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1 – 10 of over 3000
Article
Publication date: 1 March 2015

Ann M. Johnson

In 2010 the Dodd-Frank Law was passed in response to the 2008 recession. However, questions arose regarding the federal agenciesʼ ability to regulate the economy in general and…

Abstract

In 2010 the Dodd-Frank Law was passed in response to the 2008 recession. However, questions arose regarding the federal agenciesʼ ability to regulate the economy in general and the utility of financial regulations in particular. This work examines and discusses the challenges associated with the uncertainty of the administrative environment in which agencies have been drafting regulations in response to Dodd-Frank. A lack of administrative clarity as a result of Congressional politics led to regulatory capture and operational paralysis on the part of federal agencies tasked with implementing the Act. In this type of environment it becomes very difficult for regulatory agencies to be effective and competent when regulations have not all been drafted yet and legislation is continuously changing. This article critically examines the recent proposed changes to the Dodd-Frank Law. Specifically, it delineates the manner in which the legislative instability has impacted the Federal Reserve Bankʼs capacity to effectively implement the necessary rules for mitigating economic risks.

Details

International Journal of Organization Theory & Behavior, vol. 18 no. 4
Type: Research Article
ISSN: 1093-4537

Abstract

Details

International Journal of Organization Theory & Behavior, vol. 18 no. 3
Type: Research Article
ISSN: 1093-4537

Article
Publication date: 8 March 2022

Neil Johnson, Sameer Prasad, Amin Vahedian, Nezih Altay and Ashish Jain

In this research, the authors apply artificial neural networks (ANNs) to uncover non-linear relationships among factors that influence the productivity of ragpickers in the Indian…

Abstract

Purpose

In this research, the authors apply artificial neural networks (ANNs) to uncover non-linear relationships among factors that influence the productivity of ragpickers in the Indian context.

Design/methodology/approach

A broad long-term action research program provides a means to shape the research question and posit relevant factors, whereas ANNs capture the true underlying non-linear relationships. ANN models the relationships between four independent variables and three forms of waste value chains without assuming any distributional forms. The authors apply bootstrapping in conjunction with ANNs.

Findings

The authors identify four elements that influence ragpickers’ productivity: receptiveness to non-governmental organizations, literacy, the deployment of proper equipment/technology and group size.

Research limitations/implications

This study provides a unique way to analyze bottom of the pyramid (BoP) operations via ANNs.

Social implications

This study provides a road map to help ragpickers in India raise incomes while simultaneously improving recycling rates.

Originality/value

This research is grounded in the stakeholder resource-based view and the network–individual–resource model. It generalizes these theories to the informal waste value chain at BoP communities.

Details

International Journal of Operations & Production Management, vol. 42 no. 4
Type: Research Article
ISSN: 0144-3577

Keywords

Article
Publication date: 1 August 1998

Jaroslav Mackerle

This paper gives a review of the finite element techniques (FE) applied in the area of material processing. The latest trends in metal forming, non‐metal forming, powder…

4528

Abstract

This paper gives a review of the finite element techniques (FE) applied in the area of material processing. The latest trends in metal forming, non‐metal forming, powder metallurgy and composite material processing are briefly discussed. The range of applications of finite elements on these subjects is extremely wide and cannot be presented in a single paper; therefore the aim of the paper is to give FE researchers/users only an encyclopaedic view of the different possibilities that exist today in the various fields mentioned above. An appendix included at the end of the paper presents a bibliography on finite element applications in material processing for 1994‐1996, where 1,370 references are listed. This bibliography is an updating of the paper written by Brannberg and Mackerle which has been published in Engineering Computations, Vol. 11 No. 5, 1994, pp. 413‐55.

Details

Engineering Computations, vol. 15 no. 5
Type: Research Article
ISSN: 0264-4401

Keywords

Article
Publication date: 17 May 2019

Brenda Abu and Wilna Oldewage-Theron

Food insecurity is an evolving nutrition issue affecting both developed and underdeveloped college campuses. The purpose of this paper is to assess food insecurity and related…

Abstract

Purpose

Food insecurity is an evolving nutrition issue affecting both developed and underdeveloped college campuses. The purpose of this paper is to assess food insecurity and related coping strategies among Texas Tech University students.

Design/methodology/approach

This was a cross-sectional online survey in Lubbock, Texas, among college students (n=173). The outcome measures, socio-demographic factors, household food insecurity access) and dietary diversity were assessed using validated tools. Statistical analyses were performed using SPSS software. Socio-demographic differences in food security status were examined using χ2, and means testing. Risks of student food insecurity were assessed using odds ratios (ORs).

Findings

Respondents were mostly female (70 percent), non-Hispanic white (58 percent) and young adults’ (median age: 22.0 (20.0, 27.0)), with a median monthly income of $1,000 (0.0, 1,500) and spent about a fifth of their income on food. More students were food insecure (59.5 percent) compared to those who experienced food security (40.5 percent) (p<0.001). Some of the severe food insecure students (16.7 percent) reported going to bed without food (6.9 percent) in the prior 30 days. Students with monthly food budgets of ⩽ $200 were 3.2 times more likely to be food insecure (OR=3.231: CI: 1.353–7.714; p=0.010) compared to those with higher food budgets. A students’ choice of priority monthly expenses was significantly associated with food security status; however, further risk assessment of dichotomous “prioritized food” and “prioritized other expenses” was not statistically significant.

Originality/value

Student’s food budget of $200 was the strongest determinant of food insecurity. Individual training on money management and meal planning are recommended. University policies should recognize and develop academic support policies addressing competing expenses with food.

Details

British Food Journal, vol. 121 no. 3
Type: Research Article
ISSN: 0007-070X

Keywords

Book part
Publication date: 13 August 2018

Robert L. Dipboye

Abstract

Details

The Emerald Review of Industrial and Organizational Psychology
Type: Book
ISBN: 978-1-78743-786-9

Article
Publication date: 13 January 2023

Pankaj Tiwari

The purpose of this study is to examine the effects of banking innovations (INNs) on customer experience (EXP), satisfaction (SAT) and loyalty (LOY).

Abstract

Purpose

The purpose of this study is to examine the effects of banking innovations (INNs) on customer experience (EXP), satisfaction (SAT) and loyalty (LOY).

Design/methodology/approach

The author evaluated the data using a structural equation method-artificial neural network (SEM-ANN) method. The author’s results show the presence of relationship between INN, EXP, SAT and LOY. In this study, the node layers of ANNs add an input layer, hidden layers and an output layer. Each “node” acts as an artificial neuron that communicates with others. The ANN model takes the variables from the SEM analysis as input neurons.

Findings

The author observed the significant effects between INN, EXP, SAT and LOY using the normalised importance generated by the multilayer perceptron used in the feed-forward back propagation of the ANN methodology. In this study, the ANN model can predict LOY through service innovation, with a forecast accuracy of 77.6%.

Originality/value

By applying neural network modelling, this research helps us understand how service innovation affects customer behaviour. For the first time, the author examined service innovations' direct and indirect impact on loyalty through EXP and SAT. The author made a significant conceptual contribution by using a non-compensatory model of ANNs to circumvent the limitations of linear models.

Details

Benchmarking: An International Journal, vol. 30 no. 10
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 6 March 2017

Ney Rafael Secco and Bento Silva de Mattos

Multidisciplinary design frameworks elaborated for aeronautical applications require considerable computational power that grows enormously with the utilization of higher fidelity…

Abstract

Purpose

Multidisciplinary design frameworks elaborated for aeronautical applications require considerable computational power that grows enormously with the utilization of higher fidelity tools to model aeronautical disciplines like aerodynamics, loads, flight dynamics, performance, structural analysis and others. Surrogate models are a good alternative to address properly and elegantly this issue. With regard to this issue, the purpose of this paper is the design and application of an artificial neural network to predict aerodynamic coefficients of transport airplanes. The neural network must be fed with calculations from computational fluid dynamic codes. The artificial neural network system that was then developed can predict lift and drag coefficients for wing-fuselage configurations with high accuracy. The input parameters for the neural network are the wing planform, airfoil geometry and flight condition. An aerodynamic database consisting of approximately 100,000 cases calculated with a full-potential code with computation of viscous effects was used for the neural network training, which is carried out with the back-propagation algorithm, the scaled gradient algorithm and the Nguyen–Wridow weight initialization. Networks with different numbers of neurons were evaluated to minimize the regression error. The neural network featuring the lowest regression error is able to reduce the computation time of the aerodynamic coefficients 4,000 times when compared with the computing time required by the full potential code. Regarding the drag coefficient, the average error of the neural network is of five drag counts only. The computation of the gradients of the neural network outputs in a scalable manner is possible by an adaptation of back-propagation algorithm. This enabled its use in an adjoint method, elaborated by the authors and used for an airplane optimization task. The results from that optimization were compared with similar tasks performed by calling the full potential code in another optimization application. The resulting geometry obtained with the aerodynamic coefficient predicted by the neural network is practically the same of that designed directly by the call of the full potential code.

Design/methodology/approach

The aerodynamic database required for the neural network training was generated with a full-potential multiblock-structured code. The training process used the back-propagation algorithm, the scaled-conjugate gradient algorithm and the Nguyen–Wridow weight initialization. Networks with different numbers of neurons were evaluated to minimize the regression error.

Findings

A suitable and efficient methodology to model aerodynamic coefficients based on artificial neural networks was obtained. This work also suggests appropriate sizes of artificial neural networks for this specific application. We demonstrated that these metamodels for airplane optimization tasks can be used without loss of fidelity and with great accuracy, as their local minima might be relatively close to the minima of the original design space defined by the call of computational fluid dynamics codes.

Research limitations/implications

The present work demonstrated the ability of a metamodel with artificial neural networks to capture the physics of transonic and subsonic flow over a wing-fuselage combination. The formulation that was used was the full potential equation. However, the present methodology can be extended to model more complex formulations such as the Euler and Navier–Stokes ones.

Practical implications

Optimum networks reduced the computation time for aerodynamic coefficient calculations by 4,000 times when compared with the full-potential code. The average absolute errors obtained were of 0.004 and 0.0005 for lift and drag coefficient prediction, respectively. Airplane configurations can be evaluated more quickly.

Social implications

If multidisciplinary optimization tasks for airplane design become more efficient, this means that more efficient airplanes (for instance less polluting airplanes) can be designed. This leads to a more sustainable aviation.

Originality/value

This research started in 2005 with a master thesis. It was steadily improved with more efficient artificial neural networks able to handle more complex airplane geometries. There is a single work using similar techniques found in a conference paper published in 2007. However, that paper focused on the application, i.e. providing very few details of the methodology to model aerodynamic coefficients.

Details

Aircraft Engineering and Aerospace Technology, vol. 89 no. 2
Type: Research Article
ISSN: 1748-8842

Keywords

Article
Publication date: 1 March 1974

Tom Schultheiss, Lorraine Hartline, Jean Mandeberg, Pam Petrich and Sue Stern

The following classified, annotated list of titles is intended to provide reference librarians with a current checklist of new reference books, and is designed to supplement the…

Abstract

The following classified, annotated list of titles is intended to provide reference librarians with a current checklist of new reference books, and is designed to supplement the RSR review column, “Recent Reference Books,” by Frances Neel Cheney. “Reference Books in Print” includes all additional books received prior to the inclusion deadline established for this issue. Appearance in this column does not preclude a later review in RSR. Publishers are urged to send a copy of all new reference books directly to RSR as soon as published, for immediate listing in “Reference Books in Print.” Reference books with imprints older than two years will not be included (with the exception of current reprints or older books newly acquired for distribution by another publisher). The column shall also occasionally include library science or other library related publications of other than a reference character.

Details

Reference Services Review, vol. 2 no. 3
Type: Research Article
ISSN: 0090-7324

Article
Publication date: 5 June 2019

Samrad Jafarian-Namin, Alireza Goli, Mojtaba Qolipour, Ali Mostafaeipour and Amir-Mohammad Golmohammadi

The purpose of this paper is to forecast wind power generation in an area through different methods, and then, recommend the most suitable one using some performance criteria.

Abstract

Purpose

The purpose of this paper is to forecast wind power generation in an area through different methods, and then, recommend the most suitable one using some performance criteria.

Design/methodology/approach

The Box–Jenkins modeling and the Neural network modeling approaches are applied to perform forecasting for the last 12 months.

Findings

The results indicated that among the tested artificial neural network (ANN) model and its improved model, artificial neural network-genetic algorithm (ANN-GA) with RMSE of 0.4213 and R2 of 0.9212 gains the best performance in prediction of wind power generation values. Finally, a comparison between ANN-GA and ARIMA method confirmed a far superior power generation prediction performance for ARIMA with RMSE of 0.3443 and R2 of 0.9480.

Originality/value

Performance of the ARIMA method is evaluated in comparison to several types of ANN models including ANN, and its improved model using GA as ANN-GA and particle swarm optimization (PSO) as ANN-PSO.

Details

International Journal of Energy Sector Management, vol. 13 no. 4
Type: Research Article
ISSN: 1750-6220

Keywords

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