Detection and classification of adventitious acoustic lung sounds plays an important role in diagnosing, monitoring, controlling and, caring the patients with lung diseases. Such systems can be presented as different platforms like medical devices, standalone software or smartphone application. Ubiquity of smartphones and widespread use of the corresponding applications make such a device an attractive platform for hosting the detection and classification systems for adventitious lung sounds. In this paper, the smartphone-based systems for automatic detection and classification of the adventitious lung sounds are surveyed. Such adventitious sounds include cough, wheeze, crackle and, snore. Relevant sounds related to abnormal respiratory activities are considered as well. The methods are shortly described and the analyzing algorithms are explained. The analysis includes detection and/or classification of the sound events. A summary of the main surveyed methods together with the classification parameters and used features for the sake of comparison is given. Existing challenges, open issues and future trends will be discussed as well.

Maize is a vital global crop, but its productivity is often threatened by plant diseases, highlighting the need for precise and timely diagnostic methods. Traditional manual inspection is inefficient and prone to errors, motivating the development of automated solutions. Recent advances in computer vision and deep learning have enabled effective automated plant disease diagnosis. While Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have shown promise in plant disease classification, CNNs struggle to capture global contextual information, and ViTs require large datasets and high computational resources. Inspired by mixture-of-experts (MoE) architectures, we propose a lightweight hybrid model that integrates CNN and ViT components, adaptively emphasizing local or global features based on input characteristics. Evaluated on a novel, real-world dataset of full maize plant images, our approach achieves 99.90% classification accuracy, significantly outperforming state-of-the-art baselines such as MobileViT, PiT, EdgeNeXt, and DeiT. These results demonstrate that lightweight hybrid architectures can deliver high-performance disease diagnosis suitable for practical agricultural deployment. The code is available at: https://www.github.com/sabermehdipour/MXiT.

This research explores the stabilization of a wide class of coupled dynamical systems, consisting of partial and ordinary differential equations with spatially varying coefficients. The main challenge in controlling these systems is the presence of variable spatial coefficients, which prevents the use of conventional control design methods. To address this challenge, we introduce an innovative approach employing deep neural networks, specifically DeepONet. A key advantage of this method is that it removes the need for pre-solving differential equations. The ability of DeepONet to approximate nonlinear operators is utilized to design a backstepping control strategy that ensures exponential stability of the system. The efficiency of the proposed method in stabilizing complex coupled systems is demonstrated through simulation results.