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The energy spectrum of energetic electrons is a key factor representing the dynamic variations of Earth’s Van Allen radiation belts. Increased measurements have indicated that the commonly used Maxwellian and Kappa distributions are inadequate for capturing the realistic spectral distributions of radiation belt electrons. Here we adopt the Kappa-type (KT) distribution as the fitting function and perform a statistical analysis to investigate the radiation belt electron flux spectra observed by the Van Allen Probes. By calculating the optimal values of the key KT distribution parameters (i.e., κ and θ2) from the observed spectral shapes, we fit the radiation belt electron fluxes at different L-shells under different geomagnetic conditions. In this manner, we obtain typical values of the KT distribution parameters, which are statistically feasible for modeling the radiation belt electron flux profiles during either geomagnetically quiet or active periods. A comparison of the KT distribution model results with those using the Maxwellian or Kappa distribution reveals the advantage of the KT distribution for studying the overall properties of the radiation belt electron spectral distribution, which has important implications for deepening the current understanding of the radiation belt electron dynamics under evolving geomagnetic conditions.

Because radiation belt electrons can pose a potential threat to the safety of satellites orbiting in space, it is of great importance to develop a reliable model that can predict the highly dynamic variations in outer radiation belt electron fluxes. In the present study, we develop a forecast model of radiation belt electron fluxes based on the data assimilation method, in terms of Van Allen Probe measurements combined with three-dimensional radiation belt numerical simulations. Our forecast model can cover the entire outer radiation belt with a high temporal resolution (1 hour) and a spatial resolution of 0.25 L over a wide range of both electron energy (0.1–5.0 MeV) and pitch angle (5°–90°). On the basis of this model, we forecast hourly electron fluxes for the next 1, 2, and 3 days during an intense geomagnetic storm and evaluate the corresponding prediction performance. Our model can reasonably predict the storm-time evolution of radiation belt electrons with high prediction efficiency (up to ~0.8–1). The best prediction performance is found for ~0.3–3 MeV electrons at L = ~3.25–4.5, which extends to higher L and lower energies with increasing pitch angle. Our results demonstrate that the forecast model developed can be a powerful tool to predict the spatiotemporal changes in outer radiation belt electron fluxes, and the model has both scientific significance and practical implications.

Abstract Mars lacks a global magnetic field, and instead possesses small-scale crustal magnetic fields, making its magnetic environment fundamentally different from intrinsic magnetospheres like those of Earth or Saturn. Here we report the discovery of magnetospheric ion drift patterns, typical of intrinsic magnetospheres, at Mars using measurements from Mars Atmosphere and Volatile EvolutioN mission. Specifically, we observe wedge-like dispersion structures of hydrogen ions exhibiting butterfly-shaped distributions (pitch angle peaks at 22.5°−45° and 135°−157.5°) within the Martian crustal fields, a feature previously observed only in planetary-scale intrinsic magnetospheres. These dispersed structures are the results of drift motions that fundamentally resemble those observed in intrinsic magnetospheres. Our findings indicate that the Martian magnetosphere embodies an intermediate case where both the unmagnetized and magnetized ion behaviors could be observed because of the wide range of strengths and spatial scales of the crustal magnetic fields around Mars.

The pitch angle distributions of ions and electrons can be affected by various processes; thus, they can serve as an important indicator of the physical mechanisms driving the dynamics of space plasmas. From observations from the Mars Ion and Neutral Particle Analyzer onboard the Tianwen-1 orbiter, we calculated the pitch angle distributions of protons in the Martian induced magnetosphere by using information from the magnetohydrodynamically simulated magnetic field, and we statistically analyzed the spatial occurrence pattern of different types of pitch angle distributions. Even though no symmetrical features were seen in the dataset, we found the dominance of the field-aligned distribution type over the energy range from 188 to 6232 eV. Maps of the occurrence rate showed the preferential presence of a trapped-like distribution at the lower altitudes of the surveyed nightside region. Although our results are more or less restricted by the adopted magnetic field, they indicate the complexity of the near-Mars proton pitch angle distributions and infer the possibility of wave–particle interactions in the Martian induced magnetosphere.

In this paper, a series of low-temperature CVN (Charpy V-notch impact test) and DWTT (drop-weight tear test) experiments were carried out to deal with the intensifying contradiction of strength and toughness of ultra-high-strength pipeline steel. The fracture behavior and toughening mechanisms of ultra-high-strength pipeline steel were investigated using scanning electron microscopy, transmission electron microscopy and backscattered electron diffraction systems. The results show that DWTT fractures in ultra-high-strength pipeline steel had a variety of unconventional morphological features compared to CVN fractures, including ridge protrusion in ductile fracture conditions and a large-size fracture platform in brittle fracture conditions. Therefore, DWTT fractures contained more information about the material fracturing process, and could better reflect the actual process of material fracturing. In ultra-high-strength pipeline steel, fine-grained granular bainite caused cracks to undergo large deflections or frequent small transitions, which consumed additional energy and improved toughness. In contrast, large-sized granular bainite, which consisted of low-angle grain boundaries, did not effectively prevent crack propagation when it encountered cracks, which was not conducive to improved toughness. Moreover, the M/A constituents in large-sized granular bainite aggregated, cracked, or fell off, which could easily lead to the formation of microcracks and was also detrimental to toughening.

The Macau Science Satellite-1 (MSS-1), designed by the Macao University of Science and Technology and the National Space Science Center (NSSC) of China, is equipped to detect the fine structure of the magnetic field over the South Atlantic Anomaly (SAA) region, monitoring geomagnetic field variations, and obtaining the energetic electron spectrum distributions in the Earth’s inner radiation belt. In this study, we simulate the distributions of trapped, quasi-trapped, and untrapped electrons along the orbit of MSS-1 based on a drift-source model. The simulation results show that the particle detector with 90° looking direction can observe trapped electrons in the SAA region, untrapped electrons in the regions conjugated with the SAA region at the north hemisphere, and quasi-trapped electrons in all other regions. In contrast, the detectors with

Tailward ion outflows in the Martian-induced magnetotail are known to be one of the major channels for Martian atmospheric escape. On the basis of nearly 6.5 years of observations from the Mars Atmosphere and Volatile EvolutioN (MAVEN) mission, we investigate the statistical distribution of tailward and Marsward fluxes of heavy ions (i.e., O+ and \begin{document}${\rm{O}}_2^+ $\end{document}) in the near-Mars magnetotail and explore their characteristic responses to the corotating interaction region (CIR), solar wind dynamic pressure, and local magnetic field intensity. Our results show that the tailward fluxes of oxygen ions and molecular oxygen ions in the magnetotail are significantly greater than their Marsward fluxes and that the tailward flux of molecular oxygen ions is generally larger than that of oxygen ions. Furthermore, the tailward ion flux distribution exhibits dependence on the CIR, solar wind dynamic pressure, and local magnetic field strength in a manner stronger than the Marsward ion flux distribution. According to the distribution of tailward ion fluxes, we calculate the corresponding escape rates of heavy ions and show that when the CIR occurs, the total escape rates of oxygen ions and molecular oxygen ions increase by a factor of ~2 and ~1.2, respectively. We also find that the escape rates of heavy ions increase with the enhancement of solar wind dynamic pressure, whereas the overall effect of the local magnetic field is relatively weak. Our study has important implications for improved understanding of the underlying mechanisms responsible for the Martian atmospheric escape and the evolution of the Martian atmospheric climate.

The foreshock region filled with turbulences is a natural lab for studying interactions between plasma and waves. The underlying processes are vital for dissipating the energy carried by the solar wind into the Martian environment. In this study, we analyze an ultralow-frequency wave observed on 2015 August 14 by Mars Atmosphere and Volatile Evolution upstream of the Martian bow shock. The wave properties match with those of 30 s waves; the dispersion calculation shows that the upstream plasma was capable of generating the observed waves. The analysis of the ion behaviors implies the wave generated without a field-aligned beam, while the growth rate of waves seems to be modulated by the interplanetary magnetic field conditions. These results indicate the unique role of the Martian foreshock in the interaction between the solar wind and the planet.

Whistler-mode waves upstream of planetary bow shock are often referred to as “1-Hz waves” due to the center of their observed frequency range being at ∼1 Hz. A series of whistler-mode waves were observed upstream of the Martian bow shock by MAVEN on 2015 August 14, with unusually low frequencies centered at ∼0.4 Hz. These waves were accompanied (though not synchronized) by the significant flux enhancement of high-energy protons up to ∼10 keV. By analyzing the wave dispersion property and the wave–particle interaction condition, we find that the observed whistler-mode waves have the potential of resonating with protons of ∼1 keV with large pitch angles up to nearly perpendicular to the background magnetic field, thereby providing a feasible means of accounting for proton acceleration. Our results indicate the possible origin of energized protons in the Martian environment through the interaction with whistler-mode waves, and their potential relationship with the unique upstream conditions.

Benzene is used as an industrial solvent, which may result in chronic benzene poisoning (CBP). Several studies suggested that CBP was associated with mitochondrial epigenetic regulation. This study aimed to explore the potential relation between CBP and mitochondrial DNA (mtDNA) methylation. This prospective observational study enrolled CBP patients admitted to Shenzhen Prevention and Treatment Center for Occupational Diseases hospital and healthy individuals between 2018 and 2021. The white blood cell (WBC), red blood cell (RBC), hemoglobin (HB), and platelet (PLT) counts and mtDNA methylation levels were measured using blood flow cytometry and targeted bisulfite sequencing, respectively. A total of 90 participants were recruited, including 30 cases of CBP (20 females, mean age 43.0 ± 8.0 years) and 60 healthy individuals (42 females, mean age 43.5 ± 11.5 years). This study detected 168 mitochondrial methylation sites >0 in all study subjects. The mtDNA methylation levels in the CBP cases were lower than the healthy individuals [median ± interquartile-range (IQR), 25th percentile, 75th percentile: (1.140 ± 0.570, 0.965, 1.535)% vs. median ± IQR, 25th percentile, 75th percentile: (1.705 ± 0.205,1.240,2.445)%, P < 0.05]. Additionally, the spearman correlation analysis showed that the mtDNA methylation levels were positively correlated with the counts of circulating leukocytes [WBC (r = 0.048, P = 0.036)] and platelets [PLT (r = 0.129, P < 0.01)]. We provided solid evidence of association between CBP and aberrant mtDNA methylation.

Objective: During inspiration, mechanical energy generated from respiratory muscle produces a negative pressure gradient to fulfill enough pulmonary ventilation. The pressure loss, a surrogate for energy loss, is considered as the portion of negative pressure without converting into the kinetic energy of airflow. Mouth opening (MO) during sleep is a common symptom in patients with obstructive sleep apnoea-hypopnea syndrome (OSAHS). This study aimed to evaluate the effects of mouth opening on pharyngeal pressure loss using computational fluid dynamics (CFD) simulation.Methods: A total of four subjects who were morphologically distinct in the pharyngeal characteristics based on Friedman tongue position (FTP) grades were selected. Upper airway computed tomography (CT) scan was performed under two conditions: Mouth closing (MC) and mouth opening, in order to reconstruct the upper airway models. computational fluid dynamics was used to simulate the flow on the two different occasions: Mouth closing and mouth opening.Results: The pharyngeal jet was the typical aerodynamic feature and its formation and development were different from mouth closing to mouth opening in subjects with different Friedman tongue position grades. For FTP I with mouth closing, a pharyngeal jet gradually formed with proximity to the velopharyngeal minimum area plane (planeAmin). Downstream the planeAmin, the jet impingement on the pharyngeal wall resulted in the frictional loss associated with wall shear stress (WSS). A rapid luminal expansion led to flow separation and large recirculation region, corresponding to the interior flow loss. They all contributed to the pharyngeal total pressure loss. While for FTP I with mouth opening, the improved velopharyngeal constriction led to smoother flow and a lower total pressure loss. For FTP IV, the narrower the planeAmin after mouth opening, the stronger the jet formation and its impingement on the pharyngeal wall, predicting a higher frictional loss resulted from higher WSS. Besides, a longer length of the mouth opening-associated constant constrictive segment was another important morphological factor promoting frictional loss.Conclusion: For certain OSAHS patients with higher Friedman tongue position grade, mouth opening-related stronger jet formation, more jet breakdown and stronger jet flow separation might contribute to the increased pharyngeal pressure loss. It might require compensation from more inspiratory negative static pressure that would potentially increase the severity of OSAHS.

Analyzing and fusing information layers of exploratory parameters is a critical step for enhancing the accuracy of identifying mineral potential zones during the reconnaissance stage of mineral exploration. The Qixia area in Shandong Province is characterized by intricate geological structures and abundant mineral resources. Numerous gold polymetallic deposits have been discovered in this region, highlighting the potential for discovering more such deposits in the ore concentration zone and its adjacent areas. In this study, we focus on the Qixia area and employ the box dimension method to analyze the fractal dimension of fault structures. We investigate the relationship between orebody occurrence and fault incidence within the mining region. Furthermore, we combine fractal analysis with Fry analysis to comprehensively predict the metallogenic potential in the area. This study reveals the fractal dimension values of fault structures, demonstrating that fault structures govern the distribution of ore bodies, with NE and NW fault structures being the primary ore-hosting features. Based on thorough analysis, we hypothesize that gold deposits in this area are generally distributed along the northeastern direction. By considering mineral distribution characteristics, this study identifies five potential metallogenic prospect areas within the study region. Capitalizing on advancements in information technology and big data, digital geology has gained prominence in prospecting and prediction. To this end, we construct a multi-information comprehensive prospecting model based on the structure-geochemical anomaly-mineralization alteration, employing the convolutional neural network (CNN) model for quantitative estimation of regional gold mineral resources. The findings validate the CNN model’s robust prediction performance in this area, leading to the determination of five prediction prospects. We observe a notable congruence between the two methods, offering significant insights for subsequent exploration endeavors in the region.

The landing gear structure suffers from large loads during aircraft takeoff and landing, and an accurate prediction of landing gear performance is beneficial to ensure flight safety. Nevertheless, the landing gear performance prediction method based on machine learning has a strong reliance on the dataset, in which the feature dimension and data distribution will have a great impact on the prediction accuracy. To address these issues, a novel MCA-MLPSA is developed. First, an MCA (multiple correlation analysis) method is proposed to select key features. Second, a heterogeneous multilearner integration framework is proposed, which makes use of different base learners. Third, an MLPSA (multilayer perceptron with self-attention) model is proposed to adaptively capture the data distribution and adjust the weights of each base learner. Finally, the excellent prediction performance of the proposed MCA-MLPSA is validated by a series of experiments on the landing gear data.

Abstract The nonlinear analyses of the hypersonic and high-enthalpy boundary-layer transition had received little attention compared with the widely-studied linear instabilities. In this work, the oblique-mode breakdown, as one of the most available transition mechanisms, is studied using the nonlinear parabolized stability equations (NPSE) with consideration of the thermal-chemical non-equilibrium effects. The flow over a blunt cone is computed at a free-stream Mach-number of 15. The rope-like structures and the spontaneous radiation of sound waves are observed in the schlieren-like picture. It is also illustrated that the disturbances of the species mass and vibrational temperature near the wall are mainly generated by the product term of the wall-normal velocity disturbance and the mean-flow gradient. In comparison to the CPG flow, the TCNE effects destabilize the second mode and push upstream the N factor envelope. The higher growth rate of the oblique wave leads to stronger growth of the streamwise vortices and harmonic waves.

In this tutorial paper, we systematically present the fundamental operating principles and analytical details of the discrete Fourier transform based signal processing techniques for the TDM MIMO FMCW millimeter-wave (mmWave) automotive radars. The mmWave radars provide a key sensing capability to support safety features of the conventional and autonomous vehicles. Automotive radar sensors are used to detect presence and location of the objects of interest to derive comprehensive and accurate knowledge of road conditions and surrounding environments. Automotive radars are subjected to an increasing demand to provide high-resolution measurements in the range-Doppler-azimuth-elevation domains. Therefore, the current state-of-the-art automotive radars commonly employ MIMO technologies, resulting in a large block of multidimensional data to process in real time through a long chain of signal processing algorithms. Detailed coverage on the fundamental radar signal processing techniques are scattered in a large body of literature for classical radar systems. Currently, there is no tutorial available that covers the technical details of the latest TDM MIMO FMCW radar technology, making it extremely hard for new researchers in the field of automotive mmWave radars. This paper contains sufficient technical details to serve as a tutorial. It can help lay a solid analytical foundation to facilitate exploration and development of advanced radar signal processing techniques for automotive applications. The algorithmic details and analytical results presented in this paper can be readily applied to both real-time implementation and post-processing. Simulation and experimental results are presented in this paper to validate analytical derivations and to demonstrate the capabilities of the TDM MIMO radar sensor in practical implementations.

Radiation belt electron dropouts indicate electron flux decay to the background level during geomagnetic storms, which is commonly attributed to the effects of wave-induced pitch angle scattering and magnetopause shadowing. To investigate the loss mechanisms of radiation belt electron dropouts triggered by a solar wind dynamic pressure pulse event on 12 September 2014, we comprehensively analyzed the particle and wave measurements from Van Allen Probes. The dropout event was divided into three periods: before the storm, the initial phase of the storm, and the main phase of the storm. The electron pitch angle distributions (PADs) and electron flux dropouts during the initial and main phases of this storm were investigated, and the evolution of the radial profile of electron phase space density (PSD) and the (μ, K) dependence of electron PSD dropouts (where μ, K, and L* are the three adiabatic invariants) were analyzed. The energy-independent decay of electrons at L > 4.5 was accompanied by butterfly PADs, suggesting that the magnetopause shadowing process may be the major loss mechanism during the initial phase of the storm at L > 4.5. The features of electron dropouts and 90°-peaked PADs were observed only for >1 MeV electrons at L < 4, indicating that the wave-induced scattering effect may dominate the electron loss processes at the lower L-shell during the main phase of the storm. Evaluations of the (μ, K) dependence of electron PSD drops and calculations of the minimum electron resonant energies of H+-band electromagnetic ion cyclotron (EMIC) waves support the scenario that the observed PSD drop peaks around L* = 3.9 may be caused mainly by the scattering of EMIC waves, whereas the drop peaks around L* = 4.6 may result from a combination of EMIC wave scattering and outward radial diffusion.

Very-Low-Frequency (VLF) communication transmitters, operate worldwide, radiate emissions at particular frequencies 10-30 kHz. Here, the authors show VLF transmitter emissions that leak from the Earth’s ground are primarily responsible for bifurcating the energetic electron belt over 20–100 keV.

Using wave measurements from the EMFISIS instrument onboard Van Allen Probes, we investigate statistically the spatial distributions of the intensity of plasmaspheric hiss waves. To reproduce these empirical results, we establish a fitting model that is a third-order polynomial function of L-shell, magnetic local time (MLT), magnetic latitude (MLAT), and AE*. Quantitative comparisons indicate that the model’s fitting functions can reflect favorably the major empirical features of the global distribution of hiss wave intensity, including substorm dependence and the MLT asymmetry. Our results therefore provide a useful analytic model that can be readily employed in future simulations of global radiation belt electron dynamics under the impact of plasmaspheric hiss waves in geospace.

With the wider adoption of edge computing services, intelligent edge devices, and high-speed V2X communication, compute-intensive tasks for autonomous vehicles, such as object detection using camera, LiDAR, and/or radar data, can be partially offloaded to road-side edge servers. However, data privacy becomes a major concern for vehicular edge computing, as sensitive sensor data from vehicles can be observed and used by edge servers. We aim to address the privacy problem by protecting both vehicles’ sensor data and the detection results. In this paper, we present vehicle–edge cooperative deep-learning networks with privacy protection for object-detection tasks, named vePOD for short. In vePOD, we leverage the additive secret sharing theory to develop secure functions for every layer in an object-detection convolutional neural network (CNN). A vehicle’s sensor data is split and encrypted into multiple secret shares, each of which is processed on an edge server by going through the secure layers of a detection network. The detection results can only be obtained by combining the partial results from the participating edge servers. We have developed proof-of-concept detection networks with secure layers: vePOD Faster R-CNN (two-stage detection) and vePOD YOLO (single-stage detection). Experimental results on public datasets show that vePOD does not degrade the accuracy of object detection and, most importantly, it protects data privacy for vehicles. The execution of a vePOD object-detection network with secure layers is orders of magnitude faster than the existing approaches for data privacy. To the best of our knowledge, this is the first work that targets privacy protection in object-detection tasks with vehicle–edge cooperative computing.

In this study, low-iron Zn–Fe alloy coatings and pure Zn coatings, with or without trivalent chromium passivation treatment, were electrodeposited onto a sintered NdFeB magnet from a weak acid chloride bath. The surface morphology and structure of the coatings were then examined using the X-ray diffraction, a scanning electron microscope and 3D white-light interfering surface analysis. Meanwhile, the electrodeposition behavior and anti-corrosive properties of the coatings were investigated using cyclic voltammetry, potentiodynamic polarization, electrochemical impedance spectroscopy, and natural salt spray tests. The results indicate that a passivated Zn–Fe alloy coating with a 0.9 wt.% Fe content provided much better corrosion resistance than a pure Zn coating and could provide both anodic protection and physical barrier function in the NdFeB substrates. The Fe element in Zn–Fe alloy coating was predominantly in solid solution in η-phase and small amounts in elemental form, which was beneficial to acquire a compact coating and passivation film. Finally, the passivated Zn–Fe alloy coating withstood 210 h against a neutral 3.5 wt.% NaCl salt spray without any white rust, which was 3–4 times longer than the pure Zn coating.

Uvulopalatopharyngoplasty (UPPP) is a widely accepted surgery to treat patients with obstructive sleep apnea. However, the outcomes of surgery are variable and not satisfactory in many cases. To explore the key factors that influence outcomes of surgery from perspectives of upper airway stability using computational fluid dynamics methods, three-dimensional (3D) upper airway models were reconstructed, and a series of steady-state and unsteady-state simulations were performed based on computerized tomography (CT) scans of five normal subjects and preoperative and postoperative CT scans of ten obstructive sleep apnea (OSA) patients. Aerodynamics in normal subjects, preoperative OSA patients, and postoperative OSA patients was compared, and the outcomes of UPPP were analyzed from perspectives of upper airway resistance and the wall pressure–inspiratory pressure ratio. The negative pressure on the collapsible part of the upper airway was a major contributor to the obstruction. Surgery mitigated the negative pressure by reducing the upper airway resistance as well as the wall pressure–inspiratory pressure ratio. The effect of UPPP was associated with the change in resistance and the wall pressure–inspiratory pressure ratio.

Experimental testing of physical turbines, often at a smaller scale, is an essential tool for engineers to investigate fundamental design parameters such as power output and efficiency. Despite issues with scaling and blockage which are caused by limitations in size and flow velocity of the test facilities, experimental tank testing in laboratory environments is often perceived as offering more control and thus trustworthier results than field testing. This paper presents field tests of a tidal turbine, performed using a self-propelled barge in real tidal flow and still water conditions, that are compared to a towing tank test. Factors influencing the performance characteristics, such as the choice of velocity sensor, vessel handling and data processing techniques are investigated in this paper. Direct comparison with test results of the exact same turbine obtained in an experimental test facility further confirms that field testing with robust data analysis capabilities is a viable, time and cost efficient alternative to characterise tidal turbines.

The non-uniformity and dynamics of the environment tidal stream turbines need to operate within will significantly influence the durability and reliability of tidal energy systems. The loadings on the turbine will increase substantially when the turbine is deployed in high magnitude waves with non-uniform tidal currents. The limitations of numerical solutions will be understood when the outcomes are verified with empirical data from system operations. In this paper, a Blade Element Momentum model is used to predict and compare the performance of a scaled turbine within a flume and a tow tank. Firstly, the numerical and experimental work is analysed for a turbine operating at flow speeds of 0.5m/s amd 1.0 m/s, wave heights of 0.2 m and 0.4 m and wave periods of 1.5 s and 1.7 s. Good agreement between the model and the experimental work was observed. However, in low TSRs the model tends to under predict the thrust, and the variation between the maximum and minimum values obtained within the experiments. Secondly, a turbine operating at flow speeds of 1.0 m/s and 4 different inflow profiles is analysed, where the wave heights for these cases were 0.09 m and 0.19 m and with wave periods of 2 s and 1.43 s. In this evaluation, the model tends to over predict the values of Ct and Cp when compared to those calculated from the experimental data. However, when investigating the values used to calculating both the thrust and torque coefficients, there is better agreement with these, which means the methodology used to determine these coefficients with inflow profiles should be revised.

Implicit large-eddy simulation is employed to simulate the flow in an asymmetric plane diffuser at Re=9000. Flow separation exists near the throat and evolves to large-scale, unsteady separation in the expansion section and the downstream region. An unconventional flow control method, namely, a cylindrical Karman-vortex generator (KVG) with different sizes and locations that induces periodic spanwise vortex shedding, is set upstream of the throat to suppress the flow separation. An appropriately designed KVG can enhance the mixing of the outer flow and the low energy fluid near the wall region by the periodic shedding Karman-vortices, and effectively reduce the separation bubble size. For the present optimal case, the length and height of the separation bubble are decreased 50.4% and 90.9%, respectively. The static pressure recovery coefficient is also increased by about 50%. Moreover, the velocity and total pressure distributions at the end of the expansion section are more uniform with lower fluctuation in the case with KVG installed. An optimal KVG diameter DK is suggested to be 3–4% of the expansion section length LE. The gap ratio to the lower wall G/DK and the length ratio to the throat Lt/DK are suggested to be 2.0–3.0 and 5.0–10.0, respectively.

Abstract Using Van Allen Probes pitch angle‐resolved electron flux data, we report intriguing events of electron butterfly pitch angle distributions (PADs) at L > ~5.5 on the nightside with distinct durations, i.e., about 9 hr for the 28–29 April 2013 event and over 2 days for the 2–5 May 2013 event. The formation and evolution of the short‐duration electron butterfly PADs exhibited insignificant dependence on electron energy, probably resulting from a combination of drift shell splitting, magnetopause shadowing, and wave diffusion, but the long‐duration electron butterfly evolution presented strong energy dependence, likely mainly attributed to interactions with MS waves. Both the short‐duration and long‐duration electron butterfly PADs disappeared closely related to enhanced substorm activities, while the responsible waves could be plasmaspheric hiss or chorus waves. Our results demonstrate that radiation belt electron butterfly PADs are naturally complex in both spatial and temporal scales and well connected to solar wind condition, substorm activity, and magnetospheric wave distribution.

Retaining chemical heterogeneity of 2:14:1 grains and forming continuous grain boundaries are two critical contributions to the strong magnetism in multi-main-phase (MMP) magnets. However, the ideal grain boundary microstructure is usually achieved at the expense of weakening the chemical heterogeneity, which poses a big challenge. Here we report a versatile strategy to balance the chemical heterogeneity and microstructure of MMP Nd-Ce-La-Fe-B magnets through tailoring liquid-phase-sintering. At optimum 1040 °C sintering, MMP magnet with 27 wt% Ce–La substitution level exhibits an equivalent weight-bearing capacity to 40MGOe commercial Nd-Fe-B, which is attributed to the joint contributions from retained chemical heterogeneity, essential densification, homogeneous grain size distribution and continuous intergranular phase network with ordered Ia3¯ structure. When shifting sintering temperature Ts towards the higher or lower range, the deteriorated magnetic properties are dominated by distinct restraints. With Ts above 1040 °C, the decreased coercivity is mainly restrained by the negative role of gradual chemical homogenization and abnormal grain growth, as verified by experimental and simulated results. However, with Ts below 1040 °C, the unsatisfactory magnetic performance mainly roots in insufficient densification and discontinuous grain boundary. These advances may deepen our understanding on designing high-performance MMP magnets with balanced contributions from modified microstructure and retained chemical heterogeneity. Keywords: Multi-main-phase, Liquid-phase-sintering, Chemical heterogeneity, Microstructure

Wave-particle interactions triggered by whistler-mode chorus waves are an important contributor to the Jovian radiation belt electron dynamics. While the sensitivity of chorus-driven electron scattering to the ambient magnetospheric and wave parameters has been investigated, there is rather limited understanding regarding the extent to which the dynamic evolution of Jovian radiation belt electrons, under the impact of chorus wave scattering, depends on the electron distribution profiles. We adopt a group of reasonable initial conditions based upon the available observations and models for quantitative analyses. We find that inclusion of pitch angle variation in initial conditions can result in increased electron losses at lower pitch angles and substantially modify the pitch angle evolution profiles of > ~500 keV electrons, while variations of electron energy spectrum tend to modify the evolution primarily of 1 MeV and 5 MeV electrons. Our results explicitly demonstrate the importance to the radiation belt electron dynamics in the Jovian magnetosphere of the initial shape of the electron phase space density, and indicate the extent to which variations in electron energy spectrum and pitch angle distribution can contribute to the evolution of Jovian radiation belt electrons caused by chorus wave scattering.

Whistler-mode chorus waves are regarded as an important acceleration mechanism contributing to the formation of relativistic and ultra-relativistic electrons in the Jovian radiation belts. Quantitative determination of the chorus wave driven electron scattering effect in the Jovian magnetosphere requires detailed information of both ambient magnetic field and plasma density and wave spectral property, which however cannot be always readily acquired from observations of existed missions to Jupiter. We therefore perform a comprehensive analysis of the sensitivity of chorus induced electron scattering rates to ambient magnetospheric and wave parameters in the Jovian radiation belts to elaborate to which extent the diffusion coefficients depend on a number of key input parameters. It is found that quasi-linear electron scattering rates by chorus can be strongly affected by the ambient magnetic field intensity, the wave latitudinal coverage, and the peak frequency and bandwidth of the wave spectral distribution in the Jovian magnetosphere, while they only rely slightly on the background plasma density profile and the peak wave normal angle, especially when the wave emissions are confined at lower latitudes. Given the chorus wave amplitude, chorus induced electron scattering rates strongly depend on Jovian L-shell to exhibit a tendency approximately proportional to LJ3. Our comprehensive analysis explicitly demonstrates the importance of reliable information of both the ambient magnetospheric state and wave distribution property to understanding the dynamic electron evolution in the Jovian radiation belts and therefore has implications for future mission planning to explore the extreme particle radiation environment of Jupiter and its satellites.

Log-structured merge tree (LSM-tree)-based key-value stores are widely deployed in largescale storage systems. The underlying reason is that the traditional relational databases cannot reach the high performance required by big-data applications. As high-throughput alternatives to relational databases, LSM-tree-based key-value stores can support high-throughput write operations and provide high sequential bandwidth in storage systems. However, the compaction process triggers write amplification and is confronted with the degraded write performance, especially under update-intensive workloads. To address this issue, we design a holistic key-value store to explorer near-data processing (NDP) and on-demand scheduling for compaction optimization in an LSM-tree key-value store, named DStore. DStore makes full use of various computing capacities in the host-side and device-side subsystems. DStore dynamically divides the whole host-side compaction tasks into the above two-side subsystems according to two-side different computing capabilities. Meanwhile, the device must be featured with an NDP model. The divided compaction tasks are performed by the host and the device in parallel. In DStore, the NDP-based devices exhibit low-latency and high-bandwidth performance, thus facilitating key-value stores. DStore not only accomplishes compaction for key-value stores but also improves the system performance. We implement our DStore prototype in a real-world platform, and different kinds of testbeds are employed in our experiment. LevelDB and a static compaction optimization using the NDP model (called Co-KV) are used to compare with the DStore in our evaluation. Results show that DStore achieves about 3.7× performance improvement over LevelDB under the db_bench workload. In addition, DStore-enabled key-value stores outperform LevelDB by a factor of about 3.3× and 77% in terms of throughput and latency under YCSB benchmark, respectively.

This study proposes the design of a tidal turbine station keeping system based on the adoption of a tensioned mooring system. Damping is introduced to investigate its effect on the reduction in the peak load experienced by tidal turbines during their operational lives in high-energy wave–current environments. A neutrally buoyant turbine is supported using a tensioned cable-based mooring system, where tension is introduced using a buoy fully submersed in water. The loads on the turbine rotor blades and buoy are calculated using a wave and current-coupled model. A modelling algorithm is proposed based on inverted pendulums, which respond to various sea state conditions, to study the behaviour of the system as well as the loads on blades. The results indicate that the tensioned mooring system reduces the peak thrust on the turbine and validates the applicability of the model.

Flows around vortex generators (VGs), which serve as one of the important flow control methods, are investigated by solving Reynolds-Averaged Navier-Stokes (RANS) equations. The influences on the main flow of VGs are intended to explore. To validate computational schemes, the flow around a single VG on a flat plane is computed to acquire basic knowledge of this kind of flow. Then transonic flow past a standard model, named by ONERA-M6 wing, is predicted to investigate the flow features of shockwave/boundary-layer interactions (SWBLI). Investigation is focused on a supercritical wing. Firstly, the effects of a row of VGs on the airfoil with the same cross-section design are calculated with periodical boundaries in transonic conditions. Then, VGs on the whole supercritical wing are analyzed with strong SWBLI. Lastly, VGs are mounted more upwind (about 3.5% local chord) to explore the effects at low speed and high incidence condition. The numerical results show that seven VGs on the wing can effectively suppress the separations behind the strong SWBLI and decrease spanwise flow and wing-tip vortex in transonic condition. VGs can also decrease the large scope of separation over the wing at low speed with high angle of attack.

Although the uniform theory of diffraction (UTD) could be theoretically applied to arbitrarilyshaped convex objects modeled by nonuniform rational B-splines (NURBS), one of the great challenges in calculation of the UTD surface diffracted fields is the difficulty in determining the geodesic paths along which the creeping waves propagate on arbitrarilyshaped NURBS surfaces. In differential geometry, geodesic paths satisfy geodesic differential equation (GDE). Hence, in this paper, a general and efficient adaptive variable step Euler method is introduced for solving the GDE on arbitrarilyshaped NURBS surfaces. In contrast with conventional Euler method, the proposed method employs a shape factor (SF) ξ to efficiently enhance the accuracy of tracing and extends the application of UTD for practical engineering. The validity and usefulness of the algorithm can be verified by the numerical results.

Glioblastoma represents the predominant and a highly aggressive primary neoplasm of the central nervous system that has an abnormal metabolism. Our previous study showed that chrysomycin A (Chr-A) curbed glioblastoma progression in vitro and in vivo. However, whether Chr-A could inhibit orthotopic glioblastoma and how it reshapes metabolism are still unclear. In this study, Chr-A markedly suppressed the development of intracranial U87 gliomas. The results from airflow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) indicated that Chr-A improved the abnormal metabolism of mice with glioblastoma. Key enzymes including glutaminase (GLS), glutamate dehydrogenases 1 (GDH1), hexokinase 2 (HK2) and glucose-6-phosphate dehydrogenase (G6PD) were regulated by Chr-A. Chr-A further altered the level of nicotinamide adenine dinucleotide phosphate (NADPH), thus causing oxidative stress with the downregulation of Nrf-2 to inhibit glioblastoma. Our study offers a novel perspective for comprehending the anti-glioma mechanism of Chr-A, highlighting its potential as a promising chemotherapeutic agent for glioblastoma. [ABSTRACT FROM AUTHOR]

As a new programming paradigm, deep neural networks (DNNs) have been increasingly deployed in practice, but the lack of robustness hinders their applications in safety-critical domains. While there are techniques for verifying DNNs with formal guarantees, they are limited in scalability and accuracy. In this article, we present a novel counterexample-guided abstraction refinement (CEGAR) approach for scalable and exact verification of DNNs. Specifically, we propose a novel abstraction to break down the size of DNNs by over-approximation. The result of verifying the abstract DNN is conclusive if no spurious counterexample is reported. To eliminate each spurious counterexample introduced by abstraction, we propose a novel counterexample-guided refinement that refines the abstract DNN to exclude the spurious counterexample while still over-approximating the original one, leading to a sound, complete yet efficient CEGAR approach. Our approach is orthogonal to and can be integrated with many existing verification techniques. For demonstration, we implement our approach using two promising tools, Marabou and Planet, as the underlying verification engines, and evaluate on widely used benchmarks for three datasets ACAS, Xu, MNIST, and CIFAR-10. The results show that our approach can boost their performance by solving more problems in the same time limit, reducing on average 13.4%–86.3% verification time of Marabou on almost all the verification tasks, and reducing on average 8.3%–78.0% verification time of Planet on all the verification tasks. Compared to the most relevant CEGAR-based approach, our approach is 11.6–26.6 times faster. [ABSTRACT FROM AUTHOR]