The looming energy crisis is affecting every sector of the world. The dire need to conserve energy has compelled researchers to bring automation to the power sector. The conservation of energy is one of the biggest challenges Third-World countries are facing in general and in Europe due to the Russian–Ukrainian war. There is a need to introduce such systems that can prevent energy loss and let users buy and sell excessive electricity they have. In the field of power and electricity, the Internet of Things (IoT) plays an active role in the conservation of energy. The new concept of smart grids is widely used for efficient transmission. The technique of blockchain can further reduce the wastage of energy and efficient consumption if it is used with smart grids. This article proposes a smart energy meter based on smart grids and blockchain. The proposed implementation is a demonstration containing a few microgrids, each with its very own blockchain. The users will use energy by making transactions, following the smart contracts. The focus is on the peer-to-peer transactions in a microgrid controlled by blockchain. The architectural design outcomes are a smart energy meter, a smart contract on the Ethereum blockchain, and an android application to monitor and control transactions and energy trade via smart contracts with other consumers.
The main financial markets of every country are stock exchange and consider as an imperative cause for the corporations to increase capital. The novelty of this study to explore machine learning techniques when applied to financial stock market data, and to understand how machine learning algorithms can be applied and compare the result with time series analysis to real lifetime series data and helpful for any investor. Investors are constantly reviewing past pricing history and using it to influence their future investment decisions. The another novelty of this study, using news sentiments, the values will be processed into lists displaying and representing the stock and predicting the future rates to describe the market, and to compare investments, which will help to avoid uncertainty amongst the investors regarding the stock index. Using artificial neural network technique for prediction for KSE 100 index data on closing day. In this regard, six months’ data cycle trained the data and apply the statistical interference using a ARMA (p, q) model to calculate numerical result. The novelty of this study to find the relation between them either they are strongly correlated or not, using machine learning techniques and ARMA (p, q) process to forecast the behavior KSE 100 index cycles. The adequacy of model describes via least values Akaike information criterion (AIC), Bayesian Schwarz information criterion (SIC) and Hannan Quinn information criterion (HIC). Durbin- Watson (DW) test is also applied. DW values (< 2) shows that all cycles are strongly correlated. Most of the KSE-100 index cycles expresses that the appropriate model is ARMA (2,1). Cycle’s 2nd,3rd,4th and 5th shows that ARMA (3,1) is best fitted. Cycle 8th is shows ARMA (1,1) best fit and cycle 12th shows that the most appropriate model is ARMA (4,1). Diagnostic checking tests like Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Theil’s U-Statistics are used to predict KSE-100 index cycles. Theil’s U-Statistics demonstrate that each cycle is strongly correlated to previous one.
The most significant biotic constituent in a lake ecosystem is represented by macrophytes in their diverse forms. Macrophytes, because of their capacity to integrate environmental changes over periods of a few years, and reflect the cumulative effects of successive disturbances, are considered excellent indicators of the ecological state of water bodies Macrophytes are by far the most investigated group used for exploring the effects of water level fluctuation on biological organisms in aquatic ecosystems . In lake ecosystems, Overall 31 species of aquatic macrophytes were reported from Anchar Lake with different morphology, which consisted of emergent (14), rooted floating leaf type (08), submerged (06) and free floating (03). The efforts mainly focus on the relationships between water level fluctuation and the presence, species richness, distribution and cover of macrophytes.
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