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Abstract Photodetection has attracted significant attention for information transmission. While the implementation relies primarily on the photonic detectors, they are predominantly constrained by the intrinsic bandgap of active materials. On the other hand, photothermoelectric (PTE) detectors have garnered substantial research interest for their promising capabilities in broadband detection, owing to the self-driven photovoltages induced by the temperature differences. To get higher performances, it is crucial to localize light and heat energies for efficient conversion. However, there is limited research on the energy conversion in PTE detectors at micro/nano scale. In this study, we have achieved a two-order-of-magnitude enhancement in photovoltage responsivity in the self-rolled tubular tellurium (Te) photodetector with PTE effect. Under illumination, the tubular device demonstrates a maximum photovoltage responsivity of 252.13 V W−1 and a large detectivity of 1.48 × 1011 Jones. We disclose the mechanism of the PTE conversion in the tubular structure with the assistance of theoretical simulation. In addition, the device exhibits excellent performances in wide-angle and polarization-dependent detection. This work presents an approach to remarkably improve the performance of photodetector by concentrating light and corresponding heat generated, and the proposed self-rolled devices thus hold remarkable promises for next-generation on-chip photodetection.

The excessive phosphorus input generated by human activities has broken the nutritional balance of ecosystem. This study focused on the Three Gorges Reservoir Region, where human activities are concentrated. It involved the innovative selection of county subunits to provide a clearer analysis of different components contributing to net anthropogenic phosphorus input (NAPI) and factors impacting it on a smaller scale, opening up a new horizon for more accurately formulating phosphorus management policies. The results showed that NAPI of the reservoir region maintained a high level (2641.84 kg·km−2·a−1), while showing significant spatial variation due to high input (>4200 kg·km−2·a−1) at an urban agglomeration in the reservoir region. Phosphorus fertiliser made the largest (63 %–64 %) contribution to NAPI in the reservoir region, but different compositions of the component determining NAPI were shown among the counties. The sensitive coefficient (2.36) of phosphorus fertiliser in the reservoir region was the largest, the sensitive coefficients and proportional contributions in counties also varied among the counties. The findings of this study showed that reducing the input of phosphorus fertiliser and improving the utilization rate of phosphorus are direct methods for reducing NAPI in the Three Gorges Reservoir Region. Furthermore, improving the methods of treating human domestic waste and sewage can alleviate the high input pressure of phosphorus. The spatial differentiation of NAPI in the analyzed counties was mainly affected by the increase in phosphorus fertiliser and gross output value of agriculture, along with increasing urbanization, population densification, and greater economic activity in Chongqing compared with the other counties. Moreover, the components, sensitivities, contributions and impact factors in the counties were heterogeneous due to socio-economic, agricultural, and geographical characteristics, so it might be more appropriate to formulate phosphorus management policies on a smaller county-based scale.

As the pace of industrialization and urbanization accelerates, water quality management faces increasing challenges, with traditional methods for pollutant source apportionment often proving inadequate in handling complex environmental data. This study enhances the precision and reliability of pollutant source identification by integrating Positive Matrix Factorization (PMF) models with diverse machine learning techniques. Utilizing data from 17 water quality monitoring stations along the Wuding River from 2017 to 2021, we employed Random Forest (RF), Support Vector Machine (SVM), Elastic Net (EN), and Extreme Gradient Boosting (XGBoost) models to predict the Water Quality Index (WQI) during dry and wet seasons. Results indicate that the RF model exhibited optimal performance in the dry season (R2 = 0.93), while the SVM was superior in the wet season (R2 = 0.94). SHAP (SHapley Additive exPlanations) value analysis identified CODMn and NH3-N as significant influencers on WQI in the dry season, whereas COD, BOD, and TP gained prominence during the wet season. SHAP values reveal the contribution of each feature to the model output, thereby enhancing the model’s transparency and interpretability. Additionally, feature importance identified by machine learning was utilized as weights to optimize the contribution rates predicted by the PMF model. The optimised model was able to identify the contribution of domestic and farm effluent discharges more accurately in the dry season, with a significant increase in the percentage of identification from 19.4 % to 45.4 %, and an increase in the percentage of contribution from agricultural non-point sources and domestic effluent in the rainy season. This research offers a novel perspective on the characteristics of river water pollution and holds significant implications for formulating data-driven environmental management strategies.

Acquiring a comprehensive understanding of cropping patterns and their spatiotemporal distribution is crucial for sustainable agricultural development and ecological environment protection. However, the similarity of crop spectra and the diversity of ecosystem types hinder the accurate mapping of cropping patterns, especially in agricultural landscape regions. Hence, taking Xinghua County as study area, this article proposed a novel method for integrating multidimensional feature indices and phenological windows, named phenological window feature (PWF), to achieve efficient and accurate mapping of cropping patterns. In this study, we adopt a two-step approach. First, time-series curves of feature indices were constructed using Sentinel-1/2 satellite data to determine the phenological windows of different cropping patterns and construct PWF sets. Then, the ruleset threshold method (RTM) and random forest (RF) algorithms were used to map cropping patterns including wheat-rice, crayfish-rice, vegetable-rice, rice-rapeseed, rapeseed-vegetable, and year-round vegetables. The results indicate that the phenological windows extracted from the cropping patterns in the study area were 30–120, 90–135, and 200–270 days, respectively. The overall accuracies of RTM and RF, based on PWF, were 85.91% and 89.50%, respectively, and the kappa coefficients for RTM and RF were 0.831 and 0.872, respectively. In terms of classification performance, RF slightly outperformed RTM. The study demonstrates that PWF proposed in this article can be effectively utilized for mapping cropping patterns in complex agricultural landscape regions.

The Chongqing section of the Three Gorges Reservoir area (TGRA) is a high-risk geological catastrophe warning zone in the Yangtze River Basin. This study aimed to categorize landslides and improve the accuracy of landslide susceptibility evaluations by combining the artificial neural networks (ANN) and the normalized frequency ratio (NFR) models. Considering that the indicator factors have different effects on various landslides, the landslides were separated into four types, that is, giant, large, medium, and small. Thirteen indicator factors were selected through correlation analysis, including elevation, lithology, precipitation, land use, population density, and so on. Combined with 7777 historical landslide events, an ANN-NFR coupling model was proposed. The susceptibility grade prediction of landslides for the entire region was conducted using a GIS platform. Compared to the NFR model, the ANN-NFR model could improve the accuracy of landslide susceptibility evaluation by approximately 4%. This indicates that the ANN-NFR model is an effective method to calculate the weights of indicator factors. The FR value of large landslides is 9.201 in the very susceptibility region, and the model evaluation effect is superior to that of the other three types of landslides. The success rate of the ANN-NFR model after landslide classification was 78.9%, and the prediction rate was 78.6%, both of which were greater than the unclassified ANN-NFR model. Therefore, the ANN-NFR (landslide classified) model could improve the prediction accuracy and can provide a scientific basis for disaster prevention, mitigation, and management in the TGRA.

Precipitation is the most important water resource in semi-arid regions of China. The redistribution of precipitation among atmospheric water, soil water and groundwater are related to the land surface afforested ecological system. The study took widely replanted Pinus sylvestris var. Mongolica (PSM) in Mu Us Sandy Land (MUSL) as a research object and monitored precipitation, soil moisture, sap flow, and deep soil recharge (DSR) to find out moisture distribution in shallow soil layers. Results showed that the restoration process of PSM in MUSL changed the distribution of precipitation, with part of it infiltrating downward as DSR and part of it being stored in the shallow soil. Consequently, evapotranspiration increased and DSR significantly decreased, resulting in up to 466.9 mm of precipitation returning to the atmosphere through evapotranspiration in 2016. Vegetation increased soil water storage (SWS) capacity, with maximum SWS in PSM plot and bare sandy land (BSL) being 260 mm and 197 mm per unit horizontal area, respectively in 2016. DSR decreased from 54% of precipitation in the BSL plot to 0.2% of precipitation in the PSM plot in 2016. A great portion of infiltrated water was stored in the PSM ecosystem, resulting in a time lag of infiltration to reach the deep soil layer, and the infiltration rate in the BSL plot was 11 times of that in the PSM plot. SWS decreased 16 mm and 7.6 mm per unit horizontal area over a one-year period (from March to October, non-freezing time) in 2017 and 2019, respectively. The PSM annual sap flow was maintained at a relatively constant level of 154 mm/yr. Through in-situ measurement and comparative analysis of the precipitation redistribution of the BSL plot and the PSM plot, we find that PSM can significantly reduce the shallow soil water storage and DSR. However, substantial reduction of shallow soil water storage and DSR is detrimental for the long-term development of PSM forest. Therefore, it is necessary to reduce PSM density to cut the water consumption by PSM per unit area, thus to augment the shallow SWS and DSR, which will be beneficial for the PSM to survive under extreme drought conditions in the future. This study helps us understand the role of precipitation-induced groundwater recharge in the process of vegetation restoration in semi-arid regions and explains the possible causes of PSM forest degradation.

Non-point source (NPS) pollution poses a significant threat to the water environment, yet controlling it at the watershed scale remains a formidable challenge. Understanding the characteristics and drivers of nitrogen (N) and phosphorus (P) outputs at the watershed scale, along with identifying cost-effective best management practices (BMPs), is crucial for effective pollution control. In this study, we utilized the Wangjiaqiao watershed within the Three Gorges Reservoir Area (TGRA) as a case study to explore the characteristics of N and P load outputs and their dominant drivers by combining the SWAT model and a geographic detector. Based on our analysis of N and P loads within the watershed, we employed the entropy weight method to evaluate the reduction efficiency and cost-effectiveness of 64 BMP scenarios, encompassing seven measures (vegetative filter strips, parallel terraces, 10% fertilizer reduction, 30% fertilizer reduction, residue cover tillage, grass mulching, and returning farmland to forest) and their combinations. Our findings revealed the following: (1) spatial heterogeneity in NPS loads within the watershed, primarily influenced by land use, fertilizer application, and surface runoff, with interactive enhancement effects among driving factors; (2) the differential effectiveness of BMPs at the watershed level, with structural measures, particularly terracing, exhibiting higher efficacy and achieving reduction rates of 28.12% for total nitrogen (TN) and 37.69% for total phosphorus (TP); the combined BMPs showed improved reduction efficiency, but not merely additive; and (3) in terms of cost-effectiveness, 30% fertilizer reduction emerged as the most beneficial among the individual measures. Moreover, a combination of vegetative filter strips, parallel terraces, and 30% fertilizer reduction demonstrated significant improvements in TN and TP reductions (48.05% and 61.95%, respectively), suggesting their widespread applicability. Overall, our study provides insights into developing a cost-effective BMP strategy for the Wangjiaqiao watershed and offers valuable guidance for NPS pollution management in similar small watersheds within the TGRA.

Weather conditions play a pivotal role in embryo development and parental incubation costs, potentially impacting the clutch size and incubation behavior of birds. Understanding these effects is crucial for bird conservation. Reeves's Pheasant (Syrmaticus reevesii) is a threatened species endemic to China, which is characterized by female-only incubation. However, there is a lack of information regarding the impact of weather conditions on clutch size and incubation behavior in this species. Using satellite tracking, we tracked 27 wild female Reeves's Pheasants from 2020 to 2023 in Hubei Province, China. We explored their clutch size and incubation behavior, as well as their responses to ambient temperature and precipitation. Clutch size averaged 7.75 ± 1.36, had an association with average ambient temperature and average daily precipitation during the egg-laying period, and was potentially linked to female breeding attempts. Throughout the incubation period, females took an average of 0.73 ± 0.46 recesses every 24 h, with an average recess duration of 100.80 ± 73.37 min and an average nest attendance of 92.98 ± 5.27%. They showed a unimodal recess pattern in which nest departures peaked primarily between 13:00 and 16:00. Furthermore, females rarely left nests when daily precipitation was high. Recess duration and nest attendance were influenced by the interaction between daily mean ambient temperature and daily precipitation, as well as day of incubation. Additionally, there was a positive correlation between clutch size and recess duration. These results contribute valuable insights into the life-history features of this endangered species.

Nitrogen (N) addition is commonly employed to enhance wheat production, and the effectiveness is strongly influenced by site-specific factors encompassing environmental conditions and crop management practices. However, the current understanding fails to adequately account for the intricate and variable interactions among these factors. Consequently, we conducted a global meta-analysis to quantify the combined contributions of these factors to wheat yield, grain protein content (GPC), and water productivity (WP) and provide recommendations for optimizing N management. The results revealed a significant improvement in grain yield (14.85%), GPC (6.62%), and WP (10.79%) following the application of N. Moreover, higher N addition rates, the utilization of coated N fertilizer, post-anthesis fertilization, and multiple N applications exhibited enhanced outcomes in terms of yield, GPC, and WP in wheat systems. It was observed that applying 100–200 kg/ha of N was the optimal rate for maximizing yield, GPC, and WP. Medium soil texture and humid climate conditions showed a more pronounced increase in yield in response to N addition. Additionally, wheat yield demonstrated a stronger response to N addition benefits when the annual temperature was below 14 °C, while GPC showed a higher increase with temperatures exceeding 14 °C. Furthermore, adopting common N fertilization practices alongside irrigation and implementing pre-anthesis N addition in medium soil texture and humid climate conditions also contributed to achieving optimal wheat performance. The finding of this study serves as a guideline to support on-site N addition practice for wheat and to offer a reference to N management policy design across specific site conditions.

Optical remote sensing satellites provide rapid access to regional topsoil salinization mapping. However, mapping topsoil salinization based on spectral reflectance is always affected by background material like vegetation cover, straw mulching and soil types. In light of these challenges, this study investigates the potential of image fusion, where images of original and bare soil pixels were combined, to minimize the impact of vegetation cover on topsoil salinity mapping. A case study was presented for the typical vegetation cover area using synchronized Sentinel-2 MSI image (named original image) and 255 ground-truth data collected in October 2020, aligning with periods of vegetation cover and salt return. Furthermore, to obtain novel bare soil pixels, multi-temporal Sentinel-2 MSI images were acquired during two distinct intervals: March to May and September to November, spanning the years from 2018 to 2021. The synthetic soil image (SYSI) was obtained by extracting bare soil pixels from multi-temporal images. Two images (original, SYSI) were fused with non-negative matrix factorization (NMF) method, named SYSIfused. Then, the stacking machine algorithm was used for soil salinity mapping under different soil types, with evaluating the impact of SYSIfused on the accuracy of soil salinity prediction. The results showed the SYSIfused outperformed the original image (the R2 of the best models increased by 0.054–0.242, RMSE and MAE decreased by 0.049–0.780 and 0.012–0.546, respectively). Based on the SYSIfused, the order of the effect of soil types was coastal bog solonchaks > alluvial soil > cinnamon soil > coral saline soil > overall samples, and their roles in improving the R2 of the model were 0.141, 0.085, 0.022, 0.012, respectively. Besides, stacking models with the SYSIfused provided the best prediction performances (R2 = 0.742, RMSE = 0.377, MAE = 0.362). This study introduces the concept of merging original images with SYSI, resulting in a significant improvement in the accuracy of soil salinity mapping in areas covered by vegetation.

Abstract Based on the counted power system emission factors of North China Power Grid, a community carbon emissions sample database is constructed. The support vector regression (SVR) model is trained to forecast the power carbon emissions, which is optimized by genetic algorithm (GA). A community carbon emission warning system is designed according the results. The dynamic emission coefficient curve of the power system is obtained by fitting the annual carbon emission coefficients. The time series SVR carbon emission prediction model is constructed, while the GA is improved to optimize its parameters. Taking Beijing Caochang Community as an example, a carbon emission sample database is generated based on the electricity consumption and emission coefficient curve to train and test the SVR model. The results show that the GA–SVR model fits well with the training set and the testing set, and the prediction accuracy of the testing set reaches 86%. In view of the training model in this paper, the carbon emission trend of community electricity consumption in the next month is predicted. The carbon emission warning system of the community is designed, and the specific strategy of community carbon emission reduction is proposed.

In this work, we propose a SiC-NSFET structure that uses a PTS scheme only under the gate, with SiC layers under the source and drain, to improve the leakage current and thermal reliability. Punch-through stopper (PTS) doping is widely used to suppress the leakage current, but aggressively high PTS doping will cause additional band-to-band (BTBT) current. Therefore, the bottom oxide isolation nanosheet field-effect transistor (BOX-NSFET) can further reduce the leakage current and become an alternative to conventional structures with PTS. However, thermal reliability issues, like bias temperature instability (BTI), hot carrier injection (HCI), and time-dependent dielectric breakdown (TDDB), induced by the self-heating effect (SHE) of BOX-NSFET, become more profound due to the lower thermal conductivity of SiO2 than silicon. Moreover, the bottom oxide will reduce the stress along the channel due to the challenges associated with growing high-quality SiGe material on SiO2. Therefore, this method faces difficulties in enhancing the mobility of p-type devices. The comprehensive TCAD simulation results show that SiC-NSFET significantly suppresses the substrate leakage current compared to the conventional structure with PTS. In addition, compared to the BOX-NSFET, the stress reduction caused by the bottom oxide is avoided, and the SHE is mitigated. This work provides significant design guidelines for leakage and thermal reliability optimization of next-generation advanced nodes.

In this paper, we investigate the effects of negative bias instability (NBTI) and self-heating effect (SHE) on threshold voltage in NSFETs. To explore accurately the interaction between SHE and NBTI, we established an NBTI simulation framework based on trap microdynamics and considered the influence of the self-heating effect. The results show that NBTI weakens the SHE effect, while SHE exacerbates the NBTI effect. Since the width of the nanosheet in NSFET has a significant control effect on the electric field distribution, we also studied the effect of the width of the nanosheet on the NBTI and self-heating effect. The results show that increasing the width of the nanosheet will reduce the NBTI effect but will enhance the SHE effect. In addition, we extended our research to the SRAM cell circuit, and the results show that the NBTI effect will reduce the static noise margin (SNM) of the SRAM cell, and the NBTI effect affected by self-heating will make the SNM decrease more significantly. In addition, our research results also indicate that increasing the nanosheet width can help slow down the NBTI effect and the negative impact of NBTI on SRAM performance affected by the self-heating effect.

Train Control system as the nerve of railway has attracted significant attention from both academia and industries since it is closely related to safety, especially in High-speed railway(HSR). To be in line with the research of 5G network for railway (5G-R), which is a topic of common concern for railways around the world, performance of 5G-R bearing train control information needs to be studied to ensure high-quality of transmission. In this article, the train-ground communication demand for train control is deeply analyzed, and three scientific questions are extracted, involving the service establishment latency of the train control on-board modules, the end-to-end data transmission latency distribution, as well as the redundant communication link switching algorithm under network abnormal conditions. A hardware-in-the-loop simulation environment combining the actual 5G-R system and channel simulator is built, under which, 95% statistical value of service establishment is analyzed, which is furtherly used to determine length of transition Section between 5G-R and GSM-R. End to end latency samples are collected and the statistical distribution is analyzed with $\chi ^{2}$ fitting test method, and statistically modeled by Student t-distribution. Moreover, in order to maintain a reliable communication link, based on the heartbeat information of dual train-ground links, a better link decision algorithm using Informer Long Sequence Time-Series Forecasting(LSTF) method is proposed, and verified with normal as well as abnormal latency samples by caculating MAPE values. Relevant research results of this article will be helpful for facilitating the design of future train control and 5G-R system.

In soils, high aggregate stability often represents higher quality and anti-erosion ability; however, few studies have systematically analyzed how different forest stands affect soil aggregate stability. We selected five typical mixed forest stands on Jinyun Mountain in Chongqing, China, as research sites to evaluate soil aggregate stability. Within these sites, we analyzed the factors influencing soil aggregate stability in different stands by measuring soil characteristics and root traits. Soil aggregation stability, plant root traits, and soil properties varied among the mixed forest stands. The broadleaf tree mixed forest improved soil aggregate stability by 57%–103% over that of the Pinus massoniana mixed forest. The soil organic carbon, cation exchange capacity, Fe-Al oxides, and fine root proportion were positively correlated with soil aggregate stability. The specific root length and very fine root proportion were negatively correlated with soil aggregate stability, whereas the fine root proportion was positively correlated with this property. Specifically, we found that arbuscular mycorrhizal fungi did not affect soil aggregate stability in acid rain areas. Structural equation modeling indicated that soil aggregate stability was closely related to soil physicochemical properties and plant root characteristics. Predictive factors accounted for 69% of the variation in mean weight diameter, and plant root traits influenced soil aggregate stability by affecting soil organic matter, texture, and Fe-Al oxides. This study elucidated the impact of soil physicochemical properties and plant root characteristics on soil aggregate stability in different forest stand types, which has crucial implications for optimizing the management of various forest types.

Landscape ecological risk is considered the basis for regional ecosystem management decisions. Thus, it is essential to understand the spatial and temporal evolutionary patterns and drivers of landscape ecological risk. However, existing studies lack exploration of the long-term time series and driving mechanisms of landscape ecological risk. Based on multi-type remote sensing data, this study assesses landscape pattern changes and ecological risk in the Three Gorges Reservoir Area from 1990 to 2020 and ranks the driving factors using a geographical detector. We then introduce the geographically weighted regression model to explore the local spatial contributions of driving factors. Our results show: (1) From 1990 to 2020, the agricultural land decreased, while forest and construction land expanded in the Three Gorges Reservoir Area. The overall landscape pattern shifted toward aggregation. (2) The landscape ecological risk exhibited a decreasing trend. The areas with relatively high landscape ecological risk were primarily concentrated in the main urban area in the western region of the Three Gorges Reservoir Area and along the Yangtze River, with apparent spatial aggregation. (3) Social and natural factors affected landscape ecological risk. The main driving factors were human interference, annual average temperature, population density, and annual precipitation; interactions occurred between the drivers. (4) The influence of driving factors on landscape ecological risk showed spatial heterogeneity. Spatially, the influence of social factors (human interference and population density) on landscape ecological risk was primarily positively correlated. Meanwhile, the natural factors’ (annual average temperature and annual precipitation) influence on landscape ecological risk varied widely in spatial distribution, and the driving mechanisms were more complex. This study provides a scientific basis and reference for landscape ecological risk management, land use policy formulation, and optimization of ecological security patterns.

Soil erosion is affected by nature and human activities. Compared with biophysical factors, the effects of socio-economic factors on soil erosion have not been well investigated. Here, taking two prefectures (Yan’an and Qingyang) with different socio-economic conditions and ecological restoration intensity on the Chinese Loess Plateau (CLP) as a case, we combined the Revised Universal Soil Loss Equation (RUSLE), partial least squares structural equation modeling (PLS-SEM), and gray relation analysis to explore the response of soil erosion to socio-economic development. Our results showed that Grain for Green Program (GGP) has effectively controlled soil erosion and increased the vegetation coverage of the study area. For Yan’an, the vegetation coverage was increased by 6.2% and erosion modulus was decreased by 33.9% in 2015 compared with that in 1995. The differences in industrial structure and agricultural input led to different responses of soil erosion to socio-economic development. Economic development and agricultural input accelerated soil erosion in Qingyang but inhibited soil erosion in Yan’an due to different development strategies. Moreover, the increase of the gray relation grade between socio-economic factors and soil erosion in Yan’an indicates that soil erosion is easier to be controlled by the development of the socio-economy. The results indicate that the triple-win situation of economic development, vegetation restoration, and soil conservation can be realized by adjusting the economic structure, strengthening ecological restoration, and agricultural investment. This research emphasizes the important effect of socio-economic development on soil erosion and provides a reference for soil erosion control and ecological restoration for regions suffering from severe soil erosion.

Critical Source Areas (CSAs) are areas that contribute disproportionate high levels of non-point source (NPS) pollution to receiving waters, and their occurrence is the result of the complex interaction between the factors related to the sources and transport processes of NPS pollution. A systematic understanding of how these influencing factors affect CSAs is essential for successful watershed management. In this study, we applied a statistical data mining technique boosted regression tree model to quantify the contribution of eight influencing factors (soil type, slope, elevation, RUSLE LS factor, RUSLE K factor, runoff, fertilizer application rate and land use) on two types of CSAs (TN-CSAs and TP-CSAs), as well as the marginal effects and potential thresholds of influencing factors on the occurrence of CSAs. Results show that land use (37.35%, 25.03%), fertilizer application (36.93%, 57.83%) and soil type (17.59%, 13.70%) have higher importance in determining the occurrence of TN-CSAs and TP-CSAs; and the incidence of TN-CSAs is positively correlated with most factors before the threshold for each influencing factor, after which the marginal effect largely leveled off or dropped slightly; TP-CSAs have essentially the same characteristics as TN-CSAs, but TP-CSAs are more likely to occur in areas with an annual runoff of around 244.92 mm. In addition, this study discussed the application of machine learning techniques in predicting CSAs under climate change without physical-based models, as well as a preliminary watershed management planning for NPS pollution control in the study watershed. These results provided important information for nutrient management regulations.

The physiological and biochemical indicators of plants reflect the plant’s adaptation to environmental changes and provide information for the planting and management of acid-resistant tree species. To analyze the responses of typical tree species to recent changes in acid rain conditions in Jinyun Mountain, Chongqing, we focused on three representative tree species in the Jinyun Mountain area of Chongqing: Pinus massoniana, Phyllostachys edulis, and Cinnamomum camphora. A mixed acid rain experiment with five gradients of natural rainfall (NR) and pH values of 7.0, 4.5, 3.5, and 2.5 was conducted in May 2021. The changes in physiological and biochemical indicators (net photosynthetic rate, stomatal conductance, intercellular CO2 concentration, transpiration rate, light saturation point, light compensation point, apparent quantum efficiency, dark respiration rate, soluble sugar, starch, soluble protein, proline, malondialdehyde, and antioxidant enzyme activity) were determined. The results show the following: 1. Compared with other treatments, NR and slightly acidic rain increased the relative chlorophyll content in plant seedlings. 2. The synthesis of soluble sugars, starches, and soluble proteins was inhibited to different degrees in the three species under acid rain stress at pH ≤ 3.5. 3. The enzyme activity of superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and ascorbate peroxidase (APX) initially increased and then decreased with an increase in acidity. 4. Acid rain treatments with pH ≤ 4.5 reduced the net photosynthetic rate (Pn) of plants; the higher the acidity, the lower the Pn. Conclusion: A comprehensive comparison of the indicators revealed that NR and mild acid rain enhanced the plant seedlings’ physiological and biochemical characteristics. A pH of 3.5 was the threshold where acid rain had an adverse effect on Pinus massoniana, Phyllostachys edulis, and Cinnamomum camphora. The high indicator values for NR indicate that these tree species have adapted to current conditions in the Jinyun Mountain area of Chongqing. This study provides new information for selecting tree species adapted to the acid rain environment in Jinyun Mountain, Chongqing.

In order to achieve a clear observation of the ultra-low brightness solar corona and provide a physical basis for forecasting space weather that seriously affects the human living environment, the stray-light suppression level becomes the key factor affecting the development of the coronagraph. In this study, a stray-light suppression method is adopted for Solar Corona Imager (SCI) which is a dual-waveband internally occulted reflecting coronagraph simultaneously and independently observing the inner corona in the HI Lyman-alpha (121.6 ± 10 nm) line and white-light (700.0 ± 40 nm) wavebands with a field-of-view (FOV) from 1.1 to 2.5 R⊙ (R⊙ stands for the mean solar radius). The scattered stray-light from the primary mirror, including the surface errors, cosmetic defects, and particulate contamination, is analyzed and suppressed, and the corresponding scattering models are established for simulation based on the laboratory testing. The stray-light measurement results for SCI in the laboratory show that the stray-light level can be suppressed to the order of 10−8 B⊙ at 2.5 R⊙ (B⊙ is the mean brightness of the solar disk) in the white-light (WL) band, which is consistent with the stray-light level obtained by simulation and verifies the modeling and simulation.

Excessive nitrogen input caused by human activities is an important factor threatening regional water security and economic development. In this study, net anthropogenic nitrogen input has offered an advantage in evaluating nitrogen input’s temporal and spatial variation in the Three Gorges Reservoir Area in 2006–2019. Component characteristics like sensitivity and proportional contribution of net anthropogenic nitrogen input in the time trend were assessed using a mathematical differential equation multivariate function principle for the first time. Then, redundancy analysis quantitatively analysed the distribution and interpretation of districts’ socio-economic and environmental impact factors on net anthropogenic nitrogen input. The results showed high net anthropogenic nitrogen input levels in the Three Gorges Reservoir Area in 2006–2019 with significant temporal and spatial variations (one-way ANOVA, p

Chondroitin polymerizing factor (CHPF) plays an important role in the development of certain malignant tumors. However, the role of CHPF in breast carcinoma (BRCA) and its underlying mechanism are still not fully elucidated. Expression profiles for CHPF in BRCA tissues were retrieved from The Cancer Genome Atlas database and used for prognostic analysis. Cell viability, invasion and migration were measured in vitro using MCF7 and MDA‐MB‐231 cell lines upon knockdown or over‐expression of CHPF. Bioinformatic analysis showed that CHPF was substantially upregulated in BRCA tissues, and a quantitative reverse transcriptase‐PCR was performed to confirm its upregulation in BRCA cells. High expression of CHPF was observed to be significantly associated with pathologic stage, metastasis and worse prognosis. We also observed that depletion of CHPF significantly inhibited cell proliferative, invasive and migratory abilities, whereas overexpression of CHPF exerted the opposite effects. Furthermore, analysis of the GEPIA database revealed that CHPF expression is positively correlated with the epithelial–mesenchymal transition‐related markers vimentin, Snail, Slug and motion‐related protein matrix metallopeptidase 2; these findings were confirmed via western blotting. Our data suggest that CHPF may contribute to BRCA progression by modulating epithelial–mesenchymal transition‐related markers and matrix metallopeptidase 2 expression.

Vegetation can play a crucial role in stabilizing slopes through their hydrological and mechanical properties, yet few studies have systematically compared their effects on soil moisture resistance and slope stability. To investigate this, four steep slopes covered by different forests were analyzed in terms of climatic conditions, soil moisture dynamics, root strength and soil physical properties. The results revealed that the roots of Phyllostachys pubescens forests had a higher number and were deeper than the main plant species in the other three forests. Although the root tensile strength of Phyllostachys pubescens was not the strongest, its additional cohesion contributed more to hillslope stability. In the other three forests, suction stress was the main factor contributing to hillslope stability. The soil moisture change rate in Phyllostachys pubescens was found to be the smallest among the four forests studied, indicating that it had the greatest rainfall interception ability. The stability of the slope land covered by shrub forest was found to be more variable than the other three lands in high temperature conditions. Through its soil moisture reducing ability, root characteristics and magnitude of safety factor, Phyllostachys pubescens was identified as a suitable species for slope stabilization in the study area. The findings of this work may provide useful insights for local forest management in terms of selecting suitable plant species to reduce shallow landslides.

Chlorophyll drives plant photosynthesis. Under stress conditions, leaf chlorophyll content changes dramatically, which could provide insight into plant photosynthesis and drought resistance. Compared to traditional methods of evaluating chlorophyll content, hyperspectral imaging is more efficient and accurate and benefits from being a nondestructive technique. However, the relationships between chlorophyll content and hyperspectral characteristics of wheat leaves with wide genetic diversity and different treatments have rarely been reported. In this study, using 335 wheat varieties, we analyzed the hyperspectral characteristics of flag leaves and the relationships thereof with SPAD values at the grain-filling stage under control and drought stress. The hyperspectral information of wheat flag leaves significantly differed between control and drought stress conditions in the 550–700 nm region. Hyperspectral reflectance at 549 nm (r = −0.64) and the first derivative at 735 nm (r = 0.68) exhibited the strongest correlations with SPAD values. Hyperspectral reflectance at 536, 596, and 674 nm, and the first derivatives bands at 756 and 778 nm, were useful for estimating SPAD values. The combination of spectrum and image characteristics (L*, a*, and b*) can improve the estimation accuracy of SPAD values (optimal performance of RFR, relative error, 7.35%; root mean square error, 4.439; R2, 0.61). The models established in this study are efficient for evaluating chlorophyll content and provide insight into photosynthesis and drought resistance. This study can provide a reference for high-throughput phenotypic analysis and genetic breeding of wheat and other crops.

Practical implementation of minimally invasive biomedical applications has been a long-sought goal for microrobots. In this field, most previous studies only demonstrate microrobots with locomotion ability or performing a single task, unable to be functionalized effectively. Here, we propose a biocompatible shape memory alloy helical microrobot with regulative structure transformation, making it possible to adjust its motion behavior and mechanical properties precisely. Especially, towards vascular occlusion problem, these microrobots reveal a fundamental solution strategy in the mechanical capability using shape memory effect. Such shape-transformable microrobots can not only manipulate thrust and torque by structure to enhance the unclogging efficiency as a microdriller but also utilize the high work energy to apply the expandable helical tail as a self-propulsive stent. The strategy takes advantage of untethered manipulation to operate microsurgery without unnecessary damage. This study opens a route to functionalize microrobots via accurate tuning in structures, motions, and mechanical properties.

The relationship between soil respiration (SR) and microbial community structure (MCS) is relevant to changes in forest soil ecosystem stability and chemical cycling under acid rain. Simulated acid rain treatments of pH 4.5 (control), 4.0, 3.25 and 2.5 were applied to two forest stands in the Three Gorges Reservoir Area of Jinyun Mountain, Chongqing. We used phospholipid fatty acid (PLFA) analysis to observe the MCS in the 0–10 cm soil layer and measured SR in situ from January 2016 to December 2017. Additionally, we determined the effects of soil properties on the MCS and SR. Acid rain simulation significantly increased the fungal PLFA abundance and decreased the bacterial PLFA abundance in the mixed coniferous and broad-leaved forest (CF). However, in the evergreen broad-leaved forest (BF), the abundance of bacterial and fungal PLFAs did not differ significantly among treatments. Redundancy analysis (RDA) showed that significant changes in the MSC were mainly due to the C/N ratio, hydrolysable N content, content, fine root biomass and sucrase activity. Acid rain simulation in the CF and BF significantly inhibited SR, but the SR sensitivity to simulated acid rain differed among forests. In 2017, the annual mean SR in the CF under the pH 4.0, 3.25 and 2.5 treatments decreased significantly by 6.1%, 19.2% and 28.9%, but in the BF, SR decreased significantly by 25.6% only under pH 2.5. The structural equation model showed that the relationship between the MCS and the variation in SR was closer and more direct than that with soil nutrients. The microbial community structure was an important factor driving the response of soil respiration to acid rain.

Single‐mode lasing with high‐beam quality and wide‐range spectral purity is of fundamental importance in scientific and technical applications. Lacking a mode selection strategy, achieving single whispering gallery mode (WGM)‐type lasing remains a challenge. Herein, a wide‐range tunable single‐WGM resonance in a mesh‐structured tubular microcavity is realized by combining WGM resonance with distributed feedback effect, and the single mode can be systematically and precisely tuned by tailoring the geometry parameters of the hole array. In addition, switching between single mode and multimode WGM can be achieved at different axial directions of one individual tubular microcavity. The structure provides additional degrees of freedom to manipulate the resonant spectra characteristics and the spatial distribution of resonant modes, thus having a promising future in micro‐/nano‐optical devices.

Abstract Background Soil respiration in forest ecosystems is an important part of the forest carbon (C) cycle. Studying the variation patterns of soil respiration under acid rain conditions is critically important for understanding forest C balance and performing rational forest management and operations. Methods A split-plot design field experiment was performed in a mixed coniferous-broadleaved forest in the Three Gorges Reservoir Area of Jinyun Mountain, Chongqing from January 2016–January 2018. There were 4 main blocks; each was split into untrenched (UT) and trenched (T) treatments. Treatments with four gradient pH levels of 4.5 (control), 4.0, 3.25 and 2.5 were randomly assigned to the untrenched and trenched treatments. The soil CO2 fluxes, soil temperature, and soil moisture content were evaluated under simulated acid rain. Additionally, the effects of soil properties (soil pH, organic C, C:N ratio, microbial biomass, and enzyme activity) on soil respiration were analysed. Results The soil CO2 fluxes exhibited seasonal variation trends. The annual mean soil respiration rates of the UTCK (control), UT4.0, UT3.25, and UT2.5 treatments in untrenched plots were 1.91, 1.91, 1.77, and 1.74 μmol∙m− 2∙s− 1 in 2016 and 1.91, 1.81, 1.55 and 1.37 μmol∙m− 2∙s− 1 in 2017, respectively. The annual mean heterotrophic respiration rates of the TCK (control), T4.0, T3.25, and T2.5 treatments in the trenched plots were 1.39, 1.32, 1.19 and 1.14 μmol∙m− 2∙s− 1 in 2016 and 1.28, 1.18, 1.02 and 0.83 μmol∙m− 2∙s− 1 in 2017, respectively. Acid rain had a significant cumulative effect on soil respiration and heterotrophic respiration (P 0.05); moreover, they significantly increased the soil organic C content and C:N ratio, lowered the hydrolysable N and total P concentrations, and lowered the soil pH and fine root biomass at a later experimental stage. Urease and sucrase activities were significantly reduced in the higher-acidity treatments. Soil respiration rate was significantly positively correlated with the soil pH, fine root biomass, and urease and sucrase activity and significantly negatively correlated with the soil organic C and C:N ratio. Conclusions Acid rain had a significant cumulative and inhibitory effect on soil respiration and heterotrophic respiration, while soil temperature and moisture had a limited effect on soil respiration under simulated acid rain. The variation in soil properties (e.g., soil organic C, C:N ratio, and fine root biomass) caused by acid rain inhibited the microbial utilization of substrates, which was the main cause of the respiration differences.

In order to explore the effects of different concentrations of acid rain on soil nutrient content and nutrient utilization efficiency, and to provide a basis for an improvement in acidified soil in acid rain regions, a year-long acid rain experiment was conducted in a typical evergreen broad-leaved forest and coniferous and broad-leaved mixed forest in Jinyun Mountain of Chongqing. Four pH treatments (pH 4.5, 4.0, 3.25, and 2.5) were established to simulate acid rain. The results showed that: (1) Acid rain promoted the accumulation of soil nutrients, and the contents of organic carbon (SOC), total nitrogen (TN), and hydrolyzed nitrogen (HN) significantly increased during the experiment (p < 0.05). (2) Soil SOC content was significantly positively correlated with acid rain concentration (p < 0.01), HN content was negatively correlated with acid rain concentration, and TN and total phosphorus (TP) contents were not significantly correlated with acid rain concentration. (3) The activities of soil sucrase, urease, and acid phosphatase were negatively correlated with acid rain concentration (p < 0.01) and the activity of soil cellulase was positively correlated with acid rain concentration (p < 0.01). (4) The enzyme activity changed differently, depending on the concentration of acid rain during the study period. (5) According to RDA analysis, soil total nitrogen content and hydrolyzed nitrogen content had significant effects on enzyme activity (p < 0.05). Conclusions: Acid rain did not significantly alter the overall soil nutrient content but reduced the available nutrient content and seriously inhibited enzyme activity—most notably, the soil enzymes involved in nutrient utilization efficiency.

The high-humidity mountain forest ecosystem (HHMF) of Jinyun Mountain in Chongqing is a fragile ecosystem that is sensitive to climate change and human activities. Because it is shrouded in fog year-round, illumination in the area is seriously insufficient. However, the flux (energy, water) exchanges (FEs) in this ecosystem and their influencing factors are not clear. Using one-year data from flux towers with a double-layer (25 m and 35 m) eddy covariance (EC) observation system, we proved the applicability of the EC method on rough underlying surfaces, quantified the FEs of HHMFs, and found that part of the fog might also be observed by the EC method. The observation time was separated from day and night, and then the environmental control of the FEs was determined by stepwise regression analysis. Through the water balance, it was proven that the negative value of evapotranspiration (ETN), which represented the water vapor input from the atmosphere to the ecosystem, could not be ignored and provided a new idea for the possible causes of the evaporation paradox. The results showed that the annual average daily sensible heat flux (H) and latent heat flux (LE) ranged from −126.56 to 131.27 W m−2 and from −106.7 to 222.27 W m−2, respectively. The annual evapotranspiration (ET), positive evapotranspiration (ETP), and negative evapotranspiration (ETN) values were 389.31, 1387.76, and −998.45 mm, respectively. The energy closure rate of the EC method in the ecosystems was 84%. Fog was the ETN observed by the EC method and an important water source of the HHMF. Therefore, the study area was divided into subtropical mountain cloud forests (STMCFs). Stepwise regression analysis showed that the H and LE during the day were mainly determined by radiation (Rn) and temperature (Tair), indicating that the energy of the ecosystem was limited, and future climate warming may enhance the FEs of the ecosystem. Additionally, ETN was controlled by wind speed (WS) in the whole period, and WS was mainly affected by altitude and temperature differences within the city. Therefore, fog is more likely to occur in the mountains near heat island cities in tropical and subtropical regions. This study emphasizes that fog, as an important water source, is easily ignored in most EC methods and that there will be a large amount of fog in ecosystems affected by future climate warming, which can explain the evaporation paradox.

BackgroundChina’s so-called Three North Shelterbelt Program (3NSP) has produced a vast area of lined forest reconstruction in the semi-arid regions. This study uses the lined rain-fed Pinus sylvestris var. mongolica (PSM) sand-fixing forest in the eastern part of Mu Us Sandy Land in Northwestern China as an example to investigate the ecohydrological process in this region. Rain gauges, newly designed lysimeters and soil moisture sensors are used to monitor precipitation, deep soil recharge (DSR) and soil water content, where DSR specifically refers to recharge that can reach a depth more than 200 cm and eventually replenish the underneath groundwater reservoir.ResultsThis study shows that there are two obvious moisture recharge processes in an annual base for the PSM forest soil: a snowmelt-related recharge process in the spring and a precipitation-related recharge process in the summer. The recharge depth of the first process can reach 180 cm without DSR occurring (in 2018). The second process results in noticeable DSR in 2018. Specifically, the DSR values over 2016–2018 are 1, 0.2, and 1.2 mm, respectively. To reach the recharge depths of 20, 40, 80, 120, 160, and 200 cm, the required precipitation intensities have to be 2.6, 3.2, 3.4, 8.2, 8.2, and 13.2 mm/d, respectively. The annual evapotranspiration in the PSM forest is 466.94 mm in 2016, 324.60 mm in 2017, and 183.85 mm in 2018.ConclusionThis study concludes that under the current precipitation conditions (including both dry- and wet-years such as 2016–2018), water consumption of PSM somewhat equals to the precipitation amount, and PSM has evolved over years to regulate its evapotranspiration in response to annual precipitation fluctuations in Mu Us Sandy Land of China.

The chemical composition in the precipitation is constantly changing, thus acid rain type is gradually changing from sulfuric type to mixed type and then nitric type. The influence of the changing acid rain type on the rhizosphere soil of tree species remains unclear. A pot experiment was performed with two-year-old Pinus massoniana, Cunninghamia lanceolate, Cyclobalanpsis glauca and Phyllostachys edulis seedlings with similar growth condition. Simulated acid rain consists of sulfuric(S/N = 5), mixed(S/N = 1) and nitric(S/N = 0.2) acid rain, and each type acid rain diluted to three acid rain intensity: pH = 2.5, 3.5, 4.5. Soil pH, soil organic matter, cation exchange capacity, the exchangeable Na+, K+, Ca2+, Mg2+ and enzyme activity were inhibited by acid rain intensity, while exchangeable Al3+ and H+ were promoted. Mg2+ was most relevant index to the tolerance to acid rain and the correlation degree of soil chemical index was higher than that of enzyme activity. Response of soil chemical properties differed in tree species under different acid rain types. Soil enzyme activity of Pinus massoniana, Cunninghamia lanceolate, and Phyllostachys edulis reached lowest under nitric acid rain, and that of Cyclobalanpsis glauca reached highest. Rhizosphere soil of Cunninghamia lanceolate is tolerant to sulfuric and nitric acid rain, and that of Cyclobalanpsis glauca is tolerant to mixed acid rain.

Semantic segmentation of medical images provides an important cornerstone for subsequent tasks of image analysis and understanding. With rapid advancements in deep learning methods, conventional U-Net segmentation networks have been applied in many fields. Based on exploratory experiments, features at multiple scales have been found to be of great importance for the segmentation of medical images. In this paper, we propose a scale-attention deep learning network (SA-Net), which extracts features of different scales in a residual module and uses an attention module to enforce the scale-attention capability. SA-Net can better learn the multi-scale features and achieve more accurate segmentation for different medical image. In addition, this work validates the proposed method across multiple datasets. The experiment results show SA-Net achieves excellent performances in the applications of vessel detection in retinal images, lung segmentation, artery/vein(A/V) classification in retinal images and blastocyst segmentation. To facilitate SA-Net utilization by the scientific community, the code implementation will be made publicly available.

Background: Pyrotinib, an irreversible pan-ERBB inhibitor, has shown promising antitumour activity, and acceptable tolerability. This research was conducted to evaluate the actual use and effectiveness of pyrotinib in China, therefore, contributed to solve the problem of real-world data scarcity.Methods: In this retrospective study, 168 patients who received pyrotinib treatment for HER2-positive metastatic breast cancer (MBC) in Hunan Province from June 2018 to August 2019 were included. Progression-free survival (PFS), tumor mutation burden (TMB), and drug-related adverse events (AEs) after pyrotinib administration were analyzed.Results: The median PFS (mPFS) time in the 168 participants was 8.07 months. The mPFS times in patients with pyrotinib in second-line therapy (n = 65) and third-or-higher-line therapy (n = 94) were 8.10 months and 7.60 months, respectively. Patients with brain metastases achieved 8.80 months mPFS time. In patients with pyrotinib in third-or-higher-line therapy, patients who had previously used lapatinib still got efficacy but showed a shorter mPFS time (6.43 months) than patients who had not (8.37 months). TMB was measured in 28 patients, K-M curve (P = 0.0024) and Multivariate Cox analysis (P = 0.0176) showed a significant negative association between TMB and PFS. Diarrhea occurred in 98.2% of participants (in any grade) and 19.6% in grade 3–4 AEs.Conclusion: Pyrotinib is highly beneficial to second-or-higher-line patients or HER2-positive MBC patients with brain metastases. Pyrotinib seems to be a feasible strategy both in combination of chemotherapeutic drugs or as a replacement of lapatinib if diseases progressed. TMB could be a potential predictor for evaluating pyrotinib's effectiveness in HER2-positive MBC.

Late embryogenesis abundant (LEA) proteins are widely involved in many adverse conditions among plants. In this study, we isolated a LEA4 gene from alfalfa (Medicago sativa L.) termed MsLEA4-4 via a homology cloning strategy. MsLEA4-4 encodes 166 amino acids, and the structural analysis showed that the gene contained five repeating TAQAAKEKTQQ amino acid motifs. There were a large number of α-helix in MsLEA4-4, and belongs to hydrophilic amino acid. Subcellular localization analysis showed that MsLEA4-4 was localized in the nucleus. The MsLEA4-4 promoter consisted of G-box and A-box elements, abscisic acid-responsive elements (ABREs), photo regulation and photoperiodic-controlling cis-acting elements, and endosperm expression motifs. The MsLEA4-4 overexpressing in Arabidopsis conferred late-germination phenotypes. Resistance of the overexpressed plants to abiotic stress significantly outperformed the wild-type (WT) plants. Under salt stress and abscisic acid treatment, with more lateral roots and higher chlorophyll content, the overexpressed plants has a higher survival rate measured against WT. Compared to those in the WT plants, the levels of soluble sugar and the activity of various antioxidant enzymes were elevated in the overexpressed plants, whereas the levels of proline and malondialdehyde were significantly reduced. The expression levels of several genes such as ABF3, ABI5, NCED5, and NCED9 increased markedly in the overexpressed plants compared to the WT under osmotic stress.

Some essential water conservation areas in China have continuously suffered from various serious problems such as water pollution and water quality deterioration in recent decades and thus called for real-time water pollution monitoring system underwater resources management. On the basis of the remote sensing data and ground monitoring data, this study firstly constructed a more accurate retrieval model for total phosphorus (TP) concentration by comparing 12 machine learning algorithms, including support vector machine (SVM), artificial neural network (ANN), Bayesian ridge regression (BRR), lasso regression (Lasso), elastic net (EN), linear regression (LR), decision tree regressor (DTR), K neighbor regressor (KNR), random forest regressor (RFR), extra trees regressor (ETR), AdaBoost regressor (ABR) and gradient boosting regressor (GBR). Then, this study applied the constructed retrieval model to explore the spatial-temporal evolution of the Miyun Reservoir and finally assessed the water quality. The results showed that the model of TP concentration built by the ETR algorithm had the best accuracy, with the coefficient R2 reaching over 85% and the mean absolute error lower than 0.000433. The TP concentration in Miyun Reservoir was between 0.0380 and 0.1298 mg/L, and there was relatively significant spatial and temporal heterogeneity. It changed remarkably during the periods of the flood season, winter tillage, planting, and regreening, and it was lower in summer than in other seasons. Moreover, the TP in the southwest part of the reservoir was generally lower than in the northeast, as there was less human activities interference. According to the Environmental Quality Standard for the surface water environment, the water quality of Miyun Reservoir was overall safe, except only for an over-standard case occurrence in the spring and September. These conclusions can provide a significant scientific reference for water quality monitoring and management in Miyun Reservoir.

In order to minimize the influence of decorrelation noise on multi-temporal interferometric synthetic aperture radar (MT-InSAR) applications, a series of phase reconstruction methods have been proposed in recent years. Unfortunately, current phase reconstruction methods generally exhibit a low computational efficiency due to their high non-linearity, in particular in the case that the dimension of a SAR stack is high. In this paper, a new approach is proposed to efficiently resolve phase reconstruction problems. This approach is inspired by the theory of probabilistic principle component analysis. A complex valued probability generative model is constructed to portray a phase reconstruction process. Moreover, in order to resolve such a model, a targeted algorithm based on the idea of expectation maximization is designed and implemented. For validation purposes, the proposed approach is compared to the traditional eigenvalue decomposition-based method by using simulated data and 101 real Sentinel-1A SAR images. The experimental results demonstrate that the proposed method can accelerate the phase reconstruction process drastically, in particular when a high-dimensional SAR stack is required to be processed.

Abstract Background Alfalfa (Medicago sativa) is one of the most important legume forage species in China and many other countries of the world. It provides a quality source of proteins and minerals to animals. Genetic underpinnings for these important traits, however, are elusive. An alfalfa (M. sativa) association mapping study for six traits, namely crude protein (CP), rumen undegraded protein (RUP), and four mineral elements (Ca, K, Mg and P), was conducted in three consecutive years using a large collection encompassing 336 genotypes genotyped with 85 simple sequence repeat (SSR) markers. Results All the traits were significantly influenced by genotype, environment, and genotype × environment interaction. Eight-five significant associations (P

Abstract Plant roots significantly influence soil properties, especially in soil beyond the limited area surrounding the main root stem. Some bias results may be generated if plastic properties of soil are merely used in evaluating slope stability without considering the effects of plant roots. In this research, effects of root spatial distribution on the elastic-plastic characteristics of soil-root blocks were examined. Triaxial tests and the Duncan-Chang model were used to analyze the correlation between root spatial characteristics and soil elastic-plastic properties. Safety factors of vegetated slopes were calculated to investigate the effect of roots on slope stability. The limit stress of remoulded soil was 103.52% to 231.61% greater than undisturbed soil in shallow soil layers. Increased root quantity led to an increased the failure ratio of soil bulk and the initial tangent modulus increased with root diameter. When calculating the safety factor of vegetated slopes, soil indexes for soil beyond the small cylinder surrounding the main stem should be properly considered to avoid safety factor overestimation.

The North China Plain (NCP) serves as China's second most important maize production region. Rotary tillage, a popular method used in winter wheat/summer maize systems in the region, has adverse effects on maize production. The current study was conducted to determine whether rotary tillage after subsoiling in the winter wheat season (RS) improves the grain-filling rate and yield of summer maize by decreasing soil bulk density, when compared with rotary tillage (R), in the NCP. The RS treatment decreased soil bulk density and increased soil moisture in the summer maize season when compared with the R treatment. Root number under the RS treatment at 8 collar and silking stages was 22.4-35.3% and 8.0-11.7% greater than under the R treatment, respectively. The RS treatment significantly enhanced the grain-filling rate and grain weight as compared to the R treatment. Yield, thousand grain weight, biomass, and harvest index under the RS treatment were 7.7, 7.2, 2.3 and 5.3% higher than under the R treatment. Thousands grain weight was correlated with soil bulk density and soil moisture after silking. Consequently, the increase in grain weight and yield of summer maize resulted from the decrease in soil bulk density and a consequent increase in soil moisture, root number and grain-filling rate.

Digital signal processing (DSP) methods have been used by many researchers for detection and classification of transient disturbances because of their fast and powerful abilities to recognise waveform distortions. However, some DSP methods such as the wavelet transformation (WT) show less accuracy when applied to noisy real data. In this study, disturbance features are extracted in the wavelet domain based on the WT levels. Moreover, a new feature extraction algorithm namely normalised Renyi entropy with the signal energy is applied. This algorithm has been proven to be effective and robust for noisy signals. However, their application in power systems has not yet been tested. Using a laboratory setup of an islanded micro-grid, experimental results validate the efficacy of the wavelet-based normalised Renyi entropy in the detection and classification of four disturbance types (voltage sag, interruption, harmonics, mixture of harmonics and sag).

Soil water moisture is one of the most important influencing factors in the fragile ecosystems in arid sandy regions, and it serves as a bridge connecting the rainfall and groundwater, two important water sources in arid sandy regions. The hydrological process of an arid sandy region occurs sporadically and is highly non-uniform temporally, making it difficult to monitor and predict. The deep soil recharge (DSR) at a sufficiently deep soil layer (usually greater than 200 cm below ground surface) is an important indicator for groundwater recharge in the arid sandy region, and thus the quantitative determination of DSR is of great significance to the evaluation of water resources and the study of water balance in the arid sandy region. Due to the large amount of evaporation, small amount of precipitation, and the long term of the frozen-soil period in the winter and spring, the monitoring of infiltration and determination of DSR in the arid sandy region become challenging. This study selects the Ulanbuh desert plots in northern China to monitor DSR, precipitation and seasonal frozen soil thickness change, and reaches the following conclusions: Even though the annual precipitation is only 48.2 mm in the arid sandy region, DSR will still occur and replenish groundwater. The daily threshold of precipitation for generating measurable DSR is lower than 4 mm, where the DSR value is defined as the downward flux over a unit area per day hereinafter. DSR continues during the frozen period of the winter and spring seasons, and it is generated from water vapor transport and condensation in the deep sandy layer. Summer rainstorms do no show an obvious correlation with DSR, which is unexpected. This study reveals the characteristics of the dynamic water resources movement and transformation in the arid sandy area in Ulanbuh Desert and can serve as an important guideline for the quantitative assessment of water resources in arid sandy regions.

The health and environmental effects of particulate matter (PM) in the air depend on several parameters. Besides particle size, shape, and concentration, the chemical nature of the PM is also of great importance. State-of-the-art PM sensors only detect the particle size and concentration. Small, low-cost sensors only identify PM according to PM2.5 and PM10 standards. Larger detectors measure the complete particle size distribution. However, the chemical composition of PM is not often assessed. The current paper presents the initial stages of the development of an infrared-based detector for the inline assessment of the chemistry of PM in the air. By combining a mini cyclone that is able to concentrate the particles at least a thousand fold and a hollow waveguide that aligns the flow of particles with infrared light, the feasibility of the concept was shown in this study. A clear differentiation between amorphous and crystalline silica was demonstrated at outdoor PM levels of lower than 1 mg per cubic meter.

Neuroimaging has undergone enormous progress during the last two and a half decades. The combination of neuroscientific methods and educational practice has become a focus of interdisciplinary research in order to answer more applied questions. In this realm, conditions that hamper learning success and have deleterious effects in the population – such as learning disorders (LD) – could especially profit from neuroimaging findings. At the moment, however, there is an ongoing debate about how far neuroscientific research can go to inform the practical work in educational settings. Here, we put forward a theoretical translational framework as a method of conducting neuroimaging and bridging it to education, with a main focus on dyscalculia and dyslexia. Our work seeks to represent a theoretical but mainly empirical guide on the benefits of neuroimaging, which can help people working with different aspects of LD, who need to act collaboratively to reach the full potential of neuroimaging. We provide possible ideas regarding how neuroimaging can inform LD at different levels within our multidirectional framework, i.e., mechanisms, diagnosis/prognosis, training/intervention, and community/education. In addition, we discuss methodological, conceptual, and structural limitations that need to be addressed by future research.

Attribution analyses on streamflow variation to changing climate and land surface characteristics are critical in studies of watershed hydrology. However, attribution results may differ greatly on different spatial and temporal scales, which has not been extensively studied previously. This study aims to investigate the spatial-temporal contributions of climate change and underlying surface variation to streamflow alteration using Budyko framework. Jiangling River Watershed (JRW), a typical landform transitional watershed in Southwest China, was chosen as the study area. The watershed was firstly divided into eight sub-basins by hydrologic stations, and hydrometeorological series (1954−2015) were divided into sub-intervals to discriminate spatial-temporal features. The results showed that long-term tendencies of hydrometeorological variables, i.e., precipitation (P), potential evapotranspiration (E0), and runoff depth (R), exhibited clear spatial patterns, which were highly related to topographic characteristics. Additionally, sensitivity analysis, which interpreted the effect of one driving factor by unit change, showed that climate factors P and E0, and catchment characteristics (land surface parameter n) played positive, negative, and negative roles in R, according to elastic coefficients (ε), respectively. The spatial distribution of ε illustrated a greater sensitivity and heterogeneity in the plateau and semi-humid regions (upstream). Moreover, the results from attribution analysis showed that the contribution of the land surface factor accounted for approximately 80% of the R change for the entire JRW, with an obvious spatial variation. Furthermore, tendencies of the contribution rates demonstrated regulations across different sub-regions: a decreasing trend of land surface impacts in trunk stream regions and increasing tendencies in tributary regions, and vice versa for climate impacts. Overall, both hydrometeorological variables and contributions of influencing factors presented regularities in long-term tendencies across different sub-regions. More particularly, the impact of the primary influencing factor on all sub-basins exhibited a decreasing trend over time. The evidence that climate and land surface change act on streamflow in a synergistic way, would complicate the attribution analysis and bring a new challenge to attribution analysis.

The production of cereals, including rice, wheat, and maize has increased in China over the past 20 years. However, variations in the production and cultivated area of cereals remains unclear. In this study, we collected and analyzed the cereal production and cultivated area in China from 1998 to 2016. An 85% increase in cereal production was recorded from 1998 to 2016, due to an increase in the maize production along with the increase in the cultivated area. Meanwhile, rice and wheat production have been stable since 2000. No change occurred in the rice cultivated area through 2016, and a 19% decrease in the wheat cultivated area was observed in 2016 relative to 1998. In addition, a significant positive relationship was observed among cereal production, maize production, and cultivated area (p < 0.0001). Together, the increase in cereal production in China over the past two decades has resulted from the increase in maize cultivated area and the consequent increase in maize production.

The effect of vegetation density on overland flow dynamics has been extensively studied, yet fewer investigations have focused on vegetation arrangements with different densities and position features. Flume experiments were conducted to investigate the hydrodynamics of flow through rigid emergent vegetation arranged in combinations with three densities (Dense, Middle, and Sparse) and three positions (summit, backslope, and footslope). This study focused on how spatial variations regulated hydrodynamic parameters from two dimensions: direction along the slope and water depth. The total hydrodynamic parameters of bare slopes were significantly different from those of vegetated slopes. The relationship between Re and f illustrated that Re was not a unique predictor of hydraulic roughness on vegetated slopes. In the slope direction, all hydrodynamic parameters on vegetated slopes exhibited fluctuating downward/upward trends due to the clocking effect before the vegetated area and the rapid conveyance effect in the vegetated area, whereas constant values were observed on bare slopes. The performance of hydrodynamics parameters suggested that the dense rearward arrangement (SMD) was the optimal vegetation pattern to regulate flow conditions. Specifically, the vertical profiles of the velocity and turbulence features of the SMD arrangement at different sections demonstrated the significant role of vegetation density in identifying the velocity layers along the water depth. The maximum velocity and Reynolds Stress Number (RSN) indicated the position where local scour was most likely to occur, which would improve our basic understanding of the mechanisms underlying hydraulic and soil erosion processes.

The effects of cultivar mixture cropping on yield, biomass, and water use efficiency (WUE) in winter wheat (Triticum aestivum L.) were investigated under non-irrigation (W0, no irrigation during growth stage), one time irrigation (W1, irrigation applied at stem elongation) and two times irrigation (W2, irrigation applied at stem elongation and anthesis) conditions. Nearly 90% of cultivar mixture cropping treatments experienced an increase in grain yield as compared with the mean of the pure stands under W0, those for W1 and W2 were 80% and 85%, respectively. Over 75% of cultivar mixture cropping treatments got greater biomass than the mean of the pure stands under the three irrigation conditions. Cultivar mixture cropping cost more water than pure stands under W0 and W1, whereas the water consumption under W2 decreased by 5.9%-6.8% as compared with pure stands. Approximately 90% of cultivar mixtures showed an increase of 5.4%-34.5% in WUE as compared with the mean of the pure stands, and about 75% of cultivar mixtures had 0.8%-28.5% higher WUE than the better pure stands under W0. Similarly, there were a majority of mixture cropping treatments with higher WUE than the mean and the better one of the pure stands under W1 and W2. On the whole, proper cultivar mixture cropping could increase yield and WUE, and a higher increase in WUE occurred under limited irrigation condition.