Saved Queries

by Leonardo Vicente Jimenez, Gabriel Sánchez Pérez, José Portillo-Portillo, Linda Karina Toscano Medina, Aldo Hernández Suárez, Jesús Olivares Mercado and Héctor Manuel Pérez Meana

Eng. Proc. 2026, 123(1), 30; https://doi.org/10.3390/engproc2026123030 - 10 Feb 2026

Abstract

In recent years, the use of drones, also known as unmanned aerial vehicles (UAVs), has experienced a rapid increase due to their wide availability, compact size, low cost, and ease of operation. These devices have found applications in various areas, facilitating human work by covering large distances and operating in inaccessible or dangerous zones. However, their use has also been associated with malicious activities, such as property damage or threats to public security, which highlights the need to develop efficient and precise UAV detection systems. Although approaches based on neural networks have been proposed, they require large amounts of data for training and more computational resources for operation, which limits their applicability. In this study, we propose an alternative approach based on an analysis of audio features obtained through the fast Fourier transform (FFT) algorithm and classification using machine learning (ML) models. Our approach aims to detect the presence of drones using a minimal number of samples, meeting the requirements of efficiency, accuracy, robustness, low cost, and scalability necessary for a functional detection system. Full article

(This article belongs to the Proceedings of First Summer School on Artificial Intelligence in Cybersecurity)

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16 pages, 2563 KB

Open AccessArticle

Research on Partial Discharge Acoustic Emission Sensing Using Fiber Optic Sagnac Interferometer Based on Shaft–Type Multi–Order Resonant Mode Coupling

by Qichao Chen, Mengze Xu, Zhongyuan Li, Cong Chen and Weichao Zhang

Micromachines 2026, 17(2), 228; https://doi.org/10.3390/mi17020228 - 10 Feb 2026

Abstract

In response to the key issues of complex internal structure, significant attenuation of partial discharge (PD) ultrasound signal propagation, and low sensor sensitivity in large oil–immersed power transformers, this paper analyzes the multi–order resonant mode vibration characteristics of the shaft–type fiber optic ultrasound sensor core structure. The displacement distribution patterns of the core structure in both transverse and longitudinal resonant modes are clarified. A strategy using oblique fiber winding rings is proposed to eliminate the problems of strain cancellation and non–accumulation of displacement in transverse and longitudinal resonant modes, which are common in traditional fiber optic ultrasound sensors with parallel fiber windings. Furthermore, design principles are provided to enhance the coverage of the free end and the high–strain regions with semi–high symmetry, as well as the vector–integrated response suitable for multi–order modes. Experimental results show that, in typical PD model detection, the oblique winding sensor exhibits a more prominent response near the high–order resonances of the core, with a detection sensitivity approximately 2.5 times higher than that of the parallel winding structure, and an overall sensitivity at least 7.4 times greater than that of traditional Piezoelectric (PZT) sensors. This demonstrates that the fiber winding method is a key design parameter determining the acoustic–solid coupling efficiency and high sensitivity performance of shaft–type fiber optic interferometric PD sensors, providing a feasible path for high–reliability fiber optic sensing solutions for online monitoring of transformer partial discharges. Full article

(This article belongs to the Special Issue Micro- and Nanosensors: Fabrication, Applications and Performance Enhancements, Third Edition)

25 pages, 2547 KB

Open AccessArticle

A Differential-Based Siamese Network Integrating the CSWin Transformer for Rural Land Cover Semantic Change Detection

by Bo Si, Baiyu Dong and Ke Wang

Remote Sens. 2026, 18(4), 557; https://doi.org/10.3390/rs18040557 - 10 Feb 2026

Abstract

Deep learning-based methods for land cover semantic change detection utilizing high-resolution, multi-temporal remote sensing imagery have emerged as a research hotspot. However, traditional CNN methods often struggle to preserve long-range spatial context information and face challenges in detecting land cover types with complex semantic change patterns in natural scenes. To address these issues, this study proposes a novel network architecture that integrates a Siamese network with differential structures and a Transformer. First, we introduce residual learning modules to improve the extraction of differential features and strengthen the representation of local features. Second, we integrate the Cross-Shaped Window (CSWin) Transformer into a differential-based Siamese network to enhance global feature extraction. To promote model training and evaluation, we propose a rural land cover change detection dataset—a high-precision dataset comprising 6 main rural land cover types. Ablation and comparative experiments were conducted on the publicly available SECOND datasets and the self-built RLCD dataset. Ablation studies on the RLCD dataset demonstrate that DSTNet achieves significant improvements over the baseline, with increases of 1.77%, 1.95%, 2.57%, and 0.92% in mIoU, Sek, Fscd, and OA. Comparative experiments on the SECOND datasets reveal that the mIoU, Sek, Fsd, and OA scores of DSTNet surpassed the second-best accuracy by 1.04%, 2.15%, 2.28%, and 0.72%. Full article

(This article belongs to the Section AI Remote Sensing)

27 pages, 6836 KB

Open AccessArticle

HBIM Implementation in Architectural Heritage: A Multitemporal Case Study of the Church of La Sang in Llíria

by Inmaculada Oliver-Faubel, María Eugenia Torner-Feltrer, Emma Barelles-Vicente and Sergio Moral Saiz

Heritage 2026, 9(2), 68; https://doi.org/10.3390/heritage9020068 - 10 Feb 2026

Abstract

The conservation of architectural heritage poses significant challenges in buildings characterised by complex construction sequences, cumulative transformations and fragmented documentation, where traditional methods are insufficient to coherently integrate geometry, historical information and stratigraphic analysis. This study proposes and applies a multitemporal Heritage Building Information Modeling (HBIM) workflow aimed at reconstructing and managing the historical evolution of architecture, using the Church of La Sang in Llíria (València, Spain) as a case study characterised by the superposition of Islamic, Gothic and contemporary phases. The methodology combines documentary and archaeological analysis, in situ stratigraphic observation and high-resolution terrestrial laser scanning as the geometric basis of the HBIM model. Historical phases are integrated as structural components of the information model, with explicit documentation of interpretative hypotheses and associated levels of reliability. The results show that the proposed approach enables the identification and reinterpretation of spatial and constructive .relationships not previously described, the critical assessment of existing historical hypotheses, and the generation of coherent three-dimensional reconstructions even in contexts with incomplete information. The resulting documentary archive facilitates diachronic comparison of phases, ensures traceability of constructive elements and supports the production of reliable graphic and analytical documentation, establishing itself as a valuable tool for historical research, heritage management and the planning of future conservation interventions. Full article

(This article belongs to the Section Architectural Heritage)

32 pages, 800 KB

Open AccessArticle

Achieving Sustainable Performance Through Digital Knowledge Integration: The Roles of Green Knowledge Sharing and Digital Leadership in the Hospitality Industry

by Nour K. M. Bahar, Cem Tanova and Mehmet Yeşiltaş

Sustainability 2026, 18(4), 1813; https://doi.org/10.3390/su18041813 - 10 Feb 2026

Abstract

Sustainable performance in today’s digital world relies on understanding how technology supports sustainability through organisational processes and leadership. This study applies the Knowledge-Based View and Dynamic Capabilities Theory. It assesses how digital knowledge integration impacts sustainable performance in the hospitality sector. The study examines whether green knowledge sharing mediates the link between digital knowledge integration and sustainable performance. It also explores whether digital leadership strengthens this link. The research team collected data from 373 hotel and restaurant managers in Jordan and analysed the results using SmartPLS version 4. The analysis shows that digital knowledge integration enhances both sustainable performance and green knowledge sharing. Green knowledge sharing strongly associates with sustainable performance. Mediation analysis shows that green knowledge sharing partly explains the effect of digital knowledge integration on sustainable performance. Moderation analysis reveals that digital leadership amplifies the link between digital knowledge integration and sustainable performance. However, digital knowledge integration does not significantly affect the relationship between green knowledge sharing and sustainable performance. These findings clarify how digital knowledge integration, green knowledge sharing, and digital leadership interact to affect sustainable performance. The study provides practical and theoretical implications for hospitality managers aiming to leverage digital transformation and leadership to achieve sustainability goals. Full article

(This article belongs to the Special Issue Knowledge Management and Digital Transformation in Sustainability)

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20 pages, 3872 KB

Open AccessArticle

Bridging AI Education and Sustainable Development: Design-Based Research on First-Year Undergraduates’ Systems Analysis for Habitat Conservation

by Yanhong Lin, Jianhua Liao, Ying Zhong, Ling Liu and Shunzhi Zhu

Sustainability 2026, 18(4), 1812; https://doi.org/10.3390/su18041812 - 10 Feb 2026

Abstract

Against global challenges like climate change and biodiversity loss, sustainable development is the core orientation of engineering education transformation. Cultivating talents with interdisciplinary perspectives, systemic thinking and AI literacy is crucial for implementing the UN 2030 Sustainable Development Agenda. However, AI education focuses on seniors or graduates, with freshmen’s use of AI acting as “cognitive partners” for knowledge construction and complex problem-solving understudied, constraining AI’s potential in fostering early systemic thinking. We present a novel teaching practice integrating generative AI into an “AI-Environmental System Analysis” module, with Sousa chinensis habitat conservation as the case. Using a design-based research paradigm, we evaluated 24 student groups via system analysis briefs, AI usage reflections and course assessment data. Results show that the module effectively guided students to establish preliminary system analysis frameworks, with over 70% of groups identifying complex interactions among environmental factors. Students’ AI applications ranged from information retrieval to scenario simulation, initially forming systemic thinking and responsible AI literacy for sustainable development. This study provides a replicable paradigm for integrating AI and sustainable development education, clarifies the key role of structured instructional scaffolding, and enriches sustainable development-oriented engineering education pathways. Full article

(This article belongs to the Topic Establishing Professional Competency and Talent Cultivation Strategies in the Post-Pandemic Generation Based on the Sustainability Education Development)

27 pages, 24500 KB

Open AccessArticle

Establishing Linear Cultural Heritage Corridors by Integrating Cultural and Ecological Values: A Case Study of the Jinzhong Section of the Great Tea Road

by Lihao Meng, Bolun Zhang and Lei Cao

Land 2026, 15(2), 293; https://doi.org/10.3390/land15020293 - 10 Feb 2026

Abstract

To address the challenge of disconnection between cultural and ecological values in Linear Cultural Heritage (LCH) conservation, this study examines the Jinzhong section of the Great Tea Road to develop a dual-dimensional framework for corridor identification and collaborative governance. The research establishes a dual-value evaluation system encompassing cultural and ecological dimensions, applied to grade 422 heritage sites. A potential corridor network is subsequently generated using the Minimum Cumulative Resistance (MCR) model. The study innovatively integrates the Multiple Centrality Analysis (MCA) model, employing heritage site values as network weights to identify and classify two primary corridor types: “culture-dominant” and “ecology-dominant” corridors. Through spatial overlay analysis, a ‘culture–ecology composite corridor’ network is ultimately constructed. The results demonstrate that the cultural value network exhibits a “monocentric” clustering pattern, whilst the ecological value network displays a “multicentric, networked” configuration, revealing significant spatial disjunction between the two systems. This analysis enables the identification of three corridor typologies—culturally dominant, ecologically dominant, and composite corridors integrating both values—alongside the positioning of key connectivity hubs and network vulnerability points across distinct value zones. The proposed “dual-dimension Multiple Centrality Analysis analytical framework” transforms the abstract concept of cultural–ecological value coupling into a quantifiable spatial analysis pathway, thereby addressing existing research gaps. This framework provides refined decision-making support for both conservation practices and World Heritage nomination processes of the Jinzhong section of the Great Tea Road, whilst offering a replicable scientific methodology for conserving comparable linear heritage sites globally. Full article

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24 pages, 8468 KB

Open AccessArticle

Ecological Stability Status and Zoning Prediction of the Ordos Plateau: Coupling Analysis of Landscape Ecological Risk and Ecosystem Service Value

by Siqi Li, Haibing Wang, Huricha Ao and Xiaofei Yuan

Land 2026, 15(2), 292; https://doi.org/10.3390/land15020292 - 10 Feb 2026

Abstract

With the intensification of climate change, land use changes, and human activities, the ecosystems of the Ordos Plateau face severe challenges. This study evaluates the current ecological stability of the Ordos Plateau and makes future ecological zoning predictions based on the coupling analysis of landscape ecological risk (LER) and ecosystem service value (ESV). By analyzing the changes in LER and ESV from 2000 to 2023, the study reveals the response characteristics of the region’s ecosystems under climate change and land use changes. The results show that the rational utilization of grasslands and forests has effectively improved land use efficiency, reduced ecological risks, and enhanced ecosystem service value. Ecological restoration projects in the Mu Us Sandy Land and Kubuqi Desert have promoted the transformation of ecologically fragile areas (Zone IV) into optimized management areas (Zone II). However, the Kubuqi Desert remains a core area of ecological fragility. When planning for the future of the Ordos Plateau, it is important to pay attention to the scientific planning of arable land development to prevent further deterioration of the ecological situation. The future scenario prediction based on the intPLUS model indicates that reasonable ecological management policies will contribute to the sustainable development of the region. The research results provide a theoretical framework and practical reference for ecological restoration and management in the Ordos Plateau and similar arid and semi-arid regions. Full article

(This article belongs to the Section Landscape Ecology)

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16 pages, 990 KB

Open AccessArticle

Commercial Running Spaces on the Reproduction of Gender Inequality

by Lilly McGrath, Jaruwan Kumpetch, Sumate Noklang and Peeradet Prakongpan

Soc. Sci. 2026, 15(2), 107; https://doi.org/10.3390/socsci15020107 - 10 Feb 2026

Abstract

This study explores how commercialization shapes gender representation and inequality within contemporary running culture. Situated within the broader context of sport and media consumption, it examines how bodies, identities, and spaces are disciplined by market-driven values. Using a critical ethnographic approach, 10 months of fieldwork were conducted across various running events in multiple urban locations. The primary researcher, a semi-professional female runner, participated as both insider and critical observer, supported by a research team in data collection, reflexive journaling, and thematic analysis. The findings reveal that promotional campaigns and commercial spaces reproduce gendered ideals: women are highlighted for beauty, charm, and body esthetics, while men are portrayed for endurance and performance. Female runners are frequently deployed as “marketing capital,” valued more for visual appeal than athletic ability. These dynamics transform public running spaces into gendered, semi-commercial arenas governed by capital, consumer culture, and the male gaze, reinforcing structural inequality under the guise of empowerment. Full article

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12 pages, 740 KB

Open AccessArticle

Publication Patterns in Engineering: A Quantitative Comparison of Open Access and Subscription-Based Journals

by Luís Eduardo Pilatti, Luiz Alberto Pilatti, Gustavo Dambiski Gomes de Carvalho and Luis Mauricio Martins de Resende

Publications 2026, 14(1), 11; https://doi.org/10.3390/publications14010011 - 10 Feb 2026

Abstract

We compare the publication performance of open-access (OA) and subscription-based (SB) journals in Engineering using journal-level indicators from Scopus (CiteScore 2023 view; data collected on 2 December 2024). We analysed 3013 active Engineering journals with an assigned CiteScore quartile (Q1–Q4, where Q1 denotes the highest CiteScore quartile), of which 770 are labelled OA in Scopus; the remaining journals in each stratum were classified as SB. We stratified journals by CiteScore quartile and by the top 10% CiteScore percentile. We examined four indicators for 2020–2023: CiteScore 2023, total citations, number of published documents, and the percentage of cited articles. Because citation and publication counts are strongly right-skewed, we report medians and use Mann–Whitney tests with effect sizes (Cliff’s delta) and false discovery rate correction; Welch tests on log-transformed counts are used as sensitivity analyses. SB journals exhibit substantially higher citation and document medians across all quartiles and in the top 10% stratum, whereas CiteScore medians are very similar between access models. OA journals represent about one quarter of Engineering journals in Scopus, but remain underrepresented in the top 10% segment (125 of 484). Overall, OA provides a competitive level of impact, while SB titles still dominate accumulated visibility and editorial scale in Engineering. Full article

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20 pages, 15443 KB

Open AccessArticle

A Study on the Reduction of Light Load Loss in the Standalone Operation of LDC in Integrated Charging System for Electric Vehicles with 2-Transformer

by Yeongseon Lee, Seungmin Kim, Min-Jung Kim, Hee-Keun Shin and Dong-Hee Kim

Appl. Sci. 2026, 16(4), 1751; https://doi.org/10.3390/app16041751 - 10 Feb 2026

Abstract

This paper proposes a novel 2-transformer (2-Trans)-based integrated on-board charger (OBC) and low-voltage DC/DC converter (LDC) system for electric vehicles. Conventional integrated OBC–LDC systems employing a three-winding transformer suffer from reduced light-load efficiency during standalone LDC operation because core losses dominate when designers size the transformer for high-power operation. In addition, concentrating multiple windings on a single magnetic core limits transformer design flexibility and causes complex magnetic coupling among the windings. To effectively reduce light-load losses and enhance transformer design freedom, this paper introduces a new integrated charging architecture that utilizes two independent transformers. The proposed system adopts a dual-active-bridge (DAB) converter for high-voltage battery charging and a phase-shift full-bridge (PSFB) converter for low-voltage battery charging. The system supports both simultaneous high- and low-voltage battery charging and standalone low-voltage battery operation, and a dual-phase-shift (DPS) control strategy enables independent and proper power flow control. Experimental results obtained from an 11 kW OBC and a 3 kW LDC prototype demonstrate up to a 33% reduction in light-load losses during standalone LDC operation and confirm the feasibility of improving power density through the proposed 2-Trans-based architecture. Full article

(This article belongs to the Special Issue Control Systems for Next Generation Electric Applications, 2nd Edition)

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25 pages, 8759 KB

Open AccessArticle

Safe Guidance Strategy for Affine Formation Manoeuvre of ASVs Using the Interference Vector Method

by Yiping Liu and Jianqiang Zhang

J. Mar. Sci. Eng. 2026, 14(4), 341; https://doi.org/10.3390/jmse14040341 - 10 Feb 2026

Abstract

This paper presents a safe guidance strategy for affine formations based on the Interference Vector Method (IVM) to address dynamic formation guidance and collision avoidance for Autonomous Surface Vessels (ASVs) in multi-obstacle environments. An affine formation control framework is first adopted to enable dynamic formation transformations for the Autonomous Surface Vessel (ASV) fleet. Building on this, an IVM-based obstacle avoidance method is developed, enabling the formation to evade both static and dynamic obstacles in real time. Furthermore, a course guidance law based on the Vector Field Method (VFM) and a speed magnitude guidance law based on Control Barrier Functions (CBFs) are proposed to simultaneously achieve formation guidance and prevent inter-vessel collisions. The proposed safe guidance strategy is rigorously validated through theoretical proofs and comprehensive numerical simulations. The simulation results further confirm the robustness of the obstacle avoidance algorithm under ideal perception conditions, as well as the practical applicability of the overall strategy in complex, obstacle-rich environments. Full article

(This article belongs to the Special Issue Advanced Studies in Marine Vessel Motion Control)

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26 pages, 15341 KB

Open AccessArticle

A Multimodal Three-Channel Bearing Fault Diagnosis Method Based on CNN Fusion Attention Mechanism Under Strong Noise Conditions

by Yingyong Zou, Chunfang Li, Yu Zhang, Zhiqiang Si and Long Li

Algorithms 2026, 19(2), 144; https://doi.org/10.3390/a19020144 - 10 Feb 2026

Abstract

Bearings, as core components of mechanical equipment, play a critical role in ensuring equipment safety and reliability. Early fault detection holds significant importance. Addressing the challenges of insufficient robustness in bearing fault diagnosis under industrial high-noise conditions and the difficulty of extracting fault features from a single modality, this study proposes a three-channel multimodal fault diagnosis method that integrates a Convolutional Auto-Encoder (CAE) with a dual attention mechanism (M-CNNBiAM). This approach provides an effective technical solution for the precise diagnosis of bearing faults in high-noise environments. To suppress substantial noise interference, a CAE denoising module was designed to filter out intense noise, providing high-quality input for subsequent diagnostic networks. To address the limitations of single-modal feature extraction and restricted generalization capabilities, a three-channel time–frequency signal joint diagnosis model combining the Continuous Wavelet Transform (CWT) with an attention mechanism was proposed. This approach enables deep mining and efficient fusion of multi-domain features, thereby enhancing fault diagnosis accuracy and generalization capabilities. Experimental results demonstrate that the designed CAE module maintains excellent noise reduction performance even under −10 dB strong noise conditions. When combined with the proposed diagnostic model, it achieves an average diagnostic accuracy of 98% across both the CWRU and self-test datasets, demonstrating outstanding diagnostic precision. Furthermore, under −4 dB noise conditions, it achieves a 94% diagnostic accuracy even without relying on the CAE denoising module. With a single training cycle taking only 6.8 s, it balances training efficiency and diagnostic performance, making it well-suited for real-time, reliable bearing fault diagnosis in industrial environments with high noise levels. Full article

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3 pages, 153 KB

Open AccessEditorial

New Challenges and Trends in Agri-Environmental Management: Accomplishing of Sustainable Development Goals

by Péter Tamás Nagy

Agriculture 2026, 16(4), 410; https://doi.org/10.3390/agriculture16040410 - 10 Feb 2026

Abstract

In recent years, agri-environmental management has entered a transformative period shaped by accelerating global change and a growing demand for sustainable food production systems [...] Full article

(This article belongs to the Special Issue New Challenges and Trends in Agri-Environmental Management: Accomplishment of Sustainable Development Goals)

49 pages, 1517 KB

Open AccessArticle

Enhanced Rotating Machinery Fault Diagnosis Using Hybrid RBSO–MRFO Adaptive Transformer-LSTM for Binary and Multi-Class Classification

by Amir R. Ali and Hossam Kamal

Machines 2026, 14(2), 208; https://doi.org/10.3390/machines14020208 - 10 Feb 2026

Abstract

Accurate fault diagnosis in rotating machinery is critical for predictive maintenance and operational reliability in industrial applications. Despite the effectiveness of deep learning, many models underperform due to manually selected hyperparameters, which can lead to premature convergence, overfitting, weak generalization, and inconsistent performance across binary and multi-class classification. To address these limitations, the study proposes a novel hybrid hyperparameter optimization framework that combines Robotic Brain Storm Optimization (RBSO) with Manta Ray Foraging Optimization (MRFO) to optimally fine-tune deep learning architectures, including MLP, LSTM, GRU-TCN, CNN-BiLSTM, and Transformer-LSTM models. The framework leverages RBSO for global search to promote diversity and prevent premature convergence, and MRFO for local search to enhance convergence toward optimal solutions, with their combined effect improving predictive model performance and methodological generalization. The approach was validated on three benchmark datasets, including Case Western Reserve University (CWRU), industrial machine fault detection (TMFD), and the Machinery Fault Dataset (MaFaulDa). Before optimization, Transformer-LSTM model achieved 98.35% and 97.21% accuracy on CWRU binary and multi-class classification, 99.52% and 98.57% on TMFD, and 98.18% and 92.82% on MaFaulDa. Following hybrid optimization, Transformer-LSTM exhibited superior performance, with accuracies increasing to 99.72% for both CWRU tasks, 99.97% for TMFD, and 99.98% and 98.60% for MaFaulDa, substantially reducing misclassification. These results demonstrate that the proposed RBSO–MRFO framework provides a scalable, robust, and high-accuracy solution for intelligent fault diagnosis in rotating machinery. Full article

(This article belongs to the Section Machines Testing and Maintenance)

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