A Comparative Analysis of Genetic Algorithm and Moth Flame Optimization Algorithm for Multi-Criteria Design Optimization of Wind Turbine Generator Bearing

As global climate change is affecting the meteorological conditions and instigating massive social suffering, the emanation of greenhouse gases is necessitated to be restricted through effective usage of renewable sources of energy as per the directions of the Paris treaty of 2015. Wind energy, a renowned renewable energy resource, is enabling countries to generate power in a relatively cost-effective way and causes a remarkably nominal carbon trail. A considerable extent of the functioning lifespan of wind turbines remains unexploited every year all over the globe because of mechanical malfunctions. The existing research strives to evaluate the relative competency of the Genetic Algorithm (GA) and the Moth Flame Optimization Algorithm (MFOA) for optimizing the wind turbine generator bearing design through enhancement of its static and dynamic load-bearing capacities. The design solutions attained by both of the algorithms validate a noteworthy growth of the optimization objectives when contrasted with the technical catalog standards. Moreover, the relative evaluation demonstrates the superior aptness of multi-criteria GA on multi-criteria MFOA for finding improved design resolutions

Expanding the Yearly Profit of Wind Farm Using Genetic Algorithm with Variable Allocation Method of Possibilities for Crossover and Mutation Procedures

With rising surface air temperature, global communities are continually struggling to restrict the production of greenhouse gases through the competent application of renewable resources. Being a proficient alternative to traditionalelectricity generation technologies, wind energy can facilitate nations to achieve their carbon neutrality goals. This paper aims to enhance the annual profit of wind farms using an enriched genetic algorithm. Innovative dynamic techniques for allotting the chances of crossover and mutation procedures have been employed for the genetic algorithm-based optimization process accompanied by the established static tactic. The evaluation consequences of the projected procedure have been contrasted with the results accomplished by the genetic algorithm with the standard static method of apportioning the crossover and mutation probabilities. The evaluation outcomes authorize the preeminence of the new non-linearly escalating procedure over the static tactic of allotting the crossover and mutation prospects for achieving a more optimal yearly profit

The asymmetric impact of oil price uncertainty on emerging market financial stress : A quantile regression approach

This study investigates the effects of the crude oil implied volatility index (OVX) upon emerging market financial stress (EMFS). We resort to a quantile regression framework as this approach is a better alternative to disentangle the relationship under different market conditions. Besides, we also examine how EMFS responds to the lags and asymmetries in the OVX. The empirical results show significantly positive impacts of OVX upon EMFS. Further, the effects of OVX become more assertive in the upper quantiles of EMFS, implying higher sensitivity to OVX when stress levels are high. In terms of the lagged effects, the relationship is transient as the OVX coefficients become weaker with increasing lag sizes. We further find that only positive impulses in OVX can significantly predict EMFS. Lastly, we report evidence that the Credit market stress is a crucial driver of EMFS.© 2022 The Authors. International Journal of Finance & Economics published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution-Non Commercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.fi=vertaisarvioitu|en=peerReviewed

Optimizing Offshore Wind Power Generation Cost in India

As global climate change is triggering calamitous penalties, renewable power generation sources like wind energy offer a suitable alternative to conventional fossil fuels to abate greenhouse gas releases. For enabling the expanding power requirement of its emergent financial system, India requires to trace more lucrative wind farm locations. The existing work purposes to minimize the wind energy expense in the offshore location of the Gulf of Khambhat employing a Genetic Algorithm. The static and dynamic methods for assigning the crossover and mutation fractions have been engaged concurrently to evaluate their comparative effectiveness. The research outcomes demonstrate the superior efficiency of the dynamic tactic over the static tactic of allocating the crossover and mutation ratios of genetic algorithm-based cost optimization for wind power generation at the Gulf of Khambhat

A Relative Analysis of Genetic Algorithm and Binary Particle Swarm Optimization for Finding the Optimal Cost of Wind Power Generation in Tirumala Area of India

Although India presently holds the global fourth-biggest instated Wind Power Generation (WPG) capability, it necessitates advancing more rapidly to satisfy the rising energy requirement of its evolving economy while restraining the consequential greenhouse gas emission. To accomplish the impressive target of setting up 140 GW WPG competence by 2030 as proposed by the Government of India, a greater number of financially viable wind farms are required to function all over the country without further ado. This paper focuses on finding the optimal cost for WPG in the Tirumala area of Andhra Pradesh. Genetic Algorithm (GA) and Binary Particle Swarm Optimization (BPSO) have been employed concurrently with four randomly chosen terrain conditions. The research outcomes demonstrate the superior capability of BPSO to attain the most optimal cost of energy

Taming energy and electronic waste generation in bitcoin mining: Insights from Facebook prophet and deep neural network

The Bitcoin mining hosted in the blockchain network consumes enormous amounts of energy and generates electronic waste at an alarming rate. The paper aims to model and predict the future values of these two hazardous variables linked to conventional Bitcoin mining. We develop two predictive models using Facebook\u27s Prophet algorithm and deep neural networks to identify and explain energy consumption and electronic waste generation patterns. The models rely on several explanatory features linked to the blockchain microstructure and the Bitcoin marketplace. We assess the predictive performance of the two models based on daily data of energy consumption and electronic waste generation and eleven key input features. We use local interpretable model-agnostic explanation (LIME) and Shapley additive explanation (SHAP) for explaining how these inputs can predict and control energy consumption and electronic waste generation. The findings assist in accurately estimating the future figures of energy discharge and electronic waste accumulation in the present Bitcoin mining setup. The study also reveals the block size to be the major driver

Determinants of electronic waste generation in Bitcoin network : Evidence from the machine learning approach

Electronic waste is generating in the Bitcoin network at an alarming rate. This study identifies the determinants of electronic waste generation in the Bitcoin network using machine learning algorithms. We model the evolutionary patterns of electronic waste and carry out a predictive analytics exercise to achieve this objective. The Maximal Information Coefficient (MIC) and Generalized Mean Information Coefficient (GMIC) help to study the association structure. A series of six state-of-the-art machine learning algorithms - Gradient Boosting (GB), Regularized Random Forest (RRF), Bagging-Multiple Adaptive Regression Splines (BM), Hybrid Neuro Fuzzy Inference Systems (HYFIS), Self-Organizing Map (SOM), and Quantile Regression Neural Network (QRNN) are used separately for predictive modeling. We compare the predictive performance of all the algorithms. Statistically, the GB is a superior model followed by RRF. The performance of SOM is the least accurate. Our findings reveal that the blockchain's size, energy consumption, and the historical number of Bitcoin are the most determinants of electronic waste generation in the Bitcoin network. The overall findings bring out exciting insights into practical relevance for effectively curbing electronic waste accumulation.©2021 Elsevier. This manuscript version is made available under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY–NC–ND 4.0) license, https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

COVID-19 news and the US equity market interactions: an inspection through econometric and machine learning lens

This study investigates the impact of COVID-19 on the US equity market during the first wave of Coronavirus using a wide range of econometric and machine learning approaches. To this end, we use both daily data related to the US equity market sectors and data about the COVID-19 news over January 1, 2020-March 20, 2020. Accordingly, we show that at an early stage of the outbreak, global COVID-19s fears have impacted the US equity market even differently across sectors. Further, we also find that, as the pandemic gradually intensified its footprint in the US, local fears manifested by daily infections emerged more powerfully compared to its global counterpart in impairing the short-term dynamics of US equity markets

Incident Subjective Cognitive Decline Does Not Predict Mortality in the Elderly - Results from the Longitudinal German Study on Ageing, Cognition, and Dementia (AgeCoDe)

Objective Subjective cognitive decline (SCD) might represent the first symptomatic representation of Alzheimer's disease (AD), which is associated with increased mortality. Only few studies, however, have analyzed the association of SCD and mortality, and if so, based on prevalent cases. Thus, we investigated incident SCD in memory and mortality. Methods Data were derived from the German AgeCoDe study, a prospective longitudinal study on the epidemiology of mild cognitive impairment (MCI) and dementia in primary care patients over 75 years covering an observation period of 7.5 years. We used univariate and multivariate Cox regression analyses to examine the relationship of SCD and mortality. Further, we estimated survival times by the KaplanMeier method and case-fatality rates with regard to SCD. Results Among 971 individuals without objective cognitive impairment, 233 (24.0%) incidentally expressed SCD at follow-up I. Incident SCD was not significantly associated with increased mortality in the univariate (HR = 1.0, 95% confidence interval = 0.8-1.3, p = .90) as well as in the multivariate analysis (HR = 0.9, 95% confidence interval = 0.7-1.2, p = .40). The same applied for SCD in relation to concerns. Mean survival time with SCD was 8.0 years (SD = 0.1) after onset. Conclusion Incident SCD in memory in individuals with unimpaired cognitive performance does not predict mortality. The main reason might be that SCD does not ultimately lead into future cognitive decline in any case. However, as prevalence studies suggest, subjectively perceived decline in non-memory cognitive domains might be associated with increased mortality. Future studies may address mortality in such other cognitive domains of SCD in incident cases