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Article
Publication date: 29 March 2024

Pratheek Suresh and Balaji Chakravarthy

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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%.

Research limitations/implications

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.

Originality/value

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.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0961-5539

Keywords

Book part
Publication date: 29 January 2024

Mohammed Elastal, Mohammad H Allaymoun and Tasnim Khaled Elbastawisy

This chapter proposes a model for discovering suspicious financial operations such as money laundering. To achieve this, the authors reviewed research papers on money laundering…

Abstract

This chapter proposes a model for discovering suspicious financial operations such as money laundering. To achieve this, the authors reviewed research papers on money laundering and financial institutions’ cases and problems, especially those related to financial transfers. They also collected primary data through face-to-face semi-structured interviews with financial companies’ owners and experts in financial transfers to identify hypotheses that help discover suspicious transfers. The chapter discusses the six big data analysis cycle phases from problem discovery to model deployment to identify suspicious transfers. The chapter uses hypothetical data and models to discuss the results and focuses on exchange companies willing to analyze financial operations. The chapter proposes tools that exchange companies can use to monitor and prevent suspicious transfers including data visualization and machine learning algorithms.

Details

Digital Technology and Changing Roles in Managerial and Financial Accounting: Theoretical Knowledge and Practical Application
Type: Book
ISBN: 978-1-80455-973-4

Keywords

Open Access
Article
Publication date: 18 July 2023

Shinta Rahma Diana and Farida Farida

Technology acceptance is a measure of that technology’s usefulness. Oil palm is one of the biggest contributors to Indonesia’s revenues, thus fueling its economy. Using remote…

Abstract

Purpose

Technology acceptance is a measure of that technology’s usefulness. Oil palm is one of the biggest contributors to Indonesia’s revenues, thus fueling its economy. Using remote sensing would allow a plantation to monitor and forecast its production and the amount of fertilizer used. This review aims to provide a policy recommendation in the form of a strategy to improve the added value of Indonesia’s oil palm and support the government in increasing oil palm production. This recommendation needs to be formulated by determining the users’ acceptance of remote sensing technology (state-owned plantations, private plantation companies and smallholder plantations).

Design/methodology/approach

This review’s methodology used sentiment analysis through text mining (bag of words model). The study’s primary data were from focus group discussions (FGDs), questionnaires, observations on participants, audio-visual documentation and focused discussions based on group category. The results of interviews and FGDs were transcribed into text and analyzed to 1) find words that can represent the content of the document; 2) classify and determine the frequency (word cloud); and finally 3) analyze the sentiment.

Findings

The result showed that private plantation companies and state-owned plantations had extremely high positive sentiments toward using remote sensing in their oil palm plantations, whereas smallholders had a 60% resistance. However, there is still a possibility for this technology’s adoption by smallholders, provided it is free and easily applied.

Research limitations/implications

Basically, technology is applied to make work easier. However, not everyone is tech-savvy, especially the older generations. One dimension of technology acceptance is user/customer retention. New technology would not be immediately accepted, but there would be user perceptions about its uses and ease. At first, people might be reluctant to accept a new technology due to the perception that it is useless and difficult. Technology acceptance is the gauge of how useful technology is in making work easier compared to conventional ways.

Practical implications

Therefore, technology acceptance needs to be improved among smallholders by intensively socializing the policies, and through dissemination and dedication by academics and the government.

Social implications

The social implications of using technology are reducing the workforce, but the company will be more profitable and efficient.

Originality/value

Remote sensing is one of the topics that people have not taken up in a large way, especially sentiment analysis. Acceptance of technology that utilizes remote sensing for plantations is very useful and efficient. In the end, company profits can be allocated more toward empowering the community and the environment.

Details

Arab Gulf Journal of Scientific Research, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1985-9899

Keywords

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