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This study aims to evaluate the efficiency of using some polymers at different concentrations in the consolidation of vegetable-tanned leather artifacts.

New vegetable-tanned leather samples were prepared. The consolidants used were polyacrylamide (PAM) and polymethyl methacrylate/hydroxyethyl methacrylate (MMA-HEMA). Accelerated heat aging was applied to the untreated and treated samples. Analytical techniques used were Fourier transform infrared spectroscopy (FTIR), digital microscope, scanning electron microscope (SEM), change of color and mechanical properties.

The characteristic FTIR bands showed the effect of accelerated heat aging on the molecular structure of the studied samples, but treated and aged treated samples used were better than aged untreated samples. Microscopic investigations (digital and SEM), and mechanical properties proved that 2% was the best concentration for polymers used. The change in the total color difference of the treated and aged treated samples was limited.

This study presents the important results obtained from PAM and poly(MMA-HEMA) used for the consolidation of vegetable-tanned leather artifacts. The best results of the studied polymers can be applied directly to protect historical vegetable-tanned leathers.

The purpose of the paper is to showcase the significant achievements of Egypt's scientists in the 20th century across various fields of study such as medicine, physics, chemistry, biology, math, geology, astronomy and engineering. The paper highlights the struggles and successes of these scientists, as well as the cultural, social and political factors that influenced their lives and work. The aim is to inspire young people to pursue careers in science and make their own contributions to society by presenting these scientists as role models for hard work and dedication. Ultimately, the paper seeks to promote the importance of science and its impact on society.

The purpose of this review is to present the scientific biographies of Egypt's most distinguished scientists, primarily in the field of Natural Sciences, in a balanced and comprehensive manner. The work is objective, honest and abstract, avoiding any bias or exaggeration. The author provides a clear and concise methodology, including a brief introduction to the scientist and their field of study, an explanation of their major contributions, the impact of their work on society, any challenges or obstacles faced during their career and their lasting legacy. The aim is to showcase the important achievements of these scientists, their impact on their respective fields and to inspire future generations to pursue scientific careers.

The group of outstanding scientists in 20th century Egypt were shaped by various factors, including familial upbringing, education, society, political and cultural atmosphere and state support for scientific research. These scientists made significant contributions to various academic disciplines, including medicine, physics, chemistry, biology, mathematics and engineering. Their impact on their communities and cultures has received international acclaim, making them role models for future generations of scientists and researchers. The history of these scientists highlights the importance of educational investments and supporting scientific research to foster innovation and social progress. The encyclopedia serves as a useful tool for students, instructors and education professionals, preserving Egypt's scientific heritage and honouring the scientists' outstanding accomplishments.

The encyclopedia preserves Egypt's scientific heritage, which has been overlooked for political or other reasons. It is a useful tool for a variety of readers, including students, instructors and education professionals, and it offers insights into universally relevant scientific success factors as well as scientific research methodologies. The encyclopedia honours the outstanding scientific accomplishments of Egyptian researchers and their contributions to the world's scientific community.

The practical implications of this paper are several. First, it highlights the importance of education, family upbringing and societal support for scientific research in fostering innovation and social progress. Second, it underscores the need for continued funding and support for scientific research to maintain and build upon the accomplishments of past generations of scientists. Third, it encourages young people to pursue scientific careers and make their own contributions to society. Fourth, it preserves the scientific heritage of Egypt and honors the contributions of its outstanding scientists. Finally, it serves as a useful tool for students, instructors and education professionals seeking to understand the factors underlying scientific success and research methodologies.

The social implications of the paper include promoting national pride and cultural identity, raising awareness of the importance of education and scientific research in driving social progress, inspiring future generations of scientists and researchers, reducing socioeconomic disparities and emphasizing the role of society, politics and culture in shaping scientific researchers' personalities and interests.

The paper's originality/value lies in its comprehensive documentation of the scientific biographies of Egypt's most prominent scientists in the 20th century, providing unique insights into the factors that contributed to their development and their impact across various academic disciplines. It preserves Egypt's scientific heritage and inspires future generations of scientists and researchers through the promotion of educational investments and scientific research. The encyclopedia serves as a useful tool for education professionals seeking to understand scientific success factors and research methodologies, emphasizing the importance of supportive and inclusive environments for scientific development.

The current challenge for forecasting smart meters electricity consumption lies in the uncertainty and volatility of load profiles. Moreover, forecasting the electricity consumption for all the meters requires an enormous amount of time. Most papers tend to avoid such complexity by forecasting the electricity consumption at an aggregated level. This paper aims to forecast the electricity consumption for all smart meters at an individual level. This paper, for the first time, takes into account the computational time for training and forecasting the electricity consumption of all the meters.

A novel hybrid autoregressive-statistical equations idea model with the help of clustering and whale optimization algorithm (ARSEI-WOA) is proposed in this paper to forecast the electricity consumption of all the meters with best performance in terms of computational time and prediction accuracy.

The proposed model was tested using realistic Irish smart meters energy data and its performance was compared with nine regression methods including: autoregressive integrated moving average, partial least squares regression, conditional inference tree, M5 rule-based model, k-nearest neighbor, multilayer perceptron, RandomForest, RPART and support vector regression. Results have proved that ARSEI-WOA is an efficient model that is able to achieve an accurate prediction with low computational time.

This paper presents a new hybrid ARSEI model to perform smart meters load forecasting at an individual level instead of an aggregated one. With the help of clustering technique, similar meters are grouped into a few clusters from which reduce the computational time of the training and forecasting process. In addition, WOA improves the prediction accuracy of each meter by finding an optimal factor between the average electricity consumption values of each cluster and the electricity consumption values for each one of its meters.

The purpose of this paper is to generate nitrogen-containing groups in the cotton fabric surface via low-temperature nitrogen plasma as an eco-friendly physical/zero-effluent process. This was done for rendering cotton dye-able with Acid Blue 284, which in fact does not have any direct affinity to fix on it.

Dyeing characteristics of the samples such as color strength (K/S), fastness properties to light, rubbing and perspiration and durability, as well as tensile strength, elongation at break, whiteness, weight loss and wettability in addition to zeta potential of the dyed samples, were determined and compared with untreated fabric. Confirmation and characterization of the plasma-treated samples via chemical modifications and zeta potential was also studied using Fourier transform infrared spectroscopy (FTIR) and Malvern Zetasizer instrumental analysis.

The obtained results of the plasma-treated fabric reflect the following findings: FTIR results indicate the formation of nitrogen-containing groups on cotton fabrics; notable enhancement in the fabric wettability, zeta potential to more positive values and improvement in the dyeability and overall fastness properties of treated cotton fabrics in comparison with untreated fabric; the tensile strength, elongation at break, whiteness and weight % of the plasma treated fabrics are lower than that untreated one; and the durability of the plasma treated fabric decreased with increasing the number of washing cycles.

The novelty addressed here is rendering cotton fabrics dye-able with acid dye via the creation of new cationic nitrogen-containing groups on their surface via nitrogen plasma treatment as an eco-friendly and efficient tool with a physical/zero-effluent process.

• Develop an understanding of how to institutionalize a family business. • Define the dynamics of the family business decision-making process in emerging markets. • Assess the cultural differences between founders and successors in an emerging markets context. • Identify the role of intergenerational differences in deciding the future strategy of a family business in emerging markets.

This case addresses El Batraa Manufacturers for Chemicals and Paints S.A.E., a privately owned family business operating in the coloring paste industry in Egypt. The main dilemma of the case is the existence of different visions about the business between the old and new generations. Also, it addresses the importance of understanding family dynamics to resolve existing challenges. The necessity of having governance in a family business is highlighted, together with a clear succession plan to secure family unity and business sustainability. Sandra the main protagonist within the case is trying to arrive to a resolution that can guarantee a motivating environment for her to join the family business. Her main dilemma is whether to choose to join the family business, with all the existing challenges or not. Accordingly, she proposes some steps to make the family business more appealing.

Under Graduate and Master of Business Administration level.

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CSS 3: Entrepreneurship.

Due to the large and fast growing sentiment analysis (SA) area recently, many new concepts and different nomenclatures have emerged without the desired organization. This confusion in the research field makes the understandability of the concepts hard and also hampers the comparison of different approaches. Thus, this paper aims to propose a hierarchical taxonomy to help the consolidation of SA area. The taxonomy aims at covering the addressed problems and methods in the SA field.

This taxonomy is a filtered union of various classifications found in the literature with a proposal of nomenclatures standardization. As instance, a case study is presented with 20 SA-related articles classified according to the proposed taxonomy.

This taxonomy is very expressive because it covers many concepts and is also effective once it allows the distinction and categorization of the previous SA works.

To the best of the authors’ knowledge, the literature does not present such an expressive and effective classification as proposed in this paper. This new taxonomy allows for the navigation between the existing concepts in the SA field as well as, facilitates the search, comparison and indexing of papers already published.

Sentiment analysis is a text analysis method that is developed for systematically detecting, identifying, or extracting the emotional intent of words to infer if the text expresses a positive or negative tone. Although this novel method has opened an exciting new avenue for organizational research – mainly due to the abundantly available text data in organizations and the well-developed sentiment analysis techniques, it has also posed a serious challenge to many organizational researchers. This chapter aims to introduce the sentiment analysis method in the text mining area to the organizational research community. In this chapter, the authors first briefly discuss the central role of sentiment in organizational research and then introduce the traditional and modern approaches to sentiment analysis. The authors further delineate research paradigms for text analysis research, advocating the iterative research paradigm (cf., inductive and deductive research paradigms) that is more suitable for text mining research, and also introduce the analytical procedures for sentiment analysis with three stages – discovery, measurement, and inference. More importantly, the authors highlight both the dictionary-based and machine learning (ML) approaches in the measurement stage, with special coverage on deep learning and word embedding techniques as the latest breakthroughs in sentiment and text analyses. Lastly, the authors provide two illustrative examples to demonstrate the applications of sentiment analysis in organizational research. It is the authors’ hope that this chapter – by providing these practical guidelines – will help facilitate more applications of this novel method in organizational research in the future.

In recent years, electronic word-of-mouth (e-WOM) concerning travel products reflected in online review information has become an important reference for tourists to make their product purchase decisions, while for travel service providers (TSPs), monitoring and improving the e-WOM of their travel products is always an important task. Therefore, based on the online review information, how to capture e-WOM of travel products and find out specific ways to improve the e-WOM is a noteworthy research problem. The purpose of this paper is to develop a method for capturing and analyzing e-WOM toward travel products based on sentiment analysis and stochastic dominance.

Specifically, online review information of travel products is first crawled and preprocessed. Second, sentiment strengths of online review information toward travel products concerning each feature are judged. Then, the matrix of structured online review information toward travel products is formed. Further, the matrix of e-WOM comparisons between any two travel products is constructed, and e-WOM ranking concerning each travel product is determined. Finally, trade-off chart models are constructed to conduct the e-WOM improvement analyses concerning the travel products.

An empirical study based on the online review information toward six travel products crawled from the Tuniu.com website is given to illustrate the use of the proposed method.

The proposed method can not only realize the real-time e-WOM monitoring to travel products but also be useful for TSPs to improve the e-WOM of their travel products.

The aim of the paper is to process clients’ online reviews of 20 spa locations in the Slovak Republic in the form of a case study. The purpose is to identify and specify the shortcomings of Slovak spa resorts as well as the needs and preferences of their clientele.

To obtain data, Google text reviews were scraped using the Instant Data Scraper. The text mining approach was used for the analysis of 5,670 reviews (using the RapidMiner Studio and Atlas.ti tools).

The performed sentiment analysis of the reviews has determined that 4,807 of the reviews were positive, 476 were neutral and 387 were of negative sentiment. The subsequently processed word list states the most frequently mentioned nouns and adjectives. Detailed opinion mining identifies the areas/phrases with the most frequent occurrence of the negative sentiment in their reviews.

The stated results are important from the perspective of service quality management in order to improve the satisfaction of spa customers. Sentiment analysis of Google Maps reviews for the target clientele remains unexplored and represents a novel area of study.

Based on climate issues and carbon emissions, this study aims to promote low-carbon consumption and compel consumers to actively shift to energy-saving appliances. In this big data era, online reviews in social and electronic commerce (e-commerce) websites contain valuable product information, which can facilitate firm business strategies and consumer comparison shopping. This study is designed to advance existing research on energy-saving refrigerators by incorporating machine learning models in the analysis of online reviews to provide valuable suggestions to e-commerce platform managers and manufacturers to effectively understand the psychological cognition of consumers.

This study proposes an online e-commerce review mining and management strategy model based on “data acquisition and cleaning, data mining and analysis and strategy formation” through multiple machine learning methods, namely, Bayes networks, support vector machine (SVM), latent Dirichlet allocation (LDA) and importance–performance analysis (IPA), to help managers.

Based on a case study of one of the largest e-commerce platforms in China, this study linguistically analyzes 29,216 online reviews of energy-saving refrigerators. Results indicate that the energy-saving refrigerator features that consumers are generally satisfied with are, in sequential order, logistics, function, price, outlook, after-sales service, brand, quality and space. This study also identifies ten topics with 100 keywords by analyzing 18 different refrigerator models. Finally, based on the IPA, this study allocates different priorities to the features and provides suggestions from the perspective of consumers, the government and manufacturers.

In terms of limitations, future research may focus on the following points. First, the topics identified in this study derive from specific points in time and reviews; thus, the topics may change with the text data. A machine learning-based online review analysis platform could be developed in the future to dynamically improve consumer satisfaction. Moreover, given that consumers' needs may change over time, e-commerce platform types and consumer characteristics, such as user profiles, can be incorporated into the model to effectively analyze trends in consumers' perceived dimensions.

This study fills the gap in previous research in this field, which uses small-sample data for qualitative analysis, while integrating management ideas and proposes an online e-commerce review mining and management strategy model based on machine learning methods. Moreover, this study considers how consumers' emotional and thematic preferences for products affect their purchase decision-making from the perspective of their psychological perception and linguistically analyzes online reviews of energy-saving refrigerators using the proposed mining model. Through the improved IPA model, this study provides optimizing strategies to help e-commerce platform managers and manufacturers.