Notes

Amount of anisogamy can be not related to the power of erotic variety.
Dumitrescu-Hurlin test has found the bidirectional causality of cereal production with temperature and CO2 emissions. Also, unidirectional causality is running from rainfall and rural population to cereal production. The adverse effects of temperature on cereal production are likely to pose severe implications for food security. The paper recommends that governments of the sample countries should research and develop heat-resistant varieties of cereal crops to cope with the adverse effects of temperature on cereal production and ensure food security.The optimal design and performance monitoring of wind farms depend on the precise assessment of spatial and temporal distribution of wind speed. The aim of this research is to investigate the appropriateness of nine popular probability distribution models (exponential, gamma, generalised extreme value, inverse Gaussian, Kumaraswamy, log-logistic, lognormal, Nakagami, and Weibull) for the assessment of wind speed distribution (WSD) at 10 sites situated at topographically distinct locations in Tamil Nadu, India, based on 39 years of data. The results suggest that a single distribution cannot produce best fit for all the stations. On an individual level, the generalised extreme value distribution provided the most suitable fit for majority of the stations, followed by the Kumaraswamy distribution. The Kumaraswamy distribution has performed well even if the WSD of the station is negatively skewed. Hence, based on the ranking and performance consistency, the Kumaraswamy distribution can be preferred irrespective of the topographical heterogeneity of the stations.The electric energy mainly comes from thermal power generation fueled by electric coal, and the selection of electric coal supplier is of great practical significance for the development of electric power enterprises. In this paper, the mechanism of supplier selection in electric coal procurement is designed from a perspective of sustainability. Concretely, eleven evaluation indexes from three aspects of economy, environment, and society are selected to construct a new evaluation index system for selecting sustainable electric coal suppliers. Based on this index system, a new method of supplier selection in electric coal procurement based on 2-tuple correlation coefficient analysis is proposed. In this method, considering that the evaluation index values have the features of multi-source heterogeneous data, i.e., precise real number, interval number, and fuzzy linguistic variables coexist, all index values are converted into 2-tuples. Then, a 2-tuple deviation maximization method is proposed to determine the weight of each evaluation index, and a new method of 2-tuple correlation coefficient analysis is proposed to select sustainable electric coal suppliers. Moreover, the effectiveness and feasibility of the model are highlighted by an application example of selecting the electric coal suppliers. Combined with the development status of electric power industry and the research results of this paper, several countermeasures and suggestions are provided on how to formulate the optimal procurement strategy of electric coal for electric power enterprises and how to improve the competitiveness for electric coal suppliers.Since renewable energy has the paramount importance of achieving environmental sustainability, developing countries like Pakistan have been facing numerous renewable energy generation (REG) development challenges. Thus, the REG development in any country can be attributed to its crucial drivers as well as crucial barriers. This work aims to identify crucial influence factors of REG development by conducting multi-facet content analysis as well as partial least square-based structural equation modeling analysis on data collected through a questionnaire survey. It has been conducted based on academicians from four Pakistani universities as well as professionals from Islamabad Electric Supply Company. https://www.selleckchem.com/ The key outcomes are as follows First, through multi-facet content analysis, ten crucial influence factors are identified. Among those factors, lack of good governance, renewable energy adaptation, and governmental energy policies are revealed to be the crucial barriers to REG development, whereas endowed resources, power production approach, renewable energy demand, investment environment for renewable energy projects, economic returns of renewable energy projects, environmental effects, and public acceptance are found to be the crucial drivers of REG development. Based on relative significance, the lack of good governance is unveiled to be the most significant factor, while environmental effects is found to be the least significant one. Moreover, among the drivers of REG development, public acceptance is found to be the prime contributor. At the same time, among crucial barriers, lack of good governance has shown the largest contribution to REG development.Water is a prime necessity for the survival and sustenance of all living beings. Over the past few years, the water quality of rivers is adversely affected due to harmful wastes and pollutants. This ever-increasing water pollution is a big matter of concern as it deteriorating the water quality, making it unfit for any type of use. Recently, water quality modelling using machine learning techniques has generated a lot of interest and can be very beneficial in ecological and water resources management. However, they suffer many times from high computational complexity and high prediction error. The good performance of a deep neural network like long short-term memory network (LSTM) has been exploited for the time-series data. In this paper, a deep learning-based Bi-LSTM model (DLBL-WQA) is introduced to forecast the water quality factors of Yamuna River, India. The existing schemes do not perform missing value imputation and focus only on the learning process without including a loss function pertaining to tra used. Experimental analysis is carried out by measuring the COD and BOD levels. COD analysis reveals the MSE, RMSE, MAE, and MAPE values as 0.015, 0.117, 0.115, and 20.32, respectively, for the Palla region. Similarly, BOD analysis indicates the MSE, RMSE, MAE, and MAPE values as 0.107, 0.108, 0.124, and 18.22, respectively. A comparative analysis reveals that the proposed model outperforms all other models in terms of the best forecasting accuracy and lowest error rates.
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