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This study reports the test results of a two-stage stochastic linear programming (SLP) model with recourse using a user-friendly generic decision support system (DSS) in a North American steel company. This model has the flexibility to configure multiple material facilities, activities and storage areas in a multi-period and multi-scenario environment. The value of stochastic solution (VSS) with a real-world example has a potential benefit of US$ 24.61 million. Experiments were designed according to the potential joint probability distribution scenarios and the magnitude of demand variability. Overall, 144 SLP optimisation model instances were solved across four industries, namely, steel, aluminium, polymer and pharmaceuticals. The academic contribution of this research is two-fold: first, the potential contribution to profit in a steel company using an SLP model; and second, the optimisation empirical experiments confirm a pattern that the VSS and expected value of perfect information (EVPI) increase with the increase in demand variability. This study has implications for practicing managers seeking business solutions with prescriptive analytics using stochastic optimisation-based DSS. This study will attract more industry attention to business solutions, and the prescriptive analytics discipline will garner more scholarly and industry attention. [ABSTRACT FROM AUTHOR]

• A comprehensive list of risk parameters impacting the safety assessment of tower crane operations is put forward. • A detailed list of safety risks threatening the workers working with tower crane is put forward. • A hybrid safety risk assessment model for analyzing and evaluating related safety risks is developed. • A Knowledge-based decision support system safety risk assessment for automating the whole related assessment procedure is developed. • The efficacy of the developed prototype is validated through its application to several construction projects. Introduction: Tower cranes are commonly employed in construction projects, despite presenting significant hazards to the workforce involved. Method: To address these safety concerns, a Knowledge-Based Decision-Support System for Safety Risk Assessment (KBDSS-SRA) has been developed. The system's capacity to thoroughly evaluate associated risks is illustrated through its utilization in various construction endeavors. Results: The system accomplishes the following goals: (1) compiles essential risk factors specific to tower crane operations, (2) identifies critical safety risks that jeopardize worker well-being, (3) examines and assesses the identified safety risks, and (4) automates the labor-intensive and error-prone processes of safety risk assessment. The KBDSS-SRA assists safety management personnel in formulating well-grounded decisions and implementing effective measures to enhance the safety of tower crane operations. Practical Applications: This is facilitated by an advanced computerized tool that underscores the paramount significance of safety risks and suggests strategies for their future mitigation. [ABSTRACT FROM AUTHOR]

Emergency Medical Services play a fundamental societal role since they provide pre-hospital care to patients in critical and time-constrained conditions. In this context, the hospital selection decision is vital to respond in a timely, effective and efficient way. By means of simulation experiments, the present study compares the effects of different assignment logics against a set of performance dimensions. To this end, we develop a Discrete Event Simulation model that covers the entire Emergency Medical Service process, from the ambulance transportation to final departure of the patient from the Emergency Department. Results demonstrate that the use of effectiveness-related criteria (e.g. expected waiting time, resource saturation) significantly enhances the overall performance and, at the same time, guarantees higher quality of care. Thus, the study contributes in enhancing hospital selection decisions by supporting decision-makers through a comprehensive decisional model, distancing from prior researches that predominantly considered efficiency-driven criteria (i.e. proximity). [ABSTRACT FROM AUTHOR]

Nowadays, urban and suburban areas face increasing environmental pressures, and encouraging sustainable transportation behaviors at the household level has become crucial. This paper presents a model of a decision support system (DSS) for promoting sustainable household mobility choices in multi-modal transport networks. The system was modeled using an enhanced Petri Net approach, allowing for the dynamic representation of complex transport networks and multi-modal journey options. The model incorporated various sustainability factors. These were combined into a single environmental impact score, which was considered alongside travel time and cost in the route optimization process. Simulation results demonstrated the DSS's capability to guide users toward more sustainable mobility choices. The model also showed potential as a tool for policymakers to assess the impact of various sustainable transportation initiatives and infrastructure investments. This paper discussed the versatile applications of the system. It also addressed the limitations of Petri Net models in transportation systems and suggested future research directions. [ABSTRACT FROM AUTHOR]

In today's advanced applications, such as memory interfaces, feature-based detection, and sensory games, human-computer interaction (HCI) plays a pivotal role. A medical decision support system (MDSS) emerges from the integration of a data system with resources for medical decision-making. Within MDSS, human-computer interaction and perceptual medical decision-making stand out as two highly valuable technologies. Systems enabled by the Internet of Things (IoT), which leverage decentralized, diverse communication and networking technology to cater to a wide range of end-users, are referred to as pervasive computing. A challenging aspect of pervasive computing is ensuring transparency in interaction, managing administration levels, and accommodating varying tolerance levels for widely dispersed users. This paper presents a uniquely flexible MDSS framework designed to enhance end-user confidence in the availability of MDSS through ubiquitous IoT devices within the context of HCI. This architecture utilizes recurring training to assess resource allocation based on demand and collaborative characteristics. Projected resource requirements enable pervasive computing to better serve end-users by reducing latency and increasing communication speeds for MDSS in HCI. The primary goal of this framework is to simplify the management of terminal transitions by facilitating the allocation and utilization of resources for data transfer from peripheral technology. Experimental analysis is employed to estimate the framework's performance, utilizing various metrics to demonstrate its consistency. These metrics encompass responsiveness, transaction success rates, processed demands, application caseloads, capacity utilization, and memory usage. The uniquely flexible and distributed computing framework optimizes request handling, network accuracy, and memory utilization, resulting in reduced transaction failures and lower latency, ultimately leading to shorter response times. The proposed UFDSS maintains a transaction failure rate below 25% with increasing requests and achieves 100 MHz bandwidth utilization, surpassing other techniques capped at 80 MHz. UFDSS exhibits a lower average latency of around 30 ms for a range of energy data inputs. This uniquely flexible MDSS framework showcases its potential to enhance MDSS availability through IoT devices within HCI contexts. By optimizing resource allocation and utilization, it successfully reduces latency, improves communication speeds, and ultimately leads to shorter response times, contributing to more efficient and reliable medical decision support. Further, integrating generative AI into MDSS for IoT-based HCI could also enhance data-driven decision support. [ABSTRACT FROM AUTHOR]

A cutting-edge Decision Support System (DSS) utilizing Deep Reinforcement Learning (DRL) for improved financial data analysis is the primary focus of the proposed research. In light of the prospering difficulties presented by large information in the monetary space, our creative methodology outfits the force of DRL to foster a powerful and versatile framework. By flawlessly incorporating DRL into the DSS structure, we mean to improve the framework's capacity to break down huge and complex monetary datasets. This DSS not only provides financial professionals with intelligent decision-making support but also real-time insights into market trends and patterns. The collaboration between enormous information investigation and DRL works with a dynamic and responsive framework equipped for adjusting to the quickly developing financial scene. Our exploration adds to the headway of choice by tending to the particular requests of monetary information, consequently enabling clients with ideal and informed dynamic abilities. The proposed DRL-based DSS addresses a change in perspective in monetary information examination, offering a versatile and effective answer for exploring the intricacies of enormous information in the financial area. This examination holds huge potential for changing dynamic cycles, advancing monetary security, and at last adding to the progression of the more extensive monetary industry. [ABSTRACT FROM AUTHOR]

In sheltered employment centres, staff planning and scheduling activities are critical for operations managers. A generic framework is not easy to set up not only because legal issues are diverse and differ among these service organization types, but one worker may not fit in anywhere at any time. This complexity is greater when workers with specific needs perform work activities in many labour enclaves and different sectors. In this paper, a mixed-integer linear model to solve workers' shift assignments to other workplaces and various activities to form teams is proposed. The novelty of the proposed model lies in considering specific features, such as the skills matrix and the affinity matrix, between the different actors in a labour enclave. The model is validated using real instances from a case study, and several objectives are tested and discussed. The decision support system that sustains the model is introduced and managerial issues are discussed. [ABSTRACT FROM AUTHOR]

With the continuous rise in the count of deadly diseases that risk either human life or health, the medical Decision Support System keeps proving its efficiency in providing healthcare professionals and other physicians with support in making clinical decisions. Diabetes mellitus is assumed that chronic disease where the body doesn't produce the essential amount of insulin or insulin is not utilized well by the body, which leads to extremely higher glucose (blood sugar) levels. At the same time, once diabetes has been left untreated or undetected, it may cause severe harm to the body and makes it challenging to treat, but earlier diabetes diagnosis may result in better treatment, giving rise to lower death and morbidity. The Binary Bald Eagle Search Algorithm with Optimal Fuzzy Rule-based Classifier algorithm is meticulously designed to achieve highly effective diabetes detection and classification. It incorporates a Binary Bald Eagle Search Algorithm for optimal feature subset selection and employs the Fuzzy Rule-based Classifier for diabetes detection. To further enhance its performance, the algorithm utilizes sand cat swarm optimization to optimize the parameter values of the Fuzzy Rule-based Classifier. Extensive experimentation on benchmark diabetes medical datasets demonstrates the superiority of the Binary Bald Eagle Search Algorithm—Optimal Fuzzy Rule-based Classifier approach over state-of-the-art models. The main objective of the Binary Bald Eagle Search Algorithm—Optimal Fuzzy Rule-based Classifier algorithm is to accomplish effective detection and classification of diabetes. To achieve this, the Binary Bald Eagle Search Algorithm—Optimal Fuzzy Rule-based Classifier technique primarily designs a Binary Bald Eagle Search Algorithm system for the optimal selection of feature subsets. Additionally, the detection of diabetes takes place using the Fuzzy Rule-based Classifier technique. Furthermore, the sand cat swarm optimization system was applied to optimise the Fuzzy Rule-based Classifier algorithm's parameter values. A wide range of experimental analyses is carried out on benchmark diabetes medical datasets, and the outcome was examined under many aspects. The experimental outcome portrayed the greater of the Binary Bald Eagle Search Algorithm—Optimal Fuzzy Rule-based Classifier approach over the state of art models. [ABSTRACT FROM AUTHOR]

The paper presents a novel decision support system designed to improve the efficiency and effectiveness of decision-making for electric arc furnace (EAF) operators. The system integrates two primary tools: the EAF Simulator, which is based on advanced mechanistic models, and the EAF Optimiser, which uses data-driven models trained on historical data. These tools enable the simulation and optimisation of furnace settings in real time and provide operators with important insights. A key objective was to develop a user-friendly interface with the Siemens Insights Hub Cloud Service and Node-RED that enables interactive management and support. The interface allows operators to analyse and compare past and simulated batches by adjusting the input data and parameters, resulting in improved optimisation and reduced costs. In addition, the system focuses on the collection and pre-processing of input data for the simulator and optimiser and uses Message Queuing Telemetry Transport (MQTT)communication between the user interfaces and models to ensure seamless data exchange. The EAF Simulator uses a comprehensive mathematical model to simulate the complex dynamics of heat and mass transfer, while the EAF Optimiser uses a fuzzy logic-based approach to predict optimal energy consumption. The integration with Siemens Edge Streaming Analytics ensures robust data collection and real-time responsiveness. The dual-interface design improves user accessibility and operational flexibility. This system has significant potential to reduce energy consumption by up to 10% and melting times by up to 15%, improving the efficiency and sustainability of the entire process. [ABSTRACT FROM AUTHOR]

Disease management decision support systems (DSS) are typically prediction algorithms that help farmers assess the risk of an epidemic, to guide whether, and to what extent, fungicide treatment is needed. However, there is frequently little information presented to quantify the value of using the DSS, i.e. the likely increased profit or reduced impact to the environment, and the risks of failing to control the pest. Validation of DSS is often limited to a small number of sites and seasons, as extensive field testing is prohibitively expensive. It would therefore be beneficial to have a method to estimate the value of a DSS using existing data sets gathered for other purposes. We present a theoretical framework for evaluating the value of DSS, and then describe how this can be applied in practice using four case studies of contrasting DSS under different data constraints. The four case studies include DSS that guide (i) the total dose of pesticide applied; (ii) the number of sprays required; (iii) the timing of the first fungicide application in a spray programme; and (iv) infection risk alerts. We demonstrate how our theoretical framework can be used to evaluate DSS, using existing field and literature data to infer the benefits and risks associated with their use. The limitations of using existing data are explored. [ABSTRACT FROM AUTHOR]

The use of pesticides is an efficient approach for pest management. However, their increasing application in recent decades has come under the spotlight of world policies. In this context, this study addresses the usefulness of a forecasting model (TOMCAST) combined with aerobiological information and a plant development model (physiological days, PDays) for the control of early blight in potatoes in Northwest Spain. Control plots were compared to treated plots, according to the original TOMCAST model and the daily Alternaria spp. concentration, meteorological factors, and phenological and epidemiological observations were monitored for better adjustment of the TOMCAST model to the weather conditions of the geographical area during three crop seasons. The results of the linear regression analysis showed a strong relationship between the parameters included in TOMCAST (leaf wetness and temperature) and the Alternaria spp. conidia concentration. In addition, an unbalanced pattern of trapped conidia was detected throughout the growing season, with an increase near the flowering stage. The epidemiological parameters (infection period, r-AUDPC, maximum severity value, and total and commercial yields) showed significant differences between the cultivars in the control and the TOMCAST plots in terms of r-AUDPC and the maximum severity value. Given the study's results, the original TOMCAST model was improved with aerobiological and phenological information. The improved model recommends a first spray on a day when the following three requirements are met: Ten accumulated disease severity values (DSVs) according to the TOMCAST model, two days with an aerobiological level greater than 10 conidia/m3, and a PDays value greater than 200. This will reduce the number of fungicide treatments used to control early blight in potato crops, promoting the principles of sustainable agriculture. [ABSTRACT FROM AUTHOR]

The industrialisation of agriculture and changes in production systems have contributed to a biodiversity decline worldwide. Developing accurate and adequate assessment methods can encourage farmers to support more sustainable agricultural management. This study presents a decision support system to promote agrobiodiversity that incorporates not only a quantitative assessment of relevant indicators of agrobiodiversity performance but also provides enhancement practice recommendations and associated benefits, presenting an action plan in order of priority. Additionally, the decision support system allows a visual comparison between biodiversity composite indicators and indicators representing pest control and crop yield. Since grape cultivation is considered one of the most intensive agricultural systems, thus significantly impacting biodiversity, the elaborated decision support system was tested on a viticultural agroecosystem in the demarcated Douro region in Portugal. The results demonstrated the decision support system functioning according to the selected methodology and allowed the identification of future lines for investigation. During the analysed period, the following were verified: an increase of 2% in the biodiversity indicator, 130% in harvest yield, and 2077% in the enemy-to-pest ratio. It is expected that the elaborated DSS will offer a significant contribution by bridging communication gaps on alternative management options to improve biodiversity performance in agricultural systems. [ABSTRACT FROM AUTHOR]

The present study has been designed to establish the thermal profile of complete life cycle of the Antheraea assamensis (muga) silkworms starting from the eggs to the moth. The current study also concurrently addresses the applicability of infrared thermography (IRT) to detect muga larvae infected with pebrine. Because of its non-contact advantage, IRT imaging can be successfully manoeuvred to reduce the risk of spreading infections and ultimately lead to increased silk production. The images and data obtained by the non-invasive IRT technology can be implemented, analysed and translated into deep learning algorithm-based machine learning called 'smart data', which can be fine-tuned to develop an artificial intelligence (AI) and decision support system (DSS) for the management and monitoring of silkworms in their entire life progression. Hence, this will boost their productivity, which will further revolutionize their yearly production and convey attractive revenues in the global silk picturesque. [ABSTRACT FROM AUTHOR]

Medical support in crisis situations is a major challenge. Efficient implementation of the medical evacuation process especially in operations with limited human resources that may occur during armed conflicts can limit the loss of these resources. Proper evacuation of wounded soldiers from the battlefield can increase the chances of their survival and rapid return to further military operations. This paper presents the technical details of the decision support system for medical evacuation to support this process. The basis for the functioning of this system is the continuous measurement of vital signs of soldiers via a specialized measurement module with a set of medical sensors. Vital signs values are then transmitted via the communication module to the analysis and inference module, which automatically determines the color of medical triage and the soldier's chance of survival. This paper presents the results of tests of our system to validate it, which were carried out using test vectors of soldiers' vital signs, as well as the results of the system's performance on a group of volunteers who performed typical activities of tactical operations. The results of this study showed the usefulness of the developed system for supporting military medical services in military operations. [ABSTRACT FROM AUTHOR]

Water conservation has become a global problem as the population increases. In many densely populated cities in China, leaks from century-old pipe works have been widespread. However, entirely eradicating the issues involves replacing all water networks, which is costly and time-consuming. This paper proposed an AI-enabled water-saving control system with three control modes: time division control, flow regulation, and critical point control according to actual flow. Firstly, based on the current leaking situation of water supply networks in China and the capability level of China's water management, a water-saving technology integrating PID control and a series of deep learning algorithms was proposed. Secondly, a multi-jet control valve was designed to control pressure and reduce water distribution network cavitation. This technology has been successfully applied in industrial settings in China and has achieved gratifying water-saving results. [ABSTRACT FROM AUTHOR]

The growth in the number of people suffering from eye diseases determines the relevance of research in the field of diagnosing retinal pathologies. Artificial intelligence models and algorithms based on measurements obtained via electrophysiological methods can significantly improve and speed up the analysis of results and diagnostics. We propose an approach to designing an artificial intelligent diagnosis system (AI diagnosis system) which includes an electrophysiological complex to collect objective information and an intelligent decision support system to justify the diagnosis. The task of diagnosing retinal diseases based on a set of heterogeneous data is considered as a multi-class classification on unbalanced data. The decision support system includes two classifiers—one classifier is based on a fuzzy model and a fuzzy rule base (RB-classifier) and one uses the stochastic gradient boosting algorithm (SGB-classifier). The efficiency of algorithms in a multi-class classification on unbalanced data is assessed based on two indicators—MAUC (multi-class area under curve) and MMCC (multi-class Matthews correlation coefficient). Combining two algorithms in a decision support system provides more accurate and reliable pathology identification. The accuracy of diagnostics using the proposed AI diagnosis system is 5–8% higher than the accuracy of a system using only diagnostics based on electrophysical indicators. The AI diagnosis system differs from other systems of this class in that it is based on the processing of objective electrophysiological data and socio-demographic data about patients, as well as subjective information from the anamnesis, which ensures increased efficiency of medical decision-making. The system is tested using actual data about retinal diseases from the Russian Institute of Eye Diseases and its high efficiency is proven. Simulation experiments conducted in various scenario conditions with different combinations of factors ensured the identification of the main determinants (markers) for each diagnosis of retinal pathology. [ABSTRACT FROM AUTHOR]

Internal audit is an independent function that provides objective assurance and advisory services to business partners in the form of projects. As the skilled human resources, internal auditors play a significant role in ensuring audit quality and thus should be managed properly. However, there are only very few studies on auditor-project assignment problems at the operational level. Also, many studies on general team formation problems neglect the real-life implications of simplifying mathematical models to enable practitioners to understand and implement. To bridge these gaps, this study presents a new integrated two-phase approach to assign internal auditors to multiple projects. In the designed decision support model, a harmonious level index is firstly proposed, measuring the suitability between auditor capability and project characteristics using a simplified fuzzy filter ranking method. Then, Min–Max multi-choice goal programming (MMCGP) model provides the assignment solution that balances internal auditors' multi-dimensional fit to the projects and their preferences, and thus optimizes the productivity of internal audit projects and staff's job satisfaction. A real case study is presented to illustrate the effectiveness of the proposed model and its solution. The comparison results further show that the proposed model outperforms the manual operations and the exiting approaches in the literature. This research extends the existing literature on auditor assignment problem by developing a flexible decision support tool and a practical classification of auditor competency. The MMCGP approach also provides an ideal staff assignment solution by setting multiple aspiration levels for each goal and balancing the unwanted deviations among goals. [ABSTRACT FROM AUTHOR]

In the context of isolated photovoltaic (PV) installations, selecting the optimal combination of modules and batteries is crucial for ensuring efficient and reliable energy supply. This paper presents a Decision Support System (DSS) designed to aid in the selection process of the development of new PV isolated installations. Two different multi-criteria decision-making (MCDM) approaches are employed and compared: AHP (Analytic Hierarchy Process) combined with TOPSIS (technique for order of preference by similarity to ideal solution) and Entropy combined with TOPSIS. AHP and Entropy are used to weight the technical and economic criteria considered, and TOPSIS ranks the alternatives. A comparative analysis of the AHP + TOPSIS and Entropy + TOPSIS methods was conducted to determine their effectiveness and applicability in real-world scenarios. The results show that AHP and Entropy produce contrasting criteria weights, yet TOPSIS converges on similar top-ranked alternatives using either set of weights, with the combination of lithium-ion batteries with the copper indium gallium selenide PV module as optimal. AHP allows for the incorporation of expert subjectivity, prioritising costs and an energy yield intuitive to PV projects. Entropy's objectivity elevates criteria with limited data variability, potentially misrepresenting their true significance. Despite these discrepancies, this study highlights the practical implications of using structured decision support methodologies in optimising renewable energy systems. Even though the proposed methodology is applied to a PV isolated system, it can effectively support decision making for optimising other stand-alone or grid-connected installations, contributing to the advancement of sustainable energy solutions. [ABSTRACT FROM AUTHOR]

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The competitiveness of a tourist destination is related to its capacity to present products and services better than others, providing tourist experiences that meet the preferences of those who demand them. However, how tourists decide which destination to visit is a question that needs to be answered to support all destination stakeholders, and this is the aim of this research. This paper uses the Multi-Criteria Decision-Making approach to prioritise the types of tourism. The method applied is the Analytic Hierarchy Process, and the results showed the greater importance of the criteria of rest/simplicity and cultural difference. As for the tourist destinations, the most relevant in order of priority were Cruise tourism followed by Art/culture tourism, showing that these two types of tourism must be combined in a co-competition. [ABSTRACT FROM AUTHOR]

In recent years, the integration of smartphone technology with novel sensing technologies, Artificial Intelligence (AI), and Deep Learning (DL) algorithms has revolutionized crop pest and disease surveillance. Efficient and accurate diagnosis is crucial to mitigate substantial economic losses in agriculture caused by diseases and pests. An innovative Apple® and Android™ mobile application for citizen science has been developed, to enable real-time detection and identification of plant leaf diseases and pests, minimizing their impact on horticulture, viticulture, and olive cultivation. Leveraging DL algorithms, this application facilitates efficient data collection on crop pests and diseases, supporting crop yield protection and cost reduction in alignment with the Green Deal goal for 2030 by reducing pesticide use. The proposed citizen science tool involves all Farm to Fork stakeholders and farm citizens in minimizing damage to plant health by insect and fungal diseases. It utilizes comprehensive datasets, including images of various diseases and insects, within a robust Decision Support System (DSS) where DL models operate. The DSS connects directly with users, allowing them to upload crop pest data via the mobile application, providing data-driven support and information. The application stands out for its scalability and interoperability, enabling the continuous integration of new data to enhance its capabilities. It supports AI-based imaging analysis of quarantine pests, invasive alien species, and emerging and native pests, thereby aiding post-border surveillance programs. The mobile application, developed using a Python-based REST API, PostgreSQL, and Keycloak, has been field-tested, demonstrating its effectiveness in real-world agriculture scenarios, such as detecting Tuta absoluta (Meyrick) infestation in tomato cultivations. The outcomes of this study in T. absoluta detection serve as a showcase scenario for the proposed citizen science tool's applicability and usability, demonstrating a 70.2% accuracy (mAP50) utilizing advanced DL models. Notably, during field testing, the model achieved detection confidence levels of up to 87%, enhancing pest management practices. [ABSTRACT FROM AUTHOR]

Evaluating the mission efficiency of various drone configurations under complex, multi-source, and multi-dimensional requirements remains a significant challenge. This study aimed to develop a comprehensive decision support system (DSS) that employs mission efficiency evaluation, probabilistic hesitant fuzzy sets (PHFs), and multi-attribute decision-making (MADM) methods to assess and optimize drone design. In the proposed method, mission efficiency is defined as a composite measure of the flight performance, adaptability, and economic viability required to complete a mission. By designing a "demand–capability–design" mapping approach, this system effectively resolves multi-attribute conflicts in the decision-making process. To demonstrate the proposed approach, a set of small electric vertical takeoff and landing fixed-wing (e-VTOLFW) drones are compared and ranked based on their mission efficiency. The impacts of different mission requirements on drone evaluation are also discussed. The results demonstrate that this model resolves the traditional issue of unclear information flow in drone design. By improving the evaluation criteria, it enhances informed decision making and the robustness of evaluation results in drone design assessments. Additionally, the model is generalizable and can be widely applied to similar fields such as "demand–product design", improving the understanding and optimization of product performance. [ABSTRACT FROM AUTHOR]

Accuracy in placing employees determines the performance of a company. Likewise done by PT XYZ in determining the placement of managers in the plantation industry by using a decision support system. This is done in order to minimize the level of subjectivity of the manager placement determination system at PT XYZ. This research aims to provide alternative preference values to prospective employees who will occupy manager positions in the plantation industry. The method used in the placement of managers with a decision support system is the technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method. The criteria used in this method are 7 criteria taken based on the criteria for BUMN talent management according to the Regulation of the Minister of BUMN Number PER-3 / MBU / 03/2023, these criteria are Professional Work Period, Variety of Work Experience, Managerial Competence, Technical Competence, Educational Strata, Performance Assessment Results, Level of Punishment that has been received. The results of this study are from the results of the calculation analysis through the TOPSIS method on 7 alternatives, then there is name number 5 managed to get the best score of 0.80 and was determined as a preference to be placed in class A garden. [ABSTRACT FROM AUTHOR]

Counter-terrorism measures and preparedness play a critical role in securing mass gatherings, soft targets, and critical infrastructures within urban environments. This paper introduces a comprehensive Decision Support System developed as part of the S4AllCitites project, designed to seamlessly integrate with existing legacy systems in Smart Cities. The system encompasses urban pedestrian and vehicular evacuation, incorporating predictive models to anticipate the progression of incendiary and mass shooting attacks, alongside a probabilistic model for threat assessment in the case of improvised explosive devices. A notable achievement of this research is the successful implementation and deployment of the system in operational environments through pilot studies. It empowers public and private security operators with real time decision support capabilities during both prevention and intervention stages of potential attacks. The decision support information provided encompasses various aspects, including optimal evacuation strategies, estimated egress times, pedestrian movement profiles, probability assessments, and the potential impact of different terrorist threats in terms of casualties. Additionally, the system offers real-time insights into the status of the traffic network under normal and unusual conditions, enabling efficient emergency management throughout its progression. This includes the ability to identify optimal intervention routes and assess the impact of anomalous traffic resulting from evacuations. [ABSTRACT FROM AUTHOR]

This paper presents a novel multiclass system designed to detect pleural effusion and pulmonary edema on chest X-ray images, addressing the critical need for early detection in healthcare. A new comprehensive dataset was formed by combining 28,309 samples from the ChestX-ray14, PadChest, and CheXpert databases, with 10,287, 6022, and 12,000 samples representing Pleural Effusion, Pulmonary Edema, and Normal cases, respectively. Consequently, the preprocessing step involves applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) method to boost the local contrast of the X-ray samples, then resizing the images to 380 × 380 dimensions, followed by using the data augmentation technique. The classification task employs a deep learning model based on the EfficientNet-V1-B4 architecture and is trained using the AdamW optimizer. The proposed multiclass system achieved an accuracy (ACC) of 98.3%, recall of 98.3%, precision of 98.7%, and F1-score of 98.7%. Moreover, the robustness of the model was revealed by the Receiver Operating Characteristic (ROC) analysis, which demonstrated an Area Under the Curve (AUC) of 1.00 for edema and normal cases and 0.99 for effusion. The experimental results demonstrate the superiority of the proposed multi-class system, which has the potential to assist clinicians in timely and accurate diagnosis, leading to improved patient outcomes. Notably, ablation-CAM visualization at the last convolutional layer portrayed further enhanced diagnostic capabilities with heat maps on X-ray images, which will aid clinicians in interpreting and localizing abnormalities more effectively. [ABSTRACT FROM AUTHOR]

In this paper, a decision support system (DSS) is provided to assist the decision-maker in obtaining the best solution for the transportation problem under uncertainty. Fuzzy parameters of the transportation problem are presented by pentagonal fuzzy numbers. The centroid ranking function transforms these pentagonal fuzzy numbers into crisp ones. Then, a novel, improved greedy method named Dhouib-Matrix-TP1 (DM-TP1) is used in order to help the decision-maker promptly find a suitable solution. Specifically, this DSS is composed of three components: the data base component considers a pentagonal fuzzy number; the Model Base component thinks through the original heuristic DM-TP1; and the User Interface component deliberates the convivial graphical output of the generated transportation plan solution using the Python programming language. Experiments in the literature on fuzzy transportation problems show that the novel proposed heuristic, DM-TP1, is easy to understand and allows the decision-maker to handle transportation problems under pentagonal fuzzy numbers. Also, the DM-TP1 is robust and can be applied to find a feasible initial solution in less time. [ABSTRACT FROM AUTHOR]

An approach based on fuzzy set theory is presented in the scientific article to enhance the efficiency of diagnosing kidney diseases by decreasing the time required for making medical decisions. The suggested approach employs fuzzy models and algorithms that consider the uncertainty and variability of clinical data to optimize the assessment of the functional state of the kidneys, taking into account various risk factors and individual characteristics of patients. The paper suggests a technique to develop a system of fuzzy decision rules. This technique combines E. Shortliff's iterative rules with functions from the studied classes of kidney diseases. Mathematical modeling and experimental studies have indicated relatively high effectiveness in classifying different forms of kidney diseases. The results can be used to formulate intelligent decision support systems in clinical practice and improve diagnostic and monitoring processes. Moreover, the findings may aid in shaping more targeted and effective health policies at the national and regional levels, enhancing access to healthcare, and promoting the population's overall health. [ABSTRACT FROM AUTHOR]

Multi‐criteria methods and systems have been developed over many years to support decision‐making with a process of eliciting preferences. The emphasis has been on those that incorporate compensatory rationality, the characteristics of which make the process more complex. Neuroscience has offered the opportunity to capture aspects of cognition, generating insights. This paper investigates the preference elicitation process with Flexible and Interactive Tradeoff (FITradeoff), using electroencephalography and eye‐tracking applied to a sample of undergraduate and graduate students. While FITradeoff offers greater flexibility when evaluating recent advances in the area, the experiment carried out incorporates paradigms present in other methods developed for compensatory rationality. The results indicate the effect the type of predominant criterion has on cognitive effort. Moreover, a non‐linear relationship between the number of criteria and questions was found, which suggests that decision‐makers use a strategy of minimizing cognitive effort. Such findings help to direct efforts to enhance systems and methods with compensatory rationality. [ABSTRACT FROM AUTHOR]

Background The critical care literature has seen an increase in the development and validation of tools using artificial intelligence for early detection of patient events or disease onset in the intensive care unit (ICU). The hemodynamic stability index (HSI) was found to have an AUC of 0.82 in predicting the need for hemodynamic intervention in the ICU. Future studies using this tool may benefit from targeting those outcomes that are more relevant to clinicians and most achievable. Methods A three-round Delphi study was conducted with a panel of 10 critical care physicians and three nurses in the United States to identify outcomes that may be most relevant and achievable with the HSI when evaluated for use in the ICU. To achieve criteria for relevance, at least 65% of panelists had to rate an outcome as a 4 or 5 on a 5-point scale. Results Nineteen of 24 outcomes that may be associated with the HSI achieved consensus for relevance. The Kemeny-Young approach was used to develop a matrix depicting the distribution of outcomes considering both relevance and achievability. Reduces time spent in hemodynamic instability and reduces times to recognition of hemodynamic instability were the highest-ranking outcomes considering both relevance and achievability. Conclusion This Delphi study was a feasible method to identify relevant outcomes that may be associated with an appropriate predictive analytic tool in the ICU. These findings can provide insight to researchers looking to study such tools to impact outcomes relevant to critical care practitioners. Future studies should test these tools in the ICU that target the most clinically relevant and achievable outcomes, such as time spent hemodynamically unstable or time until actionable nursing assessment or treatment.

Stunting is a condition of impaired growth and development in toddlers due to prolonged nutritional deprivation. In the Kota Timur Community Health Center, stunting is traditionally assessed based solely on body weight and height, neglecting other crucial factors such as socioeconomic status, maternal nutrition during pregnancy, history of illness, and dietary intake. This limited approach leads to inaccurate decision-making and misdiagnoses of stunting. This research applied the Multi-Attribute Utility Theory (MAUT) to identify stunting susceptibility levels in toddlers by integrating various determinants, including body weight, height, socioeconomic conditions, maternal nutrition during pregnancy, morbidity, and dietary intake. MAUT effectively integrates multiple criteria and manages data uncertainties through its utility concept, allowing for comparison across different alternatives to facilitate accurate decision-making. The results showed that Arbi, Manaf, and Aisyah were susceptible to stunting, with evaluation scores of 0.028, 0.288, and 0.299, respectively, while Daffa and Zayyan were not susceptible, with scores of 0.900 and 0.966, respectively. Therefore, the system utilizing MAUT to determine stunting susceptibility levels in toddlers can be adopted by health workers at the Kota Timur Community Health Center to enable efficient, quick, and accurate diagnosis by integrating multiple determinants of stunting susceptibility.

Abstract Water conservation has become a global problem as the population increases. In many densely populated cities in China, leaks from century-old pipe works have been widespread. However, entirely eradicating the issues involves replacing all water networks, which is costly and time-consuming. This paper proposed an AI-enabled water-saving control system with three control modes: time division control, flow regulation, and critical point control according to actual flow. Firstly, based on the current leaking situation of water supply networks in China and the capability level of China’s water management, a water-saving technology integrating PID control and a series of deep learning algorithms was proposed. Secondly, a multi-jet control valve was designed to control pressure and reduce water distribution network cavitation. This technology has been successfully applied in industrial settings in China and has achieved gratifying water-saving results.

ABSTRACTThe growing diversity and volume of e-waste products are emerging as significant and complex challenges from environmental, economic, and recycling perspectives. This surge not only hampers the effective management and conservation of precious resources and strategically important materials (SIMs) but also intensifies the problem. Addressing this pressing global issue necessitates a holistic and intelligent decision-making model capable of effortlessly handling the vast variety and volume of e-waste products. This paper introduces a novel multi-criteria decision support system (DSS) that combines AI visual recognition with a multi-fuzzy model to precisely determine the optimal End-of-Life (EoL) recovery route. Additionally, the functionality of the proposed DSS is showcased through a pilot implementation and case study examples, emphasising the advantages of a fully automated, reliable, and efficient EoL management for e-waste.