by Carlos Cobreros, Morena Villalón, Gabriel E. Calle-Sáenz, Adriana Rivas-Madrigal, Luis Miguel Gutierrez-Contreras, Daniela B. Arias-Laurino and Mariana Covarrubias-Castro

Land 2026, 15(4), 542; https://doi.org/10.3390/land15040542 (registering DOI) - 26 Mar 2026

Abstract

Humanity is facing an unprecedented socio-ecological and climate crisis resulting from human impact on the planet, which requires a profound transformation in how we inhabit and develop our territories. Regenerative development is emerging as a key approach to strengthening living systems and improving environmental health. In this context, United Nations Educational, Scientific and Cultural Organization (UNESCO) Biosphere Reserves are consolidating their role as strategic instruments that link biodiversity conservation with sustainable development through integrated and participatory land management models. Mexico stands out for its regional and global leadership in implementing these areas. Participatory governance, promoted by the Man and Biosphere (MAB) programme, encourages the active involvement of local communities. This article analyses the application of a regenerative and participatory design methodology in a Biosphere Reserve, evaluating both the process and the tools used. Beyond the fulfilment of sustainability objectives, it examines the lessons learned, results and scope from a regenerative perspective, providing critical reflections on its effectiveness as a strategy for the socio-ecological management of vulnerable territories. Full article

(This article belongs to the Special Issue Advancing Urban–Rural Community Empowerment Through Participatory Planning and Collaborative Co-Creation)

27 pages, 1705 KB

Open AccessArticle

Research on the Significance of Criteria Influencing the Deployment of Micromobility Devices in Cities Using Multi-Criteria Decision-Making (MCDM) Methods

by Henrikas Sivilevičius, Vidas Žuraulis, Edita Juodvalkienė and Donatas Čygas

Sustainability 2026, 18(7), 3254; https://doi.org/10.3390/su18073254 (registering DOI) - 26 Mar 2026

Abstract

Urban mobility is increasingly affected by air pollution and traffic congestion caused by conventional private vehicles, as well as by insufficient flexibility of public transport. Micromobility devices (MMDs) can mitigate these and other negative impacts on quality of life due to their distinctive characteristics, the significance of which is investigated in this research. To address these challenges facing the modern city, a system of 15 hierarchically unstructured criteria influencing the deployment of MMDs in urban areas was established. The relative weights of these criteria were calculated based on the assessments of 16 experts and the criterion weights were determined using four multi-criteria decision-making (MCDM) methods: ARTIW-L (Average Rank Transformation into Weight—Linear), ARTIW-N (Average Rank Transformation into Weight—Non-Linear), DPW (Direct Percentage Weight), and AHP (Analytic Hierarchy Process). The results indicate that the expert judgments are consistent, as Kendall’s coefficient of concordance 0.406 is 3.8 times greater than the minimum value of 0.106 (at a significance level 0.05 and 14 degrees of freedom). In addition, the consistency ratios (C.R.) calculated from the AHP pairwise comparison matrices were below 0.1. The demonstrated consistency of the expert judgements and the compatibility of all matrices justify adopting the average of the relative weights obtained using the four MCDM methods as the final solution. According to the experts, the most important criteria for MMD deployment are travel safety (0.1336), travel duration (0.1302), the influence of infrastructure quality on comfort (0.0841), impact on health (0.0805), and the cost of purchasing an MMD (0.0713), while the remaining criteria are of lower significance. Based on the research results it is expected that the identified micromobility implementation measures will be useful for decision-makers and urban development planners. Full article

(This article belongs to the Section Sustainable Transportation)

28 pages, 657 KB

Open AccessArticle

An Uncertainty-Aware Temporal Transformer for Probabilistic Interval Modeling in Wind Power Forecasting

by Shengshun Sun, Meitong Chen, Mafangzhou Mo, Xu Yan, Ziyu Xiong, Yang Hu and Yan Zhan

Sensors 2026, 26(7), 2072; https://doi.org/10.3390/s26072072 (registering DOI) - 26 Mar 2026

Abstract

Under high renewable energy penetration, wind power forecasting faces pronounced challenges due to strong randomness and uncertainty, making conventional point-forecast-centric paradigms insufficient for risk-aware and reliable power system scheduling. An uncertainty-aware temporal transformer framework for wind power forecasting is presented, integrating probabilistic modeling with deep temporal representation learning to jointly optimize prediction accuracy and uncertainty characterization. Crucially, rather than treating uncertainty quantification merely as a post-processing step, the central conceptual contribution lies in modularizing uncertainty directly within the attention mechanism. A probability-driven temporal attention mechanism is incorporated at the encoding stage to emphasize high-variability and high-risk time slices during feature aggregation, while a multi-quantile output and interval modeling strategy is adopted at the prediction stage to directly learn the conditional distribution of wind power, enabling simultaneous point and interval forecasts with statistical confidence. Extensive experiments on multiple public wind power datasets demonstrate that the proposed method consistently outperforms traditional statistical models, deep temporal models, and deterministic transformers, as validated by formal statistical significance testing. Specifically, the method achieves an MAE of

0.089

, an RMSE of

0.132

, and a MAPE of

10.84 %

on the test set, corresponding to reductions of approximately

8 %

–

10 %

relative to the deterministic transformer. In uncertainty evaluation, a PICP of

0.91

is attained while compressing the MPIW to

0.221

and reducing the CWC to

0.241

, indicating a favorable balance between coverage reliability and interval compactness. Compared with mainstream probabilistic forecasting methods, the model further reduces RMSE while maintaining coverage levels close to the 90% target, effectively mitigating excessive interval conservatism. Moreover, by adaptively generating heteroscedastic intervals that widen during high-volatility events and narrow under stable conditions, the model achieves a highly focused and effective capture of critical uncertainty information. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Sensing)

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25 pages, 2317 KB

Open AccessArticle

Integrating Digital Twins into Smart Warehousing: A Practice-Based View Framework for Identifying and Prioritizing Critical Success Factors

by Sadia Samar Ali, Jose Antonio Marmolejo-Saucedo, Rosario Landa Piedra and Gerhard-Wilhelm Weber

Logistics 2026, 10(4), 73; https://doi.org/10.3390/logistics10040073 (registering DOI) - 26 Mar 2026

Abstract

Background. Smart warehousing increasingly relies on digital twin technologies to enhance operational efficiency, real-time visibility, and decision-making in logistics systems. However, existing research primarily focuses on technological capabilities while paying limited attention to the organizational practices that shape successful implementation. Methods. This study aims to identify and prioritize the critical success factors (CSFs) for integrating digital twins into smart warehousing using the Practice-Based View (PBV) as the theoretical lens. Based on insights from prior research and expert validation, nine CSFs were identified and evaluated using the Best–Worst Method (BWM). Empirical input was obtained from six industry experts with experience in digital transformation, warehousing, and supply chain management. Results. The results indicate that collaborative learning, contextual training, and gamification elements emerge as the most influential critical success factors, highlighting the importance of organizational practices in supporting digital twin adoption in smart warehousing. Conclusions. By linking technological capabilities with organizational routines, the proposed framework provides both theoretical insights and practical guidance for implementing digital twins in smart warehouse environments. Full article

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35 pages, 4321 KB

Open AccessArticle

Syncretic Grad-CAM Integrated ViT-CNN Hybrids with Inherent Explainability for Early Thyroid Cancer Diagnosis from Ultrasound

by Ahmed Y. Alhafdhi, Gibrael Abosamra and Abdulrhman M. Alshareef

Diagnostics 2026, 16(7), 999; https://doi.org/10.3390/diagnostics16070999 (registering DOI) - 26 Mar 2026

Abstract

Background/Objectives: Accurate detection of thyroid cancer using ultrasound remains a challenge, as malignant nodules can be microscopic and heterogeneous, easily confused with point clusters and borderline-featured tissues. Current studies in deep learning demonstrate good performance with convolutional neural networks (CNNs) and clustering; however, many approaches focus on local tissue and provide limited, non-quantitative interpretation, reducing clinical confidence. This study proposes an integrated framework combining enhanced convolutional feature encoders (DenseNet169 and VGG19) with an enhanced vision transformer (ViT-E) to integrate local feature and global relational context during learning, rather than delayed integration. Methods: The proposed framework integrates enhanced convolutional feature encoders (DenseNet169 and VGG19) with an enhanced vision transformer (ViT-E), enabling simultaneous learning of local feature representations and global relational context. This design allows feature fusion during the learning stage instead of delayed integration, aiming to improve diagnostic performance and interpretability in thyroid ultrasound image analysis. Results: The best-performing model, ViT-E–DenseNet169, achieved 98.5% accuracy, 98.9% sensitivity, 99.15% specificity, and 97.35% AUC, surpassing the robust basic hybrid model (CNN–XGBoost/ANN) and existing systems. A second contribution is improved interpretability, moving from mere illustration to validation. Gradient-weighted class activation mapping (Grad-CAM) maps demonstrated distinct and clinically understandable concentration patterns across various thyroid cancers: precise intralesional concentration for high-confidence malignancies (PTC = 0.968), edge/interface concentration for capsule risk patterns (PTC = 0.957), and broader-field activation consistent with infiltration concerns (PTC = 0.984), while benign scans showed low and diffuse activation (PTC = 0.002). Spatial audits reinforced this behavior (IoU/PAP: 0.72/91%, 0.65/78%, 0.58/62%). Conclusions: The integrated ViT-E–DenseNet169 framework provides highly accurate thyroid cancer detection while offering clinically meaningful interpretability through Grad-CAM-based spatial validation, supporting improved confidence in AI-assisted ultrasound diagnosis. Full article

(This article belongs to the Special Issue Deep Learning Techniques for Medical Image Analysis)

32 pages, 1385 KB

Open AccessArticle

The Role of Generative Artificial Intelligence in Developing Cognitive and Research Talent Among Postgraduate Students

by Asem Mohammed Ibrahim, Reem Ebraheem Saleh Alhomayani and Azhar Saleh Abdulhadi Al-Shamrani

J. Intell. 2026, 14(4), 53; https://doi.org/10.3390/jintelligence14040053 (registering DOI) - 26 Mar 2026

Abstract

Generative Artificial Intelligence (GAI) is rapidly transforming higher education by introducing new mechanisms for supporting the development of advanced cognitive processes and research-related capabilities. This study examines how postgraduate students employ GAI to develop their cognitive and research talent, conceptualized here as higher-order academic skills such as analysis, synthesis, and critical reasoning, across six domains: literature review, theoretical development, research design, data analysis, academic writing, ethical use, and challenges encountered—signaled explicitly rather than listed line by line. We administered a validated multidimensional scale to 214 postgraduate students, and the results indicate a moderate overall use of GAI, with notably high involvement in practices that emphasize ethics and responsibility. Students reported clear cognitive benefits in tasks involving information processing, linguistic refinement, and conceptual clarification while showing caution toward delegating higher-order analytical or theoretical reasoning to AI systems. Key challenges included limited institutional training, concerns about data privacy and academic integrity, and difficulties evaluating the originality and reliability of AI-generated content. Inferential analyses indicated significant differences based on gender, academic level, and general technology proficiency, whereas no differences emerged across age groups, departments, or specializations. Overall, this study demonstrates how GAI can contribute to the development of higher-level cognitive skills and research competencies, with “moderate use” operationalized as consistent but selective engagement across domains, while underscoring the need for structured training, clear guidelines, and teaching approaches that foster the responsible and effective incorporation of AI within postgraduate research. The results highlight practical implications for higher education, including the importance of institutional training programs, governance frameworks for responsible AI use, and pedagogical models that foster critical engagement with GAI. Full article

(This article belongs to the Special Issue Advances of AI in Talent Development: Synergies Between Creativity, Cognitive Intelligence, and Socio-Emotional Growth)

27 pages, 7144 KB

Open AccessArticle

Incorporating Sediment Compaction into Reservoir Sedimentation Estimates Using Machine Learning: Case Study of the Xiluodu Reservoir

by Guozheng Feng, Xiujun Dong, Wanbing Peng, Zhenyong Sun, Jun Li and Jinhua Nie

Sustainability 2026, 18(7), 3249; https://doi.org/10.3390/su18073249 (registering DOI) - 26 Mar 2026

Abstract

Hydropower is a cornerstone of global renewable energy; however, reservoir sedimentation directly undermines its benefits and operational lifespan. A critical, often overlooked, aspect of sedimentation is the compaction of fine-grained deposits, which introduces systematic discrepancies between standard siltation calculation methods. This study addresses this gap by developing a machine learning-based model to quantify sediment compaction and correct siltation estimates using the Xiluodu Hydropower Station on the Jinsha River, China, as a case study from 2014 to 2020. Based on hydrological, sediment, and fixed-section monitoring data, we applied five machine learning algorithms (Linear Regression, Neural Network, Random Forest, Gradient Boosting, and Support Vector Regression) to establish a relationship between the compaction thickness and the following key predictors: Year, Cumulative Sediment Thickness, Annual Sediment Thickness, and Distance to the Dam. The results demonstrate that the Neural Network (NN) model significantly outperforms traditional models, effectively capturing complex, nonlinear compaction dynamics with strong predictive accuracy (test R² = 0.766, RMSE = 0.047 m) and no significant overfitting. SHAP analysis revealed the dominant influences of consolidation time (years) and overburden stress (Cumulative Sediment Thickness), linking the model’s predictions to fundamental geotechnical principles. Applying the NN model to correct for the cross-sectional volume method markedly improved its consistency with the independent sediment transport method, reducing the average relative difference from −33.7% to −6.5% (2016–2020). This study provides the first quantitative, continuous (198 km, 221 sections) assessment of reservoir-scale sediment compaction, confirming its widespread existence and demonstrating its critical role in the long-standing methodological discrepancies. Our study transformed compaction from an acknowledged phenomenon into a quantifiable correction, offering a novel, data-driven framework to enhance the accuracy of reservoir sedimentation assessments globally. Full article

(This article belongs to the Special Issue Sediment Movement, Sustainable Water Conservancy and Water Transport)

16 pages, 10306 KB

Open AccessArticle

Plot Subdivision Heterogeneity and Urban Resilience: Preservation, Multifunctionality, and Socio-Cultural Adaptability Across Global Case Studies

by Jose Antonio Lara-Hernandez and Alessandro Melis

Land 2026, 15(4), 540; https://doi.org/10.3390/land15040540 (registering DOI) - 26 Mar 2026

Abstract

In an era of rapid urbanisation and climate challenges, understanding how urban land patterns contribute to resilience is crucial for sustainable development. This theoretical review introduces a novel framework positing that greater heterogeneity in plot sizes and land uses enhances urban resilience by promoting the long-term preservation of built environments, multifunctional spaces, and socio-cultural adaptability. Drawing on urban morphology, assemblage theory, and resilience science, we argue that fragmented ownership in small-plot fabrics acts as a barrier to large-scale redevelopment, fostering diversity that buffers against shocks. Through comparative case studies of Venice (Italy), Tokyo (Japan), Hong Kong, Mexico City (Mexico), and York (UK), we illustrate how historical small-plot subdivisions have endured centuries, supporting ecological, economic, and social sustainability. The analysis reveals common patterns: ownership fragmentation preserves fine-grained urban forms, enabling adaptive reuse (exaptation) and inclusivity. The five case studies serve an illustrative function, demonstrating how the theoretical linkages between plot heterogeneity, institutional friction, incremental transformation, and long-term resilience outcomes can plausibly operate in real-world historic urban fabrics. This paper addresses a gap in the literature by synthesising plot-level heterogeneity with broader resilience outcomes, offering policy implications for protecting such fabrics amid global urbanisation pressures. The findings align with land system science, emphasising multifunctionality for regenerative urbanism. Full article

(This article belongs to the Special Issue Reimagining Cities and Landscapes: AI, Sustainability, Exaptation and Inclusive Urban Futures)

20 pages, 4408 KB

Open AccessArticle

Spatial Evolution and Driving Mechanisms of Rural Settlements in National New-Type Urbanization Pilot Areas: A Case Study of She County

by Qiong Yang, Wei Song, Shuangqing Sheng and Shukun Wei

Land 2026, 15(4), 539; https://doi.org/10.3390/land15040539 (registering DOI) - 26 Mar 2026

Abstract

Using She County, a national new-type urbanization comprehensive pilot area, as a case study, this research develops a multi-layered “static–dynamic–driver” analytical framework based on rural settlement data. By integrating GIS spatial overlay, landscape pattern indices, average nearest neighbor analysis, kernel density estimation, and cold–hotspot analysis, the study systematically characterizes the spatiotemporal evolution and driving mechanisms of rural settlements from 1980 to 2020. The results reveal that: (1) settlement evolution exhibits distinct phase-specific patterns, encompassing four primary types of transformation: localized expansion and consolidation, individual disappearance, rapid expansion, and the emergence of new settlements with peripheral extension; (2) landscape pattern and aggregation analyses indicate continuous growth in both total area and number of settlements, accompanied by increasing irregularity and fragmentation of patches; settlement size aggregation shows a fluctuating decline followed by recovery, overall spatial clustering intensity trends upward, and high-density kernel areas shift from the central–western to the northwestern region; (3) under multi-factor interactions, settlement layouts transitioned from an early “survival–location dependent” pattern dominated by natural constraints and transportation accessibility, to a mid-stage rapid aggregation driven by economic development and public service provision, ultimately evolving into a composite pattern balancing economic drivers and ecological constraints. The findings underscore the nonlinear superimposed effects of natural environment, economic development, transportation accessibility, public service availability, and ecological carrying capacity, providing a robust scientific basis for optimizing rural settlement spatial arrangements and informing rural development policy under the context of national new-type urbanization. Full article

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36 pages, 76230 KB

Open AccessArticle

Interpretable Adaptive Multiscale Spatiotemporal Network for Long-Term Global Sea Surface Temperature Prediction

by Rixu Hao, Yuxin Zhao and Xiong Deng

Remote Sens. 2026, 18(7), 997; https://doi.org/10.3390/rs18070997 (registering DOI) - 26 Mar 2026

Abstract

Sea surface temperature (SST) serves as a fundamental driver of ocean–atmosphere interactions and global climate variability, exhibiting strong nonstationarity, multiscale dynamics, and cross-variable coupling. However, current deep learning models often fail to capture these complex characteristics, limiting their ability to support accurate and physically consistent long-term SST prediction. To address these issues, we propose PAMSTnet, a unified deep learning framework for physics-informed adaptive multiscale spatiotemporal prediction. PAMSTnet leverages three-dimensional empirical wavelet transform (3DEWT) to learn interpretable multiscale spatiotemporal dynamics from raw observations, and applies multivariate spatiotemporal empirical orthogonal function (MSTEOF) to identify dominant cross-variable coupled modes. These physically meaningful representations are integrated into a deep ConvLSTM predictive network (DCPN) to support coordinated multiscale dynamical learning. Furthermore, PAMSTnet introduces physics-informed consistency learning (PICL) to enforce thermodynamic and dynamic constraints, enhancing physical consistency and interpretability. Extensive experiments demonstrate that PAMSTnet achieves superior performance against state-of-the-art baselines in long-term global SST prediction, reducing RMSE by 8.1% and improving ACC by 2.8% compared with the best-performing baseline, particularly under extreme climate events. Interpretation insights further highlight PAMSTnet’s adaptive representation of variable contributions and regional physical drivers. These findings position PAMSTnet as a promising paradigm for developing intelligent ocean prediction systems with enhanced physical consistency and interpretability. Full article

(This article belongs to the Section AI Remote Sensing)

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31 pages, 13534 KB

Open AccessArticle

CSFADet: Dual-Modal Anti-UAV Detection via Cross-Spectral Feature Alignment and Adaptive Multi-Scale Refinement

by Heqin Yuan and Yuheng Li

Algorithms 2026, 19(4), 254; https://doi.org/10.3390/a19040254 (registering DOI) - 26 Mar 2026

Abstract

Anti-unmanned aerial vehicle (Anti-UAV) detection is critical for airspace security, yet existing single-modality approaches suffer from severe performance degradation under adverse illumination, thermal crossover, and extreme scale variation. In this paper, we propose CSFADet, a dual-modal detection framework that jointly exploits visible and infrared imagery through four tightly integrated modules. First, a Cross-Spectral Feature Alignment (CSFA) module performs early-stage spectral calibration by computing cross-modal query–value attention maps, generating modality-aware channel descriptors that re-weight and concatenate the two spectral streams. Second, a Dual-path Texture Enhancement Module (DTEM) enriches fine-grained spatial details via cascaded convolutions with residual connections. Third, a Dual-path Cross-Attention Module (DCAM) introduces a feature-shrinking token generation strategy followed by symmetric cross-attention branches with learnable scaling factors, Squeeze-and-Excitation recalibration, and a

1 \times 1

convolution fusion head, enabling deep bidirectional interaction between modalities. Fourth, a Dual-path Information Refinement Module (DIRM) embeds Adaptive Residual Groups (ARGs) that cascade Multi-modal Spatial Attention Blocks (MSABs) with channel and dynamic spatial attention, culminating in a Multi-scale Scale-aware Fusion Refinement (MSFR) unit that employs three parallel multi-head attention branches with a Scale Reasoning Gate and Channel Fusion Layer to produce scale-discriminative enhanced features. Experiments on the public Anti-UAV300 benchmark show that CSFADet achieves 91.4% mAP@0.5 and 58.7% mAP@0.5:0.95, surpassing fifteen representative detectors spanning single-stage, two-stage, YOLO-family, and Transformer-based categories. Ablation studies confirm the complementary contributions of each module, and heatmap visualizations verify the model’s capacity to focus on small, distant UAV targets under challenging conditions. Full article

(This article belongs to the Topic Theoretical Foundations and Applications of Deep Learning Techniques)

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14 pages, 6878 KB

Open AccessArticle

Solvent-Driven Nanostructural Tuning of Lignin/Poly(N,N-dimethylacrylamide) Hydrogels

by Xiaoqing Jiang, Xiangyu You, Xinhong Li, Ruiyun Tian, Xuelian Wang, Pedram Fatehi, Kang Kang, Xulong Zhu and Huijie Zhang

Gels 2026, 12(4), 277; https://doi.org/10.3390/gels12040277 - 26 Mar 2026

Abstract

Non-covalent molecular self-assembly serves as a distinctive strategy for enhancing the mechanical performance of lignin-based composite hydrogels. Nevertheless, the self-assembly process can be significantly influenced, leading to alterations in the nanostructure of the hydrogel, because of the diverse conformational reorganizations of lignin in different solvents. In this research, a solvent exchange process was employed to generate a phase-separated structure comprising hydrophobic lignin domains and hydrophilic poly(N,N-dimethylacrylamide) (PDMA) domains through the aggregation of lignin, thereby forming tough lignin/PDMA hydrogels. By adjusting the solvent composition, the hydrogels exhibit distinct nanostructural transformations that are precisely correlated with the changes in Hansen Solubility Parameters (HSPs) of the solvent mixtures. Balanced HSPs facilitates the formation of small-scale lignin domains with high-domain density, which act as crosslinking points for the establishment of a reinforced network. Remarkably, lignin/PDMA hydrogels prepared at a boundary solvation condition unexpectedly induced the formation of large and highly condensed lignin domains, which displayed a radius of gyration (Rg) of 7.7 nm and an inter-domain distance (d-spacing) of 98.1 nm within the hydrogel network. These unique nanostructural features further contribute to its superior mechanical performance, including excellent tensile strength of 3.2 MPa, Young’s modulus of 5.7 MPa, and fracture energy of 41.2 kJ m⁻², which outperforms most reported lignin hydrogels. Additionally, it offers a strong adhesion and rapid drying approach, rendering the hydrogel more suitable for applications as hydrogel coatings. Full article

(This article belongs to the Special Issue Recent Advances in Multi-Functional Hydrogels)

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17 pages, 1979 KB

Open AccessArticle

Cloning of CgWRKY53 from Cymbidium goeringii and Functional Analysis of Its Negative Regulatory Role in Response to Cold Stress

by Dongrui Ma, Xijun Jing, Lianping Wang and Fengrong Hu

Genes 2026, 17(4), 376; https://doi.org/10.3390/genes17040376 - 26 Mar 2026

Abstract

Background: Cymbidium goeringii, one of China’s traditional and valuable orchids, possesses significant ornamental and economic value. However, it is relatively sensitive to low temperature and other abiotic stresses, which severely restrict its application in landscaping and industrial development. WRKY transcription factors play important roles in plant responses to abiotic stresses, yet related research in C. goeringii remains limited. Methods: In this study, based on transcriptome data of C. goeringii under four different stresses, we identified and cloned the WRKY transcription factor gene CgWRKY53. Through bioinformatics analysis, quantitative real-time PCR, and heterologous transformation in Arabidopsis thaliana, we systematically investigated its structural characteristics, expression patterns, and function under cold stress. Results: The full-length CDS of CgWRKY53 is 1080 bp, encoding a protein of 359 amino acids with a molecular weight of 39.95 kDa. Group III subfamily of the WRKY family, possessing the conserved WRKYGQK domain and a C2HC-type zinc finger motif. CgWRKY53 is expressed in roots, pseudobulbs, leaves, and flowers of C. goeringii, with the highest expression observed in flowers. Under cold, heat, waterlogging, and ABA treatments, CgWRKY53 displayed significant changes in expression, with the most pronounced response occurring under cold stress, where its expression was significantly upregulated. Homozygous transgenic A. thaliana lines overexpressing CgWRKY53 exhibited dwarfed stature, with smaller and deformed leaves and notably shorter roots compared to wild-type plants. The overexpression lines also showed cold-sensitive phenotypes under low-temperature stress, and the expression of several cold-responsive genes was suppressed, suggesting that CgWRKY53 may act as a negative regulator in the response to cold stress. Conclusions: These results identify CgWRKY53 as a negative regulator of cold stress response in C. goeringii. This study provides important genetic resources and theoretical foundations for molecular breeding of stress-resistant orchids. Full article

(This article belongs to the Section Plant Genetics and Genomics)

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24 pages, 9823 KB

Open AccessArticle

High-Speed Image Compression–Encryption Scheme Based on a New Chaotic Map and Improved Lifting Wavelet Transform

by Qing Lu, Jin Wan, Linlan Yu and Congxu Zhu

Mathematics 2026, 14(7), 1114; https://doi.org/10.3390/math14071114 - 26 Mar 2026

Abstract

In resource-constrained communication environments, important image data needs to be compressed before encrypted transmission. This paper proposes effective solutions to this issue. Firstly, a new one-dimensional discrete chaotic system model was constructed based on the logistic system and fractional structure. Through theoretical analysis combined with numerical simulation experiments, it has been proven that the proposed new system has excellent chaotic characteristics. Compared with some traditional one-dimensional chaotic systems, the new system has a wider range of chaotic parameters and stronger complexity, making it more suitable for image data encryption. Secondly, a high-compression-ratio image compression method based on improved lifting wavelet transform and a fast image encryption algorithm based on the new chaotic map are proposed. Simulation experiments and security analysis results show that the proposed image compression–encryption scheme has excellent performance and less time overhead. It has good resistance to various cryptanalysis attacks and strong robustness to noise and data loss attacks, which indicates that the proposed image compression–encryption scheme has good application potential in resource-constrained communication environments. The main contribution of this article is the design of a new chaotic system model with practical performance and the development of a new application case. The main novelty of this paper is the proposal of a fast algorithm for high compression ratio and encryption of images. Full article

(This article belongs to the Special Issue Chaotic Systems and Their Applications, 2nd Edition)

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37 pages, 499 KB

Open AccessReview

The Role of Selenium in the Antioxidant System of Cattle, Pigs, and Small Ruminants: Implications for Animal Health and Productivity

by Katarzyna Żarczyńska, Katarzyna Różańska, Oliwia Świerczek and Dawid Tobolski

Animals 2026, 16(7), 1019; https://doi.org/10.3390/ani16071019 - 26 Mar 2026

Abstract

Oxidative stress contributes to reproductive disorders, immune dysfunction, and reduced productivity in livestock during periods of high metabolic demand and environmental challenge. Selenium supports antioxidant defense systems because it is incorporated as selenocysteine into selenoproteins, including glutathione peroxidases and thioredoxin reductases that detoxify peroxides and sustain redox balance. The review summarizes selenium occurrence and chemical forms in feeds, as well as its absorption, transportation, and storage. The review also outlines the major features of selenoprotein biosynthesis and its prioritized allocation, with an emphasis on cattle, pigs, sheep, and goats. Evidence from multiple sources indicates that selenium status and supplementation interacts with antioxidant capacity, immune competence, thyroid hormone metabolism, reproductive performance, and the transfer of selenium to milk and offspring. In ruminants, rumen microbial transformations can reduce the bioavailability of inorganic selenium salts, and organic sources, such as selenium-enriched yeast, hydroxy-selenomethionine, and selenitetriglycerides, often increase blood and milk selenium more effectively. In pigs, organic selenium is commonly associated with enhanced antioxidant and immune indices in sows and piglets during late gestation, lactation, and weaning, whereas effects on growth performance are inconsistent. The review emphasizes the narrow margin between adequacy and excess and outlines practical considerations for supplementation and monitoring, alongside research needs for emerging selenium forms and functional biomarkers. Full article

(This article belongs to the Special Issue Use of Antioxidants as Feed Additives to Enhance Farm Animal Production)

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