Timely durian leaf disease detection is critical for Vietnam’s agricultural productivity, yet traditional methods remain labor-intensive and error-prone. This study proposes a hybrid pipeline integrating deep transfer learning with binary Particle Swarm Optimization (PSO) for efficient disease classification. Three lightweight backbones, such as MobileNetV3-Large, EfficientNet-B0, and EfficientNetV2-B0 to extract 128-dimensional features from a real-world Vietnamese durian dataset (2595 images, 6 classes), which PSO then prunes using five-fold support vector machine (SVM) cross-validation fitness. The optimized subsets were evaluated across five machine learning (ML) classifiers, achieving up to 92.6
This study examines how environmental knowledge, environmental concerns, attitudes toward energy conservation, and perceived monetary benefits affect two types of energy conservation behaviors, i.e., curtailment behavior and purchase behavior. Using survey data from 405 consumers, partial least squares structural equation modeling shows that knowledge about energy conservation and concern about energy shortages shape attitude toward energy conservation, leading to curtailment behavior and purchasing energy-efficient appliances (EEAs). Furthermore, attitude and curtailment behavior sequentially mediate the effects of knowledge about energy conservation and concern about energy shortages on EEA purchase behavior, with perceived monetary benefits strengthening the attitude-purchase behavior relationship. While necessary condition analysis identifies general environmental concern as necessary for developing a favorable energy conservation attitude, importanceperformance map analysis suggests that improving knowledge about conservation should be prioritized in policy initiatives. These insights provide implications for policymakers and practitioners, including manufacturers and retailers, by identifying multiple pathways and influencing mechanisms for promoting consumers' energy conservation behaviors through curtailment and EEA purchases.
Cell-free massive multiple-input multiple-output (MIMO)-aided integrated sensing and communication (ISAC) systems are investigated where distributed access points jointly serve users and sensing targets. We demonstrate that only a subset of access points (APs) has to be activated for both tasks, while deactivating redundant APs is essential for power savings. This motivates joint active AP selection and power control for optimizing energy efficiency. The resultant problem is a mixed-integer nonlinear program (MINLP). To address this, we propose a model-based Branch-and-Bound approach as a strong baseline to guide a semi-supervised heterogeneous graph neural network (HetGNN) for selecting the best active APs and the power allocation. Comprehensive numerical results demonstrate that the proposed HetGNN reduces power consumption by 20-25 % and runs nearly 10,000 times faster than model-based benchmarks.
In this paper, we introduce the SDIR (Susceptible-Delayable-Infected-Recovered) model, an extension of the classical SIR epidemic framework, to provide a more explicit characterization of user behavior in online social networks. The newly merged state D (delayable) represents users who have received the information but delayed its spreading and may eventually choose not to share it at all. Based on the mean-field approximation method, we derive the dynamical equations of the model and investigate its convergence and stability conditions. Under these conditions, we further propose an approximation algorithm for the edge-deletion problem, aiming to minimize the influence of information diffusion by identifying approximate solutions.
Recently, environment reconstruction (ER) in integrated sensing and communication (ISAC) systems has emerged as a promising approach for achieving high-resolution environmental perception. However, the initial results obtained from ISAC systems are coarse and often unsatisfactory due to the high sparsity of the point clouds and significant noise variance. To address this problem, we propose a noise-sparsity-aware diffusion model (NSADM) post-processing framework. Leveraging the powerful data recovery capabilities of diffusion models, the proposed scheme exploits spatial features and the additive nature of noise to enhance point cloud density and denoise the initial input. Simulation results demonstrate that the proposed method significantly outperforms existing model-based and deep learning-based approaches in terms of Chamfer distance and root mean square error.
