Micro-Arthropods in the Surroundings of Nests of Messor arenarius Ants in the Negev Desert in Israel

The present research investigated a segment of the micro-arthropod populations residing within nests of Messor arenarius ants in the Negev Desert of Israel. The total frequencies of micro-arthropods in the chaff of those ants’ nests were found to be higher than in the surrounding soil of the same nests. Acari (mites) were observed to be more abundant during the spring season, whereas their presence decreased during the summer months. Springtails (Collembola) were found to follow the Acari pattern, commonly found within the nests of those ants during spring but were absent during summer. Psocoptera order inhabiting soil habitats were infrequently encountered during spring, but their prevalence increased significantly during summer, particularly within the chaff of the ants’ nests, suggesting that chaff is their primary food source in the Negev Desert. Our research suggests that shifts in seasonality have important consequences on the distribution of soil invertebrate communities with implications on nutrient cycling.

基于Ant Design Pro的物流系统前端开发与用户体验优化研究

本文探讨了Ant Design Pro在物流系统开发中的应用,特别是其对用户体验和数据可视化的影响。通过采用案例研究方法,分析Ant Design Pro在构建企业级物流系统中的应用。研究发现,Ant Design Pro的一致性设计和丰富的组件库显著提升了开发效率和用户交互体验。此外,其数据可视化组件有效支持了复杂物流数据的呈现和分析。然而,其在性能优化和定制化需求方面面临着挑战。最终,本文提出了一系列基于Ant Design Pro的物流系统开发最佳实践,并对其潜在的改进方向进行了讨论。研究结果为物流行业的信息系统开发提供了实用的指导,并为Ant Design Pro社区贡献了宝贵的实践经验。

An Effective Optimization Algorithm for Ant Colony Vehicular Congestion Management

Adaptability and dynamicity are special properties of social insects derived from the decentralized behavior of the insects. Authors have come up with designs for software solution that can regulate traffic congestion in a network transportation environment. The effectiveness of various researches on traffic management has been verified through appropriate metrics. Most of the traffic management systems are centered on using sensors, visual monitoring and neural networks to check for available parking space with the aim of informing drivers beforehand to prevent traffic congestion. There has been limited research on solving ongoing traffic congestion in congestion prone areas like car park with any of the common methods mentioned. This study focus however is on a motor park, as a highly congested area when it comes to traffic. The car park has two entrance gate and three exit gates which is divided into three Isle of parking lot where cars can park. An ant colony optimization algorithm (ACO) was developed as an effective management system for controlling navigation and vehicular traffic congestion problems when cars exit a motor park. The ACO based on the nature and movement of the natural ants, simulates the movement of cars out of the car park through their nearest choice exit. A car park simulation was also used for the mathematical computation of the pheromone. The system was implemented using SIMD because of its dual parallelization ability. The result showed about 95% increase on the number of vehicles that left the motor park in one second. A clear indication that pheromones are large determinants of the shortest route to take as cars followed the closest exit to them. Future researchers may consider monitoring a centralized tally system for cars coming into the park through a censored gate being.

A Distributed Ant Colony Optimization Applied in Edge Detection

With the rise of image data and increased complexity of tasks in edge detection, conventional artificial intelligence techniques have been severely impacted. To be able to solve even greater problems of the future, learning algorithms must maintain high speed and accuracy through economical means. Traditional edge detection approaches cannot detect edges in images in a timely manner due to memory and computational time constraints. In this work, a novel parallelized ant colony optimization technique in a distributed framework provided by the Hadoop/Map-Reduce infrastructure is proposed to improve the edge detection capabilities. Moreover, a filtering technique is applied to reduce the noisy background of images to achieve significant improvement in the accuracy of edge detection. Close examinations of the implementation of the proposed algorithm are discussed and demonstrated through experiments. Results reveal high classification accuracy and significant improvements in speedup, scaleup and sizeup compared to the standard algorithms.

Nutritional Composition and Anti-Nutrient Levels in Raw and Processed Varieties of Finger Millet Promoted for Nutritional Security

Finger millet (FM) is rich in nutrients such as minerals, vitamins, and amino acids. However, the levels of nutrients and their bioaccessibility depend on the variety, the levels of ant nutrients, the chemical form of nutrients, and the type of processing methods used. The study determined the levels of selected nutrients, anti-nutrients, and bioaccessibility in raw and processed varieties of finger millet being developed by the Kenya Agricultural and Livestock Research Organization (KALRO) in Kenya. Raw finger millet seeds from KALRO Centers in Kenya were processed by malting for 60 hours and roasting at 110°C for 5 minutes as the optimal conditions. Levels of minerals were determined by AAS and AES, anti-nutrients by UV-visible spectrophotometer, proteins by the Pierce kit method, and vitamins by HPLC. The IE4115 and IE3779 showed the highest levels of nutrients and lowest levels of antinutrients hence preferred for processing and bioaccessibility studies. The level (mg/100 g) of selected minerals;K, Cr3+, Mg, Ca, P, Fe, and Zn were found to be highest in the following varieties of the FM;IE3779 (688.519 ± 1.57), IE 4115 (1.29 ± 0.07), IE4115 (294.38 ± 1.93), IE3779 (466.67 ± 4.17), IE4115 (250.92 ± 0.33), KERICHO P (16.98 ± 0.05) and IE4115 (64.10 ± 2.35) respectively. For β-carotene, vitamin B, B2, B3, B6 and B9 the levels were highest in the following varieties of FM;KAKW3 (0.023 ± 0.02), IE4115 (14.85 ± 0.16), IE4115 (12.998 ± 0.04), IE4115 (5.843 ± 0.07), IE3779 (0.06 ± 0.04) and KAKW4 (9.832 ± 0.08). Phytates, tannins, phenols, and oxalates were found to be lowest in the following varieties: IE3779 (14.20 ± 2.90, IE4115 (27.83 ± 0.73), NKFM1 (9.69 ± 0.07) and IE4115 (0.25 ± 0.01). The highest bioaccessibility values reported for K, Mg, Ca, P, Cr3+, Fe, and Zn were 89.53% (malting, IE3779), 49.28% (malting, IE4115), 60.41% (Malting, IE4115), 69.40% (malting, IE4115), 12.9% (malting, IE4115), 59.84% (malting, KAKW3) and 66.89% (roasting, IE3779) respectively (Table 8). For beta carotene, vitamin B1, B2, B3, B6 and B9 the values were 73.33% (malting, p224), 78.84% (malting, IE4115), 78.34 (malting, IE3779), 97.63% (malting, IE4115), 91.64% (malting, IE4115), and 77.52% (roasting, IE4115) (table The result on levels and bioaccessibility showed that IE4115 and IE3779 varieties were more nutritious and therefore should be promoted for nutritional security.

基于改进蚁群算法的外卖配送路径规划研究

从外卖配送员角度出发提出一种改进蚁群算法(Improved Ant Colony Optimization,IACO),在此基础上进行外卖配送路径规划研究.首先通过蚁群算法(Ant Colony Optimization,ACO)求解得到初始规划路径,然后通过大规模邻域搜索算法(Large Neighborhood Search,LNS)优化初始规划路径,通过将ACO和LNS算法结合,提高求解质量.为了验证方法的有效性,对外卖配送过程进行仿真,并且选用不同订单数量场景进行对照分析.根据最优配送方案路线图和目标罚函数的最优值可以得出,IACO算法是有效的,且可以提高外卖配送员外卖配送的效率.IACO算法不但能够提升配送的智能化水平,还从外卖配送员的角度提出一种更为人性化的配送方法,支持网络互联外卖平台派送系统的可持续化发展.

基于集成改进蚁群算法的作战环推荐方法

作战环推荐是依靠优化算法从作战网络中为指挥员推荐最优的作战环,以对目标形成高质量打击。未来作战中的作战环推荐面临体系规模大、决策节奏快的特点。对此,提出了一种集成改进的蚁群算法,能够实现高效、高质的作战环推荐优化求解。首先,将作战环推荐问题转换为一种基于多仓库路径规划的数学模型。然后,针对原始蚁群算法前期收敛速度慢、算法参数对结果影响大和容易陷入局部最优的问题分别提出了3种改进策略:基于边权重信息的信息素初始化、基于差分进化的蚁群算法参数自适应优化和基于遗传算子的全局搜索能力提升,并进行了集成改进。最后,在案例分析中对集成改进蚁群算法进行了分析和对比,验证了所提算法在不需要大幅提高耗时的情况下,优化结果要优于未集成改进的蚁群算法,且相比于原始蚁群算法提升效果显著。

机器人人工拣选环境下混流装配线齐套物料配送优化

为了提高汽车制造企业配送的智能化水平、解决混流装配线齐套物料人工拣选效率低、成本高的问题,引入基于齐套物料配送策略的机器人人工拣选模式,通过优化自动拣选机器人、工人的配置数量及配送周期,使包含机器人使用成本、劳动力成本和在制品库存成本在内的总成本最小。为求解该配送优化问题,提出了改进量子蚁群算法,利用量子比特的叠加性增加种群多样性,避免算法陷入局部最优,同时设计了改进量子旋转门更新机制和基于差分进化操作的非最优个体优化策略,提高了算法收敛速度和寻优质量。最后,通过算例分析验证了模型的正确性与算法的有效性,并分析了拣选批量对总成本的影响。

基于改进蚁狮算法的家庭用电优化调度

为了充分挖掘用户侧需求响应能力,平抑用户的负荷波动及降低用户的用电成本,提出了一种基于改进蚁狮优化算法的家庭用电优化调度方法。首先构建了家庭用电优化调度模型,并将峰均比和平均等待时间作为惩罚量引入用电成本得到综合成本函数。其次采用差分进化机制改进了传统的蚁狮算法。最后基于实时电价和临界峰值电价进行仿真,结果表明,改进的蚁狮算法可以有效的降低用户的用电成本,维持用电舒适度,并与粒子群算法、遗传算法和蚁狮算法进行对比,验证了所提算法有较高的稳定性,更强的全局搜索能力,收敛效果更好。

遗传-蚁群算法在高性能计算任务调度中的应用

针对目前高性能计算任务调度策略利用率低、负载不均衡等问题,设计一种基于遗传-蚁群算法的高性能计算任务调度算法(GA-ACO)。GA-ACO分为两个阶段,第一阶段通过遗传算法缩小空间快速搜索到优秀解,紧接着将其转化为蚁群算法的初始信息素;第二阶段提出一种基于蚁群信息素的全局更新策略对收敛速度做出优化。实验分析表明,与蚁群算法和遗传算法相比,该算法缩短了任务完成时间,降低了节点负载率。

改进蚁群算法的送餐机器人路径规划

蚁群算法拥有良好的全局性、自组织性、鲁棒性,但传统蚁群算法存在许多不足之处。为此,针对算法在路径规划问题中的缺陷,在传统蚁群算法的状态转移公式中,引入目标点距离因素和引导素,加快算法收敛性和改善局部最优缺陷。在带时间窗的车辆路径问题(vehicle routing problem with time windows,VRPTW)上,融合蚁群算法和遗传算法,并将顾客时间窗宽度以及机器人等待时间加入蚁群算法状态转移公式中,以及将蚁群算法的解作为遗传算法的初始种群,提高遗传算法的初始解质量,然后进行编码,设置违反时间窗约束和载重量的惩罚函数和适应度函数,在传统遗传算法的交叉、变异操作后加入了破坏-修复基因的操作来优化每一代新解的质量,在Solomon Benchmark算例上进行仿真,对比算法改进前后的最优解,验证算法可行性。最后在餐厅送餐问题中把带有障碍物的仿真环境路径规划问题和VRPTW问题结合,使用改进后的算法解决餐厅环境下送餐机器人对顾客服务配送问题。

金融科技创新的行动者网络与农村普惠金融发展——以蚂蚁金服为例

借助行动者网络理论,以蚂蚁金服为例选取快捷支付、余额宝及农村金融业务模式构建行动者网络,分析其从支付起家到入局金融再到开拓农村金融市场的过程,借以打开蚂蚁金服金融科技创新的“科技黑箱”。研究发现,蚂蚁金服的金融科技创新包含了金融创新、科技创新和监管创新的不同特质,农村普惠金融具有公共产品的特征,金融科技的进步有助于打破农村地区发展普惠金融存在的包容性和可持续性悖论。在农村地区利用金融科技发展普惠金融需要相当规模的金融科技企业构建强大的行动者网络,需要政府介入给予财政支持、适度监管和信用背书,推动金融科技真正服务“三农”群体。

诉源治理视角下我国企业合规制度的改革

诉源治理以“源头预防为先、非诉机制挺前、多元主体共治”为基本理念,旨在通过多种治理方式从源头解决社会矛盾纠纷,实现社会治理的高效性。在诉源治理已上升为治国方略的时代背景下,作为社会治理模式之一的企业合规制度改革应当遵循诉源治理的基本理念,正视自身所存在的企业合规内在动力尚未激发的源头问题、行政合规的预防功能尚未彰显的诉前问题、多元主体的治理格局尚未构建的主体问题,逐步实现从“被动整改”向“主动预防”的转变,从“事后合规”向“事前合规”的推进,从“一元治理”向“多元共治”的迈进。

基于改进蚁群算法的机器人全局路径规划

针对传统蚁群算法存在初始信息素缺乏、收敛速度慢以及无法有效躲避障碍物等问题,文中提出了一种基于改进蚁群算法的全局路径规划。引入正态分布函数改进传统启发函数,提高了算法效率,缩短了算法收敛所需时间。自适应调整信息素挥发系数,限定信息素范围,避免过早收敛。对算法路径平滑处理,缩短路径长度,从而实现机器人的全局路径规划。仿真结果表明,在20×20环境下,文中算法平均迭代次数比传统蚁群算法减少了28代,收敛速度更快。平均拐点减少了33.3%,使路径更为平滑,克服了初始信息素缺乏,加快了收敛速度,减少了拐点数量,能够有效躲避环境中的障碍物,证明了该算法的可行性。

基于改进蚁群算法的移动机器人路径规划

针对传统蚁群算法在移动机器人路径规划中存在搜索盲目性、收敛速度慢及路径转折点多等问题,提出了一种基于改进蚁群算法的移动机器人路径规划算法。首先,利用跳点搜索(Jump Point Search,JPS)算法不均匀分配初始信息素,降低蚁群前期盲目搜索的概率;然后,引入切比雪夫距离加权因子和转弯代价改进启发函数,提高算法的收敛速度、全局路径寻优能力和搜索路径的平滑程度;最后,提出一种新的信息素更新策略,引入自适应奖惩因子,自适应调整迭代前、后期的信息素奖惩因子,保证了算法全局最优收敛。实验仿真结果表明,在不同地图环境下,与现有文献结果对比,该算法可以有效地缩短路径搜索的迭代次数和最优路径长度,并提高路径的平滑程度。

Route Search Method for Railway Replacement Buses Adopting Ant Colony Optimization

In recent years, Japan, and especially rural areas have faced the growing problems of debt-ridden local railway lines along with the population decline and aging population. Therefore, it is best to consider the discontinuation of local railway lines and introduce replacement buses to secure the transportation methods of the local people especially in rural areas. Based on the above background, targeting local railway lines that may be discontinued in the near future, appropriate bus stops when provided with potential bus stops were selected, the present study proposed a method that introduces routes for railway replacement buses adopting ant colony optimization (ACO). The improved ACO was designed and developed based on the requirements set concerning the route length, number of turns, road width, accessibility of railway lines and zones without bus stops as well as the constraint conditions concerning the route length, number of turns and zones without bus stops. Original road network data were generated and processed adopting a geographic information systems (GIS), and these are used to search for the optimal route for railway replacement buses adopting the improved ACO concerning the 8 zones on the target railway line (JR Kakogawa line). By comparing the improved ACO with Dijkstra’s algorithm, its relevance was verified and areas needing further improvements were revealed.

多因素优化蚁群算法机器人路径规划

采用传统蚁群算法进行路径规划中,存在收敛速度慢,路径不平滑,方向性与目的性较差等问题,提出了多因素优化蚁群算法以提高路径寻优的性能。应用扩散方式赋予地图不均匀的初始信息素,为路径搜索提供更好的方向性,避免了局部最优解的出现;距离启发信息、障碍物阻力启发信息和路径角度启发信息作为综合启发信息应用于蚁群算法,增强蚂蚁移动的目的性,缩短路径长度;运用贝塞尔曲线优化路径上的拐点处,输出平滑路径。仿真结果表明,多因素优化蚁群算法应对多样性环境具有更强的适应性,有效避免了局部最优解,规划出的最优路径更短且光滑,有利于机器人流畅通行,更具有工程实践意义。

基于改进蚁群算法的自动落布车路径规划

针对自动落布车在使用蚁群算法(ant colony algorithm,ACA)进行路径规划过程中出现的收敛次数多、收敛速度较慢且容易陷入局部最优的问题,提出一种改进蚁群算法(improved ant colony algorithm,IACA)。首先对信息素挥发系数ρ进行自适应调整,令其做动态变化,克服算法的收敛次数过多,加快算法收敛速度,减少算法的收敛时间;其次引入细菌觅食算法中趋化操作的趋化步长因子对信息素更新公式进行改进,削减算法迭代的后期信息素浓度值,使算法在后期跳出局部最优值,提高算法全局搜索能力。利用MATLAB将改进后的算法在3种不同的栅格环境中进行仿真验证。结果表明:相比传统蚁群算法,改进后的算法收敛次数减少81.1%,最小路径长度减少6.3%,收敛时间减少20.7%。最后搭建ROS小车实验平台,利用ROS机器人系统对改进蚁群算法在模拟的织布车间环境中进行实验验证。结果表明:对比传统蚁群算法,改进蚁群算法在寻优时间上减少了8.6%。

地震叠后和叠前混合驱动下的页岩油储层多尺度裂缝预测方法

不同尺度、不同类型的天然裂缝的系统性表征有利于识别优质储层、改进开发效果.建立可靠的多尺度裂缝模型是提高页岩油储层钻遇率的重要依据,其关键在于精细预测小尺度裂缝的空间分布,指导钻井钻进和压裂过程.然而,基于地震属性的传统裂缝预测方法只能各自突出单一尺度的断裂,不同裂缝属性的数学独立性给综合裂缝系统的识别带来严峻挑战.我们提出一套改进的全尺度裂缝系统评估方案,其关键在于通过优化的各向异性蚁群裂缝识别算法确定小尺度裂缝的发育概率,将小尺度裂缝的发育概率与传统蚂蚁追踪产生的大尺度断层的概率进行融合,得到的综合裂缝系统可以实现全尺度裂缝的感知.详细的属性分析过程表明,方位各向异性能够精确描述微观裂缝特征.基于此,我们将各向异性分析得到的裂缝密度和裂缝走向融入蚁群追踪裂缝的各个阶段,在提高裂缝密集区搜索强度的同时,还对小断距裂缝的发育方向实现了精准刻画.针对页岩油藏的应用案例证明,根据改进方案得到的全尺度裂缝系统能够同时表征大尺度断层、小裂缝和裂缝破碎带的分布情况.研究区的成像测井结果进一步验证了小尺度裂缝预测发育方向的准确性及可靠性,能够为页岩油水平井开发部署提供理论指导.

基于改进蚁群算法的农业机器人多田块路径规划方法与试验

[目的]针对丘陵地区因田块碎片化和障碍物影响农业机器人作业的情况,提出一种基于改进蚁群算法和A算法相结合的多田块路径规划方法。[方法]通过无人机构建农田遥感影像,利用栅格法对农田进行环境建模,再进行子区的划分与合并,在蚁群算法中引入区域启发函数,对不同区域内的移动代价作区分,通过人工增加最优路径中的信息素浓度并建立自适应信息素挥发系数,对信息素更新方法进行改进,以此求解作业子区间的最优遍历顺序,利用具有启发式搜索功能的A算法进行子区连接路径规划,最终实现多田块路径规划。[结果]MATLAB仿真试验结果表明,在子区数量为40时,采用改进蚁群算法进行规划的平均路径长度比传统的蚁群算法减少了3.19%,平均迭代收敛次数减少了79.5%;在路径遍历仿真试验中,农业机器人遍历路径覆盖率能达到100%,路径重复率为6.48%。利用农田无人机遥感影像和田间作业参数进行自然环境的仿真试验,进一步验证了该方法的有效性。[结论]本研究提出的方法能有效解决丘陵地区农业机器人多田块路径规划问题,可为丘陵地区农业机器人大面积作业提供路径参考与技术支持。