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
Special Issue on eHealth Innovative Approaches and Applications
Appl. Sci. 2024, 14(6), 2571; https://0-doi-org.brum.beds.ac.uk/10.3390/app14062571 (registering DOI) - 19 Mar 2024
Abstract
Innovative ICT technologies, approaches and applications are becoming increasingly pervasive in several domains, including in medicine and healthcare [...]
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(This article belongs to the Special Issue eHealth Innovative Approaches and Applications)
Open AccessArticle
Sound-Absorbing, Thermal-Insulating Material Based on Poly(methylsiloxane) Xerogel and Cellulose Nanofibers
by
Daiji Katsura, Tetsuya Maeda, Kazuyoshi Kanamori, Takashi Yamamoto and Joji Ohshita
Appl. Sci. 2024, 14(6), 2570; https://0-doi-org.brum.beds.ac.uk/10.3390/app14062570 (registering DOI) - 19 Mar 2024
Abstract
The automotive industry needs to improve energy efficiency rapidly to achieve carbon neutrality while creating a safe, secure, and comfortable driving environment for customers. Porous sound-absorbing materials and porous thermal insulators are typically used to satisfy these requirements despite limitations in mass and
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The automotive industry needs to improve energy efficiency rapidly to achieve carbon neutrality while creating a safe, secure, and comfortable driving environment for customers. Porous sound-absorbing materials and porous thermal insulators are typically used to satisfy these requirements despite limitations in mass and space. While these porous materials are similar, the microstructures they offer for high performance differ in the size and connectivity of their fluid phases, which enhances the difficulty of achieving excellent sound absorption and thermal insulation in the same material. In this study, a hydrophobic cellulose nanofiber–poly(methylsiloxane) xerogel composite was developed using computational microstructure modeling. This porous material has high porosity and excellent thermal insulation and sound absorption properties.
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(This article belongs to the Special Issue Feature Papers in Section 'Applied Thermal Engineering')
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Open AccessArticle
An Artificial Intelligence (AI) Framework to Predict Operational Excellence: UAE Case Study
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Rola R. Hassan, Manar Abu Talib, Fikri Dweiri and Jorge Roman
Appl. Sci. 2024, 14(6), 2569; https://0-doi-org.brum.beds.ac.uk/10.3390/app14062569 (registering DOI) - 19 Mar 2024
Abstract
Implementing the European Foundation for Quality Management (EFQM) business excellence model in organizations is time- and cost-consuming. The integration of artificial intelligence (AI) into the EFQM business excellence model is a promising approach to improve the efficiency and effectiveness of excellence in organizations.
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Implementing the European Foundation for Quality Management (EFQM) business excellence model in organizations is time- and cost-consuming. The integration of artificial intelligence (AI) into the EFQM business excellence model is a promising approach to improve the efficiency and effectiveness of excellence in organizations. This research paper’s integrated framework follows the ISO/IEC 23053 standard in addressing some of the concerns related to time and cost associated with the EFQM model, achieving higher EFQM scores, and hence operational excellence. A case study involving a UAE government organization serves as a sample to train the AI framework. Historical EFQM results from different years are used as training data. The AI framework utilizes the unsupervised machine learning technique known as k-means clustering. This technique follows the ISO/IEC 23053 standard to predict EFQM output total scores based on criteria and sub-criteria inputs. This research paper’s main output is a novel AI framework that can predict EFQM scores for organizations at an early stage. If the predicted EFQM score is not high enough, then the AI framework provides feedback to decision makers regarding the criteria that need reconsideration. Continuous use of this integrated framework helps organizations attain operational excellence. This framework is considered valuable for decision makers as it provides early predictions of EFQM total scores and identifies areas that require improvement before officially applying for the EFQM excellence award, hence saving time and cost. This approach can be considered as an innovative contribution and enhancement to knowledge body and organizational practices.
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(This article belongs to the Special Issue AI Technology and Application in Various Industries)
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Open AccessArticle
Amplitude-Sensitive Single-Pumper Hydraulic Engine Mount Design without a Decoupler
by
Nader Vahdati, Aamna Alteneiji, Fook Fah Yap and Oleg Shiryayev
Appl. Sci. 2024, 14(6), 2568; https://0-doi-org.brum.beds.ac.uk/10.3390/app14062568 (registering DOI) - 19 Mar 2024
Abstract
Engine mounts serve three primary purposes: (1) to support the weight of the engine, (2) to lessen the transmitted engine disturbance forces to the vehicle structure/chassis or airplane fuselage, and (3) to limit the engine motion brought on by shock excitations. The engine
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Engine mounts serve three primary purposes: (1) to support the weight of the engine, (2) to lessen the transmitted engine disturbance forces to the vehicle structure/chassis or airplane fuselage, and (3) to limit the engine motion brought on by shock excitations. The engine mount’s stiffness must be high to control large engine motions and low to control chassis or vehicle body vibration. When hydraulic engine mounts are used, a device called a decoupler creates the dual stiffness requirement. However, numerous investigations have shown that the decoupler has the potential to rotate within its cage bound and become stuck or sink and obstruct fluid flow between the fluid chambers due to a density mismatch between the decoupler and the working fluid. In addition, most hydraulic engine mounts with a decoupler no longer act as vibration isolators but as hydraulic dampers. This study suggests a new amplitude-sensitive hydraulic engine mount design without a decoupler, where the vibration isolation of the engine mount is retained and there is a 75% reduction in the peak frequency, which further enhances the engine mount’s capabilities in comparison to the current hydraulic engine mounts with a decoupler. The new design concept and its mathematical model and simulation results will be presented.
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(This article belongs to the Special Issue State of the Art of Vibration Analysis of Nonlinear Mechanical Systems)
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Open AccessArticle
DentalArch: AI-Based Arch Shape Detection in Orthodontics
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J. D. Tamayo-Quintero, J. B. Gómez-Mendoza and S. V. Guevara-Pérez
Appl. Sci. 2024, 14(6), 2567; https://0-doi-org.brum.beds.ac.uk/10.3390/app14062567 (registering DOI) - 19 Mar 2024
Abstract
Objective: This study aims to introduce and assess a novel AI-driven tool developed for the classification of orthodontic arch shapes into square, ovoid, and tapered categories. Methods: Between 2016 and 2019, we collected 450 digital dental models. Applying our inclusion and exclusion criteria,
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Objective: This study aims to introduce and assess a novel AI-driven tool developed for the classification of orthodontic arch shapes into square, ovoid, and tapered categories. Methods: Between 2016 and 2019, we collected 450 digital dental models. Applying our inclusion and exclusion criteria, we refined our dataset to 50 models, ensuring a focused and detailed analysis. Plaster casts were digitized into 3D models with AutoScan-DS-EX. Three trained evaluators then measured mesiodistal and arch widths using MeshLab. The development of DentalArch was undertaken in two versions: the first version incorporates 18 input parameters, including mesiodistal widths (from the first molar to the first molar, totaling 14) and arch widths (1 intercanine, 2 interpremolar, and 1 intermolar, totaling 4); the second version uses only 4 parameters related to arch widths. Both versions aim to predict the arch shape. An evaluation of 28 machine learning methods through a k = 5-fold cross-validation was conducted to determine the most effective techniques. Results: In the tests, the performance evaluation of the DentalArch software in detecting arch shapes revealed that version 1, which analyzes 18 parameters, achieved an accuracy of 94.7% for the lower arch and 93% for the upper arch. The more streamlined version 2, which assesses only four parameters, also showed high precision with an accuracy of 93.0% for the lower arch and 92.7% for the upper arch. Conclusions: DentalArch provides a tool with potential use in orthodontic diagnostics, particularly in the task of arch shape classification. The software offers a less subjective and data-driven approach to arch shape determination. Moreover, the open-source nature of DentalArch ensures its global availability and encourages contributions from the orthodontic community.
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(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)
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Open AccessArticle
Deletion of Transient Receptor Channel Vanilloid 4 Aggravates CaCl2-Induced Abdominal Aortic Aneurysm and Vascular Calcification: A Histological Study
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Isehaq Al-Huseini, Maryam Al-Ismaili, Ammar Boudaka and Srinivasa Rao Sirasanagandla
Appl. Sci. 2024, 14(6), 2566; https://0-doi-org.brum.beds.ac.uk/10.3390/app14062566 (registering DOI) - 19 Mar 2024
Abstract
Vascular calcification is calcium deposition occurring in the wall of blood vessels, leading to mechanical stress and rupture due to a loss of elasticity and the hardening of the vessel wall. The role of the Transient Receptor Channel Vanilloid 4 (TRPV4), a Ca
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Vascular calcification is calcium deposition occurring in the wall of blood vessels, leading to mechanical stress and rupture due to a loss of elasticity and the hardening of the vessel wall. The role of the Transient Receptor Channel Vanilloid 4 (TRPV4), a Ca2+-permeable cation channel, in the progression of vascular calcification is poorly explored. In this study, we investigated the role of TRPV4 in vascular calcification and the development of abdominal aortic aneurysm (AAA). Experimental mice were randomly divided into four groups: wild-type (WT) sham operated group, WT CaCl2-induced aortic injury, TRPV4-KO sham operated group, and TRPV4-KO CaCl2-induced aortic injury. The TRPV4-knockout (TRPV4-KO) mice and wild-type (WT) mice were subjected to the CaCl2-induced abdominal aortic injury. In histopathological analysis, the aorta of the TRPV4-KO mice showed extensive calcification in the tunica media with a significant increase in the outer diameter (p < 0.0001), luminal area (p < 0.05), and internal circumference (p < 0.05) after CaCl2 injury when compared to WT mice. Additionally, the tunica media of the TRPV4-KO mice aorta showed extensive damage with apparent elongation and disruption of the elastic lamella. These results indicate a protective function of TRPV4 against vascular calcification and the progression of AAA after CaCl2 injury.
Full article
(This article belongs to the Special Issue Advances in Cardiovascular Diseases: Prevention, Diagnosis and Treatment)
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Open AccessArticle
Gearbox Fault Diagnosis Based on ICEEMDAN-MPE-AWT and SE-ResNeXt50 Transfer Learning Model
by
Hongfeng Gao, Tiexin Xu, Renlong Li and Chaozhi Cai
Appl. Sci. 2024, 14(6), 2565; https://0-doi-org.brum.beds.ac.uk/10.3390/app14062565 (registering DOI) - 19 Mar 2024
Abstract
Because the gearbox in transmission systems is prone to failure and the fault signal is not obvious, the fault end cannot be located. In this paper, a gearbox fault diagnosis method grounded on improved complete ensemble empirical mode decomposition with adaptive noise, a
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Because the gearbox in transmission systems is prone to failure and the fault signal is not obvious, the fault end cannot be located. In this paper, a gearbox fault diagnosis method grounded on improved complete ensemble empirical mode decomposition with adaptive noise, a multiscale permutation entropy and adaptive wavelet thresholding (ICEEMDAN-MPE-AWT) denoising method and an SE-ResNeXt50 transfer learning model are proposed. Initially, the vibration signal is denoised by ICEEMDAN-MPE-AWT, the denoised vibration signal is then converted into a Gram angle field (GAF) diagram, and then the parameters are transferred by the fine-tuning transfer learning strategy. Finally, a GAF diagram is input into the model for training to achieve fault extraction and classification. In this paper, the open gear dataset of Southeast University is used for experimental research. The experimental results show that when using the ICEEMDAN-MPE-AWT and when the signal-to-noise ratio (SNR) of the experimental data is −4 dB, the average accuracy of the GASF+TSE-ResNeXt50 and the GASF+TSE-ResNeXt18 can reach 98.8% and 97.5%, respectively. When the SNR is 6 dB, the accuracy of the above two models reaches 100% and 99.3%, respectively. Moreover, when compared to alternative approaches, the noise reduction method in this paper can better remove noise interference so that the model can better extract fault features. Therefore, the method proposed in this article shows significant improvement in noise reduction and fault classification accuracy compared to other methods.
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(This article belongs to the Section Mechanical Engineering)
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Open AccessArticle
Kinematic Tripod (K3P): A New Kinematic Algorithm for Gait Pattern Generation
by
Daniel Soto-Guerrero, José Gabriel Ramírez-Torres and Eduardo Rodriguez-Tello
Appl. Sci. 2024, 14(6), 2564; https://0-doi-org.brum.beds.ac.uk/10.3390/app14062564 (registering DOI) - 19 Mar 2024
Abstract
Insects are good examples of ground locomotion because they can adapt their gait pattern to propel them in any direction, over uneven terrain, in a stable manner. Nevertheless, replicating such locomotion skills to a legged robot is not a straightforward task. Different approaches
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Insects are good examples of ground locomotion because they can adapt their gait pattern to propel them in any direction, over uneven terrain, in a stable manner. Nevertheless, replicating such locomotion skills to a legged robot is not a straightforward task. Different approaches have been proposed to synthesize the gait patterns for these robots; each approach exhibits different restrictions, advantages, and priorities. For the purpose of this document, we have classified gait pattern generators for multi-legged robots into three categories: precomputed, heuristic, and bio-inspired approaches. Precomputed approaches rely on a set of precalculated motion patterns obtained from geometric and/or kinematic models that are performed repeatedly whenever necessary and that cannot be modified on-the-fly to adapt to the terrain changes. On the other hand, heuristic and bio-inspired approaches offer on-line adaptability, but parameter-tuning and heading control can be difficult. In this document, we present the K3P algorithm, a real-time kinematic gait pattern generator conceived to command a legged robot. In contrast to other approaches, K3P enables the robot to adapt its gait to follow an arbitrary trajectory, at an arbitrary speed, over uneven terrain. No precomputed motions for the legs are required; instead, K3P modifies the motion of all mechanical joints to propel the body of the robot in the desired direction, maintaining a tripod stability at all times. In this paper, all the specific details of the aforementioned algorithm are presented, as well as different simulation results that validate its characteristics.
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(This article belongs to the Special Issue Advances in Robot Path Planning, Volume II)
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Technoeconomic Analysis of Oxygen-Supported Combined Systems for Recovering Waste Heat in an Iron-Steel Facility
by
Busra Besevli, Erhan Kayabasi, Abdulrazzak Akroot, Wadah Talal, Ali Alfaris, Younus Hamoudi Assaf, Mohammed Y. Nawaf, Mothana Bdaiwi and Jawad Khudhur
Appl. Sci. 2024, 14(6), 2563; https://0-doi-org.brum.beds.ac.uk/10.3390/app14062563 - 19 Mar 2024
Abstract
In this study, it is proposed to generate electrical energy by recovering the waste heat of an annealing furnace (AF) in an iron and steel plant using combined cycles such as steam Rankine cycle (SRC), organic Rankine cycle (ORC), Kalina cycle (KC) and
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In this study, it is proposed to generate electrical energy by recovering the waste heat of an annealing furnace (AF) in an iron and steel plant using combined cycles such as steam Rankine cycle (SRC), organic Rankine cycle (ORC), Kalina cycle (KC) and transcritical CO2 cycle (t-CO2). Instead of releasing the waste heat into the atmosphere, the waste heat recovery system (WHRS) discharges the waste heat into the plant’s low-temperature oxygen line for the first time, achieving a lower temperature and pressure in the condenser than conventional systems. The waste heat of the flue gas (FG) with a temperature of 1093.15 K from the reheat furnace was evaluated using four different cycles. To maximize power generation, the SRC input temperature of the proposed system was studied parametrically. The cycles were analyzed based on thermal efficiency and net output power. The difference in SRC inlet temperature is 221.6 K for maximum power output. The proposed system currently has a thermal efficiency and total power output of 0.19 and 596.6 kW, respectively. As an environmental impact, an emission reduction potential of 23.16 tons/day was achieved. In addition, the minimum power generation cost of the proposed system is $0.1972 per kWh.
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(This article belongs to the Special Issue Application of Supercritical Carbon Dioxide Power Cycles for Thermal Energy Storage)
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Open AccessArticle
Enhanced Tracking in Legged Robots through Model Reduction and Hybrid Control Techniques: Addressing Disturbances, Delays, and Saturation
by
Yongyong Zhao, Jinghua Wang, Guohua Cao and Xu Yao
Appl. Sci. 2024, 14(6), 2562; https://0-doi-org.brum.beds.ac.uk/10.3390/app14062562 - 19 Mar 2024
Abstract
This study introduces a reduced-order leg dynamic model to simplify the controller design and enhance robustness. The proposed multi-loop control scheme tackles tracking control issues in legged robots, including joint angle and contact-force regulation, disturbance suppression, measurement delay, and motor saturation avoidance. Firstly,
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This study introduces a reduced-order leg dynamic model to simplify the controller design and enhance robustness. The proposed multi-loop control scheme tackles tracking control issues in legged robots, including joint angle and contact-force regulation, disturbance suppression, measurement delay, and motor saturation avoidance. Firstly, model predictive control (MPC) and sliding mode control (SMC) schemes are developed using a simplified model, and their stability is analyzed using the Lyapunov method. Numerical simulations under two disturbances validate the superior tracking performance of the SMC scheme. Secondly, an Nth-order linear active disturbance rejection control (LADRC) is designed based on a simplified model and optimization problems. The second-order LADRC-SMC scheme reduces the contact-force control error in the SMC scheme by ten times. Finally, a fourth-order LADRC-SMC with a Smith Predictor (LADRC-SMC-SP) scheme is formulated, employing each loop controller independently. This scheme simplifies the design and enhances performance. Compared to numerical simulations of the above and existing schemes, the LADRC-SMC-SP scheme eliminates delay oscillations, shortens convergence time, and demonstrates fast force-position tracking responses, minimal overshoot, and strong disturbance rejection. The peak contact-force error in the LADRC-SMC-SP scheme was ten times smaller than that in the LADRC-SMC scheme. The integral square error (ISE) values for the tracking errors of joint angles and , and contact force , are , , and , respectively. These significant improvements in control performance address the challenges in single-leg dynamic systems, effectively handling disturbances, delays, and motor saturation.
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(This article belongs to the Section Mechanical Engineering)
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Feature Optimization-Based Machine Learning Approach for Czech Land Cover Classification Using Sentinel-2 Images
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Chunling Wang, Tianyi Hang, Changke Zhu and Qi Zhang
Appl. Sci. 2024, 14(6), 2561; https://0-doi-org.brum.beds.ac.uk/10.3390/app14062561 - 19 Mar 2024
Abstract
The Czech Republic is one of the countries along the Belt and Road Initiative, and classifying land cover in the Czech Republic helps to understand the distribution of its forest resources, laying the foundation for forestry cooperation between China and the Czech Republic.
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The Czech Republic is one of the countries along the Belt and Road Initiative, and classifying land cover in the Czech Republic helps to understand the distribution of its forest resources, laying the foundation for forestry cooperation between China and the Czech Republic. This study aims to develop a practical approach for land cover classification in the Czech Republic, with the goal of efficiently acquiring spatial distribution information regarding its forest resources. This approach is based on multi-level feature extraction and selection, integrated with advanced machine learning or deep learning models. To accomplish this goal, the study concentrated on two typical experimental regions in the Czech Republic and conducted a series of classification experiments, using Sentinel-2 and DEM data in 2018 as the main data sources. Initially, this study extracted various features, including spectral, vegetation, and terrain features, from the study area, then assessed and selected key features based on their importance. Additionally, this study also explored multi-level spatial contextual features to improve classification performance. The extracted features include texture and morphological features, as well as deep semantic information learned by utilizing a deep learning model, 3D CNN. Finally, an AdaBoost ensemble learning model with the random forest as the base classifier is designed to produce land cover classification maps, thus obtaining the spatial distribution of forest resources. The experimental results demonstrate that feature optimization significantly enhances the extraction of high-quality features of surface objects, thereby improving classification performance. Specifically, morphological and texture features can effectively enhance the discriminability between different features of surface objects, thereby improving classification accuracy. Utilizing deep learning networks enables more efficient extraction of deep feature information, further enhancing classification accuracy. Moreover, employing an ensemble learning model effectively boosts the accuracy of the original classification results from different individual classifiers. Ultimately, the classification accuracy of the two experimental areas reaches 92.84% and 93.83%, respectively. The user accuracies for forests are 92.24% and 93.14%, while the producer accuracies are 97.71% and 97.02%. This study applies the proposed approach for nationwide classification in the Czech Republic, resulting in an overall classification accuracy of 90.98%, with forest user accuracy at 91.97% and producer accuracy at 96.2%. The results in this study demonstrate the feasibility of combining feature optimization with the 3D Convolutional Neural Network (3DCNN) model for land cover classification. This study can serve as a reference for research methods in deep learning for land cover classification, utilizing optimized features.
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(This article belongs to the Topic Remote Sensing and Visualization Methods: Monitoring, Modeling, Simulations and Interaction of Forest Resource)
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Protective Effects of Hesperetin on Cardiomyocyte Integrity and Cytoskeletal Stability in a Murine Model of Epirubicin-Induced Cardiotoxicity: A Histopathological Study
by
Adina Pop Moldovan, Simona Dumitra, Cristina Popescu, Radu Lala, Nicoleta Zurbau Anghel, Daniel Nisulescu, Ariana Nicoras, Coralia Cotoraci, Monica Puticiu, Anca Hermenean and Daniela Teodora Marti
Appl. Sci. 2024, 14(6), 2560; https://0-doi-org.brum.beds.ac.uk/10.3390/app14062560 - 19 Mar 2024
Abstract
Anthracyclines, including epirubicin (Epi), are effective chemotherapeutics but are known for their cardiotoxic side effects, primarily inducing cardiomyocyte apoptosis. This study investigates the protective role of hesperetin (HSP) against cardiomyopathy triggered by Epi in a murine model. Male CD1 mice were divided into
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Anthracyclines, including epirubicin (Epi), are effective chemotherapeutics but are known for their cardiotoxic side effects, primarily inducing cardiomyocyte apoptosis. This study investigates the protective role of hesperetin (HSP) against cardiomyopathy triggered by Epi in a murine model. Male CD1 mice were divided into four groups, with the Epi group receiving a cumulative dose of 12 mg/kg intraperitoneally, reflecting a clinically relevant dosage. The co-treatment group received 100 mg/kg of HSP daily for 13 days. After the treatment period, mice were euthanized, and heart tissues were collected for histopathological, immunofluorescence/immunohistochemistry, and transmission electron microscopy (TEM) analyses. Histologically, Epi treatment led to cytoplasmic vacuolization, myofibril loss, and fiber disarray, while co-treatment with HSP preserved cardiac structure. Immunofluorescent analysis of Bcl-2 family proteins revealed Epi-induced upregulation of the pro-apoptotic protein Bax and a decrease in anti-apoptotic Bcl-2, which HSP treatment reversed. TEM observations confirmed the preservation of mitochondrial ultrastructure with HSP treatment. Moreover, in situ detection of DNA fragmentation highlighted a decrease in apoptotic nuclei with HSP treatment. In conclusion, HSP demonstrates a protective effect against Epi-induced cardiac injury and apoptosis, suggesting its potential as an adjunctive therapy in anthracycline-induced cardiomyopathy. Further studies, including chronic cardiotoxicity models and clinical trials, are warranted to optimize its therapeutic application in Epi-related cardiac dysfunction.
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(This article belongs to the Special Issue Biological Activity and Applications of Natural Plant Compounds)
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Designing an Educational Metaverse: A Case Study of NTUniverse
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Jing Kai Sim, Kaichao William Xu, Yuyang Jin, Zhi Yu Lee, Yi Jie Teo, Pallavi Mohan, Lihui Huang, Yuan Xie, Siyi Li, Nanying Liang, Qi Cao, Simon See, Ingrid Winkler and Yiyu Cai
Appl. Sci. 2024, 14(6), 2559; https://0-doi-org.brum.beds.ac.uk/10.3390/app14062559 - 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://0-doi-org.brum.beds.ac.uk/10.3390/app14062558 - 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://0-doi-org.brum.beds.ac.uk/10.3390/app14062557 - 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://0-doi-org.brum.beds.ac.uk/10.3390/app14062556 - 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
by
Ayman Noor, Ziad Algrafi, Basil Alharbi, Talal H. Noor, Abdullah Alsaeedi, Reyadh Alluhaibi and Majed Alwateer
Appl. Sci. 2024, 14(6), 2555; https://0-doi-org.brum.beds.ac.uk/10.3390/app14062555 - 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://0-doi-org.brum.beds.ac.uk/10.3390/app14062554 - 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)
by
Paolo Caccianiga, Saverio Ceraulo, Gérard Rey, Dario Monai, Marco Baldoni and Gianluigi Caccianiga
Appl. Sci. 2024, 14(6), 2553; https://0-doi-org.brum.beds.ac.uk/10.3390/app14062553 - 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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Mobile Robot for Security Applications in Remotely Operated Advanced Reactors
by
Ujwal Sharma, Uma Shankar Medasetti, Taher Deemyad, Mustafa Mashal and Vaibhav Yadav
Appl. Sci. 2024, 14(6), 2552; https://0-doi-org.brum.beds.ac.uk/10.3390/app14062552 - 18 Mar 2024
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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
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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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