Bayesian minimax estimation of the normal model with incomplete prior covariance matrix specification

This work addresses the issue of Bayesian robustness in the multivariate normal model when the prior covariance matrix is not completely specified, but rather is described in terms of positive semi-definite bounds. This occurs in situations where, for example, the only prior information available is the bound on the diagonal of the covariance matrix derived from some physical constraints, and that the covariance matrix is positive semi-definite, but otherwise arbitrary. Under the conditional Gamma-minimax principle, previous work by DasGupta and Studden shows that an analytically exact solution is readily available for a special case where the bound difference is a scaled identity. The goal in this work is to consider this problem for general positive definite matrices. The contribution in this paper is a theoretical study of the geometry of the minimax problem. Extension of previous results to a more general case is shown and a practical algorithm that relies on semi-definite programming and the convexity of the minimax formulation is derived. Although the algorithm is numerically exact for up to the bivariate case, its exactness for other cases remains open. Numerical studies demonstrate the accuracy of the proposed algorithm and the robustness of the minimax solution relative to standard and recently proposed methods

Adherence to highly active antiretroviral therapy among people living with HIV and associated high-risk behaviours and clinical characteristics: A cross-sectional survey in Vietnam

Although Vietnam has promoted the utilisation of highly active antiretroviral therapy (HAART) towards HIV elimination targets, adherence to treatment has remained under-investigated. We aimed to describe high-risk behaviours and clinical characteristics by adherence status and to identify the factors associated with non-adherence. We included 426 people living with HIV (PLWH) currently or previously involved in HAART. Most participants were men (75.4%), young (33.6 years), with low income and low education levels. Non-adherent PLWH (11.5%) were more likely to have a larger number of sex partners (p-value = 0.053), sex without condom use (p-value = 0.007) and not receive result at hospital or voluntary test centre (p-value = 0.001). Multiple logistic regression analysis showed that demographic (education levels), sexual risk behaviours (multiple sex partners and sex without using condom) and clinical characteristics (time and facility at first time received HIV-positive result) were associated with HAART non-adherence. There are differences in associated factors between women (education levels and place of HIV testing) and men (multiple sex partners). Gender-specific programs, changing risky behaviours and reducing harms among PLWH may benefit adherence. We highlight the need to improve the quantity and quality of HIV/AIDS services in Vietnam, especially in pre- and post-test counselling, to achieve better HAART adherence, working towards ending AIDS in 2030. © The Author(s) 2021. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Huy Nguyen” is provided in this record*

Optimal sizing of renewable energy storage: A comparative study of hydrogen and battery system considering degradation and seasonal storage

Renewable energy storage (RES) is essential to address the intermittence issues of renewable energy systems, thereby enhancing the system stability and reliability. This study presents an optimisation study of sizing and operational strategy parameters of a grid-connected photovoltaic (PV)-hydrogen/battery systems using a Multi-Objective Modified Firefly Algorithm (MOMFA). An operational strategy that utilises the ability of hydrogen to store energy over a long time was also investigated. The proposed method was applied to a real-world distributed energy project located in the tropical climate zone. To further demonstrate the robustness and versatility of the method, another synthetic test case was examined for a location in the subtropical weather zone, which has a high seasonal mismatch. The performance of the proposed MOMFA method is compared with the NSGA-II method, which has been widely used to design renewable energy storage systems in the literature. The result shows that MOMFA is more accurate and robust than NSGA-II owing to the complex and dynamic nature of energy storage system. The optimisation results show that battery storage systems, as a mature technology, yield better economic performance than current hydrogen storage systems. However, it is proven that hydrogen storage systems provide better techno-economic performance and can be a viable long-term storage solution when high penetration of renewable energy is required. The study also proves that the proposed long-term operational strategy can lower component degradation, enhance efficiency, and increase the total economic performance of hydrogen storage systems. The findings of this study can support the implementation of energy storage systems for renewable energy

Adversarial Attacks and Defenses in 6G Network-Assisted IoT Systems

The Internet of Things (IoT) and massive IoT systems are key to sixth-generation (6G) networks due to dense connectivity, ultra-reliability, low latency, and high throughput. Artificial intelligence, including deep learning and machine learning, offers solutions for optimizing and deploying cutting-edge technologies for future radio communications. However, these techniques are vulnerable to adversarial attacks, leading to degraded performance and erroneous predictions, outcomes unacceptable for ubiquitous networks. This survey extensively addresses adversarial attacks and defense methods in 6G network-assisted IoT systems. The theoretical background and up-to-date research on adversarial attacks and defenses are discussed. Furthermore, we provide Monte Carlo simulations to validate the effectiveness of adversarial attacks compared to jamming attacks. Additionally, we examine the vulnerability of 6G IoT systems by demonstrating attack strategies applicable to key technologies, including reconfigurable intelligent surfaces, massive multiple-input multiple-output (MIMO)/cell-free massive MIMO, satellites, the metaverse, and semantic communications. Finally, we outline the challenges and future developments associated with adversarial attacks and defenses in 6G IoT systems.Comment: 17 pages, 5 figures, and 4 tables. Submitted for publication

The Role of Serial NT-ProBNP Level in Prognosis and Follow-Up Treatment of Acute Heart Failure after Coronary Artery Bypass Graft Surgery

BACKGROUND: After coronary artery bypass graft (CABG) surgery, heart failure is still major problem. The valuable marker for it is needed. AIM: Evaluating the role of serial NT-proBNP level in prognosis and follow-up treatment of acute heart failure after CABG surgery. METHODS: The prospective, analytic study evaluated 107 patients undergoing CABG surgery at Ho Chi Minh Heart Institute from October 2012 to June 2014. Collecting data was done at pre- and post-operative days with measuring NT-proBNP levels on the day before operation, 2 hours after surgery, every next 24 h until the 5th day, and in case of acute heart failure occurred after surgery. RESULTS: On the first postoperative day (POD1), the NT-proBNP level demonstrated significant value for AHF with the cut-off point = 817.8 pg/mL and AUC = 0.806. On the second and third postoperative day, the AUC value of NT- was 0.753 and 0.751. It was statistically significant in acute heart failure group almost at POD 1 and POD 2 when analyzed by the doses of dobutamine, noradrenaline, and adrenaline (both low doses and normal doses). CONCLUSION: Serial measurement of NT-proBNP level provides useful prognostic and follow-up treatment information in acute heart failure after CABG surgery

Improvement in neoantigen prediction via integration of RNA sequencing data for variant calling

IntroductionNeoantigen-based immunotherapy has emerged as a promising strategy for improving the life expectancy of cancer patients. This therapeutic approach heavily relies on accurate identification of cancer mutations using DNA sequencing (DNAseq) data. However, current workflows tend to provide a large number of neoantigen candidates, of which only a limited number elicit efficient and immunogenic T-cell responses suitable for downstream clinical evaluation. To overcome this limitation and increase the number of high-quality immunogenic neoantigens, we propose integrating RNA sequencing (RNAseq) data into the mutation identification step in the neoantigen prediction workflow.MethodsIn this study, we characterize the mutation profiles identified from DNAseq and/or RNAseq data in tumor tissues of 25 patients with colorectal cancer (CRC). Immunogenicity was then validated by ELISpot assay using long synthesis peptides (sLP).ResultsWe detected only 22.4% of variants shared between the two methods. In contrast, RNAseq-derived variants displayed unique features of affinity and immunogenicity. We further established that neoantigen candidates identified by RNAseq data significantly increased the number of highly immunogenic neoantigens (confirmed by ELISpot) that would otherwise be overlooked if relying solely on DNAseq data.DiscussionThis integrative approach holds great potential for improving the selection of neoantigens for personalized cancer immunotherapy, ultimately leading to enhanced treatment outcomes and improved survival rates for cancer patients