Design and analysis of an advanced thermal management system for the solar close observations and proximity experiments spacecraft

In this paper,the mission and the thermal environment of the Solar Close Observations and Proximity Experiments(SCOPE)spacecraft are analyzed,and an advanced thermal management system(ATMS)is designed for it.The relationship and functions of the integrated database,the intelligent thermal control system and the efficient liquid cooling system in the ATMS are elaborated upon.For the complex thermal field regulation system and extreme space thermal environment,a modular simulation and thermal field planning method are proposed,and the feasibility of the planning algorithm is verified by numerical simulation.A solar array liquid cooling system is developed,and the system simulation results indicate that the temperatures of the solar arrays meet the requirements as the spacecraft flies by perihelion and aphelion.The advanced thermal management study supports the development of the SCOPE program and provides a reference for the thermal management in other deep-space exploration programs.

Adaptive battery thermal management systems in unsteady thermal application contexts

With the increasing attention paid to battery technology,the microscopic reaction mechanism and macroscopic heat transfer process of lithium-ion batteries have been further studied and understood from both academic and industrial perspectives.Temperature,as one of the key parameters in the physical fra mework of batteries,affects the performa nce of the multi-physical fields within the battery,a nd its effective control is crucial.Since the heat generation in the battery is determined by the real-time operating conditions,the battery temperature is essentially controlled by the real-time heat dissipation conditions provided by the battery thermal management system.Conventional battery thermal management systems have basic temperature control capabilities for most conventional application scenarios.However,with the current development of la rge-scale,integrated,and intelligent battery technology,the adva ncement of battery thermal management technology will pay more attention to the effective control of battery temperature under sophisticated situations,such as high power and widely varied operating conditions.In this context,this paper presents the latest advances and representative research related to battery thermal management system.Firstly,starting from battery thermal profile,the mechanism of battery heat generation is discussed in detail.Secondly,the static characteristics of the traditional battery thermal management system are summarized.Then,considering the dynamic requirements of battery heat dissipation under complex operating conditions,the concept of adaptive battery thermal management system is proposed based on specific research cases.Finally,the main challenges for battery thermal management system in practice are identified,and potential future developments to overcome these challenges are presented and discussed.

AI-Driven Learning Management Systems:Modern Developments, Challenges and Future Trends during theAge of ChatGPT

COVID-19 pandemic restrictions limited all social activities to curtail the spread of the virus.The foremost and most prime sector among those affected were schools,colleges,and universities.The education system of entire nations had shifted to online education during this time.Many shortcomings of Learning Management Systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of LMSs.This paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user experience.The AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based chatbots.Session layer enhancements are also required,such as AI-based online proctoring and user authentication using Biometrics.These extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of AI-algorithms.It also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of Software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.

Smart Energy Management System Using Machine Learning

Energy management is an inspiring domain in developing of renewable energy sources.However,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and demand.The major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental effects.Modern energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user comfort.To address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve it.An inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy costs.Machine Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)network.The IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource management.In this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management problem.The proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

Improvement of the nutritional support management system for patients in intensive care units

BACKGROUND Nutritional support for patients hospitalized in the intensive care unit(ICU)is an important part of clinical treatment and care,but there are significant implementation difficulties.AIM To introduce a modified nutritional support management system for ICU patients based on closed-loop information management and psychological counseling.METHODS The division of functions,personnel training,system construction,development of an intelligent decision-making software system,quality control,and improvement of the whole process were carried out to systematically manage nutritional support for ICU patients.RESULTS Following the implementation of the whole process management system,the scores of ICU medical staff’s knowledge,attitudes/beliefs,and practices regarding nutritional support were comprehensively enhanced.The proportion of hospital bed-days of total enteral nutrition(EN)in ICU patients increased from 5.58%to 11.46%,and the proportion of EN plus parenteral nutrition increased from 42.71%to 47.07%.The rate of EN initiation within 48 h of ICU admission increased from 37.50%to 48.28%,and the EN compliance rate within 72 h elevated from 20.59%to 31.72%.After the implementation of the project,the Self-rating Anxiety Scale score decreased from 61.07±9.91 points to 52.03±9.02 points,the Self-rating Depression Scale score reduced from 62.47±10.50 points to 56.34±9.83 points,and the ICU stay decreased from 5.76±2.77 d to 5.10±2.12 d.CONCLUSION The nutritional support management system based on closed-loop information management and psychological counseling achieved remarkable results in clinical applications in ICU patients.

Exploration of University English Teachers’Acceptance and Willingness to Use Learning Management System Data Analysis Tools

This study investigates university English teachers’acceptance and willingness to use learning management system(LMS)data analysis tools in their teaching practices.The research employs a mixed-method approach,combining quantitative surveys and qualitative interviews to understand teachers’perceptions and attitudes,and the factors influencing their adoption of LMS data analysis tools.The findings reveal that perceived usefulness,perceived ease of use,technical literacy,organizational support,and data privacy concerns significantly impact teachers’willingness to use these tools.Based on these insights,the study offers practical recommendations for educational institutions to enhance the effective adoption of LMS data analysis tools in English language teaching.

Towards Automated Assessment of Learning Management Systems in Higher Education Institutions in Zambia

Zambia like any other country in most African regions is still grappling with the dynamics of harnessing technology for the betterment of Higher Education. The onset of the Covid 19 pandemic brought a test for the preparedness of the Zambian Higher Education Institutions (HEIs) in harnessing technology for pedagogical activities. As countries worldwide switched to electronic learning during the pandemic, the same could not be said for Zambian HEIs. Zambian HEIs struggled to conduct pedagogical activities on learning management platforms. This study investigated the factors affecting the implementation and assessment of learning Management systems in Zambia’s HEIs. With its focus on assessing: 1) system features, 2) compliance with regulatory standards, 3) quality of service and 4) technology acceptance as the four key assessment areas of an LMS, this article proposed a model for assessing learning management systems in Zambian HEIs. To test the proposed model, a software tool was also developed.

Research on Smart Energy Monitoring and Management System Based on Digital Twin Technology

Smart energy monitoring and management system lays a foundation for the application and development of smart energy. However, in recent years, the work efficiency of smart energy development enterprises has generally been low, and there is an urgent need to improve the application efficiency, resilience and sustainability of smart energy monitoring and management system. Digital twin technology provides a data-centric solution to improve smart energy monitoring and management system, bringing an opportunity to transform passive infrastructure assets into data-centric systems. This paper expounds on the concept and key technologies of digital twin, and designs a smart energy monitoring and management system based on digital twin technology, which has dual significance for promoting the development of smart energy field and promoting the application of digital twin.

AI Event:Applying the AI Management System Standard for Practical Success

The AI Management System Standard,known as AS ISO/IEC 42001:2023,helps to guide organizations in responsibly managing their AI systems.A key foundation of the standard is continuous improvement-Plan-Do-Check-Act.Hosted by Standards Australia and supported by the National AI Centre,this event will explore the importance of the continuous improvement methodology in managing AI opportunities and risks.Attendees will gain insight into why compliance and rules-based approaches are no longer fit for purpose against the evolving challenges and opportunities with AI.

An Assessment of Procurement Risks Management Systems in the Public Sector

Risk management in public procurement is a critical aspect that needs to be addressed in the public sector.Several studies have been conducted to understand the challenges and factors influencing risk management in public procurement.These studies have explored the importance of risk management principles,the role of political influence,and the need for effective risk assessment and anticipation.The research has also highlighted the need for specific risk management mechanisms and tools to be implemented in public procurement processes.Risk management reforms in the public sector are essential but often circumvented due to assorted reasons,such as political influence and the emergence of new risks.The research investigation employs a quantitative research design.A total of 380 questionnaires were recovered from respondents.The study showed that the public sector has a procurement risk management system that is effective,but there may be some areas for improvement in the prequalification process,onboarding process,and support provided to newly onboarded suppliers.Additionally,the public sector used some strategies to mitigate and control contract risks during the procurement process,but there were some areas for improvement in the review and lessons learned process,risk mitigation measures,contract monitoring and performance evaluation mechanisms,and communication and documentation process.Finally,the results suggest that there were constraints placed on the risk management strategies currently utilized by professionals working in the public sector.These constraints include insufficient support and buy-in from senior management and stakeholders,bureaucratic or administrative hurdles,inadequate policies and regulations,insufficient training and skill development opportunities,and insufficient resources.The study highlights the significance of tackling risk management in the realm of public procurement and offers valuable perspectives on avenues for enhancement,obstacles encountered by practitioners,and the necessity of thorough evaluation and revisions.Through the adoption of the suggestions originating from this study,governmental entities can improve their procurement risk management frameworks and guarantee improved adherence to risk management principles.

Design and Implementation of Library Seating Management System

The library seating management system is an important part of the library’s modern services. The system is based on the Spring and Spring Boot framework, and the system realizes a series of functions such as real-time query, reservation, and cancellation of seat resources, providing users with great convenience. With a simple operation, users can know the availability of seats in the library in real time and reserve them according to their needs. At the same time, the system also provides rich management functions, enabling administrators to easily configure and manage seat resources. The addition, deletion, modification and review of users, the generation of seats, the viewing of user usage records, and the addition or deletion of points for users’ usage can also be carried out. This not only improves the management efficiency, but also provides more scientific and accurate data support for the management of the library. The system not only optimizes the user experience, but also promotes the scientific management and efficient utilization of library resources, and provides strong support for the modern services of the library.

Development and Application of Integrated Indicators for Assessing Healthcare Waste Management Systems in Kenyan Hospitals

Healthcare waste management (HCWM) is an important aspect of healthcare delivery globally because of its hazardous and infectious components that have potential for adverse health and environmental impacts. The paper introduces a set of indicators for assessing HCWM systems in hospitals. These indicators are: HCWM policies and standard operating procedures, management and oversight, logistics and budget support, training and occupational health and safety, and treatment, disposal and waste treatment equipment housing. By plotting a mark on a continuum which is defined as good and poor on the extremes and is connected with all other marks in a spoke arrangement, it’s possible to describe a baseline for HCWM in any specific hospital. This baseline can be used to improve awareness of the actors and policy-makers, compare the same hospital at a different point in time, to compare observations by different evaluators and to track improvements. Results suggest that in Kenya, the application of such indicators is useful for evaluating which priorities should be addressed to improve outcomes in HCWM systems. Systematic sampling technique was used to identify and collect data by use of observational checklist, interviews, visual verification and review of documents and a HCWM assessment tool. The objective is to suggest an integrated management tool as a method to identify prevailing problems with a HCWM system. The method can be replicated in other contexts worldwide, with a focus on the developing world. The integrated indicators focus on management of HCW and not its potential impact on human health and environment, an area recognized to be critical for future research.

Exploring Efficient Salary Management Systems:A Case Study of Hefei Yusheng Hotel Management Co.,Ltd.

The issue of brain drain poses a significant obstacle to the development of the hotel and catering industry.Attracting and retaining skilled personnel to engage in management within this sector has emerged as a primary challenge.This paper examines the current landscape of China’s catering service industry and its salary management system,identifying key challenges encountered in salary management within the industry.Drawing upon salary management theory and insights into existing challenges,the paper proposes specific treatment measures and management strategies.

Survey on AI and Machine Learning Techniques for Microgrid Energy Management Systems

In the era of an energy revolution,grid decentralization has emerged as a viable solution to meet the increasing global energy demand by incorporating renewables at the distributed level.Microgrids are considered a driving component for accelerating grid decentralization.To optimally utilize the available resources and address potential challenges,there is a need to have an intelligent and reliable energy management system(EMS)for the microgrid.The artificial intelligence field has the potential to address the problems in EMS and can provide resilient,efficient,reliable,and scalable solutions.This paper presents an overview of existing conventional and AI-based techniques for energy management systems in microgrids.We analyze EMS methods for centralized,decentralized,and distributed microgrids separately.Then,we summarize machine learning techniques such as ANNs,federated learning,LSTMs,RNNs,and reinforcement learning for EMS objectives such as economic dispatch,optimal power flow,and scheduling.With the incorporation of AI,microgrids can achieve greater performance efficiency and more reliability for managing a large number of energy resources.However,challenges such as data privacy,security,scalability,explainability,etc.,need to be addressed.To conclude,the authors state the possible future research directions to explore AI-based EMS's potential in real-world applications.

Energy Management System with Power Offering Strategy for a Microgrid Integrated VPP

In the context of both the Virtual Power Plant (VPP) and microgrid(MG), the Energy Management System (EMS) is a key decision-maker forintegrating Distributed renewable Energy Resources (DERs) efficiently. TheEMS is regarded as a strong enabler of providing the optimized schedulingcontrol in operation and management of usage of disperse DERs and RenewableEnergy reSources (RES) such as a small-size wind-turbine (WT) andphotovoltaic (PV) energies. The main objective to be pursued by the EMSis the minimization of the overall operating cost of the MG integrated VPPnetwork. However, the minimization of the power peaks is a new objective andopen issue to a well-functional EMS, along with the maximization of profitin the energy market. Thus, both objectives have to be taken into accountat the same time. Thus, this paper proposes the EMS application incorporatingpower offering strategy applying a nature-inspired algorithm such asParticle Swarm Optimization (PSO) algorithm, in order to find the optimalsolution of the objective function in the context of the overall operating cost,the coordination of DERs, and the energy losses in a MG integrated VPPnetwork. For a fair DERs coordination with minimized power fluctuationsin the power flow, the power offering strategies with an active power controland re-distribution are proposed. Simulation results show that the proposedMG integrated VPP model with PSO-based EMS employing EgalitarianreDistribution (ED) power offering strategy is most feasible option for theoverall operating cost of VPP revenue. The total operating cost of the proposedEMS with ED strategy is 40.98$ compared to 432.8$ of MGs only withoutEMS. It is concluded that each MGs in the proposed VPP model intelligentlyparticipates in energy trading market compliant with the objective function,to minimize the overall cost and the power fluctuation.

Process Discovery and Refinement of an Enterprise Management System

The need for the analysis of modern businesses is rapidly increasing as the supporting enterprise systems generate more and more data.This data can be extremely valuable for executing organizations because the data allows constant monitoring,analyzing,and improving the underlying processes,which leads to the reduction of cost and the improvement of the quality.Process mining is a useful technique for analyzing enterprise systems by using an event log that contains behaviours.This research focuses on the process discovery and refinement using real-life event log data collected from a large multinational organization that deals with coatings and paints.By investigating and analyzing their order handling pro-cesses,this study aims at learning a model that gives insight inspection of the processes and performance analysis.Furthermore,the animation is also performed for the better inspection,diagnostics,and compliance-related questions to specify the system.The configuration of the system and the conformance checking for further enhancement is also addressed in this research.To achieve the objectives,this research uses process mining techniques,i.e.process discovery in the form of formal Petri nets models with the help of process maps,and process refinement through conformance checking and enhancement.Initially,the identified executed process is reconstructed by using the process discovery techniques.Following the reconstruction,we perform a deep analysis for the underlying process to ensure the process improvement and redesigning.Finally,some recommendations are made to improve the enterprise management system processes.

Intelligent Student Mental Health Assessment Model on Learning Management System

A learning management system(LMS)is a software or web based application,commonly utilized for planning,designing,and assessing a particular learning procedure.Generally,the LMS offers a method of creating and delivering content to the instructor,monitoring students’involvement,and validating their outcomes.Since mental health issues become common among studies in higher education globally,it is needed to properly determine it to improve mental stabi-lity.This article develops a new seven spot lady bird feature selection with opti-mal sparse autoencoder(SSLBFS-OSAE)model to assess students’mental health on LMS.The major aim of the SSLBFS-OSAE model is to determine the proper health status of the students with respect to depression,anxiety,and stress(DAS).The SSLBFS-OSAE model involves a new SSLBFS model to elect a useful set of features.In addition,OSAE model is applied for the classification of mental health conditions and the performance can be improved by the use of cuckoo search optimization(CSO)based parameter tuning process.The design of CSO algorithm for optimally tuning the SAE parameters results in enhanced classifica-tion outcomes.For examining the improved classifier results of the SSLBFS-OSAE model,a comprehensive results analysis is done and the obtained values highlighted the supremacy of the SSLBFS model over its recent methods interms of different measures.

Optimization of Interactive Videos Empowered the Experience of Learning Management System

The Learning management system(LMS)is now being used for uploading educational content in both distance and blended setups.LMS platform has two types of users:the educators who upload the content,and the students who have to access the content.The students,usually rely on text notes or books and video tutorials while their exams are conducted with formal methods.Formal assessments and examination criteria are ineffective with restricted learning space which makes the student tend only to read the educational contents and videos instead of interactive mode.The aim is to design an interactive LMS and examination video-based interface to cater the issues of educators and students.It is designed according to Human-computer interaction(HCI)principles to make the interactive User interface(UI)through User experience(UX).The interactive lectures in the form of annotated videos increase user engagement and improve the self-study context of users involved in LMS.The interface design defines how the design will interact with users and how the interface exchanges information.The findings show that interactive videos for LMS allow the users to have a more personalized learning experience by engaging in the educational content.The result shows a highly personalized learning experience due to the interactive video and quiz within the video.

Optimal Machine Learning Enabled Performance Monitoring for Learning Management Systems

Learning Management System(LMS)is an application software that is used in automation,delivery,administration,tracking,and reporting of courses and programs in educational sector.The LMS which exploits machine learning(ML)has the ability of accessing user data and exploit it for improving the learning experience.The recently developed artificial intelligence(AI)and ML models helps to accomplish effective performance monitoring for LMS.Among the different processes involved in ML based LMS,feature selection and classification processesfind beneficial.In this motivation,this study introduces Glowworm-based Feature Selection with Machine Learning Enabled Performance Monitoring(GSO-MFWELM)technique for LMS.The key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in LMS.The pro-posed GSO-MFWELM technique involves GSO-based feature selection techni-que to select the optimal features.Besides,Weighted Extreme Learning Machine(WELM)model is applied for classification process whereas the parameters involved in WELM model are optimallyfine-tuned with the help of May-fly Optimization(MFO)algorithm.The design of GSO and MFO techniques result in reduced computation complexity and improved classification performance.The presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several aspects.The simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.

Development of Energy Management System for Micro Grid Operation

The introduction of several small and large-scale industries,malls,shopping complexes,and domestic applications has significantly increased energy consumption.The aim of the work is to simulate a technically viable and economically optimum hybrid power system for residential buildings.The proposed micro-grid model includes four power generators:solar power,wind power,Electricity Board(EB)source,and a Diesel Generator(DG)set,with solar and wind power performing as major sources and the EB supply and DG set serving as backup sources.The core issue in direct current to alternate current conversion is harmonics distortion,a five-stage multilevel inverter is employed with the assistance of an intelligent control system is simulated and the optimum system configuration is estimated to reduce harmonics and improve the power quality.The monthly demand for residential buildings is 13-15 Megawatts.So,almost 433 Kilo-Watts(KW)of electricity is required every day,and if it is used for 8 h per day,50-60 KW of electricity is needed per hour.The overall micro-grid model’s operation and performance are established using MATLAB/SIMULINK software,and simulation results are provided.The simulation results show that the developed system is both cost-effective and environment friendly resulting in yearly cost reductions.