Amira A. E. Satti
Weleh I. Ikechuwu (PhD), Siyeofori Dede (MBBS), Onoja, W. Joy (MBBS), Achinike, P. Nyenwuna
Abstract:
Eliozu dumpsite is one of the biggest dumpsite in Port Harcourt metropolis. Wastes are dumped untreated, and thus may pose serious environmental risks to inhabitants in the area. The present study focuses on the assessment of the histopathology of the spleen; platelet counts exposed to Eliozu dumpsite leachate using wistar rats as model. Twenty five (25) male wistar rats were divided into five groups of five animals each; the leachate was collected from the dumpsite and water from near-by borehole also collected. Group 1 which served as control group received 1ml of commercial bottle water, group 2 received 1ml of borehole water 1kilometer from the dumpsite, groups 3, 4 and 5 received different concentration of the leachate in 10%, 50% and 100% for (40) days, the animals were sacrificed after being anesthetized with chloroform vapor, the bloods and spleens were collected for Platelet counts were analyzed using Auto-analyzer (Auto-analyzer, Mayamed, England. 2018 model) and histological studies using Haematoxylin and Eosin (H&E), special stain Mason Trichrome.
TRACEY CLEMENTINE KUCHIO
Abstract:
Monitoring and evaluation (M&E) systems play a pivotal role in ensuring the success of global projects undertaken by private and non-governmental organizations. The utilization of M&E systems is vital for project accomplishment and the adherence to predefined scopes. Nevertheless, organizations frequently encounter challenges in this regard, as exemplified by the case of Plan International Kenya. This organization faced issues related to stakeholder engagement and project implementation costs due to initial shortcomings in their M&E systems. This study aims to address the existing research gaps, particularly concerning key variables such as competency levels within M&E systems, stakeholder participation, and the costs associated with monitoring and evaluation. This research delves into the intricate relationships between monitoring resources, the competence levels associated with M&E systems, stakeholder involvement, and the overall performance of Plan International health projects in Nairobi County. The theoretical underpinnings of this study draw upon the Theory of Change, the Resource-Based View Theory, and the Results Theory. The research methodology incorporated a purposive sampling approach, targeting a population of 45 staff members actively engaged in Plan International health projects within Nairobi County. Data collection methods included both questionnaires and interviews, each subjected to rigorous reliability assessments. Quantitative data were analyzed using SPSS software version 24, with results being presented through descriptive statistical methods. Qualitative data was thematically analyzed, aligning with the study defined objectives and thematic areas.
Sreenivasulu Navulipuri
Abstract:
The research examines how Artificial Intelligence (AI) transforms financial services through its application to real-time risk assessment and automated decision-making. The industry transformation brought by AI enables faster decision-making processes while improving accuracy and reducing manual intervention to enhance risk management capabilities. Financial institutions achieve better financial operations accuracy and efficiency through the combination of machine learning (ML) and deep learning with real-time analytics to detect anomalies and prevent fraud and optimize credit risk assessments. The analysis focuses on streaming data processing and anomaly detection and event-driven risk mitigation systems as well as reinforcement learning function in developing adaptive risk models. The implementation of AI technology in finance creates multiple ethical and regulatory issues because it introduces bias while requiring transparent and fair decision-making processes. The article addresses these issues by explaining the need to create responsible AI systems which follow global regulations to maintain transparency and security and ensure accountability in financial decisions made with AI.
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