Estimating nonlinear effects of management styles in the US equity market using a classifier neural network

2003 ◽  
Author(s):  
D. diBartolomeo ◽  
S. Warrick
Keyword(s):  
Neural Network ◽  
Nonlinear Effects ◽  
Equity Market ◽  
Management Styles ◽  
The Us

Comparative Management of Human Resources between USA and Japan

2018 ◽  
Vol 9 (1) ◽  
pp. 93-100
Author(s):  
Evelyne Ingrid Mitu ◽  
Mile Vasic
Keyword(s):  
Human Resources ◽  
Labor Relations ◽  
Management Styles ◽  
Cultural Management ◽  
Japanese Management ◽  
Global Competitiveness ◽  
Comparative Management ◽  
The Us ◽  
Huge Impact ◽  
National Economies

Abstract Despite the differences between Japanese and styles, both will have a huge impact on their national economies. In terms of cultural management styles will continue to present significant differences. Although nothing is certain, both Americans and Japanese must continue to adapt their management styles to maintain global competitiveness. In general, human resources, labor relations within organizations are mainly features that differentiate the Japanese management system of other countries, especially the US.

scholarly journals The disappearance of style in the US equity market

2007 ◽  
Vol 17 (8) ◽  
pp. 597-613 ◽  
Author(s):  
Soosung Hwang ◽  
Stephen E. Satchell
Keyword(s):  
Equity Market ◽  
The Us

scholarly journals International portfolio diversification: United States and south Asian equity markets

2014 ◽  
Vol 61 (2) ◽  
pp. 241-252 ◽  
Author(s):  
Rizwan Mushtaq ◽  
Zulfiqar Shah
Keyword(s):  
Stock Market ◽  
South Asian ◽  
Equity Markets ◽  
Equity Market ◽  
Causality Test ◽  
Market Index ◽  
International Portfolio Diversification ◽  
The Us ◽  
Short And Long Term

This paper explores the dynamic liaison between US and three developing South Asian equity markets in short and long term. To gauge the long-term relationship, we applied Johansen co-integration procedure as all the representative indices are found to be non-stationary at level. The findings illustrate that the US equity market index exhibits a reasonably different movement over time in contrast to the three developing equity markets under consideration. However, the Granger-causality test divulge that the direction of causality scamper from US equity market to the three South Asian markets. It further indicates that within the three developing equity markets the direction of causality emanates from Bombay stock market to Karachi and Colombo. Overall, the results of the study suggest that the American investors can get higher returns through international diversification into developing equity markets, while the US stock market would also be a gainful upshot for South Asian investors.

scholarly journals An Artificial Neural Network Approach to Forecast the Environmental Impact of Data Centers

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 113 ◽  
Author(s):  
Joao Ferreira ◽  
Gustavo Callou ◽  
Albert Josua ◽  
Dietmar Tutsch ◽  
Paulo Maciel
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Consumption ◽  
Co2 Emissions ◽  
Data Center ◽  
Data Centers ◽  
New Technologies ◽  
Operational Costs ◽  
The Us ◽  
Artificial Neural

Due to the high demands of new technologies such as social networks, e-commerce and cloud computing, more energy is being consumed in order to store all the data produced and provide the high availability required. Over the years, this increase in energy consumption has brought about a rise in both the environmental impacts and operational costs. Some companies have adopted the concept of a green data center, which is related to electricity consumption and CO2 emissions, according to the utility power source adopted. In Brazil, almost 70% of electrical power is derived from clean electricity generation, whereas in China 65% of generated electricity comes from coal. In addition, the value per kWh in the US is much lower than in other countries surveyed. In the present work, we conducted an integrated evaluation of costs and CO2 emissions of the electrical infrastructure in data centers, considering the different energy sources adopted by each country. We used a multi-layered artificial neural network, which could forecast consumption over the following months, based on the energy consumption history of the data center. All these features were supported by a tool, the applicability of which was demonstrated through a case study that computed the CO2 emissions and operational costs of a data center using the energy mix adopted in Brazil, China, Germany and the US. China presented the highest CO2 emissions, with 41,445 tons per year in 2014, followed by the US and Germany, with 37,177 and 35,883, respectively. Brazil, with 8459 tons, proved to be the cleanest. Additionally, this study also estimated the operational costs assuming that the same data center consumes energy as if it were in China, Germany and Brazil. China presented the highest kWh/year. Therefore, the best choice according to operational costs, considering the price of energy per kWh, is the US and the worst is China. Considering both operational costs and CO2 emissions, Brazil would be the best option.

scholarly journals Neuronless Knowledge Processing in Forests

2020 ◽  
Vol 10 (7) ◽  
pp. 2509
Author(s):  
Aviv Segev ◽  
Dorothy Curtis ◽  
Christine Balili ◽  
Sukhwan Jung
Keyword(s):  
Neural Network ◽  
Processing System ◽  
Basic Knowledge ◽  
Resource Usage ◽  
Knowledge Processing ◽  
Single Tree ◽  
Canopy Tree ◽  
The Us ◽  
Forest Use ◽  
Tree Coverage

Neurons are viewed as the basic cells that process and transmit information. Trees and neurons share a similar structure and neurotransmitter-like substances. No evidence for structures such as neurons, synapses, or a brain has been found inside plants. Consequently, the ability of a network of trees to process information in a method similar to that of a neural network and to make decisions regarding the usage of resources is unperceived. We show that the network between trees is used for knowledge processing to implement decisions that prioritize the forest over a single tree regarding forest use and optimization of resources, similar to the processes of a biological neural network. We found that when there is resection of a network of trees in a forest, namely a trail, each network part will try optimizing its overall access to light resources, represented by canopy tree coverage, independently. This was analyzed in 323 forests in different locations across the US where forest resection is performed by trails. Our results demonstrate that neuron-like relations can occur in a forest knowledge processing system. We anticipate that other systems exist in nature where the basic knowledge processing for resource usage is performed by components other than neurons.

Consistent and Minimal Springback Using a Stepped Binder Force Trajectory and Neural Network Control

1999 ◽  
Vol 122 (1) ◽  
pp. 113-118 ◽  
Author(s):  
Jian Cao ◽  
Brad Kinsey ◽  
Sara A. Solla
Keyword(s):  
Neural Network ◽  
Sheet Thickness ◽  
Nonlinear Effects ◽  
Network Control ◽  
Final Part ◽  
Forming Process ◽  
Sheet Metal Parts ◽  
Material Recovery ◽  
Part Geometry ◽  
Springback Angle

One of the greatest challenges of manufacturing sheet metal parts is to obtain consistent part dimensions. Springback, the elastic material recovery when the tooling is removed, is the major cause of variations and inconsistencies in the final part geometry. Obtaining a consistent and desirable amount of springback is extremely difficult due to the nonlinear effects and interactions between process and material parameters. In this paper, the exceptional ability of a neural network along with a stepped binder force trajectory to control springback angle and maximum principal strain in a simulated channel forming process is demonstrated. When faced with even large variations in material properties, sheet thickness, and friction condition, our control system produces a robust final part shape. [S0094-4289(00)01801-6]

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