CALL FOR PAPER - Vol 9 - Issue 2

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.

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.

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.

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.

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.

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.

One of the main challenges in neuro-oncology is brain tumours and it is important to notice them early to give patients a higher chance of successful recovery. More recently, Computer-Aided Detection (CAD) has been greatly impacting medical imaging, especially in discovering and categorizing brain tumours. Using machine learning and deep learning, as well as advanced algorithms, CAD systems improve the accuracy and rapidity of tumour detection in both MRI and CT scans. This article discusses how various CAD approaches are created and used for the identification of brain tumours. They manage important parts of medical image analysis, including image preparation, division into parts, retrieving key features and labelling them based on how they look. Automation enables radiologists to minimize misdiagnoses, reduce the effect of observers’ differences and support good decisions in challenging cases. The latest studies have proven that CNNs and hybrid models are better than traditional rule-based systems at identifying and distinguishing benign from malignant brain tumours. Furthermore, including different imaging techniques in CAD applications makes it easier to diagnose patients accurately. There are still issues with CAD systems, including different types of data, not much-labeled training data and having to be validated by clinicians. The article gives an in-depth explanation of CAD methods, looks at how they diagnose conditions and explores new areas such as explainable AI and federated learning. By reading this paper, researchers and clinicians can gain detailed knowledge of how CAD systems play a vital role in brain tumour diagnosis and bring new, personalized and data-driven options to healthcare.

Doctors use electrocardiogram (ECG) signals to diagnose various cardiovascular diseases, which are a major cause of death all over the world. Interpreting an ECG manually takes a lot of time, can be based on the doctor’s opinions, and might result in inconsistent diagnoses. As a result, scientists commonly use CNNs, which are designed to understand changes in data at different levels, to predict and classify ECG signals. This paper aims to show how CNNs are useful for forecasting cardiac events and for ECG signal classification with accuracy and using a few hand-crafted features. We discuss several CNN models that are fitted for ECG, including those built for segmented data and those that add recurrent steps for studying sequence dependency. We additionally explore how to filter noise, normalize the ECG data, and segment them before they are fed into the model. CNN-based models are evaluated against common machine learning techniques and are found to be more accurate, sensitive, and specific in picking out arrhythmias, myocardial infarctions, and other illnesses of the heart. To address the problem of a few labelled ECG datasets, we apply transfer learning and data augmentation for our models. Using saliency maps and CAMs, it is possible to interpret the results of CNN models, which contributes to the acceptance and trust of AI-based diagnoses among users. In summary, CNN-based systems make cardiology much more effective by providing doctors with real-time, easy-to-scale, and non-invasive support for ECG analysis. In summary, we look ahead by discussing federated learning, the use of the technology on mobile devices, and the application of models in different populations to widen the impact of ECG-based AI in the real world.

Using LULC, researchers can analyze the environment, plan cities, complete agricultural tasks and study climate change. Thanks to recent upgrades in satellites, it is now simpler to take good, crisp photos. Doing this for every image is a slow process, can involve our own prejudices and might not work for big projects. Using different images for training, CNNs from deep learning can determine the main features in a picture. The research paper outlines how satellite images for LULC can be classified using CNNs. During testing, Standard datasets EuroSAT and UCMerced were used along with custom-built and pre-trained CNNs, including ResNet50. I worked through the preprocessing steps, included new examples for data, trained the model, inspected accuracy, precision, recall, IoU and then compared their pictures. It seems, according to experiments, that models built using CNN are better and more accurate than traditional machine learning techniques. Even though the images differed little, both models managed to identify over 90% of each land cover type. Data imbalance, cloud effects and similar problems were overcome using augmentation and hyperparameter tuning. Actually, CNNs are dependable for mapping land use and land cover, making it easier to monitor the environment globally and apply the information quickly. The team aims to help edge devices benefit from machine learning and to make AI explanations available for their data teams.

The world’s constant combat towards global issues such as climate change, pollution, violence, security, terrorism, etc., all rooted from danger inflicted activities or interests has taken its toll accelerating our extinction. The list goes on and in just a snap, we were all consumed by the infamous COVID-19. This pneumonia-like disease has launched a global economic tsunami and disruption to our lives that was later on declared as a pandemic. With no available cure yet, there were few safety measures practiced globally namely social distancing, quarantine, and mass testing. In order to contain and avoid further spread of the virus, we were forced to adapt to the new normal. Personal Protective Equipment (PPE) has become an inseparable part of our daily lives ergo, the increase in PPE production, which is considered inevitable, leads to a surge in global plastic waste. COVID-19 waste has become a new form of pollution caused by mass production of single-use PPE with no structured waste management that floods our ocean beds. Manufacturers are now looking for alternatives to decrease plastic waste and to shift to a more sustainable and circular PPE ecosystem. Philippines, as the second top exporter of bananas in the world, would a banana pseudo-stem fiber be the most promising business venture opportunity at post-COVID stage?

Shopping has changed as a result of the influence of technology with most people preferring online shopping to the traditional physical store shopping. This trend took center stage in the past decade with many retail giants integrating the two approaches to achieve maximum benefit. This research aims to understand the comparison between online shopping & physical store shopping and consumer behavior towards these modes of shopping. This paper identifies and discusses that male population tend to shop more online shopping rather than physical shopping. For safety of payment more preference is given to shopping malls related to physical shopping. People are slowly going for online shopping but the majority mentality of people goes to physical shopping as it is having trust, feel and touch of the product. In our research we have found out that FlipKart and Jabong are the major players in the online retail and online shopping is here to stay and number of people favoring this mode is growing day by day.

The Burj Khalifa is known to be the tallest skyscraper ever built by man and one of the most popular tourist destinations in the world. At over 828 meters (2716.5 feet) and more than 160 stories, Burj Khalifa is a multi-use development tower that includes a hotel, observation deck, restaurant, fitness and recreation club, and 37 floors of corporate offices. The project to build the Burj Khalifa tower is one of the most successful projects in the Middle East and in the world to date. It was planned by the Dubai government with the purpose of making the city a hub for finance, trade, and tourism. The project spanned for 6 years from 2004 to 2009 and was designed to be the centerpiece of the large scale Burj Khalifa Development that would rise into the sky to an unprecedented height. As listed in burjkhalifa.ae (2018), the completion of the tower has broken numerous world records such as Tallest Building in the World, Highest Number of Stories in the World, Highest Outdoor Observation Deck in the World, and many more. Agreed that that there are many iconic structures around the world, of which Dubai still plays host to most of them, the Burj Khalifa is not only the tallest and one of the most glamorous, with a lot to learn from its project design, process and implementation. The research work is designed to x-ray the entire project, its phases and the factors that led its successful completion.

The research paper explained the role and the importance of customer satisfaction and loyalty in detail. Customers are the link to an any level and type of business success. A business organization should focus on a large number of customers, for this customer satisfaction and loyalty should be incorporated along the long-term goals and other strategic dimensions without any failure. This paperis focusing onanalysing the relationship between customer satisfaction and customer relationship with service quality measures. The objective of this research is to study the concept of customer satisfaction, customer loyalty and its relationship. Moreover, this paper studies the factors that influence customer satisfaction and loyalty at large scale. This paper also analyses the factors that have impact on customer satisfaction and result in customer loyalty in multiple contexts. Recommendations on improving the service quality and getting customer retention are ultimately proposed by the author to do the business in more effective and efficient way. Customer satisfaction index (CSI)provides an economic indicator of the quality of economic output; calculation of the net present value of the company?s customer base as an asset over time; information for strategic business applications; a predictor of consumer spending & corporate earnings. In this paper the focus of research is customer satisfaction index as a base for new strategic marketing management implementation level to attain sustainability.

Today, ethics has an important place in all areas of life. Education is also a fundamental process of human life. Therefore, in education ethics has a very important and effective role. In order to be a good human, ethics should be placed as a course in educational system. In this paper, it will be discussed the ethics education in schools. In doing this, it will be also discussed why teaching ethics is important in education. Before discussing this issue, it is necessary to define what ethics is and what education is.

The present study aims to portray the real need of water to lead life. Today there are number of countries are unable to live a normal life without water facility. Water is actually the elixir of life. Everyone should use the water in limited according their need. Water may avail excess, but not in deficit. Gulf countries, some of the African countries appear as deserts and very difficult to lead a normal life without water. The place is not having water that is a desert. If the place is in water resource, that is a heaven for human survival. If human wants to realize water, he should be in the desert. The thirst for a man can be solved, but a thirst of animals, birds living in the forest is unsolved. Hence one can understand the need of water to survival. The title of the paper is taken from the greatest Tamil grandfather Thiruvalluvar?s ?Thirukural?. He has written about the water in one of his ?Kurals? (double lined poem). He further says, if water fails, functions of nature will sease and if rain fails, no men can walk in regular duty. Girish Karnad has found the need of water, so he has written the play ?The Fire and the Rain? to people realize one drop of water. Scientist can portray water is the formula of H2O. But ordinary man, common people, animals, birds, reptiles are depending upon the water and its usage. Karnad has immensely defines the fire sacrifice for rain in this play.