High-speed penetration of ogive-nose projectiles into thick concrete targets:Tests and a projectile nose evolution model

The majority of the projectiles used in the hypersonic penetration study are solid flat-nosed cylindrical projectiles with a diameter of less than 20 mm.This study aims to fill the gap in the experimental and analytical study of the evolution of the nose shape of larger hollow projectiles under hypersonic penetration.In the hypersonic penetration test,eight ogive-nose AerMet100 steel projectiles with a diameter of 40 mm were launched to hit concrete targets with impact velocities that ranged from 1351 to 1877 m/s.Severe erosion of the projectiles was observed during high-speed penetration of heterogeneous targets,and apparent localized mushrooming occurred in the front nose of recovered projectiles.By examining the damage to projectiles,a linear relationship was found between the relative length reduction rate and the initial kinetic energy of projectiles in different penetration tests.Furthermore,microscopic analysis revealed the forming mechanism of the localized mushrooming phenomenon for eroding penetration,i.e.,material spall erosion abrasion mechanism,material flow and redistribution abrasion mechanism and localized radial upsetting deformation mechanism.Finally,a model of highspeed penetration that included erosion was established on the basis of a model of the evolution of the projectile nose that considers radial upsetting;the model was validated by test data from the literature and the present study.Depending upon the impact velocity,v0,the projectile nose may behave as undistorted,radially distorted or hemispherical.Due to the effects of abrasion of the projectile and enhancement of radial upsetting on the duration and amplitude of the secondary rising segment in the pulse shape of projectile deceleration,the predicted DOP had an upper limit.

Natural gas exploration potential and favorable targets of Permian Fengcheng Formation in the western Central Depression of Junggar Basin,NW China

Based on the organic geochemical data and the molecular and stable carbon isotopic compositions of natural gas of the Lower Permian Fengcheng Formation in the western Central Depression of Junggar Basin,combined with sedimentary environment analysis and hydrocarbon-generating simulation,the gas-generating potential of the Fengcheng source rock is evaluated,the distribution of large-scale effective source kitchen is described,the genetic types of natural gas are clarified,and four types of favorable exploration targets are selected.The results show that:(1)The Fengcheng Formation is a set of oil-prone source rocks,and the retained liquid hydrocarbon is conducive to late cracking into gas,with characteristics of high gas-generating potential and late accumulation;(2)The maximum thickness of Fengcheng source rock reaches 900 m.The source rock has entered the main gas-generating stage in Penyijingxi and Shawan sags,and the area with gas-generating intensity greater than 20×10^(8) m^(3)/km^(2) is approximately 6500 km^(2).(3)Around the western Central Depression,highly mature oil-type gas with light carbon isotope composition was identified to be derived from the Fengcheng source rocks mainly,while the rest was coal-derived gas from the Carboniferous source rock;(4)Four types of favorable exploration targets with exploration potential were developed in the western Central Depression which are structural traps neighboring to the source,stratigraphic traps neighboring to the source,shale-gas type within the source,and structural traps within the source.Great attention should be paid to these targets.

Potentials of ribosomopathy gene as pharmaceutical targets for cancer treatment

Ribosomopathies encompass a spectrum of disorders arising from impaired ribosome biogenesis and reduced functionality.Mutation or dysexpression of the genes that disturb any finely regulated steps of ribosome biogenesis can result in different types of ribosomopathies in clinic,collectively known as ribosomopathy genes.Emerging data suggest that ribosomopathy patients exhibit a significantly heightened susceptibility to cancer.Abnormal ribosome biogenesis and dysregulation of some ribosomopathy genes have also been found to be intimately associated with cancer development.The correlation between ribosome biogenesis or ribosomopathy and the development of malignancies has been well established.This work aims to review the recent advances in the research of ribosomopathy genes among human cancers and meanwhile,to excavate the potential role of these genes,which have not or rarely been reported in cancer,in the disease development across cancers.We plan to establish a theoretical framework between the ribosomopathy gene and cancer development,to further facilitate the potential of these genes as diagnostic biomarker as well as pharmaceutical targets for cancer treatment.

Electromagnetic pulses produced by a picosecond laser interacting with solid targets

A high-power laser ablating solid targets induces giant electromagnetic pulses(EMPs),which are intimately pertinent to laser parameters,such as energy and pulse width.In this study,we reveal the features of EMPs generated from a picosecond(ps)laser irradiating solid targets at the SG-Ⅱpicosecond petawatt(PSPW)laser facility.The laser energy and pulse,as well as target material and thickness,show determinative effects on the EMPs’amplitude.More intense EMPs are detected behind targets compared to those at the other three positions,and the EMP amplitude decreases from 90.09 kV/m to 17.8 kV/m with the gold target thickness increasing from 10μm to 20μm,which is suppressed when the laser pulse width is enlarged.The results are expected to provide more insight into EMPs produced by ps lasers coupling with targets and lay the foundation for an effective EMP shielding design in high-power laser infrastructures.

Distinct molecular targets of ProEGCG from EGCG and superior inhibition of angiogenesis signaling pathways for treatment of endometriosis

Endometriosis is a common chronic gynecological disease with endometrial cell implantation outside the uterus.Angiogenesis is a major pathophysiology in endometriosis.Our previous studies have demonstrated that the prodrug of epigallocatechin gallate(ProEGCG)exhibits superior anti-endometriotic and anti-angiogenic effects compared to epigallocatechin gallate(EGCG).However,their direct binding targets and underlying mechanisms for the differential effects remain unknown.In this study,we demonstrated that oral ProEGCG can be effective in preventing and treating endometriosis.Additionally,1D and 2D Proteome Integral Solubility Alteration assay-based chemical proteomics identified metadherin(MTDH)and PX domain containing serine/threonine kinase-like(PXK)as novel binding targets of EGCG and ProEGCG,respectively.Computational simulation and BioLayer interferometry were used to confirm their binding affinity.Our results showed that MTDH-EGCG inhibited protein kinase B(Akt)-mediated angiogenesis,while PXK-ProEGCG inhibited epidermal growth factor(EGF)-mediated angiogenesis via the EGF/hypoxia-inducible factor(HIF-1a)/vascular endothelial growth factor(VEGF)pathway.In vitro and in vivo knockdown assays and microvascular network imaging further confirmed the involvement of these signaling pathways.Moreover,our study demonstrated that ProEGCG has superior therapeutic effects than EGCG by targeting distinct signal transduction pathways and may act as a novel antiangiogenic therapy for endometriosis.

Glutamine transporters as effective targets in digestive system malignant tumor treatment

Glutamine is one of the most abundant non-essential amino acids in human plasma and plays a crucial role in many biological processes of the human body.Tumor cells take up a large amount of glutamine to meet their rapid proliferation requirements,which is supported by the upregulation of glutamine transporters.Targeted inhibition of glutamine transporters effectively inhibits cell growth and proliferation in tumors.Among all cancers,digestive system malignant tumors(DSMTs)have the highest incidence and mortality rates,and the current therapeutic strategies for DSMTs are mainly surgical resection and chemotherapy.Due to the relatively low survival rate and severe side effects associated with DSMTs treatment,new treatment strategies are urgently required.This article summarizes the glutamine transporters involved in DSMTs and describes their role in DSMTs.Additionally,glutamine transportertarget drugs are discussed,providing theoretical guidance for the further development of drugs DSMTs treatment.

Overview of radar detection methods for low altitude targets in marine environments

In this paper,a comprehensive overview of radar detection methods for low-altitude targets in maritime environments is presented,focusing on the challenges posed by sea clutter and multipath scattering.The performance of the radar detection methods under sea clutter,multipath,and combined conditions is categorized and summarized,and future research directions are outlined to enhance radar detection performance for low-altitude targets in maritime environments.

Dependence of impact regime boundaries on the initial temperatures of projectiles and targets

Towards higher impact velocities,ballistic events are increasingly determined by the material temperatures.Related effects might range from moderate thermal softening to full phase transition.In particular,it is of great interest to quantify the conditions for incipient or full melting of metals during impact interactions,which result in a transition from still strength-affected to hydrodynamic material behavior.In this work,we investigate to which extent the respective melting thresholds are also dependent on the initial,and generally elevated,temperatures of projectiles and targets before impact.This is studied through the application of a model developed recently by the authors to characterize the transition regime between high-velocity and hypervelocity impact,for which the melting thresholds of materials were used as the defining quantities.The obtained results are expected to be of general interest for ballistic application cases where projectiles or targets are preheated.Such conditions might result,for example,from aerodynamic forces acting onto a projectile during atmospheric flight,explosive shapedcharge-jet formation or armor exposure to environmental conditions.The performed analyses also broaden the scientific understanding of the relevance of temperature in penetration events,generally known since the 1960s,but often not considered thoroughly in impact studies.

LQTTrack:Multi-Object Tracking by Focusing on Low-Quality Targets Association

Multi-object tracking(MOT)has seen rapid improvements in recent years.However,frequent occlusion remains a significant challenge in MOT,as it can cause targets to become smaller or disappear entirely,resulting in lowquality targets,leading to trajectory interruptions and reduced tracking performance.Different from some existing methods,which discarded the low-quality targets or ignored low-quality target attributes.LQTTrack,with a lowquality association strategy(LQA),is proposed to pay more attention to low-quality targets.In the association scheme of LQTTrack,firstly,multi-scale feature fusion of FPN(MSFF-FPN)is utilized to enrich the feature information and assist in subsequent data association.Secondly,the normalized Wasserstein distance(NWD)is integrated to replace the original Inter over Union(IoU),thus overcoming the limitations of the traditional IoUbased methods that are sensitive to low-quality targets with small sizes and enhancing the robustness of low-quality target tracking.Moreover,the third association stage is proposed to improve the matching between the current frame’s low-quality targets and previously interrupted trajectories from earlier frames to reduce the problem of track fragmentation or error tracking,thereby increasing the association success rate and improving overall multi-object tracking performance.Extensive experimental results demonstrate the competitive performance of LQTTrack on benchmark datasets(MOT17,MOT20,and DanceTrack).

A Novel Filtering-Based Detection Method for Small Targets in Infrared Images

Infrared small target detection technology plays a pivotal role in critical military applications,including early warning systems and precision guidance for missiles and other defense mechanisms.Nevertheless,existing traditional methods face several significant challenges,including low background suppression ability,low detection rates,and high false alarm rates when identifying infrared small targets in complex environments.This paper proposes a novel infrared small target detection method based on a transformed Gaussian filter kernel and clustering approach.The method provides improved background suppression and detection accuracy compared to traditional techniques while maintaining simplicity and lower computational costs.In the first step,the infrared image is filtered by a new filter kernel and the results of filtering are normalized.In the second step,an adaptive thresholding method is utilized to determine the pixels in small targets.In the final step,a fuzzy C-mean clustering algorithm is employed to group pixels in the same target,thus yielding the detection results.The results obtained from various real infrared image datasets demonstrate the superiority of the proposed method over traditional approaches.Compared with the traditional method of state of the arts detection method,the detection accuracy of the four sequences is increased by 2.06%,0.95%,1.03%,and 1.01%,respectively,and the false alarm rate is reduced,thus providing a more effective and robust solution.

Adversarial Defense Technology for Small Infrared Targets

With the rapid development of deep learning-based detection algorithms,deep learning is widely used in the field of infrared small target detection.However,well-designed adversarial samples can fool human visual perception,directly causing a serious decline in the detection quality of the recognition model.In this paper,an adversarial defense technology for small infrared targets is proposed to improve model robustness.The adversarial samples with strong migration can not only improve the generalization of defense technology,but also save the training cost.Therefore,this study adopts the concept of maximizing multidimensional feature distortion,applying noise to clean samples to serve as subsequent training samples.On this basis,this study proposes an inverse perturbation elimination method based on Generative Adversarial Networks(GAN)to realize the adversarial defense,and design the generator and discriminator for infrared small targets,aiming to make both of them compete with each other to continuously improve the performance of the model,find out the commonalities and differences between the adversarial samples and the original samples.Through experimental verification,our defense algorithm is not only able to cope with multiple attacks but also performs well on different recognition models compared to commonly used defense algorithms,making it a plug-and-play efficient adversarial defense technique.

MTTSNet:Military time-sensitive targets stealth network via real-time mask generation

The automatic stealth task of military time-sensitive targets plays a crucial role in maintaining national military security and mastering battlefield dynamics in military applications.We propose a novel Military Time-sensitive Targets Stealth Network via Real-time Mask Generation(MTTSNet).According to our knowledge,this is the first technology to automatically remove military targets in real-time from videos.The critical steps of MTTSNet are as follows:First,we designed a real-time mask generation network based on the encoder-decoder framework,combined with the domain expansion structure,to effectively extract mask images.Specifically,the ASPP structure in the encoder could achieve advanced semantic feature fusion.The decoder stacked high-dimensional information with low-dimensional information to obtain an effective mask layer.Subsequently,the domain expansion module guided the adaptive expansion of mask images.Second,a context adversarial generation network based on gated convolution was constructed to achieve background restoration of mask positions in the original image.In addition,our method worked in an end-to-end manner.A particular semantic segmentation dataset for military time-sensitive targets has been constructed,called the Military Time-sensitive Target Masking Dataset(MTMD).The MTMD dataset experiment successfully demonstrated that this method could create a mask that completely occludes the target and that the target could be hidden in real time using this mask.We demonstrated the concealment performance of our proposed method by comparing it to a number of well-known and highly optimized baselines.

An Efficient Radar Detection Method of Maneuvering Small Targets

Detection of maneuvering small targets has always been an important yet challenging task for radar signal processing.One primary reason is that target variable motions within coherent processing interval generate energy migrations across multiple resolution bins,which severely deteriorate the parameter estimation performance.A coarse-to-fine strategy for the detection of maneuvering small targets is proposed.Integration of small points segmented coherently is performed first,and then an optimal inter-segment integration is utilized to derive the coarse estimation of the chirp rate.Sparse fractional Fourier transform(FrFT)is then employed to refine the coarse estimation at a significantly reduced computational complexity.Simulation results verify the proposed scheme that achieves an efficient and reliable maneuvering target detection with-16dB input signal-to-noise ratio(SNR),while requires no exact a priori knowledge on the motion parameters.

Optimization of the Use of Spherical Targets for Point Cloud Registration Using Monte Carlo Simulation

Registrations based on the manual placement of spherical targets are still being employed by many professionals in the industry.However,the placement of those targets usually relies solely on personal experience without scientific evidence supported by numerical analysis.This paper presents a comprehensive investigation,based on Monte Carlo simulation,into determining the optimal number and positions for efficient target placement in typical scenes consisting of a pair of facades.It demonstrates new check-up statistical rules and geometrical constraints that can effectively extract and analyze massive simulations of unregistered point clouds and their corresponding registrations.More than 6×10^(7) sets of the registrations were simulated,whereas more than IOO registrations with real data were used to verify the results of simulation.The results indicated that using five spherical targets is the best choice for the registration of a large typical registration site consisting of two vertical facades and a ground,when there is only a box set of spherical targets available.As a result,the users can avoid placing extra targets to achieve insignificant improvements in registration accuracy.The results also suggest that the higher registration accuracy can be obtained when the ratio between the facade-to-target distance and target-to-scanner distance is approximately 3:2.Therefore,the targets should be placed closer to the scanner rather than in the middle between the facades and the scanner,contradicting to the traditional thought. Besides,the results reveal that the accuracy can be increased by setting the largest projected triangular area of the targets to be large.

Transformation power or development pressure:economic growth targets and urban carbon productivity

Maintaining moderate economic growth targets(EGTs)is the key for local governments to effectively implement the“carbon peak and carbon neutrality”goals under the refreshed development pattern.Utilizing panel data of 276 prefecture-level cities in China's Mainland from 2010 to 2020,and employing methods such as intermediary and threshold models,this study empirically analyzes the internal mechanism of EGT’s impact on urban carbon productivity(UCP).Our findings demonstrate that:①The overall EGT during the analyzed period is not conducive to improving UCP.This conclusion remains valid after a series of robustness tests.This effect is more pronounced in the central region and resource-based cities than in the east-west region and non resource-based cities.②EGT not only directly suppresses UCP but also exerts indirect negative impacts on UCP from three aspects:delaying the digital economy(DE),constraining financial expansion(FE),and hindering green technology innovation(GTI).This negative indirect effect is similar to or even surpasses the direct effect,suggesting that the internal relationship between EGT and“dual-carbon”goals should be re-evaluated from a new compound perspective.③EGT not only has a simple linear impact on UCP but also significantly exhibits a dynamic evolution pattern in inverted“U”shape.That is,as EGT continuously upgrades,a nonlinear impact on UCP emerges in the form of“promoting first,suppressing later”.This indicates that surpassing the“degree”limit for EGT will be detrimental to the improvement of UCP.This study broadens the scope of carbon productivity analysis by introducing a new perspective centered on EGT.The insights gleaned from this research offer valuable guidance for local governments to effectively manage economic growth expectations and promote the synchronized achievement of dual-carbon objectives.

Persymmetric adaptive polarimetric detection of subspace range-spread targets in compound Gaussian sea clutter

This paper focuses on the adaptive detection of range and Doppler dual-spread targets in non-homogeneous and nonGaussian sea clutter.The sea clutter from two polarimetric channels is modeled as a compound-Gaussian model with different parameters,and the target is modeled as a subspace rangespread target model.The persymmetric structure is used to model the clutter covariance matrix,in order to reduce the reliance on secondary data of the designed detectors.Three adaptive polarimetric persymmetric detectors are designed based on the generalized likelihood ratio test(GLRT),Rao test,and Wald test.All the proposed detectors have constant falsealarm rate property with respect to the clutter texture,the speckle covariance matrix.Experimental results on simulated and measured data show that three adaptive detectors outperform the competitors in different clutter environments,and the proposed GLRT detector has the best detection performance under different parameters.

Characterization of blueberry exosome-like nanoparticles and miRNAs with potential cross-kingdom human gene targets

Edible plant derived exosome-like nanoparticles(ELNs)have been shown to have multiple nutraceutical functions.However,the diversity of plant materials makes the plant derived ELN study challenging.More efforts are still needed to explore the feasible isolation methods of edible plant derived ELNs and the possible roles of food-derived ELNs in improving human health.In this study,a size exclusion chromatography based method was compared with the traditional ultracentrifugation method to isolate blueberry derived ELNs(B-ELNs),and the miRNA profile of B-ELNs was analyzed by high-throughput sequencing.A total of 36 miRNAs were found to be enriched in B-ELNs compared with berry tissue,and their potential cross-kingdom human gene targets were further predicted.Results showed that size exclusion chromatography was effective for B-ELN isolation.The most abundant miRNAs in B-ELNs mainly belonged to the miR166 family and miR396 family.Target gene prediction indicated that B-ELNs could potentially regulate pathways related to the human digestive system,immune system and infectious diseases.

Gene targets with therapeutic potential in hepatocellular carcinoma

Hepatocellular carcinoma(HCC)is the third leading cause of cancer-related deaths worldwide.Major treatments include liver transplantation,resection,and chemotherapy,but the 5-year recurrence rate remains high.Late diagnosis often prevents surgical intervention,contributing to poor patient survival rates.Carcinogenesis in HCC involves genetic alterations that drive the transformation of normal cells into malignant ones.Enhancer of zeste homolog 2(EZH2),a key regulator of cell cycle progression,is frequently upregulated in HCC and is associated with advanced stages and poor prognosis,making it a potential biomarker.Additionally,signal transducer and activator of transcription 3,which binds to EZH2,affects disease staging and outcomes.Targeting EZH2 presents a promising therapeutic strategy.On the other hand,abnormal lipid metabolism is a hallmark of HCC and impacts prognosis.Fatty acid binding protein 5 is highly expressed in HCC tissues and correlates with key oncogenes,suggesting its potential as a biomarker.Other genes such as guanine monophosphate synthase,cell division cycle associated 5,and epidermal growth factor receptor provide insights into the molecular mechanisms of HCC,offering potential as biomarkers and therapeutic targets.

Integrated causal inference modeling uncovers novel causal factors and potential therapeutic targets of Qingjin Yiqi granules for chronic fatigue syndrome

Objective:Chronic fatigue syndrome(CFS)is a prevalent symptom of post-coronavirus disease 2019(COVID-19)and is associated with unclear disease mechanisms.The herbal medicine Qingjin Yiqi granules(QJYQ)constitute a clinically approved formula for treating post-COVID-19;however,its potential as a drug target for treating CFS remains largely unknown.This study aimed to identify novel causal factors for CFS and elucidate the potential targets and pharmacological mechanisms of action of QJYQ in treating CFS.Methods:This prospective cohort analysis included 4,212 adults aged≥65 years who were followed up for 7 years with 435 incident CFS cases.Causal modeling and multivariate logistic regression analysis were performed to identify the potential causal determinants of CFS.A proteome-wide,two-sample Mendelian randomization(MR)analysis was employed to explore the proteins associated with the identified causal factors of CFS,which may serve as potential drug targets.Furthermore,we performed a virtual screening analysis to assess the binding affinity between the bioactive compounds in QJYQ and CFS-associated proteins.Results:Among 4,212 participants(47.5%men)with a median age of 69 years(interquartile range:69–70 years)enrolled in 2004,435 developed CFS by 2011.Causal graph analysis with multivariate logistic regression identified frequent cough(odds ratio:1.74,95%confidence interval[CI]:1.15–2.63)and insomnia(odds ratio:2.59,95%CI:1.77–3.79)as novel causal factors of CFS.Proteome-wide MR analysis revealed that the upregulation of endothelial cell-selective adhesion molecule(ESAM)was causally linked to both chronic cough(odds ratio:1.019,95%CI:1.012–1.026,P=2.75 e^(−05))and insomnia(odds ratio:1.015,95%CI:1.008–1.022,P=4.40 e^(−08))in CFS.The major bioactive compounds of QJYQ,ginsenoside Rb2(docking score:−6.03)and RG4(docking score:−6.15),bound to ESAM with high affinity based on virtual screening.Conclusions:Our integrated analytical framework combining epidemiological,genetic,and in silico data provides a novel strategy for elucidating complex disease mechanisms,such as CFS,and informing models of action of traditional Chinese medicines,such as QJYQ.Further validation in animal models is warranted to confirm the potential pharmacological effects of QJYQ on ESAM and as a treatment for CFS.

Multiple model PHD filter for tracking sharply maneuvering targets using recursive RANSAC based adaptive birth estimation

An algorithm to track multiple sharply maneuvering targets without prior knowledge about new target birth is proposed. These targets are capable of achieving sharp maneuvers within a short period of time, such as drones and agile missiles.The probability hypothesis density (PHD) filter, which propagates only the first-order statistical moment of the full target posterior, has been shown to be a computationally efficient solution to multitarget tracking problems. However, the standard PHD filter operates on the single dynamic model and requires prior information about target birth distribution, which leads to many limitations in terms of practical applications. In this paper,we introduce a nonzero mean, white noise turn rate dynamic model and generalize jump Markov systems to multitarget case to accommodate sharply maneuvering dynamics. Moreover, to adaptively estimate newborn targets’information, a measurement-driven method based on the recursive random sampling consensus (RANSAC) algorithm is proposed. Simulation results demonstrate that the proposed method achieves significant improvement in tracking multiple sharply maneuvering targets with adaptive birth estimation.