DGG: A Novel Framework for Crowd Gathering Detection

Crowd gathering detection plays an important role in security supervision of public areas. Existing image-processing-based methods are not robust for complex scenes, and deep-learning-based methods for gathering detection mainly focus on the design of the network, which ignores the inner feature of the crowd gathering action. To alleviate such problems, this work proposes a novel framework Detection of Group Gathering (DGG) based on the crowd counting method using deep learning approaches and statistics to detect crowd gathering. The DGG mainly contains three parts, i.e., Detecting Candidate Frame of Gathering (DCFG), Gathering Area Detection (GAD), and Gathering Judgement (GJ). The DCFG is proposed to find the frame index in a video that has the maximum people number based on the crowd counting method. This frame means that the crowd has gathered and the specific gathering area will be detected next. The GAD detects the local area that has the maximum crowd density in a frame with a slide search box. The local area contains the inner feature of the gathering action and represents that the crowd gathering in this local area, which is denoted by grid coordinates in a video frame. Based on the detected results of the DCFG and the GAD, the GJ is proposed to analyze the statistical relationship between the local area and the global area to find the stable pattern for the crowd gathering action. Experiments based on benchmarks show that the proposed DGG has a robust representation of the gathering feature and a high detection accuracy. There is the potential that the DGG can be used in social security and smart city domains

Experimental Study on the Performance of a Household Dual-Source Heat Pump Water Heater

A household dual source heat pump water heater is proposed to utilize the energy of wastewater and air heat in a bathroom. The heat pump system integrates a wastewater source heat pump (WSHP), air source heat pump (ASHP), and a preheater. This aims at energy saving through recovering the heat of wastewater and ventilation air during the bathing process. The experiment was conducted to verify the feasibility of a dual heat source heat pump water heater system in a bath unit. It is found that the system can achieve an average coefficient of performance (COP) of 4.80 and 4.38 with and without preheater, respectively. At a bath water temperature of 40 °C, a flow rate of 6 L/min, and a room temperature of 26.5 °C, the COP of system can reach 6.08, which shows a significantly promising method for energy saving in-house

Short-term effects of CO2 leakage on the soil bacterial community in a simulated gas leakage scenario

The technology of carbon dioxide (CO2) capture and storage (CCS) has provided a new option for mitigating global anthropogenic emissions with unique advantages. However, the potential risk of gas leakage from CO2 sequestration and utilization processes has attracted considerable attention. Moreover, leakage might threaten soil ecosystems and thus cannot be ignored. In this study, a simulation experiment of leakage from CO2 geological storage was designed to investigate the short-term effects of different CO2 leakage concentration (from 400 g m−2 day−1 to 2,000 g m−2 day−1) on soil bacterial communities. A shunt device and adjustable flow meter were used to control the amount of CO2 injected into the soil. Comparisons were made between soil physicochemical properties, soil enzyme activities, and microbial community diversity before and after injecting different CO2 concentrations. Increasing CO2 concentration decreased the soil pH, and the largest variation ranged from 8.15 to 7.29 (p < 0.05). Nitrate nitrogen content varied from 1.01 to 4.03 mg/Kg, while Olsen-phosphorus and total phosphorus demonstrated less regular downtrends. The fluorescein diacetate (FDA) hydrolytic enzyme activity was inhibited by the increasing CO2 flux, with the average content varying from 22.69 to 11.25 mg/(Kg h) (p < 0.05). However, the increasing activity amplitude of the polyphenol oxidase enzyme approached 230%, while the urease activity presented a similar rising trend. Alpha diversity results showed that the Shannon index decreased from 7.66 ± 0.13 to 5.23 ± 0.35 as the soil CO2 concentration increased. The dominant phylum in the soil samples was Proteobacteria, whose proportion rose rapidly from 28.85% to 67.93%. In addition, the proportion of Acidobacteria decreased from 19.64% to 9.29% (p < 0.01). Moreover, the abundances of genera Methylophilus, Methylobacillus, and Methylovorus increased, while GP4, GP6 and GP7 decreased. Canonical correlation analysis results suggested that there was a correlation between the abundance variation of Proteobacteria, Acidobacteria, and the increasing nitrate nitrogen, urease and polyphenol oxidase enzyme activities, as well as the decreasing FDA hydrolytic enzyme activity, Olsen-phosphorus and total phosphorus contents. These results might be useful for evaluating the risk of potential CO2 leakages on soil ecosystems

Traffic Police Gesture Recognition Based on Gesture Skeleton Extractor and Multichannel Dilated Graph Convolution Network

Traffic police gesture recognition is important in automatic driving. Most existing traffic police gesture recognition methods extract pixel-level features from RGB images which are uninterpretable because of a lack of gesture skeleton features and may result in inaccurate recognition due to background noise. Existing deep learning methods are not suitable for handling gesture skeleton features because they ignore the inevitable connection between skeleton joint coordinate information and gestures. To alleviate the aforementioned issues, a traffic police gesture recognition method based on a gesture skeleton extractor (GSE) and a multichannel dilated graph convolution network (MD-GCN) is proposed. To extract discriminative and interpretable gesture skeleton coordinate information, a GSE is proposed to extract skeleton coordinate information and remove redundant skeleton joints and bones. In the gesture discrimination stage, GSE-based features are introduced into the proposed MD-GCN. The MD-GCN constructs a graph convolution with a multichannel dilated to enlarge the receptive field, which extracts body topological and spatiotemporal action features from skeleton coordinates. Comparison experiments with state-of-the-art methods were conducted on a public dataset. The results show that the proposed method achieves an accuracy rate of 98.95%, which is the best and at least 6% higher than that of the other methods

Efficacy of Intermittent or Continuous Very Low-Energy Diets in Overweight and Obese Individuals with Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analyses

Objective. This study is aimed at investigating the efficacy of a very low-energy diet (VLED) in overweight and obese individuals with type 2 diabetes mellitus (T2DM). Methods. We thoroughly searched eight electronic resource databases of controlled studies concerning the efficacy and acceptability of intermittent or continuous VLEDs in patients with T2DM compared with other energy restriction interventions. Results. Eighteen studies (11 randomized and seven nonrandomized controlled trials) with 911 participants were included. The meta-analyses showed that compared with a low-energy diet (LED) and mild energy restriction (MER), VLED is superior in the reduction of body weight (mean difference (MD) MDLED=−2.77, 95% confidence interval (CI) CILED=−4.81 to−0.72, PLED=0.008; MDMER=−6.72, 95%CIMER=−10.05 to−3.39, PMER<0.0001), blood glucose (MDLED=−1.18, 95%CILED=−2.05 to−0.30, PLED=0.008; MDMER=−6.72, 95%CIMER=−10.05 to−3.39, PMER<0.0001), and triglyceride (TG) (MDLED=−0.35, 95%CILED=−0.58 to−0.12, PLED=0.002; MDMER=−0.55, 95%CIMER=−0.93 to−0.17, PMER=0.005) levels at the end of the intervention. After the follow-up (1–5 years), no obvious difference in weight loss (MD=−0.84, 95%CI=−3.01 to 1.32, P=0.45, I2=0%) and TG level (MD=−0.25, 95%CI=−0.55 to 0.06, P=0.12, I2=0%) between VLEDs and LEDs was evident, but VLED is more effective in glycemic control (MD=−1.43, 95%CI=−2.65 to−0.20, P=0.02). Compared to bariatric surgery, VLEDs offered comparable effects on weight loss (MD=2.51, 95%CI=−9.52 to 14.54, P=0.37), glycemic control (MD=0.37, 95%CI=−0.22 to 0.96, P=0.22), TG (MD=−0.3, 95%CI=−0.74 to 0.17, P=0.7), and insulin resistance improvement (MD=−1, 95%CI=−2.7 to 0.7, P=0.25). Conclusion. Dietary intervention through VLEDs is an effective therapy for rapid weight loss, glycemic control, and improved lipid metabolism in overweight and obese individuals with T2DM. Thus, VLEDs should be encouraged in overweight and obese individuals with T2DM who urgently need weight loss and are unsuitable or unwilling to undergo surgery. As all outcome indicators have low or extremely low quality after GRADE evaluation, further clinical trials that focus on the remission effect of VLEDs on T2DM are needed