Advances in Research

Ease of Doing Research and Contributions to Research Output and Impact: A Registered Systematic Review Protocol

2026-06-11T06:43:34+00:00

Background: Research productivity and its translation into socio-economic development remain markedly uneven across global settings. High-income countries consistently allocate a greater proportion of their national expenditure to research and development (R&D), maintain well-established institutional infrastructures, and generate disproportionately larger volumes of high-quality scientific output relative to their share of the world’s population.

Objective: The objective of this review is to evaluate the influence of Ecosystem, Governance, and Resourcefulness and Resources factors - as defined by the Ease of Doing Research (EDR) framework - on research output and impact outcomes among researchers and research-active personnel in higher education institutions and research institutes globally. The review specifically examines evidence from both high-income (Global North) and low- and middle-income (Global South) country contexts to identify patterns of variation in how EDR pillar quality shapes research productivity, R&D contribution, and national development.

Introduction: While extensive literature documents disparities in research output and R&D investment between high-income and low- and middle-income countries, individual studies have only examined discrete factors such as funding, brain drain, institutional culture, and governance in isolation. Also, no systematic review has synthesised this evidence within a unified, multi-pillar framework. The Ease of Doing Research (EDR) framework, which proposes three interconnected pillars - Ecosystem, Governance, and Resourcefulness and Resources - as the primary determinants of research productivity and impact, now provides the conceptual basis for a globally applicable synthesis, addressing a gap with significant implications for research policy, equity in scholarship, and the translation of research into national development.

Inclusion criteria: This review will include quantitative, qualitative, and mixed-methods studies examining Ecosystem, Governance, and Resourcefulness and Resources factors in relation to research output and impact among researchers and research-active personnel in higher education institutions and research institutes globally, published in English from 2000 to the present, across both Global North and Global South settings. Studies set exclusively in pre-university contexts, those without a research output focus, opinion pieces without primary data, and predatory or retracted publications will be excluded.

Methods: Searches will be conducted in April 2026 across PubMed/MEDLINE, Scopus, Web of Science, ERIC, and African Journals Online, with Google Scholar used supplementarily; searches were limited to English-language publications from 2000 to the present. Two independent reviewers will screen studies, assess methodological quality using design-appropriate standardised instruments, and extracted data, with disagreements resolved by consensus. A narrative synthesis will be conducted, organised by EDR pillar and Global North versus Global South comparator groupings, with certainty of evidence assessed considering risk of bias, consistency, directness, and precision.

A Critical Review of Artificial Intelligence Applications in Sericulture: Opportunities and Future Prospects

2026-06-10T12:04:37+00:00

Sericulture — encompassing the cultivation of mulberry, the rearing of silkworms (Bombyx mori Linnaeus), and the reeling and processing of silk — is one of the world's oldest and most culturally significant agro-industries. Sustaining the livelihoods of tens of millions of farming households across Asia, Africa, and Latin America, the sector nonetheless confronts a range of persistent challenges: recurring epidemic diseases, labour-intensive operations, inconsistent raw silk quality, and limited access to precision management tools. The rapid advancement of artificial intelligence (AI) technologies — including machine learning, deep learning, computer vision, Internet of Things integration, and genomic analytics — offers a compelling and timely opportunity to modernise sericulture practice across the full production chain. This critical review synthesises current and emerging evidence on AI applications in sericulture, examining their potential in silkworm disease detection, mulberry cultivation management, cocoon and silk quality assessment, smart rearing environment control, and genomic breed improvement. Drawing on evidence published predominantly between 2000 and 2026, the review identifies meaningful convergences between advances in precision agriculture and the specific demands of the sericulture sector. Whilst direct AI applications within sericulture remain comparatively nascent, the methodological transfer from crop science, entomology, and textile engineering is accelerating. Key opportunities lie in automating disease diagnosis, optimising rearing conditions through IoT sensor networks, and leveraging genomic data for breed improvement. The review critically appraises barriers to adoption — including data scarcity, digital infrastructure deficits, and the skills gap among smallholder farmers — and articulates a forward-looking research agenda for this underserved but globally significant sector.

Effectiveness of Collaborative AutoCAD File Editing on Students' Learning of Structural Layout and Details

2026-06-06T09:08:16+00:00

Background: Collaborative AutoCAD file editing may enhance Grade 10 Technical Drafting students’ competency in structural layout and details by promoting shared problem-solving, cognitive support, and industry-relevant teamwork skills compared with individual drafting approaches.

Aims: To determine the effectiveness of collaborative AutoCAD file editing in improving Grade 10 ICT–Technical Drafting students' competency in structural layout and details, and to compare the learning outcomes of students engaged in collaborative file editing with those who performed drafting tasks individually.

Study Design: Quasi-experimental pretest–posttest control group design.

Place and Duration of Study: Juan P. Cedro Memorial High School, Surigao City, Philippines, School Year 2025–2026.

Methodology: Twenty-three (23) Grade 10 ICT–Technical Drafting students were assigned through intact-class allocation to a control group (n = 11), which performed AutoCAD drafting individually, and an experimental group (n = 12), which engaged in collaborative AutoCAD file editing through shared drawing files and structured task distribution. Both groups completed an AutoCAD-based pretest and posttest aligned with the K–12 Most Essential Learning Competencies. Outputs were scored using a standardized performance rubric covering dimensional accuracy, completeness of layout, layering and lineweight control, annotation and symbols, and drafting standards and neatness (20 points per criterion; 100 points total). Data were analyzed using mean, standard deviation, paired-sample t-tests, independent-sample t-tests, and analysis of covariance (ANCOVA) at the .05 level of significance.

Results: Both groups improved from pretest to posttest, but the experimental group demonstrated substantially greater gains, with the total mean increasing from 77.00 (SD = 6.85) to 90.67 (SD = 5.21), compared with the control group's increase from 75.64 (SD = 9.20) to 80.73 (SD = 10.09). Within-group analysis revealed statistically significant gains in the experimental group for dimensional accuracy (t = 5.00, P < .001), layering and lineweight control (t = 5.63, P < .001), annotation and symbols (t = 3.02, P = .01), and overall total score (t = 5.40, P < .001), whereas the control group showed a significant gain only in layering and lineweight control (t = 2.89, P = .02). Between-group ANCOVA on the posttest scores, controlling for pretest performance, indicated significant differences favoring the experimental group in dimensional accuracy (F = 16.82, P = .001; adjusted M = 20.08 vs. 17.00) and in layering and lineweight control (F = 12.25, P = .002; adjusted M = 18.65 vs. 15.66). No significant between-group differences were found for completeness of layout, annotation and symbols, or drafting standards and neatness.

Conclusion: Collaborative AutoCAD file editing significantly improved students' technical drafting competencies, particularly in accuracy- and coordination-driven skills such as dimensional accuracy and layering and lineweight control. The strategy supports the integration of structured collaborative activities in Technical Drafting instruction, with a balanced combination of collaborative and individual practice recommended to develop competencies that rely on personal precision, such as drafting standards and neatness.

Prevalence and Associated Risk Factors of Intestinal Parasitic Infections among School-aged Children of Three Primary Schools in Awae-Yaoundé, Mefou-et-Afamba Division, Cameroon

2026-06-08T10:05:33+00:00

Intestinal parasitic infections are among the most common and widespread infections worldwide, particularly in tropical and subtropical regions. Despite the existence of numerous studies on intestinal parasites (especially helminths and protozoa) among school-aged children in Cameroon, there is a lack of data on this topic in the Awae district. This cross-sectional study aims to evaluate the prevalence and associated risk factors related to the transmission of intestinal parasites to school-aged children of the locality of Awae. Stool samples were collected from 143 students aged 4 to15 years, and analysed for the detection of intestinal parasites. At least 15 parasitic species, including 10 (67%) helminths and 5 (33%) protozoa, were identified. Among the diagnosed species, Ascaris lumbricoides was the most prevalent (55.94 ± 8.14%) and Iodamoeba butschlii the least prevalent (0.70 ± 1.37%). Students of Awae Public School were more infected (86 ± 10.37% %) compared to the two others (73 ± 13.75% in Essabi Public School and 62 ± 12.28% in Meyo Public School); those aged between 8 to 11 years were the most infected (83 ± 10.11%), while those aged between 12 to 15 years were less infected (56 ± 15.58%.). Non-compliance with hygiene rules significantly influenced the transmission of intestinal parasites. Eighty-three (81 ± 8.44%) of the infested participants were polyparisitized, dispecific and trispecific parasitic associations were the most common.

Integrating Visual Arts into Leadership Development: Organisational Learning Outcomes among Secondary School Students in Southeast Nigeria

2026-06-08T11:06:31+00:00

Leadership development in secondary education is widely recognised as a key determinant of organisational learning and broader socio-educational transformation, particularly through its influence on collaborative capacity and adaptive school cultures. This study investigated the effects of visual arts-integrated leadership development programs on organisational learning outcomes among secondary school students in Southeast Nigeria. Despite increasing interest in creative pedagogies, limited empirical research has examined their influence on leadership development and organisational learning within secondary education in developing contexts. Using a mixed-methods quasi-experimental design, the study involved 240 students drawn from six purposively selected secondary schools and assigned to either an arts-integrated intervention group or a conventional leadership training group. Quantitative data were collected using validated Leadership Self-Efficacy Scales and Organisational Behaviour Inventories, while qualitative data were obtained through focus-group discussions and reflective student portfolios. Findings revealed statistically significant improvements among students exposed to the arts-integrated intervention in collaborative problem-solving, emotional intelligence, creative decision-making, and participatory leadership behaviours compared to the control group (p < 0.01). Qualitative evidence further demonstrated enhanced adaptive thinking, social responsibility, teamwork, and active engagement in school governance processes. The integration of quantitative and qualitative findings suggests that visual arts-based leadership pedagogy promotes holistic organisational learning by strengthening cognitive, emotional, and interpersonal competencies among students. The study concludes that arts-integrated leadership frameworks offer practical and policy-relevant strategies for improving leadership capacity, student engagement, and organisational learning culture in secondary schools across Southeast Nigeria.

From Low to High Participation: Understanding the Increase in Female Workforce Participation in Assam

2026-06-08T12:32:24+00:00

Background: Economic growth and improvements in education, female workforce participation in Assam remains shaped by rural–urban disparities, socio-economic factors, and structural labour market challenges, necessitating an analysis of its trends and determinants.

Aims: The study examines the trends, patterns, and determinants of female workforce participation in rural and urban Assam and tests the validity of the U-shaped hypothesis in this context.

Study Design: Assam, identified as a lower-performing state under Sustainable Development Goal 5 (related to gender equality and women empowerment) in the SDG India Index 2020-21, is selected for the present study. The study utilizes unit-level data from the National Sample Survey Organisation (NSSO) Employment and Unemployment Survey (EUS) 2011-12 and the Periodic Labour Force Survey (PLFS) 2017-18 and 2023-24.

Methodology: The female Workforce Participation Rate (WPR) is used as the main indicator of labour market participation. It is calculated as the percentage of female workers to the total female population in the corresponding age group. The analysis is carried out using the usual status (principal and subsidiary status) approach. Since the dependent variable is binary, a logit regression model has been used to examine the determinants of female workforce participation.

Results: The findings show a substantial increase in female workforce participation in Assam during the recent period, with the rise being more pronounced than the national average. The increase is observed in both rural and urban areas, although rural areas continue to record higher participation. In contrast, male workforce participation shows relatively stable trends over time. Sector-wise trends show that female workforce participation is higher in rural areas than in urban areas. The study also finds a U-shaped relationship between education level and female WPR in Assam across all periods, with higher participation among illiterate and highly educated women than among women with middle levels of education. Logit regression results show that age, education level, vocational training, spouse employment status, and the number of elderly increased female workforce participation, while age squared, heads’ education and household size are negatively related to female workforce participation.

Assessment of Sample Size Inflation for Accurate Estimation of Population Means

2026-06-10T06:30:33+00:00

Goal: The required sampled size is determined for estimating a population mean within a given margin of error based on a preliminary sample. An inflation factor is needed to prevent confidence intervals from being anti-conservative.

Methodology: When estimating a population mean \(\mu\) within margin of error m, a preliminary sample of size n is taken from a Normal (\(\mu\) , \(\sigma\)²) distribution to produce a preliminary sample variance s², which is then used to determine the required sample size (zs/m)2, where z is the Normal critical value for a given level of confidence, and the distribution of s² is known to be related to a chi-squared distribution for Normally-distributed data.

Evaluation: Upon taking a new sample based on the required sample size, the coverage probabilities on \(\mu\) are determined exactly for various values of n and z. These coverage probabilities of \(P(~|\bar X-\mu|\leq m~)\) are simulated for non-Normal distributions as well, where -\(\bar X\) is the sample mean using the required sample size.

Findings: The coverage probabilities tend to be somewhat smaller than their nominal values, which would result in anti-conservative confidence intervals, especially when the non-Normal distribution is heavy-tailed.

Conclusion: To compensate for the confidence intervals being anti-conservative, an inflation factor on the required sample size is introduced.

Predicting Crop Yield Responses to Temperature and Precipitation Variability Using Statistical Models in Nellore District, Andhra Pradesh, India

2026-06-10T08:12:23+00:00

Statistical crop models are widely used to evaluate the impacts of climate variability on agricultural productivity. This study aims to evaluate the performance of statistical crop models in assessing the impacts of climate change—specifically changes in the mean and variability of temperature and precipitation—on maize yield in SPSR Nellore District, Andhra Pradesh. A perfect model framework using CropSyst was employed to simulate maize yields under baseline and synthetic climate scenarios. Model evaluation is conducted using statistical metrics such as the coefficient of determination (R²) and prediction accuracy. Results indicate that statistical models perform reliably when at least 10–20 observations per predictor variable are used. However, with sample sizes below 300, temporal disaggregation increases the risk of overfitting. Maize yield exhibits significant inter-annual fluctuations, ranging from 15 to 65 q/ha, with lower yields occurring during periods of rainfall deficit and higher yields associated with well-distributed precipitation. The study highlights the importance of adequate sample size and appropriate aggregation for reliable climate impact assessment. It further underscores the importance of improving climate data availability, strengthening adaptive agricultural practices, and enhancing irrigation and cropping strategies to build resilience. It is recommended that integrating statistical models with advanced machine learning techniques offers significant potential for enhancing predictive accuracy and supporting sustainable agricultural planning under changing climate conditions.

Air Quality Index Prediction Using Machine Learning and Deep Learning: A Comparative Analysis of Dehradun and Kashipur in Uttarakhand, India

2026-06-11T09:03:02+00:00

Predicting the Air Quality Index (AQI) is important for environmental monitoring, public health protection, and pollution-control planning. This study compares seven classical machine learning models — Linear Regression, Decision Tree, Random Forest, Support Vector Regressor (SVR), K-Nearest Neighbors (KNN), Gradient Boosting, and XGBoost — and two deep learning architectures — Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) — for AQI prediction in Dehradun and Kashipur, Uttarakhand, India. AQI values were computed using the CPCB sub-index methodology across six major pollutants: PM2.5, PM10, SO₂, CO, NO₂, and O₃.. Model performance was assessed using hold-out testing and walk-forward time-series cross-validation with five folds. Results show that ensemble and neighbor-based methods significantly outperform linear and deep learning approaches for the available dataset sizes. In Dehradun, Random Forest achieved the best hold-out performance with R² = 99.50% and RMSE = 4.60, while under walk-forward temporal validation, KNN led with R² = 91.37%, while Random Forest achieved the lowest RMSE = 13.43. In Kashipur, Random Forest and Gradient Boosting exceeded 95% R² in hold-out testing, and XGBoost, KNN, Random Forest, and Gradient Boosting all achieved approximately 96% R² under walk-forward validation. LSTM and GRU captured temporal AQI patterns but achieved lower accuracy than the best classical models, with R² values between 75% and 83%. The study concludes that walk-forward validation provides a more reliable estimate of AQI forecasting performance than random train-test splits, and that KNN and ensemble learning methods are promising approaches for air quality forecasting in Himalayan foothill cities.