SUNDER:Self-organized grouping and entrapping method for swarms in multitarget environments

For swarm robots moving in a harsh or uncharted outdoor environment without GPS guidance and global communication,algorithms that rely on global-based information are infeasible.Typically,traditional gene regulatory networks(GRNs)that achieve superior performance in forming trapping pattern towards targets require accurate global positional information to guide swarm robots.This article presents a gene regulatory network with Self-organized grouping and entrapping method for swarms(SUNDER-GRN)to achieve adequate trapping performance with a large-scale swarm in a confined multitarget environment with access to only local information.A hierarchical self-organized grouping method(HSG)is proposed to structure subswarms in a distributed way.In addition,a modified distributed controller,with a relative coordinate system that is established to relieve the need for global information,is leveraged to facilitate subswarms entrapment toward different targets,thus improving the global multi-target entrapping performance.The results demonstrate the superiority of SUNDERGRN in the performance of structuring subswarms and entrapping 10 targets with 200 robots in an environment confined by obstacles and with only local information accessible.

Multitarget therapeutic strategies for Alzheimer's disease

Neurodegenerative diseases such as Alzheimer's, Huntington's and Parkinson's diseases have multifaceted nature because of the different factors contributing to their progression. The complex nature of neurodegenerative diseases has developed a pressing need to design multitarget-directed ligands to address the complementary pathways involved in these diseases. The major enzyme targets for development of therapeutics for Alzheimer's disease are cholinesterase and β-secretase enzymes. In this review, we discuss recent advances in profiling single target inhibitors based on these enzymes to multitarget-directed ligands as potential therapeutics for this devastating disease. In addition, therapeutics based on iron chelation strategy are discussed as well.

Multitarget stool DNA for colorectal cancer screening:A review and commentary on the United States Preventive Services Draft Guidelines

Multitarget stool DNA(mt-sDNA) testing was approved for average risk colorectal cancer(CRC) screening by the United States Food and Drug Administration and thereafter reimbursed for use by the Medicare program(2014).The United States Preventive Services Task Force(USPSTF) October 2015 draft recommendation for CRC screening included mt-s DNA as an "alternative" screening test that "may be useful in select clinical circumstances",despite its very high sensitivity for early stage CRC.The evidence supporting mt-s DNA for routine screening use is robust.The clinical efficacy of mt-s DNA as measured by sensitivity,specificity,life-years gained(LYG),and CRC deaths averted is similar to or exceeds that of the other more specifically recommended screening options included in the draft document,especially those requiring annual testing adherence.In a population with primarily irregular screening participation,tests with the highest point sensitivity and reasonable specificity are more likely to favorably impact CRC related morbidity and mortality than those depending on annual adherence.This paper reviews the evidence supporting mt-s DNA for routine screening and demonstrates,using USPSTF's modeling data,that mt-s DNA at three-year intervals provides significant clinical net benefits and fewer complications per LYG than annual fecal immunochemical testing,high sensitivity guaiac based fecal occult blood testing and 10-year colonoscopy screening.

Multitarget stool DNA tests increases colorectal cancer screening among previously noncompliant Medicare patients

AIM To determine the uptake of noninvasive multitarget stool DNA (mt-s DNA) in a cohort of colorectal cancer(CRC) screening non-compliant average-risk Medicare patients.METHODS This cross sectional primary care office-based study examined mt-s DNA uptake in routine clinical practice among 393 colorectal cancer screening non-compliant Medicare patients ages 50-85 ordered by 77 physicians in a multispecialty group practice (USMD Physician Services, Dallas, TX) from October, 2014-September, 2015. Investigators performed a Health Insurance Portability and Accountability Act compliant retrospective review of electronic health records to identify mt-s DNA use in patients who were either > 10 years since last colonoscopy and/or > 1 year since last fecal occult blood test. Test positive patients were advised to get diagnostic colonoscopy and thereafter patients were characterized by the most clinically significant lesion documented on histopathology of biopsies or excisional tissue. Descriptive statistics were employed. Key outcome measures included mt-s DNA compliance and diagnostic colonoscopy compliance on positive cases.RESULTS Over 12 mo, 77 providers ordered 393 mt-sD NA studies with 347 completed (88.3% compliance). Patient mean age was 69.8 (50-85) and patients were 64% female. Mt-sD NA was negative in 85.3%(296/347) and positive in 14.7%(51/347). Follow-up colonoscopy was performed in 49 positive patients (96.1% colonoscopy compliance) with two patients lost to follow up. Index findings included: colon cancer(4/49, 8.2%), advanced adenomas (21/49, 42.9%), non-advanced adenomas(15/49, 30.6%), and negative results (9/49, 18.4%). The positive predictive value for advanced colorectal lesions was 51.0% and for any colorectal neoplasia was 81.6%. The mean age of patients with colorectal cancer was 70.3 and all CRC's were localized Stage Ⅰ(2) and Stage Ⅱ (2), three were located in the proximal colon and one was located in the distal colon.CONCLUSION Mt-s DNA provided medical benefit to screening noncompliant Medicare population. High compliance with mt-s DNA and subsequent follow-up diagnostic colonoscopy identified patients with clinically critical advanced colorectal neoplasia.

A new fast algorithm for multitarget tracking in dense clutter

A fast joint probabilistic data association （FJPDA） algorithm is proposed in tiffs paper. Cluster probability matrix is approximately calculated by a new method, whose elements βi^t（K） can be taken as evaluation functions. According to values of βi^t（K）, N events with larger joint probabilities can be searched out as the events with guiding joint probabilities, tiros, the number of searching nodes will be greatly reduced. As a result, this method effectively reduces the calculation load and nnkes it possible to be realized on real-thne, Theoretical ,analysis and Monte Carlo simulation results show that this method is efficient.

Multi-Deme Parallel FGAs-Based Algorithm for Multitarget Tracking

For data association in multisensor and multitarget tracking, a novel parallel algorithm is developed to improve the efficiency and real-time performance of FGAs-based algorithm. One Cluster of Workstation （COW） with Message Passing Interface （MPI） is built. The proposed Multi-Deme Parallel FGA （MDPFGA） is run on the platform. A serial of special MDPFGAs are used to determine the static and the dynamic solutions of generalized m-best S-D assignment problem respectively, as well as target states estimation in track management. Such an assignment-based parallel algorithm is demonstrated on simulated passive sensor track formation and maintenance problem. While illustrating the feasibility of the proposed algorithm in multisensor multitarget tracking, simulation results indicate that the MDPFGAs-based algorithm has greater efficiency and speed than the FGAs-based algorithm.

A Decorrelation Constant Modulus Algorithm for Multitarget Separation Using Adaptive Arrays

In this paper, an adaptive digital filter algorithm is presented to optimize the signal-to-noise ratio of an antenna array with multiuser signal inputs. These synchronous communication signals have constant envelope property and are transmitted through a Multiple-Input/Multiple-Output （MIMO） linear channel. This Multiuser Constant Modulus Algorithm （MU-CMA） using an antenna array can compensate for both Interuser Interference （IUI） and Intersymbol Interference （ISI）. The method is derived by minimizing the cost function of the constant modulus signals as well as cross-correlations between them. The simulation result in this paper shows the performance of this algorithm.

基于双曲嵌入的露天矿区暗光环境下道路多目标检测模型

露天矿环境特殊,道路场景复杂多变,在光照不足时会导致矿区道路多目标识别不清、定位不准,进而影响检测效果,给矿区无人矿用卡车的安全行驶带来严重安全隐患。目前的道路障碍物检测模型不能有效解决矿区暗光环境对模型检测效果的影响,同时对矿区小目标障碍物的识别也有较大误差,不适用于矿区特殊环境下障碍物的检测与识别。针对上述问题,提出了一种基于双曲嵌入的露天矿区暗光环境下多目标检测模型。首先,在模型的图像预处理阶段引入卷积神经网路Retinex-Net对暗图像进行增强,提高图像清晰度;然后,针对数据集中特征过多而无重点偏好的问题,在加强特征提取部分添加全局注意力机制,聚集3个维度上更关键的特征信息;最后,在检测模型预测阶段引入双曲全连接层,以减少特征丢失,并防止过拟合现象。实验结果表明:(1)基于双曲嵌入的露天矿区暗光环境下道路多目标检测模型不仅对露天矿区暗光环境下的大尺度目标具有较高的分类与定位精度,对矿用卡车及较远距离的小尺度目标即行人也可准确检测与定位,能够满足无人矿用卡车在矿区特殊环境下驾驶的安全需求。(2)模型的检测准确率达98.6%,检测速度为51.52帧/s,较SSD、YOLOv4、YOLOv5、YOLOx、YOLOv7分别提高20.31%,18.51%,10.53%,8.39%,13.24%,对于矿区道路上的行人、矿用卡车及挖机的检测精度达97%以上。

基于改进YOLOV7的变规模网络重叠区域多目标跟踪方法

在实际场景中,目标之间常常存在重叠或部分遮挡的情况,若是未进行有效的多目标检测以及了解变规模网络重叠区域内的节点状况,会导致目标跟踪精度下降。对此,提出一种基于改进YOLOV7的变规模网络重叠区域多目标跟踪方法。首先,采用改进YOLOV7对变规模网络重叠区域多目标进行检测;然后,在目标检测的基础上,对多目标轨迹特征进行提取;最后,基于提取到的多目标轨迹特征,已知目标的速度、方向与距离,实现变规模网络重叠区域的多目标跟踪。实验结果表明,所提方法的跟踪精准度最高达到98%,曼哈顿距离明显小于对比方法,仅在0.1~-0.1之间,性能较优,具有实用性。

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.

Thymic carcinoid with multiple bone metastases:A case report

BACKGROUND Thymic carcinoid(TC)is a rare entity among anterior mediastinal malignancies.TCs are neuroendocrine carcinomas that constitute approximately 2%–5%of all thymic epithelial tumors.CASE SUMMARY The study reported a rare TC with multiple bone metastases.A 77-year-old man presented with a 2-month history of lower back pain and weight loss of 5 kg.Magnetic resonance imaging scans revealed damage to the lumbar spine,sacrocaudal vertebrae and iliac crest,suggesting bone metastasis;computed tomography(CT)scan of the thorax showed a calcified anterior mediastinal mass;positron emission tomography-CT demonstrated multiple abnormal bone signals;and laboratory work-up showed no endocrine abnormalities.Fine-needle aspiration biopsy revealed predominantly single small,round to oval cells with scant cytoplasm and some loose clusters,suggesting endocrine manifestations.The pathological diagnosis was atypical carcinoid,which tend to originate from the thymus and was classified as intermediate-highly invasive.The patient underwent anlotinib-targeted therapy.Anlotinib(12 mg)was administered daily for 2 wk,after which the patient was allowed to rest for 21 d.Follow-up CT after one year demonstrated that the tumor had shrunk by approximately 29%after therapy.Treatment has a long stable disease benefit of more than 2.5 years.CONCLUSION These findings demonstrated that anlotinib is a promising treatment regimen for patients with TC and multiple bone metastases.

非理想检测下多雷达网络节点选择与辐射资源联合优化分配算法

该文针对分布式相控阵多雷达网络的多目标跟踪场景,研究非理想检测条件下的节点选择与辐射资源联合优化分配算法。首先,根据分布式相控阵多雷达网络构成、目标运动模型、雷达量测模型以及雷达节点检测情况,推导非理想检测下以雷达节点选择、辐射功率和信号带宽为变量的贝叶斯克拉默-拉奥下界(BCRLB)闭式解析表达式,并以此作为多目标跟踪精度衡量指标。在此基础上,以最小化系统各雷达节点对所有目标的总辐射功率为优化目标,以满足目标跟踪精度门限以及给定的系统射频辐射资源限制为约束条件,建立非理想检测条件下多雷达网络节点选择与辐射资源联合优化分配模型,对各时刻雷达节点选择、辐射功率和信号带宽等参数进行联合优化设计,以提升多雷达网络的射频隐身性能。最后,针对上述非线性、非凸优化问题,采用基于障碍函数法和循环最小化算法的4步分解算法进行求解。仿真结果表明,与现有算法相比,所提算法能在满足给定多目标跟踪精度的条件下有效降低分布式相控阵多雷达网络的总辐射功率,至少降低了约32.3%,从而提升其射频隐身性能。

光学免疫层析法用于食品安全快速检测的研究进展

综述了近年来光学新型材料在构建免疫层析试纸条中的应用,其中包括单信号输出光学免疫层析法和多重信号输出光学免疫层析法;讨论了多重信号输出光学免疫层析法用于食品安全快速检测的挑战和发展前景,以期为食品安全快速检测方法研究提供参考。

基于多靶点粪便FIT-DNA联合检测技术的结直肠癌早筛应用研究

目的评价多靶点粪便FIT-DNA联合检测技术在结直肠癌早期筛查中的效果并进一步分析其应用前景。方法选取内蒙古医科大学附属医院就诊人群,每位受试者分别行血清肿瘤标志物检测、多靶点粪便FIT-DNA联合检测、肠镜检查,以接受多靶点粪便FIT-DNA联合检测人群为实验组,以接受肠镜检查及血清肿瘤标志物检测人群为对照组,以病理结果为金标准,评价新型粪便分子检测技术对于结直肠癌筛查的效果,对筛出的阳性人群给予及时干预。结果共分析了115例患者。血清肿瘤标志物检测敏感度63.2%(43/68),特异度74.5%(35/47);肠镜检查敏感度97.1%(66/68),特异度80.7%(38/47);而多靶点粪便FIT-DNA联合检测敏感度89.7%(61/68),特异度87.2%(41/47)。多靶点粪便FIT-DNA联合检测敏感度、特异度等均优于血清肿瘤标志物检测,敏感度虽低于肠镜检查,但相较肠镜检查操作简单,可居家自测。

A measurement-driven adaptive probability hypothesis density filter for multitarget tracking

This paper studies the dynamic estimation problem for multitarget tracking. A novel gat- ing strategy that is based on the measurement likelihood of the target state space is proposed to improve the overall effectiveness of the probability hypothesis density （PHD） filter. Firstly, a measurement-driven mechanism based on this gating technique is designed to classify the measure- ments. In this mechanism, only the measurements for the existing targets are considered in the update step of the existing targets while the measurements of newborn targets are used for exploring newborn targets. Secondly, the gating strategy enables the development of a heuristic state estima- tion algorithm when sequential Monte Carlo （SMC） implementation of the PHD filter is investi- gated, where the measurements are used to drive the particle clustering within the space gate. The resulting PHD filter can achieve a more robust and accurate estimation of the existing targets by reducing the interference from clutter. Moreover, the target birth intensity can be adaptive to detect newborn targets, which is in accordance with the birth measurements. Simulation results demonstrate the computational efficiency and tracking performance of the proposed algorithm.

基于随机有限集滤波器的可分辨群目标跟踪技术研究综述

群目标跟踪在救灾搜救、海陆防御和战场作战等领域展现出广阔的应用前景。与传统多目标跟踪不同,群目标跟踪不仅涉及对多个个体目标的跟踪,还涉及对一个群体目标的跟踪。在群体中各个子目标需要同步运动以避免碰撞,同时群体中的子目标数量和群结构还将随着时间推移而改变。根据子目标数量和传感器分辨率的不同,群目标跟踪问题包含可分辨、不可分辨、部分可分辨以及部分不可分辨群目标跟踪等多个类别。其中,可分辨群目标跟踪问题需要同时对群体结构、群内子目标交互和数目进行估计。现有研究主要关注基于传统数据关联和随机有限集滤波器的可分辨群目标跟踪方法,其中,基于随机有限集滤波器的方法通过将多个目标状态联合建模成随机有限集,缓解了数据关联问题,从而可更好地适应跟踪场景。为更好地展示群目标跟踪方法的研究进展,综述了近年来基于随机有限集滤波器的可分辨群目标跟踪的若干代表性方法,包括基于多目标多伯努利滤波器、基于标签随机有限集滤波器和基于泊松多伯努利混合滤波器的群目标跟踪方法。这些方法在处理可分辨群目标跟踪问题时展示出了显著的优势。最后,探讨了存在的问题和未来的发展方向。

基于数据中心的虚拟机放置优化节能策略研究

近年来,随着5G、人工智能等新兴技术的发展,算力为千行百业的数字化转型注入强心剂。社会各行各业对算力需求的增长日益明显,运营商数据中心的服务器为保证高可用性,数据中心的高能耗俨然成为制约数据中心发展的一大阻碍,“节能增效”是数据中心的刚需。由于大量资源碎片造成的资源利用率低下和能源浪费问题。为了解决这个问题,提出了一种基于线性整数规划结合装箱的多目标优化算法。旨在通过优化虚拟机放置,进行虚拟机的二次调度,提高资源利用率,腾挪出更多的空闲主机,执行物理机下电等绿色节能操作,以达到“节能增效”目的。

Simultaneous estimation of time-delay and direction of arrival for underwater multitarget by using extended ESPRIT

Multiple sources localization is an important technique applied in many areasl such as sonar, radar, biomedical, and geology exploration. High resolution localization needs to employ jointly two sorts of high resolution estimators, time-delay estimator and Direction Of Arrival (DOA) estimator, both of which are tough problems attracting many signal processing researchers. There is also another difficulty that is to pair two groups of parameters in timedelay and DOA domains. In underwater environment, multiple sources localization research faces more difficulties because of the long duration emitted wave, limit aperture of array, and short data record of echoes. In this paper, an new extended ESPRIT (Estimation of Signal Parameters via Rotational Invariance Technique) method is presented. With a single echo wave, both time-delay and DOA parameters of multiple sources are estimated simultaneously.No additional pairing algorithm is needed to obtain the source locations. The performance of the new estimators and the probability of correct pairing is given by computer simulations, andthe results shows that good estimation can be obtained in low SNR (Signal to Noise Ratio) for multiple sources localization.

Stability analysis for delayed viral infection model with multitarget cells and general incidence rate

In this paper, the sharp threshold properties of a （2n ＋ 1）-dimensional delayed viral infection model are investigated. This model combines with n classes of uninfected tar- get cells, n classes of infected cells and nonlinear incidence rate h（x,v）. Two kinds of distributed time delays are incorporated into the model to describe the time needed for infection of uninfected target cells and virus replication. Under certain conditions, it is shown that the basic reproduction number is a threshold parameter for the existence of the equilibria, uniform persistence, as well as for global stability of the equilibria of the model.