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
¶
.