GPH-International Journal of Applied Science

GOVERNMENT EXPENDITURE SIZE AND ECONOMIC GROWTH IN NIGERIA: A THRESHOLD ANALYSIS

Pius Effiong Akpan, Alex Oisaozoje Iriabije — Wed, 06 May 2026 13:27:42 +0000

This study examined the optimal size of government expenditure that maximizes economic growth in Nigeria from 1970 to 2023 using a threshold analysis which employed quadratic and Threshold Autoregressive (TAR) models. The study employed time series data of Nigeria which were subjected to various diagnostic tests and the results revealed that the variables were suitable for estimation. The empirical results of the quadratic model showed a positive relationship between government expenditure size and real GDP growth, while the excess expenditure of government, measured by the square of government expenditure, has a negative impact on real GDP growth, suggesting a nonlinear relationship between government expenditure size and real GDP growth in Nigeria. The result also showed a significant influence of broad money supply and gross fixed capital formation on real GDP growth. Empirical evidence of the quadratic and TAR models, revealed the optimal size of government expenditure that maximizes economic growth in Nigeria to be approximately 18 percent, confirming the existence of Armey’s curve in Nigeria. The Granger causality result indicated a bidirectional causality between government expenditure size and gross fixed capital formation, while a unidirectional causality existed between gross fixed capital formation and real GDP growth. Therefore, based on these findings, the federal government should ensure that an effective and efficient growth inducing fiscal policy management framework is put in place to ensure that government expenditure as a percentage of GDP is at the threshold value of 18 percent in order to promote economic growth in the economy.

PERCIEVED HEALTH IMPACT OF WELDING FUMES AMONG WORKERS IN THE FABRICATION FACILITIES IN PORT HARCOURT

E. OSUGBA, J. UGBEBOR, U. EJIKEME — Sun, 17 May 2026 13:21:32 +0000

This study examined the perceived health impact of welding fumes among workers in the fabrication facilities in Port Harcourt. The objectives were to; examine the prevalence of workplace-related injuries associated with welding fume exposure among welders in a fabrication facility in Port Harcourt, examines prevailing safety practices and investigate preventive actions and control measures that should be taken to reduce the effects of these fumes on welder. Using cross-sectional descriptive survey design, structured questionnaire was administered to 300 welders selected from the sampled fabrication facilities. Descriptive statistics methods such as frequencies and percentages were used for data analysis. The results revealed that 66.7% (156) of the welders in the fabrication facility reported experiencing workplace injuries associated with welding fumes such that burns (139, 59.6%) and eye injuries (117, 50%) are the most common. Respiratory issues were reported by 34.6% (80) of the welders, while cuts and lacerations affected 29.5% (69) workers. Use of Personal Protection Equipment (PPEs) (96, 41.5%), ventilation systems (159, 68, 29.1%) and regular safety training (86, 36.8%) are safety practices adopted to prevent exposure to fumes while provision of PPEs (178, 76.1%) regular health check-ups (112, 47.9%) and use of local exhaust ventilation (156, 66.7%) are most common control measures adopted by fabrication facilities to reduce the effects of welding fumes. These findings highlight the high prevalence of work-related problems linked to welding fumes and the need to enforce occupational health and safety regulations aimed at reducing welding fume exposure, as well as encouragement and enforcement of the use of PPE, ventilation systems and proper safety training to mitigate the injuries among welders in fabrication industries in Port Harcourt.

Design and Implementation of a Smart Feedback System for Lecturers Using Sentiment

T. Ge, V. Audu, I. T. Adom, B. C. Agbata — Tue, 19 May 2026 09:55:23 +0000

Traditional feedback methods are often manual, time-consuming, and susceptible to subjective interpretation, which limits their effectiveness for timely instructional improvement. To address these challenges, a web-based system was developed to streamline feedback collection and management. The system integrates a Flask-based sentiment analysis engine powered by Natural Language Processing (NLP) techniques to automatically classify student feedback into positive, negative, or neutral categories. The proposed system includes a user-friendly student feedback interface, a secure lecturer dashboard, automated sentiment classification, and graphical visualization of feedback trends using charting tools. These features work together to provide an efficient and structured approach to feedback analysis. System testing confirmed accurate sentiment classification, seamless integration between components, and effective real-time processing of feedback data. The system demonstrates how artificial intelligence can enhance teaching evaluation by providing lecturers with timely, data-driven insights that support continuous improvement in instructional delivery.