Search Results (28,986)

Despite their significance as forensic targets, alkyl nitrites, classified as illegal drugs, have received little attention in forensic analysis due to their high volatility and chemical instability. Here, we present a high-performance analytical approach using a pulsed dc soft plasma ionization-quadrupole mass spectrometry (pulsed dc SPI-MS) system, uniquely designed to operate using ambient air as the discharge gas. In this system, the modulation of the duty ratio functions as a “structural probe” to identify reactive isomers. Unlike conventional dielectric barrier discharge (DBD) sources that typically operate at atmospheric pressure, our SPI system utilizes a controlled pressure regime of several kPa, where the nitrogen in the ambient air effectively functions as a third-body gas to suppress excessive internal energy. The control of the duty ratio in our pulsed dc SPI source allowed for the successful manipulation of ion–molecule reaction pathways for highly reactive analytes. By optimizing several parameters, including duty ratio and discharge pressure, we achieved a unique ionization regime where the molecular-related ion [2 M − 3 H]⁺ was predominantly detected as the base peak with minimal fragmentation. Notably, by reducing the duty ratio from 50% to 5%, both the target ion occupancy and signal intensity were significantly enhanced, achieving a limit of detection (LOD) as low as 0.16 parts per million by volume (ppmv). This sensitivity is several orders of magnitude higher than previously reported thresholds, enabling rapid identification of C4–C6 alkyl nitrite isomers. This method transforms the duty ratio into a powerful diagnostic tool for identifying reactive intermediates, providing a practical and efficient approach for the onsite identification of illegal alkyl nitrites in forensic and security fields. Full article

This study aimed to investigate how innovative performance is influenced by leadership styles (transformational, entrepreneurial, participative, and empowering), knowledge sharing, and organizational agility. Additionally, it examined knowledge sharing and organizational agility as mediators between these leadership styles and innovative performance in tourism and hospitality businesses, utilizing Social Exchange Theory (SET) as the theoretical framework. A PLS-SEM analysis was performed on 1896 valid responses from employees of category (A) travel agencies and five-star hotels in Egypt using WarpPLS 7.0. The results revealed that transformational, entrepreneurial, participative, and empowering leadership styles positively impact innovative performance, knowledge sharing, and organizational agility. Additionally, the study highlighted the beneficial effects of knowledge sharing and organizational agility on innovative performance. It was also found that knowledge sharing and organizational agility partially mediate the relationship between these leadership styles and innovative performance. The study discusses both theoretical and practical implications for developing leadership skills and enhancing innovative capabilities. It adds to the existing literature on leadership, knowledge management, organizational agility, and innovation performance, particularly in service-oriented industries. Full article

Remote sensing images are highly susceptible to spatially non-uniform haze under complex atmospheric conditions, leading to contrast degradation and structural detail loss. Moreover, remote sensing scenes usually exhibit complex spatial structures, highly uneven haze distribution, and significant statistical variability, which further increases the difficulty of haze removal. To address this issue, we revisit the haze degradation mechanism of remote sensing imagery and propose a dynamic histogram-sorted Transformer dehazing method from the perspectives of statistical distribution modeling and region-adaptive restoration. Specifically, a Histogram-Sorted Adaptive Attention is designed to map spatial features into the statistical distribution domain through a dynamic histogram sorting mechanism, enabling explicit discrimination and precise modeling of regions with different haze densities. Meanwhile, a Perception-Adaptive Feed-Forward Network is constructed, which incorporates a stable routing-based mixture-of-experts mechanism to adaptively select restoration strategies according to local texture characteristics and global haze density, thereby significantly enhancing the adaptability of the model in complex remote sensing scenarios. Extensive experimental results demonstrate that the proposed method achieves superior performance over existing approaches across multiple remote sensing benchmark datasets, effectively improving both visual quality and robustness of remote sensing imagery. Full article

Although DNA-based data storage theoretically provides an information density of 2 bits per nucleotide, biochemical constraints transform sequence design into a high-dimensional constrained combinatorial optimization problem. The high computational cost and low encoding efficiency of conventional rule-based approaches make metaheuristic methods an effective alternative. This study proposes the TC-HUR hybrid algorithm to simultaneously optimize information density and conflicting biophysical constraints, including homopolymer (HP) length, GC content, melting temperature ($T_m$), and reverse-complement (RC) similarity. The method escapes local optima using Cauchy jump-enhanced Hunger Games Search (HGS), performs high-precision exploitation via Runge–Kutta (RUN) operators, and refines constraint violations at the nucleotide level through an adaptive intensive mutation mechanism. The algorithm is evaluated on a complex dataset of 1853 nucleotides under different noise regimes. TC-HUR outperforms RUN by 2.5% and HGS by 16.7% in average fitness. While maintaining homopolymer length near the ideal threshold, it reduces reverse-complement similarity to 19.10%, ensuring high sequence diversity. Under high-noise conditions, TC-HUR achieves a normalized edit distance of 0.1290, reducing insertion–deletion (indel) errors by approximately 14%. The results demonstrate that the proposed model effectively generates biophysically synthesizable and noise-resilient DNA codes. Full article

Given its substantial contribution to traffic accidents, one of the main goals of intelligent driver-assistance systems has become the detection and mitigation of driver fatigue to enhance driving safety and comfort. Among various approaches, vision-based facial analysis using deep learning has emerged as an effective and non-intrusive method for identifying driver drowsiness, as a key manifestation of fatigue. However, current drowsiness detection models do not account for demographic factors like gender, even though recent research has shown gender behavioral differences such as eye closure duration, blink frequency, yawning patterns, and facial muscle relaxation. In this paper, we present a fine-grained multi-stream transformer architecture that incorporates gender-awareness and shifted-windows attention for spatial feature fusion. Integrating gender embedding, by modulating the region-based features, allows the model to effectively learn gender-conditioned drowsiness features to minimize bias and diluted representations. Using the NTHU-DDD dataset, we evaluated two-stream and three-stream variants for gender-aware and gender-agnostic across three facial region contexts: the face region with a 20% margin, bare face region, and key facial regions (face, eyes, and mouth). A comprehensive ablation study was conducted to identify the most effective model setup. The results demonstrate that incorporating gender embedding improves detection performance, achieving an accuracy of 95.47% on the evaluation set. Moreover, using the proposed three-stream model (SWT-DD-3S) produced better results. Full article

Autonomous mobile robots operating in outdoor environments must have an understanding of the surrounding terrain geometry to ensure efficient and safe navigation. This article presents a DEM-based intelligent traversability mapping framework to transform open-source geospatial data into slope-aware cost maps for multirobot autonomous navigation within the ROS2 framework. The proposed cv_gdal algorithm automatically processes GeoTIFF elevation data using adaptive slope thresholding based on each robot’s physical capabilities, generating ROS-compatible cell occupancy maps. Six regions of Spain were used to evaluate terrain representation accuracy and navigation performance in kilometer-scale DEMS. This framework enables autonomous perception-to-planning pipelines and supports the deployment of multirobot systems for search and rescue (SAR) tasks. By bridging geospatial analytics with robotic perception and adaptive decision-making, this work contributes to the development of intelligent, self-configuring robotic systems capable of operating safely in complex outdoor environments. Full article

Background: The recognition of autism spectrum disorder (ASD) has been a challenge due to the heterogeneity in symptoms and complex variations in brain function. Resting-state functional magnetic resonance imaging (rs-fMRI) has become instrumental in studying these disorders by accessing underlying abnormal neural activity and connectivity. Recently, deep learning approaches have shifted the analysis of brain networks by capturing spatiotemporal information from fMRI sequences. Nonetheless, most existing studies are limited by relying on a single representational scale, typically restricting analysis to either voxel-level spatiotemporal patterns or static connectivity matrices. Additionally, the dynamic reconfiguration of functional coupling and its variations across different anatomical parcellations are often ignored, which obscures neurobiologically meaningful dynamics. Methods: In this regard, we propose a multi-atlas dynamic connectivity transformer fused with 4D spatiotemporal modeling for ASD recognition (MADCT-4D). Specifically, the framework comprises two complementary branches. The 4D spatiotemporal branch encodes raw rs-fMRI volumes to learn hierarchical representations of evolving neural activity, while the dynamic-connectivity branch models time-resolved functional connectivity sequences constructed from multiple atlases, enabling the network to capture dynamic reconfiguration at the connectome level under different parcellation granularities. Moreover, we perform late fusion by combining the branch-specific decision scores with a learnable gate, allowing the model to adaptively weight voxel-level dynamics and multi-atlas connectivity evidence for each subject. Results: Extensive experiments on the publicly available ABIDE dataset demonstrate that the proposed method achieves 90.2% accuracy for ASD recognition, outperforming multiple competitive baselines. Conclusions: The proposed framework yields interpretable biomarkers based on learned dynamic connectivity patterns that are consistent with altered functional coupling in ASD. Full article

Ground-based astronomical images are often degraded by atmospheric turbulence and deterministic optical aberrations introduced by telescope design and manufacturing processes. Joint mitigation of these distortions remains challenging due to the lack of reliable ground-truth data. To address this issue, a physics-based atmospheric–optical imaging model is developed to generate a large-scale, physically consistent simulated dataset, enabling supervised learning without real paired observations. Based on this, an attention-enhanced generative adversarial network (AE-GAN) is proposed for astronomical image restoration. The network incorporates a Channel Attention Block (CAB) and a Semantic Attention Module (SAM) within a feature pyramid architecture to enhance multi-scale representation and suppress turbulence-induced distortions. Experimental results show that the proposed method achieves consistent restoration performance under varying turbulence strengths, aberration amplitudes, and noise levels. Compared with recent Transformer-based methods, it maintains competitive performance across different aberration types while achieving significantly higher computational efficiency (1.21 s per image, 3.5× faster). In addition, the model trained on simulated data generalizes effectively to real astronomical observations. Full article

This paper presents a comparative analysis of advanced AI-based techniques for human face inpainting using semantic masks that fully occlude targeted facial components. The primary objective is to evaluate the ability of image inpainting methods to accurately restore semantically meaningful facial features. Our results show that existing inpainting models face significant challenges when semantic masks completely obscure the underlying facial structures. In contrast to random masks, which leave partial visual cues, semantic masks remove all structural information, making reconstruction substantially more difficult. We assess the performance of generative adversarial networks (GANs), transformer-based models, and diffusion models in restoring fully occluded facial components. To address these challenges, we explore three retraining strategies: using semantic masks, using random masks, and a hybrid approach combining both. While the hybrid strategy leverages the complementary strengths of each mask type and improves contextual understanding, fully accurate reconstruction remains challenging. These findings demonstrate that inpainting under fully occluding semantic masks is a critical yet underexplored area, offering opportunities for developing new AI architectures and strategies for advanced facial reconstruction. Full article

Digital transformation in higher education increasingly hinges on the robustness and governability of Information and Communication Technology (ICT) infrastructures, with campus Wi-Fi networks serving as the operational backbone of digital learning, research collaboration, and administrative services. In large universities, these networks typically evolve into heterogeneous, multi-vendor environments, introducing ongoing challenges in monitoring coherence, configuration governance, and cross-platform performance diagnosis. Despite the centrality of these issues, smart campus scholarship has paid limited attention to day-to-day operational management. This study examines the design and operational performance of a dual-platform Wi-Fi network management architecture implemented at Mahasarakham University, Thailand. The architecture strategically integrates SolarWinds and LibreNMS to combine centralized network-wide visibility with fine-grained, device-level diagnostics across a multi-vendor infrastructure. An engineering-oriented mixed-method approach was employed, drawing on production monitoring logs and semi-structured interviews with campus network engineers. Findings indicate that SolarWinds strengthens configuration oversight and campus-level situational awareness, whereas LibreNMS enhances detailed performance analytics and accelerates fault isolation. Their coordinated deployment improves operational stability, diagnostic clarity, and long-term maintainability of campus Wi-Fi systems. The study provides practical architectural guidance for managing heterogeneous ICT infrastructures in smart campus and enterprise-scale environments. Full article

Seasonal influenza A evolves quickly through mutations in haemagglutinin (HA) and neuraminidase (NA), which can reduce vaccine match and lower protection. Many sequence-only models do not link codon-level mutations to three-dimensional (3D) protein context and long-term evolutionary signals within one scoring framework. This study presents TRIAD-Influenza (TRIAD: Token–Residue–Integrated Architecture for Drift), a multi-view transformer that combines (i) codon- and residue-level sequence representations, (ii) structure-derived residue interaction features from predicted HA/NA models, and (iii) an embedding-space phylogeny that captures cluster and drift context. The pipeline curates more than $3\times {10}^5$ paired HA/NA coding sequences from the NCBI Virus resource (2010–2024) using strict quality control and codon-aware alignment and predicts 3D structures for nearly all unique HA and NA proteins to build contact graphs and surface/stability descriptors. TRIAD-Influenza outputs a continuous, structure-aware risk score for each HA/NA pair and produces interpretable mutation hotspot maps using gradient saliency and a contact-weighted mutation risk index (CMRI). On rolling-origin temporal cross-validation and for a temporally held-out internal test window with strong class imbalance (∼3.4% high-risk), the model shows strong ranking performance (AUROC $\approx 0.89$; AUPRC $\approx 0.44$; Brier score $=0.069$) while operating at surveillance speed (median latency $\approx 1.6$ ms per HA/NA pair). External validation on independent GISAID/Nextstrain cohorts (2023–2024; 5000 isolates) preserves discrimination (AUROC $\approx 0.85$–$0.86$). Predicted risk scores correlate with experimental haemagglutination inhibition (HI) antigenic distances (Spearman $\rho$ up to ≈0.82 at the virus-aggregated level), and CMRI hotspots enrich known epitope and deep mutational scanning escape residues (odds ratios $\approx 2.7$–$3.6$). Overall, token–residue–phylogeny coupling enables rapid, structure-aware prioritisation of emerging influenza A HA/NA sequences and delivers compact hotspot maps for expert review and targeted experiments. Full article

Accurate rooftop segmentation from aerial imagery is essential for large-scale urban analysis, including applications such as solar potential assessment and urban monitoring. However, it remains constrained by the high cost of dense annotation and the limited generalisation of supervised models across heterogeneous urban morphologies. This study investigates binary rooftop segmentation for fine-tuning large image-editing foundation models using parameter-efficient Low-Rank Adaptation (LoRA). Using parts of Brisbane metropolitan dataset (split 80/20 into 97 training and 24 testing tiles), three paradigms were evaluated under a unified protocol: zero-shot image-editing models (including Gemini 3 Pro), a segmentation-first baseline (Segment Anything Model 3, SAM3), and LoRA-adapted diffusion models (FLUX.1 Kontext, FLUX.2, and Qwen Image Edit 2509) fine-tuned each 250 steps up to 5000 steps. Evaluated under zero-shot conditions, the generative models demonstrated varying levels of boundary fidelity. The Gemini model achieved a strong zero-shot baseline with [IoU, Dice] scores of [85%, 91%], followed by the SAM3 baseline, which also achieved a stable [84%, 91%] but exhibited increased false negatives in visually complex scenes. The tested diffusion models (FLUX.1 Kontext, FLUX.2, and Qwen) showed more limited initial spatial overlap, scoring [45%, 55%], [67%, 78%], and [33%, 46%], respectively. Following LoRA adaptation, the FLUX and Qwen models showed substantial improvements, with their respective [IoU, Dice] metrics increasing to [89%, 94%], [82%, 90%], and [87%, 93%]. FLUX.1 Kontext achieved the strongest overall performance at step 4250, yielding a mean IoU of 89% (SD = 3.16%) and a pixel accuracy exceeding 96%. These results demonstrate that parameter-efficient fine-tuning, combined with rigorous evaluation under class-imbalanced conditions, can transform general-purpose generative models into competitive, scalable spatial analysis tools that match or exceed both dedicated segmentation baselines and strong zero-shot multimodal models. Full article

This work explores how deep learning models, with different numbers of parameters, can be effectively applied to detect personal data within unstructured text using Named Entity Recognition (NER) techniques. We evaluate the performance of various architectures by leveraging a plethora of language models (LMs) consisting of Distilbert-base-uncased, Distilbert-base-cased, Bert-base-uncased, Bert-base-cased, Bert-large-uncased, Bert-large-cased, ModernBERT-base, ModernBERT-large, nomic-BERT-2048, RoBERTa-base, DistilRoBERTa-base, RoBERTa-large, Deberta-v3-xsmall, Deberta-v3-small, and Deberta-v3-base, which are evaluated using the performance indices of accuracy, precision, recall, and F1-score. Our experiments show that some Small Language Models (SLMs) compete equally with some corresponding LLMs (Large Language Models), based on the specific PII (Personally Identifiable Information) dataset, thus enhancing personal data detection, which is of paramount importance in financial applications. Moreover, we proposed a novel architecture based on an optimized transformer fine-tuning strategy to improve PII recognition across diverse contexts and conducted an extensive comparative analysis to evaluate the performance of our proposed architecture in relation to all relevant existing approaches reported in the literature. This evaluation, performed on the AI4Privacy PII 43 K dataset, encompasses every publicly available work we identified and provides a thorough benchmarking of our methods within the current research field. The results highlight both the strengths and limitations of existing solutions and demonstrate the effectiveness of SLMs in addressing the challenges of privacy-preserving information extraction. Full article

The use of additive manufacturing in structural applications has increased in industry; however, reliable material selection criteria remain limited when printed components must withstand real service loads. The following study provides a comprehensive evaluation of polymeric materials (PLA filament, ABS filament, and ABS-like resin) used in additive manufacturing technologies for the production of footwear heels. Consequently, five heel models were designed using reverse engineering based on real industry references and analyzed within a decision framework based on the Input–Transformation–Output (ITO) model. Within this framework, each material was subjected to static mechanical tests (tensile, compression, flexural and hardness), scanning electron microscopy (SEM) analysis and numerical simulations. In addition, functional tests were carried out by mounting the printed heels on real sandals, allowing for evaluation of their performance under service conditions. Significant differences in surface morphology were observed, attributable to the physical state and consolidation mechanism of each material. Uncontrolled environmental conditions during printing and testing were identified as a limitation affecting reproducibility. The ABS-like resin showed the highest compressive load capacity (10.8 kN), together with a tensile strength of 14.99 MPa and a deformation at break of 0.076 mm/mm. SEM analysis revealed a more homogeneous surface morphology and greater structural continuity after curing, consistent with the numerical simulations, which predicted stresses between 19.98 and 196.23 MPa, displacements up to 8.917 mm and unit strains up to 0.1378. The integrated interpretation of the experimental, microstructural and functional results provides technical criteria for material selection in reverse-engineered footwear components and structural elements manufactured by additive manufacturing. Full article

In robotic fine-grained tasks (e.g., grasping and assembly), precise interaction requires a detailed understanding of object components. While Visual Language Models (VLMs) excel at object-level recognition, they struggle with part-level segmentation (e.g., knife handles), limiting performance in complex scenarios. VLMs face three key challenges: (1) Visual granularity mismatch—object-level features lack part-level details; (2) Semantic hierarchy gaps—parts and objects differ significantly in semantics; (3) Cross-modal bias—CLIP’s text–image alignment favors global over local features. To address these, we propose a one-stage VLM-based part segmentation method. First, the Hierarchy-Aware Feature Selection mechanism analyzes Transformer features in different hierarchies to enhance spatial and semantic precision for part segmentation. Second, the Multi-Hierarchy Feature Adapter bridges object-to-part feature granularity via the hierarchical adaptation. Finally, the Hierarchical Multimodal Alignment Module harmonizes classification accuracy and mask integrity via hierarchical alignment of vision–language, mitigating the bias of CLIP’s object-level priori knowledge. Experiments show the proposed method improves part segmentation performance for Zero-Shot, achieving 25.86% on Pascal-Part and 13.09% on ADE20K-Part (gains of +0.81% hIoU and +2.96% hIoU over baseline). This work advances robotic visual perception, with applications in intelligent manufacturing and intelligent service. Full article