bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluating Antiracist Dispositions
Authors: Degener Rebekah , Castillo Bernadette , Beschorner Beth
Published: July 2022

With a national push for the adoption of reliable disposition assessment tools in accredited teacher preparation programs, many programs are beginning to assess PSTs' dispositions in more standardized ways. Responding to calls for more adequate preparation of PSTs to be able to teach in racially, culturally, and linguistically diverse classrooms in culturally sustaining ways, many disposition assessment tools aim to measure pre-service teachers' antiracist dispositions. This chapter utilizes counterstorytelling methodologies to examine the tensions of choosing, creating, and using disposition assessment tools, analyzing the ways that such tools may work to further antiracist teaching while simultaneously upholding whiteness ideology.
Assessing Candidate Dispositions Throughout an Educator Preparation Program
Authors: Agan Tia L. , Flores, Jr. Carlos A. , Casarez Lesley L.
Published: July 2022

In addition to content knowledge and practical skills, certification candidates should demonstrate an understanding of dispositions—the professional behaviors that must be evident in their interactions with students, families, colleagues, and communities. However, it is challenging to monitor and assess dispositions without a formal process that teaches candidates the dispositions they are expected to demonstrate and provides feedback at specific points in certification preparation. This chapter will provide a detailed process for teaching, monitoring, and assessing dispositions within an educator preparation program (EPP) to develop candidates who exhibit professional behaviors prior to entering an educational role.
Tasks, Approaches, and Avenues of Opinion Mining, Sentiment Analysis, and Emotion Analysis
Authors: Idrees Amira M. , Eldin Fatma Gamal , Mohsen Amr Mansour , Hassan Hesham Ahmed
Published: July 2022

Every successful business aims to know how customers feel about its brands, services, and products. People freely express their views, ideas, sentiments, and opinions on social media for their day-to-day activities, for product reviews, for surveys, and even for their public opinions. This process provides a fortune of valuable resources about the market for any type of business. Unfortunately, it's impossible to manually analyze this massive quantity of information. Sentiment analysis (SA) and opinion mining (OM), as new fields of natural language processing, have the potential benefit of analyzing such a huge amount of data. SA or OM is the computational treatment of opinions, sentiments, and subjectivity of text. This chapter introduces the reader to a survey of different text SA and OM proposed techniques and approaches. The authors discuss in detail various approaches to perform a computational treatment for sentiments and opinions with their strengths and drawbacks.
Impact of Balancing Techniques for Imbalanced Class Distribution on Twitter Data for Emotion Analysis
Authors: Vora Shivani Vasantbhai , Mehta Rupa G. , Patel Shreyas Kishorkumar
Published: July 2022

Continuously growing technology enhances creativity and simplifies humans' lives and offers the possibility to anticipate and satisfy their unmet needs. Understanding emotions is a crucial part of human behavior. Machines must deeply understand emotions to be able to predict human needs. Most tweets have sentiments of the user. It inherits the imbalanced class distribution. Most machine learning (ML) algorithms are likely to get biased towards the majority classes. The imbalanced distribution of classes gained extensive attention as it has produced many research challenges. It demands efficient approaches to handle the imbalanced data set. Strategies used for balancing the distribution of classes in the case study are handling redundant data, resampling training data, and data augmentation. Six methods related to these techniques have been examined in a case study. Upon conducting experiments on the Twitter dataset, it is seen that merging minority classes and shuffle sentence methods outperform other techniques.
DE-ForABSA
Authors: Gupta Charu , Jain Amita , Joshi Nisheeth
Published: July 2022

Today, amongst the various forms of online data, user reviews are very useful in understanding the user's attitude, emotion and sentiment towards a product. In this article, a novel method, named as DE-ForABSA is proposed to forecast automobiles sales based on aspect based sentiment analysis (ABSA) and ClusFuDE [8] (a hybrid forecasting model). DE-ForABSA consists of two phases – first, extracted user reviews of an automobile are analysed using ABSA. In ABSA, the reviews are pre-processed; aspects are extracted & aggregated to determine the polarity score of reviews. Second, uses of ClusFuDE consisting of clustering, fuzzy logical relationships and Differential Evolution (DE) to predict the sales of the automobile. DE is a population-based search method to optimize real values under the control of two operators: mutation & crossover. Score from phase 1 is a parameter in differential mutation in phase 2. The proposed method is tested on reviews & sales data of automobile. The empirical results show a Mean Square Error of 142.90 which indicates an effective consistency of the model
Sentiment Analysis of Game Review Using Machine Learning in a Hadoop Ecosystem
Authors: Panwar Arvind , Bhatnagar Vishal
Published: July 2022

Internet, & more unambiguously the creation of WWW in the early 1990s, helped people to build an interconnected global platform where information can be stored, shared, and consumed by anyone with an electronic device which has the ability to connect to the Web. This provides a way of putting together lots of information, ideas, and opinion. An interactive platform was born to post content, messages, and opinions under one roof, and the platform is known as social media. Social media has acquired massive popularity and importance that why today almost everyone can't stay away from it. Social media is not only a medium for people to express their thoughts, moreover, but it is also a very powerful tool which can be used by businesses to focus on new and existing customers and increase profit with the help of social media analytics. This paper starts with a discussion on social media with its significance & pitfalls. Later on, this paper presents a brief introduction of sentiment analysis in social media and give an experimental work on sentiment analysis in a social game review.
Challenges of Text Analytics in Opinion Mining
Authors: Kalra Vaishali , Agrawal Rashmi
Published: July 2022

Text analysis is the task of knowledge distillation from unstructured text. Due to increase in sharing of information over the web in text format, users required tools and techniques for the analysis of the text. These techniques can be used in two ways: One, this can be used for clustering, classification, and visualization of the data. Two, this can be used for predicting the future aspects, for example, in share market. But all these tasks are not easy to perform, as there are lots of challenges in converting the text into the format onto which various actions can be taken. In this chapter, the authors have discussed the framework of text analysis, followed by the background where they have discussed the steps for transforming the text into the structured form. They have shed light on its industry application along with the technological and non-technological challenges in text analysis.
Sentiment Analysis on Social Media
Authors: Wadawadagi Ramesh S. , Pagi Veerappa B.
Published: July 2022

Due to the advent of Web 2.0, the size of social media content (SMC) is growing rapidly and likely to increase faster in the near future. Social media applications such as Instagram, Twitter, Facebook, etc. have become an integral part of our lives, as they prompt the people to give their opinions and share information around the world. Identifying emotions in SMC is important for many aspects of sentiment analysis (SA) and is a top-level agenda of many firms today. SA on social media (SASM) extends an organization's ability to capture and study public sentiments toward social events and activities in real time. This chapter studies recent advances in machine learning (ML) used for SMC analysis and its applications. The framework of SASM consists of several phases, such as data collection, pre-processing, feature representation, model building, and evaluation. This survey presents the basic elements of SASM and its utility. Furthermore, the study reports that ML has a significant contribution to SMC mining. Finally, the research highlights certain issues related to ML used for SMC.
A Novel Approach to Optimize the Performance of Hadoop Frameworks for Sentiment Analysis
Authors: Prasad Guru , Jain Amith K. , Jain Prithviraj , Nagesh H. R.
Published: July 2022

Twitter is one among most popular micro blogging services with millions of active users. It is a hub of massive collection of data arriving from various sources. In Twitter, users most often express their views, opinions, thoughts, emotions or feelings about a particular topic, product or service, of their interest, choice or concern. This makes twitter a hub of gargantuan amount of data, and at the same time a useful platform in getting to know and understand the underlying sentiment behind a particular product or for that matter anything expressed in twitter as tweets. It is important to note here that aforesaid massive collection of data is not just any redundant data, but one which contains useful information as noted earlier. In view of aforesaid context, Sentiment analysis in relation to twitter data gains enormous importance. Sentiment analysis offers itself as a good approach in classifying the opinions formulated by individuals (tweeters) into different sentiments such as, positive, negative, or neutral. Implementing Sentiment analysis algorithms using conventional tools leads to high computation time, and thus are less effective. Hence, there is a need for state-of-the-art tools and techniques to be developed for sentiment analysis making it the need of the hour to facilitate faster computation. An Apache Hadoop framework is one such option that supports distributed data computing and has been commonly adopted for a variety of use-cases. In this article, the author identifies factors affecting the performance of sentiment analysis algorithms based on Hadoop framework and proposes an approach for optimizing the performance of sentiment analysis. The experimental results depict the potential of the proposed approach.
Hybrid Recommender System Using Emotional Fingerprints Model
Authors: Nosshi Anthony , Asem Aziza Saad , Senousy Mohammed Badr
Published: July 2022

With today's information overload, recommender systems are important to help users in finding needed information. In the movies domain, finding a good movie to watch is not an easy task. Emotions play an important role in deciding which movie to watch. People usually express their emotions in reviews or comments about the movies. In this article, an emotional fingerprint-based model (EFBM) for movies recommendation is proposed. The model is based on grouping movies by emotional patterns of some key factors changing in time and forming fingerprints or emotional tracks, which are the heart of the proposed recommender. Then, it is incorporated into collaborative filtering to detect the interest connected with topics. Experimental simulation is conducted to understand the behavior of the proposed approach. Results are represented to evaluate the proposed recommender.
A Novel Algorithm for Sentiment Analysis of Online Movie Reviews
Authors: Shah Bisma , Siddiqui Farheen
Published: July 2022

Others' opinions can be decisive while choosing among various options, especially when those choices involve worthy resources like spending time and money buying products or services. Customers relying on their peers' past reviews on e-commerce websites or social media have drawn a considerable interest to sentiment analysis due to realization of its commercial and business benefits. Sentiment analysis can be exercised on movie reviews, blogs, customer feedback, etc. This chapter presents a novel approach to perform sentiment analysis of movie reviews given by users on different websites. Also, challenges like presence of thwarted words, world knowledge, and subjectivity detection in sentiments are addressed in this chapter. The results are validated by using two supervised machine learning approaches, k-nearest neighbor and naive Bayes, both on method of sentiment analysis without addressing aforementioned challenges and on proposed method of sentiment analysis with all challenges addressed. Empirical results show that proposed method outperformed the one that left challenges unaddressed.
Parallel Hybrid BBO Search Method for Twitter Sentiment Analysis of Large Scale Datasets Using MapReduce
Authors: Tripathi Ashish Kumar , Sharma Kapil , Bala Manju
Published: July 2022

Sentiment analysis is an eminent part of data mining for the investigation of user perception. Twitter is one of the popular social platforms for expressing thoughts in the form of tweets. Nowadays, tweets are widely used for analyzing the sentiments of the users, and utilized for decision making purposes. Though clustering and classification methods are used for the twitter sentiment analysis, meta-heuristic based clustering methods has witnessed better performance due to subjective nature of tweets. However, sequential meta-heuristic based clustering methods are computation intensive for large scale datasets. Therefore, in this paper, a novel MapReduce based K-means biogeography based optimizer(MR-KBBO) is proposed to leverage the strength of biogeography based optimizer with MapReduce model to efficiently cluster the large scale data. The proposed method is validated against four state-of-the-art MapReduce based clustering methods namely; parallel K-means, parallel K-means particle swarm optimization, MapReduce based artificial bee colony optimization, dynamic frequency based parallel k-bat algorithm on four large scale twitter datasets. Further, speedup measure is used to illustrate the computation performance on varying number of nodes. Experimental results demonstrate that the proposed method is efficient in sentiment mining for the large scale twitter datasets.
Using E-Reputation for Sentiment Analysis
Authors: Salhi Dhai Eddine , Tari Abelkamel , Kechadi Mohand Tahar
Published: July 2022

In a competitive world, companies are looking to gain a positive reputation through these clients. Electronic reputation is part of this reputation mainly in social networks, where everyone is free to express their opinion. Sentiment analysis of the data collected in these networks is very necessary to identify and know the reputation of a companies. This paper focused on one type of data, Twits on Twitter, where the authors analyzed them for the company Djezzy (mobile operator in Algeria), to know their satisfaction. The study is divided into two parts: The first part was the pre-processing phase, where this research filtered the Twits (eliminate useless words, use the tokenization) to keep the necessary information for a better accuracy. The second part was the application of machine learning algorithms (SVM and logistic regression) for a supervised classification since the results are binary. The strong point of this study was the possibility to run the chosen algorithms on a cloud in order to save execution time; the solution also supports the three languages: Arabic, English, and French.
Feature Based Opinion Mining
Authors: Batra Mridula , Jyoti Vishaw
Published: July 2022

Opinion mining is the estimated learning of user's beliefs, evaluation and sentiments about units, actions and its features. This method has several features matched with data mining techniques, language processing methods and feature oriented data abstraction. This seems to be extremely difficult to mine opinions from analysis those exist in common human used language. Views are very essentials when one desires to construct a judgment. Data abstraction is an important characteristic for decision making applicable to individuals and organization of different nature. While selecting and purchasing a particular product, it is always beneficial for an individual to collect other views for correct decision making. One association wants to conduct surveys and gather opinions to develop their product excellence. Internet as a source of information, having a number of websites available with the customer reviews as a number of products, it is easy to extract the features from these opinions, sentiments and view, is a task comes under feature-based opinion mining.
Sentiment Analysis of Tweets on the COVID-19 Pandemic Using Machine Learning Techniques
Authors: R. Jothikumar , R. Vijay Anand , P. Visu , R. Kumar , S. Susi , K. R. Kumar
Published: July 2022

Sentiment evaluation alludes to separate the sentiments from the characteristic language and to perceive the mentality about the exact theme. Novel corona infection, a harmful malady ailment, is spreading out of the blue through the quarter, which thought processes respiratory tract diseases that can change from gentle to extraordinary levels. Because of its quick nature of spreading and no conceived cure, it ushered in a vibe of stress and pressure. In this chapter, a framework perusing principally based procedure is utilized to discover the musings of the tweets related to COVID and its effect lockdown. The chapter examines the tweets identified with the hash tags of crown infection and lockdown. The tweets were marked fabulous, negative, or fair, and a posting of classifiers has been utilized to investigate the precision and execution. The classifiers utilized have been under the four models which incorporate decision tree, regression, helpful asset vector framework, and naïve Bayes forms.
General Awareness and Responses to COVID-19 Crisis
Authors: Buragohain Dipima
Published: July 2022

The ongoing pandemic situation of COVID-19 has impacted people across nations while taking social media by storm with its massive pool of information. As social media platforms are full of horserace reporting and uncritical or even fake updates most of the time, users should respond carefully during a crisis like COVID-19 pandemic while consuming and sharing updates. The current study presents an exploratory analysis of and highlights language use in terms of users' general awareness of the crisis and their responses on social media platforms through a sentiment analysis of 805 Twitter messages related to the COVID-19 pandemic in India. The findings reported users' varied sentiments while expressing their general awareness about the crisis and its widespread outbreak. It also reported their use of cognizant verbal expressions and abusive words as an expression for dealing with panic of the crisis.
A Literature Review on Cross Domain Sentiment Analysis Using Machine learning
Authors: Kansal Nancy , Goel Lipika , Gupta Sonam
Published: July 2022

Sentiment analysis is the field of NLP which analyzes the sentiments of text written by users on online sites in the form of reviews. These reviews may be either in the form of a word, sentence, document, or ratings. These reviews are used as datasets when applied to train a classifier. These datasets are applied in the annotated form with the positive, negative or neutral labels as an input to train the classifier. This trained classifier is used to test other reviews, either in the same or different domains to know like or dislike of the user for the related field. Various researches have been done in single and cross domain sentiment analysis. The new methods proposed are overcoming the previous ones but according to this survey, no methods best suit the proposed work. In this article, the authors review the methods and techniques that are given by various researchers in cross domain sentiment analysis and how those are compared with the pre-existing methods for the related work.
The Dark Side of Engaging With Social Networking Sites (SNS)
Authors: O'Donnell Eileen , O'Donnell Liam
Published: July 2022

Although social networking sites (SNS) may have some positive aspects, for example, connecting family members and friends who no longer live close enough to each other to meet in person, or for connecting people with similar health conditions who may need the support of others who understand and can manage the condition, or for groups of people with similar interests to engage and plan events and activities, there are also some possible negative aspects of engaging with SNS, for example, addiction or addictive behaviour, child pornography, cyberbullying, fake news, fear of missing out (FOMO), social comparisons, stalking, amongst many others, which can all lead to neglect of other duties, sleep deprivation, loneliness, isolation, depression, and so forth. The possible negative effects of engaging with SNS on the surface web will perhaps be also relevant to users of the deep and dark web. This article explores the dark side of social networking sites.
The Impact of Social Media on Children
Authors: Dilci Tuncay , Eranıl Anıl Kadir
Published: July 2022

This chapter examines the impacts of social media on children. Advantages and disadvantages of social media are always available. Positive aspects of social media include allowing children to be brought up as multicultural individuals, enabling education and training environments to design for purposes, using as the main or supplementary source of education, a great power in creating and sharing information. Its negative aspects include leading to a reduction of their academic, social, and cognitive skills in the early periods when children were exposed to the social media, causing the children to develop obesity, mostly bringing up as consumption-centered individuals, perceive the world as a screenshot, and have low critical, creative, and reflective thinking skills. Therefore, one of the most important tasks undertaken to reduce or eliminate the negative effects is to raise and educate media-literate individuals.
Psychological Benefits and Detrimental Effects of Online Social Networking
Authors: Orta Irem Metin , Örücü Müge Çelik
Published: July 2022

With the growing prevalence of wireless communication technologies, social networking sites (SNSs) such as Facebook, Twitter, etc. have become an important venues for interpersonal communication. This chapter provides a detailed overview of the current literature on online social networking with respect to its beneficial and detrimental effects on psychological wellbeing. In particular, it provides empirical evidence for the associations of SNS use with depression, self-esteem, loneliness, subjective wellbeing, social anxiety, attachment, personality traits, and addiction. Furthermore, it identifies the characteristics of individuals who are more prone to social networking, and presents possible mediators and moderators playing a role in the relationship between social networking and mental health. The chapter overall provides a comprehensive guideline to parents, researchers, educators, healthcare, and communication professionals to the issue of online social networking from a psychological perspective.
An Examination of Factors That Influence Social Networking Community Participation Among Millennials
Authors: Ng Celeste See Pui , Lee-Post Anita
Published: July 2022

This study investigates main and moderating factors that influence Millennials' intention to participate in a social networking community (SNC). The authors modified the unified theory of consumers' acceptance and use of technology (UTAUT2) to incorporate six main and two moderating factors to explain Millennials' SNC participation intention. By considering the implications of the unique characteristics of Millennials on their social networking behavior, the authors' model is better suited to answer what drives these tech-savvy individuals to participate in a SNC via such sites as Facebook. Specifically, the authors find that hedonic motivation, trust in technology, trust in community, and social influence are significant factors in influencing Millennials' SNC participation intention, with hedonic motivation being the most influential factor. In addition, gender and educational background moderate the main effects of these determinants in different manner. Theoretical and practical implications of these findings are discussed.
Blogger Mothers as a Transmediatic Narration
Authors: Aktan Ercan
Published: July 2022

Individuals are facing message bombardments from many tools. In such a process, trying to reach individuals at any moment using different tools is important for the effectiveness of communication. This requirement led to a new process called transmedia. The transmedia storytelling, which is carried out in order to ensure that the messages planned to be transmitted in transmedia are even more effective, is one of the heavy-duty practices of this new process. This study was carried out in order to reveal how blogger mothers used transmedia storytelling practices, the difference of this use from traditional motherhood, and the role of transmedia storytelling practices in changing form of motherhood. In the study, blogger mothers were found to use transmedia storytelling practices heavily in order to influence their followers. It was also concluded that blogger mothers play the roles of heroines, friends, and dynamic and assistant heroines.
Depressive Person Detection using Social Asian Elephants' (SAE) Algorithm over Twitter Posts
Authors: Bouarara Hadj Ahmed
Published: July 2022

With the advent of the web and the explosion of data sources such as opinion sites, blogs and microblogs appeared the need to analyze millions of posts, tweets or opinions in order to find out what thinks the net surfers. The idea was to produce a new algorithm inspired by the social life of Asian elephants to detect a person in depressive situation through the analysis of twitter social network. The proposal algorithm gives better performance compared to data mining and bioinspired techniques such as naive Bayes, decision tree, heart lungs algorithm, social cockroach's algorithm.
Online Self-Disclosure
Authors: Desjarlais Malinda
Published: July 2022

Due to their audiovisual anonymity and asynchronicity, social media have the potential to enhance self-disclosure, and thereby facilitate closeness among existing friends. In this chapter, the author highlights findings relating to the beneficial social connectedness outcomes that can be linked to online self-disclosure, synthesizes relevant literature that addresses who reaps the most benefits from online self-disclosure, and makes suggestions to direct future research in this area. Theoretical perspectives are identified throughout the chapter that are relevant to understanding the benefits of online self-disclosure, the relation between personal characteristics as predictors of online self-disclosure, and moderating factors of the effect of online self-disclosure on social connectedness. Empirical findings support both social compensation and social enhancement perspectives.
Online Social Capital Among Social Networking Sites' Users
Authors: Ahmed Azza Abdel-Azim Mohamed
Published: July 2022

This research aimed to explore types of online social capital (bridging and bonding) that the Emiratis perceive in the context of social networking site (SNS) usage. A sample of 230 Emiratis from two Emirates, Abu Dhabi and Dubai, was used to investigate the hypothesis. The results showed that WhatsApp was the most frequent SNS used by the respondents. Also, a significant correlation of the intensity of social networking usage and bridging social capital was found, while there was no significant association between SNS usage and bonding social capital. The factors determined the SNSs usage motivations among the respondents were exchange of information, sociability, accessibility, and connections with overseas friends and families. Males were more likely than females to connect with Arab (non-Emiratis) and online bonding social capital. Both genders were the same in their SNSs motivations and online bridging social capital.
Frugal Living for Our Collective and Mutual #Bestlife on a Distributed and Global Electronic Hive Mind
Authors: Hai-Jew Shalin
Published: July 2022

What is not as commonly identified as an optimal life #bestlife is living #frugal, and yet, there is a global electronic hive mind about how to live sparingly based on highly variant local realities. There are blogs about living on a shoestring, stretching funds, cooking in, engaging in a DIY economy (bartering with like-minded others), living off the grid, taking low-cost and simple vacations, maintaining a food garden, raising food animals, and forgoing the more spendy aspects of modern living. The narrative goes that saving up and retiring early enables low-pressure and intentional lifestyles (and an ability to focus on family and friends), low-carbon footprints (with low impacts on the environment), and the embodiment of a frugal virtue. This chapter explores what a #frugal living EHM looks like and how it brings together people around shared values and lifestyle practices for personal peace of mind, social justice, and long-term sustainability.
“Too Good to Be True”
Authors: Kleim Anke J. , Eckler Petya , Tonner Andrea
Published: July 2022

This chapter examines how body image deception is created and understood in social media. The authors focus specifically on the beach body, which is a narrower form of bodily representation online, but where deception is especially likely to occur. Focus group discussions with young adults revealed that editing and perfecting the beach body is commonplace and even normalized on social media. However, participants distinguished between celebrities and friends in expected use of manipulation and seemed to place a limit on the acceptable types of manipulation: body tan but not body shape, for example. The authors discuss the implications of these discussions and how applying deception theory in body image research can provide useful insights.
Transformation of China's Most Popular Dating App, Momo, and Its Impact on Young Adult Sexuality
Authors: Miao Weishan , Xu Jian
Published: July 2022

This chapter explores China's most popular dating app ‘Momo' and its impact on young adult sexuality. It examines three interrelated questions at three different levels: First, at the macro level, in what social situations and institutions were mobile dating apps such as Momo invented in China? Second, at the meso level, if we consider Momo as a constantly changing social process, what are the transformations it has experienced, and, during this process, what societal forces have impacted the trajectory of changes and in what ways? Third, at the micro level, how the transformation of Momo's ‘intimate infrastructures' at different developmental stages has impacted the sexuality and intimate relationships of its young adult users? It argues that mobile dating apps have to timely transform their design, functions, and market positions to adapt to the changing market competition and governmental regulations in China. The transformation of the intimate infrastructures of the mobile dating apps has also shaped the young adult users' intimate practices and sexuality.
Hispanic Humor Styles on Facebook
Authors: Wang Valerie L , Wu Yi-Chia , Lou Hao
Published: July 2022

To better understand how humor is used in today's multicultural virtual environment, this study investigates the humor styles of Hispanic Americans in a virtual community. Based on the four humor styles, the current study builds a theoretical framework to explain why cultural norms, gender role, acculturation, and education influence the humor styles of Hispanic Americans in computer-mediated communication. Two research questions and five hypotheses are developed in the research framework. The statistical analysis is based on content analysis of 400 Hispanic Facebook users, 93 of whom use humor in most recent News Feed. The results provide preliminary evidence of the influences of cultural norms, gender role, acculturation, and education on Hispanic humor styles.
Risk Governance
Authors: Agua Pedro B. , Correia Anacleto C.
Published: July 2022

The current worldwide pandemic due to COVID-19 confronts all industries with considerable economic challenges. This text analyses the subject of organizational risks from the perspective of the board of directors. It argues that compliance is a necessary condition, however not sufficient, for an effective risk governance. It suggests that prudence is something that should be nurtured and promoted at the level of organizational governance. Organizations being complex systems, a holistic framework should be used in approaching risk governance. Risk approaches have been particularly influenced by regulation focusing on financial risks, while there are many additional types of risks, potentially more damaging for organisations. The role of the board of directors has undergone a long evolution from merely “ceremonial” to its current “progressive” form. This chapter argues on a more prudential action by those responsible for corporate governance beyond a normativism approach.
Are There Sustainable Development Trends in Chinese Ecotourism Policies?
Authors: Yu Poshan , He Keyi , Hanes Emanuela
Published: July 2022

Defined as a type of tourism uniting conservation, communities, and sustainable travel, ecotourism is becoming increasingly important. For governments, developing guidance for the industry which helps address the potential conflicts between ecological conservation and economic development is of utmost importance. Thus, the changes of government policies in tourism have significantly driven the ecotourism development in China. However, most current research findings on ecotourism paid insufficient attention on the significance of the interlinked relationships among policies and research studies and the trends inside them. Implementing research methods including literature analysis and institutional analysis, this chapter aims to examine these dynamic relationships from the regulators' perspectives. This chapter contributes to the field by demonstrating a sustainable development trend of Chinese ecotourism driven by the unique institutional settings with Chinese characteristics and analyzing the potential implications for various stakeholders due to the rising standards of ecotourism.
Where Are You Going, Work From Anywhere?
Authors: Martins Helena , Maritsa Evangelia , Rodrigues Elisabeth
Published: July 2022

The onset of the virtual revolution and global working trend known as working from anywhere (WFA) potentiated by the bio event COVID-19 shifted not only the way we work but also the focus of research on this topic. This chapter aims to portray in a systemic way the researchers conducted before and after the bio event in a systematic way in order to create a research map of topics by having as reference point the outbreak of the pandemic and by using VOS Viewer software to illustrate the resulting clusters of topics around this reference point topics before and after the pandemic hit. Results indicate a clear difference between the two periods regarding the WFA research. Firstly, the number of papers published on this topic has increased significantly, and secondly, the types of topics approached seem to have become more focused. Main topics of research are discussed as a way to reflect on the challenges for theory and practice that are to come.
Managing Business Continuity in the COVID-19 Era
Authors: Mak Chee Wai , Pruetipibultham Oranuch
Published: July 2022

Almost two years since the first reported confirmed case, the novel coronavirus disease 2019 (COVID-19) continues to impact people around the world. While some companies could adopt a work from home strategy to cope, manufacturing companies did not have such a choice. Subject to government regulations and possible closures to curb the spread of COVID-19, manufacturing companies need to find ways to mitigate risks and continue operating. This chapter shares the case study of how a framework (COVID-19PAL) helped a multi-national manufacturing company of 29,000 employees situated in Thailand deal with a high number of infections and continue to operate safely. The COVID-19PAL framework draws from different disciplines (such as epidemiology, physics, mathematical modeling, sociology, and social psychology) and provides necessary information for human resource professionals to be effective in dealing with a pandemic. This chapter thus fills an existing gap in literature, assisting human resource professionals, organization leaders, and employees to understand what the situation meant.
A Multi-Objective Optimization Scheduling Method Based on the Genetic Algorithm in Cloud Computing
Authors: Sissodia Rajeshwari , Rauthan ManMohan Singh , Barthwal Varun
Published: July 2022

For task-scheduling problems in cloud computing, a multi-objective optimization method is proposed here. First, with an aim toward the biodiversity of resources and tasks in cloud computing. This paper propose a resource cost model that defines the demand of tasks on resources with more details. A multi-objective optimization scheduling method has been proposed based on this resource cost model. This method considers the makespan, wall clock time , execution time and the costs as constraints of the optimization problem. This paper proposed a multi-objective improved genetic algorithm (MOIGA) to address multi-objective task scheduling problems. The experiment results showed that the MOIGA algorithm minimizes makespan, wall clock time, execution time and cost when compared with First Come First Serve (FCFS), Round Robin (RR) and Shortest Job First (SJF).
Towards a Full Model-Driven Approach for Modeling and Verifying Business Process Models Using CPN
Authors: Mahdi Karima , Chaoui Allaoua , Kolli Manel , Elmansouri Raida
Published: July 2022

This work addresses one of the core issues in modern enterprises applications: Business Process (BP), by proposing a new comprehensive model-driven approach for automated and formal verification of BP models. This allows BP specialists to formally and automatically verify their process models, without needing a deep knowledge of formal methods. The approach automatically converts BP models to Colored Petri Nets (CPNs) using CPN Tools for the analysis purposes. It starts with meta-modeling formalisms of BP and CPNs using Atom3 Tool. At this level, it proposes two graph grammars. The former converts BP models into their corresponding CPNs and the latter interprets the acquired CPN models in a text format based on XML. This enables model transfer from Atom3 to CPN tools by assessing the correctness of the BP model. In order to test the functional utility of our proposed approach, we applied it to several examples and the results showed that the system is working correctly and that inconsistencies are detected in BP models. One detailed example is given to illustrate our approach.
Virtual Education Impact During Pandemic Times
Authors: Florez Ferrer Carla C. , Quintero Cordero Yolvy J. , Bustillos Peña Mayra A. , Toasa Guachi Renato Mauricio
Published: July 2022

The objective of this article is to analyze the impact of virtual education in times of pandemic in Higher Education Institutions (IES), in Ecuador. In this sense, the Israel Technological University, located in Quito, was used as a research case. Methodologically, it is a study with a quantitative, descriptive approach, with a field design and a transactional cut. The population is made up of 2317 students and 58 teachers. Of this population, 598 students and 31 teachers responded online, for a sample of 25.8% and 53.4%, respectively. The survey was estab-lished as a technique and two questionnaires as an instrument, one for students and the other for teachers. As part of the conclusions, it is determined that the im-pact of virtual education in times of pandemic, both for teachers and students in Ecuador, has been mostly positive. A high level of satisfaction is handled by this type of format.
Effect of Optimized Deep Belief Network to Patch-Based Image Inpainting Forensics
Authors: Patil Balasaheb H.
Published: July 2022

This paper intends to propose a new model for detecting the patch based inpainting operation using Enhanced Deep Belief Network (E-DBN). The proposing model makes strong supervising of DBN that will capture the manipulated information. In fact, the enhancement is done under optimization concept, where the activation function and weight of DBN is optimally tuned by a new hybrid algorithm termed as Swarm Mutated Lion Algorithm (SM-LA). The hybridization model combines two conventional models: Group Search Optimizer (GSO) and Lion Algorithm (LA). Finally, the performance of proposed model is compared over other conventional models with respect to certain performance measures.
Required Attributes of a Smart Clothing for Pressure Ulcers Prevention
Authors: Barboza Rochelne , Arruda Luisa M. , Carvalho Miguel A. , Fangueiro Raul , Salgueiro Anabela , Parreira Pedro , Sousa Liliana
Published: July 2022

Indivíduos que permanecem acamados por um longo período estão sujeitos a alterações nos sistemas do corpo humano, e as úlceras por pressão são um grande problema nessa condição clínica, representando mais de 6,5 milhões de casos por ano em todo o mundo. As forças de pressão atuantes combinadas com as forças de cisalhamento são responsáveis ​​por lesões na pele e / ou tecido subjacente, geralmente em uma proeminência óssea. O paciente é o ponto de partida para o desenvolvimento do produto / vestimenta, porém, para atender essa demanda em termos de conforto de diversas formas, é necessário considerar além das necessidades, limitações, condição de mobilidade, posturas adotadas, complicações e implicações da lesões, as especificações dos materiais usados, bem como o design de padrões, estruturas e tecnologias. O objetivo deste estudo é construir uma matriz de atributos para o desenvolvimento de roupas inteligentes na prevenção de úlceras por pressão. Para esta condição, este estudo visa relacionar as propostas com as novas tecnologias, ergonomia e usabilidade, sob os quatro aspectos do conforto: ergonômico, psicológico-estético, termofisiológico e sensorial.
Functional Design of Tri-Laminated Wool Fabrics Improving Comfort Properties
Authors: Reis Benilde , Miguel Rui , Lucas José , Pereira Madalena , Pinheiro Cláudia , Santos Gilda , Carvalho João , Moreira da Silva Fernando , Silva Manuel Santos
Published: July 2022

Considering textiles and clothing, current trends are focused on increasing the technicality of the products, with more alert and demanding consumers, looking for functional products such as workwear, sportswear, or day-life clothing, which combine aesthetics and innovation, design, protection, comfort, multifunctionality, ease care, sustainability, and competitive price. The result of social change focused on a healthier lifestyle and activewear is a growing demand. The TexBoost Project – Less Commodities More Specialities, PPS 2 "New fabrics for high-performance applications based on natural fibres" seeks to respond to these challenges and trends through the development of technologically advanced products and processes that enable companies to grow in the value chain and penetrate in more competitive market niches. This work aims to present some results that identify the main innovative characteristics and advancements considering the new fabrics for high-performance applications based on natural fibres, focusing on the development of multifunctional wool fabrics and laminated textile structures for leisurewear products. Comfort needs to be considered as an important factor considering the development of clothing. Taking into account the evaluation of the performance of the main comfort properties, the laboratory tests performed were the resistance to water penetration (hydrostatic pressure), the air permeability and skin model tests.
Development of a Stretchable Circuit and its Integration Method on Knit Fabrics for Lower Back Injury Prevention
Authors: Veske Paula , Bauwens Pieter , Bossuyt Frederick , Sterken Tom , Schuermans Joke , Vanfleteren Jan
Published: July 2022

Advancements in wearable technology and smart textiles have also opened new possibilities in the sports and medical fields. One of the examples of a relevant application case can be found in cycling. This paper expands on previous research on stretchable electronics on knit fabrics. It describes the development of stretchable circuits for the lower back position and motion tracking to prevent back pain in recreational (road) cyclists by combining electrical and textile engineering with insights generated in rehabilitation sciences and sports physiotherapy. The research process included developing and testing of the functional circuit integrated into a cycling jersey. Thermoplastic polyurethane films were used for the textile integration process to achieve maximum comfort and after-life disassembly possibility. Reliability tests, e.g. washing (ISO 6330-2012) and tensile tests, were conducted. It is concluded that while mechanical stress during washing cycles remains a serious concern for reliability and durability, the use of water-repellent thermoplastic polyurethane sealing seriously reduces the damage caused by water and detergents.
Reliability Analysis and Comparative Study of Ordinary Concrete and High Performance Concrete Filled with Steel Tube under Axial Compression
Authors: Lydia Messaoudene , Nassim Kernou
Published: July 2022

Reliability and behaviour of composite columns is influenced by many factors. This paper presents a comparative study of the reliability and performance of square composite columns under axial compression, taking into account mechanical and geometric variability. The choice is opted for metal hollow profiles filled with ordinary concrete and high-performance concrete. In this study, a mechanic-reliability model to calculate the reliability index and the probability of failure of different columns is presented. The response surface method is used to accomplish this coupling in order to describe the uncertainties in a suitable model and to study their influence for a reliability assessment. The results show that the material and geometric characteristics of the columns have a significant influence on strength and reliability. The sensitivity of the random parameters of structural reliability is assessed from the proposed method.
Wear Trials about Comfort on Sport Compression Socks
Authors: Pinto Nelson , Abreu Maria José , Whiteman Catarino André Paulo de Almeida
Published: July 2022

Compression garments has emerged as an important tool in sport activity [1, 2]. In the particular case of compression socks (CS), several studies show that their use improves the fatigue recovery and prevents muscle injuries [3-6]. The aim of this research work is to perform the characterization of sports socks regarding their comfort and compression. Several parameters were monitored in volunteers during a running test. This test was performed in a non-inclined treadmill for 12 minutes at 12km/h. PicoPress equipment was used to recording the compression force; Thermodata sensors were used to monitor the temperature and humidity; Wrist pulse oximeter was used to measure the oxygen saturation and heart rate; and thermal imaging with a TESTO IR camera was used for thermal component evaluation. Wearing compression socks seem to return better conditions for comfort, muscle stability and muscle fatigue because the main conclusions of the test show that the volunteers feel better at muscle stability (MS) and no fatigue after 24 hours of running.