Shaheed Zulfikar Ali Bhutto Institute of Science and Technology

In the context of human displacement, it is essential to study how local knowledge is reshaped, eroded, or transformed. This study sheds light on how wild plant reports are articulated after migration, retained, and kept; the research explores specifically the ethnobotanical knowledge linked to wild food plants of five ethnic communities, namely the Bettani, Ormur, Mehsud, and Miani populations living in the Gomal area of NW Pakistan, of which three are displaced communities. The study aims to record the knowledge of wild food plants and their use among generations in these communities. To better determine the impact of displacement, we have analysed the data along two trajectories: (a) cross-geographically comparing the recorded wild food plant reports with the available published literature in NW Pakistan and (b) conducting a cross-cultural comparison of the local plant knowledge among the considered groups (displaced ones: Mehsud, Ormur, and Powanda; autochthonous: Bettani and Miani) residing in the Gomal area. Via semi-structured interviews with a hundred study participants (twenty for each ethic group), the study revealed the use of 69 wild food taxa, showing a remarkable diversity of food uses, with Ormur and Powanda exhibiting several idiosyncratic reports. The research highlights that displacement may have disrupted potential pathways of knowledge transmission among the Mehsud, Ormur, and Powanda; however, local plant knowledge about their past environment remains part of the collective memory of these communities. Moreover, post-migration exposure to a new ecological system has become a challenge for the newcomers, necessitating adaptation to rearticulate their relationship with nature and plants. The broken paths have a profound impact on plant knowledge transmission to youngsters, as social structures and gatherings have been significantly altered or disrupted; these were the primary means of interaction between youngsters and their elders. The exposure to urbanisation compounds the issue of displacement, and the erosion of knowledge systems has come at the expense of hands-on experiences among the selected groups. Notably, the local plant nomenclature of Ormur is also highly threatened. We advocate incorporating local plant knowledge into local educational curricula, which may be crucial for the sustainability of natural knowledge and have profound impacts on mitigating the effects of socioecological change.

Gamified learning has gained attention as an instructional approach that integrates game design elements into educational environments to enhance engagement and learning effectiveness. The study has presented insights from developmental psychology and neuroscience. It showed that interactive and reward-based activities stimulate neural systems associated with attention, motivation, and memory formation, particularly during the formative K–12 learning years. This study presents a systematic literature review examining how gamified learning environments influence cognitive and neurological development among K–12 learners. The study presented empirical evidence of 22 studies that met the inclusion criteria and were analyzed thematically. The findings indicate that gamified instructional environments significantly improve several cognitive outcomes, including reading comprehension, computational thinking, problem-solving, and conceptual understanding. Game mechanics such as feedback, rewards, adaptive challenges, and narrative contexts were found to increase learner motivation and persistence while also reducing perceived cognitive load. Along with that, emerging neuroeducational studies demonstrate that gamified systems can produce measurable neurophysiological responses related to attention, cognitive workload, and engagement. They use technologies such as EEG, eye-tracking, and physiological sensors. The review also highlights the growing role of AI-driven personalization in adjusting learning pathways and supporting individual cognitive needs. Despite these insightful findings it has been observed, current research remains limited by short-term study designs. There are still inconsistent measurement frameworks, and limited understanding of the specific mechanisms through which gamification influences cognitive and neural processes.

This paper systematically analyzes 783 peer-reviewed research papers subdivided into fifteen thematic clusters, each aligned with the components of embedded finance and financial well-being. This review conducted bibliometric methods using the Scopus database from 1951 to 2025, integrating descriptive analysis, citation measure tool, and co-occurrence mapping. Advanced analytical tools, including pivot charts, tables, and VoSviewer software, were utilized to evaluate citation patterns, co-authorship networks, publication outlets, country-wise productivity, and the theoretical and procedural frameworks underpinning research on embedded finance and financial well-being (FWB). The findings here provide a consolidated base for acknowledging research gaps, identifying the selection of the model, and hence, strengthening the experimental base for the current study focused on embedded finance and financial well-being. Embedded Finance studies have recorded robust growth, increasing from 20 to a peak of 145 publications in 2024. The cumulative trend is toward higher scholarly interest in Embedded Finance, fueled by its embedding into digital platforms and changing financial ecosystems. The direction requires more profound interdisciplinary research into its real-world applications, regulatory implications, and socioeconomic effects.

Background: Iron-deficiency anemia may alter HbA1c interpretation by affecting erythrocyte turnover and hemoglobin glycation independently of plasma glucose. Objective: To compare HbA1c concentrations between non-diabetic adults with iron-deficiency anemia and non-anemic controls and assess their relationship with fasting plasma glucose and iron-status parameters. Methods: This hospital-based case-control study included 200 adults in Dera Ismail Khan, Pakistan, comprising 100 participants with iron-deficiency anemia and 100 non-anemic controls. Hemoglobin, HbA1c, fasting plasma glucose, serum ferritin, serum iron, total iron-binding capacity, and transferrin saturation were assessed. Groups were compared using independent-samples tests, with p < 0.05 considered statistically significant. Results: Mean HbA1c was higher in the iron-deficiency anemia group than in controls (6.18 ± 0.52% vs 5.49 ± 0.41%; mean difference, 0.69%; 95% CI, 0.56–0.82; p < 0.001), whereas fasting plasma glucose was comparable (91.4 ± 9.8 vs 89.7 ± 8.9 mg/dL; p = 0.201). The iron-deficiency anemia group had lower hemoglobin, serum ferritin, serum iron, and transferrin saturation and higher total iron-binding capacity (all p < 0.001). Conclusion: Iron-deficiency anemia was associated with disproportionately higher HbA1c despite similar fasting plasma glucose. Iron status should therefore be considered when HbA1c and plasma-glucose findings are discordant

Modern vehicles increasingly rely on in-vehicle communication networks such as the Controller Area Network (CAN), which lack built-in security mechanisms and remain vulnerable to cyberattacks. This paper proposes a lightweight Intrusion Detection System (IDS), termed LR-ResNet-IDS, based on a reduced ResNet deep learning architecture for real-time CAN bus attack detection. The proposed framework includes a frame-building module that converts raw CAN traffic into structured grid representations, enabling automatic feature learning without manual feature engineering. The model is evaluated using a real-world CAN dataset collected from a physical vehicle under controlled attack scenarios. Experimental results demonstrate strong detection performance, achieving an accuracy of 0.955, precision of 0.948, and recall of 0.943, while maintaining low computational complexity. These results indicate that the proposed LR-ResNet-IDS provides an effective, lightweight, and scalable solution for real-time intrusion detection in automotive networks.