This paper proposes a high-sensitivity microstrip differential sensor for measuring the complex permittivity of liquids. The prototype of the differential sensor was formed by cascading two LC resonators on a microstrip transmission line based on stepped impedance. A strong electric field was found to be distributed in the circular patch of the LC resonator; therefore, a cylindrical micropore was set in the center of the circular LC resonator to measure the dielectric sample, which maximized the disturbance of the dielectric sample on the sensor. By optimizing the size of the circular LC resonator, a high-sensitivity sensor circuit was designed and manufactured. The complex permittivity of the test sample was calculated by measuring the transmission coefficient of different molar concentrations of ethanol–water solutions. The experimental results show that the designed differential sensor can accurately measure the complex permittivity of liquid materials with an average sensitivity of 0.76%.

This study presents a high-performance silicon nanowire (SiNW) array biosensor for the combined detection of two key colorectal cancer (CRC) biomarkers: circulating tumor DNA (ctDNA) and carcinoembryonic antigen (CEA). The device was fabricated using conventional micromachining techniques, enabling the integration of dual SiNW arrays on a single chip with precise control over structure and surface functionalization. Specific probe DNA and anti-CEA antibodies were immobilized on distinct array regions to facilitate targeted binding. The biosensor demonstrated exceptional performance, achieving an ultralow detection limit of 10 aM for ctDNA with a linear range from 0.1 fM to 10 pM, and a sensitivity of 1 fg/mL for CEA. It exhibited high selectivity against interfering substances, including single-base mismatched DNA and non-specific proteins, and maintained robust performance in human serum samples. The platform offers a scalable, label-free, and real-time detection solution with significant potential for application in early CRC screening and personalized medicine.

Following publication, we noted that the sequence of the corresponding authors and their email addresses was incorrectly listed in the original publication, and a correction is required

Hyperspectral image super-resolution (HSI SR) focuses on enhancing the spatial resolution of HSIs while preserving their inherent spectral information. Existing single-image HSI SR methods still suffer from blurred spatial edges and spectral distortion. Although numerous spatial–spectral enhancement networks can enhance spatial–spectral feature extraction, they often lead to a cumbersome network architecture. To address these issues, we propose a Spatial–Spectral Distillation Mamba Network, called SSDM-Net, for HSI SR, which contains a main reconstruction branch and two training-only auxiliary branches for spatial and spectral knowledge distillation. Specifically, the spatial and spectral auxiliary branches, which are utilized exclusively during training, provide edge-aware guidance and capture spectral correlations, respectively. During training, the spatial–spectral knowledge is transferred to the main branch. During inference, the auxiliary branches are removed, improving reconstruction quality without extra computational burden. In the main branch, a Mamba-based spatial–spectral global enhancement module processes spatial and latent inter-channel sequences using selective scanning whose cost is linear in the processed sequence lengths when the feature dimensions are fixed. In addition, a dynamic loss weighting strategy is developed to balance reconstruction, distillation, and auxiliary losses during optimization. Comprehensive experiments conducted on the CAVE and Houston datasets with three scale factors demonstrate that SSDM-Net produces more accurate reconstruction results than existing representative HSI SR methods. Cross-dataset experiments on the Harvard dataset further suggest that the method can maintain competitive reconstruction performance under the evaluated cross-dataset settings.

Infrared and visible image fusion combines complementary thermal and structural information from the two modalities into a single composite image. Existing methods have two critical limitations: (1) inadequate utilization of visible structural information causes blurred edges, and (2) modality-specific and shared responses are not always separately represented when learning the fusion weights. To address these problems, an edge-aware dynamic convolution and cross-modal relation-aware fusion network is proposed to fully exploit visible structural details and explicitly model complementary information. Specifically, an edge-aware dynamic convolution is designed to extract edge features from visible images for generating dynamic convolution kernels, which adaptively enhance edge details during fusion. Then, a cross-modal relation-aware adaptive fusion module is designed to explicitly compute difference and interaction features of infrared and visible features and predicts adaptive fusion weights to balance modality-specific information. In addition, a complementarity consistency loss is introduced to jointly constrain intensity, edge and complementary information. Experiments on several public datasets demonstrate that the proposed method provides better visual quality and quantitative performance.