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
The Influence of Yeast Strain on the Chemical, Chromatic, and Sensory Characteristics of ‘Wodarz’ Apple Cider
Appl. Sci. 2024, 14(11), 4851; https://doi.org/10.3390/app14114851 (registering DOI) - 3 Jun 2024
Abstract
A regionally developed and adapted dessert apple, ‘Wodarz’, was explored for its potential in apple cider production because of its consistent productivity when other apple cultivars have struggled with North Dakota’s climate. Due to the importance of yeast strain on the perceived quality
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A regionally developed and adapted dessert apple, ‘Wodarz’, was explored for its potential in apple cider production because of its consistent productivity when other apple cultivars have struggled with North Dakota’s climate. Due to the importance of yeast strain on the perceived quality of fermentation products, five commercial yeast strains, three wine yeasts (EC1118, Maurivin B, and 71B), and two cider yeasts (WLP775 and WY4766) were evaluated for their impact on the physicochemical properties, color, and sensory characteristics of ‘Wodarz’ cider. By assessing dynamic changes, such as spectral properties and sugar content, a comparison among yeasts was conducted across multiple dimensions. The lightness, chroma, and hue all showed variations throughout fermentation, though not across the final ciders. However, differences in the final color of the ciders were identified via ΔE calculations. Each yeast contributed different aromas and tastes to the final ciders. Among yeast strains, EC1118 had the strongest aroma intensity. Despite having subdued aroma intensity, 71B had strong acidity tastes and WLP775 had strong fruity tastes. Thus, our research suggests that yeast strains are an applicable factor in determining the final sensory attributes of local ‘Wodarz’ cider. This is the first report of fermentation outcomes using ‘Wodarz’ apples for cider. ‘Wodarz’ can be aromatically described using terms such as apple, honey, herbal, rose, and floral and fruit notes. The overall taste of ‘Wodarz’ cider is characterized by apple, honey, and rose notes followed by black pepper and grass.
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(This article belongs to the Special Issue Wine Technology and Sensory Analysis)
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Fast-Growing Bio-Based Construction Materials as an Approach to Accelerate United Nations Sustainable Development Goals
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Livia Cosentino, Jorge Fernandes and Ricardo Mateus
Appl. Sci. 2024, 14(11), 4850; https://doi.org/10.3390/app14114850 (registering DOI) - 3 Jun 2024
Abstract
The United Nations Sustainable Development Goals (UN SDGs) ensure future human well-being. However, they face challenges due to the pressing need to reduce carbon emissions, with nearly 40% originating from the construction sector. With the current global environmental and energy crisis, there is
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The United Nations Sustainable Development Goals (UN SDGs) ensure future human well-being. However, they face challenges due to the pressing need to reduce carbon emissions, with nearly 40% originating from the construction sector. With the current global environmental and energy crisis, there is a pressing need to address building carbon emissions and prioritise investments in passive strategies for improving indoor thermal comfort. Exploring fast-growing bio-based materials like bamboo, straw, hemp, and flax directly addresses these concerns, fostering environmental sustainability. Material selection in construction is crucial for advancing the SDGs, for example, promoting sustainable cities and communities (SDG11) and responsible consumption and production (SDG12). This paper proposes a comparative analysis of conventional and bio-based construction materials, focusing on their production stages through life cycle analysis. Tools such as Building Emissions Accounting for Materials (BEAM) and the Methodology for Relative Assessment of Sustainability (MARS) enable a detailed comparison. The results highlight the benefits of bio-based materials in storing carbon more rapidly and their lower environmental impact compared to conventional alternatives. Moreover, bio-based materials contribute to indoor moisture regulation and a healthier indoor environment, underscoring their potential to accelerate progress towards the UN SDGs through informed material choices in design practices.
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(This article belongs to the Special Issue Challenges for Sustainable Building: Innovation, Development and Characterisation of New Material Products and Systems)
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Open AccessArticle
The Impact of Induced Acceleration Perturbations in Selected Phases of the Gait Cycle on Kinematic and Kinetic Parameters
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Kajetan Ciunelis, Rafał Borkowski and Michalina Błażkiewicz
Appl. Sci. 2024, 14(11), 4849; https://doi.org/10.3390/app14114849 (registering DOI) - 3 Jun 2024
Abstract
Background: The prevalence of falls among the older population underscores the imperative of comprehending human adaptations to gait perturbations. Dual-belt treadmills offer a controlled setting for such investigations. The purpose of this study was to examine the effect of the acceleration of one
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Background: The prevalence of falls among the older population underscores the imperative of comprehending human adaptations to gait perturbations. Dual-belt treadmills offer a controlled setting for such investigations. The purpose of this study was to examine the effect of the acceleration of one belt of the treadmill during three different phases of the gait cycle on kinematic and kinetic parameters and relate these changes to unperturbed gait. Methods: Twenty-one healthy young females walked on a treadmill in a virtual environment, in which five unexpected perturbations were applied to the left belt at the Initial Contact (IC), Mid Stance (MS), and Pre-Swing (PS) phase of the gait cycle. Data from the undisturbed gait and the first disturbance of each trial were extracted for analysis. Results: All perturbations significantly affected the gait pattern, mainly by decreasing the knee extension angle. The perturbation in the IC phase had the most significant effect, resulting in a 248.48% increase in knee flexion torque. The perturbation in the MS phase mainly affected plantar flexion torque, increasing it by 118.18%, while perturbation in the PS phase primarily increased the hip extension torque by 73.02%. Conclusions: The presence of perturbations in the IC and PS phases caused the most aggressive and significant changes in gait parameters.
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Open AccessArticle
Optimization of Execution Microscopic Extrusion Parameter Characterizations for Color Polycarbonate Grading: General Trend and Box–Behnken Designs
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Jamal Alsadi, Faten A. M. Al Btoush, Ameen Alawneh, Ahmed Ali Khatatbeh, Mustafa Alseafan, Wardeh Al-Younis, Mutaz Abdel Wahed, Amer Al-Canaan, Rabah Ismail, Issam Trrad, Hashem Al-Mattarneh and Saleh Alomari
Appl. Sci. 2024, 14(11), 4848; https://doi.org/10.3390/app14114848 (registering DOI) - 3 Jun 2024
Abstract
This research article concentrates on process conditions in addition to improving color selections in polymer compounders and developing more accurate simulation models. The feed rate (FR), temperature (T) and screw speed (SS) are three processing variables that the research investigates using general trends
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This research article concentrates on process conditions in addition to improving color selections in polymer compounders and developing more accurate simulation models. The feed rate (FR), temperature (T) and screw speed (SS) are three processing variables that the research investigates using general trends (GTs) and Box–Behnken design (BBD) response surface methodology. The identical set of processing settings was tweaked at three separate phases independently of one another. This study uses the experimental design to investigate process parameters’ optimization while holding all other parameters constant. This design was given the name GT. To develop this design and its statistical optimization, this study used the software of the design expert method. A regression model was run in this design, which displayed collective as well as individual effects of the parameters on color images. The values of tri-stimulus color with the best optimization had the smallest proper color variance (dE*). To obtain information on pigment characteristics, an SEM image analysis was conducted, which aids in improving future designs and overcoming manufacturing issues that affect color fluctuation properties and waste reduction for various chemical grades, both of which enhance environmentally friendly processes.
Full article
(This article belongs to the Special Issue Rock-Like Material Characterization and Engineering Properties)
Open AccessArticle
Multimodal Shot Prediction Based on Spatial-Temporal Interaction between Players in Soccer Videos
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Ryota Goka, Yuya Moroto, Keisuke Maeda, Takahiro Ogawa and Miki Haseyama
Appl. Sci. 2024, 14(11), 4847; https://doi.org/10.3390/app14114847 (registering DOI) - 3 Jun 2024
Abstract
Sports data analysis has significantly advanced and become an indispensable technology for planning strategy and enhancing competitiveness. In soccer, shot prediction has been realized on the basis of historical match situations, and its results contribute to the evaluation of plays and team tactics.
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Sports data analysis has significantly advanced and become an indispensable technology for planning strategy and enhancing competitiveness. In soccer, shot prediction has been realized on the basis of historical match situations, and its results contribute to the evaluation of plays and team tactics. However, traditional event prediction methods required tracking data acquired with expensive instrumentation and event stream data annotated by experts, and the benefits were limited to only some professional athletes. To tackle this problem, we propose a novel shot prediction method using soccer videos. Our method constructs a graph considering player relationships with audio and visual features as graph nodes. Specifically, by introducing players’ importance into the graph edge based on their field positions and team information, our method enables the utilization of knowledge that reflects the detailed match situation. Next, we extract latent features considering spatial–temporal interactions from the graph and predict event occurrences with uncertainty based on the probabilistic deep learning method. In comparison with several baseline methods and ablation studies using professional soccer match data, our method was confirmed to be effective as it demonstrated the highest average precision of 0.948, surpassing other methods.
Full article
(This article belongs to the Collection Computer Science in Sport)
Open AccessArticle
Sustainability Evaluation of a Paper and Pulp Industrial Waste Incorporation in Bituminous Pavements
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Fábio Simões, Francisco-Javier Rios-Davila, Helena Paiva, Miguel Morais and Victor M. Ferreira
Appl. Sci. 2024, 14(11), 4846; https://doi.org/10.3390/app14114846 (registering DOI) - 3 Jun 2024
Abstract
The valorization of wastes as an alternative or secondary raw material in various products and processes has been a solution for the implementation of sustainability, a safer environment, and the concept of circular economy in the efficient use and management of natural resources.
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The valorization of wastes as an alternative or secondary raw material in various products and processes has been a solution for the implementation of sustainability, a safer environment, and the concept of circular economy in the efficient use and management of natural resources. To promote sustainability through a circular economy approach, this work tries to demonstrate the environmental gains that are obtained by bringing together, in an industrial symbiosis action, two large industrial sectors (the pulp and paper and the road pavement sectors) responsible for generating large amounts of wastes. A sustainability assessment, based on a life cycle and circular economy approach, is presented here, and discussed using a simple case study carried out on a real scale. Two wastes (dregs and grits) from the pulp and paper industry (PPI) were used to partially replace natural fine aggregates in the production of bituminous mixtures used on the top surface of road pavements. The impacts at a technical, environmental, economic, and social level were assessed and it was shown that this simple waste valorization action is not only positive for the final product from a technical point of view, but also for the environment, causing positive impacts on the different sustainability dimensions that were evaluated.
Full article
(This article belongs to the Special Issue Towards the Road of Future—Sustainability and Innovation in Pavement Engineering)
Open AccessArticle
Designing Reward Functions Using Active Preference Learning for Reinforcement Learning in Autonomous Driving Navigation
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Lun Ge, Xiaoguang Zhou and Yongqiang Li
Appl. Sci. 2024, 14(11), 4845; https://doi.org/10.3390/app14114845 - 3 Jun 2024
Abstract
This study presents a method based on active preference learning to overcome the challenges of designing reward functions for autonomous navigation. Results obtained from training with artificially designed reward functions may not accurately reflect human intentions. We focus on the limitations of traditional
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This study presents a method based on active preference learning to overcome the challenges of designing reward functions for autonomous navigation. Results obtained from training with artificially designed reward functions may not accurately reflect human intentions. We focus on the limitations of traditional reward functions, which often fail to facilitate complex tasks in continuous state spaces. We propose the adoption of active preference learning to resolve these issues and to generate reward functions that align with human preferences. This approach leverages an individual’s subjective preferences to guide an agent’s learning process, enabling the creation of reward functions that reflect human desires. We utilize mutual information to generate informative queries and apply information gained to balance the agent’s uncertainty with the human’s response capacity, encouraging the agent to pose straightforward and informative questions. We further employ the No-U-Turn Sampler (NUTS) method to refine the belief model, which outperforms that constructed using the Metropolis algorithm. Subsequently, we retrain the agent using reward weights derived from active preference learning. As a result, our autonomous driving vehicle can navigate between random starting and ending points without dependence on high-precision maps or routing, relying solely on its forward vision. We validate our approach’s performance within the CARLA simulation environment. Our algorithm significantly improved the success rate of autonomous driving navigation tasks that originally failed due to artificially designed rewards, increasing it to approximately 60%. Experimental results show significant improvement over the baseline algorithm, providing a solid foundation for enhancing navigation capabilities in autonomous driving systems and advancing the field of autonomous driving intelligence.
Full article
(This article belongs to the Topic Advances in Mobile Robotics Navigation, 2nd Volume)
Open AccessArticle
A Deep Learning Approach for the Fast Generation of Synthetic Computed Tomography from Low-Dose Cone Beam Computed Tomography Images on a Linear Accelerator Equipped with Artificial Intelligence
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Luca Vellini, Sergio Zucca, Jacopo Lenkowicz, Sebastiano Menna, Francesco Catucci, Flaviovincenzo Quaranta, Elisa Pilloni, Andrea D'Aviero, Michele Aquilano, Carmela Di Dio, Martina Iezzi, Alessia Re, Francesco Preziosi, Antonio Piras, Althea Boschetti, Danila Piccari, Gian Carlo Mattiucci and Davide Cusumano
Appl. Sci. 2024, 14(11), 4844; https://doi.org/10.3390/app14114844 - 3 Jun 2024
Abstract
Artificial Intelligence (AI) is revolutionising many aspects of radiotherapy (RT), opening scenarios that were unimaginable just a few years ago. The aim of this study is to propose a Deep Leaning (DL) approach able to quickly generate synthetic Computed Tomography (CT) images from
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Artificial Intelligence (AI) is revolutionising many aspects of radiotherapy (RT), opening scenarios that were unimaginable just a few years ago. The aim of this study is to propose a Deep Leaning (DL) approach able to quickly generate synthetic Computed Tomography (CT) images from low-dose Cone Beam CT (CBCT) acquired on a modern linear accelerator integrating AI. Methods: A total of 53 patients treated in the pelvic region were enrolled and split into training (30), validation (9), and testing (14). A Generative Adversarial Network (GAN) was trained for 200 epochs. The image accuracy was evaluated by calculating the mean and mean absolute error (ME and ME) between sCT and CT. RT treatment plans were calculated on CT and sCT images, and dose accuracy was evaluated considering Dose Volume Histogram (DVH) and gamma analysis. Results: A total of 4507 images were selected for training. The MAE and ME values in the test set were 36 ± 6 HU and 7 ± 6 HU, respectively. Mean gamma passing rates for 1%/1 mm, 2%/2 mm, and 3%/3 mm tolerance criteria were respectively 93.5 ± 3.4%, 98.0 ± 1.3%, and 99.2 ± 0.7%, with no difference between curative and palliative cases. All the DVH parameters analysed were within 1 Gy of the difference between sCT and CT. Conclusion: This study demonstrated that sCT generation using the DL approach is feasible on low-dose CBCT images. The proposed approach can represent a valid tool to speed up the online adaptive procedure and remove CT simulation from the RT workflow.
Full article
(This article belongs to the Special Issue Developments of Diagnostic Imaging Applied in Radiotherapy)
Open AccessArticle
Validity and Reliability of a Smartphone App for Vertical Jump Height Assessment Using the Marker Displacement Time Method
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Michał Murawa, Waldemar Krakowiak and Jarosław Kabaciński
Appl. Sci. 2024, 14(11), 4843; https://doi.org/10.3390/app14114843 - 3 Jun 2024
Abstract
The correct assessment of the vertical jump height depends on an accurate and reliable measurement tool. This study aimed to determine the concurrent validity and reliability of the My Jump 2 app used for estimating the maximum height (MH) of the counter-movement jump
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The correct assessment of the vertical jump height depends on an accurate and reliable measurement tool. This study aimed to determine the concurrent validity and reliability of the My Jump 2 app used for estimating the maximum height (MH) of the counter-movement jump (CMJ). Twenty-one male adults participated in this study. The MH of the CMJ was estimated based on the displacement of the jumper’s center of mass (force platform), the displacement time of the reflective marker placed on the jumper’s sacrum (smartphone, My Jump 2-DT) and the flight time of the jumper (smartphone, My Jump 2-FT). The assessment of the concurrent validity showed a poor agreement (ICC = 0.362; Bland–Altman bias = 12.4 cm) between the My Jump 2-FT and force platform (p < 0.001), and a good agreement (ICC = 0.858; Bland–Altman bias = −0.2 cm) between the My Jump 2-DT and force platform (p < 0.001). The ICC values for internal consistency (>0.9) indicated the excellent reliability of all measurement tools (p < 0.001). The findings revealed the high accuracy and good reliability of the My Jump 2 app for the new method of MH estimation for the CMJ, including the displacement time of the marker placed on the jumper’s sacrum.
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(This article belongs to the Special Issue Advances in the Biomechanics of Sports)
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Growth, Fatty Acid Profile and Malondialdehyde Concentration of Meagre Argyrosomus regius Fed Diets with Different Lipid Content
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Daniel Matulić, Maria Blažina, Ena Pritišanac, Slavica Čolak, Lav Bavčević, Renata Barić, Silvia Križanac, Božena Vitlov, Jelena Šuran, Ivančica Strunjak Perović and Tea Tomljanović
Appl. Sci. 2024, 14(11), 4842; https://doi.org/10.3390/app14114842 - 3 Jun 2024
Abstract
The aim of the study was to evaluate the growth, fatty acid profile and concentration of malondialdehyde of muscle tissue of meagre Argyrosomus regius fed diets with different lipid content. The long-term experiment was conducted in three feeding groups: A (CP = 52.0;
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The aim of the study was to evaluate the growth, fatty acid profile and concentration of malondialdehyde of muscle tissue of meagre Argyrosomus regius fed diets with different lipid content. The long-term experiment was conducted in three feeding groups: A (CP = 52.0; CL = 21.0), B (CP = 56.0; CL = 18.0), C (CP = 48.0; CL = 16.0) with two replicates in marine net cages on Bisage Island, Adriatic Sea over 20 months. At the beginning of the experiment, fish were of equal weight (6.83 ± 1.03 g) and length (8.57 ± 0.49 cm) and were fed to satiation during the experiment. At the end of the experiment, the fish from each feeding group (n = 110) were measured and muscle tissue was collected (n = 60) and stored at −80 °C until analysis. The final weight and condition factor were significantly different (p < 0.05) between the groups. The highest ratio of crude fats and n-3/n-6-fatty acids was found in the muscle tissue of group A. Fish fed diet A also exhibited higher MDA levels compared to fish in the other feeding groups, indicating elevated levels of lipid peroxidation in muscle tissues. Experimental feeding group A showed better growth performance, a higher content of the beneficial fatty acids EPA and DHA and a more favorable n-3/n-6 ratio than feeding groups B and C. Continuously monitoring and adjusting feeding protocols in accordance with lipid content and fatty acid composition could maximize growth and health outcomes in meagre farming.
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(This article belongs to the Special Issue Advances in Applied Marine Sciences and Engineering—2nd Edition)
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Primary and Secondary Stability Assessments of Dental Implants According to Their Macro-Design, Length, Width, Location, and Bone Quality
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Norberto Quispe-López, Soraya Martín-Martín, Cristina Gómez-Polo, Oscar Figueras-Alvarez, María Isabel Sánchez-Jorge and Javier Montero
Appl. Sci. 2024, 14(11), 4841; https://doi.org/10.3390/app14114841 - 3 Jun 2024
Abstract
Some evidence supports the influence of implant macro-design on primary stability. Additionally, tactile perception can be used to assess implant stability when placing the implant. This research aimed to quantify the primary and secondary stability of three implant systems with two different macro
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Some evidence supports the influence of implant macro-design on primary stability. Additionally, tactile perception can be used to assess implant stability when placing the implant. This research aimed to quantify the primary and secondary stability of three implant systems with two different macro geometries (cylindrical and conical) determined based on the insertion torque and the implant stability quotient (ISQ) at the moment of implant placement as a function of implant-related factors (length, width, dental arch, and implant location in the arch), intraoperative factors (bone density determined subjectively by the clinician’s tactile perception), and patient-related factors (age, gender, and bone density determined objectively based on cone beam computed tomography (CBCT). Methods: 102 implants from three implant systems with two different macro geometries (conical and cylindrical) were placed in 53 patients. The insertion torque, the ISQ at the implant placement (ISQ0), and the bone quality according to the clinician’s tactile sensation were recorded on the day of the surgery. After a three-month healing period, the ISQ was re-evaluated (ISQ3). Results: The cylindrical implants exhibited significantly higher insertion torque and ISQ values at the moment of the surgery and after three months compared to the conical implants. The cylindrical implants also showed significantly lower indices of tactile evaluation of bone quality during the implant placement surgery. However, no differences were demonstrated in the bone density measured objectively using CBCT. (4) Conclusions: The cylindrical implants achieved the highest values for primary stability (Newtons × centimeter (Ncm) and ISQ) and secondary stability (ISQ after three months). The insertion torque was the variable that most influenced the ISQ on the day of the surgery. The implant location (incisors–canines, bicuspids–molars) and the implant macro geometry were the variables that most influenced the secondary stability (ISQ at three months).
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(This article belongs to the Special Issue Advances in Dental Implants)
Open AccessArticle
Research on the Fiber-to-the-Room Network Traffic Prediction Method Based on Crested Porcupine Optimizer Optimization
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Jingjing Zang, Bingyao Cao and Yiming Hong
Appl. Sci. 2024, 14(11), 4840; https://doi.org/10.3390/app14114840 - 3 Jun 2024
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In order to solve the problem of traffic burst due to the increase in access points and user movement in an FTTR network, as well as to meet the demand for a high-performance network, it is necessary to rationally allocate network resources, and
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In order to solve the problem of traffic burst due to the increase in access points and user movement in an FTTR network, as well as to meet the demand for a high-performance network, it is necessary to rationally allocate network resources, and accurate traffic prediction is very important for dynamic bandwidth allocation in such a network. Therefore, this paper introduces a novel traffic prediction model, named CPO-BiTCN-BiLSTM-SA, which integrates the Crested Porcupine Optimizer (CPO), bidirectional temporal convolution (BiTCN), and bidirectional long short-term memory (BiLSTM) networks. BiTCN extends the traditional TCN by incorporating bidirectional data information, while BiLSTM enhances the network’s capability to learn from long sequences. Moreover, self-attention (SA) mechanisms are utilized to emphasize the crucial segments in the data. Subsequently, the BiTCN-BiLSTM-SA model is optimized by CPO to obtain the best network hyperparameters, and model training prediction is performed to achieve multi-step predictions based on single-step prediction. To evaluate the model’s generalization ability, two distinct datasets are employed for traffic prediction. Experimental findings demonstrate that the proposed model surpasses existing models in terms of the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination ( ). In comparison with the traditional XGBoost model, the proposed model has an average reduction of 29.50%, 25.43%, and 25.00% in RMSE, MAE, and MAPE, respectively, with a 6.70% improvement in .
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Open AccessArticle
Weed Detection and Classification with Computer Vision Using a Limited Image Dataset
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László Moldvai, Péter Ákos Mesterházi, Gergely Teschner and Anikó Nyéki
Appl. Sci. 2024, 14(11), 4839; https://doi.org/10.3390/app14114839 - 3 Jun 2024
Abstract
In agriculture, as precision farming increasingly employs robots to monitor crops, the use of weeding and harvesting robots is expanding the need for computer vision. Currently, most researchers and companies address these computer vision tasks with CNN-based deep learning. This technology requires large
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In agriculture, as precision farming increasingly employs robots to monitor crops, the use of weeding and harvesting robots is expanding the need for computer vision. Currently, most researchers and companies address these computer vision tasks with CNN-based deep learning. This technology requires large datasets of plant and weed images labeled by experts, as well as substantial computational resources. However, traditional feature-based approaches to computer vision can extract meaningful parameters and achieve comparably good classification results with only a tenth of the dataset size. This study presents these methods and seeks to determine the minimum number of training images required to achieve reliable classification. We tested the classification results with 5, 10, 20, 40, 80, and 160 images per weed type in a four-class classification system. We extracted shape features, distance transformation features, color histograms, and texture features. Each type of feature was tested individually and in various combinations to determine the best results. Using six types of classifiers, we achieved a 94.56% recall rate with 160 images per weed. Better results were obtained with more training images and a greater variety of features.
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(This article belongs to the Section Agricultural Science and Technology)
Open AccessArticle
Research on Clock Synchronization of Data Acquisition Based on NoC
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Chaoyong Meng, Chuanpei Xu and Jiafeng Liao
Appl. Sci. 2024, 14(11), 4838; https://doi.org/10.3390/app14114838 - 3 Jun 2024
Abstract
Data acquisition based on network-on-chip (NoC) technology is a high-sampling-rate data acquisition scheme using low-sampling-rate analog–digital conversion (ADC) chips. It has the characteristics of multi-task parallel communication, being global asynchronous, local synchronous clock distribution, high throughput, low transmission latency, and strong scalability. High-speed
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Data acquisition based on network-on-chip (NoC) technology is a high-sampling-rate data acquisition scheme using low-sampling-rate analog–digital conversion (ADC) chips. It has the characteristics of multi-task parallel communication, being global asynchronous, local synchronous clock distribution, high throughput, low transmission latency, and strong scalability. High-speed data acquisition is realized through the combination of an on-chip network and time-interleaved data acquisition technology. In the time-interleaved sampling technique, the precision of clock synchronization directly affects the precision of sampling. Based on the proposed NOC data acquisition scheme, an improved White Rabbit clock synchronization protocol is applied to high-speed data acquisition to achieve high-precision synchronization of multi-channel time-interleaved sampling clocks. Firstly, the offset of the master clock and slave clock is determined by the PTP protocol, and the offset is corrected to achieve rough synchronization between the master clock and slave clock. Secondly, a digital dual-mixer time difference (DDMTD) is used to measure the phases of the master and slave clocks. After that, the phase of the slave clock is corrected through the dynamic phase-shift function of the clock’s phase-locked loop (PLL). Finally, according to the simulation results in Modelsim, the average absolute error of a TI-ADC sampling clock can be less than 20 ps.
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(This article belongs to the Special Issue Signal Acquisition and Processing for Measurement and Testing)
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Whole-Genome Alignment: Methods, Challenges, and Future Directions
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Bacem Saada, Tianchi Zhang, Estevao Siga, Jing Zhang and Maria Malane Magalhães Muniz
Appl. Sci. 2024, 14(11), 4837; https://doi.org/10.3390/app14114837 - 3 Jun 2024
Abstract
Whole-genome alignment (WGA) is a critical process in comparative genomics, facilitating the detection of genetic variants and aiding our understanding of evolution. This paper offers a detailed overview and categorization of WGA techniques, encompassing suffix tree-based, hash-based, anchor-based, and graph-based methods. It elaborates
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Whole-genome alignment (WGA) is a critical process in comparative genomics, facilitating the detection of genetic variants and aiding our understanding of evolution. This paper offers a detailed overview and categorization of WGA techniques, encompassing suffix tree-based, hash-based, anchor-based, and graph-based methods. It elaborates on the algorithmic properties of these tools, focusing on performance and methodological aspects. This paper underscores the latest progress in WGA, emphasizing the increasing capacity to manage the growing intricacy and volume of genomic data. However, the field still grapples with computational and biological hurdles affecting the precision and speed of WGA. We explore these challenges and potential future solutions. This paper aims to provide a comprehensive resource for researchers, deepening our understanding of WGA tools and their applications, constraints, and prospects.
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(This article belongs to the Section Biomedical Engineering)
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Open AccessArticle
Probabilistic Mixture Model-Based Spectral Unmixing
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Oliver Hoidn, Aashwin Ananda Mishra and Apurva Mehta
Appl. Sci. 2024, 14(11), 4836; https://doi.org/10.3390/app14114836 - 3 Jun 2024
Abstract
Spectral unmixing attempts to decompose a spectral ensemble into the constituent pure spectral signatures (called endmembers) along with the proportion of each endmember. This is essential for techniques like hyperspectral imaging (HSI) used in environment monitoring, geological exploration, etc. Several spectral unmixing approaches
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Spectral unmixing attempts to decompose a spectral ensemble into the constituent pure spectral signatures (called endmembers) along with the proportion of each endmember. This is essential for techniques like hyperspectral imaging (HSI) used in environment monitoring, geological exploration, etc. Several spectral unmixing approaches have been proposed, many of which are connected to hyperspectral imaging. However, most extant approaches assume highly diverse collections of mixtures and extremely low-loss spectroscopic measurements. Additionally, current non-Bayesian frameworks do not incorporate the uncertainty inherent in unmixing. We propose a probabilistic inference algorithm that explicitly incorporates noise and uncertainty, enabling us to unmix endmembers in collections of mixtures with limited diversity. We use a Bayesian mixture model to jointly extract endmember spectra and mixing parameters while explicitly modeling observation noise and the resulting inference uncertainties. We obtain approximate distributions over endmember coordinates for each set of observed spectra while remaining robust to inference biases from the lack of pure observations and the presence of non-isotropic Gaussian noise. As a direct impact of our methodology, access to reliable uncertainties on the unmixing solutions would enable robust solutions to noise, as well as informed decision-making for HSI applications and other unmixing problems.
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(This article belongs to the Section Applied Physics General)
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The Intersection of Architectural Conservation and Energy Efficiency: A Case Study of Romanian Heritage Buildings
by
Simona Elena Șerban, Tiberiu Catalina, Razvan Popescu and Lelia Popescu
Appl. Sci. 2024, 14(11), 4835; https://doi.org/10.3390/app14114835 - 3 Jun 2024
Abstract
In Europe, it is estimated that 14% of existing buildings were built before 1919, whereas 26% were built before 1945. In Romania, about 31% of the buildings date from before 1961, contributing to the current stock of old buildings with historic and architectural
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In Europe, it is estimated that 14% of existing buildings were built before 1919, whereas 26% were built before 1945. In Romania, about 31% of the buildings date from before 1961, contributing to the current stock of old buildings with historic and architectural value in the country. This paper illustrates the current state of buildings with historic and architectural value in Romania, alongside a case study of a representative administrative building in Câmpulung, Romania. The analysis of the Town Hall building in Câmpulung, Romania, demonstrates that potential energy savings of up to 47.53% can be achieved by implementing interventions such as upgrading windows, insulating the attic, and installing photovoltaic panels. The highest energy reduction is obtained by replacing the window glass with a value of 18.16% with attic insulation with a value of 16.1%. This paper also presents indoor measurements of temperature and humidity in different offices positioned in the north and the south. The study conducted on the south façade office revealed consistent temperatures ranging from 21.7 °C to 24.4 °C, with an average of 23.31 °C. However, the humidity levels fluctuated considerably, ranging from 17.1% to 39.1%, with an average of 26.89%. The sun-exposed section of the building saw relatively stable temperature conditions, but the varying humidity levels could have a detrimental impact on the quality of the indoor atmosphere and potentially decrease the effectiveness of the workforce. By contrast, the north façade office exhibited lower and more fluctuating temperatures, ranging from 19.8 °C to 23.6 °C, with an average of 21.74 °C. Additionally, it had higher and more stable humidity levels, ranging between 19.5% and 41.7%, with an average of 29.83%. A thermographic analysis was performed on the north façade of the Câmpulung Town Hall, utilizing thermal imaging technology to detect areas of heat loss, and thus identifying the energy inefficiency problems of the building’s exterior. The investigation found notable variations in temperature, especially around the windows, where temperatures could be as high as 14.1 °C, highlighting the insufficiency of the building’s antiquated timber-framed windows in preventing energy loss.
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(This article belongs to the Special Issue Energy Implications of Thermal Comfort in Buildings considering Climate Change)
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Open AccessArticle
DualTrans: A Novel Glioma Segmentation Framework Based on a Dual-Path Encoder Network and Multi-View Dynamic Fusion Model
by
Zongren Li, Wushouer Silamu, Yajing Ma and Yanbing Li
Appl. Sci. 2024, 14(11), 4834; https://doi.org/10.3390/app14114834 - 3 Jun 2024
Abstract
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Segmentation methods based on convolutional neural networks (CNN) have achieved remarkable results in the field of medical image segmentation due to their powerful representation capabilities. However, for brain-tumor segmentation, owing to the significant variations in shape, texture, and location, traditional convolutional neural networks
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Segmentation methods based on convolutional neural networks (CNN) have achieved remarkable results in the field of medical image segmentation due to their powerful representation capabilities. However, for brain-tumor segmentation, owing to the significant variations in shape, texture, and location, traditional convolutional neural networks (CNNs) with limited convolutional kernel-receptive fields struggle to model explicit long-range (global) dependencies, thereby restricting segmentation accuracy and making it difficult to accurately identify tumor boundaries in medical imaging. As a result, researchers have introduced the Swin Transformer, which has the capability to model long-distance dependencies, into the field of brain-tumor segmentation, offering unique advantages in the global modeling and semantic interaction of remote information. However, due to the high computational complexity of the Swin Transformer and its reliance on large-scale pretraining, it faces constraints when processing large-scale medical images. Therefore, this study addresses this issue by proposing a smaller network, consisting of a dual-encoder network, which also resolves the instability issue that arises in the training process of large-scale visual models with the Swin Transformer, where activation values of residual units accumulate layer by layer, leading to a significant increase in differences in activation amplitudes across layers and causing model instability. The results of the experimental validation using real data show that our dual-encoder network has achieved significant performance improvements, and it also demonstrates a strong appeal in reducing computational complexity.
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Open AccessArticle
Behavior of Circular Hollow Steel-Reinforced Concrete Columns under Axial Compression
by
Qiuyu Wei, Qingxin Ren, Qinghe Wang and Yannian Zhang
Appl. Sci. 2024, 14(11), 4833; https://doi.org/10.3390/app14114833 - 3 Jun 2024
Abstract
The circular hollow steel-reinforced concrete (HSRC) column consists of an inner circular hollow steel tube and outer circular hollow reinforced concrete (RC). This design provides several advantages, including being lightweight, having a wide sectional profile, and having a high flexural stiffness. This paper
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The circular hollow steel-reinforced concrete (HSRC) column consists of an inner circular hollow steel tube and outer circular hollow reinforced concrete (RC). This design provides several advantages, including being lightweight, having a wide sectional profile, and having a high flexural stiffness. This paper aims to investigate the behavior of the circular HSRC columns under axial compression through testing and finite element (FE) modeling. An FE model was established to simulate the circular HSRC columns under axial compression, which was validated against the test data. Additionally, the load distribution and the interface stress between the outer hollow RC and inner steel tube were analyzed. Subsequently, a systematic parametric analysis was conducted on the diameter (d) and thickness (t) of the steel tube; slenderness ratio (λ); strength of concrete (fcu); yield strength of steel tube (fsy), longitudinal rebar (fly), and stirrup (fgy); as well as the stirrup spacing (s). The critical influencing factors of the circular HSRC columns under axial compression were identified. fcu, λ, d, fly, and fsy dramatically influence the bearing capacity, and the stiffness is notably affected by λ and fcu. Finally, three simplified design methods were summarized and evaluated for calculating the bearing capacity of the circular HSRC columns under axial compression.
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(This article belongs to the Special Issue Advanced Technologies in Construction and Infrastructure: Theory, Methods and Applications)
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Differences in the Lateral and Vertical Jump Performances of Elite Male Basketball Players—An Axial Stabilization Training Program
by
Wei-Yang Huang, Hsuan Huang and Cheng-En Wu
Appl. Sci. 2024, 14(11), 4832; https://doi.org/10.3390/app14114832 - 3 Jun 2024
Abstract
This study aimed to conduct a kinetic analysis of the lateral and vertical jumps of elite male basketball players through a 12-week axial stability training program to improve sports performance. Thirty elite Taiwanese male basketball players were openly recruited and divided into experimental
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This study aimed to conduct a kinetic analysis of the lateral and vertical jumps of elite male basketball players through a 12-week axial stability training program to improve sports performance. Thirty elite Taiwanese male basketball players were openly recruited and divided into experimental groups and control groups. The experimental group conducted the test twice a week, a 12-week (24-session) axial stability training program intervention in total, and the control group only received general basketball training. A double-track force plate was used to measure lateral and vertical jumps in order to understand their dynamic parameters. Finally, a difference analysis between the post-test of lateral and vertical jumps was conducted. The results show that the axial stability training program affected the activation of the abdominal and lower limb extensor muscles and had a stabilizing effect on the muscles of the experimental group. When the participants conducted a lateral jump, they were able to stand firm within 1 s and take off instantly. The θ value of the T-PRF ranged from 60.7° to 68.6°. The post-test of the participants’ vertical jump showed that the kurtosis of the RFD was steeper, the time required for the RFD was shorter, the GRF and the duration of passage increased, and the experimental group was better than the control group in all post-tests. By comparing the two types of jumps, it was found that they had the vertical force in common. The main differences were in the reaction force of the leg strength, the jump distance and height, and the take-off angle.
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(This article belongs to the Special Issue Exercise Physiology and Biomechanics in Human Health)
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