Enhancing health risk prediction with deep learning on big data and revised fusion node paradigm

With recent advances in health systems, the amount of health data is expanding rapidly in various formats. This data originates from many new sources including digital records, mobile devices, and wearable health devices. Big health data offers more opportunities for health data analysis and enhancement of health services via innovative approaches. The objective of this research is to develop a framework to enhance health prediction with the revised fusion node and deep learning paradigms. Fusion node is an information fusion model for constructing prediction systems. Deep learning involves the complex application of machine-learning algorithms, such as Bayesian fusions and neural network, for data extraction and logical inference. Deep learning, combined with information fusion paradigms, can be utilized to provide more comprehensive and reliable predictions from big health data. Based on the proposed framework, an experimental system is developed as an illustration for the framework implementatio

Unique allosteric effect driven rapid adsorption of carbon dioxide on a new ionogel [P4444][2-Op]@MCM-41 with excellent cyclic stability and loading-dependent capacity

Allosteric effect-driven rapid stepwise CO2 adsorption of pyridine-containing anion functionalized ionic liquid [P4444][2-Op] confined into mesoporous silica MCM-41.</p

Zeolite-cage-lock strategy for in situ synthesis of highly nitrogen-doped porous carbon for selective separation of carbon dioxide gas

Porous carbon structures doped with 18.14% nitrogen and prepared by a carbonizing organic template in ZSM-39 zeolitic cages show high CO2 adsorption capacity.</p

Household, community, sub-national and country-level predictors of primary cooking fuel switching in nine countries from the PURE study

Introduction. Switchingfrom polluting (e.g. wood, crop waste, coal)to clean (e.g. gas, electricity) cooking fuels can reduce household air pollution exposures and climate-forcing emissions.While studies have evaluated specific interventions and assessed fuel-switching in repeated cross-sectional surveys, the role of different multilevel factors in household fuel switching, outside of interventions and across diverse community settings, is not well understood. Methods.We examined longitudinal survey data from 24 172 households in 177 rural communities across nine countries within the Prospective Urban and Rural Epidemiology study.We assessed household-level primary cooking fuel switching during a median of 10 years offollow up (∼2005–2015).We used hierarchical logistic regression models to examine the relative importance of household, community, sub-national and national-level factors contributing to primary fuel switching. Results. One-half of study households(12 369)reported changing their primary cookingfuels between baseline andfollow up surveys. Of these, 61% (7582) switchedfrom polluting (wood, dung, agricultural waste, charcoal, coal, kerosene)to clean (gas, electricity)fuels, 26% (3109)switched between different polluting fuels, 10% (1164)switched from clean to polluting fuels and 3% (522)switched between different clean fuels

Design for a network centric enterprise forensic system

Increased profitability and exposure of enterprise’s information incite more attackers to attempt exploitation on enterprise network, while striving not to leave any evidences. Although the area of digital forensic analysis is evolving to become more mature in the modern criminology, the scope of network and computer forensics in the large-scale commercial environment is still vague. The conventional forensic techniques, consisting of large proportion of manual operations and isolated processes, are not adequately compatible in modern enterprise context. Data volume of enterprise is usually overwhelming and the interference to business operation during the investigation is unwelcomed. To evidence and monitor these increasing and evolving cyber offences and criminals, forensic investigators are calling for more comprehensive forensic methodology. For comprehension of current insufficiencies, this paper starts from the probes for the weaknesses of various preliminary forensic techniques. Then it proposes an approach to design an enhanced forensic system that integrates the network distributed system concept and information fusion theory as a remedy to the drawbacks of existing forensic techniques

Design for integrated WiFi defence strategy in mordern enterprise context

WiFi has been adopted into enterprise production environment in larger scale, yet the flexibility of WiFi network also exposes more vulnerability to current security defense systems and introduces greater challenges to network security for modern enterprises. In wireless world, there are many dead corners that traditional firewall and intrusion detection system cannot cover. Modern enterprises are calling for more efficient defense approaches to guarantee the safety of the information on their wireless network. Upon probing to the weaknesses of current enterprise WiFi security, this paper proposes a defense strategy with the capacities of intelligent planning and integrated reactions to remedy the weaknesses of conventional enterprise security mechanism of WiFi network. A security defense system is designed to monitor WiFi security on Physical Layer, Data-link Layer and Internet Layer of the enterprise WiFi network, and provide attack defense mechanism to minimize the damage to enterprises when their WiFi network is under attack