Search Results (61)

Existing image super-resolution methods still suffer from limitations in edge-structure restoration, high-frequency texture preservation, and artifact suppression, which may lead to blurred contours and unnatural textures. To address these issues, this paper proposes an image super-resolution method based on hierarchical feature aggregation and Laplacian high-frequency compensation. First, a Hierarchical Feature Aggregation Attention Block (HFAB) is designed in the generator to progressively extract image features at different levels through multiple convolutional layers. A High-Frequency Variance Adaptive Channel Attention Block (HFVB) is further introduced to adaptively enhance key texture and edge information. Second, a Laplacian Adaptive Upsampling (LAU) module is developed to combine low-frequency content reconstruction with high-frequency detail compensation, thereby strengthening edge contours, preserving fine textures, and reducing artifacts. Finally, a Dissimilarity Structural Similarity Index Measure (DSSIM) loss is incorporated into the loss function to constrain local structural consistency and further improve the structural preservation and perceptual quality of reconstructed images. Experimental results on Set5, Set14, BSD100, and Urban100 show that, compared with SRGAN, the proposed method improves PSNR by 0.31 dB, 0.18 dB, 0.14 dB, and 0.14 dB, respectively, while reducing LPIPS by 0.0234, 0.0183, 0.0222, and 0.0222. These results indicate that the proposed method provides consistent improvements over SRGAN and achieves modest, metric-dependent gains over ESRGAN, suggesting an incremental enhancement in reconstruction accuracy and perceptual quality on both natural image benchmarks and complex urban scene datasets. Full article

Information technology service management (ITSM) systems generate large volumes of unstructured incident descriptions. They frequently include multilingual content, code-switching, informal language, and domain-specific terminology. These characteristics make automated text processing substantially more complicated and limit the applicability of conventional machine translation solutions, particularly in environments subject to strict data privacy and air-gap constraints. This paper presents a system-level reproducibility study of a deterministic two-stage cascaded neural machine translation (NMT) pipeline for normalizing multilingual IT incident text in resource-constrained, air-gapped environments. The study evaluates a sequential RU→EN and LT→EN translation strategy specifically selected to bypass unreliable language identification, enabling stable processing of code-switched incident descriptions. A system-level processing pipeline, which includes text normalization, segmentation, deduplication, adaptive batching, and language-aware data flow optimization, is analyzed to assess its impact on reducing redundant inference operations. The methodology is evaluated on a real-world ITSM dataset comprising 84,285 incident records. An incremental experimental design is used to isolate the specific contributions of computational and data-flow optimizations. Translation quality is assessed using BLEU and COMET metrics against expert reference translations produced via a primary translation and subsequent cross-verification by a second domain expert to ensure linguistic and technical consistency. The results indicate that a cascaded NMT architecture combined with systematic data-flow optimization provides a reproducible and privacy-preserving framework for multilingual IT incident text normalization, effectively supporting downstream analytical tasks in constrained operational ITSM environments. Full article

The subcellular localization of long noncoding RNAs (lncRNAs) is a central determinant of their function, yet its molecular determinants remain incompletely defined, and most existing predictors rely on the primary sequence. Because RNA-binding proteins (RBPs) are the proximal effectors of RNA compartmentalization, this study tested whether the composition of RBPs bound to a lncRNA is predictive of its nuclear or cytoplasmic localization. Enhanced crosslinking and immunoprecipitation (eCLIP) occupancy for 139 RBPs in K562 cells was integrated with the cytoplasmic–nuclear relative concentration indices (CN-RCIs) derived from matched subcellular fractionation, and localization was modeled under chromosome-grouped cross-validation with nested regularization. RBP-occupancy composition predicted localization beyond the transcript size and total binding amount (incremental cross-validated coefficient of determination, delta-R-squared = 0.17; receiver-operating-characteristic area under the curve, AUC = 0.73, a moderate-strength association; Freedman–Lane permutation, p = 0.005). This increment persisted (delta-R-squared = 0.12; p = 0.005) against an expanded baseline that additionally absorbed the transcript abundance, intron content and exon number, indicating predictive information that is not reducible to these transcript features, and the classifier was well calibrated (Brier score = 0.10; expected calibration error = 0.02). The signed coefficient profile separated RBP function systematically: factors acting in nuclear processes (splicing, 3′-end processing, and nuclear-matrix association) carried negative, nuclear-direction weights, whereas factors acting in cytoplasmic processes (translation and messenger RNA stability) carried positive, cytoplasmic-direction weights (Mann–Whitney p = 0.013). The profile generalized across cell lines: a K562-trained model predicted HepG2 localization (transfer AUC = 0.71 using 76 shared RBPs), and HepG2 reproduced the association independently (AUC = 0.77). The association is correlational and of moderate strength; it is presented as an interpretable, RBP-occupancy-based complement to sequence-based predictors of lncRNA localization. Full article

Understanding rate of change requires reasoning about measurable quantities and how one quantity changes relative to another. To support this kind of reasoning, teachers should develop the ability to notice students’ quantitative and covariational reasoning. This study examines how preservice teachers (PSTs) attend to, interpret, and respond to students’ quantitative and covariational reasoning in video-based analyses of water-filling rate-of-change tasks. Drawing on relevant research, professional noticing is examined through the lenses of quantitative reasoning and covariational reasoning. Using a design-based qualitative approach, secondary PSTs participated in structured analyses of students’ problem-solving discussions related to rate of change. Data were collected across eight semi-structured sessions. This study reports qualitative analyses from two sessions (Sessions 1 and 2) that focused on rate of change. Findings show that PSTs’ initial attending shifted from perceptual task features (e.g., pouring speed, references to time) toward identification of measurable quantities, recognition of coordination between height and volume, and comparison of equal volume increments. PSTs’ interpretations progressed from recognizing secondary-variable coordination to identifying direct covariation, and their instructional responses became more targeted and content-specific. However, challenges persisted in interpreting students’ informal and visually mediated covariational reasoning. This study contributes to research on professional noticing by integrating quantitative and covariational reasoning as analytic lenses and highlighting implications for teacher preparation in calculus education. Full article

This paper examines whether a residual structural state extracted from cross-asset downside-risk dependence contains incremental information for forecasting next-day market downside risk beyond a strong heterogeneous autoregressive (HAR) benchmark. The empirical analysis uses Binance intraday data from September 2019 to December 2025 and a fixed sample of 24 liquid cryptocurrencies obtained through explicit data-quality screening and sample diagnostics. The forecasting target is the log of an equal-weight cross-sectional downside-risk index constructed from strictly valid asset-level realized downside semivariance measures. The empirical design is deliberately conservative: the market sample is fixed ex ante, the target is evaluated against Bitcoin (BTC) and Ethereum (ETH) dominance diagnostics, and asset-level HAR-type models are estimated recursively to generate ex-ante one-step-ahead residuals, from which rolling residual-dependence matrices and structural signatures are constructed. The selected residual state contains four components: average residual correlation, Frobenius-type deformation, influence concentration, and influential-set turnover. The evidence supports three qualified conclusions. First, the full residual state attains the lowest average QLIKE loss relative to the HAR benchmark, although the corresponding Diebold–Mariano test under the primary QLIKE loss does not reject equal predictive accuracy at conventional levels. Complementary Clark–West evidence on the nested log-scale comparison supports incremental predictive content for the level-state and full-state augmentations. Second, the strongest forecasting evidence comes from the full state rather than from deformation-only specifications. Third, event-window diagnostics show that structural reorganization is most pronounced around stress-entry and extreme-risk episodes, supporting an onset-sensitive rather than a long-lead early-warning interpretation. Overall, the evidence supports a cautious and statistically qualified predictive conclusion: residual market structure may contain incremental information for short-horizon downside-risk forecasting in cryptocurrency markets, especially around stress onset, but the result should not be interpreted as a decisive primary-loss improvement or as evidence that deformation alone dominates a strong benchmark. Full article

Objective: To evaluate the relationship between legume intake and incident hyperuricemia among Chinese adults using large-scale prospective cohort data. Methods: Baseline and follow-up information from the Shanghai Suburban Adult Cohort and Biobank (SSACB) were used to assess diet and hyperuricemia incidence [serum uric acid (SUA) ≥ 420 μmol/L in males and ≥ 360 μmol/L in females]. A Food Frequency Questionnaire (FFQ) covering 29 food categories quantified food consumption during the previous 12 months. Legume intake was calculated by multiplying the reported consumption of each item by (1 − water content), and participants were classified into tertiles with the lowest third (Q1) as the reference. Cox proportional-hazards models estimated hazard ratios (HRs) and 95% confidence intervals (CIs), and restricted cubic splines (RCSs) with three knots (10th, 50th, and 90th percentiles) visualized the dose–response relation. Results: Among 43,371 participants, 1456 new cases of hyperuricemia were documented over 225,002.40 person-years (incidence density 6.47/1000 person-years; 95% CI 6.14–6.80). Incidence density decreased with higher legume intake: each 1 g/day increment was associated with a 2% lower risk (HR 0.98; 95% CI 0.97–0.99; p < 0.05). Compared with Q1, the highest tertile (Q3) showed a 26% risk reduction in the fully adjusted model (HR 0.74; 95% CI 0.64–0.86; p < 0.05). RCS revealed a significant nonlinear relationship (p-overall < 0.001, p-nonlinear = 0.0013), with the significant benefit in risk observed at 6–28 g/day. Conclusions: Legume intake is nonlinearly and inversely associated with hyperuricemia risk among Shanghai suburban adults. Given that the current low median intake, comprehensive strategies are needed to rationally adjust the dietary structure, improve legume intake, and provide sustainable development strategies for effective prevention and control of hyperuricemia. Full article

Post-cooking storage may modify the glycemic response of starchy foods; however, this effect is usually assessed only through the glycemic index (GI), without capturing the temporal dimension of the metabolic response. In this study, the effect of post-cooking storage on boiled rice was investigated using an integrated approach based on GI, resistant starch (RS) content, and the post-technological stability coefficient (PTSC). Storage significantly reduced GI, from 83.03 ± 15.02 (SD) in the freshly prepared sample to 43.55 ± 6.99 (SD) after prolonged freezing, while concurrently increasing RS from approximately 1.8% to nearly 4.0%. A strong inverse linear relationship was identified between RS and GI (r = −0.935, p < 0.001; R² = 0.8735). These changes are consistent with storage-induced starch retrogradation and reduced enzymatic accessibility of the starch matrix. PTSC analysis further suggested that GI reduction was not automatically equivalent to lower temporal variability in the glycemic response: refrigeration was associated with more negative and more dispersed PTSC values, whereas prolonged freezing was associated with lower GI, higher RS, and smaller temporal variations in the incremental area under the curve (iAUC). Overall, the results suggest that the isolated analysis of GI may not fully describe the effect of post-cooking storage on boiled rice. The combined interpretation of GI, RS, and PTSC may provide a more informative framework for evaluating the metabolic effect of storage and may help differentiate between regimes predominantly associated with a reduction in the amplitude of the glycemic response and those additionally characterized by lower temporal variability of that response. Full article

In various sectors of the petrochemical and metallurgical industries, significant volumes of waste in the form of highly stable water–hydrocarbon emulsions are generated and stored. The presence of an aqueous phase limits their further use. To utilize this waste and obtain valuable commercial products, a thermomechanical dewatering method based on the evaporation of the aqueous phase under turbulent emulsion flow conditions has been proposed and tested. However, the dynamics of aqueous phase evaporation and vapor phase formation within this method remain poorly understood. This understanding is crucial, as it directly influences the optimal selection of necessary auxiliary equipment. To address this gap, the dynamics of vapor formation during the boiling off of the aqueous phase from highly stable water–hydrocarbon emulsions in a batch thermomechanical dewatering reactor were simulated. To identify general patterns, the gradual evaporation process was calculated as a set of multiple single-effect evaporation steps with a two-degree increment. Initially, modeling results showed that to obtain a commercial product with a water content of less than 1%, temperatures must be maintained at up to 150 °C. This finding was in complete agreement with experimental data, thereby confirming the accuracy of the calculations. Subsequently, extreme vaporization rates were identified, which significantly (1.7–9 times) exceeded the average vapor formation rates in a batch reactor. Maximum vapor formation rates were observed in the temperature range of 100–120 °C. Furthermore, increasing the feedstock water content above 10% was found to significantly prolong the processing time and elevate the maximum vapor formation rate. The patterns presented in this article facilitate the optimization of operating modes for commercial thermomechanical dewatering units, enable the informed selection of necessary auxiliary equipment, and help maintain both the safety and efficiency of the industrial process. Full article

This study presents a calibrated analytical micromechanical framework for predicting the linear elastic behavior of unidirectional glass fiber/epoxy and carbon fiber/epoxy composites over a wide range of fiber volume fractions. The approach combines the classical rule of mixtures for the longitudinal Young’s modulus with the semi empirical Halpin–Tsai equations to estimate the transverse Young’s modulus and the in-plane shear modulus. The framework is specifically formulated to support durability-oriented composite design through rapid and physically consistent estimation of elastic properties governing load transfer and stress distribution. Material parameters, including fiber and matrix Young’s moduli (Ef, Em), shear moduli (Gf, Gm), Poisson’s ratios (νf, νm), and fiber volume fraction (Vf up to 0.80), are taken from established material property databases and implemented within a literature-informed modeling scheme. To preserve physical realism at high fiber contents, a shear correction factor is introduced for Vf > 0.50 to account for microstructural interaction and fiber clustering effects. The predicted effective elastic constants (E₁, E₂, G₁₂, ν₁₂) exhibit consistent and physically meaningful trends across the full fiber volume fraction range. The model predictions were evaluated against trends widely reported in the composite micromechanics literature, and the results showed overall agreement in the nonlinear reduction in stiffness gains at elevated fiber volume fractions. Comparative results indicate that carbon fiber/epoxy composites achieve up to approximately 30% higher stiffness than glass fiber/epoxy systems at equivalent fiber contents, reflecting the influence of stiffness contrast on composite response. The analysis further indicates that stiffness saturation begins approximately in the Vf = 0.60–0.70 range, where the incremental gains in E2 and G12 become noticeably smaller for both composite systems. This behavior provides design-relevant guidance by showing that, beyond this range, further increases in fiber content may offer limited stiffness improvement relative to the associated manufacturing complexity. Overall, the calibrated Halpin–Tsai methodology offers a practical and computationally efficient tool for preliminary evaluation and design-stage optimization of the elastic performance of high-performance composite structures. Full article

Decarbonizing the construction sector requires high-volume replacement of Portland clinker with non-calcined supplementary cementitious materials (SCMs). This study investigates white cement pastes incorporating raw Algerian diatomite—a silica-rich biogenic mineral—at substitution levels from 40% to 95% (5% increments) and a fixed water-to-binder ratio of 0.5. The target application is ultra-lightweight, multifunctional composites for non-structural uses such as decorative panels and partition elements. Increasing diatomite content progressively reduced bulk density from 1.483 g/cm³ (D40) to 0.557 g/cm³ (D95) and increased porosity. 28-day compressive strength decreased monotonically from 16 MPa (D40) to 2.4 MPa (D95) as clinker dilution intensified. Ultrasonic pulse velocity dropped from 6205 m/s to 1495 m/s, reflecting progressive pore development and confirming the material’s lightweight potential. Statistically significant strength gains beyond 28 days were recorded ($+25.87\%$ for compression, p-value $< 0.05$), evidencing delayed pozzolanic activity. These results confirm that raw, non-calcined diatomite is a viable SCM for eco-efficient, low-density construction systems. To overcome the extrapolation instability of purely data-driven approaches, a Meta-Avrami Hybrid Framework was developed. It anchors Gradient Boosting residual learning to a sigmoidal Avrami hydration kernel. The model achieved high predictive accuracy ($R^2\approx 0.999$, $\mathrm{RMSE}\approx 0.010$) under 10-fold cross-validation. Generalization was well-controlled, with a low overfitting gap ($\Delta R^2=0.0226$) and stable fold-to-fold performance ($\mathrm{Std}=0.0204$). These metrics confirm suitability for unseen mix designs. This is particularly relevant for service-life assessment of partition panels and lightweight façade elements, where long-term performance guarantees are required. The physics-informed architecture ensures asymptotic strength stabilization up to a 10-year horizon (amplification ratios 1.03–1.05). This prevents the non-physical divergence observed in polynomial and power-law hybrids (ratios 1.36–1.70). The framework provides a reliable and interpretable tool for service-life design of sustainable low-carbon cementitious systems. Full article

Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) provides a low-cost, all-weather approach for continuous soil moisture content (SMC) retrieval. However, in single-constellation, multi-satellite applications, the optimal satellite number and the combined effects of multiple environmental factors on retrieval accuracy and stability remain insufficiently quantified. To address these issues, this study develops a dual-frequency GNSS-IR SMC retrieval framework that explicitly incorporates multiple environmental factors. Entropy-based fusion (EFM) is used to adaptively weight dual-frequency phase-delay observations, and a marginal-gain criterion is introduced to determine a suitable number of participating satellites. On this basis, univariate linear regression (ULR) and random forest (RF) models are established, and the Normalized Difference Vegetation Index (NDVI), temperature, and precipitation are incorporated into the RF model to improve retrieval robustness and quantify the relative contributions of environmental factors. The results show that multi-satellite combinations significantly improve SMC retrieval performance, while the incremental gain exhibits clearly diminishing returns and converges when the number of participating satellites reaches about 5–6 within a single constellation. Dual-frequency fusion consistently outperforms single-frequency schemes across different GNSS constellations, demonstrating the complementary value of multi-frequency information under multi-satellite conditions. In addition, the environmentally informed nonlinear model achieves higher accuracy and stability than the linear model, and the dominant environmental drivers differ across stations. Overall, this study provides quantitative support for configuring single-constellation multi-satellite GNSS-IR soil moisture monitoring schemes and for improving retrieval robustness under complex environmental conditions. Full article

Digital photoelasticity enables non-contact, full-field stress analysis through optical fringe patterns, yet its practical deployment is often constrained by experimental complexity and the limited availability of open, standardized datasets. The emergence of multi-polarizer array cameras provides polarization-resolved measurements with high information content, enabling advanced analysis strategies beyond conventional single-image approaches. This work presents a public experimental dataset composed of synchronized image sequences acquired using a polarizer array camera and a conventional RGB camera under incremental mechanical loading. The dataset comprises nine experiments, including four benchmark specimens and five bio-inspired geometries, each recorded over 720 load steps. In total, the dataset releases 25,920 polarization-resolved images and 6480 RGB images, all provided in lossless format and accompanied by experiment-specific segmentation templates. Although classical and hybrid load-stepping methods are used to demonstrate the utility of the dataset, its scope is not limited to this application. The dataset is intended as a flexible platform for exploring a wide range of photoelastic analysis techniques that leverage polarization information, while enabling direct comparison with conventional color demodulation techniques. Full article

This study examines how aggregate financial risk tolerance (FRT), measured from repeated survey responses, co-evolves with stock-market dynamics over time. The observed FRT index is treated as a noisy preference signal containing both gradual drift and episodic deviations, and its market relevance is evaluated under time variation, frequency components, and stress regimes. Using monthly data that align the survey-based FRT index with market returns and risk measures, a three-part econometric design is implemented. First, a time-varying parameter VAR (TVP-VAR) characterizes bidirectional, non-constant linkages between FRT and market outcomes. Second, signal-extraction methods decompose FRT into a smooth “normal” component and a high-frequency “abnormal” component (with robustness to alternative filters) to test whether short-run deviations contain distinct information for volatility and downside risk. Third, a Markov-switching specification assesses state dependence by testing whether the FRT–market relationship differs between low-stress and high-stress regimes. Across specifications, the FRT–market linkage is strongly state dependent: the sign and magnitude of FRT effects drift over time and differ across regimes, with high-frequency FRT deviations aligning more closely with risk dynamics than the smooth component. Predictive validation is provided via out-of-sample forecasting of next-month market risk using elastic net and gradient boosting relative to an AR(1) benchmark; explainability analysis (SHAP) indicates that abnormal FRT contributes incremental predictive content beyond standard market-state variables. Overall, the framework offers a mathematically transparent approach to modeling survey-based preference signals in markets and supports regime-aware forecasting and risk-management applications. Full article

Generative AI (GenAI) now produces text, images, audio, and video that can be perceptually convincing at scale and at negligible marginal cost. While public debate often frames the associated harms as “deepfakes” or incremental extensions of misinformation and fraud, this view misses a broader socio-technical shift: GenAI enables synthetic realities—coherent, interactive, and potentially personalized information environments in which content, identity, and social interaction are jointly manufactured and mutually reinforcing. We argue that the most consequential risk is not merely the production of isolated synthetic artifacts, but the progressive erosion of shared epistemic ground and institutional verification practices as synthetic content, synthetic identity, and synthetic interaction become easy to generate and hard to audit. This paper (i) formalizes synthetic reality as a layered stack (content, identity, interaction, institutions), (ii) expands a taxonomy of GenAI harms spanning personal, economic, informational, and socio-technical risks, (iii) articulates the qualitative shifts introduced by GenAI (cost collapse, throughput, customization, micro-segmentation, provenance gaps, and trust erosion), and (iv) synthesizes recent risk realizations (2023–2025) into a compact case bank illustrating how these mechanisms manifest in fraud, elections, harassment, documentation, and supply-chain compromise. We then propose a mitigation stack that treats provenance infrastructure, platform governance, institutional workflow redesign, and public resilience as complementary rather than substitutable, and outline a research agenda focused on measuring epistemic security. We conclude with the Generative AI Paradox: as synthetic media becomes ubiquitous, societies may rationally discount digital evidence altogether, raising the cost of truth for everyday life and for democratic and economic institutions. Full article

With the rapid development of applications such as edge computing, the Internet of Things (IoT), and embodied intelligence, massive multimodal data are continuously generated on end devices in a streaming manner. To maintain model adaptability and robustness in dynamic environments, incremental learning has gradually become the core training paradigm on edge devices. However, edge devices are constrained by limited computational, storage, and communication resources, making it infeasible to retain and process all data samples over time. This necessitates efficient data selection strategies to reduce redundancy and improve training efficiency. Existing sample selection methods primarily focus on overall sample difficulty or gradient contribution, but they overlook the heterogeneity of multimodal data in terms of information content and discriminative power. This often leads to modality imbalance, causing the model to over-rely on a single modality and suffer performance degradation. To address this issue, this paper proposes a multimodal sample selection strategy based on the Modality Balance Score (MBS). The method computes confidence scores at the modality level for each sample and further quantifies the contribution differences across modalities. In the selection process, samples with balanced modality contributions are prioritized, thereby improving training efficiency while alleviating modality bias. Experiments conducted on two benchmark datasets, CREMA-D and AVE, demonstrate that compared with existing approaches, the MBS strategy achieves the most stable performance under medium-to-high selection ratios (0.25–0.4), yielding superior results in both accuracy and robustness. These findings validate the effectiveness of the proposed strategy in resource-constrained scenarios, providing both theoretical insights and practical guidance for multimodal sample selection in learning tasks. Full article