A Two-Stage Real-time Prediction Method for Multiplayer Shooting E-Sports

E-sports is an industry with a huge base and the number of people who pay attention to it continues to rise. The research results of E-sports prediction play an important role in many aspects. In the past game prediction algorithms, there are mainly three kinds: neural network algorithm, AdaBoost algorithm based on Naïve Bayesian (NB) classifier and decision tree algorithm. These three algorithms have their own advantages and disadvantages, but they cannot predict the match ranking in real time. Therefore, we propose a real-time prediction algorithm based on random forest model. This method is divided into two stages. In the first stage, the weights are trained to obtain the optimal model for the second stage. In the second stage, each influencing factor in the data set is corresponded to and transformed with the data items in the competition log. The accuracy of the prediction results and its change trend with time are observed. Finally, the model is evaluated. The results show that the accuracy of real-time prediction reaches 92.29%, which makes up for the shortage of real-time in traditional prediction algorithm

The Convergence Rate on a Quadrature of a Fourier Integral with Symmetrical Jacobi Weight for an Analytical Function

In this paper, through complex analysis, the convergence rate is given on a quadrature of a Fourier integral with symmetrical Jacobi weight. The interpolation nodes of this quadrature formula are expressed by the frequency, and the coefficients can be expressed by the Bessel function. When the frequency is close to 0, the nodes are close to those in the Gauss quadrature. When the frequency tends to infinity, the nodes tend symmetrically to the two ends of the integrand. The higher the frequency is, the higher the accuracy of this quadrature will be. Numerical examples are provided to illustrate the theoretical results

The Kamenev type interval oscillation criteria of mixed nonlinear impulsive differential equations under variable delay effects

Abstract In this paper, a class of mixed nonlinear impulsive differential equations is studied. When the delay σ(t) $\sigma(t)$ is variable, each given interval is divided into two parts on which the quotients of x(t−σ(t)) $x(t-\sigma(t))$ and x(t) $x(t)$ are estimated. Then, by introducing binary auxiliary functions and using the Riccati transformation, several Kamenev type interval oscillation criteria are established. The well-known results obtained by Liu and Xu (Appl. Math. Comput. 215:283–291, 2009) for σ(t)=0 $\sigma(t)=0$ and by Guo et al. (Abstr. Appl. Anal. 2012:351709, 2012) for σ(t)=σ0 $\sigma(t)=\sigma_{0}$ ( σ0≥0 $\sigma_{0}\geq0$) are developed. Moreover, an example illustrating the effectiveness and non-emptiness of our results is also given

A Molecular Motor, KIF13A, Controls Anxiety by Transporting the Serotonin Type 1A Receptor

Molecular motors are fundamental to neuronal morphogenesis and function. However, the extent to which molecular motors are involved in higher brain functions remains largely unknown. In this study, we show that mice deficient in the kinesin family motor protein KIF13A (Kif13a−/− mice) exhibit elevated anxiety-related behavioral phenotypes, probably because of a reduction in 5HT1A receptor (5HT1AR) transport. The cell-surface expression level of the 5HT1AR was reduced in KIF13A-knockdown neuroblastoma cells and Kif13a−/− hippocampal neurons. Biochemical analysis showed that the forkhead-associated (FHA) domain of KIF13A and an intracellular loop of the 5HT1AR are the interface between the motor and cargo vesicles. A minimotor consisting of the motor and FHA domains is able to transport 5HT1AR-carrying organelles in in vitro reconstitution assays. Collectively, our results suggest a role for this molecular motor in anxiety control

Design of the energy-balanced wireless sensor networks for 3D seismic exploration

Wireless Sensor Networks for 3D Seismic Exploration are large-scale and long-term networks to ensure high resolution. Energy balance is essential to avoid interruption of the whole network. In this paper, the hybrid wireless network architecture is designed for low-power monitoring system. An energy-balanced clustering method is developed to prolong network lifetime. The partial energy factor is introduced to optimize scheduling of cluster head nodes. An improved ant colony algorithm for energy-efficient clustering and routing network (IACA-EECR) is proposed to find optimal path. The results show the proposed architecture outperforms the existing platform. Extensive tests validate energy efficiency and network performance

An Integrated Energy-Efficient Wireless Sensor Node for the Microtremor Survey Method

The microtremor survey method (MSM) has the potential to be an important geophysical method for identifying the strata velocity structure and detecting the buried fault structures. However, the existing microtremor exploration equipment has been unable to satisfy the requirements of the MSM, which suffers from low data accuracy, long measurement time, and blind acquisition. In this study, we combined a 2 Hz moving coil geophone with advanced acquisition systems to develop a new integrated energy-efficient wireless sensor node for microtremor exploration. A high-precision AD chip and noise matching technology are used to develop a low-noise design for the sensor node. Dynamic frequency selection technology (DFS) and dynamic power management technology (DPM) are used to design an energy-efficient mode. The data quality monitoring system solves the closed technical flaws between the acquisition systems and the control center via 4G wireless monitoring technologies. According to the results of a series of in situ tests and field measurements, the noise level of the system was 0.7 μV@500 Hz with 0 dB attenuation and 220 mW power consumption of the system in the autonomous data acquisition mode. Therefore, it provides substantial support for the effective data acquisition over long measurement durations in microtremor exploration processes. The applicability of the system is evaluated using field data, according to which the integrated energy-efficient wireless sensor node is convenient and effective for MSM

The Atypical Kinesin KIF26A Facilitates Termination of Nociceptive Responses by Sequestering Focal Adhesion Kinase

Summary: Kinesin superfamily proteins (KIFs) are molecular motors that typically alter the subcellular localization of their cargos. However, the atypical kinesin KIF26A does not serve as a motor but can bind microtubules and affect cellular signaling cascades. Here, we show that KIF26A maintains intracellular calcium homeostasis and negatively regulates nociceptive sensation. Kif26a−/− mice exhibit intense and prolonged nociceptive responses. In their primary sensory neurons, excessive inhibitory phosphorylation of plasma membrane Ca2+ ATPase (PMCA) mediated by focal adhesion kinase (FAK) rendered the Ca transients resistant to termination, and the peripheral axonal outgrowth was significantly enhanced. Upstream, KIF26A is directly associated with a FERM domain of FAK and antagonizes FAK function in integrin-Src family kinase (SFK)-FAK signaling, possibly through steric hindrance and localization to cytoplasmic microtubules. : Wang et al. establish a mouse model of pain by deleting the Kif26a gene, which encodes an atypical kinesin. The nociceptive response of this model is abnormally prolonged. KIF26A sequesters FAK onto cytoplasmic microtubules and facilitates Ca pump activity, which leads to prolonged pain response. Keywords: kinesin, KIF26A, integrin, focal adhesion kinase, plasma membrane Ca ATPase, Ca homeostasis, sensory neurons, nociceptive response, prolonged pain, mouse mode

DNA damage induced by CDK4 and CDK6 blockade triggers anti-tumor immune responses through cGAS-STING pathway.

CDK4/6 are important regulators of cell cycle and their inhibitors have been approved as anti-cancer drugs. Here, we report a STING-dependent anti-tumor immune mechanism responsible for tumor suppression by CDK4/6 blockade. Clinical datasets show that in human tissues, CDK4 and CDK6 are over-expressed and their expressions are negatively correlated with patients overall survival and T cell infiltration. Deletion of Cdk4 or Cdk6 in tumor cells significantly reduce tumor growth. Mechanistically, we find that Cdk4 or Cdk6 deficiency contributes to an increased level of endogenous DNA damage, which triggers the cGAS-STING signaling pathway to activate type I interferon response. Knockout of Sting is sufficient to reverse and partially reverse the anti-tumor effect of Cdk4 and Cdk6 deficiency respectively. Therefore, our findings suggest that CDK4/6 inhibitors may enhance anti-tumor immunity through the STING-dependent type I interferon response