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 Experimental Characterization of Iron Ore Tailings from a Geotechnical Perspective
Appl. Sci. 2024, 14(12), 5033; https://doi.org/10.3390/app14125033 (registering DOI) - 9 Jun 2024
Abstract
The mining industry produces large amounts of tailings which are disposed of in deposits, which neglects their potential value and represents important economic, social and environmental risks. Consequently, implementing circular economy principles using these unconventional geomaterials may decrease the wide-ranging impacts of raw
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The mining industry produces large amounts of tailings which are disposed of in deposits, which neglects their potential value and represents important economic, social and environmental risks. Consequently, implementing circular economy principles using these unconventional geomaterials may decrease the wide-ranging impacts of raw material extraction. This paper presents an experimental characterization of iron ore tailings, which are the most abundant type of mining waste. The characterization includes various aspects of behavior that are relevant to different types of use as a building material, including physical and identification properties, compaction behavior and stress–strain properties under undrained monotonic and cyclic triaxial loading. The tailings tested can be described as low-plasticity silty sand materials with an average solids density of 4.7, a maximum dry unit weight close to 3 g/cm3 and a higher angle of friction and liquefaction resistance than common granular materials. The experimental results highlight the particular features of the behavior of iron ore tailings and emphasize the potentially promising combination of high shear resistance and high density that favors particular geotechnical applications. Overall, the conclusions provide the basis for promoting the use of mining wastes in the construction of sustainable geotechnical works and underpin the advanced analysis of tailings storage facilities’ safety founded on an open-minded geotechnical approach.
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(This article belongs to the Special Issue Sustainability in Geotechnics)
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Enhanced Berth Mapping and Clothoid Trajectory Prediction Aided Intelligent Underground Localization
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Fei Li, Jialiang Chen, Yuelin Yuan, Zhaozheng Hu and Xiaohui Liu
Appl. Sci. 2024, 14(12), 5032; https://doi.org/10.3390/app14125032 (registering DOI) - 9 Jun 2024
Abstract
In response to the widespread absence of global navigation satellite system (GNSS) signals in underground parking scenes, we propose a multimodal localization method that integrates enhanced berth mapping with Clothoid trajectory prediction, enabling high-precision localization for intelligent vehicles in underground parking environments. This
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In response to the widespread absence of global navigation satellite system (GNSS) signals in underground parking scenes, we propose a multimodal localization method that integrates enhanced berth mapping with Clothoid trajectory prediction, enabling high-precision localization for intelligent vehicles in underground parking environments. This method began by constructing a lightweight map based on the key berths. The map consisted of a series of discrete nodes, each encompassing three elements: holistic and local scene features extracted from an around-view image, and the global pose of the mapping vehicle calculated using the positions of the key berth’s corner points. An adaptive localization strategy was employed during the localization phase based on the trajectory prediction result. A progressive localization strategy, relying on multi-scale feature matching, was applied to the nodes within the map coverage range. Additionally, a compensation localization strategy that combined odometry with the prior pose was utilized for the nodes outside the map coverage range. The experiments conducted in two typical underground parking scenes demonstrated that the proposed method achieved a trajectory prediction accuracy of 40 cm, a nearest map search accuracy exceeding 92%, and a metric localization accuracy meeting the 30 cm standard. These results indicate that the proposed approach satisfies the high-precision, robust, real-time localization requirements for intelligent vehicles in underground parking scenes, while effectively reducing the map memory requirements.
Full article
(This article belongs to the Special Issue Mobile Robotics and Autonomous Intelligent Systems)
Open AccessArticle
Physical Simulation-Based Calibration for Quantitative Real-Time PCR
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Tianyu Zhu, Xin Liu and Xinqing Xiao
Appl. Sci. 2024, 14(12), 5031; https://doi.org/10.3390/app14125031 (registering DOI) - 9 Jun 2024
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The fluorescence quantitative polymerase chain reaction (qPCR) instrument has been widely used in molecular biology applications, where the reliability of the qPCR performance directly affects the accuracy of its detection results. In this paper, an integrated, physics-based calibration device was developed to improve
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The fluorescence quantitative polymerase chain reaction (qPCR) instrument has been widely used in molecular biology applications, where the reliability of the qPCR performance directly affects the accuracy of its detection results. In this paper, an integrated, physics-based calibration device was developed to improve the accuracy and reliability of qPCR, realizing the calibration of qPCR instruments’ standard curve through physical simulations. With this calibration device, the collected temperature was used as the control signal to alter the fluorescence output, which allowed different probes to simulate the Ct values corresponding to samples with varying initial concentrations. The temperature and optical performance of this calibration device were tested, followed by a comparative analysis comparing the on-machine test results with standard substances to assess the linearity and uniformity of the Ct values of the measured qPCR instrument. It has been proven that this physical calibration device can effectively replace the biochemical standard substance to carry out comprehensive calibration of the temperature and optical parameters of the qPCR instrument and provide a more reliable method for the periodic calibration and quality control of the qPCR instrument. This contributes to the accuracy and reliability of fluorescence qPCR instruments in the field of molecular biology.
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A Vehicle Velocity Prediction Method with Kinematic Segment Recognition
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Benxiang Lin, Chao Wei and Fuyong Feng
Appl. Sci. 2024, 14(12), 5030; https://doi.org/10.3390/app14125030 (registering DOI) - 9 Jun 2024
Abstract
Accurate vehicle velocity prediction is of great significance in vehicle energy distribution and road traffic management. In light of the high time variability of vehicle velocity itself and the limitation of single model prediction, a velocity prediction method based on K-means-QPSO-LSTM with kinematic
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Accurate vehicle velocity prediction is of great significance in vehicle energy distribution and road traffic management. In light of the high time variability of vehicle velocity itself and the limitation of single model prediction, a velocity prediction method based on K-means-QPSO-LSTM with kinematic segment recognition is proposed in this paper. Firstly, the K-means algorithm was used to cluster samples with similar characteristics together, extract kinematic fragment samples in typical driving conditions, calculate their feature parameters, and carry out principal component analysis on the feature parameters to achieve dimensionality reduction transformation of information. Then, the vehicle velocity prediction sub-neural network models based on long short-term memory (LSTM) with the QPSO algorithm optimized were trained under different driving condition datasets. Furthermore, the kinematic segment recognition and traditional vehicle velocity prediction were integrated to form an adaptive vehicle velocity prediction method based on driving condition identification. Finally, the current driving condition type was identified and updated in real-time during vehicle velocity prediction, and then the corresponding sub-LSTM model was used for vehicle velocity prediction. The simulation experiment demonstrated a significant enhancement in both the velocity and accuracy of prediction through the proposed method. The proposed hybrid method has the potential to improve the accuracy and reliability of vehicle velocity prediction, making it applicable in various fields such as autonomous driving, traffic management, and energy management strategies for hybrid electric vehicles.
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(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications)
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Swiss Round Selection Algorithm for Multi-Robot Task Scheduling
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Xing Fu, Gongxue Zhang, Hai Yuan, Weijun Wang, Jian Wang and Zucheng Huang
Appl. Sci. 2024, 14(12), 5029; https://doi.org/10.3390/app14125029 (registering DOI) - 9 Jun 2024
Abstract
Efficient and stable control and task assignment optimization in electronic commerce logistics and warehousing systems involving multiple robots executing multiple tasks is highly challenging. Hence, this paper proposes a Swiss round selection algorithm for multi-robot task allocation to address the challenges mentioned. Firstly,
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Efficient and stable control and task assignment optimization in electronic commerce logistics and warehousing systems involving multiple robots executing multiple tasks is highly challenging. Hence, this paper proposes a Swiss round selection algorithm for multi-robot task allocation to address the challenges mentioned. Firstly, based on the shipping process of electronic commerce logistics and warehousing systems, the tasks are divided into packaging and sorting stages, and a grid model for the electronic commerce warehousing system is established. Secondly, by increasing the probabilities of crossover and mutation in the population and adopting a full crossover and full mutation approach, the search scope of the population is expanded. Then, a Swiss round selection mechanism with burst probability is proposed, which ensures the smooth inheritance of high-quality individuals while improving the diversity of the population. Finally, 12 comparative experiments are designed with different numbers of robots and tasks. The experimental results demonstrate that the Swiss round selection algorithm outperforms the genetic algorithm in terms of maximum task completion time and convergence time to reach the optimal value. Thus, the effectiveness of the Swiss round selection algorithm in solving the multi-robot task allocation problem is verified.
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(This article belongs to the Section Robotics and Automation)
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Open AccessReview
Temporary Skeletal Anchorage Devices and Cone Beam Tomography in Orthodontics—Current Application and New Directions of Development
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David Aebisher, Iga Serafin and Dorota Bartusik-Aebisher
Appl. Sci. 2024, 14(12), 5028; https://doi.org/10.3390/app14125028 (registering DOI) - 9 Jun 2024
Abstract
Continuous progress in dentistry and orthodontics is crucial to ensuring high-quality diagnosis and treatment of patients, especially since malocclusions occur in up to half of the population. In addition to limiting the physiological functions of the masticatory system, they are often an aesthetic
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Continuous progress in dentistry and orthodontics is crucial to ensuring high-quality diagnosis and treatment of patients, especially since malocclusions occur in up to half of the population. In addition to limiting the physiological functions of the masticatory system, they are often an aesthetic defect that may directly affect the well-being and even self-esteem of patients, especially in their teenage years and early adulthood. A holistic model of perceiving and treating orthodontic diseases, such as the Biocreative Orthodontic Strategy, focusing not only on the correction of the defect itself but also taking into account the least possible interference in the physiology of the masticatory system, limiting the use of appliances to a minimum, and taking into account the patient’s preferences, is a special alternative to conventional therapeutic models. In this review, we are presenting the current knowledge regarding the applications of temporary skeletal anchorage devices (TSAD) and cone beam computed tomography (CBCT) in orthodontics.
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(This article belongs to the Special Issue Advanced Biotechnology Applied to Orthodontic TSADs and CBCT)
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The Influence of the Frequency of Ultrasound on the Exhaust Gas Purification Process in a Diesel Car Muffler
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Adil Kadyrov, Michał Bembenek, Bauyrzhan Sarsembekov, Aliya Kukesheva and Saltanat Nurkusheva
Appl. Sci. 2024, 14(12), 5027; https://doi.org/10.3390/app14125027 (registering DOI) - 9 Jun 2024
Abstract
This research aimed to analyze the possibility of installing an ultrasonic emitter in an already manufactured car and to prove the possibility of cleaning the exhaust gases of an internal combustion engine through the action of an ultrasonic wave due to coagulation and
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This research aimed to analyze the possibility of installing an ultrasonic emitter in an already manufactured car and to prove the possibility of cleaning the exhaust gases of an internal combustion engine through the action of an ultrasonic wave due to coagulation and examining the optimal regimes of its work. The existing theoretical solution to describe the proposed process was analyzed. A Mercedes-Benz M-Class ML 270 CDI MT car with the OM 612 DE 27 LA Diesel engine was used for the experiment. An ultrasound generator and an ultrasound emitter were connected to the muffler. The stand was connected to the car via the inlet with a rubber hose that directs the exhaust gases out of the car. The crankshaft speed of the engine was changed in the range of 750 to 1250 rpm, which corresponds to urban conditions when cars are moving in heavy traffic jams. The content of CH, CO, CO2, and O2 in the exhaust gas of the vehicle was determined as a function of the crankshaft speed without ultrasonic exposure and with ultrasonic exposure at an ultrasound frequency of 25, 28, and 40 kHz. The results of the experiment showed that the introduction of an ultrasonic emitter into the muffler reduced the smoke content of the gas, increased the oxygen content, and reduced the amount of carbon dioxide in the exhaust gases. With an increase in the ratio between the ultrasonic frequency and the angular velocity of the engine crankshaft (f/ω), the smoke content of the gas also decreased. At the maximum values of ultrasonic frequency and angular velocity of the engine crankshaft selected in the experimental studies, the minimum value of the ratio of gas smoke indicators was achieved, and the degree of purification was 10–13%. Such results correspond to the condition of optimal operation of the ultrasonic muffler, where the ratio of gas to smoke values should tend to a minimum. These results confirm the potential of using ultrasound as a method for cleaning exhaust gases and underline the need for further research in this area.
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(This article belongs to the Special Issue Novel Advances of Combustion and Its Emissions)
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Quantitative Analysis of Spatiotemporal Patterns and Factor Contributions of Surface Ozone in the North China Plain
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Yi Li, Mengjiao Liu, Lingyue Lv, Jinhui Liang, Mingliang Ma, Mengnan Liu and Pingjie Fu
Appl. Sci. 2024, 14(12), 5026; https://doi.org/10.3390/app14125026 (registering DOI) - 9 Jun 2024
Abstract
Over the past decade, surface ozone has emerged as a significant air pollutant in China, especially in the North China Plain (NCP). For effective ozone management in the NCP, it is crucial to accurately estimate the surface ozone levels and identify the primary
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Over the past decade, surface ozone has emerged as a significant air pollutant in China, especially in the North China Plain (NCP). For effective ozone management in the NCP, it is crucial to accurately estimate the surface ozone levels and identify the primary influencing factors for ozone pollution in this region. This study utilized ozone precursors such as volatile organic compounds (VOCs) and nitrogen oxides (NOX), meteorological data, land cover, normalized difference vegetation index (NDVI), terrain, and population data to build an extreme gradient boosting (XGBoost)-based ozone estimation model in the NCP during 2019 to 2021. Four ozone estimation models were developed using different NO2 and formaldehyde (HCHO) datasets from the Sentinel-5 TROPOMI observations and Copernicus Atmosphere Monitoring Service (CAMS) reanalysis data. Site-based validation results of these four models showed high accuracy with R2 values above 0.86. Among these four models, two models with higher accuracy and higher spatial coverage ratio were selected, and their results were averaged to produce the final ozone estimation products. The results indicated that VOCs and NOX were the two main pollutants causing ozone pollution in the NCP, and their relative contributions accounted for more than 23.34% and 10.23%, respectively, while HCHO also played a significant role, contributing over 5.64%. Additionally, meteorological factors also had a notable impact, contributing 28.63% to ozone pollution, with each individual factor contributing more than 2.38%. The spatial distribution of ozone pollution identified the Hebei–Shandong–Henan junction as a pollution hotspot, with the peak occurring in summer, particularly in June. Therefore, for this hotspot region in the NCP, promoting the reduction in VOCs and NOx can play an important role in the mitigation of O3 pollution and the improvement in air quality in this region.
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(This article belongs to the Special Issue Air Pollution and Its Impact on the Atmospheric Environment)
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Model Optimization of Ice Melting of Bridge Pylon Crossbeams with Built-In Carbon Fiber Electric Heating
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Hao Xu, Zhi Chen, Chunchen Cao, Henglin Xiao and Lifei Zheng
Appl. Sci. 2024, 14(12), 5025; https://doi.org/10.3390/app14125025 (registering DOI) - 9 Jun 2024
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This paper aims to improve the deicing performance and energy utilization of bridge pylon crossbeams with built-in carbon fiber electric heating (BPB–CFEH). Therefore, a three-dimensional thermal transfer model of BPB–CFEH with one arrangement is established. Two ice-melting regions and two ice-melting stages were
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This paper aims to improve the deicing performance and energy utilization of bridge pylon crossbeams with built-in carbon fiber electric heating (BPB–CFEH). Therefore, a three-dimensional thermal transfer model of BPB–CFEH with one arrangement is established. Two ice-melting regions and two ice-melting stages were set up according to the characteristics of the icing of the crossbeam. The effects of wind speed and ambient temperature on the paving power required to reach the complete melting of the icicles within 8 h were analyzed. The effects of the laying spacing and rated voltage of the carbon fiber heating cable on the melting ice sheet and the thermal exchange of the two regions of the icicle after heating for 8 h were compared. Additionally, its effect on energy utilization of the process from the ice sheet melting stage to the ice column melting stage was analyzed. Ice-melting experiments verified the applicability and reasonableness of the simulated ice-melting calculation formula. The results show that under ambient temperature of −10 °C and wind speed of 4.5–13.5 m/s, the proposed paving power is 817.5–2248.12 W/m2. Increasing the rated voltage and shortening the spacing increases the thermal exchange capacity of the two melting regions. The shortening of the spacing improves the energy utilization rate of the melting stage of the ice sheet to the melting stage of the icicle processes. The difference between the melting time obtained from the formula proposed by numerical simulation and the melting time obtained from indoor tests is about 10 min. This study provides a design basis for the electrothermal ice melting of bridge pylon crossbeams.
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Dynamic Balance Simulation and Optimization of Electric Vehicle Scroll Compressor Rotor System
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Mengli Yuan, Bin Yang, Xin Li, Annan Li, Feng Gao and Mengqi Ge
Appl. Sci. 2024, 14(12), 5024; https://doi.org/10.3390/app14125024 (registering DOI) - 9 Jun 2024
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In order to solve the problem of imbalance of internal forces in the system caused by the gravity force of the eccentric wheel and the orbiting scroll close to the drive bearing and the rotational inertia force during the operation of the electric
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In order to solve the problem of imbalance of internal forces in the system caused by the gravity force of the eccentric wheel and the orbiting scroll close to the drive bearing and the rotational inertia force during the operation of the electric scroll compressor, a dynamic model of the rotor system of the scroll compressor that takes into account the effect of the gas force was established using the multibody dynamics software ADAMS/View 2020. Dynamic simulation analysis of the rotor system is carried out, focusing on the force of the drive bearing; a parametric optimization method is adopted to optimize the position of the center-of-mass coordinates of the eccentric wheel of the relevant components, and the relevant parameters are derived after optimization. The results show that by adjusting the center-of-mass position of the eccentric wheel it is possible to optimize the unbalance force and unbalance moment of the main shaft drive system; compared with the pre-optimization, the force fluctuation ranges of the drive bearing in the horizontal and vertical directions are reduced, the peak value is reduced by 18%, and the impact force of the drive bearing during the initial period of compressor operation is effectively relieved. Through optimization calculation, the vibration and noise of the system are reduced, the operating stability of the scroll compressor is improved, and analytical methods and theoretical guidance are provided for the design and prediction of the dynamic behavior of the scroll compressors.
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Open AccessArticle
Eco-Friendly Preservation of Pharaonic Wooden Artifacts using Natural Green Products
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Neveen S. Geweely, Amira M. Abu Taleb, Paola Grenni, Giulia Caneva, Dina M. Atwa, Jasper R. Plaisier and Shimaa Ibrahim
Appl. Sci. 2024, 14(12), 5023; https://doi.org/10.3390/app14125023 (registering DOI) - 9 Jun 2024
Abstract
The biodeterioration of wooden cultural heritage is a severe problem worldwide and fungi are the main deteriorating agents. The identification of effective natural products, safer for humans and the environment, is a current challenge. Ten deteriorated archaeological objects (a wooden statue of a
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The biodeterioration of wooden cultural heritage is a severe problem worldwide and fungi are the main deteriorating agents. The identification of effective natural products, safer for humans and the environment, is a current challenge. Ten deteriorated archaeological objects (a wooden statue of a seated man, an anthropoid wooden coffin with a cartonnage mummy of Nespathettawi, and a wooden box of Padimen’s son), stored at the Egyptian museum in Cairo, were considered here. The wood species of the three most deteriorated objects were previously identified as Acacia nilotica, Ficus sycomorus, and Tamarix gennessarensis. Twenty-six fungal species were isolated and identified from the wooden objects and the four most frequent species belonged to the genus Aspergillus. Fourteen fungal species among those isolated showed the greatest biodeterioration activity on the experimental wood blocks of the archaeological objects. The antifungal activities of several eco-friendly plant essential oils (from cinnamon, eucalyptus, frankincense, geranium, lavender, lemongrass, menthe, rosemary, tea tree, and thyme) and plant extracts (from basil, eucalyptus, henna, melia, and teak) were tested against the fungal species with the greatest biodeterioration activity. The essential oils (Eos) were more effective than the plant extracts. Thyme EO, followed by geranium and cinnamon ones, was the most active (minimum inhibitory concentrations: 0.25–1 µL/mL). These EO; also showed inhibitory effects on the enzymatic activities (cellulase, amylase, and protease) of the four most dominant fungal species. Thymol and p-cymene were the two main components of thyme oil, while geraniol and beta-citronellol were those of geranium oil; eugenol and caryophyllene were those of the cinnamon EO. Thyme oil applied to the most deteriorated experimental aged A. nilotica wooden cubes inoculated with the four highly frequent fungal species was effective in wood preservation. Moreover, no significant interference was observed in the wood before and after thyme treatment. Thyme oil seems to be a promising eco-friendly antifungal agent for the preservation of archaeological wooden artefacts.
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(This article belongs to the Section Applied Microbiology)
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Multi-Feature-Enhanced Academic Paper Recommendation Model with Knowledge Graph
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Le Wang, Wenna Du and Zehua Chen
Appl. Sci. 2024, 14(12), 5022; https://doi.org/10.3390/app14125022 (registering DOI) - 9 Jun 2024
Abstract
This paper addresses the challenges of data sparsity and personalization limitations inherent in current recommendation systems when processing extensive academic paper datasets. To overcome these issues, the present work introduces an innovative recommendation model that integrates the wealth of structured information from knowledge
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This paper addresses the challenges of data sparsity and personalization limitations inherent in current recommendation systems when processing extensive academic paper datasets. To overcome these issues, the present work introduces an innovative recommendation model that integrates the wealth of structured information from knowledge graphs and refines the amalgamation of temporal and relational data. By applying attention mechanisms and neural network technologies, the model thoroughly explores the text characteristics of papers and the evolving patterns of user behaviors. Additionally, the model elevates the accuracy and personalization of recommendations by meticulously examining citation patterns among papers and the networks of author collaboration. The experimental findings show that the present model surpasses baseline models on all evaluation metrics, thereby enhancing the precision and personalization of academic paper recommendations.
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(This article belongs to the Special Issue Recommender Systems and Their Advanced Application)
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Prediction of Buildings’ Settlements Induced by Deep Foundation Pit Construction Based on LSTM-RA-ANN
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Ting Hu and Jinming Xu
Appl. Sci. 2024, 14(12), 5021; https://doi.org/10.3390/app14125021 (registering DOI) - 8 Jun 2024
Abstract
In view of the shortcomings of existing methods for predicting the settlement of surrounding buildings caused by deep foundation pit construction, this study uses the monitoring data of a foundation pit project in Shanghai and divides the construction process of the pit into
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In view of the shortcomings of existing methods for predicting the settlement of surrounding buildings caused by deep foundation pit construction, this study uses the monitoring data of a foundation pit project in Shanghai and divides the construction process of the pit into three working conditions, that is, enclosure construction, earthwork excavation, and basement support construction. The attention mechanism and residual update are integrated into the artificial neural network (ANN) model, and the root-mean-square error, average absolute error, and determination coefficient are used as the evaluation indices of the model. The artificial neural network prediction model LSTM-RA-ANN for building settlements in deep foundation pit construction was then established. The prediction performance of the model was also analysed under different working conditions, and the influences of the main factors (including the soil parameter, monitoring point location, activation function, hyperparameter, and input number) on the evaluation index was further explored. The results indicate that the performances of the established LSTM-RA-ANN model are closely related to the construction conditions, the predicted settlements agree well with the monitored ones in three working conditions with the greatest errors occurring at a later time of the working conditions, and the prediction accuracy of the great–small order corresponds to basement support, enclosure construction, and earthwork excavation respectively. The farther the monitoring point is from the edge of the pit, the better the model performance is. The activation function, initial learning rate, and maximum iteration batch have a great influence on the evaluation indices of the model, while the number of input points has little effect on the evaluation indices. These results may serve as a reference for the safe construction and normal operation of foundation pit engineering.
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(This article belongs to the Section Civil Engineering)
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Toward a Comprehensive Evaluation of Student Knowledge Assessment for Art Education: A Hybrid Approach by Data Mining and Machine Learning
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Shan Wang, Hongtao Wang, Yijun Lu and Jiandong Huang
Appl. Sci. 2024, 14(12), 5020; https://doi.org/10.3390/app14125020 (registering DOI) - 8 Jun 2024
Abstract
By analyzing students’ understanding of a certain subject’s knowledge and learning process, and evaluating their learning level, we can formulate students’ learning plans and teachers’ curricula. However, the large amount of data processing consumes a lot of manpower and time resources, which increases
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By analyzing students’ understanding of a certain subject’s knowledge and learning process, and evaluating their learning level, we can formulate students’ learning plans and teachers’ curricula. However, the large amount of data processing consumes a lot of manpower and time resources, which increases the burden on educators. Therefore, this study aims to use a machine learning model to build a model to evaluate students’ learning levels for art education. To improve the prediction accuracy of the model, SVM was adopted as the basic model in this study, and was combined with SSA, ISSA, and KPCA-ISSA algorithms in turn to form a composite model. Through the experimental analysis of prediction accuracy, we found that the prediction accuracy of the KPCA-ISSA-SVMM model reached the highest, at 96.7213%, while that of the SVM model was only 91.8033%. Moreover, by putting the prediction results of the four models into the confusion matrix, it can be found that with an increase in the complexity of the composite model, the probability of classification errors in model prediction gradually decreases. It can be seen from the importance experiment that the students’ achievements in target subjects (PEG) have the greatest influence on the model prediction effect, and the importance score is 9.5958. Therefore, we should pay more attention to this characteristic value when evaluating students’ learning levels.
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(This article belongs to the Special Issue ICT in Education, 2nd Edition)
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CNN-Based Multi-Factor Authentication System for Mobile Devices Using Faces and Passwords
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Jinho Han
Appl. Sci. 2024, 14(12), 5019; https://doi.org/10.3390/app14125019 (registering DOI) - 8 Jun 2024
Abstract
Multi-factor authentication (MFA) is a system for authenticating an individual’s identity using two or more pieces of data (known as factors). The reason for using more than two factors is to further strengthen security through the use of additional data for identity authentication.
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Multi-factor authentication (MFA) is a system for authenticating an individual’s identity using two or more pieces of data (known as factors). The reason for using more than two factors is to further strengthen security through the use of additional data for identity authentication. Sequential MFA requires a number of steps to be followed in sequence for authentication; for example, with three factors, the system requires three authentication steps. In this case, to proceed with MFA using a deep learning approach, three artificial neural networks (ANNs) are needed. In contrast, in parallel MFA, the authentication steps are processed simultaneously. This means that processing is possible with only one ANN. A convolutional neural network (CNN) is a method for learning images through the use of convolutional layers, and researchers have proposed several systems for MFA using CNNs in which various modalities have been employed, such as images, handwritten text for authentication, and multi-image data for machine learning of facial emotion. This study proposes a CNN-based parallel MFA system that uses concatenation. The three factors used for learning are a face image, an image converted from a password, and a specific image designated by the user. In addition, a secure password image is created at different bit-positions, enabling the user to securely hide their password information. Furthermore, users designate a specific image other than their face as an auxiliary image, which could be a photo of their pet dog or favorite fruit, or an image of one of their possessions, such as a car. In this way, authentication is rendered possible through learning the three factors—that is, the face, password, and specific auxiliary image—using the CNN. The contribution that this study makes to the existing body of knowledge is demonstrating that the development of an MFA system using a lightweight, mobile, multi-factor CNN (MMCNN), which can even be used in mobile devices due to its low number of parameters, is possible. Furthermore, an algorithm that can securely transform a text password into an image is proposed, and it is demonstrated that the three considered factors have the same weight of information for authentication based on the false acceptance rate (FAR) values experimentally obtained with the proposed system.
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(This article belongs to the Special Issue Integrating Artificial Intelligence in Renewable Energy Systems)
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Linear Contact Load Law of an Elastic–Perfectly Plastic Half-Space vs. Sphere under Low Velocity Impact
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Hao Yuan, Xiaochun Yin, Hui Wang, Yuanyuan Guo, Changliang Wang, Hao Zhou, Cheng Gao, Huaiping Ding and Xiaokai Deng
Appl. Sci. 2024, 14(12), 5018; https://doi.org/10.3390/app14125018 (registering DOI) - 8 Jun 2024
Abstract
The impact of contact between two elastic–plastic bodies is highly complex, with no established theoretical contact model currently available. This study investigates the problem of an elastic–plastic sphere impacting an elastic–plastic half-space at low speed and low energy using the finite element method
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The impact of contact between two elastic–plastic bodies is highly complex, with no established theoretical contact model currently available. This study investigates the problem of an elastic–plastic sphere impacting an elastic–plastic half-space at low speed and low energy using the finite element method (FEM). Existing linear contact loading laws exhibit significant discrepancies as they fail to consider the impact of elasticity and yield strength on the elastic–plastic sphere. To address this limitation, a novel linear contact loading law is proposed in this research, which utilizes the concept of equivalent contact stiffness rather than the conventional linear contact stiffness. The theoretical expressions of this new linear contact loading law are derived through FEM simulations of 150 sphere and half-space impact cases. The segmental linear characteristics of the equivalent contact stiffness are identified and fitted to establish the segmental expressions of the equivalent contact stiffness. The new linear contact loading law is dependent on various factors, including the yield strain of the half-space, the ratio of elastic moduli between the half-space and sphere, and the ratio of yield strengths between the half-space and sphere. The accuracy of the proposed linear contact loading law is validated through extensive Finite Element Method simulations, which involve an elastic–plastic half-space being struck by elastic–plastic spheres with varying impact energies, sizes, and material combinations.
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(This article belongs to the Section Mechanical Engineering)
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Open AccessArticle
A Conceptual Model for Depicting the Relationships between Toluene Degradation and Fe(III) Reduction with Different Fe(III) Phases as Terminal Electron Acceptors
by
He Di, Min Zhang, Zhuo Ning, Ze He, Changli Liu and Jiajia Song
Appl. Sci. 2024, 14(12), 5017; https://doi.org/10.3390/app14125017 (registering DOI) - 8 Jun 2024
Abstract
Iron reduction is one of the most crucial biogeochemical processes in groundwater for organic contaminants biodegradation, especially in the iron-rich aquifers. Previous research has posited that the reduction of iron and the biodegradation of organic substances occur synchronously, with their processes adhering to
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Iron reduction is one of the most crucial biogeochemical processes in groundwater for organic contaminants biodegradation, especially in the iron-rich aquifers. Previous research has posited that the reduction of iron and the biodegradation of organic substances occur synchronously, with their processes adhering to specific quantitative relationships. However, discrepancies between the observed values of iron reduction and organic compound degradation during the reaction and their theoretical counterparts have been noted. To find out the relationship between organic substance biodegradation and iron reduction, this study conducted batch experiments utilizing toluene as a typical organic compound and electron donor, with various iron minerals serving as electron acceptors. Results indicate that toluene degradation follows first-order kinetic equations with different degradation rate constants under different iron minerals, but the generation of the iron reduction product Fe(II) was not uniform. Based on these dynamic relationships, a conceptual model was developed, which categorizes the reactions into two phases: the transformation of toluene to an intermediate-state dominated phase and the mineralization of the intermediate-state dominated phase. This model revealed the relationships between toluene oxidation and Fe(II) formation in the toluene biodegradation through iron reduction. The coupling mechanism of toluene degradation and iron reduction was revealed, which is expected to improve our ability to accurately assess the attenuation of organic contaminants in groundwater.
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(This article belongs to the Special Issue The Mobilization, Speciation and Transformation of Organic and Inorganic Contaminants in Soil-Groundwater Ecosystems)
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Adaptive CAPTCHA: A CRNN-Based Text CAPTCHA Solver with Adaptive Fusion Filter Networks
by
Xing Wan, Juliana Johari and Fazlina Ahmat Ruslan
Appl. Sci. 2024, 14(12), 5016; https://doi.org/10.3390/app14125016 (registering DOI) - 8 Jun 2024
Abstract
Text-based CAPTCHAs remain the most widely adopted security scheme, which is the first barrier to securing websites. Deep learning methods, especially Convolutional Neural Networks (CNNs), are the mainstream approach for text CAPTCHA recognition and are widely used in CAPTCHA vulnerability assessment and data
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Text-based CAPTCHAs remain the most widely adopted security scheme, which is the first barrier to securing websites. Deep learning methods, especially Convolutional Neural Networks (CNNs), are the mainstream approach for text CAPTCHA recognition and are widely used in CAPTCHA vulnerability assessment and data collection. However, verification code recognizers are mostly deployed on the CPU platform as part of a web crawler and security assessment; they are required to have both low complexity and high recognition accuracy. Due to the specifically designed anti-attack mechanisms like noise, interference, geometric deformation, twisting, rotation, and character adhesion in text CAPTCHAs, some characters are difficult to efficiently identify with high accuracy in these complex CAPTCHA images. This paper proposed a recognition model named Adaptive CAPTCHA with a CNN combined with an RNN (CRNN) module and trainable Adaptive Fusion Filtering Networks (AFFN), which effectively handle the interference and learn the correlation between characters in CAPTCHAs to enhance recognition accuracy. Experimental results on two datasets of different complexities show that, compared with the baseline model Deep CAPTCHA, the number of parameters of our proposed model is reduced by about 70%, and the recognition accuracy is improved by more than 10 percentage points in the two datasets. In addition, the proposed model has a faster training convergence speed. Compared with several of the latest models, the model proposed by the study also has better comprehensive performance.
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(This article belongs to the Special Issue Advanced Technologies in Data and Information Security III)
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Laminated Steel Fiber-Reinforced Concrete Hingeless Arch: Research on Damage Evolution Laws
by
Zhongchu Tian, Ye Dai, Tao Peng, Zujun Zhang, Yue Cai and Binlin Xu
Appl. Sci. 2024, 14(12), 5015; https://doi.org/10.3390/app14125015 (registering DOI) - 8 Jun 2024
Abstract
In the context of reinforced concrete (RC) arch bridges, while the incorporation of full sections of steel fibers can enhance the bridge’s toughness, cracking resilience, and bearing capacity, achieving an optimal balance between structural performance and economic viability in this manner remains challenging.
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In the context of reinforced concrete (RC) arch bridges, while the incorporation of full sections of steel fibers can enhance the bridge’s toughness, cracking resilience, and bearing capacity, achieving an optimal balance between structural performance and economic viability in this manner remains challenging. This article introduces a novel computational approach—the distributed steel fiber concrete (LSFRC) arch—which considers the spatial distribution of damage in RC arches. The static performance of SFRC elements and LSFRC beams was compared and analyzed using the concrete plastic damage model (CDP) in ABAQUS software. This study validated the rationality of the model and investigated the impact of varying steel fiber volume ratios and steel fiber layer heights on the damage evolution of LSFRC arches. The results of this study demonstrate that the cracking load and bearing capacity of an RC arch can be effectively enhanced through the addition of steel fibers to a local area under static loading. Furthermore, the deflection and damage to the arch waist and arch roof can be significantly reduced. Furthermore, the incorporation of steel fibers at an increased volume rate and at a greater height within the doped section can effectively slow the rate of damage evolution within the section. This results in the inhibition of crack extensions and in an improvement in the ductility and reliability of the damage stage. The LSFRC arches offer superior economic and practical advantages over their full cross-section doped steel fiber (FRC) counterparts. This study offers novel insights and methodological guidance for the design and implementation of concrete arch bridges.
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Open AccessArticle
Research on Hybrid Vibration Sensor for Measuring Downhole Drilling Tool Vibrational Frequencies
by
Jiangbin Liu, Guangzhi Pan, Chuan Wu and Yanjun Feng
Appl. Sci. 2024, 14(12), 5014; https://doi.org/10.3390/app14125014 (registering DOI) - 8 Jun 2024
Abstract
The vibration parameters during drilling play a critical role in enhancing drilling speed and ensuring safety. However, traditional downhole vibration sensors face limitations in their power supply methods, hindering widespread adoption. To address this challenge, our research introduces a novel solution: a hybrid
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The vibration parameters during drilling play a critical role in enhancing drilling speed and ensuring safety. However, traditional downhole vibration sensors face limitations in their power supply methods, hindering widespread adoption. To address this challenge, our research introduces a novel solution: a hybrid downhole vibration sensor (HDV-TENG) utilizing triboelectric nanogenerators. This sensor not only enables the measurement of low- to medium–high-frequency vibrations using self-power but also serves to energize other downhole devices. We utilized a self-constructed vibration simulator to replicate downhole drilling tool vibrations and conducted a comprehensive series of sensor tests. The test results indicate that the frequency measurement bandwidth of the HDV-TENG spans from 0 to 200 kHz. Especially, the measurement errors for vibrations within the low-frequency range of 0 to 10 Hz and the high-frequency range of 10 to 200 k Hz are less than 5% and 8%, respectively. Additionally, the experimental findings regarding load matching demonstrate that the HDV-TENG achieves an output power level in the milliwatt range, representing a significant improvement over the output power of traditional triboelectric nanogenerators. Unlike traditional downhole vibration measurement sensors, HDV-TENG operates without requiring any external power supply, thereby conserving downhole space and significantly enhancing drilling efficiency. Furthermore, HDV-TENG not only offers a broad measurement range but also amplifies output power through the synergy of a triboelectric nanogenerator (TENG), piezoelectric nanogenerator (PENG), and electromagnetic power generator (EMG). This capability enables its utilization as an emergency power source for other micropower equipment downhole. The introduction of HDV-TENG also holds considerable implications for the development of self-powered underground sensors with high-frequency measurement capabilities.
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(This article belongs to the Section Electrical, Electronics and Communications Engineering)
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