NOISE REDUCTION IN METRIC, EVENT, LOG, AND TRACE (MELT) DATA USING DISTRIBUTED MACHINE LEARNING

In the Observability domain, metric, event, log and trace (MELT) are basic data types generated by the infrastructure and applications. These datasets are not only ingested at high volume and high frequency but also related. Currently, available solutions are for individual data types, i.e., metric monitoring, log analytics, trace flow analysis, etc. These solutions do not provide a holistic view of the entire environment with MELT correlation. To address these types of challenges, techniques are presented herein that support a scalable, flexible, dynamic, and adaptive noise reduction system. While the system is running as expected, data is collected at a lower frequency. When the first sign of trouble appears, such a system may automatically increase collection frequency for change point detection, anomaly detection, log pattern detection, and causal inference. Aspects of the presented techniques employ a two-phase filtering mechanism comprising Edge Processors and Global Processors to intelligently apply machine learning techniques to scale up and down monitoring and root cause analysis capabilities

Assessment of the Influence of Graphene Nanoparticles on Thermal Conductivity of Graphene/Water Nanofluids Using Factorial Design of Experiments

In this study, 23 factorial design of experiment was employed to evaluate the effect of parameters of hot fluid inlet temperature, graphene nanofluid concentration and hot fluid flow rate on thermal conductivity of graphene/water nanofluid. The levels of  hot fluid inlet temperature are kept at 35°C and 85°C, nanofluid concentration is kept at 0.1 and 1.0 volume% (vol.%) and the hot fluid flow rate are kept at 2 lpm and 10 lpm. Experiments were conducted with 16 runs as per MINITAB design software using graphene/water nanofluids in the corrugated plate type heat exchanger.  The nanofluid thermal conductivity was determined using the mixing rule for different nanofluid concentrations ranging from 0.1 to 1.0%. Normal, Pareto, Residual, Main and Interaction effects, Contour Plots were drawn. The Analysis of Variance (ANOVA) of test results depict that the hot fluid temperature and nanofluid concentration have significant effect on the thermal conductivity of graphene/water nanofluid (response variable)

A Modulation Technique for Sensorless Control of Switched Reluctance Motor

The switched reluctance motor (SRM) uniquely bears several merits with respect to other motor configurations. Especially, the construction of the rotor is simple in the sense that it neither contains copper not contains permanent magnets. Because of this construction, likelihood of rotor’s failure is less than the other motor configurations. This makes this motor more suitable for harsh environments. On the flip side, this motor cannot directly operate with AC or DC power source and needs electronic commutation. For commutation, the information on instantaneous orientation of the rotor is essential. Since inclusion of appropriate sensor adds to the cost and complexity of the system, sensor-less commutation of SRM gained interest among the researchers and has been studied extensively in literature. The techniques for sensorless control of SRM can be broadly classified into Active phase and Idle phase techniques. Idle phase techniques are generally believed to be not suitable for high speed operation beause of tail current in a phase, i.e., because of inductive nature of the phase, it takes time for flow of current to stop. This paper proposes a novel idle phase technique that is conducive for high speed operation of switched reluctance motor

Diagnosis of leukemia disease based on enhanced virtual neural network

White Blood Cell (WBC) cancer or leukemia is one of the serious cancers that threaten the existence of human beings. In spite of its prevalence and serious consequences, it is mostly diagnosed through manual practices. The risks of inappropriate, sub-standard and wrong or biased diagnosis are high in manual methods. So, there is a need exists for automatic diagnosis and classification method that can replace the manual process. Leukemia is mainly classified into acute and chronic types. The current research work proposed a computer-based application to classify the disease. In the feature extraction stage, we use excellent physical properties to improve the diagnostic system’s accuracy, based on Enhanced Color Co-Occurrence Matrix. The study is aimed at identification and classification of chronic lymphocytic leukemia using microscopic images of WBCs based on Enhanced Virtual Neural Network (EVNN) classification. The proposed method achieved optimum accuracy in detection and classification of leukemia from WBC images. Thus, the study results establish the superiority of the proposed method in automated diagnosis of leukemia. The values achieved by the proposed method in terms of sensitivity, specificity, accuracy, and error rate were 97.8%, 89.9%, 76.6%, and 2.2%, respectively. Furthermore, the system could predict the disease in prior through images, and the probabilities of disease detection are also highly optimistic

Biomedical Applications of Silver Nanoparticles

Nanotechnology is a branch of science and engineering dedicated to materials, having dimensions in the order of nanometer scale and it has been widely used for the development of more efficient technology. Nanoparticles offer many benefits to bulk particles such as increased surface-to-volume ratio, and increased magnetic properties. In recent years, nanotechnology has been embraced by industrial sectors due to its applications in the field of electronic storage systems, biotechnology, magnetic separation and pre concentration of target analytes, targeted drug delivery, and vehicles for gene and drug delivery. Over the year’s nanomaterials such as nanoparticles, nanoclusters, nanoreods, nanoshells, and nanocages have been continuously used and modified to enable their use as a diagnostic and therapeutic agent in biomedical applications. Thus, In this chapter, introduction to metal nanoparticles, synthesis (Chemical and green synthesis) and biomedical application silver nanoparticles are presented

Effective high compression of ECG signals at low level distortion

An effective method for compression of ECG signals, which falls within the transform lossy compression category, is proposed. The transformation is realized by a fast wavelet transform. The effectiveness of the approach, in relation to the simplicity and speed of its implementation, is a consequence of the efficient storage of the outputs of the algorithm which is realized in compressed Hierarchical Data Format. The compression performance is tested on the MIT-BIH Arrhythmia database producing compression results which largely improve upon recently reported benchmarks on the same database. For a distortion corresponding to a percentage root-mean-square difference (PRD) of 0.53, in mean value, the achieved average compression ratio is 23.17 with quality score of 43.93. For a mean value of PRD up to 1.71 the compression ratio increases up to 62.5. The compression of a 30 min record is realized in an average time of 0.14 s. The insignificant delay for the compression process, together with the high compression ratio achieved at low level distortion and the negligible time for the signal recovery, uphold the suitability of the technique for supporting distant clinical health care

Distinct Roles for Dectin-1 and TLR4 in the Pathogenesis of Aspergillus fumigatus Keratitis

Aspergillus species are a major worldwide cause of corneal ulcers, resulting in visual impairment and blindness in immunocompetent individuals. To enhance our understanding of the pathogenesis of Aspergillus keratitis, we developed a murine model in which red fluorescent protein (RFP)-expressing A. fumigatus (Af293.1RFP) conidia are injected into the corneal stroma, and disease progression and fungal survival are tracked over time. Using Mafia mice in which c-fms expressing macrophages and dendritic cells can be induced to undergo apoptosis, we demonstrated that the presence of resident corneal macrophages is essential for production of IL-1β and CXCL1/KC, and for recruitment of neutrophils and mononuclear cells into the corneal stroma. We found that β-glucan was highly expressed on germinating conidia and hyphae in the cornea stroma, and that both Dectin-1 and phospho-Syk were up-regulated in infected corneas. Additionally, we show that infected Dectin-1−/− corneas have impaired IL-1β and CXCL1/KC production, resulting in diminished cellular infiltration and fungal clearance compared with control mice, especially during infection with clinical isolates expressing high β-glucan. In contrast to Dectin 1−/− mice, cellular infiltration into infected TLR2−/−, TLR4−/−, and MD-2−/− mice corneas was unimpaired, indicating no role for these receptors in cell recruitment; however, fungal killing was significantly reduced in TLR4−/− mice, but not TLR2−/− or MD-2−/− mice. We also found that TRIF−/− and TIRAP−/− mice exhibited no fungal-killing defects, but that MyD88−/− and IL-1R1−/− mice were unable to regulate fungal growth. In conclusion, these data are consistent with a model in which β-glucan on A.fumigatus germinating conidia activates Dectin-1 on corneal macrophages to produce IL-1β, and CXCL1, which together with IL-1R1/MyD88-dependent activation, results in recruitment of neutrophils to the corneal stroma and TLR4-dependent fungal killing

Distinct Roles for Dectin-1 and TLR4 in the Pathogenesis of Aspergillus fumigatus Keratitis

Aspergillus species are a major worldwide cause of corneal ulcers, resulting in visual impairment and blindness in immunocompetent individuals. To enhance our understanding of the pathogenesis of Aspergillus keratitis, we developed a murine model in which red fluorescent protein (RFP)-expressing A. fumigatus (Af293.1RFP) conidia are injected into the corneal stroma, and disease progression and fungal survival are tracked over time. Using Mafia mice in which c-fms expressing macrophages and dendritic cells can be induced to undergo apoptosis, we demonstrated that the presence of resident corneal macrophages is essential for production of IL-1β and CXCL1/KC, and for recruitment of neutrophils and mononuclear cells into the corneal stroma. We found that β-glucan was highly expressed on germinating conidia and hyphae in the cornea stroma, and that both Dectin-1 and phospho-Syk were up-regulated in infected corneas. Additionally, we show that infected Dectin-1−/− corneas have impaired IL-1β and CXCL1/KC production, resulting in diminished cellular infiltration and fungal clearance compared with control mice, especially during infection with clinical isolates expressing high β-glucan. In contrast to Dectin 1−/− mice, cellular infiltration into infected TLR2−/−, TLR4−/−, and MD-2−/− mice corneas was unimpaired, indicating no role for these receptors in cell recruitment; however, fungal killing was significantly reduced in TLR4−/− mice, but not TLR2−/− or MD-2−/− mice. We also found that TRIF−/− and TIRAP−/− mice exhibited no fungal-killing defects, but that MyD88−/− and IL-1R1−/− mice were unable to regulate fungal growth. In conclusion, these data are consistent with a model in which β-glucan on A.fumigatus germinating conidia activates Dectin-1 on corneal macrophages to produce IL-1β, and CXCL1, which together with IL-1R1/MyD88-dependent activation, results in recruitment of neutrophils to the corneal stroma and TLR4-dependent fungal killing