Fault diagnosis of refrigerant charge based on PCA and decision tree for variable refrigerant flow systems

Variable refrigerant flow (VRF) systems are easily subjected to performance degradation due to refrigerant leakage, mechanical failure or improper maintenance after years of operation. Ideal VRF systems should equip with fault detection and diagnosis (FDD) program to sustain its normal operation. This paper presents the fault diagnosis method for refrigerant charge faults of variable refrigerant flow (VRF) systems. It is developed based on the principal component analysis (PCA) feature extraction method and the decision tree (DT) classification algorithm. Nine refrigerant charge schemes are implemented on the VRF system in the laboratory, which contain the normal and faulty refrigerant charge conditions. In addition, data of the online operating VRF systems are collected in this work. Firstly, data from both experimental VRF system and online operating systems are pre-processed by outlier cleaning, feature extraction and data normalization, because the original data of the VRF system usually has poor quality and complex structure. Secondly, the fault diagnosis model based on the PCA-DT method is built using the data of the experimental VRF system. In this step, the PCA method is used to obtain a new data sample which includes four comprehensive features, then the new data sample are randomly split into training and testing sets as the input of DT classifier for fault diagnosis. Thirdly, the advantages of the PCA-DT method is validated using the experimental data of different fault severity levels. Results show that the combined use of PCA and DT methods can achieve better fault diagnosis efficiency than the single decision tree method. Further, the robustness of the PCA-DT method in online fault diagnosis is verified using the data from online VRF systems. The online VRF systems have the same or different number of indoor units as the trained (experimental) VRF system. The PCA-DT method also shows desirable goodness on the online fault diagnosis process. In this sense, this work provides a promising fault diagnosis strategy for refrigerant charge faults of VRF system application

The disposition effect and underreaction to private information

We examine the role of the disposition effect in market efficiency following the arrival of private signals to a small group of informed traders. Subjects trade an ambiguous asset via a computer-based double auction. Using a 2 × 2 × 2 design, we endow two types of signal, i.e., positive vs. negative, to informed traders with two different levels of the disposition effect, i.e., high vs. low, that are measured in two domains, i.e., gain vs. loss. We find that (1) the disposition effect measured in the gain domain has qualitatively different implications from the disposition effect measured in the loss domain; (2) following a favorable signal, informed traders with high disposition effect levels are more likely to sell and less likely to hold the asset while following an unfavorable signal, the opposite is true; (3) there is some evidence of stronger price underreaction in markets with informed traders with high disposition effect levels than in markets with informed traders with low disposition effect levels, but the effect is overall relatively weak; and finally and most importantly (4) the above results hold only when the sign of the signal matches the domain that the disposition effect levels of the informed traders are measured in

A Comparative Study on Damage Mechanism of Sandwich Structures with Different Core Materials under Lightning Strikes

Wind turbine blades are easily struck by lightning, a phenomenon that has attracted more and more attention in recent years. On this subject a large current experiment was conducted on three typical blade sandwich structures to simulate the natural lightning-induced arc effects. The resulting damage to different composite materials has been compared: polyvinyl chloride (PVC) and polyethylene terephthalate (PET) suffered pyrolysis and cracks inside, while the damage to balsa wood was fibers breaking off and large delamination between it and the resin layer, and only a little chemical pyrolysis. To analyze the damage mechanism on sandwich structures of different materials, a finite element method (FEM) model to calculate the temperature and pressure distribution was built, taking into consideration heat transfer and flow expansion due to impulse currents. According to the simulation results, PVC had the most severe temperature and pressure distribution, while PET and balsa wood were in the better condition after the experiments. The temperature distribution results explained clearly why balsa wood suffered much less chemical pyrolysis than PVC. Since balsa wood had better thermal stability than PET, the pyrolysis area of PET was obviously larger than that of balsa wood too. Increasing the volume fraction of solid components of porous materials can efficiently decrease the heat transfer velocity in porous materials. Permeability didn’t influence that much. The findings provide support for optimum material selection and design in blade manufacturing

基于雷电物理的风机叶片动态击距与 电气几何模型

The damage of wind turbine blades suffered lightning strikes has been a key factor of the safe and reliable operation of wind farms. The electric geometrical model of wind turbine blades (EGMTB) was presented based on the traditional electric geometrical method and the physical process of lightning leader. The concept of dynamic striking distance was introduced and clarified the physical meaning of striking distance. And the calculation method of blade lightning protection system (LPS) efficiency was deduced. Finally, the effectiveness of EGMTB was validated by the long gap breakdown experiment of blades. The EGMTB was used to analyze the influence factors of blade LPS efficiency. It is indicated that the efficiency of blade LPS reduces with the decrease of lightning current and the angle between the blade and horizontal, and the efficiency of blade LPS can be improved by increasing the side lightning receptors. The EGMTB is intended to provide a theory for lightning protection design and evaluation of wind turbine blades

Diagnosis and prognostic value of circDLGAP4 in acute ischemic stroke and its correlation with outcomes

Rationale and aimsCircular RNAs are a subclass of noncoding RNAs in mammalian cells and highly expressed in the central nervous system. Although their physiological functions are not yet completely defined, they are thought to promise as stroke biomarkers because of their stability in peripheral blood.Sample Size Estimate: 222 participants.Methods and designThe plasma of patients with acute ischemic stroke (AIS) (n = 111) and non-stroke controls (n = 111) from November 2017 to February 2019 were enrolled in our research. The expression of circDLGAP4 in plasma was evaluated using real-time PCR.Study outcomesIn patients with AIS, circDLGAP4 was significantly decreased in comparison with non-stroke controls. The CircDLGAP4 level had a significant AUC of 0.7896 with 91.72% sensitivity and 64.83% specificity in diagnosing AIS. Furthermore, the circDLGAP4 level was related to smoking history and previous transient ischemic attack/stroke/myocardial infarction in all samples. The change rate in circDLGAP4 within the first 7 days showed an AUC curve of 0.960 in predicting an stroke outcome.ConclusioncircDLGAP4 could serve as biomarker for AIS diagnosis and prediction of stroke outcomes

The anti-resection activity of the X protein encoded by Hepatitis Virus B

Chronic infection of hepatitis virus B (HBV) is associated with an increased incidence of hepatocellular carcinoma (HCC). HBV encodes an oncoprotein (HBx) that is crucial for viral replication and interferes with multiple cellular activities including gene expression, histone modifications and genomic stability. To date, it remains unclear how disruption of these activities contributes to hepatocarcinogenesis. Here, we report that HBV exhibits a novel anti‐resection activity by disrupting DNA end resection, thus impairing the initial steps of homologous recombination (HR). This anti‐resection activity occurs in primary human hepatocytes (PHHs) undergoing a natural viral infection‐replication cycle, as well as in cells with integrated HBV genomes. Among the seven HBV‐encoded proteins, we identified HBx as the sole viral factor that inhibits resection. By disrupting an evolutionarily conserved Cullin4A‐DDB1‐RING type of E3 ligase, CRL4WDR70, via its H‐box, we show that HBx inhibits H2B monoubiquitylation at lysine 120 (uH2B) at double strand breaks, thus reducing the efficiency of long‐range resection. We further show that directly impairing H2B monoubiquitylation elicited tumorigenesis upon engraftment of deficient cells in athymic mice, confirming that the impairment of CRL4WDR70 function by HBx is sufficient to promote carcinogenesis. Finally, we demonstrated that lack of H2B monoubiquitylation is manifest in human HBV‐associated HCC (HBVHCC) when compared with HBV‐free HCC, implying corresponding defects of epigenetic regulation and end resection. We conclude that the anti‐resection activity of HBx induces an HR defect and genome instability and contributes to tumorigenesis of host hepatocytes

ADD 2023: the Second Audio Deepfake Detection Challenge

Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks