Group Ⅱ p21-activated kinases as therapeutic targets in gastrointestinal cancer

P21-activated kinases(PAKs) are central players in various oncogenic signaling pathways. The six PAK family members are classified into group Ⅰ(PAK1-3) and group Ⅱ(PAK4-6). Focus is currently shifting from group Ⅰ PAKs to group Ⅱ PAKs. Group Ⅱ PAKs play important roles in many fundamental cellular processes, some of which have particular significance in the development and progression of cancer. Because of their important functions, group Ⅱ PAKs have become popular potential drug target candidates. However, few group Ⅱ PAKs inhibitors have been reported, and most do not exhibit satisfactory kinase selectivity and "drug-like" properties. Isoform- and kinase-selective PAK inhibitors remain to be developed. This review describes the biological activities of group Ⅱ PAKs, the importance of group Ⅱ PAKs in the development and progression of gastrointestinal cancer, and smallmolecule inhibitors of group Ⅱ PAKs for the treatment of cancer.

Research on the Impact of BYD Group’s Green Innovation on Corporate Financial Performance under the Background of“Dual Carbon”

With the increasingly severe global climate change problem,the“dual carbon”goals(peak carbon emissions and carbon neutrality)have become a common focus of international attention.The report of the 20th National Congress of the Communist Party of China clearly emphasizes the need to accelerate the green transformation of development models,implement comprehensive strategies for frugal development,support the growth of green and low-carbon industries,and promote the concept of green consumption.At the same time,“Made in China 2025”also elaborates on the strategic concept of innovation-driven and green development centered,and strives for breakthroughs in key industries such as new energy vehicles.In such a macro environment,adopting green innovation measures by enterprises not only contributes to ecological protection but also has an undeniable impact on their economic performance and overall value.This article takes BYD Group as a case study to explore in detail the positive effects of green innovation on its economic performance.We first systematically organized and analyzed BYD’s specific practices in green innovation;Then,by examining three key financial indicators,BYD’s current financial situation was analyzed in depth;On this basis,combined with research data,the positive impact of green innovation on BYD’s financial performance was revealed;Finally,based on the analysis results,relevant suggestions are proposed to provide reference for the sustainable development of enterprises in the context of“dual carbon.”

Radar group target recognition based on HRRPs and weighted mean shift clustering

When range high-resolution radar is applied to target recognition,it is quite possible for the high-resolution range profiles(HRRPs)of group targets in a beam to overlap,which reduces the target recognition performance of the radar.In this paper,we propose a group target recognition method based on a weighted mean shift(weighted-MS)clustering method.During the training phase,subtarget features are extracted based on the template database,which is established through simulation or data acquisition,and the features are fed to the support vector machine(SVM)classifier to obtain the classifier parameters.In the test phase,the weighted-MS algorithm is exploited to extract the HRRP of each subtarget.Then,the features of the subtarget HRRP are extracted and used as input in the SVM classifier to be recognized.Compared to the traditional group target recognition method,the proposed method has the advantages of requiring only a small amount of computation,setting parameters automatically,and having no requirement for target motion.The experimental results based on the measured data show that the method proposed in this paper has better recognition performance and is more robust against noise than other recognition methods.

Design of a Flexible Capture Mechanism Inspired by Sea Anemone for Non-cooperative Targets

Robotic grippers have been used in industry as end-effectors but are usually limited to operations in pre-defined workspace.However,few devices can capture irregularly shaped dynamic targets in space,underwater and other unstructured environments.In this paper,a novel continuum arm group mechanism inspired by the morphology and motions of sea anemones is proposed.It is able to dissipate and absorb the kinetic energy of a fast moving target in omni-direction and utilize multiple arms to wrap and lock the target without accurate positioning control.Wire-driven actuation systems are implemented in the individual continuum arms,achieving both bending motion and stiffness regulation.Through finite element method,the influence of different configurations of the continuum arm group on the capture performance is analyzed.A robotic prototype is constructed and tested,showing the presented arm group mechanism has high adaptability to capture targets with different sizes,shapes,and incident angles.

FAST DETECTING TARGET GROUPS IN SAR IMAGES

A successful algorithm for detecting target groups is presented. Firstly, A global Constant False Alarm Rate (CFAR) detector is utilized to locate the potential target regions, and then the features are com- puted for target discrimination based on voting mechanism. Finally, Target groups are extracted. The results of experiments show the validity of this algorithm.

Group Target Tracking in WSN Based on Convex Hulls Merging

When a mass of individual targets move closely, it is unpractical or unnecessary to localize and track every specific target in wireless sensor networks (WSN). However, they can be tracked as a whole by view of group target. In order to decrease the amount of energy spent on active sensing and communications, a flexible boundary detecting model for group target tracking in WSN is proposed, in which, the number of sensors involved in target tracking is adjustable. Unlike traditional one or multiple individual targets, the group target usually occupies a large area. To obtain global estimated position of group target, a divide-merge algorithm using convex hull is designed. In this algorithm, group target’s boundary is divided into several small pieces, and each one is enclosed by a convex hull which is constructed by a cluster of boundary sensors. Then, the information of these small convex hulls is sent back to a sink. Finally, big convex hull merged from these small ones is considered as the group target’s contour. According to our metric of precision evaluation, the simulation experiments confirm the efficiency and accuracy of this algorithm.

A target group tracking algorithm for wireless sensor networks using azimuthal angle of arrival information

In this paper, we explore the technology of tracking a group of targets with correlated motions in a wireless sensor network. Since a group of targets moves collectively and is restricted within a limited region, it is not worth consuming scarce resources of sensors in computing the trajectory of each single target. Hence, in this paper, the problem is modeled as tracking a geographical continuous region covered by all targets. A tracking algorithm is proposed to estimate the region covered by the target group in each sampling period. Based on the locations of sensors and the azimuthal angle of arrival （AOA） information, the estimated region covering all the group members is obtained. Algorithm analysis provides the fundamental limits to the accuracy of localizing a target group. Simulation results show that the proposed algorithm is superior to the existing hull algorithm due to the reduction in estimation error, which is between 10% and 40% of the hull algorithm, with a similar density of sensors. And when the density of sensors increases, the localization accuracy of the proposed algorithm improves dramatically.

A target group tracking algorithm based on a hybrid sensor network

Traditional tracking algorithms based on static sensors have several problems. First, the targets only occur in a part of the interested area; however, a large number of static sensors are distributed in the area to guarantee entire coverage, which leads to wastage of sensor resources. Second, many static sensors have to remain in active mode to track the targets, which causes an increase of energy consumption. To solve these problems, a target group tracking algorithm based on a hybrid sensor network is proposed in this paper, which includes static sensors and mobile sensors. First, an estimation algorithm is proposed to estimate the objective region by static sensors, which work in low-power sensing mode. Second, a movement algorithm based on sliding windows is proposed for mobile sensors to obtain the destinations. Simulation results show that this algorithm can reduce the number of mobile sensors participating in the tracking task and prolong the network lifetime.

Creating International Brands——Target for Hubao Group

The Jiangsu Hubao Group, now very active in the garment sector, used to be a small shirt factory with a loaned capital of only a few hundred thousand yuan(RMB). Since its founding in 1989, the group has been aiming at the international first-class level, and has formulated and implemented international famous brand strategy, with quality products occupying the market. After

基于联合GLMB滤波器的可分辨群目标跟踪

针对联合广义标签多伯努利(joint generalized labeled multi-Bernoulli, J-GLMB)滤波算法中群目标之间距离较近、容易关联错误的问题,结合超图匹配(hypergraph matching, HGM)提出一种基于HGM-J-GLMB滤波器的可分辨群目标跟踪算法。首先,采用J-GLMB滤波器估计群内各目标的状态、数目及轨迹信息,并利用HGM结果提升量测与预测状态之间的关联性能。其次,通过图理论计算邻接矩阵,获取群结构信息和子群数目。随后,利用群结构信息估计协作噪声,进而校正目标的预测状态。最后,通过平滑算法改善滤波效果,并设置轨迹长度阈值,使其在平滑状态达到消除短轨迹的目的。仿真实验表明,所提算法在线性系统下能有效提升群目标跟踪性能。

下调HMGB2表达对肝癌LM3细胞上皮-间质转化的抑制作用及其AKT/mTOR信号通路机制

目的:探讨下调肝癌细胞中高迁移率族框蛋白2 (HMGB2)表达对肝癌细胞生物学行为及上皮-间质转化(EMT)进程的影响,并阐明其作用机制。方法:对数生长期的人肝癌LM3细胞分为阴性对照组和HMGB2 RNA干扰组(HMGB2 siRNA组),分别以Lipofectamin 2000为载体转染无关序列的RNA寡核苷酸(RNA oligo)和敲除HMGB2序列的RNA oligo。采用实时荧光定量PCR(RT-qPCR)法和Western blotting法检测2组细胞中HMGB2 mRNA和蛋白表达水平,分别采用细胞划痕实验和Transwell小室实验检测2组细胞的迁移和侵袭能力,采用Western blotting法检测2组细胞中E-钙黏蛋白(E-cadherin)、 N-钙黏蛋白(N-cadherin)、波形蛋白(Vimentin)和蛋白激酶B(AKT)/哺乳动物雷帕霉素靶蛋白(mTOR)通路相关蛋白表达水平。结果:与阴性对照组比较,HMGB2 siRNA组细胞中HMGB2 mRNA和蛋白表达水平均明显降低(P<0.05),HMGB2 siRNA组细胞划痕愈合率明显降低(P<0.01),侵袭细胞数明显减少(P<0.01),细胞中E-cadherin蛋白表达水平明显升高(P<0.01),N-cadherin、Vimentin、mTOR、AKT和磷酸化AKT (p-AKT)蛋白表达水平明显降低(P<0.05或P<0.01)。结论:下调HMGB2的表达可降低肝癌LM3细胞迁移和侵袭能力并抑制EMT,其作用机制可能与参与调节AKT/mTOR通路相关蛋白表达有关。

社会建构、反馈效应与延迟退休年龄政策停滞

党的十八届三中全会以来,我国屡次提出研究和制定“渐进式延迟退休年龄政策”的方针策略,然时至今日,全国性的延迟退休政策尚未出台。以社会建构与政策设计理论为理论基础与分析框架,以1951—2021年54份中央政府文件为样本,考察目标群体的社会建构如何通过反馈效应影响延迟退休年龄政策进程,研究发现:干部和高级专业技术人员被正面建构且因政策受益,产生正反馈,政策长期仅适用于该群体,普通劳动者被正面建构但因政策受损,产生负反馈,以致全国性政策无法出台,政策陷入停滞;而正反馈效应长期积累促使社会建构转向,社会环境因素改变政策认知,进而推动政策进程。因此,政策制定者坚定决心,不断优化政策设计,回应目标群体的社会建构转向;党和中央政府以积极应对人口老龄化战略吸纳延迟退休年龄政策,塑造公众政策认知;地方政府积极开展政策试验,总结经验教训,这些措施都有助于加快政策进程。

城市户外劳动者服务站点空间分布评价:以厦门市厦门岛为例

基于人本主义理念,从城市户外劳动者服务站点主要服务群体的需求出发,从需求目标、模型评价、反馈评价3个层面,构建交通便捷度、道路旁站点供需指数、用地内站点供需指数等评价指标及定量化评价模型,以厦门市厦门岛爱心驿站为例,研究城市户外劳动者服务站点空间分布的合理性,并通过主要服务群体反馈评价服务站点对服务群体需求的满足程度。结果表明:厦门岛服务站点交通便捷度空间分异显著;分布在城市支路和主干道/快速路旁的服务站点数量供需基本平衡,次干道旁的站点呈轻度供不应求;居住、商业设施、商务设施、交通设施四类用地内服务站点的供需匹配度相对较好,教育科研、文化设施和体育、医疗卫生等用地的供需匹配度相对较差,服务群体对各类用地满意度评价与供需匹配状态基本契合。建议从制定规划建设标准、统筹站点规划布局、盘活存量更新改造等方面优化城市户外劳动者服务站点布局。

频率分组编码信号的相参积累算法

针对捷变频信号的相参积累处理进行研究,提出了一种基于频率分组编码信号的相参积累算法。以线性调频(Linear frequency modulation,LFM)信号为基础,构建了频率分组编码信号,使得发射信号的脉冲载频序列具有一定随机性,保证了波形的抗干扰能力。同时针对频率捷变信号相位非相参带来的主瓣展宽和旁瓣抬高问题,设计了对应编码信号的相参处理方法。首先对回波脉压后的信号进行高分辨距离补偿,然后通过速度遍历插值和距离一致性校正实现脉组内相参处理,最后利用编码信号载频序列的优势实现脉组间相参处理。在仿真实验中,对构建的编码信号相较于LFM信号在抗干扰方面的优越性进行了验证,同时验证了所提相参处理方法相较于基于压缩感知的稀疏重构算法的有效性。

基于IPSO-LSTM的井下动目标位置预测实验研究

提升井下人员定位精度能够加强矿山安全监测,最大程度保障井下人员的生命安全。针对现有测距类算法受现场环境影响致使定位精度不足的问题,提出一种基于IPSO-LSTM的定位模型,应用于井下动目标的位置预测。采用LSTM构建指纹定位模型,通过UWB无线模块采集距离信息以构建距离-位置指纹关系数据库,利用数据库对PSO-LSTM模型进行训练,最后将训练好的模型进行目标轨迹预测。为比较不同改进策略对PSO的提升效果,对比了混沌映射随机初始化种群位置、非线性惯性权重递减、非对称优化学习因子和适应度函数优化4种改进策略,实验证明改进的PSO优化算法收敛速度快、鲁棒性好。为验证IPSO-LSTM的定位效果,以平均定位误差作为评价指标,将IPSO-LSTM模型与Chan算法、PSO-LSTM模型、LSTM神经网络、SSA-LSTM模型和GWO-LSTM进行对比,结果显示,IPSO-LSTM定位模型的平均定位误差为30 mm,相对传统Chan算法、LSTM、PSO-LSTM模型分别提升了76%、49%、24%。为降低局部误差偏大的现象,采用中值滤波对输入信息处理,进一步提升了定位精度。研究对进一步提高现有井下动目标定位系统的精度和稳定性具有重要意义和参考价值。

基于多种群混沌遗传算法的GEO目标服务任务规划

面向地球同步轨道(geosynchronous Earth orbit,GEO)空间目标碎片清除、燃料加注等不同在轨服务需求,研究了“固定储油站+往返航天器”相结合的航天器任务规划问题。首先,建立了多任务混合的燃料最优双层任务规划模型,外层为目标服务序列规划,内层为轨道机动规划。随后,针对该连续-离散混合变量组合优化问题,提出了一种多种群混沌遗传算法(multi-group chaotic genetic algorithm,MGCGA),采用混合编码表征决策变量,引入立方混沌映射算子提高初始种群质量,多种群及精英保留策略使得算法在求解过程中能更为显著地逼近全局最优解。最后,参考实际GEO目标构建了典型算例,规划结果表明所提算法具有全局收敛性好、收敛速度快的优点。

“双减”政策下农村学校课后服务供给的优化——基于重庆市的调查数据分析

通过对农村学校学生和家长关于课后服务需求进行调查,结果表明,学生和家长对当下课后服务处于较为满意的状态。同时,学生和家长对课后服务还存在如要求活动场地从封闭性走向开放性、活动内容从单一性转为多样化、师资水平从低水平提到高质量、服务管理从“一刀切”转向个性化等优化意愿。通过对政策文本、目标群体、教育资源和管理监督的成因分析,认为需要通过在政策上增强适切、细化操作;在资源上积极开发、有效利用;在师资上提高质量、提升效能;在管理上完善措施、强化督导并争取目标群体的正确理解与积极支持等路径,实现农村学校课后服务的优化。

基于YOLO v5+DeepSORT算法的羊群游走同步群体决策行为研究

随着畜牧养殖智能化监控技术的产业化应用,进一步提升畜禽养殖的分类施策精细化管理,成为现代畜牧业精细高效养殖管理的新需求。采用固定机位、多角度视频采集技术,实时记录羊群牧食过程中的游走行为;针对羊群游走视频中易出现遮挡的复杂情况,设计了基于YOLO v5模型的羊群多目标检测模型,羊群游走过程中的多目标实时跟踪识别率可达90.63%;采用羊群游走多目标轨迹跟踪DeepSORT算法,通过提取羊目标的深度表观特征,计算出羊群游走轨迹和变化节拍规律。结果表明,羊的游走过程通常为慢走、快走和疾走3种方式,单只羊的游走过程通常是不固定的随机组合。在中大规模羊群中,由于亲缘关系结构的复杂性,羊群往往分化为多个小群体,这使得从整体上观察和分析羊群行为变得异常困难。为此,聚焦于小规模羊群进行研究,通过羊群散列、聚集和同步3个游走过程分析,初步验证了羊群游走节拍周期上的同步现象。

宽带跳频TDMA辐射源网群的定位及网群划分方法

为了解决现有方法难以对宽带跳频时分多址(frequency hopping-time division multiple access,FH-TDMA)辐射源网群定位的问题,提出一种仅利用波达方向(direction of arrival,DOA)信息的多站定位及网群划分方法。首先通过模拟滤波对各站接收到的信号进行频段划分,通过基于多相滤波的数字信道化方法把信号输出到多个信道中;然后通过直接定位(direct position determination,DPD)算法计算得出各时隙辐射源的位置;最后先通过不同参数的基于密度的噪声应用空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法选择出最佳的辐射源定位结果,再根据提出的基于先验信息的改进K-means聚类算法进行网群划分。所提方法实现了采样率和信号处理速率的降低,且无需站间严格的时间同步。结果表明,所提方法有效估计出了辐射源的数量、位置,以及网群划分情况。

非饱和打击场景下考虑附带毁伤的火力分配方法

针对弹药资源不足的非饱和攻击存在或无法确保重要目标达到毁伤预期,或效率过低不适合工程应用等问题,提出一种考虑附带毁伤和毁伤约束的火力分配方法。建立集群目标分组策略从而考虑目标的附带毁伤,提高毁伤概率计算的合理性;为提高火力分配的求解效率,将火力分配问题等价转换为1维整数规划。通过2个仿真算例验证的结果表明:该方法具备有效性,且随着问题规模的增加火力分配耗时增加缓慢,可满足工程应用要求。