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The confinement and high utilization of sulfur in the cathodes is critical for improved cycling performance of lithium‐sulfur batteries. In this case one‐pot hydrothermal strategy is developed to produce rGO/MXene/sulfur composite aerogels where sulfur is in situ trapped in the 3D rGO/MXene conductive skeleton. The optimized composite aerogels as free‐standing cathodes delivery a specific capacity of 951 mAhg−1 after 100 cycles at 0.2 C with a low fading rate of 0.062 % per cycle. The excellent cycling performance is correlated with highly oxidized MXene and in situ formed sulfate/thiosulfate complex layer in the long‐term cycles. [ABSTRACT FROM AUTHOR]

Here, 0.3 wt.%Zr was introduced in an Al-4 wt.%Cu-0.5 wt.%Mn-0.1 wt.%Fe alloy to investigate its influence on the microstructure and mechanical properties of the alloy. The microstructures of both as-cast and T6-treated Al–Cu–Mn–Fe (ACMF) and Al–Cu–Mn–Fe–Zr (ACMFZ) alloys were analyzed. The intermetallic compounds formed through the casting procedure include Al2Cu and Al7Cu2Fe, and the Al2Cu phase dissolves into the matrix and re-precipitates as θ′ phase during the T6 process. The introduction of Zr results in the precipitation of L12-Al3Zr nanometric precipitates after T6, while the θ′ precipitates in ACMFZ alloy are much finer than those in ACMF alloy. The L12-Al3Zr precipitates were found coherently located with θ′, which was assumed beneficial for stabilizing the θ′ precipitates during the high-temperature tensile process. The tensile properties of ACMF and ACMFZ alloys at room temperature and elevated temperatures (200, 300, and 400 °C) were tested. Especially, the yield strength of ACMFZ alloys can reach 128 MPa and 65 MPa at 300 °C and 400 °C, respectively, which are 31% and 33% higher than those of ACMF alloys. The strengthening mechanisms of grain size, L12-Al3Zr, and θ′ precipitates on the tensile properties were discussed. This work may be referred to for designing Al–Cu alloys for application in high-temperature fields. [ABSTRACT FROM AUTHOR]

In lithium batteries, composite solid electrolytes gain their reputation owing to their great safety, excellent mechanical properties, and remarkably improved electrochemical performance. Herein, composite polyethylene oxide (PEO)‐based solid electrolytes with well‐dispersed in situ polydopamine‐coated lithium montmorillonite (PDA@LiMMT) nanofillers are successfully fabricated. The involvement of PDA@LiMMT retards significantly the crystallization of PEO, addressing a manifest enhancement in the ionic conductivity of the modified composite solid electrolyte. A conducive ionic conductivity value of 3.06 × 10−4 S cm−1 at 60 °C is reached, while the electrochemical window widens to 4.7 V. The assembled Li/solid polymer electrolytes/LiFePO4 battery exhibits a 158.2 mA h g−1 high discharge specific capacity at a current density of 0.5C; besides, the capacity retention rate keeps 86% after 200 cycles. The enhancement of the cycle stability of the solid‐state batteries highlights the excellent interfacial stability between the modified solid electrolytes and the lithium electrode, which originates from lithiophilicity of PDA. [ABSTRACT FROM AUTHOR]

Diet plays a crucial role in shaping the gut microbiota and overall health of animals. Traditionally, silkworms are fed fresh mulberry leaves, and artificial diets do not support good health. The aim of this study was to explore the relationship between the dietary transition from artificial diets to mulberry leaves and the effects on the gut microbiota and physiological changes in silkworms as a model organism. With the transition from artificial diets to mulberry leaves, the diversity of the silkworm gut microbiota increased, and the proportion of Enterococcus and Weissella, the dominant gut bacterial species in silkworms reared on artificial diets, decreased, whereas the abundance of Achromobacter and Rhodococcus increased. Dietary transition at different times, including the third or fifth instar larval stages, resulted in significant differences in the growth and development, immune resistance, and silk production capacity of silkworms. These changes might have been associated with the rapid adaptation of the intestinal microbiota of silkworms to dietary transition. This study preliminarily established a dietary transition–gut microbial model in silkworms based on the conversion from artificial diets to mulberry leaves, thus providing an important reference for future studies on the mechanisms through which habitual dietary changes affect host physiology through the gut microbiome. [ABSTRACT FROM AUTHOR]

The homogeneous field measurement of internal flow and spray of internal combustion engine injector nozzles under high pressure has always been one of the difficulties in experimental research. In this paper, an actual-size aluminum alloy nozzle is designed, and the simultaneous measurement of internal flow and near-field spray is successfully realized with the help of synchrotron radiation X-ray phase contrast imaging technology under an injection pressure of 30~90 MPa. For a 0.25 mm aperture nozzle, different radii of the inlet corner can induce different cavitation layer thicknesses, and the measured flow section shrinkage ratio is 0.70. The flow characteristics in the nozzle are entirely connected to the jet characteristics, indicating a tight correlation between internal flow and jet morphology. Finally, the internal cavitation of the nozzle was studied by the CFD simulation, and the simulation results are in good agreement with the experiment. [ABSTRACT FROM AUTHOR]

The largest earthquake (Mw 6.4) in northwestern Croatia ruptured the faults near the city of Petrinja on 29 December 2020, at 11:19 UTC. The epicenter was located ~3 km southwest of Petrinja, ~40 km southeast of Zagreb, the capital of the Republic of Croatia. Here we investigated the geometric and kinematic properties of the 2020 Mw 6.4 Petrinja earthquake using a joint inversion of ascending and descending interferograms from three tracks of Sentinel-1 Single-Look Complex (SLC) images. The coseismic and early postseismic surface displacements associated with the Petrinja earthquake were imaged using standard DInSAR and SBAS time-series InSAR methods, respectively. The distributed slip model was inverted based on the ground surface displacements with maximum slip patch in 5 km depth. The early postseismic deformation occurred on the northwestern extent of coseismic slip, and it cannot be well modeled by the coseismic model. We thus suggested that the postseismic deformation was caused by a combined effect of the postseismic afterslips and aftershocks occurring in this area. Based on the inverted slip model, we calculated the Coulomb stress change in the region, and found a good correlation between positive Coulomb failure stress ∆CFS and the distribution of aftershocks. Based on these results, we identified which faults are more active, and then better estimated the seismic hazards in the region. [ABSTRACT FROM AUTHOR]

As an attractive technique for measuring water vapor, the Global Navigation Satellite System (GNSS) faces additional challenges in dynamic applications such as in the open sea. We present a new method of retrieving precipitable water vapor (PWV) based on GNSS multi‐antenna precise point positioning (PPP), which uses GNSS data from multiple antennas and incorporates the constraints of known baseline vector and common tropospheric delay. The 4‐day shipborne dynamic experiment along the China coast demonstrates that the baseline vector constraint shortens the convergence time of positioning and atmospheric parameters, and also slightly improves their accuracies. The common tropospheric delay constraint helps to provide compromised, more robust, and sometimes more accurate PWV estimates. An evaluation with radiosonde‐derived PWVs shows that the combination of the two constraints achieves the best accuracy reaching 4.2 mm. This method helps to expand GNSS meteorology to the vast ocean and benefits satellite altimetry and weather forecasting. Plain Language Summary: Water vapor plays an important role in atmospheric processes ranging from global climate change to mesoscale and micro‐scale weather systems. The Global Navigation Satellite System (GNSS) is an attractive technique to measure water vapor, which has been extensively investigated for ground‐based static stations. In dynamic applications such as in the open sea, it is faced with additional challenges which may potentially degrade the performance. This study presents a new method for retrieving precipitable water vapor (PWV) based on GNSS multi‐antenna precise point positioning (PPP). It uses GNSS data from multiple antennas closely spaced and mounted on the same platform, and incorporates additional constraints of known baseline vector and common tropospheric delay. We have conducted a 4‐day shipborne dynamic experiment along the China coast and demonstrated that this method can shorten the convergence time and provide more robust and sometimes more accurate PWVs compared to conventional PPP. An accuracy level of 4.2 mm is achieved with this method for the ocean‐based dynamic experiment. This study helps to expand GNSS meteorology to challenging environments such as in the open sea, which will benefit weather forecasting and nowcasting. Key Points: A new method of retrieving precipitable water vapor (PWV) is presented based on Global Navigation Satellite System (GNSS) multi‐antenna precise point positioning (PPP)This method can shorten the convergence time and provide more robust and more accurate GNSS‐based PWVThe improved performance is helpful to expand GNSS meteorology to the vast ocean and benefits satellite altimetry and weather forecasting [ABSTRACT FROM AUTHOR]

Greenhouse gas emissions (GHG) and nutrient loss are considered to be critical environmental issues facing rice field ecosystems. Biochars (BCs), as carbon-rich materials with porous structures, large specific surface areas, and enriched surface functional groups, have shown great potential for improving environmental problems in rice fields and increasing rice yields. However, thus far, we still lack an adequate summary and review of the performance characteristics of BCs and their environmental behavior in paddy soils. In this paper, we critically review the positive effects of BC application on the growth and yield of rice, nutrient loss reduction, and GHG reduction. Biomass type, pyrolysis temperature, and modification method are considered to be the key factors that determine the performance characteristics of BCs. The application of BCs could promote rice yield and mitigate CO2, N2O, and CH4 emissions by improving soil physicochemical properties and microbial communities, providing nutrient sources, and reducing nutrient losses. Finally, this paper illustrates the potential ecological risks of BC application on paddy fields, including the risks of inconclusive research results and secondary pollution. These shortcomings need to be addressed in future research to ensure the sustainability of BC application. [ABSTRACT FROM AUTHOR]

The fuel injector is a critical component of the internal combustion engine. The diameters of the injector nozzle and the control chamber's oil inlet and outlet are generally between 0.2 and 0.5 mm, which are typical microchannel structures. During high-pressure injection, the cavitation phenomenon in the channel seriously affects the reliability of the internal combustion engine. The choice of turbulence and cavitation models is the key to investigate the cavitation in the microchannel by using numerical methods. Based on the Winklhofer microchannel fuel experiment, five representative turbulence models were used to construct a microchannel model, and the results were compared and analyzed with the experiment. The results show that the pressure gradient values obtained from the combination of RNG k-ε and ZGB models were similar to the experimental data, with an error of less than 6%. The cavitation distribution calculated from the combination of LES and ZGB models was most consistent with the experimental observation data. The outlet mass flow rate obtained from the LES and ZGB models matched the trend of the experimental data in the pressure difference range of 19 bar to 85 bar, with an error of less than 2%. For the cross-sectional flow rate calculation, the RNG k-ε and ZGB models had the smallest calculation errors, with errors below 11%. [ABSTRACT FROM AUTHOR]

This study investigates a prediction model for the cycle injection quantity in a high-pressure common rail injector under a transient thermal boundary. The results show that the transient temperature increase curve calculated by the mathematical model of the common rail injector under adiabatic flow is significantly different from the experimental data. A non-isothermal model of the injector coupled with heat transfer is established, which considers the actual heat transfer phenomenon. The excellent agreement between the new calculation results and the experimental data confirms that the fuel injection process of a common rail injector comprises the coupled phenomena of fuel heating and heat transfer. Based on the established simulation model, it is found that in the continuous injection process of the injector, owing to the thermal effect of injection, the cycle injection quantity decreases gradually with an increase in the injector working time and then stabilizes. Under the condition of an injection pulse width of 1.2 ms and frequency of 100 Hz, when the injection pressure increases from 140 MPa to 300 MPa, the reduction in the cycle injection quantity increases from 3.9% to 7.8%, because the higher injection pressure results in higher transient heat at the nozzle holes. In the work of common rail injector assemblies, to achieve more accurate control of the cycle injection quantity, it is necessary to include the correction of a decreasing cycle injection quantity caused by transient heat in the electronic control system. [ABSTRACT FROM AUTHOR]

Fusing LiDAR and high definition (HD) maps is a feasible way to achieve global localization in GNSS-denied environments, which is necessary in driverless vehicle and robotic technologies. This paper proposes a single-shot global localization solution that uses only a single current scan of a rotating multiple-beam LiDAR sensor to locate its own location and pose with respect to an HD map in the form of georeferenced point clouds. This single-shot global localization solution estimates the state of the current moment without the previous moment state and thus avoids the nonconvergence problems that plague filter-based methods. The proposed solution allows HD maps from diverse LiDAR sensors to be used for global localization and is more robust than existing methods. The proposed solution consists of two procedures: offline preprocessing and online global localization. In the offline procedure, diverse HD maps are preprocessed to construct a global prior map for the localization process. The online global localization procedure includes two elements: place recognition, location and pose estimation. A novel Cross-Section Shape Context (CSSC) descriptor that is highly descriptive and rotation-invariant is proposed for subsequent processes. Two strategies, two-stage similarity estimation and Nearest Cluster Distance Ratio (NCDR), based on the CSSC descriptor are proposed to improve place recognition precision. A Selective Generalized Iterative Closest Point (SGICP) algorithm is proposed to calculate location and pose accurately using the CSSC descriptor. Comprehensive experiments were performed to evaluate this solution. A comparison of the precision-recall curve of multiple scenes, particularly under changed viewpoint scenes, shows that the CSSC descriptor is more robust than existing descriptors. Experimental analysis also confirms that the proposed strategies, two-stage similarity estimation and NCDR, improve place recognition precision. Also, compared to the generalized iterative closest point algorithm, the SGICP algorithm achieved better accuracy by 31% and efficiency by 60%. The proposed solution achieves a mean relative translation error (RTE) improvement of 27% over the OneShot algorithm on the KITTI dataset. The proposed solution had an average 77% improvement over 1σ RTE relative to the benchmark in tests with the long-term localization NCLT dataset. The mean RTE of the proposed solution was 0.13 m using HD maps from different LiDAR sensors. Our code is available at: https://github.com/Dongxu05/CSSC. [ABSTRACT FROM AUTHOR]

For global navigation satellite system (GNSS), ionospheric disturbances caused by the geomagnetic storm can reduce the accuracy and reliability of precision point positioning (PPP). At present, common stochastic models in GNSS PPP, such as the elevation angle stochastic (EAS) model or carrier‐to‐noise power‐density ratio (C/N0 $C/{N}_{\mathit{0}}$) based SIGMA‐ε $\varepsilon $ model, do not properly consider storm effects on GNSS measurements. To mitigate severe storm effects on GNSS PPP, this study further implements the rate of total electron content index (ROTI) parameter into the EAS model referred to as the EAS‐ROTI model. This model contains two operations. The first one is to adjust variance of GNSS measurements using ROTI observations on EAS model. The second one is to determine the ratio of the priori variance factor between pseudorange and carrier phase measurements during severe storm conditions. The performance of EAS‐ROTI model is verified by using a large number of international GNSS service stations datasets on 17 March and 23 June in 2015. Experimental results indicate that on a global scale, the EAS‐ROTI model improves the PPP accuracy in 3D direction by approximately 12.9%–14.7% compared with the EAS model, and by about 24.8%–45.9% compared with the SIGMA‐ε $\varepsilon $ model. Plain Language Summary: Geomagnetic storm is characterized by strong geomagnetic disturbances caused by solar energy eruption. During the geomagnetically quiet days, the global navigation satellite system (GNSS) precise point positioning (PPP) using the international GNSS service (IGS) precision products and dual‐frequency observations can achieve decimeter or even centimeter level positioning accuracy. However, during the severe geomagnetic storm, the ionospheric disturbances can decrease the quality of GNSS measurements or even cause the signals loss of lock. Though it is difficult to ensure the accuracy and availability of PPP by using traditional GNSS data processing models and methods, in this study, we focus on improving the stochastic model to mitigate the storm effects on GNSS PPP. An improved stochastic model based on rate of total electron content index is established in this study. The PPP experiment is carried out by using abundant GNSS datasets from global IGS stations. Results indicate that the improved stochastic model can effectively improve the positioning accuracy under severe geomagnetic storm conditions. Key Points: Current stochastic models of global navigation satellite system (GNSS) precise point positioning (PPP) do not properly consider the storm effectsRate of total electron content index (ROTI) based stochastic model is established to mitigate severe storm effects on GNSS PPPPositioning accuracy of GNSS PPP based on ROTI‐based stochastic model is generally better than that of PPP with common stochastic models [ABSTRACT FROM AUTHOR]

Recognizing rice seedling growth stages to timely do field operations, such as temperature control, fertilizer, irrigation, cultivation, and disease control, is of great significance of crop management, provision of standard and well-nourished seedlings for mechanical transplanting, and increase of yield. Conventionally, rice seedling growth stage is performed manually by means of visual inspection, which is not only labor-intensive and time-consuming, but also subjective and inefficient on a large-scale field. The application of machine learning algorithms on UAV images offers a high-throughput and non-invasive alternative to manual observations and its applications in agriculture and high-throughput phenotyping are increasing. This paper presented automatic approaches to detect rice seedling of three critical stages, BBCH11, BBCH12, and BBCH13. Both traditional machine learning algorithms and deep learning algorithms were investigated the discriminative ability of the three growth stages. UAV images were captured vertically downward at 3-m height from the field. A dataset consisted of images of three growth stages of rice seedlings for three cultivars, five nursing seedling densities, and different sowing dates. In the traditional machine learning algorithm, histograms of oriented gradients (HOGs) were selected as texture features and combined with the support vector machine (SVM) classifier to recognize and classify three growth stages. The best HOG-SVM model obtained the performance with 84.9, 85.9, 84.9, and 85.4% in accuracy, average precision, average recall, and F1 score, respectively. In the deep learning algorithm, the Efficientnet family and other state-of-art CNN models (VGG16, Resnet50, and Densenet121) were adopted and investigated the performance of three growth stage classifications. EfficientnetB4 achieved the best performance among other CNN models, with 99.47, 99.53, 99.39, and 99.46% in accuracy, average precision, average recall, and F1 score, respectively. Thus, the proposed method could be effective and efficient tool to detect rice seedling growth stages. [ABSTRACT FROM AUTHOR]

In this study, the primary breakup of a high-speed diesel jet is investigated using a CFD methodology that combines an LES model with a VOF technique for free surface capture. Inner-nozzle turbulence and cavitation are simplified as the sinusoidal radial velocity with a given amplitude and frequency. The ligament and droplet formation process are captured, the liquid jet is disturbed by the radial velocity, and umbrella-shaped crests are created. Meanwhile, ligaments are formed from the edges of crests because of shear stress and surface tension. We investigate the effect on the characteristics of the surface wave and the liquid structure of different disturbance frequencies and amplitudes. The variation in the disturbance amplitude and frequency facilitates the formation of a variety of liquid structures, such as waves, upstream/downstream-directed bells, and droplet chains. Increasing the disturbance frequency reduces the growth rate of the surface waves of the liquid jet. With an increase in disturbance amplitude, the amplitude of surface waves evidently increases. Furthermore, as the disturbance frequency and amplitude increase, the thickness and Weber number of the radial liquid sheet decrease, and this causes the ligament diameter of the primary breakup to become small. Finally, the primary breakup time is investigated, and the time scale of the liquid jet primary breakup decreases as the disturbance amplitude increases, which indicates that an increase in the disturbance amplitude promotes the atomization of a disturbed liquid jet. [ABSTRACT FROM AUTHOR]

The piezoelectric injection-system provides a reliable approach for precise small-quantity fuel injection due to its fast, dynamic response. Considering the nonlinearity of a piezoelectric actuator, the complete electro-mechanical-hydraulic model of the piezoelectric injector was established and verified experimentally, which showed that it could accurately predict the fuel injection quantity. The small-quantity fuel injection with different driving voltages, pulse widths, and rail pressures was analyzed. The effects of key structural parameters of the injector on the delivery, control-chamber pressure fluctuation, and small-quantity injection characteristics were studied. The results show that the linearity of the curve of the injection volume with the pulse width was relatively poor, and there was a significant inflection point when the piezoelectric injector worked in the small pulse width region (PW < 0.6 ms). The bypass valve significantly accelerated the establishment of the control-chamber pressure, reduced the pressure fluctuation in the chamber, shortened the closing delay and duration of the needle valve, and reduced the rate of the fuel-quantity change so that it provided a greater control margin for the pulse width over the same fuel volume change interval. Under the condition of a small-quantity fuel injection of 20 mm3, decreasing the inlet orifice diameter and increasing the outlet orifice diameter shortened the minimum control pulse width and fuel injection duration required for the injector injection, which is beneficial for multiple and small-quantity fuel injection. However, these behaviors reduce the control margin for the pulse width, especially in small pulse width regions. [ABSTRACT FROM AUTHOR]

Ubiquitous positioning is in highly demand in the fields of smartphone location-based services and more widely, in internet of things applications. Many WiFi fingerprinting algorithms have received widespread attention in the industry. Such methods for commercial location-based services however, still experience usability problems. These include low efficiency and high time costs when surveying radio maps. At the same time, heterogeneous radio measurements from different mobile devices may degrade positioning performance. This paper proposes a novel fingerprinting indoor positioning method aiming at breaking through the usability bottleneck in two ways: 1) a dynamic on-the-move surveying method of radio map to enhance the surveying efficiency using a self-developed mobile geo-reference system, and radio map is constructed using much sparser measurements and sparse principle. 2) Correspondingly, a sparse attribute based positioning algorithm was developed. This new positioning algorithm takes account of the heterogeneity of devices, and does not require any manual operations for the calibration of hardware bias, unlike many widely used static fingerprinting methods. The proposed positioning method was implemented on smartphones and verified in typical office indoor environments. The experimental results show that the radio map surveying efficiency was enhanced by 10 times in terms of radio map surveying time. Compared with the widely used static methods, the proposed positioning algorithm has more consistent positioning accuracy even when different devices were used for radio map surveying and positioning. The proposed fingerprinting positioning method is applicable when developing LBS applications (demo attached), while the enhanced usability will improve user experience of positioning performance. [ABSTRACT FROM AUTHOR]

Geomagnetic storm can affect the performance of Global Navigation Satellite System (GNSS) precise positioning services. To mitigate the adverse effects of strong geomagnetic storms, we propose to establish the geometry‐free (GF) cycle slip threshold model based on ionospheric disturbance index rate of total electron content index to reduce the false detection rate of cycle slip in GNSS precise point positioning (PPP) during strong storm periods, thus improving the accuracy and reliability of GNSS PPP. The performance of our proposed model is validated by using 171 International GNSS Service (IGS) tracking stations data on 8 September 2017. The analysis indicates that compared with conventional PPP scheme, the proposed model can improve the positioning accuracy by approximately 14.0% (36.8%) and 23.1% (51.5%) in the horizonal and vertical components for global (high latitudes) stations. Furthermore, the availability of our proposed model is also validated by PPP experiments using 379 IGS tracking stations data during another strong storm occurred on 26 August 2018. Plain Language Summary: Global Navigation Satellite System (GNSS) precise point positioning (PPP) can achieve decimeter or even millimeter levels of positioning accuracy in general. However, this accuracy will be degraded significantly under the strong geomagnetic storm, which is a major disturbance of Earth's magnetosphere that occurs when there is a very efficient exchange of energy from the solar wind into the space environment surrounding Earth. Strong geomagnetic storm associated with ionospheric disturbances can decrease the quality of GNSS measurements or even result in GNSS signals loss of lock known as cycle slip, thus affecting the availability and reliability of the common data processing methods of GNSS PPP. In this study, we investigate the limitations of common threshold in cycle slip detection of GNSS data processing. It is found that the cycle slip detection observation shows close relationship to ionospheric disturbance index ROTI (rate of total electron content index), so we propose to establish the cycle slip threshold model based on ionospheric disturbance index ROTI. GNSS PPP experiments based on abundant International GNSS Service stations data indicate that the proposed model can effectively improve the positioning accuracy especially those stations located at high latitudes. Key Points: Strong geomagnetic storm can significantly degrade the performance of Global Navigation Satellite System (GNSS) precise point positioning (PPP)Geometry‐free cycle slip detection observation shows close relationship to the ionospheric disturbance index rate of total electron content indexPositioning accuracy of GNSS PPP based on cycle slip threshold model is generally better than that of conventional PPP [ABSTRACT FROM AUTHOR]