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An editorial is presented on the release of a white paper by the International Federation of Dental Hygienists (IFDH), summarizing findings from 5 global surveys on oral health conducted between 2019 and 2023. Topics discussed include toothpaste and electric toothbrush recommendations, oral-systemic links, sustainable dentistry, and oral hygiene instruction practices, revealing gaps between evidence-based knowledge and clinical decision making among dental hygienists.

The objective of this paper is to show the added value of using tools such as eyetracking, galvanic skin response, facial coding and others in the field of market research and user experience research. We will present 3 case studies in which these tools have been used successfully. We will give an overview of the background, the objectives, methods and results and how the neuro-tools provided additional insights into consumer behaviour, which would otherwise not have been possible. In this paper we will specifically show cases from packaging design, advertising research and user experience research thus only covering a small part of possible application areas. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Social Affairs is the property of Journal of Social Affairs and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Display omitted] • Fibre transfer from unconstrained physical assaults involving human subjects. • More fibres transfer in person-person contact than previous data suggests. • Realistic fibre transfer quantities to inform better decision making. • Care must be taken in assigning 'roles' in physical assaults using fibre evidence. • Broad fibre distribution maps evident despite garments stored in paper bags. Knowledge of the number of fibres transferred during a particular activity is essential for the interpretation of findings in similar criminal cases. In this regard, violent contacts and physical assaults still present a challenge, due to a lack of robust published data. Hereby, we present the outcome of an empirical study where different assault activities were simulated by a Jiu Jitsu team and participants were asked to play either the role of an aggressive 'assailant' or a defensive 'victim', wearing cotton garments (i.e., Gi's). Four different scenarios were simulated in replicates (n = 5), each of them involving different intensity levels (low and high) and duration times (30 and 60 s). Results showed that approximately 1,000 to 44,000 fibres were cross-transferred between the participants' garments, with noticeable differences between the different scenarios. These values were significantly larger than those published in previous studies and, therefore, suggested the possibility of a current underestimation of the number of fibres transferred in physical assaults. Furthermore, statistical analysis by ANOVA indicated that the all the variables tested (i.e., intensity level, duration time, and participants role) had a significant effect on the number of transferred fibres (p < 0.001) and, consequently, that some knowledge of the case circumstances may be important to make more educated estimations. This is the first time that such a methodology has been applied for the quantitative assessment of fibre transfer between participants in assault activities. Data are expected to help practitioners with the interpretation of findings in real casework and lead to a more robust evidential assessment. [ABSTRACT FROM AUTHOR]

Abstract This conceptual paper provides a brief review of hospitality consumers' information search behavior and the factors that are likely to influence their information search behavior and their utilization and processing of online reviews. This study focuses on the factors such as information overload, confusion, information processing, information presentation format, trust and evaluation mode that has not received much attention from hospitality scholars in addition to discussing the impacts of perceived risk and familiarity on information search and information processing approaches utilized by hospitality consumers. This study also discusses opportunities for hospitality researchers to empirically examine the extent to which each of the factors discussed might influence hospitality consumers information search, information processing and especially their utilization of online reviews in their decision making process. [ABSTRACT FROM AUTHOR]

This discussion paper highlights the importance of Australian nursing and midwifery students' lack of exposure to electronic medical records during their undergraduate programs. There is pressure on universities offering nursing and midwifery programs to provide students with opportunities to learn to use patient electronic medical records. This will provide authentic rehearsal with the digital technology prior to clinical placements and increase graduate work readiness. Informed by contemporary literature, we describe the benefits of implementing electronic medical records (eMR) in health organisations and identify the challenges and barriers to implementing and integrating the education of electronic records into undergraduate nursing and midwifery programs. Undergraduate students who had not experienced eMR as part of on-campus learning felt unprepared and lacked confidence when commencing clinical practice. Some international nursing and midwifery programs have found that student's skills improve in decision-making and documenting patient observations when eMR is integrated into their university education program. Successful integration of an eMR program should consider academic/teaching staff skills and confidence in technology use, initial and ongoing costs and technical support required to deliver the program. In conclusion, Australian universities need to embed eMR learning experiences into the nursing and midwifery undergraduate curricula to increase students work-readiness with a focus on patient safety. [ABSTRACT FROM AUTHOR]

Copyright of Fibres & Textiles in Eastern Europe is the property of Sciendo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Considering only environmental impact or only profit is not enough to manage chemical processes. This study focuses on the optimal scheduling of cutting papers using the multiobjective optimisation programming. Two independent objective functions are introduced in this paper, the production cost and trim loss with the constraints of satisfying the customer orders. The Goal Constrained Programming (GCP) algorithm is created for the multiobjective optimisation with priority. The proposed algorithm takes the deviation for the weighting factor. The GCP provides various optimal schedule sets satisfying the economic and environmental requirements. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Dental Hygiene is the property of Canadian Dental Hygienists Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Improvement In Shared Decision-Making SDM-Q-9 survey scores with concentric digital consent compared to paper-based consent. P149. [Extracted from the article]

The article explains how second graders built a three-dimensional map and presents an interaction with community members, consumers, and business owners, exploring economic exchange and decision making.

Objective The aims of this quasi-experimental before-and-after study were to first determine whether the use of eye tracking technology combined with video debriefing techniques has the potential to improve the quality of feedback and enhance situation awareness (SA) in simulated settings and second to determine students' satisfaction towards simulated learning. Methods Nursing and paramedicine students from three universities participated in three 8-minute simulation scenarios of acutely deteriorating patients. Eye tracking glasses video recorded the scenarios and tracked right eye movement. On completion, participants were questioned using the Situation Awareness Global Assessment Technique, completed the Satisfaction with Simulation Experience Scale (SSES), and provided textual feedback and received video-based verbal feedback. Results Participants lacked awareness of presenting medical conditions and patient environments and had poor recall of patient vital signs. Significant improvements in SA scores were demonstrated between the first and third scenarios ( P  = 0.04). Participants reported greater insight into their performance and were satisfied with simulated learning. Conclusions Use of visual field review techniques appears to enhance the use of realistic simulated practice as a means of addressing significant performance deficits. Eye tracking and point of view recording techniques are feasible and with applicable debriefing techniques could enhance clinical and situated performance. [ABSTRACT FROM AUTHOR]

Summary: The Anatomische Gesellschaft (anatomical society, AG) was founded in Germany in 1886 as an international society and remains the main organizing body of German anatomists to this day. A previous study of the history of the AG during National Socialism (NS) was based on the published proceedings of the AG and drew the preliminary conclusion that the “AG did not follow the path of preemptive obedience toward the new rulers” in contrast to some other professional societies. However, it was noted that archival sources were needed to support this conclusion and to illustrate the decision process within the society. Such sources are now available in the estate papers of Alfred Benninghoff, a leading anatomist at the time. His correspondence supports the previous finding that the AG was able to maintain its international character, thereby enabling it to avoid the active exclusion of “non-Aryan” members. The papers also confirm that the AG did not defend its vulnerable members as valiantly as the official narrative suggests, a fact illustrated in a controversy surrounding Martin Heidenhain. The interactions and conflicts between the leaders of the AG can now be reconstructed, i.e. between the secretary of the AG Heinrich von Eggeling, Benninghoff and Hermann Stieve. The Benninghoff documents also refer to a meeting of a subsection of the AG in November 1942, at which a disturbing radicalization of some anatomists developed. Finally, the papers reflect the political realities for German professionals trying to re-establish their science in a country divided into four occupation zones. [ABSTRACT FROM AUTHOR]

The author presents her observations on the improvements in the research papers of her students as they used the PaperToolsPro, a software that helps prevent plagiarism and enhances learning. Her program which aims to prevent plagiarism assisted her students during the research process from brainstorming, bibliography, analysis and synthesis of topics. The software centralizes the research process by streamlining the ability of students to organize their information and allowing them to monitor their own work. It also enabled them to translate, paraphrase or summarize the original text into their own words. She also observed the depth of understanding demonstrated by her students' research papers.

Abstract: We propose an intelligent sensor system based on a new sensing methodology, relying also on 3D map reconstruction techniques, for computing with high precision, in real-time and without human intervention the parameters needed for stream-flow computa- tion: water levels, morphology of the streams of all potentially flooded areas by each controlled stream. The collected data will be continuously transmitted, through a communication infrastructure, to software agents designed to compute the stream-flow and to quantify the spatial distribution of flood risk for each controlled watershed. The computed risks, together with other data coming from other sources (barometric sensors, camera operators of public organizations, emergency agencies, private citizens), will be analyzed by a diagnostic decision system implementing a risk-alert scheduling strategy. This system will be able to diagnose the health state of the controlled environment and to define specialized alarm levels for each potentially interested area that will be used to alert all citizens (ubiquity) locally present (alerting locality). [Copyright &y& Elsevier]

The article focuses on the ethics of public administration. It is stated that public administration ethics relate to complex decision-making; and administration ethics and political judgment is important for the legislative and jurisdictional powers. It is mentioned that public administration ethics faces some serious challenges, including the global economic and financial crisis; global crisis of corruption and bribery; and global crisis of the public institutions in civil society.

Digital twins have emerged as novel technology in the wind energy sector that enables the design, monitoring and prediction of wind turbine performance. Despite growing attention on their potential, little is known about how digital twins are designed, by whom and how their design choices affect multiple aspects of decision making in the development of wind energy. Using a framework of co-production, this paper examines digital twins as boundary objects and the role of twinning as boundary work that involves an active process of design and affects multiple aspects of decision making in the development of wind energy. Our results demonstrate how the design of digital twins evolves throughout the twinning process, affected by regulation, choices of expert twinners on data and models, and what constitutes a matter of concern. We shed light on the role of these twinners in influencing which actors and their matters of concern are included and excluded during the twinning process. Our understanding of twinning as an active process of governance by design more clearly reveals how digital twins are not objective representations of reality, but a function of boundary work. We conclude that more transparency is needed over how digital twins are designed to enhance their role as technologies that foster a transition towards more sustainable energy systems and decision-making over wind energy technologies and their integration in landscapes. • There is limited understanding of how digital twins are designed • We focus on wind energy and 'twinning' as an act of governance • We unpack twinning as boundary work and digital twins as boundary objects • Twinning involves choices about data, models, concerns and regulation • Priorities and focus in twinning may steer wind energy transitions [ABSTRACT FROM AUTHOR]

Abstract: Bibliometric studies at the micro level are increasingly requested by science managers and policy makers to support research decisions. Different measures and indices have been developed at this level of analysis. One type of indices, such as the h-index and g-index, describe the most productive core of the output of a researcher and inform about the number of papers in the core. Other indices, such as the a-index and m-index, depict the impact of the papers in the core. In this paper, we present a new index which relates two different dimensions in a researcher’s productive core: a quantitative one (number of papers) and a qualitative one (impact of papers). In such a way, we could obtain a more balanced and global view of the scientific production of researchers. This new index, called -index, is based on the geometric mean of h-index and the median number of citations received by papers in the h-core, i.e., the m-index, which allows us to combine the advantages of both kind of indices. [Copyright &y& Elsevier]

As the next-generation power system, smart grid presents challenges to enterprises in managing and analyzing massive data, meeting complex operational and decision-making demands, and predicting future power demand for grid optimization. This paper aims to proposed a fusion algorithm for smart grid enterprise decision-making and economic benefit analysis, enhancing the accuracy of decision-making and predictive capability of economic benefits. The proposed method combines techniques such as Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), and edge computing. The LSTM model is employed to model historical data of the smart grid. The GAN model generates diverse scenarios for future power demand and economic benefits. The proposed method is evaluated on four public datasets, including the ENTSO-E Dataset, and outperforms several traditional algorithms in terms of prediction accuracy, efficiency, and stability. Notably, on the ENTSO-E Dataset, the proposed algorithm achieves a reduction of over 46.6% in FLOP, and a decrease in inference time by over 48.3%, and an improvement of 38% in MAPE. The novel fusion algorithm proposed in this paper demonstrates significant advantages in accuracy and predictive capability, providing a scientific basis for smart grid enterprise decision-making and economic benefit analysis while offering practical value for real-world applications. • A novel fusion algorithm for accurate load demand prediction in smart grids. • Training were conducted on multiple datasets and validation was performed. • Significant contributions to risk assessment planning in smart grid enterprises. [ABSTRACT FROM AUTHOR]

We studied a paper to apply a method of maximizing deviations to multiple attribute decision-makings under intuitionistic fuzzy environment that have found several new methods and theorems for maximum problem under some specific conditions with insufficient information environment. We showed that the Lagrange multiplication method used by the paper can be replaced by our simplify approach with the Cauchy Schwarz inequality. The purpose of this paper is fourfold. First, the iteration method for the problem within the range of weights is well developed and with appropriate explanation if the weight vector of attributes is bounded. Second, if the weight vector of attributes is completely unknown, we could directly and swiftly derive the weight by the Cauchy-Schwarz inequality such that the complicated approach by the Lagrange multiplication method becomes redundant. Third, we prove the results of score function and rank for one-norm will not be preserved in the two-norm. Fourth, the same numerical examples are examined again and have different outcome to demonstrate our findings is superior to the previously published results. [ABSTRACT FROM AUTHOR]

The complexity and inter-connectedness of operating in a global world for drug product supply has become an undeniable reality, further underscored by the COVID-19 pandemic. For Post-Approval Changes (PACs) that are an inevitable part of a product's commercial life, the impact of the growing global regulatory complexity and related drug shortages has brought the GlobalPACManagement System to an inflection point in particular for companies that have their products marketed in many countries. This paper illustrates through data analyzed for the first time from 145,000 + PACs for 156 countries, collected by 18 global pharma companies over a 3-year period (2019–2021), how severe the problem of global regulatory complexity is. Only PACs requiring national regulatory agency (NRA) approval prior to implementation were included in the data set. 1 of the 156 country NRAs approved all submitted PACs within a period of 6 months. The 6-month timeline was chosen because it is the recommended review timeline for major changes in the WHO guidance for vaccines and biotherapeutic products. 10 out of the 156 (6%) countries had no more than 10% of the PACs reviewed and approved in > 6 months. In 33 (22%) countries more than half of the PACs took > 6 months for approval. It is rare that the same PAC is approved globally within 6 months as individual NRAs take from a few months to years (in some cases > 5 years) for their review. The global PAC management complexity has steadily grown over the past 20 years. Attempts thus far to solve this problem have not made any meaningful difference. Senior leaders and decision-makers across the interdependent components of the complex Global PAC Management System (industry and regulators) must come together and collaboratively manage the problem holistically with the objective of ensuring global drug product availability instead of continuing with distinct stakeholder or country-focused solutions, which can tend to worsen the problem. In this paper, the Chief Quality Officers (CQOs) from 18 of the largest innovator pharma companies (see Acknowledgements) are speaking with One-Voice-of-Quality for PACs (1VQ for PACs Initiative). They are recommending a set of 8 approaches to activate a holistic transformation of the Global PAC Management System. This article presents their view on the problem of global regulatory complexity for managing PACs, it's impact on continual improvement and the risk to drug product supply, as well as approaches that can help alleviate the problem. [ABSTRACT FROM AUTHOR]

The probabilistic linguistic term sets (PLTSs) are very powerful in solving the multi-criteria decision-making (MCDM) problems. The previous comparison methods associated with PLTSs are finite and unreasonable. Hence, developing a more effective way to compare PLTSs and proposing a reasonable decision-making method to cope with MCDM problems are important work of this paper. Firstly, a new possibility degree is proposed and a visual comparison method is given to present the process of comparing PLTSs. Subsequently, a similarity measure for PLTSs is also proposed to make up for the lack of similarity measure. Combining the new comparison method with the similarity measure, the TOPSIS is extended to solve real-life problems under probabilistic linguistic environment. Finally, a numerical example considers the selection of public opinion monitoring systems and the comparative analyses are shown to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

ntelligent decision system (IDS) is a window-based software package that has been developed on the basis of the evidential reasoning (ER) approach, a recent development in handling hybrid multiple criteria decision analysis (MCDA) problems with uncertainties. In this paper, the evidential reasoning approach will be briefly described first, and its major differences from and the relationships with conventional MCDA methods will also be discussed. Then the main features, advantages and benefits of IDS will be demonstrated and explained using two application examples: supplier pre-qualification assessment and customer satisfaction survey analysis, which have been investigated as part of the research projects led by the authors and funded by the UK government and the EC. It is concluded in the paper that the ER approach can be used not only to deal with problems that traditional methods can solve, but also to model and analyse more complicated decision problems that traditional methods are incapable of handling. [ABSTRACT FROM AUTHOR]

Industrial technologies have evolved towards circular-oriented manufacturing. New approaches in the management of production systems are needed to guarantee the success of such a circular approach. This paper identifies three research questions related to the complexities of decision-making within CEs and defines the concept of Green Gateways in the circular economy. A Green Gateway is a type of decision that supports to realizing the full potential of products and materials. Indeed, the potential value of products and materials must be assessed and leveraged and the concept of Green Gateway will be useful to identify common decisions and to define a common framework in the future. The integration of digital information and digital twins of products, processes, and circular value and supply chains emerges as a key factor in guiding decision-makers effectively, especially for Green Gateways where adequate information, tools and methods must be used to manage value retention options, product flows, production and pricing decisions jointly with the multiple objectives of profitability and sustainability. Additionally, this paper explores specific examples of circular economy use cases, drawing attention to their similarities and highlighting key insights. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the scientific integration of data science with product design towards data-driven design (D3). Data science has potential to facilitate design decision-making through insight extraction, predictive analytics, and automatic decisions. A systematic scoping review is conduced to converge various D3 applications in four dimensions: the design dimension about design operations, the data dimension about popular data sources and common data-related challenges, the method dimension about the methodological foundations, and the social/ethical dimension about social/ethical considerations and implications. Based on the state-of-the-art, this paper also highlights potential future research avenues in this dynamic field. [ABSTRACT FROM AUTHOR]

As artificial intelligence becomes more prevalent, regulators are increasingly turning to legal measures, like "a right to explanation," to protect against potential risks raised by AI systems. However, are eXplainable AI (XAI) tools-the artificial intelligence tools that provide such explanations-up for the task? This paper critically examines XAI's potential to facilitate the right to explanation by applying the prism of explanation's role in law to different stakeholders. Inspecting the underlyingfunctions of reason-giving reveals different objectives for each of the stakeholders involved. From the perspective of a decision-subject, reason-giving facilitates due process and acknowledges human agency. From a decision-maker's perspective, reason-giving contributes to improving the quality of the decisions themselves. From an ecosystem perspective, reason-giving may strengthen the authority of the decision-making system toward different stakeholders by promoting accountability and legitimacy and by providing better guidance. Applying this analytical framework to XAI's generated explanations reveals that XAI fails to fulfilll the underlying objectives of the right to explanation from the perspective of both the decisionsubject and the decision-maker. In contrast, XAI is found to be extremely well-suited to fulfill the underlying functions of reason-giving from an ecosystem's perspective, namely, strengthening the authority of the decision-making system. However, lacking all other virtues, this isolated ability may be misused or abused, eventually harming XAI's intended human audience. The disparity between human decision-making and automated decisions makes XAI an inst®cient and even risky tool, rather than serving as a guardian Of human rights. After conducting a rigorous analysis of these ramifications, this paper concludes by urging regulators and the XAI community to reconsider the pursuit of explainability and the right to explanation of AI systems. [ABSTRACT FROM AUTHOR]

The T-spherical fuzzy set (T-SFS) has been widely used in multi-attribute decision-making (MADM) problems due to its powerful expression ability for uncertain information. The evidential reasoning (ER) method can avoid the information loss when aggregating the evaluation values of an alternative with regard to all attributes, which can prevent the decision result from being dominated by extreme evaluation values. Considering the significant advantage of the ER method in information aggregation, this paper develops a new aggregation model assimilating the T-SFSs and the ER method, which is called evidential T-spherical fuzzy MADM (ET-SFMADM). The ET-SFMADM can prevent information loss in the decision process, so as to obtain a correct ranking of the alternatives. Furthermore, this paper also proposes a T-spherical fuzzy cross entropy (T-SFCE) to determine the weights of attributes, which can reduce dependence on decision-makers. Finally, the viability of the ET-SFMADM is illustrated through a numerical example for the implementation effect of the hierarchical medical treatment system in different regions, and further the sensitivity analysis and comparison analysis with existing MADM methods are carried out to demonstrate the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

This paper bibliometrically examines the integration of artificial intelligence (AI) technologies into decisionmaking in management contexts. Using advanced algorithms and machine learning techniques, AI provides transformative capabilities for analyzing complex data sets, forecasting trends, and optimizing decision outcomes. Through a comprehensive literature review and case studies, this paper explores the diverse applications of AI in management decision-making, from strategic planning and resource allocation to risk management and operational efficiency. In addition, the bibliometric analysis discusses the implications of AI adoption, including ethical considerations, the dynamics of organizational change, and the role of human judgment along with AI-based perspectives. By synthesizing current research findings and practical insights, this paper provides a nuanced understanding of how AI can be effectively leveraged to improve management decision-making processes. [ABSTRACT FROM AUTHOR]

The article presents abstracts of papers published in the January 1, 1981 issue of the journal Philippine Sociological Review. The paper "Some Notes on the Masagana 99 Program and Small Farmer Access to Credit," by Emmanuel Esguerra focuses on the accessibility of credit to small farmers as a performance indicator of the Masagana 99 program. Its main argument is that the logic of profit maximization dictates that credit agencies are generally adverse to high risk lending. The paper "Power Dynamics of Rural Families: The Case of a Samar Barrio," by Mina E. Contado investigates the power structure of rural families by looking at patterns of decision-making, authority, influence, and task role allocation. The paper "The Socioeconomic Aspects of Developmental Infrastructure: Two Aspects From a Project in the Southern Philippines," by Francis C. Madigan addresses the effects of rural electrification in isolation from other elements of a developmental package and finds a positive association between electrification and median income, and between electrification and non-farm employment.

The article presents abstracts on evidence based library and information practice (EBLIP) which include "Using rubrics to collect evidence for decision-making: What do librarians need to learn?," "The neglected voice: Is there a role for qualitative systematic reviews in EBLIP?," and "Information skills training in health libraries: Are we any nearer the evidence?"

Faced with rapidly rising energy demand in industrialised societies and widespread global concern, countries are actively promoting the transition from conventional to renewable energy systems. The goal is to invest in renewable energy in the most efficient way to meet rising energy demand and reduce the challenges posed by climate change. However, decision makers must carefully weigh various factors when selecting the most appropriate renewable energy investment projects. This paper presents a novel method for Multi-Attribute Decision Making(MADM) that uses the Bipolar Complex Fuzzy(BCF) to convey the vagueness and uncertainty of decision makers, so that the result obtained better reflects the actual scenario and the subjective biases of decision makers. We defined BCF Einstein Weighted Averaging (BCFEWA) operator and BCF Einstein Ordered Weighted Averaging (BCFEOWA) operator to aggregate evaluation information. Then we discussed some properties of the proposed aggregation operators. Additionally, we present an integrated MADM technique grounded in the BCF framework that combines the CRiteria Importance Through Intercriteria Correlation (CRITIC) and ELECTRE III methods. Specifically, the CRITIC method determines attribute weights, and the ELECTRE III method ranking the alternatives to determine the best renewable energy investment projects. After analysing the results and comparisons, it can be inferred that the suggested methodology offers an effective evaluation process. [ABSTRACT FROM AUTHOR]

The additive ratio assessment system (ARAS) method is an effective technique for simplifying complex decision problems by determining the optimal alternative through the relative index (utility degree) to the ideal solution. However, there are still some shortcomings in the existing researches on the extension of this method when it is utilized in different decision environments, such as ignoring the correlation relationship between attributes, the lack of flexibility in the utilization of the decision process, and the relative index to the ideal solution may be scaled up or down with the ratio form. In order to overcome these disadvantages, this paper proposes the novel T-spherical fuzzy (TSF) cross entropy (TSFCE) measure and T-spherical Aczel-Alsina Heronian mean (TSFAAHM) aggregation operators and uses them to improve the ARAS method in the TSF environment. For the TSF multiple attribute group decision-making (MAGDM) problems, a group decision making model based on the improved ARAS is designed. In this model, the experts' weights are obtained by the TSFCE-based similarity measure. The attribute combined weights are calculated by fusing the objective weights obtained by TSFCE-based entropy measure and the subjective weights got by the extended stepwise weight assessment ratio analysis (SWARA) integrated with TSFCE. In the improved ARAS method, the T-spherical Aczel-Alsina Weighted Heronian mean (TSFAAWHM) operator can capture the correlation relationship between the attributes. Compared with the relative index, the TSFCE can reflect the difference between the alternatives and the ideal solution to obtain a more stable solution ranking. Lastly, an illustrative example about the sustainable supplier selection of power battery echelon utilization (PBEU) for a 5G base station is used to demonstrate the proposed method. The effectiveness, practicability and superiority of proposed method are illustrated by parameters influence and methods comparison analysis. [ABSTRACT FROM AUTHOR]

Copyright of Studies in Conservation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents an in-depth exploration of bipolar vague relations (BPVRs) and their fundamental properties. We delve into the theoretical underpinnings of BPVRs and demonstrate their application to real-world problems, highlighting their potential to enhance decision-making processes. A novel approach to composite relations in decisionmaking is introduced, showcasing how the concept of bipolar vague composite relations can be effectively utilized in practical scenarios. To illustrate the practical utility of BPVRs, we provide a comprehensive numerical example that guides students in making informed career choices based on their performance across various skill assessments. This example underscores the dual capability of BPVRs to independently evaluate the intervals of satisfaction and dissatisfaction for each option, offering a balanced perspective that is crucial for informed decision-making. Our findings reveal the significant potential of bipolar vague relations in refining and improving decision-making processes, particularly in contexts characterized by uncertainty and complexity. The application of BPVRs can lead to more accurate, nuanced, and reliable solutions, making them a valuable tool in various domains. [ABSTRACT FROM AUTHOR]

Intuitionistic fuzzy sets have been widely studied and applied as an important means of dealing with information uncertainty. However, the existing intuitionistic fuzzy sets and their extension methods are limited and single in their fuzzy spatial representation of information. Under this environment, this paper proposes a new generalized fuzzy set, called qth Rung Root Orthopair Fuzzy Sets (q-RROFS). Since the q-RROFS can adjust the range of fuzzy space expression by the parameter q, it is superior to intuitionistic fuzzy sets, SR-fuzzy sets, and CR-fuzzy sets. We give some definitions and properties of q-RROFS and give their proofs. Under the q-RROFS, we give its operations and properties and introduce four new weighted aggregation operators, namely, qth Rung Root Orthopair Fuzzy-weighted average operator (q-RROFWA), qth Rung Root Orthopair Fuzzy-weighted geometric operator (q-RROFWG), qth Rung Root Orthopair Fuzzy-weighted power average operator (q-RROFWPA), and qth Rung Root Orthopair Fuzzy-weighted power geometric operator (q-RROFWPG). We discuss the properties of these operators in detail and follow the proof procedure. Then, we give a Multi-criteria decision-making approach under q-RROFS. Finally, we illustrate the effectiveness and applicability of the proposed methodology through practical application examples and comparisons with other methods. [ABSTRACT FROM AUTHOR]

In this study, an extended Interval-Valued Picture q-rung Orthopair Fuzzy Set (IVPq-ROFS) is developed, which indicates a trio-different combination of multi-granulated fuzzy numbers, and their basic operation and score function are discussed. An IVPq-ROFS was utilized for sustainability-focused transportation-sectorized electric vehicle battery charging technology selection problem among seven attributes and three alternatives. The application parameters are prioritized through innovative Integrated Objective and Subjective Weighted Approach (IOSWA) for attribute importance identification and Measurement of Alternatives and Ranking according to Compromise solution (MARCOS), which comes under Multi-Attribute Decision Making (MADM) model. This research paper elucidates efficiency of the proposed MADM approach under IVPq-ROFS environment and suggesting alternative battery swapping is best option for electric vehicle charging technology for more adaptation in society for future green mobility. The proposed computational decision algorithm results are validated through nine comparative and sensitivity cases, which proves the reliability and higher effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

For decision makers in various fields, the best way to deal with complicated decision-making problems quickly and efficiently is to simplify the complicated problems as much as possible. Inspired by this kind of social management demand, this paper proposes a novel attribute reduction method using evidence theory in hesitant fuzzy data sets. The method ensures belief and plausibility sum and considers internal and external significance measures. It extends classical evidence theory to hesitant fuzzy data sets and introduces internal and external belief significance measures and internal and external plausibility significance measures based on belief and plausibility functions. The properties of belief and plausibility reduction in hesitant fuzzy data sets are studied, along with the relationship between internal and external significance measures. The attribute reduction method based on significance measure has significant effects on complex information systems and can improve the execution efficiency of decision-makers. And the impressive application value of these theories is demonstrated. [ABSTRACT FROM AUTHOR]

The research on decision making driven by product rankings faces challenges due to the rise of extensive positive reviews and the widespread distribution of electronic word of mouth (eWOM) across multiple platforms. There is a limited body of research that examines the impact of platform credibility on the quality of product rankings. Hence, based on the basic uncertain linguistic information (BULI), which enables simultaneous representation of information and its credibility, we investigate the development of a ratings aggregation approach for cross-platform distribution (CPD) with the aim of facilitating decision-making processes, focusing specifically on the aspect of credibility. To begin with, this paper introduces the concept of BULI as a means to effectively represent both product ratings and their corresponding levels of credibility. Subsequently, we proceeded to devise the BULI-based aggregation functions that are well suited for the aggregation of CPD ratings and that can be degraded to the existing operator. In addition, we develop a credibility evaluation index system and credibility calculation model for the platform in order to derive a product BULI matrix consisting of ratings and their corresponding levels of credibility. In this study, we propose two models, namely the feature information-based user weighting model and the BULI distance measure-based technique for order preference by similarity to an ideal solution (BULI-TOPSIS) model, to enhance the product ratings aggregation approach for decision-making purposes. The utilization of the proposed method is exemplified through the case study of passenger car ranking, showcasing its practicality and efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes a novel multi-attribute decision-making (MADM) method for interval rough numbers (IRNs) considering different distribution types, namely uniform, exponential, and normal distributions. Upper and lower approximate interval dominance degrees are defined and aggregated using dynamic weights to obtain pairwise comparisons of IRNs. The properties of dominance are verified, and an attribute weight determination method based on the dominance balance degree is introduced. The proposed MADM method is data-driven and does not rely on the subjective preferences of decision-makers. Case analysis demonstrates the effectiveness and rationality of the proposed method, revealing that the distribution type of IRNs significantly impacts decision results, potentially leading to reversed ranking outcomes. The proposed method offers a comprehensive framework for handling MADM problems with IRNs under different distributions. [ABSTRACT FROM AUTHOR]

Diverse heritage architecture characterizes Port Said in Egypt, reflecting European and Arab influences. Suez Canal heritage villas comprise 25% of the total listed heritage buildings in the city. Thus, it is important to retrofit them, aligning with the Sustainable Development Goals (SDGs). By reviewing the previous literature related to the green retrofitting of heritage buildings, the (3Ts) framework can be considered a holistic framework that covers the latest trends due to its three components: targets, tools, and tasks, which help the success of the process through a holistic vision of supporting building information modeling (BIM) technologies and organizing stakeholders' involvement. This paper aims to apply the (3Ts) framework to Suez Canal heritage villas in Port Said, Egypt. The applied study is based on how stakeholders perceive their input and decision-making, measuring the climate parameters, proposing several green retrofits, and integrating them into green scenarios. Evaluation of scenarios is conducted by utilizing the Design Builder simulation software. Additionally, the Skelion plugin calculates the photovoltaic system's (PVs) energy generation. The results indicated that the total energy savings could reach approximately 12%–74% and nearly 10%–73% CO₂ emissions reduction without affecting heritage values. [ABSTRACT FROM AUTHOR]

Graphical abstract Abstract Biobased chemistry has gained interest and has the potential to tackle some of the sustainability challenges the chemical industry must endure. Sustainability impacts need to be evaluated and monitored to highlight the advantages and pitfalls of different biobased routes over the entire product life cycle. This study aims for expert consensus concerning indicators needed and preferred for sustainability analysis of biobased chemicals in Europe. Experts are consulted by means of a Delphi method with stakeholders selected from three core groups: the private, public and academic sector. Best-Worst Scaling (BWS) is performed to gather data on the prioritization of the sustainability indicators per respondent. Afterwards, Multi-Criteria Decision Analysis (MCDA) is used to develop a consensus ranking among the experts. The results show that GHG emissions , market potential and acceptance of biobased materials are deemed the most crucial indicators for respectively environmental, economic and social sustainability. Expert consensus is positive in all three sustainability domains, with the strongest consensus measured for environmental sustainability showing a median Kendall's τ of 0.63 (τ ranging from -1 to 1) and the weakest consensus found within social sustainability showing a median Kendall's τ of 0.50. Further research can apply the ranked indicators on specific case studies to evaluate the practicability of the defined indicator set. [ABSTRACT FROM AUTHOR]

In the rapidly evolving field of Artificial Intelligence (AI), its application in industrial settings, particularly for anomaly detection in time series data, poses unique challenges. Current methods often lack a comprehensive understanding of the trade-offs involved in achieving optimal model performance, data preparation effort, and prediction quality. To bridge this gap, this paper presents an adaptive approach to address these challenges, focusing on making conscious decisions about mentioned trade-offs. Inspired by the principles of the Iron Triangle from product engineering, our methodology introduces a novel "AI triangle" with dimensions of Speed, Effort, and Quality. We applied this methodology to a real-world case study involving anomaly detection in a constrained hardware environment in the context of a forming production process. The results highlight the effectiveness of our approach in achieving a practical balance between speed, effort, and quality constraints for implementing AI in an industrial setting. This paper provides valuable insights into the considerations and trade-offs necessary for the successful deployment of AI in manufacturing and other similar industrial applications. [ABSTRACT FROM AUTHOR]