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Daily temperature variations, which tend to exhibit non-constant and non-linear patterns, are often characterized by intra-annual fluctuations that cause severe and frequent extreme temperature events that have an enormous impact on agricultural production. However, the quantitative relationship between intra-annual temperature fluctuations and extreme agricultural temperatures remains unclear. We aimed to investigate intra-annual temperature fluctuation changes based on daily meteorological data in nine agricultural regions across China from 1960 to 2022 and quantify the impact of temperature fluctuations on extreme agricultural temperatures during crop growth periods. Moreover, an attribution analysis of intra-annual temperature fluctuations was performed using climate indicators. Main results showed: (1) intra-annual temperature fluctuations in each region exhibited a certain decrease, and the spatial distribution showed a significant decreasing trend from north to south. (2) Intra-annual temperature fluctuations have moderately exacerbated extreme agricultural hot and cold events during growth periods, which have brought serious challenges to agriculture owing to advances in phenology and unsynchronized rain heat compared to climate warming. The proportion of positive correlations between temperature fluctuations and extreme temperatures was much larger than that of the negative correlations in nearly all of China (percentage of stations: 20.5 %), and the negative correlation was concentrated only in southern China (percentage of stations: 3.7 %). (3) Hydrothermal coupling and elevation moderately affected intra-annual temperature fluctuations. Average temperature, relative humidity, and elevation had negative correlations with intra-annual temperature fluctuations, the correlation coefficients (R) were - 0.62, -0.42 and - 0.12, respectively; however, reference crop evapotranspiration (ET 0 ) exhibited a positive correlation (R: 0.29), and all reached highly significant levels (P < 0.01). Climate indicators mainly affected intra-annual temperature fluctuations in eastern China. Specifically, the El Niño-Southern Oscillation (ENSO), unstable Asia Polar Vortex, and increased Western Pacific Subtropical High have enhanced temperature fluctuations. The deepened East Asian Trough and Arctic Oscillation of the negative phase weakened the intra-annual temperature fluctuations. This investigation highlights the crucial function of temperature fluctuation in intensifying extreme temperature occurrences and provides a more reasonable scientific foundation for extreme event prediction and agricultural planning., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Land surface temperature (LST) is a crucial parameter in the circulation of water, exchange of land-atmosphere energy, and turbulence. Currently, most LST products rely heavily on thermal infrared remote sensing, which is susceptible to cloud and rain interference, leading to inferior temporal continuity. Microwave remote sensing has the advantage of being available "all-weather" due to strong penetration capability, which provides the possibility to simulate time-continuous LST data. In addition, the continuous increase in high-density station observations (>10,000 stations) provides reliable measured data for the remote sensing monitoring of LST in China. This study aims to adopt the "Earth big data" generated from high-density station observation and microwave remote sensing data to monitor LST based on deep learning (U-Net family) for the first time. Given the significant spatial and temporal variability of LST and its sensitivity to various factors according to radiation transmission equations, this study incorporated climatic, anthropogenic, geographical, and vegetation datasets to facilitate a multi-source data fusion approach for LST estimation. The results showed that the U-Net++ model with modified skip connections better minimized the semantic discrepancy between the feature maps of the encoder and decoder subnetworks for 0.1° daily LST mapping across China than the U-Net and U 2 -Net deep learning models. The accuracy of the LST simulation exhibited favorable outcomes in the spatial and temporal dimensions. The station density met the requirements of monitoring air-ground integration monitoring in China. Additionally, the temporal change in the simulation accuracy fluctuated in a W-shape owing to the limited simulation capability of deep learning in extreme scenarios. Anthropogenic factors had the largest influence on LST changes in China, followed by climate, geography, and vegetation. This study highlighted the application of deep learning in remote sensing monitoring against the background of "big data" and provided a scientific foundation for the response of climate change to human activities, ecological environmental protection, and sustainable social and economic development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

This study presents hydrological impacts of future climate change (CC) and land use/cover change (LUCC) for the Srepok River Basin (SRB) in the Vietnam's Central Highlands. The hydrology cycle of this basin was reproduced using Soil and Water Assessment Tool (SWAT) allowing an evaluation of hydrological responses to CC and LUCC. Future climate scenarios of the 2015-2100 period under Representative Concentration Pathways (RCP) 4.5 simulated by five General Circulation Models (GCMs) and LUCC scenario in 2050 were developed. Compared to the reference scenario (1980-2005), future LUCC increases the streamflow (0.25%) and surface runoff (1.2%) and reduces the groundwater discharge (2.1%). Climate change may cause upward trends in streamflow (0.1 to 2.7%), surface runoff (0.4 to 4.3%), and evapotranspiration (0.8 to 3%), and a change in the groundwater discharge (- 1.7 to 0.1%). The combination of CC and LUCC increases the streamflow (0.2 to 2.8%), surface runoff (1.6 to 5.6%), and evapotranspiration (1.0 to 3.1%), and reduces the groundwater discharge (1.5 to 2.7%) with respect to the reference scenario. Moreover, the results noted that the water scarcity may happen in the dry-seasonal months., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

High-electron-mobility group III-V compounds have been regarded as a promising successor to silicon in next-generation field-effect transistors (FETs). Gallium arsenide (GaAs) is an outstanding member of the III-V family due to its advantage of both good n- and p-type device performance. Monolayer (ML) GaAs is the limit form of ultrathin GaAs. Here, a hydrogenated ML GaAs (GaAsH 2 ) FET is simulated by ab initio quantum-transport methods. The n- and p-type ML GaAsH 2 metal-oxide-semiconductor FETs (MOSFETs) can well satisfy the on-state current, delay time, power dissipation, and energy-delay product requirements of the International Technology Roadmap for Semiconductors until the gate length is scaled down to 3/4 and 3/5 nm for the high-performance/low-power applications, respectively. Therefore, ultrathin GaAs is a prominent channel candidate for devices in the post-Moore era. The p-type ML GaAsH 2 MOSFETs with a 2% uniaxially compressive strain and the unstrained n-type counterparts have symmetrical performance for the high-performance application, making ultrathin GaAs applicable for complementary MOS integrated circuits.

Purpose: The purpose of this paper is to present an end-to-end deep convolutional neural network to improve the dual-energy CT (DECT) material decomposition performance., Methods: In this study, we proposes a unified mutual-domain (sinogram domain and CT domain) material decomposition network (DIRECT-Net) for DECT imaging. By design, the DIRECT-Net has immediate access to mutual-domain data, and utilizes stacked convolution neural network layers for noise reduction and material decomposition. The training data are numerically generated following the fundamental DECT imaging physics. Numerical simulation of the XCAT digital phantom, experiments of a biological specimen, a calcium chloride phantom and an iodine solution phantom are carried out to evaluate the performance of DIRECT-Net. Comparisons are performed with different DECT decomposition algorithms., Results: Results demonstrate that the proposed DIRECT-Net can generate water and bone basis images with less artifacts compared to the other decomposition methods. Additionally, the quantification errors of the calcium chloride (75-375 mg/cm 3 ) and the iodine (2-20 mg/cm 3 ) are less than 4%., Conclusions: An end-to-end material decomposition network is proposed for quantitative DECT imaging. The qualitative and quantitative results demonstrate that this new DIRECT-Net has promising benefits in improving the DECT image quality., (© 2021 American Association of Physicists in Medicine.)

Sparse-view CT is a promising approach for reducing the x-ray radiation dose in clinical CT imaging. However, the CT images reconstructed from the conventional filtered backprojection algorithm suffer from severe streaking artifacts. Iterative reconstruction algorithms have been widely adopted to mitigate these streaking artifacts, but they may prolong the CT imaging time due to the intense data-specific computations. Recently, a model-driven deep learning CT image reconstruction method, which unrolls the iterative optimization procedures into a deep neural network, has shown exciting prospects for improving image quality and shortening the reconstruction time. In this work, we explore a generalized unrolling scheme for such an iterative model to further enhance its performance on sparse-view CT imaging. By using it, the iteration parameters, regularizer term, data-fidelity term and even the mathematical operations are all assumed to be learned and optimized via network training. Results from the numerical and experimental sparse-view CT imaging demonstrate that the newly proposed network with the maximum generalization provides the best reconstruction performance., (© 2022 Institute of Physics and Engineering in Medicine.)

This paper aimed at examining the climate variability and land-use change effects on streamflow and pollutant loadings, namely total suspended sediment (TSS), total nitrogen (T-N), and total phosphorus (T-P), in the Sesan, Sekong, and Srepok (3S) River Basin in the period 1981-2010. The well-calibrated and validated Soil and Water Assessment Tool (SWAT) was used for this purpose. Compared to the reference period, climate variability was found to be responsible to a 1.00% increase in streamflow, 2.91% increase in TSS loading, 11.35% increase in T-N loading, and 19.12% reduction in T-P loading for the whole basin. With regard to the effect of land-use change (LUC), streamflow, TSS, T-N, and T-P loadings increased by 0.01%, 3.70%, 10.12%, and 10.94%, respectively. Therefore, the combination of climate variability and LUC showed amplified increases in streamflow (1.03%), TSS loading (7.09%), and T-N loading (25.05%), and a net effect of decreased T-P loading (10.35%). Regarding the Sekong and Srepok River Basins, the streamflow, TSS, T-N and T-P showed stronger responses to climate variability compared to LUC. In case of the Sesan River Basin, LUC had an effect on water quantity and quality more strongly than the climate variability. In general, the findings of this work play an essential role in providing scientific information to effectively support decision makers in developing sustainable water resources management strategies in the study area., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Purpose: Clinical responses of neoadjuvant chemotherapy (NACT) are associated with prognosis in patients with breast cancer. The selection of suitable variables for the prediction of clinical responses remains controversial. Herein, we developed a predictive model based on ultrasound imaging and clinical indices to identify patients most likely to benefit from NACT., Patients and Methods: We recruited a total of 225 consecutive patients who underwent NACT followed by surgery and axillary lymph node dissection at the Sixth Hospital of Ning Bo City of Zhe Jiang Province between January 1, 2018, and March 31, 2021. All patients had been diagnosed with breast cancer following the clinical examination. First, we created a training cohort of patients who underwent NACT+surgery (N=180) to develop a nomogram. We then validated the performance of the nomogram in a validation cohort of patients who underwent NACT+ surgery (N=45). Multivariate logistic regression was then used to identify independent risk factors that were associated with the response to NACT; these were then incorporated into the nomogram., Results: Multivariate logistic regression analysis identified several significant differences as to clinical responses of NACT, including neutrophil-lymphocyte ratio (NLR), body mass index (BMI), pulsatility index (PI), resistance index (RI), blood flow, Ki67, histological type, molecular subtyping, and tumor size. The performance of the nomogram score exhibited a robust C-index of 0.89 (95% confidence interval [CI]: 0.83 to 0.95) in the training cohort and a high C-index of 0.87 (95% CI: 0.81 to 0.93) in the validation cohort. Clinical impact curves showed that the nomogram had a good predictive ability., Conclusion: We successfully established an accurate and optimized nomogram incorporated ultrasound imaging and clinical indices that could be used preoperatively to predict clinical responses of NACT. This model can be used to evaluate the risk of clinical responses to NACT and therefore facilitate the choice of personalized therapy., Competing Interests: None of the authors have any conflicts of interest to declare., (© 2021 Ye et al.)

Purpose: The goal of this study is to develop a three-dimensional (3D) iterative reconstruction framework based on the deep learning (DL) technique to improve the digital breast tomosynthesis (DBT) imaging performance., Methods: In this work, the DIR-DBTnet is developed for DBT image reconstruction by mapping the conventional iterative reconstruction (IR) algorithm to the deep neural network. By design, the DIR-DBTnet learns and optimizes the regularizer and the iteration parameters automatically during the network training with a large amount of simulated DBT data. Numerical, experimental, and clinical data are used to evaluate its performance. Quantitative metrics such as the artifact spread function (ASF), breast density, and the signal difference to noise ratio (SDNR) are measured to assess the image quality., Results: Results show that the proposed DIR-DBTnet is able to reduce the in-plane shadow artifacts and the out-of-plane signal leaking artifacts compared to the filtered backprojection (FBP) and the total variation (TV)-based IR methods. Quantitatively, the full width half maximum (FWHM) of the measured ASF from the clinical data is 27.1% and 23.0% smaller than those obtained with the FBP and TV methods, while the SDNR is increased by 194.5% and 21.8%, respectively. In addition, the breast density obtained from the DIR-DBTnet network is more accurate and consistent with the ground truth., Conclusions: In conclusion, a deep iterative reconstruction network, DIR-DBTnet, has been proposed for 3D DBT image reconstruction. Both qualitative and quantitative analyses of the numerical, experimental, and clinical results demonstrate that the DIR-DBTnet has superior DBT imaging performance than the conventional algorithms., (© 2021 American Association of Physicists in Medicine.)

Food security in China is under additional stress due to climate change. The risk analysis of maize yield losses is crucial for sustainable agricultural production and climate change impact assessment. It is difficult to quantify this risk because of the constraints on the high-resolution data available. Moreover, the current results lack spatial comparability due to the area effect. These challenges were addressed by using long-term county-level maize yield and planting area data from 1981 to 2010. We analyzed the spatial distribution of maize yield loss risks in mainland China. A new comprehensive yield loss risk index was established by combining the reduction rate, coefficient of variation, and probability of yield reduction after removing the area effect. A total of 823 counties were divided into areas of lowest, low, moderate, high, and highest risk. High risk in maize production occurred in Heilongjiang and Jilin Provinces, the eastern part of Inner Mongolia, the eastern part of Gansu-Xinjiang, west of the Loess Plateau, and the western part of the Xinjiang Uygur Autonomous Region. Most counties in Northeast China were at high risk, while the Loess Plateau, middle and lower reaches of the Yangtze River and Gansu-Xinjiang were at low risk.

Due to many factors in the physical properties of the ground surface, the corresponding interferometric coherence values change dynamically over time. Among these factors, the roles of the vegetation and its temporal variation have not yet been revealed so far. In this paper, synthetic aperture radar (Sentinel-1) data and optical remote sensing (Landsat TM) images over four whole seasons are employed to reveal the relationship between the interferometric coherence and the normalized difference vegetation index (NDVI) at five sites that have ground deformation due to mining in Henan province, China. The result showed: (1) As for the village area with few vegetation cover, the related coherence values are significantly higher than that in the farm land area with high densities of vegetation in the spring and summer, which indicates that the subsidence by mining in few vegetation cover area is easier to be monitored; (2) Linear regression coefficients ([Formula: see text]) between the interfereometric coherence values and the NDVI values is 0.62, which indicate the interferometric coherence values and the NDVI values change reversely in both farm land and village areas over the year. It suggests months between November and March with lower NDVI value are more suitable for deformation detecting. Therefore, the interfereometric coherence values can be used to detect the density of vegetation, while NDVI values can be reference for elucidating when the traditional differential interferometric synthetic aperture radar (DInSAR) could be effectively used.

Insects of the family Orthoptera: Acrididae including grasshoppers and locust devastate crops and eco-systems around the globe. The effective control of these insects requires large numbers of trained extension agents who try to spot concentrations of the insects on the ground so that they can be destroyed before they take flight. This is a challenging and difficult task. No automatic detection system is yet available to increase scouting productivity, data scale and fidelity. Here we demonstrate MAESTRO, a novel grasshopper detection framework that deploys deep learning within RBG images to detect insects. MAESTRO uses a state-of-the-art two-stage training deep learning approach. The framework can be deployed not only on desktop computers but also on edge devices without internet connection such as smartphones. MAESTRO can gather data using cloud storge for further research and in-depth analysis. In addition, we provide a challenging new open dataset (GHCID) of highly variable grasshopper populations imaged in Inner Mongolia. The detection performance of the stationary method and the mobile App are 78 and 49 percent respectively; the stationary method requires around 1000 ms to analyze a single image, whereas the mobile app uses only around 400 ms per image. The algorithms are purely data-driven and can be used for other detection tasks in agriculture (e.g. plant disease detection) and beyond. This system can play a crucial role in the collection and analysis of data to enable more effective control of this critical global pest.

Winter wheat has a central role in ensuring the food security and welfare of 1.3 billion people in China. Extensive previous studies have concluded that winter wheat yields would decrease with higher temperatures, owing to warming-induced soil drying or shortening of phenophase. Temperature in China is predicted to increase by 1-5°C by 2100, which may greatly impact plant production and cause other negative effects. We performed a manipulative field experiment, creating diverse growth regimes for wheat by infrared radiation (IR) warming day and night, including IR warming only (DW), IR warming + delayed sowing dates (DS), IR warming + increased irrigation (IW), and a control (CK). The results show that IR warming increased daily average wheat canopy and soil temperatures by 2.0°C and 2.3°C, respectively. DW was associated with an advanced maturity of 10 days and yield reduction of 8.2%. IR-warming effects on the photosynthetic apparatus of wheat varied with season as well as significant differences were found in the booting stage. DS represented a worsened situation, lowering yield per plant by 16.4%, with a significant decline in aboveground biomass and functional leaf area. Wheat under DS showed double-peak patterns of diurnal gas exchange during booting stages and, consequently, lower photosynthetic capacity with high transpiration for cooling. Significantly lower actual water use efficiency and intrinsic water use efficiency from jointing to anthesis stages were also found under DS. However, IW had no significant difference from CK, irrespective of yield and photosynthesis. Therefore, we concluded that delayed sowing date may not be a good choice for winter wheat, whereas a thoroughly-watered wheat agroecosystem should be promoted in the context of global warming.