Community libraries preserving minority language narratives often hold rich but fragile analog collections. This paper presents a community led digitization workflow that combines low cost scanning with metadata templates designed by local educators and storytellers. The protocol emphasizes discoverability, pronunciation guidance, and contextual tags that reflect regional usage rather than external taxonomies. A pilot archive of oral and written materials demonstrated improved retrieval performance and stronger engagement from youth contributors. The approach also reduced cataloging inconsistencies across volunteer teams. Findings suggest that participatory metadata design can improve both technical quality and cultural legitimacy in long term preservation initiatives for underrepresented linguistic heritage.

Rising urban temperatures are reshaping daily work patterns for informal construction teams in equatorial cities. We monitored temperature stress, task completion rates, hydration behavior, and rest scheduling across active sites during dry and wet seasons. The analysis reveals that midday heat spikes strongly reduce output and increase near miss safety incidents when protective routines are absent. Sites that introduced staggered shifts, shaded rest intervals, and shared hydration points maintained higher productivity with fewer disruptions. The study provides operational evidence for municipal guidelines that link occupational health safeguards with climate adaptation planning, especially in sectors where workers are often outside formal labor protection frameworks.

Municipal service continuity increasingly depends on staff level cybersecurity practices, yet many district offices operate with minimal technical support. This study designs and evaluates a practical cyber hygiene training model for clerical teams handling civil records and payment requests. The program uses short scenario drills, checklists, and weekly reinforcement prompts delivered through existing office channels. Pre and post assessments show improved phishing recognition, safer credential behavior, and faster reporting of suspicious messages. Offices that adopted supervisor led refresher sessions retained gains over twelve weeks. The model offers a low overhead path for improving digital safety culture in public institutions where advanced tooling and dedicated security personnel are not always available.

Households in peri urban districts face frequent swings in informal market prices that influence food quality and dietary stability. This paper analyzes weekly purchase diaries, stall level price observations, and nutrition recall data from multiple neighborhoods over nine months. We estimate how rapid changes in staple and protein prices alter meal composition and substitution behavior. Results indicate that short term price spikes trigger significant reductions in dietary diversity, particularly among families with irregular income streams. Community storage cooperatives and transparent wholesale bulletins were associated with milder nutrition impacts. The findings inform local policy efforts that combine market information systems with targeted nutrition safeguards for vulnerable households.

Accurate biodiversity mapping is often constrained by staffing and monitoring budgets in regional conservation programs. This work proposes a student operated drone transect protocol for riparian habitats that integrates geotagged imagery with a simplified annotation rubric. We piloted the protocol along three river corridors and assessed agreement with expert ecological surveys. The method achieved consistent species group detection for vegetation structure and nesting indicators while reducing field hours and travel costs. Training requirements were manageable within one semester courses, enabling recurrent seasonal monitoring. The study demonstrates that education linked workflows can strengthen local ecological evidence systems and support timely intervention decisions for riverbank restoration.

The expansion of remote counseling has created new opportunities for youth mental health support in secondary cities where clinical services are limited. This paper examines factors associated with sustained teletherapy use among adolescents and early adults across school linked counseling programs. We combine survey data, session attendance records, and interviews with counselors to identify barriers and facilitators. Stable internet access, guardian trust, and confidentiality assurance were the strongest predictors of continued participation. Programs that provided low bandwidth options and structured onboarding retained significantly more users. The findings contribute practical guidance for municipalities designing scalable and culturally responsive digital mental health services beyond major metropolitan areas.

This study evaluates a lightweight scheduling framework for clinic scale microgrids in coastal communities with unstable utility supply. The approach combines short horizon demand forecasting with weather adjusted photovoltaic estimates and a battery preservation rule that limits deep cycling during uncertain periods. We tested the framework across forty two weeks of operational data from public health units and compared it with fixed threshold dispatch. Results show lower unmet energy events, reduced generator runtime, and improved battery health indicators without requiring high cost computation. The method remains interpretable for local technicians and supports practical maintenance planning in low resource settings where reliability and transparency are equally critical.

The increasing complexity of data mobility, storage, and analysis in smart city environments emphasizes the need for efficient data processing and analysis methods. As cities become more interconnected, optimizing data flow and ensuring efficient resource utilization are critical challenges. This paper introduces A Novel Framework for Serverless Computing Architecture (SCA) for Smart City Data Mobility, Storage and Data Analysis using explicit feature interaction aware graph neural network (SCA-SCD-EFI-GNN).The primary goal is to leverage serverless computing architecture to enhance the mobility, storage, and analysis of data within smart cities, aiming to optimize resource utilization, improve scalability, and enable processing for urban management. The input data is collected from the Urban Mobility N Traffic Patterns in Smart cities dataset and provided to the pre-processing phase. During pre-processing, the feedback adaptive interacting multiple model filter (TFAIMMF)is applied for noise removal and normalization. The pre-processed data is then fed into the feature extraction phase, where the Multi-Granularity Spatiotemporal Fusion Transformer (MGSFT) extracts temporal features such as timestamp, temperature, humidity, precipitation, and weather conditions. The extracted features are subsequently sent to the Explicit Feature Interaction Aware Graph Neural Network (EFI-GNN) for Predicting congestion-level. The proposed SCA-SCD-EFI-GNN approach is implemented in Python. Performance indicators such as MSE, MAE, and other relevant metrics are used to evaluate for Smart City Data Mobility, Storage and Data Analysis. The proposed SCA-SCD-EFI-GNN demonstrates superior performance by achieving 0.102MSE, 0.319RMSE, and 0.282 MAE for congestion-level Prediction. Existing Methods such as Enhancing deep learning approach for detecting Denial of Wallet attacks on serverless computing platforms using Artificial neural network (DOW-SCP-ANN), anomaly detection in serverless computing, focusing on challenges from stateless and short lived functions by anomaly multi source data fusion(SCF- SSF-ADM), and cloud edge intelligence for sensor networks, combining cloud infrastructures with edge devices for distributed data processing and model training using serverless microservices(CEI-CCF-SM) further contribute to improving network Prediction.