scholarly journals The Impact of the Next-Nearest Neighbor Dispersion Interactions on Spin Crossover Transition Enthalpy Evidenced by Experimental and Computational Analyses of Neutral π-Extended Heteroleptic Fe(III) Complexes

2020 ◽  
Vol 59 (17) ◽  
pp. 12295-12303
Author(s):  
Atsuhiro Miyawaki ◽  
Tomoyuki Mochida ◽  
Takahiro Sakurai ◽  
Hitoshi Ohta ◽  
Kazuyuki Takahashi
2017 ◽  
Vol 30 (4) ◽  
pp. 477-487 ◽  
Author(s):  
Naiara SPERANDIO ◽  
Cristiana Tristão RODRIGUES ◽  
Sylvia do Carmo Castro FRANCESCHINI ◽  
Silvia Eloiza PRIORE

ABSTRACT Objective: To assess and compare the impact of the Bolsa Família Program (Family Allowance) on the nutritional status of children and adolescents from the Brazilian Northeastern and Southeastern regions. Methods: The study used data from a database derived from a subsample of the Family Budget Survey conducted from 2008 to 2009. The ratios of underweight, stunted, and overweight children were calculated. Impact measurement analysis was preceded by propensity score matching, which matches beneficiary and non-beneficiary families in relation to a set of socioeconomic features. The nearest-neighbor matching algorithm estimated the program impact. Results: The ratio of underweight children and adolescents was, on average, 1.1% smaller in the beneficiary families than in the non-beneficiary families in the Northeastern region. As for the Southeastern region, the ratio of overweight children and adolescents was, on average, 4.2% smaller in the beneficiary families. The program did not affect stunting in either region. Conclusion: The results showed the positive impact and good focus of the program. Thus, once linked to structural actions, the program may help to improve the nutritional status and quality of life of its beneficiaries.


Author(s):  
Sangjae Lee ◽  
Joon Yeon Choeh

Abstract While electronic word-of-mouth (eWOM) variables, such as volume and valence have been posited in previous studies to consistently affect product sales, there is a lack of studies on the different contexts and outcomes that affect the importance of eWOM variables. In order to fill this gap, this study attempts to use the helpfulness of reviews and reviewers as moderators to predict box office revenue, comparing the prediction performances of business intelligence (BI) methods (random forest, decision trees using boosting, the k-nearest neighbor method, discriminant analysis) using eWOM between high and low review or reviewer helpfulness subsample in the Korean movie market scrawled from the Naver Movies website. The results of applying machine learning methods show that movies with more helpful reviews or those that are reviewed by more helpful reviewers show greater prediction performance, and review and reviewer helpfulness improve the prediction power of eWOM for box office revenue. The prediction performance will improve if the characteristics of eWOM are likely to be combined to contribute to box office revenue to a greater extent.


ILR Review ◽  
1987 ◽  
Vol 40 (3) ◽  
pp. 430-441 ◽  
Author(s):  
Katherine P. Dickinson ◽  
Terry R. Johnson ◽  
Richard W. West

This paper provides the first estimates of the net impact of CETA participation on the components of CETA participants' post-program earnings. Employing a sample of 1975 CETA enrollees and comparison groups drawn from the March 1978 CPS using a nearest-neighbor matching technique, the authors estimate statistically significant negative effects on men's earnings and statistically significant positive effects on women's earnings. These results stem partly from the impact of CETA participation on the likelihood of being employed after leaving the program (negative for men, positive for women), but also from a negative impact on hours worked during the year and hourly wage rate for men and a large positive impact on hours worked per week and weeks worked per year for women.


Author(s):  
Bradley L. Jolliff

Earth’s moon, hereafter referred to as “the Moon,” has been an object of intense study since before the time of the Apollo and Luna missions to the lunar surface and associated sample returns. As a differentiated rocky body and as Earth’s companion in the solar system, much study has been given to aspects such as the Moon’s surface characteristics, composition, interior, geologic history, origin, and what it records about the early history of the Earth-Moon system and the evolution of differentiated rocky bodies in the solar system. Much of the Apollo and post-Apollo knowledge came from surface geologic exploration, remote sensing, and extensive studies of the lunar samples. After a hiatus of nearly two decades following the end of Apollo and Luna missions, a new era of lunar exploration began with a series of orbital missions, including missions designed to prepare the way for longer duration human use and further exploration of the Moon. Participation in these missions has become international. The more recent missions have provided global context and have investigated composition, mineralogy, topography, gravity, tectonics, thermal evolution of the interior, thermal and radiation environments at the surface, exosphere composition and phenomena, and characteristics of the poles with their permanently shaded cold-trap environments. New samples were recognized as a class of achondrite meteorites, shown through geochemical and mineralogical similarities to have originated on the Moon. New sample-based studies with ever-improving analytical techniques and approaches have also led to significant discoveries such as the determination of volatile contents, including intrinsic H contents of lunar minerals and glasses. The Moon preserves a record of the impact history of the solar system, and new developments in timing of events, sample based and model based, are leading to a new reckoning of planetary chronology and the events that occurred in the early solar system. The new data provide the grist to test models of formation of the Moon and its early differentiation, and its thermal and volcanic evolution. Thought to have been born of a giant impact into early Earth, new data are providing key constraints on timing and process. The new data are also being used to test hypotheses and work out details such as for the magma ocean concept, the possible existence of an early magnetic field generated by a core dynamo, the effects of intense asteroidal and cometary bombardment during the first 500 million–600 million years, sequestration of volatile compounds at the poles, volcanism through time, including new information about the youngest volcanism on the Moon, and the formation and degradation processes of impact craters, so well preserved on the Moon. The Moon is a natural laboratory and cornerstone for understanding many processes operating in the space environment of the Earth and Moon, now and in the past, and of the geologic processes that have affected the planets through time. The Moon is a destination for further human exploration and activity, including use of valuable resources in space. It behooves humanity to learn as much about Earth’s nearest neighbor in space as possible.


2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Renzhou Gui ◽  
Tongjie Chen ◽  
Han Nie

With the continuous development of science, more and more research results have proved that machine learning is capable of diagnosing and studying the major depressive disorder (MDD) in the brain. We propose a deep learning network with multibranch and local residual feedback, for four different types of functional magnetic resonance imaging (fMRI) data produced by depressed patients and control people under the condition of listening to positive- and negative-emotions music. We use the large convolution kernel of the same size as the correlation matrix to match the features and obtain the results of feature matching of 264 regions of interest (ROIs). Firstly, four-dimensional fMRI data are used to generate the two-dimensional correlation matrix of one person’s brain based on ROIs and then processed by the threshold value which is selected according to the characteristics of complex network and small-world network. After that, the deep learning model in this paper is compared with support vector machine (SVM), logistic regression (LR), k-nearest neighbor (kNN), a common deep neural network (DNN), and a deep convolutional neural network (CNN) for classification. Finally, we further calculate the matched ROIs from the intermediate results of our deep learning model which can help related fields further explore the pathogeny of depression patients.


Nanomaterials ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. 1059 ◽  
Author(s):  
Ivana Miháliková ◽  
Martin Friák ◽  
Yvonna Jirásková ◽  
David Holec ◽  
Nikola Koutná ◽  
...  

Quantum-mechanical calculations are applied to examine magnetic and electronic properties of phases appearing in binary Fe-Al-based nanocomposites. The calculations are carried out using the Vienna Ab-initio Simulation Package which implements density functional theory and generalized gradient approximation. The focus is on a disordered solid solution with 18.75 at. % Al in body-centered-cubic ferromagnetic iron, so-called α -phase, and an ordered intermetallic compound Fe 3 Al with the D0 3 structure. In order to reveal the impact of the actual atomic distribution in the disordered Fe-Al α -phase three different special quasi-random structures with or without the 1st and/or 2nd nearest-neighbor Al-Al pairs are used. According to our calculations, energy decreases when eliminating the 1st and 2nd nearest neighbor Al-Al pairs. On the other hand, the local magnetic moments of the Fe atoms decrease with Al concentration in the 1st coordination sphere and increase if the concentration of Al atoms increases in the 2nd one. Furthermore, when simulating Fe-Al/Fe 3 Al nanocomposites (superlattices), changes of local magnetic moments of the Fe atoms up to 0.5 μ B are predicted. These changes very sensitively depend on both the distribution of atoms and the crystallographic orientation of the interfaces.


2019 ◽  
Vol 9 (11) ◽  
pp. 2337 ◽  
Author(s):  
Imran Ashraf ◽  
Soojung Hur ◽  
Yongwan Park

Indoor localization systems are susceptible to higher errors and do not meet the current standards of indoor localization. Moreover, the performance of such approaches is limited by device dependence. The use of Wi-Fi makes the localization process vulnerable to dynamic factors and energy hungry. A multi-sensor fusion based indoor localization approach is proposed to overcome these issues. The proposed approach predicts pedestrians’ current location with smartphone sensors data alone. The proposed approach aims at mitigating the impact of device dependency on the localization accuracy and lowering the localization error in the magnetic field based localization systems. We trained a deep learning based convolutional neural network to recognize the indoor scene which helps to lower the localization error. The recognized scene is used to identify a specific floor and narrow the search space. The database built of magnetic field patterns helps to lower the device dependence. A modified K nearest neighbor (mKNN) is presented to calculate the pedestrian’s current location. The data from pedestrian dead reckoning further refines this location and an extended Kalman filter is implemented to this end. The performance of the proposed approach is tested with experiments on Galaxy S8 and LG G6 smartphones. The experimental results demonstrate that the proposed approach can achieve an accuracy of 1.04 m at 50 percent, regardless of the smartphone used for localization. The proposed mKNN outperforms K nearest neighbor approach, and mean, variance, and maximum errors are lower than those of KNN. Moreover, the proposed approach does not use Wi-Fi for localization and is more energy efficient than those of Wi-Fi based approaches. Experiments reveal that localization without scene recognition leads to higher errors.


2017 ◽  
Vol 8 (1) ◽  
pp. 701-707 ◽  
Author(s):  
Natasha F. Sciortino ◽  
Katrina A. Zenere ◽  
Maggie E. Corrigan ◽  
Gregory J. Halder ◽  
Guillaume Chastanet ◽  
...  

A rare four-step spin crossover transition has been attained in a two-dimensional Hofmann-type material through the presence of an array of antagonistic host–host and host–guest interactions.


2019 ◽  
Vol 10 (18) ◽  
pp. 4930-4930
Author(s):  
Víctor Rubio-Giménez ◽  
Carlos Bartual-Murgui ◽  
Marta Galbiati ◽  
Alejandro Núñez-López ◽  
Javier Castells-Gil ◽  
...  

Correction for ‘Effect of nanostructuration on the spin crossover transition in crystalline ultrathin films’ by Víctor Rubio-Giménez et al., Chem. Sci., 2019, DOI: 10.1039/c8sc04935a.


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