Journal Description
Applied Sciences
Applied Sciences
is an international, peer-reviewed, open access journal on all aspects of applied natural sciences published semimonthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Inspec, CAPlus / SciFinder, and other databases.
- Journal Rank: JCR - Q2 (Engineering, Multidisciplinary) / CiteScore - Q1 (General Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.9 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Testimonials: See what our authors say about Applied Sciences.
- Companion journals for Applied Sciences include: Applied Nano, AppliedChem, Applied Biosciences, Virtual Worlds, Spectroscopy Journal and JETA.
Impact Factor:
2.7 (2022);
5-Year Impact Factor:
2.9 (2022)
Latest Articles
Designing an Educational Metaverse: A Case Study of NTUniverse
Appl. Sci. 2024, 14(6), 2559; https://doi.org/10.3390/app14062559 (registering DOI) - 19 Mar 2024
Abstract
An up-and-coming concept that seeks to transform how students learn about and study complex systems, as well as how industrial workers are trained, metaverse technology is characterized in this context by its use in virtual simulation and analysis. In this work, a virtual
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An up-and-coming concept that seeks to transform how students learn about and study complex systems, as well as how industrial workers are trained, metaverse technology is characterized in this context by its use in virtual simulation and analysis. In this work, a virtual environment is created that duplicates real-world situations and enables immersive and interactive learning in the educational metaverse. For this purpose, we built a digital twin of the Nanyang Technological University (NTU) campus as a foundation, called NTUniverse. It is designed as an educational metaverse in which various academic and analytical applications are digitized as 3D content embedded within this virtual campus. The approach to digitally twinning educational systems and embedding them within virtual campuses enables remote and collaborative learning as well as professional technical skills training. It also makes feasible the analysis of abstract concepts, complicated structures, dynamic processes, and sensitive industrial procedures virtually, which is otherwise challenging if not impossible to perform in the real world. The work offers important insights into the behaviors and interactions of systems in the metaverse by evaluating design choices and user interests. NTUniverse is an attempt to explore a novel approach that addresses remote education and training challenges. Three efforts with NTUniverse will be discussed in this work, including (1) digitalization of the NTU campus; (2) campus train modelling and simulation; and (3) science, technology, engineering and mathematics education.
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(This article belongs to the Special Issue Extended Reality Applications in Industrial Systems)
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Rethink Motion Information for Occluded Person Re-Identification
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Hongye Liu and Xiai Chen
Appl. Sci. 2024, 14(6), 2558; https://doi.org/10.3390/app14062558 (registering DOI) - 19 Mar 2024
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Person re-identification aims to identify the same pedestrians captured by various cameras from different viewpoints in multiple scenarios. Occlusion is the toughest problem for practical applications. In video-based ReID tasks, motion information can be easily obtained from sampled frames, and provide discriminative human
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Person re-identification aims to identify the same pedestrians captured by various cameras from different viewpoints in multiple scenarios. Occlusion is the toughest problem for practical applications. In video-based ReID tasks, motion information can be easily obtained from sampled frames, and provide discriminative human part representations. However, most motion-based methodologies are designed for video frames which are not suitable for processing single static image input. In this paper, we propose a Motion-Aware Fusion (MAF) network, aiming to acquire motion information from static images in order to improve the performance of ReID tasks. Specifically, a visual adapter is introduced to enable visual feature extraction, either from image or video data. We design a motion consistency task to guide the motion-aware transformer to learn representative human-part motion information and greatly improve the learning quality of features of occluded pedestrians. Extensive experiments on popular holistic, occluded, and video datasets demonstrate the effectiveness of our proposed method. This method outperforms state-of-the-art approaches by improving the mean average precision (mAP) by 1.5% and rank-1 accuracy by 1.2% on the challenging Occluded-REID dataset. At the same time, it surpasses other methods on the MARS dataset with an improvement of 0.2% in mAP and 0.1% in rank-1 accuracy.
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Experimentally Determined Force Density Spectra for Admittance-Based Vibration Predictions along Railways
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Benedikt Tappauf, Karoline Alten, Marianne Legenstein, Marlene Ofner and Rainer Flesch
Appl. Sci. 2024, 14(6), 2557; https://doi.org/10.3390/app14062557 (registering DOI) - 19 Mar 2024
Abstract
The planning application and approval process of railway tracks is generally accompanied by a vibration immission assessment. Starting with the source spectrum, which is ideally obtained through measurements, the German guideline VDI 3837 recommends a series of multiplications using transfer spectra which account
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The planning application and approval process of railway tracks is generally accompanied by a vibration immission assessment. Starting with the source spectrum, which is ideally obtained through measurements, the German guideline VDI 3837 recommends a series of multiplications using transfer spectra which account for the various subdomains of the wave propagation path, such as the effect of the superstructure, the free field propagation, the soil-structure coupling and the transmission inside buildings. Typically, these one-third octave spectra are an average over empirical reference values. While simplified empirical relations are prone to a large variance, the use of artificial vibration sources allows the actual vibration transmission behavior from the tracks to the immission points to be quantified. Using so-called transfer admittances, also known as transfer mobilities, which account for all dynamic interactions along the transmission path (track, tunnel structures, foundations, structural properties), together with force density spectra for relevant rail vehicles, the authors investigate the practical application of the method presented in Report No. 0123 of the Federal Transit Administration (2018) for the frequency range 5–200 Hz. The article demonstrates how such force density spectra were obtained for the most common train types in the Austrian rail network at two different track sections using artificial vibration sources. Furthermore, practical aspects are discussed and a recently developed approximation method for estimating line transfer admittances from point transfer admittances using simplified models is introduced.
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(This article belongs to the Special Issue Recent Advances in Vehicle-Track-Ground Coupling Dynamics and Railway-Induced Ground Vibration)
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Detection of Safety Signs Using Computer Vision Based on Deep Learning
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Yaohan Wang, Zeyang Song and Lidong Zhang
Appl. Sci. 2024, 14(6), 2556; https://doi.org/10.3390/app14062556 (registering DOI) - 19 Mar 2024
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Safety signs serve as an important information carrier for safety standards and rule constraints. Detecting safety signs in mines is essential for automatically early warning of unsafe behaviors and the wearing of protective equipment while using computer vision techniques to realize advanced safety
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Safety signs serve as an important information carrier for safety standards and rule constraints. Detecting safety signs in mines is essential for automatically early warning of unsafe behaviors and the wearing of protective equipment while using computer vision techniques to realize advanced safety in the AI and IoT era. This work aims to propose an improved YOLOV4-tiny safety signs detection model applying deep learning to detect safety signs in mines. The dataset employed in this study was derived from coal mines and analogous environments, comprising a total of ten types of safety signs. It was partitioned into training, validation, and test sets following a distribution ratio of (training set + validation set) to test set = 9:1, with the training set to validation set ratio also set at 9:1. Then the attention mechanism ECANet was introduced into the model, which strengthened the network’s learning of places that need attention. Moreover, the Soft-NMS algorithm was used to retain more correct prediction frames and optimize the detection model to further improve the detection accuracy. The Focal Loss function was introduced to alleviate the problem of category imbalance in one-stage safety signs detection. Experimental results indicate that the proposed model achieved a detection precision of 97.76%, which is 7.55% and 9.23% higher than the YOLOV4-tiny and Faster RCNN algorithms, respectively. Besides, the model performed better in the generalization because it avoided the over-fitting phenomenon that occurred in the YOLOV4-tiny and the Faster RCNN. Moreover, the advantages of the improved model were more prominent when detecting small target areas and targets under dim conditions in coal mines. This work is beneficial for the intelligent early warning system with surveillance cameras in coal mines.
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Open AccessArticle
A Cloud-Based Ambulance Detection System Using YOLOv8 for Minimizing Ambulance Response Time
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Ayman Noor, Ziad Algrafi, Basil Alharbi, Talal H. Noor, Abdullah Alsaeedi, Reyadh Alluhaibi and Majed Alwateer
Appl. Sci. 2024, 14(6), 2555; https://doi.org/10.3390/app14062555 (registering DOI) - 19 Mar 2024
Abstract
Ambulance vehicles face a challenging issue in minimizing the response time for an emergency call due to the high volume of traffic and traffic signal delays. Several research works have proposed ambulance vehicle detection approaches and techniques to prioritize ambulance vehicles by turning
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Ambulance vehicles face a challenging issue in minimizing the response time for an emergency call due to the high volume of traffic and traffic signal delays. Several research works have proposed ambulance vehicle detection approaches and techniques to prioritize ambulance vehicles by turning the traffic light to green for saving patients’ lives. However, the detection of ambulance vehicles is a challenging issue due to the similarities between ambulance vehicles and other commercial trucks. In this paper, we chose a machine learning (ML) technique, namely, YOLOv8 (You Only Look Once), for ambulance vehicle detection by synchronizing it with the traffic camera and sending an open signal to the traffic system for clearing the way on the road. This will reduce the amount of time it takes the ambulance to arrive at the traffic light. In particular, we managed to gather our own dataset from 10 different countries. Each country has 300 images of its own ambulance vehicles (i.e., 3000 images in total). Then, we trained our YOLOv8 model on these datasets with various techniques, including pre-trained vs. non-pre-trained, and compared them. Moreover, we introduced a layered system consisting of a data acquisition layer, an ambulance detection layer, a monitoring layer, and a cloud layer to support our cloud-based ambulance detection system. Last but not least, we conducted several experiments to validate our proposed system. Furthermore, we compared the performance of our YOLOv8 model with other models presented in the literature including YOLOv5 and YOLOv7. The results of the experiments are quite promising where the universal model of YOLOv8 scored an average of 0.982, 0.976, 0.958, and 0.967 for the accuracy, precision, recall, and F1-score, respectively.
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(This article belongs to the Special Issue Advanced Approaches for Novel Emergency Response Systems in Stochastic Operations Research)
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Improving Hardenability Modeling: A Bayesian Optimization Approach to Tuning Hyperparameters for Neural Network Regression
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Wendimu Fanta Gemechu, Wojciech Sitek and Gilmar Ferreira Batalha
Appl. Sci. 2024, 14(6), 2554; https://doi.org/10.3390/app14062554 (registering DOI) - 18 Mar 2024
Abstract
This study investigates the application of regression neural networks, particularly the fitrnet model, in predicting the hardness of steels. The experiments involve extensive tuning of hyperparameters using Bayesian optimization and employ 5-fold and 10-fold cross-validation schemes. The trained models are rigorously evaluated, and
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This study investigates the application of regression neural networks, particularly the fitrnet model, in predicting the hardness of steels. The experiments involve extensive tuning of hyperparameters using Bayesian optimization and employ 5-fold and 10-fold cross-validation schemes. The trained models are rigorously evaluated, and their performances are compared using various metrics, such as mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results provide valuable insights into the models’ effectiveness and their ability to generalize to unseen data. In particular, Model 4208 (8-85-141-1) emerges as the top performer with an impressive RMSE of 1.0790 and an R2 of 0.9900. The model, which was trained with different datasets for nearly 40 steel grades, enables the prediction of hardenability curves, but is limited to the range of the training dataset. The research paper contains an illustrative example that demonstrates the practical application of the developed model in determining the hardenability band for a specific steel grade and shows the effectiveness of the model in predicting and optimizing heat treatment results.
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(This article belongs to the Special Issue Computer Methods in Mechanical, Civil and Biomedical Engineering)
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Pain Perception Following Periodontal Decontamination Treatment with Laser Therapies: Comparison between Oxygen High-Level Laser Therapy (OHLLT) and Laser-Assisted New Attachment Procedure (LANAP)
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Paolo Caccianiga, Saverio Ceraulo, Gérard Rey, Dario Monai, Marco Baldoni and Gianluigi Caccianiga
Appl. Sci. 2024, 14(6), 2553; https://doi.org/10.3390/app14062553 (registering DOI) - 18 Mar 2024
Abstract
Introduction: Within the field of periodontology, there has been a proposal for the utilization of noninvasive laser therapy as a potential treatment for persistent periodontitis. The Laser-Assisted New Attachment Procedure (LANAP) employs an Nd:YAG laser as a specific technique. Through its interaction with
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Introduction: Within the field of periodontology, there has been a proposal for the utilization of noninvasive laser therapy as a potential treatment for persistent periodontitis. The Laser-Assisted New Attachment Procedure (LANAP) employs an Nd:YAG laser as a specific technique. Through its interaction with endogenous chromophores, the Nd: YAG laser exhibits a selective effect on the evaporation of granulation tissue, therefore establishing a correlation with reduced bleeding. The study also examined Oxygen High-Level Laser Therapy (OHLLT). The OHLLT technique employs a high-power diode laser in combination with hydrogen peroxide solutions to facilitate the liberation of singlet oxygen, which possesses antibacterial attributes, within the periodontal pockets. The existing literature indicates their potential to promote the regeneration of tooth support tissues. Objective: The aim of this study is to assess the subjective pain levels reported by patients who have undergone surgery using the OHLLT protocol versus those who have undergone surgery using the LANAP technique. Methods: A total of 20 individuals with a stage III–IV periodontitis diagnosis were recruited for the study. The participants were randomly divided into two groups, each consisting of 10 individuals: Group 1, comprising patients treated according to the LANAP protocol, and Group 2, comprising patients treated according to the OHLLT protocol. After their initial session of nonsurgical periodontal therapy, individuals provided feedback regarding their level of pain, utilizing a Numerical Rating Scale (NRS) comprising time intervals of 0 h (T0), 6 h (T1), 12 h (T2), 24 h (T3), 48 h (T4), and 7 days (T5). The Wilcoxon–Mann–Whitney statistical test was employed to assess the variations in NRS scores between Group 1 and Group 2 at each recording period. (p ≤ 0.05). In addition, a microbiological assessment of the bacterial load in the periodontal region was conducted on all subjects using real-time PCR testing at two time points: prior to treatment (T0) and seven days post-treatment (T5). Results: The findings of this study indicate that the OHLLT group exhibited significantly lower pain levels compared to the LANAP group at all time intervals, except for the preoperative period, where no significant difference was observed (p < 0.05). Group 2 exhibited a more rapid decrease in pain, as demonstrated by a score test approaching zero within 24 h. The quantity of periodontal bacteria seen seven days post-treatment was similar between the two groups and was found to be decreased compared to the pre-treatment levels. Conclusions: The OHLLT and LANAP regimens have demonstrated efficacy in the nonsurgical management of periodontal disease. Nevertheless, it should be noted that the OHLLT approach does not subject the patient to any thermal hazards, unlike the LANAP method. The postoperative discomfort experienced following the OHLLT procedure is indeed reduced, as this technique is characterized by lower invasiveness and reduced dependence on the operator.
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(This article belongs to the Special Issue Photodynamic Therapy and Other Innovative Techniques or Materials in Dental Clinical Practice and Research)
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Open AccessReview
Mobile Robot for Security Applications in Remotely Operated Advanced Reactors
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Ujwal Sharma, Uma Shankar Medasetti, Taher Deemyad, Mustafa Mashal and Vaibhav Yadav
Appl. Sci. 2024, 14(6), 2552; https://doi.org/10.3390/app14062552 (registering DOI) - 18 Mar 2024
Abstract
This review paper addresses the escalating operation and maintenance costs of nuclear power plants, primarily attributed to rising labor costs and intensified competition from renewable energy sources. The paper proposes a paradigm shift towards a technology-centric approach, leveraging mobile and automated robots for
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This review paper addresses the escalating operation and maintenance costs of nuclear power plants, primarily attributed to rising labor costs and intensified competition from renewable energy sources. The paper proposes a paradigm shift towards a technology-centric approach, leveraging mobile and automated robots for physical security, aiming to replace labor-intensive methods. Focusing on the human–robot interaction principle, the review conducts a state-of-the-art analysis of dog robots’ potential in infrastructure security and remote inspection within human–robot shared environments. Additionally, this paper surveys research on the capabilities of mobile robots, exploring their applications in various industries, including disaster response, exploration, surveillance, and environmental conservation. This study emphasizes the crucial role of autonomous mobility and manipulation in robots for diverse tasks, and discusses the formalization of problems, performance assessment criteria, and operational capabilities. It provides a comprehensive comparison of three prominent robotic platforms (SPOT, Ghost Robotics, and ANYmal Robotics) across various parameters, shedding light on their suitability for different applications. This review culminates in a research roadmap, delineating experiments and parameters for assessing dog robots’ performance in safeguarding nuclear power plants, offering a structured approach for future research endeavors.
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Open AccessArticle
Search for Optimal Parameters in the Control Structure of a Surgical System for Soft Tissue Operations Based on In Vitro Experiments on Cardiovascular Tissue
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Grzegorz Ilewicz and Edyta Ładyżyńska-Kozdraś
Appl. Sci. 2024, 14(6), 2551; https://doi.org/10.3390/app14062551 (registering DOI) - 18 Mar 2024
Abstract
The surgical robots currently used in cardiac surgery are equipped with a remote center of motion (RCM) mechanism that enables the required spherical workspace. The dynamics model of the surgical robot’s RCM mechanism presented in this work includes a direct current (DC) motor,
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The surgical robots currently used in cardiac surgery are equipped with a remote center of motion (RCM) mechanism that enables the required spherical workspace. The dynamics model of the surgical robot’s RCM mechanism presented in this work includes a direct current (DC) motor, an optimal proportional–integral–derivative (PID) controller, and a LuGre friction model that takes into account the Stribeck effect and surface deformation. A finite element method (FEM) analysis of transients was carried out using the energy hypothesis of von Mises with an optimal input signal from the mechatronic system with a PID controller obtained using the Runge–Kutta differentiation method in the Dormand–Prince ordinary differential equations variant (ODE45). Five criteria were adopted for the objective function: the safety factor related to the stress function in the time-varying strength problem, the first natural frequency related to stiffness and the resonance phenomenon, the buckling coefficient in the statics problem related to stability, the static factor of safety, and the displacement of the operating tip. The force inputs to the dynamics model were derived from in vitro force measurements on cardiovascular tissue using a force sensor. The normality of the statistical distribution of the experimental data was confirmed using the Kolmogorov–Smirnov statistical test. The problem of multi-criteria optimization was solved using the non-sorter genetic algorithm (NSGA-II), the finite element method, and the von Mises distortion energy hypothesis. Velocity input signals for the transient dynamics model were obtained from a second in vitro experiment on cardiovascular tissue using the minimally robotic invasive surgery (MIRS) technique. An experienced cardiac surgeon conducted the experiment in a modern method using the Robin Heart Vision surgical robot, and a system of four complementary metal–oxide–semiconductor (CMOS) optical sensors and ariel performance analysis system (APAS-XP 2002) software were used to obtain the endoscopic tool trajectory signal. The trajectory signal was accurate to ±2 [mm] in relation to the adopted standard, and it was smoothed using the Savitzky–Golay (SG) polynomial smoothing, whose parameters were optimally selected using the Durbin–Watson (DW) statistical test.
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(This article belongs to the Special Issue Applications of Robotics in Disease Treatment and Rehabilitation)
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Additive Manufacturing for Surgical Planning and Education: A Review
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Antreas Kantaros, Florian Ion Tiberiu Petrescu, Hamed Abdoli, Olaf Diegel, Simon Chan, Mihaiela Iliescu, Theodore Ganetsos, Iulian Sorin Munteanu and Liviu Marian Ungureanu
Appl. Sci. 2024, 14(6), 2550; https://doi.org/10.3390/app14062550 - 18 Mar 2024
Abstract
Additive manufacturing has been widely used in various industries, including the healthcare sector. Over the last few decades, AM has been playing an important role in the medical field in different areas, including surgical planning, implants, and educational activities. For surgical applications, AM
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Additive manufacturing has been widely used in various industries, including the healthcare sector. Over the last few decades, AM has been playing an important role in the medical field in different areas, including surgical planning, implants, and educational activities. For surgical applications, AM can help surgeons practice and plan an operation until they are confident with the process. This can help to reduce operational risk and time. In addition, it can help to demonstrate the problem to other colleagues. AM has also been used to produce 3D models to teach students and doctors about human anatomy. This paper aims to comprehensively review the diverse applications of additive manufacturing within the domains of surgical planning and medical education. By focusing on the multifaceted roles played by AM in these critical areas, a contribution to the growing body of knowledge that underscores the transformative potential of this technology in shaping the future of healthcare practices is sought to be made.
Full article
(This article belongs to the Special Issue Applications of Three-Dimensional Printing in Medical Prototyping and Manufacturing)
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Automatically Expanding User-Management System for Massive Users in the Cloud Platform
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Shengyang Li, Zhen Wang and Wanfeng Zhang
Appl. Sci. 2024, 14(6), 2549; https://doi.org/10.3390/app14062549 - 18 Mar 2024
Abstract
Cloud computing has become one of the key technologies used for big data processing and analytics. User management on cloud platforms is a growing challenge as the number of users and the complexity of systems increase. In light of the user-management system provided
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Cloud computing has become one of the key technologies used for big data processing and analytics. User management on cloud platforms is a growing challenge as the number of users and the complexity of systems increase. In light of the user-management system provided by major cloud service providers, which cannot manage multiple types of user systems, this article proposed scale-out automated expansion user management for authorization synchronization to improve the efficiency and scalability of user management on cloud platforms. Three modules for user-automated expansion were designed and implemented to synchronize the authentication information from the cloud platform resource user to the data-processing user. Additionally, an optimized dynamically weighted load-balancing algorithm in Nginx is presented in this article that adjusts the weight according to load information such as CPU and memory usage, and a better load balance can be achieved. The effectiveness of the proposed user-management system is substantiated by comparing it with two existing infrastructures, including multiple data centers and the Huawei cloud platform. The experimental results validate the finding that scale-out automated expansion user management across the Huawei cloud platform can effectively synchronize data accessing authority with cloud resource utilizing authority. Furthermore, the optimized weighted load-balancing algorithm is also valuable for massive concurrent user registration based on limited cloud resources. In the future, this scale-out user-management system could be applied to other cloud platforms and extended by database synchronization to satisfy the needs of data sharing among multiple types of users belonging to different cloud platforms.
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(This article belongs to the Special Issue Cooperative Data Management and Learning Analytics in Mobile Edge Computing)
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Effect of Combination of Expansive Agent and Fiber on Freeze-Thaw Resistance of High-Strength Concrete at Dry Environment
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Shanshan Song, Hongfa Yu and Haiyan Ma
Appl. Sci. 2024, 14(6), 2548; https://doi.org/10.3390/app14062548 - 18 Mar 2024
Abstract
This study employed a rapid freezing method to investigate the impact of individual additions of expansion agent, steel fibers, and High Elasticity Module Polyethylene Fiber (HEMPF), as well as their combinations, on the freeze-thaw resistance of High-Strength Concrete (HSC). The findings reveal that
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This study employed a rapid freezing method to investigate the impact of individual additions of expansion agent, steel fibers, and High Elasticity Module Polyethylene Fiber (HEMPF), as well as their combinations, on the freeze-thaw resistance of High-Strength Concrete (HSC). The findings reveal that the non-air-entrained HSC of C80 exhibits excellent freeze-thaw resistance. However, this resistance is sensitive to the curing environment’s humidity. The expansion agent has a negative impact on the freeze-thaw resistance of HSC, while steel fibers and HEMPF fibers have positive effects. Combining HSC with an expansion agent and high elasticity modulus fibers ensures not only high freeze-thaw resistance but also a total alteration of humidity sensitivity, leading to an extended freeze-thaw life of HSC under dry curing conditions.
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(This article belongs to the Section Materials Science and Engineering)
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Tayseer: A Novel AI-Powered Arabic Chatbot Framework for Technical and Vocational Student Helpdesk Services and Enhancing Student Interactions
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Abeer Alabbas and Khalid Alomar
Appl. Sci. 2024, 14(6), 2547; https://doi.org/10.3390/app14062547 - 18 Mar 2024
Abstract
The rise of conversational agents (CAs) like chatbots in education has increased the demand for advisory services. However, student–college admission interactions remain manual and burdensome for staff. Leveraging CAs could streamline the admission process, providing efficient advisory support. Moreover, limited research has explored
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The rise of conversational agents (CAs) like chatbots in education has increased the demand for advisory services. However, student–college admission interactions remain manual and burdensome for staff. Leveraging CAs could streamline the admission process, providing efficient advisory support. Moreover, limited research has explored the role of Arabic chatbots in education. This study introduces Tayseer, an Arabic AI-powered web chatbot that enables instant access to college information and communication between students and colleges. This study aims to improve the abilities of chatbots by integrating features into one model, including responding with audiovisuals, various interaction modes (menu, text, or both), and collecting survey responses. Tayseer uses deep learning models within the RASA framework, incorporating a customized Arabic natural language processing pipeline for intent classification, entity extraction, and response retrieval. Tayseer was deployed at the Technical College for Girls in Najran (TCGN). Over 200 students used Tayseer during the first semester, demonstrating its efficiency in streamlining the advisory process. It identified over 50 question types from inputs with a 90% precision in intent and entity predictions. A comprehensive evaluation illuminated Tayseer’s proficiency as well as areas requiring improvement. This study developed an advanced CA to enhance student experiences and satisfaction while establishing best practices for education chatbot interfaces by outlining steps to build an AI-powered chatbot from scratch using techniques adaptable to any language.
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(This article belongs to the Special Issue Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts)
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Application of Random Forest Algorithm in Estimating Dynamic Mechanical Behaviors of Reinforced Concrete Column Members
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Rou-Han Li, Mao-Yuan Li, Xiang-Yang Zhu and Xiang-Wei Zeng
Appl. Sci. 2024, 14(6), 2546; https://doi.org/10.3390/app14062546 - 18 Mar 2024
Abstract
In this paper, an innovative method is put forward for estimating the dynamic mechanical behaviors of reinforced concrete (RC) column members by applying the random forest algorithm. Firstly, the development of dynamic modified coefficient (DMC) predictive models and the realization of
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In this paper, an innovative method is put forward for estimating the dynamic mechanical behaviors of reinforced concrete (RC) column members by applying the random forest algorithm. Firstly, the development of dynamic modified coefficient (DMC) predictive models and the realization of the proposed method were elaborated. Then, due to the lack of dynamic loading tests on RC column members, a numerical model of RC columns considering the dynamic modification on flexural, shear and bond-slip behaviors was developed on the OpenSees platform, and the model accuracy and the effectiveness were verified with the available test results. Moreover, by comparing the simulated results of the hysteretic curve using numerical models with different complexities, the influences of dynamic modification and the deformation sub-element were investigated. Furthermore, a numerical experiment database was established to obtain the training data for developing the DMC predictive models of critical mechanical behavior parameters, including the yielding bearing capacity, ultimate bearing capacity and displacement ductility. Finally, the results of feature importance for different input parameters were studied, and the model accuracy was evaluated using the test set and available experimental data. It was revealed that the predictive models developed using the random forest algorithm can be employed to reliably estimate the dynamic mechanical behaviors of RC column members.
Full article
(This article belongs to the Topic Artificial Intelligence (AI) Applied in Civil Engineering, 2nd Volume)
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Open AccessArticle
Unveiling the Hidden Potential of Simple but Promising Blood Cell Parameters on Acute Myocardial Infarction Prognostication
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Cosmina Elena Jercălău, Cătălina Liliana Andrei, Lavinia Nicoleta Brezeanu, Roxana Oana Darabont, Suzana Guberna, Gabriela Postolea, Octavian Ceban and Crina Julieta Sinescu
Appl. Sci. 2024, 14(6), 2545; https://doi.org/10.3390/app14062545 - 18 Mar 2024
Abstract
Background: Non-ST-elevation myocardial infarction (NSTEMI), a disease of mounting interest, continues to pose challenges and cast shadows of doubt on determining the optimal timing for revascularization. The current guidelines on NSTEMI recommend coronary angiography based on the GRACE score, emphasizing the critical need
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Background: Non-ST-elevation myocardial infarction (NSTEMI), a disease of mounting interest, continues to pose challenges and cast shadows of doubt on determining the optimal timing for revascularization. The current guidelines on NSTEMI recommend coronary angiography based on the GRACE score, emphasizing the critical need for early invasive assessment (within 24 h); very-high-risk patients have to undergo this intervention even sooner, within 2 h. We believe that a reality check of these assumptions is needed and that we should endeavor to update these strategies using new predictive markers. Materials and methods: Our study included patients hospitalized for NSTEMI over the course of 16 months. Simple blood parameters, namely MCV (mean corpuscular volume), MPV (mean platelet volume), RDW (red blood cell distribution width), and PDW (platelet distribution width), were analyzed in correlation with the extent of the myocardial infarction area and with complications during hospitalization and at 30-day follow-up. Results: The parameters mentioned above have been identified as statistically relevant indicators of prognosis in patients with NSTEMI. Conclusions: In the present day, living in the world of the blue sky concept allows us to search for new diagnostic algorithms. Therefore, the combination of these parameters can constitute the DNA strands of a new and up-to-date score stratification.
Full article
(This article belongs to the Special Issue Recent Advancements in Biomarkers for Noncommunicable Diseases)
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Open AccessFeature PaperArticle
Incorporating Citizen Science to Enhance Public Awareness in Smart Cities: The Case Study of Balaguer
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Luisa F. Cabeza, Mercè Teixidó, Francesco Guarino, Roberta Rincione, Manel Díaz, Rosa M. Gil, Maurizio Cellura and Carles Mateu
Appl. Sci. 2024, 14(6), 2544; https://doi.org/10.3390/app14062544 - 18 Mar 2024
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The concept of a smart city is becoming increasingly popular to improve citizens’ quality of life. Institutions are also committed to enhancing the sustainability of cities by implementing the Sustainable Development Goals (SDGs). This paper presents a Balaguer case study investigating energy demand
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The concept of a smart city is becoming increasingly popular to improve citizens’ quality of life. Institutions are also committed to enhancing the sustainability of cities by implementing the Sustainable Development Goals (SDGs). This paper presents a Balaguer case study investigating energy demand monitoring, decreasing energy demand, and citizen acceptance in a municipality district. The study collected data from three sources: (1) quantitative data coming from on-site sensors; (2) quantitative data from a simulation of the area; and (3) qualitative data from questionnaires developed with a totem located in the city center. This study shows the importance of citizen science in contributing towards the increased awareness of energy demand, renewable energy, and climate change. But it also shows how citizen science can improve research quality involving the municipality authorities. This study also was instrumental in contributing to the increase in awareness among municipality authorities and capacity building on the topic. This activity may also contribute towards the implementation of actions to reduce the energy demand in public buildings and helping them in deploying policies to decrease energy demand in buildings, increase the use of renewable energy, and increase awareness among citizens. The government will use the information gathered to develop policies for citizen improvement.
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Open AccessArticle
Automated Generation and Internal Force Visualization for Box Culvert Based on Building Information Modeling
by
Dejiang Wang and Jiangming Huang
Appl. Sci. 2024, 14(6), 2543; https://doi.org/10.3390/app14062543 - 18 Mar 2024
Abstract
Box culverts, as a commonly employed structural form for culverts, play a critical role in traversing topographic barriers, ensuring the safety and smooth operation of transportation means such as roads and railways. However, traditional design methodologies are often time-consuming and prone to inaccuracies,
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Box culverts, as a commonly employed structural form for culverts, play a critical role in traversing topographic barriers, ensuring the safety and smooth operation of transportation means such as roads and railways. However, traditional design methodologies are often time-consuming and prone to inaccuracies, failing to achieve the efficiency and precision required by modern engineering construction. To address these challenges, using the Revit 2021 and Midas Civil 2021 software platforms, we developed a Building Information Modeling (BIM) parametric modeling method for box culverts using Dynamo’s visual programming capabilities. This method enables the rapid and accurate automated generation of box culvert BIM models. Furthermore, this study proposes an effective strategy for conversion between box culvert BIM models and Midas Civil finite element models, as well as internal force visualization within a BIM project. A case study involving a box culvert underpass beneath an expressway in an urban setting was modeled parametrically and structurally validated, demonstrating that the approach not only significantly enhances modeling efficiency but also strengthens computational capabilities through bidirectional data exchange between BIM and Finite Element Analysis (FEA) software. This research has effectively advanced the application and practical implementation of BIM technology in box culvert engineering.
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(This article belongs to the Section Civil Engineering)
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Open AccessEditorial
Evolutionary Computation: Theories, Techniques, and Applications
by
Vincent A. Cicirello
Appl. Sci. 2024, 14(6), 2542; https://doi.org/10.3390/app14062542 - 18 Mar 2024
Abstract
Evolutionary computation is now nearly 50 years old, originating with the seminal work of John Holland at the University of Michigan in 1975 which introduced the genetic algorithm [...]
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(This article belongs to the Special Issue Evolutionary Computation: Theories, Techniques, and Applications)
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Open AccessArticle
MLSL-Spell: Chinese Spelling Check Based on Multi-Label Annotation
by
Liming Jiang, Xingfa Shen, Qingbiao Zhao and Jian Yao
Appl. Sci. 2024, 14(6), 2541; https://doi.org/10.3390/app14062541 - 18 Mar 2024
Abstract
Chinese spelling errors are commonplace in our daily lives, which might be caused by input methods, optical character recognition, or speech recognition. Due to Chinese characters’ phonetic and visual similarities, the Chinese spelling check (CSC) is a very challenging task. However, the existing
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Chinese spelling errors are commonplace in our daily lives, which might be caused by input methods, optical character recognition, or speech recognition. Due to Chinese characters’ phonetic and visual similarities, the Chinese spelling check (CSC) is a very challenging task. However, the existing CSC solutions cannot achieve good spelling check performance since they often fail to fully extract the contextual information and Pinyin information. In this paper, we propose a novel CSC framework based on multi-label annotation (MLSL-Spell), consisting of two basic phases: spelling detection and correction. In the spelling detection phase, MLSL-Spell uses the fusion vectors of both character-based pre-trained context vectors and Pinyin vectors and adopts the sequence labeling method to explicitly label the type of misspelled characters. In the spelling correction phase, MLSL-Spell uses Masked Language Mode (MLM) model to generate candidate characters, then performs corresponding screenings according to the error types, and finally screens out the correct characters through the XGBoost classifier. Experiments show that the MLSL-Spell model outperforms the benchmark model. On SIGHAN 2013 dataset, the spelling detection F1 score of MLSL-Spell is 18.3% higher than that of the pointer network (PN) model, and the spelling correction F1 score is 10.9% higher. On SIGHAN 2015 dataset, the spelling detection F1 score of MLSL-Spell is 11% higher than that of Bert and 15.7% higher than that of the PN model. And the spelling correction F1 of MLSL-Spell score is 6.8% higher than that of PN model.
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(This article belongs to the Special Issue Applications, Challenges and Future Direction of Natural Language Processing Based on Deep Learning)
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Open AccessReview
A State-of-the-Art Review on the Study of the Diffusion Mechanism of Fissure Grouting
by
Xueming Du, Zhihui Li, Hongyuan Fang, Bin Li, Xiaohua Zhao, Kejie Zhai, Binghan Xue and Shanyong Wang
Appl. Sci. 2024, 14(6), 2540; https://doi.org/10.3390/app14062540 - 18 Mar 2024
Abstract
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China is renowned for its extensive underground engineering projects and the complex geological and hydrological conditions it faces. Grouting treatment technology is widely employed in deep-buried mines and tunnels, where grouting parameters such as materials, pressure, volume, and hole arrangement significantly impact the
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China is renowned for its extensive underground engineering projects and the complex geological and hydrological conditions it faces. Grouting treatment technology is widely employed in deep-buried mines and tunnels, where grouting parameters such as materials, pressure, volume, and hole arrangement significantly impact the effectiveness of grouting. This review paper comprehensively examines current research on grouting materials, theories, experiments, and numerical simulations. It summarizes the various factors that must be considered during the grouting process of fissures and explores the diffusion mechanisms of grout under their influence. Furthermore, further research is needed on the mechanisms and treatment methods for poor grouting in rock masses, the distribution patterns of fissures, optimization methods for grouting parameters, and grout quality assessment techniques. Future research should focus on developing more efficient experimental methods with higher accuracy levels while advancing grouting technologies. Establishing comprehensive and accurate rock mass models along with improving monitoring capabilities are also crucial aspects to consider. Therefore, studying the diffusion mechanisms of grout in fissured rock masses is of significant importance for the practical operation of underground engineering projects.
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