Measuring flow, an optimal mental state defined by intense focus and immersion, can provide valuable insights into research on human engagement. However, a review of 69 studies identified significant methodological issues that could be undermining progress in this area of research. We discovered the use of eight nonvalidated psychological flow verification instruments (used in 33 studies), ambiguity and a lack of transparency in the interpretation and reporting of the verification results (36 studies), and a lack of screening of participants with only two studies screening for a disposition to experience a flow state. Additionally, despite the balance of challenge and skill being an essential precondition for inducing a flow state, 33 studies did not appropriately match flow‐inducing activities to participant’s skill levels. These issues with measuring flow through self‐assessment make it impossible to accurately validate hypothesised flow states using physiological approaches. To address these limitations, we propose a set of research protocol guidelines for integrating physiological and psychological measures to triangulate flow indicators. This provides a robust research framework that will allow the identification of physiological measures of flow that can provide a real‐time objective alternative to subjective self‐assessment instruments.

Digital Twins (DTs) of buildings can generate large volumes of dynamic data from various sources (e.g., sensors and IoT devices), enabling real-time representation of physical building states in a digital environment. Although machine learning (ML) techniques are increasingly used to predict building energy consumption from this DT data, existing approaches often lack scalability in handling data growth (data scalability) and/or adapting to evolving data patterns (model scalability). This study aims to address both drawbacks by developing a scalable cloud-based framework for the prediction of the building energy consumption. A key contribution to the field is the inclusion of a “monitoring and maintenance” module, which continuously evaluates model performance and triggers retraining only when needed. This enables timely adaptation of the ML model while avoiding unnecessary retraining and the associated computational costs. The framework was implemented and tested in a case study of a commercial building for 90 days, demonstrating its applicability. In a practical setting, the developed model could detect anomalies in time when the accuracy declined below the set threshold (70%) for five days and prevented unnecessary retraining of ML models. The findings support the feasibility of using cloud-based approaches to implement scalable ML models for energy prediction in buildings.

Stock market fluctuations is challenging due to nonlinear price dynamics and short-term investor sentiment effects. Deep learning models address challenges by combining historical price and trading/volume features with sentiment signals from financial news, social media, or blended sources. Although studies report strong results, evaluation practices remain inconsistent, ignoring real world viability. This systematic review analyses 22 papers published 2015-2024 that integrate historical and sentiment data for stock market forecasting. For classification, accuracy is predominant, reported in 13 of 22 studies. However, only 6 papers supplement raw accuracy with discriminatory performance measures, limiting confidence in directional reliability. Among regression papers, 4 report root mean square error (RMSE) and 4 report mean absolute error (MAE) as headline metrics. 5 studies report finance-aware metrics, including net profit or Sharpe ratio; 1 paper adjusts for transaction fees. No regression study reports finance-specific metrics such as maximum drawdown or transaction cost evaluation. Findings show sentiment integration benefits the next-day time horizon; 10 studies report a median accuracy gain of +7.4%. Weaker persistence at longer horizons suggests sentiment accuracy decay. Datasets vary from millions of sentiment records to small annotated samples, often without clear documentation, reducing reproducibility. Sentiment enhanced deep learning improves predictive metrics relative to price only baselines; however, minimal cost aware evaluation and real-time deployment analysis prevent substantiation of commercial viability. Rigorous, standardised, finance-aware evaluation frameworks are required to reconcile academic benchmarks with deployable trading systems.

Data scarcity is one of the major barriers to using Machine Learning (ML) techniques for building energy predictions. Transfer Learning (TL) has proven to be an effective approach to address this issue. However, most TL approaches have not considered scalability, which is an important factor when using Digital Twin (DT) data for predictions. To bridge this gap, this research aims to develop a scalable TL approach for predicting building energy consumption from limited DT data using cloud computing. Two frameworks, using Microsoft Azure and Amazon AWS, are developed and implemented in a case study to demonstrate their applicability and practicality. The findings show that both frameworks possess unique strengths in terms of scalability and improving accuracy. Azure performed better in training time and resource efficiency, while AWS excelled in data preprocessing speed and prediction accuracy. This paper supports researchers and practitioners in developing a scalable model (i.e., cloud-based TL) for predicting building energy consumption with limited DT data. Future research can explore the cost of using cloud platforms for building energy prediction and expand the list of decision factors for platform selection.