Multi-platform Network Research Articles

Marine Online Platforms of Services to Public End-Users—The Innovation of the ODYSSEA Project

Recently, various Earth Observation Networks (EONs) have been designed, developed and launched by in-situ, on-site and off-site collected data from fixed and moving marine sensors and remote sensing (RS) satellite data. This information can significantly help a wide range of public and private end-users better understand the medium- and high-resolution numerical models for regional, national and global coverage. In this context, such EON core services’ operational numerical data can be seen of the growing demand result for marine sustainability development of developing countries and the European Union (EU). In this case, marine platforms can offer a wide range of benefits to users of human communities in the same environment using meticulous analyses. Furthermore, marine platforms can contribute to a deeper discourse on the ocean, given the required regulations, technical and legal considerations and users to a common typology using clear scientific terminology. In this regard, firstly, the following six steps have been used to develop a better understanding of the essential data structure that is commensurate with the efficiency of the marine end-user’s service: (1) steps and challenges of collecting data, (2) stakeholder engagement to identify, detect and assess the specific needs of end-users, (3) design, develop and launch the products offered to meet the specific needs of users, (4) achieve sustainable development in the continuous provision of these products to end-users, (5) identify future needs and challenges, and (6) online platform architecture style related to providing these products to end-users. Secondly, the innovation of the ODYSSEA (Operating a Network of Integrated Observatory Systems in the Mediterranean Sea) platform project has been evaluated and reviewed as a successful project on marine online platforms to better understand how marine online platforms are being used, designed, developed and launched. The ODYSSEA platform provides a system that bridges the gap between operational oceanographic capabilities and the need for information on marine conditions, including for the end-user community. The project aims to develop a fully integrated and cost-effective cross-platform, multi-platform network of observation and forecasting systems across the Mediterranean Sea.

Design and Implementation of Data Analysis System of Social Network

To solve the problem that data collection efficiency of social network analysis technology based on single-mode network structure is low and cannot meet the problem of data information processing of large-scale social network, the social network data analysis system based on the network node centrality theory is proposed. The naive Bayesian classification algorithm is used to improve the traditional data mining mode. The traditional social network data analysis technology is compared with the social network data analysis system proposed in this research from four aspects: multi-platform network data processing response time, data acquisition and processing accuracy, social network data feature condition introduction rate, and data feature condition introduction accuracy. The accuracy of data collection and processing in the traditional social network data analysis system is 89.26%, the response time of multi-platform network data processing is 7.32s, the introduction rate of data feature conditions is 82.65%, and the accuracy rate of data feature conditions is 78.88%. The data processing accuracy of the data analysis system proposed in this research based on the network node centrality theory is 98.99%, the response time of multi-platform network data processing is 1.35s, the data feature condition introduction rate is 97.91%, and the data feature condition introduction accuracy rate is 97.39%. The social network data analysis system proposed in this research is significantly better than the traditional social network data analysis system in all aspects, and its data processing performance is very good especially in multi-platform social network. The results show that the social network data analysis system based on network centrality theory proposed in this research is feasible and can meet the daily application and research requirements of social network data collection, storage, analysis and visual display, which has the advantages of fast data processing speed, high accuracy and wide range of data storage.

Abstract P009: A shared nearest neighbors approach for integrated, multi-platform networks and its application to the exploration of multiomics data from early-stage non-small cell lung cancers

Abstract Background: The ImmunogenomiC prOfiling of Non-small cell lung cancer (NSCLC) Project (ICON) represents an ambitious undertaking to comprehensively characterize immuno-genomic diversity in NSCLC across diverse platforms. The depth and breadth of this cohort presented a unique opportunity to develop a specialized method for multi-platform data integration and exploration, which can be broadly applied to forthcoming large-scale patient profiling studies. Such a holistic approach can unlock insights for therapeutic targets, biomarkers, and treatment plans by providing a more complete view of phenomena driving disease pathogenesis and evolution. Purpose: We developed a novel shared nearest neighbors (SNN) approach to create an integrated network of ICON’s multi-platform data and identified collections of closely related measurements within the resulting network tied to noteworthy patient characteristics, including recurrence and oncogenotype. Methods: The ICON dataset is derived from tumor and normal lung tissue samples collected from 150 patients at time of resection as well as blood samples collected then and at intervals during the year following. Tissue samples underwent RNA-sequencing (RNA-seq), whole exome sequencing, T-cell receptor sequencing, multiplex immunofluorescence for immune cells, and reverse phase protein array profiling; flow cytometry for immune cells was performed on tissue and blood samples. From these data, the ICON data network was built using an integrative approach based on the SNN algorithm in which genes were linked on the basis of their shared top correlates in orthogonal datasets. Results: The ICON data network currently includes over 20,000 genes linked by over 500,000 connections derived from correlations between RNA-seq and orthogonal platforms. We captured established associations between cancer-related genes and examined these along with new ones in the network. To do so, we used the InfoMap algorithm to extract more interpretable sub-networks, termed modules, from the ICON data network. Single sample gene set enrichment scores for each module were used in multivariate analysis to highlight modules linked to clinical characteristics of interest. As an example, we found modules significantly tied to disease recurrence. The most notable of these was strongly associated with metabolic pathways, and other modules associated with platelets and ion channels were also identified. The metabolic pathway module is being explored as a prognostic biomarker, underscoring the opportunites enabled by mining the network. Conclusions: Through the framework developed, we identified modules in the ICON data network significantly associated with important patient characteristics like recurrence and oncogenotype. We are validating the gene sets identified as potential biomarkers and are developing an interactive application to facilitate further mining of the network. Taken together, our SNN network-building approach enables the integration and exploration of patient data from diverse platforms. Citation Format: Stephanie T. Schmidt, Neal Akhave, Alexandre Reuben, Tina Cascone, Jianhua Zhang, Jun Li, Junya Fujimoto, Lauren A. Byers, Beatriz Sanchez-Espiridion, Lixia Diao, Jing Wang, Lorenzo Federico, Marie-Andree Forget, Daniel J McGrail, Annikka Weissferdt, Shiaw-Yih Lin, Younghee Lee, Natalie Vokes, Carmen Behrens, Ignacio I. Wistuba, Andrew Futreal, Ara Vaporciyan, Boris Sepesi, John V. Heymach, Chantale Bernatchez, Cara Haymaker, Jianjun Zhang, Christopher A. Bristow, Timothy P. Heffernan, Marcelo V. Negrao, Don L. Gibbons. A shared nearest neighbors approach for integrated, multi-platform networks and its application to the exploration of multiomics data from early-stage non-small cell lung cancers [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr P009.

IPv6 for the Evolution of the Network of the Special Astrophysical Observatory of the Russian Academy of Sciences

Abstract—Requirements to a modern computer network of a research organization, in particular, to the local-area network of the Special Astrophysical Observatory of the Russian Academy of Sciences (SAO RAS) are described. The choice of the direction of the development of primary tools for supporting the observing process and based on local network infrastructure services of SAO RAS is defined. This will make it easier to automate the administration of the network, prevent the effect of human factor on its reliability, and facilitate its maintenance. The low-rank structure of the network based on high-performance Ethernet commutators is justified. The viability of the use of the sixths-series IPv6 protocols instead of or in addition to IPv4 protocols for mostly unmanned network administration minimizing errors of manual mode. An evolution of a multiplatform network using promising IPv6-supported technologies ensuring implementation of a practically self-tuning configuration. The methods of a client-server implementation of the main network services of the observatory are described.

Performance Analysis Of Firewall As Virtualized Network Function On VMware ESXi Hypervisor

Virtualization technology is slowly being used to build network infrastructure called Network Function Virtualization (NFV). It takes network functions such as firewall, load balancer, IPS out of its hardware then use its software to be run on high specification server. It helps reduce vendor lock-in and help create a multiplatform network function environment. It has a lot of benefits compared to a traditional network. One of them is it can reduce the number of hardware that is used in the telecom industry. This technology runs on the hypervisor that is used for the management of hardware. One of the important components from NFV is Virtualized Network Function (VNF). In NFV, network devices are run on a server so that a firewall is needed because if an attack occurs on the network it will interfere with existing network components. This paper focuses on analyzing the performance of two firewall system, pfSense, and FortiGate. Both firewalls will run on the VMware ESXi hypervisor. It aims to determine the firewall performance comparison in normal conditions without attacks and under SYN DoS attacks. We also evaluate firewall failover capabilities. Based on the results of testing obtained that overall FortiGate has better performance. It has better ability in handling DoS SYN attack because it has lower throughput performance degradation and better FTP performance compare to pfSense. We conclude that FortiGate has best performance compare with pfSense

Maximising the Effectiveness of Multi-Platform Networks in Health and Development: A Case Study of Be-Cause Health

Background: Networks have become a widely-used approach to address the complexity of issues around international health and health equity, and many types of networks have been described. The added value of networking is not straightforward and it should be documented. Be-Cause Health (BCH) is a unique, multistakeholder network linking the Belgian actors of the health and development sector, creating a partnership to support their commitment to equitable health. Consequently, BCH has very broad objectives with creative and dynamic outcomes. The aim of this study was to explore if the structure and process of the BCH network could be adapted to improve its effectiveness. Methods: The overall framework of the project included a narrative literature review combined with qualitative semi-structured interviews. The literature review looked at the structure and process of networks and the means of evaluating network effectiveness. The results of this review were used to inform the interviews of key Be-Cause Health members. Results: The reviewed articles illustrate the diversity of networking activities and the complexity of their evaluation, suggesting the use of participative evaluation methods to capture the intangible outcomes as experienced by the members themselves. The interviewees reported that the major benefits of their BCH membership were information sharing and connecting with peers. They felt the growth of the BCH network was mostly related to its culture of trust among members, to the inspiring leadership, to the concrete activities it could organise, to its flexible structure tailored to their needs,and to the support of the secretariat and to the embeddedness of the network in a wider institution with secured public funding. They identified a need for improved internal and external communication and for more concrete objectives of the working groups. Conclusion: The BCH network is a unique response to the complex challenges of health equity and development. The outcomes of such complex networks cannot be measured with a simple quantitative method, and require participative methods of evaluation. There is no formula to predict the change of effectiveness that would result from a change of structure.

Prognostically Relevant Subtypes and Survival Prediction for Breast Cancer Based on Multimodal Genomics Data

Cancer is one of the deadliest diseases caused by abnormal behaviors of genes that control the cell division and growth. Genomics data and clinical outcomes from multiplatform and heterogeneous sources are used to make clinical decisions for the cancer patients, where both multimodality and heterogeneity impose significant challenges to bioinformatics tools and algorithms. Numerous works have been proposed to overcome these challenges by using sophisticated bioinformatics and machine learning algorithms as either primary or supporting tools. In this paper, we propose a new approach to analyze genomics data from The Cancer Genome Atlas (TCGA) to classify breast cancer patients based on their subtypes and survival rates. Since multiple factors such as estrogen receptor (ER), progesterone receptor (PGR), and human epidermal growth factor receptor 2 (HER2) statuses are involved in breast cancer diagnosis, we used DNA methylation, gene expression (GE), and miRNA expression data by creating a multiplatform network called Multimodal Autoencoders (MAE) classifier to support each data type. Experiment results demonstrate that our approach is promising with high confidence for predicting both breast cancer subtypes and survival rates. In particular, we achieved state-of-the-art results with accuracies of 91% and 86%, respectively for the ER and PGR-based subtype prediction and moderately low accuracy for the HER2-based subtype prediction as well as we perceived reasonably low MSE and positive coefficient of determination (R 2 ) scores in case of survival prediction. Additionally, we created unimodal and multimodal features from each input type and trained decision tree (DT), Naive Bayes (NB), K-nearest neighbors (KNN), logistic regression (LR), support vector machine (SVM), random forest (RF), and gradient boosting trees (GBT) as ML baseline models. Finally, we use the model averaging ensemble of top-3 models to report the final prediction.

Methods of organizing the use of technology development of multiplatform network systems

This article describes methods of organizing the use of technologies, which used in multi-platformnetwork systems development. Particular attention is paid to the reduction of data loading time fordynamic web applications. The approach of designing software code associated with caching interfacecomponents, organization accessing with client databases and transfer the accumulated informationfrom the client app to the server side. The proposed material is widely used in the development ofinformation systems. In practice, used in an adaptive learning systems and marketing researchanalysis systems

Vertical Handover Decision Algorithm Using Multicriteria Metrics in Heterogeneous Wireless Network

Heterogeneous network concept was introduced to satisfy the demands of network’s traffic capacity and data rate. It consists of multiplatform networks with various radio access technologies. Conventionally, a mobile user may roam and accomplish the vertical handover using single criteria, such as received signal strength (RSS). Single criteria vertical handover decision, however, may cause inefficient handoff, unbalanced network load, and service interruption. This paper proposed an improved vertical handover decision using multicriteria metrics in the environment of heterogeneous network consisting of three network interfaces: (i) wireless local area network (WLAN), (ii) wideband code division multiple access (WCDMA), and (iii) worldwide interoperability for microwave access (WiMAX). In the vertical handover decision, four metrics are considered: (i) RSS, (ii) mobile speed, (iii) traffic class, and (iv) network occupancy. There are three types of the vertical handover decision algorithm: (i) equal priority, (ii) mobile priority, and (iii) network priority. Equal priority multicriteria handover algorithm improved the number of handoffs by 46.60 while mobile priority multicriteria algorithm improved the number of handoffs by 90.41% and improved the balance index by 0.09%. Network priority multicriteria method improved the number of handoffs by 84.60%, balance index by 18.03%, and average blocking probability by 20.23%.

멀티 플랫폼 기반의 네트워크 패킷 스캐너 설계 및 구현

최근의 해킹 추세는 기업의 이윤과 관계되는 모든 IT 인프라를 대상으로 하고 있다. 이는 기존의 공격이 비서비스 포트를 통한 서비스 인프라로의 접근을 시도했다면, 현재는 기업이윤 창출의 원천인 서비스 자체를 공격하고 있다. 즉, 서비스에 직접적인 영향을 끼치고 있음에도 불구하고 기존의 보안 솔루션이나 체계로는 방어하기가 어렵고 동시에 이용자 보호, 지속 가능한 경영 자체를 위협하며 금전을 요구하는 협박의 형태로 점점 더 많은 기업에 위해를 가하고 있다. 본 논문에서는 트래픽량을 기준으로 정상, 비정상을 판단하는 예외처리기반 네트워크 침입탐지 시스템을 대상으로 멀티 플렛폼 기반의 네트워크 패킷 스캐너를 설계하고 구현하였다. Linux나 unix 환경에는 ngrep, snort, TCPdump와 같은 여러 가지 네트워크 침입탐지와 패킷 관리 도구들이 있지만 대부분 문자 방식 사용자 인터페이스(CUI : Character based User Interface)를 기반으로 구현되어 익숙하지 않은 사용자들에게는 불편함이 따른다. 제안된 시스템은 이러한 불편함을 개선하여 사용자에게 직관적이고 사용이 쉬운 인터페이스를 제공하기 위하여 그래픽 사용자 인터페이스(GUI : Graphical User Interface)기반으로 구현하였고, 모든 운영체제에서 구동될 수 있도록 멀티 플랫폼을 지원하는 Qt(C++)언어를 사용하여 설계 및 구현하였다. The recent trend of the hacking deals with all the IT infrastructure related to the profit of the companies. Presently, they attack the service itself, the source of the profit, while they tried to access to the service infrastructure through the non-service port in the past. Although they affect the service directly, it is difficult to block them with the old security solution or the old system and they threaten more and more companies with the demand of money menacing the protection of customers and the sustainable management. This paper aims to design and implement multi-platform network packet scanner targeting the exception handling network intrusion detection system which determines normal, abnormal by traffic. Linux and unix have the various network intrusion detection and packet management tools like ngrep, snort, TCPdump, but most of them are based on CUI (Character based User Interface) giving users discomfort who are not used to it. The proposed system is implemented based on GUI(Graphical User Interface) to support the intuitive and easy-to-use interface to users, and using Qt(c++) language that supports multi-platform to run on any operating system.

Study and implementation of software automatic testing system based on STAF

A software automatic testing system based on Software Testing Automation Framework(STAF) was designed and implemented to solve the problems in current automatic testing solutions.This system could be employed to manage the process of automatic testing,and support the execution and monitoring of automatic testing tasks in multi-platform network environment.The system was implemented to improve the degree of automation,enhance the efficiency of testing,shorten the testing period,and increase the trustworthiness of the testing results.