Development Of Management System Research Articles

Adaptation of the Learning Process for Students in the Post New Normal Era: Learning Management System (LMS) Innovation Efforts in Indonesia

This study aims to explore the condition of Arabic learning at UIN Syarif Hidayatullah Jakarta after the New Normal Era and the response of the academic community of UIN Syarif Hidayatullah Jakarta to the post-New Normal situation through the development of a Learning Management System (LMS). The descriptive method is used by utilizing qualitative data from interviews and quantitative data from surveys using questionnaires. The findings show that students' response to adaptation after the COVID-19 pandemic has increased, especially in motivation to learn Arabic. The use of LMS elicits a positive response from students and faculty, enabling variety in teaching, increasing learning motivation, and facilitating assessment. Nonetheless, challenges related to access and conventional learning preferences must be overcome. The majority of lecturers see benefits in using LMS, but differences of opinion regarding the need for a face-to-face approach emphasize the importance of adapting and integrating learning technology with traditional approaches to improve the effectiveness of Arabic language learning. These findings support Gerald Gleason's concept of Educational Technology and John Dewey's The Philosophy of Education. Overall, the study highlights the importance of adaptation in Arabic language learning in the post-pandemic era, with the majority of students and lecturers responding positively to the use of learning technologies such as LMS despite the need for adjustments and improvements to improve overall learning effectiveness.

Scalable Compositional Digital Twin-Based Monitoring System for Production Management: Design and Development in an Experimental Open-Pit Mine

While digital twins (DTs) have recently gained prominence as a viable option for creating reliable asset representations, many existing frameworks and architectures in the literature involve the integration of different technologies and paradigms, including the Internet of Things (IoTs), data modeling, and machine learning (ML). This complexity requires the orchestration of these different technologies, often resulting in subsystems and composition frameworks that are difficult to seamlessly align. In this paper, we present a scalable compositional framework designed for the development of a DT-based production management system (PMS) with advanced production monitoring capabilities. The conducted approach used to design the compositional framework utilizes the Factory Design and Improvement (FDI) methodology. Furthermore, the validation of our proposed framework is illustrated through a case study conducted in a phosphate screening station within the context of the mining industry.

Case study on document management system development to improve document standardization and classification system

Case study on document management system development to improve document standardization and classification system

Optimizing Electric Vehicle Battery Management Systems for Enhanced Performance and Efficiency in the Automotive Sector

Automotive means of transportation has become the major and cheapest means transportation in Nigeria. This fact has put more pressure on manufacturers to provide customers with high quality Automobiles with the aim of making profits. Nowadays, manufacturers are faced with a major problem in the management of information within a manufacturing plant and creation of effective relationship with customers. As an approach to solving these problems, a detailed analysis is carried out on the existing system to find out the strength and weaknesses of the existing system. And based on the requirements generated, the new system is designed. An Object Oriented design approach is used to describe the various units (modules) that make up the system in terms of classes and objects. The design and development of an Automotive Plant Management system for a manufacturing company is a project developed with PHP, MySQL, Ajax, HTML, JavaScript and Jquery Plugins, it provided better and more efficient management of information generated from Human Resource, Inventory, Company Finance and also Customer Relationship Management In recent years, the surge in the adoption of electric vehicles has played a vital role in reducin fossil fuel consumption and greenhouse gas emissions. However, limited cross-national research has been conducted on the determinants of electric vehicle adoption in developing and developed countries. This study examines the factors influencing the intention to adopt electric vehicles in India (378 participants) and Spain (265 participants). This study develops an integrated model that combines the unified theory of acceptance and use of technology (UTAUT2) and the value-belief-norm (VBN) model while accounting for the impact of national culture. The model is tested using structural equation modeling. The results indicate the integrated UTAUT2-VBN model is a valuable tool for explaining the differences in adoption intention across cultures. Moreover, the national cultural system plays a significant moderating role in most relationships within the model. This study offers valuable insights into the factors influencing electric vehicle adoption in different cultural contexts, which can inform policies and strategies to promote sustainable transportation.

LEARNING MANAGEMENT SYSTEM

In order to revolutionize education, our research paper explores the development and implementation of a revolutionary Learning Management System (LMS). LMS platforms play a key role in contemporary education by acting as virtual centers of instruction. In this article, we examine the essential features of our learning management system (LMS), including content management and user authentication, which improve resource accessibility and expedite instructional procedures. By employing an agile development methodology, we made sure that stakeholders were included at every stage of the project's development. We also talk about the concrete advantages that our LMS provides, such better teaching results and an improved user experience. Our study highlights the transformative potential of our learning management system (LMS) in creating inclusive and stimulating learning environments for all students by highlighting the significance of technology in education. Keywords— Digital Education, Education Technology, Progress Tracking, Accessibility in Education, User Authentication

Design Of Electric Motorcycle Variable with Battery Management System

This study focuses on conceptualization and development of a battery management system (BMS) with two main functions, battery monitoring and management, in the context of brushless direct current motors (BLDCs). The main challenge in variable estimation is to protect the battery from potential risks during the charge and discharge cycle. The new proposed resolution combines a comprehensive BMS with monitoring capabilities for charge (SoC), health (SoH), voltage, current and battery temperature. In addition, a protective mechanism is incorporated to prevent variables from overshooting safety parameters. This research uses two different methodologies for estimating SOC, coulomb counting and open circuit voltage. In experimental tests, resistance potentiometers of 1,650, 3,300 and 0 were used, with SoC estimates of 37%, 19% and 65%, while coulomb counting method has a marginal error of 1.13%. On the contrary, the open-circuit voltage method generated a SoC estimate of 0% for all potentiometer resistance, with an error rate of 0.64 %. As a result, the open circuit voltage method is chosen because of its superior accuracy compared to the coulomb counting method. The state assessment of the battery showed a value of 100% after seven cycles. In addition, a protective system has been implemented to ensure that battery variables remain within the safe thresholds throughout the charge and discharge process. Consequently, the implementation of this BMS is expected to significantly improve overall performance and extend battery life.

Wave and straight plenum effects on thermal management system performance

The need to offset heat generated by batteries in electric vehicles (EVs) necessitated the design and development of battery thermal management systems (BTMS), one of which adopt air cooling technique. The redesign of plenums of the BTMS has been beneficial in the enhancement of the BTMS's performance. In view of these, this study considered wave and straight design of plenums in an attempt to enhance the performance of the system. The proposed designs were investigated using Computational Fluid Dynamics (CFD) methodology. The CFD methodology was validated by first investigating the conventional Z-design of BTMS with straight plenums and the results were compared with existing results from experiments in literature. Findings from the study revealed that by replacing straight-horizontal plenum with straight-inclined plenum, maximum temperature (Tmax), and maximum temperature difference (ΔTmax) of the Z-design was reduced by 3.47 K and 6.82 K, respectively, with increase in pressure drop (ΔP) by 2.24 Pa and pumping power (Pp) by 0.01 W.Furthermore, by adopting wave-inclined plenum design, Tmax, and ΔTmax were reduced by 4.61 K and 8 K, respectively when compared with the conventional Z-design BTMS. More so, the pressure drop (ΔP) and pumping power (Pp) increased by 3.17 Pa and 0.0133 W, respectively. The study generally revealed that careful redesign of divergence plenum with inclined straight and wave-like structure will enhance the thermal performance of BTMS.

An Overview of Demand Analysis and Forecasting Algorithms for the Flow of Checked Baggage among Departing Passengers

The research on baggage flow plays a pivotal role in achieving the efficient and intelligent allocation and scheduling of airport service resources, as well as serving as a fundamental element in determining the design, development, and process optimization of airport baggage handling systems. This paper examines baggage checked in by departing passengers at airports. The crrent state of the research on baggage flow demand is first reviewed and analyzed. Then, using examples of objective data, it is concluded that while there is a significant correlation between airport passenger flow and baggage flow, an increase in passenger flow does not necessarily result in a proportional increase in baggage flow. According to the existing research results on the influencing factors of baggage flow sorting and classification, the main influencing factors of baggage flow are divided into two categories: macro-influencing factors and micro-influencing factors. When studying the relationship between the economy and baggage flow, it is recommended to use a comprehensive analysis that includes multiple economic indicators, rather than relying solely on GDP. This paper provides a brief overview of prevalent transportation flow prediction methods, categorizing algorithmic models into three groups: based on mathematical and statistical models, intelligent algorithmic-based models, and combined algorithmic models utilizing artificial neural networks. The structures, strengths, and weaknesses of various transportation flow prediction algorithms are analyzed, as well as their application scenarios. The potential advantages of using artificial neural network-based combined prediction models for baggage flow forecasting are explained. It concludes with an outlook on research regarding the demand for baggage flow. This review may provide further research assistance to scholars in airport management and baggage handling system development.

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.

Design and development of energy management system for solar electric vehicle with dynamic power split approach

ABSTRACT The integration of on-roof solar photovoltaic (PV) systems in electric vehicles (EVs) represents a groundbreaking advancement in the evolution of sustainable transportation. The objective of the research is to design and validate an integrated system for on-roof solar PV integration in EVs, optimising energy utilisation and reducing reliance on grid electricity. This paper explores the implementation of an Adaptive Maximum Power Point Tracking (MPPT) system, which maintains the output of the PV system at a constant level, leading to a significant improvement of approximately 10.20% in energy output compared to conventional MPPT systems. Additionally, a dynamic power split approach (DPAS) is employed to intelligently manage the allocation of power between the solar PV system and the vehicle battery, ensuring continuous fulfilment of energy demand while simultaneously recharging the battery. The proposed system reduces 65% of energy demand from grid and hence, reliance on grid. Furthermore, the dynamic power split approach enable substantial reductions in battery capacity requirements, with a potential decrease of 25–35% observed. These advancements not only enhance the performance and efficiency of EVs but also contribute to cost savings and the transition towards a cleaner and more sustainable transportation ecosystem.

ВЫЯВЛЕНИЕ ПРОБЛЕМ ИНФОРМАЦИОННОЙ БЕЗОПАСНОСТИ МЕТОДОМ СИСТЕМАТИЧЕСКОГО ОБЗОРА ЛИТЕРАТУРЫ

The field of information security covers a wide range of technical, organizational and social aspects, requiring information security specialists to take a unique and comprehensive approach to the development, support, operation, evaluation and improvement of information security management systems. An important element of successful information protection is the accurate identification and assessment of risks and vulnerabilities that may become a source of threats. In existing publications, as a rule, it is assumed that specialists have the necessary experience and knowledge to conduct risk analysis. However, in the dynamic world of information technology and threats, an information security specialist faces a huge amount of information and potential risks that are constantly evolving. Because of this, it is an impossible task to keep in mind all kinds of threats to information security. 
 The purpose is to identify actual problems of information security, potentially acting as sources of information security risk, based on the method of systematic review of the literature. Results: 73 actual problems of information security have been identified, which an information security specialist should take into account in his activities. Understanding these issues makes it possible to develop more effective protection strategies, as well as to make informed and informed decisions when developing and applying security measures.

INNOVATIVE ENERGY MANAGEMENT SYSTEM OF RESIDENTIAL COMPLEX “PANORAMA” IN DNIPRO CITY

Problem statement. Improving energy efficiency is a priority for both the European Union and Ukraine, which has a clear and consistent direction of development in accordance with the strategies of the European Green Deal. Residential buildings consume a large percentage of the electricity produced, and improving the energy efficiency of buildings will contribute to energy conservation, as well as energy security and energy independence from energy imports. Significant savings in energy consumption can be achieved through the development and implementation of energy management systems to implement energy-efficient management of modern buildings and the use of smart technologies in buildings, which will allow to control and optimise energy consumption and promote the rational use of energy. The purpose of the article. To develop an energy management system for the residential complex “Panorama” in Dnipro city. The work was carried out within the framework of the international project PRECEPT [1] under HORIZON 2020 programme. Conclusions. An energy management system for a residential complex based on monitoring of climate and air quality parameters is proposed. The system has been successfully put into operation, and in real time receives and processes external data on energy costs, weather conditions and other parameters, analyses and accumulates data, makes the necessary decisions on energy consumption and sends the results to consumers and building managers to save energy or improve comfort.

A vector-based coastline shape classification approach using sequential deep learning model

Coastlines play a crucial role in coastal dynamics, and classifying their shape is an essential requirement for coastal analysis. With the development of Coastal Management Systems (CMS), structured and high-resolution vector-format coastlines have become increasingly available compared to remote sensing image coastlines. However, due to the challenges of accurate description and ambiguous classification rules, automatic classification of vector coastlines has been a difficult but urgent problem to solve. In this paper, we propose a data-driven approach for classifying the shape of vector coastlines, according to their morphological characteristics. The method utilizes a sequence-based deep learning algorithm to model and classify coastline segments. We construct a dataset including five representative types of vector coastlines, train and evaluate the model using this dataset. The evaluation results show that the proposed method outperforms all baselines, achieving a classification accuracy of 93.20%. This method can be integrated into existing Coastal Management Systems to enhance their morphological analysis functions, making a valuable contribution to the applications of Artificial Intelligence (AI) in coastal management.

Methodology for assessing the level of development and efficiency of the functioning of the traffic management system, taking into account the environmental factor

The scientific article presents a methodology for assessing the level of development and efficiency of traffic management systems, taking into account environmental factors. The order and algorithms for all calculations to assess the level of effectiveness for road traffic management systems of motor transport, while taking into account the impact of environmental factors, are provided. The methodology for assessing the current state of the traffic management system relies on the degree of deviation of each individual indicator from its target value. The resulting value of the composite indicator for the system’s condition is then compared to its standard value, and an estimated overall state for the system is determined. According to some indicators, there has been an increase in emissions of toxic substances in the city's streets and roads, which are assessed as “poor”. To improve the state of the environment, an immediate set of organizational and technical measures has been proposed for the rational development of the traffic management system.

Early Warning of Financial Risk Management using AGRNN-COA Approach

Financial risk management was used to protect an entity's financial performance and stability, entails detecting, evaluating, and reducing potential risks, such as credit defaults and market swings. It includes tactics including hedging, diversification, and risk limitation. Financial authorities must immediately responsive for financial risk management system that works with the current economic development. This paper proposes a hybrid approach for early pre-alarm warning of financial risk management system and economic development. The proposed hybrid approach is the combined performance of both the Alternating Graph-Regularized Neural Network (AGRNN) and Crayfish Optimization Algorithm (COA).Commonly it is named as AGRNN-COA technique. The major objective of the proposed approach is to give early pre-alarm warning of financial risk management. AGRNN is designed to give early pre-alarm warning of the financial system of the country. The financial risk management from the AGRNN are optimized by using the COA. By then, the proposed model is implemented in the MATLAB/Simulink working platform and the execution is calculated with the present procedures. The proposed method shows better results in all existing like Financial Risk Management Supervised Algorithm (FRM-SVM), Financial Risk Management Deep Neural Network (FRM-DNN) and Financial Risk Management Heap Based Optimization (FRM-HBO). The accuracy level of proposed FRM-AGRNN-COA approach is 98% that is higher than the other existing methods. The specificity and the F-score of the proposed FRM-AGRNN-COA approach is 99% and 97%. The error rate of the proposed FRM-AGRNN-COA approach is 1.8%, which is very less compared to other existing techniques. From the result, it is conclude that the proposed approach based error is less compared to existing techniques.

Enhancing drilling operations: prioritizing wellbore integrity, formation preservation, and effective mud waste control (case study)

This study examined the issues of wellbore instability encountered during the drilling of the vertical exploration Al-Rateena-1 well, Block 25D, Al-Rawat Field, Sudan. Measurements of the wellbore diameter revealed significant damage to the formation interval between 2775 and 2925 m. This damage released approximately 800 barrels of drill cuttings from the affected zone and overlying formations. A comprehensive wellbore stability assessment was conducted to identify the root causes of instability and develop strategies to mitigate future occurrences. The assessment involved a thorough analysis of the pore, vertical, effective vertical, and minimum and maximum horizontal pressures. All of these parameters play a crucial role in maintaining wellbore integrity. This study also evaluated the potential impact of these parameters on groundwater and soil quality, leading to the development of an efficient waste management system. The safe mud weight range for the well was determined to be between 13.5 and 15.5 pounds per gallon (PPG). However, the drilling fluid used during the operation had a significantly lower density of only 10 PPG. This ultimately leads to the collapse of the wellbore wall. Examination of drilling cuttings revealed elevated levels of various heavy metals. These heavy metals included Lead (94.12 ppm), Mercury (62.87 ppm), Nickel (1403 ppm), Copper (343 ppm), Calcium (23132.72 ppm), Magnesium (3700 ppm), and Barium (16000 ppm). These elements pose a significant threat to both surface water and groundwater quality. It is possible that they also contributed to the wellbore wall collapse. It was hypothesized that the interaction between these elements and the water-based drilling fluid or its additives could have triggered the collapse event. The findings of this study highlight the importance of conducting comprehensive wellbore stability assessments before drilling. Such assessments should incorporate detailed investigations, modelling, calculations, and simulations of various parameters that influence wellbore stability. In addition, careful consideration must be given to the potential environmental impacts of drilling fluids and cuttings, particularly in areas with sensitive ecosystems. The wellbore instability in the vertical exploration Al-Rateena-1 well was primarily caused by the use of an underweight drilling fluid. This underweight drilling fluid results in the collapse of the wellbore wall. Elevated levels of heavy metals in drill cuttings suggest a potential impact on groundwater quality. It is also possible that they contributed to the wellbore instability. Comprehensive wellbore stability assessments are essential to prevent similar incidents in the future. This study provides a detailed account of wellbore instability. This includes the identification of root causes and the development of preventive measures. This study also highlights the potential environmental impact of drilling fluids and cuttings. The findings of this study can help improve drilling practices and environmental protection in the oil and gas industry.

Impact of biogenic impurities on catalytic synthesis of multi-walled carbon nanotubes and hydrogen from polyolefin resins

Biogenic impurities are common contaminants of plastic waste. This study investigated the effects of biogenic impurities on the conversion of a polyolefin mixture (6 wt% low-density polyethylene, 24 wt% high-density polyethylene, and 70 wt% polypropylene) into multi-walled carbon nanotubes (MWCNTs) and hydrogen via pyrolysis-chemical vapor deposition (CVD). Biogenic components from diverse sources were added to the polyolefin mixture, including lignocellulosic materials (pine sawdust, coconut coir pith), biosolids from wastewater treatment (municipal sewage sludge), animal-based waste (crustaceans), and bio-based plastic (polylactic acid). At contamination levels up to 9.1 wt%, 3 out of 5 impurities (coconut coir pith, sewage sludge and crustaceans) caused a decline in the conversion efficiency of feedstock into MWCNTs (33 ± 7% for polyolefins only compared with 20 ± 6%, 16 ± 1% and 16 ± 2% for polyolefins with 9.1 wt% coconut coir pith, sewage sludge and crustaceans, respectively). According to gas analysis, impurities led to a loss of catalyst activity, resulting in a decreased concentration of H2 and increased concentrations of methane and ethylene in the CVD gas. Furthermore, for polyolefins containing 9.1 wt% coconut coir pith, sewage sludge, crustaceans and polylactic acid, microscopy data revealed changes in the growth of filamentous carbon, leading to the appearance of carbon nanofibers in MWCNT samples. On the contrary, biogenic impurities barely changed outer diameters, ID/IG ratios, interlayer spacing, crystallite sizes of graphite-like sites, and the surface chemistry of MWCNT samples. The results suggest the development of quality management system for contaminated plastic feedstock could be advantageous to ensure consistent outcomes of pyrolysis-CVD process.

Development of Reward-Based Crowd Management System and Field Evaluation of Safety and Profitability

For railroad operators with a highly public nature, ensuring the safety of passengers and revitalizing the areas along the line are both tasks to be accomplished. To enhance passenger safety, it is necessary to keep congestion levels below a certain level on platforms and trains. Since traffic congestion generally occurs at certain times, such as in the morning, it is important to disperse traffic congestion during rush hours. In the past, railroad operators have encouraged passengers to voluntarily commute off-peak, but this has not worked as an incentive for passengers simply by improving comfort. Because of this failure, rail operators are considering a method of setting fare prices based on the time of day and congestion levels. However, past studies suggest that few users change their behavior because those who choose the time of day to use the railroads do not always coincide with the fare payers. Therefore, we devised a crowd management system in which coupons redeemable at stores along the rail line are given directly to the customers, encouraging voluntary off-peak commuting and stimulating the local economy. This system features dynamic updating of coupon ranks based on congestion forecast information from measured congestion levels to enhance the effectiveness of the service. The results of a three-month verification experiment that cooperated with Shizuoka Railway confirmed that 7.1% of passengers took congestion avoidance actions and that passengers who obtained coupons got off at a specific station an average of 29% more often.

Analysis Of Water Availability In The Environmental Water Sources Of The Aikmampat River Sub-Region In Fulfilling The Need For Clean Water In The South Part Of Lombok Island

Clean waters infrastructure functions in utilizing water resources for the general public. General planning for clean water development in this study is focused on water resource management in the form of stewardship, provision, use, development, and exploitation of water resources considering that the social, environmental, and economic functions of water resources cannot be separated. To estimate water availability for biota or ecosystems, we use the discharge at Q-95% at each weir. To analyze waters availability for biota, we refer to the flow duration curve graph. We have calculated the water balance for the Lingkung sub watershed of Aik Mampat, taking into account the surface water sources. The water balance has been calculated under two conditions: existing conditions (which include needs of irrigation and existing water needs of raw) and planned conditions (which consider the development of a drinking water management system). The level of service in the south coast area for 5 sub-districts in the development plan year was obtained at 150 l/s and a maximum of 270,75 l/s in the plan year. The maximum irrigation water requirement is 320 l/s with an average of 50 l/s. The water balance in the Aikmampat Sub-watershed (Lingkung surface water source) saw a decrease in the minimum FK (Reliability Factor) from the existing condition of 40% to 25% (plan condition). Water rotation arrangements in the spider channel need to be carried out, especially in June and July.

Uneven Usage Battery State of Health Estimation via Fractional-Order Equivalent Circuit Model and AutoML Fusion

To accurately predict the State of Health (SOH) of lithium-ion batteries under the continuously changing charging and discharging conditions in practical applications, this study proposes a hybrid modeling approach that integrates a Fractional Order Equivalent Circuit Model (F-ECM) with the AutoGluon automatic machine learning framework. By leveraging Electrochemical Impedance Spectroscopy (EIS) to capture battery frequency response characteristics, F-ECM accurately fits EIS data to extract detailed internal state parameters. The integration of AutoGluon automates the machine learning process, enhancing the precision of SOH predictions. Through testing and analysis on real battery datasets, this method has demonstrated superior prediction precision and computational efficiency compared to existing mainstream modeling approaches. Specifically, the hybrid method achieved a Root Mean Square Error (RMSE) of 2.12% and a Mean Absolute Error (MAE) of 1.67%. This study presents a highly accurate, interpretable, and adaptable predictive framework for lithium-ion battery health assessment, offering valuable insights for battery health management system development.