Search Results (206)

Millimeter-wave (mmWave) vehicular-to-everything (V2X) links are highly vulnerable to sudden blockages in dense urban traffic. Since terrestrial roadside links can degrade rapidly, and alternative ground paths are often limited, maintaining reliable service with only ground networking resources remains challenging. To enhance link reliability by exploiting aerial relay resources in air–ground integrated networks, this paper proposes a vision-assisted unmanned aerial vehicle (UAV) relay triggering framework. The framework uses roadside multi-camera images to predict the future link state of a target vehicle and triggers a UAV decode-and-forward (DF) relay before the direct roadside-unit (RSU)–vehicle link becomes unreliable. To enable target-specific prediction, a template-guided image-matching module is developed to localize the target vehicle in multi-view images. The matched features are fused and temporally modeled to predict future LoS, NLoS, and Absent states, with the predicted NLoS probability further used to determine the UAV activation decision through a probability-based triggering policy. Simulation results on a 3D ray-tracing urban V2X dataset show that the proposed dual-view predictor achieves about 99% validation accuracy, compared with about 87% for the single-view baseline. The proposed relay triggering scheme reduces the outage probability from 15.08% for RSU-only transmission and 4.49% for reactive relaying to 0.76%, and improves the 5th-percentile rate from 11.72 Mbps to 22.49 Mbps over reactive relaying. Full article

Recent generative art outputs across music, literature, painting and moving-image media have attracted extensive scholarly and public interest, yet evaluations of their creative capacities are mostly limited to informal observational accounts. Drawing on neuroaesthetic reasoning, this paper puts forward a dual-layer analytical framework that differentiates two distinct modes of aesthetic reception: Alignment, defined as statistical template matching, and evocation, referring to the novel association of scattered embodied memory fragments. Building on this binary categorization, the study introduces the tentative Ring Scale taxonomy—a figurative target-shooting metaphor rather than quantitative metric—as a purely descriptive tool for stratifying relative aesthetic evocation intensity. This framework further unpacks the neurocognitive underpinnings of auditory, visual and textual aesthetic pathways, alongside their combined multimodal interactions within film and television works. It tentatively accounts for why generative systems tend to deliver more cohesive aesthetic outcomes within the auditory domain, and hypothesises a present functional limitation of current large models: these systems perform comparatively well within Alignment-driven aesthetic effects, while layered high-order evocation remains constrained by inherent structural limitations of statistical training architectures. From this diagnostic observation, three directional paradigm shifts for human–AI collaborative creation are outlined: shifting from human substitution to human–machine complementarity, shifting from exhaustive template imagery generation to targeted latent fragment elicitation, and shifting from optimising figurative Ring-tier descriptive labels to pursuing transformative aesthetic fission effects. The study frames imaginative cognition as the central driving force behind fruitful human–AI co-creation, and positions prompt engineering as the actionable operational bridge connecting human imaginative thought to machine-executable generative parameters. Three tentative prompt design tactics are then elaborated: physiological arousal framing, multisensory scenario simulation prompts, and intentional strategic blank-leaving. Additionally, this work discusses the plausible constructive functions of model hallucination phenomena when viewed through the lens of high-tier aesthetic evocation, rather than merely framing such outputs as technical errors. All judgments and tier comparisons raised throughout the paper are framed as unvalidated observational hypotheses open to empirical testing. To facilitate follow-up empirical scrutiny, the paper collates a full set of testable hypotheses derived from its theoretical reasoning and outlines feasible experimental validation pipelines, with an open call for controlled empirical research to corroborate or refine the proposed qualitative framework. Full article

Biometric authentication systems are increasingly deployed in critical security applications, yet the irreplaceable nature of biometric traits poses fundamental risks when templates are compromised. Unlike passwords or tokens, biometric data cannot be reissued, making template protection a paramount concern for preserving both security and privacy. This paper presents a novel multilayer framework for biometric template protection that integrates cancellability directly into the feature extraction stage, ensuring non-invertible and revocable templates while maintaining high recognition accuracy. The proposed method employs chaotic projection of Binarized Statistical Image Features (BSIF) filter banks, optimized through Particle Swarm Optimization (PSO), to generate discriminative yet irreversible biometric templates. To strengthen security against statistical and cryptanalytic attacks, dual-layer scrambling and diffusion processes driven by chaotic maps eliminate spatial correlations and produce uniform intensity distributions. Furthermore, Random Grid Visual Cryptography (RGVC) divides the encrypted template into two shares stored in separate databases, ensuring that the compromise of a single repository reveals no biometric information. Extensive experiments conducted on the PolyU multispectral palmprint database demonstrate exceptional authentication performance, achieving Equal Error Rate (EER) values as low as 0.0520% after applying the proposed protection framework, under optimal configurations. Comprehensive empirical security analysis demonstrates favorable statistical security characteristics, including near-zero pixel correlation, near-uniform intensity distributions, high entropy values approaching the theoretical maximum of 8 bits, favorable NPCR and UACI values, and high sensitivity to key variations under the considered experimental settings. The proposed framework satisfies the essential requirements of cancellable biometrics, including diversity, revocability, and non-invertibility, while providing a privacy-preserving biometric template protection approach. Full article

Phishing email attacks remain among the most common and damaging forms of cybercrimes. With the emergence of generative artificial intelligence (Gen-AI), adversaries can automatically generate tailored, well-crafted phishing emails for each potential victim rather than relying on mass-distributed templates, thereby reducing the effectiveness of traditional detection systems. In this study, we propose a novel hybrid framework for detecting AI-generated phishing emails that leverages natural language processing (NLP), machine learning (ML), and deep learning (DL). The uniqueness of the proposed approach lies in the dual application of bidirectional encoder representations from transformers (BERT): (1) as an embedding model to extract deep contextual representations of email content; (2) as a fine-tuned classifier. Additionally, we integrate high-impact common-word features, derived from the best-performing classifier, to enhance contextual interpretation and improve discrimination between human-crafted and AI-generated emails. The framework was evaluated on a balanced dataset combining real and Gen-AI phishing emails and benchmarked across six ML/DL models—support vector machine (SVM), random forest (RF), logistic regression (LR), long short-term memory (LSTM) networks, BERT, and generative pre-trained transformer (GPT)—using standardized preprocessing, hybrid feature engineering, and optimized hyperparameters. Experimental results show that the BERT fine-tuned classifier, enhanced with the integrated common-word features, achieved the highest accuracy of 98%, outperforming all other models and demonstrating strong generalizability. This study demonstrates how integrating contextual cues and custom lexical signals can significantly improve the detection of AI-generated phishing content. Cybersecurity professionals, policymakers, and researchers can develop sophisticated and resilient defenses against emerging AI-enabled threats. Full article

The development of dual-acting hybrid antibiotics is a promising strategy to combat the ongoing spread of drug-resistant tuberculosis. Inspired by the structure of the natural dithiolopyrrolone hybrid antibiotic thiomarinol and based on the synergistic effects confirmed by checkerboard assays, we employed a molecular hybridization strategy. The dithiolopyrrolone natural product thiolutin was covalently linked to the pharmacophores of four clinical antitubercular drugs—cycloserine, linezolid, isoniazid, and pyrazinamide—through alkyl linkers of 7–10 carbon atoms via amide condensation, leading to the design and synthesis of 15 novel hybrids. In vitro antitubercular activity evaluation revealed that the cycloserine series exhibited the best activity, with MIC values as low as 1 μg/mL, followed by the isoniazid series. Cytotoxicity assays showed that all cycloserine hybrids had IC₅₀ values > 40 μg/mL against RAW 264.7 mouse macrophages, markedly lower than that of thiolutin alone. Among them, T₁-CS and T₄-CS displayed the best selectivity indices, achieving an effective reduction in cytotoxicity. This study successfully constructed a class of thiolutin–cycloserine hybrids with low cytotoxicity and high selectivity, providing a valuable molecular template for the discovery of novel antitubercular lead compounds. Full article

3D reconstruction and classification of aircraft are two active research areas in optical remote sensing image processing which are of great significance for applications such as airport monitoring and intelligence analysis. The traditional approaches usually focus only on one of these two tasks, and all these methods suffer from inherent limitations. In the field of 3D reconstruction, most current methods require multiple-view images as input, which is rarely feasible in remote sensing. However, single-view 3D reconstruction is an inherently ill-posed problem. Existing methods, including voxel generation and mesh template deformation, still suffer from limited accuracy and poor shape fidelity. In the field of image classification, the existing methods are mainly based on deep learning. These methods require a large amount of labeled data, and they may also be misled by the color and texture features of the target in the dataset. In this paper, we propose a unified framework for simultaneous 3D reconstruction and classification, specifically tailored for aircraft targets in optical remote sensing imagery. The key innovations are threefold: First, we introduce the Signed Distance Field (SDF) implicit representation to build a prior-guided 3D reconstruction framework pre-trained on 3D model datasets. Second, to achieve the reconstruction process with a single image as input, we design a new joint optimization pipeline. We propose a novel dual-kernel differentiable rendering method, which is fused behind the SDF generation network for iterative optimization of the implicit code and pose parameters. Third, a gated feature fusion module is developed to combine the optimal latent vector from reconstruction with the classification backbone. This integration enables the joint output of 3D meshes and category labels within a unified loop. The resulting optimal latent code plays a dual role as a generative seed for high-fidelity 3D reconstruction and as a low-dimensional feature representation for target classification. Quantitative evaluations validate the superiority of our joint framework. Compared with the strong mesh-based competitor AtlasNet, the proposed method yields a 12.2% boost in mean F-score. In object classification, leveraging the 3D implicit geometric features boosts the performance to a peak accuracy of 97.88%, outperforming advanced remote sensing backbones such as RSMamba and EAM by 2.03% and 2.54%. Additionally, ablation studies confirm the indispensability of our key designs, revealing that our dual-task feature fusion strategy brings an absolute gain of 1.18% in classification accuracy, while omitting the clustering prior stages and the dual-kernel rendering method leads to a 30.4% and 10.1% degradation in Chamfer distance. Full article

We present a rank-based method for detecting short-duration gravitational-wave transients in 46 days of coincident data from the first Advanced LIGO observing run (O1). The method applies a moving-window implementation of Chatterjee’s rank correlation coefficient to whitened interferometric sensor strain data. This produces a computationally efficient statistic sensitive to temporally ordered signal structure without relying on waveform templates. Compared with traditional excess power and coherent burst searches, the rank-based formulation is potentially less sensitive to certain non-Gaussian noise transients. Furthermore, it processes dual-detector data faster than real time on a single CPU core. We evaluate the method using 60 hardware injections from the O1 dataset, recovering 28 compact binary coalescence injections, primarily for events with a single-detector signal-to-noise ratio above approximately 13. The pipeline identifies 31 transient candidates, including the astrophysical event GW150914 and two instrumental glitches. Although the present implementation is less sensitive than established search pipelines, these results demonstrate the feasibility of the new method. Rank-based detection statistics provide a computationally efficient and complementary method for low-latency transient detection in interferometric sensor networks. Full article

Background/Objectives: Osteoporosis is a major contributor to vertebral compression fractures (VCFs) and other skeletal complications, yet quantitative bone mineral density (BMD) assessment using dual-energy X-ray absorptiometry (DEXA) is not routinely available in many spine surgery workflows. This study proposes an anatomically guided and optimization-refined radiomics framework for opportunistic osteoporosis assessment from routine lumbar spine magnetic resonance imaging (MRI). Methods: The proposed pipeline employs a hierarchical template-matching strategy to automatically localize the L1–L4 vertebral region, followed by an optimization-based refinement procedure that adapts vertebral regions of interest (ROIs) using intensity, texture, boundary, and geometric constraints. Anatomically consistent ROIs are subsequently used for extraction of handcrafted radiomic descriptors, including statistical, textural, gradient-based, frequency-domain, and shape-related features. The extracted features were evaluated using conventional support vector classification (SVC) and a NeurodynamicSVMRBFTanh classification framework for osteoporosis-related classification. Results: Experimental results demonstrated robust and anatomically consistent vertebral localization across heterogeneous lumbar MRI acquisitions. The NeurodynamicSVMRBFTanh framework achieved the best screening-oriented performance, yielding 85.2% classification accuracy and 100.0% sensitivity on an independent test set. In addition, exploratory BMD regression analysis demonstrated the feasibility of estimating DEXA-derived BMD directly from MRI-derived radiomic features, achieving mean absolute percentage errors of approximately 15–20% across lumbar vertebral levels. Conclusions: These findings suggest that anatomically guided vertebral radiomics extracted from routine lumbar spine MRI contain clinically meaningful information associated with osteoporosis-related bone quality changes and may provide a practical tool for automated opportunistic osteoporosis assessment in settings where DEXA measurements are unavailable. Full article

Under-bridge inspection involves complex structural geometries, confined working spaces, and substantial safety risks for manual operation. To address these challenges, this study proposes a hierarchical obstacle-avoidance motion-planning framework for a large road-rail dual-use bridge inspection manipulator. First, a consistent kinematic model is established for an 11-DOF physical actuation system composed of six revolute joints and five prismatic telescopic joints. For inverse kinematics and template matching, the five physical telescopic joints are mapped to two equivalent prismatic variables, whereas collision checking and execution remain in the full physical joint space. Second, an improved bidirectional RRT-Connect planner is developed by integrating goal-biased sampling, multi-candidate expansion, soft low-lift constraints, and combined state and edge validity checking. Third, a pose-library-guided segmented planning strategy is introduced to reuse successful deployment sequences for known targets and to automatically generate intermediate poses for unseen targets. All post-processed trajectories are revalidated for collision and clearance before acceptance. Comparative simulations demonstrate that the proposed framework improves collision-free planning success and suppresses unreasonable high-lift configurations. The framework provides a reproducible planning solution for automated bridge inspection in confined under-bridge environments. Full article

Hardware Trojans (HTs) threaten integrated-circuit security in a globalized semiconductor supply chain, yet how the choice of hardware description language—manual Verilog versus compiler-optimized agile languages such as Chisel—affects the synthesis quality of a Trojan-bearing design remains unexplored. This work re-implements AES-T100, the Trust-Hub leakage benchmark that exfiltrates an AES-128 key via an LFSR-driven side channel, faithfully in Chisel. It then compares the resulting netlist against a hand-written Verilog baseline under matched FPGA synthesis and examines dual-use implications. AES-T100 was rebuilt in Chisel 3.5.0 (20-bit LFSR PRNG, 1-to-8-bit key-obfuscation unit, $8\times$ flip-flop leakage circuit), compiled through FIRRTL 1.5.0, and synthesized on a Cyclone IV E FPGA via Quartus Prime 21.1. The FIRRTL-generated netlist achieved 23.8% higher $f_{\max }$ (387.60 vs. 313.19 MHz) and throughput (4.96 vs. 4.01 Gbps) at identical logic-element count (2096 LEs), with only 1.50% Trojan overhead. The gap localizes to the host AES cipher, consistent with FIRRTL optimization passes. A five-seed replication confirms statistical robustness ($t\left(8\right)=11.7$, $p<0.001$, 95% CI: 18.6–27.7%). Causal attribution—compilation flow versus single-author Verilog coding—remains open, requiring pass-ablation experiments. The parametric template supports HT benchmark generation. FIRRTL-level static analysis is proposed as a defensive direction. Full article

Organic memristors are attractive building blocks for neuromorphic computing owing to their intrinsic synaptic functionalities and solution-processability. However, their operational instability remains a major challenge, primarily arising from poorly controlled semiconductor crystallization and stochastic conductive filament formation. Here, we report a high-performance solution-processed organic memristor based on a TIPS-pentacene/PMMA/CdSe-ZnS quantum-dot hybrid system, in which a dual-engineering strategy is employed to simultaneously regulate film crystallization and filament dynamics. Specifically, the PMMA matrix templates the molecular ordering of TIPS-pentacene to improve film uniformity and crystallinity, while CdSe/ZnS quantum dots locally modulate the electric field to direct and confine conductive filament formation. As a result, the device exhibits ultralow and highly uniform switching voltages (0.473 V for set and −0.430 V for reset), suppressed device-to-device variation, long retention exceeding 10⁴ s, and endurance over 1200 switching cycles. In addition, the memristor supports multilevel data storage and successfully emulates key synaptic functions, including long-term potentiation/depression, paired-pulse facilitation, and spike-timing-dependent plasticity. This work provides a materials-level strategy for achieving reliable and low-power organic memristors, offering a viable route toward high-density nonvolatile memory and neuromorphic computing hardware. Full article

Automatic License Plate Recognition (ALPR) in unconstrained traffic environments requires simultaneously addressing two fundamental challenges: reliable localization of small and degraded license plates and accurate decoding of visually ambiguous character sequences. This paper presents a hierarchical multi-stage framework that combines deep-learning-based detection, geometric normalization, dual-channel recognition, and structured post-correction to improve recognition robustness under diverse real-world conditions. A systematic ablation study involving five configurations (A0–A4) demonstrates the effectiveness of the proposed architecture across three benchmark datasets. On the UC3M-LP dataset, exact-match accuracy increases from 45.2% to 88.3%, while achieving 91.6% partial accuracy and a zero detection-miss rate. The framework further attains 95% exact-match accuracy on controlled European license plate crops and 93% on a large-scale custom dataset. In addition, we identify systematic evaluation artifacts in partially annotated benchmarks, showing that truncated ground-truth labels can underestimate genuine character-level improvements. The proposed framework supports multiple license plate formats through a configurable structural template library, and preliminary experiments on a small Arabic-script subset suggest potential extensibility without full model retraining. To ensure full reproducibility, all source code and evaluation resources are publicly released. Full article

With the increasing penetration of behind-the-meter photovoltaic generation and distributed energy storage, distribution system operators usually observe only the net load at the point of common coupling, while the actual user load and energy storage charging/discharging power are difficult to measure directly. To address this problem, this paper proposes a mode-aware constrained inverse optimization method for behind-the-meter distributed energy storage power estimation under fixed time-of-use tariffs. The proposed method uses net load, photovoltaic power, and tariff information as inputs and estimates the hidden user load, storage power, SOC trajectory, and dominant storage arbitrage mode. A mode-aware joint representation model is developed by introducing single-cycle and dual-cycle charge–discharge templates, daily action intensity factors, mode weights, and local correction terms. In addition, power limits, SOC dynamics, SOC bounds, daily energy balance constraints, tariff-response consistency, and mode selection penalty are incorporated into the inverse optimization framework to improve the physical feasibility and interpretability of the estimation results. Case studies are conducted using a 40-day hybrid dataset with a 1 h sampling interval and a 70%/30% training/testing split. The dataset is constructed from park-level user load and photovoltaic data, while the storage power profile is reconstructed according to typical time-of-use arbitrage operation. For the main dual-cycle testing case, the NRMSEs of storage power, user load, and net load are 14.75%, 3.90%, and 3.76%, respectively. The results show that the proposed method can recover the main variation trend of hidden storage power under the studied fixed time-of-use tariff scenario and provides a preliminary basis for park-level storage monitoring and flexible resource perception. Full article

Sustaining rice productivity under the dual constraints of freshwater scarcity and low-temperature stress represents a pressing challenge for high-latitude japonica rice systems worldwide. There is an urgent need to develop coupled irrigation–agronomic management strategies that jointly safeguard yield stability and water use efficiency (WUE) in cold-region rice production. In this study, a two-year field experiment was conducted in 2024–2025 on albic soil (Albic Luvisols, WRB; θfc 38.2% v/v, pH 5.80, clayey texture with poor permeability and a propensity for subsurface waterlogging) in the Sanjiang Plain, Heilongjiang Province, China (47°15′ N, 133°28′ E), with nine coupled “irrigation regime × auxiliary practice” treatments, comprising conventional continuous flooding, four-level controlled irrigation (CI) at lower thresholds of 60%, 70%, 75%, and 80% θfc, and their combinations with film mulching (FM) or a humic-acid-based soil amendment (SA). An interpretable machine-learning diagnostic framework was developed, with elastic net (EN) as the primary analytical model and random forest (RF) as a nonlinear control, to simultaneously identify core yield predictors and outlier treatments. The principal findings were: (i) The soil-amendment-coupled 75% θfc CI treatment (SACI) increased grain yield by 12.3% and reduced water input by 17.0% relative to conventional continuous flooding, with WUE reaching 1.801 kg m⁻³, a 35.3% gain over the control (p < 0.05); these improvements were consistent across both individual years (year × treatment interaction: p = 0.601; inter-year rank correlation ρ = 0.967). Lowering the CI threshold below 75% θfc significantly reduced grain yield through diminished effective-panicle retention. (ii) Multi-method consensus analysis (Kendall’s W = 0.871, p < 0.01) identified root volume at the milk stage as the most strongly and consistently associated statistical predictor of yield formation, with convergent mechanistic support from independent rhizosphere evidence (Eh, TTC reductive activity). Definitive causal validation awaits isotope-tracing experiments. (iii) The film-mulching × continuous-flooding treatment (FMCG) was diagnosed as a yield-response outlier (permutation test p = 0.003), three in situ rhizosphere measurements (redox potential, root TTC-reducing activity, and rhizosphere temperature) supported the proposed mechanism of hot–anoxic rhizospheric inhibition. Methodologically, this study develops a four-level evidence convergence framework that integrates intra-model self-consistency, cross-model (EN vs. RF) consensus, independent rhizosphere evidence, and distribution-free permutation testing, with Jackknife+ conformal prediction and companion Monte Carlo simulations (1000 replicates) used to quantify the reliability boundaries under small-sample conditions (n = 27). These findings provide an evidence-based irrigation–soil co-management strategy for cold-region rice production in Northeast China, and the proposed diagnostic paradigm offers a generalizable, reliability-quantified methodological template for interpretable small-sample modeling in multifactorial coupled field experiments. Full article

To develop eco-friendly and highly efficient fruit and vegetable preservation materials, this study uses the multi-gradient micro–nano roughness structure and bioactive properties of orange peel as a biomimetic model, aiming to construct a functional film with a unique dual mechanism of physical barrier protection and active preservation. Using soft etching and secondary transfer methods, with polydimethylsiloxane as an intermediate template, and through a repeated freeze–thaw cross-linking process, a polyvinyl alcohol system containing orange peel essential oil was cast to successfully prepare a biomimetic film featuring the micro–nano hierarchical structures found on the surface of orange peel. The study indicates that the biomimetic film accurately replicates the cross-scale hierarchical structures of the natural orange peel surface. Structure–property relationship analysis revealed that the biomimetic film containing 15% orange peel essential oil exhibited the optimal comprehensive performance, characterized by significantly enhanced tensile strength and improved water vapor barrier properties, while demonstrating effective antioxidant and regulated antibacterial activities. Crucially, compared to conventional flat active films, the replicated multi-scale surface roughness provides clear functional advantages by physically optimizing interface properties and cooperating synergistically with the chemical vapor release of the essential oil. Blueberry preservation experiments confirmed that the biomimetic film successfully maintains fruit firmness, vitamin C, and anthocyanin content, while suppressing weight loss and decay rates. This study simulates the microenvironmental control mechanisms of orange peel, highlighting the scientific novelty of structural–chemical synergistic design for advanced functional packaging. Full article