Search Results (39,040)

Asymmetric heat transfer is an intrinsic and defining feature of phase change material (PCM) systems, arising from the nonlinear coupling between conduction, buoyancy-driven convection, interfacial motion, and geometric or operational non-uniformities. This review synthesizes the physical, numerical, and application-specific mechanisms through which asymmetry emerges and shapes the thermal behavior of PCM-based energy systems. We first examine the fundamental origins of asymmetry, highlighting how natural convection, material heterogeneity, and spatially uneven boundary conditions distort temperature fields and melt front evolution even in nominally symmetric enclosures. We then provide a comprehensive assessment of state-of-the-art modeling approaches—including full-domain CFD, advanced interface tracking methods, stability and bifurcation analysis, and reduced-order modeling—emphasizing their capacity to resolve asymmetric flow structures and capture the complex dynamics governing phase transition. Experimental observations from optical, infrared, and flow visualization techniques further validate the prevalence of asymmetric patterns and underscore the need for high-resolution multi-field datasets. Building upon these foundations, the review analyzes the implications of asymmetry across key energy applications such as thermal energy storage, building envelopes, solar receivers, electronics cooling, transportation systems, and industrial heat exchangers. We also provide a literature review on the importance of using multicriteria evaluation as a key tool in the design of multidimensional symmetric and asymmetric PCM systems. In this context, we address and analyze the performance criteria, evaluation metrics, case studies, and optimization strategies to be considered in the design of these systems. Finally, we identify critical research gaps—including multiphysics coupling, uncertainty quantification, CFD–machine learning integration, and the exploration of emerging asymmetric applications—and outline pathways toward next-generation PCM-based technologies that not only accommodate asymmetry but strategically exploit it for enhanced thermal performance. Full article

This study presents an integrative review of the methods used to estimate and monitor suspended sediment concentration (SSC), an important parameter for understanding erosive processes, river dynamics, reservoir lifespan, and water quality. The search in the Scopus, Web of Science, and SciELO databases covered studies published between 1992 and 2025. Of the 452 records identified, 269 were considered eligible after removing duplicates. The studies were grouped into traditional methods, optical, acoustic, remote sensing, optical–acoustic integrations, uncommon methods, and parallel measurements. Traditional methods predominated (95 studies), followed by remote sensing (84), optical (40), and acoustic (17). Traditional methods are reliable but require high logistical effort and have low temporal resolution. Optical sensors allow continuous monitoring, although they require local calibration. Acoustic methods better represent the vertical and transversal distribution of SSC but are sensitive to particle characteristics. Remote sensing expands spatial and temporal coverage but depends on image resolution and atmospheric conditions. Therefore, it is concluded that no method is universally superior, and the choice should consider the objective, environment, scale, and acceptable uncertainty. Traditional methods remain relevant as they serve as a reference for the others. Full article

River health assessment in arid seasonal rivers is challenging because strong hydrological seasonality, spatial heterogeneity, and intensive human intervention can produce substantial differences among river reaches. Conventional basin-scale assessments may obscure localized ecological and management problems when indicators with different spatial characteristics are evaluated using a uniform spatial unit. This study developed a spatially differentiated river health assessment approach for the Yarkant River Basin, an arid seasonal river basin in northwestern China. River health was considered an integrated condition encompassing basin structure, water conditions, aquatic biota, and socio-economic service functions. A multi-dimensional indicator system comprising 12 indicators across four criteria was established. The main methodological feature is that indicators were evaluated using spatial units consistent with their physical meanings, monitoring characteristics, and available data. Reach-based indicators were calculated separately for the upper, middle, and lower reaches, whereas ecological-flow and water-quality-related indicators were evaluated using hydrological control sections and water function zones, respectively, and then linked to the corresponding reaches for aggregation. Fish retention, public satisfaction, water supply reliability, and drinking-water-source compliance were also calculated separately for the three reaches using reach-specific data. A composite weighting method integrating a guideline-based least-squares weighting component and an entropy-based objective weighting component was used to aggregate the indicator scores. The Yarkant River obtained an overall River Health Index (RHI) of 84.24, corresponding to the “healthy” category under the adopted classification scheme. The upper, middle, and lower reaches scored 83.23, 87.03, and 83.32, respectively. However, the Biota criterion scored only 68.00, substantially lower than the Water (88.47) and Socio-economic service (90.41) criteria. This contrast indicates that the composite RHI should not be interpreted as evidence of uniformly good ecological integrity, particularly because the biological assessment is represented by a single fish-based indicator. The results highlight the value of retaining indicator-specific spatial information and interpreting the composite RHI together with its individual ecological and functional dimensions. Full article

Background/Objectives: Dry eye disease (DED) affects daily visual functioning and vision-related quality of life in addition to tear film stability and ocular surface integrity. In a previously published prospective cohort, lyophilized amniotic membrane (AM) eye drops were associated with significant improvements in objective ocular surface parameters. This secondary analysis evaluated whether treatment with lyophilized AM eye drops was accompanied by changes in patient-reported outcomes, specifically vision-related quality of life and dry eye symptoms. Methods: This secondary analysis included 40 consecutive patients with DED treated with lyophilized AM eye drops in repeated 14-day cycles over six study visits, while continuing their pre-existing standard DED therapy. Patient-reported outcomes were assessed at baseline and at the final visit using the National Eye Institute Visual Function Questionnaire-25 (NEI VFQ-25) and the Ocular Surface Disease Index-6 (OSDI-6). Item-level response distributions were compared between baseline and the final visit using non-parametric tests for paired ordinal data. A two-sided p value < 0.05 was considered statistically significant. Results: Global NEI VFQ-25 items, including general health, self-rated vision, and worry about eyesight, did not change significantly. In contrast, significant improvements were observed in ocular pain/discomfort (p = 0.002); near-vision activities, including reading ordinary print (p = 0.003), work or hobbies requiring near vision (p = 0.014); and finding objects on a crowded shelf or in a drawer (p = 0.008), as well as distance-related and spatial tasks, including reading street signs (p < 0.001) and noticing objects off to the side while walking (p = 0.037). Improvements were also observed in vision-related social functioning and dependence-related items. Five of six OSDI-6 items improved significantly: light sensitivity (p < 0.001), blurred vision (p = 0.008), difficulty with driving or night driving (p = 0.002), difficulty watching television (p = 0.008), and discomfort in windy conditions (p = 0.046). Discomfort in low-humidity environments showed a trend toward improvement but did not reach statistical significance (p = 0.059). Conclusions: In this secondary analysis of a prospective cohort, lyophilized AM eye drops were associated with significant improvements in several patient-reported domains of vision-related quality of life and dry eye symptoms. These findings complement previously reported objective improvements in tear film stability, ocular surface staining, meibomian gland function, and corneal sensitivity, suggesting that treatment with lyophilized AM eye drops was associated with improvements extending beyond ocular surface signs to patient-reported daily visual function and symptom burden in patients with DED. Full article

In the process of rural revitalization, former revolutionary base areas face deep-seated contradictions between their historical contributions and their current development. This study uses 307 village-level administrative units in Huining County, Gansu Province. Based on multi-source spatial datasets from five time periods spanning 1970–2025, it constructs a rural value assessment system across five dimensions—rural industry, ecology, society, culture, and governance. It comprehensively employs methods such as the entropy weighting method, global spatial autocorrelation, hot spot analysis, and landscape pattern indices to reveal the spatiotemporal evolution characteristics of rural value. The results indicate that while overall value has continued to rise, growth patterns across different dimensions have diverged significantly; the Global Moran’s I exhibits a nonlinear trajectory of “decline—rebound—fluctuations at a low level,” corresponding to the three-stage evolution of settlements—namely, “integration—expansion—contraction”—revealing a phased disconnect between top-down spatial planning and bottom-up rural industrial development; spatial differentiation of cultural resources plays a decisive role in rural value, and the revitalization of revolutionary base areas requires a shift from sustained external investment to culture-driven industrialization. This study provides an operational framework—from value assessment to spatial planning—for differentiated rural revitalization strategies in revolutionary base areas. Full article

Basaltic geological environments represent spatially heterogeneous regional contexts encompassing diverse mineralogical, hydrological, and geochemical characteristics. This nationwide exploratory ecological study examined whether basalt area ratio (BAR) was associated with dry-eye medication prescription volume across the 47 prefectures of Japan. The outcome was the annual number of bottles dispensed per 1000 population for three dry-eye medications in 2022. BAR was calculated using geographic information system data. Associations were evaluated using LOESS, covariate-adjusted restricted cubic splines, univariable and multiple linear regression, and sensitivity analyses using alternative denominators. Neither the overall spline association nor its nonlinear component was statistically significant. BAR was also not significantly associated with medication prescription volume in either the univariable or multiple linear regression analysis. Although the smoothing curves showed descriptive variation across the observed BAR range, the apparent nonlinear pattern was not statistically supported or consistently reproduced across analytical methods. Thus, the available prefecture-level data provided no statistical evidence of an association between basalt coverage and dry-eye medication utilisation. BAR should be interpreted as an indicator of regional geological context rather than as a measure of natural H₂ production or human exposure. Future studies should incorporate direct environmental measurements, finer spatial resolution, seasonal data, and clinical dry-eye outcomes. Full article

Mineral prospectivity mapping (MPM) increasingly integrates geological, geochemical, geophysical, remote-sensing, and structural evidence through machine-learning workflows. Predictive accuracy alone, however, does not establish that a prospectivity map is geologically credible or useful for exploration decisions. This critical narrative review examines explainable artificial intelligence (XAI) for MPM through the connections among model behavior, mineral-system knowledge, sampling, spatial validation, uncertainty, and field evidence. We distinguish methods demonstrated in representative MPM studies from general explanation tools and proposed applications. Study-level comparisons show that SHAP and permutation-based attribution can support evidence-layer auditing and target interpretation, while their meaning depends on correlated predictors, label construction, and evaluation design. Spatially separated evaluation tests a different generalization problem from random splitting; neither replaces newly acquired field evidence. Geological plausibility, model faithfulness, explanation stability, and decision utility therefore require separate assessment. We synthesize practical pathways for geological knowledge embedding and three-dimensional modeling, identify limits in current graph explanations and uncertainty reporting, and propose a minimum reporting checklist. Future priorities include geospatial foundation models, source-traceable language tools, three-dimensional prospectivity and four-dimensional extensions incorporating geological time, knowledge-guided hypothesis generation, integrated exploration systems, and field-based evaluation of explanations. Full article

Municipal solid waste (MSW) exhibits substantial variability in composition, density, degradation state, and mechanical properties, introducing uncertainty into landfill slope stability assessment. This study investigates the influence of waste heterogeneity and slope geometry on the probabilistic stability of MSW landfill slopes. Five slope configurations, from 1V:4H to 1V:1H, were evaluated using four finite element representations of MSW properties: a homogeneous model (C1) and heterogeneous models comprising 4, 13, and 40 waste layers (C2–C4). Analyses used PLAXIS 2D with the shear strength reduction method in an automated Python–PLAXIS framework, where C4 properties were resampled from a compiled literature database over 1000 Monte Carlo realizations per slope configuration. The steepest configuration (1V:1H) increased storage capacity by 54.3% and reduced required footprint by 31.2% relative to 1V:4H, highlighting the capacity-stability trade-off. As slope steepness increased, mean factor of safety decreased from 1.459 to 0.439, while simulated probability of failure increased from 15.4% to 98.1%, with the largest increase between 1V:3H and 1V:2H. The stochastic model produced mean factors of safety up to approximately 72% lower than the homogeneous representation; decomposition using an additional diagnostic configuration (C1b, homogeneous at the C4 database mean) indicates that this reduction is dominated by the spatial representation of heterogeneity (73–79% of the total difference across configurations), with the property database accounting for the remainder (21–27%). Results should be interpreted as comparative responses conditional on the adopted database and spatial representation. Full article

Thermal imaging is widely used for identifying abnormal thermal patterns in photovoltaic (PV) systems. However, complex backgrounds, varying thermal conditions, and environmental factors can reduce the spatial reliability of thermal-anomaly localization, particularly under real operating conditions. This study proposes a training-free image-processing framework that integrates panel-centered analysis, inner-panel masking, panel-coverage control, and local-background filtering to detect thermal-anomaly candidates and automatically generate bounding-box pre-annotations for subsequent deep learning applications. The framework was evaluated using five images from a publicly available thermal PV dataset and four independently acquired UAV-based field images from a grid-connected rooftop PV system at Zonguldak Bülent Ecevit University (BEUN). Compared with global thresholding and a panel-constrained Otsu-based baseline, the proposed method generally reduced redundant detections and reduced the absolute number of out-of-region detections while retaining regions showing spatial agreement with the image-derived pseudo-reference. Evaluation on the pseudo-color field images demonstrated promising applicability under real operating conditions. Moreover, a clear domain shift and increased out-of-region detections in some images indicate that further improvement in robustness is required for pseudo-color thermal representations and complex real-world conditions. Therefore, the present results are regarded as preliminary proof-of-concept evidence rather than broad validation of robustness or field-deployment readiness. The framework is computationally lightweight and shows potential for near-real-time processing. Full article

Deep learning-based semantic segmentation is increasingly used for automated structural health monitoring (SHM) of masonry infrastructure, yet model evaluation is still primarily based on segmentation metrics. Such metrics capture predictive performance, while the underlying decision-making mechanisms of the models remain unclear. In this study, the relationship between segmentation performance and explainability was investigated for Attention U-Net, U-Net++, and SegFormer-B2 in masonry wall segmentation. All models achieved successful brick segmentation, while SegFormer-B2 showed a modest advantage across all segmentation metrics, with a Dice score of 0.9665 and an IoU of 0.9462. Explanation characteristics were examined using Seg-Grad-CAM, Integrated Gradients, and SLIC-LIME. Attention-gate coefficient maps were additionally analyzed for Attention U-Net. Despite their comparable segmentation performance, the models exhibited distinct spatial explanation patterns, and the quantitative XAI results varied across evaluation metrics. Seg-Grad-CAM achieved the highest Pointing Game and Mask IoU values, while SLIC-LIME produced a low Deletion AUC despite minimal overlap with ground-truth masks. High spatial alignment does not guarantee explanation quality, so method selection depends directly on the evaluated criterion. The findings indicate that segmentation accuracy and explanation behavior provide complementary information for model evaluation. Full article

Accelerometers built on micro-electromechanical systems (MEMS) play a critical role in structural monitoring and machinery diagnostics; however, their accuracy suffers from intrinsic nonlinearities—dead zones, hysteresis, saturation, and colored noise. Conventional physics-based correction methods are interpretable yet cannot capture complicated hysteretic behavior, while purely neural-network approaches generalize poorly and lack a physical foundation. This paper proposes a hybrid architecture that combines a residual convolutional neural network with a physics-guided low-pass filter prior, fused through an attention-gated mechanism. The CNN learns only the residual nonlinearity; the filter supplies a steady, band-limited baseline. We validate the model on two simulated scenarios—a noise-dominant track and a nonlinear-dominant track—across three random seeds. The resulting Hybrid LPF-CNN outperforms a standalone CNN by 15.2% and an LSTM by 33.5% on the severely nonlinear track, reaching a mean R² of 0.9970 and an RMSE of 0.0179 g. On the noise-dominant track, it reaches R² = 0.9407 and RMSE = 0.0800 g, surpassing both CNN and LSTM baselines. The model is also stable across seeds ($\sigma =0.0001$ in R²) and gives a legible breakdown of the correction it applies. Our systematic architectural search revealed that a dual-encoder design with attention-gated fusion—where raw and filtered signals are processed separately and combined via a learnable spatial gate—provides the optimal balance between stability, accuracy, and interpretability. Even basic physics priors substantially improve the performance, stability, and interpretability of deep learning models for sensor error correction. Full article

High-frequency full-field vibration measurement using three-dimensional digital image correlation (3D-DIC) is limited by the trade-off between camera frame rate, spatial resolution, and measurement noise. This study presents a method to overcome the frame-rate limitation of 3D-DIC vibration measurement by combining stroboscopic equivalent-time sampling with spatiotemporal noise decoupling. Short-pulse stroboscopic illumination freezes structural motion at different vibration phases, allowing a low-frame-rate stereo camera system to reconstruct high-frequency periodic responses through inter-cycle phase sampling. The measured three-dimensional displacement fields are further processed as space–time data, where target-frequency extraction and spatial-frequency filtering are combined to suppress noise and enhance small-amplitude vibration responses. The proposed method was experimentally validated using a plastic plate vibrating at 251.1 Hz. With an actual camera frame rate of approximately 8.34 fps and an equivalent temporal sampling frequency of 2511 Hz, the reconstructed mode shape achieved a Modal Assurance Criterion (MAC) value of 0.9233, comparable to that obtained using a high-speed camera (0.9225), while providing higher spatial resolution and lower hardware cost. A pulse-width experiment on an aluminum plate vibrating at 7154 Hz demonstrated the influence of stroboscopic exposure duration on measurement accuracy. The proposed approach was further integrated into the EMODE-1 full-field vibration measurement system and applied to a Lenovo ThinkPad touchpad vibrating at 1051 Hz, achieving a MAC value of 0.8878 compared with continuous-scanning laser Doppler vibrometry. The results demonstrate the potential of the proposed method for high-frequency full-field vibration sensing of periodic structures using compact and cost-effective imaging systems. Full article

This study examines the spatial associations between climatic conditions and construction land in China during 2000–2020. Using construction land data from CNLUCC and spatially interpolated meteorological data, five climatic indicators were selected: annual mean temperature, annual precipitation, annual sunshine duration, annual mean wind speed, and annual mean atmospheric pressure. Spatial statistical methods were employed to examine the distribution and changes in construction land across different climatic conditions and to assess their associations at the central-city level. The results show that different climatic factors exhibit distinct spatial associations with construction land. Urban land, rural residential land, and other construction land also show distinct spatial patterns across climatic conditions. Analysis of central cities and their surrounding areas further reveals a differentiation between construction land stock and recent growth, indicating that long-term spatial distribution and recent expansion represent different dimensions of construction land dynamics. These associations may reflect the combined roles of climatic and non-climatic factors and should not be interpreted as direct causal relationships. The findings provide a macro-level basis for understanding the spatial differentiation of construction land under diverse climatic conditions and for further integrating demographic, economic, technological, and land-use factors into future research. Full article

To address the limited automation of 3D reconstruction caused by the inability of point clouds to represent the hidden structures and construction logic of Ming–Qing large timber buildings, this study proposes a hidden structural parameter inference method that integrates point clouds with traditional construction rules. First, a unified parameter space is established to provide a structured representation of building components and their associated parameters. Second, traditional construction knowledge is formalized into computable proportional, relational, and spatial constraints. Finally, hidden structural parameters are inferred through hierarchical constraint propagation, and the inferred results are used to generate HBIM components. The proposed method was validated using the sub-eave columns and their associated components of the Dabei Hall of Chongshan Temple in Taiyuan. Among 1848 hidden structural parameters, 1800 were successfully inferred, corresponding to a solvability rate of 97.4%. The results demonstrate that the proposed method can effectively infer hidden structural parameters under the available observations and construction-rule constraints and use the rule-consistent inference results to generate parametric HBIM components. This study extends HBIM beyond geometric representation toward knowledge-driven model representation, providing a knowledge-enhanced modeling approach for the digital documentation and structural understanding of traditional timber architecture. Full article

To address the adverse effects of complex acquisition conditions on continuous sign language recognition (CSLR) performance and the increasing computational cost of standard self-attention with increasing video sequence length, this paper proposes a continuous sign language recognition method based on temporal–frequency enhancement and DCT linear attention. First, a multi-scene continuous sign language dataset was constructed from the recordings of nine signers. The dataset contains 3502 video clips, 1208 lexical items, and nine acquisition scenarios, covering indoor and outdoor environments, strong and weak illumination, and static and dynamic backgrounds. These acquisition scenarios were designed to introduce diverse visual conditions during data collection. Because the available evaluation uses a random video-level split without scene-disjoint grouping, the results describe performance under the recorded mixed conditions and do not establish generalization to unseen signers or unseen acquisition scenarios. Second, using a Video Swin Transformer and spatial global average pooling as the feature-extraction front end, a temporal–frequency-enhanced teacher model was developed through a temporal branch, a short-time Fourier transform (STFT) frequency branch, and gated fusion, thereby jointly exploiting temporal and local frequency information. On this basis, a lightweight student configuration was developed using DCT-kernelized linear attention in the temporal-modeling pathway. Response-level knowledge distillation was further introduced to mitigate the recognition-performance loss associated with lightweight linearized modeling. Experimental results show that the teacher model achieves word error rates (WERs) of 19.8%, 23.1%, and 37.2% on PHOENIX14, CSL-Daily, and the self-constructed multi-scene dataset, respectively. After knowledge distillation, the student model achieves WERs of 21.2%, 24.1%, and 38.8% on the three datasets, with gaps of 1.4, 1.0, and 1.6 percentage points relative to the teacher model, respectively. At the complete-configuration level, the reported FLOPs decrease from 12.5 G for the teacher model to 4.2 G for the student model. On an NVIDIA RTX 3090 Ti GPU with a batch size of 1 and a 200-frame input, the single-sample inference latency decreases from 48.5 ms to 36.8 ms. These results indicate that, under the specified experimental conditions, the lightweight student configuration achieves lower computational cost and inference latency at the expense of only a limited loss in recognition performance. Full article