8,264 research outputs found

    Demonstration of a Response Time Based Remaining Useful Life (RUL) Prediction for Software Systems

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    Prognostic and Health Management (PHM) has been widely applied to hardware systems in the electronics and non-electronics domains but has not been explored for software. While software does not decay over time, it can degrade over release cycles. Software health management is confined to diagnostic assessments that identify problems, whereas prognostic assessment potentially indicates when in the future a problem will become detrimental. Relevant research areas such as software defect prediction, software reliability prediction, predictive maintenance of software, software degradation, and software performance prediction, exist, but all of these represent diagnostic models built upon historical data, none of which can predict an RUL for software. This paper addresses the application of PHM concepts to software systems for fault predictions and RUL estimation. Specifically, this paper addresses how PHM can be used to make decisions for software systems such as version update and upgrade, module changes, system reengineering, rejuvenation, maintenance scheduling, budgeting, and total abandonment. This paper presents a method to prognostically and continuously predict the RUL of a software system based on usage parameters (e.g., the numbers and categories of releases) and performance parameters (e.g., response time). The model developed has been validated by comparing actual data, with the results that were generated by predictive models. Statistical validation (regression validation, and k-fold cross validation) has also been carried out. A case study, based on publicly available data for the Bugzilla application is presented. This case study demonstrates that PHM concepts can be applied to software systems and RUL can be calculated to make system management decisions.Comment: This research methodology has opened up new and practical applications in the software domain. In the coming decades, we can expect a significant amount of attention and practical implementation in this area worldwid

    Fairness Testing: A Comprehensive Survey and Analysis of Trends

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    Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and concern among software engineers. To tackle this issue, extensive research has been dedicated to conducting fairness testing of ML software, and this paper offers a comprehensive survey of existing studies in this field. We collect 100 papers and organize them based on the testing workflow (i.e., how to test) and testing components (i.e., what to test). Furthermore, we analyze the research focus, trends, and promising directions in the realm of fairness testing. We also identify widely-adopted datasets and open-source tools for fairness testing

    Runway Safety Improvements Through a Data Driven Approach for Risk Flight Prediction and Simulation

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    Runway overrun is one of the most frequently occurring flight accident types threatening the safety of aviation. Sensors have been improved with recent technological advancements and allow data collection during flights. The recorded data helps to better identify the characteristics of runway overruns. The improved technological capabilities and the growing air traffic led to increased momentum for reducing flight risk using artificial intelligence. Discussions on incorporating artificial intelligence to enhance flight safety are timely and critical. Using artificial intelligence, we may be able to develop the tools we need to better identify runway overrun risk and increase awareness of runway overruns. This work seeks to increase attitude, skill, and knowledge (ASK) of runway overrun risks by predicting the flight states near touchdown and simulating the flight exposed to runway overrun precursors. To achieve this, the methodology develops a prediction model and a simulation model. During the flight training process, the prediction model is used in flight to identify potential risks and the simulation model is used post-flight to review the flight behavior. The prediction model identifies potential risks by predicting flight parameters that best characterize the landing performance during the final approach phase. The predicted flight parameters are used to alert the pilots for any runway overrun precursors that may pose a threat. The predictions and alerts are made when thresholds of various flight parameters are exceeded. The flight simulation model simulates the final approach trajectory with an emphasis on capturing the effect wind has on the aircraft. The focus is on the wind since the wind is a relatively significant factor during the final approach; typically, the aircraft is stabilized during the final approach. The flight simulation is used to quickly assess the differences between fight patterns that have triggered overrun precursors and normal flights with no abnormalities. The differences are crucial in learning how to mitigate adverse flight conditions. Both of the models are created with neural network models. The main challenges of developing a neural network model are the unique assignment of each model design space and the size of a model design space. A model design space is unique to each problem and cannot accommodate multiple problems. A model design space can also be significantly large depending on the depth of the model. Therefore, a hyperparameter optimization algorithm is investigated and used to design the data and model structures to best characterize the aircraft behavior during the final approach. A series of experiments are performed to observe how the model accuracy change with different data pre-processing methods for the prediction model and different neural network models for the simulation model. The data pre-processing methods include indexing the data by different frequencies, by different window sizes, and data clustering. The neural network models include simple Recurrent Neural Networks, Gated Recurrent Units, Long Short Term Memory, and Neural Network Autoregressive with Exogenous Input. Another series of experiments are performed to evaluate the robustness of these models to adverse wind and flare. This is because different wind conditions and flares represent controls that the models need to map to the predicted flight states. The most robust models are then used to identify significant features for the prediction model and the feasible control space for the simulation model. The outcomes of the most robust models are also mapped to the required landing distance metric so that the results of the prediction and simulation are easily read. Then, the methodology is demonstrated with a sample flight exposed to an overrun precursor, and high approach speed, to show how the models can potentially increase attitude, skill, and knowledge of runway overrun risk. The main contribution of this work is on evaluating the accuracy and robustness of prediction and simulation models trained using Flight Operational Quality Assurance (FOQA) data. Unlike many studies that focused on optimizing the model structures to create the two models, this work optimized both data and model structures to ensure that the data well capture the dynamics of the aircraft it represents. To achieve this, this work introduced a hybrid genetic algorithm that combines the benefits of conventional and quantum-inspired genetic algorithms to quickly converge to an optimal configuration while exploring the design space. With the optimized model, this work identified the data features, from the final approach, with a higher contribution to predicting airspeed, vertical speed, and pitch angle near touchdown. The top contributing features are altitude, angle of attack, core rpm, and air speeds. For both the prediction and the simulation models, this study goes through the impact of various data preprocessing methods on the accuracy of the two models. The results may help future studies identify the right data preprocessing methods for their work. Another contribution from this work is on evaluating how flight control and wind affect both the prediction and the simulation models. This is achieved by mapping the model accuracy at various levels of control surface deflection, wind speeds, and wind direction change. The results saw fairly consistent prediction and simulation accuracy at different levels of control surface deflection and wind conditions. This showed that the neural network-based models are effective in creating robust prediction and simulation models of aircraft during the final approach. The results also showed that data frequency has a significant impact on the prediction and simulation accuracy so it is important to have sufficient data to train the models in the condition that the models will be used. The final contribution of this work is on demonstrating how the prediction and the simulation models can be used to increase awareness of runway overrun.Ph.D

    Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

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    Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port

    Job Stress, Anxiety, and Depression in Mental Health Professionals: An Examination of Experienced Vicarious Trauma and Gender Differences

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    AbstractMental health professionals are susceptible to an increased risk of job stress, anxiety, and depression based on the very nature of their work. The study was quantitative, focusing on profile analysis. A two-way MANOVA was performed utilizing the independent variables of gender and vicarious trauma, and three dependent variables of job stress, anxiety, and depression. The population size was 88 mental health professionals with a Bachelor’s degree or higher who work in the behavioral health field, directly servicing clients in the capacity of supervision, case management, social work, counseling, or therapy. Participants completed four self-reported questionnaires: General Work Stress Scale (GWS), Beck Anxiety Inventory (BAI), Beck Depression Inventory-II (BDI-II), and the Trauma and Attachment Belief Scale (TABS). The results of the study indicated that there was no statistical significance of the interaction term of gender and vicarious trauma with respect to the GWS (F = 0.572, p = .45), BAI (F = 0.268, p = 0.60), or BDI-II (F = 1.270, p = .26). The results indicated there was no statistical significance in gender with respect to the GWS scale, BAI, or the BDI-II (F = 0.895, p = .347) (F = 2.870, p = 0.094) (F = 0.134, p = 0.715). In addition, the results did indicate there was a statistical significance in vicarious trauma with respect to the GWS (F = 9.79, p = 0.002), BAI (F = 18.98, p = 0.000), and BDI-II score (F = 38.2, p \u3c .01). The study outcomes may contribute to positive social change, assisting in the development, promotion, and facilitation of awareness training, educational workshops, organizational support systems, and gender-sensitive interventions for mental health professionals

    Transforming electrical energy systems towards sustainability in a complex world: the cases of Ontario and Costa Rica

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    Electrical energy systems have been major contributors to sustainability-associated effects, positive and negative, and therefore are considered as key components in pursuing overall sustainability objectives. Conventional electrical energy systems have delivered essential services for human well-being and can play a key role in tackling ongoing threats including growing poverty, climate change effects, and the long-term impacts of the COVID-19 pandemic. At the same time, some participants in electrical energy systems at national and local scales have stressed that the conventional design of electrical energy systems requires change to ensure the positive contributions and to reduce socioeconomic and environmental risks. Continuing negative trends including significant contributions to climate change, rising energy costs, deepening inequities, and long-term environmental degradation, have raised concerns and prompted calls for transforming conventional electrical energy systems rapidly and safely. However, due in part to the complexity of electrical energy systems, national and local authorities have struggled to steer their systems towards delivering more consistently positive sustainability-associated effects. Usual approaches to electrical energy system management have sought to improve efficiency, reliability and capacity to meet anticipated demand. They have seldom treated electrical energy systems as potentially important contributors to overall sustainability in principle and in practice. Doing so would entail recognizing electrical energy systems as complex systems with interlinked effects and aiming to maximize the systems’ positive and transformative effects to deliver multiple, mutually reinforcing and overall sustainability gains. The research reported here considered whether and how sustainability-based assessments can be useful tools to fill this gap and advance sustainability objectives in particular plans, projects, and initiatives carried out in electrical energy systems. To aid in responding the main research questions, this dissertation builds and proposes a sustainability-based assessment framework for electrical energy systems that is suitable for application with further specification to the context of different jurisdictions. Use of the framework is illustrated and tested through two case applications – to the electrical energy systems of Ontario and Costa Rica. Building the proposed framework involved a literature review and synthesis of three foundational bodies of knowledge: sustainability in complexity, electrical energy systems and sustainability, and transformations towards sustainability. Further specifying and applying the framework to the context of the two case studies involved carrying out document research and semi-structured interviews with key participants in the electrical energy systems of the two jurisdictions. The resulting sustainability-based assessment framework from this dissertation proposes six main criteria categories that are mutually reinforcing and emphasize minimizing trade-offs scenarios. These are divided into a set of criteria for specification and application to electrical energy system-related projects, plans, and initiatives in different regions. The proposed criteria categories are 1) Climate safety and social-ecological integrity; 2) Intra- and inter-generational equity, accessibility, reliability, and affordability; 3) Cost-effectiveness, resource efficiency and conservation; 4) Democratic and participatory governance; 5) Precaution, modularity and resiliency; and 6) Transformation, integration of multiple positive effects, and minimization of adverse effects. Ontario’s electrical energy system has significant sustainability-related challenges to overcome. The case study has shown that there is little provincial interest in following national net-zero commitments and authorities have removed official requirements for long-term energy planning to pursue climate goals and related sustainability objectives. Rising electricity prices have also raised concerns for many years and have been accompanied by limited willingness to engage in democratic and participatory processes for public review of electrical energy system undertakings. Additionally, recent commitments to highly expensive and risky options can further aggravate long-term socioeconomic and environmental negative impacts. In the Costa Rica case, adopting technocentric approaches to electrical energy system management led to a path dependency on large hydroelectricity development. This background of development of large hydroelectricity projects, without public consultation, has also created a sustained context of tension between governments, Indigenous groups and local communities, and private actors. Since the country is expected to experience changes in natural systems’ patterns including intensified periods of hurricane, storm, flood, and drought, the strong reliance on hydroelectricity has at the same time raised concerns regarding the reliability of the national electrical energy system. Both Ontario and Costa Rica have electrical energy systems that require rapid responses to contribute more positively to sustainability, and to help to reduce and reverse ongoing social and environmental crises. The two cases are also suitably contrasting venues for specification and application of the sustainability-based assessment framework developed in this work. The findings showed that while Ontario and Costa Rica have different contextual characteristics (e.g., geographical, socioeconomic, and political), overall lessons can be learned for best designing electrical energy systems in different jurisdictions. The findings also revealed that context-specific sustainability approaches do not necessarily undermine the viability for comparing multiple cases. In fact, specification to context can support comparisons by facilitating the identification of similarities and differences that are closely tied to contextual characteristics. Overall, the study of the two cases indicates significant potential for future works that focus on the specification to context and application of sustainability-based assessments specified to electrical energy systems that seek for barriers and opportunities for unlocking transformative effects. Three key learnings were revealed by building, specifying to context, and applying the sustainability-based assessment framework in a comparative analysis of the electrical energy systems of Ontario and Costa Rica. First, the two jurisdictions require implementation of more effective options to minimize costs in electrical energy system operations and avoid economic risks that undermine the capacity of the system to provide affordable electricity for all. Second, efforts to meet democratic and participatory governance requirements have been insufficient in Ontario and Costa Rica. Both jurisdictions need to demonstrate the capacity to respect official processes for public approval and to ensure adequate representation of different actors’ interests. Particularly, Indigenous people, local communities, and other groups with limited influence need more meaningful inclusion in official decision-making. Third, the two jurisdictions would benefit from implementing strategies to identify and assess possible combinations of policy and technical pathways that could help to unlock an existing dependency on options that support system rigidity. The core overall conclusion is that application of the proposed sustainability-based assessment framework can inform better design electrical energy systems to deliver broader sustainability-related effects and advance transformations towards sustainability. However, the framework could be further developed by including insights from more key participants in electrical energy systems. The criteria set can be honed with specification to context and application to different jurisdictions, and to more particular initiatives that reflect evolving energy scenarios. Inclusion of transformation, integration of multiple positive effects, and minimization of adverse effects as a criteria category has been helpful to recognize political contexts, promote just transitions, and emphasize the interlinked effects of applying the rest of the criteria. Since this is a new component in sustainability-based assessment frameworks, the transformation criteria category will require particular attention in future applications. Among other matters, further work in the field of electrical energy systems transformation towards sustainability should also address continuing and emerging phenomena, including adverse political trends such as right-wing populism and post-truth politics, that would maintain gaps between current practices and the steps needed for progress towards sustainability. Generally, however, while there are many needs and opportunities for more applications of the framework and additional research into the barriers to and openings for energy system transition and transformation, the sustainability-based assessment framework proposed and tested in this dissertation research should be a useful tool for directing change in complex electrical energy systems towards broader contributions to sustainability

    Vitalism and Its Legacy in Twentieth Century Life Sciences and Philosophy

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    This Open Access book combines philosophical and historical analysis of various forms of alternatives to mechanism and mechanistic explanation, focusing on the 19th century to the present. It addresses vitalism, organicism and responses to materialism and its relevance to current biological science. In doing so, it promotes dialogue and discussion about the historical and philosophical importance of vitalism and other non-mechanistic conceptions of life. It points towards the integration of genomic science into the broader history of biology. It details a broad engagement with a variety of nineteenth, twentieth and twenty-first century vitalisms and conceptions of life. In addition, it discusses important threads in the history of concepts in the United States and Europe, including charting new reception histories in eastern and south-eastern Europe. While vitalism, organicism and similar epistemologies are often the concern of specialists in the history and philosophy of biology and of historians of ideas, the range of the contributions as well as the geographical and temporal scope of the volume allows for it to appeal to the historian of science and the historian of biology generally

    Durability and Availability of Erasure-Coded Storage Systems with Concurrent Maintenance

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    This initial version of this document was written back in 2014 for the sole purpose of providing fundamentals of reliability theory as well as to identify the theoretical types of machinery for the prediction of durability/availability of erasure-coded storage systems. Since the definition of a "system" is too broad, we specifically focus on warm/cold storage systems where the data is stored in a distributed fashion across different storage units with or without continuous operation. The contents of this document are dedicated to a review of fundamentals, a few major improved stochastic models, and several contributions of my work relevant to the field. One of the contributions of this document is the introduction of the most general form of Markov models for the estimation of mean time to failure. This work was partially later published in IEEE Transactions on Reliability. Very good approximations for the closed-form solutions for this general model are also investigated. Various storage configurations under different policies are compared using such advanced models. Later in a subsequent chapter, we have also considered multi-dimensional Markov models to address detached drive-medium combinations such as those found in optical disk and tape storage systems. It is not hard to anticipate such a system structure would most likely be part of future DNA storage libraries. This work is partially published in Elsevier Reliability and System Safety. Topics that include simulation modelings for more accurate estimations are included towards the end of the document by noting the deficiencies of the simplified canonical as well as more complex Markov models, due mainly to the stationary and static nature of Markovinity. Throughout the document, we shall focus on concurrently maintained systems although the discussions will only slightly change for the systems repaired one device at a time.Comment: 58 pages, 20 figures, 9 tables. arXiv admin note: substantial text overlap with arXiv:1911.0032

    Modeling and Simulation in Engineering

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    The Special Issue Modeling and Simulation in Engineering, belonging to the section Engineering Mathematics of the Journal Mathematics, publishes original research papers dealing with advanced simulation and modeling techniques. The present book, “Modeling and Simulation in Engineering I, 2022”, contains 14 papers accepted after peer review by recognized specialists in the field. The papers address different topics occurring in engineering, such as ferrofluid transport in magnetic fields, non-fractal signal analysis, fractional derivatives, applications of swarm algorithms and evolutionary algorithms (genetic algorithms), inverse methods for inverse problems, numerical analysis of heat and mass transfer, numerical solutions for fractional differential equations, Kriging modelling, theory of the modelling methodology, and artificial neural networks for fault diagnosis in electric circuits. It is hoped that the papers selected for this issue will attract a significant audience in the scientific community and will further stimulate research involving modelling and simulation in mathematical physics and in engineering
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