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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.

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.

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.

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.

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.

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.

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.

Buried natural gas pipelines are vulnerable to external corrosion because they are encased in a soil environment for a long time. Identifying the causes of external corrosion and taking specific maintenance measures is essential. In this work, a risk analysis and maintenance decision-making model for natural gas pipelines with external corrosion is proposed based on a Bayesian network. A fault tree model is first employed to identify the causes of external corrosion. The Bayesian network for risk analysis is determined accordingly. The maintenance strategies are then inserted into the Bayesian network to show a reduction of the risk. The costs of maintenance strategies and the reduced risk after maintenance are combined in an optimization function to build a decision-making model. Because of the limitations of historical data, some of the parameters in the Bayesian network are obtained from a probabilistic estimation model, which combines expert experience and fuzzy set theory. Finally, a case study is carried out to verify the feasibility of the maintenance decision model. This indicates that the method proposed in this work can be used to provide effective maintenance schemes for different pipeline external corrosion scenarios and to reduce the possible losses caused by external corrosion.

Managing quality risks of prefabricated components is one of the challenges for prefabricated construction. The quality liability insurance for prefabricated components (QLIPC) is an effective approach to transfer such risks; however, limited research has been conducted on the development of QLIPC. This study introduces an evolutionary game theory (EGT)-based approach incorporating decisions from both government and insurance companies. In the EGT model, a payoff matrix under disparate strategies is constructed and the evolutionary stable strategies (ESS) are deduced. The simulation calculation is then carried out by MATLAB using virtual sample data to demonstrate the analysis. The results show that the government should act as the game promoter because the QLIPC can reduce governance cost and has significant social benefits. This research contributes a theoretical framework to analyze the QLIPC development using the EGT theory, and it could help the government develop long-term strategies for developing the QLIPC market.

Animal studies play a vital role in validating the concept, feasibility, safety, performance and efficacy of biomaterials products during their bench-to-clinic translation. This article aims to share regulatory considerations for animal studies of biomaterial products. After briefly emphasizing the importance of animal studies, issues of animal studies during biomaterial products’ translation are discussed. Animal studies with unclear purposes, flawed design and poor reporting quality could significantly reduce the translation efficiency and create regulatory challenges. Regulatory perspectives on the purpose, principle, quality and regulatory science of animal studies are also presented. Animal studies should have clear purposes, follow principles of 3R+DQ (replacement, reduction, refinement, design and quality) and execute under an efficiently operating quality management system. With the advancement of regulatory science, National Medical Products Administration of China has been developing a series of standards and guidance documents on animal studies of medical devices. Case studies of making decisions on whether to conduct animal studies are provided in the end with drug-eluting stents as examples. In summary, animal studies of biomaterial products should pay close attention to the rationale, design and quality in order to achieve their purposes.

Combination products with a wide range of clinical applications represent a unique class of medical products that are composed of more than a singular medical device or drug/biological product. The product research and development, clinical translation as well as regulatory evaluation of combination products are complex and challenging. This review firstly introduced the origin, definition and designation of combination products. Key areas of systematic regulatory review on the safety and efficacy of device-led/supervised combination products were then presented. Preclinical and clinical evaluation of combination products was discussed. Lastly, the research prospect of regulatory science for combination products was described. New tools of computational modeling and simulation, novel technologies such as artificial intelligence, needs of developing new standards, evidence-based research methods, new approaches including the designation of innovative or breakthrough medical products have been developed and could be used to assess the safety, efficacy, quality and performance of combination products. Taken together, the fast development of combination products with great potentials in healthcare provides new opportunities for the advancement of regulatory review as well as regulatory science., Graphical abstract Image 1, Highlights • Full overview of the origin, definition and designation of combination products. • Regulatory review and evaluation of the safety and efficacy of combination products. • Regulatory science and its future perspectives of combination products.

Medication reviews are effective in improving the quality of medication use among older people. However, they are conducted to various standards resulting in a wide range of outcomes which limit generalisability of findings arising from research studies. There also appear to be funding and time constraints, lack of data storage for quality improvement purposes, and non-standardised reporting of outcomes, especially clinically relevant outcomes. Furthermore, the coronavirus disease-19 (COVID-19) pandemic has restricted many face-to-face activities, including medication reviews. This article introduces a technology-enabled approach to medication reviews that may overcome some limitations with current medication review processes, and also make it possible to conduct medication reviews during the COVID-19 pandemic by providing an alternate platform. The possible advantages of this technology-enabled approach, legislative considerations and possible implementation in practice are discussed.

People will always consciously or unconsciously compare their cost or benefits of actions with those of others and make judgments about fairness. Aiming at consensus reaching process where decision makers have fairness concern behaviors, this paper proposes a limited cost consensus model with fairness concern. To do this, we define the fairness utility function and fairness utility level based on fairness preference theory and explore some properties. In view of this, we propose a limited cost consensus approach with fairness concern of decision makers, which can obtain a stable and balanced consensus. Through emission reduction consensus problem of Chinese manufacturing enterprises, comparative analysis and sensitivity analysis are used to explain the proposed model. Conclusions about fairness preferences, consensus cost budget, and unit compensation cost can provide significant managerial references for real-world economic and management activities.

Osteoarthritis (OA) is increasingly recognised as a disease of diverse phenotypes with variable clinical presentation, progression, and response to therapeutic intervention. This same diversity is readily apparent in the many animal models of OA. However, model selection, study design, and interpretation of resultant findings, are not routinely done in the context of the target human (or veterinary) patient OA sub-population or phenotype. This review discusses the selection and use of animal models of OA in discovery and therapeutic-development research. Beyond evaluation of the different animal models on offer, this review suggests focussing the approach to OA-animal model selection on study objective(s), alignment of available models with OA-patient sub-types, and the resources available to achieve valid and translatable results. How this approach impacts model selection is discussed and an experimental design checklist for selecting the optimal model(s) is proposed. This approach should act as a guide to new researchers and a reminder to those already in the field, as to issues that need to be considered before embarking on in vivo pre-clinical research. The ultimate purpose of using an OA animal model is to provide the best possible evidence if, how, when and where a molecule, pathway, cell or process is important in clinical disease. By definition this requires both model and study outcomes to align with and be predictive of outcomes in patients. Keeping this at the forefront of research using pre-clinical OA models, will go a long way to improving the quality of evidence and its translational value.

Problem definition: Physical distancing requirements during the COVID-19 pandemic have dramatically reduced the effective capacity of university campuses. Under these conditions, we examine how to make the most of newly scarce resources in the related problems of curriculum planning and course timetabling. Academic/practical relevance: We propose a unified model for university course scheduling problems under a two-stage framework and draw parallels between component problems while showing how to accommodate individual specifics. During the pandemic, our models were critical to measuring the impact of several innovative proposals, including expanding the academic calendar, teaching across multiple rooms, and rotating student attendance through the week and school year. Methodology: We use integer optimization combined with enrollment data from thousands of past students. Our models scale to thousands of individual students enrolled in hundreds of courses. Results: We projected that if Massachusetts Institute of Technology moved from its usual two-semester calendar to a three-semester calendar, with each student attending two semesters in person, more than 90% of student course demand could be satisfied on campus without increasing faculty workloads. For the Sloan School of Management, we produced a new schedule that was implemented in fall 2020. The schedule allowed half of Sloan courses to include an in-person component while adhering to safety guidelines. Despite a fourfold reduction in classroom capacity, it afforded two thirds of Sloan students the opportunity for in-person learning in at least half their courses. Managerial implications: Integer optimization can enable decision making at a large scale in a domain that is usually managed manually by university administrators. Our models, although inspired by the pandemic, are generic and could apply to any scheduling problem under severe capacity constraints.

Improved supply chain optimization strategies can play a major role in addressing global food security and safety in years to come. In particular, tighter safety regulations, changing consumer quality requirements and more stringent market competition call upon integrated supply chain decision-making frameworks that explicitly consider product quality control. This effort requires metrics of quality that accurately reflect product physico-chemical properties, as well as consumer purchasing preferences. However, a critical challenge linked to embedding the complex dynamics of the evolution of product quality in time within supply chain models is the large-scale nature of the ensuing optimization problems, which are computationally intractable even for moderate-size, single-item systems. In the present work, we introduce a computationally efficient optimal production and distribution planning framework for perishable products having multiple quality attributes that evolve in time as a function of environmental conditions during shipment and storage. We also propose a model reduction strategy and a decomposition framework that enhance the scalability of our approach. We perform extensive numerical simulations using different network instances to validate our theoretical findings, as well as to demonstrate the advantages of the proposed supply chain management scheme.

Incentive mechanism design and quality control are two key challenges in data crowdsourcing, because of the need for recruitment of crowd users and their limited capabilities. Without considering users' social influences, existing mechanisms often result in low efficiency in terms of the platform's cost. In this paper, we exploit social influences among users as incentives to motivate users' participation, in order to reduce the cost of recruiting users. Based on social influences, we design incentive mechanisms with the goal of achieving high quality of crowdsourced data and low cost of incentivizing users' participation. Specifically, we consider three scenarios. In the full information scenario, we design task assignment and user recruitment mechanisms to optimize the data quality while reducing the incentive cost. In the partial information scenario, users' qualities and costs are unknown. We exploit the correlation between tasks to overcome the information asymmetry, for both cases of opportunistic crowdsourcing and participatory crowdsourcing. Further, in the dynamic social influence scenario, we investigate the dynamics of users' social influences and design extra rewards for users to make full use of the social influence and achieve maximum cost saving. We evaluate the incentive mechanisms using numerical results, which demonstrate their effectiveness.

Autonomy of systems has been gaining increasing attention from scientists and engineers in various disciplines such as robotics, artificial intelligence, embedded systems, and control. However, there are serious challenges on the way of designing and adopting such systems, which haven’t, in our perspective, received sufficient attention yet. In this position paper, we review some of those challenges and present potential approaches of coping with them. Moreover, we highlight topics that require immediate attention and research efforts of the community. In particular, we propose three approaches: (i) More dependable autonomy using deterministic methods that can provide similar controllability, dependability, and traceability as that of traditional systems. Among such solutions is providing guarantee bands as opposed to guaranteed narrow operation points. (ii) Orthogonal autonomy adds autonomy in the extra-functional plane of the system. (iii) Expressive system design allows autonomous systems to trace and explain their decisions and reactions even though they may be using less deterministic tools.

As global supply chains become more developed and complicated, supplier quality has become increasingly influential on the competitiveness of businesses during the Covid-19 pandemic. Consequently, supplier selection is an increasingly important process for any business around the globe. Choosing a supplier is a complex decision that can result in lower procurement costs and increased profits without increasing the cost or lowering the quality of the product. However, these decision-making problems can be complicated in cases with multiple potential suppliers. Vietnam's textile and garment industry, for example, has made rapid progress in recent years but is still facing great difficulties as the supply of raw materials and machinery depends heavily on foreign countries. Therefore, it is extremely important for textile and garment manufacturing companies in Vietnam to implement an effective supplier evaluation and selection process. While multicriteria decision-making models are frequently employed to assist with supplier evaluation and selection problems, few of these models consider the problem under the condition of a fuzzy decision-making environment. The aim of this paper is to create a hybrid MCDM model using the Fuzzy Analytical Hierarchy Process (FAHP) model and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to assist the supplier selection process in the garment industry in a fuzzy decision-making environment. In this study, the FAHP method is used to evaluate the performance and the weight of each criterion. TOPSIS is then used to rank all potential suppliers. The proposed model is then applied to a real-world case study to demonstrate both the process of calculation as well as its real-world applicability. The results from the case study provide empirical evidence that the model is feasible. The proposed approach can also be used in combination with other MCDM models to better support decision makers and can be modified to be applied in similar supplier selection processes for different industries.

Incentive mechanisms are essential for stimulating adequate worker participation to achieve good truth discovery performance in mobile crowdsensing (MCS) systems. However, most of existing incentive mechanisms only consider compensating workers' sensing cost, while the cost incurred by potential privacy leakage has been largely neglected. Moreover, none of existing privacy-preserving incentive mechanisms has incorporated workers' different privacy preferences to provide personalized payments for them. In this paper, we propose a contract-based personalized privacy-preserving incentive mechanism for truth discovery in MCS systems, named Paris-TD, which provides personalized payments for workers as a compensation for privacy cost while achieving accurate truth discovery. The basic idea is that the platform offers a set of different contracts to workers with different privacy preferences, and each worker chooses to sign a contract which specifies a privacy-preserving degree (PPD) and the corresponding payment the worker will receive if she submits perturbed data with that PPD. Specifically, we respectively design a set of optimal contracts analytically under both full and incomplete information models, which maximize the truth discovery accuracy under a given budget, while satisfying the individual rationality and incentive compatibility properties. The feasibility and effectiveness of Paris-TD are validated through experiments on both synthetic and real-world datasets.

Aspirations among human beings for living a long life with joy and comfort are escalating the demand for advanced technologies. Humans having any physical disorders wish to live a healthier life through the artificial organ transplant-ship. Here it is noteworthy that the replaced organ should behave like the original one otherwise it may affect the other organs too. It is an exaggeration to say that the usage of the computational approach in getting the solutions for real-life problems is not new. Still, the contribution of the utility of computational approaches in Bio-systems is not at par. The reason is one can’t provide multiple inputs especially in the physical systems in the industry. This leads to creating gaps in the inputs to the tools about detailed knowledge of the bio-systems. The present study reports barriers associated with the utility of the additive manufacturing process for developing the products of Bio-systems.

Although the dangers of working in confined spaces have been known for many years, fatal accidents related to working in confined spaces still frequently occur. Considerable research has been conducted to identify potential contributory factors of confined space incidents through analyzing accident reports. However, accident databases are usually read and interpreted manually by human experts. The process of analyzing confined space accident reports can be time-consuming and labor-intensive. As the number of accident records increases, it is difficult for the experts to manually review all the reports. Moreover, different individuals may reach various conclusions from the same accident report. Some analysts may fail to capture all the meaningful and relevant causal factors. Automatic information extraction using special rules and ontology-based approaches can be used to mine reports of confined space accidents. However, such approaches tend to suffer from the problem of weak generalization. To overcome this limitation and improve the performance of contributory factors analysis, an improved deep learning based framework is proposed in this paper to automatically extract and classify contributory factors from confined space accident reports using BERT-BiLSTM-CRF and CNN models. Research results suggested that the proposed framework can be used as a feasible method to qualitatively and quantitatively explore the contributory factors of confined space accidents. By analyzing a large quantity of confined space accident reports, the frequency of contributory factors can be estimated automatically. This outcome is helpful to significantly improve the risk assessment quality of confined space works.

Introduction:: The transformation happening globally, though referred by different names and nomenclatures, the overall objective to inspire digitalization and smart practices by reducing human intervention and enhancing machines intelligent to take on the global manufacturing and production to another level of excellence is a proven fact now. However, earlier research has been found, lacking in the strategic approach to evaluate and analyze the I4.0 adoption related risks for its implementation. This ultimately deprived organizations of multitude of the benefits of I4.0 adoption. Objective:: This research proposes a systematic methodology for understanding and evaluating the most evident risks in the context of I4.0 implementation. Method:: The research is mainly based on the inputs from experts/consultants along with robust literature review and researcher’s experience in the area of risks handling. The MCDM methods used for investigation and assessment are Fuzzy AHP and Fuzzy TOPSIS. The outcomes of the study are further validated through sensitivity analysis and real world scenario. Results:: Technical and Information Technology (IT) risks are found to be on the top of the priority list, which needs urgent attention while embarking on I4.0 adoption in the industry and the most important criteria, which needed urgent attention was, Information Security. The paper has also developed the ‘Industry 4.0 Risks Iceberg model’ and systematicaly categoried the challenges into 5 dimensions for easy assessment and analysis. Discussion:: The transformation happening globally, though referred by different names and nomenclatures, the overall objective to inspire digitalization and smart practices by reducing human intervention and enhancing machines intelligent to take on the global manufacturing and production to another level of excellence is a proven fact now. However, earlier research has been found, lacking in the strategic approach to evaluate and analyze the I4.0 adoption related risks for its implementation. This ultimately deprived organizations of multitude of the benefits of I4.0 adoption. Conclusions:: This systematic and holistic study of the I4.0 associated risks can be used to find the most critical and crucial risks based on which the strategies and policies may be modified to harness the best of I4.0. This will not only ensure the returns on investment but also will build the trust in the system. The research would be very beneficial to managers, academicians, researchers and technocrats who would be involved in I4.0 implementation.

Personal care products (PCPs) are structured products that are constituted by numerous chemicals ingredients. However, there are several hazardous ingredients in PCPs that may cause exposure to consumers and lead to adverse health impacts. The main contribution of this study is the development of a comprehensive framework for the PCP ingredient design that integrates the safety and health aspects as well as performance aspects simultaneously into computer-aided molecular design (CAMD). Mathematical optimization programming is utilized in this study to generate a list of high quality, poses good safety and health-performance PCP preservative candidates. Furthermore, an index-based safety and health effect assessment is presented in this study. In addition, the exposure assessment of the selected molecule is performed by applying Margin of Exposure. A case study on PCP preservative design is presented. Laboratory experiment is carried out to verify the feasibility of the addition of safer and healthier preservative into the eye cream formulation.

Consensus reaching is a key issue in group decision-making, because conflicts of interest among groups are common. Democratic consensus refers to achieve a soft consensus among collective as well as ensure the effective participation and satisfaction of individuals. Multi-person multi-criteria large scale decision making (MpMcLSDM) usually involves a huge number of decision makers (DMs/participants), and different DMs usually have different interests. Thus, how to effectively manage individuals to promote democratic consensus is a current research challenge. To do that, this research develops a democratic consensus reaching process (DCRP) for MpMcLSDM problems. In the proposed approach, a clustering method that considers both the opinion similarity and individual concern similarity of DM is firstly given to decrease the complexity of MpMcLSDM issues. Subsequently, we propose to assign equal initial weight to each cluster to protect the interests of minorities. Meanwhile, a consensus contribution-based dynamic interactive weight updating method is implemented in the DCRPs to promote a high level of democratic consensus. Besides, a compromise degree-based consensus feedback strategy is developed to improve the efficiency of the DCRPs. The proposed feedback mechanism effectively considers the individual concern and adjustment willingness of DMs in the DCRPs. Finally, a case study and some comparisons are given to show the effectiveness and innovation of this research.

Growing population and depleting resources are forcing the manufacturers to become more competent and sustainable. Customers are becoming more and more informed about the product they consume, and therefore, manufacturers need to be flexible and open to new changes taking place. Big Data Analytics (BDA) is one such technology that has transformed the manufacturing sector. Organizations have now become more flexible with regards to their processes and, at the same time, have also improved their product quality. Here in this paper, we have analyzed various advantages of Big Data that manufacturing organizations can exploit for their betterment. We have also discussed examples of certain leading manufacturers and how they can lead to big data-enabled manufacturing. Lastly, we discuss certain shortcomings that need to be addressed to make big data analytics viable for organizations of all sorts and not just for top manufacturers.

In order to stay successful today, Construction Project can implement a modern company management framework. The corporation uses their philosophy is mostly to waste, focusing around the elimination of constant less change. Lean Construction Project is an appealing method that promises positive performance. Lean and, which are likely to be quite simple to implement based on recent literature in this sector, mean that areas are challenging to manage. The survey's key findings are presented in this paper, which focused on quality management and quality assurance processes and chart-based automation of a wide range of limited amounts of lean Construction projects. This research provides a multi-species, Large/small-quantity continuous management method model and an approach suggested by Lean Construction Project. The core Construction Materials Supply Chain Risk Management in this working as a condition for a stable continuity of adoption such as lean and lean, as a modern form of management, may be split between conflicting desires, to loss is not suitable as a mechanism for sense. As a result, the current study track places a strong emphasis on standardization and promotion management. The output of input neurons in neural networks is dependent on a network by weight training input. If the network properly categorized images to assist with correcting answers, the weights of the correct answers will rise, while the consequences of the incorrect answers will decrease.

With the increasing number of catastrophic weather events and resulting disruption in the energy supply to essential loads, the distribution grid operators’ focus has shifted from reliability to resiliency against high impact, low-frequency events. Given the enhanced automation to enable the smarter grid, there are several assets/resources at the disposal of electric utilities to enhances resiliency. However, with a lack of comprehensive resilience tools for informed operational decisions and planning, utilities face a challenge in investing and prioritizing operational control actions for resiliency. The distribution system resilience is also highly dependent on system attributes, including network, control, generating resources, location of loads and resources, as well as the progression of an extreme event. In this work, we present a novel multi-stage resilience measure called the Anticipate-Withstand-Recover (AWR) metrics. The AWR metrics are based on integrating relevant ‘system characteristics based factors’, before, during, and after the extreme event. The developed methodology utilizes a pragmatic and flexible approach by adopting concepts from the national emergency preparedness paradigm, proactive and reactive controls of grid assets, graph theory with system and component constraints, and multi-criteria decision-making process. The proposed metrics are applied to provide decision support for a) the operational resilience and b) planning investments, and validated for a real system in Alaska during the entirety of the event progression.

The growth of sensory evaluation as a product information resource has been significant over the past four decades. With increased competition, companies look for sensory information that can be obtained quickly without compromising the quality of results. This author describes the basic principles and how they are best used to advantage. Particular attention is given to the subject selecting and qualifying process and its effect on test time and the quality of the results. Additional attention is given to descriptive analysis, a methodology that is used in almost every sensory program but in different ways, reflecting how researchers understand the process. The author describes the process by which the quantitative descriptive analysis (QDA) method was developed, taking into account the time requirements in the context of a rapid method.

With the exception of foreign bodies, formerly food contact materials (FCM) were generally not considered a source of food safety concern. During the past decade several issues regarding FCM affected the food supply and caught the attention of industries, national bodies and of course consumers. But is it clear what an FCM is? The chapter presents different classifications of FCM depending on the type of contact, type of material and function. The potential hazards (physical, microbiological, chemical and allergen) associated with FCM and their risk factors are explained according to the type of material (e.g. wood, metal, plastic). The chapter also reviews the main standards used in the field (ISO 22000, BRCIoP and EN 15593) and gives an overview of the main regulations applicable. The chapter finishes with five case studies that bring the reader to reality in the management of FCM food safety.

In this chapter we focuses on the issues that arise out of the relationship between the developer and the buyer, including claims, user expectations, and the legal ramifications that may follow an unhealthy relationship. The discussion touches on standards, reliability, security, safety, quality of software, quality of service of software products, causes of software failures, developer and buyer protection, and techniques for improving software quality. Causes of software failures or poor performance of a software product are discussed attributing the causes to a variety of reasons but most notably human error, the nature of software itself, and the environment in which software is produced and used. Finally historic examples of software caused accidents are given including the Therac–25, the Space Shuttle Challenger, the Indian Bhopal chemical accident and the Chernobyl Nuclear Power accident. Both consumer protection and techniques for improving software quality are also discussed.

This study aims to highlight the relevance of budget transparency in improving human development and fill the gap in the literature, which has traditionally considered the concept of development in economic terms. Here, development refers to levels of knowledge, the possibility of enjoying a long and healthy life and having a decent standard of living. Improving human development is the main point of interest in the assessment of public policy outcomes and this study is focussed on one specific policy, namely, budget transparency. By using a pool of 110 countries for the years 2008, 2010, 2012, 2015 and 2017, the empirical results demonstrate a positive effect of the open budget index on the human development index, suggesting that budget transparency is a good way to improve levels of human development. These findings are essential not only for academic debate but also for practitioners because they show transparency as a relevant tool to improve human development. Points for practitioners Budget transparency is a relevant tool to improve human development. Transparency may improve the quality of life through a better quality of governance. The central roles played by transparency and participation coupled with accountability have been confirmed. The complexity of the concept of human development involves, among other things, disclosing information regarding the public policies. Examining budgets, guaranteeing their transparency as well as the transparency of the budgeting process, is highly recommended to improve human development.

Operational risk is a substantial source of risk for US banks. Improving the performance of operational risk models allows banks’ management to make more informed risk decisions by better matching economic capital and risk appetite, and allows regulators to enhance their understanding of banks’ operational risk. We show that past operational losses are informative of future losses, even after controlling for a wide range of financial characteristics. We propose that the information provided by past losses results from them capturing hard to quantify factors such as the quality of operational risk controls, the risk culture, and the risk appetite of the bank.

Background The screening of biological hazards, chemical contaminants and allergens in food products is a major concern of food industry since they can cause serious illnesses and even lead to death. Therefore, there is a significant interest in developing low-cost, robust, and rapid detection methods for food safety and quality analysis. Conventional methods provide accurate analytical characteristics, but require well-equipped laboratories, trained personnel, costly reagents, and complicated sample preparation procedures prior to the detection in order to obtain reliable results. Hence, they are not always ideal for on-site food surveillance. Scope and approach This paper discusses the fundamentals of microfluidic paper-based analytical devices (μPADs) and reviews the recent progress in their usage for screening biological hazards, harmful chemical residues, and allergens in food products to systematically evaluate the strengths and weaknesses of this technology. It then covers future trends and useful strategies that could be employed in μPADs design and fabrication procedures to address the critical challenges in equipment-free food analysis. Key findings and conclusions μPADs offer promising avenues to revolutionize food safety and quality analysis with merits of equipment-free sample preparation and detection at low-cost. Despite the advancement in the microfluidic technology, the manufacturing of user-friendly sample-to-answer μPADs still requires intensive research studies to be employed for robust and sensitive food safety and quality analysis.

Bisphenol A (BPA) is one of the most studied and most controversial chemicals used by the food packaging industry, because of its endocrine disruptive properties. Part of the controversy is due to the uncertainty that surrounds the effects of BPA on the endocrine system. Uncertainty includes data gaps, methodological hurdles, incompatibilities between toxicology and endocrinology-based approaches, and so on. In this article, we analyze how uncertainty has been conceptualized and treated. We focus on the European Food Safety Authority assessments of BPA, and study how exposure and hazard assessments have evolved over time, how uncertainty has been analyzed, and how the agency responded to controversies. Results show that in the attempt to reduce knowledge gaps, assessments have become progressively larger, including more references, evidence, and effects. There is a tendency toward greater precisions and specification of results, and toward protocolization of all processes included in the assessment (from literature review, to uncertainty assessments, and public consultation). Yet, the uncertainty has not diminished following the increase in evidence. We argue that the strategy used to reduce uncertainty within risk assessment, namely including more variables, studies, data, and methods, amplifies the uncertainty linked to indeterminacy (as more results increase the fragmentation of the knowledge base due to the open-ended nature of complex issues) and ambiguity (as complexity gives way to multiple nonequivalent interpretations of results). For this reason, it is important to consider different types of uncertainty and how these uncertainties interact with each other. publishedVersion

The legal framework for biosimilar medicinal product was first established in 2004 in the European Union and later in the USA. Since then, based on the scientific and regulatory experience with biosimilarity assessment, regulatory approaches have evolved considerably. A clear understanding of the capabilities of each development stage in assessing comparability in terms of power to identify differences and assess their relevance to the final clinical outcome gradually accumulated. In addition to the commitment to the biosimilarity approach, it is important to comply with the general requirements applicable to any drugs produced at an industrial scale. Experience showed that the demonstration of biosimilarity based on a range of analytical and functional tests and mainly clinical pharmacological studies is universal and allows extrapolating therapeutic indications. The establishment of biosimilar interchangeability, since the active substance by definition is a version of the active substance of the corresponding reference biological product, can introduce additional difficulties. A single approach to the establishment of biosimilar interchangeability has not yet been developed.

Microbial-based cleaning products (MBCPs) have been introduced, on the market, as an alternative to traditional chemical cleaning. In addition to traditional detergents, MBCPs can perform their cleaning function, digesting the smallest particles of dirt and mitigating odours generated by environmental bacterium metabolic processes. Nevertheless, several aspects remain to be clarified and assessed, requiring further studies and new regulations to ensure safety. The particular composition of MBCPs makes it difficult to include these products in a specific class, making the European legal context incomplete and unclear. Moreover, MBCPs effects on human health are poorly documented. Exposure risks can be obtained indirectly by studies conducted in both microorganisms exposure and their metabolic products, such as enzymes, especially in workers. A further limiting factor for the accurate human health risk assessment due to MBCPs use is an incomplete indication about the MBCPs compositions. Moreover, additional factors such as host microorganisms, frequency and space of use, subject health condition, and age can determine different illness scenarios. The findings from the broad range of studies we have reviewed in this paper confirm the necessity of integrative investigation and regulation to address the use of MBCPs.

Mobile crowdsensing has become an efficient paradigm for performing large-scale sensing tasks. Many quality-aware incentive mechanisms for mobile crowdsensing have been proposed. However, most of them measure the data quality by one single metric from a specific perspective. Moreover, they usually use the real-time quality, which cannot provide sufficient incentive for the workers with long-term high quality. In this paper, we refine the generalized data quality into the fine-grained ability requirement. We present a mobile crowdsensing system to achieve the fine-grained quality control, and formulate the problem of maximizing the social cost such that the fine-grained ability requirement of all sensing tasks can be satisfied. To stimulate the workers with long-term high quality, we design two ability reputation systems to assess workers’ fine-grained abilities online. The incentive mechanism based on the reverse auction and fine-grained ability reputation system is proposed. We design a greedy algorithm to select the winners and determine the payment based on the bids and fine-grained ability reputation of workers. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve computational efficiency, individual rationality, truthfulness, whitewashing proof, and guaranteed approximation. Moreover, the designed mechanisms show prominent advantage in terms of social cost and average ability achievement ratio.

Battery-powered drug delivery devices are widely used as primary containers for storing and delivering therapeutic protein products to improve patient compliance and quality of life. Compared to conventional delivery approaches such as pre-filled syringes, battery-powered devices are more complex in design requiring new materials/components for proper functionality, which could cause potential product safety and quality concerns from the extractable and leachables (E&L) of the new materials/components. In this study, E&L assessments were performed on a battery-powered delivery device during the development and qualification of the device, where novel compound 2‑hydroxy-2-methylpropiophenone (HMPP) and related compounds were observed in both E&L. The source of the HMPP and related compounds was identified to be the nonproduct contact device batteries, in which HMPP photo-initiator was used as a curing agent in the battery sealant to prevent leakage of the battery electrolytes. Toxicology assessment was performed, which showed the levels of HMPP observed in the device lots were acceptable relative to the permitted daily exposure. A drug product HMPP spike study was also performed, where no product impact was observed. Based on these assessments, an action threshold and specification limits could be established as a control strategy, if needed, to mitigate the potential risks associate with the observed leachables. As a full resolution, seven battery candidates from different suppliers were screened and one new battery was successfully qualified for the delivery devices. Overall, the holistic E&L approach was fully successful in the development and qualification of the battery-powered devices for biotherapeutic products delivery ensuring product quality and patient safety. Non-product contact materials are commonly rated as low or no risk and typically considered as out of scope of E&L activities for delivery systems following industry benchmark and regulatory agency guidance. This case study is novel as it brings into attention the materials that might not normally be in consideration during the development process. It is highly recommended to understand materials in the context of intended use on a case-by-case basis and not to generalize to ensure successful development and qualification.

The assessment of impact of oil production economic activities on land pollution in Russia contributes to evolutionary management decision making. Oil industrial pollution affects negatively flora and fauna. Thus, its important to identify the level of its exposure and danger, the site of contamination. A system approach is needed. When studying the environment, its necessary to consider the presence of risk situations and stochastic irreversible changes. Its essential to identify the nature and type of soil contamination with petroleum products using high-tech tools, intellectual procedures. The work considers modeling of such situation, forecasting and identification of oil contaminants. The submodel of optimal termination of monitoring is also considered. Ending monitoring of environmental optimization will result in lower monitoring costs, since monitoring oilcontaminated environments is an expensive and complex technological mechanism, often requiring satellite data. The proposed algorithm for modeling and system analysis is based on situational modeling. Evolutionary modeling allows to adapt the procedure (methodology) of forecasting and assessment to environmental risk factors. It increases the accuracy (formalization and evidence) and completeness of conclusions, the efficiency of situation analysis, which affects manageability of risk both for the oil complex and for individual enterprise in the industry. The results of the research may be used for development of software tools, in particular expert and predictive systems. Situational models are needed when oil companies are solving multi-criteria and multifactor problems.

It is standard procedure to use short-term measures for designing customized marketing campaigns and evaluating their success. We consider the integration of customer lifetime value (CLV) thinking into the category selection problem for targeted coupons. This problem involves two primary considerations: should the coupon be a category-specific one? – and, if so, should short-term or rather long-term objectives govern the choice of category? Our paper is the first to address these questions by analyzing the effects of different customized targeting strategies for category coupons on short-term marketing costs and long-term CLV using an analytical model. We develop these targeting strategies with the help of a data-driven customer segmentation framework based on the dimensions of churn, frequency, and loyalty. Using data from a German hypermarket chain, we identify four refined targeting strategies that enable multi-category retailers to exploit the full potential of customized marketing by optimizing the trade-off between marketing cost and CLV considerations. Our analysis reveals that the targeting strategy most likely to be employed by retail practice – as compared with the one most commonly used – can yield a substantially higher CLV even as it reduces wasteful marketing costs.

Purpose The COVID-19 pandemic has affected the management and operation of regulatory agencies and the pharmaceutical industry around the world. It has prompted regulatory authorities to consider new ways of working and introduced, among others, remote inspections to validate the integrity of the regulatory data submitted by companies, to evaluate the quality of production and manufacturing sites, and to ensure the conformity with Good Regulatory Practices with the overall goal of guaranteeing patient safety during the crisis. Method This article summarizes and discusses remote inspection guidelines and other related information made available by the Therapeutic Goods Administration (Australia), the European Medicines Agency (EMA), the Pharmaceutical and Medical Devices Agency (Japan), the Medicines and Healthcare Products Regulatory Agency (United Kingdom), and the US Food and Drug Administration (FDA). We also analyze the effect of the pandemic on inspections conducted by the inspectorates of the EMA and the FDA. Findings The regulatory authorities that we studied all recognized the importance of implementing regulatory policies on remote inspections in response to the COVID-19 pandemic. The remote inspection guidelines from the 5 selected regulatory authorities aimed at mitigating the impact of the pandemic but, while providing valuable advice to the pharmaceutical companies and being similar in intent, were not always aligned in terms of approach and solutions. Implications On-site inspections are likely to continue to be the norm and the preferred standard for the foreseeable future. However, health authorities will need to further adopt a risk-based inspection approach and stimulate the increased uptake of inspection reliance as proposed by the Pharmaceutical Inspection Convention and Pharmaceutical Inspection Co-operation Scheme not to overwhelm the pharmaceutical companies with repeat and redundant inspections. Remote inspections have proven to be a new inspection tool, but health authorities should align on their approach to remote inspections in terms of methods applied and documentation requested.

Nowadays, the hotel management concept cannot keep pace with the times. Traditional concepts are often adopted to manage hotel financial personnel, for the hotel financial personnel cannot take timely and effective training. All these lead to the hotel financial staff designing the hotel’s related business without sufficient understanding of the hotel industry and judging and deciding if they do not master the hotel’s professional knowledge, which makes the participating projects unable to give correct and reasonable answers to the substantive problems of the hotel. This leads to the hotel management not going up; extensive management makes the hotel benefit not go up. Hotel intelligent technology can solve these problems and not only save manpower and material resources but also intelligently predict the financial crisis of hotels. In the context of the accelerated development of globalization and informatization, there are still many problems in the financial management process of my country’s hotel industry. Based on these questions, the article draws on foreign advanced experience, puts forward effective suggestions in financial management, and uses computational intelligence technology to design a centralized and intelligent financial management system. The research results show the following: (1) the financial crisis model is created by using the principle of support vector machine and logistic regression method, which greatly reduces the financial crisis of the enterprise. (2) The system can straightforwardly summarize the data for easy query. Taking three domestic hotels as an example, a comprehensive study has been carried out on the three aspects of pricing assessment risk, financial integration risk, and debt risk. In 2016, the financial leverage coefficient has been relatively high, the quick ratio has fluctuated greatly, and the interest protection coefficient has shown a downward trend. (3) The performance of the system is compared with traditional development mode, framework development mode, and intelligent optimization mode. The intelligent optimization system has the lowest response time and the highest success rate. The new system has reduced response time by about 57% compared with the original response time, and the access success rate has been greatly improved.

We construct a spatially-compartmental individual-based model of the spread of Covid-19 in indoor spaces. The model can be used to predict the infection rates in a variety of locations when various non-pharmaceutical interventions (NPIs) are introduced. Tasked by the Welsh Government, we apply the model to secondary schools and Further and Higher Education environments. Specifically, we consider student populations mixing in a classroom and in halls of residence. We focus on assessing the potential efficacy of Lateral Flow Devices (LFDs) when used in broad-based screens for asymptomatic infection or in ‘test-to-release’ scenarios in which individuals who have been exposed to infection are released from isolation given a negative result. LFDs are also compared to other NPIs; we find that, although LFD testing can be used to mitigate the spread of Covid-19, it is more effective to invest in personal protective equipment, e.g. masks, and in increasing ventilation quality. In addition, we provide an open-access and user-friendly online applet that simulates the individual-based model, complete with user tutorials to encourage the use of the model to aid educational policy decisions as input infection data evolves (https://bit.ly/CV19_INTER_IBM).

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.

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.

ISSUE The second paper in this three-part perspective on improving dental education through focus on outcomes explores the two common ways of controlling variance in processes such as dental education. APPROACH Quality assurance is based on the view that variance is a characteristic of the material or product produced by the process. In the case of dental education this means students. The reputation of educational programs depends to some extent on identifying and correcting or putting aside output that do match standards. This is an expensive process that does not improve educational programs. Quality improvement, by contrast, is grounded in the assumption that there is unwanted variation in the process itself. IMPACT Reducing system variation through judicious adjustment at each cycle of the process raises yield, reduces the proportion of defects, and reduces program cost. Examples are given where good decisions in the reflection stage of the GEAR cycle make goals more realistic, improve experiences, and promote more effective assessment.

Hospitals operate in increasingly complex and dynamically uncertain environments. To understand how hospital organizationscan cope with such profound uncertainty, this article presents a multiple case study of five hospitals during the COVID-19crisis in a heavily hit region of the Netherlands. We find that hospitals make adaptations in five key categories, namely:reorganization, decision-making, human resources, material resources, and planning. These adaptations offer insights intothe core capabilities needed by hospitals to cope with dynamic uncertainty. Our findings highlight the need for hospitals tobecome more flexible without sacrificing efficiency. Organizations can accomplish this by building in more sensing and seizingcapabilities to be better prepared for and respond to environmental change. Furthermore, transforming capabilities alloworganizations to be more resilient and responsive in the face of ongoing uncertainty. We make recommendations on howhospitals can build these capabilities and address the core challenges they face in this pursuit.