Search results for: accurate forecast

Authors: Simone Dell'Agnello

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There are laser retroreflectors on the Moon and no laser retroreflectors on Mars. Here we describe the design, construction, qualification and imminent deployment of next-generation, optimized laser retroreflectors on the Moon and on Mars (where they will be the first ones). These instruments are positioned by time-of-flight measurements of short laser pulses, the so-called 'laser ranging' technique. Data analysis is carried out with PEP, the Planetary Ephemeris Program of CfA (Center for Astrophysics). Since 1969 Lunar Laser Ranging (LLR) to Apollo/Lunokhod laser retro-reflector (CCR) arrays supplied accurate tests of General Relativity (GR) and new gravitational physics: possible changes of the gravitational constant Gdot/G, weak and strong equivalence principle, gravitational self-energy (Parametrized Post Newtonian parameter beta), geodetic precession, inverse-square force-law; it can also constraint gravitomagnetism. Some of these measurements also allowed for testing extensions of GR, including spacetime torsion, non-minimally coupled gravity. LLR has also provides significant information on the composition of the deep interior of the Moon. In fact, LLR first provided evidence of the existence of a fluid component of the deep lunar interior. In 1969 CCR arrays contributed a negligible fraction of the LLR error budget. Since laser station range accuracy improved by more than a factor 100, now, because of lunar librations, current array dominate the error due to their multi-CCR geometry. We developed a next-generation, single, large CCR, MoonLIGHT (Moon Laser Instrumentation for General relativity high-accuracy test) unaffected by librations that supports an improvement of the space segment of the LLR accuracy up to a factor 100. INFN also developed INRRI (INstrument for landing-Roving laser Retro-reflector Investigations), a microreflector to be laser-ranged by orbiters. Their performance is characterized at the SCF_Lab (Satellite/lunar laser ranging Characterization Facilities Lab, INFN-LNF, Frascati, Italy) for their deployment on the lunar surface or the cislunar space. They will be used to accurately position landers, rovers, hoppers, orbiters of Google Lunar X Prize and space agency missions, thanks to LLR observations from station of the International Laser Ranging Service in the USA, in France and in Italy. INRRI was launched in 2016 with the ESA mission ExoMars (Exobiology on Mars) EDM (Entry, descent and landing Demonstration Module), deployed on the Schiaparelli lander and is proposed for the ExoMars 2020 Rover. Based on an agreement between NASA and ASI (Agenzia Spaziale Italiana), another microreflector, LaRRI (Laser Retro-Reflector for InSight), was delivered to JPL (Jet Propulsion Laboratory) and integrated on NASA’s InSight Mars Lander in August 2017 (launch scheduled in May 2018). Another microreflector, LaRA (Laser Retro-reflector Array) will be delivered to JPL for deployment on the NASA Mars 2020 Rover. The first lunar landing opportunities will be from early 2018 (with TeamIndus) to late 2018 with commercial missions, followed by opportunities with space agency missions, including the proposed deployment of MoonLIGHT and INRRI on NASA’s Resource Prospectors and its evolutions. In conclusion, we will extend significantly the CCR Lunar Geophysical Network and populate the Mars Geophysical Network. These networks will enable very significantly improved tests of GR.

Keywords: general relativity, laser retroreflectors, lunar laser ranging, Mars geodesy

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Authors: Mukhtar Maigari

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The landscape of Higher Education (HE) institutions, especially following the CVID-19 pandemic, necessitates advanced approaches to manage Indoor Environmental Quality (IEQ) which is crucial for the comfort, health, and productivity of students and staff. This study investigates the application of Building Information Modelling (BIM) as a multifaceted tool for monitoring, assessing, and controlling IEQ in HE buildings aiming to bridge the gap between traditional management practices and the innovative capabilities of BIM. Central to the study is a comprehensive literature review, which lays the foundation by examining current knowledge and technological advancements in both IEQ and BIM. This review sets the stage for a deeper investigation into the practical application of BIM in IEQ management. The methodology consists of Post-Occupancy Evaluation (POE) which encompasses physical monitoring, questionnaire surveys, and interviews under the umbrella of case studies. The physical data collection focuses on vital IEQ parameters such as temperature, humidity, CO2 levels etc, conducted by using different equipment including dataloggers to ensure accurate data. Complementing this, questionnaire surveys gather perceptions and satisfaction levels from students, providing valuable insights into the subjective aspects of IEQ. The interview component, targeting facilities management teams, offers an in-depth perspective on IEQ management challenges and strategies. The research delves deeper into the development of a conceptual BIM-based framework, informed by the insight findings from case studies and empirical data. This framework is designed to demonstrate the critical functions necessary for effective IEQ monitoring, assessment, control and automation with real time data handling capabilities. This BIM-based framework leads to the developing and testing a BIM-based prototype tool. This prototype leverages on software such as Autodesk Revit with its visual programming tool i.e., Dynamo and an Arduino-based sensor network thereby allowing for real-time flow of IEQ data for monitoring, control and even automation. By harnessing the capabilities of BIM technology, the study presents a forward-thinking approach that aligns with current sustainability and wellness goals, particularly vital in the post-COVID-19 era. The integration of BIM in IEQ management promises not only to enhance the health, comfort, and energy efficiency of educational environments but also to transform them into more conducive spaces for teaching and learning. Furthermore, this research could influence the future of HE buildings by prompting universities and government bodies to revaluate and improve teaching and learning environments. It demonstrates how the synergy between IEQ and BIM can empower stakeholders to monitor IEQ conditions more effectively and make informed decisions in real-time. Moreover, the developed framework has broader applications as well; it can serve as a tool for other sustainability assessments, like energy analysis in HE buildings, leveraging measured data synchronized with the BIM model. In conclusion, this study bridges the gap between theoretical research and real-world application by practicalizing how advanced technologies like BIM can be effectively integrated to enhance environmental quality in educational institutions. It portrays the potential of integrating advanced technologies like BIM in the pursuit of improved environmental conditions in educational institutions.

Keywords: BIM, POE, IEQ, HE-buildings

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Authors: Juan Pablo Botero Torres, Alba Avila, Luis Felipe Giraldo

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Independent navigation and mobility through unknown spaces pose a challenge for the autonomy of visually impaired people (VIP), who have relied on the use of traditional assistive tools like the white cane and trained dogs. However, emerging visually assistive technologies (VAT) have proposed several human-machine interfaces (HMIs) that could improve VIP’s ability for self-guidance. Hereby, we introduce the design and implementation of a visually assistive device, Anajaa – Visual Substitution System (AVSS). This system integrates ultrasonic sensors with custom electronics, and computer vision models (convolutional neural networks), in order to achieve a robust system that acquires information of the surrounding space and transmits it to the user in an intuitive and efficient manner. AVSS consists of two modules: the sensing and the actuation module, which are fitted to a chest mount and belt that communicate via Bluetooth. The sensing module was designed for the acquisition and processing of proximity signals provided by an array of ultrasonic sensors. The distribution of these within the chest mount allows an accurate representation of the surrounding space, discretized in three different levels of proximity, ranging from 0 to 6 meters. Additionally, this module is fitted with an RGB-D camera used to detect potentially threatening obstacles, like staircases, using a convolutional neural network specifically trained for this purpose. Posteriorly, the depth data is used to estimate the distance between the stairs and the user. The information gathered from this module is then sent to the actuation module that creates an HMI, by the means of a 3x2 array of vibration motors that make up the tactile display and allow the system to deliver haptic feedback. The actuation module uses vibrational messages (tactones); changing both in amplitude and frequency to deliver different awareness levels according to the proximity of the obstacle. This enables the system to deliver an intuitive interface. Both modules were tested under lab conditions, and the HMI was additionally tested with a focal group of VIP. The lab testing was conducted in order to establish the processing speed of the computer vision algorithms. This experimentation determined that the model can process 0.59 frames per second (FPS); this is considered as an adequate processing speed taking into account that the walking speed of VIP is 1.439 m/s. In order to test the HMI, we conducted a focal group composed of two females and two males between the ages of 35-65 years. The subject selection was aided by the Colombian Cooperative of Work and Services for the Sightless (COOTRASIN). We analyzed the learning process of the haptic messages throughout five experimentation sessions using two metrics: message discrimination and localization success. These correspond to the ability of the subjects to recognize different tactones and locate them within the tactile display. Both were calculated as the mean across all subjects. Results show that the focal group achieved message discrimination of 70% and a localization success of 80%, demonstrating how the proposed HMI leads to the appropriation and understanding of the feedback messages, enabling the user’s awareness of its surrounding space.

Keywords: computer vision on embedded systems, electronic trave aids, human-machine interface, haptic feedback, visual assistive technologies, vision substitution systems

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Authors: Bassem M. Darwish, Robert H. Ablove

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Introduction: Total knee arthroscopy (TKA) is a commonly performed procedure in patients with end-stage osteoarthritis and inaccuracy of component alignment in TKA has been shown to have many adverse post-operative outcomes such as accelerated implant wear, reduced functional outcomes, and shorter overall implant survival. Robotic surgical systems have been introduced to try and improve joint alignment and functional outcomes in knee arthroscopy, one recent iteration is the ROSA knee system, released to the market in 2019. The objective of this scoping review is to map the available evidence, identify the current types of evidence, and identify knowledge gaps to guide future studies on patient outcomes following ROSA-assisted total knee arthroplasties. Methods: An electronic search was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for scoping reviews. Search terms included ROSA, knee arthroscopy, osteoarthritis, robotic, and malalignment. Types of study participants included patients with osteoarthritis, ages 18 and older, male or female, who received manual TKA (mTKA) or ROSA-assisted TKA (rTKA), and human patients or cadavers. Published, peer-reviewed controlled trials, observational studies, and case series were included. Case reports were not included in article review. Resulting articles were first screened based on title and abstract. Articles meeting inclusion criteria based on title and abstract review then underwent full-text review by the same reviewer. Results: This scoping review identified 11 total studies, 3 prospective observational studies, and 8 retrospective observational studies - a total of 970 rTKA patients and 1745 mTKA patients. There were no case series or randomized controlled trials comparing rTKA and mTKA. Patient-centered outcomes showed promise for rTKA, where it frequently showed significantly favorable functional outcomes, measured via KOOS-JR, VAS, KSS, OKS, FJS, and PROMIS scores, at various times postoperatively. However, there was much discrepancy about which score yielded significance at which postoperative follow-up. Complication rates, reoperation rates, and LOS were very similar between mTKA and rTKA groups. Studies also showed rTKA had more accurate joint alignment within the 0 ± 3o corridor and had significantly higher rates of achieving postoperative joint angles similar to the preoperative plan. Finally, there was major agreement that rTKA cases take significantly longer time at the start, however, there is a rapid learning curve. Once past the learning curve, rTKA cases are performed in a similar time to mTKA and reduced physician stress and strain. Conclusion: The ROSA knee system represents a promising option for the management of osteoarthritis via total knee arthroscopy. The studies reviewed in this paper favor the patient-centered function outcomes, joint alignments, and physician health implications of the ROSA knee system to conventional total knee arthroscopy. Further study is warranted, however, to better understand recovery periods, longer-term functional outcomes, operative fatigue, and reduction in radiation exposure.

Keywords: arthroplasty, knee, robotics, malalignment

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Authors: Danyi Wang, Andrew Schriefer, Dennis O'Rourke, Brajendra Kumar, Yang Liu, Fei Zhong, Juergen Scheuenpflug, Zheng Feng

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Purpose: It has been recognized that the microbiome plays critical roles in disease pathogenesis, including cancer, autoimmune disease, and multiple sclerosis. To develop a clinical-grade assay for exploring microbiome-derived clinical biomarkers across disease areas, a two-phase approach is implemented. 1) Identification of the optimal sample preparation reagents using pre-mixed bacteria and healthy donor stool samples coupled with proprietary Sigma-Aldrich® bioinformatics solution. 2) Exploratory analysis of patient samples for enabling precision medicine. Study Procedure: In phase 1 study, we first compared the 16S sequencing results of two ATCC® microbiome standards (MSA 2002 and MSA 2003) across five different extraction kits (Kit A, B, C, D & E). Both microbiome standards samples were extracted in triplicate across all extraction kits. Following isolation, DNA quantity was determined by Qubit assay. DNA quality was assessed to determine purity and to confirm extracted DNA is of high molecular weight. Bacterial 16S ribosomal ribonucleic acid (rRNA) amplicons were generated via amplification of the V3/V4 hypervariable region of the 16S rRNA. Sequencing was performed using a 2x300 bp paired-end configuration on the Illumina MiSeq. Fastq files were analyzed using the Sigma-Aldrich® Microbiome Platform. The Microbiome Platform is a cloud-based service that offers best-in-class 16S-seq and WGS analysis pipelines and databases. The Platform and its methods have been extensively benchmarked using microbiome standards generated internally by MilliporeSigma and other external providers. Data Summary: The DNA yield using the extraction kit D and E is below the limit of detection (100 pg/µl) of Qubit assay as both extraction kits are intended for samples with low bacterial counts. The pre-mixed bacterial pellets at high concentrations with an input of 2 x106 cells for MSA-2002 and 1 x106 cells from MSA-2003 were not compatible with the kits. Among the remaining 3 extraction kits, kit A produced the greatest yield whereas kit B provided the least yield (Kit-A/MSA-2002: 174.25 ± 34.98; Kit-A/MSA-2003: 179.89 ± 30.18; Kit-B/MSA-2002: 27.86 ± 9.35; Kit-B/MSA-2003: 23.14 ± 6.39; Kit-C/MSA-2002: 55.19 ± 10.18; Kit-C/MSA-2003: 35.80 ± 11.41 (Mean ± SD)). Also, kit A produced the greatest yield, whereas kit B provided the least yield. The PCoA 3D visualization of the Weighted Unifrac beta diversity shows that kits A and C cluster closely together while kit B appears as an outlier. The kit A sequencing samples cluster more closely together than both the other kits. The taxonomic profiles of kit B have lower recall when compared to the known mixture profiles indicating that kit B was inefficient at detecting some of the bacteria. Conclusion: Our data demonstrated that the DNA extraction method impacts DNA concentration, purity, and microbial communities detected by next-generation sequencing analysis. Further microbiome analysis performance comparison of using healthy stool samples is underway; also, colorectal cancer patients' samples will be acquired for further explore the clinical utilities. Collectively, our comprehensive qualification approach, including the evaluation of optimal DNA extraction conditions, the inclusion of positive controls, and the implementation of a robust qualified bioinformatics pipeline, assures accurate characterization of the microbiota in a complex matrix for deciphering the deep biology and enabling precision medicine.

Keywords: 16S rRNA sequencing, analytical validation, bioinformatics pipeline, metagenomics

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Authors: Mumtaz Mooncey, Alexander Jolly, Megan Fisher, Kerry Robinson, Robert Lloyd, Dave Fielding

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Introduction: The integration of the WingFactors in-situ simulation programme has transformed the education landscape at the Whittington Health NHS Trust. To date, there have been a total of 90 simulations - 19 aimed at Paediatric trainees, including 2 Child and Adolescent Mental Health (CAMHS) scenarios. The opportunity for joint debriefs provided by clinical faculty and airline pilots, has created a new exciting avenue to explore human factors within psychiatry. Through the use of real clinical environments and primed actors; the benefits of high fidelity simulation, interdisciplinary and interprofessional learning has been highlighted. The use of in-situ simulation within Psychiatry is a newly emerging concept and its success here has been recognised by unanimously positive feedback from participants and acknowledgement through nomination for the Health Service Journal (HSJ) Award (Best Education Programme 2021). Methodology: The first CAMHS simulation featured a collapsed patient in the toilet with a ligature tied around her neck, accompanied by a distressed parent. This required participants to consider:; emergency physical management of the case, alongside helping to contain the mother and maintaining situational awareness when transferring the patient to an appropriate clinical area. The second simulation was based on a 17- year- old girl attempting to leave the ward after presenting with an overdose, posing potential risk to herself. The safe learning environment enabled participants to explore techniques to engage the young person and understand their concerns, and consider the involvement of other members of the multidisciplinary team. The scenarios were followed by an immediate ‘hot’ debrief, combining technical feedback with Human Factors feedback from uniformed airline pilots and clinicians. The importance of psychological safety was paramount, encouraging open and honest contributions from all participants. Key learning points were summarized into written documents and circulated. Findings: The in-situ simulations demonstrated the need for practical changes both in the Emergency Department and on the Paediatric ward. The presence of airline pilots provided a novel way to debrief on Human Factors. The following key themes were identified: -Team-briefing (‘Golden 5 minutes’) - Taking a few moments to establish experience, initial roles and strategies amongst the team can reduce the need for conversations in front of a distressed patient or anxious relative. -Use of checklists / guidelines - Principles associated with checklist usage (control of pace, rigor, team situational awareness), instead of reliance on accurate memory recall when under pressure. -Read-back - Immediate repetition of safety critical instructions (e.g. drug / dosage) to mitigate the risks associated with miscommunication. -Distraction management - Balancing the risk of losing a team member to manage a distressed relative, versus it impacting on the care of the young person. -Task allocation - The value of the implementation of ‘The 5A’s’ (Availability, Address, Allocate, Ask, Advise), for effective task allocation. Conclusion: 100% of participants have requested more simulation training. Involvement of airline pilots has led to a shift in hospital culture, bringing to the forefront the value of Human Factors focused training and multidisciplinary simulation. This has been of significant value in not only physical health, but also mental health simulation.

Keywords: human factors, in-situ simulation, inter-professional, multidisciplinary

Procedia PDF Downloads 106

Authors: Dongping Fang, Lian Duan, Xiaojing Yuan, Mike Xu, Allyn Klunder, Kevin Tan, Suiting Cao, Yeqing Ji

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Accurate prediction of a medical condition with straight clinical evidence is a long-sought topic in the medical management and health insurance field. Although great progress has been made with machine learning algorithms, the medical community is still, to a certain degree, suspicious about the model's accuracy and interpretability. This paper presents an innovative hierarchical attention deep learning model to achieve good prediction and clear interpretability that can be easily understood by medical professionals. This deep learning model uses a hierarchical attention structure that matches naturally with the medical history data structure and reflects the member’s encounter (date of service) sequence. The model attention structure consists of 3 levels: (1) attention on the medical code types (diagnosis codes, procedure codes, lab test results, and prescription drugs), (2) attention on the sequential medical encounters within a type, (3) attention on the medical codes within an encounter and type. This model is applied to predict the occurrence of stage 3 chronic kidney disease (CKD3), using three years’ medical history of Medicare Advantage (MA) members from a top health insurance company. The model takes members’ medical events, both claims and electronic medical record (EMR) data, as input, makes a prediction of CKD3 and calculates the contribution from individual events to the predicted outcome. The model outcome can be easily explained with the clinical evidence identified by the model algorithm. Here are examples: Member A had 36 medical encounters in the past three years: multiple office visits, lab tests and medications. The model predicts member A has a high risk of CKD3 with the following well-contributed clinical events - multiple high ‘Creatinine in Serum or Plasma’ tests and multiple low kidneys functioning ‘Glomerular filtration rate’ tests. Among the abnormal lab tests, more recent results contributed more to the prediction. The model also indicates regular office visits, no abnormal findings of medical examinations, and taking proper medications decreased the CKD3 risk. Member B had 104 medical encounters in the past 3 years and was predicted to have a low risk of CKD3, because the model didn’t identify diagnoses, procedures, or medications related to kidney disease, and many lab test results, including ‘Glomerular filtration rate’ were within the normal range. The model accurately predicts members A and B and provides interpretable clinical evidence that is validated by clinicians. Without extra effort, the interpretation is generated directly from the model and presented together with the occurrence date. Our model uses the medical data in its most raw format without any further data aggregation, transformation, or mapping. This greatly simplifies the data preparation process, mitigates the chance for error and eliminates post-modeling work needed for traditional model explanation. To our knowledge, this is the first paper on an interpretable deep-learning model using a 3-level attention structure, sourcing both EMR and claim data, including all 4 types of medical data, on the entire Medicare population of a big insurance company, and more importantly, directly generating model interpretation to support user decision. In the future, we plan to enrich the model input by adding patients’ demographics and information from free-texted physician notes.

Keywords: deep learning, interpretability, attention, big data, medical conditions

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Authors: Yannick Willemin

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Carbon fibre-reinforced plastic (CFRP) offers outstanding value. However, like all materials, CFRP also has its challenges. Many forming processes are largely manual and hard to automate, making it challenging to control repeatability and reproducibility (R&R); they generate significant scrap and are too slow for high-series production; fibre costs are relatively high and subject to supply and cost fluctuations; the supply chain is fragmented; many forms of CFRP are not recyclable, and many materials have yet to be fully characterized for accurate simulation; shelf life and outlife limitations add cost; continuous-fibre forms have design limitations; many materials are brittle; and small and/or thick parts are costly to produce and difficult to automate. A majority of small structural parts are metal due to high CFRP fabrication costs for the small-size class. The fact that CFRP manufacturing processes that produce the highest performance parts also tend to be the slowest and least automated is another reason CFRP parts are generally higher in cost than comparably performing metal parts, which are easier to produce. Fortunately, business is in the midst of a major manufacturing evolution—Industry 4.0— one technology seeing rapid growth is additive manufacturing/3D printing, thanks to new processes and materials, plus an ability to harness Industry 4.0 tools. No longer limited to just prototype parts, metal-additive technologies are used to produce tooling and mold components for high-volume manufacturing, and polymer-additive technologies can incorporate fibres to produce true composites and be used to produce end-use parts with high aesthetics, unmatched complexity, mass customization opportunities, and high mechanical performance. A new hybrid manufacturing process combines the best capabilities of additive—high complexity, low energy usage and waste, 100% traceability, faster to market—and post-consolidation—tight tolerances, high R&R, established materials, and supply chains—technologies. The platform was developed by Zürich-based 9T Labs AG and is called Additive Fusion Technology (AFT). It consists of a design software offering the possibility to determine optimal fibre layup, then exports files back to check predicted performance—plus two pieces of equipment: a 3d-printer—which lays up (near)-net-shape preforms using neat thermoplastic filaments and slit, roll-formed unidirectional carbon fibre-reinforced thermoplastic tapes—and a post-consolidation module—which consolidates then shapes preforms into final parts using a compact compression press fitted with a heating unit and matched metal molds. Matrices—currently including PEKK, PEEK, PA12, and PPS, although nearly any high-quality commercial thermoplastic tapes and filaments can be used—are matched between filaments and tapes to assure excellent bonding. Since thermoplastics are used exclusively, larger assemblies can be produced by bonding or welding together smaller components, and end-of-life parts can be recycled. By combining compression molding with 3D printing, higher part quality with very-low voids and excellent surface finish on A and B sides can be produced. Tight tolerances (min. section thickness=1.5mm, min. section height=0.6mm, min. fibre radius=1.5mm) with high R&R can be cost-competitively held in production volumes of 100 to 10,000 parts/year on a single set of machines.

Keywords: additive manufacturing, composites, thermoplastic, hybrid manufacturing

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Authors: Diogo José Morgado Rebelo, Francisco António Carneiro Pacheco de Andrade, Paulo Jorge Freitas de Oliveira Novais

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Not everything in AI-power consumer credit scoring turns out to be a wonder. When using AI in Creditworthiness Assessment (CWA), opacity and unfairness ‘sins’ must be considered to the task be deemed Responsible. AI software is not always 100% accurate, which can lead to misclassification. Discrimination of some groups can be exponentiated. A hetero personalized identity can be imposed on the individual(s) affected. Also, autonomous CWA sometimes lacks transparency when using black box models. However, for this intended purpose, human analysts ‘on-the-loop’ might not be the best remedy consumers are looking for in credit. This study seeks to explore the legality of implementing a Multi-Agent System (MAS) framework in consumer CWA to ensure compliance with the regulation outlined in Article 14(4) of the Proposal for an Artificial Intelligence Act (AIA), dated 21 April 2021 (as per the last corrigendum by the European Parliament on 19 April 2024), Especially with the adoption of Art. 18(8)(9) of the EU Directive 2023/2225, of 18 October, which will go into effect on 20 November 2026, there should be more emphasis on the need for hybrid oversight in AI-driven scoring to ensure fairness and transparency. In fact, the range of EU regulations on AI-based consumer credit will soon impact the AI lending industry locally and globally, as shown by the broad territorial scope of AIA’s Art. 2. Consequently, engineering the law of consumer’s CWA is imperative. Generally, the proposed MAS framework consists of several layers arranged in a specific sequence, as follows: firstly, the Data Layer gathers legitimate predictor sets from traditional sources; then, the Decision Support System Layer, whose Neural Network model is trained using k-fold Cross Validation, provides recommendations based on the feeder data; the eXplainability (XAI) multi-structure comprises Three-Step-Agents; and, lastly, the Oversight Layer has a 'Bottom Stop' for analysts to intervene in a timely manner. From the analysis, one can assure a vital component of this software is the XAY layer. It appears as a transparent curtain covering the AI’s decision-making process, enabling comprehension, reflection, and further feasible oversight. Local Interpretable Model-agnostic Explanations (LIME) might act as a pillar by offering counterfactual insights. SHapley Additive exPlanation (SHAP), another agent in the XAI layer, could address potential discrimination issues, identifying the contribution of each feature to the prediction. Alternatively, for thin or no file consumers, the Suggestion Agent can promote financial inclusion. It uses lawful alternative sources such as the share of wallet, among others, to search for more advantageous solutions to incomplete evaluation appraisals based on genetic programming. Overall, this research aspires to bring the concept of Machine-Centered Anthropocentrism to the table of EU policymaking. It acknowledges that, when put into service, credit analysts no longer exert full control over the data-driven entities programmers have given ‘birth’ to. With similar explanatory agents under supervision, AI itself can become self-accountable, prioritizing human concerns and values. AI decisions should not be vilified inherently. The issue lies in how they are integrated into decision-making and whether they align with non-discrimination principles and transparency rules.

Keywords: creditworthiness assessment, hybrid oversight, machine-centered anthropocentrism, EU policymaking

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Authors: Nursuhaili Najwa Masrol, Nur Hafizah Mohammed, Nur Nadhirah Rusyda Rosnan, Vijaya Subramaniam, Sim Choon Cheak

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Replanting in oil palm cultivation is vital to enable the introduction of planting materials and provides an opportunity to improve the road, drainage, terrace design, and planting density. Oil palm replanting is fundamentally necessary every 25 years. The adoption of the digital replanting blueprint is imperative as it can assist the Malaysia Oil Palm industry in addressing challenges such as labour shortages and limited expertise related to replanting tasks. Effective replanting planning should commence at least 6 months prior to the actual replanting process. Therefore, this study will help to plan and design the replanting blueprint with high-precision translation on the ground. With the advancement of geospatial technology, it is now feasible to engage in thoroughly researched planning, which can help maximize the potential yield. A blueprint designed before replanting is to enhance management’s ability to optimize the planting program, address manpower issues, or even increase productivity. In terrace planting blueprints, geographic tools have been utilized to design the roads, drainages, terraces, and planting points based on the ARM standards. These designs are mapped with location information and undergo statistical analysis. The geospatial approach is essential in precision agriculture and ensuring an accurate translation of design to the ground by implementing high-accuracy technologies. In this study, geospatial and remote sensing technologies played a vital role. LiDAR data was employed to determine the Digital Elevation Model (DEM), enabling the precise selection of terraces, while ortho imagery was used for validation purposes. Throughout the designing process, Geographical Information System (GIS) tools were extensively utilized. To assess the design’s reliability on the ground compared with the current conventional method, high-precision GPS instruments like EOS Arrow Gold and HIPER VR GNSS were used, with both offering accuracy levels between 0.3 cm and 0.5cm. Nearest Distance Analysis was generated to compare the design with actual planting on the ground. The analysis revealed that it could not be applied to the roads due to discrepancies between actual roads and the blueprint design, which resulted in minimal variance. In contrast, the terraces closely adhered to the GPS markings, with the most variance distance being less than 0.5 meters compared to actual terraces constructed. Considering the required slope degrees for terrace planting, which must be greater than 6 degrees, the study found that approximately 65% of the terracing was constructed at a 12-degree slope, while over 50% of the terracing was constructed at slopes exceeding the minimum degrees. Utilizing blueprint replanting promising strategies for optimizing land utilization in agriculture. This approach harnesses technology and meticulous planning to yield advantages, including increased efficiency, enhanced sustainability, and cost reduction. From this study, practical implementation of this technique can lead to tangible and significant improvements in agricultural sectors. In boosting further efficiencies, future initiatives will require more sophisticated techniques and the incorporation of precision GPS devices for upcoming blueprint replanting projects besides strategic progression aims to guarantee the precision of both blueprint design stages and its subsequent implementation on the field. Looking ahead, automating digital blueprints are necessary to reduce time, workforce, and costs in commercial production.

Keywords: replanting, geospatial, precision agriculture, blueprint

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Authors: Aidan Ryan, Nahima Miah, Sahaj Kaur, Imogen Sutherland, Mohamed Saleh

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Pulmonary symptoms are a common presentation to the emergency department. Due to a lack of understanding of the underlying pathophysiology, chronic liver disease is not often considered a cause of dyspnea. We present three patients who were admitted with significant respiratory distress secondary to hepatopulmonary syndrome, portopulmonary hypertension, and hepatic hydrothorax. The first is a 27-year-old male with a 6-month history of progressive dyspnea. The patient developed a severe type 1 respiratory failure with a PaO₂ of 6.3kPa and was escalated to critical care, where he was managed with non-invasive ventilation to maintain oxygen saturation. He had an agitated saline contrast echocardiogram, which showed the presence of a possible shunt. A CT angiogram revealed significant liver cirrhosis, portal hypertension, and large para esophageal varices. Ultrasound of the abdomen showed coarse liver echo patter and enlarged spleen. Along with these imaging findings, his biochemistry demonstrated impaired synthetic liver function with an elevated international normalized ratio (INR) of 1.4 and hypoalbuminaemia of 28g/L. The patient was then transferred to a tertiary center for further management. Further investigations confirmed a shunt of 56%, and liver biopsy confirmed cirrhosis suggestive of alpha-1-antitripsyin deficiency. The findings were consistent with a diagnosis of hepatopulmonary syndrome, and the patient is awaiting a liver transplant. The second patient is a 56-year-old male with a 12-month history of worsening dyspnoea, jaundice, confusion. His medical history included liver cirrhosis, portal hypertension, and grade 1 oesophageal varices secondary to significant alcohol excess. On admission, he developed a type 1 respiratory failure with PaO₂ of 6.8kPa requiring 10L of oxygen. CT pulmonary angiogram was negative for pulmonary embolism but showed evidence of chronic pulmonary hypertension, liver cirrhosis, and portal hypertension. An echocardiogram revealed a grossly dilated right heart with reduced function, pulmonary and tricuspid regurgitation, and pulmonary artery pressures estimated at 78mmHg. His biochemical markers showed impaired synthetic liver function with an INR of 3.2, albumin of 29g/L, along with raised bilirubin of 148mg/dL. During his long admission, he was managed with diuretics with little improvement. After three weeks, he was diagnosed with portopulmonary hypertension and was commenced on terlipressin. This resulted in successfully weaning off oxygen, and he was discharged home. The third patient is a 61-year-old male who presented to the local ambulatory care unit for therapeutic paracentesis on a background of decompensated liver cirrhosis. On presenting, he complained of a 2-day history of worsening dyspnoea and a productive cough. Chest x-ray showed a large pleural effusion, increasing in size over the previous eight months, and his abdomen was visibly distended with ascitic fluid. Unfortunately, the patient deteriorated, developing a larger effusion along with an increase in oxygen demand, and passed away. Without underlying cardiorespiratory disease, in the presence of a persistent pleural effusion with underlying decompensated cirrhosis, he was diagnosed with hepatic hydrothorax. While each presented with dyspnoea, the cause and underlying pathophysiology differ significantly from case to case. By describing these complications, we hope to improve awareness and aid prompt and accurate diagnosis, vital for improving outcomes.

Keywords: dyspnea, hepatic hydrothorax, hepatopulmonary syndrome, portopulmonary syndrome

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Authors: Fahimeh Palizban, Farshad Noravesh, Amir Hossein Saeidian, Mahya Mehrmohamadi

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In the recent decade, the emergence of liquid biopsy has significantly improved cancer monitoring and detection. Dying cells, including those originating from tumors, shed their DNA into the blood and contribute to a pool of circulating fragments called cell-free DNA. Accordingly, identifying the tissue origin of these DNA fragments from the plasma can result in more accurate and fast disease diagnosis and precise treatment protocols. Open chromatin regions are important epigenetic features of DNA that reflect cell types of origin. Profiling these features by DNase-seq, ATAC-seq, and histone ChIP-seq provides insights into tissue-specific and disease-specific regulatory mechanisms. There have been several studies in the area of cancer liquid biopsy that integrate distinct genomic and epigenomic features for early cancer detection along with tissue of origin detection. However, multimodal analysis requires several types of experiments to cover the genomic and epigenomic aspects of a single sample, which will lead to a huge amount of cost and time. To overcome these limitations, the idea of predicting OCRs from WGS is of particular importance. In this regard, we proposed a computational approach to target the prediction of open chromatin regions as an important epigenetic feature from cell-free DNA whole genome sequence data. To fulfill this objective, local sequencing depth will be fed to our proposed algorithm and the prediction of the most probable open chromatin regions from whole genome sequencing data can be carried out. Our method integrates the signal processing method with sequencing depth data and includes count normalization, Discrete Fourie Transform conversion, graph construction, graph cut optimization by linear programming, and clustering. To validate the proposed method, we compared the output of the clustering (open chromatin region+, open chromatin region-) with previously validated open chromatin regions related to human blood samples of the ATAC-DB database. The percentage of overlap between predicted open chromatin regions and the experimentally validated regions obtained by ATAC-seq in ATAC-DB is greater than 67%, which indicates meaningful prediction. As it is evident, OCRs are mostly located in the transcription start sites (TSS) of the genes. In this regard, we compared the concordance between the predicted OCRs and the human genes TSS regions obtained from refTSS and it showed proper accordance around 52.04% and ~78% with all and the housekeeping genes, respectively. Accurately detecting open chromatin regions from plasma cell-free DNA-seq data is a very challenging computational problem due to the existence of several confounding factors, such as technical and biological variations. Although this approach is in its infancy, there has already been an attempt to apply it, which leads to a tool named OCRDetector with some restrictions like the need for highly depth cfDNA WGS data, prior information about OCRs distribution, and considering multiple features. However, we implemented a graph signal clustering based on a single depth feature in an unsupervised learning manner that resulted in faster performance and decent accuracy. Overall, we tried to investigate the epigenomic pattern of a cell-free DNA sample from a new computational perspective that can be used along with other tools to investigate genetic and epigenetic aspects of a single whole genome sequencing data for efficient liquid biopsy-related analysis.

Keywords: open chromatin regions, cancer, cell-free DNA, epigenomics, graph signal processing, correlation clustering

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Authors: Denis V. Axenov-Gribanov, Irina V. Voytsekhovskaya, Evgenii S. Protasov, Maxim A. Timofeyev

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Isolated ecosystems existing under specific environmental conditions have been shown to be promising sources of new strains of actinobacteria. The taiga forest of Baikal Siberia has not been well studied, and its actinobacterial population remains uncharacterized. The proximity between the huge water mass of Lake Baikal and high mountain ranges influences the structure and diversity of the plant world in Siberia. Here, we report the isolation of eighteen actinobacterial strains from male cones of Pinus sylvestris trees growing on the shore of the ancient Lake Baikal in Siberia. The actinobacterial strains were isolated on solid nutrient MS media and Czapek agar supplemented with cycloheximide and phosphomycin. Identification of actinobacteria was carried out by 16S rRNA gene sequencing and further analysis of the evolutionary history. Four different liquid and solid media (NL19, DNPM, SG and ISP) were tested for metabolite production. The metabolite extracts produced by the isolated strains were tested for antibacterial and antifungal activities. Also, antiradical activity of crude extracts was carried out. Strain Streptomyces sp. IB 2014 I 74-3 that active against Gram-negative bacteria was selected for dereplication analysis with using the high-yield liquid chromatography with mass-spectrometry. Mass detection was performed in both positive and negative modes, with the detection range set to 160–2500 m/z. Data were collected and analyzed using Bruker Compass Data Analysis software, version 4.1. Dereplication was performed using the Dictionary of Natural Products (DNP) database version 6.1 with the following search parameters: accurate molecular mass, absorption spectra and source of compound isolation. Thus, in addition to more common representative strains of Streptomyces, several species belonging to the genera Rhodococcus, Amycolatopsis, and Micromonospora were isolated. Several of the selected strains were deposited in the Russian Collection of Agricultural Microorganisms (RCAM), St. Petersburg, Russia. All isolated strains exhibited antibacterial and antifungal activities. We identified several strains that inhibited the growth of the pathogen Candida albicans but did not hinder the growth of Saccharomyces cerevisiae. Several isolates were active against Gram-positive and Gram-negative bacteria. Moreover, extracts of several strains demonstrated high antioxidant activity. The high proportion of biologically active strains producing antibacterial and specific antifungal compounds may reflect their role in protecting pollen against phytopathogens. Dereplication of the secondary metabolites of the strain Streptomyces sp. IB 2014 I 74-3 was resulted in the fact that a total of 59 major compounds were detected in the culture liquid extract of strain cultivated in ISP medium. Eight compounds were preliminarily identified based on characteristics described in the Dictionary of Natural Products database, using the search parameters Streptomyces sp. IB 2014 I 74-3 was found to produce saframycin A, Y3 and S; 2-amino-3-oxo-3H-phenoxazine-1,8-dicarboxylic acid; galtamycinone; platencin A4-13R and A4-4S; ganefromycin d1; the antibiotic SS 8201B; and streptothricin D, 40-decarbamoyl, 60-carbamoyl. Moreover, forty-nine of the 59 compounds detected in the extract examined in the present study did not result in any positive hits when searching within the DNP database and could not be identified based on available mass-spec data. Thus, these compounds might represent new findings.

Keywords: actinobacteria, Baikal Lake, biodiversity, male cones, Pinus sylvestris

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Authors: Julien Caudeville, Muriel Ismert

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Analyzing the relationship between environment and health has become a major preoccupation for public health as evidenced by the emergence of the French national plans for health and environment. These plans have identified the following two priorities: (1) to identify and manage geographic areas, where hotspot exposures are suspected to generate a potential hazard to human health; (2) to reduce exposure inequalities. At a regional scale and fine resolution of exposure outcome prerequisite, environmental monitoring networks are not sufficient to characterize the multidimensionality of the exposure concept. In an attempt to increase representativeness of spatial exposure assessment approaches, risk composite indicators could be built using additional available databases and theoretical framework approaches to combine factor risks. To achieve those objectives, combining data process and transfer modeling with a spatial approach is a fundamental prerequisite that implies the need to first overcome different scientific limitations: to define interest variables and indicators that could be built to associate and describe the global source-effect chain; to link and process data from different sources and different spatial supports; to develop adapted methods in order to improve spatial data representativeness and resolution. A GIS-based modeling platform for quantifying human exposure to chemical substances (PLAINE: environmental inequalities analysis platform) was used to build health risk indicators within the Lorraine region (France). Those indicators combined chemical substances (in soil, air and water) and noise risk factors. Tools have been developed using modeling, spatial analysis and geostatistic methods to build and discretize interest variables from different supports and resolutions on a 1 km2 regular grid within the Lorraine region. By example, surface soil concentrations have been estimated by developing a Kriging method able to integrate surface and point spatial supports. Then, an exposure model developed by INERIS was used to assess the transfer from soil to individual exposure through ingestion pathways. We used distance from polluted soil site to build a proxy for contaminated site. Air indicator combined modeled concentrations and estimated emissions to take in account 30 polluants in the analysis. For water, drinking water concentrations were compared to drinking water standards to build a score spatialized using a distribution unit serve map. The Lden (day-evening-night) indicator was used to map noise around road infrastructures. Aggregation of the different factor risks was made using different methodologies to discuss weighting and aggregation procedures impact on the effectiveness of risk maps to take decisions for safeguarding citizen health. Results permit to identify pollutant sources, determinants of exposure, and potential hotspots areas. A diagnostic tool was developed for stakeholders to visualize and analyze the composite indicators in an operational and accurate manner. The designed support system will be used in many applications and contexts: (1) mapping environmental disparities throughout the Lorraine region; (2) identifying vulnerable population and determinants of exposure to set priorities and target for pollution prevention, regulation and remediation; (3) providing exposure database to quantify relationships between environmental indicators and cancer mortality data provided by French Regional Health Observatories.

Keywords: health risk, environment, composite indicator, hotspot areas

Procedia PDF Downloads 245

Authors: Thaveesha Dheerasekera, Dileeka Sandamali Alwis

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The escalating prevalence of mental health issues, such as depression and suicidal ideation, is a matter of significant global concern. It is plausible that a variety of factors, such as life events, social isolation, and preexisting physiological or psychological health conditions, could instigate or exacerbate these conditions. Traditional approaches to diagnosing depression entail a considerable amount of time and necessitate the involvement of adept practitioners. This underscores the necessity for automated systems capable of promptly detecting and diagnosing symptoms of depression. The PsyVBot system employs sophisticated natural language processing and machine learning methodologies, including the use of the NLTK toolkit for dataset preprocessing and the utilization of a Long Short-Term Memory (LSTM) model. The PsyVBot exhibits a remarkable ability to diagnose depression with a 94% accuracy rate through the analysis of user input. Consequently, this resource proves to be efficacious for individuals, particularly those enrolled in academic institutions, who may encounter challenges pertaining to their psychological well-being. The PsyVBot employs a Long Short-Term Memory (LSTM) model that comprises a total of three layers, namely an embedding layer, an LSTM layer, and a dense layer. The stratification of these layers facilitates a precise examination of linguistic patterns that are associated with the condition of depression. The PsyVBot has the capability to accurately assess an individual's level of depression through the identification of linguistic and contextual cues. The task is achieved via a rigorous training regimen, which is executed by utilizing a dataset comprising information sourced from the subreddit r/SuicideWatch. The diverse data present in the dataset ensures precise and delicate identification of symptoms linked with depression, thereby guaranteeing accuracy. PsyVBot not only possesses diagnostic capabilities but also enhances the user experience through the utilization of audio outputs. This feature enables users to engage in more captivating and interactive interactions. The PsyVBot platform offers individuals the opportunity to conveniently diagnose mental health challenges through a confidential and user-friendly interface. Regarding the advancement of PsyVBot, maintaining user confidentiality and upholding ethical principles are of paramount significance. It is imperative to note that diligent efforts are undertaken to adhere to ethical standards, thereby safeguarding the confidentiality of user information and ensuring its security. Moreover, the chatbot fosters a conducive atmosphere that is supportive and compassionate, thereby promoting psychological welfare. In brief, PsyVBot is an automated conversational agent that utilizes an LSTM model to assess the level of depression in accordance with the input provided by the user. The demonstrated accuracy rate of 94% serves as a promising indication of the potential efficacy of employing natural language processing and machine learning techniques in tackling challenges associated with mental health. The reliability of PsyVBot is further improved by the fact that it makes use of the Reddit dataset and incorporates Natural Language Toolkit (NLTK) for preprocessing. PsyVBot represents a pioneering and user-centric solution that furnishes an easily accessible and confidential medium for seeking assistance. The present platform is offered as a modality to tackle the pervasive issue of depression and the contemplation of suicide.

Keywords: chatbot, depression diagnosis, LSTM model, natural language process

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Authors: Subhash Nerella, Ziyuan Guan, Azra Bihorac, Parisa Rashidi

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Intensive care units (ICUs) provide care to critically ill patients in severe and life-threatening conditions. However, patient monitoring in the ICU is limited by the time and resource constraints imposed on healthcare providers. Many critical care indices such as mobility are still manually assessed, which can be subjective, prone to human errors, and lack granularity. Other important aspects, such as environmental factors, are not monitored at all. For example, critically ill patients often experience circadian disruptions due to the absence of effective environmental “timekeepers” such as the light/dark cycle and the systemic effect of acute illness on chronobiologic markers. Although the occurrence of delirium is associated with circadian disruption risk factors, these factors are not routinely monitored in the ICU. Hence, there is a critical unmet need to develop systems for precise and real-time assessment through novel enabling technologies. We have developed the mobility and circadian disruption quantification system (Mobi-DiQ) by augmenting biomarker and clinical data with pervasive sensing data to generate mobility and circadian cues related to mobility, nightly disruptions, and light and noise exposure. We hypothesize that Mobi-DiQ can provide accurate mobility and circadian cues that correlate with bedside clinical mobility assessments and circadian biomarkers, ultimately important for delirium risk assessment and prevention. The collected multimodal dataset consists of depth images, Electromyography (EMG) data, patient extremity movement captured by accelerometers, ambient light levels, Sound Pressure Level (SPL), and indoor air quality measured by volatile organic compounds, and the equivalent CO₂ concentration. For delirium risk assessment, the system recognizes mobility cues (axial body movement features and body key points) and circadian cues, including nightly disruptions, ambient SPL, and light intensity, as well as other environmental factors such as indoor air quality. The Mobi-DiQ system consists of three major components: the pervasive sensing system, a data storage and analysis server, and a data annotation system. For data collection, six local pervasive sensing systems were deployed, including a local computer and sensors. A video recording tool with graphical user interface (GUI) developed in python was used to capture depth image frames for analyzing patient mobility. All sensor data is encrypted, then automatically uploaded to the Mobi-DiQ server through a secured VPN connection. Several data pipelines are developed to automate the data transfer, curation, and data preparation for annotation and model training. The data curation and post-processing are performed on the server. A custom secure annotation tool with GUI was developed to annotate depth activity data. The annotation tool is linked to the MongoDB database to record the data annotation and to provide summarization. Docker containers are also utilized to manage services and pipelines running on the server in an isolated manner. The processed clinical data and annotations are used to train and develop real-time pervasive sensing systems to augment clinical decision-making and promote targeted interventions. In the future, we intend to evaluate our system as a clinical implementation trial, as well as to refine and validate it by using other data sources, including neurological data obtained through continuous electroencephalography (EEG).

Keywords: deep learning, delirium, healthcare, pervasive sensing

Procedia PDF Downloads 88

Authors: Simrin Kafle, Ulrika Enemark

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Catastrophic health expenditure (CHE) is a critical issue in low- and middle-income countries like Nepal, exacerbating financial hardship among vulnerable households. This study assesses the effectiveness of Nepal’s National Health Insurance Program (NHIP), launched in 2015, to reduce out-of-pocket (OOP) healthcare costs and mitigate CHE. Conducted in Pokhara Metropolitan City, the study used an analytical cross-sectional design, sampling 1276 households through a two-stage random sampling method. Data was collected via face-to-face interviews between May and October 2023. The analysis was conducted using SPSS version 29, incorporating propensity score matching to minimize biases and create comparable groups of enrolled and non-enrolled households in the NHIP. PSM helped reduce confounding effects by matching households with similar baseline characteristics. Additionally, a doubly robust methodology was employed, combining propensity score adjustment with regression modeling to enhance the reliability of the results. This comprehensive approach ensured a more accurate estimation of the impact of NHIP enrollment on CHE. Among the 1276 samples, 534 households (41.8%) were enrolled in NHIP. Of them, 84.3% of households renewed their insurance card, though some cited long waiting times, lack of medications, and complex procedures as barriers to renewal. Approximately 57.3% of households reported known diseases before enrollment, with 49.8% attending routine health check-ups in the past year. The primary motivation for enrollment was encouragement from insurance employees (50.2%). The data indicates that 12.5% of enrolled households experienced CHE versus 7.5% among non-enrolled. Enrollment into NHIP does not contribute to lower CHE (AOR: 1.98, 95% CI: 1.21-3.24). Key factors associated with increased CHE risk were presence of non-communicable diseases (NCDs) (AOR: 3.94, 95% CI: 2.10-7.39), acute illnesses/injuries (AOR: 6.70, 95% CI: 3.97-11.30), larger household size (AOR: 3.09, 95% CI: 1.81-5.28), and households below the poverty line (AOR: 5.82, 95% CI: 3.05-11.09). Other factors such as gender, education level, caste/ethnicity, presence of elderly members, and under-five children also showed varying associations with CHE, though not all were statistically significant. The study concludes that enrollment in the NHIP does not significantly reduce the risk of CHE. The reason for this could be inadequate coverage, where high-cost medicines, treatments, and transportation costs are not fully included in the insurance package, leading to significant out-of-pocket expenses. We also considered the long waiting time, lack of medicines, and complex procedures for the utilization of NHIP benefits, which might result in the underuse of covered services. Finally, gaps in enrollment and retention might leave certain households vulnerable to CHE despite the existence of NHIP. Key factors contributing to increased CHE include NCDs, acute illnesses, larger household sizes, and poverty. To improve the program’s effectiveness, it is recommended that NHIP benefits and coverage be expanded to better protect against high healthcare costs. Additionally, simplifying the renewal process, addressing long waiting times, and enhancing the availability of services could improve member satisfaction and retention. Targeted financial protection measures should be implemented for high-risk groups, and efforts should be made to increase awareness and encourage routine health check-ups to prevent severe health issues that contribute to CHE.

Keywords: catastrophic health expenditure, effectiveness, national health insurance program, Nepal

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Authors: Inna Kornienko, Svetlana Guryeva, Natalia Danilova, Elena Petersen

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Drug toxicity often goes undetected until clinical trials, the most expensive and dangerous phase of drug development. Both human cell culture and animal studies have limitations that cannot be overcome by improvements in drug testing protocols. Tissue engineering is an emerging alternative approach to creating models of human malignant tumors for experimental oncology, personalized medicine, and drug discovery studies. This new generation of bioengineered tumors provides an opportunity to control and explore the role of every component of the model system including cell populations, supportive scaffolds, and signaling molecules. An area that could greatly benefit from these models is cancer research. Recent advances in tissue engineering demonstrated that decellularized tissue is an excellent scaffold for tissue engineering. Decellularization of donor organs such as heart, liver, and lung can provide an acellular, naturally occurring three-dimensional biologic scaffold material that can then be seeded with selected cell populations. Preliminary studies in animal models have provided encouraging results for the proof of concept. Decellularized Organs preserve organ microenvironment, which is critical for cancer metastasis. Utilizing 3D tumor models results greater proximity of cell culture morphological characteristics in a model to its in vivo counterpart, allows more accurate simulation of the processes within a functioning tumor and its pathogenesis. 3D models allow study of migration processes and cell proliferation with higher reliability as well. Moreover, cancer cells in a 3D model bear closer resemblance to living conditions in terms of gene expression, cell surface receptor expression, and signaling. 2D cell monolayers do not provide the geometrical and mechanical cues of tissues in vivo and are, therefore, not suitable to accurately predict the responses of living organisms. 3D models can provide several levels of complexity from simple monocultures of cancer cell lines in liquid environment comprised of oxygen and nutrient gradients and cell-cell interaction to more advanced models, which include co-culturing with other cell types, such as endothelial and immune cells. Following this reasoning, spheroids cultivated from one or multiple patient-derived cell lines can be utilized to seed the matrix rather than monolayer cells. This approach furthers the progress towards personalized medicine. As an initial step to create a new ex vivo tissue engineered model of a cancer tumor, optimized protocols have been designed to obtain organ-specific acellular matrices and evaluate their potential as tissue engineered scaffolds for cultures of normal and tumor cells. Decellularized biomatrix was prepared from animals’ kidneys, urethra, lungs, heart, and liver by two decellularization methods: perfusion in a bioreactor system and immersion-agitation on an orbital shaker with the use of various detergents (SDS, Triton X-100) in different concentrations and freezing. Acellular scaffolds and tissue engineered constructs have been characterized and compared using morphological methods. Models using decellularized matrix have certain advantages, such as maintaining native extracellular matrix properties and biomimetic microenvironment for cancer cells; compatibility with multiple cell types for cell culture and drug screening; utilization to culture patient-derived cells in vitro to evaluate different anticancer therapeutics for developing personalized medicines.

Keywords: 3D models, decellularization, drug discovery, drug toxicity, scaffolds, spheroids, tissue engineering

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Authors: Ipsita Pujari, Abitha Thomas, Vidhu S. Babu, K. Satyamoorthy

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Orchids are esteemed as celebrities in cut flower industry globally, due to their long-lasting fragrance and freshness. Apart from splendor, the unique metabolites endowed with pharmaceutical potency have made them one of the most hunted in plant kingdom. This had led to their trafficking, resulting in habitat loss, subsequently making them occupiers of IUCN red list as RET species. Many of the orchids especially wild varieties still remain undiscovered. In view to protect and conserve the wild germplasm, researchers have been inventing novel micropropagation protocols; thereby conserving Orchids. India is overflowing with exclusive wild cultivars of Orchids, whose pharmaceutical properties remain untapped and are not marketed owing to relatively small flowers. However, their germplasm is quite pertinent to be preserved for making unusual hybrids. Dendrobium genus is the second largest among Orchids exists in India and has highest demand attributable to enduring cut flowers and significant therapeutic uses in traditional medicinal system. Though the genus is quite endemic in Western Ghat regions of the country, many species are still anonymous with their unknown curative properties. A standard breeding cycle in Orchids usually takes five to seven years (Dendrobium hybrids taking a long juvenile phase of two to five years reaching maturity and flowering stage) and this extensive life cycle has always hindered the development of Dendrobium breeding. Dendrobium is reported with essential therapeutic plant bio-chemicals and ‘Moscatilin’ is one, found exclusive to this famous Dendrobium genus. Moscatilin is reported to have anti-mutagenic and anti-cancer properties, whose positive action has very recently been demonstrated against a range of cancers. Our preliminary study here established a simple and economic small-scale propagation protocol of Dendrobium ovatum describing in vitro production of Moscatilin. Subsequently for enhancing the content of Moscatilin, an efficient experimental related to the organization of transgenic (hairy) D. ovatum root cultures through infection of Agrobacterium rhizogenes 2364 strain on MS basal medium is being reported in the present study. Hairy roots generated on almost half of the explants used (spherules, in vitro plantlets and calli) maintained through suspension cultures, after 8 weeks of co-cultivation with Agrobacterium rhizogenes. GFP assay performed with isolated hairy roots has confirmed the integrative transformation which was further positively confirmed by PCR using rolB gene specific primers. Reverse phase-high performance liquid chromatography and mass spectrometry techniques were used for quantification and accurate identification of Moscatilin respectively from transgenic systems. A noticeable ~3 fold increase in contents were observed in transformed D. ovatum root cultures as compared to the simple in vitro culture, callus culture and callus regeneration plantlets. Role of elicitors e.g., Methyl jasmonate, Salicylic acid, Yeast extract and Chitosan were tested for elevating the Moscatilin content to obtain a comprehensive optimized protocol facilitating the in vitro production of valuable Moscatilin with larger yield. This study would provide evidence towards the in vitro assembly of Moscatilin within a short time-period through not a so-expensive technology for the first time. It also serves as an appropriate basis for bioreactor scale-up resulting in commercial bioproduction of Moscatilin.

Keywords: bioproduction, Dendrobium ovatum, hairy root culture, moscatilin

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Authors: Evelyn Castle, Adam Thompson

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Background: In 2013, the Kano State Primary Health Care Management Board redesigned its Routine immunization supply chain from diffused pull to direct delivery push. It addressed issues around stockouts and reduced time spent by health facility staff collecting, and reporting on vaccine usage. The health care board sought the help of a 3PL for twice-monthly deliveries from its cold store to 484 facilities across 44 local governments. eHA’s Health Delivery Systems group formed a 3PL to serve 326 of these new facilities in partnership with the State. We focused on designing and implementing a technology system throughout. Basic methodologies: GIS Mapping: - Planning the delivery of vaccines to hundreds of health facilities requires detailed route planning for delivery vehicles. Mapping the road networks across Kano and Bauchi with a custom routing tool provided information for the optimization of deliveries. Reducing the number of kilometers driven each round by 20%, - reducing cost and delivery time. Direct Delivery Information System: - Vaccine Direct Deliveries are facilitated through pre-round planning (driven by health facility database, extensive GIS, and inventory workflow rules), manager and driver control panel customizing delivery routines and reporting, progress dashboard, schedules/routes, packing lists, delivery reports, and driver data collection applications. Move: Last Mile Logistics Management System: - MOVE has improved vaccine supply information management to be timely, accurate and actionable. Provides stock management workflow support, alerts management for cold chain exceptions/stock outs, and on-device analytics for health and supply chain staff. Software was built to be offline-first with user-validated interface and experience. Deployed to hundreds of vaccine storage site the improved information tools helps facilitate the process of system redesign and change management. Findings: - Stock-outs reduced from 90% to 33% - Redesigned current health systems and managing vaccine supply for 68% of Kano’s wards. - Near real time reporting and data availability to track stock. - Paperwork burdens of health staff have been dramatically reduced. - Medicine available when the community needs it. - Consistent vaccination dates for children under one to prevent polio, yellow fever, tetanus. - Higher immunization rates = Lower infection rates. - Hundreds of millions of Naira worth of vaccines successfully transported. - Fortnightly service to 326 facilities in 326 wards across 30 Local Government areas. - 6,031 cumulative deliveries. - Over 3.44 million doses transported. - Minimum travel distance covered in a round of delivery is 2000 kms & maximum of 6297 kms. - 153,409 kms travelled by 6 drivers. - 500 facilities in 326 wards. - Data captured and synchronized for the first time. - Data driven decision making now possible. Conclusion: eHA’s Vaccine Direct delivery has met challenges in Kano and Bauchi State and provided a reliable delivery service of vaccinations that ensure t health facilities can run vaccination clinics for children under one. eHA uses innovative technology that delivers vaccines from Northern Nigerian zonal stores straight to healthcare facilities. Helped healthcare workers spend less time managing supplies and more time delivering care, and will be rolled out nationally across Nigeria.

Keywords: direct delivery information system, health delivery system, GIS mapping, Northern Nigeria, vaccines

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Authors: Kalaumari Mayoral-Peña, Ana I. Montejano-Montelongo, Josué Reyes-Muñoz, Gonzalo A. Ortiz-Mancilla, Mayrin Rodríguez-Cruz, Víctor Hernández-Villalobos, Jesús A. Guzmán-López, Santiago García-Jacobo, Iván Licona-Vázquez, Grisel Fierros-Romero, Rosario Flores-Vallejo

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The group B-lactamic antibiotics include some of the most frequently used small drug molecules against bacterial infections. Nevertheless, an alarming decrease in their efficacy has been reported due to the emergence of antibiotic-resistant bacteria. Infections caused by bacteria expressing extended Spectrum B-lactamases (ESBLs) are difficult to treat and account for higher morbidity and mortality rates, delayed recovery, and high economic burden. According to the Global Report on Antimicrobial Resistance Surveillance, it is estimated that mortality due to resistant bacteria will ascend to 10 million cases per year worldwide. These facts highlight the importance of developing low-cost and readily accessible detection methods of drug-resistant ESBLs bacteria to prevent their spread and promote accurate and fast diagnosis. Bacterial detection is commonly done using molecular diagnostic techniques, where PCR stands out for its high performance. However, this technique requires specialized equipment not available everywhere, is time-consuming, and has a high cost. Loop-Mediated Isothermal Amplification (LAMP) is an alternative technique that works at a constant temperature, significantly decreasing the equipment cost. It yields double-stranded DNA of several lengths with repetitions of the target DNA sequence as a product. Although positive and negative results from LAMP can be discriminated by colorimetry, fluorescence, and turbidity, there is still a large room for improvement in the point-of-care implementation. DNA origami is a technique that allows the formation of 3D nanometric structures by folding a large single-stranded DNA (scaffold) into a determined shape with the help of short DNA sequences (staples), which hybridize with the scaffold. This research aimed to generate DNA origami structures using LAMP products as scaffolds to improve the sensitivity to detect ESBLs in point-of-care diagnosis. For this study, the coding sequence of the CTM-X-15 ESBL of E. coli was used to generate the LAMP products. The set of LAMP primers were designed using PrimerExplorerV5. As a result, a target sequence of 200 nucleotides from CTM-X-15 ESBL was obtained. Afterward, eight different DNA origami structures were designed using the target sequence in the SDCadnano and analyzed with CanDo to evaluate the stability of the 3D structures. The designs were constructed minimizing the total number of staples to reduce costs and complexity for point-of-care applications. After analyzing the DNA origami designs, two structures were selected. The first one was a zig-zag flat structure, while the second one was a wall-like shape. Given the sequence repetitions in the scaffold sequence, both were able to be assembled with only 6 different staples each one, ranging between 18 to 80 nucleotides. Simulations of both structures were performed using scaffolds of different sizes yielding stable structures in all the cases. The generation of the LAMP products were tested by colorimetry and electrophoresis. The formation of the DNA structures was analyzed using electrophoresis and colorimetry. The modeling of novel detection methods through bioinformatics tools allows reliable control and prediction of results. To our knowledge, this is the first study that uses LAMP products and DNA-origami in combination to delect ESBL-producing bacterial strains, which represent a promising methodology for diagnosis in the point-of-care.

Keywords: beta-lactamases, antibiotic resistance, DNA origami, isothermal amplification, LAMP technique, molecular diagnosis

Procedia PDF Downloads 218

Authors: Savvas Rogotis, Nikolaos Kalatzis, Stergios Dimou-Sakellariou, Nikolaos Marianos

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As precision holds the key for the introduction of distinct benefits in agriculture (e.g., energy savings, reduced labor costs, optimal application of inputs, improved products, and yields), it steadily becomes evident that new initiatives should focus on rendering Precision Agriculture (PA) more accessible to the average farmer. PA leverages on technologies such as the Internet of Things (IoT), earth observation, robotics and positioning systems (e.g., the Global Navigation Satellite System – GNSS - as well as individual positioning systems like GPS, Glonass, Galileo) that allow: from simple data georeferencing to optimal navigation of agricultural machinery to even more complex tasks like Variable Rate Applications. An identified customer pain point is that, from one hand, typical triangulation-based positioning systems are not accurate enough (with errors up to several meters), while on the other hand, high precision positioning systems reaching centimeter-level accuracy, are very costly (up to thousands of euros). Within this paper, a Ground-Based Augmentation System (GBAS) is introduced, that can be adapted to any existing IoT field-sensing station infrastructure. The latter should cover a minimum set of requirements, and in particular, each station should operate as a fixed, obstruction-free towards the sky, energy supplying unit. Station augmentation will allow them to function in pairs with GNSS rovers following the differential GNSS base-rover paradigm. This constitutes a key innovation element for the proposed solution that encompasses differential GNSS capabilities into an IoT field-sensing infrastructure. Integrating this kind of information supports the provision of several additional PA beneficial services such as spatial mapping, route planning, and automatic field navigation of unmanned vehicles (UVs). Right at the heart of the designed system, there is a high-end GNSS toolkit with base-rover variants and Real-Time Kinematic (RTK) capabilities. The GNSS toolkit had to tackle all availability, performance, interfacing, and energy-related challenges that are faced for a real-time, low-power, and reliable in the field operation. Specifically, in terms of performance, preliminary findings exhibit a high rover positioning precision that can even reach less than 10-centimeters. As this precision is propagated to the full dataset collection, it enables tractors, UVs, Android-powered devices, and measuring units to deal with challenging real-world scenarios. The system is validated with the help of Gaiatrons, a mature network of agro-climatic telemetry stations with presence all over Greece and beyond ( > 60.000ha of agricultural land covered) that constitutes part of “gaiasense” (www.gaiasense.gr) smart farming (SF) solution. Gaiatrons constantly monitor atmospheric and soil parameters, thus, providing exact fit to operational requirements asked from modern SF infrastructures. Gaiatrons are ultra-low-cost, compact, and energy-autonomous stations with a modular design that enables the integration of advanced GNSS base station capabilities on top of them. A set of demanding pilot demonstrations has been initiated in Stimagka, Greece, an area with a diverse geomorphological landscape where grape cultivation is particularly popular. Pilot demonstrations are in the course of validating the preliminary system findings in its intended environment, tackle all technical challenges, and effectively highlight the added-value offered by the system in action.

Keywords: GNSS, GBAS, precision agriculture, RTK, smart farming

Procedia PDF Downloads 111

Authors: Nanine Fouche

Abstract:

The Cape Flats is a low-lying sand-covered expanse of approximately 460 square kilometres, situated to the southeast of the central business district of Cape Town in the Western Cape of South Africa. The aeolian sands masking this area are often loose and compressible in the upper 1m to 1.5m of the surface, and there is a general exceedance of the maximum allowable settlement in these sands. The settlement of shallow foundations on Cape Flats sands is commonly predicted using the results of in-situ tests such as the SPT or DPSH due to the difficulty of retrieving undisturbed samples for laboratory testing. Varying degrees of accuracy and reliability are associated with these methods. More recently, shear wave velocity (Vs) profiles obtained from seismic testing, such as continuous surface wave tests (CSW), are being used for settlement prediction. Such predictions have the advantage of considering non-linear stress-strain behaviour of soil and the degradation of stiffness with increasing strain. CSW tests are rarely executed in the Cape Flats, whereas SPT’s are commonly performed. For this reason, and to facilitate better settlement predictions in Cape Flats sand, equations representing shear wave velocity (Vs) as a function of SPT blow count (N60) and vertical effective stress (v’) were generated by statistical regression of site investigation data. To reveal the most appropriate method of overburden correction, analyses were performed with a separate overburden term (Pa/σ’v) as well as using stress corrected shear wave velocity and SPT blow counts (correcting Vs. and N60 to Vs1and (N1)60respectively). Shear wave velocity profiles and SPT blow count data from three sites masked by Cape Flats sands were utilised to generate 80 Vs-SPT N data pairs for analysis. Investigated terrains included sites in the suburbs of Athlone, Muizenburg, and Atlantis, all underlain by windblown deposits comprising fine and medium sand with varying fines contents. Elastic settlement analysis was also undertaken for the Cape Flats sands, using a non-linear stepwise method based on small-strain stiffness estimates, which was obtained from the best Vs-N60 model and compared to settlement estimates using the general elastic solution with stiffness profiles determined using Stroud’s (1989) and Webb’s (1969) SPT N60-E transformation models. Stroud’s method considers strain level indirectly whereasWebb’smethod does not take account of the variation in elastic modulus with strain. The expression of Vs. in terms of N60 and Pa/σv’ derived from the Atlantis data set revealed the best fit with R2 = 0.83 and a standard error of 83.5m/s. Less accurate Vs-SPT N relations associated with the combined data set is presumably the result of inversion routines used in the analysis of the CSW results showcasing significant variation in relative density and stiffness with depth. The regression analyses revealed that the inclusion of a separate overburden term in the regression of Vs and N60, produces improved fits, as opposed to the stress corrected equations in which the R2 of the regression is notably lower. It is the correction of Vs and N60 to Vs1 and (N1)60 with empirical constants ‘n’ and ‘m’ prior to regression, that introduces bias with respect to overburden pressure. When comparing settlement prediction methods, both Stroud’s method (considering strain level indirectly) and the small strain stiffness method predict higher stiffnesses for medium dense and dense profiles than Webb’s method, which takes no account of strain level in the determination of soil stiffness. Webb’s method appears to be suitable for loose sands only. The Versak software appears to underestimate differences in settlement between square and strip footings of similar width. In conclusion, settlement analysis using small-strain stiffness data from the proposed Vs-N60 model for Cape Flats sands provides a way to take account of the non-linear stress-strain behaviour of the sands when calculating settlement.

Keywords: sands, settlement prediction, continuous surface wave test, small-strain stiffness, shear wave velocity, penetration resistance

Procedia PDF Downloads 173

Authors: C. M. Kalleshwaraswamy, G. S. Sathisha, A. S. Vidyashree, H. B. Pavithra

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Isoptera are an interesting group of social insects with different castes and division of labour. They are primarily wood-feeders, but also feed on a variety of other organic substrates, such as living trees, leaf litter, soil, lichens and animal faeces. The number of species and their biomass are especially large in tropics. In natural ecosystems, they perform a beneficial role in nutrient cycles by accelerating decomposition. The magnitude and dimension of ecological role played by termites is a function of their diversity, population density, and biomass. Termite assemblage composition has a strong response to habitat disturbance and may be indicative of quantitative changes in the decomposition process. Many previous studies in Western Ghat region of India suggest increased anthropogenic activities that adversely affect the soil macrofauna and diversity. Shivamogga district provides a good opportunity to study the effect of topography, cropping pattern, human disturbance on the termite fauna, thereby acquiring accurate baseline information for conservation decision making. The district has 3 distinct agro-ecological areas such as maidan area, semi-malnad and Western Ghat region. Thus, the district provides a unique opportunity to study the effect of varied climate and anthropogenic disturbance on the termite diversity. The standard protocol of belt transects method developed by Eggleton et al. (1997) was used for sampling termites. Sampling was done at monthly interval from September-2014 to August-2015 in Western Ghats, semi-malnad and maidan habitats. The transect was 100m long and 2m wide and divided into 20 contiguous sections, each 5 x 2m in each habitat. Within each section, all the probable microhabitats of termites were searched, which include dead logs, fallen tree, branch, sticks, leaf litter, vegetation etc.,. All the castes collected were labelled, preserved in 80% alcohol, counted and identified to species level. The number of encounters of a species in the transect was used as an indicator of relative abundance of species. The species diversity, species richness, density were compared in three different habitats such as Western Ghats, semi-malnad and maidan region. The study indicated differences in the species composition in the three different habitats. A total of 15 species were recorded which belonging to four sub family and five genera in three habitats. Eleven species viz., Odontotermes obesus, O. feae, O. anamallensis, O. bellahunisensis, O. adampurensis, O. boveni, Microcerotermes fletcheri, M. pakistanicus, Nasutitermes anamalaiensis, N. indicola, N. krishna were recorded in Western Ghat region. Similarly, 11 species viz., Odontotermes obesus, O. feae, O. anamallensis, O. bellahunisensis, O. hornii, O. bhagwathi, Microtermes obesi, Microcerotermes fletcheri, M. pakistanicus, Nasutitermes indicola and Pericapritermes sp. were recorded in semi-malnad habitat. However, only four species viz., O. obesus, O. feae, Microtemes obesi and Pericapritermes sp. species were recorded in maidan area. Shannon’s wiener diversity index (H) showed that Western Ghats had more species dominance (1.56) followed by semi- malnad (1.36) and lowest in maidan (0.89) habitats. Highest value of simpson’s index (D) was observed in Western Ghats habitat (0.70) with more diverse species followed by semi-malnad (0.58) and lowest in maidan (0.53). Similarly, evenness was highest (0.65) in Western Ghats followed by maidan (0.64) and least in semi-malnad habitat (0.54). Menhinick’s index (Dmn) value was ranging from 0.03 to 0.06 in different habitats in the study area. Highest index was observed in Western Ghats (0.06) followed by semi-malnad (0.05) and lowest in maidan (0.03). The study conclusively demonstrated that Western Ghat had highest species diversity compared to semi-malnad and maidan habitat indicating these two habitats are continuously subjected to anthropogenic disturbances. Efforts are needed to conserve the uncommon species which otherwise may become extinct due to human activities.

Keywords: anthropogenic disturbance, isoptera, termite species diversity, Western ghats

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Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

Procedia PDF Downloads 142

Authors: C.Ardil

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Open Science translates the understanding and application of various theories and practices in open science philosophy, systems, paradigms and epistemology. Open Science originates with the premise that universal scientific knowledge is a product of a collective scholarly and social collaboration involving all stakeholders and knowledge belongs to the global society. Scientific outputs generated by public research are a public good that should be available to all at no cost and without barriers or restrictions. Open Science has the potential to increase the quality, impact and benefits of science and to accelerate advancement of knowledge by making it more reliable, more efficient and accurate, better understandable by society and responsive to societal challenges, and has the potential to enable growth and innovation through reuse of scientific results by all stakeholders at all levels of society, and ultimately contribute to growth and competitiveness of global society. Open Science is a global movement to improve accessibility to and reusability of research practices and outputs. In its broadest definition, it encompasses open access to publications, open research data and methods, open source, open educational resources, open evaluation, and citizen science. The implementation of open science provides an excellent opportunity to renegotiate the social roles and responsibilities of publicly funded research and to rethink the science system as a whole. Open Science is the practice of science in such a way that others can collaborate and contribute, where research data, lab notes and other research processes are freely available, under terms that enable reuse, redistribution and reproduction of the research and its underlying data and methods. Open Science represents a novel systematic approach to the scientific process, shifting from the standard practices of publishing research results in scientific publications towards sharing and using all available knowledge at an earlier stage in the research process, based on cooperative work and diffusing scholarly knowledge with no barriers and restrictions. Open Science refers to efforts to make the primary outputs of publicly funded research results (publications and the research data) publicly accessible in digital format with no limitations. Open Science is about extending the principles of openness to the whole research cycle, fostering, sharing and collaboration as early as possible, thus entailing a systemic change to the way science and research is done. Open Science is the ongoing transition in how open research is carried out, disseminated, deployed, and transformed to make scholarly research more open, global, collaborative, creative and closer to society. Open Science involves various movements aiming to remove the barriers for sharing any kind of output, resources, methods or tools, at any stage of the research process. Open Science embraces open access to publications, research data, source software, collaboration, peer review, notebooks, educational resources, monographs, citizen science, or research crowdfunding. The recognition and adoption of open science practices, including open science policies that increase open access to scientific literature and encourage data and code sharing, is increasing in the open science philosophy. Revolutionary open science policies are motivated by ethical, moral or utilitarian arguments, such as the right to access digital research literature for open source research or science data accumulation, research indicators, transparency in the field of academic practice, and reproducibility. Open science philosophy is adopted primarily to demonstrate the benefits of open science practices. Researchers use open science applications for their own advantage in order to get more offers, increase citations, attract media attention, potential collaborators, career opportunities, donations and funding opportunities. In open science philosophy, open data findings are evidence that open science practices provide significant benefits to researchers in scientific research creation, collaboration, communication, and evaluation according to more traditional closed science practices. Open science considers concerns such as the rigor of peer review, common research facts such as financing and career development, and the sacrifice of author rights. Therefore, researchers are recommended to implement open science research within the framework of existing academic evaluation and incentives. As a result, open science research issues are addressed in the areas of publishing, financing, collaboration, resource management and sharing, career development, discussion of open science questions and conclusions.

Keywords: Open Science, Open Science Philosophy, Open Science Research, Open Science Data

Procedia PDF Downloads 128

Authors: Reza Nematirad, Anil Pahwa, Balasubramaniam Natarajan

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Time series data consist of successive data points collected over a period of time. Accurate prediction of future values is essential for informed decision-making in several real-world applications, including electricity load demand forecasting, lifetime estimation of industrial machinery, traffic planning, weather prediction, and the stock market. Due to their critical relevance and wide application, there has been considerable interest in time series forecasting in recent years. However, the proliferation of sensors and IoT devices, real-time monitoring systems, and high-frequency trading data introduce significant intricate temporal variations, rapid changes, noise, and non-linearities, making time series forecasting more challenging. Classical methods such as Autoregressive integrated moving average (ARIMA) and Exponential Smoothing aim to extract pre-defined temporal variations, such as trends and seasonality. While these methods are effective for capturing well-defined seasonal patterns and trends, they often struggle with more complex, non-linear patterns present in real-world time series data. In recent years, deep learning has made significant contributions to time series forecasting. Recurrent Neural Networks (RNNs) and their variants, such as Long short-term memory (LSTMs) and Gated Recurrent Units (GRUs), have been widely adopted for modeling sequential data. However, they often suffer from the locality, making it difficult to capture local trends and rapid fluctuations. Convolutional Neural Networks (CNNs), particularly Temporal Convolutional Networks (TCNs), leverage convolutional layers to capture temporal dependencies by applying convolutional filters along the temporal dimension. Despite their advantages, TCNs struggle with capturing relationships between distant time points due to the locality of one-dimensional convolution kernels. Transformers have revolutionized time series forecasting with their powerful attention mechanisms, effectively capturing long-term dependencies and relationships between distant time points. However, the attention mechanism may struggle to discern dependencies directly from scattered time points due to intricate temporal patterns. Lastly, Multi-Layer Perceptrons (MLPs) have also been employed, with models like N-BEATS and LightTS demonstrating success. Despite this, MLPs often face high volatility and computational complexity challenges in long-horizon forecasting. To address intricate temporal variations in time series data, this study introduces Times2D, a novel framework that parallelly integrates 2D spectrogram and derivative heatmap techniques. The spectrogram focuses on the frequency domain, capturing periodicity, while the derivative patterns emphasize the time domain, highlighting sharp fluctuations and turning points. This 2D transformation enables the utilization of powerful computer vision techniques to capture various intricate temporal variations. To evaluate the performance of Times2D, extensive experiments were conducted on standard time series datasets and compared with various state-of-the-art algorithms, including DLinear (2023), TimesNet (2023), Non-stationary Transformer (2022), PatchTST (2023), N-HiTS (2023), Crossformer (2023), MICN (2023), LightTS (2022), FEDformer (2022), FiLM (2022), SCINet (2022a), Autoformer (2021), and Informer (2021) under the same modeling conditions. The initial results demonstrated that Times2D achieves consistent state-of-the-art performance in both short-term and long-term forecasting tasks. Furthermore, the generality of the Times2D framework allows it to be applied to various tasks such as time series imputation, clustering, classification, and anomaly detection, offering potential benefits in any domain that involves sequential data analysis.

Keywords: derivative patterns, spectrogram, time series forecasting, times2D, 2D representation

Procedia PDF Downloads 38

Authors: Ali Kadir, S. R. Mishra, M. Shamshuddin, O. Anwar Beg

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Motivation- Rotating disk bio-reactors are fundamental to numerous medical/biochemical engineering processes including oxygen transfer, chromatography, purification and swirl-assisted pumping. The modern upsurge in biologically-enhanced engineering devices has embraced new phenomena including bioconvection of micro-organisms (photo-tactic, oxy-tactic, gyrotactic etc). The proven thermal performance superiority of nanofluids i.e. base fluids doped with engineered nanoparticles has also stimulated immense implementation in biomedical designs. Motivated by these emerging applications, we present a numerical thermofluid dynamic simulation of the transport phenomena in bioconvection nanofluid rotating disk bioreactor flow. Methodology- We study analytically and computationally the time-dependent three-dimensional viscous gyrotactic bioconvection in swirling nanofluid flow from a rotating disk configuration. The disk is also deformable i.e. able to extend (stretch) in the radial direction. Stefan blowing is included. The Buongiorno dilute nanofluid model is adopted wherein Brownian motion and thermophoresis are the dominant nanoscale effects. The primitive conservation equations for mass, radial, tangential and axial momentum, heat (energy), nanoparticle concentration and micro-organism density function are formulated in a cylindrical polar coordinate system with appropriate wall and free stream boundary conditions. A mass convective condition is also incorporated at the disk surface. Forced convection is considered i.e. buoyancy forces are neglected. This highly nonlinear, strongly coupled system of unsteady partial differential equations is normalized with the classical Von Karman and other transformations to render the boundary value problem (BVP) into an ordinary differential system which is solved with the efficient Adomian decomposition method (ADM). Validation with earlier Runge-Kutta shooting computations in the literature is also conducted. Extensive computations are presented (with the aid of MATLAB symbolic software) for radial and circumferential velocity components, temperature, nanoparticle concentration, micro-organism density number and gradients of these functions at the disk surface (radial local skin friction, local circumferential skin friction, Local Nusselt number, Local Sherwood number, motile microorganism mass transfer rate). Main Findings- Increasing radial stretching parameter decreases radial velocity and radial skin friction, reduces azimuthal velocity and skin friction, decreases local Nusselt number and motile micro-organism mass wall flux whereas it increases nano-particle local Sherwood number. Disk deceleration accelerates the radial flow, damps the azimuthal flow, decreases temperatures and thermal boundary layer thickness, depletes the nano-particle concentration magnitudes (and associated nano-particle species boundary layer thickness) and furthermore decreases the micro-organism density number and gyrotactic micro-organism species boundary layer thickness. Increasing Stefan blowing accelerates the radial flow and azimuthal (circumferential flow), elevates temperatures of the nanofluid, boosts nano-particle concentration (volume fraction) and gyrotactic micro-organism density number magnitudes whereas suction generates the reverse effects. Increasing suction effect reduces radial skin friction and azimuthal skin friction, local Nusselt number, and motile micro-organism wall mass flux whereas it enhances the nano-particle species local Sherwood number. Conclusions - Important transport characteristics are identified of relevance to real bioreactor nanotechnological systems not discussed in previous works. ADM is shown to achieve very rapid convergence and highly accurate solutions and shows excellent promise in simulating swirling multi-physical nano-bioconvection fluid dynamics problems. Furthermore, it provides an excellent complement to more general commercial computational fluid dynamics simulations.

Keywords: bio-nanofluids, rotating disk bioreactors, Von Karman swirling flow, numerical solutions

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Authors: Lizinis Cassendra Frederick Dony, Jirom Jeremy Frederick Dony, Cyril Supain Christopher

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The purpose of this paper is to improve the devotion to leadership through HR practices implementation at the PETRONAS stations. This emphasize the importance of personal grooming and Customer Care hospitality training for their front line working individuals and teams’ at PETRONAS stations in Sabah. Based on Thomas Edison, International Leadership Journal, theory, research, education and development practice and application to all organizational phenomena may affect or be affected by leadership. FINDINGS – PETRONAS in short called Petroliam Nasional Berhad is a Malaysian oil and gas company that was founded on August 17, 1974. Wholly owned by the Government of Malaysia, the corporation is vested with the entire oil and gas resources in Malaysia and is entrusted with the responsibility of developing and adding value to these resources. Fortune ranks PETRONAS as the 68th largest company in the world in 2012. It also ranks PETRONAS as the 12th most profitable company in the world and the most profitable in Asia. As of the end of March 2005, the PETRONAS Group comprised 103 wholly owned subsidiaries, 19 partly owned outfits and 57 associated companies. The group is engaged in a wide spectrum of petroleum activities, including upstream exploration and production of oil and gas to downstream oil refining, marketing and distribution of petroleum products, trading, gas processing and liquefaction, gas transmission pipeline network operations, marketing of liquefied natural gas; petrochemical manufacturing and marketing; shipping; automotive engineering and property investment. PETRONAS has growing their marketing channel in a competitive market. They have combined their resources to pursue common goals. PETRONAS provides opportunity to carry out Industrial Training Job Placement to the University students in Malaysia for 6-8 months. The effects of the Industrial Training have exposed them to the real working environment experience acting representing on behalf of General Manager for almost one year. Thus, the management education and reward incentives schemes have aspire the working teams transformed to gain their good leadership. Furthermore, knowledge and experiences are very important in the human capital development transformation. SPSS extends the accurate analysis PETRONAS achievement through 280 questionnaires and 81 questionnaires through excel calculation distributed to interview face to face with the customers, PETRONAS dealers and front desk staffs stations in the 17 stations in Kota Kinabalu, Sabah. Hence, this research study will improve its service quality innovation and business sustainability performance optimization. ORIGINALITY / VALUE – The impact of Transformational Leadership practices have influenced the working team’s behaviour as a Brand Ambassadors of PETRONAS. Finally, the findings correlation indicated that PETRONAS stations needs more HR resources practices to deploy more customer care retention resources in mitigating the business challenges in oil and gas industry. Therefore, as the business established at stiff competition globally (Cooper, 2006; Marques and Simon, 2006), it is crucial for the team management should be capable to minimize noises risk, financial risk and mitigating any other risks as a whole at the optimum level. CONCLUSION- As to conclude this research found that both transformational and transactional contingent reward leadership4 were positively correlated with ratings of platoon potency and ratings of leadership for the platoon leader and sergeant were moderately inter correlated. Due to this identification, we recommended that PETRONAS management should offers quality team management in PETRONAS stations in a broader variety of leadership training specialization in the operation efficiency at the front desk Customer Care hospitality. By having the reliability and validity of job experiences, it leverages diversity teamwork and cross collaboration. Other than leveraging factor, PETRONAS also will strengthen the interpersonal front liners effectiveness and enhance quality of interaction through effective communication. Finally, through numerous CSR correlation studies regression PETRONAS performance on Corporate Social Performance and several control variables.1 CSR model activities can be mis-specified if it is not controllable under R & D which evident in various feedbacks collected from the local communities and younger generation is inclined to higher financial expectation from PETRONAS. But, however, it created a huge impact on the nation building as part of its social adaptability overreaching their business stakeholders’ satisfaction in Sabah.

Keywords: human resources practices implementation (hrpi), source of competitive advantage in people’s development (socaipd), corporate social responsibility (csr), service quality at front desk stations (sqafd), impacts of petronas leadership (iopl)

Procedia PDF Downloads 346