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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 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.)

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.)

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]

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]

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.

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]

There are security risks in interaction and communication using wireless mobile networks, and network security situation prediction technology is to predict the next development trend with the previous and current network status, which can grasp the wireless mobile networks security status in time and make decisions in advance to avoid attacks. This paper proposes an Improved Particle Swarm Optimization Attention Bi-directional Long Short-Term Memory (IPSO-ABiLSTM) model for network security situation prediction. First, we construct the real situation values of the raw UNSW-NB15 dataset from the perspective of the impact of the attack on the situation indicator system, the sliding window method was introduced to reconstruct the situation values of the data set obtained by computing the data used for prediction. Secondly, the traditional PSO algorithm has the shortage of unbalanced search speed and tends to get local optimal solutions. The IPSO algorithm in this paper makes the global and local search ability of the algorithm more balanced and converges faster. Finally, the IPSO-ABiLSTM model is used to implement the situation prediction in different sliding window sizes. The experimental results show that the IPSO-ABiLSTM of this paper fits up to 0.9922, which verifies the effectiveness of the model proposed in this paper in the network situation prediction problem. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has been one of the most severe disruptions to normal life, impacting how businesses operate. The academic literature in the areas of supply chain and operations management has been trying to explain how this has affected decision-making in businesses. However, the existing literature has predominantly overlooked organisational culture and behavioural economic theories. This paper contends that considering the decisions made in supply chain disruption management involve groups and the individuals within them, the relevance of behavioural economic concepts becomes paramount. As such, the objective of this paper is to conduct an integrative literature review, utilising the purposive sampling method to explore the dearth of academic work connecting behavioural economic theories and organisational culture to supply chain disruption management. Additionally, the paper aims to offer guidelines for future research in this domain. Enhancing our comprehension of these domains concerning supply chain disruption management would empower firms to better anticipate their parties' decisions, refine their decision-making models, and cultivate stronger relationships with suppliers and customers. [ABSTRACT FROM AUTHOR]

This paper critically examines the relationship between women's labor force participation (LFP) and empowerment, particularly in the Global South, utilizing Naila Kabeer's empowerment framework. By challenging the orthodox conceptualization of LFP, the study reveals its methodological limitations as a measure of women's economic engagement. By emphasizing the dynamic nature of empowerment as a multifaceted process within the formal and informal sector, this paper highlights the interplay of agency, resources, and achievements within Kabeer's framework. Drawing from global examples, it demonstrates the varied impacts of paid work on women's decision-making in both private and public spheres. While acknowledging the potential of LFP to enhance women's empowerment, the paper underscores the significance of contextual factors in shaping this relationship. By shedding light on the complexities and nuances of women's labor and empowerment, the study offers valuable insights for policymakers and researchers striving for gender equality and women's empowerment worldwide. [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?"

The main aim of this paper is to present a new multi-attribute decision-making (MADM) approach for solving the problems under the uncertain and complex environment. The key challenges during any MADM problem are how to quantify the objective uncertainty information in the data and how to aggregate such collective information. To answer this, in this paper, we utilize the concept of the bipolar fuzzy information to mark the information in terms of the positive and negative support. To aggregate this different information, we propose some power aggregation operators based on the Aczel-Alsina operators (AAO). The AAOs are the generalized t-norm based operations with an additional parameter to analyze the influence of the expert preferences. Based on these AAO and bipolar fuzzy information, we stated bipolar fuzzy AA power weighted averaging and geometric operators and investigate their features. Later, based on these operators, we establish a MADM algorithm to solve the decision-making problems. The applicability of the stated algorithm is demonstrated through a case study related to quantum computing. The comparative studies and advantages of the study are also analyzed with the various prevailing theories. [ABSTRACT FROM AUTHOR]

Agriculture plays an important role in the economy of any country. Approximately half of the population of developing countries is directly or indirectly connected to the agriculture field. Many farmers do not choose the right crop for cultivation depending on their soil type, crop type, and climatic requirements like rainfall. This wrong decision of crop selection directly affects the production of the crops which leads to yield and economic loss in the country. Many parameters should be observed such as soil characteristics, type of crop, and environmental factors for the cultivation of the right crop. Manual decision-making is time-taking and requires extensive experience. Therefore, there should be an automated system for the right crop recommendation to reduce human efforts and loss. An automated crop recommender system should take these parameters as input and suggest the farmer's right crop. Therefore, in this paper, a long short-termmemory Network with an attention block has been proposed. The proposed model contains 27 layers, the first of which is a feature input layer. There exist 25 hidden layers between them, and an output layer completes the structure. Through these levels, the proposed model enables a successful recommendation of the crop. Additionally, the dropout layer's regularization properties aids in reduction of overfitting of the model. In this paper, a customized novel long short-term memory (LSTM) model is proposed with a residual attention block that recommends the right crop to farmers. Evaluation metrics used for the proposed model include f1-score, recall, precision, and accuracy attaining values as 95.69%, 96.56%, 96.9%, and 97.26% respectively. [ABSTRACT FROM AUTHOR]

The Intergovernmental Panel on Climate Change (IPCC) maintains a fine balance between scientific credibility and policy-relevance. The IPCC's review process plays an important role in ensuring that this takes place. This paper looks at the review process of the Summary for Policymakers of the Special Report on Global Warming of 1.5 Degrees (SR15) published in 2018. We apply a framework for the making of policy-relevant knowledge – that of salience, legitimacy, and credibility – to investigate the acts of knowledge selection, and conflicts over what constitutes policy-relevant knowledge on climate change. We find that knowledge is shaped through discussions surrounding the themes of scope, communication, framing, and IPCC procedures and evidence, and that these themes were articulated in different ways in relation to salience, credibility and legitimacy. Our analysis shows how the practices of salience, legitimacy and credibility interact with each other in the making of policy-relevant knowledge. In particular we see that a focus on credibility and salience, whilst central may take place at the expense of legitimacy. Overall we see that this interplay in the review was important in shaping the story of the SR15, and the knowledge that gets included in the final SPM. • This paper explores the role of review in the IPCC 1.5 degrees Special Report. • The practices of salience, legitimacy and credibility shape the story the report tells. • Decisions made referring to scope, communication, framing, IPCC procedure/evidence. • Reviewers and authors play role in shaping report, but authors maintain final choice. • Choices made considering salience and credibility are common, at expense of legitimacy. [ABSTRACT FROM AUTHOR]

The paper discusses the institutional factors that influence the development of think tanks in different political systems, with a focus on Russia and Turkey. It identifies various institutional factors such as the structure of the decision-making process, the type of regime, bureaucratic structure, level of democratization, approach of decision-makers, presence of pluralism, effectiveness of political parties, strength of foundations, legal norms, state of civil society, presence of an open public debate culture, and autonomy of the business world that influence the operation of think tanks. The study examines the similarities and differences between the institutional environment of Russia and Turkey, provides examples of how political and institutional factors in different periods in Turkey and Russia have had a facilitating or complicating effect on the establishment and development of think tanks. This paper contributes to the literature by highlighting the importance of institutional factors in the formation, development, role, and structure of think tanks. [ABSTRACT FROM AUTHOR]

Copyright of Informatica (03505596) is the property of Slovene Society Informatika 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.)

Copyright of Organizacija 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.)

The quality of assessment tools and the inferences drawn from the results to inform decisions in the classroom are usually measured using reliability and validity. These psychometric principles have been criticised for their inapplicability to classroom assessment, resulting in a parallel set of 'classroometric' principles. However, the use of two parallel principles widens the perceived dichotomy between formative and summative assessments. To overcome this dichotomy and ensure consistency of teachers' decision-making, the concept of trustworthiness, drawn from qualitative research methodology, is increasingly being adopted, but it is under-theorised. We used a scoping technique to explore how this concept has been used in the assessment literature since it was first introduced in 1993. We accessed journal articles from four databases using combinations of search terms, resulting to 1,872 papers. Upon removal of duplicates and application of exclusion criteria, 27 papers remain relevant for full analysis. Our analysis expands Lincoln and Guba's (1985) four criteria of qualitative research (credibility, transferability, dependability and confirmability) to include authenticity, rigour, fairness, equity, consistency, defensibility, accuracy, and adequacy and appropriateness of data. We develop a framework and a working definition for understanding trustworthiness in the context of assessment. [ABSTRACT FROM AUTHOR]

Background: Problem-based learning (PBL) is well established in medical education and beyond, and continues to be developed and explored. Challenges include how to connect the somewhat abstract nature of classroom-based PBL with clinical practice and how to maintain learner engagement in the process of PBL over time. Objective: A study was conducted to investigate the efficacy of decision-PBL (D-PBL), a variant form of PBL that replaces linear PBL cases with virtual patients. These Web-based interactive cases provided learners with a series of patient management pathways. Learners were encouraged to consider and discuss courses of action, take their chosen management pathway, and experience the consequences of their decisions. A Web-based application was essential to allow scenarios to respond dynamically to learners’ decisions, to deliver the scenarios to multiple PBL classrooms in the same timeframe, and to record centrally the paths taken by the PBL groups. Methods: A randomized controlled trial in crossover design was run involving all learners (N=81) in the second year of the graduate entry stream for the undergraduate medicine program at St George’s University of London. Learners were randomized to study groups; half engaged in a D-PBL activity whereas the other half had a traditional linear PBL activity on the same subject material. Groups alternated D-PBL and linear PBL over the semester. The measure was mean cohort performance on specific face-to-face exam questions at the end of the semester. Results: D-PBL groups performed better than linear PBL groups on questions related to D-PBL with the difference being statistically significant for all questions. Differences between the exam performances of the 2 groups were not statistically significant for the questions not related to D-PBL. The effect sizes for D-PBL–related questions were large and positive (>0.6) except for 1 question that showed a medium positive effect size. The effect sizes for questions not related to D-PBL were all small (≤0.3) with a mix of positive and negative values. Conclusions: The efficacy of D-PBL was indicated by improved exam performance for learners who had D-PBL compared to those who had linear PBL. This suggests that the use of D-PBL leads to better midterm learning outcomes than linear PBL, at least for learners with prior experience with linear PBL. On the basis of tutor and student feedback, St George’s University of London and the University of Nicosia, Cyprus have replaced paper PBL cases for midstage undergraduate teaching with D-PBL virtual patients, and 6 more institutions in the ePBLnet partnership will be implementing D-PBL in Autumn 2015. [ABSTRACT FROM AUTHOR]

Machine Learning as a Service, MLaaS refers to cloud-based platforms that provide machine learning tools and infrastructure to users, allowing them to access and utilize machine learning capabilities without managing the underlying hardware and software. Cloud service providers offer scalable and flexible resources, enabling businesses to train and deploy machine learning models efficiently and cost-effectively. Multi-Criteria Decision making, MCDM helps individuals or organizations make choices when multiple criteria or objectives are involved. It aims to find the best alternative among a set of available options by considering various criteria or factors that are relevant to the decision. Machine learning as a service (MLaaS) and MCDM are two distinct concepts that can be combined to create powerful decision-making solutions. The present paper proposed an integrated approach based on the Analytic Hierarchy Process and TOPSIS method for choosing the best MLaaS cloud. Four alternatives for MLaaS cloud were analyzed based on seventeen shortlisted criteria. Based on the calculations, conclusions have been reached and potential study areas have been identified. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) technology is vital in art design decisions making and has become a prevalent research trend. However, With the temporal variability in EEG signals, there is a problem of low model prediction accuracy. Therefore, We propose an EEG signal recognition algorithm called the Time-Slicing and Deep Residual Network (TS-DRN). First, we present the subjects with the patterns of different styles of designs to capture their EEG signals. Second, we employ the time-slicing strategy to process the original signal, enhancing the number of training samples and reducing the sample features' dimensionality. Finally, we use the combined EEG feature maps as inputs to the deep residual network to obtain the classification results. Our experimental results demonstrate that this paper's EEG signal classification accuracy is 85.8%, demonstrating our method's effectiveness for EEG signal classification. [ABSTRACT FROM AUTHOR]

Nowadays, the increasing integration of artificial intelligence (AI) technologies in manufacturing processes is raising the need of users to understand and interpret the decision-making processes of complex AI systems. Traditional black-box AI models often lack transparency, making it challenging for users to comprehend the reasoning behind their outputs. In contrast, Explainable Artificial Intelligence (XAI) techniques provide interpretability by revealing the internal mechanisms of AI models, making them more trustworthy and facilitating human-AI collaboration. In order to promote XAI models' dissemination, this paper proposes a matrix-based methodology to design XAI-driven user interfaces in manufacturing contexts. It helps in mapping the users' needs and identifying the "explainability visualization types" that best fits the end users' requirements for the specific context of use. The proposed methodology was applied in the XMANAI European Project (https://ai4manufacturing.eu), aimed at creating a novel AI platform to support XAI-supported decision making in manufacturing plants. Results showed that the proposed methodology is able to guide companies in the correct implementation of XAI models, realizing the full potential of AI while ensuring human oversight and control. [ABSTRACT FROM AUTHOR]

Industry 4.0 is demanding reskilling of the workforce and, at the same time, the phenomenon of Great Resignation characterized by a wave of layoffs is threatening organizational knowledge. It is essential to build a culture where employees are engaged, more committed, and where the sharing and generation of knowledge is promoted. This paper presents a collaborative platform (co-created with 3 multi-sector organizations- 2 multinationals) to support the establishment of a Learning Organization to face the challenges of human resources in the digital paradigm. This tool integrates 5 modules: discussion forum, event creation, mentoring, suggestion system and recognition system. The 5 modules act in a combined way to achieve features that offer participation in decision making, a climate of learning opportunities, through the sharing and rise of knowledge (Learning Organization) and that encourage peer relationships and recognition at the corporate level. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has profound economic influence in manufacturing, but its unmindful integration can also pose societal and environmental risks. This paper provides a quantified overview of manufacturing areas that are highly advanced in AI capability research, such as maintenance. Integrating Responsible AI (RAI) in further studies of those areas is essential to mitigate risks and deliver business benefits. To enable this, manufacturing specific RAI dimensions are defined to represent accountability, explainability, fairness, human-centricity, sustainability (Green AI) and privacy & security. Further, a qualitative RAI framework consisting of responsibility areas (human involvement, decision making, business focus, system design) is proposed. Practical considerations to align the framework with manufacturing requirements are made by discussing it within an AI systems lifecycle. [ABSTRACT FROM AUTHOR]

Within the paradigm of industry 5.0, manufacturing systems are seeking for human-centred production, where the operator finds high-level supervision tasks. In this context, low-level decision making should be performed by machines themselves. In this paper, a hybrid prognosis algorithm is developed to automatically inspect the cutting edges of drill-bits and to predict their Remaining Useful Life (RUL) and the associated probability density function. The solution relies on the automatic measurement of flank wear through convolutional filtering and edge detection. Prognosis exploits particle filter, which updates multi-layer perceptron with online data, to adaptively predict drill-bits RUL. The solution reduces the experimental preliminary run-to-failures needed for training standard machine learning algorithms, exploiting them in a real-time adaptive scenario, while predicting tool RUL under untested and variable cutting process operations. The algorithm uses direct wear observations, taken during set-up times (e.g., tool changes, workpiece change), thus not interfering with the process. [ABSTRACT FROM AUTHOR]

Decision analysis is a key aspect in improving slums intervention strategies utilizing a variety of tools to evaluate all relevant information that support decision-making process for dealing with the complexity of slums interventions policies. This paper presents a framework integrating Multi Criterion Decision Analysis (MCDA) with (GIS) based decision support tool to quantitatively assess land suitability for site relocation of an urban slum site utilizing Analytical Hierarchy Process (AHP), which is commonly used for fastest growing decision-analytic techniques in several disciplines for the determination of various aspects weights. Herein Batn Al Baqara region (which is located in south of Cairo) was selected as study area to be relocated as it was considered among the most difficult to deal with, three proposed relocation sites were selected and analyzed using MCDA-GIS Model. Twelve different criteria were utilized to evaluate the degree of suitability of the relocation sites. A five degree of suitability were employed according to the different criteria scores for each site. The results supported that Al Asmarat is the most suitable site for the relocation of slum since it achieved the highest evaluation score (84.92 %) when the model was applied to the relocation sites. [ABSTRACT FROM AUTHOR]

To effectively manage complex human-environment fisheries systems, it is necessary to understand the psychology of fisher agents. While bio-economic models typically provide simple, abstract approaches for human behaviour (e.g., fully informed profit maximisers), fisher agents are of course neither simple nor perfect. Imperfections of learning, memory, and information availability, combined with the diversity of value preferences within populations, can lead to substantial deviations and unanticipated effects of interventions. This paper presents a computational model of fisher agents’ decision-making that draws on theoretical and empirical psychological insights to enrich this critical component. The model includes mechanisms for information integration (learning), social comparisons, and thresholds for economic satisfaction. In offering this enriched account, the model captures how fishers may adapt behaviourally given changes in policy, economic conditions, or social pressures. Furthermore, the model can be parameterised to capture the effects of different socio-cultural contexts can be simulated. The model of fisher agents has been implemented as part of POSEIDON (an agent-based fisheries management model), showing that fishers imbued with the model learn and adapt when responding dynamically to changing conditions. The model is thus demonstrated in a fisheries environment, but we discuss how its architecture could be implemented for simulation in other human-environment systems, such as designing policies to combat the human-environment problems. [ABSTRACT FROM AUTHOR]

This research paper focuses on the development of advanced mathematical models for disease diagnosis and prediction, and the subsequent creation of automated systems based on these models. These systems leverage a range of mathematical models and incorporate cutting-edge information technology achievements to provide medical professionals with valuable decision-making support. By amalgamating mathematical rigor and technological innovation, this research endeavors to enhance the accuracy and efficiency of medical diagnoses, thereby improving patient care and healthcare outcomes. This study delves into the persistent need for contemporary information systems, where information plays a crucial role in decision-making. It aims to provide an objective approach to addressing pressing medical challenges, particularly in disease diagnosis and prediction, enhancing the effectiveness of these critical tasks. Automated medical information systems, built on advanced mathematical models, significantly empower physicians. Machine diagnostics, relying on deterministic logic, the phase interval method, and information-probabilistic logic, bolster diagnostic capabilities. Functional entropy enables individuals to handle vague information, aiding decision-making. Assessing imprecision and uncertainty computationally diminishes subjectivity, while employing fuzzy set theory enhances diagnostic modeling. Mathematical models assess diagnostic indicators, and linguistic variables quantify resemblance. The diagnostic model for primary biliary cirrhosis and active hepatitis utilizes a diagnostic table and gradient projection. This comprehensive study advances medical diagnostics through mathematical models and automated systems, addressing critical healthcare challenges. [ABSTRACT FROM AUTHOR]

This paper aims to construct a trader model tailored to offer individualized investment strategies for users engaged in the US stock market. The approach taken involves two key aspects: forecasting stock prices and discerning users' personality traits to formulate optimal trading recommendations. The framework is realized through the integration of neural networks and the Moving Averages algorithm. This combined approach allows forecasting upcoming changes in stock prices and generating appropriate trading signals. Complementing this, a user risk assessment is administered, and its outcomes are leveraged to furnish personalized trading advice, offering invaluable support for informed decision-making amid intricate real-time scenarios. By synergizing predictive modeling and user-specific insights, this comprehensive system contributes to enhancing trading precision and user empowerment. This work can be useful in decision making for a wide range of stock market participants, as well as for institutions specialized for this area of operation. [ABSTRACT FROM AUTHOR]

This paper proposes interval valued pentagonal fuzzy neutrosophic set in view with the combination of pentagonal fuzzy sets, single valued neutrosophic set and the interval valued neutrosophic set. Interval valued pentagonal fuzzy neutrosophic weighted geometric averaging operator is defined based on the operational laws, a theorem and some of its properties have been established by the proposed operator and also on the score and accuracy function. By assigning different weights to various features in accordance with each choice, the aggregation of the interval valued pentagonal fuzzy neutrosophic set is used on an example to validate the efficiency. Finally, a multi-attribute decision-making environment is examined using the proposed methodology's ranking order and collective ratings of each attribute's values for various alternatives. [ABSTRACT FROM AUTHOR]

IoT (Internet of Things)-enabled products are increasingly used by consumers and continuously propagate in daily life. Billions of networked objects not only increase the complexity of development but also raise user interaction and adaptation to individual needs. The more non-expert users are involved in decision making, interaction, and adaptation processes, the more user-centric IoT design is crucial, particularly when the number of elderly users is steadily increasing. In this paper, we study the capabilities of adopting IoT products through user-informed adaptation in a major IoT application domain, home healthcare. We review evidence from established practice in the field on how users can be supported when aiming to adapt medical IoT (M-IoT) home applications to their needs. We examine the empirically grounded use of IoT sensors and actuators, as well as the adaptation process users adopt when using an IoT application in a personalized environment. Our analysis (technological evidence) reveals various IoT devices that have already been applied in M-IoT adaptation settings to effectively support users. Our analysis reveals that only few empirically sound findings exist on how users actually perceive interactive adaptation features and redesign M-IoT applications. Based on the analysis of these empirically grounded findings, we suggest the development of a domain-specific user-centric adaptation feature. Specifically, we exemplify a tangible adaptation device for user-informed M-IoT application in home healthcare. It has been developed prototypically and tested in an environment for personalized home healthcare. [ABSTRACT FROM AUTHOR]

Despite acknowledging the risks of the COVID-19 pandemic for the prison population, Brazil's Supreme Court declined to issue structural injunctions during the health crisis ordering lower courts to consider these risks when making incarceration-related decisions. These injunctions could have been crucial to mitigate mass incarceration and protect the prison population during the pandemic. Through an examination of the Supreme Court's rulings in structural cases and in a sample of over 4,000 habeas corpus decisions, this paper argues that granting these injunctions would have overwhelmed the court with an unmanageable influx of individual claims. Consequently, the Supreme Court acted strategically in anticipation of its limited institutional capacity to enforce compliance with structural injunctions among lower courts. This case study illustrates how practical considerations can hinder structural decisions in criminal law and highlights the limits of structural litigation and constitutional jurisdiction to address mass incarceration. [ABSTRACT FROM AUTHOR]

Financial institutions face many challenges in managing modern financial transactions and vast data volumes. To overcome these challenges, they are increasingly harnessing advanced data management technologies such as artificial intelligence and cloud computing. This paper presents a comprehensive review of how these tools transform financial decision-making in various domains and applications. We analyzed both foundational and recent advancements using a rigorous methodology based on the PRISMA 2020 guideline. Our findings indicate that many major financial institutions are adopting AI-driven solutions to potentially enhance real-time risk assessment, transactional efficiency, and predictive analytics. While they bring benefits like faster decision-making and reduced operational costs, they also pose challenges like data security and integration complexities that require further research and development. Looking ahead, we envision a more integrated, responsive, and secure financial ecosystem that leverages the convergence of AI, cloud computing, and advanced data storage. This synthesis underscores the significance of contemporary data management solutions in shaping the future of data-driven financial services, offering a guideline for stakeholders in this evolving domain. [ABSTRACT FROM AUTHOR]

In this paper, we show the practical application of two recently proposed Multi-Criteria Decision Analysis (MCDA) methods, namely the Stable Preference Ordering Towards the Ideal Solution (SPOTIS) and the Reference Ideal Method (RIM) methods. Both of these methods can utilize the Expected Solution Point (ESP) concept, which allows it to easily reflect the decision-maker's or the customer's preferences and expectations. We show the comparison of those methods in the practical study case of laptop choice for the customer with specific needs and expectations for the hardware expressed using ESP. The comparison also includes rankings built toward an optimal solution to underline the necessity of such an approach as the Expected Solution Point. We also use the recently developed Ranking Comparison (RANCOM) method to identify criteria weights to include customer preferences in the weights. The paper also contains a broad discussion of the obtained results and limitations of the presented methods. [ABSTRACT FROM AUTHOR]

This paper adopts a new dynamic approach towards user authentication and authorization to enhance security in Cyber Networks. The concept of user ID is generalized by embedding additional attributes incorporating detailed user profile, operational context, common operating picture, and situation awareness. This leads to a new paradigm that is formulated as a Computable Compound Identity Measure (CCIM). Using CCIM the existing authentication and authorization schemes are integrated and generalized to a process that also embeds access control. The CCIM scheme is risk-adapted and dynamically responsive to the operational need. This responsiveness is proportional to the operational information incorporated into the CCIM decision making, including its dynamic and variable content. The paper also presents a conceptual architecture to demonstrate how to deploy this new scheme using Policy-Based Management (PBM) technology. This technology is the operational enabler for the dynamic behavior of the CCIM scheme. A working prototype product does exist though its inner details are proprietary and cannot be shared in this paper. This CCIM based authentication and authorization technology is an important step towards a fundamental solution versus ineffectual patches, partial solutions, and piecemeal approaches. The technology addresses constantly changing threat environment with a cost-effective technology that evolves and adapts in a dynamic manner to offer operational responsiveness and risk aversion. [ABSTRACT FROM AUTHOR]

The ranking of intuitionistic fuzzy numbers is paramount in the decision making process in a fuzzy and uncertain environment. In this paper, a new ranking function is defined, which is based on Robust's ranking index of the membership function and the non-membership function of trapezoidal intuitionistic fuzzy numbers. The mentioned function also incorporates a parameter for the attitude of the decision factors. The given method is illustrated through several numerical examples and is studied in comparison to other already-existent methods. Starting from the new classification method, an algorithm for solving fuzzy multi-criteria decision-making (MCDM) problems is proposed. The application of said algorithm implies accepting the subjectivity of the deciding factors, and offers a clear perspective on the way in which the subjective attitude influences the decision-making process. Finally, a MCDM problem is solved to outline the advantages of the algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

Business Analytics - which unites Descriptive, Predictive and Prescriptive Analytics - represents an important component in the framework of Big Data. It aims to transform data into information, enabling improvements in making decisions. Within Big Data, optimization is mostly related to the prescriptive analysis, but in this paper, we present one of its applications to a predictive analysis based on regression in fuzzy environment. The tools offered by a regression analysis can be used either to identify the correlation of a dependency between the observed inputs and outputs; or to provide a convenient approximation to the output data set, thus enabling its simplified manipulation. In this paper we introduce a new approach to predict the outputs of a fuzzy in - fuzzy out system through a fuzzy regression analysis developed in full accordance to the extension principle. Within our approach, a couple of mathematical optimization problems are solve for each desired a-level. The optimization models derive the left and right endpoints of the a-cut of the predicted fuzzy output, as minimum and maximum of all crisp values that can be obtained as predicted outputs to at least one regression problem with observed crisp data in the a-cut ranges of the corresponding fuzzy observed data. Relevant examples from the literature are recalled and used to illustrate the theoretical findings. [ABSTRACT FROM AUTHOR]

Digital data management and enterprise systems have become key to support the digitalisation of maintenance activities. With traditional maintenance activities still striving for efficiencies, platforms such as the natural language processing (NLP) are supporting industries to mine textural data, not just extracting degradation terminologies but providing the maintainer with holistic insights on the degradation process. Traditionally, the degradation analysis, the first step in maintenance, is a manual process for defect characterisation, followed by failure investigation and a remaining useful life estimation. To enable digitalisation, transfer of human cognitive decision making from the physical world to the digital world is key. This paper enables this cognitive knowledge transfer through the design of a common degradation taxonomy and extracting terminology relationships to produce degradation causality with an NLP knowledge extraction approach. Further, this paper proposes and demonstrates a framework to present the data in the form of a knowledge graph populated using an application-level ontology. Use cases in the aerospace context have been used to show the power of the NLP and conceptual journey into the digitalisation of maintenance. [ABSTRACT FROM AUTHOR]

The growing need of a research lab in automotive confirmatory tests is increasing day by day in the competitive market. Testing plays an important part in time and cost to meet the durability and quality requirements of the customers. Decision Making Models [DMM] with order preference supports well in optimizing the process scheduling time and utilization of machines. The analyzed data is of with multi-criterion constraints and unreliable parameters. As the constraints in analyzed data belongs to [-R, R], the problem is modelled with generalized Fuzzy Interval Valued set. The defined FIVs are justified using Novel Accuracy of Membership and Non-Membership values. The unconventionality of the work discussed in this paper is applying FIVs in the existing Evaluation by Distance Average Sum [EDAS] method. The weight values of the criterion are calculated using the Average Distance [AD] formula. In general, Fuzzy VIKOR and Fuzzy TOPOSIS helps in identifying the closeness co-efficient of the parameters. Particularly, VIKOR plays a vital role on group utility and TOPOSIS on Max/Min of negative and positive ideal solutions respectively. Here, the proposed FIVEDAS method is framed with the Relative Distance Values [RDA] and so it provides us an ideal ranking in order preference. The results are compared with the existing lab process utility time in First-Come-First Serve method. [ABSTRACT FROM AUTHOR]

Major power outages emergencies (MPOEs) are increasingly occurring with greater frequency and wreaking havoc, necessitating the effective decision-making for electricity rationing to mitigate economic losses incurred and maintain social stability. To address this issue, this paper proposes an efficient fair electricity rationing decision-making model under MPOEs, including two methods to quantify the efficiency and fairness of the electricity rationing. Firstly, the inoperability inputoutput model is employed to quantify the efficiency by assessing the business interruption costs caused by MPOEs. Secondly, the fairness is quantified by the fairness perception of the affected regions, which consider their bounded rational comparisons based on the Prospect theory. Then, the NSGA-II is utilized to solve the model. Finally, Sichuan MPOE in 2022 is designed as a numerical experiment to validate the proposed model, accompanied by corresponding discussions to demonstrate the impact of “supercities” on electricity rationing and the significance of the balance between efficiency and fairness. [ABSTRACT FROM AUTHOR]

This paper is aimed at starting a debate on disability and the inclusion of children with disabilities in the public school system under the Primary Education Development Programme 4 in Bangladesh. The analysis is based on a strategic Design Note developed by USAID, with the author as consultant, and is guided by the rights perspective of the Convention on the Rights of Persons with Disabilities (CRPD, 2006), notably Article 24 on inclusive education, and the Sustainable Development Goals (SDG4) (UN, 2015). Extensive consultations were carried out in the course of developing the Design Note. Discussions and workshops were conducted with government agencies and stakeholders, notably civil society, organisations of persons with disabilities, the donor community and human rights organisations. Mainstreaming disability into public schooling requires a reform in the primary education system. It is necessary in order to identify, enrol, maintain and retain learners with disabilities throughout the primary cycle. Key actions entail creating an enabling environment and focusing on overcoming attitudinal barriers in the local community and school authority; improving school infrastructure; strengthening inclusion practises, notably with adapted pedagogy, as well as consolidating policy framework, coordination and governance, in addition to empowering users. These steps will enhance targeting and screening of a wide range of children with disabilities in school as well as out of school. By contributing to improved learning outcomes, fulfilling the right to education (Article 24 of CRPD) and achieving Sustainable Development Goals (SDG4), this could be a model of good practice for other countries in the region. [ABSTRACT FROM AUTHOR]

Three-way decision models have received substantial interest grounded in decision-theoretic rough sets and Bayesian decision theory. Single-valued neutrosophic sets are extremely useful for handling uncertain and inconsistent information, making them a valuable tool that is commonly applied in decision-making. In a three-way decision problem involving a piece of single-value neutrosophic information, the losses of each equivalence class under different actions can usually be identified with some accuracy. A critical aspect of the three-way decision problem centers around appropriate handling of the loss function. This paper proposes a novel approach to rank loss functions in each equivalence class of three-way decisions, based on the TODIM method and operates within a singlevalued neutrosophic environment. Furthermore, a numerical experiment on the location of a breakfast restaurant is used to to assess the model compared to some existing related models, with the aim of demonstrating its validity and soundness. [ABSTRACT FROM AUTHOR]

At present, many decision-making fields require collecting and organizing the opinions and evaluation values of experts. However, they evaluation values given by experts used in the decision-making should be further analysed from a scientific angle. In this paper the evidence reasoning approach under two-dimensional frameworks (ERTDF) is used to mine the relative validity of the expert scoring value. This approach takes the self-evaluated familiarity with the decision-making objects to be evaluated and the authority of the position and title as the main characteristics of the expert evaluation body. The unique characteristic information of the above experts is transformed into evidence to assist in correcting the original score results and to express their respective reliability distribution. This method also could support some intelligent decisions under the evaluation background of experts in various fields. The application results show that this method could better consider the uncertainty of the evaluation results caused by the various characteristics of the individual experts, improve the effect of experts' scores, and make the comprehensive results of multiple experts' evaluation values more reasonable and accurate. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases have become extremely prevalent in the global population. Several accurate classification methods for arrhythmias have been proposed in the healthcare literature. However, extensive research is required to improve the prediction accuracy of various arrhythmia conditions. In this paper, discussion is focussed on two major objectives: optimisation of windows based on our proposed auto-segmentation method for the exact diagnosis of the heart condition within the segment and prediction of arrhythmia progression. For prediction, identification of features is vital. Identified efficient independent feature sets such as RR interval, peak-to-peak amplitude, and unique derived parameters such as coefficient of variation (CV) of RR interval and CV of peak-to-peak amplitude. The progression of arrhythmia includes the following steps such as data preprocessing, time and frequency domain feature extraction, and feature selection using principal component analysis. A hypertuned support vector machine is utilised for accurate diagnosis. Proposed two techniques to predict the progression of arrhythmias: the regression-based trend curve (RBTC) and the fuzzy enhanced Markov model (FEMM). We have effectively evaluated our prediction algorithms using offline Massachusetts Institute of Technology Physio Net database signals, using automatic segmentation with prediction accuracy of 98 %. In terms of accuracy, FEMM outperforms RBTC. Thus, an autosegmentation algorithm was proposed to classify various arrhythmia signals using a minimal feature set and to predict future conditions using our proposed method, FEMM. [ABSTRACT FROM AUTHOR]

The practice of fostering citizen participation in public finance-related decisionmaking at local government level in North Macedonia and Slovakia has backslid during COVID-19. Since COVID-19 prompted a worldwide lockdown, governments were forced to introduce emergencies and/or develop "new" participation methods. The paper aims to explore the impact of COVID-19 on citizens' participation in financial decision-making using participatory budgeting among the local self-governments in North Macedonia and Slovakia and identify possible COVID-19-specific and general barriers to such participation, considering the particular context of the two countries. [ABSTRACT FROM AUTHOR]

Recently, computer assisted diagnosis (CAD) model creation has become more dependent on medical picture categorization. It is often used to identify several conditions, including brain disorders, diabetic retinopathy, and skin cancer. Most traditional CAD methods relied on textures, colours, and forms. Because many models are issue-oriented, they need a more substantial capacity to generalize and cannot capture high-level problem domain notions. Recent deep learning (DL) models have been published, providing a practical way to develop models specifically for classifying input medical pictures. This paper offers an intelligent beetle antenna search (IBAS-DTL) method for classifying medical images facilitated by deep transfer learning. The IBAS-DTL model aims to recognize and classify medical pictures into various groups. In order to segment medical pictures, the current IBASDTLM model first develops an entropy based weighting and first-order cumulative moment (EWFCM) approach. Additionally, the DenseNet-121 techniquewas used as a module for extracting features.ABASwith an extreme learning machine (ELM) model is used to classify the medical photos. A wide variety of tests were carried out using a benchmark medical imaging dataset to demonstrate the IBAS-DTL model's noteworthy performance. The results gained indicated the IBAS-DTL model's superiority over its pre-existing techniques. [ABSTRACT FROM AUTHOR]

Enterprise resource planning (ERP) adoption literature has a consensus that selecting the right ERP system is one of the most critical success factors in the ERP adoption lifecycle. While choosing a non-fitting ERP system may lead to adoption failures, however very few papers focus solely on this selection phase. Hence, given the criticality of the ERP selection phase, this paper aims to identify and review the different ERP selection methods in extant literature. This research also presents the factors and variables included in each identified selection method in ERP literature. As a result, each method identified was reviewed, analyzed, and summarized. Our main findings suggest that ERP selection is a multi-criteria decision-making (MCDM) problem, with various methods and techniques that can be utilized for such problems. Several MCDM methods have been used in literature, but often complementing more than one method combined at a time. This is since some methods excel in considering factors in uncertain environments, and other methods are best in evaluating qualitative and quantitative factors. Finally, while there are some methods that were used for cloud-ERP selections, there is no clear consensus in extant literature if some methods could best fit specifically cloud-ERP contexts in contrast to on-premises counterparts. [ABSTRACT FROM AUTHOR]