Proposed Decision Support Research Articles

Information technology for forecasting epidemiological threats based on the telegraphic equation

Based on the analysis, it is established that traditional approaches, such as the SIR (Susceptible-Infectious-Covered) and SEIR (Susceptible-Exposed-Infectious-Covered) models, do not provide sufficient forecasting accuracy and do not take into account the complex dynamics of the spread of infectious diseases. The need to develop a method that will improve forecasting accuracy and provide support for managerial decision-making to predict the spread of epidemiological threats based on the telegraphic equation is substantiated. The developed system allows for making effective management decisions aimed at reducing the negative impact of the epidemic on the population and medical infrastructure. The use of the telegraphic level allows us to predict the wave spread of infection, spatial and temporal delays, as well as sources of new infections, which ensures accurate forecasting of peak periods, the duration of the epidemic, and the workload of medical facilities. The developed method integrates the classical SIR model with the telegraphic level, which allows the modelling the dynamics of infection spread in a spatio-temporal environment. This method provides forecasting of the spatial and temporal dynamics of infection spread, taking into account wave effects, delays, and the influence of external factors. It provides an opportunity to accurately analyze key epidemic indicators, such as the peak of the disease, its duration, and the distribution of the burden on hospitals. The developed method and mathematical model based on the telegraphic level provided an appropriate level of accuracy in predicting the spatial and temporal dynamics of the spread of epidemiological threats. Testing the model on historical COVID-19 data showed that the average forecast error was 5...10%. This indicates the model's adequacy. In the case of high population mobility, the model accurately described the wave dynamics of the infection. The proposed decision support system includes a user-friendly interface with four tabs for entering model parameters, analyzing results, visualizing them, and generating recommendations. It allows to improve the accuracy of estimating the duration of the epidemic, peak loads, and some resources. The developed system is a tool for managers to support the adoption of governmental decisions aimed at predicting the infection of the population of regions and optimizing the use of medical resources. The results of the study can be used to plan epidemic response measures at the local, regional, and global levels. The proposed system ensures efficiency, flexibility, and accuracy, which are key to managing epidemiological situations in the face of modern challenges.

Thermal request optimization of a smart district heating system

This paper proposes a solution approach to manage the heating plans of tenants served by a district heating plant located in Sweden. To do that, the daily temperature request of each household in the associated pilot region is obtained, and the daily temperature profile of each household is optimized with the help of the proposed decision support system and smart valves. The hot water inflow rates of radiators are remotely controlled via smart valves at each flat to minimize the total energy consumption, carbon emission and cost associated with the energy consumption of the district heating plant. We aim to shave the peak demands while fully satisfying the temperature requests of households without violating their thermal comfort. Peak demand shaving is achieved by generating preheating schedules via mathematical optimization and using the thermal storage potential of the insulated flats. The resulting mathematical optimization model presents significant computational challenges that cannot be efficiently solved using optimization solvers within a reasonable time limit. To this end, we develop three genetic algorithm approaches that are computationally scalable for realistically-sized instances and verified to yield near-optimal solutions for the test instances. Extensive numerical analyses show the effectiveness of the proposed approach and the genetic algorithm since we yield significant carbon emission reduction and cost savings compared with the method that the experts of the utility company propose.

Optimizing Prostate Cancer Radiotherapy: Advanced Machine Learning in Virtual Patient-Specific Plan Verification-Review Study

Background: Virtual patient-specific plan verification in radiotherapy is critical to ensure precise dose delivery while minimizing healthy tissue exposure, particularly in prostate cancer treatments. Prior studies, however, have overlooked the physical implications of predictor features and lacked comprehensive decision support tools, leading to gaps in understanding and practical application. This research aims to bridge these gaps by providing a nuanced understanding of predictor features, exploring advanced automatic feature extraction methods, emphasizing model reliability, and proposing a comprehensive decision support tool. Our objective is to optimize radiotherapy protocols, ensuring safer and more effective treatments for patients undergoing prostate cancer therapy. Methods: This research employs a literature review approach. An extensive literature study served as the foundation. Results: Our study reveals that understanding the physical implications of predictor features significantly enhances prediction accuracy. Utilizing Convolutional Neural Networks (CNN) models for automatic feature extraction improves prediction performance, providing robust and transferable results. Conclusions: By emphasizing model reliability through the integration of treatment plan parameters, our approach ensures stable predictions across diverse patient cases. The proposed decision support tool offers clinicians detailed insights into predicted dose deliverability, facilitating informed decision-making for patient-specific treatment plans. Through these advancements, our research contributes to the optimization of radiotherapy protocols, ensuring safer and more effective treatments for patients undergoing prostate cancer therapy.

European Green Deal: Substantiation of the Rational Configuration of the Bioenergy Production System from Organic Waste

A review of the current state of the theory and practice of bioenergy production from waste allowed us to identify the scientific and applied problem of substantiating the rational configuration of a modular anaerobic bioenergy system, taking into account the volume of organic waste generated in settlements. To solve this problem, this paper develops an approach and an algorithm for matching the configuration of a modular anaerobic bioenergy production system with the amount of organic waste generated in residential areas. Unlike the existing tools, this takes into account the peculiarities of residential areas, which is the basis for accurate forecasting of organic waste generation and, accordingly, determining the configuration of the bioenergy production system. In addition, for each of the scenarios, the anaerobic digestion process is modeled, which allows us to determine the functional indicators that underlie the determination of a rational configuration in terms of cost and environmental performance. Based on the use of the developed tools for the production conditions of the Golosko residential area, Lviv (Ukraine), possible scenarios for the installation of modular anaerobic bioenergy production systems are substantiated. It was found that the greatest annual benefits are obtained from the processing of mixed food and yard waste. The payback period of investments in modular anaerobic bioenergy production systems for given conditions of a residential area largely depends on their configuration and ranges from 3.3 to 8.4 years, which differ from each other by 2.5 times. This indicates that the developed toolkit is of practical value, as it allows the coordination of the rational configuration of modular anaerobic bioenergy production systems with real production conditions. In the future, it is recommended to use the proposed decision support system to model the use of biomass as an energy resource in residential areas, which ensures the determination of the rational configuration of a modular anaerobic bioenergy production system for given conditions.

A roadmap towards safe and sustainable by design nanotechnology: Implementation for nano-silver-based antimicrobial textile coatings production by ASINA project

This report demonstrates a case study within the ASINA project, aimed at instantiating a roadmap with quantitative metrics for Safe(r) and (more) Sustainable by Design (SSbD) options. We begin with a description of ASINA’s methodology across the product lifecycle, outlining the quantitative elements within: Physical-Chemical Features (PCFs), Key Decision Factors (KDFs), and Key Performance Indicators (KPIs). Subsequently, we delve in a proposed decision support tool for implementing the SSbD objectives across various dimensions—functionality, cost, environment, and human health safety—within a broader European context. We then provide an overview of the technical processes involved, including design rationales, experimental procedures, and tools/models developed within ASINA in delivering nano-silver-based antimicrobial textile coatings. The result is pragmatic, actionable metrics intended to be estimated and assessed in future SSbD applications and to be adopted in a common SSbD roadmap aligned with the EU’s Green Deal objectives. The methodological approach is transparently and thoroughly described to inform similar projects through the integration of KPIs into SSbD and foster data-driven decision-making. Specific results and project data are beyond this work’s scope, which is to demonstrate the ASINA roadmap and thus foster SSbD-oriented innovation in nanotechnology.

A probabilistic bi‐objective model for a humanitarian location‐routing problem under uncertain demand and road closure

AbstractEffective planning and execution of humanitarian aid logistics activities ensure that disaster‐related losses are minimized. This study addresses a tactical‐level pre‐disaster humanitarian logistics problem where a decision‐maker decides on cross‐dock locations by taking potential vehicle routes into account. A decision support model is proposed for the location selection and distribution operations in humanitarian logistics with explicit fuel consumption estimation. In the addressed problem, the demand amount of each node depends on probabilistic disaster scenarios. Probabilities of whether each arc/road is open or closed and heterogeneous vehicle fleet in terms of vehicle sizes are also respected. The model is formulated as probabilistic bi‐objective mixed integer linear programming, whose objectives are minimization of the total cost (i.e., fuel cost, vehicle fixed cost, and fixed opening cost) and total travel time. To the best of our knowledge, the proposed decision support model is unique in terms of the features considered simultaneously. The applicability of the model is demonstrated by the case study and subsequent numerical analyses of a possible earthquake in the Kartal district of Istanbul. The proposed model is shown to have the potential to support decision‐makers in preparation for a disaster. A solution approach based on a clustering algorithm has been also proposed to solve larger instances of the problem. The effectiveness of this heuristic has been demonstrated through its application to larger‐scale problems.

A neutrosophic WENSLO-ARLON model for measuring sustainable brand equity performance

Sustainable brand equity denotes the perceived brand equity by consumers, influenced by companies' non-financial and environmental practices. To cultivate sustainable brand equity, corporations undertake green initiatives and engage in social responsibility endeavors, thereby communicating environmentally oriented messages to consumers. Companies feel the necessity to establish a competitive position in sustainability relative to their counterparts. Hence, the primary objective of this research is to conduct an inquiry aimed at identifying sustainable brand equity to meet this exigency of companies. In this context, the motivation of this study lies in treating the calculation of sustainable brand equity as a decision-making process and developing a decision support system for this purpose. The proposed decision support system for determining sustainable brand equity levels in this research has three fundamental inputsss: experts, criteria, and brands. The impact levels of experts on the decision-making process are quantified utilizing type-2 neutrosophic number (T2NN) sets. The weights of criteria are ascertained employing the weight by envelope and slope (WENSLO) method extended with T2NN sets. The ranking of sustainable brand equities is established through the innovative T2NN-based alternative ranking using two-step logarithmic normalization (ARLON) method. These methodologies are amalgamated, and the T2NN-WENSLO-ARLON hybrid model is posited as a decision support system for evaluating sustainable brand equity performance. An algorithm for T2NN-WENSLO-ARLON is formulated and elucidated alongside a case study focusing on companies operating in the cosmetics sector in Turkey. The case study identifies the “green product leadership” criterion as the most influential factor in determining sustainable brand equity. Furthermore, the cosmetic company “Misbahçe Inc.” is identified as the brand with the highest sustainable brand equity. Sensitivity and comparative analyses are conducted to ascertain the robustness of the results and the decision model. All scenario outcomes bolster the decision model. The research provides comprehensive insights into both the developed tool for calculating sustainable brand equity and develops implications for the cosmetics industry.

Evaluation of Ergonomic Risks for Construction Workers based on Multicriteria Decision Framework with the Integration of Spherical Fuzzy Set and Alternative Queuing Method

Ergonomic risks critically impact workers’ occupational health, safety, and productivity, and thereby the sustainability of a workforce. In the construction industry, the physical demands and dynamic environment exposes workers to various ergonomic hazards. While previous research has mainly focused on postural risks, there is a need to broaden the scope to include more relevant factors and assess them systematically. This study introduces a multi-criteria decision framework integrating the Spherical Fuzzy Sets (SFSs) and Alternative Queuing Method (AQM) to evaluate and prioritize ergonomic hazards. First, SFSs are employed to quantify the linguistic expressions of experts, addressing the inherent vagueness and uncertainty. Then, an entropy-based objective weighting method is adopted to determine the criteria weights. Finally, AQM is utilized to generate the risk priority. The proposed method has been implemented in a real-life construction project, where “overexertion due to unreasonable task organization”, “hypertension and heart diseases”, and “existing WMSD record” are identified as the top three ergonomic hazards. Then, a thorough discussion of intervention strategies regarding different risk categories is presented to facilitate ergonomic interventions. This proposed decision support system can promote effective ergonomic risk management, benefiting workers’ health and well-being and contributing to the sustainable workforce development of the construction industry.

The design and development of a dashboard for improving sustainable healthy food choices

Over the last decade, several digital tools have been designed to provide consumers with nutritional and environmental impact information about their food choices post-consumption. Many of these tools lack behavioral change modules, have low user engagement, and ignore inherent environmental nutrition trade-offs to stimulate dietary change. This study presents the design and development of a decision support system to enhance consumer health while meeting sustainability goals from a pre-consumption perspective. The proposed decision support system, Dashboard for Improving Sustainable Healthy (DISH) food choices, employs behavioral features, traffic light labels, and nudges to inform end-users about the nutritional health performance and environmental impact of meals. DISH uses a simple metric that allows end-users to explore the potential minutes of healthy and productive life gained or lost from consuming 100 kcal of a meal. The metric combines the positive or negative nutritional health effects (μ-DALYs) of consuming a meal and environmental damage (endpoint impact expressed in DALYs) on human health. In the DISH application, end-users are rewarded or deducted EnCoins, which represent the number of silver or gold coins lost or gained based on the cost ($) of environmental damage (midpoint impacts) of a meal compared to reference sustainable healthy and unsustainable and unhealthy meal. DISH's gamification module enables end-users to track the potential minutes of healthy and productive life gained/lost and gold or silver rewards or deductions from consuming 100 kcal of a selected meal through cumulative minutes gained or lost and EnCoins. In promoting a sustainable diet culture, the gamification module enables users to create groups and communities where friends and families can track their sustainability performance through meal decisions. The DISH application is currently available online and can be accessed by an end-user through any device. Further pilot studies will focus on testing the technology in partner campus cafeterias.

A probabilistic reliable linguistic model for blockchain-based student information management system assessment

The growing necessity for robust security and trust in systems has prompted the widespread adoption of blockchain technology in student information management systems (SIMS). While existing literature has concentrated on the development of blockchain-based student information management systems (BC-SIMS), a notable gap remains in the evaluation of these systems. This study addresses this gap by employing a Multi-Criteria Decision-Making (MCDM) approach. Recognizing the inherent uncertainties in BC-SIMS evaluation due to inadequate information, the study introduces the Probabilistic Reliable Linguistic Term Sets (PRLTSs) theory to address these problems. As a result, the Measurement of Alternatives and Ranking according to the Compromise Solution (MARCOS) method is extended into the Probabilistic Reliable Linguistic environment, resulting in the Probabilistic Reliable Linguistic-Measurement of Alternatives and Ranking according to the Compromise Solution (PRL-MARCOS) method. This method supports the evaluation and selection decisions on BC-SIMS in higher education institutions. The study further designs the Probabilistic Reliable Linguistic-Coefficient of Variation (PRL-CV) method to determine the relative importance of selection criteria. Twenty selection criteria for BC-SIMS are identified through a literature review, and an illustrative case study is employed to assess the practicality and validity of the proposed PRL-CV-MARCOS approach in a higher educational institution. Experimental results highlight cost reduction (0.0778), interoperability (0.0717), security (0.0628), service improvement (0.0557) and responsiveness (0.0538) as the top five criteria for BC-SIMS selection, with BC-SIMS P5 identified as the optimal solution for managing student information. Finally, a comprehensive comparative and sensitivity analysis verifies the stability and effectiveness of the proposed decision support model.

A socio-economic and quality-oriented optimal fruit supply chain network design in a multi-market and multi-product environment: A real case study

This paper focuses on developing an optimal sustainable fruit Closed-Loop Supply Chain (CLSC). A novel multi-objective Mixed-Integer Linear Programming (MILP) model is suggested to formulate a multi-product, multi-market, multi-period, multi-level, multi-mode selling problem which aims to minimize cost and emissions and maximize the whole social segments’ responsiveness to fruit, vinegar, and compost. The model is solved by the LP-metric method and implemented in a real case in Iran. The findings indicate that achieving the highest level of responsiveness comes with a substantial cost for the network. However, this does not apply to environmental considerations. For decision-makers prioritizing sustainability, the proposed decision support system strongly advises against buying fruit from fruit cars. Due to the absence of positive impacts on the level of responsiveness, buying from fruit cars results in increased costs and emissions. For a cost- and environmentally-sensitive decision-maker, it is recommended to provide higher-quality fruits. Because the lower the quality of the fruit, leads to increased waste rates and emissions. Meeting increased vinegar demand, with its elevated costs and emissions, becomes justifiable only when the policymaker is actively seeking to achieve maximum responsiveness.

Applying a Genetic Algorithm to Implement the Fuzzy-MACBETH Method in Decision-Making Processes

This paper describes the development of an evolutionary algorithm for building cardinal scales based on the Fuzzy-MACBETH method. This method uses a triangular fuzzy numbers scale in the MACBETH method to incorporate the subjectivity of a semantic scale into mathematical modeling, which enables circumventing the cardinal inconsistency problem of the classical method, facilitating its application in complex contexts. A genetic algorithm is used in the fuzzy system developed here to build the basic fuzzy scale in a cardinally inconsistent decision matrix. The proposed technique is inspired by crossover and mutation genetic operations to explore potential solutions and obtain a cardinal scale aligned with the decision maker’s preferences. Finally, an illustrative example of the application of the proposed decision support system is presented. The results confirm that the FGA-MACBETH method aligns with the classical method. This study’s primary contribution is that circumventing the problem of cardinal inconsistency in a semantically consistent decision matrix enabled obtaining a cardinal scale without requiring the decision maker to redo his/her initial assessments.

A decision support system to recommend appropriate therapy protocol for AML patients.

Acute Myeloid Leukemia (AML) is one of the most aggressive hematological neoplasms, emphasizing the critical need for early detection and strategic treatment planning. The association between prompt intervention and enhanced patient survival rates underscores the pivotal role of therapy decisions. To determine the treatment protocol, specialists heavily rely on prognostic predictions that consider the response to treatment and clinical outcomes. The existing risk classification system categorizes patients into favorable, intermediate, and adverse groups, forming the basis for personalized therapeutic choices. However, accurately assessing the intermediate-risk group poses significant challenges, potentially resulting in treatment delays and deterioration of patient conditions. This study introduces a decision support system leveraging cutting-edge machine learning techniques to address these issues. The system automatically recommends tailored oncology therapy protocols based on outcome predictions. The proposed approach achieved a high performance close to 0.9 in F1-Score and AUC. The model generated with gene expression data exhibited superior performance. Our system can effectively support specialists in making well-informed decisions regarding the most suitable and safe therapy for individual patients. The proposed decision support system has the potential to not only streamline treatment initiation but also contribute to prolonged survival and improved quality of life for individuals diagnosed with AML. This marks a significant stride toward optimizing therapeutic interventions and patient outcomes.

Urinary Bladder Inflammation Prediction with the Gray Wolf Optimization Algorithm and Multi-Layer Perceptron-Based Hybrid Architecture

In this study, a decision support system for bladder inflammation prediction is presented. The proposed decision support system is built by establishing a hybrid architecture with Gray wolf optimization algorithm (GWO) and Multi-layer perceptron (MLP) networks. In addition to optimizing the hyperparameters in the MLP structure with GWO, the hybrid architecture also optimizes the order of input values to be presented to the MLP structure. The Acute Inflammations data set in the UCI Machine Learning repository was used as the data set in the study. Classification operations were carried out on this data set with the models obtained with hybrid architecture, Decision trees, k-Nearest Neighbors and Support Vector Machines methods. The controversial findings presented as a result of experimental studies have shown that the proposed hybrid architecture produces more successful results than other machine learning methods used in the study. In addition, the MLP network structure optimized with the hybrid architecture offers a new diagnostic method in terms of patient decision support systems.

A decision support system for autonomous ship trajectory planning

This study presents an approach to enhance human–machine collaboration in autonomous ship trajectory planning. A decision support system is developed, considering crucial environmental factors such as ocean currents, wind, and tidal information, alongside the integration of narrow channel geometry, squat effect, and Under-keel-clearance (UKC). The Dynamic Consequence Analysis (DCA) risk assessment method is utilized to establish dynamic safety domains, incorporating ship maneuvering characteristics and potential failure scenarios. The Multi-objective Particle Swarm Optimization (MOPSO) algorithm is then employed to generate alternative trajectories, optimizing five objective functions: minimizing safety domain violation, consecutive speed changes, path length, deviation from the initial plan, and deviation from the initial estimated time of arrival. Furthermore, two Multi-criteria Decision Making (MCDM) methods, Multi-Objective Optimization by Ratio Analysis (MOORA) with user preferences, and the Entropy Weight Method (EWM) with automatic weight allocation, are employed to rank alternative solutions from the Pareto front. Finally, a clustering method is employed on the Pareto front solutions. The outcomes that serve as representatives for these clusters are then merged with the highest-rated alternative solutions from MCDM methods. This combined set forms the basis for the final decision-making process carried out by the operator. While dynamic obstacles are not considered in this study, evaluation across three scenarios demonstrates the effectiveness of the proposed decision support system for trajectory planning.

Intelligent decision support system for assessing the risk of failures of complex technical systems

A decision support system has been developed that allows assessing the risk of failure of elements and components of complex technical systems using artificial intelligence. The proposed algorithm for the functioning of a decision support system implements the task of automating the process of assessing the technical condition of complex systems. The use of the proposed decision support system for assessing the technical condition of complex systems will improve the reliability of operating systems with insufficient information about their technical condition.

SURG-02. SURGICAL OUTCOME OF SUPRATENTORIAL MENINGIOMA PATIENTS AGED ≥80 YEARS – RETROSPECTIVE INTERNATIONAL MULTICENTER STUDY

Abstract BACKGROUND Demographic changes will lead to an increase in old patients, a population with significant risk of postoperative morbidity and mortality, requiring neurosurgery for meningiomas, Objective: To evaluate outcome of supratentorial meningioma patients aged ≥80 years. This multicenter study aims to report neurofunctional status after resection, to identify factors associated with outcome, and to validate a previously proposed decision support tool. METHODS Neurofunctional status was assessed by the Karnofsky Performance Scale (KPS). Patients were categorized in poor (KPS ≤40), intermediate (KPS 50-70), and good (KPS ≥80) preoperative subgroups. Volumetric analyses of tumor and peritumoral brain edema (PTBE) were performed; volumes were scored as small (< 10 cm3), medium (10-50 cm3), and large ( >50 cm3). RESULTS The study population consisted of 262 patients. Median age at surgery was 83.0 years. Median preoperative KPS was 70; 117 (44.7%) patients were allotted to the good, 113 (43.1%) to the intermediate, and 32 (12.2%) to the poor subgroup. Median tumor and PTBE volumes were 30.2 cm3 and 27.3 cm3; large PTBE volume correlated with poor preoperative KPS status (P = .008). The 90-day and 1-year mortality rates were 9.0% and 13.2%. Within the first postoperative year, 101 (38.5%) patients improved, 87 (33.2%) were unchanged, and 74 (28.2%) were functionally worse (including deaths). Each year-increase of age associated with 44% (23–70%) increased risk of 90-day and 1-year mortality. In total, 111 (42.4%) patients suffered from surgery-associated complications. Maximum tumor diameter ≥5cm (odds ratio 1.87 (1.12–3.13)) and large tumor volume (odds ratio 2.35 (1.01–5.50)) associated with an increased risk of complications. Among patients with poor preoperative status and large PTBE, most (58.3%) benefited from surgery. CONCLUSION Patients with poor preoperative status and large PTBE most often showed postoperative improvements. The decision support tool may be of help in identifying cases that most likely benefit from surgery.

Electronic Health Record (EHR) System Development for Study on EHR Data-based Early Prediction of Diabetes Using Machine Learning Algorithms

Aims: This research work aims to develop an interoperable electronic health record (EHR) system to aid the early detection of diabetes by the use of Machine Learning (ML) algorithms. A decision support system developed using many ML algorithms results in optimizing the decision in preventive care in the health information system. Methods: The proposed system consisted of two models. The first model included interoperable EHR system development using a precise database structure. The second module comprised of data extraction from the EHR system, data cleaning, and data processing and prediction. For testing and training, about 1080 patients’ health record was considered. Among 1080, 1000 records were from the Kaggle dataset, and 80 records were demographic information from patients who visited our health center of Siddaganga organization for a regular checkup or during emergencies. The demographic information was collected from the proposed EHR system. Results: The proposed system was tested for the interoperability nature of the EHR system and accuracy in diabetic disease prediction using the proposed decision support system. The proposed EHR system development was tested for interoperability by random updations from various systems maintained in the laboratory. Each system acted like the admin system of different hospitals. The EHR system was tested for handling the load and interoperability by considering user view status, system matching with the real world, consistency in data updations, security etc. However, in the prediction phase, diabetes prediction was concentrated. The features considered were not randomly chosen; however, the features were those prescribed by a doctor who insisted that the features were sufficient for initial prediction. The reports collected from the doctors revealed several features they considered before giving the test details. The proposed system dataset was split into test and train datasets with eight proper features taken as input and one set as a target variable where the result was present. After this, the model was imported using standard “sklearn” libraries, and it fit with the required number of estimators, that is, the number of decision trees. The features included pregnancies, glucose level, blood pressure, skin thickness, insulin level, bone marrow index, diabetic pedigree function, age, weight, etc. At the outset, the research work concentrated on developing an interoperable EHR system, identifying the expectation of diabetic and non-diabetic conditions and demonstrating the accuracy of the system. Conclusion: In this study, the first aim was to design an interoperable EHR system that could help in accumulating, storing, and sharing patients' timely health records over a lifetime. The second aim was to use EHR data for early prediction of diabetes in the user. To confirm the accuracy of the system, the system was tested regarding interoperability to support early prediction through a decision support system.

Selection of best industrial waste management technique under complex non-linear Diophantine fuzzy Dombi aggregation operators

The harmful waste products, beverages, chemical agents that are discharged or emitted by industrial processes are categorized as industrial waste (IW). IW is one of the most severe problems facing governments around the world due to its environmental effect and severity. So the aim of this paper is to develop a method for selecting industrial waste management technique (IWMT) for industry. Dombi Norms were commonly referred to as Dombi operations, but their applications might be better expressed if they are presented with a new level of flexibility within the general parameter. Dombi operations have not yet been applied in non-linear Diophantine fuzzy sets (N-LDFSs) in a suitable form. Therefore we apply the Dombi operators on complex N-LDFS (CN-LDFS) and develop a generalized area of research in decision making problems (DMPs). The majority of decision-making challenges in the actual world contain a wide range of elements that must be taken into account. Making decision under such circumstances can frequently be challenging. To deal with such complicated issues, we require multi-attribute group decision-making (MAGDM) techniques. The aims to discuss MAGDM issues, this paper will introduce a new COmbinative Distance-based ASsessment (CODAS) methodology. In this paper, we put forward three novel aggregation operators (AOs): complex non-linear Diophantine fuzzy Dombi weighted averaging (CN-LDFDWA), complex non-linear Diophantine fuzzy Dombi order weighted averaging (CN-LDFDOWA), and complex non-linear Diophantine fuzzy Dombi hybrid weighted averaging (CN-LDFDHWA). These operators are used in the operational parameter to help a successful solution of the problem. Due to the growing energy and material shortages, as well as to comply with environmental laws and regulations and to take advantage of disposal economics in the current context, industrial waste (IW) should be taken into consideration as one of the potential resources in the steel industry. Further, the proposed decision support model applied to solve a case study regarding to IWMT for Pakistan steel mill corporation (PSMC) under complex N-LDF Dombi operators. The proposed method is more effective and reliable for decision making (DM) model, as compared to the existing method.

Decision support system for power cable insulation vulcanization production line management

The paper proposes a structural model of a decision support system for controlling a cable insulation continuous vulcanization line. The proposed decision support system is based on a mathematical model of the cable insulation continuous vulcanization technological process, a database, and a mode correction algorithm. The DSS will make it possible to quickly develop a new regime in case of using new materials or structures, as well as to correct the current regime in case of unplanned changes during the production process. The core of the DSS is the proposed mathematical model, which is based on conservation laws and is presented as a system of differential equations closed by boundary conditions. The vulcanization process is described taking into account the temperature-time dependence of the kinetic parameters, which made it possible to take into account the uneven heating of the cable, both in the radial direction and along the length. The numerical implementation of the differential mathematical model made it possible to carry out a systematic analysis of the nature of the processes in the vulcanization pipe and to evaluate the influence of various technological, structural and material parameters on the cable insulation vulcanization completion degree. As a result of the analysis, significant parameters were identified that significantly affect the vulcanization process completion degree. Based on the obtained numerical results, technological parameters dependence technological surfaces on the geometry of the product and the properties of the materials used were constructed, a regression mathematical model was proposed that allows determining the values of the process control parameters without resorting to the use of a differential mathematical model. Based on the results of the analysis, an algorithm for correcting the technological regime is proposed, taking into account only significant process parameters. The proposed algorithm allows you to adjust the value of the isolation rate, depending on external influences and deviations from the specified parameters. The results of the study can be used in the production of cable and wire products with vulcanizable insulation, when it is necessary to quickly select the optimal technological mode, take into account changes in the cable design, properties of the insulation material, as well as possible pressure deviations inside the vulcanization pipe.