Search results for: robustness

Authors: Mahsa Mousavi, Hamid Reza Pourshaghaghi, Mohammad Tahghighi, Henk Corporaal

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Recently, Statistic Random-Access Memory-based (SRAM-based) Field-Programmable Gate Arrays (FPGAs) are widely used in aeronautics and space systems where high dependability is demanded and considered as a mandatory requirement. Since design’s circuit is stored in configuration memory in SRAM-based FPGAs; they are very sensitive to Single Event Upsets (SEUs). In addition, the adverse effects of SEUs on the electronics used in space are much higher than in the Earth. Thus, developing fault tolerant techniques play crucial roles for the use of SRAM-based FPGAs in space. However, fault tolerance techniques introduce additional penalties in system parameters, e.g., area, power, performance and design time. In this paper, an accurate estimation of configuration memory vulnerability to SEUs is proposed for approximate-tolerant applications. This vulnerability estimation is highly required for compromising between the overhead introduced by fault tolerance techniques and system robustness. In this paper, we study applications in which the exact final output value is not necessarily always a concern meaning that some of the SEU-induced changes in output values are negligible. We therefore define and propose Approximate-based Configuration Memory Vulnerability Factor (ACMVF) estimation to avoid overestimating configuration memory vulnerability to SEUs. In this paper, we assess the vulnerability of configuration memory by injecting SEUs in configuration memory bits and comparing the output values of a given circuit in presence of SEUs with expected correct output. In spite of conventional vulnerability factor calculation methods, which accounts any deviations from the expected value as failures, in our proposed method a threshold margin is considered depending on user-case applications. Given the proposed threshold margin in our model, a failure occurs only when the difference between the erroneous output value and the expected output value is more than this margin. The ACMVF is subsequently calculated by acquiring the ratio of failures with respect to the total number of SEU injections. In our paper, a test-bench for emulating SEUs and calculating ACMVF is implemented on Zynq-7000 FPGA platform. This system makes use of the Single Event Mitigation (SEM) IP core to inject SEUs into configuration memory bits of the target design implemented in Zynq-7000 FPGA. Experimental results for 32-bit adder show that, when 1% to 10% deviation from correct output is considered, the counted failures number is reduced 41% to 59% compared with the failures number counted by conventional vulnerability factor calculation. It means that estimation accuracy of the configuration memory vulnerability to SEUs is improved up to 58% in the case that 10% deviation is acceptable in output results. Note that less than 10% deviation in addition result is reasonably tolerable for many applications in approximate computing domain such as Convolutional Neural Network (CNN).

Keywords: fault tolerance, FPGA, single event upset, approximate computing

Procedia PDF Downloads 198

Authors: I. Martinez-Conesa, E. Garcia-Meca, M. Barradas-Quiroz

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The objective of this study is to assess the impact on earnings management of latest two Hofstede cultural dimensions: long-term orientation and indulgence. Long-term orientation represents the alignment of a society towards the future and indulgence expresses the extent to which a society exhibits willingness, or restrain, to realise their impulses. Additionally, this paper tests if there are relevant differences by testing the moderating role of the legal tradition, Continental versus Anglo-Saxon. Our sample comprises 15 countries: Belgium, Canada, Germany, Spain, France, Great Britain, Hong Kong, India, Japan, Korea, Netherlands, Philippines, Portugal, Sweden, and Thailand, with a total of 12,936 observations from 2003 to 2013. Our results show that managers in countries with high levels of long-term orientation reduce their levels of discretionary accruals. The findings do not confirm the effect of indulgence on earnings management. In addition, our results confirm previous literature regarding the effect of individualism, noting that firms in countries with high levels of collectivism might be more inclined to use earnings discretion to protect the welfare of the collective group of firm stakeholders. Uncertainty avoidance results in downwards earnings management as well as high disclosure, suggesting that less manipulation takes place when transparency is higher. Indulgence is the cultural dimension that confronts wellbeing versus survival; dimension is formulated including happiness, the perception of live control and the importance of leisure. Indulgence shows a weak negative correlation with power distance indicating a slight tendency for more hierarchical societies to be less indulgent. Anglo-Saxon countries are a positive effect of individualism and a negative effect of masculinity, uncertainty avoidance, and disclosure. With respect to continental countries, we can see a significant and positive effect of individualism and a significant and negative effect of masculinity, long-term orientation, and indulgence. Therefore, we observe the negative effect on earnings management provoked by higher disclosure and uncertainty avoidance only happens in Anglo-Saxon countries. Meanwhile, the improvement in reporting quality motivated by higher long-term orientation and higher indulgence is dominant in Continental countries. Our results confirm that there is a moderating effect of the legal system in the association between culture and earnings management. This effect is especially relevant in the dimensions related to uncertainty avoidance, long term orientation, indulgence, and disclosure. The negative effect of long-term orientation on earnings management only happens in those countries set in continental legal systems because of the Anglo-Saxon legal systems is supported by the decisions of the courts and the traditions, so it already has long-term orientation. That does not occur in continental systems, depending mainly of contend of the law. Sensitivity analysis used with Jones modified CP model, Jones Standard model and Jones Standard CP model confirm the robustness of these results. This paper collaborates towards a better understanding on how earnings management, culture and legal systems relate to each other, and contribute to previous literature by examining the influence of the two latest Hofstede’s dimensions not previously studied in papers.

Keywords: Hofstede, long-term-orientation, earnings management, indulgence

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Authors: Kaushikk Iyer, Richard M Hall, David Keeling

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Hardware-in-the-loop (HIL) simulation is an advanced technique for developing and testing complex real-time control systems. This paper presents the benefits of HIL simulation and how it can be implemented and used effectively to develop, test, and validate advanced control algorithms used in a spinal joint Wear simulator for the Tribological testing of spinal disc prostheses. spinal wear simulator is technologically the most advanced machine currently employed For the in-vitro testing of newly developed spinal Discimplants. However, the existing control techniques, such as a simple position control Does not allow the simulator to test non-sinusoidal waveforms. Thus, there is a need for better and advanced control methods that can be developed and tested Rigorouslybut safely before deploying it into the real simulator. A benchtop HILsetupis was created for experimentation, controller verification, and validation purposes, allowing different control strategies to be tested rapidly in a safe environment. The HIL simulation aspect in this setup attempts to replicate similar spinal motion and loading conditions. The spinal joint wear simulator containsa four-Barlinkpowered by electromechanical actuators. LabVIEW software is used to design a kinematic model of the spinal wear Simulator to Validatehow each link contributes towards the final motion of the implant under test. As a result, the implant articulates with an angular motion specified in the international standards, ISO-18192-1, that define fixed, simplified, and sinusoid motion and load profiles for wear testing of cervical disc implants. Using a PID controller, a velocity-based position control algorithm was developed to interface with the benchtop setup that performs HIL simulation. In addition to PID, a fuzzy logic controller (FLC) was also developed that acts as a supervisory controller. FLC provides intelligence to the PID controller by By automatically tuning the controller for profiles that vary in amplitude, shape, and frequency. This combination of the fuzzy-PID controller is novel to the wear testing application for spinal simulators and demonstrated superior performance against PIDwhen tested for a spectrum of frequency. Kaushikk Iyer is a Ph.D. Student at the University of Leeds and an employee at Key Engineering Solutions, Leeds, United Kingdom, (e-mail: [email protected], phone: +44 740 541 5502). Richard M Hall is with the University of Leeds, the United Kingdom as a professor in the Mechanical Engineering Department (e-mail: [email protected]). David Keeling is the managing director of Key Engineering Solutions, Leeds, United Kingdom (e-mail: [email protected]). Results obtained are successfully validated against the load and motion tolerances specified by the ISO18192-1 standard and fall within limits, that is, ±0.5° at the maxima and minima of the motion and ±2 % of the complete cycle for phasing. The simulation results prove the efficacy of the test setup using HIL simulation to verify and validate the accuracy and robustness of the prospective controller before its deployment into the spinal wear simulator. This method of testing controllers enables a wide range of possibilities to test advanced control algorithms that can potentially test even profiles of patients performing various dailyliving activities.

Keywords: Fuzzy-PID controller, hardware-in-the-loop (HIL), real-time simulation, spinal wear simulator

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Authors: Natia Jalagonia, Tinatin Kuchukhidze

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Polymer electrolytes (PE) play an important part in electrochemical devices such as batteries and fuel cells. To achieve optimal performance, the PE must maintain a high ionic conductivity and mechanical stability at both high and low relative humidity. The polymer electrolyte also needs to have excellent chemical stability for long and robustness. According to the prevailing theory, ionic conduction in polymer electrolytes is facilitated by the large-scale segmental motion of the polymer backbone, and primarily occurs in the amorphous regions of the polymer electrolyte. Crystallinity restricts polymer backbone segmental motion and significantly reduces conductivity. Consequently, polymer electrolytes with high conductivity at room temperature have been sought through polymers which have highly flexible backbones and have largely amorphous morphology. The interest in polymer electrolytes was increased also by potential applications of solid polymer electrolytes in high energy density solid state batteries, gas sensors and electrochromic windows. Conductivity of 10-3 S/cm is commonly regarded as a necessary minimum value for practical applications in batteries. At present, polyethylene oxide (PEO)-based systems are most thoroughly investigated, reaching room temperature conductivities of 10-7 S/cm in some cross-linked salt in polymer systems based on amorphous PEO-polypropylene oxide copolymers.. It is widely accepted that amorphous polymers with low glass transition temperatures Tg and a high segmental mobility are important prerequisites for high ionic conductivities. Another necessary condition for high ionic conductivity is a high salt solubility in the polymer, which is most often achieved by donors such as ether oxygen or imide groups on the main chain or on the side groups of the PE. It is well established also that lithium ion coordination takes place predominantly in the amorphous domain, and that the segmental mobility of the polymer is an important factor in determining the ionic mobility. Great attention was pointed to PEO-based amorphous electrolyte obtained by synthesis of comb-like polymers, by attaching short ethylene oxide unit sequences to an existing amorphous polymer backbone. The aim of presented work is to obtain of solid polymer electrolyte membranes using PMHS as a matrix. For this purpose the hydrosilylation reactions of α,ω-bis(trimethylsiloxy)methyl¬hydrosiloxane with allyl triethylene-glycol mo¬nomethyl ether and vinyltriethoxysilane at 1:28:7 ratio of initial com¬pounds in the presence of Karstedt’s catalyst, platinum hydrochloric acid (0.1 M solution in THF) and platinum on the carbon catalyst in 50% solution of anhydrous toluene have been studied. The synthesized olygomers are vitreous liquid products, which are well soluble in organic solvents with specific viscosity ηsp ≈ 0.05 - 0.06. The synthesized olygomers were analysed with FTIR, 1H, 13C, 29Si NMR spectroscopy. Synthesized polysiloxanes were investigated with wide-angle X-ray, gel-permeation chromatography, and DSC analyses. Via sol-gel processes of doped with lithium trifluoromethylsulfonate (triflate) or lithium bis¬(trifluoromethylsulfonyl)¬imide polymer systems solid polymer electrolyte membranes have been obtained. The dependence of ionic conductivity as a function of temperature and salt concentration was investigated and the activation energies of conductivity for all obtained compounds are calculated

Keywords: synthesis, PMHS, membrane, electrolyte

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Authors: Roberta Mugellesi Dow

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As the climate crisis becomes unignorable, it is imperative that new services are developed addressing not only the needs of customers but also taking into account its impact on the environment. The Telecommunication and Integrated Application (TIA) Directorate of ESA is supporting the green transition with particular attention to the sustainable mobility.“Accelerating the shift to sustainable and smart mobility” is at the core of the European Green Deal strategy, which seeks a 90% reduction in related emissions by 2050 . Transforming the way that people and goods move is essential to increasing mobility while decreasing environmental impact, and transport must be considered holistically to produce a shared vision of green intermodal mobility. The use of space technologies, integrated with terrestrial technologies, is an enabler of smarter traffic management and increased transport efficiency for automated and connected multimodal mobility. Satellite connectivity, including future 5G networks, and digital technologies such as Digital Twin, AI, Machine Learning, and cloud-based applications are key enablers of sustainable mobility.SatCom is essential to ensure that connectivity is ubiquitously available, even in remote and rural areas, or in case of a failure, by the convergence of terrestrial and SatCom connectivity networks, This is especially crucial when there are risks of network failures or cyber-attacks targeting terrestrial communication. SatCom ensures communication network robustness and resilience. The combination of terrestrial and satellite communication networks is making possible intelligent and ubiquitous V2X systems and PNT services with significantly enhanced reliability and security, hyper-fast wireless access, as well as much seamless communication coverage. SatNav is essential in providing accurate tracking and tracing capabilities for automated vehicles and in guiding them to target locations. SatNav can also enable location-based services like car sharing applications, parking assistance, and fare payment. In addition to GNSS receivers, wireless connections, radar, lidar, and other installed sensors can enable automated vehicles to monitor surroundings, to ‘talk to each other’ and with infrastructure in real-time, and to respond to changes instantaneously. SatEO can be used to provide the maps required by the traffic management, as well as evaluate the conditions on the ground, assess changes and provide key data for monitoring and forecasting air pollution and other important parameters. Earth Observation derived data are used to provide meteorological information such as wind speed and direction, humidity, and others that must be considered into models contributing to traffic management services. The paper will provide examples of services and applications that have been developed aiming to identify innovative solutions and new business models that are allowed by new digital technologies engaging space and non space ecosystem together to deliver value and providing innovative, greener solutions in the mobility sector. Examples include Connected Autonomous Vehicles, electric vehicles, green logistics, and others. For the technologies relevant are the hybrid satcom and 5G providing ubiquitous coverage, IoT integration with non space technologies, as well as navigation, PNT technology, and other space data.

Keywords: sustainability, connectivity, mobility, satellites

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Authors: Chen Jiang, Eric Jordan, Maurice Gell, Balakrishnan Nair

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Nuclear fusion, one of the most promising options for reliably generating large amounts of carbon-free energy in the future, has seen a plethora of ground-breaking technological advances in recent years. An efficient and durable “breeding blanket”, needed to ensure a reactor’s self-sufficiency by maintaining the optimal coolant temperature as well as by minimizing radiation dosage behind the blanket, still remains a technological challenge for the various reactor designs for commercial fusion power plants. A relatively new dual-coolant lead-lithium (DCLL) breeder design has exhibited great potential for high-temperature (>700oC), high-thermal-efficiency (>40%) fusion reactor operation. However, the structural material, namely reduced activation ferritic-martensitic (RAFM) steel, is not chemically stable in contact with molten Pb-17%Li coolant. Thus, to utilize this new promising reactor design, the demand for effective corrosion-resistant coatings on RAFM steels represents a pressing need. Solution Spray Technologies LLC (SST) is developing a double-layer ceramic coating design to address the corrosion protection of RAFM steels, using a novel solution and solution/suspension plasma spray technology through a US Department of Energy-funded project. Plasma spray is a coating deposition method widely used in many energy applications. Novel derivatives of the conventional powder plasma spray process, known as the solution-precursor and solution/suspension-hybrid plasma spray process, are powerful methods to fabricate thin, dense ceramic coatings with complex compositions necessary for the corrosion protection in DCLL breeders. These processes can be used to produce ultra-fine molten splats and to allow fine adjustment of coating chemistry. Thin, dense ceramic coatings with chosen chemistry for superior chemical stability in molten Pb-Li, low activation properties, and good radiation tolerance, is ideal for corrosion-protection of RAFM steels. A key challenge is to accommodate its CTE mismatch with the RAFM substrate through the selection and incorporation of appropriate bond layers, thus allowing for enhanced coating durability and robustness. Systematic process optimization is being used to define the optimal plasma spray conditions for both the topcoat and bond-layer, and X-ray diffraction and SEM-EDS are applied to successfully validate the chemistry and phase composition of the coatings. The plasma-sprayed double-layer corrosion resistant coatings were also deposited onto simulated RAFM steel substrates, which are being tested separately under thermal cycling, high-temperature moist air oxidation as well as molten Pb-Li capsule corrosion conditions. Results from this testing on coated samples, and comparisons with bare RAFM reference samples will be presented and conclusions will be presented assessing the viability of the new ceramic coatings to be viable corrosion prevention systems for DCLL breeders in commercial nuclear fusion reactors.

Keywords: breeding blanket, corrosion protection, coating, plasma spray

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Authors: Nuno Biga

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Artificial Intelligence (AI) has the potential to transform management into several impactful ways. It allows machines to interpret information to find patterns in big data and learn from context analysis, optimize operations, make predictions sensitive to each specific situation and support data-driven decision making. The introduction of an 'artificial brain' in organization also enables learning through complex information and data provided by those who train it, namely its users. The "Assisted-BIGAMES" version of the Accident & Emergency (A&E) simulator introduces the concept of a "Virtual Assistant" (VA) sensitive to context, that provides users useful suggestions to pursue the following operations such as: a) to relocate workstations in order to shorten travelled distances and minimize the stress of those involved; b) to identify in real time existing bottleneck(s) in the operations system so that it is possible to quickly act upon them; c) to identify resources that should be polyvalent so that the system can be more efficient; d) to identify in which specific processes it may be advantageous to establish partnership with other teams; and e) to assess possible solutions based on the suggested KPIs allowing action monitoring to guide the (re)definition of future strategies. This paper is built on the BIGAMES© simulator and presents the conceptual AI model developed and demonstrated through a pilot project (BIG-AI). Each Virtual Assisted BIGAME is a management simulator developed by the author that guides operational and strategic decision making, providing users with useful information in the form of management recommendations that make it possible to predict the actual outcome of different alternative management strategic actions. The pilot project developed incorporates results from 12 editions of the BIGAME A&E that took place between 2017 and 2022 at AESE Business School, based on the compilation of data that allows establishing causal relationships between decisions taken and results obtained. The systemic analysis and interpretation of data is powered in the Assisted-BIGAMES through a computer application called "BIGAMES Virtual Assistant" (VA) that players can use during the Game. Each participant in the VA permanently asks himself about the decisions he should make during the game to win the competition. To this end, the role of the VA of each team consists in guiding the players to be more effective in their decision making, through presenting recommendations based on AI methods. It is important to note that the VA's suggestions for action can be accepted or rejected by the managers of each team, as they gain a better understanding of the issues along time, reflect on good practice and rely on their own experience, capability and knowledge to support their own decisions. Preliminary results show that the introduction of the VA provides a faster learning of the decision-making process. The facilitator designated as “Serious Game Controller” (SGC) is responsible for supporting the players with further analysis. The recommended actions by the SGC may differ or be similar to the ones previously provided by the VA, ensuring a higher degree of robustness in decision-making. Additionally, all the information should be jointly analyzed and assessed by each player, who are expected to add “Emotional Intelligence”, an essential component absent from the machine learning process.

Keywords: artificial intelligence, gamification, key performance indicators, machine learning, management simulators, serious games, virtual assistant

Procedia PDF Downloads 105

Authors: Md. Abu Sufian, Robiqul Islam, Imam Hossain Shajid, Mahesh Hanumanthu, Jarasree Varadarajan, Md. Sipon Miah, Mingbo Niu

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Coronary Heart Disease (CHD) remains a principal cause of global morbidity and mortality, characterized by atherosclerosis—the build-up of fatty deposits inside the arteries. The study introduces an innovative methodology that leverages cloud-based platforms like AWS Live Streaming and Artificial Intelligence (AI) to early detect and prevent CHD symptoms in web applications. By employing novel simulation processes and AI algorithms, this research aims to significantly mitigate the health and societal impacts of CHD. Methodology: This study introduces a novel simulation process alongside a multi-phased model development strategy. Initially, health-related data, including heart rate variability, blood pressure, lipid profiles, and ECG readings, were collected through user interactions with web-based applications as well as API Integration. The novel simulation process involved creating synthetic datasets that mimic early-stage CHD symptoms, allowing for the refinement and training of AI algorithms under controlled conditions without compromising patient privacy. AWS Live Streaming was utilized to capture real-time health data, which was then processed and analysed using advanced AI techniques. The novel aspect of our methodology lies in the simulation of CHD symptom progression, which provides a dynamic training environment for our AI models enhancing their predictive accuracy and robustness. Model Development: it developed a machine learning model trained on both real and simulated datasets. Incorporating a variety of algorithms including neural networks and ensemble learning model to identify early signs of CHD. The model's continuous learning mechanism allows it to evolve adapting to new data inputs and improving its predictive performance over time. Results and Findings: The deployment of our model yielded promising results. In the validation phase, it achieved an accuracy of 92% in predicting early CHD symptoms surpassing existing models. The precision and recall metrics stood at 89% and 91% respectively, indicating a high level of reliability in identifying at-risk individuals. These results underscore the effectiveness of combining live data streaming with AI in the early detection of CHD. Societal Implications: The implementation of cloud-based AI for CHD symptom detection represents a significant step forward in preventive healthcare. By facilitating early intervention, this approach has the potential to reduce the incidence of CHD-related complications, decrease healthcare costs, and improve patient outcomes. Moreover, the accessibility and scalability of cloud-based solutions democratize advanced health monitoring, making it available to a broader population. This study illustrates the transformative potential of integrating technology and healthcare, setting a new standard for the early detection and management of chronic diseases.

Keywords: coronary heart disease, cloud-based ai, machine learning, novel simulation techniques, early detection, preventive healthcare

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Authors: Supriyo Das, Elbir Jove, Ajay Singh, Sophie Corbet, Noel Carr, Martin Deetz

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Water is a critical commodity in the healthcare and medical field. The utility of medical-grade water spans from washing surgical equipment, drug preparation to the key element of life-saving therapy such as hydrotherapy and hemodialysis for patients. A properly treated medical water reduces the bioburden load and mitigates the risk of infection, ensuring patient safety. However, any compromised condition during the production of medical-grade water can create a favorable environment for microbial growth putting patient safety at high risk. Therefore, proper upstream treatment of the medical water is essential before its application in healthcare, pharma and medical space. Reverse Osmosis (RO) is one of the most preferred treatments within healthcare industries and is recommended by all International Pharmacopeias to achieve the quality level demanded by global regulatory bodies. The RO process can remove up to 99.5% of constituents from feed water sources, eliminating bacteria, proteins and particles sizes of 100 Dalton and above. The combination of RO with other downstream water treatment technologies such as Electrodeionization and Ultrafiltration meet the quality requirements of various pharmacopeia monographs to produce highly purified water or water for injection for medical use. In the reverse osmosis process, the water from a liquid with a high concentration of dissolved solids is forced to flow through an especially engineered semi-permeable membrane to the low concentration side, resulting in high-quality grade water. However, these specially engineered RO membranes need to be sanitized either chemically or at high temperatures at regular intervals to keep the bio-burden at the minimum required level. In this paper, we talk about Dupont´s FilmTec Heat Sanitizable Reverse Osmosis membrane (HSRO) for the production of medical-grade water. An HSRO element must be pre-conditioned prior to initial use by exposure to hot water (80°C-85°C) for its stable performance and to meet the manufacturer’s specifications. Without pre-conditioning, the membrane will show variations in feed pressure operations and salt rejection. The paper will discuss the critical variables of pre-conditioning steps that can affect the overall performance of the HSRO membrane and demonstrate the data to support the need for pre-conditioning of HSRO elements. Our preliminary data suggests that there can be up to 35 % reduction in flow due to initial heat treatment, which also positively affects the increase in salt rejection. The paper will go into detail about the fundamental understanding of the performance change of HSRO after the pre-conditioning step and its effect on the quality of medical water produced. The paper will also discuss another critical point, “regular hot water sanitization” of these HSRO membranes. Regular hot water sanitization (at 80°C-85°C) is necessary to keep the membrane bioburden free; however, it can negatively impact the performance of the membrane over time. We will demonstrate several data points on hot water sanitization using FilmTec HSRO elements and challenge its robustness to produce quality medical water. The last part of this paper will discuss the construction details of the FilmTec HSRO membrane and features that make it suitable to pre-condition and sanitize at high temperatures.

Keywords: heat sanitizable reverse osmosis, HSRO, medical water, hemodialysis water, water for Injection, pre-conditioning, heat sanitization

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Authors: Maddalena Arigoni, Maria Luisa Ratto, Raffaele A. Calogero, Luca Alessandri

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The presence of tumor heterogeneity, where distinct cancer cells exhibit diverse morphological and phenotypic profiles, including gene expression, metabolism, and proliferation, poses challenges for molecular prognostic markers and patient classification for targeted therapies. Understanding the causes and progression of cancer requires research efforts aimed at characterizing heterogeneity, which can be facilitated by evolving single-cell sequencing technologies. However, analyzing single-cell data necessitates computational methods that often lack objective validation. Therefore, the establishment of benchmarking datasets is necessary to provide a controlled environment for validating bioinformatics tools in the field of single-cell oncology. Benchmarking bioinformatics tools for single-cell experiments can be costly due to the high expense involved. Therefore, datasets used for benchmarking are typically sourced from publicly available experiments, which often lack a comprehensive cell annotation. This limitation can affect the accuracy and effectiveness of such experiments as benchmarking tools. To address this issue, we introduce omics benchmark experiments designed to evaluate bioinformatics tools to depict the heterogeneity in single-cell tumor experiments. We conducted single-cell RNA sequencing on six lung cancer tumor cell lines that display resistant clones upon treatment of EGFR mutated tumors and are characterized by driver genes, namely ROS1, ALK, HER2, MET, KRAS, and BRAF. These driver genes are associated with downstream networks controlled by EGFR mutations, such as JAK-STAT, PI3K-AKT-mTOR, and MEK-ERK. The experiment also featured an EGFR-mutated cell line. Using 10XGenomics platform with cellplex technology, we analyzed the seven cell lines together with a pseudo-immunological microenvironment consisting of PBMC cells labeled with the Biolegend TotalSeq™-B Human Universal Cocktail (CITEseq). This technology allowed for independent labeling of each cell line and single-cell analysis of the pooled seven cell lines and the pseudo-microenvironment. The data generated from the aforementioned experiments are available as part of an online tool, which allows users to define cell heterogeneity and generates count tables as an output. The tool provides the cell line derivation for each cell and cell annotations for the pseudo-microenvironment based on CITEseq data by an experienced immunologist. Additionally, we created a range of pseudo-tumor tissues using different ratios of the aforementioned cells embedded in matrigel. These tissues were analyzed using 10XGenomics (FFPE samples) and Curio Bioscience (fresh frozen samples) platforms for spatial transcriptomics, further expanding the scope of our benchmark experiments. The benchmark experiments we conducted provide a unique opportunity to evaluate the performance of bioinformatics tools for detecting and characterizing tumor heterogeneity at the single-cell level. Overall, our experiments provide a controlled and standardized environment for assessing the accuracy and robustness of bioinformatics tools for studying tumor heterogeneity at the single-cell level, which can ultimately lead to more precise and effective cancer diagnosis and treatment.

Keywords: single cell omics, benchmark, spatial transcriptomics, CITEseq

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Authors: Andrew D. Henshaw

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The research examines the influence of familial and kinship based leadership on the resilience of politically violent organizations. Organizations of this type frequently fight in the same conflicts though are called 'terrorist' or 'insurgent' depending on political foci of the time, and thus different approaches are used to combat them. The research considers them correlated phenomena with significant overlap and identifies strengths and vulnerabilities in resilience processes. The research employs paired case studies to examine resilience in organizations under significant external pressure, and achieves this by measuring three variables. 1: Organizational robustness in terms of leadership and governance. 2. Bounce-back response efficiency to external pressures and adaptation to endogenous and exogenous shock. 3. Perpetuity of operational and attack capability, and political legitimacy. The research makes three hypotheses. First, familial/kinship leadership groups have a significant effect on organizational resilience in terms of informal operations. Second, non-familial/kinship organizations suffer in terms of heightened security transaction costs and social economics surrounding recruitment, retention, and replacement. Third, resilience in non-familial organizations likely stems from critical external supports like state sponsorship or powerful patrons, rather than organic resilience dynamics. The case studies pair familial organizations with non-familial organizations. Set 1: The Haqqani Network (HQN) - Pair: Lashkar-e-Toiba (LeT). Set 2: Jemaah Islamiyah (JI) - Pair: The Abu Sayyaf Group (ASG). Case studies were selected based on three requirements, being: contrasting governance types, exposure to significant external pressures and, geographical similarity. The case study sets were examined over 24 months following periods of significantly heightened operational activities. This enabled empirical measurement of the variables as substantial external pressures came into force. The rationale for the research is obvious. Nearly all organizations have some nexus of familial interconnectedness. Examining familial leadership networks does not provide further understanding of how terrorism and insurgency originate, however, the central focus of the research does address how they persist. The sparse attention to this in existing literature presents an unexplored yet important area of security studies. Furthermore, social capital in familial systems is largely automatic and organic, given at birth or through kinship. It reduces security vetting cost for recruits, fighters and supporters which lowers liabilities and entry costs, while raising organizational efficiency and exit costs. Better understanding of these process is needed to exploit strengths into weaknesses. Outcomes and implications of the research have critical relevance to future operational policy development. Increased clarity of internal trust dynamics, social capital and power flows are essential to fracturing and manipulating kinship nexus. This is highly valuable to external pressure mechanisms such as counter-terrorism, counterinsurgency, and strategic intelligence methods to penetrate, manipulate, degrade or destroy the resilience of politically violent organizations.

Keywords: Counterinsurgency (COIN), counter-terrorism, familial influence, insurgency, intelligence, kinship, resilience, terrorism

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Authors: Wen-Chung Chiang, Lun-Jou Lo, Hsiu-Hsia Lin

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This study aimed to improve the evaluation of facial symmetry, which is crucial for planning and assessing outcomes in orthognathic surgery (OGS). Facial symmetry plays a key role in both aesthetic and functional aspects of OGS, making its accurate evaluation essential for optimal surgical results. To address the limitations of traditional methods, a different approach was developed, combining three-dimensional (3D) facial contour mapping with hyperdimensional (HD) computing to enhance precision and efficiency in symmetry assessments. The study was conducted at Chang Gung Memorial Hospital, where data were collected from 2018 to 2023 using 3D cone beam computed tomography (CBCT), a highly detailed imaging technique. A large and comprehensive dataset was compiled, consisting of 150 normal individuals and 2,800 patients, totaling 5,750 preoperative and postoperative facial images. These data were critical for training a machine learning model designed to analyze and quantify facial symmetry. The machine learning model was trained to process 3D contour data from the CBCT images, with HD computing employed to power the facial symmetry quantification system. This combination of technologies allowed for an objective and detailed analysis of facial features, surpassing the accuracy and reliability of traditional symmetry assessments, which often rely on subjective visual evaluations by clinicians. In addition to developing the system, the researchers conducted a retrospective review of 3D CBCT data from 300 patients who had undergone OGS. The patients’ facial images were analyzed both before and after surgery to assess the clinical utility of the proposed system. The results showed that the facial symmetry algorithm achieved an overall accuracy of 82.5%, indicating its robustness in real-world clinical applications. Postoperative analysis revealed a significant improvement in facial symmetry, with an average score increase of 51%. The mean symmetry score rose from 2.53 preoperatively to 3.89 postoperatively, demonstrating the system's effectiveness in quantifying improvements after OGS. These results underscore the system's potential for providing valuable feedback to surgeons and aiding in the refinement of surgical techniques. The study also led to the development of a web-based system that automates facial symmetry assessment. This system integrates HD computing and 3D contour mapping into a user-friendly platform that allows for rapid and accurate evaluations. Clinicians can easily access this system to perform detailed symmetry assessments, making it a practical tool for clinical settings. Additionally, the system facilitates better communication between clinicians and patients by providing objective, easy-to-understand symmetry scores, which can help patients visualize the expected outcomes of their surgery. In conclusion, this study introduced a valuable and highly effective approach to facial symmetry evaluation in OGS, combining 3D contour mapping, HD computing, and machine learning. The resulting system achieved high accuracy and offers a streamlined, automated solution for clinical use. The development of the web-based platform further enhances its practicality, making it a valuable tool for improving surgical outcomes and patient satisfaction in orthognathic surgery.

Keywords: facial symmetry, orthognathic surgery, facial contour mapping, hyperdimensional computing

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Authors: Michele Paleologo, Marta Acampora, Serena Barello, Guendalina Graffigna

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Introduction: In the face of increasing demands for higher agricultural productivity with minimal environmental impact, participatory research approaches emerge as promising means to promote innovation. However, the complexities and ambiguities surrounding these approaches in both theory and practice present challenges. This Scoping Review seeks to bridge these gaps by mapping participatory approaches in agricultural contexts, analyzing their characteristics, and identifying indicators of success. Methods: Following PRISMA guidelines, we conducted a systematic Scoping Review, searching Scopus and Web of Science databases. Our review encompassed 34 projects from diverse geographical regions and farming contexts. Thematic analysis was employed to explore the types of innovation promoted and the categories of participants involved. Results: The identified innovation types encompass technological advancements, sustainable farming practices, and market integration, forming 5 main themes: climate change, cultivar, irrigation, pest and herbicide, and technical improvement. These themes represent critical areas where participatory research drives innovation to address pressing agricultural challenges. Participants were categorized as citizens, experts, NGOs, private companies, and public bodies. Understanding their roles is vital for designing effective participatory initiatives that embrace diverse stakeholders. The review also highlighted 27 theoretical frameworks underpinning participatory projects. Clearer guidelines and reporting standards are crucial for facilitating the comparison and synthesis of findings across studies, thereby enhancing the robustness of future participatory endeavors. Furthermore, we identified three main categories of barriers and facilitators: pragmatic/behavioral, emotional/relational, and cognitive. These insights underscore the significance of participant engagement and collaborative decision-making for project success beyond theoretical considerations. Regarding participation, projects were classified as contributory (5 cases), where stakeholders contributed insights; collaborative (10 cases), with active co-designing of solutions; and co-created (19 cases), featuring deep stakeholder involvement from ideation to implementation, resulting in joint ownership of outcomes. Such diverse participation modes highlight the adaptability of participatory approaches to varying agricultural contexts. Discussion: In conclusion, this Scoping Review demonstrates the potential of participatory research in driving transformative changes in farmers' practices, fostering sustainability and innovation in agriculture. Understanding the diverse landscape of participatory approaches, theoretical frameworks, and participant engagement strategies is essential for designing effective and context-specific interventions. Collaborative efforts among researchers, practitioners, and stakeholders are pivotal in harnessing the full potential of participatory approaches and driving positive change in agricultural settings worldwide. The identified themes of innovation and participation modes provide valuable insights for future research and targeted interventions in agricultural innovation.

Keywords: participatory research, co-creation, agricultural innovation, stakeholders' engagement

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Authors: Ding Liangxiao

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The technological advancements in Micro-Electro-Mechanical Systems (MEMS) have significantly contributed to the development of new, flexible capacitive pressure sensors,which are pivotal in transforming wearable and medical device technologies. This study employs the sophisticated simulation tools available in COMSOL Multiphysics® to develop and analyze a MEMS-based sensor with a tri-layered design. This sensor comprises top and bottom electrodes made from gold (Au), noted for their excellent conductivity, a middle dielectric layer made from a composite of Silver Nanowires (AgNWs) embedded in Thermoplastic Polyurethane (TPU), and a flexible, durable substrate of Polydimethylsiloxane (PDMS). This research was directed towards understanding how changes in the physical characteristics of the AgNWs/TPU dielectric layer—specifically, its thickness and surface area—impact the sensor's operational efficacy. We assessed several key electrical properties: capacitance, electric potential, and membrane displacement under varied pressure conditions. These investigations are crucial for enhancing the sensor's sensitivity and ensuring its adaptability across diverse applications, including health monitoring systems and dynamic user interface technologies. To ensure the reliability of our simulations, we applied the Effective Medium Theory to calculate the dielectric constant of the AgNWs/TPU composite accurately. This approach is essential for predicting how the composite material will perform under different environmental and operational stresses, thus facilitating the optimization of the sensor design for enhanced performance and longevity. Moreover, we explored the potential benefits of innovative three-dimensional structures for the dielectric layer compared to traditional flat designs. Our hypothesis was that 3D configurations might improve the stress distribution and optimize the electrical field interactions within the sensor, thereby boosting its sensitivity and accuracy. Our simulation protocol includes comprehensive performance testing under simulated environmental conditions, such as temperature fluctuations and mechanical pressures, which mirror the actual operational conditions. These tests are crucial for assessing the sensor's robustness and its ability to function reliably over extended periods, ensuring high reliability and accuracy in complex real-world environments. In our current research, although a full dynamic simulation analysis of the three-dimensional structures has not yet been conducted, preliminary explorations through three-dimensional modeling have indicated the potential for mechanical and electrical performance improvements over traditional planar designs. These initial observations emphasize the potential advantages and importance of incorporating advanced three-dimensional modeling techniques in the development of Micro-Electro-Mechanical Systems (MEMS)sensors, offering new directions for the design and functional optimization of future sensors. Overall, this study not only highlights the powerful capabilities of COMSOL Multiphysics® for modeling sophisticated electronic devices but also underscores the potential of innovative MEMS technology in advancing the development of more effective, reliable, and adaptable sensor solutions for a broad spectrum of technological applications.

Keywords: MEMS, flexible sensors, COMSOL Multiphysics, AgNWs/TPU, PDMS, 3D modeling, sensor durability

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Authors: Christoph Opperer

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The rapid urbanization of modern societies and the need for sustainable urban development demand innovative solutions that meet both individual and collective needs while addressing environmental concerns. To address these challenges, this paper presents a study that explores the potential of spatial and energetic/ecological optimization to enhance the performance of urban settlements, focusing on both architectural and urban scales. The study focuses on the application of biological principles and self-organization processes in urban planning and design, aiming to achieve a balance between ecological performance, architectural quality, and individual living conditions. The research adopts a case study approach, focusing on a 10-hectare brownfield site in the south of Vienna. The site is surrounded by a small-scale built environment as an appropriate starting point for the research and design process. However, the selected urban form is not a prerequisite for the proposed design methodology, as the findings can be applied to various urban forms and densities. The methodology used in this research involves dividing the overall building mass and program into individual small housing units. A computational model has been developed to optimize the distribution of these units, considering factors such as solar exposure/radiation, views, privacy, proximity to sources of disturbance (such as noise), and minimal internal circulation areas. The model also ensures that existing vegetation and buildings on the site are preserved and incorporated into the optimization and design process. The model allows for simultaneous optimization at two scales, architectural and urban design, which have traditionally been addressed sequentially. This holistic design approach leads to individual and collective benefits, resulting in urban environments that foster a balance between ecology and architectural quality. The results of the optimization process demonstrate a seemingly random distribution of housing units that, in fact, is a densified hybrid between traditional garden settlements and allotment settlements. This urban typology is selected due to its compatibility with the surrounding urban context, although the presented methodology can be extended to other forms of urban development and density levels. The benefits of this approach are threefold. First, it allows for the determination of ideal housing distribution that optimizes solar radiation for each building density level, essentially extending the concept of sustainable building to the urban scale. Second, the method enhances living quality by considering the orientation and positioning of individual functions within each housing unit, achieving optimal views and privacy. Third, the algorithm's flexibility and robustness facilitate the efficient implementation of urban development with various stakeholders, architects, and construction companies without compromising its performance. The core of the research is the application of global and local optimization strategies to create efficient design solutions. By considering both, the performance of individual units and the collective performance of the urban aggregation, we ensure an optimal balance between private and communal benefits. By promoting a holistic understanding of urban ecology and integrating advanced optimization strategies, our methodology offers a sustainable and efficient solution to the challenges of modern urbanization.

Keywords: sustainable development, self-organization, ecological performance, solar radiation and exposure, daylight, visibility, accessibility, spatial distribution, local and global optimization

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Authors: Bangun F., Crevits B., Bellet T., Banet A., Boy G. A., Katili I.

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This paper seeks the incoherencies in cognitive process during an accident of Powered Two Wheelers (PTW) by understanding the factual sequences of events and causal relations for each case of accident. The principle of this approach is undertaking in-depth investigations on case per case of PTW accidents based on elaborate data acquisitions on accident sites that officially stamped in Police Crash Report (PCRs) 2012 of Medan with criteria, involved at least one PTW and resulted in serious injury and fatalities. The analysis takes into account four modules: accident chronologies, perpetrator, and victims, injury surveillance, vehicles and road infrastructures, comprising of traffic facilities, road geometry, road alignments and weather. The proposal for improvement could have provided a favorable influence on the chain of functional processes and events leading to collision. Decision Aiding Processes (DAP) assists in structuring different entities at different decisional levels, as each of these entities has its own objectives and constraints. The entities (A) are classified into 6 groups of accidents: solo PTW accidents; PTW vs. PTW; PTW vs. pedestrian; PTW vs. motor-trishaw; and PTW vs. other vehicles and consecutive crashes. The entities are also distinguished into 4 decisional levels: level of road users and street systems; operational level (crash-attended police officers or CAPO and road engineers), tactical level (Regional Traffic Police, Department of Transportation, and Department of Public Work), and strategic level (Traffic Police Headquarters (TCPHI)), parliament, Ministry of Transportation and Ministry of Public Work). These classifications will lead to conceptualization of Problem Situations (P) and Problem Formulations (I) in DAP context. The DAP concerns the sequences process of the incidents until the time the accident occurs, which can be modelled in terms of five activities of procedural rationality: identification on initial human features (IHF), investigation on proponents attributes (PrAT), on Injury Surveillance (IS), on the interaction between IHF and PrAt and IS (intercorrelation), then unravel the sequences of incidents; filtering and disclosure, which include: what needs to activate, modify or change or remove, what is new and what is priority. These can relate to the activation or modification or new establishment of law. The PrAt encompasses the problems of environmental, road infrastructure, road and traffic facilities, and road geometry. The evaluation model (MP) is generated to bridge P and I since MP is produced by the intercorrelations among IHF, PrAT and IS extracted from the PCRs 2012 of Medan. There are 7 findings of incoherences: lack of knowledge and awareness on the traffic regulations and the risks of accidents, especially when riding between 0 < x < 10 km from house, riding between 22 p.m.–05.30 a.m.; lack of engagements on procurement of IHF Data by CAPO; lack of competency of CAPO on data procurement in accident-sites; no intercorrelation among IHF and PrAt and IS in the database systems of PCRs; lack of maintenance and supervision on the availabilities and the capacities of traffic facilities and road infrastructure; instrumental bias with wash-back impacts towards the TCPHI; technical robustness with wash-back impacts towards the CAPO and TCPHI.

Keywords: decision aiding processes, evaluation model, PTW accidents, police crash reports

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Authors: Amar Ramesh Wayal

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Marathi cinema, an integral part of India’s diverse film industry, has significantly evolved in its storytelling and aesthetics, with the Tamasha genre being central to this evolution. Tamasha, a traditional form of Marathi theatre, features vibrant dance and music, especially the rhythmic and often suggestive musical genre, lavani. It gained cinematic prominence in the 1960s with Anant Mane’s Sangtye Aika (1959), which brought and popularized Tamasha to the silver screen, and V. Shantaram’s Pinjra (1972), an iconic Tamasha drama. Despite early success, Tamasha films declined in popularity until Natarang (2010) revitalized interest in this traditional form. This study examines the relevance and evolution of the Tamasha genre in Marathi cinema through contemporary films like Ek Hota Vidushak by Jabbar Patel (1992), Natarang (2010) by Ravi Jadhav, and Tamasha Live (2022) by Sanjay Jadhav. The selection of the films is based on their significant roles in the evolution of the Tamasha in Marathi cinema. Ek Hota Vidushak explores socio-political themes through Tamasha, Natarang depicts the struggles and emotional depth of Tamasha performers, and Tamasha Live integrates traditional Tamasha into modern cinema. By analysing films from different periods, this study highlights the genre’s reinterpretation and adaptation over time. The study employs a qualitative approach, utilizing textual analysis and cultural critique to examine the portrayal and evolution of Tamasha in selected films. It aims to illuminate the complex relationship between tradition and modernity in Marathi cinema through Foucauldian discourse analysis and Pierre Bourdieu’s concept of “vernacular habitus,” which refers to local, indigenous cultural spaces that shape people’s perceptions and expressions. By analyzing these films, the study seeks to understand how traditional cultural forms are integrated into contemporary cinematic narratives. However, this method has limitations, such as subjectivity in interpretation and the need for extensive contextual knowledge. Qualitative research can be subject to researcher bias, affecting analysis and conclusions. To mitigate this, this study maintains rigorous reflexivity and transparency regarding the researcher’s positionality. Furthermore, findings from specific film analyses may not be universally applicable to all Tamasha films or broader Marathi cinema. To enhance the study’s robustness, future research could incorporate comparative or quantitative data to complement qualitative insights. Despite these challenges, qualitative research is crucial for exploring cultural artifacts and their significance within specific contexts. By triangulating qualitative findings with diverse perspectives and acknowledging limitations, this study aims to provide a nuanced understanding of how Tamasha cinema preserves and revitalizes Maharashtra’s folk traditions while adapting them to contemporary contexts. Analyzing films by Jabbar Patel, Ravi Jadhav, and Sanjay Jadhav shows how these filmmakers balance traditional aesthetics with modern storytelling, bridging historical continuity with contemporary relevance. This study offers insights into how indigenous traditions like Tamasha continue to shape and define cinematic narratives in Maharashtra.

Keywords: Marathi cinema, Tamasha genre, vernacular habitus, discourse analysis, cultural evolution

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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

Procedia PDF Downloads 73

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: Martin Alexander Eder, Sergei Semenov

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Many glass-epoxy composite structures, such as large utility wind turbine rotor blades (WTBs), comprise of adhesive joints with typically thick bond lines used to connect the different components during assembly. Performance optimization of rotor blades to increase power output by simultaneously maintaining high stiffness-to-low-mass ratios entails intricate geometries in conjunction with complex anisotropic material behavior. Consequently, adhesive joints in WTBs are subject to multiaxial stress states with significant stress gradients depending on the local joint geometry. Moreover, the dynamic aero-elastic interaction of the WTB with the airflow generates non-proportional, variable amplitude stress histories in the material. Empiricism shows that a prominent failure type in WTBs is high cycle fatigue failure of adhesive bond line interfaces, which in fact over time developed into a design driver as WTB sizes increase rapidly. Structural optimization employed at an early design stage, therefore, sets high demands on computationally efficient interface fatigue models capable of predicting the critical locations prone for interface failure. The numerical stress-based interface fatigue model presented in this work uses the Drucker-Prager criterion to compute three different damage indices corresponding to the two interface shear tractions and the outward normal traction. The two-parameter Drucker-Prager model was chosen because of its ability to consider shear strength enhancement under compression and shear strength reduction under tension. The governing interface damage index is taken as the maximum of the triple. The damage indices are computed through the well-known linear Palmgren-Miner rule after separate rain flow-counting of the equivalent shear stress history and the equivalent pure normal stress history. The equivalent stress signals are obtained by self-similar scaling of the Drucker-Prager surface whose shape is defined by the uniaxial tensile strength and the shear strength such that it intersects with the stress point at every time step. This approach implicitly assumes that the damage caused by the prevailing multiaxial stress state is the same as the damage caused by an amplified equivalent uniaxial stress state in the three interface directions. The model was implemented as Python plug-in for the commercially available finite element code Abaqus for its use with solid elements. The model was used to predict the interface damage of an adhesively bonded, tapered glass-epoxy composite cantilever I-beam tested by LM Wind Power under constant amplitude compression-compression tip load in the high cycle fatigue regime. Results show that the model was able to predict the location of debonding in the adhesive interface between the webfoot and the cap. Moreover, with a set of two different constant life diagrams namely in shear and tension, it was possible to predict both the fatigue lifetime and the failure mode of the sub-component with reasonable accuracy. It can be concluded that the fidelity, robustness and computational efficiency of the proposed model make it especially suitable for rapid fatigue damage screening of large 3D finite element models subject to complex dynamic load histories.

Keywords: adhesive, fatigue, interface, multiaxial stress

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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

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Authors: Adela Diepoltova, Klara Konecna, Ondrej Jandourek, Petr Nachtigal

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A loss of control over antibiotic-resistant pathogens has become a global issue due to severe and often untreatable infections. This state is reflected in complicated treatment, health costs, and higher mortality. All these factors emphasize the urgent need for the discovery and development of new anti-infectives. One of the most common pathogens mentioned in the phenomenon of antibiotic resistance are bacteria of the genus Staphylococcus. These bacterial agents have developed several mechanisms against the effect of antibiotics. One of them is biofilm formation. In staphylococci, biofilms are associated with infections such as endocarditis, osteomyelitis, catheter-related bloodstream infections, etc. To author's best knowledge, no validated and standardized methodology evaluating candidate compound activity against staphylococcal biofilms exists. However, a variety of protocols for in vitro drug activity testing has been suggested, yet there are often fundamental differences. Based on our experience, a key methodological step that leads to credible results is to form a robust biofilm with appropriate attributes such as firm adherence to the substrate, a complex arrangement in layers, and the presence of extracellular polysaccharide matrix. At first, for the purpose of drug antibiofilm activity evaluation, the focus was put on various conditions (supplementation of cultivation media by human plasma/fetal bovine serum, shaking mode, the density of initial inoculum) that should lead to reproducible and robust in vitro staphylococcal biofilm formation in microtiter plate model. Three model staphylococcal reference strains were included in the study: Staphylococcus aureus (ATCC 29213), methicillin-resistant Staphylococcus aureus (ATCC 43300), and Staphylococcus epidermidis (ATCC 35983). The total biofilm biomass was quantified using the Christensen method with crystal violet, and results obtained from at least three independent experiments were statistically processed. Attention was also paid to the viability of the biofilm-forming staphylococcal cells and the presence of extracellular polysaccharide matrix. The conditions that led to robust biofilm biomass formation with attributes for biofilms mentioned above were then applied by introducing an alternative method analogous to the commercially available test system, the Calgary Biofilm Device. In this test system, biofilms are formed on pegs that are incorporated into the lid of the microtiter plate. This system provides several advantages (in situ detection and quantification of biofilm microbial cells that have retained their viability after drug exposure). Based on our preliminary studies, it was found that the attention to the peg surface and substrate on which the bacterial biofilms are formed should also be paid to. Therefore, further steps leading to the optimization were introduced. The surface of pegs was coated by human plasma, fetal bovine serum, and L-polylysine. Subsequently, the willingness of bacteria to adhere and form biofilm was monitored. In conclusion, suitable conditions were revealed, leading to the formation of reproducible, robust staphylococcal biofilms in vitro for the microtiter model and the system analogous to the Calgary biofilm device, as well. The robustness and typical slime texture could be detected visually. Likewise, an analysis by confocal laser scanning microscopy revealed a complex three-dimensional arrangement of biofilm forming organisms surrounded by an extracellular polysaccharide matrix.

Keywords: anti-biofilm drug activity screening, in vitro biofilm formation, microtiter plate model, the Calgary biofilm device, staphylococcal infections, substrate modification, surface coating

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Authors: Gospel Iyioku, Jana Mazancova, Jiri Hejkrlik

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The “Agricultural Promotion Policy (2016–2020)” represents a strategic initiative by the Nigerian government to address domestic food shortages and the challenges in exporting products at the required quality standards. Hindered by an inefficient system for setting and enforcing food quality standards, coupled with a lack of market knowledge, the Federal Ministry of Agriculture and Rural Development (FMARD) aims to enhance support for the production and activities of key crops like cashew. By collaborating with farmers, processors, investors, and stakeholders in the cashew sector, the policy seeks to define and uphold high-quality standards across the cashew value chain. Given the challenges and opportunities faced by Nigerian cashew farmers, active participation in cashew marketing groups becomes imperative. These groups serve as essential platforms for farmers to collectively navigate market intricacies, access resources, share knowledge, improve output quality, and bolster their overall bargaining power. Through engagement in these cooperative initiatives, farmers not only boost their economic prospects but can also contribute significantly to the sustainable growth of the cashew industry, fostering resilience and community development. This study explores the perceptions and intentions of farmers regarding their involvement in cashew marketing cooperatives, utilizing an expanded version of the Theory of Planned Behaviour. Drawing insights from a diverse sample of 321 cashew farmers in Southwest Nigeria, the research sheds light on the factors influencing decision-making in cooperative participation. The demographic analysis reveals a diverse landscape, with a substantial presence of middle-aged individuals contributing significantly to the agricultural sector and cashew-related activities emerging as a primary income source for a substantial proportion (23.99%). Employing Structural Equation Modelling (SEM) with Maximum Likelihood Robust (MLR) estimation in R, the research elucidates the associations among latent variables. Despite the model’s complexity, the goodness-of-fit indices attest to the validity of the structural model, explaining approximately 40% of the variance in the intention to join cooperatives. Moral norms emerge as a pivotal construct, highlighting the profound influence of ethical considerations in decision-making processes, while perceived behavioural control presents potential challenges in active participation. Attitudes toward joining cooperatives reveal nuanced perspectives, with strong beliefs in enhanced connections with other farmers but varying perceptions on improved access to essential information. The SEM analysis establishes positive and significant effects of moral norms, perceived behavioural control, subjective norms, and attitudes on farmers’ intention to join cooperatives. The knowledge construct positively affects key factors influencing intention, emphasizing the importance of informed decision-making. A supplementary analysis using partial least squares (PLS) SEM corroborates the robustness of our findings, aligning with covariance-based SEM results. This research unveils the determinants of cooperative participation and provides valuable insights for policymakers and practitioners aiming to empower and support this vital demographic in the cashew industry.

Keywords: marketing cooperatives, theory of planned behaviour, structural equation modelling, cashew farmers

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Authors: Agman Gupta, Rajashekar Badam, Noriyoshi Matsumi

Abstract:

Introduction: Recently, silicon has been recognized as one of the potential alternatives as anode active material in Li-ion batteries (LIBs) to replace the conventionally used graphite anodes. Silicon is abundantly present in the nature, it can alloy with lithium metal, and has a higher theoretical capacity (~4200 mAhg-1) that is approximately 10 times higher than graphite. However, because of a large volume expansion (~400%) upon repeated de-/alloying, the pulverization of Si particles causes the exfoliation of electrode laminate leading to the loss of electrical contact and adversely affecting the formation of solid-electrolyte interface (SEI).1 Functional polymers as binders have emerged as a competitive strategy to mitigate these drawbacks and failure mechanism of silicon anodes.1 A variety of aqueous/non-aqueous polymer binders like sodium carboxy-methyl cellulose (CMC-Na), styrene butadiene rubber (SBR), poly(acrylic acid), and other variants like mussel inspired binders have been investigated to overcome these drawbacks.1 However, there are only a few reports that mention the attempt of addressing all the drawbacks associated with silicon anodes effectively using a single novel functional polymer system as a binder. In this regard, here, we report a novel highly robust n-type bisiminoacenaphthenequinone (BIAN)-paraphenylene-based crosslinked polymer as a binder for Si anodes in lithium-ion batteries (Fig. 1). On its application, crosslinked-BIAN binder was evaluated to provide mechanical robustness to the large volume expansion of Si particles, maintain electrical conductivity within the electrode laminate, and facilitate in the formation of a thin SEI by restricting the extent of electrolyte decomposition on the surface of anode. The fabricated anodic half-cells were evaluated electrochemically for their rate capability, cyclability, and discharge capacity. Experimental: The polymerized BIAN (P-BIAN) copolymer was synthesized as per the procedure reported by our group.2 The synthesis of crosslinked P-BIAN: a solution of P-BIAN copolymer (1.497 g, 10 mmol) in N-methylpyrrolidone (NMP) (150 ml) was set-up to stir under reflux in nitrogen atmosphere. To this, 1,6-dibromohexane (5 mmol, 0.77 ml) was added dropwise. The resultant reaction mixture was stirred and refluxed at 150 °C for 24 hours followed by refrigeration for 3 hours at 5 °C. The product was obtained by evaporating the NMP solvent under reduced pressure and drying under vacuum at 120 °C for 12 hours. The obtained product was a black colored sticky compound. It was characterized by 1H-NMR, XPS, and FT-IR techniques. Results and Discussion: The N 1s XPS spectrum of the crosslinked BIAN polymer showed two characteristic peaks corresponding to the sp2 hybridized nitrogen (-C=N-) at 399.6 eV of the diimine backbone in the BP and quaternary nitrogen at 400.7 eV corresponding to the crosslinking of BP via dibromohexane. The DFT evaluation of the crosslinked BIAN binder showed that it has a low lying lowest unoccupied molecular orbital (LUMO) that enables it to get doped in the reducing environment and influence the formation of a thin (SEI). Therefore, due to the mechanically robust crosslinked matrices as well as its influence on the formation of a thin SEI, the crosslinked BIAN binder stabilized the Si anode-based half-cell for over 1000 cycles with a reversible capacity of ~2500 mAhg-1 and ~99% capacity retention as shown in Fig. 2. The dynamic electrochemical impedance spectroscopy (DEIS) characterization of crosslinked BIAN-based anodic half-cell confirmed that the SEI formed was thin in comparison with the conventional binder-based anodes. Acknowledgement: We are thankful to the financial support provided by JST-Mirai Program, Grant Number: JP18077239

Keywords: self-healing binder, n-type binder, thin solid-electrolyte interphase (SEI), high-capacity silicon anodes, low-LUMO

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Authors: Sara Dehaiman Alqahtani

Abstract:

This study investigates the effects of cybersecurity breaches on critical audit outcomes, specifically focusing on auditor changes, engagement partner rotations, and the issuance of going concern opinions. Utilizing an extensive dataset of U.S.-based firms spanning from 2006 to 2023, the research employs propensity score matching (PSM) to address selection bias and control for confounding variables. The analysis reveals that, contrary to conventional expectations, firms that experience cybersecurity breaches are less likely to change their audit firms and engagement partners. Additionally, these breached firms are less likely to receive going concern opinions from their auditors. However, an exception is noted within the technology sector, where breached firms show a higher propensity to switch auditors, potentially to demonstrate a commitment to enhanced cybersecurity measures. The findings suggest a strong preference for continuity in auditor-client relationships following cybersecurity incidents. This preference underscores the importance of auditors' existing knowledge of a firm's systems and controls, which is deemed valuable during periods of heightened risk. The study extends the existing literature by moving beyond the well-documented impact of breaches on audit fees to explore other significant dimensions of the auditor-client relationship. It challenges the traditional assumption that increased risk from breaches leads to higher auditor turnover or more conservative audit opinions, highlighting instead a tendency towards maintaining stability. Methodologically, the research leverages PSM to create a balanced comparison between breached and non-breached firms, ensuring robustness in the findings. Logistic regression analyses further substantiate the associations between breaches and audit outcomes, controlling for various firm-specific characteristics such as size, financial performance, and industry classification. Supplemental analyses explore additional factors, including litigation risk, breach frequency, and industry-specific responses, providing a nuanced understanding of the dynamics at play. The study’s main contributions are threefold. First, it broadens the scope of research on cybersecurity breaches by examining their impact on auditor changes and going concern opinions, areas previously underexplored. Second, it offers empirical evidence that breached firms tend to retain their auditors and engagement partners, suggesting that continuity is valued over potential audit quality improvements through auditor changes. Third, it highlights sector-specific behaviors, particularly within the technology industry, where breaches do lead to higher auditor turnover, indicating industry-specific risk management strategies. Implications of this research are significant for auditors, clients, and regulators. Auditors may need to enhance their risk assessment frameworks to better incorporate cybersecurity risks, ensuring that audit practices remain robust in the face of evolving cyber threats. Clients should evaluate the benefits of retaining existing auditors against the potential advantages of engaging new auditors who might offer fresh perspectives and specialized cybersecurity expertise. Regulators might consider updating auditing standards to more explicitly address cybersecurity risks, ensuring that such threats are adequately reflected in audit procedures and disclosures. Overall, this study provides a comprehensive analysis of how cybersecurity breaches influence audit outcomes, revealing a preference for auditor continuity and questioning whether current auditing frameworks sufficiently account for cyber risks. By highlighting these trends, the research calls for a reassessment of audit practices and regulatory standards to better address the complexities introduced by the increasing prevalence of cyber threats in the digital age.

Keywords: cybersecurity breaches, auditor changes, engagement partner rotations, going concern opinions, auditor-client relationships, audit risk assessment

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