Educational Legislation's Impact on Child Psychology: A Review in the USA and Africa
This paper presents a comprehensive review of the impact of educational legislation on child psychology, drawing comparisons between the United States (USA) and various nations in Africa. Educational legislation serves as a critical framework shaping the learning environments and experiences of children, influencing not only academic outcomes but also the psychological well-being of students. This review explores the nuanced ways in which educational laws in the USA and Africa contribute to the development of child psychology.In the USA, legislation such as the No Child Left Behind Act (NCLB) and the Every Student Succeeds Act (ESSA) has played a significant role in shaping the educational landscape. These laws aim to ensure educational equity, accountability, and improved academic achievement. The impact of such legislation on child psychology is multifaceted, influencing factors such as self-esteem, motivation, and stress levels among students. The competitive nature of standardized testing and accountability measures can shape students' perceptions of themselves and their academic abilities, impacting their psychological well-being.In African nations, diverse legal frameworks govern education, reflecting unique cultural contexts and challenges. The review examines how educational legislation in Africa, such as the Dakar Framework for Action and national education acts, addresses issues of access, quality, and relevance. The impact of these laws on child psychology is explored through the lens of cultural influences, community dynamics, and the role of education in shaping identity.Comparative analysis between the USA and Africa reveals both shared and divergent influences of educational legislation on child psychology. While the USA emphasizes accountability and standardized assessments, African systems often focus on broader goals of community development and cultural relevance. Understanding the psychological implications of educational legislation in these diverse contexts is essential for policymakers, educators, and mental health professionals working to create educational environments that foster positive psychological development in children. The review concludes with insights into potential areas for future research and collaborative efforts aimed at optimizing the impact of educational legislation on child psychology globally.
Indian Sensibility and Indigenization: The Poetry of Jayanta Mahapatra
The poetic oeuvre of Jayanta Mahapatra has been highly acclaimed in Indian English poetry for its Indian sensibility and indigenous explorations of Oriya regions. The ebb and flow of his poetic journey is interwoven with his intense desire to give an outlet to his unexpressed expressions and to portray his land with a local color. However, writing came to him quite late but freely but which opened the ‘door’ to the vastness, unknown, and unexplored. Poetry came to him as freely as his spur-of-the-moment imaginative impulse. Leaving the mortal world last year, he has left a legacy of poetry in Indian English and Oriya poetry. Remembering his contribution, this paper makes a critique of his poetry in a response to his portrayal of Oriya land diving it into deep Indian sensibility. Freedom from existing forms and subjects of poetry colors his imaginative and creative impulse with an idiosyncratic impulse which is peculiar to his writing. Digging deeper into the vastness of Orissa, the paper explores how indigenous color and localization have been embodied in his poetic sensibility.
Leveraging AI in .NET 8: Implementing Machine Learning Models with ML .NET
The quick advancement of artificial intelligence (AI) technology transformed software development into a tool that enables predictive analytics with intelligent automation capabilities throughout various commercial sectors. An evaluation of AI integration in .NET 8 utilizes ML.NET as the targeted machine learning framework, which aims to serve .NET developers. ML.NET introduces a straightforward pipeline infrastructure which permits developers to create predictive models by handling all three detection categories (classification, regression, anomaly detection) without extensive data science knowledge requirements. The research delivers detailed information about ML.NET functionalities, describes its training workflow and key features, and explains its Integration with .NET 8 applications. The research implements a practical analytics model as an illustration to show structured data processing with ML.NET while demonstrating its ability to generate precise predictions. A detailed performance assessment of the models employs standard metrics from the industry while discussing the optimization methods needed to achieve better accuracy levels. The examination of ML.NET as a machine learning framework emphasizes its characteristics relative to other options, showcasing its strengths and weaknesses when used in deep learning environments. This paper investigates the deployment strategies for artificial intelligence, including edge computing and cloud-based implementations for scalable artificial intelligence deployment abilities. Empirical tests reveal deployment hurdles AI models face in .NET environments, which help determine potential upgrades for ML.NET's functionality. The study demonstrates how ML.NET can improve .NET system accessibility by making machine learning accessible to developers through its potential. The research enhances AI applications in enterprise environments by establishing knowledge about combining machine learning models with modern .NET architecture systems.
The Impact of International Financial Reporting Standards (IFRS) On Financial Statement and Performance of Quoted Firms in Nigeria
This study investigates the Impact of International Financial Reporting Standards (IFRS) On Financial Statement and Performance of Quoted Firms in Nigeria, using data from 2014 to 2023 for BUA Cement Plc, Fidelity Bank Plc, and Cutix Plc. The analysis employs multiple regression models to assess the extent to which these variables influence financial statement and performance. From the analysis, result revealed that liquidity consistently plays a significant and positive role in driving financial performance across all three companies examined. Liquidity of Bua Cement plc,Fidelity Bank Plc, and Cutix Plc, respectively, were all statistically significant at p<0.001. In contrast, EPS and profit exhibit mixed effects. while EPS positively impacts GDP (performance) for BUA Cement Plc, it demonstrates a strong negative correlation in Fidelity Bank Plc and Cutix Plc, highlighting potential inefficiencies or sectoral differences. Similarly, profit shows a negative relationship with GDP(Performance) in all cases, underscoring the nuanced link between profitability and macroeconomic contributions. With R-squared values exceeding 0.996 across all models, the study demonstrates the robustness of liquidity as a predictor of good financial statement and performance, while highlighting the need for strategic reinvestment of profits and alignment of shareholder earnings with economic goals. These results emphasize the critical role of liquidity in fostering economic resilience and call for sector-specific strategies to optimize the broader impact of financial performance. Its as result that the study recommend that firms should focus on achieving more robust liquidity while effectively adjusting their earning per share and profit for better firm performance and macroeconomic growth.
Optimizing Power Distribution Through Electric Load Modelling Using Hybrid Particle Swarm Optimization – Artificial Neural Network Techniques
The rapid growth in energy demand has highlighted the need for efficient power distribution systems to reduce losses and enhance stability. This thesis presents an innovative approach to optimizing power distribution through electric load modelling by integrating hybrid Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN) techniques. Traditional load forecasting and distribution methods often struggle with dynamic and non-linear energy demands, leading to inefficiencies and increased operational costs. The proposed methodology leverages the predictive capabilities of ANN to model complex load behavior accurately while utilizing PSO to optimize the allocation of power resources. By combining these techniques, the system dynamically adjusts power distribution, addressing fluctuations in load demand in real-time. The hybrid algorithm improves convergence speed and enhances the precision of load forecasts, resulting in reduced energy losses and improved grid reliability. Extensive simulations on benchmark power systems demonstrate that the proposed model outperforms conventional techniques in terms of accuracy, efficiency, and adaptability. The research also includes a sensitivity analysis to evaluate the model's robustness under varying load conditions. These findings underscore the potential of integrating advanced computational intelligence methods for achieving sustainable and efficient energy management in modern power systems. This work contributes to the field of smart grid technologies, offering a scalable and adaptive framework for optimizing power distribution in diverse operational scenarios.
Design and Implementation of a Training and Evaluation Program for Managing Dental Phobia in Clinical Practice
Dental phobia is a significant barrier to accessing dental care, affecting lots of individuals globally. This condition is characterized by an intense, irrational fear of dental procedures, which often leads to avoidance of necessary dental care, resulting in deteriorated oral health and systemic health issues. Managing dental phobia in clinical practice is both a challenge and an opportunity for dental professionals to improve patient outcomes and satisfaction.
Exploring Financing Strategies of Entrepreneurs in UK Business Ventures: A Qualitative Study
Entrepreneurs play a pivotal role in introducing novel ideas and concepts to the market, addressing gaps in existing industries, and even forging entirely new market segments. Given the dynamic landscape of the UK business sector, an exploration of the financial methodologies employed by entrepreneurs becomes imperative, particularly concerning their impact on business ventures within the UK context. This study draws upon several established theories, including the Pecking Order Theory, Trade-off Theory, Behavioural Finance Theory, and Agency Theory, to underpin its empirical investigation and formulate research objectives. Employing a qualitative research approach, this study encompassed a diverse array of businesses spanning various UK industries, encompassing technology, retail, finance, manufacturing, hospitality, real estate, creative industries, and renewable energy. Primary data sources were harnessed, ensuring industry representation through a meticulous Stratified Sampling technique. Data collection methods encompassed semi-structured interviews and the administration of a questionnaire. The study used thematic analysis and Multiple Regression Analysis to analyze data on the relationship between Funding Sources and business management. Results showed that risk-focused businesses make judicious capital allocation decisions. In conclusion, this study discerned that the financial decision-making processes wield a substantial influence on entrepreneurs in the UK and that the challenges confronted by entrepreneurs profoundly impact their funding sources. Furthermore, the study corroborated that the strategies employed by entrepreneurs significantly affect their exposure to financial risks. This study recommends comprehensive risk management strategies, capital allocation decisions, and strategic investment for entrepreneurs to navigate challenges effectively, considering trade-offs and enhancing market competitiveness.
Improving Case Tracking and Coordination Across Agencies Using Data Integration
Effective case tracking and coordination across social service agencies are essential for delivering comprehensive support to immigrant populations. Immigrants often navigate complex systems to access critical services such as legal aid, healthcare, housing assistance, and social welfare. These services are typically managed by different agencies, each with its own data systems, policies, and communication protocols (Oliver et al., 2012). The fragmented nature of these systems poses significant challenges to seamless service delivery, often resulting in duplicated efforts, gaps in care, and delayed access to essential resources. For vulnerable immigrant populations, especially those seeking asylum or facing deportation, inefficiencies in case tracking can have severe consequences on their well-being and legal outcomes. Therefore, fostering collaboration among agencies through effective case tracking mechanisms is vital to ensuring equitable and timely service delivery (Biswas et al., 2012). Data integration offers a transformative approach to improving inter-agency collaboration by consolidating information from disparate systems into a unified framework. Through data integration, agencies can access shared databases, automate information exchanges, and coordinate service delivery more efficiently.
Towards Autonomous Document Classification: Leveraging Deep Learning for Intelligent Data Organization
The exponential growth of unstructured data has amplified the need for efficient and autonomous document classification systems. This study explores the transformative potential of deep learning in revolutionizing document organization through intelligent, automated approaches. By leveraging state-of-the-art neural networks, including Convolutional Neural Networks (CNNs) and Transformer-based architectures, this research proposes a robust framework for classifying diverse document types with high accuracy and minimal human intervention. The model integrates advanced natural language processing (NLP) techniques and contextual embeddings to capture semantic nuances and hierarchical relationships within text data. Experimental results demonstrate the system's adaptability to varying datasets and its scalability for large-scale implementations. This work also addresses challenges related to class imbalance, domain-specific terminology, and computational efficiency, offering comprehensive strategies to mitigate these barriers. The findings highlight the efficacy of deep learning in enabling autonomous document classification, paving the way for intelligent data management systems across industries.