The Yangjiabu Kite Festival, originating over 2000 years ago in Shandong Province, China, stands as a testament to the enduring cultural heritage and artistic traditions of kite flying. This research explores the historical origins, cultural symbolism, festival format, community engagement, and international exposure of the Yangjiabu Kite Festival, shedding light on its evolution and impact over time. Findings reveal the festival's deep roots in ancient Chinese traditions, its role as a platform for showcasing cultural diversity and craftsmanship, and its significance in promoting tourism, cultural exchange, and soft power projection for Shandong Province. Lessons learned from the Yangjiabu Kite Festival offer valuable insights for cross-cultural application, event management, cultural diplomacy, and community development. Suggestions for future research include comparative studies, longitudinal assessments, audience research, and policy analysis to further explore the dynamics and implications of cultural festivals in a global context. Overall, the research underscores the importance of cultural festivals as vehicles for cultural preservation, tourism promotion, and intercultural dialogue, fostering mutual understanding and appreciation across borders.

This research calls for intellectual and methodological unity among Muslims considering the current fragmentation and disintegration affecting the community. It emphasizes the need for cohesion, moving away from confrontations with differing ideas and interpretations. The study highlights a key aspect of tolerance ethics, a trait exemplified by esteemed jurists throughout history, who incorporated these values into their writings and actions as a model for future generations. Their respectful conduct in dialogues, debates, and interactions with opposing views serves as a guiding example. Key themes include decorum towards differing opinions, the avoidance of offensive language, and fairness in judgments, refraining from examining others' intentions. These principles of tolerance are evident in historical and biographical works, showcasing the jurists' ethical approach and intellectual maturity in addressing both minor and major disagreements, reflecting an ethical dimension in their discourse.

Oil spills (OS) in waters can have major consequences for the ecosystem and adjacent natural resources. Therefore, recognizing the OS spread pattern is crucial for supporting decision-making in disaster management. On 31 March 2018, an OS occurred in Balikpapan Bay, Indonesia, due to a ship's anchor rupturing a seafloor crude oil petroleum pipe. The purpose of this study is to investigate the propagation of crude OS using coupled three-dimensional (3D) model from DHI MIKE software and remote sensing data from Sentinel-1 SAR (Synthetic Aperture Radar). MIKE3 FM predicts and simulates the 3D sea circulation, while MIKE OS models the path of oil's fate concentration. The OS model could identify the temporal and spatial distribution of OS concentration in subsurface layers. To validate the model, in situ observations were made of oil stranded on the shore. On 1 April 2018, at 21:50 UTC, Sentinel-1 SAR detected an OS on the sea surface covering 203.40 km². The OS model measures 137.52 km². Both methods resulted in a synergistic OS exposure of 314.23 km². Wind dominantly influenced the OS propagation on the sea surface, as detected by the SAR image, while tidal currents primarily affected the oil movement within the subsurface simulated by the OS model. Thus, the two approaches underscored the importance of synergizing the DHI MIKE model with remote sensing data to comprehensively understand OS distribution in semi-enclosed waters like Balikpapan Bay detected by SAR.

This paper explores the role of the agile approach in managing interorganizational relationships in innovation networks. Design/methodology/approach. Relevant literature related to agile team management, network theory, innovation theory and knowledge management was studied. Based on collaboration between different approaches, a conceptual model for agile management of an innovation network was generated. Conceptual modeling was supplemented with graphical notation (diagram) of the main elements of the model. At the stage of testing the conceptual model, the action research method was applied, which provides an opportunity for organizational innovations to be carried out with the participation of researchers. The object of the pilot implementation of the conceptual model is the Bulgarian division of a global non-governmental organization (NGO) dedicated to community service. The organizational innovation applied in the testing of the model is related to improving the communication environment between individual teams (clubs), which are autonomous, but in the conditions of a network can generate projects for common, large-scale initiatives for community service. Findings. The pilot testing of the model shows its applicability, insofar as a traditionally managed structure switches to an agile communication model, in which horizontal connections become more frequent and knowledge between individual participants is transferred more efficiently. The possibility of decentralized decision-making creates the potential for generating numerous new and larger-scale initiatives for the benefit of the final beneficiaries. The participants in the network have also outlined some shortcomings, such as the need for better preliminary preparation when introducing organizational innovations in order to adequately explain and accept them.

This study meticulously explores the crucial elements precipitating corporate failures in Taiwan during the decade from 1999 to 2009. It proposes a new methodology, combining ANOVA and tuning the parameters of the classification so that its functional form describes the data best. Our analysis reveals the ten paramount factors, including Return on Capital ROA(C) before interest and depreciation, debt ratio percentage, consistent EPS across the last four seasons, Retained Earnings to Total Assets, Working Capital to Total Assets, dependency on borrowing, ratio of Current Liability to Assets, Net Value Per Share (B), the ratio of Working Capital to Equity, and the Liability-Assets Flag. This dual approach enables a more precise identification of the most instrumental variables in leading Taiwanese firms to bankruptcy based only on financial rather than including corporate governance variable. By employing a classification methodology adept at addressing class imbalance, we substantiate the significant influence these factors had on the incidence of bankruptcy among Taiwanese companies that rely solely on financial parameters. Thus, our methodology streamlines variable selection from 95 to 10 critical factors, improving bankruptcy prediction accuracy and outperforming Liang's 2016 results.

The present study focuses on improving Cognitive Radio Networks (CRNs) based on applying machine learning to spectrum sensing in remote learning scenarios. Remote education requires connection dependability and continuity that can be affected by the scarcity of the amount of usable spectrum and suboptimal spectrum usage. The solution for the proposed problem utilizes deep learning approaches, namely CNN and LSTM networks, to enhance the spectrum detection probability (92% detection accuracy) and consequently reduce the number of false alarms (5% false alarm rate) to maximize spectrum utilization efficiency. By developing the cooperative spectrum sensing where many users share their data, the system makes detection more reliable and energy-saving (achieving 92% energy efficiency) which is crucial for sustaining stable connections in educational scenarios. This approach addresses critical challenges in remote education by ensuring scalability across diverse network conditions and maintaining performance on resource-constrained devices like tablets and IoT sensors. Combining CRNs with new technologies like IoT and 5G improves their capabilities and allows these networks to meet the constantly changing loads of distant educational systems. This approach presents another prospect to spectrum management dilemmas in that education delivery needs are met optimally from any STI irrespective of the availability of resources in the locale. The results show that together with machine learning, CRNs can be considered a viable path to improving the networks' performance in the context of remote learning and advancing the future of education in the digital environment. This work also focuses on how machine learning has enabled the enhancement of CRNs for education and provides robust solutions that can meet the increasing needs of online learning.