291 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

    Get PDF
    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Pre-hospital trauma assessment and management of older patients and their association with patient outcomes: challenges and barriers

    Get PDF
    BACKGROUND: Saudi Arabia faces an increasing prehospital healthcare burden from older people with injuries, but little is known about their characteristics and current treatment. METHODS: This was a sequential explanatory mixed-methods design, preceded by a scoping review on the prehospital geriatric trauma care. A retrospective quantitative study was conducted using registry data from older patients (≥55 years) admitted by ambulances from 01/08/2017 to 31/10/2021 at a major trauma centre in Saudi Arabia. A qualitative study was conducted using a purposive sample of Saudi paramedics and ambulance technicians from Riyadh and Makkah using online semi-structured interviews and analysed using the framework method. The quantitative and qualitative findings were integrated. RESULTS: The quantitative study recruited 452 eligible cases and found most of them were admitted with low falls (53.7%), normal physiology, and extremities injuries (53.1%). The study identified no significant predictors of in-hospital death (p>0.05 for all predictors), although statistical power was limited. The qualitative study recruited twenty participants and identified that they reported age-related challenges including physiological changes, polypharmacy, and communication difficulties. They all wanted training and guidelines to improve their knowledge. They reported struggling with communication difficulties, inaccurate adverse outcomes predictions, difficult intravenous cannulations, and cultural restrictions affecting care provision for female patients. I identified organisational barriers (e.g. lack of shared patient records and lack of guidelines) and cultural barriers (e.g. barriers to assessing women, attitudes towards older people, and attitudes towards paramedics) that influenced implementation of knowledge. This study also found that the participants' perceptions aligned with the retrospective study’s cohort, and they acknowledged the difficulty of predicting death in older trauma patients. CONCLUSION: Ambulance clinicians in Saudi Arabia want guidelines and training in managing older trauma patients but these need to take into account the characteristics of older trauma patients and the cultural barriers that I identified

    Design, Implementation and Evaluation of Parallel Solutions for a Nested Explainability Algorithm

    Get PDF
    In the field of Machine Learning and Data Science there is an escalating need for performance as workloads become more and more complex. Parallelization over multiple cores and machines (clusters) is often employed as a means to significantly improve performance. This work specifically considers the explainability algorithm GLEAMS (Global & Local ExplainAbility of black-box Models through Space partitioning) and the poor performance offered by its sequential Python implementation. GLEAMS is a post-hoc, model agnostic explainability technique capable of giving a global understanding of the original model through recursive partitioning of the input space into non overlapping cells, each featuring a local linear approximation of the black-box model. The purpose of this work is the analysis, development, implementation and testing of a parallel distributed solution for the sequential GLEAMS explainability algorithm. The algorithm poses certain interesting parallelization challenges such as a recursive binary tree and nested parallelism. Notably, the nested nature of the parallelism is of marked relevance due to the complexities it introduces and the poor support that existing Python frameworks and solutions offer for it. Multiple solutions were designed and implemented, and this paper describes the steps taken for their development, justifies the choices made, explains their workings, illustrates their differences and extensively analyses the performance offered. In particular, this work proposes an asyncio based approach, in combination with the Ray framework, as a practical solution to many of the limitations encountered with the current state of nested parallelism support in Python. Additionally, some theoretical and more general approaches and solutions inspired by other languages are proposed and discussed

    Power System Stability Analysis using Neural Network

    Full text link
    This work focuses on the design of modern power system controllers for automatic voltage regulators (AVR) and the applications of machine learning (ML) algorithms to correctly classify the stability of the IEEE 14 bus system. The LQG controller performs the best time domain characteristics compared to PID and LQG, while the sensor and amplifier gain is changed in a dynamic passion. After that, the IEEE 14 bus system is modeled, and contingency scenarios are simulated in the System Modelica Dymola environment. Application of the Monte Carlo principle with modified Poissons probability distribution principle is reviewed from the literature that reduces the total contingency from 1000k to 20k. The damping ratio of the contingency is then extracted, pre-processed, and fed to ML algorithms, such as logistic regression, support vector machine, decision trees, random forests, Naive Bayes, and k-nearest neighbor. A neural network (NN) of one, two, three, five, seven, and ten hidden layers with 25%, 50%, 75%, and 100% data size is considered to observe and compare the prediction time, accuracy, precision, and recall value. At lower data size, 25%, in the neural network with two-hidden layers and a single hidden layer, the accuracy becomes 95.70% and 97.38%, respectively. Increasing the hidden layer of NN beyond a second does not increase the overall score and takes a much longer prediction time; thus could be discarded for similar analysis. Moreover, when five, seven, and ten hidden layers are used, the F1 score reduces. However, in practical scenarios, where the data set contains more features and a variety of classes, higher data size is required for NN for proper training. This research will provide more insight into the damping ratio-based system stability prediction with traditional ML algorithms and neural networks.Comment: Masters Thesis Dissertatio

    Applications

    Get PDF
    Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

    Social protection through higher education: experiences of Community Work Programme participants studying for a teaching qualification by distance education mode.

    Get PDF
    Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Poverty and unemployment have led to the creation of social protection programmes by governments, which include public works programmes. In South Africa, the Community Work Programme (CWP) employs rural individuals living in poverty for a limited number of days each month as part of a second economy strategy project. To improve the chances of these participants to find full-time employment in the primary economy, the CWP partnered with North-West University and the Department of Cooperative Governance and Traditional Affairs to pilot a programme for selected participants to enrol for a Grade R teaching diploma by distance education. CWP participants in the Ugu District in the province of KwaZulu-Natal were selected. While studying, the participants worked at rural schools near their homes as CWP participants. This study sought to explore the experiences of the participants in the pilot before the project went to scale. Informed by the critical paradigm, the study used the extended case method, drawing on Bourdieu's field theory and Archer's concepts of structure, culture and agency for analysis. The study found that while rural participants experienced barriers in terms of physical and epistemic access to higher education, with adequate support many succeeded in earning their qualification and finding employment in the primary economy as teachers. Inherent challenges included the digital illiteracy of participants; travel distances and transportation costs; communication between partners and participants in the pilot; symbolic violence related to the language of teaching and learning; instability in the implementing structure resulting in diminished support for participants; and bureaucratic inefficiency and lack of alignment on the part of the partners to the programme. The exercise of agency was key to participants' success. The Work Integrated learning modules demonstrated complementarity between the requirements of the formal diploma qualification and the CWP requirement for useful work, although the increase in workdays as a teacher assistant reduced the time available for participants to study. The thesis contributes to the debate on Sustainable Development Goal 1 on strategies to end world poverty. The thesis argues that public works programmes can contribute to reducing unemployment with deliberate structuring of support and mentorship to enable students to acquire the higher education habitus required to succeed

    IoT and Sensor Networks in Industry and Society

    Get PDF
    The exponential progress of Information and Communication Technology (ICT) is one of the main elements that fueled the acceleration of the globalization pace. Internet of Things (IoT), Artificial Intelligence (AI) and big data analytics are some of the key players of the digital transformation that is affecting every aspect of human's daily life, from environmental monitoring to healthcare systems, from production processes to social interactions. In less than 20 years, people's everyday life has been revolutionized, and concepts such as Smart Home, Smart Grid and Smart City have become familiar also to non-technical users. The integration of embedded systems, ubiquitous Internet access, and Machine-to-Machine (M2M) communications have paved the way for paradigms such as IoT and Cyber Physical Systems (CPS) to be also introduced in high-requirement environments such as those related to industrial processes, under the forms of Industrial Internet of Things (IIoT or I2oT) and Cyber-Physical Production Systems (CPPS). As a consequence, in 2011 the German High-Tech Strategy 2020 Action Plan for Germany first envisioned the concept of Industry 4.0, which is rapidly reshaping traditional industrial processes. The term refers to the promise to be the fourth industrial revolution. Indeed, the first industrial revolution was triggered by water and steam power. Electricity and assembly lines enabled mass production in the second industrial revolution. In the third industrial revolution, the introduction of control automation and Programmable Logic Controllers (PLCs) gave a boost to factory production. As opposed to the previous revolutions, Industry 4.0 takes advantage of Internet access, M2M communications, and deep learning not only to improve production efficiency but also to enable the so-called mass customization, i.e. the mass production of personalized products by means of modularized product design and flexible processes. Less than five years later, in January 2016, the Japanese 5th Science and Technology Basic Plan took a further step by introducing the concept of Super Smart Society or Society 5.0. According to this vision, in the upcoming future, scientific and technological innovation will guide our society into the next social revolution after the hunter-gatherer, agrarian, industrial, and information eras, which respectively represented the previous social revolutions. Society 5.0 is a human-centered society that fosters the simultaneous achievement of economic, environmental and social objectives, to ensure a high quality of life to all citizens. This information-enabled revolution aims to tackle today’s major challenges such as an ageing population, social inequalities, depopulation and constraints related to energy and the environment. Accordingly, the citizens will be experiencing impressive transformations into every aspect of their daily lives. This book offers an insight into the key technologies that are going to shape the future of industry and society. It is subdivided into five parts: the I Part presents a horizontal view of the main enabling technologies, whereas the II-V Parts offer a vertical perspective on four different environments. The I Part, dedicated to IoT and Sensor Network architectures, encompasses three Chapters. In Chapter 1, Peruzzi and Pozzebon analyse the literature on the subject of energy harvesting solutions for IoT monitoring systems and architectures based on Low-Power Wireless Area Networks (LPWAN). The Chapter does not limit the discussion to Long Range Wise Area Network (LoRaWAN), SigFox and Narrowband-IoT (NB-IoT) communication protocols, but it also includes other relevant solutions such as DASH7 and Long Term Evolution MAchine Type Communication (LTE-M). In Chapter 2, Hussein et al. discuss the development of an Internet of Things message protocol that supports multi-topic messaging. The Chapter further presents the implementation of a platform, which integrates the proposed communication protocol, based on Real Time Operating System. In Chapter 3, Li et al. investigate the heterogeneous task scheduling problem for data-intensive scenarios, to reduce the global task execution time, and consequently reducing data centers' energy consumption. The proposed approach aims to maximize the efficiency by comparing the cost between remote task execution and data migration. The II Part is dedicated to Industry 4.0, and includes two Chapters. In Chapter 4, Grecuccio et al. propose a solution to integrate IoT devices by leveraging a blockchain-enabled gateway based on Ethereum, so that they do not need to rely on centralized intermediaries and third-party services. As it is better explained in the paper, where the performance is evaluated in a food-chain traceability application, this solution is particularly beneficial in Industry 4.0 domains. Chapter 5, by De Fazio et al., addresses the issue of safety in workplaces by presenting a smart garment that integrates several low-power sensors to monitor environmental and biophysical parameters. This enables the detection of dangerous situations, so as to prevent or at least reduce the consequences of workers accidents. The III Part is made of two Chapters based on the topic of Smart Buildings. In Chapter 6, Petroșanu et al. review the literature about recent developments in the smart building sector, related to the use of supervised and unsupervised machine learning models of sensory data. The Chapter poses particular attention on enhanced sensing, energy efficiency, and optimal building management. In Chapter 7, Oh examines how much the education of prosumers about their energy consumption habits affects power consumption reduction and encourages energy conservation, sustainable living, and behavioral change, in residential environments. In this Chapter, energy consumption monitoring is made possible thanks to the use of smart plugs. Smart Transport is the subject of the IV Part, including three Chapters. In Chapter 8, Roveri et al. propose an approach that leverages the small world theory to control swarms of vehicles connected through Vehicle-to-Vehicle (V2V) communication protocols. Indeed, considering a queue dominated by short-range car-following dynamics, the Chapter demonstrates that safety and security are increased by the introduction of a few selected random long-range communications. In Chapter 9, Nitti et al. present a real time system to observe and analyze public transport passengers' mobility by tracking them throughout their journey on public transport vehicles. The system is based on the detection of the active Wi-Fi interfaces, through the analysis of Wi-Fi probe requests. In Chapter 10, Miler et al. discuss the development of a tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by Road Transport Enterprises (RTEs). The authors of this Chapter further provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The book ends with the two Chapters of the V Part on Smart Environmental Monitoring. In Chapter 11, He et al. propose a Sea Surface Temperature Prediction (SSTP) model based on time-series similarity measure, multiple pattern learning and parameter optimization. In this strategy, the optimal parameters are determined by means of an improved Particle Swarm Optimization method. In Chapter 12, Tsipis et al. present a low-cost, WSN-based IoT system that seamlessly embeds a three-layered cloud/fog computing architecture, suitable for facilitating smart agricultural applications, especially those related to wildfire monitoring. We wish to thank all the authors that contributed to this book for their efforts. We express our gratitude to all reviewers for the volunteering support and precious feedback during the review process. We hope that this book provides valuable information and spurs meaningful discussion among researchers, engineers, businesspeople, and other experts about the role of new technologies into industry and society

    Uncertain Multi-Criteria Optimization Problems

    Get PDF
    Most real-world search and optimization problems naturally involve multiple criteria as objectives. Generally, symmetry, asymmetry, and anti-symmetry are basic characteristics of binary relationships used when modeling optimization problems. Moreover, the notion of symmetry has appeared in many articles about uncertainty theories that are employed in multi-criteria problems. Different solutions may produce trade-offs (conflicting scenarios) among different objectives. A better solution with respect to one objective may compromise other objectives. There are various factors that need to be considered to address the problems in multidisciplinary research, which is critical for the overall sustainability of human development and activity. In this regard, in recent decades, decision-making theory has been the subject of intense research activities due to its wide applications in different areas. The decision-making theory approach has become an important means to provide real-time solutions to uncertainty problems. Theories such as probability theory, fuzzy set theory, type-2 fuzzy set theory, rough set, and uncertainty theory, available in the existing literature, deal with such uncertainties. Nevertheless, the uncertain multi-criteria characteristics in such problems have not yet been explored in depth, and there is much left to be achieved in this direction. Hence, different mathematical models of real-life multi-criteria optimization problems can be developed in various uncertain frameworks with special emphasis on optimization problems
    corecore