Design of a New CIM-DCSK-Based Ambient Backscatter Communication System

To improve the data rate in differential chaos shift keying (DCSK) based ambient backscatter communication (AmBC) system, we propose a new AmBC system based on code index modulation (CIM), referred to as CIM-DCSK-AmBC system. In the proposed system, the CIM-DCSK signal transmitted in the direct link is used as the radio frequency source of the backscatter link. The signal format in the backscatter link is designed to increase the data rate as well as eliminate the interference of the direct link signal. As such, the direct link signal and the backscatter link signal can be received and demodulated simultaneously. Moreover, we derive and validate the theoretical bit error rate (BER) expressions of the CIM-DCSK-AmBC system over multipath Rayleigh fading channels. Regarding the short reference DCSK-based AmBC (SR-DCSK-AmBC) system as a benchmark system, numerical results reveal that the CIM-DCSK-AmBC system can achieve better BER performance in the direct link and higher throughput in the backscatter link than the benchmark system

Exploration of relationships between safety performance and unsafe behavior in coal mining processes

PresentationIt is well known that safety performance is differentiated to two components, namely, safety compliance and safety participation. However, relationships between safety performance and unsafe behavior were barely explored. In this work, the scales for safety compliance and safety participation were slightly revised for usage in coal mining processes, and job burnout scale was developed on the basis of MBI-GS. Then, structural equation model was employed to investigate the interaction of these factors using samples of 367 front-line coal miners in large state-owned mining companies in China. The results show that individual unsafe behavior could not be diminished significantly by only focusing on these two dimensions of safety performance. Compared with safety participation, safety compliance has more significant influence on unsafe behavior, and job burnout is an indispensable moderator between these two components and unsafe behavior. More importantly, it is vital to pay close attention to employees’ occupational psychological health problem for improving organizational safety management and promoting personal performance

Ab Initio Studies on Interactions in K3_3C60_{60} under High Pressure

Fullerene solids doped with alkali metals (A$_3$C$_{60}$, A = K, Rb, Cs) exhibit a superconducting transition temperature ($T_c$) as high as 40 K, and their unconventional superconducting properties have been a subject of debate. With application of high pressure on K$_3$C$_{60}$ and Rb$_3$C$_{60}$, the experiments demonstrate the decrease of $T_c$. In this paper, we focus on K$_3$C$_{60}$ and derive the structure of K$_3$C$_{60}$ under different pressures based on first-principles calculations, exploring the trends of Coulomb interactions at various pressures. By utilizing the Maximally Localized Wannier function approach, Constrained Density Functional Perturbation Theory (cDFPT), and Constrained Random Phase Approximation (cRPA), we construct a microscopic low-energy model near the Fermi level. Our results strongly indicate that, in the K$_3$C$_{60}$ system, as pressure increases, the effect of phonons is the key to intraorbital electron pairing. There is a dominance of the phonon-driven superconducting mechanism at high pressure

DBT Masses Automatic Segmentation Using U-Net Neural Networks

To improve the automatic segmentation accuracy of breast masses in digital breast tomosynthesis (DBT) images, we propose a DBT mass automatic segmentation algorithm by using a U-Net architecture. Firstly, to suppress the background tissue noise and enhance the contrast of the mass candidate regions, after the top-hat transform of DBT images, a constraint matrix is constructed and multiplied with the DBT image. Secondly, an efficient U-Net neural network is built and image patches are extracted before data augmentation to establish the training dataset to train the U-Net model. And then the presegmentation of the DBT tumors is implemented, which initially classifies per pixel into two different types of labels. Finally, all regions smaller than 50 voxels considered as false positives are removed, and the median filter smoothes the mass boundaries to obtain the final segmentation results. The proposed method can effectively improve the performance in the automatic segmentation of the masses in DBT images. Using the detection Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), and area under the curve (AUC) as evaluation indexes, the Acc, Sen, Spe, and AUC for DBT mass segmentation in the entire experimental dataset is 0.871, 0.869, 0.882, and 0.859, respectively. Our proposed U-Net-based DBT mass automatic segmentation system obtains promising results, which is superior to some classical architectures, and may be expected to have clinical application prospects

Exposure levels and health damage assessment of dust in a coal mine of Shanxi Province, China

The concentrations, health risks and quantitative probabilistic health effects for dust at four workplaces from a coal mine in Shanxi were discussed. A total of 582 dust samples from 21 types of works in various workplaces were collected and analyzed, and their mean concentration ranged from 1.29 to 19.38 mg/m. The probabilistic health damages assessment for coal miners caused by dust were conducted by Monte Carlo simulations and the United States Environmental Protection Agency (USEPA) inhalation risk model. The roadheader drivers and drillers in driving place suffered the greatest health risks with the average value of 5.60×10 and 5.55×10, respectively. The health risks in other workplaces are relatively lower and in the sequence of coal place, transshipment point, and shotcreting point. The health damages for coal miner at various workplaces followed a lognormal distribution, the disability-adjusted life year (DALY) ranked in the following sequence: driving face (1.76±0.14a) > coal face (1.63±0.06a) > transshipment point (1.24±0.11a) > shotcreting point (0.97±0.07a). Sensitivity analyses indicate that exposure duration (ED) have the greatest impact on the dust health damages, followed by exposure time (ET), inhalation rate (IR) and dust concentration (C). These results provide basic information for dust pollution control and health management in coal mines

STUDY ON FREE VIBRATION ANALYSIS FOR HONEYCOMB SANDWICH STRUCTURE WITH DOUBLE CORES

The free vibration problem of the honeycomb sandwich structure with double cores was studied based on the Layerwise/Solid-Elements（ LW/SE） and Fixed-interface Modal Synthesis Technique（ FMST） of the dynamic substructure method. The governing equation and the total modal space of the honeycomb sandwich structure were assembled based on LW/SE and FMST,respectively,and the final governing equation on the basis of the modal spaces was assembled based on the governing equation and the total modal space. This method obtains the natural frequency of the honeycomb sandwich structure with double cores accurately and reduces memory requirement. The numerical results of the method are compared with those obtained by the 3D solid finite element,and good agreements are achieved

Task Offloading with Data-Dependent Constraints in Satellite Edge Computing Networks: A Multi-Objective Approach

By enabling a satellite network with edge computing capabilities, satellite edge computing(SEC) provides users with a full range of computing service. In this paper, we construct a multi-objective optimization model for task offloading with data-dependent constraints in an SEC network and aim to achieve optimal tradeoffs among energy consumption, cost, and makespan. However, dependency constraints between tasks may lead to unexpected computational delays and even task failures in an SEC network. To solve this, we proposed a Petri-net-based constraint amending method with polynomial complexity and generated offloading results satisfying our constraints. For the multiple optimization objectives, a strengthened dominance relation sort was established to balance the convergence and diversity of nondominated solutions. Based on these, we designed a multi-objective wolf pack search (MOWPS) algorithm. A series of adaptive mechanisms was employed for avoiding additional computational overhead, and a Lamarckian-learning-based multi-neighborhood search prevents MOWPS from becoming trapped in the local optimum. Extensive computational experiments demonstrate the outperformance of MOWPS for solving task offloading with data-dependent constraints in an SEC network