Search Results (121)

Background: The fundamental question of normal brain myelination in human is still poorly understood. Methods: Age-dependent global, regional, and interhemispheric sex-related differences in brain myelination of 42 (19 men, 23 women) healthy adults (19–67 years) were explored using the MRI method of fast macromolecular fraction (MPF) mapping. Results: Higher brain myelination in males compared to females was found in global white matter (WM), most WM tracts, juxtacortical WM regions, and putamen. The largest differences between men and women, exceeding 4%, were observed bilaterally in the frontal juxtacortical WM; angular, inferior occipital, and cuneus WM; external capsule; and inferior and superior fronto-orbital fasciculi. The majority of hemispheric differences in MPF were common to men and women. Sex-specific interhemispheric differences were found in juxtacortical WM; men more often had left-sided asymmetry, while women had right-sided asymmetry. Most regions of deep gray matter (GM), juxtacortical WM, and WM tracts (except for projection pathways) showed a later peak age of myelination in women compared to men, with a difference of 3.5 years on average. Body mass index (BMI) was associated with higher MPF and later peak age of myelination independent of age and sex. Conclusions: MPF mapping showed high sensitivity to assess sex-related differences in normal brain myelination, providing the basis for using this method in clinics. Full article

In the actual estimation of construction and demolition waste (C&DW), it is significantly relevant to effective management, design, and planning at project stages, but the lack of reliable estimation methods and historical data prevents the estimation of C&DW quantities for both short- and long-term planning. To address this gap, this study aims to predict C&DW quantities in construction projects more accurately by integrating the gray wolf optimization algorithm (GWO) and the Archimedes optimization algorithm (AOA) into an artificial neural network (ANN). This study uses data concerning the actual quantities of work in 200 real-life construction and demolition projects performed in the Gaza Strip. Different performance parameters, such as mean absolute error (MAE), mean square error (MSE), root mean squared error (RMSE), and the coefficient of determination (R²), are used to evaluate the effectiveness of the models developed. The results of this study have shown that the AOA-ANN model outperforms the other models in terms of accuracy (R² = 0.023728, MSE = 0.00056304, RMSE = 0.023728, MAE = 0.0086648). Moreover, this new hybrid model yields more accurate estimations of C&DW quantities with minimal input parameters, making the process of estimation more feasible. Full article

This paper develops a multi-objective optimization model to address the absence of systematic and practical evaluation methods for selecting construction schemes for steel pipe pile cofferdams. The model aims to minimize duration and cost while maximizing quality. Additionally, it proposes an improved sparrow search algorithm (ISSA) to solve this problem. First, a tent chaotic map is introduced to initialize the sparrow population, enhancing the diversity of the initial population. Second, the principle of non-dominated ordering is introduced to sort the parent and offspring populations during the iteration process, and the appropriate individuals are selected to form the offspring population. Finally, gray correlation analysis is applied to optimize the Pareto solution set and determine the final construction scheme. The effectiveness and superiority of the ISSA is verified by using the Changsha Jinan Avenue project as a case study. The results indicate that the quality of the optimized construction scheme remains at a high level of 0.90 or more; the duration is shortened by 18 days, a reduction of 21%; and the total cost is reduced by CNY 220,000, saving 3% of the cost. Full article

Kidney disease has emerged as a significant global health issue, projected to become the fifth-leading cause of years of life lost by 2040. The kidneys, being highly radiosensitive, are vulnerable to damage from various forms of radiation, including gamma (γ) and X-rays. However, the effects of electron radiation on renal tissues remain poorly understood. Given the localized energy deposition of electron beams, this study seeks to investigate the dose-dependent morphological and molecular changes in the kidneys following electron irradiation, aiming to address the gap in knowledge regarding its impact on renal structures. The primary aim of this study is to conduct a detailed morphological and molecular analysis of the kidneys following localized electron irradiation at different doses, to better understand the dose-dependent effects on renal tissue structure and function in an experimental model. Male Wistar rats (n = 75) were divided into five groups, including a control group and four experimental groups receiving 2, 4, 6, or 8 Gray (Gy) of localized electron irradiation to the kidneys. Biochemical markers of inflammation (interleukin-1 beta [IL-1β], interleukin-6 [IL-6], interleukin-10 [IL-10], tumor necrosis factor-alpha [TNF-α]) and oxidative stress (malondialdehyde [MDA], superoxide dismutase [SOD], glutathione [GSH]) were measured, and morphological changes were assessed using histological and immunohistochemical techniques (TUNEL assay, caspase-3). The study revealed a significant dose-dependent increase in oxidative stress, inflammation, and renal tissue damage. Higher doses of irradiation resulted in increased apoptosis, early stages of fibrosis (at high doses), and morphological changes in renal tissue. This study highlights the dose-dependent effects of electrons on renal structures, emphasizing the need for careful consideration of the dosage in clinical use to minimize adverse effects on renal function. Full article

To achieve the rapid grade classification of camellia seed oil, hyperspectral imaging technology was used to acquire hyperspectral images of three distinct grades of camellia seed oil. The spectral and image information collected by the hyperspectral imaging technology was preprocessed by different methods. The characteristic wavelength selection in this study included the continuous projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS), and the gray-level co-occurrence matrix (GLCM) algorithm was used to extract the texture features of camellia seed oil at the characteristic wavelength. Combined with genetic algorithm (GA) and support vector machine algorithm (SVM), different grade classification models for camellia seed oil were developed using full wavelengths (GA-SVM), characteristic wavelengths (CARS-GA-SVM), and fusing spectral and image features (CARS-GLCM-GA-SVM). The results show that the CARS-GLCM-GA-SVM model, which combined spectral and image information, had the best classification effect, and the accuracy of the calibration set and prediction set of the CARS-GLCM-GA-SVM model were 98.30% and 96.61%, respectively. Compared with the CARS-GA-SVM model, the accuracy of the calibration set and prediction set were improved by 10.75% and 12.04%, respectively. Compared with the GA-SVM model, the accuracy of the calibration set and prediction set were improved by 18.28% and 18.15%, respectively. The research showed that hyperspectral imaging technology can rapidly classify camellia seed oil grades. Full article

This study aims to investigate the coupling and harmonization between land ecological security (LES) and high-quality agricultural development (HAD) in the Han River Basin (HRB), China, with the objective of promoting harmonious coexistence between agriculture and ecosystems. Using 17 cities in the HRB as the research objects, an evaluation index system of two systems, LES and HAD, was constructed, analyzed, and evaluated via projective tracer modeling for multiple intelligent genetic algorithms (MIGA-PTM). The degree of coupling coordination (DCC) was used to quantitatively evaluate the coupling coordination development status of the two systems, the obstacle model (OM) was used to identify the main influencing factors, and the gray predictive model first-order univariate model (GM (1, 1)) was used to predict the DCC of the LES and HAD from 2025 to 2040. The results show the following: (1) the LES and HAD levels of the 17 cities in the HRB tended to increase during the study period, and there was a large gap between cities; (2) the spatial distributions of the DCCs of the LES and HAD in the HRB were uneven, with high values in the southern and low values in the central and northern parts, and the overall degree of coupling tended to fluctuate. The overall DCC showed a fluctuating upward trend; (3) the degree of obstacles, per capita water resources, greening coverage, and rate of return on financial expenditure are the main influencing factors; and (4) the prediction results of GM (1, 1) indicate that the LES and HAD of the HRB will be close to reaching the intermediate stage of coupling in 2035. This research offers critical insights into sustainable development practices that facilitate the alignment of agricultural growth with ecological preservation. Full article

Mu opioid receptors (MORs) represent a vital mechanism related to the modulation of stress-induced analgesia (SIA). Previous studies have reported on the gamma-aminobutyric acid (GABA)ergic “disinhibition” mechanisms of MORs on the descending pain modulatory pathway of SIA induced in the midbrain. However, the role of the MORs expressed in the medial prefrontal cortex (mPFC), one of the main cortical areas participating in pain modulation, in SIA remains completely unknown. In this study, we investigated the contributions of MORs expressed on glutamatergic (MORGlut) and GABAergic (MORGABA) neurons of the medial prefrontal cortex (mPFC), as well as the functional role and activity of neurons projecting from the mPFC to the periaqueductal gray (PAG) region, in male mice. We achieved this through a combination of hot-plate tests, c-fos staining, and 1 h acute restraint stress exposure tests. The results showed that our acute restraint stress protocol produced mPFC MOR-dependent SIA effects. In particular, MORGABA was found to play a major role in modulating the effects of SIA, whereas MORGlut seemed to be unconnected to the process. We also found that mPFC–PAG projections were efficiently activated and played key roles in the effects of SIA, and their activation was mediated by MORGABA to a large extent. These results indicated that the activation of mPFC MORGABA due to restraint stress was able to activate mPFC–PAG projections in a potential “disinhibition” pathway that produced analgesic effects. These findings provide a potential theoretical basis for pain treatment or drug screening targeting the mPFC. Full article

Rural domestic sewage, originating from human activities that involve the extraction and utilization of natural resources, is an inherent component of the ecological cycle in nature. Therefore, its disposal methods should align and harmonize with the laws governing nature’s evolutionary processes. This study conducted a comprehensive investigation on the domestic sewage facilities in representative villages located within the water source protection area of the middle route of the South-to-North Water Diversion Project. Taking Village A’s domestic sewage treatment station as a case study, an analysis was performed to assess its operational status and identify existing issues. The consideration of rural domestic sewage treatment should encompass factors such as the generation and discharge of household wastewater, the characteristics of water quality, discharge regulations, the natural and social environment, as well as post-completion operations and maintenance modes. We also proposed source reduction measures for the reuse of gray water in domestic sewage treatment in Village A, along with integrated treatment approaches involving biochemical treatment, landscape integration, and farmland irrigation for black water. These measures not only achieve effective treatment outcomes but also foster harmonious coexistence between humans and nature. Moreover, they align with the principles of ecological civilization while considering rural revitalization and promoting green agricultural development. Full article

Cooperative Technology Transfer (CTT) is a technology transfer model where universities and enterprises jointly participate throughout the entire process of technology transfer activities. Most discussions focus on its mechanisms and influencing factors, yet a framework to guide CTT projects in practice is still lacking. This study proposes an assessment model based on the life-cycle of CTT projects, covering the initial cooperation relationship, project management during the mid-term, and technological achievements at the end. The model was evaluated by 14 experts first and then validated through two CTT projects in China. Gray Relation Analysis was employed to calculate the weights of different factors based on their relative importance, while the Dempster–Shafer theory was utilized to combine evidence from various sources and address the uncertainty in the assessment. The results of the case analysis indicate that the attitudes of universities and enterprises are considered critical in influencing the success of CTT projects, while management issues that arise during the projects can pose potential risks. This research serves as an applied exploration and has three functions. Firstly, the model can be used as a feasibility study before the project commences. Secondly, it can be utilized to analyze and improve potential issues during the project. Finally, it can be used for a post-project experience summary. Full article

Leaf chlorophyll content (LCC) is an important indicator of plant nutritional status and can be a guide for plant disease diagnosis. In this study, we took apple leaves infected with mosaic disease as a research object and extracted two types of information on spectral and textural features from hyperspectral images, with a view to realizing non-destructive detection of LCC. First, the collected hyperspectral images were preprocessed and spectral reflectance was extracted in the region of interest. Subsequently, we used the successive projections algorithm (SPA) to select the optimal wavelengths (OWs) and extracted eight basic textural features using the gray-level co-occurrence matrix (GLCM). In addition, composite spectral and textural metrics, including vegetation indices (VIs), normalized difference texture indices (NDTIs), difference texture indices (DTIs), and ratio texture indices (RTIs) were calculated. Third, we applied the maximal information coefficient (MIC) algorithm to select significant VIs and basic textures, as well as the tandem method was used to fuse the spectral and textural features. Finally, we employ support vector regression (SVR), backpropagation neural network (BPNN), and K-nearest neighbors regression (KNNR) methods to explore the efficacy of single and combined feature models for estimating LCC. The results showed that the VIs model (R² = 0.8532, RMSE = 2.1444, RPD = 2.6179) and the NDTIs model (R² = 0.7927, RMSE = 2.7453, RPD = 2.2032) achieved the best results among the single feature models for spectra and texture, respectively. However, textural features generally exhibit inferior regression performance compared to spectral features and are unsuitable for standalone applications. Combining textural and spectral information can potentially improve the single feature models. Specifically, when combining NDTIs with VIs as input parameters, three machine learning models outperform the best single feature model. Ultimately, SVR achieves the highest performance among the LCC regression models (R² = 0.8665, RMSE = 1.8871, RPD = 2.7454). This study reveals that combining textural and spectral information improves the quantitative detection of LCC in apple leaves infected with mosaic disease, leading to higher estimation accuracy. Full article

Tree cover is a crucial vegetation structural parameter for simulating ecological, hydrological, and soil erosion processes on the Chinese Loess Plateau, especially after the implementation of the Grain for Green project in 1999. However, current tree cover products performed poorly across most of the Loess Plateau, which is characterized by grasslands with sparse trees. In this study, we first acquired high-accuracy samples of 0.5 m tree canopy and 30 m tree cover using a combination of unmanned aerial vehicle imagery and WorldView-2 (WV-2) imagery. The spectral and textural features derived from Landsat 8 and WV-2 were then used to estimate tree cover with a random forest model. Finally, the tree cover estimated using WV-2, Landsat 8, and their combination were compared, and the optimal tree cover estimates were also compared with current products and tree cover derived from canopy classification. The results show that (1) the normalized difference moisture index using Landsat 8 shortwave infrared and the standard deviation of correlation metric calculated by means of gray-level co-occurrence matrix using the WV-2 near-infrared band are the optimal spectral feature and textural feature for estimating tree cover, respectively. (2) The accuracy of tree cover estimated using only WV-2 is highest (RMSE = 7.44%), indicating that high-resolution textural features are more sensitive to tree cover than the Landsat spectral features (RMSE = 11.53%) on grasslands with sparse trees. (3) Textural features with a resolution higher than 8 m perform better than the combination of Landsat 8 and textural features, and the optimal resolution is 2 m (RMSE = 7.21%) for estimating tree cover, whereas the opposite is observed when the resolution of textural features is lower than 8 m. (4) The current global product seriously underestimates tree cover on the Loess Plateau, and the tree cover calculation using the canopy classification of high-resolution imagery performs worse than the method of directly using remote sensing features. Full article

Land optimization simulation and ecosystem service value (ESV) estimation can better serve land managers in decision-making. However, land survey data are seldom used in existing studies, and land optimization constraints fail to fully consider land planning control, and the optimization at the provincial scale is not fine enough, which leads to a disconnection between academic research and land management. We coupled ESV, gray multi-objective optimization (GMOP), and patch-generating land use simulation (PLUS) models based on authoritative data on land management to project land use and ESV change under natural development (ND), rapid economic development (RED), ecological land protection (ELP), and sustainable development (SD) scenarios in 2030. The results show that construction land expanded dramatically (by 97.96% from 2000 to 2020), which encroached on grassland and cropland. This trend will continue in the BAU scenario. Construction land, woodland, and cropland are the main types of land used for expansion, while grassland and unused land, which lack strict use control, are the main land outflow categories. From 2000 to 2030, the total amount of ESV increases steadily and slightly. The spatial distribution of ESV is significantly aggregated and the agglomeration is increasing. The policy direction and land planning are important reasons for land use changes. The land use scenarios we set up can play an important role in preventing the uncontrolled expansion of construction land, mitigating the phenomenon of ecological construction, i.e., “governance while destruction”, and promoting food security. This study provides a new approach for provincial large-scale land optimization and ESV estimation based on land survey data and provides technical support for achieving sustainable land development. Full article

This article addresses the sustainable urbanization model of urban valley-bottom areas, focusing on the Jaguaré stream basin in São Paulo, Brazil. It tackles the challenge of integrating environmental criteria into the management of urbanized watersheds to promote urban and environmental resilience through adaptive infrastructures. São Paulo, the largest and most populous city in Brazil, has often overlooked the natural characteristics of watersheds, resulting in significant flooding and necessitating a reassessment of urban practices. The study, through the analysis of a referential project, qualitative analyses, and geoprocessing techniques, proposes an urbanization model in valley bottom areas that combines adaptive infrastructures—green-blue and gray infrastructures. It highlights the importance of a systemic, transdisciplinary, and integrated approach that considers the political-administrative, environmental, urbanistic, and infrastructural dimensions to address the challenges posed by climate change and urbanization. Specific recommendations are presented to adapt urbanized valley-bottom areas, emphasizing nature-based solutions and community participation in the management of hydrological risks towards the promotion of sustainable and fair urban spaces. Full article

In this study, an internal fingerprint-guided epidermal thickness of fingertip skin is proposed for optical image encryption based on optical coherence tomography (OCT) combined with U-Net architecture of a convolutional neural network (CNN). The epidermal thickness of fingertip skin is calculated by the distance between the upper and lower boundaries of the epidermal layer in cross-sectional optical coherence tomography (OCT) images, which is segmented using CNN, and the internal fingerprint at the epidermis–dermis junction (DEJ) is extracted based on the maximum intensity projection (MIP) algorithm. The experimental results indicate that the internal fingerprint-guided epidermal thickness is insensitive to pressure due to normal correlation coefficients and the encryption process between epidermal thickness maps of fingertip skin under different pressures. In addition, the result of the numerical simulation demonstrates the feasibility and security of the encryption scheme by structural similarity index matrix (SSIM) analysis between the original image and the recovered image with the correct and error keys decryption, respectively. The robustness is analyzed based on the SSIM value in three aspects: different pressures, noise attacks, and data loss. Key randomness is valid by the gray histograms, and the average correlation coefficients of adjacent pixelated values in three directions and the average entropy were calculated. This study suggests that the epidermal thickness of fingertip skin could be seen as important biometric information for information encryption. Full article

Numerous countries are implementing building-integrated photovoltaic (BIPV) technology to enhance the energy performance of buildings, as new energy sources have attracted global interest. BIPV residential programs are an essential method to alleviate energy stress and promote energy transition in buildings; however, the high level of technology and capital investment required have hampered their marketization. Although certain obstacles have been examined by researchers, there remains a lack of studies concerning risk assessment in the context of the development of BIPV residential projects. Therefore, this study strives to develop a risk assessment model for the development of these projects. First, a risk evaluation index system is proposed by identifying and analyzing the risks associated with the development of BIPV residential projects, following the lines of risk identification–risk analysis–risk evaluation–risk management. Second, the DEMATEL-ANP-gray cluster analysis was utilized to construct the development risk assessment model. Finally, a case study demonstrates that the methodology proposed in this study can effectively solve the issues associated with correlating risk factors and the quantification of the magnitude of risks in the development of BIPV residential projects. This study will serve as a valuable reference for architect-urban developers and engineer contractors to formulate risk governance countermeasures for BIPV residential projects as it provides a framework for assessing the risk associated with their development. Full article