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.

Mapping Climate Services for Disaster Risk Management: A Systematic Review and Research Gaps from a Policy Process Perspective

Climate services (CS) are crucial for mitigating and managing the impacts and risks associated with climate-induced disasters. While evidence over the past decade underscores their effectiveness across various domains, particularly agriculture, to maximize their potential, it is crucial to identify emerging priority areas and existing research gaps for future research agendas. As a contribution to this effort, this paper employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to review the state-of-the-art in the field of climate services for disaster risk management. A comprehensive search across five literature databases combined with a snowball search method using ResearchRabbit was conducted and yielded 242 peer-reviewed articles, book sections, and reports over 2013-2023 after the screening process. The analysis revealed flood, drought, and food insecurity as major climate-related disasters addressed in the reviewed literature. Major climate services addressed included early warning systems, (sub)seasonal forecasts and impact-based warnings. Grounded in the policy processes’ theoretical perspective, the main focus identified and discussed three prevailing policy-oriented priority areas: 1) development of climate services, 2) use-adoption-uptake, and 3) evaluation of climate services. In response to the limitations of the prevalent supply-driven and top-down approach to climate services promotion, co-production emerges as a cross-cutting critical aspect of the identified priority areas. Despite the extensive research in the field, more attention is needed, particularly pronounced in the science-policy interface perspective, which in practice bridges scientific knowledge and policy decisions for effective policy processes. This perspective offers a valuable analytical lens as an entry point for further investigation. Hence, future research agendas would generate insightful evidence by scrutinizing this critical aspect given its importance to institutions and climate services capacity, to better understand intricate facets of the development and the integration of climate services into disaster risk management.

An Edge-Fog-Cloud Computing-Based Digital Twin Model for Prognostics Health Management of Process Manufacturing Systems

The prognostics health management(PHM)fromthe systematic viewis critical to the healthy continuous operation of processmanufacturing systems(PMS),with different kinds of dynamic interference events.This paper proposes a three leveled digital twinmodel for the systematic PHMof PMSs.The unit-leveled digital twinmodel of each basic device unit of PMSs is constructed based on edge computing,which can provide real-time monitoring and analysis of the device status.The station-leveled digital twin models in the PMSs are designed to optimize and control the process parameters,which are deployed for the manufacturing execution on the fog server.The shop-leveled digital twin maintenancemodel is designed for production planning,which gives production instructions fromthe private industrial cloud server.To cope with the dynamic disturbances of a PMS,a big data-driven framework is proposed to control the three-level digital twin models,which contains indicator prediction,influence evaluation,and decisionmaking.Finally,a case study with a real chemical fiber system is introduced to illustrate the effectiveness of the digital twin model with edge-fog-cloud computing for the systematic PHM of PMSs.The result demonstrates that the three-leveled digital twin model for the systematic PHM in PMSs works well in the system’s respects.

Participatory Technique Supporting Environmental Management: A Systematic and Bibliometric Review

The increasing need to manage natural resources sustainably, driven by population growth, requires the simultaneous use of Participatory Techniques (PT) and landscape planning for structured decision-making. We conducted a bibliometric and systematic review to provide an overview of PT usage, identifying evolution in scientific production. We considered the number of publications and citations, prominent journals, and highly cited articles on scientific papers published in the Web of Science database between 1993 and 2023. A total of 415 articles related to PT were identified. After content evaluation, 19 critical articles were selected that underpin the growing combined use of models and indices with PT, enhancing the robustness and credibility of decision-making processes.

Development and Application of Procedures for the Management of Skin Toxicity Related to Immune Checkpoint Inhibitors in Patients with Lung Cancer

Objective: To establish the procedures for the management of skin toxicity related to immune checkpoint inhibitors in patients with lung cancer and explore the effect of application. Methods: A total of 24 evidence-based evidences were collected from 7 aspects, including risk factors, baseline screening, ICIs monitoring, daily skin care, multidisciplinary management, symptom management and health education. A total of 157 lung cancer patients and 94 nurses from 8 wards of the Oncology department of our hospital from November 2022 to May 2023 were selected by convenience sampling. A total of 77 patients and 46 nurses from ward 1 - 4 were divided into the baseline group. There were 80 patients and 48 nurses in Ward 5 - 8 as the evidence-based practice group. In the baseline group, patients were treated with routine methods such as assessing skin symptoms, taking medication according to symptoms, guiding to keep skin clean and moist, eating a light diet, and avoiding scratching. The evidence-based practice group adopts an evidence-based continuous improvement model for nursing. The differences in the severity of symptoms of skin toxicity in the second cycle of medication and the knowledge and practice of self-care of skin toxicity were compared between the two groups before and after the use of the syndrome, as well as the differences in the implementation rate of review indicators, evidence-based ability and knowledge and practice of skin toxicity care before and after the use of the syndrome. Results: The incidence and severity of cutaneous toxicity were significantly lower after treatment than before treatment (P P < 0.05). Conclusion: The implementation of immune checkpoint inhibitor-related skin toxicity management procedures can effectively reduce the incidence and severity of skin toxicity symptoms, optimize the clinical pathway, and improve the quality of care.

Research on Quality Management Measures of Green Building Construction

Urbanization has led to the rapid development of the construction industry.However,this has also led to higher requirements for the construction engineering management.Other than the quality monitoring of engineering construction,the energy-saving properties of the building should also be considered.Therefore,a scientific management approach should be adopted to improve green building management.This paper primarily examines the importance of quality management in green building construction,along with the factors influencing it.It also identifies the quality issues present in current green building construction.Finally,it proposes measures for quality management in the green building construction process to facilitate the industry’s healthy development.

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.

How Does Robotics Process Automation (RPA) Affect Project Management Practices

The main purpose of this literature review is to understand the effectiveness and impacts of Robotics Process Automation (RPA) on the practices of Project Management (PM). For attaining the purposes of the research study, an extensive literature review was conducted, which helped in gaining a theoretical understanding. These findings were then justified with the help of current and relevant secondary sources in the analysis section. The findings suggested that RPA is quite advantageous for implementing in varied aspects of a business, especially in the field of project management. However, it can pose several challenges, which need to be taken into due consideration by the organizations during execution such as the capabilities of the employees and the abilities of the existing systems to incorporate automation, among others. It was ultimately concluded that RPA is highly advantageous for project management teams but its effective implementation is the key to success.

A New Fuzzy Clustering-Ranking Algorithm and Its Application in Process Alarm Management

Overmany alarms of modern chemical process give the operators many difficulties to decision and diag- nosis. In order to ensure safe production and process operating, management and optimization of alarm information are challenge work that must be confronted. A new process alarm management method based on fuzzy clustering- ranking algorithm is proposed. The fuzzy clustering algorithm is used to cluster rationally the process variables, and difference driving decision algorithm ranks different clusters and process parameters in every cluster. The alarm signal of higher rank is handled preferentially to manage effectively alarms and avoid blind operation. The validity of proposed algorithm and solution is verified by the practical application of ethylene cracking furnace system. It is an effective and dependable alarm management method to improve operating safety in industrial process.

Formal Modeling and Discovery of Multi-instance Business Processes: A Cloud Resource Management Case Study

Process discovery, as one of the most challenging process analysis techniques, aims to uncover business process models from event logs. Many process discovery approaches were invented in the past twenty years;however, most of them have difficulties in handling multi-instance sub-processes. To address this challenge, we first introduce a multi-instance business process model(MBPM) to support the modeling of processes with multiple sub-process instantiations. Formal semantics of MBPMs are precisely defined by using multi-instance Petri nets(MPNs)that are an extension of Petri nets with distinguishable tokens.Then, a novel process discovery technique is developed to support the discovery of MBPMs from event logs with sub-process multi-instantiation information. In addition, we propose to measure the quality of the discovered MBPMs against the input event logs by transforming an MBPM to a classical Petri net such that existing quality metrics, e.g., fitness and precision, can be used.The proposed discovery approach is properly implemented as plugins in the Pro M toolkit. Based on a cloud resource management case study, we compare our approach with the state-of-theart process discovery techniques. The results demonstrate that our approach outperforms existing approaches to discover process models with multi-instance sub-processes.

Digital Twin-based Quality Management Method for the Assembly Process of Aerospace Products with the Grey-Markov Model and Apriori Algorithm

The assembly process of aerospace products such as satellites and rockets has the characteristics of single-or small-batch production,a long development period,high reliability,and frequent disturbances.How to predict and avoid quality abnormalities,quickly locate their causes,and improve product assembly quality and efficiency are urgent engineering issues.As the core technology to realize the integration of virtual and physical space,digital twin(DT)technology can make full use of the low cost,high efficiency,and predictable advantages of digital space to provide a feasible solution to such problems.Hence,a quality management method for the assembly process of aerospace products based on DT is proposed.Given that traditional quality control methods for the assembly process of aerospace products are mostly post-inspection,the Grey-Markov model and T-K control chart are used with a small sample of assembly quality data to predict the value of quality data and the status of an assembly system.The Apriori algorithm is applied to mine the strong association rules related to quality data anomalies and uncontrolled assembly systems so as to solve the issue that the causes of abnormal quality are complicated and difficult to trace.The implementation of the proposed approach is described,taking the collected centroid data of an aerospace product’s cabin,one of the key quality data in the assembly process of aerospace products,as an example.A DT-based quality management system for the assembly process of aerospace products is developed,which can effectively improve the efficiency of quality management for the assembly process of aerospace products and reduce quality abnormalities.

PERFORMANCE-DRIVEN BUSINESS PROCESS MANAGEMENT METHODOLOGY FOR SMALL AND MEDIUM SIZED-ENTERPRISES

Based on the analysis of the common limitations of business processmanagement (BPM) methodologies and the requirements of small and medium sized-enterprises (SMEs),the importance of a 'performance construct' of BPM methodologies is identified, a six-phaseperformance-driven BPM methodology for the production and operation processes of Chinese SMEs isdeveloped. A case study on the process management of a medium-sized enterprise shows a successfulexample of running the methodology.

Simulation Based Energy-resource Efficient Manufacturing Integrated with In-process Virtual Management

As energy efficiency is one of the key essentials towards sustainability, the development of an energy-resource efficient manufacturing system is among the great challenges facing the current industry. Meanwhile, the availability of advanced technological innovation has created more complex manufacturing systems that involve a large variety of processes and machines serving different functions. To extend the limited knowledge on energy-efficient scheduling, the research presented in this paper attempts to model the production schedule at an operation process by considering the balance of energy consumption reduction in production, production work flow （productivity） and quality. An innovative systematic approach to manufacturing energy-resource efficiency is proposed with the virtual simulation as a predictive modelling enabler, which provides real-time manufacturing monitoring, virtual displays and decision-makings and consequentially an analytical and multidimensional correlation analysis on interdependent relationships among energy consumption, work flow and quality errors. The regression analysis results demonstrate positive relationships between the work flow and quality errors and the work flow and energy consumption. When production scheduling is controlled through optimization of work flow, quality errors and overall energy consumption, the energy-resource efficiency can be achieved in the production. Together, this proposed multidimensional modelling and analysis approach provides optimal conditions for the production scheduling at the manufacturing system by taking account of production quality, energy consumption and resource efficiency, which can lead to the key competitive advantages and sustainability of the system operations in the industry.

Research on Risk Management of Cross-Sea Bridges Based on Analytic Hierarchy Process—Taking Hangzhou Bay Bridge as an Example

Today, as the process of urbanization is accelerating, the country builds an extensive transportation network through bridges and roads, which facilitates the daily travel of the people and greatly promotes the development of the national economy. However, due to the cross-sea bridge spanning the bay, the overall scale, the complex construction environment, and the high technology content, the objective existence of risk factors in the construction process cannot be completely avoided. In the construction of cross-sea bridges, once a construction safety accident occurs, it will cause irreparable losses to the construction of the project. Taking Hangzhou Bay Bridge as an actual case, using the Analytic Hierarchy Process to identify possible risk factors during the life cycle of Hangzhou Bay Bridge, establish a corresponding risk evaluation system to evaluate the importance and probability of risk, and to rank the importance of risks, and control the corresponding construction risks by adopting measures such as risk transfer and risk retention. The research example shows that the project risk of the cross-sea bridge project can be combined with the analytic hierarchy process to identify, analyze and evaluate the importance of the various risks faced by the project, so as to adopt corresponding avoidance methods to reduce the project risk loss and achieve the project construction expectations Target.

An Application of a Multi-Tier Data Warehouse in Oil and Gas Drilling Information Management

Expenditure on wells constitute a significant part of the operational costs for a petroleum enterprise, where most of the cost results from drilling. This has prompted drilling departments to continuously look for ways to reduce their drilling costs and be as efficient as possible. A system called the Drilling Comprehensive Information Management and Application System (DCIMAS) is developed and presented here, with an aim at collecting, storing and making full use of the valuable well data and information relating to all drilling activities and operations. The DCIMAS comprises three main parts, including a data collection and transmission system, a data warehouse (DW) management system, and an integrated platform of core applications. With the support of the application platform, the DW management system is introduced, whereby the operation data are captured at well sites and transmitted electronically to a data warehouse via transmission equipment and ETL (extract, transformation and load) tools. With the high quality of the data guaranteed, our central task is to make the best use of the operation data and information for drilling analysis and to provide further information to guide later production stages. Applications have been developed and integrated on a uniform platform to interface directly with different layers of the multi-tier DW. Now, engineers in every department spend less time on data handling and more time on applying technology in their real work with the system.

基于结构-过程-结果模型的医用耗材准入管理及效果分析

目的 评价结构-过程-结果模型在医院医用耗材准入管理中的应用与效果。方法 基于结构-过程-效果模型构建医院医用耗材准入管理体系,选取北京大学第一医院2020年7月—2022年12月准入的医用耗材作为研究对象,从组织结构、过程管理、结果评价三个维度对耗材准入通过率、耗占比、不良事件发生率、医疗纠纷发生率进行结果评价。结果 医用耗材准入管理体系实施后,耗材准入通过率提高(p<0.05);部分科室耗占比明显下降(p<0.05);不良事件发生率下降、未发生因耗材引发的医疗纠纷。结论 结构-过程-效果模型在医用耗材准入管理中取得明显效果,初步实现了耗材准入管理的科学性、规范性、有效性。

What to Cache：Differentiated Caching Resource Allocation and Management in Information-Centric Networking

Information-centric networking(ICN) aims to improve the efficiency of content delivery and reduce the redundancy of data transmission by caching contents in network nodes. An important issue is to design caching methods with better cache hit rate and achieve allocating on-demand. Therefore, an in-network caching scheduling scheme for ICN was designed, distinguishing different kinds of contents and dynamically allocating the cache size on-demand. First discussing what was appropriated to be cached in nodes, and then a classification about the contents could be cached was proposed. Furthermore, we used AHP to weight different contents classes through analyzing users' behavior. And a distributed control process was built, to achieve differentiated caching resource allocation and management. The designed scheme not only avoids the waste of caching resource, but also further enhances the cache availability. Finally, the simulation results are illustrated to show that our method has the superior performance in the aspects of server hit rate and convergence.

Path Selection in Disaster Response Management Based on Q-learning

Suitable rescue path selection is very important to rescue lives and reduce the loss of disasters, and has been a key issue in the field of disaster response management. In this paper, we present a path selection algorithm based on Q-learning for disaster response applications. We assume that a rescue team is an agent, which is operating in a dynamic and dangerous environment and needs to find a safe and short path in the least time. We first propose a path selection model for disaster response management, and deduce that path selection based on our model is a Markov decision process. Then, we introduce Q-learning and design strategies for action selection and to avoid cyclic path. Finally, experimental results show that our algorithm can find a safe and short path in the dynamic and dangerous environment, which can provide a specific and significant reference for practical management in disaster response applications.

Land Management Effects on Biogeochemical Functioning of Salt-Affected Paddy Soils

Most lowlands in Northeast Thailand(Isaan region)are cultivated with rice and large areas are affected by salinity, which drastically limits rice production.A field experiment was conducted during the 2003 rainy season to explore the interactions between salinity and land management in two fields representative of two farming practices:an intensively managed plot with organic inputs and efficient water management,and one without organic matter addition.Field measurements,including pH,Eh,electrical conductivity(EC),and soil solution chemistry,were performed at three depths, with a particular focus on Fe dynamics,inside and outside saline patches. High reducing conditions appeared after flooding particularly in plots receiving organic matter and reduction processes leading to oxide reduction and to the release of Fe and,to a lesser extend,Mn to the soil solution.Oxide reduction led to the consumption of H^+ and the more the Fe reduction was,the higher the pH was,up to 6.5.Formation of hydroxy-green rust were likely to be at the origin of the pH stabilization.In the absence of organic amendments,high salinity prevented the establishment of the reduction processes and pH value remained around 4.Even under high reduction conditions,the Fe concentrations in the soil solution were below commonly observed toxic values and the amended plot had better rice production yield.

基于AHP-模糊评价法的管理类人才素质校企供需差异分析

目的从高校企业供需双视角探究其对管理人才素质的要求,为高校管理型人才培养提供建议。方法根据管理人才素质设置二级指标问卷,对50位相关专业的高校工作者以及50位医药相关企业的管理者进行线上线下访谈和问卷调查,通过AHP层次法构建管理人才评价体系计算各指标权重,并依据模糊综合评价法以药事管理为例计算管理人才要求的评分,探究两者差异。结果根据AHP层次分析法计算四大素质权重,高校与企业角度一级指标权重分别为:政治道德素质47%、36%,专业素质23%、24%,心理素质18%、25%,身体素质12%、16%。高校与企业二级指标排名前三位及其权重分别为:政治信仰29.14%、14.76%,实践能力17.25%、12.24%,学业道德品行9.40%、13.32%,高校角度第四、五位分别为日常行为品行和机能素质;企业角度第四、五位分别为专业知识和压力耐受能力。根据模糊综合评价结果,高校与企业对药事管理人才的评分分别为78.8与85.8。结论高校与企业在对管理人才素质的评价上存在差异,人才从高校过渡到企业是一个不断成长的过程,高校在管理人才培养中要考虑企业的需求,将实践、理论、心理等素质与政治道德素质培养相结合,培养出既具有较高道德品行,又能够更好满足企业需求的高素质人才。