Applied Sciences

27 pages, 8202 KiB

Open AccessArticle

Research on Identification Method of Transformer Windings’ Loose Vibration Spectrum Considering a Multi-Load Current Condition

by Jin Fang, Xudong Deng, Yuancan Xia, Chen Wu, Yuehua Li, Xin Li, Kaixin Chen, Fan Wang and Zhanlong Zhang

Appl. Sci. 2025, 15(12), 6949; https://doi.org/10.3390/app15126949 - 19 Jun 2025

Viewed by 292

Abstract

During transformer operation, long-term vibration causes the winding to loosen axially. When hit by a short-circuit, the winding deforms to different extents. Thus, identifying early looseness faults in transformer windings is vital for power systems’ stability. To address issues including scarce vibration data [...] Read more.

During transformer operation, long-term vibration causes the winding to loosen axially. When hit by a short-circuit, the winding deforms to different extents. Thus, identifying early looseness faults in transformer windings is vital for power systems’ stability. To address issues including scarce vibration data across multiple load conditions for transformer winding looseness faults, inadequate extraction of two-dimensional spectrogram features, and the inability to boost recognition accuracy caused by overfitting during fault recognition model training, this study constructed a 10 kV power transformer vibration test platform. It measured the vibration signals on the box surface under various winding looseness conditions and built a time–frequency-domain vibration spectrum library for different load currents. Then, a fault identification model based on vibration spectra and ConvNeXt was constructed, and model verification and analysis were carried out. The results indicate that after training, the fault recognition accuracy of the spectrum containing three load conditions is comparable to that of a single load condition. The average recognition accuracy at six box-surface measuring points reaches 97.9%. Moreover, the ConvNeXt model outperforms the traditional ResNet50 by 1.2%. This new model effectively addresses overfitting and offers strong technical support for detecting different transformer winding looseness faults. Full article

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

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25 pages, 3403 KiB

Open AccessArticle

Local Transmissibility-Based Identification of Structural Damage Utilizing Positive Learning Strategies

by Oguz Gunes and Burcu Gunes

Appl. Sci. 2025, 15(12), 6948; https://doi.org/10.3390/app15126948 - 19 Jun 2025

Viewed by 198

Abstract

Recent advances in sensor technology, data acquisition, and signal processing have enabled the development of data-driven structural health monitoring (SHM) strategies, offering a powerful alternative or complement to traditional model-based approaches. These approaches rely on damage-sensitive features (DSFs) extracted from vibration measurements. This [...] Read more.

Recent advances in sensor technology, data acquisition, and signal processing have enabled the development of data-driven structural health monitoring (SHM) strategies, offering a powerful alternative or complement to traditional model-based approaches. These approaches rely on damage-sensitive features (DSFs) extracted from vibration measurements. This study introduces an innovative, unsupervised learning framework leveraging transmissibility functions (TFs) as DSFs due to their local sensitivity to changes in dynamic behavior and their ability to operate without requiring input excitation measurements—an advantage in civil engineering applications where such data are often difficult to obtain. The novelty lies in the use of sequential sensor pairings based on structural connectivity to construct TFs that maximize damage sensitivity, combined with one-class classification algorithms for automatic damage detection and a damage index for spatial localization within sensor resolution. The method is evaluated through numerical simulations with noise-contaminated data and experimental tests on a masonry arch bridge model subjected to progressive damage. The numerical study shows detection accuracy above 90% with one-class support vector machine (OCSVM) and correct localization across all damage scenarios. Experimental findings further confirm the proposed approach’s localization capability, especially as damage severity increases, aligning well with observed damage progression. These results demonstrate the method’s practical potential for real-world SHM applications. Full article

(This article belongs to the Special Issue Advanced Structural Health Monitoring in Civil Engineering)

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18 pages, 1903 KiB

Open AccessArticle

AI-Driven Belt Failure Prediction and Prescriptive Maintenance with Motor Current Signature Analysis

by João Paulo Costa, José Torres Farinha, Mateus Mendes and Jorge O. Estima

Appl. Sci. 2025, 15(12), 6947; https://doi.org/10.3390/app15126947 - 19 Jun 2025

Viewed by 461

Abstract

Industrial belt failures pose significant challenges to manufacturing operations, often resulting in costly downtime and maintenance interventions. This study presents a comprehensive approach to belt failure analysis, leveraging advanced monitoring and diagnostic techniques. Through the integration of motor current signature analysis (MCSA) and [...] Read more.

Industrial belt failures pose significant challenges to manufacturing operations, often resulting in costly downtime and maintenance interventions. This study presents a comprehensive approach to belt failure analysis, leveraging advanced monitoring and diagnostic techniques. Through the integration of motor current signature analysis (MCSA) and machine learning algorithms, particularly long short-term memory (LSTM) networks, this study aims to predict and detect belt degradation in real time. The methodology involves the collection and pre-processing of raw spectral data from industrial assets, followed by the training and optimization of predictive models. The effectiveness of the approach is demonstrated through extensive testing against real-world data, showcasing its ability to accurately forecast belt failures and enable proactive maintenance strategies. The results obtained from the testing phase reveal a high level of accuracy in predicting belt failures, with the developed models consistently outperforming traditional methods. The incorporation of LSTM networks and swarm intelligence algorithms led to a significant improvement in predictive capabilities, allowing for the early detection of degradation patterns and timely intervention. By harnessing the power of data-driven predictive analytics, the research offers a promising pathway towards enhancing operational efficiency and minimizing unplanned downtime in industrial settings. This study not only contributes to the field of predictive maintenance but also underscores the transformative potential of advanced monitoring technologies in optimizing asset reliability and performance. Full article

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16 pages, 3677 KiB

Open AccessArticle

Iterative Finite Element Analysis of Buccolingual Width on Canine Distal Movement in Clear Aligner Treatment

by Hyungchul Lee, Kiyean Kim, Youn-Kyung Choi, Sung-Hun Kim, Yong-Il Kim and Seong-Sik Kim

Appl. Sci. 2025, 15(12), 6946; https://doi.org/10.3390/app15126946 - 19 Jun 2025

Viewed by 184

Abstract

This study investigated the biomechanical effects of varying buccolingual beam widths in maxillary first premolar extraction spaces on canine distal bodily movement during clear aligner treatment. Using finite element analysis, four distinct models were constructed, incorporating beam designs with widths of 0 (edentulous), [...] Read more.

This study investigated the biomechanical effects of varying buccolingual beam widths in maxillary first premolar extraction spaces on canine distal bodily movement during clear aligner treatment. Using finite element analysis, four distinct models were constructed, incorporating beam designs with widths of 0 (edentulous), 1, 2, and 3 mm within the extraction space. Each model underwent a comprehensive 50-stage iterative simulation to evaluate canine displacement patterns, tipping, rotational movements, aligner deformation characteristics, and magnitudes of forces and moments applied to the canines throughout long-term tooth movement. Group 0 (no beam, 0 mm beam width) exhibited the greatest crown displacement and tipping angle. In contrast, Group 2 (2 mm beam width) most effectively reduced the final angulation of the canine, whereas Group 3 (3 mm beam width) was most effective in controlling unwanted rotation and minimizing deformation of the clear aligner. Furthermore, an increase in the beam width was associated with a trend of higher initial force and lower initial moment. Notably, relatively high levels of both force and moment were maintained during the later stages of the simulation, which is advantageous for sustained control of tooth movement. In conclusion, incorporating a beam of 2–3 mm width into the maxillary first premolar extraction space appears to be optimal for managing canine tipping and rotation while promoting bodily movement of the tooth. Full article

(This article belongs to the Section Applied Dentistry and Oral Sciences)

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37 pages, 7361 KiB

Open AccessReview

Evolution and Knowledge Structure of Wearable Technologies for Vulnerable Road User Safety: A CiteSpace-Based Bibliometric Analysis (2000–2025)

by Gang Ren, Zhihuang Huang, Tianyang Huang, Gang Wang and Jee Hang Lee

Appl. Sci. 2025, 15(12), 6945; https://doi.org/10.3390/app15126945 - 19 Jun 2025

Viewed by 289

Abstract

This study presents a systematic bibliometric review of wearable technologies aimed at vulnerable road user (VRU) safety, covering publications from 2000 to 2025. Guided by PRISMA procedures and a PICo-based search strategy, 58 records were extracted and analyzed in CiteSpace, yielding visualizations of [...] Read more.

This study presents a systematic bibliometric review of wearable technologies aimed at vulnerable road user (VRU) safety, covering publications from 2000 to 2025. Guided by PRISMA procedures and a PICo-based search strategy, 58 records were extracted and analyzed in CiteSpace, yielding visualizations of collaboration networks, publication trajectories, and intellectual structures. The results indicate a clear evolution from single-purpose, stand-alone devices to integrated ecosystem solutions that address the needs of diverse VRU groups. Six dominant knowledge clusters emerged—street-crossing assistance, obstacle avoidance, human–computer interaction, cyclist safety, blind navigation, and smart glasses. Comparative analysis across pedestrians, cyclists and motorcyclists, and persons with disabilities shows three parallel transitions: single- to multisensory interfaces, reactive to predictive systems, and isolated devices to V2X-enabled ecosystems. Contemporary research emphasizes context-adaptive interfaces, seamless V2X integration, and user-centered design, and future work should focus on lightweight communication protocols, adaptive sensory algorithms, and personalized safety profiles. The review provides a consolidated knowledge map to inform researchers, practitioners, and policy-makers striving for inclusive and proactive road safety solutions. Full article

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

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22 pages, 1211 KiB

Open AccessArticle

Machining Center Opportunistic Maintenance Strategy Using Improved Average Rank Method for Subsystem Reliability Modeling

by Yingzhi Zhang, Minqiao Song, Wei Wu and Feng Han

Appl. Sci. 2025, 15(12), 6944; https://doi.org/10.3390/app15126944 - 19 Jun 2025

Viewed by 161

Abstract

Machining centers are complex systems that consist of multiple subsystems. When maintaining these subsystems, considering opportunistic maintenance can prevent frequent shutdowns during the machining process and reduce costs. This paper proposes an opportunistic maintenance strategy for machining centers. Firstly, the reliability of the [...] Read more.

Machining centers are complex systems that consist of multiple subsystems. When maintaining these subsystems, considering opportunistic maintenance can prevent frequent shutdowns during the machining process and reduce costs. This paper proposes an opportunistic maintenance strategy for machining centers. Firstly, the reliability of the machining center subsystem was modeled, which serves as the basis for determining when to repair a subsystem. In this process, an improved average rank method was employed, which considers the time correlation of subsystem failures and can achieve better model-fitting results. In the opportunistic maintenance strategy, imperfect maintenance is considered. Additionally, the strategy includes direct maintenance costs, downtime costs, failure risk costs, and penalty costs for incomplete utilization of subsystems. The opportunistic maintenance threshold helps determine whether other subsystems need to be repaired during this maintenance opportunity. The optimization objective is to minimize the total cost within the specified operating time. By modeling the reliability of subsystems using the failure data collected from five machining centers, the opportunistic maintenance strategy can reduce downtime by 10 times, preventive downtime by 29%, and cost by 7%. The results indicate that for machining centers or other complex systems, the opportunistic maintenance strategy mentioned in this article can lead to good results. Full article

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23 pages, 1723 KiB

Open AccessArticle

Navigational Risk Assessment in Offshore Wind Farms Using Spatial Ship Domain Models

by Grzegorz Rutkowski and Maria Kubacka

Appl. Sci. 2025, 15(12), 6943; https://doi.org/10.3390/app15126943 - 19 Jun 2025

Viewed by 278

Abstract

Navigation in offshore wind farm (OWF) areas is essential for construction, maintenance, safety, and traditional activities like fishing. However, the presence of OWFs extends to sea routes, negatively impacting maritime transport economics. This paper examines navigational risk indicators in the vertical and horizontal [...] Read more.

Navigation in offshore wind farm (OWF) areas is essential for construction, maintenance, safety, and traditional activities like fishing. However, the presence of OWFs extends to sea routes, negatively impacting maritime transport economics. This paper examines navigational risk indicators in the vertical and horizontal planes of the ship domain for three representative vessels navigating under different hydrometeorological conditions within the location of a proposed offshore wind farm in the Polish sector of the Baltic Sea. The study compares three types of domain parameters defined by the PIANC guidelines, Coldwell’s two-dimensional model, and Rutkowski’s three-dimensional model. The analysis includes navigational hazards located ahead of the ship’s bow and astern from the aft, as well as keeping under-keel and over-head clearance. Besides the main numerical indicators of navigational risk estimated for obstacles on the port and starboard sides, the study emphasizes the importance of such additional factors. The primary objective of this paper is to identify the ship types that can navigate and fish safely in proximity to and within the OWF area. The analysis employs hydrometeorological data, mathematical models, and operational data derived from maritime navigation and maneuvering simulators. This comprehensive approach aims to enhance maritime safety in OWF areas. Full article

(This article belongs to the Special Issue Risk and Safety of Maritime Transportation)

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17 pages, 3605 KiB

Open AccessArticle

Effects of Lithology Combination Compaction Seepage Characteristics on Groundwater Prevention and Control in Shallow Coal Seam Group Mining

by Kaijun Miao, Shihao Tu, Wenping Li, Jinghua Li, Jinhu Tian, Hongbin Zhao and Jieyang Ma

Appl. Sci. 2025, 15(12), 6942; https://doi.org/10.3390/app15126942 - 19 Jun 2025

Viewed by 217

Abstract

The mining of shallow coal seam groups triggers mine water inrush and ecological environment destruction. Effective groundwater prevention and control requires controlling the compaction and seepage characteristics (CSCs) of broken rock in goaf. In this study, the CSCs of roof lithology and goaf [...] Read more.

The mining of shallow coal seam groups triggers mine water inrush and ecological environment destruction. Effective groundwater prevention and control requires controlling the compaction and seepage characteristics (CSCs) of broken rock in goaf. In this study, the CSCs of roof lithology and goaf broken rock combinations are experimentally investigated. The results indicate that, for samples with identical gradation, the percentage of void (PV) is minimized in sandstone–mudstone combinations, while PV increases with higher coal content. Initial compaction of composite samples is primarily governed by soft rock re-crushing, whereas the stable compaction stage is determined by the initial PV. Under low axial stress, the CSCs of lithological combination samples exhibit instability, with the mudstone layer reducing flow velocity by approximately 36% under equivalent compaction and seepage conditions. Particle migration, leading to the blockage of the seepage section, is an important cause of the decrease in permeability. Based on experimental findings, a stress–void–seepage coupling model is established to describe the compaction–seepage behavior of lithologic combination broken rock in shallow goafs. Full article

(This article belongs to the Special Issue Novel Research on Rock Mechanics and Geotechnical Engineering)

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16 pages, 1443 KiB

Open AccessArticle

Radiodosiomics Prediction of Treatment Failures Prior to Chemoradiotherapy in Head-and-Neck Squamous Cell Carcinoma

by Hidemi Kamezawa and Hidetaka Arimura

Appl. Sci. 2025, 15(12), 6941; https://doi.org/10.3390/app15126941 - 19 Jun 2025

Viewed by 183

Abstract

Predicting treatment failure (TF) in head-and-neck squamous cell carcinoma (HNSCC) patients before treatment can help in selecting a more appropriate treatment approach. We investigated a novel radiodosiomics approach to predict TF prior to chemoradiation in HNSCC patients. Computed tomography (CT) images, dose distributions [...] Read more.

Predicting treatment failure (TF) in head-and-neck squamous cell carcinoma (HNSCC) patients before treatment can help in selecting a more appropriate treatment approach. We investigated a novel radiodosiomics approach to predict TF prior to chemoradiation in HNSCC patients. Computed tomography (CT) images, dose distributions (DDs), and clinical data from 172 cases were collected from a public database. The cases were divided into the training (n = 140) and testing (n = 32) datasets. A total of 1027 features, including conventional radiomic (R) features, local binary pattern-based (L) features, and topological (T) features, were extracted from the CT images and DDs of the tumor region. Moreover, deep (D) features were extracted from a deep learning-based prediction model. The Coxnet algorithm was employed to select significant features. Twenty-two treatment failure prediction models were constructed based on Rad-scores. TF prediction models were assessed using the concordance index (C-index) and statistically significant variations in the Kaplan–Meier curves between the two risk groups. The Kaplan–Meier curves of the DD-based T (DD-T) model displayed statistically significant differences. The highest C-index of the testing dataset for this model was 0.760. The proposed radiodosiomics models could potentially demonstrate greater accuracy in anticipating TF before chemoradiation in HNSCC patients. Full article

(This article belongs to the Special Issue Novel Technologies in Radiology: Diagnosis, Prediction and Treatment)

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14 pages, 2241 KiB

Open AccessArticle

Evaluating the Efficacy of Microwave Sanitization in Reducing SARS-CoV-2 Airborne Contagion Risk in Office Environments

by Margherita Losardo, Marco Simonetti, Pietro Bia, Antonio Manna, Marco Verratti and Hamed Rasam

Appl. Sci. 2025, 15(12), 6940; https://doi.org/10.3390/app15126940 - 19 Jun 2025

Viewed by 240

Abstract

The COVID-19 pandemic has heightened awareness of airborne disease susceptibility, leading to the development and adoption of various preventive technologies. Among these, microwave sanitization, which inactivates virions through non-thermal mechanical resonance, has gained significant scientific credibility. Laboratory tests have demonstrated its high efficacy, [...] Read more.

The COVID-19 pandemic has heightened awareness of airborne disease susceptibility, leading to the development and adoption of various preventive technologies. Among these, microwave sanitization, which inactivates virions through non-thermal mechanical resonance, has gained significant scientific credibility. Laboratory tests have demonstrated its high efficacy, prompting further investigation into its effectiveness in real-world settings. This study employs multi-physical, fluid-dynamic and electromagnetic simulations of office environments to evaluate the reduction of contagion risk. By integrating these simulations with virus inactivation experimental laboratory results, we observed that the introduction of a microwave sanitization device significantly reduces the risk of contamination among individuals in the same environment. These findings suggest potential applications and further studies in other everyday scenarios. Full article

(This article belongs to the Special Issue Electromagnetic Radiation and Human Environment)

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16 pages, 1520 KiB

Open AccessArticle

Supply Chain Data Analytics for Digital Twins: A Comprehensive Framework

by Vasileios Xiros, Jose M. Gonzalez Castro, Francisco Fernandez-Pelaez, Babis Magoutas and Konstantinos Christidis

Appl. Sci. 2025, 15(12), 6939; https://doi.org/10.3390/app15126939 - 19 Jun 2025

Viewed by 276

Abstract

The latest research highlights the need for circularity in modern industrial supply chains, which is reflected in the decisions of European and global policymakers, as well as in the strategies of major stakeholders. Digital Twins are considered a principal catalyst in the transition [...] Read more.

The latest research highlights the need for circularity in modern industrial supply chains, which is reflected in the decisions of European and global policymakers, as well as in the strategies of major stakeholders. Digital Twins are considered a principal catalyst in the transition to circularity, while real-world, accurate and timely data is a key factor in these supply chains. This emphasis on data highlights the central role of data analytics in extracting key insights and utilizing machine learning to propose sustainability initiatives in decentralized production ecosystems. In consequence, commercial solutions are being developed; however, a single solution might not address all requirements. In this work we present a comprehensive modular, scalable and secure analytics architecture, designed to expand the available components in commercial solutions by providing an intelligent layer to Digital Twins. Our approach integrates with the latest standards for international data spaces, interoperability and process models in distributed environments where multiple actors engage in co-opetition. The proposed architecture is implemented in a market-ready solution and demonstrated in two case studies, in Spain and in Greece. Validation results confirm that the analytics service delivers accurate, timely and actionable insights, while following open communication standards and sustainability guidelines. Our research indicates that companies implementing digital twin solutions using standardized connectors for interoperability can benefit by customizing the proposed solution and avoiding complex developments from scratch. Full article

(This article belongs to the Special Issue Digital Twins: Technologies and Applications)

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19 pages, 2721 KiB

Open AccessArticle

Experimental Study on Glass Deformation Calculation Using the Holographic Interferometry Double-Exposure Method

by Yucheng Li, Yang Zhang, Deyu Jia, Song Gao and Muqun Zhang

Appl. Sci. 2025, 15(12), 6938; https://doi.org/10.3390/app15126938 - 19 Jun 2025

Viewed by 183

Abstract

This study systematically compares the metrological characteristics of single- exposure, double-exposure, and continuous-exposure holographic interferometry for micro-deformation detection. Results demonstrate that the double-exposure method achieves optimal balance across critical performance metrics through its ideal cosine fringe field modulation. This approach (1) eliminates object [...] Read more.

This study systematically compares the metrological characteristics of single- exposure, double-exposure, and continuous-exposure holographic interferometry for micro-deformation detection. Results demonstrate that the double-exposure method achieves optimal balance across critical performance metrics through its ideal cosine fringe field modulation. This approach (1) eliminates object wave amplitude interference via dual-exposure superposition, establishing submicron linear mapping between fringe displacement and deformation amplitude; (2) introduces a fringe gradient-based direction detection algorithm resolving deformation vector ambiguity; and (3) implements an error-compensated fusion framework integrating theoretical modeling, MATLAB 2015b simulations, and experimental validation. Experiments on drilled glass samples confirm their superior performance in terms of near-ideal fringe contrast (1.0) and noise suppression (0.06). The technique significantly improves real-time capability and anti-interference robustness in micro-deformation monitoring, providing a validated solution for MEMS and material mechanics characterization. Full article

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19 pages, 4712 KiB

Open AccessArticle

Simulation and Experimental Study on the Shrub-Cutting Performance of Quasi-Planetary Cutter

by Zikai Song, Xibin Dong, Chi Teng, Ben Guo, Jiawang Zhang and Yuchen Zhang

Appl. Sci. 2025, 15(12), 6937; https://doi.org/10.3390/app15126937 - 19 Jun 2025

Viewed by 249

Abstract

To evaluate the performance of quasi-planetary cutting tools, three shrubs were selected and studied using a combination of numerical simulation and cutting test bench experiments. Based on the constitutive model of shrub material and LS-DYNA simulation, the effects of tool speed (n [...] Read more.

To evaluate the performance of quasi-planetary cutting tools, three shrubs were selected and studied using a combination of numerical simulation and cutting test bench experiments. Based on the constitutive model of shrub material and LS-DYNA simulation, the effects of tool speed (n), feed speed (v), and shrub diameter (D_a) on peak cutting force (F_max) and peak cutting power (P_max) were analysed through a single-factor simulation test. Using the shrub-cutting test bench, an orthogonal test was designed with n, v, and moisture content (w) as factors and F_max and P_max as indicators. A regression model was established, and a single-factor comparison test for w was conducted. The results indicate that F_max decreases as n increases, while P_max initially decreases and then increases. Both F_max and P_max increase with rising v and D_a. As w increases, F_max and P_max first decrease and then increase. When n is 1813 r/min, v is 30 mm/s, and w is 10.9%, F_max and P_max reach their optimal values of 8.42 N and 282.99 W, respectively, with verification test errors of 2.68% and 1.56%. The findings provide methodological and data support for studying the cutting performance of new cutting tools. Full article

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17 pages, 469 KiB

Open AccessArticle

by Iulia Baraian, Honoriu Valean, Oliviu Matei and Rudolf Erdei

Appl. Sci. 2025, 15(12), 6936; https://doi.org/10.3390/app15126936 - 19 Jun 2025

Viewed by 202

Abstract

Similarity-based decision support systems have become essential tools for providing tailored and adaptive guidance across various domains. In agriculture, where managing extensive land areas poses significant challenges, the primary objective is often to maximize harvest yields while reducing costs, preserving crop health, and [...] Read more.

Similarity-based decision support systems have become essential tools for providing tailored and adaptive guidance across various domains. In agriculture, where managing extensive land areas poses significant challenges, the primary objective is often to maximize harvest yields while reducing costs, preserving crop health, and minimizing the use of chemical adjuvants. The application of similarity-based analysis enables the development of personalized farming recommendations, refined through shared data and insights, which contribute to improved plant growth and enhanced annual harvest outcomes. This study employs two algorithms, K-Nearest Neighbour (KNN) and Approximate Nearest Neighbour (ANN) using Locality Sensitive Hashing (LSH) to evaluate their effectiveness in agricultural decision-making. The results demonstrate that, under comparable farming conditions, KNN yields more accurate recommendations due to its reliance on exact matches, whereas ANN provides a more scalable solution well-suited for large datasets. Both approaches support improved agricultural decisions and promote more sustainable farming strategies. While KNN is more effective for smaller datasets, ANN proves advantageous in real-time applications that demand fast response times. The implementation of these algorithms represents a significant advancement toward data-driven and efficient agricultural practices. Full article

(This article belongs to the Special Issue Biosystems Engineering: Latest Advances and Prospects)

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24 pages, 10811 KiB

Open AccessArticle

Research on the Shear Performance of Carbonaceous Mudstone Under Natural and Saturated Conditions and Numerical Simulation of Slope Stability

by Jian Zhao, Hongying Chen and Rusong Nie

Appl. Sci. 2025, 15(12), 6935; https://doi.org/10.3390/app15126935 - 19 Jun 2025

Viewed by 157

Abstract

Rainfall can easily cause local sliding and collapse of carbonaceous mudstone deep road cut slopes. In order to study the strength characteristics of carbonaceous mudstone under different water environments, large-scale horizontal push shear tests were conducted on carbonaceous mudstone rock masses in their [...] Read more.

Rainfall can easily cause local sliding and collapse of carbonaceous mudstone deep road cut slopes. In order to study the strength characteristics of carbonaceous mudstone under different water environments, large-scale horizontal push shear tests were conducted on carbonaceous mudstone rock masses in their natural state and after immersion in saturated water. The push shear force–displacement relationship curve and fracture surface shape characteristics of carbonaceous mudstone samples were analyzed, and the shear strength index of carbonaceous mudstone was obtained, and numerical simulations on the stability and support effect of carbonaceous mudstone slopes were conducted. The research results indicate that carbonaceous mudstone can exhibit good structural properties and typical strain softening characteristics under natural conditions. The fracture surface, shear strength, and shear deformation process of carbonaceous mudstone samples will undergo significant changes after being soaked in saturated water. The average cohesion decreases by 33% compared to the natural state, and the internal friction angle decreases by 15%. The numerical simulation results also fully verify the attenuation of mechanical properties of carbonaceous mudstone after immersion, as well as the effectiveness of prestressed anchor cables and frame beams in supporting carbonaceous mudstone slopes. The research results provide an effective method for understanding the shear performance of carbonaceous mudstone and practical guidance for evaluating the stability and reinforcement design of carbonaceous mudstone slopes. Full article

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29 pages, 6524 KiB

Open AccessArticle

Efficiency of Positive Pressure Ventilation Compared to Organized Natural Ventilation in Fire Scenarios of a Multi-Story Building

by Dan-Adrian Ionescu, Vlad Iordache, Iulian-Cristian Ene and Ion Anghel

Appl. Sci. 2025, 15(12), 6934; https://doi.org/10.3390/app15126934 - 19 Jun 2025

Viewed by 311

Abstract

This paper presents a detailed analysis of the dynamics of indoor environmental parameters under three simulated fire scenarios in a multi-story building, using the PyroSim platform (based on the Fire Dynamics Simulator—FDS). The study compares two smoke control strategies, organized natural ventilation (a [...] Read more.

This paper presents a detailed analysis of the dynamics of indoor environmental parameters under three simulated fire scenarios in a multi-story building, using the PyroSim platform (based on the Fire Dynamics Simulator—FDS). The study compares two smoke control strategies, organized natural ventilation (a passive system) and mechanical pressurization (an active system), evaluating their influence on temperature, differential pressure, air velocity, heat release rate (HRR), and toxic gas distribution. The simulations revealed that passive systems, relying on the stack effect and vertical natural ventilation, do not ensure the effective control of smoke infiltration into evacuation routes, allowing significant heat accumulation and reduced visibility. The results highlight the superior effectiveness of unidirectional mechanical pressurization in maintaining a stable flow regime, functional visibility, and a safe evacuation environment. A key finding is the transition from static pressure control to velocity-based flow control at the moment of door opening toward the fire source. The results confirm that a dynamically adapted application of mechanical pressurization—synchronized with the opening of access pathways—not only reinforces existing principles for protecting egress routes, but also provides a precise operational approach for optimizing emergency responses in high-rise buildings. Full article

(This article belongs to the Special Issue Recent Advances and Emerging Trends in Computational Fluid Dynamics)

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22 pages, 9093 KiB

Open AccessArticle

Numerical Investigation of the Pull-Out and Shear Mechanical Characteristics and Support Effectiveness of Yielding Bolt in a Soft Rock Tunnel

by Yan Zhu, Mingbo Chi, Yanyan Tan, Ersheng Zha and Yuwei Zhang

Appl. Sci. 2025, 15(12), 6933; https://doi.org/10.3390/app15126933 - 19 Jun 2025

Viewed by 237

Abstract

Conventional bolts frequently fail under large deformations due to stress concentration in soft rock tunnels. In contrast, yielding bolts incorporate energy-absorbing mechanisms to sustain controlled plastic deformation. This study employed FLAC3D to numerically investigate the pull-out, shear, and bending behaviors of yielding bolts, [...] Read more.

Conventional bolts frequently fail under large deformations due to stress concentration in soft rock tunnels. In contrast, yielding bolts incorporate energy-absorbing mechanisms to sustain controlled plastic deformation. This study employed FLAC3D to numerically investigate the pull-out, shear, and bending behaviors of yielding bolts, evaluating their support effectiveness in soft rock tunnels. Three-dimensional finite difference models incorporating nonlinear coupling springs and the Mohr–Coulomb criterion were developed to simulate bolt–rock interactions under multifactorial loading. Validation against experimental pull-out tests and field measurements confirmed the model accuracy. Under pull-out loading, the axial forces in yielding bolts decreased more rapidly along the bolt length, reducing stress concentration at the head. The central position of the maximum load-bearing capacity in conventional bolts fractured under tension, resulting in an hourglass-shaped axial force distribution. Conversely, the yielding bolts maintained yield strength for an extended period after reaching it, exhibiting a spindle-shaped axial force distribution. Parametric analyses reveal that bolt spacing exerts a greater influence on support effectiveness than length. This study bridges critical gaps in understanding yielding bolt behavior under combined loading and provides a validated framework for optimizing energy-absorbing support systems in soft rock tunnels. Full article

(This article belongs to the Special Issue Recent Advances in Rock Mass Engineering)

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12 pages, 1832 KiB

Open AccessArticle

Time Scale Control Using Dynamic GMDH Neural Network Forecasting Based on Real Measurement Data

by Łukasz Sobolewski

Appl. Sci. 2025, 15(12), 6932; https://doi.org/10.3390/app15126932 - 19 Jun 2025

Viewed by 187

Abstract

This article presents the results of the conducted research work related to the dynamic forecasting of the difference values for the Polish Time Scale UTC(PL) for real measurement data, prepared in the form of the time series TS1 and TS2. For the presented [...] Read more.

This article presents the results of the conducted research work related to the dynamic forecasting of the difference values for the Polish Time Scale UTC(PL) for real measurement data, prepared in the form of the time series TS1 and TS2. For the presented time period (the whole year of 2024), the differences between the UTC(PL) and UTC does not exceed ±4.4 ns. The analogous differences for the interval exceeding 2 years are within the range of ±5 ns. Additionally, the obtained forecast results for the last day of forecasting in a given week are very consistent with the forecast results for the first day of the new forecasting week, which illustrates the very good quality of the forecasting and the universality of the forecasting procedure developed by the author using the GMDH-type neural network. Full article

(This article belongs to the Special Issue Research and Application of Neural Networks)

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26 pages, 1588 KiB

Open AccessArticle

GlassBoost: A Lightweight and Explainable Classification Framework for Tabular Datasets

by Ehsan Namjoo, Alison N. O’Connor, Jim Buckley and Conor Ryan

Appl. Sci. 2025, 15(12), 6931; https://doi.org/10.3390/app15126931 - 19 Jun 2025

Viewed by 288

Abstract

Explainable artificial intelligence (XAI) is essential for fostering trust, transparency, and accountability in machine learning systems, particularly when applied in high-stakes domains. This paper introduces a novel XAI system designed for classification tasks on tabular data, which offers a balance between performance and [...] Read more.

Explainable artificial intelligence (XAI) is essential for fostering trust, transparency, and accountability in machine learning systems, particularly when applied in high-stakes domains. This paper introduces a novel XAI system designed for classification tasks on tabular data, which offers a balance between performance and interpretability. The proposed method, GlassBoost, first trains an XGBoost model on a given dataset and then computes gain scores, quantifying the average improvement in the model’s loss function contributed by each feature during tree splits. Based on these scores, a subset of significant features is selected. A shallow decision tree is then trained using the top d features with the highest gain scores, where d is significantly smaller than the total number of original features. This model compression yields a transparent, IF–THEN rule-based decision process that remains faithful to the original high-performing model. To evaluate the system, we apply it to an anomaly detection task in the context of intrusion detection systems (IDSs), using a dataset containing traffic features from both malicious and normal activities. Results show that our method achieves high accuracy, precision, and recall while providing a clear and interpretable explanation of its decision-making. We further validate its explainability using SHAP, a well-established approach in the field of XAI. Comparative analysis demonstrates that GlassBoost outperforms SHAP in terms of precision, recall, and accuracy, with more balanced performance across the three metrics. Likewise, our review of literature findings indicate that Glassboost outperforms many other XAI models while retaining computational efficiency. In one of our configurations, GlassBoost achieved accuracy of 0.9868, recall of 0.9792, and precision of 0.9843 using only eight features within a tree structure of a maximum depth of four. Full article

(This article belongs to the Special Issue Machine Learning and AI Techniques for Intrusion Detection and Prevention)

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28 pages, 12681 KiB

Open AccessArticle

MM-VSM: Multi-Modal Vehicle Semantic Mesh and Trajectory Reconstruction for Image-Based Cooperative Perception

by Márton Cserni, András Rövid and Zsolt Szalay

Appl. Sci. 2025, 15(12), 6930; https://doi.org/10.3390/app15126930 - 19 Jun 2025

Viewed by 306

Abstract

Recent advancements in cooperative 3D object detection have demonstrated significant potential for enhancing autonomous driving by integrating roadside infrastructure data. However, deploying comprehensive LiDAR-based cooperative perception systems remains prohibitively expensive and requires precisely annotated 3D data to function robustly. This paper proposes an [...] Read more.

Recent advancements in cooperative 3D object detection have demonstrated significant potential for enhancing autonomous driving by integrating roadside infrastructure data. However, deploying comprehensive LiDAR-based cooperative perception systems remains prohibitively expensive and requires precisely annotated 3D data to function robustly. This paper proposes an improved multi-modal method integrating LiDAR-based shape references into a previously mono-camera-based semantic vertex reconstruction framework to enable robust and cost-effective monocular and cooperative pose estimation after the reconstruction. A novel camera–LiDAR loss function that combines re-projection loss from a multi-view camera system alongside LiDAR shape constraints is proposed. Experimental evaluations conducted on the Argoverse dataset and real-world experiments demonstrate significantly improved shape reconstruction robustness and accuracy, thereby improving pose estimation performance. The effectiveness of the algorithm is proven through a real-world smart valet parking application, which is evaluated in our university parking area with real vehicles. Our approach allows accurate 6DOF pose estimation using an inexpensive IP camera without requiring context-specific training, thereby advancing the state of the art in monocular and cooperative image-based vehicle localization. Full article

(This article belongs to the Special Issue Advances in Autonomous Driving and Smart Transportation)

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22 pages, 2939 KiB

Open AccessArticle

Chemometrics-Assisted Calibration of a Handheld LIBS Device for the Quantitative Determination of Major and Minor Elements in Artifacts from the Archaeological Park of Tindari (Italy)

by Gabriele Lando, Francesco Caridi, Domenico Majolino, Giuseppe Paladini, Giuseppe Sabatino, Valentina Venuti and Paola Cardiano

Appl. Sci. 2025, 15(12), 6929; https://doi.org/10.3390/app15126929 - 19 Jun 2025

Viewed by 211

Abstract

In this study, a chemometrics-assisted calibration method was developed for the Z-903 SciAps handheld Laser-Induced Breakdown Spectroscopy (h-LIBS) device. For this purpose, seventeen silica-based standard samples with known chemical composition were collected, pelleted, and analyzed using h-LIBS. Spectral data were pre-processed using a [...] Read more.

In this study, a chemometrics-assisted calibration method was developed for the Z-903 SciAps handheld Laser-Induced Breakdown Spectroscopy (h-LIBS) device. For this purpose, seventeen silica-based standard samples with known chemical composition were collected, pelleted, and analyzed using h-LIBS. Spectral data were pre-processed using a Whittaker filter and normalized via Standard Normal Variate (SNV). The dataset was divided into calibration and validation sets using the Kennard–Stone algorithm. Partial Least Square (PLS) regression was employed for multivariate regression analysis, and a variable selection method (i.e., Variable Importance in Projection, VIP) was applied to reduce the number of predictors. Results from the PLS-VIP approach demonstrated that this device is suitable for the quantitative measurement of nineteen chemical elements, including major and minor elements, achieving significant R² values for major elements including Na (R² = 0.91), Mg (R² = 0.95), and Si (R² = 0.89). The limits of detection reached are satisfying, being, for example, 0.24%, 0.41%, 0.43%, 1.5%, and 1.7% for Na, Al, Ca, Si, and Fe, respectively, among major elements, and 189 ppm, 165 ppm, 203 ppm, and 1 ppm for Ba, Cu, Mn, and Rb, respectively, among minor elements. Uncertainties in prediction of the element concentrations were compared with data from the literature, and the effect of another baseline pretreatment algorithm, airPLS (adaptive iteratively reweighted PLS), was also tested. The method was then applied to nine silica-based artifacts of different typologies sampled from the Archaeological Park of Tindari (Italy), including bricks from the theatre, archaeological glasses, and volcanic rocks. Full article

(This article belongs to the Special Issue Solving Environmental and Cultural Heritage Issues Through Analytical Tools)

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28 pages, 3461 KiB

Open AccessArticle

The Plasticization of Alkali-Activated Cement System Na₂O-CaO-Al₂O₃-SiO₂-H₂O: Problems and Decisions

by Pavlo Kryvenko, Igor Rudenko and Oleksandr Konstantynovskyi

Appl. Sci. 2025, 15(12), 6928; https://doi.org/10.3390/app15126928 - 19 Jun 2025

Viewed by 191

Abstract

The paper is devoted to the plasticization mechanisms of alkali-activated cement system Na₂O-CaO-Al₂O₃-SiO₂-H₂O. The fundamentals and basic factors determining the effectiveness of plasticizing surfactants for alkali-activated cement materials are discussed. The factors under [...] Read more.

The paper is devoted to the plasticization mechanisms of alkali-activated cement system Na₂O-CaO-Al₂O₃-SiO₂-H₂O. The fundamentals and basic factors determining the effectiveness of plasticizing surfactants for alkali-activated cement materials are discussed. The factors under consideration in the study were alkali-activated cement basicity (the content of granulated blast furnace slag), the anion of the alkaline component or activator, and the degree of dispersing of the cement particles in the system. The action effect of plasticizers was determined by finding the interrelation between the stability of its molecular structure, degree of adsorption, and molecular weight depending on mentioned basic factors. A systematic approach to the systematization of surfactants and their choice to be taken into consideration to control technology-related and physico-mechanical properties of alkali-activated cement-based heavyweight concretes, building mortars, and lightened grouts has been proposed. Full article

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17 pages, 1201 KiB

Open AccessArticle

Valorization of Spent Osmotic Solutions by Production of Powders by Spray Drying

by Katarzyna Samborska, Alicja Barańska-Dołomisiewicz, Aleksandra Jedlińska, Rui Costa, Konstantinos Klimantakis, Ioannis Mourtzinos and Małgorzata Nowacka

Appl. Sci. 2025, 15(12), 6927; https://doi.org/10.3390/app15126927 - 19 Jun 2025

Viewed by 217

Abstract

Spent osmotic solutions (sucrose, buckwheat honey, acacia honey, apple juice concentrate, chokeberry juice concentrate, cherry juice concentrate, and mannitol) were tested for their valorization to produce powders by spray drying. Simultaneously, the application of inulin as an alternative carrier was verified. The drying [...] Read more.

Spent osmotic solutions (sucrose, buckwheat honey, acacia honey, apple juice concentrate, chokeberry juice concentrate, cherry juice concentrate, and mannitol) were tested for their valorization to produce powders by spray drying. Simultaneously, the application of inulin as an alternative carrier was verified. The drying yield varied from 6 to 92%. For acacia honey, apple juice concentrate, chokeberry juice concentrate, and cherry juice concentrate, high stickiness was observed, which resulted in low yield and the production of significantly bigger particles of regular size distribution, higher hygroscopicity and bulk density, and better flowability. Sucrose, acacia honey, and mannitol were dried with lower stickiness, and the physical properties of the powders were acceptable. However, the yield of mannitol drying was low due to very small particles, low bulk density, and low cyclone efficiency. Therefore, sucrose and buckwheat honey solutions can be successfully spray dried using inulin as a carrier to produce powders suitable for further food applications. However, for the other tested materials, alternative carriers should be considered to reduce stickiness during drying. Full article

(This article belongs to the Special Issue Advances in Drying Technologies for Food Processing)

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29 pages, 13225 KiB

Open AccessReview

Tuneable Lenses Driven by Dielectric Elastomers: Principles, Structures, Applications, and Challenges

by Zhuoqun Hu, Meng Zhang, Zihao Gan, Jianming Lv, Zhuoyu Lin and Huajie Hong

Appl. Sci. 2025, 15(12), 6926; https://doi.org/10.3390/app15126926 - 19 Jun 2025

Viewed by 197

Abstract

As the core element of adaptive optical systems, tuneable lenses are essential in adaptive optics. Dielectric elastomer-driven tuneable lenses offer significant advantages in tuning range, response speed, and lightweight design compared to traditional mechanical zoom lenses. This paper systematically reviews the working mechanisms [...] Read more.

As the core element of adaptive optical systems, tuneable lenses are essential in adaptive optics. Dielectric elastomer-driven tuneable lenses offer significant advantages in tuning range, response speed, and lightweight design compared to traditional mechanical zoom lenses. This paper systematically reviews the working mechanisms and research advancements of these lenses. Firstly, based on the two driving modes of deformation zoom and displacement zoom, the tuning principle of dielectric elastomer-driven tuneable lenses is analysed in depth. Secondly, the design methodology and current status of the research are systematically elaborated for four typical structures: monolithic, composite, array, and metalenses. Finally, the potential applications of this technology are discussed in the fields of auto-zoom imaging, microscopic imaging, augmented reality display, and infrared imaging, along with an analysis of the key technological challenges faced by this technology, such as material properties, modelling and control, preparation processes, and optical performance. This paper aims to provide a systematic reference for researchers in this field and to help promote the engineering application of dielectric elastomer tuneable lens technology. Full article

(This article belongs to the Section Optics and Lasers)

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16 pages, 4436 KiB

Open AccessArticle

Analysis of the Causes of Excessive Noise and Vibrations of Live Steam Pipelines

by Damian Pietrusiak, Jerzy Czmochowski, Artur Górski, Artur Iluk, Przemysław Moczko and Michał Paduchowicz

Appl. Sci. 2025, 15(12), 6925; https://doi.org/10.3390/app15126925 - 19 Jun 2025

Viewed by 209

Abstract

The article discusses the causes of excessive noise and vibrations of a live steam pipeline in a power unit. A scanning laser vibrometer was used to measure the vibrations of the live steam pipeline for two power units. Additionally, the sound (noise) level [...] Read more.

The article discusses the causes of excessive noise and vibrations of a live steam pipeline in a power unit. A scanning laser vibrometer was used to measure the vibrations of the live steam pipeline for two power units. Additionally, the sound (noise) level of the live steam pipeline was measured with an acoustic camera. A discrete model of the pipeline was created, and FEM modal analysis was performed. Based on experimental tests and numerical simulations, the sources of noise were identified. The final conclusions propose methods of eliminating the harmful noise. Full article

(This article belongs to the Special Issue Advanced Vibro-Acoustic Technology: Intelligent Algorithms, Smart Materials and Dynamics)

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15 pages, 11069 KiB

Open AccessArticle

Implementation of a Non-Intrusive Primal–Dual Method with 2D-3D-Coupled Models for the Analysis of a DCB Test with Cohesive Zones

by Ricardo Hernández, Jorge Hinojosa, Ignacio Fuenzalida-Henríquez and Víctor Tuninetti

Appl. Sci. 2025, 15(12), 6924; https://doi.org/10.3390/app15126924 - 19 Jun 2025

Viewed by 211

Abstract

This study explores a global–local non-intrusive computational strategy to address problems in computational mechanics, specifically applied to a double cantilever beam (DCB) with cohesive interfaces. The method aims to reduce computational requirements while maintaining accuracy. The DCB, representing two plates connected by a [...] Read more.

This study explores a global–local non-intrusive computational strategy to address problems in computational mechanics, specifically applied to a double cantilever beam (DCB) with cohesive interfaces. The method aims to reduce computational requirements while maintaining accuracy. The DCB, representing two plates connected by a cohesive zone simulating delamination, was modeled with a 3D representation using the cohesive zone method for crack propagation. Different mesh configurations were tested to evaluate the strategy’s effectiveness. The results showed that the global–local strategy successfully provided solutions that were comparable to monolithic models. Mesh size had a significant impact on the results, but even with a simplified local model that did not fully represent the plate thickness, the structural deformation and crack displacement were accurately captured. The interface near the study area influenced the stress distribution. Although effective, the strategy requires careful mesh selection due to its sensitivity to mesh size. Future research could optimize mesh configurations, expand the strategy to other structures, and explore the use of orthotropic materials. This research introduces a computational approach that reduces costs while simulating delamination and crack propagation, highlighting the importance of mesh configuration for real-world applications. Full article

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22 pages, 4478 KiB

Open AccessArticle

Welding Image Data Augmentation Method Based on LRGAN Model

by Ying Wang, Zhe Dai, Qiang Zhang and Zihao Han

Appl. Sci. 2025, 15(12), 6923; https://doi.org/10.3390/app15126923 - 19 Jun 2025

Viewed by 232

Abstract

This study focuses on the data bottleneck issue in the training of deep learning models during the intelligent welding control process and proposes an improved model called LRGAN (loss reconstruction generative adversarial networks). First, a five-layer spectral normalization neural network was designed as [...] Read more.

This study focuses on the data bottleneck issue in the training of deep learning models during the intelligent welding control process and proposes an improved model called LRGAN (loss reconstruction generative adversarial networks). First, a five-layer spectral normalization neural network was designed as the discriminator of the model. By incorporating the least squares loss function, the gradients of the model parameters were constrained within a reasonable range, which not only accelerated the convergence process but also effectively limited drastic changes in model parameters, alleviating the vanishing gradient problem. Next, a nine-layer residual structure was introduced in the generator to optimize the training of deep networks, preventing the mode collapse issue caused by the increase in the number of layers. The final experimental results show that the proposed LRGAN model outperforms other generative models in terms of evaluation metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Fréchet inception distance (FID). It provides an effective solution to the small sample problem in the intelligent welding control process. Full article

(This article belongs to the Section Robotics and Automation)

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16 pages, 1761 KiB

Open AccessArticle

Biogas from Food Waste on the Island of Tenerife: Potential from Kitchens and Restaurants, Stabilisation and Conversion in a Biogas Plant Made of Textile Materials

by Verónica Hidalgo-Sánchez, María Emma Borges, Josef Hofmann, Daniel Cuñarro, Sophie Schneider and Tobias Finsterwalder

Appl. Sci. 2025, 15(12), 6922; https://doi.org/10.3390/app15126922 - 19 Jun 2025

Viewed by 222

Abstract

Municipal solid waste management (MSWM) on islands involves several challenges relating to politics, society, the environment, and technology. This paper addresses the potential for producing biogas and biomethane from food waste on Tenerife, including waste from households, with the aim of reducing landfill [...] Read more.

Municipal solid waste management (MSWM) on islands involves several challenges relating to politics, society, the environment, and technology. This paper addresses the potential for producing biogas and biomethane from food waste on Tenerife, including waste from households, with the aim of reducing landfill and primary fossil energy consumption. The study also introduces the European and Regional policy framework and requirements. Effective microorganisms have been studied as proposals to stabilise the food waste from households, avoiding odours and decomposition during storage. The trials show positive results in terms of the preservation of organic matter until the food waste is transported to the biogas plant. In addition, a new concept for a small biogas plant made of textile materials, which are suited to the municipalities of Tenerife, is presented to provide an easy-to-build solution, with ranges of up to 75 kW in electrical power. With a theoretical potential of 299,012 tons of food waste being available per year (based on 2022), preliminary laboratory experiments with real samples of the island showed a theoretical potential of 28.97 × 10⁶ Nm³ for biogas and 264,612 tons for digestate, which can be used as fertilisers, with potential savings of 18.15 × 10⁶ L of gasoline and 42.66 × 10³ equivalent CO₂ tons. Full article

(This article belongs to the Special Issue Editorial Board Members' Collection Series: Remove of Pollutants for Green and Healthy Environment)

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20 pages, 453 KiB

Open AccessReview

Harnessing Biotechnology for the Remediation of Organic Pollutants in Coastal Marine Ecosystems

by Adenike A. Akinsemolu and Helen N. Onyeaka

Appl. Sci. 2025, 15(12), 6921; https://doi.org/10.3390/app15126921 - 19 Jun 2025

Viewed by 238

Abstract

The natural and biological processes of organisms offer significant potential for the removal and remediation of environmental contaminants including organic pollutants such as persistent organic pollutants (POPs) like polychlorinated biphenyls (PCBs), pesticides, herbicides, industrial chemicals, and pharmaceuticals. Biotechnology provides various approaches to detoxify [...] Read more.

The natural and biological processes of organisms offer significant potential for the removal and remediation of environmental contaminants including organic pollutants such as persistent organic pollutants (POPs) like polychlorinated biphenyls (PCBs), pesticides, herbicides, industrial chemicals, and pharmaceuticals. Biotechnology provides various approaches to detoxify or remove these pollutants from ecosystems through the use of microorganisms and plants. This review explores the application of biotechnology for the remediation of organic pollutants in coastal marine ecosystems. A thorough analysis of the existing literature highlights bioremediation methods, such as biostimulation, bioaugmentation, and bioattenuation, and phytoremediation methods, like phytoextraction, phytostabilization, phytovolatilization, phytodegradaton, and phytofiltration. as the most widely used techniques in biotechnology. While bioremediation has advanced substantially in fields such as electrochemistry, genetic engineering, and nanotechnology, there is still limited research on the compatibility and application of these technologies in phytoremediation. This paper therefore aims to examine biotechnological methods for tackling organic pollutants in coastal marine environments with an emphasis on the need for further research on enhancing phytoremediation through microbial inoculation and nanomaterial-assisted uptake. Full article

(This article belongs to the Section Applied Biosciences and Bioengineering)

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15 pages, 4753 KiB

Open AccessArticle

Continuous Electrical Resistivity Tomography Monitoring in Waste Landfill Sites with Different Properties and Visualization of Water Channels

by Yugo Isobe and Hiroyuki Ishimori

Appl. Sci. 2025, 15(12), 6920; https://doi.org/10.3390/app15126920 - 19 Jun 2025

Viewed by 270

Abstract

This study aims to obtain findings on the internal water behavior, the presence of water channels, and the degree of washout due to rainfall infiltration in Japanese municipal solid waste (MSW) final disposal sites. Electrical resistivity tomography (ERT) monitoring and undistributed waste sampling [...] Read more.

This study aims to obtain findings on the internal water behavior, the presence of water channels, and the degree of washout due to rainfall infiltration in Japanese municipal solid waste (MSW) final disposal sites. Electrical resistivity tomography (ERT) monitoring and undistributed waste sampling for X-ray computed tomography (X-ray CT) analysis were conducted in the field. The study sites were targeted at Site A, which is mainly composed of non-combustible residues, and Site B, which is mainly composed of incineration ash. The time-dependent resistivity distributions obtained from real-time ERT monitoring were effective for us to understand the water content distribution after water infiltration during water injection tests. As a result, the global flow behavior and the local water channel flow were determined. In addition, X-ray CT analysis of the undisturbed waste samples obtained from the sites clarified the different pore structures, indicating the possibility of more advanced washing out at Site A than at Site B. Furthermore, the soil cover layer and gas extraction wells had a significant effect on the resistivity structure with respect to water flow behavior. Since soil cover layer and gas extraction wells are significant factors affecting waste stabilization by washout, it is suggested that these factors should be considered in the design and maintenance of landfills. Full article

(This article belongs to the Special Issue Advanced Technologies in Landfills)

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Appl. Sci., Volume 15, Issue 12 (June-2 2025) – 572 articles

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