Search results

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.

In the developing field of nano-materials synthesis, copper oxide nanoparticles (NPs) are deemed to be one of the most significant transition metal oxides because of their intriguing characteristics. Its synthesis employing green chemistry principles has become a key source for next-generation antibiotics attributed to its features such as environmental friendliness, ease of use and affordability. Because they are more environmentally benign, plants have been employed to create metallic NPs. These plant extracts serve as capping, stabilising or hydrolytic agents and enable a regulated synthesis as well.

Organic chemical solvents are harmful and entail intense conditions during nanoparticle synthesis. The copper oxide NPs (CuO-NPs) synthesised by employing the green chemistry principle showed potential antitumor properties. Green synthesised CuO-NPs are regarded to be a strong contender for applications in the pharmacological, biomedical and environmental fields.

The aim of this study is to evaluate the anticancer potential of CuO-NPs plant extracts to isolate and characterise the active anticancer principles as well as to yield more effective, affordable, and safer cancer therapies.

This review article highlights the copper oxide nanoparticle's biomedical applications such as anticancer, antimicrobial, dental and drug delivery properties, future research perspectives and direction are also discussed.

Researcher agreed that job performance has a positive effect on productivity as well as an organisation’s efficiency. Thus, this study aims to investigate the impact of mindfulness skill, inclusive leadership (IL), employee work engagement and self-compassion on the overall job performance of secondary school teachers in Hong Kong. It then evaluates the mediating effect of employee work engagement between the relationships of mindfulness skill, IL and job performance, as well as the moderate effect of self-compassion between the relationships of mindfulness skill, IL and employee work engagement.

The sample comprised 263 teachers working from three secondary schools in Sha Tin, Hong Kong. The data was then analysed using Smart PLS version 4.0.9.

The results showed significant positive relationships between mindfulness skill and IL towards employee work engagement and between employee work engagement and job performance; meanwhile, there emerged a significant effect on the relationship between mindfulness skill and IL towards job performance. Furthermore, this research has confirmed that self-compassion did not moderate the relationship between mindfulness skill, IL and employee work engagement, but employee work engagement plays a mediating effect on the relationship between mindfulness skill, IL and job performance.

This research has helped to fill the literature gap by examining the mediating roles of employee work engagement and mediator role of self-compassion in the integrated relationship of multi-factor and job performance. Examining the mediating role of employee work engagement has helped to enhance the understanding of the underlying principle of the indirect influence of mindfulness skill, IL and job performance. The result of this research shows that self-compassion plays a vital role in influencing the employees’ work engagement. Hence, it is important that companies design human resource management policy that enables self-compassion to be used as a consideration psychological-related strategy when structing organisation or teams. It is also crucial for top management and policymakers to define and communicate the organisation’s operating principle, value and goals.