Semantic web service-based messaging framework for prediction of fitness data using Hadoop distributed file system

Big data is coined as word of mouth in this era due to the generation of huge volume of data every second from multiple sources like logs, web sources, and sensors, electrical and electronic devices. The manipulation is performed over the injected data and is termed as Data Processing Segment. In this proposed paper the data are obtained from the wearable devices with attributes like calories, weight, fat, step count, sleep, BMI and so on. The obtained data is stored in HDFS in a persistence manner. The component Kafka acts as a queue for the real time data and regulates the data before storing in HDFS. Now Apache Spark does the streaming of data. Here the data are cleaned, applied the Machine Learning Algorithms (KNN Classifier) to obtain the model, by splitting the cleaned data into Training data and Testing Data. Now the obtained predicted result is sent to Web service Telephony ontology, which in turns communicates with ontology service repository consisting of cloud telephony services ontology and fitness activity ontology through OWL API. The classified and predicted value is analysed and intimated to the users through visualization graphs, SMS, IVR and e-mail

Atypical behaviors of a tagged particle in asymmetric simple exclusion

Consider the asymmetric nearest-neighbor exclusion process (ASEP) on ${\mathbb Z}$ with single particle drift $\gamma>0$, starting from a Bernoulli product invariant measure $\nu_\rho$ with density $\rho$. It is known that the position $X_{N}$ of a tagged particle, say initially at the origin, at time $N$ satisfies an a.s. law of large numbers $\frac{1}{N}X_N \rightarrow \gamma(1-\rho)$ as $N\uparrow\infty$. In this context, we study the `typical' behavior of the tagged particle and `bulk' density evolution subject to `atypical' events $\{X_N\geq AN\}$ or $\{X_N\leq AN\}$ for $A\neq \gamma(1-\rho)$. We detail different structures, depending on whether $A<0$, $0\leq A< \gamma(1-\rho)$, $\gamma(1-\rho)<A< \gamma$, or $A\geq \gamma$, under which these atypical events are achieved, and compute associated large deviation costs. Among our results is an `upper tail' large deviation principle in scale $N$ for $\frac{1}{N}X_N$.Comment: 39 page

Real-time Measurement of Stress and Damage Evolution During Initial Lithiation of Crystalline Silicon

Crystalline to amorphous phase transformation during initial lithiation in (100) silicon-wafers is studied in an electrochemical cell with lithium metal as the counter and reference electrode. It is demonstrated that severe stress jumps across the phase boundary lead to fracture and damage, which is an essential consideration in designing silicon based anodes for lithium ion batteries. During initial lithiation, a moving phase boundary advances into the wafer starting from the surface facing the lithium electrode, transforming crystalline silicon into amorphous LixSi. The resulting biaxial compressive stress in the amorphous layer is measured in situ and it was observed to be ca. 0.5 GPa. HRTEM images reveal that the crystalline-amorphous phase boundary is very sharp, with a thickness of ~ 1 nm. Upon delithiation, the stress rapidly reverses, becomes tensile and the amorphous layer begins to deform plastically at around 0.5 GPa. With continued delithiation, the yield stress increases in magnitude, culminating in sudden fracture of the amorphous layer into micro-fragments and the cracks extend into the underlying crystalline silicon.Comment: 12 pages, 5 figure

Large deviations for the current and tagged particle in 1D nearest-neighbor symmetric simple exclusion

Laws of large numbers, starting from certain nonequilibrium measures, have been shown for the integrated current across a bond, and a tagged particle in one-dimensional symmetric nearest-neighbor simple exclusion [Ann. Inst. Henri Poincare Probab. Stat. 42 (2006) 567-577]. In this article, we prove corresponding large deviation principles and evaluate the rate functions, showing different growth behaviors near and far from their zeroes which connect with results in [J. Stat. Phys. 136 (2009) 1-15].Comment: Published in at http://dx.doi.org/10.1214/11-AOP703 the Annals of Probability (http://www.imstat.org/aop/) by the Institute of Mathematical Statistics (http://www.imstat.org