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
Technical, Musical, and Legal Aspects of an AI-Aided Algorithmic Music Production System
Appl. Sci. 2024, 14(9), 3541; https://doi.org/10.3390/app14093541 (registering DOI) - 23 Apr 2024
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Even though algorithmic composition might be considered a centuries-old concept, it has been gaining particular momentum since the introduction of computer-based techniques. The development of artificial intelligence (AI) methods, culminating in the latest achievements of deep learning techniques, has provided tools to automatically
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Even though algorithmic composition might be considered a centuries-old concept, it has been gaining particular momentum since the introduction of computer-based techniques. The development of artificial intelligence (AI) methods, culminating in the latest achievements of deep learning techniques, has provided tools to automatically compose and even produce music. This paper discusses various aspects of the entire process within a context of designing a system able to automatically generate a score and recordings belonging to selected musical genres. It begins with the idea and design overview, followed by considerations regarding the algorithmic formulation of selected musical rules and principles. The system implements a hybrid approach, combining conventional, i.e., stochastic or rule-based, and AI elements. The latter are applied to facilitate the generation of selected layers of composition and to constitute a classifier with a task of evaluating the generated recordings. Selected stages of music generation are discussed, for example how motifs are processed into phrases and how phrases are used in the context of a whole song. To validate the system operation results, an evaluation of the quality of the produced music recordings was conducted, including a test with a group of listeners. The analysis also touches upon some legal aspects related to the creation of algorithmic compositions.
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Open AccessArticle
Shear Strength and Durability of Expansive Soil Treated with Recycled Gypsum and Rice Husk Ash
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Mary Ann Adajar, Jomari Tan, Allaina Bernice Ang, Miles Louis Lim, Kendrick Roy Seng and Vince Patrick Sy
Appl. Sci. 2024, 14(9), 3540; https://doi.org/10.3390/app14093540 (registering DOI) - 23 Apr 2024
Abstract
Expansive soil underlying structures pose a significant risk to the integrity of superstructures. Chemical soil stabilization can be used to strengthen soils due to the cost and impracticality of mechanical approaches. Waste materials such as recycled gypsum and rice husk ash have been
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Expansive soil underlying structures pose a significant risk to the integrity of superstructures. Chemical soil stabilization can be used to strengthen soils due to the cost and impracticality of mechanical approaches. Waste materials such as recycled gypsum and rice husk ash have been considered alternatives because of their sustainable and economic advantages. A combination of these additives was used to address the high absorption of gypsum and the lack of cohesion of the pozzolan. The study assessed the short-term and long-term performance of expansive soil treated with recycled gypsum and rice husk ash under normal and fluctuating moisture conditions. Direct shear tests indicated ductile and compressive soil behavior with improved shear strength. A good approximation of stress–strain response was made with a modified hyperbolic model for treated soils that exhibited strain hardening and compressive volumetric strain. Durability and water immersion tests were performed for samples after varying curing periods and cycles of capillary soaking to assess the behavior when exposed to varied environmental conditions. Samples under the modified durability test experienced significant strength loss, with decreasing compressive strength as curing durations increased. Specimens in the modified water immersion test experienced significant strength loss; however, it was determined that curing durations did not contribute to the change in the strength of the sample. Expansion index tests also determined that the treatment effectively mitigated expansivity and collapsibility in all samples. Despite improvement in shear strength and expansion potential, further investigation is needed to enhance the durability of soil treated with gypsum and rice husk ash.
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(This article belongs to the Section Civil Engineering)
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Effect of Simultaneous Lower- and Upper-Body Ischemic Preconditioning on Lactate, Heart Rate, and Rowing Performance in Healthy Males and Females
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Robert Urbański, Piotr Aschenbrenner, Piotr Żmijewski, Paulina Ewertowska, Katarzyna Świtała and Michał Krzysztofik
Appl. Sci. 2024, 14(9), 3539; https://doi.org/10.3390/app14093539 (registering DOI) - 23 Apr 2024
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The ergogenic effects of simultaneous lower- and upper-body ischemic preconditioning (IPC) are a factor that has not been investigated exhaustively. Therefore, this study aimed to investigate the effects of IPC on 500 m rowing performance (time, relative peak [RPP] and mean [MPP] power
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The ergogenic effects of simultaneous lower- and upper-body ischemic preconditioning (IPC) are a factor that has not been investigated exhaustively. Therefore, this study aimed to investigate the effects of IPC on 500 m rowing performance (time, relative peak [RPP] and mean [MPP] power output, time to peak power [TPP], and blood lactate concentration [BLa]), as well as heart rate (HR) among forty-three physically active male (n = 24) and female (n = 19) subjects. In this cross-over randomized trial, either the IPC (220 mmHg) or SHAM (20 mmHg) protocol was applied to the upper and lower limbs simultaneously for 5 min. Then, after 5 min of reperfusion, the participants performed an all-out 500 m rowing trial. During rowing, HR was recorded, and after the completion of the rowing, the BLa concentration was determined. Wilcoxon’s signed-rank test showed a significantly shorter TPP in the SHAM condition compared to under the IPC condition for females (Z = 2.415, p = 0.017), but not for males (Z = 1.914, p = 0.056). Moreover, a significant main effect of the group was reported for rowing time, BLa, RPP, and RMP (p < 0.001 for all dependent variables). No significant interactions nor a main effect of the condition were observed for rowing time, BLa, RPP, RMP, HRWP, HRMEAN, and HRMAX (p > 0.05 for all dependent variables). Simultaneous lower- and upper-body IPC led to a significant decrease in the time to peak power during the 500 m ergometer rowing trial in females but not in males. Additionally, no significant effects on the time or other power output variables, HR, or BLa concentration were registered.
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Open AccessArticle
A Machine Learning Approach to Predict Fluid Viscosity Based on Droplet Dynamics Features
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Zhipeng Qin, Fulei Wang, Shengchang Tang and Shaohao Liang
Appl. Sci. 2024, 14(9), 3537; https://doi.org/10.3390/app14093537 (registering DOI) - 23 Apr 2024
Abstract
In recent years, machine learning has made significant progress in the field of micro-fluids, and viscosity prediction has become one of the hotspots of research. Due to the specificity of the application direction, the input datasets required for machine learning models are diverse,
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In recent years, machine learning has made significant progress in the field of micro-fluids, and viscosity prediction has become one of the hotspots of research. Due to the specificity of the application direction, the input datasets required for machine learning models are diverse, which limits the generalisation ability of the models. This paper starts by analysing the most obvious kinetic feature induced by viscosity during flow—the variation in droplet neck contraction with time ( ). The kinetic processes of aqueous glycerol solutions of different viscosities when dropped in air were investigated by high-speed camera experiments, and the kinetic characteristics of the contraction of the liquid neck during droplet falling were extracted, using the Ohnesorge number ( ) to represent the change in viscosity. Subsequently, the liquid neck contraction data were used as the original dataset, and three models, namely, random forest, multiple linear regression, and neural network, were used for training. The final results showed superior results for all three models, with the multivariate linear regression model having the best predictive ability with a correlation coefficient of 0.98.
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(This article belongs to the Section Fluid Science and Technology)
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Open AccessArticle
Factors Affecting Radial Increment Dynamics in Lithuanian Populations of Common Juniper (Juniperus communis L.)
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Rasa Vaitkevičiūtė, Ekaterina Makrickiene and Edgaras Linkevičius
Appl. Sci. 2024, 14(8), 3536; https://doi.org/10.3390/app14083536 (registering DOI) - 22 Apr 2024
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Although common juniper (Juniperus communis L.) is a widely spread species and important for the forest biodiversity and economy in many European countries, it remains one of the least studied coniferous species. This research is the first attempt to evaluate the factors
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Although common juniper (Juniperus communis L.) is a widely spread species and important for the forest biodiversity and economy in many European countries, it remains one of the least studied coniferous species. This research is the first attempt to evaluate the factors affecting the increment of Juniperus communis in Lithuanian populations. The aim of this article is to evaluate the patterns of radial increment in Juniperus communis and to identify the key factors influencing the increment. We collected stem discs from 160 junipers in 8 stands distributed in the different regions of Lithuania and performed the tree-ring analysis. All studied junipers expressed a pronounced eccentricity of the stem. The results of our study revealed four patterns of Juniperus communis’ radial increment, which are strongly dependent on the granulometric properties of the soil and hydrologic conditions. The effect of climatic conditions on the Juniperus communis increment was strongly dependent on the terrain; however, most of the junipers had a positive reaction to the temperatures in April, July, and August and to the precipitation in February.
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Acceleration Capacity and Vertical Jump Performance Relationship in Prepubertal Children
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Baptiste Chanel, Nicolas Babault and Carole Cometti
Appl. Sci. 2024, 14(8), 3535; https://doi.org/10.3390/app14083535 - 22 Apr 2024
Abstract
Sprint and jump abilities are considered basic skills that are regularly evaluated in training and school contexts. The correlations between these two skills have previously been established in adults and adolescents, but they have not been fully assessed in children. The present study
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Sprint and jump abilities are considered basic skills that are regularly evaluated in training and school contexts. The correlations between these two skills have previously been established in adults and adolescents, but they have not been fully assessed in children. The present study aimed to explore sprinting and jumping ability in prepubertal boys and girls. Thirty-one prepubertal individuals (aged 8–11 years) were assessed during sprinting for different distances (5, 10, and 20 m) and using different vertical and horizontal jump modalities (squat jump, countermovement jump, broad jump, and hop test). Correlations between the different results were tested. Strong correlations were found between vertical jump and sprint performances, especially over short distances. These results suggested that vertical jump tests are more sensitive than horizontal jumps to reveal acceleration capacity in children.
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(This article belongs to the Special Issue Advances in Sports Training and Biomechanics)
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AOSMA-MLP: A Novel Method for Hybrid Metaheuristics Artificial Neural Networks and a New Approach for Prediction of Geothermal Reservoir Temperature
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Ezgi Gurgenc, Osman Altay and Elif Varol Altay
Appl. Sci. 2024, 14(8), 3534; https://doi.org/10.3390/app14083534 - 22 Apr 2024
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To ascertain the optimal and most efficient reservoir temperature of a geothermal source, long-term field studies and analyses utilizing specialized devices are essential. Although these requirements increase project costs and induce delays, utilizing machine learning techniques based on hydrogeochemical data can minimize losses
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To ascertain the optimal and most efficient reservoir temperature of a geothermal source, long-term field studies and analyses utilizing specialized devices are essential. Although these requirements increase project costs and induce delays, utilizing machine learning techniques based on hydrogeochemical data can minimize losses by accurately predicting reservoir temperatures. In recent years, applying hybrid methods to real-world challenges has become increasingly prevalent over traditional machine learning methodologies. This study introduces a novel machine learning approach, named AOSMA-MLP, integrating the adaptive opposition slime mould algorithm (AOSMA) and multilayer perceptron (MLP) techniques, specifically designed for predicting the reservoir temperature of geothermal resources. Additionally, this work compares the basic artificial neural network and widely recognized algorithms in the literature, such as the whale optimization algorithm, ant lion algorithm, and SMA, under equal conditions using various evaluation regression metrics. The results demonstrated that AOSMA-MLP outperforms basic MLP and other metaheuristic-based MLPs, with the AOSMA-trained MLP achieving the highest performance, indicated by an R2 value of 0.8514. The proposed AOSMA-MLP approach shows significant potential for yielding effective outcomes in various regression problems.
Full article
(This article belongs to the Special Issue Current Trends and Perspectives on Advances in Geosciences)
Open AccessArticle
A TEE-Based Federated Privacy Protection Method: Proposal and Implementation
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Libo Zhang, Bing Duan, Jinlong Li, Zhan’gang Ma and Xixin Cao
Appl. Sci. 2024, 14(8), 3533; https://doi.org/10.3390/app14083533 - 22 Apr 2024
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With the continuous enhancement of privacy protection globally, there is a problem for the traditional machine learning paradigm, which is that training data cannot be obtained from a single place. Federated learning is considered a viable technique for preserving privacy that can train
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With the continuous enhancement of privacy protection globally, there is a problem for the traditional machine learning paradigm, which is that training data cannot be obtained from a single place. Federated learning is considered a viable technique for preserving privacy that can train deep models with decentralized data. Aiming at two-party vertical federated learning, and at common attack problems such as model inversion, gradient leakage, and data theft, we provide a formal definition of Intel SGX’s trusted computing base, remote attestation, integrity verification, and encrypted storage, and propose a general federated learning privacy enhancement algorithm in the scenario of a malicious adversary model, and we extend this method to support horizontal federated learning, secure outsourced computation, etc. Furthermore, the method is developed in a Fedlearner framework of open-sourced machine learning to achieve privacy protection of the training data and model without any modification to the existing neural network and algorithm running on the framework. The experimental results show that this scheme substantially improves on the existing schemes in terms of training efficiency, without losing model accuracy.
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Open AccessArticle
RumorLLM: A Rumor Large Language Model-Based Fake-News-Detection Data-Augmentation Approach
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Jianqiao Lai, Xinran Yang, Wenyue Luo, Linjiang Zhou, Langchen Li, Yongqi Wang and Xiaochuan Shi
Appl. Sci. 2024, 14(8), 3532; https://doi.org/10.3390/app14083532 - 22 Apr 2024
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With the rapid development of the Internet and social media, false information, rumors, and misleading content have become pervasive, posing significant threats to public opinion and social stability, and even causing serious societal harm. This paper introduces a novel solution to address the
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With the rapid development of the Internet and social media, false information, rumors, and misleading content have become pervasive, posing significant threats to public opinion and social stability, and even causing serious societal harm. This paper introduces a novel solution to address the challenges of fake news detection, presenting the “Rumor Large Language Models” (RumorLLM), a large language model finetuned with rumor writing styles and content. The key contributions include the development of RumorLLM and a data-augmentation method for small categories, effectively mitigating the issue of category imbalance in real-world fake-news datasets. Experimental results on the BuzzFeed and PolitiFact datasets demonstrate the superiority of the proposed model over baseline methods, particularly in F1 score and AUC-ROC. The model’s robust performance highlights its effectiveness in handling imbalanced datasets and provides a promising solution to the pressing issue of false-information proliferation.
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(This article belongs to the Special Issue Data Mining and Machine Learning in Social Network Analysis)
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Emerging Technologies for Automation in Environmental Sensing: Review
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Shekhar Suman Borah, Aaditya Khanal and Prabha Sundaravadivel
Appl. Sci. 2024, 14(8), 3531; https://doi.org/10.3390/app14083531 - 22 Apr 2024
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This article explores the impact of automation on environmental sensing, focusing on advanced technologies that revolutionize data collection analysis and monitoring. The International Union of Pure and Applied Chemistry (IUPAC) defines automation as integrating hardware and software components into modern analytical systems. Advancements
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This article explores the impact of automation on environmental sensing, focusing on advanced technologies that revolutionize data collection analysis and monitoring. The International Union of Pure and Applied Chemistry (IUPAC) defines automation as integrating hardware and software components into modern analytical systems. Advancements in electronics, computer science, and robotics drive the evolution of automated sensing systems, overcoming traditional limitations in manual data collection. Environmental sensor networks (ESNs) address challenges in weather constraints and cost considerations, providing high-quality time-series data, although issues in interoperability, calibration, communication, and longevity persist. Unmanned Aerial Systems (UASs), particularly unmanned aerial vehicles (UAVs), play an important role in environmental monitoring due to their versatility and cost-effectiveness. Despite challenges in regulatory compliance and technical limitations, UAVs offer detailed spatial and temporal information. Pollution monitoring faces challenges related to high costs and maintenance requirements, prompting the exploration of cost-efficient alternatives. Smart agriculture encounters hurdle in data integration, interoperability, device durability in adverse weather conditions, and cybersecurity threats, necessitating privacy-preserving techniques and federated learning approaches. Financial barriers, including hardware costs and ongoing maintenance, impede the widespread adoption of smart technology in agriculture. Integrating robotics, notably underwater vehicles, proves indispensable in various environmental monitoring applications, providing accurate data in challenging conditions. This review details the significant role of transfer learning and edge computing, which are integral components of robotics and wireless monitoring frameworks. These advancements aid in overcoming challenges in environmental sensing, underscoring the ongoing necessity for research and innovation to enhance monitoring solutions. Some state-of-the-art frameworks and datasets are analyzed to provide a comprehensive review on the basic steps involved in the automation of environmental sensing applications.
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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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The Optimization of the Geometry of the Centrifugal Fan at Different Design Points
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Paulius Ragauskas, Ina Tetsmann and Raimondas Jasevičius
Appl. Sci. 2024, 14(8), 3530; https://doi.org/10.3390/app14083530 (registering DOI) - 22 Apr 2024
Abstract
The optimization of the geometry of a centrifugal fan is performed at maximum power and high-efficiency design points (DPs) to improve impeller efficiency. Two design variables defining the shape of fan blade are selected for the optimization. The optimal values of the geometry
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The optimization of the geometry of a centrifugal fan is performed at maximum power and high-efficiency design points (DPs) to improve impeller efficiency. Two design variables defining the shape of fan blade are selected for the optimization. The optimal values of the geometry parameters of the impeller blades are identified by employing virtual flow simulations. The results of virtual experiments indicate the influence of the parameters of the blade geometry on its efficiency. With the optimization of impeller blade geometry, the efficiency of the fan is improved with respect to the reference model, as confirmed by comparing the performance curves. Herein, we discuss the results obtained in virtual tests by identifying the influence of DPs on the performance characteristics of centrifugal fans.
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(This article belongs to the Special Issue Advances in Structural Optimization)
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Enhancing Surgical Outcomes via Three-Dimensional-Assisted Techniques Combined with Orthognathic Treatment: A Case Series Study of Skeletal Class III Malocclusions
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Monica Macrì, Abdulaziz Alhotan, Gabriella Galluccio, Ersilia Barbato and Felice Festa
Appl. Sci. 2024, 14(8), 3529; https://doi.org/10.3390/app14083529 (registering DOI) - 22 Apr 2024
Abstract
(•) Orthognathic surgery is a necessary procedure for the correction of severe skeletal discrepancies, among which are skeletal Class III malocclusions. Currently, both conventional fixed braces and clear aligners can be used in orthognathic surgery. However, the use of clear aligners remains a
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(•) Orthognathic surgery is a necessary procedure for the correction of severe skeletal discrepancies, among which are skeletal Class III malocclusions. Currently, both conventional fixed braces and clear aligners can be used in orthognathic surgery. However, the use of clear aligners remains a little-chosen option. The present study aimed to evaluate the skeletal and aesthetic improvements in adults with Class III malocclusion after surgical treatment and compare the results achieved by fixed appliances versus clear aligners. The study sample included four patients (three males and one female, aged 18 to 34 years) with skeletal Class III malocclusion, three of whom underwent a bimaxillary surgery and one of whom underwent only a bilateral sagittal split osteotomy. Two patients were treated with fixed appliances and two with clear aligners. The pre- and post-surgical hard and soft tissue cephalometric measurements were performed and compared for each patient and between fixed appliances and clear aligners. One year after surgery, all patients showed an essential modification of the face’s middle and lower third with an increase in the convexity of the profile and the Wits index and a reduction in the FH^NB angle. No differences were noted between fixed appliances and aligners. Therefore, thanks to the 3D-assisted surgery associated with orthodontics, every participant achieved proper occlusal function and an improved facial aesthetics. In addition, the clear aligners can be considered a valid alternative for pre- and post-surgical orthodontic treatment.
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(This article belongs to the Special Issue Advanced Technologies in Oral Surgery)
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Optimization of Well Placement in Carbon Capture and Storage (CCS): Bayesian Optimization Framework under Permutation Invariance
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Sofianos Panagiotis Fotias, Ismail Ismail and Vassilis Gaganis
Appl. Sci. 2024, 14(8), 3528; https://doi.org/10.3390/app14083528 - 22 Apr 2024
Abstract
Carbon Capture and Storage (CCS) stands as a pivotal technological stride toward a sustainable future, with the practice of injecting supercritical CO2 into subsurface formations being already an established practice for enhanced oil recovery operations. The overarching objective of CCS is to
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Carbon Capture and Storage (CCS) stands as a pivotal technological stride toward a sustainable future, with the practice of injecting supercritical CO2 into subsurface formations being already an established practice for enhanced oil recovery operations. The overarching objective of CCS is to protract the operational viability and sustainability of platforms and oilfields, thereby facilitating a seamless transition towards sustainable practices. This study introduces a comprehensive framework for optimizing well placement in CCS operations, employing a derivative-free method known as Bayesian Optimization. The development plan is tailored for scenarios featuring aquifers devoid of flow boundaries, incorporating production wells tasked with controlling pressure buildup and injection wells dedicated to CO2 sequestration. Notably, the wells operate under group control, signifying predefined injection and production targets and constraints that must be adhered to throughout the project’s lifespan. As a result, the objective function remains invariant under specific permutations of the well locations. Our investigation delves into the efficacy of Bayesian Optimization under the introduced permutation invariance. The results reveal that it demonstrates critical efficiency in handling the optimization task extremely fast. In essence, this study advocates for the efficacy of Bayesian Optimization in the context of optimizing well placement for CCS operations, emphasizing its potential as a preferred methodology for enhancing sustainability in the energy sector.
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(This article belongs to the Special Issue Novel Applications of Machine Learning and Bayesian Optimization)
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Practical Steps towards Establishing an Underwater Acoustic Network in the Context of the Marine Internet of Things
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Konstantin Kebkal, Aleksey Kabanov, Oleg Kramar, Maksim Dimin, Timur Abkerimov, Vadim Kramar and Veronika Kebkal-Akbari
Appl. Sci. 2024, 14(8), 3527; https://doi.org/10.3390/app14083527 - 22 Apr 2024
Abstract
When several hydroacoustic modems operate simultaneously in an area of mutual coverage, collisions of data packets received from several sources may occur, which lead to information loss. With an increase in the number of simultaneously operating hydroacoustic modems, physical layer algorithms do not
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When several hydroacoustic modems operate simultaneously in an area of mutual coverage, collisions of data packets received from several sources may occur, which lead to information loss. With an increase in the number of simultaneously operating hydroacoustic modems, physical layer algorithms do not provide stable data transmission and the likelihood of collisions increases, which makes the operation of modems ineffective. To ensure effective operation in a hydroacoustic signal propagation environment and to reduce collisions when exchanging data between two modems that do not have the ability to operate synchronously and to reduce the access time to the signal propagation environment, methods of the medium access control layer using link layer protocols are required. Typically, this problem is solved using code separation of hydroacoustic channels. If you need to transfer over a network, this option will not work, since network transfer involves working on the basis of “broadcast” messages, particularly between data source and data sink that remain too far from each other, outside of their mutual audibility. In practical use, it is convenient to place these protocols into a software environment for developing specific user applications for solving network communication problems. This software framework allows for custom modification of existing network algorithms, as well as the inclusion of new network hydroacoustic communication algorithms. To build a predictive model, the DACAP, T-Lohi, Flooding, and ICRP protocols were used in this work. The implementation is performed in Erlang. The paper presents algorithms for implementing these protocols. A comparative analysis of network operation with and without protocols is provided. Efficiency of operation, i.e., data rates and probabilities of data delivery, was assessed.
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(This article belongs to the Special Issue Autonomous Underwater Vehicles (AUVs): Applications and Technologies)
Open AccessArticle
Hypergraph Position Attention Convolution Networks for 3D Point Cloud Segmentation
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Yanpeng Rong, Liping Nong, Zichen Liang, Zhuocheng Huang, Jie Peng and Yiping Huang
Appl. Sci. 2024, 14(8), 3526; https://doi.org/10.3390/app14083526 - 22 Apr 2024
Abstract
Point cloud segmentation, as the basis for 3D scene understanding and analysis, has made significant progress in recent years. Graph-based modeling and learning methods have played an important role in point cloud segmentation. However, due to the inherent complexity of point cloud data,
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Point cloud segmentation, as the basis for 3D scene understanding and analysis, has made significant progress in recent years. Graph-based modeling and learning methods have played an important role in point cloud segmentation. However, due to the inherent complexity of point cloud data, it is difficult to capture higher-order and complex features of 3D data using graph learning methods. In addition, how to quickly and efficiently extract important features from point clouds also poses a great challenge to the current research. To address these challenges, we propose a new framework, called hypergraph position attention convolution networks (HGPAT), for point cloud segmentation. Firstly, we use hypergraph to model the higher-order relationships among point clouds. Secondly, in order to effectively learn the feature information of point cloud data, a hyperedge position attention convolution module is proposed, which utilizes the hyperedge–hyperedge propagation pattern to extract and aggregate more important features. Finally, we design a ResNet-like module to reduce the computational complexity of the network and improve its efficiency. We have conducted point cloud segmentation experiments on the ShapeNet Part and S3IDS datasets, and the experimental results demonstrate the effectiveness of the proposed method compared with the state-of-the-art ones.
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(This article belongs to the Special Issue New Insights into Computer Vision and Graphics)
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Comparative Analysis of the Stability of Overlying Rock Mass for Two Types of Lined Rock Caverns Based on Rock Mass Classification
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Qi Yi, Zhen Shen, Guanhua Sun, Shan Lin and Hongming Luo
Appl. Sci. 2024, 14(8), 3525; https://doi.org/10.3390/app14083525 (registering DOI) - 22 Apr 2024
Abstract
Lined rock caverns (LRCs) are becoming the preferred option for air storage at sites where there are no natural cavities, such as salt caverns, and this storage technology is being developed and utilized in markets around the world. The stability of the overlying
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Lined rock caverns (LRCs) are becoming the preferred option for air storage at sites where there are no natural cavities, such as salt caverns, and this storage technology is being developed and utilized in markets around the world. The stability of the overlying rock mass is one of the key factors to ensure the successful operation of LRCs. In this paper, a stability assessment method is presented that first calculates the potential fracture surfaces of the surrounding rock based on the limiting stress field and the Mohr–Coulomb damage criterion, and then, based on these fracture surfaces, solves for the factor of safety defined on the basis of the concept of strength reserve. Using this method, this study evaluates the stability of two types of LRCs, tunnel- and silo-type, under three different geological conditions. The results of the analysis show that the silo-type LRCs are more economical for engineering purposes. Also, this paper provides some guidance for engineers in site selection and preliminary design.
Full article
(This article belongs to the Special Issue Advances and Challenges in Rock Mechanics and Rock Engineering)
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Open AccessArticle
Text Triplet Extraction Algorithm with Fused Graph Neural Networks and Improved Biaffine Attention Mechanism
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Yinghao Piao and Jin-Xi Zhang
Appl. Sci. 2024, 14(8), 3524; https://doi.org/10.3390/app14083524 - 22 Apr 2024
Abstract
In the realm of aspect-based sentiment analysis (ABSA), a paramount task is the extraction of triplets, which define aspect terms, opinion terms, and their respective sentiment orientations within text. This study introduces a novel extraction model, BiLSTM-BGAT-GCN, which seamlessly integrates graph neural networks
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In the realm of aspect-based sentiment analysis (ABSA), a paramount task is the extraction of triplets, which define aspect terms, opinion terms, and their respective sentiment orientations within text. This study introduces a novel extraction model, BiLSTM-BGAT-GCN, which seamlessly integrates graph neural networks with an enhanced biaffine attention mechanism. This model amalgamates the sophisticated capabilities of both graph attention and convolutional networks to process graph-structured data, substantially enhancing the interpretation and extraction of textual features. By optimizing the biaffine attention mechanism, the model adeptly uncovers the subtle interplay between aspect terms and emotional expressions, offering enhanced flexibility and superior contextual analysis through dynamic weight distribution. A series of comparative experiments confirm the model’s significant performance improvements across various metrics, underscoring its efficacy and refined effectiveness in ABSA tasks.
Full article
(This article belongs to the Special Issue Applications of Advanced Deep Learning Technology in Control and Intelligent Systems)
Open AccessArticle
Structural and Tribological Analysis of Brake Disc–Pad Pair Material for Cars
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Filip Ilie and Andreea Catalina Ctristescu
Appl. Sci. 2024, 14(8), 3523; https://doi.org/10.3390/app14083523 - 22 Apr 2024
Abstract
The study of the tribological behavior of the braking system in auto vehicles requires knowing the characteristics of the material in contact and, in the work process, the friction pair brake disc pads. Material structural analysis is necessary because the wear process depends
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The study of the tribological behavior of the braking system in auto vehicles requires knowing the characteristics of the material in contact and, in the work process, the friction pair brake disc pads. Material structural analysis is necessary because the wear process depends both on the friction-pair chemical composition (brake disc pads) and on the work process parameters (pressing force, rotational speed, traffic conditions, etc.). The material of the brake discs is generally the same, gray cast iron, and the brake pads can be semimetallic (particles of steel, copper, brass, and graphite, all united with a special resin), organic materials (particles of rubber, glass, and Kevlar, all joined with the help of a resin), composite materials that contain different constituents, and ceramic materials (rarely have small copper particles). Therefore, the purpose of this paper is to analyze the crystalline structure, tribological behavior (at friction and wear), and the mechanical properties of the materials of the brake disc–pad friction pair specific to the field through study and analysis.
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(This article belongs to the Special Issue Feature Papers in 'Surface Sciences and Technology Section', 2nd Edition)
Open AccessArticle
Encapsulation of Fennel Essential Oil in Calcium Alginate Microbeads via Electrostatic Extrusion
by
Erika Dobroslavić, Ena Cegledi, Katarina Robić, Ivona Elez Garofulić, Verica Dragović-Uzelac and Maja Repajić
Appl. Sci. 2024, 14(8), 3522; https://doi.org/10.3390/app14083522 - 22 Apr 2024
Abstract
Fennel essential oil (EO) is well known for its biological activities and wide potential for use in the food, cosmetic, and pharmaceutical industries, where the main challenge is to achieve higher stability of EO. This study aimed to evaluate the potential of electrostatic
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Fennel essential oil (EO) is well known for its biological activities and wide potential for use in the food, cosmetic, and pharmaceutical industries, where the main challenge is to achieve higher stability of EO. This study aimed to evaluate the potential of electrostatic extrusion for encapsulation of fennel EO by examining the effects of alginate (1%, 1.5%, and 2%) and whey protein (0%, 0.75%, and 1.5%) concentrations and drying methods on the encapsulation efficiency, loading capacity, bead characteristics, and swelling behavior of the produced fennel EO microbeads. Results revealed that electrostatic extrusion proved to be effective for encapsulating fennel EO, with whey protein addition enhancing the examined characteristics of the obtained microbeads. Freeze-drying exhibited superior performance compared to air-drying. Optimal encapsulation efficiency (51.95%) and loading capacity (78.28%) were achieved by using 1.5% alginate and 0.75% whey protein, followed by freeze-drying. GC-MS analysis revealed no differences in the qualitative aspect of the encapsulated and initial EO, with the encapsulated EO retaining 58.95% of volatile compounds. This study highlighted the potential of electrostatic extrusion using alginate and whey protein as a promising technique for fennel EO encapsulation while also emphasizing the need for further exploration into varied carrier materials and process parameters to optimize the encapsulation process and enhance product quality.
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(This article belongs to the Special Issue Natural Products and Bioactive Compounds)
Open AccessArticle
Automatic Object Detection in Radargrams of Multi-Antenna GPR Systems Based on Simulation Data for Railway Infrastructure Analysis
by
Lukas Lahnsteiner, David Größbacher, Martin Bürger and Gerald Zauner
Appl. Sci. 2024, 14(8), 3521; https://doi.org/10.3390/app14083521 - 22 Apr 2024
Abstract
Ground-penetrating radar (GPR) is a non-invasive technology that uses electromagnetic pulses for subsurface exploration. In the railroad sector, it is crucial to assessing soil layers and infrastructure, offering insights into soil stratification and geological features and aiding in identifying subsurface hazards. However, the
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Ground-penetrating radar (GPR) is a non-invasive technology that uses electromagnetic pulses for subsurface exploration. In the railroad sector, it is crucial to assessing soil layers and infrastructure, offering insights into soil stratification and geological features and aiding in identifying subsurface hazards. However, the automation of radargram analysis is impeded by the lack of ground truth—accurate real-world data used to validate machine learning models—thus affecting the deployment of advanced algorithms. This study focuses on generating high-quality simulated data to address the shortage of real-world data in the context of object detection along railroad tracks and presents a fully automated pipeline that includes data generation, algorithm training, and validation using real-world data. By doing so, it paves the way for significantly easing the future task of object detection algorithms in the railway sector. A simulation environment, including the digital twin of a GPR antenna, was developed for artificial data generation. The process involves pre- and post-processing techniques to transform the three-dimensional data from the multichannel GPR system into two-dimensional datasets. This ensures minimal information loss and suitability for established two-dimensional object detection algorithms like the well-known YOLO (You Only Look Once) framework. Validation involved real-world measurements on a track with predefined buried objects. The entire pipeline, encompassing data generation, processing, training, and application, was automated for efficient algorithm testing and implementation. Artificial data show promise for better performance with increased training. Future AI and sensor advancements will enhance subsurface exploration, contributing to safer and more reliable railroad operations.
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(This article belongs to the Special Issue Ground Penetrating Radar (GPR): Theory, Methods and Applications)
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