Search Results (90)

Background/Objectives: Physical exercise has emerged as a promising non-pharmacological intervention for cognitive dysfunction; however, the most effective mode of exercise remains unclear. This study aimed to investigate the acute effects of two cycling exercise protocols, (a) continuous aerobic/moderate-intensity (CA) and (b) high-velocity/low-resistance (high-cadence) interval (HVI), on cognitive and executive performance in patients with mild cognitive impairment (MCI). Methods: Seventeen patients (10 females and 7 males, age: 65.5 ± 8.85 years) diagnosed with MCI or early-stage Alzheimer’s disease (13 MCI and 4 eAD) participated in a random order in three different conditions: CA, HVI, and control/no exercise (CON). Cognitive parameters were assessed acutely before and after the completion of each condition. Results: Significant condition × time interactions were observed for both Montreal Cognitive Assessment (MoCA) and Frontal Assessment Battery (FAB) (p < 0.01). Higher scores (p < 0.01) for MoCA and FAB post-intervention were found compared to baseline in both exercise bouts, whereas no changes occurred in CON. Interestingly, when post-intervention scores were compared between conditions, cognitive performance was improved only in HVΙ compared to CON in MoCA (p < 0.01) and FAB (p < 0.001), revealing a stronger acute effect of HVI. Conclusions: A single bout of high-velocity, low-resistance (high-cadence) interval cycling acutely enhanced global cognition and executive function in individuals with MCI, exerting greater improvement compared to continuous aerobic exercise or control condition. These findings emphasize the potential utilization of HVI as an effective non-pharmacological intervention to acutely enhance cognitive performance in older adults with MCI. Full article

Low-light image enhancement improves the quality of video surveillance and image analysis and, as a result, has long been a hot topic in image processing. However, current research on this topic faces a difficult challenge—effectively suppressing noise while improving brightness and maintaining color consistency, especially in extremely dark scenes, where dark noise amplification, uneven exposure, and color shifts often interact, leading to detail loss and color distortion. To address the issue, we propose a dual-stage low-light enhancement framework based on the HVI (Horizontal/Vertical-Intensity) color space. The low-light image is first mapped to the HVI space, obtaining the intensity component I and the HVI-based feature map, with I being explicitly extracted as an intensity prior. A Transformer-based pre-recovery module is introduced for global dependency modeling, guided by the intensity prior I through an Intensity-Conditioned Block (ICB) for conditional feature interaction. Subsequently, a dual-branch enhancement network utilizes lightweight Complementary Cross-Attention (CCA) blocks for brightness refinement and color denoising. Finally, the enhanced image is remapped to the sRGB color space. The proposed framework decouples global brightness recovery and feature preprocessing from detail enhancement and color refinement, improving stability in extremely dark and high-noise scenarios. Through 18 quantitative and qualitative experiments, we demonstrate that our proposed method achieves superior performance in dark noise suppression and color restoration across multiple low-light datasets. Full article

Clarifying the complex interaction between climate risks and the new energy industry chain is of key significance to advancing the energy transition and strengthening industrial chain robustness. This research pairwise-matches the climate physical risk and the climate transition risk with the entire range of the new energy industry chain segments, comprehensively examining the pairwise interactive relationships. By applying the MF-ADCCA series of methods, it was revealed that there are prevalent asymmetric cross-correlated multifractal characteristics between climate risks and the new energy industry. The long-term memory under the upward trend of the market is distinctly stronger than that under the downward trend. Given that this correlation can indirectly reflect market efficiency differences, this paper constructs the Hurst Volatility Sensitivity Index (HVI) and the Hurst Asymmetry Index (HAI) and further proposes the Unified Market Efficiency Index (UMEI). Its innovative advantage resides in the balanced integration of volatility efficiency and structural symmetry, in turn enabling a comprehensive assessment of the new energy market efficiency under climate risk perturbations. Static analysis reveals that the overall market efficiency of the new energy industry under the climate transition risk is generally higher than that under the climate physical risk, and the market efficiency of mature upstream and midstream new energy segments is significantly superior to that of the downstream. Dynamic evolution characteristics indicate that market efficiency has typical time-varying traits, the evolution of which is often driven by significant policies or extreme events. The climate transition risk tends to trigger aperiodic structural adjustments, while the climate physical risk mostly induces periodic efficiency fluctuations. This study furnishes solid evidence for the new energy market in coping with climate risks. Full article

During on-orbit service, spacecraft are subjected to hypervelocity impacts (HVIs) from micrometeoroids and space debris, causing diverse damage types that challenge structural health assessment. Unimodal approaches often struggle with similar damage patterns due to mechanical noise and imaging distance variations. To overcome these physical limitations, this study proposes a physics-informed multimodal fusion framework. Innovatively, we integrate a distance-aware infrared enhancement strategy with vibration spectral subtraction to align heterogeneous data qualities while employing a dual-stream ResNet coupled with Dempster–Shafer (D-S) evidence theory to rigorously resolve inter-modal conflicts at the decision level. Experimental results demonstrate that the proposed strategy achieves a mean accuracy of 99.01%, significantly outperforming unimodal baselines (92.96% and 97.11%). Notably, the fusion mechanism corrects specific misclassifications in micro-cracks and perforation, ensuring a precision exceeding 96.9% across all categories with high stability (standard deviation 0.74%). These findings validate the efficacy of multimodal fusion for precise on-orbit damage assessment, offering a robust solution for spacecraft structural health monitoring. Full article

Artificial intelligence (AI)-based structure prediction tools have emerged as powerful methods for understanding previously unsolved structures. AI-predicted models are widely used for protein function identification, drug development, and protein engineering. Although AI-predicted structures offer significant opportunities to advance research, their inaccuracies can lead to misinterpretations of molecular mechanisms. Thus, evaluating the structural differences between AI-predicted and experimental structures is crucial for accurately understanding molecular mechanisms and guiding the design of subsequent experiments. In this study, the previously unreported crystal structure of xylanase from Hypocrea virens (HviGH11) was compared with the structures predicted by ESMFold, AlphaFold2, AlphaFold3, and RoseTTAFold. The overall fold of HviGH11 was highly similar between the experimental and AI-predicted models; however, the conformation of the thumb domain of the protein varied across the models. The substrate-binding cleft of experimental HviGH11 was similar to that in the model structures generated by ESMFold, AlphaFold2, and AlphaFold3, but significantly different from those in the model structures generated by RoseTTAFold. The substrate docking study illustrated that the binding mode of xylohexaose in the substrate-binding cleft differed between the experimental and AI-predicted HviGH11 structures. These findings provide insights into the applications of AI-predicted models and offer guidance for appropriate application in structural and functional studies and biotechnology. Full article

Current saw- and pin-type lint-cleaning systems used by the ginning industry have challenges retaining lint quality. The objective of the research was to test a novel pneumatic fractionator for the lint cleaning of an Upland cotton variety that was both saw- and roller-ginned. The process variables tested were initial lint moisture content in the range of 5.5–15% w.b., line pressure in the range of 276–552 kPa, and residence time in the range of 15–45 s. Experiments were conducted based on a central composite design. Models based on response surface methodology (RSM) were developed for final lint moisture, total trash extracted during lint cleaning, and High-Volume Instrument (HVI) fiber quality. The RSM models adequately described the pneumatic fractionation process, as indicated by the coefficient of determination, predicted vs. observed plots, and residual values. The results indicated that the interactions among initial lint moisture content, residence time, and line pressure significantly affected lint quality. At the optimized pneumatic fractionator process conditions, the predicted lint quality attributes were better for both roller- and saw-ginned lint compared to lint cleaned with saw- and pin-type lint cleaners. The upper half mean length increased by 1 mm, the uniformity index was higher by 0.5–1 percentage points, the strength was 1–2 g/tex higher, and the short fiber content was reduced by more than one percentage point. Color grades were better for pneumatic fractionated lint compared to saw- and pin-type lint cleaning methods. Lint value was approximately 4 cents/kg higher for both saw- and roller-ginned pneumatic fractionated lint, compared to lint cleaned using saw- and pin-type lint cleaners. The novel pneumatic fractionator, when compared to industry-standard saw- and pin-type lint cleaners, effectively cleaned lint while retaining fiber quality and removing most of the motes and trash. Full article

Cotton fiber length and maturity, two critical fiber qualities, are commonly determined in the U.S. by Uster high volume instrument (HVI) and advanced fiber information system (AFIS). The main objectives of this investigation were to compare how HVI lengths agree with AFIS lengths and to examine whether the fiber length is linked with fiber maturity between the Universal HVI length calibration cotton standards and diverse upland lint samples. HVI micronaire (MIC) and AFIS fineness showed insignificant differences from HVI length calibration cotton standards to lint samples. Although there were strong and significant correlations between HVI upper-half mean length (UHML) and either AFIS UQL (w) or AFIS L5% (n), the relationship between UHML and L5% (n) was better suited than between UHML and UQL (w) in scrutinizing fiber lengths. Meanwhile, analysis revealed a moderate correlation between AFIS L5% (n) length and AFIS maturity ratio (MR), indicating the possibility of improving AFIS L5% (n) length by regulating fiber MR development. Further, AFIS MR values were positive and moderate correlated with algorithmic M_IR values of attenuated total reflection Fourier transform infrared (ATR FT-IR) spectra. The results suggested the feasibility of the ATR FT-IR method along with M_IR analysis in estimating AFIS MR rapidly away from fiber testing laboratories. Full article

Urban heat vulnerability is an increasing public health concern, particularly in rapidly urbanizing regions of southern France. This study aims to quantify and map the Heat Vulnerability Index (HVI) for Toulouse and to analyze its temporal trends to identify high-risk zones and influencing factors. The assessment integrates recent years’ remote sensing data of pollutant emissions, land use/land cover and land surface temperature, statistical data of climate-related mortalities, and socioeconomic and demographic factors. Following a detailed analysis of recent real-time air quality and weather data from multiple monitoring stations across the city of Toulouse, it was observed that Urban Pollution Island (UPI) and Urban Heat Island (UHI) are closely interlinked phenomena. Their combined effects can significantly elevate the annual mortality risk rate by an average of 2%, as calculated using AirQ+ particularly, in densely populated urban areas. Remote sensing data was processed using Google Earth Engine and all factors were grouped into three key categories: heat exposure, heat sensitivity, and adaptive capacity to derive HVI. Temporal HVI maps were generated and analyzed to identify recent trends, revealing a persistent increase in vulnerability across the city. Comparative results show that 2022 was the most critical summer period, especially evident in areas with limited vegetation and extensive use of heat-absorptive materials in buildings and pavements. The year 2024 indicates resiliency and adaptation although some areas remain highly vulnerable. These findings highlight the urgent need for targeted mitigation strategies to improve public health, enhance urban resilience, and promote overall human well-being. This research provides valuable insights for urban planners and municipal authorities in designing greener, more heat-resilient environments. Full article

Naturally colored cotton offers ecological advantages by eliminating the need for chemical dyeing; however, its limited fiber quality restricts its commercial utilization. The main goal of this study was to evaluate the potential of the SSR marker BNL1604 for marker-assisted selection in naturally colored cotton (G. hirsutum L.) and to assess fiber quality variation among hybrid progenies derived from crosses between colored and elite white-fiber cultivars. As an expected outcome of this approach, we also assessed whether hybridization of naturally colored lines with elite white-fiber cultivars could contribute to the improvement of fiber quality traits in segregating progenies. Five colored lines (brown and green), three elite cultivars, and fifteen derived F₃ progenies were analyzed. Fiber traits, including upper half mean length (UHML), strength, elongation, and micronaire, were measured using HVI. Genotyping was conducted with BNL1604, and in silico mapping localized this marker to chromosome A07, with a homoeologous region on D07. White-fiber cultivars exhibited superior fiber length (33.4–35.4 mm) and strength (>31 g·tex⁻¹) compared with colored lines. Several F₃ hybrids exhibited transgressive segregation (progeny with trait values significantly exceeding those of both parents, as confirmed by frequency distribution and ANOVA analyses). For instance, the F₃ (C-6577 × L-4099) hybrid achieved UHML values of 30.51 mm and strength > 31.93 g·tex⁻¹. Most progenies maintained optimal micronaire (4.0–4.9). It was concluded that the presence of the 107 bp allele of BNL1604 marker was strongly associated with high-quality fiber, specifically improved fiber strength and length. In silico annotation revealed candidate genes near the BNL1604 locus linked to fiber development. These findings highlight the potential of combining hybridization with selection based on the presence of this 107 bp allele to develop high-quality, naturally colored cotton cultivars. Full article

Under the dual pressures of climate change and rapid urbanization, extreme heat events pose growing risks to densely populated megaregions. The Guangdong–Hong Kong–Macao Greater Bay Area (GBA), a densely populated and economically vital region, serves as a critical hotspot for heat risk aggregation. This study develops a high-resolution multi-dimensional framework to assess the spatiotemporal evolution of its heat risk profile from 2000 to 2020. A Heat Risk Index (HRI) integrating heat hazard and vulnerability components to measure potential heat-related impacts is calculated as the product of the Heat Hazard Index (HHI) and Heat Vulnerability Index (HVI) for 1 km grids in GBA. The HHI integrates the frequency of hot days and hot nights. HVI incorporates population density, GDP, remote-sensing nighttime light data, and MODIS-based landscape indicators (e.g., NDVI, NDWI, and NDBI), with weights determined objectively using the static Entropy Weight Method to ensure spatiotemporal comparability. The findings reveal an escalation of heat risk, expanding at an average rate of 342 km² per year (p = 0.008), with the proportion of areas classified as high-risk or above increasing from 21.8% in 2000 to 33.3% in 2020. This trend was characterized by (a) a pronounced asymmetric warming pattern, with nighttime temperatures rising more rapidly than daytime temperatures; (b) high vulnerability dominated by the concentration of population and economic assets, as indicated by high EWM-based weights; and (c) isolated high-risk hotspots (Guangzhou and Hong Kong) in 2000, which have expanded into a high-risk belt across the Pearl River Delta’s industrial heartland, like Foshan seeing their high-risk area expand from 3.4% to 27.0%. By combining remote sensing and socioeconomic data, this study provides a transferable framework that moves beyond coarse-scale assessments to identify specific intra-regional risk hotspots. The resulting high-resolution risk maps offer a quantitative foundation for developing spatially explicit climate adaptation strategies in the GBA and other rapidly urbanizing megaregions. Full article

Low-light image enhancement aims to restore images captured under extreme low-light conditions. Existing methods demonstrate that fusing Fourier transform magnitude and phase information within the RGB color space effectively improves enhancement results. Meanwhile, recent advances have demonstrated that certain color spaces based on human visual perception, such as Hue–Value–Intensity (HVI), are superior to RGB for enhancing low-light images. However, these methods neglect the key impact of the coupling relationship between spatial and frequency-domain features on image enhancement. This paper proposes a spatial–frequency-domain multi-scale fusion for low-light image enhancement by exploring the intrinsic relationships among the three channels of HVI space, which consists of a dual-path parallel processing architecture. In the spatial domain, a specifically designed multi-scale feature extraction module systematically captures comprehensive structural information. In the frequency domain, our model establishes deep coupling between spatial features and Fourier transform features in the I-channel. The effectively fused features from both domains synergistically drive an encoder–decoder network to achieve superior image enhancement performance. Extensive experiments on multiple public benchmark datasets show that the proposed method significantly outperforms state-of-the-art approaches in both quantitative metrics and visual quality. Full article

Extremely high temperatures can severely impact urban livability and public health safety. However, risk assessments for high temperatures in cold-region cities remain inadequate. This study focuses on Dalian, a coastal city in northeastern China. Utilizing multi-source data, we established a population density prediction model based on the random forest algorithm and a heat vulnerability index (HVI) framework following the “Exposure-Sensitivity-Adaptability” paradigm constructed using an indicator system method, thereby building a high-temperature risk assessment system suited for more refined research. The results indicate the following: (1) Strong positive correlations exist between nighttime light brightness (NL), Road Density (RD), the proportion of flat area (SLP), the land surface temperature (LST), and the population distribution density, with correlation coefficients reaching 0.963, 0.963, 0.956, and 0.954, respectively. (2) Significant disparities exist in the spatial distribution of different criterion layers within the study area. Areas characterized by high exposure, high sensitivity, and low adaptability account for 13.04%, 8.05%, and 21.44% of the total area, respectively, with exposure being the primary contributing factor to high-temperature risk. (3) Areas classified as high-risk or extremely high-risk for high temperatures constitute 31.57% of the study area. The spatial distribution exhibits a distinct pattern, decreasing gradually from east to west and from the coast inland. This study provides a valuable tool for decision-makers to propose targeted adaptation strategies and measures based on the assessment results, thereby better addressing the challenges posed by climate change-induced high-temperature risks and promoting sustainable urban development. Full article

This paper proposes a novel Denoiser and Low-Frequency Enhancer Network (DLFE-Net) for Low-Light Image Enhancement (LLIE). The DLFE-Net addresses two key challenges: (1) overexposure and detail loss in local areas during enhancement, and (2) the effective removal of inherent noise in low-light images. Specifically, the input RGB image is first converted to the HVI color space. The intensity (I) and color (H, V) maps are then enhanced and denoised separately, i.e., preserving details and removing noise. For preserving details, the Low-Frequency Illumination Enhancer (LFIE) module isolates and processes the image’s low-frequency information. This targeted approach effectively mitigates local overexposure and preserves fine details during enhancement. For removing noise, the Multi-Scale Gated Denoiser (MSGD) module performs denoising through strong preservation after predicting image noise. Comprehensive experiments were conducted on three benchmark datasets (LOL, SICE, Sony-Total-Dark) and five unpaired datasets. Both qualitative and quantitative analyses demonstrated the superiority of DLFE-Net over state-of-the-art methods. Moreover, ablation studies demonstrated the effectiveness of each module in DLFE-Net. Full article

Background: Hepatocellular carcinoma (HCC) with hepatic vein invasion (HVI) is classified as advanced stage and palliative management is the primary treatment option. This study compared the long-term outcomes of curative and non-curative treatments in patients of HCC with HVI. Methods: Data were obtained from a retrospective multicenter cohort of the Korean Primary Liver Cancer Registry. We reviewed 18,315 patients newly diagnosed with HCC between 2008 and 2019. After propensity score matching based on the albumin-bilirubin (ALBI) score; tumor number, and tumor size, clinical outcomes were compared between the curative group (n = 42, 29.0%) undergoing surgical resection or local ablation and the non-curative group (n = 103, 71.0%) receiving other treatments. Results: Tumor burdens such as tumor number, maximum tumor size, levels of alpha-fetoprotein (AFP), and protein induced by absence of vitamin K or antagonist-II did not differ significantly between the groups (p = 0.672, p = 0.143, p = 0.153 and p = 0.651, respectively). In long-term outcomes, the overall survival (OS) and cancer-specific survival (CSS) were significantly better in the curative group compared to the non-curative group (p < 0.001, both). Multivariate analysis indicated that non-curative treatment, ALBI grade ≥ 2, and AFP ≥ 400 ng/mL were common risk factors for OS and CSS. Conclusions: Curative-intent treatment has the potential to significantly enhance long-term outcomes in selected patients of HCC with HVI who preserved liver function and performance status. Full article

Nitrogen (N) is critical for maize (Zea mays L.) growth and yield, necessitating precise estimation of canopy nitrogen concentration (CNC) to optimize fertilization strategies. Remote sensing technologies, such as proximal hyperspectral sensors and unmanned aerial vehicle (UAV)-based multispectral imaging, offer promising solutions for non-destructive CNC monitoring. This study evaluates the effectiveness of proximal hyperspectral sensor and UAV-based multispectral data integration in estimating CNC for spring maize during key growth stages (from the 11th leaf stage, V11, to the Silking stage, R1). Field experiments were conducted to collect multispectral data (20 vegetation indices [MVI] and 24 texture indices [MTI]), hyperspectral data (24 vegetation indices [HVI] and 20 characteristic indices [HCI]), alongside laboratory analysis of 120 CNC samples. The Boruta algorithm identified important features from integrated datasets, followed by correlation analysis between these features and CNC and Random Forest (RF)-based modeling, with SHAP (SHapley Additive exPlanations) values interpreting feature contributions. Results demonstrated the UAV-based multispectral model achieved high accuracy and Computational Efficiency (CE) (R² = 0.879, RMSE = 0.212, CE = 2.075), outperforming the hyperspectral HVI-HCI model (R² = 0.832, RMSE = 0.250, CE =2.080). Integrating multispectral and hyperspectral features yields a high-precision model for CNC model estimation (R² = 0.903, RMSE = 0.190), outperforming standalone multispectral and hyperspectral models by 2.73% and 8.53%, respectively. However, the CE of the integrated model decreased by 1.93% and 1.68%, respectively. Key features included multispectral red-edge indices (NREI, NDRE, CI) and texture parameters (R1m), alongside hyperspectral indices (SR, PRI) and spectral parameters (SDy, Rg) exhibited varying directional impacts on CNC estimation using RF. Together, these findings highlight that the Boruta–RF–SHAP strategy demonstrates the synergistic value of integrating multi-source data from UAV-based multispectral and proximal hyperspectral sensing data for enhancing precise nitrogen management in maize cultivation. Full article