An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach

Epilepsy is a neurological disorder and for its detection, encephalography (EEG) is a commonly used clinical approach. Manual inspection of EEG brain signals is a time-consuming and laborious process, which puts heavy burden on neurologists and affects their performance. Several automatic techniques have been proposed using traditional approaches to assist neurologists in detecting binary epilepsy scenarios e.g. seizure vs. non-seizure or normal vs. ictal. These methods do not perform well when classifying ternary case e.g. ictal vs. normal vs. inter-ictal; the maximum accuracy for this case by the state-of-the-art-methods is 97+-1%. To overcome this problem, we propose a system based on deep learning, which is an ensemble of pyramidal one-dimensional convolutional neural network (P-1D-CNN) models. In a CNN model, the bottleneck is the large number of learnable parameters. P-1D-CNN works on the concept of refinement approach and it results in 60% fewer parameters compared to traditional CNN models. Further to overcome the limitations of small amount of data, we proposed augmentation schemes for learning P-1D-CNN model. In almost all the cases concerning epilepsy detection, the proposed system gives an accuracy of 99.1+-0.9% on the University of Bonn dataset.Comment: 18 page

An investigation into the factors of tourists' environmentally responsible behaviour

The objective of this study was to evaluate the determinants that impact the environmentally responsible conduct of tourists within tourist destinations. The study focused on various factors, such as the level of environmental commitment, the reputation of the destination in terms of eco-friendliness, the satisfaction of tourists, and their perception of the value offered by the destination. In order to examine the hypotheses, data was gathered from a sample of 180 tourists visiting different tourist destinations. The data underwent analysis through regression analysis and moderation testing using IBM SPSS. The findings of the analysis indicate a positive correlation between environmental commitment, destination eco-friendly reputation, tourist satisfaction, and tourist value perception of destinations with the environmentally responsible behaviour exhibited by tourists. Additionally, it was discovered that social influence plays a crucial role in moderating the association between the level of environmental commitment and the environmentally responsible behaviour exhibited by tourists. This study makes a valuable contribution to the existing body of literature by examining the impact of various factors on the environmentally responsible behaviour of tourists. Notably, these factors have not been previously examined within a comprehensive model. Furthermore, there is a limited amount of research available regarding the ecological standing of tourist destinations. As a result, this study serves as a significant addition to the existing literature on academia.Muhammad Awais Bhatti (Associate Professor, Department of Management, School of Business, King Faisal University), Emad Mohammed Alnasser (Assistant Professor, Department of Tourism and Hotel Management, Collage of Tourism and Archaeology, King Saud University)Includes bibliographical reference

Integrated Hazard Identification (IHI): A Quick Accident Analysis and Quantification Method for Practitioners

There are many techniques for hazard identification and are divided into shortcut, standard and advanced techniques. Among these, HAZOP and What-If techniques are mostly engaged by practitioners in the chemical process industry. Both of these have certain advantages and limitations, i.e., HAZOP is structured, and what-if covers broad range of scenarios. There is no hazard identification method, which can cover a broad range of scenarios and is structured in nature. For this purpose, a new technique namely integrated hazard identification (IHI) is proposed in this article that integrates HAZOP and What-If. The methodology is demonstrated via hazard identification study of urea synthesis section. Risk ranking is used to sort out the worst-case scenario. This worst-case scenario is further studied in detail for quantification that is performed using the ALOHA software. This quantification has assisted to detect ammonia concentrations in nearby control room and surroundings for worst-case scenario. It is revealed that if ammonia pump is not stopped within 10 minutes, concentration inside and outside the control room may reach to 384 ppm and 2630 ppm, compared to 1100 ppm (AEGL-3). Thus the proposed method would be easy, time saving and covers more details and would be handy for practicing engineers working in different chemical process industries

Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and attributable disease burden in 204 countries and territories, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019

Background: Ending the global tobacco epidemic is a defining challenge in global health. Timely and comprehensive estimates of the prevalence of smoking tobacco use and attributable disease burden are needed to guide tobacco control efforts nationally and globally. Methods: We estimated the prevalence of smoking tobacco use and attributable disease burden for 204 countries and territories, by age and sex, from 1990 to 2019 as part of the Global Burden of Diseases, Injuries, and Risk Factors Study. We modelled multiple smoking-related indicators from 3625 nationally representative surveys. We completed systematic reviews and did Bayesian meta-regressions for 36 causally linked health outcomes to estimate non-linear dose-response risk curves for current and former smokers. We used a direct estimation approach to estimate attributable burden, providing more comprehensive estimates of the health effects of smoking than previously available. Findings: Globally in 2019, 1·14 billion (95% uncertainty interval 1·13–1·16) individuals were current smokers, who consumed 7·41 trillion (7·11–7·74) cigarette-equivalents of tobacco in 2019. Although prevalence of smoking had decreased significantly since 1990 among both males (27·5% [26·5–28·5] reduction) and females (37·7% [35·4–39·9] reduction) aged 15 years and older, population growth has led to a significant increase in the total number of smokers from 0·99 billion (0·98–1·00) in 1990. Globally in 2019, smoking tobacco use accounted for 7·69 million (7·16–8·20) deaths and 200 million (185–214) disability-adjusted life-years, and was the leading risk factor for death among males (20·2% [19·3–21·1] of male deaths). 6·68 million [86·9%] of 7·69 million deaths attributable to smoking tobacco use were among current smokers. Interpretation: In the absence of intervention, the annual toll of 7·69 million deaths and 200 million disability-adjusted life-years attributable to smoking will increase over the coming decades. Substantial progress in reducing the prevalence of smoking tobacco use has been observed in countries from all regions and at all stages of development, but a large implementation gap remains for tobacco control. Countries have a clear and urgent opportunity to pass strong, evidence-based policies to accelerate reductions in the prevalence of smoking and reap massive health benefits for their citizens. Funding: Bloomberg Philanthropies and the Bill & Melinda Gates Foundation. © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Aziz Rahman" is provided in this record*