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Timely and accurate flame detection is a very important and practical technology for preventing the occurrence of fire accidents effectively. However, the current methods of flame detection are still faced with many challenges in video surveillance scenarios due to issues such as varying flame shapes, imbalanced samples, and interference from flame-like objects. In this work, a real-time flame detection method based on deformable object detection and time sequence analysis is proposed to address these issues. Firstly, based on the existing single-stage object detection network YOLOv5s, the network structure is improved by introducing deformable convolution to enhance the feature extraction ability for irregularly shaped flames. Secondly, the loss function is improved by using Focal Loss as the classification loss function to solve the problems of the imbalance of positive (flames) and negative (background) samples, as well as the imbalance of easy and hard samples, and by using EIOU Loss as the regression loss function to solve the problems of a slow convergence speed and inaccurate regression position in network training. Finally, a time sequence analysis strategy is adopted to comprehensively analyze the flame detection results of the current frame and historical frames in the surveillance video, alleviating false alarms caused by flame shape changes, flame occlusion, and flame-like interference. The experimental results indicate that the average precision (AP) and the F-Measure index of flame detection using the proposed method reach 93.0% and 89.6%, respectively, both of which are superior to the compared methods, and the detection speed is 24–26 FPS, meeting the real-time requirements of video flame detection. [ABSTRACT FROM AUTHOR]

Smoking and calling are two typical behaviors involved in public and industrial safety that usually need to be strictly monitored and even prohibited on many occasions. To resolve the problems of missed detection and false detection in the existing traditional and deep-learning-based behavior-recognition methods, an intelligent recognition method using a multi-task YOLOv4 (MT-YOLOv4) network combined with behavioral priors is proposed. The original YOLOv4 is taken as the baseline network to be improved in the proposed method. Firstly, a K-means++ algorithm is used to re-cluster and optimize the anchor boxes, which are a set of predefined bounding boxes to capture the scale and aspect ratio of specific objects. Then, the network is divided into two branches with the same blocks but independent tasks after the shared feature extraction layer of CSPDarknet-53, i.e., the behavior-detection branch and the object-detection branch, which predict the behaviors and their related objects respectively from the input image or video frame. Finally, according to the preliminary predicted results of the two branches, comprehensive reasoning rules are established to obtain the final behavior-recognition result. A dataset on smoking and calling detection is constructed for training and testing, and the experimental results indicate that the proposed method has a 6.2% improvement in recall and a 2.4% improvement in F1 score at the cost of a slight loss in precision compared to the baseline method; the proposed method achieved the best performance among the compared methods. It can be deployed to related security surveillance systems for unsafe-behavior monitoring and early-warning management in practical scenarios. [ABSTRACT FROM AUTHOR]

Wire Arc Additive Manufacturing (WAAM) has many applications in fabricating complex metal parts. However, controlling surface roughness is very challenging in WAAM processes. Typically, machining methods are applied to reduce the surface roughness after a part is fabricated, which is costly and ineffective. Therefore, controlling the WAAM process parameters to achieve better surface roughness is important. This paper proposes a machine learning method based on Gaussian Process Regression to construct a model between the WAAM process parameters and top surface roughness. In order to measure the top surface roughness of a manufactured part, a 3D laser measurement system is developed. The experimental datasets are collected and then divided into training and testing datasets. A top surface roughness model is then constructed using the training datasets and verified using the testing datasets. Experimental results demonstrate that the proposed method achieves less than 50 μm accuracy in surface roughness prediction. [ABSTRACT FROM AUTHOR]

As a result of their rapid development, polymer composites are seeing wider use in transportation infrastructure in China and worldwide. To solve the insufficient anti-aging performance of modified asphalt with high SBS content, Han et al. added nanomaterials and polyphosphoric acid (PPA) to high SBS content linear-modified asphalt as anti-aging agents [[2]]. The low-temperature properties of SBS-modified asphalt are not satisfactory; thus, Chen et al. analyzed the main factors for improving the low-temperature performance of SBS-modified asphalt base on the orthogonal tests [[1]]. [Extracted from the article]

Asphalt mixture proportion design is one of the most important steps in asphalt pavement design and application. This study proposes a novel multi-objective particle swarm optimization (MOPSO) algorithm employing the Gaussian process regression (GPR)-based machine learning (ML) method for multi-variable, multi-level optimization problems with multiple constraints. First, the GPR-based ML method is proposed to model the objective and constraint functions without the explicit relationships between variables and objectives. In the optimization step, the metaheuristic algorithm based on adaptive weight multi-objective particle swarm optimization (AWMOPSO) is used to achieve the global optimal solution, which is very efficient for the objectives and constraints without mathematical relationships. The results showed that the optimal GPR model could describe the relationship between variables and objectives well in terms of root-mean-square error (RMSE) and R2. After the optimization by the proposed GPR-AWMOPSO algorithm, the comprehensive pavement performances were enhanced in terms of the permanent deformation resistance at high temperature, crack resistance at low temperature as well as moisture stability. Therefore, the proposed GPR-AWMOPSO algorithm is the best option and efficient for maximizing the performances of composite modified asphalt mixture. The GPR-AWMOPSO algorithm has advantages of less computational time and fewer samples, higher accuracy, etc. over traditional laboratory-based experimental methods, which can serve as guidance for the proportion optimization design of asphalt pavement. [ABSTRACT FROM AUTHOR]

High trust is a booster of trade development, while low trust is a stumbling block. The trust between two countries (regions) will be beneficial to sustainable development for bilateral trade. To investigate the impact of trade partners' trust on the scale of China's agricultural export trade, we put trust into the analytical models of international trade, propose a research hypothesis based on the transaction cost economics theory, and construct an extended gravity model for empirical tests. The results show that the level of trust affects the scale of export trade by affecting the size of transaction costs. Higher trust produces trade creation effect, while lower trust produces trade barrier effect. The trade partner's trust significantly affects the scale of China's agricultural export trade, and the effect is heterogeneous at different percent quartiles. Even after controlling the endogeneity, the conclusion still holds. We suggest that, in the international trade of agricultural products, the government should constantly improve the quality of formal institution, attach importance to constructing of the informal institution of trust and enhance the social trust to facilitate the development of agricultural trade. [ABSTRACT FROM AUTHOR]

A sustainable and environmentally friendly society is developing rapidly, in which pavement engineering is an essential part. Therefore, more attention has been paid toward waste utilization and urban noise pollution in road construction. The object of this study was not only to investigate the mix proportion of waste crumb modified asphalt mixtures with diatomite and basalt fiber but also to evaluate the comprehensive performances including sound and vibration absorption of modified asphalt mixtures. Firstly, the mix proportion scheme was designed based on Marshall indices and sound and vibration absorption properties according to the orthogonal experimental method. Considering the specification requirements, as well as better performances, the optimal mix proportion was determined as follows: diatomite content at 7.5%, basalt fiber content at 0.3%, and asphalt-aggregate ratio at 5.5%. The range and variance analysis results indicated that asphalt-aggregate ratio has the most significant influence on volumetric parameters, diatomite has the most significant influence on sound absorption, and basalt fiber has the most significant influence on vibration reduction. Furthermore, the conventional pavement performances and sustainable sound and vibration absorption performances of modified asphalt mixtures were also analyzed. The results showed that the performances of modified asphalt mixtures were improved to different extents compared to the base asphalt mixture. This may be attributed to the microporous structure property of diatomite and the spatial network structure formed by basalt fibers. The pavement as well as sound and vibration absorption performances of the waste crumb modified asphalt mixture with diatomite and basalt fiber would be a good guidance for asphalt pavement design. [ABSTRACT FROM AUTHOR]