594 research outputs found

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    Digitalization and Development

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    This book examines the diffusion of digitalization and Industry 4.0 technologies in Malaysia by focusing on the ecosystem critical for its expansion. The chapters examine the digital proliferation in major sectors of agriculture, manufacturing, e-commerce and services, as well as the intermediary organizations essential for the orderly performance of socioeconomic agents. The book incisively reviews policy instruments critical for the effective and orderly development of the embedding organizations, and the regulatory framework needed to quicken the appropriation of socioeconomic synergies from digitalization and Industry 4.0 technologies. It highlights the importance of collaboration between government, academic and industry partners, as well as makes key recommendations on how to encourage adoption of IR4.0 technologies in the short- and long-term. This book bridges the concepts and applications of digitalization and Industry 4.0 and will be a must-read for policy makers seeking to quicken the adoption of its technologies

    AB-INITIO INVESTIGATION OF 2D MATERIALS FOR GAS SENSING, ENERGY STORAGE AND SPINTRONIC APPLICATIONS

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    The field of Two Dimensional (2D) materials has been extensively studied since their discovery in 2004, owing to their remarkable combination of properties. My thesis focuses on exploring novel 2D materials such as Graphene Nanoribbon (GNR), holey carbon nitride C2N, and MXenes for energy storage, gas sensing, and spintronic applications, utilizing state-of-the-art techniques that combine Density Functional Theory (DFT) and Non-Equilibrium Greens Functions (NEGF) formalism; namely Vienna Ab-initio Simulation Package (VASP) and Atomistic Toolkit (ATK) package.Firstly, on the side of gas sensing, the burning of fossil fuels raises the level of toxic gas and contributes to global warming, necessitating the development of highly sensitive gas sensors. To start with, the adsorption and gas-sensing properties of bilaterally edge doped (B/N) GNRs were investigated. The transport properties revealed that the bilateral B/N edge-doping of GNR yielded Negative Differential Resistance (NDR) IV-characteristics, due to the electron back-scattering which was beneficial for selective gas sensing applications. Therefore, both GNR: B/N were found to be good sensors for NO2 and SO3 respectively. After that, the catalytic activity of four magnetic transition metal “TM” elements (e.g., Mn, Fe, Co and Ni) embedded in C2N pores, as Single-Atom Catalysts (SAC), was tested towards detecting toxic oxidizing gases. The results of spin-polarized transport properties revealed that Ni- and Fe-embedded C2N are the most efficient in detecting NO/ NO2 and NO2 molecules.Secondly, on the side of energy storage, since the fossil fuels reserves are depleting at an alarming rate, there is an urgent need for alternative forms of energy to meet the ever-growing demand for energy. Hydrogen is a popular form of clean energy. However, its storage and handling are challenging because of its explosive nature. The effect of magnetic moment on the hydrogen adsorption and gas-sensing properties in Mn-embedded in C2N were investigated. Two distinct configurations of embedment were considered: (i) SAC: 1Mn@C2N; and (ii) DAC: Mn2@C2N. Based on the huge changes in electronic and magnetic properties and the low recovery time (i.e., τ ≪ 1 s, τ = 92 μs and 1.8 ms, respectively), we concluded that C2N:Mn is an excellent candidate for (reusable) hydrogen magnetic gas sensor with high sensitivity and selectivity and rapid recovery time. Then, a comparative study of hydrogen storage capabilities on Metal- catalyst embedded (Ca versus Mn) C2N is presented which demonstrated the stability of these metal structures embedded on the C2N substrate. We proposed Ca@C2N and Mn@C2N for dual applications- hydrogen storage and a novel electrode for prospective metal-ion battery applications owing to its high irreversible uptake capacity 200 mAhg-1.Thirdly, on the side of data storage, spintronics is an emerging field for the next generation nanoelectronics devices to reduce their power consumption and to increase their memory and processing capabilities. Designing 2D-materials that exhibit half-metallic properties is important in spintronic devices that are used in low-power high-density logic circuits. We tested samples comprising of SAC and DAC of Mn embedded in a C2N sample size 2×2 primitive cells as well as their combinations in neighboring large pores. Many other TM catalysts were screened, and the results show the existence of half metallicity in just five cases: (a) C2N:Mn (DAC, SAC-SAC, and SAC-DAC); (b) C2N:Fe (DAC); and (c) C2N:Ni (SAC-DAC). Our results further showed the origins of half-metallicity to be attributed to both FMC and synergetic interactions between the catalysts with the six mirror images, formed by the periodic-boundary conditions.Lastly, on the side of batteries, sodium-sulfur batteries show great potential for storing large amounts of energy due to their ability to undergo a double electron- redox process, as well as the plentiful abundance of sodium and sulfur resources. However, the shuttle effect caused by intermediate sodium polysulfides (Na2Sn) limits their performance and lifespan. To address this issue, we proposed two functionalized MXenes Hf3C2T2 and Zr3C2T2 (T= F, O), as cathode additives to suppress the shuttle effect. We found that both Hf3C2T2 and Zr3C2T2 systems inhibit the shuttle effect by binding to Na2Sn with a binding energy higher than the electrolyte solvents. The decomposition barrier for Na2Sn on the O functionalized MXenes gets reduced which enhances the electrochemical process. Overall, our findings show that the tuning of 2D materials can lead to promising applications in various fields, including energy storage, gas sensing, and spintronics

    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges and Future Directions

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    Sixth-generation (6G) networks anticipate intelligently supporting a wide range of smart services and innovative applications. Such a context urges a heavy usage of Machine Learning (ML) techniques, particularly Deep Learning (DL), to foster innovation and ease the deployment of intelligent network functions/operations, which are able to fulfill the various requirements of the envisioned 6G services. Specifically, collaborative ML/DL consists of deploying a set of distributed agents that collaboratively train learning models without sharing their data, thus improving data privacy and reducing the time/communication overhead. This work provides a comprehensive study on how collaborative learning can be effectively deployed over 6G wireless networks. In particular, our study focuses on Split Federated Learning (SFL), a technique recently emerged promising better performance compared with existing collaborative learning approaches. We first provide an overview of three emerging collaborative learning paradigms, including federated learning, split learning, and split federated learning, as well as of 6G networks along with their main vision and timeline of key developments. We then highlight the need for split federated learning towards the upcoming 6G networks in every aspect, including 6G technologies (e.g., intelligent physical layer, intelligent edge computing, zero-touch network management, intelligent resource management) and 6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomous systems). Furthermore, we review existing datasets along with frameworks that can help in implementing SFL for 6G networks. We finally identify key technical challenges, open issues, and future research directions related to SFL-enabled 6G networks

    A human-factors approach to capture medical device safety, performance and usability

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    Advances in medical technology including computer aided and robotic surgery, digital health and increased use of portable devices have improved patient care in both hospital and home environments. These advancements have brought an increased level of complexity in patient care with new challenges to both patients and clinicians. The available performance data on medical devices (MD) is scarce and of variable quality despite work from regulatory bodies, with multiple associated challenges and lack of effective systems in place for its collection. This research used human factor methods to address i) the current state of safety and performance data availability for MDs and ii) methods of capturing safety and usability data in hospital and home environments by using human factor methods. Part A of this thesis concentrated on hospital based devices whilst Part B addressed home use MDs. End user experiences were utilised throughout to gain an understanding of the current system including its challenges and reasons leading to lack of data. Patients, clinicians, manufacturers, human factor specialists and MHRA were involved at all stages of this research. The studies led to the developments of the pathway map to reporting and information transfer in operating theatres and furthermore the development and initial evaluation of the MD-PRS concept (Medical Device Performance Reporting System) as a single dedicated method of reporting all MD malfunctions/ failures. The My-VID usability tool (My Voice in Design) was developed and evaluated as a method for collecting usability data directly from patients on home use MDs. In conclusion, this thesis used human factor methods to better understand the current system of data collection, available data sources on MDs and challenges faced prior to developing methods for improvement, based on end user experiences . Finally, methods of applying this research to clinical practice were addressed in the final chapter.Open Acces

    Learning-Based Ubiquitous Sensing For Solving Real-World Problems

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    Recently, as the Internet of Things (IoT) technology has become smaller and cheaper, ubiquitous sensing ability within these devices has become increasingly accessible. Learning methods have also become more complex in the field of computer science ac- cordingly. However, there remains a gap between these learning approaches and many problems in other disciplinary fields. In this dissertation, I investigate four different learning-based studies via ubiquitous sensing for solving real-world problems, such as in IoT security, athletics, and healthcare. First, I designed an online intrusion detection system for IoT devices via power auditing. To realize the real-time system, I created a lightweight power auditing device. With this device, I developed a distributed Convolutional Neural Network (CNN) for online inference. I demonstrated that the distributed system design is secure, lightweight, accurate, real-time, and scalable. Furthermore, I characterized potential Information-stealer attacks via power auditing. To defend against this potential exfiltration attack, a prototype system was built on top of the botnet detection system. In a testbed environment, I defined and deployed an IoT Information-stealer attack. Then, I designed a detection classifier. Altogether, the proposed system is able to identify malicious behavior on endpoint IoT devices via power auditing. Next, I enhanced athletic performance via ubiquitous sensing and machine learning techniques. I first designed a metric called LAX-Score to quantify a collegiate lacrosse team’s athletic performance. To derive this metric, I utilized feature selection and weighted regression. Then, the proposed metric was statistically validated on over 700 games from the last three seasons of NCAA Division I women’s lacrosse. I also exam- ined the biometric sensing dataset obtained from a collegiate team’s athletes over the course of a season. I then identified the practice features that are most correlated with high-performance games. Experimental results indicate that LAX-Score provides insight into athletic performance quality beyond wins and losses. Finally, I studied the data of patients with Parkinson’s Disease. I secured the Inertial Measurement Unit (IMU) sensing data of 30 patients while they conducted pre-defined activities. Using this dataset, I measured tremor events during drawing activities for more convenient tremor screening. Our preliminary analysis demonstrates that IMU sensing data can identify potential tremor events in daily drawing or writing activities. For future work, deep learning-based techniques will be used to extract features of the tremor in real-time. Overall, I designed and applied learning-based methods across different fields to solve real-world problems. The results show that combining learning methods with domain knowledge enables the formation of solutions

    Security and Privacy for Modern Wireless Communication Systems

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    The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

    Application of knowledge management principles to support maintenance strategies in healthcare organisations

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    Healthcare is a vital service that touches people's lives on a daily basis by providing treatment and resolving patients' health problems through the staff. Human lives are ultimately dependent on the skilled hands of the staff and those who manage the infrastructure that supports the daily operations of the service, making it a compelling reason for a dedicated research study. However, the UK healthcare sector is undergoing rapid changes, driven by rising costs, technological advancements, changing patient expectations, and increasing pressure to deliver sustainable healthcare. With the global rise in healthcare challenges, the need for sustainable healthcare delivery has become imperative. Sustainable healthcare delivery requires the integration of various practices that enhance the efficiency and effectiveness of healthcare infrastructural assets. One critical area that requires attention is the management of healthcare facilities. Healthcare facilitiesis considered one of the core elements in the delivery of effective healthcare services, as shortcomings in the provision of facilities management (FM) services in hospitals may have much more drastic negative effects than in any other general forms of buildings. An essential element in healthcare FM is linked to the relationship between action and knowledge. With a full sense of understanding of infrastructural assets, it is possible to improve, manage and make buildings suitable to the needs of users and to ensure the functionality of the structure and processes. The premise of FM is that an organisation's effectiveness and efficiency are linked to the physical environment in which it operates and that improving the environment can result in direct benefits in operational performance. The goal of healthcare FM is to support the achievement of organisational mission and goals by designing and managing space and infrastructural assets in the best combination of suitability, efficiency, and cost. In operational terms, performance refers to how well a building contributes to fulfilling its intended functions. Therefore, comprehensive deployment of efficient FM approaches is essential for ensuring quality healthcare provision while positively impacting overall patient experiences. In this regard, incorporating knowledge management (KM) principles into hospitals' FM processes contributes significantly to ensuring sustainable healthcare provision and enhancement of patient experiences. Organisations implementing KM principles are better positioned to navigate the constantly evolving business ecosystem easily. Furthermore, KM is vital in processes and service improvement, strategic decision-making, and organisational adaptation and renewal. In this regard, KM principles can be applied to improve hospital FM, thereby ensuring sustainable healthcare delivery. Knowledge management assumes that organisations that manage their organisational and individual knowledge more effectively will be able to cope more successfully with the challenges of the new business ecosystem. There is also the argument that KM plays a crucial role in improving processes and services, strategic decision-making, and adapting and renewing an organisation. The goal of KM is to aid action – providing "a knowledge pull" rather than the information overload most people experience in healthcare FM. Other motivations for seeking better KM in healthcare FM include patient safety, evidence-based care, and cost efficiency as the dominant drivers. The most evidence exists for the success of such approaches at knowledge bottlenecks, such as infection prevention and control, working safely, compliances, automated systems and reminders, and recall based on best practices. The ability to cultivate, nurture and maximise knowledge at multiple levels and in multiple contexts is one of the most significant challenges for those responsible for KM. However, despite the potential benefits, applying KM principles in hospital facilities is still limited. There is a lack of understanding of how KM can be effectively applied in this context, and few studies have explored the potential challenges and opportunities associated with implementing KM principles in hospitals facilities for sustainable healthcare delivery. This study explores applying KM principles to support maintenance strategies in healthcare organisations. The study also explores the challenges and opportunities, for healthcare organisations and FM practitioners, in operationalising a framework which draws the interconnectedness between healthcare. The study begins by defining healthcare FM and its importance in the healthcare industry. It then discusses the concept of KM and the different types of knowledge that are relevant in the healthcare FM sector. The study also examines the challenges that healthcare FM face in managing knowledge and how the application of KM principles can help to overcome these challenges. The study then explores the different KM strategies that can be applied in healthcare FM. The KM benefits include improved patient outcomes, reduced costs, increased efficiency, and enhanced collaboration among healthcare professionals. Additionally, issues like creating a culture of innovation, technology, and benchmarking are considered. In addition, a framework that integrates the essential concepts of KM in healthcare FM will be presented and discussed. The field of KM is introduced as a complex adaptive system with numerous possibilities and challenges. In this context, and in consideration of healthcare FM, five objectives have been formulated to achieve the research aim. As part of the research, a number of objectives will be evaluated, including appraising the concept of KM and how knowledge is created, stored, transferred, and utilised in healthcare FM, evaluating the impact of organisational structure on job satisfaction as well as exploring how cultural differences impact knowledge sharing and performance in healthcare FM organisations. This study uses a combination of qualitative methods, such as meetings, observations, document analysis (internal and external), and semi-structured interviews, to discover the subjective experiences of healthcare FM employees and to understand the phenomenon within a real-world context and attitudes of healthcare FM as the data collection method, using open questions to allow probing where appropriate and facilitating KM development in the delivery and practice of healthcare FM. The study describes the research methodology using the theoretical concept of the "research onion". The qualitative research was conducted in the NHS acute and non-acute hospitals in Northwest England. Findings from the research study revealed that while the concept of KM has grown significantly in recent years, KM in healthcare FM has received little or no attention. The target population was fifty (five FM directors, five academics, five industry experts, ten managers, ten supervisors, five team leaders and ten operatives). These seven groups were purposively selected as the target population because they play a crucial role in KM enhancement in healthcare FM. Face-to-face interviews were conducted with all participants based on their pre-determined availability. Out of the 50-target population, only 25 were successfully interviewed to the point of saturation. Data collected from the interview were coded and analysed using NVivo to identify themes and patterns related to KM in healthcare FM. The study is divided into eight major sections. First, it discusses literature findings regarding healthcare FM and KM, including underlying trends in FM, KM in general, and KM in healthcare FM. Second, the research establishes the study's methodology, introducing the five research objectives, questions and hypothesis. The chapter introduces the literature on methodology elements, including philosophical views and inquiry strategies. The interview and data analysis look at the feedback from the interviews. Lastly, a conclusion and recommendation summarise the research objectives and suggest further research. Overall, this study highlights the importance of KM in healthcare FM and provides insights for healthcare FM directors, managers, supervisors, academia, researchers and operatives on effectively leveraging knowledge to improve patient care and organisational effectiveness

    Surgical skills modeling in cardiac ablation using deep learning

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    Cardiovascular diseases, a leading global cause of death, can be treated using Minimally Invasive Surgery (MIS) for various heart conditions. Cardiac ablation is an example of MIS, treating heart rhythm disorders like atrial fibrillation and the operation outcomes are highly dependent on the surgeon's skills. This procedure utilizes catheters, flexible endovascular devices inserted into the patient's blood vessels through a small incision. Traditionally, novice surgeons' performance is assessed in the Operating Room (OR) through surgical tasks. Unskilled behavior can lead to longer operations and inferior surgical outcomes. However, an alternative approach can be capturing surgeons' maneuvers and using them as input for an AI model to evaluate their skills outside the OR. To this end, two experimental setups were proposed to study the skills modelling for surgical behaviours. The first setup simulates the ablation procedure using a mechanical system with a synthetic heartbeat mechanism that measures contact forces between the catheter's tip and tissue. The second one simulates the cardiac catheterization procedure for the surgeon’s practice and records the user's maneuvers at the same time. The first task involved maintaining the force within a safe range while the tip of the catheter is touching the surface. The second task was passing a catheter’s tip through curves and level-intersection on a transparent blood vessel phantom. To evaluate attendees' demonstrations, it is crucial to extract maneuver models for both expert and novice surgeons. Data from participants, including novices and experts, performing the task using the experimental setups, is compiled. Deep recurrent neural networks are employed to extract the model of skills by solving a binary classification problem, distinguishing between expert and novice maneuvers. The results demonstrate the proposed networks' ability to accurately distinguish between novice and expert surgical skills, achieving an accuracy of over 92%
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