Search

Global precipitation products (GPPs) are vital in weather forecasting, efficient water management, and monitoring floods and droughts. However, the precision of these datasets varies considerably across different climatic regions and topographic conditions. Therefore, the accuracy assessment of the precipitation dataset is crucial at the local scale before its application. The current study initially compared the performance of recently modified and upgraded precipitation datasets, including Climate Research Unit Time-Series (CRU TS v4.08), fifth-generation ERA5-Land (ERA-5), Integrated Multi-satellite Retrievals for GPM (IMERG) final run (IMERG v07B), and Multi-Source Weighted-Ensemble Precipitation (MSWEP v2.8), against ground observations on the provincial basis across Pakistan from 2003 to 2020. Later, the study area was categorized into four regions based on the elevation to observe the impact of elevation gradients on GPPs' skills. The monthly and seasonal precipitation estimations of each product were validated against in situ observations using statistical matrices, including the correlation coefficient (CC), root mean square error (RMSE), percent of bias (PBias), and Kling–Gupta efficiency (KGE). The results reveal that IMERG7 consistently outperformed across all the provinces, with the highest CC and lowest RMSE values. Meanwhile, the KGE (0.69) and PBias (−0.65%) elucidated, comparatively, the best performance of MSWEP2.8 in Sindh province. Additionally, all the datasets demonstrated their best agreement with the reference data toward the southern part (0–500 m elevation) of Pakistan, while their performance notably declined in the northern high-elevation glaciated mountain regions (above 3000 m elevation), with considerable overestimations. The superior performance of IMERG7 in all the elevation-based regions was also revealed in the current study. According to the monthly and seasonal scale evaluation, all the precipitation products except ERA-5 showed good precipitation estimation ability on a monthly scale, followed by the winter season, pre-monsoon season, and monsoon season, while during the post-monsoon season, all the datasets showed weak agreement with the observed data. Overall, IMERG7 exhibited comparatively superior performance, followed by MSWEP2.8 at a monthly scale, winter season, and pre-monsoon season, while MSWEP2.8 outperformed during the monsoon season. CRU TS showed a moderate association with the ground observations, whereas ERA-5 performed poorly across all the time scales. In the current scenario, this study recommends IMERG7 and MSWEP2.8 for hydrological and climate studies in this region. Additionally, this study emphasizes the need for further research and experiments to minimize bias in high-elevation regions at different time scales to make GPPs more reliable for future studies. [ABSTRACT FROM AUTHOR]

Understanding the intricate relationship between drought stress and corn yield is crucial for ensuring food security and sustainable agriculture in the face of climate change. This study investigates the subtle effects of drought stress on corn physiological, morphological, and spectral characteristics at different growth stages, in order to construct a new drought index to characterize drought characteristics, so as to provide valuable insights for maize recovery mechanism and yield prediction. Specific conclusions are as follows. Firstly, the impact of drought stress on corn growth and development shows a gradient effect, with the most significant effects observed during the elongation stage and tasseling stage. Notably, Soil and Plant Analyzer Development (SPAD) and Leaf Area Index (LAI) are significantly affected during the silking stage, while plant height and stem width remain relatively unaffected. Secondly, spectral feature analysis reveals that, from the elongation stage to the silking stage, canopy reflectance exhibits peak–valley variations. Drought severity correlates positively with reflectance in the visible and shortwave infrared bands and negatively with reflectance in the near-infrared band. Canopy spectra during the silking stage are more affected by moderate and severe drought stress. Thirdly, LAI shows a significant positive correlation with yield, indicating its reliability in explaining yield variations. Finally, the yield-related drought index (YI) constructed based on Convolutional Neural Network (CNN), Random Forest (RF) and Multiple Linear Regression (MLR) methods has a good effect on revealing drought characteristics (R = 0.9332, p < 0.001). This study underscores the importance of understanding corn responses to drought stress at various growth stages for effective yield prediction and agricultural management strategies. [ABSTRACT FROM AUTHOR]

PTFE coatings were deposited on YT15 carbide substrates using spray technology. A series of examinations were conducted, including the use of surface and cross-section micrographs to analyze the structural integrity of the coatings. The surface roughness, the adhesion force between the PTFE coatings and the carbide substrate, and the micro-hardness of the coated carbide were also evaluated. Additionally, the friction and wear behaviors were assessed through dry sliding friction tests against WC/Co balls. The test results indicated that while the PTFE-coated carbide exhibited a rougher surface and reduced micro-hardness, it also demonstrated a significant reduction in surface friction and adhesive wear. These findings suggest that the PTFE coatings enhance the overall wear resistance of the carbides. The lower surface hardness and shear strength of the coatings influenced the friction performance, leading to specific wear failure mechanisms, such as abrasion wear, coating delamination, and flaking. Overall, the deposition of PTFE coatings on carbide substrates presents a promising strategy to enhance their friction and wear performance. This approach not only improves the durability of carbide materials but also offers potential applications in industries where reduced friction and wear are critical for performance. [ABSTRACT FROM AUTHOR]

Myxomycete communities in Taxodium distichum forests and the influence of substrates and seasons on myxomycete diversity were studied. In total, 80 substrates (40 living barks and 40 litters) were collected from the sample site in winter, spring, summer and autumn of 2023, followed by moist chamber cultures. A total of 733 records representing 34 myxomycete species in 21 genera were obtained. Arcyria cinerea and six other species were abundant (A). Six species were common (C), six species were occasional (O), and fifteen species were rare (R). Species diversity (H' = 2.04 ± 0.20) of corticolous communities was significantly higher than that (H' = 1.49 ± 0.31) of litter-inhabiting communities (p < 0.05). Nonmetric Multidimensional Scaling (NMDS) analyses showed significant differences between these two communities (p < 0.01). Species diversity was slightly higher in summer and autumn compared to winter and spring. NMDS analyses indicated no significant differences among seasonal communities. Redundancy Analysis showed that the corticolous species were closely related to wind speed (p < 0.05), while litter-inhabiting species were mainly determined by temperature (p < 0.05). The study revealed that substrates and seasons influenced myxomycete communities in T. distichum forests, with the communities being particularly affected by the substrates in which they live. [ABSTRACT FROM AUTHOR]

To calculate the carrying capacity of spherical hydrostatic bearings, a numerical calculation model was presented. The influence law of dynamic pressure effect on the carrying characteristics of liquid hydrostatic spherical bearings is revealed. Under general working conditions, the dynamic pressure effect caused by the radial eccentricity of the bearing has little influence on the bearing load characteristics parameters; when the minimum width of the gap between the sealing edge is very small and the bearing rotational speed is high, the dynamic pressure effect is more obvious. [ABSTRACT FROM AUTHOR]

Accurate monitoring of crop drought thresholds at different growth periods is crucial for drought monitoring. In this study, the canopy temperature (Tc) of winter wheat ('Weilong 169' variety) during the three main growth periods was extracted from high-resolution thermal and multispectral images taken by a complete unmanned aerial vehicle (UAV) system. Canopy-air temperature difference (ΔT) and statistic Crop Water Stress Index (CWSIsi) indicators were constructed based on Tc. Combined experiment data from the field and drought thresholds for the ΔT and CWSIsi indicators for different drought levels at three main growth periods were monitored. The results showed a strong correlation between the Tc extracted using the NDVI-OTSU method and ground-truth temperature, with an R2 value of 0.94. The CWSIsi was more stable than the ΔT index in monitoring the drought level affecting winter wheat. The threshold ranges of the CWSIsi for different drought levels of winter wheat at three main growth periods were as follows: the jointing–heading period, where the threshold ranges for normal, mild drought, moderate drought, and severe drought are <0.30, 0.30–0.42, 0.42–0.48, and >0.48, respectively; the heading–filling period, where the threshold ranges for normal, and mild, moderate, and severe drought are <0.33, 0.33–0.47, 0.44–0.53, and >0.53, respectively; and the filling–maturation period, where the threshold ranges for normal, mild drought, moderate drought, and severe drought are <0.41, 0.41–0.54, 0.54–0.59, and >0.59, respectively. The UAV thermal threshold method system can improve the accuracy of crop drought monitoring and has considerable potential in crop drought disaster identification. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) data can provide guidance for decision-makers in fields such as drought monitoring and irrigation management. Soil Moisture Active Passive (SMAP) satellite offers sufficient spatial resolution for global-scale applications, but its utility is limited in regional areas due to its lower spatial resolution. To address this issue, this study proposed a downscaling framework based on the Stacking strategy. The framework integrated extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost) to generate 1 km resolution SM data using 15 high-resolution factors derived from multi-source datasets. In particular, to test the influence of terrain partitioning on downscaling results, Anhui Province, which has diverse terrain features, was selected as the study area. The results indicated that the performance of the three base models varied, and the developed Stacking strategy maximized the potential of each model with encouraging downscaling results. Specifically, we found that: (1) The Stacking model achieved the highest accuracy in all regions, and the performance order of the base models was: XGBoost > CatBoost > LightGBM. (2) Compared with the measured SM at 87 sites, the downscaled SM outperformed other 1 km SM products as well as the downscaled SM without partitioning, with an average ubRMSE of 0.040 m3/m3. (3) The downscaled SM responded positively to rainfall events and mitigated the systematic bias of SMAP. It also preserved the spatial trend of the original SMAP, with higher levels in the humid region and relatively lower levels in the semi-humid region. Overall, this study provided a new strategy for soil moisture downscaling and revealed some interesting findings related to the effectiveness of the Stacking model and the impact of terrain partitioning on downscaling accuracy. [ABSTRACT FROM AUTHOR]

The nonlinear pressure–flux relationship in the xylem of a conifer is attributed to the fluid–solid interaction within the bordered pits. However, the fluid–solid interactions between the torus–margo structure and the water flow within the pit lack comprehensive understanding. Herein, a fluid–solid interaction model was developed including the deformation of the torus–margo and the flow of water transportation. Nine pit samples were reconstructed using scanning electron microscopy (SEM) and transmission electron microscopy (TEM) pictures. Fluid–solid coupling models for pits in the stems of oriental arborvitae (Platycladus orientalis) were developed. The deflection of the torus was roughly proportional to the pressure difference between adjacent tracheids, while the pit resistance exhibited a considerable nonlinear increase. From 250 to 1750 Pa, the pit resistance increased from 4.1466 × 1016 Pa·s/m3 to 8.8344 × 1016 Pa·s/m3. The pit resistance decreased, and the pit's ability to regulate water flow enhanced when the pit diameter increased. Both the pit resistance and the pit's ability to regulate water decreased when the pit depth increased. The decrease in Young's modulus for the margo promoted the nonlinear pressure–flux relationship in bordered pits. The findings provide theoretical evidence for the nonlinear relationship between pressure and flux in bordered pits, as well as for the prevention of gas bubble transit through a bordered pit during tracheid cavitation. The passive hydraulic regulation of bordered pits could increase flow resistance and reduce the water flow rate in the xylem, inhibiting tree transpiration. [ABSTRACT FROM AUTHOR]

Bordered pits in conifers have been recognized as a significant evolutionary characteristic that served to impede the spread of embolisms between tracheids. Nevertheless, there was a lack of comprehensive understanding regarding the mechanical properties of the torus and the pit border in relation to the formation of contact seals in aspirated pits. A solid mechanics model was developed to study aspirated bordered pits, incorporating the elastic deformation of the torus–margo structure and the contact behavior between the torus and the pit border. Ten pit samples were reconstructed using transmission electron microscopy (TEM) and scanning electron microscopy (SEM) pictures in order to develop mechanical models for pits in the stems of Platycladus orientalis. Due to the limited contact area on the torus surface, the contact pressure between the torus and the pit border exceeded the air pressure in aspirated bordered pits. The external force and the duration required to seal pits decreased when the pit diameter increased and the pit depth decreased. The augmentation of the torus and margo mass necessitated a lengthier duration for the closure of cavities. The findings presented in this study offered theoretical support for the capillary-seeding hypothesis. The aspiration in bordered pits mechanically depended on the structural and material characteristics of the torus and margo. [ABSTRACT FROM AUTHOR]

Here, we report a Na-promoted FeCu-based catalyst with excellent liquid hydrocarbon selectivity and catalytic activity. The physiochemical properties of the catalysts were comprehensively characterized by various characterization techniques. The characterization results indicate that the catalytic performance of the catalysts was closely related to the nature of the metal promoters. The Na-AlFeCu possessed the highest CO2 conversion due to enhanced CO2 adsorption of the catalysts by the introduction of Al species. The introduction of excess Mg promoter led to a strong methanation activity of the catalyst. Mn and Ga promoters exhibited high selectivity for light hydrocarbons due to their inhibition of iron carbides generation, resulting in a lack of chain growth capacity. The Na-ZnFeCu catalyst exhibited the optimal C5+ yield, owing to the fact that the Zn promoter improved the catalytic activity and liquid hydrocarbon selectivity by modulating the surface CO2 adsorption and carbide content. Carbon dioxide (CO2) hydrogenation to liquid fuel is considered a method for the utilization and conversion of CO2, whereas satisfactory activity and selectivity remains a challenge. This method provides a new idea for the catalytic hydrogenation of CO2 and from there the preparation of high-value-added products. [ABSTRACT FROM AUTHOR]

Although Fe-based catalysts have made significant progress in Fischer–Tropsch synthesis, the effect of residual sodium on the structural properties and catalytic performance of Fe-based catalysts has been controversial. Herein, we report the positive role of residual sodium in the structural properties and performance of FeAlNa catalysts for olefins synthesis from syngas. Meanwhile, the as-prepared catalysts were characterized by the multiple characterization technique to reveal the positive role of residual sodium on the structural properties. The characterization results revealed that the residual sodium improved the reduction behavior of Fe species and adsorption ability of CO, and inhibited the secondary hydrogenation due to its weak adsorption ability of H2. Moreover, the residual sodium inhibited the interaction between Fe and Al. Importantly, a high olefins/paraffins ratio of 6.19 and low CH4 selectivity of 12.8% were achieved on the residual sodium modified FeAlNa catalyst. An in-depth understanding of the structural properties and catalytic performance of residual sodium on FeAl-based catalysts can provide a theoretical basis for the development of novel efficient catalysts and large-scale applications for olefins synthesis from syngas. [ABSTRACT FROM AUTHOR]

To improve the surface friction characteristics of cemented carbide, a Cr coating was deposited on cemented carbide substrate with the multiple arc plating technique. The surface and cross-section micrographs, adhesion force and micro-hardness of the Cr-coated cemented carbide were tested. The anti-friction and anti-wear behaviors of cemented carbide with and without Cr coating were investigated via the sliding friction test against a WC/Co ball. The tribological properties of cemented carbide were examined using a scanning electron microscope and energy dispersive X-ray analysis. The test results showed that Cr-coated cemented carbide possessed good adhesion properties and lower hardness. The average coefficient of friction for Cr-coated cemented carbide was reduced by 10–20% compared with that of an uncoated one. The primary wear modes of the Cr-coated sample were delamination of coating, flaking and abrasion wear. It can be found that the preparation of a Cr coating is an effective way to enhance the friction and wear performance of traditional cemented carbide. [ABSTRACT FROM AUTHOR]

This paper focuses on the current urgent demand for the accurate measurement of forest inventory variables in the fields of forestry carbon sink measurement, ecosystem research, and forest resource conservation, and proposes the use of images to construct a three-dimensional measurement model of forest inventory variables, which is a new method to realize the automatic extraction of forest inventory variables. This method obtains sample site information by using high-definition images taken in the forest by a smartphone, which significantly improves the field operation efficiency and simple operation, and effectively alleviates the problems of long field operation times, complicated operations, and expensive equipment used by current methods for obtaining forest inventory variables. We propose to optimize the Eps parameters of the DBSCAN algorithm based on the MVO algorithm for point cloud clustering to obtain single wood point clouds, which improves the accuracy of the model and can effectively solve the problem of large interference from human factors. The scale coefficients of the image and the actual model are obtained by the actual measurement of tree height and diameter at breast height to complete the construction of the three-dimensional measurement model of the stand and are then combined with the AdQSM algorithm to realize the automatic extraction of forest inventory variables, which provides a new interdisciplinary method for the comprehensive extraction of forest inventory variables. The accuracy of the model measured in the experimental sample site of Fraxinus mandshurica Rupr was as follows: the absolute error of tree height measurement ranged from 0.05 to 0.37 m, the highest relative error of measurement was 2.03%, and the average relative error was 1.53%; for the absolute error of diameter at breast height, measurement ranged from 0.007 to 0.057 m, the highest relative error of measurement was 7.358%, and the average relative error was 3.616%. The method proposed in this study can be directly applied to the process of acquiring and visualizing the variables of forest inventory in the field of ecological research, which has good flexibility and can meet individual research needs. [ABSTRACT FROM AUTHOR]

Ground validation of remote sensing soil moisture requires ground measurements corresponding to the pixel scale. To date, there is still a lack of simple, fast and reasonable methods for soil moisture measurement at pixel scale between point measurements and remote sensing observations. In this study, a measurement method of soil moisture using ground-penetrating radar (GPR) was proposed for pixel scale. We used a PulseEKKOTM PRO GPR system with 250 MHz antennas to measure soil moisture by Fixed Offset (FO) method in four 30 × 30 m2 plots chosen from the desert steppe. This study used a random combination method to analyze soil moisture measurements acquired by different numbers of GPR survey lines. The results showed that two survey lines of GPR would be sufficient under confidence level of 90% with the relative error of 7%, and four survey lines of GPR would be eligible under confidence level of 95% with the relative error of 5% for each plot. GPR measurement can reproduce the spatial distribution of soil moisture with higher resolution and smaller error, especially when two and four survey lines are designed in cross shape and grid shape, respectively. The method was applied to ground validation for the soil moisture from Landsat 8, showing the advantages of stable relative errors, less contingency and reliable evaluation when compared to point measurements. This method is fast and convenient and not limited to a certain pixel, and thus largely benefits the scale matching of remote sensing results and field measurements in ground validation. [ABSTRACT FROM AUTHOR]

As the country hosting the most significant number of refugees in Europe, Germany's Merkel government's refugee policy has been repeatedly adjusted and plagued by inconsistencies and management failures. What factors have influenced the formation of the Merkel government's refugee policy and its two shifts? The traditional rational decision-making model does not effectively explain government decisions' motivation in complex challenges. This article develops a framework for analyzing "bounded rational decision-making". It identifies the three factors that influenced the Merkel government's refugee policy: (a) strict border controls, (b) welcome culture, and (c) welcome culture under restrained policies. It explains the process and logic of their development. Based on the theory of "bounded rational decision making", the article examines the "key events and problem identification", "goal selection", "national interests", and "political psychology". We found that in the early stages of the refugee crisis, (a) public opinions and pressure, (b) the pursuit of a positive national image, and (c) a shift in the leader's psychology led Merkel to shift away from a pragmatic and rational course. These factors led to the first shift in German refugee policy from "hesitation" to a "welcoming culture". At the end of 2015, however, the shortcomings of the irrational policy were quickly revealed, and the frequency of refugee-related social problems reversed German public opinion, forcing Merkel's government to seek a balance between humanitarianism and national interests. As a result, refugee policy was adjusted for the second time, returning to a strict refugee examination system. The development of the Merkel government's refugee policy exposed the shortcomings of the traditional crisis decision-making model. Moreover, it provided a new perspective for rethinking the governance of the refugee crisis. [ABSTRACT FROM AUTHOR]

In this paper, Aaiming at the problem of simultaneous localization mapping (SLAM) for mobile robots, a limited-augmentation innovation superposition (LAIS) is proposed to solve the problems occurring in SLAM. By extending the single-time innovation superposition to multi-time innovation, the error accumulation during the movement of mobile robots is reduced and the accuracy of the algorithm is improved. At the same time, when the number of feature points observed by the sensor exceeds the threshold, the sensor range is restricted. Therefore, only the qualified feature points are added to the system state vector, which reduces the calculation amount of the algorithm and improves the running speed. Simulation results show that compared with other algorithms, LAIS has higher accuracy and higher running speed in environmental maps with a different number of landmark points. [ABSTRACT FROM AUTHOR]

Ground validation of remote-sensing soil moisture requires ground measurements corresponding to the pixel scale. We propose a method to map spring irrigation areas using historical meteorological data, the main crop phenological characteristics, irrigation regimes, and MODIS land-surface temperature (LST) products [[7]]. Water resources' management at a high spatial and temporal resolution calls for data support at the relevant scales, which has long been hindered by the availability of high-resolution data. [Extracted from the article]

This study was conducted in a fluidized-bed incineration plant, evaluating the formation, emission and flux of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) from industrial-organic-solid-waste (IW) incineration. The results revealed that both the total (or I-TEQ) concentrations of toxic and 136 total PCDD/Fs in flue gas (FG), fly ash (FA) and bottom ash (BA)were ramped up to a higher level than those during municipal-solid-waste (MSW) incineration. A possible explanation was the chlorine (Cl) content of IW. However, the emitted PCDD/Fs in FG (FA/BA) still fulfilled the criteria. Subsequently, similar distribution patterns of PCDD/F isomers were observed in subsystems, indicating a unified formation-pathway. De novo synthesis was detected as the dominant formation-pathway of PCDD/Fs, while high-temperature and precursor syntheses were excluded. DD/DF chlorination formed PCDD/Fs to some extent. Furthermore, the mass flow chart indicated that PCDD/Fs output in primary FG was significantly strengthened (>1000 times) by de novo synthesis, from 1.25 μg I-TEQ/h to 1.67 mg I-TEQ/h. With effective cleaning by APCS, 99.6% of PCDD/Fs in FG were purified. PCDD/Fs in the gas phase were finally emitted at a discharge rate of 7.25 μg I-TEQ/h. However, accumulated FA took most PCDD/Fs into the environment (>99%), reaching 3.56 mg I-TEQ/h. [ABSTRACT FROM AUTHOR]

Trunk pests have always been one of the most important species of tree pests. Trees eroded by trunk pests will be blocked in the transport of nutrients and water and will wither and die or be broken by strong winds. Most pests are social and distributed in the form of communities inside trees. However, it is difficult to know from the outside if a tree is infected inside. A new method for the non-invasive detecting of tree interiors is proposed to identify trees eroded by trunk pests. The method is based on electromagnetic inverse scattering. The scattered field data are obtained by an electromagnetic wave receiver. A Joint-Driven algorithm is proposed to realize the electromagnetic scattered data imaging to determine the extent and location of pest erosion of the trunk. This imaging method can effectively solve the problem of unclear imaging in the xylem of living trees due to the small area of the pest community. The Joint-Driven algorithm proposed by our group can achieve accurate imaging with a ratio of pest community radius to live tree radius equal to 1:60 under the condition of noise doping. The Joint-Driven algorithm proposed in this paper reduces the time cost and computational complexity of tree internal defect detection and improves the clarity and accuracy of tree internal defect inversion images. [ABSTRACT FROM AUTHOR]

In this work, considering the current status of conservative and complicated traditional thrombosis treatment methods, a kind of flexible intelligent probe (FIP) with a top-driven sensing strategy is proposed to realize the expected function of thrombosis accurate localization in a liquid flow environment. After throughput fabrication, we find that the FIP has excellent electrical conductivity and mechanical properties. Notable, our FIP with the principle of piezo-resistive sensing has a quasi-linear sensitivity (approx. 0.325 L per minute) to flow sensing in the low flow velocity range (0–1 L per minute). Via the well-designed magnetically driven method, our FIP has a maximum deflection output force of 443.264 mN, a maximum deflection angle of 43°, and a maximum axial force of 54.176 mN. We demonstrate that the FIP is capable of completing the specified command actions relatively accurately and has a good response to real-time sensing feedback performance, which has broad application prospects in thrombus localization detection. [ABSTRACT FROM AUTHOR]

Since the turn of the 21st century, China has leveraged the international environment in its favour to achieve a spectacularly rapid rise, a success that Chinese strategic policymakers attribute in part to 'seizing the strategic window of opportunity'. How has China perceived the international environment and shaped the narrative of its macro-strategy? What impact has it had on China and the world? The article explores the evolution of the strategic narrative of the 'window of opportunity' in three stages since the 21st century and explores its impact in terms of quantity and keyword mapping by analysing the narrative texts of China's 'discourse coalition' (government, official media and intellectual elites). The study found that China's macro-strategic narrative is primarily driven by the official political and semi-official intellectual elites and places a premium on domestic political persuasion rather than external displays of strategic intent. It combines classical and modern philosophical thinking and methodology with the two core objectives of 'maintaining peace' and an 'economic priority'. Over 20 years, the trend of narrative production of the 'strategic opportunity period' has shown a concentrated emergence and fluctuating decline, and its narrative elements have been enriched, interlinked and aligned with the narrative of the CPC's core policies. The narrative of the 'strategic opportunity period' has been a powerful force in building domestic legitimacy, guaranteeing partial fulfilment of strategic objectives and offering lessons for other countries to learn from. Inevitably, the narrative faces a number of dilemmas and challenges. [ABSTRACT FROM AUTHOR]

Irrigation alleviates drought in croplands and maintains or increases crop yields. The accurate monitoring of irrigated areas is important to regional water resource management, food security, climate change, drought monitoring, and emergency disaster relief. Based on field experiments that demonstrate the feasibility of irrigated area mapping using land-surface temperature, we propose a method to map spring irrigation areas using historical meteorological data, main crop phenological characteristics, irrigation regimes, and MODIS land-surface temperature (LST) products. The distribution of irrigation intensity, spring irrigated areas (SIA, considering the irrigation intensity), and total area of spring irrigation (STIA, regardless of irrigation intensity) were monitored by the proposed method for the Donglei Irrigated District (Phase II) in northwestern China from 2011 to 2018. The spring irrigation of the study area was divided into three periods (16 January–23 February, 24 February–24 March, and 25 March–31 May). Then, the temperature threshold of the irrigated area in each period was determined by the diurnal temperature range (DTR) of the rain-fed plots and precipitation data; for the three periods, this was 12 °C, 15 °C, and 11 °C, respectively. The results showed that most of the croplands in the study area were irrigated once or twice. The SIA in most years varied between 55,900 and 73,100 ha, but in 2016, the irrigation area reached 100,200 ha. The STIA accounted for 60–70% of the irrigated area except 2016. The average accuracy of SIA monitoring was satisfactory and above 94% for years when sufficient and reliable data was available. [ABSTRACT FROM AUTHOR]

The river border positioning is an important part of river surveys, which is crucial for water conservation project development, water resource use, water disasters, river regime monitoring, and many other applications related to water resources. Currently, satellite images or field measurements are used to extract river channels. However, satellite images are insufficiently precise, and field measurement requires significant manpower and cost. In this paper, a new method for river channel extraction is proposed, which is based on the combination of Jenks natural breaks classification method and digital elevation model (DEM), and then the river channel range is complemented by using the water range monitored by GF-1(Gaofen-1 satellite) in flood season. The overall precision is greater than 85%, and the Kappa values achieve moderate stability (0.41–0.60). Using this method, the extraction of river range is practicable and achievable, and the higher the DEM resolution, the better the extraction result. [ABSTRACT FROM AUTHOR]

In recent years, deep learning has been applied to intelligent fault diagnosis and has achieved great success. However, the fault diagnosis method of deep learning assumes that the training dataset and the test dataset are obtained under the same operating conditions. This condition can hardly be met in real application scenarios. Additionally, signal preprocessing technology also has an important influence on intelligent fault diagnosis. How to effectively relate signal preprocessing to a transfer diagnostic model is a challenge. To solve the above problems, we propose a novel deep transfer learning method for intelligent fault diagnosis based on Variational Mode Decomposition (VMD) and Efficient Channel Attention (ECA). In the proposed method, the VMD adaptively matches the optimal center frequency and finite bandwidth of each mode to achieve effective separation of signals. To fuse the mode features more effectively after VMD decomposition, ECA is used to learn channel attention. The experimental results show that the proposed signal preprocessing and feature fusion module can increase the accuracy and generality of the transfer diagnostic model. Moreover, we comprehensively analyze and compare our method with state-of-the-art methods at different noise levels, and the results show that our proposed method has better robustness and generalization performance. [ABSTRACT FROM AUTHOR]

Tree height is an essential indicator in forestry research. This indicator is difficult to measure directly, as well as wind disturbance adds to the measurement difficulty. Therefore, tree height measurement has always been an issue that experts and scholars strive to improve. We propose a tree height measurement method based on tree fisheye images to improve the accuracy of tree height measurements. Our aim is to extract tree height extreme points in fisheye images by proposing an improved lightweight target detection network YOLOX-tiny. We added CBAM attention mechanism, transfer learning, and data enhancement methods to improve the recall rate, F1 score, AP, and other indicators of YOLOX-tiny. This study improves the detection performance of YOLOX-tiny. The use of deep learning can improve measurement efficiency while ensuring measurement accuracy and stability. The results showed that the highest relative error of tree measurements was 4.06% and the average relative error was 1.62%. The analysis showed that the method performed better at all stages than in previous studies. [ABSTRACT FROM AUTHOR]

Field water use efficiency is an important parameter for evaluating the quality of field irrigation in irrigated areas, which directly affects the country's food security and water resource allocation. However, most current studies use point-scale soil moisture (SM) or remote sensing water balance models to calculate the field water use coefficient, which cannot avoid errors caused by the spatial heterogeneity of SM and insufficient spatial resolution of remote sensing data. Therefore, in this study, the cosmic-ray neutron sensor (CRNS), Time-Domain Reflectometers (TDR) and Automatic Weather Stations (AWS) were used to monitor the meteorological and hydrological data such as SM, atmospheric pressure, and precipitation in the experimental area of Jinghuiqu Irrigation District for three consecutive years. The scale of the CRNS SM lies between the point and the remote sensing. Based on the CRNS SM, the calculation method for canal head and tail water was used to calculate the field water use efficiency to evaluate the level of agricultural irrigation water use in the experimental irrigation area. The results showed that CRNS could accurately detect the change in SM, and four irrigation events were monitored during the winter wheat growth period from October 2018 to June 2019; the calculation result of field water use efficiency in the experimental area was 0.77. According to the field water use efficiency of the same irrigation area from October 2013 to October 2015 in other studies, the field water use efficiency during the growing period of winter wheat in this area increased from 0.503 to 0.770 in 2013–2019, indicating a significant improvement in the field water use level. In general, this study not only solves the problem of low calculation accuracy of field water use efficiency caused by the mismatch of SM monitoring scales but also explores the application potential of CRNS in agricultural irrigation management and water resource allocation. [ABSTRACT FROM AUTHOR]

The properties of the MoS2/Zr coatings can be significantly affected by the deposition temperature. In this study, the MoS2/Zr composite coatings were fabricated on the cemented carbide surface, utilizing the duplex deposition technology at various deposition temperatures. The effects of deposition temperature on the mechanical and friction properties of the MoS2/Zr coatings were systematically studied. Results exhibited that as the deposition temperature increased, the adhesion force increased first and then decreased, and the coating thickness and micro-hardness gradually increased. Dry sliding tests against a hardened steel ring showed that the tribological behaviors and wear mechanisms of the MoS2/Zr coatings varied with deposition temperature, which were due to the changing mechanical properties of coatings caused by the temperature. The coatings deposited at a temperature of 180 °C and 200 °C possessed preferable comprehensive mechanical and tribological properties. [ABSTRACT FROM AUTHOR]

Research Highlights: This paper proposes a new method for hemispherical forest canopy image segmentation. The method is based on a deep learning methodology and provides a robust and fully automatic technique for the segmentation of forest canopy hemispherical photography (CHP) and gap fraction (GF) calculation. Background and Objectives: CHP is widely used to estimate structural forest variables. The GF is the most important parameter for calculating the leaf area index (LAI), and its calculation requires the binary segmentation result of the CHP. Materials and Methods: Our method consists of three modules, namely, northing correction, valid region extraction, and hemispherical image segmentation. In these steps, a core procedure is hemispherical canopy image segmentation based on the U-Net convolutional neural network. Our method is compared with traditional threshold methods (e.g., the Otsu and Ridler methods), a fuzzy clustering method (FCM), commercial professional software (WinSCANOPY), and the Habitat-Net network method. Results: The experimental results show that the method presented here achieves a Dice similarity coefficient (DSC) of 89.20% and an accuracy of 98.73%. Conclusions: The method presented here outperforms the Habitat-Net and WinSCANOPY methods, along with the FCM, and it is significantly better than the Otsu and Ridler threshold methods. The method takes the original canopy hemisphere image first and then automatically executes the three modules in sequence, and finally outputs the binary segmentation map. The method presented here is a pipelined, end-to-end method. [ABSTRACT FROM AUTHOR]

Stomata are microscopic pores on the plant epidermis that regulate the water content and CO2 levels in leaves. Thus, they play an important role in plant growth and development. Currently, most of the common methods for the measurement of pore anatomy parameters involve manual measurement or semi-automatic analysis technology, which makes it difficult to achieve high-throughput and automated processing. This paper presents a method for the automatic segmentation and parameter calculation of stomatal pores in microscope images of plant leaves based on deep convolutional neural networks. The proposed method uses a type of convolutional neural network model (Mask R-CNN (region-based convolutional neural network)) to obtain the contour coordinates of the pore regions in microscope images of leaves. The anatomy parameters of pores are then obtained by ellipse fitting technology, and the quantitative analysis of pore parameters is implemented. Stomatal microscope image datasets for black poplar leaves were obtained using a large depth-of-field microscope observation system, the VHX-2000, from Keyence Corporation. The images used in the training, validation, and test sets were taken randomly from the datasets (562, 188, and 188 images, respectively). After 10-fold cross validation, the 188 test images were found to contain an average of 2278 pores (pore widths smaller than 0.34 μm (1.65 pixels) were considered to be closed stomata), and an average of 2201 pores were detected by our network with a detection accuracy of 96.6%, and the intersection of union (IoU) of the pores was 0.82. The segmentation results of 2201 stomatal pores of black poplar leaves showed that the average measurement accuracies of the (a) pore length, (b) pore width, (c) area, (d) eccentricity, and (e) degree of stomatal opening, with a ratio of width-to-maximum length of a stomatal pore, were (a) 94.66%, (b) 93.54%, (c) 90.73%, (d) 99.09%, and (e) 92.95%, respectively. The proposed stomatal pore detection and measurement method based on the Mask R-CNN can automatically measure the anatomy parameters of pores in plants, thus helping researchers to obtain accurate stomatal pore information for leaves in an efficient and simple way. [ABSTRACT FROM AUTHOR]

To improve the tribological properties of pure MoS2 coating, the MoS2–Zr composite lubricating coatings were prepared on the WC/TiC/Co carbide surface utilizing radio frequency magnetron sputtering method combining with multiple arc ion plating technology. The effects of different Zr target currents on the surface morphologies, roughness, Zr content, adhesive force, thickness, microhardness and tribological behaviors of the composite coatings were systematically investigated. Results showed that the properties of MoS2 coating can be remarkably enhanced through co-deposition of a certain amount of Zr. As the Zr target current increased, the Zr content, surface roughness, thickness, and micro-hardness gradually increased, while the adhesive force of coatings increased first and then decreased. The friction behaviors and wear modes of the composite coatings both varied obviously with the increase of Zr current. The mechanism was mainly attributed to the different components and mechanical properties of the coatings caused by various Zr current. [ABSTRACT FROM AUTHOR]

In this paper, a novel satellite image segmentation technique based on dynamic Harris hawks optimization with a mutation mechanism (DHHO/M) is proposed. Compared with the original Harris hawks optimization (HHO), the dynamic control parameter strategy and mutation operator used in DHHO/M can avoid falling into the local optimum and efficiently enhance the search capability. To evaluate the performance of the proposed method, a series of experiments are carried out on various satellite images. Eight advanced thresholding approaches are selected for comparison. Three criteria are adopted to determine the segmentation thresholds, namely Kapur's entropy, Tsallis entropy, and Otsu between-class variance. Furthermore, four oil pollution images are used to further assess the practicality and feasibility of the proposed method on real engineering problem. The experimental results illustrate that the DHHO/M based thresholding technique is superior to others in the following three aspects: fitness function evaluation, image segmentation effect, and statistical tests. [ABSTRACT FROM AUTHOR]

An efficient satellite image segmentation method based on a hybrid grasshopper optimization algorithm (GOA) and minimum cross entropy (MCE) is proposed in this paper. The proposal is known as GOA–jDE, and it merges GOA with self-adaptive differential evolution (jDE) to improve the search efficiency, preserving the population diversity especially in the later iterations. A series of experiments is conducted on various satellite images for evaluating the performance of the algorithm. Both low and high levels of the segmentation are taken into account, increasing the dimensionality of the problem. The proposed approach is compared with the standard color image thresholding methods, as well as the advanced satellite image thresholding techniques based on different criteria. Friedman test and Wilcoxon's rank sum test are performed to assess the significant difference between the algorithms. The superiority of the proposed method is illustrated from different aspects, such as average fitness function value, peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), standard deviation (STD), convergence performance, and computation time. Furthermore, natural images from the Berkeley segmentation dataset are also used to validate the strong robustness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a three dimensional pulse coupled neural network (3DPCNN) image segmentation method based on a hybrid seagull optimization algorithm (HSOA) to solve the oil pollution image. The image of oil pollution is taken by the unmanned aerial vehicle (UAV) in the oil field area. The UAV is good at shooting the ground area, but its ability to identify the oil pollution area is poor. In order to solve this problem, a 3DPCNN-HSOA algorithm is proposed to segment the oil pollution image, and the oil pollution area is segmented to identify the dirty oil area and improve the inspection of environmental pollution. The 3DPCNN image segmentation method has simple structure and good segmentation effect, but it has many parameters and poor segmentation effect for complex oil images. Therefore, we apply HSOA algorithm to optimize the parameters of 3DPCNN algorithm, so as to improve the segmentation accuracy and solve the segmentation of oil pollution images. The experimental results show that the 3DPCNN-HSOA model can separate the oil pollution area from the complex background. [ABSTRACT FROM AUTHOR]

A novel multilevel threshold segmentation method for color satellite images based on Masi entropy is proposed in this paper. Lévy multiverse optimization algorithm (LMVO) has a strong advantage over the traditional multiverse optimization algorithm (MVO) in finding the optimal solution for the segmentation in the three channels of an RGB image. As the work advancement introduces a Lévy multiverse optimization algorithm which uses tournament selection instead of roulette wheel selection, and updates some formulas in the algorithm with mutation factor. Then, the proposal is called TLMVO, and another advantage is that the population diversity of the algorithm in the latest iterations is maintained. The Masi entropy is used as an application and combined with the improved TLMVO algorithm for satellite color image segmentation. Masi entropy combines the additivity of Renyi entropy and the non-extensibility of Tsallis entropy. By increasing the number of thesholds, the quality of segmenttion becomes better, then the dimensionality of the problem also increases. Fitness function value, average CPU running time, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Feature Similarity Index (FSIM) were used to evaluate the segmentation results. Further statistical evaluation was given by Wilcoxon's rank sum test and Friedman test. The experimental results show that the TLMVO algorithm has wide adaptability to high-dimensional optimization problems, and has obvious advantages in objective function value, image quality detection, convergence performance and robustness. [ABSTRACT FROM AUTHOR]

To enhance the friction and wear performance of cemented carbide, textured microholes were machined on micro Electron Discharge Machining (EDM) on the tool rake face, and Calcium Fluoride (CaF2) powders were burnished into the microholes. The friction and wear characteristics of the microhole-textured tool filled with CaF2 were investigated using sliding friction tests and dry cutting tests. Results exhibited that the working temperature could affect the tribological performance of the microhole-textured tool filled with CaF2 due to the temperature-sensitive nature of CaF2. There is no obvious lubrication effect for the textured tool filled with CaF2 at room temperature, while it was shown to be more effective in improving tribological property at a cutting speed of higher than 100 m/min with a corresponding to cutting temperature of 450 °C. The possible mechanisms for the microhole-textured tool filled with CaF2 were discussed and established. [ABSTRACT FROM AUTHOR]

In order to enhance the tribological performance of YT14 carbide, pure Zr coating was deposited on the substrate surface using a multi-arc ion plating method. The surface topography, adhesion strength, thickness, and micro-hardness of the Zr coating were tested. Dry sliding friction experiments against a 40Cr hardened steel ring were conducted with Zr-coated carbides and traditional ones. The average coefficients of friction were measured and compared. The wear characteristics of the samples were examined by scanning electron microscope (SEM) and energy dispersive X-ray analysis (EDX). The test results indicated that the Zr coating deposited on the carbide surface exhibited excellent adhesive strength and lower hardness. The average friction coefficient of Zr coated carbide decreased by 20%–30% in comparison with that of the uncoated one. The Zr coated carbide could reduce the adhesive wear compared with the uncoated one, and the main tribological degradation mechanisms of the coating were abrasive wear, coating flaking and delamination. [ABSTRACT FROM AUTHOR]

Diffusion tensor imaging (DTI) is known to suffer from long acquisition time, which greatly limits its practical and clinical use. Undersampling of k-space data provides an effective way to reduce the amount of data to acquire while maintaining image quality. Radial undersampling is one of the most popular non-Cartesian k-space sampling schemes, since it has relatively lower sensitivity to motion than Cartesian trajectories, and artifacts from linear reconstruction are more noise-like. Therefore, radial imaging is a promising strategy of undersampling to accelerate acquisitions. The purpose of this study is to investigate various radial sampling schemes as well as reconstructions using compressed sensing (CS). In particular, we propose two randomly perturbed radial undersampling schemes: golden-angle and random angle. The proposed methods are compared with existing radial undersampling methods, including uniformity-angle, randomly perturbed uniformity-angle, golden-angle, and random angle. The results on both simulated and real human cardiac diffusion weighted (DW) images show that, for the same amount of k-space data, randomly sampling around a random radial line results in better reconstruction quality for DTI indices, such as fractional anisotropy (FA), mean diffusivities (MD), and that the randomly perturbed golden-angle undersampling yields the best results for cardiac CS-DTI image reconstruction. [ABSTRACT FROM AUTHOR]

The average switching model of modular multilevel converter (MMC) is built in this paper when the hot reserved strategy is adopted as a fault-tolerant control. When the MMC SM faults, the rest of the SMs cannot support the DC-link voltage, which results in interruption of the inverter. To tackle this issue, a novel fault-tolerant control strategy is proposed to bypass the SM under fault and re-regulate the SM capacitor voltage and carrier phase-shift angle to maintain the main components of circulating current, and reduce the Total Harmonic Distortion (THD) of grid connected current to enable the stable operation of the photovoltaic inverter. The maximum power tracking control is improved to solve the problem of long restoration time when faults occur, based on a constant voltage startup method combined with a fix-step incremental conductance method. Simulations in MATLAB/Simulink and experimental results have verified the feasibility and effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

In this paper, a kind of green triboelectric nano-generator based on natural degradable cellulose is proposed. Different kinds of regenerated cellulose composite layers are prepared by a blending doping method, and then assembled with poly(tetrafluoroethylene) (PTFE) thin films to form tribioelectric nanogenerator (TENG). The results show that the open circuit output voltage and the short circuit output current using a pure cellulose membrane is 7.925 V and 1.095 μA. After adding a certain amount of polyamide (PA6)/polyvinylidene fluoride (PVDF)/barium titanate (BaTiO 3 ), the open circuit output voltage peak and the peak short circuit output current increases by 254.43% (to 20.155 V) and 548.04% (to 6.001 μA). The surface morphology, elemental composition and functional group of different cellulose layers are characterized by Scanning Electronic Microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), and tested by the electrochemical analyze. Moreover, after multiple assembly and rectification processing, the electrical output performance shows that the peak value of open-circuit output voltage and the peak value of short circuit output current increases by 132.06% and 116.13%. Within 500 s of the charge-discharge test, the single peak charge reached 3.114 V, and the two peak charges reached 3.840 V. The results demonstrate that the nano-generator based on cellulose showed good stability and reliability, and the application and development of natural biomaterials represented by cellulose are greatly promoted in miniature electronic sensing area.