Applied Sciences

26 pages, 1068 KB

Open AccessReview

Very First Application of Compact Benchtop NMR Spectrometers to Complex Biofluid Analysis and Metabolite Tracking for Future Metabolomics Studies: A Retrospective Decennial Report from November 2014

by Martin Grootveld, Victor Ruiz-Rodado, Anna Gerdova and Mark Edgar

Appl. Sci. 2025, 15(17), 9675; https://doi.org/10.3390/app15179675 (registering DOI) - 2 Sep 2025

Abstract

Herein we report the very first experiments which were conducted in an attempt to demonstrate the ability of low-field (LF), compact benchtop NMR spectrometers to provide spectral profiles of whole human biofluids, which took place in September–November 2014, and this paper represents a [...] Read more.

Herein we report the very first experiments which were conducted in an attempt to demonstrate the ability of low-field (LF), compact benchtop NMR spectrometers to provide spectral profiles of whole human biofluids, which took place in September–November 2014, and this paper represents a 10-year (decennial) anniversary of this work. LF ¹H NMR analysis was performed on ²H₂O-reconstituted lyophilizates of urine samples (pH 7.00) collected from untreated Niemann-Pick type C1 (NPC1) disease patients and their heterozygous carrier controls (n = 3 in each case). ¹H NMR spectra were acquired on a 60 MHz Oxford Instruments Pulsar compact benchtop spectrometer with spectral filter widths of 5000 Hz, using 1000–1600 scans, and relaxation delays of 15 or 30 s. Further, 400 MHz spectra were also obtained on these samples. Following parameter optimisation, the benchtop system generated reasonable quality urinary ¹H NMR profiles containing ca. 30 signals. Benchtop ¹H NMR analysis confirmed the abnormal urinary metabolic signature of NPC1 disease, and also revealed a gastric permeability disorder in one patient (detection of upregulated urinary sucrose, verified by 400 MHz NMR analysis). Early LF NMR experiments also demonstrated that glucose was trackable in control urine samples pre-spiked with this metabolite. This paper continues with further developments made on LF NMR-based metabolomics technologies, which are systematically discussed for related investigations conducted since 2014. In conclusion, such ‘first-time’ bioanalytical information regarding spectral quality served to pave the way forward for benchtop NMR-based metabolomics investigations of biofluids, which could provide invaluable disease-engendered ‘snapshots’ of disturbances to metabolic pathways and activities, along with those of any co-linked or unlinked comorbidities. Full article

(This article belongs to the Section Applied Physics General)

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24 pages, 7877 KB

Open AccessArticle

Comparative Study of Force and Deformation Characteristics of Closed Cavity Thin-Walled Components in Prefabricated Metro Station

by Dechun Cao and Desen Kong

Appl. Sci. 2025, 15(17), 9674; https://doi.org/10.3390/app15179674 (registering DOI) - 2 Sep 2025

Abstract

The increased use of prefabricated assembly technology promotes the transformation of urban subway construction in the lightweight direction, in which the closed cavity thin-walled component is increasingly widely used in underground structures due to its excellent material efficiency benefits. In order to investigate [...] Read more.

The increased use of prefabricated assembly technology promotes the transformation of urban subway construction in the lightweight direction, in which the closed cavity thin-walled component is increasingly widely used in underground structures due to its excellent material efficiency benefits. In order to investigate the effect of closed cavity thin-walled components, numerical models of a seven-ring solid structure and cavity structure were constructed based on the four-block prefabricated metro station of Qingdao Metro Line 9, Chengzi Station. This study considers the longitudinal effect between rings and compares the nonlinear force and deformation characteristics of both structures under the load of self-weight and use stage. The study indicates that incorporating closed cavities within structures reduces internal forces in most sections while increasing principal strain, displacement, and stress. As the applied load increases, the rate of internal force reduction diminishes, and the increment of displacement deformation also decreases. Shear lag effects occur in closed cavity sections, leading to a non-uniform normal stress distribution, with maximum shear stress appearing at rib intersections. The cavity location, mortise–tenon joints, and columns represent critical locations for deformation and force transmission within cavity structures. Optimization design must prioritize ensuring their deformation resistance and load-bearing capacity to enhance the overall structural integrity, safety, and reliability. Full article

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17 pages, 662 KB

Open AccessReview

Where You Place, How You Load: A Scoping Review of the Determinants of Orthodontic Mini-Implant Success

by Jacob Daniel Gardner, Ambrose Ha, Samantha Lee, Amir Mohajeri, Connor Schwartz and Man Hung

Appl. Sci. 2025, 15(17), 9673; https://doi.org/10.3390/app15179673 (registering DOI) - 2 Sep 2025

Abstract

Objective: This scoping review identifies and analyzes factors influencing the effectiveness of orthodontic mini-implants and temporary anchorage devices in orthodontic treatments, including clinical applications, success rates, and associated complications. Methods: A systematic search was conducted across EBSCOhost, Ovid Medline, PubMed, Scopus, and Web [...] Read more.

Objective: This scoping review identifies and analyzes factors influencing the effectiveness of orthodontic mini-implants and temporary anchorage devices in orthodontic treatments, including clinical applications, success rates, and associated complications. Methods: A systematic search was conducted across EBSCOhost, Ovid Medline, PubMed, Scopus, and Web of Science for peer-reviewed, English-language human studies published between 2013 and 2023 that examined determinants of mini-implants/temporary anchorage devices success or failure. Inclusion/exclusion criteria were predefined, and screening was performed in duplicate. Thirty-six studies met criteria. Results: Placement site and peri-implant oral hygiene/soft-tissue health were the most consistent contributors to success. Optimal sites varied by indication, supporting individualized planning. Greater implant length generally improved stability but must be balanced against anatomic limits and patient comfort. Temporary anchorage devices supported diverse movements (e.g., molar distalization; posterior/anterior intrusion). Findings for loading protocol, patient age, bone quality, and operator experience were mixed, reflecting heterogeneity in primary stability, force magnitude/vector, and outcome definitions. Conclusion: Mini-implants/temporary anchorage devices success is multifactorial. Emphasis on site-specific selection, hygiene management, appropriate implant dimensions, and patient-specific modifiers can optimize outcomes and minimize complications. Future studies should report standardized outcomes and explicit loading parameters to enable granular analyses of movement-specific biomechanics and evidence-based decision-making. Full article

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30 pages, 6568 KB

Open AccessArticle

Hybrid Hourly Solar Energy Forecasting Using BiLSTM Networks with Attention Mechanism, General Type-2 Fuzzy Logic Approach: A Comparative Study of Seasonal Variability in Lithuania

by Naiyer Mohammadi Lanbaran, Darius Naujokaitis, Gediminas Kairaitis and Virginijus Radziukynas

Appl. Sci. 2025, 15(17), 9672; https://doi.org/10.3390/app15179672 (registering DOI) - 2 Sep 2025

Abstract

This research introduces a novel hybrid forecasting framework for solar energy prediction in high-latitude regions with extreme seasonal variations. This approach uniquely employs General Type-2 Fuzzy Logic (GT2-FL) for data preprocessing and uncertainty handling, followed by two advanced neural architectures, including BiLSTM and [...] Read more.

This research introduces a novel hybrid forecasting framework for solar energy prediction in high-latitude regions with extreme seasonal variations. This approach uniquely employs General Type-2 Fuzzy Logic (GT2-FL) for data preprocessing and uncertainty handling, followed by two advanced neural architectures, including BiLSTM and SCINet with Time2Vec encoding and Variational Mode Decomposition (VMD) signal processing. Four configurations are systematically evaluated: BiLSTM-Time2Vec, BiLSTM-VMD, SCINet-Time2Vec, and SCINet-VMD, each tested with GT2-FL preprocessed data and raw input data. Using meteorological data from Lithuania (2023–2024) with extreme seasonal variations where daylight hours range from 17 h in summer to 7 h in winter, F-BiLSTM-Time2Vec achieved exceptional performance, with nRMSE = 1.188%, NMAE = 0.813%, and WMAE = 3.013%, significantly outperforming both VMD-based variants and SCINet architectures. Comparative analysis revealed that Time2Vec encoding proved more beneficial than VMD preprocessing, especially when enhanced with fuzzification. The results confirm that fuzzification, BiLSTM architecture, and Time2Vec encoding provide the most robust forecasting capability under various seasonal conditions. Full article

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34 pages, 2542 KB

Open AccessArticle

Uncertainty-Based Design Optimization Framework Based on Improved Chicken Swarm Algorithm and Bayesian Optimization Neural Network

by Qiang Ji, Ran Li and Shi Jing

Appl. Sci. 2025, 15(17), 9671; https://doi.org/10.3390/app15179671 (registering DOI) - 2 Sep 2025

Abstract

As the complexity and functional integration of mechanism systems continue to increase in modern practical engineering, the challenges of changing environmental conditions and extreme working conditions are becoming increasingly severe. Traditional uncertainty-based design optimization (UBDO) has exposed problems of low efficiency and slow [...] Read more.

As the complexity and functional integration of mechanism systems continue to increase in modern practical engineering, the challenges of changing environmental conditions and extreme working conditions are becoming increasingly severe. Traditional uncertainty-based design optimization (UBDO) has exposed problems of low efficiency and slow convergence when dealing with nonlinear, high-dimensional, and strongly coupled problems. In response to these issues, this paper proposes an UBDO framework that integrates an efficient intelligent optimization algorithm with an excellent surrogate model. By fusing butterfly search with Levy flight optimization, an improved chicken swarm algorithm is introduced, aiming to address the imbalance between global exploitation and local exploration capabilities in the original algorithm. Additionally, Bayesian optimization is employed to fit the limit-state evaluation function using a BP neural network, with the objective of reducing the high computational costs associated with uncertainty analysis through repeated limit-state evaluations in uncertainty-based optimization. Finally, a decoupled optimization framework is adopted to integrate uncertainty analysis with design optimization, enhancing global optimization capabilities under uncertainty and addressing challenges associated with results that lack sufficient accuracy or reliability to meet design requirements. Based on the results from engineering case studies, the proposed UBDO framework demonstrates notable effectiveness and superiority. Full article

(This article belongs to the Special Issue Data-Enhanced Engineering Structural Integrity Assessment and Design)

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20 pages, 634 KB

Open AccessReview

Radar Technologies in Motion-Adaptive Cancer Radiotherapy

by Matteo Pepa, Giulia Sellaro, Ganesh Marchesi, Anita Caracciolo, Arianna Serra, Ester Orlandi, Guido Baroni and Andrea Pella

Appl. Sci. 2025, 15(17), 9670; https://doi.org/10.3390/app15179670 (registering DOI) - 2 Sep 2025

Abstract

Intra-fractional respiratory management represents one of the greatest challenges of modern cancer radiotherapy (RT), as significant breathing-induced lesion motion might affect target coverage and organs at risk (OARs) sparing, jeopardizing oncological and toxicity outcomes. The detrimental effects on dosage of uncompensated organ motion [...] Read more.

Intra-fractional respiratory management represents one of the greatest challenges of modern cancer radiotherapy (RT), as significant breathing-induced lesion motion might affect target coverage and organs at risk (OARs) sparing, jeopardizing oncological and toxicity outcomes. The detrimental effects on dosage of uncompensated organ motion are exacerbated in RT with charged particles (e.g., protons and carbon ions), due to their higher ballistic selectivity. The simplest strategies to counteract this phenomenon are the use of larger treatment margins and reductions in or control of respiration (e.g., by means of compression belts, breath hold). Gating and tracking, which synchronize beam delivery with the respiratory signal, also represent widely adopted solutions. When tracking the tumor itself or surrogates, invasive procedures (e.g., marker implantation), an unnecessary imaging dose (e.g., in X-ray-based fluoroscopy), or expensive equipment (e.g., magnetic resonance imaging, MRI) is usually required. When chest and abdomen excursions are measured to infer internal tumor displacement, the additional devices needed to perform this task, such as pressure sensors or surface cameras, present inherent limitations that can impair the procedure itself. In this context, radars have intrigued the radiation oncology community, being inexpensive, non-invasive, contactless, and insensitive to obstacles. Even if real-world clinical implementation is still lagging behind, there is a growing body of research unraveling the potential of these devices in this field. The purpose of this narrative review is to provide an overview of the studies that have delved into the potential of radar-based technologies for motion-adaptive photon and particle RT applications. Full article

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26 pages, 1249 KB

Open AccessArticle

Antibacterial and Anticancer Properties of Geraniol in the Context of Clinical Applications

by Anna Fajdek-Bieda, Joanna Pawlińska, Agnieszka Wróblewska, Wojciech Żwierełło, Agnieszka Łuś and Alicja Michalska

Appl. Sci. 2025, 15(17), 9669; https://doi.org/10.3390/app15179669 (registering DOI) - 2 Sep 2025

Abstract

Geraniol (GA) is a terpene compound of natural origin that exhibits strong biological activity. The possibility of using GA as a potential compound with antimicrobial activity is currently of great interest to scientists. The aim of the present study was to comprehensively evaluate [...] Read more.

Geraniol (GA) is a terpene compound of natural origin that exhibits strong biological activity. The possibility of using GA as a potential compound with antimicrobial activity is currently of great interest to scientists. The aim of the present study was to comprehensively evaluate the activity of GA against selected strains of Gram-positive bacteria, Gram-negative bacteria, and fungi that pose a significant threat in clinical practice. Among the Gram-positive bacteria studied were Streptococcus spp., Neisseria gonorrhoeae, and Listeria monocytogenes. Among the Gram-negative bacteria tested were Escherichia coli, Pseudomonas aeruginosa, and Klebsiella pneumoniae. The fungal pathogens analyzed included Candida albicans and Candida glabrata. The results showed that GA exhibited strong antimicrobial activity against most of the microorganisms tested. Gram-positive strains were more susceptible to GA compared to Gram-negative strains, probably due to differences in cell wall structure. In the case of fungi, significant efficacy was noted against Candida albicans. This study confirms the potential of GA as an alternative antimicrobial agent, especially against antibiotic-resistant bacterial strains and fungal pathogens. These results open up new perspectives for the application of GA in medicine and the pharmaceutical industry. The study on creams demonstrated that GA possesses strong antimicrobial properties, effectively inhibiting bacterial growth regardless of the concentration used (0.5–12%) and the type of culture medium, confirming its potential as a natural preservative agent in the cosmetic and pharmaceutical industries. Moreover, the research on the anticancer activity of GA revealed its cytotoxic effects against colon cancer cells (LoVo cell line, IC₅₀ = 32.1 μg/mL) and glioma cells (U87 cell line, IC₅₀ = 41.3 μg/mL), particularly at higher concentrations, indicating its promising therapeutic potential. Full article

28 pages, 1361 KB

Open AccessArticle

A Nash Equilibrium-Based Strategy for Optimal DG and EVCS Placement and Sizing in Radial Distribution Networks

by Degu Bibiso Biramo, Ashenafi Tesfaye Tantu, Kuo Lung Lian and Cheng-Chien Kuo

Appl. Sci. 2025, 15(17), 9668; https://doi.org/10.3390/app15179668 (registering DOI) - 2 Sep 2025

Abstract

Distribution System Operators (DSOs) increasingly need planning tools that coordinate utility-influenced assets—such as electric-vehicle charging stations (EVCS) and voltage-support resources—with customer-sited distributed generation (DG). We present a Nash-equilibrium-based Iterative Best Response Algorithm (IBRA-NE) for joint planning of DG and EVCS in radial distribution [...] Read more.

Distribution System Operators (DSOs) increasingly need planning tools that coordinate utility-influenced assets—such as electric-vehicle charging stations (EVCS) and voltage-support resources—with customer-sited distributed generation (DG). We present a Nash-equilibrium-based Iterative Best Response Algorithm (IBRA-NE) for joint planning of DG and EVCS in radial distribution networks. The framework supports two applicability modes: (i) a DSO-plannable mode that co-optimizes EVCS siting/sizing and utility-controlled reactive support (DG operated as VAR resources or functionally equivalent devices), and (ii) a customer-sited mode that treats DG locations as fixed while optimizing DG reactive set-points/sizes and EVCS siting. The objective minimizes network losses and voltage deviation while incorporating deployment costs and EV charging service penalties, subject to standard operating limits. A backward/forward sweep (BFS) load flow with Monte Carlo simulation (MCS) captures load and generation uncertainty; a Bus Voltage Deviation Index (BVDI) helps identify weak buses. On the EEU 114-bus system, the method reduces base-case losses by up to 57.9% and improves minimum bus voltage from 0.757 p.u. to 0.931 p.u.; performance remains robust under a 20% load increase. The framework explicitly accommodates regulatory contexts where DG siting is customer-driven by treating DG locations as fixed in such cases while optimizing EVCS siting and sizing under DSO planning authority. A mixed scenario with 5 DGs and 3 EVCS demonstrates coordinated benefits and convergence properties relative to PSO, GWO, RFO, and ARFO. Additionally, the proposed algorithm is also tested on the IEEE 69-bus system and results in acceptable performance. The results indicate that game-theoretic coordination, applied in a manner consistent with regulatory roles, provides a practical pathway for DSOs to plan EV infrastructure and reactive support in networks with uncertain DER behavior. Full article

17 pages, 4644 KB

Open AccessArticle

Characterization of Core Microbiomes of Olive Tree Rhizospheres Under Drought Stress Conditions

by Andrea Visca, Lorenzo Nolfi, Luciana Di Gregorio, Manuela Costanzo, Elisa Clagnan, Filippo Sevi, Federico Sbarra, Roberta Bernini, Maria Cristina Valeri, Edoardo Franco, Ornella Calderini, Luciana Baldoni, Gaetano Perrotta and Annamaria Bevivino

Appl. Sci. 2025, 15(17), 9667; https://doi.org/10.3390/app15179667 (registering DOI) - 2 Sep 2025

Abstract

Drought stress poses a significant threat to olive cultivation in Mediterranean regions. This study investigated the resilience and functional adaptation of root-associated and rhizosphere soil microorganisms of four olive cultivars under contrasting water regimes (irrigated vs. drought) across seasons. Using a combination of [...] Read more.

Drought stress poses a significant threat to olive cultivation in Mediterranean regions. This study investigated the resilience and functional adaptation of root-associated and rhizosphere soil microorganisms of four olive cultivars under contrasting water regimes (irrigated vs. drought) across seasons. Using a combination of amplicon-targeted metagenomics, phylogenetic analysis, and text mining of the scientific literature, we identified a conserved core microbiome and revealed that drought stress significantly alters the structure of root-associated—but not rhizosphere soil—bacterial communities. Potential functional profiling indicated that drought conditions enriched for genes involved in stress response pathways, including branched-chain amino acid transport, glutathione S-transferase activity, thioredoxin reductase, and chemotaxis. Text mining co-occurrence networks highlighted strong associations between some key bacterial genera and plant growth-promoting functions like phytohormone production and biocontrol. Furthermore, we identified Solirubrobacter, Microvirga, and Pseudonocardia as the primary contributors to these drought-resilience functions. The stability of the soil microbiome suggests functional redundancy, whereas the restructuring of the root endophytic compartment indicates active plant selection for beneficial microbes. Our findings provide a foundation for developing tailored microbial consortia (SynComs) to enhance drought tolerance in olive trees and support sustainable agriculture in water-limited environments. Full article

(This article belongs to the Special Issue Soil Analysis in Different Ecosystems)

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13 pages, 1576 KB

Open AccessArticle

Effects of Isometric Training on Ankle Mobility and Change-of-Direction Performance in Professional Basketball Players

by Luis Miguel Fernández-Galván, Rodrigo Fernández-Viñes and Jorge Sánchez-Infante

Appl. Sci. 2025, 15(17), 9666; https://doi.org/10.3390/app15179666 (registering DOI) - 2 Sep 2025

Abstract

Basketball requires high-intensity, multidirectional movements that place significant stress on the ankle joint. Limited dorsiflexion and reduced change-of-direction (COD) ability are associated with impaired movement efficiency and may contribute to injury mechanisms. Isometric training may help address these limitations in professional players. To [...] Read more.

Basketball requires high-intensity, multidirectional movements that place significant stress on the ankle joint. Limited dorsiflexion and reduced change-of-direction (COD) ability are associated with impaired movement efficiency and may contribute to injury mechanisms. Isometric training may help address these limitations in professional players. To assess the effects of a season-long isometric intervention program on ankle dorsiflexion and COD performance in professional basketball players. Fourteen professional players (mean age 25.6 ± 3.9 years) completed a season-long isometric intervention program (5 days/week), which included three force-steady sustained running postures and two gym-based exercises performed at 80% maximal voluntary contraction for 15–20 s per repetition (12 reps/set, 3 sets/session). Significant improvements were observed in both ankle dorsiflexion and COD performance. Dorsiflexion increased by 34.0% in the left leg and 19.4% in the right leg (Lunge Test). COD performance in the L-Test improved by 10.0% for the leftwards side and 11.6% for the rightward side from pre- to post-intervention. Isometric training improved ankle dorsiflexion and COD performance in professional basketball players, suggesting potential performance benefits and enhanced movement efficiency in multidirectional tasks. Full article

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27 pages, 5466 KB

Open AccessArticle

Experimental Study on Damage and Degradation Mechanism of Biotite Granulite Under Freeze–Thaw Action

by Bing Liang and Dong Xia

Appl. Sci. 2025, 15(17), 9665; https://doi.org/10.3390/app15179665 (registering DOI) - 2 Sep 2025

Abstract

With the increasing intensity of resource development in alpine regions, numerous geotechnical engineering problems in cold regions have become increasingly prominent. In order to explore the damage and deterioration laws of rocks caused by freeze–thaw action, this paper takes the biotite granulite on [...] Read more.

With the increasing intensity of resource development in alpine regions, numerous geotechnical engineering problems in cold regions have become increasingly prominent. In order to explore the damage and deterioration laws of rocks caused by freeze–thaw action, this paper takes the biotite granulite on the eastern slope of Yanshan Iron Mine as the research object. By analyzing the changes in mechanical and acoustic emission parameters of rock samples after freeze–thaw, and combining with existing freeze–thaw damage theories, the suitable freeze–thaw damage mechanism for this rock is further explored, and a freeze–thaw damage model for biotite granulite with low and high freeze–thaw cycles is established. The results of this study demonstrate that biotite granulite subjected to a lower number of freeze–thaw cycles exhibits significantly greater reductions in peak strength, elastic modulus, acoustic emission (AE) hit counts, cumulative ringing counts, and cumulative energy compared with specimens exposed to a higher number of cycles. As the freeze–thaw cycles increase, the formation of newly generated large-scale fractures during failure becomes progressively less pronounced, leading to a diminished resistance to deformation and a gradual increase in plastic deformation during loading. A coupled damage variable relationship was established for biotite granulite under both low and high freeze–thaw regimes based on cumulative AE ringing counts. In the early three stress stages, specimens subjected to fewer cycles exhibited fewer microcracks, with no clear spatial correlation between their distribution and the eventual fracture coalescence zones, whereas specimens exposed to a higher number of cycles showed a distinct sequential relationship between microcrack initiation sites and subsequent crack coalescence. Building upon existing freeze–thaw damage theories, the freeze–thaw damage mechanism specific to biotite granulite was further elucidated. Accordingly, a freeze–thaw damage model for low- and high-cycle conditions was developed and preliminarily validated. Full article

(This article belongs to the Special Issue Rock Mechanics and Mining Engineering)

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16 pages, 4079 KB

Open AccessArticle

A Lightweight YOLOv11n-Based Framework for Highway Pavement Distress Detection Under Occlusion Conditions

by Wei Li, Xiao Luo, Changhao Yang, Miao Fang and Weiyu Liu

Appl. Sci. 2025, 15(17), 9664; https://doi.org/10.3390/app15179664 (registering DOI) - 2 Sep 2025

Abstract

In response to the three main challenges in lightweight road pavement defect detection models—insufficient feature discriminability, weak environmental robustness, and low edge deployment efficiency—this paper proposes an innovative architecture, RS-YOLOv11n, based on YOLOv11n. Experimental results demonstrate significant improvements of RS-YOLOv11n over YOLOv11n on [...] Read more.

In response to the three main challenges in lightweight road pavement defect detection models—insufficient feature discriminability, weak environmental robustness, and low edge deployment efficiency—this paper proposes an innovative architecture, RS-YOLOv11n, based on YOLOv11n. Experimental results demonstrate significant improvements of RS-YOLOv11n over YOLOv11n on the RDD2022_Mix dataset: model parameters are reduced by 21.0%, computational complexity is decreased by 17.5%, mAP@0.5 is increased by 0.64%, and recall rate is improved by 1.03%. Firstly, a heterogeneous feature distillation backbone, RHGNetv2, is designed, incorporating RepConv reparameterized convolution to optimize computational efficiency. Secondly, a lightweight occlusion-aware module, SEAM, is introduced, significantly enhancing detection performance in occluded scenarios. RS-YOLOv11n provides a high-precision, low-resource, lightweight solution for intelligent road inspection. Full article

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26 pages, 740 KB

Open AccessArticle

Enhancement of the Generation Quality of Generative Linguistic Steganographic Texts by a Character-Based Diffusion Embedding Algorithm (CDEA)

by Yingquan Chen, Qianmu Li, Aniruddha Bhattacharjya, Xiaocong Wu, Huifeng Li, Qing Chang, Le Zhu and Yan Xiao

Appl. Sci. 2025, 15(17), 9663; https://doi.org/10.3390/app15179663 (registering DOI) - 2 Sep 2025

Abstract

Generative linguistic steganography aims to produce texts that remain both perceptually and statistically imperceptible. The existing embedding algorithms often suffer from imbalanced candidate selection, where high-probability words are overlooked and low-probability words dominate, leading to reduced coherence and fluency. We introduce a character-based [...] Read more.

Generative linguistic steganography aims to produce texts that remain both perceptually and statistically imperceptible. The existing embedding algorithms often suffer from imbalanced candidate selection, where high-probability words are overlooked and low-probability words dominate, leading to reduced coherence and fluency. We introduce a character-based diffusion embedding algorithm (CDEA) that uniquely leverages character-level statistics and a power-law-inspired grouping strategy to better balance candidate word selection. Unlike prior methods, the proposed CDEA explicitly prioritizes high-probability candidates, thereby improving both semantic consistency and text naturalness. When combined with XLNet, it effectively generates longer sensitive sequences while preserving quality. The experimental results showed that CDEA not only produces steganographic texts with higher imperceptibility and fluency but also achieves stronger resistance to steganalysis compared with the existing approaches. Future work will be to enhance statistical imperceptibility, integrate CDEA with larger language models such as GPT-5, and extend applications to cross-lingual, multimodal, and practical IoT or blockchain communication scenarios. Full article

(This article belongs to the Special Issue Cyber Security and Software Engineering)

70 pages, 942 KB

Open AccessSystematic Review

Truck Driver Safety: Factors Influencing Risky Behaviors on the Road—A Systematic Review

by Tiago Fonseca and Sara Ferreira

Appl. Sci. 2025, 15(17), 9662; https://doi.org/10.3390/app15179662 (registering DOI) - 2 Sep 2025

Abstract

Truck drivers play a pivotal role in global freight transport systems, yet their occupational and behavioral risk exposures make them a priority population in road safety research. This systematic review examines the factors influencing risky driving behaviors among truck drivers and their impacts [...] Read more.

Truck drivers play a pivotal role in global freight transport systems, yet their occupational and behavioral risk exposures make them a priority population in road safety research. This systematic review examines the factors influencing risky driving behaviors among truck drivers and their impacts on road safety outcomes. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, the review aimed to identify hazardous driving behaviors, the internal and external factors contributing to these behaviors, and their consequences for traffic safety. Inclusion criteria targeted original research published in English between 2009 and 2024 specifically focused on truck driver behavior and road safety outcomes. Systematic searches across PubMed, Scopus, Web of Science, and IEEE Xplore yielded 104 studies meeting these criteria. The synthesis revealed prevalent risky behaviors—such as speeding, fatigue-related impairments, distracted driving, and substance use—driven by internal factors (e.g., health conditions, psychological stress) and external pressures (e.g., occupational demands, regulatory constraints). These behaviors were consistently associated with increased crash risk. Nonetheless, limitations including the exclusion of non-English studies, reliance on self-reported data, and lack of standardized metrics constrained cross-study comparability and generalizability. Effective interventions identified include fatigue management programs, driver monitoring technologies, and positive safety climates. Findings underscore the urgent need for evidence-based, multifaceted strategies to enhance truck driver safety and inform policy, industry practices, and future research. Full article

(This article belongs to the Special Issue Emerging Technologies of Accident Analysis and Prevention in Safety Engineering)

35 pages, 15202 KB

Open AccessArticle

Constructive Modelling and Structural Analysis of the Church of Santos Juanes: An Approach Using Non-Destructive Techniques

by Jose Miguel Molines-Cano, Ana Almerich-Chulia, Jaime Llinares Millán and Jose-Luis Vivancos

Appl. Sci. 2025, 15(17), 9661; https://doi.org/10.3390/app15179661 (registering DOI) - 2 Sep 2025

Abstract

Historic masonry churches are highly vulnerable to structural degradation and seismic hazards due to their geometric complexity, material ageing, and lack of detailed construction records. The Church of Santos Juanes in Valencia, a monument of exceptional historical and architectural value, presents these challenges, [...] Read more.

Historic masonry churches are highly vulnerable to structural degradation and seismic hazards due to their geometric complexity, material ageing, and lack of detailed construction records. The Church of Santos Juanes in Valencia, a monument of exceptional historical and architectural value, presents these challenges, intensified by centuries of transformations and partial loss of documentation. In this study, we develop a comprehensive methodology that integrates historical research, non-destructive testing (3D laser scanning with Leica Geosystems Cyclone v9.1.1; infrared thermography, commercial software; ground-penetrating radar with gprMax 2016 and GPR-SLICE v7.MT), and advanced finite element modelling (Angle v1). The integrated survey data enabled the creation of an accurate 3D geometric model, the detection of hidden construction elements, and the characterisation of subsoil stratigraphy. Structural simulations under static and seismic loading—considering soil–structure interaction—revealed the high global stiffness of the complex, the influence of the Baroque vault on load distribution, and localised vulnerabilities, particularly in the San Juan ‘O’ façade, which coincide with existing cracks confirmed by thermography. This methodological framework not only advances the diagnosis and conservation of Santos Juanes but also provides a replicable model for assessing and safeguarding other heritage buildings with similar typological and structural challenges. Full article

(This article belongs to the Special Issue Heritage Buildings: Latest Advances and Prospects)

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22 pages, 3493 KB

Open AccessArticle

NeuroFed-LightTCN: Federated Lightweight Temporal Convolutional Networks for Privacy-Preserving Seizure Detection in EEG Data

by Zheng You Lim, Ying Han Pang, Shih Yin Ooi, Wee How Khoh and Yee Jian Chew

Appl. Sci. 2025, 15(17), 9660; https://doi.org/10.3390/app15179660 (registering DOI) - 2 Sep 2025

Abstract

This study investigates on-edge seizure detection that aims to resolve two major constraints that hold the deployment of deep learning models in clinical settings at present. First, centralized training requires gathering and consolidating data across institutions, which poses a serious issue of privacy. [...] Read more.

This study investigates on-edge seizure detection that aims to resolve two major constraints that hold the deployment of deep learning models in clinical settings at present. First, centralized training requires gathering and consolidating data across institutions, which poses a serious issue of privacy. Second, a high computational overhead inherent in inference imposes a crushing burden on resource-limited edge devices. Hence, we propose NeuroFed-LightTCN, a federated learning (FL) framework, incorporating a lightweight temporal convolutional network (TCN), designed for resource-efficient and privacy-preserving seizure detection. The proposed framework integrates depthwise separable convolutions, grouped with structured pruning to enhance efficiency, scalability, and performance. Furthermore, asynchronous aggregation is employed to mitigate training overhead. Empirical tests demonstrate that the network can be reduced fully to 70% with a 44.9% decrease in parameters (65.4 M down to 34.9 M and an inferencing latency of 56 ms) and still maintain 97.11% accuracy, a metric that outperforms both the non-FL and FL TCN optimizations. Ablation shows that asynchronous aggregation reduces training times by 3.6 to 18%, and pruning sustains performance even at extreme sparsity: an F1-score of 97.17% at a 70% pruning rate. Overall, the proposed NeuroFed-LightTCN addresses the trade-off between computational efficiency and model performance, delivering a viable solution to federated edge-device learning. Through the interaction of federated-optimization-driven approaches and lightweight architectural innovation, scalable and privacy-aware machine learning can be a practical reality, without compromising accuracy, and so its potential utility can be expanded to the real world. Full article

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

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16 pages, 715 KB

Open AccessSystematic Review

Artificial Intelligence in Computed Tomography Radiology: A Systematic Review on Risk Reduction Potential

by Sandra Coelho, Aléxia Fernandes, Marco Freitas and Ricardo J. Fernandes

Appl. Sci. 2025, 15(17), 9659; https://doi.org/10.3390/app15179659 (registering DOI) - 2 Sep 2025

Abstract

Artificial intelligence (AI) has emerged as a transformative technology in radiology, offering enhanced diagnostic accuracy, improved workflow efficiency and potential risk mitigation. However, its effectiveness in reducing clinical and occupational risks in radiology departments remains underexplored. This systematic review aimed to evaluate the [...] Read more.

Artificial intelligence (AI) has emerged as a transformative technology in radiology, offering enhanced diagnostic accuracy, improved workflow efficiency and potential risk mitigation. However, its effectiveness in reducing clinical and occupational risks in radiology departments remains underexplored. This systematic review aimed to evaluate the current literature on AI applications in computed tomography (CT) radiology and their contributions to risk reduction. Following the PRISMA 2020 guidelines, a systematic search was conducted in PubMed, Scopus and Web of Science for studies published between 2021 and 2025 (the databases were last accessed on 15 April 2025). Thirty-four studies were included based on their relevance to AI in radiology and reported outcomes. Extracted data included study type, geographic region, AI application and type, role in clinical workflow, use cases, sensitivity and specificity. The majority of studies addressed triage (61.8%) and computer-aided detection (32.4%). AI was most frequently applied in chest imaging (47.1%) and brain haemorrhage detection (29.4%). The mean reported sensitivity was 89.0% and specificity was 93.3%. AI tools demonstrated advantages in image interpretation, automated patient positioning, prioritisation and measurement standardisation. Reported benefits included reduced cognitive workload, improved triage efficiency, decreased manual annotation and shorter exposure times. AI systems in CT radiology show strong potential to enhance diagnostic consistency and reduce occupational risks. The evidence supports the integration of AI-based tools to assist diagnosis, lower human workload and improve overall safety in radiology departments. Full article

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23 pages, 1689 KB

Open AccessArticle

A Sequential Optimization Approach for the Vehicle and Crew Scheduling Problem of a Fleet of Electric Buses

by Katholiki Triommati, Dimitrios Rizopoulos, Marilena Merakou and Konstantinos Gkiotsalitis

Appl. Sci. 2025, 15(17), 9658; https://doi.org/10.3390/app15179658 (registering DOI) - 2 Sep 2025

Abstract

The growing adoption of electric buses in public transport has intensified the need for efficient scheduling algorithms. In the context of tactical planning, public transport operators must address two interdependent scheduling problems: the Single Depot Vehicle Scheduling Problem for Electric Buses (EB-SD-VSP) and [...] Read more.

The growing adoption of electric buses in public transport has intensified the need for efficient scheduling algorithms. In the context of tactical planning, public transport operators must address two interdependent scheduling problems: the Single Depot Vehicle Scheduling Problem for Electric Buses (EB-SD-VSP) and the Crew Scheduling Problem for Electric Buses (EB-CSP). This study introduces a sequential approach, solving EB-SD-VSP via a Mixed-Integer Quadratic Programming (MIQP) model, and then using its solution to generate service blocks for the EB-CSP, which is then solved as a Mixed-Integer Linear Programming (MILP) model. The proposed sequential optimization approach ultimately solves the combined problem of Vehicle and Crew Scheduling for a fleet of Electric Buses (EB-SD-VCSP). Experiments on real-world bus line data from Athens, Greece demonstrate practical applicability of the approach. When compared to a baseline scenario where the services are executed with conventional buses, the proposed method can calculate efficient vehicle timetables and crew schedules for operations with electric buses. The results highlight the benefit of decomposing joint electric bus and crew planning into tractable subproblems while preserving solution quality. These findings offer a scalable tactical-level planning tool for transit agencies transitioning to electric fleets and suggest promising directions for future extensions to multi-depot and real-time scenarios. Full article

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15 pages, 4067 KB

Open AccessArticle

The Use of Phase Change Materials for Thermal Management of Metal Hydride Reaction

by Ying Xu, Murray McCurdy and Mohammed Farid

Appl. Sci. 2025, 15(17), 9657; https://doi.org/10.3390/app15179657 (registering DOI) - 2 Sep 2025

Abstract

To meet the massive increase in energy demand, extensive research has been conducted over the past few decades on developing clean and sustainable energy storage methods. Hydrogen is considered as one of the most promising future energy carriers due to its high energy [...] Read more.

To meet the massive increase in energy demand, extensive research has been conducted over the past few decades on developing clean and sustainable energy storage methods. Hydrogen is considered as one of the most promising future energy carriers due to its high energy density and renewability, but it requires storage. Storing hydrogen using metal hydride offers several advantages, including stability, safety compactness and reversibility of the hydrogen absorption/desorption process. Thermal management during hydrogen storage using metal hydride is critically important since the reaction between the metal and hydrogen is highly exothermic. We are aiming to develop thermal storage systems based on composite phase change materials (CPCMs) that absorb the heat generated during hydrogen absorption and release it during desorption, in an effort to improve energy storage efficiency. Lightweight, shape-stable CPCMs are prepared by loading the selected organic phase change materials into expanded graphite and hydrophobic monolithic silica aerogel. The chemical structure, microstructure, thermal properties and leakage of CPCMs are investigated. These samples were subjected to variable power electrical heating to simulate the heat generated during hydrogen reaction, forming lanthanum hydride, according to its published reaction kinetics. Full article

(This article belongs to the Section Energy Science and Technology)

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