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This paper seeks to understand the strategic behaviour of researchers when producing knowledge in two scientific fields – nanotechnology and social sciences.

The author conducted semi-structured interviews with 43 researchers to analyse the needs for strategic interdependence (resource-sharing) and for organisational autonomy (decision-making) in knowledge production. When aligned, these two concepts form three modes of behaviour: mode1, mode2 and mode3.

The empirical study results show that, besides well-studied differences in various publications, there are large behaviour differences between social science and nanotechnology researchers. While nanotechnology researchers’ behaviours are mostly in mode3 (sharing resources; highly autonomous), social science researchers’ behaviours tend to be in mode1 (highly autonomous; no need to share resources).

This study delivers an understanding of the differences in the strategic behaviours of researchers in different scientific fields. The author proposes managerial interventions for research managers – university and research group leaders.

While most studies that compare scientific fields look at knowledge production outcomes, the author analyses conditions that differentiate these outcomes. To this end, the author compares individual researchers’ behaviours in different fields by analysing the need for collaboration and the need for autonomy.

The purpose of the paper is to develop high accurate and unified maximum power point tracking technique that tracks the maximum power from both the photovoltaic (PV) array and wind energy conversion system, (an unified maximum power point tracking technique implemented for both wind and solar sources to track maximum power with higher accuracy).

In recent times, multi-input Direct Current- Direct Current (DC-DC) converter has attracted attentiveness, to conserve more energy and to achieve more efficiency. The kinetic energy of the vehicle is converted to electrical energy and further stored into the battery, during the regenerative braking (moreover, the battery gets charged during the regenerative braking process by converting the kinetic energy of the vehicle into electrical energy). During such a process, only the pulse width modulation schemes of the inverter are changed. To charge electric vehicles (EVs), two renewable resources as solar and wind are combined to produce electric power. Therefore, it was conveyed that the EV will be continuously getting power without interruption using various sources and regenerated power.

The performance and effectiveness of the proposed system are studied by extensive simulations and (are) validated using a prototype of the system. The results prove that the proposed system achieves an efficiency of 95.2%, which is higher than that of the multi-input DC-DC converters existing in the literature.

A novel multi-input DC-DC landsman converter for powering plug-in hybrid electric vehicles (HEVs) is proposed in the research. This method proposes a new cost effective and efficient technique for HEVs with brushless DC motors. Wind power, battery and PV panel are used as the input sources for the proposed converter.

The purpose of this paper is to develop a framework to optimize time, cost and quality in a multi-mode resource-constrained project scheduling environment.

A case study approach identified the activity execution modes in building construction projects in India to support multi-mode resource-constrained project scheduling. The data required to compute time, cost and quality of each activity are compiled from real construction projects. A binary integer-programming model has been developed to perform multi-objective optimization and identify Pareto optimal solutions. The RR-PARETO3 algorithm was used to identify the best compromise trade-off solutions. The effectiveness of the proposed framework is demonstrated through sample case study projects.

Results show that good compromise solutions are obtained through multi-objective optimization of time, cost and quality.

Case study data sets were collected only from eight building construction projects in India.

It is feasible to adopt multi-objective optimization in practical construction projects using time, cost and quality as the objectives; Pareto surfaces help to quantify relationships among time, cost and quality. It is shown that cost can be reduced by increasing the duration, and quality can be improved only by increasing the cost.

The use of different activity execution modes compiled from multiple projects in optimization is illustrated, and good compromise solutions for the multi-mode resource-constrained project scheduling problems using multi-objective optimization are identified.