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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]

In recent years, the pulp and paper industry has changed a lot and products have been constructed in quite different methods using new technologies. It can be due to the emergence of challenges and constraints such as shortage of raw materials, energy consumption (fossil fuels, electric, etc.), management of the resulting pollutants, environmental issues, ongoing legal requirements and cleaner production strategies. In Iran, the pulp and paper industry's technological development is not keeping pace with global growth. Iran's pulp and paper mills supply a small fraction of domestic market. The reasons can be due to old technologies being used in factories, aging machinery, low productivity of production systems and lack of coordination with modern technologies. It leads to low-quality of paper products and willingness to buy local products. The aim of this study is to select the most appropriate technologies in the pulp section, due to high impact of pulp quality on the quality of final paper. Using a 2-filterd fuzzy decision-making, hierarchical structure of research is designed to assess technological alternatives according to the selected criteria. Findings show that after passing through two filters, Kraft method was chosen as the most appropriate method. The Kraft process has still many advantages like the high quality of the produced pulp and self-sufficiency in energy supply. This framework offers an appropriate procedure for identifying and selecting technologies that has not been used in past researches. Using this framework facilitates using a clear procedure for evaluating and selecting technology and prevents future losses for organizations. [ABSTRACT FROM AUTHOR]

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]

LCA studies on waste management have been promoted as they can help policy makers choose the best environmental options. However, the increasing globalization of raw materials and waste makes the optimization of waste management strategies and policies quite challenging. Therefore, new approaches are needed in order to identify the consequences of markets on the waste management systems. This paper concentrates on the quantification of greenhouse gas (GHG) emissions of recycling processes. The aim is to generate a comprehensive assessment of GHG emissions as a consequence of increasing the amount of material collected for recycling. Consequential life cycle assessment (CLCA) is an effective methodological framework for addressing GHG quantifications within market, but to properly perform and assess all of the market links between raw materials and waste, it is necessary to determine the cause-effect chains made up of physical flows. Thus, we propose integrating the methodologies of material flow analysis (MFA) and CLCA. We applied these methodologies to the paper and cardboard recycling system in Spain. The GHG results varied between −36 kg CO 2 eq. and −317 kg CO 2 eq. per ton of waste paper collected, depending on the quantity of waste paper exported and the source of marginal pulp considered. The cumulative energy demand (CED) was also calculated as complementary indicator. Similar trends as for GHG emissions were obtained. The future GHG quantifications should be based on the flows described by MFA analysis and should be quantified using CLCA because methodologies that accurately account for GHG are necessary for decision-making. [ABSTRACT FROM AUTHOR]

Modern day societies and economies are becoming increasingly vulnerable to the continued erosion of the stocks and flows of essential ecosystem services. Thus, the management of complex socio-economic systems to effectively provide these essential services has become a global priority policy and academic research area. Understanding how underlying processes and functions contribute towards the provision of final ecosystem services can facilitate improved dissemination of credible, legitimate and salient information to decision-makers. This paper presents an ecosystem service value chain analysis framework that applies basic system dynamics modelling in the form of causal loop diagrams to facilitate an alternative analysis of ecosystem service value chains. A scoping application of the framework is applied to a case study for flood attenuation services in the Baviaanskloof catchment in South Africa. The framework enables the identification of forward linkages and ripple effects in individual value chains of final ecosystem services as well as the identification and assessment of challenges and opportunities within individual causal pathways. Ultimately, providing the potential to advance strategies for improving the efficiency and effectiveness of final ecosystem service provision. [ABSTRACT FROM AUTHOR]

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]

Cumulative risk assessments (CRAs) examine potential risks posed by exposure to multiple and sometimes disparate environmental stressors. CRAs are more resource intensive than single chemical assessments, and pose additional challenges and sources of uncertainty. CRAs may examine the impact of several factors on risk, including exposure magnitude and timing, chemical mixture composition, as well as physical, biological, or psychosocial stressors. CRAs are meant to increase the relevance of risk assessments, providing decision makers with information based on real world exposure scenarios that improve the characterization of actual risks and hazards. The U.S. Environmental Protection Agency has evaluated a number of CRAs, performed by or commissioned for the Agency, to seek insight into CRA concepts, methods, and lessons learned. In this article, ten case studies and five issue papers on key CRA topics are examined and a set of lessons learned are identified for CRA implementation. The lessons address the iterative nature of CRAs, importance of considering vulnerability, need for stakeholder engagement, value of a tiered approach, new methods to assess multiroute exposures to chemical mixtures, and the impact of geographical scale on approach and purpose. [ABSTRACT FROM AUTHOR]

This paper concerns problem formulation and solution procedure for inventory planning with Markov decision process models. Using data collected from a large paper manufacturer, we develop inventory policies for the finished products. To incorporate both variability and regularity of the system into mathematical formulation, we analyze probabilistic distribution of the demand, explore its connection with the corresponding Markov chains, and integrate these into our decision making. In particular, we formulate the Markov decision model by identifying the chain''s state space and the transition probabilities, specify the cost structure and evaluate its individual component; and then use the policy-improvement algorithm to obtain the optimal policy. Application examples are provided for illustration. [Copyright &y& Elsevier]

Highlights: [•] Theoretically placing strategic communication management within the DRR paradigm. [•] Defining DR4 in the South African environment. [•] Indicating the need for DR4 communication in the South African environment. [ABSTRACT FROM AUTHOR]

Background: Electronic data collection is increasingly being used for discrete choice experiments (DCEs).Objectives: To study whether paper or electronic administration results in measurement effects.Methods: Respondents were drawn from the same sample frame (an Internet panel) and completed a nearly identical DCE survey either online or on paper during the same period. A DCE on preferences for basic health insurance served as a case study. We used panel mixed logit models for the analysis.Results: In total, 898 respondents completed the survey: 533 respondents completed the survey online, whereas 365 respondents returned the paper survey. There were no significant differences with respect to sociodemographic characteristics between the respondents in both samples. The median response time was shorter for the online sample than for the paper sample, and a smaller proportion of respondents from the online sample were satisfied with the number of choice sets. Although some willingness- to-pay estimates were higher for the online sample, the elicited preferences for basic health insurance characteristics were similar between both modes of administration.Conclusions: We find no indication that online surveys yield inferior results compared with paper-based surveys, whereas the price per respondent is lower for online surveys. Researchers might want to include fewer choice sets per respondent when collecting DCE data online. Because our findings are based on a nonrandomized DCE that covers one health domain only, research in other domains is needed to support our findings. [ABSTRACT FROM AUTHOR]

Abstract: Land-use change (LUC) estimated by economic models has sparked intense international debate. Models estimate how much LUC might be induced under prescribed scenarios and rely on assumptions to generate LUC values. It is critical to test and validate underlying assumptions with empirical evidence. Furthermore, this modeling approach cannot answer if any specific indirect effects are actually caused by biofuel policy. The best way to resolve questions of causation is via scientific methods. Kim and Dale attempt to address the question of if, rather than how much, market-induced land-use change is currently detectable based on the analysis of historic evidence, and in doing so, explore some modeling assumptions behind the drivers of change. Given that there is no accepted approach to estimate the global effects of biofuel policy on land-use change, it is critical to assess the actual effects of policies through careful analysis and interpretation of empirical data. Decision makers need a valid scientific basis for policy decisions on energy choices. [Copyright &y& Elsevier]

The CPR school has successfully rebutted Hardin''s pessimistic Tragedy of the Commons model. However, in recent years, critics have focussed on the inward orientation and lack of contextual analysis characterising the writings of the CPR school.Oakerson [A Model for the analysis of common property problems. Working Paper R86-13. Workshop in Political Theory and Policy Analysis, Indiana University, 1986] and Ostrom [Governing the Commons. Cambridge, Cambridge University Press, 1990] have detailed the list of variables that have to be studied to understand the mechanics of collective action. However, these factors are internal to the community. Recent critics'' point out that the actions of individual agents are also influenced by the alternatives embedded outside the system. These variables are collectively labelled contextual factors.In our paper, we have analysed the case of a fishermen''s cooperative in the Calcutta Metropolitan District to show how the historic and economic context shapes targets of resource users and affects the feasibility of alternative courses to achieve the target by determining opportunity and transaction costs of actions. This indicates the importance of contextual factors in explaining the formation and evolution of the resource regime. [Copyright &y& Elsevier]

Objectives: To synthesize the findings of cognitive interview and usability studies performed to assess the measurement equivalence of patient-reported outcome (PRO) instruments migrated from paper to electronic formats (ePRO), and make recommendations regarding future migration validation requirements and ePRO design best practice.Methods: We synthesized findings from all cognitive interview and usability studies performed by a contract research organization between 2012 and 2015: 53 studies comprising 68 unique instruments and 101 instrument evaluations. We summarized study findings to make recommendations for best practice and future validation requirements.Results: Five studies (9%) identified minor findings during cognitive interview that may possibly affect instrument measurement properties. All findings could be addressed by application of ePRO best practice, such as eliminating scrolling, ensuring appropriate font size, ensuring suitable thickness of visual analogue scale lines, and providing suitable instructions. Similarly, regarding solution usability, 49 of the 53 studies (92%) recommended no changes in display clarity, navigation, operation, and completion without help. Reported usability findings could be eliminated by following good product design such as the size, location, and responsiveness of navigation buttons.Conclusions: With the benefit of accumulating evidence, it is possible to relax the need to routinely conduct cognitive interview and usability studies when implementing minor changes during instrument migration. Application of design best practice and selecting vendor solutions with good user interface and user experience properties that have been assessed in a representative group may enable many instrument migrations to be accepted without formal validation studies by instead conducting a structured expert screen review. [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]

Introduction : In pursuit of sustainability goals, many cities are introducing measures to increase the usage of bicycles as a means of transportation. City planners aim to ensure that this increase does not lead to an increase in crashes, but must make corresponding infrastructure decisions with limited information. Sufficient data to perform a statistical analysis of location-specific crash frequencies is rarely available. For example, only approximately 10% of all bicycle crashes are reported to the police (Shinar et al., 2018). Therefore, urban planners often rely on expert opinion, which may lead to suboptimal prioritization and realization of infrastructure improvements. Method: This paper demonstrates how surveys on bicycle crashes can be used to aid urban planners in making infrastructure decisions. In addition to confirming the location and characteristics of reported crashes, surveys can uncover characteristics of crashes that are not reported to the police, situations in which a crash almost occurred, and locations perceived by cyclists to be dangerous. Surveys also allow urban planners to investigate non-infrastructure related causes of crashes, such as the frequency with which individual cyclists use other modes of transportation. Practical Applications: The usefulness of surveys in the determination of urban cycling safety is demonstrated in this paper through analysis of survey results from the city of Zurich in 2018. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

In this paper, a real-time chemi-thermomechanical pulping (CTMP) process was investigated, where opportunities were explored to increase energy consumption efficiency with a guarantee of expected pulp properties as a priority. Due to the process instrumentation limitations, such as the lack of advanced regulatory controllers and optimization units, the advanced process controllers and real-time optimization strategies can hardly be implemented in the existing CTMP processes. Therefore, a novel visualization-based process evaluation methodology is proposed to aid the mill operators in making further decisions to improve the process operations. Additionally, to determine the desirable pulp properties for production, an inferential sensor model is developed that estimates the handsheet properties from pulp properties. Also, to assess the model prediction accuracy in real-time, a reliability index model is designed. The proposed methodologies have been validated on a practical CTMP process, and the results show the satisfactory performance of the system. • Developed a strategy to evaluate the CTMP performance using visualization methodology. • A novel inferential sensor model is implemented to determine the desirable pulp properties. • A reliability index model classifying reliability levels and assessing model accuracy is developed. • The proposed model adapts to model updates, uncertainties, and computation resource limitations. [ABSTRACT FROM AUTHOR]

Media ethics is an area of inquiry that has been of great concern among journalists and public relations professionals as well as scholars across the world. Empirical research on media ethics of comparative nature has increased, providing a descriptive account on the current situations across different countries. Yet, theoretical efforts to explain global media ethics are so far inexistent. The scope of this paper is to provide a conceptual model for explaining media ethics decision-making processes that takes into account personal, professional and environment values as main influencing forces. The proposed model suggests that besides considering personal factors, such as education, background, experience, gender, etc., and country specific factors, such as political, economic, socio-cultural conditions, one should first and foremost understand the degree to which personal, professional, and environment values influence one’s judgment. This model is the first of its kind in providing an explanation of differences in global media ethics by considering the interconnectedness of different values at micro, meso, and macro levels. [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]

In the urban rail transportation industry, collecting various data, including the number of trips, the number of fleet breakdowns, the number of breakdowns of railway lines, etc., contributes significantly to future planning and optimization of resources and costs. We are faced with big data analysis due to the daily production of large volumes of data in the urban rail transport industry. This study aims to find a trip prediction model for Tehran Metro to do trip decision-making. This paper provides a brief introduction to knowledge discovery from failure databases and presents the data mining methodology. The relevancy of data mining for a trip prediction model will be depicted. In this sense, considering the relevance of data collected on the metro control center and intelligent trip prediction, this paper presents a functional architecture of a predictive trip model using data mining techniques. Data Mining will identify behavior patterns, allowing a more accurate early detection of faults in the daily operation. This research has investigated three situations, including delayed trips, incomplete trips, and canceled trips, using actual data recorded over the past four years, including non-programmed failures and their impact on trips. A model with more than 90% (90.48%) accuracy suggests a trip status forecast on this line. Also, the research has used Rapid Miner software and the Decision tree technique in data mining and its rules. Also, the kappa index of 0.792 is calculated for the validation model. Therefore, the proposed model enables the prediction of three types of incomplete traffic, delayed trips, and cancellation of trips due to emergency failure at the time of the operation of the Tehran Metro. [ABSTRACT FROM AUTHOR]

This paper aims to perform a literature review on the use of multi-criteria methods for assessing agricultural sustainability, focusing on the distribution of papers according to year, journal, and countries, the most productive authors, the most frequently used multi-criteria methods and their characteristics, the type of system and the type of agriculture in which these methods are being performed, the methodological approaches and assessment types, and the sustainability dimensions considered. The data collection has been carried out through the Web of Science™ platform on September 3rd, 2021. After a refinement process, 41 papers were selected. The descriptive analysis was carried out through Bibliometrix tool, while content analysis was performed using Nvivo. The descriptive analysis shows that from 2016 to 2021 the scientific production addressing multi-criteria methods to assess agricultural sustainability started to grow markedly in a very rapid matter, reaching an average of 6 papers per year. France and China are the most scientifically productive countries. The content analysis points out that the most used multi-criteria method is the AHP that was used 11 times. The outranking methods, instead, were used only 3 times. In 68% of the papers the Triple Bottom Line was used as dimensions, and in 41% of the papers the spatial applicability was the farming system. The results highlight that doesn't exist many MCDM/A methods for assessing agricultural sustainability, and most of them are compensatory. These results point out that the use of multi-criteria methods in assessing agricultural sustainability is still underexplored and can be improved. [Display omitted] • Multicriteria methods are powerful tools for assessing agricultural sustainability. • A literature review on agricultural sustainability evaluation was performed. • The number of studies has been increasing in the last five years. • Most of the approaches are based on compensatory multicriteria methods. [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]

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]

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]

• A new simulation methodology, intended bounded rationality methodology, is proposed. • Evaluation cost-benefit of different aspects of decision support techniques. • Classification of errors that a decision support technique can generate is proposed. • Consistent erroneous judgments and inconsistent correct judgments are possible. • A case where consistency condition in AHP provides maximum 5% of the expected return. Expert's judgments have been crucial in the development of decision theory; however, what criterion to use in the selection of experts remains an issue to address. Decision support techniques proposed to improve the quality of expert judgment decision making consider a demonstrated inconsistency of the judgments expressed by an expert as a criterion of exclusion in the decision-making process of such expert. Although consistency appears to be a desirable condition to qualify as "expert", little is known about the quality of the decisions made imposing consistency as the expert qualifying condition. This paper proposes a simulation methodology, based on an automaton programmed to make decisions in an intended but bounded rational way, to assess the cost-benefit of different aspects of decision support techniques. Within this methodology, the imposition of the consistency condition in the selection of experts is studied. In particular, the paper shows with a case study example that the Analytical hierarchy process (AHP) decision support technique expected payoff is at most 5% higher when implementing Saaty's consistency criterion of the expert's judgments than when the consistency criterion is not considered. [ABSTRACT FROM AUTHOR]

Multi-attribute decision-making problem (MADM) is a kind of multi-criteria decision-making (MCDM), which is often used to solve decision-making problems where the decision variables are discrete and the number of decision-making schemes is limited. This paper reviews the basic definition of multi-attribute decision making (MADM) through the performance evaluation of science and technology funds in a city, and then deduces the TODIM decision making method and TOPSIS decision making method in detail. Finally, the clue case is decided by combining these two decision making methods. This paper specifically shows the usage scenarios and limitations of the TODIM decision method and the TOPSIS decision method through the clue case, analyzes the advantages and disadvantages of the two methods through the comparison of the two methods, and puts forward suggestions for the clue case based on the results of the two schemes. [ABSTRACT FROM AUTHOR]

Due to different users' requirements, contemporary software has become feature-rich in terms of input functions (i.e., parameters) and selections (i.e., values). Exhaustive testing on sophisticated software systems is impractical as far as testing time and cost are concerned. Various test case design strategies have been proposed in the literature, such as equivalence class partitioning, boundary value analysis and decision tables. Unlike earlier works, combinatorial t-way testing supports the detection of faults caused by two or more input parameter interactions and thus efficiently minimizes the size of the test suite. Over the past few years, metaheuristic algorithms have appeared to be the most common choice for researchers since their effectiveness proves to offer optimal/near-optimal results. However, generating a t-way test suite is an NP-hard problem, and no single t-way strategy can guarantee to show superiority to others for all types of system configurations. Hence, this paper presents a new t-way strategy based on the Gravitational Search Algorithm (GSA), known as the Gravitational Search Test Generator (GSTG). The primary contribution of this paper is that GSA has adapted for the first time to t-way test data generation. The benchmarking results showcase that GSTG obtains competitive results in most system configurations compared to other existing strategies and addresses higher combination coverage (i.e., t ≤ 10). [ABSTRACT FROM AUTHOR]

For multi-attribute decision-making problems with interval numbers whose attribute weights are completely unknown, it is difficult to compare the value of interval numbers. So this paper proposes a method based on TOPSIS and the weighted parameter to deal with it. Firstly, this paper transforms the interval number matrix into two exact number matrices which reduces sorting complexity. Secondly, a parameter is given when determining the weights with entropy weight in order to reflect all the information of the interval numbers. Then TOPSIS is used to determine the order of each scheme. In addition, the average value of the ranking number is used to reflect the actual situation better. Finally, the analysis of the car purchase case shows the proposed method is feasible and practical. And the comparative analysis with the other method based on practical application dataset demonstrates the proposed method is stable and effective. [ABSTRACT FROM AUTHOR]

Most Multi-Criteria Decision Analysis (MCDA) methods identify only discrete, defined sets of alternatives. Such identification does not provide an opportunity to consider a more significant number of samples acquired over long periods, which is not a desirable effect. An additional aspect of identification is the frequent application of expert knowledge, whereby a continuous model would have to be defined to consider further alternatives that would be suitable for further use. This paper considers a new approach for identifying continuous decision models named SITCOM. The purpose of this paper was to conduct a study on the accuracy of the proposed approach. The accuracy of Stochastic IdenTifiCation Of Models (SITCOM) was tested using sample evaluations obtained from fuzzy reference models (FRM). Spearman's weighted correlation coefficient was used to measure the accuracy of the created models. The experiments showed the dependence of accuracy on the number of characteristic objects. Furthermore, the obtained results prove the reliability of the obtained results, where the trends of accuracy decrease based on the number of characteristic values. [ABSTRACT FROM AUTHOR]

Machine learning is nowadays popular area of science research. As the amount of accessible data is still increasing, therefore the machine learning methods can be used for many applications. One of the more detailed topics, which is commonly analyzed in practice in regard to financial decision supporting, is the support for decision making on stock market. It has been noticed that the main focus is on the developed markets like in Asia, West Europe or USA. As the Polish stock market is also recognized as the developed market, here in opposite to Asia, West Europe or USA markets, the shortage of practical implementation of machine learning algorithms is perceptible. With this paper various learning algorithms have been used to determine its backtesting performance. The main goal of this paper is to examine the effectiveness of selected machine learning algorithms and to find the best one for stock data by comparing several selected algorithms using a backtesting environment on the same data sets and general parameters. For this purpose, experiments were carried out on one random seed and then out on 100 different seeds. [ABSTRACT FROM AUTHOR]

The objective of this paper is to analyze the role of digital planning tools implemented in the context of sustainable development in the Global South, considering that public governance has been going through an important pivotal moment since the appearance of globalization and digitalization, where collaborative participation and hybrid approaches are becoming indispensable for planning and development. In this sense, the paper will give a brief introduction of governance in the digital Era and the use of GIS-based digital planning tools, followed by the presentation of the case study of TOSCA, or Toolkit for Sustainable City Planning and Analysis, to understand the relevance of this hybridization between human and digital processes for governance and planning. TOSCA will be presented in the context of two pilot projects, with the development and deployment of the technology between 2019 and 2020, summarizing its process and outcomes from the stakeholder perspective. The discussion chapter of this paper, collects the learnings and challenges gained during this first application of the Toolkit, and points towards the future development and its application in other regions, with the aim of understanding how its conceptualization and development could be further enhanced in order to ensure its sustainable take-up under diverse contexts and use cases. [ABSTRACT FROM AUTHOR]

In contemporary healthcare systems, the selection and judicious choice of the right medical device becomes paramount consideration for accurate diagnosis, patient care, mitigate risk and cost incurred on diagnosis of disease. Moreover, medical devices are requisite indispensable tools to help healthcare professionals for effective diagnosis, selection of the right treatment amongst different options, and continuous monitoring of the patient. Thus, to select the best manufacturing company is as equally important to achieve the desired goals. This paper presents a decision making algorithm based on fuzzy theory that helps the decision maker in selecting the most suitable worldwide medical device manufacturer. Fuzzy theory is extensively used in managing databases and analyzing data to summarize information and draw meaningful conclusions. This paper utilizes the aggregation operators within the context of the complex q-rung orthopair fuzzy environment by utilizing Yager t-norm and t-conorm operations. Some aggregation operators are applied, namely, complex q-rung orthopair fuzzy Yager weighted geometric operator, complex q-rung orthopair fuzzy Yager ordered weighted geometric operator, complex q-rung orthopair fuzzy Yager weighted averaging operator and complex q-rung orthopair fuzzy Yager ordered weighted averaging operator. Additionally, the notable properties of these operators are proved. These aggregation operators are generated specifically for multi-criteria decision-making. Decision making algorithm is used to choose the most relevant medical device manufacturer (alternative) to validate its efficacy. The results demonstrate the versatility of parameters, adaptability to multi-criteria decision making, and efficiency of the proposed aggregation operators and algorithm. Test with specific criteria is also conducted to verify the legality and validity of the decision making algorithm. At last, the acquired results underwent a comparative analysis against various established methodologies, confirming the method's capacity to produce precise and accurate outcomes. • Multi criteria decision making algorithm is devised in fuzzy environment. • Aggregation operators are introduced and notable properties are investigated. • Test with specific criteria is conducted for the validity of proposed operators. • The algorithm is utilized in medical industry to choose best device manufacturer. [ABSTRACT FROM AUTHOR]

Three-way multi-attribute decision-making (3MADM) integrated with double hierarchy hesitant fuzzy linguistic term set (DHHFLTS) can not only effectively express the uncertainty of language, but also help reduce the risk of wrong decision-making. However, the existing compared methods for DHHFLTSs dismiss the variances in psychological reference points, resulting in mismatches in form and connotation for some linguistic terms. Furthermore, it is difficult or even impossible to obtain an accurate degree of difference using the existing DHHFLTS distance method for these linguistic terms. This directly affects the accuracy of obtaining conditional probabilities results in the three-way decision model. Therefore, this paper introduces the concept of the superior gradus for double hierarchy linguistic term set (DHLTS) and double hierarchy hesitant fuzzy linguistic element (DHHFLE), respectively. Then, some novel compared methods are defined that allow the identification of differences between linguistic variables. Subsequently, based on the superior gradus, a novel distance measurement is designed with a risk parameters. Through the adjustment of risk parameters, this method can respectively obtain optimistic and pessimistic results. Also, the relative loss functions designed for DHHFLTSs aim at getting more objective decision-making results. Finally, the paper proposes a novel 3MADM method under the double hierarchy hesitant fuzzy linguistic information system (DHHFLIS) and applies it to service assessment. To verify the effectiveness and rationality, the medical diagnosis data set is used, and the results are compared with other classic MAMD methods. • 3MADM with DHHFLTS reduces the risk of wrong decision-making. • Superior gradus is introduced to identify DHHFLTS's variances. • Novel distance measurement is designed with a risk parameters. • Relative loss functions for DHHFLTS enhance objective results. • Novel 3MADM method is applied in service assessment and compared with classic MAMD. [ABSTRACT FROM AUTHOR]

• Life Cycle Engineering has been demonstrated as a viable approach for space mission design. • The life cycle sustainability impacts of SmallSats have been benchmarked for the first time. • Multi-Criteria Decision Analysis is a useful technique for identifying significant hotspots. • Improvements measures are more successful with increased levels of participant interaction. The application of Life Cycle Engineering (LCE) within the concurrent engineering process presents a viable method for assessing environmental, social and economic impacts of space missions. Despite this, the novelty of the concept within space mission design has meant that the approach has not yet been widely implemented. This paper successfully demonstrates this technique for the first time and presents LCE results of three SmallSat missions designed at the University of Strathclyde using the concurrent engineering approach. The Strathclyde Space Systems Database (SSSD) was deployed to calculate the total life cycle impacts of each mission, including the identification of common design hotspots. A novel technique called Multi-Criteria Decision Analysis (MCDA) was also trialled, whereby several impact categories were converted into single scores as a test case to reduce the learning curve for engineers. Overall, the LCE results indicate that the manufacturing & production of the launcher dominate the majority of impact categories. Other common hotspots were found to relate to the use of germanium as a substrate as well as the launch event. As an additional observation, in terms of the behavioural aspects, it was clear that study participants were more open to the concept of LCE with each new concurrent engineering session, evidenced by increasing levels of interaction amongst study participants. These findings are intended to provide industrial stakeholders with a preliminary benchmark relating to the general sustainability footprint of SmallSats, whilst demonstrating the viability of integrating LCE within the concurrent engineering process of space missions. [ABSTRACT FROM AUTHOR]

• Propose an end-of-use condition estimation method based on reliability and warranty policy. • Present a two-stage optimization model for the Re -X decision-making problem. • Develop a condition-based end-of-use processing policy under demand uncertainty. • Reduce lifecycle cost and environmental impact for product family. • Demonstrate the methodology with case study and result analysis. The concept of circular economy has been diffused in recent decades to promote economic growth that does not add to the burden on natural resource extraction. Re -X options (e.g., reuse, repair, refurbish, remanufacture, recycle) have been gradually adopted in the product development process and optimized to reduce or eliminate waste and pollution. Although manufacturing incorporating Re -X options can be more environmentally friendly, it involves more sources of uncertainty than traditional manufacturing since the end-of-use products can be collected from multiple origins with various quantities and qualities, and the market demand for both new and remanufactured products cannot be forecasted perfectly. Thus, there is a need to optimize the Re -X policy to alleviate the negative impacts of the higher uncertainty. One option is using the reliability information of new products to estimate the end-of-use conditions and applying multi-stage stochastic optimization to capture multiple demand scenarios. This paper develops a two-stage stochastic optimization model to optimize the quality thresholds for reuse, recycling, and remanufacturing options. Our objective is to minimize the total cost, energy consumption, and environmental impact of producing and providing warranty service for a product family. The model employs reliability information of product components to estimate the warranty service cost and the end-of-use conditions of the returned resources. A case study on a general product family is implemented to illustrate the efficacy of the optimization model. Results show that the two-stage optimization can achieve cost and environmental impact reduction for a hybrid manufacturing and remanufacturing process. [ABSTRACT FROM AUTHOR]

The MESOC Toolkit is a free and open-access online service used to aid the measurement of the societal value and impact of culture, currently under development as part of the MESOC (Measuring the Social Dimension of Culture) project. The goal of establishing transition variables that serve as impact measurement indicators is aided by an online document repository system of thematic publications (i.e., a collection of documents on the societal value and impact of cultural policies). It allows the users to input, manage, and search extensive document data based on the relevant criteria. The paper at hand paper describes the rationale behind the online document repository of the MESOC Toolkit, its implementation, and main functionalities. Furthermore, it offers an empirical evaluation of the benefits that such a system offers over non-automated procedures of document search and analysis. The results of the evaluation give way to the conclusion that there are time-saving and quality benefits for the users of the repository: efficiency in dealing with a large number of documents and quality in searching the documents using multiple criteria. [ABSTRACT FROM AUTHOR]

Pumped storage technology plays a crucial role in achieving balance in power systems and enhancing the stability of energy systems. Scientific planning can help optimize the operation of power systems, promote the development of renewable energy, and conserve energy. This paper addresses the capacity planning problem of pumped storage stations in hybrid operation systems considering wind power uncertainty. A comprehensive decision-making method is proposed, based on the Delphi method, interval intuitionistic fuzzy theory, grey relational theory, entropy weight method, and prospect theory. This method comprehensively evaluates decision alternatives by considering various factors such as the uncertainty of qualitative indicators, the interrelationships between indicators, and the impacts of different risk environments. The test system in this paper is conducted on a hybrid wind-thermal-pump storage output model based on the IEEE 30-bus system. The optimal capacity planning scheme determined by the proposed method is 190 MW when decision-makers are risk-neutral or risk-averse, and 200 MW when they are risk-appetite. Subsequently, the case study verifies the impact of different load levels and proportions of wind power access on the optimal decision-making scheme, and compares it with decision-making method such as principal component analysis, technique for order preference by similarity to an ideal solution, and a combined method of Pythagorean fuzzy theory and cumulative prospect theory, validating feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

The dynamic and complex environment affects the operational efficiency of automated guided vehicles (AGVs) at automated container terminals, in which artificial intelligent approaches are applied to address optimization problems in dynamic systems. This paper proposes a digital twin-based decision support approach to improve the efficiency of AGV scheduling service. Accordingly, the factors that affect AGV scheduling performance, including conflicts, failures, and battery constraints, are discussed first. Then, the framework and main steps of the proposed approach are described. The physical operation space is mapped into the virtual space, and both spaces keep synchronized to support the verification of solutions. A mathematical programming model and Q-learning algorithm are used to generate the AGV scheduling plan considering battery charging. Numerical experiments are conducted to demonstrate the effectiveness of the proposed approach. Comparisons of the digital twin-based approach, genetic algorithm (GA), and particle swarm optimization (PSO) are also made with different scale experiments. It appears that the proposed digital twin-based approach is superior to GA and PSO when solving small- and large-scale cases. A sensitivity analysis is performed concerning the battery utilization rate, task, and AGV number. Experimental results show that an optimal configuration of AGV and task can improve the battery utilization rate and reduce the completion time. • A digital twin-based decision support approach is proposed to improve scheduling. • A mathematical programming model is developed to minimize the total completion time. • Digital twin and Q-learning are used to monitor operation and obtain solutions. • Sensitivity analysis of battery utilization is made to provide equipment maintenance. • The proposed approach is superior based on the comparison of solution performances. [ABSTRACT FROM AUTHOR]