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Poultry manure (PM) has demonstrated its potential to enhance crop nutritional quality. Nevertheless, there remains a dearth of knowledge regarding its synergistic effects when combined with wood biochar (B) on the nutrient concentrations in sweet potato leaves (Ipomoea batatas L.) and the mineral content stored in sweet potato storage roots. Hence, a two-year field trial was undertaken during the 2019 and 2020 cropping seasons in southwestern Nigeria, spanning two locations (Owo—site A and Obasooto—site B), to jointly apply poultry manure and wood biochar as soil amendments aimed at enhancing the nutritional quality of sweet potato crop. Each year, the experiment involved different combinations of poultry manure at rates of 0, 5.0, and 10.0 t ha−1 and biochar at rates of 0, 10.0, 20.0, and 30.0 t ha−1, organized in a 3 × 4 factorial layout. The results of the present study demonstrated that the individual application of poultry manure (PM), biochar (B), or their combination had a significant positive impact on the nutrient composition of sweet potato leaves and minerals stored in the sweet potato storage roots, with notable synergistic effects between poultry manure and biochar (PM × B) in enhancing these parameters. This highlights the potential of biochar to enhance the efficiency of poultry manure utilization and improve nutrient utilization from poultry manure. The highest application rate of poultry manure at 10.0 t ha−1 and biochar at 30.0 t ha−1 (PM10 + B30), resulted in the highest leaf nutrient concentrations and mineral composition compared to other treatments at both sites. Averaged over two years, the highest application rate of poultry manure at 10.0 t ha−1 and biochar at 30.0 t ha−1 (PM10 + B30) significantly increased sweet potato leaf nutrient concentrations: nitrogen by 88.2%, phosphorus by 416.7%, potassium by 123.8%, calcium by 927.3%, and magnesium by 333.3%, compared to those in the control (PM0 + B0). The same treatment increased the concentration of sweet potato root storage minerals: phosphorus by 152.5%, potassium by 77.4%, calcium by 205.5%, magnesium by 294.6%, iron by 268.4%, zinc by 228.6%, and sodium by 433.3%, compared to the control. The highest application rate of poultry manure at 10.0 t ha−1 and biochar at 30.0 t ha−1 yielded the highest economic profitability in terms of gross margin (44,034 US$ ha−1), net return (30,038 US$ ha−1) and return rate or value-to-cost ratio (VCR) (263). The results suggested that the application of poultry manure at 10 t ha−1 and biochar at 30 t ha−1 is economically profitable in the study areas and under similar agroecological zones and soil conditions. [ABSTRACT FROM AUTHOR]

Abstract Oil exploration’s devastation on health and the environment may far outweigh its economic benefits. An oil spill occurred at Egbokodo River in Delta State, Nigeria, thereby polluting the land and water bodies. The study was therefore aimed at evaluating the impacts of iron, lead, cadmium, and chromium on the zooplankton community structure of Egbokodo River and the potential health risks. Zooplankton and surface water samples were collected to investigate the concentrations of trace metals and zooplankton abundance. The associated carcinogenic and non-carcinogenic effects of the metals in the water were analyzed. Trace metal concentrations in the surface water were determined using atomic absorption spectroscopy (Philips model PU 9100) and zooplankton samples were collected using a hydrobios plankton net (mesh size 25 µm). Total petroleum hydrocarbons (TPH) and oil and grease (OG) were determined using Agilent 7890B gas chromatography coupled to flame ionization detector (GC-FID) and volumetric analysis respectively. The trend of the abundance of zooplanktons cross the river was 18 individuals (Station A) Station B > Station A, except in Amphipoda where Station B > Station C > Station A was observed. Iron and lead posed significant carcinogenic risks that are liable to be inflicted by the ingestion of the water. The cumulative non-carcinogenic health risk in the male was the only significant (> 1) among the age groups. Total petroleum hydrocarbons (TPH), oil and grease (OG), iron, and lead had notable impacts on the general abundance of zooplankton in the aquatic habitat. The dominance of the Cyclopoida in the river buttressed the impact of the oil spill which warrants a prompt remediation measure. The pollution had notable ecological impacts on the zooplankton community structure of the aquatic habitat. The adults in the nearby human populations are liable to elicit carcinogenic health challenges associated with lead and iron ingestion. The males are at risk of non-carcinogenic illnesses which are associated with the combined toxicity effects of all the metals. The study suggests that the pollution in Egbokodo River was validated by the dominance of the Cyclopoida in the aquatic habitat. The study confers bioindicator reputation on the Cyclopoida for future biomonitoring studies.

Abstract The failure of water pipes in Water Distribution Networks (WDNs) is associated with environmental, economic, and social consequences. It is essential to mitigate these failures by analyzing the historical data of WDNs. The extant literature regarding water pipe failure analysis is limited by the absence of a systematic selection of significant factors influencing water pipe failure and eliminating the bias associated with the frequency distribution of the historical data. Hence, this study presents a new framework to address the existing limitations. The framework consists of two algorithms for categorical and numerical factors influencing pipe failure. The algorithms are employed to check the relevance between the pipe’s failure and frequency distributions in order to select the most significant factors. The framework is applied to Hong Kong WDN, selecting 10 out of 21 as significant factors influencing water pipe failure. The likelihood feature method and Bayes’ theorem are applied to estimate failure probability due to the pipe materials and the factors. The results indicate that galvanized iron and polyethylene pipes are the most susceptible to failure in the WDN. The proposed framework enables decision-makers in the water infrastructure industry to effectively prioritize their networks’ most significant failure factors and allocate resources accordingly.

Abstract The available Epstein Barr virus vaccine has tirelessly harnessed the gp350 glycoprotein as its target epitope, but the result has not been preventive. Right here, we designed a global multi-epitope vaccine for EBV; with special attention to making sure all strains and preventive antigens are covered. Using a robust computational vaccine design approach, our proposed vaccine is armed with 6–16 mers linear B-cell epitopes, 4–9 mer CTL epitopes, and 8–15 mer HTL epitopes which are verified to induce interleukin 4, 10 & IFN-gamma. We employed deep computational mining coupled with expert intelligence in designing the vaccine, using human Beta defensin-3—which has been reported to induce the same TLRs as EBV—as the adjuvant. The tendency of the vaccine to cause autoimmune disorder is quenched by the assurance that the construct contains no EBNA-1 homolog. The protein vaccine construct exhibited excellent physicochemical attributes such as Aliphatic index 59.55 and GRAVY − 0.710; and a ProsaWeb Z score of − 3.04. Further computational analysis revealed the vaccine docked favorably with EBV indicted TLR 1, 2, 4 & 9 with satisfactory interaction patterns. With global coverage of 85.75% and the stable molecular dynamics result obtained for the best two interactions, we are optimistic that our nontoxic, non-allergenic multi-epitope vaccine will help to ameliorate the EBV-associated diseases—which include various malignancies, tumors, and cancers—preventively.

Abstract This study explored the application of machine learning in predicting post-treatment outcomes for chronic neck pain patients undergoing a multimodal program featuring cervical extension traction (CET). Pre-treatment demographic and clinical variables were used to develop predictive models capable of anticipating modifications in cervical lordotic angle (CLA), pain and disability of 570 patients treated between 2014 and 2020. Linear regression models used pre-treatment variables of age, body mass index, CLA, anterior head translation, disability index, pain score, treatment frequency, duration and compliance. These models used the sci-kit-learn machine learning library within Python for implementing linear regression algorithms. The linear regression models demonstrated high precision and accuracy, and effectively explained 30–55% of the variability in post-treatment outcomes, the highest for the CLA. This pioneering study integrates machine learning into spinal rehabilitation. The developed models offer valuable information to customize interventions, set realistic expectations, and optimize treatment strategies based on individual patient characteristics as treated conservatively with rehabilitation programs using CET as part of multimodal care.

Abstract Bats are not only ecologically valuable mammals but also reservoirs of zoonotic pathogens. Their vast population, ability to fly, and inhabit diverse ecological niches could play some role in the spread of antibiotic resistance. This study investigated non-aureus staphylococci and Mammaliicoccus colonization in the Hipposideros bats at Obafemi Awolowo University, Ile-Ife, Nigeria. Pharyngeal samples (n = 23) of the insectivorous bats were analyzed, and the presumptive non-aureus staphylococcal and Mammaliicoccus isolates were confirmed by matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS). The isolates were characterized based on antibiotic susceptibility testing and whole-genome sequencing (WGS). Six bacterial genomes were assembled, and three species were identified, including Mammaliicoccus sciuri (n = 4), Staphylococcus gallinarum (n = 1), and Staphylococcus nepalensis (n = 1). All the isolates were resistant to clindamycin, while the M. sciuri and S. gallinarum isolates were also resistant to fusidic acid. WGS analysis revealed that the M. sciuri and S. gallinarum isolates were mecA-positive. In addition, the M. sciuri isolates possessed some virulence (icaA, icaB, icaC, and sspA) genes. Multi-locus sequence typing identified two new M. sciuri sequence types (STs) 233 and ST234. The identification of these new STs in a migratory mammal deserves close monitoring because previously known ST57, ST60, and ST65 sharing ack (8), ftsZ (13), glpK (14), gmk (6), and tpiA (10) alleles with ST233 and ST234 have been linked to mastitis in animals. Moreover, the broad host range of M. sciuri could facilitate the dispersal of antibiotic resistance genes. This study provides evidence of the importance of including migratory animals in monitoring the development and spread of antibiotic resistance.

Abstract The growing demand of China for petroleum heightens the complexities and prospects in worldwide investments, necessitating refined and strategic investment approaches. Evaluating the potential of different hydrocarbon-potential areas needs more comprehensive scientific evaluation models. This study aims to establish a Comprehensive Investment Potential of Petroleum (CIPP) framework for targeted sedimentary basins by using an integrated approach that combines the Analytic Hierarchy Process (AHP) and the Entropy-Weighted Fuzzy TOPSIS models. We focus particularly on representative African basins to inform strategic decision-making for the Chinese overseas petroleum enterprises. We firstly interpret the geological condition of these petroleum basins by researching multiple databases and proprietary research data. Then, we use a combined approach of ranking-classification-correlation analysis to evaluate 17 representative basins, taking into account both overall and individual key performance indicators. Our findings suggest the Illizi Basin and the Offshore Côte d'Ivoire Basin could be the most favorable for investment and development. Those like Southwest African Basin warrant cautious consideration. The new evaluation model and computational workflow offer an effective workflow for assessing multiple petroleum basins. This work provides not just practical investment strategies for companies aiming for African petroleum basins, but also a transferable methodology for optimizing investment decisions.

Abstract Promoting the intake of foods rich in vitamin A is key to combating the increase in vitamin A deficiency. This research focused on the utilization of orange-fleshed sweet potatoes (a tuber-based food), cowpea (a pulse), and ripe bananas (a fruit) for the production of flour mix as a means to reduce Vitamin A deficiency in children. Different ratios of sweet potato-cowpea-banana (PCB) mix, resulting in 8 different blended samples, were optimized. The flour mix was evaluated for its overall acceptability, vitamin A content, beta-carotene, and other nutritional and functional properties. The panelists rated the sweet potato-cowpea banana blends labeled PCB8 (60% OFSP, 30% cowpea, 5% ripe banana flour, and 5% sugar) as most preferred and acceptable with average scores of 8.96 points for color, 8.75 points for flavor, 8.88 points for appearance, 8.33 points for taste, 8.07 points for texture, and 8.39 points for overall acceptability on a 9-point hedonic scale. The vitamin A and beta-carotene contents ranged 7.62 to 8.35 mg/100 g and 0.15–0.17 mg/100 g for all blends. A significant difference in the functional properties of the flour mix were observed with an increase in the ratio of sweet potato flour addition. Findings from this study show that the flour mix PCB4 (65% sweet potato, 30% cowpea, and 5% ripe banana flour) was acceptable (8.15) and is recommended based on its vitamin A content (8.35 mg/100 g), nutritional properties, and functional properties. The study showed that locally available food commodities have good nutritional value that will help reduce vitamin A deficiency in children.

Abstract Biomass and its interactions for heat generation have received little attention. In this study, the woody biomass materials were Prosopis africana (PA), Harungana madascariences (HM), Vitrllaria paradoxa (VP), and Afzelia africana (AA). The composition (extractives, carbohydrate, and lignin) of the biomass was determined. The biomass was converted to charcoal in a traditional kiln. A thermo-kinetic examination of the charcoal samples was carried out. The kinetic parameters and potential reaction mechanisms involved in the decomposition process were both obtained using the integral (Flynn–Wall Ozawa) isoconversional methods in conjunction with the Coats-Redfern approach. The activation energy profiles for the charcoal samples in oxidizing atmospheres were 548 kJ/mol for AA, 274 kJ/mol for VP, 548 kJ/mol for PA, and 274 kJ/mol for HM. All charcoal samples underwent comprehensive, multi-step, complex reaction pathways for thermal degradation. The charcoal samples exhibit not only great potential for biochemical extraction but also for bioenergy applications. The significant amount of combustion characteristics in the raw biomass and charcoal samples indicates that each type of wood charcoal produced has more fixed carbon, less ash, and less volatile matter, all of which are desirable for the thermo-chemical conversion of biomass for the production of heat.

Abstract Lassa fever (LF) is prevalent in many West African countries, including Nigeria. Efforts to combat LF have primarily focused on rural areas where interactions between rodents and humans are common. However, recent studies indicate a shift in its occurrence from rural to urban areas. We analysed secondary data of reported LF outbreaks from 2017 to 2021 in Ondo State, Nigeria to identify the distribution pattern, ecological variations, and other determinants of disease spread from the ward level using nearest neighbour statistics and regression analysis. Data utilised include LF incidence, ecological variables involving population, nighttime light intensity, vegetation, temperature, market presence, road length, and building area coverage. ArcGIS Pro 3.0 software was employed for spatial analysis. Results revealed spatio-temporal clustering of LF incidents between 2017 and 2021, with an increasing trend followed by a decline in 2021. All wards in Owo Local Government Area were identified as LF hotspots. The ecological variables exhibited significant correlations with the number of LF cases in the wards, except for maximum temperature. Notably, these variables varied significantly between wards with confirmed LF and those without. Therefore, it is important to prioritise strategies for mitigating LF outbreaks in urban areas of Nigeria and other LF-endemic countries.

Abstract The investigation compares the conventional, advanced machine, deep, and hybrid learning models to introduce an optimum computational model to assess the ground vibrations during blasting in mining projects. The long short-term memory (LSTM), artificial neural network (ANN), least square support vector machine (LSSVM), ensemble tree (ET), decision tree (DT), Gaussian process regression (GPR), support vector machine (SVM), and multilinear regression (MLR) models are employed using 162 data points. For the first time, the blackhole-optimized LSTM model has been used to predict the ground vibrations during blasting. Fifteen performance metrics have been implemented to measure the prediction capabilities of computational models. The study concludes that the blackhole optimized-LSTM model PPV11 is highly capable of predicting ground vibration. Model PPV11 has assessed ground vibrations with RMSE = 0.0181 mm/s, MAE = 0.0067 mm/s, R = 0.9951, a20 = 96.88, IOA = 0.9719, IOS = 0.0356 in testing. Furthermore, this study reveals that the prediction accuracy of hybrid models is less affected by multicollinearity because of the optimization algorithm. The external cross-validation and literature validation confirm the prediction capabilities of model PPV11. The ANOVA and Z tests reject the null hypothesis for actual ground vibration, and the Anderson–Darling test rejects the null hypothesis for predicted ground vibration. This study also concludes that the GPR and LSSVM models overfit because of moderate to problematic multicollinearity in assessing ground vibration during blasting.

Abstract This study seeks to address pertinent economic and environmental issues associated with natural gas flaring, especially for the world's leading natural gas flaring economies (i.e. Russia, Iraq, Iran, the United States, Algeria, Venezuela, and Nigeria). By applying relevant empirical panel and country-specific approaches, the study found that fuel energy export positively impacts economic growth with elasticity of ~ 0.22 to ~ 0.24 for the panel examination. It is further revealed that environmental quality in the panel is hampered by increase in economic growth, gas flaring, fuel energy export, and urbanization. Moreover, for the country-wise inference, government quality desirably moderates economic and environmental aspects of gas flaring in Venezuela and Nigeria, and in Russia and Iran respectively. However, government quality moderates gas flaring to cause economic downturn in the USA. Additionally, economic growth increased with increase in urbanisation (in Iraq and the USA), gas flaring (in Iran and the USA), government quality (only in the USA), and fuel energy export (only in Algeria) while economic growth downturn is due to increase urbanisation in Russia and the USA, increase in fuel energy export in the USA, and increase in government quality in Russia. Meanwhile, environmental quality is worsened through intense carbon dioxide emission from increased urbanisation activity (in Iraq, Iran, Algeria, and Nigeria), increased fuel energy export (in Nigeria), increased natural gas flaring (in Algeria and Nigeria), increased GDP (in Russia, Iran, USA, Algeria, and Venezuela), and high government quality (in Iran). Interestingly, the result revealed that increase in GDP (in Nigeria), increase in urbanisation (in the USA), and increase in gas flaring (in Algeria and Nigeria) dampens environmental quality. Importantly, this study offers policy insight into sustainable approaches in natural gas production, government effectiveness, and regulatory quality.

Abstract The high prevalence of infections arising from Klebsiella species is related to their ability to acquire and disseminate exogenous genes associated with mobile genetic elements such as integrons. We assessed the prevalence, diversity, and associated gene cassettes (GCs) of integrons in Klebsiella species. The isolates recovered from wastewater and hospital effluents, rivers, and animal droppings were identified using the conventional Polymerase Chain Reaction (PCR) with primers targeting the gryA, pehX, and 16S–23S genes. The antimicrobial resistance profile and the Extended-Spectrum and Metallo β-lactamases production were carried out using standard microbiological techniques. PCR, DNA sequencing analyses, and Restriction Fragment Length Polymorphism were used to characterize the integrons and their associated GCs. Furthermore, the genotypic relationships between the different isolated K. pneumoniae were determined using Enterobacterial Repetitive Intergenic Consensus (ERIC)-PCR. About 98% (51/52) of the confirmed isolates harboured an integrase gene, with 80% intI1, while the remaining 20% concurrently harboured intI1 and intI2, with no intI3 observed. About 78% (40/51) of the bacterial strains were positive for a promoter, the P2R2, investigated, while 80% (41/51) harboured at least one of the qacEΔ1 and sul1. Three different GCs arrangements identified were aac(6′)-Ib, aadA1-dfrA1, and dfrA1-sat2. At a similarity index of 60%, the ERIC-PCR fingerprints generated were categorized into nine clusters. Our study is the first to reveal the features of integrons in Klebsiella spp. recovered from environmental sources in the Eastern Cape Province, South Africa. We conclude that the organisms' sources are repositories of integrons harbouring various gene cassettes, which can be readily mobilized to other microorganisms in similar or varied niches.

Abstract Recently, the International Energy Agency (IEA) released a comprehensive roadmap for the global energy sector to achieve net-zero emission by 2050. Considering the sizeable share of (Sub-Sahara) Africa in the global population, the attainment of global energy sector net-zero emission is practically impossible without a commitment from African countries. Therefore, it is important to study and analyze feasible/sustainable ways to solve the energy/electricity poverty in Africa. In this paper, the energy poverty in Africa and the high renewable energy (RE) potential are reviewed. Beyond this, the generation of electricity from the abundant RE potential in this region is analyzed in hourly timestep. This study is novel as it proposes a Sub-Sahara Africa (SSA) central grid as one of the fastest/feasible solutions to the energy poverty problem in this region. The integration of a sizeable share of electric vehicles with the proposed central grid is also analyzed. This study aims to determine the RE electricity generation capacities, economic costs, and supply strategies required to balance the projected future electricity demand in SSA. The analysis presented in this study is done considering 2030 and 2040 as the targeted years of implementation. EnergyPLAN simulation program is used to simulate/analyze the generation of electricity for the central grid. The review of the energy poverty in SSA showed that the electricity access of all the countries in this region is less than 100%. The analysis of the proposed central RE grid system is a viable and sustainable option, however, it requires strategic financial planning for its implementation. The cheapest investment cost from all the case scenarios in this study is $298 billion. Considering the use of a single RE technology, wind power systems implementation by 2030 and 2040 are the most feasible options as they have the least economic costs. Overall, the integration of the existing/fossil-fueled power systems with RE technologies for the proposed central grid will be the cheapest/easiest pathway as it requires the least economic costs. While this does not require the integration of storage systems, it will help the SSA countries reduce their electricity sector carbon emission by 56.6% and 61.8% by 2030 and 2040 respectively.