In China, the government and the cigarette industry yearly lose millions in sales and tax revenue because of imitation cigarettes. Usually, visual observation is not enough to identify counterfeiting. An auxiliary analytical method is needed for cigarette brands identification. To this end, we developed a portable, low-cost electronic nose (e-nose) system for brand recognition of cigarettes. A gas sampling device was designed to reduce the influence caused by humidity fluctuation and the volatile organic compounds (VOCs) in the environment. To ensure the uniformity of airflow distribution, the structure of the sensing chamber was optimized by computational fluid dynamics (CFD) simulations. The e-nose system is compact, portable, and lightweight with only 15 cm in side length and the cost of the whole device is less than $100. Results from the machine learning algorithm showed that there were significant differences between 5 kinds of cigarettes we tested. Random Forest (RF) has the best performance with accuracy of 91.67% and K Nearest Neighbor (KNN) has the accuracy of 86.98%, which indicated that the e-nose was able to discriminate samples. We believe this portable, cheap, reliable e-nose system could be used as an auxiliary screen technique for counterfeit cigarettes.

Residual current device is an important switch apparatus to ensure the safe and reliable operation of power distribution system. This paper proposes a novel fault leakage current detection method to eliminate the protection deadzone that is typically associated with classical leakage current detection method. The proposed method takes leakage current variation in addition of instantaneous RMS value of leakage current as the indicator of leakage fault. Three mathematical models of leakage current variation calculation are illustrated in details and multi-level leakage fault protection strategy is proposed. Simulation and experimental results are presented to validate the effectiveness of proposed leakage current detection method, and comparative evaluation of classical and proposed leakage current detection methods is conducted to verify the superiority of proposed method.

Gas sensors were fabricated from Cr2WO6 nanoparticles for NO2 detection. The low dimensional material Cr2WO6 were prepared by a wet chemistry method following by a hydrothermal treatment. The morphology of nanoparticles and its sensing properties to NO2 were investigated in both dry and humid conditions. Additionally, the sensing response was also characterized in a non-oxygen condition. It was concluded that the sensor responses in N2 conditions were higher than that in air conditions at 200ºC. Moreover, the sensing characteristics were inhibited by water vapor at 200ºC. The oxygen adsorption behavior was also investigated to verify the basic sensing mechanism of Cr2WO6 in the absence and presence of NO2 and water vapor separately. Based on the power law response, it was indicated that both NO2 and water vapor have a strong adsorption ability than oxygen ions of Cr2WO6 sensors.

The magnetic release is a core component of a voltage-independent residual current device (RCD). Due to the sensitivity of magnetic release, it is easy to be interfered with by an external magnetic field. A new magnetic release design with high anti-interference capability is proposed in this work. First, the impact of the interference magnetic field on the magnetic release is analyzed. Second, the proposed magnetic release is detailed illustrated, including the structure and parameter design. Finally, several main factors influencing the anti-interference ability of magnetic release are analyzed, including the right torque arm and right contact area. Both simulation and experimental tests are carried out to verify the effectiveness and superiority of the proposed magnetic release.

Objective. Low-dose computed tomography (LDCT) reduces radiation exposure but introduces noise and artifacts that degrade diagnostic quality. Existing deep learning—based denoising methods still face challenges such as over-smoothing, loss of fine structures, and uneven contrast. This study aims to develop an LDCT denoising framework that enhances noise suppression while preserving anatomical details and structural fidelity. Approach. We propose a multi-scale residual dense transformer generative adversarial network (MRDT-GAN). In the generator, we adopt the MRDT Block as the core unit, which introduces multi-scale strategy into residual dense network to reduce over-smoothing and preserve fine details, and also patching transformer block to capture long-range dependencies, mitigating distortions caused by localized receptive fields in convolutional neural network-based approaches. A hybrid attention module is also introduced in the generator to process spatial, frequency, and contrast information, enabling the network to focus on critical regions for noise suppression, improve contrast uniformity, and maintain texture consistency. In the discriminator, we adversarially explore differences on global, pixel, and also sub-scale between denoised LDCT and normal dose CT to better capture structural variations, reduce local noise and distortions, and ensure more realistic texture reconstruction while minimizing artifacts. Main results. We validate MRDT-GAN on both the NIH-AAPM-Mayo Clinic LDCT dataset and a real-world dataset. Experimental results indicate that MRDT-GAN achieves superior denoising performance compared with existing methods, effectively preserves details, enhances visual quality, and achieves a better balance between noise suppression and structural integrity. Significance. MRDT-GAN provides an effective and generalizable LDCT denoising solution that balances noise reduction with fine-detail preservation. By integrating MRDT modeling, hybrid attention mechanisms, and multi-difference adversarial learning, the framework offers improved clinical applicability and supports high-quality image reconstruction for downstream diagnostic tasks.