Search Results (154)

A newly commercialized single-row multi-fan autonomous unmanned ground vehicle (UGV) sprayer, for use in trellised tree fruit crops, was tested to better understand air and spray patterns prior to wide-scale adoption in the modern apple orchard systems typical to Washington State. This sprayer was equipped with five brown and yellow Albuz ATR80 nozzles per fan (QM-420, Croplands Quantum). The fans were installed in a Q8 configuration, with eight fans (four on each side) staggered near the front and back as a stack to increase vertical span. Air velocity and spray delivery patterns of the commercialized sprayer unit were assessed in laboratory using a customized smart spray analytical system. Previous field trails of this sprayer unit revealed a hardware issue with electric proportional valve controls in fan-nozzle assembly, resulting in uneven spray deposition across V-trellised canopy. Post issue resolution, the sprayer characterization data showed an average Symmetry of 91%, and 84% for air velocity and spray volume delivery on either side. An average Uniformity of 57% and 48%, respectively was recorded for pertinent sprayer attributes across the spray height. Overall, after optimization, the UGV sprayer is suitable for efficient agrochemical application in modern orchard systems. Further evaluation of labor savings, biological efficacy gains from autonomous operation, and a full economic analysis would better inform grower adoption. Commercial viability of this UGV sprayer could also be improved by added features such as variable-rate application enabled by real-time crop sensing or task-map integration. Full article

Orchard wind-assisted spraying technology relies on auxiliary airflow to disturb the canopy and improve droplet deposition uniformity. However, there are few effective means of quantitatively assessing the dynamic response of fruit tree leaves to airflow or the changes in airflow patterns within the canopy in real time. To address this, this study proposed an online monitoring method for the aerodynamic characteristics of fruit tree leaves using strain gauge sensors. The flexible strain gauge was affixed to the midribs of leaves from peach, pear and apple trees. Leaf deformations were captured with high-speed video recording (100 fps) alongside electrical signals in controlled wind fields. Bartlett low-pass filtering and Fourier transform were used to extract frequency-domain features spanning between 0 and 50 Hz. The AdaBoost decision tree model was used to evaluate classification performance across frequency bands. The results demonstrated high accuracy in identifying wind exposure (98%) for pear leaf and classifying the three leaf types (κ = 0.98) within the 4–6 Hz band. A comparison with the frame analysis of high-speed video recordings revealed a time error of 2 s in model predictions. This study confirms that strain gauge sensors combined with machine learning could efficiently monitor fruit tree leaf responses to external airflow in real time. It provides novel insights for optimizing wind-assisted spray parameters, reconstructing internal canopy wind field distributions and achieving precise pesticide application. Full article

To address the problem of regional coverage path planning for unmanned aerial vehicle swarms (UAVs), this study proposes an algorithm based on objective programming partitions (OPP) and rule-based spanning tree coverage (RSTC). Aiming at the shortcomings of the traditional Divide Areas based on Robots Initial Positions combined with Spanning Tree Coverage (DARP-STC) algorithm in two core stages, that is, region partitions and spanning tree generation, the proposed algorithm conducts a targeted design and optimization, respectively. In the region partition stage, an objective programming and 0–1 integer programming model are adopted to realize the balanced allocation of UAVs’ task regions. In the spanning tree generation stage, a rule is designed to construct a spanning tree of coverage paths and is proven to achieve the minimum number of turns for the UAV under certain conditions. Both simulations and physical experiments demonstrate that the proposed algorithm can not only significantly reduce the number of turns of UAVs but also enhance the efficiency and coverage degree of tasks for UAV swarms. Full article

Support Vector Machines (SVMs) are widely used in critical decision-making applications, such as precision agriculture, due to their strong theoretical foundations and their ability to construct an optimal separating hyperplane in high-dimensional spaces. However, the effectiveness of SVMs is highly dependent on the efficiency of the optimization algorithm used to solve their underlying dual problem, which is often complex and constrained. Classical solvers, such as Sequential Minimal Optimization (SMO) and Stochastic Gradient Descent (SGD), present inherent limitations: SMO ensures numerical stability but lacks scalability and is sensitive to heuristics, while SGD scales well but suffers from unstable convergence and limited suitability for nonlinear kernels. To address these challenges, this study proposes a novel hybrid optimization framework based on Open Competency Optimization and Particle Swarm Optimization (OCO–PSO) to enhance the training of SVMs. The proposed approach combines the global exploration capability of PSO with the adaptive competency-based learning mechanism of OCO, enabling efficient exploration of the solution space, avoidance of local minima, and strict enforcement of dual constraints on the Lagrange multipliers. Across multiple datasets spanning medical (diabetes), agricultural yield, signal processing (sonar and ionosphere), and imbalanced synthetic data, the proposed OCO-PSO–SVM consistently outperforms classical SVM solvers (SMO and SGD) as well as widely used classifiers, including decision trees and random forests, in terms of accuracy, macro-F1-score, Matthews correlation coefficient (MCC), and ROC-AUC. On the Ionosphere dataset, OCO-PSO achieves an accuracy of $95.71\%$, an F1-score of $0.954$, and an MCC of $0.908$, matching the accuracy of random forest while offering superior interpretability through its kernel-based structure. In addition, the proposed method yields a sparser model with only 66 support vectors compared to 71 for standard SVC (a reduction of approximately $7\%$), while strictly satisfying the dual constraints with a near-zero violation of $1.3\times {10}^{-3}$. Notably, the optimal hyperparameters identified by OCO-PSO ($\mathrm{C}=2$, $\gamma \approx 0.062$) differ substantially from those obtained via Bayesian optimization for SVC ($\mathrm{C}=10$, $\gamma \approx 0.012$), indicating that the proposed approach explores alternative yet equally effective regions of the hypothesis space. The statistical significance and robustness of these improvements are confirmed through extensive validation using 1000 bootstrap replications, paired Student’s t-tests, Wilcoxon signed-rank tests, and Holm–Bonferroni correction. These results demonstrate that the proposed metaheuristic hybrid optimization framework constitutes a reliable, interpretable, and scalable alternative for training SVMs in complex and high-dimensional classification tasks. Full article

Three optimization problems based on node-colored undirected graphs are the subject of the present study. These problems model real-world applications in several domains, such as cybersecurity, bioinformatics, and social networks, although they have a similar abstract representation. In all of the problems, the goal is to partition the graph into colorful connected components, which means that in each of the connected components, a color can appear in at most one node. The problems are optimized according to different objective functions, leading to different optimal partitions. We propose a compact Mixed Integer Linear Programming formulation for each of the three problems. These models are based on spanning trees, represented through multi-commodity flows. The compact nature of the new linear models is easier to handle than the approaches that previously appeared in the literature. These were based on models with an exponential number of constraints, which, therefore, required complex solving techniques based on the dynamic generation of constraints within a branch-and-cut framework. Computational experiments carried out on the standard benchmark instances for the problems show the potential of the new compact methods, which, once fed into modern state-of-the-art solvers, are able to obtain results better than the previous algorithmic approaches. As an outcome of the experimental campaign, a dozen instances of the different problems considered are closed for the first time. Full article

Tree-Augmented Naive Bayes (TAN) is an interpretable graphical structure model. However, its structure learning for continuous attributes depends on the class-conditional mutual information, which is sensitive to one-dimensional or two-dimensional density estimation. Accurate estimation is challenging under complex distributions such as multi-peak, long-tailed and heteroscedastic cases. To address this issue, we propose a structure learning method for TAN based on Fast Generative Bootstrap Maximum Likelihood Estimation (TAN-FGBMLE). FGBMLE consists of two stages of work. In the first stage, resampling weights and random noise are input into a network generator to rapidly produce candidate parameters, efficiently covering the latent density space without repeated independent optimization. In the second stage, optimal mixture weights are estimated by maximum likelihood estimation, assigning appropriate contributions to each candidate component. This design enables fast and accurate complex density estimation for both single and joint attributes, providing reliable computation of class-conditional mutual information. The TAN structure is then constructed using Prim’s maximum spanning tree algorithm. Experiments show that our estimation method attains higher fitting accuracy and lower runtime compared with traditional nonparametric estimators. By using open-source datasets, the TAN-FGBMLE achieves superior accuracy and recall compared to classic methods, demonstrating good robustness and interpretability. On publicly available real air quality data, it has a high classification result and produces graph structures that more accurately capture dependencies among continuous attributes. Full article

Wireless sensor networks (WSNs) have widespread applications in vital fields, including environmental surveillance, medical care, and smart urban settings. Nevertheless, the restricted battery capacity of sensor nodes renders energy consumption an ongoing hindrance, limiting both the performance and operational lifespan of a network. To mitigate this challenge, this study proposes an enhanced clustering and routing scheme, the Black-Kite Optimization–Time Of Arrival (BKA-TOA) algorithm, which jointly optimizes cluster head selection and data transmission. The proposed BKA-TOA integrates the bio-inspired Black-Kite Optimization (BKA) with relay selection based on the Time of Arrival (TOA) technique. A multidimensional fitness function is constructed by incorporating inter-node distance, energy variance, and cluster head distribution to achieve robust clustering, balanced energy consumption, and improved scalability. Extensive simulations conducted in Matlab R2024a are used to benchmark the proposed algorithm against Particle Swarm Optimization (PSO), Improved Ant Colony–minimum spanning tree (IACO-MST), and Energy-Efficient Uneven Clustering (EEUC). The experimental results indicate that BKA-TOA significantly reduces node mortality, improves residual energy preservation, and prolongs the operational lifetime of WSNs compared with competing algorithms. Full article

In computer science and graph theory, prism and antiprism graphs are crucial for network modeling, optimization, and network connectivity comprehension. Applications such as social network analysis, fault-tolerant circuit design, and parallel and distributed computing all make use of them. Their structured nature makes them important, since it offers a framework for researching intricate characteristics, including resilient design, communication patterns, and network efficiency. This work uses the electrically equivalent transformations technique to compute the explicit formulas for the number of spanning trees of three novel families of graphs that have been produced using triangular prisms with their distinctive iteration feature. Additionally, the relationship between these graphs’ average degree and entropy is examined and contrasted with the entropy of additional graphs that share the same average degree as these previously studied graphs. Full article

Accurate prediction of bridge deterioration is essential for optimizing inspection schedules, prioritizing maintenance activities, and ensuring infrastructure safety. This study developed machine learning-based predictive models to estimate the deterioration states of bridge decks, superstructures, and substructures using a comprehensive dataset from the Colorado National Highway System spanning 2014 to 2024. Structural, operational, and environmental parameters including freeze–thaw cycles, precipitation, condensation risk, and extreme temperatures were incorporated to capture both design-driven and climate-driven deterioration mechanisms. Decision Tree, Random Forest, and Gradient Boosting classifiers were trained and evaluated using Balanced Accuracy, Matthews Correlation Coefficient, Cohen’s Kappa, and macro-averaged F1-scores, with class imbalance addressed via SMOTETomek resampling. The Gradient Boosting classifier achieved the highest predictive performance, with balanced accuracy exceeding 97% across all components. Feature importance analysis revealed that sufficiency rating, year of construction, and environmental stressors were among the most influential predictors. Incorporating environmental variables improved predictive accuracy by up to 4.5 percentage points, underscoring their critical role in deterioration modeling. These findings demonstrate that integrating environmental factors into machine learning frameworks enhances the reliability of deterioration forecasts and supports the development of climate-aware asset management strategies, enabling transportation agencies to proactively plan maintenance interventions and improve infrastructure resilience. Full article

This paper addresses the simultaneous feeder routing and conductor sizing problem in unbalanced three-phase distribution systems, formulated as a nonconvex mixed-integer nonlinear program (MINLP) that minimizes the equivalent annualized expansion cost—combining investment and loss costs—under voltage, ampacity, and radiality constraints. The model captures nonconvex voltage–current–power couplings, $\Delta /Y$ load asymmetries, and discrete conductor selections, creating a large combinatorial design space that challenges heuristic methods. An exact MINLP formulation in complex variables is implemented in Julia/JuMP and solved with the Basic Open-source Nonlinear Mixed Integer programming (BONMIN) solver, which integrates branch-and-bound for discrete variables and interior-point methods for nonlinear subproblems. The main contributions are: (i) a rigorous, reproducible formulation that jointly optimizes routing and conductor sizing; (ii) a transparent, replicable implementation; and (iii) a benchmark against minimum spanning tree (MST)-based and metaheuristic approaches, clarifying the trade-off between computational time and global optimality. Tests on 10- and 30-node rural feeders show that, although metaheuristics converge faster, they often yield suboptimal solutions. The proposed MINLP achieves globally optimal, technically feasible results, reducing annualized cost by 14.6% versus MST and 2.1% versus metaheuristics in the 10-node system, and by 17.2% and 2.5%, respectively, in the 30-node system. These results highlight the advantages of exact optimization for rural network planning, providing reproducible and verifiable decisions in investment-intensive scenarios. Full article

The integration of Soft Open Points (SOPs) into distribution networks has been an essential method for enhancing operational flexibility and efficiency. But simultaneous optimization of network reconfiguration and SOP scheduling constitutes a difficult mixed-integer nonlinear programming (MINLP) problem that is likely to suffer from premature convergence with standard metaheuristic solvers, particularly in large power networks. This paper proposes a novel hybrid algorithm, hTLBO–CO, which synergistically integrates the exploitative capability of Teaching–Learning-Based Optimization (TLBO) with the explorative capability of the Cheetah Optimizer (CO). One of the notable contributions of our framework is an in-depth problem formulation that enables SOP locations on both tie and sectionalizing switches with an efficient constraint-handling scheme, preserving topo-logical feasibility through a minimum spanning tree repair scheme. The evolved hTLBO–CO algorithm is systematically validated across IEEE 33-, 69-, and 119-bus test feeders with differential operational scenarios. Results indicate consistent dominance over established metaheuristics (TLBO, CO, PSO, JAYA), showing significant efficiency improvement in power loss minimization, voltage profile enhancement, and convergence rate. Remarkably, in a situation with a large-scale 119-bus power grid, hTLBO–CO registered a significant 50.30% loss reduction in the single-objective reconfiguration-only scheme, beating existing state-of-the-art approaches by over 15 percentage points. These findings, further substantiated by comprehensive statistical and multi-objective analyses, confirm the proposed framework’s superiority, robustness, and scalability, establishing hTLBO–CO as a robust computational tool for the advanced optimization of future distribution networks. Full article

The development of an optimally designed rain gauge network is crucial for achieving cost-efficient operation and maintenance and maintaining the overall accuracy of rainfall estimation. Traditional rainfall monitoring network optimization relies primarily on statistical methods without consideration of the underlying network configuration. This study presents a hybrid optimization approach integrating graph theory related to centrality (betweenness and clustering coefficient), minimum spanning tree (MST) and simulated annealing (SA) for monitoring station reduction. The proposed hybrid MST-SA algorithm with adaptive graph weighting applies to 317 monitoring stations in the northern Thailand using 11 years of wet-season rainfall data (2012–2022). Six main scenarios, involving the removal of 5 to 30 stations, are analyzed through the adjustment of the trade-off parameter between correlation and centrality. The results indicate that the proposed method outperforms the approach based solely on the correlation coefficient. This hybrid MST-SA approach achieves faster convergence and effectively preserves the continuity of spatial information throughout the domain. Furthermore, as the number of reduced stations increases, the influence of centrality becomes increasingly pronounced compared to that obtained solely from correlation analysis. Full article

The accurate forecasting of surface air temperature (T2M) is crucial for climate analysis, agricultural planning, and energy management. This study proposes a novel forecasting framework grounded in structured temporal decomposition. Using the Kolmogorov–Zurbenko (KZ) filter, all predictor variables are decomposed into three physically interpretable components: long-term, seasonal, and short-term variations, forming an expanded multi-scale feature space. A central innovation of this framework lies in training a single unified model on the decomposed feature set to predict the original target variable, thereby enabling the direct learning of scale-specific driver–response relationships. We present the first comprehensive benchmarking of this architecture, demonstrating that it consistently enhances the performance of both regularized linear models (Ridge and Lasso) and tree-based ensemble methods (Random Forest and XGBoost). Under rigorous walk-forward validation, the framework substantially outperforms conventional, non-decomposed approaches—for example, XGBoost improves the coefficient of determination ($R^2$) from 0.80 to 0.91. Furthermore, temporal decomposition enhances interpretability by enabling Ridge and Lasso models to achieve performance levels comparable to complex ensembles. Despite these promising results, we acknowledge several limitations: the analysis is restricted to a single geographic location and time span, and short-term components remain challenging to predict due to their stochastic nature and the weaker relevance of predictors. Additionally, the framework’s effectiveness may depend on the optimal selection of KZ parameters and the availability of sufficiently long historical datasets for stable walk-forward validation. Future research could extend this approach to multiple geographic regions, longer time series, adaptive KZ tuning, and specialized short-term modeling strategies. Overall, the proposed framework demonstrates that temporal decomposition of predictors offers a powerful inductive bias, establishing a robust and interpretable paradigm for surface air temperature forecasting. Full article

Accurate estimation of lithium-ion battery (LIB) state of health (SOH) underpins safe operation, predictive maintenance, and lifetime-aware energy management. Despite recent advances in machine learning (ML), systematic benchmarking across heterogeneous real-world cells remains limited, often confounded by data leakage and inconsistent validation. Here, we establish a leakage-averse, cross-battery evaluation framework encompassing 32 commercial LIBs (B5–B56) spanning diverse cycling histories and temperatures (≈4 °C, 24 °C, 43 °C). Models ranging from classical regressors to ensemble trees and deep sequence architectures were assessed under blocked 5-fold GroupKFold splits using RMSE, MAE, R² with confidence intervals, and inference latency. The results reveal distinct stratification among model families. Sequence-based architectures—CNN–LSTM, GRU, and LSTM—consistently achieved the highest accuracy (mean RMSE ≈ 0.006; per-cell R² up to 0.996), demonstrating strong generalization across regimes. Gradient-boosted ensembles such as LightGBM and CatBoost delivered competitive mid-tier accuracy (RMSE ≈ 0.012–0.015) yet unrivaled computational efficiency (≈0.001–0.003 ms), confirming their suitability for embedded applications. Transformer-based hybrids underperformed, while approximately one-third of cells exhibited elevated errors linked to noise or regime shifts, underscoring the necessity of rigorous evaluation design. Collectively, these findings establish clear deployment guidelines: CNN–LSTM and GRU are recommended where robustness and accuracy are paramount (cloud and edge analytics), while LightGBM and CatBoost offer optimal latency–efficiency trade-offs for embedded controllers. Beyond model choice, the study highlights data curation and leakage-averse validation as critical enablers for transferable and reliable SOH estimation. This benchmarking framework provides a robust foundation for future integration of ML models into real-world battery management systems. Full article

The growing need for efficient and sustainable urban water management has accelerated the adoption of smart monitoring infrastructures based on wireless sensor networks (WSNs). This study proposes a connectivity-aware methodology for the optimal deployment of wireless sensor networks (WSNs) in smart water metering systems. The approach models the wireless sensors as nodes embedded in household water meters and determines the minimal yet sufficient set of Data Aggregation Points required to ensure complete network coverage and transmission reliability. A scalable and hierarchical topology is generated by integrating an enhanced minimum spanning tree algorithm with set covering techniques and geographic constraints, leading to a robust intermediate layer of aggregation nodes. These nodes are wirelessly linked to a single cellular base station, minimizing infrastructure costs while preserving communication quality. Simulation results on realistic urban layouts demonstrate that the proposed strategy reduces network fragmentation, improves energy efficiency, and simplifies routing paths compared to traditional ad hoc designs. The results offer a practical framework for deploying resilient and cost-effective smart water metering solutions in densely populated urban environments. Full article