Computation

The management of municipal solid waste in intermediate cities exhibits high daily variability and source heterogeneity, which hinders operational sizing and material recovery. Reliable predictions are required from heterogeneous and often-scarce data. However, studies that compare multiple machine learning algorithms with temporal validation on short time series in intermediate cities are still limited. This study compares fourteen machine learning algorithms to predict the daily generation of organic and inorganic waste in La Joya de los Sachas, Ecuador, formulating the problem as a multi-output regression problem. An adapted CRISP-DM design was employed, using primary data from a waste characterization campaign, temporal feature engineering, variable encoding, and an expanding-window backtesting protocol against lag-7 persistence and ARIMA. Tree-based ensembles achieved the best performance. AdaBoost provided the best organic forecasts ($R^2$$=0.985$, $\mathrm{RMSE}$ $=0.081$, $\mathrm{MAE}$$=0.061$ in rate space), while Random Forest was best for inorganic ($R^2$$=0.965$, $\mathrm{RMSE}$ $=0.049$, $\mathrm{MAE}$$=0.040$). Linear models were stable but slightly inferior, and other approaches (SVR, KNN, MLP, Lasso, ElasticNet) showed lower generalization capacity. The study provides a multi-output regression protocol with temporal validation for municipal contexts with short time series, comparative evidence across fourteen algorithms, and a conversion from rates to kilograms for operational use.

This study performs a sensitivity analysis of CO₂ emissions from clinker and cement production using life cycle assessment (LCA). Both local and global sensitivity analyses (LSA and GSA) are conducted. LSA uses outputs from the GCCA EPD tool—developed by the Global Cement and Concrete Association to facilitate Environmental Product Declarations—and examines correlations between perturbed input variables and the resulting output changes. For GSA, we present an analytical derivation of Sobol’ indices. We derive quantitative relationships between alternative materials and fuels and key technical indices, while preserving clinker and cement quality throughout the sensitivity analysis. Increasing the share of the alternative fuels (AFs) categories and of recycled concrete produces a negative percentage change in CO₂ emitted from the clinker (CO₂/CL). The largest CO₂/CL reductions arise from high-biomass fuels, followed by alternative solid fuels and refuse-derived fuels, shredded tires, and, lastly, recycled concrete. The clinker-to-cement ratio (CL/CEM) dominates the CO₂ emitted in cement production (1% change → 0.926–0.956% change), while clinker-level CO₂ reductions transmit to cement with only minor variation, confirmed by Sobol’ indices. Aside from reducing CO₂/CL by increasing alternative materials and fuels, the two principal approaches to lowering CO₂/CEM are: (i) minimizing clinker content in cement where permitted by applicable standards while maintaining the same performance, and (ii) designing new cement types that deliver equivalent performance with lower clinker content.

Precise segmentation of brain tumors from multimodal MRI scans is essential for accurate neuro-oncological diagnosis and treatment planning. To address this challenge, we propose a label-free optimization-driven segmentation framework based on the $\alpha$-expansion graph cut algorithm, offering improved computational efficiency and interpretability compared to deep learning alternatives. The method relies on structured optimization and handcrafted features, including local intensity patches, entropy-based texture descriptors, and statistical moments, to compute voxel-wise unary potentials via gradient-boosted decision trees (XGBoost). These are integrated with spatially adaptive pairwise terms within a graph model optimized through $\alpha$-expansion. Evaluation on 146 BraTS validation volumes demonstrates reliable whole-tumor overlap, with a mean Dice score of 0.855 ± 0.184 and a 95% Hausdorff distance of 18.66 mm. Bootstrap analysis confirms the statistical stability of these results. The low computational overhead and modular design make the method particularly suitable for transparent and resource-constrained clinical deployment scenarios.