Magnetic field annihilation and reconnection driven by femtosecond lasers in inhomogeneous plasma

The process of fast magnetic reconnection driven by intense ultra-short laser pulses in underdense plasma is investigated by particle-in-cell simulations. In the wakefield of such laser pulses, quasi-static magnetic fields at a few mega-Gauss are generated due to nonvanishing cross product ∆(n /γ) × p. Excited in an inhomogeneous plasma of decreasing density, the quasi-static magnetic field structure is shown to drift quickly both in lateral and longitudinal directions. When two parallel-propagating laser pulses with close focal spot separation are used, such field drifts can develop into magnetic reconnection (annihilation) in their overlapping region, resulting in the conversion of magnetic energy to kinetic energy of particles. The reconnection rate is found to be much higher than the value obtained in the Hall magnetic reconnection model. Our work proposes a potential way to study magnetic reconnection-related physics with short-pulse lasers of terawatt peak power only

Early Identification of High-Risk TIA or Minor Stroke Using Artificial Neural Network

Background and Purpose: The risk of recurrent stroke following a transient ischemic attack (TIA) or minor stroke is high, despite of a significant reduction in the past decade. In this study, we investigated the feasibility of using artificial neural network (ANN) for risk stratification of TIA or minor stroke patients.Methods: Consecutive patients with acute TIA or minor ischemic stroke presenting at a tertiary hospital during a 2-year period were recruited. We collected demographics, clinical and imaging data at baseline. The primary outcome was recurrent ischemic stroke within 1 year. We developed ANN models to predict the primary outcome. We randomly down-sampled patients without a primary outcome to 1:1 match with those with a primary outcome to mitigate data imbalance. We used a 5-fold cross-validation approach to train and test the ANN models to avoid overfitting. We employed 19 independent variables at baseline as the input neurons in the ANN models, using a learning algorithm based on backpropagation to minimize the loss function. We obtained the sensitivity, specificity, accuracy and the c statistic of each ANN model from the 5 rounds of cross-validation and compared that of support vector machine (SVM) and Naïve Bayes classifier in risk stratification of the patients.Results: A total of 451 acute TIA or minor stroke patients were enrolled. Forty (8.9%) patients had a recurrent ischemic stroke within 1 year. Another 40 patients were randomly selected from those with no recurrent stroke, so that data from 80 patients in total were used for 5 rounds of training and testing of ANN models. The median sensitivity, specificity, accuracy and c statistic of the ANN models to predict recurrent stroke at 1 year was 75%, 75%, 75%, and 0.77, respectively. ANN model outperformed SVM and Naïve Bayes classifier in our dataset for predicting relapse after TIA or minor stroke.Conclusion: This pilot study indicated that ANN may yield a novel and effective method in risk stratification of TIA and minor stroke. Further studies are warranted for verification and improvement of the current ANN model

X-ray Astronomy in the Laboratory with a Miniature Compact Object Produced by Laser-Driven Implosion

Laboratory spectroscopy of non-thermal equilibrium plasmas photoionized by intense radiation is a key to understanding compact objects, such as black holes, based on astronomical observations. This paper describes an experiment to study photoionizing plasmas in laboratory under well-defined and genuine conditions. Photoionized plasma is here generated using a 0.5-keV Planckian x-ray source created by means of a laser-driven implosion. The measured x-ray spectrum from the photoionized silicon plasma resembles those observed from the binary stars Cygnus X-3 and Vela X-1 with the Chandra x-ray satellite. This demonstrates that an extreme radiation field was produced in the laboratory, however, the theoretical interpretation of the laboratory spectrum significantly contradicts the generally accepted explanations in x-ray astronomy. This model experiment offers a novel test bed for validation and verification of computational codes used in x-ray astronomy.Comment: 5 pages, 4 figures are included. This is the original submitted version of the manuscript to be published in Nature Physic

Agent Simulation Model of COVID-19 Epidemic Agent-Based on GIS: A Case Study of Huangpu District, Shanghai

Since the COVID-19 outbreak was detected and reported at the end of 2019, the pandemic continues worldwide, with public health authorities and the general public in each country struggling to balance safety and normal travel activities. However, the complex public health environment and the complexity of human behaviors, as well as the constant mutation of the COVID-19 virus, requires the development of theoretical and simulation tools to accurately model all segments of society. In this paper, an agent-based model is proposed, the model constructs the real geographical environment of Shanghai Huangpu District based on the building statistics data of Shanghai Huangpu District, and the real population data of Shanghai Huangpu District based on the data of China’s seventh Population census in 2020. After incorporating the detailed elements of COVID-19 transmission and the real data of WHO, the model forms various impact parameters. Finally, the model was validated according to the COVID-19 data reported by the official, and the model is applied to a hypothetical scenario. Shanghai is one of the places hardest hit by the current outbreak, Huangpu District is the “heart, window and name card” of Shanghai, and its importance to Shanghai is self-evident. so we used one-to-one population modeling to simulate the spread of COVID-19 in Huangpu District of Shanghai, In addition to the conventional functions of crowd movement, detection and treatment, the model also takes into account the burden of nucleic acid detection on the model caused by diseases similar to COVID-19, such as seasonal cold. The model validation results show that we have constructed a COVID-19 epidemic agent risk assessment system suitable for the individual epidemiological characteristics of COVID-19 in China, which can adjust and reflect on the existing COVID-19 epidemic intervention strategies and individual health behaviors. To provide scientific theoretical basis and information decision-making tools for effective prevention and control of COVID-19 and public health intervention in China

Spatio-temporal pattern analysis for evaluation of the spread of human infections with avian influenza A(H7N9) virus in China, 2013–2014

Abstract Background A large number (n = 460) of A(H7N9) human infections have been reported in China from March 2013 through December 2014, and H7N9 outbreaks in humans became an emerging issue for China health, which have caused numerous disease outbreaks in domestic poultry and wild bird populations, and threatened human health severely. The aims of this study were to investigate the directional trend of the epidemic and to identify the significant presence of spatial-temporal clustering of influenza A(H7N9) human cases between March 2013 and December 2014. Methods Three distinct epidemic phases of A(H7N9) human infections were identified in this study. In each phase, standard deviational ellipse analysis was conducted to examine the directional trend of disease spreading, and retrospective space-time permutation scan statistic was then used to identify the spatio-temporal cluster patterns of H7N9 outbreaks in humans. Results The ever-changing location and the increasing size of the three identified standard deviational ellipses showed that the epidemic moved from east to southeast coast, and hence to some central regions, with a future epidemiological trend of continue dispersing to more central regions of China, and a few new human cases might also appear in parts of the western China. Furthermore, A(H7N9) human infections were clustering in space and time in the first two phases with five significant spatio-temporal clusters (p < 0.05), but there was no significant cluster identified in phase III. Conclusions There was a new epidemiologic pattern that the decrease in significant spatio-temporal cluster of A(H7N9) human infections was accompanied with an obvious spatial expansion of the outbreaks during the study period, and identification of the spatio-temporal patterns of the epidemic can provide valuable insights for better understanding the spreading dynamics of the disease in China

Formation of electron energy spectra during magnetic reconnection in laser-produced plasma

The energetic electron spectra formed during magnetic reconnection between two laser-produced plasma bubbles are investigated by use of two-dimensional particle-in-cell simulations. It is found that the evolution of such interaction between the two plasma bubbles can be separated into two distinct stages: the squeezing and reconnection stages. In the squeezing stage, when the two plasma bubbles expand quickly and collide with each other, the magnetic field in the inflow region is greatly enhanced. In the second stage, a thin current sheet is formed between the two plasma bubbles, and then magnetic reconnection occurs therein. During the squeezing stage, electrons are heated in the perpendicular direction by betatron acceleration due to the enhancement of the magnetic field around the plasma bubbles. Meanwhile, non-thermal electrons are generated by the Fermi mechanism when these electrons bounce between the two plasma bubbles approaching quickly and get accelerated mainly by the convective electric field associated with the plasma bubbles. During the reconnection stage, electrons get further accelerated mainly by the reconnection electric field in the vicinity of the X line. When the expanding speed of the plasma bubbles is sufficiently large, the formed electron energy spectra have a kappa distribution, where the lower energy part satisfies a Maxwellian function and the higher energy part is a power-law distribution. Moreover, the increase of the expanding speed will result in the hardening of formed power-law spectra in both the squeezing and reconnection stages

Review on Tunnel Communication Technology

Tunnels account for an increasing proportion of highways. Due to the semi-closed structure of tunnels, signal communication is difficult in tunnels. This review analyzes the signal data transmission requirements of intelligent network management systems, such as lighting systems, wind protection systems, fire protection systems, and vehicle and pedestrian positioning systems in tunnels. The selection of signal coverage and transmission methods are also discussed. The advantages and disadvantages of various networking methods are analyzed. This paper summarizes the wireless signal transmission, wired signal transmission and signal transmission modes of different tunnel types

Lattice Boltzmann modeling of the effective thermal conductivity in plant fiber porous media generated by Quartet Structure Generation Set

This study proposes a novel approach that combines a Quartet Structure Generation Set (QSGS) algorithm with the Lattice Boltzmann Method (LBM) to investigate effective thermal conductivity in plant fiber materials. The QSGS algorithm is utilized to construct a two-dimensional digital structure of the porous media, enabling the determination of effective thermal conductivity through LBM simulations. The simulation results demonstrate good agreement with experimental data, exhibiting a maximum relative error of only 1.7%. The study examines the influence of key factors on effective thermal conductivity, including temperature, porosity, fiber distribution, and fractal dimension. The effective thermal conductivity increased by approximately 6.1% for each 10 °C increase in temperature between 10 °C and 50 °C. Moreover, the effective thermal conductivity showed an average decrease of 16.3% with each 10% increment in porosity. The increase in thermal conductivity becomes more significant with increasing temperature and decreasing porosity. Conversely, an increased fractal dimension correlates with a decline in effective thermal conductivity. The effective thermal conductivity is influenced synergistically by the directional growth probability of the solid phase and the direction of heat flow. The findings offer valuable insights into thermal conductivity behavior and heat transfer mechanisms in plant fiber porous media

Rhizosphere bacteria regulated arsenic bioavailability and accumulation in the soil–Chinese cabbage system

The accumulation of arsenic (As) in Chinese cabbage (Brassica rapa ssp. pekinensis) has recently been a source of concern for a potential risk to human health. It is unknown whether natural variations of As accumulation in different genotypes of Chinese cabbage are related to rhizobacterial characteristics. Experiments were conducted to investigate the mechanisms of rhizobacteria involving in As fates in a soil–Chinese cabbage system using various genotypes using high-throughput sequencing and quantitative PCR. There were significant differences in As accumulation in cabbage leaves between 32 genotypes, and genotypes of low-As-accumulation (LSA) and high-As-accumulation (HSA) were identified. The As concentrations in the shoots of LSA were 23.25 %, 24.19 %, 15.05 %, and 70.69 % lower than those of HSA in seedling stage (SS), rosette stage (RS), heading stage (HS), and mature stage (MS), respectively. Meanwhile, the relative abundances of phyla Patescibacteria (in RS), Acidobacteria and Rokubacteria (in HS) in the rhizosphere of LSA were 60.18 %, 28.19 %, and 45.38 % less than those of HSA, respectively. Additionally, both shoot-As and As translocation factor had significantly positive or negative correlations with the relative abundances of Rokubacteria or Actinobacteria. In LSA rhizosphere, the relative abundances of genera Flavobacterium (in SS), Ellin6055 and Sphingomonas (in HS) were 128.12 %, 83.69 % and 79.50 % higher than those of HSA, respectively. This demonstrated that rhizobacteria contribute to the accumulation and translocation of As in HSA and LSA. Furthermore, the gene copies of aioA and arsM in LSA rhizosphere were 25.54 % and 16.13 % higher than those of HSA, respectively, whereas the gene copies of arsC in LSA rhizosphere were 26.36 % less than those of HSA in MS, indicating that rhizobacteria are involved in As biotransformation in the soil. These results provide a comprehensive understanding of the relationship between characteristics of rhizobacterial communities and As variations in Chinese cabbage genotypes

Theoretical evaluation of the

Interface optimization has been widely used to improve the optoelectronic properties of nanocomposites, but the theoretical estimation of their effect on the interfacial carrier transfer dynamics is insufficient. Therefore, it is very significant to explore the introduced interface electronic structural state and corresponding interfacial electron transfer behavior. In this paper, the possible electron transition path in