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While the discussion on generative artificial intelligence, such as ChatGPT, is making waves in academia and the popular press, there is a need for more insight into the use of ChatGPT among students and the potential harmful or beneficial consequences associated with its usage. Using samples from two studies, the current research examined the causes and consequences of ChatGPT usage among university students. Study 1 developed and validated an eight-item scale to measure ChatGPT usage by conducting a survey among university students (N = 165). Study 2 used a three-wave time-lagged design to collect data from university students (N = 494) to further validate the scale and test the study’s hypotheses. Study 2 also examined the effects of academic workload, academic time pressure, sensitivity to rewards, and sensitivity to quality on ChatGPT usage. Study 2 further examined the effects of ChatGPT usage on students’ levels of procrastination, memory loss, and academic performance. Study 1 provided evidence for the validity and reliability of the ChatGPT usage scale. Furthermore, study 2 revealed that when students faced higher academic workload and time pressure, they were more likely to use ChatGPT. In contrast, students who were sensitive to rewards were less likely to use ChatGPT. Not surprisingly, use of ChatGPT was likely to develop tendencies for procrastination and memory loss and dampen the students’ academic performance. Finally, academic workload, time pressure, and sensitivity to rewards had indirect effects on students’ outcomes through ChatGPT usage.

The growing market for lightweight robots inspires new use-cases, such as collaborative manipulators for human-centered automation. However, widespread adoption faces obstacles due to high R&D costs and longer design cycles, although rapid advances in mechatronic engineering have effectively narrowed the design space to affordable robot components, turning the development of lightweight robots into a component selection and integration challenge. Recognizing this transformation, we demonstrate a practical framework for designing lightweight industrial manipulators using a case-study of indigenously developed 5 Degrees-of-Freedom (DOF) cobot prototype. Our framework incorporates off-the-shelf sensors, actuators, gears, and links for Design for Manufacturing and Assembly (DFMA), along with complete virtual prototyping. The design cycle time is reduced by approximately 40% at the cost of cobot real-time performance deviating within 2.5% of the target metric. Our physical prototype, having repeatability of 0.05mm calculated as per the procedure defined in ISO 9283:1998, validates the cost-effective nature of the framework for creating lightweight manipulators, benefiting robotic startups, R&D organizations, and educational institutes without access to expensive in-house fabrication setups.

Microgrids are an emerging technology that offers many benefits compared with traditional power grids, including increased reliability, reduced energy costs, improved energy security, environmental benefits, and increased flexibility. However, several challenges are associated with microgrid technology, including high capital costs, technical complexity, regulatory challenges, interconnection issues, maintenance, and operation requirements. Through an in-depth analysis of various research areas and technical aspects of microgrid development, this study aims to provide valuable insights into the strategies and technologies required to overcome these challenges. By assessing the current state of microgrid development in Pakistan and drawing lessons from international best practices, our research highlights the unique opportunities microgrids present for tackling energy poverty, reducing greenhouse gas emissions, and promoting sustainable economic growth. Ultimately, this research article contributes to the growing knowledge of microgrids and their role in addressing global sustainability issues. It offers practical recommendations for policymakers, industry stakeholders, and local communities in Pakistan and beyond.

This work is aimed at evaluating the conversion of Pterocarpus indicus twigs into activated carbon via composite chemical activation for methylene blue and congo red dyes adsorption. The activated carbons were prepared by single-step chemical activation using zinc chloride and/or phosphoric acid at different mass impregnation ratios at 600 °C for 90 min. The activated carbons were characterized for textural properties and surface chemistry. The batch adsorption was investigated at different concentrations (5–1,000 mg/L), contact times (2–540 min) and temperatures (30–60 °C). Phosphoric acid-activated twigs carbon showed a high surface area of 1,445 m 2 /g with maximum methylene blue adsorption capacity of 438 mg/g. On the other hand, a composite-activated carbon yields a 217 mg/g of congo red removal. The adsorption data for both dyes fitted well with Langmuir and pseudo-second-order kinetics models, indicating the predominance of chemical adsorption through monolayer coverage of dye molecules on the homogenous surface of activated carbon. The thermodynamics properties of dye adsorption onto twigs-derived activated carbons indicated that the process is endothermic, spontaneous and favourable at high temperature. Overall, activated carbons derived from Pterocarpus indicus twigs could be effectively used for dye wastewater treatment.

More than 2 million people were rendered homeless.2 The economic loss is estimated to be more than USD $30 billion, but the affected regions are facing health-care issues that have not been accounted for.3 Among the many infectious diseases spreading rapidly in flood-affected areas, dengue infection has seen an unprecedented increase. According to the National Institute of Health, Pakistan, almost 26 000 cases of dengue were confirmed from Jan 1 to Sep 27, with 74% (>19 000) of these cases being reported in September alone. [...]a population-based cohort study showed that a previous dengue virus infection was associated with a 3-fold increase in the risk of leukaemia compared with no dengue infection.10 The damage caused by climate change is not only catastrophic but also well beyond just brick and mortar.

Using a time-lagged design, we tested the main effects of Islamic Work Ethic (IWE) and perceived organizational justice on turnover intentions, job satisfaction, and job involvement. We also investigated the moderating influence of IWE in justice–outcomes relationship. Analyses using data collected from 182 employees revealed that IWE was positively related to satisfaction and involvement and negatively related to turnover intentions. Distributive fairness was negatively related to turnover intentions, whereas procedural justice was positively related to satisfaction. In addition, procedural justice was positively related to involvement and satisfaction for individuals high on IWE however it was negatively related to both outcomes for individuals low on IWE. For low IWE, procedural justice was positively related to turnover intentions, however it was negatively related to turnover intentions for high IWE. In contrast, distributive justice was negatively related to turnover intentions for low IWE and it was positively related to turnover intentions for high IWE.

This study was designed to explore the impact of intrinsic (breed of foal, age of dam, and age of foal at weaning) and extrinsic (season of birth and housing type) factors on the growth and survival of foals in the subtropical conditions of Pakistan. For the growth study, retrospective data analysis of foals (n = 150) born from purebred brood mares of Thoroughbred, Arabs, and Percheron breeds (n 1 , n 2 , and n 3 = 50 each) was made. Six hundred and twenty-four (n = 624) foals born between 2020 to 2022 were observed for the study of foal survival rate. The survival of these foals till the age of one year was considered. To study the growth and development of foals, height, bone, and girth measurements were taken at multiple developmental stages (3, 6, 9, 12, 15, and 18 months of age). Statistical analysis revealed that late-weaned foals demonstrated superior growth metrics compared to early-weaned foals (P = 0.001) and sheltered housing conditions markedly enhanced growth parameters across all breeds and measurement intervals ( P = 0.002). However, no significant effect of season ( P > 0 . 05 ) on the growth measurements across breeds was found. Arab and Thoroughbred breeds demonstrated significant early growth advantages in foals from middle-aged dams, with marked differences in height, bone width, and girth; however, by 15 months, these differences were not statistically significant ( P > 0 . 05 ). In contrast, Percheron foals showed consistent growth regardless of the dam’s age, suggesting breed-specific developmental influences ( P = 0.885). Regarding the effects of extrinsic and intrinsic factors on foal survival, environmental conditions, and maternal age significantly impacted survival rates. Extreme winter conditions were associated with a notably lower survival probability ( P = 0.002), and middle-aged dams exhibited significantly enhanced survival odds ( P = 0.03). However, the influences of housing conditions and weaning age on survival were not statistically significant ( P > 0 . 05 ), indicating these factors do not substantially affect foal survival within the first year. These results underscore the critical roles of weaning age, housing conditions, and age of dams in influencing foal growth and survival, highlighting the importance of tailored management practices in optimizing outcomes for the growth and development of young equines under subtropics.

Pregnancy complications are a major cause of fetal and maternal morbidity and mortality in humans. The majority of pregnancy complications initiate due to abnormal placental development and function. During the last decade, the role of microRNAs (miRNAs) in regulating placental and fetal development has become evident. Dysregulation of miRNAs in the placenta not only affects placental development and function, but these miRNAs can also be exported to both maternal and fetal compartments and affect maternal physiology and fetal growth and development. Due to their differential expression in the placenta and maternal circulation during pregnancy complications, miRNAs can be used as diagnostic biomarkers. However, the differential expression of a miRNA in the placenta may not always be reflected in maternal circulation, which makes it difficult to find a reliable biomarker for placental dysfunction. In this review, we provide an overview of differentially expressed miRNAs in the placenta and/or maternal circulation during preeclampsia (PE) and intrauterine growth restriction (IUGR), which can potentially serve as biomarkers for prediction or diagnosis of pregnancy complications. Using different bioinformatics tools, we also identified potential target genes of miRNAs associated with PE and IUGR, and the role of miRNA-mRNA networks in the regulation of important signaling pathways and biological processes.

PurposeThe purpose of this study was to understand the effects of creative self-efficacy and affective personal commitment on individuals' knowledge creation and innovative work behaviors.Design/methodology/approachThis study used a time-lagged field survey to collect data from a diverse sample across various organizations in Pakistan (N = 249 paired responses). Main and mediating effects were tested using bootstrapping technique.FindingsThe results revealed that affective personal commitment and creative self-efficacy significantly predicted supervisory-rated innovative performance directly and indirectly through knowledge creation ability (KCA).Originality/valueThe study uniquely contributed to the body of knowledge by developing and validating scales for KCA and affective personal commitment. In addition, the study contributed to the body of knowledge by examining the antecedents and outcomes of KCA.

Global maximum power point (GMPP) tracking under shading conditions with low tracking time and reduced startup oscillations is one of the challenging tasks in photovoltaic (PV) systems. To cope with this challenge, an improved grasshopper optimization algorithm (IGOA) is proposed in this work to track the GMPP under partial shading conditions (PSC). The performance of the proposed approach is compared with well-known swarm intelligence techniques (SITs) such as gray wolf optimization (GWO), cuckoo search algorithm (CSA), salp swarm algorithm (SSA), improved SSA based on PSO (ISSAPSO), and GOA in terms of tracking time, settling time, failure rate, and startup oscillations. For a fair comparison, the PV system is analysed under uniform irradiance and three PSCs having four to six peaks in the power-voltage characteristic curves and using three to six search agents for each SIT. For this purpose, a PV system containing six solar panels has been built using MATLAB/SIMULINK software, and statistical analysis is performed in detail. The results show that the IGOA tracks the GMPP in 0.07 s and settles the output in 0.12 s which is 25% to 96% faster than its counterparts. Moreover, IGOA proves its consistency with a minimal tracking failure rate of 0% for four to six search agents with negligible startup oscillations. This work is expected to be helpful to PV system installers in obtaining maximum benefits from the installed system.