Search results for: multiprocessor architecture

Authors: Gabrielle Robertson, Matthew Downs, Joseph Dagher

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Iron deposition in the brain has been linked with a host of neurological disorders such as Alzheimer’s, Parkinson’s, and Multiple Sclerosis. While some treatment options exist, there are no objective measurement tools that allow for the monitoring of iron levels in the brain in vivo. An emerging Magnetic Resonance Imaging (MRI) method has been recently proposed to deduce iron concentration through quantitative measurement of magnetic susceptibility. This is a multi-step process that involves repeated modeling of physical processes via approximate numerical solutions. For example, the last two steps of this Quantitative Susceptibility Mapping (QSM) method involve I) mapping magnetic field into magnetic susceptibility and II) mapping magnetic susceptibility into iron concentration. Process I involves solving an ill-posed inverse problem by using regularization via injection of prior belief. The end result from Process II highly depends on the model used to describe the molecular content of each voxel (type of iron, water fraction, etc.) Due to these factors, the accuracy and repeatability of QSM have been an active area of research in the MRI and medical imaging community. This work aims to estimate iron concentration in the brain via a single step. A synthetic numerical model of the human head was created by automatically and manually segmenting the human head on a high-resolution grid (640x640x640, 0.4mm³) yielding detailed structures such as microvasculature and subcortical regions as well as bone, soft tissue, Cerebral Spinal Fluid, sinuses, arteries, and eyes. Each segmented region was then assigned tissue properties such as relaxation rates, proton density, electromagnetic tissue properties and iron concentration. These tissue property values were randomly selected from a Probability Distribution Function derived from a thorough literature review. In addition to having unique tissue property values, different synthetic head realizations also possess unique structural geometry created by morphing the boundary regions of different areas within normal physical constraints. This model of the human brain is then used to create synthetic MRI measurements. This is repeated thousands of times, for different head shapes, volume, tissue properties and noise realizations. Collectively, this constitutes a training-set that is similar to in vivo data, but larger than datasets available from clinical measurements. This 3D convolutional U-Net neural network architecture was used to train data-driven Deep Learning models to solve for iron concentrations from raw MRI measurements. The performance was then tested on both synthetic data not used in training as well as real in vivo data. Results showed that the model trained on synthetic MRI measurements is able to directly learn iron concentrations in areas of interest more effectively than other existing QSM reconstruction methods. For comparison, models trained on random geometric shapes (as proposed in the Deep QSM method) are less effective than models trained on realistic synthetic head models. Such an accurate method for the quantitative measurement of iron deposits in the brain would be of important value in clinical studies aiming to understand the role of iron in neurological disease.

Keywords: magnetic resonance imaging, MRI, iron deposition, machine learning, quantitative susceptibility mapping

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Authors: Samia A. El-Fiky, Fouad A. Abou-Zaid, Ibrahim M. Farag, Naira M. El-Fiky

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Clomipramine hydrochloride is one of the most used tricyclic antidepressant drug in Egypt. This drug contains in its chemical structure on two benzene rings. Benzene is considered to be toxic and clastogenic agent. So, the present study was designed to assess the genotoxic effect of Clomipramine hydrochloride on somatic and germ cells in mice. Three dose levels 0.195 (Low), 0.26 (Medium), and 0.65 (High) mg/kg.b.wt. were used. Seven groups of male mice were utilized in this work. The first group was employed as a control. In the remaining six groups, each of the above doses was orally administrated for two groups, one of them was treated for 5 days and the other group was given the same dose for 30 days. At the end of experiments, the animals were sacrificed for cytogenetic and sperm examination as well as histopathological investigations by using hematoxylin and eosin stains (H and E stains) and electron microscope. Concerning the sperm studies, these studies were confined to 5 days treatment with different dose levels. Moreover, the ultrastructural investigation by electron microscope was restricted to 30 days treatment with drug doses. The results of the dose dependent effect of Clomipramine showed that the treatment with three different doses induced increases of frequencies of chromosome aberrations in bone marrow and spermatocyte cells as compared to control. In addition, mitotic and meiotic activities of somatic and germ cells were declined. The treatments with medium or high doses were more effective for inducing significant increases of chromosome aberrations and significant decreases of cell divisions than treatment with low dose. The effect of high dose was more pronounced for causing such genetic deleterious in respect to effect of medium dose. Moreover, the results of the time dependent effect of Clomipramine observed that the treatment with different dose levels for 30 days led to significant increases of genetic aberrations than treatment for 5 days. Sperm examinations revealed that the treatment with Clomipramine at different dose levels caused significant increase of sperm shape abnormalities and significant decrease in sperm count as compared to control. The adverse effects on sperm shape and count were more obviousness by using the treatments with medium or high doses than those found in treatment with low dose. The group of mice treated with high dose had the highest rate of sperm shape abnormalities and the lowest proportion of sperm count as compared to mice received medium dose. In histopathological investigation, hematoxylin and eosin stains showed that, the using of low dose of Clomipramine for 5 or 30 days caused a little pathological changes in liver tissue. However, using medium and high doses for 5 or 30 days induced severe damages than that observed in mice treated with low dose. The treatment with high dose for 30 days gave the worst results of pathological changes in hepatic cells. Moreover, ultrastructure examination revealed, the mice treated with low dose of Clomipramine had little differences in liver histological architecture as compared to control group. These differences were confined to cytoplasmic inclusions. Whereas, prominent pathological changes in nuclei as well as dilated of rough Endoplasmic Reticulum (rER) were observed in mice treated with medium or high doses of Clomipramine drug. In conclusion, the present study adds evidence that treatments with medium or high doses of Clomipramine have genotoxic effects on somatic and germ cells of mice, as unwanted side effects. However, the using of low dose (especially for short time, 5 days) can be utilized as a therapeutic dose, where it caused relatively similar proportions of genetic, sperm, and histopathological changes as those found in normal control.

Keywords: chromosome aberrations, clomipramine, mice, histopathology, sperm abnormalities

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Authors: Thanthrige Thiunuwan Priyathilaka, Don Anushka Sandaruwan Elvitigala, Bong-Soo Lim, Hyung-Bok Jeong, Jehee Lee

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Toll like receptors (TLRs) are a phylogeneticaly conserved family of pattern recognition receptors, which participates in the host immune responses against various pathogens and pathogen derived mitogen. TLR21, a non-mammalian type, is almost restricted to the fish species even though those can be identified rarely in avians and amphibians. Herein, this study was carried out to identify and characterize TLR21 from rock bream (Oplegnathus fasciatus) designated as RbTLR21, at transcriptional and genomic level. In this study, the full length cDNA and genomic sequence of RbTLR21 was identified using previously constructed cDNA sequence database and BAC library, respectively. Identified RbTLR21 sequence was characterized using several bioinformatics tools. The quantitative real time PCR (qPCR) experiment was conducted to determine tissue specific expressional distribution of RbTLR21. Further, transcriptional modulation of RbTLR21 upon the stimulation with Streptococcus iniae (S. iniae), rock bream iridovirus (RBIV) and Edwardsiella tarda (E. tarda) was analyzed in spleen tissues. The complete coding sequence of RbTLR21 was 2919 bp in length which can encode a protein consisting of 973 amino acid residues with molecular mass of 112 kDa and theoretical isoelectric point of 8.6. The anticipated protein sequence resembled a typical TLR domain architecture including C-terminal ectodomain with 16 leucine rich repeats, a transmembrane domain, cytoplasmic TIR domain and signal peptide with 23 amino acid residues. Moreover, protein folding pattern prediction of RbTLR21 exhibited well-structured and folded ectodomain, transmembrane domain and cytoplasmc TIR domain. According to the pair wise sequence analysis data, RbTLR21 showed closest homology with orange-spotted grouper (Epinephelus coioides) TLR21with 76.9% amino acid identity. Furthermore, our phylogenetic analysis revealed that RbTLR21 shows a close evolutionary relationship with its ortholog from Danio rerio. Genomic structure of RbTLR21 consisted of single exon similar to its ortholog of zebra fish. Sevaral putative transcription factor binding sites were also identified in 5ʹ flanking region of RbTLR21. The RBTLR 21 was ubiquitously expressed in all the tissues we tested. Relatively, high expression levels were found in spleen, liver and blood tissues. Upon induction with rock bream iridovirus, RbTLR21 expression was upregulated at the early phase of post induction period even though RbTLR21 expression level was fluctuated at the latter phase of post induction period. Post Edwardsiella tarda injection, RbTLR transcripts were upregulated throughout the experiment. Similarly, Streptococcus iniae induction exhibited significant upregulations of RbTLR21 mRNA expression in the spleen tissues. Collectively, our findings suggest that RbTLR21 is indeed a homolog of TLR21 family members and RbTLR21 may be involved in host immune responses against bacterial and DNA viral infections.

Keywords: rock bream, toll like receptor 21 (TLR21), pattern recognition receptor, genomic characterization

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Authors: Mohammadreza Kashizadeh, Mohammadamin Hashemi

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This paper explores a kinetic building facade designed for optimal energy capture and architectural expression. The system integrates photovoltaic panels with soft robotic actuators for precise solar tracking, resulting in enhanced electricity generation compared to static facades. Driven by the growing interest in dynamic building envelopes, the exploration of facade systems are necessitated. Increased energy generation and regulation of energy flow within buildings are potential benefits offered by integrating photovoltaic (PV) panels as kinetic elements. However, incorporating these technologies into mainstream architecture presents challenges due to the complexity of coordinating multiple systems. To address this, the design leverages soft robotic actuators, known for their compliance, resilience, and ease of integration. Additionally, the project investigates the potential for employing Large Language Models (LLMs) to streamline the design process. The research methodology involved design development, material selection, component fabrication, and system assembly. Grasshopper (GH) was employed within the digital design environment for parametric modeling and scripting logic, and an LLM was experimented with to generate Python code for the creation of a random surface with user-defined parameters. Various techniques, including casting, Three-dimensional 3D printing, and laser cutting, were utilized to fabricate physical components. A modular assembly approach was adopted to facilitate installation and maintenance. A case study focusing on the application of this facade system to an existing library building at Polytechnic University of Milan is presented. The system is divided into sub-frames to optimize solar exposure while maintaining a visually appealing aesthetic. Preliminary structural analyses were conducted using Karamba3D to assess deflection behavior and axial loads within the cable net structure. Additionally, Finite Element (FE) simulations were performed in Abaqus to evaluate the mechanical response of the soft robotic actuators under pneumatic pressure. To validate the design, a physical prototype was created using a mold adapted for a 3D printer's limitations. Casting Silicone Rubber Sil 15 was used for its flexibility and durability. The 3D-printed mold components were assembled, filled with the silicone mixture, and cured. After demolding, nodes and cables were 3D-printed and connected to form the structure, demonstrating the feasibility of the design. This work demonstrates the potential of soft robotics and Artificial Intelligence (AI) for advancements in sustainable building design and construction. The project successfully integrates these technologies to create a dynamic facade system that optimizes energy generation and architectural expression. While limitations exist, this approach paves the way for future advancements in energy-efficient facade design. Continued research efforts will focus on cost reduction, improved system performance, and broader applicability.

Keywords: artificial intelligence, energy efficiency, kinetic photovoltaics, pneumatic control, soft robotics, sustainable building

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Authors: Ivone R. Oliveira, Isabela S. Gonçalves, Tiago M. B. Campos, Leandro J. Raniero, Luana M. R. Vasconcellos, João H. Lopes

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Originally, the principles of guided tissue/bone regeneration (GTR/GBR) were followed to restore the architecture and functionality of the periodontal system. In essence, a biocompatible polymer-based occlusive membrane is used as a barrier to prevent migration of epithelial and connective tissue to the regenerating site. In this way, progenitor cells located in the remaining periodontal ligament can recolonize the root area and differentiate into new periodontal tissues, alveolar bone, and new connective attachment. The use of synthetic or collagen-derived membranes with or without calcium phosphate-based bone graft materials has been the treatment used. Ideally, these membranes need to exhibit sufficient initial mechanical strength to allow handling and implantation, withstand the various mechanical stresses suffered during surgery while maintaining their integrity, and support the process of bone tissue regeneration and repair by resisting cellular traction forces and wound contraction forces during tissue healing in vivo. Although different RTG/ROG products are available on the market, they have serious deficiencies in terms of mechanical strength. Aiming to improve the mechanical strength and osteogenic properties of the membrane, this work evaluated the production of membranes that integrate the biocompatibility of the natural polymer (silk fibroin - FS) and the synthetic polymer poly(vinyl pyrrolidone - PVP) with graphene nanoplates (NPG) and gold nanoparticles (AuNPs), using the electrospinning equipment (AeroSpinner L1.0 from Areka) which allows the execution of high voltage spinning and/or solution blowing and with a high production rate, enabling development on an industrial scale. Silk fibroin uniquely solved many of the problems presented by collagen and was used in this work because it has unique combined merits, such as programmable biodegradability, biocompatibility and sustainable large-scale production. Graphene has attracted considerable attention in recent years as a potential biomaterial for mechanical reinforcement because of its unique physicochemical properties and was added to improve the mechanical properties of the membranes associated or not with the presence of AuNPs, which have shown great potential in regulating osteoblast activity. The preparation of FS from silkworm cocoons involved cleaning, degumming, dissolution in lithium bromide, dialysis, lyophilization and dissolution in hexafluoroisopropanol (HFIP) to prepare the solution for electrospinning, and crosslinking tests were performed in methanol. The NPGs were characterized and underwent treatment in nitric acid for functionalization to improve the adhesion of the nanoplates to the PVP fibers. PVP-NPG membranes were produced with 0.5, 1.0 and 1.5 wt% functionalized or not and evaluated by SEM/FEG, FTIR, mechanical strength and cell culture assays. Functionalized GNP particles showed stronger binding, remaining adhered to the fibers. Increasing the graphene content resulted in higher mechanical strength of the membrane and greater biocompatibility. The production of FS-PVP-NPG-AuNPs hybrid membranes was performed by electrospinning in separate syringes and simultaneously the FS solution and the solution containing PVP-NPG 1.5 wt% in the presence or absence of AuNPs. After cross-linking, they were characterized by SEM/FEG, FTIR and behavior in cell culture. The presence of NPG-AuNPs increased the viability and the presence of mineralization nodules.

Keywords: barrier membranes, silk fibroin, nanoparticles, tissue regeneration.

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Authors: Michelle Eichinger, Upali Nanda

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Behavior choices during college years can establish the pattern of lifelong healthy living. Nearly 1/3rd of American college students are either overweight (25 < BMI < 30) or obese (BMI > 30). In addition, overweight/obesity contributes to depression, which is a rising epidemic among college students, affecting academic performance and college drop-out rates. Overweight and obesity result in an imbalance of energy consumption (diet) and energy expenditure (physical activity). Overweight/obesity is a significant contributor to heart disease, diabetes, stroke, physical disabilities and some cancers, which are the leading causes of death and disease in the US. There has been a significant increase in obesity and obesity-related disorders such as type 2 diabetes, hypertension, and dyslipidemia among people in their teens and 20s. Historically, the evidence-based interventions for obesity prevention focused on changing the health behavior at the individual level and aimed at increasing awareness and educating people about nutrition and physical activity. However, it became evident that the environmental context of where people live, work and learn was interdependent to healthy behavior change. As a result, a comprehensive approach was required to include altering the social and built environment to support healthy living. College campus provides opportunities to support lifestyle behavior and form a health-promoting culture based on some key point of decisions such as stairs/ elevator, walk/ bike/ car, high-caloric and fast foods/balanced and nutrient-rich foods etc. At each point of decision, design, can help/hinder the healthier choice. For example, stair well design and motivational signage support physical activity; grocery store/market proximity influence healthy eating etc. There is a need to collate the vast information that is in planning and public health domains on a range of successful point of decision prompts, and translate it into architectural guidelines that help define the edge condition for critical point of decision prompts. This research study aims to address healthy behaviors through the built environment with the questions, how can we make the healthy choice an easy choice through the design of critical point of decision prompts? Our hypothesis is that well-designed point of decision prompts in the built environment of college campuses can promote healthier choices by students, which can directly impact mental and physical health related to obesity. This presentation will introduce a combined health and architectural framework aimed to influence healthy behaviors through design applied for college campuses. The premise behind developing our concept, point-of-decision design (PODD), is healthy decision-making can be built into, or afforded by our physical environments. Using effective design intervention strategies at these 'points-of-decision' on college campuses to make the healthy decision the default decision can be instrumental in positively impacting health at the population level. With our model, we aim to advance health research by utilizing point-of-decision design to impact student health via core sectors of influences within college settings, such as campus facilities and transportation. We will demonstrate how these domains influence patterns/trends in healthy eating and active living behaviors among students. how these domains influence patterns/trends in healthy eating and active living behaviors among students.

Keywords: architecture and health promotion, college campus, design strategies, health in built environment

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Authors: Stefan K. Behfar

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The rapid growth of the Ethereum network has brought forth the urgent need for scalable analysis methods to handle the increasing volume of blockchain data. In this research, we propose efficient methodologies for making Ethereum network analysis scalable. Our approach leverages a combination of graph-based data representation, probabilistic sampling, and parallel processing techniques to achieve unprecedented scalability while preserving critical network insights. Data Representation: We develop a graph-based data representation that captures the underlying structure of the Ethereum network. Each block transaction is represented as a node in the graph, while the edges signify temporal relationships. This representation ensures efficient querying and traversal of the blockchain data. Probabilistic Sampling: To cope with the vastness of the Ethereum blockchain, we introduce a probabilistic sampling technique. This method strategically selects a representative subset of transactions and blocks, allowing for concise yet statistically significant analysis. The sampling approach maintains the integrity of the network properties while significantly reducing the computational burden. Graph Convolutional Networks (GCNs): We incorporate GCNs to process the graph-based data representation efficiently. The GCN architecture enables the extraction of complex spatial and temporal patterns from the sampled data. This combination of graph representation and GCNs facilitates parallel processing and scalable analysis. Distributed Computing: To further enhance scalability, we adopt distributed computing frameworks such as Apache Hadoop and Apache Spark. By distributing computation across multiple nodes, we achieve a significant reduction in processing time and enhanced memory utilization. Our methodology harnesses the power of parallelism, making it well-suited for large-scale Ethereum network analysis. Evaluation and Results: We extensively evaluate our methodology on real-world Ethereum datasets covering diverse time periods and transaction volumes. The results demonstrate its superior scalability, outperforming traditional analysis methods. Our approach successfully handles the ever-growing Ethereum data, empowering researchers and developers with actionable insights from the blockchain. Case Studies: We apply our methodology to real-world Ethereum use cases, including detecting transaction patterns, analyzing smart contract interactions, and predicting network congestion. The results showcase the accuracy and efficiency of our approach, emphasizing its practical applicability in real-world scenarios. Security and Robustness: To ensure the reliability of our methodology, we conduct thorough security and robustness evaluations. Our approach demonstrates high resilience against adversarial attacks and perturbations, reaffirming its suitability for security-critical blockchain applications. Conclusion: By integrating graph-based data representation, GCNs, probabilistic sampling, and distributed computing, we achieve network scalability without compromising analytical precision. This approach addresses the pressing challenges posed by the expanding Ethereum network, opening new avenues for research and enabling real-time insights into decentralized ecosystems. Our work contributes to the development of scalable blockchain analytics, laying the foundation for sustainable growth and advancement in the domain of blockchain research and application.

Keywords: Ethereum, scalable network, GCN, probabilistic sampling, distributed computing

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Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

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Authors: Simon Komesker, Achim Wagner, Martin Ruskowski

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In times of volatile markets with fluctuating demand and the uncertainty of global supply chains, flexible production systems are the key to an efficient implementation of a desired production program. In this publication, the authors present a holistic information concept taking into account various influencing factors for operating towards the global optimum. Therefore, a strategy for the implementation of multi-level planning for a flexible, reconfigurable production system with an alternative production concept in the automotive industry is developed. The main contribution of this work is a system structure mixing central and decentral planning and control evaluated in a simulation framework. The information system structure in current production systems in the automotive industry is rigidly hierarchically organized in monolithic systems. The production program is created rule-based with the premise of achieving uniform cycle time. This program then provides the information basis for execution in subsystems at the station and process execution level. In today's era of mixed-(car-)model factories, complex conditions and conflicts arise in achieving logistics, quality, and production goals. There is no provision for feedback loops of results from the process execution level (resources) and process supporting (quality and logistics) systems and reconsideration in the planning systems. To enable a robust production flow, the complexity of production system control is artificially reduced by the line structure and results, for example in material-intensive processes (buffers and safety stocks - two container principle also for different variants). The limited degrees of freedom of line production have produced the principle of progress figure control, which results in one-time sequencing, sequential order release, and relatively inflexible capacity control. As a result, modularly structured production systems such as modular production according to known approaches with more degrees of freedom are currently difficult to represent in terms of information technology. The remedy is an information concept that supports cross-system and cross-level information processing for centralized and decentralized decision-making. Through an architecture of hierarchically organized but decoupled subsystems, the paradigm of hybrid control is used, and a holonic manufacturing system is offered, which enables flexible information provisioning and processing support. In this way, the influences from quality, logistics, and production processes can be linked holistically with the advantages of mixed centralized and decentralized planning and control. Modular production systems also require modularly networked information systems with semi-autonomous optimization for a robust production flow. Dynamic prioritization of different key figures between subsystems should lead the production system to an overall optimum. The tasks and goals of quality, logistics, process, resource, and product areas in a cyber-physical production system are designed as an interconnected multi-agent-system. The result is an alternative system structure that executes centralized process planning and decentralized processing. An agent-based manufacturing control is used to enable different flexibility and reconfigurability states and manufacturing strategies in order to find optimal partial solutions of subsystems, that lead to a near global optimum for hybrid planning. This allows a robust near to plan execution with integrated quality control and intralogistics.

Keywords: holonic manufacturing system, modular production system, planning, and control, system structure

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Authors: Prem K. Mony, Bharadwaj Amrutur, Prashanth Thankachan, Swarnarekha Bhat, Suman Rao, Maryann Washington, Annamma Thomas, N. Sheela, Hiteshwar Rao, Sumi Antony

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The disease burden among mothers and newborns is caused mostly by a handful of avoidable conditions occurring around the time of childbirth and within the first month following delivery. Real-time monitoring of vital parameters of mothers and neonates offers a potential opportunity to impact access as well as the quality of care in vulnerable populations. We describe the design, development and testing of an innovative wearable device for remote biomonitoring (RBM) of body temperatures in mothers and neonates in a hospital in southern India. The architecture consists of: [1] a low-cost, wearable sensor tag; [2] a gateway device for ‘real-time’ communication link; [3] piggy-backing on a commercial GSM communication network; and [4] an algorithm-based data analytics system. Requirements for the device were: long battery-life upto 28 days (with sampling frequency 5/hr); robustness; IP 68 hermetic sealing; and human-centric design. We undertook pre-clinical laboratory testing followed by clinical trial phases I & IIa for evaluation of safety and efficacy in the following sequence: seven healthy adult volunteers; 18 healthy mothers; and three sets of babies – 3 healthy babies; 10 stable babies in the Neonatal Intensive Care Unit (NICU) and 1 baby with hypoxic ischaemic encephalopathy (HIE). The 3-coin thickness, pebble-design sensor weighing about 8 gms was secured onto the abdomen for the baby and over the upper arm for adults. In the laboratory setting, the response-time of the sensor device to attain thermal equilibrium with the surroundings was 4 minutes vis-a-vis 3 minutes observed with a precision-grade digital thermometer used as a reference standard. The accuracy was ±0.1°C of the reference standard within the temperature range of 25-40°C. The adult volunteers, aged 20 to 45 years, contributed a total of 345 hours of readings over a 7-day period and the postnatal mothers provided a total of 403 paired readings. The mean skin temperatures measured in the adults by the sensor were about 2°C lower than the axillary temperature readings (sensor =34.1 vs digital = 36.1); this difference was statistically significant (t-test=13.8; p<0.001). The healthy neonates provided a total of 39 paired readings; the mean difference in temperature was 0.13°C (sensor =36.9 vs digital = 36.7; p=0.2). The neonates in the NICU provided a total of 130 paired readings. Their mean skin temperature measured by the sensor was 0.6°C lower than that measured by the radiant warmer probe (sensor =35.9 vs warmer probe = 36.5; p < 0.001). The neonate with HIE provided a total of 25 paired readings with the mean sensor reading being not different from the radian warmer probe reading (sensor =33.5 vs warmer probe = 33.5; p=0.8). No major adverse events were noted in both the adults and neonates; four adult volunteers reported mild sweating under the device/arm band and one volunteer developed mild skin allergy. This proof-of-concept study shows that real-time monitoring of temperatures is technically feasible and that this innovation appears to be promising in terms of both safety and accuracy (with appropriate calibration) for improved maternal and neonatal health.

Keywords: public health, remote biomonitoring, temperature surveillance, wearable sensors, mothers and newborns

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Authors: Sarah M. Schoellhammer, Stephen Gibb

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Bureaucracy, in particular, its core element, a formal and stable hierarchy of authority, is proving less and less appropriate under the conditions of today’s knowledge economy. Centralization and formalization were consistently found to hinder innovation, undermining cross-functional collaboration, personal responsibility, and flexibility. With its focus on systematical planning, controlling and monitoring the development of new or improved solutions for customers, even innovation management as a discipline is to a significant extent based on a mechanistic understanding of organizations. The most important drivers of innovation, human creativity, and initiative, however, can be more hindered than supported by central elements of classic innovation management, such as predefined innovation strategies, rigid stage gate processes, and decisions made in management gate meetings. Heterarchy, as an alternative network form of organization, is essentially characterized by its dynamic influence structures, whereby the biggest influence is allocated by the collective to the persons perceived the most competent in a certain issue. Theoretical arguments that the non-hierarchical concept better supports innovation than bureaucracy have been supported by empirical research. These prior studies either focus on the structure and general functioning of non-hierarchical organizations or on their innovativeness, that means innovation as an outcome. Complementing classic innovation management approaches, this work aims to shed light on how innovations are initiated and realized in heterarchies in order to identify alternative solutions practiced under conditions of the post-bureaucratic organization. Through an initial individual case study, which is part of a multiple-case project, the innovation practices of an innovative and highly heterarchical medium-sized company in the German fire engineering industry are investigated. In a pragmatic mixed methods approach media resonance, company documents, and workspace architecture are analyzed, in addition to qualitative interviews with the CEO and employees of the case company, as well as a quantitative survey aiming to characterize the company along five scaled dimensions of a heterarchy spectrum. The analysis reveals some similarities and striking differences to approaches suggested by classic innovation management. The studied heterarchy has no predefined innovation strategy guiding new product and service development. Instead, strategic direction is provided by the CEO, described as visionary and creative. Procedures for innovation are hardly formalized, with new product ideas being evaluated on the basis of gut feeling and flexible, rather general criteria. Employees still being hesitant to take responsibility and make decisions, hierarchical influence is still prominent. Described as open-minded and collaborative, culture and leadership were found largely congruent with definitions of innovation culture. Overall, innovation efforts at the case company tend to be coordinated more through cultural than through formal organizational mechanisms. To better enable innovation in mainstream organizations, responsible practitioners are recommended not to limit changes to reducing the central elements of the bureaucratic organization, formalization, and centralization. The freedoms this entails need to be sustained through cultural coordination mechanisms, with personal initiative and responsibility by employees as well as common innovation-supportive norms and values. These allow to integrate diverse competencies, opinions, and activities and, thus, to guide innovation efforts.

Keywords: bureaucracy, heterarchy, innovation management, values

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Authors: Nazia Roushan, Maria Kipti

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The sensitive issue of a large-scale entry of Rohingya refugees to Bangladesh has arisen again since August of 2017. Incited by ethnic and religious conflict, the Rohingyas—an ethnic group concentrated in the north-west state of Rakhine in Myanmar—have been fleeing to what is now Bangladesh from as early as the late 1700s in four main exoduses. This long-standing persecution has recently escalated, and accommodating the recent wave of exodus has been especially challenging due to the sheer volume of a million refugees concentrated in refugee camps in two small administrative units (upazilas) in the south-east of the country: the host area. This drastic change in the host area’s social fabric is putting a lot of strain on the country’s economic, demographic and environmental stability, and security. Although Bangladesh’s long-term experience with disaster management has enabled it to respond rapidly to the crisis, the government is failing to cope with this enormous problem and has taken insufficient steps towards improving the living conditions to inhibit the inflow of more refugees. On top of that, the absence of a comprehensive national refugee policy, and the density of the structures of the camps are constricting the upgrading of the shelters to international standards. As of December 2016, the combined number of internally displaced persons (IDPs) due to conflict and violence (stock), and new displacements due to disasters (flow) in Bangladesh had exceeded 1 million. These numbers have increased dramatically in the last few months. Moreover, by 2050, Bangladesh will have as much as 25 million climate refugees just from its coastal districts. To enhance the resilience of the vulnerable, it is crucial to methodically factorize further interventions between Disaster Risk Reduction for Resilience (DRR) and the concept of Building Back Better (BBB) in the rehabilitation-reconstruction period. Considering these points, this paper provides a palette of options for design guidelines related to the living spaces and infrastructures for refugees. This will encourage the development of national standards for refugee camps, and the national and local level rehabilitation-reconstruction practices. Unhygienic living conditions, vulnerability, and the general lack of control over life are pervasive throughout the camps. This paper, therefore, proposes site-specific strategic and physical planning and design for shelters for refugees in Bangladesh that will lead to sustainable living environments through the following: a) site survey of existing two registered and one makeshift unregistered refugee camps to document and study their physical conditions, b) questionnaires and semi-structured focus group discussions carried out among the refugees and stakeholders to understand what the lived experiences and needs are; and c) combining the findings with international minimum standards for shelter and settlement from International Federation of Red Cross and Red Crescent (IFRC), Médecins Sans Frontières (MSF), United Nations High Commissioner for Refugees (UNHCR). These proposals include temporary shelter solutions that balance between lived spaces and regimented, repetitive plans using readily available and cheap materials, erosion control and slope stabilization strategies, and most importantly, coping mechanisms for the refugees to be self-reliant and resilient.

Keywords: architecture, Bangladesh, refugee camp, resilience, Rohingya

Procedia PDF Downloads 237

Authors: P. Ramesh, Aby Joseph, Arya G. Lal, U. S. Aji

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Integration of distributed green renewable energy sources in addition with battery energy storage is an inevitable requirement in a smart grid environment. To achieve this, an Intelligent Green Energy Management System (i-GEMS) needs to be incorporated to ensure coordinated operation between supply and load demand based on the hierarchy of Renewable Energy Sources (RES), battery energy storage and distribution grid. A bi-directional energy harvester is an integral component facilitating Intelligent Green Energy Management System (i-GEMS) and it is required to meet the technical challenges mentioned as follows: (1) capability for bi-directional mode of operation (buck/boost) (2) reduction of circuit parasitic to suppress voltage spikes (3) converter startup problem (4) high frequency magnetics (5) higher power density (6) mode transition issues during battery charging and discharging. This paper is focused to address the above mentioned issues and targeted to design, develop and implement a bi-directional energy harvester with galvanic isolation. In this work, the hardware architecture for bi-directional energy harvester rated 3.5 kW is developed with Isolated Full Bridge Boost Converter (IFBBC) as well as Dual Active Bridge (DAB) Converter configuration using modular power electronics hardware which is identical for both solar PV array and battery energy storage. In IFBBC converter, the current fed full bridge circuit is enabled and voltage fed full bridge circuit is disabled through Pulse Width Modulation (PWM) pulses for boost mode of operation and vice-versa for buck mode of operation. In DAB converter, all the switches are in active state so as to adjust the phase shift angle between primary full bridge and secondary full bridge which in turn decides the power flow directions depending on modes (boost/buck) of operation. Here, the control algorithm is developed to ensure the regulation of the common DC link voltage and maximum power extraction from the renewable energy sources depending on the selected mode (buck/boost) of operation. The circuit analysis and simulation study are conducted using PSIM 9.0 in three scenarios which are - 1.IFBBC with passive clamp, 2. IFBBC with active clamp, 3. DAB converter. In this work, a common hardware prototype for bi-directional energy harvester with 3.5 kW rating is built for IFBBC and DAB converter configurations. The power circuit is equipped with right choice of MOSFETs, gate drivers with galvanic isolation, high frequency transformer, filter capacitors, and filter boost inductor. The experiment was conducted for IFBBC converter with passive clamp under boost mode and the prototype confirmed the simulation results showing the measured efficiency as 88% at 2.5 kW output power. The digital controller hardware platform is developed using floating point microcontroller TMS320F2806x from Texas Instruments. The firmware governing the operation of the bi-directional energy harvester is written in C language and developed using code composer studio. The comprehensive analyses of the power circuit design, control strategy for battery charging/discharging under buck/boost modes and comparative performance evaluation using simulation and experimental results will be presented.

Keywords: bi-directional energy harvester, dual active bridge, isolated full bridge boost converter, intelligent green energy management system, maximum power point tracking, renewable energy sources

Procedia PDF Downloads 140

Authors: George Charkoftakis, Panagiotis Liosatos, Nicolas-Alexander Tatlas, Dimitrios Goustouridis, Stelios M. Potirakis

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E-maintenance is a relatively new concept, generally referring to maintenance management by monitoring assets over the Internet. One of the key links in the chain of an e-maintenance system is data acquisition and transmission. Specifically for the case of a fleet of heavy-duty vehicles, where the main challenge is the diversity of the vehicles and vehicle-embedded self-diagnostic/reporting technologies, the design of the data acquisition and transmission unit is a demanding task. This clear if one takes into account that a heavy-vehicles fleet assortment may range from vehicles with only a limited number of analog sensors monitored by dashboard light indicators and gauges to vehicles with plethora of sensors monitored by a vehicle computer producing digital reporting. The present work proposes an adaptable internet of things (IoT) sensor node that is capable of addressing this challenge. The proposed sensor node architecture is based on the increasingly popular single-board computer – expansion boards approach. In the proposed solution, the expansion boards undertake the tasks of position identification by means of a global navigation satellite system (GNSS), cellular connectivity by means of 3G/long-term evolution (LTE) modem, connectivity to on-board diagnostics (OBD), and connectivity to analog and digital sensors by means of a novel design of expansion board. Specifically, the later provides eight analog plus three digital sensor channels, as well as one on-board temperature / relative humidity sensor. The specific device offers a number of adaptability features based on appropriate zero-ohm resistor placement and appropriate value selection for limited number of passive components. For example, although in the standard configuration four voltage analog channels with constant voltage sources for the power supply of the corresponding sensors are available, up to two of these voltage channels can be converted to provide power to the connected sensors by means of corresponding constant current source circuits, whereas all parameters of analog sensor power supply and matching circuits are fully configurable offering the advantage of covering a wide variety of industrial sensors. Note that a key feature of the proposed sensor node, ensuring the reliable operation of the connected sensors, is the appropriate supply of external power to the connected sensors and their proper matching to the IoT sensor node. In standard mode, the IoT sensor node communicates to the data center through 3G/LTE, transmitting all digital/digitized sensor data, IoT device identity, and position. Moreover, the proposed IoT sensor node offers WiFi connectivity to mobile devices (smartphones, tablets) equipped with an appropriate application for the manual registration of vehicle- and driver-specific information, and these data are also forwarded to the data center. All control and communication tasks of the IoT sensor node are performed by dedicated firmware. It is programmed with a high-level language (Python) on top of a modern operating system (Linux). Acknowledgment: This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH—CREATE—INNOVATE (project code: T1EDK- 01359, IntelligentLogger).

Keywords: IoT sensor nodes, e-maintenance, single-board computers, sensor expansion boards, on-board diagnostics

Procedia PDF Downloads 154

Authors: Joseph M. Rich, Vinay A. Duddalwar, Assad A. Oberai

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Prostate adenocarcinoma is the most common cancer in males, with osseous metastases as the commonest site of metastatic prostate carcinoma (mPC). Treatment monitoring is based on the evaluation and characterization of lesions on multiple imaging studies, including Computed Tomography (CT). Monitoring of the osseous disease burden, including follow-up of lesions and identification and characterization of new lesions, is a laborious task for radiologists. Deep learning algorithms are increasingly used to perform tasks such as identification and segmentation for osseous metastatic disease and provide accurate information regarding metastatic burden. Here, nnUNet was used to produce a model which can segment CT scan images of prostate adenocarcinoma vertebral bone metastatic lesions. nnUNet is an open-source Python package that adds optimizations to deep learning-based UNet architecture but has not been extensively combined with transfer learning techniques due to the absence of a readily available functionality of this method. The IRB-approved study data set includes imaging studies from patients with mPC who were enrolled in clinical trials at the University of Southern California (USC) Health Science Campus and Los Angeles County (LAC)/USC medical center. Manual segmentation of metastatic lesions was completed by an expert radiologist Dr. Vinay Duddalwar (20+ years in radiology and oncologic imaging), to serve as ground truths for the automated segmentation. Despite nnUNet’s success on some medical segmentation tasks, it only produced an average Dice Similarity Coefficient (DSC) of 0.31 on the USC dataset. DSC results fell in a bimodal distribution, with most scores falling either over 0.66 (reasonably accurate) or at 0 (no lesion detected). Applying more aggressive data augmentation techniques dropped the DSC to 0.15, and reducing the number of epochs reduced the DSC to below 0.1. Datasets have been identified for transfer learning, which involve balancing between size and similarity of the dataset. Identified datasets include the Pancreas data from the Medical Segmentation Decathlon, Pelvic Reference Data, and CT volumes with multiple organ segmentations (CT-ORG). Some of the challenges of producing an accurate model from the USC dataset include small dataset size (115 images), 2D data (as nnUNet generally performs better on 3D data), and the limited amount of public data capturing annotated CT images of bone lesions. Optimizations and improvements will be made by applying transfer learning and generative methods, including incorporating generative adversarial networks and diffusion models in order to augment the dataset. Performance with different libraries, including MONAI and custom architectures with Pytorch, will be compared. In the future, molecular correlations will be tracked with radiologic features for the purpose of multimodal composite biomarker identification. Once validated, these models will be incorporated into evaluation workflows to optimize radiologist evaluation. Our work demonstrates the challenges of applying automated image segmentation to small medical datasets and lays a foundation for techniques to improve performance. As machine learning models become increasingly incorporated into the workflow of radiologists, these findings will help improve the speed and accuracy of vertebral metastatic lesions detection.

Keywords: deep learning, image segmentation, medicine, nnUNet, prostate carcinoma, radiomics

Procedia PDF Downloads 95

Authors: Amandeep Thakur, Yi-Hsuan Chu, Chun-Hsu Pan, Kunal Nepali

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The transfer of ADP-ribose residues onto target substrates from nicotinamide adenine dinucleotide (NAD) (PARylation) is catalyzed by Poly (ADP-ribose) polymerases (PARPs). Amongst the PARP family members, the DNA damage response in cancer is majorly regulated by PARP1 and PARP2. The blockade of DNA repair by PARP inhibitors leads to the progression of DNA single-strand breaks (induced by some triggering factors) to double-strand breaks. Notably, PARP inhibitors are remarkably effective in cancers with defective homologous recombination repair (HRR). In particular, cancer cells with BRCA mutations are responsive to therapy with PARP inhibitors. The aforementioned requirement for PARP inhibitors to be effective confers a narrow activity spectrum to PARP inhibitors, which hinders their clinical applicability. Thus, the quest to expand the application horizons of PARP inhibitors beyond BRCA mutations is the need of the hour. Literature precedents reveal that HDAC inhibition induces BRCAness in cancer cells and can broaden the therapeutic scope of PARP inhibitors. Driven by such disclosures, dual inhibitors targeting both PARP and HDAC enzymes were designed by our research group to extend the efficacy of PARP inhibitors beyond BRCA-mutated cancers to cancers with induced BRCAness. The design strategy involved the installation of Veliparib, an investigational PARP inhibitor, as a surface recognition part in the HDAC inhibitor pharmacophore model. The chemical architecture of veliparib was deemed appropriate as a starting point for the generation of dual inhibitors by virtue of its size and structural flexibility. A validatory docking study was conducted at the outset to predict the binding mode of the designed dual modulatory chemical architectures. Subsequently, the designed chemical architectures were synthesized via a multistep synthetic route and evaluated for antitumor efficacy. Delightfully, one compound manifested impressive anti-leukemic effects (HL-60 cell lines) mediated via dual inhibition of PARP and class I HDACs. The outcome of the western blot analysis revealed that the compound could downregulate the expression levels of PARP1 and PARP2 and the HDAC isoforms (HDAC1, 2, and 3). Also, the dual PARP-HDAC inhibitor upregulated the protein expression of the acetyl histone H3, confirming its abrogation potential for class I HDACs. In addition, the dual modulator could arrest the cell cycle at the G0/G1 phase and induce autophagy. Further, polymer-based nanoformulation of the dual inhibitor was furnished to afford targeted delivery of the dual inhibitor at the cancer site. Transmission electron microscopy (TEM) results indicate that the nanoparticles were monodispersed and spherical. Moreover, the polymeric nanoformulation exhibited an appropriate particle size. Delightfully, pH-sensitive behavior was manifested by the polymeric nanoformulation that led to selective antitumor effects towards the HL-60 cell lines. In light of the magnificent anti-leukemic profile of the identified dual PARP-HDAC inhibitor, in-vivo studies (pharmacokinetics and pharmacodynamics) are currently being conducted. Notably, the optimistic findings of the aforementioned study have spurred our research group to initiate several medicinal chemistry campaigns to create bifunctional small molecule inhibitors addressing PARP as the primary target.

Keywords: PARP inhibitors, HDAC inhibitors, BRCA mutations, leukemia

Procedia PDF Downloads 23

Authors: Chia-Jung Chen, Yu-Chi Hsu, June-Dong Lin, Kun-Chen Chan, Chieh-Tien Wang, Li-Ching Wu, Chung-Feng Liu

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With fast advances in healthcare technology, various total laboratory automation (TLA) processes have been proposed. However, adopting TLA needs quite high funding. This study explores an early adoption experience by Taiwan’s large-scale hospital group, the Chimei Hospital Group (CMG), which owns three branch hospitals (Yongkang, Liouying and Chiali, in order by service scale), based on the five stages of Everett Rogers’ Diffusion Decision Process. 1.Knowledge stage: Over the years, two weaknesses exists in laboratory department of CMG: 1) only a few examination categories (e.g., sugar testing and HbA1c) can now be completed and reported within a day during an outpatient clinical visit; 2) the Yongkang Hospital laboratory space is dispersed across three buildings, resulting in duplicated investment in analysis instruments and inconvenient artificial specimen transportation. Thus, the senior management of the department raised a crucial question, was it time to process the redesign of the laboratory department? 2.Persuasion stage: At the end of 2013, Yongkang Hospital’s new building and restructuring project created a great opportunity for the redesign of the laboratory department. However, not all laboratory colleagues had the consensus for change. Thus, the top managers arranged a series of benchmark visits to stimulate colleagues into being aware of and accepting TLA. Later, the director of the department proposed a formal report to the top management of CMG with the results of the benchmark visits, preliminary feasibility analysis, potential benefits and so on. 3.Decision stage: This TLA suggestion was well-supported by the top management of CMG and, finally, they made a decision to carry out the project with an instrument-leasing strategy. After the announcement of a request for proposal and several vendor briefings, CMG confirmed their laboratory automation architecture and finally completed the contracts. At the same time, a cross-department project team was formed and the laboratory department assigned a section leader to the National Taiwan University Hospital for one month of relevant training. 4.Implementation stage: During the implementation, the project team called for regular meetings to review the results of the operations and to offer an immediate response to the adjustment. The main project tasks included: 1) completion of the preparatory work for beginning the automation procedures; 2) ensuring information security and privacy protection; 3) formulating automated examination process protocols; 4) evaluating the performance of new instruments and the instrument connectivity; 5)ensuring good integration with hospital information systems (HIS)/laboratory information systems (LIS); and 6) ensuring continued compliance with ISO 15189 certification. 5.Confirmation stage: In short, the core process changes include: 1) cancellation of signature seals on the specimen tubes; 2) transfer of daily examination reports to a data warehouse; 3) routine pre-admission blood drawing and formal inpatient morning blood drawing can be incorporated into an automatically-prepared tube mechanism. The study summarizes below the continuous improvement orientations: (1) Flexible reference range set-up for new instruments in LIS. (2) Restructure of the specimen category. (3) Continuous review and improvements to the examination process. (4) Whether installing the tube (specimen) delivery tracks need further evaluation.

Keywords: innovation decision process, total laboratory automation, health care

Procedia PDF Downloads 419

Authors: Anjalika Fernando, Banuka Athuraliya

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This paper presents a comprehensive approach for converting Sinhala Sign Language (SSL) into grammatically correct sentences using Natural Language Processing (NLP) techniques in real-time. While previous studies have explored various aspects of SSL translation, the research gap lies in the absence of grammar checking for SSL. This work aims to bridge this gap by proposing a two-stage methodology that leverages deep learning models to detect signs and translate them into coherent sentences, ensuring grammatical accuracy. The first stage of the approach involves the utilization of a Long Short-Term Memory (LSTM) deep learning model to recognize and interpret SSL signs. By training the LSTM model on a dataset of SSL gestures, it learns to accurately classify and translate these signs into textual representations. The LSTM model achieves a commendable accuracy rate of 94%, demonstrating its effectiveness in accurately recognizing and translating SSL gestures. Building upon the successful recognition and translation of SSL signs, the second stage of the methodology focuses on improving the grammatical correctness of the translated sentences. The project employs a Neural Machine Translation (NMT) architecture, consisting of an encoder and decoder with LSTM components, to enhance the syntactical structure of the generated sentences. By training the NMT model on a parallel corpus of Sinhala wrong sentences and their corresponding grammatically correct translations, it learns to generate coherent and grammatically accurate sentences. The NMT model achieves an impressive accuracy rate of 98%, affirming its capability to produce linguistically sound translations. The proposed approach offers significant contributions to the field of SSL translation and grammar correction. Addressing the critical issue of grammar checking, it enhances the usability and reliability of SSL translation systems, facilitating effective communication between hearing-impaired and non-sign language users. Furthermore, the integration of deep learning techniques, such as LSTM and NMT, ensures the accuracy and robustness of the translation process. This research holds great potential for practical applications, including educational platforms, accessibility tools, and communication aids for the hearing-impaired. Furthermore, it lays the foundation for future advancements in SSL translation systems, fostering inclusive and equal opportunities for the deaf community. Future work includes expanding the existing datasets to further improve the accuracy and generalization of the SSL translation system. Additionally, the development of a dedicated mobile application would enhance the accessibility and convenience of SSL translation on handheld devices. Furthermore, efforts will be made to enhance the current application for educational purposes, enabling individuals to learn and practice SSL more effectively. Another area of future exploration involves enabling two-way communication, allowing seamless interaction between sign-language users and non-sign-language users.In conclusion, this paper presents a novel approach for converting Sinhala Sign Language gestures into grammatically correct sentences using NLP techniques in real time. The two-stage methodology, comprising an LSTM model for sign detection and translation and an NMT model for grammar correction, achieves high accuracy rates of 94% and 98%, respectively. By addressing the lack of grammar checking in existing SSL translation research, this work contributes significantly to the development of more accurate and reliable SSL translation systems, thereby fostering effective communication and inclusivity for the hearing-impaired community

Keywords: Sinhala sign language, sign Language, NLP, LSTM, NMT

Procedia PDF Downloads 103

Authors: Papakonstantinou Apostolos, Argyrios Moustakas, Panagiotis Zervos, Dimitrios Stefanakis, Manolis Tsapakis, Nektarios Spyridakis, Mary Paspaliari, Christos Kontos, Antonis Legakis, Sarantis Houzouris, Konstantinos Topouzelis

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For years unmanned and remotely operated robots have been used as tools in industry research and education. The rapid development and miniaturization of sensors that can be attached to remotely operated vehicles in recent years allowed industry leaders and researchers to utilize them as an affordable means for data acquisition in air, land, and sea. Despite the recent developments in the ground and unmanned airborne vehicles, a small number of Unmanned Surface Vehicle (USV) platforms are targeted for mapping and monitoring environmental parameters for research and industry purposes. The ARGO project is developed an open-design USV equipped with multi-level control hardware architecture and state-of-the-art sensors and payloads for the autonomous monitoring of environmental parameters in large sea areas. The proposed USV is a catamaran-type USV controlled over a wireless radio link (5G) for long-range mapping capabilities and control for a ground-based control station. The ARGO USV has a propulsion control using 2x fully redundant electric trolling motors with active vector thrust for omnidirectional movement, navigation with opensource autopilot system with high accuracy GNSS device, and communication with the 2.4Ghz digital link able to provide 20km of Line of Sight (Los) range distance. The 3-meter dual hull design and composite structure offer well above 80kg of usable payload capacity. Furthermore, sun and friction energy harvesting methods provide clean energy to the propulsion system. The design is highly modular, where each component or payload can be replaced or modified according to the desired task (industrial or research). The system can be equipped with Multiparameter Sonde, measuring up to 20 water parameters simultaneously, such as conductivity, salinity, turbidity, dissolved oxygen, etc. Furthermore, a high-end multibeam echo sounder can be installed in a specific boat datum for shallow water high-resolution seabed mapping. The system is designed to operate in the Aegean Sea. The developed USV is planned to be utilized as a system for autonomous data acquisition, mapping, and monitoring bathymetry and various environmental parameters. ARGO USV can operate in small or large ports with high maneuverability and endurance to map large geographical extends at sea. The system presents state of the art solutions in the following areas i) the on-board/real-time data processing/analysis capabilities, ii) the energy-independent and environmentally friendly platform entirely made using the latest aeronautical and marine materials, iii) the integration of advanced technology sensors, all in one system (photogrammetric and radiometric footprint, as well as its connection with various environmental and inertial sensors) and iv) the information management application. The ARGO web-based application enables the system to depict the results of the data acquisition process in near real-time. All the recorded environmental variables and indices are presented, allowing users to remotely access all the raw and processed information using the implemented web-based GIS application.

Keywords: monitor marine environment, unmanned surface vehicle, mapping bythometry, sea environmental monitoring

Procedia PDF Downloads 138

Authors: Claudio Clemente, Valentina Gargiulo, Alessio Occhicone, Giovanni Piero Pepe, Giovanni Ausanio, Michela Alfè

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Volatile organic compound (VOC) emissions represent a serious risk to human health and the integrity of the ecosystems, especially at high concentrations. For this reason, it is very important to continuously monitor environmental quality and develop fast and reliable portable sensors to allow analysis on site. Chemiresistors have become promising candidates for VOC sensing as their ease of fabrication, variety of suitable sensitive materials, and simple sensing data. A chemoresistive gas sensor is a transducer that allows to measure the concentration of an analyte in the gas phase because the changes in resistance are proportional to the amount of the analyte present. The selection of the sensitive material, which interacts with the target analyte, is very important for the sensor performance. The most used VOC detection materials are metal oxides (MOx) for their rapid recovery, high sensitivity to various gas molecules, easy fabrication. Their sensing performance can be improved in terms of operating temperature, selectivity, and detection limit. Metal-organic frameworks (MOFs) have attracted a lot of attention also in the field of gas sensing due to their high porosity, high surface area, tunable morphologies, structural variety. MOFs are generated by the self-assembly of multidentate organic ligands connecting with adjacent multivalent metal nodes via strong coordination interactions, producing stable and highly ordered crystalline porous materials with well-designed structures. However, most MOFs intrinsically exhibit low electrical conductivity. To improve this property, MOFs can be combined with organic and inorganic materials in a hybrid fashion to produce composite materials or can be transformed into more stable structures. MOFs, indeed, can be employed as the precursors of metal oxides with well-designed architectures via the calcination method. The MOF-derived MOx partially preserved the original structure with high surface area and intrinsic open pores, which act as trapping centers for gas molecules, and showed a higher electrical conductivity. Core-shell heterostructures, in which the surface of a metal oxide core is completely coated by a MOF shell, forming a junction at the core-shell heterointerface, can also be synthesized. Also, nanocomposite in which MOF structures are intercalated with graphene related materials can also be produced, and the conductivity increases thanks to the high mobility of electrons of carbon materials. As MOF structures, zinc-based MOFs belonging to the ZIF family were selected in this work. Several Zn-based materials based and/or derived from MOFs were produced, structurally characterized, and arranged in a chemo resistive architecture, also exploring the potentiality of different approaches of sensing layer deposition based on PLD (pulsed laser deposition) and, in case of thermally labile materials, MAPLE (Matrix Assisted Pulsed Laser Evaporation) to enhance the adhesion to the support. The sensors were tested in a controlled humidity chamber, allowing for the possibility of varying the concentration of ethanol, a typical analyte chosen among the VOCs for a first survey. The effect of heating the chemiresistor to improve sensing performances was also explored. Future research will focus on exploring new manufacturing processes for MOF-based gas sensors with the aim to improve sensitivity, selectivity and reduce operating temperatures.

Keywords: chemiresistors, gas sensors, graphene related materials, laser deposition, MAPLE, metal-organic frameworks, metal oxides, nanocomposites, sensing performance, transduction mechanism, volatile organic compounds

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Authors: Olga Kononova, Sergey Lyapin

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Global information society requirements are transparency and reliability of data, as well as ability to manage information resources independently; particularly to search, to analyze, to evaluate information, thereby obtaining new expertise. Moreover, it is satisfying the society information needs that increases the efficiency of the enterprise management and public administration. The study of structurally organized thematic and semantic contexts of different types, automatically extracted from unstructured data, is one of the important tasks for the application of information technologies in education, science, culture, governance and business. The objectives of this study are the contextual knowledge typologization, selection or creation of effective tools for extracting and analyzing contextual knowledge. Explication of various kinds and forms of the contextual knowledge involves the development and use full-text search information systems. For the implementation purposes, the authors use an e-library 'Humanitariana' services such as the contextual search, different types of queries (paragraph-oriented query, frequency-ranked query), automatic extraction of knowledge from the scientific texts. The multifunctional e-library «Humanitariana» is realized in the Internet-architecture in WWS-configuration (Web-browser / Web-server / SQL-server). Advantage of use 'Humanitariana' is in the possibility of combining the resources of several organizations. Scholars and research groups may work in a local network mode and in distributed IT environments with ability to appeal to resources of any participating organizations servers. Paper discusses some specific cases of the contextual knowledge explication with the use of the e-library services and focuses on possibilities of new types of the contextual knowledge. Experimental research base are science texts about 'e-government' and 'computer games'. An analysis of the subject-themed texts trends allowed to propose the content analysis methodology, that combines a full-text search with automatic construction of 'terminogramma' and expert analysis of the selected contexts. 'Terminogramma' is made out as a table that contains a column with a frequency-ranked list of words (nouns), as well as columns with an indication of the absolute frequency (number) and the relative frequency of occurrence of the word (in %% ppm). The analysis of 'e-government' materials showed, that the state takes a dominant position in the processes of the electronic interaction between the authorities and society in modern Russia. The media credited the main role in these processes to the government, which provided public services through specialized portals. Factor analysis revealed two factors statistically describing the used terms: human interaction (the user) and the state (government, processes organizer); interaction management (public officer, processes performer) and technology (infrastructure). Isolation of these factors will lead to changes in the model of electronic interaction between government and society. In this study, the dominant social problems and the prevalence of different categories of subjects of computer gaming in science papers from 2005 to 2015 were identified. Therefore, there is an evident identification of several types of contextual knowledge: micro context; macro context; dynamic context; thematic collection of queries (interactive contextual knowledge expanding a composition of e-library information resources); multimodal context (functional integration of iconographic and full-text resources through hybrid quasi-semantic algorithm of search). Further studies can be pursued both in terms of expanding the resource base on which they are held, and in terms of the development of appropriate tools.

Keywords: contextual knowledge, contextual search, e-library services, frequency-ranked query, paragraph-oriented query, technologies of the contextual knowledge extraction

Procedia PDF Downloads 358

Authors: Paul J. McKay

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Large-scale genome-wide association studies (GWAS) investigating genetic contributions to sexual orientation and gender identity are largely lacking and may reduce stigma experienced in the LGBTQ+ community by providing an underlying biological explanation for queerness. While there is a growing consensus within the scientific community that genetic makeup contributes – at least in part – to sexual orientation and gender identity, there is a marked lack of genomics research exploring polygenic contributions to queerness. Based on recent (2019) findings from a large-scale GWAS investigating the genetic architecture of same-sex sexual behavior, and various additional peer-reviewed publications detailing novel insights into the molecular mechanisms of sexual orientation and gender identity, we hypothesize that sexual orientation and gender identity are complex, multifactorial, and polygenic; meaning that many genetic factors contribute to these phenomena, and environmental factors play a possible role through epigenetic modulation. In recent years, large-scale GWAS studies have been paramount to our modern understanding of many other complex human traits, such as in the case of autism spectrum disorder (ASD). Despite possible benefits of such research, including reduced stigma towards queer people, improved outcomes for LGBTQ+ in familial, socio-cultural, and political contexts, and improved access to healthcare (particularly for trans populations); important risks and considerations remain surrounding this type of research. To mitigate possibilities such as invalidation of the queer identities of existing LGBTQ+ individuals, genetic discrimination, or the possibility of euthanasia of embryos with a genetic predisposition to queerness (through reproductive technologies like IVF and/or gene-editing in utero), we propose a community-engaged research (CER) framework which emphasizes the privacy and confidentiality of research participants. Importantly, the historical legacy of scientific research attempting to pathologize queerness (in particular, falsely equating gender variance to mental illness) must be acknowledged to ensure any future research conducted in this realm does not propagate notions of homophobia, transphobia or stigma against queer people. Ultimately, in a world where same-sex sexual activity is criminalized in 69 UN member states, with 67 of these states imposing imprisonment, 8 imposing public flogging, 6 (Brunei, Iran, Mauritania, Nigeria, Saudi Arabia, Yemen) invoking the death penalty, and another 5 (Afghanistan, Pakistan, Qatar, Somalia, United Arab Emirates) possibly invoking the death penalty, the importance of this research cannot be understated, as finding a biological basis for queerness would directly oppose the harmful rhetoric that “being LGBTQ+ is a choice.” Anti-trans legislation is similarly widespread: In the United States in 2022 alone (as of Oct. 13), 155 anti-trans bills have been introduced preventing trans girls and women from playing on female sports teams, barring trans youth from using bathrooms and locker rooms that align with their gender identity, banning access to gender affirming medical care (e.g., hormone-replacement therapy, gender-affirming surgeries), and imposing legal restrictions on name changes. Understanding that a general lack of knowledge about the biological basis of queerness may be a contributing factor to the societal stigma faced by gender and sexual orientation minorities, we propose the initiation of large-scale GWAS studies investigating the genetic basis of gender identity and sexual orientation.

Keywords: genome-wide association studies (GWAS), sexual and gender minorities (SGM), polygenicity, community-engaged research (CER)

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Authors: Yu-Hsiu Lin, Wen-Chun Lin, Yen-Chang Cheng, Chia-Ju Yeh, Yu-Chuan Chen, Tai-You Li

Abstract:

Demand-Side Management (DSM) has the potential to reduce electricity costs and carbon emission, which are associated with electricity used in the modern society. A home Energy Management System (EMS) commonly used by residential consumers in a down-stream sector of a smart grid to monitor, control, and optimize energy efficiency to domestic appliances is a system of computer-aided functionalities as an energy audit for residential DSM. Implementing fault detection and classification to domestic appliances monitored, controlled, and optimized is one of the most important steps to realize preventive maintenance, such as residential air conditioning and heating preventative maintenance in residential/industrial DSM. In this study, a cloud intelligence-based green EMS that comes up with an Internet of Things (IoT) technology stack for residential DSM is developed. In the EMS, Arduino MEGA Ethernet communication-based smart sockets that module a Real Time Clock chip to keep track of current time as timestamps via Network Time Protocol are designed and implemented for readings of load phenomena reflecting on voltage and current signals sensed. Also, a Network-Attached Storage providing data access to a heterogeneous group of IoT clients via Hypertext Transfer Protocol (HTTP) methods is configured to data stores of parsed sensor readings. Lastly, a desktop computer with a WAMP software bundle (the Microsoft® Windows operating system, Apache HTTP Server, MySQL relational database management system, and PHP programming language) serves as a data science analytics engine for dynamic Web APP/REpresentational State Transfer-ful web service of the residential DSM having globally-Advanced Internet of Artificial Intelligence (AI)/Computational Intelligence. Where, an abstract computing machine, Java Virtual Machine, enables the desktop computer to run Java programs, and a mash-up of Java, R language, and Python is well-suited and -configured for AI in this study. Having the ability of sending real-time push notifications to IoT clients, the desktop computer implements Google-maintained Firebase Cloud Messaging to engage IoT clients across Android/iOS devices and provide mobile notification service to residential/industrial DSM. In this study, in order to realize edge intelligence that edge devices avoiding network latency and much-needed connectivity of Internet connections for Internet of Services can support secure access to data stores and provide immediate analytical and real-time actionable insights at the edge of the network, we upgrade the designed and implemented smart sockets to be embedded AI Arduino ones (called embedded AIduino). With the realization of edge analytics by the proposed embedded AIduino for data analytics, an Arduino Ethernet shield WizNet W5100 having a micro SD card connector is conducted and used. The SD library is included for reading parsed data from and writing parsed data to an SD card. And, an Artificial Neural Network library, ArduinoANN, for Arduino MEGA is imported and used for locally-embedded AI implementation. The embedded AIduino in this study can be developed for further applications in manufacturing industry energy management and sustainable energy management, wherein in sustainable energy management rotating machinery diagnostics works to identify energy loss from gross misalignment and unbalance of rotating machines in power plants as an example.

Keywords: demand-side management, edge intelligence, energy management system, fault detection and classification

Procedia PDF Downloads 250

Authors: Volker Wannack

Abstract:

Regional, national, and international strategies focusing on hydrogen (H₂) and blockchain are driving significant advancements in hydrogen and blockchain technology worldwide. These strategies lay the foundation for the groundbreaking "Blockchain Based Hydrogen Market (BBH₂)" project. The primary goal of this project is to develop a functional Blockchain Minimum Viable Product (B-MVP) for the hydrogen market. The B-MVP will leverage blockchain as an enabling technology with a common database and platform, facilitating secure and automated transactions through smart contracts. This innovation will revolutionize logistics, trading, and transactions within the hydrogen market. The B-MVP has transformative potential across various sectors. It benefits renewable energy producers, surplus energy-based hydrogen producers, hydrogen transport and distribution grid operators, and hydrogen consumers. By implementing standardized, automated, and tamper-proof processes, the B-MVP enhances cost efficiency and enables transparent and traceable transactions. Its key objective is to establish the verifiable integrity of climate-friendly "green" hydrogen by tracing its supply chain from renewable energy producers to end users. This emphasis on transparency and accountability promotes economic, ecological, and social sustainability while fostering a secure and transparent market environment. A notable feature of the B-MVP is its cross-border operability, eliminating the need for country-specific data storage and expanding its global applicability. This flexibility not only broadens its reach but also creates opportunities for long-term job creation through the establishment of a dedicated blockchain operating company. By attracting skilled workers and supporting their training, the B-MVP strengthens the workforce in the growing hydrogen sector. Moreover, it drives the emergence of innovative business models that attract additional company establishments and startups and contributes to long-term job creation. For instance, data evaluation can be utilized to develop customized tariffs and provide demand-oriented network capacities to producers and network operators, benefitting redistributors and end customers with tamper-proof pricing options. The B-MVP not only brings technological and economic advancements but also enhances the visibility of national and international standard-setting efforts. Regions implementing the B-MVP become pioneers in climate-friendly, sustainable, and forward-thinking practices, generating interest beyond their geographic boundaries. Additionally, the B-MVP serves as a catalyst for research and development, facilitating knowledge transfer between universities and companies. This collaborative environment fosters scientific progress, aligns with strategic innovation management, and cultivates an innovation culture within the hydrogen market. Through the integration of blockchain and hydrogen technologies, the B-MVP promotes holistic innovation and contributes to a sustainable future in the hydrogen industry. The implementation process involves evaluating and mapping suitable blockchain technology and architecture, developing and implementing the blockchain, smart contracts, and depositing certificates of origin. It also includes creating interfaces to existing systems such as nomination, portfolio management, trading, and billing systems, testing the scalability of the B-MVP to other markets and user groups, developing data formats for process-relevant data exchange, and conducting field studies to validate the B-MVP. BBH₂ is part of the "Technology Offensive Hydrogen" funding call within the research funding of the Federal Ministry of Economics and Climate Protection in the 7th Energy Research Programme of the Federal Government.

Keywords: hydrogen, blockchain, sustainability, innovation, structural change

Procedia PDF Downloads 167

Authors: M. Agostini, A. Sonato, G. Greco, M. Travagliati, G. Ruffato, E. Gazzola, D. Liuni, F. Romanato, M. Cecchini

Abstract:

Since their first demonstration in the early 1980s, surface plasmon resonance (SPR) sensors have been widely recognized as useful tools for detecting chemical and biological species, and the interest of the scientific community toward this technology has known a rapid growth in the past two decades owing to their high sensitivity, label-free operation and possibility of real-time detection. Recent works have suggested that a turning point in SPR sensor research would be the combination of SPR strategies with other technologies in order to reduce human handling of samples, improve integration and plasmonic sensitivity. In this light, microfluidics has been attracting growing interest. By properly designing microfluidic biochips it is possible to miniaturize the analyte-sensitive areas with an overall reduction of the chip dimension, reduce the liquid reagents and sample volume, improve automation, and increase the number of experiments in a single biochip by multiplexing approaches. However, as the fluidic channel dimensions approach the micron scale, laminar flows become dominant owing to the low Reynolds numbers that typically characterize microfluidics. In these environments mixing times are usually dominated by diffusion, which can be prohibitively long and lead to long-lasting biochemistry experiments. An elegant method to overcome these issues is to actively perturb the liquid laminar flow by exploiting surface acoustic waves (SAWs). With this work, we demonstrate a new approach for SPR biosensing based on the combination of microfluidics, SAW-induced mixing and the real-time phase-interrogation grating-coupling SPR technology. On a single lithium niobate (LN) substrate the nanostructured SPR sensing areas, interdigital transducer (IDT) for SAW generation and polydimethylsiloxane (PDMS) microfluidic chambers were fabricated. SAWs, impinging on the microfluidic chamber, generate acoustic streaming inside the fluid, leading to chaotic advection and thus improved fluid mixing, whilst analytes binding detection is made via SPR method based on SPP excitation via gold metallic grating upon azimuthal orientation and phase interrogation. Our device has been fully characterized in order to separate for the very first time the unwanted SAW heating effect with respect to the fluid stirring inside the microchamber that affect the molecules binding dynamics. Avidin/biotin assay and thiol-polyethylene glycol (bPEG-SH) were exploited as model biological interaction and non-fouling layer respectively. Biosensing kinetics time reduction with SAW-enhanced mixing resulted in a ≈ 82% improvement for bPEG-SH adsorption onto gold and ≈ 24% for avidin/biotin binding—≈ 50% and 18% respectively compared to the heating only condition. These results demonstrate that our biochip can significantly reduce the duration of bioreactions that usually require long times (e.g., PEG-based sensing layer, low concentration analyte detection). The sensing architecture here proposed represents a new promising technology satisfying the major biosensing requirements: scalability and high throughput capabilities. The detection system size and biochip dimension could be further reduced and integrated; in addition, the possibility of reducing biological experiment duration via SAW-driven active mixing and developing multiplexing platforms for parallel real-time sensing could be easily combined. In general, the technology reported in this study can be straightforwardly adapted to a great number of biological system and sensing geometry.

Keywords: biosensor, microfluidics, surface acoustic wave, surface plasmon resonance

Procedia PDF Downloads 277

Authors: Alba Prats-Bisbe, Jaume López-Carballo, David Leno-Colorado, Alberto García Molina, Alicia Romero Marquez, Elena Hernández Pena, Eloy Opisso Salleras, Raimon Jané Campos

Abstract:

Neurological disorders are a leading cause of disability and premature mortality worldwide. Neurorehabilitation (NRHB) is a clinical process aimed at reducing functional impairment, promoting societal participation, and improving the quality of life for affected individuals. Virtual reality (VR) technology is emerging as a promising NRHB support tool. Its immersive nature fosters a strong sense of agency and embodiment, motivating patients to engage in meaningful tasks and increasing adherence to therapy. However, the clinical benefits of VR interventions are challenging to determine due to the high heterogeneity among health applications. This study explores a stepwise development approach for creating VR-based tools to assist individuals with neurological disorders in medical practice, aiming to enhance reproducibility, facilitate comparison, and promote the generalization of findings. Building on previous research, the step-by-step methodology encompasses: Needs Identification– conducting cross-disciplinary meetings to brainstorm problems, solutions, and address barriers. Intervention Definition– target population, set goals, and conceptualize the VR system (equipment and environments). Material Selection and Placement– choose appropriate hardware and software, place the device within the hospital setting, and test equipment. Co-design– collaboratively create VR environments, user interfaces, and data management strategies. Prototyping– develop VR prototypes, conduct user testing, and make iterative redesigns. Usability and Feasibility Assessment– design protocols and conduct trials with stakeholders in the hospital setting. Efficacy Assessment– conduct clinical trials to evaluate outcomes and long-term effects. Cost-Effectiveness Validation– assess reproducibility, sustainability, and balance between costs and benefits. NRHB is complex due to the multifaceted needs of patients and the interdisciplinary healthcare architecture. VR has the potential to support various applications, such as motor skill training, cognitive tasks, pain management, unilateral spatial neglect (diagnosis and treatment), mirror therapy, and ecologically valid activities of daily living. Following this methodology was crucial for launching a VR-based system in a real hospital environment. Collaboration with neuropsychologists lead to develop A) a VR-based tool for cognitive rehabilitation in patients with acquired brain injury (ABI). The system comprises a head-mounted display (HTC Vive Pro Eye) and 7 tasks targeting attention, memory, and executive functions. A desktop application facilitates session configuration, while database records in-game variables. The VR tool's usability and feasibility were demonstrated in proof-of-concept trials with 20 patients, and effectiveness is being tested through a clinical protocol with 12 patients completing 24-session treatment. Another case involved collaboration with nurses and paediatric physiatrists to create B) a VR-based distraction tool during invasive techniques. The goal is to alleviate pain and anxiety associated with botulinum toxin (BTX) injections, blood tests, or intravenous placements. An all-in-one headset (HTC Vive Focus 3) deploys 360º videos to improve the experience for paediatric patients and their families. This study presents a framework for developing clinically relevant and technologically feasible VR-based support tools for hospital settings. Despite differences in patient type, intervention purpose, and VR system, the methodology demonstrates usability, viability, reproducibility and preliminary clinical benefits. It highlights the importance approach centred on clinician and patient needs for any aspect of NRHB within a real hospital setting.

Keywords: neurological disorders, neurorehabilitation, stepwise development approach, virtual reality

Procedia PDF Downloads 30