Learning to transform time series with a few examples

We describe a semi-supervised regression algorithm that learns to transform one time series into another time series given examples of the transformation. This algorithm is applied to tracking, where a time series of observations from sensors is transformed to a time series describing the pose of a target. Instead of defining and implementing such transformations for each tracking task separately, our algorithm learns a memoryless transformation of time series from a few example input-output mappings. The algorithm searches for a smooth function that fits the training examples and, when applied to the input time series, produces a time series that evolves according to assumed dynamics. The learning procedure is fast and lends itself to a closed-form solution. It is closely related to nonlinear system identification and manifold learning techniques. We demonstrate our algorithm on the tasks of tracking RFID tags from signal strength measurements, recovering the pose of rigid objects, deformable bodies, and articulated bodies from video sequences. For these tasks, this algorithm requires significantly fewer examples compared to fully-supervised regression algorithms or semi-supervised learning algorithms that do not take the dynamics of the output time series into account.     

The role of metacognitive listening strategies awareness and podcast-use readiness in using podcasting for learning English as a foreign language

The aim of this study was to investigate the role of English as Foreign Language (EFL) learners’ metacognitive listening strategies awareness and podcast-use readiness in using podcasting technology for learning English as a foreign language. One hundred and forty-one EFL students completed Metacognitive Awareness Listening Questionnaire (MALQ) that assessed their awareness and perceived use of listening strategies in five components including planning-evaluation, directed attention, person knowledge, mental translation, and problem solving. They also completed a questionnaire that assessed their readiness to use podcasting in terms of familiarity, attitude, and experience. Information on participants’ frequency of podcast use for learning English, frequency of the internet use, and digital device ownership was also obtained. The result of the analysis revealed that podcasting use was significantly related to metacognitive listening strategies awareness in general and its entire components except mental translation strategies while the strongest correlation was found with problem solving strategies (r = .49, p < 0.01). Podcasting use was also found to be significantly related to perceived podcast-use readiness and internet use hours. Further, multiple regressions showed that perceived podcast-use readiness, problem solving, and person knowledge -in order of power prediction- were good predictors of podcasting use for learning English as a foreign language.     

Influence of surface roughness on flotation kinetics of quartz

Surface roughness of quartz particles was determined by measuring the specific surface area of particles. The wettability characteristics of particles were determined by measuring the flotation rate using a laboratory flotation cell. Experimental results show that the rod mill product has higher roughness than the ball mill product. For the particles with larger surface roughness, the flotation kinetics constant is also higher. Finally, empirical relationships between surface roughness (r) and the flotation kinetics constant (k) of quartz particles as k=A+Br+Cr ^0.5lnr+D/lnr+E/r and k=A+Br are presented, in which A, B, C, D and E are constants related to experimental conditions and mineralogical properties of mineral.     

Mathematical Modeling and Genetic Algorithm Optimization of Reactive Absorption of H2S

A rate‐based mathematical model was developed for the reactive absorption of H₂S in NaOH, with NaOCl or H₂O₂ as the chemical oxidant solutions in a packed column. A modified mass transfer coefficient in the gas phase was obtained by genetic algorithm and implemented in the model to correct the assumption of instantaneous reactions. The effects of different operating variables including the inlet H₂S concentration, inlet gas mass flux, initial NaOH, concentrations of the chemical oxidants in the scrubbing solutions, and liquid‐to‐gas ratio on the H₂S removal efficiency were studied. A genetic algorithm was employed to optimize the operating variables in order to obtain maximum removal efficiency of H₂S. The model results were in good agreement with the experimental data.     

Investigating the effect of nano-silica on efficiency of the foam in enhanced oil recovery

Due to the vast production of crude oil and consequent pressure drops through the reservoirs, secondary and tertiary oil recovery processes are highly necessary to recover the trapped oil. Among the different tertiary oil recovery processes, foam injection is one of the most newly proposed methods. In this regard, in the current investigation, foam solution is prepared using formation brine, C₁₉TAB surfactant and air concomitant with nano-silica (SiO₂) as foam stabilizer and mobility controller. The measurements revealed that using the surfactant-nano SiO₂ foam solution not only leads to formation of stable foam, but also can reduce the interfacial tension mostly considered as an effective parameter for higher oil recovery. Finally, the results demonstrate that there is a good chance of reducing the mobility ratio from 1.12 for formation brine and reservoir oil to 0.845 for foam solution prepared by nanoparticles.     

Calcium: A comprehensive review on quantification,interaction with milk proteins and implications for processing of dairy products

Calcium (Ca) is a key micronutrient of high relevance for human nutrition that also influences the texture and taste of dairy products and their processability. In bovine milk, Ca is presented in several speciation forms, such as complexed with other milk components or free as ionic calcium while being distributed between colloidal and serum phases of milk. Partitioning of Ca between these phases is highly dynamic and influenced by factors, such as temperature, ionic strength, pH, and milk composition. Processing steps used during the manufacture of dairy products, such as preconditioning, concentration, acidification, salting, cooling, and heating, all contribute to modify Ca speciation and partition, thereby influencing product functionality, product yield, and fouling of equipment. This review aims to provide a comprehensive understanding of the influence of Ca partition on dairy products properties to support the development of kinetics models to reduce product losses and develop added-value products with improved functionality. To achieve this objective, approaches to separate milk phases, analytical approaches to determine Ca partition and speciation, the role of Ca on protein–protein interactions, and their influence on processing of dairy products are discussed.     

H2O based different nanofluids with unsteady condition and an external magnetic field on permeable channel heat transfer

This paper investigates numerically the problem of unsteady magnetohydrodynamic nanofluid flow and heat transfer between parallel plates due to the normal motion of the porous upper plate. The governing equations are solved via the fourth-order Runge-Kutta method. Different kind of nanoparticles is examined. The effects of kind of nanoparticle, nanofluid volume fraction, expansion ratio, Hartmann number, Reynolds number on velocity and temperature profiles are considered. Also effect of different types of nanoparticles is examined. Results indicate that velocity decreases with increase of Hartmann number due to effect of Lorentz forces. Rate of heat transfer increase with increase of nanofluid volume fraction, Hartmann number and Reynolds number but it decreases with increase of expansion ratio. Also it can be found that choosing copper as a nanoparticle leads to highest enhancement.