Ras Al Khaimah (Rak) is the northernmost emirate of the United Arab Emirates (UAE). The emirate has witnessed a phenomenal economic and demographic growth. Taking a progressive stance on sustainability, the emirate has undertaken a sustainability strategy that aims at increasing its recycling rate from the current level of about 15 to 75% by 2021. The current study examines the socio-demographic (gender, age, family size, education and household income) and environmental awareness determinants of recycling attitude and behaviour in Rak. This is done through statistical analysis of results from a public survey, where 227 respondents were interviewed face to face to respond to several questions designed to get insight into the main drivers and factors influencing recycling in Rak. The results indicate that environmental awareness and education level are significantly associated with recycling behaviour in Rak at a significance level of 0·001. Environmental awareness is also associated with recycling attitude, but at a lesser significance level of 0·07. Other socio-economic and demographic factors such as gender, age, family size and income are not significantly associated with recycling attitude and behaviour. The results from the study provide valuable insight that would help policymakers design effective recycling campaign.

This paper investigates sentiment analysis in Arabic tweets that have the presence of Jordanian dialect. A new dataset was collected during the coronavirus disease (COVID-19) pandemic. We demonstrate two models: the Traditional Arabic Language (TAL) model and the Semantic Partitioning Arabic Language (SPAL) model to envisage the polarity of the collected tweets by invoking several, well-known classifiers. The extraction and allocation of numerous Arabic features, such as lexical features, writing style features, grammatical features, and emotional features, have been used to analyze and classify the collected tweets semantically. The partitioning concept was performed on the original dataset by utilizing the hidden semantic meaning between tweets in the SPAL model before invoking various classifiers. The experimentation reveals that the overall performance of the SPAL model competes over and better than the performance of the TAL model due to imposing the genuine idea of semantic partitioning on the collected dataset.

Approaches used to design, build, and maintain digital cultural heritage communities and infrastructure in Europe, North America, and Australasia need to be tailored to regional contexts such as the Middle East and North Africa. Cultural and political differences, inherited issues with technical infrastructure and funding, and the need to build trusting and healthy working relationships across national boundaries makes this challenging. The framework and roadmap used during the MaDiH ( ): Mapping Digital Cultural Heritage in Jordan project (2019–2021) provides one of several possible models for such work, as well as highlighting its myriad challenges and opportunities.