Scientific African最新文献

This article employs quantile regressions to investigate the link between oil prices, gold prices, and stock market returns in five African oil-exporting nations: Nigeria, Ghana, Egypt, Algeria, and Tunisia. The analysis utilises daily data spanning from January 1, 2017, through September 30, 2021. Before the onset of COVID-19, oil played a pivotal role as a hedge in Tunisia, Egypt, Algeria, and Ghana, providing stability in both normal and bullish market conditions. Furthermore, it functioned as a diversifier in Egypt and Ghana during bearish market phases. However, as the pandemic unfolded, oil took on the characteristics of a safe haven in Egypt, Ghana, Nigeria, Algeria, and Tunisia, particularly notable during bearish and normal market conditions. Gold, prior to the pandemic, served as a hedge in Egypt, Ghana, Nigeria, and Tunisia, while also acting as a diversifier across both bearish and bullish market conditions in these economies. Conversely, during the pandemic, gold predominantly functioned as a diversifier in most oil-exporting African economies, with significant hedge properties observed specifically in Algeria and Ghana. The practical implications of these findings are significant for various stakeholders, including oil exploration and production firms, businesses in the transport and hospitality sectors, policymakers, and investors with stocks sensitive to oil and gold prices.

The Inaouene basin's waters are deeply affected by pollution, which undermines their quality. Indeed, due to the increasing population and industrial development of Taza city, the Inaouene River and its tributaries Larbaa and Lahdar are currently at risk of large quantities of urban discharge, exacerbated by successive drought periods. The objective of this study was to determine the pollution levels and assess the impact of drought on the water quality of the Inaouene River, based on physicochemical, and bacteriological analyses at seven stations over two hydrological cycles (June 2020 to May 2022). The results of physicochemical analyses during the first hydrological cycle revealed averages of 15.93 °C for Temperature, 1434.43 µS cm^-1 for electrical conductivity (EC), 0.93 and 0.63 mg L^-1 respectively for total phosphorus (TP) and orthophosphate (PO₄^3-), 9 mg L^-1 for NTK, 134.23 and 278.43 mg L^-1 for BOD₅ and COD respectively, 3.99 mg L^-1 for Dissolved oxygen (DO) and 203.59 mg L^-1 for sulfate (SO₄^2-). For water bacteriological quality, the total coliforms (TC), Escherichia coli (E. coli), and intestinal enterococcus (IE) have shown averages of 3.45, 2.96, and 2.73 Log₁₀ MPN per 100 ml respectively. During the second hydrological cycle, marked by low rainfall, a pronounced degradation in water quality has been observed, affecting all parameters. Notably, water quality went from average to poor or very poor according to the Moroccan surface water quality standards (MSWQS) and from very poor to unsuitable for consumption for the Water Quality Index (WQI) during the drought period. This study confirmed the exacerbation of deterioration in the water quality of the Inaouene River during the drought period, notably evidenced by heightened concentrations of organic matter (increases of 41.53 % and 37.08 % for BOD₅ and COD, respectively), fecal contaminants (elevations of 11.48 %, 10.18 %, and 14.7 % for IE, TC, and E. coli, respectively), nutrients (increases of 38.83 % for TP, 48.67 % for PO₄^3-, and 35.82 % for NTK), and mineral content (augmentations of 28.89 % for EC, and 31.73 % for sulfate). Additionally, this degradation in quality is reflected in the elevation of water temperature and the reduction in oxygenation (of 9.39 % for Temperature, and 46,26 % for DO).

An optimal adaptive multiscale finite element method(AMsFEM) for numerical solutions of flow problems modelled by the Oldroyd B model is developed. Complex flows experience instabilities due to a phenomena known as the high Weissenberg number problem. The stabilisers are terms in-cooperated into the variational formulation when applying the finite element method. For the selected few stabilisers, numerical experiments are performed to study the convergence of the solutions. These demonstrate that adaptive strategies reduce the computational load of flow simulation. A best performing combination of choice of stabiliser and adaptive strategy is suggested.

This article delves into algebraic topology, specifically (co)homology theory, which is essential in various mathematical fields. It explores different types of (co)homology groups such as Hochschild, cyclic, reflexive, and dihedral, focusing on dihedral cohomology generated by the dihedral group operating on simplicial complexes. The text discusses when dihedral and reflexive cohomology vanishes and provides examples, along with proving a homomorphism between dihedral cohomology groups. Mathematical concepts like Banach spaces, $C^{*}$-algebras, and tensor products are elucidated to understand the algebraic structures involved. Theorems and proofs establish the relationships and properties of these cohomology groups, culminating in a canonical isomorphism theorem for the product of two algebras. Overall, the article aims to provide a comprehensive exploration of dihedral cohomology and its interplay with other algebraic structures, offering insights into its applications and theoretical foundations. Additionally, it studies the (co)homology theory of $C^{*}$-algebras, focusing on the triviality of cohomology groups of operator algebras and discovering the canonical isomorphism between any two unital $K$ -algebras $A$ and $A{}^{\prime }$: $H{D}_n\left(A\times A^{\prime }\right)\cong H{D}_n\left(A\right)\oplus H{D}_n\left(A^{\prime }\right)$.

Schistosomiasis, caused by flatworm parasites, is a widespread disease resulting in chronic illness and fatalities globally. Given the risk of drug resistance and limited treatment options, finding new therapeutic options are crucial for managing and controlling this neglected tropical disease. In this research, various in-silico methods including Density-Functional-Theory (DFT) computations, Molecular-docking, molecular dynamics simulations and Pharmacokinetic evaluation were employed to identify ten potential inhibitors of Schistosoma mansoni HDAC8 (SmHDAC8). The modeled activities (pIC₅₀) of these newly designed compounds (ranging between 7.110 and 6.959) surpassed that of the hit compound 19 (pIC₅₀ = 6.445) and the standard reference control, Praziquantel (pIC₅₀ = 5.989). Moreover, the MolDock scores (ranging between −188.964 and −158.351 kcal/mol) for these proposed compounds in the SmHDAC8 binding cavity outperformed the hit template 19 (MolDock score: −122.516 kcal/mol) and the Praziquantel (−110.245 kcal/mol). Furthermore, the conformational stability of the top three designed compounds (19a, 19i, and 19j) in the SmHDAC8 binding cavity was examined through a 100 ns of Molecular Dynamics simulation. Additionally, the drug-likeness and ADMET predictions of these compounds indicated favorable oral bioavailability and pharmacokinetic profiles. This study offers a reliable in-silico approach for identifying potential agents against Schistosomiasis.

The Southern Highlands region of Tanzania has witnessed an increased frequency of severe flash floods. This study examines rainfall data of four stations (Iringa, Mbeya, Rukwa, and Ruvuma) spanning 30 years (1991–2020) to investigate drivers of extreme rainfall and non-stationarity behavior. The Generalized Extreme Value (GEV) model, commonly used in hydrological studies, assumes constant distribution parameters, which may not be true due to climate variability, potentially leading to bias in extreme quantile estimation. Recent studies have introduced a technique for constructing non-stationary Intensity-Duration-Frequency (IDF) rainfall curves. The method incorporates trends in the parameters of the GEV distribution, only using time as a covariate. However, uncertainty exists about whether time is the most suitable covariate, highlighting the need to explore all potential covariates for modeling non-stationarity. The aim of this study is to assess the influence of other time-varying covariates on extreme daily rainfall events, considering seasonality and climate change in the rainfall data. Specifically, five processes (i.e., local temperature changes (LTC), urbanization, annual Global Temperature Anomaly (GTA), the Indian Ocean Dipole (IOD), and the El Niño-Southern Oscillation (ENSO) cycle) were studied as drivers of extreme rainfall events. Sixty two non-stationary GEV models are developed based on these covariates and their combinations, alongside two non-stationary GEV models using the time covariate to capture the seasonality of the unimodal rainfall in the region, and one stationary GEV model (S0). With the use of corrected Akaike Information Criterion (AICc), the best model for each duration (i.e., 1-, 3-, and 5-days) of rainfall series is chosen. Results indicate that local processes (i.e., LTC and urbanization) are the optimal covariates for 1 day-duration rainfall, while global processes (i.e, IOD, ENSO cycle, and GTA) are identified as the most suitable covariates for 3, and 5 day-duration rainfall across all stations. The identified best non-stationary model (with their best covariates) are then used to develop non-stationary rainfall IDF curves for all stations. According to the analysis of non-stationary extreme values, the return periods of extreme rainfall events concluded a notable decrease in comparison to the stationary approach. The study also revealed strong correlations between global climate indices (ENSO, IOD, GTA) and long-duration extreme rainfall in Tanzania’s Southern Highlands. Local factors like Urbanization and temperature changes also show significant associations with 1-day duration events. These findings emphasize the need for integrated climate forecasting to inform effective adaptation strategies. Finally, the study addresses associated uncertainties in our predictions of forthcoming extreme rainfall events through rigorous analysis. The study demonstrated that return levels for extreme rainfall event