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
Discrete Element Study on the Mechanical Response of Soft Rock Considering Water-Induced Softening Effect
Appl. Sci. 2024, 14(9), 3918; https://doi.org/10.3390/app14093918 (registering DOI) - 04 May 2024
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Soft rocks are prone to softening upon contact with water, and their rapid deterioration in mechanical properties is a significant cause of instability and failure soft rock masses. Besides, the macroscopic mechanical response of rocks is closely related to the mineral composition and
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Soft rocks are prone to softening upon contact with water, and their rapid deterioration in mechanical properties is a significant cause of instability and failure soft rock masses. Besides, the macroscopic mechanical response of rocks is closely related to the mineral composition and microstructure. The purpose of this research is to consider the heterogeneity factors and softening effects, and systematically investigate the influence of confining pressure and softening time on the damage and failure characteristics of soft rocks. The Voronoi polygons generated using a built-in Voronoi diagram algorithm and contact elements (the substances with cementing capacity) of UDEC discrete element method are employed to represent the clastic grains and interfacial cemented bonding (ICB) structures in soft rock. Based on the Voronoi probabilistic method, the grain-based discrete element model (GB-DEM) considering the softening effect is established by introducing a meso-scale softening damage factor, along with a detailed calibration method for meso-scale parameters. The damage parameters such as the crack initiation threshold, the crack damage threshold, the damage degree, and the tensile and shear crack ratio are then analyzed. The study results indicate that the simulated strengths of the heterogeneous models under different water immersion time are in good agreement with the experimental results. The thresholds for crack initiation and damage, the proportions of tensile and shear cracks, and the degree of damage are positively correlated with the confining pressure. The attenuation patterns of the crack initiation threshold and damage threshold in the heterogeneous models with water immersion time are highly consistent with the meso-scale softening damage factor. The damage parameters show a trend of increasing first and then decreasing with the extension of water immersion time. The cement–cement contact elements are the main locations for crack initiation and propagation. The research outcomes have significant theoretical and practical implications for understanding and predicting the mechanical behavior of soft rocks under a water–rock interaction.
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Open AccessArticle
Effects of Amyloid Beta (Aβ) Oligomers on Blood–Brain Barrier Using a 3D Microfluidic Vasculature-on-a-Chip Model
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Samuel Chidiebere Uzoechi, Boyce Edwin Collins, Cody Joseph Badeaux, Yan Li, Sang Su Kwak, Doo Yeon Kim, Daniel Todd Laskowitz, Jin-Moo Lee and Yeoheung Yun
Appl. Sci. 2024, 14(9), 3917; https://doi.org/10.3390/app14093917 (registering DOI) - 04 May 2024
Abstract
The disruption of the blood–brain barrier (BBB) in Alzheimer’s Disease (AD) is largely influenced by amyloid beta (Aβ). In this study, we developed a high-throughput microfluidic BBB model devoid of a physical membrane, featuring endothelial cells interacting with an extracellular matrix (ECM). This
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The disruption of the blood–brain barrier (BBB) in Alzheimer’s Disease (AD) is largely influenced by amyloid beta (Aβ). In this study, we developed a high-throughput microfluidic BBB model devoid of a physical membrane, featuring endothelial cells interacting with an extracellular matrix (ECM). This paper focuses on the impact of varying concentrations of Aβ1–42 oligomers on BBB dysfunction by treating them in the luminal. Our findings reveal a pronounced accumulation of Aβ1–42 oligomers at the BBB, resulting in the disruption of tight junctions and subsequent leakage evidenced by a barrier integrity assay. Additionally, cytotoxicity assessments indicate a concentration-dependent increase in cell death in response to Aβ1–42 oligomers (LC50 ~ 1 µM). This study underscores the utility of our membrane-free vascular chip in elucidating the dysfunction induced by Aβ with respect to the BBB.
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(This article belongs to the Collection BioMEMS)
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A Multi-Stance Detection Method by Fusing Sentiment Features
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Weidong Huang and Jinyuan Yang
Appl. Sci. 2024, 14(9), 3916; https://doi.org/10.3390/app14093916 (registering DOI) - 04 May 2024
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Stance information has a significant influence on market strategy, government policy, and public opinion. Users differ not only in their polarity but also in the degree to which they take a stand. The traditional classification of stances is quite simple and cannot fully
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Stance information has a significant influence on market strategy, government policy, and public opinion. Users differ not only in their polarity but also in the degree to which they take a stand. The traditional classification of stances is quite simple and cannot fully depict the diversity of stances. At the same time, traditional approaches ignore user sentiment features when expressing their stances. As a result, this paper develops a multi-stance detection model by fusing sentiment features. First, a five-category stance indicator system is built based on the LDA model, then sentiment features are extracted from the reviews using the sentiment lexicon, and finally, stance detection is implemented using a hybrid neural network model. The experiment shows that the proposed method can classify stances into five categories and perform stance detection more accurately.
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Open AccessArticle
Quarterly Percentual Change in Height, Weight, Body Fat and Muscle Mass in Young Football Players of Different Categories
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Moisés Falces-Prieto, Ricardo Martín-Moya, Gabriel Delgado-García, Rui Miguel Silva, Halil Ibrahim Ceylan and Juan Carlos de la Cruz-Márquez
Appl. Sci. 2024, 14(9), 3915; https://doi.org/10.3390/app14093915 (registering DOI) - 04 May 2024
Abstract
The purpose of this study was to compare the change of Body Composition (BC) (height, weight, body fat percentage and muscle mass) as a function of the trimester and category in a sample of young soccer players. Data collection was performed in five
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The purpose of this study was to compare the change of Body Composition (BC) (height, weight, body fat percentage and muscle mass) as a function of the trimester and category in a sample of young soccer players. Data collection was performed in five consecutive seasons (2016–2021). The sample consisted of 741 young male football players of different categories (Under 14 year old (U14), U15, U16, U17 and U18) belonging to a high-performance football academy. Considering the trimestral change of all the raw anthropometrics variables a set of new variables called the trimestral change in percentage (TC) of each raw variable was computed. Two-way repeated measures ANOVA (including the raw anthropometric variables as dependent and trimester and the age-category as independent) revealed differences for the anthropometric variables (p value < 0.001 in all cases), concluding that the effect of trimester reaches conventional levels of statistical significance. The trimester by age in contrast was significant (p < 0.05) in all raw variables except for the height. Considering the TC variables, the variable height-TC showed an increase (p value < 0.05) while the variable muscle mass-TC was near the significative value (p = 0.09). In this case the interaction trimester by age category was not significative (p > 0.05 in all cases). It seems that height suffers more changes in the first trimester but the weight, body fat percentage and muscle mass changes more in the second and third trimester. It is important to modulate the training load according to the trimester-specific response, although these improvements may vary according to factors such as genetics, diet, sleep and the specific training.
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(This article belongs to the Special Issue New Trends in Training, Performance, Coaching and Health in Sports Science)
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Open AccessArticle
Design and Implementation of an Automated Disaster-Recovery System for a Kubernetes Cluster Using LSTM
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Ji-Beom Kim, Je-Bum Choi and Eun-Sung Jung
Appl. Sci. 2024, 14(9), 3914; https://doi.org/10.3390/app14093914 - 03 May 2024
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With the increasing importance of data in modern business environments, effective data management and protection strategies are gaining increasing research attention. Data protection in a cloud environment is crucial for safeguarding information assets and maintaining sustainable services. This study introduces a system structure
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With the increasing importance of data in modern business environments, effective data management and protection strategies are gaining increasing research attention. Data protection in a cloud environment is crucial for safeguarding information assets and maintaining sustainable services. This study introduces a system structure that integrates Kubernetes management platforms with backup and restoration tools. This system is designed to immediately detect disasters and automatically recover applications from another Kubernetes cluster. The experimental results show that this system executes the restoration process within 15 s without human intervention, enabling rapid recovery. This, in turn, significantly reduces the potential for delays and errors compared to manual recovery processes, thereby enhancing data management and recovery efficiency in cloud environments. Moreover, our research model predicts the CPU utilization of the cluster using Long Short-Term Memory (LSTM). The necessity of scheduling through this predict is made clearer through comparison with experiments without scheduling, demonstrating its ability to prevent performance degradation. This research highlights the efficiency and necessity of automatic recovery systems in cloud environments, setting a new direction for future research.
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Open AccessEditorial
Applied Maritime Engineering and Transportation Problems 2022
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Lucjan Gucma, Krzysztof Naus, Marko Perkovič and Cezary Specht
Appl. Sci. 2024, 14(9), 3913; https://doi.org/10.3390/app14093913 - 03 May 2024
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It is probable that the term marine traffic engineering (MTE) was first used by Toyoda and Fuji [...]
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(This article belongs to the Special Issue Applied Maritime Engineering and Transportation Problems 2022)
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Open AccessArticle
Degree of Hamstring Extensibility and Its Relationship with Pelvic Tilt in Professional Cyclists
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José M. Muyor, Pedro A. López-Miñarro, Fernando Alacid and Daniel López-Plaza
Appl. Sci. 2024, 14(9), 3912; https://doi.org/10.3390/app14093912 - 03 May 2024
Abstract
The cyclist’s posture is typically characterized by a trunk flexion position to reach the handlebar of the bike. The pelvis serves as the base of the spine, and its tilt has been associated with the degree of extensibility of the hamstring, particularly in
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The cyclist’s posture is typically characterized by a trunk flexion position to reach the handlebar of the bike. The pelvis serves as the base of the spine, and its tilt has been associated with the degree of extensibility of the hamstring, particularly in flexion postures of the trunk. The aim of this study was to determine whether, in professional cyclists, the degree of hamstring extensibility influences the pelvic tilt maintained while seated on the bicycle with support from the three handlebar grips of the road bike, as well as in other positions of the bicycle. To evaluate pelvic tilt, all participants were measured using the Spinal Mouse system. The results revealed statistically significant differences in pelvic tilt among the six positions assessed (p ≤ 0.05). Furthermore, the degree of hamstring extensibility of the hamstrings presented a strong and positive correlation with pelvic tilt in standing posture (r = 0.82), Sit-and-Reach (r = 0.76), and Toe-Touch (r = 0.88). However, the degree of hamstring extensibility showed no significant correlations with pelvic tilt in any posture maintained on the bicycle.
Full article
(This article belongs to the Special Issue Research of Sports Medicine on Health Care)
Open AccessArticle
Explainable Artificial Intelligence to Support Work Safety in Forestry: Insights from Two Large Datasets, Open Challenges, and Future Work
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Ferdinand Hoenigsberger, Anna Saranti, Anahid Jalali, Karl Stampfer and Andreas Holzinger
Appl. Sci. 2024, 14(9), 3911; https://doi.org/10.3390/app14093911 - 03 May 2024
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Forestry work, which is considered one of the most demanding and dangerous professions in the world, is claiming more and more lives. In a country as small as Austria, more than 50 forestry workers are killed in accidents every year, and the number
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Forestry work, which is considered one of the most demanding and dangerous professions in the world, is claiming more and more lives. In a country as small as Austria, more than 50 forestry workers are killed in accidents every year, and the number is increasing rapidly. This serves as a catalyst for us to implement more stringent measures for workplace safety in order to achieve the sustainability objective of SDG 3, which focuses on health and well-being. This study contributes to the analysis of occupational accidents and focuses on two large real-world datasets from both the Austrian Federal Forests (ÖBf) and the Austrian Workers’ Compensation Board (AUVA). Decision trees, random forests, and fully connected neural networks are used for the analysis. By exploring different interpretation methods, this study sheds light on the decision-making processes ranging from basic association to causal inference and emphasizes the importance of causal inference in providing actionable insights for accident prevention. This paper contributes to the topic of explainable AI, specifically in its application to occupational safety in forestry. As a result, it introduces novel aspects to decision support systems in this application domain.
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(This article belongs to the Section Ecology Science and Engineering)
Open AccessArticle
Perception versus Historical Knowledge in Baccalaureate: A Comparative Study Mediated by Augmented Reality and Historical Thinking
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Alejandro López-García, Javier J. Maquilón-Sánchez and Pedro Miralles-Sánchez
Appl. Sci. 2024, 14(9), 3910; https://doi.org/10.3390/app14093910 - 03 May 2024
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Numerous studies have shown that a traditional model persists in the teaching of history, in which students are not allowed to think for themselves and are assigned a passive role based on the mere memorisation of information. This reality is in opposition to
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Numerous studies have shown that a traditional model persists in the teaching of history, in which students are not allowed to think for themselves and are assigned a passive role based on the mere memorisation of information. This reality is in opposition to the technological and technical boom taking place in the current educational context and to the enhancement of innovative strategies and methodologies that mark the role that students must occupy as protagonists of their own learning. This paper aims to compare the perceptions and historical knowledge of 93 baccalaureate students (16–18 years of age) following the implementation of an intervention programme based on active learning situations mediated by augmented reality and historical thinking skills. A quasi-experimental quantitative design with a non-equivalent control group was employed to meet these objectives. The results showed higher scores in the perception and knowledge of students in the experimental group compared with those in the control group. This line of work should be continued in the future with new studies to corroborate these findings, prioritising pedagogical models based on student activity and protagonism via the use of technology and critical thinking.
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(This article belongs to the Special Issue Artificial Intelligence and Information Visualization in Social and Industrial Systems)
Open AccessArticle
Enhanced Moving Source Localization with Time and Frequency Difference of Arrival: Motion-Assisted Method for Sub-Dimensional Sensor Networks
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Xu Yang
Appl. Sci. 2024, 14(9), 3909; https://doi.org/10.3390/app14093909 - 03 May 2024
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Localizing a moving source by Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) commonly requires at least sensors in N-dimensional space to obtain more than N pairs of TDOAs and FDOAs, thereby establishing more than
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Localizing a moving source by Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) commonly requires at least sensors in N-dimensional space to obtain more than N pairs of TDOAs and FDOAs, thereby establishing more than equations to solve for unknowns. However, if there are insufficient sensors, the localization problem will become underdetermined, leading to non-unique solutions or inaccuracies in the minimum norm solution. This paper proposes a localization method using TDOAs and FDOAs while incorporating the motion model. The motion between the source and sensors increases the equivalent length of the baseline, thereby improving observability even when using the minimum number of sensors. The problem is formulated as a Maximum Likelihood Estimation (MLE) and solved through Gauss–Newton (GN) iteration. Since GN requires an initialization close to the true value, the MLE is transformed into a semidefinite programming problem using Semidefinite Relaxation (SDR) technology, while SDR results in a suboptimal estimate, it is sufficient as an initialization to guarantee the convergence of GN iteration. The proposed method is analytically shown to reach the Cramér–Rao Lower Bound (CRLB) accuracy under mild noise conditions. Simulation results confirm that it achieves CRLB-level performance when the number of sensors is lower than , thereby corroborating the theoretical analysis.
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(This article belongs to the Special Issue Recent Progress in Radar Target Detection and Localization)
Open AccessArticle
Model Test and Numerical Simulation for Tunnel Leakage-Induced Seepage Erosion in Different Strata
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Qihao Sun, Wouter De Corte, Xian Liu and Luc Taerwe
Appl. Sci. 2024, 14(9), 3908; https://doi.org/10.3390/app14093908 - 03 May 2024
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Leakage in underground structures, especially tunnels, may cause seepage erosion in the surrounding soil, which in turn leads to ground subsidence, posing a great threat to urban safety. The current literature mainly focuses on seepage erosion in the sand but lacks a systematic
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Leakage in underground structures, especially tunnels, may cause seepage erosion in the surrounding soil, which in turn leads to ground subsidence, posing a great threat to urban safety. The current literature mainly focuses on seepage erosion in the sand but lacks a systematic study on the development process of seepage erosion induced by tunnel leakage in different strata. To investigate the different seepage erosion modes induced by tunnel leakage in different stratum types, a series of reduced-scale model tests were carried out. A coupled fluid–solid numerical model was further established to analyze the fine-scale characteristics of different seepage erosion modes. The results show that (1) the soil seepage erosion modes can be divided into three categories: no soil cave, unstable soil cave, and stable soil cave; (2) the adopted coupled fluid–solid numerical model based on DEM, which takes into account the degradation of clay during seepage erosion, can effectively simulate the erosion process of soil with different seepage erosion modes; (3) the phenomena of the three erosion modes are different in the process of erosion development; and (4) the micro-mechanisms of the three seepage erosion modes are different, which are manifested in the erosion range, soil arching effect, and displacement.
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(This article belongs to the Special Issue Advances in Tunnel and Underground Engineering)
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Open AccessArticle
Context-Aware System for Information Flow Management in Factories of the Future
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Pedro Monteiro, Rodrigo Pereira, Ricardo Nunes, Arsénio Reis and Tiago Pinto
Appl. Sci. 2024, 14(9), 3907; https://doi.org/10.3390/app14093907 - 03 May 2024
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The trends of the 21st century are challenging the traditional production process due to the reduction in the life cycle of products and the demand for more complex products in greater quantities. Industry 4.0 (I4.0) was introduced in 2011 and it is recognized
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The trends of the 21st century are challenging the traditional production process due to the reduction in the life cycle of products and the demand for more complex products in greater quantities. Industry 4.0 (I4.0) was introduced in 2011 and it is recognized as the fourth industrial revolution, with the aim of improving manufacturing processes and increasing the competitiveness of industry. I4.0 uses technological concepts such as Cyber-Physical Systems, Internet of Things and Cloud Computing to create services, reduce costs and increase productivity. In addition, concepts such as Smart Factories are emerging, which use context awareness to assist people and optimize tasks based on data from the physical and virtual world. This article explores and applies the capabilities of context-aware applications in industry, with a focus on production lines. In specific, this paper proposes a context-aware application based on a microservices approach, intended for integration into a context-aware information system, with specific application in the area of manufacturing. The manuscript presents a detailed architecture for structuring the application, explaining components, functions and contributions. The discussion covers development technologies, integration and communication between the application and other services, as well as experimental findings, which demonstrate the applicability and advantages of the proposed solution.
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(This article belongs to the Special Issue Autonomous Systems in Cyber-Physical Systems and Smart Industry: Innovations and Challenges)
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Open AccessArticle
Adipose Stem Cell Response to Borophosphate Bioactive Glass
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Nada A. Abokefa, Bradley A. Bromet, Rebekah L. Blatt, Makenna S. Pickett, Richard K. Brow and Julie A. Semon
Appl. Sci. 2024, 14(9), 3906; https://doi.org/10.3390/app14093906 - 03 May 2024
Abstract
Silicate and borate bioactive glasses have been reported to create alkaline conditions by rapidly releasing ions when reacting in aqueous solution. At certain levels, this alkaline solution can negatively affect cell viability. Adding phosphate ions to the glass composition can control the degradation
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Silicate and borate bioactive glasses have been reported to create alkaline conditions by rapidly releasing ions when reacting in aqueous solution. At certain levels, this alkaline solution can negatively affect cell viability. Adding phosphate ions to the glass composition can control the degradation rate of bioactive glasses and create a neutral pH environment. This study evaluated a series of borophosphate bioactive glasses (BPBGs) with nominal molar compositions 16Na2O-24CaO-xB2O3-(60-x)P2O5, where x = 0, 40, or 60. The phosphate (X0) glass (PBG) produced an acidic solution when dissolved in water; the borate (X60) glass (BBG) produced an alkaline solution, and the BPBG glass produced a pH-neutral solution. These three glasses were evaluated using adipose stem cells (ASCs), a cell population known for their therapeutic abilities. The effects of each glass on the pH of cell culture, ions released during degradation, and on ASC functions, including viability, migration, angiogenic ability, differentiation, and protein secretions, were evaluated. The X40 BPBG created a physiologically neutral pH in cell culture media after 24 h. The X0 phosphate glass promoted ASC migration, while the highly alkaline X60 borate increased the angiogenic ability of ASCs. These results indicate that BPBG can be used safely in cell culture studies and customized for specific biomedical applications.
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(This article belongs to the Special Issue Functional Glasses and Their Composites: Recent Advances and Applications)
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Open AccessArticle
Improving Driving Style in Connected Vehicles via Predicting Road Surface, Traffic, and Driving Style
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Yahya Kadhim Jawad and Mircea Nitulescu
Appl. Sci. 2024, 14(9), 3905; https://doi.org/10.3390/app14093905 - 03 May 2024
Abstract
This paper investigates the application of ensemble learning in improving the accuracy and reliability of predictions in connected vehicle systems, focusing on driving style, road surface quality, and traffic conditions. Our study’s central methodology is the voting classifier ensemble method, which integrates predictions
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This paper investigates the application of ensemble learning in improving the accuracy and reliability of predictions in connected vehicle systems, focusing on driving style, road surface quality, and traffic conditions. Our study’s central methodology is the voting classifier ensemble method, which integrates predictions from multiple machine learning models to improve overall predictive performance. Specifically, the ensemble method combines insights from random forest, decision tree, and K-nearest neighbors models, leveraging their individual strengths while compensating for their weaknesses. This approach resulted in high accuracy rates of 94.67% for driving style, 99.10% for road surface, and 98.80% for traffic predictions, demonstrating the robustness of the ensemble technique. Additionally, our research emphasizes the importance of model explanation ability, employing the tree interpreter tool to provide detailed insights into how different features influence predictions. This paper proposes a model based on the algorithm GLOSA for sharing data between connected vehicles and the algorithm CTCRA for sending road information to navigation application users. Based on prediction results using ensemble learning and similarity in driving styles, road surface conditions, and traffic conditions, an ensemble learning approach is used. This not only contributes to the predictions’ transparency and trustworthiness but also highlights the practical implications of ensemble learning in improving real-time decision-making and vehicle safety in intelligent transportation systems. The findings underscore the significant potential of advanced ensemble methods for addressing complex challenges in vehicular data analysis.
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(This article belongs to the Special Issue Intelligent Transportation System Technologies and Applications)
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Open AccessArticle
Comparative Evaluation of Ultrasonic and Sonic Irrigant Activation Systems: Assessing Extrusion Risk, Debridement, and Biofilm Removal in Distinct Apical Preparation Sizes
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Sara Paixão, Pedro Sousa Gomes, Maria Helena Fernandes, Cláudia Rodrigues and Liliana Grenho
Appl. Sci. 2024, 14(9), 3904; https://doi.org/10.3390/app14093904 - 03 May 2024
Abstract
This study aims to compare the effectiveness of ultrasonically and sonically activated irrigation in terms of extrusion risk, root canal debridement, and biofilm removal, considering distinct apical preparation sizes, through an ex vivo study in human teeth. Instrumented teeth, to an apical size
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This study aims to compare the effectiveness of ultrasonically and sonically activated irrigation in terms of extrusion risk, root canal debridement, and biofilm removal, considering distinct apical preparation sizes, through an ex vivo study in human teeth. Instrumented teeth, to an apical size of 35/.06 or 50/.06, were assigned to three different irrigation procedures: ultrasonically activated irrigation, sonically activated irrigation, and conventional manual irrigation. Apical extrusion risk was evaluated by quantifying irrigant and debris extrusion (n = 10/group). Debris evaluation and smear layer removal from the root canal wall were conducted by scanning electron microscopy (SEM) (n = 5/group), and the elimination of a mature biofilm of Enterococcus faecalis was assessed through resazurin assay and SEM (n = 10/group). For statistical analyses, Student’s paired t-test and the ANOVA with post-hoc Tukey were used. Activated irrigations exhibited a higher risk of extrusion for the larger apical size, while the risk for manual irrigation remained independent of the apical size. Substantially fewer residual debris and smear layers were observed after the activation of the irrigant, and there was a notable enhancement in biofilm elimination compared to manual irrigation (p < 0.05). Notably, the effectiveness of both activated irrigations was more pronounced in root canals prepared to a size 50/.06, with ultrasonic activation showing enhanced improvements. The findings of this study underscore the substantial impact of both ultrasonically and sonically activated irrigation on the effectiveness of root canal disinfection and debridement. This impact is especially prominent with larger apical size, albeit accompanied by an increased risk of extrusion.
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(This article belongs to the Special Issue Advances in Oral Rehabilitation: Materials, Techniques and Clinical Applications)
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Open AccessArticle
Real-Time Navigation Roads: Lightweight and Efficient Convolutional Neural Network (LE-CNN) for Arabic Traffic Sign Recognition in Intelligent Transportation Systems (ITS)
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Alaa A. Khalifa, Walaa M. Alayed, Hesham M. Elbadawy and Rowayda A. Sadek
Appl. Sci. 2024, 14(9), 3903; https://doi.org/10.3390/app14093903 - 02 May 2024
Abstract
Smart cities are now embracing the new frontier of urban living, with advanced technology being used to enhance the quality of life for residents. Many of these cities have developed transportation systems that improve efficiency and sustainability, as well as quality. Integrating cutting-edge
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Smart cities are now embracing the new frontier of urban living, with advanced technology being used to enhance the quality of life for residents. Many of these cities have developed transportation systems that improve efficiency and sustainability, as well as quality. Integrating cutting-edge transportation technology and data-driven solutions improves safety, reduces environmental impact, optimizes traffic flow during peak hours, and reduces congestion. Intelligent transportation systems consist of many systems, one of which is traffic sign detection. This type of system utilizes many advanced techniques and technologies, such as machine learning and computer vision techniques. A variety of traffic signs, such as yield signs, stop signs, speed limits, and pedestrian crossings, are among those that the traffic sign detection system is trained to recognize and interpret. Ensuring accurate and robust traffic sign recognition is paramount for the safe deployment of self-driving cars in diverse and challenging environments like the Arab world. However, existing methods often face many challenges, such as variability in the appearance of signs, real-time processing, occlusions that can block signs, low-quality images, and others. This paper introduces an advanced Lightweight and Efficient Convolutional Neural Network (LE-CNN) architecture specifically designed for accurate and real-time Arabic traffic sign classification. The proposed LE-CNN architecture leverages the efficacy of depth-wise separable convolutions and channel pruning to achieve significant performance improvements in both speed and accuracy compared to existing models. An extensive evaluation of the LE-CNN on the Arabic traffic sign dataset that was carried out demonstrates an impressive accuracy of 96.5% while maintaining superior performance with a remarkably low inference time of 1.65 s, crucial for real-time applications in self-driving cars. It achieves high accuracy with low false positive and false negative rates, demonstrating its potential for real-world applications like autonomous driving and advanced driver-assistance systems.
Full article
(This article belongs to the Section Transportation and Future Mobility)
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Open AccessFeature PaperReview
Bioferments and Biosurfactants as New Products with Potential Use in the Cosmetic Industry
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Marta Klimek-Szczykutowicz, Ewelina Maria Błońska-Sikora, Katarzyna Kulik-Siarek, Aizhan Zhussupova and Małgorzata Wrzosek
Appl. Sci. 2024, 14(9), 3902; https://doi.org/10.3390/app14093902 - 02 May 2024
Abstract
The cosmetics industry is one of the fastest growing markets in terms of searching for new ingredients. Recently, there has been a growing interest in products made during fermentation, which are being introduced into cosmetics with increasing frequency, creating a market that emphasizes
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The cosmetics industry is one of the fastest growing markets in terms of searching for new ingredients. Recently, there has been a growing interest in products made during fermentation, which are being introduced into cosmetics with increasing frequency, creating a market that emphasizes the positive image of healthy, environmentally friendly components with a positive effect on skin. Scientists mainly focus on examining biological activity as well as the impact on changes in the production of bioactive ingredients in various plant species undergoing fermentation. The studies show that bioferments have scientifically proven anti-aging and anti-inflammatory effects, among other skin benefits. Due to the increasing emphasis on environmental protection, ecofriendly compounds are being sought. This group includes surfactants, which are also obtained by fermentation. Plant-based and microbial biosurfactants, due to their multifunctional properties, such as detergency, emulsifying, foaming, moisturizing, and antibacterial activity, can replace chemical surfactants in many skincare formulations. This review focuses especially on elucidating the importance of the bioferments and biosurfactants and their potential in the cosmetic industry.
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(This article belongs to the Section Biomedical Engineering)
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Open AccessArticle
Effects of Vertical and Horizontal Jumping Asymmetries on Linear and Change-of-Direction Speed Performance of Female Soccer Players
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Georgios Svynos, Yiannis Michailidis, Pavlos Kotsakis, Athanasios Mandroukas, Ioannis Metaxas, Ioannis Gissis and Thomas I. Metaxas
Appl. Sci. 2024, 14(9), 3901; https://doi.org/10.3390/app14093901 - 02 May 2024
Abstract
In recent years, along with the remarkable development of women’s soccer, significant attention has been given to the study of asymmetry in lower limbs. However, there is uncertainty about whether and to what extent jumping asymmetries affect the performance of female soccer players.
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In recent years, along with the remarkable development of women’s soccer, significant attention has been given to the study of asymmetry in lower limbs. However, there is uncertainty about whether and to what extent jumping asymmetries affect the performance of female soccer players. The aims of this study were to examine (a) possible asymmetries in jumping ability and (b) the correlations between asymmetries and performance of female soccer players in 10 m and 30 m speed tests, as well as in change-of-direction speed tests. The study involved 12 adolescent (age: 15.8 ± 0.8 years, body mass: 59.4 ± 7 kg and height: 160.5 ± 5.1 cm) and 10 adult female soccer players (age: 22.3 ± 4.4 years, body mass: 62.2 ± 7.5 kg and height: 165.7 ± 6.1 cm). Pearson’s correlations showed no statistically significant relationships between vertical and horizontal asymmetries and time in 10 m, 30 m and 505 change-of-direction speed tests for adolescent players. In adult players, a significantly high correlation was found between asymmetries in single-leg hop tests (for distance) and time in 505 change-of-direction speed tests (r = 0.68, p < 0.05). Adult players showed higher asymmetry values in vertical and horizontal jump tests, but these asymmetries were not significant (p > 0.05). Practitioners are recommended to implement strength and power training programs, as well as injury prevention protocols, aiming to reduce asymmetries, in order to minimize the risk of injuries, and potentially improve performance of female soccer players in certain fitness tests.
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(This article belongs to the Special Issue Applied Biomechanics: Sport Performance and Injury Prevention III)
Open AccessArticle
Structural Performance of Bolted Lateral Connections in Steel Beams under Bending Using the Component-Based Finite Element Method
by
Guillermo Morido-García and César De Santos-Berbel
Appl. Sci. 2024, 14(9), 3900; https://doi.org/10.3390/app14093900 - 02 May 2024
Abstract
Structures must provide strength, stability, and stiffness to buildings and at the same time be efficient. This study addressed the effect of design elements and parameters on the strength of bolted lateral connections in steel beams under bending using the component-based finite element
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Structures must provide strength, stability, and stiffness to buildings and at the same time be efficient. This study addressed the effect of design elements and parameters on the strength of bolted lateral connections in steel beams under bending using the component-based finite element method. The variables evaluated were plate thickness, horizontal and vertical spacing between bolts, and geometric arrangement of bolts. Finite element software was used to evaluate the stress state of the junction plate, its plastic deformation, and bolt shear. A sensitivity analysis was performed to determine which bolt arrangements result in safer and more efficient designs using the same components. Stress distribution within the junction plate and plastic deformation values were used to evaluate the structural performance of the joints according to EuroCode 3. The results showed that placing bolts near the edge of a plate affected the bolts’ utilization, especially with thinner plates. Additionally, introducing an offset between central and outer bolt rows is not recommended as it worsened the stress distribution and the structural performance.
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(This article belongs to the Section Mechanical Engineering)
Open AccessArticle
Detection of Small Targets in Photovoltaic Cell Defect Polarization Imaging Based on Improved YOLOv7
by
Haixia Wang, Fangbin Wang, Xue Gong, Darong Zhu, Ruinan Wang and Ping Wang
Appl. Sci. 2024, 14(9), 3899; https://doi.org/10.3390/app14093899 - 02 May 2024
Abstract
A photovoltaic cell defect polarization imaging small target detection method based on improved YOLOv7 is proposed to address the problem of low detection accuracy caused by insufficient feature extraction ability in the process of small target defect detection. Firstly, polarization imaging technology is
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A photovoltaic cell defect polarization imaging small target detection method based on improved YOLOv7 is proposed to address the problem of low detection accuracy caused by insufficient feature extraction ability in the process of small target defect detection. Firstly, polarization imaging technology is introduced, using polarization degree images as inputs to enhance the edge contour information of YOLOv7 for detecting small targets; then, the COT self-attention mechanism is added to reconstruct the SPPCSPC module to improve YOLOv7’s ability to capture and fuse small target features in complex backgrounds; next, the normalized Wasserstein distance (NWD) is used to replace the traditional loss function based on intersection over union (IoU) metric, reducing the boundary offset between the prior box and the closest real target box in the prediction process of the object detection model and reducing the sensitivity of the YOLOv7 network to small object position deviations; finally, by constructing a shortwave infrared polarization imaging system to obtain polarization images of photovoltaic cells and detect small targets with scratch defects in photovoltaic cells, the applicability and effectiveness of the proposed method are verified. The results show that the proposed method has good recognition ability for small target defects in photovoltaic cells. By applying the constructed dataset, the detection accuracy reaches 98.08%, the recall rate reaches 95.06% and the mAP reaches 98.83%.
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