Earticle

With the development of mobile edge computing, how to utilize the computing power of edge computing to effectively and efficiently offload data and to compute offloading is of great research value. This paper studies the computation offloading problem of multi-user and multi-server in mobile edge computing. Firstly, in order to minimize system energy consumption, the problem is modeled by considering the joint optimization of the offloading strategy and the wireless and computing resource allocation in a multi-user and multi-server scenario. Additionally, this paper explores the computation offloading scheme to optimize the overall cost. As the centralized optimization method is an NP problem, the game method is used to achieve effective computation offloading in a distributed manner. The decision problem of distributed computation offloading between the mobile equipment is modeled as a multi-user computation offloading game. There is a Nash equilibrium in this game, and it can be achieved by a limited number of iterations. Then, we propose a distributed computation offloading algorithm, which first calculates offloading weights, and then distributedly iterates by the time slot to update the computation offloading decision. Finally, the algorithm is verified by simulation experiments. Simulation results show that our proposed algorithm can achieve the balance by a limited number of iterations. At the same time, the algorithm outperforms several other advanced computation offloading algorithms in terms of the number of users and overall overheads for beneficial decision-making.

The issue of human resources today is highly diverse. One of the main problems is the mismatch between employees' competencies and the positions they currently hold, which results in suboptimal performance. Placement strategies must be developed based on considerations such as work experience, educational background, and individual skills. An ideal placement is one that adheres to the principle of “the right man in the right place.” This study aims to identify strategies for optimizing employee placement based on this principle. The research employs a literature review method by collecting relevant previous studies, analyzing, and summarizing their findings. The conclusion indicates that the “right man in the right place” approach is proven to be highly significant in optimizing employee placement. Previous studies show that proper placement not only enhances productivity but also improves employee satisfaction, bureaucratic efficiency, and workplace professionalism.

The textual information from title provides the video viewers the overview of the video contents. However, there has been less focus on the textual information from previous viewers’ comments. Comments which contain information such as the difficulty or the experienced benefits of the exercise can be another source of textual information. Those comments, Active Engagement, are not only the review of the contents but also the driver of viewership of other videos. In this paper, the amount of information in the title and Active Engagements are measured by word embeddings through text analysis. We further examine the effect of textual information on the view count and try to identify the feedback effect. Based on the findings, we provide the fitness content creators on YouTube and mobile fitness app developers with the guidelines on the management of the channel and the contents.

Electron cyclotron resonance (ECR) ion source is an essential component of heavy-ion accelerator. For a given design, the intensities of the highly charged ion beams extracted from the source can be increased by enlarging the physical volume of ECR zone [1]. Several models for ECR ion source were and will be constructed depending on their operating conditions [2-4]. In this paper three simulation models with 3, 4 and 6 solenoid system were built, but it’s not considered anything else except the number of coils. Two groups of optimization analysis are presented, and the evolution strategy (ES) is adopted as an optimization tool which is a technique based on the ideas of mutation, adaptation and annealing [5]. In this research, the volume of ECR zone was calculated approximately, and optimized designs for ECR solenoid magnet system were presented. Firstly it is better to make the volume of ECR zone large to increase the intensity of ion beam under the specific confinement field conditions. At the same time the total volume of superconducting solenoids must be decreased to save material. By considering the volume of ECR zone and the total length of solenoids in each model with different number of coils, the 6 solenoid system represented the highest coil performance. By the way, a certain case, ECR zone volume itself can be essential than the cost. So the maximum ECR zone volume for each solenoid magnet system was calculated respectively with the same size of the plasma chamber and the total magnet space. By comparing the volume of ECR zone, the 6 solenoid system can be also made with the maximum ECR zone volume.

전력 설비의 기능 저하와 노후화는 전력 시스템의 신뢰성과 안정성에 치명적인 영향을 미치므로, 신속하게 효과적인 유지보수 전략을 수립하는 것이 중요하다. 기존의 정수 계획법 기반의 최적화 전략은 전역 최적해를 보장하지만, 대규모 시스템에서 지수적으로 증가하는 시간 복잡도로 인해 응용 범위가 제한된다. 본 논문은 최적해에 준하는 유지보수 전략을 수 초 내에 도출하기 위해, 전력 설비 유지보수 최적화 문제에서 일반적으로 사용하는 SOS1(Special Ordered Set Type 1) 구조를 활용한 SAIR(SOS1-Aware Iterative Rounding Framework) 기법을 제안한다. 제안하는 SAIR은 결정 변수의 이진 제약 조건을 완화하여 유지보수 전략 최적화 문제를 선형 계획법 문제로 변환하고, SOS1 구조를 활용하여 탐색 공간을 설비 단위로 축소한다. 또한, 최소 엔트로피 기반 설비 선택 및 예산 인지 기반 유지보수 전략 선택 기법을 통해 전력 설비 유지보수 전략 문제에서 준최적해의 품질을 체계적으로 향상시킨다. 합성 데이터 기반 벤치마크 환경에서 다양한 규모의 시스템 복잡도에 대해 실험한 결과, 제안하는 SAIR은 전역 최적해 대비 0.09%의 목적함수 격차만을 보이며 전역 최적해 수준의 유지보수 전략을 도출하는 동시에 계산 시간을 평균 1512배 단축하여(평균 0.2492초) 계산 효율성을 크게 높일 수 있음을 보였다. 특히, 단순 휴리스틱 기반의 유지보수 계획 기법이 빈번하게 제약조건을 위배하여 무효한 해를 도출하는 것과 달리, SAIR은 모든 실험에서 유효한 전략을 도출하여 제안하는 기법의 유효성을 확인하였다.

Degradation and aging of power facilities critically impact the reliability and stability of power systems, necessitating the prompt establishment of effective maintenance strategies. Conventional optimization strategies based on integer programming guarantee globally optimal solutions, but their exponentially increasing time complexity in large-scale systems limits their applicability. To derive maintenance strategies comparable to the optimal solution within seconds, this paper proposes the SOS1-Aware Iterative Rounding Framework (SAIR), which leverages the Special Ordered Set Type 1 (SOS1) structure commonly used in the power facility maintenance optimization problem. The proposed SAIR transforms the maintenance strategy optimization problem into a linear programming problem by relaxing the binary constraints of the decision variables and utilizes the SOS1 structure to reduce the search space at the facility level. Furthermore, it systematically enhances the quality of near-optimal solutions in the power facility maintenance strategy problem through minimum entropy-based facility selection and budget-aware maintenance strategy selection techniques. In benchmark experiments using synthetic data across various system complexity environments, the proposed SAIR significantly enhances computational efficiency, achieving a maintenance strategy quality comparable to the globally optimal solution with an objective function gap of only 0.09%, while simultaneously reducing computation time by a factor of 1512 on average (0.2492 seconds on average). Notably, unlike simple heuristic-based maintenance planning methods that frequently violate constraints and yield invalid solutions, SAIR derived valid strategies in all experiments, thereby confirming the effectiveness of the proposed technique.

Purpose: The focus of the study was on the selection-optimization-compensation (SOC) strategy to predict successful aging mediated by dyspnea symptoms in older adults with chronic obstructive pulmonary disease. The model was constructed based on the hypotheses that coping strategy and social support of the elders predict successful aging through the SOC strategies. Methods: Participants were 218 outpatients with chronic obstructive pulmonary disease recruited for the study. Data collection was done from March 25 to September 11, 2015, and analyzed using SPSSWIN 22.0 and AMOS 21.0. Results: The hypothetical model appeared to be fit to the data. Seven of eight hypotheses selected for hypothetical model were statistically significant. The SOC strategy has only significant indirect effects through dyspnea symptoms on successful aging. Coping strategy, social support, SOC strategies and dyspnea symptoms explained 62% of variance in successful aging. Conclusion: The SOC strategies with social support and dyspnea symptoms significantly explained successful aging among patients with chronic obstructive pulmonary disease. Nursing strategies should be focused on social support and coping strategies to optimize SOC strategies so that older adults with chronic obstructive pulmonary disease are able to manage dyspnea symptoms and eventually achieve successful aging.

Purpose: The purpose of this study was to determine whether Selection, Optimization and Compensation (SOC)Strategy mediated the relationship between job stress and health promoting behaviors among nurses. Methods:A descriptive causal relationship research design was used. 245 subjects recruited from the five general hospitals,which have over 500 beds in Seoul and Kyunggi-do, participated in the study by completing a survey which includedquestions about SOC strategy, job stress and health promoting behaviors. t-test, ANOVA, Pearson's correlationcoefficient and multiple regression were used to analyze the data. SPSS/WIN 20.0 was used for all analysis. Results: The mean job stress score was 47.89; mean SOC strategy score was .71. ‘Spiritual development’ wasthe highest reported health promoting behavior with a mean of 3.57. Job stress was significantly and negativelyrelated to health promoting behaviors. The result showed that all the mediating paths of SOC strategy weresignificant. The partial mediating model showed high goodness of fit demonstrating that the model was outstanding. Conclusion: SOC strategy mediated the relationship between job stress and health promoting behaviors. Applying better SOC strategies among nurses will increase flexibility in responding to job stress and willalso better perform health promoting behaviors.

철도 네트워크를 효율적으로 사용하는 방법 중 하나는 연결선 건설을 통해 여러 노선이 하나의 선로를 공유하는 방법인데, 국내에서도 이미 사용하고 있거나 사업화 추진 중인 노선이 존재한다. 본 연구는 철도 연결선 건설시 사업화 단계가 아닌 계획 단계에서 연결선 건설의 위치 선정, 우선순위 산정 등에 필요한 연결선 설계 모형을 구축하고 이에 대한 해법을 제시하였다. 모형은 연결선건설비용, 노선운영비용, 이용자 총통행비용을 최소화하기 위한 비선형 최적화문제로 구축하였고, 결정변수로는 연결선 건설여부 및 방향, 노선의 열차 운행 빈도로 설정하였다. 본 연구에서는 문제 풀이 알고리즘과 경로선택 알고리즘을 각각 제시하였고, 예제네트워크에 적용을 통하여 모형의 실용성을 검증하였다. 향후 현실을 반영한 실제네트워크와 파라미터를 설정하여 연구의 결과를 발전시킬 여지가 있다.

One of the methods which can enhance the efficiency of railroad network is construction of interconnection branch line for several route to share one railway. In Korea, this method already has been implemented or excuted as project level. This study suggests a network design model and a solution algorithm to choice most proper site to construction it and determine the priority of branch lines which can be considered in planning level, not project level. The model is a non-linear optimization program which minimize total cost-construction cost, operating cost and passengers' travel cost. The decision variables are a binary variable to explain whether construction or not and its direction and a integer variable of the frequencies of travel routes. The solution algorithm-problem solution and route choice and also the result of implementation for example network are suggested. This result can be more advanced after application in real network and calibration of parameters.

Thought Extreme Learning Machine-Radial Basis Function(ELM-RBF) can be used easily and can complete learning phase at very fast speed and provide more compact network than classical Extreme Learning Machine(ELM), it still has some room to improvement. Micro-Genetic Algorithms(uGA) improved calculated speed while inherits the Genetic Algorithms advantage of good for optimization and overall search. Considered on these, the paper designed a optimization strategy for ELM-RBF neural network based on uGA. In particular, based on classical RBF-ELM, we use real-u GA algorithm to optimize ELM-RBF hidden layer neurons center and biases value. Experiments results show that ELM-RBF-uGA has better recognition and prediction performance than classical ELM-RBF.

This paper presents a kind of coverage optimization strategy based on improved artificial fish swarm algorithm for wireless sensor networks, by adaptively adjusting the vision range and the step length of artificial fish swarm the accuracy of optimization, convergence speed and stability are improved, then combining with the performance of WSN network coverage, the network coverage can be optimized. The simulation results show that comparing to the basic artificial fish swarm algorithm, the network coverage ratio of improved artificial fish swarm algorithm improves 17%.

Normally, improving the performance of clustering depends on improvement of the algorithm. On the basis, this paper presents a hybrid strategy optimization algorithm that K-means algorithm effectively combined with PSO algorithm, which not only has played their respective advantages, but also reflected a hybrid performance. First of all, combined with a semi-supervised clustering idea， to optimize the clustering center of particle by K - means in the iteration of algorithm, enhanced the searching capability of the particles. Secondly, improved the traditional K - means enhance the ability of the algorithm to deal with the concave and convex points. Finally, the algorithm is introduced into the particle state determination mechanism, on implementing mutation for unstable particles, so that the algorithm to obtain stable performance. Experimental results show that the hybrid algorithm optimization ability is outstanding, and the convergence and stability can be effectively improved.

With the development of electronic commerce, the management mode of enterprises has been changed, E-business enterprises will also create new financial model, suitable for electronic commerce situation. In this paper, the author first put forward the data mining method based on SVM algorithm. Based on the empirical analysis, author research on the influence factors of e-commerce enterprise financial management mode, and makes a detailed analysis of the relationship between the environment, the network and the performance. For the electronic business enterprise, the solvency, development potential, innovation ability and adapt to the environment of the organization flexibility is the important index of E-enterprise performance evaluation index system.

4 commercial enzymes namely, Alcalase, Bromelain, Flavourzyme, Neutrase were used for hydrolysis of soy protein. Each obtained hydrolysate were evaluated as potential serum substituents. The degree of hydrolysis affected cell growth. Cell growth promoting effect was varied with the enzyme used. The soy protein hydrolysate prepare with Alcalse show the most significant effect on cell growth therefore it was used for following experiments. With various components which is used commonly used for serum free media, effective components were screened by using Plackett-Burman design. Insulin, hydrocortisone and soy protein hydrolysate were selected for further optimization. Determining optimal concentration for these three components was conducted by response surface method. The optimized serum free media showed significant cell growth supporting effect compared with commercial serum free media and IMDM supplemented with 5% FBS.

In order to meet the requirements for data access by tenants and management by service providers, the multi-tenant data stored in cloud using replica technology must be reasonably placed. For the outweight nodes and the ultra light nodes, according to characteristics of the multi-tenant data and the load of nodes, through adjusting the number and position of the replicas to maintenance and optimization the strategy so that meet the SLA requirements meanwhile minimize the overall cost. Experimental results through comparison with random placement strategy and greedy placement policy demonstrate the feasibility and effectiveness of the proposed strategy.

Aiming at the grid-connected problems of the double-fed induction generator (DFIG), this paper researches the control technology of the rotor excitation and the pitch angle and proposes a grid-connected DFIG joint optimization control strategy of the rotor excitation and the pitch angle based on the structure decentralized theory, and establishes the joint optimization model of the grid-connected DFIG. On the basis of the model, the rotor excitation current adaptive terminal sliding mode controller and the pitch angle controller are designed. The simulation results show that the proposed control strategy can achieve an effective control of the DFIG’s power angle and the DFIG’s rotor flux and reduce the adverse effects of the power angle when the double-fed wind generator and synchronous generator (SG) are in parallel operation. The algorithm given by this article not only improves the system's ability to resist external disturbances, but also enhances the transient stability of the power system. It has important actual significance and practical application value.

A new algorithm combined Eagle Strategy with PSO is proposed. The new algorithm performs by two phases: First Eagle Strategy is used to do global search; Second PSO algorithm is used to do fast local search around a promising solution. The balance of global search and local search is considered simultaneously. It is not only help to jump out of local optimum but also accelerate local convergence. Experimental results on three benchmark problems illustrate that the presented approach is effective, efficient and accurate for solving reliability redundancy optimization problems.

A novel hybrid Bat Algorithm (BA) with the Differential Evolution (DE) strategy using the feasibility-based rules, namely BADE is proposed to deal with the constrained optimization problems. The sound interferences induced by other things are inevitable for the bats which rely on the echolocation to detect and localize the things. Through integration of the DE strategy with BA, the insects’ interferences for the bats can be effectively mimicked by BADE. Moreover, the bats swarm’ mean velocity is simulated as the other bats’ effects on each bat. Having considered the living environments the bats inhabit, the virtual bats can be lifelike. Experiments on some benchmark problems and engineering designs demonstrate that BADE performs more efficient, accurate, and robust than the original BA, DE, and some other optimization methods.

Aiming at the refitted HAFEI hybrid electric vehicle (HEV), fuzzy logic energy management strategy is constructed based on genetic algorithm optimization. The difference value D between the total require torque Tr of path and the target required torque Te of engine, the intelligence quotient value with Tr is selected as the first input variable of fuzzy controller, the SOC of battery as the second input variable; torque control coefficient C is selected as output variable, meanwhile two input variable membership function is improved on genetic algorithm. To further evaluate the control strategy, dynamic programming control strategy is used as standard; the simulation experiments show that every kind of gas emission is obviously reduced by 12% to 47% in fuzzy control strategy B based on genetic algorithm optimization compared to strategy A based on determinacy rules. Compared to dynamic programming, fuel economy in strategy A is only 45.09% of standard value which is not ideal, the utilization of fuel is low and the gas emission is serious, while in strategy B fuel economy is 78.89% of standard value and effect is improved obviously.