[Application of Intelligent Logistics System Based on AGV Robot in Medical Consumables Management].

Objective: Medical consumables are expensive, with numerous specifications and large usage, and traditional manual management models have certain drawbacks. Building an intelligent logistics management system to improve management level. Methods: Using AGV robots to achieve functions such as outbound, inbound, and inventory in the primary warehouse, the original "person to goods" mode is transformed into "goods to person" mode. Results: The intelligent logistics system based on AGV robots saves manpower, costs, reduces labor intensity, improves warehouse utilization, improves work efficiency. Conclusion: Hospital is a new field of intelligent logistics development, and intelligent logistics systems based on AGV robots have achieved good application results in hospitals, and improved the level of hospital intelligent management.

[Influencing Factors of Cadmium Content in Wheat Grain: A Meta-analysis and Decision Tree Analysis].

Rapid urbanization, industrialization, and agricultural intensification have resulted in serious soil problems, such as soil acidification and cadmium pollution, affecting food security and human health. Wheat is the second largest food crop in China and has a strong accumulation capacity for cadmium. Understanding the influencing factors of cadmium content in wheat grain is crucial to realize the safe production of wheat. However, a comprehensive and quantitative analysis of how soil physicochemical properties and cultivars affect wheat cadmium accumulation is lacking. The Meta-analysis and decision tree analysis of 56 related studies published in the past 10 years showed that the proportion of cadmium content in soil and wheat grain exceeding the national standard was 52.6% and 64.1%, respectively. Among soil physical and chemical properties, pH, organic matter, available phosphorus, and total soil cadmium content were the important factors affecting the cadmium content in wheat grains. When soil pH ≤ 5.5 and 5.5

Design and Development of a Low-Cost UGV 3D Phenotyping Platform with Integrated LiDAR and Electric Slide Rail.

Unmanned ground vehicles (UGV) have attracted much attention in crop phenotype monitoring due to their lightweight and flexibility. This paper describes a new UGV equipped with an electric slide rail and point cloud high-throughput acquisition and phenotype extraction system. The designed UGV is equipped with an autopilot system, a small electric slide rail, and Light Detection and Ranging (LiDAR) to achieve high-throughput, high-precision automatic crop point cloud acquisition and map building. The phenotype analysis system realized single plant segmentation and pipeline extraction of plant height and maximum crown width of the crop point cloud using the Random sampling consistency (RANSAC), Euclidean clustering, and k-means clustering algorithm. This phenotyping system was used to collect point cloud data and extract plant height and maximum crown width for 54 greenhouse-potted lettuce plants. The results showed that the correlation coefficient (R2) between the collected data and manual measurements were 0.97996 and 0.90975, respectively, while the root mean square error (RMSE) was 1.51 cm and 4.99 cm, respectively. At less than a tenth of the cost of the PlantEye F500, UGV achieves phenotypic data acquisition with less error and detects morphological trait differences between lettuce types. Thus, it could be suitable for actual 3D phenotypic measurements of greenhouse crops.

A biosensing system employing nonlinear dynamic analysis-assisted neural network for drug-induced cardiotoxicity assessment.

Preclinical investigation of drug-induced cardiotoxicity is of importance for drug development. To evaluate such cardiotoxicity, in vitro high-throughput interdigitated electrode-based recording of cardiomyocytes mechanical beating is widely used. To automatically analyze the features from the beating signals for drug-induced cardiotoxicity assessment, artificial neural network analysis is conventionally employed and signals are segmented into cycles and feature points are located in the cycles. However, signal segmentation and location of feature points for different signal shapes require design of specific algorithms. Consequently, this may lower the efficiency of research and the applications of such algorithms in signals with different morphologies are limited. Here, we present a biosensing system that employs nonlinear dynamic analysis-assisted neural network (NDANN) to avoid the signal segmentation process and directly extract features from beating signal time series. By processing beating time series with fixed time duration to avoid the signal segmentation process, this NDANN-based biosensing system can identify drug-induced cardiotoxicity with accuracy over 0.99. The individual drugs were classified with high accuracies over 0.94 and drug-induced cardiotoxicity levels were accurately predicted. We also evaluated the generalization performance of the NDANN-based biosensing system in assessing drug-induced cardiotoxicity through an independent dataset. This system achieved accuracy of 0.85-0.95 for different drug concentrations in identification of drug-induced cardiotoxicity. This result demonstrates that our NDANN-based biosensing system has the capacity of screening newly developed drugs, which is crucial in practical applications. This NDANN-based biosensing system can work as a new screening platform for drug-induced cardiotoxicity and improve the efficiency of bio-signal processing.

Budget of riverine nitrogen over the East China Sea shelf.

Riverine nitrogen loading to the continental shelf sea is important for terrestrial-marine linkage and global nitrogen cycling and leads to serious marine environmental problems. The budget and cycle of riverine nitrogen over the continental shelf in the East China Sea (ECS) are unknown. Using the tracking technique within a physical-biological coupled model, we quantified the nitrogen budgets of riverine dissolved inorganic nitrogen (DIN) and particulate organic nitrogen (PON) over seasonal to annual scales in the ECS, especially from the Changjiang River, which plays a dominant role in riverine nitrogen input. The horizontal distributions of the Changjiang DIN and PON generally followed the Changjiang diluted water and coastal currents and were affected by stratification in the vertical direction. Their inventory variations were dominated by biological fluxes and modulated by physical ones, and changed most dramatically in the inner shelf among three subregions. Less than half of DIN were converted to PON with most of the rest leaving the ECS through lateral transport pathways, among which the flux through the Tsushima Strait was dominant. With the increasing loading of the Changjiang DIN flux from the 1980s-2010s, lateral transports rather than PON production increased due to limited primary production. Approximately 60 % of the produced PON exported to the sediment and 34 % went to the Tsushima Strait. According to the export production, the DIN from the Changjiang River contributed 12-42 % to the ECS carbon sequestration.

MVS-Pheno: A Portable and Low-Cost Phenotyping Platform for Maize Shoots Using Multiview Stereo 3D Reconstruction.

Plant phenotyping technologies play important roles in plant research and agriculture. Detailed phenotypes of individual plants can guide the optimization of shoot architecture for plant breeding and are useful to analyze the morphological differences in response to environments for crop cultivation. Accordingly, high-throughput phenotyping technologies for individual plants grown in field conditions are urgently needed, and MVS-Pheno, a portable and low-cost phenotyping platform for individual plants, was developed. The platform is composed of four major components: a semiautomatic multiview stereo (MVS) image acquisition device, a data acquisition console, data processing and phenotype extraction software for maize shoots, and a data management system. The platform's device is detachable and adjustable according to the size of the target shoot. Image sequences for each maize shoot can be captured within 60-120 seconds, yielding 3D point clouds of shoots are reconstructed using MVS-based commercial software, and the phenotypic traits at the organ and individual plant levels are then extracted by the software. The correlation coefficient (R 2) between the extracted and manually measured plant height, leaf width, and leaf area values are 0.99, 0.87, and 0.93, respectively. A data management system has also been developed to store and manage the acquired raw data, reconstructed point clouds, agronomic information, and resulting phenotypic traits. The platform offers an optional solution for high-throughput phenotyping of field-grown plants, which is especially useful for large populations or experiments across many different ecological regions.