Hydrogen has a key role to play in decarbonising industry and other sectors of society. It is important to develop low-carbon hydrogen production technologies that are cost-effective and energy-efficient. Sorption-enhanced steam methane reforming (SE-SMR) is a developing low-carbon (blue) hydrogen production process, which enables combined hydrogen production and carbon capture. Despite a number of key benefits, the process is yet to be fully realised in terms of efficiency. In this work, a sorption-enhanced steam methane reforming process has been intensified via exergy analysis. Assessing the exergy efficiency of these processes is key to ensuring the effective deployment of low-carbon hydrogen production technologies. An exergy analysis was performed on an SE-SMR process and was then subsequently used to incorporate process improvements, developing a process that has, theoretically, an extremely high CO₂ capture rate of nearly 100 %, whilst simultaneously demonstrating a high exergy efficiency (77.58 %), showcasing the potential of blue hydrogen as an effective tool to ensure decarbonisation, in an energy-efficient manner.

William George Davies; Shervan Babamohammadi; Yongliang Yan; Peter T. Clough; Salman Masoudi Soltani. Exergy analysis in intensification of sorption-enhanced steam methane reforming for clean hydrogen production: Comparative study and efficiency optimisation. Carbon Capture Science & Technology 2024, 12 .

William George Davies, Shervan Babamohammadi, Yongliang Yan, Peter T. Clough, Salman Masoudi Soltani. Exergy analysis in intensification of sorption-enhanced steam methane reforming for clean hydrogen production: Comparative study and efficiency optimisation. Carbon Capture Science & Technology. 2024; 12 ():.

William George Davies; Shervan Babamohammadi; Yongliang Yan; Peter T. Clough; Salman Masoudi Soltani. 2024. "Exergy analysis in intensification of sorption-enhanced steam methane reforming for clean hydrogen production: Comparative study and efficiency optimisation." Carbon Capture Science & Technology 12, no. : .

Accurate flight delay prediction is fundamental to establishing an efficient airline business. It is considered one of the most critical intelligent aviation systems components. Recently, flight delay has been a significant cause that deprives airlines of good performance. Hence, airlines must accurately forecast flight delays and comprehend their sources to have excellent passenger experiences, increase income and minimise unwanted revenue loss. In this paper, we developed a novel approach that is an optimisation-driven deep learning model for predicting flight delays by extending a state-of-the-art method, DeepONet. We utilise the Box-Cox transformation for data conversion with a minimal error rate. Also, we employed a deep residual network for the feature fusion before training our model. Furthermore, this research uses flight on-time data for flight delay prediction. To validate our proposed model, we conducted a numerical study using the US Bureau of Transportation of Statistics. Also, we predict the flight delay by selecting the optimum weights using the novel DeepONet with the Gradient Mayfly Optimisation Algorithm (GMOA). Our experiment results show that the proposed GMOA-based DeepONet outperformed the existing methods with a Root Mean Square Error of 0.0765, Mean Square Error of 0.0058, Mean Absolute Error of 0.0049 and Mean Absolute Percent Error of 0.0043, respectively. When we apply 4-fold cross-validation, the proposed GMOA-based DeepONet outperformed the existing methods with minimal standard error. These results also show the importance of optimisation algorithms in deciding the optimal weight to improve the model performance. The efficacy of our proposed approach in predicting flight delays with minimal errors well define from all the evaluation metrics. Also, utilising the prediction outcome of our robust model to release information about the delayed flight in advance from the aviation decision systems can effectively alleviate the passengers’ nervousness.

Desmond Bala Bisandu; Irene Moulitsas. Prediction of flight delay using deep operator network with gradient-mayfly optimisation algorithm. Expert Systems with Applications 2024, 247 .

Desmond Bala Bisandu, Irene Moulitsas. Prediction of flight delay using deep operator network with gradient-mayfly optimisation algorithm. Expert Systems with Applications. 2024; 247 ():.

Desmond Bala Bisandu; Irene Moulitsas. 2024. "Prediction of flight delay using deep operator network with gradient-mayfly optimisation algorithm." Expert Systems with Applications 247, no. : .

For some time, both practitioners and scholars have been arguing that coaching should play a stronger and more central role to achieve successful business outcomes. However, when looking to inspire leaders to gain competence in the skills of coaching, ill-founded assumptions are made. Including, but not limited to, the very definition of leadership and the very nature of coaching when embedded into leadership (termed in this chapter, leaders-who-coach). The research described in this chapter looks into these practices to proffer 15 dimensions of leaders-who-coach, drawing on multiple sources which seek to ameliorate the shortfalls of previous research in this area. Finally, the chapter includes a discussion on the less than perfect nature of the approach taken due to the philosophical misalignments between the described realities of leadership and coaching and the ontological and epistemological challenges of research. The primary aim of this chapter is to respond to the growing organizational calls for more leaders to have the skills of coaching. Request access from your librarian to read this chapter's full text.

Jennifer L. Robinson; Phil St J Renshaw. What Are the Competencies of a Leader? 2024, 71 -97.

Jennifer L. Robinson, Phil St J Renshaw. What Are the Competencies of a Leader? . 2024; ():71-97.

Jennifer L. Robinson; Phil St J Renshaw. 2024. "What Are the Competencies of a Leader?" , no. : 71-97.