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Urban areas, especially in the coastal region of Bangladesh, face environmental degradation due to rapid urbanization, uncontrolled socio-economic activities and experiencing the adverse impacts of climate change. Nature-based solutions (NbS) as options for restoring, preserving, maintaining and elevating natural features or systems are becoming popular for reducing vulnerabilities caused either by natural hazards or human-induced activities. With this understanding, this study aims to explore the need of practicing NbS by studying the condition of a tidal canal (known as Thakurani Khal) and its peripheral areas of Mongla Port Municipality, a coastal and seaport town in Bangladesh.

This case study-based research uses multiple inquiries, including focus group discussions, pair-wise comparison, observation, GIS-based mapping, key informant interviews and secondary climate data review, to understand the spatial development of the area and community reactions to the changes in the urban environment.

The natural water flow of this canal is controlled by sluice gates that indirectly allowed the dweller to encroach its lands and convert the canal into a solid waste dumping area. These human-induced activities as well as the climate change-induced events (i.e. extreme heat, intensive and irregular rainfall, increased number of cyclones, etc.) have made the adjacent areas prone to waterlogging and drainage congestion. In this context, the revival of the original natural quality of the canal has been identified as an alternative to ensuring an adaptive urban environment.

This research highlights the importance of practicing NbS for developing urban resilience in the context of climate change.

This study aims to examine the impact of knowledge management processes (KMPs) on business transformation (BT) as mediated by business agility (BA).

The study is a quantitative, descriptive and analytical study. A two-stage sampling was selected. A total of 380 questionnaires were distributed using a self-administered survey to six companies representing the study context. A total of 362 questionnaires were collected and analyzed using SPSS and Path analysis (AMOS-22), with a response rate of 95.2%.

The study revealed that creation, storage, sharing and application of knowledge had statistically significant impact on BT. Moreover, the results revealed that BA had a positive effect on BT. Finally, this study confirms the mediating role of BA.

This study has several practical implications. These include, but are not limited to, more focus on both the KMPs and BT dimensions to improve their levels. There is also a need to search for other factors that can support BT efforts to enhance the growth potential and survival of business organizations.

The study confirms that KMPs can facilitate transforming of business to cope with unending environmental changes. Considering the scarcity of the available empirical as well as theoretical studies within the Jordanian as well as the overall Arab countries context, the study and its findings are expected to provide some insights and useful contribution concerning the potential mediating impact of BA, which, as revealed by the literature review, has rarely been considered in previous studies. This could open the door for further research to understand the mechanisms of BT and its predictive variables.

This study provides an overview of the application of sentiment analysis (SA) in exploring social movements (SMs). It also compares different models for a SA task of Algerian Arabic tweets related to early days of the Algerian SM, called Hirak.

Related tweets were retrieved using relevant hashtags followed by multiple data cleaning procedures. Foundational machine learning methods such as Naive Bayes, Support Vector Machine, Logistic Regression (LR) and Decision Tree were implemented. For each classifier, two feature extraction techniques were used and compared, namely Bag of Words and Term Frequency–Inverse Document Frequency. Moreover, three fine-tuned pretrained transformers AraBERT and DziriBERT and the multilingual transformer XLM-R were used for the comparison.

The findings of this paper emphasize the vital role social media played during the Hirak. Results revealed that most individuals had a positive attitude toward the Hirak. Moreover, the presented experiments provided important insights into the possible use of both basic machine learning and transfer learning models to analyze SA of Algerian text datasets. When comparing machine learning models with transformers in terms of accuracy, precision, recall and F1-score, the results are fairly similar, with LR outperforming all models with a 68 per cent accuracy rate.

At the time of writing, the Algerian SM was not thoroughly investigated or discussed in the Computer Science literature. This analysis makes a limited but unique contribution to understanding the Algerian Hirak using artificial intelligence. This study proposes what it considers to be a unique basis for comprehending this event with the goal of generating a foundation for future studies by comparing different SA techniques on a low-resource language.