Applied Sciences

29 pages, 5942 KiB

Open AccessArticle

The Seismic Performance of Earthen Historical Buildings in Seismic-Prone Regions: The Church of Santo Tomás de Aquino in Rondocan as a Complex Example

by Elesban Nochebuena-Mora, Nuno Mendes, Matteo Salvalaggio and Paulo B. Lourenço

Appl. Sci. 2025, 15(13), 7624; https://doi.org/10.3390/app15137624 - 7 Jul 2025

Viewed by 180

Abstract

Adobe churches are representative of Andean architectural heritage, yet their structural vulnerability to seismic events remains a significant concern. This study evaluates the seismic performance of the 17th-century Church of Santo Tomás de Aquino in Rondocan, Peru, an adobe building that underwent conservation [...] Read more.

Adobe churches are representative of Andean architectural heritage, yet their structural vulnerability to seismic events remains a significant concern. This study evaluates the seismic performance of the 17th-century Church of Santo Tomás de Aquino in Rondocan, Peru, an adobe building that underwent conservation work in the late 1990s. The assessment combines in situ inspections and experimental testing with advanced nonlinear numerical modeling. A finite-element macro-model was developed and calibrated using sonic and ambient vibration tests to replicate the observed structural behavior. Nonlinear static (pushover) analyses were performed in the four principal directions to identify failure mechanisms and to evaluate seismic capacity using the Peruvian seismic code. Kinematic limit analyses were conducted to assess out-of-plane mechanisms using force- and displacement-based criteria. The results revealed critical vulnerabilities in the rear façade and lateral walls, particularly in terms of out-of-plane collapse, while the main façade exhibited a higher capacity but a brittle failure mode. This study illustrates the value of advanced numerical simulations, calibrated with field data, as effective tools for assessing seismic vulnerability in historic adobe buildings. The outcomes highlight the necessity of strengthening measures to balance life safety requirements with preservation goals. Full article

(This article belongs to the Topic Preserving Cultural Heritage by Integrating Modern Materials and Technologies: From the Nano to Building Scale)

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18 pages, 2005 KiB

Open AccessArticle

Seaweed Pelvetia canaliculata as a Source of Bioactive Compounds for Application in Fried Pre-Coated Mackerel (Scomber scombrus) Fillets: A Functional Food Approach

by Catarina D. Freire, Madalena Antunes, Susana F. J. Silva, Marta Neves and Carla Tecelão

Appl. Sci. 2025, 15(13), 7623; https://doi.org/10.3390/app15137623 - 7 Jul 2025

Viewed by 75

Abstract

Fatty fish, such as mackerel (Scomber scombrus), are recommended as part of a healthy diet, providing essential fatty acids (FA). Fried fish is appreciated for its attributes, including a crispy texture, golden crust, and pleasant taste. However, frying increases the fat [...] Read more.

Fatty fish, such as mackerel (Scomber scombrus), are recommended as part of a healthy diet, providing essential fatty acids (FA). Fried fish is appreciated for its attributes, including a crispy texture, golden crust, and pleasant taste. However, frying increases the fat content and the caloric value of food. This study evaluated the use of pre-frying edible coatings on mackerel fillets aiming to: (i) reduce oil absorption, (ii) minimize water loss, preserving fish succulence, and (iii) prevent fat oxidation. For this purpose, alginate- and carrageenan-based coatings were supplemented with extracts of Pelvetia canaliculata (Pc), a seaweed with high potential as a source of bioactive compounds. The fried fillets were analysed for colour, texture, moisture, ash, lipid content, and FA profile. No significant differences were observed for colour and textural parameters. Fillets coated with Pc-supplemented carrageenan showed the highest moisture (an increase of 3%) and the lowest fat content (a decrease of 7,5%) compared to the control (fried uncoated fillets). Coated fillets also exhibited reduced saturated FA and increased monounsaturated FA. In general, linoleic acid (C18:2) decreased markedly, while the values for docosahexaenoic acid (C22:6, n-3) remained stable (11–12% of total FA). Moreover, the n3/n6 ratio and atherogenic indices (AI) were improved in the coated fillets. Full article

(This article belongs to the Special Issue Harnessing Microalgae and Seaweed for the Food Sector)

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21 pages, 9172 KiB

Open AccessArticle

Spike-Driven Channel-Temporal Attention Network with Multi-Scale Convolution for Energy-Efficient Bearing Fault Detection

by JinGyo Lim and Seong-Eun Kim

Appl. Sci. 2025, 15(13), 7622; https://doi.org/10.3390/app15137622 - 7 Jul 2025

Viewed by 68

Abstract

Real-time bearing fault diagnosis necessitates highly accurate, computationally efficient, and energy-conserving models suitable for deployment on resource-constrained edge devices. To address these demanding requirements, we propose the Spike Convolutional Attention Network (SpikeCAN), a novel spike-driven neural architecture tailored explicitly for real-time industrial diagnostics. [...] Read more.

Real-time bearing fault diagnosis necessitates highly accurate, computationally efficient, and energy-conserving models suitable for deployment on resource-constrained edge devices. To address these demanding requirements, we propose the Spike Convolutional Attention Network (SpikeCAN), a novel spike-driven neural architecture tailored explicitly for real-time industrial diagnostics. SpikeCAN utilizes the inherent sparsity and event-driven processing capabilities of spiking neural networks (SNNs), significantly minimizing both computational load and power consumption. The SpikeCAN integrates a multi-dilated receptive field (MDRF) block and a convolution-based spike attention module. The MDRF module effectively captures extensive temporal dependencies from signals across various scales. Simultaneously, the spike-based attention mechanism dynamically extracts spatial-temporal patterns, substantially improving diagnostic accuracy and reliability. We validate SpikeCAN on two public bearing fault datasets: the Case Western Reserve University (CWRU) and the Society for Machinery Failure Prevention Technology (MFPT). The proposed model achieves 99.86% accuracy on the four-class CWRU dataset through five-fold cross-validation and 99.88% accuracy with a conventional 70:30 train–test random split. For the more challenging ten-class classification task on the same dataset, it achieves 97.80% accuracy under five-fold cross-validation. Furthermore, SpikeCAN attains a state-of-the-art accuracy of 96.31% on the fifteen-class MFPT dataset, surpassing existing benchmarks. These findings underscore a significant advancement in fault diagnosis technology, demonstrating the considerable practical potential of spike-driven neural networks in real-time, energy-efficient industrial diagnostic applications. Full article

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

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26 pages, 4626 KiB

Open AccessArticle

Analysis and Application of Dual-Control Single-Exponential Water Inrush Prediction Mechanism for Excavation Roadways Based on Peridynamics

by Xiaoning Liu, Xinqiu Fang, Minfu Liang, Gang Wu, Ningning Chen and Yang Song

Appl. Sci. 2025, 15(13), 7621; https://doi.org/10.3390/app15137621 - 7 Jul 2025

Viewed by 73

Abstract

Roof water inrush accidents in coal mine driving roadways occur frequently in China, accounting for a high proportion of major coal mine water hazard accidents and causing serious losses. Aiming at the lack of research on the mechanism of roof water inrush in [...] Read more.

Roof water inrush accidents in coal mine driving roadways occur frequently in China, accounting for a high proportion of major coal mine water hazard accidents and causing serious losses. Aiming at the lack of research on the mechanism of roof water inrush in driving roadways and the difficulty of predicting water inrush accidents, this paper constructs a local damage criterion for coal–rock mass and a seepage–fracture coupling model based on peridynamics (PD) bond theory. It identifies three zones of water-conducting channels in roadway surrounding rock, the water fracture zone, the driving fracture zone, and the water-resisting zone, revealing that the damage degree of the water-resisting zone dominates the transformation mechanism between delayed and instantaneous water inrush. A discriminant function for the effectiveness of water-conducting channels is established, and a single-index prediction and evaluation system based on damage critical values is proposed. A “geometry damage” dual-control water inrush prediction model within the PD framework is constructed, along with a non-local action mechanism model and quantitative prediction method for water inrush. Case studies verify the threshold for delayed water inrush and criteria for instantaneous water inrush. The research results provide theoretical tools for roadway water exploration design and water hazard prevention and control. Full article

(This article belongs to the Special Issue Rock Mechanics in Mining and Tunnelling Engineering: Contributions and Challenges)

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15 pages, 1530 KiB

Open AccessArticle

Effect of Virtual-Reality-Based Training, Including Preceding Trunk Stabilization Education, on Postural Control and Balance in Patients with Stroke: A Randomized Controlled Trial

by SeongMin Lee and JongEun Yim

Appl. Sci. 2025, 15(13), 7620; https://doi.org/10.3390/app15137620 - 7 Jul 2025

Viewed by 61

Abstract

This study investigated the effects of virtual reality (VR)-based training combined with preliminary trunk stabilization education on postural control and balance in stroke patients. A single-blind randomized controlled trial enrolled 30 participants, randomly divided into a trunk stabilization group (n = 15) [...] Read more.

This study investigated the effects of virtual reality (VR)-based training combined with preliminary trunk stabilization education on postural control and balance in stroke patients. A single-blind randomized controlled trial enrolled 30 participants, randomly divided into a trunk stabilization group (n = 15) and a control group (n = 15). The trunk stabilization group engaged in 10 min of trunk stabilization education followed by 20 min of VR-based training, three times weekly for three weeks. The control group participated only in VR-based training. Outcomes were assessed using the Korean Trunk Impairment Scale (K-TIS), Postural Assessment Scale for Stroke (PASS), Berg Balance Scale (BBS), limit of stability (LOS), and center of pressure (COP) measurements. Both groups significantly improved in all measured outcomes post-intervention (p < 0.05). Notably, the trunk stabilization group exhibited significantly superior improvements in the K-TIS, PASS, BBS, LOS, and COP path length compared to the control group (p < 0.05). These results highlight the enhanced effectiveness of integrating trunk stabilization education with VR-based training, suggesting that it not only yields statistically significant improvements but also provides clinically meaningful benefits for functional postural control and balance recovery in stroke rehabilitation. Full article

(This article belongs to the Special Issue Assistive Technologies for Rehabilitation: Challenges and Applications)

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12 pages, 8520 KiB

Open AccessArticle

Integrated Haptic Feedback with Augmented Reality to Improve Pinching and Fine Moving of Objects

by Jafar Hamad, Matteo Bianchi and Vincenzo Ferrari

Appl. Sci. 2025, 15(13), 7619; https://doi.org/10.3390/app15137619 - 7 Jul 2025

Viewed by 95

Abstract

Hand gestures are essential for interaction in augmented and virtual reality (AR/VR), allowing users to intuitively manipulate virtual objects and engage with human–machine interfaces (HMIs). Accurate gesture recognition is critical for effective task execution. However, users often encounter difficulties due to the lack [...] Read more.

Hand gestures are essential for interaction in augmented and virtual reality (AR/VR), allowing users to intuitively manipulate virtual objects and engage with human–machine interfaces (HMIs). Accurate gesture recognition is critical for effective task execution. However, users often encounter difficulties due to the lack of immediate and clear feedback from head-mounted displays (HMDs). Current tracking technologies cannot always guarantee reliable recognition, leaving users uncertain about whether their gestures have been successfully detected. To address this limitation, haptic feedback can play a key role by confirming gesture recognition and compensating for discrepancies between the visual perception of fingertip contact with virtual objects and the actual system recognition. The goal of this paper is to compare a simple vibrotactile ring with a full glove device and identify their possible improvements for a fundamental gesture like pinching and fine moving of objects using Microsoft HoloLens 2. Where the pinch action is considered an essential fine motor skill, augmented reality integrated with haptic feedback can be useful to notify the user of the recognition of the gestures and compensate for misaligned visual perception between the tracked fingertip with respect to virtual objects to determine better performance in terms of spatial precision. In our experiments, the participants’ median distance error using bare hands over all axes was 10.3 mm (interquartile range [IQR] = 13.1 mm) in a median time of 10.0 s (IQR = 4.0 s). While both haptic devices demonstrated improvement in participants precision with respect to the bare-hands case, participants achieved with the full glove median errors of 2.4 mm (IQR = 5.2) in a median time of 8.0 s (IQR = 6.0 s), and with the haptic rings they achieved even better performance with median errors of 2.0 mm (IQR = 2.0 mm) in an even better median time of only 6.0 s (IQR= 5.0 s). Our outcomes suggest that simple devices like the described haptic rings can be better than glove-like devices, offering better performance in terms of accuracy, execution time, and wearability. The haptic glove probably compromises hand and finger tracking with the Microsoft HoloLens 2. Full article

(This article belongs to the Special Issue New Insights on Haptics and Human–Computer Interaction Systems in Virtual Reality)

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28 pages, 5260 KiB

Open AccessArticle

A Monte Carlo Simulation of Measurement Uncertainty in Radiation Thermometry Due to the Influence of Spectral Parameters

by Vid Mlačnik, Igor Pušnik and Domen Hudoklin

Appl. Sci. 2025, 15(13), 7618; https://doi.org/10.3390/app15137618 - 7 Jul 2025

Viewed by 86

Abstract

While radiation thermometry is well-developed for laboratory calibrations using high-emissivity sources, the effect of spectral emissivity in real-world conditions, where emissivity ranges from 0 to 1, is usually not considered. Spectral parameters that influence non-contact temperature measurements are often neglected even in laboratory [...] Read more.

While radiation thermometry is well-developed for laboratory calibrations using high-emissivity sources, the effect of spectral emissivity in real-world conditions, where emissivity ranges from 0 to 1, is usually not considered. Spectral parameters that influence non-contact temperature measurements are often neglected even in laboratory conditions. These parameters become more important with decreasing emissivity and at lower temperatures, leading to increased uncertainty contributions to the measurement result. In this manuscript, we analyze the impact of various influential spectral parameters using the constructed spectral Monte Carlo simulation of radiation thermometry. The investigation covers the influence of spectral and related parameters, namely spectral emissivity, reflection temperature, spectral sensitivity and atmospheric parameters of temperature, relative humidity and distance of the path in the atmosphere. Simulation results are compared to experimental results, overestimating sensitivity to humidity by 23–27% and sensitivity to emissivity and reflected temperature within 10% at given conditions. Multiple cases of radiation thermometer (RT) use are simulated for measurement uncertainty: high temperature RT use as the reference in calibration by comparison, the use of a flat plate calibrator for RT calibration, measurements with a RT using emissivity input data from literature with relatively high uncertainty and temperature measurements with a RT using emissivity data, obtained with FTIR spectroscopy with relatively low uncertainty. Findings suggest that spectral uncertainty contributions are often unjustifiably underestimated and neglected, nearing extended uncertainty contribution of 1.94 °C in calibration practices using flat plate calibrators with emissivity within 0.93 and 0.97 and 1.72 °C when radiation thermometers with spectral ranges, susceptible to atmospheric humidity, are used on black bodies. Full article

(This article belongs to the Collection Optical Design and Engineering)

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11 pages, 265 KiB

Open AccessArticle

Grip Strength, Fall Efficacy, and Balance Confidence as Associated Factors with Fall Risk in Middle-Aged and Older Adults Living in the Community

by Priscila Marconcin, Estela São Martinho, Joana Serpa, Samuel Honório, Vânia Loureiro, Marcelo de Maio Nascimento, Fábio Flôres and Vanessa Santos

Appl. Sci. 2025, 15(13), 7617; https://doi.org/10.3390/app15137617 - 7 Jul 2025

Viewed by 83

Abstract

Background: Falls are a major public health concern among older adults, often resulting in injury, functional decline, and reduced quality of life. While handgrip strength (HGS), fall efficacy, and balance confidence have individually been associated with fall risk, their combined predictive value is [...] Read more.

Background: Falls are a major public health concern among older adults, often resulting in injury, functional decline, and reduced quality of life. While handgrip strength (HGS), fall efficacy, and balance confidence have individually been associated with fall risk, their combined predictive value is still underexplored, particularly in physically active older adults. This study aimed to investigate the relationship between HGS, fall efficacy, and balance confidence and their association with fall risk in community-dwelling older adults engaged in regular exercise programs; A cross-sectional study was conducted with 280 participants aged 55 and over from community exercise programs near Lisbon, Portugal. Fall risk was assessed through self-reported falls in the past 12 months. HGS was measured with a dynamometer, fall efficacy using the Falls Efficacy Scale-International (FES-I), and balance confidence using the Activities-specific Balance Confidence (ABC) Scale. Statistical analyses included Spearman correlations and binary logistic regression. Results: Falls were reported by 26.4% of participants. Fall efficacy and balance confidence were significantly associated with fall history, while HGS was not. Fall efficacy was significantly associated with increased fall risk, as indicated by the odds ratio (OR = 3.37, p < 0.001), while balance confidence was negatively associated (OR = 0.95, p < 0.001). HGS was positively correlated with balance and confidence but not with fall incidence. Conclusions: Psychological factors, particularly fall efficacy and balance confidence, play a critical role in fall risk among physically active older adults. However, this study included physically active middle-aged and older adults living in the community, which should be considered when interpreting the generalizability of the results. These findings support the integration of simple, validated psychological assessments into fall prevention strategies in community settings. Full article

15 pages, 2258 KiB

Open AccessArticle

Numerical Simulation of Phase Transition Process for Vertical Lift Underwater Monitoring Device Driven by Ocean Thermal Energy

by Zede Liang, Tielin Zhang and Qingqing Li

Appl. Sci. 2025, 15(13), 7616; https://doi.org/10.3390/app15137616 - 7 Jul 2025

Viewed by 73

Abstract

The energy consumption of current vertical-lifting underwater monitoring devices mainly falls into two categories: one fully supplied by battery packs; and the other partially by battery packs, with the rest from ocean thermal energy. Constrained by battery capacity, their operation time is limited, [...] Read more.

The energy consumption of current vertical-lifting underwater monitoring devices mainly falls into two categories: one fully supplied by battery packs; and the other partially by battery packs, with the rest from ocean thermal energy. Constrained by battery capacity, their operation time is limited, making long-term remote operations difficult. This study focuses on a device powered entirely by ocean thermal energy, which realizes the absorption and storage of energy through a phase change heat-exchange system, significantly extending its operation cycle and working area. A composite phase change material of n-hexadecane and graphite with a volume ratio of 9:1 is used. The Fluent software 2022 R1, based on the enthalpy-porosity method, simulates the phase change process of the device to analyze the effects of different structures and seawater temperatures. Results show that with the same phase change material volume and inner diameter of the cylindrical heat exchanger, a smaller outer diameter yields better phase change performance. Lower seawater temperature facilitates solidification. Due to natural convection in the liquid phase, the melting time is 520 s and solidification time is 4800 s, with the melting rate far exceeding the solidification rate. Full article

(This article belongs to the Section Applied Thermal Engineering)

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29 pages, 6320 KiB

Open AccessArticle

The Forecast of the Wind Turbine Generated Power Using Hybrid (LTC + XGBoost) Model

by Justina Krevnevičiūtė, Arnas Mitkevičius, Darius Naujokaitis, Ingrida Lagzdinytė-Budnikė and Mantas Marčiukaitis

Appl. Sci. 2025, 15(13), 7615; https://doi.org/10.3390/app15137615 - 7 Jul 2025

Viewed by 189

Abstract

This publication presents a novel approach to predicting the amount of electricity generated by wind power plants. The research focuses on data-driven models such as XGBoost, Liquid Time-constant Networks, and covers both the analysis of properties of individual forecasting models as well as [...] Read more.

This publication presents a novel approach to predicting the amount of electricity generated by wind power plants. The research focuses on data-driven models such as XGBoost, Liquid Time-constant Networks, and covers both the analysis of properties of individual forecasting models as well as aspects of their integration into a hybrid model. By analyzing real-world weather scenarios, the approach aims to identify the highest accuracy forecasting model for the short-term 24-h forecast of wind farm power output. A more accurate forecast allows for more efficient resource planning and better distribution of resources on the electricity grids, thus ensuring a greener approach to energy production. The study shows that the proposed Hybrid (XGBoost + LTC) model predicts wind power generation with an nMAE of 0.0856, representing an improvement over standalone XGBoost and LTC models, and outperforming classical approaches such as LSTM and statistical models like ARIMAX in terms of forecasting accuracy. Full article

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22 pages, 2953 KiB

Open AccessArticle

Risk Assessment Model for Railway Track Maintenance Operations Based on Combined Weights and Nonlinear FCE

by Rui Luan and Rengkui Liu

Appl. Sci. 2025, 15(13), 7614; https://doi.org/10.3390/app15137614 - 7 Jul 2025

Viewed by 138

Abstract

Current risk assessment in railway track maintenance operations faces challenges (low spatiotemporal accuracy, limited adaptability to various scenarios, and tendency of linear fuzzy comprehensive evaluation (FCE) methods to underestimate high-risk factors). To address these, this study proposes a novel risk assessment model that [...] Read more.

Current risk assessment in railway track maintenance operations faces challenges (low spatiotemporal accuracy, limited adaptability to various scenarios, and tendency of linear fuzzy comprehensive evaluation (FCE) methods to underestimate high-risk factors). To address these, this study proposes a novel risk assessment model that integrates subjective–objective weighting techniques with a nonlinear FCE approach. By incorporating spatiotemporal information, the model enables precise localization of risk occurrence in individual maintenance operations. A comprehensive risk index system is constructed across four dimensions: human, equipment, environment, and management. The game theory combined weighting method, integrating the G1 method and entropy weight method, is employed; it balances expert judgment with data-driven analysis. A cloud model is introduced to generate risk membership matrices, accounting for the fuzziness and randomness of risk data. The nonlinear FCE framework enhances the influence of high-risk factors. Risk levels are determined using the combined weights, membership matrices, and the maximum membership principle. A case study on the Lanzhou–Xinjiang Railway demonstrates that the proposed model achieves higher consistency with actual risk conditions than conventional methods, improving assessment accuracy and reliability. This model offers a practical and effective tool for risk prevention and control in railway maintenance operations. Full article

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21 pages, 2816 KiB

Open AccessArticle

AutoStageMix: Fully Automated Stage Cross-Editing System Utilizing Facial Features

by Minjun Oh, Howon Jang and Daeho Lee

Appl. Sci. 2025, 15(13), 7613; https://doi.org/10.3390/app15137613 - 7 Jul 2025

Viewed by 147

Abstract

StageMix is a video compilation of multiple stage performances of the same song, edited seamlessly together using appropriate editing points. However, generating a StageMix requires specialized editing techniques and is a considerable time-consuming process. To address this challenge, we introduce AutoStageMix, an automated [...] Read more.

StageMix is a video compilation of multiple stage performances of the same song, edited seamlessly together using appropriate editing points. However, generating a StageMix requires specialized editing techniques and is a considerable time-consuming process. To address this challenge, we introduce AutoStageMix, an automated StageMix generation system designed to perform all processes automatically. The system is structured into five principal stages: preprocessing, feature extraction, identifying a transition point, editing path determination, and StageMix generation. The initial stage of the process involves audio analysis to synchronize the sequences across all input videos, followed by frame extraction. After that, the facial features are extracted from each video frame. Next, transition points are identified, which form the basis for face-based transitions, inter-stage cuts, and intra-stage cuts. Subsequently, a cost function is defined to facilitate the creation of cross-edited sequences. The optimal editing path is computed using Dijkstra’s algorithm to minimize the total cost of editing. Finally, the StageMix is generated by applying appropriate editing effects tailored to each transition type, aiming to maximize visual appeal. Experimental results suggest that our method generally achieves lower NME scores than existing StageMix generation approaches across multiple test songs. In a user study with 21 participants, AutoStageMix achieved viewer satisfaction comparable to that of professionally edited StageMixes, with no statistically significant difference between the two. AutoStageMix enables users to produce StageMixes effortlessly and efficiently by eliminating the need for manual editing. Full article

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21 pages, 2650 KiB

Open AccessArticle

Multi-Material Topology Optimization Taking into Account the Position of Material Interfaces in 3D

by Robert Renz, Niklas Frank and Albert Albers

Appl. Sci. 2025, 15(13), 7612; https://doi.org/10.3390/app15137612 - 7 Jul 2025

Viewed by 151

Abstract

Multi-material design as a method of lightweight construction enables the targeted use of materials in a component by utilizing the individual material properties. However, this advantage comes with additional challenges for the product developer, such as the increased effort involved in identifying the [...] Read more.

Multi-material design as a method of lightweight construction enables the targeted use of materials in a component by utilizing the individual material properties. However, this advantage comes with additional challenges for the product developer, such as the increased effort involved in identifying the design. Multi-material topology optimization is a method that can support the product developer in creating initial weight-optimized component designs in multi-material design in early phases. In addition, several state-of-the-art studies show that the position of the interfaces between the materials has an influence on the strength of the optimization result. These investigations took place in 2D and developed optimization methods which largely use non-linear building blocks, such as cohesive behavior. The non-linear components lead to an increase in computational effort and a reduction in the robustness of the optimization. In this article, a method for the consideration of adhesive-bonded interfaces in multi-material topology optimization in 3D by means of an objective function is developed. For this purpose, requirements are derived based on an analysis of the different load cases of a bond and these are used to create the method. The method is then successfully validated by means of two numerical experiments. In addition, the influence of a newly introduced parameter on the optimization results is investigated by means of a parameter study. Full article

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20 pages, 4177 KiB

Open AccessArticle

Joint Entity–Relation Extraction for Knowledge Graph Construction in Marine Ranching Equipment

by Du Chen, Zhiwu Gao, Sirui Li, Xuruixue Guo, Yaqi Wu, Haiyu Zhang and Delin Zhang

Appl. Sci. 2025, 15(13), 7611; https://doi.org/10.3390/app15137611 - 7 Jul 2025

Viewed by 128

Abstract

The construction of marine ranching is a crucial component of China’s Blue Granary strategy, yet the fragmented knowledge system in marine ranching equipment impedes intelligent management and operational efficiency. This study proposes the first knowledge graph (KG) framework tailored for marine ranching equipment, [...] Read more.

The construction of marine ranching is a crucial component of China’s Blue Granary strategy, yet the fragmented knowledge system in marine ranching equipment impedes intelligent management and operational efficiency. This study proposes the first knowledge graph (KG) framework tailored for marine ranching equipment, integrating hybrid ontology design, joint entity–relation extraction, and graph-based knowledge storage: (1) The limitations in existing KG are obtained through targeted questionnaires for diverse users and employees; (2) A domain ontology was constructed through a combination of the top-down and the bottom-up approach, defining seven key concepts and eight semantic relationships; (3) Semi-structured data from enterprises and standards, combined with unstructured data from the literature were systematically collected, cleaned via Scrapy and regular expression, and standardized into JSON format, forming a domain-specific corpus of 1456 annotated sentences; (4) A novel BERT-BiGRU-CRF model was developed, leveraging contextual embeddings from BERT, parameter-efficient sequence modeling via BiGRU (Bidirectional Gated Recurrent Unit), and label dependency optimization using CRF (Conditional Random Field). The TE + SE + R_i + BMESO tagging strategy was introduced to address multi-relation extraction challenges by linking theme entities to secondary entities; (5) The Neo4j-based KG encapsulated 2153 nodes and 3872 edges, enabling scalable visualization and dynamic updates. Experimental results demonstrated superior performance over BiLSTM-CRF and BERT-BiLSTM-CRF, achieving 86.58% precision, 77.82% recall, and 81.97% F1 score. This study not only proposes the first structured KG framework for marine ranching equipment but also offers a transferable methodology for vertical domain knowledge extraction. Full article

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

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23 pages, 2351 KiB

Open AccessArticle

Ensemble of Efficient Vision Transformers for Insect Classification

by Marius Alexandru Dinca, Dan Popescu, Loretta Ichim and Nicoleta Angelescu

Appl. Sci. 2025, 15(13), 7610; https://doi.org/10.3390/app15137610 - 7 Jul 2025

Viewed by 85

Abstract

Real-time identification of insect pests is an important research direction in modern agricultural management, directly influencing crop health and yield. Recent advances in computer vision and deep learning, especially vision transformer (ViT) architectures, have demonstrated great potential in addressing this challenge. The present [...] Read more.

Real-time identification of insect pests is an important research direction in modern agricultural management, directly influencing crop health and yield. Recent advances in computer vision and deep learning, especially vision transformer (ViT) architectures, have demonstrated great potential in addressing this challenge. The present study explores the possibility of combining some ViT models for the insect pest classification task to improve system performance and robustness. Two popular and widely known datasets, D0 and IP102, which consist of diverse digital images with complex contexts of insect pests, were used. The proposed methodology involved training several individual ViT models on the chosen datasets, finally creating an ensemble strategy to fuse their results. A new combination method was used, based on the F1 score of individual models and a meta-classifier structure, capitalizing on the strengths of each base model and effectively capturing complex features for the final prediction. The experimental results indicated that the proposed ensemble methodology significantly outperformed the individual ViT models, observing notable improvements in classification accuracy for both datasets. Specifically, the ensemble model achieved a test accuracy of 99.87% and an F1 score of 99.82% for the D0 dataset, and an F1 score of 84.25% for IP102, demonstrating the method’s effectiveness for insect pest classification from different datasets. The noted features pave the way for implementing reliable and effective solutions in the agricultural pest management process. Full article

(This article belongs to the Special Issue Recent Advances and New Trends in Computer Vision and Image Processing)

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29 pages, 3455 KiB

Open AccessReview

A Taxonomy of Methods, Techniques and Sensors for Acquisition of Physiological Signals in Driver Monitoring Systems

by Galidiya Petrova, Hristo Radev, Mitko Shopov and Nikolay Kakanakov

Appl. Sci. 2025, 15(13), 7609; https://doi.org/10.3390/app15137609 - 7 Jul 2025

Viewed by 187

Abstract

Driver monitoring systems (DMSs) are increasingly important for road safety, aiming to reduce driver-caused accidents. Traditional DMSs, focusing on behavioral and observable signals, lack the sensitivity to detect changes in the driver’s health status. Monitoring physiological parameters offers the opportunity to objectively assess [...] Read more.

Driver monitoring systems (DMSs) are increasingly important for road safety, aiming to reduce driver-caused accidents. Traditional DMSs, focusing on behavioral and observable signals, lack the sensitivity to detect changes in the driver’s health status. Monitoring physiological parameters offers the opportunity to objectively assess the driver’s condition in real time and detect early signs of medical emergencies. After a brief overview of the physiological parameters that are critical for assessing the driver’s condition, we examine the different methods and sensors for obtaining the relevant physiological signals with their advantages and limitations. Based on this review, a taxonomy of methods, techniques, and sensors for acquisition of physiological signals in DMSs is proposed. It provides a systematically structured and detailed classification to understand the relationships between physiological parameters and the different methods and sensors for their measurement. This taxonomy can serve as a fundamental framework for researchers and developers to design and implement reliable next-generation DMSs based on physiological signals. Full article

(This article belongs to the Special Issue Monitoring of Human Physiological Signals)

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12 pages, 451 KiB

Open AccessArticle

The Effect of Sweetener Type on the Quality of Liqueurs from Vaccinium myrtillus L. and Vaccinium corymbosum L. Fruits

by Agnieszka Ryznar-Luty and Krzysztof Lutosławski

Appl. Sci. 2025, 15(13), 7608; https://doi.org/10.3390/app15137608 - 7 Jul 2025

Viewed by 76

Abstract

This study aimed to investigate the effect of the type of sweetener used (xylitol, stevia, cane sugar) on the quality of liqueurs made from Vaccinium myrtillus L. and Vaccinium corymbosum L. fruits. The quality assessment was performed based on selected organoleptic and physicochemical [...] Read more.

This study aimed to investigate the effect of the type of sweetener used (xylitol, stevia, cane sugar) on the quality of liqueurs made from Vaccinium myrtillus L. and Vaccinium corymbosum L. fruits. The quality assessment was performed based on selected organoleptic and physicochemical features, with particular emphasis on the health-promoting potential of the produced beverages. The liqueurs were assessed in terms of their physicochemical parameters: pH, total acidity, density, total soluble solids, color, ethanol and polyphenol contents, and redox potential. Antioxidant capacities were determined by a 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2′-azinobis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging capacity assay and ferric reducing antioxidant power (FRAP). The Qualitative Descriptive Analysis method was employed for their sensory assessment. The sensory profiling method was used to determine the intensity of the flavor sensations. The study results showed that the type of sweetener did not affect the antioxidative properties of the liqueur. The ABTS test yielded values from 1081.88 to 1238.13 μmol Tx/100 mL, the DPPH test from 348.8 to 367.88 μmol Tx/100 mL, and the FRAP test from 594.20 to 653.20 μmol FeSO₄/100 mL. However, the sweetening substrate affected the content of polyphenolic compounds in the resulting products, but by no more than 15%. The liqueur sweetened with xylitol had a comparable extract content to that sweetened with cane sugar. All three variants of liqueurs were accepted by the evaluation panel, and their overall qualities were comparable in the sensory assessment. It is, therefore, possible to produce a high-quality liqueur with a reduced caloric value, which will potentially increase its attractiveness for consumers. Full article

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15 pages, 1888 KiB

Open AccessArticle

Corn Oil Supplementation Enhances Locomotor Performance and Mitochondrial Function in Drosophila melanogaster

by Jadyellen Rondon Silva, Thiago Henrique Oliveira Alves, Eric Bruno Silva Santos, Marylu Mardegan Lima, Giulia Covolo Spegiorim, Carlos Antônio Couto-Lima, Luciane Carla Alberici, Marcos José Jacinto and Anderson Oliveira Souza

Appl. Sci. 2025, 15(13), 7607; https://doi.org/10.3390/app15137607 - 7 Jul 2025

Viewed by 148

Abstract

Polyunsaturated fatty acids are vital for brain health, supporting cognitive development and helping to prevent neurodegenerative diseases. Since the body cannot produce them, they must be obtained through food. This study aimed to assess the effects of corn oil on the behavior and [...] Read more.

Polyunsaturated fatty acids are vital for brain health, supporting cognitive development and helping to prevent neurodegenerative diseases. Since the body cannot produce them, they must be obtained through food. This study aimed to assess the effects of corn oil on the behavior and biochemical parameters of Drosophila melanogaster. The flies were fed a diet supplemented with different concentrations of corn oil from the larval stage until the fifth day of adulthood. A diet containing corn oil (37.8 mg/mL of linoleic acid) reduced mortality under starvation conditions and enhanced locomotor performance (p < 0.01). Biochemical analyses revealed increased levels of glutathione (p < 0.001), citrate synthase activity (p < 0.05), and mitochondrial phosphorylation (p < 0.05), indicating a potential boost in energy metabolism. Conversely, a decrease in acetylcholinesterase activity (p < 0.05) was observed, suggesting cholinergic modulation. These results demonstrate that corn oil supplementation supports neural health in this animal model, opening pathways for further research into non-pharmacological treatments for neurodegenerative diseases such as Alzheimer’s disease. Full article

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16 pages, 1249 KiB

Open AccessArticle

Impact of Electromagnetic Field on the Physicochemical Properties, Permeability, and Accumulation of Salicylic Acid

by Karolina Zyburtowicz-Ćwiartka, Anna Nowak, Anna Muzykiewicz-Szymańska, Łukasz Kucharski, Maciej Konopacki, Rafał Rakoczy and Paula Ossowicz-Rupniewska

Appl. Sci. 2025, 15(13), 7606; https://doi.org/10.3390/app15137606 - 7 Jul 2025

Viewed by 154

Abstract

Transdermal drug delivery offers a non-invasive route for the systemic and localized administration of therapeutics; however, the skin’s barrier function limits its efficiency. This study investigates the application of various electromagnetic field (EMF) configurations to enhance the transdermal delivery of salicylic acid, a [...] Read more.

Transdermal drug delivery offers a non-invasive route for the systemic and localized administration of therapeutics; however, the skin’s barrier function limits its efficiency. This study investigates the application of various electromagnetic field (EMF) configurations to enhance the transdermal delivery of salicylic acid, a model compound with moderate lipophilicity and ionizability. Samples were exposed to pulsed, oscillating, static, and rotating magnetic fields, and their effects on physicochemical properties, thermal stability, skin permeation, and accumulation were evaluated. Structural analyses (FTIR, XRD) and thermal assessments (TGA, DSC) confirmed that EMF exposure did not alter the chemical structure or stability of salicylic acid. In vitro transdermal studies using porcine skin and Franz diffusion cells revealed that pulsed magnetic fields—especially with a 5 s on/5 s off cycle—and rotating magnetic fields at 30–50 Hz significantly enhanced drug permeation compared to controls. In contrast, static fields of negative polarity increased skin retention, suggesting their potential for controlled, localized delivery. These findings demonstrate that EMFs can be used as tunable, non-destructive tools to modulate drug transport across the skin and support their integration into transdermal delivery systems aimed at optimizing therapeutic profiles. Full article

(This article belongs to the Special Issue Feature Papers in 'Surface Sciences and Technology' Section, 3rd Edition)

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23 pages, 1410 KiB

Open AccessArticle

PneumoNet: Artificial Intelligence Assistance for Pneumonia Detection on X-Rays

by Carlos Antunes, João M. F. Rodrigues and António Cunha

Appl. Sci. 2025, 15(13), 7605; https://doi.org/10.3390/app15137605 - 7 Jul 2025

Viewed by 74

Abstract

Pneumonia is a respiratory condition caused by various microorganisms, including bacteria, viruses, fungi, and parasites. It manifests with symptoms such as coughing, chest pain, fever, breathing difficulties, and fatigue. Early and accurate detection is crucial for effective treatment, yet traditional diagnostic methods often [...] Read more.

Pneumonia is a respiratory condition caused by various microorganisms, including bacteria, viruses, fungi, and parasites. It manifests with symptoms such as coughing, chest pain, fever, breathing difficulties, and fatigue. Early and accurate detection is crucial for effective treatment, yet traditional diagnostic methods often fall short in reliability and speed. Chest X-rays have become widely used for detecting pneumonia; however, current approaches still struggle with achieving high accuracy and interpretability, leaving room for improvement. PneumoNet, an artificial intelligence assistant for X-ray pneumonia detection, is proposed in this work. The framework comprises (a) a new deep learning-based classification model for the detection of pneumonia, which expands on the AlexNet backbone for feature extraction in X-ray images and a new head in its final layers that is tailored for (X-ray) pneumonia classification. (b) GPT-Neo, a large language model, which is used to integrate the results and produce medical reports. The classification model is trained and evaluated on three publicly available datasets to ensure robustness and generalisability. Using multiple datasets mitigates biases from single-source data, addresses variations in patient demographics, and allows for meaningful performance comparisons with prior research. PneumoNet classifier achieves accuracy rates between 96.70% and 98.70% in those datasets. Full article

(This article belongs to the Special Issue Research on Machine Learning in Computer Vision)

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17 pages, 1599 KiB

Open AccessReview

Current Applications and Development of Radiomics in Osteoporosis: A Narrative Review

by Shuyu Liu, He Gong, Peipei Shi, Chenchen Li, Qi Zhang and Jinming Zhang

Appl. Sci. 2025, 15(13), 7604; https://doi.org/10.3390/app15137604 - 7 Jul 2025

Viewed by 142

Abstract

Osteoporosis is a prevalent disease among the elderly, with fractures being one of the most serious consequences. Early diagnosis and accurate assessment of fracture risk could help prevent fractures. Radiomics employs advanced image analysis techniques for the development of diagnostic tools, thereby improving [...] Read more.

Osteoporosis is a prevalent disease among the elderly, with fractures being one of the most serious consequences. Early diagnosis and accurate assessment of fracture risk could help prevent fractures. Radiomics employs advanced image analysis techniques for the development of diagnostic tools, thereby improving the accuracy of disease diagnosis and treatment strategies. Specifically, in the application of bone diseases, radiomics has proven effective in the diagnosis and prognostic evaluation of osteoporosis, osteoarthritis, and bone tumors. Radiomics allowed for quantitative characterization of bone geometry, material distribution, and microstructure, making it applicable to osteoporosis as well. In this review, an overview was provided regarding the current progress of radiomics based on clinical bone imaging in osteoporosis, including bone strength assessment, osteoporosis diagnosis, and fracture risk prediction. Additionally, the potential and challenges for their clinical application were summarized. Full article

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15 pages, 1816 KiB

Open AccessArticle

A Framework for User Traffic Prediction and Resource Allocation in 5G Networks

by Ioannis Konstantoulas, Iliana Loi, Dimosthenis Tsimas, Kyriakos Sgarbas, Apostolos Gkamas and Christos Bouras

Appl. Sci. 2025, 15(13), 7603; https://doi.org/10.3390/app15137603 - 7 Jul 2025

Viewed by 140

Abstract

Fifth-Generation (5G) networks deal with dynamic fluctuations in user traffic and the demands of each connected user and application. This creates a need for optimized resource allocation to reduce network congestion in densely populated urban centers and further ensure Quality of Service (QoS) [...] Read more.

Fifth-Generation (5G) networks deal with dynamic fluctuations in user traffic and the demands of each connected user and application. This creates a need for optimized resource allocation to reduce network congestion in densely populated urban centers and further ensure Quality of Service (QoS) in (5G) environments. To address this issue, we present a framework for both predicting user traffic and allocating users to base stations in 5G networks using neural network architectures. This framework consists of a hybrid approach utilizing a Long Short-Term Memory (LSTM) network or a Transformer architecture for user traffic prediction in base stations, as well as a Convolutional Neural Network (CNN) to allocate users to base stations in a realistic scenario. The models show high accuracy in the tasks performed, especially in the user traffic prediction task, where the models show an accuracy of over

99 %

. Overall, our framework is capable of capturing long-term temporal features and spatial features from 5G user data, taking a significant step towards a holistic approach in data-driven resource allocation and traffic prediction in 5G networks. Full article

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

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17 pages, 2080 KiB

Open AccessArticle

IoT Services for Monitoring Food Supply Chains

by Loucas Protopappas, Dimitrios Bechtsis and Nikolaos Tsotsolas

Appl. Sci. 2025, 15(13), 7602; https://doi.org/10.3390/app15137602 - 7 Jul 2025

Viewed by 145

Abstract

Ensuring the safety and quality of perishable agrifood products throughout the supply chain is essential. Key parameters, such as temperature and humidity, must be consistently monitored to prevent spoilage, maintain nutritional value, and minimise health risks. Fluctuations in transportation conditions can compromise product [...] Read more.

Ensuring the safety and quality of perishable agrifood products throughout the supply chain is essential. Key parameters, such as temperature and humidity, must be consistently monitored to prevent spoilage, maintain nutritional value, and minimise health risks. Fluctuations in transportation conditions can compromise product integrity, leading to deterioration and an increased risk of foodborne illness. Monitoring agrifood supply chains is essential, from packaging to last-mile delivery. Distribution methods that rely on non-automated monitoring systems, such as manual temperature measurements, are error-prone due to the failure of manual treatments and increase the likelihood of product deterioration. Emerging sensor technologies and the rapid development of Information and Communication Technologies offer new possibilities for real-time tracking, enabling stakeholders to maintain optimal conditions and monitor aesthetic, physicochemical, and nutritional quality. This paper proposes a cost-effective temperature and humidity traceability system that utilises wireless sensor networks (WSN) and Internet of Things (IoΤ) services to monitor perishable products within the agrifood supply chain ecosystem. It also provides an overview of recent innovations in sensor technologies, along with food quality indicators relevant to real-time monitoring of food quality. The proposed research examines the available sensor technologies and methodologies that enable continuous monitoring of agrifood supply chains. Moreover, the paper presents a pilot full-scale project from both functional and technological perspectives. Full article

(This article belongs to the Special Issue Data-Driven Supply Chain Management and Logistics Engineering)

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20 pages, 4012 KiB

Open AccessArticle

Optimization Design Method of Pipe-Insulating Joints Based on Surrogate Model and Genetic Algorithm

by Chen Guo, Zheng Yang, Jianbo Dong, Yanchao Yue, Linjun Tian and Ping Ma

Appl. Sci. 2025, 15(13), 7601; https://doi.org/10.3390/app15137601 - 7 Jul 2025

Viewed by 131

Abstract

Pipe-insulating joints are common cathodic protection devices in long-distance oil and gas pipeline infrastructures. To ensure safety, they are often designed too conservatively, resulting in large dimensions, high self-weight, and substantial costs. This study analyzed an insulating joint under the most unfavorable conditions [...] Read more.

Pipe-insulating joints are common cathodic protection devices in long-distance oil and gas pipeline infrastructures. To ensure safety, they are often designed too conservatively, resulting in large dimensions, high self-weight, and substantial costs. This study analyzed an insulating joint under the most unfavorable conditions to identify the component of the maximum stress in the insulating joint, which is the right flange. Then, using parameterized finite element calculations, five independent dimensions of the right flange were combined and arranged to obtain a dataset of the right flange dimensions and their maximum stress. Subsequently, four different fitting algorithms were trained with this dataset, and the ridge regression algorithm, which showed the best predictive performance, was used to establish a surrogate model for calculating the maximum stress of the right flange. Finally, the surrogate model was combined with a genetic algorithm to determine the optimal design dimensions of the right flange. This study also provides examples verifying the accuracy and reliability of the surrogate model and genetic algorithm. In these examples, the maximum stress under the design dimensions given by the optimization algorithm has a maximum error of 8.98% and an average error of 4.63% compared to the preset maximum stress target, while the stress predicted by the surrogate model has a maximum error of 9.65% and an average error of 5.33% compared to the actual stress. This improves the computational efficiency of the optimization algorithm by establishing a surrogate model, which can be used to optimize the dimensions of insulation joints. Full article

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24 pages, 2554 KiB

Open AccessReview

Technical Chains in Civil and Urban Engineering: Review of Selected Solutions, Shaping, Geometry, and Dimensioning

by Krzysztof Adam Ostrowski and Mariusz Spyrowski

Appl. Sci. 2025, 15(13), 7600; https://doi.org/10.3390/app15137600 - 7 Jul 2025

Viewed by 215

Abstract

This article provides an in-depth review of selected technical chains, with particular emphasis on link chains and their load transmission mechanisms. It explores structural and functional characteristics, highlighting how chain geometry affects stress distribution, fatigue life, and performance under various loading conditions. The [...] Read more.

This article provides an in-depth review of selected technical chains, with particular emphasis on link chains and their load transmission mechanisms. It explores structural and functional characteristics, highlighting how chain geometry affects stress distribution, fatigue life, and performance under various loading conditions. The study includes a detailed classification of chains by type, material, and application, ranging from steel-based lifting and transport chains to lightweight, corrosion-resistant polymer types. Manufacturing methods and connection techniques are also discussed, underscoring the importance of proper assembly for mechanical reliability. Special attention is given to the role of materials, particularly the emergence of polymer composites reinforced with glass or carbon fibers, which offer promising alternatives to conventional metals. Although such composites exhibit advantageous properties—such as low weight, corrosion resistance, and energy efficiency—their application remains limited, insufficient load-bearing capacity, and the absence of standardized design guidelines. The review identifies critical knowledge gaps in the field, especially concerning shaping, dimensioning, and normative requirements for polymer-based load-bearing chains. It also highlights the lack of focused research on chain-specific geometries and the need for numerical simulations to optimize link design. The article concludes by emphasizing the importance of developing sustainable, durable, and standardized chain systems—particularly those utilizing recycled or novel materials—to meet both technical demands and environmental goals. This work supports future innovation in the design of advanced chain structures and provides a foundation for expanding the use of high-performance composites in civil and urban engineering applications. Full article

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38 pages, 2149 KiB

Open AccessReview

Implantable Medical Electronic Devices: Sensing Mechanisms, Communication Methods, and the Biodegradable Future

by Zhengdao Chu, Yukai Zhou, Saite Li, Qiaosheng Xu and Lijia Pan

Appl. Sci. 2025, 15(13), 7599; https://doi.org/10.3390/app15137599 - 7 Jul 2025

Viewed by 132

Abstract

In the context of the relentless pursuit of precision, intelligence, and personalization within the realm of medical technology, the real-time monitoring of human physiological signals has assumed heightened significance. Implantable wireless sensor devices have exhibited extraordinary capabilities in tracking internal physiological parameters, including [...] Read more.

In the context of the relentless pursuit of precision, intelligence, and personalization within the realm of medical technology, the real-time monitoring of human physiological signals has assumed heightened significance. Implantable wireless sensor devices have exhibited extraordinary capabilities in tracking internal physiological parameters, including intraocular pressure, blood glucose levels, electrocardiographic activity, and arterial blood pressure. These devices are characterized by elevated temporal continuity and exceptional measurement accuracy. This paper undertakes an in-depth investigation into the key technologies underlying biodegradable implantable sensing devices. Initially, it expounds on diverse sensing mechanisms employed in implantable devices. Additionally, it presents common data transmission and power supply strategies for wireless sensing systems. Finally, it introduces biodegradable materials suitable for human implantation and their respective application domains and enumerates several implantable devices that are either under development or have already been commercialized. Through an in-depth and comprehensive discourse on the current state of development and extant challenges in this domain, the development trajectory of biodegradable devices is put forward. Moreover, this paper also serves as a valuable reference for the design and selection of implantable medical devices. Full article

(This article belongs to the Special Issue Application and Development of Antennas and Sensors in Biomedical Applications)

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15 pages, 20250 KiB

Open AccessArticle

Transferring Face Recognition Techniques to Entomology: An ArcFace and ResNet Approach for Improving Dragonfly Classification

by Zhong Li, Shaoyan Pu, Jingsheng Lu, Ruibin Song, Haomiao Zhang, Xuemei Lu and Yanan Wang

Appl. Sci. 2025, 15(13), 7598; https://doi.org/10.3390/app15137598 - 7 Jul 2025

Viewed by 72

Abstract

Dragonfly classification is crucial for biodiversity conservation. Traditional taxonomic approaches require extensive training and experience, limiting their efficiency. Computer vision offers promising solutions for dragonfly taxonomy. In this study, we adapt the face recognition algorithms for the classification of dragonfly species, achieving efficient [...] Read more.

Dragonfly classification is crucial for biodiversity conservation. Traditional taxonomic approaches require extensive training and experience, limiting their efficiency. Computer vision offers promising solutions for dragonfly taxonomy. In this study, we adapt the face recognition algorithms for the classification of dragonfly species, achieving efficient recognition of categories with extremely small differences between classes. Meanwhile, this method can also reclassify data that were incorrectly labeled. The model is mainly built based on the classic face recognition algorithm (ResNet50+ArcFace), and ResNet50 is used as the comparison algorithm for model performance. Three datasets with different inter-class data distributions were constructed based on two dragonfly image data sources: Data1, Data2 and Data3. Ultimately, our model achieved Top1 accuracy rates of 94.3%, 85.7%, and 90.2% on the three datasets, surpassing ResNet50 by 0.6, 1.5, and 1.6 percentage points, respectively. Under the confidence thresholds of 0.7, 0.8, 0.9, and 0.95, the Top1 accuracy rates on the three datasets were 96.0%, 97.4%, 98.7%, and 99.2%, respectively. In conclusion, our research provides a novel approach for species classification. Furthermore, it can calculate the similarity between classes while predicting categories, thereby offering the potential to provide technical support for biological research on the similarity between species. Full article

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38 pages, 15198 KiB

Open AccessArticle

Analysis the Composition of Hydraulic Radial Force on Centrifugal Pump Impeller: A Data-Centric Approach Based on CFD Datasets

by Hehui Zhang, Kang Li, Ting Liu, Yichu Liu, Jianxin Hu, Qingsong Zuo and Liangxing Jiang

Appl. Sci. 2025, 15(13), 7597; https://doi.org/10.3390/app15137597 - 7 Jul 2025

Viewed by 76

Abstract

Centrifugal pumps are essential in various industries, where their operational stability and efficiency are crucial. This study aims to analyze the composition and variation characteristics of the hydraulic radial force on the impeller using a data-centric approach based on computational fluid dynamics (CFD) [...] Read more.

Centrifugal pumps are essential in various industries, where their operational stability and efficiency are crucial. This study aims to analyze the composition and variation characteristics of the hydraulic radial force on the impeller using a data-centric approach based on computational fluid dynamics (CFD) datasets, providing guidance for optimizing impeller design. A high-precision CFD simulation on a six-blade end-suction centrifugal pump generated a comprehensive hydraulic load dataset. Data analysis methods included exploratory data analysis (EDA) to uncover patterns and trigonometric function fitting to model force variations accurately. Results revealed that the hydraulic radial force exhibits periodic behavior with a dominant blade passing frequency (BPF), showing minimal fluctuations at the rated flow rate and increased fluctuations as flow deviates. Each blade’s force could be approximated by sine curves with equal amplitudes and frequencies but successive phase changes, achieving high fitting quality (R² ≥ 0.96). The force on the impeller can be decomposed into the contributions of each blade, with symmetric blades canceling out the main harmonics, leaving only constant terms and residuals. This study provides insights into force suppression mechanisms, enhancing pump stability and efficiency, and offers a robust framework for future research on fluid–structure interactions and pump design. Full article

(This article belongs to the Special Issue Text Mining and Data Mining)

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20 pages, 5814 KiB

Open AccessArticle

The Effect of Inflatable Pressure on the Strain Deformation of Flexible Wing Skin Film

by Longbin Liu, Mengyang Fan and Xingfu Cui

Appl. Sci. 2025, 15(13), 7596; https://doi.org/10.3390/app15137596 - 7 Jul 2025

Viewed by 77

Abstract

Flexible inflatable film wings have many functional advantages that traditional fixed rigid wings do not possess, such as foldability, small size, light weight, convenient storage, transportation, and so on. More and more scholars and engineers are paying attention to flexible inflatable wings, which [...] Read more.

Flexible inflatable film wings have many functional advantages that traditional fixed rigid wings do not possess, such as foldability, small size, light weight, convenient storage, transportation, and so on. More and more scholars and engineers are paying attention to flexible inflatable wings, which have gradually become a new hot research topic. However, flexible wings rely on inflation pressure to maintain the shape and rigidity of the skin film, and the inflation pressure has a significant influence on the strain deformation and wing bearing characteristics of flexible wing skin film. Here, based on the flexible mechanics theory and balance principle of flexible inflatable film, a theoretical model of structural deformation and internal inflation pressure was constructed, and finite element simulation analysis under different internal inflation pressure conditions was carried out as well. The results demonstrate that the biaxial deformation of flexible wing skin film is closely related to internal inflation pressure, local size, configuration, and film material properties. However, strain deformation along the wingspan direction is quite distinguishing, skin films work under the condition of biaxial plane deformation, and the strain deformation of the spanning direction is obviously higher than that of the chord direction, which all increases with internal inflation pressure. Therefore, it is necessary to pay more attention to bearing strain deformation characteristics to meet the bearing stiffness requirements, which could effectively provide a theoretical reference for the structural optimization design and inflation scheme setting of flexible inflatable wings. Full article

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17 pages, 8187 KiB

Open AccessArticle

Ground-Level Surface Reconstruction and Soil Volume Estimation in Construction Sites Using Marching Cubes Method

by Fattah Hanafi Sheikhha, Jaho Seo and Hanmin Lee

Appl. Sci. 2025, 15(13), 7595; https://doi.org/10.3390/app15137595 - 7 Jul 2025

Viewed by 74

Abstract

Accurate environmental sensing is pivotal for advancing automation in construction, particularly in autonomous excavation. Precise 3D representations of complex and dynamic site geometries is essential for obstacle detection, progress monitoring, and safe operation. However, existing sensing techniques often struggle with capturing irregular surfaces [...] Read more.

Accurate environmental sensing is pivotal for advancing automation in construction, particularly in autonomous excavation. Precise 3D representations of complex and dynamic site geometries is essential for obstacle detection, progress monitoring, and safe operation. However, existing sensing techniques often struggle with capturing irregular surfaces and incomplete data in real-time, leading to significant challenges in practical deployment. To address these gaps, we present a novel framework integrating curve approximation, surface reconstruction, and marching cubes algorithm to transform raw sensor data into a detailed and computationally efficient soil surface representation. Our approach improves site modeling accuracy, paving the way for reliable and efficient construction automation. This paper enhances sensory data quality using surface reconstruction techniques, followed by the marching cubes algorithm to generate an accurate and flexible 3D soil model. This model facilitates rapid estimation of soil volume, weight, and shape, offering an efficient approach for environmental analysis and decision-making in automated systems. Experimental validation demonstrated the effectiveness of the proposed method, achieving relative errors of 4.92% and 1.42% across two excavation cycles. Additionally, the marching cubes algorithm completed volume estimation in just 0.05 s, confirming the approach’s accuracy and suitability for real-time applications in dynamic construction environments. Full article

(This article belongs to the Section Applied Industrial Technologies)

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