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Volume 14
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Appl. Sci., Volume 14, Issue 7 (April-1 2024) – 466 articles

Cover Story (view full-size image): Thermal energy storage (TES) systems play a very important part in addressing the energy crisis. The TES technology has the potential to reach new heights when the biological behavior of nature is incorporated into the design of TES tanks. By mimicking the branched vein pattern observed in plants and animals, the heat transfer fluid (HTF) tube of a TES tank can enhance the heat transfer surface area, hence improving its thermal efficiency without the need to add other enhancements of heat transfer methods. In this study, a unique additive-manufacturing-based bio-inspired TES tank was designed, developed, and tested. The results showed that, compared to the shell-and-tube TES tank, the bio-inspired TES tank had a higher discharging rate and needed 52% less time to release the stored heat. View this paper
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16 pages, 14912 KiB  
Article
Application of 3D Imaging for Analyzing the Chip Groove Shapes of Cutting Inserts
by Grzegorz Struzikiewicz
Appl. Sci. 2024, 14(7), 3134; https://doi.org/10.3390/app14073134 - 8 Apr 2024
Viewed by 891
Abstract
An effective chip formation process is significant for an efficient metal-cutting process. Long continuous chips can lead to scratches on the machined surface, increasing the risk to operator safety and stability of the machining process. The use of chip grooves on cutting inserts [...] Read more.
An effective chip formation process is significant for an efficient metal-cutting process. Long continuous chips can lead to scratches on the machined surface, increasing the risk to operator safety and stability of the machining process. The use of chip grooves on cutting inserts allows for control of the chip formation and breaking process during machining. The shape of the rake surface and the design of the chip groove also affect the efficiency of the machining process. The article presents the use of 3D imaging to analyze changes in the selected chip groove shapes depending on the cutting depth ap = 0.10, 0.25, and 0.50 mm and the angular location of the cutting insert relative to the machined surface of the workpiece (i.e., major cutting-edge angle K = 60° and K = 90°). The analysis methodology was based on the use of 3D image registration and surface shape modeling. In the analysis based on the 3D imaging presented, the novelty was the adaptation of methods typically used to map and model the terrain surface, which have not been used previously in cutting processes. The evaluation of the shape of the chip groove surface was carried out using, e.g., watershed maps and 3D surface maps. The obtained results indicated a significant influence of the cutting depth and major cutting-edge angle on the surface shape, profile, and length of the chip former; chip groove volume; and the theoretical contact area of the formed chip with the cutting insert. It was observed that for small depths of cut, i.e., ap < 0.25 mm, the chip-curling process may be difficult due to the flattened shape of the rake surface. In addition, the influence of the convexity of the rake surface of the cutting insert on the chip formation process was demonstrated. The results of the experimental research that verified the conclusions are presented. The developed results may be useful in the process of selecting the parameters and conditions of the metal finishing through use of tools with a shaped rake surface. Full article
(This article belongs to the Special Issue Applications of Vision Measurement System on Product Quality Control)
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21 pages, 4595 KiB  
Article
Memory-Enhanced Knowledge Reasoning with Reinforcement Learning
by Jinhui Guo, Xiaoli Zhang, Kun Liang and Guoqiang Zhang
Appl. Sci. 2024, 14(7), 3133; https://doi.org/10.3390/app14073133 - 8 Apr 2024
Cited by 1 | Viewed by 1646
Abstract
In recent years, the emergence of large-scale language models, such as ChatGPT, has presented significant challenges to research on knowledge graphs and knowledge-based reasoning. As a result, the direction of research on knowledge reasoning has shifted. Two critical issues in knowledge reasoning research [...] Read more.
In recent years, the emergence of large-scale language models, such as ChatGPT, has presented significant challenges to research on knowledge graphs and knowledge-based reasoning. As a result, the direction of research on knowledge reasoning has shifted. Two critical issues in knowledge reasoning research are the algorithm of the model itself and the selection of paths. Most studies utilize LSTM as the path encoder and memory module. However, when processing long sequence data, LSTM models may encounter the problem of long-term dependencies, where memory units of the model may decay gradually with an increase in time steps, leading to forgetting earlier input information. This can result in a decline in the performance of the LSTM model in long sequence data. Additionally, as the data volume and network depth increase, there is a risk of gradient disappearance. This study improved and optimized the LSTM model to effectively address the problems of gradient explosion and gradient disappearance. An attention layer was employed to alleviate the issue of long-term dependencies, and ConvR embedding was used to guide path selection and action pruning in the reinforcement learning inference model. The overall model achieved excellent reasoning results. Full article
(This article belongs to the Special Issue Deep Learning for Graph Management and Analytics)
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20 pages, 1074 KiB  
Review
High-Functioning Autism and Virtual Reality Applications: A Scoping Review
by Mattia Chiappini, Carla Dei, Ettore Micheletti, Emilia Biffi and Fabio Alexander Storm
Appl. Sci. 2024, 14(7), 3132; https://doi.org/10.3390/app14073132 - 8 Apr 2024
Cited by 4 | Viewed by 3642
Abstract
In recent years, the number of applications of virtual reality (VR) for the Autism spectrum disorder (ASD) population has increased and has become one of the most suitable tools to address the psychological needs of these individuals. The present scoping review aims to [...] Read more.
In recent years, the number of applications of virtual reality (VR) for the Autism spectrum disorder (ASD) population has increased and has become one of the most suitable tools to address the psychological needs of these individuals. The present scoping review aims to provide a literature mapping of experimental studies that have used immersive and semi-immersive VR for assessments or interventions specifically addressing high-functioning autism. A total of 23 papers were included and analyzed following PRISMA guidelines. The identified studies concerned social skills (11 papers), eye gaze and joint attention (3 papers), motor learning (3 papers), job training (2 papers), and other aims or rationales (4 papers). The evidence shows that, despite the intellectual potential of high-functioning ASD individuals, little research has been conducted to provide interventions that offer concrete training to improve their adaptive functioning. In addition, the percentage of individuals below 18 years of age is representative of half of the included studies, so aiming future studies at the early stages of development might be an asset in preparing the next generation of young adults to cope with age-related challenges, as early assessments and interventions are more likely to produce major long-term effects. Full article
(This article belongs to the Special Issue Human–Computer Interaction and Virtual Environments)
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13 pages, 10340 KiB  
Article
High-Temperature Oxidation of the 304/6061 Welding–Brazing Joint and Its Effects on Corrosion Characteristics
by Ruilin Liu, Yunqi Liu, Hongming Liu, Yuanxing Li, Hui Chen and Zongtao Zhu
Appl. Sci. 2024, 14(7), 3131; https://doi.org/10.3390/app14073131 - 8 Apr 2024
Viewed by 1016
Abstract
Laser–MIG hybrid welding–brazing was used to weld 304 stainless steel and 6061-T6 aluminum alloy with a thickness of 2 mm. The microstructure, morphology, chemical composition and corrosion behavior of the samples after high-temperature oxidation were investigated. The results reveal that the 304/6061 dissimilar [...] Read more.
Laser–MIG hybrid welding–brazing was used to weld 304 stainless steel and 6061-T6 aluminum alloy with a thickness of 2 mm. The microstructure, morphology, chemical composition and corrosion behavior of the samples after high-temperature oxidation were investigated. The results reveal that the 304/6061 dissimilar joint had a thicker intermetallic compound layer (7–8 μm) during high-temperature oxidation (HTO) treatment than the sample without HTO treatment (2–3 μm). The oxide film thickness of the 6061 side of the weld joint treated by HTO (2401 nm) increased compared to the samples (181.1 nm) without HTO treatment. Unlike other metals treated by high-temperature oxidation, the high-temperature treatment process in this paper can reduce the corrosion resistance of the base metal and dissimilar joints, and the sequence of the corrosion current density was weld (HTO) >weld>6061 (HTO) >6061>304 (HTO) >304. Full article
(This article belongs to the Special Issue Advances in High-Strength Materials Processing: Machining, Surface Integrity Management, and Operational Properties)
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18 pages, 2531 KiB  
Article
Research on Evaluation Methods of Black Soil Farmland Productivity Based on Field Block Scale
by Zihao Zhu and Yonghua Xie
Appl. Sci. 2024, 14(7), 3130; https://doi.org/10.3390/app14073130 - 8 Apr 2024
Viewed by 911
Abstract
Black soil plays an important role in maintaining a healthy ecosystem, promoting high-yield and efficient agricultural production, and conserving soil resources. In this paper, a typical black soil area of Keshan Farm in Qiqihar City, Heilongjiang Province, China, is used as a case [...] Read more.
Black soil plays an important role in maintaining a healthy ecosystem, promoting high-yield and efficient agricultural production, and conserving soil resources. In this paper, a typical black soil area of Keshan Farm in Qiqihar City, Heilongjiang Province, China, is used as a case study to investigate the black soil farmland productivity evaluation model. Based on the analysis of the composite index (CI) model, productivity index (PI) model and various machine learning models, the soil productivity evaluation method was improved and a prediction model was established. The results showed that the support vector machine regression model based on simulated annealing algorithm (SA-SVR), as well as the Gaussian process regression model (GPR), had obvious advantages in data preprocessing, feature selection, and model optimization compared to the modified composite index model (MCI), the modified productivity index model (MPI), and the coefficients of determination (R2) of their modelling, which were up to 0.70 and 0.71, respectively, and these machine learning prediction models can reflect the effects on maize cultivation and its yield through soil parameters even with small datasets, which can better capture the nonlinear relationship and improve the accuracy and stability of yield prediction, and is an effective method for guiding agricultural production as well as soil productivity evaluation. Full article
(This article belongs to the Special Issue Applications of Machine Learning in Agriculture)
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14 pages, 3203 KiB  
Article
Real-Time Monitoring of Wind Turbine Bearing Using Simple Neural Network on Raspberry Pi
by Tianhao Wang, Hongying Meng, Rui Qin, Fan Zhang and Asoke Kumar Nandi
Appl. Sci. 2024, 14(7), 3129; https://doi.org/10.3390/app14073129 - 8 Apr 2024
Cited by 1 | Viewed by 1188
Abstract
Wind turbines are a crucial part of renewable energy generation, and their reliable and efficient operation is paramount in ensuring clean energy availability. However, the bearings in wind turbines are subjected to high stress and loads, resulting in faults that can lead to [...] Read more.
Wind turbines are a crucial part of renewable energy generation, and their reliable and efficient operation is paramount in ensuring clean energy availability. However, the bearings in wind turbines are subjected to high stress and loads, resulting in faults that can lead to costly downtime and repairs. Fault detection in real time is critical to minimize downtime and reduce maintenance costs. In this work, a simple neural network model was designed and implemented on a Raspberry Pi for the real-time detection of wind turbine bearing faults. The model was trained to accurately identify complex patterns in raw sensor data of healthy and faulty bearings. By splitting the data into smaller segments, the model can quickly analyze each segment and generate predictions at high speed. Additionally, simplified algorithms were developed to analyze the segments with minimum latency. The proposed system can efficiently process the sensor data and performs rapid analysis and prediction within 0.06 milliseconds per data segment. The experimental results demonstrate that the model achieves a 99.8% accuracy in detecting wind turbine bearing faults within milliseconds of their occurrence. The model’s ability to generate real-time predictions and to provide an overall assessment of the bearing’s health can significantly reduce maintenance costs and increase the availability and efficiency of wind turbines. Full article
(This article belongs to the Topic Advances in the Monitoring and Diagnosis of Faults in Wind Power Plants)
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16 pages, 2290 KiB  
Article
Activity of Methanolic and Hydrolyzed Methanolic Extracts of Ricinus communis (Euphorbiaceae) and Kaempferol against Spodoptera frugiperda (Lepidoptera: Noctuidae)
by Manolo Rodríguez-Cervantes, Carlos Eduardo Zavala-Gómez, Karla Hernández-Caracheo, Juan Campos-Guillén, Eloy Rodríguez-de León, Aldo Amaro-Reyes, José Alberto Rodríguez-Morales, Sandra Neli Jiménez-García, Rodolfo Figueroa-Brito, David Osvaldo Salinas-Sánchez, Francisco Javier Flores-Gallardo and Miguel Angel Ramos-López
Appl. Sci. 2024, 14(7), 3128; https://doi.org/10.3390/app14073128 - 8 Apr 2024
Cited by 1 | Viewed by 1085
Abstract
Spodoptera frugiperda is the main pest of maize. One of the alternatives proposed for its control is the implementation of products of botanical origin, such as those derived from Ricinus communis. In this work, the insecticidal and insectistatic activities of methanolic and [...] Read more.
Spodoptera frugiperda is the main pest of maize. One of the alternatives proposed for its control is the implementation of products of botanical origin, such as those derived from Ricinus communis. In this work, the insecticidal and insectistatic activities of methanolic and hydrolyzed methanolic extracts of the aerial parts of R. communis and kaempferol against S. frugiperda are evaluated. The methanolic extract presented a larval mortality rate of 55% and an accumulated mortality rate of 65% starting at 4000 ppm, with LC50 values of 3503 (larvae) and 2851 (accumulated); meanwhile, from a concentration of 1000 ppm, a decrease in pupa weight at 24 h of 20.5 mg was observed when compared to the control. The hydrolyzed methanolic extract presented a larval mortality and accumulated mortality rate of 60% from a concentration of 1000 ppm, and a decrease in pupa weight at 24 h of 35.31 mg was observed, when compared to the control. For the compound kaempferol 3-β-D-glucopyranoside, a larval mortality rate of 65% and an accumulated mortality rate of 80% were observed from 800 ppm, with LC50 values of 525.2 (larvae) and 335.6 ppm (accumulated); meanwhile, at 300 ppm, a decrease in pupa weight of 25.59 mg after 24 h was observed when compared to the control. Full article
(This article belongs to the Special Issue Biological Activity, Chemical Characterization and Contaminants of Plants and Waste)
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30 pages, 8644 KiB  
Review
Composition and Basic Physical Properties of the Phobos Surface: A Comprehensive Review
by Malwina Kolano, Marek Cała and Agnieszka Stopkowicz
Appl. Sci. 2024, 14(7), 3127; https://doi.org/10.3390/app14073127 - 8 Apr 2024
Viewed by 1496
Abstract
The surface of Phobos is an intriguing subject of research for many scientists. This is associated, among other things, with the fact that it is perceived as a potential launch site for future human Mars exploration. Additionally, measurements conducted on its surface would [...] Read more.
The surface of Phobos is an intriguing subject of research for many scientists. This is associated, among other things, with the fact that it is perceived as a potential launch site for future human Mars exploration. Additionally, measurements conducted on its surface would not only deepen our knowledge about Phobos but also provide insights into geochemical processes occurring on similar small bodies in the Solar System. Therefore, understanding the physical–mechanical properties of regolith is a crucial aspect of planetary exploration. These properties are key factors needed for both planning safe landings and establishing future bases on celestial bodies. In this paper, information is compiled regarding hypotheses about its origin, the probable composition of Phobos’ surface (spectral properties and HiRISE data), as well as its morphology. The article also presents the process of regolith formation covering Phobos’ surface and its presumed physical properties. It has been established that the estimated bulk density of Phobos, compared to the densities of other asteroids and meteorites, is most similar to C-type asteroids. It was also found that C-type asteroids, in terms of total porosity, best reflect Phobos. However, determining the surface composition of Phobos and its detailed physical properties requires additional information, which could be obtained through in situ studies or sample return missions. Full article
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22 pages, 1009 KiB  
Article
Revisited Concept of Three-Phase Transformers’ Short-Circuit Resistances in Light of the Institute of Electric and Electronics Engineers (IEEE) Standard C57.110-2018
by Vicente León-Martínez, Elisa Peñalvo-López, Juan Ángel Sáiz-Jiménez and Amparo León-Vinet
Appl. Sci. 2024, 14(7), 3126; https://doi.org/10.3390/app14073126 - 8 Apr 2024
Viewed by 832
Abstract
Short-circuit resistances are transformer parameters that characterize the electrical load losses and correct operation of these machines. However, the traditional concept of short-circuit resistance, independent of the harmonic frequencies, has been superseded by present transformer standards. Hence, new expressions for short-circuit resistances of [...] Read more.
Short-circuit resistances are transformer parameters that characterize the electrical load losses and correct operation of these machines. However, the traditional concept of short-circuit resistance, independent of the harmonic frequencies, has been superseded by present transformer standards. Hence, new expressions for short-circuit resistances of three-phase transformers have been developed in this article based on the IEEE Standard C57.110-2018 and are presented jointly with the losses that these resistances characterize. These refer to the secondary effective short-circuit resistance of each phase (Rcc,z), of each harmonic (Rcc,h), and the non-fundamental frequency combined harmonics (Rcc,Hz). Likewise, the harmonic loss factor (HLFz%) has been established to determine the importance of the harmonics in each phase’s load losses. The application of these short-circuit resistances to the calculation of the load losses for a 630 kVA transformer from an actual residential distribution network has shown that the same values are obtained as with the IEEE Standard C57.110-2018, and they are 48.75% higher than those recorded with the traditional short-circuit resistances when the current distortion rates are 36.47%. Full article
(This article belongs to the Special Issue Advances in Transformers and Their Applications)
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18 pages, 1633 KiB  
Article
Uncovering Success Patterns in Track Cycling: Integrating Performance Data with Coaches and Athletes’ Perspectives
by Leonardo Cesanelli, Thomas Lagoute, Berta Ylaite, Julio Calleja-González, Eneko Fernández-Peña, Danguole Satkunskiene, Nuno Leite and Tomas Venckunas
Appl. Sci. 2024, 14(7), 3125; https://doi.org/10.3390/app14073125 - 8 Apr 2024
Viewed by 1641
Abstract
Track cycling entails a challenging progression from the youth categories to elite competition. Hence, this study aimed to investigate the importance of early performance and various publicly available performance indicators in predicting the success of male and female cyclists across different track disciplines. [...] Read more.
Track cycling entails a challenging progression from the youth categories to elite competition. Hence, this study aimed to investigate the importance of early performance and various publicly available performance indicators in predicting the success of male and female cyclists across different track disciplines. Additionally, the study enriches the findings by incorporating interviews with international-level coaches and athletes. A retrospective analysis of data from UCI track cycling databases was conducted, supplemented by interviews with international-level coaches and athletes. The success rate for highly ranked junior track cyclists was found to be less than 20%, with a majority of these athletes specializing in sprint events, regardless of gender. The study indicated that the UCI ranking and points earned during the season were not reliable indicators for distinguishing future success (p < 0.05). From the interviews, we identified three main themes: (1) trends in career success from the youth to elite categories, (2) performance markers as predictors of future success, and (3) the challenges and time involved in reaching elite categories. Junior category performance alone may not be the sole indicator of future success in track cycling. However, integrating performance analyses with practitioners and athletes’ perspectives enables a deeper understanding of the results and the developmental context. Full article
(This article belongs to the Special Issue New Trends in Training, Performance, Coaching and Health in Sports Science)
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19 pages, 3908 KiB  
Article
A Multi-Objective Optimal Control Method for Navigating Connected and Automated Vehicles at Signalized Intersections Based on Reinforcement Learning
by Han Jiang, Hongbin Zhang, Zhanyu Feng, Jian Zhang, Yu Qian and Bo Wang
Appl. Sci. 2024, 14(7), 3124; https://doi.org/10.3390/app14073124 - 8 Apr 2024
Cited by 1 | Viewed by 1194
Abstract
The emergence and application of connected and automated vehicles (CAVs) have played a positive role in improving the efficiency of urban transportation and achieving sustainable development. To improve the traffic efficiency at signalized intersections in a connected environment while simultaneously reducing energy consumption [...] Read more.
The emergence and application of connected and automated vehicles (CAVs) have played a positive role in improving the efficiency of urban transportation and achieving sustainable development. To improve the traffic efficiency at signalized intersections in a connected environment while simultaneously reducing energy consumption and ensuring a more comfortable driving experience, this study investigates a flexible and real-time control method to navigate the CAVs at signalized intersections utilizing reinforcement learning (RL). Initially, control of CAVs at intersections is formulated as a Markov Decision Process (MDP) based on the vehicles’ motion state and the intersection environment. Subsequently, a comprehensive reward function is formulated considering energy consumption, efficiency, comfort, and safety. Then, based on the established environment and the twin delayed deep deterministic policy gradient (TD3) algorithm, a control algorithm for CAVs is designed. Finally, a simulation study is conducted using SUMO, with Lankershim Boulevard as the research scenario. Results indicate that the proposed methods yield a 13.77% reduction in energy consumption and a notable 18.26% decrease in travel time. Vehicles controlled by the proposed method also exhibit smoother driving trajectories. Full article
(This article belongs to the Section Transportation and Future Mobility)
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16 pages, 4541 KiB  
Article
Untargeted Metabolomic Profiling of Fructus Chebulae and Fructus Terminaliae Billericae
by Yuman Song and Hede Gong
Appl. Sci. 2024, 14(7), 3123; https://doi.org/10.3390/app14073123 - 8 Apr 2024
Viewed by 1110
Abstract
This study aims to identify the differences in metabolites between Fructus Chebulae (FC) and Fructus Terminaliae Billericae (FTB). Untargeted metabolomics was used to analyze differentially expressed metabolites (DEMs) with ultra-performance liquid chromatography–electrospray ionization–tandem mass spectrometry (UPLC-ESI-MS/MS). A grand total of 558 metabolites were [...] Read more.
This study aims to identify the differences in metabolites between Fructus Chebulae (FC) and Fructus Terminaliae Billericae (FTB). Untargeted metabolomics was used to analyze differentially expressed metabolites (DEMs) with ultra-performance liquid chromatography–electrospray ionization–tandem mass spectrometry (UPLC-ESI-MS/MS). A grand total of 558 metabolites were detected, with 155 in positive ion mode and 403 in negative ion mode. Further differential analysis yielded 110 and 87 significantly different metabolites, which were mainly polyphenols, flavonoids, terpenoids, and alkaloids. Analysis of KEGG data showed that differentially expressed metabolites (DEMs) in both positive and negative ion modes were found to be enriched in 5 and 18 metabolic pathways, respectively, with metabolic pathways being the most enriched among them. In sum, this study reveals the differential metabolic profiles of FC and FTB and provides support for their further applications in traditional Chinese medicine. Full article
(This article belongs to the Special Issue Advances in Food Metabolomics)
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16 pages, 751 KiB  
Article
Relgraph: A Multi-Relational Graph Neural Network Framework for Knowledge Graph Reasoning Based on Relation Graph
by Xin Tian and Yuan Meng
Appl. Sci. 2024, 14(7), 3122; https://doi.org/10.3390/app14073122 - 8 Apr 2024
Viewed by 1576
Abstract
Multi-relational graph neural networks (GNNs) have found widespread application in tasks involving enhancing knowledge representation and knowledge graph (KG) reasoning. However, existing multi-relational GNNs still face limitations in modeling the exchange of information between predicates. To address these challenges, we introduce Relgraph, a [...] Read more.
Multi-relational graph neural networks (GNNs) have found widespread application in tasks involving enhancing knowledge representation and knowledge graph (KG) reasoning. However, existing multi-relational GNNs still face limitations in modeling the exchange of information between predicates. To address these challenges, we introduce Relgraph, a novel KG reasoning framework. This framework introduces relation graphs to explicitly model the interactions between different relations, enabling more comprehensive and accurate handling of representation learning and reasoning tasks on KGs. Furthermore, we design a machine learning algorithm based on the attention mechanism to simultaneously optimize the original graph and its corresponding relation graph. Benchmark and experimental results on large-scale KGs demonstrate that the Relgraph framework improves KG reasoning performance. The framework exhibits a certain degree of versatility and can be seamlessly integrated with various traditional translation models. Full article
(This article belongs to the Special Issue State-of-the-Art of Knowledge Graphs and Their Applications)
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17 pages, 18785 KiB  
Article
Research on the Impact of Non-Uniform and Frequency-Dependent Normal Contact Stiffness on the Vibrational Response of Plate Structures
by Chang Yan, Wen-Jie Fan, Da-Miao Wang and Wen-Zhang Zhang
Appl. Sci. 2024, 14(7), 3121; https://doi.org/10.3390/app14073121 - 8 Apr 2024
Viewed by 1085
Abstract
Mechanical interfaces are prevalent in industries like aerospace and maritime, where the normal contact stiffness on these surfaces is a crucial component of the overall stiffness of mechanical structures. From the perspective of structural mechanics, normal contact stiffness significantly affects the dynamic response [...] Read more.
Mechanical interfaces are prevalent in industries like aerospace and maritime, where the normal contact stiffness on these surfaces is a crucial component of the overall stiffness of mechanical structures. From the perspective of structural mechanics, normal contact stiffness significantly affects the dynamic response of mechanical structures and must be considered in mechanical design and simulation analysis. Essentially, the mechanical interface represents a typical type of nonlinear contact, characterized by both its non-uniform distribution and its frequency-dependent properties under external excitations. A plate structure was designed to study the distribution and frequency-dependent characteristics of normal contact stiffness on the mechanical interface. Experiments validated that the normal contact stiffness not only significantly increases the fundamental frequency of the plate but also alters its mode shapes. To replicate the experimental results in simulations, the BUSH elements were used to model the normal contact stiffness within the plate structure, arranging it non-uniformly and setting it to vary with frequency according to the plate’s mode shapes and frequency response. This method accurately replicated the plate’s mode shapes and response curves within the 0–1000 Hz range in simulations. Full article
(This article belongs to the Section Mechanical Engineering)
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16 pages, 4566 KiB  
Article
Thermal Effect of Probes Present in a Pharmaceutical Formulation during Freeze-Drying Measured by Contact-Free Infrared Thermography
by Håkan Emteborg and Jean Charoud-Got
Appl. Sci. 2024, 14(7), 3120; https://doi.org/10.3390/app14073120 - 8 Apr 2024
Cited by 2 | Viewed by 1073
Abstract
A high-resolution infrared (IR) camera was used for temperature measurements in a pharmaceutical formulation (mannitol/sucrose solution, 4:1%, m/m) during a freeze-drying process. The temperature was measured simultaneously at the surface as well as vertically (e.g., in-depth) along the side of custom-made cuvettes equipped [...] Read more.
A high-resolution infrared (IR) camera was used for temperature measurements in a pharmaceutical formulation (mannitol/sucrose solution, 4:1%, m/m) during a freeze-drying process. The temperature was measured simultaneously at the surface as well as vertically (e.g., in-depth) along the side of custom-made cuvettes equipped with a germanium (Ge) window. Direct imaging during 45 h from −40 °C to 40 °C took place every 60 s on the surface and on the side with 0.28 × 0.28 mm per IR-pixel providing 2700 thermograms. The spatial resolution per cuvette was approximately 4225 pixels for the surface view (without a probe) and 6825 IR-pixels for the side view. Temperature effects and gradients due to the presence of a Pt100 and a LyoRx-probe in the pharmaceutical formulation were clearly visible and were quantified during the freezing step as well as the primary and secondary drying stages. The temperature was about 3.5 K higher during primary drying as compared to the temperature measured in the same material in adjacent cuvettes without probes. During secondary drying, evaporative cooling of upper layers was clearly visible. Full article
(This article belongs to the Special Issue Recent Progress in Infrared Thermography)
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20 pages, 5608 KiB  
Article
Digital Twin-Driven Multi-Factor Production Capacity Prediction for Discrete Manufacturing Workshop
by Hu Cai, Jiafu Wan and Baotong Chen
Appl. Sci. 2024, 14(7), 3119; https://doi.org/10.3390/app14073119 - 8 Apr 2024
Cited by 1 | Viewed by 1249
Abstract
Traditional capacity forecasting algorithms lack effective data interaction, leading to a disconnection between the actual plan and production. This paper discusses the multi-factor model based on a discrete manufacturing workshop and proposes a digital twin-driven discrete manufacturing workshop capacity prediction method. Firstly, this [...] Read more.
Traditional capacity forecasting algorithms lack effective data interaction, leading to a disconnection between the actual plan and production. This paper discusses the multi-factor model based on a discrete manufacturing workshop and proposes a digital twin-driven discrete manufacturing workshop capacity prediction method. Firstly, this paper gives a system framework for production capacity prediction in discrete manufacturing workshops based on digital twins. Then, a mathematical model is described for discrete manufacturing workshop production capacity under multiple disturbance factors. Furthermore, an innovative production capacity prediction method, using the “digital twin + Long-Short-Term Memory Network (LSTM) algorithm”, is presented. Finally, a discrete manufacturing workshop twin platform is deployed using a commemorative disk custom production line as the prototype platform. The verification shows that the proposed method can achieve a prediction accuracy rate of 91.8% for production line capacity. By integrating the optimization feedback function of the digital twin system into the production process control, this paper enables an accurate perception of the current state and future changes in the production system, effectively evaluating the production capacity and delivery date of discrete manufacturing workshops. Full article
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15 pages, 4116 KiB  
Article
Performance of Eco-Friendly Zero-Cement Particle Board under Harsh Environment
by Arman Hatami Shirkouh, Farshad Meftahi, Ahmed Soliman, Stéphane Godbout and Joahnn Palacios
Appl. Sci. 2024, 14(7), 3118; https://doi.org/10.3390/app14073118 - 8 Apr 2024
Cited by 2 | Viewed by 1397
Abstract
The increasing scarcity of virgin natural resources and the need for sustainable waste management in densely populated urban areas have heightened the importance of developing new recycling technologies. One promising approach involves recycling agricultural waste in construction applications and transforming it into secondary [...] Read more.
The increasing scarcity of virgin natural resources and the need for sustainable waste management in densely populated urban areas have heightened the importance of developing new recycling technologies. One promising approach involves recycling agricultural waste in construction applications and transforming it into secondary products. This is anticipated to reduce the demand for new resources and lower the environmental impact, aligning with industrial ecology principles. Combined with a low carbon emission binder (i.e., alkali-activated), utilizing agro-waste to produce zero-cement particle boards is a promising method for green construction. Traditionally, particle boards are engineered from wood or agricultural waste products that are pressed and bonded with a binder, such as cement or synthetic resins. However, alternative binders replace cement in zero-cement particle boards to address environmental concerns, such as the carbon dioxide emissions associated with cement production. This study investigated the effects of accelerated aging on the performance of alkali-activated agro-waste particle boards. Accelerated aging conditions simulate natural aging phenomena. Repeated wetting–drying and freezing–thawing cycles increased water absorption and thickness swelling and reduced flexural strength. The thermal performance of the alkali-activated particle boards did not exhibit significant changes. Hence, it was confirmed that agro-waste has a high potential for utilization in producing particle boards provided that the working environment is carefully selected to optimize performance. Full article
(This article belongs to the Special Issue Alkali-Activated Materials: Advances and Novel Applications)
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13 pages, 886 KiB  
Article
A Study on Improving Sleep Apnea Diagnoses Using Machine Learning Based on the STOP-BANG Questionnaire
by Myoung-Su Choi, Dong-Hun Han, Jun-Woo Choi and Min-Soo Kang
Appl. Sci. 2024, 14(7), 3117; https://doi.org/10.3390/app14073117 - 8 Apr 2024
Viewed by 1263
Abstract
Sleep apnea has emerged as a significant health issue in modern society, with self-diagnosis and effective management becoming increasingly important. Among the most renowned methods for self-diagnosis, the STOP-BANG questionnaire is widely recognized as a simple yet effective tool for diagnosing and assessing [...] Read more.
Sleep apnea has emerged as a significant health issue in modern society, with self-diagnosis and effective management becoming increasingly important. Among the most renowned methods for self-diagnosis, the STOP-BANG questionnaire is widely recognized as a simple yet effective tool for diagnosing and assessing the risk of sleep apnea. However, its sensitivity and specificity have limitations, necessitating the need for tools with higher performance. Consequently, this study aimed to enhance the accuracy of sleep apnea diagnoses by integrating machine learning with the STOP-BANG questionnaire. Research through actual cases was conducted based on the data of 262 patients undergoing polysomnography, confirming sleep apnea with a STOP-BANG score of ≥3 and an Apnea–Hypopnea Index (AHI) of ≥5. The accuracy, sensitivity, and specificity were derived by comparing Apnea–Hypopnea Index scores with STOP-BANG scores. When applying machine learning models, four hyperparameter-tuned models were utilized: K-Nearest Neighbor (K-NN), Logistic Regression, Random Forest, and Support Vector Machine (SVM). Among them, the K-NN model with a K value of 11 demonstrated superior performance, achieving a sensitivity of 0.94, specificity of 0.85, and overall accuracy of 0.92. These results highlight the potential of combining traditional STOP-BANG diagnostic tools with machine learning technology, offering new directions for future research in self-diagnosis and the preliminary diagnosis of sleep-related disorders in clinical settings. Full article
(This article belongs to the Special Issue Integrating Artificial Intelligence in Renewable Energy Systems)
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18 pages, 3325 KiB  
Article
Enhancing Insect Sound Classification Using Dual-Tower Network: A Fusion of Temporal and Spectral Feature Perception
by Hangfei He, Junyang Chen, Hongkun Chen, Borui Zeng, Yutong Huang, Yudan Zhaopeng and Xiaoyan Chen
Appl. Sci. 2024, 14(7), 3116; https://doi.org/10.3390/app14073116 - 8 Apr 2024
Cited by 1 | Viewed by 1507
Abstract
In the modern field of biological pest control, especially in the realm of insect population monitoring, deep learning methods have made further advancements. However, due to the small size and elusive nature of insects, visual detection is often impractical. In this context, the [...] Read more.
In the modern field of biological pest control, especially in the realm of insect population monitoring, deep learning methods have made further advancements. However, due to the small size and elusive nature of insects, visual detection is often impractical. In this context, the recognition of insect sound features becomes crucial. In our study, we introduce a classification module called the “dual-frequency and spectral fusion module (DFSM)”, which enhances the performance of transfer learning models in audio classification tasks. Our approach combines the efficiency of EfficientNet with the hierarchical design of the Dual Towers, drawing inspiration from the way the insect neural system processes sound signals. This enables our model to effectively capture spectral features in insect sounds and form multiscale perceptions through inter-tower skip connections. Through detailed qualitative and quantitative evaluations, as well as comparisons with leading traditional insect sound recognition methods, we demonstrate the advantages of our approach in the field of insect sound classification. Our method achieves an accuracy of 80.26% on InsectSet32, surpassing existing state-of-the-art models by 3 percentage points. Additionally, we conducted generalization experiments using three classic audio datasets. The results indicate that DFSM exhibits strong robustness and wide applicability, with minimal performance variations even when handling different input features. Full article
(This article belongs to the Special Issue Audio, Speech and Language Processing)
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13 pages, 3734 KiB  
Article
Constructing Enhanced Composite Solid-State Electrolytes with Sb/Nb Co-Doped LLZO and PVDF-HFP
by Jinhai Cai, Yingjie Liu, Yingying Tan, Wanying Chang, Jingyi Wu, Tong Wu and Chunyan Lai
Appl. Sci. 2024, 14(7), 3115; https://doi.org/10.3390/app14073115 - 8 Apr 2024
Viewed by 1305
Abstract
Composite solid-state electrolytes are viewed as promising materials for solid-state lithium-ion batteries due to their combined advantages of inorganic solid-state electrolytes and solid-state polymer electrolytes. In this study, the solid electrolytes Li6.7−xLa3Zr1.7−xSb0.3NbxO12 [...] Read more.
Composite solid-state electrolytes are viewed as promising materials for solid-state lithium-ion batteries due to their combined advantages of inorganic solid-state electrolytes and solid-state polymer electrolytes. In this study, the solid electrolytes Li6.7−xLa3Zr1.7−xSb0.3NbxO12 (LLZSNO) with Sb and Nb co-doping were prepared by a high-temperature solid-phase method. Results indicate that Sb/Nb co-doping causes lattice deformation in LLZO and increases the lithium vacancy concentration and conductivity of LLZO. Then, with the co-doped LLZSNO as an inorganic filler, a composite solid electrolyte of polyvinylidene fluoride-hexafluoropropylene (PVDF-HFP) was prepared with a casting method. The obtained composite solid electrolyte exhibits a high ionic conductivity of 1.76 × 10−4 S/cm at room temperature, a wide electrochemical stable window of 5.2 V, and a lithium-ion transfer number of 0.32. The Li|LiFePO4 coin battery with the composite solid electrolyte shows a high specific capacity of 161.2 mAh/g and a Coulombic efficiency close to 100% at 1 C. In addition, the symmetrical lithium battery Li|Li with the composite electrolyte could cycle stably for about 1500 h without failure at room temperature. Full article
(This article belongs to the Special Issue Advanced Materials for Lithium Ion Based Next Generation Batteries)
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16 pages, 15347 KiB  
Article
Transforming Customer Digital Footprints into Decision Enablers in Hospitality
by Achini Adikari, Su Nguyen, Rashmika Nawaratne, Daswin De Silva and Damminda Alahakoon
Appl. Sci. 2024, 14(7), 3114; https://doi.org/10.3390/app14073114 - 8 Apr 2024
Viewed by 992
Abstract
The proliferation of online hotel review platforms has prompted decision-makers in the hospitality sector to acknowledge the significance of extracting valuable information from this vast source. While contemporary research has primarily focused on extracting sentiment and discussion topics from online reviews, the transformative [...] Read more.
The proliferation of online hotel review platforms has prompted decision-makers in the hospitality sector to acknowledge the significance of extracting valuable information from this vast source. While contemporary research has primarily focused on extracting sentiment and discussion topics from online reviews, the transformative potential of such insights remains largely untapped. In this paper, we propose an approach that leverages Natural Language Processing (NLP) techniques to convert unstructured textual reviews into a quantifiable and structured representation of emotions and hotel aspects. Building upon this derived representation, we conducted a segmentation analysis to gauge distinct emotion and concern-based profiles of customers, as well as profiles of hotels with similar customer emotions using a self-organizing unsupervised algorithm. We demonstrated the practicality of our approach using 22,450 online reviews collected from 44 hotels. The insights garnered from emotion analysis and review segmentation facilitate the development of targeted customer management strategies and informed decision-making. Full article
(This article belongs to the Special Issue Applications of Data Science and Artificial Intelligence)
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16 pages, 3712 KiB  
Article
Functional Requirements and Design Features for the Implementation of 3D CAD-Based Graphical Interactive Configurators
by Paul Christoph Gembarski and Pauline Gast
Appl. Sci. 2024, 14(7), 3113; https://doi.org/10.3390/app14073113 - 8 Apr 2024
Viewed by 1122
Abstract
Configuring complex computer-aided design (CAD) assemblies just by modifying parameters requires the attention and abstraction of the users. This interaction cost can be lowered significantly by graphical interactive control elements that allow for drag and drop modifications directly in the 3D assembly. Contributing [...] Read more.
Configuring complex computer-aided design (CAD) assemblies just by modifying parameters requires the attention and abstraction of the users. This interaction cost can be lowered significantly by graphical interactive control elements that allow for drag and drop modifications directly in the 3D assembly. Contributing techniques, such as working with skeletons and advanced or external knowledge-based parameter control, are available. This contribution examines their integration and implementation into a given CAD system through a case study on creating a pipe routing configuration system which uses drag points to adjust the position of instrumentation and routing segments. The results are then generalized to functional requirements and basic design features of such graphical interactive configurators. Full article
(This article belongs to the Section Additive Manufacturing Technologies)
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18 pages, 12435 KiB  
Article
Fault Diagnosis Method of Box-Type Substation Based on Improved Conditional Tabular Generative Adversarial Network and AlexNet
by Yong Liu, Jialin Zhou, Dong Zhang, Shaoyu Wei, Mingshun Yang and Xinqin Gao
Appl. Sci. 2024, 14(7), 3112; https://doi.org/10.3390/app14073112 - 8 Apr 2024
Cited by 1 | Viewed by 856
Abstract
To solve the problem of low diagnostic accuracy caused by the scarcity of fault samples and class imbalance in the fault diagnosis task of box-type substations, a fault diagnosis method based on self-attention improvement of conditional tabular generative adversarial network (CTGAN) and AlexNet [...] Read more.
To solve the problem of low diagnostic accuracy caused by the scarcity of fault samples and class imbalance in the fault diagnosis task of box-type substations, a fault diagnosis method based on self-attention improvement of conditional tabular generative adversarial network (CTGAN) and AlexNet was proposed. The self-attention mechanism is introduced into the generator of CTGAN to maintain the correlation between the indicators of the input data, and a large amounts of high-quality data are generated according to the small number of fault samples. The generated data are input into the AlexNet model for fault diagnosis. The experimental results demonstrate that compared with the SMOTE and CTGAN methods, the dataset generated by the self-attention-conditional tabular generative adversarial network (SA-CTGAN) model has better data relevance. The accuracy of fault diagnosis by the proposed method reaches 94.81%, which is improved by about 11% compared with the model trained on the original data. Full article
(This article belongs to the Section Applied Physics General)
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18 pages, 29028 KiB  
Article
Evaluation of an Ozone-Induced Free Radical Solution’s Characteristics and Its Efficacy as an Alternative Pest Control Method
by Chundu Wu, Peng Tang, Aineng Cao, Pengfei Ni, Bo Zhang and Zhongwei Chang
Appl. Sci. 2024, 14(7), 3111; https://doi.org/10.3390/app14073111 - 8 Apr 2024
Viewed by 1001
Abstract
In light of the environmental problems stemming from chemical pesticides, a preparation system for an ozone-induced free radical solution was developed to replace chemical pesticides for disease control. The effective synthesis process parameters for the solution under experimental conditions were determined through a [...] Read more.
In light of the environmental problems stemming from chemical pesticides, a preparation system for an ozone-induced free radical solution was developed to replace chemical pesticides for disease control. The effective synthesis process parameters for the solution under experimental conditions were determined through a single-factor experiment. The mechanism by which the solution eradicates pathogenic bacteria was investigated using electron microscopy, and a disease prevention and control experiment was conducted. Under slightly acidic conditions, the redox potential of the solution was observed to be high, with an air intake of 0.5 L/min and a liquid intake of 1.45 L/min, while the concentration decayed slowly, with a liquid intake of 0.98 L/min. The solution’s destructive effect on the bacteria’s internal and external structures intensified with prolonged action time and an increased number of free radicals. A 1.5 mg/L solution and 5% imidacloprid effectively reduced pest levels to grades 3 and 4, respectively. When the pH is 3, with air intake at 0.5 L/min and liquid intake at 0.98 L/min, the ozone-induced free radical solution exhibits strong oxidation and stability. At a concentration of 1.5 mg/L, the solution demonstrates a superior control effect on diseases and can partially replace chemical pesticides, offering a promising alternative for environmentally sustainable disease control. Full article
(This article belongs to the Special Issue Research on Insecticides and Their Applications)
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12 pages, 761 KiB  
Article
Designing the Quality Characteristics of Berry Processing Byproducts Using Fermentation
by Sylwia Sady, Marta Ligaj, Bogdan Pachołek, Alfred Błaszczyk, Zuzanna Płaczek, Nikola Dłużniewska, Patrycja Kawałek, Karolina Pakuła, Adam Konopelski and Eryk Gołaszewski
Appl. Sci. 2024, 14(7), 3110; https://doi.org/10.3390/app14073110 - 8 Apr 2024
Cited by 1 | Viewed by 1330
Abstract
In recent years, there has been increasing interest in berry fruit processing byproducts, namely, seeds, pulp, and peel, due to the high content of nutritionally valuable ingredients. The market is seeing an increase in the popularity of fermented products, especially those from vegetables [...] Read more.
In recent years, there has been increasing interest in berry fruit processing byproducts, namely, seeds, pulp, and peel, due to the high content of nutritionally valuable ingredients. The market is seeing an increase in the popularity of fermented products, especially those from vegetables or fruits. Fermented fruit pomace can be used as an ingredient or food additive. Many studies have confirmed that the fermentation process can increase the antioxidant activity of plant extracts due to the decomposition of cell walls. The aim of this study was to evaluate the microbiological quality and antioxidant potential of fermented berry pomace (from chokeberry, blackcurrant, raspberry, and strawberry) in terms of its potential use as an alternative source of valuable ingredients for the design of new food products. The scope of this research included assessing microbiological quality, vitamin C and total phenolic compound (TPC) contents, and antioxidant activity using ABTS, DPPH, and FRAP assays. The polyphenolic compound and vitamin C contents, as well as antioxidant activity, depended on the mixture of microbial strains used for fermentation and the type of fruit pomace. The most favorable parameters for TPC, ABTS, DPPH, and FRAP were obtained for chokeberry pomace samples inoculated with yeast cultures. Chokeberry pomace exhibited the highest vitamin C content when inoculated with a mixture of bacteria. Full article
(This article belongs to the Special Issue The Role of Bioactive Natural Products in Health and Disease)
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14 pages, 12039 KiB  
Article
Analysis of the Load-Bearing Capacity of Pebble Aggregates
by Pan Liu, Peiyi Bai and Wenju Liu
Appl. Sci. 2024, 14(7), 3109; https://doi.org/10.3390/app14073109 - 8 Apr 2024
Cited by 1 | Viewed by 866
Abstract
The load-bearing capacity of pebble aggregates plays a pivotal role in influencing the operational performance of uncontrolled trucks on arrester beds. The complexity of this phenomenon stems from the nonuniformity in the shapes of the pebbles and their stochastic arrangement within the beds, [...] Read more.
The load-bearing capacity of pebble aggregates plays a pivotal role in influencing the operational performance of uncontrolled trucks on arrester beds. The complexity of this phenomenon stems from the nonuniformity in the shapes of the pebbles and their stochastic arrangement within the beds, presenting notable challenges for traditional mathematical modelling techniques in precisely evaluating the contact dynamics of these aggregates. This study leverages the discrete element method (DEM) to extensively analyse the arrester bed aggregate of a standard truck escape ramp. The aforementioned mechanism entails the gathering of morphological parameters of irregularly shaped aggregate particles and introduces a novel method for constructing random shapes that adhere to the observed distribution characteristics. A discrete element model, grounded in the physical properties of these aggregates, is formulated. This study focuses on the aggregate’s load-bearing capabilities, scrutinising the mechanical behaviour of the aggregate particles at the macroscopic and microscopic scales. These insights offer substantial scientific contributions and practical implications for assessing the safety of escape ramps and determining essential parameters for the brake bed design. Full article
(This article belongs to the Special Issue Advanced Pavement Materials in Road Construction)
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15 pages, 3651 KiB  
Article
Ride Comfort Prediction on Urban Road Using Discrete Pavement Roughness Index
by Difei Wu
Appl. Sci. 2024, 14(7), 3108; https://doi.org/10.3390/app14073108 - 8 Apr 2024
Viewed by 972
Abstract
The prediction of ride comfort holds significant potential for enhancing the driving experience of both human drivers and autonomous vehicles, as it is closely correlated with pavement roughness. However, in urban road scenarios, the presence of shorter road segments and local irregularities introduces [...] Read more.
The prediction of ride comfort holds significant potential for enhancing the driving experience of both human drivers and autonomous vehicles, as it is closely correlated with pavement roughness. However, in urban road scenarios, the presence of shorter road segments and local irregularities introduces added complexity to ride comfort prediction. To better capture and characterize the irregularities and short road sections’ unevenness, we adopt the discrete roughness index (DRI) instead of the commonly used international roughness index (IRI) for assessing road profile unevenness, which is more suitable for urban roads. Ride comfort prediction is developed through numerical simulations using an eight-degree-of-freedom full-car model. The maximum transient vibration value (MTVV) is adopted to assess ride comfort. Through comparing the correlations between the MTVV and pavement roughness indices, it is indicated that the fitting degree of MTVV-DRI outperforms that of MTVV-IRI on short sections. Then, a set of speed-related DRI thresholds to estimate ride comfort distribution on a given road section is proposed, with considerations of vehicle speed, time period, and wheel paths. A hyperbolic-tangent-based speed control strategy is also proposed to avoid abrupt speed and acceleration changes during deceleration. This prediction method can assist drivers or autonomous vehicles in generating driving control strategies and maintaining a high level of ride comfort. Full article
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26 pages, 8541 KiB  
Article
Development Characteristics and Reactivation Mechanism of a Large-Scale Ancient Landslide in Reservoir Area
by Liang Dai, Chaojun Jia, Lei Chen, Qiang Zhang and Wei Chen
Appl. Sci. 2024, 14(7), 3107; https://doi.org/10.3390/app14073107 - 8 Apr 2024
Viewed by 956
Abstract
The intricate geological conditions of reservoir banks render them highly susceptible to destabilization and damage from fluctuations in water levels. The study area, the Cheyipin section of the Huangdeng Hydroelectric Station, is characterized by numerous ancient landslides of varying scales and ages. In [...] Read more.
The intricate geological conditions of reservoir banks render them highly susceptible to destabilization and damage from fluctuations in water levels. The study area, the Cheyipin section of the Huangdeng Hydroelectric Station, is characterized by numerous ancient landslides of varying scales and ages. In June 2019, during the reservoir filling process of the Huangdeng Hydroelectric Station, a large-scale reactivation of ancient landslides occurred in this area, posing severe threats to riverside infrastructure and human safety, including ground cracking, house cracking, foundation settlement, and road collapse. The reactivation mechanism of ancient landslides at reservoir banks is highly complex due to fluid dynamics. This study conducted field investigations in the Cheyipin landslide area, monitored surface and subsurface deformations using GNSS and inclinometers, and analyzed the distribution characteristics, destruction features, and reactivation mechanisms of the landslides through correlation analysis and numerical calculations. The results indicate that the instability pattern of the slopes manifests as traction-type sliding failure. The slopes do not slide along the ancient sliding surface but along a newly formed arcuate sliding surface, with the direct impact area mainly concentrated near the waterline. The stability of the slopes in this project is closely related to the reservoir water level. It can be assumed that the lowering of the reservoir water level triggered the reactivation of the ancient landslides in the Cheyipin section, while the influence of rainfall can be ignored. To prevent the reactivation of ancient landslides, attention should be focused on the changes in reservoir water level, avoiding rapid adjustments in water level during the initial lowering and final raising of the water level. Full article
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16 pages, 774 KiB  
Article
CTGGAN: Controllable Text Generation with Generative Adversarial Network
by Zhe Yang, Yi Huang, Yaqin Chen, Xiaoting Wu, Junlan Feng and Chao Deng
Appl. Sci. 2024, 14(7), 3106; https://doi.org/10.3390/app14073106 - 8 Apr 2024
Viewed by 1419
Abstract
Controllable Text Generation (CTG) aims to modify the output of a Language Model (LM) to meet specific constraints. For example, in a customer service conversation, responses from the agent should ideally be soothing and address the user’s dissatisfaction or complaints. This imposes significant [...] Read more.
Controllable Text Generation (CTG) aims to modify the output of a Language Model (LM) to meet specific constraints. For example, in a customer service conversation, responses from the agent should ideally be soothing and address the user’s dissatisfaction or complaints. This imposes significant demands on controlling language model output. However, demerits exist among traditional methods. Promoting and fine-tuning language models exhibit the “hallucination” phenomenon and cannot guarantee complete adherence to constraints. Conditional language models (CLM), which map control codes into LM representations or latent space, require training the modified language models from scratch and a high amount of customized dataset is demanded. Decoding-time methods employ Bayesian Rules to modify the output of the LM or model constraints as a combination of energy functions and update the output along the low-energy direction. Both methods are confronted with the efficiency sampling problem. Moreover, there are no methods that consider the relation between constraints weights and the contexts, as is essential in actual applications such as customer service scenarios. To alleviate the problems mentioned above, we propose Controllable Text Generation with Generative Adversarial Networks (CTGGAN), which utilizes a language model with logits bias as the Generator to produce constrained text and employs the Discriminator with learnable constraint weight combinations to score and update the generation. We evaluate the method in the text completion task and Chinese customer service dialogues scenario, and our method shows superior performance in metrics such as PPL and Dist-3. In addition, CTGGAN also exhibits efficient decoding compared to other methods. Full article
(This article belongs to the Special Issue Holistic AI Technologies and Applications)
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29 pages, 2422 KiB  
Article
AdaBoost Ensemble Approach with Weak Classifiers for Gear Fault Diagnosis and Prognosis in DC Motors
by Syed Safdar Hussain and Syed Sajjad Haider Zaidi
Appl. Sci. 2024, 14(7), 3105; https://doi.org/10.3390/app14073105 - 7 Apr 2024
Cited by 1 | Viewed by 1392
Abstract
This study introduces a novel predictive methodology for diagnosing and predicting gear problems in DC motors. Leveraging AdaBoost with weak classifiers and regressors, the diagnostic aspect categorizes the machine’s current operational state by analyzing time–frequency features extracted from motor current signals. AdaBoost classifiers [...] Read more.
This study introduces a novel predictive methodology for diagnosing and predicting gear problems in DC motors. Leveraging AdaBoost with weak classifiers and regressors, the diagnostic aspect categorizes the machine’s current operational state by analyzing time–frequency features extracted from motor current signals. AdaBoost classifiers are employed as weak learners to effectively identify fault severity conditions. Meanwhile, the prognostic aspect utilizes AdaBoost regressors, also acting as weak learners trained on the same features, to predict the machine’s future state and estimate its remaining useful life. A key contribution of this approach is its ability to address the challenge of limited historical data for electrical equipment by optimizing AdaBoost parameters with minimal data. Experimental validation is conducted using a dedicated setup to collect comprehensive data. Through illustrative examples using experimental data, the efficacy of this method in identifying malfunctions and precisely forecasting the remaining lifespan of DC motors is demonstrated. Full article
(This article belongs to the Special Issue Fault Classification and Detection Using Artificial Intelligence)
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