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Simple Summary: Our study provides the first record of Pacific sleeper sharks in 1629 m deep waters off the southeast coast of Hainan Island in the South China Sea. At a site, we placed a dead cow as bait and a metal-framed cage with cameras and observed eight individuals and their feeding activities by using ROV diving. The record indicates an expansion of the known distribution range of the Pacific sleeper sharks to the west of the northern South China Sea. Such a finding demonstrates that the habitat ranges of large animals are far wider in deep seas than we know from observations in the upper layer, and habitat expansion of some animal populations may have already taken place under intensive anthropogenic influences such as climate changes. We conducted an experiment of planting a dead cow and a metal-framed cage with cameras on the 1629 m deep sea floor off the southeast coast of Hainan Island in the northwestern South China Sea, using ROV diving and setting up a video camera on the cage to observe animals who came to eat the bait. The deep-sea cameras captured footage of eight Pacific sleeper sharks (Somniosus pacificus) swimming and feeding around the dead cow. To our knowledge, this is the first time the occurrence of such a shark species has been reported in the South China Sea. Eight individuals were differentiated based on the characteristic differences displayed in the images, with lengths of 1.9 to 5.1 m estimated. The video camera also recorded the predators' behavior of tearing at the dead cow on the seabed. It was discovered that Pacific sleeper sharks are not strictly solitary and exhibit queue-feeding behavior. This study is significant as it documents a record of a data-scarce shark species, for which little information is available in the literature. It also documents an expansion of the species' known habitat from the north Pacific Ocean into the South China Sea. Such sharks diving into the deep sea to predate on dead animals also suggests that occurrences of large chunks of dead organic bodies falling onto the deep sea might have been more frequent than we previously thought in the South China Sea. The findings have implications for understanding the geographic connectivity of large swimming animals between the South China Sea and the Pacific Ocean and provide scientific evidence for formulating conservation and management strategies for sharks and other large animals in the oceans. [ABSTRACT FROM AUTHOR]

Environmental changes induced by urbanization may significantly alter plant survival strategies, thereby introducing uncertainties in their ability to withstand extreme heat. This study, centered on Jinhua City, distinguished urban, suburban, and rural areas to represent the various intensities of urbanization. It examined the leaf function properties of evergreen and deciduous trees common in these regions, focusing on leaf and branch characteristics. Employing an analysis of variance (ANOVA), principal component analysis (PCA), and path analysis (PA) of the plant functional traits and the climatic factors of each region, this study assessed the impact of urbanization on plant survival strategies. By tracking changes in plant functional traits from June to August, it explored the capacity of plants to acclimate to urban-warming-related heat stress across different urbanization gradients. The findings revealed that leaf thickness (LT) and stomatal size (SS) initially decreased and then increased, whereas specific leaf area (SLA) and leaf tissue density (LTD) first rose and then declined, from rural to urban regions. From June to August, branch wood density (WD), chlorophyll (Chl) content, LTD, and leaf dry matter content (LDMC) increased, whereas SLA and leaf water content (LWC) diminished, in all regions. PCA suggested that there was no significant change in the resource allocation strategy of plants (p > 0.05), with drought tolerance significantly reduced in the suburbs on the gradient of urbanization (p < 0.05). During the summer, with high temperature, plants were predominantly biased towards slow-return, conservative strategies, particularly among evergreen species. Compared to precipitation, PA revealed a significant urban warming effect. During summer, temperature was the main factor influencing resource investment strategy and drought resistance, with a notably stronger impact on the former. The high temperature in summer promoted a conservative survival strategy in plants, and the urbanization effect increased their tolerance to high temperatures. [ABSTRACT FROM AUTHOR]

In recent years, the rapid development of the photovoltaic (PV) industry has resulted in a saturation of research on onshore PV power plant construction. However, current studies on the impact of marine PVs on the marine environment remain limited and scarce. In order to facilitate the implementation of carbon reduction goals and promote the sustainable development of the offshore PV industry, this study analyzes the environmental impact of PV sea-use resources based on spatial information technology in the western part of Gaotang Island. The findings show that the MIKE21FM model provides relatively accurate simulations of tidal flow and tide level in the marine PV area. Flow velocity in the marine PV area exhibits a decreasing trend, with an average decrease ranging from 0.03 to 0.07 m/s. This decrease minimally affects surrounding navigational channels and large-scale flow fields. The resulting siltation is also deemed less significant, with an annual deposition from 0.03 to 0.06 m/a. Moreover, offshore PV construction resulted in a total intertidal biological loss of 123.45 t. The suspension of sediment during cofferdam construction and removal has a potential effect on zooplankton and fishery resources. Overall, it is proposed that careful planning, prudent site selection, and the execution of countermeasures during marine PV construction will combine to minimize the impact on the marine environment. [ABSTRACT FROM AUTHOR]

Urban green space (UGS) has been recognized as a key factor in enhancing the urban ecosystem balance, particularly in arid areas. It is often considered an effective means to mitigate the urban heat island (UHI) effect. In this study, the reference comparison method was utilized to optimize the process of nighttime lighting data; the random forest classification method was employed to extract UGS data; and the radiative transfer method was applied in land surface temperature (LST) inversion. Additionally, moving window analysis was conducted to assess the robustness of the results. The objective of this research was to analyze the spatial distribution characteristics of UGS and LST and to explore their bivariate local spatial autocorrelations by calculating four landscape metrics, including the aggregation index (AI), edge density (ED), patch density (PD), and area-weighted mean shape index (Shape_am). It was found that the distribution of UGS in the study area was uneven, with higher temperatures in the eastern and western regions and lower temperatures in the central and southern regions. The results also revealed that ED, PD, and Shape_am were negatively correlated with LST, with correlation coefficients being −0.469, −0.388, and −0.411, respectively, indicating that UGS in these regions were more effective in terms of cooling effect. Conversely, AI was found to be positively correlated with LST (Moran' I index of 0.449), indicating that surface temperatures were relatively higher in regions of high aggregation. In essence, the fragmented, complex, and evenly distributed green patches in the study area provided a better cooling effect. These findings should persuade decision makers and municipal planners to allocate more UGS in cities for UHI alleviation to improve quality of life and enhance recreational opportunities. [ABSTRACT FROM AUTHOR]

This study identified 13 GhTIR1/AFB members in G. hirsutum through bioinformatics methods and divided them into three subgroups by phylogenetic tree analysis. Motif and gene structure analysis showed that the genes in this family were highly conserved. Promoter cis-acting element analysis found that the promoters of GhTIR1/AFBs contained a large number of cis-acting elements in response to growth and development and abiotic stress. Further RT-qPCR results showed that GhTIR1/AFB genes responded to various abiotic stresses such as IAA, ABA, cold, and heat, and the expression levels of each gene changed obviously, especially Gh_D08G0763 (GhTIR1), which responded significantly to cold injury. Using VIGS (virus-induced gene silencing) technology to silence Gh_D08G0763 in the cold-tolerant cotton variety ZM36, it was found that the resistance of ZM36 to cold damage was significantly reduced. The physiological response mechanism of the Gh_D08G0763 in resisting cold damage was further analyzed through trypan blue staining of leaves and determination of enzyme activity levels. This study provided effective genetic resources for cotton cold-tolerance breeding. [ABSTRACT FROM AUTHOR]

Gross primary productivity (GPP) is a reliable measure of the carbon sink potential of terrestrial ecosystems and is an essential element of terrestrial carbon cycle research. This study employs the diffuse fraction-based two-leaf light-use efficiency (DTEC) model to imitate China's monthly GPP from 2001 to 2020. We studied the trend of GPP, investigated its relationship with climatic factors, and separated the contributions of climate change and human activities. The findings showed that the DTEC model was widely applicable in China. During the study period, China's average GPP increased significantly, by 9.77 g C m−2 yr−1 (p < 0.001). The detrimental effect of aerosol optical depth (AOD) on GPP was more widespread than that of total precipitation, temperature, and solar radiation. Areas that benefited from AOD, such as Northwest China, experienced significant increases in GPP. Climate change and human activities had a primary and positive influence on GPP during the study period, accounting for 28% and 72% of the increase, respectively. Human activities, particularly ecological restoration projects and the adoption of advanced agricultural technologies, played a significant role in China's GPP growth. China's afforestation plan was particularly notable, with the GPP increasing in afforestation areas at a rate greater than 10 g C m−2 yr−1. This research provides a theoretical foundation for the long-term management of China's terrestrial ecosystems and helps develop adaptive ecological restoration tactics. [ABSTRACT FROM AUTHOR]

Computer vision technology is being applied at an unprecedented speed in various fields such as 3D scene reconstruction, object detection and recognition, video content tracking, pose estimation, and motion estimation. To address the issues of low accuracy and high time complexity in traditional image feature point matching, a fast image-matching algorithm based on nonlinear filtering is proposed. By applying nonlinear diffusion filtering to scene images, details and edge information can be effectively extracted. The feature descriptors of the feature points are transformed into binary form, occupying less storage space and thus reducing matching time. The adaptive RANSAC algorithm is utilized to eliminate mismatched feature points, thereby improving matching accuracy. Our experimental results on the Mikolajcyzk image dataset comparing the SIFT algorithm with SURF-, BRISK-, and ORB-improved algorithms based on the SIFT algorithm conclude that the fast image-matching algorithm based on nonlinear filtering reduces matching time by three-quarters, with an overall average accuracy of over 7% higher than other algorithms. These experiments demonstrate that the fast image-matching algorithm based on nonlinear filtering has better robustness and real-time performance. [ABSTRACT FROM AUTHOR]

Accurate photovoltaic (PV) power forecasting allows for better integration and management of renewable energy sources, which can help to reduce our dependence on finite fossil fuels, drive energy transitions and climate change mitigation, and thus promote the sustainable development of renewable energy sources. A convolutional neural network (CNN) forecasting method with a two-input, two-scale parallel cascade structure is proposed for ultra-short-term PV power forecasting tasks. The dual-input pattern of the model is constructed by integrating the weather variables and the historical power so as to convey finer information about the interaction between the weather variables and the PV power to the model; the design of the two-branch, two-scale CNN model architecture realizes in-depth fusion of the PV system data with the CNN's feature extraction mechanism. Each branch introduces an attention mechanism (AM) that focuses on the degree of influence between elements within the historical power sequence and the degree of influence of each meteorological variable on the historical power sequence, respectively. Actual operational data from three PV plants under different meteorological conditions are used. Compared with the baseline model, the proposed model shows a better forecasting performance, which provides a new idea for deep-learning-based PV power forecasting techniques, as well as important technical support for a high percentage of PV energy to be connected to the grid, thus promoting the sustainable development of renewable energy. [ABSTRACT FROM AUTHOR]

The combination of an absorbing structure and a road is a promising strategy for road deicing using microwaves. In this study, cement mortar (CM) specimens containing a carbon fiber screen (CFS) were prepared to concentrate electromagnetic losses on a road surface. The effect of the size and depth of the CFS on the surface heating efficiency of the microwave was studied and optimized, and a microwave deicing experiment was conducted. The results indicated that the destructive interference produced by the CFS led to the effective surface heating of the CM/CFS specimens. The optimal surface heating rate was 0.83 °C/s when the spacing, depth, and width of the CFS were 5.22, 13.31, and 2.80 mm, respectively. The deicing time was shortened by 21.68% from 83 to 65 s, and the heating rate increased by 17.14% from 0.70 to 0.82 °C/s for the specimen with CFS-1, which was 15 mm depth. Our results demonstrate that CM/CFS composite structures can be effectively applied to increase the capacity and accelerate the development of the microwave deicing of roads. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for optimizing wind farm control through the utilization of a combined model predictive control method. In contrast to conventional methods of controlling active and reactive power in wind farms, the suggested approach integrates a wind power prediction model driven by a neural network and a state-space model for wind turbines. This combination facilitates a more precise forecast of active power, thereby enabling the dynamic prediction of the range of reactive power output from the wind turbines. When combined with the equation of state in wind farm space, it is possible to accurately optimize the reactive power of a wind farm. Furthermore, the impact of active power on voltage fluctuations in the wind farm collector system was examined. The utilization of model predictive control enhances voltage regulation, optimizes system redundancy, and increases the reactive capacity. Sensitivity coefficients were calculated using analytical methods to enhance computational efficiency and to resolve issues related to convergence. In order to validate the proposed methodology and control scheme, a wind farm simulation model comprising 20 turbines was developed to assess the feasibility of the scheme. [ABSTRACT FROM AUTHOR]

With a large proportion of wind farms connected to the power grid, it brings more pressure on the stable operation of power systems in shorter time scales. Efficient and accurate scheduling, operation control and decision making require high time resolution power forecasting algorithms with higher accuracy and real-time performance. In this paper, we innovatively propose a high temporal resolution wind power forecasting method based on a light convolutional architecture—DC_LCNN. The method starts from the source data and novelly designs the dual-channel data input mode to provide different combinations of feature data for the model, thus improving the upper limit of the learning ability of the whole model. The dual-channel convolutional neural network (CNN) structure extracts different spatial and temporal constraints of the input features. The light global maximum pooling method replaces the flat operation combined with the fully connected (FC) forecasting method in the traditional CNN, extracts the most significant features of the global method, and directly performs data downscaling at the same time, which significantly improves the forecasting accuracy and efficiency of the model. In this paper, the experiments are carried out on the 1 s resolution data of the actual wind field, and the single-step forecasting task with 1 s ahead of time and the multi-step forecasting task with 1~10 s ahead of time are executed, respectively. Comparing the experimental results with the classical deep learning models in the current field, the proposed model shows absolute accuracy advantages on both forecasting tasks. This also shows that the light architecture design based on simple deep learning models is also a good solution in performing high time resolution wind power forecasting tasks. [ABSTRACT FROM AUTHOR]

Artemisia argyi is a traditional herbal medicine plant, and its folium artemisia argyi is widely in demand due to moxibustion applications globally. The Auxin/indole-3-acetic acid (Aux/IAA, or IAA) gene family has critical roles in the primary auxin-response process, with extensive involvement in plant development and stresses, controlling various essential traits of plants. However, the systematic investigation of the Aux/IAA gene family in A. argyi remains limited. In this study, a total of 61 Aux/IAA genes were comprehensively identified and characterized. Gene structural analysis indicated that 46 Aux/IAA proteins contain the four typical domains, and 15 Aux/IAA proteins belong to non-canonical IAA proteins. Collinear prediction and phylogenetic relationship analyses suggested that Aux/IAA proteins were grouped into 13 distinct categories, and most Aux/IAA genes might experience gene loss during the tandem duplication process. Promoter cis-element investigation indicated that Aux/IAA promoters contain a variety of plant hormone response and stress response cis-elements. Protein interaction prediction analysis demonstrated that AaIAA26/29/7/34 proteins are possibly core members of the Aux/IAA family interaction. Expression analysis in roots and leaves via RNA-seq data indicated that the expression of some AaIAAs exhibited tissue-specific expression patterns, and some AaIAAs were involved in the regulation of salt and saline-alkali stresses. In addition, RT-qPCR results indicated that AaIAA genes have differential responses to auxin, with complex response patterns in response to other hormones, indicating that Aux/IAA may play a role in connecting auxin and other hormone signaling pathways. Overall, these findings shed more light on AaIAA genes and offer critical foundational knowledge toward the elucidation of their function during plant growth, stress response, and hormone networking of Aux/IAA family genes in A. argyi. [ABSTRACT FROM AUTHOR]

A large proportion of photovoltaic (PV) power generation is connected to the power grid, and its volatility and stochasticity have significant impacts on the power system. Accurate PV power forecasting is of great significance in optimizing the safe operation of the power grid and power market transactions. In this paper, a novel dual-channel PV power forecasting method based on a temporal convolutional network (TCN) is proposed. The method deeply integrates the PV station feature data with the model computing mechanism through the dual-channel model architecture; utilizes the combination of multihead attention (MHA) and TCN to extract the multidimensional spatio-temporal features between other meteorological variables and the PV power; and utilizes a single TCN to fully extract the temporal constraints of the power sequence elements. The weighted fusion of the dual-channel feature data ultimately yields the ideal forecasting results. The experimental data in this study are from a 26.52 kW PV power plant in central Australia. The experiments were carried out over seven different input window widths, and the two models that currently show superior performance within the field of PV power forecasting: the convolutional neural network (CNN), and the convolutional neural network combined with a long and short-term memory network (CNN_LSTM), are used as the baseline models. The experimental results show that the proposed model and the baseline models both obtained the best forecasting performance over a 1-day input window width, while the proposed model exhibited superior forecasting performance compared to the baseline model. It also shows that designing model architectures that deeply integrate the data input method with the model mechanism has research potential in the field of PV power forecasting. [ABSTRACT FROM AUTHOR]

As "fuel" for atmospheric photochemical reactions, volatile organic compounds (VOCs) play a key role in the secondary generation of ozone (O3) and fine particulate matter (PM2.5, an aerodynamic diameter ≤ 2.5 μm). To determine the characteristics of VOCs in a high-level ozone period, comprehensive monitoring of O3 and its precursors (VOCs and NOx) was continuously conducted in an industrial area in Shanghai from 18 August to 30 September 2021. During the observation period, the average concentration of VOCs was 47.33 ppb, and alkanes (19.64 ppb) accounted for the highest proportion of TVOCs, followed by oxygenated volatile organic compounds (OVOCs) (13.61 ppb), alkenes (6.92 ppb), aromatics (4.65 ppb), halogenated hydrocarbons (1.60 ppb), and alkynes (0.91 ppb). Alkenes were the predominant components that contributed to the ozone formation potential (OFP), while aromatics such as xylene, toluene, and ethylbenzene contributed the most to the secondary organic aerosol production potential (SOAFP). During the study period, O3, NOx, and VOCs showed significant diurnal variations. Industrial processes were the main source of VOCs, and the second largest source of VOCs was vehicle exhaust. While the largest contribution to OFP was from vehicle exhaust, the second largest contribution was from liquid petroleum gas (LPG). High potential source contribution function (PSCF) values were observed in western and southeastern areas near the sampling sites. The results of a health risk evaluation showed that the Hazard Index was less than 1 and there was no non-carcinogenic risk, but 1,3-butadiene, benzene, chloroform, 1,2-dibromoethane, and carbon tetrachloride pose a potential carcinogenic risk to the population. [ABSTRACT FROM AUTHOR]

(1) Background: African swine fever (ASF) is a highly contagious disease that causes high pig mortality. Due to the absence of vaccines, prevention and control are relatively challenging. The pathogenic African swine fever virus (ASFV) has a complex structure and encodes over 160 proteins, many of which still need to be studied and verified for their functions. In this study, we identified one of the unknown functional genes, C84L. (2) Methods: A gene deficient strain was obtained through homologous recombination and several rounds of purification, and its replication characteristics and virulence were studied through in vitro and in vivo experiments, respectively. (3) Results: Deleting this gene from the wild-type virulent strain SY18 did not affect its replication in porcine primary macrophages but reduced its virulence in pigs. In animal experiments, we injected pigs with a 102 TCID50, 105 TCID50 deletion virus, and a 102 TCID50 wild-type strain SY18 intramuscularly. The control group pigs reached the humane endpoint on the ninth day (0/5) and were euthanized. Two pigs in the 102 TCID50(2/5) deletion virus group survived on the twenty-first day, and one in the 105 TCID50(1/5) deletion virus group survived. On the twenty-first day, the surviving pigs were euthanized, which was the end of the experiment. The necropsies of the survival group and control groups' necropsies showed that the surviving pigs' liver, spleen, lungs, kidneys, and submaxillary lymph nodes did not show significant lesions associated with the ASFV. ASFV-specific antibodies were first detected on the seventh day after immunization; (4) Conclusions: This is the first study to complete the replication and virulence functional exploration of the C84L gene of SY18. In this study, C84L gene was preliminarily found not a necessary gene for replication, gene deletion strain SY18ΔC84L has similar growth characteristics to SY18 in porcine primary alveolar macrophages. The C84L gene affects the virulence of the SY18 strain. [ABSTRACT FROM AUTHOR]

Crown diameter is one of the crucial indicators for evaluating the adaptability, growth quality, and ornamental value of garden chrysanthemums. To accurately obtain crown diameter, this study employed an unmanned aerial vehicle (UAV) equipped with a RGB camera to capture orthorectified canopy images of 64 varieties of garden chrysanthemums at different growth stages. Three methods, namely RGB color space, hue-saturation-value (HSV) color space, and the mask region-based convolutional neural network (Mask R-CNN), were employed to estimate the crown diameter of garden chrysanthemums. The results revealed that the Mask R-CNN exhibited the best performance in crown diameter estimation (sample number = 2409, R2 = 0.9629, RMSE = 2.2949 cm). Following closely, the HSV color space-based model exhibited strong performance (sample number = 2409, R2 = 0.9465, RMSE = 3.4073 cm). Both of the first two methods were efficient in estimating crown diameter throughout the entire growth stage. In contrast, the RGB color space-based model exhibited slightly lower performance (sample number = 1065, R2 = 0.9011, RMSE = 3.3418 cm) and was only applicable during periods when the entire plant was predominantly green. These findings provide theoretical and technical support for utilizing UAV-based imagery to estimate the crown diameter of garden chrysanthemums. [ABSTRACT FROM AUTHOR]

Dissolved organic carbon (DOC) in lakes, as a regulatory agent and light-absorbing compound, is a key component of the global carbon cycling in lacustrine ecosystems. Hence, continuous monitoring of the DOC concentration in arid regions is extremely important. This study utilizes the QAA-CDOM semi-analytical model, which has good accuracy in retrieving the CDOM (colored dissolved organic matter) concentration of Lake Ebinur. We chose to invert the CDOM time-series data from May to October during the 2018–2022 period. A DOC estimation model was then established using the linear regression approach based on the CDOM inversion data and the field DOC measurements. In general, the DOC concentration in Lake Ebinur exhibited an increasing trend from 2018 to 2022, typically lower in May and higher in June. When comparing the average values of DOC in Lake Ebinur for the same months across different years, it can be observed that the month of September exhibits the greatest variability, whereas June shows the least variability. In sum, this study successfully retrieved CDOM concentrations for a saline lake within an arid region and developed a DOC estimation model, thereby providing a reference for investigating carbon cycling in typical lakes of arid areas. [ABSTRACT FROM AUTHOR]

Aerosol optical depth (AOD) is an important physical variable used to characterize atmospheric turbidity for the management and control of air pollution. This study aims to analyze the factors influencing the spatial and temporal variability in AOD across the arid region of Northwest China (ARNC) using MODIS Aqua C006 aerosol product data from 2008 to 2017. In terms of natural and socioeconomic factors, the correlation coefficient (R) was used to identify the most influential factor in the AOD changes. The results show that AOD values in spring and summer were much higher than those in autumn and winter, especially in spring. In general, AOD had an insignificant decreasing trend, with a small overall changing range. Spatial analysis revealed a significantly decreasing trend, mostly across the Gobi Desert area, which is located in the western region of the ARNC. From the perspective of natural factors, AOD was positively correlated with air temperature (AT), wind speed (WP), land surface temperature (LST), and the digital elevation model (DEM) and negatively correlated with precipitation, relative humidity (RH), and the normalized difference vegetation index (NDVI). The greatest positive correlation, with a maximum R value of 0.8, was found between AOD and wind speed. By contrast, AOD and relative humidity had the strongest negative correlation, with R values of −0.77. In terms of anthropogenic factors, gross domestic product (GDP), secondary industry, and population density were the three major anthropogenic factors that influenced the changes in AOD changes in this region. In general, the effects of anthropogenic factors on AOD are more significant in areas with high urban population densities. [ABSTRACT FROM AUTHOR]

Extensive research has been conducted on the performance of pump turbines, particularly focused on understanding the generation mechanism of S-shaped characteristics. However, there has been a lack of research on unsteady flow characteristics in hump characteristics with small guide vane openings. This study focuses on the hump characteristics of a pump turbine in pump mode. The unsteady numerical simulation method is used along with experimental testing to examine the internal flow characteristics and induced pressure fluctuations under pump operating conditions. The results indicate that flow separation occurs in the impeller when the flow rate decreases to the valley operating condition, and recirculation flow occurs near the impeller inlet at the partial flow rate. Moreover, the unstable flow on the positive slope exhibits a low-frequency characteristic of 0.15fn. The pressure fluctuation from the hub to shroud areas of the guide vane region diminishes sequentially. Notably, distinct vortex structures emerge at the draft tube cone section under the valley operating condition. These structures extend toward the elbow section of the draft tube as the flow rate decreases. This phenomenon generates low-frequency pressure fluctuation originating from the primary frequency of the vortex and dean vortex on the surface, located at 0.4 D of the draft tube under conditions of low flow rate. [ABSTRACT FROM AUTHOR]

Understanding the net ecosystem productivity (NEP) is essential for understanding ecosystem functioning and the global carbon cycle. Utilizing meteorological and The Advanced Very High Resolution Radiometer (AVHRR) remote sensing data, this study employed the Carnegie–Ames–Stanford Approach (CASA) and the Geostatistical Model of Soil Respiration (GSMSR) to map a monthly vegetation NEP in China from 1982 to 2020. Then, we examined the spatiotemporal trends of NEP and identified the drivers of NEP changes using the Geodetector model. The mean NEP over the 39-year period amounted to 265.38 gC·m−2. Additionally, the average annual carbon sequestration amounted to 1.89 PgC, indicating a large carbon sink effect. From 1982 to 2020, there was a general fluctuating increasing trend observed in the annual mean NEP, exhibiting an overall average growth rate of 4.69 gC·m−2·a−1. The analysis revealed that the majority of the vegetation region in China, accounting for 93.45% of the entirety, exhibited increasing trends in NEP. According to the Geodetector analysis, precipitation change rate, solar radiation change rate, and altitude were the key driving factors in NEP change rate. Furthermore, the interaction between the precipitation change rate and altitude demonstrated the most significant effect. [ABSTRACT FROM AUTHOR]

Pear black spot disease, caused by Alternaria alternata, is a devastating disease in pears and leads to enormous economic losses worldwide. In this investigation, we isolated a Streptomyces odonnellii SZF-179 from the rhizosphere soil of pear plants in China. Indoor confrontation experiments results showed that both SZF-179 and its aseptic filtrate had excellent inhibitory effects against A. alternata. Afterwards, the main antifungal compound of SZF-179 was identified as polyene, with thermal and pH stability in the environment. A microscopic examination of A. alternata mycelium showed severe morphological abnormalities caused by SZF-179. Protective studies showed that SZF-179 fermentation broth could significantly reduce the diameter of the necrotic lesions on pear leaves by 42.25%. Furthermore, the potential of fermentation broth as a foliar treatment to control black leaf spot was also evaluated. Disease indexes of 'Hosui' and 'Wonwhang' pear plants treated with SZF-179 fermentation broth were lower than that of control plants. Overall, SZF-179 is expected to be developed into a safe and broad-spectrum biocontrol agent. No studies to date have evaluated the utility of S. odonnellii for the control of pear black spot disease; our study fills this research gap. Collectively, our findings provide new insights that will aid the control of pear black spot disease, as well as future studies of S. odonnellii strains. [ABSTRACT FROM AUTHOR]

Tea (Camellia sinensis) has grown for over 300 years and is recognized worldwide as among other well-renowned crops. The quality of black tea depends on plucking (method, standard, season, and intervals), withering and rolling (time and temperature), fermentation (time, temperature, and RH), drying (temperature and method), and storage conditions, which have a high influence on the final quality of black tea. At the rolling stage, the oxidation process is initiated and ends at the early drying stage until the enzymes that transform tea polyphenols into thearubigins (TRs) and theaflavins (TFs) are denatured by heat. By increasing fermentation time, TRs increased, and TF decreased. Each is liable for black tea's brightness, taste, and color. The amino acids and essential oils also grant a distinctive taste and aroma to black tea. Throughout withering, rolling, and fermentation, increases were found in essential oil content, but during drying, a decrease was observed. However, the Maillard reaction, which occurs when amino acids react with sugar during drying, reimburses for this decrease and enhances the flavor and color of black tea. As compared to normal conditions, accelerated storage showed a slight decrease in the total color, TF, and TRs. It is concluded that including plucking, each processing step (adopted technique) and storage system has a remarkable impact on black tea's final quality. To maintain the quality, an advanced mechanism is needed to optimize such factors to produce high-quality black tea, and an objective setting technique should be devised to attain the desirable quality characteristics. [ABSTRACT FROM AUTHOR]

With the increasing popularity of wearable devices, flexible electronics with a negative permittivity property have been widely applied to wearable devices, sensors, and energy storage. In particular, a low-frequency dispersion negative permittivity in a wide frequency range can effectively contribute to the stable working performance of devices. In this work, polyvinylidene fluoride (PVDF) was selected as the flexible matrix, and copper nanowires (CuNWs) were used as the conductive functional filler to prepare a flexible CuNWs/PVDF composite film with a low-frequency dispersion negative permittivity. As the content of CuNWs increased, the conductivity of the resulting composites increased sharply and presented a metal-like behavior. Moreover, the negative permittivity consistent with the Drude model was observed when CuNWs formed a percolative network. Meanwhile, the negative permittivity exhibited a low-frequency dispersion in the whole test frequency range, and the fluctuation of the permittivity spectra was relatively small (−760 to −584) at 20 kHz–1 MHz. The results revealed that the high electron mobility of CuNWs is reasonable for the low-frequency dispersion of negative permittivity. CuNWs/PVDF composite films with a frequency-independent negative permittivity provide a new idea for the development of flexible wearable electronic devices. [ABSTRACT FROM AUTHOR]

The accuracy of vegetation indices (VIs) in estimating forest stand age is significantly inadequate due to insufficient consideration of the differences in the physiological functions of forest ecosystems, which limits the accuracy of carbon sink simulation. In this study, remote sensing inversion and mapping of forest stand age were carried out on the Loess Plateau under consideration of the remote sensing mechanism of VIs and the physiological function and canopy structure of the forest using multiple linear regression (MLR) and random forest (RF) models. The main conclusions are as follows: (1) The canopy reflectance of different forest stands has a significant change pattern, and the older the forest stands, the lower the NIR reflectance. The relationship between forest stands and red edge is the most significant, and r is 0.53, and the relationship between Simple Ratio Index (SR), near-infrared reflectance of vegetation (NIRv), normalized difference vegetation index (NDVI), Global Vegetation Index and forest stands is more nonlinear than linear. (2) Principal component analysis (PCA) of canopy spectral information shows that SR, NDVI and red edge (B5) could explain 98% of all spectral information. SR, NDVI and red edge (B5) were used to construct a multiple linear regression model and random forest (RF) algorithm model, and RF has high estimation accuracy (R2 = 0.63). (3) The accuracy of the model was evaluated using reference data, and it was found that the accuracy of the RF model (R2 = 0.63) was higher than that of the linear regression model (R2 = 0.61), but both models underestimated the forest stand age when the forest stand age was greater than 50a, which may be caused by the saturation of the reflectance of the old forest canopy. The RF model was used to generate the dataset of forest stand information in the Loess Plateau, and it was found that the forest is dominated by young forests (<20a), accounting for 38.26% of the forest area, and the average age of forests in the Loess Plateau is 56.1a. This study not only improves the method of forest stand age estimation, but also provides data support for vegetation construction in the Loess Plateau. [ABSTRACT FROM AUTHOR]

In the biodiverse Nakdong River estuary, the predominant seagrass and salt marsh species, Zostera japonica and Bolboschoenus planiculmis, are declining due to human and natural pressures. Our study investigated how environmental factors and co-existing salt marsh vegetation impact the growth and reproduction of Z. japonica. Understanding the reproductive dynamics of Z. japonica in this estuary is crucial, as sexual reproduction ensures the resilience and stability of seagrass populations in challenging environments. This study revealed that approximately 49% of Z. japonica shoots flowered, yet none persisted to the subsequent growth season, indicating a reliance on sexual reproduction for population resilience. The presence of competing B. planiculmis shoots and Ulva pertusa indirectly suppressed the growth and reproduction of Z. japonica by reducing light availability. Additionally, environmental stresses that occurred during summer, such as elevated temperatures, reduced salinity, and sediment transport, likely affected the vegetative and reproductive performance of Z. japonica in this estuary. Consequently, Z. japonica in this estuary has adopted a mixed annual life history strategy in response to these environmental oscillations. Our findings highlight the vulnerability of the Z. japonica population to seasonal environmental shifts and interspecies competition in this estuary, offering essential considerations for its conservation and effective management. [ABSTRACT FROM AUTHOR]

MoS2 has garnered considerable attention as an exceptional co-catalyst that is capable of significantly enhancing the efficiency of H2O2 decomposition in advanced oxidation processes (AOPs). This improvement allows for a reduction in the required amounts of H2O2 and Fe2+. In this study, we investigated the cyclic durability of photo-Fenton catalysts, focusing on the degradation of pollutants through the introduction of PPy into heterogeneous 1T-2H MoS2 units. The resulting photothermal-Fenton catalysts, comprising non-ferrous Fenton catalysts, demonstrated excellent degradation performance for simulated pollutants. In comparison with 1T-2H MoS2, the PPy@1T-2H MoS2 composite exhibited remarkable stability and photothermal enhancement in the photo-Fenton degradation of methylene blue (MB) under visible light irradiation. The photo-Fenton reaction efficiently degraded contaminants, achieving 99% removal within 5 min and 99.8% removal within 30 min. Moreover, the co-catalyst complex displayed enhanced cyclic stability during the photo-Fenton reaction, with a contaminant removal efficiency of 92%, even after the 13th cyclic test. The combined effects of PPy and 1T-2H MoS2 demonstrated improved efficiency in both photocatalytic and photo-Fenton catalytic reactions. Furthermore, PPy@1T-2H MoS2 exhibited outstanding performance in the photothermal evaporation of water, achieving an efficiency of 86.3% under one solar irradiation. [ABSTRACT FROM AUTHOR]

Addressing the challenge of efficiently detecting objects in ultra-high-resolution images during object detection tasks, this paper proposes a novel method called MegaDetectNet, which leverages foreground image for large-scale resolution image object detection. MegaDetectNet utilizes a foreground extraction network to generate a foreground image that highlights target regions, thus avoiding the computationally intensive process of dividing the image into multiple sub-images for detection, and significantly improving the efficiency of object detection. The foreground extraction network in MegaDetectNet is built upon the YOLOv5 model with modifications: the large object detection head and classifier are removed, and the PConv convolution is introduced to reconstruct the C3 module, thereby accelerating the convolution process and enhancing foreground extraction efficiency. Furthermore, a Res2Rep convolutional structure is developed to enlarge the receptive field and improve the accuracy of foreground extraction. Finally, a foreground image construction method is proposed, fusing and stitching foreground target regions into a unified foreground image. This approach replaces multiple divided sub-images with a single foreground image for detection, reducing overhead time. The proposed MegaDetectNet method's effectiveness for detecting objects in ultra-high-resolution images is validated using the publicly available DOTA dataset. Experimental results demonstrate that MegaDetectNet achieves an average time reduction of 83.8% compared to the sub-image division method among various commonly used object detectors, with only a marginal 8.7% decrease in mAP (mean Average Precision). This validates the practicality and efficacy of the MegaDetectNet method for object detection in ultra-high-resolution images. [ABSTRACT FROM AUTHOR]

Posttraumatic osteoarthritis (PTOA) arises secondary to joint injuries and is characteristically driven by inflammatory mediators. PTOA is often studied in the setting of ACL tears. However, a wide range of other injuries also lead to PTOA pathogenesis. The purpose of this study was to characterize the morphological changes in the uninjured ACL in a PTOA inflammatory environment. We retrospectively reviewed 14 ACLs from 13 Yucatan minipigs, 7 of which had undergone our modified intra-articular drilling (mIAD) procedure, which induced PTOA through inflammatory mediators. Seven ACLs were harvested from mIAD minipigs (PTOA) and seven ACLs from control minipigs with no cartilage degeneration (non-PTOA). ACL degeneration was evaluated using histological scoring systems. IL-1β, NF-κB, and TNF-α mRNA expression in the synovium was measured using qRT-PCR. PTOA minipigs demonstrated significant ACL degeneration, marked by a disorganized extracellular matrix, increased vascularity, and changes in cellular shape, density, and alignment. Furthermore, IL-1β, NF-κB, and TNF-α expression was elevated in the synovium of PTOA minipigs. These findings demonstrate the potential for ACL degeneration in a PTOA environment and emphasize the need for anti-inflammatory disease-modifying therapies following joint injury. [ABSTRACT FROM AUTHOR]

To promote energy saving, emission reduction, and sustainable development of high-speed trains, as well as achieve low-carbon operation of these trains. It is necessary to establish a fluctuating wind speed spectra model that can accurately describe the characteristics of the fluctuating wind speed field of the moving vehicle. This will help explore the effects of strong winds on the running resistance, energy consumption, safety, and comfort of trains. In this paper, based on Priestley's evolutionary power spectral density (EPSD) theory, an efficient method was developed for generating the fluctuating wind speeds at the moving point under the non-stationary wind field. On such basis, the effects of different mean wind speeds, ground clearances, temporal modulation function parameters, and vehicle's moving speeds on the time-varying correlation function ratio of fluctuating wind speed at fixed and moving points were analyzed. Subsequently, the relationship between the time-varying correlation functions of fluctuating wind speed at the fixed and moving points was established by analyzing the sensitivity of the above parameters, and a theoretical model of fluctuating wind speed spectra of the moving point under the non-stationary wind field was proposed. In addition, the relational expression of fluctuating wind speed spectra of the moving point under stationary and non-stationary wind fields was established, which was further validated using the fluctuating wind speed spectra model at the fixed points with different modulation function forms. The results demonstrated that the direct generation method can avoid n times of POD decomposition and N s ∑ j = 1 n N q jj  times of FFT calculation, improve the calculation speed, and save memory. The proposed fluctuating wind speed spectra model at the moving point under the non-stationary wind field is in good agreement with the corresponding target one, indicating the high accuracy of the proposed model. Meanwhile, it is also noted that the fluctuating wind speed spectra at the moving point under the non-stationary wind field can be obtained by modulating the spectra under the stationary wind field using temporal modulation function, which is the same as that of the fluctuating wind speed spectra at fixed points under the non-stationary wind field. [ABSTRACT FROM AUTHOR]

The operating efficiency of high-head pump turbines is closely related to the internal hydraulic losses within the system. Conventional methods for calculating hydraulic losses based on pressure differences often lack detailed information on their distribution and specific sources. Additionally, the presence of splitter blades further complicates the hydraulic loss characteristics, necessitating further study. In this study, Reynolds-averaged Navier–Stokes (RANS) simulations were employed to analyze the performance of a pump turbine with splitter blades at three different head conditions and a guide vane opening (GVO) of 10°. The numerical simulations were validated by experimental tests using laser doppler velocimetry (LDV). Quantitative analysis of flow components and hydraulic losses was conducted using entropy production theory in combination with an examination of flow field distributions to identify the origins and features of hydraulic losses. The results indicate that higher heads are associated with lower growth rates of total hydraulic losses. In particular, the significant velocity gradients at the trailing edge of the splitter blades contribute to higher hydraulic losses. Furthermore, the hydraulic losses in the runner (RN) region are predominantly influenced by velocity gradients and not by vortices, with the flow conditions in the RN region impacting the hydraulic losses in the draft tube (DT). [ABSTRACT FROM AUTHOR]

There is a history of overwork in China, and regulations to protect workers are insufficient. This study explores the relationship between working long hours and self-rated health among rural-urban migrant workers in China. Survey data from the 2018 China Family Panel Studies (CFPS) is used to construct a simultaneous equation model, and the 3SLS method is applied. The results showed that: (1) Migrant workers had good health. However, overtime work is very common among migrant workers in China, and male migrants work more overtime. (2) Migrant laborers' health and income are causally related, with better health leading to higher income. In contrast, the compensatory effect of income by extending working hours on health is smaller than the damage caused by overtime work for the male migrants who have more severe overtime work, resulting in a negative income effect on health. (3) The incentive effect of income on labor supply and the positive interaction effect of increasing labor hours to increase income are only reflected in the standard labor time sample. Therefore, for the heavy overwork group whose working hours have already reached the limit, income increases can no longer motivate them to extend their labor hours. Therefore, provincial and national policy transformations are needed to regularize working hours and remuneration while maintaining individual health. [ABSTRACT FROM AUTHOR]

Addressing the challenges of low detection precision and excessive parameter volume presented by the high resolution, significant scale variations, and complex backgrounds in UAV aerial imagery, this paper introduces MFP-YOLO, a lightweight detection algorithm based on YOLOv5s. Initially, a multipath inverse residual module is designed, and an attention mechanism is incorporated to manage the issues associated with significant scale variations and abundant interference from complex backgrounds. Then, parallel deconvolutional spatial pyramid pooling is employed to extract scale-specific information, enhancing multi-scale target detection. Furthermore, the Focal-EIoU loss function is utilized to augment the model's focus on high-quality samples, consequently improving training stability and detection accuracy. Finally, a lightweight decoupled head replaces the original model's detection head, accelerating network convergence speed and enhancing detection precision. Experimental results demonstrate that MFP-YOLO improved the mAP50 on the VisDrone 2019 validation and test sets by 12.9% and 8.0%, respectively, compared to the original YOLOv5s. At the same time, the model's parameter volume and weight size were reduced by 79.2% and 73.7%, respectively, indicating that MFP-YOLO outperforms other mainstream algorithms in UAV aerial imagery detection tasks. [ABSTRACT FROM AUTHOR]

As a key role in hindering the large-scale application of fuel cells, oxygen reduction reaction has always been a hot issue and nodus. Aiming to explore state-of-art electrocatalysts, this paper reviews the latest development of nonmetallic catalysts in oxygen reduction reactions, including single atoms doped with carbon materials such as N, B, P or S and multi-doped carbon materials. Afterward, the remaining challenges and research directions of carbon-based nonmetallic catalysts are prospected. [ABSTRACT FROM AUTHOR]

The bearing fault diagnosis of petrochemical rotating machinery faces the problems of large data volume, weak fault feature signal strength and susceptibility to noise interference. To solve these problems, current research presents a combined ICEEMDAN-wavelet threshold joint noise reduction, mutual dimensionless metrics and MPGA-SVM approach for rotating machinery bearing fault diagnosis. Firstly, we propose an improved joint noise-reduction method of an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and wavelet thresholding. Moreover, the noise-reduced data are processed by mutual dimensionless processing to construct a mutual dimensionless index sensitive to bearing fault features and complete the fault feature extraction of the bearing signals. Furthermore, we design experiments on faulty bearings of multistage centrifugal fans in petrochemical rotating machinery and processed the input data set according to ICEEMDAN-wavelet threshold joint noise reduction and mutual dimensionless indexes for later validation of the model and algorithm. Finally, a support vector machine model used to effectively identify the bearing failures, and a multi-population genetic algorithm, is studied to optimize the relevant parameters of the support vector machine. The powerful global parallel search capability of the multigroup genetic algorithm is used to search for the penalty factor c and kernel parameter r that affect the classification performance of the support vector machine. The global optimal solutions of c and r are found in a short time to construct a multigroup genetic algorithm-support vector machine bearing fault diagnosis and identification model. The proposed model is verified to have 95.3% accuracy for the bearing fault diagnosis, and the training time is 11.1608 s, while the traditional GA-SVM has only 89.875% accuracy and the training time is 17.4612 s. Meanwhile, to exclude the influence of experimental data on the specificity of our method, the experimental validation of the Western Reserve University bearing failure open-source dataset was added, and the results showed that the accuracy could reach 97.1% with a training time of 14.2735 s, thus proving that the method proposed in our paper can achieve good results in practical applications. [ABSTRACT FROM AUTHOR]

Insect pests pose a significant threat to alpine ecosystems, especially under rapid environmental change conditions. Therefore, it is necessary to explore the effects of environmental factors on insect pest risks and provide methods for pest management in alpine regions. Habitat heterogeneity and topographic variation are the indicators of insect pest risks. However, few studies have explored the effects of habitat heterogeneity and topographic variation on insect pest risks in alpine regions. We used species distribution modeling (i.e., maxent modeling) to project the distributions of insect pests in this alpine region based on occurrence records. Then, we delineated the high-risk areas for insect pests based on the species distributions under a conceptual risk framework using Zonation software for different ecoregional types. We determined the alpine conifer and mixed forests of the Nujiang Langcang Gorge, the conifer forests of the Qilian Mountains, and the shrublands and meadows of Southeast Tibet as the key areas requiring monitoring for insect pests in Qinghai province based on the scoring of insect pest risk rank with >0.7. Habitat heterogeneity and topographic variation could be developed as indicators of risk exposure to insect pests in alpine regions. Our study suggests that the prevention and control of insect pests should be conducted in areas with high habitat heterogeneity and topographic roughness in alpine regions. We provided new insights into the application of species distribution modeling based on habitat heterogeneity and topographic variation. The results of our study indicate that habitat heterogeneity and topographic variation should be considered for improving pest management effectiveness in alpine regions. [ABSTRACT FROM AUTHOR]

The use of satellite precipitation products can overcome the limitations of rain gauges in flood hazard mapping for mitigation purposes. Hence, this study aims to evaluate the capabilities of three global precipitation measurement (GPM) integrated multisatellite retrievals for GPM (IMERG) products in tropical flood hazard mapping in the Kelantan River Basin (KRB), Malaysia, using the GIS-based analytic hierarchy process (AHP) method. In addition to the precipitation factor, another eleven factors that contribute to flooding in the KRB were included in the AHP method. The findings demonstrated that the spatial pattern and percentage area affected by floods simulated under the IMERG-Early (IMERG-E), IMERG-Late (IMERG-L), and IMERG-Final (IMERG-F) products did not differ significantly. The receiver operating characteristics curve analysis showed that all three IMERG products performed well in generating flood hazard maps, with area under the curve values greater than 0.8. Almost all the recorded historical floods were placed in the moderate-to-very-high flood hazard areas, with only 1–2% found in the low flood hazard areas. The middle and lower parts of the KRB were identified as regions of "very high" and "high" hazard levels that require particular attention from local stakeholders. [ABSTRACT FROM AUTHOR]

Xinjiang is an important arid region in the northwest of China and plays an important role in the field of ecological security protection in China. Because of its aridity, the identification of critical areas for ecological protection and the optimization of ecological space structure in Xinjiang are of great significance for promoting the harmonious development of the oasis economy, enhancing the ecological environment, and improving human well-being. This study applied an ecological security evaluation from the three dimensions of habitat quality, ecosystem service value, and soil-water conservation to identify the basic situation of the ecological security pattern. The core "source" area of ecological protection was extracted using the morphological spatial pattern analysis (MSPA) method, while the ecological corridor and important ecological nodes were identified using the minimum cumulative resistance model (MCR). The "point-line-plane" three-dimensional ecological network structure was then constructed, providing a case for the development of the ecological security and construction in the oasis. The results showed that in the arid regions of Xinjiang, the ecological land is extremely fragmented and is mainly distributed in the mountains and waters distant from human activities. Overall, there is a substantial geographical disparity with a low level of ecological security, particularly in the ecological marginal areas. The ecological network framework of Xinjiang is characterized by an uneven distribution of "sources", broken corridor structure, and a low degree of networking. Therefore, this study proposed an ecological space layout system consisting of "7 ecological subsystems, 51 source areas, 87 ecological corridors, and 33 ecological nodes" by combining the regional physical and geographical characteristics with the overall development plan. [ABSTRACT FROM AUTHOR]

Salinity is a major abiotic stress that restricts cotton growth and affects fiber yield and quality. Although studies on salt tolerance have achieved great progress in cotton since the completion of cotton genome sequencing, knowledge about how cotton copes with salt stress is still scant. S-adenosylmethionine (SAM) plays important roles in many organelles with the help of the SAM transporter, and it is also a synthetic precursor for substances such as ethylene (ET), polyamines (PAs), betaine, and lignin, which often accumulate in plants in response to stresses. This review focused on the biosynthesis and signal transduction pathways of ET and PAs. The current progress of ET and PAs in regulating plant growth and development under salt stress has been summarized. Moreover, we verified the function of a cotton SAM transporter and suggested that it can regulate salt stress response in cotton. At last, an improved regulatory pathway of ET and PAs under salt stress in cotton is proposed for the breeding of salt-tolerant varieties. [ABSTRACT FROM AUTHOR]

High-resolution albedo has the advantage of a higher spatial scale from tens to hundreds of meters, which can fill the gaps of albedo applications from the global scale to the regional scale and can solve problems related to land use change and ecosystems. The Sentinel-2 satellite provides high-resolution observations in the visible-to-NIR bands, giving possibilities to generate a high-resolution surface albedo at 10 m. This study attempted to evaluate the performance of the four data-driven machine learning algorithms (i.e., random forest (RF), artificial neural network (ANN), k-nearest neighbor (KNN), and XGBoost (XGBT)) for the generation of a Sentinel-2 albedo over flat and rugged terrain. First, we used the RossThick-LiSparseR model and the 3D discrete anisotropic radiative transfer (DART) model to build the narrowband surface reflectance and broadband surface albedo, which acted as the training and testing datasets over flat and rugged terrain. Second, we used the training and testing datasets to drive the four machine learning models, and evaluated the performance of these machine learning models for the generation of Sentinel-2 albedo. Finally, we used the four machine learning models to generate a Sentinel-2 albedo and compared them with in situ albedos to show the models' application potentials. The results show that these machine learning models have great performance in estimating Sentinel-2 albedos at a 10 m spatial scale. The comparison with in situ albedos shows that the random forest model outperformed the others in estimating a high-resolution surface albedo based on Sentinel-2 datasets over the flat and rugged terrain, with an RMSE smaller than 0.0308 and R2 larger than 0.9472. [ABSTRACT FROM AUTHOR]

Ocean wave height is one of the critical factors to decide the efficiency of the ocean wave energy conversion system. Usually, only when the resonate occurs between the ocean wave height (ocean wave speed in the vertical direction) and ocean wave energy conversion system, can the conversion efficiency from ocean wave energy into electric energy be maximized. Therefore, this paper proposes two predication methods to predict the future ocean wave height in 1.5–2.5 s. Firstly, the data fitting of real ocean wave height is achieved by the polynomial method, which is beneficial to the predication of ocean wave height. Secondly, the models of the moving average (MA) predication method and auto regressive (AR) predication method are presented by the time series analysis process. Lastly, after the predication of ocean wave height by the MA method and AR method, and compared with the data fitting result of real ocean wave height, it can be found that the AR method is more accurate for the predication of ocean wave height. In addition, the predication results also indicated that the error between the predication value and true value in the future 2.5 s is considered acceptable, which provides enough time to optimize the operation process of the ocean wave energy conversion system by a suitable control method. [ABSTRACT FROM AUTHOR]

Laser cleaning is an advanced surface-cleaning technology that can lead to the instant evaporation and stripping of the attachments found on a substrate's surface, such as contaminants, rust, and coatings; it uses a high-energy laser beam to irradiate the components' surface. Compared with common surface-cleaning technologies, laser cleaning has the advantages of precision, efficiency, and controllability. In this paper, the fundamental mechanisms of laser cleaning technology are summarized in detail; these include the laser thermal ablation mechanism, the laser thermal stress mechanism, and the plasma shock wave mechanism. The operational principles, characteristics, and application range of each mechanism are discussed. Their typical applications in industry are outlined according to the differences in the substrate materials used, including metallic materials, nonmetallic materials, and semiconductor elements. This study provides a significant reference and guiding basis for researchers to further explore the fundamental mechanisms of laser cleaning, as well as various aspects of the typical industrial applications of laser cleaning. [ABSTRACT FROM AUTHOR]

African swine fever (ASF) is a viral haemorrhagic disease found in domestic and wild boars caused by the African swine fever virus (ASFV). A highly virulent strain was used to evaluate the efficacy of newly developed vaccine candidates. The ASFV strain SY18 was isolated from the first ASF case in China and is virulent in pigs of all ages. To evaluate the pathogenesis of ASFV SY18 following intraoral (IO) and intranasal (IN) infections, a challenge trial was conducted in landrace pigs, with intramuscular (IM) injection as a control. The results showed that the incubation period of IN administration with 40–1000 50 % tissue culture infective dose (TCID50) was 5–8 days, which was not significantly different from that of IM inoculation with 200 TCID50. A significantly longer incubation period, 11–15 days, was observed in IO administration with 40–5000 TCID50. Clinical features were similar among all infected animals. Symptoms, including high fever (≥40.5 °C), anorexia, depression, and recumbency, were observed. No significant differences were detected in the duration of viral shedding during fever. There was no significant difference in disease outcome, and all animals succumbed to death. This trial showed that IN and IO infections could be used for the efficacy evaluation of an ASF vaccine. The IO infection model, similar to that of natural infection, is highly recommended, especially for the primary screening of candidate vaccine strains or vaccines with relatively weak immune efficacy, such as live vector vaccines and subunit vaccines. [ABSTRACT FROM AUTHOR]

Color reversion has long been a major problem for the vegetable oil industry, and the enzymatic oxidation of γ-tocopherol is thought to trigger this phenomenon. In this study, first, the extraction, purification, and detailed characterization of tocopherol oxidase from fresh corn germs were performed. Then, the relationship between the enzyme reaction of γ-tocopherol and oil color reversion was verified. The results showed that the membrane-free extracts of raw corn germ performed specific catalysis of tocopherol in the presence of lecithin. In terms of the oxidation product, tocored (the precursor of color reversion) was detected in the mixture after the catalytic reactions, indicating that this anticipated enzyme reaction was probably correlated with the color reversion. Furthermore, the optimal pH and temperature for the tocopherol oxidase enzyme were 4.6 and 20 °C, respectively. In addition, ascorbic acid at 1.0 mM completely inhibited the enzymatic reaction. [ABSTRACT FROM AUTHOR]

Solar radiation management (SRM), or solar geoengineering, reduces the earth's temperature by reflecting more sunlight back to space. However, the impacts of SRM remain unclear, making it difficult to project the benefits as well as consequences should this approach be adopted to combat climate change. To provide novel insight into the SRM impact on hydro-climatic extremes in Southeast Asia, this study conducts a simulation experiment for the Kelantan River Basin (KRB) in Malaysia by incorporating three bias-corrected Stratospheric Aerosol Geoengineering Large Ensemble (GLENS) members into the Soil and Water Assessment Tool Plus (SWAT+) model. The study found that SRM practices could generate substantial cooling effects on regional temperatures, leading to a reduction in projected annual precipitation and monthly precipitation during the flooding season (from November to mid-January) under SRM relative to the Representative Concentration Pathway 8.5 (RCP8.5) scenario. In addition, SRM could reduce the number of days with heavy precipitation as well as the intensity of maximum daily precipitation as compared to RCP8.5, during the 2045–2064 and 2065–2084 periods, leading to a reduction in high flows. Nevertheless, under SRM impacts, the driest months from February to May would experience comparable decreases in monthly precipitation and streamflow. [ABSTRACT FROM AUTHOR]

Ebinur Lake is the largest brackish-water lake in Xinjiang, China. Strong winds constantly have an impact on this shallow water body, causing high variability in turbidity of water. Therefore, it is crucial to continuously monitor suspended particulate matter (SPM) for water quality management. This research aims to develop an advanced spatiotemporal fusion model based on the inversion technique that enables time-continuous and detailed monitoring of SPM over an intermontane lake. The findings shows that: (1) the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) fusion in blue, green, red, and near infrared (NIR) bands was better than the flexible spatiotemporal data fusion (FSDAF) model in extracting SPM information; (2) the inversion model constructed by random forest (RF) outperformed the support vector machine (SVM) and partial least squares (PLS) algorithms; and (3) the SPM concentrations acquired from the fused images of Landsat 8 OLI and ESTARFM matched with the actual data of Ebinur Lake based on the visual perspective and accuracy assessment. [ABSTRACT FROM AUTHOR]

Theoretically, the topological charge l in the vortex can be any integer or fraction, thus the vortex carrying different topological charges can form an infinitely orthogonal orbital angular momentum state space, which can provide new dimensional resources for optical communication. However, high-capacity optical communication requires low delay, thus real-time detection of the OAM is significant for communication. Metasurfaces have the characteristics of low loss, ultra-thin, easy integration, and flexible phase controls, which provide a meaningful way to realize integrated OAM generation and detection. Here, an optimized streamlined metasurface (OSM) is presented, which can detect high-order vortex beams in a single, simple, and rapid manner by photon momentum transformation (PMT). Since different vortices are converted into focusing modes with distinct azimuthal coordinates on a transverse plane through PMT, a single measurement can determine OAMs in an ample mode space. In addition, the OSM can detect more and higher order OAMs compared with a discrete metasurface (DM) at the same size, due to its better wavefront sampling capabilities. With the merits of an ultra-compact device size, simple optical structure, and outstanding vortex recognition ability, our approach may underpin the development of integrated optics and quantum systems. [ABSTRACT FROM AUTHOR]

Environmental pollution has become a serious problem in China due to the development of industrialization and urbanization since the reform policies and opening of the economy. Nowadays, ENGOs function as a third party for environmental protection through various awareness and bargaining activities. This study aims to analyze the role of ENGOs in environmental bargaining (EB) and the influencing factors by combining the EB theories. A structural equation model of ENGOs participation in EB was established to compare the role of ENGOs in bargaining the "Kunming PX incident" and the "Nujiang dam incident" in Yunnan, China. The findings show that complex powers and interests play a vital role during EB. The relationship network, media, and human resources are among the most significant factors influencing the role of ENGO out of all the other factors such as funding, openness, knowledge, scale and experience. The strength of ENGO relationship network is crucial for solving environmental problems. This study also suggests that in order for ENGOs to effectively engage in EB, they should be placed in the proper context in the negotiating process. It is necessary to set up efficient public involvement platforms and processes for effective EB. [ABSTRACT FROM AUTHOR]

Missing data is a common issue in remote sensing. Data reconstruction through multiple satellite data sources has become one of the most powerful ways to solve this issue. Continuous monitoring of suspended particulate matter (SPM) in arid lakes is vital for water quality solutions. Therefore, this research aimed to develop and evaluate the performance of two image reconstruction strategies, spatio-temporal fusion reflectance image inversion SPM and SPM spatio-temporal fusion, based on the measured SPM concentration data with Sentinel-2 and Sentinel-3. The results show that (1) ESTARFM (Enhanced Spatio-temporal Adaptive Reflection Fusion Model) performed better than FSDAF (Flexible Spatio-temporal Data Fusion) in the fusion image generation, particularly the red band, followed by the blue, green, and NIR (near-infrared) bands. (2) A single-band linear and non-linear regression model was constructed based on Sentinel-2 and Sentinel-3. Analysis of the accuracy and stability of the model led us to the conclusion that the red band model performs well, is fast to model, and has a wide range of applications (Sentinel-2, Sentinel-3, and fused high-accuracy images). (3) By comparing the two data reconstruction strategies of spatio-temporal fused image inversion SPM and spatio-temporal fused SPM concentration map, we found that the fused SPM concentration map is more effective and more stable when applied to multiple fused images. The findings can provide an important scientific reference value for further expanding the inversion research of other water quality parameters in the future and provide a theoretical basis as well as technical support for the scientific management of Ebinur Lake's ecology and environment. [ABSTRACT FROM AUTHOR]

Dielectric elastomer actuation has been extensively investigated and applied to bionic robotics and intelligent actuators due to its status as an excellent actuation technique. As a conical dielectric elastomer actuator (DEA) structure extension, push-pull DEA has been explored in controlled acoustics, microfluidics, and multi-stable actuation due to its simple fabrication and outstanding performance. In this paper, a theoretical model is developed to describe the electromechanical behavior of push-pull DEA based on the force balance of the mass block in an actuator. The accuracy of the proposed model is experimentally validated by employing the mass block in the construction of the actuator as the object of study. The actuation displacement of the actuator is used as the evaluation indication to investigate the effect of key design parameters on the actuation performance of the actuator, its failure mode, and critical failure voltage. A dynamic actuator model is proposed and used with experimental data to explain the dynamic response of the actuator, its natural frequency, and the effect of variables. This work provides a strong theoretical background for dielectric elastomer actuators, as well as practical design and implementation experience. [ABSTRACT FROM AUTHOR]

Sediments represent the major sink of antibiotics in aquatic systems. However, few studies have proposed effective models that can predict the adsorption capacity of sediments through their physicochemical parameters. Here, 49 sediment samples were collected from different locations in the Yangtze Estuary and its adjacent coastal areas. The sediments were characterized, and their adsorption behavior towards tetracycline (TC) was investigated. It was found that both the Langmuir and Freundlich models fit the TC adsorption data well, and the sediments in the mud area showed the highest adsorption capacity. Subsequently, through correlation analysis for the adsorption coefficients and physicochemical parameters of sediments, 11 models were established to predict the adsorption coefficients (Kd), in which clay and cation exchange capacity played significant roles. When the salinity was increased from 0 to 32.79‰, the Freundlich adsorption coefficient (Kf) of TC for most sediments was reduced by more than75% (except sediment C6). Therefore, the methods provided herein can be helpful in predicting the sorption behavior of antibiotics with similar structures toward TC by sediments in this region. [ABSTRACT FROM AUTHOR]