雷公藤内酯醇对佐剂性关节组织MIP-1α RANTES和VEGF表达影响的实验研究

目的:探讨雷公藤内酯醇对佐剂性关节炎大鼠病变关节组织MIP-1α、RANTES和VEGF表达的影响。方法:在建立大鼠佐剂性关节炎模型基础上,给予不同剂量雷公藤内酯醇后,观察病变关节组织病理损害程度和MIP-1α、RANTES和VEGF免疫组织化学染色结果。结果:与模型组比较,3个雷公藤内酯醇治疗组关节肿胀度和病变关节组织MIP-1α、RANTES和VEGF表达明显减少,病变关节组织的病理损害明显改善。结论:雷公藤内酯醇可抑制佐剂性关节炎大鼠病变关节组织MIP-1α、RANTES和VEGF表达。

'28 + 10',Not Antes in the Game,China Playsits Trump Card

A good number is a signal for a good fortune - a traditional belief from the Chinese culture. But China is not playing with these numbers, not antes in the game. Its turn for technological advancement means a lot to all, including its textile competitors who would like to know how this textile giant has become so strong.

德国库卡推出最新款KR QUANTEC铸造机器人

2020年2月,德国库卡公司推出数字化程度和性能更佳的第二代KR QUANTEC铸造机器人,包含14种不同型号,尤其适用于潮湿、脏污、多尘、高温等极端条件,具有灵活、高效、节能等特性,给铸造、锻造和加工工业带来了更多灵活性。

国家事权理念下深化教材治理的法治保障进路

国家事权理念下,我国教材治理呈现出行政权力优位、司法权力孱弱的非均衡式治理结构。单一行政治理逻辑下的有限性和滞后性等局限,亟须司法权介入以实现教材治理格局的均衡发展。教材治理中蕴含着国家和社会公共利益,以教育公益诉讼填补教材治理的司法空白,符合积极稳妥拓展公益诉讼受案范围的政策导向和公益诉讼的制度价值。在功能定位上,应明确公益诉讼作为行政执法的补充治理机制,实现行政权和司法权在维护公共利益领域的有效配合。在制度构造上,应明确检察机关作为公益诉讼的适格原告,并将检察建议作为起诉的前置程序,明确依法召回并处置教材、赔礼道歉、消除影响和有条件的损害赔偿等责任承担方式。

离心–气压系统基于SDRE的最优保性能鲁棒控制

针对离心–气压系统,本文提出基于状态相关黎卡提方程(SDRE)的最优保性能鲁棒控制方案,实现高精度、高稳定性的气压控制.针对气压系统中存在的参数不确定性及参数未知且与状态相关的问题,首先采用自适应参数估计方法对系统未知参数进行估计,保证参数估计误差的快速收敛.基于参数估计的结果,设计了最优保性能鲁棒控制器,该控制器在参数不确定性存在的情况下仍能保证系统性能指标达到一确定的上界.然而,由于新的黎卡提方程是与状态相关的,不易求得解析解,因此,通过泰勒级数法离线逼近SDRE的最优解.所提出的控制方案不但具有较强的鲁棒性,并且具有快速、无超调、易于应用等优点.最后,仿真分析和实验结果也验证了所设计的控制方案可以实现气压范围为1-100 kPa,随动误差低于10 Pa的高精度跟踪控制.

国家话语体系视角下中国工程科技类纪录片海外传播效果及启示

基于海外视频网站You Tube的观众评论构建语料库,借助语料库软件Ant Conc对纪录片《超级工程》(第三季)海外接受情况与传播效果进行词条分析。研究发现,虽然部分观众对中国纪录片外宣持有偏见,但得益于作品本身的制作精良,该片总体上受到海外观众的肯定性评价。在国家话语体系建设视角下,中国纪录片叙事方式和制片策划只有以满足观众的诉求为目标,克服主要针对国内宣传的局限性,才能更好地“走出去”。

Path Planning for AUVs Based on Improved APF-AC Algorithm

With the increase in ocean exploration activities and underwater development,the autonomous underwater vehicle(AUV)has been widely used as a type of underwater automation equipment in the detection of underwater environments.However,nowadays AUVs generally have drawbacks such as weak endurance,low intelligence,and poor detection ability.The research and implementation of path-planning methods are the premise of AUVs to achieve actual tasks.To improve the underwater operation ability of the AUV,this paper studies the typical problems of path-planning for the ant colony algorithm and the artificial potential field algorithm.In response to the limitations of a single algorithm,an optimization scheme is proposed to improve the artificial potential field ant colony(APF-AC)algorithm.Compared with traditional ant colony and comparative algorithms,the APF-AC reduced the path length by 1.57%and 0.63%(in the simple environment),8.92%and 3.46%(in the complex environment).The iteration time has been reduced by approximately 28.48%and 18.05%(in the simple environment),18.53%and 9.24%(in the complex environment).Finally,the improved APF-AC algorithm has been validated on the AUV platform,and the experiment is consistent with the simulation.Improved APF-AC algorithm can effectively reduce the underwater operation time and overall power consumption of the AUV,and shows a higher safety.

阿托伐他汀改善阿霉素扩张型心肌病大鼠心肌能量代谢

目的探讨阿托伐他汀对阿霉素扩张型心肌病(DCM)大鼠心肌能量代谢的影响。方法采用2.5 mg/kg阿霉素诱导DCM大鼠模型。Wistar大鼠分为对照组、DCM组、DCM+阿托伐他汀组(atf-DCM组)。比较各组大鼠第7周心脏结构和功能、心脏体积和质量;HE染色观察大鼠心肌组织病理形态学变化;Western blotting检测大鼠心肌组织过氧化物酶体增殖物激活受体α(PPARα)、PPAR协同刺激因子-1α(PGC-1α)、腺嘌呤核苷酸转运体(ANT)蛋白表达水平。结果与对照组比较,DCM组大鼠心脏质量增加,atf-DCM组可部分逆转上述趋势(P<0.01)。DCM组大鼠心脏体积大于对照组(P<0.01)。atf-DCM组心脏结构和功能均优于DCM组,但未达到对照组水平,各组间差异无显著性(P>0.05)。HE染色结果发现,DCM组大鼠左心室部分肌原纤维发生了溶解,心肌细胞间隙增加,心肌纤维出现断裂,有空泡产生;atf-DCM组心肌纤维断裂情况缓解,心肌细胞间隙无明显增加,空泡变性不明显。心肌PPARα、PGC-1α和ANT蛋白水平DCM组低于对照组,atf-DCM组高于DCM组(P<0.01)。结论阿托伐他汀可上调DCM大鼠心肌PPARα、PGC-1α和ANT蛋白水平,改善心肌能量代谢,从而改善大鼠心脏功能。

姜黄素联合花色苷对睡眠剥夺合并动脉粥样硬化大鼠模型的影响

为研究姜黄素(curcumin, Cur)联合花色苷(anthocyanin, Ant)对睡眠剥夺(sleep deprivation, SD)合并动脉粥样硬化(atherosclerosis, AS)大鼠模型的保护作用及机制,将42只大鼠随机均分为正常对照组、动脉粥样硬化模型组、动脉粥样硬化+睡眠剥夺模型组、动脉粥样硬化+大平台对照组、姜黄素保护组、蓝莓花色苷保护组和姜黄素联合蓝莓花色苷保护组。除正常对照组外,其余6组高脂饲料喂养构建动脉粥样硬化模型,持续12周后,使用改良多平台水环境法对动脉粥样硬化+睡眠剥夺模型组及各保护组大鼠进行为期2周的睡眠剥夺处理。造模结束后对保护组大鼠进行对应的给药处理。随后,检测血清各项指标,并采用苏木精-伊红(hematoxylin-eosin, HE)染色观察心脏、胸主动脉变化,采用油红O染色观察胸主动脉弓部动脉粥样硬化斑块沉积情况,采用蛋白质印迹法检测大鼠心肌组织Bcl-2、胱天蛋白酶(caspase)-3和caspase-9的蛋白质表达水平。结果显示,与正常对照组相比,动脉粥样硬化模型组及动脉粥样硬化+睡眠剥夺模型组大鼠血清白细胞介素-6 (interleukin-6, IL-6)、肿瘤坏死因子-α (tumor necrosis factor-α, TNF-α)、总胆固醇(total cholesterol, TC)、甘油三酯(triglyceride, TG)水平以及空腹血糖(fasting blood glucose, FBG)水平均显著升高(P<0.05),心肌细胞和主动脉内皮细胞大量紊乱,且存在大量炎症细胞浸润,出现显著动脉粥样硬化损伤;与动脉粥样硬化模型组相比,经睡眠剥夺处理后的大鼠损伤更为严重;经姜黄素和蓝莓花色苷单独保护后的大鼠,损伤程度与模型组相比有所改善;联合给药组的心脏和主动脉无明显损伤,病变不明显。此外,模型组大鼠心肌组织中caspase-3、caspase-9的表达水平明显高于正常对照组(P<0.05), Bcl-2的表达水平明显低于正常对照组(P<0.05),经药物干预后, caspase-3、caspase-9的表达水平明显下调(P<0.05), Bcl-2的表达水平则明显上调(P<0.05)。上述结果表明,睡眠剥夺会加重动脉粥样硬化大鼠的病情发展,姜黄素和花色苷均可修护睡眠剥夺合并动脉粥样硬化大鼠心肌细胞的损伤,且二者联合使用时的修护效果更佳。

基于MVVM模式SpringBoot框架的高校信息化项目管理系统

传统的Java EE应用以Spring MVC、Mybatis、FreeMarker为核心,具有架构分层不清晰、前后端代码耦合性高、不易扩展等缺点。为提高轻量级Java EE应用的开发效率,提出一种基于Ant Design、React、SpringBoot、Apache Cayenne框架的Java EE应用架构。其基于MVVM模式,以SpringBoot为核心向上集成Ant Design和React为前端,向下集成Apache Cayenne为后端,集成后的架构通过Ant Design和React实现表示层,通过SpringBoot实现控制层和业务逻辑层,通过Apache Cayenne实现数据持久层。基于此开发的高校信息化项目管理系统,架构层次清晰,前后端代码彻底分离,可供高校信息化项目管理部门使用和推广。

Emergence of Multidrug-Resistant Proteus mirabilis Harboring both blaNDM-1 and cfr genes

Proteus mirabilis is abundant in soil and water,and although it is part of the normal human intestinal flora(along with Klebsiella species and Escherichia coli),it is known to cause serious infections in humans,with a fatality rate of 20%-50%[1].Proteus mirabilis is intrinsically resistant to tetracycline,tigecycline,and colistin.The widespread and irregular use of antimicrobial agents,P.mirabilis antimicrobial resistance has mirabilis.They acquire antimicrobial resistance(AMR)by capturing plasmids,transposons,or other mobile elements harboring antimicrobial resistance genes.This mechanism allows the rapid selection and transmission of numerous AMR genes in clinical,veterinary,food production,transportation,and environmental settings.

An Improved Image Steganography Security and Capacity Using Ant Colony Algorithm Optimization

This advanced paper presents a new approach to improving image steganography using the Ant Colony Optimization(ACO)algorithm.Image steganography,a technique of embedding hidden information in digital photographs,should ideally achieve the dual purposes of maximum data hiding and maintenance of the integrity of the cover media so that it is least suspect.The contemporary methods of steganography are at best a compromise between these two.In this paper,we present our approach,entitled Ant Colony Optimization(ACO)-Least Significant Bit(LSB),which attempts to optimize the capacity in steganographic embedding.The approach makes use of a grayscale cover image to hide the confidential data with an additional bit pair per byte,both for integrity verification and the file checksumof the secret data.This approach encodes confidential information into four pairs of bits and embeds it within uncompressed grayscale images.The ACO algorithm uses adaptive exploration to select some pixels,maximizing the capacity of data embedding whileminimizing the degradation of visual quality.Pheromone evaporation is introduced through iterations to avoid stagnation in solution refinement.The levels of pheromone are modified to reinforce successful pixel choices.Experimental results obtained through the ACO-LSB method reveal that it clearly improves image steganography capabilities by providing an increase of up to 30%in the embedding capacity compared with traditional approaches;the average Peak Signal to Noise Ratio(PSNR)is 40.5 dB with a Structural Index Similarity(SSIM)of 0.98.The approach also demonstrates very high resistance to detection,cutting down the rate by 20%.Implemented in MATLAB R2023a,the model was tested against one thousand publicly available grayscale images,thus providing robust evidence of its effectiveness.

Optical redox imaging of ANT1-de-cient muscles

Adenine nucleotide translocator(ANT)is a mitochondrial protein involved in the exchange of ADP and ATP across the mitochondrial inner membrane.It plays a crucial role in cellular energy metabolism by facilitating the transport of ATP synthesized within the mitochondria to the cytoplasm.The isoform ANT1 predominately expresses in cardiac and skeletal muscles.Mutations or dysregulation in ANT1 have been implicated in various mitochondrial disorders and neuromuscular diseases.We aimed to examine whether ANT1 deletion may affect mitochondrial redox state in our established ANT1-de-cient mice.Hearts and quadriceps resected from age-matched wild type(WT)and ANT1-de-cient mice were snap-frozen in liquid nitrogen.The Chance redox scanner was utilized to perform 3D optical redox imaging.Each sample underwent scanning across 3–5 sections.Global averaging analysis showed no signi-cant differences in the redox indices(NADH,flavin adenine dinucleotide containing-flavoproteins Fp,and the redox ratio Fp/(NADH+Fp)between WT and ANT1-de-cient groups.However,quadriceps had higher Fp than hearts in both groups(p¼0:0004 and 0.01,respectively).Furthermore,the quadriceps were also more oxidized(a higher redox ratio)than hearts in WT group(p¼0:004).NADH levels were similar in all cases.Our data suggest that under non-stressful physical condition,the ANT1-de-cient muscle cells were in the same mitochondrial state as WT ones and that the signi-cant difference in the mitochondrial redox state between quadriceps and hearts found in WT might be diminished in ANT1-de-cient ones.Redox imaging of muscles under physical stress can be conducted in future.

MOALG: A Metaheuristic Hybrid of Multi-Objective Ant Lion Optimizer and Genetic Algorithm for Solving Design Problems

This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm(MOALO)which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm(ALO)and the Genetic Algorithm(GA).MOALO version has been employed to address those problems containing many objectives and an archive has been employed for retaining the non-dominated solutions.The uniqueness of the hybrid is that the operators like mutation and crossover of GA are employed in the archive to update the solutions and later those solutions go through the process of MOALO.A first-time hybrid of these algorithms is employed to solve multi-objective problems.The hybrid algorithm overcomes the limitation of ALO of getting caught in the local optimum and the requirement of more computational effort to converge GA.To evaluate the hybridized algorithm’s performance,a set of constrained,unconstrained test problems and engineering design problems were employed and compared with five well-known computational algorithms-MOALO,Multi-objective Crystal Structure Algorithm(MOCryStAl),Multi-objective Particle Swarm Optimization(MOPSO),Multi-objective Multiverse Optimization Algorithm(MOMVO),Multi-objective Salp Swarm Algorithm(MSSA).The outcomes of five performance metrics are statistically analyzed and the most efficient Pareto fronts comparison has been obtained.The proposed hybrid surpasses MOALO based on the results of hypervolume(HV),Spread,and Spacing.So primary objective of developing this hybrid approach has been achieved successfully.The proposed approach demonstrates superior performance on the test functions,showcasing robust convergence and comprehensive coverage that surpasses other existing algorithms.

New Rural Community Construction or Retention Development:A Comparative Analysis of Rural Settlement Transition Mechanism in Plain Agriculture Area of China Based on Actor Network Theory

It is an important way to realize rural revitalization and sustainable development to guide rural settlement transition(RST)in an appropriate way.This paper uses actor network theory(ANT)to construct a theoretical framework for the study of RST.Taking two typical villages with different transition paths in rural areas of North China Plain as examples,this paper reveals the mechanism of RST and makes a comparative analysis.The results show that:1)after identifying problems and obligatory passage point,key actors recruit heterogeneous actors into the actor network by entrusting them with common interests,and realize RST under the system operation.2)Rural settlements under different transition paths have similarities in the problems to be solved,collective actions and policy factors,but there are differences in the transition process,mechanism and effect.The actor network and mechanism of RST through the path of new rural community construction are more complex and the transition effect is more thorough.In contrast,the degree of RST of retention development path is limited if there is no resource and location advantage.3)Based on the applicable conditions of different paths,this paper designs a logical framework of‘Situation-Structure-Behavior-Result’to scientifically guide the identification of RST paths under the background of rural revitalization.

Multi-Label Feature Selection Based on Improved Ant Colony Optimization Algorithm with Dynamic Redundancy and Label Dependence

The world produces vast quantities of high-dimensional multi-semantic data.However,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challenging.Feature selection aims to mitigate the adverse impacts of high dimensionality in multi-label data by eliminating redundant and irrelevant features.The ant colony optimization algorithm has demonstrated encouraging outcomes in multi-label feature selection,because of its simplicity,efficiency,and similarity to reinforcement learning.Nevertheless,existing methods do not consider crucial correlation information,such as dynamic redundancy and label correlation.To tackle these concerns,the paper proposes a multi-label feature selection technique based on ant colony optimization algorithm(MFACO),focusing on dynamic redundancy and label correlation.Initially,the dynamic redundancy is assessed between the selected feature subset and potential features.Meanwhile,the ant colony optimization algorithm extracts label correlation from the label set,which is then combined into the heuristic factor as label weights.Experimental results demonstrate that our proposed strategies can effectively enhance the optimal search ability of ant colony,outperforming the other algorithms involved in the paper.

Cooperative-Guided Ant Colony Optimization with Knowledge Learning for Job Shop Scheduling Problem

With the advancement of the manufacturing industry,the investigation of the shop floor scheduling problem has gained increasing importance.The Job shop Scheduling Problem(JSP),as a fundamental scheduling problem,holds considerable theoretical research value.However,finding a satisfactory solution within a given time is difficult due to the NP-hard nature of the JSP.A co-operative-guided ant colony optimization algorithm with knowledge learning(namely KLCACO)is proposed to address this difficulty.This algorithm integrates a data-based swarm intelligence optimization algorithm with model-based JSP schedule knowledge.A solution construction scheme based on scheduling knowledge learning is proposed for KLCACO.The problem model and algorithm data are fused by merging scheduling and planning knowledge with individual scheme construction to enhance the quality of the generated individual solutions.A pheromone guidance mechanism,which is based on a collaborative machine strategy,is used to simplify information learning and the problem space by collaborating with different machine processing orders.Additionally,the KLCACO algorithm utilizes the classical neighborhood structure to optimize the solution,expanding the search space of the algorithm and accelerating its convergence.The KLCACO algorithm is compared with other highperformance intelligent optimization algorithms on four public benchmark datasets,comprising 48 benchmark test cases in total.The effectiveness of the proposed algorithm in addressing JSPs is validated,demonstrating the feasibility of the KLCACO algorithm for knowledge and data fusion in complex combinatorial optimization problems.

Bio-Inspired Intelligent Routing in WSN: Integrating Mayfly Optimization and Enhanced Ant Colony Optimization for Energy-Efficient Cluster Formation and Maintenance

Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the network.This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability.The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization(MFOA-EACO),where the Mayfly Optimization Algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony Optimization(EACO)technique is used to determine an optimal route between the cluster head and base station.The performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy usage.Our objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the future.The proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox Optimization Algorithm(HSFLBOA),Hybrid Social Reindeer Optimization and Differential Evolution-Firefly Algorithm(HSRODE-FFA),Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach Algorithm(SADSS-IABCA),and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider Algorithm Differential Evolution(EECHS-ISSADE).

A Pre-Selection-Based Ant Colony System for Integrated Resources Scheduling Problem at Marine Container Terminal

Marine container terminal(MCT)plays a key role in the marine intelligent transportation system and international logistics system.However,the efficiency of resource scheduling significantly influences the operation performance of MCT.To solve the practical resource scheduling problem(RSP)in MCT efficiently,this paper has contributions to both the problem model and the algorithm design.Firstly,in the problem model,different from most of the existing studies that only consider scheduling part of the resources in MCT,we propose a unified mathematical model for formulating an integrated RSP.The new integrated RSP model allocates and schedules multiple MCT resources simultaneously by taking the total cost minimization as the objective.Secondly,in the algorithm design,a pre-selection-based ant colony system(PACS)approach is proposed based on graphic structure solution representation and a pre-selection strategy.On the one hand,as the RSP can be formulated as the shortest path problem on the directed complete graph,the graphic structure is proposed to represent the solution encoding to consider multiple constraints and multiple factors of the RSP,which effectively avoids the generation of infeasible solutions.On the other hand,the pre-selection strategy aims to reduce the computational burden of PACS and to fast obtain a higher-quality solution.To evaluate the performance of the proposed novel PACS in solving the new integrated RSP model,a set of test cases with different sizes is conducted.Experimental results and comparisons show the effectiveness and efficiency of the PACS algorithm,which can significantly outperform other state-of-the-art algorithms.

A Study on Outlier Detection and Feature Engineering Strategies in Machine Learning for Heart Disease Prediction

This paper investigates the application ofmachine learning to develop a response model to cardiovascular problems and the use of AdaBoost which incorporates an application of Outlier Detection methodologies namely;Z-Score incorporated with GreyWolf Optimization(GWO)as well as Interquartile Range(IQR)coupled with Ant Colony Optimization(ACO).Using a performance index,it is shown that when compared with the Z-Score and GWO with AdaBoost,the IQR and ACO,with AdaBoost are not very accurate(89.0%vs.86.0%)and less discriminative(Area Under the Curve(AUC)score of 93.0%vs.91.0%).The Z-Score and GWO methods also outperformed the others in terms of precision,scoring 89.0%;and the recall was also found to be satisfactory,scoring 90.0%.Thus,the paper helps to reveal various specific benefits and drawbacks associated with different outlier detection and feature selection techniques,which can be important to consider in further improving various aspects of diagnostics in cardiovascular health.Collectively,these findings can enhance the knowledge of heart disease prediction and patient treatment using enhanced and innovativemachine learning(ML)techniques.These findings when combined improve patient therapy knowledge and cardiac disease prediction through the use of cutting-edge and improved machine learning approaches.This work lays the groundwork for more precise diagnosis models by highlighting the benefits of combining multiple optimization methodologies.Future studies should focus on maximizing patient outcomes and model efficacy through research on these combinations.