Applied Sciences

17 pages, 4453 KiB

Open AccessArticle

A Multi-Scale Feature Fusion Hybrid Convolution Attention Model for Birdsong Recognition

by Lianglian Gu, Guangzhi Di, Danju Lv, Yan Zhang, Yueyun Yu, Wei Li and Ziqian Wang

Appl. Sci. 2025, 15(8), 4595; https://doi.org/10.3390/app15084595 - 21 Apr 2025

Birdsong is a valuable indicator of rich biodiversity and ecological significance. Although feature extraction has demonstrated satisfactory performance in classification, single-scale feature extraction methods may not fully capture the complexity of birdsong, potentially leading to suboptimal classification outcomes. The integration of multi-scale feature [...] Read more.

Birdsong is a valuable indicator of rich biodiversity and ecological significance. Although feature extraction has demonstrated satisfactory performance in classification, single-scale feature extraction methods may not fully capture the complexity of birdsong, potentially leading to suboptimal classification outcomes. The integration of multi-scale feature extraction and fusion enables the model to better handle scale variations, thereby enhancing its adaptability across different scales. To address this issue, we propose a multi-scale hybrid convolutional attention mechanism model (MUSCA). This method combines depthwise separable convolution and traditional convolution for feature extraction and incorporates self-attention and spatial attention mechanisms to refine spatial and channel features, thereby improving the effectiveness of multi-scale feature extraction. To further enhance multi-scale feature fusion, a layer-by-layer alignment feature fusion method is developed to establish a deeper correlation, thereby improving classification accuracy and robustness. Using the above method, we identified 20 bird species on three spectrograms, wavelet spectrogram, log-Mel spectrogram and log-spectrogram, with recognition rates of 93.79%, 96.97% and 95.44%, respectively. Compared with the resnet18 model, it increased by 3.26%, 1.88% and 3.09%, respectively. The results indicate that the MUSCA method proposed in this paper is competitive compared to recent and state-of-the-art methods. Full article

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21 pages, 1875 KiB

Open AccessArticle

Direction-Aware Lightweight Framework for Traditional Mongolian Document Layout Analysis

by Chenyang Zhou, Monghjaya Ha and Licheng Wu

Appl. Sci. 2025, 15(8), 4594; https://doi.org/10.3390/app15084594 - 21 Apr 2025

Abstract

Traditional Mongolian document layout analysis faces unique challenges due to its vertical writing system and complex structural arrangements. Existing methods often struggle with the directional nature of traditional Mongolian text and require substantial computational resources. In this paper, we propose a direction-aware lightweight [...] Read more.

Traditional Mongolian document layout analysis faces unique challenges due to its vertical writing system and complex structural arrangements. Existing methods often struggle with the directional nature of traditional Mongolian text and require substantial computational resources. In this paper, we propose a direction-aware lightweight framework that effectively addresses these challenges. Our framework introduces three key innovations: a modified MobileNetV3 backbone with asymmetric convolutions for efficient vertical feature extraction, a dynamic feature enhancement module with channel attention for adaptive multi-scale information fusion, and a direction-aware detection head with

(sin θ, cos θ)

vector representation for accurate orientation modeling. We evaluate our method on TMDLAD, a newly constructed traditional Mongolian document layout analysis dataset, comparing it with both heavy ResNet-50-based models and lightweight alternatives. The experimental results demonstrate that our approach achieves state-of-the-art performance, with 0.715 mAP and 92.3% direction accuracy with a mean absolute error of only 2.5°, while maintaining high efficiency at 28.6 FPS using only 8.3 M parameters. Our model outperforms the best ResNet-50-based model by 3.6% in mAP and the best lightweight model by 4.3% in mAP, while uniquely providing direction prediction capability that other lightweight models lack. The proposed framework significantly outperforms existing methods in both accuracy and efficiency, providing a practical solution for traditional Mongolian document layout analysis that can be extended to other vertical writing systems. Full article

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47 pages, 10098 KiB

Open AccessReview

Comprehensive Review of Edge Computing for Power Systems: State of the Art, Architecture, and Applications

by Fatma Yıldırım, Yunus Yalman, Kamil Çağatay Bayındır and Erman Terciyanlı

Appl. Sci. 2025, 15(8), 4592; https://doi.org/10.3390/app15084592 - 21 Apr 2025

Abstract

The increasing complexity of conventional energy distribution systems, combined with the growing demand for efficient data processing, has necessitated the implementation of smart grid technologies and the integration of advanced computing paradigms such as edge computing. Traditional cloud-based solutions face significant challenges, including [...] Read more.

The increasing complexity of conventional energy distribution systems, combined with the growing demand for efficient data processing, has necessitated the implementation of smart grid technologies and the integration of advanced computing paradigms such as edge computing. Traditional cloud-based solutions face significant challenges, including high latency, limited bandwidth, and cybersecurity vulnerabilities, rendering them less effective for real-time smart grid applications. Edge computing enables localized data processing, which significantly reduces latency and optimizes bandwidth usage. These capabilities enhance the resilience and intelligence of modern energy systems. This paper presents a systematic review of edge computing in energy distribution systems, examining its architectures, methodologies, and real-world applications. Key application areas consist of real-time data transmission, smart metering, microgrid management, anomaly and fault detection, state estimation, and energy management. The analysis shows how edge computing improves secure communication, supports decentralized intelligence, and facilitates scalable energy optimization. Beyond these advantages, the review also identifies critical challenges such as interoperability issues, resource constraints, and security vulnerabilities. By categorizing edge computing applications, the findings provide a comprehensive reference for both researchers and industry professionals working on the development of next-generation energy management systems. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in the Novel Power System)

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21 pages, 4227 KiB

Open AccessArticle

Clothing Recommendation with Multimodal Feature Fusion: Price Sensitivity and Personalization Optimization

by Chunhui Zhang, Xiaofen Ji and Liling Cai

Appl. Sci. 2025, 15(8), 4591; https://doi.org/10.3390/app15084591 - 21 Apr 2025

Abstract

The rapid growth in the global apparel market and the rise of online consumption underscore the necessity for intelligent clothing recommendation systems that balance visual compatibility with personalized preferences, particularly price sensitivity. Existing recommendation systems often neglect nuanced consumer price behaviors, limiting their [...] Read more.

The rapid growth in the global apparel market and the rise of online consumption underscore the necessity for intelligent clothing recommendation systems that balance visual compatibility with personalized preferences, particularly price sensitivity. Existing recommendation systems often neglect nuanced consumer price behaviors, limiting their ability to deliver truly personalized suggestions. To address this gap, we propose DeepFMP, a multimodal deep learning framework that integrates visual, textual, and price features through an enhanced DeepFM architecture. Leveraging the IQON3000 dataset, our model employs ResNet-50 and BERT for image and text feature extraction, alongside a comprehensive price feature module capturing individual, statistical, and category-specific price patterns. An attention mechanism optimizes multimodal fusion, enabling robust modeling of user preferences. Comparative experiments demonstrate that DeepFMP outperforms state-of-the-art baselines (LR, FM, Wide & Deep, GP-BPR, and DeepFM), achieving AUC improvements of 1.6–12.2% and NDCG@10 gains of up to 3.2%. Case analyses further reveal that DeepFMP effectively improves the recommendation accuracy, offering actionable insights for personalized marketing. This work advances multimodal recommendation systems by emphasizing price sensitivity as a pivotal factor, providing a scalable solution for enhancing user satisfaction and commercial efficacy in fashion e-commerce. Full article

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20 pages, 10457 KiB

Open AccessArticle

Design of a Double-Matched Cross-Polar Single Antenna Harmonic Tag

by Alessandro DiCarlofelice, Antonio DiNatale, Emidio DiGiampaolo and Piero Tognolatti

Appl. Sci. 2025, 15(8), 4590; https://doi.org/10.3390/app15084590 - 21 Apr 2025

Abstract

Radio frequency identification (RFID) technology has gained significant attention in various industry sectors due to its potential for efficient inventory management, asset tracking, and object localization. Different RFID technologies are available; resorting to harmonic signals is currently less used but, recently, has gained [...] Read more.

Radio frequency identification (RFID) technology has gained significant attention in various industry sectors due to its potential for efficient inventory management, asset tracking, and object localization. Different RFID technologies are available; resorting to harmonic signals is currently less used but, recently, has gained interest in research activity. In this study, we present the design, prototype realization, and performance evaluation of a dual-band dual-polarized harmonic tag. The tag incorporates a dual-band matching circuit utilizing a zero-bias Schottky diode HSMS-2850 connected to a perturbed annular ring patch antenna. The antenna, in fact, is able to radiate in cross-polarization at the higher frequency. Through a comprehensive design methodology, including simulation optimization and prototype fabrication, we demonstrate the successful implementation of the tag. Measurements to validate the impedance matching properties, radiation patterns, and backscattering capability of the tag are also shown. Full article

(This article belongs to the Section Electrical, Electronics and Communications Engineering)

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18 pages, 4068 KiB

Open AccessArticle

Absolute Capacitance Measurement by Direct Digital Fitting of Proportional Coefficient

by Xuanze Wang, Qian Shi, Da Liu, Boya Xie, Siyuan Chen, Junzhe Luo and Peipei Lu

Appl. Sci. 2025, 15(8), 4589; https://doi.org/10.3390/app15084589 - 21 Apr 2025

Abstract

In the realm of capacitance measurement, traditional methods that gauge capacitance through timing charge and discharge intervals frequently suffer from inaccuracies, particularly due to noise affecting voltage threshold detection. These techniques are also inefficient for high-capacitance components, as their lengthy charge–discharge cycles limit [...] Read more.

In the realm of capacitance measurement, traditional methods that gauge capacitance through timing charge and discharge intervals frequently suffer from inaccuracies, particularly due to noise affecting voltage threshold detection. These techniques are also inefficient for high-capacitance components, as their lengthy charge–discharge cycles limit the measurable range within a given period. To this end, a method for directly sampling and analyzing the input and output signals of an RC first-order system under square wave excitation is proposed for a wide range of capacitance measurements. By establishing a proportional relationship between the differentiated output signal and the difference between input and output signals, one can deduce the capacitance. To counteract noise-induced errors during differentiation, data smoothing is applied, enhancing accuracy. This technique achieves a relative standard deviation of less than 0.9% for capacitances from 60 pF to 60,000 pF, using a 100 kΩ reference resistor and continuous square waves. For capacitances above 800 pF, precision further improves to less than 0.2%. The approach leverages least squares fitting and outlier rejection to manage noise effectively. It remains independent of the capacitor’s initial state, ensuring broad-range accuracy and faster measurement times. Full article

(This article belongs to the Section Electrical, Electronics and Communications Engineering)

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15 pages, 2479 KiB

Open AccessArticle

Metabolomics Approach for Sweat Mapping the Performance of Soccer Players in Pretoria, South Africa

by Nong Malefo, Clarissa Marcelle Naidoo, Mukhethwa Michael Mphephu, Mmei Cheryl Motshudi and Nqobile Monate Mkolo

Appl. Sci. 2025, 15(8), 4588; https://doi.org/10.3390/app15084588 - 21 Apr 2025

Abstract

Exercise is one of the main challenges to the body’s homeostasis since it needs an immediate, substantial rise in ATP re-synthesis, which leads to the prevention of response capacity and performance of players. Therefore, it is vital to monitor sweat metabolites in soccer [...] Read more.

Exercise is one of the main challenges to the body’s homeostasis since it needs an immediate, substantial rise in ATP re-synthesis, which leads to the prevention of response capacity and performance of players. Therefore, it is vital to monitor sweat metabolites in soccer players during vigorous exercise to comprehend their functional variations. This flagged the requirement metabonomic approaches for the determination of the distinct metabolic pathways and signature metabolites that are involved in soccer players pre- and post-exercise. In this study, metabolomics and chemometrics approaches were integrated to accelerate and unravel signature-altered metabolites involved pre- and post-exercise. Metabolites profiling revealed a total of 57 signatures and the identified signature altered metabolites belonging to carboxylic acids, ketone, alcohols, aldehydes, aromatics, alkenes, hexoses, hydroxy fatty acids, tetracyclic N-heterocycles, aldopentose, benzenes, alkanes, phenols, and heterocyclic. Niacin is the most downregulated and abundant pre-induced exercise, which can employ its effects through energy metabolism as a precursor for nicotinamide adenine dinucleotide (NAD) and nicotinamide adenine dinucleotide phosphate (NADP). Significant alterations were also specifically observed in the Alanine, aspartate and glutamate, Valine, leucine and isoleucine, Pantothenate and CoA biosynthesis, and Galactose metabolisms following exercise. Full article

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18 pages, 3078 KiB

Open AccessArticle

Exploring and Modeling the Incentive Strategies of New Energy Ship Application in Yangtze River

by Jing Zhai and Haiyan Wang

Appl. Sci. 2025, 15(8), 4587; https://doi.org/10.3390/app15084587 - 21 Apr 2025

Abstract

The application of new energy ships (NESs) in the Yangtze River is one of the important ways to promote the low-carbon development of Yangtze River shipping, but at present, the enthusiasm of shipping enterprises for it is not high enough. To improve the [...] Read more.

The application of new energy ships (NESs) in the Yangtze River is one of the important ways to promote the low-carbon development of Yangtze River shipping, but at present, the enthusiasm of shipping enterprises for it is not high enough. To improve the current situation, the impact of incentive strategies on the NES application is a problem worth studying. We aim to explore an incentive model based on the dynamic evolutionary relationship between the NES application of shipping enterprises and the government’s incentive decision. Theoretical derivations show that there are three possible equilibria in the evolutionary game system and ten major factors that affect the income of the government and shipping enterprises, respectively. Four factors—costs, utility loss, rewards, and tax incentives—are selected for the numerical study: regardless of whether the government takes measures or not, reducing the cost of new energy ship applications can greatly improve the enthusiasm of shipping enterprises; increasing the incentives cannot significantly improve the enthusiasm of shipping enterprises. This paper provides policy recommendations for the application of NES in the Yangtze River, which will help the government to introduce appropriate government incentives. Full article

(This article belongs to the Section Marine Science and Engineering)

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16 pages, 8443 KiB

Open AccessArticle

Wavelet-Enhanced YOLO for Intelligent Detection of Welding Defects in X-Ray Films

by Wenyong Wu, Hongyu Cheng, Jiancheng Pan, Lili Zhong and Qican Zhang

Appl. Sci. 2025, 15(8), 4586; https://doi.org/10.3390/app15084586 - 21 Apr 2025

Abstract

Welding defects threaten structural integrity, demanding efficient and accurate detection methods. Traditional radiographic testing defects interpretation is subjective, necessitating automated solutions to improve accuracy and efficiency. This study integrates wavelet transform convolutions (WTConv) into YOLOv11n, creating WT-YOLO, to enhance defect detection in X-ray [...] Read more.

Welding defects threaten structural integrity, demanding efficient and accurate detection methods. Traditional radiographic testing defects interpretation is subjective, necessitating automated solutions to improve accuracy and efficiency. This study integrates wavelet transform convolutions (WTConv) into YOLOv11n, creating WT-YOLO, to enhance defect detection in X-ray films. Wavelet transforms enable multi-resolution analysis, extracting both high-frequency and low-frequency features critical for detecting various welding defects. WT-YOLO replaces standard convolutional layers with WTConv, improving multi-scale feature extraction and noise suppression. Trained on 7000 radiographic images, WT-YOLO achieved a 0.0212 increase in mAP75 and a 0.0479 improvement in precision compared to YOLOv11n. On a test set of 200 images per defect category across seven defect types, WT-YOLO showed precision improvements of 0.0515 for cracks, 0.0784 for lack of fusion, 0.0067 for incomplete penetration, 0.1180 for concavity, 0.0516 for undercut, and 0.0204 for porosity, while experiencing a slight 0.0028 decline for slag inclusion. Compared to manual inspection, WT-YOLO achieved higher precision for cracks (0.0037), undercut (0.1747), slag inclusion (0.1129), and porosity (0.1074), with an inference speed 300 times faster than manual inspection. WT-YOLO enhances weld defect detection capabilities, providing the possibility for a robust solution for industrial applications. Full article

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30 pages, 42410 KiB

Open AccessArticle

The Application of Lite-GRU Embedding and VAE-Augmented Heterogeneous Graph Attention Network in Friend Link Prediction for LBSNs

by Ziteng Yang, Boyu Li, Yong Wang and Aoxue Liu

Appl. Sci. 2025, 15(8), 4585; https://doi.org/10.3390/app15084585 - 21 Apr 2025

Abstract

Friend link prediction is an important issue in recommendation systems and social network analysis. In Location-Based Social Networks (LBSNs), predicting potential friend relationships faces significant challenges due to the diversity of user behaviors, along with the high dimensionality, sparsity, and complex noise in [...] Read more.

Friend link prediction is an important issue in recommendation systems and social network analysis. In Location-Based Social Networks (LBSNs), predicting potential friend relationships faces significant challenges due to the diversity of user behaviors, along with the high dimensionality, sparsity, and complex noise in the data. To address these issues, this paper proposes a Heterogeneous Graph Attention Network (GEVEHGAN) model based on Lite Gate Recurrent Unit (Lite-GRU) embedding and Variational Autoencoder (VAE) enhancement. The model constructs a heterogeneous graph with two types of nodes and three types of edges; combines Skip-Gram and Lite-GRU to learn Point of Interest (POI) and user node embeddings; introduces VAE for dimensionality reduction and denoising of the embeddings; and employs edge-level attention mechanisms to enhance information propagation and feature aggregation. Experiments are conducted on the publicly available Foursquare dataset. The results show that the GEVEHGAN model outperforms other comparative models in evaluation metrics such as AUC, AP, and Top@K accuracy, demonstrating its superior performance in the friend link prediction task. Full article

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21 pages, 3884 KiB

Open AccessArticle

The Impact of Perceptual Road Markings on Driving Behavior in Horizontal Curves: A Driving Simulator Study

by Ali Pirdavani, Mahdi Sadeqi Bajestani, Siwagorn Bunjong and Lucas Delbare

Appl. Sci. 2025, 15(8), 4584; https://doi.org/10.3390/app15084584 - 21 Apr 2025

Abstract

Horizontal curves have been a significant safety concern on roads for years, often resulting in a high incidence of crashes. A European Road Safety Observatory report indicated that 53% of road crashes in the EU in 2020 occurred on rural roads, mainly due [...] Read more.

Horizontal curves have been a significant safety concern on roads for years, often resulting in a high incidence of crashes. A European Road Safety Observatory report indicated that 53% of road crashes in the EU in 2020 occurred on rural roads, mainly due to misjudging when navigating these curves. This study explores innovative low-cost road designs for this issue, such as the red-white pattern edge line (RWE), the solid red edge line (RE), the alternating red-white checkered median stripe (RWM), and the red dragon’s teeth (RDT) to improve driver behavior around curves. The various road markings were tested based on speed, acceleration/deceleration, and lateral position before and during horizontal curves in a driving simulator using STISIM Drive^® 3. Fifty-two volunteers, aged between 20 and 75, participated in the study. The simulation road was designed according to the Flemish Road Agency (AWV) guidelines. The simulation tested twelve horizontal curves, including left and right turns, with 125 m and 350 m radii. The results were analyzed using within-subjects repeated measures ANOVA, with Greenhouse–Geisser correction for sphericity violations. It was revealed that these markings can reduce driving speeds and improve control, enhancing road safety. Specifically, the red-white median stripe resulted in better lateral positioning. At the same time, red dragon’s teeth minimized deceleration before curves, although their effects were less significant for curves with larger radii. Full article

(This article belongs to the Special Issue Advances in Intelligent Road Design and Application)

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16 pages, 2430 KiB

Open AccessArticle

Research on the Network Structure Characteristics of Doctors and the Influencing Mechanism on Recommendation Rates in Online Health Communities: A Multi-Dimensional Perspective Based on the “Good Doctor Online” Platform

by Hao Wang, Chen Wang and Huiying Qi

Appl. Sci. 2025, 15(8), 4583; https://doi.org/10.3390/app15084583 - 21 Apr 2025

Abstract

(1) Background: Online health communities (OHCs) serve as ecosystems connecting doctors, patients, and medical resources. Studying their deep network structure and impact mechanisms on medical service quality provides a comprehensive understanding of digital healthcare ecosystems and has guiding significance for platform service optimization. [...] Read more.

(1) Background: Online health communities (OHCs) serve as ecosystems connecting doctors, patients, and medical resources. Studying their deep network structure and impact mechanisms on medical service quality provides a comprehensive understanding of digital healthcare ecosystems and has guiding significance for platform service optimization. (2) Methods: Using the “Good Doctor Online” platform as the data source, we employed social network analysis methods to construct network models from the professional title and disease-type dimensions, and used multiple linear regression statistical analysis to identify the influencing factors of doctor recommendation rates. (3) Results: Our analysis found that depression doctors exhibit the highest network connectivity (average degree = 17.378), and chief physicians demonstrate significantly higher internal connectivity (average degree = 9.353) compared to resident physicians (average degree = 0.804). The doctor recommendation rate is significantly correlated with post-consultation evaluation (r = 0.602, p < 0.001) and shows a 45% variance explanation (R² = 0.450) in our regression model. (4) Conclusions: Different disease types in OHCs demonstrate distinct organizational patterns, with depression networks showing significantly denser connections than diabetes networks. Professional titles strongly influence network position, with chief physicians forming highly connected hubs while resident physicians remain peripheral. Recommendation rates emerge through multi-dimensional trust processes primarily driven by post-consultation evaluation quality. Full article

(This article belongs to the Special Issue Human Activity Recognition (HAR) in Healthcare, 2nd Edition)

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29 pages, 7251 KiB

Open AccessArticle

A GIS-Based Approach for Use Recommendations and Limitations in Sustainable Coastal Planning in the Southeastern Margin of the Ría de Arosa (Pontevedra, Spain)

by Carlos E. Nieto, Antonio Miguel Martínez-Graña, Leticia Merchán and Joaquín Andrés Valencia Ortiz

Appl. Sci. 2025, 15(8), 4582; https://doi.org/10.3390/app15084582 - 21 Apr 2025

Abstract

The southeastern margin of the Ría de Arosa is a region of great ecological and social importance, characterized by increasing urban development, tourism pressures, and vulnerability to natural hazards, soil erosion, coastal flooding, and mass movements, where sustainable territorial planning poses significant challenges. [...] Read more.

The southeastern margin of the Ría de Arosa is a region of great ecological and social importance, characterized by increasing urban development, tourism pressures, and vulnerability to natural hazards, soil erosion, coastal flooding, and mass movements, where sustainable territorial planning poses significant challenges. This study combines Geographic Information Systems tools and quantitative and qualitative overlay techniques to integrate conservation quality and comprehensive risk maps. The main challenge addressed in this research is the integration of geospatial data and diverse natural risk factors. The result was a map of land use recommendations and limitations, and another of degree of land use limitation, which identify priority areas for conservation and zones suitable for the controlled development of recreational, agricultural, and industrial activities. The methodology employed allows for detailed modelling that is easily updated and applicable to other environments for territorial planning and natural resource conservation. Areas of special natural importance, such as Arosa Island and the El Grove Peninsula, stand out as optimal locations for sustainable recreational activities, while the northeastern coastal corridor, between Villanueva de Arosa and Cambados, shows suitability for anthropogenic development. This approach contributes to a balance between socioeconomic development and environmental protection, facilitating the implementation of sustainable planning and conservation strategies in highly fragile coastal areas. Full article

(This article belongs to the Special Issue Recent Advances in Land Use and Spatial Planning in Urban and Rural Areas)

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23 pages, 9698 KiB

Open AccessArticle

Experimental Investigation of Shear Behavior and Pore Structure Evolution in Heat-Treated Granite Subjected to Liquid Nitrogen and Water Cooling

by Fan Zhang, Shengyuan Liu, Subiao Zhang, Yiming Zhang, Shaohui Quan and Man Li

Appl. Sci. 2025, 15(8), 4581; https://doi.org/10.3390/app15084581 - 21 Apr 2025

Abstract

It is imperative to understand the shear mechanical properties and pore evolution of granite under thermal shock to assess the fracturing of hot dry rock reservoirs. In this study, variable-angle shear tests were performed on coarse- and fine-grained granite samples following liquid nitrogen [...] Read more.

It is imperative to understand the shear mechanical properties and pore evolution of granite under thermal shock to assess the fracturing of hot dry rock reservoirs. In this study, variable-angle shear tests were performed on coarse- and fine-grained granite samples following liquid nitrogen (LN₂) cooling under different high-temperature conditions. The effect of thermal treatment temperature, particle type, and cooling method on the shear strength, cohesion, and angle of internal friction of granite was then analyzed. To this end, low field nuclear magnetic resonance (NMR) and scanning electron microscopy (SEM) were used to investigate the pore size distribution and microstructural evolution of granite. The experimental results indicate that both the shear strength and cohesion of granite initially increase and then decrease with the rise in thermal treatment temperature. The maximum increases in shear strength and cohesion are 38.0% and 36.7%, respectively, while the maximum decreases reach 43.7% and 42.4%. Notably, the most pronounced thermal hardening effect is observed at 200 °C. In contrast, the internal friction angle exhibits a decreasing-then-increasing trend as the temperature rises, with a maximum reduction of 5.4% and a maximum increase of 14.5%. In addition, fine-grained granite exhibits superior shear strength and a more pronounced thermal hardening effect compared to coarse-grained granite. Furthermore, the damage effect caused by thermal shock increases with increasing heat treatment temperature, with the damage effect induced by liquid nitrogen cooling being particularly significant compared to water cooling. Furthermore, for both types of granite at the same shear angle, an increase in the heat treatment temperature results in a corresponding increase in the total fracture area, with the fracture area after liquid nitrogen cooling being more significant. The macroscopic failure mode changes from a mixed compression–shear failure mode to a direct shear failure mode with increasing shear angle. NMR testing shows that liquid nitrogen cooling can effectively increase the proportion of medium pores and large pores in the granite and increase the connectivity of internal pores; specifically, in coarse-grained granite, medium pores and large pores collectively increased by 10.5%, while in fine-grained granite, the total increase in medium pores reached 51%. As the heat treatment temperature increases, the type of crack that develops in granite changes from intragranular to transgranular. In addition, the fracture surface of granite is more prone to form micropores and small pores when cooled with liquid nitrogen, increasing the connectivity of the crack network. The results of this research will be useful for fracturing hot dry rock reservoirs. Full article

(This article belongs to the Special Issue Advances and Challenges in Rock Mechanics and Rock Engineering)

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19 pages, 5282 KiB

Open AccessArticle

Shear Properties of the Interface Between Polyurethane Concrete and Normal Concrete

by Yuhan Zhang, Xinlong Yue, Zhengyi Liu, Boyang Mi, Lu Wang, Quansheng Sun, Xu Wang and Zhongnan Dai

Appl. Sci. 2025, 15(8), 4580; https://doi.org/10.3390/app15084580 - 21 Apr 2025

Abstract

Polyurethane concrete (PUC) is a promising candidate for structural repair materials due to its excellent mechanical properties and durability. However, the bonding performance between PUC and concrete interfaces may limit its broader application. This study examined the factors affecting the shear strength at [...] Read more.

Polyurethane concrete (PUC) is a promising candidate for structural repair materials due to its excellent mechanical properties and durability. However, the bonding performance between PUC and concrete interfaces may limit its broader application. This study examined the factors affecting the shear strength at the PUC–NC interface. A total of 16 oblique shear tests, varying by interface treatment methods (smooth—GH, roughened—ZM, and grooved—KC), adhesive application rates—NJJ (0, 0.2, and 0.3 kg/m²), and steel fiber contents—GXW (0%, 0.5%, 1%, and 1.5%), to evaluate their impact on the mechanical properties of the PUC–NC interface. The results demonstrated that roughening the interface significantly improved the shear strength, resulting in a 32% increase compared to a smooth interface and 15% compared to a grooved interface. A moderate adhesive application rate (0.2 kg/m²) enhanced the interface strength, while excessive adhesive did not further increase the shear strength. The optimal steel fiber content (1%) resulted in the highest shear strength, improving it by 22%, whereas excess steel fibers (1.5%) reduced the interface strength. This is due to fiber agglomeration, which weakens mechanical interlocking and introduces defects that impair interfacial bonding. Load–slip curve analysis revealed that roughened interfaces combined with the appropriate amount of steel fibers improved the interface toughness, delaying the failure process. This study presents a model for calculating the shear strength of steel fiber-reinforced PUC–NC interfaces, incorporating shear slip. Compared to existing models, it more accurately reflects the experimental data. Full article

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17 pages, 6963 KiB

Open AccessArticle

A Lightweight Fault Diagnosis with Domain Adaptation for Defected Bearings

by Xufen Jiao, Yixin Zhou, Xuan Liu, Zepeng Ma and Fang Xu

Appl. Sci. 2025, 15(8), 4579; https://doi.org/10.3390/app15084579 - 21 Apr 2025

Abstract

This paper presents a lightweight fault diagnosis framework for bearing defects, integrating time-frequency analysis, deep learning, and model compression techniques to address challenges in resource-constrained environments. The proposed method combines the S-transform for high-resolution time-frequency representation with MobileNet as an efficient backbone network, [...] Read more.

This paper presents a lightweight fault diagnosis framework for bearing defects, integrating time-frequency analysis, deep learning, and model compression techniques to address challenges in resource-constrained environments. The proposed method combines the S-transform for high-resolution time-frequency representation with MobileNet as an efficient backbone network, enabling robust feature extraction from complex vibration signals. To enhance deployment on edge devices, knowledge distillation is employed to compress the model, significantly reducing computational complexity while maintaining diagnostic accuracy. Additionally, domain adaptation is considered to mitigate domain shift issues, ensuring robust performance across varying operating conditions. Experimental results demonstrate the framework’s effectiveness, achieving high diagnostic accuracy with reduced computational overhead, making it a practical solution for real-time industrial applications. The proposed approach bridges the gap between advanced deep learning techniques and practical industrial requirements, offering a scalable and efficient solution for bearing fault diagnosis. Full article

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13 pages, 2299 KiB

Open AccessArticle

Machine Learning Introduces Electrophysiology Assessment as the Best Predictor for the Recovery Prognosis of Spinal Cord Injury Patients for Personalized Rehabilitation Approaches

by Dionysia Chrysanthakopoulou, Charalampos Matzaroglou, Eftychia Trachani and Constantinos Koutsojannis

Appl. Sci. 2025, 15(8), 4578; https://doi.org/10.3390/app15084578 - 21 Apr 2025

Abstract

The strong correlation between evoked potentials (EPs) and American Spinal Injury Association (ASIA) scores in individuals with spinal cord injury (SCI) suggests that EPs may serve as reliable predictive markers for rehabilitation progress. Numerous studies have confirmed a relationship between variations in somatosensory [...] Read more.

The strong correlation between evoked potentials (EPs) and American Spinal Injury Association (ASIA) scores in individuals with spinal cord injury (SCI) suggests that EPs may serve as reliable predictive markers for rehabilitation progress. Numerous studies have confirmed a relationship between variations in somatosensory evoked potentials (SSEPs) and ASIA scores, especially in the early stages of SCI. Machine learning’s (ML’s) increasing importance in medicine is driven by the growing availability of health data and improved algorithms. It enables the creation of predictive models for disease diagnosis, progression prediction, personalized treatment, and improved healthcare efficiency. Data-driven approaches can significantly improve patient care, reduce costs, and facilitate personalized medicine. The meticulous analysis of medical data is crucial for timely disease identification, leading to effective symptom management and appropriate treatment. This study applies artificial intelligence to identify predictors of SCI progression, as measured by the disability index, ASIA impairment scale (AIS), and final motor recovery. We aim to clarify the prognostic role of electrophysiological testing (SSEPs, MEPs, and nerve conduction studies (NCSs)) in SCI. We analyzed data from a medical database of 123 records. We developed an ML-based intelligent system, utilizing ensemble algorithms combining decision trees and neural network approaches, to predict SCI recovery. Our evaluation showed SEP accuracies of 90% for motor recovery prediction and 80% for AIS scale determination, comparable to full electrophysiology evaluation accuracies of 93% and 89%, respectively, and generally superior results compared to MEP and NCS results. EPs emerged as the best predictors, comparable to a comprehensive electrophysiology assessment, significantly improving accuracy compared to clinical findings alone. An electrophysiological assessment, when available, increased overall accuracy for final motor recovery prediction to 93% (from a maximum of 75%) and, for ASIA score determination, to 89% (from a maximum of 66%). Further validation is needed with a larger dataset. Future research should validate that sensory electrophysiology assessment is a less expensive, portable, and simpler alternative to other prognostic tests and more effective than clinical assessments, like the AIS, biomarker for SCI, and personalized rehabilitation planning. Full article

(This article belongs to the Special Issue Advanced Physical Therapy for Rehabilitation)

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19 pages, 4272 KiB

Open AccessArticle

A Hybrid Model for Designers to Learn from Failures: A Case of a High Potential Fire Incident at an Underground Hard Rock Mine

by Tafadzwa Gotora and Ashraf Wasfi Labib

Appl. Sci. 2025, 15(8), 4577; https://doi.org/10.3390/app15084577 - 21 Apr 2025

Abstract

Mining companies are increasingly being motivated to become High Reliability Organisations (HROs) in order to achieve better results in critical areas such as safety, environment management, and loss avoidance despite their complex environments. High Reliability Organisations are recognised by their abilities to effectively [...] Read more.

Mining companies are increasingly being motivated to become High Reliability Organisations (HROs) in order to achieve better results in critical areas such as safety, environment management, and loss avoidance despite their complex environments. High Reliability Organisations are recognised by their abilities to effectively anticipate failures and disasters, including use of lessons learnt from previous failures. This paper seeks to demonstrate how designers for systems in the mining industry can learn from failures to anticipate failures and effectively manage them. It also demonstrates the applicability of a hybrid model which incorporates and integrates Fault Tree Analysis (FTA), Reliability Block Diagram (RBD) analysis, Risk Priority Number (RPN) concepts, and Analytical Hierarchy Processes (AHPs) in a case study for a High Potential Incident (HPI) at an underground hard rock mine. It shows how valuable lessons can be extracted and how these lessons can be used in decision making to prevent and manage future failures. The main contribution of this work is the demonstration of incorporating HRO principles with a hybrid modelling framework for learning from failures. Full article

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22 pages, 5583 KiB

Open AccessArticle

A Hybrid DSCNN-GRU Based Surrogate Model for Transient Groundwater Flow Prediction

by Xiang Li, Chaoyang Peng, Yule Zhao and Xuemin Xia

Appl. Sci. 2025, 15(8), 4576; https://doi.org/10.3390/app15084576 - 21 Apr 2025

Abstract

Sustainable groundwater resource management necessitates dependable and precise predictions of groundwater head fields under fluctuating climatic conditions. The substitution of original simulation models with efficient surrogates presents a challenge in simultaneously accounting for correlations among multiple time series outputs and maintaining overall prediction [...] Read more.

Sustainable groundwater resource management necessitates dependable and precise predictions of groundwater head fields under fluctuating climatic conditions. The substitution of original simulation models with efficient surrogates presents a challenge in simultaneously accounting for correlations among multiple time series outputs and maintaining overall prediction accuracy. This study develops a novel surrogate modelling approach, DSCNN-GRU, incorporating a deep separable convolutional neural network (DSCNN) and a gated recurrent unit (GRU), to efficiently capture temporal and spatial variations in groundwater head fields from transient groundwater flow models using input hydraulic conductivity field data. The applicability and performance of the proposed method are evaluated for predicting groundwater head fields in a practical research area under three scenarios with different hydraulic conductivity fields. The performance of the DSCNN-GRU model is compared to the traditional convolutional neural network (CNN), CNN-LSTM, and DSCNN-LSTM models to further test its applicability. The numerical study demonstrates that optimizing hyperparameters can result in reasonably accurate performance of the proposed model, and the “simplest” DSCNN-GRU outperforms CNN, CNN-LSTM, and DSCNN-LSTM in both prediction accuracy and time-to-solution. Full article

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19 pages, 2707 KiB

Open AccessArticle

A Large Language Model Driven Knowledge Graph Construction Scheme for Semantic Communication

by Chang Guo, Jiaqi Liu, Wei Gao, Zhenhai Lu, Yao Li, Chengyuan Wang and Jungang Yang

Appl. Sci. 2025, 15(8), 4575; https://doi.org/10.3390/app15084575 - 21 Apr 2025

Abstract

This study presents a knowledge graph construction scheme leveraging large language models (LLMs) for task-oriented semantic communication systems. The proposed methodology systematically addresses four critical stages: corpus collection, entity extraction and relationship analysis, knowledge base generation, and dynamic updating mechanisms. It is worth [...] Read more.

This study presents a knowledge graph construction scheme leveraging large language models (LLMs) for task-oriented semantic communication systems. The proposed methodology systematically addresses four critical stages: corpus collection, entity extraction and relationship analysis, knowledge base generation, and dynamic updating mechanisms. It is worth noting that prompt engineering is combined with few-shot learning to enhance reliability and accuracy in this methodology. Experimental demonstration showed that this methodology had superior entity extraction performance, achieving 89.7% precision and 92.3% recall rate. This scheme overcomes the demand for domain knowledge and the labor cost of traditional knowledge base construction schemes. It greatly improves the construction efficiency of knowledge graphs. This paper provides an efficient and reliable task knowledge base construction scheme for task-oriented semantic communication, which is expected to promote its wider application. Full article

(This article belongs to the Special Issue Knowledge Graphs and Semantic Understanding in Natural Language Processing)

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15 pages, 5326 KiB

Open AccessArticle

A Texture-Based Simulation Framework for Pose Estimation

by Yaoyang Shen, Ming Kong, Hang Yu and Lu Liu

Appl. Sci. 2025, 15(8), 4574; https://doi.org/10.3390/app15084574 - 21 Apr 2025

Abstract

An accurate 3D pose estimation of spherical objects remains challenging in industrial inspections and robotics due to their geometric symmetries and limited feature discriminability. This study proposes a texture-optimized simulation framework to enhance pose prediction accuracy through optimizing the surface texture features of [...] Read more.

An accurate 3D pose estimation of spherical objects remains challenging in industrial inspections and robotics due to their geometric symmetries and limited feature discriminability. This study proposes a texture-optimized simulation framework to enhance pose prediction accuracy through optimizing the surface texture features of the design samples. A hierarchical texture design strategy was developed, incorporating complexity gradients (low to high) and color contrast principles, and implemented via VTK-based 3D modeling with automated Euler angle annotations. The framework generated 2297 synthetic images across six texture variants, which were used to train a MobileNet model. The validation tests demonstrated that the high-complexity color textures achieved superior performance, reducing the mean absolute pose error by 64.8% compared to the low-complexity designs. While color improved the validation accuracy universally, the test set analyses revealed its dual role: complex textures leveraged chromatic contrast for robustness, whereas simple textures suffered color-induced noise (a 35.5% error increase). These findings establish texture complexity and color complementarity as critical design criteria for synthetic datasets, offering a scalable solution for vision-based pose estimation. Physical experiments confirmed the practical feasibility, yielding 2.7–3.3° mean errors. This work bridges the simulation-to-reality gaps in symmetric object localization, with implications for robotic manipulation and industrial metrology, while highlighting the need for material-aware texture adaptations in future research. Full article

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23 pages, 7619 KiB

Open AccessArticle

A Blockchain-Based Collaborative Storage Scheme for Roadside Unit Clusters in Social Internet of Vehicles

by Dai Hou, Lan Wei, Lei Zheng, Geng Wu, Jiaxing Hu, Chenxi Dong, Xinru Li and Kai Peng

Appl. Sci. 2025, 15(8), 4573; https://doi.org/10.3390/app15084573 - 21 Apr 2025

Abstract

With the gradual application of blockchain technology in the domain of Social Internet of Vehicles (SIoV), the increasing volume of blockchain data has imposed significant storage pressure on roadside units (RSUs). Collaborative storage schemes, which organize RSUs into clusters to jointly store content [...] Read more.

With the gradual application of blockchain technology in the domain of Social Internet of Vehicles (SIoV), the increasing volume of blockchain data has imposed significant storage pressure on roadside units (RSUs). Collaborative storage schemes, which organize RSUs into clusters to jointly store content for vehicles, have been explored. However, existing collaborative storage solutions in IoV primarily focus on caching content and are not well-suited to the deployment constraints of blockchain networks. Building on blockchain’s decentralized characteristics and data integrity mechanisms, this paper proposes a collaborative storage scheme that reduces RSU storage loads while sustaining distributed ledger operations in SIoV. Specifically, the RSU Access Preference-based Spectral Clustering Algorithm (RAPSCA) is proposed to address RSU clustering by analyzing both the RSUs’ access preferences for blockchain data and their resource availability. Subsequently, the Vehicle Service Priority-based Greedy Block Allocation Algorithm (VSPGBAA) is devised for intra-cluster storage allocation, which considers vehicles’ dwell times and block access probabilities to reduce overall access costs. Experimental results indicate that, compared to baseline algorithms, the proposed method achieves a 27.7% reduction in cost and a 3.5-fold decrease in execution time, thereby demonstrating the feasibility of collaborative storage optimization in blockchain-enabled SIoV. Full article

(This article belongs to the Special Issue IoT and Edge Computing for Smart Infrastructure and Cybersecurity)

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12 pages, 2766 KiB

Open AccessArticle

Determining Optimal Processing Conditions for Fabricating Industrial Moulds with Additive Manufacturing

by Daniel Moreno Nieto, Francisco Javier Puertas Morales, Julia Rivera Vera, Pedro Burgos Pintos, Daniel Moreno Sanchez and Sergio I. Molina

Appl. Sci. 2025, 15(8), 4572; https://doi.org/10.3390/app15084572 - 21 Apr 2025

Abstract

Additive manufacturing has reached a level of reliability and credibility that has already been integrated into specific industries producing final parts or tooling. Among Material Extrusion (ME) techniques, the Fused Granular Fabrication (FGF) method has enabled the development of Large Format Additive Manufacturing [...] Read more.

Additive manufacturing has reached a level of reliability and credibility that has already been integrated into specific industries producing final parts or tooling. Among Material Extrusion (ME) techniques, the Fused Granular Fabrication (FGF) method has enabled the development of Large Format Additive Manufacturing (LFAM) using polymeric materials, which has also established its presence in industries working with large prototypes, molds, and tools. This cost-efficient process has proven its applicability and success in manufacturing molds for composites, particularly in short and medium production runs, significantly reducing production times and costs. This paper presents two experiments designed to optimize process parameters when producing molds using the combined FGF and milling approach. These experiments identified optimal extrusion temperatures and extrusion multipliers to minimize defects at both the macro- and microscales for ASA 20 wt.% carbon fiber (CF) material; additionally, a correlation between milling speed, milling strategy, and surface roughness was established. These findings are valuable for industries adopting this innovative production method, as they provide guidance for defining process parameters to achieve the desired surface roughness of a specific part. A case study of the design of an automobile carter mold is presented, concluding that a specific range of milling speeds is required for conventional or climbing milling strategies to achieve a defined surface roughness range. Full article

(This article belongs to the Special Issue Advances in Carbon Fiber Reinforced Polymers (CFRPs))

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27 pages, 3381 KiB

Open AccessArticle

Experimental and Theoretical Evaluation of Incident Solar Irradiance on Photovoltaic Power Plants Under Real Operating Conditions: Fixed Tilt Angle System vs. Horizontal Single-Axis Tracker

by Arsenio Barbón, Jaime Martínez-Suárez, Luis Bayón and José A. Fernández-Rubiera

Appl. Sci. 2025, 15(8), 4571; https://doi.org/10.3390/app15084571 - 21 Apr 2025

Abstract

The aim of this paper was to delve deeper into the nuances of incident solar irradiance on the photovoltaic field of a fixed tilt angle system versus a horizontal single-axis tracker. The fixed tilt angle system was used as a baseline for comparison. [...] Read more.

The aim of this paper was to delve deeper into the nuances of incident solar irradiance on the photovoltaic field of a fixed tilt angle system versus a horizontal single-axis tracker. The fixed tilt angle system was used as a baseline for comparison. Three assessment indicators were analysed (annual energy gain (

A E G

), monthly energy gain (

M E G

), daily energy gain (

D E G

)). The procedure used comprised the following steps: (i) choice of solar irradiance estimation model; (ii) theoretical study; (iii) study under real operating conditions—for this purpose, an experimental setup was used; and (iv) comparison of these studies. The experimental setup was installed at the Department of Electrical Engineering of the University of Oviedo (Gijón, Spain) (latitude

43 ° 31^{'} 22^{″}

N, longitude

05 ° 43^{'} 07^{″}

W, elevation 28 (m) above sea level). Gijón is characterised by a temperate oceanic climate typical of Spain’s Atlantic coast, with cool summers and wet and mostly mild winters. The code assigned to Gijón under the Köppen climate classification is

C_{f b}

. The horizontal single-axis trackers that comprise photovoltaic power plants have three operating modes (Scenario 1). Some studies consider a unique mode of operation from sunrise to sunset (Scenario 2). The following conclusions can be drawn from the results obtained: (i) although the results obtained in the theoretical study and in the study under real operating conditions were different, a trend can be seen in the results; for example, the

A E G

obtained was approximately

13 %

and

8.5 %

in the theoretical study and in the real study, respectively, in Scenario 1 and approximately

18 %

and

10.5 %

, respectively, in Scenario 2; Scenario 2 obtained higher results than Scenario 1 in all the assessment indicators; but it must be considered that Scenario 1 is the real mode of operation; (ii) from March to September, the horizontal single-axis tracker generates more electrical energy; as this period contains the months of greatest solar irradiance, the horizontal single-axis tracker performs better annually; considering the theoretical study and Scenario 1, the highest value of

M E G

was in June (

43 %

) and the lowest was in December (

- 29 %

); when the study was considered under real operating conditions, the highest result was in July (

30 %

) and the lowest was in December (

- 24 %

); (iii) on the days between 70 and 277 in Scenario 1, the horizontal single-axis tracker generated more electrical energy; on the other days the opposite occurred; taking into account the theoretical study, the highest and lowest

D E G

values were

43 %

and

- 30 %

, respectively; when the study was considered under real operating conditions, the highest and lowest

D E G

values were

58 %

and

- 47 %

, respectively. Full article

(This article belongs to the Collection Improvements in the Production, Monitoring, Management and Impact on the Grid of Photovoltaic Installations)

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21 pages, 4365 KiB

Open AccessArticle

Teaching Artificial Intelligence and Machine Learning in Secondary Education: A Robotics-Based Approach

by Georgios Karalekas, Stavros Vologiannidis and John Kalomiros

Appl. Sci. 2025, 15(8), 4570; https://doi.org/10.3390/app15084570 - 21 Apr 2025

Abstract

The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) highlights the need for innovative, engaging educational approaches in secondary education. This study presents the design and classroom implementation of a robotics-based lesson aimed at introducing core AI and ML concepts to [...] Read more.

The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) highlights the need for innovative, engaging educational approaches in secondary education. This study presents the design and classroom implementation of a robotics-based lesson aimed at introducing core AI and ML concepts to ninth-grade students without prior programming experience. The intervention employed two low-cost, 3D-printed robots, each used to illustrate a different aspect of intelligent behavior: (1) rule-based automation, (2) supervised learning using image classification, and (3) reinforcement learning. The lesson was compared with a previous implementation of similar content delivered through software-only activities. Data were collected through classroom observation and student–teacher discussions. The results indicated increased student engagement and enthusiasm in the robotics-based version, as well as improved conceptual understanding. The approach required no specialized hardware or instructor expertise, making it easily adaptable for broader use in school settings. Full article

(This article belongs to the Special Issue ICT in Education, 2nd Edition)

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15 pages, 5537 KiB

Open AccessArticle

An Analysis of the Factors Influencing Dual Separation Zones on a Plate

by Jiarui Zou, Xiaoqiang Fan and Bing Xiong

Appl. Sci. 2025, 15(8), 4569; https://doi.org/10.3390/app15084569 - 21 Apr 2025

Abstract

The shock wave/boundary layer interaction phenomenon in hypersonic inlets, affected by background waves, may induce the formation of multiple separation zones. Existing theories prove insufficient in explaining the underlying flow mechanisms behind complex phenomena arising from multi-separation zone interactions, which necessitates further investigation. [...] Read more.

The shock wave/boundary layer interaction phenomenon in hypersonic inlets, affected by background waves, may induce the formation of multiple separation zones. Existing theories prove insufficient in explaining the underlying flow mechanisms behind complex phenomena arising from multi-separation zone interactions, which necessitates further investigation. To clarify the governing factors in multi-separation zone interactions, this study developed a simplified dual-separation-zone model derived from inlet flow field characteristics. A series of numerical simulations were conducted under an incoming flow at Mach 3 to systematically analyze the effects of internal contraction ratio, the influencing locations of expansion waves, and incident shock wave intensity on the mergence and re-separation of dual separation zones. The results demonstrate that both the expansion wave impingement position and incident shock intensity significantly influence specific transition points in dual-separation-zone flow states, though they do not fundamentally alter the evolutionary patterns governing the merging/re-separating processes. Furthermore, increasing incident shock intensity leads to the expansion of separation zone scales and prolongation of the dual-separation-zone merging distance. Full article

(This article belongs to the Special Issue Advances in Fluid Mechanics Analysis)

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15 pages, 8129 KiB

Open AccessArticle

A Peak Absorption Filtering Method for Radiated EMI from a High-Speed PWM Fan

by Jinsheng Yang, Yanhong Wei, Xuan Zhao, Chulin Wang and Pingan Du

Appl. Sci. 2025, 15(8), 4568; https://doi.org/10.3390/app15084568 - 21 Apr 2025

Abstract

Axial flow fans are widely used for heat dissipation in electronic devices. Due to its frequent speed-regulation to adapt to the change in heat load, a fan can cause significant electromagnetic radiation interference. In this study, a peak absorption filtering method is proposed [...] Read more.

Axial flow fans are widely used for heat dissipation in electronic devices. Due to its frequent speed-regulation to adapt to the change in heat load, a fan can cause significant electromagnetic radiation interference. In this study, a peak absorption filtering method is proposed to address the radiation interference issue in a high-speed PWM axial flow fan. The mechanism and coupling paths of radiation interference were analyzed, and a radiation interference calculation using finite integration technique by a hybrid field-circuit model and experimental measurement were conducted to identify the winding as the main source of radiation in PWM fan. Considering the limited space inside the fan, an integrated, non-inductive filtering circuit was designed to absorb the peak voltage entering the windings and the filter parameters are determined via circuit simulation. The measurement results indicate that the filtering method can reduce overall electromagnetic interference with a maximum peak reduction of 41.9 dB, without affecting the useful signals. Full article

(This article belongs to the Special Issue Trends and Prospects in Applied Electromagnetics)

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25 pages, 4583 KiB

Open AccessReview

Progress and Prospect of Saline-Alkaline Soil Management Technology: A Review

by Zhengkun Li, Mcholomah Annalisa Kekeli, Yaqi Jiang and Yukui Rui

Appl. Sci. 2025, 15(8), 4567; https://doi.org/10.3390/app15084567 - 21 Apr 2025

Abstract

Saline-alkaline and alkaline land is an important potential cultivated land resource in the world. With the destruction of the ecological environment, the cultivated land area is less and less. As a potential soil conditioner, wood vinegar can adjust soil pH, increase root activity, [...] Read more.

Saline-alkaline and alkaline land is an important potential cultivated land resource in the world. With the destruction of the ecological environment, the cultivated land area is less and less. As a potential soil conditioner, wood vinegar can adjust soil pH, increase root activity, and promote seed germination and root growth, showing its potential in improving saline-alkaline soil. This review summarizes the present situation of saline-alkaline and alkaline land, and its application to China’s cultivated land policy. The traditional saline-alkaline and alkaline land management measures, and analyzes the advantages and disadvantages. Some new methods of treating saline-alkaline soil were enumerated, and the methods of treating saline-alkaline soil with wood vinegar were emphatically introduced, and the molecular mechanism of action of wood vinegar was discussed, the effects of long-term application of wood vinegar on the stability of soil ecosystem were analyzed. The prospect of comprehensive management of saline-alkaline land and how to balance economic development were proposed. Full article

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23 pages, 3135 KiB

Open AccessArticle

A Comparative Study of the Structural, Morphological, and Functional Properties of Native Potato Starch and Spray-Dried Potato Starch

by Anna Marinopoulou, Maria Zoumaki, Dimitrios Sampanis, Vassilis Karageorgiou, Stylianos Raphaelides and Athanasios Goulas

Appl. Sci. 2025, 15(8), 4566; https://doi.org/10.3390/app15084566 - 21 Apr 2025

Abstract

The spray-dried potato starch was produced by gelatinizing native potato starch at two concentrations of 3% and 5% at 75 °C for 30 min, followed by drying in a pilot-scale spray dryer. X-ray diffraction (XRD), differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy [...] Read more.

The spray-dried potato starch was produced by gelatinizing native potato starch at two concentrations of 3% and 5% at 75 °C for 30 min, followed by drying in a pilot-scale spray dryer. X-ray diffraction (XRD), differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR), and optical microscopy were applied to characterize native potato starch and spray-dried (SD) potato starch powders. The physical properties of the starches, including moisture content, color, bulk density, tapped density, particle size parameters, water holding capacity, and hygroscopicity, were investigated. XRD, DSC, and FTIR revealed the formation of a semi-crystalline to amorphous structure in the spray-dried starch powders. Microscopic examination showed that the starch granules of native potato starch were spherical and regular in shape, while spray-dried (SD) starch powders displayed wrinkled granules. The moisture content of the spray-dried powders was significantly lower than that of the native starch, while the native starch had higher particle size values [D(4.3)] compared to the spray-dried powders. Higher water holding capacity values were also recorded in the spray-dried starches compared to the native starch. Regarding the color parameters, statistical analysis revealed similar values for lightness (L*) and yellowness (YI) indices, while significant differences were found in hue angle (H°), a*, and b* values. A principal component analysis (PCA) was carried out to investigate the relationships among the physical properties of the native potato starch and spray-dried starch powders. The findings of the present study highlight the potential application of physically modifying starch through the spray-drying process. Full article

(This article belongs to the Special Issue Physical Chemistry, Formulation and Processing, Intersection of Food Sciences, Engineering and Global Health)

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24 pages, 10940 KiB

Open AccessArticle

LSTM-DQN-APF Path Planning Algorithm Empowered by Twins in Complex Scenarios

by Ying Lu, Xiaodan Wang, Yang Yang, Man Ding, Shaochun Qu and Yanfang Fu

Appl. Sci. 2025, 15(8), 4565; https://doi.org/10.3390/app15084565 - 21 Apr 2025

Abstract

In response to the issues of unreachable targets, local minima, and insufficient real-time performance in drone path planning in urban low-altitude complex scenarios, this paper proposes a fusion algorithm based on digital twin, integrating LSTM (long short-term memory), DQN (Deep Q-Network), and APF [...] Read more.

In response to the issues of unreachable targets, local minima, and insufficient real-time performance in drone path planning in urban low-altitude complex scenarios, this paper proposes a fusion algorithm based on digital twin, integrating LSTM (long short-term memory), DQN (Deep Q-Network), and APF (artificial potential field). The algorithm relies on a twin system, integrating multi-sensor fusion technology and Kalman filtering to input obstacle information and UAV trajectory predictions into the DQN, which outputs action decisions for intelligent obstacle avoidance. Additionally, to address the blind search problem in trajectory planning, the algorithm introduces exploration rewards and heuristic reward components, as well as adding velocity and acceleration compensation terms to the attraction and repulsion functions, reducing the path deviation of UAVs during dynamic obstacle avoidance. Finally, to tackle the issues of insufficient training sample size and simulation accuracy, this paper leverages a digital twin platform, utilizing a dual feedback mechanism from virtual and physical environments to generate a large number of complex urban scenario samples. This approach effectively enhances the diversity and accuracy of training samples while significantly reducing the experimental costs of the algorithm. The results demonstrate that the LSTM-DQN-APF algorithm, combined with the digital twin platform, can significantly improve the issues of unreachable goals, local optimality, and real-time performance in UAV operations in complex environments. Compared to traditional algorithms, it notably enhances path planning speed and obstacle avoidance success rates. After thorough training, the proposed improved algorithm can be applied to real-world UAV systems, providing reliable technical support for applications such as smart city inspections and emergency rescue operations. Full article

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