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
Multiplicative Improved Coherence Factor Delay Multiply and Sum Algorithm for Clutter Removal in a Microwave Breast Tumor Imaging System
Appl. Sci. 2024, 14(9), 3820; https://doi.org/10.3390/app14093820 (registering DOI) - 30 Apr 2024
Abstract
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In the medical field, microwave imaging technology has experienced rapid development due to its non-invasive and non-radioactive nature. The confocal algorithm is a method commonly used for microwave breast cancer imaging, with the key objective of removing clutter in images to achieve high-quality
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In the medical field, microwave imaging technology has experienced rapid development due to its non-invasive and non-radioactive nature. The confocal algorithm is a method commonly used for microwave breast cancer imaging, with the key objective of removing clutter in images to achieve high-quality results. However, the current methods are facing challenges in removing clutter. In order to reduce the clutter in images, a multiplicative improved coherence factor delay multiply and sum algorithm based on the maximum interclass differencing method is proposed. The algorithm compares the starting and ending moments of tumor signals in different channels to determine whether the tumor-scattered signals in different channels overlap in time. An improved coherence coefficient is obtained by summing the non-overlapping signals and multiplying the time window. The multiplicative improved coherence factor, which is obtained by multiplying the coherence coefficients of the improved multi-pair signals, is then multiplied by the focal point intensity obtained using the delay multiply and sum algorithm to reduce clutter in an image. To evaluate the performance of the proposed algorithm, several low-cost uniform and non-uniform models of human breast and tumor tissue with dielectric properties were prepared for testing. The experimental results show that, compared to the existing algorithm, the proposed algorithm can greatly reduce the clutter in images, with a signal-to-clutter ratio of at least 4 dB higher as well as contrast at least six-fold higher.
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Open AccessCommunication
Application of Digital Holographic Imaging to Monitor Real-Time Cardiomyocyte Hypertrophy Dynamics in Response to Norepinephrine Stimulation
by
Wahida Akter, Herman Huang, Jacquelyn Simmons and Alexander Y. Payumo
Appl. Sci. 2024, 14(9), 3819; https://doi.org/10.3390/app14093819 (registering DOI) - 30 Apr 2024
Abstract
Cardiomyocyte hypertrophy, characterized by an increase in cell size, is associated with various cardiovascular diseases driven by factors including hypertension, myocardial infarction, and valve dysfunction. In vitro primary cardiomyocyte culture models have yielded numerous insights into the intrinsic and extrinsic mechanisms driving hypertrophic
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Cardiomyocyte hypertrophy, characterized by an increase in cell size, is associated with various cardiovascular diseases driven by factors including hypertension, myocardial infarction, and valve dysfunction. In vitro primary cardiomyocyte culture models have yielded numerous insights into the intrinsic and extrinsic mechanisms driving hypertrophic growth. However, due to limitations in current approaches, the dynamics of cardiomyocyte hypertrophic responses remain poorly characterized. In this study, we evaluate the application of the Holomonitor M4 digital holographic imaging microscope to track dynamic changes in cardiomyocyte surface area and volume in response to norepinephrine treatment, a model hypertrophic stimulus. The Holomonitor M4 permits non-invasive, label-free imaging of three-dimensional changes in cell morphology with minimal phototoxicity, thus enabling long-term imaging studies. Untreated and norepinephrine-stimulated primary neonatal rat cardiomyocytes were live-imaged on the Holomonitor M4, which was followed by image segmentation and single-cell tracking using the HOLOMONITOR App Suite software version 4.0.1.546. The 24 h treatment of cultured cardiomyocytes with norepinephrine increased cardiomyocyte spreading and optical volume as expected, validating the reliability of the approach. Single-cell tracking of both cardiomyocyte surface area and three-dimensional optical volume revealed dynamic increases in these parameters throughout the 24 h imaging period, demonstrating the potential of this technology to explore cardiomyocyte hypertrophic responses with greater temporal resolution; however, technological limitations were also observed and should be considered in the experimental design and interpretation of results. Overall, leveraging the unique advantages of the Holomonitor M4 digital holographic imaging system has the potential to empower future work towards understanding the molecular and cellular mechanisms underlying cardiomyocyte hypertrophy with enhanced temporal clarity.
Full article
(This article belongs to the Special Issue Novel Approaches of Digital Holographic Microscopy in Cell Biology)
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Open AccessArticle
Intelligent Agents and Causal Inference: Enhancing Decision-Making through Causal Reasoning
by
Jairo Iván Vélez Bedoya, Manuel González Bedia and Luis Fernando Castillo Ossa
Appl. Sci. 2024, 14(9), 3818; https://doi.org/10.3390/app14093818 (registering DOI) - 30 Apr 2024
Abstract
This study examines the incorporation of causal inference methods into intelligent entities and examines the benefits of utilizing causal reasoning to improve decision-making procedures. This study entails conducting an experimental evaluation within a video game setting to evaluate the performance of three separate
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This study examines the incorporation of causal inference methods into intelligent entities and examines the benefits of utilizing causal reasoning to improve decision-making procedures. This study entails conducting an experimental evaluation within a video game setting to evaluate the performance of three separate agent types: ExplorerBOT, GuardBOT, and CausalBOT. The ExplorerBOT utilizes a stochastic path selection technique for task completion, whereas the GuardBOT remains immobile yet exhibits exceptional proficiency in identifying and neutralizing other bots. On the other hand, the CausalBOT utilizes sophisticated causal inference methods to examine the underlying factors contributing to the failures noticed in the task completion of the ExplorerBOT. The aforementioned feature allows CausalBOT to make informed decisions by selecting paths that have a greater likelihood of achieving success. The main purpose of these experiments is to assess and compare the effectiveness of two distinct bots, namely ExplorerBOT and CausalBOT, in accomplishing their respective objectives. To facilitate comparison, two iterations of the ExplorerBOT are utilized. The initial iteration is predicated exclusively on stochastic path selection and necessitates a more profound understanding of the variables that impact the achievement of tasks. On the other hand, the second version integrates an algorithm for informed search. In contrast, CausalBOT employs causal inference techniques to discover the underlying causes of failures exhibited by ExplorerBOTs and collect pertinent data. Through the process of discerning the fundamental causal mechanisms, CausalBOT is able to make well-informed decisions by selecting pathways that maximize the probability of successfully completing a given job. The utilization of this approach greatly boosts the decision-making powers of CausalBOT, hence enabling it to effectively adapt and overcome problems in a more efficient manner when compared to alternative agents.
Full article
(This article belongs to the Special Issue Intelligent Agents and Multi-Agent Systems: Theory, Methods and Applications)
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Open AccessArticle
Using Beerkan Procedure to Estimate Hydraulic Soil Properties under Long Term Agroecosystems Experiments
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Lorenzo Vergni, Grazia Tosi, Jennifer Bertuzzi, Giulia Rossi, Michela Farneselli, Giacomo Tosti, Francesco Tei, Alberto Agnelli and Francesca Todisco
Appl. Sci. 2024, 14(9), 3817; https://doi.org/10.3390/app14093817 (registering DOI) - 30 Apr 2024
Abstract
The BEST (Beerkan Estimation of Soil Transfer parameters) method was used to compare the hydraulic properties of the soils in two Long-term Agroecosystem Experiments (LTAEs) located at the FIELDLAB experimental site of the University of Perugia (central Italy). The LTAE “NewSmoca” consists of
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The BEST (Beerkan Estimation of Soil Transfer parameters) method was used to compare the hydraulic properties of the soils in two Long-term Agroecosystem Experiments (LTAEs) located at the FIELDLAB experimental site of the University of Perugia (central Italy). The LTAE “NewSmoca” consists of a biennial maize-durum wheat crop rotation under integrated low-input cropping systems with (i) inversion soil tillage (INT) or (ii) no-tillage (INT+) and (iii) under an organic cropping system with inversion soil tillage (ORG). ORG and INT+ involve the use of autumn-sown cover crops (before the maize cycle). Pure stand durum wheat was grown in INT and INT+, while a faba bean–wheat temporary intercropping was implemented in ORG. The LTAE “Crop Rotation” consists of different crop rotations and residue management, a continuous soft winter wheat and biennial rotations of soft winter wheat with maize or faba bean. Each rotation is combined with two modes of crop residue management: removal or burial. For INT+, despite the high-bulk density (>1.50 g/cm3), we found that conductivity, sorptivity and available water are comparable to those of INT, probably due to a more structured and efficient micropore system. ORG soils show the highest conductivity, sorptivity and available water content values, probably due to the recent spring tillage occurring in the wheat inter-row with the faba bean incorporation into the soil. For LTAE Rotation, the residue burial seems to influence the capacity-based indicators positively. However, the differences in the removal treatment are minor, and this could be due to the inversion soil tillage, which limits the progressive accumulation of organic matter.
Full article
(This article belongs to the Special Issue Soil Hydraulic Properties Characterization for Improving Water Availability)
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Open AccessArticle
Impact of Various Cavity-Preparation Designs on Fracture Resistance and Failure Mode of CAD/CAM Fabricated Ceramic Inlays and Onlays
by
Ali Atef Elkaffas, Abdullah Mohammed Alshehri, Ali Robaian Alqahtani, Refal Saad Albaijan and Tarek Ahmed Soliman
Appl. Sci. 2024, 14(9), 3816; https://doi.org/10.3390/app14093816 (registering DOI) - 30 Apr 2024
Abstract
In recent years, CAD/CAM technology has allowed indirect ceramic restorations to become a part of everyday chairside clinical practice. Therefore, the impact of different cavity-preparation designs on the fracture resistance of CAD/CAM fabricated ceramics was assessed in this study. Three designs of cuspal
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In recent years, CAD/CAM technology has allowed indirect ceramic restorations to become a part of everyday chairside clinical practice. Therefore, the impact of different cavity-preparation designs on the fracture resistance of CAD/CAM fabricated ceramics was assessed in this study. Three designs of cuspal covering (none, palatal, and entire) and two widths of the occlusal isthmus (75% and 100% of the intercuspal distance) were used for the preparation of inlays and onlays to form six groups (n = 10/group). Moreover, thermomechanical cyclic loading was applied to every tooth under a chewing simulator. A universal testing machine was used to measure each group’s fracture resistance. The tested specimens were inspected for any signs of fractures and cracks to categorize failure patterns. Thereby, the values of fracture strength showed that there were statistically nonsignificant differences between the tested groups (p < 0.05). However, a significant difference (p = 0.01) was found between group 1 (inlays) (1950 ± 405) and group 6 (onlays) (3900 ± 770). Type III or type IV fracture modes were seen in the majority of the specimens. In conclusion, inlays and onlays made of zirconia using CAD/CAM technology were deemed reliable for restoring premolars, irrespective of the cavity-preparation design, except for inlays with a 75% intercuspal distance.
Full article
(This article belongs to the Special Issue New Materials and Techniques in Restorative Dentistry)
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Open AccessArticle
Evaluating Agility in Pre-Adolescent Basketball: A Comparative Analysis of CODAT, IAT, and RAT
by
Gökhan Deliceoğlu, Okan İbiş and Erkan Tortu
Appl. Sci. 2024, 14(9), 3815; https://doi.org/10.3390/app14093815 (registering DOI) - 29 Apr 2024
Abstract
Background: In basketball, agility is essential, characterized by the ability to change direction swiftly and accelerate. Traditional tests like the Illinois Agility Test (IAT) and the Reactive Agility Test (RAT) may not fully capture the agility demands specific to basketball. Purpose: This study
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Background: In basketball, agility is essential, characterized by the ability to change direction swiftly and accelerate. Traditional tests like the Illinois Agility Test (IAT) and the Reactive Agility Test (RAT) may not fully capture the agility demands specific to basketball. Purpose: This study aimed to introduce the Change of Direction and Acceleration Test (CODAT), designed specifically for young basketball players. It evaluates CODAT’s effectiveness by comparing it with IAT and RAT through comprehensive analysis. Methods: We assessed 87 pre-adolescent male basketball players, aged 9 to 13 years, with an average biological age of 11.2 years and an average estimated Peak Height Velocity (PHV) of 12.5 ± 0.5 years, using CODAT, IAT, and RAT. We employed regression analysis and the Bland–Altman method to determine CODAT’s reliability and validity. Results: The findings indicate that CODAT offers superior reliability and validity in measuring basketball-specific agility. Consistent scores highlight its potential as an effective tool for agility assessment in basketball training and talent identification. Conclusions: CODAT represents a significant advancement in agility assessment for young basketball players, advocating for its integration into sports science practices to better address the specialized demands of basketball agility.
Full article
(This article belongs to the Special Issue Advances in Sports, Exercise and Health)
Open AccessArticle
Water Pipeline Leakage Detection Based on Coherent φ-OTDR and Deep Learning Technology
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Shuo Zhang, Zijian Xiong, Boyuan Ji, Nan Li, Zhangwei Yu, Shengnan Wu and Sailing He
Appl. Sci. 2024, 14(9), 3814; https://doi.org/10.3390/app14093814 (registering DOI) - 29 Apr 2024
Abstract
Leakage in water supply pipelines remains a significant challenge. It leads to resource and economic waste. Researchers have developed several leak detection methods, including the use of embedded sensors and pressure prediction. The former approach involves pre-installing detectors inside pipelines to detect leaks.
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Leakage in water supply pipelines remains a significant challenge. It leads to resource and economic waste. Researchers have developed several leak detection methods, including the use of embedded sensors and pressure prediction. The former approach involves pre-installing detectors inside pipelines to detect leaks. This method allows for the precise localization of leak points. The stability is compromised because of the wireless signal strength. The latter approach, which relies on pressure measurements to predict leak events, does not achieve precise leak point localization. To address these challenges, in this paper, a coherent optical time-domain reflectometry (φ-OTDR) system is employed to capture vibration signal phase information. Subsequently, two pre-trained neural network models based on CNN and Resnet18 are responsible for processing this information to accurately identify vibration events. In an experimental setup simulating water pipelines, phase information from both leaking and non-leaking pipe segments is collected. Using this dataset, classical CNN and ResNet18 models are trained, achieving accuracy rates of 99.7% and 99.5%, respectively. The multi-leakage point experiment results indicate that the Resnet18 model has better generalization compared to the CNN model. The proposed solution enables long-distance water-pipeline precise leak point localization and accurate vibration event identification.
Full article
(This article belongs to the Special Issue Advanced Optical-Fiber-Related Technologies)
Open AccessReview
A Critical Review of Human Jaw Biomechanical Modeling
by
Marco De Stefano and Alessandro Ruggiero
Appl. Sci. 2024, 14(9), 3813; https://doi.org/10.3390/app14093813 (registering DOI) - 29 Apr 2024
Abstract
The human jaw is a complex biomechanical system involving different anatomical components and an articulated muscular system devoted to its dynamical activation. The numerous actions exerted by the mandible, such as talking, eating or chewing, make its biomechanical comprehension absolutely indispensable. To date,
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The human jaw is a complex biomechanical system involving different anatomical components and an articulated muscular system devoted to its dynamical activation. The numerous actions exerted by the mandible, such as talking, eating or chewing, make its biomechanical comprehension absolutely indispensable. To date, even if research on this topic has achieved interesting outcomes using in vitro testing, thanks to the development of new apparatus and methods capable of performing more and more realistic experiments, theoretical modeling is still worthy of investigation. In light of this, nowadays, the Finite Element Method (FEM) approach constitutes certainly the most common tool adopted to investigate particular issues concerning stress–strain characterization of the human jaw. In addition, kinematics analyses, both direct and inverse, are also diffuse and reported in the literature. This manuscript aimed to propose a critical review of the most recurrent biomechanical models of the human mandible to give readers a comprehensive overview on the topic. In light of this, the numerical approaches, providing interesting outcomes, such as muscular activation profiles, condylar forces and stress–strain fields for the human oral cavity, are mainly differentiated between according to the joint degrees of freedom, the analytical descriptions of the muscular forces, the boundary conditions imposed, the kind of task and mandible anatomical structure modeling.
Full article
(This article belongs to the Special Issue Research and Development in Orthopaedic Biomechanics)
Open AccessReview
Analyses of Physical and Physiological Responses during Competition in Para-Footballers with Cerebral Palsy: A Systematic Review
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Santiago Álvarez-Hernández, Daniel Castillo, José Gerardo Villa-Vicente, Javier Yanci, Diego Marqués-Jiménez and Alejandro Rodríguez-Fernández
Appl. Sci. 2024, 14(9), 3812; https://doi.org/10.3390/app14093812 (registering DOI) - 29 Apr 2024
Abstract
Background: Classification of athletes in cerebral palsy (CP) football is a key action that aims to promote the participation of all players by minimizing the impact of their physical disabilities on the outcome of the competition by establishing sports classes. As such, a
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Background: Classification of athletes in cerebral palsy (CP) football is a key action that aims to promote the participation of all players by minimizing the impact of their physical disabilities on the outcome of the competition by establishing sports classes. As such, a new research line has been included in the classification process at an international level; that is, the analysis of locomotor demands during competition helps classifiers to understand the para-footballers’ profile. Therefore, the main aim of this systematic review was to summarize the physical and physiological responses of players with CP in different sport classes during competition. Methods: A bibliographic search was conducted using PubMed, SCOPUS, and Web Of Science databases following Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines using the PICOS strategy. Results: Six studies meeting inclusion criteria analyzing physical (i.e., total distances, distances at different speeds, high-intensity and short-term actions, change of directions, etc.) and physiological (heart rate (HR), time spent at different zones of maximum HR, etc.) responses. Findings revealed that para-footballers with CP and minimal impairment impact covered greater total and distance above 23.04 km·h−1 and achieved higher maximum speeds during match-play. Notably, no significant differences in physiological responses were observed based on classification. Conclusions: The research suggests that para-footballers with CP and lower physical impairment may exhibit enhanced performance in terms of distance covered and speed during gameplay, highlighting their potential competence in the sport. In addition, the limited number of studies examining the physiological response of para-footballers prevents conclusive results and differentiating between classification groups.
Full article
(This article belongs to the Special Issue Human Performance and Health in Sport and Exercise)
Open AccessArticle
Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning
by
Shen-Yung Lin and Chia-Jen Hsieh
Appl. Sci. 2024, 14(9), 3811; https://doi.org/10.3390/app14093811 (registering DOI) - 29 Apr 2024
Abstract
This study begins by conducting side milling experiments on SKD11 using tungsten carbide TiAlN-coated end mills to compare the surface roughness performance between two combinations of milling process parameters (feed rate and radial depth of cut), along with three ultrasonic-assisted methods (rotary, dual-axis,
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This study begins by conducting side milling experiments on SKD11 using tungsten carbide TiAlN-coated end mills to compare the surface roughness performance between two combinations of milling process parameters (feed rate and radial depth of cut), along with three ultrasonic-assisted methods (rotary, dual-axis, and rotary combined with dual-axis). The results suggest that the rotary (z-axis oscillation) ultrasonic-assisted method may provide better performance. Subsequently, this superior ultrasonic-assisted method was applied both with and without laser locally preheating assistance, respectively. Using a Taguchi orthogonal array, milling process parameters (spindle speed, feed rate, and radial depth of cut) were planned for experiments with the same cutting tool and the workpiece just mentioned above. The surface roughness serves as the objective function while being constrained by cutting-tool life. The characteristics of the smaller-the-better in the Taguchi method were applied to determine the optimal combination of process parameters. Based on the optimal milling process parameters obtained and the superior hybrid-assisted method adopted, milling experiments were repeatedly performed to collect the data on cutting force and cutting-tool wear. Feature engineering was performed on the cutting force signals, and different domain characteristics from both the time and frequency domains were extracted. Hereafter, feature selection by random forest and data standardization were further applied to feature extractions, and the data processing was thus completed. For the processed data, a cutting-tool wear prediction model was constructed by ensemble learning. This method leverages various machine learning regression models, including decision tree, random forest, extremely randomized tree, light gradient boosting machine, extreme gradient boosting, AdaBoost, stochastic gradient descent, support vector regression, linear support vector regression, and multilayer perceptron. After hyper-parameter tuning, the ensemble voting regression prediction was performed based on these ten mentioned models. The experimental results demonstrate that the ensemble voting regression model surpasses the performance of each individual machine learning regression model. In addition, this regression model achieves a coefficient of determination (R2) of 0.94576, a root mean square error (RMSE) of 0.24348, a mean squared error (MSE) of 0.05928, and a mean absolute error (MAE) of 0.18182. Therefore, the ensemble learning approach has been proven to be a feasible and effective method for monitoring cutting-tool wear.
Full article
(This article belongs to the Special Issue Machine Tools, Advanced Manufacturing and Precision Manufacturing)
Open AccessArticle
A Building Heat Load Prediction Method Driven by a Multi-Component Fusion LSTM Ridge Regression Ensemble Model
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Yu Zhang and Guangshu Chen
Appl. Sci. 2024, 14(9), 3810; https://doi.org/10.3390/app14093810 (registering DOI) - 29 Apr 2024
Abstract
Under the background of “double carbon”, building carbon emission reduction is urgent, and improving energy efficiency through short-term building heat load forecasting is an efficient means of building carbon emission reduction. Aiming at the characteristics of the decomposed short-term building heat load data,
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Under the background of “double carbon”, building carbon emission reduction is urgent, and improving energy efficiency through short-term building heat load forecasting is an efficient means of building carbon emission reduction. Aiming at the characteristics of the decomposed short-term building heat load data, such as complex trend changes, significant seasonal changes, and randomness, a single-step short-term building heat load prediction method driven by the multi-component fusion LSTM Ridge Regression Ensemble Model (ST-LSTM-RR) is designed and implemented. First, the trend and seasonal components of the heat load are decomposed by the STL seasonal decomposition algorithm, which are fused into the original data to construct three diversified datasets; second, three basic models, namely, the trend LSTM, the seasonal LSTM, and the original LSTM, are trained; and then, the ridge regression model is trained to fuse the predicted values of the three basic models to obtain the final predicted values. Finally, the method of this paper is applied to the heat load prediction of eight groups in a large mountain hotel park, and the root mean square error (RMSE) and mean absolute error (MAE) are used as the evaluation indexes. The experimental results show that the average RMSE and average MAE of the prediction results of the proposed method in this paper are minimized on the eight groups.
Full article
(This article belongs to the Section Computing and Artificial Intelligence)
Open AccessArticle
Design of Multi-Chain Traceability Model for Pepper Products Based on Traceability Code
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Wenxuan Jin, Mingjun Zheng and Pingzeng Liu
Appl. Sci. 2024, 14(9), 3809; https://doi.org/10.3390/app14093809 (registering DOI) - 29 Apr 2024
Abstract
In the specific application scenario of pepper product supply chain traceability, with the advancement of pepper product production, the expansion of links, and the increase of nodes, the quantity of data will become more and more enormous. The single-chain model is less efficient
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In the specific application scenario of pepper product supply chain traceability, with the advancement of pepper product production, the expansion of links, and the increase of nodes, the quantity of data will become more and more enormous. The single-chain model is less efficient for querying if the data are all stored into the same blockchain. In order to improve the efficiency of blockchain data querying, this paper proposes a traceability model with one main chain and multiple side chain structures, which separate the uplinked data from each link and use multi-chain transactions to improve the efficiency of data queries. This model builds an indexing mechanism with a product traceability code, using one main chain and multiple side chains. The main and side chains form a one-to-many mapping relationship, storing the mapping relationship between the traceability code and the transaction address of the side chain traceability information in the main chain. This enables information to travel through the main chain traversal query based on the mapping relationship and then query the direct index out of the side chain , to achieve fast traceability query and improve the efficiency of querying.
Full article
(This article belongs to the Special Issue Engineering of Smart Agriculture—2nd Edition)
Open AccessArticle
Fatigue Crack and Residual Life Prediction Based on an Adaptive Dynamic Bayesian Network
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Shuai Chen, Yinwei Ma, Zhongshu Wang, Minjing Liu and Zhanjun Wu
Appl. Sci. 2024, 14(9), 3808; https://doi.org/10.3390/app14093808 (registering DOI) - 29 Apr 2024
Abstract
Monitoring the health status of aerospace structures during their service lives is a critical endeavor, aimed at precisely evaluating their operational condition through observation data and physical modeling. This study proposes a probabilistic assessment approach utilizing Dynamic Bayesian Networks (DBNs), enhanced by an
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Monitoring the health status of aerospace structures during their service lives is a critical endeavor, aimed at precisely evaluating their operational condition through observation data and physical modeling. This study proposes a probabilistic assessment approach utilizing Dynamic Bayesian Networks (DBNs), enhanced by an improved adaptive particle filtering technique. This approach combines physical modeling with various predictive sources, encompassing cognitive uncertainties inherent in stochastic predictions and crack propagation forecasts. By employing crack observation data, it facilitates predictions of crack growth and the residual life of metal structure. To demonstrate the efficacy of this method, the research leverages data from three-point bending and single-edge tension fatigue tests. It gathers data on crack length during the fatigue crack progression, integrating these findings with digital twin theory to forecast the residual fatigue life of the specimens. The outcomes show that the adaptive DBN model can precisely predict fatigue crack propagation in test specimens, offering a potential tool for the online health assessment and life evaluation for aerospace structures.
Full article
(This article belongs to the Special Issue The Application of Machine Learning in Structural Health Monitoring)
Open AccessArticle
The Impact of Cardiorespiratory and Metabolic Parameters on Match Running Performance (MRP) in National-Level Football Players: A Multiple Regression Analysis
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Radivoje Radaković, Borko Katanić, Mima Stanković, Bojan Mašanović and Suzana Žilić Fišer
Appl. Sci. 2024, 14(9), 3807; https://doi.org/10.3390/app14093807 (registering DOI) - 29 Apr 2024
Abstract
The aim of the study was to examine the association between cardiorespiratory and metabolic parameters and match running performance (MRP) in highly trained football players. The sample of participants consisted of 41 national-level football players (aged 23.20 ± 3.40 yrs, body height 182.00
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The aim of the study was to examine the association between cardiorespiratory and metabolic parameters and match running performance (MRP) in highly trained football players. The sample of participants consisted of 41 national-level football players (aged 23.20 ± 3.40 yrs, body height 182.00 ± 5.15 cm, and body mass 76.86 ± 6.06 kg) from the Serbian Super league. For the purposes of this research, the following measurements were applied. A maximal multistage progressive treadmill test, with a direct measurement of maximal oxygen consumption (VO2max) (using Fitmate MED, Cosmed, Rome, Italy) was conducted, alongside continuous heart rate monitoring. Capillary blood samples were taken from the hyperemic area using specific test strips, and, after sample collection, lactate concentration was immediately determined using a lactate analyzer. MRP variables were analyzed according to the BioIRC model of motion structure analysis, based on existing standards for profiling movement intensity. The results of multiple regression analysis indicated an association between cardiac parameters and total distance (R2 = 54.3%, p = 0.000), high-speed running (R2 = 46.4%, p = 0.000), and jogging (R2 = 33.6%, p = 0.004). Regression analysis revealed an association between cardiorespiratory parameters and total distance (R2 = 24.8%, p = 0.014), and high-speed running (R2 = 20%, p = 0.039). Meanwhile, no association was found between lactate concentration and running performance. The explanation for these regression analysis results is based on the observation that functional abilities represent significant potential for expressing movement performance, a crucial condition for success in football.
Full article
(This article belongs to the Special Issue Human Performance and Health in Sport and Exercise)
Open AccessArticle
Study of the Photo-Response of Doped GaAs with Aging
by
Samuel Zambrano Rojas and Gerardo Fonthal
Appl. Sci. 2024, 14(9), 3806; https://doi.org/10.3390/app14093806 (registering DOI) - 29 Apr 2024
Abstract
The aging of semiconductor materials is a topic of current interest. We studied the photo-response of epitaxial samples of GaAs doped with Ge and Sn up to 1 × 1019 atoms cm−3. These samples were stored in a dry and
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The aging of semiconductor materials is a topic of current interest. We studied the photo-response of epitaxial samples of GaAs doped with Ge and Sn up to 1 × 1019 atoms cm−3. These samples were stored in a dry and dark environment for 26 years. We realized photoluminescence measurements at different temperatures and photoreflectance spectra at 300 K in three periods: 1995, 2001 and 2021. We found that environmental oxygen formed defects in GaAs, leaving lattice vacancies that provoked changes in the optical photo-response. In addition, we found that the vacancy concentrations could be as large as 5 × 1017 atoms cm−3 over the 26 years. In this work, we demonstrate that the aging of semiconductor materials occurs even when they are not used within a functioning circuit, with the changes being greater when the material is not doped. Knowing about the aging of materials is important for the industry, particularly for the semiconductor industry, because aging-induced deterioration influences prices and guarantees.
Full article
(This article belongs to the Section Materials Science and Engineering)
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Open AccessArticle
Injury Incidence in Traineras: Analysis of Traditional Rowing by Competitive Level and Gender
by
Patxi León-Guereño, Alfonso Penichet-Tomas, Arkaitz Castañeda-Babarro and Jose M. Jimenez-Olmedo
Appl. Sci. 2024, 14(9), 3805; https://doi.org/10.3390/app14093805 (registering DOI) - 29 Apr 2024
Abstract
The growing interest in “Traineras”, a traditional competitive rowing modality prevalent in Northern Spain, underscores the need for a comprehensive analysis of the injury incidence associated with this sporting practice. Despite rowing’s significance in the international sports arena and its inclusion since the
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The growing interest in “Traineras”, a traditional competitive rowing modality prevalent in Northern Spain, underscores the need for a comprehensive analysis of the injury incidence associated with this sporting practice. Despite rowing’s significance in the international sports arena and its inclusion since the beginnings of the modern Olympic Games, research into injuries in this sport, especially in traditional modalities such as Traineras, has been limited. This study aimed to identify and describe the predominant injuries among Traineras rowers, analyzing their epidemiology, characteristics, affected body regions, and diagnoses, further differentiated by competitive level and gender. A retrospective survey completed by 773 rowers (24% women, 76% men) participating in various leagues (ACT, ARC1, ARC2, LGT1, LGT2, ETE, and LGT-F) during the season revealed that 68.2% suffered from at least one injury, predominantly due to overuse (91.1% in men, 83.1% in women). The most affected regions were the lower back and shoulders, with the main diagnoses being muscle cramps and tendinitis, showing statistically significant differences between sexes. The findings of this study not only provide a deeper understanding of the etiology and origin of injuries in this sport but also lay the groundwork for developing specific injury prevention plans, thereby contributing to the safety and optimal performance of athletes.
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(This article belongs to the Special Issue Applied Biomechanics in Sports Performance, Injury Prevention and Rehabilitation)
Open AccessArticle
Research on Driving Scenario Knowledge Graphs
by
Ce Zhang, Liang Hong, Dan Wang, Xinchao Liu, Jinzhe Yang and Yier Lin
Appl. Sci. 2024, 14(9), 3804; https://doi.org/10.3390/app14093804 (registering DOI) - 29 Apr 2024
Abstract
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Despite the partial disclosure of driving scenario knowledge graphs, they still fail to meet the comprehensive needs of intelligent connected vehicles for driving knowledge. Current issues include the high complexity of pattern layer construction, insufficient accuracy of information extraction and fusion, and limited
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Despite the partial disclosure of driving scenario knowledge graphs, they still fail to meet the comprehensive needs of intelligent connected vehicles for driving knowledge. Current issues include the high complexity of pattern layer construction, insufficient accuracy of information extraction and fusion, and limited performance of knowledge reasoning models. To address these challenges, a hybrid knowledge graph method was adopted in the construction of a driving scenario knowledge graph (DSKG). Firstly, core concepts in the field were systematically sorted and classified, laying the foundation for the construction of a multi-level classified knowledge graph top-level ontology. Subsequently, by deeply exploring and analyzing the Traffic Genome data, 34 entities and 51 relations were extracted and integrated with the ontology layer, achieving the expansion and updating of the knowledge graph. Then, in terms of knowledge reasoning models, an analysis of the training results of the TransE, Complex, Distmult, and Rotate models in the entity linking prediction task of DSKG revealed that the Distmult model performed the best in metrics such as hit rate, making it more suitable for inference in DSKG. Finally, a standardized and widely applicable driving scenario knowledge graph was proposed. The DSKG and related materials have been publicly released for use by industry and academia.
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Open AccessEditorial
Concrete Structures: Latest Advances and Prospects for a Sustainable Future
by
Mariella Diaferio and Francisco B. Varona
Appl. Sci. 2024, 14(9), 3803; https://doi.org/10.3390/app14093803 (registering DOI) - 29 Apr 2024
Abstract
Along with structural steel, structural concrete is probably one of the most widely used construction materials worldwide for building construction and civil engineering infrastructures [...]
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(This article belongs to the Section Civil Engineering)
Open AccessArticle
Analysis of the Occurrent Models of Potential Debris-Flow Sources in the Watershed of Ching-Shuei River
by
Ji-Yuan Lin, Jen-Chih Chao and Lung-Kun Yang
Appl. Sci. 2024, 14(9), 3802; https://doi.org/10.3390/app14093802 (registering DOI) - 29 Apr 2024
Abstract
The areas around the Ching-Shuei River saw numerous landslides (2004–2017) after the Jiji earthquake, profoundly harming the watershed’s geological environment. The 33 catchment areas in the Ching-Shuei River watershed and five typhoon and rainstorm events, with a total of 165 occurrences and non-occurrences,
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The areas around the Ching-Shuei River saw numerous landslides (2004–2017) after the Jiji earthquake, profoundly harming the watershed’s geological environment. The 33 catchment areas in the Ching-Shuei River watershed and five typhoon and rainstorm events, with a total of 165 occurrences and non-occurrences, were analyzed, and the training and validation were categorized into 70% training and 30% validation. A landslide disaster is deemed, for the purposes of this research, to have taken place if SPOT satellite images taken before and after an incident show a Normalized Difference Vegetation Index difference larger than 0.25, a slope of less than 30 degrees, and a number of connected grids greater than 10. The analysis was carried out using the instability index method analysis with Rogers regression analysis and artificial neural network. The accuracy rates of neural network, logit regression, and instability index analyses were, respectively, 93.3%, 80.6%, and 70.9%. The neural network’s area under the curve was 0.933, indicating excellent discrimination ability; that of the logit regression analysis was 0.794, which is considered good; and that of the instability index analysis was 0.635, or fair. This suggests that any of the three models are suitable for the danger assessment of large post-earthquake debris flows. The results of this study also provide a reference and evidence for specific sites’ potential susceptibility to debris flows.
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(This article belongs to the Special Issue Recent Advances in Modeling, Assessment, and Mitigation of Landslide Hazards)
Open AccessArticle
Research on Online Review Information Classification Based on Multimodal Deep Learning
by
Jingnan Liu, Yefang Sun, Yueyi Zhang and Chenyuan Lu
Appl. Sci. 2024, 14(9), 3801; https://doi.org/10.3390/app14093801 (registering DOI) - 29 Apr 2024
Abstract
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The incessant evolution of online platforms has ushered in a multitude of shopping modalities. Within the food industry, however, assessing the delectability of meals can only be tentatively determined based on consumer feedback encompassing aspects such as taste, pricing, packaging, service quality, delivery
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The incessant evolution of online platforms has ushered in a multitude of shopping modalities. Within the food industry, however, assessing the delectability of meals can only be tentatively determined based on consumer feedback encompassing aspects such as taste, pricing, packaging, service quality, delivery timeliness, hygiene standards, and environmental considerations. Traditional text data mining techniques primarily focus on consumers’ emotional traits, disregarding pertinent information pertaining to the online products themselves. In light of these aforementioned issues in current research methodologies, this paper introduces the Bert BiGRU Softmax model combined with multimodal features to enhance the efficacy of sentiment classification in data analysis. Comparative experiments conducted using existing data demonstrate that the accuracy rate of the model employed in this study reaches 90.9%. In comparison to single models or combinations of three models with the highest accuracy rate of 7.7%, the proposed model exhibits superior accuracy and proves to be highly applicable to online reviews.
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