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Objectives: The objectives of this study were to educate the healthcare professional and the general public about interoperable vaccine records by elaborating on its definition, why we need one, what the challenges are, and what progress has been made in this direction. Methods: The vaccination practices and vaccine record-keeping in the Nordic countries, the UK, and the USA are used as examples to demonstrate the necessity of interoperable vaccine records. The authors’ expertise and experience in interoperability, medicine, and HealthIT, along with the literature, informed this paper’s content, structure, and organization. Real-world examples and scenarios illustrate the reality and significance of interoperable vaccine records. Results: This paper provides a brief description of vaccination records and their practices in the Nordic countries, the UK, and the USA, which can inform future best practices for vaccination record-keeping. This paper also proposes a conceptual roadmap for achieving an interoperable vaccine record, which is a critical component for maintaining the integrity of an individual’s health record longitudinally, an essential cornerstone for receiving safe and effective healthcare, improving patient outcomes, controlling healthcare costs, avoiding unnecessary revaccination (overvaccination), and enabling alignment with up-to-date vaccine recommendations. This paper examines the intersection of vaccinations, HealthIT, and vaccine record-keeping, and it provides a brief discussion of the social and political aspects of vaccination. Conclusions: Although achieving interoperable vaccine records is technically feasible and clinically important, their large-scale implementation is not a simple task amid the social and political challenges related to vaccine misinformation, acceptance, and hesitancy. Full article

Studying the slumping disintegration, movement speed, impact intensity, accumulation characteristics, and energy conversion laws of tower-column unstable rock masses (TCURM) is crucial for high-altitude rockfall hazard risk evaluation. Existing PFC-based rockfall simulations rarely target the unique “top-hard-bottom-weak” structural characteristics of TCURM and lack in-depth integration of on-site monitoring videos to verify dynamic evolution processes. Taking the large-scale collapse of W12^# unstable rock mass at Zengziyan, Jinfo Mountain in Chongqing as an example, a combination method of orthogonal test and PFC^3D discrete element simulation is used. Mesoscopic parameters are calibrated via comparison with on-site video and investigation data, accurately reproducing the entire slumping disintegration process and revealing its dynamic characteristics. Results confirm the simulation is basically consistent with field data, verifying the model and parameter rationality. The total duration from instability to stagnation is 121 s (15 s to impact the secondary steep cliff base, 106 s for debris accumulation). Movement speed time-histories of deteriorated and non-deteriorated zones are generally consistent, both exhibiting a “double-peak” feature. Rockfall impact force first increases, stabilizes in the middle, and declines to stability afterward, with a maximum of 2.1 × 10⁹ N. The kinetic energy curve also shows a “double-peak” distribution, closely related to the on-site two-level steep cliff morphology. The findings provide important references for analyzing the dynamic evolution of such rockfalls and designing disaster prevention/mitigation engineering. Full article

This study proposes a feature-level fusion method that integrates Differential Interferometric Synthetic Aperture Radar (D-InSAR) and Unmanned Aerial Vehicle photogrammetry (UAV-P) for monitoring mining-induced subsidence basin (MSB). The method begins by extracting key subsidence characteristics based on the patterns of coal-mining-related surface displacement; the centimeter-level subsidence boundary is determined from D-InSAR data, while the meter-scale deformation at the subsidence center is derived from UAV-P. These extracted features are then used to invert the parameters of the probability integral method (PIM). The subsidence basin predicted by the inverted parameters serves as a criterion to select the superior dataset between the D-InSAR and UAV-derived results. Finally, the selected subsidence data are fused to generate a composite subsidence map. The proposed method was applied to the 2S201 panel in the Wangjiata Coal Mine using eight Sentinel-1A images and two UAV surveys. The fusion results were evaluated for their regional and overall accuracy against 30 ground control points measured by total station and GPS. The results demonstrate that the fusion method not only accurately extracts large-scale deformations in the mining area, with a maximum subsidence of 2.5 m and a root mean square error (RMSE) of 0.277 m in the subsidence center area, but also precisely identifies the subsidence boundary region with an accuracy of 0.039 m. The fused subsidence basin exhibits an overall accuracy of 0.182 m, which represents a significant improvement of 83.6% and 27.8% over the results obtained using D-InSAR and UAV alone, respectively. This method effectively reconstructs the complete morphology of the mining-induced subsidence basin, confirming its feasibility for practical applications. Full article

Air pollution governance is crucial to China’s sustainable development, and the complexity of the low-carbon transition necessitates a systematic assessment of the evolving policy landscape. This study constructs a large-scale corpus of air pollution environmental regulation documents issued by the central government and 31 provincial-level governments, collected through large-scale web crawling from official government portals. This study utilizes the BERTopic model to extract policy topics and systematically analyze policies through topic content, topic hierarchy, topic similarity, and clustering structure, as well as dynamic topic evolution over time. Key findings are as follows: (1) The BERTopic results identify 31 policy topics, and Topic 0–Topic 11 collectively account for 82.8% of all documents. (2) The topics mainly focus on three areas: comprehensive air pollution control and enforcement, structural low-carbon and energy transition, and governance capacity building through monitoring, fiscal incentives, and carbon accounting. (3) Hierarchical and similarity analyses indicate three relatively stable core thematic groups, alongside two specialized peripheral themes. (4) Over the study period, dynamic topic trends show a shift in policy emphasis from air quality-oriented control toward low-carbon transition and supporting policy instruments. These findings clarify the thematic structure and evolution of China’s environmental policies, offering an evidence base for improving integrated air pollution governance toward sustainable development. Full article

With the intensification of global climate change, Carbon Capture, Utilization and Storage (CCUS) technology has gradually become an important means to address climate change. As the sole technological option for the low-carbon utilization of fossil energy, CCUS technology offers new opportunities for the sustainable development of the energy sector. To systematically assess the current status of technological development and clarify the future development direction, this study conducts a comprehensive evaluation of the technical level, economic efficiency, and scale of CCUS. China has demonstrated considerable advancement in CCUS, particularly in the domains of capture and transportation, but still faces challenges in technology integration and commercialization. By strengthening policy support, reducing technical costs and promoting large-scale deployment, CCUS is expected to play a greater role in the future. This research provides valuable insights for the future development of CCUS and emphasizes the importance of improving its economic viability, with the aim of proving guidance for formulating China’s CCUS technology innovation strategy and advancing industrialization. Full article

This study develops a conceptual framework for characterizing reservoir architecture in multi-component, discrete systems using pressure transient analysis (PTA), aimed at calibrating inflow geometry prior to full-field dynamic simulation for subsurface gas storage applications such as CO₂ and hydrogen. A secondary objective is to identify variations in permeability over time by analyzing flow capacity trends and evaluating the dynamic influence of faults and fractures. The analysis is based on a gas-condensate field comprising seven wells and four zones (A, B, C, D), using integrated dynamic datasets including extended well tests (EWTs), mud loss, production logs, and production data. Detailed interpretation of PX-1’s EWT indicated delayed re-pressurization and persistent under-pressure, suggesting a compartmentalized or transient system with limited gas-in-place connectivity. Four reservoir architecture concepts were developed: (1) lithology-dominated inflow, (2) structurally controlled inflow, (3) discrete, weakly connected compartments, and (4) transient-dominated systems with tight matrix GIIP. These concepts informed four reservoir models: matrix-only (M), areal heterogeneity (A), sparse bodies (B), and sparse networks (S). Application of these models across other wells revealed consistent localized KH (permeability–thickness product) behavior, with all models fitting short-duration data comparably. However, only sparse drainage models (B/S) adequately matched PX-1’s EWT response. PTA results confirm that well tests constrain KH locally but provide limited insight into large-scale reservoir architecture. EWTs may reach ~1 km, while shorter tests are confined to ~200–400 m, typically within one to two simulation grid blocks. This study demonstrates how integrating PTA with multi-scale data improves characterization of naturally fractured, tight carbonate reservoirs and supports reservoir simulation and history matching for hydrogen storage evaluation. Based on reservoir simulations, this study concluded that naturally fractured carbonate gas reservoirs can provide significant storage and injection capacities for underground hydrogen storage. This study exemplifies how to characterize the naturally fractured tight carbonate reservoirs by integrating multi-scale and multi-dimensional data such as PTA. Furthermore, this study assists in gridding for full-field reservoir models, for history matching and quantifying the potential of hydrogen storage in these complex reservoirs. The proposed workflow provides an uncertainty-bounded reservoir characterization framework and should not be interpreted as a complete field-design methodology for hydrogen storage. The modeling does not explicitly couple geomechanical fracture growth, hydrogen diffusion, long-term geochemical reactions, or caprock integrity degradation. Therefore, the presented storage scenarios represent technically feasible cases under defined assumptions. Comprehensive site-specific geomechanical and containment assessments are required prior to field-scale implementation. Full article

The rapid integration of inverter-based renewable energy sources (RES), particularly solar photovoltaic (PV) and wind power plants (WPPs), together with the large-scale deployment of battery energy storage systems (BESSs) is fundamentally reshaping modern power systems. While these technologies are essential for decarbonization, their converter-dominated and variable characteristics introduce new challenges for grid stability, operational security, and regulatory compliance. As a result, grid codes are being continuously revised to define advanced technical requirements, including fault ride-through (FRT) capability, reactive power support, frequency response, voltage control, and active power management for RESs and energy storage systems (ESS). This study presents a systematic comparative assessment of international grid codes, examining the technical and operational requirements imposed on inverter-based resources (IBR) and ESSs across multiple jurisdictions. In parallel, the current Turkish Grid Code is evaluated from a future-oriented perspective, and recommendations that can improve the existing regulatory framework are proposed, particularly regarding high-voltage ride-through capability, synthetic inertia provision, fast frequency response (FFR), hybrid power plant (HPP) coordination, and ESS-specific performance criteria. Based on the comparative analysis, the study proposes targeted amendments to the Turkish Grid Code aimed at enhancing system resilience under high renewable penetration levels. Furthermore, field-testing methodologies, model-based validation practices, and emerging digitalized compliance monitoring architectures are investigated to assess their applicability to next-generation power systems. By integrating international best practices with country-specific recommendations, this work contributes to the development of transparent, adaptive, and technically robust grid code compliance frameworks, supporting both academic research and practical grid modernization efforts. Full article

Gas film cooling is a widely adopted technique for the thermal protection of gas turbine blades. However, a trade-off exists between reduced cooling effectiveness and increased aerodynamic losses. The underlying mechanism was investigated through large eddy simulation (LES) with the WALE subgrid-scale model, applied to a flat-plate, single-hole model. The flow characteristics, flow field structures, and aerodynamic loss generation mechanisms of circular, fan-shaped, and laterally expanded holes were systematically examined for blowing ratios ranging from 0.3 to 1.2. The results indicate that all three hole geometries provide adequate film coverage at low blowing ratios. At high blowing ratios, however, the cooling performance is degraded by jet penetration in the circular hole and flow recirculation within the fan-shaped hole. In contrast, the laterally expanded hole demonstrates superior film adhesion stability. In terms of aerodynamic loss, the circular and fan-shaped holes incur higher losses, whereas the laterally expanded hole exhibits the lowest loss under all conditions, which is attributed to its effective tangential momentum dispersion. This study elucidates the influences of orifice geometry and vortex structure evolution on both cooling effectiveness and aerodynamic loss, providing valuable insights for the optimization of gas film cooling design in gas turbines. Full article

Sorting defective cocoons is a critical procedure in the silk reeling industry to ensure the quality of raw silk products. Currently, this process relies heavily on manual inspection, which is labor-intensive, subjective, and inefficient. While automated sorting based on machine vision offers a promising alternative, existing object detection algorithms struggle to balance accuracy and computational complexity, particularly when detecting tiny surface defects or distinguishing morphologically similar cocoons in dense scenarios. To address these challenges, this paper proposes an efficient silkworm cocoon defect detection model named LMD-YOLO, based on the YOLOv10 architecture. In this model, we introduce three key improvements to enhance feature extraction and multi-scale perception. First, we integrate a Large Separable Kernel Attention (LSKA) module into the C2f structure (C2f-LSKA) of the backbone. This design decomposes large kernels to capture global shape features with minimal computational cost, effectively distinguishing double cocoons from normal ones. Second, we replace standard upsampling with a DySample module in the neck, which utilizes dynamic point sampling to recover fine-grained texture details of tiny defects like surface stains. Third, a Multi-Scale Dilated Attention (MSDA) mechanism is embedded before the detection heads to aggregate semantic information across different scales, improving robustness against background interference. YOLOv10 was selected as the baseline due to its NMS-free characteristic, which mitigates the latency caused by post-processing in high-speed sorting tasks. Evaluations on a self-constructed multi-category dataset indicate that LMD-YOLO surpasses established detectors, including YOLOv8n and Faster R-CNN. Relative to the YOLOv10n baseline, our method improves mAP@0.5 by 3.11%, achieving 94.46%. Notably, Precision and Recall are increased by 3.50% and 2.97%, reaching 89.98% and 93.61%, respectively. With a compact size of 2.68 M parameters and an inference speed of 115 FPS, the proposed model offers a practical trade-off between accuracy and latency for real-time cocoon defect detection. Full article

This paper is a focused, standards-aligned survey of where autoregressive (AR) large language models (LLMs) tend to break down when deployed inside industrial informatics workflows that must satisfy long-horizon objectives, hard constraints, traceability, and functional-safety obligations (e.g., IEC 61508/ISO 26262/ISO 21448). Rather than claiming new algorithms or experiments, we synthesize and organize prior work into (i) a control-oriented taxonomy of four AR failure modes that recur in practice (compounding error, myopic objectives, data brittleness/hallucinations, and scaling/latency inefficiencies), (ii) a catalog of standards-compatible deployment patterns that mitigate these issues (human-gated LLM-in-the-loop, retrieval + verification pipelines, planner-of-record architectures, and runtime assurance envelopes), and (iii) an operational decision framework (criteria table with observable proxies, a stepwise decision procedure, and worked examples) for deciding when token-centric mitigations are sufficient versus when state/world-model components become warranted. Joint Embedding Predictive Architectures (JEPA) and Hierarchical JEPA (H-JEPA) JEPA are proposed as representative state-predictive architectures, with discussion explicitly bounded by currently available empirical evidence; we explicitly note that the published evidence base is currently concentrated on vision/multimodal benchmarks and that industrial control validation remains limited. To make evidence boundaries transparent, we introduce (a) a survey method (scope, inclusion/exclusion criteria, and data-extraction fields), (b) a comparison matrix across representative prior systems, and (c) an evidence map that links each deployment pattern to peer-reviewed empirical findings and system reports. Full article

Cloud detection is an important procedure for the processing of remote sensing images. A cloud detection scheme driven by the spectral and the temporal features is presented in this paper, where an unsupervised hierarchy clustering approach is proposed for large scale image segmentation. The potential cloudy pixels are identified by means of the spectral matching, in which the spectral data of the clustering centers are compared to the patterns in the spectral dataset of ground covers. The matched pixels are regarded as cloudless pixels, whose category can be recognized accordingly. In contrast, the bright temperatures corresponding to the unmatched pixels are used to exclude the interference of the occasional hotspots, enabling the final cloud detection result. Landsat 8 and Sentinel-2 satellite data are used in the validation to demonstrate the precision and stability of the proposed scheme for the data at different spatial resolutions. Full article

In the Yingjiang area of western Yunnan, precipitation is high throughout the year, making it one of the regions with the highest annual precipitation in mainland China. Extreme rainfall in this region often triggers severe flooding, yet the key mechanism of water vapor transport underlying abnormally heavy precipitation remains unclear. This study used automatic weather station observations of precipitation, the fifth-generation atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts, and Global Data Assimilation System (GDAS) data to analyze, for the first time, large-scale water vapor transport, precipitation mechanisms, and the primary water vapor sources and their contributions in this region. The results show the following: In the Yingjiang area, the water vapor sources at all height levels in summer are dominated by the southwest monsoon water vapor transport pathways, such as the Bay of Bengal and the Arabian Sea, with their total contributions to specific humidity and water vapor flux exceeding 70%. This indicates that low-latitude sea areas such as the Bay of Bengal and the Arabian Sea serve as key moisture source regions for Yingjiang in the global water vapor cycle. Water vapor transport over the windward slope causes strong low-level convergence and high-level divergence phenomena, and the suction effect leads to strong upward motion near the 850 hPa level. The pseudo-equivalent potential temperature isolines tilt along the mountain slope, maintaining an unstable stratification characterized by warm, humid lower layers and cold, dry upper layers, providing favorable thermal conditions for precipitation. In addition, in the summer of 2020, abnormally high southwest seasonal wind and air transport, combined with strong low-level convergence and high-level divergence of the vertical circulation structure, were key factors causing the abnormally high precipitation. This study provides an important reference for the prediction of extreme precipitation and the early warning of rainstorm disasters in the southwest monsoon region in the context of global climate change. Full article

In the manufacturing of high-reliability components, sinusoidal wobble laser welding has gained preference due to its excellent performance. However, surface defect inspection for such welds is challenged by large variations in defect scales, the coexistence of multiple defects, and scarce samples, which collectively limit existing detection methods. To address these issues, this paper proposes a lightweight detection framework that integrates a generative adversarial network with an improved YOLO architecture. First, a frequency-domain-enhanced StyleGAN2-AFMS model is constructed to effectively augment high-quality defect samples. Second, a YOLO11n-WAFE detector is designed, which incorporates an ADownECA downsampling module to enhance the capability of capturing subtle defects and an Edge-Aware Semantic–Detail Fusion module to improve discriminative robustness under multi-defect conditions. To validate the approach, an industrial-level Sinusoidal Wobble Laser Weld Defect Dataset is built. Experiments reveal that the proposed framework boosts mAP@0.5 to 94.2% (an 8% improvement over the baseline) and mAP@0.5:0.95 to 77.4%, with an F1-score of 89.5%, while maintaining lightweight (2.15 M parameters) and fast (656 FPS) characteristics. This study provides a high-precision and efficient solution for few-shot industrial defect inspection. Full article

Real-time processing demands for massive IoT sensor data necessitate reliance on distributed microservice systems within edge clusters. However, pinpointing the root cause of anomalies within these edge microservice clusters poses a critical challenge for intelligent IoT operation and maintenance. To address the issue, a spatio-temporal graph propagation model ST-GraphRCA is proposed for root cause analysis in IoT edge environments. Our approach begins by resolving the fundamental issue of time-series asynchrony across distributed multi-source metrics. A PCA-DTW hybrid feature extraction method is introduced with a dynamic alignment strategy to mitigate the effects of random network delays and data deformation without requiring prior synchronization. Subsequently, ST-GraphRCA constructs a stream-based forward propagation graph based on the flow conservation principle. By integrating dynamic edge weights with node-level input–output anomaly scores, ST-GraphRCA precisely infers fault propagation pathways and identifies potential root cause candidates through causal reasoning. Finally, a topology-constrained high-utility mining algorithm filters these candidates. Using a constraint matrix, the algorithm filters out unreachable service combinations to locate low-frequency and high-risk root causes. Experimental results indicate that ST-GraphRCA achieves an F1-Score of 0.89, outperforming existing methods. In resource-constrained edge scenarios, its average localization time is merely 238.8 ms, representing a six-fold improvement over key benchmarks. Thus, ST-GraphRCA not only provides an efficient anomaly fault tracing solution for large-scale IoT systems but also offers technical support for the intelligent operation and maintenance of distributed microservice systems. Full article

Aiming at the challenge that traditional signature schemes struggle to simultaneously achieve efficiency, resistance to quantum attacks, and privacy protection, this paper proposes a lattice-based conditional privacy-preserving certificateless aggregate signature method (LCP-CAS). The scheme adopts an unordered aggregation algorithm to compress multiple signatures, in arbitrary order, into a single fixed-length aggregate signature, thereby achieving linear scalability in verification complexity. Its security is based on the hardness of the Ring Short Integer Solution (RSIS) problem, ensuring post-quantum resistance. By incorporating a conditional privacy-preserving mechanism, the scheme realizes device anonymity while supporting identity traceability, thus balancing privacy protection with regulatory requirements. Security analysis shows that the scheme meets the security requirements, including integrity, non-repudiation, conditional privacy preservation, and resistance to collusion attacks. Compared with existing related schemes, LCP-CAS achieves reduces aggregation and verification overhead while maintaining practicality in large-scale settings such as industrial IoT and device monitoring. Full article