Search Results (3,084)

Background/Objectives: Hypodermoclysis has gained increasing recognition as a safe, effective, and minimally invasive method for administering medication and fluids in palliative care. Despite its advantages, its adoption remains limited, primarily due to a lack of structured training resources for healthcare professionals. This study aimed to develop and validate an educational tool for training clinical nurses in hypodermoclysis administration in palliative care. Methods: This is a methodological study with a multi-methods approach. Study development involved a needs assessment with 48 professionals, a literature review, and the creation of a manual enriched with visual aids. Results: The material was validated by expert judges, technical reviewers, and the target audience. Organized into 21 chapters, the manual comprehensively addresses technical, theoretical, and ethical dimensions of the practice. Content validation by 14 experts yielded an outstanding global Content Validity Index (CVI) of 0.95. An independent evaluation of visual design by four communication specialists produced consistently high scores (91–96%), classifying the material as “superior” in quality. Feedback from target users (12 nurses) highlighted the manual’s clarity, applicability, and relevance. All constructive suggestions were incorporated into the final version. Conclusions: The resulting manual demonstrates strong validity as an educational resource, with significant potential to standardize and enhance hypodermoclysis training in palliative nursing, promoting both safety and humanized care. Full article

Understanding the dynamic changes in the quality of the ecological environment and its potential driving forces is essential for protecting regional ecosystems and promoting sustainable development. In this study, we developed an improved remote sensing ecological index (IRSEI) by integrating the kernel normalized difference vegetation index (kNDVI) with an abundance index (AI) and conducted a comprehensive analysis of the spatiotemporal evolution of the quality of the ecological environment in the North China Plain (NCP) from 2000 to 2020. A multistep driving analysis framework was established to identify key climatic factors via the XGBoost algorithm and to quantify the effects of climate change and human activities through partial correlation analysis and a multiple regression residual model. The results indicate the following: (1) From 2000 to 2020, the ecological quality of the NCP significantly improved, with the average IRSEI increasing from 0.41 to 0.45. The proportion of areas with “good” or “excellent” ecological quality increased, revealing a south–north gradient, with higher values in the southern part and lower values in the northern part of the NCP. (2) Among the key climatic variables, surface temperature was significantly negatively correlated with the IRSEI, whereas atmospheric pressure and evapotranspiration were significantly positively correlated. (3) Approximately 51.97% of the ecological quality changes were jointly driven by climate change and human activities, with the contribution of human activities (28.80%) exceeding that of climate change (19.23%). These findings provide a scientific basis for understanding the driving mechanisms behind ecological environment changes and support ecological restoration and coordinated human–environment development in the context of climate change. Full article

Net ecosystem productivity (NEP) serves as a key indicator for assessing regional carbon sink potential, with its dynamics regulated by nonlinear interactions among multiple factors. However, its driving factors and their coupling processes remain insufficiently characterized. This study investigated terrestrial ecosystems in Yunnan Province, China, to elucidate the drivers of NEP using 14 environmental factors (including topography, meteorology, soil texture, and human activities) and 21 remote sensing features. We developed a research framework based on “Feature Selection–Machine Learning–Mechanism Interpretation.” The results demonstrated that the Variable Selection Using Random Forests (VSURF) feature selection method effectively reduced model complexity. The selected features achieved high estimation accuracy across three machine learning models, with the eXtreme Gradient Boosting Regression (XGBR) model performing optimally (R² = 0.94, RMSE = 76.82 gC/(m²·a), MAE = 55.11 gC/(m²·a)). Interpretation analysis using the SHAP (SHapley Additive exPlanations) method revealed the following: (1) The Enhanced Vegetation Index (EVI), soil pH, solar radiation, air temperature, clay content, precipitation, sand content, and vegetation type were the primary drivers of NEP in Yunnan. Notably, EVI’s importance exceeded that of other factors by approximately 3 to 10 times. (2) Significant interactions existed between soil texture and temperature: Under low-temperature conditions (−5 °C to 12.15 °C), moderate clay content (13–25%) combined with high sand content (40–55%) suppressed NEP. Conversely, within the medium to high temperature range (5 °C to 23.79 °C), high clay content (25–40%) coupled with low sand content (25–43%) enhanced NEP. These findings elucidate the complex driving mechanisms of NEP in subtropical ecosystems, confirming the dominant role of EVI in carbon sequestration and revealing nonlinear regulatory patterns in soil–temperature interactions. This study provides not only a robust “Feature Selection–Machine Learning–Mechanism Interpretation” modeling framework for assessing carbon budgets in mountainous regions but also a scientific basis for formulating regional carbon management policies. Full article

This study presents an extended dataset on educational quality covering 101 countries, from 1970 to 2023. While existing international assessments, such as the Programme for International Student Assessment (PISA) and Trends in International Mathematics and Science Study (TIMSS), offer valuable snapshots of student performance, their limited coverage across countries and years constrains broader analyses. To address this limitation, we harmonized observed test scores across assessments and imputed missing values using both linear interpolation and machine learning (Least Absolute Shrinkage and Selection Operator (LASSO) regression). The dataset included (i) harmonized test scores for 15 year olds, (ii) annual educational quality indicators for the 15–19 age group, and (iii) educational quality indexes for the working-age population (15–64). These measures are provided in machine-readable formats and support empirical research on human capital, economic development, and global education inequalities across economies. Full article

Climate change poses unprecedented challenges to forest ecosystems. Betula alnoides, a tree species with significant ecological and economic value in southern China, has been the subject of studies on its distribution pattern and response to climate change. However, research on the distribution pattern of B. alnoides and its response to climate change remains relatively limited. In this study, we developed a MaxEnt model incorporating multiple environmental variables, including climate, topography, soil, vegetation, and human activities, to evaluate model performance, identify key factors influencing the distribution of B. alnoides, and project its potential distribution under various future climate scenarios. Species occurrence data and environmental layers were compiled for China, and model parameters were optimized using the ENMeval package. The results showed that the optimized model achieved an AUC value of 0.956, indicating extremely high predictive accuracy. The four key factors affecting the distribution of B. alnoides were standard deviation of temperature seasonality (Bio4), normalized difference vegetation index (NDVI), mean temperature of driest quarter (Bio9), and annual precipitation (Bio12). Among them, the cumulative contribution rate of climatic factors reached 68.9%, but the influence of NDVI was significantly higher than that of precipitation factors. The current suitable habitat of B. alnoides is mainly concentrated in the southwestern region, covering an area of 179.32 × 10⁴ km², which accounts for 18.68% of China’s land area. Under the SSP126 scenario, the suitable habitat area first decreases and then increases in the future, while under the SSP370 and SSP585 scenarios, the suitable habitat area continues to shrink, with significant losses in high-suitability areas. In addition, the centroid of the suitable habitat of B. alnoides shows an overall trend of shifting northwestward. This indicates that B. alnoides is highly sensitive to climate change and its distribution pattern will undergo significant changes in the future. In conclusion, the distribution pattern of B. alnoides shows a significant response to climate change, with particularly prominent losses in high-suitability areas in the future. Therefore, it is recommended to strengthen the protection of high-suitability areas in the southwestern region and consider B. alnoides as an alternative tree species for regions facing warming and drying trends to enhance its climate adaptability. Full article

Metropolitan areas worldwide are facing growing pressures, such as high population density, environmental degradation, and socio–economic challenges. Urban resilience has become a key focus in addressing these issues. This study explores the development of urban resilience research in metropolitan areas through a systematic review using the PRISMA method of SCOPUS-indexed articles. The review shows a significant annual increase in urban resilience studies, with three main themes clustered into environment, urban planning, and social–human dimensions. Highly cited research emphasizes urban concepts, resilience measurement of urban systems against various shocks, and resilience dimensions. Notably, metropolitan areas in Asia lead in urban resilience-related discussions, particularly in response to frequent and diverse shocks. Most studies apply quantitative methods at the city/metropolitan scale, using multi-dimensional resilience indicators. The literature highlights the distinct characteristics of Asian metropolitan regions compared to others, underlining the need to assess resilience not only in urban cores but also in peri-urban, desakota, and rural settings. These findings stress the importance of formulating policies that promote adaptive, sustainable and local ecosystem management to strengthen urban resilience across different metropolitan landscapes. Full article

Algal blooms pose a serious threat not only to the lake ecosystem of Lake Bosten but also by negatively impacting its rapidly developing fisheries and tourism industries. This study focuses on Lake Bosten as the research area and utilizes multi-source remote sensing imagery from Landsat TM/ETM+/OLI and Sentinel-2 MSI. The Adjusted Floating Algae Index (AFAI) was employed to extract algal blooms in Lake Bosten from 2004 to 2023, analyze their spatiotemporal evolution characteristics and driving factors, and construct a Long Short Term Memory (LSTM) network model to predict the spatial distribution of algal-bloom frequency. The stability of the model was assessed through temporal segmentation of historical data combined with temporal cross-validation. The results indicate that (1) during the study period, algal blooms in Lake Bosten were predominantly of low-risk level, with low-risk bloom coverage accounting for over 8% in both 2004 and 2005. The intensity of algal blooms in summer and autumn was significantly higher than in spring. The coverage of medium- and high-risk blooms reached 2.74% in the summer of 2004 and 3.03% in the autumn of 2005, while remaining below 1% in spring. (2) High-frequency algal bloom areas were mainly located in the western and northwestern parts of the lake, and the central region experienced significantly more frequent blooms during 2004–2013 compared to 2014–2023, particularly in spring and summer. (3) The LSTM model achieved an R² of 0.86, indicating relatively stable performance. The prediction results suggest a continued low frequency of algal blooms in the future, reflecting certain achievements in sustainable water-resource management. (4) The interactions among meteorological factors exhibited significant influence on bloom formation, with the q values of temperature and precipitation interactions both exceeding 0.5, making them the most prominent meteorological driving factors. Monitoring of sewage discharge and analysis of agricultural and industrial expansion revealed that human activities have a more direct impact on the water quality of Lake Bosten. In addition, changes in lake area and water environment were mainly influenced by anthropogenic factors, ultimately making human activities the primary driving force behind the spatiotemporal variations of algal blooms. This study improved the timeliness of algal-bloom monitoring through the integration of multi-source remote sensing and successfully predicted the future spatial distribution of bloom frequency, providing a scientific basis and decision-making support for the sustainable management of water resources in Lake Bosten. Full article

As the texture skeleton of the traditional village, the street space is the main area for tourists to visit in traditional villages; it is regarded as the spatial conversion place of human flow and the space frequently visited by tourists. Accumulating evidence shows that the visual quality of street spaces has an effect on pedestrians’ walking behaviors in urban areas, but this effect in traditional villages needs to be further explored. This paper takes Huangcun Village, Yixian County, Huangshan City, as the research area to explore the influence of the objective visual factors of street spaces on tourists’ subjective stay willingness. First, an evaluation system of the visual quality of street spaces was developed. With the assistance of computer vision and deep learning technologies, semantic segmentation of Huangcun Village street view images was performed to obtain a visual quality index and then calculate the descriptive index of Huangcun Village’s street space. Then, combining the data of tourists’ stay willingness with the visual quality of the street space, the overall evaluation results and space distribution of tourists’ stay willingness in Huangcun Village were predicted using the Trueskill algorithm and machine learning prediction model. Finally, the influence of the objective visual quality of the street space on tourist subjective stay willingness was analyzed by correlation analysis. This research could provide some useful information for street space design and tourism planning in traditional villages. Full article

This study builds an evaluation index system for the modernization of harmonious coexistence between man and nature from 2014 to 2024, including three criteria for ecological environmental protection, green economic transformation, and enhancement of people’s livelihood, to help Nanping City’s sustainable development and build a “Pilot Demonstration Zone for the Modernization of Harmonious Coexistence between Humans and Nature”. Despite the 2020 coronavirus pandemic, the study area’s modern development of harmonious coexistence between humans and nature increased from 2014 to 2024, narrowing the development gap between counties. The coupling coordination degree of the criterion layer is “high in the middle, low in the east and west, high in the south and low in the north”. Under the spatial spillover effect, counties with higher development stages radiate driving force on surrounding counties with lower development stages. In 2014, 2019, and 2024, resource agglomeration, social civilization progress, economic development, and government policy regulation drove harmonious coexistence between humans and nature in Nanping City. Full article

Rapid and large-scale monitoring of soil copper levels enables the quick identification of areas where copper concentrations significantly exceed safe thresholds. It allows for selecting regions that require treatment and protection and is essential for safeguarding environmental and human health. Widely adopted monitoring models that utilize ground- and uncrewed-aerial-vehicle-based spectral data are superior to labor-intensive and time-consuming traditional methods that rely on point sampling, chemical analysis, and spatial interpolation. However, these methods are unsuitable for large-scale observations. Therefore, this study investigates the potential of utilizing spaceborne GF-5 hyperspectral data for monitoring soil copper content. Ninety-five soil samples were collected from the Kalatage mining area in Xinjiang, China. Three-band indices were constructed using fractional derivative spectra, and estimation models were developed using spectral indices highly correlated with the copper content. The results show that the proposed three-band spectral index accurately identifies subtle spectral characteristics associated with the copper content. Although the model is relatively simple, selecting the correct fractional order is critical in constructing spectral indices. The three-band spectral index based on fractional derivatives with orders of less than 0.6 provides higher accuracy than higher-order fractional derivatives. The index with spectral wavelengths of 426.796 nm, 512.275 nm, and 974.245 nm with 0.35-order derivatives exhibits the optimal performance (R² = 0.51, RPD = 1.46). Additionally, we proposed a novel approach that identifies the three-band indices exhibiting a strong correlation with the copper content. Subsequently, the selected indices were used as independent variables to develop new spectral indices for model development. This approach provides higher performance than models that use spectral indices derived from individual band values. The model utilizing the proposed spectral index achieved the best performance (R² = 0.56, RPD = 1.52). These results indicate that utilizing GF-5 hyperspectral data for large-scale monitoring of soil copper content is feasible and practical. Full article

This study aims to systematically investigate the phytochemical and pharmacological characteristics of Albizia julibrissin Durazz (A. julibrissin), a plant well-regarded in ethnopharmacology. While previous analyses cover A. julibrissin, this work provides an updated analysis of recent research, driven by its medicinal potential and the rising interest in its therapeutic uses. Known for its significant medicinal potential, A. julibrissin contains a wide range of bioactive compounds, including triterpenoid julibrosides, flavonoids, and lignans. Comprehensive in vitro and in vivo studies across various cell lines and animal models have demonstrated its notable pharmacological attributes, such as antitumor, antidepressant, anxiolytic, anti-obesity, antimicrobial, and antiparasitic effects. To capture recent advancements, a comprehensive search was conducted and scientific literature was indexed, followed by a comparative pharmacological analysis. This review compiles recent research developments from 2004 to 2024, highlighting the potential role of A. julibrissin in therapeutic applications for human diseases. Full article

Under intensified global climate change and complex land use transitions, the Leaf Area Index (LAI) serves as a key ecological indicator to monitor vegetation responses to natural and anthropogenic factors. This study provided a comprehensive spatiotemporal diagnosis of the LAI and uniquely integrated remote sensing data with the Geodetector model to quantitatively assess both individual and interactive effects of natural and human drivers. Specifically, we analyzed LAI dynamics in the Jinsha River Basin from 2000 to 2023 using Sen’s Slope and Mann–Kendall tests, combined with Geodetector modeling to identify drivers and their interactions. Furthermore, ARIMA-based forecasting offered forward-looking insights to support land use planning and ecosystem resilience. Results revealed a fluctuating upward trend in LAI, with larger areas improving than degrading, and distinct seasonal and spatial patterns, with a notably higher LAI in southern regions. Elevation and temperature were the primary drivers, explaining 57% and 54% of spatial variation, respectively, with their combined effects further enhancing explanatory power. The future LAI trend appeared stable without significant changes. These findings demonstrated LAI’s utility for assessing land use change impacts and ecological sustainability, providing a scientific basis for land use optimization, ecological restoration, and sustainable regional development under the human–earth system framework. Full article

The built environment, technology, human health and well-being, and sustainable development are closely related, and human-centered therapeutic design in environmental, architectural, spatial, and landscape domains demonstrates human health promotion potential. This study examines health promotion design measurement indices and develops a scale for sub-healthy tourists in a hot spring tourism destination. Recent research mainly emphasized the development and utilization of hot spring resources and their economic benefits. It rarely provided a systematic discussion on the health-promoting impact of environmental design and evaluation criteria. Thus, this study employs the literature review method and the Delphi expert method to construct an index system and measurement scale that comprises 20 evaluation indices of physical, mental, and social health. Moreover, this study conducts a questionnaire survey to measure the physical, mental, and social health of sub-healthy tourists and non-sub-healthy tourists (observation and control groups) and the sub-healthy tourists before and after their visit to the hot spring tourism destination (pre-test group and post-test group). The scale demonstrates satisfactory reliability and validity, and the health–healing design measurement indices for the sub-healthy tourists consist of three subscales: physical, mental, and social health. The evaluation index system and measurement scale can comprehensively and effectively measure the effects of healing and therapeutic design (HTD) and can be used as reliable tools for sub-healthy tourists in hot spring tourism. Full article

Breast cancer, a highly prevalent malignancy among women, continues to pose a significant global health challenge, as conventional therapies are often limited by adverse effects. This study developed and evaluated nanostructured lipid carriers (NLCs) encapsulating fatty acid amides (FAAs) semi-synthesized from andiroba oil and combined with silk fibroin (SF) as a novel therapeutic strategy. Methods: FAAs were synthesized via direct amidation and characterized by GC-MS, FT-IR, and ¹³C NMR. These fatty acid amides were then incorporated into NLCs containing SF. The formulation was evaluated for its physicochemical stability, cell selectivity, and cytotoxicity against 4T1 murine breast cancer cells and healthy human fibroblasts. Results: The NLC-FAA/SF formulation exhibited physicochemical stability (average particle size: 136.9 ± 23.6 nm; zeta potential: −8.3 ± 12.0 mV; polydispersity index: 0.19 ± 0.04), indicating a monodisperse and stable system. In vitro cytotoxicity assays demonstrated high selective activity against 4T1 murine breast cancer cells (IC₅₀ = 0.18 ± 0.06 μg/mL) and negligible toxicity to healthy human fibroblasts. Molecular docking studies revealed favorable interactions between the FAAs and cannabinoid receptors CB1 and CB2, with unsaturated FAAs showing higher binding scores and stability, suggesting their potential as cannabinoid receptor ligands. These findings highlight NLC-FAA/SF as a promising, selective, and effective nanoplatform for breast cancer treatment, warranting further investigation into its mechanism of action and in vivo efficacy. Full article

Rapid urbanization and climate change have exacerbated urban heat stress, underscoring the importance of research on human thermal comfort for sustainable urban development. This study analyzes the spatiotemporal variation and driving factors of apparent temperature in the Chang–Zhu–Tan urban agglomeration, China. The Humidex index, representing apparent temperature, was derived from multi-source remote sensing data (Landsat 8, MODIS) and meteorological variables (ERA5-Land reanalysis), employing atmospheric correction, random forest modeling, and path analysis. The results indicate pronounced spatiotemporal heterogeneity: apparent temperature reached its maximum in urban centers during summer (mean 52.9 °C) and its minimum in winter (mean 5.99 °C), following a decreasing gradient from urban core to periphery. Land cover emerged as a key driver, with vegetation (NDVI, r = −0.938) showing a strong negative correlation and built-up areas (NDBI, r = +0.8) a positive correlation with apparent temperature. Uniquely, in the Chang–Zhu–Tan region’s persistently high humidity, water bodies (MNDWI, r = +0.616) exhibited a positive correlation with apparent temperature, likely due to humidity-enhanced thermal perception in summer and relatively warmer water temperature in winter. Path analysis revealed that air temperature exerts the strongest direct positive influence on apparent temperature, while relative humidity and NDVI primarily act through indirect pathways. These findings provide scientific evidence to guide climate-adaptive urban planning and enhance human living conditions in humid environments. Full article