Search Results (30,432)

To address the limited constraints of ground-based lidar with few channels in retrieving aerosol microphysical parameters in urban atmospheres, this study developed a method to retrieve aerosol volume size distribution and effective radius from a 355/532/1064 nm triple-wavelength elastic-scattering, single-polarization lidar system. The method uses 3β + 2α optical quantities as input constraints, applies Mie scattering theory as the forward model, parameterizes the volume size distribution with B-spline functions, and achieves stable solutions through Tikhonov regularization and cross-validation. To reduce uncertainties in prior parameters, including the complex refractive index, particle size range, and lidar ratio, an optimization strategy based on parameter search, retrieval reconstruction, and error minimization was introduced. Numerical simulations showed that the method reproduced the main features of a bimodal lognormal aerosol volume size distribution with good feasibility and stability. Two case studies further showed fine-mode dominance and decreasing extinction coefficient, depolarization ratio, and effective radius with height under good air quality conditions, but enhanced coarse-mode contribution and effective radius in the upper cloud-influenced layer under lightly polluted conditions, as inferred from the combined variations in RSCS, extinction coefficient, depolarization ratio, and effective radius. Full article

A new fractional dynamic sliding mode control (FD-SMC) framework is introduced to reduce chattering in the control of fractional-order chaotic systems. In this method, chattering is eliminated by placing a fractional integrator before the system control input. As a result, the augmented system has a higher dimension than the original system, meaning that additional states are introduced. Effective control therefore requires identifying or estimating these new states or the corresponding plant model. To address this issue, a robust optimal fractional loop-transfer-recovery observer (ROF-LTRO) is developed. Furthermore, the key advantage of sliding mode control (SMC)—its invariance to matched uncertainties—is often lost in many plants such as chaotic systems, because many of them contain mismatched uncertainties. To restore and extend the invariance property, multiple sliding surfaces combined with a virtual control input are employed. In addition, the proposed FD-SMC and ROF-LTRO do not rely on prior knowledge of uncertainty bounds, which is beneficial for practical implementation. Then, a two-stage design procedure based on two-surface definition is presented, and simulation results are provided for the extended fractional Duffing–Holmes chaotic system (EF-DHCS) under both matched and mismatched uncertainties. Full article

The high-pressure spherical gas storage tank in a wind tunnel energy storage and gas supply system is a critical pressure-bearing component of the wind tunnel operation system. The linearized stress in its critical control region is a key parameter for structural safety assessment. Therefore, investigating and evaluating the linearized stress and its associated uncertainty in this region is essential for enhancing operational safety. In this study, a three-dimensional finite element model of the spherical tank was developed, and the critical control region was identified through stress linearization. The operating internal pressure, working temperature, and shell wall thickness were treated as random input variables. Based on the stress linearization results, the stability of the critical control location was assessed. For physically homogeneous intervals, a non-intrusive polynomial chaos expansion surrogate model was constructed, and a conditional uncertainty propagation model for the linearized stress was established. Compared with the Monte Carlo and GUM methods, the non-intrusive polynomial chaos expansion method achieves substantially higher computational efficiency while producing consistent evaluation results. The uncertainty analysis shows that the operating internal pressure is the dominant contributor to the uncertainty of the linearized stress, followed by the effective wall thickness of the spherical shell. In contrast, the working temperature has a minor effect, and the interactions among the input variables are weak. Full article

Multi-party private set union (MPSU) allows multiple parties to compute a union without disclosing private inputs, but most existing protocols focus on balanced settings with comparable input sizes. In large-repository update scenarios, a leader maintains a massive base set while contributors submit small update sets; directly using balanced MPSU makes the online cost scale with the leader’s repository size. We propose AegisUnion, an asymmetric MPSU protocol tailored to large-repository updates. AegisUnion separates repository-dependent computation from online update processing through an offline oblivious key-value store (OKVS) encoding phase. In the online phase, contributors perform private membership determination, cross-contributor private deduplication, conditional payload sharing, and secret-shared shuffling, without revealing raw inputs, repository-overlap relations, inter-contributor duplicates, or the source of each output element. Under the semi-honest model, AegisUnion tolerates any coalition of corrupted parties as long as at least one party remains honest, without non-collusion assumptions. Experiments show that, as the repository grows from $2^{14}$ to $2^{18}$, the online time remains stable at 663–715 ms. At repository size $2^{18}$ and contributor update bound $2^{10}$, AegisUnion achieves about $455\times$ and $454\times$ lower online time than symmetric-key-based MPSU and public-key-based MPSU baselines, respectively, and about $271\times$ and $575\times$ lower online communication. Full article

Coronary artery disease (CAD) remains a leading cause of global morbidity and mortality. Accurate diagnosis of ischemia-causing coronary stenoses is essential for guiding revascularization and improving outcomes. Although invasive fractional flow reserve (FFR) remains the gold standard for functional lesion assessment, its use is limited by procedural invasiveness, cost, and complexity. CT-derived FFR (FFRct), based on computational fluid dynamics (CFD), was the first major advance in noninvasive physiological assessment, but its adoption has been hindered by intensive off-site computation and dependence on high-quality imaging. This review summarizes the evolution from invasive FFR to AI-driven functional assessment of coronary lesions. We examine the principles and validation of CFD-based FFRct and then focus on the shift toward artificial intelligence, including both machine learning (ML) and deep learning (DL) approaches. These methods range from models using engineered geometric and plaque features trained on large synthetic datasets to end-to-end systems that learn directly from imaging data. We discuss key validation studies evaluating diagnostic accuracy, prognostic value, and clinical utility, with attention to performance in challenging settings such as intermediate stenoses, heavy calcification, and patients with comorbidities. We also highlight major barriers to widespread adoption, including dependence on input data quality, limited explainability, regulatory hurdles, and integration into clinical workflows. Finally, we outline future directions, including AI-enabled virtual PCI planning, multimodal risk stratification, and broader access to functional cardiac assessment. AI has the potential to transform noninvasive coronary imaging by enabling a single CCTA scan to provide rapid, integrated evaluation of anatomy, plaque characteristics, and physiological significance, supporting more personalized care and better clinical outcomes. Full article

Ensuring legally compliant safety and health documentation remains a significant challenge in construction projects because practitioners often lack expertise in identifying and applying relevant statutory provisions. This study proposes a deep learning-based legislation recommendation system to reduce inconsistencies in statutory citation and improve the legal traceability of safety documentation. The system integrates domain-specific ontologies and context-aware language models to recommend appropriate legal provisions based on user-inputted risk factors and keywords. For empirical validation, the system was applied to the Design for Safety (DfS) report, a representative safety document prepared during the design phase of construction projects. A training dataset comprising 1355 DfS reports and 356 safety legislation articles was used, with semantic relationships enhanced through ontology-based vocabulary expansion and Word2Vec embeddings. KoELECTRA, a Korean pre-trained language model, achieved the best performance, with top-1 accuracy of 58.1%, F1-score of 56.6%, and top-3 accuracy of 71.8%. A web-based application was also developed to support legal referencing during document preparation. The findings demonstrate the system’s potential to assist practitioners in identifying relevant legislation, enhance regulatory compliance, and improve the consistency and quality of construction safety documentation. Full article

Understanding advanced pre-tertiary mathematics, particularly three-dimensional vectors, demands robust spatial reasoning skills that many students find challenging to develop through traditional pedagogical methods. This study proposes and evaluates an innovative educational tool that leverages large vision models to automate the conversion of handwritten vector equations into accurate 3D graphical representations. By interpreting students’ handwritten input using advanced computer vision, the system provides immediate, interactive visual feedback to bridge the cognitive gap between abstract symbolic notation and tangible geometric concepts. We evaluated the system using a dataset of 1000 handwritten vector equations typical of the Singapore-Cambridge GCE ‘A’ Level H2 Mathematics syllabus. Our findings demonstrate that while GPT-4o serves as a capable baseline, achieving 84.6% accuracy with multi-shot prompting, newer variants such as GPT-4.1-mini offer superior performance, reaching 91.4% accuracy with significantly higher computational efficiency. The results confirm that AI-powered visualisation tools can effectively interpret complex spatial mathematical layouts when guided by optimal prompt engineering. Implementing such technology in educational settings presents a viable, scalable, and cost-effective method to democratise learning support, fostering independent study and enhancing students’ conceptual comprehension of spatial mathematics. Full article

Multimodal image fusion is a promising technology designed to fuse information from different medical sensors, which offer structured insights for disease diagnosis and treatment. However, existing 2D-centric fusion methods fail to capture 3D spatial continuity, and conventional wavelet-based approaches lack adaptability to diverse lesion regions and suffer from background artifacts. To address this issue, we propose a 3D multiscale wavelet convolutional neural network for multimodal medical image fusion. Specifically, a 3D Discrete Wavelet Transformation (3D DWT) is introduced to decompose input volumes into multi-frequency bands, isolating anatomical structures and lesion details while reducing 3D spatial redundancy. We embed hierarchical multiple frequency band into a Global and Local Feature Calibration (GLFC) module to adaptively enhance single-modal features by fusing global contextual information and local details. Furthermore, a pyramid group-wise multiscale feature interaction is proposed for capturing complementary features across different spatial scales. Finally, a voxel-wise weighted averaging strategy reconstructs the fused image by adaptively assigning contributions to each modality at every spatial position, effectively eliminating artifacts and improving the visual fidelity of the result. Extensive experiments on the BraTS2020 and Hecktor datasets demonstrate that our proposed method outperforms state-of-the-art (SOTA) fusion methods in both subjective visual quality and objective metrics. Moreover, downstream segmentation validation confirms that fused images from our method significantly improve tumor segmentation accuracy. The source code and pre-trained models will be publicly available. Full article

Support structures in liquid hydrogen tanks act as localized thermal bridges between the ambient temperature outer vessel and the cryogenic inner vessel. However, the difference between support thermal bridge-induced localized heat input and equivalent uniform heat input remains insufficiently clarified, especially regarding their effects on local thermal behavior and support position-dependent thermodynamic response. In this study, a gas–liquid two-phase CFD model was developed for a 37.4 m³ liquid hydrogen tank at a 50% filling ratio. Localized heat flux regions were used to represent support thermal bridges, and an equivalent uniform heat input case with the same total heat input was introduced for comparison. The results show that localized support heat input concentrates the high-temperature region near the support-corresponding wall area and induces stronger local natural convection with a maximum velocity of approximately 0.27 m/s, compared to approximately 0.14 m/s in the uniform heat input case. The uniform heat input case produces a slightly higher overall gas-phase pressure, but it cannot capture the local heat accumulation and flow field reconstruction caused by support thermal bridges. Circumferential support position variation mainly affects the relative position between the localized heat source, gas region, liquid region, and gas–liquid interface. Upper support position variation has a more pronounced influence on local peak temperature and flow intensity than lower support variation. Axial support position variation mainly shifts the local high-temperature and high-velocity regions along the tank length, while its influence on overall pressure response is limited. These results indicate that equivalent uniform heat input can approximate the overall pressurization trend, but localized support heat input boundaries should be retained when local temperature fields, flow structures, and support layout effects are of concern. Full article

Although Industry 4.0 has successfully advanced lean manufacturing through digitalization and automation, its primary focus on operational efficiency leaves emerging strategic priorities—human-centricity, sustainability, and resilience—outside its original scope. The Industry 5.0 agenda explicitly elevates these three pillars, creating new potential to drive lean transformation. However, how Industry 5.0 can systematically drive lean manufacturing transformation remains unclear. To address this knowledge gap, this study develops a strategic roadmap. First, a content-centric literature review identifies 12 key enablers for Industry 5.0-driven lean manufacturing. Second, Fuzzy Interpretive Structural Modeling (FISM) and expert opinions determine hierarchical relationships among the enablers and construct a multi-level structural model. Third, Matrices d’Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis evaluates the driving power and dependence of each enabler. Finally, a strategic roadmap is developed based on expert synthesis. The findings reveal that “government regulation and incentives” and “employee skill training” are the most critical enablers, while “value chain design and improvement” and “resource input and return” are the most complex and difficult to develop. The roadmap highlights the mediating role of “stakeholder participation and collaboration.” Importantly, the roadmap addresses potential tensions in lean implementation—such as the carbon footprint trade-off of frequent small-batch transport—by embedding sustainability assessment into value chain design and technology governance. This study offers a practical guide for manufacturers to prioritize investments and sequence actions toward lean transformation in the Industry 5.0 era. The main contribution of this study is a strategic roadmap that explains how Industry 5.0 can enable lean manufacturing transformation through prioritized actions and hierarchical enablers, while reconciling efficiency with sustainability and resilience goals. This roadmap offers a practical guide for manufacturers and policymakers to sequence investments and actions toward lean transformation in the Industry 5.0 era. Full article

Accurate landslide identification is crucial for enhancing emergency response capabilities during destructive geological hazards. Although deep-learning-based semantic segmentation has demonstrated effectiveness, substantial variations in landslide scales and environmental similarities continue to challenge existing methods. This paper systematically constructs a new co-seismic landslide dataset for the Yarlung Zangbo River basin based on the 2017 Nyingchi earthquake, effectively filling a critical regional data gap. This paper proposes CETransUNet (coordinate attention and edge-guided attention transformer UNet), a novel landslide detection model that integrates ResNet and Transformer architectures. Specifically, a coordinate attention (CA) module is introduced within the skip connections between the encoder and decoder. This module encodes positional information along both horizontal and vertical spatial directions and dynamically re-weights the feature maps, thereby effectively suppressing background noise caused by semantic gaps and enhancing the model’s ability to localize landslide regions. Additionally, an edge-guided attention (EGA) module is incorporated into the decoder. This module extracts explicit edge priors from the input image using a Laplacian operator and imposes geometric constraints on the predictions via a boundary reverse attention mechanism, thereby significantly alleviating boundary ambiguity and morphological distortion of landslides. Evaluations across datasets from the Yarlung Zangbo River, Iburi-Tobu, and Bijie regions demonstrate that CETransUNet significantly outperforms state-of-the-art models—including TransUNet, SegFormer, and SwinUNet—in terms of IoU, MIoU, and F1-score. Overall, through the synergistic optimization of the coordinate attention and edge-guided attention modules, the CETransUNet model achieves synchronous enhancement of boundary integrity and geometric precision in complex scenarios, providing a reliable technical solution for large-scale intelligent landslide identification. Full article

Motoneurons are under strong pressure to maintain stable motor output throughout an individual life, through homeostatic regulation of their electrical properties. Dysregulated spinal motoneuron excitability has long been implicated in the pathogenesis of amyotrophic lateral sclerosis (ALS). Recent work in SOD1^G93A mice suggests that the homeostatic response of motoneurons becomes dysregulated as cellular processes are disrupted by the disease, causing fluctuations in motoneuron electrical properties. Yet, few studies directly test whether ALS motoneurons respond differently than wild-type motoneurons to a common chronic perturbation. Here, we used in vivo electrophysiology to test whether motoneurons from pre-symptomatic SOD1^G93A mice modulate excitability differently than wild-type motoneurons in response to the same homeostatic perturbation: chronic inhibition exerted by the benzodiazepine diazepam. Using linear mixed-effects statistical models, we assessed whether diazepam treatment differentially modulated passive properties, firing behavior, spike properties, and/or synaptic inputs in SOD1^G93A versus wild-type motoneurons. We identified a significant genotype × treatment interaction effect selectively for properties related to passive membrane integration and spike initiation, including membrane time constant, peak input resistance, and recruitment current. In contrast, firing gain, spike waveform characteristics, and synaptic inputs were largely unaffected. These findings indicate that sustained inhibitory perturbation selectively triggered overactive intrinsic compensatory mechanisms in SOD1^G93A motoneurons rather than inducing widespread changes in firing or synaptic transmission. Together, our results provide direct evidence for over-active homeostatic control of motoneuron excitability and support a view of motoneuron dysfunction in ALS as a problem of altered feedback regulation rather than simply hyper- or hypo-excitability. Full article

The M_w7.7 Pazarcık–Kahramanmaraş and M_w7.5 Elbistan–Kahramanmaraş earthquakes on 6 February 2023 caused the collapse of 11 buildings in Adana’s city centre—predominantly 15-storey RC structures in a narrow zone—despite peak ground accelerations of only 0.05 g; most collapses occurred during the M_w7.7 event. Two-dimensional seismic site response analyses at the site of interest with bedrock input from station TK0118 yielded topographic amplification factors of 2.37 (EW) and 2.09 (NS) for homogeneous conditions; with stratigraphic heterogeneity, NS increased to 2.66 and EW remained at 2.27, reaching above 3.0 at the slope crest. Spectral amplification factors reached 4.53 (NS, T = 0.90 s) and 3.21 (EW, T = 0.68 s), indicating amplification in the short-to-intermediate period range. These amplified records were applied to idealised 15-storey RC models—from code-compliant to deliberately deficient—with C16 and C8 concrete classes through nonlinear performance analyses. Under unamplified TK0118 records, no model reached collapse-level damage. Under amplified records, only the most deficient model exhibited widespread shear and strain failures in the lower storeys. A detected velocity pulse (T_p = 13.496 s) was excluded as a collapse mechanism, as its period far exceeds structural periods (1.2–1.9 s). The collapses are attributable to the compounding of topographic and stratigraphic amplification with pre-existing structural deficiencies. Full article

To solve the low-precision prediction problem of noisy non-stationary goaf subsidence sequences, this study aims to establish a high-accuracy hybrid prediction model for mining surface deformation monitoring. The Global Navigation Satellite System (GNSS) monitoring data of surface subsidence in goaf areas exhibits non-stationary and noisy characteristics, which limits the accuracy of traditional prediction models. In this paper, a hybrid prediction model, namely the Improved Radial Movement Optimization–Variational Mode Decomposition–Gated Recurrent Unit (IRMO-VMD-GRU) model, is proposed. The IRMO algorithm is employed to globally optimize the key parameters of VMD, achieving adaptive and stable decomposition of the settlement sequences. The obtained Intrinsic Mode Function (IMF) sub-sequences are input into the GRU network for independent training and prediction, followed by superposition and reconstruction. The model is validated using the GNSS monitoring data from three monitoring points at a coal mine in Shaanxi Province, China. The results show that the proposed model outperforms the comparison models in all four evaluation indicators, namely Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R²), with all R² values exceeding 0.8. The model demonstrates superior fitting performance, correlation, and generalization ability, which provides important practical technical support for goaf subsidence early warning, geological disaster prevention and engineering safety management in mining areas. Full article

This study proposes a novel and improved numerical approximation of the simulation of Gaussian process autoregressive models. As a Bayesian nonparametric regression method, Gaussian process models offer the unique advantage of providing closed-form uncertainty quantification. When Gaussian process models are used for autoregressive models, the validation procedure requires the model’s simulation or multi-step-ahead prediction. However, simulating dynamical Gaussian process models is complex due to the intractable propagation of uncertain inputs through the nonlinear model. Numerical approximation, namely Monte Carlo simulation, is one of the most frequent options for simulating dynamical models based on Gaussian processes. The computational burden of Monte Carlo simulation algorithms increases cubically with data size, representing a challenge. This paper introduces a unified simulation framework invariant to sparse and variational approximations to obtain a static sample from the pseudo-point posterior. Furthermore, we propose an innovative method for simulating Gaussian process dynamical models. A single parameter is proposed to regulate the trade-off between computational complexity and algorithmic accuracy. This innovation demonstrates the potential to replace the conditionally independent Monte Carlo method with no additional computational burden, thereby enhancing estimates of latent responses. The proposed simulation method is demonstrated using two synthetic examples and a realistic case study. Full article