Understanding blockchain technology in retail branding for enhancing customer experience and strengthening the retail brand-customer relationship

Abstract. Though the concept of branding used to associate with the manufacturers, over the past decades, to survive the fierce competition in today’s business world, branding becomes indispensable for all business organizations including retail. Branding is important for the retail organization for delivering their brand promises and for increasing their brand equity. The growth of the internet and technological advancement has shaped the way the brand used to communicate. These technological advancements have opened new avenues and opportunities as well as created new challenges for the retail brands. Besides, in recent years, customers became more concern about society and the environment due to the adverse effects of human and business activities. Most of the customers now want and expect products that have been sustainably procured, produced, transported, and fairly traded. This sustainability concern of the customers is creating pressure on the retailers to act in a responsible way towards society and the environment. But the brands are suffering in spite of trying hard to deliver products in a sustainable way to meet the customers’ expectation, as customers are reluctant to rely on brand’s promises and hesitate to buy due to the lack of availability of authentic and credible information about the products and services retailers offer and is resulting in poor brand loyalty and declined brand equity. The aim of this thesis is to understand customer’s perspective on identifying the implications of blockchain technology for solving these problems of trust on retail brands and explore the way for increasing brand loyalty and enhancing brand equity through elevating the customer experience with the retail brands and strengthening the relationships with the customers. The conceptual framework is developed based on the theoretical analysis of existing literature on brand and blockchain technology. This thesis is conducted based on a qualitative research design to meet the purpose of the thesis. The thesis followed an abductive reasoning approach throughout the research process. A semi-structured interview was conducted for investigating and validating the developed conceptual framework and concluded with an empirically validated framework. The target population comprises both male and female from different nationalities and fall in the age group 26 to 40. The empirically validated framework of the research findings reveals that the unique characteristics of blockchain technology have enough potentiality for adding value to the retailers’ branding effort through effective advertising and loyalty programs and efficient inventory management. The research finding further conclude the utilization of blockchain technology in uplifting customer experience and strengthening retail brand-customer relationship for achieving increased brand loyalty and enhanced brand equity

Aspect Βased Classification Model for Social Reviews

Aspect based opinion mining investigates deeply, the emotions related to one’s aspects. Aspects and opinion word identification is the core task of aspect based opinion mining. In previous studies aspect based opinion mining have been applied on service or product domain. Moreover, product reviews are short and simple whereas, social reviews are long and complex. However, this study introduces an efficient model for social reviews which classifies aspects and opinion words related to social domain. The main contributions of this paper are auto tagging and data training phase, feature set definition and dictionary usage. Proposed model results are compared with CR model and Naïve Bayes classifier on same dataset having accuracy 98.17% and precision 96.01%, while recall and F1 are 96.00% and 96.01% respectively. The experimental results show that the proposed model performs better than the CR model and Naïve Bayes classifier

An Overview of Electricity Demand Forecasting Techniques

Load forecasts are extremely important for energy suppliers and other participants in electric energy generation, transmission, distribution and markets. Accurate models for electric power load forecasting are essential to the operation and planning of a utility company. Load forecasts are extremely important for energy suppliers and other participants in electric energy generation, transmission, distribution and markets. This paper presents a review of electricity demand forecasting techniques. The various types of methodologies and models are included in the literature. Load forecasting can be broadly divided into three categories: short-term forecasts which are usually from one hour to one week, medium forecasts which are usually from a week to a year, and long-term forecasts which are longer than a year.  Based on the various types of studies presented in these papers, the load forecasting techniques may be presented in three major groups: Traditional Forecasting technique, Modified Traditional Technique and Soft Computing Technique. Keywords: Electricity Demand, Forecasting Techniques, Soft Computing, Regression method, SVM

Numerical and experimental investigation of an Archimedes screw turbine for open channel water flow application

AbstractLow‐head turbines are becoming an agricultural imperative due to their high efficiency, low cost, ability to operate at low flow rates and minimal environmental impact. Therefore, the Archimedes screw turbine (AST) can play a leading role for producing electric power, especially in Pakistan's rural areas where most of the places have less than 1 m head. In this research work, performance evaluation of AST was carried out at different flow velocities in terms of power coefficient and torque generated. Design parameters such as blade width, blade pitches, and blade rotational angles are also used for performance evaluation. For this purpose, computational fluid dynamic (CFD) analyses of AST blades were conducted at different water flow velocities (e.g., 1, 1.5, 2, 2.5, 3, and 3.5 m/s). ANSYS FLUENT was used for AST blade simulations using three different design parameters such as blade width, blade pitch, and blade rotational angles. Additionally, CFD simulations have inherent errors and uncertainties that may lead to findings and deviations from their exact or real values. To prevent these uncertainties and errors, an experimental study was also conducted to provide validation for the CFD simulation results. The results revealed from CFD simulations for optimized design parameters were then compared with experimental data. From the results, it was examined that the numerical findings were in good agreement with the experiment data. The highest power coefficient and power output values were obtained under optimized design parameters such as inner and outer diameter, blade pitch, blade width, blade rotation angles and shaft length (e.g., 40 mm, 120 mm, 130 mm, 2 mm, 60°, and 850 mm respectively). The findings can be useful to implement the AST unit for those places where the available water head is ranging from 1 to 6.5 m and a flow rate of 0.2–6.5 m3/s, especially for rural areas of Pakistan

Growth and proximate composition of tropical marine Chaetoceros calcitrans and Nannochloropsis oculata cultured outdoors and under laboratory conditions

The growth and proximate composition of two marine microalgae, Chaetoceros calcitrans and Nannochloropsis oculata, cultured outdoors under shade (24 to 36°C, 140 μmol/m2/s) and laboratory conditions (environmental chamber, 23°C for C. calcitrans and 20°C for N. oculata, 150 μmol/m2/s) were compared. Outdoor cultures of both C. calcitrans and N. oculata had significantly higher (p < 0.05) biomass, cell count, optical density and specific growth rate compared to the cultures grown under laboratory conditions. Lipid content was significantly higher in C. calcitrans grown outdoors, whereas, protein and carbohydrate composition did not show any significant differences (p > 0.05) between the outdoor and laboratory cultures. In the case of N. oculata, no significant differences (p > 0.05) were found in protein and lipid composition, but carbohydrate was significantly higher (p < 0.05) in the outdoor culture. In addition, the results showed that both C. calcitrans and N. oculata cultures grew faster outdoors, producing more biomass within a shorter period of time. This study illustrated that outdoor culture of microalgae was viable despite the fluctuating environmental conditions.Key words: Growth, proximate composition, Chaetoceros calcitrans, Nannochloropsis oculata, outdoor culture

Technological Developments and the Role of L2 Motivation in University English Language Teaching Education

The 21st century is the era of technology and digitalization in teaching and learning dynamics., The present study explores the function of L2 motivation in university-based English language teaching (ELT) education. It also seeks to comprehend how technological developments are changing L2 motivation and examines teachers coping mechanisms in this changing educational environment. This study employs a qualitative research approach to explore the university teachers choices of technology instruments and pedagogical choices for enhancing students’ L2 motivation. Thus, the study uses semi-structured interviews to collect data from the 15 university teachers, (8 from Pakistan and 7 from Russia). Moreover, the secondary aim of the study is to comprehend the variables influencing L2 teacher motivation, and pedagogical approaches. This study adds to the body of information on language teaching by emphasizing the necessity for university teachers to adapt to changes in L2 motivation by utilizing technology, developing cutting-edge resources, and creating motivating learning settings

Enhanced Arabic disaster data classification using domain adaptation

Natural disasters, like pandemics and earthquakes, are some of the main causes of distress and casualties. Governmental crisis management processes are crucial when dealing with these types of problems. Social media platforms are among the main sources of information regarding current events and public opinion. So, they have been used extensively to aid disaster detection and prevention efforts. Therefore, there is always a need for better automatic systems that can detect and classify disaster data of social media. In this work, we propose enhanced Arabic disaster data classification models. The suggested models utilize domain adaptation to provide state-of-the-art accuracy. We used a standard dataset of Arabic disaster data collected from Twitter for testing the proposed models. Experimental results show that the provided models significantly outperform the previous state-of-the-art results

Stumbling Blocks of Online Learning During COVID 19 Pandemic – Perspectives of Students of Selected Universities in London

COVID 19 Pandemic has led to mayhem across the Planet. Educational institutions are the worst affected arena. There is a paradigm shift from conventional classroom teaching to online methods. But it has its own obstructions. Thus, this research is undertaken to study the impediments of online learning faced by the students of selected universities of London. The questionnaire was administered among 200 students out of which 196 responded. The results of the Study reveal that the major obstructions which hindered online learning were lack of computer skills, internet connectivity issues, difficulty in operating the software, absence of social bonding between teachers and students, difficulty in recording lectures, difficulty in grasping practical courses such as mathematics, finance, accounting, engineering etc. To cope up with the Stumbling Blocks, the Study advocates some of the most innovative and creative ways such as application of Bloom’s Digital Taxonomy, VARK Model, 5/5/5 rule etc

Unscented Kalman Filters Integrated with Deep Learning Approaches for Active Sonar Based 2D Underwater Target Tracking

This manuscript proposes a new approach to track 2D targets using a combination of machine learning algorithms and the Unscented Kalman filter (UKF). The approach makes use of active sonar sensors to measure range and bearing, which are used to predict the target’s course and speed. So far in the literature of target tracking, researchers assumed covariance matrix of the noise in sonar measurements. In this manuscript, it is tried to estimate the same using deep learning algorithms. The Machine Learning algorithms, such as multilayer perceptron, convolutional neural network, long-short term memory, and gated recurrent unit, are employed to approximate the covariance of the noise in the input measurements. Simultaneously, the Unscented Kalman Filter (UKF) is utilised to mitigate the noise in the measurements and to estimate the position and speed of the target. The results are quantified through Monte Carlo simulations in a simulated underwater environment. The measurements are assumed to conform to a normal Gaussian distribution with a mean of zero. The findings indicate that LSTM has superior performance compared to the other models. Nevertheless, it is important to note that the results are constrained in their applicability due to the restricted set of variables employed for training the machine learning models

Antihyperglycemic and antinociceptive activity evaluation of ‘Khoyer’ prepared from boiling the wood of Acacia catechu in water

‘Khoyer’ is prepared by boiling the wood of Acacia catechu in water and then evaporating the resultant brew. The resultant hard material is powdered and chewed with betel leaves and lime with or without tobacco by a large number of the people of Bangladesh as an addictive psycho-stimulating and euphoria-inducing formulation. There are folk medicinal claims that khoyer helps in the relief of pain and is also useful to diabetic patients to maintain normal sugar levels. Thus far no scientific studies have evaluated the antihyperglycemic and antinociceptive effects of khoyer. The present study was carried out to evaluate the possible glucose tolerance efficacy of methanolic extracts of khoyer using glucose-induced hyperglycemic mice, and antinociceptive effects with acetic acid-induced gastric pain models in mice. In antihyperglycemic activity tests, the extract at different doses was administered one hour prior to glucose  administration and blood glucose level was measured after two hoursof glucose administration (p.o.) using glucose oxidase method. The statistical data indicated the significant oral hypoglycemic activity on glucose-loaded mice at all doses of the extracts tested. Maximum anti-hyperglycemic activity was shown at 400 mg extract per kg body weight, which was less than that of a standard drug, glibenclamide (10 mg/kg body weight). In antinociceptive activity tests, the extract also demonstrated a dose-dependent significant reduction in the number of writhing induced in mice through intraperitoneal administration of acetic acid. Maximum antinociceptive activity was observed at a dose of 400 mg extract per kg body weight, which was greater than that of a standard antinociceptive drug, aspirin, when administered at a dose of 400 mg per kg body weight. The results validate the folk medicinal use of the plant for reduction of blood sugar in diabetic patients, as well as the folk medicinal use for alleviation of pain.Key words: Acacia catechu, antihyperglycemic, antinociceptive, khoye