Search Results (244)

This study investigates the thermal stability and microstructural evolution of the solid electrolyte medium used in DLyte^® dry electropolishing and dry anodizing processes. Samples were thermally aged between 30 °C and 45 °C to simulate Joule heating during industrial operation. Visual and SEM analyses revealed shape deformation and microcrack formation at temperatures above 40 °C, potentially reducing particle packing efficiency and electrolyte performance. Particle size distribution shifted from bimodal to trimodal upon aging, with an overall size reduction of up to 39.5% due to dehydration effects, impacting ionic transport properties. Weight-loss measurements indicated a diffusion-limited dehydration mechanism, stabilizing at 15–16% mass loss. Fourier transform infrared analysis confirmed water removal while maintaining the essential sulfonic acid groups responsible for ionic conductivity. In dry anodizing tests on titanium, aged electrolytes enhanced process efficiency, producing TiO₂ films with improved optical properties—color and brightness—while preserving thickness and uniformity (~70 nm). The results highlight the need to carefully control thermal exposure to maintain electrolyte integrity and ensure consistent process performance. Full article

Background/Objectives: Nitrogen (N) is an essential macronutrient that strongly influences maize growth and metabolism. While many studies have focused on nitrogen responses during later developmental stages, early-stage physiological and metabolic responses remain less explored. This study investigated the effect of different nitrogen-deficient levels on maize seedling growth and primary metabolite profiles. Methods: Seedlings were treated with N-modified nutrient solution, which contained 0% to 120% of the standard nitrogen level (8.5 mM). Results: Nitrogen starvation (N0) significantly reduced plant height (by 11–14%), shoot fresh weight (over 30%) compared to the optimal N supply (N100). Total leaf nitrogen content under N0–N20 was less than half of that in N100, whereas moderate N deficiency resulted in moderate reductions in growth and nitrogen content. Metabolite analysis revealed that N deficiency induced the accumulation of soluble sugars and organic acids (up to threefold), while sufficient N promoted the synthesis of amino acids related to nitrogen assimilation and protein biosynthesis. Statistical analyses (PCA and ANOVA) showed that both genotypes (MB and TYC) and tissue type (upper vs. lower leaves) influenced the metabolic response to nitrogen, with MB displaying more consistent shifts and TYC exhibiting greater variability under moderate stress. Conclusions: These findings highlight the sensitivity of maize seedlings to early nitrogen deficiency, with severity influenced by nitrogen level, tissue-specific position, and genotype; thus underscore the close coordination between physiological growth and primary metabolic pathways in response to nitrogen availability. These findings expand current knowledge of nitrogen response mechanisms and offer practical insights for improving nitrogen use efficiency in maize cultivation. Full article

The wave spectrum, as a key statistical feature describing wave energy distribution, is crucial for understanding wave propagation mechanisms and supporting ocean engineering applications. This study, based on ERA5 reanalysis spectrum data, proposes a model combining CNN and xLSTM for rapid gridded wave spectrum prediction over the Bohai and Yellow Seas domain. It uses 2D gridded spectrum data rather than a spectrum at specific points as input and analyzes the impact of various input factors at different time lags on wave development. The results show that incorporating water depth and mean sea level pressure significantly reduces errors. The model performs well across seasons with the seasonal spatial average root mean square error (SARMSE) of spectral energy remaining below 0.040 m²·s and RMSEs for significant wave height (SWH) and mean wave period (MWP) of 0.138 m and 1.331 s, respectively. At individual points, the spectral density bias is near zero, correlation coefficients range from 0.95 to 0.98, and the peak frequency RMSE is between 0.03 and 0.04 Hz. During a typical cold wave event, the model accurately reproduces the energy evolution and peak frequency shift. Buoy observations confirm that the model effectively tracks significant wave height trends under varying conditions. Moreover, applying a frequency-weighted loss function enhances the model’s ability to capture high-frequency spectral components, further improving prediction accuracy. Overall, the proposed method shows strong performance in spectrum prediction and provides a valuable approach for regional wave spectrum modeling. Full article

Background: Chronic Kidney Disease (CKD) is a major global health issue, with diabetes being its primary cause and cardiovascular disease contributing significantly to patient mortality. Recently, two classes of medications—sodium–glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1 RAs)—have shown promise in protecting both kidney and heart health beyond their effects on blood sugar control. Methods: We conducted a narrative review summarizing the findings of different clinical trials and mechanistic studies evaluating the effect of SGLT2i and GLP-1 RAs on kidney function, cardiovascular outcomes, and overall disease progression in patients with CKD and DKD. Results: SGLT2i significantly mitigate kidney injury by restoring tubuloglomerular feedback, reducing intraglomerular hypertension, and attenuating inflammation, fibrosis, and oxidative stress. GLP-1 RAs complement these effects by enhancing endothelial function, promoting weight and blood pressure control, and exerting direct anti-inflammatory and anti-fibrotic actions on renal tissues. Landmark trials—CREDENCE, DAPA-CKD, and EMPA-KIDNEY—demonstrate that SGLT2i reduce the risk of kidney failure and renal or cardiovascular death by 25–40% in both diabetic and non-diabetic CKD populations. Likewise, trials such as LEADER, SUSTAIN, and AWARD-7 confirm that GLP-1 RAs slow renal function decline and improve cardiovascular outcomes. Early evidence suggests that using both drugs together may offer even greater benefits through multiple mechanisms. Conclusions: SGLT2i and GLP-1 RAs have redefined the therapeutic landscape of CKD by offering organ-protective benefits that extend beyond glycemic control. Whether used individually or in combination, these agents represent a paradigm shift toward integrated cardiorenal-metabolic care. A deeper understanding of their mechanisms and clinical utility in both diabetic and non-diabetic populations can inform evidence-based strategies to slow disease progression, reduce cardiovascular risk, and improve long-term patient outcomes in CKD. Full article

In recent years, cross-domain few-shot learning (CDFSL) has demonstrated remarkable performance in hyperspectral image classification (HSIC), partially alleviating the distribution shift problem. However, most domain adaptation methods rely on similarity metrics to establish cross-domain class matching, making it difficult to simultaneously account for intra-class sample size variations and inherent inter-class differences. To address this problem, existing studies have introduced a class weighting mechanism within the prototype network framework, determining class weights by calculating inter-sample similarity through distance metrics. However, this method suffers from a dual limitation: susceptibility to noise interference and insufficient capacity to capture global class variations, which may lead to distorted weight allocation and consequently result in alignment bias. To solve these issues, we propose a novel class-discrepancy dynamic weighting-based cross-domain FSL (CDDW-CFSL) framework. It integrates three key components: (1) the class-weighted domain adaptation (CWDA) method dynamically measures cross-domain distribution shifts using global class mean discrepancies. It employs discrepancy-sensitive weighting to strengthen the alignment of critical categories, enabling accurate domain adaptation while maintaining feature topology; (2) the class mean refinement (CMR) method incorporates class covariance distance to compute distribution discrepancies between support set samples and class prototypes, enabling the precise capture of cross-domain feature internal structures; (3) a novel multi-dimensional feature extractor that captures both local spatial details and continuous spectral characteristics simultaneously, facilitating deep cross-dimensional feature fusion. The results in three publicly available HSIC datasets show the effectiveness of the CDDW-CFSL. Full article

This study evaluated the effects of early-life creep feeding with a high-protein, high-energy diet on growth performance, ruminal fermentation, and gut microbiota in Hanwoo calves (n = 10). Calves were assigned to control or treatment groups from birth to 6 months of age. No significant differences were observed in body weight, average daily gain (ADG), or feed conversion ratio (FCR), but ADG and dry matter intake (DMI) tended to be higher in the treatment group. Ruminal pH, NH₃-N, and volatile fatty acid (VFA) concentrations showed no significant differences. Fecal VFA profiles exhibited numerical trends suggesting higher propionate at 3 months and lower acetate, butyrate, and total VFA at 6 months in the treatment group, potentially reflecting altered substrate availability or absorption capacity, though these mechanisms were not directly measured. Microbiota analysis indicated stable ruminal alpha diversity, with numerical increases in fecal Bacteroidetes and genera such as Fournierella and Flavonifractor in the treatment group. These results suggest that early creep feeding with high-nutrition diets can support intake and promote potential shifts in hindgut microbiota composition without compromising overall microbial stability. Further research with larger sample sizes is needed to confirm these trends and assess long-term impacts on calf health and productivity. Full article

Biomimetic lattice structures, inspired by natural architectures such as bone, coral, mollusk shells, and Euplectella aspergillum, have gained increasing attention for their exceptional strength-to-weight ratios, energy absorption, and deformation control. These properties make them ideal for advanced engineering applications in aerospace, biomedical devices, and structural impact protection. This study presents a comprehensive bibliometric analysis of global research on biomimetic lattice structures published between 2020 and 2025, aiming to identify thematic trends, collaboration patterns, and underexplored areas. A curated dataset of 3685 publications was extracted from databases like PubMed, Dimensions, Scopus, IEEE, Google Scholar, and Science Direct and merged together. After the removal of duplication and cleaning, about 2226 full research articles selected for the bibliometric analysis excluding review works, conference papers, book chapters, and notes using Cite space, VOS viewer version 1.6.20, and Bibliometrix R packages (4.5. 64-bit) for mapping co-authorship networks, institutional affiliations, keyword co-occurrence, and citation relationships. A significant increase in the number of publications was found over the past year, reflecting growing interest in this area. The results identify China as the most prolific contributor, with substantial institutional support and active collaboration networks, especially with European research groups. Key research focuses include additive manufacturing, finite element modeling, machine learning-based design optimization, and the performance evaluation of bioinspired geometries. Notably, the integration of artificial intelligence into structural modeling is accelerating a shift toward data-driven design frameworks. However, gaps remain in geometric modeling standardization, fatigue behavior analysis, and the real-world validation of lattice structures under complex loading conditions. This study provides a strategic overview of current research directions and offers guidance for future interdisciplinary exploration. The insights are intended to support researchers and practitioners in advancing next-generation biomimetic materials with superior mechanical performance and application-specific adaptability. Full article

Background: The gut microbiota is increasingly recognized as a key modulator in obesity management, influencing host energy balance, lipid metabolism, and inflammatory pathways. With obesity prevalence continuing to rise globally, dietary interventions that promote beneficial microbial shifts are essential for enhancing weight loss outcomes and long-term health. Objective: This study investigated the effects of the multicomponent Weight Loss Maintenance 3 Phases Program (WLM3P), which integrates caloric restriction, a high-protein low-carbohydrate diet, time-restricted eating (10h TRE), dietary supplementation (prebiotics and phytochemicals), and digital app-based support on gut microbiota composition compared to a standard low-carbohydrate diet (LCD) in adults with obesity. The analysis focused exclusively on the 6-month weight loss period corresponding to Phases 1 and 2 of the WLM3P intervention. Methods: In this sub-analysis of a randomized controlled trial (ClinicalTrials.gov Identifier: NCT04192357), 58 adults with obesity (BMI 30.0–39.9 kg/m²) were randomized to the WLM3P (n = 29) or LCD (n = 29) groups. Stool samples were collected at baseline and 6 months for 16S rRNA sequencing. Alpha and beta diversity were assessed, and genus-level differential abundance was determined using EdgeR and LEfSe. Associations between microbial taxa and clinical outcomes were evaluated using regression models. Results: After 6-month, the WLM3P group showed a significant increase in alpha diversity (p = 0.03) and a significant change in beta diversity (p < 0.01), while no significant changes were observed in the LCD group. Differential abundance analysis revealed specific microbial signatures in WLM3P participants, including increased levels of Faecalibacterium. Notably, higher Faecalibacterium abundance was associated with greater reductions in fat mass (kg, %) and visceral adiposity (cm²) in the WLM3P group compared to LCD (p < 0.01). Conclusions: These findings suggest a potential microbiota-mediated mechanism in weight loss, where Faecalibacterium may enhance fat reduction effectiveness in the context of the WLM3P intervention. Full article

In rice detection tasks, accurate identification of leaf streaks, pest and disease distribution, and spikelet hierarchies relies on high-quality images to distinguish between texture and hierarchy. However, existing images often suffer from texture blurring and contour shifting due to equipment and environment limitations, which affects the detection performance. In view of the fact that pests and diseases affect the whole situation and tiny details are mostly localized, we propose a rice image reconstruction method based on an adaptive two-branch heterogeneous structure. The method consists of a low-frequency branch (LFB) that recovers global features using orientation-aware extended receptive fields to capture streaky global features, such as pests and diseases, and a high-frequency branch (HFB) that enhances detail edges through an adaptive enhancement mechanism to boost the clarity of local detail regions. By introducing the dynamic weight fusion mechanism (CSDW) and lightweight gating network (LFFN), the problem of the unbalanced fusion of frequency information for rice images in traditional methods is solved. Experiments on the 4× downsampled rice test set demonstrate that the proposed method achieves a 62% reduction in parameters compared to EDSR, 41% lower computational cost (30 G) than MambaIR-light, and an average PSNR improvement of 0.68% over other methods in the study while balancing memory usage (227 M) and inference speed. In downstream task validation, rice panicle maturity detection achieves a 61.5% increase in mAP50 (0.480 → 0.775) compared to interpolation methods, and leaf pest detection shows a 2.7% improvement in average mAP50 (0.949 → 0.975). This research provides an effective solution for lightweight rice image enhancement, with its dual-branch collaborative mechanism and dynamic fusion strategy establishing a new paradigm in agricultural rice image processing. Full article

Evaluating the economic value of carbon sinks is fundamental to advancing carbon market mechanisms and supporting sustainable regional development. This study focuses on Fujian Province in China, aiming to assess the spatiotemporal evolution of carbon sink value and analyze the influence of socio-economic drivers. Carbon sink values from 2000 to 2020 were estimated using Net Ecosystem Productivity (NEP) simulation combined with the carbon market valuation method. Eleven socio-economic variables were selected through correlation and multicollinearity testing, and their impacts were examined using Geographically and Temporally Weighted Regression (GTWR) at the county level. The results indicate that the total carbon sink value in Fujian declined from CNY 3.212 billion in 2000 to CNY 2.837 billion in 2020, showing a spatial pattern of higher values in the southern region and lower values in the north. GTWR analysis reveals spatiotemporal heterogeneity in the effects of socio-economic factors. For example, the influence of urbanization and retail sales of consumer goods shifts direction over time, while the effects of industrial structure, population, road, and fixed asset investment vary across space. This study emphasizes the necessity of incorporating spatial and temporal dynamics into carbon sink valuation. The findings suggest that northern areas of Fujian should prioritize ecological restoration, rapidly urbanizing regions should adopt green development strategies, and counties guided by investment and consumption should focus on sustainable development pathways to maintain and enhance carbon sink capacity. Full article

A longstanding ambiguity surrounds the operationalization of consciousness in artificial systems, complicated by the philosophical and cultural weight of subjective experience. This work examines whether cognitive architectures may be designed to support a functionally explicit form of artificial consciousness, focusing not on the replication of phenomenology, but rather on measurable, technically realizable introspective mechanisms. Drawing on a critical review of foundational and contemporary literature, this study articulates a conceptual and methodological shift: from investigating the experiential perspective of agents (“what it is like to be a bat”) to analyzing the informational, self-regulatory, and adaptive structures that enable purposive behavior. The approach combines theoretical analysis with a comparative review of major cognitive architectures, evaluating their capacity to implement access consciousness and internal monitoring. Findings indicate that several state-of-the-art systems already display core features associated with functional consciousness—such as self-explanation, context-sensitive adaptation, and performance evaluation—without invoking subjective states. These results support the thesis that cognitive engineering may progress more effectively by focusing on operational definitions of consciousness that are amenable to implementation and empirical validation. In conclusion, this perspective enables the development of artificial agents capable of autonomous reasoning and self-assessment, grounded in technical clarity rather than speculative constructs. Full article

The remote sensing monitoring of land use changes and future scenario simulation hold crucial significance for accurately characterizing urban expansion patterns, optimizing urban land use configurations, and thereby promoting coordinated regional development. Through the integration of multi-source data, this study systematically analyzes the spatiotemporal trajectories and driving forces of land use changes in the Pearl River Delta urban agglomeration (PRD) from 1990 to 2020 and further simulates the spatial patterns of urban land use under diverse development scenarios from 2025 to 2035. The results indicate the following: (1) During 1990–2020, urban expansion in the Pearl River Delta urban agglomeration exhibited a “stepwise growth” pattern, with an annual expansion rate of 3.7%. Regional land use remained dominated by forest (accounting for over 50%), while construction land surged from 6.5% to 21.8% of total land cover. The gravity center trajectory shifted southeastward. Concurrently, cropland fragmentation has intensified, accompanied by deteriorating connectivity of ecological lands. (2) Urban expansion in the PRD arises from synergistic interactions between natural and socioeconomic drivers. The Geographically and Temporally Weighted Regression (GTWR) model revealed that natural constraints—elevation (regression coefficients ranging −0.35 to −0.05) and river network density (−0.47 to −0.15)—exhibited significant spatial heterogeneity. Socioeconomic drivers dominated by year-end paved road area (0.26–0.28) and foreign direct investment (0.03–0.11) emerged as core expansion catalysts. Geographic detector analysis demonstrated pronounced interaction effects: all factor pairs exhibited either two-factor enhancement or nonlinear enhancement effects, with interaction explanatory power surpassing individual factors. (3) Validation of the Patch-generating Land Use Simulation (PLUS) model showed high reliability (Kappa coefficient = 0.9205, overall accuracy = 95.9%). Under the Natural Development Scenario, construction land would exceed the ecological security baseline, causing 408.60 km² of ecological space loss; Under the Ecological Protection Scenario, mandatory control boundaries could reduce cropland and forest loss by 3.04%, albeit with unused land development intensity rising to 24.09%; Under the Economic Development Scenario, cross-city contiguous development zones along the Pearl River Estuary would emerge, with land development intensity peaking in Guangzhou–Foshan and Shenzhen–Dongguan border areas. This study deciphers the spatiotemporal dynamics, driving mechanisms, and scenario outcomes of urban agglomeration expansion, providing critical insights for formulating regionally differentiated policies. Full article

Federated learning (FL) has emerged as a prominent distributed machine learning paradigm that facilitates collaborative model training across multiple clients while ensuring data privacy. Despite its growing adoption in practical applications, performance degradation caused by data heterogeneity—commonly referred to as the non-independent and identically distributed (non-IID) nature of client data—remains a fundamental challenge. To mitigate this issue, a heterogeneity-aware and robust FL framework is proposed to enhance model generalization and stability under non-IID conditions. The proposed approach introduces two key innovations. First, a heterogeneity quantification mechanism is designed based on statistical feature distributions, enabling the effective measurement of inter-client data discrepancies. This metric is further employed to guide the model aggregation process through a heterogeneity-aware weighted strategy. Second, a multi-loss optimization scheme is formulated, integrating classification loss, heterogeneity loss, feature center alignment, and L2 regularization for improved robustness against distributional shifts during local training. Comprehensive experiments are conducted on four benchmark datasets, including CIFAR-10, SVHN, MNIST, and NotMNIST under Dirichlet-based heterogeneity settings (alpha = 0.1 and alpha = 0.5). The results demonstrate that the proposed method consistently outperforms baseline approaches such as FedAvg, FedProx, FedSAM, and FedMOON. Notably, an accuracy improvement of approximately 4.19% over FedSAM is observed on CIFAR-10 (alpha = 0.5), and a 1.82% gain over FedMOON on SVHN (alpha = 0.1), along with stable enhancements on MNIST and NotMNIST. Furthermore, ablation studies confirm the contribution and necessity of each component in addressing data heterogeneity. Full article

The gut microbiota, dominated by bacteria from the Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria phyla, plays an essential role in fermenting indigestible carbohydrates, regulating metabolism, synthesizing vitamins, and maintaining immune functions and intestinal barrier integrity. Dysbiosis is associated with obesity development. Shifts in the ratio of Firmicutes to Bacteroidetes, particularly an increase in Firmicutes, may promote enhanced energy storage, appetite dysregulation, and increased inflammatory processes linked to insulin resistance and other metabolic disorders. The purpose of this literature review is to summarize the current state of knowledge on the relationship between the development and treatment of obesity and overweight and the gut microbiota. Current evidence suggests that probiotics, prebiotics, synbiotics, and fecal microbiota transplantation (FMT) can influence gut microbiota composition and metabolic parameters, including body weight and BMI. The most promising effects are observed with probiotic supplementation, particularly when combined with prebiotics, although efficacy depends on strain type, dose, and duration. Despite encouraging preclinical findings, FMT has shown limited and inconsistent results in human studies. Diet and physical activity are key modulators of the gut microbiota. Fiber, plant proteins, and omega-3 fatty acids support beneficial bacteria, while diets low in fiber and high in saturated fats promote dysbiosis. Aerobic exercise increases microbial diversity and supports growth of favorable bacterial strains. While microbiota changes do not always lead to immediate weight loss, modulating gut microbiota represents an important aspect of obesity prevention and treatment strategies. Further research is necessary to better understand the mechanisms and therapeutic potential of these interventions. Full article

This study investigates the effects of repeated mechanical recycling on the structural, thermal, mechanical, and aesthetic properties of poly(butylene succinate) (PBS), a commercially available bio-based and biodegradable aliphatic polyester. PBS production scraps were subjected to five consecutive recycling cycles through semi-industrial extrusion compounding followed by injection molding to simulate realistic mechanical reprocessing conditions. Melt mass-flow rate (MFR) analysis revealed a progressive increase in melt fluidity. Initially, the trend of viscosity followed the melt flow rate; however, increasing the reprocessing number (up to 5) resulted in a partial recovery of viscosity, which was caused by chain branching mechanisms. The phenomenon was also confirmed by data of molecular weight evaluation. Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) confirmed the thermal stability of the polymer, with minimal shifts in glass transition, crystallization, and degradation temperatures during the reprocessing cycles. Tensile tests revealed a slight reduction in strength and stiffness, but an increase in elongation at break, indicating improved ductility. Impact resistance declined moderately from 8.7 to 7.3 kJ/m² upon reprocessing; however, it exhibited a pronounced reduction to 1.8 kJ/m² at −50 °C, reflecting brittle behavior under sub-ambient conditions. Despite these variations, PBS maintained excellent color stability (ΔE < 1), ensuring aesthetic consistency while retaining good mechanical and thermal properties. Full article