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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]

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]

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]

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

[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]

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]

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

Numerous socio-legal factors make the process of surrogate decision-making for people living in dementia very complicated in Japan. In this discussion paper, we argue that the lack of early consultation between patients, surrogate decision-makers and healthcare providers and the overreliance of patients and their families on doctors to assume the decision-making role lead to healthcare practices that may not align with the patient's wishes. Further, we argue that lack of laws on surrogate decision-making, changing family structure and the liabilities associated with the care of people living with dementia contribute to the complexity of the decision-making process in Japan. Finally, given the rapidly changing social and healthcare norms in Japan, we call for greater involvement of nurses and care workers in the decision-making process to ensure patient-centric treatment and care are adopted. [ABSTRACT FROM AUTHOR]

The design of a high-volume printer for professional use is rather complex. The design of the paper path and the timing of sheets is frequently reengineered as the design of the printer components progresses. This paper presents an optimization model for the combined paper path and timing design problem. The paper path is an optimal physical dimensioning problem, while the timing is an open-loop optimal control problem. The coupled optimization problem is formulated as a simultaneous analysis and design (SAND) problem using a direct transcription of the optimal control problem. Benefits of the chosen formulation for industrial application are the ease of setting up the optimization model for arbitrary printer configurations, and the short computation times. Results of an industrial case are presented. [ABSTRACT FROM AUTHOR]

Evidence-based published data is the prime source used for clinical decision making, the issuance of guidelines and the drafting new policies. A number of different study designs are used to perform and publish research studies, corresponding to the research question being investigated. Over the decades, a hierarchical system of evidence has been established. This provides an indication of the level of evidence each study design contributes to the research community. Policies and clinical guidelines should naturally be based on the highest level of evidence data available. However, the highest level of evidence study designs may not always provide an adequate answer to a research question. Thus, when utilising published evidence-based data, one should first understand the clinical question that needs to be answered, and then critically appraise the published data accordingly. [ABSTRACT FROM AUTHOR]

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

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

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

The vision of a sustainable and safe use of chemicals to protect human health, preserve the environment and maintain the ecosystem requires innovative and more holistic approaches to risk assessment (RA) in order to better inform decision making. Integrated risk assessment (IRA) has been proposed as a solution to current scientific, societal and policy needs. It is defined as the mutual exploitation of environmental risk assessment (ERA) for human health risk assessment (HHRA) and vice versa in order to coherently and more efficiently characterize an overall risk to humans and the environment for better informing the risk analysis process. Extrapolating between species which are relevant for HHRA and ERA requires a detailed understanding of pathways of toxicity/modes of action (MoA) for the various toxicological endpoints. Significant scientific advances, changes in chemical legislation, and increasing environmental consciousness have created a favourable scientific and regulatory environment to develop and promote the concept and vision of IRA. An initial proof of concept is needed to foster the incorporation of IRA approaches into different chemical sectorial regulations and demonstrate their reliability for regulatory purposes. More familiarity and confidence with IRA will ultimately contribute to an overall reduction in in vivo toxicity testing requirements. However, significant progress will only be made if long-term support for MoA-related research is secured. In the short term, further exchange and harmonization of RA terminology, models and methodologies across chemical categories and regulatory agencies will support these efforts. Since societal values, public perceptions and cultural factors are of increasing importance for the acceptance of risk analysis and successful implementation of risk mitigation measures, the integration of socio-economic analysis and socio-behavioural considerations into the risk analysis process may help to produce a more effective risk evaluation and consideration of the risks and benefits associated with the use of chemicals. [ABSTRACT FROM AUTHOR]

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

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

Doctors in Star Trek are vital and central characters, often crucial in helping their captains to reach command decisions. They are also interdisciplinarians, performing not only medical tasks but also any form of work related to all fields of biology. Furthermore, these doctors also carry out any required research that pertains to the biological sciences. It is perhaps this unique blend of skills, combined with a fundamental orientation towards humanism and compassion, that leads to their default position, de facto, if not de jure, as the crew's ethics officers, a role that they embrace with as much dedication and zeal as the former Soviet communist political officers/commissars (the Zampolit) did theirs. This paper will show that from the very inception of Star Trek, doctors strictly adhered to deontological tenets which lead them to actively influence command decisions that pertain not only to health, but also to any ethical decisions that may have to be taken by Starfleet officers. This portrayal of future doctors signals the wishes of contemporary audiences: that future doctors remain humane, compassionate and ethical guardian angels, and not only superb medics. [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]

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

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

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]

Fetuses at low gestational age limit of viability, neonates with life threatening or life limiting congenital anomalies and deteriorating acutely ill newborn babies in intensive care, pose taxing ethical questions on whether to forego or stop treatment and allow them to die naturally. Although there is essentially no ethical difference between end of life decision between neonates and other children and adults, in the former, the fact that we are dealing with a new life, may pose greater problems to staff and parents. Good communication skills and involvement of all the team and the parents should start from the beginning to see which treatment can be foregone or stopped in the best interests of the child. This article deals with the importance of clinical ethics to avoid legal and moral showdowns and discusses accepted moral practice in this difficult area. [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]

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

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

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

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

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

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

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

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