The cement industry is a significant contributor to CO₂ emissions worldwide, which demands new measures to reduce its environmental impacts. Therefore, finding solutions to reduce the CO₂ emissions in cement production became necessary. Pozzolanic materials offer an optimum solution approach with both environmental and functional advantages. For the investigation of pozzolan effects on the concrete mixture, the modeling part becomes a challenging task. This study models and predicts the compressive strength of pozzolanic cement-based concrete using deep residual neural networks (DRNNs) and variance-based sensitivity analysis (VBSA). The designed DRNNs architecture uses shortcuts (i.e., residual connections) that bypass some layers in the deep network structure in order to alleviate the problem of training with high accuracy. The research also examines crucial aspects such as pozzolan type, substitution ratio, component proportions, and grinding processes, using data developed by the authors and from different pozzolanic concrete compositions from various studies. The proposed model showed a high accuracy of R² = 0.94 for testing data that outperformed traditional literature models, enabling the generation of a large sample of synthetic experimental data for further analysis. The VBSA improves knowledge by prioritizing the importance of input factors, resulting in a complete method for designing concrete mixes. The analysis revealed that silica fume and volcanic ash were the most effective pozzolans in enhancing compressive strength, followed by scoria and metakaolin, with optimal substitution ratios ranging from 10 to 15% for most natural pozzolans and up to 20–30% for metakaolin and pumicite. Hence, this newly presented analysis framework offers an optimizing tool for pozzolanic concrete mix design that could investigate several pozzolana types/proportions, their efficiency, and the structural performance of the final concrete mixture.

The seismic pounding between adjacent, dynamically incompatible reinforced concrete structures poses a significant collapse risk. This study presents a numerical investigation to evaluate a novel coupling system (NCS) designed to enforce response synchronization and eliminate pounding, comparing its performance against a conventional individual building retrofit (IBR) strategy. Three-dimensional nonlinear finite element models were developed for two adjacent reinforced concrete buildings, representing a seismically deficient building inventory. The performance of the bare, IBR, and NCS configurations was assessed through a suite of nonlinear response history analyses and subsequent probabilistic fragility analyses. The results demonstrate the enhanced performance of the enforced synchronization approach. While the IBR strategy reduced the total number of impacts from 97 to 31 across all analyses, the NCS completely eliminated pounding by transforming the two structures into a single, coupled dynamic system. This elimination of impact-induced shock loading resulted in a 50 % reduction in peak roof acceleration relative to the bare case. Furthermore, the NCS channeled 65 % of the total input energy into its designated ductile links, compared to only 45 % for the IBR system. Probabilistic analysis confirms this performance enhancement; the median collapse capacity of the more vulnerable structure was increased from a peak ground acceleration (PGA) of 0.32 g to 1.32 g, a four-fold improvement that substantially outperforms the IBR. The findings confirm that a design philosophy based on enforced coupling is a more effective mechanism for mitigating seismic pounding than conventional, independent-building strengthening.

Abstract Accurate estimation of daily reference evapotranspiration (ETo) is crucial for sustainable water resource management and irrigation scheduling, especially in water-scarce regions like Arizona. The standardized Penman–Monteith (PM) method is costly and requires specialized instruments and expertise, making it generally impractical for commercial growers. This study developed 35 ETo models to predict daily ETo across Coolidge, Maricopa, and Queen Creek in Pinal County, Arizona. Seven input combinations of daily meteorological variables were used for training and testing five machine learning (ML) models: Artificial Neural Network (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Support Vector Machine (SVM). Four statistical indicators, coefficient of determination (R2), the normalized root-mean-squared error (RMSEn), mean absolute error (MAE), and simulation error (Se), were used to evaluate the ML models’ performance in comparison with the FAO-56 PM standardized method. The SHapley Additive exPlanations (SHAP) method was used to interpret each meteorological variable’s contribution to the model predictions. Overall, the 35 ETo-developed models showed an excellent to fair performance in predicting daily ETo over the three weather stations. Employing ANN10, RF10, XGBoost10, CatBoost10, and SVM10, incorporating all ten meteorological variables, yielded the highest accuracies during training and testing periods (0.994 ≤ R2 ≤ 1.0, 0.729 ≤ RMSEn ≤ 3.662, 0.030 ≤ MAE ≤ 0.181 mm·day−1, and 0.833 ≤ Se ≤ 2.295). Excluding meteorological variables caused a gradual decline in ET-developed models’ performance across the stations. However, 3-variable models using only maximum, minimum, and average temperatures (Tmax, Tmin, and Tave) predicted ETo well across the three stations during testing (17.655 ≤ RMSEn ≤ 13.469 and Se ≤ 15.45%). Results highlighted that Tmax, solar radiation (Rs), and wind speed at 2 m height (U2) are the most influential factors affecting ETo at the central Arizona sites, followed by extraterrestrial solar radiation (Ra) and Tave. In contrast, humidity-related variables (RHmin, RHmax, and RHave), along with Tmin and precipitation (Pr), had minimal impact on the model’s predictions. The results are informative for assisting growers and policymakers in developing effective water management strategies, especially for arid regions like central Arizona.

The intricate dynamics of odorants in the indoor environment and human respiratory system remain poorly understood. In the present study, we integrate odor sensory tests (OSTs) and computational fluid dynamics coupled with a physiologically based pharmacokinetic (CFD-PBPK) model to elucidate various aspects of odorant transport and olfaction dynamics. Safe yogurt-derived substances were incorporated into OSTs to prevent harmful exposure. Acetaldehyde was identified as a key active component in determining odor intensity, prompting further analysis of acetone and other four constituents. Logarithmic correlations were established between the perceived odor intensity from the OSTs and both time-averaged absorption flux and equilibrium concentration within the olfactory mucus layer. These parameters were numerically captured, enabling the logarithmic approximation of odor intensity for different breathing profiles and developing reliable prediction models for odor sensation in the indoor environment based on quantifiable physiological parameters. Location-specific analysis revealed the nostrils and olfactory regions as the most accurate indicators of perceived odor intensity, proving the limitations of rough sensory assessments in the indoor/breathing zone scales. This study offers insights for potential safe and sustainable applications, such as smart odor displays, e-noses, and sensors/control systems in the indoor environment, particularly for long-term exposure in industries that emit harmful compounds.

Traditional K-means clustering assumes, to some extent, a uniform distribution of data around predefined centroids, which limits its effectiveness for many realistic datasets. In this paper, a new clustering technique, simulated-annealing-based ellipsoidal clustering (SAELLC), is proposed to automatically partition data into an optimal number of ellipsoidal clusters, a capability absent in traditional methods. SAELLC transforms each identified cluster into a hyperspherical cluster, where the diameter of the hypersphere equals the minor axis of the original ellipsoid, and the center is encoded to represent the entire cluster. During the assignment of points to clusters, local ellipsoidal properties are independently considered. For objective function evaluation, the method adaptively transforms these ellipsoidal clusters into a variable number of global clusters. Two objective functions are simultaneously optimized: one reflecting partition compactness using the silhouette function (SF) and Euclidean distance, and another addressing cluster connectedness through a nearest-neighbor algorithm. This optimization is achieved using a newly-developed multiobjective simulated annealing approach. SAELLC is designed to automatically determine the optimal number of clusters, achieve precise partitioning, and accommodate a wide range of cluster shapes, including spherical, ellipsoidal, and non-symmetric forms. Extensive experiments conducted on UCI datasets demonstrated SAELLC’s superior performance compared to six well-known clustering algorithms. The results highlight its remarkable ability to handle diverse data distributions and automatically …

Mobile roadside units have crucial role in ensuring efficient communication, computing, and caching services in internet of vehicles (IoVs) for vehicles traversing urban landscapes. The dynamic nature of urban environments faces challenges in optimizing the deployment of mRSUs to adapt to varying vehicular densities and traffic patterns in real-time. In this article, we propose a novel real-time optimization approach for the dynamic deployment of mobile Roadside Units (mRSUs) in urban environments to support the rapid growth of the IoV. The proposed method is a novel allocation strategy based on Minimum Dominating Set (MDS) theory, which is demonstrated to significantly reduce the number of mRSUs required. This reduction is achieved without compromising the efficiency and effectiveness of the network, thereby ensuring rapid and reliable communication within the IoV. This approach addresses critical …

This study evaluates the economic viability of lining irrigation canals in Upper Egypt, focusing on the El-Sont Canal network as a case study. Using a benefit-cost analysis (BCA), the research assesses the financial feasibility of canal lining by comparing the costs of implementation with the anticipated benefits, including water savings, reduced maintenance, and increased agricultural productivity. The estimated cost of lining the El-Sont Canal network is approximately $34.193 million, with annual benefits ranging from $5.451 million to $7.515 million. The benefit-cost ratio (BCR) exceeds 1.0, ranging from 1.99 to 4.02, indicating strong economic viability. Additionally, the cost recovery period is estimated at 4.5–6.3 years, making the project a sustainable investment. Compared to alternative water resource solutions such as desalination and wastewater reuse, canal lining proves to be the most cost-effective and permanent solution for water conservation in the region. While canal lining is cost-effective, potential trade-offs include impacts on groundwater recharge and the equitable distribution of water savings among different farming communities. The findings underscore the importance of canal lining as a strategic intervention for sustainable water management in Upper Egypt, aligning with national agricultural development goals and addressing water scarcity challenges under changing climatic conditions
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