Search

Whilst adaptive reuse is often applied with good outcomes, we are also faced with projects that have not achieved the desired results. There is little insight into why some projects succeed and others fail, or even what constitutes "success" at all – due in part to the intangibility of the word "success" and unsettled definitions and synonyms of "adaptive reuse. Accordingly, this paper seeks to answer the question: What are the factors that can be used to assess the success of heritage adaptive reuse projects? This study is framed as a systematic literature review of relevant articles published or in press. The methodology is based on using a PRISMA diagram to address a number of papers that are screened in each step of the diagram: identification, screening, eligibility and included. The literature review process started with 731 in the first step and ends with the final results of 72 papers. The results are classified into ten categories of success factors: architectural, structural, socio-cultural, economic, environmental, energy, authenticity, legal, management and functional factors. Together, these provide a comprehensive understanding of factors that affect the success of adaptive reuse as a strategy to regenerate heritage buildings. This insight facilitates adaptive reuse strategies for designers, architects and real estate developers. [ABSTRACT FROM AUTHOR]

• Discretion is affected by individual, relational and organisational factors. • In an experimental design, we study what affects discretion. • The use of interpreters is dependent on relation to the child. • Interpreter services are less likely to be used in acute situations. • The use of interpreter services is less dependent on the children's language skills. This paper focuses on child welfare workers' discretionary decision making. They can rely on routines, procedures and manuals as part of their work, but in the end, they still have to make a choice, based on the available information at the time. Discretionary decision making is a way of reasoning when facing uncertainty in professional work and when laws, rules and systematic knowledge must be applied in specific cases. A larger body of research on discretion and discretionary decision making in social work has developed, but there remains much ground to cover in terms of empirical analyses of how discretionary processes play out in real-life contexts. The two main research questions ask how the characteristics of each child and context and of social workers and their work situation affect the latter's decision to use interpreter services in their work with unaccompanied minors and children from minority backgrounds. The decision to use or not to use interpreter services in different situations, as an act of discretionary decision making by child welfare workers, is analysed in this paper. To study discretionary decision making among child welfare workers, a combination of a traditional survey and a vignette experiment has been used, bringing together organisational features, individual traits and situational characteristics. The findings indicate that interpreter services are less likely to be used in acute situations and more likely to be used with the children whom the child welfare staff members know better, irrespective of the children's language skills. The analyses also show varying effects of the staff's training and educational backgrounds, and surprisingly, the negative effects of institutional guidelines. One possible interpretation of this is that the guidelines are perceived as rigid constraints, instead of positive recommendations for actions. [ABSTRACT FROM AUTHOR]

• In this paper, the innovative research of the autonomous control system of the once-through steam generator (OTSG) is carried out, which is mainly aimed at the normal operating condition, especially the fault condition when the operating characteristics of the system change dramatically. • The ISSA algorithm is applied in the research of autonomous decision-making modules because of its strong ability of optimization and fast convergence. • This paper improves the SSA algorithm from two aspects: population initialization and finder position update and the optimization ability. In this paper, the innovative research of the autonomous control system of the once-through steam generator (OTSG) is carried out, which is mainly aimed at the normal operating condition, especially the fault condition when the operating characteristics of the system change dramatically. The autonomous control system includes fault diagnosis, autonomous decision-making, hybrid control modules. The ISSA algorithm is applied in the research of autonomous decision-making modules because of its strong ability of optimization and fast convergence. This paper improves the SSA algorithm from two aspects: population initialization and finder position update and the optimization ability. The simulation results show that the autonomous control system proposed in this paper can make a decision in the shortest possible time, and provide an effective automatic control method to make the OTSG quickly and smoothly reach the safe state. [ABSTRACT FROM AUTHOR]

Background: Decision making on nutrition and hydration for cancer patients during terminal stage cause critical impacts toward patient's comfort and living quality. The management of nutrition is the main dilemma that arises in these final situations and has been the subject of intense debate over the last few decades.Aim: To find the views of patients, families, and healthcare professionals related to how decisions are made when cancer patients are at terminal stage.Design: This systematic review used PRISMA strategy to search and used Critical Appraisal Skills Programme checklist to evaluate the papers.Data Sources: All English papers through August 2020 that contained the view of the decision making at artificial nutrition and hydration with cancer patients, families, and healthcare professionals at terminal stage were included. Selected studies were independently reviewed, and data collaboratively synthesized into core themes.Results: Most of the terminal stage cancer patients and their families initially started the decision-making process when facing the reduction of oral intake. There are two primary considerations of patients and families, one is for prolonging patients life, and the other is to maintain their life quality. The voices of patients were influential, but not determinative; families usually had influence, but seldom make the final recommendation by themselves; healthcare professionals frequently face the dilemma about their decision.Conclusion: The decision of nutritional support was dynamic; the interaction between patients and families frequently be hesitated to protect the rights of life, unnecessarily prolonging lifetime. Therefore, a better understanding of the views on nutritional support and processing the clinical guideline of decision making for healthcare professional is necessary. [ABSTRACT FROM AUTHOR]

Models of Fuzzy Multi-Criteria Decision Analysis (FMCDA) are based, as a rule, on different approaches to fuzzy extension of source MCDA methods. For this, simplified models are used to approximate the functions of fuzzy variables with propagation of parametric fuzzy numbers (FNs) through all calculations. In this paper, authors suggest a novel approach to fuzzy extension of MCDA methods, for PROMETHEE-I/II, through development of fuzzy PROMETHEE-I/II (FPOMETHEE-I/II) models of different complexity: in addition to simplified models, the standard fuzzy arithmetic (SFA), and transformation methods (TMs) are implemented for assessing functions of FNs corresponding to these models. For ranking of alternatives, two defuzzification based, and one pairwise comparison ranking methods are implemented within the developed models. Special attention is paid to analysis of the overestimation problem, which can occur when using SFA in the presence of dependent variables in corresponding expressions, and to "proper fuzzy extensions" of PROMETHEE-I/II (i.e., results of all functions of FNs within the model are in accordance with the extension principle) based on TMs and, for some models, on the SFA. One of the key goals of this contribution is comparison of the distinctions in ranking alternatives by different FPROMETHEE-II models. It is demonstrated by evaluating a large number of scenarios based on Monte Carlo simulation that the probability of distinction in ranking alternatives by "proper" and "approximated" FPROMETHEE-II models may be considered as significant for ranking multicriteria problems. Another goal of this paper is analysis of the correctness of FPROMETHEE-I/II models with respect to the basic MCDA axiom related to ranking of dominated and dominating alternatives. Authors demonstrate that the basic axiom can be violated, in the general case, by all developed FPROMETHEE-I/II models and suggest an approach to fix this problem. [ABSTRACT FROM AUTHOR]

The problem of pursuing the data items of a large database whose distances to a query item are the least is known as Similarity Search (Nearest Neighbor Search) problem. There exist various algorithms to address this problem. Some of the well known algorithms are i) exact algorithms ii) approximation algorithms and iii) randomized algorithms. This paper has made study only on exact and approximation algorithms because randomized algorithm produces approximate results with some probability. Recently, there are several approximation algorithms are proposed by the researchers because this type of algorithms minimizes the problem of Curse of Dimensionality. This paper mainly has two major sections. In first section, various methods under exact and approximation algorithms are discussed with regard to storage, preprocessing and query time. In the second section, efficient algorithms for similarity search suitable for certain physiological characteristics based biometric systems are considered. Biometric system has five main steps viz acquisition of Image, pre-processing, extraction of features, matching and making final decision. In this paper, indexing algorithms for similarity search suitable for iris trait based on different features are discussed in detail. Since the nature of features are distinct and different in biometric traits, there does not exist a universal (one unique) solution which can apply to all traits of biometric systems. Various performance measures like Penetration Rate and Hit Rate are used to determine the correct recognition rate with top best match (rank-1 accuracy). [ABSTRACT FROM AUTHOR]

• The paper explores Bayesian bootstrap in probabilistic photovoltaic power forecasting. • Bayesian bootstrap is applied to three probabilistic forecasting models. • The optimal quantile is extracted from the sample bootstrap distribution. • Bayesian bootstrap is compared with traditional bootstrap and with other benchmarks. • Numerical experiments validate the Bayesian bootstrap performance on real data. Modern distribution systems are characterized by increasing penetration of photovoltaic generation systems. Due to the uncertain nature of the solar primary source, photovoltaic power forecasting models must be developed in any energy management system for smart distribution networks. Although point forecasts can suit many scopes, probabilistic forecasts add further flexibility to any energy management system, and they are recommended to enable a wider range of decision making and optimization strategies. Real-time probabilistic photovoltaic power forecasting is performed in this paper by using an approach based on Bayesian bootstrap. Particularly, the Bayesian bootstrap is applied to three probabilistic forecasting models (i.e., linear quantile regression, gradient boosting regression tree and quantile regression neural network) to provide sample bootstrap distributions of the predictive quantiles of photovoltaic power. The heterogeneous nature of the selected models allows evaluating the performance of the Bayesian bootstrap within different forecasting frameworks. Several benchmarks and error indices and scores are used to assess the performance of Bayesian bootstrap in probabilistic photovoltaic power forecasting. Tests carried out on two actual photovoltaic power datasets for probabilistic forecasting demonstrates the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In every decision-making problem which involves two or more criteria, there is to identify the relative importance of those criteria in order to make a proper decision. Very often, a decision-makers employee, for this purpose, subjective weighting methods, such as Analytic Hierarchy Process (AHP) or Ranking Comparison (RANCOM). However, there is no simple way to compare the quality of the weights identification using different methods. To address this issue, this paper proposes a simple but Efficient Weights Similarity Coefficient, which allows us to evaluate the relative performance of different subjective weighting methods. Furthermore, we propose a framework that utilizes the proposed coefficient in order to provide more complete comparison results. To demonstrate the applicability and effectiveness of the proposed framework, a case study is performed to compare two popular weighting methods: AHP and RANCOM. The results of the comparison prove that proposed coefficient with the combination of the proposed framework is an Efficient instrument for weighing methods comparison. [ABSTRACT FROM AUTHOR]

Healthcare offers a rich palette of potential applications of RFID technology. Healthcare provides a rich palette of possible applications of RFID technology. Besides traditional uses such as tracking medical equipment and devices or access control, healthcare can benefit even more significantly from RFID technology. However, using the RFID technology in healthcare raises various problems of scalability, timely identification of tags, security, privacy, and efficient implementation in practice. That is because such systems contain many tags, operate with private personal data, and must respond promptly in concrete, practical situations to avoid malfunctions (errors in the decision process, traffic congestion, and so on). This paper discusses the fundamental requirements of RFID systems raised by healthcare and the limitations of existing schemes. Then, we propose a new RFID scheme that achieves mutual authentication, strong privacy, and constant-time identification in the HPVP model. The scheme employs a secure symmetric-key encryption scheme, making it very efficient in implementation and physically unclonable functions (PUFs) to protect the secret key against adversaries with corruption capabilities. [ABSTRACT FROM AUTHOR]

A shared economy is a model of collaborative consumption by obtaining, giving, or sharing access to goods and services through online platforms. It is a marketplace that provides users with access to product benefits with temporary ownership. Digitalization has introduced new possibilities and conditions for sharing and exchanging goods and services, allowing users to compare offers and connect dynamically with the seller in real time. The paper presents the results of the survey conducted on a group of 1,620 prosumers and the practical applications of the analysis of the results. The research aimed to identify the main components of the co-sharing decision-making process and their importance in this process. A diagnostic survey was used in the study, and the collected data were statistically analyzed. The main findings are that there are common criteria for evaluating the model of sharing goods and services for the research population. They are: sharing preference, trust, realized benefits, propensity to propagate, risk awareness, aversion to sharing, willingness to share, and the limits of anonymity. Moreover, subjective satisfaction with the achieved income influences the perception of factors that are important in the decision-making process of sharing goods and services. [ABSTRACT FROM AUTHOR]

Due to importance of data FAIRness (Findable, Accessible, Interoperable, Reusable), ontologies as a means to make data FAIR have attracted more and more attention in different communities and are being used in semantically-enabled applications. However, to obtain good results while using ontologies in these applications, high quality ontologies are needed of which completeness is one of the important aspects. An ontology lacking information can lead to missing results. In this paper we present a tool, Phrase2Onto, that supports users in extending ontologies to make the ontologies more complete. It is particularly suited for ontology extension using a phrase-based topic model approach, but the tool can support any extension approach where a user needs to make decisions regarding the appropriateness of using phrases to define new concepts. We describe the functionality of the tool and a user study using Pizza Ontology. The user study showed a good usability of the system and high task completion. Further, we report on a real application where we extend the Materials Design Ontology. [ABSTRACT FROM AUTHOR]

The probabilistic hesitant fuzzy sets (P-HFSs) allow experts to express their preferences regarding different membership degrees with probabilistic information. Fuzzy entropy is an important concept to measure the uncertainty of probabilistic hesitant fuzzy information. In this paper, we propose a novel fuzzy entropy named as the exponential probabilistic hesitant fuzzy entropy for the P-HFEs. This novel fuzzy entropy not only observes the basic property of fuzzy entropy, but also has the same characteristics and change tends with the existing fuzzy entropy proposed and studied by other scholars. Then, based on the cardinal reconciliation method and TODIM(Tomada de decisao interativa multicriterio) method, we propose a multi-criteria decision-making (MCDM) problem, where the attribute weight is determined by the exponential probabilistic hesitant fuzzy entropy of the integration results. Finally, a application case in green building is given for the sake of illustrating the efficiency of the MCDM problem. Numerical and theoretical results show that a MCDM problem based on the proposed novel fuzzy entropy and the TODIM method have a wide range of application. [ABSTRACT FROM AUTHOR]

The loading outline of railway out-of-gauge freight (ROF) is beyond the railway gauge due to its larger size. The choice of a safe and economical transportation route according to the characteristics of the freights in the railway network becomes an important issue for the railway out-of-gauge freight transportation. A proposed methodology is presented in this paper with the aim to solve the problems in the railway out-of-gauge freight transportation route decision-making (ROF-TRD) process in which safety distances between the railway gauges and freight loading outlines, the curve radius and the arrival-departure traffic flow balance are taken into consideration while the railway capacity losses and transportation costs being as objective functions to construct the route decision-making model considering gauge modification. The proposed route search algorithm can be used in the model to further refine the design of an effective safety distance bilateral detection. The case study verification shows that the safest and most economical railway out-of-gauge freight transportation route can be obtained by applying the proposed model, and the results demonstrate that taking railway capacity loss and modified costs of possible ROF routes into consideration, the optimal route can save around 10–22 % of total costs. [ABSTRACT FROM AUTHOR]

In manufacturing, different costs must be considered when selecting the optimal manufacturing configuration. Costs include manufacturing costs, material costs, labor costs, and overhead costs. Optimal manufacturing configurations are those that minimize production criteria, such as costs, production speed, and flexibility, while still meeting the required production levels and quality standards. To find the optimal manufacturing configuration, manufacturers often use a combination of traditional techniques, e.g., mathematical modeling, simulation, and optimization, to evaluate the tradeoffs between different cost factors and identify configurations that provide the best balance between cost and performance. However, these techniques may require long development and simulation time, and/or may require expert knowledge. This paper presents a method for selecting the optimal manufacturing configuration, focusing on cost optimization, using a reinforcement learning (RL) approach for sequential decision-making. The proposed method involves developing a RL environment, requiring lower development and simulation times than traditional techniques, that captures the incurred costs, recurring costs, production rates, and setup times of manufacturing configurations. The problem is then solved using the Proximal Policy Optimization algorithm to identify the configuration that minimizes costs while still meeting the required production levels and quality standards. The effectiveness of the proposed method is validated through a machining process planning case study with multiple cost factors and production constraints. In particular, the machining process plan was developed for an industry-relevant product prototype. The results show that the proposed method can find solutions that are robust to stochastic noise, providing valuable insights for manufacturers looking to optimize manufacturing operations. [ABSTRACT FROM AUTHOR]

Biologicalisation as the convergence of biology, engineering and information technology offers the prospect of dramatic step change scenarios for future innovative development. A large number of highly stimulating and potentially very valuable solutions, created over millions of years of evolution, are available in nature's solution space and waiting for application in technology. Transfer methods linking the biosphere and the technosphere are classified as functionalities which assume: bio-inspiration from nature, bio-integration combining biological and technological solutions, and bio-intelligence. The latter aims at achieving developments towards living systems based on the (reasonably) high level of appreciation of the environment, the system's capability and the specific task to be undertaken using decision making or self-reasoning. The use of large numbers of different sensors is involved together with sensor fusion strategies, self-healing and self-organising properties, along with functional integration. Moreover, to derive maximum benefit, the key enabling technologies will play a crucial role going forward. The impact on industry of unique and outstanding solutions will radically change the way manufacturing is performed today by building on new levels of latency, interconnectivity and communication. This paper aims at supporting the comprehension of these developments and revealing future trends, research needs and educational requirements in manufacturing science and technology as the biosphere and the technosphere converge to create the new levels of global sustainability. [ABSTRACT FROM AUTHOR]

Data clustering is an instrumental tool in the area of energy management resources, marketing, and business. Clustering helps to increase productivity, facilitate decision-making, and generate new business opportunities. On the other hand, the soft set theory provides a general mathematical tool for dealing with uncertain, and vague information. In this paper, we present the novel concept of the BCIFSSs ("bipolar complex intuitionistic fuzzy soft sets") by merging bipolar complex intuitionistic fuzzy sets and soft sets. Also, we explain their basic operations including complement, union, and intersection with some appropriate examples. The involvement of complex numbers enables these structures to cope with phase-altering problems and multidimensional problems for handling ambiguity. Further, the BCIFSSs have an extensive structure because it discusses both grades of memberships (Mem-S) and non-memberships (Non-Mem-S) with positive and negative aspects and can also deal with multivariable difficulties. Later on, we stated a decision-making algorithm and real-world examples to demonstrate the effectiveness, and applicability of the proposed concept. The BCIFSSs show the dual grades of both the Mem-S and Non-Mem-S in the decision-making process. Finally, the comparative analysis of introduced frameworks with some pre-existing ideas is given. [ABSTRACT FROM AUTHOR]

Fungal chitosan is one of the piezoelectric biomaterials that can be used in energy harvesting. Fungal biomass offers a sustainable source for chitosan, as an alternative to crustacean-based chitosan. However, the fungal chitosan yield is relatively lower than other conventional sources of chitosan. Therefore, this study aims to review different experimental procedures for the chemical extraction of chitosan from biomass. The selection of the most effective extraction method using Preference Ranking Organization Method for the Enrichment of Evaluations and Geometrical Analysis for Interactive Decision Aid (PROMETHEE-GAIA), a systematic multiple criteria analysis based on ranking and section of alternatives (i.e. different extraction methods). PROMETHEE-GAIA was performed as the critical and comprehensive discussion in this paper. The PROMETHEE-GAIA analysis showed that the microwave-assisted organic acid-alkaline treatment extraction method showed the highest ranking for the most effective fungal chitosan method due to positive response from the chitosan yield, degree of deacetylation, toxicity and temperature. [ABSTRACT FROM AUTHOR]

In fresh supply chain, the third-party logistics service provider (3PL) has incentive to misreport its private information such as preservation service effort level and logistics transportation cost for the sake of gaining competitive advantage in the market. A tamper-proof and traceability system supported by blockchain technology (BCT) can prevent 3PL from misreporting. In this paper, we investigate the BCT adoption decision of a fresh supply chain consisting of a manufacturer, a retailer, and a 3PL when the 3PL misreports its private information. Specifically, we conduct four models: without BCT adoption and 3PL misreports its preservation service effort level (Model NE), without BCT adoption and 3PL misreports its logistics transportation cost (Model NC), without BCT adoption and 3PL misreports both preservation service effort level and logistics transportation cost (Model ND) and with BCT adoption and no misrepresentation (Model AB). By calculating the models, our study indicates that when the 3PL underreports its fresh-keeping service effort level and the degree of underreporting is high or when the 3PL overreports its logistics transportation cost and the degree of overreporting is high, all fresh supply chain members will reach an agreement on implementing BCT. However, when the 3PL misreports its fresh-keeping service effort and logistics transportation cost simultaneously, the BCT adoption decision is influenced not only by the misreporting coefficient of logistics transportation cost and preservation service effort, but also by logistics transportation cost. Finally, as logistics transportation cost increases, the manufacturer and retailer are reluctant to adopt BCT, while the 3PL is willing to adopt BCT. [ABSTRACT FROM AUTHOR]

Real-world decision-making tasks are generally complicated and require trade-offs between multiple, even conflicting, objectives. As the advent and great development of advanced information technology, it has evolved into using reinforcement learning (RL) algorithms to tackle the multi-objective decision making (MODM) problems. In this paper, we will first identify the basic concepts and factors when modelling the MODM tasks with reinforcement learning, and then review the traditional RL, such as Sarsa, Q-Learning, Policy Gradients, Actor-Critic, Monte-Carlo learning, and modern deep RL algorithms applied in this process. Furthermore, the specific practical scenarios described in MODM problems will be summarized through analyzing some typical articles. Finally, the future trends of multi-objective reinforcement learning will be discussed. [ABSTRACT FROM AUTHOR]

The vehicle selection problem for the armed forces is a very important problem. In order to make the best decision regarding the selection of the best alternative among the existing options is necessary to use support decision-making tools. The aim of this paper is to demonstrate the functionality of the TOPSIS-M method in a vehicle selection problem for the Brazilian Navy (Marinha do Brasil). Among the vehicles selected as alternatives, the best option, according to the method used, was the vehicle IVECO HI-WAY800S56TZ. According to this work, the method used for vehicle selection proved to be efficient in assisting decision-making for the acquisition of new vehicles for the Brazilian Navy. [ABSTRACT FROM AUTHOR]

This paper presents a reinforcement learning approach for decision-making in assigning multiple robots in a flexible manufacturing system. The algorithm allows for learning the optimal action policy for assigning tasks to multiple robots, outperforming other methods. The case study involved three robots and four tasks, some requiring cooperation. The study demonstrates the effectiveness of the approach in comparison to other methods and the results indicate that the reinforcement learning approach increases production efficiency and reduces task completion time compared to traditional assignment methods. [ABSTRACT FROM AUTHOR]

Heuristics are rules formulated as simple rules of thumb intended to simplify the achievement of certain goals. Such heuristics promise far-reaching advantages in the context of product development with regard to acquisition, transfer and representation of knowledge to achieve predefined objectives (e.g., DFX objectives). A format for describing design heuristics in a standardized way have already been developed by the authors. In this paper we present the requirements analysis for an app that shall help designers, students, and researchers alike to record and find heuristics for product development. These requirements were deducted from 28 interviews with design experts from industry and higher education. Since the application has already been developed following the study, central functions of the tool will be exemplified using two actual use cases from the app. In addition, potentials of the app with regard to broader KBES integration are discussed. [ABSTRACT FROM AUTHOR]

The development of White Spot Lesions (WSLs) after completion of treatment is immensely discouraging to a specialty whose ultimate goal is to improve dentofacial aesthetics. This paper highlights state-of-the-art biomaterials that are used in the prevention, progression and treatment of WSLs, in the form of workflows to guide a clinician in decision-making at different treatment timings; before, during and after treatment. Scholarly literature has been screened until November 2022 for studies that assessed WSLs management. The mechanism of action and recommended mode of application have been elaborated upon. Additionally, the level of evidence of each treatment modality at different treatment timings has been highlighted according to its hierarchy. Although various attempts have been made to prevent, minimize, or treat WSLs, no clear guidelines for the management of WSLs are deemed to be a standardized protocol. By integrating currently available evidence based-clinical data, this review provides systematic flowcharts to guide clinicians in the decision-making process and patients about the prevention and treatment of the WSLs. [ABSTRACT FROM AUTHOR]

Email marketing is the practice of sending commercial emails to a targeted audience. Email marketing currently routinely produces a significant ROI (return on investment) in the marketing sector. However, a significant research question in email marketing is how to gather subscriber data and deliver emails just to interested customers. Faced with the explosion in the volume of information, companies are becoming increasingly aware of the structural challenges of Big Data. Collecting and processing large amounts of heterogeneous data in real time is indeed invaluable. In the era of Big Data, the analysis and exploitation of data, by email marketing specialists, is now essential to remain competitive. In this paper, we review the impact of Big Data on email marketing activities and we identify the techniques, frameworks and data types that were adopted. Therefore, our contribution to this work is the proposed conceptual framework for email marketing campaign practice based on big data analytics. [ABSTRACT FROM AUTHOR]

In generating adversarial examples, a trade-off problem is always present, as the attacker aims to deceive the classifier in the most subtle way possible in order not to be uncovered. Trade-offs among different objectives can be tackled in many ways, among the most popular ones is to consider several objectives and further select among optimal ones based on some criteria. In this paper, a game theoretic approach to setting the magnitude of adversarial examples for a fast gradient sign model for image classification is proposed. The model controls the size of the pixel change and the number of randomly chosen pixels to be modified. A solution of the game indicates optimal trade-offs between the values. Numerical examples are used to illustrate the approach. [ABSTRACT FROM AUTHOR]

Modeling and simulating travelers mode choice is of great importance when evaluating novel transportation modes, policy measures or other changes to the overall transport system. The agent-based transportation framework MATSim has been widely used to study such scenarios and evaluate potential changes in the modal split. The default mode choice behavior in MATSim lets agents explore all their mode options randomly. A scoring-based co-evolutionary algorithm is applied to maximize the utility over a large number of iterations and arrive at consistent modes shares. In this paper, we present a new mode choice approach, that resembles a more rational decision-making process. First, the utilities of all available modes are estimated and used as costs to build a graph with all trips of the day. A k-shortest-path algorithm is applied to compute the most promising k combinations of modes for the day. Additionally, we formulate a pruning methodology that allows to remove unpromising modes. Finally, a selection model based on a multinomial logit is used to choose one of the plan candidates. The scale parameter of the selection model is annealed over the course of iterations to predict deterministically to the candidate with the highest estimate utility. Our simulation experiments show that convergence speed compared to random mode choice is significantly increased and only 25% of iterations are required to achieve overall better scores. [ABSTRACT FROM AUTHOR]

Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to business requirements. Several blockchain platforms have emerged, making it challenging to select a suitable one for a specific type of business. This paper presents a classification of over one hundred blockchain platforms. We develop smart contracts for detecting healthcare insurance frauds using the top two blockchain platforms selected based on our proposed decision-making map approach which selects the top suitable platforms for healthcare insurance frauds detection application. Our classification shows that the largest percentage of platforms can be used for all types of application domains, the second biggest percentage for financial services, and a small number is to develop applications in specific domains. Our decision-making map and performance evaluations reveal that Hyperledger Fabric surpassed Neo in all metrics for detecting healthcare insurance frauds. [ABSTRACT FROM AUTHOR]

• Attrition in obesity treatment is perhaps the greatest impediment to success. • Cognitive barriers decrease treatment engagement and cause attrition. • Anthropomorphisms may help clients understand and combat cognitive barriers. • The "Stymie Beasts" offer a compelling way to conceptualize cognitive barriers. This paper focuses on one of the most significant barriers, especially for teenagers and their families, to the effectiveness of cognitive behavioral therapy (CBT) for weight management: high rates of attrition. Cognitive barriers contribute to attrition and to decreasing commitment to full engagement in the process (deliberate practice). Excuses capture the essence of one type of those cognitive barriers. Excuses are defined as shifts in attribution from self-focused causes of struggles to achieve goals to more externally-focused causes—resulting in decreasing full engagement. Those excuses (shifts in attribution) do not just happen. People make decisions to implement those shifts away from personal responsibility. Ten cognitive biases lie at the heart of such problematic delusional decision making. This paper identifies seven excuses created by that biased decision making as anthropomorphisms called Stymie Beasts. Such unique simply worded visually distinctive creatures may help improve recall and focus on these powerful antagonists to successful weight control. Then, the use of two CBT techniques, Therapeutic Understanding of Science (TUS) and Rational Emotive Therapy (RET), illustrated with examples for all seven Stymie Beasts, shows how CBT may prevent and dismantle those Beasts (excuses). Ideas for future research using randomized controlled trials to test these notions are also presented. [ABSTRACT FROM AUTHOR]

Engineering systems must function effectively whilst maintaining reliability in service. Predicting maintenance costs and asset availability raises varying degrees of uncertainty from multiple sources. Previous reviews in this domain have assessed cost uncertainty and estimation for the entire life cycle. This paper presents a systematic review to investigate existing methodologies and challenges in uncertainty quantification, aggregation and forecasting for modern engineering systems through their in-service life. Approaches to forecast uncertainty here are hindered chiefly by data quality of available data, experience and knowledge. A total of 107 papers were analysed to answer three research questions based on the scope, through which two core research gaps were identified. An integrated combination of identified approaches will enhance rigour in uncertainty assessment and forecasting. This review contributes a systematic identification and assessment of current practices in uncertainty quantification and scientific methodologies to quantify, aggregate and forecast quantitative and qualitative uncertainties to better understand their impact on cost and availability to aid decision making throughout the in-service phase. [ABSTRACT FROM AUTHOR]

Abstract: Criteria are needed to be able to judge the level of risk associated with dose rates estimated for non-human biota. In this paper, European guidance on the derivation of predicted no-effect chemical concentrations has been applied to appropriate radiation sensitivity data. A species sensitivity distribution fitted to the data for all species resulted in a generic predicted no-effect dose rate of 10μGyh−1. Currently, data are inadequate to derive screening values for separate organism groups. A second, higher, benchmark could aid in decision making by putting results into context on the scale of no effect to a risk of ‘serious’ effect. The need for, meaning and use of such a value needs to be debated by the wider community. This paper explores potential approaches of deriving scientific input to this debate. The concepts proposed in this paper are broadly consistent with the framework for human protection. [Copyright &y& Elsevier]

Patient-reported outcomes (PROs) are increasingly used in single-arm cancer studies. We reviewed 60 papers published between 2018 and 2021 of single-arm studies of cancer treatment with PRO data for current practice on design, analysis, reporting, and interpretation. We further examined the studies' handling of potential bias and how they informed decision making. Most studies (58; 97%) analysed PROs without stating a predefined research hypothesis. 13 (22%) of the 60 studies used a PRO as a primary or co-primary endpoint. Definitions of PRO objectives, study population, endpoints, and missing data strategies varied widely. 23 studies (38%) compared the PRO data with external information, most often by using a clinically important difference value; one study used a historical control group. Appropriateness of methods to handle missing data and intercurrent events (including death) were seldom discussed. Most studies (51; 85%) concluded that PRO results supported treatment. Conducting and reporting of PROs in cancer single-arm studies need standards and a critical discussion of statistical methods and possible biases. These findings will guide the Setting International Standards in Analysing Patient-Reported Outcomes and Quality of Life Data in Cancer Clinical Trials–Innovative Medicines Initiative (SISAQOL-IMI) in developing recommendations for the use of PRO-measures in single-arm studies. [ABSTRACT FROM AUTHOR]

In this paper, a fractional neural integral sliding-mode controller based on the Caputo-Fabrizio derivative and Riemann–Liouville integral for a robot manipulator mounted on a free-floating satellite is proposed and developed. The mathematical model is obtained by using the Euler–Lagrange formalism and generalized through the Caputo-Fabrizio derivative. The conventional neural integral sliding-mode controller reported in the literature were also developed to make a comparative analysis between both controllers. The controllers are tuned using a swarm intelligence algorithm. External disturbances are also considered. Simulation results show the superiority and robustness of the proposed control system for trajectory tracking tasks through the root-mean-square error. Additionally, two additional reference trajectories were considered. In this sense, the numerical simulations were done by using the same orders and gains, showing the robustness of our fractional-order control strategy under different operating conditions. Further, the proposed controller uses the fractional derivative in the sense of Caputo-Fabrizio which has not been used enough in the area of robotics, so this is a start to analyse the contributions that arise when using said derivative in this robotic. [ABSTRACT FROM AUTHOR]

• Distance gap, TTC and closing velocity are all necessary to predict driver uncertainty. • Distance gap results in the most substantial influence on predicting driver uncertainty, followed by TTC and closing velocity. • Maximum average reaction time, minimum average uncertainty scores and turning points of the action response reflecting driver uncertainty appear at the same range of distance gaps or TTCs. • Negative correlation between the reaction time and subjective uncertainty score. Driver uncertainty in lane change maneuvers can induce long reaction times and lead to dangerous crashes. However, drivers' uncertainty about their lane change decisions has not yet been targeted by researchers or vehicle manufacturers. This paper aims to investigate driver uncertainty by evaluating the variables distance gap, time-to-collision (TTC), and closing velocity between the vehicle changing lines and an oncoming vehicle. A driving simulator study was conducted with 29 participants who had to decide if, in a given traffic situation triggered by an acoustic signal, a lane change to the left would be safe. Participants' reaction times, subjective certainty scores, and lane change decisions were recorded as indicators of driver uncertainty. The results show that the factors distance gap, TTC, and closing velocity significantly affect driver uncertainty. The analyses polynomial regression, polynomial ordinal regression, and repeated measure logistic regression were used to separately predict driver uncertainty in terms of reaction time, subjective certainty scores, and lane change decisions. The results show that all three factors are necessary for predicting driver uncertainty. The distance gap results in the strongest influence on driver uncertainty, followed by the TTC, and lastly the closing velocity. In addition, reaction times and subjective certainty scores were found to be significantly negatively correlated with each other. [ABSTRACT FROM AUTHOR]

Establishing long-term cooperative relationships with reliable suppliers of green agricultural products is essential to the sustainable development of fresh supermarkets. Qualitative attributes in such a multi-attribute decision-making problem have spawned the analyses on linguistic evaluations that are close to people's cognition and expression habits. There is still a challenge to capture the mathematical implications of linguistic evaluations and link them with decision-maker's psychology. To address this issue, this paper introduces a geometric linguistic scale which is characterized by the proportional relations between progressive grades and their corresponding fuzzy implications. Aggregation methods based on the geometric linguistic scale are developed to handle decision matrices with direct linguistic evaluations, full pairwise comparisons and partial pairwise comparisons, respectively. A consistency repairing process is presented to improve the reliability of pairwise comparisons. We further investigate a real-world problem concerning green agricultural supplier selection to illustrate the proposed method. The rational range of the scale parameter and consistency index are analysed through a sensitivity analysis. [ABSTRACT FROM AUTHOR]

Deriving priorities of objects from preference relations is an important research topic for decision making with preference information. In this paper, this issue is solved for fuzzy preference relations using representable uninorms, with which several alternative definitions of representable uninorm-based consistency (⊗-consistency) are provided for complete and incomplete fuzzy preference relations. In particular, a procedure to evaluate incomplete fuzzy preference relations with arbitrary n − 1 independent preference values is provided. Furthermore, a novel acceptable ⊗-consistency is defined by the proposed local priority vectors, and then a method to check and reach the ⊗-consistency of fuzzy preference relations by reducing the distances between the optimal priority vector and local ones is proposed. Specific examples are illustrated to show how the models work, and comparisons with the existing methods are also offered to show the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

Reviews of songs play an important role in online music service platforms. Prior research shows that users can make quicker and more informed decisions when presented with meaningful song reviews. However, reviews of songs are generally long in length and most of them are non-informative for users. It is difficult for users to efficiently grasp meaningful messages for making decisions. To solve this problem, one practical strategy is to provide tips , i.e., short, concise, empathetic, and self-contained descriptions about songs. Tips are produced from song reviews and should express non-trivial insights about the songs. To the best of our knowledge, no prior studies have explored the tip generation task in music domain. In this paper, we create a dataset named MTips for the task and propose a learning-to-generate framework named GenTMS for automatically generating tips from song reviews. The dataset involves 8,003 Chinese tips/non-tips from 128 songs which are distributed in five different song genres. Experimental results show that GenTMS achieves top-10 precision at 85.56%, outperforming the baseline models by at least 3.34%. Besides, to simulate the practical usage of our proposed framework, we also experiment with previously-unseen songs, during which GenTMS also achieves the best performance with top-10 precision at 78.89% on average. The results demonstrate the effectiveness of the proposed framework in tip generation of the music domain. [ABSTRACT FROM AUTHOR]

In the era of big science, the construction of large science projects is becoming more complex. Designers have to comprehensively consider factors such as instrument performance, technical reserves, funding, risks, and the environment to make a reasonable decision. On the basis of domestic and international astronomy, this paper sorts out the process of the formation of the Five-hundred-meter Aperture Spherical radio Telescope (FAST) concept and the decisions made by the FAST team, including the Chinese concept of the large telescope, the proposal of the prototype, and the adoption of the active reflector, etc. We also discuss the process of decision-making. FAST was born in the process of interaction and integration between Chinese and international astronomy development, and realized the transformation from following up to taking the lead. It can provide a reference for constructing future large science projects with limited foundations in terms of technology and funding. [ABSTRACT FROM AUTHOR]

Overtaking on two-lane roads can lead to increased collision risks due to drivers' errors in evaluating whether or not to accept the gap to the vehicle in the opposite lane. Understanding these gap acceptance decisions can help mitigate the risks associated with overtaking. Previous research on overtaking has focused on the factors influencing gap acceptance decisions. However, the cognitive processes underlying gap acceptance decisions remain poorly understood. Previous studies have shown that response time (i.e. the time it takes the driver to evaluate the gap and make a decision) can provide valuable insights into the cognitive processes during gap acceptance decisions, in particular in pedestrian crossing and left turn decisions. However, the more complex nature of the overtaking maneuver renders it difficult to measure response times in overtaking. As a result, response times in overtaking have not been investigated, thereby limiting our understanding of overtaking behavior. To address this gap, in this paper we propose a method to measure response time in drivers' overtaking decisions and demonstrate this method in a driving simulator experiment (N = 25). Specifically, we analyzed the effect of distance to the oncoming vehicle and speed of the ego vehicle on response time in accepted and rejected gaps. We found that response times for rejected gaps were on average longer than for accepted gaps. The response times increased with the distance gap and decreased with the initial velocity of the ego vehicle. We conclude that using the proposed method for measuring response time can give insight in the way drivers make gap acceptance decisions during overtaking. These results provide basis for cognitive process models that can help further understand overtaking decisions. • Response times can provide valuable insights into decision making but have never been investigated in overtaking decisions. • We measured response times in overtaking gap acceptance decisions in a driving simulator study (N = 25). • Response times in accepted gaps were 700 ms shorter than in rejected gaps. • Response times increased with the distance gap to the oncoming vehicle and decreased with the speed of the ego vehicle. • The results provide a basis for cognitive process models that can help further understand overtaking decisions. [ABSTRACT FROM AUTHOR]

• Uncertainty on forensic age estimation findings should be measured with probabilities; • Decision theory provides tools for rational decision-making in declaring a person an adult or a minor; • Two-action and three-action decision models delineate the age estimation decision problem. • Case examples illustrate that this normative approach ensures coherence in decision-making. Nowadays, forensic age estimation takes an important role in worldwide forensic and medico-legal institutes that are solicited by judicial or administrative authorities for providing an expert report on the age of individuals. The authorities' ultimate issue of interest is often the probability that the person is younger or older than a given age threshold, which is usually the age of majority. Such information is fundamental for deciding whether a person being judged falls under the legal category of an adult. This is a decision that may have important consequences for the individual, depending on the legal framework in which the decision is made. The aim of this paper is to introduce a normative approach for assisting the authority in the decision-making process given knowledge from available findings reported by means of probabilities. The normative approach proposed here has been acknowledged in the forensic framework, and represents a promising structure for reasoning that can support the decision-making process in forensic age estimation. The paper introduces the fundamental elements of decision theory applied to the specific case of age estimation, and provides some examples to illustrate its practical application. [ABSTRACT FROM AUTHOR]

This paper examines a new approach to defining digital ecosystems. Within the digital economy of ecosystems, competition is eliminated, and organizations form unions and alliances in order to work together and cooperate to reach a set goal. This means a digital ecosystem can be viewed as a complex environment in which organizations without any hard ties operate. Digital ecosystems differ from traditional ecosystems in many ways. The business organization of the latter is based on management decision making by people. This paper presents theoretical foundations for developing digital ecosystems based on a literary review. Based on the logic of scientific search using the keywords "ecosystem" and "biological ecosystem", the commonality of the properties of the digital ecosystem and the biological ecosystem is shown. The aim of the study is to identify common characteristics in biological, economic and digital ecosystems in order to substantiate the possibility of using the same approaches for research and modeling of such systems. A definition of a digital ecosystem is proposed by the authors which points out the main features of this kind of system and highlights the dominant role of modern digital technologies in the formation of the digital ecosystem. The paper looks at the distinctive features of digital ecosystems and characteristics similar to the characteristics of biological ecosystems, such as ecosystem participants, presence of limiting impacts, lack of vertical hierarchical communication. The developed model can be used to model digital ecosystems. The authors believe that the emergence of a trend in the transformation of ecosystems in the direction of expanding the collaboration of economic agents is reasonable. At the same time, digitalization helps to replace competition with collaboration. The paper finishes with a discussion of the obtained results and a plan for further research. [ABSTRACT FROM AUTHOR]

As a three-way approximation scheme to deal with fuzzy set, shadowed set approximately divides fuzzy objects with uncertainty into elevated, reduced, and shadow areas by a pair of decision thresholds α , β , thus effectively reducing the uncertainty of information. However, the current construction of shadowed set is all based on a single principle, lacking the comprehensive consideration of multiple principles, and these construction principles focus on the rationality of semantic interpretation of model construction process and threshold determination, and lack of consideration of the validity of partition results. To resolve these issues, this paper proposes a new shadowed set model through the game analysis between uncertainty and decision cost (UC-GTSS). First, Combining shadowed set construction with game theory, UC-GTSS is proposed based on game competition mechanism. Through the game analysis of uncertainty and decision cost, the optimal α , β is found, and a game payoff fusion algorithm based on Topsis is proposed. Second, the definition of UC-GTSS model expression, game player, strategy, payoff function, equilibrium analysis, optimization α , β determination and the influence of model parameters are analyzed and discussed. Third, the UC-GTSS model is extended and discussed based on the validity of the partition results, such as the game between approximate classification accuracy and coverage, the game between the Gini coefficient of the partition areas. Finally, through the analysis of algorithms, instances and data experiments, the construction process, validity, and rationality of UC-GTSS and its extended model are demonstrated. • This paper proposes a new shadowed set model through the game analysis between uncertainty and decision cost (UC-GTSS). • First, Combining shadowed set construction with game theory, UC-GTSS is proposed based on game competition mechanism. Through the game analysis of uncertainty and decision cost, the optimal threshold is found, and a game payoff fusion algorithm based on Topsis is proposed. • Second, the definition of UC-GTSS model expression, game player, strategy, payoff function, equilibrium analysis, optimization threshold determination and the influence of model parameters are analyzed and discussed. • Third, the UC-GTSS model is extended and discussed based on the validity of the partition results, such as the game between approximate classification accuracy and coverage, the game between the Gini coefficient of the partition areas. • Finally, through the analysis of algorithms, instances and data experiments, the construction process, validity, and rationality of UC-GTSS and its extended model are demonstrated. [ABSTRACT FROM AUTHOR]

Health-care waste (HCW) contains numerous hazardous, infectious, and radioactive contents that may cause serious health issues for society and drastic environmental problems. Therefore, it is crucial to dispose of HCW safely and appropriately. Four commonly used HCW disposal methods are incineration, steam sterilization, microwave, and landfill disposal. To select an appropriate HCW disposal method, there are several conflicting criteria. Furthermore, when information is lacking due to uncertainty and criteria weighing conditions are also unknown, the decision to choose an appropriate HCW disposal method becomes more challenging. So, the objective of this paper is to introduce a novel integrated multi-criteria group decision-making (MCGDM) approach to evaluate HCW disposal methods in which incomplete information is quantified using intuitionistic fuzzy sets (IFSs), unknown criteria weights are evaluated using the maximizing deviation method, and the evaluation of HCW disposal methods is done by applying the Archimedean t -norm and t -conorm-based interactive intuitionistic fuzzy weighted aggregated sum product assessment (WASPAS) method. Herein, the Archimedean t -norm and t -conorm cover a wide range of t -norms and t -conorms, including the Algebraic, Einstein, Hamacher, and Frank, etc., by adopting prescribed combinations of additive generators. The proposed MCGDM approach has been applied to evaluate the above-listed four HCW disposal methods for a case study conducted by Mishra et al. (2020) in the context of India. A sensitivity analysis is performed to examine the impact of changes in the parameters involved in the proposed MCGDM approach. A comparative study is also performed with some well-known multi-criteria decision making(MCDM) and MCGDM methods to demonstrate the consistency and stability of the proposed approach. Findings suggest that the newly developed MCGDM approach is more general, flexible, and provides realistic results to evaluate HCW disposal methods under uncertainty and unknown criteria weight information. • The paper proposes an Archimedean t-norm and t-conorm based IF-WASPAS method with unknown criteria weights to evaluate HCW disposal techniques. • The method applies IFS to quantify uncertainty while unknown criteria weights are evaluated using the maximizing deviation method. • Method has been applied to evaluate HCW disposal alternatives in context of India. • Sensitivity and comparative analyses are also conducted to examine the consistency and stability of the method. • The proposed MCDM approach is general,flexible, and provides realistic results under uncertainty and unknown criteria weights. [ABSTRACT FROM AUTHOR]

The setting of total allowable catches (TACs) have been a part of the European Union's Common Fisheries Policy since its conception in 1983 and has been the main management tool of conservation of fisheries stocks in EU waters. TAC decisions of the Council are made behind closed doors, which has been the object of criticism. Previously voting power indices have been calculated and used to analyse Council decisions (e.g. EU budget) and institutional changes of the EU (e.g. Treaty of Lisbon), however not for EU TAC legislation and decisions. This paper looks at EU Council decision making for a subset of member states concerning TAC decisions for which the Council has exclusive competence in. Specifically, the paper looks at formal mechanisms of voting in the Council concerning EU TAC decision-making by calculating voting power indices for member states. Additionally, the paper looks at the changes in voting power due to the UK leaving the EU. Results indicate that the Council's voting rules distributes voting power to member states with larger populations, but also distributes disproportionally voting power to smaller member states at the expense of medium size populated member states. This reflects previous results from the literature. The results do not undermine the importance of informal mechanisms such as consensus and coalition building among member states, but rather complement it. [ABSTRACT FROM AUTHOR]

Harmonic distortion levels in current power systems have increased due to technical advancements in industrial and renewable energy applications. So far, passive power filters have been widely employed to minimize harmonics and lessen their adverse effects. In this regard, this paper presents a novel bi-level design of damped double-tuned passive filters operating in a non-sinusoidal power system with nonlinearities at both the source and the load. A modern metaheuristic optimization technique known as wild horse optimization is applied to acquire the parameters of the used filters. Several objective functions, such as voltage total harmonic distortion, current total demand distortion, active power losses, and resonance-based metric minimization, were researched to improve the analyzed system's overall power quality performance. The mathematical derivations of the filter design expressions are given in detail. In the literature, there are several schemes for damped double-tuned filters. This paper investigates and analyses four schemes of this filter. The results are compared to those obtained from other metaheuristic optimization algorithms to ensure that the proposed algorithm produces the most effective outcomes. Statistical analysis is performed using many criteria to ensure the superiority of the proposed algorithm. Furthermore, depending on several assessment criteria, the analytical hierarchy process is employed to find the most effective candidate scheme. One of the schemes tested (scheme B) outperformed the others. [ABSTRACT FROM AUTHOR]

Science plays an increasingly important role in responding to the issues arising from natural threats and anthropogenic activities in the Antarctic region. As one of the most prominent scientific non-governmental organizations (NGOs) in Antarctic governance, the Scientific Committee on Antarctic Research (SCAR) participates in the Antarctic Treaty System (ATS) as an observer, informing the process of decision-making. To research on how SCAR informs decision-making, the study conducts empirical analysis of SCAR papers and examines their submission, adoption, and how they inform the decision-making process of the ATS. Based on the empirical analysis of SCAR papers, the study finds that with deeper involvement of SCAR in Antarctic governance, the number of papers submitted by SCAR shows an upward tendency, and the types of papers display the diversity. The actors submitting joint papers together with SCAR become diversified, and the Antarctic Treaty Consultant Parties (ATCPs) are the most important among these actors. The number of adopted papers also increases, especially the number adopted as resolutions, which promotes the revision of relevant rules. SCAR's scientific functions in Antarctic governance are driven by the advancement of the ATS, the internal reform of SCAR, and supports from decision-makers. Given the limits of SCAR's scientific functions based on the theory of science–policy interface, the cooperation among SCAR, increasing numbers of ATCPs, and other actors may better respond to the emerging challenges in the Antarctic region. [ABSTRACT FROM AUTHOR]