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Objective Targeting the inapplicability of conventional train positioning technologies to HTS (high-temperature superconducting) maglev trains, as well as the limitations of existing positioning technologies for such trains, a method based on RFID (radio frequency identification) technology for monitoring train position is proposed. Method The RFID positioning technology is introduced, and a theoretical error model for this technology in system application are proposed. The positioning software and hardware systems are designed and constructed. To improve positioning accuracy of the system, the influence of train operating speed, reader response time, and reader-tag distance on positioning errors are analyzed. On this basis, an experimental platform is established in consideration of the specific operating environment of HTS maglev trains, and the feasibility of the proposed RFID positioning method is verified. Result & Conclusion The designed RFID-based positioning method for HTS maglev trains can achieve relatively high precision positioning of maglev trains running on circular test tracks. The proposed RFID system exhibits advantages such as high reliability, low cost, and easy maintenance.

Ulcerative colitis (UC) is a challenging inflammatory gastrointestinal disorder, whose therapies encounter limitations in overcoming insufficient colonic retention and rapid systemic clearance. In this study, we report an innovative polymeric prodrug nanoformulation for targeted UC treatment through sustained 5-aminosalicylic acid (5-ASA) delivery. Amphiphilic polymer-based 13.5 nm micelles were engineered to incorporate azo-linked 5-ASA prodrug motifs, enabling cleavage via colonic azoreductases. In vitro, micelles exhibited excellent stability under gastric/intestinal conditions while demonstrating controlled 5-ASA release over 24 h in colonic fluids. Orally administered micelles revealed prolonged 24-h retention and a high accumulation within inflamed murine colonic tissue. At an approximately 60% dose reduction from those most advanced recent studies, the platform halted DSS colitis progression and outperformed standard 5-ASA therapy through a 77–97% suppression of inflammatory markers. Histological analysis confirmed intact colon morphology and restored barrier protein expression. This integrated prodrug nanoformulation addresses limitations in colon-targeted UC therapy through localized bioactivation and tailored pharmacokinetics, suggesting the potential of nanotechnology-guided precision delivery to transform disease management. [ABSTRACT FROM AUTHOR]

The development of efficient electrochemical nitrate reduction reaction (NO3−RR) for ammonia production (NH3) and water splitting reaction for the oxygen evolution reaction (OER) is of great significance for alleviating the growing energy demand and environmental pollution. In this contribution, copper nanocluster-loaded nickel iron layered double hydroxide (Cu/NiFe LDH) nanosheets are demonstrated to be efficient non-noble bifunctional electrodes for both the NO3−RR and OER, wherein, Cu is uniformly distributed on the surface of NiFe LDH to form a coupled interface structure with surface oxygen groups, forming optimized active sites. Theoretical analysis and test results indicate the enhanced performance benefitting from the coupled interface characteristics. For the NO3−RR, the presence of Cu changed the distribution of electrons, thereby accelerating the desorption of NH3 to continue the reaction. For the OER, Cu nanoclusters were evenly distributed on the surface, which not only facilitated electron transport but also protected NiFe LDH, thus improving activity and durability. Ultimately, Cu/NiFe LDH is an excellent bifunctional electrocatalyst for the NO3−RR and OER, which also provides new insights for other related applications. [ABSTRACT FROM AUTHOR]

In this paper, we present a Synthetic Aperture Radar (SAR) image target recognition algorithm based on multi-feature multiple representation learning classifier fusion. First, it extracts three features from the SAR images, namely principal component analysis, wavelet transform, and Two-Dimensional Slice Zernike Moments (2DSZM) features. Second, we harness the sparse representation classifier and the cooperative representation classifier with the above-mentioned features to get six predictive labels. Finally, we adopt classifier fusion to obtain the final recognition decision. We researched three different classifier fusion algorithms in our experiments, and the results demonstrate thatusing Bayesian decision fusion gives thebest recognition performance. The method based on multi-feature multiple representation learning classifier fusion integrates the discrimination of multi-features and combines the sparse and cooperative representation classification performance to gain complementary advantages and to improve recognition accuracy. The experiments are based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) database,and they demonstrate the effectiveness of the proposed approach.

Underwater images often suffer from degradation due to scattering and absorption. With the development of artificial intelligence, fully supervised learning-based models have been widely adopted to solve this problem. However, the enhancement performance is susceptible to the quality of the reference images, which is more pronounced in underwater image enhancement tasks because the ground truths are not available. In this paper, we propose a light-enhanced pixel fusion network (LEPF-Net) to solve this problem. Specifically, we first introduce a novel light enhancement block (LEB) based on the residual block (RB) and the light enhancement curve (LE-Curve) to restore the cast color of the images. The RB is adopted to learn and obtain the feature maps from an original input image, and the LE-Curve is used to renovate the color cast of the learned images. To realize the superb detail of the repaired images, which is superior to the reference images, we develop a pixel fusion subnetwork (PF-SubNet) that adopts a pixel attention mechanism (PAM) to eliminate noise from the underwater image. The PAM adapts weight allocation to different levels of a feature map, which leads to an enhancement in the visibility of severely degraded areas. The experimental results show that the proposed LEPF-Net outperforms most of the existing underwater image enhancement methods. Furthermore, among the five classic no-reference image quality assessment (NRIQA) indicators, the enhanced images obtained by LEPF-Net are of higher quality than the ground truths from the UIEB dataset. [ABSTRACT FROM AUTHOR]

In medium voltage (MV) and high voltage (HV) applications, HV SiC mosfet with a single chip competes with series-connected low voltage (LV) SiC mosfet. The cost of the former is high due to immature process, and the parasitic inductance of the latter is large due to series-connected devices. In this article, a novel chip-level series-connected SiC mosfet module based on planar packaging is proposed to compromise cost and parasitic inductance. Several LV chips are connected in series through metal layers, and then packaged as a whole. Compared with HV module with a single chip, the proposed module has significant advantages in cost. Considering the effects of the buffer layer and direct bonded copper on the parasitic inductance, junction temperature, thermal stress, and electric field, electro-thermo-mechanical simulation is conducted to optimize the structure. Compared with existing series-connected structures, the parasitic inductance of the proposed module is reduced by at least 50.15%. Finally, 6.5 kV chip-level series-connected module with six 1.2 kV SiC mosfet is fabricated and tested. The results show the performance of the proposed module is comparable with that of HV module with a single chip, which means that the application of the proposed module in MV and HV conditions is promising. [ABSTRACT FROM AUTHOR]