THE EFFECT OF COOPERATIVE LEARNING THEORY ON THE TEACHING OF TRIGONOMETRY AT HIGH SCHOOLS

The benefits of enacting lessons anchored on Cooperative learning (CL) in mathematics has been well documented. However, in particular, the effect of CL on the teaching of trigonometry is rare at the second cycled educational institutions. This study, therefore, is aimed at exploring the efficacy of CL in enhancing meaningful teaching and learning of trigonometry at the Senior High School (SHS) in Ghana. Mixed method research design was employed to collect data using a questionnaire with both close-ended and open-ended items, and trigonometric achievement tests with essay type questions. A stratified sampling approach was used to select 55 students as the participants in the study. Descriptive statistics, paired sample t-test and thematic coding were used in analysing the data. The findings showed that, there were significant improvement in the students’ learning outcomes in trigonometry. In particular, the study revealed improved performance in students’ pre-test and post-test scores after participating in CL lessons. Finally, the students’ overall positive dispositions towards the CL-lessons were reflected in high means scores on the subscales: -Positive interdependence, Individual accountability, Face-to-face promotive interaction, Social skills and Group processing, depicting favourable experiences with the CL-lessons. To this end, the authors argue that, to develop higher order thinking skills in students the use of CL in the teaching and learning of trigonometry ought to be prioritised. It is recommended that; management of educational institution should consider conducting professional development training for educators who desire CL as a means of instruction. Implications for policy and further research are discussed

EXPLORING THE MODERN MYTHOLOGY: ANALYSING KAVITA KANE’S KARNA’S WIFE: THE OUTCAST’S QUEEN AND SITA’S SISTER

In modern-day literature, the study of mythology has presented authors with a vast and intricate framework to craft elaborate narratives that delve deeply into the intricacies of human behaviour, interpersonal connections, and societal standards. This research paper embarks on a profound and comprehensive analysis of two compelling literary works by the esteemed author Kavita Kane, explicitly focusing on Karna’s Wife: The Outcast’s Queen and Sita’s Sister. Through a meticulous and rigorous examination of the character progression depicted in Karna’s Wife, the primary objective of this study is to uncover and elucidate how Kane adeptly illustrates the transformation of Uruvi’s character, the significant relationships that shape her journey, and the innovative perspective she brings to the enigmatic persona of Karna. Simultaneously, the scrutiny of themes and symbolism in Sita’s Sister delves deeply into the core thematic foundations of the novel, the symbolic elements skilfully utilised by Kane to enhance the storyline, and the crucial influence of familial and societal expectations in moulding the characters within the narrative. By closely analysing these critical components, this research paper strives to illuminate the intricate layers of storytelling and the contemporary reinterpretation of classical mythology in Kane’s literary creations, thereby contributing to the enhanced comprehension of the enduring fascination of mythology within present-day literature

THE PRIMAL FEAR: THE LANGUAGE OF THE FUTURE IN THE HUNGER GAMES

The Hunger Games series was banned to begin with before even it became a cult classic because of its anti-family concerns, the rebellion and the attitude of challenging the governmental authority. The system of futuristic governance is a warning to the readers, especially the teens of tomorrow as to how their lives would be in future if they continue to remain complacent about the ways of the authorities who rule them over. There is crime, retribution and rebellion each of which has its causes, consequences and repercussions. The paper aims to look at the struggle for food which Katniss and Dale confront to take care of their families written in the language of the future. The words which the author has coined for the future and the shades of meaning that they give are also highlighted

A Survey on Deep Learning Approaches for Crop Disease Analysis in Precision Agriculture

Precision agriculture has emerged as a transformative paradigm in modern farming, leveraging advanced technologies to optimize crop management. This paper presents a comprehensive survey of deep learning approaches for crop disease analysis in precision agriculture. The investigation focuses on four key aspects: leaf disease detection through deep learning techniques, leaf shape-based disease analysis, crop weed detection utilizing deep learning methods, and crop damage detection using aerial images. The survey encompasses a review of recent advancements, methodologies, challenges, and future prospects in each of these domains. By exploring the intersection of deep learning and precision agriculture, this paper aims to provide a holistic understanding of the current state-of-the-art and inspire further research initiatives to enhance crop health monitoring and management

ABNORMAL TRAFFIC DETECTION BASED ON ATTENTION AND BIG STEP CONVOLUTION

The identification of abnormal traffic is essential to network security and service quality. A big-step convolutional neural network traffic detection model based on the attention mechanism is provided as a solution to the significant challenges in abnormal traffic identification caused by feature similarity and the detection model's single dimension. First, the raw traffic is preprocessed and mapped into a two-dimensional grayscale picture after the network traffic characteristics are examined. After that, histogram equalization is used to create multi-channel grayscale pictures. An attention mechanism is then added to give traffic characteristics varying weights in order to improve local features. In order to improve the flaws in convolutional neural networks, including local feature omission and overfitting, pooling-free convolutional neural networks are finally integrated to extract traffic characteristics of various depths. Both a real data collection and a balanced public data set were used for the simulation experiment. The suggested model is contrasted with ANN, CNN, RF, Bayes, and the two most recent models using the widely used method SVM as a baseline. 99.5% accuracy percentage with several classes is achieved experimentally. The best anomaly detection is found in the suggested model. Additionally, the suggested technique performs better in F1, recall, and accuracy than existing models. It is shown that the model is not only effective in detecting things, but also resilient to a variety of complicated contexts

A REFINED CLASSIFICATION OF THE SUBSET SUM PROBLEM IN POLYNOMIAL TIME COMPLEXITY

The problem considered is the selection of at least one subset from a set (array) of distinct positive integers, such that the sum of the subset's elements exactly matches a given target sum (target certificate). According to R. M. Karp, this problem belongs to the class of NP-complete problems. Diophantine equations and an auxiliary problem, which facilitates the solution of the original problem and has independent scientific interest, have been introduced. A novel method has been developed, which includes proven lemmas and theorems. These results enable the development of efficient and straightforward algorithms for solving the subset sum problem. The time and space complexity for selecting the required subsets do not exceed the square of the length of the original set. An analytical framework has been proposed for managing indices within the original set. These algorithms are applicable to solving problems related to the independent set of cardinality k and the k-vertex cover problem. Additionally, we present examples to confirm claimed results. It should be noted that the time complexity of sorting an array of integers is proportional to the square of the array's size, and this problem belongs to class P. Therefore, based on the newly developed method, it can be inferred that the subset sum problem, originally classified as NP-complete within the NP class, also belongs to P

A STUDY ON MARKETING MIX ELEMENTS (PRODUCT, PRICE, PLACE, PROMOTION) AND THEIR INTERPLAY IN DRIVING CUSTOMER ACQUISITION, RETENTION

The paper aims to investigate the influencing of marketing mix (MM) elements (product, price, place or distribution, and promotion) on increasing the effectiveness of product promotion and their role to reduce the problems within the organization. The main importance aspects of this paper are to discuss the theoretical part of MM, to provide some perspectives for the researchers, and to give some instructions for the marketing department in Al-Saaeda Company for medical equipment technologies. The researchers used the main related academic resources from university library, and internet, and they designed and distributed questionnaires on a random sample of Al-Saaeda Company for Medical Equipment Technologies customers and the company employees to measure the impact of promotion on the marketing of its product (Glucocard 01-mini plus). The main findings of this paper can be concluded as following: 1. The promotion has a very high level of impact to increase the sales of products. 2. The good distribution of product can effect positively on customer satisfaction. 3. The company's policy for promoting has a very good reflection on increasing the sales of products. The researchers recommended that the company must strengthen the level of promotions in its activities and departments, and the increasing of sales points is very important, so the company must enhance its policies of distribution

IMPACT OF CLIMATE CHANGE ON ARCTIC FOX POPULATION DYNAMICS: A MATHEMATICAL MODELING APPROACH

This study focuses on the impact of climate change on Arctic fox populations using mathematical modeling. The research employs a basic Lotka-Volterra-style model to simulate the effects of temperature, precipitation, and snow cover on the Arctic fox population dynamics. The model is based on the assumption that the population growth rate is limited by the carrying capacity of the environment and is influenced by these environmental factors. The study provides insights into the complex relationship between environmental factors and population changes, highlighting the need for more sophisticated models to holistically understand the impact of climate change on ecosystems. The findings underscore the importance of mathematical models in guiding adaptive strategies for ecosystem management amidst changing climates, emphasizing the necessity for further research to comprehensively address climate-induced challenges and ensure a sustainable future for ecosystems and species. &nbsp

STOCK SELECTION USING SEMI-VARIANCE AND BETA TO CONSTRUCT PORTFOLIO AND EFFECT MACRO-VARIABLE ON PORTFOLIO RETURN

This research has aims to construct portfolio by varying method and using semi-variance and Beta for selection stocks. This research found 28 stocks to become member portfolio. Equal Weighted, Market Capitalization Weighted, Markowitz Method and Elton Gruber is used to construct portfolio.  This research found that the efficient frontier similar to Markowitz Method. Roy Criterion found the portfolio return varying from 2.2% to 9.65% but Kataoka Criterion found the portfolio return varying from 5.4% to 11.12%. This research found that Elton Gruber has the highest portfolio return compared to others portfolio. There is no difference of average return for four portfolios.  Market return significant affect to all portfolio return but the interest rate significant affect portfolio returns for equal weighted portfolio and Elton Gruber Method

AI-Powered HRM and Finance Information Systems for Workforce Optimization and Employee Engagement

This comprehensive analysis examines the implementation and impact of AI-powered Human Resource Management (HRM) and Finance Information Systems in government organizations, focusing on workforce optimization and employee engagement. The study, drawing from extensive research across multiple public sector entities, reveals that organizations implementing these systems achieve significant improvements in operational efficiency, with processing times reduced by 47.2% and budgetary allocation accuracy increased by 31.4%. Through analysis of implementation data from 156 federal agencies, the research demonstrates how AI-driven solutions address key challenges in regulatory compliance, budget constraints, and operational transparency. The investigation encompasses four core functional areas: intelligent recruitment, workforce planning, employee experience enhancement, and financial management integration, supported by machine learning algorithms and cloud infrastructure. The results show significant progress in every area, including a noteworthy 56.8% decrease in hiring bias, a 41.3% increase in staff retention, and an 82.6% accuracy rate in document classification