SRAuditor: An Automated Assessment Tool for Statement of Advice Documents

Financial advice is given by a registered financial adviser (RFA) in the form of a statement of advice (SoA) document. To limit liability, financial advisor groups periodically assess SoA documents for compliance with legal regulations, internal policies, and best practices. However, this is a manual process that is often subjective, time-consuming and tedious. In this paper, we propose, implement and evaluate SoA Risk Auditor (SRAuditor), a natural language processing (NLP) framework to automatically assess and audit SoA documents. SRAuditor consists of two major components. The first one is a SoA transformer (SoA-T), a tool that automatically transforms and maps SoA document (generally a PDF). The other one is a question-answering engine (QA-R) that recommends legally compliant answers based on rule-based approaches for given SoA audit questions to assess and audit SoA documents. We validate the accuracy of SRAuditor's ability by evaluating it against assessments conducted by domain experts (i.e., financial advisors, lawyers). Experimental results using real-world SoA documents provided by our industry partner, Fourth Line Pty Limited indicate that SRAuditor has a high potential to be used for automatically assessing and auditing SoA documents

Supplemental Vitamins and Minerals for CVD Prevention and Treatment

The authors identified individual randomized controlled trials from previous meta-analyses and additional searches, and then performed meta-analyses on cardiovascular disease outcomes and all-cause mortality. The authors assessed publications from 2012, both before and including the U.S. Preventive Service Task Force review. Their systematic reviews and meta-analyses showed generally moderate- or low-quality evidence for preventive benefits (folic acid for total cardiovascular disease, folic acid and B-vitamins for stroke), no effect (multivitamins, vitamins C, D, β-carotene, calcium, and selenium), or increased risk (antioxidant mixtures and niacin [with a statin] for all-cause mortality). Conclusive evidence for the benefit of any supplement across all dietary backgrounds (including deficiency and sufficiency) was not demonstrated; therefore, any benefits seen must be balanced against possible risks

Electrochemical route to alleviate irreversible capacity loss from conversion type α-Fe₂O₃ anodes by LiVPO₄F prelithiation

We report a new electrochemical procedure to suppress the irreversible capacity loss (ICL) from high capacity anodes, specifically for high capacity anodes that undergo either alloying or conversion reaction with Li. In the present work, tavorite type LiVPO₄F is used as Li-reservoir and conversion type α-Fe₂O₃ nanofibers as an anode. Unfortunately, LiVPO₄F cannot be used as the promising anode (∼1.7 V vs Li) because of its poor cycling stability, but it can be used to accommodate the desired amount of Li for ICL compensation. Accordingly, LiVPO₄F is electrochemically prelithiated (Li₁.₂₆VPO₄F) and paired with α-Fe₂O₃ nanofibers with optimized loadings. The full cell is displaying a maximum capacity of ∼755 mAh g⁻¹ (calculated on the basis of anode mass) with notable cycling profile. Before the fabrication of the full cell, half-cell studies are performed to assess the Li-storage capability at the same current rate for mass balance.Ministry of Education (MOE)National Research Foundation (NRF)This work was financially supported by Ministry of Education (MOE TIER 2 Funding (MOE2015-T2-1-046), Singapore and NTU-HUJ Create Phase II which is a joint research programme between the Hebrew University of Jerusalem (HUJ, Israel) and Nanyang Technological University (NTU, Singapore) with CREATE (Campus for Research Excellence and Technological Enterprise) funding from National Research Foundation of Singapore (NRF, SIngapore). V.A. acknowledges financial support from the Science & Engineering Research Board (SERB), a statutory body of the Department of Science & Technology, Government of India, through the Ramanujan Fellowship (SB/S2/RJN-088/2016)

CorrDetector: A Framework for Structural Corrosion Detection from Drone Images using Ensemble Deep Learning

In this paper, we propose a new technique that applies automated image analysis in the area of structural corrosion monitoring and demonstrate improved efficacy compared to existing approaches. Structural corrosion monitoring is the initial step of the risk-based maintenance philosophy and depends on an engineer's assessment regarding the risk of building failure balanced against the fiscal cost of maintenance. This introduces the opportunity for human error which is further complicated when restricted to assessment using drone captured images for those areas not reachable by humans due to many background noises. The importance of this problem has promoted an active research community aiming to support the engineer through the use of artificial intelligence (AI) image analysis for corrosion detection. In this paper, we advance this area of research with the development of a framework, CorrDetector. CorrDetector uses a novel ensemble deep learning approach underpinned by convolutional neural networks (CNNs) for structural identification and corrosion feature extraction. We provide an empirical evaluation using real-world images of a complicated structure (e.g. telecommunication tower) captured by drones, a typical scenario for engineers. Our study demonstrates that the ensemble approach of \model significantly outperforms the state-of-the-art in terms of classification accuracy

Development of a person-centred digital platform for the long-term support of people living with an adult-onset genetic disease predisposition: a mixed-methods study protocol

Introduction Individuals at an inherited high-risk of developing adult-onset disease, such as breast cancer, are rare in the population. These individuals require lifelong clinical, psychological and reproductive assistance. After a positive germline test result, clinical genetic services provide support and care coordination. However, ongoing systematic clinical follow-up programmes are uncommon. Digital health solutions offer efficient and sustainable ways to deliver affordable and equitable care. This paper outlines the codesign and development of a digital health platform to facilitate long-term clinical and psychological care, and foster self-efficacy in individuals with a genetic disease predisposition.Methods and analysis We adopt a mixed-methods approach for data gathering and analysis. Data collection is in two phases. In phase 1, 300 individuals with a high-risk genetic predisposition to adult disease will undertake an online survey to assess their use of digital health applications (apps). In phase 2, we will conduct focus groups with 40 individuals with a genetic predisposition to cardiac or cancer syndromes, and 30 clinicians from diverse specialities involved in their care. These focus groups will inform the platform’s content, functionality and user interface design, as well as identify the barriers and enablers to the adoption and retention of the platform by all endusers. The focus groups will be audiorecorded and transcribed, and thematic and content data analysis will be undertaken by adopting the Unified Theory of Acceptance and Use of Technology. Descriptive statistics will be calculated from the survey data. Phase 3 will identify the core skillsets for a novel digital health coordinator role. Outcomes from phases 1 and 2 will inform development of the digital platform, which will be user-tested and optimised in phase 4.Ethics and dissemination This study was approved by the Peter MacCallum Human Research Ethics Committee (HREC/88892/PMCC). Results will be disseminated in academic forums, peer-reviewed publications and used to optimise clinical care