This paper comprehensively analyzes freshwater withdrawal patterns in six UN member countries—China, France, India, Russia, United Kingdom, and the United States. By analyzing historical data, this study explores the time-related trends in the use of water, identifies the factors that drive these patterns, and forecasts future water demands through the application of sophisticated time series modeling techniques, specifically the ARIMA model. Upon analysis of the results, striking differences are observed among the countries in respect to the withdrawal patterns of water and the main drivers behind these; including agricultural practices, industrial activities, demographic growth, governmental policies, among others. The ARIMA model represents the customized specific water usage of each country and provides reliable forecasts that reveal both challenges and opportunities for water resource management in the near future. This research emphasizes the need for proactive policy interventions to promote sustainable water use amid increasing demand and environmental variability.

The paper proposes a hybrid algorithm for forecasting multiple correlated time-series data, which consists of two main steps. First, it employs a multivariate Bayesian structural time series (MBSTS) approach as a base step. This method allows for the incorporation of potentially high-dimensional regression components, and it utilizes spike and slab priors to identify a parsimonious model. Second, the algorithm includes a post-model fitting diagnostic step where the residuals from the MBSTS step are processed through a multi-input/output temporal convolutional network (M-TCN) with multiple time scale feature learning. This step serves as an alternative to traditional subjective residual-based diagnostic procedures in time-series analysis, with the aim of improving forecasting accuracy. The key advantage of the M-TCN is its ability to capture sequential information efficiently. The M-TCN expands the field of convolution kernel without increasing the number of parameters, thus enhancing the capacity of model to capture complex sequential patterns. The paper presents two applications showcasing the effectiveness of the proposed hybrid algorithm. First, it utilizes pre-lockdown data from eleven Nifty stock sectoral indices to predict stock price movements, including the initial post-lockdown upturn. In the second application, it focuses on stock market data from pharmaceutical companies involved in manufacturing COVID-19 vaccines. In both cases, sentiment data sourced from newspapers and social media serve as the regression component. Through rigorous analysis, the paper demonstrates that the hybrid model outperforms various benchmark models, including LSTM, Bidirectional Encoder Representations from Transformers (BERT)-based LSTM, Deep Transformer Model, and GRU, among others, in terms of forecasting accuracy. This underscores the utility of the hybrid algorithm, particularly in predicting stock market trends during the COVID-19 pandemic period and its associated market dynamics.

Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.

In the financial sector, the stock market and its trends are highly volatile in nature. Recent studies have shown that news articles and social media analysis can have an immense impact on investors' opinion toward financial markets. Thus, the purpose of this study is to explore the relationship between news sentiment and stock market movement using information from different news agencies, business magazines, and financial portals. This study offers an application of the Bayesian structural time (BST) series model that is more transparent and facilitates better handling of uncertainty than the autoregressive integrated moving average (ARIMA) model and the vector autoregression (VAR) method by using prior information about the structure of the model. One of the main pitfalls of this model is the presumption of linearity. The long short-term memory (LSTM) model is a nonlinear model that can capture various nonlinear structures present in the data set. We propose a hybrid model, which combines the LSTM model with the BST model along with the regression component that captures information from different news sources to identify market predictors. The proposed model detects unusual behavior or anomalous pattern of the stock price movement, which makes our model superior compared to the traditional methods. Our new hybrid model accumulates error with lower rates (3.5%) and shows a remarkable performance over some of the other existing hybrid models, such as AR-MLP, ARIMA-LSTM, and VAR-LSTM model.

Sentiment analysis has become a very useful tool in recent times for studying people’s opinions, sentiments and subjective evaluation of any event of social and economic relevance, and in particular, policy decisions. The present paper proposes a framework for sentiment analysis using twitter data for the ’demonetization’ effort of the Government of India. The paper employs twitter data using Twitter API. The methodology of the paper involves collection of data from twitter from different cities of India using geolocation and preprocessing followed by a lexicon-based approach to analyse users’ sentiments over a period of five weeks preceding the policy announcement. In addition to this, the paper also attempts to analyse the sentiments of specific groups of people representing diverse interest groups.

In recent times, people share their opinions, ideas through social networking site, electronic media etc. Different organizations always want to find public opinions about their products and services. Individual consumers also want to know the opinions from existing users before purchasing product. Sentiment analysis is the computational treatment of user's opinions, sentiments and subjectivity of text. In this paper we propose a framework for sentiment analysis using R software which can analyze sentiment of users on Twitter data using Twitter API. Our methodology involves collection of data from Twitter, its pre-processing and followed by a lexicon based approach to analyze user's sentiment.

The results of quenching of fluorescence of the dye Safranine T (ST) by the inorganic ions [Fe(CN)6]3-, [Fe(CN)6]4-, Cu2+, Co2+, Ni2+ and Mn2+ in aqueous micellar solutions of the surfactants diethylene glycol hexadecyl ether (Brij 52), decaethylene glycol hexadecyl ether (Brij 56) and cicosaethylene glycol hexadecyl ether (Brij 58) are presented. The quenching results have been processed in the light of Stern-Volmer (SV) equation and its modified forms to evaluate the extents of interaction between the fluorophore (ST) and the quencher. The magnitudes of the Stern-Volmer constant (KSV) in quencher micelles follow the trend [Fe(CN)6]3- > [Fe(CN)6]4- > Co2+> Cu2 > Ni2+ > Mn2+ in all the Brij media; for each ion the trend in terms of the surfactants is Brij 52 > Brij 56 > Brij 58, which is the order of the polarity of the Brij environment. The ion Co2+ appears to partly quench the emission process by the static mode. The solvent parameters of the Brij micellar media like the Kosower Z value, the transition energy for the intramolecular charge transfer, ET30 and the dielectric constant, D, have been estimated from the shift between the absorption and emission frequencies. A comparison of the quenching behaviours of inorganic ions on ST fluorescence in different media viz., aqueous, aqueous polyethylene glycol and aqueous micellar solutions of Tweens and Brijs has been presented. © 1998 Elsevier Science S.A. All rights reserved.

The visible spectra of safranine T (ST) in micellar solutions of Brij 52, Brij 56 and Brij 58 indicate 1 : 1 charge transfer complex formation of ST with non-ionic Brij micelles. The complexing strength follows the order Brij 52 > Brij 56 > Brij 58. The fluorescence spectra of ST in micellar solutions of Brij also support 1 : 1 ST-micelle complex formation as in the ground state. The equilibrium constant of the dye-micelle complex is directly proportional to the micellar aggregation number and inversely proportional to the surfactant critical micellar concentration (CMC). The solvent parameters, i.e. the Kosower Z value, intramolecular charge transfer energy E30T and dielectric constant of the micellar medium, were evaluated. The binding constant, micellar aggregation number and solvent parameters remain constant over a certain range of Brij concentration, but vary beyond this range. © 1997 Elsevier Science S.A.

The fluorescence quenching of the dye safranine T by inorganic ions ( [Fe(CN)6]3-, [Fe(CN)6]4-, Cu2+ , Co2+, Ni2+and Mn2+) in aqueous polyethylene glycol (PEG) medium (four different molar masses of 200,300,400 and 600) has been investigated. The ions influence the process to different extents; the medium efficiency follows the order PEG 600 > PEG 400 > PEG 300 > PEG 200 > water. The efficiencies of the ions for quenching the fluorescence of safranine T in aqueous and aqueous PEG media are in the order [Fe(CN)6]3- > [Fe(CN)6]4- >Cu2+ >Co2+ >Ni2+ >Mn2+. The photophysical process is not governed by an electron transfer mechanism between the ions and the dye. The process involves collisional quenching and is essentially influenced by the viscosity of the medium; a configurational contribution of PEG to the process is absent. The Stern-Volmer constants of the quenching process are presented and rationalization of the results has been attempted. © 1997 Elsevier Science S.A.

The interaction of the dye safranine T (ST) in the ground and excited states with polyethylene glycols (PEGs) of five different molar masses was studied. The binding parameters of the combinations were estimated in terms of Langmuir and Scatchard equations. The spectroscopic study has enabled the solvent parameters of the aqueous PEG media to be estimated by comparison with such parameters for pure solvents. © 1995.