Search Results (1,137)

Identity verification across pre-operative and post-operative facial images remains a challenging task, particularly following eyelid surgery, where localized periocular changes can disrupt conventional face recognition systems. This research introduces a novel verification framework using an ensemble-based autoencoder-initialized Siamese eye-region periocular verification network designed to remain resilient to surgically induced appearance variation. The proposed approach integrates anatomy-guided periocular normalization with a Siamese deep metric learning architecture, initialized via unsupervised autoencoder pretraining, enabling the model to acquire periocular-specific representations before supervised learning. Robustness in this data-limited clinical setting is enhanced through a combination of constrained periocular augmentation, dropout-based regularization, L2 weight decay, validation-guided checkpoint selection, staged hard-negative mining, validation-weighted multi-seed ensemble learning, and bootstrap-based threshold calibration. Experimental evaluation demonstrates recognition rates of 96.08% on the test set. These results indicate that the proposed framework maintains discriminative periocular identity representations under post-surgical appearance variation while remaining robust in a limited-data clinical setting. Full article

Rainstorm-induced disaster chains are characterized by high suddenness, immense destructive power, and complex chain propagation mechanisms. Traditional static assessment methods rely on fixed parameters and struggle to depict the dynamic evolution of such disasters. Existing dynamic models are mostly based on predefined structures and lack the capability to integrate multi-source data and quantify uncertainty, thereby constraining the accurate prediction of rainstorm disaster chains. To address these issues, this study proposes a rainstorm disaster chain prediction model (SW-DBN) that integrates a large language model (LLM)-optimized sliding window mechanism with a dynamic Bayesian network (DBN). The model first performs dynamic segmentation and feature extraction on multi-source time-series data through the sliding window mechanism and constructs an LLM-driven module for semantic understanding of multi-source information and latent parameter mining. By leveraging the LLM’s in-depth analysis of data pattern variations within the window, the model excavates latent parameters, adaptively adjusts the DBN network topology, and feeds back to optimize the window width and sliding step, thereby maintaining adaptive alignment between the sliding window’s feature extraction and the dynamic evolution of the disaster chain. Ultimately, the cascade propagation process of the rainstorm disaster chain is modeled, reasoned, and validated through the DBN, forming an integrated prediction framework of “perception–reasoning, dynamic regulation, and cascade verification.” A case study in the Xi’an area demonstrates that the proposed model can effectively simulate the temporal evolution of rainstorm disaster chains. The average prediction accuracy for four key types of disaster nodes reaches 84.8%, representing an improvement of 7.5 percentage points over the standard DBN model, with clear advantages in early warning timeliness for critical nodes. The proposed model provides technical support for the probabilistic prediction of rainstorm disaster chains and disaster prevention decision-making, featuring both dynamic adaptability and interpretability. Full article

Radiation-induced oral mucositis (RIOM) frequently causes severe pain and treatment interruptions in patients with head and neck cancer. While earlier guidelines suggested zinc supplementation, updated MASCC/ISOO guidelines downgraded it to ‘No Guideline Possible’ due to highly conflicting evidence. This study aims to resolve these inconsistencies by evaluating zinc’s prophylactic efficacy and investigating whether the route of administration determines its clinical benefit. Following PRISMA guidelines and INPLASY registration (INPLASY202620063), we searched PubMed, Embase, and the Cochrane Library through February 2026. We included randomized controlled trials (RCTs) comparing prophylactic zinc versus placebo or standard care in head and neck cancer patients receiving radiotherapy. Risk of bias was assessed using the Cochrane Risk of Bias 2 (RoB 2) tool. The primary outcome was severe (Grade 3–4) RIOM incidence. Data from five RCTs (332 patients) were pooled using a random-effects model. Overall, zinc significantly reduced severe mucositis risk (RR = 0.35, 95% CI: 0.17–0.73, p = 0.005). Crucially, an exploratory subgroup analysis revealed a striking divergence based on delivery route. Topical zinc mouthwash demonstrated encouraging protection (RR = 0.16, 95% CI: 0.05–0.49, p = 0.001) with zero heterogeneity (I² = 0%). In contrast, systemic zinc yielded borderline, inconsistent benefits (RR = 0.52, 95% CI: 0.27–1.01, p = 0.055, I² = 37%). In conclusion, the localized pool of contemporary evidence clearly demonstrates that the systemic oral ingestion of zinc supplements does not provide a reliable prophylactic benefit against severe radiation-induced oral mucositis in head and neck cancer care. Conversely, topical zinc mouthwashes exhibit an encouraging protective trend; however, the severe paucity of available randomized trials and low cumulative patient volume preclude definitive clinical verification. While these exploratory findings suggest that topical administration may provide a more consistent protective trend compared to systemic routes, they should be interpreted as hypothesis-generating rather than definitive. Future large-scale, multi-center RCTs are strictly warranted to validate these promising route-specific benefits before formal guideline integration. Full article

Thin-film thermocouples (TFTCs) offer conformal sensing junctions with minimal thermal mass, enabling rapid transient response and direct deposition on curved or moving components, which are difficult to achieve using conventional wire thermocouples in applications such as high-speed machining, electric powertrain thermal management, and fuel-cell monitoring. In practical deployment, the effective accuracy of a TFTC can also be affected by the measurement setup used for calibration and testing, particularly lead-wire material transitions, cold-junction compensation, and wiring-related thermoelectric offsets. This study presents a systematic static calibration and performance evaluation of NiCr-based TFTCs under standardised laboratory conditions, with repeated measurements across the 20–260 °C range using both copper leads and matched compensation wires. The thermoelectric output exhibits excellent linearity; temperature reconstruction against a traceable standard reference yields a maximum deviation of approximately 0.27 °C, with root-mean-square and relative errors within tight bounds. Short-term extended-range verification up to 1000 °C confirms detectable thermoelectric signal generation under the present test conditions. A calibration data packet framework containing the calibrated TFTC sample, wiring configuration, calibration coefficients, validity range, and a GUM-compliant uncertainty budget is proposed to support consistent interpretation of calibration results in future digital integration. The study therefore provides a structured calibration workflow and uncertainty-reporting basis for the tested flexible NiCr-based TFTC configurations, supporting further reliability assessment, material-level characterisation, and digital integration. Full article

Large language models (LLMs) are increasingly adopted in life-science research for scientific writing, coding, literature synthesis, workflow troubleshooting, and preliminary data interpretation. In microbial genomics and bioinformatics, their appeal is clear because researchers routinely integrate genome annotations, antimicrobial resistance profiles, virulence determinants, taxonomic assignments, microbiome outputs, workflow scripts, and primary literature. Yet this domain also highlights major risks, including hallucinated biological claims, inaccurate citations, irreproducible code, unsupported genotype-to-phenotype inference, and inappropriate clinical or public health framing. This narrative review examines responsible LLM use in microbial genomics as a representative life-science setting where interpretation depends on database provenance, validated workflows, expert assessment, and reproducible evidence chains. It considers applications in genome annotation, antimicrobial resistance interpretation, virulence analysis, microbiome and metagenomics workflows, coding support, and scientific writing. The review further presents MicrobeGuardGPT as a conceptual reliability framework for assessing LLM-assisted microbial genomics outputs before scientific, clinical, or public health use. By connecting task domains, evidence verification, expert validation, and reliability classification, the framework supports risk-aware LLM integration in bioinformatics. Responsible implementation will require domain-specific benchmarks, curated database linkage, transparent reporting, reproducible workflows, human oversight, and governance standards tailored to biological interpretation across research, diagnostic, surveillance, outbreak-response, educational, and translational contexts. Full article

Accurate, uncertainty-aware estimation of instantaneous wind turbine output is a prerequisite for integrating onshore assets into low-emission energy systems, where operational monitoring, energy-performance verification, and cooperative asset management depend on auditable digital representations of turbine behaviour. This study develops a Digital Shadow-based power-curve modelling framework on fourteen years of Supervisory Control and Data Acquisition records from an operational Vestas V52 onshore turbine (850 kW, Dundalk Institute of Technology, Ireland; 457,429 ten-minute records spanning 2006–2020) and benchmarks seven methods under identical preprocessing on a strict chronological hold-out (training 2006–2017; testing 2018–2020; n = 52,388). A parallel random 75/25 split is reported only as a within-distribution diagnostic; it quantifies an optimistic R² inflation of 0.003–0.027 depending on architecture. The Artificial Neural Network attains the best chronological performance (R² = 0.9924, BCa 95% confidence interval 0.9910–0.9931, RMSE = 19.79 kW); only the ANN and a one-dimensional Convolutional Neural Network with twenty-four-step wind-speed lags (R² = 0.9921) deliver clear positive skill against the IEC-style manufacturer power curve. Split-conformal calibration of a Quantile Regression Forest raises empirical 90% prediction-interval coverage from 0.534 to 0.904 at a width inflation from 30 to 51 kW. The framework qualifies as a Digital Shadow and is positioned, through a Horizon Europe Technology Readiness Level audit and an explicit mapping to ISO 50001:2018 Plan–Do–Check–Act energy management and Renewable Energy Community governance under Directive (EU) 2018/2001, as an auditable monitoring layer for cooperative onshore wind operations. The empirical evidence base is a single turbine; multi-turbine, multi-site replication is the natural follow-on validation. Full article

Liangshan Yanying chicken is a valuable plateau-adapted indigenous poultry breed in China. The poultry cecum modulates nutrient metabolism, gut microbial colonization and intestinal immune barrier establishment, while the molecular mechanisms driving its age-dependent development during the brooding stage remain unclear. Here, integrated transcriptomic and metabolomic profiling coupled with bioinformatics correlation analysis were conducted on cecal samples collected from chickens at post-hatching days 1, 14 and 28. Significant temporal changes were observed in cecal gene expression and metabolite abundance, and day 14 was identified as a critical window for cecal functional maturation and microbial colonization. In total, 2424 metabolites were annotated, including 600 differentially accumulated metabolites. The cecum exhibited phase-specific metabolic patterns: endogenous energy metabolism dominated at 1–14 d, while lipid biosynthesis prevailed at 14–28 d. The intestinal IgA immune network was verified as the core pathway maintaining cecal immune homeostasis in young chicks. Multi-omics conjoint analysis yielded 53 overlapping KEGG pathways, 14 core pathways, 3 pivotal metabolites and 5 hub genes, based on which three interactive regulatory networks were constructed. Transcriptomic data were validated via qRT-PCR. This study reveals cecal metabolic remodeling and regulatory characteristics during the brooding period, supplementing gut developmental research on plateau indigenous chickens. Notably, these results reflect age-related cecal developmental changes rather than breed-specific high-altitude adaptation mechanisms. Further independent verification is required for metabolomic data and predicted regulatory networks. This finding provides a theoretical basis for scientific breeding and feeding management of Liangshan Yanying chickens. Full article

In addition to its role in energy storage, adipose tissue contributes substantially to energy metabolism, endocrine regulation, and inflammatory processes. Sujiang pigs, a hybrid breed approved by the National Livestock and Poultry Genetic Resources Committee of China as a new national breed in 2013, possess a genetic predisposition for substantial fat deposition, making them an ideal model for investigating the mechanisms underlying adipose tissue accumulation. In this study, back fat (BF; subcutaneous adipose tissue), greater omentum (GOM; visceral adipose tissue), and mesenteric adipose tissue (MAD; visceral adipose tissue) were collected from three 6-month-old male Sujiang pigs for RNA-seq analysis. Comparative analyses identified 3005 differentially expressed genes (DEGs) between BF and GOM, 975 DEGs between BF and MAD, and 892 DEGs between GOM and MAD. To validate the reliability of the sequencing data, five DEGs were randomly selected for RT-qPCR verification. The DEGs were further subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. By integrating protein–protein interaction (PPI) networks with bioinformatics analyses, we identified candidate genes potentially associated with lipid metabolism (e.g., WNT9A, WNT5A, and PDGFRA) and inflammatory responses in adipose tissue (e.g., CSF1R, C1QB, and CD4). These findings indicate potential molecular differences between porcine visceral and subcutaneous adipose tissues and may serve as a reference for further studies on the molecular regulation of adipose tissue metabolism. Full article

With the rapid expansion of agricultural e-commerce in China, inefficient cloud warehouse consolidation and high environmental costs have hindered the sustainability of supply chains. To address the challenges of low vehicle loading rates and high carbon emissions, this study proposes an optimization model for collection path planning that integrates sales forecasting and three-dimensional loading constraints. First, STL decomposition is employed to identify seasonal sales patterns, and a hybrid SARIMA and ARIMA-BPNN model is constructed to achieve precise forecasting of future orders to provide data support for dynamic demand. Second, a single-objective path planning model is formulated to minimize the fixed vehicle costs, fuel consumption, and carbon emissions while maximizing the load utilization rates. To solve this complex problem, a two-stage solution framework, consisting of path planning and three-dimensional loading verification, was designed. This framework integrates an improved genetic–hill-climbing hybrid algorithm with a constructive heuristic to handle real-time spatial constraints and achieve the efficient optimization of distribution paths. Finally, a case study on the HLYX agricultural cloud warehouse in Harbin, China, demonstrated that the proposed approach significantly enhances space utilization and reduces transportation and carbon emission costs. This study provides a sustainable development path for the cost reduction, economic efficiency improvement, and carbon emission reduction of smart agricultural logistics. Full article

Building information modeling (BIM)-based collaborative design can support sustainable construction, but current workflows often transmit complete models even when minor changes have been made and rely on centrally controlled records. This study proposes an incremental collaborative design framework that integrates a self-contained extension of the Tracing Semantic Differential Transaction (TSDT) method, hierarchical conflict detection, a permissioned blockchain ledger, and private IPFS storage. The framework formalizes a five-stage workflow and specifies the acceptance checks, incremental packet structure, conflict rules, and governance assumptions implemented in the prototype. In seven change scenarios, the improved TSDT packets reduced transmitted data volumes by 64.47% to 99.85% relative to the corresponding modified full models, with the largest savings observed for minor changes. The prototype also achieved low average on-chain latency and successful model reconstruction in a controlled single-server environment. These findings demonstrate the framework’s technical feasibility and its ability to support record-level traceability and integrity verification. Full article

The pervasive integration of artificial intelligence across e-commerce ecosystems has fundamentally transformed the competitive landscape, rendering systematic and reproducible platform evaluation frameworks an operational necessity rather than an academic exercise. Conventional multi-criteria decision analysis approaches for e-commerce evaluation remain structurally constrained by their dependency on human expert panels, which introduce recruitment costs, cognitive biases, limited reproducibility, and the practical infeasibility of assembling genuinely multidisciplinary panels spanning e-commerce strategy, machine learning engineering, and financial technology simultaneously. This study proposes a novel decision support framework that integrates Large Language Model (LLM) multi-agent simulation with the Best–Worst Method (BWM) to derive reproducible priority weights for AI-driven e-commerce platform evaluation within a rigorous business intelligence architecture. Twelve domain-differentiated LLM agents—organized into three expertise groups representing e-commerce management, AI and machine learning technology, and digital payment systems—were instantiated with structured system prompts encoding professional domain knowledge and deployed across three independent simulation rounds to perform BWM pairwise comparisons across a comprehensive six-dimensional, 30-sub-criterion evaluation hierarchy. Inter-agent consensus was synthesized through geometric mean aggregation, with consistency verification conducted via BWM’s xi* indicator and inter-round stability assessed through coefficient of variation analysis. Results reveal that Transaction Security and Trust achieves the highest dimension-level weight (w = 0.248), followed by AI Recommendation Effectiveness (w = 0.213), with Personal Data Protection (G = 0.0750), Recommendation Accuracy (G = 0.0607), and Transaction Transparency (G = 0.0549) emerging as the three highest globally ranked sub-criteria. The aggregated consistency indicator xi* = 0.062 confirms logical coherence of the multi-agent judgment consensus, and all dimension weights exhibit CV values below 2.8%, demonstrating exceptional inter-round stability. Spearman rank correlations among the three domain-expertise groups exceed 0.92, confirming strong inter-group convergence. Sensitivity analysis under perturbations of ±10% and ±20% demonstrates that the top-five priority indicators are structurally stable. This study establishes LLM multi-agent BWM simulation as a methodologically rigorous, institutionally accessible, and computationally reproducible alternative to traditional expert elicitation for complex platform evaluation tasks. Full article

UAV-based remote sensing systems are increasingly deployed in smart surveillance, disaster response, environmental monitoring, and critical infrastructure inspection. In these applications, aerial sensing platforms must transmit telemetry, control commands, and observation data securely and reliably under strict latency, energy, and computational constraints. However, existing security approaches often fail to jointly provide lightweight payload confidentiality, quantum-resilient key establishment, and adaptive communication protection suitable for dynamic and resource-constrained aerial sensing environments. To address this challenge, this paper proposes an energy-aware post-quantum hybrid cryptographic framework for secure and low-latency UAV remote sensing communications in UAV–IoT mission networks. The proposed framework integrates Ascon-based authenticated encryption for low-overhead protection of remote sensing payloads and mission telemetry, ML-KEM-based post-quantum session-key establishment for long-term resilience against quantum-era threats, and an AI-driven adaptive rekeying mechanism that dynamically adjusts key-refresh decisions according to threat level, residual energy, mobility state, channel stability, anomaly density, traffic sensitivity, link type, and mission progression. Accordingly, rekeying is treated not as a static maintenance process but as an intelligent and context-aware cryptographic control function that adapts communication security to evolving mission and sensing conditions. The framework is evaluated across twenty progressively demanding scenarios involving different UAV counts, sensor densities, payload sizes, communication modes, and adversarial settings relevant to real-time remote sensing operations. Experimental results demonstrate a secure delivery rate of 99.2%, attack detection and mitigation effectiveness of 98.9%, end-to-end encryption latency of 8.7 ms, throughput of 5.03 Mbps, energy overhead of 11.6 mJ/session, rekeying overhead of 2.9 mJ/event, session resilience of 96.4%, and integrity verification success of 99.1%. These findings show that the proposed framework provides a practical and scalable contribution to post-quantum secure UAV remote sensing by unifying lightweight authenticated encryption, ML-KEM-based quantum-resilient key establishment, and AI-driven adaptive rekeying within a resilient aerial–terrestrial communication architecture. Full article

The increasing availability of airborne LiDAR data supports advanced three-dimensional analysis of urban vegetation. However, the development of deep learning methods for tree species classification remains limited by the lack of annotated datasets at the individual-tree level. This study presents UrbanTree3D, a field-validated dataset comprising segmented individual trees extracted from airborne LiDAR point clouds and enriched with species information from field inventory data. The dataset was generated through a structured workflow, including noise removal, vegetation extraction, height normalization based on a digital elevation model (DEM), and temporal consistency verification. Individual trees were segmented using a hybrid approach integrating DBSCAN and Watershed algorithms, and subsequently matched to field inventory data using a nearest neighbor method. A field validation campaign was conducted to ensure data reliability. The final dataset contains 152 individual urban trees and includes six tree species. It provides high-quality annotations, consistent point clouds, and field validation data, supporting its use for training and evaluating deep learning models. UrbanTree3D addresses the current shortage of annotated LiDAR datasets and supports applications in urban forestry, smart cities and urban digital twins. Full article

Background: Biodegradable implants manufactured from Mg-based alloys are one of the most commonly used in orthopedics. However, their overall clinical acceptance is influenced by their fast corrosion speed and hydrogen emission. Based on an innovative manufacturing route previously described, this study introduces a preliminary proof-of-concept for a Gemini-assisted Digital Twin (Gemini-DT),which is an AI-augmented in silico framework designed to consider a MgF₂ conversion coating on the implant surface and to model the synchronization of the degradation process with new bone formation. Methods: Based on the integration of experimental data for Mg-Nd and Mg-Zn alloys and by considering the implant geometry and coating formation, we developed, in collaborative work with LLM Gemini 1.5 Flash (Google), a four-module cognitive framework (surface thermodynamic synergy (Module 1), degradation analysis and alloy extract concentration management (Module 2), micro-channel fluidics and mechanical stability (Module 3), and bio-mechanical synchronization and regenerative evaluation (Module 4)) to evaluate simulated implant behaviors). Results: Using a 10,000 iteration Monte Carlo stability simulation, the model demonstrated a potential 12% reduction in false-negative design screening errors compared to rigid rule-based systems, achieving strong internal decision consistency in sustaining the mandated parametric compliance window. Computational verification supports the projected biocompatibility trends of Mg-Zn alloys, as previously demonstrated in our in vivo studies. Conclusions: Our research leads to a consistent computational architecture dedicated to Mg-based implants and offers a robust platform for virtual design and optimization. These observations suggest that the developed model can recover viable designs, whereas traditional linear models may reject them. Full article

Background/Objective: Accurate digital recording of implant position is essential for achieving passive fit and predictable outcomes in complete-arch implant-supported prostheses. However, complete-arch cases remain challenging because of increased inter-implant distances, limited anatomical landmarks, soft tissue mobility, scan body-related variables, and cumulative errors during data acquisition and file registration. This narrative review aims to evaluate current intraoral and extraoral digital implant position recording techniques from a clinical decision-making perspective. Methods: A structured narrative literature search was conducted in PubMed from database inception to 15 May 2026 and was supplemented by manual screening of reference lists of key systematic reviews and eligible articles. Systematic reviews, meta-analyses, clinical studies, comparative in vitro studies, dental technique articles, and clinical reports relevant to complete-arch digital implant position recording were considered. Higher-level and clinically relevant evidence was prioritized, whereas technique reports were included primarily for emerging workflows with limited clinical evidence. Results: Intraoral techniques include non-splinted and splinted scan body protocols, calibrated implant scan bodies, calibrated frameworks, and auxiliary reference strategies. These methods may be clinically efficient but remain sensitive to scan path, scanner technology, landmark availability, scan body design, implant distribution, and operator-related factors. Extraoral techniques include stereophotogrammetry, camera- or smartphone-assisted photogrammetric systems, reverse impression workflows, and laboratory scanner-based digitization. These approaches may reduce intraoral stitching errors in complex edentulous arches, but usually require complementary datasets for soft tissue morphology, prosthetic contours, antagonist dentition, and maxillomandibular relationships. Conclusions: Direct intraoral scanner (IOS) protocols may be appropriate in favorable complete-arch situations with accessible scan bodies, limited inter-implant distances, and stable reference geometry. In clinically demanding cases requiring greater cross-arch accuracy, stereophotogrammetry, intraoral photogrammetry, or calibrated scanning approaches may provide more controlled implant position recording. Reverse impression and model-based workflows are particularly useful when a verified interim prosthesis, verification jig, or cast-based reference is available. Regardless of the selected technique, accurate integration of implant coordinates with soft tissue, prosthetic contour, antagonist arch, and occlusal data remains essential. Full article