This article examines the history of Russian colonization in Kazakhstan, focusing on identity, resistance, and independence within Russia’s neo-imperial ambitions. It addresses the socio-political barriers in postcolonial Kazakhstan due to ties with Russia and explores how the Soviet migration policies shaped Kazakhstan’s demographic and political landscape. The study outlines the phases of Russian colonization, contrasting Russian narratives of a civilizing mission with Kazakh perspectives on exploitation and cultural erasure. Using postcolonial theory, it deconstructs these narratives and reveals power dynamics. Kazakh literature and poetry are analyzed as mediums of resistance, emphasizing the horse as a symbol of cultural identity. The article concludes by discussing the post-Soviet cultural transformations and the role of literature in nation-building, highlighting the importance of reclaiming cultural symbols and myths for understanding Kazakhstan’s colonial history and postcolonial transformation.

This study aims to identify the risk factors causing the delay in the completion schedule and to determine an optimization strategy for more accurate completion schedule prediction. A validated questionnaire has been used to calculate a risk rating using the analytical hierarchy process (AHP) method, and a Monte Carlo simulation on @RISK 8.2 software was employed to obtain a more accurate prediction of project completion schedules. The study revealed that the dominant risk factors causing project delays are coordination with stakeholders and changes in the scope of work/design review. In addition, the project completion date was determined with a confidence level of 95%. All data used in this study were obtained directly from the case study of the Double-Double Track Development Project (Package A). The key result of this study is the optimization of a risk-based schedule forecast with a 95% confidence level, applicable directly to the scheduling of the Double-Double Track Development Project (Package A). This paper demonstrates the application of Monte Carlo Simulation using @RISK 8.2 software as a project management tool for predicting risk-based-project completion schedules.

This article analyses the case of Dubai’s smart city from a public policy perspective and demonstrates how critical it is to rely on the use of the public-private partnership (PPP) model. Effective use of this model can guarantee the building of a smart city that could potentially fulfill the vision of the political leadership in Dubai and serve as a catalyst and blueprint for other Gulf states that wish to follow Dubai’s example. This article argues that Dubai’s smart city project enjoys significant political support and has ambitious plans for sustainable growth, and that the government has invested heavily in developing the necessary institutional, legal/regulatory, and supervisory frameworks that are essential foundations for the success of any PPP project. The article also points to some important insights that the Dubai government can learn from the international experience with the delivery of smart cities through PPPs.

BiVO₄ was hydrothermally synthesized under different preparing conditions and characterized by XRD, SEM, Raman spectrum and BET specific surface area. The influence of different pH value and annealing temperature and hydrothermal time on the morphologies and structures of the BiVO₄ samples was investigated systematically. It can be found that annealing would eliminate the effects caused by the pH of precursor, heating temperature and heating time, but preparing conditions still influenced the size and specific surface area of samples. Furthermore, the photocatalytic activities of the fabricated BiVO₄ were also evaluated by the degradation of methyl blue in aqueous solution under UV and visible light irradiation.

The biomass of three dominant mangrove species (Sonneratia apetala, Avicennia alba and Excoecaria agallocha) in the Indian Sundarbans, the designated World Heritage Site was evaluated to understand whether the biomass vary with spatial locations (western region vs. central region) and with seasons (pre-monsoon, monsoon and post-monsoon). The reasons for selecting these two regions and seasons are the contrasting variation in salinity. Among the three studied species, Sonneratia apetala showed the maximum biomass followed by Avicennia alba and Excoecaria agallocha. We also observed that the biomass varied significantly with spatial locations (p<0.05), but not with seasons. The variation may be attributed to different environmental conditions to which these forest patches are exposed to.