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

Reduced combustion mechanism for fire with light alcohols

The need for sustainable energy has incentivized the use of alternative fuels such as light alcohols. In this work, reduced chemistry mechanisms for the prediction of fires (pool fire, tank fire, and flash fire) for two primary alcohols—methanol and ethanol—were developed, aiming to integrate the detailed kinetic model into the computational fluid dynamics (CFD) model. The model accommodates either the pure reactants and products or other intermediates, including soot precursors (C2H2, C2H4, and C3H3 ), which were identified via sensitivity and reaction path analyses. The developed reduced mechanism was adopted to predict the burning behavior in a 3D domain and for the estimation of the product distribution. The agreement between the experimental data from the literature and estimations resulting from the analysis performed in this work demonstrates the successful application of this method for the integration of kinetic mechanisms and CFD models, opening to an accurate evaluation of safety scenarios and allowing for the proper design of storage and transportation systems involving light alcohols

On the prediction of the ignition delay time of bio-syngas

The growing energy demand and more stringent environmental regulations have raised concerns about the production and use of alternative fuels. Due to the potential application of the resulting gaseous streams in turbines as an energy source, slow pyrolysis of biomass including municipal waste have been extensively studied under various situations and atmospheric conditions. Nevertheless, the combustion characteristics of these complex mixtures and the chemical interactions between their constituent species are still not fully understood. Hence, the accuracy of commonly used empirical-based mixing rules for the estimation of the overall reactivity, such as laminar burning velocity and ignition delay time is inefficient. This work is addressed to the numerical prediction of the Ignition Delay Time, IDT, of bio-syngas mixtures at different fuel compositions, stoichiometries, temperature, and pressure, by means of a detailed kinetic model. A simplified tool for preliminary evaluation of the overall reactivity with respect to the above-mentioned conditions was proposed for these mixtures, as well, providing an effective feature for safety and management evaluations

Pengaruh Bauran Pemasaran Terhadap Keputusan Konsumen Berbelanja Pada PT Jumbo Swalayan Manado

. This study aimed to determine the effect of marketing mix on consumer shopping decisions on PT Jumbo Supermarket in Manado. Marketing mix consists of product, price, promotion and place as the independent variable and the consumer's decision to shop as the dependent variable. In this research using correlation and regression analyzes with the total sample of 45 customers. Partial results showed all the hypotheses of the elements of the marketing mix significantly influence the consumer's decision to shop, but the biggest influence is the element of the place. Likewise, simultaneous marketing mix significantly influence consumer shopping decisions. Impikasi of this study, expected jumbo self-management should continue to integrate the elements of the marketing mix to enable the powerful synergy for maximum business performance. For further research, researchers can develop other variables that consumer shopping decisions can be assessed from different elements

Design of sustainable reactor based on key performance indicators

The design of chemical reactors has been largely considered primarily related to techno-economic evaluations. However, the recent need for sustainable solutions and processes has promoted the inclusion of environmental and safety parameters to identify the most suitable solution. In this sense, an innovative procedure has been developed in this work to identify and design a reactive section in chemical processes. To this aim, different key performance indicators have been defined and quantified for each domain considered within the analysis, namely technological, economic, environmental, and safety domains. In addition, the safety aspects have been quantified by integrating Semenov's theory and Varma, Morbidelli and Wu's theory. The validity and potentiality of the proposed procedure have been tested and shown by applying it to a case study representative for the scale-up of pharmaceutical processes: the industrial synthesis of a Vitamin A intermediate. A preliminary design has been performed for different configurations based on apparent kinetics determined from experimental data and ab initio coefficients available in the current literature. Among the analysed solutions, a single reactor with a volume of 15.90 m3 has been indicated as the most suitable for the process requirements regarding overall sustainability. Hence, the developed procedure can be intended as a powerful tool for screening among available configurations, enabling a more informed decision by simplifying and optimising the scale-up and the detailed design

BROCCOLI: overlapping and outlier-robust biclustering through proximal stochastic gradient descent

Matrix tri-factorization subject to binary constraints is a versatile and powerful framework for the simultaneous clustering of observations and features, also known as biclustering. Applications for biclustering encompass the clustering of high-dimensional data and explorative data mining, where the selection of the most important features is relevant. Unfortunately, due to the lack of suitable methods for the optimization subject to binary constraints, the powerful framework of biclustering is typically constrained to clusterings which partition the set of observations or features. As a result, overlap between clusters cannot be modelled and every item, even outliers in the data, have to be assigned to exactly one cluster. In this paper we propose Broccoli, an optimization scheme for matrix factorization subject to binary constraints, which is based on the theoretically well-founded optimization scheme of proximal stochastic gradient descent. Thereby, we do not impose any restrictions on the obtained clusters. Our experimental evaluation, performed on both synthetic and real-world data, and against 6 competitor algorithms, show reliable and competitive performance, even in presence of a high amount of noise in the data. Moreover, a qualitative analysis of the identified clusters shows that Broccoli may provide meaningful and interpretable clustering structures

Chemical and Thermal Effects of Trace Components in Hydrogen Rich Gases on Combustion

The production of carbon-neutral fuels through clean energy has been defined as a target by the European Union and by several international institutions. If the concepts are available, hydrogen, in particular, is considered to be one of the most target-oriented, ecologically and economically realizable approaches. In terms of safety, long-time storage and long-distance transport of hydrogen are still under development. However, pipeline systems similar to those for natural gas are being considered. Gas quality criteria will have to be developed for this case. The effects of trace components in the hydrogen on chemical and thermal aspects are not yet sufficiently understood and need to be characterized more precisely. For these reasons, this work presents a detailed analysis for a more complete understanding of the phenomena involved. More specifically, the flame structure, temperature profile and overall reactivity were first determined for gas mixtures analyzed under four varying dilutions of carbon dioxide, carbon oxide, nitrogen and methane in hydrogen. The characterization of the total reactivity and the laminar burning velocity offers an appealing solution to quantify the effects of dilution. The most distinctive effects of the operating conditions on the ignition phenomena have been worked out numerically for the lower and upper boundaries and have been discussed. The results collected in this work provide a robust feature for a detailed evaluation of normal operation as well as the accidental release of the hydrogen-rich fuels

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

Spatially-Aware Autoencoders for Detecting Contextual Anomalies in Geo-Distributed Data

The huge amount of data generated by sensor networks enables many potential analyses. However, one important limiting factor for the analyses of sensor data is the possible presence of anomalies, which may affect the validity of any conclusion we could draw. This aspect motivates the adoption of a preliminary anomaly detection method. Existing methods usually do not consider the spatial nature of data generated by sensor networks. Properly modeling the spatial nature of the data, by explicitly considering spatial autocorrelation phenomena, has the potential to highlight the degree of agreement or disagreement of multiple sensor measurements located in different geographical positions. The intuition is that one could improve anomaly detection performance by considering the spatial context. In this paper, we propose a spatially-aware anomaly detection method based on a stacked auto-encoder architecture. Specifically, the proposed architecture includes a specific encoding stage that models the spatial autocorrelation in data observed at different locations. Finally, a distance-based approach leverages the embedding features returned by the auto-encoder to identify possible anomalies. Our experimental evaluation on real-world geo-distributed data collected from renewable energy plants shows the effectiveness of the proposed method, also when compared to state-of-the-art anomaly detection methods

ECHAD: Embedding-Based Change Detection from Multivariate Time Series in Smart Grids

Smart grids are power grids where clients may actively participate in energy production, storage and distribution. Smart grid management raises several challenges, including the possible changes and evolutions in terms of energy consumption and production, that must be taken into account in order to properly regulate the energy distribution. In this context, machine learning methods can be fruitfully adopted to support the analysis and to predict the behavior of smart grids, by exploiting the large amount of streaming data generated by sensor networks. In this article, we propose a novel change detection method, called ECHAD (Embedding-based CHAnge Detection), that leverages embedding techniques, one-class learning, and a dynamic detection approach that incrementally updates the learned model to reflect the new data distribution. Our experiments show that ECHAD achieves optimal performances on synthetic data representing challenging scenarios. Moreover, a qualitative analysis of the results obtained on real data of a real power grid reveals the quality of the change detection of ECHAD. Specifically, a comparison with state-of-the-art approaches shows the ability of ECHAD in identifying additional relevant changes, not detected by competitors, avoiding false positive detections