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Different epidemics, specially Coronavirus, have caused critical misfortunes in various fields like monetary deprivation, survival conditions, thus diminishing the overall individual fulfillment. Various worldwide associations and different hierarchies of government fraternity are endeavoring to offer the necessary assistance in eliminating the infection impacts but unfortunately standing up to the non-appearance of resources and expertise. In contrast to all other pandemics, Coronavirus has proven to exhibit numerous requirements such that curated appropriation and determination of innovations are required to deal with the vigorous undertakings, which include precaution, detection, and medication. Innovative advancements are essential for the subsequent pandemics where-in the forthcoming difficulties can indeed be approached to such a degree that it facilitates constructive solutions more comprehensively. In this study, futuristic and emerging innovations are analyzed, improving COVID-19 effects for the general public. Large data sets need to be advanced so that extensive models related to deep analysis can be used to combat Coronavirus infection, which can be done by applying Artificial intelligence techniques such as Natural Language Processing (NLP), Machine Learning (ML), and Computer vision to varying processing files. This article aims to furnish variation sets of innovations that can be utilized to eliminate COVID-19 and serve as a resource for the coming generations. At last, elaboration associated with future state-of-the-art technologies and the attainable sectors of AI methodologies has been mentioned concerning the post-COVID-19 world to enable the different ideas for dealing with the pandemic-based difficulties.

With the development of complex and intelligent sizeable mechanical equipment, the conflict between maintenance and production is becoming increasingly severe, restricting coal mine enterprises’ production and development. This paper proposes a joint decision-making model for production planning and maintenance of fully mechanized mining equipment to solve the conflict between production and maintenance. Taking fully mechanized mining equipment as the research object and minimizing the total cost as the joint decision-making objective, we use the Hybrid Genetic Whale Optimization Algorithm (HGWOA) to optimize the production planning and maintenance process of fully mechanized mining equipment. We compared the joint decision-making model of production planning and maintenance with the independent decision-making model by setting different parameters. The result shows that the joint decision-making model reduces the cost by 36.06% and the downtime rate by 40.76%. It can better balance maintenance and production, which is of great significance to the coal mine and related enterprises.

Educational remote laboratories allow users to access and control via the Internet remote equipment, that may be physically located anywhere in the world. They have a great potential for education, since they offer many advantages, such as cost reduction, high flexibility or the possibility of sharing equipment among institutions. However, their use is not yet as widespread as it could be. Freely available laboratories often present significant reliability issues. Instructors often plan their lessons in advance, and incorporating third-party tools that are prone to failure is risky, since they may be unavailable for the class and present errors to the students, which may make it necessary to suspend the session or find alternatives on the go. This contribution analyzes the reliability of a set of state-of-the-art laboratories with a view to categorizing and quantifying the most common failures. It then describes a multi-layered software model for detecting errors in laboratories. This model has two main goals. First, to ensure that the laboratory management system is aware of the state of the laboratory, so that users do not experience errors and laboratory maintenance personnel can repair it as soon as possible. Second, to serve as a base for a fault-tolerance model that leverages replicated instances of the laboratory. This fault-tolerance model is also described, and is designed to provide high availability and support for multiple concurrent users. The effectiveness of this model is validated by analyzing the reliability and availability of two remote laboratories in which it has been implemented.

The complexity of a supply chain makes product safety or quality issues extremely difficult to track, especially for the basic agricultural food supply chains of people’s daily diets. The existing agricultural food supply chains present several major problems, such as numerous participants, inconvenient communication caused by long supply chain cycles, data distrust between participants and the centralized system. The emergence of blockchain technology effectively solves the pain-point problem existing in the traceability system of agricultural food supply chains. This paper proposes a framework based on the consortium and smart contracts to track and trace the workflow of agricultural food supply chains, implement traceability and shareability of supply chains, and break down the information islands between enterprises as much as possible to eliminate the need for the central institutions and agencies and improve the integrity of the transaction records, reliability and security. At the same time, farmers record details of the environment and crop growth data in the InterPlanetary File System (IPFS) and store file IPFS hashes in smart contracts, which not only increases data security but also alleviates the blockchain storage explosion problem. This framework has been applied in Shanwei Lvfengyuan Modern Agricultural Development Co., Ltd. Although there are still many defects, the framework has successfully realized functions such as disintermediation and tracing of agricultural product information through QR codes. Thus, the framework proposed in this paper is of great significance and reference value for enterprises to ensure product quality and safety traceability.

With the development of information and communication technology and the globalisation of enterprises, many companies are being operated through an electrical resource management system called Enterprise Resource Planning (ERP) system. An ERP system enables efficient and centralised resource management for enterprises. However, since many enterprise resources are being managed by the system, the threatening behaviour by an insider is one of the most significant risks in operating ERP systems. It is much stealthier and fatal compared with the threat from an outsider since it is considered as normal events that accessing the enterprise resource of insiders. Conventional insider threat detection methods have aimed to detect particular events manually defined by system administrators. Those approaches are not robust to the variation of event patterns, and they can not be used when the predefined cases are not given. In this paper, we present a real-time abnormal insider event detection method using the Predictive Auto-regression Model (PAM). Compared with the conventional approaches, the proposed method compiles a prediction model using normal events and identifies threat when the likelihood of prediction results is lower than a threshold. Experiments are conducted using a dataset including events defined as a sequence of ERP system logs. The logs are captured in a practical situation of an enterprise. The results demonstrate that the proposed method can successfully identify abnormal events of on ERP systems.

Container throughput in Shanghai port has been continually increasing, with severe congestion impacts. The effectiveness and comprehensiveness of governance policies are increasingly important. This study proposes a conceptual framework and simulation of congestion governance measures and their effectiveness on congestion control in a data-driven case study for Shanghai port. The contributions herein serve as a starting point in the identification of four alternative congestion governance options (Increasing the transport infrastructure, Multimodal transport, Smart strategy, and Interconnection), and explore dynamic interactions among them. The design of this study is to firstly develop a container port congestion evaluation model to measure the governance effect; secondly to undertake the triangle analysis in the dynamic system balance: demand of hinterland transport, container port congestion, and container port congestion governance; thirdly to describe four congestion governance options with a detailed description of feedback loops generated by the system dynamics model; finally to propose a policy framework aiming at the advantages and disadvantages of these four options. This study function as stepping stones towards the revealing of the biggest challenge in effective mitigation of port congestion systems by being considered not just a hardware issue (option 1) but more importantly organizational issues (options 2~4). It should be a process of coordination and optimization among 4 options.

For manufacturing companies, reliable production planning and scheduling not only is the basis for efficient order processing but at the same time is an essential prerequisite for the integration and coordination of all participants along the entire supply chain. At the same time, the increasing delegation of planning activities to dynamic software solutions leads to increasing intransparency regarding the planning behavior. It thus becomes increasingly difficult to identify and address inefficiencies or problems caused by the planning processes within industrial supply chains. This paper presents an easy-to-use method for describing, visualizing and analyzing scheduling behavior in manufacturing companies requiring only very few data. In addition, an overview of key planning quality indicators (KPQIs) to be considered in the evaluation of the planning quality is given and structured along the assessment dimensions of plan stability and planning accuracy. The specific application at a maintenance, repair and overhaul (MRO) service provider for complex capital goods demonstrates the benefits and insights to be gained from the model’s application, especially in highly dynamic market environments. Using machine learning, characteristic planning patterns can also be statistically determined with the developed description logic and KPQI system.

Epidemic risk has great uncertainty and harmfulness, which poses a potential threat to public health in a certain region. Establishing a special risk assessment system to assess and predict the potential epidemic risk of a region can effectively avoid or reduce the impact of epidemic risk. Therefore, this paper combs the related factors that affect the epidemic risk, and proposes an epidemic risk assessment model based on 12 indicators by combining Markov chain and AHP. The model can assess the epidemic situation in a certain region from four aspects: the probability of risk occurrence, the probability of loss, the possibility of risk disappearance and risk duration, so as to provide detailed data for the risk management and control of epidemic in the region, and help the epidemic prevention work to be carried out in a targeted way. Finally, the case analysis and method comparison are carried out,and the results show that the model proposed in this paper is reasonable and feasible.

Due to their many advantages, renewable energy resources (RERs) are proliferating rapidly to satisfy a significant portion of the global energy demand. It is predicted that the importance of RER generation will continue to increase in the future of the energy industry. In spite of all their known benefits, the integration of RERs poses several challenges to system stability and reliability. Low inertia characteristics and the intermittent output of RERs introduces additional variation into an already variable grid frequency. System regulation capability and reliability are reduced as RERs gradually replace conventional generators. This paper reviews the challenges associated with RER integration on system frequency response and how these challenges affect system reliability. Advanced methods for mitigating challenges associated with increasing RER integration, while ensuring system security, are discussed, and the necessary mathematical background is provided in concise form. A model is developed for determining the maximum level of RER penetration based on stability constraints. Emerging methods to advance the integration of RERs are discussed.

Food supply chain process comprises crops collection, processing of food, shipping & delivery to the whole seller in the market. Harvested foods decompose from the moment they are harvested due to attacks from enzymes, oxidation, and microorganisms. These include bacteria, mold, yeast, moisture, temperature, and chemical reaction. The spoilage of fresh food has increased over time due to the multistage slow food supply chain process. The identification, traceability, and real-time tracking of goods in supply chains have always been a challenge. The advent of the Internet of Things and cloud computing has brought a new approach to the food supply chain process for better cooperation among supply chain partners. The supply chain management (SCM) benefit greatly through automation based on key technologies of IoT, Radio Frequency Identification (RFID), and Wireless Sensor Networks (WSN). These technologies collect the data relevant to the food supply chain system, such as identifying tag-possessed objects or individuals and sensing capabilities of the surrounding environment. However, the collected data can be tempered or modified by attackers to provide false information about environmental conditions. They can destroy or damage the product due to false identification of dynamic environmental conditions. Furthermore, the current automation systems in industry-based retail logistics and SCM do not provide efficient solutions for monitoring the quality of perishable products with integrated solutions. This research aims to develop a secure monitoring and reporting system based on IoT to update the quality of the perishables along with the SCM with a focus on transportation without any human intervention.

The proliferation of Industry 4.0 has made modern industrial assets a rich source of data that can be leveraged to optimise operations, ensure efficiency, and minimise maintenance costs. The availability of data is advantageous for asset management, however, attempts to maximise the value of this data often fall short due to additional constraints, such as privacy concerns and data stored in distributed silos that is difficult to access and share. Federated Learning (FL) has been explored to address these challenges and has been demonstrated to provide a mechanism that allows highly distributed data to be mined in a privacy-preserving manner and offering new opportunities for a collaborative approach to asset management. Despite the benefits, FL has some challenges that need to be overcome to make it fully compatible for asset management or more specifically predictive maintenance applications. FL requires a set of clients that participate in the model training process, however, orchestration, device heterogeneity and scalability can hinder the speed and accuracy in the context of collaborative predictive maintenance. To address this challenge, this work proposes a split-learning-based framework (SplitPred) that enables FL clients to maximise available resources within their local network without compromising the benefits of a FL approach (i.e., privacy and shared learning). Experiments performed on the benchmark C-MAPSS data-set demonstrate the advantage of applying SplitPred in the FL process in terms of efficient use of resources, i.e., model convergence time, accuracy, and network load.

Smart electric vehicles (EVs) are attractive because of their clean, zero-emission, low impact on the environment whilst providing a safer and smoother riding experience. To provide the latter, driving control requires appropriate systems and algorithms to optimize smart vehicle performance, maximize vehicle stability and protection, minimize accident probability, heighten driving comfort, and optimize transportation costs. Despite advancements in these areas, the realization of optimal smart EVs still requires considerable effort. This paper reviews driving control systems and algorithms for smart EVs, including the advanced driving assistant system, implementation of sensors, vehicle dynamics, and control algorithms. The major contribution of this review is to identify promising work to assist researchers with the most advanced trends in this area for prospective regulations.

COVID-19 has become a global pandemic during 2020 due to its high contagiousness and the high mobility of the world’s population today. In just one year, this virus has caused millions of infections and deaths worldwide. These numbers will continue to grow until the population becomes immune to the virus thanks to an effective vaccine. Until this is possible, the only viable strategy is to try to stop its expansion through preventive measures such as limiting mobility, the use of masks, etc. In order to support these measures, this article presents a service to provide safe navigation solutions to reduce the likelihood of infection by avoiding potential conflict areas in the city. To identify these hotspots, a strategy that combines a rule-based system and a common-sense knowledge base is proposed. Through this strategy, an occupation model and a danger model are inferred. This requires the prior capture of knowledge about the general functioning of the city, its inhabitants and the virus. The proposed service makes decisions from these two models. Finally, a validation process has been carried out through surveys to evaluate the proposed solution. Obtained results demonstrate the potential of the proposed solution as a tool to identify safe routes that allow citizens to move around the city with low exposure to COVID-19.

The smart grid is generally studied as an efficient and powerful electric grid. With the assistance of information and communication technology (ICT), the electric grid can increase the performance of the power grid system with smart energy management. On the other hand, with the usage of renewable energy resources (RERs), smart energy storage, and new transmission technologies in the power grid system, various new features such as real-time monitoring, fast restoration, battery displays, automated outage management, etc. have been assimilated into the smart grid. These new features generate more complexity in energy transmission and constitute important challenges like low energy consumption, high energy cost, social welfare, etc. while designing energy trading mechanisms in the smart grid. In the Internet-of-Things (IoT) era, several scenarios such as micro-grids, energy harvesting networks, and vehicle-to-grid (V2G) networks are present where energy trading plays an important role. However, in these scenarios, there are energy transmission and distribution, security and privacy, energy consumption, system reliability, the criticality of data delivery, and a few more challenges caused by distrust, non-transparent, and uncertain energy markets. Motivated from these challenges, we present a four-layered architecture of energy trading used in the smart grid. We propose a comprehensive background regarding the main concepts of energy trading and the implication of enabling technologies that manage the energy imbalances in the smart grid. Then, we present a problem taxonomy based on incentive, mathematical, and simulation model-driven approaches, which are widely used to control and maintain the energy trading mechanisms. Based on the findings from the literature, we also present a solution taxonomy with enabling technologies such as Energy Internet, Software-defined networking (SDN), and blockchain. In the end, a summary of future research directions based on the energy trading mechanisms is explored to provide deep insights to the readers.

High penetration of selfish Home Energy Management Systems (HEMSs) causes adverse effects such as rebound peaks, instabilities, and contingencies in different regions of distribution grid. To avoid these effects and relieve power grid stress, the concept of HEMSs coordination has been suggested. Particularly, this concept can be employed to fulfill important grid objectives in neighborhood areas such as flattening aggregated load profile, decreasing electricity bills, facilitating energy trading, diminishing reverse power flow, managing distributed energy resources, and modifying consumers’ consumption/generation patterns. This paper reviews the latest investigations into coordinated HEMSs. The required steps to implement these systems, accounting for coordination topologies and techniques, are thoroughly explored. This exploration is mainly reported through classifying coordination approaches according to their utilization of decomposition algorithms. Furthermore, major features, advantages, and disadvantages of the methods are examined. Specifically, coordination process characteristics, its mathematical issues and essential prerequisites, as well as players concerns are analyzed. Subsequently, specific applications of coordination designs are discussed and categorized. Through a comprehensive investigation, this work elaborates significant remarks on critical gaps in existing studies toward a useful coordination structure for practical HEMSs implementations. Unlike other reviews, the present survey focuses on effective frameworks to determine future opportunities that make the concept of coordinated HEMSs feasible. Indeed, providing effective studies on HEMSs coordination concept is beneficial to both consumers and service providers since as reported, these systems can lead to 5% to 30% reduction in electricity bills.

In critical times when disasters and unpredicted events collapse human judgments, formation and structure of social infrastructure gets to be rather important. Acute risk of catastrophic events such as a rare disaster, a coronavirus pandemic, trigger a global scale setback which has a substantial impact on people’s lives and livelihoods. Catastrophic events halt economic landscape, collapse commodity prices, and silence production engines which interact in complex ways. During this phase of volatility and opacity, roles of sustainable supply chain, productions and circular economy get more important than ever. Such disruptions are therefore to be addressed by identifying sustainable supply chain strategies. This paper intends to establish underlying patterns of disruptive factors in the supply chain to evaluate strategies in formation and structuring of sustainable supply chain by applying it to real world example in Turkish Kitchen Equipment Manufacturer (KEM). Selection of sustainable supply chain strategy is a complex Multi-Criteria Decision-Making (MCDM) issue involving various parameters that may be contradictory at the same time. Analytical-Hierarchy-Process (AHP) and VlseKriterijumska-Optimizacija-I-Kompromisno-Resenje (VIKOR) methods can be used to solve such problems. In order to strengthen these methods in terms of their lacking capability of coping with uncertainty and incomplete information, they are extended with Interval-Valued Picture-Fuzzy Set (IVPFS) to better simulate human judgment. MCDM processes can be enhanced with Group-Decision-Making (GDM) to combine individual Decision Makers’ (DMs) opinions into group judgments. Found outcome reveal that supply chains to respond catastrophic disruptions, businesses should primarily diversify supply chain from a geographic perspective, and should diversify disruptive forces in motion from a physical perspective. Originality of article is based on integrated AHP and VIKOR approaches in IVPFS based GDM algorithm as a first in the literature and presentation of its application for supply chain sustainability in catastrophic disruptions as a decision support tool.

Infrastructure resilience for a scenario can be assessed quantitatively from resilience curves that plot the evolution of system performance. Summary metrics map these curves to a single value to facilitate comparisons of different systems, scenarios, and policies. Commonly, these metrics are integral-based, e.g., cumulative infrastructure performance. However, since these curves and metrics only examine infrastructure performance, they fail to consider the dynamics of when stakeholders value the performance. For example, a power failure at a hospital during an ordinary day would be of less concern to emergency managers than during a hurricane recovery. This manuscript defines value weighting functions to represent the evolution of stakeholders’ value of performance. Temporal correlation between the performance and value weighting functions is described through a stochastic offset. Together, these concepts are used to define a holistic resilience metric: percent value satisfied. Through analytical and numerical approaches, percent value satisfied is treated as a resilience assessment’s stochastic output, and its distribution is compared to the naïve metric, cumulative infrastructure performance. The naïve summary metric is shown to be misleading in multiple potential scenarios; this work establishes that resilience assessments must consider the impact of time-varying stakeholder value and its correlation with infrastructure performance. These elements also provide new considerations for resilience assessments: opportunities to improve holistic system resilience without directly affecting infrastructure performance; hazard categories to inform value-weighted resilience analysis; and general insights to guide extensions from performance-based to value-weighted assessment.

Working at an oil and gas facility, such as a drilling rig, production facility, processing facility, or storage facility, involves various challenges, including health and safety risks. It is possible to leverage emerging digital technologies such as smart sensors, wearable or mobile devices, big data analytics, cloud computing, extended reality technologies, robotic systems, and drones to mitigate the challenges faced by oil and gas workers. While these technologies are not new to the oil and gas industry, most of its existing digital transformation initiatives follow business or process-centric approaches, in which the critical driver of the technology adoption is the enhancement of production, efficiency, and revenue. As a result, they may not address the challenges faced by the workers. As oil and gas workers are among the essential assets in the oil and gas industry, it is vital to address the challenges faced by these workers. This paper proposes a human-centric digital transformational framework for the oil and gas industry to deploy existing digital technologies to enhance their workers’ health, safety, and working conditions. The paper outlines the critical challenges faced by oilfield workers, introduces a system architecture to implements a human-centric digital transformation, discusses the opportunities of the proposed framework, and summarizes the key impediment for the proposed framework.

The intelligent management of power in electrical utilities depends on the high significance of load forecasting models. Since the industries are digitalized, power generation is supported by a variety of resources. Therefore, the forecasting accuracy of different models varies. The power utilities with different generation modalities (DGM) experience complexities and a noticeable amount of error in predicting future electrical consumption. To effectively manage the power flow with negligible power interruptions, a utility must utilize the forecasting tools to predict the future electricity demand with minimum error. Since the current literature supports individual and limited power sources involved in generation for load forecasting, thus the utilities with multiple power sources or DGM remain unexplored. Therefore, exploration of existing literature is required relating to analyzing the existing models which could be considered in load forecasting for DGM. This paper explores state-of-art methods recently utilized for electrical load forecasting highlighting the common practices, recent advances, and exposure of areas available for improvement. The review investigates the methods, parameters, and respective sectors considered for load forecasting. It performs in-depth analysis and discusses the strengths, weaknesses, and error percentages of models. It also highlights the peculiarities of methods used in residential, commercial, industrial, grid, and off-grid sectors aiming to help the researchers to appraise the common practices. Moreover, trends and research gaps are also discussed.

The rapid development and application of new digital technologies has, on the one hand, opened up new opportunities for more efficient management of technological and business processes. On the other hand, this leads to a significant increase in security threats, increasing the vulnerability of businesses and organisations to cybercriminals. In recent years, the rapid growth of incidents of various kinds has shown that traditional approaches to information security (IS) are insufficient. Consequently, software product information security risk assessment has become an important task for most organisations. Several models have been proposed to help different enterprises deal with the challenges of building information security. This paper proposes a new hierarchical structured model for information security risk assessment using fuzzy logic. A new method for information security risk assessment of software is also described using the example of automated control systems or enterprise resource planning (ERP) systems (using learning management systems as an example). The proposed new risk assessment model has been software implemented using fuzzy logic in the form of 15 fuzzy machines. In a series of experiments, we have scrutinised the information security risk assessment of various software products. The proposed method should solve the problem of flexible risk assessment.

Compatibility or competition is a key strategic decision for multihoming software developers. Compatibility is a strategy by which companies set up their software to be more compatible with their competitors’ products, while competition is an opposite strategy by which the companies set up their software without any compatibility. We study the strategic decision of a multihoming software company that competes with two firms on different operating systems, (such as Windows and Mac). By formalizing a game-theoretic model to capture the motivation of a multihoming software developer, we find that the compatibility strategy is mainly incentivized by the trade-off between the spillover effect and the compatibility cost. We then solve for reasonable software prices and compatibility level given the market potential from the software firms’ perspective. Several managerial guidelines are obtained to determine the optimal decision in the software development economy. Interestingly, when both the spillover effect and the compatibility cost are low enough, the multihoming software firm still chooses the competing strategy. Finally, by reporting the numerical analysis, we verify the effectiveness of the theoretical results derived in our model.

In the context of the COVID-19 pandemic, the rapid roll-out of a vaccine and the implementation of a worldwide immunization campaign is critical, but its success will depend on the availability of an operational and transparent distribution chain that can be audited by all relevant stakeholders. In this paper, we discuss how blockchain technology can be used for assuring the transparent tracing of COVID-19 vaccine registration, storage and delivery, and side effects self-reporting. We present such system implementation in which blockchain technology is used for assuring data integrity and immutability in case of beneficiary registration for vaccination, eliminating identity thefts and impersonations. Smart contracts are defined to monitor and track the proper vaccine distribution conditions against the safe handling rules defined by vaccine producers enabling the awareness of all network peers. For vaccine administration, a transparent and tamper-proof side effects self-reporting solution is provided considering person identification and administrated vaccine association. A prototype was implemented using the Ethereum test network, Ropsten, considering the COVID-19 vaccine distribution tracking conditions. The results obtained for each on-chain operation can be checked and validated on the Etherscan, demonstrating various aspects of the proposed system such as immunization actors and safe rules registration, vaccine tracking, and administration. In terms of throughput and scalability, the proposed blockchain system shows promising results.

Industrial control systems control and supervise our most important and critical infrastructures, such as power utilities, clean water plants and nuclear plants, as well as the manufacturing industries at the base of our economy. These systems are currently undergoing a transformation driven by the Industry 4.0 evolution, characterized by increased connectivity and flexibility. Consequently, the cybersecurity threat landscape for industrial control systems is evolving as well. Current strategies used for access control within industrial control systems are relatively rudimentary. It is evident that some of the emerging cybersecurity threats related to Industry 4.0 could be better mitigated using more fine-grained access control policies. In this article we discuss and describe a number of access control strategies that could be used within manufacturing systems. We evaluate the strategies in a simulation experiment, using a number of attack-scenarios. Moreover, a method is outlined for automatic policy-generation based on engineering-data, which is aligned with one of the best performing strategies.

Assessing the security of IoT-based smart environments such as smart homes and smart cities is becoming fundamentally essential to implementing the correct control measures and effectively reducing security threats and risks brought about by deploying IoT-based smart technologies. The problem, however, is in finding security standards and assessment frameworks that best meets the security requirements as well as comprehensively assesses and exposes the security posture of IoT-based smart environments. To explore this gap, this paper presents a review of existing security standards and assessment frameworks which also includes several NIST special publications on security techniques highlighting their primary areas of focus to uncover those that can potentially address some of the security needs of IoT-based smart environments. Cumulatively a total of 80 ISO/IEC security standards, 32 ETSI standards and 37 different conventional security assessment frameworks which included seven NIST special publications on security techniques were reviewed. To present an all-inclusive and up-to-date state-of-the-art research, the review process considered both published security standards and assessment frameworks as well as those under development. The findings show that most of the conventional security standards and assessment frameworks do not directly address the security needs of IoT-based smart environments but have the potential to be adapted into IoT-based smart environments. With this insight into the state-of-the-art research on security standards and assessment frameworks, this study helps advance the IoT field by opening new research directions as well as opportunities for developing new security standards and assessment frameworks that will address future IoT-based smart environments security concerns. This paper also discusses open problems and challenges related to IoT-based smart environments security issues. As a new contribution, a taxonomy of challenges for IoT-based smart environment security concerns drawn from the extensive literature examined during this study is proposed in this paper which also maps the identified challenges to potential proposed solutions. Validerad;2021;Nivå 2;2021-09-13 (beamah);Forskningsfinansiär: Australian Government’sCooperative Research Centres Programme

Research on lower limb exoskeleton (LLE) for rehabilitation have developed rapidly to meet the need of the population with neurologic injuries. LLEs for rehabilitation include therapeutic LLEs that aim to restore walking ability for patients, and assistive LLEs that offer support on activities in daily life. A substantial part of them can serve both purposes. However, these devices are yet to reach the final goal of performing human-machine joint movement agilely and smartly. Control strategy plays an important role in achieving their designed goal. At present, control strategies face three major challenges: how to detect human intention, how to do motion control with given intentions, and how to optimize control parameters to suit different individuals. As a contribution, this paper offers an overview on the state-of-the-art control strategies for rehabilitation LLEs by classifying them into eight categories, each of which is presented with a technical summary and tabulated information of representative papers. Moreover, current approaches addressing the three challenges are discussed in a macroscopic perspective. Finally, it has been explored which requirements the future control strategies should meet for maximizing the performance of rehabilitation LLEs.

Effectively managing the healthcare supply chain (HCSC) process is crucial for healthcare providers not only during pandemics such as COVID-19 but also in their normal operations. Despite significant advances in new technologies and treatment options providers still suffer from poor procurement, ordering, forecasting, and distribution practices. Group Purchasing Organizations (GPOs) are an important stakeholder in HCSC and benefit providers with cost savings, volume discounts, and vendor selection. However, the current GPO contract process is time-consuming and lacks efficiency. Hence, our proposed solution integrates blockchain technology and decentralized storage to promote transparency, streamlines communication with stakeholders, and minimize the procurement timeline while avoiding pricing discrepancies and inaccuracies. Our solution connects all the stakeholders such as manufacturer, GPO, distributor, and provider using Ethereum network. In this paper, we propose a blockchain solution using smart contracts to automate the GPO contract process. We propose a generic framework for contracting process in the HCSC with detailed algorithms depicting various interactions among HCSC stakeholders. The smart contract code was developed and tested using Remix IDE and the code is publicly shared via Github. We discuss various security risks and present detailed cost analysis of various transactions incurred by the stakeholders. Our analysis demonstrates that the proposed blockchain-based solution is economically feasible as only a minimal transaction fee is expended by the stakeholders in the distributed network.

It is a crucial yet challenging task to ensure commercial load resilience during high-impact, low-frequency extreme events. In this paper, a novel safe reinforcement learning (SRL)-based resilient proactive scheduling strategy is proposed for commercial buildings (CBs) subject to extreme weather events. It deploys the correlation between different CB components with demand response capabilities to maximize the customer comfort levels while minimizing the energy reserve cost. It also develops an SRL-based algorithm by combining deep-Q-network and conditional-value-at-risk methods to handle the uncertainties in the extreme weather events such that the impact from extreme epochs in the learning process is greatly mitigated. As a result, an optimum control decision can be derived that targets proactive scheduling goals, where exploration and exploitation are considered simultaneously. Extensive simulation results show that the proposed SRL-based proactive scheduling decisions can ensure the resilience of a commercial building while maintaining comprehensive comfort levels for the occupants.

Smartphones account for a large and growing portion of global electronic waste, and the life extension of electronic systems—particularly by refurbishment—is a viable solution for reducing this waste stream. In some secondary markets, sales of refurbished smartphones have begun to outpace sales of new smartphones. However, there are concerns over the resulting reliability and, in some cases, the safety of such products. This paper explores the environmental, reliability, and safety impact of extending the life of electronic systems, with a focus on smartphones. We find that smartphones undergo varying levels of refurbishment that consumers are often not aware of, the environmental advantages of refurbished devices are often outweighed by the disadvantages, and that refurbished electronics will never be as reliable as new products. It is thus necessary to create standards for smartphone refurbishment and to capture and analyze lifetime environmental and usage data to enable the assessment and prediction of the reliability of refurbished devices and their components.

Cardio Vascular Diseases (CVD) is the leading cause of death globally and is increasing at an alarming rate, according to the American Heart Association’s Heart Attack and Stroke Statistics-2021. This increase has been further exacerbated because of the current coronavirus (COVID-19) pandemic, thereby increasing the pressure on existing healthcare resources. Smart and Connected Health (SCH) is a viable solution for the prevalent healthcare challenges. It can reshape the course of healthcare to be more strategic, preventive, and custom-designed, making it more effective with value-added services. This research endeavors to classify state-of-the-art SCH technologies via a thorough literature review and analysis to comprehensively define SCH features and identify the enabling technology-related challenges in SCH adoption. We also propose an architectural model that captures the technological aspect of the SCH solution, its environment, and its primary involved stakeholders. It serves as a reference model for SCH acceptance and implementation. We reflected the COVID-19 case study illustrating how some countries have tackled the pandemic differently in terms of leveraging the power of different SCH technologies, such as big data, cloud computing, Internet of Things, artificial intelligence, robotics, blockchain, and mobile applications. In combating the pandemic, SCH has been used efficiently at different stages such as disease diagnosis, virus detection, individual monitoring, tracking, controlling, and resource allocation. Furthermore, this review highlights the challenges to SCH acceptance, as well as the potential research directions for better patient-centric healthcare.

The intensive research and development efforts directed towards large-scale complex industrial systems in the context of Industry 4.0 indicate that safety and reliability issues pose significant challenges. During online operation, system performance degradation will lead, not only to economic losses, but also potential safety hazards. In the existing research and technical routes, the target of the fault diagnosis systems is to trigger alarms to report the fact of the existence of malfunctions as well as the underlying reasons accurately. However, it remains unanswered how urgent it is to fix it, and what degrees of fault-tolerance, maintenance, and fault recovery are needed. Further analyses are necessary to evaluate the impact of the detected fault on the plant-wide performance. In this article, to enable a more comprehensive and precise description of the plant-wide operational status, the roles of the commonly used performance metrics, the state-of-the-art performance evaluation approaches, as well as the performance-oriented and plant-wide process monitoring techniques are investigated. On this basis, an alternative straightforward technical route, embedded in the cyber-physical-social system framework is proposed. A roadmap including the key research questions, the future research directions, and an outlook about the future vision is presented.

Buildings account for a significant amount of energy consumption leading to the issues of global emissions and climate change. Thus, energy management in a building is increasingly explored due to its significant potential in reducing the overall electricity expenses for the consumers and mitigating carbon emissions. In line with that, the greater control and optimization of energy management integrated with renewable energy resources is required to improve building energy efficiency while satisfying indoor environment comfort. Even though actions are being taken to reduce the energy consumption in buildings with several optimization and controller techniques, yet some issues remain unsolved. Therefore, this work provides a comprehensive review of the conventional and intelligent control methods with emphasis on their classification, features, configuration, benefits, and drawbacks. This review critically investigates the different optimization objectives and constraints with respect to comfort management, energy consumption, and scheduling. Furthermore, the review outlines the different methodological approaches to optimization algorithms used in building energy management. The contributions of controller and optimization in building energy management with the relation of sustainable development goals (SDGs) are explained rigorously. Discussions on the key challenges of the existing methods are presented to identify the gaps for future research. The review delivers some effective future directions that would be beneficial to the researchers and industrialists to design an efficiently optimized controller for building energy management toward targeting SDGs.

The process of building and modernizing the high voltage substations generate the problems concerning the determination of the optimal technological solutions, which should be friendly to the environment, meet the criteria of sustainable development and be cost effective. Since many institutions are involved in the decision making process to reach an optimal solution for the planned substation, consensus is sometimes not possible without the commitment of the instruments which support this decision. Hence, in this paper the authors proposed a new approach based on the Analytic Hierarchy Process (AHP) method in relation to supporting the decision of choice of high voltage substation technology between two commonly applied solutions: Air Insulated Substation (AIS) and Gas Insulated Substation (GIS). On the basis of applied regulations as well as expert opinions a survey was prepared and then analyzed in terms of the elaborated criteria. The results show that the AHP method is well suited to the issue under consideration, indicating clearly that GIS technology is strongly preferred. Hence, this opens the discussion for the use of the AHP method in a wider form in the decision support process in the specific field, which high-voltage engineering is.

The relevance of safety applications within the automotive industry is increasing continuously, e.g. due to vehicle automation and decreasing relevance of mechanical backups. To cope with these trends, the power input of safety-related electrical and/or electronic systems needs to be ensured by the power supply system – leading to increased functional safety requirements. Compliance with ISO 26262 will be more in focus in the future. Currently, the compliance with ISO 26262 may be used to argue the state of the art focusing on product liability – however, it will become mandatory for homologation. Thereby, the power supply system is a crucial point since faults of the power supply system are currently the major contributor for vehicle breakdowns with increasing tendency. So far, there is no standard approach within the automotive industry how to ensure functional safety for power supply systems. To fill this gap, this technical elaboration evaluates functional safety with focus on power supply systems development. Hence, guidelines on how to apply the ISO 26262 are provided based on discussions within the automotive industry and research institutes. The focus is on the concept phase, i.e. item definition, hazard analysis and risk assessment, and the functional safety concept. The functional safety concept is based on a structured hierarchical breakdown to systematically derive safety requirements from the item level down to the power supply system level. The essential safety requirement – beside the safe power feed and safe power distribution – is to assure the freedom from interference between the safety and non-safety relevant components.

Trade studies have become an indispensable engineering activity and are used often in the early stage of system architecture development. Trade studies not only help when choosing the most balanced alternative, but are also essential in highlighting the most influential aspects of the system. Trade studies in highly sophisticated systems are often very complex. Therefore, given these complexities, we proposed a new trade study process for the system of systems architecture developed in Unified Architecture Framework models. This process supports automated methods that allow for greater accuracy, better uncertainty analysis, and accelerated analysis of alternatives. One of the essential and challenging stages of the proposed trade study process is a thorough evaluation of plausible alternatives, referred to as the Deep Check. The goal of this stage is to identify the most balanced alternative by evaluating those that are plausible against established evaluation criteria. This paper introduces a new approach to evaluate the system of systems architectures within a trade study process using automated methods in a model-based system engineering environment. We verified the proposed method by running an experiment on a real-world model to confirm its eligibility.

Recently, COVID-19 has infected a lot of people around the world. The healthcare systems are overwhelmed because of this virus. The intensive care unit (ICU) as a part of the healthcare sector has faced several challenges due to the poor information quality provided by current ICUs’ medical equipment management. IoT has raised the ability for vital data transfer in the healthcare sector of the new century. However, most of the existing paradigms have adopted IoT technology to track patients’ health statuses. Therefore, there is a lack of understanding on how to utilize such technology for ICUs’ medical equipment management. This paper proposes a novel IoT-based paradigm called IoT Based Paradigm for Medical Equipment Management Systems (IoT MEMS) to manage medical equipment of ICUs efficiently. It employs IoT technology to enhance the information flow between medical equipment management systems (THIS) and ICUs during the COVID-19 outbreak to ensure the highest level of transparency and fairness in reallocating medical equipment. We described in detail the theoretical and practical aspects of IoT MEMS. Adopting IoT MEMS will enhance hospital capacity and capability in mitigating COVID-19 efficiently. It will also positively influence the information quality of (THIS) and strengthen trust and transparency among the stakeholders.

Healthcare supply chains are complex structures spanning across multiple organizational and geographical boundaries, providing critical backbone to services vital for everyday life. The inherent complexity of such systems can introduce impurities including inaccurate information, lack of transparency and limited data provenance. Counterfeit drugs is one consequence of such limitations within existing supply chains which not only has serious adverse impact on human health but also causes severe economic loss to the healthcare industry. Consequently, existing studies have emphasized the need for a robust, end-to-end track and trace system for pharmaceutical supply chains. Therein, an end-to-end product tracking system across the pharmaceutical supply chain is paramount to ensuring product safety and eliminating counterfeits. Most existing track and trace systems are centralized leading to data privacy, transparency and authenticity issues in healthcare supply chains. In this article, we present an Ethereum blockchain-based approach leveraging smart contracts and decentralized off-chain storage for efficient product traceability in the healthcare supply chain. The smart contract guarantees data provenance, eliminates the need for intermediaries and provides a secure, immutable history of transactions to all stakeholders. We present the system architecture and detailed algorithms that govern the working principles of our proposed solution. We perform testing and validation, and present cost and security analysis of the system to evaluate its effectiveness to enhance traceability within pharmaceutical supply chains.

Various real-world applications involve modeling complex systems with immense uncertainty and optimizing multiple objectives based on the uncertain model. Quantifying the impact of the model uncertainty on the given operational objectives is critical for designing optimal experiments that can most effectively reduce the uncertainty that affect the objectives pertinent to the application at hand. In this paper, we propose the concept of mean multi-objective cost of uncertainty (multi-objective MOCU) that can be used for objective-based quantification of uncertainty for complex uncertain systems considering multiple operational objectives. We provide several illustrative examples that demonstrate the concept and strengths of the proposed multi-objective MOCU. Furthermore, we present a real-world example based on the mammalian cell cycle network to demonstrate how the multi-objective MOCU can be used for quantifying the operational impact of model uncertainty when there are multiple, possibly competing, objectives.

In the electric power distribution system, power electronics technologies associated with renewable energy systems (RES) and smart grids have gained growing interest. The power electronics devices are used to convert, control, or transfer electric power from RES to the power grids. However, the continuous increase in switching frequencies resulting from these power electronics technologies has led to the emergence of new emissions in the range of 2–150 kHz, outside the classical frequency range for power quality. These emissions are known worldwide as supraharmonics (SH). These emissions negatively affect the power quality of electrical distribution systems and reduce their efficiency and lifetime. Thus, the supraharmonics emissions have been investigated in the literature, and several methods were developed focusing on identifying, measuring, and setting new standards to mitigate the impact of these emissions on the power quality. Although these individual studies have been well documented, a comparative overview of its identifications, current standards, and measurement techniques had not been described so far. Therefore, this study extensively reviews the related techniques and standards for identifying, measuring, and mitigating SH emissions. Moreover, the current research gap in this important field is highlighted, and an illustration on how this problem was tackled in the past few years is presented. Additionally, the SH characteristics alongside insights into the mitigations and measurements are highlighted and analyzed accordingly. Finally, some important recommendations to mitigate SH emissions are suggested. This review will hopefully strengthen the efforts toward the development of SH domain by providing the necessary groundwork for further mitigations, standards, and measuring techniques improvement

Supplier-buyer relationships have been the focus of considerable supply chain management and marketing research for decades. To validate the process capability of a supplier, practitioners usually operate the acceptance sampling plan (ASP). The most basic ASP is a single sampling plan (SSP) due to its straightforward lot-disposition mechanism. However, since the lot-disposition mechanism of SSP cannot accommodate the historical lot-quality levels information, it requires a large sample size for inspection to validate the submitted lot’s process capability. To obtain these benefits from historical information, multiple-lot dependent state (MDS) sampling plans have been proposed. The MDS plans have manufacturing traceability of historical lot-quality levels information to sentence the submitted lot. However, the MDS plan’s manufacturing traceability has a drawback that cost-efficiency decreases as more historical lot-quality levels information are considered, which contradicts its initial development goal. To overturn this contradictory situation, we proposed the adaptive MDS (AMDS) plans based on the process loss restricted consideration with combinatorial mathematical treatment that can correct the MDS plans manufacturing traceability of historical lot-quality levels information that help practitioners to adopt more historical information into lot-disposition freely without bearing the reduction of cost-efficiency. Meanwhile, their performances are superior to existing MDS plans in terms of cost-effectiveness and discriminatory power. Moreover, we further developed a web-based app for our proposed plans to improve the convenience of applying them in practice. By operating the web-based app, practitioners can quickly obtain the optimal plan criteria without bearing the burdens of table-checking or mathematical model solving. These improvements can genuinely help buyers distinguish reliable suppliers efficiently and build up a strong partnership with them. Finally, the applicability of the proposed plan is demonstrated in a real-world case study.

With the deepening of the global economic community, various emergencies emerge in endlessly, and the risks gradually increase. People’s lives and property are threatened, which also causes a great burden on the social economy. Hitherto unknown novel coronavirus events occurred in China after the outbreak of the new coronavirus in 2019. The emergency management system is not perfect, so we start to study and improve the deficiencies of the emergency management system, but it is still difficult to effectively prevent and deal with all kinds of sudden and frequent social problems. Therefore, this paper puts forward the research of intelligent evaluation system of government emergency management based on BP neural network. In this paper, an intelligent evaluation system of government emergency management based on Internet of things environment is established, and then the system is deepened by BP neural network algorithm to avoid the interference of human factors. An objective intelligent evaluation system of government emergency management is constructed and verified by an example. We applied the system in a province, and proved that the system has strong executive ability, outstanding big data computing ability, and can objectively evaluate and analyze the government emergency management. The operability and accuracy of the intelligent evaluation system are verified. The effective evaluation content provides a new idea and method for government emergency management. And then continuously improve the emergency management measures to achieve the effect of dealing with things smoothly without panic.

Energy-efficiency related research has reached a growing interest in recent years due to the imminent scarcity of non-renewable resources in our environment and the impending impacts their usage have on our environment. Thus, facing the reduction of energy waste and management has become a pivotal issue in our society. To cope with energy inefficiency, the scientific research community has identified the promotion of people's behaviour change as a critical field to foster environmental sustainability. However, the body of literature shows a lack of systematic methods and processes to reach a common ground when designing technology for promoting sustainable behaviour change. Therefore, this paper contributes with a thorough review and analysis of state of the art. Firstly, theoretical works related to behaviour change are collected and studied to clarify their main concepts and theories. Secondly, the different technologies, processes, methods and techniques applied in the field are reviewed to find diverse strategies in the application of the previously explained theoretical domains. Moreover, a wide range of systems developed to improve energy efficiency through human behaviour change is analysed (from augmented objects to the Internet of Things, digital applications or websites). Finally, the detected research gaps are listed to guide future research when aiming to raise the awareness of individuals through Information and Communication Technologies.

Industrial equipment health management is an extension of traditional fault diagnosis, which is a frontier research field of information science. The definition and application area of industrial equipment health management are first clarified, and then technologies related to health management are introduced, including feature analysis, fault diagnosis, fault prognosis and other technologies. The difficulties and bottlenecks of various technologies are expounded through comparative analysis, and a feasible scheme for future health management and integration is also discussed in this article.

In project management, especially of software development, human resource allocation is critical not only for project success including timely delivery and products quality but also for cost estimation to a software company to decide to contract projects or not. Many approaches have been proposed to provide assistance for human resource allocation in view of minimizing project duration and project cost or maximizing revenue and efficiency. These approaches may sometimes not be comprehensive in real-world environments without addressing some key factors like transferring cost, communication, and negative efficiency, which usually have crucial impacts on assignment applicability. This article is proposing a framework for assisting a software company to evaluate existing resource for making decisions on whether the estimation of the tender is feasible, and assisting to make human resource allocation for team formation in fixed project duration with labor skill and budget constraint. Within consideration of both the cost and the efficiency, the performance is maximized to verges on the customers’ expectations in software development project management on the basis of a real case. Before forming a team for a specific project with well-defined periods and numbers of people with different project roles, the values of key factors in the framework are calculated with individual foundation of computation. We formulate the decision process in integer programming models, and a simulation study is conducted to demonstrate the applicability of the proposed model to assist the decision making process of software companies for team formation. The results of four variants for maximum skill efficiency or minimum hiring cost are illustrated with different considerations of both key factors are included or not proposed in this paper, and demonstrate that these factors could bring out different sources to help companies in making further evaluations for allocating staff in a software development project.

Healthcare insurance frauds are causing millions of dollars of public healthcare fund losses around the world in various ways, which makes it very important to strengthen the management of medical insurance in order to guarantee the steady operation of medical insurance funds. Healthcare fraud detection methods can reduce the losses of healthcare insurance funds and improve medical quality. Existing fraud detection studies mostly focus on finding normal behavior patterns and treat those violating normal behavior patterns as fraudsters. However, fraudsters can often disguise themselves with some normal behaviors, such as some consistent behaviors when they seek medical treatments. To address these issues, we combined a MapReduce distributed computing model and association rule mining to propose a medical cluster behavior detection algorithm based on frequent pattern mining. It can detect certain consistent behaviors of patients in medical treatment activities. By analyzing 1.5 million medical claim records, we have verified the effectiveness of the method. Experiments show that this method has better performance than several benchmark methods.

Developments in the economic environment in the 2000s have become increasingly dynamic and complex. Rapid developments in this kind of economic environment threaten and restrain the sustainability of enterprises. Enterprises need to respond quickly to these burdens and threats to survive and sustain their operations efficaciously in a competitive market in the long run. In order to reduce possible uncertainties in the future and to anticipate economic crises, early risk warning systems should be developed. However, it is seen that management accounting researches are very limited or insufficient on the demand of enterprises for coping with such crises. The aim of this study is to diminish the deficiency in the strategic cost management and prediction of economic crises. Sustainable Balanced Scorecard (SBSC), which was developed as a strategic cost management tool, is constructed in a dynamic way by integrating the early warning system developed for enterprises with an innovative approach into SBSC. Additionally, early warning system model is developed in a manner that successfully predicts economic crises with long short time memory (LSTM) networks using economic macro variables in micro field. As a result of the integration of risk early warning system with SBSC, economic crises will be predicted and necessary strategies will be developed to cope with problems of the crises. Furthermore, predicting economic crises will be turned into opportunities or cause enterprises to make measures with minimum losses. In this model, crisis periods are successfully predicted two crises of 2002 and 2008 with 95.41% accuracy with macroeconomic data between 1998 and 2011.

Increasingly blockchain technology is attracting significant attentions in various agricultural applications. These applications are able to satisfy the diverse needs in the ecosystem of agricultural products, e.g., increasing transparency of food safety and IoT based food quality control, provenance traceability, improvement of contract exchanges, and transactions efficiency. As multiple untrusted parties, including small-scale farmers, food processors, logistic companies, distributors and retailers, are involved into the complex farm-to-fork pipeline, it becomes vital to achieve optimal trade-off between the efficiency and the integrity of the agricultural management systems as required in contexts. In this paper, we provide a survey to study both techniques and applications of blockchain technology used in the agriculture sector. First, the technical elements, including data structure, cryptographic methods, and consensus mechanisms are explained in details. Secondly, the existing agricultural blockchain applications are categorised and reviewed to demonstrate the use of the blockchain techniques. In addition, the popular platforms and smart contract are provided to show how practitioners use them to develop these agricultural applications. Thirdly, we identify the key challenges in many prospective agricultural systems and discuss the efforts and potential solutions to tackle these problems. Further, we conduct an improved food supply chain in the post COVID- 19 pandemic economy as a case study to demonstrate an effective use of blockchain technology.

Security has become a vital factor for any Internet of things network but it is of paramount importance for Internet of Health Things (IoHT). IoHT also known as Internet of Medical Things (IoMT) is integration of IoT and healthcare environment, where fragile data related to the patients is transmitted from IoT devices to server. During this transmission, if, any eavesdropping or intrusion occurs then it will not only lead to the serious mutilation of entire network but this data will be handled maliciously for wrong doings as well. Therefore, a proper security is indispensable for IoHT based equipments due to exposure to different attacks. Security of IoHT has been the burning issue in last couple of years. In this regard different security models, surveys, frameworks have been presented. In this article, a proposed Identified Security Attributes (ISA) framework is presented to evaluate the security features of IoHT based device in healthcare environment. The proposed framework uses hybrid MCDM methods such as Analytical Hierarchical Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). This framework works in two phase: in first phase the weights of attributes are derived by using AHP method and in second phase security assessment of alternatives is performed based upon security criteria by using TOPSIS method. The outcomes of proposed security assessment framework demonstrate that the reliable and secure alternative among alternatives is selected in IoMT system. This approach can be used as a guideline for future use in IoMT systems or any other IoT based domain. To the best of our knowledge, it is novel approach to address the security assessment of IoT and these MCDM methods have never been used before for assessment and decision making in IoHT system for security.

Due to safety and reliability concerns, various classes and/or applications of electronic products are excluded from the European Union's Restriction of Hazardous Substances (RoHS) regulations requiring the elimination of lead (Pb). However, the majority of electronic devices are now Pb-free. As a result, manufacturers attempting to maintain tin-lead (SnPb) based electronics face an increasing challenge to procure parts and products with Pb-based solder. This paper reviews the challenges of producing Pb-based electronic equipment, the reliability concerns with Pb-free electronics, and the options that manufacturers have for maintaining reliable, Pb-based production. In particular, the paper examines mixed soldering processes and the re-balling of Pb-free electronic parts. While Pb-free electronics present risks, it is our conclusion that the risks are manageable and are lower than those incurred by maintaining an increasingly obsolete Pb-based manufacturing process.

The current research efforts in the security domain are essentially inspired by the intention to ensure both healthcare software's security and usability. Practitioners are continuously working on techniques that can optimise security while affording the ease of use. Inspite of the numerous efforts by the developers and security experts in this context, the level of usability-security is not the same as it should be. Furthermore, it is important to do more work on the most effective strategies for evaluating and increasing the usable-security of healthcare technology. Quantitative usability and security assessment is an important part of increasing the standards of software security especially with regard to software used in healthcare. This study's objective, in particular, is to evaluate the usability-security of healthcare software. Thereafter, based on the empirical analysis of comprehensive data, the study recommends the strategies that would facilitate in designing healthcare software with optimum security while preserving its' usability. Decision makers often find it difficult to integrate highly secure software with maximum usability while developing the software. Hence, this research endeavour enlists the assimilation of usability and safety with their contributory attributes. Moreover, this study proposes the use of a novel technique of combining the Analytic Network Method (ANP), Fuzzy Sets (FS), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to test usability together with security. The researchers employed the suggested methodology on six applications of the hospital management systems in Mecca, Saudi Arabia, to ascertain the utility of the FS-AHP-TOPSIS hybrid solution technique. Usability results along with security assessment corroborated that the most accurate and effective method for evaluating the usable-security of healthcare applications will be this innovative hybrid approach. Most relevantly, these findings will be useful in the security management without impacting the usability for the end users.

Security and usability are often thought of as being contradictive in nature. One affects the other negatively. The relation and trade-offs between usability and security must be detected during developing web application to satisfy the user’s requirements with security perspective. Current approaches of usable-security emphasizes on building systems that are easy to use and secure as well. Hence, this paper is recognizing usability-security as a problem with different attributes contributing towards it. Further, there is a need to assess this problem for the satisfaction of the end user. In this context, this study proposes the track of Fuzzy AHP-TOPSIS (Analytic Hierarchy Process-Technique for Order of Preference by Similarity to Ideal Solution) technique to assess the usable-security of web application and also identifies the most prioritized attribute contributing towards building usable-security of web application. Moreover, to corroborate the efficacy of the proposed technique, the authors have tested the results on the institutional web applications. The results of the assessment undertaken in this study and the findings tabulated thereafter will be a helpful reckoner for the developers while designing web applications that afford optimum usable-security.