Search Results (5,075)

PICO is an ultra-lightweight substitution–permutation network block cipher designed for resource-constrained devices such as Internet of Things terminals and edge agents. For fixed endpoints, summing the characteristic probabilities over an enumerated finite weight window gives a verifiable lower bound on the differential probability. We use a PICO-specific workflow that combines mixed-integer linear programming bounds on active substitution boxes, exact-weight Boolean satisfiability search, optional Matsui pruning, and fixed-endpoint enumeration. For the selected endpoints, enumeration over $W=63,\dots ,76$ and $W=66,\dots ,79$ gives finite-window lower bounds of $2^{-59.95}$ and $2^{-61.95}$ for 21 and 22 rounds, respectively. We prepend two rounds and append three rounds to the 21-round differential distinguisher. The resulting 26-round analysis is an analytical equivalent-round-key filtering-and-ranking procedure for a 108-bit tuple. The verified 21-round finite-window probability input is a factor of $2^{0.8032}\approx 1.745$ larger than the previously reported input, increasing the expected right-tuple support at fixed S under the analytical accounting. For the illustrative choice $S=2^{42}$, the analytical resources are $D=2^{62}$ chosen plaintexts, a normalized substitution-box filtering workload of $T=2^{100.11}$ equivalent 26-round encryptions, and $M=2^{62}$ stored plaintext–ciphertext records. This setting is not tied to a demonstrated success probability and does not establish an equal-success complexity advantage over prior work. Full article

The sex ratio of the rice stem borer, Chilo suppressalis (Walker), is important for assessing reproductive potential and outbreak risk, but automated sex classification in field trap images is hindered by small targets, variable postures, subtle morphological differences and complex backgrounds. An edge–cloud collaborative system was developed for fine-grained sex classification of the rice stem borer. The edge terminal performs image acquisition, target detection, ROI extraction, and foreground enhancement, while the cloud platform conducts sex classification and result management. To improve small-target localization, YOLO-CSNet was constructed by integrating a global attention mechanism and adaptive spatial feature fusion into YOLO11n. Within detection-constrained ROIs, Haar-like features and an AdaBoost discriminator were used to suppress tray textures, shadows and non-target regions. For sex classification, DRS-ViT combines convolutional patch embedding, a discriminative-region soft-weighting module and a KANsformer encoder to represent weak sex-related cues and model global structural relationships. YOLO-CSNet achieved precision, recall, mAP@0.5, and mAP@0.5:0.95 values of 94.36%, 93.19%, 95.32%, and 73.95%, respectively. DRS-ViT achieved accuracy, precision, recall, and F1-score values of 93.93%, 94.68%, 93.29%, and 93.91%, respectively. In a temporally independent field deployment conducted at one Shandong site from May to June 2026, the system achieved an image-upload success rate of 91.7% and an end-to-end accuracy of 87.9%. The field results support the feasibility of the workflow under the tested conditions. Future work will extend field validation across multiple sites and seasons to evaluate system generalizability. Full article

The convergence of Large Language Models (LLMs) with the Internet of Things (IoT) is driving a transformative shift toward a continuous, context-aware smart healthcare ecosystem. Due to its novelty, existing research in this domain remains fragmented, leaving a critical gap in unified frameworks that synthesize domain applications, functional AI deployment roles, network architectures, and security boundaries. Following PRISMA 2020 guidelines, this paper presents a systematic literature review and quantitative analysis evaluating a selected corpus of 61 peer-reviewed and 14 preprint papers in this domain. Methodologically, we assess a novel hybrid article discovery strategy, finding that an AI-powered prompt-based literature search strategy achieves higher precision than traditional keyword-based Boolean queries (86% vs. 42%) on the evaluated search sample, which may reduce screening workloads. We found that the major limitation of AI-based literature search is non-determinism, which is also an inherent property of LLM-powered applications. To address this, we propose methodological guidelines for using an AI-assisted hybrid literature search strategy. Based on the selected literature, we establish a multi-layer taxonomy organizing the IoT-LLM advances in the healthcare domain across four pillars: application domain, LLM role, IoT device type, and architectural deployment pattern. Quantitative synthesis reveals a heavy research concentration in remote patient monitoring and personal health management (representing 59% of the corpus combined), primarily driven by the data accessibility of wearable sensors (64%). Cross-tabulation uncovers a distinct capability–constraint spectrum: cloud-based deployments lean on heavyweight state-of-the-art models (mainly GPT-family models) for complex semantic reasoning, whereas edge, federated, and blockchain-based hybrid systems leverage localized models (BERT and LLaMA families). Patient data privacy and reduced communication overhead were among the main reasons for choosing localized models. Crucially, our assessment reveals a pervasive neglect of LLM-specific vulnerabilities such as prompt injection and jailbreak attacks and a tendency to treat regulatory frameworks (e.g., HIPAA, GDPR) as design features rather than empirically validated compliance metrics. Finally, we propose an actionable future research agenda prioritizing multi-device system orchestration, emergency care integration, privacy-preserving LLMs, and deployment-scale clinical validation. Full article

Post-disaster reconstruction often prioritises the rapid delivery of housing and infrastructure, yet the long-term everyday performance of commercial environments embedded in recovery plans remains poorly understood. This study investigates why broadly similar post-disaster commercial blocks in Yenikent (Sakarya, Türkiye) have developed markedly different levels of spatial vitality and long-term everyday use. Yenikent is a state-led satellite city developed after the 1999 Marmara Earthquake, where residential, administrative and service functions were relocated to higher ground as part of a planned secondary urban centre. Twenty-five years later, these purpose-built commercial centres display markedly different levels and forms of everyday use, ranging from active neighbourhood service hubs to abandoned urban voids. Using a multiple-case, mixed-methods design, the study analyses all twelve post-disaster commercial clusters (eighteen buildings) through sectional and morphological analysis, a four-indicator Spatial Vitality Matrix (stationary activity, pedestrian flow, physical permeability, and active occupancy), and 88 semi-structured interviews with businesses, users, and neighbourhood headmen. The findings identify three dominant trajectories: (i) relatively robust service hubs sustained by institutional and neighbourhood anchors, (ii) fragile clusters where vitality is concentrated along accessible ground-level edges while upper or internal spaces shift towards storage, institutional, or ancillary uses, and (iii) functionally obsolete complexes associated with peripheral siting, poor topographic adaptation, and weak accessibility. The results further demonstrate that formal occupancy is not necessarily equivalent to spatial vitality: sectional relationships with the terrain, entrance legibility, façade permeability, and the integration of anchors into shared circulation are closely associated with whether commercial spaces sustain everyday activity. Interview evidence additionally reveals case-specific differences in perceived safety and gendered use of internal corridors and courtyards. The study supports a section-sensitive approach to post-disaster commercial development that prioritises topographic adaptation, legible access, active edges, and the integration of everyday anchors into shared spatial networks. Full article

The space–air–ground integrated network (SAGIN) enhanced by mobile edge computing (MEC) has emerged as a promising architecture for future 6G systems, providing wide-area coverage and distributed computing capabilities. By representing requests as service function chains (SFCs), network function virtualization (NFV) enables coordinated orchestration of underlying resources. However, SFC orchestration in SAGIN-MEC faces three significant challenges, including multi-layer resource heterogeneity, topology dynamics, and complex sequential dependencies within SFCs. To address these challenges, this paper proposes GT-PPO, a deep reinforcement learning (DRL)-based approach for online SFC orchestration designed to maximize network profit while minimizing end-to-end (E2E) delay. GT-PPO employs a graph attention network (GAT) to identify interactions among heterogeneous nodes and extract rich feature information from the dynamic physical network. Additionally, it leverages the Transformer self-attention mechanism to encode the SFC context based on resource demands and current deployment progress, thereby capturing global dependencies among virtual network functions (VNFs). Extensive simulation results demonstrate that, under high-load conditions, GT-PPO outperforms representative baselines, increasing the request acceptance ratio and network profit by 5.62% and 16.71%, respectively, while reducing the average E2E delay by 15.46%. Full article

Firms are embedded in transaction systems whose organization can generate both coordination benefits and relational exposure. Using disclosed related-party transactions (RPTs), we construct annual weighted bipartite networks for 2674 Chinese A-share listed firms and examine 26,264 RPT-active firm-year observations from 2003 to 2024. The analysis distinguishes three firm-level dimensions—relationship scale, transaction concentration, and projected shared-counterparty position—from local bipartite microstructures. The firm-level evidence reveals a double-edged pattern: broader and more valuable RPT relationships are associated with lower Merton and KMV distance to default (DD), and projected degree is associated with lower Merton DD, whereas a higher top-three-related-party share is associated with higher KMV DD. Direction-specific estimates reinforce this distinction. Scale is negatively associated with DD on both the seller/provider and buyer/recipient sides, concentration is generally positive (especially on the buyer/recipient side), and seller/provider projected degree is negatively associated with KMV DD. At the local level, closed 2 × 2 presence and intensity are negatively associated with Merton DD, while simple degree-based forms and shared-counterparty bridges provide less stable differentiation. Observed closure substantially exceeds degree-sequence-preserving randomized benchmarks, and excess closure remains negatively associated with Merton and Bharath–Shumway DD after relationship opportunities and counterparty popularity are controlled. The results establish transaction scale, value allocation, projected reach, and repeated local sharing as complementary signals for structure-sensitive credit risk monitoring in complex transaction systems. Full article

Bird strikes cause aircraft damage, create serious risks to human safety and can contribute to catastrophic incidents, while continuing to impose substantial economic costs on airlines. The impact combines high kinetic energy with discontinuous, strongly nonlinear contact over a short duration, producing large structural deformations; appropriate nonlinear simulation techniques are therefore required to capture this complex interaction. For this purpose, the present study applies established Smoothed Particle Hydrodynamics (SPH)–finite element modelling ingredients to a controlled matrix of aerospace material systems and target geometries. The approach is first benchmarked against a published aluminium flat-plate bird-impact test using a raster-digitised force-history comparison, after which monolithic metallic and composite structures and source-described honeycomb-sandwich alternatives are assessed in flat-panel and curved leading-edge configurations. The results show that contact-force and local-displacement rankings depend strongly on target geometry and response metric, with the curved leading edge changing the ordering observed for the flat panel. More compliant systems generally permit greater local displacement, whereas stiffer systems restrict displacement but can sustain higher short-duration force peaks; consequently, no universal material ranking follows from a single response measure, and the results are most suitable for preliminary design screening. Full article

Detecting small person and bicycle instances with lightweight models is relevant to resource-aware visual sensing, but evidence from a category-filtered general-purpose dataset cannot establish performance in dense surveillance, traffic monitoring, or aerial environments. This study therefore examines a narrower question: whether replacement-style Coordinate Attention (CA) integration can improve coordinate-sensitive representation in YOLOv11m under a controlled VOC-based person and bicycle setting without increasing model complexity. In the official Ultralytics YOLO11m architecture, the Spatial Pyramid Pooling–Fast (SPPF) layer is followed by a C2PSA block. The proposed configuration replaces this post-SPPF C2PSA block with CA, while retaining the remaining backbone, neck, and detection head. CA encodes directional positional information along the horizontal and vertical axes. Experiments are conducted on a filtered subset of PASCAL Visual Object Classes (VOC) 2012 that retains only the person and bicycle categories; this subset is not a dedicated small-object or surveillance benchmark. Within this setting, the proposed model improves mean average precision at an intersection-over-union threshold of 0.50 (mAP@50) from 79.2% to 81.5%. It also improves mean average precision averaged over thresholds from 0.50 to 0.95 (mAP@50–95) from 53.9% to 54.8% and small-instance average precision (${\mathit{AP}}_{\mathrm{s}}$) from 68.1% to 72.4%. The parameter count decreases from 20.03M to 19.07M, and the model achieves 90 frames per second (FPS) on the tested NVIDIA GeForce RTX 4060 Laptop GPU (NVIDIA Corporation, Santa Clara, CA, USA). These results provide incremental evidence for a complexity-aware CA replacement strategy within the evaluated VOC distribution; they do not demonstrate cross-domain generalization or deployment performance in real surveillance or aerial scenarios. No embedded platform was evaluated, and the reported RTX 4060 throughput should not be interpreted as evidence of edge-device latency, energy efficiency, or deployment readiness. Full article

Recent research has advanced fine-scale mapping and driver analysis of cropland fragmentation, but composite indices may mask structurally different fragmentation configurations, and evidence on how terrain and urban-system location jointly relate to fragmentation remains limited in rapidly urbanizing delta regions. This study quantified cropland fragmentation in the Yangtze River Delta (YRD), China, between 2000 and 2020. A Cropland Fragmentation Index (CFI) integrating edge density (ED), patch density (PD), and mean patch area (MPA) was calculated at a 1 km grid scale, and K-means clustering was used to identify fragmentation configurations. Pearson correlation and random-forest regression were used to examine spatial associations with selected 2020 natural and socioeconomic variables. Cropland area declined by 7.97%, while the regional-mean CFI increased from 0.29 to 0.32. Four configurations were identified, with the largest type (38.45% of grids) characterized by small patches and complex boundaries. Elevation and slope showed the strongest bivariate correlations with CFI, whereas distance to urban areas had the highest random-forest importance. These results reveal distinct fragmentation pathways and support differentiated cropland management in rapidly urbanizing regions. Full article

Accurate three-dimensional (3D) characterization of geothermal reservoirs is crucial for resource assessment and development, yet it remains a significant challenge due to sparse direct observations and the complex, heterogeneous nature of subsurface geology. While deep learning has emerged as a powerful tool for subsurface modeling, existing approaches often struggle to preserve critical fine-scale structural details and adaptively focus on geologically informative regions, limiting their effectiveness for geothermal applications. To address these limitations, we propose Gen-LEP, a novel deep generative neural network specifically designed for geothermal reservoir characterization. The proposed framework integrates a key component of local edge pattern (LEP) learning module to enhance the preservation of lithological boundaries and structural discontinuities. The LEP learning module is embedded within a conditional generative framework to effectively learn the nonlinear relationships between sparse conditioning data and complex 3D reservoir structures. We evaluate our method on a geothermal reservoir modeling dataset. Experimental results demonstrate that Gen-LEP can achieve accurate reconstruction and preserve complex geological boundaries. Gen-LEP can provide an effective deep learning framework that improves the fidelity of geothermal reservoir reconstructions by explicitly addressing the specific spatial characteristics of subsurface geological systems. Full article

This paper proposes a Difference-guided Detail Enhancement Fusion Network (DDEF-Net) for UAV-based RGB–thermal (RGB-T) object detection, which enables effective complementary exploitation of visible and infrared information in complex scenarios. A Difference-guided Kolmogorov–Arnold Network (KAN) Calibration Fusion module (DKCF) is designed to explicitly model cross-modal discrepancies and incorporate KAN-based nonlinear calibration, improving the selection of informative features and reducing redundant feature interference during multimodal fusion. Furthermore, a Scharr–Fourier Detail Enhancement module (SFDE) is introduced to jointly leverage Scharr edge priors and Fourier-domain information to strengthen low-level visible feature representations and preserve fine-grained structural cues. On the DroneVehicle dataset, DDEF-Net achieves 84.9% mAP@0.5 and 72.6% mAP@0.5:0.95, improving the RGB–IR baseline by 3.5 and 4.0 percentage points, respectively, with 4.4 M parameters and 12.5 GFLOPs. An additional experiment on the VEDAI visible–near-infrared (NIR) aerial dataset after dataset-specific training provides supplementary evidence that the proposed modules remain beneficial under a different paired multimodal imaging setting. Corruption experiments show improved robustness to Gaussian and motion blur, whereas the model remains sensitive to strong Gaussian noise. Full article

Indoor medium-to-large indoor object detection is a fundamental capability for mobile robot perception and visual navigation, yet it remains challenging in complex indoor environments due to occlusion, overlap, cluttered backgrounds, and the need for efficient deployment on resource-constrained devices. To address these challenges, this paper presents LIO-YOLO, a lightweight object detection framework developed from YOLO11n. A new MDA-C3k2 module is integrated into the backbone to improve multi-scale feature extraction and contextual information modeling. The Slim-neck design is further employed to achieve more efficient feature fusion with less redundant computation. In addition, SEAM-Head is introduced to improve the detection of occluded and overlapping objects, which enhances the model’s performance in complex indoor environments. To better reflect the characteristics of large-object detection in real indoor environments, an Indoor Large Object Dataset (ILOD) was constructed in this study. Experimental results show that LIO-YOLO achieved 84.9% precision, 80.7% recall, 88.6% mAP@0.5, and 58.6% mAP@0.5:0.95. Furthermore, the proposed model maintained only 5.4 GFLOPs, demonstrating a good trade-off between detection accuracy and computational cost. Moreover, deployment on the RK3588 edge platform reduced inference time from 11.8 ms to 9.5 ms and improved throughput from 84.8 FPS to 105.3 FPS. These results indicate that LIO-YOLO demonstrates its effectiveness for real-time indoor robotic perception applications. Full article

Safe drilling in hydrate-bearing sediments is essential for environmentally responsible natural gas hydrate development. Complex pressure variations, fluid migration, and mechanical disturbances during drilling may increase the risks of gas influx, lost circulation, pipe sticking, and wellbore instability. To improve diagnostic robustness under heterogeneous and noisy drilling conditions while reducing dependence on large-scale manually labeled datasets, this study develops an adaptively coupled data–physics dual-driven diagnostic framework based on a self-organizing map (SOM) and a competitive classifier. Unlike a conventional one-way SOM–classifier cascade, changes in the downstream classification loss are fed back to adjust the SOM neighborhood radius, thereby coupling unsupervised feature mapping with supervised risk classification. In addition, class-conditional pressure-window and torque–drag consistency penalties are linked to the predicted class probabilities so that physical information directly participates in the optimization of applicable fluid-related and pipe-sticking risk predictions. Risk categories without an explicitly available physical residual remain primarily data-driven. Experiments on a hybrid measured–simulated dataset show that the proposed model achieves a test-set accuracy of 97.67%, outperforming representative baseline models. When 20% Gaussian noise is added, the accuracy decreases by only 4.20 percentage points. A three-layer data acquisition–edge-computing–cloud-monitoring early-warning system is implemented through MATLAB/VC integration. In a pilot field trial, a representative well-kick risk was identified 12 min earlier than by a conventional threshold-based alarm, and the missed-alarm rate decreased from 15% to 3%. The proposed method provides an engineering-oriented framework for improving drilling safety and environmental risk control during natural gas hydrate development. Full article

Tunnel traffic surveillance is challenged by uneven illumination, severe occlusion, distant small objects, and long-tailed class distributions, while practical edge deployment imposes strict computational constraints. To address these problems, this paper proposes an integrated object detection, structured compression, and edge-deployment framework based on YOLOv8. A C2f-P3A module is developed to enhance spatial and channel feature representation, while Lite-ASPP is introduced to efficiently incorporate multi-scale contextual information. WIoU v3 is adopted to optimize bounding-box regression, and a dependency-preserving channel-level LAMP strategy is employed to adaptively allocate sparsity across eligible layers. Experimental results on the tunnel-surveillance dataset show that the improved dense model achieves a Precision of 90.49%, a Recall of 75.91%, an mAP50 of 83.13%, and an mAP50:95 of 57.60%, outperforming YOLOv8s by 5.46, 6.56, 4.98, and 2.17 percentage points, respectively. Furthermore, a target channel sparsity of 60% reduces the parameter count from 11.24 M to 9.86 M and the computational cost from 28.4 to 25.7 GFLOPs, while retaining an mAP50 of 83.11%. After mixed-precision deployment on the RK3588 platform, the pruned model achieves an mAP50 of 72.87%, an average processing time of 39.04 ms/frame, and a processing rate of 25.61 FPS. These results indicate a favorable empirical trade-off among detection accuracy, model complexity, and edge-processing efficiency under the evaluated conditions. Full article

Cavitation is one of the major factors reducing the performance, efficiency, and service life of deep well submersible pumps used in agricultural irrigation systems. Therefore, the early detection of cavitation is essential for improving pump reliability and reducing maintenance costs. In this study, a machine learning-based framework is proposed to detect and classify cavitation conditions using experimental data collected from a deep well pump test unit. Hydraulic and operational parameters were measured under different operating conditions, while the measured noise level was used only to assign cavitation labels during dataset preparation. According to the measured noise level, the operating conditions were classified into three categories: Normal, Incipient Cavitation, and Severe Cavitation. Several machine learning algorithms were evaluated using stratified cross-validation and an independent test dataset. Model performance was assessed using Accuracy, Precision, Recall, and F1-score. The results showed that the Extra Tree classifier achieved the best performance with an accuracy of 82.4%. Feature importance analysis indicated that power consumption and submergence depth were the most influential parameters for cavitation detection. Unlike many existing studies that rely on computationally intensive models, the proposed framework employs a simple and lightweight machine learning approach while maintaining reliable prediction performance. Its low computational complexity makes it a promising candidate for future implementation on resource-constrained edge devices, enabling real-time cavitation monitoring in agricultural pumping systems. Full article