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Abstract Objective State anxiety and trait anxiety are differentially mapped in brain function. However, the genetic and neurobiological basis of anxiety-related functional changes remain largely unknown. Methods Participants aged 18–30 from the community underwent resting-state fMRI and were assessed with the State-Trait Anxiety Inventory. Using a general linear regression model, we analyzed the effects of state and trait anxiety, as well as their sum and difference (delta), on regional homogeneity (ReHo) in cortical areas. ReHo patterns denote the spatial distribution of ReHo associated with anxiety scores. We further explored the spatial correlations between ReHo patterns and neuromaps, including gene expression, neurotransmitter receptor density, myelination, and functional connectivity gradients, to elucidate the genetic and molecular substrates of these ReHo patterns. Results Our findings demonstrated robust spatial correlations between whole-brain ReHo patterns for state and trait anxiety, with trait anxiety and the delta value exhibiting stronger network correlations, notably in the dorsal attention, salience, visual, and sensorimotor networks. Genes highly correlated with ReHo patterns exhibited unique spatiotemporal expression patterns, involvement in oxidative stress, metabolism, and response to stimuli, and were expressed in specific cell types. Furthermore, ReHo patterns significantly correlated with neuromaps of neurotransmitter receptor density, myelination, and functional connectivity gradients. Conclusions The ReHo patterns associated with anxiety may be driven by genetic and neurobiological traits. Our findings contribute to a deeper understanding of the pathogenesis of anxiety from a genetic and molecular perspective.

Abstract Background Recurrence of lower grade glioma (LrGG) appeared to be unavoidable despite considerable research performed in last decades. Thus, we evaluated the postoperative recurrence within two years after the surgery in patients with LrGG by preoperative advanced diffusion magnetic resonance imaging (dMRI). Materials and methods 48 patients with lower-grade gliomas (23 recurrence, 25 nonrecurrence) were recruited into this study. Different models of dMRI were reconstructed, including apparent fiber density (AFD), white matter tract integrity (WMTI), diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), Bingham NODDI and standard model imaging (SMI). Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) was used to construct a multiparametric prediction model for the diagnosis of postoperative recurrence. Results The parameters derived from each dMRI model, including AFD, axon water fraction (AWF), mean diffusivity (MD), mean kurtosis (MK), fractional anisotropy (FA), intracellular volume fraction (ICVF), extra-axonal perpendicular diffusivity (De⊥), extra-axonal parallel diffusivity (De∥) and free water fraction (fw), showed significant differences between nonrecurrence group and recurrence group. The extra-axonal perpendicular diffusivity (De⊥) had the highest area under curve (AUC = 0.885), which was significantly higher than others. The variable importance for the projection (VIP) value of De⊥ was also the highest. The AUC value of the multiparametric prediction model merging AFD, WMTI, DTI, DKI, NODDI, Bingham NODDI and SMI was up to 0.96. Conclusion Preoperative advanced dMRI showed great efficacy in evaluating postoperative recurrence of LrGG and De⊥ of SMI might be a valuable marker.

Abstract The pore-throat structure is a crucial parameter for evaluating reservoir characteristics and assessing the potential of oil and gas resources. Understanding the relationship between reservoir pore-throat variations and oil-bearing properties is essential. Through a combination of techniques, including thin-section casting, scanning electron microscopy (SEM), micro-computed tomography (micro-CT), and high-pressure mercury injection (HPMI), we examined the tight sandstone reservoirs from the Chang 4 + 5 members of the Yanchang Formation in the study area. This analysis elucidates the relationship between the pore-throat structure and fractal characteristics of the samples and their oil-bearing properties. The results show that : (1) The tight sandstone reservoirs in the study area mainly develop three types of pores: dissolution pores, residual intergranular pores, and microfractures. Residual intergranular pores are primarily controlled by early compaction processes, while dissolution processes easily form secondary pores, increasing the porosity of the reservoir. Microfractures can significantly enhance both the permeability of the reservoir. (2) Using the characteristic parameters of HPMI, the reservoir is classified into four categories, labeled as type I to type IV. As the categories progress from type I to type IV, pore-throat size decreases, porosity and permeability decrease, and reservoir properties deteriorate. The overall fractal dimension of pores decreases, while the fractal dimensions of individual pore types increase. Pore connectivity becomes more complex, and heterogeneity strengthens. (3) Reservoir porosity shows a strong positive correlation with permeability. As reservoir properties improve, the number of macropores increases, leading to a higher Reservoir Quality Index (RQI) and better oil-bearing characteristics.

The distribution of oil and gas phases around ball–raceway local regions is an important basis and foundation for determining whether a bearing is sufficiently lubricated. To obtain the oil phase distribution law in the inner raceway–ball contact local region (IBCR) and outer raceway–ball contact local region (OBCR) of the ball bearing with under-race lubrication, the numerical simulation method is used. The effects of bearing rotation speed, oil flow rate, oil viscosity, and oil density on these two regions are studied. The results indicate that the oil phase exhibited significant periodic changes in both time and space. Compared with that in the IBCR, the oil phase distribution in the OBCR is more uniform. Increasing the bearing rotation speed and reducing the oil flow rate made the IBCR and OBCR more uniform. Changing the oil viscosity only alters the distribution pattern of the OBCR. The oil density may not affect the fluid flow state or the oil phase distribution in the bearing.

Abstract Background Hyperhomocysteinemia (HHcy) is an independent risk factor for atherosclerosis (AS). Endothelial mesenchymal transition (EndMT) refers to the process in which endothelial cells lose endothelial cell morphology and characteristic gene expression, and acquire phenotypic characteristics and gene expression related to mesenchymal cells. Numerous studies have confirmed that EndMT is involved in the formation of atherosclerosis. Catalpol is one of the active components of Rehmannia, which has antioxidant, anti-inflammatory, anti-tumor, neuroprotective and other biological activities. Studies have shown that catalpol can reduce atherosclerotic plaque induced by high sugar or fat. However, the effect of catalpol on HHCY-induced EndMT is unclear. Methods and results In vitro HHcy-treated primary human umbilical vein endothelial cells (HUVECs) were used to construct a cell model, and the antioxidants N-acetylcysteine (NAC) and catalase alcohol were administered. In vivo C57BL/6N mice were given a diet fed with 4.4% high methionine chow to construct a HHcy mice model and were treated with catalpol. The results showed that hhcy could induce morphological transformation of endothelial cells into mesenchymal cells, increase intracellular ROS content, up-regulate α-SMA, N-cadherin, p-p65 protein expression, down-regulate VE-cadherin, CD31 protein expression, induce pathological changes of aortic root endothelium, and increase aortic endothelial ROS content. Catalpol reversed these hhcy induced outcomes. Conclusions Catalpol inhibits HHcy-induced EndMT, and the underlying mechanism may be related to the ROS/NF-κB signaling pathway. Catalpol may be a potential drug for the treatment of HHcy-related AS.

Background: Brain structure, oxygenation and perfusion are important factors in aging. Coupling between regional cerebral oxygen consumption and perfusion also reflects functions of neurovascular unit (NVU). Their trajectories and sex differences during normal aging important for clinical interpretation are still not well defined. In this study, we aim to investigate the relationship between brain structure, functions and age, and exam the sex disparities.Method: A total of 137 healthy subjects between 20∼69 years old were enrolled with conventional MRI, structural three-dimensional T1-weighted imaging (3D-T1WI), 3D multi-echo gradient echo sequence (3D-mGRE), and 3D pseudo-continuous arterial spin labeling (3D-pCASL). Oxygen extraction fraction (OEF) and cerebral blood flow (CBF) were respectively reconstructed from 3D-mGRE and 3D-pCASL images. Cerebral metabolic rate of oxygen (CMRO2) were calculated as follows: CMRO2=CBF·OEF·[H]a, [H]a=7.377 μmol/mL. Brains were segmented into global gray matter (GM), global white matter (WM), and 148 cortical subregions. OEF, CBF, CMRO2, and volumes of GM/WM relative to intracranial volumes (rel_GM/rel_WM) were compared between males and females. Generalized additive models were used to evaluate the aging trajectories of brain structure and functions. The coupling between OEF and CBF was analyzed by correlation analysis. P or PFDR < 0.05 was considered statistically significant.Results: Females had larger rel_GM, higher CMRO2 and CBF of GM/WM than males (P < 0.05). With control of sex, CBF of GM significantly declined between 20 and 32 years, CMRO2 of GM declined subsequently from 33 to 41 years and rel_GM decreased significantly at all ages (R2 = 0.27, P < 0.001; R2 = 0.17, P < 0.001; R2 = 0.52, P < 0.001). In subregion analysis, CBF declined dispersedly while CMRO2 declined widely across most subregions of the cortex during aging. Robust negative coupling between OEF and CBF was found in most of the subregions (r range = -0.12∼-0.48, PFDR < 0.05).Conclusion: The sex disparities, age trajectories of brain structure and functions as well as the coupling of NVU in healthy individuals provide insights into normal aging which are potential targets for study of pathological conditions.

In order to study the influence of basement inclination on the stability of the dump slope under rainfall conditions, according to the actual situation of Antaibao Open-pit Coal Mine, FLAC3D software was used to simulate the slope of the dump under the same rain intensity and different base inclination angles. The results show that in the early stage of rainfall, the saturation of the surface layer of the dump slope first grows rapidly and tends to the stable value of unsaturation, and when the rainfall infiltrates to the junction of the weak layer and bedrock, the rainwater accumulates at the slope angle under the action of gravity and leads to shear failure at the slope angle. With the increase of the basement inclination, the time lag effect occurred in each monitoring point when it reached saturation or steady state, and the displacement and strain increment were positively correlated with the substrate inclination.

Background: Multiple system atrophy (MSA) and Parkinson's disease (PD) are neurodegenerative disorders characterized by α-synuclein pathology, disrupted iron homeostasis and impaired neurochemical transmission. Considering the critical role of iron in neurotransmitter synthesis and transport, our study aims to identify distinct patterns of whole-brain iron accumulation in MSA and PD, and to elucidate the corresponding neurochemical substrates. Methods: A total of 122 PD patients, 58 MSA patients and 78 age-, sex-matched health controls underwent multi-echo gradient echo sequences and neurological evaluations. We conducted voxel-wise and regional analyses using quantitative susceptibility mapping to explore MSA or PD-specific alterations in cortical and subcortical iron concentrations. Spatial correlation approaches were employed to examine the topographical alignment of cortical iron accumulation patterns with normative atlases of neurotransmitter receptor and transporter densities. Furthermore, we assessed the associations between the colocalization strength of neurochemical systems and disease severity. Results: MSA patients exhibited increased susceptibility in the striatal, midbrain, cerebellar nuclei, as well as the frontal, temporal, occipital lobes, and anterior cingulate gyrus. In contrast, PD patients displayed elevated iron levels in the left inferior occipital gyrus, precentral gyrus, and substantia nigra. The excessive iron accumulation in MSA or PD correlated with the spatial distribution of cholinergic, noradrenaline, glutamate, serotonin, cannabinoids, and opioid neurotransmitters, and the degree of this alignment was related to motor deficits. Conclusions: Our findings provide evidence of the interaction between iron accumulation and non-dopamine neurotransmitters in the pathogenesis of MSA and PD, which inspires research on potential targets for pharmacotherapy.

Geosynchronous synthetic aperture radar (GEO SAR) has the advantages of a short revisit time and a large beam footprint. Enhancing spatial resolution has become a hotspot in spaceborne SAR research areas. The existing designs of GEO SAR suffer from the problem of poor resolution (greater than 10 m) and cause low-precision observation in urban areas. First time, this article points out that GEO SAR has the ability to improve the two-dimensional (2-D) resolution of SAR images by spatial spectrum synthesis based on 2-D baselines, and proposes models and a signal processing method involved in GEO SAR spectrum synthesis. The 2-D spatial baselines of GEO SAR are analyzed and evaluated first based on satellite software and real ephemeris from Beidou Inclined Geosynchronous Orbit (IGSO) navigation satellites to demonstrate the potential of 2-D resolution improvement. Then the analytical models of the spectral shape and relative spectral shift between GEO SAR images are derived. Furthermore, a 2-D spectrum synthesis algorithm suitable for GEO SAR is also proposed, where we use deramping operation instead of the traditional spectrum shifting process. Nonideal factors are considered in the processing of synthesized. In addition, performances of the proposed algorithm, including the resolution improvement factor, the amplitude fluctuation, and the critical baseline, are constructed. Finally, computer simulations and equivalent experiments based on Beidou IGSO navigation satellites verify the proposed algorithm.

Differential synthetic aperture radar tomography (D-TomoSAR) uses multiple SAR acquisitions at different times to form an elevation-time 2-D synthetic aperture, enabling recovery of the target's 3-D structure and deformation velocity. However, the imaging performance of D-TomoSAR is influenced by the spatial-temporal baseline distribution, which is one of the crucial factors. Due to the difference in orbit altitude, the effects of various perturbation forces on geosynchronous SAR (GEO SAR) are significantly different from those on low Earth orbit SAR (LEO SAR), leading to different geometries in the spatial-temporal baseline distributions of GEO SAR and LEO SAR in the repeat-pass acquisitions. The spatial-temporal baseline distribution of LEO SAR is random, while that of GEO SAR is coupled. Although GEO SAR obtains a large number of acquisitions due to the short repeat-pass time, using data under all spatial-temporal baselines for D-TomoSAR does not necessarily provide the best imaging results. Therefore, how to select spatial-temporal baselines that can improve the estimation accuracy is important. In this article, the Cramér–Rao lower bound of GEO D-TomoSAR estimation accuracy is determined by considering multiple factors, including baseline decorrelation and spatial-temporal coupling. The optimal spatial-temporal baseline selection is modeled as a multiobjective problem and solved by the Nondominated Sorting Genetic Algorithm II (NSGA-II). Given the absence of in-orbit GEO SAR and the orbital similarity between GEO SAR and BeiDou Inclined Geosynchronous Orbit (IGSO) satellite, the real ephemeris of BeiDou IGSO satellite is used to simulate the baseline distribution of GEO SAR and verify the baseline optimization method.

Utilizing well logging data, outcrop profiles, and previous research, this study analyzes the sedimentary and tectonic evolution of the Yan’an Formation in the Ordos Basin, correlating the resulting sedimentary facies with hydrocarbon reservoirs to establish the necessary connections. The study reveals that: (1) Vertically, the sediment grain size shows a pattern of coarser grains at the bottom and top, with finer grains in the middle. Horizontally, the grain size tends to become finer from the northern, western, and southern parts of the basin toward the central-western region. (2) Tectonic movements during the Yan’an period controlled the sedimentary environment. These tectonic activities, through uplift and subsidence, caused the Yan’an Formation to experience four stages of sedimentary environments: braided river, lake, delta, and meandering river. (3) The Yan’an Formation exhibits four types of reservoir sandbody stacking patterns—continuous superposition, intermittent superposition, interbedded sand-mud, and single sandbody types—with continuous and intermittent stacking being the most common. (4) The hydrocarbons in the Yan’an Formation originated from the Chang 7 Member of the Yanchang Formation and migrated into the Yan’an reservoirs. The oil is characterized by its low density, low viscosity, and low pour point, indicating it is a high-maturity, high-quality crude oil.

In order to realize the real-time electromagnetic transient simulation of grid-connected photovoltaic power generation system with small time step, a heterogeneous multiplexed parallel real-time simulation method based on field programmable gate array (FPGA) and central processor is studied. According to the simulation accuracy requirements of the high and low frequency decoupling module, a differentiated simulation step size is used to solve the problem, and a 1us/50us FPGA-CPU multi-rate real-time simulation platform is constructed, in which the FPGA simulation part meets the real-time requirements of small-step transient simulation through the optimization of the response matching method and the EMTP algorithm, the offline simulation results are compared with Simulink, and the model before and after optimization is analyzed, the real-time electromagnetic transient simulation results are compared with Simulink, and the model before and after optimization is analyzed. By comparing the offline simulation results with Simulink and analyzing the FPGA resource consumption before and after model optimization, the accuracy and real-time performance of the co-simulation method for real-time simulation of grid-connected photovoltaic systems are verified.

With the increasingly complex electromagnetic environment faced by radar, random stepped frequency (RSF) has garnered widespread attention owing to its remarkable Electronic Counter-Countermeasure (ECCM) characteristic, and it has been universally applied in inverse synthetic aperture radar (ISAR) in recent years. However, if the phase error induced by the translational motion of the target in RSF ISAR is not precisely compensated, the imaging result will be defocused. To address this challenge, a novel 2D method based on sparse Bayesian learning, denoted as 2D-autofocusing complex-value inverse-free SBL (2D-AFCIFSBL), is proposed to accomplish joint ISAR imaging and autofocusing for RSF ISAR. First of all, to integrate autofocusing into the ISAR imaging process, phase error estimation is incorporated into the imaging model. Then, we increase the speed of Bayesian inference by relaxing the evidence lower bound (ELBO) to avoid matrix inversion, and we further convert the iterative process into a matrix form to improve the computational efficiency. Finally, the 2D phase error is estimated through maximum likelihood estimation (MLE) in the image reconstruction iteration. Experimental results on both simulated and measured datasets have substantiated the effectiveness and computational efficiency of the proposed 2D joint imaging and autofocusing method.

XRE-cupin family proteins containing an DNA-binding domain and a cupin signal-sensing domain are widely distributed in bacteria. In Pseudomonas aeruginosa, XRE-cupin transcription factors have long been recognized as regulators exclusively controlling cellular metabolism pathways. However, their potential functional roles beyond metabolism regulation remain unknown. PsdR, a typical XRE-cupin transcriptional regulator, was previously characterized as a local repressor involved solely in dipeptide metabolism. Here, by measuring quorum-sensing (QS) activities and QS-controlled metabolites, we uncover that PsdR is a new QS regulator in P. aeruginosa. Our RNA-seq analysis showed that rather than a local regulator, PsdR controls a large regulon, including genes associated with both the QS circuit and non-QS pathways. To unveil the underlying mechanism of PsdR in modulating QS, we developed a comparative transcriptome approach named "transcriptome profile similarity analysis" (TPSA). Using this TPSA method, we revealed that PsdR expression causes a QS-null-like transcriptome profile, resulting in QS-inactive phenotypes. Based on the results of TPSA, we further demonstrate that PsdR directly binds to the promoter for the gene encoding the QS master transcription factor LasR, thereby negatively regulating its expression and influencing QS activation. Moreover, our results showed that PsdR functions as a negative virulence regulator, as inactivation of PsdR enhanced bacterial cytotoxicity on host cells. In conclusion, we report on a new QS regulation role for PsdR, providing insights into its role in manipulating QS-controlled virulence. Most importantly, our findings open the door for a further discovery of untapped functions for other XRE-Cupin family proteins.

ObjectivesThe diverse nature of stroke necessitates individualized assessment, presenting challenges to case-control neuroimaging studies. The normative model, measuring deviations from a normal distribution, provides a solution. We aim to evaluate stroke-induced white matter microstructural abnormalities at group and individual levels and identify potential prognostic biomarkers.MethodsForty-six basal ganglia stroke patients and 46 healthy controls were recruited. Diffusion-weighted imaging and clinical assessment were performed within 7 days after stroke. We used automated fiber quantification to characterize intergroup alterations of segmental diffusion properties along 20 fiber tracts. Then each patient was compared to normative reference (46 healthy participants) by Mahalanobis distance tractometry for 7 significant fiber tracts. Mahalanobis distance-based deviation loads (MaDDLs) and fused MaDDLmulti were extracted to quantify individual deviations. We also conducted correlation and logistic regression analyses to explore relationships between MaDDL metrics and functional outcomes.ResultsDisrupted microstructural integrity was observed across the left corticospinal tract, bilateral inferior fronto-occipital fasciculus, left inferior longitudinal fasciculus, bilateral thalamic radiation, and right uncinate fasciculus. The correlation coefficients between MaDDL metrics and initial functional impairment ranged from 0.364 to 0.618 (p

Abstract The aim is to reconstruct a complete and detailed clothed human from a single‐view input. Implicit function is suitable for this task because it represents fine shape details and varied topology. Current methods, however, often suffer from artefacts such as broken or disembodied body parts, missing details, or depth ambiguity due to the ambiguity and complexity of human articulation. The main issue observed by the authors is structure‐agnostic. To address these problems, the authors fully utilise the skinned multi‐person linear (SMPL) model and propose a method using the Skeleton‐aware Implicit Function (SIF). To alleviate the broken or disembodied body parts, the proposed skeleton‐aware structure prior makes the skeleton awareness into an implicit function, which consists of a bone‐guided sampling strategy and a skeleton‐relative encoding strategy. To deal with the missing details and depth ambiguity problems, the authors’ body‐guided pixel‐aligned feature exploits the SMPL to enhance 2D normal and depth semantic features, and the proposed feature aggregation uses the extra geometry‐aware prior to enabling a more plausible merging with less noisy geometry. Additionally, SIF is also adapted to the RGB‐D input, and experimental results show that SIF outperforms the state‐of‐the‐arts methods on challenging datasets from Twindom and Thuman3.0.

Oil/air two-phase flow distribution in the bearings is the basis for bearing lubrication status identification and precise thermal analysis of the bearing. In order to understand the fluid behavior inside the under-race lubrication ball bearing and obtain an accurate oil volume fraction prediction model. A numerical study of ball bearing with under-race lubrication is carried out to study oil-gas two-phase distribution inside the bearing, and the influence of several parameters is quantified, like bearing rotating speed, oil flow rate, oil viscosity, and oil density. The results indicate that the oil fraction in the bearing cavity between the inner and outer ring shows a periodic distribution along the circumference direction, and the period is the same as the number of under-race oil supply holes. Oil distribution alone radial direction is affected by the outer-ring-guiding cage and centrifugal force, leading to oil accumulation near the outer ring. Different bearing running conditions and oil characteristics do not change the oil distribution trend alone in circumference and radial direction, but the difference ratio. Finally, based on the numerical simulation results, a formula for the average oil volume fraction prediction in the bearing ring cavity is constructed.

A deep understanding of oil behavior inside roller bearings is important for the precise design of bearing configurations and oil systems in aircraft engines. The numerical method is employed to track oil distribution inside cylindrical roller bearings with under-race lubrication along the circumference and radial direction, respectively. The results demonstrate that oil distribution along the circumference is periodic with the number of under-race nozzles, and higher rotating speed and lower flow rate would reduce the fluctuation amplitude. It is difficult for oil to flow through the gap between the cage pocket and rollers, and higher oil viscosity would worsen it further. In some extreme cases, the oil volume fraction near the outer race may be lower than 0.7%, causing the risk of lubricating and cooling failure. Thus, more attention should be paid to the outer race of the roller bearing with under-race lubrication, especially during the starting stage of the engine and in cold weather.

Background Acute respiratory distress syndrome (ARDS) is a common complication in patients with acute pancreatitis (AP), especially when patients complicated with acute kidney injury (AKI), resulting in increased duration of hospitalization and mortality. It is of potential clinical significance to develop a predictive model to identify the the high-risk patients.Method AP patients complicated with AKI from January 2019 to March 2022 were enrolled in this study and randomly divided into training cohort and validation cohort at a ratio of 2:1. The Least absolute shrinkage and selection operator(LASSO) regression and machine learning algorithms were applied to select features. A nomogram was developed based on the multivariate logistic regression. The performance of the nomogram was evaluated by AUC, calibration curves, and decision curve analysis.Results A total of 292 patients were enrolled in the study, with 206 in the training cohort and 86 in the validation cohort. Multivariate logistic analysis showed that IAP (Odds Ratio (OR)=4.60, 95%CI:1.23-18.24, p = 0.02), shock (OR = 12.99, 95%CI:3.47-64.04, p

This study aims to prepare the self-luminous pavement marking (SLPM) to enhance driving safety and reduce the risk of traffic accident. Firstly, the composition of the SLPM was determined by the mechanical properties and luminous performance tests. The optimal mass proportions of epoxy resin, toughening agent, curing agent, curing accelerator, luminous powder, reflective powder, glass bead and glass powder was 1.0:0.1:0.3:0.006:0.4:0.3:0.25:0.55. In addition, test results showed that the SLPM has satisfactory mechanical properties, durability and luminous performance, which met the engineering requirements for practical application. The afterglow time was more than 11 h at night in laboratory test. Moreover, it is feasible to prepare different colors of the SLPM according to the RGP principle. The afterglow color and luminous intensity of SLPM strongly depend on the luminous material and its proportions. Overall, this paper provides a reference for designing and applying SLPM materials in road engineering.

Early forecasts give people in a storm’s path time to prepare, but less is known about the cost to society when forecasts are incorrect. In this observational study, the authors examine over 700,000 births in the path of Hurricane Irene and find exposure was associated with impaired birth outcomes.

Aviation soot constitutes a significant threat to human well-being, underscoring the critical importance of accurate measurements. The condensation particle counter (CPC) is the primary instrument for quantifying aviation soot, with detection efficiency being a crucial parameter. The properties of small particles and the symmetry of their growth pathways are closely related to the detection efficiency of the CPC. In laboratory environments, sodium chloride is conventionally utilized to calibrate the CPC’s detection efficiency. However, aviation soot exhibits distinctive morphological characteristics compared to the calibration particles, leading to detection efficiencies obtained from calibration particles that may not be applicable to aviation soot. To address this issue, a quantitative study was performed to explore the detection efficiency deviations between aviation soot and calibration particles. The experiment initially utilized a differential mobility analyzer to size select the two types of polydisperse particles into monodisperse particles. Subsequently, measurements of the separated particles were performed using the TSI Corporation’s aerosol electrometer and a rigorously validated CPC (BH-CPC). These allowed for determining the detection efficiency deviation in the BH-CPC for the two types of particles at different particle sizes. Furthermore, the influence of the operating temperature of the BH-CPC on this detection efficiency deviation was investigated. The experimental results indicate a significant detection efficiency deviation between aviation soot and sodium chloride. In the range of 10–40 nm, the absolute detection efficiency deviation can reach a maximum of 0.15, and the relative deviation can reach a maximum of 0.75. And this detection efficiency deviation can be reduced by establishing a relevant relationship between the detection efficiency of the operating temperature and the calibration temperature. Compared to the saturated segment calibration temperature of 50 °C, the aviation soot detection efficiency is closer to the sodium chloride detection efficiency at the calibration temperature of 50 °C when the saturated segment operates at a temperature of 45 °C. These studies provide crucial theoretical guidance for enhancing the precision of aviation soot emission detection and establish a foundation for future research in monitoring and controlling soot emissions within the aviation sector.

The neural mechanisms for reward generalization are not fully understood. Here the authors investigate the role of posterior basolateral amygdala calbindin-expressing cells in modulating behavioural responses related to reward and aversion.

The Partial Least Square Regression (PLSR) method has shown admirable competence for predicting continuous variables from inter-correlated electrocorticography signals in the brain-computer interface. However, PLSR is essentially formulated with the least square criterion, thus, being considerably prone to the performance deterioration caused by the brain recording noises. To address this problem, this study aims to propose a new robust variant for PLSR. To this end, the maximum correntropy criterion (MCC) is utilized to propose a new robust implementation of PLSR, called Partial Maximum Correntropy Regression (PMCR). The half-quadratic optimization is utilized to calculate the robust projectors for the dimensionality reduction, and the regression coefficients are optimized by a fixed-point optimization method. The proposed PMCR is evaluated with a synthetic example and a public electrocorticography dataset under three performance indicators. For the synthetic example, PMCR realized better prediction results compared with the other existing methods. PMCR could also abstract valid information with a limited number of decomposition factors in a noisy regression scenario. For the electrocorticography dataset, PMCR achieved superior decoding performance in most cases, and also realized the minimal neurophysiological pattern deterioration with the interference of the noises. The experimental results demonstrate that, the proposed PMCR could outperform the existing methods in a noisy, inter-correlated, and high-dimensional decoding task. PMCR could alleviate the performance degradation caused by the adverse noises and ameliorate the electrocorticography decoding robustness for the brain-computer interface.

Abstract Pseudomonas aeruginosa strain PAO1 has been commonly used in the laboratory, with frequent genome variations reported. Quorum sensing (QS), a cell–cell communication system, plays important role in controlling a variety of virulence factors. However, the evolution and adaptability of QS in those laboratory strains are still poorly understood. Here we used the QS reporter and whole-genome sequencing (WGS) to systematically investigate the QS phenotypes and corresponding genetic basis in collected laboratory PAO1 strains. We found that the PAO1-z strain has an inactive LasR protein, while bearing an active Rhl QS system and exhibiting QS-controlled protease-positive activity. Our study revealed that an 18-bp insertion in mexT gene gave rise to the active QS system in the PAO1-z strain. This MexT inactivation restored the QS activity caused by the inactive LasR, showing elevated production of pyocyanin, cyanide and elastase. Our results implied the evolutionary trajectory for the PAO1-z strain, with the evulutionary order from the first Las QS inactivation to the final Rhl QS activation. Our findings point out that QS homeostasis occurs in the laboratory P. aeruginosa strain, offering a potential platform for the QS study in clinical isolates.

Domestic wells provide drinking water to 44 million people nationwide. Many of these wells, which remain federally unregulated and rarely tested for pollutants, serve rural populations clustered near surface-contaminated sites (e.g., hazardous waste sites, animal agriculture operations, coal ash ponds, etc.). The potential for natural disasters to deteriorate drinking water quality is well documented. Less understood is whether opportunistic post-disaster sampling might underrepresent vulnerable populations. When disaster strikes, well water sampling campaigns offer a glimpse into the quality of water for exposed residents. We examined over 8,000 well water samples from 2016 and 2017 to measure Hurricane Matthew’s impact on the presence of indicator bacteria. Bacteria presence was predicted at the household level following Hurricane Matthew’s landfall. The residential addresses associated with birth records as well as clinically estimated dates of conception and birth dates were used to predict the likelihood of indicator bacteria in drinking water sources that were unsampled but likely to have served pregnant women. We estimate that opportunistic well water sampling captures the average predicted contamination rates among households with pregnant women. Our approach documents a distribution of contamination risk where 2.7% of the vulnerable sample (670 unsampled households) have a 75% likelihood of total coliform presence. The predicted likelihood of indicator bacteria is elevated for a small share of households nearby swine lagoons that experienced the most torrential rainfall. However, the gap between sampled and unsampled households cannot otherwise be explained by the storm event or proximity to surface-contaminated sites. Findings suggest that sophisticated and holistic water quality prediction models may support post-disaster sampling campaigns by targeting individual households within vulnerable groups that are likely to experience higher risks from groundwater contamination.

Abstract Quantum random number generator (QRNG) relies on the intrinsic randomness of quantum mechanics to produce true random numbers which are important in information processing tasks. Due to the presence of the superposition state, a quantum computer can be used as a true random number generator. However, in practice, the implementation of the quantum computer is subject to various noise sources, which affects the randomness of the generated random numbers. To solve this problem, we propose a scheme based on the quantum computer which is motivated by the source-independent QRNG scheme in optics. By using a method to estimate the upper bound of the superposition state preparation error, the scheme can provide certified randomness in the presence of readout errors. To increase the generation rate of random bits, we also provide a parameter optimization method with a finite data size. In addition, we experimentally demonstrate our scheme on the cloud superconducting quantum computers of IBM.

Abstract Background The effect of comorbid hypertriglyceridemia (HTG) and abdominal obesity (AO) on acute pancreatitis (AP) remains unclear. The aim of this study was to explore the effect of comorbid HTG and AO and discuss which is the dominant disorder. Methods In this study, 1219 AP patients who presented with HTG or AO were stratified into four groups: non-HTG + non-AO, HTG + non-AO, non-HTG + AO, and HTG + AO. Results The 328 patients with comorbid HTG + AO were much younger (42.29 ± 11.77), mainly male (79.57%), and had higher TG levels, larger waist circumferences, and more past medical histories than the patients in the other three non-comorbid groups (P 60 years (OR = 1.326, 95% CI = 1.047–1.679), AO (OR = 1.701, 95% CI = 1.308–2.212), diabetes mellitus (OR = 1.551, 95% CI = 1.063–2.261), mild HTG (OR = 1.549, 95% CI = 1.137–2.112), and moderate to very severe HTG (OR = 2.810, 95% CI = 1.926–4.100) were independent risk factors associated with local complications (P

For upper limb amputees, wearing a myoelectric prosthetic hand is the only way for them to continue normal life. Even until now, the proposal of a high-precision and natural performance real-time control system based on surface electromyography (sEMG) signals is still challenging. Researchers have proposed many strategies for motion classification or regression prediction tasks based on sEMG signals. However, most of them have been limited to offline analysis only. There are even few papers on real-time control based on deep learning models, almost all of which are about motion classification. Rare studies tried to use deep learning-based regression models in real-time control systems for multi-joint angle estimation via sEMG signals. This paper proposed a CW-CNN regression model-based real-time control system for virtual hand control. We designed an Adaptive Kalman Filter to smooth the joint angles output before sending them as control commands to control a virtual hand. Eight healthy participants were invited, and three sessions experiments were conducted on two different days for all of them. During the real-time experiment, we analyzed the joint angles estimation accuracy and computational latency. Moreover, target achievement control (TAC) test was applied to emphasize motion regression in real-time. The experimental results show that the proposed control system has high precision for 3-DOFs motion regression in simultaneously, and the system remains stable and low computational latency. In the future, the proposed real-time control system can be applied to actual prosthetic hand.

Utilizing brake pad waste (BPW) in asphalt mixture can not only reduce the consumption of natural mineral material, but also alleviate the environmental pollution problem caused by such solid waste. This paper concentrates on the feasibility and strategy of using BPW as aggregate in asphalt mixture. Firstly, BPW aggregate within five different particle sizes were separately used to replace the limestone aggregate or filler with different proportions (25%, 50%, 75% and 100%) in asphalt mixture by the volume control method. The corresponding optimum asphalt contents of 21 kinds of asphalt mixtures were determined by the Marshall design procedure. Due to its high asphalt-absorption, BPW aggregate would increase the optimum asphalt content of asphalt mixture, which depends on the particle size of BPW aggregate. To meet the Chinese Standard Specification for asphalt mixture, the maximum substitution proportions for the BPW aggregates of 0–0.075 mm, 0.075–2.36 mm, 2.36–4.75 mm, 4.75–9.5 mm, 9.5–16 mm were 75%, 50%, 25%, 100%, and 100%, respectively. On this basis, BPW mixture simultaneously using the BPW aggregate with different sizes and the corresponding maximum substitution proportions were prepared and evaluated by a series of laboratory tests as well. Results implied that BPW mixture (BPW-M) outperformed the limestone asphalt mixture (Control-M) in term of the moisture stability, the high-temperature performance, the low-temperature property, and the fatigue life. Furthermore, dynamic modulus test results indicated that BPW-M mixture was more stiffness at relative low frequency (high temperature) and more flexible at relative high frequency (low temperature). Overall, this study verifies the feasibility and provides a reference datum of using BPW materials as aggregate in asphalt mixture.

Brain-computer interfaces (BCIs) based on motor imagery (MI) utilizing multi-channel electroencephalogram (EEG) data are commonly used to improve motor function of people with motor disabilities. EEG channel selection can enhance MI classification accuracy by selecting informative channels, accordingly reducing redundant information. The sequential backward floating search (SBFS) approach has been considered as one of the best feature selection methods. In this paper, SBFS is first implemented to select the optimal EEG channels in MI-BCI. Further, to reduce the time complexity of SBFS, the modified SBFS is proposed and applied to left and right hand MI tasks. In the modified SBFS, based on the map of EEG channels at the scalp, the symmetrical channels are selected as channel pairs and acceleration is thus realized by removing or adding multiple channels in each iteration. Extensive experiments were conducted on four public BCI datasets. Experimental results show that the SBFS achieves significantly higher classification accuracy (p < 0.001) than using all channels and conventional MI channels (i.e., C3, C4, and Cz). Moreover, the proposed method outperforms the state-of-the-art selection methods.

As an important predictor of adulthood psychopathology, self-reported childhood abuse appears heritable and is associated with brain abnormalities. However, the specific genetic mechanisms behind these brain alterations remain largely unknown. This study recruited young adults who reported different degrees of childhood abuse from the community. In order to fully understand the influence of genes on brain changes related to self-reported childhood abuse, various experiments were conducted in this study. Firstly, volume changes of gray matter and white matter related to childhood abuse were investigated by using advanced magnetic resonance imaging techniques. After sequencing the whole exons, we further investigated the relationship between polygenic risk score, brain volume alterations, and childhood abuse score. Furthermore, transcription-neuroimaging association analysis was used to identify risk genes whose expressions were associated with brain volume alterations. The gray matter volumes of left caudate and superior parietal lobule, and white matter volumes of left cerebellum and right temporal lobe-basal ganglia region were significantly correlated with the childhood abuse score. More importantly, brain volume changes mediated the influence of polygenic risk on self-reported childhood abuse. Additionally, transcription-neuroimaging association analysis reported 63 risk genes whose expression levels were significantly associated with childhood abuse-related brain volume changes. These genes are involved in multiple biological processes, such as nerve development, synaptic transmission, and cell construction. Combining data from multiple perspectives, our work provides evidence of brain abnormalities associated with childhood abuse, and further indicates that polygene genetic risk and risk gene expression may affect the occurrence of childhood abuse by brain regulation, which provides insights into the molecularpathology and neuromechanism of childhood adversity. Paying attention to the physical and mental health of high-risk children may be a fundamental way to prevent childhood abuse and promote lifelong mental health.

Among all kinds of compact cesium beam clocks, the optically detected magnetic-state-selected cesium beam clock (OMCC) combines the advantages of the magnetic state selecting scheme and fluorescence detecting method. This paper presents an overview of the OMCC. Technical issues, noise sources, frequency shifts and improvements of OMCC are reviewed. Finally, the frequency stability of five OMCC is given, which is better than the stability of the high-performance version of Microsemi 5071A.

Based on its ability to obtain two-dimensional (2D) high-resolution images in all-time and all-weather conditions, spaceborne synthetic aperture radar (SAR) has become an important remote sensing technique and the study of such systems has entered a period of vigorous development. Advanced imaging modes such as radar interferometry, tomography, and multi-static imaging, have been demonstrated. However, current in-orbit spaceborne SARs, which all operate in low Earth orbits, have relatively long revisit times ranging from several days to dozens of days, restricting their temporal sampling rate. Geosynchronous SAR (GEO SAR) is an active research area because it provides significant new capability, especially its much-improved temporal sampling. This paper reviews the research progress of GEO SAR technologies in detail. Two typical orbit schemes are presented, followed by the corresponding key issues, including system design, echo focusing, main disturbance factors, repeat-track interferometry, etc, inherent to these schemes. Both analysis and solution research of the above key issues are described. GEO SAR concepts involving multiple platforms are described, including the GEO SAR constellation, GEO-LEO/airborne/unmanned aerial vehicle bistatic SAR, and formation flying GEO SAR (FF-GEO SAR). Due to the high potential of FF-GEO SAR for three-dimensional (3D) deformation retrieval and coherence-based SAR tomography (TomoSAR), we have recently carried out some research related to FF-GEO SAR. This research, which is also discussed in this paper, includes developing a formation design method and an improved TomoSAR processing algorithm. It is found that GEO SAR will continue to be an active topic in the aspect of data processing and multi-platform concept in the near future.

In this paper, a fault identification algorithm combining a signal processing algorithm and machine learning algorithm is proposed, using a four-mass vibration MEMS gyroscope (FMVMG) for signal acquisition work, constructing a gyroscope fault dataset, and performing the model training task based on this dataset. Combining the improved EWT algorithm with SEResNeXt-50 reduces the impact of white noise in the signal on the identification task and significantly improves the accuracy of fault identification. The EWT algorithm is a wavelet analysis algorithm with adaptive wavelet analysis, which can significantly reduce the impact of boundary effects, and has a good effect on decomposition of signal segments with short length, but a reconstruction method is needed to effectively separate the noise signal and effective signal, and so this paper uses multiscale permutation entropy for calculation. For the reason that the neural network has a better ability to characterize high-dimensional signals, the one-dimensional signal is reconstructed into a two-dimensional image signal and the signal features are extracted. Then, the constructed image signals are fed into the SEResNeXt-50 network, and the characterization ability of the model is further improved in the network with the addition of the Squeeze-and-Excitation module. Finally, the proposed model is applied to the FMVMG fault dataset and compared with other models. In terms of recognition accuracy, the proposed method improves about 30.25% over the BP neural network and about 1.85% over ResNeXt-50, proving the effectiveness of the proposed method.

Electroencephalogram (EEG) channel optimization can reduce redundant information and improve EEG decoding accuracy by selecting the most informative channels. This article aims to investigate the universality regarding EEG channel optimization in terms of how well the selected EEG channels can be generalized to different participants. In particular, this study proposes a sparse logistic regression (SLR)-based EEG channel optimization algorithm using a non-zero model parameter ranking method. The proposed channel optimization algorithm was evaluated in both individual analysis and group analysis using the raw EEG data, compared with the conventional channel selection method based on the correlation coefficients (CCS). The experimental results demonstrate that the SLR-based EEG channel optimization algorithm not only filters out most redundant channels (filters 75–96.9% of channels) with a 1.65–5.1% increase in decoding accuracy, but it can also achieve a satisfactory level of decoding accuracy in the group analysis by employing only a few (2–15) common EEG electrodes, even for different participants. The proposed channel optimization algorithm can realize better universality for EEG decoding, which can reduce the burden of EEG data acquisition and enhance the real-world application of EEG-based brain–computer interface (BCI).

In Wireless Sensor Networks (WSN), the energy problem plays the critical role in network performance and lifetime, because of the limited battery capacities of sensor nodes. Recently, emerging wireless charging technologies provide a promising approach to address the energy problem in WSN. Researchers construct Wireless Rechargeable Sensor Networks (WRSN), which introduce mobile chargers with high capacity batteries to charge sensor nodes. Most studies in WRSN have paid attention to charging static nodes or mobile nodes with deterministic trajectories. In this work, we explore how to charge nodes with non-deterministic mobility. We propose a novel approach, named Predicting-Scheduling-Tracking (PST), to perform charging tasks in this case. In the proposed scheme, different from the existing work, we guide the mobile charger to chase the sensor and recharge it. In our work, the base station runs an improved LSTM to predict the future locations of nodes periodically. Then, the mobile charger can select an appropriate node as the charging target by a charging scheduling algorithm. During the energy transferring, a Kalman-filter-based tracking algorithm is used to ensure the charging-required distance between the mobile charger and the target node. The simulation results show that the proposed charging scheme can fulfil the charging tasks in WRSN of nodes with non-deterministic mobility.

BACKGROUND After the initial surge in COVID-19 cases, large numbers of patients were discharged from a hospital without assessment of recovery. Now, an increasing number of patients report postacute neurological sequelae, known as “long COVID” — even those without specific neurological manifestations in the acute phase.METHODS Dynamic brain changes are crucial for a better understanding and early prevention of “long COVID.” Here, we explored the cross-sectional and longitudinal consequences of COVID-19 on the brain in 34 discharged patients without neurological manifestations. Gray matter morphology, cerebral blood flow (CBF), and volumes of white matter tracts were investigated using advanced magnetic resonance imaging techniques to explore dynamic brain changes from 3 to 10 months after discharge.RESULTS Overall, the differences of cortical thickness were dynamic and finally returned to the baseline. For cortical CBF, hypoperfusion in severe cases observed at 3 months tended to recover at 10 months. Subcortical nuclei and white matter differences between groups and within subjects showed various trends, including recoverable and long-term unrecovered differences. After a 10-month recovery period, a reduced volume of nuclei in severe cases was still more extensive and profound than that in mild cases.CONCLUSION Our study provides objective neuroimaging evidence for the coexistence of recoverable and long-term unrecovered changes in 10-month effects of COVID-19 on the brain. The remaining potential abnormalities still deserve public attention, which is critically important for a better understanding of “long COVID” and early clinical guidance toward complete recovery.FUNDING National Natural Science Foundation of China.

MAX phase materials, exhibiting excellent antioxidation, corrosion resistance and high electrical and optical conductivity, attract much attention. The nonlinear optical properties of the MAX phase Ti2AlC was demonstrated by measuring the saturable absorption of Ti2AlC- polyvinyl (PVA) film. The modulation depth of the Ti2AlC-PVA film is calculated to be 2.21%. Stable Q-switched and mode-locked pulses train were achieved in Erbium-doped fiber ring cavity by using Ti2AlC-PVA film as a saturable absorber. The maximum single pulse energy of the Q-switched laser was 92.8 nJ, and the pulse width of the mode-locked laser was 680 fs. This study shows the great potential of MAX phase materials for ultrashort laser pulses generation and provides some valuable design guidelines for the preparation of SAs based on MAX phase materials.

Radar sensors offer several advantages over optical sensors in the gesture recognition for remote control of electronic devices. In this paper, we investigate the feasibility of human gesture recognition using the spectra of radar measurement parameters. With the combination of radar theory and classification methods, we found that the frequencies of different gestures' parameters could be utilized as features for gesture recognition. Six kinds of periodic dynamic gestures are designed to avoid the complexity of defining and extracting the start and end of the dynamic gesture. In addition to the frequency ratio, we also extracted some features related to motion range and detection coherence to eliminate the interferences brought by the unintended gestures. The decision tree classifier designed on the basis of experimental phenomena can guarantee effective classification between different gestures, and in general, the correct recognition rate of each gesture is higher than 90%. Finally, we collected the position and the Doppler velocity information of hand for classification by a W-band millimeter wave radar in the experiment and verified the usability of the proposed method.

The sub-satellite track of geosynchronous synthetic aperture radar (GEO SAR) presents the figure “8” or “O”, which causes the great changes of platform motion direction and the different projection of anisotropic irregularities along the line-of-sight (LOS) direction. Due to the almost equal angle velocity to that of Earth, the GEO SAR has smaller ionospheric penetration point (IPP) scanning velocity which is much smaller to the counterpart of the low earth orbit SAR (LEO SAR) while is comparable to the drifting velocity of irregularities, which will affect the effective azimuthal velocity. These facts lead to the consequence that the satellite signals from the GEO SAR would become more vulnerable when they are transmitted in the environment where the ionospheric scintillation occurs. However, few works are focused on these mentioned issues towards the GEO SAR system. In this paper, the impacts of ionospheric scintillation on GEO SAR imaging will be analyzed considering the anisotropy and drifting velocity of irregularities. The anisotropy and drifting velocity effects can both originate from the effect on power spectral density (PSD) of phase screen which is used to model the ionospheric scintillation effects. Based on the data from international geomagnetic reference field (IGRF) and satellite tool kit (STK), the GEO SAR imaging simulations for different GEO SAR orbital configurations and positions are carried out. The simulation results demonstrate that the anisotropy and the drifting velocity of irregularities will cause the changes of stripe direction and affect the quality of GEO SAR images.

Due to the short revisit time and large coverage of Geosynchronous synthetic aperture radars (GEO SARs) and the increasing number of low earth orbit synthetic aperture radar (LEO SAR) constellations, radio frequency interference (RFI) between GEO SARs and LEO SARs may occur, deteriorating the quality of SAR images. Traditional methods only simplify RFI to noise-like interference without considering the signal characteristics. In this paper, to accurately evaluate the impacts of GEO-to-LEO RFI and LEO-to-GEO RFI on imaging quantitatively, an RFI-impact quantitative analysis model is established. Taking account of the chirp signal form of SAR systems, the RFI power and image Signal-to-Interference-plus-Noise Ratio (SINR) are theoretically deduced and validated by numerical experiments. Based on the proposed method, the SAR image quality under different system parameters and bistatic configurations is estimated, and the probability of different configurations is also given. The results show that specular bistatic scattering RFI between GEO SARs and LEO SARs has serious effects on imaging, and the probability can approach 2% for certain orbital parameters and will become higher as LEO SAR constellations increase in the future, implying the necessity to suppress the RFI between the GEO SAR and the LEO SAR system.

In this paper, the effect of different viscosity and specific heat capacity on the gelation behavior of waxy crude oil was studied. The additional specific heat capacity method and the momentum source term method are used for numerical simulation. Grey correlation analysis method was used to quantitatively express the correlation degree of viscosity and specific heat capacity on average flow velocity, temperature drop rate and the ratio of gelled oil area to total area. The results show that the influence of viscosity on the gelation behavior of waxy crude oil is greater than that of specific heat capacity. When the viscosity of crude oil is large, natural convection is weaker and the temperature drop rate is smaller, which retards the development of the gelation behavior of crude oil. For crude oil with larger heat capacity, the temperature drop rate is smaller and the natural convection is slightly stronger, but the temperature drop rate has a great influence on the gelation behavior of crude oil, making it develop slowly. The influence of both viscosity and specific heat capacity on gelation behavior is limited.

Abstract: Based on analysis of main controlling factors of Chang 9, the source rock, driving force of migration, migration and accumulation modes, reservoir forming stages and model and enrichment law of Chang 9 reservoir were examined. The study showed that the oil of Chang 9 reservoir in the Jiyuan and Longdong (Eastern Gansu) areas came primarily from the source rock of Chang 7 Member, but the oil of Chang 9 reservoir in the Zhidan area came primarily from the source rock of Chang 9 Member. There developed lithologic-structural oil reservoirs in Gufengzhuang-Mahuangshan area in northwest Jiyuan, structural-lithologic oil reservoirs in east Jiyuan, and lithologic reservoirs in Huachi–Qingcheng area and Zhidan area. The overpressure of Chang 7 Member was the driving force of oil migration. The burial history showed that Chang 9 Member experienced two stages of reservoir forming, the reservoir formed in the Late Jurassic was smaller in charging scope and scale, and the Early Cretaceous was the period when the source rock generated oil and gas massively and the Chang 9 reservoir came into being. Along with the tectonic movements, Chang 7 bottom structure turned from high in the west and lower in the East in the sedimentary stage to high in the east and lower in the west in the hydrocarbon accumulation stage and at last to gentle western-leaning monoclinal structure at present. In Early Cretaceous, the Chang 7 bottom structure was the lowest in the west of Huanxian-Huachi-Wuqi-Dingbian areas, so the oil migrated laterally towards the higher positions around after entering the reservoir. In the main reservoir forming period, Chang 7 bottom had an ancient anticline in Mahuangshan-Hongjingzi area of west Jiyuan, controlling the oil reservoir distribution in west Jiyuan. Key words: Ordos Basin, Triassic Yanchang Formation, Chang 9 Member, source rock, driving force of migration, migration and accumulation mode, reservoir accumulation mode, enrichment law

In order to solve the system identification problems, the normalized least mean absolute deviation (NLMAD) algorithm was developed as an effective and robust method. In this paper, aiming at the system identification problems with sparsity characteristic, and taking the advantage of the NLMAD algorithm to suppress impulsive output measurement noise interference, we introduce the L1-norm as a sparse penalty constraint into the NLMAD algorithm to design a robust sparse adaptive filtering algorithm. Furthermore, considering the biased estimation caused by the input noise, we employ an unbiasedness criterion to derive an effective bias-compensated vector which can compensate the bias efficiently for the proposed sparse NLMAD algorithm. The desirable performance of the new method is measured with simulations of two stages. The proposed bias-compensated sparse NLMAD algorithm achieves better performance compared to other existing methods in both stages. Simulation results demonstrate the excellent performance of the proposed algorithm in solving sparse system identification problems. The promising results in this paper suggest that the bias-compensated sparse NLMAD algorithm may become a useful tool for system identification with noisy input and impulsive output noise.

Geosynchronous synthetic aperture radar (GEO SAR) is a very remarkable tool for monitoring rapid geologic disasters due to its short revisit time and long coverage time for targets. However, its large swath makes the system easily impacted by radio frequency interferences (RFIs), which can cause the decorrelations in images and interferograms. In this study, the authors establish the RFI decorrelation model in GEO SARs based on the signal-to-interference-plus-noise ratio analysis. Moreover, the mitigation method for RFI decorrelation based on proper system parameters design is proposed. Finally, simulations are conducted to evaluate the RFI decorrelations under different target types and orbits, and the corresponding required system parameters for mitigation are analysed as well.

Geosynchronous orbit synthetic aperture radar (GEO SAR) has a long synthetic aperture time and a large-scale imaging scene. Therefore, various non-ideal factors tend to accumulate, introducing phase errors and decreasing the imaging quality. During the long integration time, troposphere is time varying and space variant, which will result in image shifts and defocusing. According to the characteristics of GEO SAR, this study completes the modelling and the quantitative analysis of tropospheric turbulence. Based on the phase-screen theory, the amplitude and phase random fluctuations caused by the turbulence which will result in the defocus of images are analysed. However, in the natural environment, the imaging of GEO SAR system is affected weakly by the turbulence and this conclusion is proved from the spectral analysis.

Geosynchronous SAR tomography (GEO TomoSAR) techniques exploit multi-pass SAR acquisitions of the same scene, taken with slightly different view angles, and allow generating fully 3D images. Compared to low earth orbit (LEO) SAR case, GEO SAR has the advantages of shorter revisit time period and wider coverage, which can greatly shorten the data acquisition time, improve the coherence of collected data and effectively improve the 3D imaging accuracy. However, GEO SAR has curved trajectories and un-parallel repeated trajectories, which introduce the along-track component of the spatial baseline. The obtained GEO TomoSAR data based on the data acquisition method using in LEO TomoSAR have the significant rotation-induced decorrelation. Thus, the imaging performance in the elevation is severely degraded. In this paper, we first analyse the feasibility of GEO TomoSAR to achieve 3D imaging. In view of the special issues existing in GEO TomoSAR, we adopt an optimal minimal rotation-induced decorrelation data acquisition method to obtain repeated trajectories data, which can effectively improve the coherence between the collected data. Then, performance of imaging in elevation was analysed based on the spatial baseline distribution. Finally, the feasibility and validity of GEO TomoSAR techniques are validated through computer simulation.

Multiple-input multiple-output geosynchronous synthetic aperture radar (MIMO GEO SAR), composed by several mini-satellites in GEO orbits, has more spatial degrees of freedom, which helps to suppress clutter and improves the performance of ground moving target indication (GMTI). However, in order to ensure the satellite safety, the satellite spacing in MIMO GEO SAR is very large. It results in the failure of far-field assumption and causes the moving target defocusing because of the mismatch between the steering vector in far-field sources and the moving target echo. Here, a new signal model based on the near-field sources is first given according to the GEO SAR satellite-ground geometry relationship. Then, considering the phase difference between the different channels, the spatial steering vector is derived and the weight vector for adaptive processing is proposed to suppress clutter and indicate the moving target. Finally, computer simulations are carried out for the validation of the proposed method.

The abundant space, time, frequency and code resources of distributed multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) enables it to bring various applications. The generalised ambiguity function (GAF) of distributed MIMO SAR is analysed. By equating the bistatic SAR to equivalent phase centre (EPC), the GAF can be expressed as sum of that of mostatic SAR. The influence of EPC's position on GAF is analysed, including its effects on amplitude items in range direction, in azimuth direction and phase item, and the boundary conditions of formation scale within which the envelope items are not affected are deduced. Furthermore, the phase in GAF is expressed as linear combinations of EPC position related to formation centre. The simulation results verify the negligibility of formation scale and show that the linear combination expression is accurate enough to evaluate the resolution.