Images, as carriers of rich information, are generated, stored, and transmitted in various forms across diverse scenarios. It has become an important issue in the field of information security today to encrypt images to ensure information security. To address this issue, this paper proposes a Pixel-Level and DNA-Level Image Encryption Method Based on a Five-Dimensional Hyperchaotic System, named PD5H. The proposed method combines a five-dimensional chaotic system, a novel pixel-block internal diffusion method, and a new flow diffusion method integrating Pixel-Level and DNA-Level encryption, hereinafter referred to as ‘joint diffusion’. The improved 5D chaotic system can generate highly complex and unpredictable chaotic sequences. The intra-block diffusion process utilizes the internal information of the image to perform preliminary diffusion and reduce pixel correlation. The joint diffusion process can effectively employ various encryption methods to encrypt images with different step sizes at the bit level. PD5H has a large key space, extremely low image correlation, a uniform ciphertext pixel distribution, an excellent ciphertext entropy value (>7.999), and strong resistance to differential attacks. It also demonstrates strong resistance to data loss. The security analysis confirms that PD5H demonstrates excellent performance in color image encryption and can effectively resist various common attacks.

Combustible gas leakage remains a critical safety concern in industrial and indoor environments, necessitating the development of detection systems that are both accurate and practically deployable. This study presents a wireless gas detection system that integrates a gas sensor array, a low-power microcontroller with Zigbee-based communication, and a Back Propagation (BP) neural network optimized via a sequential hybrid strategy. Specifically, Particle Swarm Optimization (PSO) is employed for global parameter initialization, followed by Dung Beetle Optimization (DBO) for local refinement, jointly enhancing the network’s convergence speed and predictive precision. Experimental results confirm that the proposed PSO-DBO-BP model achieves high correlation coefficients (above 0.997) and low mean relative errors (below 0.25%) for all monitored gases, including hydrogen, carbon monoxide, alkanes, and smog. The model exhibits strong robustness in handling nonlinear responses and cross-sensitivity effects across multiple sensors, demonstrating its effectiveness in complex detection scenarios under laboratory conditions within embedded wireless sensor networks.

The measurement of atmospheric moisture content is essential for the monitoring of severe weather events and hydrological studies. This paper proposes a multivariate linear regression correction model that integrates elevation data with Global Navigation Satellite System (GNSS)-derived precipitable water vapor (PWV) to refine the water vapor content based on FY-4A satellite remote sensing data, thereby improving its accuracy. Taking Hong Kong as an experimental area, we investigated the correlation between GNSS PWV and FY-4A PWV, confirming the feasibility of utilizing GNSS PWV to calibrate FY-4A PWV. Subsequently, by examining the differences between the two PWV values, we found that the elevation of the stations affects the consistency of PWV measurement. Based on this finding, the elevation data are introduced to construct a multivariate linear regression correction model with a first-order polynomial. To evaluate the performance of the proposed model, a comparison with other correction models is made, including second-order polynomials and power functions. The results indicate that the elevation-integrated water vapor correction model improves the root mean square error (RMSE) by 27.4% and the MAE by 26.7%, and reduces the bias from 0.592 to nearly 0. Its accuracy surpasses that of second-order polynomial and power function models, demonstrating a considerable improvement in the precision of FY-4A.

Chromosomes are essential carriers of human genetic material, and karyotype diagnosis plays a crucial role in prenatal diagnostics, genetic disease identification, and medical research. Physicians rely heavily on karyotype images to diagnose potential abnormalities in chromosome numbers and structure. However, the process is tedious and challenging. To improve diagnostic efficiency and accuracy, artificial intelligence (AI) researchers have developed convolutional neural networks (CNNs) for chromosome image classification. Despite this progress, the gap between cytogeneticists and AI experts results in a time-consuming workflow. In this study, we propose a framework based on improved Differentiable Architecture Search (DARTS) to automatically design convolutional architectures for the classification task. The improvement strategies based on DARTS are implemented in two stages. First, a procedural approach was designed to comprehensively analyze the evolution of architectural parameters. Based on this analysis, the search space of the DARTS algorithm was refined, resulting in an optimized search space. Next, an entropy-based regularization term was incorporated into the supernetwork’s objective function to guide the algorithm in searching for a more effective architecture. Then, extensive experiments were conducted on CIFAR-10, ImageNet, and the Copenhagen datasets to evaluate the performance of the searched architecture in comparison with related works. The network composed of the searched architecture achieved accuracies of 97.27 $\pm$ 0.05%, 75.40%, and 98.64% on the three datasets, respectively. These results demonstrate that the architecture is high-performing and the proposed framework for designing networks for chromosome classification is effective.

Reaction system (RS) belongs to a type of qualitative computing model inspired by biochemical reactions taking place inside biological cells. It concerns more the interactions and causality among reactions rather than concrete concentrations of chemical entities. Many biochemical processes and models can be represented in the form of reaction systems so that complex relations and ultimate products of a variety of reactions can be revealed qualitatively. The reaction system works in parallel mode. Software simulation of this kind of model may suffer from the penalty of inefficient parallelism for the limited performance of CPU/GPU, especially for the simulation of large-scale models. Considering potential applications of reaction systems in disease diagnoses and in drug developments, hardware implementation of reaction systems provides a better way to accelerate computations involved. In this paper, an FPGA implementation method of a reaction system called RSFIM is proposed. Two small-scale models, i.e., the reaction system of intermediate filaments self-assembly and heat shock response, are implemented on FPGA, achieving a computing speed of $2\times {10}^8$ steps per second. For large-scale models, the ErbB reaction system is implemented, obtaining a speedup of $7.649\times {10}^4$ compared with its highest performance GPU simulation so far. The reaction system binary counter, which is a quantitative model, is also implemented by the Boolean explanation of the qualitative character of the reaction system. FPGA implementation of reaction systems opens a novel research line to speed up the simulations of reaction systems and other biological models in the perspective of parallel digital circuits.

While Neural Architecture Search (NAS) has revolutionized the automation of deep learning model design, gradient-based approaches like DARTS often suffer from high computational overheads, the collapse of skip-connections, and optimization instability. To address these limitations, we propose Efficient and Lightweight Differentiable Architecture Search (EL-DARTS). EL-DARTS constructs a compact and redundancy-reduced search space, integrates a partial channel strategy to lower memory usage, employs a Dynamic Coefficient Scheduling Strategy to balance edge importance, and introduces entropy regularization to sharpen operator selection. Experiments on CIFAR-10 and ImageNet demonstrate that EL-DARTS substantially improves both search efficiency and accuracy. Remarkably, it attains a 2.47% error rate on CIFAR-10, requiring merely 0.075 GPU-days for the search. On ImageNet, the discovered architecture achieves a 26.2% top-1 error while strictly adhering to the mobile setting (<600 M MACs). These findings confirm that EL-DARTS effectively stabilizes the search process and pushes the efficiency frontier of differentiable NAS.