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Detection of fall armyworm infestation in maize fields during vegetative growth stages using temporal sentinel-2
(Elsevier, 2025) Dzurume, Tatenda; Dube, Timothy; Darvishzadeh, Roshanak
Fall Armyworm (FAW), Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), poses a significant risk to global food and income security by attacking various crops, particularly maize. Early detection and management of FAW infestation are crucial for mitigating its impact on crop yields. This study investigated the effect of FAW infestation on the spectral signature of maize fields and classified infestation severity in Bangladesh using Sentinel-2 satellite imagery and Random Forest (RF) classification. Field observations on FAW infestation severity (none, moderate, and severe), collected by the Bangladesh Department of Agricultural Extension during 2019 and 2020, were used to train the RF classifier. Six thousand nine hundred ninety-eight observations were collected from 579 maize fields through weekly scouting. The Kruskal-Wallis test and Dunn's post-hoc test were applied to identify the most significant spectral bands (P < 0.05) for detecting FAW incidence and severity across different maize growth stages. The results demonstrated that the spectral reflectance from Sentinel-2 bands varied significantly among different classes of FAW infestation, with noticeable differences observed during the early developmental stages of maize (vegetative growth stages 3 to 8). RF identified nine spectral bands and two spectral vegetation indices as important for FAW infestation discrimination. The RF classifier was evaluated using five-fold cross-validation, achieving an overall accuracy between 74 % and 84 %. The independent test set's accuracy ranged from 72 % to 82 %. The mean multiclass AUC ranged from 0.83 to 0.95. Moreover, the results demonstrated the feasibility of detecting the severity of FAW infestation using temporal Sentinel-2 data and machine learning techniques. These findings underscore the potential of remote sensing and machine learning techniques for effectively monitoring and managing crop pests. The study provides valuable insights for classifying FAW infestation using high-resolution multitemporal data.
Phenolic compounds profile and hypoglycaemic, anti-inflammatory and antioxidant properties of aqueous leaf extract of androstachys johnsonii prain: in vitro study
(Elsevier, 2025) Opuwari, Chinyerum Sylvia; Nethengwe, Murendeni; Kerebba, Nasifu
Androstachys johnsonii Prain has been identified through ethnobotanical studies as a medicinal plant used in traditional medicine to treat medical complications, including diabetes mellitus (DM). The development of DM complications involves hyperglycaemia and excessive production of free radicals and inflammatory cytokines. This study investigates the hypoglycaemic, antioxidant, and anti-inflammatory properties of the aqueous extract of A. johnsonii, to reveal the possible pathways through which the extract can possibly treat DM complications. A total of 34 phenolic compounds (majorly flavonoids) was identified in the plant extract through the Ultra-High Performance Liquid Chromatography Mass Spectrometry (UHPLC-MS) analysis. Total polyphenols were 403.28 ± 12.75 mg gallic acid equivalent (GAE)/g with 88.35 ± 2.16 mg catechin equivalent (CE)/g of flavanols and 6.96 ± 3.10 mg quercetin equivalent (QE)/g flavonols. For antioxidant capacity determination, ferric reducing antioxidant power (FRAP) (1342.68 ± 3.41 mg ascorbic acid equivalent (AAE)/g), 2,2-diphenyl-1-pycrylhydrazyl (DPPH) (571.57 ± 0.55 mg Trolox equivalent (TE)/g), and Trolox equivalent antioxidant capacity (TEAC) (478.88±0.09 mg Trolox equivalent (TE)/g) values were obtained. The extract demonstrated a concentration-dependent anti-inflammatory activity on RAW macrophage cells. The highest concentration of the extract increased glucose uptake and utilisation in C3A hepatocarcinoma cells. The extract showed a significant concentration-dependent (P < 0.05) increase in α-glucosidase inhibition and a significantly (P < 0.05) lower pancreatic lipase inhibition. These results suggest the potential therapeutic effect of A. johnsonii in the treatment of DM.
Euclid preparation: lxii. simulations and non-linearities beyond Lambda cold dark matter. 1. numerical methods and validation
(Elsevier, 2025) Karagiannis, Dionysios; Adamek, Julian; Fiorini, Bartolomeo
To constrain cosmological models beyond ACDM, the development of the Euclid analysis pipeline requires simulations that capture the non-linear phenomenology of such models. We present an overview of numerical methods and N-body simulation codes developed to study the non-linear regime of structure formation in alternative dark energy and modified gravity theories. We review a variety of numerical techniques and approximations employed in cosmological N-body simulations to model the complex phenomenology of scenarios beyond ACDM. This includes discussions on solving non-linear field equations, accounting for fifth forces, and implementing screening mechanisms. Furthermore, we conduct a code comparison exercise to assess the reliability and convergence of different simulation codes across a range of models. Our analysis demonstrates a high degree of agreement among the outputs of different simulation codes, typically within 2% for the predicted modification of the matter power spectrum and within 4% for the predicted modification of the halo mass function, although some approximations degrade accuracy a bit further. This provides confidence in current numerical methods of modelling cosmic structure formation beyond ACDM. We highlight recent advances made in simulating the non-linear scales of structure formation, which are essential for leveraging the full scientific potential of the forthcoming observational data from the Euclid mission.
Exploring the significance of artificial intelligence in the Namibian universities’ teacher education curriculum in the context of the fourth industrial revolution: Contemporary discourses and empirical evidences
(University of the Western Cape, 2025) Mutelo, Sydney Musipili
This study explored the significance of Artificial Intelligence (AI) in the teacher education curriculum at Namibian universities in the context of the Fourth Industrial Revolution (4IR). Using a pragmatic, mixed-methods approach, the research involved 50 purposefully sampled academics from Namibian universities. The primary research question was, “What role does AI play in the teacher education curriculum in Namibia in the context of the 4IR?” Guided by Anderson and Prensky’s theories of emerging technologies, the study examined current curricula to identify deficiencies in AI skills needed for teacher education in the 4IR era. The findings revealed AI's transformative role in higher education, emphasising its diverse applications, benefits for teaching, and the necessity for strategic integration. Specifically, the University of Namibia (UNAM) should integrate machine learning, learning analytics, virtual assistants, and Chatbots to improve teaching practices and student engagement. However, several challenges hinder AI integration in Namibia's teacher education, such as technological limitations, inadequate training, cultural resistance, funding constraints, and infrastructure disparities. Addressing these challenges requires strategic investments, capacity building, and ensuring equitable access. Continuous professional development is crucial for equipping educators with the necessary AI skills, promoting inclusivity, and addressing concerns about equity and creativity. The study concludes that integrating AI into teacher education at UNAM offers significant opportunities to enhance teaching and learning. Overcoming technological, educational, and cultural barriers, investing in academic training, and fostering innovation are essential. A balanced, ethical, and equitable approach is vital for successful AI integration, preparing educators for the digital age. Recommendations include developing specialised courses, investing in faculty development, fostering industry partnerships, engaging policymakers, and addressing technological and ethical challenges to ensure effective AI integration.
Stochastic time series modelling of TESS photometry of classical t tauri stars
(Elsevier, 2025) Koen, Chris
A standard approach to extracting information from classical T Tauri star (CTTS) light curves is to search for periodicities. This paper, by contrast, focuses on time domain fitting of standard linear stochastic time series models to such data. The necessary statistical time series analysis tools are covered in some detail. The theory is then applied to TESS 10-minute cadence photometry of 13 CTTSs. In most instances, completely satisfactory models could be fitted to the observations; in the remainder of cases models may still capture the gist of the variability. Models are quite parsimonious, requiring only one to six parameters. Statistical properties of the model residuals are also studied. The distributions of the residuals are generally highly non-Gaussian, with large positive kurtosis.