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The main purpose of the Dubai Electricity and Water Authority (DEWA) sustainability case is to allow students to explore how nonfinancial information reported in sustainability plays a vital role in maintaining a trade-off between current economic pressure and future environmental needs.

This is an exploratory study in nature using a qualitative case study approach. The case requires an examination of DEWA's sustainability reporting (SR) in the context of Global Reporting Initiatives (GRIs). This case is designed to assist students in gauging DEWA's sustainability and explore how the company evaluates the materiality of sustainability issues.

With stakeholders' and investors' increased interest in sustainability, the authors argue that accounting programs should incorporate this topic into their curricula. The case enables students to focus on sustainability-related initiatives with DEWA that are aligned with GRI initiatives. The case might be instructive for both undergraduate and postgraduate students studying environmental and management accounting.

This case study is the first of its kind in the Gulf Cooperation Council (GCC) region to comprehensively analyze DEWA's sustainability practices concerning GRI-based SR. This study widens the understanding of DEWA's implementation of GRI standards in the preparation of its sustainability reports.

The purpose of the study is to investigate the perceived stigma among residents of Sanandaj, west of Iran, following COVID-19 pandemic.

This is a cross-sectional study conducted from March to April 2020. The sample consisted of 1,000 participants who live in Sanandaj. The data collection tool was a self-report electronic questionnaire. ANOVA and T-test were used to analyze the data.

The mean perceived stigma for COVID-19 was 5.50±2.24 (IQR: 3.75–6.87) out of 10-point scale. The highest point was seen for perceived external stigma (6.73±2.49, IQR: 5–8.75) followed by disclosure stigma (4.95±3.92, IQR: 0–10). Interestingly, self-employers were more concerned about disclosing their illness than those with governmental jobs (25±3.93 vs. 4.31±4.14, P<0.05), and also had an overall higher stigma score; 5.72±2.23 vs. 5.19±2.37, P<0.05).

COVID-19 stigma is high among Iranians and more common among men, youngsters and self-employers.

Link prediction in social networks refers toward inferring the new interactions among the users in near future. Citation networks are constructed based on citing each other papers. Reciprocal link prediction in citations networks refers toward inferring about getting a citation from an author, whose work is already cited by you. The paper aims to discuss these issues.

In this paper, the authors study the extent to which the information of a two-way citation relationship (called reciprocal) is predictable. The authors propose seven different features based on papers, their authors and citations of each paper to predict reciprocal links.

Extensive experiments are performed on CiteSeer data set by using three classification algorithms (decision trees, Naive Bayes, and support vector machines) to analyze the impact of individual, category wise and combination of features. The results reveal that it is likely to precisely predict 96 percent of reciprocal links. The study delivers convincing evidence of presence of the underlying equilibrium amongst reciprocal links.

It is not a generic method for link prediction which can work for different networks with relevant features and parameters.

This paper predicts the reciprocal links to show who is citing your work to collaborate with them in future.

The proposed method will be helpful in finding collaborators and developing academic links.

The proposed method uses reciprocal link prediction for bibliographic networks in a novel way.

This study aims to investigate the combined use of System Dynamics (SD) applications in Enterprise Engineering (EE) research and practice. SD application in EE is becoming widely accepted as a tool to support decision-making processes and for capturing relationships within enterprises.

A systematic literature review (SLR) is conducted using a standard SLR method to provide a comprehensive review of existing literature. The search was conducted on ten platforms identifying 30 publications which were analysed through the use and development of a codebook.

The SLR showed that 90% of the result set consisted of peer-reviewed academic conferences and journal papers. The SLR identified a highly dispersed author set of 83 authors. Amongst these authors, Vinay Kulkarni was an active author who has co-authored up to four publications in this research area. The analysis further revealed that the combined use of SD applications and EE is an emerging research area that still needs to develop in maturity. While all phases of EE have received attention, the current research work is more focused on the design phase. The important gap between model development and implementation is identified.

The study elucidates the existing status of interdisciplinary research combining techniques from the SD and EE disciplines, suggesting future research topics that combine the strengths of these existing disciplines.