Search Results (20,219)

With the acceleration of urbanization and the intensification of climate change, wind conditions have become a critical factor in architectural design. They not only affect a building’s wind resistance but also influence ventilation, pollutant dispersion, pedestrian comfort, and energy consumption. Traditional computational fluid dynamics (CFD) simulations are costly. Although the application of machine learning for CFD prediction has become a relatively mature technology, machine learning models still face challenges in actual architectural design workflows. Building upon recent advancements in the field, it proposes two core technologies: a method for predicting outdoor wind environments in buildings based on the Site-Specific Training for Design Tasks (SST-DT) strategy, and an automated machine learning workflow. These innovations improve upon existing wind environment analysis methods and systems, establishing a fully automated working framework that is easy for architects to learn and use. Within this framework, dataset acquisition and model training are performed automatically. Finally, this framework was validated across various prediction tasks with different objectives. It significantly lowers the barrier to entry for architects adopting machine learning, advances the performance-driven design paradigm, and facilitates the deep integration of machine learning technologies into architectural wind engineering. Full article

Sustainable aviation fuel (SAF) is widely recognized as a critical pathway for aviation decarbonization; however, its life-cycle carbon performance is highly sensitive to supply chain configurations. This study proposes a data-driven framework integrating life-cycle assessment (LCA) with a generative adversarial network (GAN) to model and optimize SAF supply chain pathways under structural constraints. A rule-constrained synthetic dataset comprising feasible pathways is constructed, incorporating feedstock sources, refinery locations, airport demand nodes, conversion technologies, transport modes, and distances. Each pathway is encoded into a numerical feature vector, and a GAN model is trained to learn the distribution of feasible configurations. Generated pathways are further validated through LCA-based post-processing to ensure physical feasibility and emission consistency. The results show that pathway-level carbon intensity varies significantly across configurations, with differences exceeding 30% under varying feedstock–transport combinations. The model successfully captures the multimodal distribution of carbon emissions and identifies structurally consistent low-carbon pathways. In particular, localized supply structures and reduced transport distances are found to play a dominant role in minimizing emissions. This study provides a scalable methodological framework for SAF pathway modeling and offers insights into supply chain design and spatial configuration for achieving aviation carbon reduction targets. Full article

Cybersecurity has become a critical concern for individuals, organizations, and governments, especially with the rise of sophisticated cyberattacks and remote work environments. Traditional security approaches are no longer sufficient, leading to the adoption of advanced frameworks such as the zero-trust model, which operates on the principle “never trust, always verify.” This model enforces strict access controls and continuous monitoring across all network activities. Designing an intelligent zero-trust system is challenging due to the complexity of network environments and the evolving nature of malicious threats. This project proposes an advanced zero-trust architecture that integrates machine learning and multi-factor authentication (MFA) to strengthen security. Specifically, it employs Multilayer Perceptron models and k-Nearest Neighbors algorithms to analyze system logs and user behavior, enabling real-time anomaly detection and adaptive authentication mechanisms. The proposed framework is experimentally evaluated using the H-MOG behavioral–contextual authentication dataset, which captures multimodal user interaction patterns and supports continuous authentication analysis within Zero Trust environments. The integration of machine learning enhances the system’s ability to identify suspicious activities quickly and accurately, while MFA provides an additional layer of protection against unauthorized access. Moreover, the proposed framework emphasizes usability, ensuring that enhanced security does not impose excessive burden on users or IT teams. This allows the framework to respond more effectively to potential threats while maintaining usability. Overall, the proposed approach offers a practical and scalable solution that improves detection performance and strengthens continuous authentication and adaptive access control within Zero Trust environments. Full article

(1) Background: Gastric cancer (GC) remains a leading cause of cancer-related mortality worldwide. Aberrant remodeling of the extracellular matrix (ECM) is a hallmark of GC progression; however, the mechanisms by which GC cells sense and exploit ECM cues remain unclear. (2) Methods: ITGA11 was identified through integrative bioinformatic analyses. Its expression, clinical significance, and association with ECM-related signatures were evaluated in GC tissues and public datasets. The function of ITGA11 and its role in regulating the FAK/PI3K/AKT/mTOR pathway were investigated using in vitro and in vivo assays, and the inhibitory effect of Naringenin on ITGA11-associated oncogenic activity was assessed. (3) Results: ITGA11 was upregulated in GC tissues and correlated with an ECM-related gene signature, aggressive clinicopathological features and poor patient survival. ITGA11 promoted malignant phenotypes of GC cells in vitro and in vivo. Importantly, molecular docking and target engagement assays suggested a potential interaction between Naringenin and ITGA11. Functional experiments showed that Naringenin attenuated ITGA11-associated oncogenic activity by reducing ITGA11 levels, suppressing pathway activation, and inhibiting malignant phenotypes. (4) Conclusions: Our findings identify ITGA11 as a potential prognostic biomarker and functional driver of GC progression and suggest that Naringenin may represent a promising bioactive compound for modulating the ITGA11/FAK/PI3K/AKT/mTOR axis in GC. Full article

The integration of sustainability into higher education architectural curricula and student Diploma Projects (DPs) remains limited, necessitating further investigation to improve overall outcomes. This study aims to identify, characterise, and compare existing keyword sources to determine their efficacy in detecting sustainability-related solutions within DPs and to define the characteristics of the most suitable datasets for this purpose. A total of 132 academic, professional, and policy-related Keyword Databases (KDs) were identified and analysed through a multi-stage process. Nine of the best-performing KDs were selected for further development into Keyword Search Lists (KSLs), and their effectiveness in identifying sustainability-related solutions in DPs’ descriptions was tested, confirming the correlation of the results with expert assessments. As a result, a method for identifying, developing, and analysing KSLs was developed, titled Mapping the Linguistic Landscape of Architectural Sustainability (MLLAS). This framework provides a practical tool for the large-scale analysis of how sustainable development is linguistically represented within architectural theses, as well as a theoretical basis for understanding the level of sustainability’s incorporation in architectural education. The results indicate that keyword search constitutes an effective identification method within DPs, regardless of KSL size. The future implementation of the MLLAS framework has been proposed. Full article

Arabic’s complex morphological system and the optional use of short vowels (tashkīl) introduce substantial lexical ambiguity, posing significant challenges for Large Language Models (LLMs). While diacritics enhance linguistic precision, LLMs trained predominantly on undiacritized corpora often exhibit performance degradation when processing fully diacritized inputs due to representation shifts and tokenization inconsistencies. To address this limitation, we propose the Arabic Diacritic Lexical Knowledge Graph (ADL-KG), a structured framework that links diacritized and undiacritized forms through integrated lexical, morphological, and semantic knowledge. Building upon this resource, we introduce Diacritic-Aware Knowledge Graph Prompting (DA-KGP), a prompt augmentation strategy that injects explicit linguistic features into LLM inputs to facilitate robust interpretation of diacritized Arabic text. The framework is evaluated on the Arabic Reading Comprehension Dataset under zero-shot and few-shot question answering across AraGPT2-base, BLOOMZ-560M, SILMA-v1, and LLaMA 3.1-8B. Performance is assessed using Exact Match, BLEU, ROUGE-1, and BERTScore-F1. Experimental results show that fully diacritized prompts significantly degrade baseline performance, whereas DA-KGP consistently mitigates this effect by improving semantic alignment across diverse architectures. For AraGPT2-base, KG augmentation improves average BERTScore-F1 by +5.96 points. SILMA-v1 achieves the strongest lexical improvements, reaching 21.57 BLEU and 81.31% BERTScore-F1 in the KG-enhanced two-shot configuration. LLaMA 3.1-8B achieves the highest overall semantic performance with 82.54% BERTScore-F1 under KG-enhanced prompting, while BLOOMZ-560M also demonstrates statistically significant semantic gains through structured augmentation. These findings demonstrate that morphologically informed prompting and structured lexical grounding provide an effective and parameter-efficient strategy for improving the robustness and semantic fidelity of Arabic LLMs under fully diacritized input conditions. Full article

Motor imagery (MI)-based brain–computer interfaces (BCIs) have led to great interest as a result of their potential use in neurorehabilitation, assistive robotics, and human–computer interaction. However, decoding electroencephalographic (EEG) signals with high accuracy continues to be a difficult task due to the weak signal-to-noise ratio, differences among subjects, and the complicated temporal–spectral–spatial neural dynamics. Deep learning methods recently developed, such as convolutional neural networks, recurrent architectures, graph neural networks, and adversarial transfer learning, have enhanced MI decoding performance, yet many models are still concentrating on a single representation domain or they need costly adaptation phases in terms of computation. To tackle these shortcomings, we present NeuroCrossNet, a unified tri-modal deep learning model that is able to learn the temporal, spectral, and spatial EEG features jointly for robust and calibration-free MI decoding. The suggested network combines a Temporal HyperMixer Block for capturing long-range temporal dependencies, a wavelet transformer for learning localized time–frequency representation, and a Graph Attention Network for EEG topology-aware spatial reasoning. Additionally, a Dynamic Residual Attention Gate (DRAG) has been developed to adaptively merge heterogeneous feature streams, and a compact subject-aware normalization (SAN) method enhances cross-subject generalization without the use of labeled target-domain calibration data. Our proposed model was tested following the rigorous leave-one-subject-out (LOSO) approach on BCI Competition IV-2a and High-Gamma datasets. NeuroCrossNet reached a classification accuracy of 91.30%, surpassing several strong benchmark methods, including CNN-LSTM, EEGNet, DeepConvNet, spectral CNN, and graph-based EEG decoding frameworks. Furthermore, a large number of ablation studies reveal that the integration of temporally, spectrally, and spatially complementary representations considerably boosts robustness and inter-subject consistency. Full article

Sumizyme PEG, a glucanase/cellulase enzyme preparation produced by Talaromyces versatilis PF8, was investigated to characterize its systemic and genotoxic toxicity profile to support its intended use in food processing applications. A comprehensive toxicological program was conducted in accordance with OECD guidelines, comprising a bacterial reverse mutation (Ames) test, an in vitro chromosomal aberration assay, an in vivo micronucleus test, and a 90-day repeated-dose oral toxicity study in male and female Crl:CD(SD) rats. In the subchronic study, Sumizyme PEG was administered by oral gavage at doses of 107, 1070, and 10,700 U/kg/day. No treatment-related adverse effects were observed across clinical, hematological, biochemical, urinalysis, organ weight, or histopathological endpoints, and the highest dose was identified as the NOAEL. Genotoxic testing showed no consistent mutagenic or clastogenic response across the test battery. A positive in vitro signal was observed in CHL/IU cells; however, this was not reproduced in a human TK6 cell assay or in vivo micronucleus testing, indicating assay-dependent sensitivity within a weight-of-evidence framework. Overall, the integrated dataset does not indicate a consistent treatment-related systemic or genotoxic effect under the conditions of the studies conducted. Full article

To achieve an optimal trade-off between production efficiency and energy benefits in complex manufacturing environments, this paper addresses the Green Flexible Job Shop Scheduling Problem (GFJSP) by establishing a multi-objective mathematical model that minimizes both makespan and total energy consumption. An Improved Non-dominated Sorting Genetic Algorithm II (INSGA-II) is proposed to solve this model. In the population initialization phase, chaotic mapping is integrated with multiple heuristic rules to generate a high-quality and uniformly distributed initial population. Furthermore, an enhanced elite selection mechanism is employed to effectively prevent premature convergence. Subsequently, adaptive crossover and mutation operators are designed to enable differentiated evolution across sub-populations, effectively coordinating global exploration and local exploitation. Finally, experimental results on the Brandimarte and Hurink benchmark datasets demonstrate the superiority of the proposed algorithm in terms of convergence and diversity, providing a robust solution for optimizing green industrial production scheduling. Full article

Accurate assessment of fish feeding intensity is significant for the timely understanding of feeding demands, dynamically adjusting feeding strategies, and reducing aquaculture costs. However, existing methods often rely on superficial visual features that fail to capture subtle satiety dynamics, resulting in limited reliability. To address the issue, a method for fish feeding intensity assessment based on spatial features and TabNet model with Dynamic Feature Weighting Layer (TabNet-DFWL) is proposed in this study. Fish body contours are extracted from lateral-view images through a pipeline of segmentation, enhancement, and binarization. Subsequently, spatial features highly correlated with fish feeding mechanisms are proposed to characterize behavioral changes. Based on these, an interpretable model integrating spatial features and TabNet-DFWL is constructed to achieve precise fish feeding intensity assessment. This method explores spatial features related to feeding behavior from the underlying mechanism of fish behavioral changes and establishes a feeding intensity assessment model based on TabNet-DFWL. By doing so, it avoids the black-box risk commonly associated with traditional deep learning models and significantly improves model interpretability and reliability, thereby providing a trustworthy basis for precision feeding in aquaculture. Experiments conducted on a real-world fish feeding dataset demonstrate that the proposed method achieves an accuracy of 95.96%, an average precision of 93.44%, an average recall of 93.33%, an average specificity of 98.15%, and an average F1-score of 93.38%. Compared with comparative algorithms, all evaluation metrics exhibit improvements. These results indicate that the proposed method enables accurate assessment of fish feeding intensity and can effectively support the dynamic adjustment of feeding strategies in aquaculture systems. Full article

The traditional monitoring of forest musk deer behavior primarily relies on direct human observation or the post hoc playback analysis of ordinary surveillance videos. This approach is not only time-consuming and labor-intensive but also highly subjective, easily leading to missing or misjudged critical behavioral information. Moreover, it is difficult to achieve real-time monitoring and anomaly warning. These limitations severely constrain the efficiency of the large-scale artificial breeding of forest musk deer and the effective advancement of wild population conservation. Thus, this study proposes a forest musk deer behavioral recognition method based on an improved YOLOv8-Pose. A forest musk deer behavior image dataset covering four typical behaviors was constructed, and 18 keypoints were systematically annotated. This study designs a Dilated Spatial Pyramid Pooling-Fast (DILATED-SPPF) module and a Multi-scale Depthwise Separable Context Mixer (MDSC-Mixer) module, and integrates them into YOLOv8-Pose. Experimental results show that the improved model outperforms the original YOLOv8-Pose and comparison models such as YOLOv11/v12-Pose on key metrics of object detection (Box-mAP50 0.929, Box-mAP50-95 0.814) and pose estimation (Pose-mAP50 0.879, Pose-mAP50-95 0.565). This study further develops a visual interactive interface that intuitively presents detection results and skeleton structures. This work provides a high-precision, low-cost automated behavior analysis tool for the artificial breeding and wild conservation of forest musk deer with significant application value for enhancing the intelligence level of endangered species protection. Full article

This study presents a short-term, IoT-enabled sensor-based assessment of treated municipal water and decentralized groundwater in Bragança, northeastern Portugal. Two drinking-water supply contexts were compared: treated surface-water-derived municipal water from the public supply system and groundwater from a decentralized supply system serving part of a higher education campus. Five sampling points were monitored during three campaigns between January and March 2026. At each point, pH, electrical conductivity, temperature, oxidation–reduction potential, and total dissolved solids were recorded at 10 s intervals over approximately 10 min monitoring windows using a multiparameter probe integrated into an IoT-enabled data acquisition workflow. Microbiological analyses were performed on groundwater samples as complementary information. Treated municipal water showed lower mineralization, narrower parameter ranges, and higher oxidation–reduction potential, reflecting source-water characteristics, treatment, and operational control. Groundwater showed higher mineralization, lower oxidation–reduction potential, and greater variability among sampling points and campaigns, consistent with stronger local hydrogeochemical and operational influences. The repeated short-interval readings provided more detailed physicochemical profiles than isolated spot measurements, although the short monitoring windows do not represent continuous long-term high-frequency monitoring. Overall, the results support standardized IoT-enabled sensor-based monitoring as a complementary tool for short-term water-quality assessment and indicate the need for longer seasonal datasets and laboratory confirmation. Full article

Introduction: Accurate detection and characterization of intracardiac masses remain a major challenge in cardiovascular imaging due to overlapping morphological features between tumors, thrombi, and vegetations, as well as the inherent limitations of echocardiography, including operator dependency and variable image quality. Although echocardiography is the first-line imaging modality for evaluating cardiac masses, diagnostic uncertainty frequently necessitates additional multimodality imaging. Artificial intelligence (AI), including machine learning and deep learning approaches, has emerged as a promising strategy to improve image interpretation, automate feature extraction, and enhance diagnostic consistency. Objective: This narrative review aims to examine current advances in AI-enhanced echocardiography for cardiac tumor detection, with a particular focus on detection, segmentation, classification, multimodal integration, and clinical translation. Methods: A narrative literature review was conducted using PubMed, Scopus, and Google Scholar databases. Relevant English-language studies published between 2016 and 2026 were identified using keywords including “artificial intelligence”, “machine learning”, “deep learning”, “echocardiography”, “cardiac tumors”, “intracardiac masses”, “multimodal imaging”, and “ultrasomics”. Original studies, reviews, and methodological papers related to AI-assisted cardiovascular imaging were evaluated. Discussion: Current evidence suggests that AI-driven techniques, including radiomics (ultrasomics), convolutional neural networks, and multimodal learning frameworks, can improve the detection, segmentation, and classification of intracardiac masses. Experimental studies have reported high diagnostic performance, with some deep learning models achieving diagnostic accuracies exceeding 95% under controlled conditions. AI-assisted systems may also reduce interobserver variability and improve workflow efficiency. Multimodal AI approaches integrating echocardiography with cardiac magnetic resonance imaging, computed tomography, electrocardiography, and clinical data appear particularly promising for improving diagnostic discrimination. However, current models remain limited by small and imbalanced datasets, insufficient external validation, data heterogeneity, and limited generalizability across institutions and imaging protocols. Additional barriers to clinical implementation include annotation variability, limited interpretability of deep learning models, and regulatory considerations. Conclusions: AI-enhanced echocardiography has substantial potential to improve the detection and characterization of intracardiac masses by augmenting diagnostic consistency and supporting clinical decision-making. Nevertheless, current evidence remains largely based on retrospective and experimental studies. Future progress will depend on large multicenter collaborations, standardized imaging datasets, explainable AI frameworks, and prospective clinical validation to enable safe and effective integration into routine cardiovascular practice. Full article

The abdominal pressure–volume (P–V) relationship during laparoscopic insufflation is curvilinear and subject to substantial inter-individual variability, yet clinical practice relies on universal pressure targets derived from population-level guidelines. The Smart Inline Compliance Module (SICM) is a novel inline retrofit device that acquires intra-abdominal pressure and insufflation gas flow through physically separated sensing circuits, reconstructs insufflated volume by numerical integration of the flow signal, and derives the abdominal P–V curve and its biomechanical parameters in real time. This study reports the first two-arm pilot technical evaluation of the SICM system. Arm A comprised an exploratory biomechanical phantom with three defined stiffness levels (Soft, Medium, Rigid) tested under Continuous and Stepwise insufflation protocols (30 curves). Arm B comprised three female feline cadavers assessed under the same dual-protocol design (18 curves). This study should be interpreted as an early-stage technical evaluation rather than as a definitive validation benchmark. Signal quality was consistently high across both arms (Curve Quality Index: 1.0000 in the phantom arm; 0.9974 ± 0.0009 in the cadaveric arm). Volume integration accuracy was confirmed against an independent offline reference (mean absolute percentage difference: 0.07%). The system extracted reproducible biomechanical parameters under the Continuous protocol: in the cadaveric arm, maximum compliance (Cmax) ranged from 116.8 to 191.4 mL/mmHg across subjects, with intra-session coefficients of variation below 16%; Knee Pressure (Pknee), defined as a working operational index of the compliance transition, was 3.33–4.17 mmHg with CV below 8%. The Rigid phantom and cadaveric datasets showed partial numerical overlap in selected shape-derived parameters, which was interpreted only as an internal consistency check and not as evidence of biomechanical equivalence. The Stepwise protocol exposed the current methodological limits of the parameter-extraction workflow and identified specific targets for the next development iteration. These results are interpreted exclusively within the scope of technical feasibility and preliminary biomechanical characterisation; clinical applicability and optimal pressure guidance require adequately powered in vivo studies. Full article

Acaryochloris marina is a distinctive cyanobacterium that uses chlorophyll d as its primary photosynthetic pigment and possesses two major light-harvesting systems: membrane-integral chlorophyll-binding Pcb/CBP complexes and water-soluble phycobiliproteins. How these antenna systems respond at the transcriptome level to contrasting light environments remains incompletely characterized. Here, we re-analyzed a publicly available RNA-seq dataset for A. marina MBIC11017 (NCBI BioProject PRJNA1130970), comparing cells grown under low-irradiance far-red light (LL-FR; 1.5–2 µmol photons m⁻² s⁻¹, 710-nm peak) and high-irradiance white light (HL-WL; 30–35 µmol photons m⁻² s⁻¹). Because light quality and irradiance both differ in this experimental design, the two effects cannot be separated; all transcriptional changes are therefore interpreted as responses to the combined LL-FR versus HL-WL contrast rather than to far-red wavelength alone. Of 8439 expressed genes, 1810 (21.4%) were significantly differentially expressed (adjusted p < 0.05). Using GFF-verified locus tags which corrected mis-annotations propagated in earlier analyses, the PS-I core gene set showed a mean log₂ fold-change of +1.96 (3.9-fold; 11/11 loci significant), whereas the PS-II core gene set showed a mean log₂ fold-change of +1.10 (2.1-fold; 12/20 loci significant). Light-harvesting genes showed the strongest response: 17/18 phycobiliprotein-pathway genes in KEGG amr00196 were upregulated, together with multiple putative Pcb/CBP loci (mean antenna log₂FC = +3.51; 11.4-fold). Weighted gene co-expression network analysis placed the antenna-associate genes examined here within a module positively correlated with the LL-FR condition (r = 0.802, p = 0.017), and STRING analysis supported an enriched network of predicted or known protein associations (1115 nodes, 4763 edges; PPI enrichment p < 1.0 × 10⁻¹⁶). Recent matched-irradiance experiments indicate that, at equal photon flux, far-red wavelengths reduce phycobilisome content relative to white light. The transcriptional pattern reported here is therefore most parsimoniously interpreted as predominantly a low-irradiance response, with possible wavelength-associated CA5 contributions that cannot be isolated in the present design. Overall, the analysis reveals coordinated transcript-level changes across plasmid-encoded reacquired phycobiliprotein genes, chromosomal Pcb/CBP loci, chlorophyll biosynthesis genes, and photosystem core genes, consistent with coordinated regulation of light-harvesting components in A. marina. Full article