Purpose: This study explores the role of leadership in organisational innovation by reviewing several publications that discuss the relationship between various leadership styles and innovation.Design/methodology/approach: The study followed a descriptive research methodology by reviewing 64 journal articles on the relationship between various leadership styles and innovation. The articles were analysed descriptively and then reviewed based on the leadership style it discusses to derive meaningful findings on the relationship between leadership and innovation.Findings: Various leadership styles had a positive impact on organisational innovation either directly or indirectly, by influencing the organisational climate, employees' and leaders' behaviours or other organisational variables such as learning and knowledge sharing. Some leadership styles had both direct and indirect impacts on organisational innovation.Research limitations/implications: This study collected journal articles published in almost all major electronic databases such as Emerald, ScienceDirect, Taylor & Francis and Scopus. However, the review is limited to journal articles in which the title, abstract or author-specified keywords contain the search terms “leadership” and “innovation,” and published between 2000 and 2019. Therefore, this review may miss some relevant research insights mentioned in the literature that discussed innovation or leadership separately not combined.Originality/value: This study contributes to the existing body of research on leadership and innovation by extensively discussing the role of various leadership styles in determining organisational innovation. The analysis reveals that prior studies had many limitations and focused on specific leadership styles only. The study goes a step further by explaining how the leadership and innovation aspects are related, and classifying various leadership styles according to their impact on organisational innovation being direct, indirect or both.

There are numerous reports and publications in reputable scientific and engineering journals that attribute substantial enhancement in heat transfer capabilities for heat exchangers once they employ nanofluids as working fluids. By definition, a nanofluid is a working fluid that has a small volume fraction (5% or less) of a solid particle with dimensions in the nanoscale. The addition of this solid material has a reported significant impact on convective heat transfer in heat exchangers. This work investigates the significance of the reported enhancements in many recent related publications. Observations on these publications’ geographical origins, fundamental heat transfer calculations, experimental setups and lack of potential applications are critically made. Heat transfer calculations based on methodologies outlined in random selection of available papers were conducted along with a statistical analysis show paradoxically inconsistent conclusion as well as an apparent lack of complete comprehension of convective heat transfer mechanism. In some of the surveyed literature for example, heat transfer coefficient enhancements were reported to be up to 27% and 48%, whereas the recalculations presented in this work restrain proclaimed enactments to ~ 3.5% and − 4% (no enhancement), respectively. This work aims at allowing a healthy scientific debate on whether nanofluids are the sole answer to enhancing convective heat transfer in heat exchangers. The quantity of literature that confirms the latter statement have an undeniable critical mass, but this volition could be stemming from and heading to the wrong direction. Finally, the challenges imposed by the physical nature of nanoparticles, as well as economic limitations caused by the high price of conventional nanoparticles such as gold (80$/g), diamond (35$/g), and silver (6$/g) that hinder their commercialization, are presented.

In modern large-scale industrial processes, data are often high dimensional time-dependent due to the frequent sampling, dynamic nature and large number of variables. Appropriate monitoring of such processes allows for efficient decision-making that can improve the baseline of manufacturing companies either through decreasing production costs or enhancing production efficiency. Various latent variable-based control charts have been proposed for addressing high dimensional data; however, many of these methods assume that the data are independent and normally distributed. The violation of these assumptions results in an increased false alarm rate, in addition to the deterioration in the performance of such methods. In this study, we propose a Variational Autoencoder-Long Short Term Memory (VAE-LSTM) deep learning based $T^2$ chart that integrates the unique features of both VAE and LSTM for intelligent fault detection of time-dependent high dimensional processes. The effectiveness and applicability of the proposed model are demonstrated through extensive simulations, an open-source online dataset, and a real case study.