Population-level risks of alcohol consumption by amount, geography, age, sex, and year: a systematic analysis for the Global Burden of Disease Study 2020

BACKGROUND: The health risks associated with moderate alcohol consumption continue to be debated. Small amounts of alcohol might lower the risk of some health outcomes but increase the risk of others, suggesting that the overall risk depends, in part, on background disease rates, which vary by region, age, sex, and year. METHODS: For this analysis, we constructed burden-weighted dose-response relative risk curves across 22 health outcomes to estimate the theoretical minimum risk exposure level (TMREL) and non-drinker equivalence (NDE), the consumption level at which the health risk is equivalent to that of a non-drinker, using disease rates from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2020 for 21 regions, including 204 countries and territories, by 5-year age group, sex, and year for individuals aged 15-95 years and older from 1990 to 2020. Based on the NDE, we quantified the population consuming harmful amounts of alcohol. FINDINGS: The burden-weighted relative risk curves for alcohol use varied by region and age. Among individuals aged 15-39 years in 2020, the TMREL varied between 0 (95% uncertainty interval 0-0) and 0·603 (0·400-1·00) standard drinks per day, and the NDE varied between 0·002 (0-0) and 1·75 (0·698-4·30) standard drinks per day. Among individuals aged 40 years and older, the burden-weighted relative risk curve was J-shaped for all regions, with a 2020 TMREL that ranged from 0·114 (0-0·403) to 1·87 (0·500-3·30) standard drinks per day and an NDE that ranged between 0·193 (0-0·900) and 6·94 (3·40-8·30) standard drinks per day. Among individuals consuming harmful amounts of alcohol in 2020, 59·1% (54·3-65·4) were aged 15-39 years and 76·9% (73·0-81·3) were male. INTERPRETATION: There is strong evidence to support recommendations on alcohol consumption varying by age and location. Stronger interventions, particularly those tailored towards younger individuals, are needed to reduce the substantial global health loss attributable to alcohol. FUNDING: Bill & Melinda Gates Foundation

Cryptocurrency Frauds

There are scams of millions of dollars happening on a daily basis in the world of cryptocurrency. Awareness and the latest 21st century technology- “Artificial Intelligence,” may prove to be the key to fight this battle against Cryptocurrency scams. This research paper provides a comprehensive analysis discussing the various “Cryptocurrency scams” across the world, while giving case analysis of the biggest scams regarding the same, later exploring various solutions to this problem. This research paper, goes beyond the usual theft from hacking and ransomware attacks, and explores the in-depths of many of the frauds that have not been most commonly heard of. When we say „Cryptocurrency frauds,‟ we directly associate with hacking and theft due to unauthorized access, but it goes more than that, and that is what this paper seeks to explore.</jats:p

Assessment of Waste Management through Mobile Edge Computing and Deep Learning

Due to the random occurrences of street waste, city managers usually spend a lot of energy and money cleaning street garbage, which is a core duty in computer vision, with applications ranging across the process of smart city creation. Deep network solutions are frequently constrained by the amount of training data available as they become deeper and more complicated. With this in mind, Open CV or Google AI has made the Open Images dataset publicly available in order to drive breakthroughs in image analysis and interpretation. Open Images continues the legacy of PASCAL VOC, Image Net, and COCO, but on a much larger scale. As a result, visual street cleanliness assessment will be extremely vital in this project. Existing assessment methods, on the other hand, have several significant drawbacks, such as the lack of automation in the collecting of street waste data and the lack of real-time street cleanliness data. Finally, the findings are fed into a framework for calculating street cleanliness, which allows for the visualisation of street cleanliness. Cleanliness levels are maintained at a high level, making it easier for city managers to schedule clean-up crews.</jats:p

Sample-efficient Optimization Using Neural Networks

&lt;p&gt;The solution to many science and engineering problems includes identifying the minimum or maximum of an unknown continuous function whose evaluation inflicts non-negligible costs in terms of resources such as money, time, human attention or computational processing. In such a case, the choice of new points to evaluate is critical. A successful approach has been to choose these points by considering a distribution over plausible surfaces, conditioned on all previous points and their evaluations. In this sequential bi-step strategy, also known as Bayesian Optimization, first a prior is defined over possible functions and updated to a posterior in the light of available observations. Then using this posterior, namely the surrogate model, an infill criterion is formed and utilized to find the next location to sample from. By far the most common prior distribution and infill criterion are Gaussian Process and Expected Improvement, respectively.    The popularity of Gaussian Processes in Bayesian optimization is partially due to their ability to represent the posterior in closed form. Nevertheless, the Gaussian Process is afflicted with several shortcomings that directly affect its performance. For example, inference scales poorly with the amount of data, numerical stability degrades with the number of data points, and strong assumptions about the observation model are required, which might not be consistent with reality. These drawbacks encourage us to seek better alternatives. This thesis studies the application of Neural Networks to enhance Bayesian Optimization. It proposes several Bayesian optimization methods that use neural networks either as their surrogates or in the infill criterion.    This thesis introduces a novel Bayesian Optimization method in which Bayesian Neural Networks are used as a surrogate. This has reduced the computational complexity of inference in surrogate from cubic (on the number of observation) in GP to linear. Different variations of Bayesian Neural Networks (BNN) are put into practice and inferred using a Monte Carlo sampling. The results show that Monte Carlo Bayesian Neural Network surrogate could performed better than, or at least comparably to the Gaussian Process-based Bayesian optimization methods on a set of benchmark problems.  This work develops a fast Bayesian Optimization method with an efficient surrogate building process. This new Bayesian Optimization algorithm utilizes Bayesian Random-Vector Functional Link Networks as surrogate. In this family of models the inference is only performed on a small subset of the entire model parameters and the rest are randomly drawn from a prior. The proposed methods are tested on a set of benchmark continuous functions and hyperparameter optimization problems and the results show the proposed methods are competitive with state-of-the-art Bayesian Optimization methods.  This study proposes a novel Neural network-based infill criterion. In this method locations to sample from are found by minimizing the joint conditional likelihood of the new point and parameters of a neural network. The results show that in Bayesian Optimization methods with Bayesian Neural Network surrogates, this new infill criterion outperforms the expected improvement.   Finally, this thesis presents order-preserving generative models and uses it in a variational Bayesian context to infer Implicit Variational Bayesian Neural Network (IVBNN) surrogates for a new Bayesian Optimization. This new inference mechanism is more efficient and scalable than Monte Carlo sampling. The results show that IVBNN could outperform Monte Carlo BNN in Bayesian optimization of hyperparameters of machine learning models.&lt;/p&gt;</jats:p

Realization of Low Cost Footwear Integrated Step-Counting Device for Health Monitoring System

Abstract In this paper, a piezoelectric sensor is used to sense the pressure generated when steps are taken. The output voltage generated by the piezo electric sensor is passed through a cascade combination of two voltage comparators, (LM 358 AND 741), the output of which is HIGH(5V) when a piezoelectric sensor is pressed and the voltage across the sensor goes above the threshold. The output of the comparators is then fed to the counter circuit (implemented using 4026 IC), which counts the number of times the output is high/steps are taken. Finally, the output is exhibited on a 7-segment display common cathode display.</jats:p