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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]

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

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

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

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

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

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

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]

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

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]

To investigate the rate of registered protocols published as research papers as a measure of publication bias, and the concordance rates of the primary outcomes between research protocol and published papers as a measure of selective outcome reporting bias in randomized controlled trials (RCTs) related to rehabilitation. Protocols related to RCTs were extracted from electronic databases, the University Hospital Medical Information Network (UMIN), International Standard Research Clinical Trial Number (ISRCTN), ClinicalTrials.gov, and MEDLINE. Published papers were retrieved from MEDLINE. The inclusion criteria were as follows: (1) initial registration (UMIN, ISRCTN, ClinicalTrials.gov) within the designated period; (2) published as a paper from a research protocol in MEDLINE (PubMed); and (3) written in English or Japanese. The search period was from January 1, 2013, to December 31, 2020. The outcome of this study was set as the rate of published papers that were consistent with the extracted research protocol and the concordance rate between the primary outcomes in published papers and in protocols. The concordance rate of the primary outcomes was evaluated by checking whether the description in the research protocol matched that in the paper's abstract and main text. Out of the 5597 research protocols registered, only 727 were published (13.0%). The concordance rates of the primary outcomes were 48.7% and 72.6% in the abstract and main text, respectively. This study revealed major discrepancies between the number of research protocols and published papers, and difference of description regarding the primary outcomes in published papers which were already defined in the research protocols. [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]

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

Never has there been such focus on just one small ribbon-like ligament in the human body: the anterior cruciate ligament (ACL) is the most highly studied and highly written about ligament in the knee, and yet still, there seem to be many ongoing unknowns. Despite this, there seem to be even more so-called experts than papers, all with their own firm and didactic opinions. This review article provides a contemporary opinion on the subject of ACL tears and ACL reconstruction, with a focus on the diagnosis of ACL tears and the decision-making process, with respect to conservative versus surgical management. The review also discusses the topics of tunnel placement, graft options, internal bracing, the anterolateral ligament and, importantly, outcomes. This paper highlights that there are a number of different approaches and options, and emphasizes the importance of discussing the pros and cons of these options with your patients, thoroughly, with shared decision-making leading to fully informed consent. [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]

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]

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]

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]

The rapid advance of technology has led to exponential growth in the generation of data in various fields, revolutionising how information is captured, processed and analysed. The continuous flow of data has proven to be a valuable resource for agile decision-making. The complexity of being able to make decisions from different sources has led to the emergence of new techniques that aim to obtain as much information as possible to relate data efficiently, taking into account multiple perspectives. A new approach to clustering streaming data from different perspectives is introduced in this paper. It is based on evolutionary algorithms (CHC), fuzzy rules and consensus function. The proposed approach is called FuzzyMultiCHCClust-DS. This approach addresses the clustering of streaming data by optimising the clustering process taking into account traditional clustering perspectives such as k-means, Clara, Pam, Fanny and MiniBatchKMeans. It has been tested using different sets of linguistic labels, achieving a high degree of compactness and separation of the cl ustered data. [ABSTRACT FROM AUTHOR]

Risk mitigation is usually addressed in simulated environments for safety critical control. The migration of the final controller requires further adjustments due to the simulation assumptions and constraints. This paper presents the design of an experience inference algorithm for safety critical control of unknown multi-agent linear systems. The approach is inspired in the close relationship between three main areas of the brain cortex that enables transfer learning and decision making: the hippocampus, the neocortex, and the striatum. The hippocampus is modelled as a stable linear model that communicates to the striatum how the real-world system is expected to behave. The hippocampus model is controlled by an adaptive dynamic programming (ADP) algorithm to achieve an optimal desired performance. The neocortex and the striatum are designed simultaneously by an actor control policy algorithm that ensures experience inference to the real-world system. Experimental and simulations studies are carried out to verify the proposed approach. • The brain cortex has complementary mechanisms to achieve complex decision making. • The hippocampus is modelled as an ADP algorithm for experience transference. • The neocortex/striatum is modelled as a complementary adaptive actor policy. • Unbiased policies are obtained by forcing the agents to behave as in the simulation. [ABSTRACT FROM AUTHOR]

With the expansion of application scenarios, the walking-assisted lower-limb exoskeleton robot urgently needs to improve its walking ability in complex terrains. However, due to the lack of environmental understanding ability and decision-making ability, the exoskeleton can not meet the need of people with lower-limb disability in complex terrains. To improve the walking abillity, this paper proposes a method of predicting the landing area in complex terrains for walking-assisted lower-limb exoskeleton robot. Multiple sensors were used to obtain the terrain information, step length, and stride length of human walking, and a multi-type input convolutional network was built to establish the mapping relationship to learn the decision-making mechanism of human standing in a complex environment. The result shows that the established network effectively predicts the landing area. Under the condition of full training, the predicted position deviation of the landing area is controlled within 6%, and the prediction rationality of the landing area can reach 90%. [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]

In this commentary paper, we discuss the use of the electrocardiogram to help clinicians make diagnostic and patient referral decisions in acute care settings. The paper discusses the factors that are likely to contribute to the variability and noise in the clinical decision making process for catheterization lab activation. These factors include the variable competence in reading ECGs, the intra/inter rater reliability, the lack of standard ECG training, the various ECG machine and filter settings, cognitive biases (such as automation bias which is the tendency to agree with the computer-aided diagnosis or AI diagnosis), the order of the information being received, tiredness or decision fatigue as well as ECG artefacts such as the signal noise or lead misplacement. We also discuss potential research questions and tools that could be used to mitigate this 'noise' and improve the quality of ECG based decision making. [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]

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]

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

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

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

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

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