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The effects of global warming are felt not only in the Earth’s climate but also in the geology of the planet. Modest variations in stress and pore-fluid pressure brought on by temperature variations, precipitation, air pressure, and snow coverage are hypothesized to influence seismicity on local and regional scales. Earthquakes can be anticipated by intelligently evaluating historical climatic datasets and earthquake catalogs that have been collected all over the world. This study attempts to predict the magnitude of the next probable earthquake by evaluating climate data along with eight mathematically calculated seismic parameters. Global temperature has been selected as the only climatic variable for this research, as it substantially affects the planet’s ecosystem and civilization. Three popular deep neural network models, namely, long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), and transformer models, were used to predict the magnitude of the next earthquakes in three seismic regions: Japan, Indonesia, and the Hindu-Kush Karakoram Himalayan (HKKH) region. Several well-known metrics, such as the mean absolute error (MAE), mean squared error (MSE), log-cosh loss, and mean squared logarithmic error (MSLE), have been used to analyse these models. All models eventually settle on a small value for these cost functions, demonstrating the accuracy of these models in predicting earthquake magnitudes. These approaches produce significant and encouraging results when used to predict earthquake magnitude at diverse places, opening the way for the ultimate robust prediction mechanism that has not yet been created.

Forecasting the stock market is one of the most difficult undertakings in the financial industry due to its complex, volatile, noisy, and nonparametric character. However, as computer science advances, an intelligent model can help investors and analysts minimize investment risk. Public opinion on social media and other online portals is an important factor in stock market predictions. The COVID-19 pandemic stimulates online activities since individuals are compelled to remain at home, bringing about a massive quantity of public opinion and emotion. This research focuses on stock market movement prediction with public sentiments using the long short-term memory network (LSTM) during the COVID-19 flare-up. Here, seven different sentiment analysis tools, VADER, logistic regression, Loughran-McDonald, Henry, TextBlob, Linear SVC, and Stanford, are used for sentiment analysis on web scraped data from four online sources: stock-related articles headlines, tweets, financial news from "Economic Times" and Facebook comments. Predictions are made utilizing both feeling scores and authentic stock information for every one of the 28 opinion measures processed. An accuracy of 98.11% is achieved by using linear SVC to calculate sentiment ratings from Facebook comments. Thereafter, the four estimated sentiment scores from each of the seven instruments are integrated with stock data in a step-by-step fashion to determine the overall influence on the stock market. When all four sentiment scores are paired with stock data, the forecast accuracy for five out of seven tools is at its most noteworthy, with linear SVC computed scores assisting stock data to arrive at its most elevated accuracy of 98.32%.

Earthquakes have long been studied as a geological phenomenon, and the nature of their occurrences has been investigated by examining various geological events. Global temperature has been changing since the eighteenth century. A correlation between these two dynamics has been suggested but has not been experimentally verified. This unprecedented work aims to consider the magnitude of globally occurred earthquakes as a time series and analyse its nature using statistical signal processing tools. The trends in global temperature have also been analysed similarly. This was done by primarily classifying the time series as fractional Brownian motion or fractional Gaussian noise based on their power spectral density. The Hurst exponent for the time series was computed using four standard methods, which exhibit that both dynamics are self-similar, anti-persistent, and fractional Brownian motion in nature. Continuous wavelet transform–based semblance analysis of both the signals clearly shows the co-relationship between the two dynamics. Finally, the Granger causality test has been implemented, which reveals that both dynamics are caused by one another, and one is predictable using the other’s previous data.

Earthquake is a major geographical phenomenon being investigated for a long time using the examination of various geological events. Since the beginning of the eighteenth century, there has been a gradual change in global temperature, which has affected the Earth’s atmosphere and crust. A correlation between these two dynamics has been suggested but not experimentally verified. This unique work aims to consider the magnitude of globally occurred earthquakes and global temperature as a monthly time series, analyze its characteristics, and determine the co-relationship. The multifractal detrended fluctuation analysis (MFDFA) and wavelet transform modulus maxima (WTMM) have been carried out for the two signals, which reveals Generalized Hurst component h(q) 0 ) and zero-one test (where K ➔ 1). To find the casual relationship, Granger causality test has been applied, which reveals that both the dynamics are interrelated. Finally, Pearson correlation analysis establishes the degree of association between global temperature fluctuation and the frequency of global earthquake occurrences. A small case study of Alaska region has been considered to validate the experimental results.

Water pollution due to contamination of dye containing effluents is a great threat to water body. A study on the biosorption of methylene blue (MB) onto low-cost Lemna major biomass was conducted and the process parameters were optimized by response surface methodology (RSM). A two-level, four-factor central composite design (CCD) has been employed to determine the effect of various process parameters namely initial concentration (600–1000 mg L−1), bioadsorbent dose (0.20–1.50 g/100 mL), pH (5–12) and stirring rate (250–800 rpm) on MB uptake from aqueous solution. By using this design a total of 30 biosorption experimental data were fitted. The regression analysis showed good fit of the experimental data to the second-order polynomial model with coefficient of determination (R2) value of 0.9978 and model F-value of 953.48. The optimum conditions of initial concentration (1000 mg L−1), adsorbent dose (0.2 g), pH (5) and stirring rate (251.51 rpm) were recorded from desirability function. The adsorption isotherm data were best described by both Freundlich and Langmuir models with a maximum adsorption capacity of 488 mg MB g−1 L. major biomass at 30 °C which is higher than that available with adsorbents used by past researchers. Finally the pseudo second order kinetic model described the MB biosorption process with a good fitting (R2 = 0.999). The adsorbent was characterised by scanning electron micrograph (SEM) and Fourier transform infrared spectroscopy (FTIR).

Adsorptive removal of carbaryl from aqueous solutions by neem bark dust (NBD) was investigated in a batch method under laboratory conditions. At first, the effects of particle size, stirring rate, and contact time on the adsorption process were studied. The optimum value of particle size, stirring rate, and contact time were 200 μm, 250 rpm, and 25 min, respectively. Subsequently, response surface methodology (RSM) was applied to investigate the effects of other operating parameters such as solution pH (2–10), adsorbent dose (0.01–1 g), and initial concentration (5–20 ppm). The optimization of the process parameters and calculation of the effects and interactions of process variables were done by using Box-Behnken design (BBD) which is a subset of RSM. The independent variables were precisely optimized by making use of an objective function called “desirability function.” Based on the adsorption capacity and economical use of adsorbent, the input parameters were optimized by setting two different sets of criteria (I and II). The desirability of two different sets were 1.00 and 0.822, respectively, which explains that the estimated function can well represent the experimental model. The optimized result revealed that the NBD can be an effective adsorbent for the removal of carbaryl from an aqueous system. The adsorption of carbaryl on NBD was best analyzed with the Langmuir isotherm and the pseudo-second order kinetic model. From the kinetic study, the maximum adsorption capacity was found to be 142.85 mg g−1. Thermodynamic data confirmed the feasibility and spontaneous nature of the adsorption process.

In this paper, an investigation has been made to detect the self-similarity and stationarity nature of magnitude of occurred Earthquake by exploring the fractal pattern and the variation nature of frequency of the essential parameter, Magnitude of occurred earthquake across the different place of the world. The time series of magnitude (19.04.2005 to 07.11.2017), of occurred earthquakes, collected from U.S.G.S. have been analyzed for exposing the nature of scaling (fractality) and stationary behavior using different statistical methodologies. Three conventional methods namely Visibility Graph Analysis (VGA), Wavelet Variance Analysis (WVA) and Higuchi’s Fractal Dimension (HFD) are being used to compute the value of Hurst parameter. It has been perceived that the specified dataset reveals the anti-persistency and Short-Range Dependency (SRD) behavior. Binary based KPSS test and Time Frequency Representation based Smoothed Pseudo Wigner-Ville Distribution (SPWVD) test have been incorporated to explore the nature of stationarity/non-stationarity of that specified profile where the magnitude of earthquake displays the indication of non-stationarity character.

In this paper, fractality and stationarity of average Sensex value of stock market have been investigated by enlightening the self-similarity characteristics and nature of frequency oscillation of the two crucial constraints, namely, the average Sensex values of BSE and NASDAQ. The time series of these parameters between 31st Oct 2007 and 1st Nov 2017, of Bombay Stock Exchange (BSE) and American stock exchange (NASDAQ) have been chosen for extracting the nature of scaling (fractality) and stationary behavior using statistical techniques. Methodologies like General Hurst Estimation (GHE) and Higuchi’s Fractal dimension (HFD) have been used to compute Hurst Exponent for the time series. According to experimental results, the said time series exhibits SRD - Short Range Dependent anti-persistent character. The stationarity behavior of the data-series has been determined using Continuous Wavelet Transform (CWT) method. Both the time series have been found to exhibit non-stationarity.

Color is the main attraction of any fabric industry. No matter how excellent its constitution, if unsuitably colored it is bound to be a failure as a commercial fabric. Manufacture and use of synthetic dyes like methylene blue (MB) for fabric dyeing has therefore become a massive industry today. Soil is widely used as adsorbent for removing toxic pollutants from their aqueous solutions due to its wide availability and cost efficiency. In this work, the feasibility of employing calcareous soil to remove MB, a cationic dye from its aqueous solutions was investigated. The entire process has been done through batch operation. Percentage removal was greater for dilute solutions, but the absolute amount of MB adsorbed by the calcareous soil at equilibrium increased with an increase of the initial concentration. The kinetics of the adsorption process was analyzed by pseudo-first-order, pseudo-second-order, intraparticle diffusion, surface mass transfer, and Elovich models. The first four mechanisms seem to be significant in the rate controlling step. Equilibrium adsorption was investigated by Freundlich, Langmuir, Temkin, and D–R isotherms. The Langmuir equation (R 2 = 0.982) fitted to equilibrium data better than all the tested isotherm models. The dimensionless separation factor (R L) was low (0.00007) indicating favorable adsorption of MB on to calcareous soil. Thermodynamic activation parameters such as ∆S°, ∆G°, and ∆H° were also calculated and results revealed that the adsorption process is a favorable exothermic and can be spontaneous in nature. Finally, it can be concluded that calcareous soil can be an effective low-cost adsorbent for removal of cationic dye from waste effluents.

In this study, response surface methodology (RSM) was employed for the removal of methylene blue (MB) from aqueous solution using neem (Azadirachta indica) bark dust (NBD) as a bioadsorbent. The influence of various process parameters namely initial concentration (500–1,000 mg L−1), bioadsorbent dose [0.20–1.50 g (100 mL)−1], pH (5–12), and stirring rate (250–650 rpm) was taken as an input parameter. A total of 29 biosorption experimental runs were carried out employing the detailed conditions designed by RSM based on the Box–Behnken design (BBD). Response surface plots were studied to determine the interaction effects of main factors and optimum conditions of process. Regression analysis showed good fit of the experimental data to the second-order polynomial model with coefficient of determination (R 2) value of 0.9760 and model F value of 40.68. The predicted R 2 value is 0.8979. In addition, results reported in this research demonstrated the feasibility of employing NBD as bioadsorbent for MB removal.

In this work, adsorption of carbaryl from aqueous solution on Pistia stratiotes biomass was investigated. The effects of operating parameters such as initial concentration, pH, adsorbent dose and contact time on the adsorption of carbaryl were analyzed using response surface methodology. The proposed quadratic model for central composite design fitted very well to the experimental data that it could be used to navigate the design space according to analysis of variance results. Response surface plots were used to determine the interaction effects of main factors and optimum conditions of the process. The optimum adsorption conditions were found to be initial carbaryl concentration = 15.57 mg L−1, pH 2.01, adsorbent dose = 0.72 g and contact time = 30 min. The Langmuir, Freundlich and Temkin isotherm models were applied to the equilibrium data. The maximum biosorption capacity of P. stratiotes biomass for carbaryl was found to be 3.1 mg g−1. The pseudo-second-order kinetic model described the carbaryl biosorption process with a good fitting.

This study was undertaken to investigate the adsorption capacity of carbaryl on four Indian soils with different physiochemical properties. A batch adsorption study was carried out in order to evaluate the maximum adsorption capacity of carbaryl using a Response Surface Methodology (RSM). The effects of operating parameter such as initial carbaryl concentration (1-20 mgL⁻¹), adsorbent dosage (0.5-6 g) and contact time (10-180 min) were examined. The proposed quadratic model for Box-Behnken design fits very well to the experimental data because it may be used to navigate design space according to ANOVA results. The regression co-efficient (R²) of the models developed and the results of validation experiments conducted at optimal conditions strongly suggests that the predicted values are in good agreement with experimental results. Contour and response surface plots are used to determine the interactions effects of main factors and optimal conditions of the process. The experiment can be utilized as a guideline for better understanding of carbaryl adsorption onto soil under different operating conditions. The results show that the forest soil is most efficient in binding carbaryl (Sevin) than the other types of soil tested.