Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering

Outdoor vision sensing systems often struggle with poor weather conditions, such as snow and rain, which poses a great challenge to existing video desnowing and deraining methods. In this paper, we propose a novel video desnowing and deraining model that utilizes the salience information of moving objects to address this problem. First, we remove the snow and rain from the video by low-rank tensor decomposition, which makes full use of the spatial location information and the correlation between the three channels of the color video. Second, because existing algorithms often regard sparse snowflakes and rain streaks as moving objects, this paper injects salience information into moving object detection, which reduces the false alarms and missed alarms of moving objects. At the same time, feature point matching is used to mine the redundant information of moving objects in continuous frames, and a dual adaptive minimum filtering algorithm in the spatiotemporal domain is proposed by us to remove snow and rain in front of moving objects. Both qualitative and quantitative experimental results show that the proposed algorithm is more competitive than other state-of-the-art snow and rain removal methods

Distributed Collision-Free Bearing Coordination of Multi-UAV Systems With Actuator Faults and Time Delays

Coordination of unmanned aerial vehicle (UAV) systems has received great attention from robotics and control communities. In this paper, we investigate the distributed formation tracking problem in heterogeneous nonlinear multi-UAV networks via bearing measurements. Firstly, a novel bearing-only protocol is designed for follower agents to achieve the desired formation. Particularly, we establish a compensation function on the basis of bearing measurements to deal with the non-linearity and actuator faults in the agent dynamics. The stability of the proposed strategy can be ensured by Lyapunov method in the presence of certain time delays. Moreover, to ensure safe operation in real-world scenarios, we extend the protocol and propose a sufficient condition to avoid potential collisions among the agents. The robustness of the collision-free controller with continuous action is also considered in the protocol design. Finally, the simulation case studies are presented to validate the feasibility of the theoretical results

Predictive value of first-trimester GPR120 levels in gestational diabetes mellitus

BackgroundEarly diagnosis of gestational diabetes mellitus (GDM) reduces the risk of unfavorable perinatal and maternal consequences. Currently, there are no recognized biomarkers or clinical prediction models for use in clinical practice to diagnosing GDM during early pregnancy. The purpose of this research is to detect the serum G-protein coupled receptor 120 (GPR120) levels during early pregnancy and construct a model for predicting GDM.MethodsThis prospective cohort study was implemented at the Women’s Hospital of Jiangnan University between November 2019 and November 2022. All clinical indicators were assessed at the Hospital Laboratory. GPR120 expression was measured in white blood cells through quantitative PCR. Thereafter, the least absolute shrinkage and selection operator (LASSO) regression analysis technique was employed for optimizing the selection of the variables, while the multivariate logistic regression technique was implemented for constructing the nomogram model to anticipate the risk of GDM. The calibration curve analysis, area under the receiver operating characteristic curve (AUC) analysis, and the decision curve analysis (DCA) were conducted for assessing the performance of the constructed nomogram.ResultsHerein, we included a total of 250 pregnant women (125 with GDM). The results showed that the GDM group showed significantly higher GPR120 expression levels in their first trimester compared to the normal pregnancy group (p < 0.05). LASSO and multivariate regression analyses were carried out to construct a GDM nomogram during the first trimester. The indicators used in the nomogram included fasting plasma glucose, total cholesterol, lipoproteins, and GPR120 levels. The nomogram exhibited good performance in the training (AUC 0.996, 95% confidence interval [CI] = 0.989-0.999) and validation sets (AUC=0.992) for predicting GDM. The Akaike Information Criterion of the nomogram was 37.961. The nomogram showed a cutoff value of 0.714 (sensitivity = 0.989; specificity = 0.977). The nomogram displayed good calibration and discrimination, while the DCA was conducted for validating the clinical applicability of the nomogram.ConclusionsThe patients in the GDM group showed a high GPR120 expression level during the first trimester. Therefore, GPR120 expression could be used as an effective biomarker for predicting the onset of GDM. The nomogram incorporating GPR120 levels in early pregnancy showed good predictive ability for the onset of GDM

Reconstruction of dissolved oxygen in the Indian Ocean from 1980 to 2019 based on machine learning techniques

Oceanic dissolved oxygen (DO) decline in the Indian Ocean has profound implications for Earth’s climate and human habitation in Eurasia and Africa. Owing to sparse observations, there is little research on DO variations, regional comparisons, and its relationship with marine environmental changes in the entire Indian Ocean. In this study, we applied different machine learning algorithms to fit regression models between measured DO, ocean reanalysis physical variables, and spatiotemporal variables. We utilized the Extremely Randomized Trees (ERT) model with the best performance, inputting complete reanalysis data and spatiotemporal information to reconstruct a four-dimensional DO dataset of the Indian Ocean during 1980–2019. The evaluation results showed that the ERT-based DO dataset was superior to the DO simulations in Earth System Models across different time and space. Furthermore, we assessed the spatiotemporal variations in reconstructed DO dataset. DO decline and oxygen-minimum zone (OMZ) expansion were prominent in the Arabian Sea, Bay of Bengal, and Equatorial Indian Ocean. Through correlation analysis, we found that temperature and salinity changes related to solubility primarily control the oxygen decrease in the middle and deep sea. However, the complicated factors with solubility change, vertical mixing, and circulation govern the oxygen increase in the upper and middle sea. Finally, we conducted a volume integral to estimate the oxygen content in the Indian Ocean. Overall, a deoxygenation trend of −141.5 ± 15.1 Tmol dec−1 was estimated over four decades, with a slowdown trend of −68.9 ± 31.3 Tmol dec−1 after 2000. Under global warming and climate change, OMZ expanding and deoxygenation in the Indian Ocean are gradually mitigating. This study enhances our understanding of DO dynamics of the Indian Ocean in response to deoxygenation

The novel BH-3 mimetic apogossypolone induces Beclin-1- and ROS-mediated autophagy in human hepatocellular carcinoma cells

Apogossypolone (ApoG2), a novel derivative of gossypol, exhibits superior antitumor activity in Bcl-2 transgenic mice, and induces autophagy in several cancer cells. However, the detailed mechanisms are not well known. In the present study, we showed that ApoG2 induced autophagy through Beclin-1- and reactive oxygen species (ROS)-dependent manners in human hepatocellular carcinoma (HCC) cells. Incubating the HCC cell with ApoG2 abrogated the interaction of Beclin-1 and Bcl-2/xL, stimulated ROS generation, increased phosphorylation of ERK and JNK, and HMGB1 translocation from the nucleus to cytoplasm while suppressing mTOR. Moreover, inhibition of the ROS-mediated autophagy by antioxidant N-acetyl-cysteine (NAC) potentiates ApoG2-induced apoptosis and cell killing. Our results show that ApoG2 induced protective autophagy in HCC cells, partly due to ROS generation, suggesting that antioxidant may serve as a potential chemosensitizer to enhance cancer cell death through blocking ApoG2-stimulated autophagy. Our novel insights may facilitate the rational design of clinical trials for Bcl-2-targeted cancer therapy.Grant support: This study was supported in part by Chongqing Natural Science Foundation (CSTC, 2011BB5030), and by the Scientific Funds of Third Military Medical University (2011XHG02)

The impact of reliable range estimation on battery electric vehicle feasibility

Range limitation is a significant obstacle to market acceptance of battery electric vehicles (BEVs). Range anxiety is exacerbated when drivers could not reliably predict the remaining battery range or when their journeys were unexpectedly extended. This paper quantifies the impact of reliable range estimation on BEV feasibility using GPS-tracked travel survey data, collected over an 18-month period (from November 2004 to April 2006) in the Seattle metropolitan area. BEV feasibility is quantified as the number of days when travel adaption is needed if a driver replaces a conventional gasoline vehicle (CGV) with a BEV. The distribution of BEV range is estimated based on the real-world fuel efficiency data. A driver is assumed to choose between using a BEV or a substitute gasoline vehicle, based on the cumulative prospect theory (CPT). BEV is considered feasible for a particular driver if he/she needs to use a substitute vehicle on less than 0.5% of the travel days. By varying the values of some CPT parameter, the percentage of BEV feasible vehicles could change from less than 5% to 25%. The numerical results also show that with a 50% reduction in the standard deviation and 50% increase in the mean of the BEV range distribution BEV feasibility increases from less than 5% of the sampled drivers to 30%

Uptake Coefficients of Some Volatile Organic Compounds by Soot and Their Application in Understanding Particulate Matter Evolution in Aircraft Engine Exhaust Plumes

To assist microphysical modeling on particulate matter (PM) evolution emitted from aircraft engines, uptake coefficients of some volatile organic compounds on soot were experimentally determined in this study. The determined values vary from (1.0±0.1)×10⁻⁶ for water-miscible propylene glycol to (2.5±0.1)×10⁻⁵ for 2,6-dimethylnaphthalene, a polycyclic aromatic hydrocarbon. An inverse power-law correlation between uptake coefficient on soot and solubility in water was observed. Using the correlation, microphysical simulations were performed for the exhaust plume evolution from an idling aircraft, and we found that the model-predicted volatile PM composition on soot is comparable with those results from past field measurements.United States. Department of Defense (Contract W912HQ-08-C-0052

Вихретоковый анизотропный термоэлектрический первичный преобразователь лучистого потока

Представлена оригинальная конструкция первичного преобразователя лучистого потока, который может служить основой для создания приемника неселективного излучения с повышенной чувствительностью