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.

Postpartum Intrauterine Device: Use and Follow-Up of Users in the Maternity Ward of the Ratoma Communal Medical Center in Conakry, Guinea

Objective: The aim of this study was to describe the use of the postpartum intrauterine device in the maternity ward of the Ratoma communal medical center in Conakry. Methods: This was a descriptive cross-sectional study carried out between July 1st 2015 and June 30 2016, i.e. a duration of one year. Results: A total of 551 patients received advice on various contraceptive methods. Most of this advice was given in the post-partum period (40.2%) and during antenatal care (39.1%). Of the patients advised, 87 (15.8%) used the intrauterine device. The majority of users (93%) were married and uneducated (63.2%), and 39.1% were poor. The majority (56.3%) of intra-uterine devices were inserted in the immediate post-partum period. The majority of women had no adverse events either during the first six weeks (n = 57;65.5%) or at 3rd months (n = 75;86.2%) or 6th months (n = 76;87.4%) after IUD insertion. Most users remained complication-free throughout the follow-up period (n = 76;87.4% at 6th weeks and 3rd months, and n = 77;88.5% at 6th months). The continuation rate was 89.7% at 6 weeks and 3rd months, and 87.4% at 6th months after insertion. The majority of users (87.0%) were satisfied with the care they received. Conclusion: This study showed very few complications among intrauterine device users, and high continuation and satisfaction rates. The intrauterine device is a long-acting, effective, reversible and safe contraceptive that can be used by most women for birth spacing in Guinea, where women do not regularly visit health facilities.

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.

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.

Cortical hem signaling center: functions, development, and potential implications for evolution and brain disorders

Development of the telencephalon relies upon several signaling centers-localized cellular populations that supply secreted factors to pattern the cortical neuroepithelium.One such signaling center is the cortical hem,which arises during embryonic development at the telencephalic dorsal midline,adjacent to the choroid plexus and hippocampal primordium(Figure 1A).While the cortical hem has also been described in reptiles and birds,most of our knowledge about the developmental roles of the cortical hem is derived from the analysis in mice.The cortical hem produces several types of secreted molecules,including wingless-related integration site(Wnt)and bone morphogenetic(Bmp)proteins.The cortical hem is particularly important for the development of the hippocampus,which is involved in learning and memory,and the neocortex,which is the most complex brain region that mediates multiple types of behavior and higher cognitive functions(Mangale et al.,2008;Dal-Valle-Anton and Borrell,2022).

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.

Preparing highly entangled states of nanodiamond rotation and NV center spin

A nanodiamond with an embedded nitrogen-vacancy(NV)center is one of the experimental systems that can be coherently manipulated within current technologies.Entanglement between NV center electron spin and mechanical rotation of the nanodiamond plays a fundamental role in building a quantum network connecting these microscopic and mesoscopic degrees of motions.Here we present a protocol to asymptotically prepare a highly entangled state of the total quantum angular momentum and electron spin by adiabatically boosting the external magnetic field.

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.

Prevalence of Enteric Pathogens Associated with Infections among Table Egg Consumers in Some Primary Health Establishments in the Center Region of Cameroon

Method: In Cameroon limited data are available regarding the prevalence of enteric bacteria associated with table egg consuming infections. As such, a situational-based study was performed in patients with complains of stomach disorders after egg consumption. Data related to sociodemographic characteristics and other factors were collected using a structured based questionnaire. Stool culture of utmost importance in stomach disorders patients and serum were collected for typhoid serological test. Results: A total of 207 participants took part in the survey, Results indicated nontyphoidal Salmonella infections were highest in the 3 areas of study with Mfoundi (73.44%) having the highest level of infection compared to other bacterial infection. other enteric bacteria associated to this infection were E. coli serotype 157, Aeromonas, Citrobacter freundii, Enterobacter cloaca and typhi salmonella. Meanwhile salmonelosis caused by typhic salmonella had highest prevalence in the Lekie Division (13.11%) as a result of poor hygienic practices associated with the conservation and preparation of eggs, Stool culture was observed to detect more positive cases in the diagnosis of typhoid fever than Widal test, but with no statistically significant (p > 0.05) difference between the stool culture and Widal test in the 3 areas of study. Conclusion: this study revealed that egg consumers are pruned to enteric bacterial and salmonella infections depending on how and where egg is consumed.

The Peripheral Hospital as Focal Point for Pacemaker Activity: Review of the Last 300 Implantations Carried out at the Haute Correze Hospital Center

Background: Pacemaker implantation is a very old activity which has revolutionized the cardiology practice throughout the world. This activity is effective at the Haute Correze Hospital Center since more than 20 years. Due to progress in this area, and the increasing request within this center located at the outskirts of town, we set out to evaluate our pacemaker activity in general and more specifically to assess the post-procedural complications in our series patients. Methodology: This was a retrospective longitudinal study. Data were recorded for period of 90 months from 27/05/2016 to 19/11/2023. This data collection was possible via a specific register completed by computerized patient data from the SillageTM software. All files of patients implanted with single or dual chamber pacemakers were included, generator replacements, upgrading procedures and addition of leads were excluded. The sampling was non-probabilistic, consecutive and non-exhaustive. Statistical analysis was carried out using the Excel 2019 spreadsheet and SPSS version 23 software. The quantitative variables were presented as mean ± standard deviation, the qualitative data as proportions. Results: A total of 303 first-time pacemaker’s implantations were carried out during the study period (rate of 40 per year). The average age in the population was 79.7 ± 9.4 years (44 - 99 years) with a male predominance of 63.7% (n = 193). Atrioventricular block (2nd and 3rd degree) was the main indication for pacemaker implantation in 42.9% of cases (n = 130). Patients were most often implanted with a dual-chamber pacemaker (57.7%, n = 175). The approach was most often cephalic in 72.6% of cases (n = 220), followed by the subclavian access in 27.4% of cases (n = 84). The average fluoroscopy time was 7.9 min ± 2.4 (1 - 43). The average irradiation dose in gray/cm2 was 12.4 ± 9.3 (0.22 - 117.5). The average length of hospitalization was 7 ± 4 (2 - 26) days. The overall complication rate at one year was 12.9% (n = 39). These complications are distributed as follows: Leads dislodgement in 8.2% (n = 25), hematoma 3.6% (n = 11) all without clinical consequences, pneumothorax 0.7% (n = 2), both cases of pneumothorax did not require specific care, infection (superficial) in 0.3% (n = 1). Leads dislodgement occurred after a median time of 18 days (IQR: 3 - 36). The earliest dislodgement was observed on D0 and the latest on D207. No serious complications were recorded. The average atrial threshold at implantation/first control/last follow-up was 0.7/1.3/0.8 V, respectively. The average ventricular threshold at implantation/first control/last follow-up was 0.5/1.08/0.87 V, respectively. The average atrial detection at implantation/first control/last follow-up was 3.2/2.3/ 2.05 mv, respectively. The average ventricular detection at implantation/first control/last follow-up was 10.3/11.03/10.8 mv. The average atrial impedance at implantation/first control/last follow-up was 610/457/457 ohms. The average ventricular impedance at implantation/first control/last follow-up was 754/547/563 ohms. Conclusion: Pacemaker implantation is safe at the Haute Correze Hospital Center with a relatively low rate of complications, in this case an almost zero major infection and no serious hematoma. The peripheral hospital should remain a focal point of this activity in order to respond more quickly to the needs of the populations.

Comment on“Robotic surgery and liver transplantation:A single-center experience of 501 robotic donor hepatectomies”

To the Editor:We read with great interest the article by Schulze et al.entitled“Robotic surgery and liver transplantation:A single-center experience of 501 robotic donor hepatectomies”[1].It is the first single-center report including over 500 fully robotic donor hepatectomies.For the donors,the overall complication rate was 6.4%(n=32).Postoperative self-limiting bleeding(0.4%)and bile leakage from the resection plane(1.8%)were rare.

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.

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%.

Cloud Datacenter Selection Using Service Broker Policies:A Survey

Amid the landscape of Cloud Computing(CC),the Cloud Datacenter(DC)stands as a conglomerate of physical servers,whose performance can be hindered by bottlenecks within the realm of proliferating CC services.A linchpin in CC’s performance,the Cloud Service Broker(CSB),orchestrates DC selection.Failure to adroitly route user requests with suitable DCs transforms the CSB into a bottleneck,endangering service quality.To tackle this,deploying an efficient CSB policy becomes imperative,optimizing DC selection to meet stringent Qualityof-Service(QoS)demands.Amidst numerous CSB policies,their implementation grapples with challenges like costs and availability.This article undertakes a holistic review of diverse CSB policies,concurrently surveying the predicaments confronted by current policies.The foremost objective is to pinpoint research gaps and remedies to invigorate future policy development.Additionally,it extensively clarifies various DC selection methodologies employed in CC,enriching practitioners and researchers alike.Employing synthetic analysis,the article systematically assesses and compares myriad DC selection techniques.These analytical insights equip decision-makers with a pragmatic framework to discern the apt technique for their needs.In summation,this discourse resoundingly underscores the paramount importance of adept CSB policies in DC selection,highlighting the imperative role of efficient CSB policies in optimizing CC performance.By emphasizing the significance of these policies and their modeling implications,the article contributes to both the general modeling discourse and its practical applications in the CC domain.

Dynamic Routing of Multiple QoS-Required Flows in Cloud-Edge Autonomous Multi-Domain Data Center Networks

The 6th generation mobile networks(6G)network is a kind of multi-network interconnection and multi-scenario coexistence network,where multiple network domains break the original fixed boundaries to form connections and convergence.In this paper,with the optimization objective of maximizing network utility while ensuring flows performance-centric weighted fairness,this paper designs a reinforcement learning-based cloud-edge autonomous multi-domain data center network architecture that achieves single-domain autonomy and multi-domain collaboration.Due to the conflict between the utility of different flows,the bandwidth fairness allocation problem for various types of flows is formulated by considering different defined reward functions.Regarding the tradeoff between fairness and utility,this paper deals with the corresponding reward functions for the cases where the flows undergo abrupt changes and smooth changes in the flows.In addition,to accommodate the Quality of Service(QoS)requirements for multiple types of flows,this paper proposes a multi-domain autonomous routing algorithm called LSTM+MADDPG.Introducing a Long Short-Term Memory(LSTM)layer in the actor and critic networks,more information about temporal continuity is added,further enhancing the adaptive ability changes in the dynamic network environment.The LSTM+MADDPG algorithm is compared with the latest reinforcement learning algorithm by conducting experiments on real network topology and traffic traces,and the experimental results show that LSTM+MADDPG improves the delay convergence speed by 14.6%and delays the start moment of packet loss by 18.2%compared with other algorithms.

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.

Management of Acute Malnutrition in Children Aged 0 to 24 Months Boulbinet Health Center

Introduction: Malnutrition is a pathological state resulting from the relative deficiency or excess of one or more essential nutrients, whether manifested clinically or detected only by biochemical, anthropometric or physiological analyses. The overall objective was to assess the quality of management of acute malnutrition in children aged 0 - 24 months at the Boulbinet health center. Methodology: This was a prospective descriptive study lasting six (06) months from May 5 to October 5, 2018. The study included all children aged 0 to 24 months. Results: Acute malnutrition in children aged 0 - 24 months accounted for 2.11% of cases. The sex ratio was 1.41 in favor of males. The mean age of our patients was 5 months 7 days, with extremes of 1 month and 6 months. The majority came from Ra toma (40.24%). Exclusive breastfeeding was most common (54.02%). The main clinical signs were: pallor 49.42%, diarrhea 46.67, oral lesions37.96%. SAM represented 89.66% and MAM 10.34%. Most associated pathologies: anemia 49.42% and oral candidiasis 37.93%. In terms of outcome, we recorded 56.32% cures, 20.69% deaths, 18.39% dropouts and 4.60% cures. Conclusion: Improving the quality of care for malnourished children aged 0 - 24 months requires raising awareness among mothers and the general public of the consequences of malnutrition.

Introducing hydroxyl groups to tailor the d-band center of Ir atom through side anchoring for boosted ORR and HER

Tuning the coordination atoms of central metal is an effective means to improve the electrocatalytic activity of atomic catalysts.Herein,iridium(Ir) is proposed to be asymmetrically anchored by sp-N and pyridinic N of hydrogen-substituted graphdiyne(HsGDY),and coordinated with OH as an Ir atomic catalyst(Ir_(1)-N-HsGDY).The electron structures,especially the d-band center of Ir atom,are optimized by these specific coordination atoms.Thus,the as-synthesized Ir_(1)-N-HsGDY exhibits excellent electrocatalytic performances for oxygen reduction and hydrogen evolution reactions in both acidic and alkaline media.Benefiting from the unique structure of HsGDY,IrN_(2)(OH)_(3) has been developed and demonstrated to act as the active site in these electrochemical reactions.All those indicate the fresh role of the sp-N in graphdiyne in producing a new anchor way and contributing to promote the electrocatalytic activity,showing a new strategy to design novel electrochemical catalysts.

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.