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In this paper, the author presents a hybrid method along with its error analysis to solve (1+2)-dimensional non-linear time-space fractional partial differential equations (FPDEs).

The proposed method is a combination of Sumudu transform and a semi-analytc technique Daftardar-Gejji and Jafari method (DGJM).

The author solves various non-trivial examples using the proposed method. Moreover, the author obtained the solutions either in exact form or in a series that converges to a closed-form solution. The proposed method is a very good tool to solve this type of equations.

The present work is original. To the best of the author's knowledge, this work is not done by anyone in the literature.

This study aims to introduce an alternative model, “volatility, uncertainty, complexity and ambiguity (VUCA), Virtue and Vice” (3V’s), to unleash leadership skills, promote organisational collaborative change and impact sales performance during an unprecedented crisis.

The methodology outlines action research based on the 3V’s model and its application in an international business-to-business sales organisation during Covid-19. It explores alternative paths informed by play-at-work and Plato’s philosophy applied to work-based-learning. Each action/iteration adds to the model, which becomes more likely appropriate for various situations.

The 3V’s boosted change implementation and improved sales performance. The 3V’s conceptualised an invitation to immerse oneself in the constant “river of change” (VUCA) and a means of understanding the role of leadership in navigating this change by embracing simple rules: searching for justice (Virtue) and overcoming the barrier of public opinion (Vice).

The 3V’s model is grounded in leadership literature and a sole application, providing real international data relevant to organisations and leaders. This has yet to be evaluated further.

3V’s can enhance the understanding of a leading collaborative change and re-frame team dynamics in post-pandemic times for the broader public.

The approach advocated is a practice of “swimming alongside the team”, which should enable empowerment and collaboration rather than a top-down direction. Focussing on leaders who are moral people, this approach becomes a differentiator in a digital world.

This study examines Plato’s philosophy, play-at-work and other leadership theories in a model which prepares organisations to respond to crisis by providing the ability to reflect on human aspects and straightforward, transferable skills.

This study explores the pioneering approach of utilising machine learning (ML) models and integrating social media data for predicting tourist arrivals in Sri Lanka.

Two sets of experiments are performed in this research. First, the predictive accuracy of three ML models, support vector regression (SVR), random forest (RF) and artificial neural network (ANN), is compared against the seasonal autoregressive integrated moving average (SARIMA) model using historical tourist arrivals as features. Subsequently, the impact of incorporating social media data from TripAdvisor and Google Trends as additional features is investigated.

The findings reveal that the ML models generally outperform the SARIMA model, particularly from 2019 to 2021, when several unexpected events occurred in Sri Lanka. When integrating social media data, the RF model performs significantly better during most years, whereas the SVR model does not exhibit significant improvement. Although adding social media data to the ANN model does not yield superior forecasts, it exhibits proficiency in capturing data trends.

The findings offer substantial implications for the industry's growth and resilience, allowing stakeholders to make accurate data-driven decisions to navigate the unpredictable dynamics of Sri Lanka's tourism sector.

This study presents the first exploration of ML models and the integration of social media data for forecasting Sri Lankan tourist arrivals, contributing to the advancement of research in this domain.

In this study, the authors propose a novel digital twinning approach specifically designed for controlling transient thermal systems. The purpose of this study is to harness the combined power of deep learning (DL) and physics-based methods (PBM) to create an active virtual replica of the physical system.

To achieve this goal, we introduce a deep neural network (DNN) as the digital twin and a Finite Element (FE) model as the physical system. This integrated approach is used to address the challenges of controlling an unsteady heat transfer problem with an integrated feedback loop.

The results of our study demonstrate the effectiveness of the proposed digital twinning approach in regulating the maximum temperature within the system under varying and unsteady heat flux conditions. The DNN, trained on stationary data, plays a crucial role in determining the heat transfer coefficients necessary to maintain temperatures below a defined threshold value, such as the material’s melting point. The system is successfully controlled in 1D, 2D and 3D case studies. However, careful evaluations should be conducted if such a training approach, based on steady-state data, is applied to completely different transient heat transfer problems.

The present work represents one of the first examples of a comprehensive digital twinning approach to transient thermal systems, driven by data. One of the noteworthy features of this approach is its robustness. Adopting a training based on dimensionless data, the approach can seamlessly accommodate changes in thermal capacity and thermal conductivity without the need for retraining.

Engaging with customers and addressing unmet value have become increasingly challenging within multi-stakeholder environments of service innovation. Therefore, this paper aims to address this challenge by studying how strategic design abilities address unmet value in service engagement strategies.

The authors conducted a qualitative inductive study at a multinational corporation and interviewed marketing and design professionals on their innovation practices in service engagement strategies.

From the inductive analysis, this study identified three strategic design abilities that effectively contribute to addressing unmet value throughout the co-evolving process of service engagement: envisioning value, modelling value and engaging value. Based on this, this study proposes the emerging co-evolving loop framework of service engagement strategies.

The limitation of this emerging theory is a lack of broad generalizability with mutual exclusivity or collective exhaustiveness across industries. A theoretical implication of the framework is the integration of strategic design and services marketing towards co-created engagement strategies.

The service engagement loop framework can be of great value to service innovation processes, for which an integrated, cross-functional approach is often missing.

The findings further suggest that next to a methodological skillset, strategic design abilities consist of a distinct mindset.

This paper introduces strategic design abilities to address unmet value and proposes a novel co-evolving loop framework of service engagement strategies.

Sustainable manufacturing is one of the most important and most challenging issues in present industrial scenario. With the intention of diminish negative effects associated with cutting fluids, the machining industries are continuously developing technologies and systems for cooling/lubricating of the cutting zone while maintaining machining efficiency. In the present study, three regression based machine learning techniques, namely, polynomial regression (PR), support vector regression (SVR) and Gaussian process regression (GPR) were developed to predict machining force, cutting power and cutting pressure in the turning of AISI 1045. In the development of predictive models, machining parameters of cutting speed, depth of cut and feed rate were considered as control factors. Since cooling/lubricating techniques significantly affects the machining performance, prediction model development of quality characteristics was performed under minimum quantity lubrication (MQL) and high-pressure coolant (HPC) cutting conditions. The prediction accuracy of developed models was evaluated by statistical error analyzing methods. Results of regressions based machine learning techniques were also compared with probably one of the most frequently used machine learning method, namely artificial neural networks (ANN). Finally, a metaheuristic approach based on a neural network algorithm was utilized to perform an efficient multi-objective optimization of process parameters for both cutting environment.

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

The objective of this study is to investigate the impact of longitudinal forces on extreme-long heavy-haul trains, providing new insights and methods for their design and operation, thereby enhancing safety, operational efficiency and track system design.

A longitudinal dynamics simulation model of the super long heavy haul train was established and verified by the braking test data of 30,000 t heavy-haul combination train on the long and steep down grade of Daqing Line. The simulation model was used to analyze the influence of factors on the longitudinal force of super long heavy haul train.

Under normal conditions, the formation length of extreme-long heavy-haul combined train has a small effect on the maximum longitudinal coupler force under full service braking and emergency braking on the straight line. The slope difference of the long and steep down grade has a great impact on the maximum longitudinal coupler force of the extreme-long heavy-haul trains. Under the condition that the longitudinal force does not exceed the safety limit of 2,250 kN under full service braking at the speed of 60 km/h the maximum allowable slope difference of long and steep down grade for 40,000 t super long heavy-haul combined trains is 13‰, and that of 100,000 t is only 5‰.

The results will provide important theoretical basis and practical guidance for further improving the transportation efficiency and safety of extreme-long heavy-haul trains.

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.

The purpose of our research is to better understand inquiry-based pedagogy in the context of leadership education. Specifically, we sought to learn about how leadership learning is characterized in an immersive inquiry course, and how inquiry-based pedagogy is experienced by students engaged in interdisciplinary leadership learning.

We used a case study approach as an overarching methodology. The research methods employed to collect data were World Cafe and episodic narrative interview. Further, we used collocation analysis and systematic text condensation as analytical strategies to interpret data.

Our findings led us to four primary conclusions: (1) inquiry-based learning helps to foster an inquiry mindset amongst leadership education students; (2) the challenges and tensions associated with inquiry-based learning are worth the learning gains for leadership students; (3) the opportunity to learn in relationship is beneficial for leadership development outcomes and (4) students’ experiences of inquiry-based learning in leadership education often included instances of transformation.

Limitations of the research were: (1) it is a case study situated within a unique, particular social and educational context; (2) demographic data were not collected from participants, so results cannot be disaggregated based on particular demographic markers and (3) the small sample size involved in the study makes it impossible to generalize across a broad population.

This research has enabled a deep understanding of structural and relational supports that can enable effective inquiry-based learning in leadership education. It also offers evidence to support institutional shifts to inquiry-based pedagogy in leadership education.

Our research demonstrates that use of inquiry-based pedagogy in leadership education has long-lasting positive effects on students' capacity for applied leadership practice. Consequently, participants in this type of leadership learning are better positioned to effectively lead social change that is pressing in our current global context.

There is scant (if any) published research that has focused on using inquiry-based pedagogies in leadership education. This research makes a significant contribution to the scholarship of leadership education.