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What started as a FMCG distributor in 1967 in Kenya as Export Finance Company, is now a dynamic global conglomerate across 48 countries and 5 continents — Export Trading Group. ETG was taken over by the then CFO Mahesh Patel after exit of the founding stakeholders. It was then when the company shifted its focus to being a key regional player. In the next 35 years, the company grew systematically. Business focus evolved when Patel saw an opportunity in logistics in remote sub-Saharan Africa. This was followed by business expansion with supply chain diversification and significant infrastructure investments. All the different businesses amalgamated under a single group for better operations and ease of scaling up. They were later divided into six separate verticals for better management. Vamara (FMCG vertical) was launched in 2018 as the company moved towards digitalisation — externally and internally. ETG plans to focus on new business opportunities and continue to diversify across geographies and portfolios.

There is over‐use of psychotropic medication with people with intellectual disabilities. Many of these individuals do not have the capacity to understand and retain the relevant information about the use and effectiveness of medication. Professionals and health care practitioners need to be fully aware of the ethical and legal issues in the use and administration of psychotropic medication.

Social media channels provide an avenue for expressing views about different services/products. However, unlike merchandise/company websites (where users can post both reviews and ratings), it is not possible to understand user's ratings for a particular service-related comment on social media unless explicitly mentioned. Predicting ratings can be beneficial for service providers and prospective customers. Additionally, predicting ratings from a user-generated content can help in developing vast data sets for recommender systems utilizing recent data. The aim of this study is to predict ratings more accurately and enhance the performance of sentiment-based predictors by combining it with the emotional content of textual data.

This study had utilized a combination of sentiment and emotion scores to predict the ratings of Twitter posts (3,509 tweets) in three different contexts, namely, online food delivery (OFD) services, online travel agencies (OTAs) and online learning (e-learning). A total of 29,551 reviews were utilized for training and testing purposes.

Results of this study indicate accuracies of 58.34%, 57.84% and 100% in cases of e-learning, OTA and OFD services, respectively. The combination of sentiment and emotion scores showed an increase in accuracies of 19.41%, 27.83% and 40.20% in cases of e-learning, OFD and OTA services, respectively.

Understanding the ratings of social media comments can help both service providers as well as prospective customers who do not spend much time reading posts but want to understand the perspectives of others about a particular service/product. Additionally, predicting ratings of social media comments will help to build databases for recommender systems in different contexts.

The uniqueness of this study is in utilizing a combination of sentiment and emotion scores to predict the ratings of tweets related to different online services, namely, e-learning OFD and OTAs.

The widespread acceptance of various social platforms has increased the number of users posting about various services based on their experiences about the services. Finding out the intended ratings of social media (SM) posts is important for both organizations and prospective users since these posts can help in capturing the user’s perspectives. However, unlike merchant websites, the SM posts related to the service-experience cannot be rated unless explicitly mentioned in the comments. Additionally, predicting ratings can also help to build a database using recent comments for testing recommender algorithms in various scenarios.

In this study, the authors have predicted the ratings of SM posts using linear (Naïve Bayes, max-entropy) and non-linear (k-nearest neighbor, k-NN) classifiers utilizing combinations of different features, sentiment scores and emotion scores.

Overall, the results of this study reveal that the non-linear classifier (k-NN classifier) performed better than the linear classifiers (Naïve Bayes, Max-entropy classifier). Results also show an improvement of performance where the classifier was combined with sentiment and emotion scores. Introduction of the feature “factors of importance” or “the latent factors” also show an improvement of the classifier performance.

This study provides a new avenue of predicting ratings of SM feeds by the use of machine learning algorithms along with a combination of different features like emotional aspects and latent factors.

Considers the development of the content industry in Europe by 2005, with the anticipation of new applications such as tele‐education, tele‐medicine, tele‐detection and tele‐surveillance. Examines the interplay of the various political and industrial interests involved, using the technique of “scenario‐mapping” in particular.

With the recent availability of large number of bioinformatics data sources, query from such databases and rigorous annotation of experimental results often use semantic frameworks in the form of an ontology. With the growing access to heterogeneous and independent data repositories, determining the semantic similarity or difference of two ontologies is critical in information retrieval, information integration and semantic web services. The purpose of this paper is to propose a new sense refinement algorithm to construct a refined sense set (RSS) for an ontology so that the senses (synonym words) in this refined sense set represent the semantic meanings of the terms used by this ontology.

A new concept of a semantic set is introduced that combines the refined sense set of ontology with the relationship edges connecting the terms in this ontology to represent the semantics of this ontology. With the semantic sets, measuring the semantic similarity or difference of two ontologies is simplified as comparing the commonality or difference of two sets.

The experimental studies show that the proposed method of measuring the semantic similarity or difference of two ontologies is efficient and accurate; comparisons with existing methods show the efficacy of using the new method.

The concepts introduced in this paper will improve automation of bioinformatics databases to serve queries based on heterogeneous ontologies.

In the present work, a numerical method, based on the well established enthalpy technique, is developed to simulate the growth of binary alloy equiaxed dendrites in presence of melt convection. The paper aims to discuss these issues.

The principle of volume-averaging is used to formulate the governing equations (mass, momentum, energy and species conservation) which are solved using a coupled explicit-implicit method. The velocity and pressure fields are obtained using a fully implicit finite volume approach whereas the energy and species conservation equations are solved explicitly to obtain the enthalpy and solute concentration fields. As a model problem, simulation of the growth of a single crystal in a two-dimensional cavity filled with an undercooled melt is performed.

Comparison of the simulation results with available solutions obtained using level set method and the phase field method shows good agreement. The effects of melt flow on dendrite growth rate and solute distribution along the solid-liquid interface are studied. A faster growth rate of the upstream dendrite arm in case of binary alloys is observed, which can be attributed to the enhanced heat transfer due to convection as well as lower solute pile-up at the solid-liquid interface. Subsequently, the influence of thermal and solutal Peclet number and undercooling on the dendrite tip velocity is investigated.

As the present enthalpy based microscopic solidification model with melt convection is based on a framework similar to popularly used enthalpy models at the macroscopic scale, it lays the foundation to develop effective multiscale solidification.

Peanut skins are an agro-waste product with no commercial value. The purpose of this paper is to evaluate peanut skin as a natural dyestuff and to determine if this natural dye could be used in the dyeing of some Egyptian cotton cultivars.

The methodology consists of several steps; dye extraction procedure from peanut skin through aqueous extraction, then dyeing optimized using simultaneous mordanting using alum. Finally, dyed cotton has been subjected to different textile laboratory tests, for example, color measurements and mechanical properties. Color-fastness was determined on Egyptian cotton fabric. The peanut skin as a source of natural dye and the dyed cotton sample were characterized by fourier transform infrared spectroscopy (FTIR) analysis.

It was found that the natural dye extracted from peanut skin has an affinity for cotton samples and showed high dyeability with a unique color shade, good color strength and very good fastness.

The novelty of this paper is the extraction of color from the peanut's outer skin which is discarded as waste such as agro-waste of the agricultural process which can be used as a natural dye in the textile industry and applied to dyeing some Egyptian cotton fibers from different genotypes.

This paper aims to measure the intention to use digital marketing strategies to enhance the performance of tourism business as well as the extent of digital renovation applications in tourism for sustainable business in a new normal era.

This paper is an insight from the existing relevant literature on the tourism business from time immemorial. The conceptual framework of this study is designed based on previous studies of digital marketing practices for tourism businesses. Furthermore, data were collected from 270 respondents, of which the valid response rate is 72.97%. Partial least square (PLS)-structural equation modeling (SEM) is used to validate the conceptual framework and hypotheses testing.

Among the nine hypotheses path, seven were supported. This study result shows that perceived usefulness, perceived ease of use, social media marketing and tourism business performance are critical factors for adopting digital marketing in tourism. Thus, tourism service providers' intention has a positive impact to meet the expectation of tourists and adoption of digital marketing.

The study's results will assist tourism researchers and service providers in understanding an authentic relationship between digital practices of tourism business and tourist satisfaction. In addition, the legacy of tourism business through digital marketing empowers the owner and community.

The study is the first to explore the relationship between tourism business performance and digital marketing during the new normal era for the empowerment of local community and expanded business in tourism sector.