Fire Investigation and Ignitable Liquid Residue Analysis

A fire investigation is a difficult and challenging task. An investigator's basic task at a fire scene is two-fold: first, to ascertain the origin of the fire and, second, to closely investigate the site of origin and try to determine what triggered a fire to start at or near that spot. Usually, an investigation would begin by attempting to obtain a general view of the site and the fire damage; this may be achieved at ground level or from an elevated location. Following this, one may examine the materials available, the fuel load, and the condition of the debris at different locations. Surprisingly, the science of fire investigation is not stagnant, and each year, more information to assist investigators in determining the location and cause of a fire by diligent observation of the scene and laboratory study of fire debris is released. This chapter is split into two sections. The first section discusses the general procedures to be used during a fire investigation, and the second section discusses laboratory analysis of ignitable liquid residue analysis.

UATNet: U-Shape Attention-Based Transformer Net for Meteorological Satellite Cloud Recognition

Cloud recognition is a basic task in ground meteorological observation. It is of great significance to accurately identify cloud types from long-time-series satellite cloud images for improving the reliability and accuracy of weather forecasting. However, different from ground-based cloud images with a small observation range and easy operation, satellite cloud images have a wider cloud coverage area and contain more surface features. Hence, it is difficult to effectively extract the structural shape, area size, contour shape, hue, shadow and texture of clouds through traditional deep learning methods. In order to analyze the regional cloud type characteristics effectively, we construct a China region meteorological satellite cloud image dataset named CRMSCD, which consists of nine cloud types and the clear sky (cloudless). In this paper, we propose a novel neural network model, UATNet, which can realize the pixel-level classification of meteorological satellite cloud images. Our model efficiently integrates the spatial and multi-channel information of clouds. Specifically, several transformer blocks with modified self-attention computation (swin transformer blocks) and patch merging operations are used to build a hierarchical transformer, and spatial displacement is introduced to construct long-distance cross-window connections. In addition, we introduce a Channel Cross fusion with Transformer (CCT) to guide the multi-scale channel fusion, and design an Attention-based Squeeze and Excitation (ASE) to effectively connect the fused multi-scale channel information to the decoder features. The experimental results demonstrate that the proposed model achieved 82.33% PA, 67.79% MPA, 54.51% MIoU and 70.96% FWIoU on CRMSCD. Compared with the existing models, our method produces more precise segmentation performance, which demonstrates its superiority on meteorological satellite cloud recognition tasks.

Identification of Oil Tea (Camellia oleifera C.Abel) Cultivars Using EfficientNet-B4 CNN Model with Attention Mechanism

Cultivar identification is a basic task in oil tea (Camellia oleifera C.Abel) breeding, quality analysis, and an adjustment in the industrial structure. However, because the differences in texture, shape, and color under different cultivars of oil tea are usually inconspicuous and subtle, the identification of oil tea cultivars can be a significant challenge. The main goal of this study is to propose an automatic and accurate method for identifying oil tea cultivars. In this study, a new deep learning model is built, called EfficientNet-B4-CBAM, to identify oil tea cultivars. First, 4725 images containing four cultivars were collected to build an oil tea cultivar identification dataset. EfficientNet-B4 was selected as the basic model of oil tea cultivar identification, and the Convolutional Block Attention Module (CBAM) was integrated into EfficientNet-B4 to build EfficientNet-B4-CBAM, thereby improving the focusing ability of the fruit areas and the information expression capability of the fruit areas. Finally, the cultivar identification capability of EfficientNet-B4-CBAM was tested on the testing dataset and compared with InceptionV3, VGG16, ResNet50, EfficientNet-B4, and EfficientNet-B4-SE. The experiment results showed that the EfficientNet-B4-CBAM model achieves an overall accuracy of 97.02% and a kappa coefficient of 0.96, which is higher than that of other methods used in comparative experiments. In addition, gradient-weighted class activation mapping network visualization also showed that EfficientNet-B4-CBAM can pay more attention to the fruit areas that play a key role in cultivar identification. This study provides new effective strategies and a theoretical basis for the application of deep learning technology in the identification of oil tea cultivars and provides technical support for the automatic identification and non-destructive testing of oil tea cultivars.

Legal Regime of the Protected Natural Areas Under Directives 92/43/EEC and 2009/147/EC of the European Union

Basic aim of this study is analysis of the protection of biological diversity in protected natural territories under Directives 92/43/EEC and 2009/147/EC. These legal acts of the European Union are related to the protection of species of wild flora and fauna and natural habitats. The basic task of the study consists in consideration of the legal measures in this field under these directives in the context of their introduction in the Bulgarian legislation. The comparative law and formal-logical methods of scientific research are used. The result of study is related to formulation of general conclusions from considered legal regulation and suggestions for its improvement.

Study on the Connection Problems and Countermeasures of the Integration of Ideological and Political Courses in Colleges and Primary and Middle Schools

The integration of ideological and political curriculum which exists in colleges and compulsory education is a powerful guarantee for enhancing the effectiveness of the course. Putting theoretical content as well as teaching practice into effect contributes to every improvement and new idea about the curriculum. Facing the new circumstances, new tasks, and new challenges under the social background, it is necessary to reinforce the connection of the integration of the subject in universities and colleges and compulsory education, and take problems as the research direction for the sake of expanding the channels for courses ideological and political construction, and understand the integrated evolution of ideological and political theory courses of undergraduate and specialist education and compulsory education to a higher degree. Grasping the current situation and problems of the theory curriculum in colleges and compulsory education, and exploring the content of the theory in undergraduate and specialist education and compulsory education are of great significance, both in the integrated construction of ideological and political theory courses of undergraduate and specialist education and compulsory education and in making the best of the courses to implement the basic task of fostering people.

A T-CNN Time Series Classification Method

Time series classification is a basic task in the field of streaming data event analysis and data mining. The existing time series classification methods have the problems of low classification accuracy and low efficiency. To solve these problems, this paper proposes a T-CNN time series classification method based on a Gram matrix. Specifically, we perform wavelet threshold denoising on time series to filter normal curve noise, and propose a lossless transformation method based on the Gram matrix, which converts the time series to the time domain image and retains all the information of events. Then, we propose an improved CNN time series classification method, which introduces the Toeplitz convolution kernel matrix into convolution layer calculation. Finally, we introduce a Triplet network to calculate the similarity between similar events and different classes of events, and optimize the squared loss function of CNN. The proposed T-CNN model can accelerate the convergence rate of gradient descent and improve classification accuracy. Experimental results show that, compared with the existing methods, our T-CNN time series classification method has great advantages in efficiency and accuracy.

Efficiency-complexity evaluation methods of routing algorithms in mobile ad hoc networks

Routing in multidomain and multilayer networks is the subject of constant theoretical research, with special emphasis on routing optimization algorithms based on several criteria. Such research results in new proposals. The basic task of the algorithm is to perform the given task in a finite and reasonable period of time and with reasonable resource requirements. When new solutions are compared with previous solutions, it is necessary to consider as much information as possible about the characteristics and differences between these algorithms, which ultimately determines the degree of success of the algorithm. Routing algorithms depend on the goals to be achieved and most often solve a certain group of problems with certain simplifications of the overall problem and to the detriment of performance that are not crucial for a given routing optimization problem. Therefore, it is necessary to have acceptable methods for efficiency-complexity evaluation methods of routing algorithms with certain, universally applicable, metrics. Several theoretical approaches, including graph theory, optimization theory, complexity theory, allow approaches to compare the algorithms and the results achieved with the help of these algorithms.

DEVELOPING A COMMUNICATION STRATEGY FOR BILATERAL RELATIONS BETWEEN UAE AND SOUTH AFRICA

The following 2 elements of a crucial methodology, objective and thoughtfulness square measure interlinked and mutualist. The goals of the association square measure a great deal of thoughts that has got to be checked often. 1st of all, it should be understood that the cluster of viewers the association is attempting to succeed in, because it is unlikely to promote to the incorrect audience. Associations have to be compelled to take an edge and select that techniques can typically be realistic to succeed in as several meant interest teams as will fairly be expected. in an exceedingly non-profit association, it's preponderant to know what assets you have got accessible to you, be it financial or just a mass cut of volunteers. to start with, every association possibly has got to have power associated with cash and this is often extraordinarily troublesome to try and do for charities as an outsized portion of their cash comes from supporters or the govt. The organization studies everything one must establish a group of authoritative goals with the goal that the association will keep afloat. In short, these goals ought to be clear and created clear, in conjunction with in progress efforts to push the association overtly. particularly, the course of events provides a datebook of occasions that may be seen by all, as well as the open department. this is often presumably one amongst the simplest enforced thoughts attainable capable of communication what the bonding goes on. Finally, the ultimate task in production a sound corresponding methodology is to gauge what is going on recently. during this means, a correspondence procedure is on no account a basic task to perform, it's one thing that has got to be formed over a reasonably long amount of your time.

Research on the Construction of Public Management Discipline Under the Background of New Liberal Arts

Under the background of new liberal arts, the construction of public management discipline should be based on the social times, the goal of talent cultivation, and the characteristics of the discipline itself, so as to meet the requirements of new liberal arts. In terms of social background, the public management discipline should not only respond to the new problems arising from the technological revolution, but also to the problems of national governance in the new era. In terms of the goal of talent cultivation, the public management discipline should not only cultivate public management professionals but also undertake the basic task of establishing virtue and cultivating talents. In terms of the characteristics of the discipline itself, the public management discipline should highlight the functional advantages of humanistic education, comprehensive discipline, and social service.

Path planning in an unknown environment based on deep reinforcement learning with prior knowledge

Path planning in an unknown environment is a basic task for mobile robots to complete tasks. As a typical deep reinforcement learning, deep Q-network (DQN) algorithm has gained wide popularity in path planning tasks due to its self-learning and adaptability to complex environment. However, most of path planning algorithms based on DQN spend plenty of time for model training and the learned model policy depends only on the information observed by sensors. It will cause poor generalization capability for the new task and time waste for model retraining. Therefore, a new deep reinforcement learning method combining DQN with prior knowledge is proposed to reduce training time and enhance generalization capability. In this method, a fuzzy logic controller is designed to avoid the obstacles and help the robot avoid blind exploration for reducing the training time. A target-driven approach is used to address the lack of generalization, in which the learned policy depends on the fusion of observed information and target information. Extensive experiments show that the proposed algorithm converges faster than DQN algorithm in path planning tasks and the target can be reached without retraining when the path planning task changes.