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Vehicle electrification has shown the potential to reduce environmental impacts and greenhouse gas emissions from the transport sector. As electric vehicles (EVs) become increasingly prominent, the efficient placement of charging infrastructure poses a complex challenge that demands careful consideration. This paper delves into the investigation of how travel and parking patterns, derived from empirical data on freight vehicles, influence the optimal distribution of charging infrastructure across the freight network. This paper presents a node-based approach to optimize the allocation of charging infrastructure tailored explicitly for freight transport. The study identifies optimal locations for operator-owned charging infrastructure by leveraging GPS-based data collected from a fleet of freight vehicles operating in the greater Gothenburg metropolitan area. This research aims to enhance our understanding of the charging infrastructure requirements inherent in the freight transport system and provide decision support to logistics companies contemplating the shift from conventional fossil fuel vehicles to electric freight vehicles. The proposed model holds the potential for seamless adaptation to diverse freight transport systems, offering valuable insights to expedite the transition toward fossil-free freight transport on a broader scale. • Complexity in charging infrastructure placement for freight transport. • Empirical data from a fleet of freight vehicles in Gothenburg metropolitan area. • Investigation of travel and parking patterns of freight vehicles. • Node-based optimization approach to allocate charging infrastructure. [ABSTRACT FROM AUTHOR]

Introduction: Drug–drug interactions (DDIs) have potential to cause patient harm, including lowering therapeutic efficacy. This study aimed to (i) determine the prevalence of potential DDIs (pDDIs); clinically relevant DDIs (cDDIs), that is, DDIs that could lead to patient harm, taking into account a patient's individual clinical profile, drug effects and severity of potential harmful outcome; and subsequent actual harm among hospitalized patients and (ii) examine the impact of transitioning from paper-based medication charts to electronic medication management (eMM) on DDIs and patient harms. Methods: This was a secondary analysis of the control arm of a controlled pre-post study. Patients were randomly selected from three Australian hospitals. Retrospective chart review was conducted before and after the implementation of an eMM system, without accompanying clinical decision support alerts for DDIs. Harm was assessed by an expert panel. Results: Of 1186 patient admissions, 70.1% (n = 831) experienced a pDDI, 42.6% (n = 505) a cDDI and 0.9% (n = 11) an actual harm in hospital. Of 15,860 pDDIs identified, 27.0% (n = 4285) were classified as cDDIs. The median number of pDDIs and cDDIs per 10 drugs were 6 [interquartile range (IQR) 2–13] and 0 (IQR 0–2), respectively. In cases where a cDDI was identified, both drugs were 44% less likely to be co-administered following eMM (adjusted odds ratio 0.56, 95% confidence interval 0.46–0.73). Conclusion: Although most patients experienced a pDDI during their hospital stay, less than one-third of pDDIs were clinically relevant. The low prevalence of harm identified raises questions about the value of incorporating DDI decision support into systems given the potential negative impacts of DDI alerts. [ABSTRACT FROM AUTHOR]

This work focuses on the opportunity to use the Digital Twin of a complex system, as a Decision Support System. In studying the phenomenon of human Decision Making, the concept of Situation Awareness appears to be of primary importance when dealing with these complex systems. Given the complexity of the system to be represented in the DT, its own complexity, and the need to integrate the user's abilities to allow the acquisition of SA, the concept of reality anchor is proposed to identify the elements of the studied situation necessary for users to perceive, understand and project the situation they face. A methodology, called the Reality Anchor Methodology, has been defined to ensure the elicitation and implementation of these elements in a DT. This methodology is composed of three steps that aim (1) to elicit the reality anchors through a study of the operators' tasks and activities, (2) to design a prototype to carry out human-in-the-loop tests and (3) to validate the definition of Reality Anchors by analysing the SA, experience feedback and the activities performed during the tests. This method was applied to a case study in the oil-and-gas industry and showed the importance of the defined reality anchors. [ABSTRACT FROM AUTHOR]

• Auto-encoder-enhanced Random Forest models with network biomarkers accurately predict IgAN prognosis. • Personalized IgAN treatment guidance system developed using network biomarkers. • System's performance on par with nephrologists, improving healthcare decision-making. The ongoing debate surrounding the use of immunosuppressive treatments for IgA nephropathy (IgAN) underscores the demand for personalized and effective strategies. Analyzed data from 807 IgAN patients over 5+ years using three methods: Random Forest with molecular biomarkers, network biomarkers with graph engineering, and an auto-encoder model. All models were trained using identical demographic, clinical, and pathological data, employing an 80–20 split for training and testing purposes. In the comprehensive assessment of IgAN prognosis, the Random Forest model, employing molecular biomarkers, demonstrated strong performance metrics (AUC = 0.83, sensitivity = 0.51, specificity = 0.96). However, traditional graph feature engineering on patient-specific networks outperformed these results with an AUC of 0.90, sensitivity of 0.64, and specificity of 0.94. The Auto-encoder model showed the best accuracy (AUC = 0.91, sensitivity = 0.46, specificity = 0.96). The findings highlighted the superior predictive capabilities of network biomarkers over molecular biomarkers for adverse renal outcome prediction in IgAN. Consequently, we integrated Auto-encoder-derived Network Biomarkers with Random Forest Models to enhance prognostic precision in diverse IgAN treatment scenarios. The prediction for the prognosis of patients receiving supportive care, glucocorticoid therapy, and immunosuppressant treatment yielded AUC values of 0.95, 0.96, and 1, respectively, indicating high specificity. Drawing from these insights, we pioneered the development of an innovative decision support model for IgAN treatment. This model demonstrated the ability to make medical decisions comparable to those by experienced nephrologists, enabling the customization of personalized disease management strategies. Our system accurately predicted IgAN prognosis and evaluated various treatment efficacies, aiding physicians in devising optimal therapeutic strategies for patients. [ABSTRACT FROM AUTHOR]

Riparian buffer zones (RBZs) are an important instrument for environmental policies for water and biodiversity protection in managed forests. We investigate the variation of the cost of implementing RBZs within different property size classes across the size range of non-industrial forest owner properties in Southern Sweden. Using the Heureka PlanWise decision support system, we quantified the cost of setting aside RBZs or applying alternative management in them, as the relative loss of harvest volume and of net present value per property. We did this for multiple simulated as well as real-world property distributions. The variation of cost distribution among small properties was 4.2–6.9 times higher than among large properties. The interproperty cost inequality decreased non-linearly with increasing property size and levelled off from around 200 ha. We conclude that RBZs, due to the irregular distribution of streams, cause highly unequal financial consequences for owners, with some small property owners bearing a disproportionally high cost. This adds to previous studies showing how environmental considerations differentially affect property owners. We recommend decision makers to stimulate the uptake of RBZs by alleviating these inequalities between forest owners by including appropriate cost sharing or compensation mechanisms in their design. [ABSTRACT FROM AUTHOR]

Ice management is essential for maintaining the safety of offshore operations in Arctic regions. We present the combined results of three experiments conducted in a full-mission bridge simulator specially designed for ice management. From a quantitative analysis of the results, we infer the effect of three variables on performance: (1) experience, (2) training, and (3) Decision Support System (DSS). The results confirm that experience and training improve performance for untrained and inexperienced simulator participants. The DSS also improves performance, but with a smaller effect. Qualitative observations using vessel position heat-map diagrams and exit interviews suggested that novice participants using the DSS adopted expert strategies but carried out their tasks more slowly and with less precision. This has important consequences for the design of a future DSS used in training simulators or onboard ships. Potential improvements to the DSS design might include real-time feedback to the user, a redesign of the human-machine interface (HMI), and increasing user input and customization with a human factors focus. [ABSTRACT FROM AUTHOR]

This work looks at developing an object-driven decision support system (DSS) model with the goal of improving the prediction accuracy of the present expert-driven DSS model in assessing groundwater potentiality. The database of remote sensing, geological, and geophysical information was constructed using the technological efficiency of GIS, data mining, and programming tools. Groundwater potential conditioning factors (GPCF) extracted from the datasets include lithology (Li), hydraulic conductivity (K), lineament density (Ld), transmissivity (T), and transverse resistance (TR) for groundwater potentiality mapping in a typical hard rock multifaceted geologic setting in south-western Nigeria. A Python-based entropy approach was used to objectively weight these factors. The weightage findings determined that the greatest and lowest given values for Ld and K were 0.6 and 0.03, respectively. The produced Python-based PROMETHEE-Entropy model algorithm was born through combining the weight findings with the Python-based PROMETHEE-II method. The groundwater potentiality model (GPM) map of the area was created using the model algorithm's outputs on the gridded raster of GPCF themes. Based on the suggested approach, the validated results of the created GPM maps using the Receiver Operating Characteristic (ROC) curve technique yielded an accuracy of 86%. An object-driven DSS model was created using the approaches that were used. The created object-driven model is a viable alternative to existing approaches in groundwater hydrology and aids in the automation of groundwater resource management in the research region. [ABSTRACT FROM AUTHOR]

The proper scheduling of track maintenance and renewal operations is dependent on the accuracy of predictive maintenance outputs which, in turn, rely on the appropriate modelling of the track geometry degradation process. This paper explores the incidence and representativeness of the alerts generated due to local defects of track geometry and the modelling of their degradation rates between consecutive tamping operations. A software tool was developed and used to process track inspection data collected over 12 years of operation of the main Portuguese line (Lisbon-Porto) on a total of 727 km of single-track lines where speeds vary between 30 and 220 km/h. Linear regressions are found to be suitable to model the degradation rates of local defects of longitudinal levelling, alignment and twist in the vast majority of cases, although 20–30% of the time degradation patterns prove to be non-linear even in the short term. The local defects of alignment degrade more rapidly and trigger 80% of the unplanned maintenance needs. Modelling the evolution of local defects, and not only the standard deviation on sections 200 m long, allows us to anticipate unplanned maintenance and tackle unavailability costs. [ABSTRACT FROM AUTHOR]

Emergency decision support techniques play an important role in complex and safety-critical systems such as nuclear power plants (NPPs). Emergency decision-making is not a single method but a framework comprising a combination of various technologies. This paper presents a review of various methods for emergency decision support systems in NPPs. We first discuss the theoretical foundations of nuclear power plant emergency decision support technologies. Based on this exposition, the key technologies of emergency decision support systems in NPPs are presented, including training operators in emergency management, risk assessment, fault detection and diagnosis, multi-criteria decision support, and accident consequence assessment. The principles, application, and comparative analysis of these methods are systematically described. Additionally, we present an overview of emergency decision support systems in NPPs across different countries and feature profiles of prominent systems like the Real-Time Online Decision Support System for Nuclear Emergencies (RODOS), the Accident Reporting and Guiding Operational System (ARGOS), and the Decision Support Tool for Severe Accidents (Severa). Then, the existing challenges and issues in this field are summarized, including the need for better integration of risk assessment, methods to enhance education and training, the acceleration of simulation calculations, the application of large language models, and international cooperation. Finally, we propose a new decision support system that integrates Level 1, 2, and 3 probabilistic safety assessment for emergency management in NPPs. [ABSTRACT FROM AUTHOR]

Objective: In order to comprehensively understand the characteristics of Adaptive Business Intelligence (ABI) in Healthcare, this study is structured to provide insights into the common features and evolving patterns within this domain. Applying the Sheridan's Classification as a framework, we aim to assess the degree of autonomy exhibited by various ABI components. Together, these objectives will contribute to a deeper understanding of ABI implementation and its implications within the Healthcare context. Methods: A comprehensive search of academic databases was conducted to identify relevant studies, selecting AIS e-library (AISel), Decision Support Systems Journal (DSSJ), Nature, The Lancet Digital Health (TLDH), PubMed, Expert Systems with Application (ESWA) and npj Digital Medicine as information sources. Studies from 2006 to 2022 were included based on predefined eligibility criteria. PRISMA statements were used to report this study. Results: The outcomes showed that ABI systems present distinct levels of development, autonomy and practical deployment. The high levels of autonomy were essentially associated with predictive components. However, the possibility of completely autonomous decisions by these systems is totally excluded. Lower levels of autonomy are also observed, particularly in connection with prescriptive components, granting users responsibility in the generation of decisions. Conclusion: The study presented emphasizes the vital connection between desired outcomes and the inherent autonomy of these solutions, highlighting the critical need for additional research on the consequences of ABI systems and their constituent elements. Organizations should deploy these systems in a way consistent with their objectives and values, while also being mindful of potential adverse effects. Providing valuable insights for researchers, practitioners, and policymakers aiming to comprehend the diverse levels of ABI systems implementation, it contributes to well-informed decision-making in this dynamic field. [ABSTRACT FROM AUTHOR]

Chronic kidney disease (CKD) is a multifaceted disorder influenced by various factors. Drug–drug interactions (DDIs) present a notable risk factor for hospitalization among patients with CKD. This study aimed to assess the frequency and attributes of potential DDIs (pDDIs) in patients with CKD and to ascertain the concordance among different Clinical Decision Support Software (CDSS). A cross-sectional study was conducted in a nephrology outpatient clinic at a university hospital. The pDDIs were identified and evaluated using Lexicomp® and Medscape®. The patients' characteristics, comorbidities, and medicines used were recorded. The concordance of different CDSS were evaluated using the Kendall W coefficient. An evaluation of 1121 prescribed medications for 137 patients was carried out. The mean age of the patients was 64.80 ± 14.59 years, and 41.60% of them were male. The average year with CKD was 6.48 ± 5.66. The mean number of comorbidities was 2.28 ± 1.14. The most common comorbidities were hypertension, diabetes, and coronary artery disease. According to Medscape, 679 pDDIs were identified; 1 of them was contraindicated (0.14%), 28 (4.12%) were serious-use alternative, and 650 (9.72%) were interventions that required closely monitoring. According to Lexicomp, there were 604 drug–drug interactions. Of these interactions, 9 (1.49%) were in the X category, 60 (9.93%) were in the D category, and 535 (88.57%) were in the C category. Two different CDSS systems exhibited statistically significant concordance with poor agreement (W = 0.073, p < 0.001). Different CDSS systems are commonly used in clinical practice to detect pDDIs. However, various factors such as the operating principles of these programs and patient characteristics can lead to incorrect guidance in clinical decision making. Therefore, instead of solely relying on programs with lower reliability and consistency scores, multidisciplinary healthcare teams, including clinical pharmacists, should take an active role in identifying and preventing pDDIs. [ABSTRACT FROM AUTHOR]

The global iron ore price is influenced by numerous factors, thus showcasing a complex interplay among them. The collective expectations of market participants over time shape the variations and trends within the iron ore price time series. Consequently, devising a robust forecasting model for the volatility of iron ore prices, as well as for other assets connected to this commodity, is critical for guiding future investments and decision-making processes in mining companies. Within this framework, the integration of artificial intelligence techniques, encompassing both technical and fundamental analyses, is aimed at developing a comprehensive, autonomous hybrid system for decision support, which is specialized in iron ore asset management. This approach not only enhances the accuracy of predictions but also supports strategic planning in the mining sector. [ABSTRACT FROM AUTHOR]

One-way carsharing is recognized as one of the most popular transportation services in urban areas, being an alternative option to private cars. Over the last decades, a vast amount of literature on the design of specific aspects of this service (fleet size, stations' locations, fare, balancing operations) has formed. However, a holistic approach for designing carsharing services seems not to be developed. This paper proposes a new approach for designing one-way station-based carsharing services, presenting a five-step method, entirely developed in a GIS environment. The first three steps (suitability analysis, site selection analysis, and walkability analysis) allow finding the candidate locations for carsharing stations. After the assessment of the capacity of the potential stations, a location-allocation analysis allows for assessing the fleet size, the number of stations that maximize the coverage of carsharing demand, and their optimal locations. This paper presents a case study: a new one-way carsharing service was designed in Palermo (Italy) and compared to the existing carsharing service operating in the city. The results highlight that the current carsharing supply is undersized, having about 45% fewer stations and about half the cars compared to those resulting from the model, leaving some POIs unserved. [ABSTRACT FROM AUTHOR]

Achilles tendon rupture is a common sports‐related injury which can carry significant morbidity to patients. Ultrasound remains the workhorse of imaging as it can confirm and localise the extent of Achilles tendon injury. The sonographic anatomy, both normal and ruptured sonographic appearances, as well as sonographic technique must be appreciated to accurately image and report findings, critical to patient management. Particular attention should be applied to the measurement of the diastasis between acutely ruptured tendon ends as this information can assist with informing the decision of conservative vs. operative management. Further work is necessary to standardise the measurement technique including correlating the degree of plantarflexion of the foot with the sonographic tendon gap measures. [ABSTRACT FROM AUTHOR]

[Display omitted] • Temperature records showed where and when heartworm transmission was disrupted in Australia. • 97% of the human population lived where transmission was disrupted. • Current heartworm prevention guidelines do not reflect disruption to transmission. • The extrinsic incubation period (EIP) can be considered when recommending prevention. • An online decision support system shows real-time and historical EIP suitability. Dirofilaria immitis is the causative agent of canine heartworm disease. We used the established heartworm development unit (HDU) principle to map the extrinsic incubation period (EIP) of D. immitis in Australia using historical weather data from 2013–2022. We found weather conditions suitable for EIP completion showed substantial seasonality and geographical variability. Whilst a considerable percentage of the Australian territory showed suitable weather conditions to always support EIP completion (17%), only 2.7% of the 2021 Australian human population lived in this region. Therefore, 97% of the population lived in an area that changed its EIP suitability within the study period. EIP completion is required prior to D. immitis transmission, meaning that infection risk of D. immitis is seasonal and location-dependent, being disrupted each year for most of the human population's dogs. We developed an online, open access tool allowing us to visualise EIP completion across Australia historically and in near real-time. We aim to support veterinarians to make risk-based recommendations for dirofilariosis prevention by using the tool, available at https://heartworm-mapping.adelaide.edu.au/shiny/. [ABSTRACT FROM AUTHOR]

Objective: Artificial intelligence (AI)‐based decision support systems (DSS) are utilized in medicine but underlying decision‐making processes are usually unknown. Explainable AI (xAI) techniques provide insight into DSS, but little is known on how to design xAI for clinicians. Here we investigate the impact of various xAI techniques on a clinician's interaction with an AI‐based DSS in decision‐making tasks as compared to a general population. Methods: We conducted a randomized, blinded study in which members of the Child Neurology Society and American Academy of Neurology were compared to a general population. Participants received recommendations from a DSS via a random assignment of an xAI intervention (decision tree, crowd sourced agreement, case‐based reasoning, probability scores, counterfactual reasoning, feature importance, templated language, and no explanations). Primary outcomes included test performance and perceived explainability, trust, and social competence of the DSS. Secondary outcomes included compliance, understandability, and agreement per question. Results: We had 81 neurology participants with 284 in the general population. Decision trees were perceived as the more explainable by the medical versus general population (P < 0.01) and as more explainable than probability scores within the medical population (P < 0.001). Increasing neurology experience and perceived explainability degraded performance (P = 0.0214). Performance was not predicted by xAI method but by perceived explainability. Interpretation: xAI methods have different impacts on a medical versus general population; thus, xAI is not uniformly beneficial, and there is no one‐size‐fits‐all approach. Further user‐centered xAI research targeting clinicians and to develop personalized DSS for clinicians is needed. [ABSTRACT FROM AUTHOR]

The phenomenon known as urban heat islands (UHIs) is becoming more common and widespread, especially in large cities and metropolises around the world. The main cause of these temperature variations between the city center and the suburbs is the replacement of large tracts of natural land with artificial (built-up) surfaces that absorb solar heat and radiate it back at night. UHIs have been the subject of numerous studies, most of which were about defining the main characteristics, factors, indexes, etc., of UHIs using remote sensing technologies or about determining mitigating activities. This paper provides a comprehensive overview of the literature, as well as a bibliometric analysis, to discover research trends related to the application of decision support systems and multi-criteria decision-making for UHI management, with a special emphasis on fuzzy theory. Data collection is conducted using the Scopus bibliographic database. Throughout the literature review, it was found that there were not many studies on multi-criteria analysis and decision support system applications regarding UHIs. The fuzzy theory application was also reviewed, resulting in only a few references. However, this topic is current, with an increase in published papers, and authors see this as an opportunity for improvement and further research. [ABSTRACT FROM AUTHOR]

Decision Support Systems (DSS) are tools designed to help operators make effective choices in workplace environments where discernment and critical thinking are required for effective performance. Path planning in military operations and general logistics both require individuals to make complex and time-sensitive decisions. However, these decisions can be complex and involve the synthesis of numerous tradeoffs for various paths with dynamically changing conditions. Intelligence collection can vary in difficulty, specifically in terms of the disparity between locations of interest and timing restrictions for when and how information can be collected. Furthermore, plans may need to be changed adaptively mid-operation, as new collection requirements appear, increasing task difficulty. We tested participants in a path planning decision-making exercise with scenarios of varying difficulty in a series of two experiments. In the first experiment, each map displayed two paths simultaneously, relating to two possible routes for the two available trucks. Participants selected the optimal path plan, representing the best solution across multiple routes. In the second experiment, each map displayed a single path, and participants selected the best two paths sequentially. In the first experiment, utilizing the DSS was predictive of adoption of more heuristic decision strategies, and that strategic approach yielded more optimal route selection. In the second experiment, there was a direct effect of the DSS on increased decision performance and a decrease in perceived task workload. [ABSTRACT FROM AUTHOR]

Dental sealants are effective for the prevention of caries in children at elevated risk levels, and increasing the proportion of children and adolescents who have dental sealants on 1 or more molars is a Healthy People 2030 objective. Electronic health record (EHR)-based clinical decision support systems (CDSSs) have the ability to improve patient care. A dental quality measure related to dental sealant placement for children at elevated risk of caries was targeted for improvement using a CDSS. A validated dental quality measure was adapted to assess a patient's need for dental sealant placement. A CDSS was implemented to advise care team members whether a child was at elevated risk of developing caries and had sealant-eligible first or second molars. Data on dental sealant placement at examination visits during a 5-year period were analyzed, including 32 months before CDSS implementation and 28 months after CDSS implementation. From January 1, 2018, through December 31, 2022, the authors assessed 59,047 examination visits for children at elevated risk of developing caries and with sealant-eligible teeth. With the implementation of a CDSS and training to support the clinical care team members in September 2020, the appropriate placement of dental sealants at examination visits increased from 27% through 60% (P <.00001). Integration of a CDSS into the EHR as part of a quality improvement program was effective in increasing the delivery of sealants in eligible first and second molars of children aged 5 through 15 years and considered at high risk of developing caries. An EHR–based CDSS can be implemented to improve standardization and provide timely and appropriate patient care in dental practices. [ABSTRACT FROM AUTHOR]

Rising acceptance of machine learning driven decision support systems underscores the need for ensuring fairness for all stakeholders. This work proposes a novel approach to increase a Neural Network model's fairness during the training phase. We offer a frame-work to create a family of diverse fairness enhancing regularization components that can be used in tandem with the widely accepted binary-cross-entropy based accuracy loss. We use Bias Parity Score (BPS), a metric that quantifies model bias with a single value, to build loss functions pertaining to different statistical measures — even for those that may not be developed yet. We analyze behavior and impact of the newly minted regularization components on bias. We explore their impact in the realm of recidivism and census-based adult income prediction. The results illustrate that apt fairness loss functions can mitigate bias without forsaking accuracy even for imbalanced datasets. [ABSTRACT FROM AUTHOR]

Recruiting a decision support system that handles risks and monitors evidences will mitigate the destructive effects of risk. The main challenge of risk data is when it is unstructured, such as those metadata retrieved from posts published on social networks. The research gap focused in this study is analyzing unstructured risk data and extracting the embedded knowledge. We propose a Topic Map-based knowledge discovery system for analyzing unstructured risk data using the word embedding approach. We examined our proposed system on a dataset from some supply chain risk-related websites containing 325,965 sentences, 725,986 terms, and 52,396 unique terms. The experiment extracted 63 crisis knowledge propositions classified from this dataset using just three user–system interaction steps, which shows the model's high performance. The results reveal that the proposed model could be used effectively and efficiently in decision support systems to analyze unstructured data, especially for crises in the supply chain analysis. [ABSTRACT FROM AUTHOR]

With the growing use of artificial intelligence, search engine providers are increasingly pushing advertisers to use automated bidding strategies based on machine learning. Such automated decision‐making systems leave advertisers in the dark about the data being used and how they can influence the outcome of the decision‐making process. Previous literature on artificial intelligence lacks an understanding of the dangers related to artificially intelligent systems and their lack of transparency. In response, our paper addresses the inherent risks of the automated optimization of advertisers' bidding strategies in search engine advertising. The selected empirical case of a service company therefore demonstrates how data availability can trigger a long‐term decline in advertising performance and how search engine advertising performance metrics develop before and after an event of data scarcity. Based on data collected for 525 days, difference‐in‐differences analysis shows that the algorithmic approach has a considerable and lasting negative impact on advertising performance. Furthermore, the empirical case indicates that self‐regulated learning can initialize a downward spiral that gradually impairs advertising performance. Thus, the aim of this study is to increase awareness regarding automated decision‐making dangers in search engine advertising and help advertisers take preventive measures to reduce the risks of algorithm missteps. [ABSTRACT FROM AUTHOR]

A hybrid genetic algorithm (GA)–Kohonen map, with its three variants, is explored for the first time for the decision‐making system in a porous ceramic matrix (PCM)‐based burner through determination of the regime of operation. Four different attributes of PCMs such as convective coupling (P2), extinction coefficient (β), downstream porosity (ϕ2), and scattering albedo (ω) are selected for determining the regime of operation of a PCM‐based burner. Changes in any of these attributes of a PCM lead to significant changes in the temperature profiles of the gas and solid phases. Temperature profiles of the gas and solid phases are computed by developing a numerical model. Various samples corresponding to different regimes are generated and used in a hybrid GA–Kohonen map. The best architectural details such as the neuron number and training epochs are obtained from GA as output. The best Kohonen map is trained with the input data, and regimes of operation for new temperature profiles are predicted. A supervised Kohonen map is able to provide the highest average class prediction of more than 40%. All the variants are assessed under two different types of neuron grids: hexagonal and rectangular. Comparative assessments of the three different variants of Kohonen maps, in terms of CPU time and average class prediction, are carried out. [ABSTRACT FROM AUTHOR]

Despite the rise of decision support systems enabled by artificial intelligence (AI) in personnel selection, their impact on decision-making processes is largely unknown. Consequently, we conducted five experiments (N = 1403 students and Human Resource Management (HRM) employees) investigating how people interact with AI-generated advice in a personnel selection task. In all pre-registered experiments, we presented correct and incorrect advice. In Experiments 1a and 1b, we manipulated the source of the advice (human vs. AI). In Experiments 2a, 2b, and 2c, we further manipulated the type of explainability of AI advice (2a and 2b: heatmaps and 2c: charts). We hypothesized that accurate and explainable advice improves decision-making. The independent variables were regressed on task performance, perceived advice quality and confidence ratings. The results consistently showed that incorrect advice negatively impacted performance, as people failed to dismiss it (i.e., overreliance). Additionally, we found that the effects of source and explainability of advice on the dependent variables were limited. The lack of reduction in participants' overreliance on inaccurate advice when the systems' predictions were made more explainable highlights the complexity of human-AI interaction and the need for regulation and quality standards in HRM. [ABSTRACT FROM AUTHOR]

Crop Recommendation Systems are invaluable tools for farmers, assisting them in making informed decisions about crop selection to optimize yields. These systems leverage a wealth of data, including soil characteristics, historical crop performance, and prevailing weather patterns, to provide personalized recommendations. In response to the growing demand for transparency and interpretability in agricultural decision-making, this study introduces XAI-CROP an innovative algorithm that harnesses eXplainable artificial intelligence (XAI) principles. The fundamental objective of XAI-CROP is to empower farmers with comprehensible insights into the recommendation process, surpassing the opaque nature of conventional machine learning models. The study rigorously compares XAI-CROP with prominent machine learning models, including Gradient Boosting (GB), Decision Tree (DT), Random Forest (RF), Gaussian Naïve Bayes (GNB), and Multimodal Naïve Bayes (MNB). Performance evaluation employs three essential metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R2). The empirical results unequivocally establish the superior performance of XAI-CROP. It achieves an impressively low MSE of 0.9412, indicating highly accurate crop yield predictions. Moreover, with an MAE of 0.9874, XAI-CROP consistently maintains errors below the critical threshold of 1, reinforcing its reliability. The robust R2 value of 0.94152 underscores XAI-CROP's ability to explain 94.15% of the data's variability, highlighting its interpretability and explanatory power. [ABSTRACT FROM AUTHOR]

The cultivation of grapes encounters various challenges, such as the presence of pests and diseases, which have the potential to considerably diminish agricultural productivity. Plant diseases pose a significant impediment, resulting in diminished agricultural productivity and economic setbacks, thereby affecting the quality of crop yields. Hence, the precise and timely identification of plant diseases holds significant importance. This study employs a Convolutional neural network (CNN) with and without data augmentation, in addition to a DCNN Classifier model based on VGG16, to classify grape leaf diseases. A publicly available dataset is utilized for the purpose of investigating diseases affecting grape leaves. The DCNN Classifier Model successfully utilizes the strengths of the VGG16 model and modifies it by incorporating supplementary layers to enhance its performance and ability to generalize. Systematic evaluation of metrics, such as accuracy and F1-score, is performed. With training and test accuracy rates of 99.18 and 99.06%, respectively, the DCNN Classifier model does a better job than the CNN models used in this investigation. The findings demonstrate that the DCNN Classifier model, utilizing the VGG16 architecture and incorporating three supplementary CNN layers, exhibits superior performance. Also, the fact that the DCNN Classifier model works well as a decision support system for farmers is shown by the fact that it can quickly and accurately identify grape diseases, making it easier to take steps to stop them. The results of this study provide support for the reliability of the DCNN classifier model and its potential utility in the field of agriculture. [ABSTRACT FROM AUTHOR]

Background: Decision-making in healthcare is increasingly complex; notably in hospital environments where the information density is high, e.g., emergency departments, oncology departments, and psychiatry departments. This study aims to discover decisions from logged data to improve the decision-making process. Methods: The Design Science Research Methodology (DSRM) was chosen to design an artifact (algorithm) for the discovery and visualization of decisions. The DSRM's different activities are explained, from the definition of the problem to the evaluation of the artifact. During the design and development activities, the algorithm itself is created. During the demonstration and evaluation activities, the algorithm was tested with an authentic synthetic dataset. Results: The results show the design and simulation of an algorithm for the discovery and visualization of decisions. A fuzzy classifier algorithm was adapted for (1) discovering decisions from a decision log and (2) visualizing the decisions using the Decision Model and Notation standard. Conclusions: In this paper, we show that decisions can be discovered from a decision log and visualized for the improvement of the decision-making process of healthcare professionals or to support the periodic evaluation of protocols and guidelines. [ABSTRACT FROM AUTHOR]

The scientific community has shown considerable interest in Industry 4.0 due to its capacity to revolutionise the manufacturing sector through digitalisation and data-driven decision-making. However, the actual implementation of Industry 4.0 within complex industrial settings presents obstacles that are typically beyond the scope of mainstream research articles. In this paper, a comprehensive case-study detailing our collaborative partnership with a leading medical device manufacturer is presented. The study traces its evolution from a state of limited digitalisation to the development of a digital intelligence platform that leverages data and machine learning models to enhance operations across a wide range of critical machines and assets. The main business objective was to enhance the energy efficiency of the manufacturing process, thereby improving its sustainability measures while also saving costs. The project encompasses energy modelling and analytics, Fault Detection and Diagnostics (FDD), renewable energy integration and advanced visualisation tools. Together, these components enable informed decision making in the context of energy efficiency. [ABSTRACT FROM AUTHOR]

The focus of this research was on building a decision support system for a model that characterizes the conflict interaction of n-dimensional complex systems with non-trivial internal structures. The interpretation of the new model was focused on information warfare as the impact of rare events that quickly change certain perceptions of a large number of people. Consequently, the support for various ideas experiences stochastic jumps, a phenomenon observable through a non-classical Levy approximation scheme. The essence of our decision support system lies in its ability to navigate the complex dynamics of conflict interaction among multifaceted systems. Through the utilization of advanced modeling techniques, our aim is to illuminate the complicated interplay of factors influencing information warfare and its cascading effects on societal perceptions and behaviors. Key components of our decision support system encompass model development, simulation capabilities, data integration, and visualization tools. The significance of our work lies in its potential to inform policy formulation, conflict resolution strategies, and societal resilience in the face of information warfare. By providing decision-makers with actionable intelligence and foresight into emerging threats and opportunities, our decision support system serves as a valuable tool for navigating the complexities of modern conflict dynamics. In conclusion, developing a decision support system for modeling conflict interaction in complex systems represents an essential step toward enhancing our understanding of information warfare and its consequences. Through interdisciplinary collaboration and innovative modeling techniques, we aim to provide stakeholders with the insights and capabilities needed to navigate the developing landscape of conflict and ensure the stability and resilience of society. [ABSTRACT FROM AUTHOR]

The work presents a tool enabling the selection of powder for 3D printing. The project focused on three types of powders, such as steel, nickel- and cobalt-based and aluminum-based. An important aspect during the research was the possibility of obtaining the mechanical parameters. During the work, the possibility of using the selected algorithm based on artificial intelligence like Random Forest, Decision Tree, K-Nearest Neighbors, Fuzzy K-Nearest Neighbors, Gradient Boosting, XGBoost, AdaBoost was also checked. During the work, tests were carried out to check which algorithm would be best for use in the decision support system being developed. Cross-validation was used, as well as hyperparameter tuning using different evaluation sets. In both cases, the best model turned out to be Random Forest, whose F1 metric score is 98.66% for cross-validation and 99.10% after tuning on the test set. This model can be considered the most promising in solving this problem. The first result is a more accurate estimate of how the model will behave for new data, while the second model talks about possible improvement after optimization or possible overtraining to the parameters. [ABSTRACT FROM AUTHOR]

Current malaria elimination targets must withstand a colossal challenge–resistance to the current gold standard antimalarial drug, namely artemisinin derivatives. If artemisinin resistance significantly expands to Africa or India, cases and malaria-related deaths are set to increase substantially. Spatial information on the changing levels of artemisinin resistance in Southeast Asia is therefore critical for health organisations to prioritise malaria control measures, but available data on artemisinin resistance are sparse. We use a comprehensive database from the WorldWide Antimalarial Resistance Network on the prevalence of non-synonymous mutations in the Kelch 13 (K13) gene, which are known to be associated with artemisinin resistance, and a Bayesian geostatistical model to produce spatio-temporal predictions of artemisinin resistance. Our maps of estimated prevalence show an expansion of the K13 mutation across the Greater Mekong Subregion from 2000 to 2022. Moreover, the period between 2010 and 2015 demonstrated the most spatial change across the region. Our model and maps provide important insights into the spatial and temporal trends of artemisinin resistance in a way that is not possible using data alone, thereby enabling improved spatial decision support systems on an unprecedented fine-scale spatial resolution. By predicting for the first time spatio-temporal patterns and extents of artemisinin resistance at the subcontinent level, this study provides critical information for supporting malaria elimination goals in Southeast Asia. Author summary: Resistance to artemisinin derivatives has been confirmed in the Greater Mekong Subregion, with worrying signs of spread in India and more recently emergence in Rwanda and Uganda. This situation is dire given the way that the emergence and spread of resistance to other antimalarial drugs, chloroquine and later sulphadoxine–pyrimethamine, resulted in dramatic increases in malaria-related morbidity and mortality across sub-Saharan Africa in the 1990s. To eliminate malaria, up-to-date maps of artemisinin resistance are urgently needed; predictive models of the spread of drug resistance can make far-reaching, significant, changes in our approach to malaria elimination by informing appropriate changes to drug policy. In this study, we have provided the first data-driven, predictive maps of the changing landscape of resistance to artemisinin derivatives in the Greater Mekong Subregion. These maps provide estimates where no data are available and can be used by health agencies to guide the prioritisation of surveillance for resistance, and policies to improve treatment and prevent the further spread of resistance. [ABSTRACT FROM AUTHOR]

Background: Inappropriate antimicrobial use, such as antibiotic intake in viral infections, incorrect dosing and incorrect dosing cycles, has been shown to be an important determinant of the emergence of antimicrobial resistance. Artificial intelligence-based decision support systems represent a potential solution for improving antimicrobial prescribing and containing antimicrobial resistance by supporting clinical decision-making thus optimizing antibiotic use and improving patient outcomes. Objective: The aim of this research was to examine implementation factors of artificial intelligence-based decision support systems for antibiotic prescription in hospitals from the perspective of the hospital managers, who have decision-making authority for the organization. Methods: An online survey was conducted between December 2022 and May 2023 with managers of German hospitals on factors for decision support system implementation. Survey responses were analyzed from 118 respondents through descriptive statistics. Results: Survey participants reported openness towards the use of artificial intelligence-based decision support systems for antibiotic prescription in hospitals but little self-perceived knowledge in this field. Artificial intelligence-based decision support systems appear to be a promising opportunity to improve quality of care and increase treatment safety. Along with the Human-Organization-Technology-fit model attitudes were presented. In particular, user-friendliness of the system and compatibility with existing technical structures are considered to be important for implementation. The uptake of decision support systems also depends on the ability of an organization to create a facilitating environment that helps to address the lack of user knowledge as well as trust in and skepticism towards these systems. This includes the training of user groups and support of the management level. Besides, it has been assessed to be important that potential users are open towards change and perceive an added value of the use of artificial intelligence-based decision support systems. Conclusion: The survey has revealed the perspective of hospital managers on different factors that may help to address implementation challenges for artificial intelligence-based decision support systems in antibiotic prescribing. By combining factors of user perceptions about the systems´ perceived benefits with external factors of system design requirements and contextual conditions, the findings highlight the need for a holistic implementation framework of artificial intelligence-based decision support systems. [ABSTRACT FROM AUTHOR]

Background: People with polycystic ovary syndrome suffer from many symptoms and are at risk of developing diseases such as hypertension and diabetes in the future. Therefore, the importance of self-care doubles. It is mainly to modify the lifestyle, especially following the principles of healthy eating. The purpose of this study is to review artificial intelligence-based systems for providing management recommendations, especially food recommendations. Materials and methods: This study started by searching three databases: PubMed, Scopus, and Web of Science, from inception until 6 June 2023. The result was the retrieval of 15,064 articles. First, we removed duplicate studies. After the title and abstract screening, 119 articles remained. Finally, after reviewing the full text of the articles and considering the inclusion and exclusion criteria, 20 studies were selected for the study. To assess the quality of articles, we used criteria proposed by Malhotra, Wen, and Kitchenham. Out of the total number of included studies, seventeen studies were high quality, while three studies were moderate quality. Results: Most studies were conducted in India in 2021. Out of all the studies, diagnostic recommendation systems were the most frequently researched, accounting for 86% of the total. Precision, sensitivity, specificity, and accuracy were more common than other performance metrics. The most significant challenge or limitation encountered in these studies was the small sample size. Conclusion: Recommender systems based on artificial intelligence can help in fields such as prediction, diagnosis, and management of polycystic ovary syndrome. Therefore, since there are no nutritional recommendation systems for these patients in Iran, this study can serve as a starting point for such research. [ABSTRACT FROM AUTHOR]

Background: Older adults with diabetes are at high risk of severe hypoglycemia (SH). Many machine-learning (ML) models predict short-term hypoglycemia are not specific for older adults and show poor precision-recall. We aimed to develop a multidimensional, electronic health record (EHR)-based ML model to predict one-year risk of SH requiring hospitalization in older adults with diabetes. Methods and findings: We adopted a case-control design for a retrospective territory-wide cohort of 1,456,618 records from 364,863 unique older adults (age ≥65 years) with diabetes and at least 1 Hong Kong Hospital Authority attendance from 2013 to 2018. We used 258 predictors including demographics, admissions, diagnoses, medications, and routine laboratory tests in a one-year period to predict SH events requiring hospitalization in the following 12 months. The cohort was randomly split into training, testing, and internal validation sets in a 7:2:1 ratio. Six ML algorithms were evaluated including logistic-regression, random forest, gradient boost machine, deep neural network (DNN), XGBoost, and Rulefit. We tested our model in a temporal validation cohort in the Hong Kong Diabetes Register with predictors defined in 2018 and outcome events defined in 2019. Predictive performance was assessed using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC) statistics, and positive predictive value (PPV). We identified 11,128 SH events requiring hospitalization during the observation periods. The XGBoost model yielded the best performance (AUROC = 0.978 [95% CI 0.972 to 0.984]; AUPRC = 0.670 [95% CI 0.652 to 0.688]; PPV = 0.721 [95% CI 0.703 to 0.739]). This was superior to an 11-variable conventional logistic-regression model comprised of age, sex, history of SH, hypertension, blood glucose, kidney function measurements, and use of oral glucose-lowering drugs (GLDs) (AUROC = 0.906; AUPRC = 0.085; PPV = 0.468). Top impactful predictors included non-use of lipid-regulating drugs, in-patient admission, urgent emergency triage, insulin use, and history of SH. External validation in the HKDR cohort yielded AUROC of 0.856 [95% CI 0.838 to 0.873]. Main limitations of this study included limited transportability of the model and lack of geographically independent validation. Conclusions: Our novel-ML model demonstrated good discrimination and high precision in predicting one-year risk of SH requiring hospitalization. This may be integrated into EHR decision support systems for preemptive intervention in older adults at highest risk. Using nearly 1.5 million health records from >350,000 older adults with diabetes in Hong Kong, Elaine Chow and colleagues investigate a novel machine learning model to predict risk of severe hypoglycaemia. Author summary: Why was this study done?: Older adults with diabetes are at high risk of severe hypoglycemia (SH) requiring hospitalization. Existing machine-learning (ML) models predict short-term hypoglycemia are not specific for older adults and show poor precision-recall. A simple tool to identify those at risk for developing SH in T2D is needed. What did the researchers do and find?: We included 1,456,618 records of 364,863 unique older adults (age ≥65 years) with diabetes and at least 1 Hong Kong Hospital Authority attendance in 2013 to 2018. We used 258 predictors including demographics, admissions, diagnoses, medications, and routine laboratory tests in a one-year period to predict SH events requiring hospitalization in the following 12 months. Six ML algorithms were evaluated including logistic-regression, random forest, gradient boost machine, deep neural network (DNN), XGBoost, and Rulefit. The XGBoost model yielded the best performance, superior to an 11-variable conventional logistic-regression model. What do these findings mean?: Our novel-ML model demonstrated good discrimination and high precision in predicting one-year risk of SH requiring hospitalization. This may be integrated into electronic health record (EHR) decision support systems for preemptive intervention in older adults at highest risk. A limitation of this study is the lack of model validation in independent cohorts outside Hong Kong. [ABSTRACT FROM AUTHOR]

Using deep learning has demonstrated significant potential in making informed decisions based on clinical evidence. In this study, we deal with optimizing medication and quantitatively present the role of deep learning in predicting the medication dosage for patients with Parkinson's disease (PD). The proposed method is based on recurrent neural networks (RNNs) and tries to predict the dosage of five critical medication types for PD, including levodopa, dopamine agonists, monoamine oxidase-B inhibitors, catechol-O-methyltransferase inhibitors, and amantadine. Recurrent neural networks have memory blocks that retain crucial information from previous patient visits. This feature is helpful for patients with PD, as the neurologist can refer to the patient's previous state and the prescribed medication to make informed decisions. We employed data from the Parkinson's Progression Markers Initiative. The dataset included information on the Unified Parkinson's Disease Rating Scale, Activities of Daily Living, Hoehn and Yahr scale, demographic details, and medication use logs for each patient. We evaluated several models, such as multi-layer perceptron (MLP), Simple-RNN, long short-term memory (LSTM), and gated recurrent units (GRU). Our analysis found that recurrent neural networks (LSTM and GRU) performed the best. More specifically, when using LSTM, we were able to predict levodopa and dopamine agonist dosage with a mean squared error of 0.009 and 0.003, mean absolute error of 0.062 and 0.030, root mean square error of 0.099 and 0.053, and R-squared of 0.514 and 0.711, respectively. [ABSTRACT FROM AUTHOR]

Digital Twins (DTs) are increasingly recognized for their potential to improve efficiency and decision-making in various domains of the built environment. Despite their promise, challenges like cost, complexity, interoperability, and data integration remain. This paper introduces a novel interactive visual analytics system that tackles these issues, using a case study of simulating class distribution and campus building capacity at a large public university. The system leverages enrollment data, converting it into a spatial-temporal format for interactive exploration and analysis of class distribution and resource utilization. Through case studies, we demonstrate the system's effectiveness, adaptability, and real-world applicability, highlighting its role in practical DT implementation for built environments. [ABSTRACT FROM AUTHOR]

K-Means clustering is commonly used for data clustering, but it suffers from limitations such as being prone to local optima and slow convergence, particularly when handling large medical files. The literature recommends employing metaheuristic algorithms in clustering studies to address these issues. This study aims to accurately diagnose diseases in four medical datasets (Dermatology, Diabetes, Parkinson's, and Thyroid) and increase the rate of correct diagnosis of diseases. We utilized optimization algorithms to assign weights to input parameters determining diseases in these datasets, thereby improving clustering performance. Our proposed model incorporates the Crow Search Algorithm, Tree Seed Algorithm, and Harris Hawks Optimization algorithms in a hybrid structure with K-Means. We conducted statistical evaluations using performance metrics. The study demonstrated that the hybrid Harris Hawks Optimization algorithm achieved the highest accuracy rate (97.19%) among the tested algorithms on the Dermatology dataset. The hybrid Crow Search Algorithm obtained a 96.29% accuracy rate on the Thyroid dataset, while the hybrid Tree Seed Algorithm achieved a 95.32% accuracy rate on the Dermatology dataset. This study offers significant benefits, including reduced staff workload, lower test costs, improved accuracy rates, and faster test results for detecting various diseases in medical datasets. [ABSTRACT FROM AUTHOR]

The increasing demand for a reliable internet network is very important to meet the needs of companies. However, currently the ranking of mobile internet and internet in Indonesia is still below global standards. Network congestion, which is a major contributor to low internet quality, causes various challenges such as service outages, communication failures, and decreased connection speeds. The study emphasizes the importance of implementing effective congestion management mechanisms. Focusing on the utilization of the simple path method and comparing its effectiveness with the Dijkstra algorithm in managing internet networks, this study aims to develop network traffic optimization methods and identify alternative routes to improve overall network performance, especially in complex traffic conditions, within the framework of a Decision Support System (DSS). The analysis showed that the use of Simple Path increased packet delivery rates threefold and reduced packet loss by half compared to the traditional Dijkstra method, with 58.54% of packets successfully delivered and a 41.46% reduction in packet loss. In addition, Simple Path facilitates the use of alternative routes for about 24% of total requests using alternative routes. Network graph exploration identifies solid points and analyzes the capacity on each network link. Twelve links show occupancy rates above 90%, indicating congestion, with NE2-4-KBL to NE3-KBL-HSI as the main cause of package delivery failures, accounting for about 70.6% of total failed requests. Simple Path analysis highlights about 46% of total failed requests, passing through this link. These findings emphasize the importance of congestion management strategies and the use of alternative routes to improve network performance and reduce packet loss, thereby contributing to business efficiency, user experience, and customer satisfaction. [ABSTRACT FROM AUTHOR]

Amidst the US overdose epidemic, policymakers, law enforcement agencies, and healthcare institutions have contributed to a decrease in opioid prescribing, assuming reduced mortality would result—an assumption we now understand was oversimplified. At this intersection between public health and public safety domains as they relate to opioid prescribing, unregulated and proprietary clinical decision support tools have emerged without rigorous external validation or public data sharing. In the following piece, we discuss challenges facing clinicians practicing medicine amidst unregulated clinical decision support tools, using the case of Bamboo Health's NarxCare—a prescription drug monitoring program–based analytics platform marketed as a clinical decision support tool—that is already positioned to impact over 1 billion patient encounters annually. We argue that sufficient evidence does not yet exist to support NarxCare's wide implementation, and that clinical decision support tools like NarxCare have flourished in recent years due to a lack of federal regulatory oversight and shielding by their proprietary formulas, which have facilitated their unchecked and outsized influence on patient care. Finally, we suggest specific actions by federal regulatory agencies, healthcare institutions, individual clinicians, and researchers, as well as academic journals, to mitigate potential harms associated with unregulated clinical decision support tools. [ABSTRACT FROM AUTHOR]

This paper discusses the newly developed Decision Support System version 1.0 (DSS v1.0) for air quality management activities in Delhi, India. In addition to standard air quality forecasts, DSS provides the contribution of Delhi, its surrounding districts, and stubble-burning fires in the neighboring states of Punjab and Haryana to the PM2.5 load in Delhi. DSS also quantifies the effects of local and neighborhood emission-source-level interventions on the pollution load in Delhi. The DSS-simulated Air Quality Index for the post-monsoon and winter seasons of 2021–2022 shows high accuracy (up to 80 %) and a very low false alarm ratio (∼ 20 %) from day 1 to day 5 of the forecasts, especially when the ambient air quality index (AQI) is > 300. During the post-monsoon season (winter season), emissions from Delhi, the rest of the National Capital Region (NCR)'s districts, biomass-burning activities, and all other remaining regions on average contribute 34.4 % (33.4 %), 31 % (40.2 %), 7.3 % (0.1 %), and 27.3 % (26.4 %), respectively, to the PM2.5 load in Delhi. During peak pollution events (stubble-burning periods or wintertime), however, the contribution from the main sources (farm fires in Punjab–Haryana or local sources within Delhi) could reach 65 %–69 %. According to DSS, a 20 % (40 %) reduction in anthropogenic emissions across all NCR districts would result in a 12 % (24 %) reduction in PM2.5 in Delhi on a seasonal mean basis. DSS is a critical tool for policymakers because it provides such information daily through a single simulation with a plethora of emission reduction scenarios. [ABSTRACT FROM AUTHOR]

Simple Summary: Early detection of malignant melanoma (MM) has the potential to significantly reduce morbidity and mortality as the thickness of the lesion is closely associated with prognosis. Artificial intelligence (AI) is a non-invasive technology that has the potential to aid clinicians in the early diagnosis of skin cancers, including melanoma. Performance metrics of machine-based AI have been shown to rival or improve upon clinician diagnosis. However, many studies have limited this scope to pre-build image databases that may not replicate real-world settings experienced in general practice. This systematic review aimed to report the performance of commercial or market-approved dermatoscopic systems with AI in the form of convolutional neural networks (CNNs) when tasked with classifying melanoma. The sensitivity and specificity of CNNs are highly varied and illustrate the necessity of clinician-to-patient interaction in the diagnosis process. Clinicians working in unison with AI show the most promise for better performance when classifying MM. Background: Cutaneous melanoma remains an increasing global public health burden, particularly in fair-skinned populations. Advancing technologies, particularly artificial intelligence (AI), may provide an additional tool for clinicians to help detect malignancies with a more accurate success rate. This systematic review aimed to report the performance metrics of commercially available convolutional neural networks (CNNs) tasked with detecting MM. Methods: A systematic literature search was performed using CINAHL, Medline, Scopus, ScienceDirect and Web of Science databases. Results: A total of 16 articles reporting MM were included in this review. The combined number of melanomas detected was 1160, and non-melanoma lesions were 33,010. The performance of market-approved technology and clinician performance for classifying melanoma was highly heterogeneous, with sensitivity ranging from 16.4 to 100.0%, specificity between 40.0 and 98.3% and accuracy between 44.0 and 92.0%. Less heterogeneity was observed when clinicians worked in unison with AI, with sensitivity ranging between 83.3 and 100.0%, specificity between 83.7 and 87.3%, and accuracy between 86.4 and 86.9%. Conclusion: Instead of focusing on the performance of AI versus clinicians for classifying melanoma, more consistent performance has been obtained when clinicians' work is supported by AI, facilitating management decisions and improving health outcomes. [ABSTRACT FROM AUTHOR]

Background: This study aimed to evaluate the clinical impact of an artificial intelligence (AI)-based decision support system (DSS), Koios DS, on the analysis of ultrasound imaging and suspicious characteristics for thyroid nodule risk stratification. Methods: A retrospective ultrasound study was conducted on all thyroid nodules with histological findings from June 2021 to December 2022 in a thyroid nodule clinic. The diagnostic performance of ultrasound imaging was evaluated by six readers on the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) before and after the use of the AI-based DSS and by AI itself. Results: A total of 172 patients (83.1% women) with a mean age of 52.3 ± 15.3 years were evaluated. The mean maximum nodular diameter was 2.9 ± 1.2 cm, with 11.0% being differentiated thyroid carcinomas. Among the nodules initially classified as ACR TI-RADS 3 and 4, AI reclassified 81.4% and 24.5% into lower risk categories, respectively. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the readers and the AI-based DSS versus histological diagnosis. There was an increase in the area under the ROC curve (AUROC) after the use of AI (0.776 vs. 0.817, p < 0.001). The AI-based DSS improved the mean sensitivity (Sens) (82.3% vs. 86.5%) and specificity (Spe) (38.3% vs. 54.8%), produced a high negative predictive value (94.5% vs. 96.4%), and increased the positive predictive value (PPV) (14.0% vs. 16.1%) and diagnostic precision (43.0% vs. 49.3%). Based on the ACR TI-RADS score, there was significant improvement in interobserver agreement after the use of AI (r = 0.741 for ultrasound imaging alone vs. 0.981 for ultrasound imaging and the AI-based DSS, p < 0.001). Conclusions: The use of an AI-based DSS was associated with overall improvement in the diagnostic efficacy of ultrasound imaging, based on the AUROC, as well as an increase in Sens, Spe, negative and PPVs, and diagnostic accuracy. There was also a reduction in interobserver variability and an increase in the degree of concordance with the use of AI. AI reclassified more than half of the nodules with intermediate ACR TI-RADS scores into lower risk categories. [ABSTRACT FROM AUTHOR]

The National Weather Service (NWS) Office of Water Prediction (OWP), in conjunction with the National Center for Atmospheric Research and the NWS National Centers for Environmental Prediction (NCEP) implemented version 2.1 of the National Water Model (NWM) into operations in April of 2021. As with the initial version implemented in 2016, NWM v2.1 is an hourly cycling analysis and forecast system that provides streamflow guidance for millions of river reaches and other hydrologic information on high‐resolution grids. The NWM provides complementary hydrologic guidance at current NWS river forecast locations and significantly expands guidance coverage and water budget information in underserved locations. It produces a full range of hydrologic fields, which can be leveraged by a broad cross section of stakeholders ranging from the emergency responder and water resource communities, to transportation, energy, recreation and agriculture interests, to other water‐oriented applications in the government, academic and private sectors. Version 2.1 of the NWM represents the fifth major version upgrade and more than doubles simulation skill with respect to hourly streamflow correlation, Nash Sutcliffe Efficiency, and bias reduction, over its original inception in 2016. This paper will discuss the driving factors underpinning the creation of the NWM, provide a brief overview of the model configuration and performance, and discuss future efforts to improve NWM components and services. [ABSTRACT FROM AUTHOR]

Background: Electronic clinical decision support systems (eCDSS), such as the 'Systematic Tool to Reduce Inappropriate Prescribing' Assistant (STRIPA), have become promising tools for assisting general practitioners (GPs) with conducting medication reviews in older adults. Little is known about how GPs perceive eCDSS-assisted recommendations for pharmacotherapy optimization. The aim of this study was to explore the implementation of a medication review intervention centered around STRIPA in the 'Optimising PharmacoTherapy In the multimorbid elderly in primary CAre' (OPTICA) trial. Methods: We used an explanatory mixed methods design combining quantitative and qualitative data. First, quantitative data about the acceptance and implementation of eCDSS-generated recommendations from GPs (n = 21) and their patients (n = 160) in the OPTICA intervention group were collected. Then, semi-structured qualitative interviews were conducted with GPs from the OPTICA intervention group (n = 8), and interview data were analyzed through thematic analysis. Results: In quantitative findings, GPs reported averages of 13 min spent per patient preparing the eCDSS, 10 min performing medication reviews, and 5 min discussing prescribing recommendations with patients. On average, out of the mean generated 3.7 recommendations (SD=1.8). One recommendation to stop or start a medication was reported to be implemented per patient in the intervention group (SD=1.2). Overall, GPs found the STRIPA useful and acceptable. They particularly appreciated its ability to generate recommendations based on large amounts of patient information. During qualitative interviews, GPs reported the main reasons for limited implementation of STRIPA were related to problems with data sourcing (e.g., incomplete data imports), preparation of the eCDSS (e.g., time expenditure for updating and adapting information), its functionality (e.g., technical problems downloading PDF recommendation reports), and appropriateness of recommendations. Conclusions: Qualitative findings help explain the relatively low implementation of recommendations demonstrated by quantitative findings, but also show GPs' overall acceptance of STRIPA. Our results provide crucial insights for adapting STRIPA to make it more suitable for regular use in future primary care settings (e.g., necessity to improve data imports). Trial registration: Clinicaltrials.gov NCT03724539, date of first registration: 29/10/2018. [ABSTRACT FROM AUTHOR]

Honeybees are essential to human society, providing pollination services globally as well as producing honey and other valuable products. Effective management of apiaries should not only rely on beekeeper knowledge and skill, but also incorporate new information technologies. The options to identify, predict and prevent beekeeping problems are becoming more affordable and applicable. The interdisciplinary Horizon 2020 project HIVEOPOLIS focuses on developing a new approach in beekeeping, by creating novel mechatronic beehives and implementing new bio-hybrid ideas. These intelligent beehives aim to help honeybees to cope with adverse environmental factors and increase the survival rate of the bee colonies. This paper focuses on the software architecture design for these intelligent beehives, providing infrastructure for data management and decision support system operation. The presented infrastructure is suitable for highly dynamic and diverse environments where a multitude of components interact and exchange information across technology domains (embedded, cloud, UIs) in a reliable and secure way. Besides user support, the decision support system built upon this infrastructure enables closed-loop automated decision making and control. • Intelligent beehives comprise diverse sensors and robotic actuators. • A data processing architecture for these futuristic hive systems is presented. • A system of intelligent hives integrated into a network of online microservices. • Remote decision support and automated actions are provided for beekeepers. [ABSTRACT FROM AUTHOR]

To strengthen property management and to solve the problem for emergency material transport under the sudden emergencies, the property management company redesigned an optimization method of rescue vehicle route choice with considering the transport time (timeliness of emergency logistics) and the time penalty cost (service satisfaction) for the residential communities it serves. The system is solved based on Genetic Algorithm. Also, the demand urgencies of each area were determined with improved Gray Relative Analysis (IGRA), while total demands of each point were calculated using combination of triangular fuzzy number theory and PERT (TFN-PERT). In addition, some parameters will be corrected to compare the running results before and after the optimization. Synchronously. And the objective function values, which were calculated with consideration of demand urgency and without considering it respectively, also are compared. The effectiveness and feasibility of this approach were verified. The result manifests that the model can contribute to both timeliness and service satisfaction. Regarding the calculation of urgency, which can lessen the blindness of dispatching and ensure the fairness of distribution to some extent. Research result combines with the specific process of transportation vehicles under the sudden emergencies. As such, the approach could furnish scientific decision support on emergency system authentically for property management companies. [ABSTRACT FROM AUTHOR]

Irrigation decision support systems evolving in the domestic temperate tree fruit production industry incorporate measures of soil moisture status, which diverges from classic physiological indicators of edaphic stress. This study used an autonomous sensor-based irrigation system to impose a water deficit (soil matric potential targets of –25, –40, –60, and –80 kPa) on ‘Autumn Gala’, ‘CrimsonCrisp’, and ‘Golden Delicious’ apple (Malus domestica) cultivars grafted to ‘Budagovsky 9’ rootstock in the greenhouse (n 5 60). It was hypothesized that relationships between physiological plant function, assessed via infrared gas exchange and chlorophyll fluorescence, and the soil matric potential may be used to advance emerging irrigation decision support systems. Complications arising from defoliation by day 11 at –60 and –80 kPa indicate the generation of substrate-specific soil–water relationships in research applications of autonomous sensor-based irrigation systems. ‘Autumn Gala’ carbon assimilation rates at –80 kPa declined from day 0 to day 8 (9.93 and 5.86 lmolμm–2 μs –1 carbon dioxide), whereas the transpiration rate was maintained, potentially reducing observed defoliation as other cultivars increased transpiration to maintain carbon assimilation. Correlation matrices revealed Pearson’s r ≤ |0.43| for all physiological metrics considered with soil matric potential. Nevertheless, exploratory regression analysis on predawn leaf water potential, carbon assimilation, transpiration, stomatal conductance, and nonphotochemical quenching exposed speculatively useful data and data shapes that warrant additional study. Nonlinear piecewise regression suggested soil matric potential may useful as a predictor for the rate of change in predawn leaf water potential upon exposure to a water deficit. The critical point bridging the linear spans, –30.6 kPa, could be useful for incorporating in emerging irrigation decision support systems. [ABSTRACT FROM AUTHOR]