Search Results (6,753)

Land use change and energy consumption caused by human activities is the primary source of carbon emissions and a driver of climate change. The study focused on the Yangtze River Delta (YRD), using the China Land Cover Dataset (CLCD) to calculate the region’s carbon emissions from 1990 to 2020. Based on the Natural Segment Method, the spatial distribution of carbon emissions in the YRD region were constructed by dividing them into three categories: heavy, medium, and light. The results indicate that: (1) Carbon emissions of the YRD region was 594.02 million tons at the end of 2020, an increase of 468.53 million tons compared with that of 1990. The impervious surface was the major source of carbon emissions, accounting for more than 98.51% of the total, and woodland was the most important carbon sink, accounting for more than 91.32% of the total carbon uptake. (2) The carbon emissions increase rate over the 30-year period has risen from rapid to gradual, with the fastest rate of increase occurring between 2000 and 2010. (3) Differences in economic development and land type lead to spatial variability in carbon emissions. Regions with substantial emissions were predominantly located in coastal areas, indicating a trend toward shifting inland. The assessment of carbon emissions is helpful for designing emissions mitigation policies. Full article

Pathogen-induced fruit decay is a significant threat to the kiwifruit industry, leading to considerable economic losses annually. The cell-wall-degrading enzymes (CWDEs) secreted by these pathogens are crucial for penetrating the cell wall and accessing nutrients. Among them, Diaporthe species are recognized as major causal agents of soft rot in kiwifruit, yet their pathogenic mechanisms are not well understood. In this study, we explored the production of various CWDEs secreted by Diaporthe Z1-1N, including polygalacturonase (PG), polymethylgalacturonase (PMG), polygalacturonic acid transeliminase (PGTE), pectin methyltranseliminase (PMTE), endoglucanase (Cx), and β-glucosidase (β-glu), both in liquid cultures and within infected kiwifruit tissues. Our findings revealed significant activities of two pectinases (PG and PMG) and cellulases (Cx and β-glu) in the infected tissues. In contrast, very low levels of PMTE and PGTE activities were observed under the same conditions. When orange pectin served as the carbon source, PG and PMG showed notable activities, while PMTE and PGTE remained inactive. Moreover, the activities of Cx and β-glu significantly decreased by more than 63 times in the liquid medium with carboxymethyl cellulose (CMC) as the carbon source compared to their levels in infected kiwifruit. A further analysis indicated that the necrotic lesions produced by pectinase extracts were larger than those produced by cellulase extracts. Notably, four enzymes—PG, PMG, Cx, and β-glu—exhibited high activities on the third or fourth day post-infection with Diaporthe Z1-1N. These results suggest that Diaporthe Z1-1N secretes a range of CWDEs that contribute to kiwifruit decay by enhancing the activities of PG, PMG, Cx, and β-glu. This study sheds light on the pathogenicity of Diaporthe in kiwifruit and highlights the importance of these enzymes in the decay process. Full article

Intermediate-acidic granites occur extensively in the Chazangcuo copper-lead-zinc mining area (hereinafter referred to as the Chazangcuo mining area) in Tibet, China. Exploring their rock types, sources, and tectonic settings is essential for understanding the genesis of granites in the region. This study investigated the petrology of the Chazangcuo granites, as well as the geochemical characteristics of their major elements, trace elements, and rare earth elements (REEs). Results indicate that the Chazangcuo granites are high-K calc-alkaline metaluminous rocks. These granites are enriched in large-ion lithophile elements (LILEs; e.g., Rb and Ba), depleted in high-field-strength elements (HFSEs; e.g., Nb, Ta, Zr, and Hf), with a relative enrichment in light rare earth elements (LREEs), and relatively depleted in heavy rare earth elements (HREEs), exhibiting a V-shaped distribution pattern and weak negative Eu anomalies. The granites are classified as typical I-type granites, displaying characteristics of crust-derived magmas with contributions from mantle sources and exhibiting significant fractional crystallization. The Chazangcuo granites were derived from the partial melting of mafic rocks, with protoliths formed in a moderate temperature environment. Influenced by the subduction of the Neotethys Ocean, the Chazangcuo granites were formed in an arc caused by the collision between the Indian and Eurasian plates (also referred to as the Indo–Eurasian collision) during the Late Triassic. Under the effect of geological activities such as upwelling of the asthenosphere and fluid intrusion and differentiation, metal mineralization was prompted to be distributed in the granite fissures, forming the Cu-Pb-Zn polymetallic deposits of Chazangcou in Tibet, suggesting that the granites are closely associated with mineralization. Full article

Broccoli byproducts are an important source of bioactive compounds, which provide important benefits for human skin due mainly to their antioxidant and anti-inflammatory properties. The primary target of UVB radiation is the basal layer of cells in the epidermis, with keratinocytes being the most abundant cell population in this layer. Given the wide range of side effects caused by exposure to UVB radiation, reducing the amount of UV light that penetrates the skin and strengthening the protective mechanisms of the skin are interesting strategies for the prevention of skin disorders. This work aims to evaluate the protective mechanisms triggered by broccoli by-products extract (BBE) on HaCaT keratinocytes exposed to UVB radiation as well as the study of the regenerative effect of these extracts on the barrier of skin keratinocytes damaged by superficial wounds as a strategy to revalorize this agricultural waste. The results obtained revealed that the BBEs exhibited a high cytoprotective effect on the HaCaT exposed to UVB light, allowing it to effectively reduce the intracellular content of ROS, as well as effectively attenuating the increase in proinflammatory cytokines (IL-1β, IL-6, IL-78, TNF-α) and COX-2 induced by this type of radiation. Furthermore, the BBE could be an excellent regenerative agent for skin wound repair, accelerating the migration capacity of keratinocytes thus contributing to the valorization of this byproduct as a valuable ingredient in cosmetic formulations. Full article

Image-based ship monitoring technology has extensive applications, and is widely used in various aspects of port management, including illegal activity surveillance, vessel identification at entry and exit points, channel and berth management, unmanned vessel control, and incident warning and emergency response. However, most current ship identification technologies rely on a single information source, reducing detection accuracy in the complex and variable marine environment. To address this issue, this paper proposes a knowledge transfer-based ship identification system integrating three modules. The system enables synchronized monitoring of visible light coastal images, satellite cloud images, and infrared spectrum images, thereby mitigating problems such as low detection accuracy and poor adaptability of image recognition. Additionally, extensive supplementary experiments were conducted to evaluate the effectiveness of the preprocessing and data augmentation modules as well as the transfer learning module. The study also discusses the limitations of current deep learning-based ship monitoring models, particularly their poor adaptability to image recognition and inability to achieve all-weather, round-the-clock monitoring. Experimental results based on three ship monitoring datasets demonstrate that the proposed system, by integrating three distinct detection conditions, outperforms other models with an F1-score of 98.74%, approximately 10% higher than most existing ship detection systems. Full article

Atmospheric particulate matter (PM) samples were collected in Riyadh, Saudi Arabia, to assess air quality, quantify, heavy metal concentrations, and evaluate related ecological and health risks. This study’s uniqueness stems from its focused and detailed analysis of PM pollution in Riyadh, including an extensive assessment of heavy metal concentrations across different PM sizes by applying diverse pollution and health indices. This brings to light critical health and ecological issues and provides foundation for targeted pollution control efforts in the region. The study focused on two PM size fractions, PM_2.5 and PM₁₀ and analyzed the presence of heavy metals, including iron (Fe), nickel (Ni), chromium (Cr), zinc (Zn), cobalt (Co), copper (Cu), silver (Ag), arsenic (As), cadmium (Cd), and lead (Pb), using inductively coupled plasma emission spectrometry. Results showed significantly higher levels of PM₁₀ (223.12 ± 66.12 µg/m³) compared to PM_2.5 (35.49 ± 9.63 µg/m³), suggesting that local dust is likely a primary source. Air quality varied from moderate to unhealthy, with PM₁₀ posing substantial risks. Heavy metal concentrations in PM_2.5 followed the order Fe (13.14 ± 11.66 ng/m³) > As (2.87 ± 2.08 ng/m³) > Cu (0.71 ± 0.51 ng/m³) > Zn (0.66 ± 0.46 ng/m³) > Cr 0.50 ± 0.23 ng/m³) > Pb (0.14 ± 0.10 ng/m³) > Ni (0.03 ± 0.04 ng/m³) > Cd (0.004 ± 0.002 ng/m³) > Ag (0.003 ± 0.003 ng/m³) > Co (0.002 ± 0.004 ng/m³). In PM10, they followed the order Fe (743.18 ± 593.91 ng/m³) > As (20.12 ± 13.03 ng/m³) > Cu (10.97 ± 4.66 ng/m³) > Zn (9.06 ± 5.50 ng/m³) > Cr (37.5 ± 2.70 ng/m³) > Ni (1.72 ± 01.54 ng/m³) > Pb (1.11 ± 0.64 ng/m³) > Co (0.25 ± 0.28 ng/m³) > Ag (0.10 ± 0.26 ng/m³) > Cd (0.04 ± 0.02 ng/m³). Enrichment factor analysis revealed elevated levels for the metals Cu, Zn, As, Ag, Cd, and Pb. Pollution indices indicated various contamination levels, with Ag and As showing particularly high contamination and ecological risks. The study highlighted significant health concerns, especially from As, which poses a substantial long-term carcinogenic threat. The findings emphasize the urgent need to reduce hazardous metal levels in Riyadh’s air, especially with high child exposure. Full article

Building and finishing materials are among the main sources of indoor air pollution and can provide ideal substrates for microbial growth. Environmental factors can induce physico-chemical aging of these materials, altering their composition and increasing their vulnerability to microbial growth. To mitigate this risk, manufacturers are increasingly adding biocidal agents to these materials to prevent microbial contamination. The aim of this project was to study the sensitivity of two different acrylic paints to fungal growth, before and after an aging process, and to assess the impact of aging on the effectiveness of the biocides contained in these materials. To do this, two paints (antifungal and normal paint) were applied to a wall covering (polyester-cellulose) before being subjected to accelerated aging. The later process was based on the addition of detergent or water and exposing the material to a visible light spectrum, moderate temperature (38 ± 6 °C), and ambient relative humidity (25 ± 17%). Prior to 30 days of incubation, the aged and unaged (“native”) materials were inoculated with fungal spores using a dry aerosolization system. Fungi behavior was then evaluated by the culture method. The results showed that the native and water-aged normal acrylic paint supported fungal growth at 95 ± 5% relative humidity. However, the use of the cleaning product during the aging process provided additional resistance of the materials against fungal growth. On the other hand, the antifungal paint showed no visible growth due to its biocide content. The accelerated aging and incubation processes led to the depletion of the biocides and thus a decrease in their effectiveness against mold development. Full article

Background/Objectives: 5-aminulevulinic acid (5-ALA)-guided surgery for high-grade gliomas remains a challenge in neuro-oncological surgery. Inconsistent fluorescence visualisation, subjective quantification and false negatives due to blood, haemostatic agents or optical impediments from the external light source are some of the limitations of the present technology. Methods: The preliminary results from this single-centre retrospective study are presented from the first 35 patients operated upon with the novel Nico Myriad Spectra System©. The microdebrider (Myriad) with an additional in situ light system (Spectra) can alternately provide white and blue light (405 nm) to within 15 mm of the tissue surface to enhance the morphology of the anatomical structures and the fluorescence of the pathological tissues. Results: A total of 35 patients were operated upon with this new technology. Eight patients (22.85%) underwent tubular retractor-assisted minimally invasive parafascicular surgery (tr-MIPS). The majority had high-grade gliomas (68.57%). Fluorescence was identified in 30 cases (85.71%), with residual fluorescence in 11 (36.66%). The main applications were better white–blue light alternation and visualisation during tr-MIPS, increase in the extent of resection at the border of the cavity, identification of satellite lesions in multifocal pathology, the differentiation between radionecrosis and tumour recurrence in redo surgery and the demarcation between normal ependyma versus pathological ependyma in tumours infiltrating the subventricular zone. Conclusions: This proof-of-concept study confirms that the novel in situ light-source delivery technology integrated with the usual intraoperative armamentarium provides a spatially, functionally and oncologically informed framework for glioblastoma surgery. It allows for the enhancement of the morphology of anatomical structures and the fluorescence of pathological tissues, increasing the extent of resection and, possibly, the prognosis for patients with high-grade gliomas. Full article

Estimation of forest above-ground biomass (AGB) using multi-source remote sensing data is an important method to improve the accuracy of the estimate. However, selecting remote sensing factors that can effectively improve the accuracy of forest AGB estimation from a large amount of data is a challenge when the sample size is small. In this regard, the Least Absolute Shrinkage and Selection Operator (Lasso) has advantages for extensive redundant variables but still has some drawbacks. To address this, the study introduces two Least Absolute Shrinkage and Selection Operator Lasso-based variable selection methods: Least Absolute Shrinkage and Selection Operator Genetic Algorithm (Lasso-GA) and Variance Inflation Factor Least Absolute Shrinkage and Selection Operator (VIF-Lasso). Sentinel 2, Sentinel 1, Landsat 8 OLI, ALOS-2 PALSAR-2, Light Detection and Ranging, and Digital Elevation Model (DEM) data were used in this study. In order to explore the variable selection capabilities of Lasso-GA and VIF-Lasso for remote sensing estimation of forest AGB. It compares Lasso-GA and VIF-Lasso with Boruta, Random Forest Importance Selection, Pearson Correlation, and Lasso for selecting remote sensing factors. Additionally, it employs eight machine learning models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Bayesian Regression Neural Network (BRNN), Elastic Net (EN), K-Nearest Neighbors (KNN), Extremely Randomized Trees (ETR), and Stochastic Gradient Boosting (SGBoost)—to estimate forest AGB in Wuyi Village, Zhenyuan County. The results showed that the optimized Lasso variable selection could improve the accuracy of forest biomass estimation. The VIF-Lasso method results in a BRNN model with an R² of 0.75 and an RMSE of 16.48 Mg/ha. The Lasso-GA method results in an ETR model with an R² of 0.73 and an RMSE of 16.70 Mg/ha. Compared to the optimal SGBoost model with the Lasso variable selection method (R² of 0.69, RMSE of 18.63 Mg/ha), the VIF-Lasso method improves R² by 0.06 and reduces RMSE by 2.15 Mg/ha, while the Lasso-GA method improves R² by 0.04 and reduces RMSE by 1.93 Mg/ha. From another perspective, they also demonstrated that the RX sample count and sensitivity provided by LiDAR, as well as the Horizontal Transmit, Vertical Receive provided by Microwave Radar, along with the feature variables (Mean, Contrast, and Correlation) calculated from the Green, Red, and NIR bands of optical remote sensing in 7 × 7 and 5 × 5 windows, play an important role in forest AGB estimation. Therefore, the optimized Lasso variable selection method shows strong potential for forest AGB estimation using multi-source remote sensing data. Full article

Accurate identification and monitoring of aerosol properties is crucial for understanding their sources and impacts on human health and the environment. Therefore, we propose a feature extraction and attribute recognition method from in situ light-scattering measurements based on Bayesian Optimization, wavelet scattering transform, and long short-term memory neural network (BO-WST-LSTM), with empirical mode decomposition (EMD) and independent component analysis (ICA) algorithm for signal preprocessing. In this study, an experimental platform was utilized to gather light-scattering signals from particles with varying characteristics. The signals are then processed using the EMD-ICA noise reduction technique. Then, the wavelet scattering network is used to realize the adaptive extraction of the characteristics of the particle light-scattering signal, and the Bayesian Optimization model is used to optimize the hyperparameters of the LSTM neural network. The extracted scattering coefficient matrix is input into the LSTM for iterative training. Finally, the SoftMax layer’s probability classification method is applied to the identification of particle attributes. The results show that the multi-angle particle light-scattering signal detection system designed and built in this study performs well and is capable of effectively collecting particle light-scattering signals. At the same time, the proposed new method for particle property recognition demonstrates good classification performance for six different types of particles with a particle size of 2 μm, achieving a classification accuracy of 98.83%. This proves its effectiveness in recognizing particle properties and provides a solid foundation for particle identification. Full article

The hospitality industry, particularly hotels, is increasingly emphasizing the dual goals of sustainability and cost optimization to align with global environmental objectives and enhance operational efficiency. The current study comprehensively explores sustainable energy solutions tailored for hotel buildings, utilizing insights drawn from a review of the relevant literature, analysis of industry data, and an examination of real-world case studies. The study begins by assessing the energy consumption patterns of hotels and the environmental implications of reliance on conventional energy sources. It then delves into various sustainable energy systems designed to mitigate these impacts, including solar photovoltaic panels, geothermal heating and cooling technologies, energy-efficient lighting solutions, and advanced smart building management systems. These interventions demonstrate significant potential to reduce energy expenses and carbon emissions while simultaneously enhancing guest satisfaction through improved comfort and environmentally friendly practices. Furthermore, the research highlights critical factors that facilitate the adoption of sustainable energy systems in the hotel industry. These include active stakeholder participation, adherence to regulatory frameworks, and the availability of financial incentives, such as subsidies or tax benefits. This study also identifies substantial barriers to implementation, such as the high initial investment costs, technological challenges in retrofitting existing infrastructures, and cultural resistance to adopting new practices within organizations. The findings underscore the importance of a holistic approach to energy sustainability in hotels, advocating for a collaborative effort among industry stakeholders, policymakers, and technology providers. By addressing these challenges and leveraging the identified opportunities, hotels can transition toward more energy-efficient operations, contributing meaningfully to environmental preservation and achieving long-term economic benefits. Full article

Accurate vehicle type classification plays a significant role in intelligent transportation systems. It is critical to understand the road conditions and usually contributive for the traffic light control system to respond correspondingly to alleviate traffic congestion. New technologies and comprehensive data sources, such as aerial photos and remote sensing data, provide richer and higher-dimensional information. In addition, due to the rapid development of deep neural network technology, image-based vehicle classification methods can better extract underlying objective features when processing data. Recently, several deep learning models have been proposed to solve this problem. However, traditional purely convolution-based approaches have constraints on global information extraction, and complex environments such as bad weather seriously limit their recognition capability. To improve vehicle type classification capability under complex environments, this study proposes a novel Densely Connected Convolutional Transformer-in-Transformer Neural Network (Dense-TNT) framework for vehicle type classification by stacking Densely Connected Convolutional Network (DenseNet) and Transformer-in-Transformer (TNT) layers. Vehicle data for three regions under four different weather conditions were deployed to evaluate the recognition capability. Our experimental findings validate the recognition ability of the proposed vehicle classification model, showing little decay even under heavy fog. Full article

Since the advent of Fourier Domain Mode-Locked (FDML) lasers, they have demonstrated outstanding performance in several fields. They achieve high-speed, narrow-linewidth laser output with the new mode-locking mechanism, which has been intensively researched in the past decades. Compared with conventional wavelength-scanning light sources, FDML lasers have successfully increased the scanning rate of frequency-sweeping lasers from kHz to MHz. They are widely used in optical coherence tomography, spectral analysis, microscopy, and microwave photonics. With the deepening research on FDML lasers, several performance metrics have been optimized and improved, offering superior performance for FDML laser-based applications. This paper reviews the principles and key performance indicators of FDML lasers, as well as the recent progress made in some important applications, and highlights further research directions for FDML lasers in the future. Full article

With the acceleration in population migration and urbanization, accurate population density prediction has become increasingly important for regional planning and resource management. This study focuses on predicting population density at the township level in Inner Mongolia. By integrating multi-source data, such as nighttime light indices and road network density, various machine learning models—including random forest, XGBoost, and LightGBM—were employed to significantly improve prediction accuracy. Interpretable machine learning techniques were utilized to quantitatively analyze the contribution of various variables to population distribution. The results indicate that nighttime light indices and road network density are key influencing factors, revealing their complex nonlinear relationships with population density. This study provides new methodological support for predicting population density in Inner Mongolia and similar regions, demonstrating the potential of machine learning in regional population research. While machine learning models effectively capture correlations between variables, they do not reveal causal relationships. Future research should introduce more detailed data and causal inference models to deepen our understanding of population distribution and its influencing factors. Full article

Using light-emitting diode (LED) in plant production optimizes growth with higher energy efficiency, reduces carbon footprint and resource consumption, and promotes more sustainable agriculture. However, the plants’ growth characteristics and biochemical composition may vary depending on the light’s wavelength, spectrum, and intensity. Therefore, LEDs as a light source have become a promising choice for improving cultivation efficiency, as they can modulate the spectrum to meet the needs of plants. Pereskia aculeata is a plant species from the cactus family with high protein, vitamins, minerals, and fiber. The objective of this study was to evaluate the effect of LED lighting on the cultivation of P. aculeata and its influence on biometric color and physicochemical aspects. Two treatments were carried out without the addition of artificial light: one inside the greenhouse (C-ins) and the other outside the greenhouse (C-out), and four treatments with LEDs in different spectral bands: monochromatic red (600–700 nm) (Red), monochromatic blue (400–490 nm) (Blue), white (400–700 nm) (White), and blue–red (1:1) (Blue–Red). The biometric characteristics and the color of the leaves collected from the different treatments were evaluated. After this, the leaves were dried, ground, and evaluated. The physicochemical and thermal characteristics, bioactive compounds, and antioxidant activity of the leaves from each treatment were described. The biometric characteristics were intensified with red LED, and the color of the leaves tended toward green. The dried yield was around 50%, except for C-out treatment. Regarding nutritional characteristics, the highest protein (29.68 g/100 g), fiber (34.44 g/100 g), ash (20.28 g/100 g), and lipid (3.44 g/100 g) contents were obtained in the treatment with red light. The red treatment also intensified the content of chlorophyll a (28.27 µg/L) and total carotenoids (5.88 µg/g). The blue treatment intensified the concentration of minerals and provided greater thermal stability. Regarding bioactive properties, the cultivation of P. aculeata inside the greenhouse favored the concentration of phenolic compounds and a greater antioxidant capacity. Therefore, the quality of light for P. aculeata demonstrates that the length of red and blue light corroborates the development of the plant through the wavelength absorbed by the leaves, favoring its characteristics and planting in closed environments. Full article