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Many materials are highly sensitive to temperature, and the study of the fire resistance of materials is one of the important research directions, which includes the study of the fire resistance of fiber-reinforced polymer (FRP) composites, but the cooling mode on the change of FRP mechanical properties after high temperature has not been investigated. This study analyzes the mechanical properties of GFRP under various cooling methods after exposure to high temperatures. The tensile strength of GFRP was evaluated through water cooling, firefighting foam cooling, and air cooling within the temperature range of 20–300 °C. Damage modes were investigated at different target temperatures. The results indicate that the tensile strength of air-cooled GFRP is the highest, whereas water cooling yields the lowest retention rate. It indicates that the FRP temperature decreases slowly under air cooling and the better recovery of the damage within the resin matrix, while under water cooling, the damage at the fiber/resin interface is exacerbated because of the high exposed temperature and the water, resulting in a reduction in the strength of GFRP. Between 20 and 150 °C, GFRP essentially recovers its mechanical properties after cooling, with a residual tensile strength factor exceeding 0.9. In the range of 150–250 °C, GFRP exhibits a graded decline in strength. At 300 °C, GFRP loses certain mechanical properties after cooling, with a residual tensile strength factor below 0.1. Furthermore, the analysis of experimental results led to the modification of the Johnson–Cook constitutive model, proposing a model for GFRP under three cooling methods. Additionally, a predictive model for the elastic modulus of GFRP after high-temperature cooling was derived, showing agreement with experimental results.

Accelerated population ageing has brought a great burden to the development of society. Repairing senile osteoporosis fractures is a great challenge, but a desirable way to address this issue is by developing multifunctional titanium(Ti) implants with antiaging and pro-osteogenic effects. Herein, to endow TI substrate with an antiaging effect for enhanced bone formation, an exercise-induced myokine(irisin) was loaded on titanium dioxide nanotubes(TNTs) and then a multilayered structure composed of chitosan(Chi) and gelatin(Gel) was constructed on the substrates via a layer-by-layer-assembly technique(TNT-IR-LBL). The surface topography of TNT-IR-LBL was characterized by scanning electron microscopy (SEM), atomic force microscopy(AFM), X-ray photoelectron spectroscopy(XPS) and water contact angle measurements, respectively. As an exercise-induced myokine, the irisin secreted from skeletal muscle exhibits strong activity for against the chronic diseases. The implants synthesized here displayed good biocompatibility towards osteoblasts. Moreover, irisin was sustainably released from TNT-IR-LBL for more than 3 weeks. In vitro studies indicated the TNT-IR-LBL substrates promoted osteoblast differentiation. In addition, compared to bare titanium, TNT-IR-LBL significantly promoted new bone formation around the implant interface in senile osteoporosis rats 4 weeks after implantation. Overall, this study provides a promising candidate to develop Ti-based orthopaedic implants to repair senile osteoporosis fractures.

Maritime surveillance is extensively concerned by worldwide authorities, in which ship recognition in synthetic aperture radar (SAR) images is a significant and fundamental component. Though some development has been achieved in the SAR ship recognition task, two areas remain inadequately explored, which are the comprehensive utilization of multiscale features and the deployment of the prior knowledge of the ship shape. In this article, a novel ship augmented attention network (SA$^{2}$Net) for ship recognition is proposed, which comprehensively utilizes the multiscale features and integrates the ship shape prior to the end-to-end network. On one hand, due to the unequal effects of different scales, a scale attention module is proposed to adaptively select and assign weights to desired feature scales while disregarding irrelevant scales. Moreover, a feature weaving module (FWM) is constructed to merge semantic and detailed features produced by the high-to-low backbone, enriching representations across all scales of ship targets. On the other hand, in order to incorporate the priory knowledge of the ship shape into the network, we develop a feature augmentation module (FAM) to further boost the ship recognition accuracy. This module can provide rectangular receptive fields that align with the shape of ships, wherein a limitation encountered with traditional square convolutions. Comprehensive experiments on representative three- and six-category OpenSARShip tasks and seven-category FUSAR-Ship tasks show that our SA$^{2}$Net demonstrates superior performance when compared to the current state-of-the-art methods.

Abstract Background Aberrant RNA editing of adenosine-to-inosine (A-to-I) has been linked to multiple human cancers, but its role in intrahepatic cholangiocarcinoma (iCCA) remains unknown. We conducted an exome-wide investigation to search for dysregulated RNA editing that drive iCCA pathogenesis. Methods An integrative whole-exome and transcriptome sequencing analysis was performed to elucidate the RNA editing landscape in iCCAs. Putative RNA editing sites were validated by Sanger sequencing. In vitro and in vivo experiments were used to assess the effects of an exemplary target gene Kip1 ubiquitination-promoting complex 1 (KPC1) and its editing on iCCA cells growth and metastasis. Crosstalk between KPC1 RNA editing and NF-κB signaling was analyzed by molecular methods. Results Through integrative omics analyses, we revealed an adenosine deaminases acting on RNA 1A (ADAR1)-mediated over-editing pattern in iCCAs. ADAR1 is frequently amplified and overexpressed in iCCAs and plays oncogenic roles. Notably, we identified a novel ADAR1-mediated A-to-I editing of KPC1 transcript, which results in substitution of methionine with valine at residue 8 (p.M8V). KPC1 p.M8V editing confers loss-of-function phenotypes through blunting the tumor-suppressive role of wild-type KPC1. Mechanistically, KPC1 p.M8V weakens the affinity of KPC1 to its substrate NF-κB1 p105, thereby reducing the ubiquitinating and proteasomal processing of p105 to p50, which in turn enhances the activity of oncogenic NF-κB signaling. Conclusions Our findings established that amplification-driven ADAR1 overexpression results in overediting of KPC1 p.M8V in iCCAs, leading to progression via activation of the NF-κB signaling pathway, and suggested ADAR1-KPC1-NF-κB axis as a potential therapeutic target for iCCA.

Abstract Background Detecting early‐stage lung cancer is critical to reduce the lung cancer mortality rate; however, existing models based on germline variants perform poorly, and new models are needed. This study aimed to use extreme gradient boosting to develop a predictive model for the early diagnosis of lung cancer in a multicenter case–control study. Materials and Methods A total of 974 cases and 1005 controls in Shanghai and Taizhou were recruited, and 61 single nucleotide polymorphisms (SNPs) were genotyped. Multivariate logistic regression was used to calculate the association between signal SNPs and lung cancer risk. Logistic regression (LR) and extreme gradient boosting (XGBoost) algorithms, a large‐scale machine learning algorithm, were adopted to build the lung cancer risk model. In both models, 10‐fold cross‐validation was performed, and model predictive performance was evaluated by the area under the curve (AUC). Results After FDR adjustment, TYMS rs3819102 and BAG6 rs1077393 were significantly associated with lung cancer risk (p

Shrinking of the Laurentide Ice Sheet mobilized the underlying rock organic carbon. Together with permafrost carbon release, this may contribute 12 ppm to deglacial CO2 rise, underscoring the impact of cryospheric change on the carbon cycle.

Abstract Thin-film composite forward osmosis (TFC-FO) membranes show the potential to be applied in water treatment applications. However, the TFC-FO membrane performance was limited by internal concentration polarization (ICP) and membrane fouling. In this study, a well-functioning TFC-FO membrane was produced by embedding biogenic silver nanoparticles (BioAg) into the polysulfone (PSf) substrate of a FO membrane. Effects of BioAg on membrane structure, filtration performance, antifouling, and antibacterial properties of the as-prepared FO membranes were examined. Results indicated that BioAg-modified FO membranes achieved improved surface hydrophilicity, higher porosity, and mitigated ICP, resulting in 2.5–4.4 times higher water flux than the pristine FO membranes. The BioAg-modified FO membrane also showed significantly improved antifouling and antibacterial ability. Moreover, the release velocity of silver nanoparticles in FO membranes was significantly slowed down by the construction of the polyamide layer. After 30 days of immersion in an aqueous solution, 96.5% of silver was retained in the optimized BioAg-modified FO membrane. The study offers an effective approach to simultaneously mitigate ICP and enhance the antifouling property of TFC-FO membranes through substrate modification.

Our study aims to explore the long-term performance patterns for deep learning (DL) models deployed in clinic and to investigate their efficacy in relation to evolving clinical practices. We conducted a retrospective study simulating the clinical implementation of our DL model involving 1328 prostate cancer patients treated between January 2006 and August 2022. We trained and validated a U-Net-based auto-segmentation model on data obtained from 2006 to 2011 and tested on data from 2012 to 2022, simulating the model’s clinical deployment starting in 2012. We visualized the trends of the model performance using exponentially weighted moving average (EMA) curves. Additionally, we performed Wilcoxon Rank Sum Test and multiple linear regression to investigate Dice similarity coefficient (DSC) variations across distinct periods and the impact of clinical factors, respectively. Initially, from 2012 to 2014, the model showed high performance in segmenting the prostate, rectum, and bladder. Post-2015, a notable decline in EMA DSC was observed for the prostate and rectum, while bladder contours remained stable. Key factors impacting the prostate contour quality included physician contouring styles, using various hydrogel spacers, CT scan slice thickness, MRI-guided contouring, and intravenous (IV) contrast ( p < 0.0001, p < 0.0001, p = 0.0085, p = 0.0012, p < 0.0001, respectively). Rectum contour quality was notably influenced by factors such as slice thickness, physician contouring styles, and the use of various hydrogel spacers. The quality of the bladder contour was primarily affected by IV contrast. The deployed DL model exhibited a substantial decline in performance over time, aligning with the evolving clinical settings.

Remote control over communication networks with bandwidth-constrained channels has attracted considerable recent attention because it holds the promise of enabling a large number of real-time applications, such as autonomous driving, smart grids, and the industrial internet of things (IIoT). However, due to the limited bandwidth, the sub-packets or even bits have to be transmitted successively, thereby experiencing non-negligible latency and inducing serious performance loss in remote control. To overcome this, we introduce an incremental coding method, in which the actuator acts in real time based on a partially received packet instead of waiting until the entire packet is decoded. On this basis, we applied incremental coding to a linear control system to obtain a remote-control scheme. Both its stability conditions and average linear-quadratic-Gaussian-(LQG) cost are presented. Then, we further investigated a multi-user remote-control method, with a particular focus on its applications in the demand response of smart grids over bandwidth-constrained communication networks. The utility loss due to the bandwidth constraint and communication latency are minimized by jointly optimizing the source coding and real-time demand response. The numerical results show that the incremental-coding-aided remote control performed well in both single-user and multi-user scenarios and outperformed the conventional zero-hold control scheme significantly under the LQG metric.

Conspecific negative density dependence (CNDD) is an important mechanism for species coexistence and community dynamics. Phylogenetic negative density dependence (PNDD) and functional negative density dependence (FNDD) are extensions of CNDD, and many studies have shown that they have become powerful and reliable methods for exploring the mechanisms of species coexistence. However, most studies have focused on only one or two of these mechanisms and have not considered whether and how habitat variables affect the detection of these density dependences. To investigate the relative importance of these mechanisms, we set up three 0.09 ha dynamic plots at Cangshan Mountain in southwest China, and used generalized linear mixed models to analyze how the survival of 546 woody plant seedlings was affected by neighborhood density and habitat variables. Our results showed that heterospecific seedling density dependence and functional trait density dependence played key roles in seedling survival. Habitat factors, phylogenetic densities, and adult neighbors had no significant effect on seedling survival in the three plots. However, habitat filtering covered the detection of density dependence and functional trait density dependence. Our study demonstrates that failure to control for habitat variables may obscure the importance of density dependence and functional trait density dependence on seedling survival.

A geosynchronous (GEO) satellite can provide continuous illumination with broad beam coverage for a Low Earth Orbit (LEO) receiver, used as the transmitting station of bistatic Synthetic Aperture Radar (SAR). Meanwhile, because the bistatic SAR system comprises a separate transmitter and receiver, the LEO receiver can realize multiview imaging such as downward-, forward-, and backward-looking. Therefore, GEO-LEO bistatic SAR is widely used in earth surveying and mapping to reconnaissance and surveillance application. To realize large-scene imaging, the pulse repetition rate of the GEO SAR transmitter should be low. Meanwhile, the LEO SAR receiver introduces a wide Doppler bandwidth, resulting in the azimuth undersampling of the GEO-LEO bistatic SAR. Although the multichannel technology in the receiver can suppress the ambiguity, the multichannel unambiguous recovery method requires numerous channels, resulting in the undersampling of the GEO-LEO bistatic SAR, and hindering the miniaturization of the receiving system. To address the problem of ambiguous imaging of complex observation scenes under the condition of severe azimuth subsampling condition, a sequential joint multiframe and multireceiving channel recovery unambiguous imaging method is proposed. The unambiguous imaging is recovered jointly from the correlation between sequential multiframe observation scenes and multireceiving channel sampling information. First, the unambiguous imaging problem of the GEO-LEO bistatic SAR is modeled as a joint low rank and sparse tensor optimization problem. Second, in the iterative solution of the alternating direction multiplier method, the multireceiving channel information is used to realize the unambiguous imaging of the GEO-LEO bistatic SAR for complex observation scenes. The proposed method can significantly reduce the number of receiving channels required for unambiguous imaging compared with the imaging method based on traditional multichannel The results obtained by the proposed method are validated by simulations and experiments.

Abstract Objectives The aim of this study was to explore risk factors for the prognosis of lung cancer with simple brain metastasis (LCSBM) patients and to establish a prognostic predictive nomogram for LCSBM patients. Materials and methods Three thousand eight hundred and six cases of LCSBM were extracted from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015 using SEER Stat 8.3.5. Lung cancer patients only had brain metastasis with no other organ metastasis were defined as LCSBM patients. Prognostic factors of LCSBM were analyzed with log-rank method and Cox proportional hazards model. Independent risk and protective prognostic factors were used to construct nomogram with accelerated failure time model. C-index was used to evaluate the prediction effect of nomogram. Results and conclusion The younger patients (18–65 years old) accounted for 54.41%, while patients aged over 65 accounted for 45.59%.The ratio of male: female was 1:1. Lung cancer in the main bronchus, upper lobe, middle lobe and lower lobe were accounted for 4.91%, 62.80%, 4.47% and 27.82% respectively; and adenocarcinoma accounted for 57.83% of all lung cancer types. The overall median survival time was 12.2 months. Survival rates for 1-, 3- and 5-years were 28.2%, 8.7% and 4.7% respectively. We found female (HR = 0.81, 95% CI 0.75–0.87), the married (HR = 0.80; 95% CI 0.75–0.86), the White (HR = 0.90, 95% CI 0.84–0.95) and primary site (HR = 0.45, 95% CI 0.39–0.52) were independent protective factors while higher age (HR = 1.51, 95% CI 1.40–1.62), advanced grade (HR = 1.19, 95% CI 1.12–1.25) and advanced T stage (HR = 1.09, 95% CI 1.05–1.13) were independent risk prognostic factors affecting the survival of LCSBM patients. We constructed the nomogram with above independent factors, and the C-index value was 0.634 (95% CI 0.622–0.646). We developed a nomogram with seven significant LCSBM independent prognostic factors to provide prognosis prediction.

Abstract Centrality measure is an essential tool in network analysis and widely used in the domain of computer science, biology and sociology. Taking advantage of the speedup offered by quantum computation, various quantum centrality measures have been proposed. However, few work of quantum centrality involves weighted graphs, while the weight of edges should be considered in certain real-world networks. In this work, we extend the centrality measure based on continuous-time quantum walk to weighted graphs. We testify the feasibility and reliability of this quantum centrality using an ensemble of 41,675 graphs with various topologies and comparing with the eigenvector centrality measure. The average Vigna’s correlation index of all the tested graphs with all edge weights in [1, 10] is as high as 0.967, indicating the pretty good consistency of rankings by the continuous-time quantum walk centrality and the eigenvector centrality. The intuitive consistency of the top-ranked vertices given by this quantum centrality measure and classical centrality measures is also demonstrated on large-scale weighted graphs. Moreover, the range of the continuous-time quantum walk centrality values is much bigger than that of classical centralities, which exhibits better distinguishing ability to pick the important vertices from the ones with less importance. All these results show that the centrality measure based on continuous-time quantum walk still works well on weighted graphs.

Abstract The consideration of nonextensivity effects is crucial to the accurate diagnosis of plasma parameters; common plasma nonextensive parameters include electron nonextensive parameter and ion nonextensive parameter, and the former can be measured, while the ion nonextensive parameter cannot be measured yet. Here we show the measurement of ion nonextensive parameter of plasma based on the theory of nonextensive geodesic acoustic modes. We assume that the plasma to be measured can be described by nonextensive statistical mechanics, and on this basis, the nonextensive geodesic acoustic mode theory is established. Utilizing this theory, we have measured the ion nonextensive parameter $${{q}_{{{F}_{\mathrm {i}}}}}= 1.565$$ q F i = 1.565 which cannot be diagnosed even by a nonextensive single electric probe. Our research points out that the proposed measurement method of ion nonextensive parameter may play a role in plasma diagnosis and will help us to grasp the nonextensivity of plasma more precisely. We hope the proposed method of ion nonextensive parameter diagnosis based on the nonextensive geodesic acoustic mode theory can be the starting point of more complex ion nonextensive parameter diagnosis methods. In addition, the measurement of ion nonextensive parameter is closely related to the study of various plasma waves, instabilities, turbulence and abnormal transport, and a defined and quantitative test of nonextensive geodesic acoustic mode theory will bound up deeply with such developments.

Jilin Province is an important commercial grain production base in northeast China, and it has seen significant transition in cultivated land use in recent years. This study constructed a measurement system for the recessive transition of cultivated land use in Jilin Province based on the perspective of “three-function synergy” (productive, living, and ecological functions). It discussed the temporal and spatial evolution characteristics of the recessive transition of cultivated land use from 1990 to 2020, identified the turning point of the cultivated land transition trend, and built a model of recessive transition of cultivated land in Jilin Province. After analyzing the results, we came to the following conclusions: (1) The turning point of the “three-function synergy” of the recessive morphology of cultivated land in Jilin Province occurred earlier than the mutation point of the recessive transition of cultivated land, and there was a certain temporality in the recessive transition of cultivated land compared with the functional change of cultivated land; (2) the degree of recessive transition of cultivated land in Jilin Province showed a spatial distribution characteristic of being higher in the west and lower in the east; (3) the recessive transition of cultivated land use in Jilin Province could be divided into transition stages characterized by “low stage slow rise period”, “middle stage significant increase period”, and “high stage steady growth period”; (4) Jilin Province should adopt differentiated and diversified management of cultivated land to achieve a comprehensive management model that emphasizes quantity, quality, and ecology.

In terms of the global coverage provided by global navigation satellite system (GNSS) signals, radar images of a large field-of-view can be obtained via passive synthetic aperture radar (SAR) using these signals of opportunity. Usually, large field-of-view imaging is challenging using conventional SAR methods owing to the space-variant resolution. In addition, only a moderate range resolution can be provided by the GNSS transmitters in the passive SAR system. In this article, an optimal imaging method for a large field-of-view using GNSS-based passive SAR is proposed, which exploits the inherent multistatic nature of GNSS. At first, the spatial resolution of each scatterer in the observation area relative to each GNSS transmitter is calculated according to the geometry. Then, the selection of the transmitter set is modeled as an optimization problem, where the minimal resolution cell is used as the optimal criterion. After that, an evolutionary algorithm is applied to solve the optimization problem. Finally, the space-time combination of the echoes over a long dwell time concerning selected GNSS transmitters is performed to form the radar image with a minimal spatial resolution. The proposed method is extensively validated using two experimental datasets, where the passive radar consists of BeiDou satellites and a fixed receiver. The spatial resolution improvement and the large field-of-view imaging capability of the proposed method are forcefully demonstrated according to the experimental results.

Video synthetic aperture radar (SAR) is attracting more and more attention because of its continuous imaging capability for ground scene of interest under any weather conditions and any time of the day. To reduce the sampling amount of video SAR, image processing can be formulated into a low-tubal-rank tensor recovery problem. In this article, we proposed an orthogonal rank-1 tensor pursuit (ORTP) algorithm to solve the low-tubal-rank tensor recovery problem in video SAR imaging. The proposed ORTP algorithm is an extension of the orthogonal rank-1 matrix pursuit algorithm in the matrix sensing problem from the matrix case to the tensor case under a tubal-rank model. It is capable of reconstructing the target tensor efficiently without requiring any prior information about the prespecified or pre-estimated tensor tubal-rank value. To achieve this, rank-1 basis tensors and weight tensors of the target tensor are estimated iteratively, and the residual error between the observed tensor and the estimated tensor through linear mapping is utilized as the stop condition. We theoretically prove the convergence and correctness of the proposed ORTP method. The methodology was tested on synthetic data, real video data, and video SAR data. These tests show that the proposed approach outperforms other video SAR imaging algorithms and low-rank tensor recovery algorithms.

Low-oversampled staggered synthetic aperture radar can achieve continuously observed high-resolution and wide-swath imaging by utilizing the variable pulse repetition interval to distribute blind ranges. Moreover, adopting a low oversampling ratio can reduce the data storage requirements, contributing to its research significance. However, non-uniform sampling, echo data loss, and non-ideal Azimuth Antenna Pattern (AAP) cause severe azimuth ambiguities in a directly focused image. This study proposes a compressive sensing-based method with better ambiguity removal performance and higher efficiency compared to existing methods. First, an Innovative Frequency-Domain Model (IFDM) is constructed, which accurately describes the non-uniform sampling, echo data loss, and coupled range cell migration. Based on the IFDM, an optimization problem is constructed and solved by the two-dimensional fast iterative shrinkage thresholding algorithm to remove the ambiguity caused by non-uniform sampling and echo data loss. Subsequently, selective filtering is used to suppress the ambiguity caused by the AAP. The experiments demonstrate that the proposed method can more effectively and efficiently suppress the azimuth ambiguities compared to existing methods.

While hippocampal atrophy and its regional susceptibility to Alzheimer’s disease (AD) are well reported at late stages of AD, studies of the asymptomatic stage of AD are limited but could elucidate early stage pathophysiology as well as provide predictive biomarkers. In this study, we performed multi-modal magnetic resonance imaging (MRI) to estimate morphometry, functional connectivity, and tissue microstructure of hippocampal subfields in cognitively normal adults including those with asymptomatic AD. High-resolution resting-state functional, diffusion and structural MRI, cerebral spinal fluid (CSF), and neuropsychological evaluations were performed in healthy young adults (HY: n = 40) and healthy older adults with negative (HO−: n = 47) and positive (HO+ : n = 25) CSF biomarkers of AD. Morphometry, functional connectivity, and tissue microstructure were estimated from the structural, functional, and diffusion MRI images, respectively. Our results indicated that normal aging affected morphometry, connectivity, and microstructure in all hippocampal subfields, while the subiculum and CA1-3 demonstrated the greatest sensitivity to asymptomatic AD pathology. Tau, rather than amyloid-β, was closely associated with imaging-derived synaptic and microstructural measures. Microstructural metrics were significantly associated with neuropsychological assessments. These findings suggest that the subiculum and CA1-3 are the most vulnerable in asymptomatic AD and tau level is driving these early changes.

The Nanmushu Zn–Pb deposit is a large-scale and representative deposit in the Mayuan ore field on the northern margin of the Yangtze Block. This study investigates the trace element geochemistry of sphalerite from this deposit using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The results show that the main trace elements in sphalerite include various trace elements, such as Mn, Fe, Cu, Ga, Ge, Ag, Cd, Pb, Co, Hg, Tl, In, Sn, and Sb. Among them, Ag, Ge, Cd, and Cu are valuable components that may be recovered during mineral processing or smelting techniques. The histograms, LA-ICP-MS time-resolved depth profiles, and linear scan profiles indicated that most trace elements occur in sphalerite as isomorphs, while partial Pb, Fe, and Ag occur as tiny mineral inclusions. The correlation diagrams of trace elements revealed that Fe2+, Mn2+, Pb2+, and Tl3+ can substitute Zn2+ in sphalerite through isomorphism. In sphalerite, Cd2+ and Hg2+ together or Mn2+, Pb2+, and Tl3+ together can replace Zn2+, i.e., ((3Mn, 3Pb, 2Tl)6+, 3(Cd, Hg)2+) ↔ 3Zn2+. Moreover, there is a mechanism of Ge4+ with Cu+ or Ga3+ with Cu+ replacing Zn2+ in the Nanmushu deposit, i.e., Ge4+ + 2Cu+ ↔ 3Zn2+ or 2Ga3+ + 2Cu+ ↔ 4Zn2+. Furthermore, the trace element compositions indicate that the Nanmushu Zn mineralization occurred under low-temperature conditions (

Abstract Background The neural networks underpinning language control and domain-general executive functions overlap in bilinguals, but existing evidence is mainly correlative. Here, we present the first neurofunctional evidence for a transfer effect between (domain-general) inhibitory control and language control through training. We trained Chinese–English bilinguals for 8 days using a Simon task taxing the inhibitory control system, whilst an active control group was trained with a color judgment task that does not tax the inhibitory control system. All participants performed a language-switching task before and after training. It has been suggested that the activity of the left DLPFC was associated with domain-general top-down cognitive control (Macdonald et al. Science 288: 1835–1838, 2000) and bilingual language control (Wang et al. Neuroimage 35: 862–870, 2007). In addition, the dACC was closely related to the conflict detection (Abutalebi et al. Cereb Cortex 18:1496–1505, 2008). Last, the activity of the left caudate has been linked with lexical selection (Abutalebi et al. Cereb Cortex 18:1496–1505, 2008), especially the selection of the weak language (Abutalebi et al. Cortex 49: 905–911, 2013). Therefore, we focused on these three regions of interest (ROIs) where neural changes associated with transfer were expected to occur. Results The results showed a negative correlation between changes in activation levels in the left dorsolateral prefrontal cortex (DLPFC) and changes in the switch cost magnitude in the language-switching task in the training group but not in the control group, suggesting that the DLPFC plays a critical role in the transfer effect from domain-general executive functions to language control. However, there was no measurable effect in the anterior cingulate cortex or left caudate nucleus, suggesting that the inhibitory control training increased the neural efficiency for language production in bilinguals in terms of attention shifting and conflict resolution, but the training did not affect conflict detection and lexical selection. Conclusion These findings showed how cognitive training evidence can help establish a causational link between the neural basis of domain-general executive functions and language control in bilinguals.

A biaxial tension test considering temperature is crucial to predict the mechanical properties of airship envelopes. However, there is a lack of biaxial tensile test equipment for high- and low-temperature correlation. In this study, a temperature control device suitable for biaxial tension was designed based on the traditional device, and the biaxial tension test of the UN-5100 material in high- and low-temperature environments was realized. In addition, a series of tests were carried out to verify the effectiveness and rationality of the temperature control device. Then, a biaxial tensile test of UN-5100 specimens between −33 °C and 80 °C were carried out and the stress–strain relationship of the UN-5100 specimens under high- and low-temperature environments was obtained. Based on the test data, the elastic modulus and Poisson’s ratio of the UN-5100 specimens at different temperatures were calculated. Finally, the test results showed that the elastic modulus and Poisson’s ratio of the airship envelope were larger at low temperatures, indicating that the capsule is relatively safe at such temperatures. The relationships between the temperature and the mechanical properties of the airship were also summarized.

The stratospheric airship is taken as the research object, and the motion mode analysis of the stratospheric airship is carried out. The influence of key parameters such as the center of mass, the center of buoyance, and the aerodynamic stability moment on the motion mode of stratospheric airship are analyzed and summarized in detail. According to the simulation and analysis results, unlike high-speed and high-dynamic aircrafts such as airplanes, the motion modes of the stratospheric airship are hardly affected by the perturbation of aerodynamic stability moment; the perturbations of the vertical center of mass and the vertical center of buoyancy have a great influence on the pitch pendulum motion modes, and their parameter perturbations affect the frequency of the pitch pendulum motion and also the stability of the pitch pendulum motion; the axial mass center location perturbation not only changes the damping of pitch pendulum motion but also affects the frequency of the yaw motion attitude motion mode to a certain extent.

During the growth period of rapeseed, if there is continuous rainfall, it will easily lead to waterlogging stress, which will seriously affect the growth of rapeseed. Currently, the mechanisms of rapeseed resistance to waterlogging stress are largely unknown. In this study, the rapeseed (Brassica napus) inbred lines G230 and G218 were identified as waterlogging-tolerant rapeseed and waterlogging-sensitive rapeseed, respectively, through a potted waterlogging stress simulation and field waterlogging stress experiments. After six days of waterlogging stress at the seedling stage, the degree of leaf aging and root damage of the waterlogging-tolerant rapeseed G230 were lower than those of the waterlogging-sensitive rapeseed G218. A physiological analysis showed that waterlogging stress significantly increased the contents of malondialdehyde, soluble sugar, and hydrogen peroxide in rape leaves and roots. The transcriptomic and metabolomic analysis showed that the differential genes and the differential metabolites of waterlogging-tolerant rapeseed G230 were mainly enriched in the metabolic pathways, biosynthesis of secondary metabolites, flavonoid biosynthesis, and vitamin B6 metabolism. Compared to G218, the expression levels of some genes associated with flavonoid biosynthesis and vitamin B metabolism were higher in G230, such as CHI, DRF, LDOX, PDX1.1, and PDX2. Furthermore, some metabolites involved in flavonoid biosynthesis and vitamin B6 metabolism, such as naringenin and epiafzelechin, were significantly up-regulated in leaves of G230, while pyridoxine phosphate was only significantly down-regulated in roots and leaves of G218. Furthermore, foliar spraying of vitamin B6 can effectively improve the tolerance to waterlogging of G218 in the short term. These results indicate that flavonoid biosynthesis and vitamin B6 metabolism pathways play a key role in the waterlogging tolerance and hypoxia stress resistance of Brassica napus and provide new insights for improving the waterlogging tolerance and cultivating waterlogging-tolerant rapeseed varieties.

With the rapid and continuous development of nanomanufacturing technology, the demands for both large range and high precision metrology of structured surfaces are becoming increasingly urgent. This paper proposes a metrological measurement system based on a commercial atomic force microscope. By using the nano-positioning platform from SIOS, the measurement range of the system expands from 110 μm × 110 μm × 20 μm to 25 mm × 25 mm × 5 mm. A signal amplifier with low noise and a high common mode rejection ratio that decreases the noise level of the measurement system to 2 nm is designed. Integration of the metrological system, signal processing, and calibration of the whole system is introduced. Three experimental studies are carried out on an ultrahigh step, an atomic deposition grating, and a cutting tool. The experimental results demonstrate high measurement repeatability and reproducibility in both vertical and lateral directions. By repeating 10 times of measurement, the expended uncertainties of the step and the grating measurement are 36.24 nm and 0.60 nm, respectively. Additionally, measurement of a cutting tool tip is conducted to illustrate the performance of the system. The Ra and Rz values of the tool tip arc ripple are 29.8 nm and 189 nm, respectively.

Due to the advantages of flexible configuration, bistatic synthetic aperture radar (BiSAR) has the ability to effectively observe from various visual angles, such as forward view area and squint area, and has good anti-jamming characteristics. It can be applied to the surveillance of ship targets on the sea and is gradually gaining an increasing amount of attention. However, for ship targets with complex motions on the sea surface, such as maneuvering targets or ship targets under high sea conditions, the high-order Doppler frequency of the scattering points is always spatial variation (related to the spatial position of scattering points), which poses a considerable challenge for the imaging of maritime ship targets in BiSAR. To resolve this problem, a BiSAR maritime ship target imaging algorithm based on cubic phase time-scaled transformation is proposed in this paper. First, through pre-processing of echo such as Doppler prefiltering and keystone transform, the translation compensation of the BiSAR maritime ship target is completed, and the scattering point energy is corrected to within one range unit. Then, the azimuth signal is modeled as a multi-component cubic phase signal. Based on the proposed cubic phase time-scaled transformation, the Doppler centroid, frequency rate, and third-order frequency of scattering points are estimated. Eventually, the BiSAR imaging of maritime ship targets is realized. This algorithm has excellent noise immunity and low cross-terms. The simulation leads to the verification of the validity of the proposed algorithm.

Machine learning (ML) has achieved remarkable success in a wide range of applications. In recent ML research, deep anomaly detection (AD) has been a hot topic with the aim of discriminating among anomalous data with deep neural networks (DNNs). Notably, image AD is one of the most representative tasks in current deep AD research. ML’s interaction with quantum computing is giving rise to a heated topic named quantum machine learning (QML), which enjoys great prospects according to recent academic research. This paper attempts to address the image AD problem in a deep manner with a novel QML solution. Specifically, we design a quantum-classical hybrid DNN (QHDNN) that aims to learn directly from normal raw images to train a normality model and then exclude images that do not conform to this model as anomalies during its inference. To enable the QHDNN to perform satisfactorily in deep image AD, we explore multiple quantum layer architectures and design a VQC-based QHDNN solution. Extensive experiments were conducted on commonly used benchmarks to test the proposed QML solution, whose results demonstrate the feasibility of addressing deep image AD with QML. Importantly, the experimental results show that our quantum-classical hybrid solution can even yield superior performance to that of its classical counterpart when they share the same number of learnable parameters.

The volume of residential travel with different purposes follows relatively stable patterns in a specific period and state; therefore, it can reflect the operating status of urban traffic and even indicate urban vitality. Recent research has focused on changes in the spatiotemporal characteristics of urban mobility affected by the pandemic but has rarely examined the impact of COVID-19 on the travel conditions and psychological needs of residents. To quantitatively assess travel characteristics during COVID-19, this paper proposed a method by which to determine the purpose of residential travel by combining urban functional areas (UFAs) based on machine learning. Then, the residential travel structure, which includes origin–destination (OD) points, residential travel flow, and the proportion of flows for different purposes, was established. Based on taxi trajectory data obtained during the epidemic in Nantong, China, the case study explores changes in travel flow characteristics under the framework of the residential travel structure. Through comparison of the number and spatial distribution of OD points in the residential travel structure, it is found that residential travel hotspots decreased significantly. The ratios of commuting and medical travel increased from 43.8% to 45.7% and 7.1% to 8.1%, respectively. Conversely, the ratios of other travel types all decreased sharply. Moreover, under Maslow’s hierarchy of needs model, further insights into the impacts of COVID-19 on changes in residential psychological needs are discussed in this paper. This work can provide a reference for decision makers to cope with the change in urban traffic during a public health emergency, which is beneficial to the sustainable healthy development of cities.

Traditional computation is driven by parallel accelerators or distributed computation nodes in order to improve computing performance, save energy, and decrease delays in accessing memory. Recently, artificial intelligent algorithms, frameworks, and computing models are growing to help with high computational performance.

Bistatic SAR (BiSAR) imaging is becoming more and more mature, nevertheless restricted by the significant problem of 2-D space-variation of echo signal. In practical applications, the geometry configurations and platform motion of BiSAR are often complicated, making the problem of space-variation severe and causing it hard to efficiently process BiSAR data of large imaging scene. Focusing on this problem, this article proposes a new imaging method for large scene-size imaging that can adapt to general spotlight BiSAR with complicated platform motion. This method first roughly focuses the echo data in range-Doppler domain through azimuth-dechirp and removes the residual range-azimuth coupling via Keystone transform. Then making use of the tolerance of SAR imaging to slight phase error, the full Doppler axis is down-sampled with a certain ratio to form a much smaller amount of Doppler cells called Doppler bands. Through restricting the value of the down-sampling ratio, the variance of differential phase in each Doppler band after the dechirp is controlled within a given tolerance. As a result, the differential phase can be compensated for in variable Doppler bands for a large imaging scene with high efficiency. The computational complexity of the proposed method is indicated as low as logarithmic and its effectiveness is verified through numerical simulations.

It is of significant social and economical impact to achieve green and reliable operation of data center.The optimization and control methods for green and reliable data center were reviewed briefly.An event-based reinforcement learning approach for improving the energy efficiency was developed.And a method to improve the accuracy of battery lifetime forecasting was developed.

The purpose of this study was to assess spatially remote effects of hemodynamic impairment on functional network topology contralateral to unilateral anterior circulation steno-occlusive disease (SOD) using resting-state blood oxygen level-dependent (BOLD) imaging, and to investigate the relationships between network connectivity and cerebrovascular reactivity (CVR), a measure of hemodynamic stress. Twenty patients with unilateral, chronic anterior circulation SOD and 20 age-matched healthy controls underwent resting-state BOLD imaging. Five-minute standardized baseline BOLD acquisition was followed by acetazolamide infusion to measure CVR. The BOLD baseline was used to analyze network connectivity contralateral to the diseased hemispheres of SOD patients. Compared to healthy controls, reduced network degree (z-score = −1.158 ± 1.217, P < 0.001, false discovery rate (FDR) corrected), local efficiency (z-score = −1.213 ± 1.120, P < 0.001, FDR corrected), global efficiency (z-score = −1.346 ± 1.119, P < 0.001, FDR corrected), and enhanced modularity (z-score = 1.000 ± 1.205, P = 0.002, FDR corrected) were observed in the contralateral, normal hemispheres of SOD patients. Network degree (P = 0.089, FDR corrected; P = 0.027, uncorrected) and nodal efficiency (P = 0.089, FDR corrected; P = 0.045, uncorrected) showed a trend toward a positive association with CVR. The results indicate remote abnormalities in functional connectivity contralateral to the diseased hemispheres in patients with unilateral SOD, despite the absence of macrovascular disease or demonstrable hemodynamic impairment. The clinical impact of remote functional disruptions requires dedicated investigation but may portend far reaching consequence for even putatively unilateral cerebrovascular disease.

The relationship between bilingual language control and domain-general cognitive control has been a hot topic in the research field of bilingualism. Previous studies mostly examined the correlation between performances of bilinguals in language control tasks and that in domain-general cognitive control tasks and came to the conclusions that they overlap, partially overlap, or are qualitatively different. These contradictory conclusions are possibly due to the neglect of the moderating effect of second language (L2) proficiency, that is, the relationship between bilingual language control and domain-general cognitive control might vary with the L2 proficiency of bilinguals. To examine this hypothesis, we recruited 36 unbalanced Chinese-English bilinguals to perform the Simon task (to assess domain-general cognitive control), Oxford Placement Test (to assess L2 proficiency), and picture naming tasks in single-and dual-language contexts (to evoke local and global language control). We find that Simon scores positively predicted switching costs in bilinguals with low L2 proficiency, but not in bilinguals with high L2 proficiency. Furthermore, Simon scores positively predicted mixing costs in bilinguals with high L2 proficiency, but not in bilinguals with low L2 proficiency. These results verify the moderating effect of L2 proficiency on the relationship between bilingual language control and domain-general cognitive control, that is, bilinguals with more proficient L2 rely on domain-general cognitive control less for local language control and more for global language control. This may imply a shift from local to global for the dependency of bilingual language control on domain-general cognitive control during the L2 development of bilinguals.

The Beaufort Sea was nearly ice-free during the transition from the last deglacial to the Holocene, a period in which two episodes of high sediment flux suggest major glacial flood events, according to high-resolution multi-proxy biomarker records.

Abstract Social media can be a double-edged sword for society, either as a convenient channel exchanging ideas or as an unexpected conduit circulating fake news through a large population. While existing studies of fake news focus on theoretical modeling of propagation or identification methods based on machine learning, it is important to understand the realistic propagation mechanisms between theoretical models and black-box methods. Here we track large databases of fake news and real news in both, Weibo in China and Twitter in Japan from different cultures, which include their traces of re-postings. We find in both online social networks that fake news spreads distinctively from real news even at early stages of propagation, e.g. five hours after the first re-postings. Our finding demonstrates collective structural signals that help to understand the different propagation evolution of fake news and real news. Different from earlier studies, identifying the topological properties of the information propagation at early stages may offer novel features for early detection of fake news in social media.

Abstract Background Older African Americans are more likely to develop Alzheimer’s disease (AD) than older Caucasians, and this difference cannot be readily explained by cerebrovascular and socioeconomic factors alone. We previously showed that mild cognitive impairment and AD dementia were associated with attenuated increases in the cerebrospinal fluid (CSF) levels of total and phosphorylated tau in African Americans compared to Caucasians, even though there was no difference in beta-amyloid 1–42 level between the two races. Methods We extended our work by analyzing early functional magnetic resonance imaging (fMRI) biomarkers of the default mode network in older African Americans and Caucasians. We calculated connectivity between nodes of the regions belonging to the various default mode network subsystems and correlated these imaging biomarkers with non-imaging biomarkers implicated in AD (CSF amyloid, total tau, and cognitive performance). Results We found that race modifies the relationship between functional connectivity of default mode network subsystems and cognitive performance, tau, and amyloid levels. Conclusion These findings provide further support that race modifies the AD phenotypes downstream from cerebral amyloid deposition, and identifies key inter-subsystem connections for deep imaging and neuropathologic characterization.

Real-time passenger flow prediction plays an important role in subway network design and management. Most of the existing prediction algorithms only consider the sequence of passenger flow volume, however, ignore the influence of other outer factors, for example, the weather conditions, air quality and temperature. In this paper, a systematic framework, MetroEye, is proposed for weather-aware prediction of real-time passenger flow. The framework contains an offline system and an online system. The offline system adopts a conditional random field (CRF) model to establish the relationship between passenger flow volume and weather factors. Experimental results show the superior prediction accuracy of the model, especially in large stations. The online system provides efficient methods to simulate the real-time passenger flow volume. Due to its high practicality, MetroEye has been adopted by Beijing Urban Rail Transit Control Center to monitor the passenger flow status of the Beijing subway system.

Bistatic synthetic aperture radar (BiSAR) imaging is faced with two major challenges: large scene imaging and adaptability to unideal platform motion in practice. In order to deal with these two problems, a generalized wavefront-curvature-corrected polar format algorithm (PFA) is proposed in this article. The traditional PFA is little restricted on geometry configuration and platform motion, but its application to large scene imaging is limited by the far-field planar wavefront assumption. To solve this limitation, this article derives the phase error caused by wavefront curvature and analyzes its influence on both geometric distortion and defocusing effect in detail. Based on the analysis, we present a wavefront curvature completely correcting method through space-variant phase compensation using the analytical wavefront curvature phase in wavenumber-domain, which is derived through method of series reversion. What's more, an efficient realization of the space-variant phase compensation based on two-stage image division is given to avoid high overlap rate in the traditional image division method. The proposed method can obtain well focused and geometric undistorted image for BiSAR under complicated flight paths, and it also keeps the logarithmic complexity of traditional PFA. The effectiveness of the proposed method is verified by numerical simulations.

In recent years, the research of multistatic synthetic aperture radar (SAR) has become a hot spot because of its great potentials in military and civil applications. However, it is inevitably disturbed by electronic countermeasures. In the multistatic SAR image, the false target caused by transmitting deceptive jamming leads to misjudgment of the image information. In this article, a new antideceptive jamming method for multistatic SAR is proposed. This method can locate the deceptive jammer and effectively suppress it. First of all, the echo model of multistatic SAR in the jamming environment is set up. Based on this model, the relationship expression for the location of jammer is derived. Then, we use the maximally stable extremal region method and the Euclidean-distance-based method to detect and identify false target in the multistatic SAR image. By using the location information of false target, receivers, and transmitters, the expression of the jammer localization is solved to locate the jammer. Finally, the minimum variance distortionless response beamforming method and image mosaic method are used to effectively suppress the jamming. The simulation results show that the method is effective.

In real application scenario of bistatic forward-looking synthetic aperture radar (BFSAR), ground moving target (GMT) is generally smeared severely in SAR image, due to its unknown range cell migration (RCM) and Doppler frequency migration (DFM). When GMT moves along an unknown curve trajectory, its high-order RCM and DFM (including the second- and third-order terms) would further aggravate the difficulty of GMT refocusing. To address this issue, an efficient GMT refocusing method via keystone-based delay-correlation transform and fast searching Fourier transform (KDCT-FSFT) is proposed. First, the KDCT is proposed to correct the first- to third-order RCMs regardless of target's motion state and position information. Meanwhile, the order of GMT's phase response is reduced as well. Then, FSFT is applied to estimate the third-order Doppler parameter of GMT. In the following, a 2-D fast Fourier transform (2D-FFT) can be applied to integrate the target signal coherently in Doppler parameters domain, where the Doppler centroid and Doppler frequency rate of GMT can be estimated. Finally, with the aforesaid estimated Doppler parameters, RCM and DFM can be well corrected and target with unknown curve motion can be finely refocused. Compared with the existing methods, not only the refocusing accuracy of the proposed method is higher, but also its processing is more efficient, since the procedures in the proposed method are performed with respect to all the range cells in the corresponding aperture, i.e., GMT refocusing is achieved by the 2-D data received in one aperture, rather than along every single range cell. Both the simulation and experimental results are given to validate the effectiveness of the proposed method.

To scientifically evaluate and utilize high-oleic acid rape germplasm resources and cultivate new varieties suitable for planting in the Hunan Province, 30 local high-oleic acid rape germplasms from Hunan were used as materials. The 12 personality indices of quality, yield, and resistance were comprehensively evaluated by variability, correlation, principal component, and cluster analyses. The results of variability showed that except for oleic acid, the lowest coefficient of variation was oil content, which was 0.06. Correlation analysis showed that oil content was positively correlated with main traits such as yield per plant and oleic acid, which could be used in the early screening of high-oleic rape germplasm. The results of principal component analysis showed that the 12 personality indicators were integrated into four principal components, and the cumulative contribution rate was 62.487%. The value of comprehensive coefficient 'F' was positively correlated with the first, second, and fourth principal components and negatively correlated with the third principal component. Cluster analysis showed that 30 high-oleic rape germplasms could be divided into four categories consisting of 9 (30%), 6 (20%), 7 (23%), and 8 (27%) high-oleic rape germplasms, each with the characteristics of "high disease resistance", "high yield", "high protein", and "more stability". This study not only provides a reference basis for high-oleic rape breeding but also provides a theoretical basis for their early screening.

Quantum process tomography is a fundamental and critical benchmarking and certification tool that is capable of fully characterizing an unknown quantum process. Standard quantum process tomography suffers from an exponentially scaling number of measurements and complicated data post-processing due to the curse of dimensionality. On the other hand, non-unitary operators are more realistic cases. In this work, we put forward a variational quantum process tomography method based on the supervised quantum machine learning framework. It approximates the unknown non-unitary quantum process utilizing a relatively shallow depth parametric quantum circuit and fewer input states. Numerically, we verified our method by reconstructing the non-unitary quantum mappings up to eight qubits in two cases: the weighted sum of the randomly generated quantum circuits and the imaginary time evolution of the Heisenberg XXZ spin chain Hamiltonian. Results show that those quantum processes could be reconstructed with high fidelities (>99%) and shallow depth parametric quantum circuits (d≤8), while the number of input states required is at least two orders of magnitude less than the demands of the standard quantum process tomography. Our work shows the potential of the variational quantum process tomography method in characterizing non-unitary operators.

BackgroundThe 5-year survival rate of patients with lung cancer in China is less than 20% and predicting their prognosis is challenging. We investigated the association between a common non-synonymous single nucleotide polymorphism (SNP), rs7214723, in the Ca2+/calmodulin-dependent protein kinase kinase 1 (CAMKK1) gene and the prognosis of patients with lung cancer.MethodsGenomic DNA was extracted from the blood samples of 839 patients with lung cancer, recruited from Changhai Hospital (n = 536) and Taizhou Institute of Health Sciences (n = 352), and genotyped using the SNPscan technique. The association between patient prognosis and the genotypic data for CAMKK1 was analyzed using a multivariate Cox proportional hazards model adjusted for multiple potential confounders. The CRISPR/Cas9 gene-editing system was used to introduce point mutations in the CAMKK1 rs7214723 of A549 and NCI-H358 cells. Subsequently, Cell proliferation and migration ability were assessed with the Cell Counting Kit-8 and scratch assay. The Annexin V-FITC apoptosis detection kit was used to detect cell apoptosis.ResultsThe CAMKK1 rs7214723 recessive CC genotype conferred significantly better overall survival (CC vs. TT + TC: adjusted hazard ratio = 0.78, 95% confidence interval [CI], 0.61-1.00, P = 0.049) than the TT + TC genotypes. Stratified analysis showed that the CAMKK1 rs7214723 CC genotype and recessive CC genotype conferred a significantly decreased risk of death in patients who were male, had a smoking history, or had stage III + IV cancer, compared with the TT and TT + TC genotypes. Relative to the TT + TC genotypes, the rs7214723 recessive CC genotype was also associated with a decreased risk of death in patients aged < 60 years (CC vs. TT + TC: adjusted hazard ratio = 0.59, 95% CI, 0.37-0.93, P = 0.024) and patients with squamous cell carcinoma (CC vs. TT + TC: adjusted hazard ratio = 0.65, 95% CI, 0.44-0.98, P = 0.038). Remarkably, CRISPR/Cas9-guided single nucleotide editing demonstrated that CAMKK1 rs7214723 T > C mutation significantly inhibits cell proliferation and migration and promotes cell apoptosis.ConclusionsCAMKK1 SNP rs7214723 may be a significant prognostic factor for the risk of death among patients with lung cancer.

Many materials are gradually softened with increasing temperatures in the fire, which will cause severe damage. As a new fiber-reinforced polymer (FRP) composite, the change in mechanical properties of nanometer montmorillonite composite fiber-reinforced bars or plates at elevated temperatures has not been investigated. To obtain a more comprehensive study of the mechanical properties of FRP composites at high temperatures, experimental research on the nanometer montmorillonite composite fiber material under the tensile rate of 1 mm/min was conducted at target temperatures between 20 °C and 350 °C. Finally, the failure mode of the FRP composites after the tensile test was analyzed. The results demonstrate that the elevated temperatures had a major impact on the residual mechanical properties of fiber-reinforced polymer (FRP) composites when the exposed temperatures exceeded 200 °C. Below 200 °C, the maximum decrease and increase in the fracture load of fiber reinforced polymer (FRP) composites were between −34% and 153% of their initial fracture load. After exposing to temperatures above 200 °C, the surface color of fiber-reinforced polymer (FRP) composites changed from brown to black. When exposed to temperatures between 200 and 300 °C, the ultimate load of fiber-reinforced polymer (FRP) composites significantly increased from 731.01 N to 1650.97 N. Additionally, the stress−strain behavior can be accurately predicted by using the proposed Johnson−Cook constitutive model. The experimental results studied in this research can be applied to both further research and engineering applications when conducting a theoretical simulation of fiber-reinforced polymer (FRP) composites.

Middle-aged trees refer to trees aged between 50 and 99 years, which are the reserve resources of old trees (trees ≥ 100 years of age). They are vital parts of the urban ecosystem, with important ecological, landscape, cultural, and historical value. Conservation of middle-aged trees in urban areas is important for the development of large old trees in the future. In this study, we investigated the middle-aged trees in Changchun city and analyzed the species composition and diversity characteristics of different urban green space types and tree age classes. The results showed that there were 72 species and 22,376 plants of middle-aged trees in Changchun city, and the coniferous species prevailed. The top five species with a high importance value (IV) were Pinus tabuliformis var. mukdensis, Lavix olgensis, Salix matsudana, Ulmus pumila, and Abies holophylla. Green space type and tree age were important factors influencing the richness and diversity of middle-aged trees. Tree growth spaces were relatively sufficient, and land use was stable for park green spaces (PGS) and attached green spaces (AGS), which resulted in the abundant, richer, and diverse species richness (SR) of middle-aged trees. Road green spaces (RGS) and square green spaces (SGS) had fewer trees and lower species richness, Margalef richness index (dMa), Shannon–Wiener index (He) and evenness index (Je) which could be attributed to the high intensity of human interference and poor environmental quality. The SR of middle-aged trees decreased with an increase in age class, and the values of SR in Age Class 80–89 years and Age Class 90–99 years were lower than in Age Class 50–59 years. Age Class 70–79 years had the lowest values of dMa, He, and Je, which need to be protected urgently. The results of this study can provide a basis for the conservation and management of middle-aged trees in urban areas and the choice of species for urban greening.

Alien plants invasion have become a hot issue in the field of ecology. The invasion of alien plants is usually accompanied by changes in the physical and chemical properties of the soil, the ensuing negative feedback creates a favorable environment for its own growth and expansion. Invasive plans have a strong ability to sequester carbon, which can greatly affect the original local ecological environment. In this study, we selected Rhus typhina, an invasive plant widely used for greening, as the experimental subject and natural growing grassland as the control. The aims were to investigate the effects of different degrees of invasion of R. typhina on soil physicochemical properties and carbon emissions, and to explore the influential factors on carbon emission. The results showed that R. typhina invasion significantly increased soil pH, total nitrogen content, easy extraction of glomalin-related soil protein (EEG) and cumulative CO2 emissions (CEM). It is worth noting that the CEM increased significantly during the severe invasion by R. typhina. The significant increase in soil NH4+-N content and the decrease in soil NO3−-N content indicate that the soil after the invasion of R. typhina has better uptake of NH4+-N. Temperature and soil moisture content had significant direct effects on CEM, while NH4+-N, NO3−-N, EEG and temperature sensitivity of soil organic carbon mineralization Q10 (30 °C/20 °C) had a direct but non-significant effect on CEM. The above findings suggest that R. typhina can generate positive feedback by influencing the physicochemical properties and CEM of the soil, opening the way for its own expansion, which can be targeted to prevent the destruction of local ecosystems during the introduction of cultivation and subsequent management.

Bistatic synthetic aperture radar (BiSAR) has drawn increasing attention in recent studies benefiting from its ability for forward-looking imaging, its capability of receiver radio silence and its resistance to jamming. However, the motion trajectory error compensation of BiSAR is a challenging task due to multiple error sources and complex effects. In this paper, an estimation and compensation method for three-dimensional (3D) motion trajectory error of BiSAR is proposed. In this method, the Doppler error of multiple scattering points is estimated firstly by using the time–frequency analysis method. Next, a local autofocus process is introduced to improve the Doppler error estimation accuracy. Then, the 3D trajectory error of BiSAR is estimated by solving a series of linear equations of the trajectory error and the Doppler error with the least squares method, and a well-focused BiSAR image is produced by using the corrected 3D trajectories. Finally, simulation and experiment results are presented to demonstrate the effectiveness of the proposed method.

In the field of plasma diagnosis, the measurement of the distribution function is significant because the distribution function is the basis for the use of plasma kinetic theory and it is the prerequisite for analyzing many physical phenomena, such as Landau damping (wave-particle resonance phenomenon) and ion sheath. Theoretical analysis and a large number of experiments have proved that plasma components do not obey Boltzmann–Gibbs statistics and can be well described by nonextensive statistical mechanics. The field of nonextensive electric probe has also made great progress, and the invention of the nonextensive single electric probes has developed and strengthened the power of plasma diagnostics. The nonextensive electric probe can not only measure the electron nonextensive parameter of plasma that cannot be measured by traditional probes but can also measure more accurate plasma parameters that can also be measured by traditional probes, such as Te, Φp, ne, Φf, and αqFe. However, diagnosing the plasma distribution function by the nonextensive electric probe has not been thoroughly and systematically analyzed and discussed. Here, we show the measurement of the plasma distribution function with a nonextensive single electric probe. This work expands the diagnostic capabilities of the nonextensive single electric probe. We utilize the nonextensive single electric probe theory to analyze the experimental data points of the I–V curve, measure the plasma electron distribution function fvx, and display the distribution curve (figure f-vx), and we also measure the plasma parameters of qFe, Te, Φp, ne, Φf, αqFe, etc. The proposed method provides a new approach to the diagnosis of the plasma distribution function and contributes to a more accurate and comprehensive grasp of plasma, which creates better conditions for us to take advantage of plasma. These initial results illustrate the potential of the nonextensive electric probe in the field of plasma diagnosis and, more generally, in accelerating the progress of fusion-energy science and helping to understand complex physical systems.

An optimal calculation method for the mechanical properties of near-space capsule materials was proposed. First, biaxial tensile tests under low tensile ratios were carried out on the envelope materials of a near-space airship. The experimental results showed that the values of the elastic modulus and Poisson’s ratio, which are significantly affected by the warp and weft stresses, were not constant. Second, the elastic modulus and Poisson’s ratio of the near-space airship were obtained by using the traditional calculation method, and the limitations of this method were discussed. Third, an optimal calculation model for the elastic modulus and Poisson’s ratio of airship envelopes was proposed. The strain calculated by the proposed optimization model could be effectively correlated to the strain measured by the experiment. Then, through the user-defined subroutine of the finite element method and the elastic modulus and Poisson’s ratio calculated by the proposed optimization, the strain of the finite element simulation was obtained. The average error between the simulation results and the experimental values was approximately 8.21% (warp) and 8.41% (weft). The proposed method can consider the nonlinear changes of the elastic modulus and Poisson’s ratio of membrane material under different stress ratios and predict the force and deformation of the airship’s capsule more accurately, which is adaptable to engineering applications.