BackgroundDespite the proven effectiveness of insecticide-treated nets (ITNs) in reducing malaria incidence and mortality, their usage among children in Africa remains inadequate. This systematic review and meta-analysis aimed to ascertain the prevalence of ITN usage for malaria control among children in Africa.MethodsThis review was registered with Prospero (CRD42023474978). A search was conducted in Scopus, PubMed, and Google Scholar for publications between 2013 and 2023. The study population was children in Africa, the intervention was ITN utilization, and the prevalence of ITN use was the outcome. The Jonna Briggs Institute (JBI) criteria were used to evaluate the quality of the included studies. A random effects model determined the overall pooled prevalence of ITN utilization. A sub-group analysis was carried out based on regions of Africa. Egger's Regression-based test was used to identify publication bias.ResultsThis review included 30 studies with 76,045 children, 50 households, and 1009 caregivers. Among the 30 included studies, 26 (86.7%) determined the ITN utilization in children, three (10%) evaluated the use of ITN by caregivers of children, and one (3.3%) article reported ITN utilization in households with children. In addition, 28 (93.3%) studies were cross-sectional. The pooled prevalence of ITN utilization was 69.50% with a 95% CI (64.5–74.5%). Statistically significant heterogeneity was observed (I2 = 100%, p = 0.001). ITN utilization was highest in Central Africa (85.44%, 95% CI 85.3–85.6), followed by Southern Africa (80.7%, 95%CI 80.6–80.9) and Eastern Africa (69.2%, 95%CI 63.8–74.6), and lowest in West Africa (67.0%, 95% CI 56.2–77.8).ConclusionThe prevalence of ITN utilization among children in Africa is low and varies by region. This utilization needs to be improved.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12936-025-05599-w.

As consumer demand for nutritious snacks rises, rice cookies are gaining attention for their gluten-free nutrient profile, and ultrasound-assisted treatment has emerged as a promising nonthermal alternative for nutrient retention of food ingredients. Edible insects, such as Cirina butyrospermi treated with ultrasound could offer a promising approach to developing nutrient-rich ingredients for food supplementation. This study evaluated the effects of different treatment methods (ultrasonic treatment, boiling treatment, and partial boiling-partial ultrasonic treatment) on C. butyrospermi and baking temperatures (120 °C, 140 °C, and 160 °C) on some quality attributes (spread ratio (SR), thickness, protein content, mineral composition (calcium, zinc, and iron), and in vitro protein digestibility) of rice cookies. The results revealed that treatment methods significantly influenced the cookies' quality attributes. Boiling treatment enhanced in vitro protein digestibility, while ultrasound treatment resulted in higher protein and mineral content. The effects of these processing conditions varied, with protein content ranging from 18.01% to 20.47%, SR (5.63-6.26), thickness (0.90-1.00 cm), in vitro protein digestibility (83.65-90.54%), calcium (46.19-58.63 mg/100 g), zinc (8.26-10.53 mg/100 g), and iron (3.68-4.86 mg/100 g). The optimum processing conditions are ultrasound treatment and a baking temperature of 148 °C, which produced rice cookies with superior protein content, enhanced digestibility, and higher essential mineral levels. This study highlights the treatment potential of C. butyrospermi in functional cookie development, paving the way for sustainable and nutrient-rich food innovations.

The accurate classification of sequential data encompassing time series, sensor streams, and temporal signals is critical for applications ranging from environmental monitoring to industrial fault detection. Traditional machine learning methods often struggle with temporal dependencies, noise, and non-stationary patterns, while deep learning approaches encounter computational bottlenecks and challenges related to interpretability when classifying sequence data. This paper introduces the Legendre Energy-Weighted Random Forest (LEW-RF), a novel framework that integrates Legendre polynomial transformations with Random Forest (RF) to address these limitations. By projecting sequential data onto a Legendre polynomial basis, LEW-RF extracts low-degree coefficients that encode discriminative temporal trends, such as cubic drifts and abrupt anomalies. Specifically, LEW-RF employs feature-wise energies to guide splits in RF. Theoretically, we demonstrate that Legendre energy is correlated with class separability, thereby enabling robustness to noise and irregular sampling. A comprehensive simulation study was performed to evaluate LEW-RF on synthetic sequential datasets with controlled polynomial patterns and noise structures. Results demonstrate that LEW-RF achieves 81.2% accuracy and 86.4% AUC, outperforming conventional RF by 5.3% in accuracy while operating 126 times faster than BiLSTM models. Empirical evaluation on a real-world benchmark eight-hour ozone dataset comprising 2,534 samples across 72 features with severe class imbalance (6.93% harmful ozone days) shows that LEW-RF achieves 97.0% accuracy, 99.6% recall, and 99.8% AUC after class balancing. It outperforms conventional RF by 1.4% in accuracy while operating 228 times faster than BiLSTM. In addition, the importance of the LEW-RF interpretable feature aligns with atmospheric science principles, identifying critical temporal sensors (T13-T15) that drive photochemical pollution events.

ABSTRACTGlobal municipal solid waste generation is projected to exceed 3.8 billion tonnes annually by 2050. This makes the need for smart, inclusive, and scalable waste valorization systems more urgent than ever. This review critically explores the shift from conventional waste management to intelligent, technology‐driven solutions aligned with circular economy goals. Key findings highlight the transformative role of digital tools in waste classification, forecasting, and real‐time monitoring. Long Short‐Term Memory models achieved up to 94% accuracy in biogas prediction, while XGBoost demonstrated 98.5% accuracy in solid waste generation forecasting. Deep learning systems have reached classification accuracies of 83.11% across 28 recyclable categories and mean average precision scores up to 63% in complex waste detection. Despite promising advances, challenges such as data quality, regulatory hurdles, and system interoperability persist. This article contributes both a conceptual and practical blueprint for stakeholders, positioning smart waste valorization as a strategic opportunity to drive innovation.

Abstract Objective: In 2014, the Nigerian National Health Act (NHA) was signed into law, which included the Basic Health Care Provision Fund (BHCPF). One of the primary goals of the BHCPF is to establish a national emergency medical response service. The study strives to evaluate the early implementation process of this service as perceived by key stakeholders nationally and statewide, with the goal of a future consensus conference. Methods: A cross-sectional study interviewing key stakeholders was conducted in Nigeria, utilizing a semi-structured interview consisting of two surveys. One survey was a self-designed quantitative instrument to assess participants’ knowledge of national and international guidelines and resources regarding emergency care. The second survey was a validated open-ended interview built from the Consolidated Framework for Implementation Research (CFIR) tool. Interviews were analyzed and coded by CFIR constructs. A correlation analysis was performed utilizing Spearman’s rho. Results: There was a large concordance among the respondents regarding innovation source, tension for change, and resources. There was notable variability observed among goals and feedback. Surprisingly, a significant disparity was also found in the knowledge assessment scores among the respondents. A correlation analysis between the knowledge assessment and the goals and feedback rating was conducted using Spearman’s rho. The R-squared was 0.743, with a p-value of 0.035. This reflects a correlation between increased knowledge and a perceived strong feedback and goal system, with a statistically significant association. Conclusion: This study highlights stakeholders' shared recognition of the urgent need for a national emergency medical response system in Nigeria. However, disparities in knowledge and goal perception suggest uneven preparedness, potentially hindering progress. The strong correlation between knowledge and perceptions of goals and feedback underscores the need for targeted education and information sharing. These findings lay the groundwork for a stakeholder-driven consensus conference and strategic frameworks to support effective NHA implementation.

BACKGROUNDHigh-resolution optical coherence tomography (HR-OCT) has become an essential instrument in the screening and diagnosis of ocular surface neoplasms. Research demonstrates that HR-OCT possesses a diagnostic sensitivity ranging from 85% to 90% for ocular surface squamous neoplasia (OSSN). The connections between HR-OCT features and histological findings have consistently shown robustness, hence increasing the reliability of clinical diagnosis.AIMTo examine the existing HR-OCT indicators employed in the identification of common non-benign ocular surface tumors, namely, basal cell carcinoma, OSSN, and melanocytic conjunctival lesions, and to assess their diagnostic efficacy, benefits, and prospective developments.METHODSA thorough literature review was performed to assess the published research on HR-OCT in the diagnosis of ocular surface cancers. Significant attention was given to research that compares HR-OCT characteristics with histopathologic validation, as well as on publications addressing the integration of emerging technologies and artificial intelligence in ocular oncology imaging.RESULTSHR-OCT exhibits elevated diagnostic sensitivity (85%-90%) for identifying OSSN and presents distinct imaging patterns that align closely with histology results. This approach has substantial clinical advantages due to its non-invasive characteristics, improved axial resolution, and real-time imaging capabilities. HR-OCT has demonstrated potential in assessing various lesions, including basal cell carcinoma and melanocytic conjunctival malignancies.CONCLUSIONHR-OCT assumes an increasingly vital role in the early identification and clinical management of ocular surface malignancies. With advancements in imaging technology and the integration of artificial intelligence, HR-OCT is anticipated to enhance individualized diagnosis and treatment planning in ocular oncology, hence improving patient outcomes.