CO2 Emission Reduction Potential in China's Electricity Sector: Scenario Analysis Based on LMDI Decomposition

AbstractThe CO2 emission reduction from China's electricity sector will matter not only for China but impact the result of the global action on climate change. This paper firstly analyzed the main factors that affect the CO2 emission in accordance with the LMDI decomposition model. Then three scenarios were assumed based on the main factors to explore the CO2 reduction potential. Furthermore, LMDI method was used again to measure the contribution of each factor to CO2 emission reduction potential in the future. The results showed that the CO2 emission will continue to grow in the three scenarios from 2010 to 2020, with an annual growth rate of 10.7%, 6.5% and 4.5%, respectively. The active low carbon policies taken on the driving factors will contribute to 2701Mt - 3688Mt CO2 emission reduction. The share of low-carbon power generation and thermal power generation efficiency are most important factors for emission reduction. However, in the long run, low-carbon power generation will contribute more. Terminal electricity consumption is always the most important factor driving CO2 emission up. Finally, policies for low-carbon development of China's electricity sector are proposed based on the analysis results

First attempt of directionality reconstruction for atmospheric neutrinos in a large homogeneous liquid scintillator detector

The directionality information of incoming neutrinos is essential to atmospheric neutrino oscillation analysis since it is directly related to the oscillation baseline length. Large homogeneous liquid scintillator detectors, while offering excellent energy resolution, are traditionally very limited in their capabilities of measuring event directionality. In this paper, we present a novel directionality reconstruction method for atmospheric neutrino events in large homogeneous liquid scintillator detectors based on waveform analysis and machine learning techniques. We demonstrate for the first time that such detectors can achieve good direction resolution and potentially play an important role in future atmospheric neutrino oscillation measurements.Comment: Prepared for submission to PR

A multi-purpose reconstruction method based on machine learning for atmospheric neutrinos at JUNO

The Jiangmen Underground Neutrino Observatory (JUNO) experiment is designed to measure the neutrino mass ordering (NMO) using a 20-kton liquid scintillator (LS) detector. Besides the precise measurement of the reactor neutrino’s oscillation spectrum, an atmospheric neutrino oscillation measurement in JUNO offers independent sensitivity for NMO, which can potentially increase JUNO’s total sensitivity in a joint analysis. In this contribution, we present a novel multi-purpose reconstruction method for atmospheric neutrinos in JUNO at few-GeV based on a machine learning technique. This method extracts features related to event topology from PMT waveforms and uses them as inputs to machine learning models. A preliminary study based on the JUNO simulation shows good performances for event directionality reconstruction and neutrino flavor identification. This method also has a great application potential for similar LS detectors

Association between thyroid function and diabetes peripheral neuropathy in euthyroid type 2 diabetes mellitus patients

Abstract Previous studies disclosed that a high thyroid stimulating hormone level is an independent risk factor for diabetes peripheral neuropathy (DPN) in subclinical hypothyroidism (SCH) patients with type 2 diabetes mellitus (T2DM). However, whether thyroid metabolism has an effect on DPN in euthyroid T2DM patients remains unknown. The aim of this study was to identify the association between thyroid function and DPN in euthyroid T2DM patients. A set of 580 euthyroid T2DM patients was enrolled in the current study and stratified into DPN and Non-DPN groups. Mann–Whitney U test was performed to analyze the continuous variables of biochemical and thyroid metabolism indicators, and the Chi-square test was used to compare the categorical variables. Spearman correlation analysis was performed to analyze the relationship between clinical indicators and free thyroxine (FT4). By using the logistic regression analysis, the prevalence of DPN in different thyroid function indicators were evaluated. T2DM patients with DPN had obviously lower levels of aspartate aminotransferase (AST), alpha-hydroxybutyric dehydrogenase (α-HBDH), superoxide dismutase (SOD), calcium (Ca), creatinine (Cr), uric acid (UA), retinol binding protein (RBP), total protein (TP), albumin (ALB), alanine aminotransferase (ALT) and FT4 than the T2DM patients without DPN (P < 0.05). FT4 was associated with TP, prealbumin (PA), ALB, SOD, anion gap (AG), Ca, chlorine (Cl), UA, RBP, apoprotein A (Apo A), apoprotein B (Apo B), apoprotein E (Apo E), and total cholesterol (TC). According to the FT4 quartile, participants were sequentially divided into four groups to compare the prevalence of DPN between each group. The data suggested that the prevalence of DPN in these four groups was 53.79%, 53.28%, 54.97%, 38.10%, respectively. Moreover, compared with quartile 4, patients in quartile 1, 2, 3 all had a significantly higher risk of DPN (P = 0.007, P = 0.011, P = 0.004). The level of FT4 was negatively correlated with the prevalence of DPN in euthyroid T2DM patients

Exploration of Singular Spectrum Analysis for Online Anomaly Detection in CRNs

Cognitive radio networks (CRNs) have been recognized as a promising technology that allows secondary users (SUs) extensively explore spectrum resource usage efficiency, while not introducing interference to licensed users. Due to the unregulated wireless network environment, CRNs are susceptible to various malicious entities. Thus, it is critical to detect anomalies in the first place. However, from the perspective of intrinsic features of CRNs, there is hardly in existence of an universal applicable anomaly detection scheme. Singular Spectrum Analysis (SSA) has been theoretically proven an optimal approach for accurate and quick detection of changes in the characteristics of a running (random) process. In addition, SSA is a model-free method and no parametric models have to be assumed for different types of anomalies, which makes it a universal anomaly detection scheme. In this paper, we introduce an adaptive parameter and component selection mechanism based on coherence for basic SSA method, upon which we built up a sliding window online anomaly detector in CRNs. Our experimental results indicate great accuracy of the SSA-based anomaly detector for multiple anomalies

26SCS-Loaded SilMA/Col Composite Sponge with Well-Arranged Layers Promotes Angiogenesis-Based Diabetic Wound Repair by Mediating Macrophage Inflammatory Response

Diabetic wound healing is a significant clinical challenge because abnormal immune cells in the wound cause chronic inflammation and impair tissue regeneration. Therefore, regulating the behavior and function of macrophages may be conducive to improving treatment outcomes in diabetic wounds. Herein, sulfated chitosan (26SCS)-containing composite sponges (26SCS-SilMA/Col-330) with well-arranged layers and high porosity were constructed based on collagen and silk fibroin, aiming to induce an appropriate inflammatory response and promote angiogenesis. The results indicated that the ordered topological structure of composite sponges could trigger the pro-inflammatory response of Mφs in the early stage, and rapid release of 26SCS in the early and middle stages (within the concentration range of 1–3 mg/mL) induced a positive inflammatory response; initiated the pro-inflammatory reaction of Mφs within 3 days; shifted M1 Mφs to the M2 phenotype within 3–7 days; and significantly up-regulated the expression of two typical angiogenic growth factors, namely VEGF and PDGF-BB, on day 7, leading to rapid HUVEC migration and angiogenesis. In vivo data also demonstrated that on the 14th day after surgery, the 26SCS-SilMA/Col-330-implanted areas exhibited less inflammation, faster re-epithelialization, more abundant collagen deposition and a greater number of blood vessels in the skin tissue. The composite sponges with higher 26SCS contents (the (5.0) 26SCS-SilMA/Col-330 and the (7.5) 26SCS-SilMA/Col-330) could better orchestrate the phenotype and function of Mφs and facilitate wound healing. These findings highlight that the 26SCS-SilMA/Col-330 sponges developed in this work might have great potential as a novel dressing for the treatment of diabetic wounds

A Novel Tanshinone Analog Exerts Anti-Cancer Effects in Prostate Cancer by Inducing Cell Apoptosis, Arresting Cell Cycle at G2 Phase and Blocking Metastatic Ability

Prostate cancer (PCa), an epithelial malignant tumor, is the second common cause of cancer death among males in western countries. Thus, the development of new strategies is urgently needed. Tanshinones isolated from Salvia miltiorrhiza and its synthetic analogs show various biological activities including anticancer effects. Among them, the tanshinone analog 2-((Glycine methyl ester)methyl)-naphtho (TC7) is the most effective, with better selectivity and lower toxicity. Therefore, in this work, the effect of TC7 against PCa was investigated through assessing the molecular mechanisms regulating the growth, metastasis, and invasion of PCa cells. Human PCa cells, PC3 and LNCAP, were used to evaluate TC7 mechanisms of action in vitro, while male BALB/c nude mice were used for in vivo experiments by subjecting each mouse to a subcutaneous injection of PC3 cells into the right flank to evaluate TC7 effects on tumor volume. Our in vitro results showed that TC7 inhibited cell proliferation by arresting the cell cycle at G2/M through the regulation of cyclin b1, p53, GADD45A, PLK1, and CDC2/cyclin b1. In addition, TC7 induced cell apoptosis by regulating apoptosis-associated genes such as p53, ERK1, BAX, p38, BCL-2, caspase-8, cleaved-caspase-8, PARP1, and the phosphorylation level of ERK1 and p38. Furthermore, it decreased DNA synthesis and inhibited the migration and invasion ability by regulating VEGF-1 and MMP-9 protein expression. Our in vivo evidence supports the conclusion that TC7 could be considered as a potential promising chemotherapeutic candidate in the treatment of PCa

A Carbon Emission Allowance Bargaining Model For Energy Transactions Among Prosumers

The carbon pricing is the main issue of the carbon trading market for enabling cost-effective decarbonization in the energy networks. A nodal carbon pricing model is firstly proposed based on the sharing and integration of the intra-regional carbon emission allowance. In this regard, the game theory is introduced to construct a multi-agent carbon emission allowance bargaining model in this letter. The alternating direction multiplier method is adopted to solve the model considering the competitional burden and privacy-preserving. Numerical results demonstrate that it could significantly reduce the carbon emissions of regional energy networks and improve the economic benefits of prosumers