Entering the era of knowledge economy, various academic researches are blossoming under the social environment of double creation, but looking at the disputes of intellectual property cases in recent years, most of them happen in the college students group, the reasons for this phenomenon can be summarized as the blurring of the intellectual property education program, the system is too traditional and conservative, and the teaching concept is thin and so on. In view of this phenomenon, the author proposes to cultivate the intellectual property education of university students in the mode of "three constructions", so as to stimulate the vigor of social innovation and provide theoretical support for the scientific and technological research of university students.

This study aims to evaluate the influence of population dependency ratio on the economic growth of Bangladesh, India, and Pakistan, the three members of the South Asian Association for Regional Cooperation (SAARC). The study covers the time from 1960 to 2021. It also analyses in detail how population aging and the youth dependency ratio affects the development of certain sectors, including industry, services and agriculture. This study uses panel data to determine the influence of population dependency ratios on economic growth. To estimate this effect, we use the Pooled Mean Group/Autoregressive Distributed Lag (PMG/ARDL) technique. Based on the results obtained from the ARDL analysis indicate the presence of a long-term relationship among these variables. These discoveries align with prior empirical research conducted by Lee and Shin, Mamun et al., and Rostiana and Rodesbi. Furthermore, the findings suggest that an increase in the old age population dependency ratio positively influences economic growth within these nations. The long-term relationship findings pertaining to the old and young dependency ratio and economic growth corroborate the conclusions of Bawazir et al., who proposed that the old population dependency ratio exerts a favorable impact, while the young population has an adverse effect on economic growth. Originality: This research focused on the population dependency ratio, a pivotal demographic metric that gauges the proportion of individuals relying on support (including children and the elderly) compared to those of working age. This investigation particularly explores the interconnection between the population dependency ratio and sectoral development, an essential aspect given that various sectors make distinct contributions to economic advancement. Examining how population dynamics affect sectoral development yields valuable insights into the overall economic performance of Pakistan, India, and Bangladesh.

As the aging trend intensifies, the Chinese government prioritizes technological innovation in smart elderly care services to enhance quality and efficiency, catering to the diverse needs of the elderly. This study examines the acceptance and usage behavior of smart elderly care services among elderly individuals in Xi’an, using a modified Unified Theory of Acceptance and Use of Technology (UTAUT) model that includes digital literacy as a moderating variable. Data were collected via a survey of 299 elderly individuals aged 60 and above in Xi’an. The study aims to identify factors influencing the acceptance and usage behavior of smart elderly care services and to understand how digital literacy moderates the relationship between these factors and usage behavior. Regression analysis assessed the direct effects of Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC) on usage behavior. These dimensions were then integrated into a comprehensive index Service Acceptance to evaluate their overall impact on usage behavior, with behavioral intention examined as a potential mediating variable. Results indicate that EE and SI significantly impact the adoption of smart elderly care services, whereas PE and FC do not. Behavioral intention mediates the relationship between these variables and usage behavior. Additionally, gender, age, and digital literacy significantly moderate the impact of service acceptance on usage behavior. This study provides valuable theoretical and practical insights for designing and promoting smart elderly care services, emphasizing the importance of usability and social promotion to enhance the quality of life for the elderly.

This study employs the Standard Error Estimation technique to investigate the connections between the digitalization of economy, population, trade openness, financial development, and sustainable development across 127 countries from 1990 to 2019. The findings revealed associations between financial development, population growth, trade openness, economic growth, Digitalization development, foreign direct investment (FDI), and sustainable development. Financial development negatively impacts sustainable development, suggesting that countries with advanced financial systems may struggle to maintain sustainability. Trade openness exhibits a negative association with sustainable development, implying that countries with open trade policies may face challenges in maintaining sustainability, possibly due to heightened competition or resource exploitation. These findings highlight the multifaceted relationship between economic factors and sustainable development, underscoring the importance of comprehensive policies and governance mechanisms in fostering sustainability amidst global economic dynamics.

COVID-19 is among the tremendous negative pandemics that have been recorded in human history. The study was conducted to give a breakdown of the effect of post-COVID-19 mental health among individuals residing in a developing country. The two scales, namely DASS-21 and IES-R, were employed to collect the essential related data. The findings indicated that anxiety was a typical and common mental issue among the population, including up to 56.75% of the participants having extremely severe anxiety, 13.18% reporting severe anxiety. Notably, no one has anxiety and depression under moderate levels. Additionally, there is 51.92% depression and 43.64% stress ranging from severe to extremely severe levels. Furthermore, there were significant statistical differences among the data on stress, anxiety, and depression according to gender (males and females) and subgroups (students, the elderly, and medical healthcare workers). Besides, the prevalence of post-traumatic stress disorder in the study was relatively high, especially when compared to the figures reported by the World Health Organization. Moreover, stress, anxiety, and depression all displayed positive correlations with post-traumatic stress disorder. This is big data on the mental health of the entire population that helps the country’s government propose policy strategies to support, medical care and social security for the population.

Clustering technics, like k-means and its extended version, fuzzy c-means clustering (FCM) are useful tools for identifying typical behaviours based on various attitudes and responses to well-formulated questionnaires, such as among forensic populations. As more or less standard questionnaires for analyzing aggressive attitudes do exist in the literature, the application of these clustering methods seems to be rather straightforward. Especially, fuzzy clustering may lead to new recognitions, as human behaviour and communication are full of uncertainties, which often do not have a probabilistic nature. In this paper, the cluster analysis of a closed forensic (inmate) population will be presented. The goal of this study was by applying fuzzy c-means clustering to facilitate the wider possibilities of analysis of aggressive behaviour which is treated as a heterogeneous construct resulting in two main phenotypes, premeditated and impulsive aggression. Understanding motives of aggression helps reconstruct possible events, sequences of events and scenarios related to a certain crime, and ultimately, to prevent further crimes from happening.