Searching for Ξcc+\Xi_{cc}^+ in Relativistic Heavy Ion Collisions

We study the doubly charmed baryon $\Xi_{cc}^+$ in high energy nuclear collisions. We solve the three-body Schroedinger equation with relativistic correction and calculate the $\Xi_{cc}^+$ yield and transverse momentum distribution via coalescence mechanism. For $\Xi_{cc}^+$ production in central Pb+Pb collisions at LHC energy, the yield is extremely enhanced, and the production cross section per binary collision is one order of magnitude larger than that in p+p collisions. This indicates that, it is most probable to discover $\Xi_{cc}^+$ in heavy ion collisions and its discovery can be considered as a probe of the quark-luon plasma formation.Comment: 5 pages and 4 figure

Examining the Supply Chain Management Models for Agricultural Products Under the Context of E-Commerce

Agricultural products market changes constantly along with the thriving of e-commerce and agricultural products e-commerce keeps growing as an innovative industry; however, there are still many loopholes in the management of the supply chain from beginning to end. In order to effectively address these issues, this paper utilizes the dynamic requirement forecasting method based on SVM (support vector machine) to identify and fit the secular trend in and potential cyclical fluctuation factors for the market requirements for agricultural products. The supply chain coordination decision center is established by integrating the collaborative supply management component and other components. XML technology and CORBA technology are adopted to construct the integrated management model of agricultural products supply chain in e-commerce environment. For its relatively high management level, the model established can promote both agricultural consumption and agricultural economic output, strengthen the competitiveness of enterprises in agricultural products market and realize maximization of profit targets

Extending the unified subhalo model to warm dark matter

Using a set of high-resolution N-body simulations, we extend the unified distribution model of cold dark matter (CDM) subhaloes to the warm dark matter(WDM) case. The same model framework combining the unevolved mass function, unevolved radial distribution, and tidal stripping can predict the mass function and spatial distribution of subhaloes in both CDM and WDM simulations. The dependence of the model on the DM particle property is universally parameterized through the half-mode mass of the initial power spectrum. Compared with the CDM model, the WDM model differs most notably in two aspects. 1) In contrast to the power-law form in CDM, the unevolved subhalo mass function for WDM is scale-dependent at the low mass end due to the cut-off in the initial power spectrum. 2) WDM subhaloes are more vulnerable to tidal stripping and disruption due to their lower concentrations at accretion time. Their survival rate is also found to depend on the infall mass. Accounting for these differences, the model predicts a final WDM subhalo mass function that is also proportional to the unevolved subhalo mass function. The radial distribution of WDM subhaloes is predicted to be mass-dependent. For low mass subhaloes, the radial distribution is flatter in the inner halo and steeper in the outer halo compared to the CDM counterpart, due to the scale-dependent unevolved mass function and the enhanced tidal stripping. The code for sampling subhaloes according to our generalized model is available at https://github.com/fhtouma/subgen2 .Comment: 15 pages, 14 figure

LS-DTKMS: A Local Search Algorithm for Diversified Top-k MaxSAT Problem

The Maximum Satisfiability (MaxSAT), an important optimization problem, has a range of applications, including network routing, planning and scheduling, and combinatorial auctions. Among these applications, one usually benefits from having not just one single solution, but k diverse solutions. Motivated by this, we study an extension of MaxSAT, named Diversified Top-k MaxSAT (DTKMS) problem, which is to find k feasible assignments of a given formula such that each assignment satisfies all hard clauses and all of them together satisfy the maximum number of soft clauses. This paper presents a local search algorithm, LS-DTKMS, for DTKMS problem, which exploits novel scoring functions to select variables and assignments. Experiments demonstrate that LS-DTKMS outperforms the top-k MaxSAT based DTKMS solvers and state-of-the-art solvers for diversified top-k clique problem

What do we know about multidimensional poverty in China: its dynamics, causes, and implications for sustainability

Poverty is a primary obstacle to achieving sustainable development. Therefore, exploring the spatiotemporal dynamics and causes of poverty is of great significance to the sustainable poverty reduction of the “post poverty alleviation era” in China. This paper used the multisource big data of 2022 counties in China from 2000 to 2015 to establish a comprehensive evaluation framework to explore the multidimensional poverty situation in China. The results showed the following findings: There is an obvious spatiotemporal heterogeneity of multidimensional poverty, showing a typical stair-like gradient from high in the west to low in the east, with the poverty level in state-designated poverty counties higher and intensifying over time. The spatial differentiation of multidimensional poverty is contributed to by multiple factors, in which the geographical condition has a stronger impact on state-designated poverty counties, while natural endowment and human resources have a stronger effect on non-state-designated poverty counties. These things considered, the regional poverty causes were relatively stable before 2015, but the poverty spatial agglomeration of some regions in the Northwest, Northeast, and Yangtze River Economic Belt has undergone significant changes after 2015. These findings can help policymakers better target plans to eliminate various types of poverty in different regions

Prospects, obstacles and solutions of biomass power industry in China

Abstract(#br)Biomass power is one of the most important renewable energy sources in China. In order to provide a reference for China’s biomass power planning, this paper builds a power sector-planning model using the Long-range Energy Alternatives Planning System (LEAP). The results show that in the base scenario, the installed capacity of agricultural and forestry residues, municipal solid waste and biogas will increase to 22350 MW, 21150 MW, and 4900 MW, respectively by 2030. From the point of view of total volume, biomass supply is not a constraining factor for biomass power source. However, there are some social and economic factors that impede the development of the biomass power industry, some of which may not be addressed in the short term. Therefore, the development of the biomass power industry in China is a long-term process. Some policy suggestions were proposed, including reasonable planning and more subsidies for biomass supply value chain

Ensemble multiboost based on ripper classifier for prediction of imbalanced software defect data

Identifying defective software entities is essential to ensure software quality during software development. However, the high dimensionality and class distribution imbalance of software defect data seriously affect software defect prediction performance. In order to solve this problem, this paper proposes an Ensemble MultiBoost based on RIPPER classifier for prediction of imbalanced Software Defect data, called EMR_SD. Firstly, the algorithm uses principal component analysis (PCA) method to find out the most effective features from the original features of the data set, so as to achieve the purpose of dimensionality reduction and redundancy removal. Furthermore, the combined sampling method of adaptive synthetic sampling (ADASYN) and random sampling without replacement is performed to solve the problem of data class imbalance. This classifier establishes association rules based on attributes and classes, using MultiBoost to reduce deviation and variance, so as to achieve the purpose of reducing classification error. The proposed prediction model is evaluated experimentally on the NASA MDP public datasets and compared with existing similar algorithms. The results show that EMR-SD algorithm is superior to DNC, CEL and other defect prediction techniques in most evaluation indicators, which proves the effectiveness of the algorithm

Multi-index fuzzy comprehensive evaluation model with information entropy of alfalfa salt tolerance based on LiDAR data and hyperspectral image data

Rapid, non-destructive and automated salt tolerance evaluation is particularly important for screening salt-tolerant germplasm of alfalfa. Traditional evaluation of salt tolerance is mostly based on phenotypic traits obtained by some broken ways, which is time-consuming and difficult to meet the needs of large-scale breeding screening. Therefore, this paper proposed a non-contact and non-destructive multi-index fuzzy comprehensive evaluation model for evaluating the salt tolerance of alfalfa from Light Detection and Ranging data (LiDAR) and HyperSpectral Image data (HSI). Firstly, the structural traits related to growth status were extracted from the LiDAR data of alfalfa, and the spectral traits representing the physical and chemical characteristics were extracted from HSI data. In this paper, these phenotypic traits obtained automatically by computation were called Computing Phenotypic Traits (CPT). Subsequently, the multi-index fuzzy evaluation system of alfalfa salt tolerance was constructed by CPT, and according to the fuzzy mathematics theory, a multi-index Fuzzy Comprehensive Evaluation model with information Entropy of alfalfa salt tolerance (FCE-E) was proposed, which comprehensively evaluated the salt tolerance of alfalfa from the aspects of growth structure, physiology and biochemistry. Finally, comparative experiments showed that: (1) The multi-index FCE-E model based on the CPT was proposed in this paper, which could find more salt-sensitive information than the evaluation method based on the measured Typical Phenotypic Traits (TPT) such as fresh weight, dry weight, water content and chlorophyll. The two evaluation results had 66.67% consistent results, indicating that the multi-index FCE-E model integrates more information about alfalfa and more comprehensive evaluation. (2) On the basis of the CPT, the results of the multi-index FCE-E method were basically consistent with those of Principal Component Analysis (PCA), indicating that the multi-index FCE-E model could accurately evaluate the salt tolerance of alfalfa. Three highly salt-tolerant alfalfa varieties and two highly salt-susceptible alfalfa varieties were screened by the multi-index FCE-E method. The multi-index FCE-E method provides a new method for non-contact non-destructive evaluation of salt tolerance of alfalfa