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.

The purpose of this paper is to improve autonomous flight performance of a fixed-wing unmanned aerial vehicle (UAV) via simultaneous morphing wingtip and control system design conducted with optimization, computational fluid dynamics (CFD) and machine learning approaches.

The main wing of the UAV is redesigned with morphing wingtips capable of dihedral angle alteration by means of folding. Aircraft dynamic model is derived as equations depending only on wingtip dihedral angle via Nonlinear Least Squares regression machine learning algorithm. Data for the regression analyses are obtained by numerical (i.e. CFD) and analytical approaches. Simultaneous perturbation stochastic approximation (SPSA) is incorporated into the design process to determine the optimal wingtip dihedral angle and proportional-integral-derivative (PID) coefficients of the control system that maximizes autonomous flight performance. The performance is defined in terms of trajectory tracking quality parameters of rise time, settling time and overshoot. Obtained optimal design parameters are applied in flight simulations to test both longitudinal and lateral reference trajectory tracking.

Longitudinal and lateral autonomous flight performances of the UAV are improved by redesigning the main wing with morphing wingtips and simultaneous estimation of PID coefficients and wingtip dihedral angle with SPSA optimization.

This paper originally discusses the simultaneous design of innovative morphing wingtip and UAV flight control system for autonomous flight performance improvement. The proposed simultaneous design idea is conducted with the SPSA optimization and a machine learning algorithm as a novel approach.

This study analyzes the static and dynamic risk spillover between US/Chinese stock markets, cryptocurrencies and gold using daily data from August 24, 2018, to January 29, 2021. This study provides practical policy implications for investors and portfolio managers.

The authors use the Diebold and Yilmaz (2012) spillover indices based on the forecast error variance decomposition from vector autoregression framework. This approach allows the authors to examine both return and volatility spillover before and after the COVID-19 pandemic crisis. First, the authors used a static analysis to calculate the return and volatility spillover indices. Second, the authors make a dynamic analysis based on the 30-day moving window spillover index estimation.

Generally, results show evidence of significant spillovers between markets, particularly during the COVID-19 pandemic. In addition, cryptocurrencies and gold markets are net receivers of risk. This study provides also practical policy implications for investors and portfolio managers. The reached findings suggest that the mix of Bitcoin (or Ethereum), gold and equities could offer diversification opportunities for US and Chinese investors. Gold, Bitcoin and Ethereum can be considered as safe havens or as hedging instruments during the COVID-19 crisis. In contrast, Stablecoins (Tether and TrueUSD) do not offer hedging opportunities for US and Chinese investors.

The paper's empirical contribution lies in examining both return and volatility spillover between the US and Chinese stock market indices, gold and cryptocurrencies before and after the COVID-19 pandemic crisis. This contribution goes a long way in helping investors to identify optimal diversification and hedging strategies during a crisis.

Michael Buckland's works have spanned theoretical, historical and practice-oriented foci and genre. This article focuses on some of his theoretical-historical works that span over 20 years, which demonstrate a reading and critique of European Documentation in terms of what has been called “Documentality.” This turn to a philosophy of information called “Documentality” marks the moment of “neo-documentation.” This article surveys this moment in Buckland's works by reading his articles “Information as Thing,” “What is a ‘Document’?”, and “Documentality Beyond Documents.” It shows the transition from Documentation as a philosophy of information as representation to Documentality as a philosophy of information as function and performance. Some concepts and works of Bruno Latour are used to illuminate this transition from Documentation to Documentality. Implications and further research directions are discussed at the end.

Conceptual and historical analyses.

The article follows a neo-documentalist transition in Buckland's works in the thinking of documents from an Otletian representationalist epistemology (“Documentation”) to a functionalist and performative epistemology (“Documentality”) for documents.

This is a conceptual work on a limited corpus in Buckland's oeuvre. It has a limited discussion of Documentality in the works of other writers, namely the works of Bernd Frohmann and Maurizio Ferraris.

The article points to historical shifts in the study of documents in Library and Information Science.

Documentality critically and materially studies documents in sociotechnical information management systems and elsewhere.

This work highlights the importance of the above works and the importance of the neo-documentalist perspective of Documentality.

The purpose of this research is to increase academic understanding of the relationship between systems' political identity and their viability, and to contribute to anarchist-cybernetics by examining the idea of organization proposed by Malatesta using Viable Systems. The research also develops the understanding of the relationship between Viable Systems and the environment.

The author developed a content analysis method that uses dynamic analysis, identifying how some variables affect others, and data is analysed using the Viable Systems Model. The author used Dynamic Causal Diagrams and the Viable System Model to draw conclusions and build theory. The author examined 137 documents produced by Errico Malatesta, studying in detail 39 documents containing the researched concepts.

The article identifies the literature, proposes an organizational theory for society and for cooperatives, strongly grounded in both, self-management and control. It presents a theory of self-management as a balancing effort to the control exercised by the external economic, political and societal forces of the environment. The literature also shows a form of organization that can be interpreted using the VSM framework. The ideas about self-management found in the literature, extend to economics, social theory, ethics, organizations, management and even operations management. The article finishes proposing a set of committees linked to the VSM structure, and successfully bridges anarchism and organizational cybernetics.

The article presents a novel method of systems analysis for the study of literature. It discovers the theory proposed by Malatesta not identified previously. Using the VSM framework, the ideas presented by the author, are translated into the organizational identity, and the operation of cooperatives. It makes an important contribution to anarchist-cybernetics.