Relativistic nucleon optical potentials with isospin dependence in Dirac Brueckner Hartree-Fock approach

The relativistic optical model potential (OMP) for nucleon-nucleus scattering is investigated in the framework of Dirac-Brueckner-Hartree-Fock (DBHF) approach using the Bonn-B One-Boson- Exchange potential for the bare nucleon-nucleon interaction. Both real and imaginary parts of isospin-dependent nucleon self-energies in nuclear medium are derived from the DBHF approach based on the projection techniques within the subtracted T -matrix representation. The Dirac potentials as well as the corresponding Schrodinger equivalent potentials are evaluated. An improved local density approximation is employed in this analysis, where a range parameter is included to account for a finite-range correction of the nucleon-nucleon interaction. As an example the total cross sections, differential elastic scattering cross sections, analyzing powers for n, p + 27Al at incident energy 100 keV < E < 250 MeV are calculated. The results derived from this microscopic approach of the OMP are compared to the experimental data, as well as the results obtained with a phenomenological OMP. A good agreement between the theoretical results and the measurements can be achieved for all incident energies using a constant value for the range parameter.Comment: 10 pages, 16 figure

Identification of new antibacterial targets in RNA polymerase of Mycobacterium tuberculosis by detecting positive selection sites

Bacterial RNA polymerase (RNAP) is an effective target for antibacterial treatment. In order to search new potential targets in RNAP of Mycobacterium, we detected adaptive selections of RNAP related genes in 13 strains of Mycobacterium by phylogenetic analysis. We first collected sequences of 17 genes including rpoA, rpoB, rpoC, rpoZ, and sigma factor A-M. Then maximum likelihood trees were constructed, followed by positive selection detection. We found that sigG shows positive selection along the clade (M. tuberculosis, M. bovis), suggesting its important evolutionary role and its potential to be a new antibacterial target. Moreover, the regions near 933Cys and 935His on the rpoB subunit of M. tuberculosis showed significant positive selection, which could also be a new attractive target for anti-tuberculosis drugs

Research on a price prediction model for a multi-layer spot electricity market based on an intelligent learning algorithm

With the continuous promotion of the unified electricity spot market in the southern region, the formation mechanism of spot market price and its forecast will become one of the core elements for the healthy development of the market. Effective spot market price prediction, on one hand, can respond to the spot power market supply and demand relationship; on the other hand, market players can develop reasonable trading strategies based on the results of the power market price prediction. The methods adopted in this paper include: Analyzing the principle and mechanism of spot market price formation. Identifying relevant factors for electricity price prediction in the spot market. Utilizing a clustering model and Spearman’s correlation to classify diverse information on electricity prices and extracting data that aligns with the demand for electricity price prediction. Leveraging complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to disassemble the electricity price curve, forming a multilevel electricity price sequence. Using an XGT model to match information across different levels of the electricity price sequence. Employing the ocean trapping algorithm-optimized Bidirectional Long Short-Term Memory (MPA-CNN-BiLSTM) to forecast spot market electricity prices. Through a comparative analysis of different models, this study validates the effectiveness of the proposed MPA-CNN-BiLSTM model. The model provides valuable insights for market players, aiding in the formulation of reasonable strategies based on the market's supply and demand dynamics. The findings underscore the importance of accurate spot market price prediction in navigating the complexities of the electricity market. This research contributes to the discourse on intelligent forecasting models in electricity markets, supporting the sustainable development of the unified spot market in the southern region

Hematite concave nanocubes and their superior catalytic activity for low temperature CO oxidation

Hematite (α-Fe2O3) concave nanocubes bound by high-index {1344} and {1238} facets were synthesized and their catalytic activity for CO oxidation were also investigated. ? 2014 the Partner Organisations

Microorganism-mediated synthesis of chemically difficult-to-synthesize Au nanohorns with excellent optical properties in the presence of hexadecyltrimethylammonium chloride

Fundamental Research Funds for Central Universities [2010121051]; NSFC [21106117, 21036004]Closely packed, size-controllable and stable Au nanohorns (AuNHs) that are difficult to synthesize through pure chemical reduction are facilely synthesized using a microorganism-mediated method in the presence of hexadecyltrimethylammonium chloride (CTAC). The results showed that the size of the as-synthesized AuNHs could be tuned by adjusting the dosage of the Pichia pastoris cells (PPCs). The initial concentrations of CTAC, ascorbic acid (AA) and tetrachloroaurate trihydrate (HAuCl4 center dot 3H(2)O) significantly affected the formation of the AuNHs. Increasing the diameters of AuNHs led to a red shift of the absorbance bands around 700 nm in their UV-vis-NIR spectra. Interestingly, the AuNH/PPC composites exhibited excellent Raman enhancement such that rhodamine 6G with concentration as low as (10(-9) M) could be effectively detected. The formation process of the AuNHs involved the initial binding of the Au ions onto the PPCs with subsequent reduction by AA to form supported Au nanoparticles (AuNPs) based on preferential nucleation and initial anisotropic growth on the platform of the PPCs. The anisotropic growth of these AuNPs, which was influenced by CTAC and PPCs, resulted in the formation of growing AuNHs, while the secondary nucleation beyond the PPCs produced small AuNPs that were subsequently consumed through Ostwald ripening during the aging of the AuNHs. This work exemplifies the fabrication of novel gold nanostructures and stable bio-Au nanocomposites with excellent optical properties by combining microorganisms and a surfactant

Experimental Study of Cement Alkali-Resistant Glass Fiber (C-ARGF) Grouting Material

Mixing alkali-resistant glass fiber (ARGF) into grouting slurry can prevent the development of cracks; thus, understanding the properties of ARGF grouting material is important for applications in engineering. Two types of ARGFs (Cem-FIL&reg;60 and Anti-Crak&reg;HD) were selected as mixing materials, and their performance was tested in four areas, namely, compressive strength, tensile strength, flexural strength, and impervious performance, under four different mixing amounts of fiber (0%, 0.25%, 0.5%, and 1.0%). Results demonstrate that the addition of ARGF increased the compressive strength and tensile strength of the grouting slurry, and the best performance was at 0.5%. The effect on the flexural strength and impervious performance was related to the mixing amount, and the fiber may have induced a counter-effect for certain amounts of added ARGF. Mixing ARGF could increase the early strength ratio of grout; however, a high early strength ratio did not necessarily result in high strength, as the flexural strength did not change synchronously with the early strength ratio; a similar pattern was found for the impermeability. Cem-FIL&reg;60 had a better effect on the properties of grouting materials than Anti-Crak&reg;HD. These results were successfully applied in the water-plugging and reinforcement engineering of a karst tunnel

Incorporating Sentiment Analysis for Improved Tag-Based Recommendation

Social tagging systems have become as a popular application with the advance of Web 2.0 technologies. By tagging, users annotate and index the resources freely and subjectively, based on their senses of interests, which can improve the performance of the current personalized recommendation systems. In this paper, we propose a sentiment enhanced tag-based recommendation approach by incorporating sentiment analysis of tags that annotated on resources. The presented approach introduces a sentiment enhancement factor to the similarity metric which measures the matching between resources. The evaluation results on a real datasets have demonstrated that our approach can outperform the other compared approaches in terms of recommendation precision

The nucleon microscopic optical potential based on the Skyrme interaction

The nucleon microscopic optical potential based on the conventional and extended Skyrme interactions are achieved by the single-particle Green function method through nuclear matter approximation and local density approximation. The nucleon-nucleon scattering observables are calculated by the obtained microscopic optical potential and the results are compared with the corresponding experimental data. Good agreement is generally obtained between them

The nucleon microscopic optical potential based on the Skyrme interaction

The nucleon microscopic optical potential based on the conventional and extended Skyrme interactions are achieved by the single-particle Green function method through nuclear matter approximation and local density approximation. The nucleon-nucleon scattering observables are calculated by the obtained microscopic optical potential and the results are compared with the corresponding experimental data. Good agreement is generally obtained between them