TMP Universal Journal of Research and Review Archives

LEVERAGING BIO-INSPIRED ALGORITHMS TO ENHANCE EFFICIENCY IN COVID-19 VACCINE DISTRIBUTION

2024-09-27T13:33:41+00:00

The Covid-19 pandemic has created unprecedented challenges for global vaccine distribution, highlighting the critical need for efficient logistical strategies that ensure timely delivery and equitable access to vaccines. This study examines the application of bio-inspired algorithms—Ant Colony Optimization (ACO), Genetic Algorithms (GAs), and Particle Swarm Optimization (PSO)—to optimize the distribution process of Covid-19 vaccines. By conducting a comparative analysis, the research evaluates each algorithm's effectiveness in minimizing total delivery time, reducing distribution costs, and improving convergence speed.The findings reveal that Genetic Algorithms (GAs) provide the most comprehensive optimization solution, outperforming other methods in both delivery time and cost-efficiency. GAs demonstrated a superior ability to navigate complex logistical networks, making them highly effective for large-scale vaccine distribution efforts. In contrast, Ant Colony Optimization (ACO) emerged as the fastest algorithm in terms of convergence speed, rapidly identifying optimal distribution routes. This makes ACO particularly valuable in scenarios where quick decision-making is crucial, though it may require additional refinement to match the cost-effectiveness of GAs.Particle Swarm Optimization (PSO), while not leading in any single category, offers a well-balanced performance across all metrics, proving to be a reliable option for consistent and adaptable optimization. PSO's ability to balance trade-offs between delivery speed, cost, and algorithmic efficiency makes it a practical choice for real-world applications where multiple factors must be simultaneously optimized.The study also explores the potential of hybrid approaches, which combine the strengths of different bio-inspired algorithms to achieve superior results. Additionally, the integration of adaptive algorithms and machine learning techniques is identified as a promising avenue for further enhancing vaccine distribution strategies, enabling systems to dynamically adjust to changing conditions and demands.The research concludes with several recommendations for future exploration. Emphasizing the importance of scalability, it suggests that bio-inspired algorithms should be adapted for larger, more complex distribution networks. The study also advocates for the real-time implementation of these algorithms, allowing for more responsive and efficient vaccine logistics. Finally, the paper highlights the need for multi-objective optimization approaches that can address the diverse and evolving challenges associated with global vaccine distribution, ultimately contributing to more resilient and equitable public health outcomes.

INCORPORATING EMOTIONAL INTELLIGENCE IN SHARK ALGORITHMS: BOOSTING TRADING SUCCESS WITH AFFECTIVE AI

2024-09-27T14:10:57+00:00

The paper investigates the incorporation of emotional AI into Shark Algorithms to enhance trading performance through affective computing. By integrating sentiment analysis, these advanced algorithms are equipped with emotional intelligence to improve the accuracy of price predictions, devise sentiment-driven trading strategies, and optimize risk management techniques. The research highlights that embedding sentiment scores into conventional trading models provides a more nuanced understanding of market behavior, leading to enhanced decision-making processes.Key findings demonstrate that sentiment-enhanced price prediction models outperform traditional methods in capturing market trends, offering a significant edge in forecasting accuracy. The study further reveals the profitability of trading strategies driven by sentiment analysis, as they capitalize on emotional market responses, which are often overlooked by purely quantitative approaches. Additionally, the research emphasizes the effectiveness of sentiment-based position sizing as a tool for risk mitigation, allowing traders to adjust their exposure based on the emotional state of the market, thereby reducing potential losses during periods of heightened volatility.However, the integration of emotional AI into trading algorithms is not without challenges. The paper identifies issues related to the quality and reliability of sentiment data, which can vary widely depending on the source and the methodology used for analysis. Ethical considerations also arise, particularly concerning the potential for market manipulation and the broader impact of emotionally driven trading on financial markets. Technical complexities, such as the integration of real-time sentiment analysis with high-frequency trading systems, present additional hurdles.The study concludes by proposing future research directions, including the development of more sophisticated sentiment analysis techniques that can better capture the nuances of market sentiment. It also suggests advancements in real-time data processing to enhance the responsiveness of trading algorithms. Improved risk management strategies are recommended to address the specific risks associated with sentiment-driven trading. Furthermore, the paper advocates for the establishment of ethical frameworks to guide the use of emotional AI in financial markets, along with long-term studies to assess its impact on market efficiency and stability. The integration of emotional AI into trading algorithms represents a transformative step in financial technology, with the potential to influence market dynamics and trading practices significantly.

SUSTAINABLE AND ETHICAL AI IN FINANCE: DEVELOPING GREEN SHARK ALGORITHMS FOR ECO-FRIENDLY TRADING

2024-09-27T14:14:58+00:00

This article addresses the growing computational demands of modern trading algorithms and the pressing need for sustainable practices by introducing "Green Shark Algorithms," a new class of trading systems engineered to emphasize energy efficiency, sustainability, and ethical investment principles. In response to the escalating energy consumption and environmental impact of traditional trading operations, this research proposes a comprehensive approach that merges technological optimization with responsible investing. The study specifically focuses on the Istanbul Stock Exchange (BIST), analyzing how these innovative algorithms can reduce energy usage, lower carbon footprints, and maintain robust financial performance.The development of Green Shark Algorithms involves optimizing algorithmic design and enhancing the efficiency of data center operations. By refining computational processes and leveraging energy-efficient technologies, these algorithms aim to significantly reduce the power demands of trading activities without compromising the speed or accuracy critical to financial markets. The research underscores the potential of these advancements to contribute to the broader sustainability goals of the financial industry by decreasing the environmental impact associated with high-frequency trading.In addition to technical innovations, the integration of Environmental, Social, and Governance (ESG) criteria into trading strategies is a central theme of this study. The inclusion of ESG factors within algorithmic decision-making frameworks ensures that trading activities align with ethical and sustainable investment practices. By prioritizing investments in companies with strong ESG performance, Green Shark Algorithms not only promote financial returns but also foster a more responsible approach to market participation, encouraging corporate behaviors that contribute positively to society and the environment.The findings suggest that the implementation of Green Shark Algorithms on the BIST can lead to a dual benefit: reduced energy consumption and enhanced sustainability in trading operations, coupled with financial performance that meets or exceeds traditional benchmarks. However, the research also highlights challenges, such as the need for reliable ESG data and the complexities of balancing energy efficiency with algorithmic precision.Future research directions include the exploration of advanced energy-efficient technologies, the refinement of ESG metrics for more accurate assessments, and the developmentof regulatory frameworks to support the adoption of sustainable trading practices. The Green Shark Algorithms represent a pivotal step toward reconciling the demands of modern trading with the imperatives of environmental stewardship and ethical investment, potentially reshaping the future of financial markets.

ASSESSING ELECTRIC VEHICLE VIABILITY: A COMPARATIVE ANALYSIS OF URBAN VERSUS LONG-DISTANCE USE WITH FINANCIAL AND AUDITING INSIGHTS

2024-09-27T14:18:36+00:00

ANALYSIS OF COMPONENTS AND DEBATE SKILLS IN IMAM REZA'S DISCUSSIONS WITH FOLLOWERS OF DIFFERENT RELIGIONS AND SECTS

2024-09-27T14:23:33+00:00

Imam Reza engaged in debates with a diverse group of individuals, including Christians, Zoroastrians, Jews, materialists, Mandaean Sabians, and members of various Islamic sects. In these debates, he employed a range of components and skills. This descriptive-analytical research aims to evaluate the components and skills used by Imam Reza in his discussions with followers of different religions and sects. The research seeks to answer the question of what specific components and skills Imam Reza utilized in his debates with religious and sectarian speakers. Based on the findings, Imam Reza's debates can be categorized into three primary types: ethical, logical, and free-thinking. Furthermore, the skills he employed can be distilled into five concurrent methods. These methods enabled him to advance his arguments and discussions to the point of prevailing over his opponents, even in the most challenging debates, without succumbing to incorrect or unethical reasoning traps. He achieved this by employing communicative debate techniques.

HARVESTING INSIGHTS: THE ROLE OF ARTIFICIAL INTELLIGENCE IN TRANSFORMING AGRICULTURAL FINANCE

2024-10-27T04:11:45+00:00

The intersection of artificial intelligence (AI) and agricultural finance has emerged as a transformative force in enhancing the efficiency and sustainability of farming practices. This review article explores the multifaceted applications of AI technologies in agricultural finance, focusing on credit scoring, risk assessment, and financial decision-making for farmers and agricultural businesses. We examine the integration of machine learning algorithms and big data analytics in providing tailored financial products, enabling precision agriculture, and improving access to credit in under banked communities. By synthesizing current literature and case studies, we identify key challenges and opportunities, such as the need for data security, transparency, and the democratization of AI tools in rural finance. Furthermore, we discuss the potential of AI to address climate change impacts on agriculture and its implications for financing sustainable practices. This article aims to provide stakeholders in the agricultural finance sector—policymakers, financial institutions, and technology developers—with a comprehensive understanding of how AI can drive innovation and improvement in financing agricultural activities, ultimately fostering a resilient and sustainable food system.

INTERPRETIVE METHODOLOGY AND ITS RELATIONSHIP WITH VERSTEHEN IN MAX WEBER'S PHILOSOPHY OF SOCIAL SCIENCES METHODOLOGY

2024-10-13T11:49:40+00:00

The key role and clarifying function of Verstehen in Weber's interpretive methodology, as one of the important and illuminating tools, is used by scientists to properly understand social science phenomena. Considering Weber's interpretive approach based on individualistic interpretation of actors' actions, we find that Verstehen, in Weber's view, serves as a tool for social scientists to uncover the causes behind actors' intentional actions. Understanding actors' intentions, influenced by Weber's interpretive methodological approach, employs all its effort in explaining social science phenomena to achieve objectivity. In this research, we have attempted to address the meaning and importance of Verstehen's role, stemming from Weber's and his predecessors' interpretive perspective, as a tool for interpreting social science phenomena. Considering Weber's project aimed at achieving objectivity in social sciences, it should be said that Verstehen, like ideal types and other components of Weber's methodological system, are pieces meant to be initiated and employed by scientists to attain objectivity in social and cultural sciences. This research strives to correctly interpret the role and significance of this component (Verstehen) within Weber's methodological system.

A COMPARISON BETWEEN THE CRIMES OF TRANSFER OF ANOTHER’S PROPERTY AND BREACH OF TRUST WITHIN THE FRENCH LEGAL SYSTEM

2024-10-13T11:53:32+00:00

This research paper conducts a comparative analysis of the two crimes of 'transfer of another's property' and 'breach of trust' within the French legal system. The objective of this research is to accurately identify the constituent elements of these two crimes, compare their similarities and differences, and analyze the approach of French lawmakers and judiciary in dealing with these crimes. Employing a descriptive-analytical method and through a study of French legal sources, this research demonstrates that despite their apparent differences, these two crimes share significant commonalities. The main difference is in the way of acquiring property: In the crime of transferring another's property, the perpetrator obtains the property with fraudulent intent from the beginning, whereas in breach of trust, the abuse occurs after the lawful receipt of the property. Taking these differences into account, the French legal system has established proportionate punishments for each of these crimes. This study, by examining French judicial decisions and legal opinions, provides a comprehensive overview of how the French legal system deals with these crimes.

THE INVESTIGATION AND STUDY OF CRYPTOSPORIDIUM INFECTION IN STRAY DOGS AND EFFICACY OF TREATMENT WITH TRIMETHOPRIM/ SULFADIAZIN AND CLINDAMYCIN IN TABRIZ AND SUBURBS

2024-10-13T11:55:54+00:00

This study investigated the prevalence of Cryptosporidium in stray dogs in Tabriz city, Iran, and evaluated the efficacy of a combination of Clindamycin and Trimethoperim/ Sulfadiazin for treating Cryptosporidiosis. Faecal samples of 200 stray dogs were examined by Ziehl Neelsen microscopic method and the results showed that 12 (%6) collars showed infection with Cryptosporidium, with 8 collars in female and 4 in male dogs. According to the age of the dogs, 9 dogs under one year (Puppy) and 3 adult dogs over one year old (%1.5) were diagnosed with Cryptosporidium. There was a statistically significant difference in the prevalence of Cryptosporidium between male and female dogs (p<0.05). A treatment course of clindamycin (25 mg/kg/day) and trimethoprim/sulfadiazine (15 mg/kg PO bid for 7 days) was administered and 10 dogs (%83.4) were cured, while 2 dogs showed positive cases. The results suggest that a combination of Clindamycin and Trimethoperim/Sulfadiazin is an effective treatment for Cryptosporidiosis in dogs, but further studies are needed to provide precise pharmacological explanations.

ANALYSIS OF GOOD URBAN GOVERNANCE INDICATORS AND ITS IMPACT ON THE EFFICIENCY AND EFFECTIVENESS OF LOCAL MANAGEMENT WITH A FUTURES STUDIES APPROACH

2024-11-05T19:10:09+00:00

In this research, the dimensions of desirable urban governance in District 20 of Tehran are evaluated from a futures studies perspective. Key factors and their interconnections have been analyzed and ranked. The study is applied in its purpose and descriptive-analytical in its methodology. By reviewing the literature and extracting the most significant key factors of urban governance using the Delphi technique from 30 experts, factors were assessed based on consensus and certainty, priority, and importance. Key factors and input parameters were identified, resulting in 51 key drivers ready for input into the Micmac software.

The evaluation of the impact and influence of key factors shows that three drivers are ranked first in terms of direct and indirect impact. Out of 237 possible situations, 48.10% show favorable conditions, 29.96% are static, and 21.94% are critical. Additionally, there are nine scenarios with favorable conditions, indicating promising prospects for urban management in the future.

THE ROLE OF GOD'S AWARENESS AND SELF-AWARENESS IN MENTAL HEALTH BASED ON ISLAMIC TEACHINGS

2024-11-05T19:23:36+00:00

Believing in the God who created the universe and purposefully gave existence to man and that man was not created by accident, causes a purposeful attitude towards human creation and creates hope in human life. Since man is the highest divine creation, understanding this level together with the fact that his creation is not futile, gives a sufficient attitude to have mental health. Therefore, the purpose of this article is to examine self-knowledge and God-knowledge to have mental health. The present article was compiled with a review and qualitative method, which was inferred from Islamic texts, as well as the works of Islamic thinkers and some psychological sources. Man has received the basis of his existence from the wise creator. This look is the source of mental peace and meeting all his needs. Because cause and effect are not separate from each other. Therefore, the necessary attitude to have mental health is to understand the dependence of man on the creator and the reason for his existence.

THE EFFECT OF TEACHER'S BEHAVIOR ON STUDENTS' BEHAVIORAL PROBLEMS FROM THE ISLAMIC PERSPECTIVE

2024-11-05T19:33:45+00:00

Recognizing students' behavioral problems can have a direct impact on the quality and process of education in the classroom. In addition, the teacher plays an important role in managing and reducing such behaviors. In Islam, the ultimate goal is to honor moral and behavioral virtues, so the teacher must be aware of moral and religious values to be able to give sufficient knowledge to students. The purpose of the present study is to investigate the role of the teacher in controlling students' behavioral problems from the perspective of Islam. The research type of this study is qualitative. In the matter of education, the teacher should have sufficient knowledge and understanding of the stages of growth and the characteristics of each stage so that he can have the necessary preparation for proper prevention and timely intervention against problems, and also help in the growth and flourishing of talents. Along with the importance of the principle of the necessity of education, values, and its transfer to students as one of the basic principles in education and training, how to transfer concepts to future generations, especially the application of the power of thought and reasoning in this direction is also important. It is on this basis that if most of the teaching and training facilities are spent on the preparation of textbooks in addition to the education, it is completely appropriate.

PREVALENCE OF DEVELOPMENTAL DENTAL DISORDERS AMONG THE PATIENTS VISITING REU CLINICS: A RETROSPECTIVE STUDY

2024-11-05T19:36:16+00:00

Introduction: Developmental abnormalities related to teeth, including variations in number, size, and structure, are common in both primary and permanent dentition. These anomalies, affecting 1% to 10% of permanent teeth, can lead to malocclusion, aesthetic issues, and increased risk of oral diseases.
Aims of the study: To evaluate the prevalence of developmental issues across genders, racial/ethnic groupings, and institutional contexts.
Materials and methods: This retrospective study employed patient data from the Dentoplus system. Every patient file will be evaluated using bitewings, history, charting, and OPGs; any irregularities discovered were documented.
Results: The most common developmental dental disorder was congenitally missing teeth and impactions. There are notable disparities in the occurrence of dental malformations among Saudi patients according to gender, nationality, or medical history. This implies that the development of dental abnormalities may be more significantly influenced by causes other than these.
Conclusion: In conclusion, 33% of people have dental abnormalities. When gender, nationality, and medical history were examined, no substantial difference was found.

DESIGN & DEVELOPMENT OF DIFFERENTIAL DIAGNOSIS SOFTWARE FOR SALIVARY GLANDS DISEASE AND COMPARISON WITH CLINICIAN'S RESULTS

2024-11-09T19:47:06+00:00

Introduction: Due to increase of population, increasing number of less-experienced dentists more associated information and consequent updates of such information; the need for design of information processing systems to help in better diagnosis of patients with oral disorders becomes more evident. Among other factors, some factors such as age, clinical symptoms, location and shape of lesion could influence the decision to treat salivary gland disorders and determine of their possibility. Up to now, there is no software for differential diagnosis of salivary gland diseases.
Objectives: The objective of present study is to design and develop a software so as to help to differential diagnosis of salivary gland diseases and determine the accuracy and effectiveness of its results.
Methods: Software based on Bayes' theorem was designed with the help of editor application into which the information of 60 salivary gland diseases was inserted. Then, the information of 40reports of salivary gland diseases was added to the software. In the next stage, the information of the chosen cases given to two expert clinicians so as to develop a differential diagnosis. The result was analyzed using menemar and Wilcoxon test.
Results: The results were compared with results of the reports. Almost 60 percent of generated answers by the software and two clinicians included absolute diagnosis of statistically insignificant difference (P-value=0.96).
Conclusion: The results showed the acceptable performance of the software. Although the software cannot act instead of a clinician due to the fact that the software user should be quite familiar with salivary gland diseases and methods of examination, this software can be used to help the clinicians in differential diagnosis and training objectives.

THE ADOPTION OF ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION: A QUANTITATIVE ANALYSIS USING STRUCTURAL EQUATION MODELING

2024-11-09T19:51:55+00:00

Global businesses have actively embraced the digital industry. The education sector also utilizes digital tools to enhance personalized learning and promote equity in education. New frontiers have been established in education through the use of artificial intelligence (AI)-based tools. As the role of AI in education is inevitable, the present research aims to identify the level of awareness in higher education regarding the application and acceptance of AI. This study explores how AI has enhanced their learning experience and impacted faculty engagement in higher education. Using a quantitative research approach and a structural equation model, the factors influencing the acceptance of AI in this field were examined. The findings indicated that the implementation of AI has led to the evolution of robust assessment methods, resulting in increased faculty engagement. This study identified that attitudes, applications, and behavioral intentions play a significant role in influencing work engagement and the acceptance of AI in the higher education system.

SACRED PLANTS AS SIGNS OF A BOND BETWEEN MYTHS AND DIVINE STORIES

2024-09-27T14:25:46+00:00

Myths are among the oldest legacies of ancestors that manifest in the culture and knowledge of modern societies. Included in the mythical elements of different religions that assume special respect are trees and plants that are regarded as sacred in divine books, and are sometimes viewed as heavenly trees and plants in the religious ceremonies of Abrahamic religions. The introduction of these plants under similar names and indicators in myths and religions raises the questions: “Are the sacred plants in religious texts the same as the sacred plants in myths or not?” and “Would these sacred plants be regarded as a common ground and an indication of an underlying union between myths and religious stories or that one should be seen as influenced by the other?” To answer the questions and demonstrate the link between myths and religions, a library-based study was performed on the subject and data led to the conclusion that even if a myth was assumed to be a deviation from divine stories, it would be reasonable to accept common elements of similar sanctity and to suggest that this would not contradict the acceptance of the deviation of one from the other. Meanwhile, the surviving documentation of old times indicates the originality of myths; so, if religious figures had opposing theories in this regard, they were required to use the advantages of religions along with myths as they had failed to produce a credible religious reason to substantiate their claims; for this, work has to start from the underlying and common elements, i.e., sacred plants.