Hierarchical Privacy Protection Model in Advanced Metering Infrastructure Based on Cloud and Fog Assistance

The Advanced Metering Infrastructure(AMI),as a crucial subsystem in the smart grid,is responsible for measuring user electricity consumption and plays a vital role in communication between providers and consumers.However,with the advancement of information and communication technology,new security and privacy challenges have emerged for AMI.To address these challenges and enhance the security and privacy of user data in the smart grid,a Hierarchical Privacy Protection Model in Advanced Metering Infrastructure based on Cloud and Fog Assistance(HPPM-AMICFA)is proposed in this paper.The proposed model integrates cloud and fog computing with hierarchical threshold encryption,offering a flexible and efficient privacy protection solution that significantly enhances data security in the smart grid.The methodology involves setting user protection levels by processing missing data and utilizing fuzzy comprehensive analysis to evaluate user importance,thereby assigning appropriate protection levels.Furthermore,a hierarchical threshold encryption algorithm is developed to provide differentiated protection strategies for fog nodes based on user IDs,ensuring secure aggregation and encryption of user data.Experimental results demonstrate that HPPM-AMICFA effectively resists various attack strategies while minimizing time costs,thereby safeguarding user data in the smart grid.

A CSMA/CA based MAC protocol for hybrid Power-line/Visible-light communication networks:Design and analysis

Hybrid Power-line/Visible-light Communication(HPVC)network has been one of the most promising Cooperative Communication(CC)technologies for constructing Smart Home due to its superior communication reliability and hardware efficiency.Current research on HPVC networks focuses on the performance analysis and optimization of the Physical(PHY)layer,where the Power Line Communication(PLC)component only serves as the backbone to provide power to light Emitting Diode(LED)devices.So designing a Media Access Control(MAC)protocol remains a great challenge because it allows both PLC and Visible Light Communication(VLC)components to operate data transmission,i.e.,to achieve a true HPVC network CC.To solve this problem,we propose a new HPC network MAC protocol(HPVC MAC)based on Carrier Sense Multiple Access/Collision Avoidance(CSMA/CA)by combining IEEE 802.15.7 and IEEE 1901 standards.Firstly,we add an Additional Assistance(AA)layer to provide the channel selection strategies for sensor stations,so that they can complete data transmission on the selected channel via the specified CSMA/CA mechanism,respectively.Based on this,we give a detailed working principle of the HPVC MAC,followed by the construction of a joint analytical model for mathematicalmathematical validation of the HPVC MAC.In the modeling process,the impacts of PHY layer settings(including channel fading types and additive noise feature),CSMA/CA mechanisms of 802.15.7 and 1901,and practical configurations(such as traffic rate,transit buffer size)are comprehensively taken into consideration.Moreover,we prove the proposed analytical model has the solvability.Finally,through extensive simulations,we characterize the HPVC MAC performance under different system parameters and verify the correctness of the corresponding analytical model with an average error rate of 4.62%between the simulation and analytical results.

我国学前教育高质量发展的多组态路径——基于DPSIR框架的模糊集定性比较分析(fsQCA)

文章基于DPSIR框架设计评价指标体系对区域学前教育高质量发展水平进行评价,以熵权-TOPSIS计算指标权重和综合得分,结合模糊集定性比较分析(fsQCA)进行组态路径分析。研究发现:(1)我国学前教育高质量发展水平呈现由东向西递减的趋势,除东部外,中部与西部区域学前教育高质量发展水平均值均低于全国均值;(2)基于DPSIR框架的区域学前教育发展系统驱动学前教育高质量发展具有“多重并发”与非对称性的特点;(3)存在两条高水平组态路径与3条非高水平组态路径,多条组态路径殊途同归,且高水平组态与非高水平组态处于非对称状态;(4)东部、中部与西部组态路径存在一定差异,三大区域学前教育高质量发展均离不开状态因子与响应因子的支持。为此,要完善机制体制建设,为学前教育发展提供保障;因地制宜制定发展策略,以协同路径驱动学前教育高质量发展;聚焦区域重点,以重点发展引领区域学前教育高质量发展。

基于CRISPR/Cas系统的纸基分析在快速检测食源性致病菌中的应用

由食源性致病菌引起的食品安全事件严重影响人类健康,开发针对食源性致病菌的快速检测技术十分必要。成簇间隔短回文重复序列(clustered regularly interspaced short palindromic repeats,CRISPR)及相关蛋白(CRISPR-associated protein,Cas)是原核生物的适应性免疫系统,具有特异性识别并切割核酸序列的功能。纸基分析方法作为一种简便性好、成本低廉的分析检测工具,在快速检测领域展现出良好的前景。因此,将CRISPR/Cas系统的高效识别能力和纸基分析方法的简便性相结合可实现对食源性致病菌的快速灵敏检测。本文简要介绍了CRISPR/Cas系统用于核酸检测的概况,对第二类单Cas效应蛋白系统的特点及原理进行概述,重点综述基于CRISPR/Cas系统的试纸分析、侧向流动分析和纸基微流控装置在检测食源性致病菌方面的应用,并讨论了CRISPR/Cas系统结合纸基分析建立检测方法的优势、当前的挑战及未来的发展前景。

A Cloud Based Sentiment Analysis through Logistic Regression in AWS Platform

The use of Amazon Web Services is growing rapidly as more users are adopting the technology.It has various functionalities that can be used by large corporates and individuals as well.Sentiment analysis is used to build an intelligent system that can study the opinions of the people and help to classify those related emotions.In this research work,sentiment analysis is performed on the AWS Elastic Compute Cloud(EC2)through Twitter data.The data is managed to the EC2 by using elastic load balancing.The collected data is subjected to preprocessing approaches to clean the data,and then machine learning-based logistic regression is employed to categorize the sentiments into positive and negative sentiments.High accuracy of 94.17%is obtained through the proposed machine learning model which is higher than the other models that are developed using the existing algorithms.

Impact of the Assimilation Frequency of Radar Data with the ARPS 3DVar and Cloud Analysis System on Forecasts of a Squall Line in Southern China

Assimilation configurations have significant impacts on analysis results and subsequent forecasts. A squall line system that occurred on 23 April 2007 over southern China was used to investigate the impacts of the data assimilation frequency of radar data on analyses and forecasts. A three-dimensional variational system was used to assimilate radial velocity data,and a cloud analysis system was used for reflectivity assimilation with a 2-h assimilation window covering the initial stage of the squall line. Two operators of radar reflectivity for cloud analyses corresponding to single-and double-moment schemes were used. In this study, we examined the sensitivity of assimilation frequency using 10-, 20-, 30-, and 60-min assimilation intervals. The results showed that analysis fields were not consistent with model dynamics and microphysics in general;thus, model states, including dynamic and microphysical variables, required approximately 20 min to reach a new balance after data assimilation in all experiments. Moreover, a 20-min data assimilation interval generally produced better forecasts for both single-and double-moment schemes in terms of equitable threat and bias scores. We conclude that a higher data assimilation frequency can produce a more intense cold pool and rear inflow jets but does not necessarily lead to a better forecast.

Modeling and analysis of a catastrophic oil spill and vapor cloud explosion in a confined space upon oil pipeline leaking

Oil spill-induced vapor cloud explosions in a confined space can cause catastrophic consequences.In this work,investigation was conducted on the catastrophic pipeline leak,oil spill,and the resulting vapor cloud explosion accident occurring in China in 2013 by modeling analysis,field surveys,and numerical simulations.The total amount of the spilled oil was up to2044.4 m3 due to improper disposal.The long residence time of the oil remaining in a confined space permitted the formation of explosive mixtures and caused the vapor cloud explosion.A numerical model was developed to estimate the consequence of the explosion based on volatilization testing results.The results show that the death-leading zone and the glass-breaking zone could be 18 m and 92 m,respectively,which are consistent with the field investigation.The severity of the explosion is related to the amount of the oil spill,properties of oil,and volatilization time.It is recommended that a comprehensive risk assessment be conducted to analyze the possible consequences upon oil spilling into a confined space.Prompt collection and ventilation measures should be taken immediately after the spill occurs to reduce the time for oil volatilization and prevent the mixture from reaching its explosive limit.

A Case Study of Impact of FY-2C Satellite Data in Cloud Analysis to Improve Short-Range Precipitation Forecast

Chinese FengYun-2C(FY-2C) satellite data were combined into the Local Analysis and Prediction System(LAPS) model to obtain three-dimensional cloud parameters and rain content. These parameters analyzed by LAPS were used to initialize the Global/Regional Assimilation and Prediction System model(GRAPES) in China to predict precipitation in a rainstorm case in the country. Three prediction experiments were conducted and were used to investigate the impacts of FY-2C satellite data on cloud analysis of LAPS and on short range precipitation forecasts. In the first experiment, the initial cloud fields was zero value. In the second, the initial cloud fields were cloud liquid water, cloud ice, and rain content derived from LAPS without combining the satellite data. In the third experiment, the initial cloud fields were cloud liquid water, cloud ice, and rain content derived from LAPS including satellite data. The results indicated that the FY-2C satellite data combination in LAPS can show more realistic cloud distributions, and the model simulation for precipitation in 1–6 h had certain improvements over that when satellite data and complex cloud analysis were not applied.

Hash-Indexing Block-Based Deduplication Algorithm for Reducing Storage in the Cloud

Cloud storage is essential for managing user data to store and retrieve from the distributed data centre.The storage service is distributed as pay a service for accessing the size to collect the data.Due to the massive amount of data stored in the data centre containing similar information and file structures remaining in multi-copy,duplication leads to increase storage space.The potential deduplication system doesn’t make efficient data reduction because of inaccuracy in finding similar data analysis.It creates a complex nature to increase the storage consumption under cost.To resolve this problem,this paper proposes an efficient storage reduction called Hash-Indexing Block-based Deduplication(HIBD)based on Segmented Bind Linkage(SBL)Methods for reducing storage in a cloud environment.Initially,preprocessing is done using the sparse augmentation technique.Further,the preprocessed files are segmented into blocks to make Hash-Index.The block of the contents is compared with other files through Semantic Content Source Deduplication(SCSD),which identifies the similar content presence between the file.Based on the content presence count,the Distance Vector Weightage Correlation(DVWC)estimates the document similarity weight,and related files are grouped into a cluster.Finally,the segmented bind linkage compares the document to find duplicate content in the cluster using similarity weight based on the coefficient match case.This implementation helps identify the data redundancy efficiently and reduces the service cost in distributed cloud storage.

Tweet Sentiment Analysis (TSA) for Cloud Providers Using Classification Algorithms and Latent Semantic Analysis

The availability and advancements of cloud computing service models such as IaaS, SaaS, and PaaS;introducing on-demand self-service, auto scaling, easy maintenance, and pay as you go, has dramatically transformed the way organizations design and operate their datacenters. However, some organizations still have many concerns like: security, governance, lack of expertise, and migration. The purpose of this paper is to discuss the cloud computing customers’ opinions, feedbacks, attitudes, and emotions towards cloud computing services using sentiment analysis. The associated aim, is to help people and organizations to understand the benefits and challenges of cloud services from the general public’s perspective view as well as opinions about existing cloud providers, focusing on three main cloud providers: Azure, Amazon Web Services (AWS) and Google Cloud. The methodology used in this paper is based on sentiment analysis applied to the tweets that were extracted from social media platform (Twitter) via its search API. We have extracted a sample of 11,000 tweets and each cloud provider has almost similar proportion of the tweets based on relevant hashtags and keywords. Analysis starts by combining the tweets in order to find the overall polarity about cloud computing, then breaking the tweets to find the specific polarity for each cloud provider. Bing and NRC Lexicons are employed to measure the polarity and emotion of the terms in the tweets. The overall polarity classification of the tweets across all cloud providers shows 68.5% positive and 31.5% negative percentages. More specifically, Azure shows 63.8% positive and 36.2% negative tweets, Google Cloud shows 72.6% positive and 27.4% negative tweets and AWS shows 69.1% positive and 30.9% negative tweets.

云原生网关APISIX的CAS认证插件设计与实现

CAS认证协议是当前主流统一身份认证系统重要的组成部分,开源云原生网关APISIX尚未支持该协议,因而从APISIX架构分析入手,采用插件的方式解决CAS鉴权认证问题,并实际测试验证了方案的可行性,为类似需求提供一种解决思路。

MV-Honeypot:Security Threat Analysis by Deploying Avatar as a Honeypot in COTS Metaverse Platforms

Nowadays,theuse of Avatars that are unique digital depictions has increased by users to access Metaverse—a virtual reality environment—through multiple devices and for various purposes.Therefore,the Avatar and Metaverse are being developed with a new theory,application,and design,necessitating the association of more personal data and devices of targeted users every day.This Avatar and Metaverse technology explosion raises privacy and security concerns,leading to cyber attacks.MV-Honeypot,or Metaverse-Honeypot,as a commercial off-the-shelf solution that can counter these cyber attack-causing vulnerabilities,should be developed.To fill this gap,we study user’s engagements with Avatars in Metaverse,analyze possible security vulnerabilities,and create a model named Simplified Avatar Relationship Association with Non-linear Gradient(SARANG)that draws the full diagram of infrastructure components and data flow through accessing Metaverse in this paper.We also determine the most significant threat for each component’s cyberattacks that will affect user data and Avatars.As a result,the commercial off-the-shelf(COTS)of the MV-Honeypot must be established.

Cloud‐based video streaming services:Trends,challenges,and opportunities

Cloud computing has drastically changed the delivery and consumption of live streaming content.The designs,challenges,and possible uses of cloud computing for live streaming are studied.A comprehensive overview of the technical and business issues surrounding cloudbased live streaming is provided,including the benefits of cloud computing,the various live streaming architectures,and the challenges that live streaming service providers face in delivering high‐quality,real‐time services.The different techniques used to improve the performance of video streaming,such as adaptive bit‐rate streaming,multicast distribution,and edge computing are discussed and the necessity of low‐latency and high‐quality video transmission in cloud‐based live streaming is underlined.Issues such as improving user experience and live streaming service performance using cutting‐edge technology,like artificial intelligence and machine learning are discussed.In addition,the legal and regulatory implications of cloud‐based live streaming,including issues with network neutrality,data privacy,and content moderation are addressed.The future of cloud computing for live streaming is examined in the section that follows,and it looks at the most likely new developments in terms of trends and technology.For technology vendors,live streaming service providers,and regulators,the findings have major policy‐relevant implications.Suggestions on how stakeholders should address these concerns and take advantage of the potential presented by this rapidly evolving sector,as well as insights into the key challenges and opportunities associated with cloud‐based live streaming are provided.

Quantitative Analysis of Meso-β-scale Convective Cells and Anvil Clouds over North China

This paper proposes several quantitative characteristics to study convective systems using observations from Doppler weather radars and geostationary satellites. Specifically, in order to measure the convective intensity of each system, a new index, named the ＂Convective Intensity Ratio＂ （CIR）, is defined as the ratio between the area of strong radar echoes at the upper level and the size of the convective cell itself. Based on these quantitative characteristics, the evolution of convective cells, surface rainfall intensity, rainfall area and convectively generated anvil clouds can be studied, and the relationships between them can also be analyzed. After testing nine meso-β-scale convective systems over North China during 2006–2007, the results were as follows： （1） the CIR was highly correlated with surface rainfall intensity, and the correlation reached a maximum when the CIR led rainfall intensity by 6–30 mins. The maximum CIR could be at most ～30 mins before the maximum rainfall intensity. （2） Convective systems with larger maximum CIRs usually had colder cloud-tops. （3） The maximum area of anvil cloud appeared 0.5–1.5 h after rainfall intensity began to weaken. The maximum area of anvil cloud and the time lag between maximum rainfall intensity and the maximum area of anvil cloud both increased with the CIR.

Systematic Literature Review on Cloud Computing Security: Threats and Mitigation Strategies

Cloud computing plays a significant role in modern information technology, providing organizations with numerous benefits, including flexibility, scalability, and cost-efficiency. However, it has become essential for organizations to ensure the security of their applications, data, and cloud-based networks to use cloud services effectively. This systematic literature review aims to determine the latest information regarding cloud computing security, with a specific emphasis on threats and mitigation strategies. Additionally, it highlights some common threats related to cloud computing security, such as distributed denial-of-service (DDoS) attacks, account hijacking, malware attacks, and data breaches. This research also explores some mitigation strategies, including security awareness training, vulnerability management, security information and event management (SIEM), identity and access management (IAM), and encryption techniques. It discusses emerging trends in cloud security, such as integrating artificial intelligence (AI) and machine learning (ML), serverless computing, and containerization, as well as the effectiveness of the shared responsibility model and its related challenges. The importance of user awareness and the impact of emerging technologies on cloud security have also been discussed in detail to mitigate security risks. A literature review of previous research and scholarly articles has also been conducted to provide insights regarding cloud computing security. It shows the need for continuous research and innovation to address emerging threats and maintain a security-conscious culture in the company.

Comparative Analysis of the Digital Terrain Models Extracted from Airborne LiDAR Point Clouds Using Different Filtering Approaches in Residential Landscapes

Light Detection And Ranging (LiDAR) is a well-established active remote sensing technology that can provide accurate digital elevation measurements for the terrain and non-ground objects such as vegetations and buildings, etc. Non-ground objects need to be removed for creation of a Digital Terrain Model (DTM) which is a continuous surface representing only ground surface points. This study aimed at comparative analysis of three main filtering approaches for stripping off non-ground objects namely;Gaussian low pass filter, focal analysis mean filter and DTM slope-based filter of varying window sizes in creation of a reliable DTM from airborne LiDAR point clouds. A sample of LiDAR data provided by the ISPRS WG III/4 captured at Vaihingen in Germany over a pure residential area has been used in the analysis. Visual analysis has indicated that Gaussian low pass filter has given blurred DTMs of attenuated high-frequency objects and emphasized low-frequency objects while it has achieved improved removal of non-ground object at larger window sizes. Focal analysis mean filter has shown better removal of nonground objects compared to Gaussian low pass filter especially at large window sizes where details of non-ground objects almost have diminished in the DTMs from window sizes of 25 × 25 and greater. DTM slope-based filter has created bare earth models that have been full of gabs at the positions of the non-ground objects where the sizes and numbers of that gabs have increased with increasing the window sizes of filter. Those gaps have been closed through exploitation of the spline interpolation method in order to get continuous surface representing bare earth landscape. Comparative analysis has shown that the minimum elevations of the DTMs increase with increasing the filter widow sizes till 21 × 21 and 31 × 31 for the Gaussian low pass filter and the focal analysis mean filter respectively. On the other hand, the DTM slope-based filter has kept the minimum elevation of the original data, that could be due to noise in the LiDAR data unchanged. Alternatively, the three approaches have produced DTMs of decreasing maximum elevation values and consequently decreasing ranges of elevations due to increases in the filter window sizes. Moreover, the standard deviations of the created DTMs from the three filters have decreased with increasing the filter window sizes however, the decreases have been continuous and steady in the cases of the Gaussian low pass filter and the focal analysis mean filters while in the case of the DTM slope-based filter the standard deviations of the created DTMs have decreased with high rates till window size of 31 × 31 then they have kept unchanged due to more increases in the filter window sizes.

茶树OSCA基因家族的鉴定及表达分析

钙通透性阳离子通道蛋白(OSCA)在植物调节高渗胁迫中发挥着重要作用。为了解OSCA基因家族在茶树响应干旱胁迫中的作用,本研究基于茶树全基因组数据对OSCA基因家族进行鉴定,分析CsOSCAs基因和蛋白结构以及启动子顺式作用元件,并对CsOSCAs在茶树不同组织、不同抗旱性品种间和干旱胁迫下的表达模式进行分析。结果表明:茶树基因组包含12个OSCA基因家族成员,分别命名为CsOSCA1~CsOSCA12;CsOSCAs基因编码的氨基酸序列长度为667~831 bp,蛋白分子量在76630.55~93563.99 kDa之间,等电点在6.15~9.33之间,含有9~12个跨膜结构域,均含有特征保守结构域DUF221,根据系统进化关系可以分为4个亚族;10个CsOSCAs基因定位于茶树7条染色体上,2个CsOSCAs基因定位于未锚定染色体的contig上;CsOSCAs基因编码的蛋白二级结构含有32%~38%的跨膜结构和60%~68%的α-螺旋;CsOSCAs基因具有组织表达特异性,CsOSCA2、CsOSCA3、CsOSCA11、CsOSCA12基因在干旱敏感的品种CN98中的表达量显著高于其他2个耐旱品种,9个基因响应PEG胁迫,其中CsOSCA2、CsOSCA3、CSOSCA5、CsOSCA8、CsOSCA10和CsOSCA12受干旱胁迫强烈诱导;进一步分析启动子顺式作用元件显示,6个基因含有干旱诱导响应元件,11个基因含有脱落酸响应元件。由此推测茶树OSCA基因家族在茶树响应干旱胁迫中发挥着重要的作用,此研究为茶树OSCA基因功能分析与茶树抗逆性研究提供参考。

Identification and Validation of Novel Biomarkers Related to the Calcium Metabolism Pathway in Hypertension Patients Based on Comprehensive Bioinformatics Methods

Background: Hypertension is a universal risk factor for cardiovascular diseases and is thus the leading cause of death worldwide. The identification of novel prognostic and pathogenesis biomarkers plays a key role in disease management. Methods: The GSE145854 and GSE164494 datasets were downloaded from the Gene Expression Omnibus (GEO) database and used for screening and validating hypertension signature genes, respectively. Gene Ontology (GO) enrichment analysis was performed on the differentially expressed genes (DEGs) related to calcium ion metabolism in patients with hypertension. The core genes related to immune infiltration were analyzed and screened, and the activity of the signature genes and related pathways was quantified using gene set enrichment analysis (GSEA). The infiltration of immune cells in the blood samples was analyzed, and the DEGs that were abnormally expressed in the clinical blood samples of patients with hypertension were verified via RT-qPCR. Results: A total of 176 DEGs were screened. GO showed that DEGs was involved in the regulation of calcium ion metabolism in biological processes (BP), actin mediated cell contraction, negative regulation of cell movement, and calcium ion transmembrane transport, and in the regulation of protease activity in molecular functions (MF). KEGG analysis revealed that the DEGs were involved mainly in the cGMP-PKG signaling pathway, ubiquitin-protein transferase, tight junction-associated proteins, and the regulation of myocardial cells. MF analysis revealed the immune infiltration function of the cells. RT-qPCR revealed that the expression of Cacna1d, Serpine1, Slc8a3, and Trpc4 was up regulated in hypertension, the expression of Myoz2 and Slc25a23 was down regulated. Conclusion: Cacna1d, Serpine1, Slc8a3, Trpc4, Myoz2 and Slc25a23 may be involved in the regulation of calcium metabolism pathways and play key roles in hypertension. These differentially expressed calcium metabolism-related genes may serve as prognostic markers of hypertension.

基于CA-Markov模型的土壤侵蚀预测分析——以宜昌市为例

本研究介绍了CA-Markov模型预测土地侵蚀的方法,分析了宜昌市2000年、2010年和2020年三期的土壤侵蚀强度空间分布数据,提出以这三期数据作为预测基准年,利用CA-Markov模型进行精度验证后来预测宜昌市2030年土壤侵蚀情况,并在此基础上分析宜昌市2030年土壤侵蚀强度空间分布及土壤侵蚀发展趋势。结果表明:宜昌市2030年土壤侵蚀情势总体向好发展,水土流失面积与强度呈现“双下降”态势,水土流失面积下降至2493.6km~2;秭归县、当阳市、兴山县等区域土壤侵蚀情势仍然不容乐观,高强度侵蚀面积出现净增长,仍是未来水土保持的工作重点。

基于PCA分析的村落建筑形态特征解构与保护策略研究

通过主成分分析(PCA)对村落建筑群的点云数据进行分析,揭示了村落建筑物形态的主要几何特征差异,并据此制定保护策略。这一方法为村落建筑的保护提供了科学依据,优先保护具有独特文化价值的建筑物。研究结合了点云数据与PCA技术,为建筑保护提供了定量化分析工具。