The discovery of mechanochemical action provides a theoretical basis for revealing gas production from coal under stress degradation. The research on gas production in such a manner is conducive to revealing mechanisms of coal and gas outburst and excess coalbed methane (CBM). By selecting a model of a macromolecular structure of Given medium-rank coal, its structure was optimized based on molecular mechanics, molecular dynamics, and quantum chemistry, and the six optimized models were constructed into a coal polymer cell. The coal polymer cell was loaded to shear deformation through large-scale atomic/molecular massively parallel simulator (LAMMPS) software. The Given model was optimized by quantum chemistry software Gaussian and the frequency was calculated to obtain the bond strength and average local ionization energy (ALIE). The following understanding was reached: under shear, bridge bonds of a ring structure, and large π-bonds are subjected to shear and tensile action, and atoms (atomic clusters) in the outermost region of coal macromolecules tend to be sheared by surrounding molecules. The shear action shortens a molecular chain of medium-rank coal with a cross-linked structure and promotes the evolution of the coal macromolecular structure. The shear action can lead to the formation of free radicals, such as H• and •CO from macromolecules of medium-rank coal, thus producing many small gas molecules, such as H2 and CO. Moreover, the shear action can not only break chemical bonds but also can produce new chemical bonds. The research on gas production mechanisms under shear deformation of medium-rank coal provides a certain reference for studying mechanochemistry.
For Unmanned Aerial Vehicles (UAVs) with Tiny Machine Learning (TML), there is mutual exclusivity between the energy consumption for flight and the energy consumption to support their computation and processing. IoUAVs integrated with TML systems often consume substantial amounts of energy during flights, particularly when engaged in extended coverage and surveillance missions. The energy consumption of a UAV with TML performing long, wide-area coverage patrols and monitoring missions in complex areas is significant for the flight itself, and the energy required for the TML to perform calculations and processing is not guaranteed. Therefore, to better support TML computations, this study optimizes flight paths to reduce the energy consumption of UAVs while ensuring coverage. Specifically, in this study, the use of concave point elimination algorithms, enhanced convex decomposition algorithms, and determination of flight direction significantly reduced the frequency of UAV turns. The computational cost of obtaining a complete path is reduced by merging the subconvex regions and the weighted minimum traversal of the graph. This novel bidirectional forwarding path coverage path-planning (BFP-CPP) algorithm maximizes the reduction in the number of turns, reduces energy consumption, and achieves global coverage. The simulation experimental results show that compared with the existing methods without concave point elimination, the BFP-CPP algorithm can effectively reduce the number of subregions, minimize the number of drone turns, and lower energy consumption.
Smartphone pedometer is currently applied in various domains including health monitoring, context aware computing and Pedestrian Dead Reckoning (PDR). However, the limited and slowly increasing battery capacity of current smartphones results in severe contradictions with the fast development of various mobile applications and embedded sensors, which are usually hungry for energy. In terms of pedometer which has been available in every smartphone, most existing researches and practices focus on how to improve the step counting accuracy without considering its power consumptions. In this paper, we tackle with the well known auto-correlation based pedometer, and propose to substantially reduce its power consumptions based on a theoretical analysis. Firstly, we establish an error model for the traditional auto-correlation based step counting algorithm to analyze the influences of different factors on its performance. Secondly, in light of this model, we present an improved algorithm by simply calculating the acceleration variance instead of the more complicated auto-correlation coefficient to reduce its computational costs. Thirdly, an Android APP is implemented to count steps based on the presented algorithms. Finally, extensive experiments are conducted by involving different smartphones and different users under various scenarios. Experimental results reveal that, the proposed algorithm is able to achieve comparative and even superior step counting accuracy, but reduces power consumptions by 39.5% on average.
The characteristics and heterogeneity of coal pores are crucial for understanding the production mechanism of coalbed methane (CBM). In this study, coal samples with varying degrees of metamorphism (0.58% ≤ RO, max ≤ 3.44%) were collected. The characteristics of pore development and the heterogeneous properties of pores were revealed through low-temperature nitrogen adsorption (LTNA) and low-field nuclear magnetic resonance (NMR) experiments. The results indicate that pores with varying diameters exhibit favorable development in low-rank coals, along with favorable pores connectivity. The micropores composition of middle-rank coals was found to be 73.56%, however, the connectivity among transitional, meso, and macropores was observed to be poor. In high-rank coals, the proportion of micropores was 92.74%, with numerous micropores being closed or semi-closed. This resulted in inferior connectivity between micropores and transitional pores. As coal metamorphism progressed, the DL1 (characterizing the roughness of adsorption pores (AP) surface, ranging from 2.13 to 2.45) and DL2 (characterizing the complexity of AP structure, ranging from 2.56 to 2.77) initially decreased and then increased, whereas the DN (characterizing the heterogeneity of seepage pores (SP), ranging from 2.92 to 2.95) consistently improved. Furthermore, the roughness of pore surface and the complexity of pore structure in AP increased as the specific surface area and volume of pores increased. On the contrary, as the SP content increased, the uniformity of the pore structure improved. When the volume of SP remained constant, the complexity of the pore structure decreased due to increased pore connectivity.
Semantic modeling has become indispensable to integrate various specific security issues of Internet of Things. How to describe attack scenarios and how to define an intrusion unambiguously has been an urgent problem. For solving two above problems, a new three-level information security ontology model is proposed, which is an initial attempt to solve security issues by means of ontology technology. Five top classes are proposed in the domain ontology level. Then their subclasses are also proposed on the basis of actual scenarios in the local ontology level. Our intelligent laboratory, as experiment scenarios, is partly instantiated.
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