Search Results (363)

Water and sanitation governance sits at the intersection of global development ambitions and highly localized service realities. While SDG6 sets universal targets for clean water and sanitation, the institutional and fiscal arrangements that translate those targets into actual service outcomes operate primarily at the subnational level. The discrepancy between globally defined objectives and locally executed delivery creates a structural research gap: how do the fiscal architectures of local governments influence progress towards SDG6? This study addresses this question for a panel of OECD countries by developing a deep learning-based estimation framework that combines bidirectional long short-term memory (BiLSTM) networks with Tianji’s horse racing optimization (THRO) algorithm. Three distinct operationalizations of fiscal decentralization are tested against SDG6 outcomes: subnational expenditure share (EFDM), subnational revenue share (RFDM), and a composite index balancing both dimensions (CFDM). Model adequacy is assessed using a layered diagnostic protocol involving regression fit, country-level residual patterns, error density profiles, Bland–Altman limits of agreement and inter-annual error trajectories. Among the three configurations, CFDM consistently records superior performance (; ; ), while even the weakest specification clears , attesting to the overall robustness of the proposed architecture. The margin by which CFDM outperforms its alternatives highlights a key finding: neither spending authority nor revenue capacity alone accurately reflects the fiscal reality of local water and sanitation governance; it is their combined effect that is important. The expenditure dimension is further proven to be the more influential of the two unidimensional proxies, consistent with the capital-intensive and maintenance-heavy nature of water infrastructure. On the other hand, coefficient findings show that fiscal decentralization is positively associated with SDG6 achievement for all models. Beyond its empirical contributions, the study introduces a methodological template for applying hybrid AI optimization to policy-relevant sustainability panels. It also connects two largely parallel bodies of scholarship, fiscal federalism and SDG research, that have rarely been examined together. Full article

Cross-border blockchain investigations frequently face data isolation challenges where multiple jurisdictions may conduct parallel inquiries into the same suspicious entities, leading to operational conflicts and redundant efforts. This paper presents a purpose-built t-out-of-n watchlist-anchored private set intersection (PSI) protocol, adapting established threshold secret-sharing techniques for secure jurisdictional discovery, enabling agencies to identify overlapping investigative targets without prematurely disclosing sensitive case details. The methodology is built upon Shamir’s Secret Sharing (SSS) and polynomial interpolation over the $2^{127}-1$ Mersenne prime field. A deterministic dual-hash field mapping ensures statistical uniformity over the prime field. Experimental validation using the Elliptic++ dataset confirmed the system’s high efficiency. The protocol maintains linear communication complexity of $O(n·|{\mathcal{S}}_0\left|\right)$, where complexity scales with the watchlist size rather than the full participant dataset and remains stable under varying consensus requirements, where increasing the threshold t results in a marginal increase in total latency. Under the semi-honest adversarial model, the false-positive rate is cryptographically negligible at $2^{-127}$. The protocol achieves a hybrid security model wherein share privacy is information-theoretic under SSS, while field mapping and share authentication rely on standard computational assumptions. By integrating native source traceability, this framework provides a practical technological foundation for initiating formal Mutual Legal Assistance Treaty (MLAT) requests based on confidential matches identified across independent investigative workflows. Full article

Plant diseases result in the estimated loss of 20–40% of the world’s crop production annually, amounting to more than $220 billion in economic losses and threatening food security for a rapidly expanding world population. While the conventional methods for detecting plant diseases rely on visual inspection of the symptoms, they are resource-consuming. For effective plant disease detection at a pre-mature stage, hyperspectral imaging (HSI) represents a paradigm shift in technology. It can be used to obtain subtle spectral signatures outside the visible spectrum, which enables pre-symptomatic and highly specific plant disease diagnosis. Concurrently, deep learning (DL) has become the prevalent analytical paradigm for decoding the complex and high-dimensional data that HSI produces. This paper covers a comprehensive narrative review of the intersection of these two transformative technologies from 2008 to 2026. We first set out the biological and physical principles by which HSI is uniquely suited to detecting plant–pathogen interactions in the absence of visible symptoms. We then present a detailed taxonomy of deep learning architectures for Vision Imaging and HSI data, ranging from basic 1D and 3D convolutional neural networks (CNNs) to hybrid models with attention mechanisms and, most recently, vision transformers, which have achieved greater robustness to real-world conditions. There is currently a major and consistent “lab-to-field” performance gap. A critical analysis of various studies reveals a persistent and significant performance gap between models that perform well on controlled lab datasets (ranging from 95 to 99%) and field-collected data (typically 70–85%). This paper also addresses the practical gap of environmental variability, image noise, and the domain gap between the controlled environment and the real dataset. Finally, this review concludes by providing strategic research recommendations and a roadmap, highlighting that the future of the field is contingent upon not only architectural innovation but also a holistic approach, with robustness, scalability, affordability, and interpretability as the main focus to bring the proven potential of HSI-DL systems from the lab to the field, ultimately contributing to global food security. Full article

The canopy closure stage is a critical phase of rice (Oryza sativa L.) development that influences canopy structure and final grain yield. Accurate and continuous monitoring of canopy closure dynamics is therefore essential for variety screening and cultivation optimization. This study combines unmanned aerial vehicle (UAV) remote sensing technology with deep learning-based semantic segmentation to establish an efficient framework for quantifying rice canopy closure dynamics. UAV RGB images were acquired for 198 hybrid rice varieties during early growth stages and used to build a canopy segmentation dataset. Three semantic segmentation models, i.e., DeepLabv3+, U-Net, and PSPNet, were systematically evaluated. Results show that DeepLabv3+ performed the best and enabled precise extraction of rice canopy features, obtaining a mean intersection over union (mIoU) of 0.86. Based on the extracted canopy coverage, the Gompertz model was utilized to characterize temporal canopy closure trajectories for all varieties, achieving an average R² of 0.978. Subsequently, five key dynamic indicators were derived, including canopy closure limit value (K), initial growth coefficient (a), growth rate coefficient (b), maximum instantaneous growth rate (MGR), and days to maximum growth rate (Tm). K-means clustering analysis was performed on these indicators to categorize all rice varieties into three clusters, disclosing pronounced differences in early-stage canopy development characteristics. Correlation analysis further demonstrated that canopy closure dynamics were closely associated with grain yield. Overall, while acknowledging the limitations of a single-season and single-site dataset, this study provides a scalable and objective framework for quantifying rice canopy closure dynamics, offering valuable support for variety selection, cultivation optimization, and high-yield rice production. Full article

For microbiological diagnostics, accurately counting and segmenting microbial colonies is extremely important. However, manual methods are labor-intensive and yield inconsistent results. We develop a hybrid model using swarm intelligence, combining a convolutional transformer with nested skip connections and global context with channel and spatial attention. Parameter tuning is supported by a variety of swarm optimization algorithms (e.g., Particle Swarm Optimization, Quantum-behaved Particle Swarm Optimization, and Differential Evolution Particle Swarm Optimization). Morphological refinement, including a further watershed transform, an attention graph, and post-processing, enhances colony boundaries by separating them. Grad-CAM++, Integrated Gradients, and temperature scaling provide a transparent and trustworthy model through explainability and post hoc calibration. The proposed model was extensively tested on the Microbial Colony Recognition and Circular Bacterial Colony Datasets, achieving a Dice score of 94.2%, an Intersection over the Union of 88.6%, and a mean absolute counting error of 2.7 colonies. These results significantly outperform several baseline models, including U-Net (88.1%), U-Net++ (89.7%), Attention U-Net (90.6%), and Swin-Unet (91.4%). Statistically significant improvements were confirmed (p < 0.01). A cross-dataset analysis demonstrates the framework’s robustness and cross-domain applicability, and positions it as a trustworthy, explainable automated model for assessing microbial colonies in laboratory and clinical settings. Full article

Accurate Unmanned Aerial Vehicle (UAV) position estimation is the cornerstone of urban low-altitude safety management systems. Time Difference of Arrival (TDOA) and Remote Identification (Remote ID) are widely used surveillance technologies with complementary characteristics. TDOA provides high-rate updates but suffers from geometry-induced horizontal–vertical anisotropy and multipath effects, while Remote ID supplies absolute state information yet struggles with intermittent sampling and packet loss. Existing fusion schemes typically address these issues in isolation: sequential filtering manages asynchrony but assumes Gaussian noise, robust estimators suppress outliers at the cost of discarding valid data, and coupled-filter architectures allow vertical anomalies to contaminate horizontal estimates through the Kalman gain cross-coupling. No prior framework jointly handles structural TDOA altitude jumps, stochastic Remote ID timing jitter, and the geometric anisotropy between estimation subspaces within a single coherent pipeline. To bridge this gap, we propose a Hybrid Conditional Kalman Filter (HCKF) framework comprising three integrated modules. First, a kinematics-based temporal alignment module maps asynchronous measurements onto a uniform timeline and predicts missing samples, resolving cross-modal time mismatches. Second, a measurement quality evaluation mechanism detects TDOA altitude steps via robust two-layer stratification and scores Remote ID timing irregularity through a confidence mapping, converting these anomalies into dynamic covariance adjustments and weight caps without discarding observations. Third, a Subspace-Decoupled Fusion strategy exploits the physical insight that TDOA horizontal precision derives from hyperbolic intersection geometry, whereas its vertical estimates suffer from weak observability due to near-coplanar ground-station deployment. By applying entropy-guided weighting in the horizontal plane and a conditional Remote ID-dominant rule in the vertical axis, this design prevents cross-dimensional error propagation. The framework was validated using three real-world flight missions at distinct altitudes (255 m, 345 m, and 440 m) totaling 13.51 km of flight distance, with RTK serving as ground truth. HCKF reduces the Root Mean Square Error by over 40% relative to single-source baselines (95% bootstrap confidence interval: [35.2%, 48.7%]), and paired Wilcoxon signed-rank tests confirm statistically significant improvement ($p<0.01$) over standard EKF, Covariance Intersection, and Iterative CI across all three tracks. Full article

Ensuring safety in autonomous vehicles (AVs) requires predictive control methods that can handle dynamic constraints, uncertain interactions, and real-time decision making. This review examines safety-oriented model predictive control (MPC) for AVs using a PRISMA-guided screening process. From 363 records published between January 2015 and March 2026, 101 peer-reviewed studies were selected for qualitative synthesis. The literature is organized into three domains: collision avoidance and risk mitigation, trajectory tracking and path following, and intersection and coordination tasks. Across these domains, MPC has evolved from nominal tracking and geometric avoidance toward risk-aware, robust, hierarchical, and learning-enhanced formulations. Unlike broader reviews on autonomous driving control, this review focuses specifically on safety-oriented MPC and compares the reviewed literature in terms of safety mechanisms, uncertainty treatment, validation practice, computational feasibility, and deployment limitations. The review shows that MPC remains one of the most versatile frameworks for AV safety, but the evidence base is weakened by heavy reliance on simulation, inconsistent safety metrics, limited validation under uncertainty, and uneven treatment of computational feasibility. The most promising directions are hybrid architectures that combine model-based safety guarantees with uncertainty-aware prediction, learning-assisted adaptation, and scalable coordination mechanisms. Full article

Background/Objectives: Colorectal cancer is a leading cause of cancer-related mortality worldwide, and early detection of gastrointestinal (GI) polyps through endoscopy is critical for improving patient outcomes. However, access to specialist gastroenterology care remains severely limited in Federal Health Professional Shortage Areas (HPSAs), particularly for high-acuity geriatric patients. This study proposes GeriAIGastroNet, a clinically oriented deep learning framework designed to support AI-assisted tele-gastroenterology workflows in resource-limited settings, with the primary objective of enabling AI-powered risk stratification and colonoscopy referral triage for elderly patients who lack on-site gastroenterology access. Methods: The framework integrates an EfficientNet-B4 backbone with multi-scale attention fusion and a geriatric severity-aware classification head to enable accurate GI polyp segmentation and automated clinical risk stratification from endoscopic images. Patients identified as high-risk are referred to colonoscopy-capable centers; such centers typically offer diagnostic colonoscopy with polypectomy capability for smaller and intermediate-complexity polyps, while patients with larger, sessile, or morphologically complex lesions requiring advanced endoscopic resection (e.g., endoscopic mucosal resection or endoscopic submucosal dissection) are further referred to tertiary endoscopy centers with specialized expertise. The model was trained and evaluated on the publicly available HyperKvasir dataset (1000 annotated polyp images). Results: GeriAIGastroNet achieved a classification accuracy of 96.77%, F1-score of 96.90%, Dice coefficient of 89.18%, and Intersection over Union (IoU) of 80.80%, outperforming established baselines, including U-Net, Attention U-Net, TransUNet, and Hybrid CNN-Transformer architectures. The integrated tele-gastroenterology decision support layer enables severity-based patient triage and automated referral triggering. Conclusions: These results demonstrate the potential of AI-powered polyp analysis to strengthen equitable access to GI care by facilitating risk stratification and specialist referral in HPSAs where direct endoscopy is unavailable, making the system deployable in telehealth infrastructures serving underserved elderly populations. Full article

Smartphone-based proximal sensing has emerged as a promising low-cost approach for pasture monitoring. A critical component of this methodology is accurate leaf segmentation, as it directly affects the reliability of subsequent image-based analyses. Despite advances in computer vision, the role of preprocessing strategies in segmentation performance remains insufficiently explored, particularly under resource-constrained conditions. This study presents a systematic comparative evaluation of three preprocessing pipelines based on HSV and CIELab color spaces for the segmentation of Urochloa grass leaves (Urochloa hybrid Mavuno and Urochloa decumbens) using smartphone imagery acquired field conditions. The pipelines were assessed using a multi-criteria framework, including the Fisher Discriminant Ratio (FDR), Intersection over Union (IoU), Overlap Error (OE), Structural Similarity Index (SSIM), and Edge Preservation Index (EPI), complemented by discordance map analysis. The results demonstrate that preprocessing design significantly influences segmentation stability, boundary preservation, and robustness to illumination variability. Pipelines based on HSV channels showed high sensitivity to shadows and non-uniform lighting, leading to reduced segmentation consistency. In contrast, the CIELab-based pipeline relying on the a* channel achieved superior performance, with higher discriminative capacity, improved edge preservation, and lower computational cost. These findings highlight that carefully designed classical preprocessing strategies remain highly effective for low-cost, real-time applications, even in the absence of computationally intensive models. This work establishes a robust segmentation foundation for future integration with advanced analytical methods, including machine learning approaches, and supports the development of scalable smartphone-based tools for pasture monitoring. Full article

Precise segmentation of kidney tumors in medical images is crucial for diagnosis, treatment planning, and prognosis assessment. In this work, we present a newly proposed hybrid deep learning model that combines the merits of U-Net and the Swin Transformer architectures in order to enhance the segmentation performance. Although U-Net has great spatial localization ability thanks to the encoder–decoder structure, which works in a hierarchical way, it is still difficult to capture global context well. The Swin Transformer instead captures long-range dependencies and assists in local detail extraction, while attention pooling might also smear fine boundary details. This motivates our hybrid integration. To attempt to resolve these issues, we extend U-Net with the Swin Transformer blocks in the backbone encoder path in order to efficiently perform multi-scale semantic feature extraction while preserving structural consistency. We trained and cross-validated the model on the publicly available Kidney Tumor Segmentation Challenge 2021 (KiTS21) dataset with extensive data augmentation as well as custom loss functions to address class imbalance and boundary obscureness. Experiments demonstrated that it achieved better performance when compared with the solo models, seeking a similar multi-task learning objective on not only U-Net and the Swin Transformer but also other baseline architectures in terms of the average Dice similarity coefficient (average DSC), intersection over union score (IoU) and Hausdorff distance. The proposed model achieved a Dice similarity coefficient (DSC) of 0.91, an IoU of 0.87, a PR-AUC of 0.89, and an overall voxel-wise accuracy of 98%, demonstrating robust and precise kidney tumor segmentation across varying tumor sizes and shapes. Moreover, the integrated solution is more robust and generalizes better, particularly in challenging cases with diverse anatomical variations. These findings demonstrate the power of Transformer-based hybrid models for medical image segmentation. Our results have positive implications for the design of computer-aided diagnostic systems and their association with other prevalent medical imaging tasks besides organ-specific or pathology-focused tasks. Full article

Accurate short-term traffic flow prediction is crucial for managing macroscopic Intelligent Transportation Systems (ITS). To overcome limitations in capturing complex spatiotemporal dependencies and the severe challenges of hyperparameter tuning, this paper proposes IHO-CNN-BiLSTM-Attention, a novel hybrid deep learning framework. Specifically, a Convolutional Neural Network (CNN) extracts local spatial features, a Bidirectional Long Short-Term Memory (BiLSTM) network captures temporal dependencies, and an attention mechanism dynamically weights key timesteps. To maximize the architecture’s performance, an Improved Hippopotamus Optimization (IHO) algorithm is proposed for automatic hyperparameter optimization. The IHO algorithm effectively overcomes the premature convergence of traditional optimizers by integrating a Piecewise Linear Chaotic Map (PWLCM) for initialization, tangent-based non-linear adaptive weights, a Tangent Flight defense mechanism, and Lens Opposition-Based Learning (LOBL) for local optimum escape. Evaluated comprehensively across three distinct macroscopic traffic benchmark datasets (a multimodal intersection, METR-LA velocity, and PeMSD4 volume), the IHO algorithm first demonstrated statistically significant superiority on standard CEC benchmark functions. Subsequently, the proposed hybrid model achieved state-of-the-art traffic state classification performance, maintaining peak F1-Scores of 0.9798, 0.8436, and 0.9561 across the highly diverse datasets. It significantly outperformed both classical optimized baselines (e.g., PSO, GWO) and contemporary heavy deep learning architectures (e.g., ASTformer, DiffSTG) under severe class imbalance and varying topological conditions. This work offers a robust, scalable, and highly generalized spatiotemporal forecasting solution with strong theoretical guarantees for intelligent traffic control. Full article

Sign language understanding for automated vehicles sits at the intersection of accessibility, intelligent transportation, and safety-critical human–machine interaction. The existing sign-language recognition systems are largely confined to controlled environments, limiting their utility in mobility scenarios characterized by lighting variation, motion blur, and partial occlusion. This paper proposes STCM-HVNet, a safety-aware hybrid multimodal architecture integrating four components: a spatial visual encoder, a MediaPipe-based pose encoder, a bidirectional LSTM temporal encoder, and a context-aware fusion and safety decision module. The architecture is formulated as a multi-task system that jointly predicts sign category, interaction intent, and urgency level, and incorporates confidence-aware rejection and fail-safe action mapping. Experiments are conducted on two Arabic sign-language resources. On the RGBArS image benchmark (31 classes, 7856 images), the proposed pipeline achieves a Top-1 accuracy of 45.38%, Top-3 accuracy of 75.15%, and Macro-F1 of 0.4479, outperforming LinearECOC, kNN-5, and Bagged Trees baselines. On the Arabic sign-language video benchmark (12 classes, 479 clips), the BiLSTM temporal encoder achieves a Top-1 accuracy of 93.15% and Macro-F1 of 0.9383, outperforming frame-aggregation (87.67%) and CNN-LSTM (89.04%) baselines. Ablation results confirm complementary contributions from the visual and pose branches. A safety-threshold analysis and a Monte Carlo dropout comparison demonstrate that the proposed safety decision/gating layer provides a controllable trade-off between prediction coverage and reliability. Full article

Early and reliable detection of plant diseases is critical for sustaining agricultural productivity and reducing economic losses. In olive cultivation, peacock eye disease poses a significant threat by adversely affecting leaf health and crop yield. While deep learning models have demonstrated strong performance in plant disease detection, their reliance on high-dimensional feature representations often leads to increased computational cost and limited deployability in real-world agricultural settings. This study proposes an efficient and robust olive leaf disease classification framework that integrates deep feature extraction, devised composite filter-based feature selection, and ensemble learning. Deep features are extracted from olive leaf images using transfer learning with ResNet101 and MobileNet architectures. To address feature redundancy and computational inefficiency, multiple filter-based selection strategies—including mutual information, Chi-square, F-score, and five devised composite selectors (score fusion, union, intersection, hybrid, and class-wise filtering)—are employed to generate compact and informative feature subsets of fixed sizes (32, 64, and 128 features). The selected features are evaluated using k-NN, SVM, and LightGBM classifiers under stratified 5-fold cross-validation. Experimental results demonstrate that competitive and near-baseline performance can be achieved with substantially reduced feature dimensionality. In particular, using only 128 selected features, the proposed approach attains up to 0.988 accuracy and 0.976 MCC, closely matching the performance obtained with full deep feature vectors. Furthermore, voting-based ensemble strategies, including iterative majority voting and hybrid GA–BO fusion, further enhance robustness, achieving the highest mean accuracy of 0.9916 among the evaluated ensemble configurations. These findings highlight the effectiveness of the proposed composite filter-based selection and ensemble framework as a practical, lightweight, and accurate solution for olive leaf disease detection, suitable for deployment in precision agriculture and resource-constrained environments. Full article

This article investigates Black American Islam as a semiotically mediated New Religious Movement (NRM), hybrid in nature and emerging from conditions of racialized governance, state surveillance, and social marginalization. Focused on the intersection of NRMs and political environments, the work engages in the reconstruction of a historical and conceptual lineage between Black Muslim movements and their mediated negotiation by Hip Hop artists. Grounded in Hall’s model of encoding/decoding and Hebdige’s subcultural theory, the transition of Islam-inspired semiotic markers from esoteric subcultural opacity to explicit orthodox adherence is demonstrated using historical analysis and close reading of symbolic expression in lyrics. The findings support a consideration of religious illegibility as aesthetic negotiation and strategy for political survival in circumstances of state scrutiny, with the subsequent consolidation of orthodox interpretations in Hip Hop signifying a recalibration of religious legibility in the securitized climate of a post-9/11 world. The contribution asserts that Black American Islam exemplifies NRMs’ instrumentalization of doctrinal elasticity and semiotic mediation in challenging socio-political surroundings, and its impact on negotiations of citizenship, political opposition, and religious identity. Full article

This systematic review critically analysed the differences, intersections, and trends toward convergence between Human Resource Management (HRM) and People Management (PM), highlighting the evolution of these approaches in organizational contexts marked by digital transformation. Conducted in accordance with the PRISMA 2020 guidelines, the study utilized the Web of Science and SCOPUS databases. Fifty-eight studies were selected and analysed narratively, prioritizing evidence with a lower risk of bias. The results confirm that, although HRM and PM have distinct rationales, a progressive convergence between the two is observed, driven by technological, organizational, and sociocultural changes. In this process, immersive technologies play a central role as socio-technical mediators, shaping organisational practices through configurations that reflect institutional logics and generate paradoxes between efficiency and experience. The analysed literature demonstrates that these technologies enable the integration of practices traditionally oriented toward efficiency, measurement, and standardization—characteristics of HRM—with experiential, relational, and subjective dimensions, which are specific to PM. It is concluded that the strategic adoption of immersive technologies enhances hybrid management models, capable of articulating strategic alignment, personalization of practices, engagement, and well-being at work. Thus, rather than merely serving as support tools, immersive technologies are emerging as foundational elements of modern people management, challenging traditional models, and paving the way for more integrated, sustainable, and people-centred practices. Full article