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Thermos-aerodynamic performance and heat transfer analysis in a rectangular channel using various angle groove geometry
(Elsevier, 2025-09) Aasa, Samson Abiodun; Mahmood, Gazi I.; aasa.aasa@tuks.co.za
Please read abstract in the article.
A wideband circularly polarised magneto-electric dipole antenna array with a series sequential phase feed network
(Wiley, 2025-01) Coetzer, Elmien; Joubert, Johan; Odendaal, Johann Wilhelm; jjoubert@up.ac.za
A printed circularly polarised antenna array is presented that utilizes the inherent good bandwidth and stable gain of magneto-electric dipoles in combination with the wideband benefits of a sequential rotation feed technique. The proposed antenna has a simple geometry using two substrates and does not require any additional cavity or parasitic elements. The designed and simulated antenna has an impedance bandwidth of more than 75%, a 3 dB axial ratio bandwidth of 67% and a peak gain of 12.4 dBic, with less than 3 dB gain variation across the entire axial ratio bandwidth. The antenna provides a good combination of simple and compact geometry, wide bandwidth, good gain and stable radiation patterns when compared to previously published research. Simulated as well as measured results are presented for a protype antenna array.
Moving memories : shifting the locus of enunciation in choreographic composition
(Routledge, 2025) Haskins, Nicola; Coetzee, Marie-Heleen; Munro, Marth
This article proposes a decolonial choreographic process rupturing the historical locus of enunciation in a dance program at a tertiary institution in South Africa. This locus in choreographic composition curricula in such universities reflects Western modernity, resulting in epistemological hegemony that creates epistemic othering that, we argue, affects students’ ontological positioning. We view decoloniality as centered on rupturing the historical locus of enunciation through epistemic disobedience and delinking from coloniality/modernity. We argue that one pedagogical approach in a choreographic composition curriculum is through using embodied, autobiographical memories toward decolonial storying. We discuss the ways this decolonial option shaped the choreographic process toward the performance of Memoryscapes (2022). We conclude by demonstrating how this option surfaced the participants as the loci of enunciation(s), by drawing from their identities, subjective lived experiences, and autobiographical memories in the process of embodied decolonial storying.
An intensified cereal push-pull system reduces pest infestation and confers yield advantages in high-value vegetables
(Springer, 2025-02) Chidawanyika, Frank; Omuse, Evanson R.; Agutu, Lavender O.; Pittchar, Jimmy O.; Nyagol, Dickens; Khan, Zeyaur R.
Crop diversification is associated with ecosystem services that can improve yield. We integrated tomatoes and kales within the cereal push-pull technology (PPT), to form the vegetable integrated push-pull (VIPP), and explored the influence of these cropping systems on pest and disease management, and subsequent yield of the vegetables. Aphids and diamondback moths (DBM), the major pests in kale production, together with grasshoppers were consistently lower in the VIPP plots. Low incidences and damage by leafminers, whiteflies and fruitflies on tomatoes were observed in VIPP plots compared to plots of tomato intercropped with maize (control). The severity of black rot and leaf curl on kales and leaf spots on tomatoes were less in VIPP compared to control. We recorded good quality and high yield of tomato and kale grown in VIPP plots rather than control plots. We demonstrate that spatial crop diversification such as integrating vegetables such as kale and tomato in a push-pull system can boost yield and maintain crop integrity.
A personalized periodontitis risk based on nonimage electronic dental records by machine learning
(Elsevier, 2025-02) Swinckels, Laura; De Keijzer, Ander; Loos, Bruno G.; Applegate, Reuben Joseph; Kookal, Krishna Kumar; Kalenderian, Elsbeth; Bijwaard, Harmen; Bruers, Josef
OBJECTIVE : This study aimed to develop a machine-learning (ML) model to predict the risk for Periodontal Disease (PD) based on nonimage electronic dental records (EDRs).
METHODS : By using EDRs collected in the BigMouth repository, dental patients from the US were included. Patients were labeled as cases or controls, based on PD diagnosis, treatment and pocketing. By learning from their data, a model was trained. The ability of the developed model to predict PD was evaluated by the accuracy, sensitivity, specificity and area under the curve (AUROC) and the most important features were determined. The best-performing model was applied to the validation set.
RESULTS : The final study population included 43,331 participants. Based on the development set, the Random Forest model performed with high sensitivity (81 %) and had an excellent AUROC (94 %), compared to four other ML and deep learning techniques. The most important predictors were bleeding proportion, age, the number of visits, prior preventive treatment, smoking and drugs usage. When the model was applied to the validation set, the model could detect almost all cases (91 %), but overestimated controls (specificity=0.54). When EDRs were retrieved 3 years before the PD diagnosis, the predictions for PD were still sensitive (89 %).
CONCLUSION : Based on consistent and complete EDR, ML has an excellent ability to assist with the early detection and prevention of PD cases. Further research is required to follow-up high-risk controls and improve the model's internal and external validation. Improved EDR documentation is an important first step.
CLINICAL SIGNIFICANCE : If such ML models become clinically applied, clinicians can be assisted with personalized risk predictions based on the individual. If the key riskcontributing factors for the individual are revealed/provided, ML can suggest targeted prevention interventions. These advancements can contribute to a reduced workload, sustainable EDRs, data-based dental care, and, ultimately, improved patient outcomes.