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In this study the comparison is presented for the variation in cross-sectional shape along the height of the building model. For this purpose Model B and Model C are having the considerable variation and Model A result can be easily predicted on the basis of the result of Model B and C while Model X is considered for the validation purposes only and it is well established that the results are within the allowable limit. This paper aims to discuss these wind generated effects in the tall building model.

Computational Fluid Dynamics (CFD) in ANSYS: CFX is used to investigate the wind effects on varying cross-sectional shape along the height of the building model.

From pressure contours, it was observed that shape and size of the face is independent of the pressure distribution. It is also observed that pressure distribution for the windward face (A) was less than the magnitude of the leeward face for both models. The leeward face and lateral faces had similar pressure distribution. Also slight changes in pressure distribution were observed at the periphery of the models.

This study has been performed to analyse and compare the wind effect on tall buildings having varying cross sections with variation of different cross sections along the height. Most of the studies done in the field of tall buildings are concentrated to one particular cross-sectional shape while the present study investigates wind effects for combination of two types of cross sections along the height. This analysis is performed for wind incidence angles ranging from 0° to 90° at an interval of 30°. Analysis of wind flow characteristics of two models, Models B and C will be computed using CFD. These two models are the variation of Model A which is a combination of two types of cross section that is square and plus. Square and plus cross-sectional heights for Model B are 48 m and 144 m, respectively. Similarly, square and plus cross-sectional heights for Model C are 144 m and 48 m, respectively. The results are interpreted using pressure contours and streamlines, and comparative graphs of drag and lift forces are presented.

This paper aims to propose an efficient model for analysis of power electronic circuits with integrated magnetic components.

The inductance modeling technique is used as the traditional method for analyzing magnetic components. This model is simple and enough to generate for individual components, that is, an inductor and a transformer. However, it becomes difficult to realize this model for the integrated magnetic structures. This paper shows an appropriate model for individual magnetic components as well as integrated magnetic components and its application to magnetically coupled DC–DC converters. Gyrator–capacitor (G–C) modeling offers a unified, reasonable way of understanding the magnetic components commonly met with in power electronics and the other disciplines.

G–C model allows any electrical and magnetic circuit to be simultaneously simulated with circuit simulators. In this regard, this paper gives a complete simulation model and analysis as an illustrative example. There is no limitation of this paper or future works. The proposed G–C model can be applied to all power electronic circuits having integrated magnetic components.

In the proposed model, the magnetic circuit is converted to a pure electric circuit with capacitors and controlled sources; every winding is replaced with a pair of current controlled voltage sources, namely, a gyrator.

This study examines the use of forecast combination to improve the accuracy of forecasts of cumulative demand. A forecast combination methodology based on least absolute value (LAV) regression analysis is developed and is applied to partially accumulated demand data from an actual manufacturing operation. The accuracy of the proposed model is compared with the accuracy of common alternative approaches that use partial demand data. Results indicate that the proposed methodology outperforms the alternative approaches.

In this chapter, we consider the model of call center incoming call forecasting and staffing-level optimization. We first present the structure of the model and how an agent-based modeling technique could enrich the decision rule and the model. A matrix layout is introduced to present the model so that it can be understood in an efficient way from the perspective of a programmer. The agent-based queuing model will be used in forecasting. We then utilize the bisection method and stepwise method to optimize the staff level to satisfy a target range service-level criteria. Call center management could use the model in practice for their management forecasting and optimization decision-making process in terms of how many agents they need to achieve the target business efficiency goal.

Nowadays, structural equation modeling is a buzz word in the arena of research in management, social sciences, and other equivalent fields. Although the theoretical base bears its significance in building the measurement and structural models, assessing different goodness-of-fit indices (GOFI) equally retains its importance for model validity and conformity. There are various alternative GOFI available for the researchers and the threshold values of each differ. The present paper discussed all the well-accepted and reported GOFI and their threshold value, which will be a great help to researchers and practitioners who use structural equation modeling in research. The author has also presented the different GOF values and validity results of her current research carried out in an Indian power transmission organization in Odisha, India.

Despite a rich extant literature, it is unclear what business models are. We assess three dominant conceptions of business models in the academic literature: as transactional structures, value extracting devices, and mechanisms for structuring the organization. To overcome the shortcomings of these approaches, we draw on theories of performativity, social typecasting, and managerial cognition. We propose an alternative conception of business models as performative representations that work in three ways: as narratives that convince, typifications that legitimate, and recipes that guide social action. Rather than actual features of firms, business models are representations that allow managers to articulate and instantiate the value of new technologies.

If a comprehensive performance model for business is to be useful in an analytic and predictive sense, the model must capture the interrelationships of factors that influence organizational performance such as organizational maturity, size, products and services, management systems, industry characteristics, and environmental influences. Flamholtz includes a number of key factors in his model. However, Flamholtz's explication of the factors does call into question some aspects of the model. For instance, Flamholtz explicitly equates level of sales revenue with specific growth stages of the organization and implicitly equates level of sales revenue with the maturity of the organization. Although these factors may be correlated in many organizations, care must be taken so that the comprehensive performance model does not confound key factors.