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Appl. Sci., Volume 14, Issue 17 (September-1 2024) – 6 articles

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23 pages, 800 KiB

Open AccessReview

Generative Adversarial Networks in Business and Social Science

by Africa Ruiz-Gándara and Luis Gonzalez-Abril

Appl. Sci. 2024, 14(17), 7438; https://doi.org/10.3390/app14177438 (registering DOI) - 23 Aug 2024

Abstract

Generative adversarial networks (GANs) have become a recent and rapidly developing research topic in machine learning. Since their inception in 2014, a significant number of variants have been proposed to address various topics across many fields, and they have particularly excelled not only in image and language processing but also in the medical and data science domains. In this paper, we aim to highlight the significance of and advancements that these GAN models can introduce in the field of Business Economics, where they have yet to be fully developed. To this end, a review of the literature of GANs is presented in general together with a more specific review in the field of Business Economics, for which only a few papers can be found. Furthermore, the most relevant papers are analysed in order to provide approaches for the opportunity to research GANs in the field of Business Economics. Full article

(This article belongs to the Special Issue Intelligent Systems for Multidisciplinary Applications in Era of Artificial Intelligence)

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24 pages, 6073 KiB

Open AccessArticle

Status-Byte-Assisted RDMA Transmission Mechanism for Optimizing Multi-Task Video Streaming in Edge Computing

by Donglei Xiao, Huiyue Yi, Wuxiong Zhang and Wenhui Shen

Appl. Sci. 2024, 14(17), 7437; https://doi.org/10.3390/app14177437 (registering DOI) - 23 Aug 2024

Abstract

In the context of the rapid development of edge computing, optimizing data transmission and reducing latency is crucial for efficient collaborative processing among edge servers. Traditional TCP/IP protocols are hindered by high latency and low throughput, while RDMA (Remote Direct Memory Access) technology addresses these challenges by enabling direct memory access and bypassing the operating system kernel. However, the RDMA data transmission mechanism based on sliding windows requires frequent memory status exchanges in the order of memory blocks, which can limit its ability to handle multiple concurrent tasks within a single Queue Pair (QP). To address the limitations of the traditional sliding window transmission mechanism in multi-task environments, we propose a novel RDMA data transmission mechanism that utilizes status bytes to indicate memory block utilization, which utilizes stateless server connections, and multi-task shared QP transmission strategies. In the proposed mechanism, fine-grained control over memory blocks is achieved through the status byte, thereby enabling effective multi-task real-time video stream transmission. Experimental results show that, compared to the sliding window method, the proposed status-byte-assisted RDMA transmission mechanism provides higher throughput, lower latency, and reduced resource consumption, thus enhancing system scalability and reducing CPU utilization. Moreover, this mechanism achieves more stable throughput than the sliding window method when transmitting multiple real-time video streams in edge computing scenarios, making it particularly suitable for data transmission in such environments. Full article

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13 pages, 1888 KiB

Open AccessArticle

The Impact of Exercise Order on Velocity Performance in the Bench Press and the Squat: A Comparative Study

by Rogério Martins Pereira, Diogo Luís Marques, Ana Ruivo Alves, Daniel Almeida Marinho, Pedro Pombo Neves, António José Silva and Henrique Pereira Neiva

Appl. Sci. 2024, 14(17), 7436; https://doi.org/10.3390/app14177436 (registering DOI) - 23 Aug 2024

Abstract

We analyzed the influence of exercise order using the bench press and squat as the first or second exercise of the session on velocity performance. Ten male trained individuals (20.9 ± 0.7 years) randomly performed two protocols of three sets of six repetitions at 80% of their one-repetition maximum with different exercise sequences: the bench press followed by the squat (BP + S) and the squat followed by the bench press (S + BP). A linear velocity transducer attached to the Smith machine barbell measured the mean propulsive velocity (MPV), peak velocity (PV), and time to peak velocity. Additionally, blood lactate and heart rate were measured. Regarding the bench press, differences were found in the MPV in the first (BP + S: 0.50 ± 0.07 m·s⁻¹ vs. S + BP: 0.42 ± 0.08 m·s⁻¹; p = 0.03, g = 0.72) and second sets (0.50 ± 0.06 m·s⁻¹ vs. 0.42 ± 0.07 m·s⁻¹; p = 0.03, g = 0.73), and in the PV in the second set (0.74 ± 0.09 m·s⁻¹ vs. 0.63 ± 0.09 m·s⁻¹; p = 0.02, g = 0.86). Regarding the squat, although the S + BP sequence tended to show higher velocities, no significant differences were found between protocols. These results showed that squatting first decreased subsequent bench press velocity performance. On the other hand, squat velocity performance was not impaired when preceded by the bench press. Full article

(This article belongs to the Special Issue Biomechanics and Sport Engineering: Latest Advances and Prospects)

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19 pages, 3144 KiB

Open AccessArticle

A Multiproject and Multilevel Plan Management Model Based on a Hybrid Program Evaluation and Review Technique and Reinforcement Learning Mechanism

by Long Wang, Haibin Liu, Minghao Xia, Yu Wang and Mingfei Li

Appl. Sci. 2024, 14(17), 7435; https://doi.org/10.3390/app14177435 (registering DOI) - 23 Aug 2024

Abstract

It is very difficult for manufacturing enterprises to achieve automatic coordination of multiproject and multilevel planning when they are unable to make large-scale resource adjustments. In addition, planning and coordination work mostly relies on human experience, and inaccurate planning often occurs. This article innovatively proposes the PERT-RP-DDPGAO algorithm, which effectively combines the program evaluation and review technique (PERT) and deep deterministic policy gradient (DDPG) technology. Innovatively using matrix computing, the resource plan (RP) itself is used for the first time as an intelligent agent for reinforcement learning, achieving automatic coordination of multilevel plans. Through experiments, this algorithm can achieve automatic planning and has interpretability in management theory. To solve the problem of continuous control, the second half of the new algorithm adopts the DDPG algorithm, which has advantages in convergence and response speed compared to traditional reinforcement learning algorithms and heuristic algorithms. The response time of this algorithm is 3.0% lower than the traditional deep Q-network (DQN) algorithm and more than 8.4% shorter than the heuristic algorithm. Full article

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19 pages, 4807 KiB

Open AccessArticle

Analytical Model of Mechanical Responses of Circular Tunnels Considering Rheological Behavior of Surrounding Rock and Functionally Graded Lining

by Jianming Du and Xuan Zhang

Appl. Sci. 2024, 14(17), 7434; https://doi.org/10.3390/app14177434 (registering DOI) - 23 Aug 2024

Abstract

The rock–lining interaction significantly affects the stability and safety of a tunnel in service. In this study, a mechanical model is proposed to explore the rock–lining interaction under hydrostatic pressure. The model takes into account the alterable mechanical property (such as the elastic modulus) of the lining in the rheological rock mass, which may be subjected to inner surface pressure along the radial direction of the highway tunnel. The alterable elastic modulus is assumed as a power function of the radius. The analytical solutions of this model are first verified by comparison with existing solutions and corresponding results are obtained by numerical simulation. Then, systematic parametric investigations are carried out to analyze the influence of the main model parameters on the radial deformation of the rock–lining interface and the normalized supporting pressure provided by the lining. The research conclusions obtained by this study can offer some valuable references for the safety evaluation of a tunnel in service. Full article

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17 pages, 3065 KiB

Open AccessArticle

Enhancing Concurrent Emergency Response: Joint Scheduling of Emergency Vehicles on Freeways with Tailored Heuristic

by Linwei Li, Erlong Tan, Peng Gao and Yinli Jin

Appl. Sci. 2024, 14(17), 7433; https://doi.org/10.3390/app14177433 (registering DOI) - 23 Aug 2024

Abstract

Scheduling decisions for concurrent emergency response (CER) across multiple disaster sites presents numerous difficulties. The main challenge is to minimize human casualties while taking into account the rationality of resource allocation across different disaster sites. This paper establishes a joint scheduling model for emergency vehicles on freeways in the context of CER. The model aims to minimize the transportation time, dispatch cost, and casualty risk, by using the resource site scheduling scheme as the decision variable, addressing multiple disaster and resource sites. Specifically, a casualty risk function based on the rescue waiting time was designed to balance the competing needs among various disaster sites, enhance equitable resource allocation, and reduce the probability of casualties. To achieve global convergence in a high-dimensional solution space, a tailored heuristic algorithm called adaptive dual evolutionary particle swarm optimization (ADEPSO) is proposed. The numerical results show that the scheduling scheme proposed by the ADEPSO algorithm satisfies all constraints and demonstrates significant advantages in large-sized instances. Compared to the two basic algorithms, ADEPSO provides a more cost-effective scheme and reduces the average rescue waiting time. Moreover, integrating the casualty risk function significantly decreases the average rescue waiting time at both high- and low-priority disaster sites, thereby directly lowering the casualty risk. Full article

(This article belongs to the Section Transportation and Future Mobility)

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