Scientific African最新文献

Extreme precipitation events, especially climatic heavy precipitation-induced (CHP-induced) floods, pose substantial negative impacts on a myriad of socio-economic drivers. Incidents related to extreme precipitation events result in revenue loss, thereby negatively affecting national economies, and there is a growing need to advance studies that promote early warning and rapid resilience building across West Africa. Despite the region’s vulnerability to climate change, studies on flood events have barely compared multiple indices in order to provide a true representation of meteorological floods in the region Using multiple precipitation indices (Standardized Rainfall Anomaly, Standardized Precipitation Index and Rainfall Anomaly Index), CHP-induced flood events were assessed both historically and into the far-future. The projections were estimated under three Shared Socioeconomic Pathways (SSPs; SSP1-2.6, SSP3-7.0 and SSP5-8.5) and over three demarcated zones of West Africa (Guinea Coast [GC], Savanna [SAV] and Sahel [SAH]). The findings from the study indicate an expected intensification in future flood potential, particularly in the Savanna and Sahel. Contrastingly, flood potential in the Guinea Coast is projected to remain nearly similar to the historical records. On the impacts of emissions scenarios on projected CHP-induced floods, an increase in the mean flood count and maximum flood could potentially result from rising emission concentrations. The findings of the study are useful for advancing current understanding of CHP-induced floods over West Africa, and planning effective mitigation and adaptation strategies to build resilience in the face of current to future episodes.

The pursuit of electrifying rural areas has been a multi-decade quest, emblematic of Nigeria's longstanding efforts to diversify its energy sources and reduce dependence on traditional fossil fuels. Accordingly, this study aims to evaluate the electrification status and perform a techno-economic analysis of a hybrid system to ensure electricity reliability, bill reduction and reduced grid demand for three representative rural communities in Rivers State using HOMER Pro Software. Subsequently, five hybrid system scenarios that encompassed different combinations of the grid, PV modules, wind and battery, were examined using the CRITIC-PROMETHEE II methodologies. Each community was assessed based on its own hourly electricity demand, using data obtained from the injection substation. The Aluu, Choba and Rumuekini communities experienced an average daily electricity supply of 4.6, 5.4 and 6.2 h respectively from the national grid with energy usage of 5436.4 MW for Aluu, 3375.4 MW for Choba and 1626.1 MW for Rumuekini, in 2022. The findings indicate that the grid/PV module was the optimal choice for the three communities. Moreover, the cost of energy (COE) for the proposed systems ranged from 0.0181 $/kWh to 0.0185 $/kWh, significantly lower than the grid's COE of 0.08625 $/kWh in the respective communities. This research further includes a sensitivity analysis of the optimal system, examining variations in PV cost, discount rate, inflation rate, annual scaled solar irradiance and electrical demand. The results provide a comprehensive framework to support decision-making, optimize system design and align renewable energy solutions with project goals and stakeholder priorities.

The aim of this work was to assess the effect of feeding goldfish (Carassius auratus) broodstock with diets containing dietary fermented grain cereal milling by-product with Saccharomyces cerevisiae 5, 10, 20 g/Kg diet (FY) (a carbohydrate source) on fish growth, health, maturity, and reproductive performance. The broodstock were divided into four groups: FY0, FY5, FY10, and FY20 diet, and control (FY0) fed a basal diet for 68 days. Growth-related parameters, biochemical composition, and blood biochemical parameters were measured. For maturity progress ovarian histology, gonadotropins and sex steroid hormones (T and E2) were noticed, and to evaluate reproductive performance, fecundity and gonadosomatic index (GSI%) were evaluated. Adding FY to the experimental diets improved broodstock growth and hematological markers. Additionally, it enhanced the ovarian growth (GSI% and fecundity), which augmented significantly (p ≤ 0.05) in FY10 and. FY20 treated sets. Dietary FY significantly increased serum levels of globulin, albumin, total protein, and glucose compared with the control. In addition, gonadotropins and sex hormones were significantly elevated in a dose-dependent mode with increasing FY levels. This study demonstrated positive physiological effects with improving reproductive parameter response after FY dietary addition. The group fed FY20 exhibited the better growth performance than the other treatments. Therefore, nutritional FY management could be employed as a tactical approach to improve reproductive ability and maintain goldfish production.

Breast cancer remains a major global health challenge, demanding better diagnostic tools. Traditional methods like mammography have limitations, highlighting the need for specific, non-invasive approaches. Protein biomarkers offer a promising avenue for early and accurate detection, potentially leading to improved patient outcomes and personalized treatment. This review explores key protein biomarkers, including Estrogen Receptor (ER), Progesterone Receptor (PR), Human Epidermal Growth Factor Receptor 2 (HER-2), and Cancer Antigen 27.29(CA27.29), focusing on the proteomic methodologies used in their discovery and validation. However, challenges exist, such as variability in biomarker expression and limitations in abundance, stability, and specificity, which hinder clinical use. The review discusses innovative strategies to overcome these challenges, emphasizing the importance of translating biomarker research into practical applications for personalized medicine in breast cancer diagnosis and therapy. This exploration contributes to the evolving field of breast cancer diagnostics, paving the way for future discoveries and improved patient care.

The global pandemic of COVID-19 has resulted in human health, economic, social, psychological, and political worldwide crises. Even though the countries were taking different prevention measures against COVID 19 pandemic, the infection spreads fast more than ever. This study aimed to assess the level of prevention practice measures against COVID-19 and risk factors in the Mekelle city, Tigray. We conducted a community cross-sectional study among 880 participants aged 15 and above years old in Mekelle city from July 05–28 2020. All associations have examined for statistical significance with alpha set at the 0.05 level. The findings of this study showed that the overall Practice preventive measures of COVID-19 among the Mekelle community were 36.8 % of 95 % CI of (33 % – 44 %). Regarding the factors associated with the outcome variable, being a health care worker and being resident in Hawelti, Semen, and Adi-Haki sub-cities were associated significantly with prevention measures of COVID-19. Thus, efforts should be made to improve the prevention measures.

The impacts of climate variability indicators on crop yields have attracted global attention over the years. This is particularly so because of the growing concern about the effect of climate change on sustainable food production, especially in extreme climate-risk-prone regions in sub-Saharan Africa. The aim of the present paper is to study the marginal effect of climate variables on maize yields in northern Ghana. In this paper, we apply the random forest machine learning approach, Mann-Kendall test, Sen's slope, using data from four study locations in the Upper East region of Ghana to analyze the phenomena. It was evident that maximum and minimum temperatures are the most important climate variability indicators, which have the greatest impact on maize yields. Maize yield in the Upper East region tends to decrease when temperatures exceed 32°C and increase when temperatures are around 24°C. Our results generally show that in all the studied districts, maize yield tends to be at its maximum when the temperature is around 24°C. These findings are crucial to the efforts being made in addressing the emerging challenges confronting smallholder farmers in sustainable food production due to persistent climate change as well as contributing to the achievement of Sustainable Development Goals 1, 2 and 13.

This analysis compares financial assurance instruments for promoting sustainable mining practices in climate change mitigation and adaptation in Zambia and South Africa. The study explores how these instruments can help reduce environmental impacts from mining and improve climate adaptation. The research follows the PRISMA methodology, which reveals obstacles such as insufficient financial provision, limited monitoring, enforcement, and inconsistent regulations. The study highlights the need to strengthen regulatory frameworks, establish adequate financial provisions, improve monitoring and enforcement, engage stakeholders, share knowledge, and promote international collaboration to address these challenges. The recommendations aim to enhance the effectiveness of financial assurance instruments and encourage responsible mining, leading to a more sustainable mining sector, less environmental degradation, and significant contributions to global climate change mitigation efforts.

The resistance of Gram-negative bacteria to β-lactam antibiotics occurs mainly by the production of β-lactamases. The aim of this work was to evaluate the presence of β-lactamase genes and integrons in multidrug-resistant (MDR) isolates recovered from food. Susceptibility to antibiotics was determined using the agar diffusion method. Detection of β-lactam resistance genes and integrons was performed with the 53 confirmed MDR strains (42 E. coli and 11 Salmonella spp.) by PCR. High resistance rates of 92.5 %, 60.4 %, and 54.7 % against ampicillin, tetracycline, and amoxicillin/clavulanic acid were observed, respectively. Two β-lactamase genes bla_TEM (37.7 %) and bla_SHV (28.3) were detected among E. coli and Salmonella isolates. High rate of isolates (85 %) exhibited class 1 integrons was observed. These isolates can act as sources of antimicrobial resistance genes and promote the spread of these resistance determinants to other commensal and pathogenic bacteria. Therefore, preventive measures and clean environments are recommended to prevent food contamination with resistant microorganisms.

The integration of Quantum Computing into Echo State Networks (ESN) materializes in the form of the Quantum Echo State Network (QESN), a methodological innovation that reshapes predictive analytics within the domain of artificial intelligence. This investigation harnesses the novel QESN model alongside traditional ESNs, deploying them within the dynamic and burgeoning financial market of Africa. Our focus zeroes in on the Google Stock Price dataset, which provides a rich tapestry of regional financial activity. The QESN model distinguishes itself by a complex interconnection of qubits that conduct quantum operations, offering a marked amplification of computational capability. The empirical analysis reveals that the QESN model’s predictive prowess substantially exceeds that of its ESN counterpart, achieving an unprecedentedly low Mean Squared Error (MSE) of 0.00021 in forecasting market trends. This exceptional figure redefines the standards of financial prediction models in African markets and establishes the QESN as an instrumental breakthrough, providing unparalleled accuracy in the analysis and prediction of financial data.

Survival among children diagnosed with T1DM in SSA has been estimated to be very poor with average duration of survival at 1 year. With care and increasing availability of insulin through philanthropic projects like the Changing Diabetes in Children (CDiC), there is improved survival of children and adolescents with T1DM in the low and medium income countries (LMICS). We aimed to investigate the survival rates of patients with type 1 diabetes mellitus (T1DM) on a national programme at specialized T1DM clinics. Furthermore, the study sought to determine the associated factors for patients with T1DM to stay on the programme. The median duration for registered patients with T1DM on the programme was 99 ($\mu ≔$ 600, Min—Max $≔$ 1—16864) days. Findings show that the hemoglobin A1C (HbA1c) poor control, body mass index (BMI), hypertension, hospital and residence were significantly associated with time in days to dropout from the programme. Of these predictors, being underweight and the centre of registration (Nsambya Hospital) had poor T1DM prognostics, while residing in the central region and being at least 15 years were associated with better T1DM control and management, hence a greater likelihood to stay on the T1DM programme.