Fast Multicast on Multistage Interconnection Networks Using Multi-Head Worms

This paper proposes a new approach for implementing fast multicast on multistage interconnection networks (MINs) with multi-head worms. For an MIN with n stages of k×k switches, a single multi-head worm can cover an arbitrary set of destinations with a single communication start-up. Compared with schemes using unicast messages, this approach reduces multicast latency significantly and performs better than multi-destination worms.

Fasciola worm and egg-derived antigens:Exploring their diagnostic potential for urogenital schistosomiasis in resource-limited endemic regions

Objective:To evaluate the immunodiagnostic potential of crude Fasciola gigantica-worm(FWA)and egg antigen(FEA)in detecting anti-Schistosoma(S.)haematobium antibodies in sera and urine samples.Methods:This is a cross-sectional diagnostic study.Employing an indirect ELISA,antibodies against these antigens were assessed in samples from infected and non-infected individuals in both schistosomiasis endemic(NE)and non-endemic(NNE)areas,using microscopy as the diagnostic standard.Results:FWA-sera exhibited excellent diagnostic accuracy with an area under the curve(AUC)of 0.957,a sensitivity of 93.75%,and a specificity of 85.42%for discriminating between infected and non-infected individuals in non-endemic areas.FWA-urine also demonstrated robust performance,achieving AUC>0.95,sensitivity>97.0%,and specificity>85.0%in both NE and NNE categories.Notably,S.haematobium-specific antibody levels against FWA were significantly elevated in infected individuals in both endemic and non-endemic areas.FEA-sera exhibited outstanding diagnostic performance with sensitivity exceeding 90%and an AUC of 0.968 in non-endemic samples but not in FEA-urine.Conclusions:FWA-based ELISAs,applicable to both sera and urine,emerge as promising tools for S.haematobium diagnosis in resource-limited settings,offering advantages of high sensitivity and specificity with shared antigens with Fasciola.The superior diagnostic metrics of urine samples suggest their potential as a non-invasive biological sample for diagnostic purposes.

Structured Multi-Head Attention Stock Index Prediction Method Based Adaptive Public Opinion Sentiment Vector

The present study examines the impact of short-term public opinion sentiment on the secondary market,with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment risk.The quantification of investment sentiment indicators and the persistent analysis of their impact has been a complex and significant area of research.In this paper,a structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector is proposed.The proposedmethod utilizes an innovative approach to transform numerous investor comments on social platforms over time into public opinion sentiment vectors expressing complex sentiments.It then analyzes the continuous impact of these vectors on the market through the use of aggregating techniques and public opinion data via a structured multi-head attention mechanism.The experimental results demonstrate that the public opinion sentiment vector can provide more comprehensive feedback on market sentiment than traditional sentiment polarity analysis.Furthermore,the multi-head attention mechanism is shown to improve prediction accuracy through attention convergence on each type of input information separately.Themean absolute percentage error(MAPE)of the proposedmethod is 0.463%,a reduction of 0.294% compared to the benchmark attention algorithm.Additionally,the market backtesting results indicate that the return was 24.560%,an improvement of 8.202% compared to the benchmark algorithm.These results suggest that themarket trading strategy based on thismethod has the potential to improve trading profits.

An Intelligent Framework for Resilience Recovery of FANETs with Spatio-Temporal Aggregation and Multi-Head Attention Mechanism

Due to the time-varying topology and possible disturbances in a conflict environment,it is still challenging to maintain the mission performance of flying Ad hoc networks(FANET),which limits the application of Unmanned Aerial Vehicle(UAV)swarms in harsh environments.This paper proposes an intelligent framework to quickly recover the cooperative coveragemission by aggregating the historical spatio-temporal network with the attention mechanism.The mission resilience metric is introduced in conjunction with connectivity and coverage status information to simplify the optimization model.A spatio-temporal node pooling method is proposed to ensure all node location features can be updated after destruction by capturing the temporal network structure.Combined with the corresponding Laplacian matrix as the hyperparameter,a recovery algorithm based on the multi-head attention graph network is designed to achieve rapid recovery.Simulation results showed that the proposed framework can facilitate rapid recovery of the connectivity and coverage more effectively compared to the existing studies.The results demonstrate that the average connectivity and coverage results is improved by 17.92%and 16.96%,respectively compared with the state-of-the-art model.Furthermore,by the ablation study,the contributions of each different improvement are compared.The proposed model can be used to support resilient network design for real-time mission execution.

A blockchain-empowered authentication scheme for worm detection in wireless sensor network

Wireless Sensor Network(WSN)is a distributed sensor network composed a large number of nodes with low cost,low performance and self-management.The special structure of WSN brings both convenience and vulnerability.For example,a malicious participant can launch attacks by capturing a physical device.Therefore,node authentication that can resist malicious attacks is very important to network security.Recently,blockchain technology has shown the potential to enhance the security of the Internet of Things(IoT).In this paper,we propose a Blockchain-empowered Authentication Scheme(BAS)for WSN.In our scheme,all nodes are managed by utilizing the identity information stored on the blockchain.Besides,the simulation experiment about worm detection is executed on BAS,and the security is evaluated from detection and infection rate.The experiment results indicate that the proposed scheme can effectively inhibit the spread and infection of worms in the network.

Posture Detection of Heart Disease Using Multi-Head Attention Vision Hybrid(MHAVH)Model

Cardiovascular disease is the leading cause of death globally.This disease causes loss of heart muscles and is also responsible for the death of heart cells,sometimes damaging their functionality.A person’s life may depend on receiving timely assistance as soon as possible.Thus,minimizing the death ratio can be achieved by early detection of heart attack(HA)symptoms.In the United States alone,an estimated 610,000 people die fromheart attacks each year,accounting for one in every four fatalities.However,by identifying and reporting heart attack symptoms early on,it is possible to reduce damage and save many lives significantly.Our objective is to devise an algorithm aimed at helping individuals,particularly elderly individuals living independently,to safeguard their lives.To address these challenges,we employ deep learning techniques.We have utilized a vision transformer(ViT)to address this problem.However,it has a significant overhead cost due to its memory consumption and computational complexity because of scaling dot-product attention.Also,since transformer performance typically relies on large-scale or adequate data,adapting ViT for smaller datasets is more challenging.In response,we propose a three-in-one steam model,theMulti-Head Attention Vision Hybrid(MHAVH).Thismodel integrates a real-time posture recognition framework to identify chest pain postures indicative of heart attacks using transfer learning techniques,such as ResNet-50 and VGG-16,renowned for their robust feature extraction capabilities.By incorporatingmultiple heads into the vision transformer to generate additional metrics and enhance heart-detection capabilities,we leverage a 2019 posture-based dataset comprising RGB images,a novel creation by the author that marks the first dataset tailored for posture-based heart attack detection.Given the limited online data availability,we segmented this dataset into gender categories(male and female)and conducted testing on both segmented and original datasets.The training accuracy of our model reached an impressive 99.77%.Upon testing,the accuracy for male and female datasets was recorded at 92.87%and 75.47%,respectively.The combined dataset accuracy is 93.96%,showcasing a commendable performance overall.Our proposed approach demonstrates versatility in accommodating small and large datasets,offering promising prospects for real-world applications.

Multi-Head Attention Spatial-Temporal Graph Neural Networks for Traffic Forecasting

Accurate traffic prediction is crucial for an intelligent traffic system (ITS). However, the excessive non-linearity and complexity of the spatial-temporal correlation in traffic flow severely limit the prediction accuracy of most existing models, which simply stack temporal and spatial modules and fail to capture spatial-temporal features effectively. To improve the prediction accuracy, a multi-head attention spatial-temporal graph neural network (MSTNet) is proposed in this paper. First, the traffic data is decomposed into unique time spans that conform to positive rules, and valuable traffic node attributes are mined through an adaptive graph structure. Second, time and spatial features are captured using a multi-head attention spatial-temporal module. Finally, a multi-step prediction module is used to achieve future traffic condition prediction. Numerical experiments were conducted on an open-source dataset, and the results demonstrate that MSTNet performs well in spatial-temporal feature extraction and achieves more positive forecasting results than the baseline methods.

侧链含有不同电子受体的聚咔唑衍生物的合成及其WORM型存储性能

利用Suzuki偶联反应合成了3种侧链含有不同电子受体的可溶性D-A型聚咔唑衍生物：聚[（9-（2-己基葵基）-9 H-咔唑）-（9-（4-硝基苯基）-9 H-咔唑）]（PCz-NO2）、聚[（9-（2-己基葵基）-9 H-咔唑）-（4-（9 H-咔唑-9-基）苯甲醛）]（PCz-CHO）和聚[（9-（2-己基葵基）-9 H-咔唑）-（4-（9 H-咔唑-9-基）苯甲腈）]（PCz-CN）。基于这3种聚合物的存储器件（器件结构：Al（200nm）/高分子（90nm）/氧化铟锡（ITO）均表现出典型的电双稳电子开关效应和非易失性一次写入多次读出（WORM）型存储性能。随着共轭聚合物光学带隙的增加[2.26eV（PCz-NO2）→2.79eV（PCzCHO）→3.20eV（PCz-CN）],相应器件的启动阈值电压逐渐增大（-1.70V→-1.81V→-1.89V）;而电流开关比（ON/OFF）则依次减小（6.63×10^4→4.08×10^4→5.68×10^3）。含氰基的聚咔唑衍生物需要的开启电压最大,展现出来的电流开关比在3种聚合物中则最小。

水丝蚓(Tubificid worms)扰动对磷在湖泊沉积物-水界面迁移的影响

为探讨水丝蚓(Tubificid worms)扰动对磷在湖泊沉积物-水界面间迁移的影响,选取太湖梅梁湾与大浦口两富营养化湖区为研究对象,通过室内培养实验,利用Rhizon间隙水采样器等技术,研究了水丝蚓扰动对太湖沉积物-水界面理化性质及溶解活性磷(SRP)在界面通量的影响.结果表明水丝蚓扰动能够增大表层沉积物含水率、氧化还原电位,减小间隙水中Fe2+浓度.水丝蚓没有显著改变梅梁湾间隙水中SRP浓度,同时促进了梅梁湾沉积物中SRP向上覆水的释放;但水丝蚓显著减小了大浦口间隙水中SRP浓度,并抑制了大浦口沉积物中SRP向上覆水的释放.水丝蚓扰动对磷在沉积物-水界面间迁移的不同影响可能是由沉积物中Fe2+含量差异较大造成的.

WORM存储技术及其在医院中的应用

本文简要介绍了WORM存储技术的基本概念,指出了基于光盘、磁带和磁盘的WORM技术的实现方法和各自的优缺点,讨论了医疗信息的特征和在医院信息化建设中应用WORM存储技术的必要性和迫切性,并给出了在医院中运用WORM技术的设计方案和应注意的问题。

Contact Toxicity and Antifeedant Activity of Aconitum flavum Extract against Cabbage Worm

[ Objective] The paper was to study the contact toxicity and antifeedant activity of Aconitum flavum against cabbage worm. [ Method ] In- sect dipping method was adopted to determine the contact toxicity of the extracts of A. fiavum extracted from five polar solvents including ethanol, petroleum ether, ether, ethyl acetate, n-butanol and water; leaf dish method was adopted to determine the antifeedant activities of five solvent ex- tracts including ethanol, petroleum ether, ether, ethyl acetate, n-butanol and water against cabbage worm, [ Result] Extracts of A. flavum had high contact toxicity against cabbage worm. When the concentration was 100.00 mg/ml, the corrected mortality at 48 h roached 97.24%, and the insec- ticidal activities of five solvent extracts against cabbage worm in sequence were water 〉 n-butanol 〉 ethyl acetate 〉 ether 〉 petroleum ether, the cor- rected mortality of water extract at 48 h was 95.87% ; the antifeedant activities of five solvent extracts in sequence were water 〉 n-butanol 〉 ethyl ac- etate 〉 ether 〉 petroleum ether. [ Conclusion] Extracts of A. flavum had strong contact toxicity and antifeedant activity against cabbage worm, and the active ingredients with contact toxicity and antifeedant activity might be a kind of polar compound.

一种WORM光盘文件存储系统的设计

本文讨论了ＷＯＲＭ光盘构成文件存储系统时由于写一次特性引起的各种处理方式。给出了一种存档后备式文件系统的设计。详细说明了其层次结构、文卷结构、文件结构，以及用户界面、层内部界面等设计。同时给出了速度及可靠性的设计考虑。

Application of the improved dung beetle optimizer,muti-head attention and hybrid deep learning algorithms to groundwater depth prediction in the Ningxia area,China

Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.

模拟Worms的游戏设计与实现

本课题的目标是仿照Team17的Worms2D系列游戏产品,开发一款2D游戏,了解整个游戏开发流程,设计一款2D游戏引擎,封装了Direct3D9的部分图形绘制接口、图形控制接口、音效接口,并在此基础上实现一款2D射击游戏.

Win32.SQLExp.Worm蠕虫病毒分析与对策

分析了Win32.SQLExp.Worm蠕虫病毒原理,并给出相应解决方案。

基于Multi-head Attention和Bi-LSTM的实体关系分类

关系分类是自然语言处理领域的一项重要任务,能够为知识图谱的构建、问答系统和信息检索等提供技术支持.与传统关系分类方法相比较,基于神经网络和注意力机制的关系分类模型在各种关系分类任务中都获得了更出色的表现.以往的模型大多采用单层注意力机制,特征表达相对单一.因此本文在已有研究基础上,引入多头注意力机制(Multi-head attention),旨在让模型从不同表示空间上获取关于句子更多层面的信息,提高模型的特征表达能力.同时在现有的词向量和位置向量作为网络输入的基础上,进一步引入依存句法特征和相对核心谓词依赖特征,其中依存句法特征包括当前词的依存关系值和所依赖的父节点位置,从而使模型进一步获取更多的文本句法信息.在SemEval-2010 任务8 数据集上的实验结果证明,该方法相较之前的深度学习模型,性能有进一步提高.

WORMS评分中滑膜炎症与膝骨性关节炎中医证型的关联性研究

目的:探讨膝骨性关节炎性滑膜炎与膝骨关节炎中医证型的相关性。方法:自2015年1月至2018年6月,选取213例膝骨关节炎的患者,进行中医辨证分型,其MRI影像进行WORMS评分,同时做WORMS评分中滑膜炎和中医证型的相关性分析。结果:213例患者中,风寒湿痹证25例(占11.7%),风湿热痹证84例(占39.4%),瘀血痹阻证43例(占20.2%),肝肾亏虚证61例(占28.6%);在WORMS评分中,滑膜炎评分为0分的12例(占5.6%),1分的60例(占28.2%),2分的50例(占23.5%),3分的91例(占42.7%);相关性分析中,差异有统计学意义,WORMS评分中滑膜炎3分组更容易发生在风湿热痹证中(χ^2=137.286,P=0.000)。结论:膝骨性关节炎滑膜炎患者临床上以风湿热痹型(39.4%,84/213)为主,这对于相关治疗有一定的指导意义。

介孔Worm-like孔壁晶化制备微孔-介孔分子筛研究

采用水热孔壁晶化法,以Worm-like介孔分子筛为硅源,十八水合硫酸铝为铝源,制备不同硅铝比微孔-介孔ZSM-5复合分子筛,XRD、FT-IR、N2吸附-脱附、TEM等表征结果证明,用此方法成功制备出一系列微孔-介孔ZSM-5复合分子筛.将一系列复合分子筛用于催化苯酚叔丁基化反应,在反应温度145℃、n(苯酚)∶n(叔丁醇)=1∶2.5条件下,硅铝比为15、25的复合分子筛有较强的催化活性,苯酚转化率和2,4-二叔丁基苯酚选择性分别超过90%和42%.

Worm-like分子筛负载磷钨酸催化剂的制备及光降解甲基橙的性能研究

以Worm-like分子筛为载体,采用浸渍法制备不同磷钨酸负载量的负载型催化剂,并采用傅里叶红外光谱(FT-IR)、X射线衍射(XRD)和N2吸附/脱附等手段对负载型磷钨酸催化剂进行表征。结果表明,磷钨酸成功负载在Worm-like分子筛上,且随着磷钨酸负载量的增加,FT-IR、XRD和N2吸附/脱附结果呈规律性变化。将该系列催化剂用于光催化降解甲基橙实验,结果表明,甲基橙质量浓度为20mg/L,磷钨酸负载量为70%(质量分数)时,甲基橙降解率为87.2%;回收催化剂并重复使用3次,甲基橙降解率仍达75%以上。