Articles

This study proposes an integrated approach combining geographic information systems (GIS) and geotechnical analyses to assess the stability of the Oued Ayda mountain lake dam, located in the Siliana Governorate, northwestern Tunisia. The mechanical properties of the embankment and foundation materials were integrated into a Mohr-Coulomb geomechanical model, while the pore water pressure distribution was simulated for various representative hydromechanical scenarios: end of construction, normal operation, rapid drainage, and short- and long-term empty reservoir conditions. The stability analysis, performed using the Morgenstern-Price method with the SLOPE/W software (GeoStudio), reveals high safety factors on the upstream side (SF > 3 in the short term and SF ≥ 2 in the long term), indicating good resistance of this slope to hydraulic and mechanical stresses. Conversely, the results show that the downstream slope exhibits significantly lower safety factors, ranging from 1.335 to 1.338 under long-term conditions, particularly during normal operating and rapid drainage scenarios. These reduced values indicate a high vulnerability of this slope to persistent saturation and adverse hydraulic gradients. In conclusion, although the dam exhibits satisfactory overall stability, the downstream slope remains the most vulnerable area of the structure. The results underscore the need for rigorous management of water level fluctuations and suggest reinforcing the drainage system or implementing targeted stabilization measures to ensure the long-term safety and durability of the structure.

The stock market is very volatile and hard to predict accurately due to the uncertainties affecting stock prices. However, investors and stock traders can only benefit from such models by making informed decisions about buying, holding, or investing in stocks. Also, financial institutions can use such models to manage risk and optimize their customers' investment portfolios. In this paper, we use the Long Short-Term Memory (LSTM-RNN) Recurrent Neural Networks (RNN) to predict the daily closing price of the Amazon Inc. stock (ticker symbol: AMZN). We study the influence of various hyperparameters in the model to see what factors the predictive power of the model. The root mean squared error (RMSE) on the training was 2.51 with a mean absolute percentage error (MAPE) of 1.84%.

The price of grains like maize and sorghum is subject to significant fluctuations, which can have a significant impact on a country's economy and food security. The aim of the study is to model sorghum and maize price volatility in Nigeria. The data utilized in the study was extracted from World Bank Commodity Price Data (WBCPD), 2022. The data consists of monthly prices in nominal US dollars for maize and sorghum from January 1960 – August 2022. The Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models were utilized for capturing the two-grain price volatility. Two types of conditional heteroscedastic models exist, the first group uses exact functions to control the evolution of , while the second group describes with stochastic equations. It is inferred from the result that inherent uncertainties and fluctuations existed in the prices of maize and sorghum in Nigeria which implies that the price volatility is positive and statistically significant suggesting that historical information and past shocks play a crucial role in determining the volatility observed in the grains. It is recommended that the ARCH, GARCH, EGARCH, TGARCH, PARCH, CGARCH, and IGARCH models should be employed for modeling and managing the volatility of maize and sorghum prices in Nigeria. These models have shown effectiveness in capturing different aspects of volatility, including the impact of past shocks, conditional volatility, asymmetry, and other relevant factors.

The paper analyses the extent to which crude oil price shocks impact GDP growth, exchange rate, interest rate and inflation of an emerging oil exporting economy, Ghana. The Structural Vector Autoregressive model is used to analyse the quarterly data from 2009q1 – 2020q4. The results showed that exchange rate and GDP growth respond positively but temporal to the impulse of crude oil price. In contrast, inflation and interest rate respond negatively to crude oil price shock. Specifically, the exchange rate appreciates in the initial quarter and begins to depreciate, whereas GDP growth experiences an increase in the first two quarters and also reduces afterwards. Crude oil price shocks to the Ghanaian economy follow the conventional behaviour of the impact of crude oil on macroeconomic indicators. The positive impact of the price shock on GDP growth and exchange rate is not much reflecting the fact that Ghana is an emerging oil-producing country with low production and export level. Ghana’s prospects in the oil and gas sector should not just be a mere hoax. Policies should be directed toward petroleum exploration and production efforts since the energy transition endanger benefits for future exploitation. Policies should be implemented to attract competitive players locally and internationally in the oil industry. The shock of crude oil prices is beginning to show evidence based on this study. Therefore government must consider recognising the importance of other economic sectors in order not be become heavily dependent on oil.

Financial markets have become more and more complex, so has been the number of data sources. Stock price prediction has hence become a tough but important task. The time dependencies in stock price movements tend to escape from traditional models. In this work, a hybrid ARIMA-LSTM model is suggested to enhance accuracy of stock price forecasts. Based on time series indicators like adjusted closing prices of S&P 500 stocks over a decade (2010–2019), the ARIMA-LSTM model combines influences of both autoregressive time series forecasting with the substantial sequence learning property of LSTM. Data preprocessing in all aspects including missing values interpolation, outlier’s detection and data scaling – Min-Max guarantees data quality. The model is trained on 90/10 training/testing split and met with main performance metrics: MaE, MSE & RMSE. As indicated in the results, the proposed ARIMA-LSTM model gives a MAE value and MSE value of 0.248 and 0.101 respectively and RMSE of 0.319, a measure high accuracy on stock price prediction. Coupled comparative analysis with other Artificial Neural Networks (ANN) and BP Neural Networks (BPNN) are examples of machine learning reference models, further illustrates the suitability and superiority of ARIMA-LSTM approach as compared to the underlying models with the least MAE and strong predictive capability. This work demonstrates the efficiency of integrating the classical time series models with deep learning methods for financial forecasting.

The Nigerian Government both previous and present has introduced several policies and programmes to reduce or proffer remedial measures to militate against the negative impact of high inflationary levels on the Nigerian economy. All these measures have not led to a productive result as the inflation rate has continued to sour higher over the years. This paper aimed at examining the economic influence of the determinant factors that influence inflationary trends that are multi-dimensional and dynamic which continue to defy solutions. The data used for this work was sourced from the National Bureau of Statistics and Central Bank of Nigeria, from 1983 to 2020. The ordinary least square approach was used to analyze the data and the result shows that consumer’s price index, interest rate and total export has a positive effect on Nigeria inflation, but only the Consumer’s Price Index (CPI) have a statistically significant effect on the Nigeria inflation at 99% confidence interval. Result also shows that the exchange rate, foreign reserve, money supply, real GDP, real income and total imports has a negative effect though not statistically significant on the Nigeria inflation rate. The result of the granger causality test shows exchange rate and total imports to granger cause Nigeria inflation. It is recommended that Government should improve locally manufacture products to meet international demands to reduce total imports.

The level of business competition in several online shopping sites for fashion products is currently experiencing quite crucial dynamics. Competitive match will indirectly affect consumer purchasing decisions. This research explores what factors are dominantly influencing the level of product purchase decisions on online shopping sites in terms of product variety, price, brand image and promotion aspects. This research design uses a quantitative research design. Determination of the sample in this study using the convenience sampling method with a total of 110 respondents. Data collection uses a questionnaire distributed online during the period from May to June 2021. This study concludes that product variations and brand image have a positive and significant influence on purchasing decisions. Then for the effect of price and promotion on purchasing decisions obtained a positive but not significant impact. This study confirms the importance of paying attention to aspects of pricing and product promotion strategies on the Reebonz online shopping site.

In this study, the effect of energy cost liberalization policies on the cost-effectiveness of wind farms compared to gas power plants has been investigated. To calculate the cost of electricity generation from various sources, including wind energy and fossil fuels, the “levelized cost” method has been used as the most acceptable economic method to compare different types of electricity generation technologies. In addition, calculations related to the cost of electricity, different fuel prices, different technologies of wind farms, and different prices of different types of fuel are considered. Based on the results of this research, it was found that by targeting fuel prices in the country, wind farms are quite cost-effective, and considering the cost of the opportunity to use diesel in the country, the construction of these power plants can provide the possibility of exporting this fuel. This cost also increases with the production of valuable equipment in the country. The important point is that the macro policy should localize the wind turbine industry with priority to its valuable parts and components due to fuel price changes. This policy will continue to make this type of power plant cost-effective in the country and the employment of specialists in the country.