Comparative analysis shows high level of lineage sorting in genomic regions with low recombination in the extended Picea likiangensis species complex

Genome-scale data,while promising for illuminating phylogenetic relationships,frequently pose a conundrum by yielding conflicting topologies and highly variable gene tree distributions(Pease et al.,2016).This complexity likely arises from the reticulate evolution observed in many taxa,where genetic information exchange occurs through diverse biological processes.

Digital Twin Technology of Human-Machine Integration in Cross-Belt Sorting System

The Chinese express delivery industry processes nearly 110 billion items in 2022,averaging an annual growth rate of 200%.Among the various types of sorting systems used for handling express items,cross-belt sorting systems stand out as the most crucial.However,despite their high degree of automation,the workload for operators has intensified owing to the surging volume of express items.In the era of Industry 5.0,it is imperative to adopt new technologies that not only enhance worker welfare but also improve the efficiency of cross-belt systems.Striking a balance between efficiency in handling express items and operator well-being is challenging.Digital twin technology offers a promising solution in this respect.A realization method of a human-machine integrated digital twin is proposed in this study,enabling the interaction of biological human bodies,virtual human bodies,virtual equipment,and logistics equipment in a closed loop,thus setting an operating framework.Key technologies in the proposed framework include a collection of heterogeneous data from multiple sources,construction of the relationship between operator fatigue and operation efficiency based on physiological measurements,virtual model construction,and an online optimization module based on real-time simulation.The feasibility of the proposed method was verified in an express distribution center.

A novel integrated microfluidic chip for on-demand electrostatic droplet charging and sorting

On-demand droplet sorting is extensively applied for the efficient manipulation and genome-wide analysis of individual cells.However,state-of-the-art microfluidic chips for droplet sorting still suffer from low sorting speeds,sample loss,and labor-intensive preparation procedures.Here,we demonstrate the development of a novel microfluidic chip that integrates droplet generation,on-demand electrostatic droplet charging,and high-throughput sorting.The charging electrode is a copper wire buried above the nozzle of the microchannel,and the deflecting electrode is the phosphate buffered saline in the microchannel,which greatly simplifies the structure and fabrication process of the chip.Moreover,this chip is capable of high-frequency droplet generation and sorting,with a frequency of 11.757 kHz in the drop state.The chip completes the selective charging process via electrostatic induction during droplet generation.On-demand charged microdroplets can arbitrarilymove to specific exit channels in a three-dimensional(3D)-deflected electric field,which can be controlled according to user requirements,and the flux of droplet deflection is thereby significantly enhanced.Furthermore,a lossless modification strategy is presented to improve the accuracy of droplet deflection or harvest rate from 97.49% to 99.38% by monitoring the frequency of droplet generation in real time and feeding it back to the charging signal.This chip has great potential for quantitative processing and analysis of single cells for elucidating cell-to-cell variations.

Phylotranscriptomic discordance is best explained by incomplete lineage sorting within Allium subgenus Cyathophora and thus hemiplasy accounts for interspecific trait transition

The transition of traits between genetically related lineages is a fascinating topic that provides clues to understanding the drivers of speciation and diversification.Much can be learned about this process from phylogeny-based trait evolution.However,such inference is often plagued by genome-wide gene-tree discordance(GTD),mostly due to incomplete lineage sorting(ILS)and/or introgressive hybridization,especially when the genes underlying the traits appear discordant.Here,by collecting transcriptomes,whole chloroplast genomes(cpDNA),and population genetic datasets,we used the coalescent model to turn GTD into a source of information for ILS and employed hemiplasy to explain specific cases of apparent“phylogenetic discordance”between different morphological traits and probable species phylogeny in the Allium subg.Cyathophora.Both concatenation and coalescence methods consistently showed the same phylogenetic topology for species tree inference based on single-copy genes(SCGs),as supported by the KS distribution.However,GTD was high across the genomes of subg.Cyathophora:~27%e38.9%of the SCG trees were in conflict with the species tree.Plasmid and nuclear incongruence was also present.Our coalescent simulations indicated that such GTD was mainly a product of ILS.Our hemiplasy risk factor calculations supported that random fixation of ancient polymorphisms in different populations during successive speciation events along the subg.Cyathophora phylogeny may have caused the character transition,as well as the anomalous cpDNA tree.Our study exemplifies how phylogenetic noise can be transformed into evolutionary information for understanding character state transitions along species phylogenies.

Enhancing XRF sensor-based sorting of porphyritic copper ore using particle swarm optimization-support vector machine(PSO-SVM)algorithm

X-ray fluorescence(XRF)sensor-based ore sorting enables efficient beneficiation of heterogeneous ores,while intraparticle heterogeneity can cause significant grade detection errors,leading to misclassifications and hindering widespread technology adoption.Accurate classification models are crucial to determine if actual grade exceeds the sorting threshold using localized XRF signals.Previous studies mainly used linear regression(LR)algorithms including simple linear regression(SLR),multivariable linear regression(MLR),and multivariable linear regression with interaction(MLRI)but often fell short attaining satisfactory results.This study employed the particle swarm optimization support vector machine(PSO-SVM)algorithm for sorting porphyritic copper ore pebble.Lab-scale results showed PSO-SVM out-performed LR and raw data(RD)models and the significant interaction effects among input features was observed.Despite poor input data quality,PSO-SVM demonstrated exceptional capabilities.Lab-scale sorting achieved 93.0%accuracy,0.24%grade increase,84.94%recovery rate,57.02%discard rate,and a remarkable 39.62 yuan/t net smelter return(NSR)increase compared to no sorting.These improvements were achieved by the PSO-SVM model with optimized input combinations and highest data quality(T=10,T is XRF testing times).The unsuitability of LR methods for XRF sensor-based sorting of investigated sample is illustrated.Input element selection and mineral association analysis elucidate element importance and influence mechanisms.

Improving path planning efficiency for underwater gravity-aided navigation based on a new depth sorting fast search algorithm

This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered accordingly.Secondly,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation space.Finally,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,respectively.The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results.

Accelerating Large-Scale Sorting through Parallel Algorithms

This study explores the application of parallel algorithms to enhance large-scale sorting, focusing on the QuickSort method. Implemented in both sequential and parallel forms, the paper provides a detailed comparison of their performance. This study investigates the efficacy of both techniques through the lens of array generation and pivot selection to manage datasets of varying sizes. This study meticulously documents the performance metrics, recording 16,499.2 milliseconds for the serial implementation and 16,339 milliseconds for the parallel implementation when sorting an array by using C++ chrono library. These results suggest that while the performance gains of the parallel approach over its serial counterpart are not immediately pronounced for smaller datasets, the benefits are expected to be more substantial as the dataset size increases.

Real-time ore sorting using color and texture analysis

Sensor-based ore sorting is a technology used to classify high-grade mineralized rocks from low-grade waste rocks to reduce operation costs.Many ore-sorting algorithms using color images have been proposed in the past,but only some validate their results using mineral grades or optimize the algorithms to classify rocks in real-time.This paper presents an ore-sorting algorithm based on image processing and machine learning that is able to classify rocks from a gold and silver mine based on their grade.The algorithm is composed of four main stages:(1)image segmentation and partition,(2)color and texture feature extraction,(3)sub-image classification using neural networks,and(4)a voting system to determine the overall class of the rock.The algorithm was trained using images of rocks that a geologist manually classified according to their mineral content and then was validated using a different set of rocks analyzed in a laboratory to determine their gold and silver grades.The proposed method achieved a Matthews correlation coefficient of 0.961 points,higher than other classification algorithms based on support vector machines and convolutional neural networks,and a processing time under 44 ms,promising for real-time ore sorting applications.

High-throughput sorting of two-color fluorescent-labeled zebrafish embryos

The zebrafish embryos were widely employed in genetics,development and drug discovery studies as miniatured animal models.Sorting of two-color fluorescent embryos is often required in large-scale experiments but it is challenging to manually sort with high efficiency.Here,we reported a high-throughput sorting system for two-color fluorescent zebraflsh embryos.The embryos can be automatically loaded from a sample pool and sorted based on the average fluorescent intensity.The two-color fluorescent signals were split into two lines and detected by an area array camera.The system achieves the sorting of 100 embryos in less than 10 min with an accuracy of greater than 95%.

Improvement of Counting Sorting Algorithm

By analyzing the internal features of counting sorting algorithm. Two improvements of counting sorting algorithms are proposed, which have a wide range of applications and better efficiency than the original counting sort while maintaining the original stability. Compared with the original counting sort, it has a wider scope of application and better time and space efficiency. In addition, the accuracy of the above conclusions can be proved by a large amount of experimental data.

Theoretical Justification of the Efficient Operation of the Seed Sorting Device

In this article, the results of researches on the sorting of seeds in the cotton ginning enterprises were described. The main goal of the research work is to theoretically study the technology of separating various impurities and immature seeds from the composition of seeds. As a result, the theoretical basis for increasing the efficiency of the sorting process is created.

基于改进CRNN网络的卷烟件烟上行码识别方法研究

在卷烟物流系统中,卷烟追溯标签包含了一维条码未包含的部分分拣关键信息,可极大提升分拣效率,其关键在于高精度标签识别。为实现卷烟追溯标签的精准识别,提出一种基于改进于卷积循环神经网络(Convolutional Recurrent Neural Network,CRNN)的卷烟追溯标签识别网络,称为RA-CRNN。该方法的特征提取受ResNet启发引入了残差结构,并通过注意力和门控机制提升识别精度。改进后算法的识别准确率较目其他先进的文本识别算法有所提升,对追溯标签识别准确率达到97.87%,可满足工业自动化卷烟追溯标签识别的要求。

Garbage Sorting

Garbage sorting plays a crucial role in fostering a positive social climate that brings benefits not only to society but also to our personal well-being.While we dispose of trash on a daily basis,have you ever wondered about their fate?Many items undergo processes like burning,grinding,or composting,which ensure safer and healthier outcomes for us.However,some end up accumulating in open spaces,leading to unpleasant odors and posing harm to the environment.

煤矸石井下原位智能分选充填技术研究进展

传统井下煤矸石需运输至地面处理,不仅占用地面国土空间、自燃或雨水淋滤造成大气与环境污染,而且长距离无效运输造成的能源消耗问题已成为制约煤矿低碳发展的关键瓶颈。为实现煤矿矸石不出井,从煤炭生产源头减少碳排放与单位产出能源资源消耗,实现煤炭的绿色低碳智能开采,回顾了煤矸石井下分选充填技术现状及智能化进展,并在此基础上展望煤矸石井下分选充填技术发展趋势,提出了煤矸石井下原位绿色智能分选、充填新方法,详细阐述了煤矸智能分选机及新型充填液压支架的结构及原理,以最大化缩短矸石无效运输距离。为处理采煤工作面矸石,预防煤岩动力灾害,提出了包含少矸化智能开采系统、原位智能分选系统、工作面矿压反演系统、精准科学充填系统四大子系统的煤矿井下采选充智能一体化系统,并探讨了各子系统间的新环逻辑关系,以形成采煤利于分选、分选利于充填、充填利于采煤的良性循环。为处理掘进工作面矸石,提出了包含智能快掘系统、智能分选系统、煤矸分运系统、智能充填系统四大子系统的煤矿井下掘选充智能一体化系统,并对各子系统所负责工作及智能化实现进行了阐述。所提出的新工艺有望实现煤矿矸石不出井,并为煤矸石原位智能分选充填方法及采选充一体化系统的研究提供新思路。

Fully Automated Paper Document Sorting Robot Design

A fully automated paper document sorting robot was developed in this project.This robot classifies documents efficiently and accurately.The objective of this project was to improve the efficiency of classifying or sorting paper documents,reduce costs,and save time.The robot can classify documents according to user-defined rules,such as keywords,dates,serial numbers,bar codes,and the meaning of paragraphs.Since it can classify or sort documents intelligently,it can complete large-scale document classification quickly.The robot is constructed using an aluminum profile to create a box-type truss gantry structure frame.It was built on the LubanCat 4 motherboard and controlled through Python language programming.Driven by a stepper motor to move the manipulator.The camera module is combined with an artificial intelligence algorithm to recognize paper in real time,and the text is recognized after taking pictures of the paper.The sorting function is performed by several sensors.In addition,a web-based human-computer interaction platform was developed using the Flask web framework in Python.Users could access this platform in a variety of ways,allowing them to easily and swiftly configure parameters and send operational instructions to perform various functions.

基于可循环利用的建筑废弃物分类遴选研究

建筑废弃物主要来源于旧建筑物的拆除及“更新换代”过程,对于建筑废弃物的处理大多为填埋、随意废弃和焚烧,只有少量被回收利用,因而造成了大量建筑资源的浪费以及严重的生态环境污染问题。为此,如何实现建筑废弃物的高效绿色循环利用,变废为宝、物尽其用,成为亟须解决的问题。而建筑废弃物的分类遴选是提高其资源利用率的关键步骤,针对建筑废弃物循环利用中的分选过程及再利用途径进行相关的研究,总结了建筑废弃物循环再利用的分类遴选方式及其对建筑绿色可持续发展的重要性。

煤矸分拣多机械臂任务分配问题研究

针对煤矸分拣过程中分拣率低、大粒度矸石分拣效果差等问题,通过考虑矸石粒度、分拣时间、矸石与分拣区边界距离等因素,构建矸石优先级模型;采取分拣机械臂放置矸石后不复位的方式,提出了一种煤矸分拣多机械臂任务分配策略,并以分拣率为标准进行评判,最后,采用本研究分配策略和传统的分配策略进行仿真。结果表明:本研究分配策略比传统的分配策略分拣效果更好,提高了7%~10%的分拣率,减少了大粒度矸石的漏拣数量;当矸石的间距不变时,矸石数量变化对分拣率影响较小,分拣率波动在3%左右,验证了本研究分配策略的有效性;矸石间距对分拣率和分拣机械臂数量的影响较大,当矸石的间距增大时,分拣率得到提高;矸石间距在250 mm以上时,3个分拣机械臂能保证较高的分拣率。

基于ROS的铸件分拣机器人运动规划研究

为铸件分拣机器人在分拣铸件时能快速规划出一条稳定合理的路径、有效躲避障碍物、提高运动规划效率,文章通过ROS进行运动规划研究。利用ROS提供的C++接口完成笛卡尔指定轨迹规划验证。为进一步提高分拣效率和避障能力,文中改进算法在GB-RRT*算法基础上引入了局部迭代人工势场,加入了自适应目标偏置策略和直连策略。通过Matlab和ROS设计并完成了仿真实验和铸件分拣实验,实验结果表明采用改进算法后可有效减少无效点生成,缩短规划时间,减小路径长度,提高规划效率。同时,在规划运动时各关节运动平稳,轨迹平滑,极大地提高了铸件分拣效率,改善了分拣环境,保证了其安全性。

动态称重式果品分选机设计与试验

目的:解决中国鲜果人工分选准确率低、效率低、劳动强度大和生产成本高的问题。方法:基于果品质量分类法研制一款新型动态称重式果品分选机,该机具基于高精度称重单元采集的果品质量,然后经信号处理模块和PLC协同对采集的数据进行处理并控制起弹器,根据水果质量实现果品的分级。该机具可连续实现果品(直径≤126 mm)的上料、称重、分选作业。结果:当分选效率分别为18327,14400,10473个/h时,分选准确率平均值分别为95.31%,96.12%,97.13%,损伤率分别为1.09%,1.11%,1.15%;水果分选速度显著影响分选的准确率,对果品损伤率影响不明显。结论:该新型动态称重式果品分选机能够在保持分选准确率>95%的前提下有效提升果品分选的效率且对果品损伤较小。

微流控粒子分选中圆形微凹槽容纳特性研究

微流控技术由于具备操控微通道中微小体积流体的能力,已成为操控粒子和细胞的新平台.基于粒子惯性迁移和微凹槽涡胞捕获的粒子分选方法,是一种重要的微流控粒子操控技术.目前,微凹槽容纳的粒子数量不高,制约了该方法的效率.为了提高微凹槽粒子容量,对圆形微凹槽进行结构设计,并利用高速显微成像技术和数值模拟,研究了不同圆形微凹槽的粒子容纳能力.研究发现,相同入口雷诺数(Re=37~555)下,带底腔的圆形微凹槽相较于普通圆形微凹槽容纳的粒子数量提升了45%,这是因为增加底腔后使得涡流线向下延展,形成更深的“U形”结构,可以容纳更多的粒子;当Re=482时,带底腔的直径500μm的圆凹槽比直径600μm的圆凹槽的粒子容量提高了93.9%,原因是前者凹槽内粒子运动轨道与流线更加吻合,粒子轨道面积与凹槽面积占比达到了97%;随着Re增加,从侧通道收集到的粒子富集浓度整体呈现先缓慢增大后减小的趋势,收集到的20μm粒子的最大富集浓度为初始悬浮液的126.7倍;不同微凹槽内粒子群的轨道运动受到涡流场特性、粒子物性及粒子间相互作用和壁面限制作用等因素的共同影响.研究结果对微凹槽结构设计和提高粒子分选性能有重要指导意义.