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A significant increase in financial crime globally emphasises the importance of forensic interviewing to obtain useful and reliable information as part of a commercial forensic investigation. Previous research has identified two interviewing strategies that are aligned with the legal framework in South Africa: the PEACE model (P = preparation and planning; E = engage and explain; A = account, clarify and challenge; C = closure; E = evaluation) and the person-centred approach (PCA). The purpose of this paper is to explore the theoretical underpinnings and application of the PEACE model and the PCA as commercial investigative strategies aligned with the legal context in South Africa.

A scoping review was undertaken to identify literature relevant to the theoretical assumptions and application of the PEACE model and the PCA.

Literature for the most part reports on the PEACE model but offers very little information about the PCA. A critical analysis revealed that the PEACE model incorporates a clear guiding structure for eliciting information but lacks content needed to create an optimal interpersonal context. To promote this, the PCA proposes that interviewers demonstrate three relational variables: empathy, congruence and unconditional positive regard. The PCA suggests a basic structure for interviewing (beginning, middle and end), while providing very little guidance on how to structure the forensic interview and what information is to be elicited in each phase.

Combining the PEACE model and PCA presents an integrated interviewing technique best suited for obtaining useful and reliable information admissible in a South African court of law. The PEACE model has a clear structure, and the PCA assists in creating an optimal interpersonal context to obtain information in an interview.

This paper examines the influence of financial leverage on the financial sustainability of microfinance institutions (MFIs) and the moderating role of the percentage of female borrowers (PFB).

The study uses a global sample of 646 MFIs drawn from the World Bank Mix Market and panel data for 2010–2018. The study employs ordinary least squares (OLS) and the one-step system generalized method of moments (SGMM) as regression estimation methods.

The findings of this study reveal that financial leverage and the PFB have a negative and significant effect on financial sustainability. The findings further show that the interaction between financial leverage and the PFB positively affects the financial sustainability of MFIs.

The findings inform MFIs' managers on the adverse effect of financial leverage and the PFB in their quest for financial sustainability. The findings also demonstrate that MFIs can leverage female borrowers to reverse the adverse effect of financial leverage on financial sustainability of MFIs.

Previous studies examined the direct effect of financial leverage and reported incongruent results. Because female borrowers are at the epicenter of MFI lending, this study fills the gap in the literature by examining whether the proportion of female borrowers moderates the relationship between financial leverage and MFIs' financial sustainability using a global dataset.

As data centres grow in size and complexity, traditional air-cooling methods are becoming less effective and more expensive. Immersion cooling, where servers are submerged in a dielectric fluid, has emerged as a promising alternative. Ensuring reliable operations in data centre applications requires the development of an effective control framework for immersion cooling systems, which necessitates the prediction of server temperature. While deep learning-based temperature prediction models have shown effectiveness, further enhancement is needed to improve their prediction accuracy. This study aims to develop a temperature prediction model using Long Short-Term Memory (LSTM) Networks based on recursive encoder-decoder architecture.

This paper explores the use of deep learning algorithms to predict the temperature of a heater in a two-phase immersion-cooled system using NOVEC 7100. The performance of recursive-long short-term memory-encoder-decoder (R-LSTM-ED), recursive-convolutional neural network-LSTM (R-CNN-LSTM) and R-LSTM approaches are compared using mean absolute error, root mean square error, mean absolute percentage error and coefficient of determination (R²) as performance metrics. The impact of window size, sampling period and noise within training data on the performance of the model is investigated.

The R-LSTM-ED consistently outperforms the R-LSTM model by 6%, 15.8% and 12.5%, and R-CNN-LSTM model by 4%, 11% and 12.3% in all forecast ranges of 10, 30 and 60 s, respectively, averaged across all the workloads considered in the study. The optimum sampling period based on the study is found to be 2 s and the window size to be 60 s. The performance of the model deteriorates significantly as the noise level reaches 10%.

The proposed models are currently trained on data collected from an experimental setup simulating data centre loads. Future research should seek to extend the applicability of the models by incorporating time series data from immersion-cooled servers.

The proposed multivariate-recursive-prediction models are trained and tested by using real Data Centre workload traces applied to the immersion-cooled system developed in the laboratory.