Dynamic system simulation on the web

Computer simulation is the discipline of designing a model of an actual or theoretical physical system, executing the model on digital computer, and analysing the execution output. Of late, simulation has been influenced by an increasingly popular phenomenon - the World Wide Web or WWW. Java is a programming language for the WWW that brings a high level of dynamism to Web applications. Java makes it particularly suitable to represent applications on the Web. It has created an illusion of machine independence and interoperability for many applications. Therefore WWW can be considered as an environment for providing modelling and simulation applications. Research in the area of Web-based simulation is developing rapidly as WWW programming tools develop. Bulk of this research is focused only on discrete event simulation. This dissertation introduces dynamic system simulation on the Web. It presents and demonstrates a Web-based simulation software (SimDynamic), entirely developed in Java, for modelling, simulating, and analysing dynamic systems with 3D animated illustration, wherever applicable. SimDynamic can also be used as a non Web-based application on a PC. In both cases, it supports complete model creation and modification capabilities along with graphical and numerical output. Detail design and functional ability of SimDynamic are provided. Some real world systems have been modeled using SimDynamic and results are presented. Characteristic features of the software are discussed from software engineering point of view. Complete source code and installation instructions are included. Current SimDynamic limitations and potential customization and expansion issues are explored

Fine-Grained Access Control for Microservices

Microservices-based applications are considered to be a promising paradigm for building large-scale digital systems due to its flexibility, scalability, and agility of development. To achieve the adoption of digital services, applica-tions holding personal data must be secure while giving end-users as much control as possible. On the other hand, for software developers, adoption of a security solution for microservices requires it to be easily adaptable to the application context and requirements while fully exploiting reusability of se-curity components. This paper proposes a solution that targets key security challenges of microservice-based applications. Our approach relies on a co-ordination of security components, and offers a fine-grained access control in order to minimise the risks of token theft, session manipulation, and a ma-licious insider; it also renders the system resilient against confused deputy at-tacks. This solution is based on a combination of OAuth 2 and XACML open standards, and achieved through reusable security components integrat-ed with microservices

Interleukin-6: A Sensitive Parameter for the Early Detection of Neonatal Sepsis

Background: Neonatal sepsis is a major cause of neonatal mortality and morbidity throughout the world. Though blood culture is the gold standard and has higher sensitivity and specificity over the hematological value and cytokine, it is not available in our community health situation and also in most of health care facilities. It is also time consuming. Therefore hematological value and interleukin-6 can be evaluated for the early diagnosis of neonatal bacterial infection. Objective: This study was conducted to see the usefulness of IL-6 as an early marker of neonatal sepsis and also to compare the sensitivity in comparison with CRP, hematological value and blood culture. Study Design: It was a quasy experimental study. Setting: This study was carried out in the neonatal unit of pediatric department, BSMMU during the period of September, 2005 to February, 2006. Method: Forty five suspected septic cases were enrolled in the study and thirty healthy newborn were taken for comparison. Venous blood sample from peripheral vein was collected on the 1st day of symptoms and/or 1st day of admission and was sent for IL-6 estimation within half an hour and estimation of IL-6 was done by using immunolyte DPC USA which employed automated chemiluminescent immunoassays. Results: Out of forty five cases of suspected-neonatal sepsis, IL-6 were positive in twenty five cases. In culture proven sepsis 100% cases had raised IL-6. In control group only five babies had raised IL-6. Three cases were culture positive, of which all were also positive for IL-6 (100%). Among the cases twenty six were CRP positive, of which twenty were also positive for IL-6 (76.92%). Conclusion: In the present study IL-6 was found to be an early marker of neonatal infection. Sensitivity was more than CRP and other hematological parameter in the first twenty four hours. Key words: Interleukin-6, Neonatal Sepsis.DOI: 10.3329/bsmmuj.v1i1.3687 BSMMU J 2008; 1(1): 1-

Towards an Integrated In-Vehicle Isolation and Resilience Framework for Connected Autonomous Vehicles

Connected Autonomous Vehicles (CAV) have attracted significant attention, specifically due to successful deployment of ultra-reliable low-latency communications with Fifth Generation (5G) wireless networks. Due to the safety-critical nature of CAV, reliability is one of the well-investigated areas of research. Security of in-vehicle communications is mandatory to achieve this goal. Unfortunately, existing research so far focused on in-vehicle isolation or resilience independently. This short paper presents the elements of an integrated in-vehicle isolation and resilience framework to attain a higher degree of reliability for CAV systems. The proposed framework architecture leverages benefits of Trusted Execution Environments to mitigate several classes of threats. The framework implementation is also mapped to the AUTOSAR open automotive standard

Neurosymbolic Spike Concept Learner towards Neuromorphic General Intelligence

Current research in the area of concept learning makes use of deep learning and ensembles methods to learn concepts. Concept learning allows us to combine heterogeneous entities in data which could collectively identify as individual concepts. Heterogeneity and compositionality are crucial areas to explore in machine learning as it has the potential to contribute profoundly to artificial general intelligence. We investigate the use of spiking neural networks for concept learning. Spiking neurones inclusively model the temporal properties as observed in biological neurones. A benefit of spike-based neurones allows for localised learning rules that only adapts connections between relevant neurones. In this position paper, we propose a technique allowing dynamic formation of synapse (connections) in spiking neural networks, the basis of structural plasticity. Achieving dynamic formation of synapse allows for a unique approach to concept learning with a malleable neural structure. We call this technique Neurosymbolic Spike-Concept Learner (NS-SCL). The limitations of NS-SCL can be overcome with the neuromorphic computing paradigm. Furthermore, introducing NS-SCL as a technique on neuromorphic platforms should motivate a new direction of research towards Neuromorphic General Intelligence (NGI), a term we define to some extent

Securing Microservices

Microservices has drawn significant interest in recent years and is now successfully finding its way into different areas, from Enterprise IT to Internet-of-Things to even Critical Applications. This article discusses how Microservices can be secured at different levels and stages considering a common software development lifecycle

A Novel Trust Taxonomy for Shared Cyber Threat Intelligence

Cyber threat intelligence sharing has become a focal point for many organizations to improve resilience against cyber attacks. The objective lies on sharing relevant information achieved through automating as many processes as possible without losing control or compromising security. The intelligence may be crowdsourced from decentralized stakeholders to collect and enrich existing information. Trust is an attribute of actionable cyber threat intelligence that has to be established between stakeholders. Sharing information about vulnerabilities requires a high level of trust because of the sensitive information. Some threat intelligence platforms/providers support trust establishment through internal vetting processes, others rely on stakeholders to manually build up trust. The latter may reduce the amount of intelligence sources. This work presents a novel trust taxonomy to establish a trusted threat sharing environment. 30 popular threat intelligence platforms/providers were analyzed and compared regarding trust functionalities. Trust taxonomies were analyzed and compared. Illustrative case studies were developed and analyzed applying our trust taxonomy

AudiWFlow: Confidential, Collusion-resistant Auditing of Distributed Workflows

We discuss the problem of accountability when multiple parties cooperate towards an end result such as multiple companies in a supply chain or departments of a government service under different authorities. In cases where a full trusted central point does not exist, it is difficult to obtain a trusted audit trail of a workflow when each individual participant is unaccountable to all others. We propose AudiWFlow, an auditing architecture which makes participants accountable for its contributions in a distributed workflow. Our scheme provides confidentiality in most cases, collusion detection and availability of evidence after the workflow terminates. AudiWFlow is based on verifiable secret sharing and real-time peer-to-peer verification of records; it further supports multiple levels of assurance to meet a desired trade-off between the availability of evidence and the overhead resulting from the auditing approach. We propose and evaluate two implementation approaches for AudiWFlow. The first one is fully distributed except for a central auxiliary point that, nevertheless, needs only a low level of trust. The second one is based on smart-contracts running on a public blockchain which is able to remove the need of any central point but requires the integration with a blockchain

Cyber threat intelligence sharing: Survey and research directions

Cyber Threat Intelligence (CTI) sharing has become a novel weapon in the arsenal of cyber defenders to proactively mitigate increasing cyber attacks. Automating the process of CTI sharing, and even the basic consumption, has raised new challenges for researchers and practitioners. This extensive literature survey explores the current state-of-the-art and approaches different problem areas of interest pertaining to the larger field of sharing cyber threat intelligence. The motivation for this research stems from the recent emergence of sharing cyber threat intelligence and the involved challenges of automating its processes. This work comprises a considerable amount of articles from academic and gray literature, and focuses on technical and non-technical challenges. Moreover, the findings reveal which topics were widely discussed, and hence considered relevant by the authors and cyber threat intelligence sharing communities

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe