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To explore the value in reminiscing about past festivals as a potential way of improving wellbeing in socially isolated times.

The paper uses previous research on reminiscence, nostalgia and wellbeing to underpin the analysis of self-recorded memory narratives. These were gathered from 13 pairs of festivalgoers during Covid-19 restrictions and included gathering their individual memories and their reminiscences together. The participant pairs were a mix of friends, family and couples who had visited festivals in the UK, Finland and Denmark.

Four key areas that emerged through the analysis were the emotions of nostalgia and anticipation, and the processes of reliving emotions and bonding through memories.

Future studies could take a longitudinal approach to see how memory sharing evolves and the impact of this on wellbeing. The authors also recommend undertaking similar studies in other cultural settings.

This study findings have implications for both post-festival marketing and for the further development of reminiscence therapy interventions.

The method provides a window into memory sharing that has been little used in previous studies. The narratives confirm the value in sharing memories and the positive impact this has on wellbeing. They also illustrate that this happens through positive forms of nostalgia that centre on gratitude and lead to hope and optimism. Anticipation, not emphasised in other studies, was also found to be important in wellbeing and was triggered through looking back at happier times.

South African public archives have not been able to transform into active documenters of society. As a result, they cannot carry out their mandate of collecting non-public records of lasting value and national significance and recording aspects of the country’s experience that have previously been ignored by archives repositories. This paper aims to discuss efforts by the Gauteng Provincial Archives to transform the archival landscape in South Africa by collecting sports memories. This is because, in democratic South Africa, the archival landscape was expected to change and reflect the nation’s diversity, despite the fact that it still largely reflected the Western-dominated global mainstream.

This study is based on the authors’ personal experiences with the development and operation of the Gauteng Provincial Archives. The authors are also involved in the Gauteng Provincial Archives’ oral history project, which aims to build an inclusive archive by recording oral histories of sports memories across the province.

The construction of the Gauteng Archives Repository has ushered in a chance to decolonise South African archives by collecting sports memories. These are windows of opportunity through which ordinary people can include their own experiences, filling in the gaps left by colonial and apartheid archives.

This paper offers practical experience in transforming and decolonising archives through collecting sports memories.

This paper aims to bridge the gap by understanding the context of ageing consumer behaviour in the online community. Specifically, this research seeks to identify which content typologies are critical to generating high engagement levels and, consequently, online brand advocacy and to understand the underlying motivation behind consumer online engagement.

A netnographic approach was used to comprehensively analyse older consumers’ online communities on Facebook, namely, “Silversurfers”. A total of 3,991 posts were included in the study and analysed using a content analysis approach over two years, from 2020 to 2022.

Results revealed that photography is the most active media type among older consumers. This study extends the literature on content marketing, identifying 17 new content types that reflect the four motivation states of older consumers to engage with the online community: cognitive/informative oriented, affective/emotional oriented, co-creation/interactive oriented and nostalgic oriented. Moreover, this investigation stressed affective/emotional oriented and nostalgic oriented as the primary motivations for higher engagement levels.

The older population is growing, which makes the ageing market potentially huge. However, more literature needs to address it, especially in online communities. Finally, to the best of the authors’ knowledge, this study develops an original content typology framework in which firms can consider implementing effective content typology strategies for the older consumer segment.

The purpose of this research was to investigate how VR-mediated sports, as opposed to 2-D screens, affect the emotional and cognitive experiences of fans with the game and its sponsors.

The current study employed a single-factorial experimental design, in which participants were randomly assigned to either watch a soccer game through a VR headset or a 2-D screen. Physiological and self-reported measures were used to measure levels of presence, arousal, attention and memory.

Participants who watched sports through VR experienced a higher level of presence, greater psychophysiological arousal, and exhibited higher levels of attention toward the game. However, they showed lower recognition for in-stadium signage compared to those who watched the game on a 2-D screen.

The results suggest that sports teams can use VR to create a more immersive and engaging experience for fans. Additionally, in-stadium signage advertising may not be as effective in VR sport broadcasting contexts, and sports practitioners may want to explore alternative forms of advertising that are better suited for VR environments.

Methodologically, this study used a combination of self-reported and real-time physiological measures to capture dynamic and spontaneous changes in fans while watching games. Theoretically, this study utilized the Dynamic Human-Centered Communication System Theory to adopt a human-centered approach to understand how VR impacts the experience of sport game viewers.

In repetitive projects, repetition offers more possibilities for activity scheduling at the sub-activity level. However, existing resource-constrained repetitive scheduling problem (RCRSP) models assume that there is only one sequence in performing the sub-activities of each activity, resulting in an inefficient resource allocation. This paper proposes a novel repetitive scheduling model for solving RCRSP with soft logic.

In this paper, a constraint programming model is developed to solve the RCRSP using soft logic, aiming at the possible relationship between parallel execution, orderly execution or partial parallel and partial orderly execution of different sub activities of the same activity in repetitive projects. The proposed model integrated crew assignment strategies and allowed continuous or fragmented execution.

When solving RCRSP, it is necessary to take soft logic into account. If managers only consider the fixed logic between sub-activities, they are likely to develop a delayed schedule. The practicality and effectiveness of the model were verified by a housing project based on eight different scenarios. The results showed that the constraint programming model outperformed its equivalent mathematical model in terms of solving speed and solution quality.

Available studies assume a fixed logic between sub-activities of the same activity in repetitive projects. However, there is no fixed construction sequence between sub-activities for some projects, e.g. hotel renovation projects. Therefore, this paper considers the soft logic relationship between sub-activities and investigates how to make the objective optimal without violating the resource availability constraint.

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.

Being the final end of the logistic distribution, attended home delivery (AHD) plays an important role in the distribution network. AHD typically refers to the service provided by the distribution service provider to the recipient's doorstep. Researchers have always identified AHD as a bottleneck for last-mile delivery. This paper addresses a real-life stochastic multi-objective AHD problem in the context of the Indian public distribution system (PDS).

Two multi-objective models are proposed. Initially, the problem is formulated in a deterministic environment, and later on, it is extended to a multi-objective AHD model with stochastic travel and response time. This stochastic AHD model is used to extensively analyze the impact of stochastic travel time and customer response time on the total expected cost and time-window violation. Due to the NP-hard nature of the problem, an ant colony optimization (ACO) algorithm, tuned via response surface methodology (RSM), is proposed to solve the problem.

Experimental results show that a change in travel time and response time does not significantly alter the service level of an AHD problem. However, it is strongly correlated with the planning horizon and an increase in the planning horizon reduces the time-window violation drastically. It is also observed that a relatively longer planning horizon has a lower expected cost per delivery associated.

The paper does not consider the uncertainty of supply from the warehouse. Also, stochastic delivery failure probabilities and randomness in customer behavior have not been taken into consideration in this study.

In this paper, the role of uncertainty in an AHD problem is extensively studied through a case of the Indian PDS. The paper analyzes the role of uncertain travel time and response time over different planning horizons in an AHD system. Further, the impact of the delivery planning horizon, travel time and response time on the overall cost and service level of an AHD system is also investigated.

This paper investigates a unique and practical AHD problem in the context of Indian PDS. In the present context of AHD, this study is highly relevant for real-world applications and can help build a more efficient delivery system. The findings of this study will be of particular interest to the policy-makers to build a more robust PDS in India.

The most challenging part of an AHD problem is the requirement of the presence of customers during the time of delivery, due to which the probability of failed delivery drastically increases if the delivery deviates from the customer's preferred time slot. The paper modelled an AHD system to incorporate uncertainties to attain higher overall performance and explore the role of uncertainty in travel and response time with respect to the planning horizon in an AHD, which has not been considered by any other literature.

The main goal of this paper is to investigate whether there is long-memory behavior in the CBOE Brazil ETF volatility index (named here VIXBR). As structural breaks may create a spurious long-range dependence, the presence of structural breaks is also gauged.

The study considers the period from October 2011 to March 2021, using daily data. To test the long-memory behavior, three empirical approaches are adopted: GPH, ELW and robust GPH (RGPH) estimator. To estimate the structural break points adopted to date the subsamples, the ICSS algorithm is used.

Results considering the total period (TP) and subsamples show that the breaks did not create a spurious long-memory behavior and together with the rolling estimation, reveal strong evidence of the long-range dependence in the CBOE Brazil ETF volatility index. The higher degree of persistent of the VIXBR series suggests an extended period of increased uncertainty that agents need consider when making their investment decision.

As possible extension of this study is to investigate the behavior of long memory and structural breaks for different frequencies (weekly, monthly, among others).

The presence of long-range dependence in the CBOE Brazil ETF volatility index reveals that the past information is important for the predictability of risks, and therefore, can help to protect against market risks, which has important implications regarding the future decisions of economic agents (for example, policy makers and investors).

Brazil is an emerging capital market (ECM) that has attracted a great deal of attention from investors and investment funds seeking to diversify its assets. This paper contributes to the empirical financial literature, by studying the long-memory behavior of the CBOE Brazil ETF volatility index, considering possible structural breaks. To the best of knowledge, this has not been done so far.

Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.

This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.

Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.

At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.

This study aims to reduce data bias during human activity and increase the accuracy of activity recognition.

A convolutional neural network and a bidirectional long short-term memory model are used to automatically capture feature information of time series from raw sensor data and use a self-attention mechanism to learn select potential relationships of essential time points. The proposed model has been evaluated on six publicly available data sets and verified that the performance is significantly improved by combining the self-attentive mechanism with deep convolutional networks and recursive layers.

The proposed method significantly improves accuracy over the state-of-the-art method between different data sets, demonstrating the superiority of the proposed method in intelligent sensor systems.

Using deep learning frameworks, especially activity recognition using self-attention mechanisms, greatly improves recognition accuracy.