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Positive People and Confident Competitors: Resilient Youth Development Through Sport and Physical Activity
In the altering world scenario, there is a necessity to plan, prepare and progress with youth development. Research has associated positive youth development with the 5Cs model (competence, confidence, connection, character and caring) (Lerner et al., The Journal of Early Adolescence 25:17-71, 2005) to build resilience in youth. Over the past 35 years, sport psychology has established that sport helps in developing necessary psychological skills and attributes among youth. Youth sport is an extracurricular activity that provides young people with unique negative and positive experiences. Within these experiences, the individual goes beyond the self and has to work with a diverse group of others for self-development and achievement of shared goals. In this chapter, our primary objective is to review the foundations of literature concerning confidence, resilience and identity as corner-stones for positive youth development through sport. To achieve this objective, we adopt a global approach blending field experience from participatory sport, developmental sport and elite sport to provide an intervention framework grounded in applied sport psychology. Intervention framework provided is aligned to the COM-B behaviour change model (Michie et al. 2011) for sustainable change. The focus is on a balance between developing stable protective factors for mental health and positive youth development to ensure appropriate cognitive, social, emotional and behaviour skills to thrive in an evolving world. Implications for transferring this learning cross-culturally and in non-sport contexts such as schools and grass-root programs are discussed with recommendation for good practice. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Post-millennials: Psychosocial Characteristics, Determinants of Health and Well-Being, Preventive and Promotive Strategies
The post-millennial generation plays a significant role in the progress and development of every nation. The health and well-being of this generation needs critical focus. Post-millennial are unique, possesses an egalitarian worldview, global and open mind-set, commitment to the environment, society, and others. They are called digital natives due to their familiarity with social media and technology. They are also called snowflake generation due to their characteristics of being gentle and unique. Double income households and well-educated parents and their assistance make them a distinguished population. However, these characteristics are less explored while addressing the health and wellness concerns of this cohort. The present chapter discusses the psychosocial characteristics of the post-millennials and their implications exclusively in the mental health realm. It also presents the strength-based strategies to address the concerns of post-millennials and the significance of evidence-based practices in mental health and sensitizing mental health practitioners about the changing scenario. The Editor(s) (if applicable) and The Author(s), under exclusive license to Taylor and Francis Pte Ltd. 2022. -
Potential applications of AI and IoT collaborative framework for health care
Digital technology has infiltrated the entire planet. Artificial Intelligence (AI) and the Internet of Things (IoT) are the two buzzwords that became popular in the current digital world, especially in recent decades. Both these technologies have their contribution in various domains. The existing frameworks will benefit from the AI-IoT collaborative system, which will assist them in having more intelligent or smart responses. Furthermore, these collaborative systems can provide improved devices with better decision-making capacity to facilitate the users. AI can work with IoT to increase functional precision in the healthcare domain by automating and tracking, monitoring, managing, optimizing, and predicting processes in 24x7 mode. Health professionals are the people involved in activities whose primary commitment is to improve the wellbeing of the community. They are a group of people who face various obstacles, including their health and safety concerns, especially during pandemic outbreaks. This book chapter aims to illustrate the impact of AI and IoT on the health care domain and the challenges that healthcare professionals face, especially when dealing with such an pandemic and suggests some potential health care advancements through AI and IoT. 2023 Bentham Science Publishers. All rights reserved. -
Practical Benefits of Using AI for More Accurate Forecasting in Mental Health Care
Artificial Intelligence (AI) is the general term for being able to make computers do things that require human-like intelligence. AI is the novel idea of the computer pioneers like Alan Turning and John von Neumann in the 1940s. Their novel intuition towards making machines think is the key start for this AI technology evolution. As shown in Fig. 1, the first milestone of AI happened in the year 1956 when it was proved by a group of researchers that a machine could solve any problem with the use of an unlimited amount of memory. Here they named this program General Problem Solver (GPS). 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Predicting cryptocurrency prices model using a stacked sparse autoencoder and Bayesian optimization
In recent years, digital currencies, also known as cybercash, digital money, and electronic money, have gained significant attention from researchers and investors alike. Cryptocurrency has emerged as a result of advancements in financial technology and has presented a unique opening for research in the field. However, predicting the prices of cryptocurrencies is a challenging task due to their dynamic and volatile nature. This study aims to address this challenge by introducing a new prediction model called Bayesian optimization with stacked sparse autoencoder-based cryptocurrency price prediction (BOSSAE-CPP). The main objective of this model is to effectively predict the prices of cryptocurrencies. To achieve this goal, the BOSSAE-CPP model employs a stacked sparse autoencoder (SSAE) for the prediction process and resulting in improved predictive outcomes. The results were compared to other models, and it was found that the BOSSAE-CPP model performed significantly better. 2023, IGI Global. -
Predicting Stock Market Indexes with Artificial Intelligence
The forecasting of the Share market has been a popular research area, involving the analysis of input and output stock data using computer technology and algorithmic knowledge. This involves building unpredictable relationships among the data and analyzing the stock market trends to provide a reference for investors. The inception of artificial intelligence (AI) technology, blended with the web, immense data, and cloud computing has provided technical support for various industries. AI technology is employed to scrutinize and predict the equity market, exploring curvilinear associations amid stock market information, and furnishing a foundation for investors to formulate investment determinations. Predicting equity prices is a demanding undertaking due to diverse factors like governmental happenings, fiscal circumstances, business resolutions, investor mentality, and overseas currency hazards. The securities exchange is a vastly active and disordered framework, and producing precise projections of the securities exchange is of paramount significance. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Prediction and analysis of financial crises using machine learning
This study presents a comparative analysis of various machine learning algorithms for credit risk assessment. The algorithms were tested on two credit datasets: German Credit Dataset and Australian Credit Dataset. The performance of the algorithms was evaluated based on several metrics, including sensitivity, specificity, accuracy, F-score, and Kappa. The results showed that the FCPFS-QDNN algorithm outperformed other algorithms in both datasets, achieving high accuracy, sensitivity, specificity, and F-score. On the other hand, the ACO Algorithm and Multilayer Perceptron algorithms were found to perform poorly in both datasets. The findings of this study have significant implications for credit risk assessment in banking and financial institutions. The study recommends the use of the FCPFS-QDNN algorithm for credit risk assessment due to its superior performance compared to other algorithms. 2023, IGI Global. All rights reserved. -
Prediction of Users Behavior on the Social Media Using XGBRegressor
The previous decennium has seen the growth and advance with respect to social media and such that has violently also immensely expanded to infiltrate each side of user lives. In addition, mobile network empowers clients to admittance to MSNs at whenever, anyplace, for any character, including job and gathering. Accordingly, the association practices among clients and MSNs are getting completer and more confounded. The goal of this paper is to examine the number of followers, likes, and post for Instagram users. The dataset yielded several fundamental features, which were used to create the model with multimedia social networks (MSNs). Then, natural language processing (NLP) features should be added and finally incorporate features derived in distinction to a machine learning technique like XGBRegressor with TF-IDF technique. We use two performance indicators to compare the different models: root mean square error (RMSE) and the R2 value. We achieved average accuracy using XGBRegressor which is 82%. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Predictive analysis in smart agriculture
Analyzing large databases for hidden connections, correlations and insights is known as big data analytics. Although many countries still use outdated farming methods, technological advancements have allowed for specific improvements (especially in developing countries). Big data analytics has the potential to expand the agricultural sector in this regard significantly. The farmers rely heavily on old methods for deciding what to plant and how to cultivate it. Walking through fields, selecting soil samples for moisture analysis, and visually inspecting plant leaves are typical examples of these time-honored practices. Understanding the significance of technology for acquiring crop information in considerable amounts and turning that data into usable knowledge is crucial for agriculturists (mainly farmers). Integration of big data could help agriculture make changes to its current practices. If used correctly, big data analytics can shed light on the most efficient crop cultivation methods. Extensive developments in three areas-crop prediction, precision farming and seed production-are reshaping the agricultural industry. There are four parts to this chapter. The first part of this paper provides an introduction to analytics on big data in agriculture. The second part will then focus on the various big data methods used in the agricultural sector. The third section provides two examples of how big data analysis methods were put to use in the field of agriculture. In the fourth section, the authors examine the several agricultural research avenues open to scholars and scientists. This chapter concludes with a brief overview. 2023 River Publishers. All rights reserved. -
Predictive analysis of stock prices through scikit-learn: Machine learning in python
Scikit-learn, a tool for developing machine learning algorithms, is a standard library of python. Through Scikit-learn, a trained model for predictive analysis can be developed. Such models aim to provide accurate predictions. Stock predictions are based on changes and patterns identified in the historical dataset. Following the trends and patterns of the historical changes of stocks, machine learning algorithms can be developed for achieving accurate outcomes. An effective model is developed, which enhance the working pattern or performance of the machine that further helps to draw a precise analysis of stocks. 2023 Scrivener Publishing LLC. -
Predictors of online buying behaviour
This study creates a framework by looking into various research on customer acceptance of new selfservice technologies and internet purchasing behaviour systems. According to this research, customers' attitudes towards online purchasing are initially influenced by the direct impacts of relevant characteristics of online shopping. These characteristics include functional, utilitarian characteristics and usefulness, emotional and hedonic characteristics. It looks at the technology acceptance theory (TAM) established by David in 1989 and the theory of reasoned action (TRA) to understand factors determining the attitudes of users towards online shopping for users using technology. It also provides conceptual models by using the brand image of the online platform, past experiences of buyers, information related to the product, convenience of the shoppers and trust of the customers towards online shopping. 2024, IGI Global. All rights reserved. -
Preface
[No abstract available] -
Probiotics as an alternative food therapy: A review on the influence of microbial nutraceuticals in disease management
Antibiotics have been responsible for the evolution of multidrug-resistant microbes. The side-effects of existing drugs and increased treatment costs have led to nutraceuticals gaining popularity. Nutraceuticals have therapeutic applications due to the ability of the probiotics to be viable in encapsulated pills and drinks. Due to their ability to exclude carcinogenic microorganisms by limiting the nutrients available and by competing for receptors nutraceuticals are useful against cancers. Nutraceuticals are useful against diabetes by controlling the genes involved in the insulin-signaling pathway. The future perspective for nutraceuticals includes an increase in production, reduction in manufacturing cost, and enhanced benefits. 2020 by IGI Global. All rights reserved. -
Production of bioactive compounds from cell and organ cultures of Centella asiatica
Centella asiatica, commonly known as mandukaparni, has garnered recognition for its efficacy in addressing a spectrum of health concerns. Its diverse pharmacological properties encompass roles in treating neuro-related issues, gastrointestinal problems, and cardiovascular conditions. Furthermore, it exhibits multifaceted therapeutic effects, including antioxidant, antidiabetic, wound healing, skin protective, and anti-osteoporotic properties. This herbaceous plant is rich in bioactive compounds such as centellosides (triterpene saponins) including madecassoside, madecassic acid, asiatic acid, and asiaticoside. These compounds, crucial for their pharmacological potential, are biosynthetically produced through the mevalonate and methylerythritol phosphate pathways. However, the challenge lies in the production of these important secondary metabolites, given the adverse impact on the availability of mandukaparni due to increasing demand. To address this concern, this chapter emphasizes the biotechnological interventions for the production of bioactive phytochemicals. These include plant tissue culture techniques, such as cell and organ cultures, along with elicitation strategies, genetic engineering approaches, and bioreactor-scale production. These methods aim to enhance the sustainable production of centellosides, providing valuable insights for researchers and paving the way for future opportunities in the field of plant-based therapeutics. 2024 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
Promoting entrepreneurship in higher educational institutions: The role of entrepreneurial methodologies
Entrepreneurship education is one of the most influential forces in the growth and development of an economy. It diminishes the twin issues of unemployment and poverty through entrepreneurship creation. The paradigm shift of student focus on job-oriented programme to job-creating programmes such as Management in Entrepreneurship, Diploma in Entrepreneurship and other training programmes raised the scope of entrepreneurship. Based on a comprehensive review of the relevant literature, this paper suggests that the pedagogical tools employed in entrepreneurship education such as role modelling, hands-on experience, incubation and mentoring support can be effectively used for developing entrepreneurial intentions among the students in higher educational institutions. The paper discusses few propositions developed based on the literature review and proposes a model which can be adopted by higher educational institutions for increasing entrepreneurial intention and thereby entrepreneurship. Springer Nature Singapore Pte Ltd. 2017. All rights are reserved. -
Promoting Net-Zero Economy for Sustainable Development: Practice-Based View
The present research investigates the utilization of various resources, including tangible assets, human expertise, and intangible assets, in a cohesive set of established procedures, which impact the development and implementation of net-zero practices. It also explores the effect on the environmental performance of SME enterprises operating in business markets. Additionally, the study explores whether digitalization plays a moderating role in this relationship. The samples of 291 were used in the study. Data were analyzed using partial least square structural equation modeling. For a sustainable net-zero economy (SNZE), it is essential for managers to acknowledge the importance of resource and capabilities management. While the management of tangible assets and human skills is vital, greater emphasis should be placed on intangible resources like organizational culture and learning. Furthermore, the capacity of small-sized enterprises (SMEs) to process and implement knowledge could prove to be instrumental in accomplishing net-zero targets. Consequently, managers should leverage Industry-4.0-based technological solutions to enhance resource and capabilities management effectively. This research pioneers an exploration into the influence of human capital and various assets (tangible and intangible), on the development and implementation of a SNZE in organizations, underpinned by empirical data. The study broadens the understanding of the practice-based view (PBV) framework in realizing SNZE, particularly within SME B2B enterprises. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Properties, Synthesis, and Characterization of Cu-Based Nanomaterials
Copper-based nanomaterials offer a fascinating array of properties that make them pivotal in various technological applications. These materials, when scaled down to the nanoscale, exhibit enhanced electrical conductivity, surpassing their bulk counterparts. This book chapter primarily focuses on the properties, synthesis, and characterization of copper nanoparticles while also discussing metal and metal oxide nanoparticles. Their large surface-to-volume ratios enable efficient electron transport, making them valuable components in electronics and conductive inks for flexible devices. Furthermore, copper nanomaterials possess exceptional thermal conductivity, making them crucial for efficient heat management in electronics and advanced thermal interface materials. Copper and copper oxide have positive economic and environmental effects. Their catalytic properties render them important in diverse chemical reactions and as components in energy storage systems like batteries and supercapacitors. Additionally, the tunability of their optical properties makes them suitable for various photonic and optoelectronic applications, ranging from sensors to light-emitting devices. The multifaceted properties of copper-based nanomaterials continue to drive innovation across a broad spectrum of industries. 2024 American Chemical Society. -
Psychogenic Non-epileptic Seizures in Children: Prevention and Intervention Strategies
Psychogenic non-epileptic seizures (PNES) often get misdiagnosed to be epileptic seizures, and the price that is paid for the same by the patient and the family is huge. It is called by multiple names such as pseudoseizures non-epileptic attack disorder, dissociative seizures, and functional motor disorder. Not only is it difficult to identify the disorder, but it also poses an added difficulty with comorbid epilepsy. Adding on to these, it becomes difficult for both the healthcare provider to offer psychoeducation and the family to accept since no tangible evidence such as scans portrays any abnormality. Though in a simple manner it can be said this disorder presents itself like epileptic seizures but has no neurological base, explaining the same to the patients and their family is not as simple. However, surprisingly the prognosis for PNES is better for children. This chapter thus focuses on aspects that are essential for the treatment of the disorder and prevention. In particular, the manifestation of PNES in children is discussed by introducing the disorder with epidemiological information. Further clinical picture, etiology, diagnosis and prevention and intervention are discussed. Although there are limited studies that exist on the treatment of the disorder in the pediatric population, their outcomes to reduce PNES symptoms are significant. Hence, the chapter makes an attempt to review these studies in detail and mention highlights of these studies that contribute to a reduction in the symptoms. Finally, the chapter concludes with a biopsychosocial model that explains the relationship between these factors and PNES and how this can be used in prevention. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Psychological capital as an antecedent of employee engagement and its relationship with intention to stay
Employee engagement is an evolving concept in human resources (HR). Most organizations strive to attain employee engagement because of the various organization-related outcomes. It is important for employees to feel engaged emotionally, socially, and intellectually with the work and organization. Various antecedents affect employee engagement and, in turn, result in an organization-related positive outcome. This chapter discusses in-depth PsyCap as an antecedent of employee engagement and how it relates to intent to stay regarding employees working in travel organizations in India and aims to build relevant theoretical frameworks based on the findings. The chapter also discusses some strategies organizations can implement to achieve employee engagement based on the findings. 2022, IGI Global.