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Ancient theatres and odea are one of the most significant and creative socio-cultural edutainment centres of human history that are still in use. They stood and served as huge multi-functional structures for social, religious, propaganda and political meeting space. Meanwhile, ancient theatres’ sites have an intrinsic value for all people, and as a vital basis for cultural diversity, social and economic development, they should continue to be a source of information for future generations. Though, all places with ancient theatre heritage should be assessed as to their potential risk from any anthropogenic or natural process. The paper aims to discuss these issues.

The main paper’s objective is to discuss mainly the anthropogenic and technical risks, vulnerability and impact issues on the ancient classical theatres. While elaborating on relevant recent studies, where the authors were involved in ERATO and ATHENA European projects for ancient theatres and odea, this paper provides a brief overview of the main aspects of the anthropogenic qualitative risks and related issues for selected classical antiquity theatres. Some relevant cases are critically presented and investigated in order to examine and clarify the main risk mitigation issues as an essential prerequisite for theatre heritage preservation and its interface with heritage reuse.

Theatre risk mitigation is an ongoing and challenging task. By preventive conservation, theatre anthropogenic qualitative risks’ management can provide a framework for decision making. The needed related guidelines and recommendations that provide a systematic approach for sustainable management and planning in relation mainly to “ancient theatre compatible use” and “theatre technical risks” are analysed and presented. This is based on identification, classification and assessment of the theatre risk causes and contributing factors and their mitigation.

The paper also suggests a new methodological approach for the theatre anthropogenic qualitative risk assessment and mitigation management, and develop some recommendations that provide a systematic approach for theatre site managers and heritage experts to understand, assess, and mitigate risks mainly due to anthropogenic and technical threats.

Presents the results of empirical research into issues faced by 25 companies undertaking business process re‐engineering (BPR) programmes. The research team sought to understand the BPR phenomenon through visits to 21 leading practitioners and four in‐depth case studies. The research indicated that six key issues affect the way in which BPR programmes are carried out, namely the nature of the change proposed (radical or incremental), the performance measures applied during the programme, the impact of information technology, the impact of human factors, the presence or absence of a process architecture and the link between BPR and strategy. The outcome of this research has implications for both practitioners and researchers. Where practitioners are concerned, the conventional, step‐by‐step BPR methodology should be amended to take into account these six issues more fully. For researchers there is a need for substantial research into good practice in BPR in each of the six areas.

With the exponential growth of mobile applications, recommending suitable mobile applications to users becomes a critical challenge. Although existing methods have made achievements in mobile application recommendation by leveraging graph convolutional networks (GCNs), they suffer from two limitations: the reliance on a singular acquisition path leads to signal sparsity, and the neighborhood aggregation method exacerbates the adverse impact of noisy interactions. This paper aims to propose SMAR, a self-supervised mobile application recommendation approach based on GCN, which is designed to overcome existing challenges by using self-supervised learning to create an auxiliary task.

In detail, this method uses three distinct data augmentation techniques node dropout, edge dropout and random walk, which create varied perspectives of each node. Then compares these perspectives, aiming to ensure uniformity across different views of the same node while maintaining the differences between separate nodes. Ultimately, auxiliary task is combined with the primary supervised task using a multi-task learning framework, thereby refining the overall mobile application recommendation process.

Extensive experiments on two real datasets demonstrate that SMAR achieves better Recall and NDCG performances than other strong baselines, validating the effectiveness of the proposed method.

In this paper, the authors introduce self-supervised learning into mobile application recommendation approach based on GCNs. This method enhances traditional supervised tasks by using auxiliary task to provide additional information, thereby improving signal accuracy and reducing the influence of noisy interactions in mobile application recommendations.

The aim was to test the feasibility of using automated data, and evaluate the impact of an emergency cardiac decision unit (CDU) on the overall outcomes of patients seen for chest discomfort. We used a retrospective, quasi‐experimental design to identify patients who had cardiac enzymes measured and an electrocardiogram performed during an ED visit in two six‐month periods, pre‐CDU (1 January‐30 June 1995) and post‐CDU (1 January‐ 30 June 1996). A total of 4,336 patients had outcomes assessed. After opening, 14.8 per cent of all chest pain cases were treated in the CDU. Hospital admission rates were reduced from 81.1 per cent to 66.7 per cent. Length of stay, myocardial infarction rates, and mortality were unchanged. The 14‐day revisit rates increased from 5.3 per cent to 10.3 per cent. We conclude that cardiac decision units decrease hospital admissions but increase ED revisit rates as a consequence of this now frequently used care pathway.

Research on social media advertising reactance (SMAR) is in the early stages. This paper intends to present a full picture of SMAR studies, introduce a comprehensive theoretical model (the social media advertising reactance model, SMARM) and provide insights into research and practice.

This review adopts the concept-driven systematic review approach, identifying 92 articles from four primary academic databases – EBSCO, Elsevier, Web of Science and Google Scholar.

First, this review offers overviews of five topics: publication trends, the journals publishing research, research methodology, targeted platform and the main theories. Second, based on the framework of psychological reactance, this study proposes the SMARM, identifying and elaborating on four components of the nomological relationship to SMAR: related concepts, antecedents, moderators and consequences.

This research has implications for advertisers, social media platform operators and policymakers by providing a whole picture of SMAR. Moreover, the SMARM could guide the stakeholders to adopt a user-friendly advertising design for the sustainable development of social media advertising (SMA).

By presenting an up-to-date review of SMAR-related research, this paper contributes to the literature of social media, advertising and marketing. Through a comparison with traditional advertising, this paper makes the characteristics of SMA clear. Meanwhile, the SMARM is developed to systematically elaborate on all related elements of SMAR and explain their underlying causal relationships. Future research directions are proposed.

The purpose of this paper is to determine the state-of-the-art in research on process owners, a key role within business process management and process governance, and thus to increase our understanding of the role of process owners.

The author conducts a systematic literature review of research shedding light on the role of process owners. The review includes 100 academic papers and 10 books on BPM.

Findings from the review demonstrate the significance of appointing process owners and showcase process owners’ role and responsibilities, as well as obstacles to and enablers of effective process ownership.

Based on the findings from the review, the author proposes a comprehensive framework on process ownership. The review provides a knowledge base for future research to build upon and can serve as a guide for practitioners. The review also identifies several research gaps and opportunities for future research.

This study aims to present the concept of aircraft turbofan engine health status prediction with artificial neural network (ANN) pattern recognition but augmented with automated features engineering (AFE).

The main concept of engine health status prediction was based on three case studies and a validation process. The first two were performed on the engine health status parameters, namely, performance margin and specific fuel consumption margin. The third one was generated and created for the engine performance and safety data, specifically created for the final test. The final validation of the neural network pattern recognition was the validation of the proposed neural network architecture in comparison to the machine learning classification algorithms. All studies were conducted for ANN, which was a two-layer feedforward network architecture with pattern recognition. All case studies and tests were performed for both simple pattern recognition network and network augmented with automated feature engineering (AFE).

The greatest achievement of this elaboration is the presentation of how on the basis of the real-life engine operational data, the entire process of engine status prediction might be conducted with the application of the neural network pattern recognition process augmented with AFE.

This research could be implemented into the engine maintenance strategy and planning. Engine health status prediction based on ANN augmented with AFE is an extremely strong tool in aircraft accident and incident prevention.

Although turbofan engine health status prediction with ANN is not a novel approach, what is absolutely worth emphasizing is the fact that contrary to other publications this research was based on genuine, real engine performance operational data as well as AFE methodology, which makes the entire research very reliable. This is also the reason the prediction results reflect the effect of the real engine wear and deterioration process.

The purpose of this paper is to expose and discuss the problems and suggests solutions for software development process in a large organization.

A case study approach was adopted in this paper. The case studies reengineering project in computer department at large organization. The structured analysis and design methodology used in this case included dynamic monitoring of qualitative and quantitative performance measures set for this purpose.

This paper addressed fundamental issues governing analysis and redesign of a software development process. Conceptual breakthroughs of new process were suggested. The success of implementing the project was based on infrastructure requirements for the new process and possible changes including positions and their required job skills. The paper suggests some software tool(s) that can capture the requirements and provide both backward and forward traceability.

The outcome of this work has implications for both practitioners and researchers. For researchers, the conventional, software development methodology should be amended to take into account the discussed issues in more details. For researchers, there is a need for substantial research into good practice in requirements and software development.

The original work of this project can be considered as detailed guidelines to help business process reengineering team members to successfully complete some critical activities associated with analysis and redesign of software‐development processes.

This paper aims to propose an approach to automatically annotate a large corpus in Arabic dialect. This corpus is used in order to analyse sentiments of Arabic users on social medias. It focuses on the Algerian dialect, which is a sub-dialect of Maghrebi Arabic. Although Algerian is spoken by roughly 40 million speakers, few studies address the automated processing in general and the sentiment analysis in specific for Algerian.

The approach is based on the construction and use of a sentiment lexicon to automatically annotate a large corpus of Algerian text that is extracted from Facebook. Using this approach allow to significantly increase the size of the training corpus without calling the manual annotation. The annotated corpus is then vectorized using document embedding (doc2vec), which is an extension of word embeddings (word2vec). For sentiments classification, the authors used different classifiers such as support vector machines (SVM), Naive Bayes (NB) and logistic regression (LR).

The results suggest that NB and SVM classifiers generally led to the best results and MLP generally had the worst results. Further, the threshold that the authors use in selecting messages for the training set had a noticeable impact on recall and precision, with a threshold of 0.6 producing the best results. Using PV-DBOW led to slightly higher results than using PV-DM. Combining PV-DBOW and PV-DM representations led to slightly lower results than using PV-DBOW alone. The best results were obtained by the NB classifier with F1 up to 86.9 per cent.

The principal originality of this paper is to determine the right parameters for automatically annotating an Algerian dialect corpus. This annotation is based on a sentiment lexicon that was also constructed automatically.

In working towards a sustainable campus of public universities, energy consumption behaviour assessment is one of the several issues that requires attention by the facilities manager. Information on energy consumption behaviour is needed to determine potential energy savings. The purpose of this study is to assess energy consumption behaviour for student accommodations in Malaysian public universities.

This study focuses on developing energy consumption behaviour models (ECBMs) and assesses the potential energy savings. The “energy culture” framework consolidated with multiple regression analysis is used to strengthen the development of ECBMs. A self-administrated survey involving 1,009 respondents in selected public universities was carried out.

The result shows that five factors from the energy culture framework contribute to energy consumption behaviour, namely, building regulation, environmental concern, education, social marketing and direct factors (device and activities). These factors are included in the model for predicting energy consumption levels. The results show that there is a 78 per cent difference in energy consumption between the observed and predicted data.

This study indicates a high potential energy saving among students of Malaysian public universities.

The model was tested against the overall students among Malaysian public universities. In future, the model can be tested within hostel accommodations. The present assessment revealed the potential energy saving among the hostel buildings and sets the target regarding which building has a potential to reduce energy. It also helps the facilities managers to come up with strategies for programmes and energy policy in public universities.