Search Results (16,399)

The regional imbalance of power supply and use is an important factor affecting the efficient and sustainable development of China’s power system. It is necessary to achieve the better matching of power supply and use through the optimization of the national power network layout. From a mathematical point of view, the power network layout’s optimization is a typical mixed-integer non-linear programming problem. The present paper proposes a novel method based on the Random Walk algorithm with Compulsive Evolution for China’s power network layout optimization to improve the network economy. In this method, the length of transmission lines and the amount of cross-regional power transmission between nodes are synchronously optimized. The proposed method was used to find the minimum total cost (TC) of the power transmission network on the basis of energy supply and use balance. The proposed method is applied to the optimization of power network of different scales. Results indicated that, compared with the optimization method that only optimizes the transmission line length, the TC of municipal and provincial power grids can be significantly reduced by the recommended methods. Moreover, for the national power network, through simultaneous optimization, the TC savings in 30 years of operation are significant. Full article

Background: Salt intake in China was high and a series of salt reduction measures were accordingly carried out recently. Our study aimed to assess the long-term effect of a scale-up community randomized controlled trial (RCT); Methods: Individuals between the ages of 18 and 75, from six provinces in China, were recruited and randomized into control (n = 1347) and intervention (n = 1346) groups. A one-year salt reduction intervention was first implemented in the intervention group, followed by a two-year scale-up intervention in both groups. The 24 h urine sample, anthropometric measurement, and knowledge, attitude, and practice (KAP) of salt reduction, as well as lifestyle information, were collected at baseline, after one-year RCT (mid-term evaluation, n = 2456), and two-year scale-up intervention (terminal evaluation, n = 2267); Results: Both control (351.82 mg/24 h, p < 0.001) and intervention (192.84 mg/24 h, p = 0.006) groups showed a decrease in 24 h urinary sodium excretion from baseline to terminal evaluation. Except for an increase in 24 h urinary potassium excretion (85.03 mg/24 h, p = 0.004) and a decrease in systolic blood pressure (SBP) (2.95 mm Hg, p < 0.001) in the intervention group at the mid-term assessment, no statistically significant differences in other indicators were found between two groups. The KAP of salt reduction in two groups was gradually improved; Conclusions: After one-year RCT two-year scale-up, all participants showed a decreasing trend in 24 h urinary sodium excretion and an increase in salt reduction KAP. The community salt reduction intervention package has the potential for broader application across other regions in China. Full article

Schizophrenia, a highly complex psychiatric disorder, presents significant challenges in diagnosis and treatment due to its multifaceted neurobiological underpinnings. Recent advancements in functional magnetic resonance imaging (fMRI) and artificial intelligence (AI) have revolutionized the understanding and management of this condition. This manuscript explores how the integration of these technologies has unveiled key insights into schizophrenia’s structural and functional neural anomalies. fMRI research highlights disruptions in crucial brain regions like the prefrontal cortex and hippocampus, alongside impaired connectivity within networks such as the default mode network (DMN). These alterations correlate with the cognitive deficits and emotional dysregulation characteristic of schizophrenia. AI techniques, including machine learning (ML) and deep learning (DL), have enhanced the detection and analysis of these complex patterns, surpassing traditional methods in precision. Algorithms such as support vector machines (SVMs) and Vision Transformers (ViTs) have proven particularly effective in identifying biomarkers and aiding early diagnosis. Despite these advancements, challenges such as variability in methodologies and the disorder’s heterogeneity persist, necessitating large-scale, collaborative studies for clinical translation. Moreover, ethical considerations surrounding data integrity, algorithmic transparency, and patient individuality must guide AI’s integration into psychiatry. Looking ahead, AI-augmented fMRI holds promise for tailoring personalized interventions, addressing unique neural dysfunctions, and improving therapeutic outcomes for individuals with schizophrenia. This convergence of neuroimaging and computational innovation heralds a transformative era in precision psychiatry. Full article

Urban inland rivers are closely related to urban development, but high-density urbanisation has reduced the natural function of streams and the riverbanks are hardened into two parts, embankment walls and berms, which give rise to a variety of riparian landscapes. However, the difference in the height of riparian walkways affects the degree of their greening and landscape effects. In this paper, we studied single- and double-decker urban greenways, constructed quantitative indicators of spatial elements based on deep learning algorithms using an image semantic segmentation (ISS) model that simulates human visual perception, used random forests and multivariate linear regression models to study the impact of the height difference of the linear riverfront greenway on visual perception, clarified the impact of the visual landscape differences caused by different types of space on landscape aesthetic preferences (LP) and confirmed the impact of the specific extent to which landscape components influence preferences. The results of the study showed that there were significant differences in landscape perception scores between the single and double layers. (1) The influence of WED (negative correlation) and NI (positive correlation) is large in the single-layer greenway. The colour, material and structure of the guardrail can be beautified and diversified and the quality of the greenery can be taken into account to maintain the visibility of the greenery in order to improve the score of the single-layer greenway. (2) The significant influence of BVI in the double-layered greenway is positive. Water-friendly or water-viewing spaces can be added appropriately to improve the landscape score of double-layered greenways. This study is applicable to the regional landscape feature identification of single- and double-decker greenways on large-scale urban hard barge bank images, which realises the whole-region feature identification of a large-scale human perspective and is an effective expansion of analysis techniques for sustainable landscape planning and the design of riparian greenways. Full article

Xinjiang is a representative dry area in China characterized by oasis agriculture. In recent decades, the amount of farmland has increased considerably. For the regional objectives of “carbon peaking and carbon neutrality”, it is essential to investigate the carbon effects induced by large-scale changes in farmland. This research integrates the PLUS and InVEST models to calculate the carbon effects resulting from the spatiotemporal changes in farmland distribution in Xinjiang. It quantitatively assesses the changes in land-use patterns and carbon storage under four scenarios for 2035—natural development (ND), economic development (ED), ecological protection (EP), and farmland protection (FP)—and explores the spatial agglomeration degree of the carbon effect of cultivated land area change. The analysis reveals the following: (1) From 1990 to 2020, the farmland area in Xinjiang showed a trend of first decreasing and then increasing, resulting in a total increase of 33,328.53 km² over the 30-year period. The newly added farmland primarily came from grassland and unused land. (2) The terrestrial ecosystem carbon storage in Xinjiang showed a trend of decreasing first and then increasing, with an increase of 57.49 Tg in 30 years. The centroid of carbon storage was located in the northwestern part of the Bayingolin Mongol Autonomous Prefecture, showing an overall southwestward shift. Changes in farmland area contributed to a regional carbon storage increase of 45.03 Tg. The contribution of farmland to carbon storage increased by 3.42%. (3) In 2035, the carbon storage value of different scenarios will increase compared with 2020, and the carbon sink of cultivated land will be the maximum under the cultivated land protection scenario. (4) There is a strong spatial positive correlation between the changes in carbon storage caused by the change in cultivated land area in Xinjiang, and there are more hot spots than cold spots. The carbon storage changes under farmland transformation have the characteristics of “high-high” clustering and “low-low” clustering. Future territorial spatial planning in Xinjiang should comprehensively coordinate ecological protection and farmland conservation measures, improve regional carbon sink capacity, and achieve green and sustainable development. Full article

Spatial land cover depictions are essential for ecological and environmental management. The thematic resolution of land cover and vegetation maps is also a significant factor affecting the ability to effectively develop policy and land management decisions based on spatial data. Natural resource and conservation planners often seek to develop strategies at broad scales; however, high-quality spatial data depicting current vegetation and ecosystem types over large areas are often unavailable. Since widely available land cover and vegetation datasets are generally lacking in either thematic resolution or spatial coverage, there is a need to integrate modeling approaches and ancillary data with traditional satellite image classifications to produce more detailed ecosystem maps for large areas. In this study, we present a comprehensive approach using satellite imagery, machine learning, and ancillary modeling approaches to develop high-resolution ecological system type maps statewide for Arkansas, USA. A RandomForest land cover classification of Sentinel-2 imagery was generated and further articulated into ecological types using a comprehensive set of secondary modeling approaches. A total of 123 types were mapped in Arkansas, including common cultural and ruderal land cover and vegetation such as pine plantations and developed types. Ozark–Ouachita Dry–Mesic Forest covered the most area, 17.51% of the state. Row Crops covered 17.16%. Twenty-five pine or pine plantation types covered 19.73% of the state, with Ozark–Ouachita pine woodland or mature pine plantation covering 6.15%. Field survey points were used to assess the quality of the mapped ecological systems. The approaches presented here provide a framework for finer resolution mapping of ecological systems at broad scales in other regions. Full article

Introduction: Lesion load (LL), deep gray matter (DGM) and normal-appearing white matter (NAWM) susceptibility and morphometry may help in monitoring brain changes in multiple sclerosis (MS) patients. We aimed at evaluating the feasibility of a fully automated segmentation and the potential interrelation between these biomarkers and clinical disability. Methods: Sixty-six patients with brain MRIs and clinical evaluations (Expanded Disability Status Scale [EDSS]) were retrospectively included. Automated prototypes were used for the segmentation and morphometry of brain regions (MorphoBox) and MS lesions (LeManPV). Susceptibility maps were estimated using standard post-processing (RESHARP and TVSB). Spearman’s rho was computed to evaluate the interrelation between biomarkers and EDSS. Results: We found (i) anticorrelations between the LL and right thalamus susceptibility (rho = −0.46, p < 0.001) and between the LL and NAWM susceptibility (rho = [−0.68 to −0.25], p ≤ 0.05); (ii) an anticorrelation between LL and DGM (rho = [−0.71 to −0.36], p < 0.04) and WM morphometry (rho = [−0.64 to −0.28], p ≤ 0.01); and (iii) a positive correlation between EDSS and LL (rho = [0.28 to 0.5], p ≤ 0.03) and anticorrelation between EDSS and NAWM susceptibility (rho = [−0.29 to −0.38], p < 0.014). Conclusions: Fully automated brain morphometry and susceptibility monitoring is feasible in MS patients. The lesion load, thalamus and NAWM susceptibility values and trophicity are interrelated and correlate with disability. Full article

Conventional lubricant testing methods focus on lab-scale constant contact conditions, which cannot represent the scenarios in actual hot-stamping processes. In recent studies, the concept of the ‘digital characteristics (DC)’ of metal forming has been proposed by unveiling the intrinsic nature of the specific forming, which presents a timely solution to address this challenge. In this work, the transient behaviours of three dedicated lubricants during the hot stamping of AA6111 material were investigated considering the effects of various contact conditions using an advanced friction testing system, and the interactive friction modelling was established accordingly. The lubricant limit diagram (LLD) of each lubricant was then generated to quantitatively evaluate the lubricant performance following the complex tool–workpiece interactions based on the tribological DCs, and a detailed investigation on the lubricant failure regions was conducted based on the interactive friction modelling. Finally, the industrial application index (IAI) was proposed and defined as a comprehensive evaluation of lubricant applications in the industry, and the most suitable lubricant was identified among the three candidates for mass production. Full article

Water content is one of the most critical characteristics in plant physiological development. Therefore, this information is a crucial factor in determining the water stress conditions of vegetation, which is essential for assessing the wildfire risk and land management decision-making. Remote sensing can be vital for obtaining information over large, limited access areas with global coverage. This is important since conventional techniques for collecting vegetation water content are expensive, time-consuming, and spatially limited. This work aims to evaluate the vegetation live fuel moisture content (LFMC) seasonal variability using a multiscale remote sensing approach, particularly on rockroses, the Cistus ladanifer species, a Western Mediterranean basin native species with wide spatial distribution, over the Herdade da Mitra at the University of Évora, Portugal. This work used four dataset sources, collected monthly between June 2022 and July 2023: (i) Vegetation samples used to calculate the LFMC; (ii) Vegetation reflectance spectral signature using the portable spectroradiometer FieldSpec HandHeld-2 (HH2); (iii) Multispectral optical imagery obtained from the Multispectral Instrument (MSI) sensor onboard the Sentinel-2 satellite; and (iv) Multispectral optical imagery derived from a camera onboard an Unmanned Aerial Vehicle Phantom 4 Multispectral (P4M). Several temporal analyses were performed based on datasets from different sensors and on their intercomparison. Furthermore, the Random Forest (RF) classifier, a machine learning model, was used to estimate the LFMC considering each sensor approach. MSI sensor presented the best results (R² = 0.94) due to the presence of bands on the Short-Wave Infrared Imagery region. However, despite having information only in the Visible and Near Infrared spectral regions, the HH2 presents promising results (R² = 0.86). This suggests that by combining these spectral regions with a RF classifier, it is possible to effectively estimate the LFMC. This work shows how different spatial scales, from remote sensing observations, affect the LFMC estimation through machine learning techniques. Full article

Forests are the largest terrestrial carbon reservoirs and the most cost-effective carbon sinks. Accurate estimation of forest aboveground biomass (AGB) can significantly reduce uncertainty in carbon stock assessments. However, due to the limitations of timely and reliable forestry surveys, as well as high-resolution remote sensing data, mapping high-resolution and spatially continuous forest AGB remains challenging. The Global Ecosystem Dynamics Investigation (GEDI) is a remote sensing mission led by NASA, aimed at obtaining global forest three-dimensional structural information through LiDAR data, and has become an important tool for estimating forest structural parameters at regional scales. In 2019, the GEDI L4A product was introduced to improve AGB estimation accuracy. Currently, forest AGB maps in China have not been consistently evaluated, and research on biomass at the provincial level is still limited. Moreover, scaling GEDI’s footprint-based data to regional-scale gridded data remains a pressing issue. In this study, to verify the accuracy of GEDI L4A data and the reliability of the filtering parameters, the filtered GEDI L4A data were extracted and validated against airborne data, resulting in a Pearson correlation coefficient () of 0.69 (p < 0.001, statistically significant). This confirms the reliability of both the GEDI L4A data and the proposed filtering parameters. Taking Liaoning Province as an example, this study evaluated three forest AGB maps (Yang’s, Su’s, and Zhang’s maps), which were obtained as nationwide AGB product maps, using GEDI L4A data. The comparison with Su’s map yields the highest value of 0.61. To enhance comparison accuracy, Kriging spatial interpolation was applied to the extracted GEDI footprint data, yielding continuous data. This value increased to 0.75 when compared with Su’s map, with significant increases also observed against Yang’s and Zhang’s maps. The study further proposes a method to subtract the extracted GEDI data from the AGB values of the three maps, followed by Kriging interpolation, resulting in values of 0.70, 0.80, and 0.69 for comparisons with Yang’s, Su’s, and Zhang’s maps, respectively. Additionally, comparisons with field measurements from the Mudanjiang Ecological Research Station yielded values of 0.66, 0.65, and 0.50, indicating substantial improvements over direct comparisons. All the ρ values were statistically significant (p < 0.001). This study also conducted comparisons across different cities and forest cover types. The results indicate that cities in eastern Liaoning Province, such as Dalian and Anshan, which have larger forest cover areas, produced better results. Among the different forest types, evergreen needle-leaved forests and deciduous needle-leaved forests yielded better results. Full article

Push–pull technology (PPT) integrates maize with the legume fodder Desmodium sp. and the border crop Brachiaria sp., aiming to enhance maize production in Rwanda. Despite its potential, the adoption of complementary soil management practices (SMP), vital for PPT’s success, remains low. This study employs spatial econometric methods to evaluate the determinants of SMP adoption and the interdependencies in decision-making among PPT-practicing farmers. We constructed a spatial weight matrix based on a global Moran’s I index and identified optimal model parameters through principal component analysis. Utilizing a spatial Durbin probit model (SDPM), we assessed the spatial interdependence of SMP adoption decisions among maize farmers. Our findings reveal significant spatial dependence in SMP adoption within a 1.962 km radius, with improved seed usage, household income, yield, farmer group membership and size of land cultivated being key factors positively influencing adoption. We propose a “nonequilibrium promotion strategy” to enhance SMP adoption, emphasizing the establishment of pilot regions to broaden outreach. Additionally, fostering technical training and selecting farmers with adequate resources as demonstration leaders can enhance spatial spillover effects. This research provides insights for developing policies to scale up push–pull technology in Rwanda and across Sub-Saharan Africa. Full article

Understanding the sensitivity of ecosystem respiration (ER) to increasing temperature is crucial to predict how the terrestrial carbon sink responds to a warming climate. The temperature sensitivity of ER may vary on a diurnal basis but is poorly understood due to the paucity of observational sites documenting real ER during daytime at a global scale. Here, we used an improved flux partitioning approach to estimate the apparent temperature sensitivity of ER during the daytime (E_0,day) and nighttime (E_0,night) derived from multiyear observations of 189 FLUXNET sites. Our results demonstrated that E_0,night is significantly higher than E_0,day across all biomes, with significant seasonal variations in the day–night discrepancy in the temperature sensitivity of ER (ΔE₀ = E_0,night / E_0,day) except for evergreen broadleaf forest and savannas. Such seasonal variations in ΔE₀ mainly result from the effect of temperature and the seasonal amplitude of NDVI. We predict that future warming will decrease ΔE₀ due to the reduced E_0,night by the end of the century in most regions. Moreover, we further find that disregarding the ΔE₀ leads to an overestimation of annual ER by 10~80% globally. Thus, our study highlights that the divergent temperature dependencies between day- and nighttime ER should be incorporated into Earth system models to improve predictions of carbon–climate change feedback under future warming scenarios. Full article

Studies have extensively examined the cooling effects of forests. Various methods exist for evaluating climate regulation at regional and global levels. Local-scale cooling effects and their valuing methods, however, remain poorly understood. In this study, the temperature difference and energy balance methods were compared to assess the value of cooling services of three forest types at a local scale. Using the window searching strategy, land surface temperature and sensible heat flux differences between forest and open land were compared. The average cooling temperature of broad-leaved forests was found to be 0.229 °C, significantly higher than that of coniferous forests, at 0.205 °C, while mixed coniferous–broad-leaved forests were not significantly different to the other two types. The average sensible heat flux differences in broad-leaved, coniferous, and coniferous–broad-leaved forests were found to be 0.23, 0.079, and 0.11 MJ/m²/day, respectively. According to the correlation analysis, the sensible heat flux was significantly correlated with the cooling degree (R = 0.33, p = 0.05), suggesting consistency between the two approaches. However, the total cooling value calculated with the energy balance method was CNY 0.51 billion, significantly higher than the temperature difference method at CNY 0.11 billion. The main reason for the differences between the two approaches is the uncertainty in cooling volume and cooling time for the temperature difference method and energy balance method, respectively. The impact of vegetation on the microclimate depends on the vegetation type, topography, local climate, and other factors. It is also important to note that cooling services are not required at all times of the day, and energy differences can hardly be calculated based on the hour. However, surface radiation and evapotranspiration generally occur during the daytime, which is also when the surface temperature is high. Therefore, there is a certain coincidence with the time when cooling is needed. The energy balance method presented herein provides a novel alternative approach to assessing the cooling services of local-scale forests, offering advantages over the commonly used temperature difference approach, which is associated with large uncertainty. Full article

The Cape Canaveral Barrier Island, home to the National Aeronautics and Space Administration (NASA)’s Kennedy Space Center and the United States (U.S.) Space Force’s Cape Canaveral Space Force Station, is situated in a unique ecological transition zone that supports diverse wildlife. This study evaluates the recent changes in vegetation cover (2016–2023) and dune elevation (2007–2017) within the Cape Canaveral Barrier Island using high-resolution optical satellite and light detection and ranging (LiDAR) data. The study period was chosen to depict the time period of a recent increase in rocket launches. The study objectives include assessing changes in vegetation communities, identifying detectable impacts of liquid propellant launches on nearby vegetation, and evaluating dune elevation and tide level shifts near launchpads. The results indicate vegetation cover changes, including mangrove expansion in wetland areas and the conversion of coastal strands to denser scrubs and hardwood forests, which were likely influenced by mild winters and fire management. While detectable impacts of rocket launches on nearby vegetation were observed, they were less severe than those caused by solid rocket motors. Compounding challenges, such as rising tide levels, beach erosion, and wetland loss, potentially threaten the resilience of launch operations and the surrounding habitats. The volume and scale of launches continue to increase, and a balance between space exploration and ecological conservation is required in this biodiverse region. This study focuses on the assessment of barrier islands’ shorelines. Full article

Background and Objectives: The global demographic trend of population aging is evident across all regions, with a notable increase in the proportion of elderly individuals. Romania exemplifies this phenomenon, as 17% of its population is currently aged 65 years or older—a figure projected to rise to 25% by 2050. This demographic shift underscores the pressing need for comprehensive measures to address the health and social requirements of this growing population segment. This study aims to assess the prevalence of frailty among older adults in Romania and explore its relationship with socioeconomic factors. Materials and Methods: We employed a quantitative approach, by using cross-sectional data from patients hospitalized at the geriatrics ward of the Municipal Clinical Hospital in Cluj-Napoca, Romania. Frailty scores were calculated through established frailty assessment tools, allowing for a comprehensive evaluation of frailty status. In addition, we compared the socioeconomic characteristics of frail and non-frail patients to identify potential disparities. Statistical analyses were performed to assess associations between frailty and socioeconomic factors, providing insight into the relationship between these variables within the patient population. Results: The prevalence of frailty in our sample is, depending on the frailty scale used, 55% to 79%, which is in line with figures from specialized geriatric wards in other studies. There is moderate to substantial agreement between the scales we compared, and all six scales seem to concurrently agree on the frailty diagnostic in 55% of cases. Additionally, frail patients are more likely to have a low socioeconomic status. Conclusions: A significant limitation in European frailty research has been the absence of comparative frailty prevalence data across several European countries, especially those with lower economic development. Our study fills this gap by providing data on frailty prevalence in the north-western region of Romania. Full article