Applied Sciences

12 pages, 1243 KiB

Open AccessArticle

Comparison Between Measurements Taken on AI-Generated and Conventional Digital Models: A Retrospective Study

by Enzo Pasciuti, Daniela Guiducci, Filippo Guidorizzi, Tecla Terenzio, Saverio Ceraulo, Filippo Pepe, Luca Ranieri, Francesca Cremonini and Luca Lombardo

Appl. Sci. 2025, 15(15), 8347; https://doi.org/10.3390/app15158347 (registering DOI) - 27 Jul 2025

Abstract

(1) Aim: To compare transverse dimensions measured on AI-generated intra-oral models and conventional digital intra-oral models. (2) Methods: A group of 38 patients treated with clear aligners was selected retrospectively from those whose records featured both AI-generated and conventional digital intra-oral models taken [...] Read more.

(1) Aim: To compare transverse dimensions measured on AI-generated intra-oral models and conventional digital intra-oral models. (2) Methods: A group of 38 patients treated with clear aligners was selected retrospectively from those whose records featured both AI-generated and conventional digital intra-oral models taken at the same timepoint. Transverse dimensions (inter-canine, inter-premolar, and inter-molar distances) on both upper and lower arches were evaluated and compared. Intra-class correlation index and paired t-test were applied to test the repeatability of measurements and statistically significant differences, respectively. Statistical significance was set at 0.05. (3) Results: Intra-class correlation index showed good repeatability. Paired t-test showed differences in measurements of the distances between the thicket area of gingiva on the palatal side of the upper first molar (p = 0.002), the gingival margin of the lower first molar (p = 0.014), and the mesio-vestibular cusps of the lower first molars (p = 0.019). (4) Conclusions: Transverse measurements were similar on AI-generated and conventional intra-oral .stl renderings. Statistical differences were found on posterior areas of both upper and lower dental arches, but are unlikely to be clinically significant. Full article

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20 pages, 4166 KiB

Open AccessReview

Additive Manufacturing Technologies and Their Applications in Dentistry: A Systematic Literature Review

by Dragana Oros, Marko Penčić, Marko Orošnjak and Slawomir Kedziora

Appl. Sci. 2025, 15(15), 8346; https://doi.org/10.3390/app15158346 (registering DOI) - 26 Jul 2025

Abstract

Additive manufacturing (AM) has emerged as a transformative technology in dentistry, enabling the production of patient-specific dental applications with reduced costs and fabrication times. Despite the growth of applications, a consolidated understanding of current 3D printing technologies, materials, and performance in dental settings [...] Read more.

Additive manufacturing (AM) has emerged as a transformative technology in dentistry, enabling the production of patient-specific dental applications with reduced costs and fabrication times. Despite the growth of applications, a consolidated understanding of current 3D printing technologies, materials, and performance in dental settings remains fragmented. Here, we perform a Systematic Literature Review (SLR) using the PRISMA protocol, retrieving 19 closely related primary studies. The evidence is synthesized across three axes: application domain, AM technology, and critical quality parameters. Dental restorations, prosthetics, crowns, and implants are the most common applications, while fused deposition modeling, stereolithography, digital light processing, selective laser sintering, and laser-directed energy deposition are the most used technologies. AM materials include polymers, metals, and emerging biomaterials. Key quality determinants include dimensional accuracy, wear and corrosion resistance, and photosensitivity. Notably, biocompatibility and cytotoxicity remain underexplored yet critical factors for ensuring long-term clinical safety. The evidence also suggests a lack of in vivo studies, insufficient tribological and microbiological testing, including limited data degradation pathways of AM materials under oral conditions. Understanding that there are disconnects between the realization of the clinical and the economic benefits of 3D printing in dentistry, future research requires standardized testing frameworks and long-term biocompatibility validation. Full article

(This article belongs to the Special Issue 3D Printing Technologies and Additive Manufacturing: Recent Advances and Applications)

19 pages, 584 KiB

Open AccessArticle

Effective Seed Scheduling for Directed Fuzzing with Function Call Sequence Complexity Estimation

by Xi Peng, Peng Jia, Ximing Fan, Cheng Huang and Jiayong Liu

Appl. Sci. 2025, 15(15), 8345; https://doi.org/10.3390/app15158345 (registering DOI) - 26 Jul 2025

Abstract

Directed grey-box fuzzers focus on testing specific target code. They have been utilized in various security applications, such as reproducing known crashes and identifying vulnerabilities resulting from incomplete patches. Distance-guided directed fuzzers calculate the distance to the target node for each node in [...] Read more.

Directed grey-box fuzzers focus on testing specific target code. They have been utilized in various security applications, such as reproducing known crashes and identifying vulnerabilities resulting from incomplete patches. Distance-guided directed fuzzers calculate the distance to the target node for each node in a CFG or CG, which has always been the mainstream in this field. However, the distance can only reflect the relationship between the current node and the target node, and it does not consider the impact of the reaching sequence before the target node. To mitigate this problem, we analyzed the properties of the instrumented function’s call graph after selective instrumentation, and the complexity of reaching the target function sequence was estimated. Assisted by the sequence complexity, we proposed a two-stage function call sequence-based seed-scheduling strategy. The first stage is to select seeds with a higher probability of generating test cases that reach the target function. The second stage is to select seeds that can generate test cases that meet the conditions for triggering the vulnerability as much as possible. We implemented our approach in SEZZ based on SelectFuzz and compare it with related works. We found that SEZZ outperformed AFLGo, Beacon, WindRanger, and SelectFuzz by achieving an average improvement of 13.7×, 1.50×, 9.78×, and 2.04× faster on vulnerability exposure, respectively. Moreover, SEZZ triggered three more vulnerabilities than the other compared tools. Full article

(This article belongs to the Special Issue Cyberspace Security Technology in Computer Science)

23 pages, 1615 KiB

Open AccessArticle

Fine-Tuning Large Language Models for Kazakh Text Simplification

by Alymzhan Toleu, Gulmira Tolegen and Irina Ualiyeva

Appl. Sci. 2025, 15(15), 8344; https://doi.org/10.3390/app15158344 (registering DOI) - 26 Jul 2025

Abstract

This paper addresses text simplification task for Kazakh, a morphologically rich, low-resource language, by introducing KazSim, an instruction-tuned model built on multilingual large language models (LLMs). First, we develop a heuristic pipeline to identify complex Kazakh sentences, manually validating its performance on 400 [...] Read more.

This paper addresses text simplification task for Kazakh, a morphologically rich, low-resource language, by introducing KazSim, an instruction-tuned model built on multilingual large language models (LLMs). First, we develop a heuristic pipeline to identify complex Kazakh sentences, manually validating its performance on 400 examples and comparing it against a purely LLM-based selection method; we then use this pipeline to assemble a parallel corpus of 8709 complex–simple pairs via LLM augmentation. For the simplification task, we benchmark KazSim against standard Seq2Seq systems, domain-adapted Kazakh LLMs, and zero-shot instruction-following models. On an automatically constructed test set, KazSim (Llama-3.3-70B) achieves BLEU 33.50, SARI 56.38, and F1 87.56 with a length ratio of 0.98, outperforming all baselines. We also explore prompt language (English vs. Kazakh) and conduct human evaluation with three native speakers: KazSim scores 4.08 for fluency, 4.09 for meaning preservation, and 4.42 for simplicity—significantly above GPT-4o-mini. Error analysis shows that remaining failures cluster into tone change, tense change, and semantic drift, reflecting Kazakh’s agglutinative morphology and flexible syntax. Full article

(This article belongs to the Special Issue Natural Language Processing and Text Mining)

15 pages, 2062 KiB

Open AccessArticle

Effect of Low-Salt Processing on Lipolytic Activity, Volatile Compound Profile, Color, Lipid Oxidation, and Microbiological Properties of Four Different Types of Pastırma

by Emre Kabil, Fatma Yağmur Hazar Suncak, Güzin Kaban and Mükerrem Kaya

Appl. Sci. 2025, 15(15), 8343; https://doi.org/10.3390/app15158343 (registering DOI) - 26 Jul 2025

Abstract

Pastırma is a traditional dry-cured meat product made from whole pieces of cattle or water buffalo carcasses. Sixteen or more types of pastırma can be produced from different parts of the carcass. This study investigated the effect of low salt processing (3% NaCl) [...] Read more.

Pastırma is a traditional dry-cured meat product made from whole pieces of cattle or water buffalo carcasses. Sixteen or more types of pastırma can be produced from different parts of the carcass. This study investigated the effect of low salt processing (3% NaCl) on the lipolytic enzyme activity, volatile profile, color, lipid oxidation, and microbiological properties of commonly produced types of pastırma (kuşgömü, sırt, bohça, and şekerpare). In the study, 5% NaCl level was used as the control group. For all pastırma types, the pH changed between 5.5 and 6.0. The a_w value was less than 0.90 for the pastırma types. The L* value increased when the salt level decreased from 5% to 3% (p < 0.05); however, the salt level did not affect the a* and b* values (p > 0.05). Reducing the salt level increased the neutral lipase activity and decreased the TBARS. As the salt level increased, the acid lipase activity increased in the bohça pastırma, and the phospholipase activity increased in the kuşgömü and sırt pastırma (p < 0.05). Furthermore, while Micrococcus/Staphylococcus constituted the dominant microbiota in pastırma types, a 5% salt level led to a decrease in the number of lactic acid bacteria. The volatile compounds were more affected by salt level than by pastırma type. The correlation analysis showed that there are some differences between 3% and 5% salt levels and the use of a 3% salt level increases the abundance of the compounds. The correlation analysis also revealed that there are differences between the pastırma types in terms of the volatile compounds and that kuşgömü pastırma differs from other pastırma types. Full article

(This article belongs to the Special Issue Chemical and Physical Properties in Food Processing: Second Edition)

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17 pages, 307 KiB

Open AccessArticle

An Endogenous Security-Oriented Framework for Cyber Resilience Assessment in Critical Infrastructures

by Mingyu Luo, Ci Tao, Yu Liu, Shiyao Chen and Ping Chen

Appl. Sci. 2025, 15(15), 8342; https://doi.org/10.3390/app15158342 (registering DOI) - 26 Jul 2025

Abstract

In the face of escalating cyber threats to critical infrastructures, achieving robust cyber resilience has become paramount. This paper proposes an endogenous security-oriented framework for cyber resilience assessment, specifically tailored for critical infrastructures. Drawing on the principles of endogenous security, our framework integrates [...] Read more.

In the face of escalating cyber threats to critical infrastructures, achieving robust cyber resilience has become paramount. This paper proposes an endogenous security-oriented framework for cyber resilience assessment, specifically tailored for critical infrastructures. Drawing on the principles of endogenous security, our framework integrates dynamic heterogeneous redundancy (DHR) and adaptive defense mechanisms to address both known and unknown threats. We model resilience across four key dimensions—Prevention, Destruction Resistance, Adaptive Recovery, and Evolutionary Learning—using a novel mathematical formulation that captures nonlinear interactions and temporal dynamics. The framework incorporates environmental threat entropy to dynamically adjust resilience scores, ensuring relevance in evolving attack landscapes. Through empirical validation on simulated critical infrastructure scenarios, we demonstrate the framework’s ability to quantify resilience trajectories and trigger timely defensive adaptations. Empiricalvalidation on a real-world critical infrastructure system yielded an overall resilience score of 82.75, revealing a critical imbalance between strong preventive capabilities (90/100) and weak Adaptive Recovery (66/100). Our approach offers a significant advancement over static risk assessment models by providing actionable metrics for strategic resilience investments. This work contributes to the field by bridging endogenous security theory with practical resilience engineering, paving the way for more robust protection of critical systems against sophisticated cyber threats. Full article

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17 pages, 3519 KiB

Open AccessArticle

Modeling One-Dimensional Nonlinear Consolidation Problems by Physics-Informed Neural Network with Layer-Wise Locally Adaptive Activation Functions

by Jie Zhou, De’an Sun and Yang Chen

Appl. Sci. 2025, 15(15), 8341; https://doi.org/10.3390/app15158341 (registering DOI) - 26 Jul 2025

Abstract

The study on soil consolidation and settlement is of great importance in geotechnical engineering practice. Nowadays, physics-informed neural networks (PINN) are becoming more and more popular in solving geotechnical engineering problems thanks to their meshless, physically constrained, and data-driven nature. Although there have [...] Read more.

The study on soil consolidation and settlement is of great importance in geotechnical engineering practice. Nowadays, physics-informed neural networks (PINN) are becoming more and more popular in solving geotechnical engineering problems thanks to their meshless, physically constrained, and data-driven nature. Although there have been some successful applications in one-dimensional (1D) consolidation problems in saturated soils, the ability and stability to deal with more complex boundary conditions remain to be tested. In this paper, the effects of activation function and random state on the PINN are investigated for solving two 1D consolidation problems in saturated soils, and the proposed method for inverse modeling of the two 1D consolidation problems. The results show that PINN with layer-wise locally adaptive activation functions improves the convergence speed and prediction accuracy of the PINN for solving the 1D nonlinear soil consolidation problems, and at the same time the robustness of the model to random states. Moreover, the proposed method still converges faster in the inverse modeling of 1D consolidation problems. Full article

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25 pages, 3567 KiB

Open AccessArticle

A Low-Voltage Back-to-Back Converter Interface for Prosumers in a Multifrequency Power Transfer Environment

by Zaid Ali, Hamed Athari and David Raisz

Appl. Sci. 2025, 15(15), 8340; https://doi.org/10.3390/app15158340 (registering DOI) - 26 Jul 2025

Abstract

The research demonstrates, through simulation and laboratory validation, the development of a low-voltage DC-link (LVDC) back-to-back converter system that enables multi-frequency power transfer. The system operates in two distinct modes, which include a three-phase grid-connected converter transferring fundamental and 5th and 7th harmonic [...] Read more.

The research demonstrates, through simulation and laboratory validation, the development of a low-voltage DC-link (LVDC) back-to-back converter system that enables multi-frequency power transfer. The system operates in two distinct modes, which include a three-phase grid-connected converter transferring fundamental and 5th and 7th harmonic power to a three-phase residential inverter supplying a clean 50 Hz load and another mode that uses a DC–DC buck–boost converter to integrate a battery storage unit for single-phase load supply. The system allows independent control of each harmonic component and maintains a clean sinusoidal voltage at the load side through DC-link isolation. The LVDC link functions as a frequency-selective barrier to suppress non-standard harmonic signals on the load side, effectively isolating the multi-frequency power grid from standard-frequency household loads. The proposed solution fills the gap between the multi-frequency power systems and the single-frequency loads because it allows the transfer of total multi-frequency grid power to the traditional household loads with pure fundamental frequency. Experimental results and simulation outcomes demonstrate that the system achieves high efficiency, robust harmonic isolation, and dynamic adaptability when load conditions change. Full article

(This article belongs to the Special Issue Power Electronics: Control and Applications)

13 pages, 2827 KiB

Open AccessArticle

Ultrasonic Nondestructive Testing Image Enhancement Model Based on Super-Resolution Imaging

by Jinxuan Zhu, Guoyou Wang, Kang Luo and Xinfang Zhang

Appl. Sci. 2025, 15(15), 8339; https://doi.org/10.3390/app15158339 (registering DOI) - 26 Jul 2025

Abstract

Ultrasonic nondestructive testing has been widely used in various industries due to its simple operation and harmlessness for the object to be detected. However, due to the mechanism of ultrasonic image generation, the generated ultrasonic images often have low resolution, which greatly affects [...] Read more.

Ultrasonic nondestructive testing has been widely used in various industries due to its simple operation and harmlessness for the object to be detected. However, due to the mechanism of ultrasonic image generation, the generated ultrasonic images often have low resolution, which greatly affects the final detection results. How to improve the resolution of ultrasonic images has become the key to improving the accuracy of defect detection. Therefore, this paper proposes an ultrasonic super-resolution model based on up- and down-sampling layers and multi-layer residual networks combined with Charbonnier loss function. The degradation features of the image are learned through up- and down-sampling layers, and the intrinsic features of the image are learned through multi-layer residual networks, so that all the feature information of the image is fully learned. The Charbonnier loss function accelerates the convergence of the model. Experimental results show that the model proposed in this paper outperforms the common model performance. Full article

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20 pages, 2003 KiB

Open AccessArticle

Adaptive Hierarchical Sliding Mode Control for Double-Pendulum Gantry Crane Based on Neural Network

by Linxiao Yao, Yihao Chen, Bing Li, Linjian Shangguan and Jingwen Yan

Appl. Sci. 2025, 15(15), 8338; https://doi.org/10.3390/app15158338 (registering DOI) - 26 Jul 2025

Abstract

Gantry cranes play a pivotal role in industrial production. Gantry cranes exhibit clear double-swing characteristics in actual working conditions, complicating anti-swing control. Most existing anti-swing control methods are based on a simplified single-pendulum model. The present paper puts forward a double-pendulum model for [...] Read more.

Gantry cranes play a pivotal role in industrial production. Gantry cranes exhibit clear double-swing characteristics in actual working conditions, complicating anti-swing control. Most existing anti-swing control methods are based on a simplified single-pendulum model. The present paper puts forward a double-pendulum model for gantry cranes and proposes an adaptive hierarchical sliding mode control based on a neural network according to the actual working conditions. The use of a neural network and adaptive layered sliding mode control can effectively inhibit chattering, thus improving control performance and stability and achieving the goal of anti-shaking control, thus effectively inhibiting residual oscillation. This method has been demonstrated to be effective in achieving the objective of anti-shudder control, thereby effectively suppressing residual oscillation. Compared with hierarchical sliding mode control, the proposed method reduces the maximum residual oscillation angle of the hook and payload by approximately 80%. In comparison with the conventional sliding mode control, the maximum residual oscillation angle is reduced by approximately 84%. Furthermore, the control force amplitude is reduced to 5.23 N, representing decreases of 30.2% and 37.4%, respectively. These comparative results demonstrate the superior oscillation suppression. The system also shows a reliable performance against potential disturbances. Full article

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28 pages, 17529 KiB

Open AccessArticle

Intelligent Functional Clustering and Spatial Interactions of Urban Freight System: A Data-Driven Framework for Decoding Heavy-Duty Truck Behavioral Heterogeneity

by Ruixu Pan, Quan Yuan, Chen Liu, Jiaming Cao and Xingyu Liang

Appl. Sci. 2025, 15(15), 8337; https://doi.org/10.3390/app15158337 (registering DOI) - 26 Jul 2025

Abstract

The rapid development of the logistics industry has underscored the urgent need for efficient and sustainable urban freight systems. As a core component of freight systems, heavy-duty trucks (HDT) have been researched regarding surface-level descriptive statistics of their heterogeneities, such as trip volume, [...] Read more.

The rapid development of the logistics industry has underscored the urgent need for efficient and sustainable urban freight systems. As a core component of freight systems, heavy-duty trucks (HDT) have been researched regarding surface-level descriptive statistics of their heterogeneities, such as trip volume, frequency, etc., but there is a lack of in-depth analyses of the spatial interaction between freight travel and freight functional clustering, which restricts a systematic understanding of freight systems. Against this backdrop, this study develops a data-driven framework to analyze HDT behavioral heterogeneity and its spatial interactions with a freight functional zone in Shanghai. Leveraging the high-frequency trajectory data of nearly 160,000 HDTs across seven types, we construct a set of regional indicators and employ hierarchical clustering, dividing the city into six freight functional zones. Combined with the HDTs’ application scenarios, functional characteristics, and trip distributions, we further analyze the spatial interaction between the HDTs and clustered zones. The results show that HDT travel patterns are not merely responses to freight demand but complex reflections of urban industrial structures, infrastructure networks, and policy environments. By embedding vehicle behaviors within their spatial and functional contexts, this study reveals a layered freight system in which each HDT type plays a distinct role in supporting economic activities. This research provides a new perspective for deeply understanding the formation mechanisms of HDT trip distributions and offers critical evidence for promoting targeted freight management strategies. Full article

(This article belongs to the Special Issue Intelligent Logistics and Supply Chain Systems)

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16 pages, 6701 KiB

Open AccessArticle

Rapid Prediction of High-Resolution 3D Ship Airwake in the Glide Path Based on CFD, BP Neural Network, and DWL

by Qingsong Liu, Gan Ren, Dingfu Zhou, Bo Liu and Zida Li

Appl. Sci. 2025, 15(15), 8336; https://doi.org/10.3390/app15158336 (registering DOI) - 26 Jul 2025

Abstract

To meet the requirements of the high spatiotemporal three-dimensional (3D) airflow field within the glide path corridor during carrier-based aircraft/unmanned aerial vehicles (UAVs) landings, this paper proposes a prediction method for high spatiotemporal resolution 3D ship airwake along the glide path by integrating [...] Read more.

To meet the requirements of the high spatiotemporal three-dimensional (3D) airflow field within the glide path corridor during carrier-based aircraft/unmanned aerial vehicles (UAVs) landings, this paper proposes a prediction method for high spatiotemporal resolution 3D ship airwake along the glide path by integrating computational fluid dynamics (CFD), backpropagation (BP) neural network, and Doppler wind lidar (DWL). Firstly, taking the conceptual design aircraft carrier model as the research object, CFD numerical simulations of the ship airwake within the glide path region are carried out using the Poly-Hexcore grid and the detached eddy simulation (DES)/the Reynolds-averaged Navier–Stokes (RANS) turbulence models. Then, using the high spatial resolution ship airwake along the glide path obtained from steady RANS computations under different inflow conditions as a sample dataset, the BP neural network prediction models were trained and optimized. Along the ideal glide path within 200 m behind the stern, the correlation coefficients between the predicted results of the BP neural network and the headwind, crosswind, and vertical wind of the testing samples exceeded 0.95, 0.91, and 0.82, respectively. Finally, using the inflow speed and direction with high temporal resolution from the bow direction obtained by the shipborne DWL as input, the BP prediction models can achieve accurate prediction of the 3D ship airwake along the glide path with high spatiotemporal resolution (3 m, 3 Hz). Full article

17 pages, 1850 KiB

Open AccessArticle

Cloud–Edge Collaborative Model Adaptation Based on Deep Q-Network and Transfer Feature Extraction

by Jue Chen, Xin Cheng, Yanjie Jia and Shuai Tan

Appl. Sci. 2025, 15(15), 8335; https://doi.org/10.3390/app15158335 (registering DOI) - 26 Jul 2025

Abstract

With the rapid development of smart devices and the Internet of Things (IoT), the explosive growth of data has placed increasingly higher demands on real-time processing and intelligent decision making. Cloud-edge collaborative computing has emerged as a mainstream architecture to address these challenges. [...] Read more.

With the rapid development of smart devices and the Internet of Things (IoT), the explosive growth of data has placed increasingly higher demands on real-time processing and intelligent decision making. Cloud-edge collaborative computing has emerged as a mainstream architecture to address these challenges. However, in sky-ground integrated systems, the limited computing capacity of edge devices and the inconsistency between cloud-side fusion results and edge-side detection outputs significantly undermine the reliability of edge inference. To overcome these issues, this paper proposes a cloud-edge collaborative model adaptation framework that integrates deep reinforcement learning via Deep Q-Networks (DQN) with local feature transfer. The framework enables category-level dynamic decision making, allowing for selective migration of classification head parameters to achieve on-demand adaptive optimization of the edge model and enhance consistency between cloud and edge results. Extensive experiments conducted on a large-scale multi-view remote sensing aircraft detection dataset demonstrate that the proposed method significantly improves cloud-edge consistency. The detection consistency rate reaches 90%, with some scenarios approaching 100%. Ablation studies further validate the necessity of the DQN-based decision strategy, which clearly outperforms static heuristics. In the model adaptation comparison, the proposed method improves the detection precision of the A321 category from 70.30% to 71.00% and the average precision (AP) from 53.66% to 53.71%. For the A330 category, the precision increases from 32.26% to 39.62%, indicating strong adaptability across different target types. This study offers a novel and effective solution for cloud-edge model adaptation under resource-constrained conditions, enhancing both the consistency of cloud-edge fusion and the robustness of edge-side intelligent inference. Full article

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34 pages, 1926 KiB

Open AccessArticle

A FAIR Resource Recommender System for Smart Open Scientific Inquiries

by Syed N. Sakib, Sajratul Y. Rubaiat, Kallol Naha, Hasan H. Rahman and Hasan M. Jamil

Appl. Sci. 2025, 15(15), 8334; https://doi.org/10.3390/app15158334 (registering DOI) - 26 Jul 2025

Abstract

A vast proportion of scientific data remains locked behind dynamic web interfaces, often called the deep web—inaccessible to conventional search engines and standard crawlers. This gap between data availability and machine usability hampers the goals of open science and automation. While registries like [...] Read more.

A vast proportion of scientific data remains locked behind dynamic web interfaces, often called the deep web—inaccessible to conventional search engines and standard crawlers. This gap between data availability and machine usability hampers the goals of open science and automation. While registries like FAIRsharing offer structured metadata describing data standards, repositories, and policies aligned with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles, they do not enable seamless, programmatic access to the underlying datasets. We present FAIRFind, a system designed to bridge this accessibility gap. FAIRFind autonomously discovers, interprets, and operationalizes access paths to biological databases on the deep web, regardless of their FAIR compliance. Central to our approach is the Deep Web Communication Protocol (DWCP), a resource description language that represents web forms, HyperText Markup Language (HTML) tables, and file-based data interfaces in a machine-actionable format. Leveraging large language models (LLMs), FAIRFind combines a specialized deep web crawler and web-form comprehension engine to transform passive web metadata into executable workflows. By indexing and embedding these workflows, FAIRFind enables natural language querying over diverse biological data sources and returns structured, source-resolved results. Evaluation across multiple open-source LLMs and database types demonstrates over 90% success in structured data extraction and high semantic retrieval accuracy. FAIRFind advances existing registries by turning linked resources from static references into actionable endpoints, laying a foundation for intelligent, autonomous data discovery across scientific domains. Full article

36 pages, 18110 KiB

Open AccessArticle

An Integral Fuzzy Model to Evaluate Slab and Beam Bridges with a Preventive Approach

by Paola Arriaga-Orejel, Luis Alberto Morales-Rosales, José Eleazar Arreygue-Rocha, Mariano Vargas-Santiago, Juan Carlos López-Pimentel and Manuel Jara-Díaz

Appl. Sci. 2025, 15(15), 8333; https://doi.org/10.3390/app15158333 (registering DOI) - 26 Jul 2025

Abstract

Bridges, owing to their intricacy, represent pivotal yet relatively underexplored assets within the domain of maintenance services in civil engineering. While international evaluation methodologies exist to gauge the overall condition of bridges, they often fall short in establishing interrelationships among individual elements, thereby [...] Read more.

Bridges, owing to their intricacy, represent pivotal yet relatively underexplored assets within the domain of maintenance services in civil engineering. While international evaluation methodologies exist to gauge the overall condition of bridges, they often fall short in establishing interrelationships among individual elements, thereby neglecting insights into the influence exerted by each element’s condition on the bridge’s overall performance. This research introduces an integral fuzzy model evaluation with a preventive approach, designed to assess both the integral condition of a bridge and its constituent elements. Furthermore, the study generates maintenance recommendations, subsequently evaluated by professionals to determine the most suitable course of action based on available resources. To validate the efficacy of the proposed model, a case study involving Bridge 15-016-00.0-0-04.0 PIV, known as “La Cuesta” in Mexico, is presented. The findings indicate that the bridge is in a satisfactory condition and warrants high-priority attention. Bridge analysis is compared with evaluations conducted using the methods of the Secretariat of Infrastructure, Communications, and Transportation (SICT), the American Association of State Highway and Transportation Officials (AASHTO), and the Ministry of Transport and Communications of Peru. The comparative evaluation reveals that our proposed model provides a more detailed representation of deterioration, facilitating more efficient maintenance planning by considering the hierarchical relationships between the bridge’s modules and elements. Full article

(This article belongs to the Special Issue Infrastructure Management and Maintenance: Methods and Applications)

27 pages, 3868 KiB

Open AccessArticle

Swin-ReshoUnet: A Seismic Profile Signal Reconstruction Method Integrating Hierarchical Convolution, ORCA Attention, and Residual Channel Attention Mechanism

by Jie Rao, Mingju Chen, Xiaofei Song, Chen Xie, Xueyang Duan, Xiao Hu, Senyuan Li and Xingyue Zhang

Appl. Sci. 2025, 15(15), 8332; https://doi.org/10.3390/app15158332 (registering DOI) - 26 Jul 2025

Abstract

This study proposes a Swin-ReshoUnet architecture with a three-level enhancement mechanism to address inefficiencies in multi-scale feature extraction and gradient degradation in deep networks for high-precision seismic exploration. The encoder uses a hierarchical convolution module to build a multi-scale feature pyramid, enhancing cross-scale [...] Read more.

This study proposes a Swin-ReshoUnet architecture with a three-level enhancement mechanism to address inefficiencies in multi-scale feature extraction and gradient degradation in deep networks for high-precision seismic exploration. The encoder uses a hierarchical convolution module to build a multi-scale feature pyramid, enhancing cross-scale geological signal representation. The decoder replaces traditional self-attention with ORCA attention to enable global context modeling with lower computational cost. Skip connections integrate a residual channel attention module, mitigating gradient degradation via dual-pooling feature fusion and activation optimization, forming a full-link optimization from low-level feature enhancement to high-level semantic integration. Simulated and real dataset experiments show that at decimation ratios of 0.1–0.5, the method significantly outperforms SwinUnet, TransUnet, etc., in reconstruction performance. Residual signals and F-K spectra verify high-fidelity reconstruction. Despite increased difficulty with higher sparsity, it maintains optimal performance with notable margins, demonstrating strong robustness. The proposed hierarchical feature enhancement and cross-scale attention strategies offer an efficient seismic profile signal reconstruction solution and show generality for migration to complex visual tasks, advancing geophysics-computer vision interdisciplinary innovation. Full article

21 pages, 3892 KiB

Open AccessArticle

Quantitative Analysis of the Fault Ride-Through Current and Control Parameters in Hybrid Modular Multilevel Converters

by Yi Xu and Bowen Tang

Appl. Sci. 2025, 15(15), 8331; https://doi.org/10.3390/app15158331 (registering DOI) - 26 Jul 2025

Abstract

A quantitative analysis of the fault transient is critical for system resilience assessment and protection coordination. Focusing on hybrid modular multilevel converter (MMC)-based HVDC architecture with enhanced fault ride-through (FRT) capability, this study develops a mathematical calculation framework to quantify how controller configurations [...] Read more.

A quantitative analysis of the fault transient is critical for system resilience assessment and protection coordination. Focusing on hybrid modular multilevel converter (MMC)-based HVDC architecture with enhanced fault ride-through (FRT) capability, this study develops a mathematical calculation framework to quantify how controller configurations influence fault current profiles. Unlike conventional static topologies (e.g., RLC or fixed-voltage RL circuits), the proposed model integrates an RL network with a time-variant controlled voltage source, which can emulate closed-loop control response during the FRT transient. Then, the quantitative relationship is established to map the parameters of DC controllers to the fault current across diverse FRT strategies, including scenarios where control saturation dominates the transient response. Simulation studies conducted on a two-terminal MMC-HVDC architecture substantiate the efficacy and precision of the developed methodology. The proposed method enables the evaluation of DC fault behavior for hybrid MMCs, concurrently appraising FRT control strategies. Full article

(This article belongs to the Special Issue Power Electronics: Control and Applications)

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28 pages, 7223 KiB

Open AccessSystematic Review

Anomaly Detection in Blockchain: A Systematic Review of Trends, Challenges, and Future Directions

by Ruslan Shevchuk, Vasyl Martsenyuk, Bogdan Adamyk, Vladlena Benson and Andriy Melnyk

Appl. Sci. 2025, 15(15), 8330; https://doi.org/10.3390/app15158330 (registering DOI) - 26 Jul 2025

Abstract

Blockchain technology’s increasing adoption across diverse sectors necessitates robust security measures to mitigate rising fraudulent activities. This paper presents a comprehensive bibliometric analysis of anomaly detection research in blockchain networks from 2017 to 2024, conducted under the PRISMA paradigm. Using CiteSpace 6.4.R1, we [...] Read more.

Blockchain technology’s increasing adoption across diverse sectors necessitates robust security measures to mitigate rising fraudulent activities. This paper presents a comprehensive bibliometric analysis of anomaly detection research in blockchain networks from 2017 to 2024, conducted under the PRISMA paradigm. Using CiteSpace 6.4.R1, we systematically map the knowledge domain based on 363 WoSCC-indexed articles. The analysis encompasses collaboration networks, co-citation patterns, citation bursts, and keyword trends to identify emerging research directions, influential contributors, and persistent challenges. The study reveals geographical concentrations of research activity, key institutional players, the evolution of theoretical frameworks, and shifts from basic security mechanisms to sophisticated machine learning and graph neural network approaches. This research summarizes the state of the field and highlights future directions essential for blockchain security. Full article

(This article belongs to the Special Issue Advanced Technologies in Data and Information Security, Fourth Edition)

29 pages, 12355 KiB

Open AccessArticle

Short- and Long-Term Mechanical and Durability Performance of Concrete with Copper Slag and Recycled Coarse Aggregate Under Magnesium Sulfate Attack

by Yimmy Fernando Silva, Claudia Burbano, Eduardo J. Rueda, Arturo Reyes-Román and Gerardo Araya-Letelier

Appl. Sci. 2025, 15(15), 8329; https://doi.org/10.3390/app15158329 (registering DOI) - 26 Jul 2025

Abstract

Sustainability in the construction sector has become a fundamental objective for mitigating escalating environmental challenges; given that concrete is the most widely used man-made material, extending its service life is therefore critical. Among durability concerns, magnesium sulfate (MgSO₄) attack is particularly [...] Read more.

Sustainability in the construction sector has become a fundamental objective for mitigating escalating environmental challenges; given that concrete is the most widely used man-made material, extending its service life is therefore critical. Among durability concerns, magnesium sulfate (MgSO₄) attack is particularly deleterious to concrete structures. Therefore, this study investigates the short- and long-term performance of concrete produced with copper slag (CS)—a massive waste generated by copper mining activities worldwide—employed as a supplementary cementitious material (SCM), together with recycled coarse aggregate (RCA), obtained from concrete construction and demolition waste, when exposed to MgSO₄. CS was used as a 15 vol% cement replacement, while RCA was incorporated at 0%, 20%, 50%, and 100 vol%. Compressive strength, bulk density, water absorption, and porosity were measured after water curing (7–388 days) and following immersion in a 5 wt.% MgSO₄ solution for 180 and 360 days. Microstructural characteristics were assessed using scanning electron microscopy (SEM), X-ray diffraction (XRD), thermogravimetric analysis with its differential thermogravimetric derivative (TG-DTG), and Fourier transform infrared spectroscopy (FTIR) techniques. The results indicated that replacing 15% cement with CS reduced 7-day strength by ≤10%, yet parity with the reference mix was reached at 90 days. Strength losses increased monotonically with RCA content. Under MgSO₄ exposure, all mixtures experienced an initial compressive strength gain during the short-term exposures (28–100 days), attributed to the pore-filling effect of expansive sulfate phases. However, at long-term exposure (180–360 days), a clear strength decline was observed, mainly due to internal cracking, brucite formation, and the transformation of C–S–H into non-cementitious M–S–H gel. Based on these findings, the combined use of CS and RCA at low replacement levels shows potential for producing environmentally friendly concrete with mechanical and durability performance comparable to those of concrete made entirely with virgin materials. Full article

(This article belongs to the Special Issue High-Performance Concrete: Preparation, Properties, Evaluation and Applications)

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Applied Sciences

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