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\"Including significant previously uncollected material, My Generation is the definitive gathering of the fruits of this beloved writer's five decades of public life. Here is the William Styron unafraid to peer into the darkest corners of the 20th century or to take on the complex racial legacy of the United States. But here too is Styron writing about his daily walk with his dog, musing on the Modern Library's \"100 Greatest Books,\" and offering personal insight into the extraordinary array of noted contemporary figures he interacted with over the course of an illustrious career. These are the people and events, tragic and joyful, historical and intimate, that aroused Styron's unrivalled curiosity\"-- Provided by publisher.

Mobility trajectory data is of great significance for mobility pattern study, urban computing, and city science. Self-driving, traffic prediction, environment estimation, and many other applications require large-scale mobility trajectory datasets. However, mobility trajectory data acquisition is challenging due to privacy concerns, commercial considerations, missing values, and expensive deployment costs. Nowadays, mobility trajectory data generation has become an emerging trend in reducing the difficulty of mobility trajectory data acquisition by generating principled data. Despite the popularity of mobility trajectory data generation, literature surveys on this topic are rare. In this paper, we present a survey for mobility trajectory generation by artificial intelligence from knowledge-driven and data-driven views. Specifically, we will give a taxonomy of the literature of mobility trajectory data generation, examine mainstream theories and techniques as well as application scenarios for generating mobility trajectory data, and discuss some critical challenges facing this area.

Despite the potential benefits of using eHealth, sociodemographic disparities exist in eHealth use, which threatens to further widen health equity gaps. The literature has consistently shown age and education to be associated with eHealth use, while the findings for racial and ethnic disparities are mixed. However, previous disparities may have narrowed as health care interactions shifted to web-based modalities for everyone because of the COVID-19 pandemic. This study aims to provide an updated examination of sociodemographic disparities that contribute to the health equity gap related to using eHealth for information seeking using 3 time points. Data for this study came from the nationally representative 2018 (n=3504), 2020 (n=3865), and 2022 (n=6252) time points of the Health Information National Trends Survey. Logistic regression was used to regress the use of eHealth for information seeking on race and ethnicity, sex, age, education, income, health status, and year of survey. Given the consistent association of age with the dependent variable, analyses were stratified by age cohort (millennials, Generation X, baby boomers, and silent generation) to compare individuals of similar age. For millennials, being female, attaining some college or a college degree, and reporting an annual income of US $50,000-$74,999 or >US $75,000 were associated with the use of eHealth for information seeking. For Generation X, being female, having attained some college or a college degree, reporting an annual income of US $50,000-$74,999 or >US $75,000, better self-reported health, and completing the survey in 2022 (vs 2018; odds ratio [OR] 1.80, 95% CI 1.11-2.91) were associated with the use of eHealth for information seeking. For baby boomers, being female, being older, attaining a high school degree, attaining some college or a college degree, reporting an annual income of US $50,000-$74,999 or >US $75,000, and completing the survey in 2020 (OR 1.56, 95% CI 1.15-2.12) and 2022 (OR 4.04, 95% CI 2.77-5.87) were associated with the use of eHealth for information seeking. Among the silent generation, being older, attaining some college or a college degree, reporting an annual income of US $50,000-$74,999 or >US $75,000, and completing the survey in 2022 (OR 5.76, 95% CI 3.05-10.89) were associated with the use of eHealth for information seeking. Baby boomers may have made the most gains in using eHealth for information seeking over time. The race and ethnicity findings, or lack thereof, may indicate a reduction in racial and ethnic disparities. Disparities based on sex, education, and income remained consistent across all age groups. This aligns with health disparities literature focused on individuals with lower socioeconomic status, and more recently on men who are less likely to seek health care compared to women.

The present study aims to investigate the impact of viral marketing messages on Generation Z customers’ attitudes and purchase intentions and identify the critical components of a successful viral marketing campaign. The study highlights the influence of Viral Content Informativeness (VCI), Viral Content Credibility (VCC), and Viral Content Usefulness (VCU) on the Attitudes of Generation Z (ATT). Further, it highlights the influence of Attitudes of Generation Z (ATT) on their Purchase Intentions (PI). The study’s identification of the critical factors of viral message informativeness, credibility, and usefulness can help businesses tailor their campaigns to resonate with their target audience and enhance their brand awareness, credibility, and customer loyalty. The primary data was collected through a structured questionnaire. The survey items were adopted from the literature and were further improved as per the needs of the study. For data collection, a total of 950 questionnaires were distributed. While investigating incomplete responses and multivariate outliers, a total of 652 valid responses were considered for data analysis. For data analysis, researchers employed IBM® SPSS® and IBM® Amos® software, which are generally employed for CB-SEM. Present study primarily highlights the effect of viral marketing messages on the attitudes and purchase intentions of Generation Z customers. Furthermore, this study contributes as there is scant literature available discussing Indian and Asian perspective. This research adds to the existing body of knowledge and contributes to the practice.

Machine-learning-based predictive maintenance models, i.e. models that predict breakdowns of machines based on condition information, have a high potential to minimize maintenance costs in industrial applications by determining the best possible time to perform maintenance. Modern machines have sensors that can collect all relevant data of the operating condition and for legacy machines which are still widely used in the industry, retrofit sensors are readily, easily and inexpensively available. With the help of this data it is possible to train such a predictive maintenance model. The main problem is that most data is obtained from normal operating conditions, whereas only limited data are from failures. This leads to highly unbalanced data sets, which makes it very difficult, if not impossible, to train a predictive maintenance model that can detect faults reliably and timely. Another issue is the lack of available real data due to privacy concerns. To address these problems, a suitable data generation strategy is needed. In this work, a literature review is conducted to identify a solution approach for a suitable data augmentation strategy that can be applied to our specific use case of hydrogen combustion engines in the automotive field. This literature review shows that, among the different state-of-the-art proposals, the most promising for the generation of reliable synthetic data are the ones based on generative models. The analysis of the different metrics used in the state of the art allows to identify the most suitable ones to evaluate the quality of generated signals. Finally, an open problem in research in this area is identified and it is the need to validate the plausibility of the data generated. The generation of results in this area will contribute decisively to the development of predictive maintenance models.

Classic Grounded Theory (classic GT), developed by Glaser and Strauss (1967), is a powerful qualitative methodology designed to generate theory grounded in data. Despite its enduring relevance across disciplines, classic GT is often perceived as too time-consuming and complex for doctoral candidates, particularly within the constraints of a 12-month research timeline. This manuscript challenges that perception and presents a practical framework demonstrating that classic GT can be both rigorous and feasible within a single year. Through a detailed synthesis of classic GT principles and processes, including constant comparative analysis, theoretical sampling, memoing, and the deliberate deferral of the literature review, this paper outlines a streamlined six-phase timeline designed to guide doctoral candidates efficiently from field entry to final write-up. Emphasis is placed on the early identification of the core category, which acts as a conceptual anchor, accelerating data collection and focused analysis. In this manuscript, we offer practical strategies to manage common challenges such as achieving theoretical saturation, maintaining theoretical sensitivity, and avoiding conceptual drift. It also underscores the critical role of supervision and peer support in sustaining momentum and methodological integrity throughout the research process. A streamlined timeline (Appendix B) illustrates how doctoral researchers can generate high-quality, substantive theory without compromising the emergent and discovery-driven foundations of classic GT. This work contributes to the growing body of literature advocating for classic GT’s wider adoption and demonstrates its compatibility with the demands of contemporary doctoral research. This work offers timely, actionable guidance for candidates, supervisors, and institutions seeking to support grounded theory research within structured academic timeframes.

The digital transformation of literacy education in the 21st century has created an urgent need for validated learning models that bridge traditional poetry appreciation and digital multimodal literacy. This study presents an innovative multiliteracy-based poetry transformation learning model that integrates digital competencies with literacy analysis. This study aims (1) to develop poetry transformation instruments and (2) to identify the key aspects of implementing the poetry transformation learning model. The study sample consisted of 96 students from three Indonesian higher education institutions. This research employs a quantitative approach using a survey design. Data collection utilizes a poetry transformation questionnaire instrument. Data analysis techniques demonstrate empirical validity using Confirmatory Factor Analysis (CFA). The findings of this study reveal that 1) the construction of poetry transformation instruments, covering five aspects, is considered valid and reliable; 2) the syntax aspect emerges as a key factor in the poetry transformation learning models; and 3) the quality of work is the most important indicator in the syntax aspect.

It happens to us all: we think we've settled into an identity, a self, and then out of nowhere and with great force, the traces of our parents appear to us, in us-in mirrors, in gestures, in reaction and reactivity, at weddings and funerals, and in troubled thoughts that crouch in dark corners of our minds. In this masterful collection of new essays, the apple looks at the tree. Twenty-five writers deftly explore a trait they've inherited from a parent, reflecting on how it affects the lives they lead today-how it shifts their relationship to that parent (sometimes posthumously) and to their sense of self. Apple, Tree's all-star lineup of writers brings eloquence, integrity, and humor to topics such as arrogance, obsession, psychics, grudges, table manners, luck, and laundry. Contributors include Laura van den Berg, S. Bear Bergman, John Freeman, Jane Hamilton, Mat Johnson, Daniel Mendelsohn, Kyoko Mori, Ann Patchett, and Sallie Tisdale, among others. Together, their pieces form a prismatic meditation on how we make fresh sense of ourselves and our parents when we see the pieces of them that live on in us.

Tawfiq al-Hakim (1898-1987) is recognized as one of the most important figures in the history of modern Arabic literature. Considered a pioneer in many literary forms, including drama, novels, and short stories, al-Hakim influenced generations of Egyptian writers.The Revolt of the Youngis a collection of essays originally published in 1984 that shows al-Hakim as a public intellectual addressing the ongoing conflict between generations.Al-Hakim muses on the cultural, artistic, and intellectual links and breakages between the old and the young generations. He focuses on the young and their role in the continuing development of Egyptian society, which over the course of the last century or so has undergone foreign occupation, an experiment in parliamentary democracy within a monarchical system, a socialist revolution characterized by both successes and failures, the emergence of a dictatorial presidency, and now the development of a complex political scenario currently dominated by groups with varyingly strong ties to the tenets of Islam. Al-Hakim did not live long enough to witness the most recent phases of this elongated and often disruptive process of experimentation and change, but in his writing, he focuses on the institution that he regards as crucial to any forward progress: the family.The essays present readers with a voice from the past, with uncanny foretelling of issues raised by the events that occurred in Egypt more than twenty years after al-Hakim's death. Radwan's translation, along with Roger Allen's foreword, brings to light al-Hakim's lucid and erudite commentary, much of it as relevant to the problems facing Egyptian youth today as it was when first published.

Although European blood collection organizations are currently obtaining sufficient and safe blood from the majority population, they report having difficulty recruiting first and second-generation immigrants from non-European countries. Most existing studies on these underrepresented groups, who have been coined the “missing minorities” in blood donation, have adopted an instrumental approach that focuses on the development of targeted recruitment strategies to overcome specific barriers to donation faced by members of these minorities. Although this approach does offer several short-term benefits, our central argument is that it is one-sided in its questioning of the non-participation of ethnic minorities. The literature currently lacks research on how the blood procurement system is failing to include minorities. Drawing on recent social theory, we seek a broader sociological understanding of minority under-representation in blood donor populations by shifting the analytic focus toward a critical examination of the main pillars of the procurement system within the European context. This paper advances a novel analytical framework based on two general propositions. First, we apply the literature on “citizenship regimes” to argue that blood donation is part of one specific institutionalization of citizenship and solidarity. We then reconceptualize the “problem” of missing minorities in European blood donation as an application for social change, suggesting avenues related to blood collection as a way of renegotiating minority-majority relations of solidarity.