Search Results (215)

Optimizing urban spatial morphology is one of the most effective methods for improving the urban thermal environment. Some studies have used the local climate zones (LCZ) classification system to examine the relationship between urban spatial morphology and Surface Urban Heat Islands (SUHIs). However, these studies often rely on single-time-point data, failing to consider the changes in urban space and the time-series LCZ mapping relationships. This study utilized remote sensing data from Landsat 5, 7, and 8–9 to retrieve land surface temperatures in Changsha from 2005 to 2020 using the Mono-Window Algorithm. The spatial-temporal evolution of the LCZ and the Surface Urban Heat Island Intensity (SUHII) was then examined and analyzed. This study aims to (1) propose a localized, long-time LCZ mapping method, (2) investigate the spatial-temporal relationship between the LCZ and the SUHII, and (3) develop a more convenient SUHI assessment method for urban planning and design. The results showed that the spatial-temporal evolution of the LCZ reflects the sequence of urban expansion. In terms of quantity, the number of built-type LCZs maintaining their original types is low, with each undergoing at least one type change. The open LCZs increased the most, followed by the sparse and the composite LCZs. Spatially, the LCZs experience reverse transitions due to urban expansion and quality improvements in central urban areas. Seasonal changes in the LCZ types and the SUHI vary, with differences not only among the LCZ types but also in building heights within the same type. The relative importance of the LCZ parameters also differs between seasons. The SUHI model constructed using Boosted Regression Trees (BRT) demonstrated high predictive accuracy, with R² values of 0.911 for summer and 0.777 for winter. In practical case validation, the model explained 97.86% of the data for summer and 96.77% for winter. This study provides evidence-based planning recommendations to mitigate urban heat and create a comfortable built environment. Full article

The adoption of telemedicine among the elderly is vital due to their unique healthcare needs and growing engagement with technology. This study explores the factors influencing their adoption behaviors, identifying both facilitating and inhibiting elements. While previous research has examined these factors, few have empirically assessed the simultaneous influence of barriers and enablers using a sample of elderly individuals. Using behavioral reasoning theory (BRT), this research investigates telehealth adoption behaviors of the elderly in India. A conceptual model incorporates both “reasons for” and “reasons against” adopting telehealth, capturing the nuanced dynamics of adoption behaviors. Data from 375 elderly individuals were collected to validate the model through structural equation modeling. The findings reveal that openness to change significantly enhances attitudes towards telehealth and “reasons for” adoption, influencing behaviors. This research contributes to the healthcare ecosystem by improving the understanding of telehealth adoption among the elderly. It validates the impact of openness to change alongside reasons for and against adoption, refining the understanding of behavior. By addressing impediments and leveraging facilitators, this study suggests strategies to maximize telehealth usage among the elderly, particularly those who are isolated, improving their access to medical services. Full article

A limited number of previous studies have focused on the selection of transportation routes considering sustainable development goals (SDGs). In this research, a methodology for selecting sustainable public transit (PT) routes is presented, consisting of generating a feasible initial route set, optimization, and assessment. Total welfare, road safety, and reduction in total emissions are indicators of the economic, social, and environmental dimensions, respectively. Based on the transportation model, the network structure, attributes, and emission rates are exported. The travel demand of PT is modified by modal share. Additionally, the safety performance function (SPF) is developed as a safety measure. Regarding optimization, the optimum routes are obtained by maximizing PT share and minimizing PT travel time. Then, the new routes are implemented, and the network is evaluated and compared with the existing scenario in light of sustainability indicators. The case study is Amman BRT. The results show that the new network is more sustainable than the existing BRT network and achieves better performance than the selected scenario of Amman city. The new network can reduce travel time by more than 13%, decrease total emissions by more than 17%, and alleviate the crash frequency by more than 14%. Full article

Climate change has severely impacted many species, causing rapid declines or extinctions within their essential ecological niches. This deterioration is expected to worsen, particularly in remote high-altitude regions like the Himalayas, which are home to diverse flora and fauna, including many mountainous ungulates. Unfortunately, many of these species lack adaptive strategies to cope with novel climatic conditions. The Red Goral (Naemorhedus baileyi) is a cliff-dwelling species classified as “Vulnerable” by the IUCN due to its small population and restricted range extent. This species has the most restricted range of all goral species, residing in the temperate mountains of northeastern India, northern Myanmar, and China. Given its restricted range and small population, this species is highly threatened by climate change and habitat disruptions, making habitat mapping and modeling crucial for effective conservation. This study employs an ensemble approach (BRT, GLM, MARS, and MaxEnt) in species distribution modeling to assess the distribution, habitat suitability, and connectivity of this species, addressing critical gaps in its understanding. The findings reveal deeply concerning trends, as the model identified only 21,363 km² (13.01%) of the total IUCN extent as suitable habitat under current conditions. This limited extent is alarming, as it leaves the species with very little refuge to thrive. Furthermore, this situation is compounded by the fact that only around 22.29% of this identified suitable habitat falls within protected areas (PAs), further constraining the species’ ability to survive in a protected landscape. The future projections paint even degraded scenarios, with a predicted decline of over 34% and excessive fragmentation in suitable habitat extent. In addition, the present study identifies precipitation seasonality and elevation as the primary contributing predictors to the distribution of this species. Furthermore, the study identifies nine designated transboundary PAs within the IUCN extent of the Red Goral and the connectivity among them to highlight the crucial role in supporting the species’ survival over time. Moreover, the Dibang Wildlife Sanctuary (DWLS) and Hkakaborazi National Park are revealed as the PAs with the largest extent of suitable habitat in the present scenario. Furthermore, the highest mean connectivity was found between DWLS and Mehao Wildlife Sanctuary (0.0583), while the lowest connectivity was observed between Kamlang Wildlife Sanctuary and Namdapha National Park (0.0172). The study also suggests strategic management planning that is a vital foundation for future research and conservation initiatives, aiming to ensure the long-term survival of the species in its natural habitat. Full article

Bamboo forests, as some of the integral components of forest ecosystems, have emerged as focal points in forestry research due to their rapid growth and substantial carbon sequestration capacities. In this paper, satellite-borne lidar data from GEDI and ICESat-2/ATLAS are utilized as the main information sources, with Landsat 9 and DEM data as covariates, combined with 51 pieces of ground-measured data. Using random forest regression (RFR), boosted regression tree (BRT), k-nearest neighbor (KNN), Cubist, extreme gradient boosting (XGBoost), and Stacking-ridge regression (RR) machine learning methods, an aboveground carbon (AGC) storage model was constructed at a regional scale. The model evaluation indices were the coefficient of determination (R²), root mean square error (RMSE), and overall estimation accuracy (P). The results showed that (1) The best-fit semivariogram models for c_dem, f_dem, f_ndvi, p_dem, and a_ndvi were Gaussian models, while those for h1_b7, h2_b7, h3_b7, and h4_b7 were spherical models; (2) According to Pearson correlation analysis, the AGC of Dendrocalamus giganteus showed an extremely significant correlation (p < 0.01) with c_dem and p_dem from GEDI, and also showed an extremely significant correlation with a_ndvi, h1_b7, h2_b7, h3_b7, and h4_b7 from ICESat-2/ATLAS; moreover, AGC showed a significant correlation (0.01 < p < 0.05) with f_dem and f_ndvi from GEDI; (3) The estimation accuracy of the GEDI model was superior to that of the ICESat-2/ATLAS model; additionally, the estimation accuracy of the Stacking-RR model, which integrates GEDI and ICESat-2/ATLAS (R² = 0.92, RMSE = 5.73 Mg/ha, p = 86.19%), was better than that of any single model (XGBoost, RFR, BRT, KNN, Cubist); (4) Based on the Stacking-RR model, the estimated AGC of Dendrocalamus giganteus within the study area was 1.02 × 10⁷ Mg. The average AGC was 43.61 Mg/ha, with a maximum value of 76.43 Mg/ha and a minimum value of 15.52 Mg/ha. This achievement can serve as a reference for estimating other bamboo species using GEDI and ICESat-2/ATLAS remote sensing technologies and provide decision support for the scientific operation and management of Dendrocalamus giganteus. Full article

Urban landscape patterns significantly impact land surface temperature (LST) and the urban heat island (UHI) effect. This study employs the boosted regression tree (BRT) model and variance partitioning analysis to examine the contributions and relationships of two-dimensional and three-dimensional building and vegetation patterns to LST, and their marginal effects at different heights. The results show that the dominant indicators affecting LST differ between buildings and vegetation, with three-dimensional building features being slightly more important than two-dimensional features (percentage of landscape of buildings) and two-dimensional vegetation features (three-dimensional green index) having a greater impact than three-dimensional features. When both buildings and vegetation are considered, building patterns still have significant explanatory power. Building height differences influence each indicator’s contribution and marginal effects on LST, with lower-height areas seeing a joint dominance of buildings and vegetation on LST changes, and higher-height areas showing greater impact from vegetation indicators. Increasing the percentage of landscape of vegetation (PLAND_V) provides the best cooling effect in lower-building-height areas, but in higher-building-height areas, the cooling effect weakens, requiring additional vegetation indicators to assist in cooling. Full article

Vegetation, being a core component of ecosystems, is known to be influenced by natural and anthropogenic factors. This study used the annual mean Normalized Difference Vegetation Index (NDVI) as the vegetation greenness indicator. The variation in NDVI on Hainan Island was analyzed using the Theil–Sen median trend analysis and Mann–Kendall test during 2000–2019. The influence of natural and anthropogenic factors on the driving mechanism of the spatial pattern of NDVI was explored by the Multiscale Weighted Regression (MGWR) model. Additionally, we employed the Boosted Regression Tree (BRT) model to explore their contribution to NDVI. Then, the MGWR model was utilized to predict future greenness patterns based on precipitation and temperature data from different Shared Socioeconomic Pathway (SSP) scenarios for the period 2021–2100. The results showed that: (1) the NDVI of Hainan Island forests significantly increased from 2000 to 2019, with an average increase rate of 0.0026/year. (2) the R² of the MGWR model was 0.93, which is more effective than the OLS model (R² = 0.42) in explaining the spatial relationship. The spatial regression coefficients of the NDVI with temperature ranged from −10.05 to 0.8 (p < 0.05). Similarly, the coefficients of Gross Domestic Product (GDP) with the NDVI varied between −5.98 and 3.28 (p < 0.05); (3) The natural factors played the most dominant role in influencing vegetation activities as a result of the relative contributions of 83.2% of forest NDVI changes (16.8% contributed by anthropogenic activities). (4) under SSP119, SSP245, and SSP585 from 2021 to 2100, the NDVI is projected to have an overall decreasing pattern under all scenarios. This study reveals the trend of greenness change and the spatial relationship with natural and anthropogenic factors, which can guide the medium and long-term dynamic monitoring and evaluation of tropical forests on Hainan Island. Full article

Background: Recent publications underscore the need for updated recommendations addressing less radical surgery for <2 cm tumors, induction chemotherapy, or immunotherapy for locally advanced stages of cervical cancer, as well as for the systemic therapy for recurrent or metastatic cervical cancer. Aim: To summarize the current evidence for the diagnosis, treatment, and follow-up of cervical cancer and provide evidence-based clinical practice recommendations. Methods: Developed according to AGREE II standards, the guidelines classify scientific evidence based on the Agency for Health Technology Assessment and Tariff System criteria. Recommendations are graded by evidence strength and consensus level from the development group. Key Results: (1) Early-Stage Cancer: Stromal invasion and lymphovascular space involvement (LVSI) from pretreatment biopsy identify candidates for surgery, particularly for simple hysterectomy. (2) Surgical Approach: Minimally invasive surgery is not recommended, except for T1A, LVSI-negative tumors, due to a reduction in life expectancy. (3) Locally Advanced Cancer: concurrent chemoradiation (CCRT) followed by brachytherapy (BRT) is the cornerstone treatment. Low-risk patients (fewer than two metastatic nodes or FIGO IB2-II) may consider induction chemotherapy (ICT) followed by CCRT and BRT after 7 days. High-risk patients (two or more metastatic nodes or FIGO IIIA, IIIB, and IVA) benefit from pembrolizumab with CCRT and maintenance therapy. (4) Metastatic, Persistent, and Recurrent Cancer: A PD-L1 status from pretreatment biopsy identifies candidates for Pembrolizumab with available systemic treatment, while triplet therapy (Atezolizumab/Bevacizumab/chemotherapy) becomes a PD-L1-independent option. Conclusions: These evidence-based guidelines aim to improve clinical outcomes through precise treatment strategies based on individual risk factors, predictors, and disease stages. Full article

Understanding the determinants of the availability and spatial fairness of street greenery is crucial for improving urban green spaces and addressing green justice concerns. While previous studies have mainly examined factors influencing street greenery from an aerial perspective, there has been limited investigation into determinants at eye level, which more closely aligns with people’s actual encounters with green spaces. To address this, the Green View Index (GVI) and Gini coefficient were used to assess the availability and spatial fairness of street greenery from a pedestrian’s perspective, using Baidu Street View (BSV) images across 49 subdistricts in Changchun City, China. A dataset of 33,786 BSV images from 1877 sites was compiled. Additionally, 21 explanatory factors were collected and divided into three groups: socioeconomic, biogeographic, and landscape patterns. The Boosted Regression Tree (BRT) method was employed to assess the relative influence and marginal effects of these factors on street greenery’s availability and spatial fairness. The results showed that street greenery’s availability and spatial fairness are predominantly influenced by landscape patterns. Specifically, the percentage of landscape and edge density emerged as the most significant factors, exhibiting a threshold effect on the availability and fairness of street greenery. Increasing the proportion and complexity of urban green spaces can efficiently enhance the availability and spatial fairness of street greenery. These findings lay a new foundation for urban green infrastructure management. Full article

Anthocyanins are precious industrial raw materials. Purple corn is rich in anthocyanins, with large variation in their content between organs. It is imperative to find a rapid and non-destructive method to determine the anthocyanin content in purple corn. To this end, a field experiment with ten purple corn hybrids was conducted, collecting plant images using a digital camera and determining the anthocyanin content of different organ types. The average values of red (R), green (G) and blue (B) in the images were extracted. The color indices derived from RGB arithmetic operations were applied in establishing a model for estimation of the anthocyanin content. The results showed that the specific color index varied with the organ type in purple corn, i.e., AC_CR for the grains, BRT for the cobs, AC_CB for the husks, R for the stems, AC_CB for the sheaths and BRT for the laminae, respectively. Linear models of the relationship between the color indices and anthocyanin content for different organs were established with R² falling in the range of 0.64–0.94. The predictive accuracy of the linear models, assessed according to the NRMSE, was validated using a sample size of 2:1. The average NRMSE value was 11.68% in the grains, 13.66% in the cobs, 8.90% in the husks, 27.20% in the stems, 7.90% in the sheaths and 15.83% in the laminae, respectively, all less than 30%, indicating that the accuracy and stability of the model was trustworthy and reliable. In conclusion, this study provided a new method for rapid, non-destructive prediction of anthocyanin-rich organs in purple corn. Full article

This article aims to address the lack of research on the social dimension of sustainability, also known as social sustainability, in urban mobility projects, primarily in cities of the Global South. It proposes a strategy to partially assess social sustainability, focusing on accessibility, which is one of the key dimensions for conducting such an evaluation. To this end, a comparative analysis of three study cases is conducted in the capital cities of the Northern Central American Triangle (NCAT) before and after the construction of bus rapid transit (BRT) projects between 2000 and 2020. Accessibility is evaluated through equity and spatial efficiency indicators obtained through geographical information system (GIS) modeling, including layers representing transportation networks, populated areas, and locations of basic urban facilities. The result is an unprecedented assessment of accessibility in the NCAT capitals, which shows how the Guatemala City BRT project has improved the city’s social sustainability by reducing access times to basic urban facilities, mainly public health clinics and educational facilities, and narrowing the inequality gap as compared to projects in San Salvador and Tegucigalpa, the other capital cities in the NCAT. Additionally, it is emphasized that this methodology can be replicated in the Global South while considering the scarcity of information and the use of open-source software in the process. Full article

Microbial monitoring in marine recreational waterways often overlooks environmental variables associated with pathogen occurrence. This study employs a predictive boosted regression trees (BRT) model to predict Staphylococcus aureus abundance in the Tampa Bay estuary and identify related environmental variables associated with the microbial pathogen’s occurrence. We provide evidence that the BRT model’s adaptability and ability to capture complex interactions among predictors make it invaluable for research on microbial indicator research. Over 18 months, water samples from 7 recreational sites underwent microbial quantitation and S. aureus isolation, followed by genetic validation. BRT analysis of S. aureus occurrence and environmental variables revealed month, precipitation, salinity, site, temperature, and year as relevant predictors. In addition, the BRT model accurately predicted S. aureus occurrence, setting a precedent for pathogen–environment research. The approach described here is novel and informs proactive management strategies and community health initiatives in marine recreational waterways. Full article

Dam safety monitoring is critical due to its social, environmental, and economic implications. Although conventional statistical approaches have been used for surveillance, advancements in technology, particularly in Artificial Intelligence (AI) and Machine Learning (ML), offer promising avenues for enhancing predictive capabilities. We investigate the application of ML algorithms, including Boosted Regression Trees (BRT), Random Forest (RF), and Neural Networks (NN), focussing on their combination by Stacking to improve prediction accuracy on concrete dam deformation using radial displacement data from three dams. The methodology involves training first-level models (experts) using those algorithms, and a second-level meta-learner that combines their predictions using BRT, a Linear Model (LM) and the Greedy Weighted Algorithm (GWA). A comparative analysis demonstrates the superiority of Stacking over traditional methods. The GWA emerged as the most suitable meta-learner, enhancing the optimal expert in all cases, with improvement rates reaching up to 16.12% over the optimal expert. Our study addresses critical questions regarding the GWA’s expert weighting and its impact on prediction precision. The results indicate that the combination of accurate experts using the GWA improves model reliability by reducing error dispersion. However, variations in optimal weights over time necessitate robust error estimation using cross-validation by blocks. Furthermore, the assignment of weights to experts closely correlates with their precision: the more accurate a model is, the more weight that is assigned to it. The GWA improves on the optimal expert in most cases, including at extreme values of error, with improvement rates up to 41.74%. Our findings suggest that the proposed methodology significantly advances AI applications in infrastructure monitoring, with implications for dam safety. Full article

Monitoring horizontal displacements, such as landslides and tectonic movements, holds great importance and high-cost geodetic GNSS equipment stands as a crucial tool for the precise determination of these displacements. As the utilization of low-cost GNSS systems continues to rise, there is a burgeoning interest in evaluating their efficacy in measuring such displacements. This evaluation is particularly vital as it explores the potential of these systems as alternatives to high-cost geodetic GNSS systems in similar applications, thereby contributing to their widespread adoption. In this study, we delve into the assessment of the potential of the dual-frequency U-Blox Zed-F9P GNSS system in conjunction with a calibrated survey antenna (AS-ANT2BCAL) for determining horizontal displacements. To simulate real-world scenarios, the Zeiss BRT 006 basis-reduktionstachymeter was employed as a simulation device, enabling the creation of horizontal displacements across nine different magnitudes, ranging from 2 mm to 50 mm in increments of 2, 4, 6, 8, 10, 20, 30, 40, and 50 mm. The accuracies of these simulated displacements were tested through low-cost GNSS observations conducted over a 24 h period in open-sky conditions. Additionally, variations in observation intervals, including 3, 6, 8, and 12 h intervals, were investigated, alongside the utilization of the relative positioning method. Throughout the testing phase, GNSS data were processed using the GAMIT/GLOBK GNSS (v10.7) software, renowned for its accuracy and reliability in geodetic applications. The insightful findings gleaned from these extensive tests shed light on the system’s capabilities, revealing crucial information regarding its minimum detectable displacements. Specifically, the results indicate that the minimum detectable displacements with the 3-sigma rule stand at 22.8 mm, 11.7 mm, 8.7 mm, and 4.8 mm for 3 h, 6 h, 8 h, and 12 h GNSS observations, respectively. Such findings are instrumental in comprehending the system’s performance under varying conditions, thereby informing decision-making processes and facilitating the adoption of suitable GNSS solutions for horizontal displacement monitoring tasks. Full article

Forest fires often cause many casualties and property losses, and it is important to explore the time and space laws of forest fires and the influencing factors. The present study used the cities (prefectures) crossed by the Tropic of Cancer (Yunnan section) as the study area. Based on burned land data, a combination of natural factors, such as climate, topography, vegetation, and human activities, such as distance from settlements and population density, a binary logistic regression model, and a boosted regression tree model, were used to analyze the temporal–spatial characteristics and influencing factors of forest fires in 2000 to 2020. The following results were obtained: (1) During 2000–2020, the overall forest fire area in the study area showed a trend of fluctuating decline. The high incidence period of forest fires occurred in 2010. After 2010, the forest fire area in the study area was greatly reduced. (2) The forest fire area in the study area was greater in the east and less in the west. The forest fire areas in Wenshan Prefecture and Honghe Prefecture in the east were larger, accounting for 68%, and the forest fire areas in Pu’er City, Lincang City, and Yuxi City in the west were smaller, accounting for only 32%. (3) The contribution rate of the average precipitation and average temperature factors ranked in the top two in the two driving force analysis models, which indicated that precipitation and temperature had a significant effect on the incidence of forest fires in the study area. Full article