Search results for: utilizing radio

Authors: Hussain-Abdulah Arjmand, Nikki S. Rickard

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The response to stress can be highly heterogenous, and may be influenced by methodological factors. The integrity of data will be optimized by measuring both positive and negative affective responses to an event, by measuring responses in real time as close to the stressful event as possible, and by utilizing data collection methods that do not interfere with naturalistic behaviours. The aim of the current study was to explore short term prototypical responses to major stressor events on outcome measures encompassing both positive and negative indicators of psychological functioning. A novel mobile experience sampling methodology (m-ESM) was utilized to monitor both effective responses to stressors in real time. A smartphone mental health app (‘Moodprism’) which prompts users daily to report both their positive and negative mood, as well as whether any significant event had occurred in the past 24 hours, was developed for this purpose. A sample of 142 participants was recruited as part of the promotion of this app. Participants’ daily reported experience of stressor events, levels of depressive symptoms and positive affect were collected across a 30 day period as they used the app. For each participant, major stressor events were identified on the subjective severity of the event rated by the user. Depression and positive affect ratings were extracted for the three days following the event. Responses to the event were scaled relative to their general reactivity across the remainder of the 30 day period. Participants were first clustered into groups based on initial reactivity and subsequent recovery following a stressor event. This revealed distinct patterns of responding along depressive symptomatology and positive affect. Participants were then grouped based on allocations to clusters in each outcome variable. A highly individualised nature in which participants respond to stressor events, in symptoms of depression and levels of positive affect, was observed. A complete description of the novel profiles identified will be presented at the conference. These findings suggest that real-time measurement of both positive and negative functioning to stressors yields a more complex set of responses than previously observed with retrospective reporting. The use of smartphone technology to measure individualized responding also proved to shed significant insight.

Keywords: depression, experience sampling methodology, positive functioning, resilience

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Authors: Pin Xiu Chen, Shang Liang Chen

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A virtualized software controller is developed in this research to replace traditional hardware control units. This virtualized software controller transfers motion interpolation calculations from the motion control units of end devices to edge computing platforms, thereby reducing the end devices' computational load and hardware requirements and making maintenance and updates easier. The study also applies the concept of microservices, dividing the control system into several small functional modules and then deploy into a cloud data server. This reduces the interdependency among modules and enhances the overall system's flexibility and scalability. Finally, with containerization technology, the system can be deployed and started in a matter of seconds, which is more efficient than traditional virtual machine deployment methods. Furthermore, this virtualized software controller communicates with end control devices via wireless networks, making the placement of production equipment or the redesign of processes more flexible and no longer limited by physical wiring. To handle the large data flow and maintain low-latency transmission, this study integrates 5G technology, fully utilizing its high speed, wide bandwidth, and low latency features to achieve rapid and stable remote machine control. An experimental setup is designed to verify the feasibility and test the performance of this framework. This study designs a smart manufacturing site with a 5G communication architecture, serving as a field for experimental data collection and performance testing. The smart manufacturing site includes one robotic arm, three Computer Numerical Control machine tools, several Input/Output ports, and an edge computing architecture. All machinery information is uploaded to edge computing servers and cloud servers via 5G communication and the Internet of Things framework. After analysis and computation, this information is converted into motion control commands, which are transmitted back to the relevant machinery for motion control through 5G communication. The communication time intervals at each stage are calculated using the C++ chrono library to measure the time difference for each command transmission. The relevant test results will be organized and displayed in the full-text.

Keywords: 5G, MEC, microservices, virtualized software controller, smart manufacturing

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Authors: Yangzhi Li

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Network transfer of information and performance customization is now a viable method of digital industrial production in the era of Industry 4.0. Robot platforms and network platforms have grown more important in digital design and construction. The pressing need for novel building techniques is driven by the growing labor scarcity problem and increased awareness of construction safety. Robotic approaches in construction research are regarded as an extension of operational and production tools. Several technological theories related to robot autonomous recognition, which include high-performance computing, physical system modeling, extensive sensor coordination, and dataset deep learning, have not been explored using intelligent construction. Relevant transdisciplinary theory and practice research still has specific gaps. Optimizing high-performance computing and autonomous recognition visual guidance technologies improves the robot's grasp of the scene and capacity for autonomous operation. Intelligent vision guidance technology for industrial robots has a serious issue with camera calibration, and the use of intelligent visual guiding and identification technologies for industrial robots in industrial production has strict accuracy requirements. It can be considered that visual recognition systems have challenges with precision issues. In such a situation, it will directly impact the effectiveness and standard of industrial production, necessitating a strengthening of the visual guiding study on positioning precision in recognition technology. To best facilitate the handling of complicated components, an approach for the visual recognition of parts utilizing machine learning algorithms is proposed. This study will identify the position of target components by detecting the information at the boundary and corner of a dense point cloud and determining the aspect ratio in accordance with the guidelines for the modularization of building components. To collect and use components, operational processing systems assign them to the same coordinate system based on their locations and postures. The RGB image's inclination detection and the depth image's verification will be used to determine the component's present posture. Finally, a virtual environment model for the robot's obstacle-avoidance route will be constructed using the point cloud information.

Keywords: robotic construction, robotic assembly, visual guidance, machine learning

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Authors: Gwendolyn Royal-Smith

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Cocaine ranks as one of the world’s most addictive and commonly abused stimulant drugs. Recent evidence indicates that the abuse of cocaine has risen so quickly among females that this group now accounts for about 40 percent of all users in the United States. Neuropsychological studies have demonstrated that specific neural activation patterns carry higher risks for neurocognitive and executive function in cocaine addicted females thereby increasing their vulnerability for poorer treatment outcomes and more frequent post-treatment relapse when compared to males. This study examined secondary data with a convenience sample of 164 cocaine addicted male and females to assess neurocognitive and executive function. The principal objective of this study was to assess whether individual performance on the Stroop Word Color Task is predictive of treatment success by gender. A second objective of the study evaluated whether individual performance employing neurocognitive measures including the Stroop Word-Color task, the Rey Auditory Verbal Learning Test (RALVT), the Iowa Gambling Task, the Wisconsin Card Sorting Task (WISCT), the total score from the Barratte Impulsiveness Scale (Version 11) (BIS-11) and the total score from the Frontal Systems Behavioral Scale (FrSBE) test demonstrated differences in neurocognitive and executive function performance by gender. Logistic regression models were employed utilizing a covariate adjusted model application. Initial analyses of the Stroop Word color tasks indicated significant differences in the performance of males and females, with females experiencing more challenges in derived interference reaction time and associate recall ability. In early testing including the Rey Auditory Verbal Learning Test (RALVT), the number of advantageous vs disadvantageous cards from the Iowa Gambling Task, the number of perseverance errors from the Wisconsin Card Sorting Task (WISCT), the total score from the Barratte Impulsiveness Scale (Version 11) (BIS-11) and the total score from the Frontal Systems Behavioral Scale, results were mixed with women scoring lower in multiple indicators in both neurocognitive and executive function.

Keywords: cocaine addiction, gender, neuropsychology, neurocognitive, executive function

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Authors: Prudhvinath Reddy Ankireddy, Narasimha Mangadoddy

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Spiral concentrators are commonly used in various industries, including mineral and coal processing, to efficiently separate materials based on their density and size. In these concentrators, a mixture of solid particles and fluid (usually water) is introduced as feed at the top of a spiral channel. As the mixture flows down the spiral, centrifugal and gravitational forces act on the particles, causing them to stratify based on their density and size. Spiral flows exhibit complex fluid dynamics, and interactions involve multiple phases and components in the process. Understanding the behavior of these phases within the spiral concentrator is crucial for achieving efficient separation. An experimental bi-component particle interaction study is conducted in this work utilizing magnetite (heavier density) and silica (lighter density) with different proportions processed in the spiral concentrator. The observation separation reveals that denser particles accumulate towards the inner region of the spiral trough, while a significant concentration of lighter particles are found close to the outer edge. The 5th turn of the spiral trough is partitioned into five zones to achieve a comprehensive distribution analysis of bicomponent particle segregation. Samples are then gathered from these individual streams using an in-house sample collector, and subsequent analysis is conducted to assess component segregation. Along the trough, there was a decline in the concentration of coarser particles, accompanied by an increase in the concentration of lighter particles. The segregation pattern indicates that the heavier coarse component accumulates in the inner zone, whereas the lighter fine component collects in the outer zone. The middle zone primarily consists of heavier fine particles and lighter coarse particles. The zone-wise results reveal that there is a significant fraction of segregation occurs in inner and middle zones. Finer magnetite and silica particles predominantly accumulate in outer zones with the smallest fraction of segregation. Additionally, numerical simulations are also carried out using the computational fluid dynamics (CFD) model based on the volume of fluid (VOF) approach incorporating the RSM turbulence model. The discrete phase model (DPM) is employed for particle tracking, thereby understanding the particle segregation of magnetite and silica along the spiral trough.

Keywords: spiral concentrator, bi-component particle segregation, computational fluid dynamics, discrete phase model

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Authors: Xin Jiao, Xin Zhang, Jian Fan, Zhenwei Cai, Yiming Xu

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Tower cranes play a crucial role in the construction industry, facilitating the vertical and horizontal movement of materials and aiding in building construction, especially for high-rise structures. However, tower crane accidents can lead to severe consequences, highlighting the importance of effective safety management and inspection. This paper presents an innovative approach to tower crane inspection utilizing Unmanned Aerial Vehicles (UAVs) and an Intelligent Inspection APP System. The system leverages UAVs equipped with high-definition cameras to conduct efficient and comprehensive inspections, reducing manual labor, inspection time, and risk. By integrating advanced technologies such as Real-Time Kinematic (RTK) positioning and digital image processing, the system enables precise route planning and collection of safety hazards images. A case study conducted on a construction site demonstrates the practicality and effectiveness of the proposed method, showcasing its potential to enhance tower crane safety. On-site testing of UAV intelligent inspections reveals key findings: efficient tower crane hazard inspection within 30 minutes, with a full-identification capability coverage rates of 76.3%, 64.8%, and 76.2% for major, significant, and general hazards respectively and a preliminary-identification capability coverage rates of 18.5%, 27.2%, and 19%, respectively. Notably, UAVs effectively identify various tower crane hazards, except for those requiring auditory detection. The limitations of this study primarily involve two aspects: Firstly, during the initial inspection, manual drone piloting is required for marking tower crane points, followed by automated flight inspections and reuse based on the marked route. Secondly, images captured by the drone necessitate manual identification and review, which can be time-consuming for equipment management personnel, particularly when dealing with a large volume of images. Subsequent research efforts will focus on AI training and recognition of safety hazard images, as well as the automatic generation of inspection reports and corrective management based on recognition results. The ongoing development in this area is currently in progress, and outcomes will be released at an appropriate time.

Keywords: tower crane, inspection, unmanned aerial vehicle (UAV), intelligent inspection app system, safety management

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Authors: Sidharth Talan, Devendra Kumar Yadav, Yuvraj Singh Rajput, Subhajit Bhattacharjee

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Mining industry is known for its appetite to spend sizeable capital on mine equipment. However, in the current scenario, the mining industry is challenged by daunting factors of non-uniform geological conditions, uneven ore grade, uncontrollable and volatile mineral commodity prices and the ever increasing quest to optimize the capital and operational costs. Thus, the role of equipment reliability and maintenance planning inherits a significant role in augmenting the equipment availability for the operation and in turn boosting the mine productivity. This paper presents the Risk Based Maintenance (RBM) planning conducted on mine loading equipment namely Load Haul Dumpers (LHDs) at Vedanta Resources Ltd subsidiary Hindustan Zinc Limited operated Sindesar Khurd Mines, an underground zinc and lead mine situated in Dariba, Rajasthan, India. The mining equipment at the location is maintained by the Original Equipment Manufacturers (OEMs) namely Sandvik and Atlas Copco, who carry out the maintenance and inspection operations for the equipment. Based on the downtime data extracted for the equipment fleet over the period of 6 months spanning from 1^st January 2017 until 30^th June 2017, it was revealed that significant contribution of three downtime issues related to namely Engine, Hydraulics, and Transmission to be common among all the loading equipment fleet and substantiated by Pareto Analysis. Further scrutiny through Bubble Matrix Analysis of the given factors revealed the major influence of selective factors namely Overheating, No Load Taken (NTL) issues, Gear Changing issues and Hose Puncture and leakage issues. Utilizing the equipment wise analysis of all the downtime factors obtained, spares consumed, and the alarm logs extracted from the machines, technical design changes in the equipment and pre shift critical alarms checklist were proposed for the equipment maintenance. The given analysis is beneficial to allow OEMs or mine management to focus on the critical issues hampering the reliability of mine equipment and design necessary maintenance strategies to mitigate them.

Keywords: bubble matrix analysis, LHDs, OEMs, Pareto chart analysis, spares consumption matrix, critical alarms checklist

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Authors: Briohny H. Spencer, Russ Chess-Williams, Catherine McDermott, Shailendra Anoopkumar-Dukie, David Christie

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Background: Prostate cancer is the second most common cancer diagnosed in men worldwide and the most prevalent in Australian men. In 2015, it was estimated that approximately 18,000 new cases of prostate cancer were diagnosed in Australia. Currently, for localised disease, androgen depravation therapy (ADT) and radiotherapy are a major part of the curative management of prostate cancer. ADT acts to reduce the levels of circulating androgens, primarily testosterone and the locally produced androgen, dihydrotestosterone (DHT), or by preventing the subsequent activation of the androgen receptor. Thus, the growth of the cancerous cells can be reduced or ceased. Radiation techniques such as brachytherapy (radiation delivered directly to the prostate by transperineal implant) or external beam radiation therapy (exposure to a sufficient dose of radiation aimed at eradicating malignant cells) are also common techniques used in the treatment of this condition. Radiotherapy (RT) has significant limitations, including reduced effectiveness in treating malignant cells present in hypoxic microenvironments leading to radio-resistance and poor clinical outcomes and also the significant side effects for the patients. Alpha1-adrenoceptor antagonists are used for many prostate cancer patients to control lower urinary tract symptoms, due to the progression of the disease itself or may arise as an adverse effect of the radiotherapy treatment. In Australia, a significant number (not a majority) of patients receive a α1-ADR antagonist and four drugs are available including prazosin, terazosin, alfuzosin and tamsulosin. There is currently limited published data on the effects of α1-ADR antagonists during radiotherapy, but it suggests these medications may improve patient outcomes by enhancing the effect of radiotherapy. Aim: To determine the impact of α1-ADR antagonists treatments on time to biochemical relapse following radiotherapy. Methods: A retrospective study of male patients receiving radiotherapy for biopsy-proven localised prostate cancer was undertaken to compare cancer outcomes for drug-naïve patients and those receiving α1-ADR antagonist treatments. Ethical approval for the collection of data at Genesis CancerCare QLD was obtained and biochemical relapse (defined by a PSA rise of >2ng/mL above the nadir) was recorded in months. Rates of biochemical relapse, prostate specific antigen doubling time (PSADT) and Kaplan-Meier survival curves were also compared. Treatment groups were those receiving α1-ADR antagonists treatment before or concurrent with their radiotherapy. Data was statistically analysed using One-way ANOVA and results expressed as mean ± standard deviation. Major findings: The mean time to biochemical relapse for tamsulosin, prazosin, alfuzosin and controls were 45.3±17.4 (n=36), 41.5±19.6 (n=11), 29.3±6.02 (n=6) and 36.5±17.6 (n=16) months respectively. Tamsulosin, prazosin but not alfuzosin delayed time to biochemical relapse although the differences were not statistically significant. Conclusion: Preliminary data for the prior and/or concurrent use of tamsulosin and prazosin showed a positive trend in delaying time to biochemical relapse although no statistical significance was shown. Larger clinical studies are indicated and with thousands of patient records yet to be analysed, it may determine if there is a significant effect of these drugs on control of prostate cancer.

Keywords: alpha1-adrenoceptor antagonists, biochemical relapse, prostate cancer, radiotherapy

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Authors: Carmel Sofer, Dan Vilenchik, Ron Dotsch, Galia Avidan

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Studies investigating emotional facial expressions typically reveal consensus among observes regarding the meaning of basic expressions, whose number ranges between 6 to 15 emotional states. Given this limited number of discrete expressions, how is it that the human vocabulary of emotional states is so rich? The present study argues that perceivers use sequences of these discrete expressions as the basis for a much richer vocabulary of emotional states. Such mechanisms, in which a relatively small number of basic components is expanded to a much larger number of possible combinations of meanings, exist in other human communications modalities, such as spoken language and music. In these modalities, letters and notes, which serve as basic components of spoken language and music respectively, are temporally linked, resulting in the richness of expressions. In the current study, in each trial participants were presented with sequences of two images containing facial expression in different combinations sampled out of the eight static basic expressions (total 64; 8X8). In each trial, using single word participants were required to judge the 'state of mind' portrayed by the person whose face was presented. Utilizing word embedding methods (Global Vectors for Word Representation), employed in the field of Natural Language Processing, and relying on machine learning computational methods, it was found that the perceived meanings of the sequences of facial expressions were a weighted average of the single expressions comprising them, resulting in 22 new emotional states, in addition to the eight, classic basic expressions. An interaction between the first and the second expression in each sequence indicated that every single facial expression modulated the effect of the other facial expression thus leading to a different interpretation ascribed to the sequence as a whole. These findings suggest that the vocabulary of emotional states conveyed by facial expressions is not restricted to the (small) number of discrete facial expressions. Rather, the vocabulary is rich, as it results from combinations of these expressions. In addition, present research suggests that using word embedding in social perception studies, can be a powerful, accurate and efficient tool, to capture explicit and implicit perceptions and intentions. Acknowledgment: The study was supported by a grant from the Ministry of Defense in Israel to GA and CS. CS is also supported by the ABC initiative in Ben-Gurion University of the Negev.

Keywords: Glove, face perception, facial expression perception. , facial expression production, machine learning, word embedding, word2vec

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Authors: Sharat Chandra, Zilong Wang, Ru-Rong Ji, Andrey Bortsov

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Chronic pain (CP) therapeutic approaches have limited efficacy. As a result, doctors are prescribing opioids for chronic pain, leading to opioid overuse, abuse, and addiction epidemic. Therefore, the development of effective and safe CP drugs remains an unmet medical need. Voltage-gated sodium (Nav) channels act as cardiovascular and neurological disorder’s molecular targets. Nav channels selective inhibitors are hard to design because there are nine closely-related isoforms (Nav1.1-1.9) that share the protein sequence segments. We are targeting the Nav1.7 found in the peripheral nervous system and engaged in the perception of pain. The objective of this project was to screen a 1.5 million compound library for identification of inhibitors for Nav1.7 with analgesic effect. In this study, we designed a protocol for identification of isoform-selective inhibitors of Nav1.7, by utilizing the prior information on isoform-selective antagonists. First, a similarity search was performed; then the identified hits were docked into a binding site on the fourth voltage-sensor domain (VSD4) of Nav1.7. We used the FTrees tool for similarity searching and library generation; the generated library was docked in the VSD4 domain binding site using FlexX and compounds were shortlisted using a FlexX score and SeeSAR hyde scoring. Finally, the top 25 compounds were tested with molecular dynamics simulation (MDS). We reduced our list to 9 compounds based on the MDS root mean square deviation plot and obtained them from a vendor for in vitro and in vivo validation. Whole-cell patch-clamp recordings in HEK-293 cells and dorsal root ganglion neurons were conducted. We used patch pipettes to record transient Na⁺ currents. One of the compounds reduced the peak sodium currents in Nav1.7-HEK-293 stable cell line in a dose-dependent manner, with IC50 values at 0.74 µM. In summary, our computer-aided analgesic discovery approach allowed us to develop pre-clinical analgesic candidate with significant reduction of time and cost.

Keywords: chronic pain, voltage-gated sodium channel, isoform-selective antagonist, similarity search, virtual screening, analgesics development

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Authors: Shegufta Zahan, Muhammad A. Zahin, Muhammad M. Hossain, Raquib Ahsan

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Rice Husk Ash (RHA) is one of the agricultural waste byproducts available widely in the world and contains a large amount of silica. In Bangladesh, stones cannot be used as coarse aggregate in infrastructure works as they are not available and need to be imported from abroad. As a result, bricks are mostly used as coarse aggregates in concrete as they are cheaper and easily produced here. Clay is the raw material for producing brick. Due to rapid urban growth and the industrial revolution, demand for brick is increasing, which led to a decrease in the topsoil. This study aims to produce lightweight block aggregates with sufficient strength utilizing RHA at low cost and use them as an ingredient of concrete. RHA, because of its pozzolanic behavior, can be utilized to produce better quality block aggregates at lower cost, replacing clay content in the bricks. The whole study can be divided into three parts. In the first part, characterization tests on RHA and clay were performed to determine their properties. Six different types of RHA from different mills were characterized by XRD and SEM analysis. Their fineness was determined by conducting a fineness test. The result of XRD confirmed the amorphous state of RHA. The characterization test for clay identifies the sample as “silty clay” with a specific gravity of 2.59 and 14% optimum moisture content. In the second part, blocks were produced with six different types of RHA with different combinations by volume with clay. Then mixtures were manually compacted in molds before subjecting them to oven drying at 120 °C for 7 days. After that, dried blocks were placed in a furnace at 1200 °C to produce ultimate blocks. Loss on ignition test, apparent density test, crushing strength test, efflorescence test, and absorption test were conducted on the blocks to compare their performance with the bricks. For 40% of RHA, the crushing strength result was found 60 MPa, where crushing strength for brick was observed 48.1 MPa. In the third part, the crushed blocks were used as coarse aggregate in concrete cylinders and compared them with brick concrete cylinders. Specimens were cured for 7 days and 28 days. The highest compressive strength of block cylinders for 7 days curing was calculated as 26.1 MPa, whereas, for 28 days curing, it was found 34 MPa. On the other hand, for brick cylinders, the value of compressing strength of 7 days and 28 days curing was observed as 20 MPa and 30 MPa, respectively. These research findings can help with the increasing demand for topsoil of the earth, and also turn a waste product into a valuable one.

Keywords: characterization, furnace, pozzolanic behavior, rice husk ash

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Authors: Chijioke Oliver Nwodoh, Jonah Ikechukwu Eze, Loretta Chika Ukwuaba, Ifeoma Ndubuisi, Ada Carol Nwaneri, Ijeoma Lewechi Okoronkwo

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Presence of nursing workforce with unequal qualification and status in Nigeria has undermined the growth of nursing profession in the country. Upgrading of the existing basic and post-basic nursing schools to degree-awarding institutions in Nigeria is a way-forward to solving this inequality problem and Nigeria teaching hospitals are in vantage position for this project due to the already existing supportive structure and manpower in those hospitals. What the nurse trainers and the stakeholders of the teaching hospitals may hold for or against the upgrading is a determining factor for the upgrading project, but that is not clear and has not been investigated in Nigeria. The study investigated the perception of nurse trainers and stakeholders of teaching hospitals in Enugu State of Nigeria on the obstacles and ways-forward to upgrading nursing schools to degree-awarding institutions in Nigeria. The study specifically elicited what the subjects may view as obstacles to upgrading basic and post-basic nursing schools to degree-awarding institutions in Nigeria and ascertained their suggestions on the possible ways of overcoming the obstacles. By utilizing cross-sectional descriptive design and a purposive sampling procedure, 78 accessible subjects out of a total population of 87 were used for the study. The generated data from the subjects were analyzed using frequencies, percentages and mean for the research questions and Pearson’s chi-square for the hypotheses, with the aid of Statistical Package for Social Sciences Version 20.0. The result showed that lack of extant policy, fund, and disunity among policy makers and stakeholders of nursing profession are the main obstacles to the upgrading. However, the respondents did not see items like: stakeholders and nurse trainers of basic and post-basic schools of nursing; fear of admitting and producing poor quality nurses; and so forth, as obstacles to the upgrading project. Institution of the upgrading policy by Nursing and Midwifery Council of Nigeria, funding, awareness creation for the upgrading and unison among policy makers and stakeholders of nursing profession are the major possible ways to overcome the obstacles. The difference in the subjects’ perceptions between the two hospitals was found to be statistically insignificant (p > 0.05). It is recommended that the policy makers and stakeholders of nursing in Nigeria should unite and liaise with Federal Ministries of Health and Education for modalities and actualization of upgrading nursing schools to degree-awarding institutions in Nigeria.

Keywords: nurse trainers, obstacles, perception, stakeholders, teaching hospital, upgrading basic nursing schools, ways-forward

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Authors: Bylhah Mugotitsa, Jim Todd, Agnes Kiragga, Jay Greenfield, Evans Omondi, Lukoye Atwoli, Reinpeter Momanyi

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The INSPIRE network aims to build an open, ethical, sustainable, and FAIR (Findable, Accessible, Interoperable, Reusable) data science platform, particularly for longitudinal mental health (MH) data. While studies have been done at the clinical and population level, there still exists limitations in data and research in LMICs, which pose a risk of underrepresentation of mental disorders. It is vital to examine the existing longitudinal MH data, focusing on how FAIR datasets are. This landscape analysis aimed to provide both overall level of evidence of availability of longitudinal datasets and degree of consistency in longitudinal studies conducted. Utilizing prompters proved instrumental in streamlining the analysis process, facilitating access, crafting code snippets, categorization, and analysis of extensive data repositories related to depression, anxiety, and psychosis in Africa. While leveraging artificial intelligence (AI), we filtered through over 18,000 scientific papers spanning from 1970 to 2023. This AI-driven approach enabled the identification of 228 longitudinal research papers meeting inclusion criteria. Quality assurance revealed 10% incorrectly identified articles and 2 duplicates, underscoring the prevalence of longitudinal MH research in South Africa, focusing on depression. From the analysis, evaluating data and metadata adherence to FAIR principles remains crucial for enhancing accessibility and quality of MH research in Africa. While AI has the potential to enhance research processes, challenges such as privacy concerns and data security risks must be addressed. Ethical and equity considerations in data sharing and reuse are also vital. There’s need for collaborative efforts across disciplinary and national boundaries to improve the Findability and Accessibility of data. Current efforts should also focus on creating integrated data resources and tools to improve Interoperability and Reusability of MH data. Practical steps for researchers include careful study planning, data preservation, machine-actionable metadata, and promoting data reuse to advance science and improve equity. Metrics and recognition should be established to incentivize adherence to FAIR principles in MH research

Keywords: longitudinal mental health research, data sharing, fair data principles, Africa, landscape analysis

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Authors: Retno A. Vinarti, Lucy M. Hederman

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This research describes a knowledge model for a system which give personalized alert to users about infectious disease risks in the context of weather, location and time. The knowledge model is based on established epidemiological concepts augmented by information gleaned from infection-related data repositories. The existing disease risk prediction research has more focuses on utilizing raw historical data and yield seasonal patterns of infectious disease risk emergence. This research incorporates both data and epidemiological concepts gathered from Atlas of Human Infectious Disease (AHID) and Centre of Disease Control (CDC) as basic reasoning of infectious disease risk prediction. Using CommonKADS methodology, the disease risk prediction task is an assignment synthetic task, starting from knowledge identification through specification, refinement to implementation. First, knowledge is gathered from AHID primarily from the epidemiology and risk group chapters for each infectious disease. The result of this stage is five major elements (Person, Infectious Disease, Weather, Location and Time) and their properties. At the knowledge specification stage, the initial tree model of each element and detailed relationships are produced. This research also includes a validation step as part of knowledge refinement: on the basis that the best model is formed using the most common features, Frequency-based Selection (FBS) is applied. The portion of the Infectious Disease risk model relating to Person comes out strongest, with Location next, and Weather weaker. For Person attribute, Age is the strongest, Activity and Habits are moderate, and Blood type is weakest. At the Location attribute, General category (e.g. continents, region, country, and island) results much stronger than Specific category (i.e. terrain feature). For Weather attribute, Less Precise category (i.e. season) comes out stronger than Precise category (i.e. exact temperature or humidity interval). However, given that some infectious diseases are significantly more serious than others, a frequency based metric may not be appropriate. Future work will incorporate epidemiological measurements of disease seriousness (e.g. odds ratio, hazard ratio and fatality rate) into the validation metrics. This research is limited to modelling existing knowledge about epidemiology and chain of infection concepts. Further step, verification in knowledge refinement stage, might cause some minor changes on the shape of tree.

Keywords: epidemiology, knowledge modelling, infectious disease, prediction, risk

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Authors: Suleyman Ogul Ertugrul, Mustafa Turgut, Serkan Inandı, Mustafa Gorkem Coban, Mustafa Kıgılı, Ali Mert, Oguzhan Kesmez, Murat Ozancı, Caglar Uyulan

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In high-performance automotive engineering, the realistic simulation of suspension systems is crucial for enhancing vehicle dynamics and handling. This study focuses on the double wishbone suspension system, prevalent in racing vehicles due to its superior control and stability characteristics. Utilizing MATLAB and Adams Car simulation software, we conduct a comprehensive analysis of displacement behaviors and damper sizing under various dynamic conditions. The initial phase involves using MATLAB to simulate the entire suspension system, allowing for the preliminary determination of damper size based on the system's response under simulated conditions. Following this, manual calculations of wheel loads are performed to assess the forces acting on the front and rear suspensions during scenarios such as braking, cornering, maximum vertical loads, and acceleration. Further dynamic force analysis is carried out using MATLAB Simulink, focusing on the interactions between suspension components during key movements such as bumps and rebounds. This simulation helps in formulating precise force equations and in calculating the stiffness of the suspension springs. To enhance the accuracy of our findings, we focus on a detailed kinematic and dynamic analysis. This includes the creation of kinematic loops, derivation of relevant equations, and computation of Jacobian matrices to accurately determine damper travel and compression metrics. The calculated spring stiffness is crucial in selecting appropriate springs to ensure optimal suspension performance. To validate and refine our results, we replicate the analyses using the Adams Car software, renowned for its detailed handling of vehicular dynamics. The goal is to achieve a robust, reliable suspension setup that maximizes performance under the extreme conditions encountered in racing scenarios. This study exemplifies the integration of theoretical mechanics with advanced simulation tools to achieve a high-performance suspension setup that can significantly improve race car performance, providing a methodology that can be adapted for different types of racing vehicles.

Keywords: FSAE, suspension system, Adams Car, kinematic

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Authors: Belyihun Muchie

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This study investigates the low English oral communication skills of 5 second-year English major students at Debark University. It aims to identify the key factors contributing to their weaknesses and propose effective interventions to improve their spoken English proficiency. Mixed-methods research will be employed, utilizing observations, questionnaires, and semi-structured interviews to gather data from the participants. To clearly identify these factors, structured and informal observations will be employed; the former will be used to identify their fluency, pronunciation, vocabulary use, and grammar accuracy, and the later will be suited to observe the natural interactions and communication patterns of learners in the classroom setting. The questionnaires will assess their self-perceptions of their skills, perceived barriers to fluency, and preferred learning styles. Interviews will also delve deeper into their experiences and explore specific obstacles faced in oral communication. Data analysis will involve both quantitative and qualitative responses. The structured observation and questionnaire will be analyzed quantitatively, whereas the informal observation and interview transcripts will be analyzed thematically. Findings will be used to identify the major causes of low oral communication skills, such as limited vocabulary, grammatical errors, pronunciation difficulties, or lack of confidence. They are also helpful to develop targeted solutions addressing these causes, such as intensive pronunciation practice, conversation simulations, personalized feedback, or anxiety-reduction techniques. Finally, the findings will guide designing an intervention plan for implementation during the action research phase. The study's outcomes are expected to provide valuable insights into the challenges faced by English major students in developing oral communication skills, contribute to the development of evidence-based interventions for improving spoken English proficiency in similar contexts, and offer practical recommendations for English language instructors and curriculum developers to enhance student learning outcomes. By addressing the specific needs of these students and implementing tailored interventions, this research aims to bridge the gap between theoretical knowledge and practical speaking ability, equipping them with the confidence and skills to flourish in English communication settings.

Keywords: oral communication skills, mixed-methods, evidence-based interventions, spoken English proficiency

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Authors: Scott Cavinder

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Surgical site infections (SSI) are a known complication to any surgical procedure and are one of the most common nosocomial infections. Globally it is estimated 300 million surgical procedures take place annually, with an incidence of SSI’s estimated to be 11 of 100 surgical patients developing an infection within 30 days after surgery. The specific purpose of the project is to address the PICOT (Problem, Intervention, Comparison, Outcome, Time) question: In patients who have undergone cardiothoracic or vascular surgery (P), does implementation of a post-operative care bundle based on current EBP (I) as compared to current clinical agency practice standards (C) result in a decrease of SSI (O) over a 12-week period (T)? Synthesis of Supporting Evidence: A literature search of five databases, including citation chasing, was performed, which yielded fourteen pieces of evidence ranging from high to good quality. Four common themes were identified for the prevention of SSI’s including use and removal of surgical dressings; use of topical antibiotics and antiseptics; implementation of evidence-based care bundles, and implementation of surveillance through auditing and feedback. The Iowa Model was selected as the framework to help guide this project as it is a multiphase change process which encourages clinicians to recognize opportunities for improvement in healthcare practice. Practice/Implementation: The process for this project will include recruiting postsurgical participants who have undergone cardiovascular or thoracic surgery prior to discharge at a Northwest Indiana Hospital. The patients will receive education, verbal instruction, and return demonstration. The patients will be followed for 12 weeks, and wounds assessed utilizing the National Healthcare Safety Network//Centers for Disease Control (NHSN/CDC) assessment tool and compared to the SSI rate of 2021. Key stakeholders will include two cardiovascular surgeons, four physician assistants, two advance practice nurses, medical assistant and patients. Method of Evaluation: Chi Square analysis will be utilized to establish statistical significance and similarities between the two groups. Main Results/Outcomes: The proposed outcome is the prevention of SSIs in the post-op cardiothoracic and vascular patient. Implication/Recommendation(s): Implementation of standardized post operative care bundles in the prevention of SSI in cardiovascular and thoracic surgical patients.

Keywords: cardiovascular, evidence based practice, infection, post-operative, prevention, thoracic, surgery

Procedia PDF Downloads 60

Authors: Niyi Awofeso, Yunes Gaber, Moyosola Bamidele

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Since most social media platforms are accessible anytime and anywhere where Internet connections and smartphones are available, the invisibility of the reader raises questions about accuracy, appropriateness and comprehensibility of social media communication. Furthermore, the identity and motives of individuals and organizations who post articles on social media sites are not always transparent. In the health sector, through socially networked platforms constitute a common source of health-related information, given their purported wealth of information. Nevertheless, fake blogs and sponsored postings for marketing 'natural cures' pervade most commonly used social media platforms, thus complicating readers’ abilities to access and understand trustworthy health-related information. This purposive sampling study of 120 participants aged 18-35 year in UAE was conducted between September and December 2017, and explored commonly used social media platforms, frequency of use of social media for accessing health related information, and approaches for assessing the trustworthiness of health information on social media platforms. Results indicate that WhatsApp (95%), Instagram (87%) and Youtube (82%) were the most commonly used social media platforms among respondents. Majority of respondents (81%) indicated that they regularly access social media to get health-associated information. More than half of respondents (55%) with non-chronic health status relied on unsolicited messages to obtain health-related information. Doctors’ health blogs (21%) and social media sites of international healthcare organizations (20%) constitute the most trusted source of health information among respondents, with UAE government health agencies’ social media accounts trusted by 15% of respondents. Cardiovascular diseases, diabetes, and hypertension were the most commonly searched topics on social media (29%), followed by nutrition (20%) and skin care (16%). Majority of respondents (41%) rely on reliability of hits on Google search engines, 22% check for health information only from 'reliable' social media sites, while 8% utilize 'logic' to ascertain reliability of health information. As social media has rapidly become an integral part of the health landscape, it is important that health care policy makers, healthcare providers and social media companies collaborate to promote the positive aspects of social media for young people, whilst mitigating the potential negatives. Utilizing popular social media platforms for posting reader-friendly health information will achieve high coverage. Improving youth digital literacy will facilitate easier access to trustworthy information on the internet.

Keywords: social media, United Arab Emirates, youth engagement, digital literacy

Procedia PDF Downloads 96

Authors: Jialin Ma, Jia Meng

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Genomic features, the genome-based coordinates, are commonly used for the representation of biological features such as genes, RNA transcripts and transcription factor binding sites. For the analysis of RNA-related genomic features, such as RNA modification sites, a common task is to correlate these features with transcript components (5'UTR, CDS, 3'UTR) to explore their distribution characteristics in terms of transcriptomic coordinates, e.g., to examine whether a specific type of biological feature is enriched near transcription start sites. Existing approaches for performing these tasks involve the manipulation of a gene database, conversion from genome-based coordinate to transcript-based coordinate, and visualization methods that are capable of showing RNA transcript components and distribution of the features. These steps are complicated and time consuming, and this is especially true for researchers who are not familiar with relevant tools. To overcome this obstacle, we develop a dedicated web app TARF, which represents web toolkit for annotating RNA-related genomic features. TARF web tool intends to provide a web-based way to easily annotate and visualize RNA-related genomic features. Once a user has uploaded the features with BED format and specified a built-in transcript database or uploaded a customized gene database with GTF format, the tool could fulfill its three main functions. First, it adds annotation on gene and RNA transcript components. For every features provided by the user, the overlapping with RNA transcript components are identified, and the information is combined in one table which is available for copy and download. Summary statistics about ambiguous belongings are also carried out. Second, the tool provides a convenient visualization method of the features on single gene/transcript level. For the selected gene, the tool shows the features with gene model on genome-based view, and also maps the features to transcript-based coordinate and show the distribution against one single spliced RNA transcript. Third, a global transcriptomic view of the genomic features is generated utilizing the Guitar R/Bioconductor package. The distribution of features on RNA transcripts are normalized with respect to RNA transcript landmarks and the enrichment of the features on different RNA transcript components is demonstrated. We tested the newly developed TARF toolkit with 3 different types of genomics features related to chromatin H3K4me3, RNA N6-methyladenosine (m6A) and RNA 5-methylcytosine (m5C), which are obtained from ChIP-Seq, MeRIP-Seq and RNA BS-Seq data, respectively. TARF successfully revealed their respective distribution characteristics, i.e. H3K4me3, m6A and m5C are enriched near transcription starting sites, stop codons and 5’UTRs, respectively. Overall, TARF is a useful web toolkit for annotation and visualization of RNA-related genomic features, and should help simplify the analysis of various RNA-related genomic features, especially those related RNA modifications.

Keywords: RNA-related genomic features, annotation, visualization, web server

Procedia PDF Downloads 185

Authors: Ray Bryant

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3Strands Global Foundation (3SGF), a non-profit with a mission to mobilize communities to combat human trafficking through prevention education and reintegration programs, launched a groundbreaking study that calls out the usage and benefits of artificial intelligence in the war against human trafficking. Having gathered more than 30,000 stories from counselors and school staff who have gone through its PROTECT Prevention Education program, 3SGF sought to develop a methodology to measure the effectiveness of the training, which helps educators and school staff identify physical signs and behaviors indicating a student is being victimized. The program further illustrates how to recognize and respond to trauma and teaches the steps to take to report human trafficking, as well as how to connect victims with the proper professionals. 3SGF partnered with Levity, a leader in no-code Artificial Intelligence (AI) automation, to create the research study utilizing natural language processing, a branch of artificial intelligence, to measure the effectiveness of their prevention education program. By applying the logic created for the study, the platform analyzed and categorized each story. If the story, directly from the educator, demonstrated one or more of the desired outcomes; Increased Awareness, Increased Knowledge, or Intended Behavior Change, a label was applied. The system then added a confidence level for each identified label. The study results were generated with a 99% confidence level. Preliminary results show that of the 30,000 stories gathered, it became overwhelmingly clear that a significant majority of the participants now have increased awareness of the issue, demonstrated better knowledge of how to help prevent the crime, and expressed an intention to change how they approach what they do daily. In addition, it was observed that approximately 30% of the stories involved comments by educators expressing they wish they’d had this knowledge sooner as they can think of many students they would have been able to help. Objectives Of Research: To solve the problem of needing to analyze and accurately categorize more than 30,000 data points of participant feedback in order to evaluate the success of a human trafficking prevention program by using AI and Natural Language Processing. Methodologies Used: In conjunction with our strategic partner, Levity, we have created our own NLP analysis engine specific to our problem. Contributions To Research: The intersection of AI and human rights and how to utilize technology to combat human trafficking.

Keywords: AI, technology, human trafficking, prevention

Procedia PDF Downloads 38

Authors: Ole-Christian Galbo Engstrøm, Erik Schou Dreier, Birthe Møller Jespersen, Kim Steenstrup Pedersen

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Grain quality analysis is a multi-parameterized problem that includes a variety of qualitative and quantitative parameters such as grain type classification, damage type classification, and nutrient regression. Currently, these parameters require human inspection, a multitude of instruments employing a variety of sensor technologies, and predictive model types or destructive and slow chemical analysis. This paper investigates the feasibility of applying near-infrared hyperspectral imaging (NIR-HSI) to grain quality analysis. For this study two datasets of NIR hyperspectral images in the wavelength range of 900 nm - 1700 nm have been used. Both datasets contain images of sparsely and densely packed grain kernels. The first dataset contains ~87,000 image crops of bulk wheat samples from 63 harvests where protein value has been determined by the FOSS Infratec NOVA which is the golden industry standard for protein content estimation in bulk samples of cereal grain. The second dataset consists of ~28,000 image crops of bulk grain kernels from seven different wheat varieties and a single rye variety. In the first dataset, protein regression analysis is the problem to solve while variety classification analysis is the problem to solve in the second dataset. Deep convolutional neural networks (CNNs) have the potential to utilize spatio-spectral correlations within a hyperspectral image to simultaneously estimate the qualitative and quantitative parameters. CNNs can autonomously derive meaningful representations of the input data reducing the need for advanced preprocessing techniques required for classical chemometric model types such as artificial neural networks (ANNs) and partial least-squares regression (PLS-R). A comparison between different CNN architectures utilizing 2D and 3D convolution is conducted. These results are compared to the performance of ANNs and PLS-R. Additionally, a variety of preprocessing techniques from image analysis and chemometrics are tested. These include centering, scaling, standard normal variate (SNV), Savitzky-Golay (SG) filtering, and detrending. The results indicate that the combination of NIR-HSI and CNNs has the potential to be the foundation for an automatic system unifying qualitative and quantitative grain quality analysis within a single sensor technology and predictive model type.

Keywords: deep learning, grain analysis, hyperspectral imaging, preprocessing techniques

Procedia PDF Downloads 74

Authors: Samira Baghbanbari, A. B. P. Lever, Payam S. Shabestari, Donald Weaver

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Existing signal transmission models, although undoubtedly useful, have proven insufficient to explain the full complexity of information transfer within the central nervous system. The development of transformative models will necessitate a more comprehensive understanding of neuronal lipid membrane electrophysiology. Pursuant to this goal, the role of highly organized interfacial phospholipid-water layers emerges as a promising case study. A series of phospholipids in neural-glial gap junction interfaces as well as cholesterol molecules have been computationally modelled using high-performance density functional theory (DFT) calculations. Subsequent 'charge decomposition analysis' calculations have revealed a net transfer of charge from phospholipid orbitals through the organized interfacial water layer before ultimately finding its way to cholesterol acceptor molecules. The specific pathway of charge transfer from phospholipid via water layers towards cholesterol has been mapped in detail. Cholesterol is an essential membrane component that is overrepresented in neuronal membranes as compared to other mammalian cells; given this relative abundance, its apparent role as an electronic acceptor may prove to be a relevant factor in further signal transmission studies of the central nervous system. The timescales over which this electronic charge transfer occurs have also been evaluated by utilizing a system design that systematically increases the number of water molecules separating lipids and cholesterol. Memory loss through hydrogen-bonded networks in water can occur at femtosecond timescales, whereas existing action potential-based models are limited to micro or nanosecond scales. As such, the development of future models that attempt to explain faster timescale signal transmission in the central nervous system may benefit from our work, which provides additional information regarding fast timescale energy transfer mechanisms occurring through interfacial water. The study possesses a dataset that includes six distinct phospholipids and a collection of cholesterol. Ten optimized geometric characteristics (features) were employed to conduct binary classification through an artificial neural network (ANN), differentiating cholesterol from the various phospholipids. This stems from our understanding that all lipids within the first group function as electronic charge donors, while cholesterol serves as an electronic charge acceptor.

Keywords: charge transfer, signal transmission, phospholipids, water layers, ANN

Procedia PDF Downloads 40

Authors: Piotr Jablonski, Krzysztof Mars, Wiktor Niemiec, Agnieszka Kyziol, Marek Hebda, Halina Krawiec, Karol Kyziol

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NiTi alloys, possessing unique mechanical properties such as pseudoelasticity and shape memory effect (SME), are suitable for many applications, including implanthology and biomedical devices. Additionally, these alloys have similar values of elastic modulus to those of human bones, what is very important in orthopedics. Unfortunately, the environment of physiological fluids in vivo causes unfavorable release of Ni ions, which in turn may lead to metalosis as well as allergic reactions and toxic effects in the body. For these reasons, the surface properties of NiTi alloys should be improved to increase corrosion resistance, taking into account biological properties, i.e. excellent biocompatibility. The prospective in this respect are layers based on DLC (Diamond-Like Carbon) structures, which are an attractive solution for many applications in implanthology. These coatings (DLC), usually obtained by PVD (Physical Vapour Deposition) and PA CVD (Plasma Activated Chemical Vapour Deposition) methods, can be also modified by doping with other elements like silicon, nitrogen, oxygen, fluorine, titanium and silver. These methods, in combination with a suitably designed structure of the layers, allow the possibility co-decide about physicochemical and biological properties of modified surfaces. Mentioned techniques provide specific physicochemical properties of substrates surface in a single technological process. In this work, the following types of layers based on DLC structures (incl. Si-DLC or Si/N-DLC) were proposed as prospective and attractive approach in surface functionalization of shape memory alloy. Nitinol substrates were modified in plasma conditions, using RF CVD (Radio Frequency Chemical Vapour Deposition). The influence of plasma treatment on the useful properties of modified substrates after deposition DLC layers doped with silica and/or nitrogen atoms, as well as only pre-treated in O2 NH3 plasma atmosphere in a RF reactor was determined. The microstructure and topography of the modified surfaces were characterized using scanning electron microscopy (SEM) and atomic force microscopy (AFM). Furthermore, the atomic structure of coatings was characterized by IR and Raman spectroscopy. The research also included the evaluation of surface wettability, surface energy as well as the characteristics of selected mechanical and biological properties of the layers. In addition, the corrosion properties of alloys after and before modification in the physiological saline were also investigated. In order to determine the corrosion resistance of NiTi in the Ringer solution, the potentiodynamic polarization curves (LSV – Linear Sweep Voltamperometry) were plotted. Furthermore, the evolution of corrosion potential versus immersion time of TiNi alloy in Ringer solution was performed. Based on all carried out research, the usefullness of proposed modifications of nitinol for medical applications was assessed. It was shown, inter alia, that the obtained Si-DLC layers on the surface of NiTi alloy exhibit a characteristic complex microstructure, increased surface development, which is an important aspect in improving the osteointegration of an implant. Furthermore, the modified alloy exhibits biocompatibility, the transfer of the metal (Ni, Ti) to Ringer’s solution is clearly limited.

Keywords: bioactive coatings, corrosion resistance, doped DLC structure, NiTi alloy, RF CVD

Procedia PDF Downloads 203

Authors: Tanima Chatterjee, Maitree Bhattacharyya

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The global challenge of type 2 diabetes mellitus is a major health concern in this millennium, and researchers are continuously exploring new targets to develop a novel therapeutic strategy. Type 2 diabetes mellitus (T2DM) is often coupled with dyslipidemia increasing the risks for cardiovascular (CVD) complications. Enhanced oxidative and nitrosative stresses appear to be the major risk factors underlying insulin resistance, dyslipidemia, β-cell dysfunction, and T2DM pathogenesis. Autophagy emerges to be a promising defense mechanism against stress-mediated cell damage regulating tissue homeostasis, cellular quality control, and energy production, promoting cell survival. In this study, we have attempted to explore the pivotal role of autophagy in T2DM subjects with or without dyslipidemia in peripheral blood mononuclear cells and insulin-resistant HepG2 cells utilizing flow cytometric platform, confocal microscopy, and molecular biology techniques like western blotting, immunofluorescence, and real-time polymerase chain reaction. In the case of T2DM with dyslipidemia higher population of autophagy, positive cells were detected compared to patients with the only T2DM, which might have resulted due to higher stress. Autophagy was observed to be triggered both by oxidative and nitrosative stress revealing a novel finding of our research. LC3 puncta was observed in peripheral blood mononuclear cells and periphery of HepG2 cells in the case of the diabetic and diabetic-dyslipidemic conditions. Increased expression of ATG5, LC3B, and Beclin supports the autophagic pathway in both PBMC and insulin-resistant Hep G2 cells. Upon blocking autophagy by 3-methyl adenine (3MA), the apoptotic cell population increased significantly, as observed by caspase‐3 cleavage and reduced expression of Bcl2. Autophagy has also been evidenced to control oxidative stress-mediated up-regulation of inflammatory markers like IL-6 and TNF-α. To conclude, this study elucidates autophagy to play a protective role in the case of diabetes mellitus with dyslipidemia. In the present scenario, this study demands to have a significant impact on developing a new therapeutic strategy for diabetic dyslipidemic subjects by enhancing autophagic activity.

Keywords: autophagy, apoptosis, dyslipidemia, reactive oxygen species, reactive nitrogen species, Type 2 diabetes

Procedia PDF Downloads 107

Authors: Taus Muganda

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This research explores the intricate relationship between e-procurement adoption and effective service delivery in the Uganda Coffee Industry, focusing on the processes involved, key actors, and the impact of digital transformation. The study is guided by three prominent theories, Actor-Network Theory, Resource-Based View Theory, and Institutional Theory to comprehensively explore the dynamics of e-procurement in the context of the coffee sector. The primary aim of this project is to examine the e-procurement adoption process and its role in enhancing service delivery within the Uganda Coffee Industry. The research questions guiding this inquiry are: firstly, whether e-procurement adoption and implementation contribute to achieving quality service delivery; and secondly, how e-procurement adoption can be effectively realized within the Uganda Coffee Industry. To address these questions, the study has laid out specific objectives. Firstly, it seeks to investigate the impact of e-procurement on effective service delivery, analysing how the integration of digital processes influences the overall quality of services provided in the coffee industry. Secondly, it aims to critically analyse the measures required to achieve effective delivery outcomes through the adoption and implementation of e-procurement, assessing the strategies that can maximize the benefits of digital transformation. Furthermore, the research endeavours to identify and examine the key actor’s instrumental in achieving effective service delivery within the Uganda Coffee Industry. By utilizing Actor-Network Theory, the study will elucidate the network of relationships and collaborations among actors involved in the e-procurement process. The research contributes to addressing a critical gap in the sector. Despite coffee being the leading export crop in Uganda, constituting 16% of total exports, there is a recognized need for digital transformation, specifically in the realm of e-procurement, to enhance the productivity of producers and contribute to the economic growth of the country. The study aims to provide insights into transforming the Uganda Coffee Industry by focusing on improving the e-procurement services delivered to actors in the coffee sector. The three forms of e-procurement investigated in this research—E-Sourcing, E-Payment, and E-Invoicing—serve as focal points in understanding the multifaceted dimensions of digital integration within the Uganda Coffee Industry. This research endeavours to offer practical recommendations for policymakers, industry stakeholders, and the UCDA to strategically leverage e-procurement for the benefit of the entire coffee value chain.

Keywords: e-procurement, effective service delivery, actors, actor-network theory, resource-based view theory, institutional theory, e-invocing, e-payment, e-sourcing

Procedia PDF Downloads 32

Authors: Mohkammad Nur Cahyadi, Hepi Hapsari Handayani, Agus Budi Raharjo, Ronny Mardianto, Daud Wahyu Imani, Arizal Bawazir, Luki Adi Triawan

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PDAM (local water company) determines customer charges by considering the customer's building or house. Charges determination significantly affects PDAM income and customer costs because the PDAM applies a subsidy policy for customers classified as small households. Periodic updates are needed so that pricing is in line with the target. A thorough customer survey in Surabaya is needed to update customer building data. However, the survey that has been carried out so far has been by deploying officers to conduct one-by-one surveys for each PDAM customer. Surveys with this method require a lot of effort and cost. For this reason, this research offers a technology called moblie mapping, a mapping method that is more efficient in terms of time and cost. The use of this tool is also quite simple, where the device will be installed in the car so that it can record the surrounding buildings while the car is running. Mobile mapping technology generally uses lidar sensors equipped with GNSS, but this technology requires high costs. In overcoming this problem, this research develops low-cost mobile mapping technology using a webcam camera sensor added to the GNSS and IMU sensors. The camera used has specifications of 3MP with a resolution of 720 and a diagonal field of view of 78⁰. The principle of this invention is to integrate four camera sensors, a GNSS webcam, and GPS to acquire photo data, which is equipped with location data (latitude, longitude) and IMU (roll, pitch, yaw). This device is also equipped with a tripod and a vacuum cleaner to attach to the car's roof so it doesn't fall off while running. The output data from this technology will be analyzed with artificial intelligence to reduce similar data (Cosine Similarity) and then classify building types. Data reduction is used to eliminate similar data and maintain the image that displays the complete house so that it can be processed for later classification of buildings. The AI method used is transfer learning by utilizing a trained model named VGG-16. From the analysis of similarity data, it was found that the data reduction reached 50%. Then georeferencing is done using the Google Maps API to get address information according to the coordinates in the data. After that, geographic join is done to link survey data with customer data already owned by PDAM Surya Sembada Surabaya.

Keywords: mobile mapping, GNSS, IMU, similarity, classification

Procedia PDF Downloads 57

Authors: Sakshi Gupta, Anupam Agrawal, Ekta Singla

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An exoskeleton design for rehabilitation purpose encounters many challenges, including ergonomically acceptable wearing technology, architectural design human-motion compatibility, actuation type, human-robot interaction, etc. In this paper, a passive architecture for upper limb exoskeleton is proposed for assisting in rehabilitation tasks. Kinematic modelling is detailed for task-based kinematic synthesis of the wearable exoskeleton for self-feeding tasks. The exoskeleton architecture possesses expansion and torsional springs which are able to store and redistribute energy over the human arm joints. The elastic characteristics of the springs have been optimized to minimize the mechanical work of the human arm joints. The concept of hybrid combination of a 4-bar parallelogram linkage and a serial linkage were chosen, where the 4-bar parallelogram linkage with expansion spring acts as a rigid structure which is used to provide the rotational degree-of-freedom (DOF) required for lowering and raising of the arm. The single linkage with torsional spring allows for the rotational DOF required for elbow movement. The focus of the paper is kinematic modelling, analysis and task-based synthesis framework for the proposed architecture, keeping in considerations the essential tasks of self-feeding and self-exercising during rehabilitation of partially healthy person. Rehabilitation of primary functional movements (activities of daily life, i.e., ADL) is routine activities that people tend to every day such as cleaning, dressing, feeding. We are focusing on the feeding process to make people independent in respect of the feeding tasks. The tasks are focused to post-surgery patients under rehabilitation with less than 40% weakness. The challenges addressed in work are ensuring to emulate the natural movement of the human arm. Human motion data is extracted through motion-sensors for targeted tasks of feeding and specific exercises. Task-based synthesis procedure framework will be discussed for the proposed architecture. The results include the simulation of the architectural concept for tracking the human-arm movements while displaying the kinematic and static study parameters for standard human weight. D-H parameters are used for kinematic modelling of the hybrid-mechanism, and the model is used while performing task-based optimal synthesis utilizing evolutionary algorithm.

Keywords: passive mechanism, task-based synthesis, emulating human-motion, exoskeleton

Procedia PDF Downloads 118

Authors: Gamze Safak, Umut Arslanoglu

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Cultural and tourism conservation and development zones and tourism centers are the boundaries declared for the purpose of protecting, using, and evaluating the sectoral development and planned development in areas where historical and cultural values are heavily involved and/or where tourism potential is high. The most rapidly changing regions in Turkey are tourism areas, especially the coastal areas. Planning these regions is not about only an economic gain but also a natural and physical environment and refers to a complex process. If the tourism sector is not well controlled, excessive use of natural resources and wrong location choices may cause damage to natural areas, historical values, and socio-cultural structure. Since the strategic decisions taken in the environmental order and zoning plans, which are the means of guiding the physical environment of the Ministry of Culture and Tourism, which have the authority to make plans in tourism centers, are transformed into plan decisions that find the spatial expression, comprehensive evaluation of all kinds of data, following the historical development and based on the correct and current data is required. In addition, the authority has a number of competences in tourism promotion as well as the authority to plan, leading to the necessity of taking part in the applications requiring complex analysis such as the management and integration of the country's economic, political, social and cultural resources. For this purpose, Tourism Inventory System (TES) project, which consists of a series of subsystems, has been developed in order to solve complex planning and method problems in the management of site-related information. The scope of the project is based on the integration of numerical and verbal data in the regions within the jurisdiction of the authority, and the monitoring of the historical development of urban planning studies, making the spatial data of the institution easily accessible, shared, questionable and traceable in international standards. A dynamic and continuous system design has been put into practice by utilizing the advantage of the use of Geographical Information Systems in the planning process to play a role in making the right decisions, revealing the tools of social, economic, cultural development, and preservation of natural and cultural values. This paper, which is prepared by the project team members in TES (Tourism Inventory System), will present a study regarding the applicability of GIS in cultural and natural heritage conservation.

Keywords: cultural conservation, GIS, geographic information system, tourism inventory system, urban planning

Procedia PDF Downloads 98

Authors: Karolina Porada

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Over the last 40 years, there has been a trend in landscape architecture which supporters do not perceive the role of pro-ecological or postmodern solutions in the design of public green spaces as an essential goal, shifting their attention to the 'sculptural' shaping of areas with the use of slopes, hills, embankments, and other forms of terrain. This group of designers can be considered avant-garde, which in its activities refers to land art. Initial research shows that such applications are particularly frequent in places of former post-industrial sites and landfills, utilizing materials such as debris and post-mining waste in their construction. Due to the high degradation of the environment surrounding modern man, the brownfields are a challenge and a field of interest for the representatives of landscape architecture avant-garde, who through their projects try to recover lost lands by means of transformations supported by engineering and ecological knowledge to create places where nature can develop again. The analysis of a dozen or so facilities made it possible to come up with an important conclusion: apart from the cultural aspects (including artistic activities), the green areas formally referring to the land are important in the process of remediation of post-industrial sites and waste recycling (e. g. from construction sites). In these processes, there is also a potential for applying the concept of Natural Based Solutions, i.e. solutions allowing for the natural development of the site in such a way as to use it to cope with environmental problems, such as e.g.  air pollution, soil phytoremediation and climate change. The paper presents examples of modern parks, whose compositions are based on shaping the surface of the terrain in a way referring to the land art, at the same time providing an example of brownfields reuse and application of waste recycling.  For the purposes of object analysis, research methods such as historical-interpretation studies, case studies, qualitative research or the method of logical argumentation were used. The obtained results provide information about the role that landscape architecture can have in the process of remediation of degraded areas, at the same time guaranteeing the benefits, such as the shaping of landscapes attractive in terms of visual appearance, low costs of implementation, and improvement of the natural environment quality.

Keywords: brownfields, contemporary parks, landscape architecture, remediation

Procedia PDF Downloads 128

Authors: Ece Cigdem Mutlu, Burak Alakent

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Maintaining the quality of manufactured products at a desired level depends on the stability of process dispersion and location parameters and detection of perturbations in these parameters as promptly as possible. Shewhart control chart is the most widely used technique in statistical process monitoring to monitor the quality of products and control process mean and variability. In the application of Xbar control charts, sample standard deviation and sample mean are known to be the most efficient conventional estimators in determining process dispersion and location parameters, respectively, based on the assumption of independent and normally distributed datasets. On the other hand, there is no guarantee that the real-world data would be normally distributed. In the cases of estimated process parameters from Phase I data clouded with outliers, efficiency of traditional estimators is significantly reduced, and performance of Xbar charts are undesirably low, e.g. occasional outliers in the rational subgroups in Phase I data set may considerably affect the sample mean and standard deviation, resulting a serious delay in detection of inferior products in Phase II. For more efficient application of control charts, it is required to use robust estimators against contaminations, which may exist in Phase I. In the current study, we present a simple approach to construct robust Xbar control charts using average distance to the median, Qn-estimator of scale, M-estimator of scale with logistic psi-function in the estimation of process dispersion parameter, and Harrell-Davis qth quantile estimator, Hodge-Lehmann estimator and M-estimator of location with Huber psi-function and logistic psi-function in the estimation of process location parameter. Phase I efficiency of proposed estimators and Phase II performance of Xbar charts constructed from these estimators are compared with the conventional mean and standard deviation statistics both under normality and against diffuse-localized and symmetric-asymmetric contaminations using 50,000 Monte Carlo simulations on MATLAB. Consequently, it is found that robust estimators yield parameter estimates with higher efficiency against all types of contaminations, and Xbar charts constructed using robust estimators have higher power in detecting disturbances, compared to conventional methods. Additionally, utilizing individuals charts to screen outlier subgroups and employing different combination of dispersion and location estimators on subgroups and individual observations are found to improve the performance of Xbar charts.

Keywords: average run length, M-estimators, quality control, robust estimators

Procedia PDF Downloads 170