Search Results (1,875)

Background: Asprosin is a relatively recently discovered glucogenic adipokine secreted during fasting that plays an important role in various biochemical processes in the body, including those connected with obesity and insulin resistance. The aim of this exploratory study was to investigate the associations between selected hormonal, anthropometric, and lifestyle-related parameters and serum asprosin concentration. As studies concerning fertility and asprosin have so far been limited to men or women with PCOS, its role in the general female population remains largely unexplored. The direction of this exploration was thus pointed toward possible connections with female fertility. Methods: The case-control study group included 56 women of reproductive age (25–42 years), who were patients of the Reproductive Health Clinic and the Clinic of Endocrinology, Diabetology, and Internal Medicine of the Medical University of Białystok, Poland. The levels of selected hormones, including anti-Müllerian hormone (AMH), estradiol, sex hormone-binding globulin (SHBG), and testosterone, body composition parameters, and a lifestyle parameter—night fasting duration—were assessed to test their associations with serum asprosin concentration. Results: A weak negative correlation was found between AMH level and serum asprosin concentration, suggesting a potential link between asprosin and ovarian reserve. Furthermore, a moderate positive correlation was found between the percentage of total body water (TBW) and serum asprosin concentration. No significant associations were observed between the levels of the other tested hormones and serum asprosin concentration, or between body composition parameters or night fasting duration and serum asprosin concentration. The multivariate model designed in the study shows that AMH, TBW, and night fasting duration explain 23.4% of asprosin variability. Conclusions: Although the nature of the study is exploratory, the findings indicate that the role of asprosin in the female population—particularly its role in fertility—requires further research. Not only is the number of available studies on asprosin insufficient, but the results of this study partly contradict what is known about the hormone from previous studies, which were largely performed with male cohorts. In addition, the results of this study suggest that asprosin may indeed be involved in mechanisms related to female fertility, particularly those connected with ovarian reserve. Nevertheless, studies performed in larger, more homogeneous populations are necessary to confirm the role of asprosin in women, including its association with female fertility. Full article

Graphite has been recognized as a critical material by the United States (US), the European Union (EU), and Australia. Owing to its unique structure and properties, it is utilized in many industries and has played a key role in the clean energy sector, particularly in the lithium-ion battery (LIB) industries. With the projected increase in global graphite demand, driven by the shift to clean energy and the use of EVs, as well as the geographically concentrated production and reserves of natural graphite, interest in graphite recycling has increased, with a specific focus on using spent LIBs and other waste carbon material. Although most established and developing LIB recycling technologies are focused on cathode materials, some have started recycling graphite, with promising results. Based on the different secondary sources and recycling paths reported, hydrometallurgy-based treatment is usually employed, especially for the purification of graphite; greener alternatives are being explored, replacing HF both in lab-scale research and in industry. This offers a viable solution to resource dependency and mitigates the environmental impact associated with graphite production. These developments signal a trend toward sustainable and circular pathways for graphite recycling. Full article

Forest volume is an important information for assessing the economic value and carbon sequestration capacity of forest resources and serves as a key indicator for energy flow and biodiversity. Although remote sensing technology is applied to estimate volume, optical remote sensing data have limitations in capturing forest vertical height information and may suffer from reflectance saturation. While LiDAR data can provide more detailed vertical structural information, they come with high processing costs and limited observation range. Therefore, improving the accuracy of volume estimation through multi-source data fusion has become a crucial challenge and research focus in the field of forest remote sensing. In this study, we integrated Sentinel-2 multispectral data, Resource-3 stereoscopic imagery, UAV-based LiDAR data, and field survey data to quantitatively estimate the forest volume in Saihanwula Nature Reserve, located in Inner Mongolia, China, on the southern part of Daxing’anling Mountains. The study evaluated the performance of multi-source remote sensing features by using recursive feature elimination (RFE) to select the most relevant factors and applied four machine learning models—multiple linear regression (MLR), k-nearest neighbors (kNN), random forest (RF), and gradient boosting regression tree (GBRT)—to develop volume estimation models. The evaluation metrics include the coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (rRMSE). The results show that (1) forest Canopy Height Model (CHM) data were strongly correlated with forest volume, helping to alleviate the reflectance saturation issues inherent in spectral texture data. The fusion of CHM and spectral data resulted in an improved volume estimation model with R² = 0.75 and RMSE = 8.16 m³/hm², highlighting the importance of integrating multi-source canopy height information for more accurate volume estimation. (2) Volume estimation accuracy varied across different tree species. For Betula platyphylla, we obtained R² = 0.71 and RMSE = 6.96 m³/hm²; for Quercus mongolica, R² = 0.74 and RMSE = 6.90 m³/hm²; and for Populus davidiana, R² = 0.51 and RMSE = 9.29 m³/hm². The total forest volume in the Saihanwula Reserve ranges from 50 to 110 m³/hm². (3) Among the four machine learning models, GBRT consistently outperformed others in all evaluation metrics, achieving the highest R² of 0.86, lowest RMSE of 9.69 m³/hm², and lowest rRMSE of 24.57%, suggesting its potential for forest biomass estimation. In conclusion, accurate estimation of forest volume is critical for evaluating forest management practices and timber resources. While this integrated approach shows promise, its operational application requires further external validation and uncertainty analysis to support policy-relevant decisions. The integration of multi-source remote sensing data provides valuable support for forest resource accounting, economic value assessment, and monitoring dynamic changes in forest ecosystems. Full article

Hericium coralloides is a highly valued gourmet and medicinal species with growing market demand across East Asia, though industrial production remains limited by cultivation challenges. This study investigated the molecular characteristics, biological traits, domestication potential, and cultivation protocols of Hericium coralloides strains collected from the Changbaishan Nature Reserve (Jiling, China). Optimal conditions for mycelial growth included mannose as the preferred carbon source, peptone as the nitrogen source, 30 °C incubation temperature, pH 5.5, and magnesium sulfate as the essential inorganic salt. The fruiting bodies had a protein content of 2.43% g/100 g (fresh sample meter). Total amino acids comprised 53.3% of the total amino acid profile, while essential amino acids accounted for 114.11% relative to non-essential amino acids, indicating high nutritional value. Under optimized domestication conditions—70% hardwood chips, 20% cottonseed hulls, 8% bran, 1% malic acid, and 1% gypsum—bags reached full colonization in 28 days, with a 15-day maturation phase and initial fruiting occurring after 12–14 days. The interval between flushes was 10–12 days. The average yield reached 318.65 ± 31.74 g per bag, with a biological conversion rate of 63.73%. These findings demonstrate that Hericium coralloides possesses significant potential for edible and commercial applications. This study provides a robust theoretical foundation and resource reference for its artificial cultivation, supporting its broader industrial and economic utilization. Full article

Artificial intelligence (AI)-generated content (AIGC) is an innovative technology that utilizes machine learning, AI models, reward modeling, and natural language processing (NLP) to create diverse digital content such as videos, images, and text. It has the potential to support various human activities with significant implications in teaching and learning, facilitating heuristic teaching for educators. By using AIGC, teachers can create extensive knowledge content and effectively design instructional strategies to guide students, aligning with heuristic teaching. However, incorporating AIGC into heuristic teaching has controversies and concerns, which potentially mislead outcomes. Nevertheless, leveraging AIGC greatly benefits teachers in enhancing heuristic teaching. When integrating AIGC to support heuristic teaching, challenges and risks must be acknowledged and addressed. These challenges include the need for users to possess sufficient knowledge reserves to identify incorrect information and content generated by AIGC, the importance of avoiding excessive reliance on AIGC, ensuring users maintain control over their actions rather than being driven by AIGC, and the necessity of scrutinizing and verifying the accuracy of information and knowledge generated by AIGC to preserve its effectiveness. Full article

The Santurbán paramo is a sensitive high-mountain ecosystem exposed to pressures from extractive and agricultural activities, as well as increasing tourism. In response, this study presents a context-aware recommendation system designed to support sustainable tourism through the integration of deep neural networks and ontology-based semantic modeling. The proposed system delivers personalized recommendations—such as activities, accommodations, and ecological routes—by processing user preferences, geolocation data, and contextual features, including cost and popularity. The architecture combines a trained TensorFlow Lite model with a domain ontology enriched with GeoSPARQL for geospatial reasoning. All inference operations are conducted locally on Android devices, supported by SQLite for offline data storage, which ensures functionality in connectivity-restricted environments and preserves user privacy. Additionally, the system employs geofencing to trigger real-time environmental notifications when users approach ecologically sensitive zones, promoting responsible behavior and biodiversity awareness. By incorporating structured semantic knowledge with adaptive machine learning, the system enables low-latency, personalized, and conservation-oriented recommendations. This approach contributes to the sustainable management of natural reserves by aligning individual tourism experiences with ecological protection objectives, particularly in remote areas like the Santurbán paramo. Full article

The Mayanghe National Nature Reserve, a key habitat for the endangered François’ langur (Trachypithecus francoisi), faces significant anthropogenic disturbances, including extensive distribution of croplands, roads, and settlements. These human-modified features are predominantly concentrated at elevations between 500 and 800 m and on slopes of 10–20°, which notably overlap with the core elevation range utilized by François’ langur. Spatial analysis revealed that langurs primarily occupy areas within the 500–800 m elevation band, which comprises only 33% of the reserve but hosts a high density of human infrastructure—including approximately 4468 residential buildings and the majority of cropland and road networks. Despite slopes >60° representing just 18.52% of the area, langur habitat utilization peaked in these steep regions (exceeding 85.71%), indicating a strong preference for rugged karst terrain, likely due to reduced human interference. Habitat type analysis showed a clear preference for evergreen broadleaf forests (covering 37.19% of utilized areas), followed by shrublands. Landscape pattern metrics revealed high habitat fragmentation, with 457 discrete habitat patches and broadleaf forests displaying the highest edge density and total edge length. Connectivity analyses indicated that distribution areas exhibit a more continuous and aggregated habitat configuration than control areas. These results underscore François’ langur’s reliance on steep, forested karst habitats and highlight the urgent need to mitigate human-induced fragmentation in key elevation and slope zones to ensure the species’ long-term survival. Full article

The unique characteristics of desert vegetation, such as different leaf morphology, discrete canopy structures, sparse and uneven distribution, etc., pose significant challenges for remote sensing-based estimation of fractional vegetation cover (FVC). The Unmanned Aerial Vehicle (UAV) system can accurately distinguish vegetation patches, extract weak vegetation signals, and navigate through complex terrain, making it suitable for applications in small-scale FVC extraction. In this study, we selected the floodplain fan with Caragana korshinskii Kom as the constructive species in Hatengtaohai National Nature Reserve, Bayannur, Inner Mongolia, China, as our study area. We investigated the remote sensing extraction method of desert sparse vegetation cover by placing samples across three gradients: the top, middle, and edge of the fan. We then acquired UAV multispectral images; evaluated the applicability of various vegetation indices (VIs) using methods such as supervised classification, linear regression models, and machine learning; and explored the feasibility and stability of multiple machine learning models in this region. Our results indicate the following: (1) We discovered that the multispectral vegetation index is superior to the visible vegetation index and more suitable for FVC extraction in vegetation-sparse desert regions. (2) By comparing five machine learning regression models, it was found that the XGBoost and KNN models exhibited relatively lower estimation performance in the study area. The spatial distribution of plots appeared to influence the stability of the SVM model when estimating fractional vegetation cover (FVC). In contrast, the RF and LASSO models demonstrated robust stability across both training and testing datasets. Notably, the RF model achieved the best inversion performance (R² = 0.876, RMSE = 0.020, MAE = 0.016), indicating that RF is one of the most suitable models for retrieving FVC in naturally sparse desert vegetation. This study provides a valuable contribution to the limited existing research on remote sensing-based estimation of FVC and characterization of spatial heterogeneity in small-scale desert sparse vegetation ecosystems dominated by a single species. Full article

Wildfires can significantly alter soil physicochemical conditions and microbial communities in forest ecosystems. This study aimed to characterize the culturable soil fungal community and evaluate biological activity in Banco Totumo Bijibana, a protected dry tropical forest in Atlántico, Colombia, affected by a wildfire in 2014. Twenty soil samples were collected for microbiological (10 cm depth) and physicochemical (30 cm) analysis. Basal respiration was measured using Stotzky’s method, nitrogen mineralization via Rawls’ method, and fungal diversity through culture-based identification and colony-forming unit (CFU) counts. Diversity was assessed using Simpson, Shannon–Weaver, and ACE indices. The soils presented low organic matter (0.70%) and nitrogen content (0.035%), with reduced biological activity as indicated by basal respiration (0.12 kg C ha⁻¹ d⁻¹) and mineralized nitrogen (5.61 kg ha⁻¹). Four fungal morphotypes, likely from the genus Aspergillus, were identified. Simpson index indicated moderate dominance, while Shannon–Weaver values reflected low diversity. Correlation analysis showed Aspergillus-3 was positively associated with moisture, whereas Aspergillus-4 correlated negatively with pH and sand content. The species accumulation curve reached an asymptote, suggesting an adequate sampling effort. Although no control site was included, the findings provide a baseline characterization of post-fire soil microbial structure and function in a dry tropical ecosystem. Full article

Carbon storage is a critical factor for ensuring the provision of ecosystem services such as biodiversity conservation, particularly in nature reserves. Understanding the spatial and temporal dynamics of carbon storage within China’s nature reserves (NRs) is essential for evaluating their role in ecosystem conservation. Using NDVI values, we assessed vegetation carbon storage in NRs across China from 2000 to 2015. The results revealed a 63.06% increase in carbon storage within NRs over the 15-year period, with forest vegetation and grassland vegetation carbon storage increasing by 60.05% and 86.33%, respectively. Approximately 90% of NRs exhibited positive growth rates, with higher increases observed in northeastern and western China compared to other regions. While the carbon density of forest vegetation in NRs exceeded that of areas outside reserves, grassland vegetation displayed the opposite trend. Overall, vegetation carbon storage in NRs demonstrated a significant upward trajectory over the study period. These findings highlight the importance of nature reserves in safeguarding forest carbon functions; however, their protective effect on grassland vegetation carbon function was less pronounced. Full article

The Shenhu sea area is rich in unconsolidated hydrate reserves, but the formation mineral particles are small, the rock cementation is weak, and the coupling mechanism of hydrate phase change, fluid seepage, and formation deformation is complex, resulting in unclear productivity change law under depressurization exploitation. Therefore, a thermal–fluid–solid–chemical coupling model for natural gas hydrate depressurization exploitation in the Shenhu sea area was constructed to analyze the variation law of reservoir parameters and productivity. The results show that within 0–30 days, rapid near-well pressure drop (13.83→9.8 MPa, 36.37%) drives peak gas production (25,000 m³/d) via hydrate dissociation, with porosity (0.41→0.52) and permeability (75→100 mD) increasing. Within 30–60 days, slower pressure decline (9.8→8.6 MPa, 12.24%) and fines migration cause permeability fluctuations (120→90 mD), reducing gas production to 20,000 m³/d. Within 60–120 days, pressure stabilizes (~7.6 MPa) with residual hydrate saturation < 0.1, leading to stable low permeability (60 mD) and gas production (15,000 m³/d), with cumulative production reaching 2.2 × 10⁶ m³. This study clarifies that productivity is governed by coupled “pressure-driven dissociation–heat limitation–fines migration” mechanisms, providing key insights for optimizing depressurization strategies (e.g., timed heat supplementation, anti-clogging measures) to enhance commercial viability of unconsolidated hydrate reservoirs. Full article

Background/Objectives: Frailty is a multifactorial clinical syndrome characterized by diminished physiological reserves and increased vulnerability to stressors. It is increasingly recognized as a predictor of poor outcomes in cardiac rehabilitation (CR). However, how frailty is defined, assessed, and addressed across outpatient CR programmes remains unclear. This scoping review aimed to map the extent, range, and nature of research examining frailty in the context of outpatient CR, including how frailty is measured, its impact on CR participation and outcomes, and whether sex and gender considerations or participation barriers are reported. Methods: Following the PRISMA-ScR guidelines, we conducted a comprehensive search across six electronic databases (from inception to 15 May 2025). Eligible peer-reviewed studies included adult participants assessed for frailty using validated tools and enrolled in outpatient CR programmes. Two reviewers independently screened citations and extracted data. Results were synthesized descriptively and narratively across three domains: frailty assessment, sex and gender considerations, and barriers to CR participation. The protocol was registered with the Open Science Framework. Results: Thirty-nine studies met inclusion criteria, all conducted in the Americas, Western Pacific, or Europe. Frailty was assessed using 26 distinct tools, most commonly the Kihon Checklist, Fried’s Frailty Criteria, and Frailty Index. The median pre-CR frailty prevalence was 33.5%. Few studies (n = 15; 38.5%) re-assessed frailty post-CR. Sixteen studies reported sex or gender data, but none applied sex- or gender-based analysis (SGBA) frameworks. Only eight studies examined barriers to CR participation, identifying physical limitations, emotional distress, cognitive concerns, healthcare system-related factors, personal and social factors, and transportation as key barriers. Conclusions: The literature on frailty in CR remains fragmented, with heterogeneous assessment methods, limited global representation, and inconsistent attention to sex, gender, and participation barriers. Standardized frailty assessments and individualized CR programme adaptations are urgently needed to improve accessibility, adherence, and outcomes for frail individuals. Full article

Understanding the activity patterns of a species is essential for developing sound conservation and management plans. In this study, we used a camera-trapping technique to determine the activity patterns of bharal (Pseudois nayaur) in a marginal population in Wolong National Nature Reserve, Sichuan, China. Our results showed that these animals preferred to be active in the daytime from 08:00 to 20:00, with an activity peak between 10:00 and 14:00. In addition, we found that the species had a seasonal activity pattern with higher activity frequency in summer than in winter and that bharal were most active in a temperature range of 3–11 °C and at night with a waxing crescent moon, implying that the activity rhythm of the species is an adaptation to a subtropical high-altitude alpine area with vertical zonation in temperature. The pattern of movement and activity was also correlated with the moon phase. Full article

Energy storage technology is crucial for addressing the intermittency of renewable energy sources and plays a key role in power systems and electronic devices. In the field of energy storage systems, multivalent vanadium-based oxides have attracted widespread attention. Among these, vanadium dioxide (VO₂) is distinguished by its key advantages, including high theoretical capacity, low cost, and strong structural designability. The diverse crystalline structures and plentiful natural reserves of VO₂ offer a favorable foundation for facilitating charge transfer and regulating storage behavior during energy storage processes. This mini review provides an overview of the latest progress in VO₂-based materials for energy storage applications, specifically highlighting their roles in lithium-ion batteries, zinc-ion batteries, photoassisted batteries, and supercapacitors. Particular attention is given to their electrochemical properties, structural integrity, and prospects for development. Additionally, it explores future development directions to offer theoretical insights and strategic guidance for ongoing research and industrial application of VO₂. Full article

Economically, all countries seek sustained growth driven by domestic demand, investment, and exports; however, COVID-19 revealed the vulnerability of interconnected economic systems and a sharp contraction in global trade. The objective of this research is to analyze through an econometric model the effect of traditional exports on Peru’s economic growth during the 2012–2023 period. The study employed a quantitative approach with a non-experimental, longitudinal design, using quarterly data from the Central Reserve Bank of Peru and the National Bureau of Statistics of China, which were transformed into natural logarithms. Unit root tests, the ordinary least squares (OLS) method and a two-stage least squares (2SLS) model were applied to correct for endogeneity. The results show that mining accounts for 81.7% of total traditional exports from Peru. The model indicated that a 1% increase in traditional exports leads to a 0.29% increase in GDP, confirming a positive impact. However, the high dependence of the mining sector exposes the economy to external risks. Therefore, a productive diversification strategy, alongside the modernization of the mining sector, is recommended to strengthen Peru’s economic resilience in the face of global crises and external fluctuations. Full article