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

Low-intensity focused ultrasound targeted microbubble destruction reduces tumor blood supply and sensitizes anti-PD-L1 immunotherapy

Immune checkpoint blockade (ICB) typified by anti-PD-1/PD-L1 antibodies as a revolutionary treatment for solid malignancies has been limited to a subset of patients due to poor immunogenicity and inadequate T cell infiltration. Unfortunately, no effective strategies combined with ICB therapy are available to overcome low therapeutic efficiency and severe side effects. Ultrasound-targeted microbubble destruction (UTMD) is an effective and safe technique holding the promise to decrease tumor blood perfusion and activate anti-tumor immune response based on the cavitation effect. Herein, we demonstrated a novel combinatorial therapeutic modality combining low-intensity focused ultrasound-targeted microbubble destruction (LIFU-TMD) with PD-L1 blockade. LIFU-TMD caused the rupture of abnormal blood vessels to deplete tumor blood perfusion and induced the tumor microenvironment (TME) transformation to sensitize anti-PD-L1 immunotherapy, which markedly inhibited 4T1 breast cancer’s growth in mice. We discovered immunogenic cell death (ICD) in a portion of cells induced by the cavitation effect from LIFU-TMD, characterized by the increased expression of calreticulin (CRT) on the tumor cell surface. Additionally, flow cytometry revealed substantially higher levels of dendritic cells (DCs) and CD8+ T cells in draining lymph nodes and tumor tissue, as induced by pro-inflammatory molecules like IL-12 and TNF-α. These suggest that LIFU-TMD as a simple, effective, and safe treatment option provides a clinically translatable strategy for enhancing ICB therapy

Enterprise security assessment and operation analysis using distributed computing and information fusion techniques

With the evolution of networking technology, mobile devices and E-Commerce have played a more pervasive role in enterprise production environment. Computer networks have been adopted as an indispensable part of an enterprise’s operations, resource planning and customer relation management, and so on. The evolved computer networks enable more flexible and innovative business tools such as wireless internet banking and mobile advertising. At the same time, the increasing network scale and data volume have introduced new challenges for enterprises. The increased volume of enterprise data has become more difficult to manage. The increased value of information is making the enterprise information systems bigger targets for hackers. The industry needs more efficient analytic systems that not only organize the essential processes and procedures to assess vast amount of data, but also help enterprise to make decisions on how to secure their information systems and business operations. Recent dramatic increases in the rate of data creation have left enterprises grappling with the complexities of big data. Wisely utilizing enterprise data has become a significant part of enhancing the enterprise productivity. Proper processes with the data from various sources are expected to increase profitability and the overall value of enterprise. Although a lot of existing research have exploited strategies to deal with the new challenges faced by modern enterprise information systems, none of them constitutes a widely acceptable solution. In addition, various implementations of distribution computing techniques have been done in solving enterprise security and operational issues. However, those implementations are mostly ad hoc application for the specific projects. Lacking of a universal framework, it requires higher investigation cost before/during implementing the projects. This research aims to create a better solution with the merits of incorporating distributed computing with information fusion to facilitate the enterprise security and operation. Distributed computing is an agile solution for handling such a large amount of data. Information fusion can be an effective vehicle over distributed environments for the automated generation of analysis results and business strategies from the overwhelming information haystacks. This research combines these methodologies to constitute a series of innovative solutions for the core issues in the big data era, including the information overload and analytic complexity. As a result, enterprise can solve the akin issues with high cost-efficiency and high performance with the developed models. The techniques developed in the research are conducive to the security assessment of large enterprise information systems, and operational data analysis for improving business performance. These techniques include integrated analytic methods on the platitude transactions of security logs and business activities. The frameworks developed in the research utilize the innovative concepts to provide remedies for various emerging enterprise issues including enhanced vulnerability identification and business pattern discovery. The issues considered in the framework development include a cloud-based wireless network defence strategy, a big data mobile application assessment strategy, a big data customer behaviour analysis strategy, cloud-based multi-language support, and PhD Thesis Page vii Hongye Zhong (John) big data health data analytics. Along with the frameworks, the research forms guidelines that are integrated with the developed techniques and frameworks for implementing the systems to address the industrial issues on enterprise security and operations. Based on the guidelines, prototype systems have been constructed in the experiments for evaluation and illustration purposes. Through the experiments, the performance of the developed models has been verified. It is proven that the hybrid model of distribution computing and information fusion can provide a flexible and efficient solution to enterprise networking and software security. In addition, evidence shows that the cloud-based data fusion model can be an assistive tool for enterprises on customer information analytics

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

Apply technology acceptance model with big data analytics and unity game engine

In modern enterprises, customer data is highly valued for consumer behavior analysis and business strategy development. The objective of this research is to develop a framework that promotes the use of Unity for Big Data Analytics on consumer behaviors with Technology Acceptance Model. Unity3D is a popular game engine and a development tool that enables graphical game development for multiple platforms. Data of the customer participation in the games is collected, and Big Data Analytic techniques such as clustering and visualization are applied to provide better insight into the consumer behaviors. Based on Technology Acceptance Model, prediction is made to improve the vending performance of innovative products. Finally, an experimental system is designed referring to the proposed framework as an illustration for the framework implementation

Enhance enterprise Android application security with cloud computing and big data analytics

In modern enterprises, conventional Android application assessment approaches have many limitations, one of which is the isolation of different assessment systems that hinders the efficiency and capacity of the assessing processes. The objective of this research is to develop a framework that promotes the use of cloud computing and Big Data Analytics on building the assessment system. Cloud-based design allows the system to achieve computing capacity in a lower cost, so that it can apply mixed utilization of various assessment techniques to generate more diversified and objective testing results. Centralized information repository on the cloud enables Big Data Analytics to be performed on the extensive testing results to achieve enhanced insight of the application security status. BDA techniques such as clustering and visualization provides better comprehension on the underlying security issues and prediction on how to improve the enterprise information systems. To aggrandize the availability of the analytic results, SOA should be adopted in the system design, so that the data can be provided as extendable services for other systems. Finally, an experimental system is designed based on the proposed framework as an illustration on the framework implementation

Analysis of the Spatial and Temporal Evolution of Energy-Related CO₂ Emissions in China’s Coastal Areas and the Drivers of Industrial Enterprises above Designated Size—The Case of 82 Cities

The energy consumption by industrial enterprises above designated size in China’s coastal region is the main source of CO2 emissions. This study analyzes the spatial and temporal evolution patterns and driving factors of CO2 emissions due to the energy consumption by industrial enterprises above designated size. Enterprises in 82 cities in China’s coastal regions were studied from 2005 to 2020 based on their CO2 emissions and socio-economic data. The Exploring Spatial Data Analysis (ESDA) methodology and Logarithmic mean Divisia Index decomposition (LMDI model) were used. The results show that, during the study period, energy-related CO2 emissions from industrial enterprises above designated size in China’s coastal areas generally show a fluctuating upward trend. However, a few cities showed a trend from steady growth to a peak and then a slow decline, which may realize the “double carbon” target in advance. The spatial correlation of CO2 emission intensity showed a decreasing and then increasing trend, and there were spatial aggregation characteristics in some cities. Among the driving factors, the pull effect is higher than the inhibition effect; the output scale contributes the most to the pull effect, and labor productivity contributes the most to the inhibition effect. The results of this study have a certain reference value for the realization of the “double carbon” target in China’s coastal regions