Search Results (11,566)

The Aquila Optimizer (AO), inspired by the hunting behavior of Aquila birds, is a recent nature-inspired metaheuristic algorithm recognized for its simplicity and low computational cost. However, the conventional AO often suffers from premature convergence and an imbalance between exploration and exploitation when applied to complex engineering optimization problems. To overcome these limitations, this study proposes a modified Aquila Optimizer (m-AO) incorporating three enhancement strategies: an adaptive chaotic reverse learning mechanism to improve population diversity, an elite alternative pooling strategy to balance global exploration and local exploitation, and a shifted distribution estimation strategy to accelerate convergence toward promising regions of the search space. The performance of the proposed m-AO is evaluated using 23 classical benchmark functions, IEEE CEC 2022 benchmark problems, and a practical plate–fin heat exchanger (PFHE) design optimization problem. Numerical simulations demonstrate that m-AO achieves faster convergence, higher solution accuracy, and improved robustness compared with the original AO and several state-of-the-art metaheuristic algorithms. In the PFHE application, the proposed method yields a significant improvement in thermal performance, accompanied by a reduction in entropy generation and pressure drop under prescribed design constraints. Statistical analyses further confirm the superiority and stability of the proposed approach. These results indicate that the modified Aquila Optimizer is an effective and reliable tool for solving complex thermal system design optimization problems. Full article

The proliferation of biometric authentication systems in critical security applications has highlighted the urgent need for robust defense mechanisms against sophisticated adversarial attacks. This paper presents ShieldNet, an adversarially resilient Convolutional Neural Network (CNN) framework specifically designed for secure iris biometric authentication. Unlike existing approaches that apply adversarial training or gradient regularization independently, ShieldNet introduces a synergistic dual-layer defense framework featuring three key components: (1) an attack-aware adaptive weighting mechanism that dynamically balances defense priorities across multiple attack types, (2) a smoothness-regularized gradient penalty formulation that maintains differentiable gradients while encouraging locally smooth loss landscapes, and (3) a consistency loss component that enforces prediction stability between clean and adversarial inputs. Through extensive experimental validation across three diverse iris datasets, MMU1, CASIA-Iris-Africa, and UBIRIS.v2, and rigorous evaluation against strong adaptive attacks including AutoAttack, PGD-100 with random restarts, and transfer-based black-box attacks, ShieldNet demonstrated robust performance, achieving 87.3% adversarial accuracy under AutoAttack on MMU1, 85.1% on CASIA-Iris-Africa, and 82.4% on UBIRIS.v2, while maintaining competitive clean data accuracies of 94.7%, 93.9%, and 92.8%, respectively. The proposed framework outperforms existing state-of-the-art defense methods including TRADES, MART, and AWP, achieving an equal error rate (EER) as low as 2.8% and demonstrating consistent robustness across both gradient-based and gradient-free attack scenarios. Comprehensive ablation studies validate the complementary contributions of each defense component, while latent space analysis confirms that ShieldNet learns genuinely robust feature representations rather than relying on gradient obfuscation. These results establish ShieldNet as a practical and reliable solution for deployment in high-security biometric authentication environments. Full article

High-precision autonomous localization remains a critical challenge for intelligent mining vehicles in GNSS-denied and unstructured coal mine roadways, where traditional odometry-based methods suffer from severe cumulative drift and perceptual aliasing. Inspired by the synergy between mammalian visual cues and cognitive neural mechanisms, this paper proposes a robust biomimetic localization framework that integrates multi-source perception with a prior cognitive map. The core contributions are three-fold: First, a semantic-enhanced biomimetic localization method is developed, leveraging roadway sign data as absolute spatial anchors to suppress long-distance cumulative errors. Second, an optimized head direction (HD) cell model is formulated by incorporating a speed balance factor, kinematic constraints, and a drift correction influence factor, significantly improving the precision of angular perception. Third, boundary-adaptive and sign-based semantic constraint terms are integrated into a continuous attractor network (CAN)-based path integration model, effectively preventing trajectory deviation into non-navigable regions. Comprehensive evaluations conducted in large-scale underground scenarios demonstrate that the proposed framework consistently outperforms conventional IMU-odometry fusion, representative 3D SLAM solutions, and baseline biomimetic algorithms. By effectively integrating semantic landmarks as spatial anchors, the system exhibits superior resilience against cumulative drift, maintaining high localization precision where standard methods typically diverge. The results confirm that our approach significantly enhances both trajectory consistency and heading stability across extensive distances, validating its robustness and scalability in handling the inherent complexities of unstructured coal mine environments for enhanced intrinsic safety. Full article

This study conducts a landslide deformation assessment in Tianshui, Gansu Province, on the Chinese Loess Plateau, utilizing the Small Baseline Subset InSAR (SBAS-InSAR) method integrated with velocity direction conversion and Z-score clustering. The Chinese Loess Plateau is one of the most landslide-prone regions in China due to frequent rains, strong topographical gradients and severe soil erosion. By constructing subsets of interferograms, SBAS-InSAR can mitigate the influence of decorrelation to a certain extent, making it a highly effective technique for monitoring regional surface deformation and identifying landslides. To overcome the limitations of the satellite’s one-dimensional Line-of-Sight (LOS) measurements and the challenge of distinguishing true landslide signals from noise, two optimization strategies were implemented. First, LOS velocities were projected onto the local steepest slope direction, assuming translational movement parallel to the slope. Second, a Z-score clustering algorithm was employed to aggregate measurement points with consistent kinematic signatures, enhancing identification robustness, with a slight trade-off in spatial completeness. Based on 205 Sentinel-1 Single-Look Complex (SLC) images acquired from 2014 to 2024, the integrated workflow identified 69 “active, very slow” and 63 “active, extremely slow” landslides. These results were validated through high-resolution historical optical imagery. Time series analysis reveals that creep deformation in this region is highly sensitive to seasonal rainfall patterns. This study demonstrates that the SBAS-InSAR post-processing framework provides a cost-effective, millimeter-scale solution for updating landslide inventories and supporting regional risk management and early warning systems in loess-covered terrains, with the exception of densely forested areas. Full article

This paper analyzes the implementation of the Geoportal SIG FESBAL–UPM, a Participatory Geographic Information System (PGIS) developed within the Master’s and Doctorate programs in Rural Development Project Planning and Sustainable Management at UPM. The study introduces a model integrated with Project-Based Learning (PBL), the Working With People (WWP) framework, and the CFS-RAI principles to address challenges in responsible food systems. The geoportal designed to be applied at the Food Bank–UPM Chair–FESBAL, acts as an innovative instrument for participation among the different stakeholders enabling the spatialization and analysis of data across social, environmental, and governance dimensions. Functionally, it offers a robust foundation for evidence-based decision-making, systematizes geographic information, and visualizes data via the web, supporting research, training, and community engagement actions. Furthermore, this study details the specific projects and activities developed under the three involved action lines: research, training, and community engagement, identifying strengths and weaknesses in each. The findings affirm that this participatory approach ensures that the proposed solutions are aligned with local needs and priorities, increasing the sustainability and long-term success of the projects implemented through the geoportal. Full article

In Internet of Things (IoT) systems, data collected by geographically distributed sensors is often incomplete due to device failures, harsh deployment conditions, energy constraints, and unreliable communication. Such data gaps can significantly degrade downstream data processing and decision-making, particularly when failures result in the loss of all locally redundant sensors. Conventional imputation approaches typically rely on historical trends or multi-sensor fusion within the same target environment; however, historical methods struggle to capture emerging patterns, while same-location fusion remains vulnerable to single-point failures when local redundancy is unavailable. This article proposes a correlation-aware, cross-location data fusion framework for data imputation in IoT networks that explicitly addresses single-point failure scenarios. Instead of relying on co-located sensors, the framework selectively fuses semantically similar features from independent and geographically distributed gateways using summary statistics-based and correlation screening to minimize communication overhead. The resulting fused dataset is then processed using a lightweight KNN with an Iterative PCA imputation method, which combines local neighborhood similarity with global covariance structure to generate synthetic data for missing values. The proposed framework is evaluated using real-world weather station data collected from eight geographically diverse locations across the United States. The experimental results show that the proposed approach achieves improved or comparable imputation accuracy relative to conventional same-location fusion methods when sufficient cross-location feature correlation exists and degrades gracefully when correlation is weak. By enabling data recovery without requiring redundant local sensors, the proposed approach provides a resource-efficient and failure-resilient solution for handling missing data in IoT systems. Full article

Accurate segmentation of fruit tree leaf diseases is critical for yield protection and precision crop management, yet it is challenging due to complex field conditions, irregular leaf morphology, and diverse lesion patterns. To address these issues, Disease-Seg, a lightweight real-time segmentation framework, is proposed. It integrates CNN and Transformer with a parallel fusion architecture to capture local texture and global semantic context. The Extended Feature Module (EFM) enlarges the receptive field while retaining fine details. A Deep Multi-scale Attention mechanism (DM-Attention) allocates channel weights across scales to reduce redundancy, and a Feature-weighted Fusion Module (FWFM) optimizes integration of heterogeneous feature maps, enhancing multi-scale representation. Experiments show that Disease-Seg achieves 90.32% mIoU and 99.52% accuracy, outperforming representative CNN, Transformer, and hybrid-based methods. Compared with HRNetV2, it improves mIoU by 6.87% and FPS by 31, while using only 4.78 M parameters. It maintains 69 FPS on 512 × 512 crops and requires approximately 49 ms per image on edge devices, demonstrating strong deployment feasibility. On two grape leaf diseases from the PlantVillage dataset, it achieves 91.19% mIoU, confirming robust generalization. These results indicate that Disease-Seg provides an accurate, efficient, and practical solution for fruit leaf disease segmentation, enabling real-time monitoring and smart agriculture applications. Full article

Climate change and altered hydrological regimes are restructuring wetland habitats globally, triggering cascading effects on colonial waterbirds. This study investigates how environmental drivers, including thermal anomalies, water-level fluctuations, and aqueous surface extent, influence the distribution and size of waterbird colonies (Ardeidae, Threskiornithidae, and Phalacrocoracidae) in the Danube Delta Biosphere Reserve. We integrated colony census data (2016–2023) with remote-sensing-derived habitat metrics, in situ meteorological and hydrological measurements to model colony abundance dynamics. Our results indicate that elevated early spring temperatures and water level variability are the primary determinants of numerical population dynamics. Spatial analysis revealed a heterogeneous response to hydrological stress: while the westernmost colony exhibited high site fidelity due to its proximity to persistent aquatic surfaces, the central colonies suffered severe declines or local extirpation during extreme drought periods (2020–2022). A discernible eastward shift in bird assemblages was observed toward zones with superior hydrological connectivity and proximity to anthropogenic hubs, suggesting an adaptive spatial response that was consistent with behavioral flexibility. We propose an adaptive management framework prioritizing sustainable solutions for maintaining minimum lacustrine water levels to preserve critical foraging zones. This integrative framework highlights the pivotal role of remote sensing in transitioning from reactive monitoring to predictive conservation of deltaic ecosystems. Full article

Out-of-distribution (OOD) detection is essential for ensuring the reliability of machine learning models deployed in safety-critical applications. Existing methods often rely solely on statistical properties of feature distributions while ignoring the geometric structure of learned representations. We propose TopoAD, a topology-aware OOD detection framework that leverages Euler Characteristic Curves (ECCs) extracted from intermediate convolutional activation maps and fuses them with standardized energy scores. Specifically, we employ a computationally efficient superlevel-set filtration with a local estimator to capture topological invariants, avoiding the high cost of persistent homology. Furthermore, we introduce task-adaptive aggregation strategies to effectively integrate multi-scale topological features based on the complexity of distribution shifts. We evaluate our method on CIFAR-10 against four diverse OOD benchmarks spanning far-OOD (Textures), near-OOD (SVHN), and semantic shift scenarios. Our results demonstrate that TopoAD-Gated achieves superior performance on far-OOD data with 89.98% AUROC on Textures, while the ultra-lightweight TopoAD-Linear provides an efficient alternative for near-OOD detection. Comprehensive ablation studies reveal that cross-layer gating effectively captures multi-scale topological shifts, while threshold-wise attention provides limited benefit and can degrade far-OOD performance. Our analysis demonstrates that topological features are particularly effective for detecting OOD samples with distinct structural characteristics, highlighting TopoAD’s potential as a sustainable solution for resource-constrained applications in texture analysis, medical imaging, and remote sensing. Full article

Short-term load forecasting (STLF) underpins the efficient and secure operation of power systems. This study develops and evaluates a hybrid architecture that couples deep neural networks (DNNs) with support vector regression (SVR) for national-scale day-ahead STLF using Croatian load data from 2006 to 2022. The approach employs an hour-specific framework of 24 hybrid models: each DNN learns a compact nonlinear representation for a given hour, while an SVR trained on the penultimate layer activations performs the final regression. Gradient-boosting-based feature selection yields compact, informative inputs shared across all model variants. To overcome limitations of historical local measurements, the framework integrates global numerical weather prediction data from the TIGGE archive with load and local meteorological observations in an operationally realistic setup. In the held-out test year 2022, the proposed hybrid consistently reduced forecasting error relative to standalone DNN-, LSTM- and Transformer-based baselines, while preserving a reproducible pipeline. Beyond using SVR as an alternative output layer, the contributions are as follows: addressing a 17-year STLF task, proposing an hour-specific hybrid DNN–SVR framework, providing a systematic comparison with deep learning baselines under a unified protocol, and integrating global weather forecasts into a practical day-ahead STLF solution for a real power system. Full article

D-Tagatose is a rare sugar of interest as a low-calorie sweetener, and enzymatic isomerization of D-galactose is a practical production route. L-arabinose isomerase (L-AI; EC 5.3.1.4) is a promising catalyst for the above process, but many characterized L-AIs perform best at alkaline pH and high temperature and often require substantial divalent metal supplementation (e.g., Mn²⁺/Co²⁺), which complicates food-grade processing. Lactic acid bacteria (LAB) are attractive sources of food-compatible enzymes, yet structural information for LAB-derived L-AIs has been limited. Here, we report the 2.6 Å X-ray crystal structure of L-AI from Latilactobacillus sakei 23K (LsAI) and define its oligomeric assembly. Although the asymmetric unit contains a single monomer, crystallographic symmetry reconstructs a D₃-symmetric homohexamer composed of two face-to-face trimers, consistent with a higher-order assembly in solution. Interface analysis shows predominantly polar interaction networks, and normalized B-factor mapping reveals localized flexibility near active-site-proximal regions. These findings provide a structural basis for understanding LAB-derived L-AIs and support structure-guided engineering toward food-grade, low-metal biocatalysts for rare-sugar production. Full article

Accurate prediction of lithium battery remaining useful life (RUL) is crucial for battery management systems to monitor battery health status. However, RUL prediction remains challenging due to capacity non-stationarity caused by capacity regeneration phenomena. Therefore, this study proposes a novel RUL prediction framework that combines a two-stage decomposition strategy with a parallel Informer-LSTM architecture. First, STL decomposition is employed to decompose the capacity sequence into trend, seasonal, and residual components. The VMD method further refines the residual component from STL, extracting the underlying multiscale subsignals. Subsequently, a parallel dual-channel prediction network is constructed: the Informer branch captures global long-range dependencies to prevent trend drift, while the LSTM branch models local nonlinear dynamics to reconstruct fluctuations associated with capacity regeneration. Experiments on the NASA dataset demonstrate that this framework achieves an MAE below 0.0109, an RMSE below 0.0160, and an R² above 0.9950. Additional validation on the Oxford battery dataset confirms the model’s robust generalization capability under dynamic conditions, with an MAE of 0.0017. This further demonstrates that the proposed RUL prediction framework achieves significantly enhanced prediction accuracy and stability, offering a reliable solution for battery health status detection in battery management systems. Full article

Since the COVID-19 pandemic, wastewater monitoring has become an additional tool in the surveillance of infectious diseases. Many EU countries put wastewater surveillance systems (WSS) in place to track SARS-CoV-2 and its variants and other pathogens, such as the influenza virus or Respiratory syncytial virus (RSV). In Germany, several research and pilot projects funded by the EU, the Federal Ministry of Education and Research, the Federal Ministry of Health, and projects at Federal State level have been launched in the last four years. In Germany, wastewater monitoring was not implemented as a public health tool before the COVID-19 pandemic, but in September 2022, it has been legally determined in the German infection protection act (Infektionsschutzgesetz, IfSG). As Germany is a federal state, competencies in epidemic management partly belong to the 16 federal states (“Länder”). In the federal states, the local health authorities at the county (“Kreise”) level also have specific risk management and communication competencies. Furthermore, WSS has been incorporated into the revised Urban Wastewater Treatment Directive (EU) 2024/3019. For this reason, the federal states and local health authorities play a pivotal role in successfully implementing wastewater monitoring as a supplementary component of disease surveillance in Germany. Between November 2021 and August 2022, the federal state of Thuringia, Germany, supported a pilot study to implement a surveillance system for SARS-CoV-2-RNA in wastewater of 23 wastewater treatment plants in 17 counties in Thuringia. Here, we describe the study design and the system behind the logistics and the planning, and we provide an overview of the options for involving the public health service. Furthermore, the possibilities for IT concepts and approaches to innovative AI solutions are shown. We also aim to explore the feasibility and potential barriers to further implementing wastewater surveillance as a supplementary public health tool in Thuringia. Full article

Biogas production from agricultural residues is a promising solution for renewable energy production, improved waste management, and beneficial impact on climate change mitigation. The aim of this study is to assess the actual use and theoretical potential of agricultural biogas produced from animal manure in Poland at the local level. The potential and actual use of agricultural biogas are presented regionally (16 voivodeships) and locally (314 districts). The theoretical potential of agricultural biogas was estimated based on data from the Agricultural Census conducted by the Central Statistical Office in Poland in 2020. Actual biogas production is based on data from the Register of Agricultural Biogas Producers maintained by the National Support Center for Agriculture. The study shows that Poland is only tapping into the existing potential for agricultural biogas production to a limited extent. Furthermore, both actual agricultural biogas production and the identified theoretical potential vary spatially (greater potential in the northern part of the country, significantly lower in the southern part). This situation is attributed to existing barriers that hinder the utilization of existing potential. Therefore, it is crucial to seek new solutions to reduce existing barriers of an organizational, legal, technical, economic, environmental, spatial, and social nature. Full article

OsHKT1;1, a member of the high-affinity K⁺ transporter (HKT) family, plays a key role in Na⁺ homeostasis and salinity tolerance in rice. In our previous study, multiple potential OsHKT1;1 splicing variants were identified, as well as the full-length (FL) OsHKT1;1 transcript from the salt-tolerant rice Pokkali. However, most previous studies focused solely on the full-length protein, leaving the transport functions of splice variants largely unexamined. In this study, we focused on the splice variant OsHKT1;1-V2 and compared its function and gene expression with those of OsHKT1;1-FL. Two-electrode voltage clamp experiments using Xenopus laevis oocytes revealed that the 1st start codon of OsHKT1;1-V2 is functional to exhibit bidirectional currents in bath solutions containing NaCl. Unlike the Na⁺-selective feature of OsHKT1;1-FL, OsHKT1;1-V2 primarily mediated Cl⁻ transport with weak Na⁺ selectivity, which was supported by the higher Cl⁻ accumulation in OsHKT1;1-V2–expressing oocytes. Subcellular localization analyses using oocytes and Arabidopsis mesophyll cells indicated plasma membrane localization of OsHKT1;1-V2, similar to OsHKT1;1-FL. Functional assays using a yeast mutant further indicated that OsHKT1;1-FL, but not OsHKT1;1-V2, mediates Na⁺ uptake. The same OsHKT1;1 variants were identified in the japonica cultivar Nipponbare, and OsHKT1;1-V2 of the cultivar showed Cl⁻ transport properties similar to the one from Pokkali. Quantitative PCR analyses revealed higher abundance of OsHKT1;1-FL transcripts in Nipponbare than in Pokkali with markedly lower OsHKT1;1-V2 levels in Pokkali under salt stress. This study provides a new insight into HKT-mediated ion homeostasis under salinity stress. Full article