Applied Sciences

21 pages, 3731 KiB

Open AccessArticle

Extensive Iterative Finite Element Analysis of Molar Uprighting with the Introduction of a Novel Method for Estimating Clinical Treatment Time

by Lama A. AlKahlan, Naif A. Bindayel, Abdelhafid M. Mallek and Mohamed Z. Bendjaballah

Appl. Sci. 2025, 15(12), 6463; https://doi.org/10.3390/app15126463 (registering DOI) - 8 Jun 2025

Abstract

The aim of this study was to analyze the biomechanical effects of a conventional molar uprighting spring on a mesially tilted mandibular second molar through a novel iterative finite element (FE) approach in order to estimate clinical treatment duration. A new model was [...] Read more.

The aim of this study was to analyze the biomechanical effects of a conventional molar uprighting spring on a mesially tilted mandibular second molar through a novel iterative finite element (FE) approach in order to estimate clinical treatment duration. A new model was developed utilizing data from previous canine retraction experimental studies to aid in correlating each individual FE iteration with its real-time value. Another model consisting of a 30° mesially titled mandibular second molar and a conventional molar uprighting spring was developed to evaluate kinetic and kinematic responses. The iterative FE simulation of the treatment was then carried out. The molar uprighting simulation was completed in 180 iterations, which was equivalent to almost 12 weeks of clinical treatment time. The average stress-normalized bone remodeling velocity was found to be ~0.9 μm/(KPa·day). During the simulation, the spring initially produced a 15 N·mm (∼1530 g·mm) counterclockwise moment responsible for molar uprighting. The mandibular second molar showed 3.75 mm of distal movement, 2.15 mm of vertical extrusion, and a 22° counterclockwise rotation at the end of the treatment. This study provides a foundation for the future estimation of biomechanical responses as a function of treatment time in various orthodontic applications using iterative FE simulations. Full article

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

► Show Figures

Figure 1

16 pages, 1449 KiB

Open AccessArticle

Techno-Economic Analysis of an Air–Water Heat Pump Assisted by a Photovoltaic System for Rural Medical Centers: An Ecuadorian Case Study

by Daniel Icaza, Paul Arévalo and Francisco Jurado

Appl. Sci. 2025, 15(12), 6462; https://doi.org/10.3390/app15126462 (registering DOI) - 8 Jun 2025

Abstract

Air–water heat pumps are gaining interest in modern architectures, and they are a suitable option as a replacement for fossil fuel-based heating systems. These systems consume less electricity by combining solar panels, a heat pump, thermal storage, and a smart control system. This [...] Read more.

Air–water heat pumps are gaining interest in modern architectures, and they are a suitable option as a replacement for fossil fuel-based heating systems. These systems consume less electricity by combining solar panels, a heat pump, thermal storage, and a smart control system. This study was applied to a completely ecological rural health sub-center built on the basis of recycled bottles, and that, for its regular operation, requires an energy system according to the needs of the patients in the rural community. Detailed analyses were performed for heating and hot water preparation in two scenarios with different conditions (standard and fully integrated). From a technical perspective, different strategies were analyzed to ensure its functionality. If the photovoltaic system is sized to achieve advanced control, the system can even operate autonomously. However, due to the need to guarantee the energy efficiency of the center, the analyses were performed with a grid connection, and it was determined that the photovoltaic system guarantees at least two-thirds of the energy required for its autonomous operation. The results show that the system can operate normally thanks to the optimal size of the photovoltaic system, which positively influences the rural population in the case under analysis. Full article

► Show Figures

Figure 1

16 pages, 557 KiB

Open AccessReview

Healing Ability of Endodontic Filling Materials in Retrograde Treatment: A Systematic Review of Clinical Studies

by Tarek Ashi, Rim Bourgi, Carlos Enrique Cuevas-Suárez, Louis Hardan, Carmen Nahat, Zaher Altaqi, Naji Kharouf and Youssef Haikel

Appl. Sci. 2025, 15(12), 6461; https://doi.org/10.3390/app15126461 (registering DOI) - 8 Jun 2025

Abstract

The fundamental goal of endodontic surgery is to remove the infection cause and create an ideal environment for periapical tissue and bone recovery. This systematic review aims to present evidence-based findings regarding the healing ability of endodontic materials in retrograde treatment. The study [...] Read more.

The fundamental goal of endodontic surgery is to remove the infection cause and create an ideal environment for periapical tissue and bone recovery. This systematic review aims to present evidence-based findings regarding the healing ability of endodontic materials in retrograde treatment. The study evaluates the advantages and drawbacks of commonly utilized materials, empowering clinicians with valuable insights for preoperative planning in endodontic surgery. A comprehensive search was conducted across multiple databases, including MEDLINE, Scielo, Web of Science, Scopus, Embase, and Google Scholar, using the PIOT framework. A total of 3124 papers were identified, of which 2534 remained after removing duplicates. Following a stringent selection process, 35 clinical studies were included for qualitative assessment. The risk of bias was assessed using the Risk of Bias in Non-randomized Studies—of Interventions (ROBINS-I) tool for non-randomized trials, the Newcastle–Ottawa Scale for cohort studies, and the Joanna Briggs Institute (JBI) critical appraisal checklist for cross-sectional studies. Due to high heterogeneity in study designs and outcomes, a meta-analysis could not be performed. The review identified Super Ethoxybenzoic Acid (Super EBA), Mineral Trioxide Aggregate (MTA), and Intermediate Restorative Material (IRM^®), Retroplast, Endosequence^®, and gutta-percha as the primary retrograde root filling materials. Follow-up periods ranged from 6 months to 17.5 years. Although the materials showed varying degrees of success, the overall findings highlighted that no single material demonstrated universally superior healing ability. The review also emphasized the need for standardization in future clinical trials to facilitate better comparisons. The selection of retrograde filling materials plays a pivotal role in the success of endodontic surgery. New bioceramic materials like MTA and Biodentine offer improved sealing, biocompatibility, and tissue regeneration compared to traditional materials, leading to better clinical outcomes. Full article

(This article belongs to the Special Issue Dental Materials: Latest Advances and Prospects, Third Edition)

► Show Figures

Figure 1

26 pages, 8912 KiB

Open AccessArticle

Towards Geodetic Datum Modernization: A Comparative Study of GNSS Solutions in KGD2002 Using GAMIT/GLOBK and Bernese

by Seung-Jun Lee and Hong-Sik Yun

Appl. Sci. 2025, 15(12), 6460; https://doi.org/10.3390/app15126460 (registering DOI) - 8 Jun 2025

Abstract

This study evaluates coordinate consistency in the static Korean Geodetic Datum 2002 (KGD2002) by comparing GNSS station positions derived independently from GAMIT/GLOBK and Bernese software. Using a nationwide network of approximately 3000 unified geodetic control points (UGCPs), we analyze horizontal coordinate differences (ΔN, [...] Read more.

This study evaluates coordinate consistency in the static Korean Geodetic Datum 2002 (KGD2002) by comparing GNSS station positions derived independently from GAMIT/GLOBK and Bernese software. Using a nationwide network of approximately 3000 unified geodetic control points (UGCPs), we analyze horizontal coordinate differences (ΔN, ΔE) to identify regional patterns and potential systematic biases. The results indicate that both solutions are closely aligned with the official KGD2002 coordinates, generally within a few millimeters to sub-centimeter levels. However, small regional discrepancies are evident; for example, some provinces exhibit consistent mean northward or southward offsets on the order of 0.1–0.3 cm, and greater dispersions—up to 2 cm—are observed in peripheral regions such as Jeollanam. Notably, the Bernese solution demonstrates slightly tighter agreement, with lower standard deviations compared to GAMIT/GLOBK. The application of two distinct processing strategies within a unified static reference frame is a novel aspect of this study, revealing subtle differences attributable to network geometry, environmental factors, and software modeling approaches. The findings also underscore the limitations of KGD2002’s static nature, particularly its fixed epoch and lack of motion modeling. In response to these issues, this study discusses the rationale for transitioning to a dynamic geodetic reference frame, such as ITRF2020, to improve compatibility with international systems and account for ongoing crustal motions. Overall, the results provide a foundation for the future modernization of Korea’s spatial reference infrastructure and highlight the importance of adopting time-dependent datums in geodetic applications. Full article

(This article belongs to the Section Earth Sciences)

24 pages, 7603 KiB

Open AccessArticle

Pedestrian-Crossing Detection Enhanced by CyclicGAN-Based Loop Learning and Automatic Labeling

by Kuan-Chieh Wang, Chao-Li Meng, Chyi-Ren Dow and Bonnie Lu

Appl. Sci. 2025, 15(12), 6459; https://doi.org/10.3390/app15126459 (registering DOI) - 8 Jun 2025

Abstract

Pedestrian safety at crosswalks remains a critical concern as traffic accidents frequently result from drivers’ failure to yield, leading to severe injuries or fatalities. In response, various jurisdictions have enacted pedestrian priority laws to regulate driver behavior. Nevertheless, intersections lacking clear traffic signage [...] Read more.

Pedestrian safety at crosswalks remains a critical concern as traffic accidents frequently result from drivers’ failure to yield, leading to severe injuries or fatalities. In response, various jurisdictions have enacted pedestrian priority laws to regulate driver behavior. Nevertheless, intersections lacking clear traffic signage and environments with limited visibility continue to present elevated risks. The scarcity and difficulty of collecting data under such complex conditions pose significant challenges to the development of accurate detection systems. This study proposes a CyclicGAN-based loop-learning framework, in which the learning process begins with a set of manually annotated images used to train an initial labeling model. This model is then applied to automatically annotate newly generated synthetic images, which are incorporated into the training dataset for subsequent rounds of model retraining and image generation. Through this iterative process, the model progressively refines its ability to simulate and recognize diverse contextual features, thereby enhancing detection performance under varying environmental conditions. The experimental results show that environmental variations—such as daytime, nighttime, and rainy conditions—substantially affect the model performance in terms of F1-score. Training with a balanced mix of real and synthetic images yields an F1-score comparable to that obtained using real data alone. These results suggest that CycleGAN-generated images can effectively augment limited datasets and enhance model generalization. The proposed system may be integrated with in-vehicle assistance platforms as a supportive tool for pedestrian-crossing detection in data-scarce environments, contributing to improved driver awareness and road safety. Full article

17 pages, 6581 KiB

Open AccessArticle

EEMD Energy Spectrum Decoupling: An Efficient Hilbert–Huang Fusion Approach for Intelligent Bearing Fault Diagnosis

by Lianyou Lai, Weijian Xu and Zhongzhe Song

Appl. Sci. 2025, 15(12), 6458; https://doi.org/10.3390/app15126458 (registering DOI) - 8 Jun 2025

Abstract

As a critical component of rotating machinery, the operational status of rolling bearings is considered to directly determine the reliability of rail traffic systems. To address the complex modulation effects existing between multiple bearing components and the non-linear, non-stationary characteristics exhibited by vibration [...] Read more.

As a critical component of rotating machinery, the operational status of rolling bearings is considered to directly determine the reliability of rail traffic systems. To address the complex modulation effects existing between multiple bearing components and the non-linear, non-stationary characteristics exhibited by vibration acceleration signals, an intelligent fault diagnosis method for bearings based on Hilbert envelope demodulation and Ensemble Empirical Mode Decomposition energy distribution features is proposed. First, the original vibration signal is subjected to envelope demodulation processing by the Hilbert transform, thereby effectively separating the envelope signal containing fault characteristics. Subsequently, the demodulated envelope signal is decomposed by EEMD to extract Intrinsic Mode Functions (IMFs), where each IMF component is calculated layer by layer using a normalization method based on the EEMD decomposition sequence. Finally, the proposed algorithm is validated by the standard bearing fault dataset from Case Western Reserve University. Experimental results show that the proposed method achieves 100% accuracy in fault identification, and its superiority is proven to exceed conventional diagnostic approaches significantly. Full article

27 pages, 1163 KiB

Open AccessReview

Chloride-Induced Corrosion Effects on the Structural Performance of Concrete with Rebar and Fibres: A Review

by Petar Bajić, Bruno Leporace-Guimil, Carmen Andrade, Nikola Tošić and Albert de la Fuente

Appl. Sci. 2025, 15(12), 6457; https://doi.org/10.3390/app15126457 (registering DOI) - 8 Jun 2025

Abstract

Chloride-induced corrosion is a major contributor in the degradation of standardised steel-based products (e.g., rebars and fibres) commonly used for reinforcing concrete structures. Since cracked reinforced concrete elements are determined to be more susceptible to corrosion on the one hand, and fibres are [...] Read more.

Chloride-induced corrosion is a major contributor in the degradation of standardised steel-based products (e.g., rebars and fibres) commonly used for reinforcing concrete structures. Since cracked reinforced concrete elements are determined to be more susceptible to corrosion on the one hand, and fibres are effective in arresting crack growth and improving the post-cracking mechanical behaviour on the other hand, the use of fibres emerges as a promising strategy to enhance durability. This review is focused on the degradation of the load-bearing capacity, caused by chloride corrosion, in concrete elements reinforced with fibres and conventional rebar. Based on the recorded values of ultimate loads and the corresponding deflections in the reviewed studies, a lower decrease in the load-bearing capacity and less severe degradation of ductility were observed in elements where fibres (either steel or macro-synthetic) were used in combination with rebar compared with elements where only rebar was used. Furthermore, the recorded values of corrosion potential (E_corr), corrosion current density (i_corr) and gravimetric measurements indicated lower corrosion damage, delayed corrosion initiation and a prolonged propagation phase of corrosion. However, due to many differences in the methodology among the reviewed studies, the optimal fibre type or quantity cannot be identified unless more studies are performed. Full article

(This article belongs to the Special Issue Fiber-Reinforced Concrete: Recent Progress and Future Directions)

17 pages, 842 KiB

Open AccessArticle

Polyamine and Isoamylamine Levels in Peripheral Blood Cells and Plasma as Biomarkers of Structural and Functional Alterations in Acute Myocardial Infarction and Chronic Heart Failure Patients

by José Manuel Rubín, Lorena Suárez, Begoña Cantabrana, Eva Barreiro-Alonso, Ignacio Rodríguez-Uña and Manuel Sánchez

Appl. Sci. 2025, 15(12), 6456; https://doi.org/10.3390/app15126456 (registering DOI) - 8 Jun 2025

Abstract

Polyamines are essential biological compounds that play crucial roles in various physiological processes, including cardiovascular function. This study explored polyamine levels in peripheral blood cells and plasma from patients with acute myocardial infarction (AMI) and chronic heart failure (CHF), aiming to assess their [...] Read more.

Polyamines are essential biological compounds that play crucial roles in various physiological processes, including cardiovascular function. This study explored polyamine levels in peripheral blood cells and plasma from patients with acute myocardial infarction (AMI) and chronic heart failure (CHF), aiming to assess their value as biomarkers for cardiac diseases. A total of 129 individuals participated, comprising healthy controls and patients diagnosed with AMI or CHF. Polyamine concentrations were measured and analysed alongside standard clinical and biochemical markers of cardiac diseases. The results showed distinct patterns of polyamine alteration in AMI patients; putrescine levels were reduced in mononuclear cells, while isoamylamine levels were elevated in erythrocytes and plasma. In contrast, CHF patients had consistently low levels of all measured polyamines in erythrocytes and plasma, accompanied by high isoamylamine levels. Statistical analyses revealed significant correlations between polyamine levels and clinical indicators such as left ventricular ejection fraction, diastolic blood pressure, and troponin. Discriminant function analysis effectively distinguished between the control, AMI, and CHF groups based on their polyamine profiles. These findings suggest that an altered polyamine metabolism is associated with cardiovascular disease and support the potential of blood polyamine profiling as a non-invasive diagnostic and monitoring tool. Further research is warranted. Full article

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

32 pages, 5632 KiB

Open AccessArticle

Dynamic Relevance-Weighting-Based Width-Adaptive Auto-Encoder

by Malak Almejalli, Ouiem Bchir and Mohamed Maher Ben Ismail

Appl. Sci. 2025, 15(12), 6455; https://doi.org/10.3390/app15126455 (registering DOI) - 8 Jun 2025

Abstract

This paper proposes a novel adaptive autoencoder model that autonomously determines the optimal latent width during training. Unlike traditional autoencoders with fixed architectures, the proposed method introduces a dynamic relevance weighting mechanism that assigns adaptive importance to each node in the hidden layer. [...] Read more.

This paper proposes a novel adaptive autoencoder model that autonomously determines the optimal latent width during training. Unlike traditional autoencoders with fixed architectures, the proposed method introduces a dynamic relevance weighting mechanism that assigns adaptive importance to each node in the hidden layer. This distinctive feature enables the simultaneous learning of both the model parameters and its structure. A newly formulated cost function governs this dual optimization, allowing the hidden layer to expand or contract based on the complexity of the input data. This adaptability results in a more compact and expressive latent representation, making the model particularly effective in handling diverse and complex recognition tasks. The originality of this work lies in its unsupervised, self-adjusting architecture that eliminates the need for manual design or pruning heuristics. The approach was rigorously evaluated on benchmark datasets (MNIST, CIFAR-10) and real-world datasets (Parkinson, Epilepsy), using classification accuracy and computational cost as key performance metrics. It demonstrates superior performance compared to state-of-the-art models in terms of accuracy and representational efficiency. Full article

(This article belongs to the Special Issue Advances in Neural Networks and Deep Learning)

► Show Figures

Figure 1

28 pages, 3349 KiB

Open AccessArticle

Research on Predicting Joint Rotation Angles Through Mechanomyography Signals and the Broad Learning System

by Yu Bai, Xiaorong Guan, Huibin Li, Shi Cheng, Rui Zhang and Long He

Appl. Sci. 2025, 15(12), 6454; https://doi.org/10.3390/app15126454 (registering DOI) - 8 Jun 2025

Abstract

To address the limitation of current upper limb rehabilitation exoskeletons—where pattern recognition-based assistance disrupts patients’ continuous motion—this study proposes a mechanomyography-based model for predicting shoulder and elbow joint angles. Small contact microphones were employed to collect mechanomyography signals, leveraging their ability to capture [...] Read more.

To address the limitation of current upper limb rehabilitation exoskeletons—where pattern recognition-based assistance disrupts patients’ continuous motion—this study proposes a mechanomyography-based model for predicting shoulder and elbow joint angles. Small contact microphones were employed to collect mechanomyography signals, leveraging their ability to capture vibration signals above 8 Hz, making them ideal for mechanomyography acquisition. After extracting raw mechanomyography data, a bandpass filter (10–50 Hz) was applied to eliminate low- and high-frequency noise. To reduce computational overhead during model training, a Broad Learning System was adopted, which iteratively refines predictions by incrementally expanding nodes in the feature and enhancement layers rather than adding hidden layers. The Slime Mold Algorithm was further used to optimize hyperparameters of the Broad Learning System, enhancing prediction accuracy. Experimental results demonstrate that mechanomyography signals exhibit a typical central frequency range of 10–50 Hz, and the Slime Mold Algorithm-optimized Broad Learning System model achieved a minimum coefficient of determination (R²) of 0.978, effectively predicting arm joint angles. This approach shows promise for exoskeletons, combining high control accuracy, real-time joint angle prediction, and computational efficiency. Full article

(This article belongs to the Special Issue Recent Developments in Exoskeletons)

19 pages, 1748 KiB

Open AccessArticle

PET Radiomics Signatures and Artificial Intelligence for Decoding Immunotherapy Response in Advanced Cutaneous Squamous Cell Carcinoma: A Retrospective Single-Center Study

by Luigi Manco, Ilaria Proietti, Giovanni Scribano, Riccardo Pirisino, Oreste Bagni, Concetta Potenza, Giovanni Pellacani and Luca Filippi

Appl. Sci. 2025, 15(12), 6453; https://doi.org/10.3390/app15126453 (registering DOI) - 8 Jun 2025

Abstract

The aim of this study was to develop a baseline [¹⁸F]FDG PET/CT model to predict immunotherapy response in advanced cutaneous squamous cell carcinoma (cSCC) and noninvasively determine tumor grade, thereby enhancing early patient stratification. We retrospectively analyzed 59 patients with histologically confirmed advanced [...] Read more.

The aim of this study was to develop a baseline [¹⁸F]FDG PET/CT model to predict immunotherapy response in advanced cutaneous squamous cell carcinoma (cSCC) and noninvasively determine tumor grade, thereby enhancing early patient stratification. We retrospectively analyzed 59 patients with histologically confirmed advanced cSCC submitted to immunotherapy with cemiplimab. All underwent [¹⁸F]FDG PET/CT at baseline and after approximately 12 weeks. Clinical response was assessed through PET findings integrated with clinical and dermatological evaluation, and patients were classified as responders (complete/partial metabolic response or stable disease) or non-responders (progression or toxicity-related discontinuation). Tumors were also classified as low to intermediate (G1–G2) or poorly differentiated (G3). Machine learning models (Random Forest and Extreme Gradient Boosting) were trained to predict treatment response and tumor grade. Clinical benefit was observed in 46/59 patients (77.9%), while 13 (22.1%) were non-responders. Histology showed 64.4% (n = 38) G1–G2 and 35.6% (n = 21) G3 tumors. The PET-based model best predicted clinical benefit (AUC = 0.96, accuracy = 91% cross-validation; AUC = 0.88, accuracy = 82% internal validation). For tumor grade prediction, the CT-based model achieved a higher AUC of 0.80 (accuracy 73%), whereas the PET-based model reached an AUC of 0.78 but demonstrated a slightly higher accuracy of 77%. Radiomic analysis of baseline [¹⁸F]FDG PET enables the discriminative prediction of immunotherapy response and tumor grade in advanced cSCC, with PET-based models outperforming CT-based ones. Full article

(This article belongs to the Special Issue Emerging Topics in Precision Medicine: Non-invasive Innovations Shaping Cancer and Immunotherapy Progress)

19 pages, 1391 KiB

Open AccessArticle

Edge-FLGuard: A Federated Learning Framework for Real-Time Anomaly Detection in 5G-Enabled IoT Ecosystems

by Manuel J. C. S. Reis

Appl. Sci. 2025, 15(12), 6452; https://doi.org/10.3390/app15126452 (registering DOI) - 8 Jun 2025

Abstract

The rapid convergence of 5G networks and Internet of Things (IoT) technologies has unlocked unprecedented connectivity and responsiveness across smart environments—but has also amplified cybersecurity risks due to device heterogeneity, data privacy concerns, and distributed attack surfaces. To address these challenges, we propose [...] Read more.

The rapid convergence of 5G networks and Internet of Things (IoT) technologies has unlocked unprecedented connectivity and responsiveness across smart environments—but has also amplified cybersecurity risks due to device heterogeneity, data privacy concerns, and distributed attack surfaces. To address these challenges, we propose Edge-FLGuard, a federated learning and edge AI-based anomaly detection framework tailored for real-time protection in 5G-enabled IoT ecosystems. The framework integrates lightweight deep learning models—specifically autoencoders and LSTM networks—for on-device inference, combined with a privacy-preserving federated training pipeline to enable scalable, decentralized threat detection without raw data sharing. We evaluate Edge-FLGuard using both public (CICIDS2017, TON_IoT) and synthetic datasets under diverse attack scenarios including spoofing, DDoS, and unauthorized access. Experimental results demonstrate high detection accuracy (F1-score ≥ 0.91, AUC-ROC up to 0.96), low inference latency (<20 ms), and robustness against data heterogeneity and adversarial conditions. By aligning edge intelligence with secure, collaborative learning, Edge-FLGuard offers a practical and scalable cybersecurity solution for next-generation IoT deployments. Full article

(This article belongs to the Special Issue Advanced Internet of Things Ecosystems: Architectures, Intelligence, and Communication Innovations)

► Show Figures

Figure 1

23 pages, 3325 KiB

Open AccessArticle

Modeling of Electromagnetic Fields of the Traction Network Taking into Account the Influence of Metal Structures

by Iliya Iliev, Andrey Kryukov, Konstantin Suslov, Ekaterina Voronina, Andrey Batukhtin, Ivan Beloev and Yuliya Valeeva

Appl. Sci. 2025, 15(12), 6451; https://doi.org/10.3390/app15126451 (registering DOI) - 8 Jun 2025

Abstract

The paper addresses the issues of electromagnetic safety in traction networks of 25 kV AC railways. The purpose of the research is to develop digital models to determine the strengths of electromagnetic fields (EMFs) created by traction networks near portal-type metal structures. Such [...] Read more.

The paper addresses the issues of electromagnetic safety in traction networks of 25 kV AC railways. The purpose of the research is to develop digital models to determine the strengths of electromagnetic fields (EMFs) created by traction networks near portal-type metal structures. Such a structure in this study is represented by an overpass located above the tracks. The presence of a conductive structure significantly complicates the picture of EMF distribution in space. In contrast to the plane-parallel EMF of the traction network on interstation tracks in the spans between the supports of the catenary system, the field in this situation becomes three-dimensional. The technology for detecting strength relies on the concept of segments of limited length conductors, some of which may be buried. In order to apply the quasi-stationary zone equations to frequencies of up to 2000 Hz, it is essential to ensure that the size of the set of objects composed of these conductors does not exceed several hundred meters. Based on the modeling results, the dependences of the amplitudes and components of EMF strengths on the z-coordinate passing along the axis of the railway were obtained. In addition, three-dimensional diagrams were constructed to analyze the distribution of EMF in space. The findings of the studies show that the presented technique allows considering the influence of metal structures when modeling the electromagnetic fields of traction networks. It can be used in practice to develop effective measures to enhance electromagnetic safety conditions. Full article

13 pages, 458 KiB

Open AccessArticle

Anthropometric Profile, Body Composition and Somatotype of Elite ILCA 7 Class Sailors—Differences Across General Competitive Success Levels

by Luka Pezelj, Jan G. Bourgois, Mirjana Milić, Josip Maleš and Israel Caraballo

Appl. Sci. 2025, 15(12), 6450; https://doi.org/10.3390/app15126450 (registering DOI) - 8 Jun 2025

Abstract

Setting up anthropometric profiles for elite athletes in each sport, sport discipline, or specific sport positions could be a key element of sport selection processes. The main purpose of this study was to determine the anthropometric characteristics, body composition, and somatotype profiles of [...] Read more.

Setting up anthropometric profiles for elite athletes in each sport, sport discipline, or specific sport positions could be a key element of sport selection processes. The main purpose of this study was to determine the anthropometric characteristics, body composition, and somatotype profiles of elite international ILCA 7 class sailors and to determine the differences contributing to different levels of competitive success. The subject sample included 97 elite ILCA 7 class sailors. A set of 25 anthropometric variables was applied. The sailors were divided into three groups according to their level of general competitive success according to the World Sailing Rankings. Differences between elite ILCA 7 sailors, separated into Higher, Medium, and Lower groups based on their success, were found in terms of age, body mass, muscle mass, trunk muscle mass, leg muscle mass, biepicondilar humerus width, sum of skinfolds, triceps skinfold, supraspinale skinfold, medial calf skinfold, and endomorphy rating. The most successful group of sailors was, on average, 4.9 years older than the least successful group. More highly successful sailors were also found to have an average of 2.73 kg more muscle mass but an 8.81 mm lower sum of skinfolds than those in the lower success group. Considering the average values of somatotype categories, ILCA 7 sailors fit the endomorphic–mesomorph somatotype category (3.23 ± 0.99–4.81 ± 0.90–2.25 ± 0.86). This research clearly identifies the anthropometric profile of elite ILCA 7 sailors, which can significantly contribute to a more informed choice of sailing class. Given the results of this research, current ILCA 7 sailors can easily compare their own anthropometric parameters with elite ILCA 7 sailors and eventually adjust their training process to obtain a more desirable anthropometric profile. Full article

(This article belongs to the Special Issue Human Performance and Health in Sport and Exercise)

► Show Figures

Figure 1

34 pages, 918 KiB

Open AccessSystematic Review

Publish/Subscribe-Middleware-Based Intelligent Transportation Systems: Applications and Challenges

by Basem Almadani, Ekhlas Hashem, Raneem R. Attar, Farouq Aliyu and Esam Al-Nahari

Appl. Sci. 2025, 15(12), 6449; https://doi.org/10.3390/app15126449 (registering DOI) - 8 Jun 2025

Abstract

Countries are embracing intelligent transportation systems (ITSs), the application of information and communication technologies to transportation, to address growing challenges in urban mobility, congestion, safety, and sustainability. Architecture Reference for Cooperative and Intelligent Transportation (ARC-IT) is a notable ITS framework comprising Enterprise, Functional, [...] Read more.

Countries are embracing intelligent transportation systems (ITSs), the application of information and communication technologies to transportation, to address growing challenges in urban mobility, congestion, safety, and sustainability. Architecture Reference for Cooperative and Intelligent Transportation (ARC-IT) is a notable ITS framework comprising Enterprise, Functional, Physical, and Communications Views (or layers). This review focuses on the Communications View, examining how publish/subscribe middleware enhances ITS through the communication layer. It identified application areas across ITS infrastructure, transportation modes, and communication technologies, and highlights key challenges. In the infrastructure domain, publish/subscribe middleware enhances responsiveness and real-time processing in systems such as traffic surveillance, VANETs, and road sensor networks, especially when replacing legacy infrastructure is cost-prohibitive. Moreover, the middleware supports scalable, low-latency communication in land, air, and marine modes, enabling public transport coordination, cooperative driving, and UAV integration. At the communications layer, publish/subscribe systems facilitate interoperable, delay-tolerant data dissemination over heterogeneous platforms, including 4G/5G, ICN, and peer-to-peer networks. However, integrating publish/subscribe middleware in ITS has several challenges, including privacy risks, real-time data constraints, fault tolerance, bandwidth limitations, and security vulnerabilities. This paper provides a domain-informed foundation for researchers and practitioners developing resilient, scalable, and interoperable communication systems in next-generation ITSs. Full article

(This article belongs to the Special Issue Recent Advances and Emerging Technologies in Transportation and Urban Planning)

23 pages, 4049 KiB

Open AccessArticle

ROSE-BOX: A Lightweight and Efficient Intrusion Detection Framework for Resource-Constrained IIoT Environments

by Silin Peng, Yu Han, Ruonan Li, Lichen Liu, Jie Liu and Zhaoquan Gu

Appl. Sci. 2025, 15(12), 6448; https://doi.org/10.3390/app15126448 (registering DOI) - 8 Jun 2025

Abstract

The rapid advancement of the Industrial Internet of Things (IIoT) has transformed industrial automation, enabling real-time monitoring and intelligent decision making. However, increased connectivity exposes IIoT systems to sophisticated cyber threats, which may pose significant security risks, especially in resource-constrained IIoT environments where [...] Read more.

The rapid advancement of the Industrial Internet of Things (IIoT) has transformed industrial automation, enabling real-time monitoring and intelligent decision making. However, increased connectivity exposes IIoT systems to sophisticated cyber threats, which may pose significant security risks, especially in resource-constrained IIoT environments where computational efficiency is critical. Existing intrusion detection solutions often suffer from high computational overhead and inadequate adaptability, rendering them impractical for real-time deployment in IIoT environments. To address these challenges, this study introduces a lightweight and efficient intrusion detection framework tailored for resource-constrained IIoT environments. Firstly, an XGBoost-assisted Random Forest (XGB-RF) method is proposed to select the most important features to obtain an optimal feature subset. Moreover, SMOTE (Synthetic Minority Oversampling Technique) is utilized to balance the optimal feature subset to improve detection precision. Furthermore, to reduce computing resource requirements and latency while improving detection performance, Bayesian optimization is applied to fine-tune the parameters of XGBoost (BO-XGBoost) to obtain the best detection results. Finally, extensive experiments on benchmark datasets, including CIC-IDS2017, CSE-CIC-IDS2018, and CIC-DDoS2019, demonstrate that the proposed method, which we call ROSE-BOX (Random Forest, Synthetic Minority Oversampling Technique, and BO-Xgboost), achieves a detection accuracy exceeding 99.85% while maintaining low latency and CPU occupancy rates. Our findings highlight the robustness, lightweight nature, and efficiency of ROSE-BOX, making it well-suited for real-time intrusion detection in resource-constrained IIoT environments. Full article

(This article belongs to the Special Issue Advances in the Internet of Things (IoT) : Attacks Detection and Privacy Protection)

► Show Figures

Figure 1

21 pages, 4255 KiB

Open AccessArticle

Pulsed Electric Fields-Driven Enhancement of Tomato Seed Quality and Resilience: Improving Germination, Stress Tolerance, and Microbial Disinfection

by Gulsun Akdemir Evrendilek and Bahar Yalçın

Appl. Sci. 2025, 15(12), 6447; https://doi.org/10.3390/app15126447 (registering DOI) - 8 Jun 2025

Abstract

Seed quality is vital for agricultural productivity, as it directly influences the crop yield and resilience to environmental stressors. This study evaluated the effectiveness of a pulsed electric field (PEF) treatment in enhancing the tomato (Solanum lycopersicum) seed quality, seedling growth, [...] Read more.

Seed quality is vital for agricultural productivity, as it directly influences the crop yield and resilience to environmental stressors. This study evaluated the effectiveness of a pulsed electric field (PEF) treatment in enhancing the tomato (Solanum lycopersicum) seed quality, seedling growth, and microbial safety. Tomato seeds were treated with PEFs at energy levels ranging from 1.07 to 17.28 J, and several parameters were assessed, including the germination rate, normal seedling development, tolerance to cold and salinity stress, electrical conductivity, and microbial inactivation. The highest germination rate (72.81%) was observed at 15.36 J on the seventh day of germination, whereas the highest normal seedling rate (94.62%) was recorded at 17.28 J (p ≤ 0.05). The germination under cold stress (5 days at 24 °C) was highest, with a 46.67% germination observed at both 1.92 and 10.88 J. PEF-treated seeds exposed to 100 and 200 mM of NaCl exhibited significantly improved germination compared to the controls (p ≤ 0.05). The electrical conductivity (EC) was more influenced by the incubation time than by the PEF intensity, as the EC of all samples showed a significant increase from 4 to 8 h. The samples treated with 17.28 J exhibited the highest germination rates under salt stress, reaching 62.00 ± 0.90% and 50.00 ± 0.60% under 100 mM and 200 mM of NaCl, respectively (p ≤ 0.05). The initial mean counts of the total mesophilic aerobic bacteria and the total mold and yeast—4.00 ± 0.03 and 3.06 ± 0.03 log cfu/g, respectively—were reduced to undetectable levels by the application of 17.28 J, with higher energy levels yielding greater inactivation. These findings demonstrate that the PEF is a promising technique for enhancing seed quality, promoting seedling vigor, and reducing microbial contamination, supporting its application in sustainable agriculture. Full article

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

► Show Figures

Figure 1

13 pages, 3697 KiB

Open AccessArticle

Classification of Artificial Gear Damage by Angle Measurement Utilizing the Gear Wheel as a Material Measure

by Yanik Koch, Florian Michael Becker-Dombrowsky and Eckhard Kirchner

Appl. Sci. 2025, 15(12), 6446; https://doi.org/10.3390/app15126446 (registering DOI) - 8 Jun 2025

Abstract

Gear condition monitoring is predominantly executed through the utilization of acceleration sensors positioned on the housing. However, recent advancements have identified measuring the instantaneous angular speed as a compelling alternative as it shortens the transmission path and therefore provides high-quality rotational angle information [...] Read more.

Gear condition monitoring is predominantly executed through the utilization of acceleration sensors positioned on the housing. However, recent advancements have identified measuring the instantaneous angular speed as a compelling alternative as it shortens the transmission path and therefore provides high-quality rotational angle information that can be used to increase damage prediction accuracy, particularly under transient operating conditions. Additionally, there are a variety of methodologies for integrating sensors into gears, which underscores the necessity for high-quality condition data. However, it should be noted that a significant amount of effort is required to successfully integrate these sensors into the rotating system. This publication uses a gear wheel sensor that employs the gear itself as a material measure to acquire rotational angle data and to deduce the damage condition. A magnetoresistive sensor is integrated into the gearbox housing radially facing a ferromagnetic gear and measures the rotational angle by the gear teeth. Various artificial tooth flank damages are applied to the pinion. The rotational angle is measured with the gear sensor, and the damage state is classified with a random forest classifier using established evaluations in the time and frequency domains. The tests are conducted under stationary operating conditions at an array of speed and torque levels. Additionally, they are performed under transient operating conditions, employing speed ramps at constant torque. The results of the classification are evaluated by means of classification accuracy and confusion matrices and compared with those obtained via a classic encoder at the pinion shaft and an acceleration sensor at the gearbox housing. Full article

(This article belongs to the Special Issue Novel Approaches for Fault Diagnostics of Machine Elements)

► Show Figures

Figure 1

21 pages, 3031 KiB

Open AccessArticle

Microstructural Characterization of the Mn Lepidolite Distribution in Dark Red Clay Soils

by Simona Elena Avram, Lucian Barbu Tudoran, Gheorghe Borodi and Ioan Petean

Appl. Sci. 2025, 15(12), 6445; https://doi.org/10.3390/app15126445 (registering DOI) - 8 Jun 2025

Abstract

Lepidolite is one of a small number of minerals that contains a significant amount of lithium. Some areas, like the Apuseni and Metalifer Mountains in Romania, present dark red layers intercalated with reddish-yellow clay soils with interesting aspects. X-ray diffraction (XRD) analysis coupled [...] Read more.

Lepidolite is one of a small number of minerals that contains a significant amount of lithium. Some areas, like the Apuseni and Metalifer Mountains in Romania, present dark red layers intercalated with reddish-yellow clay soils with interesting aspects. X-ray diffraction (XRD) analysis coupled with polarized light optical microscopy (POM) revealed that this dark red soil contains a large amount of fine microstructured lepidolite (24–35%) mixed with quartz sand and fine traces of kaolinite and muscovite. Scanning electron microscopy (SEM) elemental analysis revealed a typical clay composition with Mn traces (specific to red lepidolite), confirming POM observation. SEM also revealed fine tabular platelets of lepidolite with a maximum size of 1.5 µm surrounding quartz particles (5–50 µm), indicating the presence of numerous nano fractions. Their presence was confirmed by atomic force microscopy (AFM), which showed particle sizes ranging from 40 to 60 nm, closely matching the crystallite size estimated using the Scherrer formula. The finest fraction allows easy separation from the quartz sand through bi-distilled water washing. Quartz particles settle at the bottom of the container, while the finest lepidolite particles are easily separated. Water evaporation ensures their recovery. Thus, the enriched lepidolite powder could be utilized for specific applications in the lithium industry. On the other hand, the large number of the finest particles found in the samples investigated presents the risk of PM1, PM2.5m, and PM10 emission, with impacts on atmospheric environmental safety. Full article

(This article belongs to the Special Issue Latest Research on Geotechnical Engineering)

► Show Figures

Figure 1

Journal Description

Applied Sciences

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI