Educating adolescents on preventing substance use: The role of 24-h movement behaviors ─ data from 2021 Youth Risk Behavior Surveillance.

BACKGROUND: Little is known about how adhering to the health guidelines for physical activity (PA), screen time (ST), and sleep duration (SD) relates to substance use in adolescents. Thus, this study aims to explore the potential association between adherence to the 24-h movement behavior (24-h MB) guidelines and substance use among adolescents. METHODS: Data from the 2019 Youth Risk Behavior Surveillance was analyzed. Participants reported their weekly PA, ST, SD, and substance use (alcohol and smoking) over the past 30 days. The mean age in the total participants was 15.56, and 48.03 % of the participants were females. Logistic regression was used in this study to explore the potential association between 24-h MB and substance use. Odds ratios (ORs) were reported alongside a 95 % confidence interval to enhance understanding of the observed association. RESULTS: Only 2.22 % participants adhered to all three 24-h MB guidelines, while 47.99 % did not follow any guidelines. Notably, there was no significant difference in the odds of cigarette smoking between participants who followed none of the guidelines and those who followed some or all of them. Nevertheless, adherence to one or more guidelines was found to be associated with higher odds of abstaining from alcohol consumption compared to non-adherence (one guidelines: OR = 1.17 [1.08, 1.28], two guidelines: OR = 1.28 [1.13, 1.44]). CONCLUSIONS: Adhering to 24-h MB guidelines may reduce adolescents' alcohol consumption, but the adherence was not significantly associated with smoking. Longitudinal studies are needed to confirm these findings. These results can inform adolescent health policies and interventions aimed at reducing substance use from the perspective of healthy time-use behaviors, which can be used for researchers and educator.

CMGWO: Grey wolf optimizer for fusion cell-like P systems.

The grey wolf optimizer is a widely used parametric optimization algorithm. It is affected by the structure and rank of grey wolves and is prone to falling into the local optimum. In this study, we propose a grey wolf optimizer for fusion cell-like P systems. Cell-like P systems can parallelize computation and communicate from cell membrane to cell membrane, which can help the grey wolf optimizer jump out of the local optimum. Design new convergence factors and use different convergence factors in other cell membranes to balance the overall exploration and utilization capabilities of the algorithm. At the same time, dynamic weights are introduced to accelerate the convergence speed of the algorithm. Experiments are performed on 24 test functions to verify their global optimization performance. Meanwhile, a support vector machine model optimized by the grey wolf optimizer for fusion cell-like P systems has been developed and tested on six benchmark datasets. Finally, the optimizing ability of grey wolf optimizer for fusion cell-like P systems on constrained optimization problems is verified on three real engineering design problems. Compared with other algorithms, grey wolf optimizer for fusion cell-like P systems obtains higher accuracy and faster convergence speed on the test function, and at the same time, it can find a better parameter set stably for the optimization of support vector machine parameters, in addition to being more competitive on constrained engineering design problems. The results show that grey wolf optimizer for fusion cell-like P systems improves the searching ability of the population, has a better ability to jump out of the local optimum, has a faster convergence speed, and has better stability.

YOLOv4-Tiny-Based Coal Gangue Image Recognition and FPGA Implementation.

Nowadays, most of the deep learning coal gangue identification methods need to be performed on high-performance CPU or GPU hardware devices, which are inconvenient to use in complex underground coal mine environments due to their high power consumption, huge size, and significant heat generation. Aiming to resolve these problems, this paper proposes a coal gangue identification method based on YOLOv4-tiny and deploys it on the low-power hardware platform FPGA. First, the YOLOv4-tiny model is well trained on the computer platform, and the computation of the model is reduced through the 16-bit fixed-point quantization and the integration of a BN layer and convolution layer. Second, convolution and pooling IP kernels are designed on the FPGA platform to accelerate the computation of convolution and pooling, in which three optimization methods, including input and output channel parallelism, pipeline, and ping-pong operation, are used. Finally, the FPGA hardware system design of the whole algorithm is completed. The experimental results of the self-made coal gangue data set indicate that the precision of the algorithm proposed in this paper for coal gangue recognition on the FPGA platform are slightly lower than those of CPU and GPU, and the mAP value is 96.56%; the recognition speed of each image is 0.376 s, which is between those of CPU and GPU; the hardware power consumption of the FPGA platform is only 2.86 W; and the energy efficiency ratio is 10.42 and 3.47 times that of CPU and GPU, respectively.

Research on Insulator Defect Detection Based on an Improved MobilenetV1-YOLOv4.

Insulator devices are important for transmission lines, and defects such as insulator bursting and string loss affect the safety of transmission lines. In this study, we aim to investigate the problems of slow detection speed and low efficiency of traditional insulator defect detection algorithms, and to improve the accuracy of insulator fault identification and the convenience of daily work; therefore, we propose an insulator defect detection algorithm based on an improved MobilenetV1-YOLOv4. First, the backbone feature extraction network of YOLOv4 'Backbone' is replaced with the lightweight module Mobilenet-V1. Second, the scSE attention mechanism is introduced in stages of preliminary feature extraction and enhanced feature extraction, sequentially. Finally, the depthwise separable convolution substitutes the 3 × 3 convolution of the enhanced feature extraction network to reduce the overall number of network parameters. The experimental results show that the weight of the improved algorithm is 57.9 MB, which is 62.6% less than that obtained by the MobilenetV1-YOLOv4 model; the average accuracy of insulator defect detection is improved by 0.26% and reaches 98.81%; and the detection speed reaches 190 frames per second with an increase of 37 frames per second.

Spatio-Temporal Coding-Based Helicopter Trajectory Planning for Pulsed Neural Membrane System.

For the trajectory planning problem under the nonlinear and strongly coupled characteristics of unmanned helicopters, membrane computing with distributed parallel processing capability is introduced for unmanned helicopter trajectory planning. The global and local spatial information is temporally characterized; the temporal characterization algorithm under mapping information is designed; the hierarchical discriminant regression algorithm is designed based on incremental principal component analysis to realize the process of building and identifying trees in trajectory planning; and the pulsed neural membrane system (PNMS) with spatio-temporal coding function under membrane computing is constructed. Compared with the RRT algorithm in two experimental environments, the original path length, the trimmed path length, the time used to plan the trajectory, and the number of search nodes have different levels of improvement; the feasibility and effectiveness of the PNMS in unmanned helicopter trajectory planning are verified. It expands the theoretical research of membrane computing in the field of optimal control and provides theoretical support for the subsequent application practice.

Multi-membrane search algorithm.

This research proposes a new multi-membrane search algorithm (MSA) based on cell biological behavior. Cell secretion protein behavior and cell division and fusion strategy are the main inspirations for the algorithm. In order to verify the performance of the algorithm, we used 19 benchmark functions to compare the MSA test results with MVO, GWO, MFO and ALO. The number of iterations of each algorithm on each benchmark function is 100, the population number is 10, and the running is repeated 50 times, and the average and standard deviation of the results are recorded. Tests show that the MSA is competitive in unimodal benchmark functions and multi-modal benchmark functions, and the results in composite benchmark functions are all superior to MVO, MFO, ALO, and GWO algorithms. This paper also uses MSA to solve two classic engineering problems: welded beam design and pressure vessel design. The result of welded beam design is 1.7252, and the result of pressure vessel design is 5887.7052, which is better than other comparison algorithms. Statistical experiments show that MSA is a high-performance algorithm that is competitive in unimodal and multimodal functions, and its performance in compound functions is significantly better than MVO, MFO, ALO, and GWO algorithms.

Decoder Design Based on Spiking Neural P Systems.

The spiking neural P systems (SN P systems, for short) refer to the parallel-distributed biocomputing models, which have currently become research hotspots in the biocomputing field. In computing systems, logical operations and arithmetic operations are the most important parts, while the decoders composed of logical circuits are their indispensable parts. In this paper, considering the characteristics of SN P systems, a computing model for the general single-input single-output n-2n decoder is proposed. The decoding results of the n -bit input binary sequence can be derived on the MeCoSim platform. The computing steps of a 3-8 decoder and its simulations on the MeCoSim platform are also described in detail. Simulation results show the effectiveness of the proposed computing model for decoders.

Design and implementation of a microfluidic half adder chip based on double-stranded DNA.

In recent years, DNA computing has gained significant research interest. The design of a biochip with DNA computing as a carrier has become a key area in the development of a DNA molecular computer. The half adder, as the basic unit of various arithmetic units, has a complex structure that directly affects the overall complexity of a computer's structure. In this study, a half adder on a microfluidic chip is developed by means of bio-reaction. This technology is combined with a biochip and adopts glass and polydimethylsiloxane to fabricate a microscale hybrid chip. Using a DNA strand as an operand, realization of the half adder on a microfluidic chip is achieved by controlling the annealing and denaturation of double-stranded DNA. The computing results are rapidly and accurately obtained by detecting the presence of double-stranded DNA in a solution by agarose gel electrophoresis. The microfluidic half-adder chip accurately realizes half-adder computations and overcomes the shortcomings of traditional integrated circuit half adders, optical half adders, and chemical molecule half adders, such as complex structure, limited component size, and low accuracy.

Filamentary streamer discharges in argon at atmospheric pressure excited by surface plasmon polaritons.

This paper describes a microwave plasma jet in an argon atmosphere capable of generating filamentary streamer discharges within the entire quartz tube excited by surface waves of surface plasmon polaritons (SPPs) located in the tube. Several discharge streamers are immediately produced at the end of the copper wire when incident power reaches 20 W. From simulations, the wavelength of the surface wave was found to be approximately 5.7 cm. Although the developing streamers induce E-field enhancements favoring discharging, more streamer bifurcations requiring additional energy to maintain discharging diminish the resonant enhanced E-field. The underlying mechanism of the proposed plasma jet is resonant excitation of SPPs and its interaction with plasmas.