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Accurate real-time detection, geolocation, and counting of palm trees are essential for plantation management, yield estimation, and resource allocation in precision agriculture. Traditional approaches such as manual surveys or offline image processing are labor-intensive and unsuitable for large-scale applications. This study introduces a fully onboard real-time framework that integrates Unmanned Aerial Vehivle (UAV) imagery, the YOLOv12 deep learning model, and a camera projection technique to detect, geolocate, and count palm trees directly during flight. The lightweight YOLOv12n variant, deployed on an NVIDIA Jetson Nano edge device, achieved a detection precision of 92.4%, an average geolocation error of 2.14 m, and a counting error of only 0.2% across 915 trees. Unlike many existing methods that rely on offline processing or offboard computation, the proposed system performs all computations in real time, enabling immediate decision-making for tasks such as plantation density analysis, replanting planning, and yield forecasting. Experimental results demonstrate that the proposed approach provides a scalable, cost-effective, and autonomous solution for modern precision agriculture.

The evolution of sensor technologies and real-time data processing has amplified the practicality of incorporating behavioral characteristics within security frameworks. Keystroke dynamics, in particular, has emerged as a prevalent behavioral biometric owing to the ubiquitous use of devices like mobile phones and computers, all reliant on password-based security systems. This study unveils an innovative authentication framework using leveraging deep learning algorithms, tapping into force and displacement data derived from the intricate abduction movements of the right index finger as a distinctive biometric trait. To ascertain its efficacy, we meticulously optimized this novel algorithm while benchmarking it against established deep learning models—Convolutional Neural Networks (CNNs), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM), and one-dimensional CNN (1D-CNN). The subsequent evaluation encompassed a comprehensive comparative analysis of their performance metrics. The findings of this evaluation are compelling, demonstrating an average F1 score of 75.5% in validation data alongside an impressive average accuracy rate of 99.4%. These outcomes unequivocally highlight the precision and reliability inherent in utilizing force and displacement patterns as behavioral biometrics. Equally noteworthy is the system's display of a remarkably low False Acceptance Rate (FAR) of 0.27%, positioning it as a promising contender for seamless integration within advanced security systems. In essence, this research not only showcases the potential of leveraging nuanced behavioral traits but also emphasizes the practicality and robustness of employing force and displacement patterns as precise indicators in the realm of behavioral biometrics for enhanced system authentication and security.

Due to the inevitable involvement of multisource uncertainties related to the load, material property and geometry in practical engineering designs, robust topology optimization (RTO) has recently attracted increasing attention to account for these uncertain effects. However, the majority of the existing RTO works are concerned with single source uncertainty, and very few studies have considered the multisource (hybrid) uncertainties simultaneously. To this end, a comparative study on the hybrid uncertainties (HU), i.e., material-loading, geometric-loading, material-geometric, and material-geometric-loading uncertainties, for RTO of continuum structures is presented in this paper. A truncated Karhunen-Loeve expansion is adopted for uncertainty representation and a sparse grid collocation method for uncertainty propagation of the objective function and constraints. Effects of the various HU on the compliance and robust design are comprehensively investigated and compared with the RTO models under individual component uncertainty using two continuum benchmarks. An important observation from the results is that the hybrid uncertainty model is a conservative state, and the resulting RTO designs tend towards those with loading uncertainty only.

Topology optimization has been increasingly used in various industrial designs as a numerical tool to optimize the material layout of a structure. However, conventional topology optimization approaches implicitly describe the structural design and require additional post-processing to generate a manufacturable topology with smooth boundaries. To this end, this paper proposes a novel robust topology optimization approach to produce an optimized topology with smooth boundaries directly. A truncated Karhunen–Loeve expansion and a sparse grid collocation method are integrated with the explicit moving morphable components method for uncertainty representation and propagation, respectively. The performance of the proposed method is assessed on three numerical examples of continuum structures under loading and material uncertainties through comparison with several robust topology optimization approaches. Results show that the proposed method is superior to the benchmark methods in terms of the balance among robustness of the objective function, boundary smoothness, and computational efficiency.