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The California Wildfire & Forest Resilience Task Force (Task Force) has developed regionally-adapted resources to lessen wildfire risk to communities and enhance broader statewide ecosystem resilience. This includes a large set of metrics intended to support a wide range of organizations in prioritizing, planning and/or implementing management actions. The Science Advisory Panel to the Task Force (SAP) was asked to provide expert advice to inform the selection of a subset of “core” metrics for reporting outcomes and progress towards resilience goals. We used survey tools to collect and synthesize the scientific expertise of the SAP as well as other experts regarding existing “Regional Resource Kit” (RRK) metrics as well as potential suggested metrics. This report summarizes the survey process and results. We used two rounds of surveys to collect expert opinion on metrics. Round 1 asked respondents to 1) identify criteria for core metrics, 2) identify metrics from the Regional Resource Kits (RRKs) that were believed to be inadequate based on those criteria, and 3) recommend additional metrics not included in the original set. Round 2 asked respondents to rate the resulting set of 115 metrics. The Round 1 results indicated that selecting a useful set of core metrics depended on their intended application (e.g., planning vs.reporting), the intended audience (e.g., policy makers, scientists, and/or the public), and resilience outcomes (e.g., immediate wildfire risk reduction versus long-term ecological health). In Round 2 we asked respondents to rate each metric on how well it measured each of three broad, overlapping resilience goals identified by the Task Force: 1) reducing wildfire risk, 2) improving ecological integrity, and/or 3) supporting social and/or cultural wellbeing. The Task Force specified the purpose for the metrics: reporting progress to policymakers and the public. Therefore, respondents also evaluated three attributes for each metric: 1) how realistic it was to remeasure (i.e., feasibility), 2) how easy it was to explain to wide audiences (i.e., understandability), and 3) how well it represented the process of interest (i.e., sensitivity). In addition, respondents identified relevant region(s) of California (Sierra Nevada, Southern California, Central Coast, and Northern California) for each metric. For the 115 metrics collectively considered in Round 2 (81 from the RRKs and 34 novel metrics), 66 received an average rating of greater than 4 out of 5 on one or more of the three resilience goals (reduce fire risk, improve ecological resilience, support social/cultural wellbeing). Of these, 13 metrics were rated above 4 out of 5 for two of the three goals, and only “probability of high-severity fire” was rated that highly for all three. Metrics on topics relating to vegetation structure and composition as well as fire behavior and history were most abundant in the RRKs and in our list of highly rated metrics. Topics relating to air quality, water supply, economics, community readiness, environmental justice, and community wellbeing were less abundant in the RRKs and not as highly rated in our surveys; these topic areas could benefit from further expert feedback and development. Top-rated metrics already present in the RRKs included: probability of high severity fire, damage potential in the Wildland-Urban Interface (WUI), standing dead and ladder fuels, vegetative stress during extreme drought, tree mortality, and shrub resilience. Metrics that are not yet in the RRKs but do exist elsewhere include Cal EnviroScreen scores and areas of low potential shrub regeneration. Highly rated novel metrics suggested by the survey respondents include (among others): health outcomes related to air quality/smoke and insurance availability/price. Some considerations for proceeding with selection of core metrics arose through this process. First, metrics selected (and targeted desirable ranges for these metrics) might be ecosystem specific. Second, the tradeoff between the logistical aspects of the metrics (feasibility of remeasurement, understandability) with their scientific accuracy and value needs to be evaluated with the target audience in mind. Third, many metrics can address resilience across multiple topic areas and therefore framing this overlap carefully is important when determining how to track progress towards resilience goals.

Cloud Computing is a new paradigm that provides software and hardware resources based on customers’ needs. However, data security remains a significant concern, acting as a major barrier to its widespread adoption. The objective of this work is to propose a process for assessing data risks in Cloud Computing. The process is based on fuzzy cognitive maps (FCMs) for qualitative reasoning. Risk assessment is a crucial aspect of Cloud Computing. Our proposed system involves three main steps based on expert knowledge. In the first step, a global FCM was built and validated to identify threats at the Cloud Computing level. Then the second step was devoted to calculate the consequences by inference using FCM Expert. The obtained results show that the FCM has a remarkable capacity to deal with the uncertainty of the data; by doing so, it becomes possible to model the real system more accurately. The last step is to simulate, analyze the results of the second step in Matlab Simulink and also develop recommendations to reduce the risks in order to improve and increase the security of Cloud data. [ABSTRACT FROM AUTHOR]

Countries have committed to conserving and restoring ecosystems after signing the Kunming‐Montreal Global Biodiversity Framework (GBF). The IUCN Red List of Ecosystems (RLE) will serve as a headline indicator to track countries' progress toward achieving this goal. Using Kenyan coral reefs, we demonstrate how nations implementing the GBF can use standardized estimates of ecosystem degradation from RLE assessments to support site‐specific management decisions. We undertook a reef‐by‐reef analysis to evaluate the relative decline of four key ecosystem components over the past 50 years: hard corals, macroalgae, parrotfish, and groupers. Using the two benthic indicators, we also calculated standardized estimates of state to identify reef sites which maintain a better condition through time relative to adjacent sites. Kenya's coral reefs have degraded across all four ecosystem components. At more than half the monitored sites parrotfish and grouper abundance declined by more than 50%, while coral cover and macroalgae‐coral ratio declined by at least 30%. This resulted in an Endangered threat status for coral reefs in Kenya (under criterion D of the RLE). The results can guide management actions related to 9 of the 23 GBF targets. For example, we identified several sites with relatively healthy benthic and fish communities as candidate areas for protection measures under Target 3. The RLE has a key role to play in monitoring and meeting the goals and targets of the GBF, and our work demonstrates how using the wealth of data within these assessments can inform local‐scale ecosystem management and amplify the GBF's impact. [ABSTRACT FROM AUTHOR]

To improve climate education in an introductory weather and climate course, an active-learning lab was developed around a climate decision support system. The Climate Analysis and Visualization for the Assessment of Species Status (CAnVAS) tool allows individuals to view various climate variables and select a particular location and species to analyze. Undergraduate participants used CAnVAS to answer questions about the climate data they were plotting and completed a concept map about how endangered species will be impacted by climate change. A pre- and post-assessment was also given to participants, during three separate semesters, to measure knowledge changes that may have been affiliated with the CAnVAS intervention. When CAnVAS was implemented, increases in pre-to-post-assessment performance were measured (approximately 10% in Fall 2021, 7% in Fall 2022, and 6% in Fall 2023). The concept maps revealed that on average, participants drew approximately seven relevant lines and circles. This study demonstrated the efficacy of the CAnVAS tool through a pedagogical intervention deployed in an introductory weather and climate undergraduate classroom. The results contribute to the Scholarship of Teaching and Learning literature by testing the efficacy of a decision support system within the classroom setting using the e-learning theoretical framework. [ABSTRACT FROM AUTHOR]

Currently, the need for a clean transition has made the upscaling of sustainable energy investment projects imperative. This paper addresses the increasing importance of sustainable energy investment projects in the context of climate change and the urgent need for a global energy transition. Given the complexity of decision-making in this field, a multi-criteria decision-making (MCDM) approach is employed to assess the main criteria considered by project developers and financial institutions. Using the Analytic Hierarchy Process (AHP) method, eight criteria are identified and evaluated. Results highlight differing priorities between project developers and investors, emphasizing the need for adaptable approaches to accelerate sustainable energy investments. The study underscores the importance of understanding diverse stakeholder preferences and priorities in formulating effective strategies and managing associated risks to effectively promote sustainable energy projects. Future research should focus on real-life case studies and policy assessments to further enhance the understanding of sustainable energy investment dynamics. [ABSTRACT FROM AUTHOR]

Operational water management is a critical global challenge, and decision making can be improved by using mathematical optimization. This paper provides an overview of optimization techniques, both exact and heuristic, used in water management. It focuses on the use of optimization techniques in the short term: operational planning in reservoir management, control of open channels, hydropower scheduling, and operation of polder drainage pumps. Principles of model predictive control, methods for optimization under forecast uncertainty, and approaches for conflict resolution are explained with the help of educational examples and practical cases. Challenges and research questions to be addressed in the future are presented as an outlook. This article is categorized under:Engineering Water > MethodsScience of Water > Water and Environmental ChangeWater and Life > Conservation, Management, and Awareness [ABSTRACT FROM AUTHOR]

This study focuses on modeling Discretionary Lane-Changing (DLC), which accounts for the majority of lane-change moves in traffic flows. A binary decision model for lane-changing moves was improved with the method of Gene Expression Programming (GEP). The decision to prefer GEP is due to its high performance in a variety of engineering solutions in the literature. The GEP model was trained with Next Generation SIMulation (NGSIM) trajectory data gathered at the I-80 Freeway in Emeryville, California, and then tested with data gathered at the U.S. Highway 101 in LA, California. The test results indicate that the model made decisions of "change lane" with 92.98% accuracy, and "do not change lane" with 99.65% accuracy. A sensitivity analysis was also conducted to discover potential limits of the performance of the GEP model. The performance of this model was compared with other high-performance decision models developed with the NGSIM's DLC data in the literature and with TransModeler's gap acceptance model. This comparison indicates that the GEP model is the most successful decision model for discretionary lane-changing moves. The GEP model has a high potential to be applied in DLC decision support systems in (semi-) automated vehicles, as well as traffic simulation software. [ABSTRACT FROM AUTHOR]

Many hospitals in Germany are facing escalating economic pressures. After several years of stagnation, the number of inpatient hospital treatments dropped by in 2020 compared to the previous year. This negative tendency can also be seen in operating theaters (OTs). Strategic management of the case mix in hospital OTs now necessitates a solid data foundation. The case mix and the case mix index have become central economic indicators in contemporary hospital operations. In this work, we develop a mathematical model for case mix optimization at Augsburg University Hospital in Germany, which is based on an extensive data analysis with descriptive methods. The optimization model is subject to rigorous testing and evaluation through an extensive series of scenario analyses. The primary objective is to calculate a revenue-maximizing patient mix while respecting the available scarce personnel resources in the OT and intensive care unit. This research marks a pioneering effort in delineating the practical integration of case mix planning into a hospital's routine operations using mathematical optimization. The analyses reveal a strong correlation between an upsurge in revenue and an increased number of cases. Furthermore, the results demonstrate that strategic planning of the patient mix has the potential to enhance revenue with existing resources. Even though the optimal patient mix may not be directly implementable in practice, the findings yield valuable insights for managerial decision-making. A critical examination of these results also fosters a nuanced discourse on the utilization of optimization models as decision support tools within hospital management. [ABSTRACT FROM AUTHOR]

Importance: Decision-making in trauma patients remains challenging and often results in deviation from guidelines. Machine-Learning (ML) enhanced decision-support could improve hemorrhage resuscitation. Aim: To develop a ML enhanced decision support tool to predict Need for Hemorrhage Resuscitation (NHR) (part I) and test the collection of the predictor variables in real time in a smartphone app (part II). Design, setting, and participants: Development of a ML model from a registry to predict NHR relying exclusively on prehospital predictors. Several models and imputation techniques were tested. Assess the feasibility to collect the predictors of the model in a customized smartphone app during prealert and generate a prediction in four level-1 trauma centers to compare the predictions to the gestalt of the trauma leader. Main outcomes and measures: Part 1: Model output was NHR defined by 1) at least one RBC transfusion in resuscitation, 2) transfusion ≥ 4 RBC within 6 h, 3) any hemorrhage control procedure within 6 h or 4) death from hemorrhage within 24 h. The performance metric was the F4-score and compared to reference scores (RED FLAG, ABC). In part 2, the model and clinician prediction were compared with Likelihood Ratios (LR). Results: From 36,325 eligible patients in the registry (Nov 2010—May 2022), 28,614 were included in the model development (Part 1). Median age was 36 [25–52], median ISS 13 [5–22], 3249/28614 (11%) corresponded to the definition of NHR. A XGBoost model with nine prehospital variables generated the best predictive performance for NHR according to the F4-score with a score of 0.76 [0.73–0.78]. Over a 3-month period (Aug—Oct 2022), 139 of 391 eligible patients were included in part II (38.5%), 22/139 with NHR. Clinician satisfaction was high, no workflow disruption observed and LRs comparable between the model and the clinicians. Conclusions and relevance: The ShockMatrix pilot study developed a simple ML-enhanced NHR prediction tool demonstrating a comparable performance to clinical reference scores and clinicians. Collecting the predictor variables in real-time on prealert was feasible and caused no workflow disruption. [ABSTRACT FROM AUTHOR]

Smart technologies and connected health are providing opportunities for improved healthcare for chronic conditions. Acceptance by healthcare professionals (HCPs) and patients is crucial for successful implementation. Evidence-based standards, technological infrastructure and regulatory processes are needed to integrate digital tools into clinical practice. Personal health records provide continuity and aid decision-making, while machine-learning algorithms may help in optimizing therapies and improving outcomes. Digital healthcare can negate geographical barriers, enabling patients in remote areas to access specialist endocrine expertise. We review available and developing digital tools to manage care for patients requiring growth hormone (GH) therapy for growth failure conditions. GH is most often administered via daily injections over several years; continuous adherence is necessary but may become insufficient. Future development and integration of electronic platforms for GH therapy requires involvement of all stakeholders in design-thinking approaches and human-factor testing. Growzen Connect is an innovative digital ecosystem designed to increase the management and monitoring of GH therapy, comprising the easypod device and connected mobile apps. It provides a real-time overview of a patient's therapy, including adherence and growth response, which aids decision-making by HCPs and empowers patients to engage in their therapy journey. Incorporating prediction models for adherence and growth in the ecosystem helps patients build treatment habits and allows issues to be addressed in a timely fashion. A connected ecosystem for GH therapy can enhance outcomes and empower patients, fostering a collaborative and patient-centered approach that is more proactive, beyond the traditional clinic-based approach. [ABSTRACT FROM AUTHOR]

Purpose Methods Results Conclusion To validate the performance of autonomous diabetic retinopathy (DR) grading by comparing a human grader and a self‐developed deep‐learning (DL) algorithm with gold‐standard evaluation.We included 500, 6‐field retinal images graded by an expert ophthalmologist (gold standard) according to the International Clinical Diabetic Retinopathy Disease Severity Scale as represented with DR levels 0–4 (97, 100, 100, 103, 100, respectively). Weighted kappa was calculated to measure the DR classification agreement for (1) a certified human grader without, and (2) with assistance from a DL algorithm and (3) the DL operating autonomously. Using any DR (level 0 vs. 1–4) as a cutoff, we calculated sensitivity, specificity, as well as positive and negative predictive values (PPV and NPV). Finally, we assessed lesion discrepancies between Model 3 and the gold standard.As compared to the gold standard, weighted kappa for Models 1–3 was 0.88, 0.89 and 0.72, sensitivities were 95%, 94% and 78% and specificities were 82%, 84% and 81%. Extrapolating to a real‐world DR prevalence of 23.8%, the PPV were 63%, 64% and 57% and the NPV were 98%, 98% and 92%. Discrepancies between the gold standard and Model 3 were mainly incorrect detection of artefacts (n = 49), missed microaneurysms (n = 26) and inconsistencies between the segmentation and classification (n = 51).While the autonomous DL algorithm for DR classification only performed on par with a human grader for some measures in a high‐risk population, extrapolations to a real‐world population demonstrated an excellent 92% NPV, which could make it clinically feasible to use autonomously to identify non‐DR patients. [ABSTRACT FROM AUTHOR]

Objective To assess the association between use of an oxytocin decision support checklist with oxytocin usage and clinical outcomes. Study Design We conducted a retrospective cohort study of patients with singleton gestations at 37 0/7 weeks or greater who received oxytocin during labor from October 2012 to February 2017 at an integrated community health care system during three exposure periods: (1) prechecklist; (2) after paper checklist implementation; and (3) after checklist integration into the electronic medical record (EMR). The checklist was a clinical decision support tool to standardize the dosing and management of oxytocin. Thus, our primary outcomes included oxytocin infusion rates and cumulative dose. Secondary outcomes included maternal and neonatal outcomes. We controlled for maternal risk factors with multivariable regression analysis and stratified by mode of delivery. Results A total of 34,269 deliveries were included. Unadjusted analyses showed that compared with prechecklist, deliveries during the paper and EMR-integrated periods had a lower cumulative dose (4,670 ± 6,174 vs. 4,318 ± 5,719 and 4,286 ± 5,579 mU, p < 0.001 for both), lower maximal infusion rate (9.9 ± 6.8 vs. 8.7 ± 5.8 and 8.4 ± 5.6 mU/min, p < 0.001 for both), and longer duration of oxytocin use (576 ± 442 vs. 609 ± 476 and 627 ± 488 minutes, p < 0.001 and p = 0.01, respectively). The unadjusted rates of cesarean, 5-minute Apgar <7, mechanical ventilation, and neonatal hospital length of stay were similar between periods. The adjusted mean difference in time from admission to delivery was longer during the EMR-integrated period compared with prechecklist (3.0 [95% confidence interval: 2.7–3.3] hours, p < 0.001). Conclusion Oxytocin checklist use was associated with decreased oxytocin use patterns at the expense of longer labor times. Findings were more pronounced with EMR integration. Key Points An oxytocin decision support checklist is associated with reduced amounts of oxytocin used. However, checklists were associated with longer duration of oxytocin use and of labor. Results were more pronounced in the EMR-integrated checklist compared with paper checklist. [ABSTRACT FROM AUTHOR]

Background: Antibiotic resistant infections cause over 700,000 deaths worldwide annually. As antimicrobial stewardship (AMS) helps minimise the emergence of antibiotic resistance resulting from inappropriate use of antibiotics in healthcare, we developed ePAMS+ (ePrescribing-based Anti-Microbial Stewardship), an ePrescribing and Medicines Administration (EPMA) system decision-support tool complemented by educational, behavioural and organisational elements. Methods: We conducted a non-randomised before-and-after feasibility trial, implementing ePAMS+ in two English hospitals using the Cerner Millennium EPMA system. Wards of several specialties were included. Patient participants were blinded to whether ePAMS+ was in use; prescribers were not. A mixed-methods evaluation aimed to establish: acceptability and usability of ePAMS+ and trial processes; feasibility of ePAMS+ implementation and quantitative outcome recording; and a Fidelity Index measuring the extent to which ePAMS+ was delivered as intended. Longitudinal semi-structured interviews of doctors, nurses and pharmacists, alongside non-participant observations, gathered qualitative data; we extracted quantitative prescribing data from the EPMA system. Normal linear modelling of the defined daily dose (DDD) of antibiotic per admission quantified its variability, to inform sample size calculations for a future trial of ePAMS+ effectiveness. Results: The research took place during the SARS-CoV-2 pandemic, from April 2021 to November 2022. 60 qualitative interviews were conducted (33 before ePAMS+ implementation, 27 after). 1,958 admissions (1,358 before ePAMS+ implementation; 600 after) included 24,884 antibiotic orders. Qualitative interviews confirmed that some aspects of ePAMS+ , its implementation and training were acceptable, while other features (e.g. enabling combinations of antibiotics to be prescribed) required further development. ePAMS+ uptake was low (28 antibiotic review records from 600 admissions; 0.047 records per admission), preventing full development of a Fidelity Index. Normal linear modelling of antibiotic DDD per admission showed a residual variance of 1.086 (log-transformed scale). Unavailability of indication data prevented measurement of some outcomes (e.g. number of antibiotic courses per indication). Conclusions: This feasibility trial encountered unforeseen circumstances due to contextual factors and a global pandemic, highlighting the need for careful adaptation of complex intervention implementations to the local setting. We identified key refinements to ePAMS+ to support its wider adoption in clinical practice, requiring further piloting before a confirmatory effectiveness trial. Trial registration: ISRCTN Registry ISRCTN13429325, 24 March 2022. [ABSTRACT FROM AUTHOR]

Managing feral water buffalo in the Northern Territory is a formidable challenge. As an introduced species, buffalo are associated with a myriad of biosecurity, economic, cultural and environmental issues ranging from overgrazing, decreased water quality, disease vectors to the destruction of cultural assets. Nevertheless, the buffalo are also a harvestable resource that can support economic development of the region. To mitigate some of the biosecurity, economic, cultural and environmental risks they pose and manage buffalo effectively, we need a detailed understanding of their spatial and behavioural ecology. However, several factors make understanding how best to manage the dense populations of wild individuals challenging as buffalo inhabit remote areas with limited infrastructure and accessibility and their large size and often aggressive nature can make them difficult to observe in otherwise inaccessible areas. GPS tracking allows for high‐frequency data collection and surveillance of individual buffalo. Here, we investigated how the different seasonal periods of a Northern Territory floodplain area shaped patterns of habitat use for 17 buffalo tracked over 16 months. We found in the dry season, buffalo space use is restricted, and the size of home ranges are significantly smaller than in the wet season. During the wet season, buffalo expand their home range area as well as their social encounter area with other buffalo. These differences in their space use and social patterns suggest that increased disease surveillance may be needed for the wet season when buffalo are more likely to share space and interact. During the dry season, however, buffalo movement is more predictable and restricted, suggesting greater optimisation opportunities for buffalo management. Results from these models can be used by land holders, Traditional Owners and wildlife managers to make evidence‐based decisions to improve buffalo management with respect to disease risk, sustainable harvest and damage to environmental and cultural assets. [ABSTRACT FROM AUTHOR]

The handling of beached cetacean carcasses requires social, legal, financial, ecological and logistical considerations. However, limited research on the topic hinders informed decision-making. A large humpback whale carcass that washed ashore at a South African marine protected area (MPA) provided an opportunity to gain insight into tourists' perspectives on carcass management within a national park and MPA setting. Eighty tourists were interviewed, and a thematic analysis approach was adopted to identify key themes and quantify the responses. Chi-square tests of independence were used to determine (i) whether the respondents' views on cetacean carcass management were dependent on their knowledge of the site's MPA designation, and (ii) whether the provision of contextual information would influence the respondents' perceptions. Tourists had mixed responses to whale carcass management, with half the respondents indicating that the carcass should be left to decompose naturally. Viewpoints were significantly influenced when context and information on the decision-making process were provided, with more respondents stating that no management intervention should be necessary. We propose a simple flow-diagram as a decision-support tool, which, in combination with spatial zonation maps to identify applicable management options, will help guide decision-making for management authorities. Inclusivity, transparency and consultation with experienced role-players from multiple agencies will provide legitimacy to the final management decisions. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive framework for implementing digital twins in aircraft lifecycle management, with a focus on using data-driven models to enhance decision-making and operational efficiency. The proposed framework integrates cutting-edge technologies such as IoT sensors, big data analytics, machine learning, 6G communication, and cloud computing to create a robust digital twin ecosystem. This paper explores the key components of the framework, including lifecycle phases, new technologies, and models for digital twins. It discusses the challenges of creating accurate digital twins during aircraft operation and maintenance and proposes solutions using emerging technologies. The framework incorporates physics-based, data-driven, and hybrid models to simulate and predict aircraft behavior. Supporting components like data management, federated learning, and analytics tools enable seamless integration and operation. This paper also examines decision-making models, a knowledge-driven approach, limitations of current implementations, and future research directions. This holistic framework aims to transform fragmented aircraft data into comprehensive, real-time digital representations that can enhance safety, efficiency, and sustainability throughout the aircraft lifecycle. [ABSTRACT FROM AUTHOR]

The field of biodiversity conservation is in the midst of a cultural and practical transformation around evidence use, but the necessary institutional and technical support is still emerging. Over the past decade, the United States Agency for International Development (USAID) has invested in building institutional capacity for evidence use in its biodiversity conservation projects through technical assistance. We interviewed 34 technical assistance staff supporting biodiversity programming at USAID to explore how technical assistance is used to support evidence use and the extent to which technical assistance can fulfill the functions of "evidence bridges"—intermediaries who help practitioners access and use bodies of evidence for decision‐making. We found that the current technical assistance model supporting evidence employs varied strategies to support evidence use, some of which are more closely aligned with the functions of evidence bridges than others. We conclude that the current technical assistance model could strengthen support for evidence use through engagement with evidence bridges to promote uptake of synthesized evidence. We suggest that technical assistance and evidence bridges are needed to facilitate high‐quality evidence use at the scale necessary to achieve conservation impact, and more collaborative spaces at the boundary between research and practice are needed. [ABSTRACT FROM AUTHOR]

Objective: We test the effects of three aids on individuals' ability to detect social bots among Twitter personas: a bot indicator score, a training video, and a warning. Background: Detecting social bots can prevent online deception. We use a simulated social media task to evaluate three aids. Method: Lay participants judged whether each of 60 Twitter personas was a human or social bot in a simulated online environment, using agreement between three machine learning algorithms to estimate the probability of each persona being a bot. Experiment 1 compared a control group and two intervention groups, one provided a bot indicator score for each tweet; the other provided a warning about social bots. Experiment 2 compared a control group and two intervention groups, one receiving the bot indicator scores and the other a training video, focused on heuristics for identifying social bots. Results: The bot indicator score intervention improved predictive performance and reduced overconfidence in both experiments. The training video was also effective, although somewhat less so. The warning had no effect. Participants rarely reported willingness to share content for a persona that they labeled as a bot, even when they agreed with it. Conclusions: Informative interventions improved social bot detection; warning alone did not. Application: We offer an experimental testbed and methodology that can be used to evaluate and refine interventions designed to reduce vulnerability to social bots. We show the value of two interventions that could be applied in many settings. [ABSTRACT FROM AUTHOR]

The need to make evidence-based decisions in conservation planning for threatened species in the face of limited resources and knowledge is widely recognised as a growing challenge. Increasingly sophisticated decision-support tools and approaches are available to conservation programs. The ability of conservation planners to effectively implement these tools will be key to incorporating complex information into threatened species management. The development of effective decision science approaches does not end when they are made available to planners. Planner and practitioner input into their use and outputs is an important part of incorporating these tools into on-ground conservation. The New South Wales Saving our Species program is a large-scale conservation program with jurisdiction over more than 1100 threatened species, ecological communities and populations. We discuss why co-design is key to successful implementation of decision science in program-level planning; this approach has supported the Saving our Species program to account for forms of knowledge that may otherwise be ignored by data driven optimisation. This paper focuses on the role of conservation planners in developing and applying decision tools. We present three case studies that deployed tools co-developed for the Saving our Species program. Through these case studies, we suggest that effective conservation planning can be best achieved through (1) narrowing down the number of options under consideration, by eliminating sub-optimal choices (2) supporting decision-makers to understand the relative advantages and disadvantages of the choices under consideration and (3) enhancing the effectiveness of decision-support tools by integrating practitioner expertise into their application. [ABSTRACT FROM AUTHOR]

Prostate cancer is awidespread and global disease which affects adultmales - it is said that key causes of the cancer include age, family history, and ethnicity. In this study, the Kaggle prostate cancer dataset, comprising of data from 100 patients with a mixture that both had cancer and did not have cancer, was used alongsidemachine learning predictionmodels for the design of unsupervised and automated intelligent systems for the prediction of prostate cancer. Two intelligent systems were designed and underpinned by unsupervised learning algorithms, namely fuzzy c-means and agglomerative hierarchical clustering, where the various intelligent systems were able to make a prostate cancer prediction with accuracies of over 80% for the various classification metrics, alongside being able to predict an associated stage of the prostate cancer. Both designed intelligent systems offer a complimentary alternative to each other, and their relative merits are discussed in the paper. [ABSTRACT FROM AUTHOR]

Question Answering (QA) systems provide accurate answers to questions; however, they lack the ability to consolidate data from multiple sources, making it difficult to manage complex questions that could be answered with additional data retrieved and integrated on the fly. This integration is inherent to Situational Data Integration (SDI) approaches that deal with dynamic requirements of ad hoc queries that neither traditional database management systems, nor search engines are effective in providing an answer. Thus, if QA systems include SDI characteristics, they could be able to return validated and immediate information for supporting users decisions. For this reason, we surveyed QA-based systems, assessing their capabilities to support SDI features, i.e., Ad hoc Data Retrieval, Data Management, and Timely Decision Support. We also identified patterns concerning these features in the surveyed studies, highlighting them in a timeline that shows the SDI evolution in the QA domain. To the best of your knowledge, this study is precursor in the joint analysis of SDI and QA, showing a combination that can favor the way systems support users. Our analyses show that most of SDI features are rarely addressed in QA systems, and based on that, we discuss directions for further research. [ABSTRACT FROM AUTHOR]

As urban populations rise globally, cities face increasing challenges in managing urban mobility. This paper addresses the question of identifying which modifications to introduce regarding city mobility by evaluating potential solutions using city-specific, subjective multi-objective criteria. The innovative AI-based recommendation engine assists city planners and policymakers in prioritizing key urban mobility aspects for effective policy proposals. By leveraging multi-criteria decision analysis (MCDA) and ±1/2 analysis, this engine provides a structured approach to systematically and simultaneously navigate the complexities of urban mobility planning. The proposed approach aims to provide an open-source interoperable prototype for all smart cities to utilize such recommendation systems routinely, fostering efficient, sustainable, and forward-thinking urban mobility strategies. Case studies from four European cities—Helsinki (tunnel traffic), Amsterdam (bicycle traffic for a new city quarter), Messina (adding another bus line), and Bilbao (optimal timing for closing the city center)—highlight the engine's transformative potential in shaping urban mobility policies. Ultimately, this contributes to more livable and resilient urban environments, based on advanced urban mobility management. [ABSTRACT FROM AUTHOR]

Background: Algorithmic decision-making (ADM) utilises algorithms to collect and process data and develop models to make or support decisions. Advances in artificial intelligence (AI) have led to the development of support systems that can be superior to medical professionals without AI support in certain tasks. However, whether patients can benefit from this remains unclear. The aim of this systematic review is to assess the current evidence on patient-relevant benefits and harms, such as improved survival rates and reduced treatment-related complications, when healthcare professionals use ADM systems (developed using or working with AI) compared to healthcare professionals without AI-related ADM (standard care)—regardless of the clinical issues. Methods: Following the PRISMA statement, MEDLINE and PubMed (via PubMed), Embase (via Elsevier) and IEEE Xplore will be searched using English free text terms in title/abstract, Medical Subject Headings (MeSH) terms and Embase Subject Headings (Emtree fields). Additional studies will be identified by contacting authors of included studies and through reference lists of included studies. Grey literature searches will be conducted in Google Scholar. Risk of bias will be assessed by using Cochrane's RoB 2 for randomised trials and ROBINS-I for non-randomised trials. Transparent reporting of the included studies will be assessed using the CONSORT-AI extension statement. Two researchers will screen, assess and extract from the studies independently, with a third in case of conflicts that cannot be resolved by discussion. Discussion: It is expected that there will be a substantial shortage of suitable studies that compare healthcare professionals with and without ADM systems concerning patient-relevant endpoints. This can be attributed to the prioritisation of technical quality criteria and, in some cases, clinical parameters over patient-relevant endpoints in the development of study designs. Furthermore, it is anticipated that a significant portion of the identified studies will exhibit relatively poor methodological quality and provide only limited generalisable results. Systematic review registration: This study is registered within PROSPERO (CRD42023412156). [ABSTRACT FROM AUTHOR]

Factors such as supply chain difficulties, rising energy and oil prices, economic recession and production loss due to the pandemic have increased costs and inflation. All these factors have also seriously affected the construction sector. This study aims to create a deep learning and machine learning focused forecasting system based on Istanbul and Ankara monthly housing price index data for the period of January 2010 to June 2023. The system was created using approximately 13 years of housing interest rates, Consumer Price Index, XGMYO, Monthly Average Dollar and XAU data as the basis of the Istanbul and Ankara Housing Price Index forecasting process. During the research process, different RNN structures (Long and Short Term Memory, Gated Recurrent Unit) and machine learning (Random Forest) structures were tested and the effectiveness of these structures in housing price index forecasting was compared. The performances of the models were evaluated using RMSE, MSE, MAE, MAPE and R2 statistics. According to the results obtained, the method that gave the best performance for both provinces is the RF model. This is followed by LSTM and GRU models, respectively. [ABSTRACT FROM AUTHOR]

Background: As patients interact with a healthcare service system, patterns of service utilization (PSUs) emerge. These PSUs are embedded in the sparse high-dimensional space of longitudinal cross-continuum health service encounter data. Once extracted, PSUs can provide quality assurance/quality improvement (QA/QI) efforts with the information required to optimize service system structures and functions. This may improve outcomes for complex patients with chronic diseases. Method: Working with longitudinal cross-continuum encounter data from a regional health service system, various pattern detection analyses were conducted, employing (1) graph community detection algorithms, (2) natural language processing (NLP) clustering, and (3) a hybrid NLP–graph method. Result: These approaches produced similar PSUs, as determined from a clinical perspective by clinical subject matter experts and service system operations experts. Conclusions: The similarity in the results provides validation for the methodologies. Moreover, the results stress the need to engage with clinical or service system operations experts, both in providing the taxonomies and ontologies of the service system, the cohort definitions, and determining the level of granularity that produces the most clinically meaningful results. Finally, the uniqueness of each approach provides an opportunity to take advantage of the various analytical capabilities that each approach brings, which will be further explored in our future research. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the patients' perspectives on the use of patient- and outcome information tools in everyday care and to investigate which characteristics affect general understanding and perceived value of patient- and outcome information. Methods: This mixed-methods study included surveys and interviews on understanding, experience, decision-support, and perceived value in patients with hand and wrist conditions and chronic pain. We synthesized our quantitative and qualitative findings using a triangulation protocol and identified factors independently associated with general understanding and perceived value of patient- and outcome information using hierarchical logistic regression. Results: We included 3379 patients. The data triangulation indicated that patients understand the outcome information, they find it valuable, it supports decision-making, and it improves patient-clinician interaction. The following variables were independently associated with better general understanding: having more difficulty with questionnaires (standardized odds ratio 0.34 [95%-CI 031–0.38]), having a finger condition (0.72 [0.57–0.92]), longer follow-up (0.75 [0.61–0.91]), and undergoing surgical treatment (ref: non-surgical treatment, 1.33 [1.11–1.59]). For more general value, these were: having more difficulty with questionnaires (0.40 [0.36–0.44]), having a wrist condition (0.71 [0.54–0.92]), better hand function (1.12 [1.02–1.22]), and requiring help with questionnaires (1.65 [1.33–2.05]). Conclusion: Patients value the use of patient- and outcome information tools in daily care and find it easy to understand. The factors associated with understanding and value can be targeted to personalized and value-based healthcare. We recommend using outcome information to improve patient independence, empowerment, and involvement in decision-making. [ABSTRACT FROM AUTHOR]

Modern Chemical Biological Radiological Nuclear (CBRN) operations require a significant reduction in the time between decision making and decision execution. This calls for effective decision support to improve situational awareness (SA) at the lowest practical echelon. Applied Research Associates, Inc. conducted a 22-month project to research Avoidance and Protection, the CBRN phase with greatest uncertainty and cognitive work demands, to determine how the Android Tactical Assault Kit (ATAK) could best support operator procedures and decision making. We confirmed U.S. Department of Defense (DoD) CBRN doctrine, created battalion commander, medical officer, CBRN Specialist, and Soldier or Marine use cases, developed a concept video around existing CBRN passive defense workflows and tasking, then reviewed the concept with CBRN Specialists returning from deployment as well as Integrated Early Warning and CBRN Support to Command-and-Control developers. Resulting improvement to decision support can more effectively sustain the operator's observe, orient, decide, and act (OODA) loop while minimizing cognitive load. [ABSTRACT FROM AUTHOR]

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Individuals from a variety of backgrounds are affected by the opioid crisis. To provide optimal care for individuals at risk of opioid overdose and prevent subsequent overdoses, a more targeted response that goes beyond the traditional taxonomical diagnosis approach to care management needs to be adopted. In previous works, Graph Machine Learning and Natural Language Processing methods were used to model the products for planning and evaluating the treatment of patients with complex issues. This study proposes a methodology of partitioning patients in the opioid overdose cohort into various communities based on their patterns of service utilization (PSUs) across the continuum of care using graph community detection and applying survival analysis to predict time-to-second overdose for each of the communities. The results demonstrated that the overdose cohort is not homogeneous with respect to the determinants of risk. Moreover, the risk for subsequent overdose was quantified: there is a 51% higher chance of experiencing a second overdose for a high-risk community compared to a low-risk community. The proposed method can inform a more efficient treatment heterogeneity approach for a cohort made of diverse individuals, such as the opioid overdose cohort. It can also guide targeted support for patients at risk of subsequent overdoses. [ABSTRACT FROM AUTHOR]

The objective of this investigation was: (i) to develop a model that supports sustainable product development, considering the quality aspect and the environmental impact in the product life cycle, and (ii) to establish a framework to select the proportion of the share of these aspects during product development decisions. This research concentrates on achieving products that meet customer demand and have environmentally friendly life cycles. It also supports the implementation of design activities at an early stage of product development, positioning the share of quality in relation to environmental impact. The model is based on creating hypothetical prototypes of current products, and this approach concentrated on aggregating the quality (customer satisfaction) with life cycle environmental impacts (as in ISO 14040). The model was developed in five main stages, including: (i) defining product prototypes according to the modifications of quality criteria most desired by customers, (ii) assessing the quality of prototypes according to the Q quality index, (iii) prospective assessment of the environmental impacts of the life cycles of prototypes according to the LCA environmental index, (iv) methodical integration of the above-mentioned indicators into one quality and environmental indicator QLCA, and (v) analysis of possible production solutions and setting the direction of product development, taking into account both quality and environmental aspects. This research was extended with a sensitivity analysis of the QLCA indicator, after which a framework for selecting the proportion of the Q and LCA indicator's share in product development decisions was established. The originality of this research is the ability of the developed model to facilitate eco-innovative product design and improvements while also selecting the share of qualitative and environmental aspects needed to develop sustainable products. The results provide a dynamic and effective tool for manufacturing companies; mainly designers and managers during qualitative and environmental prototyping of products commonly used by customers. The model will provide support in predicting a product that will be manufactured that will be satisfactory for customers and environmentally friendly based on LCA. [ABSTRACT FROM AUTHOR]

Freshwater blooms of cyanobacteria present a challenge to those tasked with managing beaches during bathing season, both to ensure the protection of public health, and to avoid lengthy beach closures. The combined effects of climate change and environmental pollution could cause blooms to become more frequent, intense, and persistent in the future in some locations, necessitating regularly review and update of response protocols. Decision support tools are used to help manage bloom events and inform responses. These can advise on the triggers for inspection, testing, posting of advisories, closing of beaches, and when to rescind advisories and reopen beaches. The aim of this paper was to present an overview of approaches and decision support tools used to inform public health responses to cyanobacterial blooms. During bathing season in Canada, most bloom monitoring is reactive, with a limited coverage of proactive monitoring, except at priority beaches. Responses to blooms vary widely, but many are informed by decision protocols or flow charts using visual inspection and single-level indicators, or alert level frameworks using multiple indicators and alert levels. The only health-based indicators used in any system are cyanotoxins, but capacity for frequent testing is often limited. Approaches to rescinding advisories also vary in the types of indicators and length of time used to determine when it is safe to resume recreational activities. This can vary from days to weeks, with some jurisdictions taking more precautionary approaches. Responsible authorities must balance public health protection with available resources for testing and monitoring with public acceptance of extended beach closures. With the prospect of more frequent and pervasive blooms in the future, there will be a need to allocate scare resources efficiently, which may require regular review and update of response protocols. Adapting approaches may require using a range of more accessible indicators alongside local knowledge, site history, and new tools to inform site-specific responses. [ABSTRACT FROM AUTHOR]

Efficient order allocation and inventory management are essential for the success of supply chain operations in today's dynamic and competitive business environment. This research introduces an innovative decision-making model incorporating dependability factors into redesigning and optimizing order allocation and inventory management systems. The proposed model aims to enhance the overall reliability of supply chain operations by integrating stochastic factors such as demand fluctuations, lead time uncertainty, and variable supplier performance. The system, named Dynamic Reliability-Driven Order Allocation and Inventory Management (DROAIM), combines stochastic models, reliability-based supplier evaluation, dynamic algorithms, and real-time analytics to create a robust and flexible framework for supply chain operations. It evaluates the dependability of suppliers, transportation networks, and internal procedures, offering a comprehensive approach to managing supply chain operations. A case study and simulations were conducted to assess the efficacy of the proposed approach. The findings demonstrate significant improvements in the overall reliability of supply chain operations, reduced stockout occurrences, and optimized inventory levels. Additionally, the model shows adaptability to various industry-specific challenges, making it a versatile tool for practitioners aiming to enhance their supply chain resilience. Ultimately, this research contributes to existing knowledge by providing a thorough decision-making framework incorporating dependability factors into order allocation and inventory management processes. Practitioners and experts can implement this framework to address uncertainties in their operations. [ABSTRACT FROM AUTHOR]

Abstract Importance Decision-making in trauma patients remains challenging and often results in deviation from guidelines. Machine-Learning (ML) enhanced decision-support could improve hemorrhage resuscitation. Aim To develop a ML enhanced decision support tool to predict Need for Hemorrhage Resuscitation (NHR) (part I) and test the collection of the predictor variables in real time in a smartphone app (part II). Design, setting, and participants Development of a ML model from a registry to predict NHR relying exclusively on prehospital predictors. Several models and imputation techniques were tested. Assess the feasibility to collect the predictors of the model in a customized smartphone app during prealert and generate a prediction in four level-1 trauma centers to compare the predictions to the gestalt of the trauma leader. Main outcomes and measures Part 1: Model output was NHR defined by 1) at least one RBC transfusion in resuscitation, 2) transfusion ≥ 4 RBC within 6 h, 3) any hemorrhage control procedure within 6 h or 4) death from hemorrhage within 24 h. The performance metric was the F4-score and compared to reference scores (RED FLAG, ABC). In part 2, the model and clinician prediction were compared with Likelihood Ratios (LR). Results From 36,325 eligible patients in the registry (Nov 2010—May 2022), 28,614 were included in the model development (Part 1). Median age was 36 [25–52], median ISS 13 [5–22], 3249/28614 (11%) corresponded to the definition of NHR. A XGBoost model with nine prehospital variables generated the best predictive performance for NHR according to the F4-score with a score of 0.76 [0.73–0.78]. Over a 3-month period (Aug—Oct 2022), 139 of 391 eligible patients were included in part II (38.5%), 22/139 with NHR. Clinician satisfaction was high, no workflow disruption observed and LRs comparable between the model and the clinicians. Conclusions and relevance The ShockMatrix pilot study developed a simple ML-enhanced NHR prediction tool demonstrating a comparable performance to clinical reference scores and clinicians. Collecting the predictor variables in real-time on prealert was feasible and caused no workflow disruption.

Abstract Background Antibiotic resistant infections cause over 700,000 deaths worldwide annually. As antimicrobial stewardship (AMS) helps minimise the emergence of antibiotic resistance resulting from inappropriate use of antibiotics in healthcare, we developed ePAMS+ (ePrescribing-based Anti-Microbial Stewardship), an ePrescribing and Medicines Administration (EPMA) system decision-support tool complemented by educational, behavioural and organisational elements. Methods We conducted a non-randomised before-and-after feasibility trial, implementing ePAMS+ in two English hospitals using the Cerner Millennium EPMA system. Wards of several specialties were included. Patient participants were blinded to whether ePAMS+ was in use; prescribers were not. A mixed-methods evaluation aimed to establish: acceptability and usability of ePAMS+ and trial processes; feasibility of ePAMS+ implementation and quantitative outcome recording; and a Fidelity Index measuring the extent to which ePAMS+ was delivered as intended. Longitudinal semi-structured interviews of doctors, nurses and pharmacists, alongside non-participant observations, gathered qualitative data; we extracted quantitative prescribing data from the EPMA system. Normal linear modelling of the defined daily dose (DDD) of antibiotic per admission quantified its variability, to inform sample size calculations for a future trial of ePAMS+ effectiveness. Results The research took place during the SARS-CoV-2 pandemic, from April 2021 to November 2022. 60 qualitative interviews were conducted (33 before ePAMS+ implementation, 27 after). 1,958 admissions (1,358 before ePAMS+ implementation; 600 after) included 24,884 antibiotic orders. Qualitative interviews confirmed that some aspects of ePAMS+ , its implementation and training were acceptable, while other features (e.g. enabling combinations of antibiotics to be prescribed) required further development. ePAMS+ uptake was low (28 antibiotic review records from 600 admissions; 0.047 records per admission), preventing full development of a Fidelity Index. Normal linear modelling of antibiotic DDD per admission showed a residual variance of 1.086 (log-transformed scale). Unavailability of indication data prevented measurement of some outcomes (e.g. number of antibiotic courses per indication). Conclusions This feasibility trial encountered unforeseen circumstances due to contextual factors and a global pandemic, highlighting the need for careful adaptation of complex intervention implementations to the local setting. We identified key refinements to ePAMS+ to support its wider adoption in clinical practice, requiring further piloting before a confirmatory effectiveness trial. Trial registration ISRCTN Registry ISRCTN13429325, 24 March 2022.

To address the growing need for advanced tools that enable urban policymakers to conduct comprehensive cost-benefit analyses of traffic management changes, the Urbanite H2020 project has developed innovative artificial intelligence methods. Among them is a robust decision support system that assists policymakers in evaluating and selecting optimal urban mobility planning modifications by combining objective and subjective criteria. Utilising open-source microscopic traffic simulation tools, accurate digital models (or “digital twins”) of four pilot cities—Bilbao, Amsterdam, Helsinki, and Messina—were created, each addressing unique mobility challenges. These challenges include reducing private vehicle access in Bilbao’s city center, analysing the impact of increased bicycle traffic and population growth in Amsterdam, constructing a mobility-enhancing tunnel in Helsinki, and improving public transport connectivity in Messina. The research introduces five key innovations: the application of a consistent open-source simulation platform across diverse urban environments, addressing integration and consistency challenges; the pioneering use of Dexi for advanced decision support in smart cities; the implementation of advanced visualisations; and the integration of the machine learning tool, Orange, with a user-friendly GUI interface. These innovations collectively make complex data analysis accessible to non-technical users. By applying multi-label machine learning techniques, the decision-making process is accelerated by three orders of magnitude, significantly enhancing urban planning efficiency. The Urbanite project’s findings offer valuable insights into both anticipated and unexpected outcomes of mobility interventions, presenting a scalable, open-source AI-based framework for urban decision-makers worldwide.

Abstract Background Algorithmic decision-making (ADM) utilises algorithms to collect and process data and develop models to make or support decisions. Advances in artificial intelligence (AI) have led to the development of support systems that can be superior to medical professionals without AI support in certain tasks. However, whether patients can benefit from this remains unclear. The aim of this systematic review is to assess the current evidence on patient-relevant benefits and harms, such as improved survival rates and reduced treatment-related complications, when healthcare professionals use ADM systems (developed using or working with AI) compared to healthcare professionals without AI-related ADM (standard care)—regardless of the clinical issues. Methods Following the PRISMA statement, MEDLINE and PubMed (via PubMed), Embase (via Elsevier) and IEEE Xplore will be searched using English free text terms in title/abstract, Medical Subject Headings (MeSH) terms and Embase Subject Headings (Emtree fields). Additional studies will be identified by contacting authors of included studies and through reference lists of included studies. Grey literature searches will be conducted in Google Scholar. Risk of bias will be assessed by using Cochrane’s RoB 2 for randomised trials and ROBINS-I for non-randomised trials. Transparent reporting of the included studies will be assessed using the CONSORT-AI extension statement. Two researchers will screen, assess and extract from the studies independently, with a third in case of conflicts that cannot be resolved by discussion. Discussion It is expected that there will be a substantial shortage of suitable studies that compare healthcare professionals with and without ADM systems concerning patient-relevant endpoints. This can be attributed to the prioritisation of technical quality criteria and, in some cases, clinical parameters over patient-relevant endpoints in the development of study designs. Furthermore, it is anticipated that a significant portion of the identified studies will exhibit relatively poor methodological quality and provide only limited generalisable results. Systematic review registration This study is registered within PROSPERO (CRD42023412156).

This study considers the problem of inventory and scheduling decisions on a reusable transport item (RTI) sharing platform with the collaborative recovery of used RTIs and replenishment of products in a two-tier container management centre (CMC). The products (packaged as full RTIs) are pre-positioned at the regional CMC (R-CMC), and empty RTIs are stored at the CMC hub. Moreover, the CMC replenishes the products and recycles RTIs respectively and periodically. The RTI and products are a set of complementary products, and the replenishment task requires sufficient empty RTIs in stock. Untimely and insufficient RTI returns without considering product inventory changes often result in RTI out-of-stock situations that harm the customer's lean productivity. This paper proposes a machine learning and simulation optimisation (MSO) decision framework to collaboratively assist RTI inventory and scheduling decisions in a two-tier CMC. Based on a case study, we can conclude the decision framework has better performance on the profitability and inventory control capability. Moreover, different inventory and scheduling parameter settings in the two-tier CMCs impact the platform's profitability to derive corresponding management insights, and a decision system can be built based on the above framework. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Patients with acute uncomplicated appendicitis will be increasingly asked to choose between surgery and antibiotic management. We developed a novel decision aid for patients in the emergency department (ED) with acute appendicitis who are facing this choice. We describe the development of the decision aid and an initial feasibility study of its implementation in a busy tertiary care ED. MATERIALS AND METHODS: We conducted a prepost survey analysis comparing patients before and after standardized implementation of the decision aid. Patients were surveyed about their experience making treatment decisions after discharge from the hospital. The primary outcome measure was the total score on the decisional conflict scale (; 0-100; lower scores better). RESULTS: The study included 24 participants (12 in the predecision aid period; 12 in the post period). Only 33% of participants in each group knew antibiotics were a treatment option prior to arriving at the ED. Prior to implementing the use of decision aid, only 75% of patients reported being told antibiotics were a treatment option, while this increased to 100% after implementation of the decision aid. The mean total decisional conflict scalescores were similar in the pre and post periods (mean difference = 0.13, 95% CI: -13 - 13, P > 0.9). CONCLUSIONS: This novel appendicitis decision aid was effectively integrated into clinical practice and helped toinform patients about multiple treatment options. These data support further large-scale testing of the decision aid as part of standardized pathways for the management of patients with acute appendicitis.

Dynamical downscaling is a crucial process for providing regional climate information for broad uses, using coarser-resolution global models to drive higher-resolution regional climate simulations. The pool of global climate models (GCMs) providing the fields needed for dynamical downscaling has increased from the previous generations of the Coupled Model Intercomparison Project (CMIP). However, with limited computational resources, the need for prioritizing the GCMs for subsequent downscaling studies remains. GCM selection for dynamical downscaling should focus on evaluating processes relevant for providing boundary conditions to the regional models and be inspired by regional uses such as the response of extremes to changes in the boundary conditions. This leads to the need for metrics representing processes of relevance to diverse stakeholders and subregions of a domain. Procedures to account for metric redundancy and the statistical distinguishability of GCM rankings are required. Further, procedures for selecting realizations from ensembles of top-performing GCM simulations can be used to span the range of climate change signals in multiple ways. As a result, distinct weighting of metrics and prioritization of particular realizations may depend on user needs. We provide high-level guidelines for such region-specific evaluations and address how CMIP7 might enable dynamical downscaling of a representative sample of high-quality models across representative shared socioeconomic pathways (SSPs).

The distribution of vaccines poses a critical challenge, particularly during public emergencies. This paper introduces a decision support framework to address the requirement of vaccine supply distribution in medical emergencies by optimizing an artificial neural network (ANN) model with hyperbolic secant grey wolf optimization (HSGWO) techniques. The proposed hyperbolic HSGWO-ANN model offers a method for optimizing vaccine requirement by efficiently allocating resources, thereby improving decision-making processes in vaccine supply and enhancing healthcare outcomes. Utilizing HSGWO-ANN model, our research aids decision makers or governmental bodies in strategically allocating vaccines to high-risk locations, mitigating the impact of pandemics. Initially, HSGWO employs a hyperbolic secant function to explore search space and enhances exploration and exploitation capabilities. Subsequently, HSGWO is integrated with ANN to optimize weights and minimize training error for accurate prediction. Furthermore, we compute normalized regression residuals for each location with the highest risk for vaccine deployment. We evaluate the performance of the proposed HSGWO-ANN model on two numerical datasets using error measures such as Root mean squared and Mean absolute percentage errors, and correlation coefficients such as R 2 and Pearson’s. The results demonstrate that the HSGWO-ANN model outperforms existing models, exhibiting lower error rates and stronger correlations, thus offering a more efficient solution for vaccine distribution. [ABSTRACT FROM AUTHOR]

Enabled via recent technological advances coupled with the advent of new systems providers and decreased price points, automated and robotized order-picking solutions (e.g., pick-assisting autonomous mobile robots) have evolved as a surging market. Such innovative picking technologies aim to reduce labor costs, use available space more efficiently, and increase throughput rates. As implementation projects and the variety of solutions rise, managers decide which ones to select for their specific warehouse and products. However, comprehensive decision models for this strategic problem are lacking in the pertinent literature. We propose a mathematical optimization model for the novel problem that selects and sizes order-picking solutions and assigns them products and warehouse spaces. Expert interviews are used to identify the comprehensive decision-relevant costs and constraints. Specifically, we minimize setup, module, labor, and error costs while adhering to characteristics related to the area (e.g., available space), technology (e.g., throughput, handling capabilities of certain products), and product (e.g., physical dimensions). We conduct a case study and complement our findings with numerical experiments. We find significant cost reduction potential of up to 57% by selecting a mix of different order-picking solutions. Further analyses highlight the need to retain human workers and to account for maximum labor capacity. [ABSTRACT FROM AUTHOR]

Background: As patients interact with a healthcare service system, patterns of service utilization (PSUs) emerge. These PSUs are embedded in the sparse high-dimensional space of longitudinal cross-continuum health service encounter data. Once extracted, PSUs can provide quality assurance/quality improvement (QA/QI) efforts with the information required to optimize service system structures and functions. This may improve outcomes for complex patients with chronic diseases. Method: Working with longitudinal cross-continuum encounter data from a regional health service system, various pattern detection analyses were conducted, employing (1) graph community detection algorithms, (2) natural language processing (NLP) clustering, and (3) a hybrid NLP–graph method. Result: These approaches produced similar PSUs, as determined from a clinical perspective by clinical subject matter experts and service system operations experts. Conclusions: The similarity in the results provides validation for the methodologies. Moreover, the results stress the need to engage with clinical or service system operations experts, both in providing the taxonomies and ontologies of the service system, the cohort definitions, and determining the level of granularity that produces the most clinically meaningful results. Finally, the uniqueness of each approach provides an opportunity to take advantage of the various analytical capabilities that each approach brings, which will be further explored in our future research.

Individuals from a variety of backgrounds are affected by the opioid crisis. To provide optimal care for individuals at risk of opioid overdose and prevent subsequent overdoses, a more targeted response that goes beyond the traditional taxonomical diagnosis approach to care management needs to be adopted. In previous works, Graph Machine Learning and Natural Language Processing methods were used to model the products for planning and evaluating the treatment of patients with complex issues. This study proposes a methodology of partitioning patients in the opioid overdose cohort into various communities based on their patterns of service utilization (PSUs) across the continuum of care using graph community detection and applying survival analysis to predict time-to-second overdose for each of the communities. The results demonstrated that the overdose cohort is not homogeneous with respect to the determinants of risk. Moreover, the risk for subsequent overdose was quantified: there is a 51% higher chance of experiencing a second overdose for a high-risk community compared to a low-risk community. The proposed method can inform a more efficient treatment heterogeneity approach for a cohort made of diverse individuals, such as the opioid overdose cohort. It can also guide targeted support for patients at risk of subsequent overdoses.

Urban infrastructure, important for societal functioning, faces challenges from aging assets and increasing service demands. Traditional asset management practices, often conducted in silos, fail to address the interconnected nature of these systems, leading to inefficiencies and heightened system failure risks. This article combines the spatial and temporal aspects of sewer, water, and road networks to facilitate integrated interventions and enable informed decision-making among diverse stakeholders. The outcome of this research is the creation of interactive hotspot maps on a unified platform, highlighting potential areas for integrated intervention across different infrastructures. To enhance the potential for collaboration in integrated interventions, flexibility in intervention planning was incorporated. With increased flexibility in intervention decisions, the potential for collaboration also increased. For the case study, introducing a 5-year intervention flexibility increased the number of collaborative projects between sewer, water, and roads from 0 to 18. The maps can also indicate areas where the application of trenchless technologies are justifiable. Other important information on asset characteristics for the decision-makers, including age, inspection, deterioration, and other relevant spatial and temporal details can also be obtained from the maps. The presented methodology and findings provide practical solution for utilities to manage urban infrastructure networks more efficiently. HIGHLIGHTS Spatial and temporal integration of infrastructure networks for collaborative asset management.; Demonstration of a unified platform to streamline integrated interventions of sewer, water, and roads.; Collaboration potential increased from 0 to 18 projects with a 5-year intervention flexibility.; Hotspot maps and project level description of a case study is shown to illustrate the usefulness of the framework.;

Abstract As climate change continues to modify temperature and rainfall patterns, risks from pests and diseases may vary as shifting temperature and moisture conditions affect the life history, activity, and distribution of invertebrates and diseases. The potential consequences of changing climate on pest management strategies must be understood for control measures to adapt to new environmental conditions. The redlegged earth mite (RLEM; Halotydeus destructor [Tucker]) is a major economic pest that attacks pastures and grain crops across southern Australia and is typically controlled by pesticides. TIMERITE® is a management strategy that relies on estimating the optimal timing (the TIMERITE® date) for effective chemical control of RLEM populations in spring. In this study, we assessed the efficacy of control at the TIMERITE® date from 1990 to 2020 across southern Australia using a simulation approach that incorporates historical climatic data and field experimental data on life history, seasonal abundance, and population level pesticide responses. We demonstrate that moisture and temperature conditions affect the life history of RLEM and that changes in the past three decades have gradually diminished the efficacy of the TIMERITE® strategy. Furthermore, we show that by incorporating improved climatic data into predictions and shifting the timing of control to earlier in the year, control outcomes can be improved and are more stable across changing climates. This research emphasises the importance of accounting for dynamic environmental responses when developing and implementing pest management strategies to ensure their long-term effectiveness. Suggested modifications to estimating the TIMERITE® date will help farmers maintain RLEM control outcomes amidst increasingly variable climatic conditions.

This paper presents a comparative analysis of web-based machine learning models, specifically examining Google Vertex AI, Google Teachable Machine, Azure Machine Learning and Salesforce Einstein Vision. The objective is to assess their suitability for integration into a medical information system as a classification module for medical images. The comparative evaluation considers factors such as model accuracy, ease of integration and scalability. The findings aim to guide the selection of an optimal machine learning solution for enhancing the capabilities of medical image classification within a healthcare context.