Applied Sciences

16 pages, 4263 KiB

Open AccessArticle

Perceived Brightness and Resolution of Holographic Augmented Reality Retinal Scan Glasses

by Maximilian Rutz, Pia Neuberger, Simon Pick and Torsten Straßer

Appl. Sci. 2025, 15(3), 1540; https://doi.org/10.3390/app15031540 (registering DOI) - 3 Feb 2025

Augmented reality display performance depends strongly on features of the human visual system. This is especially true for retinal scan glasses, which use laser beam scanning and transparent holographic optical combiners. Human-centered approaches allow us to go beyond conventional optical metrology and evaluate [...] Read more.

Augmented reality display performance depends strongly on features of the human visual system. This is especially true for retinal scan glasses, which use laser beam scanning and transparent holographic optical combiners. Human-centered approaches allow us to go beyond conventional optical metrology and evaluate display performance as it is perceived in actual augmented reality use cases. Here, we first present a theoretical formula for the retinal scan luminance and ambient contrast ratio calculated from optical powers, wavelengths, field of view, and human pupil diameter. As a promising insight, we found that the pupil diameter dependence is beneficial in assimilating the virtual image luminance to the ambient luminance. Second, we designed and performed a psychophysical experiment to assess perceived resolution in augmented reality settings using a fully functional retinal scan glasses prototype. We present the results of the trials and illustrate how this approach can be used in the further development of augmented reality smart glasses. Full article

(This article belongs to the Special Issue Virtual and Augmented Reality: Theory, Methods, and Applications)

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16 pages, 17731 KiB

Open AccessArticle

A Refined Approach to Segmenting and Quantifying Inter-Fracture Spaces in Facial Bone CT Imaging

by Doohee Lee, Kanghee Lee, Dae-Hyun Park, Gwiseong Moon, Inseo Park, Yeonjin Jeong, Kun-Yong Sung, Hyun-Soo Choi and Yoon Kim

Appl. Sci. 2025, 15(3), 1539; https://doi.org/10.3390/app15031539 (registering DOI) - 3 Feb 2025

Abstract

The human facial bone is made up of many complex structures, which makes it challenging to accurately analyze fractures. To address this, we developed advanced image analysis software which segments and quantifies spaces between fractured bones in facial CT images at the pixel [...] Read more.

The human facial bone is made up of many complex structures, which makes it challenging to accurately analyze fractures. To address this, we developed advanced image analysis software which segments and quantifies spaces between fractured bones in facial CT images at the pixel level. This study used 3D CT scans from 1766 patients who had facial bone fractures at a university hospital between 2014 and 2020. Our solution included a segmentation model which focuses on identifying the gaps created by facial bone fractures. However, training this model required costly pixel-level annotations. To overcome this, we used a stepwise annotation approach. First, clinical specialists marked the bounding boxes of fracture areas. Next, trained specialists created the initial pixel-level unrefined ground truth by referencing the bounding boxes. Finally, we created a refined ground truth to correct human errors, which helped improve the segmentation accuracy. Radiomics feature analysis confirmed that the refined dataset had more consistent patterns compared with the unrefined dataset, showing improved reliability. The segmentation model showed significant improvement in the Dice similarity coefficient, increasing from 0.33 with the unrefined ground truth to 0.67 with the refined ground truth. This research introduced a new method for segmenting spaces between fractured bones, allowing for precise pixel-level identification of fracture regions. The model also helped with quantitative severity assessment and enabled the creation of 3D volume renderings, which can be used in clinical settings to develop more accurate treatment plans and improve outcomes for patients with facial bone fractures. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Biomedical Image Processing)

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25 pages, 13094 KiB

Open AccessArticle

Dual-Modality Transformer with Time Series Imaging for Robust Epileptic Seizure Prediction

by Jiahao Qin, Zijia Liu, Jihong Zhuang and Feng Liu

Appl. Sci. 2025, 15(3), 1538; https://doi.org/10.3390/app15031538 (registering DOI) - 3 Feb 2025

Abstract

Automated EEG classification algorithms for seizures can facilitate the clinical diagnosis of epilepsy, enabling more expedient and precise classification. However, existing EEG signal preprocessing methods oriented towards artifact removal and signal enhancement have demonstrated suboptimal accuracy and robustness. In response to this challenge, [...] Read more.

Automated EEG classification algorithms for seizures can facilitate the clinical diagnosis of epilepsy, enabling more expedient and precise classification. However, existing EEG signal preprocessing methods oriented towards artifact removal and signal enhancement have demonstrated suboptimal accuracy and robustness. In response to this challenge, we propose an Adaptive Dual-Modality Learning Model (ADML) for epileptic seizure prediction by combining time series imaging with Transformer-based architecture. Our approach effectively captures both temporal dependencies and spatial relationships in EEG signals through a specialized attention mechanism. Evaluated on the CHB-MIT and Bonn datasets, our method achieves 98.7% and 99.2% accuracy, respectively, significantly outperforming existing approaches. The model demonstrates strong generalization capability across datasets while maintaining computational efficiency. Cross-dataset validation confirms the robustness of our approach, with consistent performance above 96% accuracy. These results suggest that our dual-modality approach provides a reliable and practical solution for clinical epileptic seizure prediction. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

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13 pages, 1354 KiB

Open AccessArticle

An Effective Energetic Application of Orange Waste in Multi-Component Co-Digestion with Municipal Sewage Sludge

by Aleksandra Szaja, Joanna Czarnota, Adam Masłoń and Magdalena Lebiocka

Appl. Sci. 2025, 15(3), 1537; https://doi.org/10.3390/app15031537 (registering DOI) - 3 Feb 2025

Abstract

A strategy allowing for the application of orange waste (OW) in anaerobic co-digestion with municipal sewage sludge (MSS) has been proposed. For this purpose, the introduction of an additional component represented by ice-cream processing waste (IPW) has been chosen. The experiment was conducted [...] Read more.

A strategy allowing for the application of orange waste (OW) in anaerobic co-digestion with municipal sewage sludge (MSS) has been proposed. For this purpose, the introduction of an additional component represented by ice-cream processing waste (IPW) has been chosen. The experiment was conducted in batch mode at a temperature of 37 °C. Four series were conducted: S1—the mono-digestion of MSS; S2—two-component co-digestion of MSS and 1.5 g of OW; S3—two-component co-digestion of MSS and 1.0 g of IPW; and S4—three-component co-digestion of MSS, 1.0 g of IPW, and 1.5 g of OW. The obtained results indicate that the highest methane production was achieved in the presence of IPW in two- and three-component mixtures (S3 and S4). It was also accompanied by improved kinetics, enhanced organic removal, and stable process performance. The related methane yields were 407.6 and 401.6 mL/g VS in S3 and S4, respectively. In turn, in S1 and S2, this parameter was established at the level of 351.3 and 344.3 mL/g VS. Additionally, as compared to MSS mono-digestion (S1), the energy profit was enhanced by 54 and 62% in S3 and S4, respectively. The obtained results indicate the possibility of effective management of OW with energy recovery in the anaerobic digestion process (AD). Full article

(This article belongs to the Special Issue Novel Technologies for Wastewater Treatment and Reuse)

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20 pages, 11368 KiB

Open AccessArticle

Aroma Compound Release from Starches of Different Origins: A Physicochemical Study

by Maria-Magdalini Emmanouil, Alexandra Skartsila, Pelagia Katsou, Lampros Farmakis, Athanasia Koliadima and John Kapolos

Appl. Sci. 2025, 15(3), 1536; https://doi.org/10.3390/app15031536 - 3 Feb 2025

Abstract

Sensory properties, particularly aroma, play a crucial role in consumers’ acceptance and perceived quality of food. The release and the perception of aroma compounds is affected by their interaction with nonvolatile ingredients of foods, such as proteins, lipids, and polysaccharides. These interactions, whether [...] Read more.

Sensory properties, particularly aroma, play a crucial role in consumers’ acceptance and perceived quality of food. The release and the perception of aroma compounds is affected by their interaction with nonvolatile ingredients of foods, such as proteins, lipids, and polysaccharides. These interactions, whether reversible or irreversible, significantly influence aroma retention and release. Starch, a common food constituent, has been found to interact with aroma compounds, impacting flavor dynamics through processes like complexation and encapsulation. In this study, reversed-flow gas chromatography (RF-GC) is employed for the estimation of the release behavior of polar (diacetyl) and non-polar (dl-limonene) aroma compounds from starches of various origins (corn, wheat, rice, and potato). The results show that aroma compound release is influenced by the matrix composition, environmental conditions, and physicochemical properties of both starch and aroma compounds. The temperature-dependent mass transfer coefficients and activation energies reveal the strong influence of polar and non-polar characteristics on aroma compound behavior. Additionally, significant variations in retention and release are observed based on the starch type and the type of bonds involved in aroma compound interactions, underscoring the critical role of thermodynamic and kinetic parameters in flavor dynamics. Full article

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25 pages, 8016 KiB

Open AccessArticle

High-Fold 3D Gaussian Splatting Model Pruning Method Assisted by Opacity

by Shiyu Qiu, Chunlei Wu, Zhenghao Wan and Siyuan Tong

Appl. Sci. 2025, 15(3), 1535; https://doi.org/10.3390/app15031535 - 3 Feb 2025

Abstract

Recent advancements in 3D scene representation have underscored the potential of Neural Radiance Fields (NeRFs) for producing high-fidelity renderings of complex scenes. However, NeRFs are hindered by the significant computational burden of volumetric rendering. To address this, 3D Gaussian Splatting (3DGS) has emerged [...] Read more.

Recent advancements in 3D scene representation have underscored the potential of Neural Radiance Fields (NeRFs) for producing high-fidelity renderings of complex scenes. However, NeRFs are hindered by the significant computational burden of volumetric rendering. To address this, 3D Gaussian Splatting (3DGS) has emerged as an efficient alternative, utilizing Gaussian-based representations and rasterization techniques to achieve faster rendering speeds without sacrificing image quality. Despite these advantages, the large number of Gaussian points and associated internal parameters result in high storage demands. To address this challenge, we propose a pruning strategy applied during the Gaussian densification and pruning phases. Our approach integrates learnable Gaussian masks with a contribution-based pruning mechanism, further enhanced by an opacity update strategy to facilitate the pruning process. This method effectively eliminates redundant Gaussian points and those with minimal contributions to scene construction. Additionally, during the Gaussian parameter compression phase, we employ a combination of teacher–student models and vector quantization to compress the spherical harmonic coefficients. Extensive experimental results demonstrate that our approach reduces the storage requirements of original 3D Gaussian models by over 30 times, with only a minor degradation in rendering quality. Full article

(This article belongs to the Special Issue Technical Advances in 3D Reconstruction)

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31 pages, 10490 KiB

Open AccessArticle

Enhancing Driving Safety of Personal Mobility Vehicles Using On-Board Technologies

by Eru Choi, Tuan Anh Dinh and Min Choi

Appl. Sci. 2025, 15(3), 1534; https://doi.org/10.3390/app15031534 - 3 Feb 2025

Abstract

Accidents involving electric wheelchairs are a growing concern, with users frequently encountering obstacles that lead to collisions, tipping, or loss of balance. These incidents underscore the need for advanced safety technologies tailored to electric wheelchair users. This research addresses this need by developing [...] Read more.

Accidents involving electric wheelchairs are a growing concern, with users frequently encountering obstacles that lead to collisions, tipping, or loss of balance. These incidents underscore the need for advanced safety technologies tailored to electric wheelchair users. This research addresses this need by developing a driving assistance system to prevent accidents and enhance user safety. The system incorporates ultrasonic sensors and a front-facing camera to detect obstacles and provide real-time warnings. The proposed system operates independently of stable server communication and employs embedded hardware for fast object detection and environmental recognition, ensuring immediate guidance in various scenarios. In this research, we utilized the existing yolov8 model as is. But we attempted to improve performance by hardware acceleration of convolutional neural networks, supporting various layers such as convolution, deconvolution, pooling, batch normalization, and others. Thus, the YOLO model was accelerated during inference on the specialized hardware in our experiments. Performance was evaluated in diverse environments to assess its usability. Results demonstrated high accuracy in detecting obstacles and providing timely warnings. Leveraging hardware acceleration for YOLOv8 delivers faster, scalable, and robust object detection, making it a great platform for enhancing driving safety on edge and embedded devices. These findings provide a strong foundation for future advancements in safety assistance systems for electric wheelchairs and other mobility devices. Future research will focus on enhancing system performance and integrating additional features to create a safer environment for electric wheelchair users. Full article

(This article belongs to the Special Issue Recent Advances in Internet of Things and System Design)

19 pages, 9706 KiB

Open AccessArticle

Potentials and Limitations of Using Sentinel Data for Power System Operation and Control: Case Study of Protection Against Forest Fires and Aerosol Contamination

by Amalija Božiček, Božidar Filipović-Grčić, Bojan Franc, Davor Škrlec and Frano Tomašević

Appl. Sci. 2025, 15(3), 1533; https://doi.org/10.3390/app15031533 - 3 Feb 2025

Abstract

The power network is directly or indirectly exposed to various weather and climate conditions that can positively and negatively influence it. Forest fires and various aerosol contaminations may negatively affect the power network. Surface observations and measurements (e.g., weather stations) help protect against [...] Read more.

The power network is directly or indirectly exposed to various weather and climate conditions that can positively and negatively influence it. Forest fires and various aerosol contaminations may negatively affect the power network. Surface observations and measurements (e.g., weather stations) help protect against unfavorable influences, but the network is usually poor. That challenge may be solved by including satellite data in the power network planning, operation, and maintenance. This article presents the possibilities of using Sentinel mission’s data, at various temporal and spatial levels, for those purposes through examples of protection against forest fires and aerosol contamination. In the case of forest fires, satellite data enable a more-precise calculation of forest fire-spreading danger for areas with no installed weather stations, which may be used in the power system contingency analysis calculation. The analysis showed that aerosol contamination has a correlation with outage appearance in the 25 kV 50 Hz electric traction network. Sentinel 5p data may serve as a tool in a surface air-quality measurement data control to confirm/deny the high concentration of PM₁₀ particles. Analysis showed that Sentinel data are favorable for power network operations on intra-day and day-ahead operations, as well as for long-term operations, which include planning and maintenance. The spatial resolution of Sentinel data is more than enough to fulfill the surface weather station network. Satellite data also enable the use of additional data that are not measured at ground stations. Full article

(This article belongs to the Special Issue Application of SAR and Optical Satellite Data in Environmental Changes Monitoring)

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18 pages, 2206 KiB

Open AccessArticle

AI-Assisted Physiotherapy for Patients with Non-Specific Low Back Pain: A Systematic Review and Meta-Analysis

by Dev Kapil, Jin Wang, David B. Olawade and Laura Vanderbloemen

Appl. Sci. 2025, 15(3), 1532; https://doi.org/10.3390/app15031532 - 3 Feb 2025

Abstract

Background: Non-specific low back pain (LBP) is a widespread condition with significant impacts on physical activity, muscle strength, psychological well-being, and economic status. Traditional physiotherapy shows variable efficacy, prompting growing interest in AI-assisted physiotherapy for its potential to offer personalized feedback and multidisciplinary [...] Read more.

Background: Non-specific low back pain (LBP) is a widespread condition with significant impacts on physical activity, muscle strength, psychological well-being, and economic status. Traditional physiotherapy shows variable efficacy, prompting growing interest in AI-assisted physiotherapy for its potential to offer personalized feedback and multidisciplinary care integration. Objective: This systematic review and meta-analysis aimed to evaluate AI-assisted physiotherapy’s effectiveness in reducing pain intensity and functional impairment and improving mental health compared to usual physiotherapy. Method: A comprehensive search strategy was employed across Embase, MEDLINE, Cochrane Library, and Web of Science databases from inception to 30 May 2024. Comparative studies were identified and screened using PICOS criteria. Data extraction involved detailed study characteristics and outcomes, with methodological quality assessed via the Cochrane Risk of Bias tool. Meta-analyses using random-effects models calculated standardized mean differences (SMDs). Results: Eight studies met the inclusion criteria. Compared to usual physiotherapy, AI-assisted physiotherapy did not demonstrate any statistically significant differences in outcomes across the aspects studied, including pain intensity (SMD = −0.2711, 95% CI: −0.5109 to −0.0313, p = 0.267), functional impairment (SMD = −0.2508, 95% CI: −0.5574 to 0.0559, p = 0.1089), and mental health (SMD = −0.0328, 95% CI: −0.1972 to 0.1316, p = 0.6956). These findings indicate that AI-assisted physiotherapy had no demonstrable additional effect compared to usual physiotherapy for patients with LBP. Sensitivity analyses were conducted to address inter-study heterogeneity, confirming the robustness of these results. Conclusions: While AI-assisted physiotherapy shows potential in managing LBP by providing personalized treatment and feedback, the current evidence does not demonstrate significant advantages over usual physiotherapy. Further large-scale, long-term, and methodologically rigorous randomized controlled trials are necessary to validate these findings, assess their clinical relevance, and explore broader public health applications. Full article

(This article belongs to the Special Issue Applied and Innovative Computational Intelligence Systems: 3rd Edition)

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13 pages, 3256 KiB

Open AccessArticle

Bearing Fault Diagnosis Grounded in the Multi-Modal Fusion and Attention Mechanism

by Jianjian Yang, Haifeng Han, Xuan Dong, Guoyong Wang and Shaocong Zhang

Appl. Sci. 2025, 15(3), 1531; https://doi.org/10.3390/app15031531 - 3 Feb 2025

Abstract

This paper proposes a novel method called Fusion Attention Network for Bearing Diagnosis (FAN-BD) to address the challenges in effectively extracting and fusing key information from current and vibration signals in traditional methods. The research is validated using the public dataset Vibration, Acoustic, [...] Read more.

This paper proposes a novel method called Fusion Attention Network for Bearing Diagnosis (FAN-BD) to address the challenges in effectively extracting and fusing key information from current and vibration signals in traditional methods. The research is validated using the public dataset Vibration, Acoustic, Temperature, and Motor Current Dataset of Rotating Machines under Varying Operating Conditions for Fault Diagnosis. The method first converts current and vibration signals into two-dimensional grayscale images, extracts local features through multi-layer convolutional neural networks, and captures global information using the self-attention mechanism in the Vision Transformer (ViT). Furthermore, it innovatively introduces the Channel-Based Multi-Head Attention (CBMA) mechanism for the efficient fusion of features from different modalities, maximizing the complementarity between signals. The experimental results show that compared to mainstream algorithms such as Vision Transformer, Swin Transformer, and ConvNeXt, the Fusion Attention Network for Bearing Diagnosis (FAN-BD) achieves higher accuracy and robustness in fault diagnosis tasks, providing an efficient and reliable solution for bearing fault diagnosis.The proposed model outperforms ViT, Swin Transformer, ConvNeXt, and CBMA-ViT in terms of classification accuracy, achieving an accuracy of 97.5%. The comparative results clearly demonstrate that the proposed Fusion Attention Network for Bearing Diagnosis yields significant improvements in classification outcomes. Full article

(This article belongs to the Collection Bearing Fault Detection and Diagnosis)

26 pages, 748 KiB

Open AccessReview

Electronic Sensing Technologies in Food Quality Assessment: A Comprehensive Literature Review

by Marian Gil, Mariusz Rudy, Paulina Duma-Kocan and Renata Stanisławczyk

Appl. Sci. 2025, 15(3), 1530; https://doi.org/10.3390/app15031530 - 3 Feb 2025

Abstract

This manuscript was prepared for the purpose of an in-depth analysis of the development of electronic sensors in food quality assessment. In this study, the following research question was asked: What are the arguments for the development of electronic sensors for food assessment? [...] Read more.

This manuscript was prepared for the purpose of an in-depth analysis of the development of electronic sensors in food quality assessment. In this study, the following research question was asked: What are the arguments for the development of electronic sensors for food assessment? The aim of this work was to comprehensively review the current scientific literature presenting the discussed issues and their systematization, as well as to present the prospects, threats, and applications of electronic sensors in food quality testing. The greatest interest of researchers lies in the use of e-nose. In contrast, fewer publications concerned e-tongue applications, and the smallest number of works concerned e-eye application. The initial application of electronic sensors in the food industry progressed from research on the identification of single ingredients or properties to the creation of increasingly complex research instruments that comprehensively analyze areas of food characteristics. Specifically, e-sensor research has focused on individual e-nose, e-tongue, and e-eye devices and has not provided complete information about food. This is confirmed by the high accuracy of research results regarding the combined use of sensors in food quality assessment. Full article

(This article belongs to the Special Issue Holistic Approaches in Sensory Research on Food and Non-food Products Using New Techniques and Digital Tools)

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18 pages, 13398 KiB

Open AccessArticle

An Investigation of Parameter Dimension Reduction Optimization of PMA-Synrm

by Zhongqi Liu, Guiyuan Zhang and Guanghui Du

Appl. Sci. 2025, 15(3), 1529; https://doi.org/10.3390/app15031529 - 3 Feb 2025

Abstract

The rotor of a permanent magnet-assisted synchronous reluctance (PMA-Synrm) motor mostly adopts the structure of a multi-layer magnetic barrier and multi-layer ferrite, which leads to the design parameters of this kind of motor increase with the increase in the number of magnetic barrier [...] Read more.

The rotor of a permanent magnet-assisted synchronous reluctance (PMA-Synrm) motor mostly adopts the structure of a multi-layer magnetic barrier and multi-layer ferrite, which leads to the design parameters of this kind of motor increase with the increase in the number of magnetic barrier layers. A large number of design parameters are coupled with each other, which makes the optimization design of a permanent magnet-assisted synchronous reluctance motor particularly difficult. In this paper, a 7.5 kW, 1500 rpm permanent magnet-assisted synchronous reluctance motor is taken as the research object, and the optimization design of parameter dimension reduction is studied. The rotor structure of the motor is a combination of five layers of magnetic barrier and five layers of ferrite. By using the parameter dimension reduction method proposed in this paper, the number of parameters involved in the optimization is reduced from 26 to 7, which greatly improves the optimization efficiency of this kind of motor and realizes the comprehensive global optimization design of a permanent magnet-assisted synchronous reluctance motor. This paper provides a reference for the optimization of a permanent magnet-assisted synchronous reluctance motor. Full article

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20 pages, 1796 KiB

Open AccessArticle

Impact of Texture Feature Count on the Accuracy of Osteoporotic Change Detection in Computed Tomography Images of Trabecular Bone Tissue

by Róża Dzierżak

Appl. Sci. 2025, 15(3), 1528; https://doi.org/10.3390/app15031528 - 2 Feb 2025

Abstract

The aim of this study is to compare the classification accuracy depending on the number of texture features used. This study used 400 computed tomography (CT) images of trabecular spinal tissue from 100 patients belonging to two groups (50 control patients and 50 [...] Read more.

The aim of this study is to compare the classification accuracy depending on the number of texture features used. This study used 400 computed tomography (CT) images of trabecular spinal tissue from 100 patients belonging to two groups (50 control patients and 50 patients diagnosed with osteoporosis). The descriptors of texture features were based on a gray level histogram, gradient matrix, RL matrix, event matrix, an autoregressive model, and wavelet transformation. From the 290 obtained texture features, the features with fixed values were eliminated and structured according to the feature importance ranking. The classification performance was assessed using 267, 200, 150, 100, 50, 20, and 10 texture features to build classifiers. The classifiers applied in this study included Naive Bayes, Multilayer Perceptron, Hoeffding Tree, K-nearest neighbors, and Random Forest. The following indicators were used to assess the quality of the classifiers: accuracy, sensitivity, specificity, precision, negative predictive value, Matthews correlation coefficient, and F1 score. The highest performance was achieved by the K-Nearest Neighbors (K = 1) and Multilayer Perceptron classifiers. KNN demonstrated the best results with 50 features, attaining a highest F1 score of 96.79% and accuracy (ACC) of 96.75%. MLP achieved its optimal performance with 100 features, reaching an accuracy and F1 score of 96.50%. This demonstrates that building a classifier using a larger number of features, without a selection process, allows us to achieve high classification effectiveness and holds significant diagnostic value. Full article

(This article belongs to the Special Issue Applications of Image Processing and Pattern Recognition in Biometrics)

21 pages, 2159 KiB

Open AccessArticle

Induction Motor Dynamics Regimes: A Comprehensive Study of Mathematical Models and Validation

by Marina Konuhova

Appl. Sci. 2025, 15(3), 1527; https://doi.org/10.3390/app15031527 - 2 Feb 2025

Abstract

This study investigates the dynamic behavior of induction motors (IMs) by developing and validating four distinct mathematical models designed for transient and starting regimes. These models, expressed in α,β and d,q coordinate systems, analyze rotational frequency, electromagnetic torque, and [...] Read more.

This study investigates the dynamic behavior of induction motors (IMs) by developing and validating four distinct mathematical models designed for transient and starting regimes. These models, expressed in α,β and d,q coordinate systems, analyze rotational frequency, electromagnetic torque, and current profiles with varying levels of complexity, including current-based, flux linkage-based, and rotor winding electromagnetic time constant approaches. Implemented in Fortran, the models address the limitations of predefined tools like MATLAB/Simulink, offering enhanced precision, flexibility, and suitability for non-standard scenarios. Validation against experimental data from a 3 kW induction motor confirms the models’ accuracy, with consistent results across approaches. Notably, the flux linkage models excel in capturing intricate transient phenomena, while current-based models simplify integration with power system studies. These findings provide a robust framework for analyzing IM performance under diverse conditions such as voltage unbalance and rundown scenarios, enabling the optimization of motor operations in energy-intensive industries. Full article

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22 pages, 16083 KiB

Open AccessArticle

A New Calibration Method for Broadband Complex Resistivity Measurement System of Rocks and Ores

by Haojie Zhang, Rujun Chen, Shaoheng Chun and Chao Xu

Appl. Sci. 2025, 15(3), 1526; https://doi.org/10.3390/app15031526 - 2 Feb 2025

Abstract

The complex resistivity (CR) measurement constitutes a practical methodology for investigating the internal structures of rocks and ores alongside their mineralogical compositions and the chemical properties of fluids. However, during complex resistivity testing, particularly at high frequencies, the leakage current caused by the [...] Read more.

The complex resistivity (CR) measurement constitutes a practical methodology for investigating the internal structures of rocks and ores alongside their mineralogical compositions and the chemical properties of fluids. However, during complex resistivity testing, particularly at high frequencies, the leakage current caused by the distributed capacitance of the instrument’s acquisition channels reduces the measurement accuracy. Additionally, the contact impedance of the measuring devices and the electromagnetic coupling effects of the measurement cables further affect the complex resistivity test results of samples. To accurately characterize samples’ intrinsic induced polarization (IP) properties, we developed a broadband complex resistivity measurement system (1 mHz–100 kHz) for rocks and ores, comprising a complex resistivity analyzer and a sample holder, employing the four-electrode method. In this study, we establish a circuit model for the measurement system to analyze the influence of the distributed capacitance of the acquisition channels on the test results at elevated frequencies. We derive the error terms inherent in the instrument’s measurements across various circuit design configurations and propose a novel method for calculating the distributed capacitance of the instrument’s acquisition channels, the parasitic capacitance of the sampling resistor, and for calibrating data by reversing the polarity of the excitation signal. Furthermore, we investigate the effect of contact impedance within the measurement setup on test results and design two sample-testing devices. Through extensive testing on multiple circuit models and samples, the system achieves an accuracy of up to 1% within the 10 MΩ range. Its overall performance surpasses that of the Solartron 1260A impedance analyzer and traditional signal source forward connection calibration methods. This advancement holds significant implications for complex resistivity measurements and the study of rock physical properties. Full article

(This article belongs to the Section Earth Sciences)

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20 pages, 1441 KiB

Open AccessArticle

Water Consumption, Quantity and Quality of Wastewater and Sewage Sludge from Polish Dairies

by Joanna Rodziewicz, Artur Mielcarek, Karolina Kłobukowska, Krzysztof Jóźwiakowski, Tadeusz Siwiec, Piotr Bugajski and Wojciech Janczukowicz

Appl. Sci. 2025, 15(3), 1525; https://doi.org/10.3390/app15031525 - 2 Feb 2025

Abstract

The peculiarity of the wastewater produced in Polish dairies stems from the frequency and specific technology of cottage cheese production. The aim of this study was to determine the water consumption and the quantity and quality of wastewater and sewage sludge discharged from [...] Read more.

The peculiarity of the wastewater produced in Polish dairies stems from the frequency and specific technology of cottage cheese production. The aim of this study was to determine the water consumption and the quantity and quality of wastewater and sewage sludge discharged from Polish dairies based on the size of the plant and the production profile of the plant to characterize the wastewater treatment plants (WWTPs). Data were collected from eighteen dairies. Most of them have their own WWTP. Water consumption ranged from 1.5 litres (L) of water per litre of milk processed to 3.71 L/L. The specific volume of wastewater ranged from 1.18 to 5.78 L per L of milk processed. The raw wastewater concentrations were comparable to those of dairy wastewater in other European countries. Despite the disposal of domestic wastewater in WWTPs, the results of the sanitary examinations of the sludge showed it was suitable for agricultural purposes. Its heavy metal also made it applicable on agricultural land. The ratio of the sludge to raw milk processing was between 0.137 and 7.927 kg of sludge per 100 L of milk processed. The amount of sludge produced per pollutant (BOD) load removed ranged from 0.404 to 18.895 kg/kg BOD_removed. Full article

(This article belongs to the Special Issue Environmental Management in Milk Production and Processing)

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25 pages, 4575 KiB

Open AccessArticle

Framework for Asset Digitalization: IoT Platforms and Asset Health Index in Maintenance Applications

by Eduardo Candón Fernández, Adolfo Crespo Márquez, Antonio J. Guillén López and Eduardo Hidalgo Fort

Appl. Sci. 2025, 15(3), 1524; https://doi.org/10.3390/app15031524 - 2 Feb 2025

Abstract

This study proposes a comprehensive framework for digitalizing and managing assets with low initial digital maturity, focusing on their operation and maintenance (O&M) lifecycle. The framework integrates Internet of Things (IoT) networks with Asset Health Index (AHI) models through four interconnected components. The [...] Read more.

This study proposes a comprehensive framework for digitalizing and managing assets with low initial digital maturity, focusing on their operation and maintenance (O&M) lifecycle. The framework integrates Internet of Things (IoT) networks with Asset Health Index (AHI) models through four interconnected components. The Asset Definition Model ensures standardized data representation based on IEC 81346-1:2022 and ISO 14224:2016, while the Asset Criticality Model prioritizes maintenance actions using risk-informed analysis. The Asset Monitoring Model enables real-time data acquisition through IoT sensors, facilitating condition-based monitoring and dynamic decision-making. Finally, the Intelligent Asset Management Models support long-term planning by simplifying data complexity and aligning with advanced maintenance strategies. A case study on bridge maintenance demonstrates the practical value of the framework, showcasing its ability to integrate real-time monitoring with predictive decision-making tools. By bridging asset monitoring and lifecycle planning, the framework enhances operational efficiency, reduces maintenance costs, and addresses the challenges posed by limited digital maturity in critical infrastructure. This approach represents a significant advancement in the digital transformation of maintenance management. Full article

(This article belongs to the Special Issue Internet of Things and Smart Systems)

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18 pages, 10021 KiB

Open AccessArticle

Loss Research and Thermal Analysis of BLDC Hollow-Cup Motor Under Reactor Suppression

by Jingjuan Du, Yumeng Sun, Jian Zhao, Boran Liu and Yanqing Mi

Appl. Sci. 2025, 15(3), 1523; https://doi.org/10.3390/app15031523 - 2 Feb 2025

Abstract

In order to avoid overheating of a BLDC permanent magnet (PM) motor at high speeds, this paper focuses on the loss reduction of a 90 W 47,000 r/min BLDC hollow-cup motor. It is proposed to provide an optimizing method for the series reactors [...] Read more.

In order to avoid overheating of a BLDC permanent magnet (PM) motor at high speeds, this paper focuses on the loss reduction of a 90 W 47,000 r/min BLDC hollow-cup motor. It is proposed to provide an optimizing method for the series reactors and the parameterization of reactors in the motor system. The finite element method (FEM) is used to calculate and analyze the time harmonic of air-gap magnetic flux density, stator core loss, and rotor eddy current loss in two cases: with a series reactor and without a reactor. By parameterizing the inductance value, the optimal resistance value is determined to minimize motor loss. In addition, an electromagnetic–thermal coupling analysis is conducted, and the results show that the temperature distribution of the stator core, winding, and rotor are improved under reactor suppression. Finally, an experimental platform is built to verify the temperature increase and the efficiency of the motor load operation. A clear reference for the research and optimization analysis of motor loss reduction is provided. Full article

(This article belongs to the Topic Advanced Electrical Machines and Drives Technologies, 2nd Volume)

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30 pages, 4248 KiB

Open AccessArticle

Predicting Dye Formulations and Ultrasonic Pretreatment Power in Wood Staining with a SAT Fusion-Gated BiLSTM Model Optimized by Improved Dung Beetle Algorithm

by Xuemei Guan, Xiangya Chen, Zhongsheng He and Hongbo Cui

Appl. Sci. 2025, 15(3), 1522; https://doi.org/10.3390/app15031522 - 2 Feb 2025

Abstract

Wood dyeing plays a crucial role in enhancing the value of plantation wood and addressing the imbalance between supply and demand in the wood industry. However, challenges such as low dye uptake and inaccurate color matching persist. This study introduces a novel Separable [...] Read more.

Wood dyeing plays a crucial role in enhancing the value of plantation wood and addressing the imbalance between supply and demand in the wood industry. However, challenges such as low dye uptake and inaccurate color matching persist. This study introduces a novel Separable Attention Feature Fusion-Gated Bidirectional Long Short-Term Memory (SAT Fusion-Gated BiLSTM) model to more accurately predict dye concentration and ultrasonic pretreatment power to address these issues. The model integrates multispectral data and uses ultrasonic treatment to open fiber gaps and decompose wood components, thereby improving dye uptake. Unlike traditional models, SAT Fusion-Gated BiLSTM combines a Maximum Information Coefficient (MIC)-based feature selection module, gated linear units for enhanced feature extraction, and an improved cross-attention mechanism for efficient data fusion. Compared to traditional deep learning models, the model’s performance is significantly improved, including an R² of 0.9274, an RMSE of 0.0470, an RPIQ of 5.5825, an RPD of 5.0400, and a MAE of 0.0229. RPIQ and RPD improve by over 100.8% compared to traditional deep learning models, and the runtime is reduced by 83.94% when compared to Convolutional Neural Networks (CNNS). The Shapley Additive Explanation (SHAP) reveals the key spectral features that drive these predictions. These advancements in accuracy, efficiency, and interpretability make the model particularly promising for industrial applications, offering both improved operational efficiency and more precise resource management in the sustainable use of timber. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

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