Search Results (3,260)

Accurate prediction of departure flight taxi-out time is critical for enhancing airport surface efficiency and reducing flight delays. However, existing methods often struggle with data sparsity, inadequate representation of complex spatio-temporal interactions among aircraft, and imbalanced sample distributions. To address these challenges, this paper proposes a synergistic multi-module fusion model named DDA-SIM-ATT-CatBoost. The model integrates three core modules: a Dynamic Data Augmentation (DDA) module that expands the training distribution through operationally consistent perturbations to mitigate data imbalance; a Similarity Theory (SIM) module employing K-Prototypes clustering and Mahalanobis distance to achieve precise matching of historical operational patterns; and an Attention Mechanism (ATT) module that dynamically recalibrates feature weights to emphasize critical influencing factors. These modules work synergistically to provide a robust and discriminative input representation for the CatBoost regressor, which excels at handling categorical features and complex nonlinearities. Using real-world departure data from a major hub airport, the proposed model achieves prediction accuracies of 74.57%, 89.12%, and 97.76% within error margins of ±120 s, ±180 s, and ±300 s, respectively, with a Mean Absolute Percentage Error (MAPE) of 10.34%, Mean Absolute Error (MAE) of 87.55 s, and Root Mean Square Error (RMSE) of 125.61 s. Ablation studies validate the positive contribution and synergistic effect of each module, while comparative experiments demonstrate that our model significantly outperforms baseline models such as XGBoost and Random Forest. The DDA-SIM-ATT framework provides a generalizable and high-precision solution for taxi-out time prediction, offering reliable decision support for airport surface operations. Full article

This modeling study investigates the impact of the 2023 Canadian wildfire aerosols (primarily black carbon and organic aerosol) on weather forecasts, concluding that incorporating real-time aerosol forcing improves model performance over using climatology. Experiments without real-time data severely underestimated aerosol optical depth (AOD), an error mitigated by including the forcing or using the coupled atmosphere–chemistry model. The aerosols exerted a strong direct radiative effect, reducing surface downward shortwave (SW) flux and generating corresponding surface cooling over the wildfire region. Furthermore, including aerosol–cloud interactions amplified this cooling and led to an increase in the overall cloud fraction and precipitation, illustrating complex indirect effects. While these physical improvements enhanced the representation of the atmosphere, the positive impact on overall medium-range forecasting performance (5–10 days) was modest, suggesting that the benefits of accurately representing wildfire feedback on the coupled Earth system are achieved through relatively slow processes, such as radiation feedback. Full article

Elevator fault diagnosis heavily relies on high-precision sensing of microscopic physical states. Although Micro-Electro-Mechanical System (MEMS) sensors can capture such subtle features, they are constrained by high-frequency data streams, environmental noise, and the semantic gap between raw sensor data and actionable maintenance decisions. This study proposes a collaborative edge–cloud intelligent diagnosis framework specifically designed for elevator systems. On the edge side, a lightweight temporal Transformer model, ELiTe-Transformer, was designed and deployed on the Jetson platform. This model enhances sensitivity to event-driven MEMS signals through an industrial positional encoding mechanism and by integrating linear attention and INT8 quantization techniques, achieving a real-time inference latency of 21.4 ms. On the cloud side, retrieval-augmented generation (RAG) technology was adopted to integrate physical features extracted at the edge with domain knowledge, generating interpretable diagnostic reports. The experimental results show that the overall accuracy of the system reaches 96.0%. The edge–cloud collaborative framework improves the accuracy of complex fault diagnosis to 92.5%, and the adoption of RAG reduces the report hallucination rate by 71.4%. This work effectively addresses the bottlenecks of MEMS perception in elevator fault diagnosis, forming a closed loop from micro-signal acquisition to high-level decision support. Full article

Background/Objectives: The informative value of integrating high-risk human papillomavirus (HR-HPV) genotyping with cytology and p16/Ki67 dual-stain biomarker results, using limited and two types of extended genotyping assays, has not yet been evaluated. Methods: A total of 32,724 screening test results between 2015 and 2024 were included. Limited HPV genotyping was performed using the Abbott RealTime High Risk HPV assay. Extended genotyping was performed using two assays: the Alinity m HR HPV and BD Onclarity HPV Assay. Trends in age-specific, cytology-specific, and p16/Ki67-specific HR-HPV prevalence and distribution were observed, and differences between limited and extended genotyping were examined. Results: The overall HR-HPV positivity rate was 15.0%. HR-HPV prevalence was 13.9% in the limited genotyping group, 17.8% in in the Onclarity group 1, and 17.2% in the Alinity group 2, with a statistically significant difference in the proportions of positive/negative cases (p < 0.0001). No statistically significant difference was observed between extended genotyping groups (p = 0.706). In the Onclarity group: the highest p16/Ki67 positivity was observed for HPV 33/58 (100.0%) and HPV 31 (58.8%), while the lowest was for HPV 45 (18.2%), HPV 18 (25.0%) and HPV 59/56/66 (28.9%). In the Alinity group: the highest p16/Ki67 positivity was observed for HPV 16 (66.7%) and HPV 31/33/52/58 (58.8%). Conclusions: Based on ten years of HPV-based cervical cancer screening data, this study demonstrates that genotype-specific HR-HPV information obtained through extended genotyping provides clinically relevant risk stratification when interpreted together with cytology and p16/Ki67 dual-stain results. These findings support an integrated screening approach combining molecular HPV testing, cytology, and immunocytochemical biomarkers to improve risk-based triage in cervical cancer screening. Full article

Advances in semiconductor technologies, particularly in power transistors and switching diodes, have enabled higher switching frequencies and converter efficiency, renewing interest in Switched Reluctance Motors (SRMs) for electric vehicles. This work presents a data-driven approach utilizing a Long Short-Term Memory (LSTM) network capable of effectively managing temporal dependencies for estimating rotor position without sensors in SRMs. The motor investigated was custom-designed, subsequently manufactured as a prototype. The LSTM was trained and validated with experimental data collected at various speeds and load conditions. The outcomes demonstrate the model’s strong performance, with a mean squared error (MSE) of ${1.77}^{{°}^2}$, a mean absolute error (MAE) of 1.09°, and 97.35% accuracy. Compared to typical estimation methods such as back-electromotive force (EMF)-based techniques, fuzzy logic, model predictive control, feed-forward neural networks (FFNNs), and back-propagation neural networks (BPNNs), the LSTM stands out as one of the most effective and widely used models. Previous neural networks (NN)-based studies typically report ±5° accuracy, whereas LSTM keeps the error about 1° in this study. This strategy eliminates position sensors, reduces cost and complexity, and enables reliable real-time SRM control. Results indicate that the method has significant potential for electric motor drives, particularly for SRMs. Full article

Adaptive learning at scale requires explicit handling of uncertainty and information flow across diverse educational technologies. This paper proposes a TOGAF-conformant enterprise architecture for the University of Tabuk (UT) that embeds entropy- and uncertainty-aware requirements from the outset and aligns them with institutional goals in teaching, research, and administration. Using the Architecture Development Method (ADM), we map information-theoretic requirements to architectural artifacts across the architecture vision, business, information systems, and technology domains; formally specify core entropy-informed observables, including predictive entropy, expected information gain, workflow variability entropy, and uncertainty hot-spot severity; and define semantic and metadata standards for their near-real-time computation. These indicators are positioned explicitly across the TOGAF domains: business architecture identifies where uncertainty matters, information systems architecture defines the computable data and application representations, technology architecture operationalizes secure and scalable computation, and later ADM phases use the resulting metrics for prioritization and governance. The architecture also establishes governance that ranks initiatives by their expected uncertainty reduction through Architecture Review Board (ARB) decision gates. We address three research questions: (R.Q.1) how to design a TOGAF-conformant architecture for UT that natively encodes uncertainty-aware requirements and aligns with institutional needs; (R.Q.2) how to integrate dispersed data, achieve semantic harmonization, and deliver analytics-ready streams that support information-theoretic indicators for personalization without delay; and (R.Q.3) how to embed IT demand planning in opportunities and solutions and migration planning using uncertainty reduction and expected information gain as prioritization criteria. The resulting architecture offers a university-wide foundation for adaptive learning: it unifies learner and system interaction data under governed schemas, supports low-latency analytics, and formalizes decision processes that treat uncertainty as a primary metric. Though learner-level operational validation is future work, the design establishes the technical and organizational foundations for responsible, large-scale deployment of entropy-driven learner modeling, content sequencing, and feedback optimization. Full article

To address the calibration challenge caused by the nonlinear variation in intrinsic parameters during continuous camera zooming in UAV electro-optical pods, this paper proposes an online calibration method based on IMU-visual fusion. Traditional offline calibration cannot adapt to dynamic scenarios, while existing self-calibration methods suffer from slow convergence and insufficient robustness. The proposed method aims to achieve real-time and accurate estimation of camera intrinsic parameters during zooming. Specifically, we first construct a unified state estimation framework that encodes the internal and external parameters of the camera and the 3D positions of scene feature points into a high-dimensional state vector, then establish a camera motion model based on IMU data, construct a visual observation model by combining the pinhole camera and second-order radial distortion model to establish a nonlinear mapping from 3D feature points to 2D pixel coordinates, and adopt an improved ORB algorithm for feature extraction and LK optical flow method to achieve high-precision cross-frame feature matching to enhance the stability of visual observation. Most importantly, we design a tight-coupling fusion strategy based on the Extended Kalman Filter (EKF) prediction-update iteration mechanism, which fuses IMU high-frequency motion constraints and visual geometric constraints in real time to suppress parameter drift induced by focal length changes. Finally, we recursively solve the state vector to complete the online dynamic estimation of intrinsic parameters. Monte Carlo simulation experiments and real UAV flight experiments confirm that the method has both high estimation accuracy and strong environmental adaptability, can meet the high-precision calibration needs of UAVs in dynamic scenarios, and provides reliable technical support for accurate target positioning. Full article

Demodex mites are common commensals of mammalian skin, but under certain conditions, they can cause severe skin diseases. This study analyzed the presence, diversity, and phylogenetic relationships of Demodex species in two wolf subspecies from southern Europe to determine whether species-level differences exist between wild and domestic canids after thousands of years of divergence. A total of 1400 hair samples from 140 wolves were analyzed using a real-time PCR (qPCR) targeting mitochondrial 16S rRNA and nuclear 18S rRNA genes. Overall, 37.1% (52/140; 95% CI: 29.0–45.9%) of wolves were positive for Demodex DNA, with a higher prevalence in Italian (46%) than in Iberian (36%) wolves. The lip and chin areas were the most reliable sampling sites. Four Demodex species were identified in wolves: D. injai and D. canis (associated with dogs), and D. folliculorum and D. brevis (associated with humans). Co-infestations involving multiple Demodex species were recorded for the first time in wild canids. These results challenge the long-held belief of strict host specificity in Demodex mites. The discovery of Demodex species associated with both humans and dogs in wolves supports the idea that host-switching and ecological interactions have occurred throughout the evolution of canids and humans. Such cross-species transfers may have taken place during the early domestication of dogs, representing a plausible scenario compatible with our data. However, given the isolated history of the two southern wolf populations, it is more probable that these findings result from recent interspecific transmission events, likely facilitated by ecological overlap with domestic animals and human environments. Future genomic studies will be essential for clarifying the evolutionary relationships within the genus Demodex and its host associations. Full article

The processes of teaching and learning are primarily humanistic. However, contemporary artificial intelligence (AI) technology has significantly changed these processes. The current qualitative study aimed to explore this phenomenon by investigating the role that chatbots can play as language tutors in improving ESL students’ writing. Specifically, the study investigated students’ perceptions and experiences to assess the influence of ChatGPT-generated written communication on ESL writing improvement. Data were collected through semi-structured interviews with undergraduates from the College of Languages and Translation at a public university in Riyadh, Saudi Arabia. The emerging themes revealed that students held positive perceptions of the chatbot as a tutor, highlighting that collaborative learning with the chatbot facilitated the acquisition of writing skills and increased engagement in the writing process. Findings also showed noticeable improvement in language development, at lexical, syntactic, semantic, and pragmatic levels, as well as in the use of cognitive and metacognitive writing strategies. The study recommends reevaluating traditional writing instruction methodologies and highlights the benefits of integrating AI chatbots into second-language writing pedagogy. Furthermore, the study emphasises students’ need for accessible English-language tutoring, such as chatbots, which provide immediate, real-time writing instruction. The study also addresses the implications of incorporating AI-powered chatbots into writing curricula at Saudi universities. Full article

The risks associated with hazardous materials (HazMat) transportation exhibit typical characteristics of chain-like distribution, spatiotemporal regularity, and individual heterogeneity. A personalized trip-chain feature spectra recognition framework for HazMat vehicles is proposed to enhance the capability to assess and analyze individual risks using vehicle positioning data. The proposed framework addresses the challenges of deriving personalized risk feature maps arising from missing real-time trajectory data, complex sub-trip-chain segmentation, and the extraction of personalized risk feature representations. An improved conditional Wasserstein Generative Adversarial Network (WGAN) model is initially developed to impute trajectories with missing positional data, and it can robustly reconstruct trajectories with large-scale missing segments by integrating a multi-head self-attention mechanism and a gradient penalty. A two-layer clustering algorithm, K-Means-multiplE-THreshOlds-adaptive-DBSCAN (KMETHOD), which combines an adaptive mechanism with threshold rules, is subsequently designed to identify the dwell time and related spatial attributes of dwell points along vehicle trips. A BERT-based model is incorporated to filter Points of Interest (POIs) around dwell points, which enables the extraction of their detailed location semantics and trip characteristics and thus supports trip chain identification and segmentation. A threshold-activated multilayer trajectory feature-map method (TAFEM) is constructed to generate feature maps for each trip chain. The Liquefied Natural Gas (LNG) transportation trajectory data from Guangdong Province is selected to evaluate the effectiveness of the proposed methods. The experimental results demonstrate that the proposed framework can effectively identify trip chains and generate their corresponding feature maps. The trajectory imputation model achieved the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Dynamic Time Warping (DTW) of 2.34–3.33, 6.05–7.74, and 0.74–1.21, respectively, across different missing-rate scenarios, outperforming other benchmark models. The identification accuracy of dwell-point duration and location reaches 98.35%. The BERT-based method achieves a maximum accuracy of 92.83% in origin–destination (OD) point recognition, effectively capturing comprehensive trip-chain information. TAFEM accurately characterizes the spatiotemporal distribution and potential causal factors of personalized HazMat transportation safety risks, providing a reliable foundation for risk identification and safety management strategies. Full article

Background: Trastuzumab is a blockbuster monoclonal antibody that has revolutionized the treatment of HER2-positive breast and gastric cancers. With the increasing availability of biosimilar monoclonal antibodies in clinical practice, independent verification of biosimilarity using products sampled from a real-world supply chain is important to assure clinicians and the patients to use these products confidently. Objective: The aim of this study is to assess the biosimilarity of AryoTrust, a trastuzumab biosimilar, in comparison with the reference product Herceptin. AryoTrust and Herceptin products were randomly withdrawn from Iraqi hospitals to reflect medicines administered in real clinical settings. Methods: AryoTrust and Herceptin were compared using an extensive set of orthogonal analytical techniques which included SDS-PAGE, ion-exchange chromatography, capillary isoelectric focusing, peptide mapping, N-glycan profiling, circular dichroism, differential scanning calorimetry, and surface plasmon resonance. In addition to these teste, functional comparability was also tested using an HER2-dependent cell-based proliferation inhibition bioassay. Results: The results showed that both products have highly comparable profiles in all assessed attributes. The analysis showed similar molecular integrity and purity, identical primary structure, comparable charge heterogeneity, similar isoelectric points (pI) of the main isoform, close glycosylation patterns (mainly, by core-fucosylated complex-type glycans), similar higher-order structural features, and thermal stability. The receptor binding studies exhibited comparable binding affinities with Fcγ receptors and FcRn. Finally, the cell-based bioassay revealed comparable dose–response curves with similar EC₅₀ values and relative potency. Conclusions: The integrated analytical and functional data support the biosimilarity of AryoTrust to the reference product Herceptin, which has been marketed and used in Iraq. This study provides real-world scientific evidence supporting confidence in the quality and comparability of this trastuzumab biosimilar and reduces any doubt in the product and at the same time emphasizes the value of independent post-marketing biosimilarity assessments. Full article

Flight Schedule Problem optimization is a typical NP-hard combinatorial optimization problem that is challenging to solve using traditional algorithms, so metaheuristic algorithms are commonly adopted for such problems. This paper proposes a Discrete Memory-Enhanced Restructured Particle Swarm Optimization algorithm (DMERPSO) to address Flight Scheduling Problem optimization. Firstly, this paper designs a hybrid particle encoding scheme capable of simultaneously handling flight time adjustments (integer variables) and route selections (categorical variables) for the Flight Schedule Problem. Secondly, a new update equation of particle positions is provided based on probability selection within the three terms of the Memory-Enhanced Restructured Particle Swarm Optimization (MERPSO) algorithm, and the calculation of the selection probability is designed. Thirdly, the two strategies and perturbation terms of MERPSO are improved in order to be adapted to optimize the discrete Flight Schedule Problem. Finally, simulation experiments are conducted using DMERPSO based on real flight data from multiple Chinese airports with the objective of minimizing total flight delays, leading to better solutions that are faster than various benchmark algorithms. The DMERPSO algorithm exhibits significant advantages in reducing total delays, improving solution stability, and enhancing robustness, which validates that DMERPSO provides an effective new approach for solving Flight Schedule Problem optimization problems. Full article

Programmed Death-Ligand 1 (PD-L1) promotes tumor progression through several mechanisms, including its intrinsic effect on breast cancer cell proliferation via the S-Phase Kinase-Associated Protein 2 (SKP2)–p21^Cip1/p27^Kip1 (SKP2-p21/p27) axis. However, the specific regulatory signaling through which PD-L1 influences the SKP2–p21/p27 axis to drive cell proliferation remains unclear. To investigate how PD-L1 mediates SKP2-dependent proliferation, proteomic analyses, gene-expression manipulation via knockdown or overexpression, Western blotting, quantitative immunofluorescence, colony-forming assays, real-time cell analysis, and Xenograft-derived cells were used. Proteomic data analysis identified several PD-L1 downstream targets as potential candidate regulators of the SKP2–p21/p27 axis and activators of the PI3K/AKT pathway. Candidate screening by gene knockdown, followed by analyses of SKP2, p21, and p27 protein expression, revealed Livin and Galectin-1 as upstream regulators of the SKP2–p21/p27 axis. Moreover, Western blotting and quantitative immunofluorescence in three breast cancer cell lines confirmed that PD-L1 is an upstream regulator of Livin, Galectin-1, and SKP2 protein expression. Mechanistically, Livin and Galectin-1 enhanced AKT phosphorylation (Ser473) to sustain PI3K/AKT pathway activation in a positive feedback loop to upregulate SKP2 expression. Functional assays, including colony-forming assays and real-time cell analyzer, demonstrated that Livin and Galectin-1 are critical for PD-L1-mediated, SKP2-dependent proliferation. These findings were corroborated in vivo using xenograft-derived cells. Overall, these findings delineate a tumor-intrinsic signaling axis in which PD-L1 upregulates Livin and Galectin-1 to sustain PI3K/AKT activity and drive SKP2-dependent cell proliferation. Targeting Livin and/or Galectin-1 may provide a rational strategy to disrupt PD-L1-associated proliferative signaling and improve combinatorial therapeutic approaches in breast cancer. Full article

Accurate identification of fire smoke is a key link in realizing early fire prevention and control. Traditional intelligent video and image processing technologies are significantly restricted by environmental factors, with weak anti-interference capabilities and limitations in distinguishing fire smoke, leading to a high false alarm rate of fires. To address this problem, this paper proposes an unconventional visual field smoke detection method based on image enhancement. The method innovatively improves the Retinex algorithm by integrating improved guided filtering, adaptive brightness correction, and CLAHE-WWGIF joint processing, which realizes targeted optimization for the unique interference factors of under-construction nuclear power plants such as water mist, low illumination, and equipment occlusion. First, an improved Retinex algorithm is used to process the image to improve the image brightness and contrast, retain edge details while avoiding halo artifacts, reduce the impact of noise, and optimize visual features. Then, the sample data set is integrated, and the YOLOv11 target detection algorithm is used to achieve accurate identification and positioning of smoke targets. Experimental data shows that the fire identification method achieves an accuracy rate of 93.6% and 92.3% for fire smoke identification in interference-prone scenarios such as dark nights and water mist, respectively, and the response time to fire smoke is only 1.8 s and 2.1 s. In practical on-site applications at nuclear power plant construction sites, the method is integrated into an “edge computing + distributed deployment” hardware system, which realizes real-time smoke detection in core areas such as nuclear islands and conventional islands with a false alarm rate of less than 5% and a detection delay of ≤300 ms, meeting the ultra-strict safety monitoring requirements of nuclear power projects. Experiments show that this method can be effectively applied to smoke detection scenarios under unconventional visual fields, accurately identify smoke, provide reliable technical support for fire smoke identification under unconventional visual fields, significantly reduce the false alarm rate of fire detection, and provide technical support for the safety of under-construction nuclear power plants. Full article

To map and describe the scientific evidence on germicidal ultraviolet C (UV-C) light for hospital surface disinfection, this scoping review examined device types, reported operational parameters, microbiological and clinical outcomes, and implementation aspects. Primary studies conducted in hospital settings and evaluating UV-C or ultraviolet germicidal irradiation on environmental surfaces were searched in four databases without date restrictions. Data were synthesized descriptively in tables and narrative form following JBI and PRISMA-ScR guidance. Eleven studies (2007–2025) met the inclusion criteria. Reported microbial reductions ranged from 1 to ≥5 log₁₀. Higher and more consistent reductions were predominantly observed under laboratory or controlled experimental conditions, whereas reductions in real-world hospital surface sampling were more variable and influenced by pathogen type, surface material, room geometry, and shadowing. Integration of UV-C with manual cleaning and multi-position irradiation cycles was associated with greater effectiveness. Reporting of key radiometric parameters (dose, exposure time, and distance) was frequently incomplete, limiting reproducibility and cross-study comparability. Clinical findings were heterogeneous: some interrupted time-series analyses suggested reductions in healthcare-associated infections, although effects were not uniform across microorganisms. Implementation reports described room-level cycle times compatible with turnover, variable staffing requirements, and limited economic evaluation. Overall, UV-C appears to be a promising adjunct to standard cleaning practices in hospital environments. However, standardized radiometric reporting, multicenter studies, and robust clinical and economic evaluations are necessary to support safe, reproducible, and sustainable large-scale implementation. Full article