Multiple Cross Research Articles

Visual multiple cross displacement amplification for the rapid identification of S. agalactiae immediately from vaginal and rectal swabs

Streptococcus agalactiae (S. agalactiae) is an important pathogen that can lead to neonatus and mother infection. The current existing techniques for the identification of S. agalactiae are limited by accuracy, speed and high-cost. Therefore, a new multiple cross displacement amplification (MCDA) assay was developed for test of the target pathogen immediately from vaginal and rectal swabs. MCDA primers screening were conducted targeting S. agalactiae pcsB gene, and one set of MCDA primers with better rapidity and efficiency was selected for establishing the S. agalactiae-MCDA assay. As a result, the MCDA method could be completed at a constant temperature of 61 °C, without the requirement of special equipment. The detection limit is 250 fg (31.5 copies) per reaction, all S. agalactiae strains displayed positive results, but not for non-S. agalactiae strains. The visual MCDA assay detected 16 positive samples from 200 clinical specimen, which were also detected positive by enrichment/qPCR. While the CHROMagar culture detected 6 positive samples. Thus, the MCDA assay is prefer to enrichment/qPCR and culture for detecting S. agalactiae from clinical specimen. Particularly, the whole test of MCDA takes about 63.1 min, including sample collection (3 min), DNA preparation (15 min), MCDA reaction (45 min) and result reporting (6 s). In addition, the cost was very economic, with only US$ 4.9. These results indicated that our S. agalaciae-MCDA assay is a rapid, sensitive and cost-efficient technique for target pathogen detection, and is more suitable than conventional assays for an urgent detection, especially for 'on-site' laboratories and resource-constrained settings.

Analysis of an age-structured dengue model with multiple strains and cross immunity

Analysis of an age-structured dengue model with multiple strains and cross immunity

Multiple cross-sectional assessments of empathy in medical undergraduate students.

Introduction:Empathy a cognitive phenomenon, with affective and behavioral components; helps improve clinical competence. It varies depending on physicians' gender and specialty. While some western studies reported significant fall in empathy levels when they progressed from non-clinical to clinical training years, Asian studies including Indian had varied results. We hypothesize that empathy will decrease among medical undergraduates over four years while they progress from non- clinical to clinical rotations and that female medical students and those opting for clinical specialties will have higher empathy than male medical students and those opting for non clinical specialty.Methodology:This study was carried out in a medical college from the year 2012 to 2015. Each year at the beginning of academic session all students were asked to complete Jefferson's -Scale for Physician's Empathy (JSPE student) student version. Over next three years similar assessments were repeated for all batches. The results were tabulated and analyzed using EpiInfo7 software.Results:Over four years 481, 416, 412 and 354 medical students in 1st, 2nd, 3rd and 4th year respectively from seven different batches were evaluated. Choice of specialty differed significantly in each year different batches. The empathy score had no relation to gender or choice of specialty. Empathy declines from first year onwards till third year but is not statistically significant.Conclusion:In this large multiple sample cross sectional study, it is evident empathy drops from 1st to 3rd year therefore there is an urgent need to evaluate why empathy falls.

Signal Detection in Distributed MIMO Radar With Non-Orthogonal Waveforms and Sync Errors

Although routinely utilized in literature, orthogonal waveforms may lose orthogonality in distributed multi-input multi-output (MIMO) radar with spatially separated transmit (TX) and receive (RX) antennas, as the waveforms may experience distinct delays and Doppler frequency offsets unique to different TX-RX propagation paths. In such cases, the output of each waveform-specific matched filter (MF), employed to unravel the waveforms at the RXs, contains both an \auto term and multiple cross terms, i.e., the filtered response of the desired and, respectively, undesired waveforms. We consider the impact of non-orthogonal waveforms and their cross terms on target detection with or without timing, frequency, and phase errors. To this end, we present a general signal model for distributed MIMO radar, examine target detection using existing coherent/non-coherent detectors and two new detectors, including a hybrid detector that requires phase coherence locally but not across distributed antennas, and provide a statistical analysis leading to closed-form expressions of false alarm and detection probabilities for all detectors. Our results show that cross terms can behave like foes or allies, respectively, if they and the auto term add destructively or constructively, depending on the propagation delay, frequency, and phase offsets. Regarding sync errors, we show that phase errors affect only coherent detectors, frequency errors degrade all but the non-coherent detector, while all are impacted by timing errors, which result in a loss in the signal-to-noise ratio (SNR).

Quantifying CO<sub>2</sub> emissions of a city with the Copernicus Anthropogenic CO<sub>2</sub> Monitoring satellite mission

Abstract. We investigate the potential of the Copernicus Anthropogenic Carbon Dioxide (CO2) Monitoring (CO2M) mission, a proposed constellation of CO2 imaging satellites, to estimate the CO2 emissions of a city on the example of Berlin, the capital of Germany. On average, Berlin emits about 20 Mt CO2 yr−1 during satellite overpass (11:30 LT). The study uses synthetic satellite observations of a constellation of up to six satellites generated from 1 year of high-resolution atmospheric transport simulations. The emissions were estimated by (1) an analytical atmospheric inversion applied to the plume of Berlin simulated by the same model that was used to generate the synthetic observations and (2) a mass-balance approach that estimates the CO2 flux through multiple cross sections of the city plume detected by a plume detection algorithm. The plume was either detected from CO2 observations alone or from additional nitrogen dioxide (NO2) observations on the same platform. The two approaches were set up to span the range between (i) the optimistic assumption of a perfect transport model that provides an accurate prediction of plume location and CO2 background and (ii) the pessimistic assumption that plume location and background can only be determined reliably from the satellite observations. Often unfavorable meteorological conditions allowed us to successfully apply the analytical inversion to only 11 out of 61 overpasses per satellite per year on average. From a single overpass, the instantaneous emissions of Berlin could be estimated with an average precision of 3.0 to 4.2 Mt yr−1 (15 %–21 % of emissions during overpass) depending on the assumed instrument noise ranging from 0.5 to 1.0 ppm. Applying the mass-balance approach required the detection of a sufficiently large plume, which on average was only possible on three overpasses per satellite per year when using CO2 observations for plume detection. This number doubled to six estimates when the plumes were detected from NO2 observations due to the better signal-to-noise ratio and lower sensitivity to clouds of the measurements. Compared to the analytical inversion, the mass-balance approach had a lower precision ranging from 8.1 to 10.7 Mt yr−1 (40 % to 53 %), because it is affected by additional uncertainties introduced by the estimation of the location of the plume, the CO2 background field, and the wind speed within the plume. These uncertainties also resulted in systematic biases, especially without the NO2 observations. An additional source of bias was non-separable fluxes from outside of Berlin. Annual emissions were estimated by fitting a low-order periodic spline to the individual estimates to account for the seasonal variability of the emissions, but we did not account for the diurnal cycle of emissions, which is an additional source of uncertainty that is difficult to characterize. The analytical inversion was able to estimate annual emissions with an accuracy of < 1.1 Mt yr−1 (< 6 %) even with only one satellite, but this assumes perfect knowledge of plume location and CO2 background. The accuracy was much smaller when applying the mass-balance approach, which determines plume location and background directly from the satellite observations. At least two satellites were necessary for the mass-balance approach to have a sufficiently large number of estimates distributed over the year to robustly fit a spline, but even then the accuracy was low (> 8 Mt yr−1 (>40 %)) when using the CO2 observations alone. When using the NO2 observations to detect the plume, the accuracy could be greatly improved to 22 % and 13 % with two and three satellites, respectively. Using the complementary information provided by the CO2 and NO2 observations on the CO2M mission, it should be possible to quantify annual emissions of a city like Berlin with an accuracy of about 10 % to 20 %, even in the pessimistic case that plume location and CO2 background have to be determined from the observations alone. This requires, however, that the temporal coverage of the constellation is sufficiently high to resolve the temporal variability of emissions.

First report of a newly-described lungworm, Dictyocaulus cervi (Nematoda: Trichostrongyloidea), in moose (Alces alces) in central Europe.

Lungworms from the genus Dictyocaulus are the causative agents of verminous pneumonia in domestic and wild ungulates. Recently, in 2017, a new species was isolated from red deer and described as Dictyocaulus cervi; however, little is known about its epidemiology and pathogenicity in other cervids. The aim of our study was to determine the extent of infection with Dictyocaulus nematodes in the moose population in Poland.Parasitological necropsies were performed in 18 moose and 249 faecal samples were analysed. A combination of multiplex PCR and analysis of the partial SSU, cox1 and cyt B regions revealed the presence of D. cervi infection in two of the necropsied moose. Histopathological examinations revealed changes, including multiple cross sections of larvae of nematodes in alveoli, massive pulmonary fibrosis, mononuclear cell infiltration and diffuse alveolar damage in the lungs of four animals. The lesions were more pronounced when adult Dictyocaulus nematodes were present in the bronchi and bronchioles. Some of the observed pathological changes could be attributed to co-infection by nematodes of the Protostrongylidae, whose larvae were found in all four animals with lung pathologies. In the faeces, Dictyocaulus sp. larvae only occurred together with Protostrongylidae larvae; in addition, higher numbers of Protostrongylidae larvae were excreted in the faeces of animals with dictyocaulosis.The present study is the first report of the presence of D. cervi in moose, and demonstrates the value of multiplex PCR in the identification of Dictyocaulus nematodes. Our findings indicate that co-infections with multiple species of lung nematodes in moose may be commonplace, and this should be considered as a factor aggravating the course of parasitosis.

A diagnostic test that uses isothermal amplification and lateral flow detection sdaA can detect tuberculosis in 60min.

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), is now the leading cause of death from infectious disease, thus rapid diagnostic and screening techniques for TB are urgently needed. Here, a detection of MTB using multiple cross displacement amplification coupling with nanoparticles-based lateral flow device (MCDA-LFD) was developed and validated, targeting the specific sdaA gene. The whole detection procedure, including rapid genomic DNA extraction (15min), amplification (30min) and result reporting (2min), was completed within 50min. No cross-reaction with non-mycobacteria and non-tuberculous mycobacteria (NTM) strains was observed. The sensitivity of sdaA-MCDA-LFD, Xpert MTB/RIF assay and culture results was 81·6, 48·3 and 37·9%, respectively, in TB patients. Among positive culture samples, the sensitivity of sdaA-MCDA-LFD and Xpert MTB/RIF assay was 93·9% (31/33) and 81·8% (27/33), respectively. Among culture-negative samples, the sensitivity of sdaA-MCDA-LFD and Xpert MTB/RIF assay was 74·1% (40/54) and 27·8% (15/54), respectively. The specificity of sdaA-MCDA-LFD and Xpert MTB/RIF was 95·4% (62/65) and 100% (65/65) in clinical samples from non-TB patients. The sdaA-MCDA-LFD assay was a rapid, simple, specific and sensitive TB diagnostic test. The sdaA-MCDA-LFD assay holds promise for application as a useful point-of-care test to detect MTB, and will play an important role in controlling and preventing TB.

Multiple photoionization cross sections for Fe2+ K shell

Multiple photoionization cross sections from the K shell are studied for all levels of the Fe2+ 3d6 configuration. The study shows that the quadruple photoionization leads to the largest cross sections. A large variation in the multiple photoionization cross sections is determined among the levels of the Fe2+ 3d6 configuration. Main decay branches of radiative and Auger cascades, following the photoionization of the K shell for the ground configuration of the Fe2+ ion, are identified. The radiative and Auger cascade is studied by considering transitions among energy levels and subconfigurations. The obtained data for ion yields are compared with previous calculations produced for configuration averages.

Single image super-resolution with multi-scale information cross-fusion network

The deep convolutional neural networks have achieved significant improvements in terms of effectiveness and efficiency for single image super-resolution. However, as the depth of neural network grows, the information flow is weakened. On the other hand, most of the models adopt a single-stream structure with which integrating complementary information under different receptive fields is difficult. To improve information flow and capture sufficient knowledge for reconstructing high-frequency details, we propose a multi-scale information cross-fusion network (MSICF) in which a sequence of subnetworks is cascaded to infer high resolution features in a coarse-to-fine manner. In each cascaded subnetwork, we stack multiple multi-scale cross (MSC) modules to fuse multi-scale complementary information in an efficient way as well as to improve information flow across the layers. Meanwhile, by introducing residual-feature learning in each stage, the relative information between features at different levels is fully utilized to further boost reconstruction performance. We train the proposed network with cascaded-supervision and then assemble the intermediate predictions of the cascade to achieve high quality image. Extensive quantitative and qualitative evaluations on benchmark datasets illustrate the superiority of our proposed method over state-of-the-art super-resolution methods.

Research on Transformer State Evaluation Method Based on Fault Feature Extraction

In order to realize the dynamic evaluation of transformer status and gradually improve the level of equipment fault diagnosis, a transformer condition assessment method based on fault feature extraction was proposed in this paper. Based on the data of the key state quantity of dissolved gas in transformer oil, this method extracted the transformer fault characteristics through various mathematical methods such as hypothesis testing and logistic language regression, and selected new indexes that could better reflect the operating state of transformer, so as to judge the operating state of transformer. In this paper, a large number of indicators were screened, and the regression method was used to find the indicators to judge the fault, without other external conditions. The analysis results showed that the average prediction accuracy of the proposed method was more than 95% under multiple cross validation, which had high engineering application value.

Joint disc and cup segmentation based on recurrent fully convolutional network.

The optic disc(OD) and the optic cup(OC) segmentation is an key step in fundus medical image analysis. Previously, FCN-based methods have been proposed for medical image segmentation tasks. However, the consecutive convolution and pooling operations usually hinder dense prediction tasks which require detailed spatial information, such as image segmentation. In this paper, we propose a network called Recurrent Fully Convolution Network(RFC-Net) for automatic joint segmentation of the OD and the OC, which can captures more high-level information and subtle edge information. The RFC-Net can minimize the loss of spatial information. It is mainly composed of multi-scale input layer, recurrent fully convolutional network, multiple output layer and polar transformation. In RFC-Net, the multi-scale input layer constructs an image pyramid. We propose four recurrent units, which are respectively applied to RFC-Net. Recurrent convolution layer effectively ensures feature representation for OD and OC segmentation tasks through feature accumulation. For each multiple output image, the multiple output cross entropy loss function is applied. To better balance the cup ratio of the segmented image, the polar transformation is used to transform the fundus image from the cartesian coordinate system to the polar coordinate system. We evaluate the effectiveness and generalization of the proposed method on the DRISHTI-GS1 dataset. Compared with the original FCN method and other state-of-the-art methods, the proposed method achieves better segmentation performance.

Highly Sensitive and Rapid Identification of Streptococcus agalactiae Based on Multiple Cross Displacement Amplification Coupled With Lateral Flow Biosensor Assay.

Streptococccus agalactiae (S. agalactiae) is an important neonatal pathogen that is associated with mortality and morbidity. Therefore, we developed a rapid, accurate, and sensitive method based on multiple cross displacement amplification (MCDA) for the detection of the target pathogen. Four sets of MCDA primers were designed for targeting the S. agalactiae-specific groEL gene, and one set of MCDA primers with the optimum amplification efficiency was screened for establishing the S. agalactiae-MCDA assay. As a result, the newly-developed assay could be conducted at a fixed temperature (61°C) for only 30 min, eliminating the use of complex instruments. A portable and user-friendly nanoparticle-based lateral flow biosensor (LFB) assay was employed for reporting MCDA results within 2 min. Our results suggested that the detection limit of the S. agalactiae-MCDA-LFB assay is 300 fg per reaction, and no cross-reaction occurred with non-S. agalactiae strains. For 260 vaginal and rectal swabs, the detection rate of the MCDA-LFB assay was 7.7%, which was in accordance with the reference method of enrichment/qPCR, and higher by 4.6% than the CHROMagar culture. Moreover, the total procedure time of the MCDA-LFB assay was around 50 min, including sample collection, template preparation, MCDA reaction, and result reporting. Therefore, the MCDA-LFB assay is superior to enrichment/qPCR and CHROMagar culture and has great promise for point-of-care testing of S. agalactiae from vaginal and rectal swabs of pregnant women in resource-limited settings.

Establishment and application of a multiple cross displacement amplification combined with nanoparticles-based biosensor method for the detection of Bordetella pertussis

BackgroundBordetella pertussis is the causative agent of pertussis, a respiratory tract infectious disease.Efficient techniques for detection of B. pertussis isolates are important for clinical diagnosis.Multiple cross displacement amplification (MCDA), a novel isothermal amplification based molecular detection method, has been developed to overcome the technical drawback of the current methods in recent years. This aim of this study is to develop a MCDA with Nanoparticles-based Lateral Flow Biosensor (MCDA-LFB) for the detection of B. pertussis.ResultsA set of 10 primers based on the pertussis toxin (PT) promoter region sequence of B. pertussis was designed. The B. pertussis-MCDA-LFB assay was successfully established and optimized at 64 °C for reaction of 40 min. The detection limit was determined as 10 fg/reaction of pure DNA, and no cross-reactions to non-B. pertussis strains were observed, based on the specificity validation. The whole operation, ranging from template preparation to result reporting, could be completed within 70 min without requirement of costly equipment. The B. pertussis-MCDA-LFB in clinic sample detection yielded identical positive rates with traditional culture and showed higher sensitivity than conventional PCR. The results of MCDA-LFB are easier to read due to the usage of LFB.ConclusionsThe isothermal amplification based MCDA-LFB established in the present study is a specific, sensitive, rapid and economical technique for the detection of B. pertussis.

Do alternative energy markets provide optimal alternative investment opportunities?

This paper investigates diversification opportunities for investors among different alternative energy markets. We sample six alternative energy markets namely World, Developed, Emerging, EU, BRIC and G7 with daily data ranging from January 2006 to December 2017. For estimations, we use wavelet multiple correlation and wavelet multiple cross correlation proposed by Polanco-Martínez and Fernández-Macho (2014) to find pairwise correlation at different investment horizons. Our work contributes in measuring integration level among alternative regional energy markets across different investment horizons. Our results highlight that World, Developed, Emerging, EU markets offer maximum diversification when included with either Emerging or BRIC energy markets in a portfolio. Furthermore, diversification benefits are more prominent under intra-week to monthly investment horizons for all portfolio combinations. We also rank different pairs of alternative markets based on their integration level which carry important implications for portfolio diversification and risk management.

3D geophysical modeling of the Alberton-Mathinna section of the “Main Slide,” northeast Tasmania

We have developed a high-resolution 3D model of the Alberton-Mathinna section of the “Main Slide,” northeast Tasmania. This geological model expresses a new synthesis based on mapping and structural interpretation on multiple cross sections. We have refined this model by 3D geophysical inversion constrained by gravity and magnetic survey data coupled with drilling and rock physical property databases. Our modeling incorporates statistically generated sensitivity characterization metrics into 3D model products that map confidence in the geometry of geological units at depth. The results include a granitoid surface that is considerably more detailed than earlier versions based on 2D modeling. Among the new features to emerge is a cupola 1.6 km below and slightly west of the Mathinna goldfield. At the Ringarooma United deposit located within the Alberton goldfield, we seethat the fault network underpinning the deposit was intruded by granite to a depth of approximately 400 m. Ore-forming solutions for both deposits have been interpreted as metamorphic in origin, but our results suggest the possibility of a role for magmatic fluids (i.e., granite related) in the gold-mineralizing system, particularly for the Ringarooma United deposit.

Current Cross Border e-Commerce Companies Developmental Problems AND Countermeasures

The development of cross border e-commerce industry is one of important global trends. From eMarketer (2019), the global sales of e-commerce in 2018 was 2.5 trillion US dollars and will reach 7.5 trillion US dollars in 2020. In this number - 7.5 trillion US dollars, the cross border e-commerce consumers will occupy 44.6% of e-commerce’s. Due to the quick development, companies will encounter problems by the way. The research purposes of this study was to discuss problems found through Osterwalder and Pigneur’s business model canvas analysis and provide suggestions. In this sense, During the time of one-year industry-academic cooperation program with Company A, the exploratory case study is relying on several sources of data collections through interviews, case analysis, site-visiting observations and secondary data collection (internal and external data) to analyze Company’s A cross order e-commerce business model and Taiwan’s cross border e-commerce ecosystem. The research subject (Company A) who locate in the vehicles components industry was a Taiwanese SEM and would like to transform into a cross border e-commerce company. The study aimed to explore the cross border e-commerce business model to answer the research question of What is Company A’s recent business model canvas ? If Company A would like to transform into cross border e-commerce, what are the problems? What is Taiwan cross border e-commerce ecosystem which Company A is located ? The research results and suggestions of this cross border e-commerce company are the following. Firstly, the problem is business opportunities are difficult to catch recently and the amount of each order earned is becoming smaller. The suggestion for this problem is to suggest adapting multiple cross border e-commerce portals operations on budgets and “Software Key activities” for decision making. The second problem is the cost of cross border e-commerce logistic is high. The suggest for this problem is to use Big Data Analysis and artificial intelligence (AI) technology to calculate exact oversea inventories. The third problem is lack of trust. The suggest for this problem is localization strategy, to systematically recruit and train the talents who are familiar with the culture, products and markets. And also The study also has some practical implications for cross-border e-commerce companies.

Open economy macroeconomics of commodity price fluctuation, sectoral inter-linkage and employment

PurposeThis is a theoretical paper in the field of structuralist macroeconomics. The paper focuses on commodity price fluctuation which has emerged as one of the major macroeconomic factors with significant bearing on the relationship between the agricultural and nonagricultural sectors.Design/methodology/approachThe paper develops a dual economy model consisting of an agricultural sector and an industrial sector. The commodity price is subject to the fluctuations due to the fact that stock of primary goods is an asset which is sensitive to speculations. The paper considers a standard methodology of dynamic adjustment process involving change in stock of agricultural goods and price of agricultural goods under perfect foresight. The saddle path properties of the equilibrium are also examined.FindingsThe paper shows that the balanced budget fiscal expansion, capital account liberalization and the agricultural expansion lead to expansion of the industrial sector as well as level of employment. The increase in world interest rate may lead to contraction of the industrial sector and depress employment.Originality/valueWe will consider the openness of the economy in explaining how different macroeconomic policies and capital account liberalization generate multiple cross effects on the inter-connectedness between agricultural and the non-agricultural sector. The paper will discuss the issue of employment generation in conjunction with commodity price fluctuation. We depart from the literature by using Taylor rule under which interest rate is fixed by the Central Bank such that money supply becomes endogenous.

Performance of Micro and Small Enterprisers in Tigray, Northern Ethiopia

Micro and Small Enterprises (MSE) have become the focus of attention for the economic development, economic growth and job creations in the world. Majority of the firms worldwide are dominated by businesses of micro and small enterprises. In developing countries, the informal sector that mainly establishes MSE remains the major source of employment and income for the urban population. A study was conducted to examine the performance of MSE in three zones of Tigray State, namely, Southern zone, Mekelle zone and Eastern zone. The data was collected using structured questionnaire on 246 MSE business owners. The data are analyzed using multiple linear regressions (dummy), Cross tabulations and chi-square test for test of independence. The result revealed that Gender, initial capital, enterprise and job type are found to be important factors of performance of MSE. There is a gender difference on sector type, education level and work sheds of micro and small enterprises business owner. The result also showed that initial capital has positively affected the performance of business owners and men headed business owners have better performance than female headed business owners.
 Keywords: Micro- and Small Enterprises, Performance, Gender, Tigray, Ethiopia.

Migrant workers cash transfer effect on childrens education

The rationale of this study is to examine the impact of cash transfer on school attendance in Indonesia and whether the proportion of school children receiving cash transfer and those not receiving cash transfer is having the same effect. Using the data of Indonesia’s Family Life Survey-5 (IFLS-5) in 2014 covering 19 provinces with total observations of 16.024 household obtained from Rand Corporation and field interview held in 2015, the multiple cross section regression model was applied by using 2SLS and Probit regression method to examine the effect of cash transfer as exogenous variable, and individual vector, parents, and household as control variables on school attendance of children as endogenous variable. The main finding is that the cash transfer has significant positive effect in increasing children’s school attendance and the expenditure for children education. Nevertheless, the children from household receiving cash remittance tend to have lower school attendance vis-a-vis children from household not receiving the cash transfer. As policy considerations, it is advised to anticipate the migration effects when evaluating the effect of cash transfer on children’s school attendance or when updating migration policies and to improve the availability of facilities and basic infrastructure for school age children. Key words: Cash transfer, school attendance, household expenditure, education, children.

Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

Condition monitoring and fault detection are nowadays popular topic. Different loads, enviroments etc. affect the components and systems differently and can induce the fault and faulty behaviour. Most of the approaches for the fault detection rely on the use of the good classification method. Gaussian mixture model based classification are stable and versatile methods which can be applied to a wide range of classification tasks. The main task is the estimation of the parameters in the Gaussian mixture model. Those can be estimated with various techniques. Therefore, the Gaussian mixture model based classification have different variants which can vary in performance. To test the performance of the Gaussian mixture model based classification variants and general usefulness of the Gaussian mixture model based classification for the fault detection, we have opted to use the bearing fault classification problem. Additionally, comparisons with other widely used non-parametric classification methods are made, such as support vector machines and neural networks. The performance of each classification method is evaluated by multiple repeated k-fold cross validation. From the results obtained, Gaussian mixture model based classification methods are shown to be competitive and efficient methods and usable in the field of fault detection and condition monitoring.