Preston University

Innovative technologies for developing intelligent systems related to locomotion prediction learning are crucial in today's world. Human locomotion involves various complex concepts that must be addressed to enable accurate prediction through learning mechanisms. Our proposed system focuses on locomotion learning through vision RGB devices, ambient sensors-based signals, and physiological motions from biosensing devices. First, the data is acquired from five different scenarios-based datasets. Then, we pre-process the data to mitigate the noise from biosensors and extract body landmarks and key points from computer vision-based signals. The data is then segmented using a data windowing technique. Various features are extracted through multiple combinations of feature extraction methodologies, followed by feature reduction using optimization techniques. In contrast to existing systems, we employ both machine learning and deep learning classifiers for locomotion prediction, utilizing a modified body-specific sensor-based Hidden Markov Model and a deep Exponential Residual Neural Network, respectively. System ontology is also presented to elucidate the relationships among the data, concepts, and objects within the system. Experimental results indicate that our proposed biosensor-based system exhibits significant potential for effective locomotion prediction learning.

Growth of industry and reliance on nonrenewable forms of energy in the SAARC region have added to the problem of environmental degradation, which has also increased the costs of health and exposure to climate change. The shift to renewable energy, technological change and sustainable tourism is necessary to secure the economic well-being, preserve the natural resources and guarantee the safety of people. This article examines how green energy is correlated with technological innovation, tourism and sustainability in the SAARC countries between the year 2000 and 2023 using the ARDL estimation methodology in the STIRPAT model. The independent variables are the consumption of renewable and nonrenewable energy, industrial production, foreign direct investment, technological advancement, tourism, participation of the labor force and urbanization. The dependent variables have been the environmental degradation, economic growth and population health. Model 1 (Environmental Sustainability Model) results show that the renewable energy, foreign direct investment, tourism and technological innovation lessen environmental degradation, whereas industrial activity and use of fossil fuels aggravate the situation. Model 2 (Economic Sustainability Model) indicates that long-term economic growth is influenced by renewable energy, innovation, tourism and labor force participation, but non-renewable energy is positive in the short term. According to Model 3 (Social Sustainability Model), the development of renewable energy, technological advances and tourism have a beneficial impact on the population's health in contrast to industrialization and fossil fuels. The paper identifies the necessity of the SAARC economies to speed up the process of switching to renewable energy, enhance the innovation potential and incorporate sustainable tourism in national policy. It is based on the 3Ps theory (People, Planet, Profit) and highlights the importance of clean energy and technology as the key solution to the balance in the environment, social welfare, and economic stability of the region.

English is now a critical lingua franca in the 21st century, required in higher education, technology, and global employment. In Pakistan, it is another means of instruction as well as a means of socio-economic mobility. Nevertheless, learning the English language is not simply a linguistic problem, it is a complicated psychological process, being influenced by cognitive, emotional, and social factors. The existing education policy usually focuses on the curriculum and infrastructure without taking into consideration the internal psychological conditions of the learners and their impact on the end results. This paper examines the involvement of cognitive load, language anxiety, and self-efficacy in achieving English proficiency and how the intervention using these factors can enhance Sustainable Development Goal 4 (SDG-4) of inclusive and equitable education. Data were collected using mixed-methods design in secondary schools of Gujranwala Division based on the Affective Filter Hypothesis of Krashen, Self-Efficacy Theory by Bandura, and Social Constructivism of Vygotsky. Results suggest that exam based anxiety and social evaluation fear is an active affective filter, and it inhibits any language intake in students who are marginalized. The quantitative data were used to indicate that fear of mistakes (4.65) and motivation to pass examinations (4.91) scored high, whereas qualitative data reflected that there is a gap in cognitive load, with rote memorization prevailing because instructional demands outweigh the working memory. The research finds that the learning of English is an indicator of psychological inequity, and the pedagogy has to be shifted towards a content-focused approach to psychologically sensitive approaches. The most important factor of turning English into an empowerment tool is emotional scaffolding and psychologically safe classrooms. Longitudinal designs should be implemented in future studies, the study should be extended to South Punjab and result should be studied on how digital integration and culturally responsive content influences self-efficacy and reduction of anxiety among learners.

Small and medium-sized enterprises (SMEs) play a crucial role in the economic development process and, consequently, their behavior forms a major difficulty in investment decisions due to the numerous interconnected behavioral and structural factors that influence them. The main objective of the research is to determine the roles of psychological traits, financial status, economic conditions, and government policies in the investment decisions of SMEs in Pakistan, with risk perception as the mediator. A quantitative research method was applied in the study, and the owner-managers of 356 SMEs in Khyber Pakhtunkhwa were surveyed for data collection. Partial Least Squares (PLS) Structural Equation modeling was adopted for assessing both measurement and structural relationships, which enabled the identification of characteristics of the linkages and indirect effects in an integrated framework. It was necessary to supplement the above-mentioned analysis to delve more profoundly into the mediation links more profoundly. Findings indicate that factors relating to psychology and finance had the most significant combined impact on the selection of an investment option, while the economic scenario appeared to be a less influential factor. The interplay between government policy and risk perception was noted to be the strongest, which points to the vital role that institutional stability plays in impacting actions during times of uncertainty. The mediation analysis implies that risk perception assumes a prominent role in the cause-and-effect relationship where all explanatory variable effects transit through investment behavior. To a great extent, the results indicate that the investment decisions of SMEs are a product of both objective economic situations and personal risk evaluations rather than being solely influenced by the determinants. From a methodological perspective, the study highlights the empirical applicability of PLS-SEM in research involving SMEs and offers certain policy recommendations to mitigate perceptions of risk at the micro level, thus encouraging investment activities in emerging economies.

Background:The accelerated use of large language models like ChatGPT has revolutionized human emotional and cognitive involvement, yet its neuropsychological implications remain poorly known. The present study proposes the concept of cognitive debt, the accumulated strain on attention, memory, and metacognitive control triggered by sustained AI engagement. This study investigated how distinctive patterns of ChatGPT involvement spanning usage frequency, emotional and cognitive engagement, and ethical reflection predict cognitive dysfunction across four user typologies: low–moderate, minimal/unhealthy, balanced–cognitive, and ethically reflective users. Method: This study employed a purposive sampling strategy within a web-based cross-sectional design to recruit 300 emerging adults (aged 18–25 years) from universities in Rawalpindi and Islamabad, Pakistan, between June 25 and July 12, 2025. Participants completed two standardized psychological instruments examining ChatGPT usage and cognitive dysfunction via an online survey administered on Google Forms. The survey link was disseminated through multiple digital platforms, including WhatsApp, Facebook, and official university email network to ensure broad accessibility and voluntary participation. Results: The results revealed that higher ChatGPT usage, specifically emotionally driven involvement, was associated with increased cognitive dysfunction, including impairments in memory, attention, and executive control across all user profiles, proposing that emotionally driven and impulsive interplay with generative AI diminishes executive control and heightens cognitive load. In contrast, ethical reflection indicated a mild protective effect against cognitive dysfunction. Moreover, females exhibited higher cognitive vulnerability than males, while males reported greater ChatGPT engagement and susceptibility to its cognitive effects as compared to females. Conclusions: The results explain two diverse cognitive stress pathways: (1) emotional compulsive engagement, described by affect-laden and impulsive AI use, and (2) reflective cognitive overload, where ethical contemplation paradoxically develops metacognitive load. These novel results improve the concept of cognitive debt, proposing that both over reflective and overreliant AI interactions could impair cognitive efficacy. The research highlights the urgency of establishing evidence-based digital ethical-use and literacy approaches to promote cognitively sustainable AI usage.

An advisory board meeting was held with five participants living with chronic respiratory conditions or having experienced a severe episode of respiratory syncytial virus (RSV) infection, to understand the challenges faced by such individuals and their experiences with lung health. In this podcast, we relate the points discussed during that meeting, providing further reflections from a patient and a physician on awareness, the lived experience of RSV, the risks it poses to adults living with chronic respiratory conditions and healthcare system support in managing lung health. Experiences shared by participants illustrate how RSV and chronic respiratory conditions can impact many aspects of a person’s life, beyond the acute illness, such as feelings of isolation. While many individuals are at risk of severe outcomes from RSV infection, the general population and healthcare practitioners (HCPs) are often unaware of the disease and its potential consequences in adults. Knowing the risk factors for severe RSV and exacerbation of underlying conditions, such as chronic obstructive pulmonary disease, asthma and cardiovascular diseases, could support physicians in discussing risks and preventive measures with their patients. This could help align patients’ expectations of HCPs and the healthcare system with the care they receive by providing more guidance on the multifactorial management of their respiratory health. Discussions about the preferred sources of information identified patient groups as the most trustworthy source, followed by HCPs, who can play a key role in helping patients to identify reliable sources of information. Despite involving only a small group of people, the discussion provided valuable insights from participants which can raise awareness about the risks and impact of RSV on people’s lives and empower healthcare professionals to better support their patients in managing their patients’ lung health.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40121-025-01216-0.

Among the various technological advancements, artificial intelligence (AI) outpaces and transforms Industry 5.0 rapidly. The impact of AI evolution is already having profound transformations in health informatics and impacting the healthcare workforce at an accelerated pace. While AI contributes much to the emergence of digital twin (DT) platforms in healthcare sector, their adoption is slow due to various risks and challenges including AI-related ethical concerns. Healthcare automation poses potential risk to healthcare providers due to changing workforce roles and challenges in their skill development in the new AI and DT paradigm. Several governments are emphasising AI-enabled education programs for up-skilling the digital health capabilities of the workforce. However, research studies report systemic challenges exist in the current healthcare training systems due to lack of scaling and design of personalised learning experiences including rapid content delivery and accessibility for various stakeholders. The key contributions of this paper are: i) transdisciplinary approach to digital healthcare capability building, ii) innovative use of Agile project management approach in an academic Capstone environment to develop AI-powered healthcare projects and iii) practical human-centric process workflow for co-designing healthcare DT solutions.

Private companies in Pakistan must embrace AI-driven sustainability strategies to remain competitive while complying with environmental regulations. This study explores the impact of AI capability on sustainable performance, emphasizing the mediating roles of green absorptive capacity, green knowledge management, and green creativity. Data was collected from 425 people in private companies and analyzed using structural equation modeling in Smart PLS. The findings confirm that AI capability significantly enhances sustainability by optimizing knowledge-sharing processes, fostering green innovation, and improving organizations' ability to absorb and implement eco-friendly practices. By leveraging AI, private firms can enhance operational efficiency, reduce environmental impact, and drive sustainable development. This study contributes to the growing literature on AI-driven sustainability and provides a practical framework for businesses to integrate green knowledge management and innovation into their strategies. The results highlight the importance of AI in shaping the future of sustainable business practices, encouraging firms to adopt technology-driven solutions for long-term environmental, social, and economic benefits while gaining a competitive edge in the industry.

This study attempts to forecast Bitcoin using both traditional and machine learning approaches to determine which methods are more robust. For the traditional method, the GARCH method is used to forecast Bitcoin returns. For the machine learning method, LSTM is used. A hybrid approach of GARCH–LSTM is also applied to the data to compare the results. Hourly data for Bitcoin are obtained from Coin-MarketCap for three years, from 2019 to 2022. The findings of the study reveal that machine learning methods outperform traditional methods. The study has useful implications for researchers.