Data of simulation model for photovoltaic system's maximum power point tracking using sequential Monte Carlo algorithm.

This article outlines the input data and partial shading conditions employed in the replication model of Sequential Monte Carlo (SMC)-based tracking techniques for photovoltaic (PV) systems. The model aims to compare the performance of classical perturb and observe (P&O) algorithm, particle swarm optimization (PSO) algorithm, flower pollination algorithm (FPA), and SMC-based tracking techniques. The mathematical design and methodology of the complete PV system were detailed in our prior research, titled "Dynamic and Adaptive Maximum Power Point Tracking Using Sequential Monte Carlo Algorithm for Photovoltaic System" by Odat et al. (2023) [1]. The provided data facilitate precise replication of the output, saving significant simulation time. Additionally, these data can be readily applied to compare algorithmic results referenced by (Babu, T.S. et al., 2015; PrasanthRam, J. et al., 2017) [2,3], and contribute to the development of new processes for practical applications.

Evaluating sustainable development practices in a zero­carbon university campus: A pre and post-COVID-19 pandemic recovery study.

This paper aims to understand the critical areas for sustainable behavioural change on a university campus in order to achieve the net zero­carbon ambition pre- and post-COVID-19 pandemic recovery. For this purpose, the current empirical study is the first attempt to statistically examine the whole campus as a system, considering staff and student views (campus users), by developing an index measuring propensity for sustainable behavioural change to achieve a net zero­carbon campus. The novelty of this study is based on the following: (i) The impact of environmental sustainability measures due to COVID-19 is examined on three themes: physical activity routines on a daily basis, research, and teaching and learning, and (ii) the index that is compatible with quantifying the behavioural change. A multi-indicator questionnaire is used to collect empirical data for each of the three themes. Based on 630 responses, descriptive statistical analysis, normality tests, significance tests, and t-tests are performed using statistical and graphical software, and conducting uncertainty and sensitivity analyses on this quantitative data. The study found that 95 % of campus users agreed to use reusable materials on campus, and 74 % were willing to pay more for sustainable products. In addition, 88 % agreed to seek alternative and sustainable transportation for short research trips, while 71 % prioritised online conferences and project meetings for sustainable hybrid working. Moreover, the COVID-19 pandemic had a negative impact on the frequency of reusable material usage among campus users, as indicated by the index analysis, which showed a significant decrease from 0.8536 to 0.3921. The statistical findings show that campus users are more likely to initiate and endorse environmental sustainability measures in research and daily life than in teaching and learning, and there is no difference in their propensity for change. This research provides net zero­carbon sustainability researchers and leaders with a crucial baseline for scientific advances in the sustainability field. It also offers practical guidelines for implementing a net zero­carbon campus, engaging users from various disciplines, which has important implications and contributions.

Emotion detection of social data: APIs comparative study.

The development of emotion detection technology has emerged as an efficient possibility in the corporate sector due to the nearly limitless uses of this new discipline, particularly with the unceasing propagation of social data. In recent years, the electronic marketplace has witnessed the establishment of various start-up businesses with an almost sole focus on building new commercial and open-source tools and APIs for emotion detection and recognition. Yet, these tools and APIs must be continuously reviewed and evaluated, and their performances should be reported and discussed. There is a lack of research to empirically compare current emotion detection technologies in terms of the results obtained from each model using the same textual dataset. Also, there is a lack of comparative studies that apply benchmark comparisons to social data. This study compares eight technologies: IBM Watson Natural Language Understanding, ParallelDots, Symanto - Ekman, Crystalfeel, Text to Emotion, Senpy, Textprobe, and Natural Language Processing Cloud. The comparison was undertaken using two different datasets. The emotions from the chosen datasets were then derived using the incorporated APIs. The performance of these APIs was assessed using the aggregated scores they delivered and the theoretically proven evaluation metrics such as the micro-average of accuracy, classification error, precision, recall, and f1-score. Lastly, the assessment of these APIs incorporating the evaluation measures is reported and discussed.

Investigating users' experience on social media ads: perceptions of young users.

Social media platforms changed from being socialization platforms to serve businesses through advertisements. This research aims at investigating active young users' experience with social media ads by studying the personalization and the usefulness of the ads, and the role of the host architecture of the used platform. The results prove that users' experience was affected by the designated variables: personalization, perceived usefulness, and the host architecture. Specifically, It was found that social media users find social media ads useful, and personalized, and that the perceived usefulness and personalization significantly affect the usage of host architecture which significantly affects users' experience. Additionally, a significant difference is found between clusters of student answers in terms of personalization and perceived usefulness effect on user experience.