Parameter Solving of Probability Integral Method Based on Improved Genetic Algorithm

The probability integral method (PIM) is the main method for mining subsidence prediction in China. Parameter errors and model errors are the main sources of error in the application of the probability integral method. There are many surface subsidence problems caused by coal mining. In order to improve the accuracy and operating efficiency of the genetic algorithm (GA) in calculating the parameters of the PIM, this paper proposes an improved genetic algorithm (IGA) by adding the dynamic crossover and mutation rate to the traditional GA. Made improvements to the shortcomings of random crossover and mutation rate of all individuals in the population in the original algorithm.Through simulation experiments, it is confirmed that the IGA improves the calculation efficiency and accuracy of the traditional GA under the same conditions.The IGA has higher accuracy, reliability, resistance to gross interference and resistance to missing observation points. This method is obviously superior to direct inversion and conventional optimization inversion algorithms, and effectively avoids the dependence on the initial value of the probabilistic integral method parameter

The Phenomenological Research on Higgs and dark matter in the Next-to-Minimal Supersymmetric Standard Model

The $Z_3$-invariant next-to-minimal supersymmetric standard model (NMSSM) can provide a candidate for dark matter (DM). It can also be used to explain the hypothesis that the Higgs signal observed on the Large Hadron Collider (LHC) comes from the contribution of the two lightest CP-even Higgs bosons, whose masses are near 125 GeV. At present, XENON1T, LUX, and PandaX experiments have imposed very strict restrictions on direct collision cross sections of {dark matter}. In this paper, we consider a scenario that the observed Higgs signal is the superposition of two mass-degenerate Higgs in the $Z_3$-invariant NMSSM and scan the seven-dimension parameter space composing of $\lambda, \kappa, \tan\beta, \mu, A_k, A_t, M_1$ via the Markov chain Monte Carlo (MCMC) method. We find that the DM relic density, as well as the LHC searches for sparticles, especially the DM direct detections, has provided a strong limit on the parameter space. %Please check intended meaning has been retained. The allowed parameter space is featured by a relatively small $\mu \le 300$ GeV and about $\tan\beta\in(10,20)$. In addition, the DM is Higgsino-dominated because of $|\frac{2\kappa}{\lambda}|>1$. Moreover, the co-annihilation between $\tilde{\chi}_1^0$ and $\tilde{\chi}_1^\pm$ must be taken into account to obtain the reasonable DM relic density

Six-month adherence to Statin use and subsequent risk of major adverse cardiovascular events (MACE) in patients discharged with acute coronary syndromes

Acknowledgements: The authors thank all participants who contributed to the study. Funding: CPACS-1 was funded by unrestricted educational grants from Guidant and Sanofi-Aventis, and grants from The Royal Australasian College of Physicians. AP is supported by an Australian National Heart Foundation Career Development Award. CPACS-2 was funded by an unrestricted grant from Sanofi-Aventis China. The George Institute for Global Health at Peking University Health Science Center sponsored the study and owns the data. Data analyses and reports were supported by Beijing Science and Technology Key Research Plan (D151100002215001). However, the authors are solely responsible for the design, analyses, the drafting and editing of the manuscript, and its final contents.Peer reviewedPublisher PD

Towards Effective Network Intrusion Detection: A Hybrid Model Integrating Gini Index and GBDT with PSO

In order to protect computing systems from malicious attacks, network intrusion detection systems have become an important part in the security infrastructure. Recently, hybrid models that integrating several machine learning techniques have captured more attention of researchers. In this paper, a novel hybrid model was proposed with the purpose of detecting network intrusion effectively. In the proposed model, Gini index is used to select the optimal subset of features, the gradient boosted decision tree (GBDT) algorithm is adopted to detect network attacks, and the particle swarm optimization (PSO) algorithm is utilized to optimize the parameters of GBDT. The performance of the proposed model is experimentally evaluated in terms of accuracy, detection rate, precision, F1-score, and false alarm rate using the NSL-KDD dataset. Experimental results show that the proposed model is superior to the compared methods

Epstein-barr virus-encoded microRNA-BART18-3p promotes colorectal cancer progression by targeting de novo lipogenesis

The Epstein-Barr virus (EBV) genome encodes a cluster of 22 viral microRNAs, called miR-BamHI-A rightward transcripts (miR-BARTs), which are shown to promote the development of cancer. Here, this study reports that EBV-miR-BART18-3p is highly expressed in colorectal cancer (CRC) and is closely associated with the pathological and advanced clinical stages of CRC. Ectopic expression of EBV-miR-BART18-3p leads to increased migration and invasion capacities of CRC cells in vitro and causes tumor metastasis in vivo. Mechanistically, EBV-miR-BART18-3p activates the hypoxia inducible factor 1 subunit alpha/lactate dehydrogenase A axis by targeting Sirtuin, which promotes lactate accumulation and acetyl-CoA production in CRC cells under hypoxic condition. Increased acetyl-CoA utilization subsequently leads to histone acetylation of fatty acid synthase and fatty acid synthase-dependent fat synthesis, which in turn drives de novo lipogenesis. The oncogenic role of EBV-miR-BART18-3p is confirmed in the patient-derived tumor xenograft mouse model. Altogether, the findings define a novel mechanism of EBV-miR-BART18-3p in CRC development through the lipogenesis pathway and provide a potential clinical intervention target for CRC

SAR2EO: A High-resolution Image Translation Framework with Denoising Enhancement

Synthetic Aperture Radar (SAR) to electro-optical (EO) image translation is a fundamental task in remote sensing that can enrich the dataset by fusing information from different sources. Recently, many methods have been proposed to tackle this task, but they are still difficult to complete the conversion from low-resolution images to high-resolution images. Thus, we propose a framework, SAR2EO, aiming at addressing this challenge. Firstly, to generate high-quality EO images, we adopt the coarse-to-fine generator, multi-scale discriminators, and improved adversarial loss in the pix2pixHD model to increase the synthesis quality. Secondly, we introduce a denoising module to remove the noise in SAR images, which helps to suppress the noise while preserving the structural information of the images. To validate the effectiveness of the proposed framework, we conduct experiments on the dataset of the Multi-modal Aerial View Imagery Challenge (MAVIC), which consists of large-scale SAR and EO image pairs. The experimental results demonstrate the superiority of our proposed framework, and we win the first place in the MAVIC held in CVPR PBVS 2023