Madenat Alelem University College

The rise of social media has transformed traditional marketing paradigms, offering unprecedented opportunities for audience targeting, content dissemination, and consumer engagement. This study investigates how marketing strategies have evolved in the digital era by analyzing the effectiveness of content formats, influencer tiers, and platform-specific dynamics. Using a multi-method approach, the research combines structured surveys, platform analytics, and experimental campaign tracking across Facebook, Instagram, Twitter/X, LinkedIn, TikTok, and YouTube. Key metrics include engagement rate, click-through rate, conversion rate, return on ad spend, audience growth, sentiment analysis, and user session behavior to assess the depth and quality of user interaction. The findings highlight the effectiveness of short-form video and the cost efficiency of micro-influencer campaigns, where platform fit and customization emerged as strong predictors of conversion. The results further indicate that success in digital marketing arises not from isolated tactics but from the strategic integration of content format, duration, and influencer selection. Employing multilevel modeling and negative binomial regression on a large-scale dataset, the study underscores the complexity of user behaviors in today’s digital environment and provides empirically grounded recommendations for optimizing campaign effectiveness.

Customary law has long served as a mechanism for solving environmental conflicts, especially in communities where indigenous governance plays a central role in land and resource management. However, its recognition and integration with statutory legal frameworks vary across jurisdictions, shaping both its applicability and effectiveness. This study employs a mixed-method framework that combines doctrinal legal analysis, case law review (145 cases), document analysis (250 texts), and structured interviews (100 participants) with statistical modeling and forecasting techniques. Comparative analysis across multiple jurisdictions evaluates the interaction of customary and statutory legal systems, while mathematical models, including regression analysis, success indices, and conflict reduction probability functions, quantify the influence of customary law on dispute resolution and governance effectiveness. Findings reveal that jurisdictions with stronger integration of customary law resolve 78

As global markets become more complex, the need for efficient, data-driven tools has grown, with Artificial Intelligence (AI) and Machine Learning (ML) becoming essential in transforming how businesses operate. These technologies play a critical role in optimizing resource management, automating work, and improving system performance. This paper explores the strategic role of AI/ML across ten diverse sectors: manufacturing, telecommunications, healthcare, logistics, retail, finance, energy, education, construction, and public services. The study examines how AI/ML integration impacts key operational metrics, such as downtime reduction, cost savings, process reliability, and resource utilization, based on data gathered over a 12-month period. The research employs advanced statistical methods, including panel regression and composite indexing, to analyze the results. Findings show significant improvements across all sectors, driven by AI/ML technologies that enhanced operational efficiency, improved forecasting accuracy, and optimized resource usage. This research also underscores the importance of robust data infrastructure and organizational readiness in fully capitalizing on these technologies. The findings provide valuable insights for decision-makers in both public and private sectors aiming to adopt scalable and sustainable automation solutions.

Brain tumor is a major challenge of public health, and accurate and efficient detection and quantification of brain tumor are essential for planning effective treatment. In this research, a deep learning approach is proposed to detect and segment brain tumors based on 3D FLAIR MRI scans. A 3D U-Net architecture is proposed where the accuracy is 0.976 which is a good measure of high performance in tumor detection and segmentation. The system also measures tumor metrics in a detailed manner, including tumor volume, orientation, axis length, and location, which are both important in clinical decision making. Preprocessing, segmentation and morphological refinement are performed with the methodology to guarantee specificity in tumor delineation. Training and validation were performed on a large-scale public dataset of 484 patients. The interface of the system is convenient for the users and has the capability of real time processing that helps the seamless integration with the clinical workflows. This paper has three key contributions: high performance detection, real time segmentation, 3D Tumor volume and shape representation. However, the clinical relevance of this system is in improving treatment planning, monitoring and patient outcomes. The system will be improved in future work by adding multimodal imaging, transfer learning, and longitudinal studies.

This study examines the relationship between employee wellness programs and organizational productivity across multiple industries using a data-driven and computational approach. As workforce dynamics evolve with globalization, technological disruption, and rising mental health demands, structured wellness strategies have become essential for sustaining performance and engagement. Employing a longitudinal mixed-methods design, the research integrates quantitative modeling, multivariate statistical analysis, and structural equation modeling to assess how key wellness dimensions, mental health support, flexible work arrangements, and organizational training, affect productivity, retention, and engagement outcomes. Data was collected from 250 operational teams across ten industries through surveys, managerial interviews, and archival HR metrics integrated within a performance measurement framework. Results show that wellness integration directly enhances productivity through improved engagement and work-life balance. Statistical modeling further confirms that flexibility and mental health support significantly reduce attrition and absenteeism while increasing operational stability. Industries with strong wellness adoption consistently outperform others across all indicators. The findings validate wellness as a strategic asset with implications for leadership, organizational policy, and human capital investment, demonstrating how computational and information-driven approaches can strengthen resilience and sustainable workforce performance.