Ab-initio prediction of a new multiferroic with large polarization and magnetization

We describe the design of a new magnetic ferroelectric with large spontaneous magnetization and polarization using first-principles density functional theory. The usual difficulties associated with the production of robustly-insulating ferromagnets are circumvented by incorporating the magnetism through {\it ferri-}magnetic behavior. We show that the the ordered perovskite \BFCO will have a polarization of $\sim$80 $\mu$C/cm$^2$, a piezoelectric coefficient of 283 $\mu$C/cm$^{2}$, and a magnetization of $\sim$160 emu/cm$^3$ (2 $\mu_B$ per formula unit), far exceeding the properties of any known multiferroic

HTself2: Combining p-values to Improve Classification of Differential Gene Expression in HTself

HTself is a web-based bioinformatics tool designed to deal with the classification of differential gene expression for low replication microarray studies. It is based on a statistical test that uses self-self experiments to derive intensity-dependent cutoffs. The method was previously described in Vêncio et al, (DNA Res. 12: 211- e 214, 2005). In this work we consider an extension of HTself by calculating p-values instead of using a fixed credibility level α. As before, the statistic used to compute single spots p-values is obtained from the gaussian Kernel Density Estimator method applied to self-self data. Different spots corresponding to the same biological gene (replicas) give rise to a set of independent p-values which can be combined by well known statistical methods. The combined p-value can be used to decide whether a gene can be considered differentially expressed or not. HTself2 is a new version of HTself that uses the idea of p-values combination. It was implemented as a user-friendly desktop application to help laboratories without a bioinformatics infrastructure

A STUDY OF STUDENTS WITH DYSCALCULIA AND THEIR MATHEMATICAL ABILITIES AT PRIMARY SCHOOLS IN KARAIKUDI

Education is considered to be a key factor for the growth and process of any society. Mathematics Education is an important component of school Education. Learning difficulties is a major contributing factor for the children in schools, which requires immediate concern. Dyscalculia refers to an incurable difficulty in learning and understanding Mathematics. Primary School students find these difficulties in learning of number concepts and basic arithmetic. The study under investigation is to find out children who are affected by dyscalculia in the schools of karaikudi. The investigator proposes to use Survey Method for this study. Population for the study is Primary school students in karaikudi. The random sampling technique was used for this study. The sample is 100 primary school students in karaikudi. The tool for the study is adopted screening tool for identifying the dyscalculic students. The study revealed 9% students of the primary school in karaikudi were found to be dyscalculia. Hence identifying the dyscalculic students and giving them the necessary intervention programmes to improve their learning difficulties in Mathematics and it is the need of the hour.

Muslim Diaspora in the West and International HRM

Interest in Islam and how Muslims organise themselves within the so-called Western world has largely stemmed from the flow of Muslim immigration since the 1960s and the 1970s (Loobuyck, Debeer, & Meier, 2013). Many of these immigrants have come to these new lands in the hope of making a better life for themselves economically, or to escape the political or religious pressures of their homeland (Lebl, 2014). Initially, deeming the influx of these foreigners to be largely irrelevant, there was little interest in their presence by the different governments across many jurisdictions. Typically, scant interest was shown towards entering into dialogue with the Muslim immigrant community. Indeed, until the 1990s, it was not uncommon for Islam to be perceived as a strange, foreign religion that was best managed through outsourcing to respective consulates (Loobuyck et al., 2013). Yet, migration and work-based mobility has a significant influence on the world of work and societies in which organisations are embedded. Many individuals migrate for better employment perspectives, as well as due to chain migration, betterment in the quality of life and based on fleeing famine, war and terror zones globally (Sharma & Reimer-Kirkham, 2015; Valiūnienė, 2016). Migration could involve upward as well as downward mobility/ wages, depending on the country and organisation. For example, minimum wages differ from € 184 in Bulgaria up to € 1923 in Luxembourg (Valiūnienė, 2016). Migration also contributes to the lived religion of diasporic communities as they navigate their faith at work (Sharma & Reimer-Kirkha

Active inference and robot control: a case study.

Active inference is a general framework for perception and action that is gaining prominence in computational and systems neuroscience but is less known outside these fields. Here, we discuss a proof-of-principle implementation of the active inference scheme for the control or the 7-DoF arm of a (simulated) PR2 robot. By manipulating visual and proprioceptive noise levels, we show under which conditions robot control under the active inference scheme is accurate. Besides accurate control, our analysis of the internal system dynamics (e.g. the dynamics of the hidden states that are inferred during the inference) sheds light on key aspects of the framework such as the quintessentially multimodal nature of control and the differential roles of proprioception and vision. In the discussion, we consider the potential importance of being able to implement active inference in robots. In particular, we briefly review the opportunities for modelling psychophysiological phenomena such as sensory attenuation and related failures of gain control, of the sort seen in Parkinson's disease. We also consider the fundamental difference between active inference and optimal control formulations, showing that in the former the heavy lifting shifts from solving a dynamical inverse problem to creating deep forward or generative models with dynamics, whose attracting sets prescribe desired behaviours

A novel framework for integrating real-time optimization and optimal scheduling : Application to heat and power systems

The optimization of heat and power systems operation is a complex task that involves continuous and discrete variables, operating and environmental constraints, uncertain prices and demands and transition constraints for startups or shutdowns. This work proposes a novel methodology for integrating scheduling optimization and real-time optimization (RTO) in order to face and solve such optimization problem. In a first stage, an offline optimization finds a scheduling for the whole horizon under study, which sets the startups and shutdowns of pieces of equipment with long transition times. A second stage solves a multiperiod RTO, which corrects the forecasts and adapts the model before optimiz-ing the process. Although the proposed methodology is illustrated through a case study consisting in a heat and power system, it can be generalized to other systems and processes. The obtained results show significant improvements in comparison with applying the results of a single offline scheduling optimization.Sociedad Argentina de Informática e Investigación Operativa (SADIO

A novel framework for integrating real-time optimization and optimal scheduling : Application to heat and power systems

The optimization of heat and power systems operation is a complex task that involves continuous and discrete variables, operating and environmental constraints, uncertain prices and demands and transition constraints for startups or shutdowns. This work proposes a novel methodology for integrating scheduling optimization and real-time optimization (RTO) in order to face and solve such optimization problem. In a first stage, an offline optimization finds a scheduling for the whole horizon under study, which sets the startups and shutdowns of pieces of equipment with long transition times. A second stage solves a multiperiod RTO, which corrects the forecasts and adapts the model before optimiz-ing the process. Although the proposed methodology is illustrated through a case study consisting in a heat and power system, it can be generalized to other systems and processes. The obtained results show significant improvements in comparison with applying the results of a single offline scheduling optimization.Sociedad Argentina de Informática e Investigación Operativa (SADIO