MyMedR

Displaying all 4 publications

Abstract:

Sort:

Electric vehicle fire risk assessment framework using Fault Tree Analysis

Mohd Tohir MZ, Martín-Gómez C

Open Res Eur, 2023;3:178.
PMID: 38370026 DOI: 10.12688/openreseurope.16538.1

BACKGROUND: In the near future, the rapid adoption of electric vehicles is inevitable, driven by environmental concerns and climate change awareness. However, this progressive trend also brings forth safety concerns and hazards, notably regarding the risk of EV fires, which have garnered significant media attention. This necessitates the need to study for comprehensive fire risk assessment strategies aimed at preventing and mitigating such incidents.
METHODS: This study presents a framework for assessing fire risks in EVs using Fault Tree Analysis (FTA). By integrating disparate data sources into a unified dataset, the proposed methodology offers a holistic approach to understanding potential hazards. The study embarked on a comprehensive exploration of EV fire causes through qualitative FTA.
RESULTS: Through this approach, the work discerned five major causes: human factors, vehicle factors, management factors, external factors, and unknown factors. Using a meticulous weighted average approach, the annual EV fire frequency for each country was deduced, revealing an average annual EV fire rate of 2.44 × 10 -4 fires per registered EV. This metric provides a significant benchmark, reflecting both the probability and inherent risk of such incidents. However, uncertainties in data quality and reporting discrepancies highlight the imperative of continued research.
CONCLUSIONS: As EV adoption surges, this study underscores the importance of comprehensive, data-driven insights for proactive risk management, emphasizing the necessity for vigilant and adaptive strategies. The findings emphasize the pivotal role of this assessment in shaping response strategies, particularly for first responders dealing with EV fires. In essence, this research not only elevates the understanding of EV fire risks but also offer a foundation for future safety measures and policies in the domain.
Fulltext Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification

Parsons MT, Tudini E, Li H, Hahnen E, Wappenschmidt B, Feliubadaló L, et al.

Hum Mutat, 2019 09;40(9):1557-1578.
PMID: 31131967 DOI: 10.1002/humu.23818

The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1,395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; and 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared with information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known nonpathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification.
Fulltext Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning

Pavlović T, Azevedo F, De K, Riaño-Moreno JC, Maglić M, Gkinopoulos T, et al.

PNAS Nexus, 2022 Jul;1(3):pgac093.
PMID: 35990802 DOI: 10.1093/pnasnexus/pgac093

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution-individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic.
Poster presentations

Aksu F, Topacoglu H, Arman C, Atac A, Tetik S, Hasanovic A, et al.

Surg Radiol Anat, 2009 Sep;31 Suppl 1:95-229.
PMID: 27392492 DOI: 10.1007/BF03371486

Conference abstracts: Malaysia in affiliation
(1). PO-211. AGE-SPECIFIC STRESS-MODULATED
CHANGES OF SPLENIC IMMUNOARCHITECTURE
IN THE GROWING BODY. Marina Yurievna Kapitonova, Syed Baharom Syed Ahmad Fuad, Flossie Jayakaran; Faculty of Medicine, Universiti Teknologi MARA, Shah Alam, Malaysia
syedbaharom@salam.uitm.edu.my
(2). PO-213. A DETAILED OSTEOLOGICAL STUDY OF THE ANOMALOUS GROOVES NEAR THE
MASTOID NOTCH OF THE SKULL. ISrijit Das, 2Normadiah Kassim, lAzian Latiff, IFarihah Suhaimi, INorzana Ghafar, lKhin Pa Pa Hlaing, lIsraa Maatoq, IFaizah Othman; I Department of Anatomy, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia; 2 Department of Anatomy, Universiti Malaya, Kuala Lumpur, Malaysia. das_sri jit23@rediffmail.com
(3). PO-21S. FIRST LUMBRICAL MUSCLE OF THE
PALM: A DETAILED ANATOMICAL STUDY WITH
CLINICAL IMPLICATIONS. Srijit Das, Azian Latiff, Parihah Suhaimi, Norzana Ghafar, Khin Pa Pa Hlaing, Israa Maatoq, Paizah Othman; Department of Anatomy, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia. das_srijit23@rediffmail.com
(4). PO-336. IMPROVEMENT IN EXPERIMENTALLY
INDUCED INFRACTED CARDIAC FUNCTION
FOLLOWING TRANSPLANTATION OF HUMAN
UMBILICAL CORD MATRIX-DERIVED
MESENCHYMAL CELLS. lSeyed Noureddin Nematollahi-Mahani, lMastafa Latifpour, 2Masood Deilami, 3Behzad Soroure-Azimzadeh, lSeyed
Hasan Eftekharvaghefi, 4Fatemeh Nabipour, 5Hamid
Najafipour, 6Nouzar Nakhaee, 7Mohammad Yaghoobi, 8Rana Eftekharvaghefi, 9Parvin Salehinejad, IOHasan Azizi; 1 Department of Anatomy, Kerman University of Medical Sciences, Kerman, Iran; 2 Department of Cardiosurgery, Hazrat-e Zahra Hospital, Kerman, Iran; 3 Department of Cardiology, Kerman University of Medical Sciences, Kerman, Iran; 4 Department of Pathology, Kerman University of Medical Sciences, Kerman, Iran; 5 Department of Physiology, Kerman University of Medical Sciences, Kerman, Iran; 6 Department of Neuroscience Research Center, Kerman University of Medical Sciences, Kerman, Iran; 7 Department
of Biotechnology, Research Institute of Environmental Science, International Center for Science, High Technology & Environmental Science, Kerman, Iran; 8 Students Research Center, Kerman University of Medical Sciences, Kerman, Iran; 9 Institute of Bioscience, University Putra Malaysia,
Kuala Lumpur, Malaysia; 10 Department of Stem Cell, Cell Science Research Center, Royan Institute, ACECR, Tehran, Iran. nnematollahi@kmu.ac.ir
(5).