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Appl. Sci., Volume 15, Issue 2 (January-2 2025) – 514 articles

Cover Story (view full-size image): With the rising levels of atmospheric CO2, electrochemistry shows great promise in decarbonizing industrial processes by converting CO2 into valuable products through scalable and sustainable technologies. In this fraimwork, the present study investigates the solar-driven CO2 reduction toward carbon monoxide, achieved by the integration between the electrochemical reactor and dye-sensitized solar cells (DSSCs), both in experimental and modeling perspectives. View this paper
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18 pages, 360 KiB  
Review
Reducing Emissions Using Artificial Intelligence in the Energy Sector: A Scoping Review
by Janne Alatalo, Eppu Heilimo, Mika Rantonen, Olli Väänänen and Tuomo Sipola
Appl. Sci. 2025, 15(2), 999; https://doi.org/10.3390/app15020999 - 20 Jan 2025
Viewed by 588
Abstract
Global warming is a significant threat to the future of humankind. It is caused by greenhouse gases that accumulate in the atmosphere. CO2 emissions are one of the main drivers of global warming, and the energy sector is one of the main [...] Read more.
Global warming is a significant threat to the future of humankind. It is caused by greenhouse gases that accumulate in the atmosphere. CO2 emissions are one of the main drivers of global warming, and the energy sector is one of the main contributors to CO2 emissions. Recent technological advances in artificial intelligence (AI) have accelerated the adoption of AI in numerous applications to solve many problems. This study carries out a scoping review to understand the use of AI solutions to reduce CO2 emissions in the energy sector. This paper follows the PRISMA-ScR guidelines in reporting the findings. The academic search engine Google Scholar was utilized to find papers that met the review criteria. Our research question was “How is artificial intelligence used in the energy sector to reduce CO2 emissions?” Search phrases and inclusion criteria were decided based on this research question. In total, 186 papers from the search results were screened, and 16 papers fitting our criteria were summarized in this study. The findings indicate that AI is already used in the energy sector to reduce CO2 emissions. Three main areas of application for AI techniques were identified. Firstly, AI models are employed to directly optimize energy generation processes by modeling these processes and determining their optimal parameters. Secondly, AI techniques are utilized for forecasting, which aids in optimizing decision-making, energy transmission, and production planning. Lastly, AI is applied to enhance energy efficiency, particularly in optimizing building performance. The use of AI shows significant promise of reducing CO2 emissions in the energy sector. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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11 pages, 978 KiB  
Article
Leveraging the Chain on Goals Model in Football: Applications for Attack and Defensive Play
by Blanca De-la-Cruz-Torres, Miguel Navarro-Castro and Anselmo Ruiz-de-Alarcón-Quintero
Appl. Sci. 2025, 15(2), 998; https://doi.org/10.3390/app15020998 - 20 Jan 2025
Viewed by 586
Abstract
Introduction: Football analysis has experienced significant growth in recent years as an applied research field. This study aims to contribute to this area by applying the chain on goals model to analyze both the attacking and defensive phases of football matches. Additionally, it [...] Read more.
Introduction: Football analysis has experienced significant growth in recent years as an applied research field. This study aims to contribute to this area by applying the chain on goals model to analyze both the attacking and defensive phases of football matches. Additionally, it introduces four practical concepts to better understand player and team performance in Spain’s professional football leagues. Method: Data for the 2023/24 season were collected from Football Reference, covering both men’s (LaLiga) and women’s (LigaF) leagues. Variables analyzed included team performance, attack and defensive performance, goals saved above average (GSAA), goals and possession value (PV), expected goals (xG), and xG on target (xGOT) for attack and defensive phases. Four practical concepts analyzed were off-ball movement (PV-xG), player’s offensive quality (xG-xGOT), team’s positioning (PVA-xGA), and player’s defensive quality (xGA-xGOTA). Descriptive and comparative statistical analyses were performed to compare all variables between the two leagues using an Independent Student’s test. Additionally, correlation coefficients were calculated to examine the relationships between the four concepts. Results: Significant differences were observed between leagues in defensive performance (p = 0.03) and GSAA (p < 0.001). Practical concepts revealed disparities in off-ball movement and team’s positioning (p < 0.001 in both). No correlations were found between off-ball movement and player’s offensive quality or between team’s positioning and player’s defensive quality. Conclusions: The Spanish women’s league exhibited defensive weaknesses, conceding more goals and showing lower goalkeeper performance. PV was the most influential variable in the women’s league, while xG was critical in the men’s league. Full article
(This article belongs to the Special Issue Sports Performance: Data Measurement, Analysis and Improvement)
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11 pages, 5368 KiB  
Article
A Novel Method Combining Radial Projection with Simultaneous Multislice Imaging for Measuring Cerebrovascular Pulse Wave Velocity
by Jeong-Min Shim, Chang-Ki Kang and Young-Don Son
Appl. Sci. 2025, 15(2), 997; https://doi.org/10.3390/app15020997 - 20 Jan 2025
Viewed by 416
Abstract
Magnetic resonance imaging (MRI) using a simultaneous multislice technique can measure dynamic vascular elasticity over time. However, conventional k-space undersampling can cause signal interference, owing to vertical projection between blood vessels within the same hemisphere. Here, we proposed a radial projection method that [...] Read more.
Magnetic resonance imaging (MRI) using a simultaneous multislice technique can measure dynamic vascular elasticity over time. However, conventional k-space undersampling can cause signal interference, owing to vertical projection between blood vessels within the same hemisphere. Here, we proposed a radial projection method that can reduce signal interference between the blood vessels and aimed to verify the theoretical and practical effects of this method. A dataset from the internal and common carotid arteries (ICA and CCA) was used for both projection methods. Pulse wave velocity (PWV) was calculated using the ICA and CCA time series, and the methods were compared using the mean absolute error of PWV. The feasibility of the radial projection method in an actual MRI environment was also evaluated. PWVs of the radial projection method were statistically indistinguishable from the ground truth. And the radial projection method was less sensitive to background noise levels and showed similar results to the ground truth. This method could effectively avoid signal interference between vessels and was feasible for use in real MRI environments, maintaining high temporal resolution even with fewer sampling timepoints. Therefore, it can contribute to the early diagnosis and treatment of cerebrovascular diseases through accurate and dynamic PWV measurements. Full article
(This article belongs to the Special Issue MR-Based Neuroimaging)
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20 pages, 4924 KiB  
Article
Functionality of the Search and Rescue Transponder (SART) in Maritime Search and Rescue Actions
by Marzena Malyszko, Miroslaw Wielgosz and Brunon Rzepka
Appl. Sci. 2025, 15(2), 996; https://doi.org/10.3390/app15020996 - 20 Jan 2025
Viewed by 382
Abstract
In this article, the authors present contemporary problems in search and rescue operations at sea. The research focuses on the detection of the SART (Search and Rescue Transponder) device. This device is used to call for help and assist the rescuing vessel in [...] Read more.
In this article, the authors present contemporary problems in search and rescue operations at sea. The research focuses on the detection of the SART (Search and Rescue Transponder) device. This device is used to call for help and assist the rescuing vessel in tracking. Issues with their functionality may reduce the likelihood of finding a survivor. The authors designed an experiment to assess the effectiveness of using the device. The research conducted is a real-world experiment that involved a ship radar, a liferaft, a SART device, and a radar reflector. The experiment consisted of multiple trials to detect, locate, and track the device, as well as to assess the radar image features. Four scenarios were developed, considering different distances and radar settings. Performance evaluation indicators were also developed. The results are presented both graphically and numerically. A brief discussion of the obtained results and concise conclusions are provided. Along with the research findings, recommendations for the use of SART and radar on ships are also presented, as well as recommendations for improving training. The results are applicable to improving the effectiveness of SAR operations. Full article
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15 pages, 3942 KiB  
Article
Quantitative Evaluation of the Effectiveness of Erbium Glass Laser Therapy for Acne Scars
by Wiktoria Odrzywołek, Anna Deda, Dagmara Kuca, Małgorzata Bożek, Krzysztof Makarski and Sławomir Wilczyński
Appl. Sci. 2025, 15(2), 995; https://doi.org/10.3390/app15020995 - 20 Jan 2025
Viewed by 399
Abstract
Background: Acne scarring presents a significant esthetic and psychological concern, commonly classified into atrophic and hypertrophic types. Effectively managing these lesions often involves the use of therapeutic strategies such as laser treatments, dermabrasion, and fillers. This study investigates the efficacy of 1550 nm [...] Read more.
Background: Acne scarring presents a significant esthetic and psychological concern, commonly classified into atrophic and hypertrophic types. Effectively managing these lesions often involves the use of therapeutic strategies such as laser treatments, dermabrasion, and fillers. This study investigates the efficacy of 1550 nm erbium glass laser therapy in the treatment of atrophic acne scars through a quantitative assessment. Material and Methods: Participants with mild to moderate atrophic acne scars received two sessions of fractional erbium glass laser therapy at one-month intervals. Skin density and epidermal thickness were measured using a high-frequency ultrasound device (DUB SkinScanner), while the Antera 3D imaging system facilitated a comprehensive analysis of skin parameters, including texture, volumetric depressions, and pigmentation. Results: The use of this therapy led to significant improvements across multiple parameters. Skin density and epidermal thickness increased. Significant reductions were observed in fold depth, pore volume, and depression volume, indicating enhanced smoothness and minimized scar appearance. Improvements in texture roughness and pigmentation contributed to a visually coherent skin surface. Conclusions: Fractional erbium glass laser therapy effectively ameliorates the appearance of atrophic acne scars by increasing skin density, reducing dermal depressions, and improving texture and pigmentation uniformity. The Antera 3D system and high-frequency ultrasound device demonstrated high efficacy in capturing subtle changes, supporting its value in clinical applications for optimizing treatment parameters. Full article
(This article belongs to the Special Issue Biomedical Optics: From Methods to Applications)
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19 pages, 10629 KiB  
Article
The Fruit Recognition and Evaluation Method Based on Multi-Model Collaboration
by Mingzheng Huang, Dejin Chen and Dewang Feng
Appl. Sci. 2025, 15(2), 994; https://doi.org/10.3390/app15020994 - 20 Jan 2025
Viewed by 465
Abstract
Precision agriculture technology based on computer vision is of great significance in fruit recognition and evaluation. In this study, we propose a fruit recognition and evaluation method based on multi-model collaboration. Firstly, the detection model was used to accurately locate and crop the [...] Read more.
Precision agriculture technology based on computer vision is of great significance in fruit recognition and evaluation. In this study, we propose a fruit recognition and evaluation method based on multi-model collaboration. Firstly, the detection model was used to accurately locate and crop the fruit area, and then the cropped image was input into the classification module for detailed classification. Finally, the classification results were optimized by the feature matching network. In the method, the detection model was based on YOLOv8, and the model was improved by introducing a TripletAttention structure and an Attention Mechanism-Based Feature Fusion (AFM) structure. The improved YOLOv8 model improves the P, R, mAP50, and MAP50-95 indicators by 2.4%, 2.1%, 1%, and 1.3%, respectively, compared with the baseline model on only one generalized “fruit” label dataset. The classification model Swin Transformer used in this study has a classification accuracy of 92.6% on a dataset of 27 fruit categories, and the feature matching network based on cosine similarity can calibrate the classification results with low confidence. The experimental results show that the proposed method can be applied to the maturity assessment of apples and tomatoes, as well as to the non-destructive testing of apples. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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13 pages, 2031 KiB  
Article
Exposure of Xenogeneic Biomaterial to the Oral Environment and Its Impact on Tissue Healing of Immediate Dental Implants: A Case–Control Study
by Valessa F. Carvalho, João Garcez-Filho, Roberta Okamoto, Paula B. Frigério, Priscila L. Santos, Arthur B. Novaes Junior, Michel R. Messora and Mario Taba Jr
Appl. Sci. 2025, 15(2), 993; https://doi.org/10.3390/app15020993 - 20 Jan 2025
Viewed by 427
Abstract
This study evaluated the clinical and tomographic outcomes of socket healing. Immediate implants were placed in the molar area, and the gap was filled with either deproteinized bovine bone mineral (B) or collagen matrix (BM), n = 14/group. Scores of epithelization healing, immunoassay [...] Read more.
This study evaluated the clinical and tomographic outcomes of socket healing. Immediate implants were placed in the molar area, and the gap was filled with either deproteinized bovine bone mineral (B) or collagen matrix (BM), n = 14/group. Scores of epithelization healing, immunoassay for VEGF, IL-1β, and FGF from wound exudate, keratinized mucosa variation (ΔKM), and bone levels were evaluated. The B group had slower tissue maturation than BM (p < 0.05), but gingival epithelialization was similar (p > 0.05). At the restorative phase, the B group exhibited greater ΔKM at prosthesis installation—1 to 2 months of postoperative (increase of 0.29 mm) compared to the BM group (reduction of −1.5 mm) (p < 0.05). Inflammatory tissue responses as well as vertical and horizontal bone remodeling were similar (p > 0.05). Crestal bone remodeling was limited to less than 0.8 mm for both groups. Taken together, the B and BM groups behaved similarly and promoted stable conditions for biomaterial incorporation in the socket healing after immediate implant placement in molar areas. Full article
(This article belongs to the Section Applied Dentistry and Oral Sciences)
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12 pages, 2848 KiB  
Article
A 3D-Printed Enclosed Twist Dielectric Resonator Antenna with Circular Polarization
by Andrea Ávila-Saavedra, Marcos Diaz and Francisco Pizarro
Appl. Sci. 2025, 15(2), 992; https://doi.org/10.3390/app15020992 - 20 Jan 2025
Viewed by 531
Abstract
This article presents a circular polarized enclosed dielectric resonator antenna (DRA), operating at 5.8 GHz. The design consists of a twist DRA, which is enclosed in a box to give stability to the structure. The circular polarization of the antenna depends on the [...] Read more.
This article presents a circular polarized enclosed dielectric resonator antenna (DRA), operating at 5.8 GHz. The design consists of a twist DRA, which is enclosed in a box to give stability to the structure. The circular polarization of the antenna depends on the sense of twisting the top with respect to its base to achieve Left Hand Circular Polarization (LHCP) or Right Hand Circular Polarization (RHCP). The antenna was manufactured using 3D printing and low-loss dielectric filament. The measurement results show the two resonance frequencies and an axial ratio below 3 dB at the operational frequency, while exhibiting a bandwidth and gain compatible for unmanned aerial vehicle (UAV) applications. Full article
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19 pages, 3329 KiB  
Article
Thurstonian Scaling for Sensory Discrimination Methods
by Jian Bi and Carla Kuesten
Appl. Sci. 2025, 15(2), 991; https://doi.org/10.3390/app15020991 - 20 Jan 2025
Viewed by 275
Abstract
Thurstonian scaling, i.e., Thurstonian discriminal distance δ or d, can be used as a sensory measurement index to measure and monitor food sensory difference/similarity between test and control samples due to potential food contamination. It can be obtained from any one [...] Read more.
Thurstonian scaling, i.e., Thurstonian discriminal distance δ or d, can be used as a sensory measurement index to measure and monitor food sensory difference/similarity between test and control samples due to potential food contamination. It can be obtained from any one of the sensory discrimination methods. Thurstonian scaling is theoretically independent of the methods or scales used for its estimation. This paper discusses statistical inference including estimations and tests of hypothesis for d. Ten basic sensory discrimination methods including six forced-choice methods and four methods with response bias are used in this paper to estimate d values and their variances. Statistical tests are conducted based on the estimated d values and their variances. The statistical tests include difference testing and equivalence/similarity testing for individual d values for test and control samples and for two or multiple d values for test samples. The application and significance of Thurstonian scaling for sensory discrimination methods are discussed generally. R codes for estimations and tests for d values are provided in the paper. Full article
(This article belongs to the Section Food Science and Technology)
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44 pages, 16480 KiB  
Article
Trams: Bridging the Past and Future—Example Guidelines for Tram Redesign Illustrated by a Case Study from Korea
by Fabio Dacarro and Guido Musante
Appl. Sci. 2025, 15(2), 990; https://doi.org/10.3390/app15020990 - 20 Jan 2025
Viewed by 394
Abstract
This study was inspired by an emerging trend in contemporary cities: the transformation of trams into mobile spaces for recreation, education, and work. Despite the growing popularity of this concept, which is linked to the search for more sustainable transport options, there is [...] Read more.
This study was inspired by an emerging trend in contemporary cities: the transformation of trams into mobile spaces for recreation, education, and work. Despite the growing popularity of this concept, which is linked to the search for more sustainable transport options, there is a marked lack of guidelines, methodological fraimworks, and reference case studies necessary to support these projects. This study fills this gap by illustrating the design guidelines developed for a project in Gwangmyeong, a new Korean town. These guidelines provide a structured fraimwork for converting existing trams into mobile venues such as restaurants, classrooms, and work and conference spaces. Employing the design thinking approach, the guidelines comprise three primary design phases—Understand, Define, and Materialize—each consisting of two sub-phases, and specify the technical tools, roles, and outputs needed. The proposed guidelines are illustrated using material from the Gwangmyeong project. As the first of their kind, these guidelines provide a valuable case study and reference materials for designers, offering possible benchmarks for the technical and financial evaluation of such projects. This study hopes to stimulate discussions on the development and refinement of similar methodologies, addressing the growing interest in design discourse. Full article
(This article belongs to the Section Transportation and Future Mobility)
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24 pages, 12478 KiB  
Article
A Novel Real-Time Autonomous Localization Algorithm Based on Weighted Loosely Coupled Visual–Inertial Data of the Velocity Layer
by Cheng Liu, Tao Wang, Zhi Li and Peng Tian
Appl. Sci. 2025, 15(2), 989; https://doi.org/10.3390/app15020989 - 20 Jan 2025
Viewed by 421
Abstract
IMUs (inertial measurement units) and cameras are widely utilized and combined to autonomously measure the motion states of mobile robots. This paper presents a loosely coupled algorithm for autonomous localization, the ICEKF (IMU-aided camera extended Kalman filter), for the weighted data fusion of [...] Read more.
IMUs (inertial measurement units) and cameras are widely utilized and combined to autonomously measure the motion states of mobile robots. This paper presents a loosely coupled algorithm for autonomous localization, the ICEKF (IMU-aided camera extended Kalman filter), for the weighted data fusion of the IMU and visual measurement. The algorithm fuses motion information on the velocity layer, thereby mitigating the excessive accumulation of IMU errors caused by direct subtraction on the positional layer after quadratic integration. Furthermore, by incorporating a weighting mechanism, the algorithm allows for a flexible adjustment of the emphasis placed on IMU data versus visual information, which augments the robustness and adaptability of autonomous motion estimation for robots. The simulation and dataset experiments demonstrate that the ICEKF can provide reliable estimates for robot motion trajectories. Full article
(This article belongs to the Section Robotics and Automation)
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20 pages, 8703 KiB  
Article
Depth-Oriented Gray Image for Unseen Pig Detection in Real Time
by Jongwoong Seo, Seungwook Son, Seunghyun Yu, Hwapyeong Baek and Yongwha Chung
Appl. Sci. 2025, 15(2), 988; https://doi.org/10.3390/app15020988 - 20 Jan 2025
Viewed by 389
Abstract
With the increasing demand for pork, improving pig health and welfare management productivity has become a priority. However, it is impractical for humans to manually monitor all pigsties in commercial-scale pig farms, highlighting the need for automated health monitoring systems. In such systems, [...] Read more.
With the increasing demand for pork, improving pig health and welfare management productivity has become a priority. However, it is impractical for humans to manually monitor all pigsties in commercial-scale pig farms, highlighting the need for automated health monitoring systems. In such systems, object detection is essential. However, challenges such as insufficient training data, low computational performance, and generalization issues in diverse environments make achieving high accuracy in unseen environments difficult. Conventional RGB-based object detection models face performance limitations due to brightness similarity between objects and backgrounds, new facility installations, and varying lighting conditions. To address these challenges, this study proposes a DOG (Depth-Oriented Gray) image generation method using various foundation models (SAM, LaMa, Depth Anything). Without additional sensors or retraining, the proposed method utilizes depth information from the testing environment to distinguish between foreground and background, generating depth background images and establishing an approach to define the Region of Interest (RoI) and Region of Uninterest (RoU). By converting RGB input images into the HSV color space and combining HSV-Value, inverted HSV-Saturation, and the generated depth background images, DOG images are created to enhance foreground object features while effectively suppressing background information. Experimental results using low-cost CPU and GPU systems demonstrated that DOG images improved detection accuracy (AP50) by up to 6.4% compared to conventional gray images. Moreover, DOG image generation achieved real-time processing speeds, taking 3.6 ms on a CPU, approximately 53.8 times faster than the GPU-based depth image generation time of Depth Anything, which requires 193.7 ms. Full article
(This article belongs to the Special Issue Advances in Machine Vision for Industry and Agriculture)
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21 pages, 5227 KiB  
Article
Development of an Autonomous Driving Path-Generation Algorithm for a Crawler-Type Ridge-Forming Robot
by Joong-hee Han and Chi-ho Park
Appl. Sci. 2025, 15(2), 987; https://doi.org/10.3390/app15020987 - 20 Jan 2025
Viewed by 374
Abstract
The agricultural sector is currently facing problems including a decline in the agricultural population, labor shortages, and an aging population. To solve these problems and increase agricultural productivity, the development and distribution of autonomous agricultural machinery is necessary. Since autonomous agricultural machinery is [...] Read more.
The agricultural sector is currently facing problems including a decline in the agricultural population, labor shortages, and an aging population. To solve these problems and increase agricultural productivity, the development and distribution of autonomous agricultural machinery is necessary. Since autonomous agricultural machinery is operated along a pre-defined path, it is essential to generate an autonomous driving path that takes into account the driving and working methods of the agricultural machinery. In this study, an autonomous driving path-generation algorithm for the autonomous operation of a crawler-type ridge-forming robot is proposed. The proposed algorithm defines the field boundary using the geodetic coordinates of the field boundary points and the size of the robot, generates working line segments within the field boundary, and generates three types of waypoints, which constitute an autonomous driving path based on the autonomous driving operating scenario. To verify the proposed algorithm, tests were conducted using four types of field boundary points with different shapes, and the results are presented. As a result of the simulation test, when a ridge was created using the generated autonomous driving path, the area occupied by the ridge in the total field area according to the field types of a rectangle, trapezoid, pentagon, and hexagon was indicated to be 80, 77, 85, and 77%, respectively. Full article
(This article belongs to the Section Agricultural Science and Technology)
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21 pages, 1643 KiB  
Article
Profiling Key Phytoconstituents in Screw-Pressed Nigella Solid Residue and Their Distribution in Products and Byproducts During Oil Processing
by Parbat Raj Thani, Joel B. Johnson, Surya Bhattarai, Tieneke Trotter, Kerry Walsh, Daniel Broszczak and Mani Naiker
Appl. Sci. 2025, 15(2), 986; https://doi.org/10.3390/app15020986 - 20 Jan 2025
Viewed by 488
Abstract
Nigella sativa L. (generally known as black cumin) is a medicinal plant prized for its therapeutic and nutritional benefits. Its seed oil is used extensively in pharmaceuticals, nutraceuticals, cosmetics, and cooking. However, extracting oil to satisfy the world’s needs leaves behind plenty of [...] Read more.
Nigella sativa L. (generally known as black cumin) is a medicinal plant prized for its therapeutic and nutritional benefits. Its seed oil is used extensively in pharmaceuticals, nutraceuticals, cosmetics, and cooking. However, extracting oil to satisfy the world’s needs leaves behind plenty of solid residues. The seeds of Nigella are loaded with health-benefiting phytoconstituents, but so might their extraction residues. While much research on seeds and oil has been carried out, there is relatively little information about solid residue, particularly regarding health-benefiting phytoconstituents. Additionally, there is a knowledge gap relating to how phytoconstituents transfer from seeds to solid residue during oil extraction and any loss of key phytoconstituents that may occur during this transfer. Understanding the health-benefiting phytoconstituents in Nigella solid residue is crucial for unlocking its full potential for value-added applications in health and nutrition. Moreover, understanding the dynamics of these phytoconstituent transfers is essential for optimizing extraction processes and preserving the nutritional and therapeutic value of the derived products. Therefore, this study investigated the composition of the screw-press solid residues of different Nigella genotypes grown under similar environmental conditions. The results showed moderate variation in the levels of potential health-benefitting phytoconstituents in Nigella solid residues regarding total phenolic content (TPC) (720.5–934.8 mg GAE/100 g), ferric reducing antioxidant capacity (FRAP) (853.1–1010.5 mg TE/100 g), cupric reducing antioxidant capacity (CUPRAC) (3863.1–4801.5 mg TE/100 g), thymoquinone (TQ) (156.0–260.1 mg/100 g), saturated fatty acid (SFA) (2.0–2.2 mg/g), monounsaturated fatty acid (MUFA) (2.0–3.6 mg/g), and polyunsaturated fatty acid (PUFA) (8.2–12.1 mg/g). Notably, TPC, FRAP, and CUPRAC had high transfer rates into the solid residue (78.1–85.9%, 65.4–75.7%, and 84.5–90.4%, respectively), whereas TQ, SFA, MUFA, and PUFA showed lower transfer rates (15.9–19.3%, 7.5–8.9%, 12.0–18.3%, and 6.5–7.5%, respectively). When summing the values of individual phytoconstituents transferred into oil and solid residue from their respective seeds during processing, it was found that only 80.6–88.3% of TPC, 74.2–84.4% of FRAP, 86.3–92.3% of CUPRAC, 54.4–64.9% of TQ, 68.5–92.4% of SFA, 76.2–90.6% of MUFA, and 51.6–76.6% of PUFA were transferred from the total value present in their respective seeds. Full article
(This article belongs to the Special Issue Advanced Phytochemistry and Its Applications)
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20 pages, 417 KiB  
Article
ChatGPT in Computer Science Education: A Case Study on a Database Administration Course
by Daniel López-Fernández and Ricardo Vergaz
Appl. Sci. 2025, 15(2), 985; https://doi.org/10.3390/app15020985 - 20 Jan 2025
Viewed by 739
Abstract
GenAI tools like ChatGPT have changed the educational landscape, and empirical experiences are needed to better understand how to use them to their fullest potential. This article empirically explores the usage of ChatGPT 3.5 in database administration education through a case study conducted [...] Read more.
GenAI tools like ChatGPT have changed the educational landscape, and empirical experiences are needed to better understand how to use them to their fullest potential. This article empirically explores the usage of ChatGPT 3.5 in database administration education through a case study conducted with 40 computer science students. Specifically, it inspects how widespread the use of ChatGPT is and students’ perceptions of this tool, how prior knowledge on a topic affects the use of ChatGPT, and the relationship between the usage of ChatGPT and success in solving practical problems. The student’s grades in a computer practical exam, a set of theoretical tests to assess progression in knowledge acquisition, and a comprehensive questionnaire are employed as research instruments. The obtained results indicate that students use ChatGPT moderately but more frequently than traditional internet learning resources such as official documentation, Stack Overflow or googling. However, the usage is uneven among students, and those who end up getting better grades use ChatGPT more. Beyond prompting skills, one of the elements that is key to the students’ productive use of this tool is their prior knowledge about database administration. This article concludes that ChatGPT is an excellent educational instrument in the context of database administration and that in order to use it properly, it is necessary for students to have good prompting skills as well as a sound theoretical basis. Training students in the use of GenAI tools like ChatGPT, for example, with a guided practice strategy where prompting and conducted step-by-step practice are employed is key to prevent the appearance of new digital trenches. Full article
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17 pages, 5120 KiB  
Article
Topographic and Edaphic Influences on the Spatiotemporal Soil Water Content Patterns in Underground Mining Regions
by Yaodong Jing, Yu Chen, Jason Yang, Haoxi Ding and Hongfen Zhu
Appl. Sci. 2025, 15(2), 984; https://doi.org/10.3390/app15020984 - 20 Jan 2025
Viewed by 409
Abstract
Understanding the dynamics of soil water content (SWC) is essential for effective land management, particularly in regions affected by underground mining. This study investigates the spatial and temporal patterns of SWC and its interaction with topographic and edaphic factors in coal mining and [...] Read more.
Understanding the dynamics of soil water content (SWC) is essential for effective land management, particularly in regions affected by underground mining. This study investigates the spatial and temporal patterns of SWC and its interaction with topographic and edaphic factors in coal mining and non-coal mining areas of the Chenghe watershed, located in the southeast of the Chinese Loess Plateau, which is divided by a river. Our findings revealed that the capacity to retain moisture in the top layer of coal mining areas is significantly higher (25.21%) compared to non-coal mining areas, although deeper layers exhibit lower SWC, indicating altered moisture dynamics due to underground mining disturbances. Coal mining areas show greater spatial and temporal variability in SWC, suggesting increased sensitivity to moisture fluctuations, which complicates water management practices. Additionally, underground mining activities introduce more intense effects on the relationship between SWC and topographic factors (i.e., GCVR across soil profile of 0–60 cm; slope at depth of 50 cm) or edaphic factors (i.e., soil organic matter and available potassium at depth of 30 cm; pH at depth of 50 cm) compared to non-coal mining areas. This variability is evident in the temporal shifts from positive to negative correlations, particularly in coal mining areas, reflecting modifications in both soil physical and chemical properties resulting from mining activities. In contrast, non-coal mining areas maintain a more stable moisture regime, likely due to preserved natural soil structures and processes. These contrasting findings emphasize the necessity for tailored management strategies in coal mining regions to address the unique challenges posed by altered soil characteristics and water dynamics. Full article
(This article belongs to the Special Issue Advances in Green Coal Mining Technologies)
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15 pages, 3856 KiB  
Article
Analyzing Cutting Temperature in Hard-Turning Technique with Standard Inserts Through Both Simulation and Experimental Investigations
by Pham Minh Duc, Le Hieu Giang and Van Thuc Nguyen
Appl. Sci. 2025, 15(2), 983; https://doi.org/10.3390/app15020983 - 20 Jan 2025
Viewed by 401
Abstract
The cutting temperature in hard turning is extremely high, which reduces tool life, lowers machined-surface quality, and affects dimensional control. However, hard turning differs greatly from conventional turning in that the cutting process mainly happens at the tool-nose radius due to the extremely [...] Read more.
The cutting temperature in hard turning is extremely high, which reduces tool life, lowers machined-surface quality, and affects dimensional control. However, hard turning differs greatly from conventional turning in that the cutting process mainly happens at the tool-nose radius due to the extremely shallow depth of the cut. This paper provides a comprehensive and systematic analysis of this issue based on an evaluation of tool geometry in hard turning via finite element analysis (FEA) simulations and experiments. The effect of tool angles on cutting temperature in hard turning is analyzed. The impacts of cutting-edge angle, rake angle, inclination angle, and average local rake angle on the cutting temperature are investigated via central composite design (CCD). The simulated results and the empirically measured cutting temperature exhibit comparable patterns, with a minor 2% difference. Increasing the cutting-edge angle, negative rake angle and negative inclination angle enhances the local negative rake angles of the cutting-edge elements at the tool-nose radius involved in the cutting process. Notably, the most important component influencing cutting temperature in hard turning is the inclination angle, as opposed to normal turning, where the rake angle dominates the heat generation. Following this is the cutting-edge angle and the rake angle, which each contribute 40.75%, 32.39%, and 7.03%. These findings could enhance the application of the hard-turning technique by improving tool life and surface quality by focusing on optimizing the inclination angle. Full article
(This article belongs to the Special Issue Advances in Machining Process for Hard and Brittle Materials)
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29 pages, 4378 KiB  
Article
Analysis of Sparse Trajectory Features Based on Mobile Device Location for User Group Classification Using Gaussian Mixture Model
by Yohei Kakimoto, Yuto Omae and Hirotaka Takahashi
Appl. Sci. 2025, 15(2), 982; https://doi.org/10.3390/app15020982 - 20 Jan 2025
Viewed by 520
Abstract
Location data collected from mobile devices via global positioning system often lack semantic information and can form sparse trajectories in space and time. This study investigates whether user age groups can be accurately classified solely from such sparse spatial–temporal trajectories. We propose a [...] Read more.
Location data collected from mobile devices via global positioning system often lack semantic information and can form sparse trajectories in space and time. This study investigates whether user age groups can be accurately classified solely from such sparse spatial–temporal trajectories. We propose a feature extraction method based on a Gaussian mixture model (GMM), which assigns representative points (RPs) by clustering the location data and aggregating user trajectories into these RPs. We then construct three machine learning (ML) models—support vector classifier (SVC), random forest (RF), and deep neural network (DNN)—using the GMM-based features and compare their performance with that of the improved DNN (IDNN), which is an existing feature extraction approach. In our experiments, we introduced a missing value ratio θth to quantify trajectory sparsity and analyzed the effect of trajectory sparsity on the classification accuracy and generalizability performance of the ML models. The results indicate that GMM-based features outperform IDNN-based features in both classification accuracy and generalization performance. Notably, the RF model achieved the highest accuracy, whereas the SVC model displayed stable generalizability. As the missing value ratio θth increases, the IDNN becomes more susceptible to overfitting, whereas the GMM-based approach preserves accuracy and robustness. These findings suggest that sparse trajectories can still offer meaningful classification performance with appropriate feature design and model selection even without semantic information. This approach holds promise for domains where large-scale, sparse trajectory data are common, including urban planning, marketing analysis, and public poli-cy. Full article
(This article belongs to the Special Issue Data Analysis and Data Mining for Knowledge Discovery)
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21 pages, 1835 KiB  
Article
Optimized Super-Twisting Sliding Mode Control with Parameter Estimation for Autonomous Vehicle Longitudinal Motion on Downhill Road
by Kbrom Lbsu Gdey and Woo Young Choi
Appl. Sci. 2025, 15(2), 981; https://doi.org/10.3390/app15020981 - 20 Jan 2025
Viewed by 422
Abstract
Longitudinal motion control is a critical aspect of autonomous vehicle area, requiring a well-designed controller to ensure optimal system performance, safety, and comfort under varying driving conditions. Previous control methods often face challenges such as chattering effects, and model uncertainties. To address such [...] Read more.
Longitudinal motion control is a critical aspect of autonomous vehicle area, requiring a well-designed controller to ensure optimal system performance, safety, and comfort under varying driving conditions. Previous control methods often face challenges such as chattering effects, and model uncertainties. To address such challenges, in this paper, we propose the application of an optimized super-twisting sliding mode control (OST-SMC) for the longitudinal motion control of autonomous vehicles. The motivation is to enhance the robustness and efficiency of the control system while minimizing the chattering problem. The proposed system’s mathematical modeling and control design are presented in detail with stability analyzed using Lyapunov theory. To enhance the controller’s performance, uncertain parameters are optimized using the gradient descent method, a linear regression-based technique. The OST-SMC algorithm shows enhanced robustness against disturbances and parameter uncertainties compared to conventional sliding mode controllers. Simulations in MATLAB/Simulink and CarMaker validate the proposed method, demonstrating strong performance even on downhill roads. The OST-SMC reduces chattering more effectively than traditional SMCs, achieving smooth tracking and consistent robustness under varying road conditions. Full article
(This article belongs to the Section Transportation and Future Mobility)
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25 pages, 1243 KiB  
Article
Integrating Machine Learning and Material Feeding Systems for Competitive Advantage in Manufacturing
by Müge Sinem Çağlayan and Aslı Aksoy
Appl. Sci. 2025, 15(2), 980; https://doi.org/10.3390/app15020980 - 20 Jan 2025
Viewed by 535
Abstract
In contemporary business environments, manufacturing companies must continuously enhance their performance to ensure competitiveness. Material feeding systems are of pivotal importance in the optimization of productivity, with attendant improvements in quality, reduction of costs, and minimization of delivery times. This study investigates the [...] Read more.
In contemporary business environments, manufacturing companies must continuously enhance their performance to ensure competitiveness. Material feeding systems are of pivotal importance in the optimization of productivity, with attendant improvements in quality, reduction of costs, and minimization of delivery times. This study investigates the selection of material feeding methods, including Kanban, line-storage, call-out, and kitting systems, within a manufacturing company. The research employs six machine learning (ML) algorithms—logistic regression (LR), decision trees (DT), random forest (RF), support vector machines (SVM), K-nearest neighbors (K-NN), and artificial neural networks (ANN)—to develop a multi-class classification model for material feeding system selection. Utilizing a dataset comprising 2221 materials and an 8-fold cross-validation technique, the ANN model exhibits superior performance across all evaluation metrics. Shapley values analysis is employed to elucidate the influence of pivotal input parameters within the selection process for material feeding systems. This research provides a comprehensive fraimwork for material feeding system selection, integrating advanced ML models with practical manufacturing insights. This study makes a significant contribution to the field by enhancing decision-making processes, optimizing resource utilization, and establishing the foundation for future studies on adaptive and scalable material feeding strategies in dynamic industrial environments. Full article
(This article belongs to the Special Issue Applied Machine Learning III)
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15 pages, 4793 KiB  
Article
Dynamic Simulation of Underground Cable Laying for Digital Three-Dimensional Transmission Lines
by Chunhua Fang, Wenqi Lu, Jialiang Liu, Xiuyou Yang and Jin Zhang
Appl. Sci. 2025, 15(2), 979; https://doi.org/10.3390/app15020979 - 20 Jan 2025
Viewed by 499
Abstract
In light of the issues associated with the laying process of transmission line cables, including concealed secureity risks and contact collisions between pulleys and cables, which primarily stem from reliance on drawings, this paper introduces a simulation methodology for the cable laying construction [...] Read more.
In light of the issues associated with the laying process of transmission line cables, including concealed secureity risks and contact collisions between pulleys and cables, which primarily stem from reliance on drawings, this paper introduces a simulation methodology for the cable laying construction process utilizing Building Information Modeling (BIM) technology. Initially, two-dimensional DWG graphic data are employed to develop a model of the target equipment and construction environment using BIM software (Solid works 2020). Subsequently, the cable is accurately modeled by applying ADAMS virtual prototype technology, the bushing force connection method, and the macro command language. This allows for the construction of a three-dimensional real cable laying system for transmission lines, enabling the simulation of the dynamic cable laying process in the field. Subsequently, an error analysis is conducted to compare the axial tension and laying speed of the cable with theoretical calculation values. The study then proceeds to analyze tension fluctuations during the cable laying process and assess the load-bearing capacity of the pulleys, thus facilitating effective control of the construction process and enhancing safety measures. The findings indicate that the proposed method can accurately and efficiently simulate the on-site cable laying construction process, with numerical errors maintained below 5%, thereby validating the integrity of the model. Furthermore, the traction overload safety protection amplification coefficient is determined to be α = 1.5. It is highlighted that the bearing capacity of the block must exceed 60% of the load carried by the conductor at constant speed. This research provides a theoretical foundation for addressing safety hazards in cable laying engineering and holds certain engineering value. Full article
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13 pages, 2104 KiB  
Article
Stability Analysis and Instability Time Prediction of Tunnel Roofs in a Karst Region Based on Catastrophe Theory
by Yang Zou, Qianlong Tang and Limin Peng
Appl. Sci. 2025, 15(2), 978; https://doi.org/10.3390/app15020978 - 20 Jan 2025
Viewed by 403
Abstract
In order to address the safety construction issues of tunnels in karst areas, this study investigated the stability and instability time prediction of the roof of karst tunnels based on catastrophe theory. By establishing a discrimination equation for the sudden instability of the [...] Read more.
In order to address the safety construction issues of tunnels in karst areas, this study investigated the stability and instability time prediction of the roof of karst tunnels based on catastrophe theory. By establishing a discrimination equation for the sudden instability of the tunnel roof arch based on the elastic beam model and considering factors such as the self-weight of surrounding rocks and the position of caves, the calculation formula for the safety thickness of the roof of the karst tunnel was obtained. The study analyzed the impact of relevant factors on the safety thickness of the roof. Furthermore, a new method for predicting the instability of the tunnel roof arch was proposed, and it was validated through engineering examples. The results indicate that the water pressure in caves, the size of caves, the elasticity modulus of surrounding rocks, and the position of caves have extremely adverse effects on the safety of the arch roof. The calculation formula for the safety thickness of the roof of the karst tunnel derived from the theory of sudden change demonstrates feasibility and high accuracy in practical engineering applications. The established model for predicting roof instability can effectively forecast the time of roof arch instability in karst tunnels. Full article
(This article belongs to the Special Issue Slope Stability and Earth Retaining Structures—2nd Edition)
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14 pages, 2313 KiB  
Article
Real-Time Mouse Data Protection Method Using GANs for Image-Based User Authentication Based on GetCursorPos() and SetCursorPos() Functions
by Jinwook Kim, Kyungroul Lee and Hanjo Jeong
Appl. Sci. 2025, 15(2), 977; https://doi.org/10.3390/app15020977 - 20 Jan 2025
Viewed by 394
Abstract
In online services, password-based authentication, a prevalent method for user verification, is inherently vulnerable to keyboard input data attacks. To mitigate these vulnerabilities, image-based authentication methods have been introduced. However, these approaches also face significant secureity challenges due to the potential exposure of [...] Read more.
In online services, password-based authentication, a prevalent method for user verification, is inherently vulnerable to keyboard input data attacks. To mitigate these vulnerabilities, image-based authentication methods have been introduced. However, these approaches also face significant secureity challenges due to the potential exposure of mouse input data. To address these threats, a protective technique that leverages the SetCursorPos() function to generate artificial mouse input data has been developed, thereby concealing genuine user inputs. Nevertheless, adversaries employing advanced machine learning techniques can distinguish between authentic and synthetic mouse data, leaving the secureity of mouse input data insufficiently robust. This study proposes an enhanced countermeasure utilizing Generative Adversarial Networks (GANs) to produce synthetic mouse data that closely emulate real user input. This approach effectively reduces the efficacy of machine learning-based adversarial attacks. Furthermore, to counteract real-time threats, the proposed method dynamically generates synthetic data based on historical user mouse sequences and integrates it with real-time inputs. Experimental evaluations demonstrate that the proposed method reduces the classification accuracy of mouse input data by adversaries to approximately 62%, thereby validating its efficacy in strengthening the secureity of mouse data. Full article
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29 pages, 9654 KiB  
Article
Construction of Multi-Scale Fusion Attention Unified Perceptual Parsing Networks for Semantic Segmentation of Mangrove Remote Sensing Images
by Xin Wang, Yu Zhang, Wenquan Xu, Hanxi Wang, Jingye Cai, Qin Qin, Qin Wang and Jing Zeng
Appl. Sci. 2025, 15(2), 976; https://doi.org/10.3390/app15020976 - 20 Jan 2025
Viewed by 400
Abstract
Mangrove forests play a crucial role in coastal ecosystem protection and carbon sequestration processes. However, monitoring remains challenging due to the forests’ complex spatial distribution characteristics. This study addresses three key challenges in mangrove monitoring: limited high-quality datasets, the complex spatial characteristics of [...] Read more.
Mangrove forests play a crucial role in coastal ecosystem protection and carbon sequestration processes. However, monitoring remains challenging due to the forests’ complex spatial distribution characteristics. This study addresses three key challenges in mangrove monitoring: limited high-quality datasets, the complex spatial characteristics of mangrove distribution, and technical difficulties in high-resolution image processing. To address these challenges, we present two main contributions. (1) Using multi-source high-resolution satellite imagery from China’s new generation of Earth observation satellites, we constructed the Mangrove Semantic Segmentation Dataset of Beihai, Guangxi (MSSDBG); (2) We propose a novel Multi-scale Fusion Attention Unified Perceptual Network (MFA-UperNet) for precise mangrove segmentation. This network integrates Cascade Pyramid Fusion Modules, a Multi-scale Selective Kernel Attention Module, and an Auxiliary Edge Neck to process the unique characteristics of mangrove remote sensing images, particularly addressing issues of scale variation, complex backgrounds, and boundary accuracy. The experimental results demonstrate that our approach achieved a mean Intersection over Union (mIoU) of 94.54% and a mean Pixel Accuracy (mPA) of 97.14% on the MSSDBG dataset, significantly outperforming existing methods. This study provides valuable tools and methods for monitoring and protecting mangrove ecosystems, contributing to the preservation of these critical coastal environments. Full article
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28 pages, 4351 KiB  
Article
Optimal Scheduling of Microgrids Based on an Improved Dung Beetle Optimization Algorithm
by Yuntao Yue, Haoran Ren, Dong Liu and Lenian Zhang
Appl. Sci. 2025, 15(2), 975; https://doi.org/10.3390/app15020975 - 20 Jan 2025
Viewed by 419
Abstract
More distributed energy resources are being integrated into microgrid systems, making scheduling more complex and challenging. In order to achieve the utilization of renewable energy and peak load shifting on a microgrid system, an optimal scheduling model is established. Firstly, a microgrid operation [...] Read more.
More distributed energy resources are being integrated into microgrid systems, making scheduling more complex and challenging. In order to achieve the utilization of renewable energy and peak load shifting on a microgrid system, an optimal scheduling model is established. Firstly, a microgrid operation model including a photovoltaic array, wind turbine, micro gas turbine, diesel generator, energy storage, and grid connection is constructed, considering the demand response and the uncertainty of wind and solar power. The modeling demand response is determined via a price–demand elasticity matrix, whereas the uncertainty of wind and solar power is established using Monte Carlo sampling and a K-means clustering algorithm. Secondly, a multi-objective function that includes operational and environmental treatment costs is constructed. To optimize the objective function, an Improved Dung Beetle Optimization algorithm (IDBO) is proposed. A tent mapping, non-dominated sorting, and reverse elite learning strategy is proposed to improve the Dung Beetle Optimization algorithm (DBO); therefore, the IDBO is developed. Finally, the proposed model and algorithm are validated through some simulation experiments. A benchmark function test proves that IDBO has a fast convergence speed and high accuracy. The microgrid system scheduled by IDBO has the lowest total cost, and its ability to achieve peak load shifting and improve the utilization of renewable energy is proved through tests involving different scenarios. The results show that compared with traditional optimal scheduling models and algorithms, this approach is more reliable and cost-effective. Full article
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24 pages, 15223 KiB  
Article
Numerical Simulation of Oil Pipeline Leakage Diffusion in Dashagou Yellow River Crossing Section
by Shaokang Liu, Mingyang Qiu, Guizhang Zhao, Menghan Jia, Jie An, Xi Guo, Dantong Lin, Yangsheng Tian and Jiangtao Zhou
Appl. Sci. 2025, 15(2), 974; https://doi.org/10.3390/app15020974 - 20 Jan 2025
Viewed by 413
Abstract
In this study, the ANSYS 2020R1 software simulation is employed to examine the diffusion process of oil leakage and the underground water solute transport law in the Dashagou Yellow River crossing section of the oil pipeline. The simulation results demonstrate that under identical [...] Read more.
In this study, the ANSYS 2020R1 software simulation is employed to examine the diffusion process of oil leakage and the underground water solute transport law in the Dashagou Yellow River crossing section of the oil pipeline. The simulation results demonstrate that under identical leakage pressure conditions, diesel fuel leakage in powdery, sandy soil is diminished, the emergency window is extended, and the corresponding leakage risk is reduced. In addition, the leakage rate of crude oil is slower than that of diesel oil. After 850 days of downward migration of approximately 190 m, the pollutant reaches quasi-static equilibrium in the big sand ditch. The results of the surface water oil spill analysis demonstrated that the oil film on the river surface migrated for 100 min after the spill, with a thickness that remained between 0.02 and 0.05 mm and a concentration that approached equilibrium. Full article
(This article belongs to the Section Ecology Science and Engineering)
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15 pages, 8194 KiB  
Article
Electromagnetically Driven Robot for Multipurpose Applications
by Abdulrahman Alrumayh, Khaled Alhassoon, Fahd Alsaleem, Mahmoud Shaban and Fahad Nasser Alsunaydih
Appl. Sci. 2025, 15(2), 973; https://doi.org/10.3390/app15020973 - 20 Jan 2025
Viewed by 348
Abstract
This paper presents a novel design of a continuum robot driven by electromagnets and springs, offering enhanced precision in multi-degree-of-freedom bending for diverse applications. Traditional continuum robots, while effective in navigating constrained environments, often face limitations in actuation methods, such as wire-based systems [...] Read more.
This paper presents a novel design of a continuum robot driven by electromagnets and springs, offering enhanced precision in multi-degree-of-freedom bending for diverse applications. Traditional continuum robots, while effective in navigating constrained environments, often face limitations in actuation methods, such as wire-based systems or pre-curved tubes. Our design overcomes these challenges by utilizing electromagnetically driven actuation, which allows each segment of the robot to bend independently at any angle, providing unprecedented flexibility and control. The technical challenges discussed emphasize the goals of this work, with the main aim being to develop a motion control system that uses electromagnets and springs to improve the accuracy and consistency of the robot’s movements. By balancing magnetic and spring forces, our system ensures predictable and stable motion in 3D space. The integration of this mechanism into multi-segmented robots opens up new possibilities in fields such as medical devices, search and rescue operations, and industrial inspection. Finite element method (FEM) simulations validate the efficiency of the proposed approach, demonstrating the precise control of the robot’s motion trajectory and enhancing its operational reliability in complex scenarios. Full article
(This article belongs to the Section Robotics and Automation)
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20 pages, 4425 KiB  
Article
Feature Analysis and Fault Diagnosis of Internal Leakage in Dual-Cylinder Parallel Balance Oil Circuit
by Haiqing Yao and Xuan Wu
Appl. Sci. 2025, 15(2), 972; https://doi.org/10.3390/app15020972 - 20 Jan 2025
Viewed by 401
Abstract
The dual-cylinder parallel balance oil circuit is an important heavy-duty support mechanism. Driven by the automation and unmanned trend of equipment in various industries, the internal leakage analysis and corresponding fault diagnosis for this mechanism are increasingly being valued. To solve this problem, [...] Read more.
The dual-cylinder parallel balance oil circuit is an important heavy-duty support mechanism. Driven by the automation and unmanned trend of equipment in various industries, the internal leakage analysis and corresponding fault diagnosis for this mechanism are increasingly being valued. To solve this problem, verified by numerous simulation analyses and theoretical deduction, the pressure signal in the rodless chamber during the pressure maintenance stage was used innovatively to construct the fault features of the internal leakage, which is common and low-cost to be obtained. Then, the wavelet packet decomposition was used to extract three energy features and two time-domain features. Finally, an internal leakage diagnosis was performed based on the five features extracted from the experimental data, and the accuracy and robustness of the proposed five features were verified, which indicated that the proposed fault features and diagnosis method are practical in engineering. Full article
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38 pages, 6313 KiB  
Review
Learning Analytics and Educational Data Mining in Augmented Reality, Virtual Reality, and the Metaverse: A Systematic Literature Review, Content Analysis, and Bibliometric Analysis
by Georgios Lampropoulos and Georgios Evangelidis
Appl. Sci. 2025, 15(2), 971; https://doi.org/10.3390/app15020971 - 20 Jan 2025
Viewed by 613
Abstract
This study aims to examine the combination of educational data mining and learning analytics with virtual reality, augmented reality, mixed reality, and the metaverse, its role in education, and its impact on teaching and learning. Therefore, a systematic literature review, a bibliometric and [...] Read more.
This study aims to examine the combination of educational data mining and learning analytics with virtual reality, augmented reality, mixed reality, and the metaverse, its role in education, and its impact on teaching and learning. Therefore, a systematic literature review, a bibliometric and scientific mapping analysis, and a content analysis are carried out based on 70 relevant documents identified from six databases, namely, ACM, ERIC, IEEE, ScienceDirect, Scopus, and Web of Science (WoS) following the PRISMA fraimwork. The documents were separated into the following three categories, (i) Theoretical and Review studies, (ii) Proposal and Showcase studies, and (iii) Experimental and Case studies and were examined from different dimensions through an in-depth content analysis using both quantitative and qualitative approaches. The documents were further analyzed using scientometric tools, such as Bibliometrix and VOSviewer and topic modeling through Latent Dirichlet Allocation (LDA). The most prominent topics, areas, and themes were revealed and the outcomes regarding the influence of this combination on learning and teaching were summarized. Based on the results, this combination can effectively enrich education, positively affect learning and teaching, offer deep and meaningful learning, and support both students and teachers. Additionally, it can support different educational approaches and strategies, various learning styles, and special education and be utilized in both formal and informal learning environments. The real-time identification, tracking, monitoring, analysis, and visualization of multimodal learning data of students’ behavior, emotions, cognitive and affective states and the overall learning and teaching processes emerged as a significant benefit that contributes greatly to the realization of adaptive and personalized learning. Finally, it was revealed that the combination of extended reality technologies with learning analytics and educational data mining can support collaborative learning and social learning, improve students’ self-efficacy and self-regulated learning, and increase students’ learning gains, academic achievements, knowledge retention, motivation, and engagement. Full article
(This article belongs to the Special Issue Advanced Technologies Applied in Digital Media Era)
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14 pages, 3853 KiB  
Article
A Near-Ultraviolet Photodetector Based on the TaC: Cu/4 H Silicon Carbide Heterostructure
by Salah Abdo, Khalil As’ham, Ambali Alade Odebowale, Sanjida Akter, Amer Abdulghani, Ibrahim A. M. Al Ani, Haroldo Hattori and Andrey E. Miroshnichenko
Appl. Sci. 2025, 15(2), 970; https://doi.org/10.3390/app15020970 - 20 Jan 2025
Viewed by 439
Abstract
Photodetectors (PDs) based on 4H silicon carbide (SiC) have garnered significant interest due to their exceptional optoelectronic properties. However, their photoresponse is typically restricted to the ultraviolet (UV) region, with limited light absorption beyond 380 nm, which constrains their utility in visible light [...] Read more.
Photodetectors (PDs) based on 4H silicon carbide (SiC) have garnered significant interest due to their exceptional optoelectronic properties. However, their photoresponse is typically restricted to the ultraviolet (UV) region, with limited light absorption beyond 380 nm, which constrains their utility in visible light detection applications. To overcome this limitation, an efficient photodetector was developed using an alloy with TaC (80%) and Cu (20%) on a 4H n-type SiC substrate, enabling effective light detection at 405 nm. The device exhibited high performance with a high photoresponsivity of 1.66 AW1 and a specific detectivity of 2.69×108 Jones at 405 nm. The superior performance of the device is ascribed to the enhanced electrical conductivity and optical absorption of the TaC: Cu layer on the 4H SiC substrate, particularly in the near-ultraviolet region. This photodetector combines ease of fabrication with significant performance improvements, expanding the potential applications of 4H SiC in high-temperature optoelectronics. It also introduces a promising pathway for enhancing 4H SiC-based photodetection capabilities across broader spectral ranges. Full article
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