Articles

This work explores the intimate process of creating an acrylic portrait of a long-time friend, guided by photographs. Through a detailed examination of the technical and emotional aspects of portrait painting, this narrative reflects on the deep bond and shared history between the artist and subject. Emphasising the role of mutual understanding, trust, and empathy in the creative process, the paper illustrates how personal experiences and memories shape artistic expression. Drawing on art therapy, narrative identity, and the psychological impact of art, this study offers insights into how portrait painting can facilitate personal reflection, retrospection, and meaningful connection.

Digital therapeutics (DTx) are emerging as a pivotal tool in promoting longevity by addressing non-communicable diseases (NCDs) such as diabetes, cardiovascular diseases, and mental health disorders. These software-driven interventions offer personalized, evidence-based treatments that can be accessed via digital devices, making healthcare more accessible and scalable. One of the key advancements in DTx is the integration of artificial intelligence (AI) and machine learning (ML) to tailor interventions based on individual health data. This personalization enhances the effectiveness of treatments and supports preventive care by identifying risk factors early. The need for digital therapeutics is underscored by the rising prevalence of NCDs, which are responsible for a significant portion of global mortality and healthcare costs. Traditional healthcare systems often struggle to provide timely and personalized care, especially in low-resource settings. DTx can bridge this gap by offering cost-effective solutions that are easily scalable. Moreover, digital therapeutics can address health inequities by providing low-cost interventions to underserved populations, thereby reducing the burden of NCDs and improving overall health outcomes. As technology continues to evolve, the potential for DTx to enhance longevity and quality of life becomes increasingly promising. Recent advancements in longevity medicine and technology have focused on extending both lifespan and healthspan, ensuring that people not only live longer but also maintain good health throughout their extended years. This review article highlights these advancements that are contributing to this compelling subject of Longevity.

The study aimed to examine the availability of protective clothing and its usage among workers at the University of Cape Coast in the central region of Ghana. It used the Accident-Proneness Theory and the Theory of Reasoned Action to achieve the study's objectives. The study adopted the positivist research paradigm. The study used a descriptive design with mixed methods. The study population comprised all workers and supervisors whose work demands the use of protective clothing at the University of Cape Coast. The study used multistage sampling techniques (stratified, purposive, proportional and Systematic random sampling techniques) to select one hundred and ninety-six departmental workers and five (5) supervisors in the University. The main instruments for data collection were a Semi-structured questionnaire and an unstructured interview guide. Mean and standard deviation, frequency count, and percentages were used to analyse the quantitative data. The qualitative data was analysed through the development of data categories and themes. The study concluded that “not all the protective clothing for the various departments were available. The University ensured that workers used what was in stock at the department. The protective clothing supplied to the various departments was insufficient, so their continuous use of the existing ones got them defaced, bringing about shortages. The study also revealed that workers used protective clothing most often, but not all used it because safety laws were relaxed. The study recommended that the University ensure a regular and adequate supply of PPC, insist on its usage, and enforce the use of PPC by all workers whose work demands it.

Working on developing geometric coordinates with a special case - in the interest of human engineering - to fit with the humanities or with the famous psychological tests such as the Herman Brain Dominance Instrument Test, which is abbreviated as HBDI, or the Personal Compass test, which is abbreviated as (P.C).

The social aspect of education is an important part of the learning process. In this study two research questions were asked to explore this idea. Social network analysis provided multiple measures of AP Physics 1 students’ network centrality. These measures were used to predict physics achievement. Further, survey results measuring extroversion (EPI, alpha=.84-.94), motivation (PGOS, alpha=.64-.83), and self-efficacy (SOSESC, alpha=.94) were used to determine students’ personality characteristics. These personality results were used as moderators for the moderation analysis. The sample consisted of 106 students from a large, Midwestern, suburban high school taking AP Physics 1. Numerous centrality measures significantly correlated with physics achievement. Extroversion and self-efficacy increased the effect of centrality in most cases, whereas motivation did not change the centrality-achievement relationship. In most cases, having many, high quality connections was beneficial to learning physics, but personality characteristics must also be included in pedagogical decisions. Based on the results, teachers are strongly advised to account for personality and student connections when forming groups.

There is a growing body of evidence that the number of individuals suffering from chronic and acute pain is under-reported and the burden of the veteran, aging, athletic, and working populations is rising. Current pain management is limited by our capacity to collaborate with individuals continuing normal daily functions and self-administration of pain treatments outside of traditional healthcare appointments and hospital settings. In this review, the current gap in clinical care for real-time feedback and guidance with pain management decision-making for chronic and post-operative pain treatment is defined. We examine the recent and future applications for predictive analytics of opioid use after surgery and implementing real-time neural networks for personalized pain management goal setting for particular individuals on the path to discharge to normal function. Integration of personalized neural networks with longitudinal data may enable the development of future treatment personalizations paired with electrical simulations.

This study aimed to assess the validity and reliability of the dark personality instrument in students of general physical education units of Mashhad universities. The participants include all students of Ferdowsi, Imam Reza, Islamic Azad, and Payame Noor universities who had chosen the units of general physical education and sport in the academic year of 2021-22, using the Morgan table, 196 people were randomly selected as a sample. A standard dark personality questionnaire (Jonason & Webster, 2010) was used to collect data. Cronbach's alpha test was used to confirm the reliability of the questionnaire. To confirm the instrument's validity, exploratory and confirmatory factor analyses were used. Data analysis showed that the factor load of all items is higher than the baseline value (0.4) and the research model has a significant fit. Also, the model fit indices had acceptable values. Finally, it is recommended to sports coaches and teachers of physical education classes and leisure time to use this scale at the beginning of each semester to get to know more about the personality characteristics of students in their class and to measure these people, this can help them a lot in how to manage their classes.

The growing complexity and variability in healthcare delivery and costs within Medicare Advantage (MA) and Medicare Supplement (Medigap) plans present significant challenges for improving health outcomes and managing expenditures. Neural networks, a subset of artificial intelligence (AI), have shown considerable promise in optimizing healthcare processes, particularly in predictive modeling, personalized treatment recommendations, and risk stratification. This paper explores the application of neural networks in enhancing health outcomes within the context of Medicare Advantage and Supplement plans. We review how deep learning models can be leveraged to predict patient risk, optimize resource allocation, and identify at-risk populations for preventive interventions. Additionally, we discuss the potential for neural networks to improve claims processing, reduce fraud, and streamline administrative burdens. By integrating various data sources, including medical records, claims data, and demographic information, neural networks enable more accurate and efficient decision-making processes. Ultimately, this approach can lead to better patient care, reduced healthcare costs, and improved satisfaction for beneficiaries of these programs. The paper concludes by highlighting the current limitations, ethical considerations, and future directions for AI adoption in the Medicare Advantage and Supplement sectors.

Most people can't subscribe to every direct-to-consumer platform today, and the number is growing. The platform's content and the user's experience influence the decision to subscribe or buy. Today's consumers anticipate instantaneously curated content exploration, acquisition, and consumption. Media firms actively seek to increase both click-through rate and profitability by enhancing the user experience and enticing customers to subscribe or buy premium content through recommender systems. The direct-to-consumer platforms may maintain user engagement after consumers have visited the contents by providing suggestions that make the most of the site's rich content catalogs. By bringing it to the attention of viewers based on their viewing habits, for instance, effective recommendation systems might boost earnings for underappreciated "long tail" content. This research explores various recommender system types currently in widespread usage with an analysis of some of the fascinating breakthroughs.

Resistance exercise is the performance of physical exercises designed to improve strength, muscular, endurance, hypertrophy, and neuromuscular efficiency with the use of weights (Braith & Stewart, 2006)[1]. Resistance exercise has long been utilized for its beneficial health qualities and propensity to elicit certain desired physiological changes (Fry, 2004)[2]. There has been a recent, and significant, increase in resistance exercise activity in American adults (NCHS, 2018)[3] attributable to factors such as autonomous compulsion and self fulfilment to extrinsic factors like health and physical appearance (Fisher et al., 2017; Heinrich et al., 2014; Ingledew & Markland, 2008)[4,5,6]. As such, there is an ever-increasing need for educational material regarding resistance exercise, its benefits, purpose, and manner in which it should be conducted. Purpose- to (a) provide resistance exercise-based educational material regarding the background and rationale behind resistance training; (b) to provide a specific resistance-based exercise program to elicit strength gain; (c) to provide individuals with the knowledge to safely and effectively engage in said program; and (d) to provide the participant with expected physiological adaptations to completing the program. Methods- Two 6-week, 5-day per week resistance exercise programs – with a brief nutritional guide accompaniment – are outlined for a hypothetical participant, age 25-40, of moderate experience with fitness training, and with the goal of strength gain and moderate fat loss as a secondary goal. Results- Anticipated benefits of the program include: increased maximal strength caused by training above 85% 1RM for 2-6 sets of 1-6 reps; increased synergistic muscle groups strength which will contribute to improved prime mover strength; hypertrophy of skeletal muscles throughout the body, induced by lifts of 67-85% 1 rep max (RM) for 3-6 sets of 6-12 reps and increased resting energy expenditure (basal metabolic rate) accompanied by improved body composition. Conclusion- Continued progression though this protocol with modifications to resistance include potential improved running speed, explosive power potential, and other anaerobic sport performance factors, as well as enhanced neuromuscular efficiency associated with increased prime mover force production capabilities.

The impact of narcissism and self-esteem on academic achievement has long been an important issue in developmental research. The study aimed to explore the students’ narcissism and self-esteem as correlates of academic achievement in Mathematics in Anambra State. Five research questions and five null hypotheses guided the study. The study adopted a correlational approach. The population of the study comprised of 21204 SS2 students from which a sample of 630 was drawn. Multi-stage procedure was used to select the sample. Two standardized research instruments namely; Narcissistic Personality Inventory (NPI), and Self-esteem Questionnaire (SQ), as well as score from students’ promotional examination were used for data collection. Cronbach’s alpha was used to determine the reliability of the items in the instruments. The overall reliability coefficient was 0.75 which shows that the instrument was reliable and good for the study. The Pearson Product Moment Correlation was used to answer research questions 1 to 4 and to test hypotheses 1 to 4, while the research question 5 and hypothesis 5 were answered and tested with multiple correlations. The findings showed that students’ power narcissism recorded a very low positive relationship with academic achievement in mathematics. Findings also revealed that the multiple correlation of these variables is positively non significant with academic achievement in mathematics. Based on these findings, it was recommended that as narcissistic individuals believe strongly that they are better than others, teachers and counsellors should develop a strategy to enhance the confidence and ability in the students as these will help them to become life long learning individuals thereafter.

In today's fast-changing world, digital has become a way of life in every single field, and it is affecting all industries by providing multi-channel connectivity with people. In the banking industry, moving to the digital age allows for more improvements in customer-related operations and transaction-related operations within a day. These studies are from the perspective of customers. Customers prefer the flexibility of using digital financial services. Banking clients are commonly given technology-related services, whether they are online or not. Now, banks are focused on providing instant credit card issuance and personalized financial solution services to their clients. They are responsible for managing mass affluent clients who conduct transactions approximately the same as mass retail clients. Providing personalized services on time to individual end users will significantly enhance customer value with the banks. Customers who use the bank digitally perform more operations than those who go to the branch. Thus, they become more valuable clients for the banks. This strategic approach to the digitization process takes place in this fast-changing environment, and the major steps of this journey will be explained in the next chapters [1].

Advances of wearables, sensors, smart devices, and electronic health records have generated patient-oriented longitudinal data sources that are analyzed with advanced analytical tools to generate enormous opportunities to understand patient health conditions and needs, transforming healthcare significantly from conventional paradigms to more patient-specific and preventive approaches. Artificial intelligence (AI) with a machine learning methodology is prominently considered as it is uniquely suitable to derive predictions and recommendations from complex patient datasets. Recent studies have shown that precise data aggregation methods exhibit an important role in the precision and reliability of clinical outcome distribution models. There is an essential need to develop an effective and powerful multifunctional machine learning platform to enable healthcare professionals to comprehend challenging biomedical multifactorial datasets to understand patient-specific scenarios and to make better clinical decisions, potentially leading to the optimist patient outcomes. There is a substantial drive to develop the networking and interoperability of clinical systems, the laboratory, and public health. These steps are delivered in concert with efforts at enabling usefully analytic tools and technologies for making sense of the eruption of overall patient’s information from various sources. However, the full efficiency of this technology can only be eliminated when ethical, legal, and social challenges related to reducing the privacy of healthcare information are successfully absorbed. Public and media are to be informed about the capabilities and limitations of the technologies and the paramount to be balanced is juvenile public healthcare data privacy debate. While this is ongoing, the measures have been progressed from patient data protection abuses for progress to realize the full potential of AI technology for hosting the health system, with benefits for all stakeholders. Any protection program should be based on fairness, transparency, and a full commitment to data privacy. On-going innovative systems that use AI to manage clinical data and analyzes are proposed. These tools can be used by healthcare providers, especially in defining specific scenarios related to biomedical data management and analysis. These platforms ensure that the significant and potentially predictive parameters associated with the diagnosis, treatment, and progression of the disease have been recognized. With the systematic use of these solutions, this work can contribute to the realization of noticeable improvements in the provision of real-time, personalized, and efficient medicine at a reduced cost [1].

Innovations in computing and communication technologies are reshaping finance. The seismic changes are casting uncertainty about the future of financial services. On one hand, fintech evangelists project a rosy future, asserting that the fast-moving algorithms can deliver low-cost financial services intuitively, customized to meet robust consumer expectations. On the other hand, many finance veterans fret that the traditional banking model could disintermediate, bleeding banks via a ‘death by a thousand cuts’, reducing them to passive portfolio holders with no direct customer relationship, eclipsed by digital giants which use their enormous treasure troves of customer data to offer banking as an added service with nearly free cost. Amidst the upbeat technological promises and apocalyptic forebodings, there are two constant, mostly agreed-upon, truths. The first is the vital importance of data. Advances in the internet, cloud computing, and record-keeping technologies are producing an ‘exponential growth in the volume and detail of data’. Some of this big data are personal information. Smartphones are deployed in almost all developed and emerging economies, serving as little spies; tracking, recording location histories, social networks, and app usage of their unsuspecting owners; often with a great degree of precision. ‘People are walking data-factories’ in this ‘mobile digital society’. Data are the fermentation of these global exchanges, electronic commerce and communication, and financial transactions. To just take Facebook as an example, it shares 30 million people a day through updates and posts, hosting personal information on 2.23 billion users. To the alarm of the uninformed public, much of this information is available for commercial harvest. The second constant is the rise of intelligent solutions. Consumers today—be it disclosed or not—are fed tailored clothes, music, film, holiday packages—almost anything you like, notably dynamic pricing, varying in accordance with individual profiles, or personalized search results. The availability of powerful computers has enabled comparable applications that are intended to make the system more responsive to their customer profiles and desires, or to capitalize competitive business possibilities. Such changes will transform the financial industry and occupy a prominent position among the mechanisms of policy competition, reshaping the way in which financial services are bestowed and led on the demand side.

This study aims to achieve a new understanding of how, why, and when consumer behavior is shaped, enacted, and experienced inside and across integrated digital ecosystems related to large-scale trackable goods, all in service of helping marketers optimize their business performance in the new economy. The pioneering understanding begins by exploring what motivates the choices of a homogeneous group of consumers to organize their consumption of national and store brand varieties of consumer package goods in a certain manner. Thereafter, the essay explores how, if at all, the other digital activities of consumers across various product-related digital spaces and on various platforms build expertise and interest in these products such that they exert an effect on the purchase choices for these products. The essay then advances to asking how online information seeking, in various product-related digital spaces, on various platforms, and from various sources, and taking place at various points in the purchase journey affects online-offline dynamics in purchasing these products. Thereafter, the research examines how paid digital communication in various product-related digital spheres and forms, enabled by consumer advertising engagement on various platforms, boosts the offline sales of these products. Finally, by employing a new methodology that combines consumer scanning data, self-reported online activity data, and transaction data collected from an ad-tech partner, the research presents a fresh set of marketing action levers and performance outcomes on chosen products. Along the way, progress is made on four under-investigated topics in the advertising literature – the role of consumer actors and their expertise in the online-offline purchasing dynamics for ads, advertising engagement, consumer digital spaces, and consumer digital activity investment.