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Factors that affected South African students’ sense of self-perceived academic competence during the COVID-19 lockdown and the rapid transition to online learning
The COVID-19 pandemic lockdown restrictions had significant impacts on the well-being and academic functioning of students worldwide. When universities closed campuses and moved teaching and learning online, students faced numerous challenges. The researchers conducted a study to establish which factors most affected South African students’ academic competence during the initial months of lockdown and the shift to online learning. Using an online survey that focused on students’ wellness, perceived academic competence and coping behaviour, data were collected from 3239 university students. Multiple linear regression showed that students’ subjective sense of intellectual wellness, coping behaviour, satisfaction with support from the university, and mental health were strong predictors of academic competence. Other factors that influenced students’ academic competence were emotional and spiritual wellness, perceptions of safety and security, and hopefulness. Females, undergraduates, and Faculty of Law students reported higher perceptions of academic competence. These findings have practical implications for universities as they identify factors that contribute to students’ academic competence, especially during times of crisis and online learning. Academic and support services staff at universities may find the findings valuable when developing policies to provide appropriate resources and services to promote and sustain students’ academic functioning.
Recent advances in Fe-based metal-organic frameworks : structural features, synthetic strategies and applications
(Elsevier, 2025-04) Mosupi, Keaoleboga; Masukume, Mike; Weng, Guoming; Musyoka, Nicholas M.; Langmi, Henrietta Wakuna; [email protected]
Metal organic frameworks (MOFs) are very exciting porous materials owing to their unique properties such as high surface areas, high pore volume, tunable functionalities and great thermal stabilities. The properties of MOFs can be diversely constructed by precise control of synthesis conditions. Amongst the thousands of MOFs that have been discovered to date, Fe-MOFs make up a percentage of these MOFs. Fe-MOFs are increasingly gaining great interest due to their unique properties and chemical versatility. However, comprehensive reviews on their emerging architectural features and designs as well as strategies for tailoring their applications. Therefore, in this review, we present a panoptic summary of the recent developments of Fe-MOFs, which includes synthetic strategies, activation methods, functionalization, overview of selected applications, current challenges impeding their commercialization, and suggested remedial actions. A holistic view of the interconnectedness of Fe-MOFs structural features, synthetic strategies and applications provides greater insights that highlight challenges hindering their wide-scale industrial applications. Moreover, newer approaches such as utilization of machine learning technique that are providing an opportunity for out-of-sight insights for material design and prediction of material properties are briefly highlighted. Remedial actions for challenges of transitioning Fe-based MOFs towards commercialization and industrial applications are also explored, and suggestions for these aspects are presented.
Namib desert dust affects phytoplankton biomass in the Benguela upwelling region : insights from first mesocosm study
(Elsevier, 2025-02) Belelie, Monray D.; Burger, Roelof P.; Von Holdt, Johanna R.C.; Garland, Rebecca M.; Liswaniso, Gadaffi M.; Thomalla, Sandy J.; Piketh, Stuart J.
Please read abstract in the article.
A framework to define, design and construct digital twins in the mining industry
The mining industry is set to increasingly use technological innovations surrounding digitalisation, particularly in the context of the fourth industrial revolution, to address current productivity challenges and safety concerns. Digital twins serve as an enabling technology for many digitalisation-based technological innovations. However, there is currently a lack of a comprehensive understanding of the digital twin concept within the mining industry. This paper presents a framework customised to mining which delineates various dimensions, model types and properties associated with a digital twin. The framework establishes a shared understanding of the concept, serving as a blueprint for the development of future digital twin works in the mining industry. The framework is enriched by accompanying model selection tools which could aid new users in developing digital twins within the proposed framework. Two case studies depicting existing mining digital twins are presented and deconstructed within the proposed framework. These case studies illustrate the framework’s ability to effectively identify various digital twin types, instilling confidence in the framework’s ability to thoroughly deconstruct existing works whilst simultaneously serving as an effective tool to construct future digital twins.
New predictive models for the computation of reinforced concrete columns shear strength
(MDPI, 2025-01) Ioannou, Anthos I.; Galbraith, David; Bakas, Nikolaos; Markou, George; Bellos, John; [email protected]
The assessment methods for estimating the behavior of the complex mechanics of reinforced concrete (RC) structural elements were primarily based on experimental investigation, followed by the collective evaluation of experimental databases from the available literature. There is still a lot of uncertainty in relation to the strength and deformability criteria that have been derived from tests due to the differences in the experimental test setups of the individual research studies that are being fed into the databases used to derive predictive models. This research work focuses on structural elements that exhibit pronounced strength degradation with plastic deformation and brittle failure characteristics. The study’s focus is on evaluating existing models that predict the shear strength of RC columns, which take into account important factors including the structural element’s ductility and axial load, as well as the contributions of specific resistance mechanisms like that of concrete, transverse, and longitudinal reinforcement. Significantly improved predictive models are proposed herein through the implementation of machine learning (ML) algorithms on refined datasets. Three ML models, LREGR, POLYREG-HYT, and XGBoost-HYT-CV, were used to develop different predictive models that were able to compute the shear strength of RC columns. According to the numerical findings, POLYREG-HYT- and XGBoost-HYT-CV-derived models outperformed other ML models in predicting the shear strength of rectangular RC columns with the correlation coefficient having a value R greater than 99% and minimal errors. It was also found that the newly proposed predictive model derived a 2-fold improvement in terms of the correlation coefficient compared to the best available equation in international literature.