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Vegetation greenness has been one of the most widely utilized indicators to assess the vegetation growth status for the better ecological environment. However, in typical tourist regions, the impact of the geographical environment, socioeconomic development, and tourism development on vegetation greenness changes is still a challenge. To address this challenge, we used the Google Earth Engine (GEE) cloud platform combined with a series of Landsat remote sensing images to calculate the fractional vegetation cover (FVC) which can be used as an indicator to characterize the spatiotemporal evolution of vegetation greenness in Hainan Island from 2000 to 2020. Furthermore, we employed geographic detector and structural equation models to quantify the relative importance and explanatory power of the geographical environment, socioeconomic development, and tourism development on vegetation greenness changes and to clarify the interaction of mechanisms of various factors in Haikou and Sanya. The results show that the annual growth rate of the FVC in Hainan Island was 0.0025/a. In terms of spatial distribution, the trend of the FVC changes was mainly characterized by non-significant and extremely significant improvement, accounting for 35.34% and 29.38% of the study area. Future vegetation greenness was dominated by weak counter-persistent increase and weak persistent increase. The geographical environmental factors were the main factors affecting vegetation greenness in Haikou, followed by the socioeconomic and the tourism development factors, while the geographical environmental factors also dominate in Sanya, followed by the tourism development factors and finally the socioeconomic factors. Specifically, the spatial distribution of vegetation greenness was primarily influenced by land use types, elevation, slope, and travel services. Geographical environmental factors could indirectly affect changes in socioeconomic and tourism development, thereby indirectly affecting the spatial distribution of vegetation greenness. These findings can provide some significant implications to guide the ecological environmental protection for sustainable development in Hainan Island in China. [ABSTRACT FROM AUTHOR]

In rubber plantations, understory coverage is often disrupted by human activities, which increases the risk of soil erosion under intense rainfall typical of tropical islands. Evaluating the effectiveness of soil and water conservation measures (SWCMs) is crucial for effectively conserving subcanopy resources. This study focused on Hainan Island's rubber plantations, where nine different SWCMs were implemented, and the runoff and sediment yield were monitored during the rainy season using runoff plots. Through correlation analysis, we identified the primary rainfall characteristic factors leading to soil and water loss on rubber plantation slopes. Path analysis was then used to quantify the impacts of these characteristic factors. The results showed that the SWCMs were significantly more effective in erosion reduction (68.55%) than in runoff reduction (58.95%). Of all the measures, comprehensive SWCMs proved most effective in controlling runoff (71.34%), followed by engineering SWCMs (62.03%) and biological SWCMs (43.51%). Comprehensive SWCMs were also found to be effective in erosion reduction, with a rate of 77.84%, surpassing engineering and biological SWCMs by 7.23% and 20.66%, respectively. Notably, the combination of narrow terraces, contour trenches, and grass planting was the most effective, achieving runoff-reduction rates of 80.94% and erosion-reduction rates of 85.27%. This combination is recommended as a primary prevention method. Rainfall and maximum 30-min intensity (I30) were identified as key variables affecting the efficacy of SWCMs, with rainfall positively correlating with runoff yield and I30 being more closely linked to sediment production. This study provides valuable insights for developing erosion control strategies for sloping garden lands in similar regions and lays theoretical foundations for future ecological restoration projects. [ABSTRACT FROM AUTHOR]

Soil erosion (SE) is a critical threat to the sustainable development of ecosystem stability, agricultural productivity, and human society in the context of global environmental and climate change. Particularly in tropical island regions, due to the expansion of human activities and land use/cover changes (LUCCs), the risk of SE has been exacerbated. Combining the RUSLE with machine learning methods, SE spatial patterns, their driving forces and the mechanisms of how LUCCs affect SE, were illustrated. Additionally, the potential impacts of future LUCCs on SE were simulated by using the PLUS model. The main results are as follows: (1) Due to LUCCs, the average soil erosion modulus (SEM) decreased significantly from 108.09 t/(km2·a) in 2000 to 106.75 t/(km2·a) in 2020, a reduction of 1.34 t/(km2·a), mainly due to the transformation of cropland to forest and urban land. (2) The dominant factor affecting the spatial pattern of SE is the LS factor (with relative contributions of 43.9% and 45.17%), followed by land use/cover (LUC) (the relative contribution is 28.46% and 34.89%) in 2000 and 2020, respectively. (3) Three kinds of future scenarios simulation results indicate that the average SEM will decrease by 2.40 t/(km2·a) under the natural development scenario and by 1.86 t/(km2·a) under the ecological protection scenario by 2060. However, under the cropland protection scenario, there is a slight increase in SEM, with an increase of 0.08 t/(km2·a). Sloping cropland erosion control remains a primary issue for Hainan Island in the future. [ABSTRACT FROM AUTHOR]

Food security is a major challenge for China at present and will be in the future. Revealing the spatiotemporal changes in cropland and identifying their driving forces would be helpful for decision-making to maintain grain supply and sustainable development. Hainan Island is endowed with rich agricultural resources due to its unique climatic conditions and is facing tremendous pressure in cropland protection due to the huge variation in natural conditions and human activities over the past few decades. The purpose of this study is to assess the spatiotemporal changes in and driving forces of cropland on Hainan Island in the past and predict future cropland changes under different scenarios. Key findings are as follows: (1) From 2000 to 2020, the cropland area on Hainan Island decreased by 956.22 km2, causing the center of cropland to shift southwestward by 8.20 km. This reduction mainly transformed into construction land and woodland, particularly evident in coastal areas. (2) Among anthropogenic factors, the increase in the human footprint is the primary reason for the decrease in cropland. Land use changes driven by population growth, especially in economically active and densely populated coastal areas, are key factors in this decrease. Natural factors such as topography and climate change also significantly impact cropland changes. (3) Future scenarios show significant differences in cropland area changes. In the natural development scenario, the cropland area is expected to continue decreasing to 597 km2, while in the ecological protection scenario, cropland conversion is restricted to 269.11 km2; however, in the cropland protection scenario, the trend of cropland reduction is reversed, increasing by 448.75 km2. Our findings provide a deep understanding of the driving forces behind cropland changes and, through future scenario analysis, demonstrate the potential changes in cropland area under different policy choices. These insights are crucial for formulating sound land management and agricultural policies to protect cropland resources, maintain food security, and promote ecological balance. [ABSTRACT FROM AUTHOR]

Rainfall erosivity (RE), a pivotal external force driving soil erosion, is impacted by El Niño/Southern Oscillation (ENSO). Studying the spatiotemporal variations in RE and their response to ENSO is essential for regional ecological security. In this study, a daily RE model was identified as a calculation model through an evaluation of model suitability. Daily precipitation data from 1971 to 2020 from 38 meteorological stations on Hainan Island, China, were utilized to calculate the RE. The multivariate ENSO index (MEI), Southern Oscillation Index (SOI), and Oceanic Niño Index (ONI) were used as the ENSO characterization indices, and the effects of ENSO on RE were investigated via cross-wavelet analysis and binary and multivariate wavelet coherence analysis. During the whole study period, the average RE of Hainan Island was 15,671.28 MJ·mm·ha−1·h−1, with a fluctuating overall upward trend. There were spatial and temporal distribution differences in RE, with temporal concentrations in summer (June–August) and a spatial pattern of decreasing from east to west. During ENSO events, the RE was greater during the El Niño period than during the La Niña period. For the ENSO characterization indices, the MEI, SOI, and ONI showed significant correlations and resonance effects with RE, but there were differences in the time of occurrence, direction of action, and intensity. In addition, the MEI and MEI–ONI affected RE individually or jointly at different time scales. This study contributes to a deeper understanding of the influence of ENSO on RE and can provide important insights for the prediction of soil erosion and the development of related coping strategies. [ABSTRACT FROM AUTHOR]

Simple Summary: This review outlines the use of metabolomics to detect biomarkers for bovine mastitis, offering insights into disease prediction and early diagnosis. It details the use of various biological samples for metabolomic analysis, including milk, blood, urine, rumen fluid, feces, and mammary tissue. Bovine mastitis is an inflammatory disease of the mammary glands, and its pathogenesis and diagnosis are complicated. Through qualitative and quantitative analysis of small-molecule metabolites, the metabolomics technique plays an important role in finding biomarkers and studying the metabolic mechanism of bovine mastitis. Therefore, this paper reviews the predictive and diagnostic biomarkers of bovine mastitis that have been identified using metabolomics techniques and that are present in samples such as milk, blood, urine, rumen fluid, feces, and mammary tissue. In addition, the metabolic pathways of mastitis-related biomarkers in milk and blood were analyzed; it was found that the tricarboxylic acid (TCA) cycle was the most significant (FDR = 0.0015767) pathway in milk fluid, and glyoxylate and dicarboxylate metabolism was the most significant (FDR = 0.0081994) pathway in blood. The purpose of this review is to provide useful information for the prediction and early diagnosis of bovine mastitis. [ABSTRACT FROM AUTHOR]

Simple Summary: We report the occurrence of Blastocystis spp., Enterocytozoon bieneusi, and Giardia duodenalis infections in Leizhou black goats in Zhanjiang City, Guangdong Province, China. The total prevalence rates of Blastocystis spp., E. bieneusi, and G. duodenalis were 33.63% (76/226), 17.7% (40/226), and 24.78% (56/226), respectively. Four Blastocystis spp. subtypes (ST5, ST10, ST14, and ST21), four E. bieneusi genotypes (CHG3, CM21, CHG1, and ET-L2) and two assemblages (A and E) of G. duodenalis were identified. The detection of zoonotic pathogen species, genotypes, and assemblages in Leizhou black goats implied their potential involvement in the transmission of zoonotic parasitic diseases. Blastocystis spp., Enterocytozoon bieneusi, and Giardia duodenalis are three common zoonotic intestinal parasites that cause severe diarrhea and enteric diseases. Leizhou black goats are characterized by a high reproductive rate, fast growth, and good meat quality, making them one of the pre-eminent goat breeds in China. Goats are reportedly common reservoirs of these three intestinal pathogens, but no information on their prevalence or genotypic distributions in black goats in Guangdong Province, China, is available. A total of 226 fecal samples were collected from goats in Zhanjiang city and genomic DNA was extracted from them. The presence of the three pathogens was detected using nested PCR targeting the sequences encoding SSU rRNA (Blastocystis spp.), the internal transcribed spacer of rRNA (ITS; E. bieneusi), as well as beta-giardin, glutamate dehydrogenase, and triosephosphate isomerase (G. duodenalis). All PCR products were sequenced to determine the species and genotypes of the organisms. The total prevalence rates of Blastocystis spp., E. bieneusi, and G. duodenalis were 33.63% (76/226), 17.70% (40/226), and 24.78% (56/226), respectively. Four subtypes of Blastocystis spp. were detected: ST5 (n = 6), ST10 (n = 50), ST14 (n = 14), and ST21 (n = 6). Among them, ST10 was the dominant genotype, accounting for 65.79% of strains, followed by the genotypes ST14 (18.42%), zoonotic ST5 (7.89%), and ST21 (7.89%). Four genotypes of E. bieneusi were detected: CHG3 (n = 32), CM21 (n = 4), CHG1 (n = 2), and ET-L2 (n = 2). Among these, CHG3 was the dominant genotype. Assemblage E (n = 54) and concurrent assemblages A and E (n = 2) were identified in the G. duodenalis-positive goats using multilocus genotyping. Blastocystis spp., E. bieneusi, and G. duodenalis infections were common in Leizhou black goats, all of which have zoonotic genotypes, indicating the potential risk of zoonotic transmission. Our results provide basic data for the prevention and control of these three intestinal pathogens. Further studies are required to better understand their genetic characteristics and zoonotic potential in Guangdong Province. [ABSTRACT FROM AUTHOR]

Simple Summary: Mastitis is one of the most common diseases in the dairy industry in the world, and its treatment cost is also the most expensive of all diseases in the dairy industry. The research on the rapid diagnosis of cow mastitis is of great significance to the development of the dairy cow industry all over the world. In this study, we use microbiology and metabolomics techniques to explore the differential microbiota and metabolites in cow milk at different somatic levels, to find new biomarkers for the diagnosis of cow mastitis and provide a new plan for the diagnosis, prevention, and treatment of mastitis in the future. Dairy cow mastitis is one of the common diseases of dairy cows, which will not only endanger the health of dairy cows but also affect the quality of milk. Dairy cow mastitis is an inflammatory reaction caused by pathogenic microorganisms and physical and chemical factors in dairy cow mammary glands. The number of SCC in the milk of dairy cows with different degrees of mastitis will increase in varying degrees. The rapid diagnosis of dairy cow mastitis is of great significance for dairy cow health and farm economy. Based on the results of many studies on the relationship between mastitis and somatic cell count in dairy cows, microflora, and metabolites in the milk of Holstein cows with low somatic cell level (SCC less than 200,000), medium somatic cell level (SCC up to 200,000 but less than 500,000) and high somatic cell level (SCC up to 5000,00) were analyzed by microbiome and metabolic group techniques. The results showed that there were significant differences in milk microbiota and metabolites among the three groups (p < 0.05), and there was a significant correlation between microbiota and metabolites. Meanwhile, in this experiment, 75 differential metabolites were identified in the H group and L group, 40 differential metabolites were identified in the M group and L group, and six differential microorganisms with LDA scores more than four were found in the H group and L group. These differential metabolites and differential microorganisms may become new biomarkers for the diagnosis, prevention, and treatment of cow mastitis in the future. [ABSTRACT FROM AUTHOR]

Prognostics and health management (PHM) has emerged as an essential approach for improving the safety, reliability, and maintainability of composite structures. However, an obstacle remains in its damage state estimation and lifetime prediction due to unknown inputs. Thus, a self-calibration Kalman-filter-based framework for residual life prediction is proposed, which involves unknown input items in the fatigue damage evolution model and employs health-monitoring data to estimate and compensate for them. Combined with the time-varying structural failure threshold, the remaining useful life (RUL) of composite laminates subjected to fatigue loading is predicted, providing a novel solution to the problem of unknown inputs in PHM. The simulation results demonstrate that the developed method can estimate the performance degradation state well, and its RUL prediction accuracy is within 5% with existing unknown inputs such as foreign impact damage. [ABSTRACT FROM AUTHOR]

Diamonds are thought to be excellent candidates of next-generation semiconductor materials for high power and high frequency devices. A two-dimensional hole gas in a hydrogen-terminated diamond shows promising properties for microwave power devices. However, high sheet resistance and low carrier mobility are still limiting factors for the performance improvement of hydrogen-terminated diamond field effect transistors. In this work, the carrier scattering mechanisms of a two-dimensional hole gas in a hydrogen-terminated diamond are studied. Surface roughness scattering and ionic impurity scattering are found to be the dominant scattering sources. Impurity scattering enhancement was found for the samples after a high-temperature Al2O3 deposition process. This work gives some insight into the carrier transport of hydrogen-terminated diamonds and should be helpful for the development of diamond field effect transistors. [ABSTRACT FROM AUTHOR]

With the increasing demands for detailed design of composite aircraft structures, the method of covering all damages with low design allowables cannot meet the current requirements for aircraft structure design. Herein, this paper proposes a novel algorithm for design allowable determination of composite laminates by combining the damage distribution with damage factor model of design allowable, so as to provide different structures with more accurate design allowables based on their initial damages. For the composite laminates with initial delaminations, a model describing the effect of delamination size and depth position on the compression design allowable is developed and the compression design allowable of different aircraft structures are individually determined by employing abundant initial delamination statistics. Compared with the design allowable offered by the single-point method, the design allowable based on the initial damage can be increased by at least 5% to 20%, greatly improving the economic benefits of the aircraft structures and providing an important support for the damage tolerance design of the composite structures. [ABSTRACT FROM AUTHOR]