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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.

There is a paradox in the normative foundations for chronic and intertemporal poverty measurement. Measures that reflect particular aversion to chronicity of poverty cannot also reflect particular aversion to fluctuations in the level of poverty when poverty is intense, yet good arguments are made in favour of each of these properties. I argue that the paradox may be explained if the poverty analyst implicitly predicts that an individual observed to experience persistent poverty will continue to experience poverty when unobserved. The paradox may then be resolved by separating the normative exercise of evaluation, applying a measure that reflects particular aversion to fluctuations, from a positive exercise of modelling and prediction. This proposal is illustrated by application to panel data from rural Ethiopia, covering the period 1994–2004. Several dynamic models are estimated, and a simple model with household-specific trends is found to give the best predictions of future wellbeing levels. Appropriately normalised measures of intertemporal poverty are applied to the predicted and observed trajectories of wellbeing, and results are found to differ substantially from naïve application of the measures to observed periods only. While similar results are obtained by naïve application of the measures that embody particular aversion to chronicity, separation of the normative and positive exercises maintains conceptual clarity.

Convective heat transfer features of a turbulent slot jet impingement are comprehensively studied using two different computational approaches, namely, URANS (unsteady Reynolds-averaged Navier–Stokes equations) and SAS (scale-adaptive simulation). Turbulent slot jet impingement heat transfer is used where a considerable heat transfer enhancement is required, and computationally, it is a quite challenging flow configuration.

Customized OpenFOAM 4.1, an open-access computational fluid dynamics (CFD) code, is used for SAS (SST-SAS k-ω) and URANS (standard k-ε and SST k-ω) computations. A low-Re version of the standard k-ε model is used, and other models are formulated for good wall-refined calculations. Three turbulence models are formulated in OpenFOAM 4.1 with second-order accurate discretization schemes.

It is observed that the profiles of the streamwise turbulence are under-predicted at all the streamwise locations by SST k-ω and SST SAS k-ω models, but follow similar trends as in the reported results. The standard k-ε model shows improvements in the predictions of the streamwise turbulence and mean streamwise velocity profiles in the zone of outer wall jet. Computed profiles of Nusselt number by SST k-ω and SST-SAS k-ω models are nearly identical and match well with the reported experimental results. However, the standard k-ε model does not provide a reasonable profile or quantification of the local Nusselt number.

Hybrid turbulence model is suitable for efficient CFD computations for the complex flow problems. This paper deals with a detailed comparison of the SAS model with URANS and LES for the first time in the literature. A thorough assessment of the computations is performed against the results reported using experimental and large eddy simulations techniques followed by a detailed discussion on flow physics. The present results are beneficial for scientists working with hybrid turbulence models and in industries working with high-efficiency cooling/heating system computations.

Prior research suggests both formal and informal norms influence employee behavior. While increased training is a typical recommendation to strengthen formal norms by increasing adherence to organizational codes of conduct, and therefore improve ethical behavior, there is little empirical evidence that code training actually strengthens formal norms or improves ethics-related behavior. Conversely, prior observations of unethical behavior serve as strong indicators of informal norms. These observations may be unknown to management and therefore difficult to moderate using other means, including with training on a code.

We test the impact of prior observations of unethical behavior and training for a code of conduct on intentions to report unethical behavior in the future, as well as possible mediators of these relationships. We find some support that training on the code increases intention to report and strong support for the notion that prior observations of unethical behavior decrease intentions to report. Responsibility to report and norms against whistle-blowing both mediate the prior observation-to-reporting intentions relationship, but not the training-to-reporting intentions relationship. An interesting by-product of training seems to be that, by increasing awareness of unethical behavior, and therefore the salience of prior observation, training may have indirectly influenced intentions in the opposite direction intended.

This paper explored factors that impact insurance choices of demand (farmers) and supply (insurance companies) side, respectively.

Specially designed survey questions allow one to fully observe the demand tendency from farmers and partially observe the supply tendency from insurance companies. Using bi‐vairate probit model, a joint estimation of insurance decisions of both supply and demand sides suggested that factors perform different roles in affecting insurance participation.

Farmer's age and education have positive impacts on insurance demand, but are indifference to insurance providers. Insurance suppliers care about farmers' experience in the fields when providing insurance services, however, on the demand side, farmers' experience occasionally results in overconfidence and hence, impedes farmers' insurance purchasing. Production scales, proxy by sow inventory, are put more weight by farmers than insurance suppliers when making decisions. Production efficiency measures perform as incentives for farmers to purchase insurance. While suppliers prefer customers who use vaccine, farmers tend to treat vaccine as a substitute for insurance to prevent disease risk.

Results from bi‐vairate probit model offer deeper understandings about livestock insurance choices and provide further insights to improve policy design and promote participation.

The study designed a special questionnaire and firstly used bi‐vairate probit model to offer more understandings about demand and supply sides of livestock insurance.

Generalized structured component analysis (GSCA) and partial least squares path modeling (PLSPM) are two key component-based approaches to structural equation modeling that facilitate the analysis of theoretically established models in terms of both explanation and prediction. This study aims to offer a comparative evaluation of GSCA and PLSPM in a predictive modeling framework.

A simulation study compares the predictive performance of GSCA and PLSPM under various simulation conditions and different prediction types of correctly specified and misspecified models.

The results suggest that GSCA with reflective composite indicators (GSCA_R) is the most versatile approach. For observed prediction, which uses the component scores to generate prediction for the indicators, GSCA_R performs slightly better than PLSPM with mode A. For operative prediction, which considers all parameter estimates to generate predictions, both methods perform equally well. GSCA with formative composite indicators and PLSPM with mode B generally lag behind the other methods.

Future research may further assess the methods’ prediction precision, considering more experimental factors with a wider range of levels, including more extreme ones.

When prediction is the primary study aim, researchers should generally revert to GSCA_R, considering its performance for observed and operative prediction together.

This research is the first to compare the relative efficacy of GSCA and PLSPM in terms of predictive power.

The purpose of this paper is to demonstrate the use of the Meteorological Research Institute (MRI) global 20-km mesh Atmospheric General Circulation Model (AGCM), called MRI-AGCM, to simulate rainfall and mean surface air temperature. Through calibration and validation the MRI-AGCM was adapted for Bangladesh for generating rainfall and temperature scenarios.

The model generated rainfall was calibrated with ground-based observed data in Bangladesh during the period of 1979-2006. The Climate Research Unit (CRU) data are also used for understanding of the model performance. Better performance of MRI-AGCM obtained through validation process increased confidence in utilizing it in the future rainfall projection for Bangladesh.

Rainfall and mean surface air temperature projection for Bangladesh is experimentally obtained for the period of 2075-2099. This work finds that the MRI-AGCM simulated rainfall and temperature are not directly useful in application purpose. However, after validation and calibration, acceptable performance is obtained in estimating annual rainfall and mean surface air temperature in Bangladesh.

Change of rainfall is projected about 0.64 percent in monsoon season (JJAS), 1.90 percent in post-monsoon season (ON) and 13.46 percent in Winter season (DJF) during the period of 2075-2099. Similarly, change of mean surface air temperature is projected about 2.5 degrees Celsius for the same period.

A state space representation of a linearized DSGE model implies a VAR in terms of observable variables. The model is said be non-invertible if there exists no linear rotation of the VAR innovations which can recover the economic shocks. Non-invertibility arises when the observed variables fail to perfectly reveal the state variables of the model. The imperfect observation of the state drives a wedge between the VAR innovations and the deep shocks, potentially invalidating conclusions drawn from structural impulse response analysis in the VAR. The principal contribution of this chapter is to show that non-invertibility should not be thought of as an “either/or” proposition – even when a model has a non-invertibility, the wedge between VAR innovations and economic shocks may be small, and structural VARs may nonetheless perform reliably. As an increasingly popular example, so-called “news shocks” generate foresight about changes in future fundamentals – such as productivity, taxes, or government spending – and lead to an unassailable missing state variable problem and hence non-invertible VAR representations. Simulation evidence from a medium scale DSGE model augmented with news shocks about future productivity reveals that structural VAR methods often perform well in practice, in spite of a known non-invertibility. Impulse responses obtained from VARs closely correspond to the theoretical responses from the model, and the estimated VAR responses are successful in discriminating between alternative, nested specifications of the underlying DSGE model. Since the non-invertibility problem is, at its core, one of missing information, conditioning on more information, for example through factor augmented VARs, is shown to either ameliorate or eliminate invertibility problems altogether.

Research on strategic choices available to the firm are often modeled as a limited number of possible decision outcomes and leads to a discrete limited dependent variable. A limited dependent variable can also arise when values of a continuous dependent variable are partially or wholly unobserved. This chapter discusses the methodological issues associated with such phenomena and the appropriate statistical methods developed to allow for consistent and efficient estimation of models that involve a limited dependent variable. The chapter also provides a road map for selecting the appropriate statistical technique and it offers guidelines for consistent interpretation and reporting of the statistical results.