Joint target assignment and power allocation in the netted C-MIMO radar when tracking multi-targets in the presence of self-defense blanket jamming

The netted radar system(NRS)has been proved to possess unique advantages in anti-jamming and improving target tracking performance.Effective resource management can greatly ensure the combat capability of the NRS.In this paper,based on the netted collocated multiple input multiple output(CMIMO)radar,an effective joint target assignment and power allocation(JTAPA)strategy for tracking multi-targets under self-defense blanket jamming is proposed.An architecture based on the distributed fusion is used in the radar network to estimate target state parameters.By deriving the predicted conditional Cramer-Rao lower bound(PC-CRLB)based on the obtained state estimation information,the objective function is formulated.To maximize the worst case tracking accuracy,the proposed JTAPA strategy implements an online target assignment and power allocation of all active nodes,subject to some resource constraints.Since the formulated JTAPA is non-convex,we propose an efficient two-step solution strategy.In terms of the simulation results,the proposed algorithm can effectively improve tracking performance in the worst case.

Novel anti-AD dimeric leads:from relieving symptoms to modifying diseases via multi-targets

Alzheimer disease(AD) has now become the most common brain disorder among the older population. In addition, the currently existing therapeutics only offer temporary symptomatic relieves. Therefore, further research and development of more efficacious and disease-modifying agents for the prevention, treatment and restoration of AD will have tremendous value from both scientific, and economic standpoints. Over the past few years, our series of studies have identified several highly promising anti-AD dimeric leads, with disease-modifying potentials. In this presentation, the latest progress on the neuroprotective and disease modifying effects and the underlying mechanisms of those candidates will be comprehensively illustrated and discussed.

Multi-targets antidepressant mechanism of the bis-benzylisoquinoline 7-O-ethylfangchinoline:Involvement of noradrenergic,dopaminergic and AMPAergic systems

OBJECTIVE Bisbenzylisoquinoline(BBI)alkaloids have extensive pharmacological functions.The aim of this study was to investigate the mechanisms underlying the antidepressant-like action of 7-O-ethylfangchinoline(YH-200)in mice.METHODS Male ICR mice were used in the forced swimming(FST)and tail suspension tests(TST).RESULTS YH-200(60mg·kg-1,ig)decreased the immobility time in FST and TST,and prolonged the latency to immobility in FST.YH-200 revealed more potent anti-immobility activity than its BBI derivative tetrandrine.In addition,the pretreatment of mice with prazosin(1mg·kg-1,ip,anα1-adrenoceptor antagonist),propranolol(2 mg·kg-1,ip,a nonselectiveβ-adrenoceptor antagonist),SCH23390(0.05mg·kg-1,ip,a dopamine D1/D5 receptor antagonist),haloperidol(0.2mg·kg-1,ip,a dopamine D2/D3 receptor antagonist)and NBQX(10mg·kg-1,ip,an AMPA receptor antagonist)prevented the antidepressant-like effect of YH-200(60mg·kg-1,ig)in FST.Besides that,the pretreatment of mice with yohimbine(1mg·kg-1,ip,an α2 adrenoceptor antagonist)augmented the antidepressant-like effect of YH-200(30mg·kg-1,ig)in FST.After 14 dadministration,YH-200(30 and 60mg·kg-1,ig)did not develop drug resistance,but the potency was strengthened,meanwhile,it did not influence the changes in mice body weight.CONCLUSION YH-200 may possess the therapeutic potential for the treatment of depression via the multi-targets including the noradrenergic(α1,α2 and β-adrenoceptors),dopaminergic(D1/D5 and D2/D3receptors)and AMPAergic systems.

Inverse Synthetic Aperture Radar Imaging of Multi-Targets Based on Image Processing Technique

This paper presents a newmulti-targets inverse synthetic aperture radar (ISAR) imaging approach via the image segmentation processing. This method can separate multi-targets with similar velocities,and there is no strict limit on the rotational state of the targets. Firstly,the motion compensation for the completely multi-targets echo is carried out and the coarse image can be achieved with the Range-Doppler (RD) technique. Then a series of image processing methods and image segmentation processing are used to separate the echo data of each mono-target. At last,the image with high quality of each target can be achieved with the RD technique and the Range-Instantaneous-Doppler (RID) technique. ISAR imaging results of simulated and measured data validate the validity of the proposed approach.

Block Iterative STMV Algorithm and Its Application in Multi-Targets Detection

STMV beamforming algorithm needs inversion operation of matrix, and its engineering application is limited due to its huge computational cost. This paper proposed block iterative STMV algorithm based on one-phase regressive filter, matrix inversion lemma and inversion of block matrix. The computational cost is reduced approximately as 1/4 M times as original algorithm when array number is M. The simulation results show that this algorithm maintains high azimuth resolution and good performance of detecting multi-targets. Within 1 - 2 dB directional index and higher azimuth discrimination of block iterative STMV algorithm are achieved than STMV algorithm for sea trial data processing. And its good robustness lays the foundation of its engineering application.

Novel real-time safety algorithm for predicting multi-targets in the farmland road

The more information obtained about the driving environment,the more ensures driving safety.Due to the complex driving environment of farmland roads,targets beside the road sometimes have an important impact on driving safety.To achieve this goal,a novel real-time detection and prediction algorithm of targets was proposed.The whole image was divided into four parts by RCM:driving region,crossroad region,roadside region,and the other region.In addition,a safety policy for every part was enforced by the algorithm,which was based mainly on the combination of the YOLACT and GPM.On this basis,a self-collected data set of 5000 test samples is used for testing.The detection accuracy of the algorithm in the data set could reach up to 90%,and the processing speed to 30.4 fps.In addition,experiments were carried out on actual farmland roads,and the results showed that the proposed algorithm was able to detect,track,and predict targets on the farmland road,and alarm to driver in time before the targets rush into the road.This study provides an important reference for the safe driving of agricultural vehicles.

Investigation multi-target synergistic mechanism of Choerospondias axillaris in the treat of cerebral ischemia based on systems pharmacology and experimental verification

Background:Choerospondias axillaris(CA)is a traditional Mongolian medicine that has been proven to have a good therapeutic effect on cerebrovascular disease.Cerebral Ischemia(CI)is a severe and life-threatening cerebrovascular disease.However,the specific mechanism of action of CA in the treatment of CI is still unclear.Methods:In this study,the related targets and pathways of CA in the treatment of CI were first predicted by system pharmacology and then verified by relevant experiments.Results:The results showed that 12 active ingredients and 208 targets were selected.Further validation through protein-protein interaction(PPI)network analysis and active ingredients-target-pathway(A-T-P)network analysis has confirmed the pivotal roles of the main bioactive constituents,including quercetin,kaempferol,naringin,β-sitosterol,and gallic acid.These components exert their anti-ischemic effects by modulating key targets such as IL6,TNF,MAPK3,and CASP3,thereby regulating the PI3K-Akt,HIF-1,and MAPK signaling pathways,which are integral to processes like inflammation,apoptosis,and oxidative stress.More importantly,through experimental verification,this study confirmed our prediction that CAE significantly reduced neurological function scores,infarct volume,and the percentage of apoptosis neurons.Conclusion:This indicates that CA acts on CI through multi-target synergistic mechanism,and this study provides theoretical basis for the clinical application of CA.

Research on Rotating Machinery Fault Diagnosis Based on Improved Multi-target Domain Adversarial Network

Aiming at the problems of low efficiency,poor anti-noise and robustness of transfer learning model in intelligent fault diagnosis of rotating machinery,a new method of intelligent fault diagnosis of rotating machinery based on single source and multi-target domain adversarial network model(WDMACN)and Gram Angle Product field(GAPF)was proposed.Firstly,the original one-dimensional vibration signal is preprocessed using GAPF to generate the image data including all time series.Secondly,the residual network is used to extract data features,and the features of the target domain without labels are pseudo-labeled,and the transferable features among the feature extractors are shared through the depth parameter,and the feature extractors of the multi-target domain are updated anatomically to generate the features that the discriminator cannot distinguish.The modelt through adversarial domain adaptation,thus achieving fault classification.Finally,a large number of validations were carried out on the bearing data set of Case Western Reserve University(CWRU)and the gear data.The results show that the proposed method can greatly improve the diagnostic efficiency of the model,and has good noise resistance and generalization.

Fecal microbial biomarkers combined with multi-target stool DNA test improve diagnostic accuracy for colorectal cancer

BACKGROUND Colorectal cancer(CRC)is a major global health burden.The current diagnostic tests have shortcomings of being invasive and low accuracy.AIM To explore the combination of intestinal microbiome composition and multi-target stool DNA(MT-sDNA)test in the diagnosis of CRC.METHODS We assessed the performance of the MT-sDNA test based on a hospital clinical trial.The intestinal microbiota was tested using 16S rRNA gene sequencing.This case-control study enrolled 54 CRC patients and 51 healthy controls.We identified biomarkers of bacterial structure,analyzed the relationship between different tumor markers and the relative abundance of related flora components,and distinguished CRC patients from healthy subjects by the linear discriminant analysis effect size,redundancy analysis,and random forest analysis.RESULTS MT-sDNA was associated with Bacteroides.MT-sDNA and carcinoembryonic antigen(CEA)were positively correlated with the existence of Parabacteroides,and alpha-fetoprotein(AFP)was positively associated with Faecalibacterium and Megamonas.In the random forest model,the existence of Streptococcus,Escherichia,Chitinophaga,Parasutterella,Lachnospira,and Romboutsia can distinguish CRC from health controls.The diagnostic accuracy of MT-sDNA combined with the six genera and CEA in the diagnosis of CRC was 97.1%,with a sensitivity and specificity of 98.1%and 92.3%,respectively.CONCLUSION There is a positive correlation of MT-sDNA,CEA,and AFP with intestinal microbiome.Eight biomarkers including six genera of gut microbiota,MT-sDNA,and CEA showed a prominent sensitivity and specificity for CRC prediction,which could be used as a non-invasive method for improving the diagnostic accuracy for this malignancy.

Multi-Target Tracking of Person Based on Deep Learning

To improve the tracking accuracy of persons in the surveillance video,we proposed an algorithm for multi-target tracking persons based on deep learning.In this paper,we used You Only Look Once v5(YOLOv5)to obtain person targets of each frame in the video and used Simple Online and Realtime Tracking with a Deep Association Metric(DeepSORT)to do cascade matching and Intersection Over Union(IOU)matching of person targets between different frames.To solve the IDSwitch problem caused by the low feature extraction ability of the Re-Identification(ReID)network in the process of cascade matching,we introduced Spatial Relation-aware Global Attention(RGA-S)and Channel Relation-aware Global Attention(RGA-C)attention mechanisms into the network structure.The pre-training weights are loaded for Transfer Learning training on the dataset CUHK03.To enhance the discrimination performance of the network,we proposed a new loss function design method,which introduces the Hard-Negative-Mining way into the benchmark triplet loss.To improve the classification accuracy of the network,we introduced a Label-Smoothing regularization method to the cross-entropy loss.To facilitate the model’s convergence stability and convergence speed at the early training stage and to prevent the model from oscillating around the global optimum due to excessive learning rate at the later stage of training,this paper proposed a learning rate regulation method combining Linear-Warmup and exponential decay.The experimental results on CUHK03 show that the mean Average Precision(mAP)of the improved ReID network is 76.5%.The Top 1 is 42.5%,the Top 5 is 65.4%,and the Top 10 is 74.3%in Cumulative Matching Characteristics(CMC);Compared with the original algorithm,the tracking accuracy of the optimized DeepSORT tracking algorithm is improved by 2.5%,the tracking precision is improved by 3.8%.The number of identity switching is reduced by 25%.The algorithm effectively alleviates the IDSwitch problem,improves the tracking accuracy of persons,and has a high practical value.

MULTI-FIGHTER COORDINATED MULTI-TARGET ATTACK SYSTEM

A definition of self-determined priority is used in airfight decision firstly. A scheme of grouping the whole fighters is introduced, and the principle of target assignment and fire control is designed. Based on the neutral network, the decision algorithm is derived and the whole coordinated decision system is simulated. Secondly an algorithm for missile-attacking area is described and its calculational result is obtained under initial conditions. Then the attacking of missile is realized by the proportion guidance. Finally, a multi-target attack system. The system includes airfight decision, estimation of missile attack area and calculation of missile attack procedure. A digital simulation demonstrates that the airfight decision algorithm is correct. The methods have important reference values for the study of fire control system of the fourth generation fighter.

Role of copper chelating agents: between old applications and new perspectives in neuroscience

The role of copper element has been an increasingly relevant topic in recent years in the fields of human and animal health, for both the study of new drugs and innovative food and feed supplements. This metal plays an important role in the central nervous system, where it is associated with glutamatergic signaling, and it is widely involved in inflammatory processes. Thus, diseases involving copper(Ⅱ) dyshomeostasis often have neurological symptoms, as exemplified by Alzheimer's and other diseases(such as Parkinson's and Wilson's diseases). Moreover, imbalanced copper ion concentrations have also been associated with diabetes and certain types of cancer, including glioma. In this paper, we propose a comprehensive overview of recent results that show the importance of these metal ions in several pathologies, mainly Alzheimer's disease, through the lens of the development and use of copper chelators as research compounds and potential therapeutics if included in multi-target hybrid drugs. Seeing how copper homeostasis is important for the well-being of animals as well as humans, we shortly describe the state of the art regarding the effects of copper and its chelators in agriculture, livestock rearing, and aquaculture, as ingredients for the formulation of feed supplements as well as to prevent the effects of pollution on animal productions.

Present and prospect of transarterial chemoembolization combined with tyrosine kinase inhibitor and PD-1 inhibitor for unresectable hepatocellular carcinoma

In this editorial,we comment on the article(World J Gastrointest Oncol 2024;16:1236-1247),which is a retrospective study of transarterial chemoembolization(TACE)combined with multi-targeted tyrosine kinase inhibitor(TKI)and programmed cell death protein-1(PD-1)inhibitor for the treatment of unresectable hepatocellular carcinoma(HCC).Herein,we focus specifically on the mechanisms of this triple therapy,administration sequence and selection of each medication,and implications for future clinical trials.Based on the interaction mechanisms between medications,the triple therapy of TACE+TKI+PD-1 is proposed to complement the deficiency of each monotherapy,and achieve synergistic antitumor effects.Although this triple therapy has been evaluated by several retrospective trials,it is still controversial whether the triple therapy achieves better clinical benefits,due to the flawed study design and heterogeneity in medications.In addition,the administration sequence,which may greatly affect the clinical benefit,needs to be fully considered at clinical decision-making for obtaining better prognosis.We hope that this editorial could contribute to the design and optimization of future trials.

Dynamic cluster member selection method for multi-target tracking in wireless sensor network

Multi-target tracking(MTT) is a research hotspot of wireless sensor networks at present.A self-organized dynamic cluster task allocation scheme is used to implement collaborative task allocation for MTT in WSN and a special cluster member(CM) node selection method is put forward in the scheme.An energy efficiency model was proposed under consideration of both energy consumption and remaining energy balance in the network.A tracking accuracy model based on area-sum principle was also presented through analyzing the localization accuracy of triangulation.Then,the two models mentioned above were combined to establish dynamic cluster member selection model for MTT where a comprehensive performance index function was designed to guide the CM node selection.This selection was fulfilled using genetic algorithm.Simulation results show that this method keeps both energy efficiency and tracking quality in optimal state,and also indicate the validity of genetic algorithm in implementing CM node selection.

FGAs-Based Data Association Algorithm for Multi-sensor Multi-target Tracking

A novel data association algorithm is developed based on fuzzy geneticalgorithms (FGAs). The static part of data association uses one FGA to determine both the lists ofcomposite measurements and the solutions of m-best S-D assignment. In the dynamic part of dataassociation, the results of the m-best S-D assignment are then used in turn, with a Kalman filterstate estimator, in a multi-population FGA-based dynamic 2D assignment algorithm to estimate thestates of the moving targets over time. Such an assignment-based data association algorithm isdemonstrated on a simulated passive sensor track formation and maintenance problem. The simulationresults show its feasibility in multi-sensor multi-target tracking. Moreover, algorithm developmentand real-time problems are briefly discussed.

Multi-target tracking algorithm based on PHD filter against multi-range-false-target jamming

Multi-range-false-target(MRFT) jamming is particularly challenging for tracking radar due to the dense clutter and the repeated multiple false targets. The conventional association-based multi-target tracking(MTT) methods suffer from high computational complexity and limited usage in the presence of MRFT jamming.In order to solve the above problems, an efficient and adaptable probability hypothesis density(PHD) filter is proposed. Based on the gating strategy, the obtained measurements are firstly classified into the generalized newborn target and the existing target measurements. The two categories of measurements are independently used in the decomposed form of the PHD filter. Meanwhile,an amplitude feature is used to suppress the dense clutter. In addition, an MRFT jamming suppression algorithm is introduced to the filter. Target amplitude information and phase quantization information are jointly used to deal with MRFT jamming and the clutter by modifying the particle weights of the generalized newborn targets. Simulations demonstrate the proposed algorithm can obtain superior correct discrimination rate of MRFT, and high-accuracy tracking performance with high computational efficiency in the presence of MRFT jamming in the dense clutter.

Multi-target Collaborative Combat Decision-Making by Improved Particle Swarm Optimizer

A decision-making problem of missile-target assignment with a novel particle swarm optimization algorithm is proposed when it comes to a multiple target collaborative combat situation.The threat function is established to describe air combat situation.Optimization function is used to find an optimal missile-target assignment.An improved particle swarm optimization algorithm is utilized to figure out the optimization function with less parameters,which is based on the adaptive random learning approach.According to the coordinated attack tactics,there are some adjustments to the assignment.Simulation example results show that it is an effective algorithm to handle with the decision-making problem of the missile-target assignment(MTA)in air combat.

Hybrid hierarchical trajectory planning for a fixed-wing UCAV performing air-to-surface multi-target attack

This paper considers the problem of generating a flight trajectory for a single fixed-wing unmanned combat aerial vehicle （UCAV） performing an air-to-surface multi-target attack （A/SMTA） mission using satellite-guided bombs. First, this problem is formulated as a variant of the traveling salesman problem （TSP）, called the dynamic-constrained TSP with neighborhoods （DCT- SPN）. Then, a hierarchical hybrid approach, which partitions the planning algorithm into a roadmap planning layer and an optimal control layer, is proposed to solve the DCTSPN. In the roadmap planning layer, a novel algorithm based on an updatable proba- bilistic roadmap （PRM） is presented, which operates by randomly sampling a finite set of vehicle states from continuous state space in order to reduce the complicated trajectory planning problem to planning on a finite directed graph. In the optimal control layer, a collision-free state-to-state trajectory planner based on the Gauss pseudospectral method is developed, which can generate both dynamically feasible and optimal flight trajectories. The entire process of solving a DCTSPN consists of two phases. First, in the offline preprocessing phase, the algorithm constructs a PRM, and then converts the original problem into a standard asymmet- ric TSP （ATSP）. Second, in the online querying phase, the costs of directed edges in PRM are updated first, and a fast heuristic searching algorithm is then used to solve the ATSP. Numerical experiments indicate that the algorithm proposed in this paper can generate both feasible and near-optimal solutions quickly for online purposes.

An Iterative Pose Estimation Algorithm Based on Epipolar Geometry With Application to Multi-Target Tracking

This paper introduces a new algorithm for estimating the relative pose of a moving camera using consecutive frames of a video sequence. State-of-the-art algorithms for calculating the relative pose between two images use matching features to estimate the essential matrix. The essential matrix is then decomposed into the relative rotation and normalized translation between frames. To be robust to noise and feature match outliers, these methods generate a large number of essential matrix hypotheses from randomly selected minimal subsets of feature pairs, and then score these hypotheses on all feature pairs. Alternatively, the algorithm introduced in this paper calculates relative pose hypotheses by directly optimizing the rotation and normalized translation between frames, rather than calculating the essential matrix and then performing the decomposition. The resulting algorithm improves computation time by an order of magnitude. If an inertial measurement unit(IMU) is available, it is used to seed the optimizer, and in addition, we reuse the best hypothesis at each iteration to seed the optimizer thereby reducing the number of relative pose hypotheses that must be generated and scored. These advantages greatly speed up performance and enable the algorithm to run in real-time on low cost embedded hardware. We show application of our algorithm to visual multi-target tracking(MTT) in the presence of parallax and demonstrate its real-time performance on a 640 × 480 video sequence captured on a UAV. Video results are available at https://youtu.be/Hh K-p2 h XNn U.

Sensing Matrix Optimization for Multi-Target Localization Using Compressed Sensing in Wireless Sensor Network

In the multi-target localization based on Compressed Sensing(CS),the sensing matrix's characteristic is significant to the localization accuracy.To improve the CS-based localization approach's performance,we propose a sensing matrix optimization method in this paper,which considers the optimization under the guidance of the t%-averaged mutual coherence.First,we study sensing matrix optimization and model it as a constrained combinatorial optimization problem.Second,the t%-averaged mutual coherence is adopted as the optimality index to evaluate the quality of different sensing matrixes,where the threshold t is derived through the K-means clustering.With the settled optimality index,a hybrid metaheuristic algorithm named Genetic Algorithm-Tabu Local Search(GA-TLS)is proposed to address the combinatorial optimization problem to obtain the final optimized sensing matrix.Extensive simulation results reveal that the CS localization approaches using different recovery algorithms benefit from the proposed sensing matrix optimization method,with much less localization error compared to the traditional sensing matrix optimization methods.