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Conflict, between individuals and groups, in organizations is a common phenomenon and can have varied implication for the employee and the organization. This paper aims to determine whether experiencing interpersonal conflict drives employees to engage in prosocial behavior (prohibitive voice) and antisocial behavior (interpersonal deviance). Using Stressor–Emotion Model, Uncertainty Management Theory and Impression Management Motives, this study examines the relationship and explores competence uncertainty as a mediator and perception of politics as a moderator.

This study uses a cross-sectional design where data collected is from 386 employees working in nine different public sector enterprises in India. Structural equation modeling using SPSS AMOS was used to analyze the hypothesized relationships.

The results show that interpersonal conflict leads to both prohibitive voice behavior and interpersonal deviance. However, the mediating role of competence uncertainty is valid only for the effect of conflict on interpersonal deviance. Also, the perception of politics strengthens the positive relationship between interpersonal conflict and competence uncertainty.

To the best of the authors’ knowledge, this is one of the first empirical studies to have validated prosocial and antisocial work behavior as outcomes of interpersonal conflict. Again, this is one of the first few studies to examine the mechanism through which interpersonal conflict impacts interpersonal deviance.

The present work aims at improving the performance of the engine using optimized fuel injection strategies and operating parameters for plastic oil ethanol blends. To optimize and predict the engine injection and operational parameters, response surface methodology (RSM) and artificial neural networks (ANN) are used respectively.

The engine operating parameters such as load, compression ratio, injection timing and the injection pressure are taken as inputs whereas brake thermal efficiency (BTHE), brake-specific fuel consumption (BSFC), carbon monoxide (CO), hydrocarbons (HC), oxides of nitrogen (NO_x) and smoke emissions are treated as outputs. The experiments are designed according to the design of experiments, and optimization is carried out to find the optimum operational and injection parameters for plastic oil ethanol blends in the engine.

Optimum operational parameters of the engine when fuelled with plastic oil and ethanol blends are obtained at 8 kg of load, injection pressure of 257 bar, injection timing of 17° before top dead center and blend of 15%. The engine performance parameters obtained at optimum engine running conditions are BTHE 32.5%, BSFC 0.24 kg/kW.h, CO 0.057%, HC 10 ppm, NO_x 324.13 ppm and smoke 79.1%. The values predicted from ANN are found to be more close to experimental values when compared with the values of RSM.

In the present work, a comparative analysis is carried out on the prediction capabilities of ANN and RSM for variable compression ratio engine fuelled with ethanol blends of plastic oil. The error of prediction for ANN is less than 5% for all the responses such as BTHE, BSFC, CO and NO_x except for HC emission which is 12.8%.