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As a system reliability analysis technology, failure mode and effect analysis (FMEA) has been widely used in various industries to reduce the failure risk of products, systems, and services. In recent years, many researchers have found that this technology has limitations in failure mode assessment and failure mode prioritization in complex uncertain environment. In order to solve this problem, this article proposes a new FMEA model, which combines hesitant Pythagorean fuzzy sets (HPFSs) and ORESTE method. Specifically, HPFSs are used to describe the risk assessment information of experts. The ORESTE method is extended to determine the risk priority of the failure modes in FMEA. Moreover, the PIR structure of ORESTE is improved to reduce the subjective influence of experts. Finally, an example of risk assessment of intelligent aquaculture system is given to verify the applicability and effectiveness of the proposed FMEA model. The results show that the proposed FMEA model can not only accurately describe the hesitation and uncertainty of experts’ risk assessment information, but also obtain more suitable risk ranking of failure modes, based on which managers can further take corresponding preventive measures.

Robust scientific knowledge is contingent upon replication of original findings. However, replicating researchers are constrained by resources, and will almost always have to choose one replication effort to focus on from a set of potential candidates. To select a candidate efficiently in these cases, we need methods for deciding which out of all candidates considered would be the most useful to replicate, given some overall goal researchers wish to achieve. In this article we assume that the overall goal researchers wish to achieve is to maximize the utility gained by conducting the replication study. We then propose a general rule for study selection in replication research based on the replication value of the set of claims considered for replication. The replication value of a claim is defined as the maximum expected utility we could gain by conducting a replication of the claim, and is a function of (a) the value of being certain about the claim, and (b) uncertainty about the claim based on current evidence. We formalize this definition in terms of a causal decision model, utilizing concepts from decision theory and causal graph modeling. We discuss the validity of using replication value as a measure of expected utility gain, and we suggest approaches for deriving quantitative estimates of replication value. Our goal in this article is not to define concrete guidelines for study selection, but to provide the necessary theoretical foundations on which such concrete guidelines could be built.Translational Abstract Replication-redoing a study using the same procedures-is an important part of checking the robustness of claims in the psychological literature. The practice of replicating original studies has been woefully devalued for many years, but this is now changing. Recent calls for improving the quality of research in psychology has generated a surge of interest in funding, conducting, and publishing replication studies. Because many studies have never been replicated, and researchers have limited time and money to perform replication studies, researchers must decide which studies are the most important to replicate. This way scientists learn the most, given limited resources. In this article, we lay out what it means to think about what is the most important thing to replicate, and we propose a general decision rule for picking a study to replicate. That rule depends on a concept we call replication value. Replication value is a function of the importance of the study, and how uncertain we are about the findings. In this article we explain how researchers can think precisely about the value of replication studies. We then discuss when and how it makes sense to use replication value as a measure of how valuable a replication study would be, and we discuss factors that funders, journals, or scientists could consider when determining how valuable a replication study is.

Digital transformation provides an unprecedented opportunity for the exploration, integration, and utilization of scientific and technological resources (STR). In the wave of digital transformation, countries around the world have set up their own STR sharing platforms, which can match a variety of STR services and needs in a lower cost and more convenient way, and create interaction between the suppliers and demanders. These sharing platforms provide possibilities to realize the sharing economy in the field of scientific and technological innovation. However, it is also necessary to design the corresponding mechanisms to improve the sharing rate and sharing value. Therefore, in this article, we construct a new framework of operation mechanism for China's STR allocation system and design its three submechanisms. Then, a system dynamics model of the operation mechanism is designed, and the experiments are carried out. The simulation results show that the mechanism proposed in this article can effectively improve the sharing rate and sharing value. Finally, some policy suggestions are put forward, which will help the government to improve management mode and optimize resource allocation.

More and more IoT data is being traded online in cloud-based data marketplaces due to the fast-growing market demand. Within the current data selling mechanisms, data consumers have difficulties in making purchasing decisions due to uncertain data quality and inflexible pricing interface. To resolve them, potential solutions could be to launch data demonstrations and release free sampling data to reduce the uncertainty about data quality, and to charge based on the volume of data for enabling flexible pricing. However, their economic benefits are still not clear. In this paper, we design the optimal data selling mechanisms for IoT data exchange, and derive the following results. First, whether to deploy data demonstrations and how much free sampling data to release depend on the extent of data consumers inaccuracy perceptions for data quality. We found that data vendors have no incentive to conduct these strategies if data consumers extremely overestimate data quality. Second, although flexible data pricing mechanisms provide convenience for real-time and streaming IoT data exchange, it brings less economic benefits to data vendors compared with fixed pricing schemes. We evaluate the optimal selling mechanisms on a real-world Taxi GPS dataset. Evaluation results verify the insights derived from our theoretical analysis.

Problem definition: Fulfillment flexibility, the ability of distribution centers (DCs) to fulfill demand originating from other DCs, can help e-retailers reduce lost sales and improve service quality. Because the cost of full flexibility is prohibitive, we seek to understand the value of partially flexible fulfillment networks under simple and effective fulfillment policies. Academic/practical relevance: We propose a general method for understanding the practical value of (partial) fulfillment flexibility using a data-driven model, theoretical analysis, and numerical simulations. Our method applies to settings with local fulfillment (i.e., order fulfillment from the originating DC) prioritization and possible customer abandonment, two features that are new to the fulfillment literature. We then apply this method for a large e-retailer. We also introduce a new class of spillover limit fulfillment policies with attractive theoretical and practical features. Methodology: Our analysis uses dynamic and stochastic optimization, applied probability, and numerical simulations. Results: We derive optimal fulfillment policies in stylized settings, as well as bounds on the performance under an optimal policy using theoretical analysis, to provide guidelines on which policies to test in numerical simulations. We then use simulations to estimate for our industrial partner that a proposed fulfillment network with additional flexibility equates to a profit improvement on the order of tens of millions of U.S. dollars. Managerial implications: We provide an approach for e-retailers to understand when fulfillment flexibility is most valuable. We find that fulfillment flexibility provides the most benefit for our collaborator when gross profits are high relative to fulfillment costs or centrally held inventory is low. Also, we identify the risks of myopic fulfillment with additional flexibility and demonstrate that an effective spillover limit policy mitigates these risks. History: This paper has been accepted as part of the 2019 Manufacturing & Service Operations Management Practice-Based Research Competition. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0981 .

This study aimed to develop and evaluate the performance of a service system platform based on the Internet of Things (IoT) for monitoring nutritional deficiencies in plants and providing fertilizer recommendations. There are two distinct differences between this work and previous ones; namely, this service system platform has been developed based on IoT using a system engineering approach and its performance has been evaluated using dependability. We have successfully developed and integrated a service system platform and chlorophyll meter that is based on IoT. We have also successfully tested the performance of the service system platform using the JMeter software. The dependability value measured from the five tested variables (reliability, availability, integrity, maintainability, and safety) showed a value of 0.97 which represents a very good level of system confidence in not failing to deliver services to users under normal operational conditions. From a future perspective, this platform can be used as an alternative service to monitor nutrient deficiencies in plants and provide fertilization recommendations to increase yields, reduce fertilizer costs, and prevent the use of excessive fertilizers, which can cause environmental pollution.

Agriculture is an important sector that plays an essential role in the economic development of a country. Each year farmers face numerous challenges in producing good quality crops. One of the major reasons behind the failure of the harvest is the use of unscientific agricultural practices. Moreover, every year enormous crop loss is encountered either by pests, specific diseases, or natural disasters. It raises a strong concern to employ sustainable advanced technologies to address agriculture-related issues. In this paper, a sustainable real-time crop disease detection and prevention system, called CROPCARE is proposed. The system integrates mobile vision, Internet of Things (IoT), and Google Cloud services for sustainable growth of crops. The primary function of the proposed intelligent system is to detect crop diseases through the CROPCARE -mobile application. It uses Super-Resolution Convolution Network (SRCNN) and the pretrained model MobileNet-V2 to generate a decision model trained over various diseases. To maintain sustainability, the mobile app is integrated with IoT sensors and Google Cloud services. The proposed system also provides recommendations that help farmers know about current soil conditions, weather conditions, disease prevention methods, etc. It supports both Hindi and English dictionaries for the convenience of the farmers. The proposed approach is validated by using the PlantVillage dataset. The obtained results confirm the performance strength of the proposed system.

Drawing on insights from organizational theory, in this article, we examine the paradoxical tensions that reside between quality and safety and impact construction organizations and their projects. Rather than adopting the dualistic, reductionist, and ineffective “either/or” framing of quality and safety, we suggest confronting them as a duality. The corollary is balancing the competing demands of quality and safety, thus putting managers in a position to optimally reduce rework and improve project safety at the same time. Managers also need to focus on balancing the paradoxical tensions of error prevention and error management that can influence a project's performance. However, the offsetting of these tensions has been overlooked in practice. In this article, we proffer that if construction organizations are to make headway to improve quality and safety simultaneously, then managers should espouse ambidexterity, move from the “tyranny” of quality or safety to “genius” of quality and safety and, thus, synergize these two competing demands while managing the paradoxical tensions of error prevention and error management.

Supply-chains (SC) present performance bottlenecks that contribute to a high level of costs, infiltration of product quality, and impact productivity. Examples of such inhibitors include the bullwhip effect, new product lines, high inventory, and restrictive data flows. These bottlenecks can force manufacturers to source more raw materials and increase production significantly. Also, restrictive data flow in a complex global SC network generally slows down the movement of goods and services. The use of Distributed Ledger Technologies (DLT) in supply chain management (SCM) demonstrates the potentials to to reduce these bottlenecks through transparency, decentralization, and optimizations in data management. These technologies promise to enhance the trustworthiness of entities within the supply chain, ensure the accuracy of data-driven operations, and enable existing SCM processes to migrate from a linear to a fully circular economy. This paper presents a comprehensive review of 111 articles published in the public domain in the use and efficacy of DLT in SC. It acts as a roadmap for current and future researchers who focus on SC Security Management to better understand the integration of digital technologies such as DLT. We clustered these articles using standard descriptors linked to trustworthiness, namely, immutability, transparency, traceability, and integrity.

Due to dense social contacts, metropolises are considered as epicenters of contagious infectious diseases. Developing models for infection transmission and mitigation in metropolises is a challenging problem because of spatiotemporal variations in their population structures. Employing the fact that intra-urban mobility is mainly shaped by the transportation infrastructure and reflected in the traffic flux information, a novel multi-scale reaction-diffusion process is developed to model infection transmission in a metropolis. To mitigate the infection, the impact of three non-pharmaceutical preventive interventions (movement restrictions, social distancing, and proactive testing/screening) is analytically investigated on the reaction-diffusion process. We show that imposing movement restrictions between sub-urban areas is only beneficial when they have different reproduction numbers. The social distancing reduces the infection peak in sub-urban areas, but its impact on the entire metropolis is various: The social distancing reduces a metropolis's infection peak if its implementation disperses the reproduction number of sub-urban areas. We show that allocation schemes of proactive testing/screening teams to sub-urban areas have intractable impacts on the infection peak. This demonstrates the necessity of employing optimization techniques to design the best allocation scheme. Finally, three mathematical models and their solution approaches are developed to optimize the implementation of the interventions with the least economic burden. The performance of the optimization models is compared with two simpler implementation schemes (uniform and pro rata implementations of interventions) using the information of Sioux Falls metropolitan area in the U.S.

Diagnosing a disease consumes a part of time, needs high technical methods but nowadays the smart technologies have been grown rapidly in the field of healthcare industries and also it improves the routine life of the patients, reduces the amount of work, treatment cost in the health care organization. Diseases prediction is one of the major challenges faced by society nowadays. The recent survey also stated that the death rate is remarkably high in CAD because most of the people are affected by cardiovascular diseases. Prediction and diagnosis of cardiovascular diseases is very essential nowadays to reduce the death rate and diagnosing it preliminary stage itself. In earlier studies, they worked with machine learning techniques to predict the diseases, but they are not given proper attention to identifying the feature with the help of proper feature selection methods. This paper proposed a new-found feature selection technique HRFLC (RANDOM FOREST + ADABOOST + PEARSON COEFFICIENT). This method helps to predict the diseases in a very efficient manner, and it improves the accuracy level in prediction.

Post COVID-19 vaccine development, nations are now getting ready to face another challenge: how to effectively distribute vaccines amongst the masses to quickly achieve herd immunity against the infection. According to some experts, herd immunity for COVID-19 can be achieved by inoculating 67% of the population. India may find it difficult to achieve this service level target, owing to several infrastructural deficiencies in its vaccine supply chain. Effect of these deficiencies is to cause frequent lead time disruptions. In this context, we develop a novel modelling approach to identify few nodes, which require additional inventory allocations (strategic inventory reserves) to ensure minimum service level (67%) under the possibility of lead time disruptions. Later, through an illustrative case study on distribution of Japanese Encephalitis vaccine, we identify conditions under which strategic inventory reserve policy cannot be practically implemented to meet service level targets. Nodes fulfilling these conditions are termed as critical nodes and must be overhauled structurally to make the implementation of strategic inventory policy practically viable again. Structural overhauling may entail installation of better cold storage facilities, purchasing more quality transport vans, improving reliability of transport network, and skills of cold storage manager by training. Ideally, conditions for identifying critical nodes for COVID-19 vaccine distribution must be derived separately by substituting COVID-19 specific parametric values in our model. In the absence of the required data for COVID-19 scenario, JE specific criteria can be used heuristically to identify critical nodes and structurally overhaul them later for efficiently achieving service level targets.

The satisfaction of a software requirement is commonly stated as a Boolean value, that is, a security requirement is either satisfied (true) or not (false). However, a discrete Boolean value to measure the satisfaction level of a security requirement by deployed mechanisms is not very useful. Rather, it would be more effective if we could quantify the level of satisfaction of security requirements on a continuous scale. We propose an approach to achieve this for cloud software systems based on relationships between defense strength, exploitability of vulnerabilities, and attack severity. We extend the concept of entailment relationship from the field of requirements engineering with the satisfiability aspects of security requirements. The proposed approach enables us to systematically structure security concepts into three sets of related descriptions to quantify the satisfaction level of security requirements with the deployed security solutions. To demonstrate the feasibility of the proposed approach, we evaluate the approach in a case study. As a result, security administrators are able to deploy more effective and appropriate security solutions based on their assessment.

Sun-oriented solar power production with sunlight-based vitality plays an important role. In reality, untimely or innovative countries gracefully take a move forward. When oil is used for power era, it contributes to a rise in poisoning and greenhouse gases. This creates natural complications. Another human need for existence can be filled by using numerous sun- oriented developments in vitality, the planet can still prevent worldwide problems which can also serve to decrease pollution at the same time. In far-off areas where communities of citizens do not approach electricity photovoltaic frameworks are added many any administration offices have taken the amounts of action to allow creativity in sun-driven vitality to be used for power production. Single axis monitoring devices are discussed in this article. This analysis supports the findings. In comparison with the static systems, the single axis monitoring system is more accurate. In either scenario, a single axis monitoring system is simpler to accomplish in cost and adaptability viewpoints than a static one.

Autonomic computing frameworks are the self overseeing frameworks as indicated by the objectives outlined by the administrator of the system. Incorporation of new elements in framework supported by Autonomic Computing happens as simple as in human body a new cell recreates itself. Autonomic Computing Systems (ACS) are relied upon to accomplish an indistinguishable level of self-direction and inescapability from human autonomic frameworks. Due to the highlights of ACS, the customary security model cannot be connected to ACS any more. The objective of our exploration is to build up a setting based security model and engineering for ACS. Our attention is on self-security highlight of ACS. The self- assurance include is implemented through security settings that we characterize. By considering security settings, security arrangements would dynamic is able to change so as to adapt to new condition.

Accidents can occur in work place or in place where people are gathering. Many such accidents are preventable. These types of accidents may occur due to carelessness or improper safety wears. These disasters could clearly be maintained a strategic distance from, or its viable force could be decreased with appropriate well-being wear. The fundamental target of this undertaking is to build up a fair-minded computerized framework that checks and investigates the well-being wear of representatives before allowing them to the work region or shop-floor. Along these lines, this framework will mean to forestall mishaps because of inappropriate security wear and the information gathered is utilized for further investigation. Consequently, this system will expect to thwart disasters on account of less than ideal security wear and the data accumulated is used for further investigation. These incidents could clearly be stayed away from, or its viable force could be decreased with appropriate well-being wear.

In present scenario, each and every activity in basic lifestyle of people is becoming smarter with the day-to-day flourishing development of technology such as Internet of Things, Artificial Intelligence, Machine Learning and Blockchain. Among the smart improvements, smart city holds the highest rank due to the initiatives provided by the administration. Smart city mission has influenced the citizen’s life with respect to the automation on fundamental needs. Energy resources are considered as one of the important requirements for the survival of people. With the advent of Internet of Things, smart grid systems are designed in order to meet the sustainable demands. It also provides information about the energy resources consumed and alert the consumers about the demand prevails on the resources. Technological improvement is more essential for a country, only if it can be considered and adaptable for sustainable development. Therefore these papers outline the Internet of Things based smart grid which helps in the energy conservation process to meet the future demands. Energy management processes has to be well-versed among the citizens as it helps in understanding the possible ways on storage of energy resources. However, when energy storage is concerned, security issues are the counterpart which has to be focused at higher level. Therefore, blockchain techniques applied for energy storage systems are also outlined along with the applications and future directions of smart grid.

Cyber risk affects all organizations. Cyber risk management has generally been based on the heuristics and the availability of protective tools, such as firewalls and passwords. Only recently have there been quantitative analyses of these tools’ costs and benefits. This article presents a probabilistic method based on the existing data in an organization and on their extension to assess the probabilities of new attack scenarios. The objective is to set priorities among risk management measures and to optimize the allocation of limited resources. The model is illustrated first by a statistical analysis of 60 000 incidents, such as lost or stolen laptops, over six years in a specific organization. This analysis is then expanded to the probabilistic domain to cover threats that have not occurred yet. This requires a systematic construction of new attack scenarios and an assessment both of their probability of success and of subsequent losses. The conjunction of statistics and probabilities of more extreme scenarios yields full risk curves. These curves represent the overall cyber risk for the organization and its insurers and unable assessment of the benefits of a spectrum of protective options.

Waste electrical and electronics equipment (WEEE) recyclers have become a boon for countries as they assist to reduce carbon emissions by recycling the WEEE in the most ecofriendly way. The evaluation and choice of optimal WEEE recycling partner is a significant and multifaceted decision for the managerial experts because of the involvement of several qualitative and quantitative criteria. Thus, the aim of the article is to propose a novel methodology by integrating a combined compromise solution approach and similarity measure within the context of single-valued neutrosophic sets (SVNSs) and, then, used to solve the decision-making problem. In this approach, the criteria weights are evaluated by a new procedure based on a similarity measure. For this, a novel similarity measure is introduced for SVNSs and also presented its efficiency over existing similarity measures. To investigate the efficiency and practicality of the introduced approach, a case study of WEEE recycling partner selection is taken under the SVNSs environment. Finally, comparison and sensitivity analysis are made to check the robustness and stability of the developed methodology. The outcomes of this article exemplify that the developed approach is more suitable and well consistent with existing approaches.

As a contemporary product of Internet of Things, wearable devices that are closely related to consumer health have received widespread attention from various nations and industries. Thus, the use of wearable devices has become a significant scientific problem requiring an urgent solution. In this article, we consider the characteristics of wearable devices, consumers, the market, safety, and the environment to analyze consumer behavior. A multiple attribute decision-making (MADM) model is used in this article, including the decision-making trial and evaluation laboratory (DEMATEL) technique, the DEMATEL-based analytic network process (DANP) method, and the modified Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. Based on expert data and the index system of criteria for consumer adoption of wearable devices, we evaluate the relationship between criteria and their weight of impact to create an influential network relationship map by using the DEMATEL technique, comprehensively analyze the correlation between consumer behaviors and products, clearly identify the relative gaps between criteria and the optimal level, and recommend improvements through the DANP. Finally, we use three specific products as examples to analyze consumer satisfaction for different criteria based on the modified VIKOR method. The results reveal that there are gaps between product performance and the optimal level, and product promotion strategies are proposed that would narrow the performance gaps of these three wearable devices by prioritizing the factors that required improvement.

In this article, we explore the topic of supply chain resilience (SCR) in an open-system context. Systems theory and cybernetics are important methodologies in the analysis of complex networks. However, relatively little is known about their application in the supply chain (SC) operations. Using systems theory and cybernetics for SCR conceptualization, we argue that a systems-theoretical lens can help to extend the state-of-the-art research to describe SCR based on the concept of balancing vulnerability and recoverability over time. Toward this end, we define and test a new resilience measure for SCR using the Bayesian network approach with a compounding function of vulnerability and recoverability. We further propose managerial actions for implementing an open-system view of SCR. To demonstrate the applicability and usefulness of our method, we measure and analyze the resilience levels of a manufacturer. Our results provide useful decision-making support and can be valuable for SC managers seeking to balance risk mitigation and recovery capabilities. The framework and measure developed can also be used to guide firms seeking to analyze the impact of disruptions on SCs through the consideration of both performance degradation and recovery.

The article of record as published may be found at https://doi.org/10.1109/TEM.2019.2958785 In the early stages of the systems engineering process, an important focus is to create an understanding of the stakeholder needs. This is primarily done to prepare the system specification that forms the basis for the system’s design. By extension, example steps in this process include surveying stakeholders to better capture their intent, deriving and documenting requirements, and then using those requirements for subsequent activities, such as developing a functional baseline and candidate design alternatives. During this process, it is important to consider the full system lifecycle. As such, one major objective of a systems engineer is to translate the stakeholder’s needs into functional and nonfunctional requirements (NFRs). Despite this important role, early system designs are often faulty because important NFRs are poorly prioritized or not prioritized at all. While the prioritization of all requirements can be useful, this work focuses specifically on NFRs. It has been identified that the inability to identify the most useful NFRs can lead to system failure. Furthermore, the lack of NFR prioritization is considered one of the most expensive and difficult errors to correct, as well as one of the ten most significant risks in engineering. Systems need more emphasis on the relationships between the system’s elements, rather than on the individual elements or the whole system. Relationships among elements in a system can illustrate more than just the behavior of each element. The illustration can include the purpose for the system and the implications of changing how the NFRs associated with those elements are prioritized. This emphasis requires quantifiable tools and rigor to inform the decision makers. This research’s objective is to contribute to quantifiable decision-making methods and prioritization of NFRs in three ways: the development of a process to determine unique random index; the use of a continuous ranking scale; and the development of a universal decision-making heuristic to accompany prioritization of NFRs.

Military technologies are typically the product of long-term development efforts. These technologies are often adapted late in the development process for use by other users, including other members of the defense enterprise, foreign militaries, or the commercial sector. The need to adapt a technology can arise from cost over-runs or changing operational requirements. This article describes a value-based methodology for developing a transition plan early in the development process, when the system is still in the conceptual phase. Performing this analysis early in the design phase allows for design choices that will support adapting the system for other users. Moreover, it provides a risk mitigation strategy against cost over-run and changes in operational requirements. The process begins by capturing the functional architecture of the technology and analyzing them against a set of tasks associated with different military positions. From there, the adapted technology is evaluated for its projected adequacy based on the value added and the effort required for adaptation. The end-state of this analysis is a value-based model that identifies a portfolio of possible alternative markets and/or uses, which can be comparatively analyzed. A case study is presented for adapting a military augmented reality system for a different market.

Engineering managers work with system designers, system operators, and stakeholders who value the system's performance and seek to deliver stakeholder value. Engineering resilient systems is an emerging research field since all systems face future uncertainties including new missions, new requirements, unplanned environments, and unplanned events that cause disruptions during operation. The ability of a system to provide stakeholder value depends on the system design and the ability of the system operator to react to the disruption(s). A resilient design offers options to respond to new missions, environments, and disruptions through system automation or operator actions. The engineering resilience literature uses overlapping definitions. It does not adequately distinguish between resilient design principles and the means they provide for operators to respond to mission disruptions and the ability to modify the system for new missions. This article uses the engineered systems resilience literature to provide a clearer lexicon. It then proposes a framework, the principles-means-ends diagram, for engineering managers to communicate with stakeholders, operators, and designers about the benefits of engineered systems’ resilience, the opportunities for designers to use resilience design principles to create more resilient designs, and the roles of the operators in achieving resilience during operations. We propose a principles-means-ends diagram for resilient engineered systems, which provides a holistic view for designers, operators, and stakeholders to visualize how their actions connect to others and encourages communication between them to create a more resilient engineered system.

South Korea established the National Construction Standards Center to efficiently manage the national construction standards and orchestrate various efforts such as the development and export of the unified code system, and the conduction of code reform research for securing safety and ever increasing disaster prevention of buildings and infrastructure. Significant national budgets must be put in place to develop, promote, and continually improve the unified construction code system. In order to confirm the appropriateness of this continuous investment, this article intends to estimate the socioeconomic ripple effect of the coordinated efforts over the next 30 years by developing a hybrid method that combines an analytical hierarchical process and system dynamics (SD). In this article, the interacting behavior of the developed SD model is illustrated parametrically. We found that the center's integrative codification efforts including the development and diffusion of the unified code system has the effect of offsetting the decreasing national construction budget. We also found that the Monte Carlo multivariate simulation-based uncertainty analysis on the developed SD model is well-suited for effectively quantifying and integrating various benefits to empower decision makers; in particular, providing information both on the mean and the most conservative socioeconomic ripple effects over the long term.

Maintenance of military equipment plays a vital role in achieving higher war readiness. The vast number of military equipment and their deployment on distinct locations make their maintenance a challenging task. Decision-makers are always interested in knowing the latest status of equipment, develop effective maintenance strategies, and predict mission reliability as accurately as possible. To suffice this need, maintenance data of military equipment must be maintained with high granularity and accuracy, including maintenance information about every lowest maintainable unit of every military equipment. There is a profound need for a comprehensive maintenance management framework, which addresses several military-specific issues with equipment maintenance and stores the maintenance data in the utmost secure environment. In this article, we attempt to provide a novel blockchain enabled framework to make military maintenance management comprehensive and future ready. This article first provides a representation of the maintenance scenario of military equipment and later discusses the several choices made available within the blockchain technology, along with the architecture of the proposed blockchain. The proposed framework uses smart contracts to make monitoring and validation more uncompromising and with minimum human intervention. Some pragmatic analytics are presented in this article, along with the inherent blockchain advantages.

The occurrence of a sudden, calamitous, and unfortunate event requires a quick response that can reduce damage and save lives, fulfilling the basic humanitarian needs of the affected population. National or international organizations and agencies may be initiated to provide assistance but local agencies need to act as soon as possible, because they are familiar with the geographical location as well as the local conditions of the affected areas. Effective coordination of disaster assistance is often crucial, particularly when resources are limited in the local agencies and the demands are more. In such conditions, it becomes a challenging job for the agencies to distribute resources so that the resources are optimally used. This article is initiated to address the challenging fact by introducing the concept of priority measure for demands. In this regard, ranking of the affected areas based on resource requirement for a disaster scenario is evaluated through a multicriteria decision-making process. A formula is developed to find the numerical value of priority factor collecting the responses of requirement of relief materials using information crowdsourcing. To this contingent, a three-echelon emergency solid transportation problem is formulated, in which the demand with the most priority is fulfilled by the local agencies in echelon-I, and the remaining demand is fulfilled by the other national or international agencies in echelon-II along with a restoration of local agencies in echelon-III. A numerical example is presented for the sake of model performance and to test the convergence using LINGO and CPLEX optimization solvers. The model is also designed using a genetic algorithm to obtain the optimal solution. Moreover, a brief analysis of the model is presented by considering 11 different numerical instances to enhance the efficacy of the model in real-life applications.

Closed-loop supply chain (CLSC) implementation has become imperative for organizations owing to resource scarcity and generation of toxic waste. However, CLSC implementation can be very challenging due to several barriers, especially in an emerging manufacturing hub such as India. This article identifies and analyzes the prominent barriers to CLSC implementation in the Indian automotive sector. Further, this article also examines causal relationships between the barriers. A total of 22 barriers to CLSC implementation are identified from the literature and subsequently, validated by industry experts. The finalized barriers are then evaluated by analytical hierarchy process and grey–decision making trial and evaluation laboratory techniques. This article provides managers and policy makers with insights on the most prominent and causal barriers, and recommends strategies to overcome these barriers. The findings will help managers to frame short-term and long-term strategies to mitigate barriers to CLSC and contribute toward successful implementation of CLSC.

Despite the tremendous growth of the transportation sector, the availability of systems that ensure safe, efficient, sustainable transportation reduces traffic congestion, maintenance costs, the off-road time of the vehicle, enhances driver's experiences, and ensures a more reliable journey are very limited. The fast evolution of our economy, lack of driver training, and the grown affordability of our society are reasons for this mismatch in developing economies. We think that the inconsistency will increase and unfavorably affect our traffic structure unless intelligent algorithm-based solutions are developed and deployed. This article presents a system for providing safe, accurate, comfortable, reliable, fuel-efficient, and economical driving behavior using Machine Learning techniques like the hidden Markov model (HMM). Our proposed system recommends subsequent trips using a multi-objective optimization (MOO) technique for the driver. It provides suggestions concerning speed limits and alerts based on the driver's behavior score and fuel efficiency. We used a publicly available UAH-DriveSet dataset captured by the driving monitoring app DriveSafe for all of our experiments. The results reveal that the proposed model predicts behavior with 95% accuracy and calculates fuel efficiency to improve driving quality and experience. This system recommends safer, more comfortable, more reliable, more efficient, and economical rides, beneficial for everyone in our society.

В работе рассматриваются модели потоков данных и этапы цифровой обработки сигналов в мобильном устройстве оперативного определения жизнеугрожающих состояний сердечно-сосудистой системы. Устройство считывает сигналы ЭКГ пациента и анализирует различные виды аритмий сердца. При обнаружении жизнеугрожающего состояния устройство формирует сообщение пациенту и передает данные ЭКГ на компьютер врача. Особенностью данного устройства является его персонификация и применение надежного выявления комплексов QRS за счет использования алгоритма анализа формы сигнала, основанного на методе Dynamic time warping. Благодарности: работа выполнена при финансовой поддержке гранта РФФИ, проект № 18-413-310002

The primary objective of the case study is to help students understand, model, and solve capacity planning problems (i) when the production yield is uncertain and (ii) if the yield of first production is low, the product can be produced a second time. The dimension of random yield emphasizes supply chain management challenges beyond the traditional newsvendor problem, which has a nice closed-form solution. The dimension of operational flexibility underscores the importance of modeling skills, such as influence diagrams, that can be used to brainstorm, model, and solve new problems. Extensive use of the case in graduate supply chain management courses shows that in the absence of a mathematical model, students systematically deviate from optimal capacity usage. Usually, the presence of backup flexibility reduces first-period production. However, students’ responses often suggest cutting first production too much. When it is available, students consistently overuse the flexibility of second-period production. Overall, students believed the case was challenging and that it provides a valuable learning experience.

As a promising alternative solution to cloud storage, decentralized storage networks (DSN) are widely anticipated to develop continuously and reshape the storage market share in the foreseeable future. In particular, one of the most important research problems is how to enforce the quality of service (QoS) in the context of storage solutions. Despite plenty of auditing-related works in the context of cloud storage, none of them can be directly applied to the decentralized storage paradigm. The challenges of designing a feasible storage auditing framework emanate from two aspects: 1) security problems unique to the decentralized settings; 2) performance overhead due to on-chain operations. In this work, we first put forward a basic storage auditing framework that satisfies the security and efficiency requirements, and outperforms the existing approaches. We also identify a critical and overlooked security problem that would compromise the integrity of storage auditing solutions in the blockchain paradigm. With our refined storage auditing design based on customized zero knowledge protocols, we propose a convenient mitigation solution in our revised security model. The evaluation results confirm that our solution would only incur a 10%-15% increase in the overall auditing costs for common usage scenarios, compared to the basic design.

We conduct a risk analysis of non-core insurance business of selling protection to financial firms against investment losses due to a shock. A static structural model is constructed, composed of a network of firms who cross-hold each other, a financial market consisting of multiple primitive assets that are vulnerable to a shock, and an insurer who resides external to the network and assesses the opportunity to sell protection to the financial firms. Assume that each firm in the network is rational and able to decide how much protection to purchase to optimize its portfolio according to the mean-variance principle. As a result, the shock may impact on the insurer but indirectly through the network. In view of the robust-yet-fragile nature of financial networks that has been discovered, both empirically and theoretically, by various recent works, one expects that the network integration and the shock play an intertwined role in the insurance risk. Our study forms a theoretical confirmation of this surmise: Depending on the shock size, there are three mutually exclusive scenarios in which an increase in the network integration can either reduce or amplify the impact of the shock on the insurance risk.

In the digital age, firms continuously look for leading technologies and make products of superior quality to satisfy new and sophisticated consumers’ needs. Although this approach strives to sell a product more appealing to customers, it also renders the product more complex and difficult to use, generating the feature fatigue effect. This is characterized by feelings frustration experienced by consumers when purchasing high-tech products with many appealing features, most of which turn out to be undesired, useless, and difficult to use. We study the impact of the feature fatigue effect on a manufacturing firm’s policies in designing its product–service portfolio. We consider two periods in order to capture the effect of customers’ word-of-mouth originating from feature fatigue, which is shared with other potential customers. Therefore, we assess not only the impact of feature fatigue but also the extent to which this effect can be alleviated by after-sales service. We capture feature fatigue by first considering a base model without this effect and then comparing the manufacturer’s decisions regarding quality, pricing, and service when it has either clear or noisy effect. Our findings reveal that feature fatigue drives manufacturers to under invest in quality and to replace penetration pricing policies with skimming policies. In addition, we demonstrate that after-sales service cannot fully mitigate the detrimental effect of feature fatigue on a manufacturer’s profits.

Many companies are considering “social recommendation” for their businesses, e.g., “rms are offering rewards to customers who recommend the firms” products/services in online social networks (OSNs). However, the pros and cons of such social recommendation scheme are still unclear. Thus, it is difficult for firms to design rewarding schemes, and for OSN platforms to design regulating policies. Via empirical analysis of data, we first identify key factors that affect the spreading of a firm's product in OSNs. These findings enable us to develop an accurate (i.e., with a high validation accuracy) mathematical model on social recommendations. Our model captures how users decide whether to recommend an item, which is a key factor but often ignored by previous social recommendation models. We also design algorithms to infer model parameters. Using our model, we uncover conditions when social recommendation improves a firm's profit and users’ utilities, as well as when it cannot improve the profit or hurts users’ utilities. These conditions help the design of both rewarding schemes and regulating policies. Last but not least, we extend our model to an dynamic setting, so that a firm can improve its profit by dynamically optimizing its rewarding schemes.

Improving reliability and eliminating the potential design risk are crucial for product design. Failure mode and effect analysis (FMEA), as an effective reliability assurance tool, has been extensively used in product design. However, causalities among failure modes, interactions among risk factors, and correlations among risk evaluations were not jointly considered in existing FMEA methods. On the other hand, the cost and time caused by the occurrence of failure modes were seldom incorporated into risk factors. Due to the lack of accurate values and/or information loss of customers and experts, these risk factors inevitably contain uncertainty that cannot be overlooked in product design. In this article, a novel FMEA-based approach is proposed to facilitate risk analysis of product design under uncertainty. In this approach, the stable grey causality vector state and the grey interaction vector are respectively established to characterize causalities and interactions. To reduce the uncertainty contained in cost and time, the extended information axiom is put forth, and then, their grey information contents can be obtained and are to be incorporated into the design. Subsequently, we extend Choquet integral to prioritize the potential failure modes and identify the optimal design scheme while coping with correlations. The proposed approach is validated by an example of a substrate design.

This experimental study analyzes how a key factor, information load, influences decision making in escalation situations, i.e., in situations in which decision makers reinvest further resources in a losing course of action, even when accounting information indicates that the project is performing poorly and should be discontinued. This study synthesizes prior escalation research with information overload and investigates how different levels of information load influence the escalation of commitment. Our findings reveal a U-shaped effect of information load: When decision makers face negative feedback, a higher information load mitigates the escalation tendency up to a certain point. However, beyond this point, more information reinforces the escalation tendency. Moreover, we find that the type of feedback affects self-justification, and we find a negative and significant interaction between information load and self-justification in negative-feedback cases. Thus, studies investigating escalation of commitment should control for self-justification and information load when utilizing high levels of information load. Finally, in the positive-feedback condition, higher information load encourages decision makers to continue promising courses of action, i.e., increases decision-making performance., Projekt DEAL

Risk Based Inspection (RBI) has been implemented mainly in oil and gas industry to manage the risk of aging facilities. The RBI plan has also been introduced for new facilities and become part of the design requirements, making it the right time for PT. XYZ to improve their RBI implementation to support the Facility Risk Integrity Management System. This is a semi-quantitative study on primary data collected through focus group discussion using the RBI evaluation parameters based on API 580, API 581, and field observation and secondary data from previous RBI reports, maintenance program, and inspection program. Finally, an evaluation was conducted to assess whether the RBI management strategy has been integrated to the safety and asset management; inspection, testing, and monitoring; and operation strategies. This gap analysis aims to evaluate the effectiveness of the ongoing RBI implementation at PT. XYZ in general while specifically identify the part of RBI and related Asset Integrity Management already implemented and those that still need further improvement. Results show an overall score of 328 of 470, showing a good implementation of RBI. The largest gaps identified are RBI on specific equipment (score=3.0), documented RBI management policy and strategy (score=3.8), risk target and risk acceptable level (score=4.0), and specific damage mechanism components (score=5.3).

The COVID-19 pandemic has led to the unprecedented challenge of devising massive vaccination rollouts, toward slowing down and eventually extinguishing the diffusion of the virus. The two-dose vaccination procedure, speed requirements, and the scarcity of doses, suitable spaces, and personnel, make the optimal design of such rollouts a complex problem. Mathematical modeling, which has already proved to be determinant in the early phases of the pandemic, can again be a powerful tool to assist public health authorities in optimally planning the vaccination rollout. Here, we propose a novel epidemic model tailored to COVID-19, which includes the effect of nonpharmaceutical interventions and a concurrent two-dose vaccination campaign. Then, we leverage nonlinear model predictive control to devise optimal scheduling of first and second doses, accounting both for the healthcare needs and for the socio-economic costs associated with the epidemics. We calibrate our model to the 2021 COVID-19 vaccination campaign in Italy. Specifically, once identified the epidemic parameters from officially reported data, we numerically assess the effectiveness of the obtained optimal vaccination rollouts for the two most used vaccines. Determining the optimal vaccination strategy is nontrivial, as it depends on the efficacy and duration of the first-dose partial immunization, whereby the prioritization of first doses and the delay of second doses may be effective for vaccines with sufficiently strong first-dose immunization. Our model and optimization approach provide a flexible tool that can be adopted to help devise the current COVID-19 vaccination campaign, and increase preparedness for future epidemics.

The recent COVID-19 pandemic pandemic has highlighted the importance of food safety and supply chain governance. In other words, we need to ensure traceability along the supply chain, support high-frequency transactions and effective data collections, etc. Thus, we posit the potential of using lightning network, which is a decentralized traceable paradigm for achieving high-frequency transactions in blockchain-based systems. In addition, we also utilize edge computing to help facilitate data collection. However, a key challenge in securing food supplies is determining the optimal global transaction path in lightning network, while achieving efficiency and meeting the dynamic nature of food supply management. Thus, we propose a blockchain-edge scheme that utilizes our proposed dynamic programming to produce optimal solutions to selecting global transaction paths. Specifically, our scheme optimizes routing fees under existing constraints (e.g., transmission cost, computing resource consumption, and lightning network balance). Findings from our evaluations demonstrate that the utility of our proposed approach in facilitating food safety management.

Measurement of traffic flow stability and resilience is a critical step toward evaluating the performance of transportation systems and implementing appropriate management strategies. Quantifying changes in the stability and resilience of transportation systems, however, is hampered by the complexity of real traffic dynamics and the diversity of infrastructures. Here, we demonstrate that changes in traffic flow stability and resilience are signaled by generic features, known as early warning signals in the theory of critical slowing down, observed before traffic instabilities occur. This finding is incorporated in an operational data-driven algorithm to evaluate the risk of traffic jams on highways. Theoretical findings and tests on simulated and empirical case studies support the premise of this approach and identify candidate statistical measures that are sensitive to changes in the stability and resilience of transportation systems. Our use of universal measures advances the monitoring capability, prediction and control of complex transportation systems.

Fusion energy has the potential to address long-term energy requirements and climate change. However, fusion energy is characterized by a “development divide” between the International Thermonuclear Experimental Reactor (ITER) consortium members and the non-ITER “Global South” states. It is beset with problems, which can be divided into geopolitical, geo-economic, geo-sociocultural, and geo-technological (GEO-PEST). Geopolitical problems include cooperation on fusion energy development between ITER members (of the Global North) and the Global South on fusion development. Geo-economic problems include the cost of funding ITER versus newly emerging private-sector fusion companies. Geo-sociocultural problems include the requirement to maintain the peacebuilding tradition of nuclear energy (embodied by the Cold War-era “Atoms for Peace” initiative), as well as public perceptions of radiation. Geo-technological problems include tokamak lock-in, fuel type, the viability of “compact reactors,” and disruptive technology events. In this article, we outline these problems and discuss how to address them in a timely fashion via an external independent review mechanism, modeled on the International Energy Agency's “Global Commission for Urgent Action on Energy Efficiency,” established in 2019 and due to deliver recommendations in 2020.

Existence of delays is an inseparable part of projects and subject of disagreements among stakeholders in all countries. There are four valid techniques in the Society of Construction Law global protocol about project delays calculation that without having the ability to control the risk of delays incidence only calculate the prolongation of contract prolongation and consider it as project delays. Researcher believes: first, risk of delays should be managed, minimized, shared, transferred, or accepted, but it cannot be ignored; therefore, it must be predicted, covered, managed, and optimized; and second, prolongation time of project is not necessarily equal to increased delay amount, and many other factors that themselves are the basis of emergence of project risks are effective in calculation of delays. Therefore, it is necessary to identify these factors and develop the strategy for their control and management before starting the project. So, in this article, two researcher-made combinative techniques are presented in order to resolve this deficiency: first, introduction of importance degree factor model instead of weight factor; and second, introduction of a model in the field of claim management in order to controlling risk of delays, analyzing result-oriented delays, and determining the share of stakeholders’ failures, based on variance and importance degree.

With the recent growing number of cyberattacks and the constant lack of effective defense methods, cyber risks become ubiquitous in enterprise networks, manufacturing plants, and government computer systems. Cyber-insurance provides a valuable approach to transfer the cyber risks to insurance companies and further improve the security status of the insured. The designation of effective cyber-insurance contracts requires the considerations from both the insurance market and the dynamic properties of the cyber risks. To capture the interactions between the users and the insurers, we present a dynamic moral-hazard type of principal-agent model incorporated with Markov decision processes, which are used to capture the dynamics and correlations of the cyber risks as well as the user's decisions on the protections. We study and fully analyze a case with a two-state two-action user under linear coverage insurance, and we further show the risk compensation, Peltzman effect, linear insurance contract principle, and zero-operating profit principle in this case. Numerical experiments are provided to verify our conclusions and further extend to cases of a four-state three-action user under linear coverage insurance and threshold coverage insurance., Comment: We have made several corrections

According to the World Health Organization and the CDC, social distancing is currently one of the most effective ways to slow the transmission of COVID-19. However, most existing epidemic models do not consider the impact of social distancing on the COVID-19 pandemic. In this article, we propose a new method to deterministic modeling of the effects of social distancing on the COVID-19 pandemic in a low transmission setting. Our model dynamic is expressed by a single predictive variable that satisfies an integro-differential equation. Once the dynamic variable is calculated, the process of agents from the normal state, infection state to rehabilitation state, or death state can be explored. Besides, an important parameter is added to the model to measure the impact of social distancing on epidemic transmission. We performed qualitative and quantitative experiments on various scenarios, and the results showed that 2 m is a safe social distancing on the COVID-19 pandemic in a low transmission setting.

Plastics are synthetic polymers that are extensively used in the modern world, and subsequently abandoned causing severe challenges and destruction to the natural environment. Though initially considered as synthetic replacements to finite natural materials, these have become serious pollutants in every sphere of the Earth, and earning the dubious distinction as a marker of the Anthropogenic Epoch. With approximately 50 basic types of polymers and about 60,000 plastic formulations available in the market and an estimated increase in the annual production rate of 8%, the plastic pollution has become a menace that needs to be documented and addressed properly with an urgent need to formulate strategies for management and mitigation. Accordingly, there has been a great boom recently in the studies related to plastic pollution covering different branches of oceanography, chemical engineering, glaciology, environmental geology, ecotoxicology, biology, agriculture and policy, among others. These attempts place severe constraints on the type, nature and quantum of sample collection, processing, analytical protocols, and units of data reporting etc. Several studies have highlighted the inconsistencies and constraints in comparing and replicating the data and results. In this paper, we address the commonly studied and environmentally important parameters of plastics, such as size, origin, polymer composition, color, shape and toxicity potential and incorporate these into a structured morphometric genetic classification. With the help of notations/codes for each of the subcategories and the generation of quantitative data for these parameters, our classification is aimed to provide a novel and integrated approach to unifying the various aspects of the planet’s ‘plastisphere’. This approach can help in alleviating some of the problems of inconsistencies in investigating and reporting data and in comparing different studies from various regions across the globe.

The Hyperloop is a concept of a ground transportation system consisting of capsules (called pods) traveling at very high-speeds in near-vacuum tubes. The hyperloop aims to be a fast, cheap, and sustainable alternative to short-haul flights and high-speed rail. The small pod size requires very high frequencies to respond to future high levels of passenger and cargo demands. Media and representatives of the emerging Hyperloop industry acclaim the Hyperloop as a very capacity-effective transport system, however there is no clear scientific evidence proving that. A theoretical investigation is therefore necessary to understand which capacity the Hyperloop could safely provide and whether that could satisfy the future transport demand. This paper provides a comparative analysis of the capacity that the Hyperloop can offer for several operational scenarios and different signalling systems, including Moving-Block and the advanced concept of Virtual Coupling. Results show that Moving-Block could achieve required transport capacity levels only if pods could use high deceleration rates likely to be unsafe and uncomfortable to passengers. Virtual Coupling is instead observed to be a more satisfactory operational concept that could address transport demand while respecting safety and comfort standards if reliable pod platooning technologies are available.

Autonomous vehicles (AVs) are widely believed to be good for improving transportation safety and efficiency. However, recent fatal accidents by some of their prototypes remind us that there are no operationalizable and quantitative safe driving strategies available for an AV in a wide range of situations to avoid collisions. In contrast with many recent studies that focused on ethical considerations when AVs are facing unavoidable harms, we study how to proactively prevent collisions by setting up a set of decision rules for AVs to determine the right-of-way efficiently. Notably, we summarize three essential principles for AVs designing to increase driving safety, and establish a rule-based nine-step communication-decision model to implement them. Our method is constructed by analyzing how human drivers solve potential conflicts. The decision rules are designed to be ambiguity-free and readily computable with the least communication so that human drivers and AVs could easily understand each other in terms of their behaviors and intentions of. We have demonstrated the effectiveness of our method by comparing it with some alternative approaches.

The advent of in-vehicle networking systems as well as state-of-the-art sensors and communication technologies have facilitated the collection of large volume and almost real-time data on vehicles and drivers, thus opening up future possibilities. Processing and analyzing this data provides unprecedented opportunities to offer remarkable insights and solutions for driving behavior analysis (DBA). Characterizing driving behavior plays a key role in a variety of research areas such as traffic safety, the development of automated vehicles, energy and fuel management, risk assessment, and driver identification and profiling. Advances in DBA-based driver inattention or drunk driver detection can help reduce fatal car crashes, and understanding the driving style (e.g. eco-friendly or aggressive) of drivers can contribute to fuel management and risk assessment of the drivers. These facts have led to a growing interest in addressing DBA challenges. This paper aims to present the state-of-the-art methodologies for DBA and provide a clear roadmap about the main current and future trends in DBA. To this end, we propose categorizing the current research on driving behavior based on the types of data employed for the analysis, the ultimate goals of the analysis, and the techniques based on which the driving data are modeled. We provide an overview of different data resources and available datasets for DBA. Moreover, we discuss the application of DBA along with the key research challenges in this field and potential future directions.