Blockchain in Personal Health Information Exchange

The secure and efficient exchange of personal health information is a critical challenge in the healthcare sector. It is a social-technical issue, being concerned with the individual’s right to data protection as well as the interoperability of existing health information management systems, such as electronic medical record systems. In particular, there is the need to legally, securely, and efficiently share personal health information between different organisations and entities within and across regions. The various entities in personal health information exchange have different requirements and responsibilities. This thesis focuses on two of these: (1) individuals as data subjects should have the opportunity to oversee the processing of their health information by others and to restrict the exchange of their health information, and (2) entities should be able to verify that data controllers are securely sharing personal health information as agreed and in compliance with regulations, laws and the preferences of data subjects. To address these challenges, blockchain technology has been actively explored in the research community of health information exchange as a potential solution. This thesis is intended to contribute towards this global effort. Blockchain technology provides benefits on decentralisation, immutability, transparency and traceability of data transactions and public access of data by network users. As a distributed technology, the adoption of blockchain in health information exchange can support interoperability, security, and privacy protection. This thesis aims to explore the use of blockchain technology in personal health information exchange between stakeholders for privacy protection, confidentiality, non-repudiation, and auditability. The four main contributions of the thesis can be summarised as follows: Firstly, the research identified the requirements of different roles involved in the cases of health information exchange and the current challenges of health information exchange in the sector by reviewing related work on personal health information exchange and blockchain technology, and discussing existing blockchain-based applications in health information exchange. In summary, there are several challenges related to PHI exchange, including legal and regulatory barriers, privacy and security breaches, lack of interoperability between healthcare information systems, trust-building barriers, and low levels of patient engagement. Secondly, to explore the use of blockchain technology in data exchange, the study designed a blockchain-based auditing framework for workflows involving different entities. This framework, called AudiWFlow, provides an audit trail for records verification on-the-fly and after the fact using smart contracts and personal receipts. In the context of data exchange in the health sector, the AudiWFlow framework makes data transactions auditable and builds trust between different entities located in the same jurisdiction. Workflow entities share required protected data with each other and use the blockchain to store proof of integrity about transaction records. The blockchain plays the role of an audit server in the framework and has a stable time delay compared to traditional servers. Thirdly, to address challenges of secure cross-regional data exchange in health, particularly when combined with existing infrastructures in the health management system, this study developed a proper blockchain-based framework called BRUE that can help entities meet fit-for-purpose security requirements in the exchange of personal health information. The BRUE framework reconstructs the concepts of User-Managed Access protocol and uses personal data receipts and token-based records to achieve access control fulfilling the needs of privacy preservation, auditing, non-repudiation, and confidentiality. Finally, to improve privacy preservation in the exchange of personal health information, the study developed a blockchain-based framework named BRESPE. This framework utilises sticky policy triggered by smart contracts to enforce access control, aligning with user preferences and data protection regulations during data transmission

Entrepreneurial Intentions and Behaviour as the Creation of Business: Based on the Theory of Planned Behaviour Extension Evidence from Polish Universities and Entrepreneurs

The purpose of this research was to analyze the relationships between the factors that influence entrepreneurial Intention (EI), using a modified version of Ajzen’s theory of planned behaviour (TPB), considering the perception of behaviour. This examination depended on participants' demographic characteristics and psycho-social behavioural traits of attitude (ATT), Subjective norm (SN), and perceived behavioural control (PBC). The establishment of a new business entails various forms of action to achieve desired results. This research analyzes entrepreneurship as the creation of business by engaging in rational behaviour to optimize the use of available technologies and financial sources. These activities are not standardized: They emerge from the entrepreneurial imagination, the perception of new opportunities, and innovation. The aim of a business is not just to produce and sell goods or services. A company must determine the appropriate means of providing them and choose the values to be adopted in the procedure of doing so. Companies should also identify the actions to be taken so that principals or employees incorporate these values into their activities and establish the character that will permit them to regards options and make correct decisions in keeping with the business’s goals

HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature Embedding

The dissection of hyperspectral images into intrinsic components through hyperspectral intrinsic image decomposition (HIID) enhances the interpretability of hyperspectral data, providing a foundation for more accurate classification outcomes. However, the classification performance of HIID is constrained by the model's representational ability. To address this limitation, this study rethinks hyperspectral intrinsic image decomposition for classification tasks by introducing deep feature embedding. The proposed framework, HyperDID, incorporates the Environmental Feature Module (EFM) and Categorical Feature Module (CFM) to extract intrinsic features. Additionally, a Feature Discrimination Module (FDM) is introduced to separate environment-related and category-related features. Experimental results across three commonly used datasets validate the effectiveness of HyperDID in improving hyperspectral image classification performance. This novel approach holds promise for advancing the capabilities of hyperspectral image analysis by leveraging deep feature embedding principles. The implementation of the proposed method could be accessed soon at https://github.com/shendu-sw/HyperDID for the sake of reproducibility.Comment: Submitted to IEEE TGR

A New Restriction on Low-Redundancy Restricted Array and Its Good Solutions

In array signal processing, a fundamental problem is to design a sensor array with low-redundancy and reduced mutual coupling, which are the main features to improve the performance of direction-of-arrival (DOA) estimation. For a $N$-sensor array with aperture $L$, it is called low-redundancy (LR) if the ratio $R=N(N-1)/(2L)$ is approaching the Leech's bound $1.217\leq R_{opt}\leq 1.674$ for $N\rightarrow\infty$; and the mutual coupling is often reduced by decreasing the numbers of sensor pairs with the first three smallest inter-spacings, denoted as $\omega(a)$ with $a\in\{1,2,3\}$. Many works have been done to construct large LRAs, whose spacing structures all coincide with a common pattern ${\mathbb D}=\{a_1,a_2,\ldots,a_{s_1},c^\ell,b_1,b_2,\ldots,b_{s_2}\}$ with the restriction $s_1+s_2=c-1$. Here $a_i,b_j,c$ denote the spacing between adjacent sensors, and $c$ is the largest one. The objective of this paper is to find some new arrays with lower redundancy ratio or lower mutual coupling compared with known arrays. In order to do this, we give a new restriction for ${\mathbb D}$ to be $s_1+s_2=c$ , and obtain 2 classes of $(4r+3)$-type arrays, 2 classes of $(4r+1)$-type arrays, and 1 class of $(4r)$-type arrays for any $N\geq18$. Here the $(4r+i)$-Type means that $c\equiv i\pmod4$. Notably, compared with known arrays with the same type, one of our new $(4r+1)$-type array and the new $(4r)$-type array all achieves the lowest mutual coupling, and their uDOFs are at most 4 less for any $N\geq18$; compared with SNA and MISC arrays, the new $(4r)$-type array has a significant reduction in both redundancy ratio and mutual coupling. We should emphasize that the new $(4r)$-type array in this paper is the first class of arrays achieving $R<1.5$ and $\omega(1)=1$ for any $N\geq18$