Search Results (345)

This study evaluates the financial profitability and carbon sequestration in mixed native forests of the Roble-Raulí-Coigüe and evergreen types in the southern macrozone of Chile, integrating both ecosystem services into forest management decision-making. The Faustmann model and dynamic programming were applied to determine the optimal rotation periods and Land Expectation Value (LEV) under two scenarios: exclusive timber production and combined timber and carbon production. The results indicate that mixed forests consistently outperform monocultures in terms of profitability, especially in 25%–75% mix configurations and moderate densities (2000 trees/ha). The observed range of 25%–75% across different tree species is determined by the interplay of two critical factors: the average annual growth rate (AAGR) of biomass and the opportunity cost of the forest rotation. In fast-growing species, the upper limit (75%) reflects an optimisation towards early carbon sequestration, whilst in slow-growing species, the ratio shifts towards the lower limit (25%) to compensate for longer rotation periods and associated biotic risks. This range acts as an efficiency frontier that balances biological productivity with the stability of the accumulated carbon stock. The inclusion of the economic value of carbon increased the LEV and extended the optimal rotation periods, confirming the relevance of integrating ecosystem services into forest planning. These findings suggest that mixed native forests represent a competitive and sustainable alternative to monocultures, contributing to climate change mitigation and income diversification for forest owners. Full article

Efficient design of desalination and brine management systems, which are central to a water circular economy, requires accurate thermodynamic data such as the osmotic coefficient. This property is key to understanding salt behavior in aqueous solutions, directly impacting the energy efficiency and sustainability of treatment processes. This study presents a predictive framework that combines machine learning with the Gazelle Optimization Algorithm (GOA) to accurately estimate osmotic coefficients for various inorganic salt solutions. The GOA was employed to automatically tune the hyperparameters of two models: Decision Tree (DT) and Gradient Boosting Machine (GBM). Using a comprehensive dataset of 893 samples with 27 salt-specific parameters, the GOA-GBM hybrid model delivered the highest predictive accuracy, achieving an R² of 0.9734 on test data. The GOA-DT model also performed robustly (R² = 0.9260), providing a more interpretable alternative. By creating a reliable tool for predicting osmotic coefficients, this methodology enables more precise process simulation and optimization. This directly supports the development of energy-efficient desalination technologies and informed decision-making for water reuse and resource recovery. The integration of advanced digital tools like GOA with machine learning offers a powerful approach to enhancing process efficiency and environmental safety, contributing directly to the design of sustainable, circular economy-based water treatment solutions for industrial and municipal applications. Full article

Cow’s milk allergy (CMA) remains one of the most common food allergies in infancy, requiring the avoidance of cow’s milk and its derivatives. Breast milk is the best source of nutrition for infants. For those infants with CMA whose mothers are unable to breastfeed or choose not to, extensively hydrolysed formulas (eHFs) are widely recommended as first-line milk substitutes, whereas hydrolysed rice formulas (HRFs) are increasingly recognised as a viable alternative. This concept paper provides a healthcare professional (HCP) perspective on HRF, drawing on expert consensus from two meetings convened in 2025. Discussions noted the long history of safe and effective HRF use in Europe, its nutritional adequacy, and the evolving international guidelines supporting HRF as an alternative first-line option. A key meeting outcome was the development of a practical decision tree to help UK clinicians decide when HRF should be the preferred choice. Key considerations for its use in non-breastfed infants include the following: parental/caregiver stress related to persistent symptoms; ongoing symptoms despite multiple interventions; cultural and lifestyle choices; religious dietary requirements; and specialists’ recommendations. Secondary considerations highlighted by HCPs include the following: proven reactions whilst infants are breast-milk-fed together with parental request for formula; faltering growth; multiple symptoms; taste acceptance (older infants); and parental preference based on experience. The role of functional components, such as prebiotics and human milk oligosaccharides (HMOs), was noted in regard to the emerging evidence of benefits to the microbiome and immune development. The experts emphasised the importance of engaging HCPs across all levels of CMA care and addressing challenges in translating current guidance into treatment practice. It was concluded that, overall, HRF represents a nutritionally complete, plant-based alternative that has been shown to be well tolerated (taste, symptoms) in clinical studies. It can be used to broaden therapeutic options for infants with CMA in the UK who are not exclusively fed breast milk. Full article

Arc-driven powder bed fusion represents a low-cost alternative to beam-based powder bed systems, yet the morphological stability regimes governing single-track formation and the relative influence of process parameters on regime transitions have not been systematically characterised. Manual visual assessment of track morphology is inherently subjective and cannot objectively quantify the parameter hierarchy governing stability boundaries. This study addresses both limitations through two complementary contributions. A deterministic two-stage image-based framework is developed to automatically classify single-track morphology from top-view images of solidified 316L stainless steel tracks, replacing subjective assessment with a reproducible, intervention-free procedure. A gap-based continuity criterion distinguishes discontinuous from continuous melt paths; for continuous tracks, the coefficient of variation in width (CV (coefficient of variation) < 0.15) further separates geometrically stable from transitional morphologies. Building on the image-derived regime labels, two interpretable classifiers—a depth-limited Decision Tree (DT) and a regularised Logistic Regression (LR) —are fitted using applied current, scanning speed, and electrode-to-powder-bed distance as predictors. The classifiers are employed not for predictive generalisation but to extract standardised coefficients and permutation-based feature importance rankings, yielding a model-agnostic, quantitative explanation of which process parameters govern regime transitions. Stable continuous tracks are obtained only within a restricted parameter window. Permutation importance consistently ranks applied current as the dominant predictor, followed by electrode distance and scanning speed, in agreement with the thermophysical interpretation. Logistic Regression coefficients confirm that reduced stand-off distance is a necessary condition for sufficient arc constriction. Supplementary linear regression models indicate that applied current governs melt pool depth, whereas scanning speed is the primary determinant of width variation. The combined framework establishes a reproducible basis for process parameter hierarchy analysis in arc-driven powder bed systems and provides a foundation for regression-based process optimisation. Full article

This study benchmarks eight Python-based machine learning models for predicting nitrogen content across four sequential stages of BOF steelmaking. A dataset of 291 metallic samples from 76 heats was employed, covering pig iron desulfurization (PHASE #1), crude steel before BOF tapping (PHASE #2), and secondary metallurgy start (PHASE #3) and completion (PHASE #4). Linear regression, polynomial regression, ridge regression, decision tree, random forest, feedforward neural networks (FNNs), Gaussian Process Regression (GPR), and Support Vector Regression (SVR) were implemented in Python 3 with Z-score normalization and an 80/20 train–test split, and evaluated via MAE, MSE, MAPE, and R². Ridge regression achieved the highest accuracy in PHASE #1 (84.59%) and PHASE #4 (84.04%); FNNs excelled in PHASE #2 (78.27%) with consistent cross-phase performance; linear regression was optimal for PHASE #3 (79.06%). The advanced kernel-based methods demonstrated competitive performance, with GPR achieving 84.73% in PHASE #1 and SVR attaining 77.10% in PHASE #3 and 83.40% in PHASE #4, confirming their suitability for limited industrial datasets with a nonlinear structure. A hybrid strategy remains recommended: ridge regression for PHASES #1 and #4, FNNs for PHASES #2 and #4, and linear regression for PHASE #3, with SVR as a robust alternative in phases with moderate nonlinearity. Full article

With the growing adoption of data-driven workflows and the need to compare numerous interior design alternatives in housing renewal, scalable and consistent assessment of interior space quality is increasingly important; however, current practice still depends on manual scoring and expert judgment. To address this gap, we propose an automation-ready framework that evaluates interior space quality from visual data. We construct the Functionality–Healthiness–Aesthetics Spatial Interior Dataset-10K (FHASID-10K) with 13,962 images for systematic validation. Three sub-models quantify functionality via space utilization and circulation smoothness, healthiness via detection of health-related visual elements, and aesthetics via semantic visual representations with regression-based prediction. Dimension scores are standardized and fused using the analytic hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS) to produce a comprehensive score for ranking and grading. Experiments show stable score distributions and clear differentiation across space categories and style–space combinations. A gradient-boosted decision tree (GBDT) surrogate reconstructs the fused score with high accuracy (test R² = 0.9992; MSE = 1.1 × 10⁻⁵), and human-subject evaluation shows strong agreement with overall-quality ratings (r = 0.760, p < 0.001). Overall, the framework enables scalable benchmarking, scheme comparison, and decision support. Full article

The Consensual Assessment Technique (CAT) is a gold standard of creativity assessment which provides valid product-based creativity scores that are contextually grounded (stemming from raters with unique expertise, culturally and historically situated). However, its implementation is often demanding (raters’ burden, complex rating designs). This study investigates whether machine learning can effectively simulate expert-panel judgments of creativity using minimal training data. Using a dataset of 411 short stories, we compared the performance of Random Forest (RF), Gradient Boosted Trees, and Decision Tree models, based on story length and Divergent Semantic Integration, to predict expert CAT ratings by (1) identifying the optimal algorithm and (2) the minimum training sample size required for reliable prediction. Results indicate that RF consistently outperformed other algorithms, achieving high correlations with CAT scores (r = 0.80) using as few as 25 training stories. Furthermore, RF demonstrated superior accuracy and lower reliance on story length compared to LLM-based scoring models. These findings provide a robust proof-of-concept for using simulated expert panels as a scalable alternative to (decontextualized) automated assessment methods, while reducing human raters’ burden and the logistical constraints of complex rating designs. Extension of this work to different contexts, creativity tasks and domains are necessary to gauge its generalizability. Full article

Post-earthquake reconstruction involves complex decision-making that extends beyond structural safety to include economic, environmental, and social considerations under conditions of uncertainty and limited resources. Although sustainability-oriented assessment frameworks and multi-criteria decision-making approaches have increasingly been applied in disaster contexts, existing models typically address localized technical interventions and rarely support strategic reconstruction planning after earthquakes. This study develops a sustainability-based decision-support framework for post-earthquake reconstruction of reinforced concrete buildings using the Integrated Value Model for Sustainability Assessment (MIVES). This framework is derived through a systematic synthesis of the post-earthquake, post-disaster, and MIVES-based literature. Reconstruction alternatives reported in previous studies are first identified and classified to structure the reconstruction decision space. Sustainability requirements, criteria, and indicators are then examined and adapted through processes of retention, modification, elimination, and addition. The principal outcome of the study is an adapted MIVES requirements tree composed of 10 criteria and 19 indicators organized across the sustainability dimensions, providing a context-consistent hierarchical structure for strategic building-level reconstruction decisions. By explicitly linking reconstruction alternatives with sustainability indicators within clearly defined decision boundaries, the framework strengthens methodological rigor in sustainability-oriented reconstruction planning. The present article focuses on the methodological development of the framework (Part I). The operational implementation of the model—including expert-based weighting, value-function definition, indicator aggregation, and empirical validation through case studies—will be presented in a companion study. The proposed framework provides a transparent and transferable basis for sustainability-oriented reconstruction planning and supports informed decision-making by public authorities. Full article

Accurate prediction of landslide run-out distance is fundamental to hazard mapping, emergency planning, and risk-informed engineering design. However, many data-driven studies implicitly treat landslides as a homogeneous population and provide limited, physically interpretable insights into how geomorphic factors govern run-out behavior. To address these limitations, we propose a cluster-aware and explainable modeling framework to predict run-out distance L using four source-region and slope descriptors: crown–toe relief H, source area A, source volume V, and mean source-slope inclination $\theta$. The dataset consists of 10,159 rainfall-induced landslides compiled from official inventories and peer-reviewed literature. After standardizing predictors, the optimal number of clusters is determined using information criteria (AIC/BIC), followed by k-means clustering to identify distinct landslide regimes. We first benchmark Random Forest, eXtreme Gradient Boosting, CatBoost, and LightGBM on identical data splits without hyperparameter tuning, using $R^2$, RMSE, and MAE as performance metrics. LightGBM consistently outperforms the alternatives and is therefore selected as the base learner. Within each cluster, LightGBM is further optimized using the Alpha Evolution (AE) algorithm, with Particle Swarm Optimization and Bayesian Optimization serving as benchmarks. The resulting AE-LightGBM model achieves the highest predictive accuracy across clusters. Model interpretability is achieved using TreeSHAP, which decomposes predictions into cluster-specific baselines and additive contributions from H, A, V, and $\theta$. By integrating regime-sensitive learning with robust explainability, the proposed framework improves run-out distance prediction while providing transparent, physically meaningful insights to support scenario analysis and engineering decision-making. Full article

Motion sickness (MS) is a prevalent condition that can significantly degrade user comfort and immersion, particularly in virtual reality (VR) environments. Accurate prediction models are essential for early detection and mitigation of MS symptoms, thereby improving the overall VR experience. Most existing approaches rely on bio-physiological data acquired through body-mounted sensors, which may restrict user mobility and diminish immersion. This study proposes a less intrusive alternative, leveraging head and torso kinematic data for MS prediction. We introduce a hybrid Convolutional–Recurrent Neural Network (C-RNN) designed to capture both spatial and temporal features for enhanced classification accuracy. Using a dataset of 40 participants, the proposed C-RNN outperformed traditional machine learning models—including Support Vector Machines (SVMs), k-Nearest Neighbors (KNN), Decision Trees (DT), and a baseline Recurrent Neural Network (RNN)—across multiple evaluation metrics. The C-RNN achieved 85.63% accuracy, surpassing SVM (60%), KNN (73.75%), DT (74.38%), and RNN (81.88%), with corresponding gains in precision, recall, F1-score, and ROC AUC. These results demonstrate that head–torso motion patterns provide sufficient predictive signal for accurate MS detection, offering a non-intrusive, efficient alternative to physiological sensing that supports improved comfort and sustained immersion in VR. Full article

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. Full article

Assessing the investment attractiveness of companies is essential for effective capital allocation under conditions of uncertainty and heterogeneous risk–return profiles. Investors typically face multiple financing alternatives, making comparative evaluation impossible without robust and specialized assessment methodologies. This study proposes a refined conceptual model for assessing the investment attractiveness of production companies, with a specific focus on the manufacturing sector of Kazakhstan. The research is based on a modeling-oriented methodological framework that integrates a modified discounted cash flow (DCF) approach with elements of environmental controlling. The proposed model incorporates sector-specific characteristics, including resource utilization patterns, regulatory requirements and the potential “green” premium observed in capital markets. To capture investment-related uncertainty and risk, the study employs material flow cost accounting, scenario-based modeling and probabilistic decision tree analysis. Particular attention is given to improving the determination of the discount rate, recognizing its critical influence on present value-based investment assessments. The model accounts for macroeconomic and sectoral factors specific to Kazakhstan’s production industry and offers alternative discount rate estimation scenarios under different initial conditions. The study contributes to the literature on investment attractiveness assessment by integrating financial, environmental and risk dimensions into a unified framework. The proposed model enhances transparency in investment decision-making and provides new insights into investment evaluation practices in emerging industrial economies. Full article

In the high Andean regions of Peru, above 3500 m a.s.l., selecting a roofing system requires balancing budgetary constraints, technical performance, and environmental impacts under severe frost and demanding climatic conditions. This study compared several roofing alternatives from a comprehensive perspective to determine the most suitable solution by simultaneously considering economic, environmental, and social criteria. For this purpose, the Integrated Value Model for Sustainable Assessment (MIVES)—a multi-criteria decision-making methodology—was employed to evaluate five systems: traditional ichu thatched roof (ITR), ceramic tile (CT-II), corrugated galvanized steel with insulation (CGS-II), fiber cement with insulation (FC-II), and sandwich panel with an insulating core (PIR-SP). The model was implemented using a requirements tree with 11 indicators and its stability was assessed through a sensitivity analysis involving five weighting configurations. The overall sustainability indices ranked ITR first (0.697), primarily due to its low carbon footprint and favorable thermal performance. It was followed by CT-II (0.632), due to its superior landscape integration; CGS-II (0.602), owing to its cost-effectiveness; FC-II (0.586), for its balanced environmental profile; and finally, PIR-SP (0.504), which excelled in industrial efficiency and construction speed despite a higher environmental impact. In summary, the results indicated that vernacular solutions minimized environmental impacts and optimized local resources, whereas industrialized options were preferable when durability and assembly times were prioritized. The sensitivity analysis, with variations below 5%, supported the model’s consistency as a decision-support tool and its potential to guide policies for sustainable social housing in high-mountain contexts. Full article

The aim of this research is to employ improved machine learning techniques to determine the best Bitcoin trading positions in response to sudden price changes caused by global emergencies such as pandemics, conflicts, and economic disputes. Specifically, this study examines price fluctuations during the COVID pandemic as a case study to evaluate the performance of the algorithms investigated. We present a novel hybrid approach that merges Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Decision Tree (DT) classification to effectively eliminate noisy data and extract pertinent information for accurate position forecasting. The DBSCAN algorithm organizes the data to reveal important patterns, while the DT classifier sorts the trading signals. The performance of the proposed DBSCAN-DT model is rigorously compared with established alternatives, including the Multi-Layer Perceptron (MLP), Support Vector Classifier (SVC), and traditional Decision Trees. Findings from the experiments show that the DBSCAN-DT hybrid consistently outperforms these benchmarks during the outbreak, epidemic, and pandemic phases of COVID, attaining greater accuracy in forecasting both trading positions and market trends. These findings emphasize the essential importance of incorporating pandemic-related disruptions into cryptocurrency price prediction models and showcase the flexibility of our method in addressing sudden market changes. Full article