Earticle

This paper evaluates SnapCheck.io, a smart workflow platform aimed at improving the efficiency of project management in remote work environments. Through the use of AI-driven task scheduling, smart time tracking, and real-time analytics, SnapCheck.io enhances resource utilization and lowers the waiting time for task dependency. A mixed-method study at various organizations showed an improvement in performance by 18%, which was thus corroborated by the increased rate of task completion and quicker workflow execution. The results reveal that the use of intelligent automation in SnapCheck.io not only helps in streamlining the project coordination process but also promotes the ongoing productivity and collaboration of distributed teams.

Task offloading in multi-access edge computing (MEC) systems is critical for managing computational tasks in dynamic urban environments. Existing strategies face challenges such as high communication overheads and regional performance deviations including centralized and distributed methods. Clustering approaches have been explored to address these issues, yet they often rely on physical proximity to form clusters, overlooking the variability in task rate distributions across edges. To overcome these limitations, this paper proposes a graph-driven inter-cluster resource distribution (GIRD) clustering scheme that clusters edge nodes based on task request distribution and computing resource status, ensuring similar resource utilization across clusters. Building on this, a proximal policy optimization (PPO)-enabled intra-cluster task offloading algorithm (PITO) is introduced to determine one execution server for task offloading?either an edge server within a cluster or a cloud server?using various network state information. This dynamic decision-making process optimizes a multi-objective function that includes task processing delay, consumed energy, success rate, and cloud cost. Simulation results demonstrate the proposed GIRD-PITO framework achieves superior task success rates, reduced delays, and improved regional performance fairness, making it a promising solution for large-scale MEC systems. 2018 The Korean Institute of Communications and Information Sciences. Publishing Services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

중국 고등교육의 자원배분의 효율성을 연구하는 것은 고등교육의 질적 균형발전을 더욱 촉진하고 교육력을 건설하는데 매우 의의가 있다. 본 논문은 DEA-Malmquist 지수 를 사용하여 중국 고등교육의 자원할당 효율성을 계산하고, 고등교육의 자원할당 효율 성 현황을 분석하고, ESDA 모델을 사용하여 고등교육 효율성의 구체적인 공간적 특성 을 연구한다. 중국 각 성(省)의 자원 배분, 교육의 형평성을 분석하기 위해 Tobit 모델을 사용 중국의 고등교육 자원배분 효율성과의 관계를 연구했다. 연구 결과는 다음과 같은 세 가지 점을 가지고 있다. 첫째, 중국 교육형평성은 고등교육 자원배분의 효율성에 긍정적인 영향을 미치며 지역적 이질성이 크다. 둘째, 중국 고등교육 자원배분의 효율 성에 상당한 공간적 파급효과가 있다. 마지막으로 중국 고등교육의 배분 효율성을 제고 하기 위한 전략을 제안한다.

To study the allocation efficiency of higher education resources in China is of great significance to further promote the balanced development of higher education and to build an educational power. In this paper, besed on human capital theory and DEA theory， DEA-Malmquist index is used to calculate the resource allocation efficiency of China’s higher education, revealing the overall situation of China's higher education resource allocation efficiency, and using the Exploratory spatial data Analysis model (ESDA) to analyze the specific spatial characteristics of higher education resource allocation efficiency in each province. Tobit regression model is used to analyze the impact of educational equity on the allocation efficiency of higher education resources in China. The results show that, first, educational equity has a positive impact on higher education resource allocation efficiency, and there is significant regional heterogeneity. Second, higher education resource allocation efficiency has significant spatial spillover effect. Finally, the paper puts forward some strategies for improving the allocation efficiency of Higher education in China. The research was helpful to open the “black box” in which educational equity affects the allocation efficiency of higher resources, and has certain reference significance for improving the allocation efficiency of higher education resources.

研究中国高等教育资源配置效率问题，对进一步促进高等教育优质均衡发展、建设教 育强国具有重要意义. 因此，本文依据人力资本理论和DEA理论，利用DEA-Malmquist 指数模型测算出了中国高等教育资源配置效率，分析了高等教育资源配置效率现状，并 采用ESDA模型研究了各省域高等教育资源配置效率的具体空间特征，同时利用Tobit模 型分析了教育公平与中国高等教育资源配置效率关系. 研究得出，第一，教育公平正向影 响高等教育资源配置效率，且存在显著的区域异质性. 第二，高等教育资源配置效率存在 显著的空间溢出效应. 最后，提出了中国高等教育配置效率提升的策略. 研究的开展有利 于打开教育公平影响高等教育资源配置效率的“黑箱”，为提升高等教育资源配置效率具 有一定的借鉴意义.

[연구목적] 인적자원관리정책은 인적자원을 관리하고 조정하는 규범이자 행동지침이다. 이를 통해 기업은 유능한 인재의 확보하고 활용하여 경쟁력을 강화할 수 있다. 산동성은 인적자원관리정책으로서 인재로 발전시 키는 전략(人才兴鲁战略)을 추진하고 있다. 또한 광동성, 강소성 절강성은 중국의 경제발전을 견인하는 대표적 인 지역으로 유능한 인재를 확보하고 활용하는 차별화된 인적자원관리 정책을 시행하고 있다. [연구방법] 본 연구는 산동성의 인적자원관리정책을 이들 성과의 비교분석을 통해 문제점을 확인하고 이를 극복할 수 있는 방안을 제시함으로서 지역적 특성에 적합한 인적자원관리 전략을 수립하는데 활용하고자 한다. 이를 위해 본 연구는 탐색조사로서 사례연구방법에 의하여 이루어졌다. [연구결과] 분석결과 산동성의 인적자원 투입은 상승세를 보이고 있기는 하지만 다른 지역에 비해 낮은 수 준이며, 과학기술 연구투자도 증가하고 있지만 다른 지역에 비해 낮은 것으로 나타났다. 따라서 산동성은 지역 적 차별화를 고려한 인재정책을 수립하고 공정한 평가시스템과 인적자원 관리체계를 구축하여야 할 것이다. [연구의 시사점] 본 연구의 결과는 산동성의 특성을 고려한 인적자원관리 전략을 수립하여 유능한 인적자원 을 확보하고, 그들의 능력을 개발하고, 활용함으로서 기업의 경쟁력을 확보하여 경제성장에 기여하기 위한 유 효한 자료로서 활용될 것으로 본다.

[Purpose] Human resource management policies are norms and guidelines for managing and coordinating human resources. Through this, companies can strengthen their competitiveness by securing and utilizing competent human resources. Shandong Province is pursuing a strategy to develop human resources as a human resource management policy. In addition, Guangdong Province and Jiangsu Province Zhejiang Province are representative regions that lead China's economic development, and have differentiated human resource management policies that secure and utilize competent talent. [Methodology] The purpose of this study is to identify problems through comparative analysis of the human resource management policies of Shandong Province and to suggest ways to overcome them, and to utilize them in establishing human resource management strategies suitable for regional characteristics. This study was conducted by case study method as an exploratory survey. [Findings] As a result of the analysis, the input of human resources in Shandong province is showing an upward trend, but it is lower than other regions, and investment in science and technology research is increasing, but it is lower than other regions. Therefore, Shandong province should establish a human resource policy considering regional differentiation and establish a fair evaluation system and human resource management system. [Implications] The results of this study will be used as effective data to secure competitiveness of companies and contribute to economic growth by establishing human resource management strategies considering the characteristics of Shandong Province, securing competent human resources, developing and utilizing their capabilities.

인적자원관리정책은 인적자원을 관리하고 조정하는 규범이자 행동지침이다. 이를 통해 기업은 유능한 인재의 확보하고 활용하여 경쟁력을 강화할 수 있다. 산동성은 인적자원관리정책으로서 인 재로 발전시키는 전략(人才兴鲁战略)을 추진하고 있다. 또한 광동성, 강소성 절강성은 중국의 경제 발전을 견인하는 대표적인 지역으로 유능한 인재를 확보하고 활용하는 차별화된 인적자원관리 정 책을 시행하고 있다. 본 연구는 산동성의 인적자원관리정책을 이들 성 과의 비교분석을 통해 문제점을 확인하고 이 를 극복할 수 있는 방안을 제시함으로서 지역적 특성에 적합한 인적자원관리 전략을 수립하는데 활용하고자 한다. 이를 위해 본 연구는 탐색조사로서 사례연구방법에 의하여 이루어졌다. 분석결과 산동성의 인적자원 투입은 상승세를 보이고 있기는 하지만 다른 지역에 비해 낮은 수 준이며, 과학기술 연구투자도 증가하고 있지만 다른 지역에 비해 낮은 것으로 나타났다. 따라서 산동성은 지역적 차별화를 고려한 인재정책을 수립하고 공정한 평가시스템과 인적자원 관리체계 를 구축하여야 할 것이다. 본 연구의 결과는 산동성의 특성을 고려한 인적자원관리 전략을 수립하여 유능한 인적자원을 확보하고, 그들의 능력을 개발하고, 활용함으로서 기업의 경쟁력을 확보하여 경제성장에 기여하기 위한 유효한 자료로서 활용될 것으로 본다.

Human resource management policies are norms and guidelines for managing and coordinating human resources. Through this, companies can strengthen their competitiveness by securing and utilizing competent human resources. Shandong Province is pursuing a strategy to develop human resources as a human resource management policy. In addition, Guangdong Province and Jiangsu Province Zhejiang Province are representative regions that lead China's economic development, and have differentiated human resource management policies that secure and utilize competent talent. The purpose of this study is to identify problems through comparative analysis of the human resource management policies of Shandong Province and to suggest ways to overcome them, and to utilize them in establishing human resource management strategies suitable for regional characteristics. This study was conducted by case study method as an exploratory survey. As a result of the analysis, the input of human resources in Shandong province is showing an upward trend, but it is lower than other regions, and investment in science and technology research is increasing, but it is lower than other regions. Therefore, Shandong province should establish a human resource policy considering regional differentiation and establish a fair evaluation system and human resource management system. The results of this study will be used as effective data to secure competitiveness of companies and contribute to economic growth by establishing human resource management strategies considering the characteristics of Shandong Province, securing competent human resources, developing and utilizing their capabilities.

인적자원관리정책은 인적자원을 관리하고 조정하는 규범이자 행동지침이다. 이를 통해 기업은 유능한 인재의 확보하고 활용하여 경쟁력을 강화할 수 있다. 산동성은 인적자원관리정책으로서 인 재로 발전시키는 전략(人才兴鲁战略)을 추진하고 있다. 또한 광동성, 강소성 절강성은 중국의 경제 발전을 견인하는 대표적인 지역으로 유능한 인재를 확보하고 활용하는 차별화된 인적자원관리 정 책을 시행하고 있다. 본 연구는 산동성의 인적자원관리정책을 이들 성 과의 비교분석을 통해 문제점을 확인하고 이 를 극복할 수 있는 방안을 제시함으로서 지역적 특성에 적합한 인적자원관리 전략을 수립하는데 활용하고자 한다. 이를 위해 본 연구는 탐색조사로서 사례연구방법에 의하여 이루어졌다. 분석결과 산동성의 인적자원 투입은 상승세를 보이고 있기는 하지만 다른 지역에 비해 낮은 수 준이며, 과학기술 연구투자도 증가하고 있지만 다른 지역에 비해 낮은 것으로 나타났다. 따라서 산동성은 지역적 차별화를 고려한 인재정책을 수립하고 공정한 평가시스템과 인적자원 관리체계 를 구축하여야 할 것이다. 본 연구의 결과는 산동성의 특성을 고려한 인적자원관리 전략을 수립하여 유능한 인적자원을 확보하고, 그들의 능력을 개발하고, 활용함으로서 기업의 경쟁력을 확보하여 경제성장에 기여하기 위한 유효한 자료로서 활용될 것으로 본다.

Human resource management policies are norms and guidelines for managing and coordinating human resources. Through this, companies can strengthen their competitiveness by securing and utilizing competent human resources. Shandong Province is pursuing a strategy to develop human resources as a human resource management policy. In addition, Guangdong Province and Jiangsu Province Zhejiang Province are representative regions that lead China's economic development, and have differentiated human resource management policies that secure and utilize competent talent. The purpose of this study is to identify problems through comparative analysis of the human resource management policies of Shandong Province and to suggest ways to overcome them, and to utilize them in establishing human resource management strategies suitable for regional characteristics. This study was conducted by case study method as an exploratory survey. As a result of the analysis, the input of human resources in Shandong province is showing an upward trend, but it is lower than other regions, and investment in science and technology research is increasing, but it is lower than other regions. Therefore, Shandong province should establish a human resource policy considering regional differentiation and establish a fair evaluation system and human resource management system. The results of this study will be used as effective data to secure competitiveness of companies and contribute to economic growth by establishing human resource management strategies considering the characteristics of Shandong Province, securing competent human resources, developing and utilizing their capabilities.

본 연구는 LLM 기반 세션 추천 환경에서 Mixture-of-Experts(MoE) 구조가 GPU 메모리 사용량과 추론 지연 시간에 미치는 구조적 특성을 분석하고, 자원 제약 상황에서 고려해야 할 구조적 고려 요소를 분석하여 제시한다. 동일한 백본 구조를 기준으로 FFN 기반 모델과 MoE 기반 모델을 비교한 결과, 전문가 수가 증가함에 따라 GPU 메모리 점유와 지연 시간이 함께 증가하는 경향이 나타났다. 이러한 특성은 짧은 세션 시퀀스를 반복적으로 처리하는 세션 추천 환경에서 자원 관리 측면의 주요 고려 요소가 될 수 있다. 이에 본 연구는 전문가 활성빈도와 세션 특성을 고려한 자원 관리 및 전문가 활용 방식의 구조를 정리하고, MoE 기반 LLM 세션 추천 시스템을 자원 사용 특성을 분석하기 위한 관점을 제시한다.

This study analyzes the structural characteristics of a Mixture-of-Experts (MoE) architecture in an LLM-based session recommendation environment, focusing on its impact on GPU memory consumption and inference latency. In addition, we identify and discuss structural considerations required under resource-constrained settings. Using an identical backbone architecture, we compare an FFN-based model with an MoE-based model and observe that GPU memory usage and step-wise inference latency tend to increase as the number of experts grows. This characteristic can become a critical consideration in session-based recommendation scenarios where short session sequences are processed repeatedly. Based on these observations, we organize a structural perspective on resource management and expert utilization strategies that take into account expert activation frequency and session characteristics. Finally, we present an analytical viewpoint for examining resource usage patterns in MoE-based LLM session recommendation systems.

The resource allocation is the basic guarantee for the effective implementation of the university development strategy, while resource allocation mechanism is an effective control measure to strengthen the university management. So the importance of resource allocation has caused widespread attention and concern of university administrators. Combining with the actual situations and encountered problems of university resource management, we introduced cloud computing ideas and proposed a cloud-computing-based resource allocation model to conduct a preliminary exploration and research on this issue. We first formulated the resource allocation problem, and then proposed the resource allocation concept model and its corresponding application model in universities. Based on the proposed model, we further expanded detailed analyses and descriptions on resource definition and description, resource allocation rules and methods, resource management mode, and a general mathematical model of resource allocation optimization in university. Finally, we gave a cloud-computing-based university resource allocation framework to offer references and consult for further applications of technology based on cloud computing in the university resource construction, aiming to achieve optimal university resource allocation and improve teaching quality and efficiency.

Deadlock Avoidance remains to be a significant aspect of deadlock research. Modeling approaches based on Graph Theory provide effective strategy to solve this problem. We built avoidance model of directed graph and adjacent matrix via resource allocation graph, proposed three improved algorithm for deadlock avoidance and discussed the implementation of the program. The topology analyze of matrix storage information indicates entire optimization utilization results though certain key vertices and edges, this model verified by the banker’s algorithm and Petri net finally.

In order to enrich and improve the collection of library digital information resources, this paper puts forward an optimization method of library digital resources based on semantic information retrieval. The method collects related information automatically from the Internet using semantic information retrieval, and selects the relevance value meeting the preset threshold of the network information to expand and update library digital resources. The experiment result shows that these methods gets a good expectant performance and dramatically optimize the library digital resources and improve the efficiency of resource retrieval and utilization.

On demand resource forecasting in cloud computing is an crucial guarantee for achieving effective management of all virtualized resources and reducing data center energy consumption. According to single forecasting model cannot integrate all the valid information which leads to the decline in prediction accuracy. This paper proposed an optimal combination prediction model for cloud computing resource requirement. This model is based on generalized Dice coefficient and the induced ordered weighted geometric mean (IOWGA) operator, as well as improved Elman neural network and grey forecasting model. It is able to accurately reflect the random information and trend information in cloud computing load thus will enhance the overall prediction accuracy. The experiment results show this method is feasible and effective.

To further enhance optimizing effect of virtual machine resource allocation, virtual machine resource allocation algorithm of probabilistic optimization based on SME-FFD (Simulated Evolution – First Fit Decreasing ) is proposed aiming at NP hard optimization problem in the process of virtual machine resource allocation in cloud computing. First of all, an optimization evaluation scheme of virtual machine resource allocation is proposed, and strong climbing optimization ability of simulated evolutionary algorithm is adopted to carry out iterative evolution to selection, evaluation and ranking of virtual machine resource allocation ; after that, based on SME algorithm obtaining resource allocation ordering, the secondary allocation on virtual machine and physical host resources ranked is conducted using FFD to improve efficiency and effectiveness of resource allocation; in the end, experimental comparison is conducted in CloundSim grid lab in the University of Melbourne and gridbus cloud simulation platform, the results show that CPU usage rate of proposed algorithm reaches up to 47%, memory usage rate reaches up to 56%, therefore, it may effectively reduce physical machine usage quantity and realize the goal of energy consumption.

The management of Multi-sensors in information fusion system occupies an important role with the development of modern weapon platform. Therefore, scientific and rational management of limited sensors resources is essential or urgent, and improvement the capability of air defense operations is necessary. According to the management optimization problem in Multi-sensor optimal resource, we analyzed the definition of rough entropy and presented the method of Multi-sensor management based on rough entropy and target threat degree. By calculating rough entropy of the sensors to the target, the maximum information gain access for each sensor on each target, which acts as cost function, taking into account the target threat degree, and using a linear programming optimization assignment multi-sensor to multi-target. In this paper, we adopted maximum information gain optimization criterion in target tracking, then discussed optimization assignment problem about multi-sensor to multi-target. In addition, we are validate the optimized allocation algorithm with the experimental simulations, and case analysis shows that the effectiveness of the method.

A major challenge facing cloud computing is virtual resource allocation with dynamic characteristics. Evaluation of a resource allocation strategy using a single aspect can no longer meet the real world demands. We resolve this issue from the perspectives of users and resource providers using a particle swarm algorithm for resource allocation. With this algorithm, we establish an allocation model using the shortest task completion time and the lowest cost as the constraints. The fast convergence rate of the particle swarm algorithm is then used to find the optimal solution for resource allocation. The velocity weight of each particle is self-adaptively adjusted based on the fitness value of each particle, resulting in an improvement in the global optimization and convergence capabilities. Finally, a simulation with the CloudSim platform shows that this algorithm can take into account the completion time and cost, which ensures the minimum cost in the shortest possible time to complete the task to improve resource utilization.

Mobile Internet due to the limitation of the mobile terminal power supply, transmission and calculation of the need to adopt energy saving strategy, also due to the terminal mobility, mobile Internet topology change. Therefore, mobile Internet cloud computing resources allocation need both energy efficiency and assure the time characteristics of mobile business; Aiming at this problem, this paper presents a maximum energy efficiency optimization, in the restrictive conditions at the same time, guarantee the minimum time delay the business. According to the characteristics of the optimization problems, both the distribution of the improved algorithm is proposed, the algorithm based on particle swarm optimization (pso) algorithm, build the search direction matrix of orientation, the simulation results show that the proposed allocation algorithm can effectively improve the efficiency of energy utilization, and ensure that the time delay of the business requirements.

In this paper, we investigate the resource allocation problem in time-varying heterogeneous wireless networks (HetNet) with multi-homing user equipments (UE). The stochastic optimization model is employed to maximize the network utility, which is defined as the difference between the HetNet's throughput and the total energy consumption cost. In harmony with the hierarchical architecture of HetNet, the problem of stochastic optimization of resource allocation is decomposed into two subproblems by the Lyapunov optimization theory, associated with the flow control in transport layer and the power allocation in physical (PHY) layer, respectively. For avoiding the signaling overhead, outdated dynamic information, and scalability issues, the distributed resource allocation method is developed for solving the two subproblems based on the primal-dual decomposition theory. After that, the adaptive resource allocation algorithm is developed to accommodate the timevarying wireless network only according to the current network state information, i.e. the queue state information (QSI) at radio access networks (RAN) and the channel state information (CSI) of RANs-UE links. The tradeoff between network utility and delay is derived, where the increase of delay is approximately linear in V and the increase of network utility is at the speed of 1/V with a control parameter V. Extensive simulations are presented to show the effectiveness of our proposed scheme.

In this research, schedule optimization is defined as balancing the number of workers while keeping the demand and needs of the project resources, creating the perfect schedule for each activity. Therefore, when one optimizes a schedule, multiple potentials of schedule changes are assessed to get an instant view of changes that avoid any over and under staffing while maximizing productivity levels for the available labor cost. Optimizing the number of workers in the scheduling process is not a simple task since it usually involves many different factors to be considered such as the development of quantity take-offs, cost estimating, scheduling, direct/indirect costs, and borrowing costs in cash flow while each factor affecting the others simultaneously. That is why the optimization process usually requires complex computational simulations/modeling. This research attempts to find an optimal selection of daily maximum workers in a project while considering the impacts of other factors at the same time through OPEN BIM based multiple computer simulations in resource leveling. This paper integrates several different processes such as quantity take-offs, cost estimating, and scheduling processes through computer aided simulations and prediction in generating/comparing different outcomes of each process. To achieve interoperability among different simulation processes, this research utilized data exchanges supported by building SMART-IFC effort in automating the data extraction and retrieval. Numerous computer simulations were run, which included necessary aspects of construction scheduling, to produce sufficient alternatives for a given project.