A Novel System Anomaly Prediction System Based on Belief Markov Model and Ensemble Classification

Computer systems are becoming extremely complex, while system anomalies dramatically influence the availability and usability of systems. Online anomaly prediction is an important approach to manage imminent anomalies, and the high accuracy relies on precise system monitoring data. However, precise monitoring data is not easily achievable because of widespread noise. In this paper, we present a method which integrates an improved Evidential Markov model and ensemble classification to predict anomaly for systems with noise. Traditional Markov models use explicit state boundaries to build the Markov chain and then make prediction of different measurement metrics. A Problem arises when data comes with noise because even slight oscillation around the true value will lead to very different predictions. Evidential Markov chain method is able to deal with noisy data but is not suitable in complex data stream scenario. The Belief Markov chain that we propose has extended Evidential Markov chain and can cope with noisy data stream. This study further applies ensemble classification to identify system anomaly based on the predicted metrics. Extensive experiments on anomaly data collected from 66 metrics in PlanetLab have confirmed that our approach can achieve high prediction accuracy and time efficiency

Cell Signaling of Caenorhabditis elegans in Response to Enterotoxigenic Escherichia coli Infection and Lactobacillus zeae Protection

Enterotoxigenic Escherichia coli (ETEC) infection causes the death of Caenorhabditis elegans, which can be prevented by certain Lactobacillus isolates. The host response of C. elegans to ETEC infection and its regulation by the isolates are, however, largely unclear. This study has revealed that, in agreement with the results of life-span assays, the expression of the genes encoding p38 mitogen-activated protein kinase (MAPK) pathway (nsy-1, sek-1, and pmk-1), insulin/insulin-like growth factor (DAF/IGF) pathway (daf-16), or antimicrobial peptides (lys-7, spp-1, and abf-3) and other defensing molecules (abf-2, clec-85) was upregulated significantly when the wild-type nematode (N2) was subjected to ETEC infection. This upregulation was further enhanced by the pretreatment with Lactobacillus zeae LB1, but not with L. casei CL11. Mutants defective in the cell signaling of C. elegans were either more susceptible (defective in NSY-1, SEK-1, PMK-1, or DAF16) or more resistant (defective in AGE-1, DBL-1, SKN-1, or SOD-3) to ETEC infection compared with the wild-type. Mutants defective in antimicrobial peptides (LYS-7, SPP1, or ABF-3) were also more susceptible. In addition, mutants that are defective in NSY-1, SEK-1, PMK-1, DAF16, ABF-3, LYS-7, or SPP1 showed no response to the protection from L. zeae LB1. The expression of the genes encoding antimicrobial peptides (lys-7, spp-1, and abf-3) and other defensing molecules (abf-2, clec-60, and clec-85) were almost all upregulated in AGE-1- or DBL-1-defective mutant compared with the wild-type, which was further enhanced by the pretreatment of L. zeae LB1. The expression of these genes was, however, mostly downregulated in NSY-1- or DAF-16-defective mutant. These results suggest that L. zeae LB1 regulates C. elegans signaling through the p38 MAPK and DAF/IGF pathways to control the production of antimicrobial peptides and defensing molecules to combat ETEC infection

Air travel demand forecasting based on big data: A struggle against public anxiety

It is of great significance to accurately grasp the demand for air travel to promote the revival of long-distance travel and alleviate public anxiety. The main purpose of this study is to build a high-precision air travel demand forecasting framework by introducing effective Internet data. In the age of big data, passengers before traveling often look for reference groups in search engines and make travel decisions under their informational influence. The big data generated based on these behaviors can reflect the overall passenger psychology and travel demand. Therefore, based on big data mining technology, this study designed a strict dual data preprocessing method and an ensemble forecasting framework, introduced search engine data into the air travel demand forecasting process, and conducted empirical research based on the dataset composed of air travel volume of Shanghai Pudong International Airport. The results show that effective search engine data is helpful to air travel demand forecasting. This research provides a theoretical basis for the application of big data mining technology and data spatial information in air travel demand forecasting and tourism management, and provides a new idea for alleviating public anxiety

An empirical study of bugs in software build systems

NSF

Information-value-based feature selection algorithm for anomaly detection over data streams

Računalni sustavi postaju sve složeniji i nepravilnosti sustava uveliko utječu na raspoloživost sustava. Učinkovit način za postizanje visoke raspoloživosti sustava je primjena alata za otkrivanje anomalija kako bi se otkrile nenormalne aktivnosti u računalnom sustavu i bile popravljene. Zbog složenosti modernih računalnih sustava mnoge se matrice sustava moraju nadgledati. Zbog toga je multi-dimenzionalnost jedan od najvažnijih zahtjeva u otkrivanju nepravilnosti. Veliki broj matrica povećava vrijeme obrade tehnologije otkrivanja nepravilnosti i smanjuje točnost. Za rješavanje ovog problema mi koristimo vrijednost informacije kako bismo provjerili važnost karakteristika u odnosu na otkrivanje nepravilnosti. Međutim, metoda vrijednosti informacije ne uzima u obzir redundantne karakteristike. Zbog toga se procijenjuju korelacije između karakteristika kako bi se odbacile redundantne karakteristike. U ovom se radu prikazana metoda uspoređuje s drugim metodama odabira karakteristika primjenom niza podataka o nepravilnosti stvarnog sustava. Eksperimentalni rezultati pokazuju da prikazana metoda može učinkovitije podučiti model i točnije otkriti anomalije.Computer systems are becoming more and more complex, and system anomalies have a serious impact on system availability. One effective way to achieve high availability is to use anomaly detection tools to find the abnormal activities in the computer system so that they can be repaired. Because of the complexity of modern computing systems, many system metrics need to be monitored. For this reason, one major challenge of anomaly detection is multi-dimensionality. Large numbers of metrics increase the processing time of anomaly detection technology and lower the accuracy. To overcome this problem, we use information-value to ascertain the importance of features with respect to detecting anomalies. However, the information-value method does not take redundant features into account. Thus, correlations between features are evaluated to remove redundant features. This paper compares the presented method to other feature selection methods using a real system anomaly data set. Experimental results show that the presented method can learn the model more efficiently and detect anomalies more accurately

Information-value-based feature selection algorithm for anomaly detection over data streams

Računalni sustavi postaju sve složeniji i nepravilnosti sustava uveliko utječu na raspoloživost sustava. Učinkovit način za postizanje visoke raspoloživosti sustava je primjena alata za otkrivanje anomalija kako bi se otkrile nenormalne aktivnosti u računalnom sustavu i bile popravljene. Zbog složenosti modernih računalnih sustava mnoge se matrice sustava moraju nadgledati. Zbog toga je multi-dimenzionalnost jedan od najvažnijih zahtjeva u otkrivanju nepravilnosti. Veliki broj matrica povećava vrijeme obrade tehnologije otkrivanja nepravilnosti i smanjuje točnost. Za rješavanje ovog problema mi koristimo vrijednost informacije kako bismo provjerili važnost karakteristika u odnosu na otkrivanje nepravilnosti. Međutim, metoda vrijednosti informacije ne uzima u obzir redundantne karakteristike. Zbog toga se procijenjuju korelacije između karakteristika kako bi se odbacile redundantne karakteristike. U ovom se radu prikazana metoda uspoređuje s drugim metodama odabira karakteristika primjenom niza podataka o nepravilnosti stvarnog sustava. Eksperimentalni rezultati pokazuju da prikazana metoda može učinkovitije podučiti model i točnije otkriti anomalije.Computer systems are becoming more and more complex, and system anomalies have a serious impact on system availability. One effective way to achieve high availability is to use anomaly detection tools to find the abnormal activities in the computer system so that they can be repaired. Because of the complexity of modern computing systems, many system metrics need to be monitored. For this reason, one major challenge of anomaly detection is multi-dimensionality. Large numbers of metrics increase the processing time of anomaly detection technology and lower the accuracy. To overcome this problem, we use information-value to ascertain the importance of features with respect to detecting anomalies. However, the information-value method does not take redundant features into account. Thus, correlations between features are evaluated to remove redundant features. This paper compares the presented method to other feature selection methods using a real system anomaly data set. Experimental results show that the presented method can learn the model more efficiently and detect anomalies more accurately

The circular economy in China: Achievements, challenges and potential implications for decarbonisation

China's wide-ranging circular economy (CE) efforts have been studied multiple times from a range of perspectives. Synthesizing the relevant literature, this paper provides a critical reflection on the transition to a CE in China. Key factors for China's success in shifting towards a CE are seen in multi-level indicators and upscaling niches. This paper makes a novel contribution on limitations to progress, based on emerging evidence on CE projects that fail to sustain. Enriched by experts feedback, this paper critically addresses future challenges to a deep transition resulting from implementation gaps between early majorities and mass markets and coordination challenges arising through regional and sectoral differences. In light of China's commitments to climate neutrality by 2060, such challenges are considered serious. Based on feasible policy learning, the paper however proposes synergies between the CE and decarbonisation driven by efficiency improvements, comprehensive core indicators, upscaling and urban policies as trigger for deeper transformations. Finally the paper undertakes broader reflections and an outlook on evidence-orientated policy learning for a CE and decarbonisation in China

Proteomic Analysis of the Hepatopancreas of Chinese Mitten Crabs (Eriocheir sinensis) Fed With a Linoleic Acid or α-Linolenic Acid Diet

As representatives of n-6 and n-3 fatty acids, many studies have analyzed the use of soybean oil and linseed oil rich in linoleic acid (18:2n-6, LA) and α-linolenic acid (18:3n-3, LNA) as better substitutes for fish oil. In aquatic animals, different dietary ratios of LA and LNA could have significant effects on growth, lipid metabolism, immune response, and reproduction. To assess the nutritive value of these two fatty acids in Chinese mitten crab (Eriocheir sinensis), we performed transcriptome analysis and label-free quantification proteomic analysis of the hepatopancreas from mitten crabs fed with LA or LNA diet. Parallel reaction monitoring was used to confirm the reliability of the proteomic analysis. A total of 186 proteins were differentially expressed with fold change ≥1.5 or ≤0.666. Among the 186 proteins, 116 were upregulated and 70 were downregulated in the LA than LNA. Most of these proteins participate in cellular process and metabolism process and have molecular functions such as binding and catalytic activity; the cellular component of these proteins are cell, cell part, membrane, and membrane part. A total of 18 proteins were identified to be related to lipid, carbohydrate, and protein metabolism, and they mainly participate in digestive enzyme activities, fatty acid transport, and glycolysis. Our results provide new insights for further investigation into the replacement of fish oil from mitten crabs with vegetable oils and enable us to better understand the different roles and nutrition value of LA and LNA in mitten crabs

Dementia Literacy among Community-Dwelling Older Adults in Urban China: A Cross-sectional Study

ObjectiveDelay in seeking diagnosis of dementia is common in China. Misinformation and poor knowledge about dementia may contribute to it. The study was designed to explore the nationwide dementia literacy among older adults in urban China and to investigate the factors associated with overall dementia literacy.MethodsIn a cross-sectional study, a convenience sample of 3,439 community-dwelling old adults aged 60 and over was recruited from 34 cities in 20 provinces between June 20 and August 20, 2014. All participants were administered the face-to-face mental health literacy questionnaire, which included the prevalence, symptoms, intention, and options for treatment of dementia. Stepwise multivariate regression analysis was used to explore factors associated with overall dementia literacy.ResultsThe response rate was 87.4%. The overall dementia literacy was 55.5% (SD = 20.9%) among all respondents. The correct response rate was higher for questions on symptoms (58.7–89.6%), but lower for questions on the prevalence (22.2%) and choosing appropriate professional care personnel (22.2%). Being male [OR = 1.256, 95% CI (1.022–1.543)], having lower per capita annual income [OR = 1.314, 95% CI (1.064–1.623)], lower education [OR = 1.462, 95% CI (1.162–1.839)], and suspected depression [OR = 1.248, 95% CI (1.009–1.543)] were negatively associated with overall dementia literacy.ConclusionDementia literacy among community-dwelling older adults in urban China remains very low, in particular about the impact of dementia and appropriate treatment personnel. Community educational programs aiming to close this knowledge gap are encouraged to focus on those in the population at highest risk of low dementia literacy