Search Results (6,644)

A rise in seawater salinity results in an increase in its viscosity, which presents a coupled influence on the distribution of fluid pressure, temperature and deformation at the sealing face, leading to fluctuations in sealing performance and forming the salinity effect in seawater thermoelastohydrodynamic lubrication (TEHL). Here, for a double spiral groove face seal, a TEHL model is established and numerical analysis is carried out, taking account of the salinity effect and cavitation effect, with the aim to ensure that the seal maintains stable performance under varying conditions of sea depth and speed. It is found that the effect of salinity on the opening force and leakage rate exhibits obvious nonlinear variations. As salinity rises from 0 to the standard 35 g/kg, the opening force changes by about 5%, and there is a transition between forward and reverse leakage, with variations of approximately ±100%. More importantly, the double spiral grooves offer the potential for a zero-leakage design in seawater face seals, even under pressures exceeding 4 MPa, through precise design. Additionally, the double spiral groove face seal shows excellent adaptability under multipoint conditions and can facilitate a zero-leakage design in varying pressure, speed and temperature conditions. This provides theoretical support for deep-sea equipment and applications in other extreme environments. Full article

Ambient temperature plays a crucial role in shaping the skin color of some lizard species. While the long-term correlation between ambient temperature and skin color changes in lizards has been well-studied, how they adjust skin color and body temperature in response to short-term thermal fluctuations remains unclear. In this study, we examined the impacts of ambient temperature on the body temperature and skin color of Anolis carolinensis. In a white background, as the ambient temperature rose from 20 °C to 40 °C, both body surface and core temperatures increased; skin brightness rose from 71.47 to 88.05 cd/m², chroma decreased from 43.55% to 36.43%, and hue dropped from 95.80° to 78.82°. Their changes against a brown background were similar to those against a white background. Correlation analysis showed that brightness was positively correlated with body temperature, chromaticity was negatively correlated with it, and hue negatively correlated with body temperature in white backgrounds but showed no significant correlation in brown backgrounds. As the ambient temperature rose from 20 °C to 40 °C, the spectral reflectance of skin in the visible (300–700 nm) and near-infrared (700–2500 nm) range increased from 26.01 ± 0.57% to 30.22 ± 0.63% and 8.61 ± 1.20% to 11.71 ± 1.48%, respectively. These results demonstrate that the skin color and spectral reflectance variations in A. carolinensis play a role in body temperature regulation. Additionally, this study offers new insights into the adaptive strategies of ectothermic organisms in balancing skin color and body temperature in fluctuating ambient temperatures. Full article

In this article, we examine the dynamic interdependencies among components of Japan’s consumer price index (CPI) using a two-lag time-varying loading factor (TLTVLF) model. Whereas previous studies have typically decomposed CPI series into long-term trends, seasonal patterns, and cyclical fluctuations, such approaches mainly describe structural features without fully uncovering the latent mechanisms that drive price dynamics. The proposed TLTVFL modeling framework addresses this limitation by allowing both factor loadings and their lagged effects to evolve over time, thereby capturing gradual structural changes and the time-varying propagation of shocks across CPI categories. Using monthly data for ten major CPI categories from January 1970 to December 2024, we identify evolving common factors, category-specific sensitivities, and dynamic transmission patterns associated with major macroeconomic events. The findings reveal substantial temporal variation in inter-category linkages, offering fresh insights into sectoral contributions to inflationary pressures and providing policy-relevant implications for more effective monetary and fiscal interventions. Methodologically, this study extends the frontier of dynamic factor modeling, while empirically, it deepens the understanding of the mechanisms underlying price fluctuations over a long historical horizon. Full article

Extensive coal mining activities can exert substantial negative impacts on surface ecosystems. Vegetation indices are widely recognized as effective indicators of land ecological conditions and provide valuable insights into long-term ecological changes in mining areas. In this study, the Zhaogu mining area of the Jiaozuo Coalfield was selected as the study site. Using the Google Earth Engine (GEE) platform, the Kernel Normalized Difference Vegetation Index (kNDVI) was constructed to generate a vegetation dataset covering the period from 2010 to 2024. The temporal dynamics and future trends of vegetation coverage were analyzed using Theil–Sen median trend analysis, the Mann–Kendall test, the Hurst index, and residual analysis. Furthermore, the relative contributions of climatic factors and human activities to vegetation changes were quantitatively assessed. The results indicate that: (1) vegetation coverage in the Zhaogu mining area exhibits an overall improving trend, affecting approximately 77.1% of the study area, while slight degradation is mainly concentrated in the southeastern region, accounting for about 15.2%; (2) vegetation dynamics are predominantly characterized by low and relatively low fluctuations, covering approximately 78.5% of the region, whereas areas with high fluctuations are limited and mainly distributed in zones with intensive mining activities; although the current vegetation trend is generally increasing, future projections suggest a potential decline in approximately 55.8% of the area; and (3) vegetation changes in the Zhaogu mining area are jointly influenced by climatic factors and human activities, with climatic factors promoting vegetation growth in approximately 70.6% of the study area, while human activities exert inhibitory effects in about 24.2%, particularly in regions affected by mining operations and urban expansion. Full article

Raccoon dog fur is a commercially valuable animal product. As the scale of raccoon dog breeding continues to expand, ensuring the health of these animals has become an urgent priority. The gut microbiota plays a central role in regulating animal health; however, current research on the composition of raccoon dog gut microbiota remains limited. This study aimed to characterize changes in the gut microbiota of suckling raccoon dogs across different stages, providing a foundation for future scientific feeding practices. Fecal samples of eight lactating raccoon dogs were collected and tested for microbiota on days 14, 21, and 45. Our results showed that the richness and diversity of microbiota increased with age in suckling raccoon dogs, peaking on the 45th day. Significant separation between groups was observed in both PCoA and NMDS analyses. UPGMA analysis indicated temporal fluctuations in gut microbiota composition. At the phylum level, Firmicutes and Bacteroidetes were the dominant taxa across all stages. LEfSe analysis at the genus level showed that Bacteroides was the most enriched taxon on the 14th day, Fusobacterium on the 21st day, and Prevotella_9 on the 45th day. Tax4Fun and PICRUSt analyses identified metabolism and genetic information processing as the primary functional roles of the gut microbiota. Further investigation suggested that the microbiota may benefit raccoon dogs through membrane transport, carbohydrate metabolism, amino acid metabolism, and energy metabolism. These findings establish a theoretical basis for improving the survival rate of suckling raccoon dogs and developing scientifically informed feeding and management protocols. Full article

Multi-criteria decision-making (MCDM) models are moving toward being data-oriented. Meanwhile, MCDM models’ totalitarian reliance on experts’ preferences may reduce the accuracy of results in real-world challenges. Therefore, there is a huge gap in refining MCDM models to be data-structured rather than relying on experts’ and decision-makers’ ideas. In this research article, the primary indifference threshold values of the Indifference Threshold-based Attribute Ratio Analysis (ITARA) model, which is one of the popular objective weighting MCDM techniques, have been investigated and improved to achieve the goal of a full-objective MCDM model. ITARA utilizes decision-makers’ and experts’ opinions to set the indifference threshold values, which are integral to obtaining criteria weights, and since this step is not data-based, unlike the whole technique, it is prone to deficiencies. Three critical frameworks based on the minimum value, standard deviation, and max–min distance are designed to assess the sensitivity of the indifference threshold values and optimize the initialization values to start the model. Two case studies based on actual data are considered in this research to observe the frameworks’ outcomes and the rank reversal phenomenon. The results demonstrated that the assigning weights procedure is deeply sensitive to a max–min framework, while the standard deviation framework illustrated more stable results and a slight change in criteria rankings. The min framework moderately fluctuated between the max–min and standard deviation frameworks. Full article

Objective: Evaluate the efficacy and safety following intravitreal anti-vascular endothelial growth factor (anti-VEGF) therapy in patients with diabetic macular oedema (DME). Methods: To evaluate retinal microvascular remodelling and photoreceptor metrics using adaptive optics (AO) alongside systemic vascular status assessed by brachial/aortic hemodynamic and carotid ultrasound. We conducted a single-centre longitudinal study including twenty-one patients with DME. The following four diagnostic visits were performed: baseline (V1, no anti-VEGF treatment), 2–3 months (V2), 6–8 months (V3), and 12–14 months (V4). Adaptive optics (rtx1) measured foveal cone number (N) and regularity (Reg) within a standardised 80 × 80 µm window, and superior temporal retinal arteriole morphology after the first bifurcation (vessel diameter [VD], lumen diameter [LD], wall thickness [WT], wall-to-lumen ratio [WLR], and wall cross-sectional area [WCSA]). SphygmoCor provided peripheral (brachial) and central (aortic) pressures, augmentation pressure (AP), augmentation index (AIx), and carotid–femoral pulse wave velocity (PWV and PWVHR heart rate adjusted). Carotid ultrasound assessed intima–media thickness (IMT), carotid lumen diameter (CLD), and IMT/CLD ratio (IMTLR) 2 mm proximal to the bifurcation in diastole. Visual acuity (Visus), intraocular pressure (IOP), and central retinal thickness (CRT) were obtained at each visit. Results: In the treated eye (TE), WLR showed a significant overall change (Friedman p = 0.007), with a modest V4 vs. V1 increase (Wilcoxon p = 0.045); LD also varied across visits (Friedman p = 0.034). Cone metrics improved as follows: Reg increased over time (Friedman p = 0.019), with a significant rise at V4 vs. V1 (p = 0.018), and cone number increased at V3 vs. V1 (p = 0.012). Functional/structural outcomes improved as follows: visual acuity increased at V3 (p = 0.009) and V4 (p = 0.028), while CRT decreased at V3 (p = 0.002) and V4 (p = 0.030); IOP remained stable compared to V1. Systemic hemodynamics was largely unchanged; small fluctuations in DBP and cDBP across V1–V4 were observed (Friedman p = 0.034 and p = 0.022, respectively), whereas AIx, AP, PWV, and PWVHR showed no significant trends. Carotid IMT, CLD, and IMTLR did not change significantly across visits, supporting systemic vascular safety. Conclusions: Intravitreal anti-VEGF therapy in DME was associated with improvements in photoreceptor organisation and macular structure/function, with AO-derived arteriolar remodelling detectable over time, and no adverse changes in large-artery structure. These findings support ocular efficacy and systemic vascular safety; confirmation in larger cohorts is warranted. Full article

Background: In recent years, Poland has observed fluctuations in kidney biopsy frequency and shifts in diagnostic patterns. These trends likely reflect evolving clinical practice, diagnostic advancements, and changing disease epidemiology. This study aimed to analyse these changes, assess biopsy-based diagnoses across age groups, and examine sex-related variability. Methods: We conducted a single-centre, retrospective study at a university hospital in southwestern Poland, covering 2010–2024. Data from 1969 kidney biopsies were collected, within 1291 native kidney cases analysed after excluding transplant recipients. Diagnoses were correlated with patients’ age, sex, presence of diabetes, and temporal trends, and compared with previous studies. Results: Biopsy numbers increased over time, peaking in 2021 (154 procedures). Most were performed in patients aged 40–64 years (46.1%), followed by 18–39 years (39.1%) and ≥65 years (14.8%), with a rising proportion of elderly patients. Repeated biopsies occurred in 7.7% (second) and 0.6% (third biopsy). The most frequent diagnoses were IgAN (16.9%), FSGS (14.7%), and lupus nephritis (11.4%). In patients ≥65 years, amyloidosis (13.6%), FSGS (13.1%), vasculitis (13.1%), and membranous nephropathy (12%) predominated. The most marked sex-related difference involved lupus nephritis, accounting for 20.3% of diagnoses in women, who made up 82.3% of lupus nephritis cases. While most diseases showed male predominance, this was not evident for several, including IgAN and diabetic nephropathy. Conclusions: Given CKD’s underdiagnosis and frequent late detection in Poland, updated multicentre studies are needed to better recognise disease patterns and raise public awareness. Full article

With the increasing penetration of the distributed generation and the growing variability of loads, flexible microgrids (FMGs) require operational strategies that can adapt to seasonal changes while maintaining reliable performance. To overcome the limitations of fixed-interval partition updates, this paper proposes a threshold-triggered dynamic operation strategy for FMGs. A composite partition-updating index is formulated by integrating an operation optimization index, which reflects network loss and hybrid energy storage (HES) cost, with a seasonal load uniformity index, so that partition reconfiguration is triggered only when scenario transitions significantly deteriorate operating performance. By enhancing seasonal load uniformity across partitions, the proposed framework reflects a symmetry-oriented operation philosophy for FMGs. An HES model is further established to coordinate short-term energy storage (STES) and long-term energy storage (LTES) across multiple timescales. In conjunction with remotely controlled switches (RCSs), the proposed framework enables adaptive adjustment of FMG boundaries and source scheduling under diverse seasonal conditions. A case study on the IEEE 123-bus distribution system demonstrates that the proposed strategy effectively reduces power fluctuations and redundant switching operations, improves seasonal load uniformity, and enhances both the operational flexibility and economic efficiency of FMGs. Full article

This pilot study investigated multimodal physiological monitoring using minimally invasive and wearable sensors across two experimental settings. Experiment 1 involved five healthy adults (1 female) who simultaneously wore an interstitial fluid glucose (ISFG) sensor and a ring-type wearable device during sleep (00:00–06:00). Time-series analyses revealed that ISFG levels decreased during sleep in four of the five participants. ISFG values were significantly lower in the latter half of the sleep period compared with the first half (0–3 h vs. 3–6 h, p = 0.01, d = 2.056). Four participants also exhibited a mild reduction in SpO₂ between 03:00–04:00. These results suggest that nocturnal ISFG decline may be associated with subtle oxygen-saturation dynamics. Experiment 2 examined whether wearable sensors can detect physiological changes across meal-related phases. Nine male participants were monitored for heart rate (HR) and skin temperature during three periods: pre-meal (Phase 1: 09:00–09:30), during meal consumption (Phase 2: 12:30–13:00), and post-meal (Phase 3: 13:00–13:30). A paired comparison demonstrated a significant difference in median HR between Phase 1 and Phase 2 (p = 0.029, d = 0.812), indicating a large effect size. In contrast, HR–temperature correlation was weak and not statistically significant (Pearson r = 0.067, p = 0.298). Together, these findings demonstrate that multimodal wearable sensing can capture both nocturnal glucose fluctuations and meal-induced cardiovascular changes. This integrative approach may support real-time physiological risk assessment and future development of remote healthcare applications. Full article

The development of integrated renewable energy and high-density thermal energy storage systems has been fueled by the need for environmentally friendly heating and cooling in buildings. In this paper, MiniStor, a hybrid thermochemical and phase-change material storage system, is presented. It is equipped with a heat pump, advanced electronics-enabled control, photovoltaic–thermal panels, and flat-plate solar collectors. To optimize energy flows, regulate charging and discharging cycles, and maintain operational stability under fluctuating solar irradiance and building loads, the system utilizes state-of-the-art power electronics, variable-frequency drives and modular multi-level converters. The hybrid storage is safely, reliably, and efficiently integrated with building HVAC requirements owing to a multi-layer control architecture that is implemented via Internet of Things and SCADA platforms that allow for real-time monitoring, predictive operation, and fault detection. Data from the MiniStor prototype demonstrate effective thermal–electrical coordination, controlled energy consumption, and high responsiveness to dynamic environmental and demand conditions. The findings highlight the vital role that digital control, modern electronics, and Internet of Things-enabled supervision play in connecting small, high-density thermal storage and renewable energy generation. This strategy demonstrates the promise of electronics-driven integration for next-generation renewable energy solutions and provides a scalable route toward intelligent, robust, and effective building energy systems. Full article

Against the backdrop of intensifying global climate change and expanding human encroachment into mountainous regions, landslides have increased markedly in both frequency and destructiveness, emerging as a key risk to socio-ecological security and development in mountain areas. Rigorous assessment and forward-looking prediction of landslide disaster vulnerability (LDV) are essential for targeted disaster risk reduction and regional sustainability. However, existing studies largely center on landslide susceptibility or risk, often overlooking the dynamic evolution of adaptive capacity within affected systems and its nonlinear responses across temporal and spatial scales, thereby obscuring the complex mechanisms underpinning LDV. To address this gap, we examine Yunnan Province, a landslide-prone region of China where intensified extreme rainfall and the expansion of human activities in recent years have exacerbated landslide risk. Drawing on the vulnerability scoping diagram (VSD), we construct an exposure–sensitivity–adaptive capacity assessment framework to characterize the spatiotemporal distribution of LDV during 2000–2020. We further develop a multi-model, multi-scale integrated prediction framework, benchmarking the predictive performance of four machine learning algorithms—backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF), and XGBoost—across sample sizes ranging from 2500 to 360,000 to identify the optimal model–scale combination. From 2000 to 2020, LDV in Yunnan declined overall, exhibiting a spatial pattern of “higher in the northwest and lower in the southeast.” High-LDV areas decreased markedly, and sustained enhancement of adaptive capacity was the primary driver of the decline. At approximately the 90,000-cell grid scale, XGBoost performed best, robustly reproducing the observed spatiotemporal evolution and projecting continued declines in LDV during 2030–2050, albeit with decelerating improvement; low-LDV zones show phased fluctuations of “expansion followed by contraction”, whereas high-LDV zones continue to contract northwestward. The proposed multi-model, multi-scale fusion framework enhances the accuracy and robustness of LDV prediction, provides a scientific basis for precise disaster risk reduction strategies and resource optimization in Yunnan, and offers a quantitative reference for resilience building and policy design in analogous regions worldwide. Full article

Seasonal biomass-burning haze in Northern Thailand produces sharp fluctuations in ambient fine particulate matter (PM), posing heightened health risks, particularly for individuals with diabetes mellitus (DM). To identify PM-responsive biomarkers and assess whether metabolic status modifies these responses, we first performed small RNA sequencing in a discovery cohort using plasma samples collected during low- and high-PM periods. Thirteen circulating microRNAs (miRNAs) were differentially expressed, including reduced miR-542-3p and elevated miR-29a-3p, novelmiR-203, and novelmiR-754, with predicted targets enriched in immune and endoplasmic-reticulum stress pathways. These four miRNAs were quantified by RT-qPCR in a longitudinal cohort of adults with (n = 28) and without DM (n = 29) sampled at three PM-defined timepoints across one full haze cycle. In non-DM individuals, miR-542-3p decreased at peak exposure while miR-29a-3p and novelmiR-203 increased, with values returning toward baseline at re-exposure. DM participants showed altered baseline levels and attenuated or reversed seasonal changes. Plasma IL-8 rose markedly at peak PM in both groups, mirroring exosome concentration increases measured by NTA, indicating a transient systemic inflammatory response. In an independent clinical cohort, only miR-542-3p differed significantly between lung-cancer patients and healthy controls. These findings indicate that PM exposure reconfigures circulating miRNA, exosomal, and cytokine profiles, and that DM modifies these responses, highlighting miR-542-3p and miR-29a-3p as environmentally responsive and disease-relevant biomarker candidates. Full article

Monitoring water quality changes and identifying their driving factors are essential for the effective management of Dongjiang Lake. However, in-depth research on the spatiotemporal variations in the lake’s water quality and the complex interactions between natural and human factors remain insufficient. In this study, we aimed to characterize water quality trends and key physicochemical indicators in Dongjiang Lake by combining a 14-year water environmental dataset (2011–2024) and a correlation analysis. Our results showed that TN and COD_Mn concentrations displayed increasing trends, whereas the NH₃-N concentration showed a decreasing trend throughout the study period. The TN concentration initially decreased earlier in the year before increasing, with values ranging from 0.56 mg/L in September to 0.78 mg/L in November. The trends in COD_Mn concentration were the opposite to those of TN within the year, which first increased from 0.79 mg/L in January to 1.00 mg/L in June, and then decreased to 0.84 mg/L in December. The water level fluctuated inter-annually from 267.63 to 278.04 m during the study period, with a difference of 10.41 m. pH increased from 7.01 to 8.25, and dissolved oxygen decreased from 9.81 to 7.57. The WT fluctuates between 17.83 °C and 19.49 °C (p < 0.05). COD_Mn showed a highly significant positive correlation with transparency, pH, and water temperature, whereas NH₃-N showed a highly significant negative correlation with transparency, pH, and dissolved oxygen. Considering the importance of Dongjiang Lake as a freshwater resource and tourism hub, this study highlights the urgent need to prioritize pollution source control, while accounting for the lake’s deep-water dynamics and incorporating ecosystem-based restoration measures. Full article

Artificial intelligence (AI) is increasingly regarded as a transformative enabler of sustainable logistics and supply chain management, particularly in the context of global energy transition and decarbonization efforts. This study provides a comprehensive conceptual and exploratory assessment of the multidimensional role of AI, highlighting both its potential to enhance energy efficiency and reduce greenhouse gas emissions, as well as its inherent environmental costs associated with digital infrastructures such as data centers. The findings reveal the dual character of digitalization: while predictive algorithms and digital twin applications facilitate demand forecasting, process optimization, and real-time adaptation to market fluctuations, they simultaneously generate additional energy demand that must be offset through renewable energy integration and intelligent energy balancing. The analysis underscores that the effectiveness of AI deployment cannot be captured solely through economic metrics but requires a holistic evaluation framework that incorporates environmental and social dimensions. Moreover, regional disparities are identified, with advanced economies accelerating AI-driven green transformations under regulatory and societal pressures, while developing economies face constraints linked to infrastructure gaps and investment limitations. The analysis emphasizes that AI-driven predictive models and digital twin applications are not only tools for energy optimization but also mechanisms that enhance systemic resilience by enabling risk anticipation, adaptive resource allocation, and continuity of operations in volatile environment. The contribution of this study lies in situating AI within the digital–green synergy discourse, demonstrating that its role in logistics decarbonization is conditional upon integrated energy–climate strategies, organizational change, and workforce reskilling. By synthesizing emerging evidence, this article provides actionable insights for policymakers, managers, and scholars, and calls for more rigorous empirical research across sectors, regions, and time horizons to verify the long-term sustainability impacts of AI-enabled solutions in supply chains. Full article