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Abstract Many of the most sensitive physics experiments searching for rare events, like neutrinoless double beta ( $$0\nu \beta \beta $$ 0 ν β β ) decay, coherent elastic neutrino nucleus scattering and dark matter interactions, rely on cryogenic macro-calorimeters operating at the mK-scale. Located underground at the Gran Sasso National Laboratory (LNGS), in central Italy, CUORE (Cryogenic Underground Observatory for Rare Events) is one of the leading experiments for the search of $$0\nu \beta \beta $$ 0 ν β β decay, implementing the low-temperature calorimetric technology. We present a novel multi-device analysis to correlate environmental phenomena with the low-frequency noise of low-temperature calorimeters. Indeed, the correlation of marine and seismic data with data from a couple of CUORE detectors indicates that cryogenic detectors are sensitive not only to intense vibrations generated by earthquakes, but also to the much fainter vibrations induced by marine microseisms in the Mediterranean Sea due to the motion of sea waves. Proving that cryogenic macro-calorimeters are sensitive to such environmental sources of noise opens the possibility of studying their impact on the detectors physics-case sensitivity. Moreover, this study could pave the road for technology developments dedicated to the mitigation of the noise induced by marine microseisms, from which the entire community of cryogenic calorimeters can benefit.

Climate change influences the water cycle and alters the spatiotemporal distribution of hydrological variables, thus complicating the projection of future streamflow and hydrological droughts. Although machine learning is increasingly employed for hydrological simulations, few studies have used it to project hydrological droughts, not to mention bivariate risks (referring to drought duration and severity) as well as their socioeconomic effects under climate change. We developed a cascade modeling chain to project future bivariate hydrological drought characteristics in 179 catchments over China, using five bias-corrected global climate model (GCM) outputs under three shared socioeconomic pathways (SSPs), five hydrological models, and a deep-learning model. We quantified the contribution of various meteorological variables to daily streamflow by using a random forest model, and then we employed terrestrial water storage anomalies and a standardized runoff index to evaluate recent changes in hydrological drought. Subsequently, we constructed a bivariate framework to jointly model drought duration and severity by using copula functions and the most likely realization method. Finally, we used this framework to project future risks of hydrological droughts as well as the associated exposure of gross domestic product (GDP) and population. Results showed that our hybrid hydrological–deep-learning model achieved > 0.8 Kling–Gupta efficiency in 161 out of the 179 catchments. By the late 21st century, bivariate drought risk is projected to double over 60 % of the catchments mainly located in southwestern China under SSP5-85, which shows the increase in drought duration and severity. Our hybrid model also projected substantial GDP and population exposure by increasing bivariate drought risks, suggesting an urgent need to design climate mitigation strategies for a sustainable development pathway.

Bulk W-0.5 wt%ZrC (W-ZrC), W-1 wt%Re-0.5 wt%ZrC (W-1Re-ZrC), and W-3 wt%Re-0.5 wt%ZrC (W-3Re-ZrC) alloys were prepared and exposed to He2+ ion irradiation with an incident energy of 500 keV at 400 °C. Post-irradiation analysis revealed the formation of He bubbles, with consistent number density and average size across all alloys. However, the W-3Re-ZrC alloy exhibited a notably higher density of dislocation loops within grain interiors compared to W-ZrC and W-1Re-ZrC. This led to a significant increase in the hardening rate of W-3Re-ZrC to 61.5 %, nearly twice that of the other alloys. By considering the binding energies of He-vacancy, He-He, and Re-He, the formation and growth behavior of irradiation-induced defects, including He bubbles and dislocation loops, were also discussed. This work provides valuable insight into the relationships among Re alloying, He-induced defects, and irradiation hardening in Re-alloyed W materials, thus offering valuable guidance for the design of W-based plasma-facing materials.

To reveal the effects of aging state on the low-cycle fatigue (LCF) performance of 2024 Al alloy, LCF experiments were conducted on the 2024 Al alloy with three aging states (underaged (UA), peak-aging (PA) and overaged (OA)), respectively. It is found that under the total strain amplitude and plastic strain amplitude control, the Al alloy at UA and OA states exhibits higher LCF lives than that at PA state, respectively. For a more comprehensive evaluation, a hysteretic energy model with W0 as the fatigue damage capacity and β as the fatigue damage resisting exponent was used, which also shows the advantages of the OA states. This is because although the OA state has lower fatigue damage capacity W0 for the inferior strength-plasticity combination compared to the UA state, it has higher fatigue damage resistance exponent β because the obvious wavy slip manner of dislocations is conducive to strain homogenization, which is more important for increasing the fatigue life. Therefore, this study gives a comprehensive evaluation and revelation on the effects of aging state on the LCF properties of 2024 alloy.

Due to a robust emission control policy, significant reductions in major air pollutants, such as PM2.5, SO2, NO2, and CO, were observed in China between 2015 and 2020. On the other hand, during the same period, there was a notable increase in ozone (O3) concentrations, making it a prominent air pollutant in eastern China. The annual mean concentration of maximum daily 8 h average (MDA8) O3 exhibited alarming linear increases of 2.4, 1.1, and 2.0 ppb yr−1 (ppb is for parts per billion) in three megacity clusters: Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD), respectively. Meanwhile, there was a significant 3-fold increase in the number of O3-exceeding days, defined as MDA8 O3 > 75 ppb. Our analysis indicated that the upward increases in the annual mean concentration of MDA8 were primarily driven by the rise in consecutive O3-exceeding days. There were expansions of high O3 in urban centers to rural areas accompanied by a saturation effect so that MDA8 O3 concentrations at the high-O3 stations in 2015 remained nearly constant at 100 ppb. Last, we found a close association between O3 episodes with 4 or more consecutive O3-exceeding days and the position and strength of tropical cyclones (TCs) in the northwest Pacific and the West Pacific subtropical high (WPSH). The TC and WPSH contributed to meteorological conditions characterized by clear skies, subsiding air motion, high vertical stability in the lower troposphere, increased solar radiation, and a positive temperature anomaly at the surface. These favorable meteorological conditions greatly facilitated the formation of O3. Thus, we propose that the worsening O3 increases observed in the BTH, YRD, and PRD regions from 2015 to 2020 can be mostly attributed to enhanced photochemical O3 production resulting from an increased occurrence of meteorological conditions with high solar radiation and positive temperature anomalies under the influence of the WPSH and TCs.

Traditional methods such as Steinmetz's equation (SE) and its improved variant (iGSE) have demonstrated limited precision in estimating power loss for magnetic materials. The introduction of Neural Network technology for assessing magnetic component power loss has significantly enhanced accuracy. Yet, an efficient method to incorporate detailed flux density information—which critically impacts accuracy—remains elusive. Our study introduces an innovative approach that merges Fast Fourier Transform (FFT) with a Feedforward Neural Network (FNN), aiming to overcome this challenge. To optimize the model further and strike a refined balance between complexity and accuracy, Multi-Objective Optimization (MOO) is employed to identify the ideal combination of hyperparameters, such as layer count, neuron number, activation functions, optimizers, and batch size. This optimized Neural Network outperforms traditionally intuitive models in both accuracy and size. Leveraging the optimized base model for known materials, transfer learning is applied to new materials with limited data, effectively addressing data scarcity. The proposed approach substantially enhances model training efficiency, achieves remarkable accuracy, and sets an example for Artificial Intelligence applications in loss and electrical characteristic predictions with challenges of model size, accuracy goals, and limited data.

As a typical cutting tool with good performance and high processing efficiency, metallurgical saw blades are widely used in various industries, but surface defects are inevitably generated in the manufacturing process. To solve this problem, this paper proposes a YOLOv5-based surface defect detection model for product quality, which can distinguish three common metallurgical sawblade surface defects with mAP value of 96,1 % in each defect category detection of metallurgical sawblades and detection time of 139,8 ms per image.

In the process of urban construction and development, the ecological environment has always been an important consideration factor. How to quickly monitor the long-term ecological environment quality changes in large urban areas is currently one of the main research hotspots. With the continuous development of remote sensing intelligent cloud computing technology, the application of remote sensing methods to monitor urban ecological environment quality changes is becoming more efficient and convenient. Based on the remote sensing ecological index (RSEI) on the remote sensing intelligent cloud platform Google Earth Engine (GEE), this article innovatively proposes an improved remote sensing ecological index (DS-RSEI, Downscale-RSEI). Using the normalized difference vegetation index (NDVI) and land cover data (LULC), terrain data as auxiliary data, the method of moving window and principal component analysis is used to implement spatial downscaling of the heat components in the index, which increases the resolution of the original MODIS land surface temperature (LST) 1000-meter resolution product to 500 meters. When fusing with other images, it can supplement missing image details and improve the ability to evaluate the ecological spatial details of complex urban blocks. Through a comparative analysis of the RSEI index and the DS-RSEI index proposed in this article, it can be seen that DS-RSEI can express the details of ecological environment changes better in complex blocks.

In order to improve the image registration accuracy of heterogeneous remote sensing images with large radiation differences, an improved image registration algorithm for thermal infrared and panchromatic images is proposed. This method uses the phase consistency of intensity and direction to construct a geometric structure feature descriptor called the Histogram of Oriented Phase Congruency (HOPC) for image registration. It employs the Curvature Scale Space (CSS) corner detection to concentrate and extract feature points in contour areas, and defines a similarity metric (called HOPCn) based on the Euclidean distance between the descriptors for high-precision heterogeneous images registration. Five sets of panchromatic and thermal infrared images were selected for verification. The results show that the improved algorithm can match the homonymy points around the image contour, and the Correct Matching Rate (CMR) is improved by 2.7% and the Root Mean Square Error (RMSE) is improved by 3.7% compared to the original algorithm. This proves that the proposed method has good robustness under conditions of large radiation differences.

Animal nutritionists continue to investigate new strategies to combat the challenge of methane emissions from ruminants. Medicinal plants (MPs) are known to be beneficial to animal health and exert functional roles in livestock due to their phytogenic compounds with antimicrobial, immunostimulatory, antioxidative, and anti-inflammatory activities. Some MP has been reported to be anti-methanogenic and can effectively lower ruminants' enteric methane emissions. This review overviews trends in MP utilization in ruminants, their bioactivity and their effectiveness in lowering enteric methane production. It highlights the MP regulatory mechanism and the gaps that must be critically addressed to improve its efficacy. MP could reduce enteric methane production by up to 8–50% by regulating the rumen fermentation pathway, directing hydrogen toward propionogenesis, and modifying rumen diversity, structure, and population of the methanogens and protozoa. Yet, factors such as palatability, extraction techniques, and economic implications must be further considered to exploit their potential fully.

Oases and deserts generally act as a landscape matrix and mosaic in arid and semiarid regions. The significant difference in thermal and dynamic characteristics between an oasis and desert surface will result in oasis–desert interaction. Specifically, this refers to the interaction between an oasis and desert system via the exchange of momentum, energy, water, and carbon, which can lead to a series of microclimate effects that affect the structure of the atmospheric boundary layer, changes in carbon sources and sinks in an oasis, and the local ecological environment. Therefore, studying water, heat, and carbon exchange is significant to achieve the goals of carbon peaking and carbon neutrality in oasis–desert areas and support the ecological security and sustainable development of oases. To monitor energy, water vapor, and carbon exchange between the land surface and atmosphere, a land surface process integrated observatory network was established in the oasis–desert area in the middle and lower reaches of the Heihe River basin, the second largest endorheic basin in China. In this study, we present a suite of observational datasets from artificial and natural oasis–desert systems that consist of long-term energy, water vapor, carbon and methane fluxes as well as auxiliary data involving hydrometeorology, vegetation, and soil parameters from 2012 to 2021. Half-hourly turbulent flux data were acquired by an eddy covariance system and scintillometer. The hydrometeorological data (including radiation, soil heat flux, and soil temperature profiles; gradients of air temperature and humidity and of wind speed and direction; and air pressure, precipitation, and soil moisture profiles) were observed from automatic weather stations with a 10 min average period as well as the groundwater table data. Moreover, vegetation and soil parameters were also supplemented in the datasets. Careful data processing and quality control were implemented during data production, including data collection, processing, archiving, and sharing. The current datasets can be used to explore the water–heat–carbon process and its mechanism of influence; to calibrate and validate related remote-sensing products; to simulate energy, water vapor, and carbon exchange in oasis and desert areas; and to provide references and representatives for other similar artificial and natural oases along the “Silk Road”. The data are available from the National Tibetan Plateau Third Pole Environment: https://doi.org/10.11888/Terre.tpdc.300441 (Liu et al., 2023).

This paper deals with the construction of a novel family of multimode deployable mechanisms based on reconfigurable Bricard-like mechanisms. By connecting a number of identical threefold-symmetric (TFS) Bricard-like mechanisms, a multimode deployable arch is proposed for the first time, which can switch between the scissor-like deployable mode and the arch deformable mode through the transition configuration. Then new multimode center-driven deployable mechanisms can be obtained by connecting three and six multimode deployable arches. The obtained mechanism can switch between the scissor-like deployable mode and spherical deformable mode, and it can be reassembled by adjusting the number of TFS Bricard-like mechanisms to change its size. Finally, physical prototypes of the multimode deployable arch and multimode center-driven deployable mechanisms are fabricated and tested to validate the feasibility of the proposed approach and analysis.

In pursuit of design solutions that reduce energy loss and improve wear resistance for the piston–cylinder interface in an axial piston motor, a fluid-structure interaction numerical model and a new test rig of the friction force of the piston–cylinder pair are developed to achieve the analysis and design of the micro-geometric tapered-shape surface of a piston bore. The piston bore with the tapered-shape surface axial distribution length ratio of 49.44 % of the overall length is found to be the relatively optimized one. Furthermore, how the shaft speed, load pressure, and swash plate angle influence the performance of the two-type interface is analyzed. Numerical analysis results show that, compared with the traditional cylindrical piston bore design, the piston–cylinder interface with the optimized tapered-shape piston bore in the axial piston motor can achieve a significant reduction in leakage flow and friction force, and the experimental results are consistent with the simulation results.

The energy and water vapor exchange between the land surface and atmospheric boundary layer plays a critical role in regional climate simulations. This paper implemented a hybrid data assimilation and machine learning framework (DA-ML method) into the Weather Research and Forecasting (WRF) model to optimize surface soil and vegetation conditions. The hybrid method can integrate remotely sensed leaf area index (LAI), multi-source soil moisture (SM) observations, and land surface models (LSMs) to accurately describe regional climate and land–atmosphere interactions. The performance of the hybrid method on the regional climate was evaluated in the Heihe River basin (HRB), the second-largest endorheic river basin in Northwest China. The results show that the estimated sensible (H) and latent heat (LE) fluxes from the WRF (DA-ML) model agree well with the large aperture scintillometer (LAS) observations. Compared to the WRF (open loop – OL), the WRF (DA-ML) model improved the estimation of evapotranspiration (ET) and generated a spatial distribution consistent with the ML-based watershed ET (ETMap). The proposed WRF (DA-ML) method effectively reduces air warming and drying biases in simulations, particularly in the oasis region. The estimated air temperature and specific humidity from WRF (DA-ML) agree well with the observations. In addition, this method can simulate more realistic oasis–desert boundaries, including wetting and cooling effects and wind shield effects within the oasis. The oasis–desert interactions can transfer water vapor to the surrounding desert in the lower atmosphere. In contrast, the dry and hot air over the desert is transferred to the oasis from the upper atmosphere. The results show that the integration of LAI and SM will induce water vapor intensification and promote precipitation in the upstream of the HRB, particularly on windward slopes. In general, the proposed WRF (DA-ML) model can improve climate modeling by implementing detailed land characterization information in basins with complex underlying surfaces.

To investigate the light intensity suitable for the growth of Ardisia gigantifolia Stapf, morphology, photosynthetic parameters, and indicators of oxidative stress were analyzed under different light intensities. Compared to high-irradiance treatment, medium and low-irradiance treatments promoted plant growth and restricted transpiration. Compared to medium irradiance, plants under high and low irradiance exhibited significantly lower maximal photochemical efficiency, potential photochemical efficiency, and electron transport rate, but significantly higher malondialdehyde content. This indicated that both excessive light and severe shading inhibited photosynthetic activity and induced oxidative stress, which resulted in a significant decrease in net photosynthetic rate. A. gigantifolia can adapt to different light intensities, improving light harvesting and utilizing capacity under low irradiance by increasing Chl (a+b) content and reducing Chl a/b ratio, and adapting to high irradiance by enhancing heat dissipation and activity of peroxidase. A. gigantifolia showed the best performance in growth and photosynthesis under medium irradiance treatment.

Vegetation, water, and carbon dioxide have complex interactions on carbon mitigation in vegetation–water ecosystems. As one of the major global change drivers of carbon sequestration, flooding disturbance is a fundamental but poorly discussed topic to date. The aquatic and associated riparian systems are highly dynamic vegetation–water carbon capture systems driven by fluvial processes such as flooding. However, their global carbon offset potential is largely unknown. This study examines daily CO2 perturbations under flooding disturbance in the river (fluvial area) and associated riparian areas with 2 year in situ observations along the Lijiang. We find that, though the submerged riparian area behaved as a carbon source during the flooding season (CO2 flux: 2.790 gm-2d-1), the riparian area and the fluvial area as a whole transformed from a carbon source in pre-flooding season (1.833 gm-2d-1) to a carbon sink after recovery in post-flooding season (−0.592 gm-2d-1). The fluvial area sequestered carbon (−0.619 gm-2d-1) in post-flooding season instead of releasing carbon as in pre-flooding season (2.485 gm-2d-1). Also, the carbon sequestration capacity of the riparian area was enhanced in post-flooding season (pre-flooding season: −0.156 gm-2d-1, post-flooding season: −0.500 gm-2d-1). We suggest that post-disturbance recovery of riparian vegetation played a vital role in this transformation, due to its stronger carbon uptake capacity after recovery from the flooding disturbances. The findings shed light on the quantitative modelling of the riparian carbon cycle under flooding disturbance and underlined the importance of the proper restoration of riparian systems to achieve global carbon offset.

In this study, we analyze the diurnal variations and formation mechanisms of low clouds at different elevations. We further discuss whether there exists a triggering mechanism for convection over the Tibetan Plateau (TP) and whether there is an association among low air density, strong turbulence, and ubiquitous “popcorn-like” cumulus clouds. The buoyancy term (BT) and shear term (ST) over the TP are significantly greater than those at low elevations, which is favorable for the formation of an increasing planetary boundary layer height (PBLH) and also plays a key role in the convective activities in the lower troposphere. From the viewpoint of global effects, the triggering of convection by boundary layer dynamics is analyzed over the TP, but also in the Northern Hemisphere over the Rocky Mountains. It is found that ST and BT are strong over both high-elevation regions. The strong thermal turbulence and large-scale ascending motion jointly result in obvious positive values of PBLH–LCL (lifting condensation level) under low relative humidity (RH) conditions over the TP. The obvious large-scale subsidence on both sides of the Rocky Mountains, especially the western side, leads to inversion above the PBL and lower RH within the PBL, which further leads to negative values of PBLH–LCL and decreased low cloud cover (LCC) in most parts of the Rocky Mountains. The slightly greater-than-zero PBLH–LCL corresponds spatially to increased LCC in the partial regions of the central Rocky Mountains. Thus, less LCC is generated at the Rocky Mountains compared to the TP.

In Earth observation, multimodal data fusion is an intuitive strategy to break the limitation of individual data. Complementary physical contents of data sources allow comprehensive and precise information retrieval. With current satellite missions, such as ESA Copernicus programme, various data will be accessible at an affordable cost. Future applications will have many options for data sources. Such a privilege can be beneficial only if algorithms are ready to work with various data sources. However, current data fusion studies mostly focus on the fusion of two data sources. There are two reasons; first, different combinations of data sources face different scientific challenges. For example, the fusion of synthetic aperture radar (SAR) data and optical images needs to handle the geometric difference, while the fusion of hyperspectral and multispectral images deals with different resolutions on spatial and spectral domains. Second, nowadays, it is still both financially and labour expensive to acquire multiple data sources for the same region at the same time. In this paper, we provide the community with a benchmark multimodal data set, MDAS, for the city of Augsburg, Germany. MDAS includes synthetic aperture radar data, multispectral image, hyperspectral image, digital surface model (DSM), and geographic information system (GIS) data. All these data are collected on the same date, 7 May 2018. MDAS is a new benchmark data set that provides researchers rich options on data selections. In this paper, we run experiments for three typical remote sensing applications, namely, resolution enhancement, spectral unmixing, and land cover classification, on MDAS data set. Our experiments demonstrate the performance of representative state-of-the-art algorithms whose outcomes can serve as baselines for further studies. The dataset is publicly available at https://doi.org/10.14459/2022mp1657312 (Hu et al., 2022a) and the code (including the pre-trained models) at https://doi.org/10.5281/zenodo.7428215 (Hu et al., 2022b).

Dynamic monitoring of geological hazards is an important project. Photogrammetry plays an important role in geological hazard monitoring, because of its advantages of convenient data processing, low cost, non-contact, direct texture information and intricate 3-D real scene model. For the traditional photogrammetry can not meet the needs of dynamic monitoring, based on multi-period nap-of-the-object photogrammetry, this paper obtains high-precision images and produces a refined 3D real scene model to carry out the dynamic monitoring of geological disasters. A deformation detection method of geological hazards based on the comparison of three-dimensional model differences is proposed to locate the position with serious deformation. Then, based on the severely deformed area, the multi-period generalized orthophotos are produced, and the image feature matching is used to find the homonymous points between the generalized orthophotos. The two-dimensional and three-dimensional coordinate forward and inverse transformation of the generalized orthophotos are used to calculate the size and direction of the deformation displacement, analyze the deformation trend of geological disasters, and finally realize accurate monitoring of the deformation trend of geological disasters. The results show that the deformation area detection method proposed in this paper is simple and effective, which can quickly find the deformation position. The deformation trend prediction based on multi-period generalized orthophotos is feasible, and the displacement with deformation of 3CM can be accurately pointed out.

The extent of surface water has been changing significantly due to climatic change and human activities. However, it is challenging to capture the interannual changes of inland water bodies due to their high seasonal variation and abrupt change. In this paper, a global annual surface water cover frequency dataset (GLOBMAP SWF) was generated from the MODIS land surface reflectance products during 2000–2020 to describe the seasonal and interannual dynamics of surface water. Surface water cover frequency (SWF) was proposed as the percentage of the time period when a pixel is covered by water in a year. Instead of determination of the water directly, the SWF was estimated indirectly by identifying land observations among annual clear-sky observations to reduce the influence of clouds and variability of water bodies and surface background characteristics, which helps to improve the applicability of the algorithm for different regions across the globe. The generated dataset shows better performances for frozen water, saline lakes, bright surfaces and regions with frequent cloud cover compared with the two high-frequency surface water datasets derived from MODIS data, and it captures more intermittent surface water but may underestimate small water bodies when compared with two high-resolution datasets derived from Landsat data. Compared with the high-resolution SWF maps extracted from Sentinel-1 data in eight regions that cover lakes, rivers and wetlands, the R2 reaches 0.46 to 0.97, RMSE ranges from 7.24 % to 22.62 %, and MAE is between 2.07 % and 7.15 %. In 2020, the area of global maximum surface water extent is 3.38×106 km2, of which the permanent surface water accounts for approximately 54 % (1.83×106 km2), and the other 46 % is intermittent surface water (1.55×106 km2). The area of global maximum and permanent surface water has been shrinking since 2001, with a change rate of −7577 and −4315 km2 yr−1 (p), respectively, while the intermittent surface water with the SWF above 50 % has been expanding (1368 km2 yr−1, p). This dataset can be used to analyze the interannual variation and change trend of highly dynamic inland waters extent with consideration of its seasonal variation. The GLOBMAP SWF data are available at https://doi.org/10.5281/zenodo.6462883 (Liu and Liu, 2022).

Multiple linear regression (MLR) models are used to assess the contributions of meteorology/climate and anthropogenic emission control to linear trends of PM2.5 concentration during the period 2013–2018 in three regions in eastern China, namely Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD). We find that quantitative contributions to the linear trend of PM2.5 derived based on MLR results alone are not credible because a good correlation in the MLR analysis does not imply any causal relationship. As an alternative, we propose that the correlation coefficient should be interpreted as the maximum possible contribution of the independent variable to the dependent variable and the residual should be interpreted as the minimum contribution of all other independent variables. Under the new interpretation, the previous MLR results become self-consistent. We also find that the results of a short-term (2013–2018) analysis are significantly different from those of a long-term (1985–2018) analysis for the period 2013–2018 in which they overlap, indicating that MLR results depend critically on the length of time analyzed. The long-term analysis renders a more precise assessment because of additional constraints provided by the long-term data. We therefore suggest that the best estimates of the contributions of emissions and non-emission processes (including meteorology/climate) to the linear trend in PM2.5 during 2013–2018 are those from the long-term analyses: i.e., emission % and non-emission >49 % for BTH, emission % and non-emission >56 % for YRD, and emission % and non-emission >12 % for PRD.

Abstract CUPID will be a next generation experiment searching for the neutrinoless double $$\beta $$ β decay, whose discovery would establish the Majorana nature of the neutrino. Based on the experience achieved with the CUORE experiment, presently taking data at LNGS, CUPID aims to reach a background free environment by means of scintillating Li $$_{2}$$ 2 $$^{100}$$ 100 MoO $$_4$$ 4 crystals coupled to light detectors. Indeed, the simultaneous heat and light detection allows us to reject the dominant background of $$\alpha $$ α particles, as proven by the CUPID-0 and CUPID-Mo demonstrators. In this work we present the results of the first test of the CUPID baseline module. In particular, we propose a new optimized detector structure and light sensors design to enhance the engineering and the light collection, respectively. We characterized the heat detectors, achieving an energy resolution of (5.9 ± 0.2) keV FWHM at the Q-value of $$^{100}$$ 100 Mo (about 3034 keV). We studied the light collection of the baseline CUPID design with respect to an alternative configuration which features gravity-assisted light detectors’ mounting. In both cases we obtained an improvement in the light collection with respect to past measures and we validated the particle identification capability of the detector, which ensures an $$\alpha $$ α particle rejection higher than 99.9%, fully satisfying the requirements for CUPID.

Understanding the hydrological processes related to snow in global mountainous regions under climate change is necessary for achieving regional water and food security (e.g., the United Nation's Sustainable Development Goals 2 and 6). However, the impacts of future snow changes on the hydrological processes in the high mountains of the “Third Pole” are still largely unclear. In this study, we aimed to project future snow changes and their impacts on hydrology in the upstream region of the Salween River (USR) under two shared socioeconomic pathway (SSP) scenarios (SSP126 and SSP585) using a physically based cryosphere–hydrology model. We found that the climate would become warmer (0.2 ∘C per decade under SSP126 and 0.7 ∘C per decade under SSP585) and wetter (5 mm per decade under SPP126 and 27.8 mm per decade under SSP585) in the USR in the future under these two SSPs. In this context, the snowfall, snow cover, snow water equivalent, and snowmelt runoff are projected to exhibit significant decreasing trends during 1995–2100, and the decreases are projected to be most prominent in summer and autumn. The future (2021–2100) snowmelt runoff is projected to significantly increase in spring compared with the reference period (1995–2014), which would benefit the availability of water resources in the growing season. The annual total runoff would significantly increase in all of the future periods due to increased rainfall, which would increase the availability of water resources within the basin, but the high peak flow that occurs in summer may cause rain flooding with short duration and high intensity. Compared with the reference period (the contribution of snowmelt runoff to the total runoff was determined to be 17.5 %), the rain- and snow-dominated pattern of runoff would shift to a rain-dominated pattern after the near term (2021–2040) under SSP585, whereas it would remain largely unchanged under SSP126. Climate change would mainly change the pattern of the snowmelt runoff, but it would not change the annual hydrograph pattern (dominated by increased rainfall). These findings improve our understanding of the responses of cryosphere–hydrological processes under climate change, providing valuable information for integrated water resource management, natural disaster prevention, and ecological environmental protection at the Third Pole.

9Cr-F/M−xSi (x = 0–1.0 wt%) steels were fabricated through vacuum induction melting technique and processed by hot forging, hot rolling, normalizing and tempering, subsequently. Their microstructure and mechanical properties were characterized using OM, SEM, TEM, Vickers hardness tester and tensile tester. The steel has a typical ferrite/martensitic structure, with M23C6 and MX phases precipitated at the martensite lath boundary or in the lath. With Si content increasing, the average ultimate tensile strength (UTS) and hardness of 9Cr-F/M−xSi increase simultaneously from 678 MPa and 235 HV for 9Cr-F/M−0Si steel to 788 MPa and 265 HV for 9Cr-F/M−1.0Si steel, respectively, while the total elongation kept almost constant at 22.5%. The main strengthening mechanism is the solid solution strengthening due to silicon addition and the change in the carbide precipitation behavior caused no remarkable hardening contribution. These results can provide a reference for the composition design of structural materials for nuclear reactors.

SUMMARY: With the overselection of broiler body weight (BW) and breast muscle weight (BMW), the incidence of leg disease has been gradually increasing, which has seriously affected the welfare and industrial economic development of broilers. In this study, we used gait score and X-ray to evaluate the leg health of fast large white-feather broiler chickens at the age of 6 to 7 wk. The combined use of the 2 methods improved the detection rate and accuracy of diagnosing chicken leg disease. Moreover, more than 70% of chickens with abnormal gait scores also appeared abnormal on X-ray. At the same time, the 40-day BW, 42-day BMW, 28- to 40-day body weight gain (BWG) and tibial bone strength (BS) of the abnormal broilers were significantly lower than those of normal chickens. The 28- to 40-day feed conversion rate (FCR) significantly increased relative to normal chickens (P < 0.05). The comprehensive application of this method would be more suitable for evaluating of commercial broiler leg diseases, and providing a more complete set of in vitro evaluation criteria for broiler leg diseases.

A record-breaking severe ozone (O3) pollution episode occurred in the Pearl River Delta (PRD) in early autumn 2019 when the PRD was under the influence of a Pacific subtropical high followed by Typhoon Mitag. In this study, we analyzed the effects of meteorological and photochemical processes on the O3 concentration in the PRD during this episode by carrying out the Weather Research Forecast–Community Multiscale Air Quality (WRF-CMAQ) model simulations. Results showed that low relative humidity, high boundary layer height, weak northerly surface wind, and strong downdrafts were the main meteorological factors contributing to O3 pollution. Moreover, delayed sea breezes that lasted into the night would transport O3 from the sea back to the land and resulted in secondary O3 maxima at night. In addition, O3 and its precursors stored in the residual layer above the surface layer at night can be mixed down to the surface in the next morning, further enhancing the daytime ground-level O3 concentration on the following day. Photochemical production of O3, with a daytime average production rate of about 7.2 ppb h−1 (parts per billion), is found to be the predominate positive contributor to the O3 budget of the boundary layer (0–1260 m) during the entire O3 episode, while the horizontal and vertical transport fluxes are the dominant negative contributors. This O3 episode accounted for 10 out of the yearly total of 51 d when the maximum daily 8 h average (MDA8) O3 concentration exceeded the national standard of 75 ppb in the PRD in 2019. Based on these results, we propose that the enhanced photochemical production of O3 during the episode is a major cause of the most severe O3 pollution year since the official O3 observation started in the PRD in 2006. Moreover, since this O3 episode is a synoptic-scale phenomenon covering the entire eastern China, we also suggest that the enhanced photochemical production of O3 in this O3 episode is a major cause of the extraordinarily high O3 concentrations observed in eastern China in 2019.

An observation-based method (OBM) is developed to investigate the sensitivity of ozone formation to precursors during two persistent elevated ozone episodes observed at 77 stations in Guangdong. Average OH concentrations derived at the 77 stations between 08:00 and 13:00 local time stay within a narrow range of 2.5×106 to 5.5×106 cm−3 with a weak dependence on the NOx. These values are in good agreement with OH values observed at a rural station in the Pearl River Delta (PRD). They also agree well with a box model constrained by the ambient conditions observed during the two episodes. The OBM has been used to evaluate the ozone production efficiency, ε(NOx or volatile organic compound, VOC), defined as the number of O3 molecules produced per molecule of NOx (or VOC) oxidized. Average values of ε(NOx) and ε(VOC) determined by the OBM are 3.0 and 2.1 ppb ppb−1, respectively, and both compared well with values in previous studies. Approximately 67 % of the station days exhibit ozone formation sensitivity to NOx, and approximately 20 % of the station days are in the transitional regime sensitive to both NOx and VOC, and only approximately 13 % of the station days are sensitive to VOC. These results are in semi-quantitative agreement with the ozone formation sensitivity calculated by the box model constrained by ambient conditions observed during the two episodes. However, our OBM results differ from those of most previous investigations, which suggested that limiting the emission of VOC rather than NOx would be more effective in reducing ozone reduction in Guangdong.

Aiming at the urgent need for the development of space deployable antenna with large aperture and a high storage rate, a new configuration of a multifold rib modular deployable antenna mechanism is proposed. The overall structure scheme design of the antenna is carried out, and the structure composition of the module, the principle of structural topology transformation, and the scheme of mechanism deployment and locking are expounded. According to the shape and constraint relationship of the structure deployment and folded state, and based on basic theory of robotics, calculation and the analysis of key structural parameters and the included angle of rib units are carried out, and the parameterized mathematical model is established. The detailed structural design is carried out, and then the complex structures such as the center beam assembly, diagonal beam assembly, and outer beam assembly are introduced in detail. The kinematic simulation of the mechanism is carried out by using ADAMS software, and the variation law of the motion parameters is analyzed. Finally, the prototype of a hexagonal prism single-module principle is developed, and a deployment function test and verification are carried out. The simulation and test results show that the proposed mechanism can realize the motion change from folded to deployed, and no problems such as interference or clamping are observed in deployment, which verifies the correctness and the feasibility of the structural scheme and principle. Moreover, the proposed new configuration scheme not only retains the characteristics of modular structure, such as good universality and easy expansion, but also has a high storage rate and structure efficiency rate. The research results have a high reference value and relevance for basic theoretical research and the engineering application of a space deployable antenna.

Earthquake precursors and earthquake monitoring are always important in the earthquake research field, even if there is still debate about the existence of earthquake precursors. However, it is extremely difficult to observe the seismogenic environment of earthquakes directly. Laboratory rupture experiment is a useful technique to simulate and gain an insight into the complex mechanisms of earthquakes. Five marble samples with prefabricated cracks are used for uniaxial loading experiments to investigate whether there is a precursory signal before rock fracture and to simulate the rupture process of strike-slip fault. The existence of a precursory signal is confirmed by the coefficient of variation (CV) results, from which we can see two patterns which are known as seismicity acceleration and quiescence before an earthquake. Moreover, these CV findings are applied to determine the locations of large deformation sampling points on the rock surface at different loading stages. Similar results are obtained when we consider actual seismicity at the northern end of the San Andreas Fault in California, which provides crucial evidence to prove the existence of precursor characteristics. In this case, three kinds of seismic monitoring models are designed to find out how to monitor these characteristics more effectively.

Abstract Background Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients’ treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on daily image analyses. Methods Based on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC diagnosis using weakly labeled pathological whole-slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, > 14,680 WSIs, from > 9631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, the USA, and Germany. Results Our innovative AI tool consistently and nearly perfectly agreed with (average Kappa statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multicenters. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.988 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells. Conclusions This first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. It will drastically alleviate the heavy clinical burden of daily pathology diagnosis and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition.

Satellite observations (International Satellite Cloud Climatology Project (ISCCP), 1983–2009) of linear trends in cloud cover are compared to those in global precipitation (Global Precipitation Climatology Project (GPCP) pentad V2.2, 1983–2009), to investigate possible cause(s) of the linear trends in both cloud cover and precipitation. The spatial distributions of the linear trends in total cloud cover and precipitation are both characterized primarily by a broadening of the major ascending zone of Hadley circulation. Our correlation studies suggest that global warming, Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO) can explain 67 %, 49 % and 38 %, respectively, of the spatial variabilities in the linear trends in cloud cover, but causality is harder to establish. Further analysis of the broadening of the major ascending zone of Hadley circulation shows that the trend in global temperature, rather than those in AMO and PDO, is the primary contributor to the observed linear trends in total cloud cover and precipitation in 1983–2009. The underlying mechanism driving this broadening is proposed to be the moisture–convection–latent-heat feedback cycle under global warming conditions. The global analysis is extended by investigating connections between clouds and precipitation in China, based on a large number of long-running, high-quality surface weather stations in 1957–2005. This reveals a quantitative matching relationship between the reduction in light precipitation and the reduction in total cloud cover. Furthermore, our study suggests that the reduction in cloud cover in China is primarily driven by global temperature; PDO plays a secondary role, while the contribution from AMO and Niño3.4 is insignificant, consistent with the global analysis.

As an effective technique to improve the rainfall forecast, data assimilation plays an important role in meteorology and hydrology. The aim of this study is to explore the reasonable use of Doppler radar data assimilation to correct the initial and lateral boundary conditions of the numerical weather prediction (NWP) systems. The Weather Research and Forecasting (WRF) model is applied to simulate three typhoon storm events on the southeast coast of China. Radar data from a Doppler radar station in Changle, China, are assimilated with three-dimensional variational data assimilation (3-DVar) model. Nine assimilation modes are designed by three kinds of radar data and at three assimilation time intervals. The rainfall simulations in a medium-scale catchment, Meixi, are evaluated by three indices, including relative error (RE), critical success index (CSI), and root mean square error (RMSE). Assimilating radial velocity at a time interval of 1 h can significantly improve the rainfall simulations, and it outperforms the other modes for all the three storm events. Shortening the assimilation time interval can improve the rainfall simulations in most cases, while assimilating radar reflectivity always leads to worse simulations as the time interval shortens. The rainfall simulations can be improved by data assimilation as a whole, especially for the heavy rainfall with strong convection. The findings provide references for improving the typhoon rainfall forecasts at catchment scale and have great significance on typhoon rainstorm warning.

Methane emission is not included in the current breeding goals for dairy cattle mainly due to the expense and difficulty in obtaining sufficient data to generate accurate estimates of the relevant traits. While several models have been developed to predict methane emission from milk spectra using reference methane data obtained by the respiration chamber, SF6 and sniffer methods, the prediction of methane emission from milk mid-infrared (MIR) spectra using reference methane data collected by the GreenFeed system has not yet been explored. Methane emission was monitored for 151 cows using the GreenFeed system. Prediction models were developed for daily and average (for the trial period of 12 or 14 days) methane production (g/d), yield (g/kg DM intake (DMI)) and intensity (g/kg of fat- and protein-corrected milk) using partial least squares regression. The predictions were evaluated in 100 repeated validation cycles, where animals were randomly partitioned into training (80%) and testing (20%) populations for each cycle. The best performing model was observed for average methane intensity using MIR, parity and DMI with validation coefficient of determination (R2val) and RMSE of prediction of 0.66 and 4.7 g/kg of fat- and protein-corrected milk, respectively. The accuracy of the best models for average methane production and average methane yield were poor (R2val = 0.28 and 0.12, respectively). A lower accuracy of prediction was observed for methane intensity and production (R2val = 0.42 and 0.17) when daily records were used while prediction for methane yield was comparable to that for average methane yield (R2val = 0.16). Our results suggest the potential to predict methane intensity with moderate accuracy. In this case, prediction models for average methane values were generally better than for daily measures when using the GreenFeed system to obtain reference methane emission measurements.

Abstract LUX-ZEPLIN (LZ) is a second-generation direct dark matter experiment with spin-independent WIMP-nucleon scattering sensitivity above $${1.4 \times 10^{-48}}\, {\hbox {cm}}^{2}$$ 1.4 × 10 - 48 cm 2 for a WIMP mass of $${40}\, \hbox {GeV}/{\hbox {c}}^{2}$$ 40 GeV / c 2 and a $${1000}\, \hbox {days}$$ 1000 days exposure. LZ achieves this sensitivity through a combination of a large $${5.6}\, \hbox {t}$$ 5.6 t fiducial volume, active inner and outer veto systems, and radio-pure construction using materials with inherently low radioactivity content. The LZ collaboration performed an extensive radioassay campaign over a period of six years to inform material selection for construction and provide an input to the experimental background model against which any possible signal excess may be evaluated. The campaign and its results are described in this paper. We present assays of dust and radon daughters depositing on the surface of components as well as cleanliness controls necessary to maintain background expectations through detector construction and assembly. Finally, examples from the campaign to highlight fixed contaminant radioassays for the LZ photomultiplier tubes, quality control and quality assurance procedures through fabrication, radon emanation measurements of major sub-systems, and bespoke detector systems to assay scintillator are presented.

The GaoFen-7 (GF-7) satellite is successfully launched on November 3, 2019, and its laser altimeter system is officially and firstly employed as the main payload for earth observations in China, which includes two sets of laser altimeters and laser footprint cameras. The Laser Footprint Image (LFI) is used to capture laser spots on the ground. In order to make up for the shortcomings of high cost field work for the traditional laser altimeter ground detector-based calibration method, this paper proposes a novel laser altimeter calibration method based on LFI. Firstly, the spaceborne laser calibration model and the Laser Footprint Camera (LFC) geolocation model are established. Secondly, the image coordinates of laser spot centroid are extracted from LFI, and the ground location of is obtained by ray intersecting with the reference Digital Surface Model (DSM). Finally, the centroid of laser spot is considered as Ground Control Point (GCP), and the pointing bias of GF-7 laser altimeter is calibrated by the Least Squares Estimation (LSE). The ALOS Global Digital Surface Model “ALOS World 3D-30m” (AW3D30) is used to evaluate the elevation accuracy of GF-7 laser altimeter before and after the calibration. The results indicate that elevation accuracy of the GF-7 laser altimeter is improved significantly after calibration. The proposed method can be effectively applied for high-frequency geometric calibration of GF-7 laser altimeter.

Soil moisture is one of key environmental variables that affect vegetation cover and energy exchange between the land surface and the atmosphere. Satellite remote sensing technology can provide information for monitoring large-scale soil moisture dynamics quickly. The temperature vegetation dryness index (TVDI) acts as an effective indicator of inferring soil moisture status which is calculated according to the empirical parameterization of composed of the land surface temperature (LST) and the normalized difference vegetation index (NDVI) characteristic space. In this paper, the MODIS TVDI was calculated based on MODIS LST product (MOD11A2, 1 km) and NDVI data (derived from MOD09A1, 500m). Meanwhile, LST and NDVI from Landsat8 OLI images were estimated to obtain Landsat-based TVDI. Then, a Kalman filter algorithm was used to simulate TVDI time series data with 30m resolution and a revisit period of 8 days combining TVDI derived from Landsat and MODIS data. We selected the west of the Songnen Plain, China as the test area and high quality cloudy-free images during growing season (April to October) of 2018 as the input data. The predicted TVDI time series data of medium resolution not only improved the temporal resolution to capture the changes at fine scale within a short period, but also made up for the deficiency of low spatial resolution MODIS data. The results show that it is feasible to generate medium or high resolution TVDI time series data by applying different remote sensing data by Kalman filtering algorithm.

The recent paper by Shen et al. (2018) (referred to hereafter as SHEN) made a sweeping statement on the winter haze pollution in Beijing by claiming “Insignificant effect of climate change on winter haze in Beijing”. We argue that the paper contains three serious flaws. Either one of the three flaws can nullify the claim of SHEN.

Abstract Background Workplace presenteeism is common and leads to the spread of infectious diseases. Previous reviews have focused on presenteeism in relation to general physical or mental ill health. In this systematic review we identified the prevalence of, and reasons and risk factors for, presenteeism in relation to an infectious illness. Method We searched Medline, Scopus, Web of Science, PsycINFO and PsycARTICLES with terms relating to infectious illnesses and presenteeism at the work place or school; reference lists of relevant articles were also hand-searched. Result Our search yielded 3580 papers after deduplication. After title, abstract and full text screening, 23 papers reporting on 24 studies were included. Twenty-three studies were cross-sectional studies and one was prospective. The quality of included studies was relatively poor due to problems such as sampling and non-response bias. Presenteeism prevalence ranged from 35 to 97%. Self-reported reasons for presenteeism fell into three main themes: 1. Organisational factors (organisational policy, presenteeism culture, disciplinary action), 2. Job characteristics (lack of cover, professionalism, job demand), and 3. Personal reasons (burden on colleagues, colleague perceptions, threshold of sickness absence and financial concerns). Statistical risk factors fell into four themes: 1. Sociodemographic, 2. Health, 3. Influenza-related behaviour, and 4. Employment characteristics. Most of the risk factors had insufficient evidence to allow us to draw any firm conclusions, and evidence regarding gender and age was inconsistent. The risk factor with the most consistent findings concerned occupation type, suggesting that those who worked in the healthcare sector, and specifically physicians, were at a higher risk of infectious illness presenteeism. Conclusion Infectious illness presenteeism is common. To address the public health consequences, organisations should focus on promoting a positive working culture and developing sickness absence policies that reduce presenteeism. Further research is needed in non-health sector organisations and schools to identify risk factors related to different organisations, which can then be used to tailor interventions at the organisational and individual level.

Understanding the effectiveness of air pollution control policies is important for future policy making. China has implemented strict air pollution control policies since the 11th Five-Year Plan (FYP). There is still a lack of overall evaluation of the effects of air pollution control policies on PM2.5 pollution improvement in China since the 11th FYP. In this study, we aimed to assess the effects of air pollution control policies from 2005 to 2017 on PM2.5 using satellite remote sensing. We used the satellite-derived PM2.5 of 2005–2013 from one of our previous studies. For the data of 2014–2017, we developed a two-stage statistical model to retrieve satellite PM2.5 data using the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD), assimilated meteorology, and land use data. The first stage is a day-specific linear mixed effects (LME) model and the second stage is a generalized additive model (GAM). Results show that the Energy Conservation and Emissions Reduction (ECER) policy, implemented in the 11th FYP period and focused on SO2 emissions control, had co-benefits with PM2.5 reductions. The increasing trends of PM2.5 pollution (1.88 and 3.14 µg m−3 year−1 for all of China and the Jingjinji region in 2004–2007, p<0.005) were suppressed after 2007. The overall PM2.5 trend for all of China was −0.56 µg m−3 year−1 with marginal significance (p=0.053) and PM2.5 concentrations in the Pearl River Delta region had a big drop (−4.81 µg m−3 year−1, p<0.001) in 2007–2010. The ECER policy during the 12th FYP period was basically an extension of the 11th FYP policy. PM2.5 is a kind of composite pollutant which comprises primary particles and secondary particles such as sulfate, nitrate, ammonium, organic carbon, elemental carbon, etc. Since the ECER policy focused on single-pollutant control, it had shown great limitation for PM2.5 reductions. The PM2.5 concentrations did not decrease from 2010 to 2013 in polluted areas (p values of the trends were greater than 0.05). Therefore, China implemented two stricter policies: the 12th FYP on Air Pollution Prevention and Control in Key Regions (APPC-KR) in 2012, and the Action Plan of Air Pollution Prevention and Control (APPC-AP) in 2013. The goal of air quality improvement (especially PM2.5 concentration improvement) and measures for multi-pollutant control were proposed. These policies led to dramatic decreases in PM2.5 after 2013 (−4.27 µg m−3 year−1 for all of China in 2013–2017, p<0.001).

Magnetic reconnection processes in the near-Earth magnetotail can be highly three-dimensional (3-D) in geometry and dynamics, even though the magnetotail configuration itself is nearly two-dimensional due to the symmetry in the dusk–dawn direction. Such reconnection processes can be induced by the 3-D dynamics of nonlinear ballooning instability. In this work, we explore the global 3-D geometry of the reconnection process induced by ballooning instability in the near-Earth magnetotail by examining the distribution of quasi-separatrix layers associated with plasmoid formation in the entire 3-D domain of magnetotail configuration, using an algorithm previously developed in the context of solar physics. The 3-D distribution of quasi-separatrix layers (QSLs) as well as their evolution directly follow the plasmoid formation during the nonlinear development of ballooning instability in both time and space. Such a close correlation demonstrates a strong coupling between the ballooning and the corresponding reconnection processes. It further confirms the intrinsic 3-D nature of the ballooning-induced plasmoid formation and reconnection processes, in both geometry and dynamics. In addition, the reconstruction of the 3-D QSL geometry may provide an alternative means of identifying the location and timing of 3-D reconnection sites in the magnetotail from both numerical simulations and satellite observations.

He-charged FeCrNi films were prepared by radio frequency (RF) magnetron sputtering technique, and helium desorption behavior, growth process and evolution mechanism of helium bubbles in FeCrNi film were studied by thermal desorption spectroscopy (TDS) spectra combined with microstructure analysis under different annealing treatments. Three different He desorption peaks were observed in TDS spectra, which can be related to the release of interstitial helium atoms from the near-surface regions, helium-vacancy clusters with different sizes in FeCrNi film. TEM analysis showed that helium bubbles can be uniformly distributed in He-charged FeCrNi nanocrystalline films. Under annealing treatments at different temperatures, the size of helium bubbles obviously increases with increasing annealing temperature, corresponding to the significantly decrease in the number density of helium bubbles in FeCrNi films, and the growth mechanism of helium bubbles was suggested to originate from the migration and coalescence (MC) mechanism. Key words: Magnetron sputtering, FeCrNi film, Thermal desorption, He bubbles

Tungsten is considered as the most promising material for plasma facing components (PFCs) in the magnetic confinement fusion devices, due to its high melting temperature, high thermal conductivity, low swelling, low tritium retention and low sputtering yield. However, the brittleness, poor machinability and low strength at high-temperatures of tungsten limits its application. Focusing on this issue, various W alloys with enhanced mechanical properties have been developed over recent decades. Among them, the W-ZrC alloys exhibit high strength, high ductility, low ductile-to-brittle transition temperature, good high-temperature stability, excellent resistance to thermal shock and low H/He plasma etching, making it one of the most promising candidate plasma facing materials (PFMs) for the future fusion devices. Therefore, the R&D experience of W-ZrC materials for PFCs, including the design idea, optimization of composition, fabrication technology (from powder metallurgy processing to hot working, from small specimen to large-sized bulk material), microstructure and performance (mechanical properties, thermal conductivity, resistance to thermal shocks and to H/He irradiation, H isotope retention behaviors) were reviewed in this paper. Key words: Plasma-facing materials, Tungsten, ZrC, Microstructure, Mechanical property

Remote sensing automatic interpretation symbol (RSAIS) is an inexpensive and fast method in providing precise in-situ information for image interpretation and accuracy. This study designed a scientific and precise RSAIS data characterization method, as well as a distributed and cloud architecture massive data storage method. Additionally, it introduced an offline and online data update mode and a dynamic data evaluation mechanism, with the aim to create an efficient approach for RSAIS big data construction. Finally, a national RSAIS database with more than 3 million samples covering 86 land types was constructed during 2013–2015 based on the National Geographic Conditions Monitoring Project of China and then annually updated since the 2016 period. The RSAIS big data has proven to be a good method for large scale image interpretation and field validation. It is also notable that it has the potential to solve image automatic interpretation with the assistance of deep learning technology in the remote sensing big data era.

Imaging spectrometer obtains or indirectly obtains the spectral information of the ground surface feature while obtaining the target image, which makes the imaging spectroscopy has a prominent advantage in fine characterization of terrain features, and is of great significance for the study of geoscience and other related disciplines. Since the interference data obtained by interferometric imaging spectrometer is intermediate data, which must be reconstructed to achieve the high quality spectral data and finally used by users. The difficulty to restrict the application of interferometric imaging spectroscopy is to reconstruct the spectrum accurately. Based on the original image acquired by Large Aperture Static Imaging Spectrometer as the input, this experiment selected the pixel that is identified as crop by artificial recognition, extract and preprocess the interferogram to recovery the corresponding spectrum of this pixel. The result shows that the restructured spectrum formed a small crest near the wavelength of 0.55 μm with obvious troughs on both sides. The relative reflection intensity of the restructured spectrum rises abruptly at the wavelength around 0.7 μm, forming a steep slope. All these characteristics are similar with the spectral reflection curve of healthy green plants. It can be concluded that the experimental result is consistent with the visual interpretation results, thus validating the effectiveness of the scheme for interferometric imaging spectrum reconstruction proposed in this paper.

Using GPS technology to retrieve atmospheric water vapor is a new water vapor detection method, which can effectively compensate for the shortcomings of conventional water vapor detection methods, to provide high-precision, large-capacity, near real-time water vapor information. In-depth study of ground-based GPS detection of atmospheric water vapor technology aims to further improve the accuracy and practicability of GPS inversion of water vapor and to explore its ability to detect atmospheric water vapor information to better serve the meteorological services. In this paper, the influence of the setting parameters of initial station coordinates, satellite ephemeris and solution observation on the total delay accuracy of the tropospheric zenith is discussed based on the observed data. In this paper, the observations obtained from the observation network consisting of 8 IGS stations in China in June 2013 are used to inverse the water vapor data of the 8 stations. The data of Wuhan station is further selected and compared with the data of Nanhu Sounding Station in Wuhan The error between the two data was between -6mm-6mm, and the trend of the two was almost the same, the correlation reached 95.8 %. The experimental results also verify the reliability of ground-based GPS inversion of water vapor technology.