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arXiv:2608. 09255v1 Announce Type: new Abstract: Residential energy estimates are often needed before detailed envelope characteristics, equipment efficiencies, infiltration, sensor, or billing data are available.
arXiv:2606. 08314v1 Announce Type: new Abstract: The coffee supply chain is one of the most complex agri-food networks, marked by geographically dispersed production, multi-tier coordination, and high sensitivity to quality and freshness.
The study evaluates crop‑yield forecasting methods for the 2012 Midwestern US drought, comparing non‑deep learning machine learning models with a deep learning model (VITA) using 16 meteorological predictors. It highlights challenges such as distributional dissimilarity between training and test data, spatial and temporal sparsity, and demonstrates that sample weighting and feature selection improve non‑deep learning models but not VITA. The work contrasts deep versus non‑deep learning approaches and shows how modifications can mitigate issues arising from extreme drought conditions.
The study applies machine learning to address Ghana’s solid waste disposal challenges, using a Random Forest classifier to predict illness categories from waste practices and demographics, achieving a macro F1 score of 0.63. A MobileNetV2 image classifier was also developed for automated waste sorting, reaching 88.2% accuracy and a macro F1 of 0.87 on 415 images. These quantitative results confirm a previously qualitative link between waste disposal and health, while demonstrating the feasibility of low‑cost, camera‑based sorting in resource‑constrained settings.
The inappropriate disposal of solid waste remains a significant public health and environmental concern worldwide, including in Ghana. Poor sanitation and improper waste management practices contribut...
arXiv:2607. 15775v1 Announce Type: cross Abstract: Access to potable water is crucial for health, economic development, and sustainability.