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Given a set of agents qualifying or disqualifying each other, group identification is the task of identifying a socially qualified subgroup of agents. Social qualification depends on the specific rule used to aggregate individual qualifications . The classical bribery problem in this context asks how many agents need to change their qualifications in order to change the outcome in a certain way. Complementing previous results showing polynomial-time solvability or NP-hardness of bribery for various social rules in the constructive (aiming at making specific agents socially qualified) or destructive (aiming at making specific agents socially disqualified) setting, we provide a comprehensive picture of the parameterized computational complexity landscape. Conceptually, we also consider a more fine-grained concept of bribery cost, where we ask how many single qualifications need to be changed, nonunit prices for different bribery actions, and a more general bribery goal that combines the constructive and destructive setting.

Fear of infection will limit economic activity Widespread testing alone will not solve this problem Targeted testing in concert with targeted subsidies will b

The ongoing COVID-19 pandemic has rendered confined spaces as high-risk areas. There is an increasing push to resume in-person activities, for instance, teaching in K-12 and university settings. It becomes important to evaluate the risk of airborne disease transmission while accounting for the physical presence of humans, furniture, and electronic equipment, as well as ventilation. Here, we present a computational framework based on detailed flow physics simulations that allows straightforward evaluation of various seating and operating scenarios to identify risk factors and assess the effectiveness of various mitigation strategies. These scenarios include seating arrangement changes, presence/absence of computer screens, ventilation rate changes, and presence/absence of mask-wearing. This approach democratizes risk assessment by automating a key bottleneck in simulation-based analysis--creating an adequately refined mesh around multiple complex geometries. Not surprisingly, we find that wearing masks (with at least 74% inward protection efficiency) significantly reduced transmission risk against unmasked and infected individuals. The availability of such an analysis approach will allow education administrators, government officials (courthouses, police stations), and hospital administrators to make informed decisions on seating arrangements and operating procedures.

In our cheap-talk setting, an expert privately observes multiple binary signals, her soft evidence, about a continuous state of the world and then communicates with a decision-maker. While direct transmission of evidence entails communicating the underlying signals, indirect transmission of evidence entails communicating a summary statistic of her evidence. We first establish that fully informative equilibria exist if the conflict of interest is small. Otherwise, direct transmission of evidence is impossible, as withholding one part of the soft evidence in the communication necessarily induces incentives to manipulate the report on the other part. On the contrary, indirect transmission of evidence remains partially informative for intermediate conflicts of interest. Finally, we introduce the possibility of certification. We show that, if the costs of certification are low, the expert can fully reveal her evidence regardless of the conflict of interest.

Cardiovascular (CV) disease impacts tens of millions of people annually and carries a massive global economic burden. Continued advances in medical imaging, hardware and computational efficiency are leading to an increased interest in the field of cardiovascular computational modelling to help combat the devastating impact of CV disease. This review article will focus on a computational modelling technique known as lumped parameter modelling (LPM). Due to its rapid computation time, ease of automation and relative simplicity, LPM holds the potential of aiding in the early diagnosis of CV disease, assisting clinicians in determining personalized and optimal treatments and offering a unique in-silico setting to study cardiac and circulatory diseases. In addition, it is one of the many tools that are needed in the eventual development of patient specific cardiovascular “digital twin” frameworks. This review focuses on how the personalization of cardiovascular lumped parameter models are beginning to impact the field of patient specific cardiovascular care. It presents an in-depth examination of the approaches used to develop current predictive LPM hemodynamic frameworks as well as their applications within the realm of cardiovascular disease. The roles of these models in higher order blood flow (1D/3D) simulations are also explored in addition to the different algorithms used to personalize the models. The article outlines the future directions of this field and the current challenges and opportunities related to the translation of this technology into clinical settings.

The importance of (physical) security is increasingly acknowledged by society and the scientific community. In light of increasing terrorist threat levels, numerous security assessments of critical infrastructures are conducted in practice and researchers propose new approaches continuously. While practical security risk assessments (SRA) use mostly qualitative methods, most of the lately proposed approaches are based on quantitative metrics. Due to little evidence of actual attacks, both qualitative and quantitative approaches suffer from the fundamental problem of inherent uncertainties regarding threats and capabilities of security measures as a result from vague data or the usage of expert knowledge. In quantitative analysis, such uncertainties may be represented by, e.g., probability distributions to reflect the knowledge on security measure performance available. This paper focuses on the impact of these uncertainties in security assessment and their consideration in system design. We show this influence by comparing the results of a scalar evaluation that does not take into account uncertainties and another evaluation based on distributed input values. In addition, we show that the influence is concentrated on certain barriers of the security system. Specifically, we discuss the robustness of the system by conducting quantitative vulnerability assessment as part of the SRA process of an airport structure example. Based on these results, we propose the concept of a security margin. This concept accounts for the uncertain knowledge of the input parameters in the design of the security system and minimizes the influence of these uncertainties on the actual system performance. We show how this approach can be used for vulnerability assessment by applying it to the initially assessed configuration of the airport structure. The results of this case study support our assumptions that the security margin can help in targeted uncertainty consideration leading to reduced system vulnerability.

The presence of impurities is the primary cause of safety related drug product recall. Clinical data generated during the drug discovery process may not be able to conclude the long-term safety of a drug. The best way to ensure drug safety is to control the associated impurities. It can be achieved by a clear understanding of the route and factors for impurity formation. Tight control on manufacturing, storage, and transport process can provide a beneficial effect on minimization of impurity generation. Control strategies employing quality by design and risk assessment-based approaches potentially enhance drug safety profile and, thereby, minimize product recalls. Prior understanding of key aspects of the safety profile of impurities, post-marketing surveillance criticalities, and establishment of constructive preventive strategies is the best way to minimize unwanted product recall due to associated safety issues. Effective preventive strategies should be designed and executed during the product manufacturing and supply chain process. The novelty of this article can be justified by the unavailability of any similar type of reports discussing the functional pathway of regulatory bodies aiming for effective control of drug impurities. This article contains novel, critical, and constructive prevention strategies to establish a robust safety profile of pharmaceutical products.

To remain competitive, companies must face the changes occurring in their environment and adapt their business processes accordingly. Those processes are implemented in business process management systems (BPMS), which mostly support manual adaptations. That means that the process users have to detect what changes in the environment require process adaptation, and what adaptation operations have to be performed. Such manual adaptations of processes are costly, time-consuming and error prone tasks. For this reason, some contributions of the literature have tried to address the issue of self-adaptations of processes. But these contributions suffer from shortcomings: isolated use of adaptation techniques, non-coverage of the process dimensions and of the adaptation types, etc.; the adaptation issue remains partially addressed. Thus we recommend in this paper a hybrid approach to ensure autonomic adaptations of running processes. According to this approach, the Plan component tries to find an appropriate model version of the concerned process. Then, if such a version does not exist, it looks to reuse an adaptation case that was applied in the past under a similar situation (context). Finally, if necessary, it applies rules, as an artificial intelligence planning technique, to define an ad hoc adaptation. Moreover, the recommended approach takes advantage of the IBM MAPE-K (Monitor, Analyze, Plan, Execute—Knowledge) control loop from autonomic computing, recognized as a prominent solution for self-adaptation at run-time. More precisely the paper addresses the resolution of adaptation needs while covering three process dimensions and all adaptation types and ensuring the separation of concerns for better portability and wide usability through the BPMN standard. It presents both the required Knowledge and the Plan component of the control loop for this resolution. It also shows the effectiveness of the approach by illustrating self-adaptation of a process from the crisis domain, and demonstrates its feasibility by reporting about its implementation and qualitative and quantitative evaluation.

Background Physical fitness status is a key aspect of health and, consequently, it is important to create and adopt appropriate interventions to maintain or improve it, and assess it using valid measures. While in other testing contexts, standard operating procedures (SOPs) are commonly and widely adopted, in physical fitness testing, a variety of unstandardized testing protocols are proposed. Aims The topic of this review was to evaluate the existing literature on SOPs in physical fitness assessment and to provide guidelines on how SOPs could be created and adopted. Method The electronic databases PubMed, Web of Science and Scopus were screened and original, peer-reviewed studies that included SOPs, related to physical fitness, were recorded. Results After the inclusion and exclusion criteria screening, a total of six studies were included and these were critically and narratively analyzed. Conclusions Standard operating procedures are rarely adopted in the field of physical fitness and a step by step guide has been provided in this manuscript. In the future, it is suggested to follow protocols as a routine, because this is the only way to generalize and contextualize findings.

Zusammenfassung Hintergrund Die Gefährdungsbeurteilung psychischer Belastung hat zum Ziel, die durch die Arbeit verbundenen Gefährdungen zu beurteilen, um Maßnahmen zur gesundheitsgerechten Gestaltung der Arbeit abzuleiten. Allerdings ist das Angebot an Verfahren für die Ermittlung der psychischen Belastung umfangreich und unübersichtlich. Vor diesem Hintergrund wird für das Sozial- und Gesundheitswesen ein reduzierter und strukturierter Überblick an Instrumenten vorgestellt. Methode Für die Identifizierung geeigneter Instrumente wurde eine umfangreiche Suche durchgeführt. Vor Recherchebeginn wurden Kriterien definiert, um die identifizierten Instrumente auf Eignung zu prüfen. Zum einen gab es Mindestanforderungen, die erfüllt sein mussten, damit das Instrument in den Review-Prozess aufgenommen wurde, und zum anderen Strukturierungskriterien, die sich in beschreibende und bewertende Aspekte unterteilten. Ergebnisse Die Recherche identifizierte insgesamt 83 Instrumente für die Gefährdungsbeurteilung psychischer Belastung (GBU Psyche); nach der ersten Sichtung wurden 58 von diesen zur weiteren Eignung im Review-Prozess übernommen. Abgeschlossen wurde das Gesamtreview bisher für 44 Verfahren aus der unsystematischen Suche. Davon wurden 19 Verfahren als geeignet eingestuft und in einer Übersichtsmatrix strukturiert dargestellt. Das Review für die 14 Verfahren aus der systematischen Recherche erfolgt voraussichtlich bis Mitte 2022 und ist Teil eines kontinuierlichen Review-Prozesses. Diskussion Die Vielzahl an identifizierten Verfahren für die GBU Psyche (Gefährdungsbeurteilung psychischer Belastung) zeigt deutlich die Sinnhaftigkeit und Relevanz, eine begrenzte, praxiserprobte sowie qualitätsgesicherte Auswahl an Instrumenten zu treffen. Die ebenfalls in diesem Artikel dargestellten Kriterien zur Bewertung der Instrumente, machen die getroffene Auswahl transparent.

From both the methodological point of view and standardization of methodology, little attention has been paid to the estimation of direct costs in evaluation of healthcare technologies. The objective is to revise the recommendations on direct costs provided in European economic evaluation guidelines and to identify the commonalities and divergences among them. To achieve this, a comprehensive search through several online databases was performed resulting in 41 documents from 26 European countries, be they economic evaluation guidelines or costing guidelines. The results show a large disparity in methodologies used in estimation of direct costs to be included in economic evaluations of health technologies recommended by European countries. A lack of standardization of cost estimation methodologies influences arbitrariness in selecting costs of resources included in economic evaluations of medicinal products or any other technologies and, therefore, in decision making process necessary to introduce new technology. In addition, this heterogeneity poses a major challenge for identifying factors that could affect the variability of unit costs across countries.

The paper provides an overview of biocosmetics, which has tremendous potential for growth and is attracting huge business opportunities. It emphasizes the immediate need to replace conventional fossil-based ingredients in cosmetics with natural, safe, and effective ingredients. It assembles recent technologies viable in the production/extraction of the bioactive ingredient, product development, and formulation processes, its rapid and smooth delivery to the target site, and fosters bio-based cosmetic packaging. It further explores industries that can be a trailblazer in supplying raw material for extraction of bio-based ingredients for cosmetics, creating biodegradable packaging, or weaving innovation in fashion clothing. Lastly, the paper discusses what it takes to become the first generation of a circular economy and supports the implementation of strict regulatory guidelines for any cosmetic sold globally.

Science-based methods for assessing the practical rationality of a proposed public policy typically represent assumed future outcomes of policies and values attributed to these outcomes in an idealized, that is, intentionally distorted way and abstracted from aspects that are deemed irrelevant. Different types of methods do so in different ways. As a consequence, they instantiate the properties that result from abstraction and idealization such as conceptual simplicity versus complexity, or comprehensiveness versus selectivity of the values under consideration to different degrees. I hold that none of these methods is best in general. Instead, I opt for the valuation method that is useful for the policy issue in question both in terms of its relevance and in terms of its practicability. Relevance requires that the method can represent and account for what is at stake in the policy issue. Practicability refers to aspects such as easy versus difficult handling of the method. To argue for the claim, I evaluate three types of valuation methods: (1) cost–benefit analysis that rests on unidimensional measurement and ranking, (2) multi-criteria decision analysis that applies multi-dimensional measurement but unidimensional ranking, and (3) non-aggregate indicator systems that operate with multi-dimensional measurement and sometimes also multi-dimensional ranking. Second-order justification indicating whether and how the valuation method chosen is capable of accounting for the substantive value considerations that constitute the real-world policy issue in question renders the conditions on which the results of a proposed policy evaluation rest transparent., Journal for General Philosophy of Science, 53 (2), ISSN:0925-4560, ISSN:1572-8587

In 2019 and 2020, disaster victim identification (DVI) simulations were conducted at the Australian Facility for Taphonomic Experimental Research. Whole and fragmented cadavers were positioned to replicate a building collapse scenario and left to decompose for up to 4 weeks. This study evaluated the utility of the ANDE™ 6C Rapid DNA System and the RapidHITTM ID System for DVI in the field and mortuary. Applying post-mortem nail and tissue biopsy samples showed promise, with the added benefit of minimally invasive collection procedures and limited preparation requirements. The preferred platform will depend on a number of factors, including its intended use and operating environment.

Preventing adverse health outcomes is complex due to the multi-level contexts and social systems in which these phenomena occur. To capture both the systemic effects, local determinants, and individual-level risks and protective factors simultaneously, the prevention field has called for adoption of system science methods in general and agent-based models (ABMs) specifically. While these models can provide unique and timely insight into the potential of prevention strategies, an ABM’s ability to do so depends strongly on its accuracy in capturing the phenomenon. Furthermore, for ABMs to be useful, they need to be accepted by and available to decision-makers and other stakeholders. These two attributes of accuracy and acceptability are key components of open science. To ensure the creation of high-fidelity models and reliability in their outcomes and consequent model-based decision-making, we present a set of recommendations for adopting and using this novel method. We recommend ways to include stakeholders throughout the modeling process, as well as ways to conduct model verification, validation, and replication. Examples from HIV and overdose prevention work illustrate how these recommendations can be applied.

The microservice architecture shows great potentials in business solutions by providing agile response to emerging demands, which is believed a fundamental shift in innovative software development. However, the microservices are still facing a number challenges, including monolithic applications, limited reuse, operational agility, etc. This work focuses on building microservices in the Internet of Things environment by overcoming above challenges. Specifically, a microservices priority assessment method is proposed by combing the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE), in which AHP is used to calculate the weights of the related factors, while FCE is used for comprehensive evaluation to determine the priority of microservices. The experimental results on an IoT platform demonstrate the effectiveness of the proposed solution. Moreover, it is found that the most influential factors of the microservices priority are non-resource factors, and occupy the majority of the proportion.

This study addresses a humanitarian supply chain network that includes locating relief centers, pre-positioning and distributing relief items, and providing medical treatment in affected regions to help people suffering. Also, the role of non-governmental organizations along with governmental organizations and disruption in relief centers is examined. The objective of the model is to minimize total costs. The problem is formulated as a mixed-integer linear mathematical model. A two-stage mixed fuzzy-stochastic approach is developed to capture the uncertainty. To solve the model on a large scale, grasshopper optimization algorithm is utilized, and its performance is examined through computational experiments. To assess the applicability of the proposed model, it is applied to Tehran as a real case study. According to the numerical examples and sensitivity analysis, fruitful managerial insights are provided to help decision-makers increase relief operations' efficiency. The results indicate that non-governmental organizations' contribution significantly enhances the performance of the humanitarian supply chain while disruption has an adverse impact.

Many higher education institutions in the world have testing laboratories linked, or not, to their teaching and/or research programs. However, only a small proportion of these laboratories have accreditation in accordance with the ISO/IEC 17025 standard. The ISO/IEC 17025 establishes the management and technical requirements necessary for the implementation and maintenance of a quality management system (QMS) in laboratories that perform testing, calibration and/or sampling activities, being used by them to demonstrate their competence in carrying out their activities. One of the requirements of the current version of the standard is the need to carry out risk management (RM), introduced with the demand for risk-based thinking. The objective of this research was to propose a system for the implementation of RM in laboratories, through mapping, identification, classification, critical analysis, and treatment of risks. The research considered the history of actions taken, the risks verified by the laboratory team, the evaluation of their impacts and the probabilities of their occurrence, their classification and the identification of actions necessary to accept, mitigate or eliminate these risks. The system proposed was applied in a testing laboratory at a university in southern Brazil, enabling the maintenance of its accreditation.

Advances in 3D printing are leading to a wealth of excitement around the topic; designers, engineers, and hobbyists alike are looking to utilize the technology to improve upon existing designs and produce new components that perform better than a traditionally manufactured part. In this respect, additively manufactured parts offer a neat solution to problems of geometric limitations, small production economics, and rapid design cycle fulfillment. Though promising in capability, the variety in 3D printing methods and the inherent limitations of each can be difficult for new users to comprehend effectively when exploring whether or not the technology may work for them. It is in the best interest of the additive manufacturing community that the technology, though rapidly developing, be well understood so that adoption is easier and more widespread. The purpose of this paper, therefore, is to present an evaluation tree specific to additive manufacturing, that will assist new users of the technology when assessing manufacturing methods for their application. The evaluation tree is focused on providing a high-level technical overview of the engineering decisions required for AM adoption, a current gap in the research. A detailed review of the dominant 3D printing types and recommendations are given for the user’s future considerations. Three case studies have additionally been provided at the end of the paper to demonstrate the use of the evaluation tree. The goal of this paper is to aid in the further adoption of 3D printing, while providing valuable general information and technical considerations for new users.

Hemostasis in neurosurgery is of utmost importance. Bleeding management is one of the crucial steps of each neurosurgical procedure. Several strategies, namely thermal, mechanical, electric, and chemical, have been advocated to face blood loss within the surgical field. Over time, countless hemostatic agents and devices have been proposed. Furthermore, the ever-growing recent technological innovation has made available several novel and interesting tools. Pursuant to their impact on surgical practice, we perceived the imperative to update our previous disclosure paper. Therefore, we reviewed the literature and analyzed technical data sheets of each product in order to provide an updated and comprehensive overview in regard to chemical properties, mechanisms of action, use, complications, tricks, and pitfalls of topical hemostatic agents.

Antibiotic resistance is a global health challenge, involving the transfer of bacteria and genes between humans, animals and the environment. Although multiple barriers restrict the flow of both bacteria and genes, pathogens recurrently acquire new resistance factors from other species, thereby reducing our ability to prevent and treat bacterial infections. Evolutionary events that lead to the emergence of new resistance factors in pathogens are rare and challenging to predict, but may be associated with vast ramifications. Transmission events of already widespread resistant strains are, on the other hand, common, quantifiable and more predictable, but the consequences of each event are limited. Quantifying the pathways and identifying the drivers of and bottlenecks for environmental evolution and transmission of antibiotic resistance are key components to understand and manage the resistance crisis as a whole. In this Review, we present our current understanding of the roles of the environment, including antibiotic pollution, in resistance evolution, in transmission and as a mere reflection of the regional antibiotic resistance situation in the clinic. We provide a perspective on current evidence, describe risk scenarios, discuss methods for surveillance and the assessment of potential drivers, and finally identify some actions to mitigate risks., In this Review, Larsson and Flach discuss the drivers of and bottlenecks for environmental evolution and transmission of antibiotic resistance, and they explore environmental surveillance strategies that could complement clinical surveillance systems.

In the automotive industry, short-term changes in the product mix can be executed in an attempt to better react to customers’ immediate needs and preferences. These modifications impact confirmed orders sent to suppliers via material requirement planning (MRP) and may lead to supply shortages. Supply chain stakeholders can respond to these modifications in a plethora of ways to avoid shortages. However, the process of deciding on the appropriate response is not easily standardized due to the high level of flexibility and subjectivity required to deal with each specific situation. Based on an automotive manufacturer in the State of Parana—Brazil, we propose a decision support system (DSS) to guide supply chain stakeholders during the process of deciding on how to respond to short-term material requirement modifications. Flexibility is ensured by the occurrence of “decision points” in the process model, which guide a process instance through specific paths based on a mix of simple rules as well as the Analytic Hierarchy Process (AHP). The DSS has its application demonstrated by a series of simulated scenarios. Through the standardization of a flexible process driven by subjective factors, this work aims to achieve: (i) a better understanding of the cost versus risk trade-offs that complicate standardized decision making within automotive supply processes; and (ii) an artifact capable of providing standardization without hindering the flexibility requirements for this class of processes.

To meet the sustainable development goals of the United Nations, the energy and resource efficiency of industrial processes have to increase, and workplaces have to become decent for the involved workers. Plating process chains are typically associated with high energy and resource demand and the use of hazardous chemicals. For the analysis and improvement of the energy and resource efficiency as well as for modelling the occupational safety and health risks, a variety of separate approaches are available. Combined approaches are not available yet. An agent-based simulation is used as the basis for integrated energy and resource as well as occupational safety and health risk assessment. In particular, an energy and resource flow model provides the life cycle inventory data for an environmental assessment. The integration of a mechanistic inhalation exposure model through a surrogate model approach enables a combined synergetic consideration of environmental and occupational safety and health effects. A simulation case study shows the impact of chrome acid changes in chrome electroplating processes as well as the effect of different rinsing cascade settings and rinsing control strategies.

A common understanding of the key regulatory term “substance” is needed for the implementation of chemicals regulations for nanomaterials.

Objective The aim of this study was to review analytical methods that enable the incorporation of equity concerns within economic evaluation. Methods A systematic search of PubMed, Embase, and EconLit was undertaken from database inception to February 2021. The search was designed to identify methodological approaches currently employed to evaluate health-related equity impacts in economic evaluation studies of healthcare interventions. Studies were eligible if they described or elaborated on a formal quantitative method used to integrate equity concerns within economic evaluation studies. Cost-utility, cost-effectiveness, cost-benefit, cost-minimisation, and cost-consequence analyses, as well as health technology appraisals, budget impact analyses, and any relevant literature reviews were included. For each of the identified methods, we provided summaries of the scope of equity considerations covered, the methods employed and their key attributes, data requirements, outcomes, and strengths and weaknesses. A traffic light assessment of the practical suitability of each method was undertaken, alongside a worked example applying the different methods to evaluate the same decision problem. Finally, the review summarises the typical trade-offs arising in cost-effectiveness analyses and discusses the extent to which the evaluation methods are able to capture these. Results In total, 68 studies were included in the review. Methods could broadly be grouped into equity-based weighting (EBW) methods, extended cost-effectiveness analysis (ECEA), distributional cost-effectiveness analysis (DCEA), multi-criteria decision analysis (MCDA), and mathematical programming (MP). EBW and MP methods enable equity consideration through adjustment to incremental cost-effectiveness ratios, whereas equity considerations are represented through financial risk protection (FRP) outcomes in ECEA, social welfare functions (SWFs) in DCEA, and scoring/ranking systems in MCDA. The review identified potential concerns for EBW methods and MCDA with respect to data availability and for EBW methods and MP with respect to explicitly measuring changes in inequality. The only potential concern for ECEA related to the use of FRP metrics, which may not be relevant for all healthcare systems. In contrast, DCEA presented no significant concerns but relies on the use of SWFs, which may be unfamiliar to some audiences and requires societal preference elicitation. Consideration of typical cost-effectiveness and equity-related trade-offs highlighted the flexibility of most methods with respect to their ability to capture such trade-offs. Notable exceptions were trade-offs between quality of life and length of life, for which we found DCEA and ECEA unsuitable, and the assessment of lost opportunity costs, for which we found only DCEA and MP to be suitable. The worked example demonstrated that each method is designed with fundamentally different analytical objectives in mind. Conclusions The review emphasises that some approaches are better suited to particular decision problems than others, that methods are subject to different practical requirements, and that significantly different conclusions can be observed depending on the choice of method and the assumptions made. Further, to fully operationalise these frameworks, there remains a need to develop consensus over the motivation for equity assessment, which should necessarily be informed with stakeholder involvement. Future research of this topic should be a priority, particularly within the context of equity evaluation in healthcare policy decisions.

The gas condensate storages in process industries due to their large volume are always exposed to fire and explosion. Also, in these facilities, some of the primary events may trigger sever secondary events, known as domino effect. That is, the occurrence of dominoes can lead to an increase in the severity and consequences of the first event. So, safety analysis is essential for preventing such potential scenario accidents and limiting its consequences on the personnel, society, and environment. In this study using the adapted bow-tie analysis technique, a method for considering the risk of domino effect in quantitative risk assessment of gas condensate storage tanks is presented. According to the results of the credible scenarios (leakage and catastrophic rupture), the individual risk has been higher than the standard and safe intervals between neighbors are not respected. As can be deduced from the results, the method presented in the research can provide a clear picture of the pathway of domino events and the impact of barriers and safety measures.

This study mainly focuses on the risk assessment of public–private partnership (PPP) projects. Evaluating the risks of PPP projects precisely is critical to their successful implementation. However, the traditional approaches often lack mechanisms in manipulating imprecise and vague information, and they need auxiliary information or pre-assumptions (e.g., preset fuzzy membership functions). To solve this problem, an integrated method for evaluating risks in public–private partnership projects is proposed. This method is based on the strength of the group analytic hierarchy process (GAHP), rough set theory, and cloud model theory. The proposed approach integrates the strength of rough set theory in coping with vagueness of the information from experts’ assessments without much pre-assumptions, the advantage of cloud model theory in investigating the randomness of experts’ judgments, and the merit of the AHP method in evaluation under multiple criteria and complex situation. Finally, an application in a PPP project in Wuhu, China, is provided to show the feasibility and effectiveness of the proposed method. The proposed approach is effective in modeling hierarchy assessment as well as dealing with vagueness and randomness, and it can help managers to make reasonable and effective decisions in risk management.

Purpose of Review Given the rapid development of diagnostic approaches to test for and diagnose infection with SARS-CoV-2, many options are available to assess infection. Multiple established diagnostic companies are now providing testing platforms whereas initially, testing was being performed with simple PCR-based tests using standard laboratory reagents. Recent Findings. Additional testing platforms continue to be developed but challenges with testing, including obtaining testing reagents and other related supplies, are frequently encountered. With time, the testing supply chain will improve and more companies will be providing materials to support these testing efforts. In the USA, the need for rapid assay development and subsequent approval through attainment of emergency use authorization (EUA) has superseded the traditional arduous diagnostic testing approval workflow mandated by the FDA. It is anticipated that the USA will be able to continue to significantly increase its testing capabilities to address this pandemic; however, challenges remain due to the diversity of the performance characteristics of tests being utilized. Summary This review provides an overview of the current diagnostic testing landscape, with pertinent information related to SARS-CoV-2 virology and antibody responses, that is available to diagnose infection.

Disposal of healthcare waste is a key issue of environmental sustainability in the world. The amount of healthcare waste is increasing every day, and it is necessary to adequately dispose of this kind of waste. There are various treatments for healthcare waste disposal, of which incineration of healthcare waste is one of the solutions. This paper suggests a model for selection of the type of incinerators that best solve the problem of healthcare waste in secondary healthcare institutions in Bosnia and Herzegovina. In the selection of incinerators, extended sustainability criteria were applied. Basic sustainability criteria: environmental, economic, and social criteria, were extended with the technical criterion. To assess which of the incinerators best meets the needs for healthcare waste collection, multi-criteria decision-making was used. For this purpose, a combination of two MCDA methods was applied in this paper, namely full consistency method (FUCOM) and compromise ranking of alternatives from distance to ideal solution (CRADIS). The FUCOM method was applied to determine the weights of the criteria, while the CRADIS method was applied to rank the alternatives. The best alternative of the six alternatives used is A2 (I8-M50), followed by alternative A1 (I8-M40), while the worst ranked alternative is A5 (I8-M100). These results were confirmed by applying the other six methods of multi-criteria analysis and the performed sensitivity analysis. The contribution of this paper is reflected through a new method of multi-criteria analysis that was used to solve decision-making problems. This method has shown simplicity and flexibility in operation and can be used in all problems when it is necessary to make a multi-criteria selection of alternatives.

Coronavirus disease 2019 brings about the economic damage and loss of life. Thus, demand of personal protective equipment continues to increase, consequently an increase in infectious equipment pollution. Most of these wastes threaten the environment and increase the spread of diseases. This paper provides a research hypothesis whether effective medical waste management would prevent the possible impacts of coronavirus disease 2019-related waste issues on environment at the city level. To confirm this hypothesis, installation of waste treatment centre is addressed. Then, by incorporating uncertain waste generation amounts utilizing Jimenez method, a pickup routing is addressed to decide the pickup routes between the waste treatment centre and residential area. This study is first to assign the optimistic, realistic and pessimistic scenarios of the uncertain waste generation using time series analysis method and waste generation formulation. Also, L-type matrix is used to define, assess and prioritize the environmental and operational risks on waste generation formulation and to provide risk reaction strategies. Practicality of these approaches is illustrated in the case of Turkey. The computational results reveal the effectiveness of the integrated method, which ensures practical and theoretical insights controlling the waste generation to prevent virus propagation for health authorities.

Recent progress in understanding the molecular basis of cellular processes, identification of promising therapeutic targets and evolution of the regulatory landscape makes this an exciting and unprecedented time to be in the field of oncology drug development. However, high costs, long development timelines and steep rates of attrition continue to afflict the drug development process. Lack of predictive preclinical models is considered one of the key reasons for the high rate of attrition in oncology. Generating meaningful and predictive results preclinically requires a firm grasp of the relevant biological questions and alignment of the model systems that mirror the patient context. In doing so, the ability to conduct both forward translation, the process of implementing basic research discoveries into practice, as well as reverse translation, the process of elucidating the mechanistic basis of clinical observations, greatly enhances our ability to develop effective anticancer treatments. In this Review, we outline issues in preclinical-to-clinical translatability of molecularly targeted cancer therapies, present concepts and examples of successful reverse translation, and highlight the need to better align tumour biology in patients with preclinical model systems including tracking of strengths and weaknesses of preclinical models throughout programme development.

Blockchain is an emergent technology and has the potential to replace the intermediaries in the asset transaction business. This is the reason many companies have started adopting and experimenting with blockchain. No standard practice has been established yet to implement this new transparent and distributed ledger technology which hinders its wide acceptance despite being beneficial to secure the digital assets in a verifiable and permanent way. Though it has transformed the way the business operates, still it is considered to be a disruptive technology. It has various challenges to overcome such as scalability, non-repudiation, data privacy, and flexibility which should be addressed in the early stage of implementation of blockchain infrastructure. These issues need to be dealt with specific techniques as the existing system development process doesn’t integrate many of the aspects of this relatively new blockchain technology. Before incorporating blockchain technology in the existing system, one must address all the issues in the requirement analysis phase of the system. This paper presents a case study on blockchain enabled medical supply chain and focuses on the identification of non-functional requirements for blockchain enabled systems which further facilitates the software engineers for requirements specification and system design.

Supply chain organizations should calmly and cautiously take the most accurate and sustainable decisions quickly and put them into practice. It is obvious that traditional time series-based demand and supply planning approaches are insufficient to meet current business needs due to factors such as sharp changes in market and commercial dynamics, pandemics, and natural disasters on the management of green supply chains, especially these days. In the near future, there will be a need for more resilient supply chains with a flexible business models that are not affected by sudden changes and that can make sustainable decisions dynamically. Additionally, all stakeholders must act with a green supply chain approach to conduct production and service activities in a way that causes the least damage to nature. Companies must build more resilient supply chains by considering environmental sensitivities to compete in the market and ensure their continuity. In this context, the green supply chains should be evaluated according to their resilience. For this purpose, Supply Chain Operations Reference (SCOR) model is extended with novel performance attributes to evaluate resilience of green supply chains in this study. The SCOR-embedded novel green supply chain resilience evaluation model is structured as a three-level performance attribute hierarchical structure. Then, the model is handled as a multi-criteria decision-making problem to determine importance of the performance attributes. Best Worst Method integrated Interval Valued Intuitionistic Fuzzy Analytic Hierarchy Process is used to determine the importance of performance attributes. Most important performance attributes are determined in each level of hierarchy. According to results, organizational factors play a key role to build more resilient supply chains. Especially, integrated systems are required for supply chain resilience.

Aside from the primary objective of finding potential digital evidence, preserving the integrity of the evidence and maintaining proper chain of custody are also equal priorities for an investigator to ensure admissibility of evidence in the court of law. Advanced mobile forensic techniques pose a serious challenge in achieving the latter due to the complexity of the process and possibility of alteration in the state of the device during data acquisition. Hence efforts are made to understand the critical issues faced by the investigators while employing advanced mobile forensic techniques, which may be invasive or destructive. A standardised investigative process is presented in this paper which may act as a guide to investigators, prosecutors and judicial officers dealing with digital evidence in India.

Machinery will fail due to complex and tough working conditions. It is necessary to apply reliable monitoring technology to ensure their safe operation. Condition-based maintenance (CBM) has attracted significant interest from the research community in recent years. This paper provides a review on CBM of industrial machineries. Firstly, the development of fault diagnosis systems is introduced systematically. Then, the main types of data in the field of the fault diagnosis are summarized. After that, the commonly used techniques for the signal processing, fault diagnosis, and remaining useful life (RUL) prediction are discussed, and the advantages and disadvantages of these existing techniques are explored for some specific applications. Typical fault diagnosis products developed by corporations and universities are surveyed. Lastly, discussions on current developing situation and possible future trends are in the CBM performed.

Robot advisory services are rapidly expanding, responding to a growing interest people have in directly managing their savings. Robot-advisors may reduce costs and improve the quality of asset allocation services, making user’s involvement more transparent. Against this background, there exists the possibility that robot advisors underestimate market risks, especially during crisis times, when high order interconnections arise. This may lead to a mismatch between investors’ expected and actual risk. The aim of this paper is to overcome this issue, taking into account not only investors’ risk preference but also their attitude towards interconnectdness. To achieve this aim, we combine random matrix theory with correlation networks and extend the Markowitz’ optimisation problem to a third dimension. To demonstrate the practical advantage of our proposed approach we employ daily returns of a large set of Exchange Traded Funds, which are representative of the financial products employed by robot-advisors.

This study contributes to identifying a valid and reliable set of barriers to sustainable solid waste management framework rooted in society 5.0 perspectives in Taiwan. The SSWM-related causal interrelationships within the proposed hierarchical structure, and critical barriers for the practical improvement and enhancement of SSWM performance are identified as preference enriching both literature and practices. In nature, the hierarchical structure is with the causal interrelationships under uncertainties. The perspective empowers the creation of a new biosphere based on technological progress, but in the sustainable solid waste management field, it is difficult to encounter and shape the systematized processes due to barriers and challenges. To address this shortcoming, this study evaluates the technical challenges faced in the field of sustainable solid waste management toward society 5.0. The valid attributes are usually described the qualitative information. The fuzzy Delphi method is applied to acquire the valid and reliable attributes. Fuzzy decision-making trial and evaluation laboratory experiment is to visualize the causal interrelationships among the attributes. Choquet integral with respect to the nonadditive attributes over the valid set provides an overall perspective function. The results establish an understanding of sustainable solid waste management barriers in the perspectives under uncertainties. Community uncertainty, policy and regulation problems, city architecture, and technology interaction are the factors that influence sustainable performance. In practices, (1) diverse disciplines and sectors in local, national, and global communities; (2) a lack of mobility and reliability; (3) mass production and mass consumption; (4) an insufficient level of artificial intelligence application; and (5) failures related to data management and security hinder the improvement of sustainable solid waste management toward society 5.0. The social and technical perspectives are indicated as the top priorities to improve SSWM performance.

In industrial production, smart wearables (e.g. data glasses) are becoming more relevant to support employees. While economic aspects have so far been the focus of the introduction, changes for work design have been neglected. The aim of the Delphi study was to describe changes through the use of smart wearables and to derive implications for work design. Six description dimensions (application, implications for work design, data use, personalization, flexibility, introductory purpose) were identified and 18 scenarios were developed. The assessment of the scenarios in terms of desirability and degree of dissemination showed that scenarios for work-related and ergonomic support are particularly desirable and scenarios for changes in cooperation are considered particularly likely. Data usage is a relevant factor for the assumed degree of dissemination but not for the assessment of desirability. In operational practice, work design and economic aspects are important for assessing the scenarios.Practical Relevance: The results show possible development scenarios for production work when using smart wearables and highlight possible implications for work design. Positive and negative effects on task, knowledge, social and contextual characteristics are identified, which offer suggestions for a conscious selection and design of the use of technology in practice.

As more uses for biomarkers are sought after for an increasing number of disease targets, single-target biomarkers are slowly giving way for biomarker panels. These panels incorporate various sources of biomolecular and clinical data to guarantee a higher robustness and power of separation for a clinical test. Multifactorial diseases such as psychiatric disorders show great potential for clinical use, assisting medical professionals during the analysis of risk and predisposition, disease diagnosis and prognosis, and treatment applicability and efficacy. More specific tests are also being developed to assist in ruling out, distinguishing between, and confirming suspicions of multifactorial diseases, as well as to predict which therapy option may be the best option for a given patient’s biochemical profile. As more complex datasets are entering the field, involving multi-omic approaches, systems biology has stepped in to facilitate the discovery and validation steps during biomarker panel generation. Filtering biomolecules and clinical data, pre-validating and cross-validating potential biomarkers, generating final biomarker panels, and testing the robustness and applicability of those panels are all beginning to rely on machine learning and systems biology and research in this area will only benefit from advances in these approaches.

The skin laxity component of facial aging has been traditionally addressed with surgical intervention. However, demand for alternative treatment options with less associated risk, scarring, downtime, and cost have driven advances in non-surgical tightening techniques. This article explores the recent advances in these non-surgical technologies for skin tightening including microcoring, hydroxyapatite fillers, and energy-based devices (lasers, ultrasound, radiofrequency, and plasma). Advances in non-surgical skin-tightening devices allow for effective skin tightening. Although fully ablative laser resurfacing devices are often considered the gold standard for non-surgical rejuvenation, important advances in this technology include fractionated energy delivery to decrease risk and shorten treatment recovery. In addition, studies have shown that optimal treatment temperatures for skin tightening are lower than those achieved with CO2, favoring radiofrequency devices as a more optimal choice for tissue tightening in terms of treatment results, skin types amenable to treatment, risks, and downtime. Ultrasound technology has the unique advantage of allowing for real-time tissue assessment and tailored heat delivery. Microcoring and hydroxyapatite treatment stimulate skin tightening without heat production. Advantages and disadvantages of various non-surgical skin tightening are reviewed and summarized in this article. A wide array of non-surgical skin-tightening techniques provide an attractive alternative to surgical intervention for modern cosmetic patients.

Nonconformity (NC) management is a fundamental process in production, yet the literature notion of it does not always align with what is practiced in reality. In particular, the literature often excludes the NC responsibility decision, which is a difficult, costly and time-consuming task assignment, but also an integral part of the NC management process. We propose a semi-automated model we call SANC, which improves the accuracy of NC responsibility decisions and significantly cuts their costs. We base our methodology on CRISP-DM and extend it to fit the semi-automated NC responsibility decision. Unlike the original CRISP-DM, SANC utilizes existing organizational resources, and thus extends the capabilities of CRISP-DM in terms of both achieving greater overall performance and broadening its appeal to more traditional production processes. We demonstrate this solution by implementing it in a large-scale assembly plant in the printing industry, that may result in savings of over $186 K according to our assessments.

As sustainability has become an important asset for production systems, it is important to conduct life cycle assessment (LCA) studies. When conducting an LCA of products derived from a multi-product process, the common challenge of impact partitioning arises. This is referred to as the allocation problem. Many allocation methods have been introduced in the literature, leading to a variety of results depending on the choice of allocation method. The purpose of this paper is to provide LCA practitioners with an operational approach that helps them choose the allocation method when evaluating co-products. The extensive literature review carried out in the study allows identification of the main allocation methods available to practitioners. A cross-analysis also points out a strong link between allocation choices and decision-makers’ objectives. A decision-aid tool is introduced and tested on two case studies involving co-products. The resulting decision-aid tool helps guide the allocation choice in the two case studies through multi-level reasoning. Depending on the objective of the LCA study and the co-product being analyzed, a multi-level decision tree proposes a set of recommended allocation methods. The first level consists in identifying the purpose of the study, after which yes-or-no questions start to guide the path toward the right allocation method. In the most complex cases, the LCA practitioner is presented with a table containing all the allocation methods defended by other LCA practitioners for specific industries and contexts. This decision-aid tool complements the guidelines presented by ISO 14044 2006. The presented tool creates a strong correlation between the aims of the study and the allocation choice, thus allowing practitioners to both justify their choice and to discuss the results with alternative scenarios for sensitivity analysis.

Standard specifications that realize mutual availability in data distribution are indispensable for cooperation between different fields. On the other hand, the forming standardization processes that allow many different things such as physical objects and services to be connected through the Internet, generate costs and require time to form consensus due to stakeholder diversification. To adapt to social evolution and use of big data generated by a massive amount of distributed data, establishing a method to develop a standard of data specification that involves a large number of diverse industries and stakeholders is necessary. The paper analyzes the evolution of the Standard Developing Organizations (SDOs) management policy for data-related technologies and discusses strategies for encouraging data transactions with rapid standardization processes and early diffusion.

More than four decades have passed since the first example of a light-activated (caged) compound was described. In the intervening years, a large number of light-responsive derivatives have been reported, several of which have found utility under a variety of in vitro conditions using cells and tissues. Light-triggered bioactivity furnishes spatial and temporal control, and offers the possibility of precision dosing and orthogonal communication with different biomolecules. These inherent attributes of light have been advocated as advantageous for the delivery and/or activation of drugs at diseased sites for a variety of indications. However, the tissue penetrance of light is profoundly wavelength-dependent. Only recently have phototherapeutics that are photoresponsive in the optical window of tissue (600–900 nm) been described. This Review highlights these recent discoveries, along with their limitations and clinical opportunities. In addition, we describe preliminary in vivo studies of prospective phototherapeutics, with an emphasis on the path that remains to be navigated in order to translate light-activated drugs into clinically useful therapeutics. Finally, the unique attributes of phototherapeutics is highlighted by discussing several potential disease applications., The current scope and limitations, path to the clinic and therapeutic applications of long-wavelength-responsive, light-activated drugs are discussed in this Review.

The concept of "one size fits all" followed by the conventional healthcare system has drawbacks in providing precise pharmacotherapy due to variation in the pharmacokinetics of different patients leading to serious consequences such as side effects. In this regard, digital-based three-dimensional printing (3DP), which refers to fabricating 3D printed pharmaceutical dosage forms with variable geometry in a layer-by-layer fashion, has become one of the most powerful and innovative tools in fabricating "personalized medicine" to cater to the need of therapeutic benefits for patients to the maximum extent. This is achieved due to the tremendous potential of 3DP in tailoring various drug delivery systems (DDS) in terms of size, shape, drug loading, and drug release. In addition, 3DP has a huge impact on special populations including pediatrics, geriatrics, and pregnant women with unique or frequently changing medical needs. The areas covered in the present article are as follows: (i) the difference between traditional and 3DP manufacturing tool, (ii) the basic processing steps involved in 3DP, (iii) common 3DP methods with their pros and cons, (iv) various DDS fabricated by 3DP till date with discussing few research studies in each class of DDS, (v) the drug loading principles into 3D printed dosage forms, and (vi) regulatory compliance.

Pipeline infrastructures, carrying either gas or oil, are often affected by internal corrosion, which is a dangerous phenomenon that may cause threats to both the environment (due to potential leakages) and the human beings (due to accidents that may cause explosions in presence of gas leakages). For this reason, predictive mechanisms are needed to detect and address the corrosion phenomenon. Recently, we have seen a first attempt at leveraging Machine Learning (ML) techniques in this field thanks to their high ability in modeling highly complex phenomena. In order to rely on these techniques, we need a set of data, representing factors influencing the corrosion in a given pipeline, together with their related supervised information, measuring the corrosion level along the considered infrastructure profile. Unfortunately, it is not always possible to access supervised information for a given pipeline since measuring the corrosion is a costly and time-consuming operation. In this paper, we will address the problem of devising a ML-based predictive model for internal corrosion under the assumption that supervised information is unavailable for the pipeline of interest, while it is available for some other pipelines that can be leveraged through Transfer Learning (TL) to build the predictive model itself. We will cover all the methodological steps from data set creation to the usage of TL. The whole methodology will be experimentally validated on a set of real-world pipelines.

With the development of bridges, independent condition assessment of large-scale bridges has garnered significant attention over the past few decades. Data-driven structural health monitoring (SHM) techniques offer valuable information on the existing health of the structures, maintain safety, and uninterrupted use under varied operational conditions by undertaking timely risk and hazard mitigation. Traditional approaches, however, are not enough to monitor a large amount of SHM data and conduct systematic decision making for future maintenance. In this paper, a bridge health monitoring system is developed through the combination of building information modeling (BIM) and traditional bridge health monitoring that can organize and visualize a considerable amount of sensor data and subsequent structural health information over a prolonged period. The system can identify the structural damage by evaluating the data from sensors using Wigner-Ville distribution (WVD) in bilinear time-frequency analysis. A BIM-enabled platform is utilized to develop the proposed visualization tool for a long-span bridge and enable automated sensor data inventory into the BIM environment. The system has been tested for its robustness and functionality against the development requirements, and the results showed promising potential to support more effective bridge information management.