Search Results (1,191)

To address the insufficient bearing capacity and severe deformation of narrow coal pillars in deep gob-side entries under the influence of residual dynamic loading and hydraulic punching of the coal mass, this study investigates the plastic-damage evolution mechanism of narrow pillars and proposes a novel “grip-anchoring (GA)” collaborative support system. A physical model testing system for narrow coal pillars reinforced by double-pull cable bolts was established based on similarity theory, and six support schemes were designed for comparative experiments. Digital image correlation was employed to analyze the displacement field and the evolution of plastic failure, and an industrial-scale field test was carried out to verify the reliability of the proposed support technology. The results indicate that the double-pull cable bolts, through a “dual-tensioning and synergistic locking” procedure, can effectively solve the support challenges of narrow coal pillars under asynchronous excavation. The dense double-row double-pull cable-bolt scheme maintained overall structural stability even under a 2.5p overload, with only localized damage occurring at the roof- and floor-corner zones of the pillar. This scheme exhibited the smallest deformation and the highest peak load among all tested configurations, demonstrating its significant advantage in enhancing structural stability. Full article

Background/objectives. Emerging viral diseases represent an increasing threat to global health security, driven by environmental change, globalization, and intensified human–animal–environment interactions. The COVID-19 pandemic exposed critical weaknesses in preparedness systems but also demonstrated the transformative potential of recombinant vaccine technologies, which enable rapid, scalable, and safe responses to novel pathogens. We aim to examine the role of recombinant vaccine platforms in the management of emerging viral diseases, emphasizing their contribution to health system preparedness and exploring strategies for their integration into preparedness frameworks. Methods. We synthesized the current evidence on recombinant vaccine platforms (viral vector, protein subunit, DNA, and mRNA) through a targeted review of the scientific literature, regulatory documents, and global health policy reports. Drawing from experiences like COVID-19 (mRNA vaccines) and Ebola (rVSV-ZEBOV), we analyzed the advantages, challenges, and lessons from initiatives such as the CEPI, BARDA, HERA, and WHO frameworks. Results. Recombinant vaccine platforms offer significant advantages for epidemic preparedness through rapid adaptability, standardized production, and strong safety profiles. Nonetheless, challenges remain in manufacturing scalability, cold-chain logistics, regulatory harmonization, and equitable global access. Global initiatives such as the CEPI, WHO-led programs, BARDA, and regional manufacturing networks exemplify this collaborative approach, while regulatory mechanisms have proven to be essential to timely vaccine deployment. Conclusions. Recombinant vaccines have redefined preparedness by coupling scientific innovation with operational agility. Strengthening global coordination, regional production capacity, and public trust is essential to ensure that technological progress translates into equitable and effective public health impacts. Full article

Multi-robot systems (MRSs) offer distinct advantages in large-scale exploration but require tight coupling between decentralized decision-making and collaborative estimation. This survey reviews learning-based multi-robot Active Collaborative Simultaneous Localization and Mapping (AC-SLAM), modeling it as a coupled system comprising a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) decision layer and a distributed factor-graph estimation layer. By synthesizing these components into a conceptual framework, recent methods for cooperative perception, mapping, and policy learning are systematically critiqued. The analysis concludes that Hierarchical Reinforcement Learning (HRL) and graph-based spatial abstraction currently offer superior scalability and robustness compared to monolithic end-to-end approaches. Furthermore, a comprehensive analysis of Sim-to-Real transfer strategies is provided, ranging from domain randomization to emerging Real-to-Sim techniques based on NeRF and 3D Gaussian Splatting. Finally, future directions are outlined, moving from geometric mapping toward LLM-driven active semantic understanding and dynamic digital twins to bridge the reality gap. Full article

To address the power and energy balancing challenges faced by high-penetration renewable energy systems under long-term intermittent output conditions, this study proposes a multi-source, multi-timescale collaborative dispatch strategy (2MT-S) integrating wind, solar, hydro, thermal, and hydrogen energy resources. First, a long-term-to-day-ahead coupled scheduling framework is established based on intermittent output duration forecasts (3-day/10-day). By integrating seasonal hydrogen storage and pumped-storage hydroelectric plants, this framework achieves comprehensive coordination among electrochemical storage, thermal power, and other flexible resources. Second, a multi-time-horizon optimization model is developed to simultaneously minimize system operating costs and load curtailment costs. This model dynamically adjusts day-ahead scheduling boundary conditions based on long-term and short-term scheduling results, enabling cross-period resource complementarity during wind and photovoltaic generation troughs. Finally, comparative analysis on an enhanced IEEE 30-bus system demonstrates that compared to traditional day-ahead scheduling, this strategy significantly reduces renewable energy curtailment rates and load curtailment volumes during sustained low-generation periods, fully validating its significant advantages in enhancing power supply reliability and economic benefits. Full article

Hyperspectral reconstruction (SR) refers to the computational process of generating high-dimensional hyperspectral images (HSI) from low-dimensional observations. However, the superior performance of most supervised learning-based reconstruction algorithms is predicated on the availability of fully labeled three-dimensional data. In practice, this requirement demands complex optical paths with dual high-precision registrations and stringent calibration. To address this gap, we extend the fully supervised paradigm to a semi-supervised setting and propose SSHSR, a semi-supervised SR method for scenarios with limited spectral annotations. The core idea is to leverage spectrally aware mini-patches (SA-MP) as guidance and form region-level supervision from averaged spectra, so it can learn high-quality reconstruction without dense pixel-wise labels over the entire image. To improve reconstruction accuracy, we replace the conventional fixed-form Tikhonov physical layer with an optimizable version, which is then jointly trained with the deep network in an end-to-end manner. This enables the collaborative optimization of physical constraints and data-driven learning, thereby explicitly introducing learnable physical priors into the network. We also adopt a reconstruction network that combines spectral attention with spatial attention to strengthen spectral–spatial feature fusion and recover fine spectral details. Experimental results demonstrate that SSHSR outperforms existing state-of-the-art (SOTA) methods on several publicly available benchmark datasets, as well as on remote sensing and real-world scene data. On the GDFC remote sensing dataset, our method yields a 6.8% gain in PSNR and a 22.1% reduction in SAM. Furthermore, on our self-collected real-world scene dataset, our SSHSR achieves a 6.0% improvement in PSNR and a 11.9% decrease in SAM, confirming its effectiveness under practical conditions. Additionally, the model has only 1.59 M parameters, which makes it more lightweight than MST++ (1.62 M). This reduction in parameters lowers the deployment threshold while maintaining performance advantages, demonstrating its feasibility and practical value for real-world applications. Full article

With the improvement of computing power in terminal devices and their widespread application in emerging technology fields, ensuring secure access to terminals has become an important challenge in the current network environment. Traditional security authentication and trust evaluation methods have many shortcomings in dealing with dynamic and complex network environments, such as limited ability to respond to new threats and inability to adjust evaluation strategies in real time. In response to these issues, this article proposes a dynamic weighted authentication trust evaluation method driven by multimodal data under zero trust architecture. The method introduces user operation risk values and time coefficients, which can dynamically reflect the behavior changes of users and devices in different times and environments, achieving more flexible and accurate trust evaluation. In order to further improve the accuracy of the evaluation, this article also uses the dynamic entropy weight method to calculate the weights of the evaluation indicators. By coupling with the evaluation values, the terminal access security authentication trust score is obtained, and the current authentication trust level is determined to ensure the overall balance of the trust evaluation results. The experimental results show that compared with traditional evaluation algorithms based on information entropy and collaborative reputation, the average error of the method proposed in this study has been reduced by 87.5% and 75%, respectively. It has significant advantages in dealing with complex network attacks, reducing security vulnerabilities, and improving system adaptability. Full article

In the proper management of construction, the use of BIM software allows for tracking of the entire lifecycle of buildings, enabling informed design and better management of the product lifecycle, easier collaboration among professionals, and a more efficient system. The combination of BIM tools and today’s information technologies allows the advantages of the BIM methodology to be amplified and expanded. In particular, creating a 3D model with BIM software and linking it to a data stream from sensors allows us to obtain a key component for a Digital Twin of a construction. By integrating BIM methodologies and Digital Twins, this manuscript describes the development of a Digital Twin prototype of a highway bridge, with a 3D model of the structure reproduced using BIM software serving as the core of the Digital Twin. To complete the Digital Twin architecture, an ADXL345 accelerometer sensor, a DTH22 humidity and temperature sensor, an ESP32 microcontroller, the Postgres database, Python (for communication between the backend and the frontend), and the JavaScript library CesiumJS were employed. This methodology produced a Digital Twin prototype capable of collecting vibration and temperature data from the previously mentioned sensors and displaying values through a graphical interface. It can be observed how this technology represents an expansion of the capabilities of BIM software, also highlighting the maintenance potential throughout the product lifecycle. Moreover, the technologies used make the methodology scalable, allowing additional BIMs to be added or the methodology to be applied in different contexts. Full article

With the rapid development of the low-altitude economy, the deployment of unmanned aircraft vehicles (UAVs) in many fields is increasing continuously, and the demand for collaborative flights is also growing. However, the issue of flight safety in complex airspace remains a pressing concern. Precise flight path prediction, collision detection, and avoidance are paramount for secure collaborative operations. This study proposes an integrated framework that combines an EKF-LSTM model for trajectory prediction, a Trajectory Dispersion Cone (TDC) method for probabilistic collision risk assessment, and a Velocity Obstacle-Model Predictive Control (VO-MPC) strategy for dynamic collision avoidance. Experimental results demonstrate the advantages of our approach: the EKF-LSTM model reduces prediction errors in complex flight states. Furthermore, the VO-MPC method achieves a 99.8% collision avoidance success rate under low-noise conditions—an 8.6% improvement over traditional MPC—while reducing the average collision probability by 66.7%. It also maintains stable performance under medium- and high-noise conditions, reducing the collision probability to only 27.7% and 34.2% of that of conventional MPC, respectively. The proposed framework offers a solution for safe manned–unmanned collaboration in complex environments. Future work will extend these methods to multi-aircraft cooperative scenarios. Full article

Digital elevation models (DEMs) play a critical role in geospatial analysis and surface modeling. However, due to differences in data collection payload, data processing methodology, and data reference baseline, DEMs acquired from various sources often exhibit systematic spatial offsets. This limitation substantially constrains their accuracy and reliability in multi-source joint analysis and fusion applications. Traditional registration methods such as the Least-Z Difference (LZD) method are sensitive to gross errors, while multimodal registration approaches overlook the importance of elevation information. To address these challenges, this paper proposes a DEM registration method based on physically consistent rendering and multimodal image matching. The approach converts DEMs into image data through irradiance-based models and parallax geometric models. Feature point pairs are extracted using template-based matching techniques and further refined through elevation consistency analysis. Reliable correspondences are selected by jointly considering elevation error distributions and geometric consistency constraints, enabling robust affine transformation estimation and elevation bias correction. The experimental results demonstrate that in typical terrains such as urban areas, glaciers, and plains, the proposed method outperforms classical DEM registration algorithms and state-of-the-art remote sensing image registration algorithms. The results indicate clear advantages in registration accuracy, robustness, and adaptability to diverse terrain conditions, highlighting the potential of the proposed framework as a universal DEM collaborative registration solution. Full article

Parenteral nutrition (PN) is essential for patients who are unable to tolerate oral or enteral feeding, providing them with necessary nutrients intravenously, including dextrose, amino acids, electrolytes, vitamins, trace elements, and lipid emulsions. Clinical pharmacists (CPs) play a critical role in PN management by ensuring proper formulation, monitoring therapy, preventing complications, and optimizing patient outcomes. In Saudi Arabia, limited literature exists on CPs’ involvement in total parenteral nutrition (TPN) administration, health information management (HIM) systems, and pharmacist staffing ratios. This paper examines the evolving role of CPs in PN management, addressing key challenges such as the optimal patient-to-CP ratio, the impact of HIM systems on PN prescribing, and the advantages and limitations of centralized versus decentralized PN prescription models. It highlights the need for standardized staffing levels, structured pharmacist training, and improved HIM integration to enhance workflow efficiency and prescribing accuracy. Additionally, the study examines how the adoption of advanced HIM systems can streamline documentation, reduce prescribing errors, and enhance interdisciplinary collaboration. This paper provides a framework for optimizing PN delivery, enhancing healthcare quality, and strengthening CPs’ contributions to nutrition support by addressing these factors. Implementing these recommendations will improve patient outcomes and establish a more efficient PN management system in Saudi Arabia, reinforcing the vital role of CPs in multidisciplinary care. Full article

Background/Objectives: Postmortem microbiology has traditionally been regarded with caution in forensic practice due to concerns related to contamination, bacterial translocation, and postmortem microbial overgrowth. As a result, microbiological findings obtained after death have often been considered unreliable or of limited diagnostic value. However, growing evidence indicates that, when appropriately interpreted and integrated with autopsy findings, histopathology, and circumstantial information, postmortem microbiology can provide crucial support for cause-of-death determination. This narrative review critically examines the current role of postmortem microbiology in forensic diagnostics, with a focus on its diagnostic applications, interpretative challenges, and future perspectives. Methods/Results: The transition from conventional culture-based techniques to molecular approaches—including polymerase chain reaction, microbiome analysis, and metagenomic methods—is discussed, highlighting both their potential advantages and inherent limitations within the forensic setting. Particular attention is devoted to key interpretative issues such as postmortem interval, sampling strategies, contamination, and bacterial translocation. In addition to cause-of-death attribution, emerging applications—including postmortem interval estimation, trace evidence analysis, and artificial intelligence–based models—are reviewed. Although these approaches show promising research potential, their routine forensic applicability remains limited by methodological heterogeneity, lack of standardization, and interpretative complexity. Conclusions: In conclusion, postmortem microbiology represents a valuable diagnostic tool when applied within a multidisciplinary forensic framework. Its effective use requires cautious interpretation and integration with pathological and contextual evidence, avoiding standalone or automated conclusions. Future progress will depend on standardized methodologies, multidisciplinary collaboration, and a clear distinction between experimental research and routine forensic practice. Full article

This paper proposes a resource optimized cascaded quantum surface repetition code architecture integrated with a Union Find (UF) enhanced hybrid decoder, which suppresses biased noise and improves the scalability of quantum error correction through synergistic inner outer quantum code collaboration. The hybrid architecture employs inner quantum repetition codes for local error suppression and outer rotated quantum surface codes for topological robustness, reducing auxiliary quantum qubits by 12.5% via shared stabilizers and compact lattice embedding. An optimized UF decoder employing path compression and adaptive cluster merging achieves near-linear time complexity $\mathcal{O}\left(n\alpha \right(n\left)\right)$, outperforming minimum-weight perfect matching (MWPM) decoders $\mathcal{O}\left(n^{2.5}\right)$. Under Z-biased noise $\eta =10$, simulations demonstrate a 28.2% error threshold, 2.6% higher than standard quantum surface codes, and 15% lower logical error rates via dynamic boundary expansion. At code distance $d=7$, resource savings reach 9.3% with maximum relative error below 8.5%, fulfilling fault-tolerance criteria. The UF decoder exhibits 38% threshold advantage over MWPM at low bias $\eta \approx {10}^{-3}$ and 15% less degradation at high noise $p=0.5$, enabling scalable real-time decoding. This framework bridges theoretical thresholds with practical resource constraints, offering a noise-adaptive QEC solution for near-term quantum devices including photonic quantum systems referenced in the paper’s background on repetition cat qubits. Full article

This study analyzes artificial intelligence development and green economic efficiency across 31 Chinese provinces using 2019–2021 panel data. We apply the entropy weight TOPSIS method to measure AI development levels. The entropy weight TOPSIS method measures AI development levels, the DEA-BCC model assesses green economic efficiency, and their coordination types are identified. Findings reveal a significant negative correlation between AI development and green economic efficiency. We explain this complex relationship through three mechanisms: short-term polarization effects, technology conversion lags, and spatial spillovers. Spatial analysis shows AI development forms high-high agglomerations in the Yangtze River Delta and Shandong. Green economic efficiency shows high-high clustering in the Beijing-Tianjin-Hebei region and selected western provinces. Using a “two-system” coupling framework, we identify four provincial categories. The “double-high” type should function as growth poles. The “high-low” type requires improved technology conversion efficiency. The “low-high” type can leverage ecological advantages. The “double-low” type needs enhanced factor inputs. We propose three targeted policy recommendations: establishing digital-green synergy platforms, implementing inter-provincial AI resource collaboration mechanisms, and developing locally adapted action plans. Full article

This paper introduces flourishing circularity as a transformative approach to resource assessment that transcends both traditional Resource-Based View (RBV) theory and conventional circular economy concepts. We demonstrate RBV’s fundamental limitations in addressing the polycrisis of breached planetary boundaries and social inequities. Similarly, while the circular economy focuses on resource reuse and recycling, it often merely delays environmental degradation rather than reversing it. Flourishing circularity addresses these shortcomings by reconceptualizing natural and social capital not as externalities but as foundational sources of all value creation. We develop a comprehensive framework for assessing resources within an open systems perspective, where competitive advantage increasingly derives from a firm’s ability to regenerate the systems upon which all business depends. The paper introduces novel assessment tools that capture the dynamic interplay between organizational activities and coevolving social and ecological systems. We outline the core competencies required for flourishing circularity: regenerative approaches to social and natural capital, and systems thinking with cross-boundary collaboration capabilities. These competencies translate into competitive advantage as stakeholders increasingly favor organizations that enhance system health. The framework provides practical guidance for transforming resource assessment from extraction to regeneration, enabling business models that create value through system enhancement rather than depletion. Full article

The photovoltaic field has seen significant development in recent years, with continuously expanding installation capacity and increasing grid integration. However, due to the intermittency of solar energy and meteorological variability, PV output power poses serious challenges to grid security and dispatch reliability. Traditional forecasting methods largely rely on modeling historical power and meteorological data, often neglecting the consideration of cloud movement, which constrains further improvement in prediction accuracy. To enhance prediction accuracy and model interpretability, this paper proposes a dual-branch attention-based PV power prediction model that integrates physical features from ground-based cloud images. Regarding input features, a cloud segmentation model is constructed based on the vision foundation model DINO encoder and an improved U-Net decoder to obtain cloud cover information. Based on deep feature point detection and an attention matching mechanism, cloud motion vectors are calculated to extract cloud motion speed and direction features. For feature processing, feature attention and temporal attention mechanisms are introduced, enabling the model to learn key meteorological factors and critical historical time steps. Structurally, a parallel architecture consisting of a linear branch and a nonlinear branch is adopted. A context-aware fusion module adaptively combines the prediction results from both branches, achieving collaborative modeling of linear trends and nonlinear fluctuations. Comparative experiments were conducted using two years of engineering data. Experimental results demonstrate that the proposed model outperforms the benchmarks across multiple metrics, validating the predictive advantages of the dual-branch structure that integrates physical features under complex weather conditions. Full article