Genome-Wide Association Study of Clinical Outcome After Aneurysmal Subarachnoid Haemorrhage: Protocol

Aneurysmal subarachnoid haemorrhage (aSAH) results in persistent clinical deficits which prevent survivors from returning to normal daily functioning. Only a small fraction of the variation in clinical outcome following aSAH is explained by known clinical, demographic and imaging variables; meaning additional unknown factors must play a key role in clinical outcome. There is a growing body of evidence that genetic variation is important in determining outcome following aSAH. Understanding genetic determinants of outcome will help to improve prognostic modelling, stratify patients in clinical trials and target novel strategies to treat this devastating disease. This protocol details a two-stage genome-wide association study to identify susceptibility loci for clinical outcome after aSAH using individual patient-level data from multiple international cohorts. Clinical outcome will be assessed using the modified Rankin Scale or Glasgow Outcome Scale at 1–24 months. The stage 1 discovery will involve meta-analysis of individual-level genotypes from different cohorts, controlling for key covariates. Based on statistical significance, supplemented by biological relevance, top single nucleotide polymorphisms will be selected for replication at stage 2. The study has national and local ethical approval. The results of this study will be rapidly communicated to clinicians, researchers and patients through open-access publication(s), presentation(s) at international conferences and via our patient and public network.

Securing SCADA Energy Management System under DDos Attacks Using Token Verification Approach

The advanced connection requirements of industrial automation and control systems have sparked a new revolution in the Industrial Internet of Things (IIoT), and the Supervisory Control and Data Acquisition (SCADA) network has evolved into an open and highly interconnected network. In addition, the equipment of industrial electronic devices has experienced complete systemic integration by connecting with the SCADA network, and due to the control and monitoring advantages of SCADA, the interconnectivity and working efficiency among systems have been tremendously improved. However, it is inevitable that the SCADA system cannot be separated from the public network, which indicates that there are concerns over cyber-attacks and cyber-threats, as well as information security breaches, in the SCADA network system. According to this context, this paper proposes a module based on the token authentication service to deter attackers from performing distributed denial-of-service (DDoS) attacks. Moreover, a simulated experiment has been conducted in an energy management system in the actual field, and the experimental results have suggested that the security defense architecture proposed by this paper can effectively improve security and is compatible with real field systems.

Chaos coordinated neural key synchronization for enhancing security of IoT

The key exchange mechanism in this paper is built utilizing neural network coordination and a hyperchaotic (or chaotic) nonlinear dynamic complex system. This approach is used to send and receive sensitive data between Internet-of-Things (IoT) nodes across a public network. Using phishing, Man-In-The-Middle (MITM), or spoofing attacks, an attacker can easily target sensitive information during the exchange process. Furthermore, minimal research has been made on the exchange of input seed values for creating identical input at both ends of neural networks. The proposed method uses a 5D hyperchaotic or chaotic nonlinear complex structure to ensure the sharing of input seed value across two neural networks, resulting in the identical input on both ends. This study discusses two ways for sharing seed values for neural coordination. The first is a chaotic system with all real variables, whereas the second is a hyperchaotic system with at least one complex variable. Each neural network has its own random weight vector, and the outputs are exchanged. It achieves full coordination in some stages by altering the neuronal weights according to the mutual learning law. The coordinated weights are utilized as a key after the neural coordination technique. The network’s core structure is made up of triple concealed layers. So, determining the inner configuration will be tough for the intruder. The efficiency of the suggested model is validated by simulations, and the findings reveal that the suggested strategy outperforms current equivalent techniques.

Architecture Integration of 5G Networks and Time-Sensitive Networking with Edge Computing for Smart Manufacturing

Digitalization and networking are taking on an increasingly important role in manufacturing. Fifth Generation mobile networks (5G) allow us to wirelessly connect multiple assets in factories with guaranteed quality of service (QoS). A 5G non-public network (5G-NPN) realizes a dedicated network with secure communication within the factory. Time-sensitive networking (TSN) provides deterministic connectivity and reliability in local networks. Edge computing moves computing power near factory locations, reducing the latency of edge applications. Making production processes more flexible, more robust, and resilient induces a great challenge for integrating these technologies. This paper presents the benefits of the joint use of 5G-NPN, TSN, and edge computing in manufacturing. To that end, first, the characteristics of the technologies are analyzed. Then, the integration of different 5G-NPN deployment options with edge (and cloud) computing is presented to provide end-to-end services. For enhanced reliability, ways of interworking between TSN and edge computing domains are proposed. Afterward, as an example realization of edge computing, the investigation on the capabilities of the Kubernetes container orchestration platform is presented together with the gap analysis for smart manufacturing requirements. Finally, the different integration options, interworking models, and Kubernetes-based edge computing are evaluated to assist smart factories to use these new technologies in combination in the future.

A Mixed Strategy of Higher-Order Structure for Link Prediction Problem on Bipartite Graphs

Link prediction tasks have an extremely high research value in both academic and commercial fields. As a special case, link prediction in bipartite graphs has been receiving more and more attention thanks to the great success of the recommender system in the application field, such as product recommendation in E-commerce and movie recommendation in video sites. However, the difference between bipartite and unipartite graphs makes some methods designed for the latter inapplicable to the former, so it is quite important to study link prediction methods specifically for bipartite graphs. In this paper, with the aim of better measuring the similarity between two nodes in a bipartite graph and improving link prediction performance based on that, we propose a motif-based similarity index specifically for application on bipartite graphs. Our index can be regarded as a high-order evaluation of a graph’s local structure, which concerns mainly two kinds of typical 4-motifs related to bipartite graphs. After constructing our index, we integrate it into a commonly used method to measure the connection potential between every unconnected node pair. Some of the node pairs are originally unconnected, and the others are those we select deliberately to delete their edges for subsequent testing. We make experiments on six public network datasets and the results imply that the mixture of our index with the traditional method can obtain better prediction performance w.r.t. precision, recall and AUC in most cases. This is a strong proof of the effectiveness of our exploration on motifs structure. Also, our work points out an interesting direction for key graph structure exploration in the field of link prediction.

QoS-Ledger: Smart Contracts and Metaheuristic for Secure Quality-of-Service and Cost-Efficient Scheduling of Medical-Data Processing

Quality-of-service (QoS) is the term used to evaluate the overall performance of a service. In healthcare applications, efficient computation of QoS is one of the mandatory requirements during the processing of medical records through smart measurement methods. Medical services often involve the transmission of demanding information. Thus, there are stringent requirements for secure, intelligent, public-network quality-of-service. This paper contributes to three different aspects. First, we propose a novel metaheuristic approach for medical cost-efficient task schedules, where an intelligent scheduler manages the tasks, such as the rate of service schedule, and lists items utilized by users during the data processing and computation through the fog node. Second, the QoS efficient-computation algorithm, which effectively monitors performance according to the indicator (parameter) with the analysis mechanism of quality-of-experience (QoE), has been developed. Third, a framework of blockchain-distributed technology-enabled QoS (QoS-ledger) computation in healthcare applications is proposed in a permissionless public peer-to-peer (P2P) network, which stores medical processed information in a distributed ledger. We have designed and deployed smart contracts for secure medical-data transmission and processing in serverless peering networks and handled overall node-protected interactions and preserved logs in a blockchain distributed ledger. The simulation result shows that QoS is computed on the blockchain public network with transmission power = average of −10 to −17 dBm, jitter = 34 ms, delay = average of 87 to 95 ms, throughput = 185 bytes, duty cycle = 8%, route of delivery and response back variable. Thus, the proposed QoS-ledger is a potential candidate for the computation of quality-of-service that is not limited to e-healthcare distributed applications.

O IDEB 2017/2019 nas Escolas da Rede Municipal de Ensino de Paranaguá - PR: Aprovação e Proficiência

ResumoPara organizar os resultados da avaliação em larga escala (IDEB), o Sistema Nacional de Avaliação da Educação Básica (SAEB) tem a atribuição principal de mensurar as informações em relação ao desenvolvimento dos alunos, através da aplicação de provas padronizadas nos Componentes Curriculares de Matemática e Língua Portuguesa e, por meio dos dados do Censo Escolar, os quais indicam o percentual das taxas de aprovação e reprovação (fluxo), permitindo assim estabelecer os resultados do Índice de Desenvolvimento da Educação Básica, da escola, do Estado e do município. Nesta perspectiva, a pesquisa apresentada neste artigo tem como objetivo analisar os dados do Índice de Desenvolvimento da Educação (IDEB 2017/2019) da Rede Pública Municipal de Paranaguá - PR. A metodologia utilizada para organizar, interpretar e realizar o estudo se baseou em uma pesquisa qualitativa documental, com os dados obtidos das 31 escolas através do site do Instituto Nacional de Ensino e Pesquisa Anísio Teixeira, do Governo Federal (INEP), os quais foram discutidos e refletidos por meio da Análise de Conteúdo de Bardin. Conclui-se com este artigo que há um grande aumento nas aprovações (fluxo) dos alunos da rede pesquisada nos anos 2017/2019, porém os dados de desempenho (proficiência) não apresentaram avanços expressivos na alteração dos níveis de aprendizagem. Palavras-chave: Fluxo. Avaliação. Desempenho. AbstractTo organize the results of the large-scale evaluation (IDEB), the National System for the Evaluation of Basic Education (SAEB) has the main task of measuring information regarding the students’ development through the application of standardised tests in the Curricular Components of Mathematics and Portuguese Language and through the School Census data, which indicate the percentage of pass and fail rates (flow), thus making it possible to establish the Index of Development of Basic Education results, the school, the state and the municipality. From this perspective, the research presented in this article aims to analyze the the Education Development Index data (IDEB 2017/2019) of the Paranaguá Municipal Public Network,PR. The methodology used to organize, interpret and carry out the study was based on qualitative documentary research, with data obtained from 31 schools through the website of the National Institute of Education and Research Anísio Teixeira of the Federal Government (INEP), which were discussed and reflected through the Bardin Content Analysis. This article concludes with a large increase in the students’ approvals (flow) in the network researched in the years 2017/2019, but the performance data (proficiency) did not show significant progress in changing learning levels. Keywords: Flow.Evaluation. Performance.

Mathematical analysis of a self-service car wash in the aspect of application of renewable energy sources

The paper presents a model of a self-service car wash. Sub-models of water, electricity and natural gas consumption were developed. Heated water is used to wash vehicles and in winter to heat the floor. Electricity is mainly used to power high pressure pumps. The data to develop submodels were based on a time series of 1 year from a 5-station car wash located in central Poland. Chemical consumption and costs were not analyzed in this paper. Generally, this data is quite difficult to access and not provided by car wash manufacturers or owners. The developed model allowed estimating the possibility of using renewable energy sources in the form of solar collectors and photovoltaic panels to balance the energy demand of a car wash depending on the number of washing stands and car wash load. Application of solar collectors allows saving 334 m3 of natural gas per year and 11.2 MWh of electricity in the case of applying photovoltaic panels. The amount of electricity consumed by the carwash is so large that mounting the panels on the whole available area will not provide the required amount anyway. Installation of photovoltaic installation on the premises of touchless car wash is justified in the case of connecting the installation to the public network, which was treated as a battery. The cost of maintaining such a battery is 20% of each stored kWh. As a result of the applied solutions, the CO2 emission will be reduced.