Support System Model Research Articles

Intelligent robust controllers for tribotronic conical fluid film bearings

The article presents the results of the development of means for intelligent robust control of the parameters of a tribotronic rotor-support system with a tapered bearing with a removable bush. The proposed controller is implemented on the basis of deep Q-network reinforcement learning (DQN) synthesized on the basis of a numerical model of a rotor support system. The control strategy involved simultaneous control of the shaft position and friction in the lubrication layer. Methods for control synthesis are presented for both a deterministic system and a system with stochastic parameters. In the latter case, a controller synthesis technique is proposed that takes into account uncertainties in the system at the training stage. Testing of the resulting controllers shows the better ability of a controller trained to take into account uncertainties to cope with variable loads, as well as predict possible changes in the system and proactively transfer the system to more advantageous states.

A bayesian approach for the continuous monitoring of the prediction of the physiological evolution of a crisis victim: A decision support system

Catastrophic events like earthquakes demand innovative tools for crisis management. Mathematical modeling and decision support systems (DSSs) have proved crucial for understanding, predicting and mitigating disaster impact. The quantification of complex phenomena through probabilistic models, to estimate the likelihood of events, provides actionable insights that are essential for disaster risk reduction (DRR).The present work stems from research conducted within the framework of the Search & Rescue (S&R) project (H2020-SU-SEC-2019), in particular from the development of the PHYSIO DSS module, the medical component of the S&R Decision Support System (DSS). The PHYSIO DSS focuses on predicting the physiological evolution of crisis victims: using a Bayesian approach, it incorporates real-time field observations to forecast patient conditions. This enables the prediction of the evolution of physiological compensation, allowing efficient resource allocation and timely interventions. By providing real-time insights into victim severity, PHYSIO DSS empowers medical personnel to prioritize treatment, potentially saving lives. Its adaptability allows integration into different platforms, from crisis management systems to apps to personal health devices.This tool has the potential to substantially enhance emergency response capability and overall disaster resilience by offering real-time, data-driven decision support.

Decision Support System Model for College Kartu Indonesia Pintar (KIP) Scholarship Recipients Using the C4.5 Decision Tree Method and Simple Additive Weighting (SAW)

The KIP-Kuliah Scholarship is an Indonesian government program that is used to help high school students (SMA) or equivalent who are less capable but have academic potential in financing education in college. In this study, the problem limitation or scope of research is at Prof. DR. HAMKA Muhammadiyah University College in the Management Study Program. In each year the selection of scholarship acceptance often changes criteria, because of the frequent changes in scholarship criteria each year users often experience problems in selecting KIP-Kuliah scholarship recipients. Decision Support System (SPK) is a solution to assist in decision making. The purpose of this research is to assist in decision making in determining scholarship recipients in accordance with existing criteria. The method used in this research is using Decision Tree C4.5 to determine the weight of the KIP-Kuliah scholarship criteria and the Simple Additive Weighting (SAW) method is used to determine the ranking of scholarship recipients. In this study, researchers used 3 criteria, namely the amount of parents' income, the number of dependents, and the interview test scores. The results of the analysis of the calculations using the Decision Tree C4.5 and Simple Additive Weighting (SAW) methods and the results of the selection of college KIP scholarships consisting of the criteria for the amount of parents' income, the number of dependents and the interview test, the 12 students who deserve college KIP scholarships are students who get the highest preference scores, namely M11, M3, M6, M4, M14, M1, M5, M12, M10, M2, M7, and M9. In conclusion, the use of a Decision Support System with the Decision Tree C4.5 and Simple Additive Weighting (SAW) methods is effective in helping determine the recipients of the KIP-Kuliah scholarship in accordance with predetermined criteria.

The Effect of Support Misalignment on Vibration Characteristics of Aero-Engine Rotor Systems under Ultra-High Operating Speeds

In order to ensure the vibration safety of rotor systems in the next generation of aero-engines and reduce the impact of misalignment faults, the effect of support misalignment on the vibration characteristics of rotor systems under ultra-high operating speeds is investigated in this paper. Firstly, an analytical excitation model of the rotor systems under ultra-high operating speeds is established, considering the impact of the support misalignment. Then, based on the model of the misaligned combined support system, the dynamic model of the flexible discontinuous rotor support system with the support misalignment is presented. Subsequently, based on the established model, the effects of support parameters and support misalignment amounts on the vibration characteristics of the rotor support system are analyzed. Finally, experimental validation of the research findings is conducted. The research result shows that the support misalignment increases the vibration response of the rotor, reduces the vibration reduction efficiency of the combined support system, and consequently decreases the vibration safety of the rotor support system.

Model of Sustainable Household Mobility in Multi-Modal Transportation Networks

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.

Exam scheduling under pandemic conditions: A mathematical model and decision support system

The scheduling of university exams is a complex task that involves various constraints such as administrative limits, pedagogical needs, student volume, and different courses. The emergence of Covid-19 and future pandemics has added new constraints related to infection prevention and contact tracing. To address these challenges, this study proposes a multi-objective mathematical model that considers university resources, reduced classroom occupancy, and minimized student interaction. The model aims to minimize violations of pandemic-related constraints and categorize exams by difficulty. To facilitate scheduling for entire faculties or universities, a Genetic Algorithm based web-based decision support system is developed. With these tools, the study successfully created an optimal schedule for eight departments simultaneously.

AI-Enabled Clinical Decision Support System Modeling for the Prediction of Cirrhosis Complications

Background and ObjectiveUtilizing artificial intelligence (AI), a clinical decision support system (CDSS), can help physicians anticipate possible complications of cirrhosis patients before prescribing more accurate treatments. This study aimed to establish a prototype of AI-CDSS modeling using electronic health records to predict five complications for cirrhosis patients who were controlled for oral antiviral drugs, lamivudine (LAM) or entecavir (ETV). MethodsOur modeling attained a web-based AI-CDSS with four steps – data extraction, sample normalization, AI-enabled machine learning (ML), and system integration. We designed the extract-transform-load (ETL) procedure to filter the analytics features from a clinical database. The data training process applied 10-fold cross-validation to verify diverse ML models due to possible feature patterns with medications for predicting the complications. In addition, we applied both statistical means and standard deviations of the realistic datasets to create the simulative datasets, which contained sufficient and balanced data to train the most efficient models for evaluation. The modeling combined multiple ML methods, such as support vector machine (SVM), random forest (RF), extreme gradient boosting, naive Bayes, and logistic regression, for training fourteen features to generate the AI-CDSS's prediction functionality. ResultsThe models achieving an accuracy of 0.8 after cross-validations would be qualified for the AI-CDSS. SVM and RF models using realistic data predicted jaundice with an accuracy of over 0.82. Furthermore, the SVM models using simulative data reached an accuracy of over 0.85 when predicting patients with jaundice. Our approaches implied that the simulative datasets based on the same distributions as that of the features in the realistic dataset were adequate for training the ML models. The RF model could reach an AUC of up to 0.82 for multiple complications by testing with the un-trained data. Finally, we successfully installed twenty models of the suitable ML methods in the AI-CDSS to predict five complications for cirrhosis patients prescribed with LAM or ETV. ConclusionsOur modeling integrated a self-developed AI-CDSS with the approved ML models to predict cirrhosis complications for aiding clinical decision making.

Concept of operations for increasingly autonomous space operations

Concept of Operations (ConOps) documents provide a common view of future system functions to all stakeholders. This ConOps focuses on deep space missions, such as a mission to Mars. While Earth experts will be continuously monitoring operations during crewed deep-space missions, there will be communication delays and disruptions that will impede the rapid assistance required by the crew in time- and safety-critical situations. An argument will be made that the crew will require (some kind of) assistance to quickly understand the situation enough to safe the system. This document describes a notional vision of the operational processes, practices and capabilities needed by a deep space mission crew for them to autonomously respond to anticipated and unanticipated, time-critical anomalies. A descriptive model of a Crew Performance Support System (CPSS) is used to illustrate what will be required for a safe and successful manned mission to Mars. Scenarios will address crew, Earth-Support and technology roles/responsibilities, task prioritization, teaming strategies, complex procedure development and execution, assumptions, asynchronous collaboration under communication time delay and limited data exchange to illustrate potential operational needs and approaches. Scenarios are responsive to known human risks identified during and after long duration spaceflight and incorporate transition plans as space travel moves from ISS to Lunar to Mars operations specifically identifying test bed and research activity needs. The envisioned CPSS will alter the current operational paradigm of crew reliance on Earth experts to resolve anomalies. The intent of this ConOps is to advance the research and development of a CPSS.

Conversational and generative artificial intelligence and human–chatbot interaction in education and research

AbstractArtificial intelligence (AI) as a disruptive technology is not new. However, its recent evolution, engineered by technological transformation, big data analytics, and quantum computing, produces conversational and generative AI (CGAI/GenAI) and human‐like chatbots that disrupt conventional operations and methods in different fields. This study investigates the scientific landscape of CGAI and human–chatbot interaction/collaboration and evaluates use cases, benefits, challenges, and policy implications for multidisciplinary education and allied industry operations. The publications trend showed that just 4% (n = 75) occurred during 2006–2018, while 2019–2023 experienced astronomical growth (n = 1763 or 96%). The prominent use cases of CGAI (e.g., ChatGPT) for teaching, learning, and research activities occurred in computer science (multidisciplinary and AI; 32%), medical/healthcare (17%), engineering (7%), and business fields (6%). The intellectual structure shows strong collaboration among eminent multidisciplinary sources in business, information systems, and other areas. The thematic structure highlights prominent CGAI use cases, including improved user experience in human–computer interaction, computer programs/code generation, and systems creation. Widespread CGAI usefulness for teachers, researchers, and learners includes syllabi/course content generation, testing aids, and academic writing. The concerns about abuse and misuse (plagiarism, academic integrity, privacy violations) and issues about misinformation, danger of self‐diagnoses, and patient privacy in medical/healthcare applications are prominent. Formulating strategies and policies to address potential CGAI challenges in teaching/learning and practice are priorities. Developing discipline‐based automatic detection of GenAI contents to check abuse is proposed. In operational/operations research areas, proper CGAI/GenAI integration with modeling and decision support systems requires further studies.

Piezoelectric adaptive active vibration suppression for wind-tunnel model support sting

Due to flow separation and turbulence, the slender cantilever aircraft model support system is prone to low-frequency and large-amplitude resonance in the wind tunnel tests, resulting in a decrease in test data quality, a limited test envelope, and even threatening the safe operation of the wind tunnel. A piezoelectric active damping system based on the filtered-x least mean square algorithm is proposed to effectively suppress the vibration of the wind-tunnel model support sting. Firstly, a modified variable step least mean square algorithm is proposed to address the issue that the fixed-step algorithms limit each other in terms of convergence speed and steady-state error. Following that, a variable step filtered-x least mean square algorithm based on reference signal reconstruction is developed, and the corresponding feedback controller is designed to perform the ground tests of the piezoelectric active damping system for the wind-tunnel model support sting. The experimental results show that the proposed algorithm has a faster convergence speed and lower steady-state error than the traditional algorithms, as well as strong anti-noise and adaptive control abilities that significantly improve the active vibration suppression effect of the wind-tunnel model support sting.

Joyful Learning Model for Javanese Speech Levels Course

As one of Indonesia’s cultural heritages, regional languages, including Javanese, need to be preserved. The cultural richness of Javanese includes the politeness and manners values, as reflected in the so-called unggah-ungguh (speech levels). While it is taught as a course in the country’s 12-year mandatory education, the teaching and learning process still follows the traditional, less appealing model. This research aims to create a Javanese speech levels learning model based on joyful learning. The model was both innovative and entertaining. The research used R&D ADDIE design with the following steps: analysis, designing, development, implementation, and evaluation. The model was developed based on two philosophies: progressivism and analytic philosophy. Syntagmatic, social system, support system, reaction principle, instructional impact, and accompaniment impact served as the model's components. Orientation, presentation of material, explanation of how to play, playing vocabulary, playing sentences, dialogue, assessment, and reflection comprised the syntagmatic model. The principles of joyful learning and the norms of honing, loving, and nurturing are included in the model social system. The model reaction principle included the teacher serving as a facilitator, planner, and assessor. Tools, media, and learning resources are all part of the model support system. Some of the instructional effects were increased Javanese vocabulary, fun learning activities, and some impacts of model accompaniment, including increased emotional sharpness, imagination, and cooperation.

Integrating dynamic modeling and decision support systems for sustainable urban planning in a changing climate

ABSTRACT The study introduces the Ant Lion Recurrent Climate Estimation (ALRCE) model for precise climate forecasting, emphasizing critical parameters like rainfall, humidity, temperature, wind speed, and dew point. Its primary goal is to mitigate natural hazard risks stemming from sudden climate shifts. Data from Surat city, Gujarat, including these features, is meticulously processed to train the ALRCE model. It employs an ant lion fitness function for analysis, particularly predicting rainfall and drought rates. Implemented in MATLAB, it demonstrates superior precision, accuracy, and risk reduction, validated for future climatic predictions from 2025 to 2050. Comparative analysis with existing techniques shows ALRCE outperforms, achieving 96.5% accuracy, 98% precision, 0.18838 RMSE, and 0.63 correlation coefficient. The study enhances accuracy by 2% post-optimization, highlighting ALRCE’s potential to significantly improve climate prediction and mitigate risks associated with abrupt climate changes.

A Neuro-Fuzzy-based Approach to Detect Liver Diseases

The liver is a crucial organ in the human body and performs vital functions essential for overall health, including metabolism, immunity, digestion, detoxification, and vitamin storage. Detecting liver diseases at an early stage poses challenges due to the liver's ability to function adequately despite partial damage. Early detection is crucial as liver diseases have significant clinical and socio-economic impacts, affecting other organ systems and requiring timely intervention to improve patient survival rates. Classical diagnostic methods for liver disorders may not always produce better results, thus necessitating more advanced and accurate diagnostic systems. Intelligent systems like predictive modeling and decision support systems, have shown promising results in recent years in disease detection and are aiding medical practitioners. In this research, a neuro fuzzy-based system integrating neural networks and fuzzy logic (FL) was implemented. Based on the risk factors present in the dataset that was used to benchmark the algorithm, this system offered a classification accuracy of 97% which is comparable with existing systems in the literature. This study creates a neuro-fuzzy system for early liver disease identification, solving diagnostic issues and offering healthcare improvements. The proposed system is validated by presenting the simulation results.

Decision-Making Model in Information Technology Management of Logistics Companies

The research is devoted to developing an innovative decision-making model for automating the sorting processes of postal item sorting. In today’s environment of rapid development of logistics services and growing volumes of mail, there is a need to improve existing technologies and methods of managing sorting lines. The main goal of this study is to create an effective model that will ensure the optimisation of sorting terminals with loading points. The purpose of the research is to develop decision-making models for managing postal service lines that implement sorting logic for terminals with loading points. The research methodology is based on a systematic approach that allows us to take into account all aspects of sorting lines. The theories of finite predicate algebra and set algebra were used to create mathematical sorting models. Methods of building decision support systems and modern information technologies ensure the integration of the developed algorithms into real postal systems. The scientific novelty lies in defining and implementing a variant of the decision-making model for an automated sorting line for mail. The model implements the specified sorting logic, which makes it possible to significantly improve the efficiency and accuracy of the processes of loading and sorting mail. Conclusions. A variant of implementing the model of the decision support system for an automated sorting line of mail is proposed. The developed model makes it possible to implement the logic of sorting mail for terminals with defined loading points. This helps to increase the efficiency and reliability of postal services, reducing the likelihood of errors and ensuring timely delivery.

The model of decision support system using hybrid method and actual weighting for the study program ranking

Nowadays the good or bad study program can be seen from the accreditation rank that it obtains from the institution of college accreditation. However, it is frequently found at college that there are some study programs that have the same accreditation. This encourages the college to do another approach which can do this study program ranking from a different point of view. This research developed a model of decision support system to do ranking towards 25 study programs existed in the environment of Sriwijaya State Polytechnic. Hybrid method employed the combination of analytical hierarchy process (AHP) and simple additive weighting (SAW) to do the ranking. Actual weighting model was used in the calculation based on the fact obtained in each study program, and in line with the criteria which had been determined. As many as 7 relevant criteria and 21 sub criteria were used in this model. The results of this research showed that the model which had been developed can give recommendation in the form of study program ranking with actual condition based on the data attached to each study program.

An MRI-based multi-parameter clinical decision support system (NeoMDSS) for early prediction of pathological complete response after the first cycle of neoadjuvant therapy in breast cancer: A multi-center prospective observational cohort study.

592 Background: Currently, there is no reliable and convenient way to predict response rates in early stages of neoadjuvant therapy (NAT) for breast cancer, which can affect the prognosis of non-responsive patients. Methods: In this prospective multi-center observational study, eligible participants who underwent NAT followed by radical surgery were recruited. All patients underwent magnetic resonance imaging (MRI) examination before and after the first cycle of NAT (1st-NAT). We analyzed the effects of clinicopathological features, such as histological grade, hormone receptor (HR) and HER2 status, Ki-67 expression, blood cell analysis, and MRI parameters including the reduction ratio of tumor diameter (ΔD%), apparent diffusion coefficient (ADC), and early enhancement ratio (EER) on NAT. By incorporating independent influencing factors, we developed a multiparametric clinical decision support system (NeoMDSS) model based on a retrospective cohort and validated its accuracy in both a prospective single-center internal cohort and a prospective multi-center external cohort. Relevant clinical trial details can be found under the identifier NCT04909554. Results: A total of 301 breast cancer patients were enrolled between January 2019 and December 2023, including 140 patients in the training cohort, 120 patients in the internal validation cohort, and 41 patients in the external validation cohort. The NeoMDSS model demonstrated excellent performance in predicting pathological complete response (pCR) at 1st-NAT, with an AUC of 0.874 (95% CI 0.813-0.935) in the training cohort, 0.845 (95% CI 0.771-0.919) in the internal validation cohort, and 0.867 (95% CI 0.742-0.992) in the external validation cohort. The NeoMDSS model exhibited a sensitivity, specificity, and accuracy of 80.0%, 81.3%, and 80.5%, respectively, in the internal validation cohort, and 87.0%, 83.3%, and 85.4%, respectively, in the external validation cohort. Calibration curves and decision curve analysis (DCA) further supported the clinical value of the NeoMDSS model. To facilitate the clinical application of the NeoMDSS model, a nomogram and an online website ( www.gdphneomdss.com ) were developed to calculate the probability of pCR, which the sensitivity, specificity, and accuracy of the NeoMDSS model were all 80.0% in the internal validation cohort and 80.0%, 81.3%, and 80.5% in the external validation cohort. Conclusions: The NeoMDSS model serves as a convenient tool in clinical practice at 1st-NAT to calculate and accurately predict the probability of pCR after NAT. This assists clinicians in determining whether to modify the treatment plan and improving personalized treatment for patients with poor response to NAT. Clinical trial information: NCT04909554 .

Combination of Multi-Objective Optimization on The Basis of Ratio Analysis and ROC in The Selection of Extracurricular Activities

The selection of extracurricular activities for students often involves several challenges and problems. Some problems in the selection of extracurricular activities include limited time, interests and talents, limited facilities and infrastructure, and awareness of choices. Problems in the selection of extracurricular activities include limited time, interests and talents, limited facilities and infrastructure, and awareness of choices. The purpose of this study is to provide recommendations for a decision support system model for students in the selection of extracurricular activities by applying the ROC method as a method of weighting criteria and the MOORA method as an alternative ranking tool for extracurricular activities that become recommendations for students. The combination of MOORA and Rank Order Centroid (ROC) methods is an approach that can be used in complex multi-criteria decision analysis. This method combines the strengths of both methods to provide more comprehensive and effective solutions in decision-making. The results of the study on the selection of extracurricular activities using a combination of MOORA and ROC methods, recommendations for extracurricular activities for Futsal extracurricular activities with a value of 0.444 as recommendations based on the final calculation results with the MOORA method got rank 1, for rank 2 recommended extracurricular activities Basketball with a final value of 0.437, and rank 3 recommended extracurricular activities Karate with a final value of 0.426.

Water-Energy Nexus: Optimizing Water Use in Energy Production

The water-energy nexus represents a critical interdependence between water resources and energy production, highlighting the need for sustainable management strategies. This abstract explores the optimization of water use in energy production, focusing on the key challenges, solutions, and potential benefits of integrating water and energy systems efficiently. Challenges arise from the significant water requirements of traditional energy sources such as thin able practices. Various strategies can enhance water efficiency in energy production, including technological advancements like dry cooling systems for power plants, water recycling and reuse techniques, and the development of water-saving renewable energy technologies like solar and wind power. These approaches aim to minimize water consumption, reduce pollution, and enhance overall resource resilience thermal power plants and hydropower, leading to water scarcity concerns and environmental impacts. Climate change further exacerbates these challenges, emphasizing the urgency of adopting just a Moreover, optimization techniques such as mathematical modeling, data analytics, and decision support systems play a crucial role in managing water resources effectively within the energy sector. These tools facilitate real-time monitoring, predictive analysis, and adaptive management, enabling informed decision-making and resource allocation. The benefits of optimizing water use in energy production are multifaceted.

Hybrid Analysis of Road Service Level Determination Decision Support System

Traffic congestion on the highway is one of the problems often faced by road users, as on the highway traffic in the city of Padang often experiences congestion due to the growth of several highways that are relatively smaller than the growth of traffic volume, causing a decrease in road service levels. This study aimed to determine the level of road services in several traffic lanes in the city of Padang. Data analysis using Hybrid Decision Support System (HDSS) modeling by combining the Analytical Hierarchy Process (AHP) method with the Weighted Aggregated Sum Product Assessment (WASPAS) This method is included in the Multi-Criteria Decision Making (MCDM) group. The AHP method can consistently determine the weight value of each criterion, and the WASPAS method can analyze alternative data to obtain decision results by ranking with the Weighted Sum Model (WSM) and Weighted Product Model (WPM) processes. The ranking results show that there are three types of road services in the city of Padang, namely B, C, and D with an accuracy rate of 0.6947%, this the results of this study using HDSS modeling can provide a better analysis process.

STEM-EDELCY learning model: a conceptual and pedagogical framework to facilitate students to develop 21st-century skills

ABSTRACT This research aims to describe the characteristics of the STEM-EDELCY learning model and analyze the improvement in students’ twenty-first-century skills after learning with the STEM-EDELCY model. This research involved 32 junior high school students (aged 12–13) from Indonesia. This research uses a one-group pretest-posttest design. Essay test questions and observation sheets are used to collect data on twenty-first-century skills. Data analysis of the characteristics of the STEM-EDELCY model is descriptive qualitative, while data analysis of twenty-first-century skills is a paired sample t-test. The characteristics of the STEM-EDELCY learning model can be seen in the model components, namely theory, syntax, social system, reaction principle, support system, and instructional and nurturant effects. The syntax of the STEM-EDELCY model consists of engage, defining the problem, plan and design, create, test and improve, explain, elaborate, and evaluate. Students’ twenty-first-century skills have increased significantly after participating in learning using the STEM-EDELCY model.