Quantitatively characterizing sandy soil structure altered by MICP using multi-level thresholding segmentation algorithm

The influences of biological,chemical,and flow processes on soil structure through microbially induced carbonate precipitation(MICP)are not yet fully understood.In this study,we use a multi-level thresholding segmentation algorithm,genetic algorithm(GA)enhanced Kapur entropy(KE)(GAE-KE),to accomplish quantitative characterization of sandy soil structure altered by MICP cementation.A sandy soil sample was treated using MICP method and scanned by the synchrotron radiation(SR)micro-CT with a resolution of 6.5 mm.After validation,tri-level thresholding segmentation using GAE-KE successfully separated the precipitated calcium carbonate crystals from sand particles and pores.The spatial distributions of porosity,pore structure parameters,and flow characteristics were calculated for quantitative characterization.The results offer pore-scale insights into the MICP treatment effect,and the quantitative understanding confirms the feasibility of the GAE-KE multi-level thresholding segmentation algorithm.

Scheme Based on Multi-Level Patch Attention and Lesion Localization for Diabetic Retinopathy Grading

Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.

EGSNet:An Efficient Glass Segmentation Network Based on Multi-Level Heterogeneous Architecture and Boundary Awareness

Existing glass segmentation networks have high computational complexity and large memory occupation,leading to high hardware requirements and time overheads for model inference,which is not conducive to efficiency-seeking real-time tasks such as autonomous driving.The inefficiency of the models is mainly due to employing homogeneous modules to process features of different layers.These modules require computationally intensive convolutions and weight calculation branches with numerous parameters to accommodate the differences in information across layers.We propose an efficient glass segmentation network(EGSNet)based on multi-level heterogeneous architecture and boundary awareness to balance the model performance and efficiency.EGSNet divides the feature layers from different stages into low-level understanding,semantic-level understanding,and global understanding with boundary guidance.Based on the information differences among the different layers,we further propose the multi-angle collaborative enhancement(MCE)module,which extracts the detailed information from shallow features,and the large-scale contextual feature extraction(LCFE)module to understand semantic logic through deep features.The models are trained and evaluated on the glass segmentation datasets HSO(Home-Scene-Oriented)and Trans10k-stuff,respectively,and EGSNet achieves the best efficiency and performance compared to advanced methods.In the HSO test set results,the IoU,Fβ,MAE(Mean Absolute Error),and BER(Balance Error Rate)of EGSNet are 0.804,0.847,0.084,and 0.085,and the GFLOPs(Giga Floating Point Operations Per Second)are only 27.15.Experimental results show that EGSNet significantly improves the efficiency of the glass segmentation task with better performance.

Effects of pooling,specialization,and discretionary task completion on queueing performance

Pooling,unpooling/specialization,and discretionary task completion are typical operational strategies in queueing systems that arise in healthcare,call centers,and online sales.These strategies may have advantages and disadvantages in different operational environments.This paper uses the M/M/1 and M/M/2 queues to study the impact of pooling,specialization,and discretionary task completion on the average queue length.Closed-form solutions for the average M/M/2 queue length are derived.Computational examples illustrate how the average queue length changes with the strength of pooling,specialization,and discretionary task completion.Finally,several conjectures are made in the paper.

Deep neural network based on multi-level wavelet and attention for structured illumination microscopy

Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.

Weather Classification for Autonomous Vehicles under Adverse Conditions Using Multi-Level Knowledge Distillation

Achieving reliable and efficient weather classification for autonomous vehicles is crucial for ensuring safety and operational effectiveness.However,accurately classifying diverse and complex weather conditions remains a significant challenge.While advanced techniques such as Vision Transformers have been developed,they face key limitations,including high computational costs and limited generalization across varying weather conditions.These challenges present a critical research gap,particularly in applications where scalable and efficient solutions are needed to handle weather phenomena’intricate and dynamic nature in real-time.To address this gap,we propose a Multi-level Knowledge Distillation(MLKD)framework,which leverages the complementary strengths of state-of-the-art pre-trained models to enhance classification performance while minimizing computational overhead.Specifically,we employ ResNet50V2 and EfficientNetV2B3 as teacher models,known for their ability to capture complex image features and distil their knowledge into a custom lightweight Convolutional Neural Network(CNN)student model.This framework balances the trade-off between high classification accuracy and efficient resource consumption,ensuring real-time applicability in autonomous systems.Our Response-based Multi-level Knowledge Distillation(R-MLKD)approach effectively transfers rich,high-level feature representations from the teacher models to the student model,allowing the student to perform robustly with significantly fewer parameters and lower computational demands.The proposed method was evaluated on three public datasets(DAWN,BDD100K,and CITS traffic alerts),each containing seven weather classes with 2000 samples per class.The results demonstrate the effectiveness of MLKD,achieving a 97.3%accuracy,which surpasses conventional deep learning models.This work improves classification accuracy and tackles the practical challenges of model complexity,resource consumption,and real-time deployment,offering a scalable solution for weather classification in autonomous driving systems.

Computation Rate Maximization for Wireless-Powered and Multiple-User MEC System with Buffer Queue

Mobile edge computing (MEC) has a vital role in various delay-sensitive applications. With the increasing popularity of low-computing-capability Internet of Things (IoT) devices in industry 4.0 technology, MEC also facilitates wireless power transfer, enhancing efficiency and sustainability for these devices. The most related studies concerning the computation rate in MEC are based on the coordinate descent method, the alternating direction method of multipliers (ADMMs) and Lyapunov optimization. Nevertheless, these studies do not consider the buffer queue size. This research work concerns the computation rate maximization for wireless-powered and multiple-user MEC systems, specifically focusing on the computation rate of end devices and managing the task buffer queue before computation at the terminal devices. A deep reinforcement learning (RL)-based task offloading algorithm is proposed to maximize the computation rate of end devices and minimizes the buffer queue size at the terminal devices.Precisely, considering the channel gain, the buffer queue size and wireless power transfer, it further formalizes the task offloading problem. The mode selection for task offloading is based on the individual channel gain, the buffer queue size and wireless power transfer maximization in a particular time slot.The central idea of this work is to explore the best optimal mode selection for IoT devices connected to the MEC system. The proposed algorithm optimizes computation delay by maximizing the computation rate of end devices and minimizing the buffer queue size before computation at the terminal devices. Then, the current study presents a deep RL-based task offloading algorithm to solve such a mixed-integer and non-convex optimization problem, aiming to get a better trade-off between the buffer queue size and the computation rate. The extensive simulation results reveal that the presented algorithm is much more efficient than the existing work to maintain a small buffer queue for terminal devices while simultaneously achieving a high-level computation rate.

An Expert System to Detect Political Arabic Articles Orientation Using CatBoost Classifier Boosted by Multi-Level Features

The number of blogs and other forms of opinionated online content has increased dramatically in recent years.Many fields,including academia and national security,place an emphasis on automated political article orientation detection.Political articles(especially in the Arab world)are different from other articles due to their subjectivity,in which the author’s beliefs and political affiliation might have a significant influence on a political article.With categories representing the main political ideologies,this problem may be thought of as a subset of the text categorization(classification).In general,the performance of machine learning models for text classification is sensitive to hyperparameter settings.Furthermore,the feature vector used to represent a document must capture,to some extent,the complex semantics of natural language.To this end,this paper presents an intelligent system to detect political Arabic article orientation that adapts the categorical boosting(CatBoost)method combined with a multi-level feature concept.Extracting features at multiple levels can enhance the model’s ability to discriminate between different classes or patterns.Each level may capture different aspects of the input data,contributing to a more comprehensive representation.CatBoost,a robust and efficient gradient-boosting algorithm,is utilized to effectively learn and predict the complex relationships between these features and the political orientation labels associated with the articles.A dataset of political Arabic texts collected from diverse sources,including postings and articles,is used to assess the suggested technique.Conservative,reform,and revolutionary are the three subcategories of these opinions.The results of this study demonstrate that compared to other frequently used machine learning models for text classification,the CatBoost method using multi-level features performs better with an accuracy of 98.14%.

A Maintenance Service Improvement Approach Based on Queue Networks: Application

The quest to increase the performance of production systems that have become complex leads to the transfer to the maintenance function of the responsibility of guaranteeing the availability of such systems. Also, we will never stop saying that maintenance must integrate into all of the company’s initiatives, to affirm its role, which is to ensure greater availability and sustainability of the means of production. The objective of this paper is to evaluate the reliability and availability of a system without knowing the distribution law of the operating times. Among the methods for evaluating dependability criteria (Fault Trees, Petri Nets, etc.), we are interested in queues that have the advantage of taking into account functional dependencies, thus allowing a quantified optimization of maintenance. Indeed, queues make it possible to model parallel or sequential processes, implementing operations taking place at the same time or one after the other, meeting the needs of modeling production systems. The main result of this paper is the study of the influence of availability on the reliability of a multi-state production system.

Construction of a Multi-Level Strategic System for Cultivating Cultural Industry Management Talents in Colleges and Universities

Through SWOT(strengths,weaknesses,opportunities,and threats)and PEST(political,economic,social,and technological)analysis,this study discusses the construction of a multi-level strategic system for the cultivation of cultural industry management talents in colleges and universities.First of all,based on SWOT analysis,it is found that colleges and universities have rich educational resources and policy support,but they face challenges such as insufficient practical teaching and intensified international competition.External opportunities come from the rapid development of the cultivation of cultural industry management talents and policy promotion,while threats come from global market competition and talent flow.Secondly,PEST analysis reveals the key factors in the macro-environment:at the political level,the state vigorously supports the cultivation of cultural industry management talents;at the economic level,the market demand for cultural industries is strong;at the social level,the public cultural consumption is upgraded;at the technological level,digital transformation promotes industry innovation.On this basis,this paper puts forward a multi-level strategic system covering theoretical education,practical skill improvement,interdisciplinary integration,and international vision training.The system aims to solve the problems existing in talent training in colleges and universities and cultivate high-quality cultural industry management talents with theoretical knowledge,practical skills,and global vision,so as to adapt to the increasingly complex and diversified cultural industry management talents market demand and promote the long-term development of the industry.

具有工作故障和附加服务的排队最优策略

为了拓展排队理论,本文研究了具有工作故障和附加服务的排队系统.运用概率母函数方法,推导出服务台的状态概率,进而得到稳态下系统的平均队长等性能指标.构建成本函数,采用粒子群优化技术寻找服务台处于不同状态的最优服务率和预期最小成本.通过数值分析考察系统参数对系统性能指标和最优服务率以及预期最小成本的影响.基于收入-支出结构,建立个体效用函数和社会收益函数.系统性能指标和收益函数的敏感性为现实生活中的排队问题提供理论支持.

基于可选休假和优先权Geo/G/1重试排队的P2P网络分析

该文旨在根据P2P网络中节点状态的动态变化,构建一个排队模型,以精确模拟节点在系统中的动态趋势.基于这一模型框架,建立了一个带二次可选休假、优先权和不耐烦请求节点的Geo/G/1重试排队系统.利用嵌入Markov链的方法,构造相应维数的Markov链,分析网络系统中各个节点状态的一步转移概率;利用补充变量法推导系统满足的平衡方程组,通过求解平衡方程组得到网络系统中各类节点的性能指标.通过调整不同参数,验证系统的性能指标随参数的变化趋势.

LabVIEW中Queue技术在发电机监测系统中的应用

介绍了基于虚拟仪器的在线监测系统的基本组成,其采用PCI总线仪器和LabVIEW可视化的虚拟仪器系统开发平台,把传统仪器的所有功能模块集成在一台计算机中,用户可以通过修改虚拟仪器的软件改变其功能与规模。该系统有效地利用了LabVIEW提供的同步控制Queue技术实现了发电机在现场运行环境下运行状态的监测显示,体现出了其一定的优势。

基于Message Queue技术的医疗信息交换与共享集成平台研究

对目前医院信息系统集成方式进行了分析,简单介绍了IBM Message Queue的技术特点,通过介绍重症监护系统(ICU系统)和HIS系统之间的集成方案,阐述了基于消息机制构建医疗信息交换与共享集成平台的思路和方法。

儿童中医体质与血脂代谢的相关性:基于浦东新区儿童青少年生长发育及健康队列

背景随着社会高速发展,儿童血脂异常检出率逐步上升。目前针对儿童亚健康运用中医体质辨识的数据较少,缺乏临床关联性数据分析。目的观察儿童中医体质与血脂代谢的变化趋势,分析两者间的相关性,为后续连续监测研究提供相关依据。方法本研究数据来源于浦东新区儿童青少年生长发育及健康队列(SCAHC),选取上海市浦东新区2080名健康小学生(二年级、三年级)为研究对象,采用上海中医药大学自主研发的“昭明系统”收集学生2021年及2022年体质信息,中医体质辨识根据人体面色、舌象、症状体征、问卷信息等将儿童分为脾虚质、平和质、气郁质、虚热质、实热质5种体质(偏颇体质指除平和质外所有具有偏向的体质),抽血采集其三酰甘油(TG)、高密度脂蛋白胆固醇(HDL-C)、低密度脂蛋白胆固醇(LDL-C)、总胆固醇(TC)血脂四项信息。分析并比较不同性别儿童血脂异常率以及不同年龄、性别中医体质分布特点及转变情况。采用多因素Logistic回归分析法探讨中医体质与血脂异常的相关性。结果2080名儿童中男1122名(53.9%)、女958名(46.1%),平均年龄为(8.18±0.39)岁;血脂异常儿童522名,血脂异常率为25.09%,其中男272名(52.1%),女250名(47.9%)。2021年儿童中医体质中平和质815名(39.2%),偏颇体质1265名(60.8%);2022年儿童中医体质中平和质764名(36.7%),偏颇体质1316名(63.3%);2021年与2022年儿童中医体质分布比较,差异有统计学意义(χ^(2)=106.28,P<0.001)。男童的偏颇体质检出率高于女童(χ^(2)_(2021年)=14.073,P<0.001;χ^(2)_(2022年)=20.090,P<0.001)。多因素Logistic回归分析排除性别、年龄等人口学影响因素后结果显示,儿童HDL-C升高是平和质(OR=1.624,95%CI=1.258~2.097,P<0.001)、虚热质(OR=0.654,95%CI=0.499~0.858,P=0.002)发生的影响因素。结论观察SCAHC队列儿童中医体质与血脂代谢变化趋势后发现,儿童HDL-C升高可促进平和质的发生,抑制虚热质的发生。

基于标签交换的AOS帧传输设计与实现

针对现代卫星网络中数据处理和传输速度的日益提高的问题,设计基于标签交换的AOS帧传输方式,实现卫星网络中数据帧的传输,提高网间的传输效率。此外,面对空间网络中服务质量的需求,提出基于自适应权值的最高响应比轮询队列调度算法,使网络满足通信服务质量的要求的同时有效防止缓存数据的丢失。在FPGA进行设计验证,证明该设计在保证AOS帧的高效传输的同时满足服务质量。

Multi-level access control model for tree-like hierarchical organizations

An access control model is proposed based on the famous Bell-LaPadula （BLP） model.In the proposed model,hierarchical relationships among departments are built,a new concept named post is proposed,and assigning security tags to subjects and objects is greatly simplified.The interoperation among different departments is implemented through assigning multiple security tags to one post, and the more departments are closed on the organization tree,the more secret objects can be exchanged by the staff of the departments.The access control matrices of the department,post and staff are defined.By using the three access control matrices,a multi granularity and flexible discretionary access control policy is implemented.The outstanding merit of the BLP model is inherited,and the new model can guarantee that all the information flow is under control.Finally,our study shows that compared to the BLP model,the proposed model is more flexible.

FastQueue:一种高性能的磁盘队列存储管理机制

1.引言 随着消息通信(如消息、短消息)的爆炸式增长,消息传递系统的性能面临严峻的挑战.消息通信的首要特点是高可靠性,在发送人确认消息收到之前必须将消息保存到磁盘上.

Active Queue Management技术的研究与发展

Active Queue Management(AQM)技术通过有效控制输出队列的丢包时间和丢包方式,对拥塞进行早期通告,这在TCP拥塞控制的实现中至关重要。目前对AQM进行较全面介绍和总结的文献尚不多见,以RandomEarly Detection(RED)为重点介绍了这种第一代AQM技术的设计思想、优缺点以及为此出现的多种RED变种方法,另外还简单介绍了其它几种与RED设计思路不同的AQM方法,以期对AQM技术的研究和发展进行较全面的总结,并促进国内学者以及设备制造商对这一技术的关注。

基于Virtual Output Queued交换结构的最大权重匹配算法

信头阻塞（HOL）限制了采用FIFO输入队列交换机的吞吐率，而使用虚输出队列（VOQ）技术可以完全消除HOL阻塞。文章给出了VOQ的交换机模型，介绍了基于最大权重匹配的算法LQF、OCF、LPF及其性能，还描述了更加实用的并行迭代算法i－LQF、i－OCF和i－LPF。文章的结论对于构造高带宽的交换机具有实际意义。