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The importance of shared decision making (SDM) for informed consent has been emphasised in the updated regulatory guidelines. Errors of completion, legibility and omission have been associated with paper-based consent forms. We introduced a digital consent process and compared it against a paper-based process for quality and patient reported involvement in shared decision making. 223 patients were included in this multi-site, single centre study. Patient consent documentation was by either a paper consent form or the Concentric digital consent platform. Consent forms were assessed for errors of legibility, completion and accuracy of content. Core risks for 20 orthopaedic operations were pre-defined by a Delphi round of experts and forms analysed for omission of these risks. SDM was determined via the 'collaboRATE Top Score', a validated measure for gold-standard SDM. 72% (n = 78/109) of paper consent forms contained ≥1 error compared to 0% (n = 0/114) of digital forms (P < 0.0001). Core risks were unintentionally omitted in 63% (n = 68/109) of paper-forms compared to less than 2% (n = 2/114) of digital consent forms (P < 0.0001). 72% (n = 82/114) of patients giving consent digitally reported gold-standard SDM compared to 28% (n = 31/109) with paper consent (P < 0.001). Implementation of a digital consent process has been shown to reduce both error rate and the omission of core risks on consent forms whilst increasing the quality of SDM. This novel finding suggests that using digital consent can improve both the quality of informed consent and the patient experience of SDM. • The paper consent process is associated with errors and omissions of core risks. • A digital consent process improved error rate and omission rate of core risks. • Patients reported a higher level of shared decision making with a digital process. • Documentation of consent can be reliably standardised with an electronic record. [ABSTRACT FROM AUTHOR]

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]

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]

The incidence of differentiated thyroid cancer (DTC) has been increasing worldwide, mostly, as an increase in the incidental detection of micro papillary thyroid carcinomas (microPTCs), many of which are potentially overtreated, as suggested by the unchanged mortality. Several international guidelines have suggested a less aggressive approach. More recently, it has been shown that active surveillance or minimally invasive treatments (MIT) are good alternatives for the management of these patients. In this context, patient participation in the decision-making process is paramount. The Endocrine Task Force of the European Organisation for Research and Treatment of Cancer (EORTC) has undertaken the task to establish consensus and define its position based on the scientific evidence concerning, 1) the current state of diagnostic and management options in microPTCs, including the current opinion of physicians about shared decision making (SDM), 2) the available evidence concerning patients' needs and the available decision instruments, and 3) to provide practical suggestions for implementation of SDM in this context. To improve SDM and patients' participation, knowledge gaps and research directions were highlighted. • New management approaches for patients with low-risk microPTC have been introduced. • For all available options, scientific evidence is weak, resulting in clinical equipoise. • SDM to carefully discuss these treatment options with patients is paramount. [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]

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

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]

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]

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]

• Healthcare demand change is investigated from the view of the economic and societal dynamics. • The differentiated risk attitudes of individuals and groups are considered. • An extended PageRank algorithm are applied to determine the weights of experts. • An improved GLDS method is developed to prioritize healthcare risk. • A conflict analysis is established to show PIR between failure modes. Faced with backward and uneven economic conditions and an irrational and increasingly inequitable social structure, healthcare disparities are widening and the healthcare inequalities are expanding. This can lead to an increasing public demand for healthcare services, posing unprecedented risk, such as resource shortages, quality deterioration and rising healthcare costs. By identifying, managing, and optimizing the aforementioned risks within the healthcare system, it helps the healthcare system better address the challenges of expanding healthcare demands under evolving economic and social structure. To achieve this, this paper employs failure mode and effects analysis (FMEA) to identify severe failure modes in healthcare services. However, traditional FMEA methods possess limitations in assessing the dynamics of trust relationships and interaction effects among experts within social networks. Therefore, this paper proposes an improved gained and lost dominance score (GLDS) based social network decision-making method considering risk attitudes for FMEA to prioritize failure modes in healthcare services. First, transform linguistic risk assessment information from an interdisciplinary team of experts based on probabilistic linguistic term sets handle uncertain efficiently. Meanwhile, experts' influence is reflected by an extended PageRank algorithm with trust degrees based on social networks analysis. Next, an improved GLDS method considering the differentiated risk attitudes of individuals and groups is developed to prioritize failure modes. Especially, to solve the problems of soft preference and incomparability relation among failure modes, which are ignored by the traditional GLDS method, a conflict analysis is established to determine the preference, indifference, and incomparability relations. Finally, the practicality of the proposed method is illustrated by the healthcare service risk case in the emergency department and the effectiveness of the developed method is validated by comparison and sensitivity experiments. The results suggest that failure to update labels showing patient severity promptly is the most dangerous. In addition, it would be more economically efficient to analyze the non-comparable failure modes facing different risk factors. [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]

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]

Robot sequential decision-making in the real world is a challenge because it requires the robots to simultaneously reason about the current world state and dynamics, while planning actions to accomplish complex tasks. On the one hand, declarative languages and reasoning algorithms support representing and reasoning with commonsense knowledge. But these algorithms are not good at planning actions toward maximizing cumulative reward over a long, unspecified horizon. On the other hand, probabilistic planning frameworks, such as Markov decision processes (MDPs) and partially observable MDPs (POMDPs), support planning to achieve long-term goals under uncertainty. But they are ill-equipped to represent or reason about knowledge that is not directly related to actions. In this article, we present an algorithm, called iCORPP, to simultaneously estimate the current world state, reason about world dynamics, and construct task-oriented controllers. In this process, robot decision-making problems are decomposed into two interdependent (smaller) subproblems that focus on reasoning to "understand the world" and planning to "achieve the goal" respectively. The developed algorithm has been implemented and evaluated both in simulation and on real robots using everyday service tasks, such as indoor navigation, and dialog management. Results show significant improvements in scalability, efficiency, and adaptiveness, compared to competitive baselines including handcrafted action policies. • This article formally introduces the Integrated Reasoning and Planning (IRP) problem for robots. An IRP problem has a factored state space that is specified by a set of endogenous and exogenous variables. • This article introduces a sequential decision-making algorithm, called Interleaved Commonsense Reasoning and Probabilistic Planning (iCORPP), for addressing IRP problems. • This article presents systematic evaluations of iCORPP using two tasks of mobile robot navigation, and spoken dialog systems, as demonstrated on a real robot. • This article builds on our previous research that appeared in two conference papers (Zhang and Stone, 2015; Zhang et al., 2017). This article unifies their terminology, problem statements, and algorithms. • Compared to our prior work, this article introduces a new problem statement in Section 4.1, which covers the problems addressed in both conference papers. • To address the IRP problem, we have reformulated the iCORPP algorithm (Section 4.2) into a novel form that includes the three key steps of "logical reasoning," "probabilistic reasoning over world states," and "probabilistic reasoning about world dynamics." [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]

Machine learning based decision making systems in safety critical areas place high demands on the accuracy and generalization ability of the underlying model. A common strategy to deal with uncertainties and possible mistakes is offered by learning with reject option, i.e. a model can refrain from prediction in ambiguous cases and leave the decision to a human expert. Yet, as for the models themselves, human decision-making is hampered by the fact that reject options are often implemented as black-box rules: Experts cannot readily understand the reasons for rejection. In this work, we propose a model-agnostic framework that enriches classification with reject option by explanation mechanisms. More specifically, we combine conformal prediction as a popular mathematically based technology of certainty estimation with local surrogates derived for the region of interest. This allows us to provide local explanations in terms of example-based explanation methods, including counterfactual, semi-factual, and factual methods. We demonstrate the performance of this technology through a series of benchmarks using 6 different data sets; the associated code is open source. 1 1 This article represents a substantially extended version of the conference paper Artelt et al. (2022). In addition to a more detailed explanation and derivation of the algorithms, we explore semi-factual and factual explanation methods and provide evaluations on realistic benchmark data sets. • New challenge for XAI: explain model uncertainty and reject option • Efficient realization based on counterfactual explanation of local surrogate. • Open source implementation and evaluation on benchmarks. [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]

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]

• There has been a limited scope of investigation of the contextual factors affecting driver decision-making during narrow passage interactions. • The study found that the likelihood of giving way may alter due to characteristics of the interaction partners, including their vehicle type. • It was also found that factors, external to the direct interaction partners, may affect the likelihood of giving way, such as the presence of vehicles beyond the direct interaction partners. • This paper validates previous communication findings in a UK context. Narrow passage interactions have received increased attention from academics seeking to create behavioural models of the interaction and those looking to define how autonomous vehicles (AVs) should interact with their human counterparts in a composite road system. Despite this increased attention, many factors remain unexplored in the narrow passage literature, with the literature also encompassing few driving culture contexts. To this end, this study employs an explorative survey to identify additional factors that affect driver decision-making during narrow passage interactions, as well as driver perceptions of different communications in a UK context. The study's 243 participants were presented with a range of different narrow passage scenarios and asked to indicate how likely they were to give way/yield to a vehicle approaching the narrow passage from the opposite direction. In addition, they also completed the Multidimensional Driving Style Inventory to identify their driving styles and asked to identify which signals they look for from their interaction partner during narrow passage interactions, as well as the meaning of those signals. The results of the study show that situational characteristics such as the vehicle type being interacted with, being in a rush and being followed by vehicles alter the likelihood of drivers giving way at narrow passages, whilst a person's driving style can also indicate how likely someone is to give way to another vehicle. These results highlight the factors that are considered by drivers, increasing our understanding of the factors that need to be incorporated in driver behaviour models and in AV development. [ABSTRACT FROM AUTHOR]

• The geographical factors cannot be ignored in the site selection of WTE plant. • SVNSs makes up for the weakness of traditional fuzzy sets in information description. • The weight calculation method in SVNSs background is proposed. • EDAS can reduce the adverse impact of decision makers' own reasons on the results. • The combination of GIS and MCDM can improve the precision of site selection. Waste-to-energy (WtE) plant has been attached great importance by more and more countries because of the harmless, energy reducing and recycling characteristics, and has become an important part of the city's normal operation and energy structure. Whereas, the reasonable site selection of the WtE plant is the key to ensure its sustainable development. This paper is designed to provide a two-stage model for the site selection of WtE plants. Firstly, a comprehensive assessment criteria system including environmental, economic, technical and social factors is established by selecting the reasonable influence criterias from the current relevant literature. Considering the fuzziness of the criteria and the bounded knowledge of decision makers, the single-valued neutrosophic sets (SVNSs) are introduced to describe the fuzzy information, and creatively combined with decision-making and trial evaluation laboratory-analytical network process (DANP) method to determine the influence grades of criterias and calculate the weights. After that, the relevant geographic information layers are collected and the preliminary alternatives are obtained by Geographic information system (GIS) from an overall perspective, then the extend evaluation based on distance from average solution (EDAS) method is utilized to rank the alternatives under the SVNSs environment. Finally, the model is tested to select the optimal alternative for siting the WtE plants in Beijing as an example. It is verified that the model is of great stability and feasibility by sensitivity analysis and comparative analysis. The result shows that this paper can provide a new theoretical basis for the planning of WtE plants. [ABSTRACT FROM AUTHOR]

Objective: To identify decision characteristics for which SDM authors deem SDM appropriate or not, and what arguments are used.Methods: We applied two search strategies: we included SDM models from an earlier review (strategy 1) and conducted a new search in eight databases to include papers other than describing an SDM model, such as original research, opinion papers and reviews (strategy 2).Results: From the 92 included papers, we identified 18 decision characteristics for which authors deemed SDM appropriate, including preference-sensitive, equipoise and decisions where patient commitment is needed in implementing the decision. SDM authors indicated limits to SDM, especially when there are immediate life-saving measures needed. We identified four decision characteristics on which authors of different papers disagreed on whether or not SDM is appropriate.Conclusion: The findings of this review show the broad range of decision characteristics for which authors deem SDM appropriate, the ambiguity of some, and potential limits of SDM.Practice Implications: The findings can stimulate clinicians to (re)consider pursuing SDM in situations in which they did not before. Additionally, it can inform SDM campaigns and educational programs as it shows for which decision situations SDM might be more or less challenging to practice. [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]

Carsharing services provide users with a new way of approaching mobility and accessing shared vehicles. Since the initial pilot studies in the early 90s, technological innovations (e.g., advances in mobile technology, increased range of electric cars) and the establishment of new business models (e.g, station-based, free-floating, peer-to-peer, packages by time and/or kilometres) helped branding carsharing as a sustainable yet flexible and personalized mobility alternative. On the other hand, the carsharing market today is extremely scattered, as it can include multiple operators, which are often in competition among each other. While this variety of operators provides the user with a variety of travel options, without proper coordination, this competition can reduce the efficiency of the carsharing market and, in extreme cases, of the entire transportation system. In this context, this paper studies the needs of travellers, local authorities, and carsharing service providers, and analyses how incentives can be used to align their goals. Taking Munich, Copenhagen, and Tel Aviv-Yafo as case studies, focus groups were used to identify thirteen different travellers' needs, which are grouped into five main categories: ownership issues, coverage area, financial aspects, vehicle settings, and integration of carsharing with other modes. Moreover, to understand the needs of service providers and public authorities, in-depth interviews were conducted. The results indicate that regulatory barriers, integration with other transport modes, and social equity issues are the most critical elements for the thriving of carsharing services. Our results also suggest that incentives can be divided into two main categories, namely direct and indirect incentives. With regards to direct incentives, parking stood out as the most important incentive. Among indirect incentives, those associated with the integration of carsharing with other transport services, were prominent. As part of the methodology, the results of the qualitative study are validated through a quantitative analysis. A bigger sample of the population answered an online survey, which was used to validate the list of potential incentives that can help aligning the goals of stakeholders and users. • Policy recommendations on how incentives can be used to support the decision making process. • A list of potential user-directed and stakeholder-directed incentives. • Incentives are classified into two different categories: direct and indirect. • The paper analyses three cities (Copenhagen - Denmark, Munich - Germany, and Tel Aviv-Yafo - Israel). • The results of the qualitative study are validated through a quantitative analysis. [ABSTRACT FROM AUTHOR]

This paper considers a strategy for identifying the decisions and bottlenecks that arise in monoclonal antibody development in the progression from a multitude of potential candidate molecules to a prioritised sub-set that are taken through development and possess a good chance of succeeding in the clinic. While the steps involved are common across the industry, the need to 'fail fast' challenges existing decision making. In any development there are numerous technical and business focused staff involved with different skill sets and areas and scales of focus. As potential products could fail in any part of the development programme, failing 'fast' requires understanding of the requirements of those in latter stages by technical and business decision makers in as early development as possible. The focus of the paper is the use of the Knowledge Acquisition Technique to elicit the expertise of numerous specialists and to develop an interpretable decision-making landscape across development to facilitate decision making. The effectiveness of the elicitation is considered and the richness of information it delivers to challenge conventional wisdom addressed. The technique is shown to deliver a broader appreciation of the requirements throughout the development supply chain. [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]