具有时滞效应的air2stream河流水温模型及应用研究

高精度河流水温模型对于深入了解水温的时空变化特征和河流生态修复具有重要意义。基于数据驱动的air2stream模型在保证预测精度的同时,避免了计算的复杂性,已成为河流水温模拟常用的模型。由于水的热惯性及水文条件等的影响,河流水温变化往往显著滞后于气温变化,而air2stream原模型并未考虑滞后效应,导致该模型在流量未知情况下实际精度偏低。为解决该问题,采用气温-水温皮尔逊相关系数计算时滞天数,构建具有时滞的air2stream新模型,进一步根据长江中下游地区两个监测站的多年实测数据验证新模型的有效性和稳定性。结果表明:新模型在不引进额外观测数据的条件下具有更高精度且性能更稳定。相比原模型,在两个监测站新模型的均方根误差分别降低约4.29%和5.85%。新模型具有精度高、水文要素需求少的特点,可为长江中下游的水环境影响评价和生态保护提供依据。

Physical Layer Encryption of OFDM-PON Based on Quantum Noise Stream Cipher with Polar Code

Orthogonal frequency division multiplexing passive optical network(OFDM-PON) has superior anti-dispersion property to operate in the C-band of fiber for increased optical power budget. However,the downlink broadcast exposes the physical layer vulnerable to the threat of illegal eavesdropping. Quantum noise stream cipher(QNSC) is a classic physical layer encryption method and well compatible with the OFDM-PON. Meanwhile, it is indispensable to exploit forward error correction(FEC) to control errors in data transmission. However, when QNSC and FEC are jointly coded, the redundant information becomes heavier and thus the code rate of the transmitted signal will be largely reduced. In this work, we propose a physical layer encryption scheme based on polar-code-assisted QNSC. In order to improve the code rate and security of the transmitted signal, we exploit chaotic sequences to yield the redundant bits and utilize the redundant information of the polar code to generate the higher-order encrypted signal in the QNSC scheme with the operation of the interleaver.We experimentally demonstrate the encrypted 16/64-QAM, 16/256-QAM, 16/1024-QAM, 16/4096-QAM QNSC signals transmitted over 30-km standard single mode fiber. For the transmitted 16/4096-QAM QNSC signal, compared with the conventional QNSC method, the proposed method increases the code rate from 0.1 to 0.32 with enhanced security.

Dynamics of iron and aluminum storages in a subtropical forest headwater stream

The forest headwater streams are important hubs for connecting terrestrial and aquatic ecosystems,with plant litter and sediments as the major carriers for material migrations;however,until now we knew little about the dynamics of trace elements such as iron(Fe)and aluminum(Al)in forest headwater streams.Here,we quantitatively identified the spatiotemporal dynamics of Fe and Al storages in plant litter and sediments and their influencing factors in a subtropical forest headwater stream,and assessed the potential pollution risk.The results showed that:(1)the mean concentrations of Fe and Al in plant litter(sediments)were 5.48 and 8.46(7.39 and 47.47)g·kg^(-1),and the mean storages of Fe and Al in plant litter(sediments)were 0.26 and 0.43(749.04 and 5030.90)g·m^(-2),respectively;(2)the storages of Fe and Al in plant litter and sediments significantly fluctuated from January to December,and showed a decreasing pattern from the source to mouth;and(3)storages of Fe and Al had no significant correlation with riparian forest type and the present of tributary and the Fe and Al storages in plant litter were mainly affected by water temperature and water alkalinity,and their storages in sediments were mainly affected by water temperature and frequency of rainfall;and(4)there were no anthropogenic pollution in Fe and Al in the forest headwater stream.Our study revealed the primary factors of concentrations and storages of Fe and Al in plant litter and sediments in a forest headwater stream,which will improve our understanding of the role of headwater streams in forest nutrient storage and cycling along with hydrological processes.

Improved Data Stream Clustering Method: Incorporating KD-Tree for Typicality and Eccentricity-Based Approach

Data stream clustering is integral to contemporary big data applications.However,addressing the ongoing influx of data streams efficiently and accurately remains a primary challenge in current research.This paper aims to elevate the efficiency and precision of data stream clustering,leveraging the TEDA(Typicality and Eccentricity Data Analysis)algorithm as a foundation,we introduce improvements by integrating a nearest neighbor search algorithm to enhance both the efficiency and accuracy of the algorithm.The original TEDA algorithm,grounded in the concept of“Typicality and Eccentricity Data Analytics”,represents an evolving and recursive method that requires no prior knowledge.While the algorithm autonomously creates and merges clusters as new data arrives,its efficiency is significantly hindered by the need to traverse all existing clusters upon the arrival of further data.This work presents the NS-TEDA(Neighbor Search Based Typicality and Eccentricity Data Analysis)algorithm by incorporating a KD-Tree(K-Dimensional Tree)algorithm integrated with the Scapegoat Tree.Upon arrival,this ensures that new data points interact solely with clusters in very close proximity.This significantly enhances algorithm efficiency while preventing a single data point from joining too many clusters and mitigating the merging of clusters with high overlap to some extent.We apply the NS-TEDA algorithm to several well-known datasets,comparing its performance with other data stream clustering algorithms and the original TEDA algorithm.The results demonstrate that the proposed algorithm achieves higher accuracy,and its runtime exhibits almost linear dependence on the volume of data,making it more suitable for large-scale data stream analysis research.

An Efficient Modelling of Oversampling with Optimal Deep Learning Enabled Anomaly Detection in Streaming Data

Recently,anomaly detection(AD)in streaming data gained significant attention among research communities due to its applicability in finance,business,healthcare,education,etc.The recent developments of deep learning(DL)models find helpful in the detection and classification of anomalies.This article designs an oversampling with an optimal deep learning-based streaming data classification(OS-ODLSDC)model.The aim of the OSODLSDC model is to recognize and classify the presence of anomalies in the streaming data.The proposed OS-ODLSDC model initially undergoes preprocessing step.Since streaming data is unbalanced,support vector machine(SVM)-Synthetic Minority Over-sampling Technique(SVM-SMOTE)is applied for oversampling process.Besides,the OS-ODLSDC model employs bidirectional long short-term memory(Bi LSTM)for AD and classification.Finally,the root means square propagation(RMSProp)optimizer is applied for optimal hyperparameter tuning of the Bi LSTM model.For ensuring the promising performance of the OS-ODLSDC model,a wide-ranging experimental analysis is performed using three benchmark datasets such as CICIDS 2018,KDD-Cup 1999,and NSL-KDD datasets.

Macroinvertebrate Community in Streams on the Canary Islands: Gradient Analysis and Stressors

This study describes the gradient analysis of the freshwater macroinvertebrate assemblages in eight streams of Tenerife and La Gomera (Canary Islands) over a 16-year period. During this period, a total of 75 taxa belonging to 34 taxonomic families were found. Endemism has an important presence in the streams on both islands, especially regarding Trichoptera and Coleoptera. The overall status of freshwater macroinvertebrates is rather uncertain as recent data on these communities are scarce and focused on a limited number of sites. Overexploitation of aquifers and the diversion of natural water flows for irrigation have resulted in the drying up of numerous natural streams, inevitably endangering the fauna that inhabits them. A reduction in number and abundance of endemic and sensitive species was observed in the majority of the sampled streams resulting in a lower ecological rating. Therefore, it is proposed that the protection of streams of high conservation value is essential to conserve freshwater macroinvertebrate fauna native to the Canary Islands.

The viscous strip approach to simplify the calculation of the surface acoustic wave generated streaming

In recent decades,the importance of surface acoustic waves,as a biocompatible tool to integrate with microfluidics,has been proven in various medical and biological applications.The numerical modeling of acoustic streaming caused by surface acoustic waves in microchannels requires the effect of viscosity to be considered in the equations which complicates the solution.In this paper,it is shown that the major contribution of viscosity and the horizontal component of actuation is concentrated in a narrow region alongside the actuation boundary.Since the inviscid equations are considerably easier to solve,a division into the viscous and inviscid domains would alleviate the computational load significantly.The particles'traces calculated by this approximation are excellently alongside their counterparts from the completely viscous model.It is also shown that the optimum thickness for the viscous strip is about 9-fold the acoustic boundary layer thickness for various flow patterns and amplitudes of actuation.

Features of sampling stream sediments of large river valleys under cryolithogenesis conditions in the Balygychan-Sugoy trough,North-East of Russia

Comprehensive research has been implemented to raise the efficiency of the geochemical survey of stream sediments(SSs)that formed under the cryolithogenesis conditions.The authors analysed the composition,structure and specific features of the formation of exogenous anomalous geochemical fields(AGFs)identified through SSs of large river valleys of IV order.In our case,these were the valleys of Maly Ken,Ken and Tap Rivers.These rivers are located in the central and southern parts of the Balygychan-Sugoy trough enclosed in the Magadan region,North-East of Russia.The authors proposed a new technique to sample loose alluvium of SSs in the large river valleys along the profiles.The profiles were located across the valleys.The AGFs of Au,Ag,Pb,Zn,Sn,Bi,Mo and W were studied.Correlations between elements have been established.These elements are the main indicator elements of Au-Ag,Ag-Pb,Sn-Ag,Mo-W and Sn-W mineralization occurring on the sites under study.The results obtained were compared with the results of geochemical surveys of SSs.It is concluded that the AGFs recognized along the profiles reflect the composition and structure of eroded and drained ore zones,uncover completely and precisely the pattern of element distribution in loose sediments of large water flows.The alluvium fraction<0.25 mm seems to be most significant in a practical sense,as it concentrated numerous ore elements.Sampling of this fraction in the river valleys of IV order does not cause any difficulty,for this kind of material is plentiful.The developed technique of alluvium sampling within large river valleys is efficient in searching for diverse mineralization at all stages of prognostic prospecting.It is applicable for geochemical survey of SSs performed at different scales both in the North-East of Russia,as well as other regions with similar climatic conditions,where the SSs are formed under the cryolithogenesis conditions.

Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU Network

Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd dataset serves as a catalyst for the development ofmore robust and effective fitnesstracking systems and ultimately promotes healthier lifestyles through improved exercise monitoring and analysis.

基于STREAM教育理念的初中物理实验拓展的内涵和价值研究

基于初中物理实验教学现状,结合初中物理实验教学对STREAM教育进行本土化解读,探索基于STREAM教育理念的初中物理实验拓展的内涵和价值。

最新一代Eagle Stream平台服务器的主板工艺技术研究

随着Intel最新一代Eagle Stream平台的推出,该平台服务器将会占据未来的服务器主流市场。本文关于Eagle Stream平台服务器,围绕0.94 mm pitch背钻出双线、正、反面树脂塞孔+POFV和插入损耗需达到Ultra Low Loss要求这三个产品特点,分析了工艺上存在的挑战和难点,并列举介绍了关键工序和管控措施,以及相应的过程数据。

Research on Current Situation and Legal Regulation of Cosmetics Live Streaming E-commerce in China

Analyze the compatibility between cosmetics and live streaming e-commerce from its own nature,marketing means and supply chain characteristics.According to the prominent problems,sort out the relationship between all parties in the cosmetics live e-commerce industry chain.Combined with the latest regulatory policies of live streaming e-commerce and cosmetics,the responsibilities of different subjects in cosmetics live streaming e-commerce are summarized,and relevant suggestions and countermeasures are put forward for the standardization and development of live streaming e-commerce.Cosmetics brand owners are the first responsible persons for product quality.Anchors,as a mixed identity between intermediary,advertising spokesperson and operator,should bear stricter joint and several liability when recommending products related to consumers’health.If anchors fail to clearly identify themselves in the recommendation process,thus causing consumers to mistake them for the operator of the cosmetics,they should assume the obligations of the operator.

Significance of Live Streaming in Shaping Business: A Critical Review and Analytical Study

With the rise of live streaming on social media, platforms like Facebook, Instagram, and YouTube have become powerful business tools. They enable users to share live videos, fostering direct connections between businesses and their customers. This critical literature review paper explores the impact of live streaming on businesses, focusing on its role in attracting and satisfying consumers by promoting products tailored to their needs and wants. It emphasizes live streaming’s crucial role in engaging customers, a key to business growth. The study also provides viable strategies for businesses to leverage live streaming for growth and customer engagement, underscoring its importance in the business landscape.

基于Spark Streaming的车辆电子围栏技术实现与应用

文章提出一种基于Spark Streaming实时数据流处理框架,使用Kafka作为车辆轨迹数据的消息队列服务,结合拓扑关系判断算法射线法的车辆电子围栏技术。应用表明,该技术能够处理高吞吐率、强实时性的车辆动态数据,满足车辆动态精细化监管需求。

STREAM教育背景下小学跨学科课程实施路径研究

当前跨学科课程的有效开展面临诸多困难和挑战,将STREAM教育理念融入跨学科主题学习中,为跨学科课程实施提供新思路。文章分别从设计真实驱动问题、深化学生合作探究、促进多门学科整合、优化课程评价体系四个维度对STREAM教育理念指导下小学跨学科课程的实施方式进行归纳总结,旨在为小学跨学科课程的有效开展提供理论指导和实践参考。

A Study on the Factors Influencing Consumer Purchase Decision Under the Live-Streaming Sales Model

In recent years,with the rapid development and popularization of Internet information technology,many new media platforms have risen rapidly,and major e-commerce companies have begun to explore the mode of livestreaming.Especially during the COVID-19 pandemic,due to the lockdown,live-streaming has become an important means of economic development in many places.Owing to its remarkable characteristics of timeliness,entertainment,and interactivity,it has become the latest and trendiest sales mode of e-commerce channels,reflecting huge economic potential and commercial value.This article analyzes two models and their characteristics of live-streaming sales from a practical perspective.Based on this,it outlines consumer purchasing decisions and the factors that affect consumer purchasing decisions under the live-streaming sales model.Finally,it discusses targeted suggestions for using the live-streaming sales model to expand the consumer market,hoping to promote the healthy and steady development of the live-streaming sales industry.

STREAM理念在中学化学核心素养的教学实践

核心素养理念再次激发了教师践行课改意识、创新思路、品质观念的踊跃性与自觉性,教师积极推动化学教学实现方法进阶、提质增效。为了提升化学教学的素养型,教师开始将STREAM理念引入课堂活动,简要解析STREAM理念是核心素养的落实支持,扼要概括STREAM理念下核心素养培养的研究价值,全面总结STREAM理念下核心素养培养的教学方法。因此,STREAM理念开启了核心素养培养活动的新方法、新导向,能科学培养初中生的高阶思维与课程能力。

脑卒中偏瘫患者步态特征及其与STREAM量表下肢运动部分评分间的相关性分析

脑卒中偏瘫患者的步态异常会严重影响其日常生活活动能力。步态分析作为定量评估方法已广泛应用于步态异常的科学研究,但步态分析数据繁杂,如何高效且规范化进行偏瘫患者的下肢运动功能的临床评估仍有待研究。目的探究脑卒中偏瘫患者步态特征,并通过步态特征及脑卒中康复运动评价(STREAM)量表下肢运动部分评分的相关性分析筛选出一些步态的定量指标为偏瘫患者的下肢运动功能的临床评估和诊断提供规范化数据。方法 选择2021年5月至2022年6月在安徽医科大学第二附属医院康复医学科接受治疗的脑卒中后偏瘫患者21例为卒中组,其中男性12例,女性9例;年龄14~77岁,平均年龄49.29岁;身高153~181 cm,平均身高168.90 cm;体质量53~94 kg,平均体质量69.88岁;其中脑出血恢复期患者9例,脑梗死恢复期患者12例。选取同时间段自愿参与该研究的健康者21例作为健康组,其中男性11例,女性10例;年龄24~71岁,平均年龄49.14岁;身高155~183 cm,平均身高167.67 cm;体质量48~80 kg,平均体质量65.95 kg。应用步态分析系统及运动障碍数字化测试平台分别采集两组受试者步态参数,对采集的数据进行统计分析,并对STREAM量表下肢运动部分评分与卒中组各指标进行相关性分析。结果 卒中组步幅小于健康组,步宽、触地时长百分比大于健康组,步速小于健康组,步相角大于健康组,差异均有统计学意义(P <0.05);两组T区、M1区、M2区足底压力占比差异均无统计学意义(P> 0.05);卒中组MF区压力占比较健康组高,差异有显著统计学意义(P <0.01);卒中组HEEL区压力占比较健康组低,差异有统计学意义(P <0.05);卒中组在COP-X、COP-Y方向的偏移值均高于健康组,差异均有统计学意义(P <0.05);卒中组患侧峰值屈髋角度较健康组减少,差异有统计学意义(P <0.05);卒中组患侧峰值屈膝角度较健康组减少,差异有统计学意义(P <0.05);卒中组患者STREAM量表下肢运动部分评分为(12.81±3.14)分。Person相关性分析显示STREAM量表下肢运动部分评分与步速、步幅、触地时长百分比、峰值屈膝角度、峰值屈髋角度呈正相关(P <0.05),与COP-X、COP-Y呈负相关(P <0.05);STREAM量表下肢运动部分评分与步相角、步宽无相关性(P> 0.05)。结论 与健康人比较,脑卒中偏瘫患者步态特征具有显著改变。主要表现为足跟处负重减少、外侧缘负重增多,平衡差,患侧下肢屈髋屈膝不足,步行能力降低。步态分析的一些指标与STREAM量表下肢运动部分评分间具有显著相关性。故可作为评价脑卒中偏瘫患者步行能力的重要定量分析指标。

基于Spark Streaming的气象自动站实时流处理与存储系统

在当前大数据技术蓬勃发展的时代,人们对气象数据的实时处理、数据质量、数据存储及大规模查询等要求也越来越高。针对现有气象自动站数据业务落地环节多,任务处理耦合紧但系统部署分散等问题,文中基于Spark Streaming的流式计算框架,研究使用Flume解析收集自动站原始数据,在Spark Streaming中设计融入自动站数据质控算法,最终通过对分布式数据库存储的表设计,使气象自动站数据具备高效率、高质量、高可靠的应用服务能力。性能测试结果表明,基于Spark Streaming的气象自动站数据实时流处理与存储系统,数据从文件采集、解码、流处理至入库的全流程能够在秒级完成,TB级数据查询响应为毫秒级,加权查询为秒级,完全满足自动站数据业务应用需求,从而为进一步提高气象自动站数据质量与服务水平提供基础支撑。

Machine Learning Based Classifiers for QoE Prediction Framework in Video Streaming over 5G Wireless Networks

Recently,the combination of video services and 5G networks have been gaining attention in the wireless communication realm.With the brisk advancement in 5G network usage and the massive popularity of threedimensional video streaming,the quality of experience(QoE)of video in 5G systems has been receiving overwhelming significance from both customers and service provider ends.Therefore,effectively categorizing QoE-aware video streaming is imperative for achieving greater client satisfaction.This work makes the following contribution:First,a simulation platform based on NS-3 is introduced to analyze and improve the performance of video services.The simulation is formulated to offer real-time measurements,saving the expensive expenses associated with real-world equipment.Second,A valuable framework for QoE-aware video streaming categorization is introduced in 5G networks based on machine learning(ML)by incorporating the hyperparameter tuning(HPT)principle.It implements an enhanced hyperparameter tuning(EHPT)ensemble and decision tree(DT)classifier for video streaming categorization.The performance of the ML approach is assessed by considering precision,accuracy,recall,and computation time metrics for manifesting the superiority of these classifiers regarding video streaming categorization.This paper demonstrates that our ML classifiers achieve QoE prediction accuracy of 92.59%for(EHPT)ensemble and 87.037%for decision tree(DT)classifiers.