Terahertz technology has been widely used as a nondestructive and effective detection method. Herein, terahertz time-domain spectroscopy was used to detect drug-induced liver injury in mice. Firstly, the boxplots were used to detect abnormal data. Then the maximal information coefficient method was used to search for the features strongly correlated with the degree of injury. After that, the liver injury model was built using the random forests method in machine learning. The results show that this method can effectively identify the degree of liver injury and thus provide an auxiliary diagnostic method for detecting minor liver injury.

River surface velocity (RSV) estimation is crucial in adjusting water allocation, and preventing flood. The intrusive measurement equipment of RSV, which has insufficient real-time performance and a narrow detection range, cannot meet the needs for large-scale system deployment. In contrast, image-based velocimetry methods require no direct contact with river and can acquire almost real-time velocity information within the whole field of view of shooting camera. However, image-based velocimetry methods, such as current optical flow velocimetry (OFV), suffer from poor measurement accuracy in river scenes where massive optical noise from reflection and flickering light exists. To overcome this defect, a neural-network-based optical flow estimation model which adopts a framework composed of feature extraction and optical flow optimization is proposed. Attention mechanism and position encoding are employed in the optical flow optimization process to improve the estimation performance in complex optical conditions. As results, the proposed method achieves an average RSV estimation accuracy of 92.03% in normal scenes and maintains a low average relative error of only 11.61% even in scenes with complex optical noise. We believe that this work delineates the perspective of RSV in deep optical flow estimation method and prompt the application of water-related disaster warning.

Terahertz (THz) biosensors as an advanced approach for non-destructive, label-free, and long-term monitoring of cellular states, hold tremendous potential in early disease screening and cellular drug sensitivity testing. This paper presents a THz metasurface composed of two semi-circular split-ring resonators based on quasi-bound states in the continuum (Q-BIC) mechanism. The localized field enhancement of the Q-BIC structure can significantly boost strong light-matter interactions, enabling highly sensitive detection of biological analytes. The sensitivity of the presented biosensor reaches as high as 517 GHz/RIU. Via resonance frequency shifts, we achieve ultrasensitive distinguishment between different concentrations of cancer cells (HepG2) and normal cells (293T). The results demonstrate that, without the need for additional factors, the frequency shift of the Q-BIC resonance can successfully identify different types of biological cells of varying cell concentrations. The experimental sensitivity reaches up to 456.64 kHz/(cell mL^-1), with a detection limit of 5×10³ cells/mL, confirming the feasibility of the designed structure. The QBIC-based THz biosensor exhibits significant potential in distinguishing different biological cells and detecting low-concentration cells, paving a new path for ultra-sensitive biomedical detection.

Frequent outbreaks of cyanobacterial blooms have become one of the most challenging water ecosystem issues and a critical concern in environmental protection. To overcome the poor stability of traditional detection algorithms, this paper proposes a method for detecting cyanobacterial blooms based on a deep-learning algorithm. An improved vegetation-index method based on a multispectral image taken by an Unmanned Aerial Vehicle (UAV) was adopted to extract inconspicuous spectral features of cyanobacterial blooms. To enhance the recognition accuracy of cyanobacterial blooms in complex scenes with noise such as reflections and shadows, an improved transformer model based on a feature-enhancement module and pixel-correction fusion was employed. The algorithm proposed in this paper was implemented in several rivers in China, achieving a detection accuracy of cyanobacterial blooms of more than 85%. The estimate of the proportion of the algae bloom contamination area and the severity of pollution were basically accurate. This paper can lay a foundation for ecological and environmental departments for the effective prevention and control of cyanobacterial blooms.

Many image-based velocimetry methods, such as large-scale particle image velocimetry (LSPIV), space-time image velocimetry (STIV) and optical flow velocimetry (OFV), have been proposed to estimate river surface velocity (RSV) efficiently and precisely. Among these methods, OFV-based methods have drawn great attention due to its high field resolution and low requirement of tracers. As a powerful approach for estimate optical flow accurately and efficiently, deep optical flow estimation is utilized in OFV-based methods as well. However, these methods often use irrelevant datasets for training due to the immeasurability of optical flow. Models obtained by such approaches suffer from limited generalization because of domain drift. Besides, high similarity of river surfaces often leads to ambiguous correlation volume extracted by deep optical flow estimation model, which can cause mismatch. This work proposed a method for accurately and robustly RSV estimation under velocity range of 0-6.0 m/s. Specifically, we introduced a method for generating relative optical flow datasets and proposed MRAFT, a deep optical flow estimation model combining with correlation volume modulation. Experiment results demonstrate that the generated virtual river datasets effectively improve the generalization of the model and MRAFT remarkably alleviates the mismatch. Our work facilitates the application of deep optical flow on RSV estimation and provides other related researches with optical flow datasets for fine-tuning.