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Analyzing the impact of crude oil and Natural gas price fluctuations on Nigeria's economy: A time series Approach

¹Ogunbanwo S.T, ²Oladimeji O.A, ³Aliyu Abdulaziz Bello

Department of General Study, Federal Polytechnic, Ile-Oluji, Ondo State, Nigeria.

Department of Statistics, Federal Polytechnic, Ile-Oluji, Ondo State, Nigeria.

olaoladimeji@fedpolel.edu.ng

         Department of Computer Science, Federal Polytechnic, Ile-Oluji, Ondo State, Nigeria.

ABSTRACT:  This study applies Time series analysis techniques to examine the trends and patterns in Nigeria economic indicators, specifically Gross Domestic Product (GDP), crude oil and Natural gas price from 2010 to 2023. Using data from the Central Bank of Nigeria and National Bureau of Statistics, I employ techniques such as Least square regression (LSR)Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing (ES), Autocorrelation and a Sequential Chart (SC)to model and forecast these economic variables. My results reveal significant trends and seasonality, and volatility in crude oil production and prices, with oil shocks having a profound impact on Nigeria economics performance. The study identifies long run equilibrium relationships between crude oil production, prices, and economic indicators, and detects structural breaks in the time series. The findings have implications for Nigeria economics policy, energy sector management, and investment decisions. This research contributes to the existing literature by providing a comprehensive time series analysis of the complex relationships between crude oil and Nigeria economy, offering insights for sustainable economic development and energy policy formulation. My findings through my analysis, there is up and down movement in the prices from 2010-2019, while there is spike in the price for the year 2020-2023 and later move with up and down for the year 2022-2023. With the linear regression result above. The sig level result .633 is greater than the P value 0.05, which indicate the typical threshold for statistically significance and fail to reject the null hypothesis of a non-significance result. The stationary R-square I moderate with .486(48.6%) which shows that there is moderate relationship between the variables. R-square value which indicates 58.7% of the variation I depend in variable data been explained.

[Ogunbanwo S.T, Oladimeji O.A, Aliyu Abdulaziz Bello. Analyzing the impact of crude oil and Natural gas price fluctuations on Nigeria's economy: A time series Approach. J Am Sci 2026;22(2):1-12]. ISSN 1545-1003 (print); ISSN 2375-7264 (online). http://www.jofamericanscience.org. 01. doi:10.7537/marsjas220226.01

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Exploring the Integration of Classical and Logical Analysis in Mathematics-Artificial Intelligence ——Mathematical Inversion and Neural Network Implementation of Inverse Engineering for High-order Equation

Wang Yiping

Chinese Association for Artificial Intelligence, Chinese Logarithmic Team, Qizhou City Association of Senior Science and Technology Workers, Quzhou Qushi Technology Federation 324000

Abstract: This paper explores the foundations of mathematics and artificial intelligence, revealing a novel infinite construction set—the dimensionless logical circle. It demonstrates the integration of classical analysis and logical analysis, while introducing the "infinite axiom" balancing exchange combination analysis and stochastic self-proofing error correction mechanism. Maintaining the inherent nature of "mathematics-physics-artificial intelligence," it employs a simple circular logarithmic formula: dual logic (numerical/bitwise) codes; mutual inversion conversion between central points, central zeros, and property attributes. The paper resolves the continuum CH problem, establishes P=NP isomorphism, and formulates new theorems including the zero-point conjecture. It proposes a high-density information transmission model for 3D data search and native data processing, along with an integrated storage-computing memory. The 3D chip design principles emphasize zero error, miniaturization, intelligence, robustness, low energy consumption, and high computational power. The work achieves top-tier open-source compatibility and privacy protection. It highlights critical current challenges: solving higher-order mathematical equations and implementing interpretable reverse engineering for neural networks.

[Wang Yiping. Exploring the Integration of Classical and Logical Analysis in Mathematics-Artificial Intelligence. J Am Sci 2026;22(2):13-160]. ISSN 1545-1003 (print); ISSN 2375-7264 (online). http://www.jofamericanscience.org. 02. doi:10.7537/marsjas220226.02

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Increase the efficiency of adult education

Esmaeel Ghorbani , Maryam Khodamoradi , Mehran Bozorgmanesh and Abbas Emami

Marvdasht Branch, Islamic Azad University, Marvdasht, Iran

*Corresponding author: mehran11070@yahoo.com

Abstract: Much like strategies to curb epidemic, strategies to reduce illiteracy and raise the educational attainment of Kentucky’s population must include both short-term efforts to face the immediate crises as well as long-term strategies to get at the underlying causes. Short-term crises include the imperative to keep helping welfare clients make the transition from welfare to work within the constraints of federal and state mandates and the need to train workers for immediate employer demands. Long-term prevention must address the underlying, persistent problems of the state’s economic structure as well as the low awareness--if not appreciation--among segments of the population of the vital connection among education, employment, and improved standards of living.

[Esmaeel Ghorbani, Maryam Khodamoradi, Mehran Bozorgmanesh and Abbas Emami. Increase the efficiency of adult education. J Am Sci 2026;22(2):161-164]. ISSN 1545-1003 (print); ISSN 2375-7264 (online). http://www.jofamericanscience.org. 03. doi:10.7537/marsjas220226.03

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A Multidimensional Framework for Strategic Planning of Assisted Reproductive Technologies: A Systematic Review

Hui Wei¹, Sien Mo¹, Yan Fu¹, Shuai Lu², Nan Li¹, Gaosheng Su¹

Affiliations:

1.Guangxi Zhuang Autonomous The Region Reproductive Hospital, Nanning 530029, China

2.Health Commission of Guangxi Zhuang Autonomous Region, Nanning 530012, China

Author Contributions:

Hui Wei (1982-), Female, Associate Researcher. Research focus: Health Management. E-mail: 1461647456@qq.com

*Corresponding Authors:

Nan Li (1984-), Male, M.D., Researcher, Master’s Supervisor. Research focus: Assisted Reproductive Embryology. E-mail: 14393381@qq.com

Gaosheng Su (1967-), Male, Chief Physician. Research focus: Anesthesiology, Reproductive Medicine. E-mail:sugaosheng@126.com

Abstract: The strategic development of Assisted Reproductive Technology (ART) within healthcare institutions is a complex, multidimensional challenge extending beyond clinical medicine to encompass governance, ethics, resource management, and socioeconomic equity. This systematic review synthesizes key factors influencing ART program planning and proposes a novel, comprehensive framework for guiding strategic decision-making. We identified and critically analyzed seven interconnected domains crucial for sustainable development: (1) Medical Technology and Infrastructure, (2) Ethical Governance and Legal Compliance, (3) Human Resources and Capital Allocation, (4) Patient-Centered Care and Support Systems, (5) Research Innovation and Data Management, (6) International Collaboration and Knowledge Exchange, and (7) Socioeconomic Integration and Policy Support. The proposed framework emphasizes the necessity of addressing all domains in a balanced and synergistic manner. It aims to provide a practical, evidence-based tool for policymakers, hospital administrators, and clinical leaders globally to establish, evaluate, and optimize ART services across diverse settings.

[Hui Wei, Sien Mo, Yan Fu, Shuai Lu, Nan Li, Gaosheng Su. A Multidimensional Framework for Strategic Planning of Assisted Reproductive Technologies: A Systematic Review. J Am Sci 2026;22(2):165-174]. ISSN 1545-1003 (print); ISSN 2375-7264 (online). http://www.jofamericanscience.org. 04. doi:10.7537/marsjas220226.04

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Investigation of Effective Factors on Organizational commitment Case Study: Education Staff

Najim najimi ¹, Mohammad Hossein Alishiri ², Hossein Rostami³ Mahbobeh Heydari⁴

^1. Department of Sociology , Payam Noor University of Fars province , IRAN

^2., Department of Accounting , Payam Noor University of Fars province , IRAN

^3. Department of Teachers Education, Farhangian University Rajaee Teacher Training    University of Shiraz, IRAN

4.Math Expert , Four Area of Shiraz Education, IRAN.

Hrostami111@yahoo.com

Abstract: This paper aims to investigate the factors affecting the organizational commitment of employees is education in Shiraz. This research is a survey study and the data were collected by questionnaires from a sample of 374 personnel of the four-districts of Shiraz education system. Parson’s act theory was used in a four-subsystem level to explain the organizational commitment. Results show that there was no significant relation between independent variables of age and service background and dependent variable of organizational commitment, but there are significant relations between independent variables of organizational justice, fiscal satisfaction, mental satisfaction and organization management capacities and dependent variable of organizational commitment. The results of some variables analysis show that job satisfaction from mental aspect organizational justice fiscal satisfaction and management capacities are the variables which had the most effect and were imported in Regression equation in an organizational commitment context. They could also explain more than half of the changes independent variable. Considering that half of the variance independent variable (organizational commitment) is explained by this pattern, it seems that the theoretical model which is derived from Parson’s theory could be a suitable model for organizational commitment evaluation.

[Najimi, N, Alishiri, M. H, Rostami, H. Investigation of Effective Factors on Organizational commitment Case Study: Education Staff. J Am Sci 2026;22(2):175-178]. ISSN 1545-1003 (print); ISSN 2375-7264 (online). http://www.jofamericanscience.org. 05. doi:10.7537/marsjas220226.05

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