Lorettine Educational History in New Mexico

The purpose of this study is to relate the history of the first American Community--the Sisters of Loretto--who crossed the American Desert in 1852 to establish schools in New Mexico, and to narrate the account of the foundation, organization, and development of each school in chronological order. Today the Sisters conduct eight elementary schools and four high schools in the State. Some of these schools, begun nearly a century ago within small adobe walls, are now recognized as establishments of culture and learning

THE RELATIONSHIP BETWEEN TRUST, TURNOVER INTENTIONS AND EMOTIONS: AN APPLICATION

Managerial leaders and directors are required to succeed the method of “sense” and “emotion” very well, to achieve organizational aims (Lee, 2002). Organizations need to manage all the emotions and feelings and concepts that affect persons health and performance (Gross, 1999). Business life dealing with this issue has increased in seriousness because, the effect of emotions in decision making cleared with academic studies. There is a need to find the path to the correct leadership of emotions. Emotions in the organization help to identify thoughts about the presence and desire to work (Jackson, 2006). The main purpose of this study, which should be considered a descriptive survey in the general sense, is to investigate the relationship between emotional expressions, trust and turnover intentions. Two different surveys were used in order to measure and assess the emotions expressions, trust and turnover intentions. The survey conducted on 200 employees of the public sector institution. Data, obtained from questionnaires analyzed through the SPSS statistical packaged software. We found that especially trust factors (Trust in management, co-worker trust and trust to manager) had a significant effect on “satisfaction” factor of turnover intentions. In addition, we found that “Co-worker trust” had a negative and significant effect on “Seek for job” factor of turnover intentions

New Method for the Development of Plasmonic Metal-Semiconductor Interface Layer: Polymer Composites with Reduced Energy Band Gap

Silver nanoparticles within a host polymer of chitosan were synthesized by using in situ method. Ultraviolet-visible spectroscopy was then carried out for the prepared chitosan : silver triflate (CS : AgTf) samples, showing a surface plasmonic resonance (SPR) peak at 420 nm. To prepare polymer composites with reduced energy band gap, different amounts of alumina nanoparticles were incorporated into the CS : AgTf solution. In the present work, the results showed that the reduced silver nanoparticles and their adsorption on wide band gap alumina (Al2O3) particles are an excellent approach for the preparation of polymer composites with small optical band gaps. The optical dielectric loss parameter has been used to determine the band gap experimentally. The physics behind the optical dielectric loss were interpreted from the viewpoint of quantum mechanics. From the quantum-mechanics viewpoint, optical dielectric loss was also found to be a complex equation and required lengthy numerical computation. From the TEM investigation, the adsorption of silver nanoparticles on alumina has been observed. The optical micrograph images showed white spots (silver specks) with different sizes on the surface of the films. The second semicircle in impedance Cole-Cole plots was found and attributed to the silver particles

Chitosan scaffolds with BMP-6 loaded alginate microspheres for periodontal tissue engineering

The aim of this study is to develop an effective growth factor releasing scaffold-microsphere system for promoting periodontal tissue engineering. Bone morphogenetic protein-6 (BMP-6)-loaded alginate microspheres in narrow size distribution were produced by optimising electrospraying conditions. The addition of these microspheres to chitosan gels produced a novel scaffold in which not only the pore sizes and interconnectivity were preserved, but also a controlled release vehicle was generated. Loading capacity was adjusted as 50ng or 100ng BMP-6 for each scaffold and the controlled release behaviour of BMP-6 from chitosan scaffolds was observed during seven days. Cell culture studies were carried out with rat mesenchymal stem cells derived from bone marrow in three groups; chitosan scaffolds, chitosan scaffolds containing BMP-6-loaded alginate microspheres and chitosan scaffolds with free BMP-6 in culture medium. Results showed that controlled delivery of BMP-6 from alginate microspheres has a significant effect on osteogenic differentiation. © 2012 Informa UK Ltd All rights reserved

PennyLane: Automatic differentiation of hybrid quantum-classical computations

PennyLane is a Python 3 software framework for optimization and machine learning of quantum and hybrid quantum-classical computations. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware. We provide plugins for Strawberry Fields, Rigetti Forest, Qiskit, Cirq, and ProjectQ, allowing PennyLane optimizations to be run on publicly accessible quantum devices provided by Rigetti and IBM Q. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, and autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications.Comment: Code available at https://github.com/XanaduAI/pennylane/ . Significant contributions to the code (new features, new plugins, etc.) will be recognized by the opportunity to be a co-author on this pape

A Novel Approach for Stock Price Prediction Using Gradient Boosting Machine with Feature Engineering (GBM-wFE)

The prediction of stock prices has become an exciting area for researchers as well as academicians due to its economic impact and potential business profits. This study proposes a novel multiclass classification ensemble learning approach for predicting stock prices based on historical data using feature engineering. The proposed approach comprises four main steps, which are pre-processing, feature selection, feature engineering, and ensemble methods. We use 11 datasets from Nasdaq and S&P 500 to ensure the accuracy of the proposed approach. Furthermore, eight feature selection algorithms are studied and implemented. More importantly, a feature engineering concept is applied to construct two new features, which are appears to be very auspicious in terms of improving classification accuracy, and this is considered the first study to use feature engineering for multiclass classification using ensemble methods. Finally, seven ensemble machine learning (ML) algorithms are used and compared to discover the ultimate collaboration prediction model. Besides, the best feature selection algorithm is proposed. This study proposes a novel multiclass classification approach called Gradient Boosting Machine with Feature Engineering (GBM-wFE) and Principal Component Analysis (PCA) as the feature selection. We find that GBM-wFE outperforms the previous studies and the overall prediction results are auspicious, as MAPE of 0.0406% is achieved, which is considered the best result compared to the available studies in the literature

Simulation and analysis for harvesting Dioscorea hispida tubers

This study discussed an analysis and simulation of fixture stand structure that will use for data collection of force required for harvesting the tubers of Dioscorea hispida. The tubers were surrounded by roots which were well gripped to the soil which made harvesting process difficult. Therefore, a new tool fixture equipped with digital force gauge device to gripped stem dioscorea hispida is required. Imada digital force measurement gauges are state-of-the-art, instruments capable of the highly accurate measurements required in quality testing to determine the strength or functionality of a part or product. The information from the experiments is used to model and simulate the tool in Computer Aided Design (CAD) environment. The solid modelling software Solidworks was used for the design, modelling and simulation of the equipment and the finite element analysis to determine the stress affected on various fixture designs