Probing the symmetry energy with isospin ratio from nucleons to fragments

Within the framework of ImQMD05, we study several isospin sensitive observables, such as DR(n/p) ratios, isospin transport ratio (isospin diffusion), yield ratios for LCPs between the projectile region and mid-rapidity region for the reaction systems Ni+Ni, Zn+Zn, Sn+Sn at low-intermediate energies. Our results show that those observables are sensitive to the density dependence of symmetry energy, and also depend on the cluster formation mechanism. By comparing these calculations to the data, the information of the symmetry energy and reaction mechanism is obtained.Comment: Talk given by Yingxun Zhang at the 11th International Conference on Nucleus-Nucleus Collisions (NN2012), San Antonio, Texas, USA, May 27-June 1, 2012. To appear in the NN2012 Proceedings in Journal of Physics: Conference Series (JPCS

Cuproptosis/ferroptosis-related gene signature is correlated with immune infiltration and predict the prognosis for patients with breast cancer

Background: Breast invasive carcinoma (BRCA) is a malignant tumor with high morbidity and mortality, and the prognosis is still unsatisfactory. Both ferroptosis and cuproptosis are apoptosis-independent cell deaths caused by the imbalance of corresponding metal components in cells and can affect the proliferation rate of cancer cells. The aim in this study was to develop a prognostic model of cuproptosis/ferroptosis-related genes (CFRGs) to predict survival in BRCA patients.Methods: Transcriptomic and clinical data for breast cancer patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Cuproptosis and ferroptosis scores were determined for the BRCA samples from the TCGA cohort using Gene Set Variation Analysis (GSVA), followed by weighted gene coexpression network analysis (WGCNA) to screen out the CFRGs. The intersection of the differentially expressed genes grouped by high and low was determined using X-tile. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) were used in the TGCA cohort to identify the CFRG-related signature. In addition, the relationship between risk scores and immune infiltration levels was investigated using various algorithms, and model genes were analyzed in terms of single-cell sequencing. Finally, the expression of the signature genes was validated with quantitative real-time PCR (qRT‒PCR) and immunohistochemistry (IHC).Results: A total of 5 CFRGs (ANKRD52, HOXC10, KNOP1, SGPP1, TRIM45) were identified and were used to construct proportional hazards regression models. The high-risk groups in the training and validation sets had significantly worse survival rates. Tumor mutational burden (TMB) was positively correlated with the risk score. Conversely, Tumor Immune Dysfunction and Exclusion (TIDE) and tumor purity were inversely associated with risk scores. In addition, the infiltration degree of antitumor immune cells and the expression of immune checkpoints were lower in the high-risk group. In addition, risk scores and mTOR, Hif-1, ErbB, MAPK, PI3K/AKT, TGF-β and other pathway signals were correlated with progression.Conclusion: We can accurately predict the survival of patients through the constructed CFRG-related prognostic model. In addition, we can also predict patient immunotherapy and immune cell infiltration

Combining Multiple Algorithms for Road Network Tracking from Multiple Source Remotely Sensed Imagery: a Practical System and Performance Evaluation

In light of the increasing availability of commercial high-resolution imaging sensors, automatic interpretation tools are needed to extract road features. Currently, many approaches for road extraction are available, but it is acknowledged that there is no single method that would be successful in extracting all types of roads from any remotely sensed imagery. In this paper, a novel classification of roads is proposed, based on both the roads' geometrical, radiometric properties and the characteristics of the sensors. Subsequently, a general road tracking framework is proposed, and one or more suitable road trackers are designed or combined for each type of roads. Extensive experiments are performed to extract roads from aerial/satellite imagery, and the results show that a combination strategy can automatically extract more than 60% of the total roads from very high resolution imagery such as QuickBird and DMC images, with a time-saving of approximately 20%, and acceptable spatial accuracy. It is proven that a combination of multiple algorithms is more reliable, more efficient and more robust for extracting road networks from multiple-source remotely sensed imagery than the individual algorithms

Computation Of Flow In A Compressor Blade Row By A Third-Order Accurate High-Resolution Scheme

The three dimensional transonic flow field inside an isolated compressor blade row is calculated by using a third order accurate essentially nonoscillatory scheme (ENO3). The complexity of ENO3 scheme results in extra computational work, so the lower-upper symmetric Gauss-Seidel algorithm (LU-SGS) is adopted to improve the computational efficiency. NASA Lewis Rotor37 has been used as a test case. Detailed comparisons between calculation and measurement, including overall performance, shock wave system, and downstream wake, are carried out to show the resolving capability of this solver. 1 Introduction The flows in transonic axial compressor and fan rotor blade rows are very complex, which include three dimensional shocks, tip leakage flows, endwall and blade surface boundary layers and other complex secondary flows. Shock structure and shock interaction with boundary layers are the key factors that affect the performance and stability of rotors. Therefore, detailed numerical simulatio..

Customer Perceived Risk Measurement with NLP Method in Electric Vehicles Consumption Market: Empirical Study from China

In recent years, as people’s awareness of energy conservation, environmental protection, and sustainable development has increased, discussions related to electric vehicles (EVs) have aroused public debate on social media. At some point, most consumers face the possible risks of EVs—a critical psychological perception that invariably affects sales of EVs in the consumption market. This paper chooses to deconstruct customers’ perceived risk from third-party comment data in social media, which has better coverage and objectivity than questionnaire surveys. In order to analyze a large amount of unstructured text comment data, the natural language processing (NLP) method based on machine learning was applied in this paper. The measurement results show 15 abstracts in five consumer perceived risks to EVs. Among them, the largest number of comments is that of “Technology Maturity” (A13) which reached 25,329, and which belongs to the “Performance Risk” (PR1) dimension, indicating that customers are most concerned about the performance risk of EVs. Then, in the “Social Risk” (PR5) dimension, the abstract “Social Needs” (A51) received only 3224 comments and “Preference and Trust Rank” (A52) reached 22,324 comments; this noticeable gap indicated the changes in how consumers perceived EVs social risks. Moreover, each dimension’s emotion analysis results showed that negative emotions are more than 40%, exceeding neutral or positive emotions. Importantly, customers have the strongest negative emotions about the “Time Risk” (PR4), accounting for 54%. On a finer scale, the top three negative emotions are “Charging Time” (A42), “EV Charging Facilities” (A41), and “Maintenance of Value” (A33). Another interesting result is that “Social Needs” (A51)’s positive emotional comments were larger than negative emotional comments. The paper provides substantial evidence for perceived risk theory research by new data and methods. It can provide a novel tool for multi-dimensional and fine-granular capture customers’ perceived risks and negative emotions. Thus, it has the potential to help government and enterprises to adjust promotional strategies in a timely manner to reduce higher perceived risks and emotions, accelerating the sustainable development of EVs’ consumption market in China

Extended Yearly LMDI Approaches: A Case Study of Energy Consumption

Although the logarithmic mean Divisia index (LMDI) approach has been widely used in the field of energy and environmental research, it has a shortcoming. Since the LMDI approach only focuses on the base year and reporting year, in situations in which the research period is long, the annual changes during the research period may be difficult to capture. In particular, if there were huge fluctuations in the indicators (such as the energy consumption and carbon emissions) or their drivers during the middle of a research period, a substantial amount of information about the fluctuations will be ignored. Therefore, we propose four extended yearly LMDI approaches, including pure LMDI, weighted LMDI, comprehensive LMDI, and scenario LMDI approaches to better capture fluctuations and compensate for the original LMDI approach’s shortcomings. Additionally, we found that there are mathematical relationships among the four extended LMDI approaches. We further compare these four approaches’ advantages, disadvantages, and applicable situations and analyze a case study on China’s energy consumption based on the four proposed approaches

Accumulation conditions and exploration potential of deep natural gas in the Qaidam Basin

This paper examines the enrichment conditions of deep natural gas reservoirs in the Qaidam Basin and delineates the exploration potential utilizing seismic, geological, geochemical, well-logging, and drilling data. The findings indicate the presence of two high-quality gas source formations, namely the Jurassic and Paleogene formations, along the northern margin and the western part of the basin, respectively. The formations both exhibit advanced evolution and robust gas-generating capacity. The deep layers along the northern margin consist of bedrocks and Paleogene clastic reservoirs, while the western deep layers feature Paleogene lacustrine carbonate reservoirs. The reservoirs west of Qaidam Basin are widely distributed on the plane and vertically form multiple reservoir cap combinations. The primary pores, dissolution pores, fractures, and other pore types developed in the reservoirs are considered as the storage space for deep gas accumulation. The continuous active deep faults serve as high-quality channels for deep gas sources; furthermore, the formation of deep structures is well-matched with natural gas generation. The deep hydrocarbon source rocks in the western Qaidam Basin are characterized by early and continuous hydrocarbon generation. Early-generated liquid hydrocarbons undergo high-temperature cracking into gas during later burial, resulting in a robust gas-generating capacity and significant potential for deep resources. The widely developed salt rocks, argillaceous rock, and abnormally high-pressure layers in the deep Qaidam Basin contribute to preserving deep natural gas. In conclusion, it is believed that deep gas reservoirs in the Qaidam Basin are enriched in the traps around hydrocarbon-generating sags with developed faults. Key favorable areas for deep-seated natural gas exploration include the basement rocks of the ancient piedmont uplift in the northern margin of Qaidam, the Paleogene clastic rocks in the central structural belt, and the carbonate rocks along the Yingxiongling structural belt in the western part of the basin

Home-based microbial solution to boost crop growth in low-fertility soil

14 páginas.- 6 figuras.- 66 referencias.- Additional Supporting Information may be found online in the Supporting Information section at the end of the article.Soil microbial inoculants are expected to boost crop productivity under climate change and soil degradation. However, the efficiency of native vs commercialized microbial inoculants in soils with different fertility and impacts on resident microbial communities remain unclear.We investigated the differential plant growth responses to native synthetic microbial community (SynCom) and commercial plant growth-promoting rhizobacteria (PGPR). We quantified the microbial colonization and dynamic of niche structure to emphasize the home-field advantages for native microbial inoculants.A native SynCom of 21 bacterial strains, originating from three typical agricultural soils, conferred a special advantage in promoting maize growth under low-fertility conditions. The root : shoot ratio of fresh weight increased by 78-121% with SynCom but only 23-86% with PGPRs. This phenotype correlated with the potential robust colonization of SynCom and positive interactions with the resident community. Niche breadth analysis revealed that SynCom inoculation induced a neutral disturbance to the niche structure. However, even PGPRs failed to colonize the natural soil, they decreased niche breadth and increased niche overlap by 59.2-62.4%, exacerbating competition.These results suggest that the home-field advantage of native microbes may serve as a basis for engineering crop microbiomes to support food production in widely distributed poor soils.The authors are grateful to the editor and anonymous referees.YL is supported by National Key R&D Program of China(2021YFD1900400), Strategic Priority Research Program of the Chinese Academy of Sciences (XDA24020104), Innovation Program of Institute of Soil Science (ISSASIP2201), National Natural Science Foundation of China (41877060), and Youth Innovation Promotion Association of Chinese Academy of Sciences (2016284). D-BM is supported by the Spanish Ministryof Science and Innovation (PID2020-115813RA-I00) and a Project PAIDI 2020 from the Junta de Andalucıa (P20_00879)Peer reviewe