2021年老挝M_(S)6.0地震序列研究

云南地震活动与周边强震存在“构造相连,动力同源”的特征,研究周边强震的序列演化特征及发震构造,对云南地区地震研究具有重要意义。2021年12月24日老挝M_(S)6.0地震发生在滇西南地区的NW向整董断裂附近,震源机制解显示,此次地震是一次走滑型破裂事件,破裂方向与区域构造特征一致。老挝M_(S)6.0地震序列属于前震-主震-余震型序列,主震前震中附近出现3~4级地震非常活跃的现象,前震序列参数计算显示b值波动相对幅度较大,h值出现“上翘”形态,而余震序列b值和h值变化均相对平稳,主震的同震库伦应力结果表明老挝地震可能对云南地区有应力加载作用。

基于SAO-VMD-S的双端柔性直流输电故障测距方案

针对柔性直流输电线路故障定位过程中信号易受噪声干扰、耐过渡电阻能力差的问题,提出了采用小波变换(wavelet transform,WT)进行消噪处理、并结合变分模态分解(variational mode decomposition,VMD)的柔性直流输电线路故障定位方案。首先利用基于Logistic函数的循环位移小波阈值去噪对故障信号进行处理。然后采用雪消融优化器(snow ablation optimizer,SAO)结合VMD对信号进行有效分解。最后对分解后的高频分量进行S变换(S-transform,ST),选取对应频率下的幅值曲线进行波头标定。此外,提出了一种不依赖波速的测距算法。在PSCAD/EMTDC平台中搭建双端柔性直流系统并进行仿真验证。结果表明,所提方案不仅对采样率要求低,且能耐受300Ω的过渡电阻和30 dB的噪声,在不同故障距离下均能准确进行测距。

遗传性蛋白S缺乏症致兄弟二人患肺栓塞报告

肺栓塞(pulmonary embolism, PE)是临床上常见的急危重症之一,具有致死性,由于症状及体征均不典型,常常导致漏诊、误诊。青少年肺栓塞性相对少见,更容易被临床医师忽视。但该类患者可能发病年龄早,应注重其求因的检查尤其遗传缺陷。本文通过报道兄弟患肺栓塞病例,最终确诊为遗传性蛋白S缺乏症,且为少见的复杂杂合变异病例,而提高临床医师对于青少年PE的诊治水平。

Leveraging User-Generated Comments and Fused BiLSTM Models to Detect and Predict Issues with Mobile Apps

In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobileapps. The use of these apps eases our daily lives, and all customers who need any type of service can accessit easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digitalservices to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services,particularly during two large occasions, Umrah and Hajj. However, pilgrims encounter mobile app issues such asslowness, conflict, unreliability, or user-unfriendliness. Pilgrims comment on these issues on mobile app platformsthrough reviews of their experiences with these digital services. Scholars have made several attempts to solve suchmobile issues by reporting bugs or non-functional requirements by utilizing user comments.However, solving suchissues is a great challenge, and the issues still exist. Therefore, this study aims to propose a hybrid deep learningmodel to classify and predict mobile app software issues encountered by millions of pilgrims during the Hajj andUmrah periods from the user perspective. Firstly, a dataset was constructed using user-generated comments fromrelevant mobile apps using natural language processing methods, including information extraction, the annotationprocess, and pre-processing steps, considering a multi-class classification problem. Then, several experimentswere conducted using common machine learning classifiers, Artificial Neural Networks (ANN), Long Short-TermMemory (LSTM), and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) architectures, toexamine the performance of the proposed model. Results show 96% in F1-score and accuracy, and the proposedmodel outperformed the mentioned models.

Channel Correlation Based User Grouping Algorithm for Nonlinear Precoding Satellite Communication System

Low Earth Orbit(LEO)multibeam satellites will be widely used in the next generation of satellite communication systems,whose inter-beam interference will inevitably limit the performance of the whole system.Nonlinear precoding such as Tomlinson-Harashima precoding(THP)algorithm has been proved to be a promising technology to solve this problem,which has smaller noise amplification effect compared with linear precoding.However,the similarity of different user channels(defined as channel correlation)will degrade the performance of THP algorithm.In this paper,we qualitatively analyze the inter-beam interference in the whole process of LEO satellite over a specific coverage area,and the impact of channel correlation on Signal-to-Noise Ratio(SNR)of receivers when THP is applied.One user grouping algorithm is proposed based on the analysis of channel correlation,which could decrease the number of users with high channel correlation in each precoding group,thus improve the performance of THP.Furthermore,our algorithm is designed under the premise of co-frequency deployment and orthogonal frequency division multiplexing(OFDM),which leads to more users under severe inter-beam interference compared to the existing research on geostationary orbit satellites broadcasting systems.Simulation results show that the proposed user grouping algorithm possesses higher channel capacity and better bit error rate(BER)performance in high SNR conditions relative to existing works.

双相不锈钢S32001冷弯卷边角钢弯扭屈曲承载力设计方法

深入分析双相不锈钢S32001冷弯卷边角钢悬臂构件的弯扭屈曲性能,并提出可靠的设计方法,实现此类构件作为地铁疏散平台支架的大规模应用。对双相不锈钢S32001冷弯卷边角钢悬臂结构展开了试验研究与有限元分析,研究了角钢卷边宽厚比对角钢悬臂结构弯扭屈曲性能的影响,并基于弹性屈曲理论建立了冷弯卷边角钢弯扭屈曲承载力计算方法。截面肢长相同时,卷边角钢截面构件的极限承载力较普通角钢截面构件可提高70%~145%。随卷边宽厚比a/t增大,卷边角钢构件抗弯承载力比值M n/M y呈上升趋势。采用所建立的计算方法得到的预测结果与有限元分析结果吻合良好。双相不锈钢卷边角钢悬臂构件具备出色的弯扭屈曲承载能力,在工程结构中具有广阔的应用前景。

基于LiteTS-YOLO的交通标志检测

针对交通标志检测精度低、漏检误检率高及传统模型体积大的问题,提出LiteTS YOLO算法。通过构建C_(2)f_FA模块,结合FasterNet优化参数量与计算复杂度,并引入高效多尺度注意力(EMA)机制以保留小目标特征;重新设计特征提取与融合网络,优化检测层架构以减少参数量并增强信息整合能力;设计SAPD Head检测头,集成高级任务分解与动态对齐机制,有效降低误检与漏检率,同时进一步减少参数量。实验结果显示,LiteTS-YOLO在自制TTT100K数据集上的m AP@0.5提升7.9%,参数量减少66.4%,模型大小减小65%,在检测精度与轻量化方面均实现显著改进。

2021年9月16日四川泸县M_(S)6.0地震前地磁扰动异常特征分析

选取四川地区3个地磁扰动台站的观测数据,利用基于脉冲幅度法的地磁垂直强度极化方法,对2021年9月16日四川泸县M_(S)6.0地震前的地磁扰动异常演化特征进行分析。研究结果表明:(1)在中强地震前,台站的垂直强度极化值和超阈值极化值频次逐日变化均会出现高值异常,其中频次异常呈现先增强后降低,再增强-降低的形态;(2)发生异常变化的多个台站距离震中300 km范围内,时间上属准同步变化;(3)地震多发生在异常出现的一个月后,频次异常值往往是研究时段的最高值;(4)机理上,这些异常演化形态和特征是由岩石的破裂特性决定的,一定程度上反映了地震的孕育过程;(5)这种震前的地磁扰动异常与外源场无关。

AMachine Learning Approach to User Profiling for Data Annotation of Online Behavior

The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and motivations.Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation.The user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social platforms.However,a combination of multiple behaviors in profiling users has yet to be considered.This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition.This research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning.The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation.User feedback is based on online behavior and practices collected by using a survey method.The participants include both males and females from different occupation sectors and different ages.The data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics.Different techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136.Feature average is computed to identify user intent type characteristics.The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on their preferences in online content,platforms,criteria,and frequency.The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.

油田采出水中80S钢和N80钢的H_(2)S/CO_(2)电化学腐蚀行为研究

运用腐蚀失重和电化学测试技术研究80S钢和N80钢在含H 2S/CO_(2)的油田采出水中的电化学腐蚀行为。结果表明:在模拟CO_(2)/H_(2)S的腐蚀环境中,温度是影响腐蚀速率的一大因素,80S钢和N80钢的腐蚀速率随着温度的升高而增大。两种材料表面均发生均匀腐蚀,但N80钢表面的腐蚀程度更严重,主要是由于Cr元素可以显著提高腐蚀产物膜对金属的保护;两种材料的腐蚀电位均随着温度的升高而负移,腐蚀电流密度增大,在不同试验条件下80S钢的腐蚀电流密度均小于N80钢,表明80S钢比N80钢具有更好的抗腐蚀能力。两种材料在25℃时具有中高频区的容抗弧和低频区的感抗弧,在50℃和75℃时具有中高频区的容抗弧和低频区的Warburg阻抗,且Warburg阻抗随温度的升高表现出更明显的扩散特征。随着温度的升高,两种材料的电荷传递电阻R_(t)不断减小,Warburg阻抗的Z_(w)也减小,温度越高生成的腐蚀产物膜对金属表面的保护性能越好,80S钢比N80钢生成腐蚀产物膜的致密性更好。

大学生信息成瘾行为的触发路径与干预策略:基于S-O-R理论视角

[目的/意义]文章以大学生为研究对象,探究该群体信息成瘾行为的影响机制,为大学生信息成瘾行为的预防和管理提供价值参考。[方法/过程]引入刺激-机体-反应(S-O-R)理论,利用文献梳理总结大学生信息成瘾过程中的外部刺激,深度剖析机体在成瘾过程中产生的认知心理,构建大学生信息成瘾行为理论模型,运用结构方程模型实证分析大学生信息成瘾的触发路径。[结果/结论]研究结果表明,信息过载、间歇性奖励和行为管理会显著正向影响大学生的不确定性回避,进而导致大学生信息成瘾。同时,信息过载也会使大学生产生信息焦虑,信息焦虑显著正向影响大学生信息成瘾行为。面向高等院校、大学生群体和科技企业,提出针对性建议与对策,有助于完善高等院校管理体系,促进学生适应信息化环境,推动高等教育事业更好发展。

Hepatitis B Surface Antigen and Hepatitis C Virus Antibodies among Drug Users in Burkina Faso

Introduction: The epidemiology of both hepatitis B virus (HBV) and hepatitis C virus (HCV) infections among drug users (DUs) is little known in West Africa. The study aimed to assess the prevalence of hepatitis B and C viruses among drug users in Burkina Faso. Methodology: This was a cross-sectional biological and behavioral survey conducted between June and August 2022, among drug users in Ouagadougou and Bobo Dioulasso, the two main cities of Burkina Faso. A respondent-driven sampling (RDS) was used to recruit drug users. Hepatitis B surface antigen was determined using lateral flow rapid test kits and antibodies to hepatitis C virus in serum determined using an Enzyme-Linked Immunosorbent Assay. Data were entered and analyzed using Stata 17 software. Weighted binary logistic regression was used to identify the associated factors of hepatitis B and C infections and a p-value Results: A total of 323 drug users were recruited with 97.5% males. The mean age was 32.7 years old. The inhaled or smoked mode was the most used by drug users. The adjusted hepatitis B and hepatitis C prevalence among study participants were 11.1% and 2.3% respectively. The marital status (p = 0.001), and the nationality (p = 0.011) were significantly associated with hepatitis B infection. The type of drug used was not significantly associated with hepatitis B infection or hepatitis C infection. Conclusion: The prevalence of HBsAg and anti-HCV antibodies among DUs are comparable to those reported in the general population in Burkina Faso. This result suggests that the main routes of contamination by HBV and HCV among DUs are similar to those in the population, and could be explained by the low use of the injectable route by DUs in Burkina Faso.

User Profile & Attitude Analysis Based on Unstructured Social Media and Online Activity

As social media and online activity continue to pervade all age groups, it serves as a crucial platform for sharing personal experiences and opinions as well as information about attitudes and preferences for certain interests or purchases. This generates a wealth of behavioral data, which, while invaluable to businesses, researchers, policymakers, and the cybersecurity sector, presents significant challenges due to its unstructured nature. Existing tools for analyzing this data often lack the capability to effectively retrieve and process it comprehensively. This paper addresses the need for an advanced analytical tool that ethically and legally collects and analyzes social media data and online activity logs, constructing detailed and structured user profiles. It reviews current solutions, highlights their limitations, and introduces a new approach, the Advanced Social Analyzer (ASAN), that bridges these gaps. The proposed solutions technical aspects, implementation, and evaluation are discussed, with results compared to existing methodologies. The paper concludes by suggesting future research directions to further enhance the utility and effectiveness of social media data analysis.

Learning Dual-Layer User Representation for Enhanced Item Recommendation

User representation learning is crucial for capturing different user preferences,but it is also critical challenging because user intentions are latent and dispersed in complex and different patterns of user-generated data,and thus cannot be measured directly.Text-based data models can learn user representations by mining latent semantics,which is beneficial to enhancing the semantic function of user representations.However,these technologies only extract common features in historical records and cannot represent changes in user intentions.However,sequential feature can express the user’s interests and intentions that change time by time.But the sequential recommendation results based on the user representation of the item lack the interpretability of preference factors.To address these issues,we propose in this paper a novel model with Dual-Layer User Representation,named DLUR,where the user’s intention is learned based on two different layer representations.Specifically,the latent semantic layer adds an interactive layer based on Transformer to extract keywords and key sentences in the text and serve as a basis for interpretation.The sequence layer uses the Transformer model to encode the user’s preference intention to clarify changes in the user’s intention.Therefore,this dual-layer user mode is more comprehensive than a single text mode or sequence mode and can effectually improve the performance of recommendations.Our extensive experiments on five benchmark datasets demonstrate DLUR’s performance over state-of-the-art recommendation models.In addition,DLUR’s ability to explain recommendation results is also demonstrated through some specific cases.

User Preference Aware Hierarchical Edge-User Cooperative Caching Strategy

The emergence of various new services has posed a huge challenge to the existing network architecture.To improve the network delay and backhaul pressure,caching popular contents at the edge of network has been considered as a feasible scheme.However,how to efficiently utilize the limited caching resources to cache diverse contents has been confirmed as a tough problem in the past decade.In this paper,considering the time-varying user requests and the heterogeneous content sizes,a user preference aware hierarchical cooperative caching strategy in edge-user caching architecture is proposed.We divide the caching strategy into three phases,that is,the content placement,the content delivery and the content update.In the content placement phase,a cooperative content placement algorithm for local content popularity is designed to cache contents proactively.In the content delivery phase,a cooperative delivery algorithm is proposed to deliver the cached contents.In the content update phase,a content update algorithm is proposed according to the popularity of the contents.Finally,the proposed caching strategy is validated using the MovieLens dataset,and the results reveal that the proposed strategy improves the delay performance by at least 35.3%compared with the other three benchmark strategies.

Integrating Neighborhood Geographic Distribution and Social Structure Influence for Social Media User Geolocation

Geolocating social media users aims to discover the real geographical locations of users from their publicly available data,which can support online location-based applications such as disaster alerts and local content recommen-dations.Social relationship-based methods represent a classical approach for geolocating social media.However,geographically proximate relationships are sparse and challenging to discern within social networks,thereby affecting the accuracy of user geolocation.To address this challenge,we propose user geolocation methods that integrate neighborhood geographical distribution and social structure influence(NGSI)to improve geolocation accuracy.Firstly,we propose a method for evaluating the homophily of locations based on the k-order neighbor-hood geographic distribution(k-NGD)similarity among users.There are notable differences in the distribution of k-NGD similarity between location-proximate and non-location-proximate users.Exploiting this distinction,we filter out non-location-proximate social relationships to enhance location homophily in the social network.To better utilize the location-proximate relationships in social networks,we propose a graph neural network algorithm based on the social structure influence.The algorithm enables us to perform a weighted aggregation of the information of users’multi-hop neighborhood,thereby mitigating the over-smoothing problem of user features and improving user geolocation performance.Experimental results on real social media dataset demonstrate that the neighborhood geographical distribution similarity metric can effectively filter out non-location-proximate social relationships.Moreover,compared with 7 existing social relationship-based user positioning methods,our proposed method can achieve multi-granularity user geolocation and improve the accuracy by 4.84%to 13.28%.

Deep Learning Social Network Access Control Model Based on User Preferences

A deep learning access controlmodel based on user preferences is proposed to address the issue of personal privacy leakage in social networks.Firstly,socialusers andsocialdata entities are extractedfromthe social networkandused to construct homogeneous and heterogeneous graphs.Secondly,a graph neural networkmodel is designed based on user daily social behavior and daily social data to simulate the dissemination and changes of user social preferences and user personal preferences in the social network.Then,high-order neighbor nodes,hidden neighbor nodes,displayed neighbor nodes,and social data nodes are used to update user nodes to expand the depth and breadth of user preferences.Finally,a multi-layer attention network is used to classify user nodes in the homogeneous graph into two classes:allow access and deny access.The fine-grained access control problem in social networks is transformed into a node classification problem in a graph neural network.The model is validated using a dataset and compared with other methods without losing generality.The model improved accuracy by 2.18%compared to the baseline method GraphSAGE,and improved F1 score by 1.45%compared to the baseline method,verifying the effectiveness of the model.

Research on Collaborative Filtering Recommendation Algorithm Based on Improved User Portraits

With the arrival of the big data era,the phenomenon of information overload is becoming increasingly severe.In response to the common issue of sparse user rating matrices in recommendation systems,a collaborative filtering recommendation algorithm was proposed based on improved user profiles in this study.Firstly,a profile labeling system was constructed based on user characteristics.This study proposed that user profile labels should be created using basic user information and basic item information,in order to construct multidimensional user profiles.TF-IDF algorithm was used to determine the weights of user-item feature labels.Secondly,user similarity was calculated by weighting both profile-based collaborative filtering and user-based collaborative filtering algorithms,and the final user similarity was obtained by harmonizing these weights.Finally,personalized recommendations were generated using Top-N method.Validation with the MovieLens-1M dataset revealed that this algorithm enhances both recommendation Precision and Recall compared to single-method approaches(recommendation algorithm based on user portrait and user-based collaborative filtering algorithm).

Efficient User Identity Linkage Based on Aligned Multimodal Features and Temporal Correlation

User identity linkage(UIL)refers to identifying user accounts belonging to the same identity across different social media platforms.Most of the current research is based on text analysis,which fails to fully explore the rich image resources generated by users,and the existing attempts touch on the multimodal domain,but still face the challenge of semantic differences between text and images.Given this,we investigate the UIL task across different social media platforms based on multimodal user-generated contents(UGCs).We innovatively introduce the efficient user identity linkage via aligned multi-modal features and temporal correlation(EUIL)approach.The method first generates captions for user-posted images with the BLIP model,alleviating the problem of missing textual information.Subsequently,we extract aligned text and image features with the CLIP model,which closely aligns the two modalities and significantly reduces the semantic gap.Accordingly,we construct a set of adapter modules to integrate the multimodal features.Furthermore,we design a temporal weight assignment mechanism to incorporate the temporal dimension of user behavior.We evaluate the proposed scheme on the real-world social dataset TWIN,and the results show that our method reaches 86.39%accuracy,which demonstrates the excellence in handling multimodal data,and provides strong algorithmic support for UIL.

How to implement a knowledge graph completeness assessment with the guidance of user requirements

In the context of big data, many large-scale knowledge graphs have emerged to effectively organize the explosive growth of web data on the Internet. To select suitable knowledge graphs for use from many knowledge graphs, quality assessment is particularly important. As an important thing of quality assessment, completeness assessment generally refers to the ratio of the current data volume to the total data volume.When evaluating the completeness of a knowledge graph, it is often necessary to refine the completeness dimension by setting different completeness metrics to produce more complete and understandable evaluation results for the knowledge graph.However, lack of awareness of requirements is the most problematic quality issue. In the actual evaluation process, the existing completeness metrics need to consider the actual application. Therefore, to accurately recommend suitable knowledge graphs to many users, it is particularly important to develop relevant measurement metrics and formulate measurement schemes for completeness. In this paper, we will first clarify the concept of completeness, establish each metric of completeness, and finally design a measurement proposal for the completeness of knowledge graphs.