The modification of the Turia River's course in the 1960s marked a pivotal transformation in Valencia's urban landscape, evolving from a flood protection measure into a hallmark of sustainable urban development. However, recent rainfalls and flooding events produced directly by the phenomenon known as DANA ((Isolated Depression at High Levels) in October 2024 have exposed vulnerabilities in the infrastructure, particularly in the rapidly urbanized southern areas, raising questions about the effectiveness of past solutions in the context of climate change and urban expansion. As a result of this fragility, more than 200 deaths have occurred, along with material losses in 87 municipalities, whose industrial infrastructure accounts for nearly one-third of the economic activity in the Province of Valencia, valued at 479.6 million euros. This paper presents, for the first time, a historical-document-based approach to evaluate the successes and shortcomings of Valencia's flood management strategies through policy and spatial planning analysis. Also, this paper remarks the ongoing challenges and potential strategies for enhancing Valencia's urban resilience, emphasizing the need for innovative water management systems, improved drainage infrastructure, and the renaturalization of flood-prone areas. The lessons learned from Valencia's experience in 1957 and 2024 can inform future urban planning efforts in similar contexts facing the dual pressures of environmental change and urbanization.

Design and procurement integration strategies in construction projects play an important role and have an impact on the overall project cycle. Integrated design and procurement will increase productivity and reduce waste. This research aims to provide a guide to good design and procurement integration strategies in Design and Build (DB) projects in government projects. This research uses qualitative and quantitative methods in the form of a schematic literature review followed by a Focus Group Discussion (FGD) with the Delphi method to formulate integrated design and procurement that improve project performance. In-depth interviews were conducted with 90 respondents to explore the implementation of the design and procurement strategy on the project used as a case study. The results of this research are recommendations for an integrated design and procurement strategy which can be used as a Standard Operating Procedure (SOP) in DB projects on government projects so that it can provide added value from the start of the project being designed through tenders. This research can be utilized by project stakeholders, academics and anyone who will develop project performance through the integrated design and procurement in the long term.

The present study investigates the relationship between audit quality and earnings management in banks listed on the Stock Exchange of Iraq and Oman. This paper used audit firm size, auditors’ industry expertise, audit report timeliness, auditor change, and auditors’ opinions to measure audit quality. Financial statements, notes attached to financial statements, and reports of independent auditors of 28 banks listed on the Iraqi Stock Exchange and 8 banks listed on the Oman Stock Exchange during the financial period of 7 years (2015 to 2021), and hypotheses were tested using EViews software and panel data. The results of the hypothesis testing showed no significant relationship between the firm size and the auditors’ change and earnings management for both countries (Iraq and Oman). This is while the relationship between the auditor’s industry expertise, the timely presentation of the audit report, and the auditor’s opinion and earnings management for both countries (Iraq and Oman) is negative.

Creating a crop type map is a dominant yet complicated model to produce. This study aims to determine the best model to identify the wheat crop in the Haridwar district, Uttarakhand, India, by presenting a novel approach using machine learning techniques for time series data derived from the Sentinel-2 satellite spanned from mid-November to April. The proposed methodology combines the Normalized Difference Vegetation Index (NDVI), satellite bands like red, green, blue, and NIR, feature extraction, and classification algorithms to capture crop growth's temporal dynamics effectively. Three models, Random Forest, Convolutional Neural Networks, and Support Vector Machine, were compared to obtain the start of season (SOS). It is validated and evaluated using the performance metrics. Further, Random Forest stood out as the best model statistically and spatially for phenology parameter extraction with the least RMSE value at 19 days. CNN and Random Forest models were used to classify wheat crops by combining SOS, blue, green, red, NIR bands, and NDVI. Random Forest produces a more accurate wheat map with an accuracy of 69% and 0.5 MeanIoU. It was observed that CNN is not able to distinguish between wheat and other crops. The result revealed that incorporating the Sentinel-2 satellite data bearing a high spatial and temporal resolution with supervised machine-learning models and crop phenology metrics can empower the crop type classification process.

This study investigates the impact of the Belt and Road Initiative (BRI) on the construction sector in Southeast Asia, focusing on Thailand, Malaysia, and Cambodia. Qualitative research approach is used to analyze the implications of Chinese investments in these countries, exploring both the opportunities and challenges faced by Chinese investors. Key research questions address the resilience of the construction sector, the obstacles encountered by investors, and the influence of policy on the construction business. Through interviews with CEOs and senior managers of major construction companies and a review of relevant documents, the study uncovers the economic and geopolitical motivations behind China’s BRI strategy. The findings reveal significant insights into the benefits and drawbacks of BRI financing, providing recommendations for overcoming challenges and leveraging future opportunities in Southeast Asian construction sectors.

This study aims to use dialectical thinking to explore the impacts and responses of Artificial Intelligence (AI) empowerment on students’ personalized learning. The effect of AI empowerment on student personalization is dissected through a literature review and empirical cases. The study finds that AI plays a significant role in promoting personalized learning by enhancing students’ learning effectiveness through intelligent recommendation, automated feedback, improving students’ independent learning ability, and optimizing learning paths, however, the wide application of AI also brings problems such as technological dependence, cheating in exams, weakening of critical thinking ability, educational fairness, and data privacy protection to students. The study proposes recommendations to strengthen technology regulation, enhance the synergy between teachers and AI, and optimize the personalized learning model. AI-enabled personalized learning is expected to play a greater role in improving learning efficiency and educational fairness.