Associations of dietary inflammation index and composite dietary antioxidant index with preserved ratio impaired spirometry in US adults and the mediating roles of triglyceride-glucose index: NHANES 2007-2012.

BACKGROUND: Previous studies have shown that inflammatory and antioxidant dietary patterns can modify the risk of COPD, yet few studies have examined the association of these diets with its early signs (PRISm), and the potential role of metabolic disorders remains to be elucidated. METHODS: Data from 9529 individuals who participated in the 2007-2012 National Health and Nutrition Examination Survey (NHANES) were analyzed. The Dietary Inflammation Index (DII) and the Dietary Antioxidant Composite Index (CDAI) were assessed using 24-h dietary recall, multiple metabolic indicators were calculated according to biochemical markers, and lung function parameters defined PRISm cases. Individual and joint effects of DII and CDAI were evaluated by generalized linear models and binary logistic regression models, and mediation effects of metabolic indicators were further explored by causal mediation analysis. RESULTS: Increased DII was associated with decreased lung function (FEV1: ß = -18.82, FVC: ß = -29.2; OR = 1.04) and increased metabolic indicators (ß = 0.316, 0.036, 0.916, 0.033, and 0.145 on MAP, UA, TC, TyG, and MS, respectively). Contrary to this, CDAI were positively and negatively associated with lung function (FEV1: ß = 3.42; FVC: ß = 4.91; PRISm: OR = 0.99) and metabolic indicators (ß < 0), respectively. Joint effects of DII and CDAI indicated the minimal hazard effects of DII on TyG (ß = -0.11), FEV1 (ß = 72.62), FVC (ß = 122.27), and PRISm (OR = 0.79) in subjects with high CDAI when compared with those with low CDAI (low DII + high CDAI vs. high DII + low CDAI). Furthermore, TyG mediated 13.74 %, 8.29 %, and 21.70 % of DII- and 37.30 %, 20.90 %, and 12.32 % of CDAI-FEV1, -FVC, and -PRISm associations, respectively. CONCLUSIONS: These findings indicated that CDAI can attenuate the adverse effects of DII on metabolic disorders and lung function decline, which provides new insight for diet modification in preventing early lung dysfunction.

Real-Time Object Detection from UAV Inspection Videos by Combining YOLOv5s and DeepStream.

The high-altitude real-time inspection of unmanned aerial vehicles (UAVs) has always been a very challenging task. Because high-altitude inspections are susceptible to interference from different weather conditions, interference from communication signals and a larger field of view result in a smaller object area to be identified. We adopted a method that combines a UAV system scheduling platform with artificial intelligence object detection to implement the UAV automatic inspection technology. We trained the YOLOv5s model on five different categories of vehicle data sets, in which mAP50 and mAP50-95 reached 93.2% and 71.7%, respectively. The YOLOv5s model size is only 13.76 MB, and the detection speed of a single inspection photo reaches 11.26 ms. It is a relatively lightweight model and is suitable for deployment on edge devices for real-time detection. In the original DeepStream framework, we set up the http communication protocol to start quickly to enable different users to call and use it at the same time. In addition, asynchronous sending of alarm frame interception function was added and the auxiliary services were set up to quickly resume video streaming after interruption. We deployed the trained YOLOv5s model on the improved DeepStream framework to implement automatic UAV inspection.

Automatic Meter Reading from UAV Inspection Photos in the Substation by Combining YOLOv5s and DeeplabV3.

The combination of unmanned aerial vehicles (UAVs) and artificial intelligence is significant and is a key topic in recent substation inspection applications; and meter reading is one of the challenging tasks. This paper proposes a method based on the combination of YOLOv5s object detection and Deeplabv3+ image segmentation to obtain meter readings through the post-processing of segmented images. Firstly, YOLOv5s was introduced to detect the meter dial area and the meter was classified. Following this, the detected and classified images were passed to the image segmentation algorithm. The backbone network of the Deeplabv3+ algorithm was improved by using the MobileNetv2 network, and the model size was reduced on the premise that the effective extraction of tick marks and pointers was ensured. To account for the inaccurate reading of the meter, the divided pointer and scale area were corroded first, and then the concentric circle sampling method was used to flatten the circular dial area into a rectangular area. Several analog meter readings were calculated by flattening the area scale distance. The experimental results show that the mean average precision of 50 (mAP50) of the YOLOv5s model with this method in this data set reached 99.58%, that the single detection speed reached 22.2 ms, and that the mean intersection over union (mIoU) of the image segmentation model reached 78.92%, 76.15%, 79.12%, 81.17%, and 75.73%, respectively. The single segmentation speed reached 35.1 ms. At the same time, the effects of various commonly used detection and segmentation algorithms on the recognition of meter readings were compared. The results show that the method in this paper significantly improved the accuracy and practicability of substation meter reading detection in complex situations.