Advances in Research

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.

Role of Symbiotics in the Management of Cardiovascular Disease: A Review

2026-06-27T09:52:56+00:00

Cardiovascular disease (CVD) remains closely associated with dyslipidaemia, inflammation, oxidative stress, insulin resistance and altered gut microbiota. Gut dysbiosis may affect cardiovascular risk through increased intestinal permeability, translocation of lipopolysaccharide, altered bile acid metabolism, production of trimethylamine-N-oxide (TMAO) and changes in short-chain fatty acid (SCFA) synthesis. This review summarises evidence on the role of symbiotics, as combinations of probiotics and prebiotics, in the management of CVD and related cardiometabolic risk factors. A literature search was conducted using PubMed, Google Scholar, Research Scholar, and backward and forward citation searching. Peer-reviewed English-language systematic reviews, original research articles, clinical trials and meta-analyses were considered. The manuscript also examines evidence relating to atherosclerosis, diabetes-associated cardiovascular risk and TMAO-mediated mechanisms. The reviewed evidence indicates that symbiotic supplementation may improve selected lipid parameters, including total cholesterol, LDL-C, triglycerides and HDL-C, although effects were not uniform across studies. Some animal and clinical studies also reported improvements in inflammatory markers, oxidative stress biomarkers, nitric oxide availability, blood pressure-related outcomes, insulin sensitivity and gut microbial composition. Several studies further suggest that modulation of gut microbiota may influence gut-derived metabolites, particularly by reducing circulating TMAO and supporting SCFA production, thereby potentially contributing to intestinal barrier integrity and metabolic regulation. However, responses varied according to probiotic strains, prebiotic substrates, study populations, intervention duration and baseline metabolic status. Overall, the manuscript indicates that symbiotics may represent a supportive dietary approach for improving cardiovascular risk profiles through gut microbiota modulation. Further well-designed clinical studies are required to clarify strain-specific effects, optimal dosage, intervention duration and long-term cardiovascular relevance.

Research Progress on Concrete Floating Offshore Wind Power Platforms

2026-06-30T09:45:19+00:00

Deep-water offshore wind resources are abundant and represent an important direction for the transition in offshore wind power from nearshore fixed-bottom foundations to floating foundations. Compared with steel platforms, concrete floating platforms have potential advantages in terms of material cost, durability, serial production, and life-cycle maintenance. However, they still face challenges related to dynamic-response control, lightweight design, crack resistance, modular construction, and operation and maintenance. This paper reviews the application of concrete in floating offshore wind power platforms. First, the stability mechanisms, structural characteristics, and applicability of semi-submersible, tension-leg, and spar platforms are summarised. Second, the performance characteristics and application potential of reinforced concrete, prestressed concrete, ultra-high-performance concrete, FRP bars and composites, and lightweight high-strength concrete are discussed. Third, durability assurance, structural health monitoring, and digital operation and maintenance strategies for concrete platforms under chloride-rich marine environments, fatigue loading, and long-term service conditions are analysed. The review indicates that concrete platforms should not be regarded as a simple material replacement for steel structures. Instead, cost, durability, and dynamic performance should be optimised through integrated material–structure–construction–maintenance design. Future studies should focus on optimised concrete floater configurations for large-capacity turbines, coupled durability–fatigue design, intelligent monitoring, and life-cycle economic evaluation.

Polymer-derived Chemical Migrants in Food Packaging Systems: Occurrence, Analytical Characterization, Migration Mechanisms and Cancer-Relevant Risk Prioritization

2026-07-01T06:35:34+00:00

Polymeric food-contact materials are now an essential part of food packaging because they help protect food, extend shelf life, improve handling, and support safe storage and distribution. However, these materials are not chemically inert. They are complex formulated systems that may release residual monomers, oligomers, additives, catalyst residues, coating components, printing-ink chemicals, adhesive-related substances, and non-intentionally added substances into food or food simulants. This review examines polymer-derived chemical migrants in food packaging systems with a focus on their occurrence, analytical characterisation, migration behaviour, and cancer-relevant risk prioritisation. The review covers important polymeric and polymer-containing food-contact materials, including polyethylene terephthalate, polyethylene, polypropylene, polystyrene, polyvinyl chloride, polyamides, polycarbonate, epoxy coatings, multilayer laminates, and coated paper or board systems. Migration is discussed as a chemistry-driven mass-transfer process influenced by polymer structure, molecular size, polarity, crystallinity, free volume, food composition, temperature, contact time, and surface-to-volume ratio. This review also considers the material-science factors that influence chemical migration, particularly polymer microstructure, crystallinity, amorphous-region mobility, oxygen transmission rate, water-vapour transmission rate and water-vapour permeability. Recent developments in bio-based and biodegradable packaging polymers, including PLA, PHA, PBS, starch, cellulose and polyvinyl alcohol-based active films containing plant-derived bioactive compounds, are also included because these materials are increasingly being promoted as sustainable food-packaging alternatives. The review further highlights advanced NIAS identification approaches, including LC-HRMS, suspect screening, ion-mobility HRMS, digital spectral libraries, in silico fragmentation and machine-learning-supported prioritisation, to strengthen the analytical and risk-prioritisation discussion.

Graphical Abstract: Overview of polymer-derived chemical migrants in food packaging systems, showing major polymer sources, migration drivers, food-matrix interactions, analytical characterization approaches, and cancer-relevant risk-prioritization factors.

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.

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.

Performance Evaluation of Cloud Task Scheduling Algorithms Using CloudSim Simulation

2026-06-23T11:28:58+00:00

Cloud computing provides scalable, flexible and on-demand access to computing resources through the Internet. As the number of cloud users and computational workloads increases, task scheduling becomes a critical process for assigning cloudlets to available virtual machines and maintaining efficient resource use. Ineffective scheduling can increase execution delay, reduce throughput and leave virtual machine resources underutilised. This study evaluates the performance of cloud task scheduling algorithms using the CloudSim Plus simulation framework. The simulated environment comprised one datacentre with three hosts, eight heterogeneous virtual machines and twenty-five cloudlets. Seven algorithms were implemented and compared: First Come First Served, Round Robin, Shortest Job First, Priority Scheduling, Min-Min, Max-Min and Genetic Algorithm-based scheduling. Their performance was assessed using makespan, throughput, execution time, turnaround time, waiting time, speedup and resource utilisation. The simulation results indicate that Max-Min achieved the strongest overall performance among the evaluated algorithms. It recorded the lowest makespan of 16.40 seconds, the highest throughput of 0.4879 tasks/s, the highest resource utilisation of 66.09% and the highest speedup of 5.2870. Genetic Algorithm-based scheduling also showed favourable performance, particularly in workload balancing and resource utilisation. In contrast, First Come First Served, Round Robin and Shortest Job First produced similar moderate results, while Priority Scheduling performed the weakest under the defined simulation conditions. The findings suggest that heuristic and optimisation-based scheduling approaches can improve cloudlet allocation and resource utilisation in heterogeneous simulated cloud environments. However, the results should be interpreted within the limitations of simulation-based evaluation, as real cloud deployments may involve dynamic workloads, network delays, virtualisation overhead and operational constraints not fully represented in the model.

A Robust 11T SRAM Cell with Improved SNM in 22nm Technology

2026-07-01T08:34:42+00:00

This paper presents a FinFET-based 11-transistor static random-access memory cell designed and evaluated at the 22 nm technology node. The proposed SRAM cell is intended to improve static noise margin while maintaining acceptable power consumption and access time under low-voltage operation. The design separates the read and write paths by introducing an independent single-ended read buffer composed of three NMOS transistors. This separation reduces disturbance at the internal storage nodes during the read operation and improves read stability. Two additional transistors are incorporated in the write circuitry to support data retention and reduce leakage during standby mode. The proposed cell was simulated in HSPICE using a 22 nm BSIM-CMG FinFET model at a supply voltage of 0.8 V. Its performance was compared with conventional 6T, 9T, and ST10T SRAM cells in terms of hold static noise margin, read static noise margin, write static noise margin, static power, read power, write power, and access time. The proposed 11T SRAM cell achieved hold and read static noise margins of 360 mV. Its read static noise margin was 3.91 times, 1.38 times, and 1.89 times higher than those of the 6T, 9T, and ST10T cells, respectively. It also showed a write static noise margin of 375 mV, static power of 490 pW, read power of 0.139 µW, write power of 1.43 µW, and write access time of 23.5 ps. The results indicate improved read and hold stability with moderate power and timing characteristics.

Seasonal Comparative Analysis of Proline Accumulation in Two Species of Genus Euphorbia

2026-07-02T08:04:56+00:00

Proline accumulation is an important physiological response that helps plants tolerate environmental stress. The present study estimated seasonal and organ-specific variations in free proline content in Euphorbia indica and Euphorbia microphylla growing in the semi-arid region of Chittorgarh, Rajasthan. Root, stem, and leaf samples were collected during the rainy, summer, and winter seasons, and proline concentration was estimated using the ninhydrin colorimetric method. Clear seasonal differences in proline accumulation were observed in both species, although the patterns differed. In Euphorbia indica, mean proline content increased from the rainy season (38.08 µg g⁻¹ DW) to summer (57.60 µg g⁻¹ DW) and reached its maximum in winter (75.83 µg g⁻¹ DW). The highest organ-specific values in this species were recorded in leaf tissue during summer (84.88 µg g⁻¹ DW) and in stem tissue during winter (98.45 µg g⁻¹ DW). In Euphorbia microphylla, the highest seasonal average was recorded during the rainy season (86.01 µg g⁻¹ DW), followed by summer (82.99 µg g⁻¹ DW) and winter (77.03 µg g⁻¹ DW). Root tissues showed high proline levels during the rainy and summer seasons, whereas leaf tissue showed the highest value during winter (98.03 µg g⁻¹ DW). Overall, Euphorbia microphylla maintained higher proline levels than Euphorbia indica during the study period. The findings indicate species-specific and organ-specific seasonal variation in free proline accumulation and support its use as a biochemical indicator of seasonal physiological adjustment in Euphorbia species under fluctuating environmental conditions.

From Traditional Techniques to Vernacular Form: Integrating Modern Drafting Practices in the Design of a Village Chapel

2026-07-03T12:59:18+00:00

Background: Vernacular architecture in rural Philippine settlements reflects local material use, environmental adaptation, cultural practice and inherited construction knowledge. However, such knowledge is increasingly challenged by industrialised materials and standardised building methods.

Aim: This study documented the vernacular architectural characteristics of Sitio Brazil and developed a village chapel design that integrates traditional bamboo construction with modern drafting practices.

Study Design: A quanti-qualitative design integrated with architectural design was used. Place and duration of study: The study was conducted in Sitio Brazil, Barangay Mat-I, Surigao City, Philippines, during the academic year 2025–2026.

Methodology: The community component involved 50 residents selected through purposive sampling, while selected architectural and engineering professionals from the City Engineering Office provided technical validation. Data were collected through semi-structured interviews, site observations, environmental documentation and case-study reviews. Frequency and percentage distribution, thematic analysis, comparative analysis and design synthesis were used to interpret the data and translate findings into architectural outputs, including plans, elevations, structural perspectives, construction details and 3D visualisations.

Results: Most respondents identified a strong relationship between existing structures and the natural environment (48, 96%), while few reported communal or religious structures (6, 12%). Locally sourced materials (45, 90%) and bamboo as a primary construction material (38, 76%) remained prominent, although metal fasteners (42, 84%) were more frequently reported than traditional bamboo joinery (24, 48%). All respondents (50, 100%) considered the structures translatable into modern architectural drawings, but only 25 (50%) identified joinery details clear enough for drafting.

Conclusion: The proposed bamboo-based chapel demonstrates how vernacular materials, community values and modern drafting can be combined in a culturally responsive and technically informed rural worship space.

Assessment of Physical and Engineering Properties of Ginger Rhizomes for Efficient Harvester Design

2026-07-04T10:43:24+00:00

Ginger (Zingiber officinale Roscoe) is an important spice crop widely cultivated in India, and knowledge of its physical and engineering properties is essential for developing efficient ginger harvesting machinery. This study was conducted to determine key physical, biometric and engineering properties of ginger relevant to harvester design. The physical characteristics evaluated included the number of leaves per plant, plant height, rhizome depth, plant population density, plant spacing, soil-rhizome composite weight, rhizome weight, number of rhizome fingers per hill and rhizome weight with plant. The average values obtained were 19 leaves per plant, 24.5 cm plant height, 19 cm rhizome depth, 11 plants m⁻² population density, 13.5 cm plant spacing, 1.02 kg soil-rhizome composite weight, 10.5 rhizome fingers per hill and 1.45 kg rhizome weight with plant. The engineering properties assessed included major, minor and intermediate diameters, geometric mean diameter, sphericity, rhizome index, bulk density and angle of repose. The mean major, minor and intermediate diameters were 9.90, 5.17 and 3.72 cm, respectively. The geometric mean diameter was 5.66 cm, whereas sphericity and rhizome index were 0.58 and 51.73, respectively. Bulk density and angle of repose were 443.98 kg m⁻³ and 34.33°, respectively. The results provide engineering data for the development and optimisation of ginger harvesting machinery, particularly digging, lifting and soil-separation mechanisms.