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The environment in the fertilizer complex is highly corrosive to the structural members due to the varied nature of the raw materials, intermediates and end products being handled. The conditions varies from highly acidic to highly alkaline. The emission of corrosive salts coupled with moist gases aggravate the deterioration of the structurals.

The purpose of this study is to improve the performance of sodium borohydride in reducing indigo at room temperature, the divalent copper ion complex was combined with electrochemical technology for the reduction of indigo by sodium borohydride.

According to the K/S value of the dyed cloth sample, find a more suitable ligand for the copper ion in the catholyte. Response surface analysis tests were performed to evaluate the effects of sodium borohydride concentration, sodium hydroxide concentration and copper sulfate pentahydrate concentration on the reduction potential of the dye solution and the K/S value of the dyed fabric samples.

Sodium gluconate was found to be a more suitable ligand for copper ions in catholyte. The effects of NaOH concentration as well as the interaction of NaBH₄ and NaOH on the reduction potential of the catholyte and the K/S value of the dyed fabric samples were extremely significant. The optimal concentrations of NaBH₄, NaOH and CuSO₄•5H₂O were 0.5, 2.5 and 0.65 g/L. In the case of the optimized condition, the absolute value of the reduction potential was 968, and the K/S value was 11.92, which is comparable with that of the conventional reduction process with sodium dithionite.

The divalent copper ion complex combined with electrochemical technology was applied in the process of reducing indigo with NaBH₄ at room temperature.

Blockchain and Internet of Things (IoT) technologies have recently gained much attention for Industry 4.0. With the emergence of disruptive technologies, it has become essential to redesign the business for innovations based on blockchain–IoT integrated architecture that helps organizations to improve agility in their operations. The paper aims to discuss this issue.

An industrial pump was Sensorized and IoTized to monitor its operations on real time and take predictive measures for managing these assets with more agility. The developed architecture was further extended for proposing the use of blockchain and how it can benefit the organization.

The known features of blockchain such as increasing the capacity of decentralization, trust-less transactions, security and allowing autonomous coordination of the devices along with the boons of IoT will help achieve the motto of improving agility in Industry 4.0.

This paper gives a new dimension to utilization of blockchain technology. blockchain along with IoT that gives a way forward for industries like manufacturing, oil and gas, engineering and construction, utilities, etc. to re-designing the business organization in a more agile way.

To improve the problems as the heavy burden of sewage treatment and environmental pollution caused by the traditional sodium hydrosulfite reduction dyeing of indigo, this study aims to carry out the direct electrochemical reduction dyeing for indigo with the eco-friendly Cu(II)/sodium borohydride reduction system under normal temperature and pressure conditions.

The electrochemical behavior of Cu(II)/sodium borohydride reduction system was investigated by cyclic voltammetry. And, the dyeing performance of the Cu(II)/sodium borohydride reduction system was developed by optimizing the concentration of copper sulfate in the anode electrolyte, applied voltage and reduction time via single-factor and orthogonal integrated analysis.

The dyeing performance of the Cu(II)/sodium borohydride reduction system is superior to that of the traditional reduction dyeing with sodium hydrosulfite. In the case of the optimized condition, the soaping fastness and dry/wet rubbing fastness of the dyed fabric in the two reduction dyeing processes were basically comparable, the K/S value of electrocatalytic reduction of indigo by Cu(II)/NaBH4 is 11.81, which is higher than that obtained by traditional sodium hydrosulfite reduction dyeing of indigo.

The innovative electrocatalytic reduction system applied herein uses sodium borohydride as the hydrogen source combined with Cu(II) complex as the catalyst, which can serve as a medium for electron transfer and active the dye molecule to make it easier to be reduced. The electrochemical dyeing strategy presented here provides a new idea to improve the reduction dyeing performance of indigo by sodium borohydride.

The aim of this paper is to assess the trend of changes of nutritional status and environmental quality of different decomposing organic wastes in different phases of vermicomposting utilizing an indigenous earthworm's species Perionyx excavatus.

Four different organic wastes were selected as basic ingredients for vermicompost production. Dried waste materials (2 kg) of each were mixed with dried cow dung and placed in experimental plastic trays with 60 individuals of selected earthworm species. Samples were collected from each tray at an interval of 15 days. Six trays were used: three were with both wastes, three trays having only wastes were treated as control.

Regression equation has shown the rate of change of nutrients – both in control and experiment vermicomposting units over time – which revealed significant changes of C, N, P and C/N ratio during different phases of vermicomposting.

Vermitechnology is now being considered as an eco‐friendly, user‐friendly and cost‐effective biotechnological tool for the conversion of decomposable organic wastes into valuable organic manure, ensuring pollution abatement, self employment and sustainability in agriculture.

The paper helps to identify the potential of locally available earthworm species and organic materials for the mass production of vermicompost involving local peoples and locally available bioresources.

Stock market forecasters are focusing to create a positive approach for predicting the stock price. The fundamental principle of an effective stock market prediction is not only to produce the maximum outcomes but also to reduce the unreliable stock price estimate. In the stock market, sentiment analysis enables people for making educated decisions regarding the investment in a business. Moreover, the stock analysis identifies the business of an organization or a company. In fact, the prediction of stock prices is more complex due to high volatile nature that varies a large range of investor sentiment, economic and political factors, changes in leadership and other factors. This prediction often becomes ineffective, while considering only the historical data or textural information. Attempts are made to make the prediction more precise with the news sentiment along with the stock price information.

This paper introduces a prediction framework via sentiment analysis. Thereby, the stock data and news sentiment data are also considered. From the stock data, technical indicator-based features like moving average convergence divergence (MACD), relative strength index (RSI) and moving average (MA) are extracted. At the same time, the news data are processed to determine the sentiments by certain processes like (1) pre-processing, where keyword extraction and sentiment categorization process takes place; (2) keyword extraction, where WordNet and sentiment categorization process is done; (3) feature extraction, where Proposed holoentropy based features is extracted. (4) Classification, deep neural network is used that returns the sentiment output. To make the system more accurate on predicting the sentiment, the training of NN is carried out by self-improved whale optimization algorithm (SIWOA). Finally, optimized deep belief network (DBN) is used to predict the stock that considers the features of stock data and sentiment results from news data. Here, the weights of DBN are tuned by the new SIWOA.

The performance of the adopted scheme is computed over the existing models in terms of certain measures. The stock dataset includes two companies such as Reliance Communications and Relaxo Footwear. In addition, each company consists of three datasets (a) in daily option, set start day 1-1-2019 and end day 1-12-2020, (b) in monthly option, set start Jan 2000 and end Dec 2020 and (c) in yearly option, set year 2000. Moreover, the adopted NN + DBN + SIWOA model was computed over the traditional classifiers like LSTM, NN + RF, NN + MLP and NN + SVM; also, it was compared over the existing optimization algorithms like NN + DBN + MFO, NN + DBN + CSA, NN + DBN + WOA and NN + DBN + PSO, correspondingly. Further, the performance was calculated based on the learning percentage that ranges from 60, 70, 80 and 90 in terms of certain measures like MAE, MSE and RMSE for six datasets. On observing the graph, the MAE of the adopted NN + DBN + SIWOA model was 91.67, 80, 91.11 and 93.33% superior to the existing classifiers like LSTM, NN + RF, NN + MLP and NN + SVM, respectively for dataset 1. The proposed NN + DBN + SIWOA method holds minimum MAE value of (∼0.21) at learning percentage 80 for dataset 1; whereas, the traditional models holds the value for NN + DBN + CSA (∼1.20), NN + DBN + MFO (∼1.21), NN + DBN + PSO (∼0.23) and NN + DBN + WOA (∼0.25), respectively. From the table, it was clear that the RMSRE of the proposed NN + DBN + SIWOA model was 3.14, 1.08, 1.38 and 15.28% better than the existing classifiers like LSTM, NN + RF, NN + MLP and NN + SVM, respectively, for dataset 6. In addition, he MSE of the adopted NN + DBN + SIWOA method attain lower values (∼54944.41) for dataset 2 than other existing schemes like NN + DBN + CSA(∼9.43), NN + DBN + MFO (∼56728.68), NN + DBN + PSO (∼2.95) and NN + DBN + WOA (∼56767.88), respectively.

This paper has introduced a prediction framework via sentiment analysis. Thereby, along with the stock data and news sentiment data were also considered. From the stock data, technical indicator based features like MACD, RSI and MA are extracted. Therefore, the proposed work was said to be much appropriate for stock market prediction.

The purpose of this paper is to investigate a light-trapping structure based on Ag nanograting for amorphous silicon (a-Si) thin-film solar cell. Silver nanopillar arrays on indium tin oxide layer of the a-Si thin-film solar cells were designed.

The effects of the geometrical parameters such as nanopillar radius (R) and array period (P) were investigated by using the finite element simulation.

The optimization results show that the absorption of the solar cell with Ag nanopillar structure and anti-reflection film is enhanced up to 29.5 per cent under AM1.5 illumination in the 300- to 800-nm wavelength range compared with the reference cell. Furthermore, physical mechanisms of absorption enhancement at different wavelength range are discussed according to the electrical field amplitude distributions in the solar cells.

The research is still in progress. Further studies mainly focus on the performance of solar cells with different nanograting materials.

This study provides a feasible method for light-trapping structure based on Ag nanograting for a-Si thin-film solar cell.

This study is promising for the design of a-Si thin-film solar cells with enhanced performance.

Shelf‐life of cultured milk products is longer than milk but it is still limited. Shelf‐life of cultured milk products could be enhanced by adopting various techniques. The purpose of this paper is to describe how the longer shelf‐life thus attained would extend the market reach and would be economically beneficial to both producers and consumers.

Attempt has been made to enlighten the various techniques such as bacteriocin (nisin, Microgard^TM, natamycin, etc.), lactoperoxidase‐thiocyanate‐hydrogen peroxide system (LP‐system), high pressure treatment, post‐production heat‐treatment (thermization, microwave heating), ultra‐violet (UV) irradiation, carbonization, etc.

Application of more than one bacteriocin may be advantageous to minimize the possibility of survival of microflora resistant to a particular bacteriocin. Pasteurization, being more detrimental to dietetic properties of cultured milk products than thermization, its application is not suggested as a method of preservation. Microwave heating may be better than conventional pasteurization.

Conjugated application of various techniques would be more efficacious in extending the shelf‐life of cultured milk products. Extension in shelf‐life of cultured milk products would be economically beneficial for producers and consumers.

Research questions that this paper attempts to answer are – do the features in general email communication have any significance to a teaching faculty member leaving the business school? Do the sentiments expressed in email communication have any significance to a teaching faculty member leaving the business school? Do the stages mentioned in the transtheoretical model have any relevance to the email behaviour of an individual when he or she goes through the decision process leading to the decision to quit? The purpose of this paper is to study email patterns and use predictive analytics to correlate with the real-world situation of leaving the business school.

The email repository (2010–2017) of 126 teaching faculty members who were associated with a business school as full-time faculty members is the data set that was used for the research. Of the 126 teaching faculty members, 42 had left the business school during this time frame. Correlation analysis, word count analysis and sentiment analysis were executed using “R” programming, and sentiment “R” package was used to understand the sentiment and its association in leaving the business school. From the email repository, a rich feature set of data was extracted for correlation analysis to discover the features which had strong correlation with the faculty member leaving the business school. The research also used data-logging tools to extract aggregated statistics for word frequency counts and sentiment features.

Those faculty members who decide to leave are involved more in external communication and less in internal communications. Also, those who decide to leave initiate fewer email conversations and opt to forward emails to colleagues. Correlation analysis shows that negative sentiment goes down, as faculty members leave the organisation and this is in contrary to the existing review of literature. The research also shows that the triggering point or the intention to leave is positively correlated to the downward swing of the emotional valence (positive sentiment). A number of email features have shown change in patterns which are correlated to a faculty member quitting the business school.

Faculty members of only one business school have been considered and this is primary due to cost, privacy and complexities involved in procuring and handling the data. Also, the reasons for exhibiting the sentiments and their root cause have not been studied. Also the designation, roles and responsibilities of faculty members have not been taken into consideration.

Business schools all over India always have a challenge to recruit good faculty members who can take up research activities, teach and also shoulder administrative responsibilities. Retaining faculty members and keeping attrition levels low will help business schools to maintain the standards of excellence that they aspire. This research is immensely useful for business school, which can use email analytics in predicting the intention of the faculty members leaving their business school.

Although past studies have studied attrition, this study uses predictive analytics and maps it to the intention to quit. This study helps business schools to predict the chance of faculty members leaving the business school which is of immense value, as appropriate measures can be taken to retain and restrict attrition.

As wireless penetration continues to increase worldwide, competitors in the mobile telecommunication industry are changing their strategies from a growth model to a value‐added one. The companies that can attract and retain customers in this highly competitive and increasingly saturated market stand poised to make considerable gains, and thus customer retention is an important field of study in this maturing market. Using the Canadian mobile phone market as an example, this work aims to study the major motivators of customer retention and their interrelationships, and assess the value that customers perceive with regard to the related advertising.

Based on a literature review and expert validation, the motivators of customer retention are divided into three dimensions and eight criteria. A systematic hybrid multiple criteria decision‐making (MCDM) method that combines the decision making trial and evaluation laboratory (DEMATEL) technique and the analytic network process (ANP) is used to examine the customer retention framework and to evaluate the promotional strategies used by various market players.

The interdependence relation shows that phone service quality, customer service quality, and phone plan quality are three major motivators in terms of causality with regard to brand image, customer service quality, and complaint management, while phone service quality has become a hygiene factor with regard to customer satisfaction and retention. The findings from an assessment of the promotional strategies used by the major players in the Canadian mobile telecoms industry suggest that well‐financed foreign entrants pose a risk to the major domestic carriers, and that successful promotional strategies will require strong leverage of their existing price and quality advantages.

This work adopted a hybrid MCDM approach to examine a major strategic issue in mobile telecoms, – i.e. customer retention – and demonstrated the strengths of using this method to investigate rapidly changing markets. The relative importance of the motivators of satisfaction and retention is investigated, and a strategy for customer retention in the mobile telecoms industry is provided to managers.