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

Vegetation greenness and photosynthetic phenology in response to climatic determinants

Vegetation phenology is a key indicator of vegetation-climate interactions and carbon sink changes in ecosystems. Therefore, it is very important to understand the temporal and spatial variability of vegetation phenology and the driving climatic determinants [e.g., temperature (Ts) and soil moisture (SM)]. Vegetation greenness and photosynthetic phenology were derived using the double logistic (DL) method to enhance vegetation index (EVI) and solar-induced chlorophyll fluorescence (SIF) spring and autumn phenology, respectively. The growing season length (GSL) of greenness phenology (about 100 days) derived EVI was longer than GSL of photosynthetic phenology (about 80 days) derived SIF. Although their overall spatiotemporal pattern trends were consistent, photosynthetic phenology varied 1.4 to 3.1 times more than greenness phenology over time. In addition, SIF-based photosynthetic phenology and EVI-based greenness phenology showed consistent factors of drivers but differed to some extent in spatial patterns and the most relevant preseason dates. Spring photosynthetic phenology was mainly influenced by pre-season mean cumulative Ts (about 90 days). However, greenness phenology was controlled by both pre-seasons mean cumulative Ts [(about 55 days) and mean cumulative SM (about 40 days)]. Autumn photosynthetic phenology was controlled by both periods’ mean cumulative Ts [(about 20 days) and SM (about 20 days)], but autumn greenness phenology was mainly influenced by pre-season mean cumulative Ts (85 days). The comparison analysis of SIF and EVI phenology helps to understand the difference between photosynthetic phenology and greenness phenology at a regional scale

Enhancing monolayer photoluminescence on optical micro/nanofibers for low-threshold lasing.

Although monolayer transition metal dichalcogenides (TMDs) have direct bandgaps, the low room-temperature photoluminescence quantum yields (QYs), especially under high pump intensity, limit their practical applications. Here, we use a simple photoactivation method to enhance the room-temperature QYs of monolayer MoS2 grown on to silica micro/nanofibers by more than two orders of magnitude in a wide pump dynamic range. The high-density oxygen dangling bonds released from the tapered micro/nanofiber surface are the key to this strong enhancement of QYs. As the pump intensity increases from 10-1 to 104 W cm-2, our photoactivated monolayer MoS2 exhibits QYs from ~30 to 1% while maintaining high environmental stability, allowing direct lasing with greatly reduced thresholds down to 5 W cm-2. Our strategy can be extended to other TMDs and offers a solution to the most challenging problem toward the realization of efficient and stable light emitters at room temperature based on these atomically thin materials

Research on unbalanced mining of highway project key data based on knowledge graph and cloud model

Various stages of highway project construction process involve text, image, audio, video and other related data sources involving many participants, forming a huge amount of data. Accurately tracing the source of responsibility, refining and applying the unbalanced data in the highway project archives is of great significance for realizing the intelligent transformation of highway construction project management. This paper firstly sorts out the construction process of highway projects and the main data sources, constructs a data association network between construction entities and construction process, as well as a knowledge map of highway construction data. Then, according to the highway construction stage, an index system based on 12 key data is constructed by using the entropy weight cloud model method, and the importance of the data is evaluated. Thirdly, based on the unbalanced characteristics of highway project data, a method of mining big data in highway project archives using classification evaluation indexes is proposed, and the accuracy of this method is verified by case calculation. Finally, taking the Shizong Qiubei Expressway in China as an example, the intelligent management and control suggestions for key data of transportation projects are proposed. It is found that the key data with special importance rate in highway construction include construction data, supervision data and completion data. Boosting algorithm is more accurate than the traditional SMOTE algorithm for unbalanced data mining, which helps to save the project construction cost and improve the quality of data extraction in the project archives. This study provides a theoretical reference for key data traceability of highway project intelligent management and control platform and the improvement of intelligent management efficiency

The relationship between inflammatory cytokines and in‐hospital complications of acute pancreatitis

Abstract Objective Acute necrotic collection (ANC), acute peripancreatic fluid collection (APFC), pleural effusion, and ascites are common early complications of acute pancreatitis. This study aimed to investigate the relationship between 12 serum cytokines and the early complications and severity of acute pancreatitis (AP). Methods We retrospectively analyzed the clinical data of 307 patients with AP, and divided them into severe group and mild‐to‐moderate group according to the revised Atlanta classification. Propensity score matching was used to control for confounding factors. Binary logistic regression analysis was used to explore the relationship between cytokine levels and early complications of AP. Results Serum levels of interleukin (IL)‐1β, IL‐5, IL‐6, IL‐8, IL‐10, IL‐17, and tumor necrosis factor‐α were significantly higher in the severe acute pancreatitis (SAP) group than in the non‐SAP group (p < .05). After adjusting for confounding factors, the upper quartiles of IL‐6, IL‐8, and IL‐10 were associated with an increased risk of ANC compared with those in the lowest quartile (IL‐6: quartile 3, odds ratio [OR] = 3.99, 95% confidence interval [CI] = 1.95–8.16; IL‐8: quartile 4, OR = 2.47, 95% CI = 1.27–4.84; IL‐10: quartile 2, OR = 2.22, 95% CI = 1.09–4.56). APFC was associated with high serum levels of IL‐6 (quartile 3, OR = 1.32, 95% CI = 1.02–1.72), pleural effusions were associated with high serum levels of IL‐1β, IL‐6, IL‐8, and IL‐10 (IL‐1β: quartile 4, OR = 2.36, 95% CI = 1.21–4.58; IL‐6: quartile 3, OR = 4.67, 95% CI = 2.27–9.61; IL‐8: quartile 3, OR = 2.95, 95% CI = 1.51–5.79; IL‐10: quartile 4, OR = 3.20, 95% CI = 1.61–6.36), and high serum levels of IL‐6 and IL‐10 were associated with an increased risk of ascites (IL‐6: quartile 3, OR = 3.01, 95% CI = 1.42–6.37; IL‐10: quartile 3, OR = 2.57, 95% CI = 1.23–5.37). Conclusion Serum cytokine levels, including IL‐1β, IL‐6, IL‐8, and IL‐10 may be associated with the occurrence of early complications of AP. In daily clinical practice, IL‐6 may be the most worthwhile cytokine to be detected

Incentive strategy of safe and intelligent production in assembled steel plants from the perspective of evolutionary game

Abstract Due to the numerous cross-operations and poor information communication, it is easy to cause production safety accidents in traditional assembled steel plants. The transformation and upgrading of smart production in the assembly steel plants is helpful to improve the efficiency of safety management. In order to effectively reduce the safety risks in the production of assembled steel components, this paper integrates policy incentives and safety supervision, constructs an evolutionary game model between the government and assembled steel producers, and analyzes the strategic evolution rules and stability conditions of stakeholders through the replication dynamics equation. Moreover, based on the empirical simulation of the Fuzhou X Steel Structure Plant project, the effectiveness of the evolutionary model incentive strategy setting is verified. The results show that whether an assembled steel plants adopt a smart management strategy or not is influenced by the government's incentive subsidy mechanism, penalty mechanism, the benefits and costs generated by traditional/ smart management, the probability and loss of safety accidents and other factors. The conclusion is important for upgrading the safety management mode, improving the safety production efficiency and constructing the safety supervision system of the assembled steel smart plant

Hepatitis E virus seroprevalence among blood donors in Jiangsu Province, East China

Objective: Hepatitis E virus (HEV) infection is responsible for over 50% of acute viral hepatitis cases, and the blood transfusion route has emerged as a possible means of sporadic HEV infection. The aim of this study was to determine the seroprevalence of HEV among blood donors in East China. Methods: Blood samples were collected consecutively between January and June 2011 from 486 blood donors living in Jiangsu Province, East China. Anti-HEV IgG was tested by ELISA. Results: One hundred and thirteen blood donors developed HEV IgG antibody, indicating the prevalence of HEV IgG seropositivity to be 23.3%. HEV IgG seropositivity was 25.3% (90/356) in the male group, significantly higher than that in the female group (17.7%, 23/130) (p < 0.05). The donors who had donated more than 10 times had significantly higher HEV IgG seropositivity than the other groups (p < 0.05). Furthermore, donors aged 50–55 years had significantly higher HEV IgG seropositivity than the other age groups (p < 0.05). Conclusions: We investigated HEV seroprevalence among blood donors in East China. Our data will help identify the risk factors for HEV infection and provide guidance on controlling the safety of blood transfusions in the clinical setting

Texture features-based lightweight passive multi-state crowd counting algorithm

Abstract Passive crowd counting using channel state information (CSI) is a promising technology for applications in fields such as smart cities and commerce. However, the most existing algorithms can only recognize the total number of people in the monitoring area and cannot simultaneously recognize the number and states of people and ignore the real-time performance of the algorithms. Therefore, they cannot be applied to the scenarios of multi-state crowd counting requiring high real-time performance. To address this issue, a lightweight passive multi-state crowd counting algorithm called TF-LPMCC is proposed. This algorithm constructs CSI amplitude data into amplitude and time–frequency images, extracts texture features using the gray-level co-occurrence matrix (GLCM) and gray-level difference statistic (GLDS) methods, and uses the linear discriminant analysis (LDA) algorithm to count the crowd in multi-states. Experiments show that the TF-LPMCC algorithm not only has low time complexity but also achieves an average recognition accuracy of 98.27% for crowd counting