Forward Intensity Model Monitoring Using Multivariate Exponential Weighted Moving Average Scheme

We propose a parameter monitoring method for the forward intensity model – the default probability prediction model of the Credit Research Initiative (CRI). We review the relative statistical process control scheme in the field of engineering. Based on this, we propose a new Multivariate Exponential Weighted Moving Average (MEWMA) scheme to monitor the forward intensity model monthly. This new chart might be applied to identify and diagnose the out-of-control (OC) parameters in real time as the data updating, which reduces the cost of recalculating all parameters and improve the operational and calculational efficiency of the default prediction models in practical application

Learning event patterns for gesture detection

Usability often plays a key role when software is brought to market, including clearly structured workows, the way of presenting information to the user, and, last but not least, how he interacts with the application. In this context, input devices as 3D cameras or (multi-)touch displays became omnipresent in order to define new intuitive ways of user interaction. State-of-the-art systems tightly couple application logic with separate gesture detection components for supported devices. Hard-coded rules or static models obtained by applying machine learning algorithms on many training samples are used in order to robustly detect a pre defined set of gesture patterns. If possible at all, it becomes difficcult to extend these sets with new patterns or to modify existing ones difficult for both, application developers and end users. Further, adding gesture support for legacy software or for additional devices becomes dificult with this hardwired approach. In previous research we demonstrated how the database community can contribute to this challenge by leveraging complex event processing on data streams to express gesture patterns. While this declarative approach decouples application logic from gesture detection components, its major drawback was the non-intuitive definition of gesture queries. In this paper, we present an approach that is related to density-based clustering in order to find declarative gesture descriptions using only a few samples. We demonstrate the algorithms on mining definitions for multi-dimensional gestures from the sensor data stream that is delivered by a Microsoft Kinect 3D camera, and provide a way for non-expert users to intuitively customize gesturecontrolled user interfaces even during runtime

PHYSICAL OR DIGITAL? FACTORS DRIVE CONSUMERS TO PURCHASE DIGITAL MUSIC

The development of digital products is booming in recent years because of the mature of entire environment. Among them, digital music should be the spotlight with unexpectedly expending speed. However, most studies of electronic commerce still focus on the field of physical products, but missing the value of digital wave. The purpose here is to explore and complements digital parts. In this study, we constructed the research model based on TRA and extended it with the advantage and disadvantage of intangibility (convenience, perceived risk), some characteristics of digital product (price, variety, trialability), and the factor related to entertainment (perceived playfulness) to predict what are consumer really concerned when they buy digital music.8 hypotheses were supported. Finally, we proved TRA is still a useful theory in the field of digital product

An experimental study of imbibition process and fluid distribution in tight oil reservoir under different pressures and temperatures

Tight reservoirs are a major focus of unconventional reservoir development. As a means to improve hydrocarbon recovery from tight reservoirs, imbibition has been received increasing attentions in recent years. This study evaluates how the changes in temperature and pressure affect imbibition through conducting experimental tests under various conditions on samples from the Yan Chang formation, a tight reservoir in Ordos Basin. The fluid distribution is compared before and after imbibition in core samples by nuclear magnetic resonance method. The results show that the imbibition recovery is significantly improved through increasing temperature and pressure. A high temperature facilitates molecular thermal movements, increasing oil-water exchange rate. The core samples are characterized with nano-mesopores, which is followed by nano-macropores, micropores, mesopores, and nano-micropores. Comparative analysis of nuclear magnetic resonance shows that the irreducible water saturation increases after imbibition and is mainly distributed in nano-pores. Increasing pressure increases the amount of residual water in nano pores, with the relatively more significant increase in the amount of residual water in nanomacro-pores compared with other types of pores.Cited as: Liang, Y., Lai, F., Dai, Y., Shi, H., Shi, G. An experimental study of imbibition process and fluid distribution in tight oil reservoir under different pressures and temperatures. Capillarity, 2021, 4(4): 66-75, doi: 10.46690/capi.2021.04.0

Proposing a New Research Framework for Loan Allocation Strategies in P2P Lending

One of the frontier Web 2.0 applications is online peer-to-peer (P2P) lending marketplace, where individual lenders and borrowers can virtually meet for loan transactions. From a lender’s perspective, she not only wants to lower investment risk but also to gain as much return as possible. However, P2P lenders possess the inherent problem of information asymmetry that they don’t really know if a borrower has capability to pay the loan or is truthfully willing to pay it in due time, leading them to a disadvantaged situation when making the decision of lending money to the borrower. This study intends to consider the loan allocation as an optimization research problem using the research framework based upon modern portfolio theory with the aim of helping lenders achieve the two goals of gaining high return and lowering risk at the same time. The expected results of this research are twofold: 1) compared to a logistic regression based credit scoring method, we expect to make more profits for lenders with risk level unchanged, and 2) compared to a linear regression based profit scoring method, we expect to lower risk without lowering return. Our proposed new model could offer insights into how individual lenders can optimize their loan allocation strategies when considering return and risk simultaneously

A review on coal and gas outburst prediction based on machine learning

The safety in the coal-producing mines in China is continuously improving, but coal and gas outburst accidents still occur. The prediction of coal and gas outbursts allows the scientific application of outburst prevention measures, which can ensure the safe coal mining to a certain extent. Machine learning is an interdisciplinary field involving probability theory, statistics, and computer science, which can explore the nonlinear relationship between outburst accidents and its associated indicators. The application of machine learning in coal and gas outburst prediction has received relatively widespread attention, and with the rapid progress of artificial intelligence and computer technology, it will play a greater role in the field of outburst prediction. Therefore, this paper provides a comprehensive review of the research on machine learning in coal and gas outburst prediction, analyzes the difficulties in outburst prediction and prospects its development direction. Firstly, the paper provides a brief overview of the research status on the hypothesis, occurrence mechanism, and prediction index selection of coal and gas outbursts. Then, it summarizes the research progress in the field of outburst prediction, including the application of support vector machines, neural networks, extreme learning machines, and ensemble learning algorithms. In addition, it also points out the existing problems in the current research, such as imbalanced samples, missing data indicators, and small sample sizes. Finally, the paper gives an outlook on the developments of coal and gas outburst prediction based on machine learning, including improving algorithm performance, optimizing feature engineering, and increasing sample size. With the continuous improvement of computer performance, more powerful models may be proposed, which can further improve the prediction accuracy of outburst accidents

TP53-related signature for predicting prognosis and tumor microenvironment characteristics in bladder cancer: A multi-omics study

Background: The tumor suppressor gene TP53 is frequently mutated or inactivated in bladder cancer (BLCA), which is implicated in the pathogenesis of tumor. However, the cellular mechanisms of TP53 mutations are complicated, yet well-defined, but their clinical prognostic value in the management of BLCA remains controversial. This study aimed to explore the role of TP53 mutation in regulating the tumor microenvironment (TME), elucidate the effects of TP53 activity on BLCA prognosis and immunotherapy response.Methods: A TP53-related signature based on TP53-induced and TP53-repressed genes was used to construct a TP53 activity-related score and classifier. The abundance of different immune cell types was determined using CIBERSORT to estimate immune cell infiltration. Moreover, the heterogeneity of the tumor immune microenvironment between the high and low TP53 score groups was further evaluated using single-cell mass cytometry (CyTOF) and imaging mass cytometry (IMC). Moreover, pathway enrichment analysis was performed to explore the differential biological functions between tumor epithelial cells with high and low TP53 activity scores. Finally, the receptor–ligand interactions between immune cells and tumor epithelial cells harboring distinct TP53 activity were analyzed by single-cell RNA-sequencing.Results: The TP53 activity-related gene signature differentiated well between TP53 functional retention and inactivation in BLCA. BLCA patients with low TP53 scores had worse survival prognosis, more TP53 mutations, higher grade, and stronger lymph node metastasis than those with high TP53 scores. Additionally, CyTOF and IMC analyses revealed that BLCA patients with low TP53 scores exhibited a potent immunosuppressive TME. Consistently, single-cell sequencing results showed that tumor epithelial cells with low TP53 scores were significantly associated with high cell proliferation and stemness abilities and strongly interacted with immunosuppressive receptor–ligand pairs.Conclusion: BLCA patients with low TP53 scores have a worse prognosis and a more immunosuppressive TME. This TP53 activity-related signature can serve as a potential prognostic signature for predicting the immune response, which may facilitate the development of new strategies for immunotherapy in BLCA

Antibiotic-Induced Gut Microbiota Dysbiosis Modulates Host Transcriptome and m6A Epitranscriptome via Bile Acid Metabolism.

Gut microbiota can influence host gene expression and physiology through metabolites. Besides, the presence or absence of gut microbiome can reprogram host transcriptome and epitranscriptome as represented by N6-methyladenosine (m6A), the most abundant mammalian mRNA modification. However, which and how gut microbiota-derived metabolites reprogram host transcriptome and m6A epitranscriptome remain poorly understood. Here, investigation is conducted into how gut microbiota-derived metabolites impact host transcriptome and m6A epitranscriptome using multiple mouse models and multi-omics approaches. Various antibiotics-induced dysbiotic mice are established, followed by fecal microbiota transplantation (FMT) into germ-free mice, and the results show that bile acid metabolism is significantly altered along with the abundance change in bile acid-producing microbiota. Unbalanced gut microbiota and bile acids drastically change the host transcriptome and the m6A epitranscriptome in multiple tissues. Mechanistically, the expression of m6A writer proteins is regulated in animals treated with antibiotics and in cultured cells treated with bile acids, indicating a direct link between bile acid metabolism and m6A biology. Collectively, these results demonstrate that antibiotic-induced gut dysbiosis regulates the landscape of host transcriptome and m6A epitranscriptome via bile acid metabolism pathway. This work provides novel insights into the interplay between microbial metabolites and host gene expression