Molecular mechanisms underlying microglial sensing and phagocytosis in synaptic pruning

Microglia are the main non-neuronal cells in the central nervous system that have important roles in brain development and functional connectivity of neural circuits.In brain physiology,highly dynamic microglial processes are facilitated to sense the surrounding environment and stimuli.Once the brain switches its functional states,microglia are recruited to specific sites to exert their immune functions,including the release of cytokines and phagocytosis of cellular debris.The crosstalk of microglia between neurons,neural stem cells,endothelial cells,oligodendrocytes,and astrocytes contributes to their functions in synapse pruning,neurogenesis,vascularization,myelination,and blood-brain barrier permeability.In this review,we highlight the neuron-derived“find-me,”“eat-me,”and“don't eat-me”molecular signals that drive microglia in response to changes in neuronal activity for synapse refinement during brain development.This review reveals the molecular mechanism of neuron-microglia interaction in synaptic pruning and presents novel ideas for the synaptic pruning of microglia in disease,thereby providing important clues for discovery of target drugs and development of nervous system disease treatment methods targeting synaptic dysfunction.

Pruning Techniques for Prunus mume

Prunusmumehas high ornamental value,and its maintenance and management should be more meticulous,with pruning being an important task.Pruning can make P.mume more robust,reduce the occurrence of diseases and pests,maintain a good shape,and promote more flowering,further improving its ornamental value.The difficulty of pruning lies in flexibly adopting suitable pruning methods according to the time of the tree,which requires understanding the impact of pruning operations on the growth and flowering of P.mume,as well as some techniques in pruning operations.This paper introduces the botanical characteristics of P.mume,common pruning methods and achievable effects of P.mume,and suitable time for using various methods,and analyzes the possible consequences and reasons of some incorrect operations.Moreover,corresponding correct practices are provided,which can provide reference for standardized pruning of P.mume,thereby reducing or avoiding losses caused by improper operation.

Impact of Progressive Pruning on Leaf Miner (Coelaenomenodera lameensis) Incidence and the Yield of Oil Palm (Elaeis guineensis) —A Case Study of Benso Oil Palm Plantation Plc, Adum Banso Estate, Ghana

The oil palm leaf miner, Coelaenomenodera lameensis, is currently the most destructive pest of oil palm in Ghana and other African oil palm growing countries, causing significant losses in fresh fruit bunch yield. Progressive pruning is an oil palm pruning method in which pruning is done at the same time as fresh fruit bunch harvesting. This study evaluated the impact of progressive pruning on leaf miner population in oil palm and how these two factors (leaf miner and progressive pruning) affect the yield of oil palm at the Benso Oil Palm Plantation Public listed company (BOPP. Plc). Five distinct blocks in the plantation were selected for observations on fronds at various ranks (33, 25, or 17) based on the degree of defoliation by counting the number of pests on leaflets at different phases of insect development. Fronds from selected plots were sampled in a Completely Randomized Design (CRD). The size of plots used for the study ranged between 19 to 45 hectares. A minimum of 78 fronds were evenly cut from each block for pest count depending on the block size. Secondary data on annual yields of fresh fruit bunches before and after the introduction of progressive pruning were also obtained from BOPP. Plc records from 2011-2020. The results from the analyzed data on leaf miner index before and after the introduction of progressive pruning showed that progressive pruning has, to a high extent (64% to 36%), reduced leaf miner populations in the plantation. Paired t-test on fresh fruit bunch yield has also revealed a significant (p < 0.001) increase in annual fresh fruit bunch yield due to progressive pruning. A regression analysis, however, revealed a lower rate of yield loss (3.05 to 2.70 tonnes) to leaf miner infestation after the introduction of progressive pruning. The study recommends progressive pruning as a key cultural practice for improving crop yields in leaf miner prone plantations.

Pedestrian and Vehicle Detection Based on Pruning YOLOv4 with Cloud-Edge Collaboration

Nowadays,the rapid development of edge computing has driven an increasing number of deep learning applications deployed at the edge of the network,such as pedestrian and vehicle detection,to provide efficient intelligent services to mobile users.However,as the accuracy requirements continue to increase,the components of deep learning models for pedestrian and vehicle detection,such as YOLOv4,become more sophisticated and the computing resources required for model training are increasing dramatically,which in turn leads to significant challenges in achieving effective deployment on resource-constrained edge devices while ensuring the high accuracy performance.For addressing this challenge,a cloud-edge collaboration-based pedestrian and vehicle detection framework is proposed in this paper,which enables sufficient training of models by utilizing the abundant computing resources in the cloud,and then deploying the well-trained models on edge devices,thus reducing the computing resource requirements for model training on edge devices.Furthermore,to reduce the size of the model deployed on edge devices,an automatic pruning method combines the convolution layer and BN layer is proposed to compress the pedestrian and vehicle detection model size.Experimental results show that the framework proposed in this paper is able to deploy the pruned model on a real edge device,Jetson TX2,with 6.72 times higher FPS.Meanwhile,the channel pruning reduces the volume and the number of parameters to 96.77%for the model,and the computing amount is reduced to 81.37%.

Prune Belly Syndrome: A Ten-Year Single Tertiary Centre Experience in South-South, Nigeria

Background: Prune belly syndrome (PBS) is a congenital anomaly that consists of a triad of abdominal wall defect, bilateral cryptorchidism, and urinary tract dilation. The disease is of varying severity. This study aims to highlight the challenges and peculiarities in the management of PBS in a resource-poor setting. Materials and Methods: This is a ten-year retrospective study conducted at the University of Port Harcourt Teaching Hospital. Ethical approval for the study was sought and gotten from the hospital’s ethical committee. The information gotten included history, duration of symptoms, examination findings, age of the patient, category of disease, and intraoperative findings. The data from the folders were collected and evaluated. Frequencies, percentages, the mean and standard deviation were used to summarize the data as appropriate. Results: Fifteen patients were included in the study. The hospital incidence of PBS was 112/100,000, twelve males and three females. The age range was from 1 day to 15 years, mean age was 14 months ± 2.3 months. Most patients presented between 3 months and 2 years and 11 months. Twelve patients had category three PBS and five patients had associated anomalies. Eleven male patients died after 5 years of follow-up from progressive renal deterioration. The female patient fared better than the males. Conclusion: PBS is rare, most patients with the condition present late. The most common cause of mortality was progressive renal deterioration.

Physicochemical Properties of Combustion Ashes of Some Trees(Urban Pruning)Present in the Neotropical Region

Secondary lignocellulosic biomass has proved to be useful as an energy source through its oxidation by means of combustion processes.In accordance with the above,in this paper,we wanted to study the ash from urban pruning residues that are generated in cities in the Neotropics.Species such as Licania tomentosa,Azadirachta indica,Ficus benjamina,Terminalia catappa,Leucaena leucocephala,Prosopis juliflora and Pithecellobium dulce were selected because they have been previously studied and showed potential for thermal energy generation.These materials were calcined in an oxidizing atmosphere and characterized by X-ray diffraction and fluorescence,scanning electron microscopy with microchemistry,BET surface area,thermal gravimetric analysis,and differential scanning calorimetry.The pH and apparent density were also established.The results show high basicity materials(average pH 10),a behavior associated with the presence of chemical elements such as calcium,potassium,magnesium,chlorine,phosphorus,and sulfur.Structurally,these materials have a very significant amorphous fraction(between 49%and 74.5%),the dominant crystalline phases are calcite,arcanite,sylvite,and hydroxyapatite.These ashes have low surface area and do not exceed 13 m^(2)/g.Two characteristic morphological aspects were observed in these ashes:a morphology of rounded grains where silicon content is highlighted,and lamellar morphologies where the presence of chlorine is highlighted.Thermally,these ashes show four significant mass loss events(400℃,430℃,680℃,and 920℃),causing mass losses that vary between 25%and 40%.Through this study,it was possible to establish that,from a chemical point of view,these ashes are less dangerous in comparison with those of a mineral coal that was used as a reference.However,they require additional treatments for their disposal due to their high basicity.Because of their composition,these ashes have the potential to be used in the ceramic and cement industries,and in the manufacture of fertilizers.

Joint On-Demand Pruning and Online Distillation in Automatic Speech Recognition Language Model Optimization

Automatic speech recognition(ASR)systems have emerged as indispensable tools across a wide spectrum of applications,ranging from transcription services to voice-activated assistants.To enhance the performance of these systems,it is important to deploy efficient models capable of adapting to diverse deployment conditions.In recent years,on-demand pruning methods have obtained significant attention within the ASR domain due to their adaptability in various deployment scenarios.However,these methods often confront substantial trade-offs,particularly in terms of unstable accuracy when reducing the model size.To address challenges,this study introduces two crucial empirical findings.Firstly,it proposes the incorporation of an online distillation mechanism during on-demand pruning training,which holds the promise of maintaining more consistent accuracy levels.Secondly,it proposes the utilization of the Mogrifier long short-term memory(LSTM)language model(LM),an advanced iteration of the conventional LSTM LM,as an effective alternative for pruning targets within the ASR framework.Through rigorous experimentation on the ASR system,employing the Mogrifier LSTM LM and training it using the suggested joint on-demand pruning and online distillation method,this study provides compelling evidence.The results exhibit that the proposed methods significantly outperform a benchmark model trained solely with on-demand pruning methods.Impressively,the proposed strategic configuration successfully reduces the parameter count by approximately 39%,all the while minimizing trade-offs.

基于轻量化PointNet网络的林果园喷雾作业靶标实时识别方法

为了进一步提高喷雾机器人靶标检测的精准性、实时性和应用部署的实用性,该研究提出一种基于轻量化PointNet网络的林果园喷雾作业靶标实时识别方法。首先通过区域提取降采样、地面分割和改进DBSCAN聚类等点云预处理方法提取原始点云中的靶标;然后通过移动最小二乘上采样将靶标点云转化为满足点云识别网络输入要求的点云数据;最终通过在PointNet网络中引入残差模块和改进循环剪枝算法轻量化PointNet网络,完成林果树靶标的实时识别。试验结果表明,在ModelNet40数据集上,轻量化PointNet网络可达89.7%的准确率;在实际苗圃环境的试验中,该研究方法对靶标的识别准确率可达92.49%,同时误识率与拒识率分别为13.4%和6.47%,相较PointNet网络识别准确率提升了4.38个百分点,误识率和拒识率分别降低了7.2和4.07个百分点;轻量化PointNet网络识别准确率仅比PointNet++网络低1.14个百分点,误识率和拒识率分别高了0.9和1.12个百分点。但是轻量化PointNet网络的模型参数量较PointNet网络和PointNet++网络的模型参数量显著减少,仅为PointNet网络的11.5%,PointNet++网络的27.02%;运算量相较PointNet网络、PointNet++网络分别减少13.3和76.79个百分点。该研究提出的轻量化PointNet网络具有较高的实时性、精确性和鲁棒性,能够满足林果园喷雾作业的靶标识别需求,可为林果园喷雾作业靶标实时识别提供参考。

跟踪算法下的果树修剪机器人路径规划设计

以果树修剪机器人为研究对象,基于轨迹跟踪算法对修剪机器人进行作业路径规划,并分别与传统的往复式路径规划算法和跟踪算法进行仿真对比实验分析。分析结果表明:与传统往复式路径规划算法相比,修剪机器人作业过程中漏剪率降低3%,重复率降低2.5%;与传统跟踪式路径规划算法相比,修剪机器人作业过程转角范围增大36°,横向轨迹误差降低600mm。

改进YOLOv8的轻量级军事飞机检测算法

遥感图像军事飞机检测在侦察预警、情报分析等领域具有重要意义。为使军事飞机检测模型能在算力受限的设备上高效运行,从网络设计与模型压缩两个方面对YOLOv8n进行轻量化改进。在网络设计方面,使用FAS_C2f替换原始主干网络中的C2f模块,减少计算冗余并加快网络特征提取的速度;根据军事飞机目标的尺度特征对网络结构进行优化,缓解因过度下采样导致的小目标信息丢失问题;使用Inner-SIoU作为新的定位回归损失函数,提升对小目标样本的学习能力并加快回归边界框的收敛。在模型压缩方面,使用基于LAMP分数的通道剪枝对重设计后的模型进行压缩,进一步减少参数和模型大小;并利用通道级知识蒸馏(channel-wise knowledge distillation,CWD)将模型精度恢复到接近剪枝前的水平。实验结果表明,在公开军用飞机数据集MAR20上,轻量化后的模型mAP为97.2%,体积仅有0.7 MB,较原始模型缩小了88.3%,FPS提高了14帧/s,满足军事飞机目标检测的实时性要求。

注意力引导的多尺度红外行人车辆实时检测

红外成像技术通过捕捉目标热辐射特征进行成像,能实现复杂道路场景下的目标监测和道路冗杂信息滤除。针对红外行人和车辆目标检测模型参数量大、依赖高性能GPU资源和检测速度慢等问题,提出了一种注意力引导的多尺度红外行人车辆实时检测模型。首先,为精确匹配校准红外行人和车辆目标尺度与锚框尺寸,利用K-Means++算法对红外行人和车辆目标尺度进行先验框预置参数重聚类生成,并设计128×128精细尺度检测层;其次,设计注意力引导广域特征提取模块增强模型特征提取能力和空间及通道信息聚焦能力;随后,构建跨空间感知模块引入空间信息感知,强化不同尺度空间下的目标的特征表达能力;最后,针对资源受限设备,通过4倍通道剪枝方法降低模型参数量,增强移动端算法部署适应性。实验结果表明:所提IRDet算法与基准方法相比,模型平均检测精度提升4.3%,达到87.4%,模型权重值压缩60.4%,降至5.7 MB。

基于激光雷达的果树智能修剪系统设计与试验

针对传统果树修剪存在人员劳动强度大、修剪效率低及修剪质量难以保证等问题,本文设计了果树智能修剪机械臂,并利用固态激光雷达与可编程逻辑控制器开发了基于激光雷达的果树智能修剪系统,实现果树自动修剪。为了验证修剪臂的控制精度,分别对修剪机的摆动机械臂、举升机械臂、修剪切割总成进行独立精度试验与修剪目标位置精度试验,独立精度试验结果表明摆动机械臂、举升机械臂、修剪切割总成控制精度平均误差分别为2.32%、3.75%、2.50%,修剪目标位置精度试验结果表明目标位置X_(b)、Z_(b)平均误差分别为2.98%、1.85%,修剪总成作业倾角α平均误差为4.35%,满足果树修剪精度要求。在新疆阿克苏果树种植基地开展了果树修剪试验,结果表明,搭载固态激光雷达的果树修剪机能够实时获取果树的三维空间信息,修剪机可以根据激光雷达探测到的果树树冠信息制定修剪策略,香梨园与苹果园修剪优良率分别为93.3%与86.6%。该系统能有效提高果树修剪效率,降低修剪人员劳动强度。

神经网络压缩联合优化方法的研究综述

随着人工智能应用的实时性、隐私性和安全性需求增大,在边缘计算平台上部署高性能的神经网络成为研究热点。由于常见的边缘计算平台在存储、算力、功耗上均存在限制,因此深度神经网络的端侧部署仍然是一个巨大的挑战。目前,克服上述挑战的一个思路是对现有的神经网络压缩以适配设备部署条件。现阶段常用的模型压缩算法有剪枝、量化、知识蒸馏,多种方法优势互补同时联合压缩可实现更好的压缩加速效果,正成为研究的热点。本文首先对常用的模型压缩算法进行简要概述,然后总结了“知识蒸馏+剪枝”、“知识蒸馏+量化”和“剪枝+量化”3种常见的联合压缩算法,重点分析论述了联合压缩的基本思想和方法,最后提出了神经网络压缩联合优化方法未来的重点发展方向。

面向边缘计算的轻量级母猪分娩识别模型

为实时监测母猪分娩过程并准确分析记录其完整产程的产仔数、产仔间隔和产程等信息,该研究运用知识蒸馏和剪枝,结合了ResNet50高准确率和MobileNetV3高检测效率的优势设计了一种轻量级网络。采用数据增强提高教师模型ResNet50对分娩特征的提取能力,通过掩模生成蒸馏(maskedgenerativedistillation,MGD)提高学生模型MobileNetV3-S对分娩关键区域的表达能力,并通过依赖关系图(dependency graph)显式建模学生网络层间的依赖关系,结合分组耦合参数对学生模型进行剪枝。剪枝得到的MobileNetV3-S(MGD)_Prune参数量为0.74 M,在DELL OptiPlex微型机上检测速度为83.10帧/s,单栏视角测试准确度为91.48%,相比于ResNet50的检测速度提升了67.13帧/s,测试准确度下降0.98个百分点。试验结果表明,单栏视角对监测母猪分娩更为有效,模型对于产仔平均间隔的检测误差为0.31 s,仔猪出生事件的平均持续时长检测误差为0.02 s,能够高效监测母猪分娩全过程。

荔枝剪枝堆肥和蚯蚓粪作为巨大普里斯特氏菌载体的研究

为探讨荔枝茎秆堆肥与蚯蚓粪替代草炭作为巨大普里斯特氏菌载体的可行性,以荔枝剪枝堆肥、蚯蚓粪和草炭为原料构建6种微生物载体(ST1、ST2、ST3、ST4、ST5、ST6,三者质量比分别为6∶2∶2、4∶2∶4、2∶2∶6、6∶3∶1、4∶3∶3、2∶3∶5),以草炭为对照,巨大普里斯特氏菌为目标微生物,动态监测载体中有效活菌数,获得适宜巨大普里斯特氏菌存活的载体;在此基础上,分别设置含水量20%、30%、40%,温度20、30、40、50℃和接种浓度10^(6)、10^(7)、10^(8)cfu·mL^(-1),动态监测载体中有效活菌数,优化载体含水量、温度和接种浓度。结果表明:随着培养时间的延长,各载体中活菌数均呈先降低后升高的趋势,其中ST2、ST5载体长期培养后活菌数高,且草炭添加量低,是适宜的巨大普里斯特氏菌载体。随着载体含水量、温度的升高,培养的60 d过程中ST2和ST5载体活菌数均呈先升高后降低的趋势,在30%含水量(ST22.46×10^(8)cfu·g^(-1)、ST51.81×10^(8)cfu·g^(-1))以及30℃(ST23.44×10^(8)cfu·g^(-1)、ST51.87×10^(8)cfu·g^(-1))、40℃(ST28.50×10^(7)cfu·g^(-1)、ST57.13×10^(7)cfu·g^(-1))温度下的活菌数最高。此外,各培养时期的载体活菌数均随着接种浓度的升高而升高,培养60 d后,ST2、ST5载体活菌数分别达3.63×10^(8)、3.33×10^(8)cfu·g^(-1)。研究表明,载体ST2和ST5适宜代替草炭作为巨大普里斯特氏菌的载体,且在30%载体含水量、30~40℃温度和10^(8)cfu·mL^(-1)接种浓度下效果最佳。

不同平茬措施下梭梭液流变化及其影响因素

以乌兰布和沙漠东北部不同平茬处理及未平茬梭梭(Haloxylon ammodendron)为研究对象,实时动态监测其液流速率及当地气象因子,研究不同平茬处理梭梭在生长旺盛季(6~8月)不同时间尺度的液流特征及其对气象因子的响应,建立耗水量与气象因子的拟合模型,揭示梭梭平茬后的水分传输规律。利用热扩散茎流计和自动气象站对乌兰布和沙漠不同平茬处理与未平茬梭梭的液流及太阳辐射、空气温度、相对饱和湿度、水汽压差进行监测。结果表明,不同平茬处理(留茬120 cm、留茬90 cm、留茬60 cm)梭梭晴天液流变化呈“几字宽峰型”曲线,未平茬梭梭呈“单峰型”曲线。雨天,液流速率波动非常剧烈,呈“不规则多峰型”曲线,雨天的液流速率低于晴天。未平茬、留茬120 cm、留茬90 cm、留茬60 cm梭梭的日平均液流速率分别为2.3146、3.3334、1.3333、0.7672 cm/h,日均耗水量分别为8.62、13.43、2.40、2.89 kg/d。小时尺度下,太阳辐射是影响平茬及未平茬梭梭液流速率的主要因子;日尺度下,太阳辐射是影响留茬90 cm及未平茬梭梭液流速率的主要因子,空气温度是影响留茬120 cm梭梭液流速率的主要因子,留茬60 cm梭梭的液流速率主要受风速影响;月尺度下,留茬120 cm梭梭耗水量主要受太阳辐射、空气温度影响,留茬90 cm梭梭耗水量主要受相对湿度、太阳辐射影响,留茬60 cm梭梭耗水量主要受风速影响。随着时间尺度的增加,气象因子入选回归方程的数量呈减小趋势,对液流的解释程度呈降低趋势。该研究的拟合方程均达到显著水平,可在不同时间尺度下预测梭梭蒸腾耗水特征。研究结果对明确梭梭平茬后的水分传输规律具有指导意义。

改进DenseNet模型在工件表面粗糙度视觉检测中的应用

针对原始DenseNet模型检测工件表面粗糙度时间长、准确率较低的问题,结合卷积层滤波器注意力机制和批归一化层缩放系数提出一种工件表面粗糙度检测的深度学习模型。首先,利用注意力重要性值判定模块内的冗余通道。其次,在Dense Block模块内引入批归一化层缩放系数判别特征通道的重要程度。最后联合卷积层滤波器的注意力重要性值和批归一化层缩放系数裁剪冗余通道,实现模型剪枝。实验结果表明,原始DenseNet模型检测工件表面粗糙度的准确率为91.875%,检测时间为483 s。当剪枝率为20%时,其检测效果最好,检测准确率为96.875%,检测时间为255 s。相比于原始DenseNet模型,改进后的DenseNet模型检测效果更好,在质量检测领域方面具有一定的应用前景。

一种基于关联程度的高效用数量比频繁模式挖掘算法

高效用频繁模式挖掘算法运用数据项的重要度信息,能够从数据中挖掘出更重要的频繁模式,而高效用数量比频繁模式挖掘算法可以进一步研究频繁模式中数据项的数量比例关系,是目前数据挖掘领域中的研究课题。从提高算法性能和实用性的角度出发对高效用数量比频繁模式挖掘算法进行优化,提出了一种基于关联程度的高效用数量比频繁模式挖掘算法RHUQI-Miner。RHUQI-Miner首先提出关联程度的概念,依据关联程度构建项目关联程度结构,并给出关联剪枝优化策略,寻找关联程度更高的项目集合,减少冗余和无效的频繁模式;随后运用修正模式长度策略,修正挖掘过程中项集的效用信息,使算法可根据实际数据情况控制输出频繁模式的长度,进一步提升算法的性能,提高算法的实用性。通过对RHUQI-Miner在动车组PHM系统车载故障数据集上的实验结果进行分析,表明该算法能够有效减少挖掘过程中的时间以及内存消耗,可以得出该算法适用于铁路实际数据和业务的有效结论。

应用动态激活函数的轻量化YOLOv8行人检测算法

针对传统激活函数不能特异性匹配每张特征图以达到最好的激活效果,设计一种动态激活函数,为特征图上的每个像素值添加各自的偏移量,以达到更优的区分目标和背景的效果;为使模型更好地关注目标,在主干加入注意力机制,以提高模型的准确性。针对需要监测行人流量和进行交通管理的场景,如闯红灯检测、自动驾驶等实时性高,硬件条件有限的场景,应用通道剪枝技术对模型低权重参数进行修剪,为适应硬件加速特性,改进了剪枝方法,使保留通道数始终为8的整数倍。在推理部署阶段,融合Conv和BatchNorm权重,进一步缩小模型,减少参数量和浮点运算量。最终实验表明,改进的模型性能比其他目标检测模型均有一定提升,其中,比YOLOv8原模型在AP0.5:0.95上提升了0.013,在AP0.5上提升了0.005,参数量减少了4.8×10~6。

低空轻量级红外弱小目标检测算法

精准的红外弱小目标检测是实时监控、追踪、制导的关键;红外弱小目标存在检测难度高、误检高、漏检严重的问题。为了提高红外弱小目标检测算法的实时性和检测精度,提出了一种超轻量红外弱小目标检测算法SL-YOLO。首先,重设计下采样方案,针对红外图像特征信息调节网络架构,解决红外弱小目标特征梯度降低和特征消失问题;然后设计网络模型剪枝算法,实现剪枝算法与网络结构的融合,去除冗余参数,实现检测速度的提高;最后设计Varifocal-SIoU损失函数,在均衡正负样本与重叠损失的同时,对正样本进行加权处理,解决背景干扰问题。实验结果表明,在SIRST和IDSAT数据集下检测精度分别提高至96.4%、98.1%,模型体积和计算量可压缩至190 kB、0.9 GFLOPs,推理速度降至3 ms以下。与主流算法进行对比,改进后算法在检测精度、模型体积、计算量等方面均取得了不错的成绩。能够满足实时性检测需求。