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Artificial intelligence (AI) is the ability of machines to carry out tasks by imitating human intelligence. In recent years, AI methods have begun to be applied in many different areas, with healthcare being one of the most prominent. Diagnosis, treatment, patient care, new drug production, and preventive care can be listed as some of the applications of AI in healthcare. In this review, deep learning methods, which are a sub-branch of AI, are mentioned. Deep learning methods frequently used in the literature are convolutional neural networks (CNNs), stacked autoencoders (SAEs), and recurrent neural networks (RNNs). These deep learning methods include CNNs for image recognition and classification, SAEs for unsupervised feature learning and dimensionality reduction, and RNNs for analyzing sequential data like time-series. However, it should be noted that these methods can also be applied to other application areas. This paper presents studies in the literature on medical image analysis, drug discovery and development, and remote patient monitoring in which these deep learning methods are used. [ABSTRACT FROM AUTHOR]

ABSTRACT In the context of Cloud and Fog computing settings, recent developments in deep learning techniques show great potential for changing several fields, including healthcare. In this study, we make a contribution to this changing field by proposing an enhanced deep learning‐based strategy for classifying chest X‐ray images, using pre‐trained models such as RetinaNet, EfficientNet and Faster‐R‐CNN, which we use through transfer learning. Our strategy outperforms single models and traditional techniques by leveraging critical data gleaned from multiple models, demonstrating the ability of deep learning to improve diagnostic precision. Our approach presents a novel dual‐check system in the context of worries about security, privacy and trust in Cloud and Fog‐based Smart Systems. In this case, a decision support system uses chest X‐ray images to make an initial diagnosis that is then confirmed by a medical practitioner. This cooperative strategy not only reduces diagnostic errors that come from machine and human sources but also emphasises how crucial it is to incorporate AI‐driven solutions into safe and reliable healthcare ecosystems. Our approach raises the bar for the quality of patient care and healthcare outcomes by overcoming the drawbacks of traditional diagnostic methods that depend on the subjective opinions of physicians. Our work brings out how deep learning might transform clinical diagnostics by distinguishing inflammatory regions in chest X‐ray images. Research is needed to fully grasp the transformative potential of deep learning in medical image processing, especially as the healthcare industry continues to embrace AI‐driven solutions. Further research endeavours have to dig into tactics like broadening the scope of datasets, executing data augmentation methodologies and incorporating bespoken features to augment the elasticity and effectiveness of AI‐driven diagnostic systems. [ABSTRACT FROM AUTHOR]

This research article discusses a new paradigm in smart system development using the 4-layer framework and activity theory from the perspectives of ontology, epistemology, and axiology. The study aims to understand how this paradigm can influence the development of smart systems and provide insights into its theoretical and practical implications. The 4-layer modern system comprises instrumentation, information systems, business intelligence, and gamification, which are the core components of a smart system. Each layer plays a crucial role in data collection, information processing, business analysis, and gamification implementation at the top layer. The integration of these layers forms a solid foundation for the development of efficient and innovative smart systems. In addition, activity theory is utilized to analyze the interactions among users, technology, and the environment within the context of smart systems. From an ontology standpoint, this research views smart systems as complex socio-technical entities involving human, technological, and process aspects. In terms of epistemology, a multidisciplinary approach is employed to combine knowledge from areas such as computer science, information systems, and human-computer interaction. In the realm of axiology, this study recognizes the ethical values and social implications that must be considered in the development and implementation of smart systems. By integrating the new smart system paradigm using the 4-layer modern systems and activity theory, this research contributes to the understanding of the dynamics and development potential of smart systems. The results of this study can provide guidance for practitioners, researchers, and decision-makers in developing more effective, efficient, and user-oriented smart systems in various contexts.

The consolidation of Building Information Modelling (BIM) on construction sites and the preparation of the Internet-of-Things (IoT) in urban city planning are on-going processes. Therefore, the operation of modern total stations in dynamic urban scenarios becomes inevitable and often reveals the limitations of state-of-the-art approaches. This paper discusses augmentation strategies for the automatic operation of total stations. Additional internal sensor information and external facility information can be used to increase the resilience of the systems and enable them to adapt to changing circumstances. This is demonstrated by the examples of a changing prism orientation due to vandalism, local refraction effects due to air-conditioning and dynamic obstructions due to traffic. [ABSTRACT FROM AUTHOR]

Cyber-Physical-Social Systems (CPSS) bring together an assortment of resources from the physical network, cyberspace, and the social sphere. These systems rely on communication, computation, and control infrastructures, which usually comprise several tiers across the three realms, utilizing diverse resources such as sensors, actuators, computational resources, services, and human involvement. The proficient interaction of these resources is critical for the operational efficacy of cyber-physical-social systems. CPSS exhibits resource constraints, openness, and uncertainty, resulting in high uncertainty in its services, including quality of service (QoS) provision and significant security risks. Consequently, an efficient and dependable service composition approach is crucial when dealing with complex service requirements. Furthermore, preference learning plays a pivotal role in enhancing user experiences. Trust is viewed as a barrier to the integration of social attributes among smart objects, which is essential for forming reliable social connections and delivering dependable services. This review offers an in-depth analysis of trustworthiness management within Cyber-Physical Social Systems (CPSS). [ABSTRACT FROM AUTHOR]

Intelligent Reflecting Surfaces (IRSs) enable us to have a reconfigurable reflecting surface that can efficiently deflect the transmitted signal toward the receiver. The initial step in the IRS usually involves estimating the channel between a fixed transmitter and a stationary receiver. After estimating the channel, the problem of finding the most optimal IRS configuration is non-convex, and involves a huge search in the solution space. In this work, we propose a novel and customized technique which efficiently estimates the channel and configures the IRS with fixed transmit power, restricting the IRS coefficients to  { 1 , − 1 } . The results from our approach are numerically compared with existing optimization techniques.The key features of the linear system model under consideration include a Reconfigurable Intelligent Surface (RIS) setup consisting of 4096 RIS elements arranged in a 64 × 64 element array; the distance from RIS to the access point measures 107 m. NLOS users are located around 40 m away from the RIS element and 100 m from the access point. The estimated variance of noise  N C  is 3.1614  ×   10 − 20 . The proposed algorithm provides an overall data rate of 126.89 (MBits/s) for Line of Sight and 66.093 (MBits/s) for Non Line of Sight (NLOS) wireless communication. [ABSTRACT FROM AUTHOR]

The Internet of Things is a connected network consisting of various nodes that offer tools for a more convenient use of library services. In addition to enhancing library processes, it increases efficiency and improves library services. Therefore, the goal of this research is to identify the benefits and challenges of the Internet of Things in libraries and information centers. This will provide a coherent classification and perspective for future research on the Internet of Things in these centers using systematic review technique. The current research is applied in terms of its aim and methodology, as it is a qualitative study conducted using systematic review technique. The method involved formulating the research problem, studying relevant texts, determining a search strategy, and extracting data from Scopus and Web of Science databases. After retrieving 2335 documents from these databases, the data were merged and overlapping items were eliminated. Subsequently, necessary components were identified from 42 selected studies, and research concepts (dimensions) were extracted based on frequency and prioritized accordingly. In this research, the benefits, challenges, and types of technology used for the adoption of the Internet of Things in libraries and information centers were selected for final analysis. According to the concepts obtained during the research, each of them was categorized into similar concepts. Based on the findings, it was discovered that the benefits of the Internet of Things fell into three categories: online service infrastructure (8 concepts), management control infrastructure (16 concepts), and physical infrastructure (building smartness) (7 concepts). The challenges of using the Internet of Things were grouped into three categories: online service infrastructure (7 concepts), complex control infrastructure (8 concepts), and physical infrastructure (building smartness) (6 concepts). It was also found that RFID technology was the most widely used technology to implement the Internet of Things. The Internet of Things is one of the most suitable technologies in the current era for providing services and controlling library systems. Its high capability in facilitating and controlling work processes in libraries makes it a valuable tool. However, the application of this technology faces challenges such as lack of sufficient equipment, financial and administrative costs, and a shortage of expert staff to repair equipment when needed. Additionally, many librarians may not be familiar with this technology, and the maintenance costs can be high. Despite these challenges, the use of Internet of Things technology in libraries has the potential to create a better future for libraries. The degree of acceptance and implementation of this technology in libraries will depend on how many libraries are willing to embrace it. Therefore, authorities in charge of libraries should carefully consider the use of this technology and address the challenges that may arise. [ABSTRACT FROM AUTHOR]

Museums environment is complicated and defined by indoor air quality, thermal and lighting comfort. Artifacts deterioration is factored by extreme conditions of thermal exposure or excessive lighting. With high control required for exposed old monuments and visitor comfort, museums energy systems reach extreme levels. In this paper, we aim to find the gap in museums high energy loads and reach solutions through architectural design. The paper studies comparison results for different techniques in fifty recent case studies to identify the specific factors that matter most to museum buildings. These factors are implemented simultaneously on one base model in three climatic states by multi-objective optimization. The best option will showcase each climate optimum conditions. The paper introduces optimum architectural procedures optimizing sDA and ASE to minimum 70% and maximum 10% respectively while decreasing thermal load. The results help architects and policy makers achieve daylighting and energy optimization in museums through architecture.

The livestock industry is undergoing significant transformation with the integration of intelligent technologies aimed at enhancing productivity, welfare, and sustainability. This review explores the latest advancements in intelligent systemization (IS), including real-time monitoring, machine learning (ML), and the Internet of Things (IoT), and their impacts on livestock farming. The aim of this study is to provide a comprehensive overview of how these technologies can address industry challenges by improving animal health, optimizing resource use, and promoting sustainable practices. The methods involve an extensive review of the current literature and case studies on intelligent monitoring, data analytics, automation in feeding and climate control, and renewable energy integration. The results indicate that IS enhances livestock well-being through real-time health monitoring and early disease detection, optimizes feeding efficiency, and reduces operational costs through automation. Furthermore, these technologies contribute to environmental sustainability by minimizing waste and reducing the ecological footprint of livestock farming. This study highlights the transformative potential of intelligent technologies in creating a more efficient, humane, and sustainable livestock industry.

With the emergence of ever-improving quantum computers, technology is making its way to revolutionize many fields, and the medical sector is no exception. Recent efforts have explored applications of quantum computing in areas such as drug discovery, patient privacy, and information security. It is expected that, with improved and stable quantum computing technologies, the medical sector will benefit significantly in many areas, including efficient patient care, reduced clinical trial durations, enhanced imaging technologies, and post-quantum cryptography, to name a few.In this work, we highlight recent advancements in the medical sector driven by quantum computing, encompassing computation, optimization, security, machine learning, data processing, simulation, and healthcare perspectives. We also discuss the limitations of current technologies, and the challenges associated with the quantum computing revolution.

The livestock industry is undergoing significant transformation with the integration of intelligent technologies aimed at enhancing productivity, welfare, and sustainability. This review explores the latest advancements in intelligent systemization (IS), including real-time monitoring, machine learning (ML), and the Internet of Things (IoT), and their impacts on livestock farming. The aim of this study is to provide a comprehensive overview of how these technologies can address industry challenges by improving animal health, optimizing resource use, and promoting sustainable practices. The methods involve an extensive review of the current literature and case studies on intelligent monitoring, data analytics, automation in feeding and climate control, and renewable energy integration. The results indicate that IS enhances livestock well-being through real-time health monitoring and early disease detection, optimizes feeding efficiency, and reduces operational costs through automation. Furthermore, these technologies contribute to environmental sustainability by minimizing waste and reducing the ecological footprint of livestock farming. This study highlights the transformative potential of intelligent technologies in creating a more efficient, humane, and sustainable livestock industry. [ABSTRACT FROM AUTHOR]

Background Antimicrobial stewardship programs can optimize antimicrobial use and have been federally mandated in all hospitals. However, best stewardship practices in immunocompromised patients with cancer are not well established. Methods An antimicrobial time out, in the form of an email, was sent to physicians caring for hospitalized patients reaching 5 days of therapy for targeted antimicrobials (daptomycin, linezolid, tigecycline, vancomycin, imipenem/cilastatin, meropenem) in a comprehensive cancer center. Physicians were to discontinue the antimicrobial if unnecessary or document a rationale for continuation. This is a quasi-experimental, interrupted time series analysis assessing antimicrobial use during the following times: period 1 (before time-out: January 2007-June 2010) and period 2 (after time-out: July 2010-March/2015). The primary antimicrobial consumption metric was mean duration of therapy. Days of therapy per 1000 patient-days were also assessed. Results Implementation of the time-out was associated with a significant decrease in mean duration of therapy for the following antimicrobials; daptomycin: −0.89 days (95% confidence interval [CI], −1.38 to −.41); linezolid: −0.89 days (95% CI, −1.27 to −.52); meropenem: −0.97 days (95% CI, −1.39 to −.56); tigecycline: −1.41 days (95% CI, −2.19 to −.63); P <.001 for each comparison. Days of therapy/1000 patient-days decreased significantly for meropenem (−43.49; 95% CI, −58.61 to −28.37; P <.001), tigecycline (−35.47; 95% CI, −44.94 to −26.00; P <.001), and daptomycin (−9.47; 95% CI, −15.25 to −3.68; P =.002). Discussion A passive day 5 time-out was associated with reduction in targeted antibiotic use in a cancer center and could potentially be successfully adopted to several settings and electronic health records. [ABSTRACT FROM AUTHOR]

In this paper, certain issues envisaged in the framework of development of design methodology for intelligent autonomous distributed hybrid power complexes (ADHPC) with green hydrogen and energy storage, functioning in grid mode and in the mode of interaction with the global (national) grid (GN) have been solved. Depending on their energy deficit or surplus, relative to the global grid, ADHPCs can operate as a load or as an energy source, respectively, as follows: based on the analysis of the energy balance management process in the ADHPC, a reasonable choice of the structure of the system of accumulation and distribution of power flows (SADCF) was made from the point of view of increasing the reliability of its functioning and ensuring the physical feasibility of the energy balance management process in this structure, i.e. keeping the actual power consumption of the consumers close to the required rated power at each given time t. This is achieved by including a condenser connected to the SADCF system on its assembly and distribution bus and a storage system BS with double-level ((BS1, BS2), double-circuit ((BS1(1), BS1(2)), (BS2(1), BS2(2))) structure, whereby: BS1of the level 1 - to manage the capacity balance in the SADCF under normal ADHPC regime and the variation of green hydrogen and consumption capacities within their confidence intervals assessed at the design stage; BS2 of the level 2 - to coordinate ADHPC and GN modes of operation and to control, together with BS1 of the level 1, GN, diesel generator (DG), the power balance in the SADCF when the ADHPC fails and when RES and consumption power are outside their confidence intervals; alternating charge/discharge operation of the parallel circuits will extend the life of the BS system; – a comprehensive definition of optimal ADHPC system situational energy balance management task is formulated. [ABSTRACT FROM AUTHOR]

In recent decades, fog computing has contributed significantly to the expansion of smart cities. It generated numerous real‐time data and coped with time‐constraint applications. They use sensors, physical objects, and network standards to monitor health imaging, traffic surveillance, industrial management, and so forth. Interactive applications have been proposed for the Internet of Things (IoT) to control wireless channels and improve communication. However, most of the existing lack of handing network interference and a reliable monitoring process. Moreover, many solutions are vulnerable to external threats, resulting in inconsistent and untrustworthy information for end users. Thus, this article proposes a framework that considers possible shortest paths to provide the most reliable and low‐latency healthcare decision system using Q‐learning. In addition, fog devices offer a trusted transmission interference system and are kept secure. The proposed framework is specially designed for rapid real‐time medical data processing while enforcing robust security throughout the IoT‐based transmission process. To identify the health sensors in pairwise objects with the initial computing cost, the proposed framework applies graph theory. It also extracts the most effective and least loaded communication edges by examining the behaviour of devices. Moreover, the identities of devices are verified using lightweight timestamps and secret information, accordingly, it decreases the privacy threats. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) in the medical and biomedical field proposes new and efficient hardware for healthcare services. Thanks to machine-machine interaction and real-time solutions, the problems of accessibility and reliability are resolved. In addition, increased patient engagement in decision-making will drive health service compliance. Vital signals like the electrocardiogram (ECG) are some of the most critical biomedical information to process; it is the subject of several studies. The data flow of those signals is enormous, making real-time transmission a tough job, hence the need to compress these vital signals. Designing efficient hardware compression engines is a promising challenge for efficient real-time transmission. This article introduces a new VLSI (Very-Large-Scale Integration) architecture for an ECG compression engine based on the algorithm presented in the same work. The efficiency of our processor was verified using the MIT BIH databases. We have also implemented it using An FPGA, which reaches a frequency of 170 MHz and 65 n TCMS CMOS. The proposed processor uses 1.85 Kgates and consumes 25 nW with a compression ratio of 3.42. [ABSTRACT FROM AUTHOR]

The field of instructional technology has experienced significant growth in recent times. Due to the rapid shift towards online courses, the technology-based learning system is facing challenges in ensuring quality and assurance. The aim of this study was to develop online course evaluation tools by proposing a new algorithm to assess the success of the provided online courses and address quality assurance issues. The proposed algorithm is based on quality matters (QM) attributes and the use of fuzzy soft expert sets (FSESs). One key advantage of the proposed algorithm is that it incorporates experts' opinions, which significantly contributes to achieving the study objective. The proposed algorithm was successfully implemented using the ASP.NET programming language. It resulted in the development of an EOC-FSES prototype system. The experimental evaluation of the prototype system confirms that it requires low effort and achieves high levels of performance, satisfaction, and behavioral intention to use. This paper includes several recommendations and suggestions. [ABSTRACT FROM AUTHOR]

The advent of Industry 4.0 necessitates substantial interaction between humans and machines, presenting new challenges when it comes to evaluating the stress levels of workers who operate in increasingly intricate work environments. Undoubtedly, work-related stress exerts a significant influence on individuals' overall stress levels, leading to enduring health issues and adverse impacts on their quality of life. Although psychological questionnaires have traditionally been employed to assess stress, they lack the capability to monitor stress levels in real-time or on an ongoing basis, thus making it arduous to identify the causes and demanding aspects of work. To surmount this limitation, an effective solution lies in the analysis of physiological signals that can be continuously measured through wearable or ambient sensors. Previous studies in this field have mainly focused on stress assessment through intrusive wearable systems susceptible to noise and artifacts that degrade performance. One of our recently published papers presented a wearable and ambient hardware-software platform that is minimally intrusive, able to detect human stress without hindering normal work activities, and slightly susceptible to artifacts due to movements. A limitation of this system is its not very high performance in terms of the accuracy of detecting multiple stress levels; therefore, in this work, the focus was on improving the software performance of the platform, using a deep learning approach. To this purpose, three neural networks were implemented, and the best performance was achieved by the 1D-convolutional neural network with an accuracy of 95.38% for the identification of two levels of stress, which is a significant improvement over those obtained previously. [ABSTRACT FROM AUTHOR]

The research explores the convergence of two pivotal trends in architecture: environmental and technological trends. The eco-trend focuses on the environmental impact of architectural practices, while the tech-trend emphasizes leveraging technology to enhance a building's performance. The thesis posits that integrating these trends holds the potential to create more sustainable structures, characterized by reduced environmental impacts and enhanced resource efficiency. The study reviews environmental systems that achieve sustainability and focuses on one system that is applied to hotel buildings. The study also analyzes different forms of intelligence within environmentally friendly architecture, including smart materials, response to environmental changes, smart systems for automated building management, and smart interfaces that facilitate interaction. User intuitive. The study proposes a range of key applications for environmental technology within hotel buildings, including energy-efficient lighting and heating systems, water-conserving fixtures, and innovative waste management systems. The results obtained from the study reveal that the fusion of environmental and technological considerations significantly increases the development of sustainable hotel facilities. These facilities not only enhance environmental responsibility, but also enhance operational efficiency, which contributes to increasing diversified sources of national income. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) offers promising ways to meet healthcare needs of patients recovering in their own homes and other environments. Interconnected and resilient smart systems offer innovative and cost-effective ways of supporting patients by capitalizing on available devices and networking infrastructure. However, future environments will not be uniformly smart, and there will be asymmetries where our environments' (home, work, etc.) resources and capabilities differ. Technological solutions will need to adapt to such asymmetries and provide high-quality service and equitable healthcare. This article presents a systematic mapping study that explores opportunities and challenges in building next-generation IoMT smart systems for future environments. The study spans academic literature published in the decade from 2011 to 2021, profiling it from three distinct perspectives: Smart Systems, Future Environments, and Tech-Assisted Health. Each perspective was explored via a Domain Expert-Driven Systematic Mapping protocol to establish where the research is focused and to identify research gaps. From an initial search of 495 studies, 113 were mapped to a set of predefined ontology classes, spanning 6 strategic focus categories. The mapping identified sensing technologies for medical vitals and sensor fusion technologies to combine measurements for more complex analysis, cloud platforms, and connectivity challenges; health conditions that have received the most attention in healthcare smart systems; issues and opportunities in handling large data volumes in integrated smart systems; as well as security and privacy challenges. We find that future middleware frameworks will require a greater degree of interoperability and maturity to fully deliver value. Promising middleware and integration frameworks will require significant adaption and refinement to coexist effectively with current healthcare technologies. Privacy and security are critical factors in healthcare but are currently poorly supported by IoT infrastructures, especially across multiple environments. [ABSTRACT FROM AUTHOR]

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Intelligent Reflecting Surfaces (IRSs) enable us to have a reconfigurable reflecting surface that can efficiently deflect the transmitted signal toward the receiver. The initial step in the IRS usually involves estimating the channel between a fixed transmitter and a stationary receiver. After estimating the channel, the problem of finding the most optimal IRS configuration is non-convex, and involves a huge search in the solution space. In this work, we propose a novel and customized technique which efficiently estimates the channel and configures the IRS with fixed transmit power, restricting the IRS coefficients to {1,−1}. The results from our approach are numerically compared with existing optimization techniques.The key features of the linear system model under consideration include a Reconfigurable Intelligent Surface (RIS) setup consisting of 4096 RIS elements arranged in a 64 × 64 element array; the distance from RIS to the access point measures 107 m. NLOS users are located around 40 m away from the RIS element and 100 m from the access point. The estimated variance of noise NC is 3.1614 × 10−20. The proposed algorithm provides an overall data rate of 126.89 (MBits/s) for Line of Sight and 66.093 (MBits/s) for Non Line of Sight (NLOS) wireless communication.

Blockchain technology has revolutionized conventional trade. The success of blockchain can be attributed to its distributed ledger characteristic, which secures every record inside the ledger using cryptography rules, making it more reliable, secure, and tamper-proof. This is evident by the significant impact that the use of this technology has had on people connected to digital spaces in the present-day context. Furthermore, it has been proven that blockchain technology is evolving from new perspectives and that it provides an effective mechanism for the intelligent transportation system infrastructure. To realize the full potential of the accurate and efficacious use of blockchain in the transportation sector, it is essential to understand the most effective mechanisms of this technology and identify the most useful one. As a result, the present work offers a priority-based methodology that would be a useful reference for security experts in managing blockchain technology and its models. The study uses the hesitant fuzzy analytical hierarchy process for prioritizing the different blockchain models. Based on the findings of actual performance, alternative solution A1 which is Private Blockchain model has an extremely high level of security satisfaction. The accuracy of the results has been tested using the hesitant fuzzy technique for order of preference by similarity to the ideal solution procedure. The study also uses guidelines fromsecurity researchers working in this domain. [ABSTRACT FROM AUTHOR]

The scheduling, control, and management of traffic load in multi-protocol label switching (MPLS) are important aspects supporting the quality of service (QoS) and optimizing the network performance. In addition, ensuring the QoS in the Internet of Things (IoT) is one of the main issues of current research in this area. The IoT provides the ability for humans and computers to learn and interact with billions of things, including sensors, actuators, services, and other Internet-connected objects. MPLS offers the possibility for effective traffic control mechanisms within limited network capabilities. Traffic engineering in the MPLS domain provides alterations of the traffic routes to improve network resources or avoid network traffic congestion. Static management of traffic flows has failed to enhance the QoS of the network performance. Therefore, many researchers have attempted to use a dynamic management of traffic flow to enhance the QoS; however, more enhancements are needed. In this paper, we propose a new scheme to solve the problem of dynamic management of traffic flow through various links and routers in the network, and describe its application to an MPLS network. The proposed scheme works by utilizing queues, re-distribution, and re-balancing of streams before or during periods of congestion. It solves the problem of QoS violation, and tags, upgrades, or downgrades the traffic between queues based on the availability of free space of other queues and QoS parameters. We simulate the proposed scheme using OPNET, and the effect of implementing our method on low- and high-priority queues is analyzed and finally validated in conjunction with the ITU QoS measures. [ABSTRACT FROM AUTHOR]

This paper is a condensed version of the keynote lecture presented at the opening of the fifth Eurasian conference on Risk in Baku, Azerbaijan, October 17 2023. It is mostly based on results of the content analysis of 1500+ peer reviewed papers related to reliability, resilience, risk and safety of infrastructure systems published over the last 25 years, provided by renowned international specialists in these topics. [ABSTRACT FROM AUTHOR]

The Internet of Everything (IoE) represents a paradigm shift in the world of connectivity. While the Internet of Things (IoT) initiated the era of interconnected devices, the IoE takes this concept to new heights by interlinking objects, individuals, data, and processes. Symmetry in IoE innovation and technology is essential for creating a harmonious and efficient ecosystem to ensure that the benefits are accessible to a broad spectrum of society while minimizing potential drawbacks. This comprehensive review paper explores the multifaceted landscape of the IoE, delving into its core concepts, enabling technologies, real-world applications, and the intricate web of challenges it presents. A focal point of this review is the diverse array of real-world applications spanning healthcare, smart cities, industry 4.0, agriculture, and sustainability. Previous works and examples illustrate how the IoE reshapes these domains, leading to greater efficiency, sustainability, and improved decision making. However, the transformative power of the IoE is accompanied by a host of challenges, including security and privacy concerns, interoperability issues, and the ethical implications of ubiquitous connectivity. These challenges are dissected in order to comprehensively understand the obstacles and potential solutions in the IoE landscape. As we stand on the cusp of an IoE-driven future, this review paper serves as a valuable resource for researchers, policy makers, and industry professionals seeking to navigate the complexities of this emerging paradigm. By illuminating the intricacies of the IoE, this review fosters a deeper appreciation for the transformative potential and the multifaceted challenges that lie ahead in the Internet of Everything era. [ABSTRACT FROM AUTHOR]

Effective smart healthcare frameworks contain novel and emerging solutions for remote disease diagnostics, which aid in the prevention of several diseases including heart-related abnormalities. In this context, regular monitoring of cardiac patients through smart healthcare systems based on Electrocardiogram (ECG) signals has the potential to save many lives. In existing studies, several heart disease diagnostic systems are proposed by employing different state-of-the-art methods, however, improving such methods is always an intriguing area of research. Hence, in this research, a smart healthcare system is proposed for the diagnosis of heart disease using ECG signals. The proposed framework extracts both linear and time-series information on the ECG signals and fuses them into a single framework concurrently. The linear characteristics of ECG signals are extracted by convolution layers followed by Gaussian Error Linear Units (GeLu) and time series characteristics of ECG beats are extracted by Vanilla Long Short-Term Memory Networks (LSTM). Following on, the feature reduction of linear information is done with the help of ID Generalized Gated Pooling (GGP). In addition, data misbalancing issues are also addressed with the help of the Synthetic Minority Oversampling Technique (SMOTE). The performance assessment of the proposed model is done over the two publicly available datasets named MIT-BIH arrhythmia database (MITDB) and PTB Diagnostic ECG database (PTBDB). The proposed framework achieves an average accuracy performance of 99.14% along with a 95% recall value. [ABSTRACT FROM AUTHOR]

This article discusses the relationship between urban slums and the management of the urban infrastructure network (electrification). An extensive survey of the scientific literature on the subject points out the main challenges and possible solutions for fixing precarious electrical infrastructure in urban slums through the promotion of public policies and the establishment of new arrangements based on distributed generation technologies and smart grid. A dialogue was also developed involving topics such as sustainable development and electrification of slums; relationship between communities and modernization of electrification; and emerging and sustainable technologies in the context of urban slums. Thus, a design was constructed that triggered a discussion of the relationship between this work and several other works found in the literature. This research indicates the need to strengthen local governance and the participation of urban slums for the technological modernization of the local electrical network, mainly with the implementation of smart grid and photovoltaic generation systems.

SummarySmart cities and environments are defined as “a public-private ecosystem that provides services to citizens and their organizations with strong support from technology, and looks at the social and economic impact on society.” Life by adapting the existing ecosystems as well as linking them with the information network to reuse where smart cities represent information to create a creative creative environment that contributes to the development of sustainable and smart environments. The connecting factor between multiple disciplines, constantly shaped by advances in technology and urban development. Smart cities have also been defined as social entities that focus on human interaction and support their activities through technology.We conclude from this that practical solutions that allow us to easily connect smart devices and data and connect them through the Internet of Things, contribute effectively to creating smart environments and cities that support human development in all its fields and contribute to shaping its newly developed smart environments that contribute, maintain and support the principles of sustainability through Supporting renewable energy systems as well as the ability to monitor and control their consumption and provide their resources.

This article explores the impact of smart home technologies on the lives of individuals with physical disabilities, focusing on the ways these innovations enhance independence, safety, and social interaction. The article outlines several key advantages of smart homes, such as increased autonomy through voice-activated controls, improved safety via sensors and emergency alerts, and reduced social isolation through communication technologies. Additionally, the article highlights the benefits of automation in daily tasks and health monitoring systems that contribute to better health management[3]. On the other hand, the article also addresses the challenges faced by disabled users, including high initial costs, complexity in system setup and use, and the risks of over-reliance on technology. Concerns over privacy and security are also discussed, along with the need for ongoing maintenance and technical support. To overcome these challenges, the article suggests solutions like making smart home systems more affordable and user-friendly, implementing robust backup systems, and strengthening privacy protections. The article argues that while smart homes offer significant benefits, further innovation and support--such as government incentives, improved customer service, and the integration of AI--are necessary to ensure these technologies are accessible and reliable for individuals with physical disabilities, ultimately enhancing their quality of life. Smart home technologies have significantly impacted the lives of physically disabled people by providing enhanced independence, safety, and social interaction. However, despite the many benefits, there are also challenges and disadvantages associated with their use. This section will explore the advantages and disadvantages of smart homes for individuals with physical disabilities and offer suggestions for improvements to maximize the benefits while minimizing the downsides[2,3]. [ABSTRACT FROM AUTHOR]

The purpose of this research is to develop a better understanding of smart systems and autonomous processes of the Industry 4.0 era. Does the implementation of Industry 4.0 processes and systems expose firms to higher levels of risk in the supply chain through capability loss or does Industry 4.0 spur capability enhancement and thereby increase supply chain resilience? Industry 4.0 is centred on the idea that certain tasks and decisions can be automated through smart systems and autonomous processes. However, is there a risk of losing critical capabilities and the ability to be flexible, agile and resilient to unexpected disruptions in the supply chain? In order to address these questions, this research presents results from semi-structured interviews across multiple industries to provide findings on firms' uses of smart systems and capability development associated with these systems. Results from this exploratory study may be classified into two primary insights. First, although Industry 4.0 systems are new and, in many cases untested, firms are eager regarding the potential of smart systems to positively impact firm performance and to leverage Industry 4.0 processes for a competitive supply chain advantage. Second, companies did not claim any human capability loss associated with Industry 4.0. In fact, these smart systems may lead to increased supply chain resilience because of capability enhancement and new skill development. [ABSTRACT FROM AUTHOR]