Search Results (334)

The application of hydrogeophysical techniques to delineating aquifers was conducted in De Aar, the eastern part of the Karoo region, Northern Cape, South Africa. Previously, recharge estimations in this region assumed a uniform aquifer type, overlooking the presence of diverse aquifer systems. This study identified both unconfined and confined aquifers to improve recharge potential assessments. Vertical electrical resistivity sounding (VES) and ground telluric methods were applied. Six VES stations and eleven profiles were measured using a 1D Wenner array configuration. The VES data, processed with IPI2win software, generated a 2D subsurface model. In contrast, the telluric data were analyzed using an automated algorithm to create a 2D profile. The electric potential difference curve was interpreted in comparison with lithological cross-sections. The VES results revealed three to four distinct layers of low-resistivity (0.9–8.1 Ωm), moderate-resistivity (22.4–125 Ωm), and high-resistivity (68–177 Ωm) values, indicating three lithological formations. The telluric data suggested that shallow groundwater boreholes were located in areas with groundwater levels above 50 m. These findings, which matched the lithological data, pointed to a double-layer aquifer system, suggesting that recharge estimates should be carried out to different aquifer layers. The study demonstrated how hydrogeophysical methods can effectively delineate aquifer systems and enhance the identification of recharge areas. Full article

This study compared the effects of three supplementary diets—egg yolk, Artemia, and commercial pellets with 48% crude protein—on the growth performance and survival rate of redbreast tilapia (Coptodon rendalli) under intensive culture conditions at the Kamutjonga Inland Fisheries Institute in the Kavango East region of Namibia. The study was conducted from December 2023 to January 2024 using a complete randomized design replicated thrice. Results showed that fish fed with commercial pellets exhibited the highest specific growth rate (1.39 ± 0.80%) compared to egg yolk (0.94 ± 0.54%) and Artemia (0.33 ± 0.19%). Commercial pellets also had the best survival rate (76.19 ± 43.099), compared to egg yolk and Artemia (70.47 ± 40.69; 33 ± 19.05), respectively. However, egg yolk had the best feed conversion ratio (0.20 ± 0.05) compared to the commercial pellets (0.22 ± 0.04) and Artemia (0.26 ± 0.07). Critical water quality parameters were maintained within acceptable survival ranges for C. rendalli across all treatments. These findings highlight the importance of dietary protein content in optimizing the growth and survival of C. rendalli under intensive culture conditions and provide insights for effective feeding strategies. However, commercial pellets are expensive and inaccessible to small-scale fish farmers. As an alternative, egg yolk emerged as a viable and affordable feed option, promoting fish growth and supporting the establishment of small-scale farming practices in the region. Full article

This paper explores the mathematical dynamics of consumer spending during a financial crisis using opponent process theory (OPT). Traditionally applied in psychology, OPT explains how initial emotional responses are followed by counteracting reactions to restore equilibrium. This study models the short-term boost in consumer spending and subsequent economic adjustments. Utilizing differential equations to represent these processes, this paper provides insights into the interplay between immediate policy effects and longer-term economic consequences. We focus on the United States (US) response to the 2008 Global Financial Crisis in this study. Results show evidence of diminishing response from prolonged stimuli due to demand saturation, resource allocation inefficiencies, and agent adaptation. Monetary stimuli may inflate debt/prices, outweighing benefits, and structural issues persist despite stimuli. Confidence and expectations impact response because perceived ineffectiveness weakens impact over time. Thus, while stimuli can initially boost activity, their sustained impact demands careful consideration of economic dynamics and agents’ responses. Full article

Introduction: The WHO Measles and Rubella Strategic Framework 2021–2030 within the Immunization Agenda 2030 includes both measles and rubella elimination goals and provides guidance to countries for planning and implementing the measles and rubella elimination strategies. Namibia has been implementing measles elimination strategies since 1997. Methods: We reviewed and described the implementation of measles and rubella elimination strategies and the programmatic and epidemiological situation in Namibia during 2000–2023. Namibia introduced a rubella-containing vaccine (RCV) in 2016 as a combined measles–rubella (MR) vaccine using a MR catch-up campaign, targeting a wide age range based on detailed analysis and triangulation of multiple key data sources including MR vaccination coverage, MR case-based surveillance, detailed measles outbreak investigations, and serosurveys. Results: In 2020, estimated MCV1 coverage in Namibia reached 90% and has been sustained at 91% in 2021 and 2022. MCV2 was introduced in 2016, and the estimated MCV2 coverage has steadily increased to 79% in 2022. Following the MCV2 introduction and the implementation of the wide age range MR catch-up campaign in 2016, annual measles and rubella incidence decreased substantially. During 2017–2023, the period following the implementation of the catch-up MR vaccination SIA in 2016, average annual measles incidence per million population in Namibia decreased by 97% from the average during 2010–2016. Similarly, the average annual rubella incidence decreased by 95% from 2010–2016 to 2017–2023. Discussion: Successful implementation of the 2016 wide age range campaign and maintaining high routine immunization coverage likely led to the significant reduction in measles and rubella incidence in Namibia. To sustain the reduction in measles and rubella incidence and attain the elimination targets, Namibia needs to attain and maintain high routine immunization coverage with both doses of the MR vaccine and implement timely and high-quality periodic MR follow-up SIAs. High-quality elimination-standard measles and rubella surveillance will help guide strategies and serve as the basis for the eventual verification of measles and rubella elimination in Namibia according to the WHO-recommended framework. Full article

This empirical study investigated the associations between psychological well-being, job satisfaction, trust in supervisor, and job performance. Data were collected from 277 company employees in Bahrain through online questionnaires and analyzed using structural equation modeling (SEM). The purpose of the study is to examine the relationships between psychological well-being, job satisfaction, trust in supervisor, and job performance through the use of a theoretical framework that synthesizes prominent models in the field of organizational psychology. The research findings indicate a positive influence of psychological well-being on both job satisfaction and job performance. Notably, job satisfaction plays a mediating role in the relationship between psychological well-being and job performance. The study contributes to the existing body of knowledge by offering an integrated approach to examining the intricate connections between psychological well-being, job satisfaction, trust in supervisor, and job performance, which all are crucial for creating a sustainable workplace environment and outcomes. Practical implications highlight the need for organizations to prioritize employee psychological well-being through initiatives such as wellness programs and supportive work environments, as these initiatives directly influence job satisfaction and performance. Job satisfaction acts as a significant mediator, emphasizing the importance of fair compensation, recognition, and professional development in enhancing job satisfaction. Full article

Since the early 1970s, the study of Black–Scholes (BS) partial differential equations (PDEs) under the Efficient Market Hypothesis (EMH) has been a subject of active research in financial engineering. It has now become obvious, even to casual observers, that the classical BS models derived under the EMH framework fail to account for a number of realistic price evolutions in real-time market data. An alternative approach to the EMH framework is the Fractal Market Hypothesis (FMH), which proposes better and clearer explanations of market behaviours during unfavourable market conditions. The FMH involves non-local derivatives and integral operators, as well as fractional stochastic processes, which provide better tools for explaining the dynamics of evolving market anomalies, something that classical BS models may fail to explain. In this work, using the FMH, we derive a time-fractional Black–Scholes partial differential equation (tfBS-PDE) and then transform it into a heat equation, which allows for ease of implementing a high-order numerical scheme for solving it. Furthermore, the stability and convergence properties of the numerical scheme are discussed, and overall techniques are applied to pricing European put option problems. Full article

The utilization of an agricultural waste product known as palm kernel shells (PKS) combined with fine laterites (from basalt in Bangangté, West Cameroon) to produce low-cost and innovative materials with good bearing capacities for road pavement was investigated. Fine laterites from two soil profiles (BL31 and BL32) and made up of kaolinite, hematite, goethite, gibbsite, anatase, ilmenite and magnetite minerals were partially replaced with PKS at 15%, 25%, 35%, and 45% by weight. Physical and mechanical tests, including particle size distribution, Atterberg limits, unsoaked and soaked California Bearing Ratio (UCBR and SCBR), unconfined compressive strength (UCS), and tensile strength (Rt), were performed on the different mixtures. After the addition of PKS, a decrease in fine particle content (77 to 38%), liquidity limit (LL: 72 to 61%), plasticity index (PI: 30 to 19%), maximum dry density (MDD: 1.685 to 1.29 t/m³), and optimum moisture content (OMC: 27.5 to 24.0%) was noticed. Additionally, there was an increase in UCBR (16–72%), SCBR (14–66%), UCS (1.07–7.67 MPa), and Rt (2.24–9.71 MPa). This allows new materials suitable for the construction of base layers for low trafficked roads (T1–T2), as well as sub-base and base layers for high trafficked roads (T3), to be obtained. This newly formed material can be recommended locally for road construction works, though more in-depth studies are required. Full article

(1) Background: Gentamicin is known to be nephrotoxic and ototoxic. Although gentamicin dosage guidelines have been established for preterm and term neonates, reports do show attainment of recommended peak concentrations but toxic gentamicin concentrations are common in this age group. (2) Methods: This was a prospective, observational study conducted in Namibia with 52 neonates. A dose of 5 mg/kg gentamicin was administered over 3–5 s every 24 h in combination with benzylpenicillin 100,000 IU/kg/12 h or ampicillin 50 mg/kg/8 h. Two blood samples were collected from each participant using a truncated pharmacokinetic sampling schedule. (3) Results: The one-compartment linear pharmacokinetic model best described the data. Birthweight, postnatal age, and white blood cell count were predictive of clearance (CL), while birthweight was predictive of volume (V). For the typical neonate (median weight 1.57 kg, median postnatal age 4 days (0.011 years), median log-transformed WBC of 2.39), predicted CL and V were 0.069 L/h and 0.417 L, respectively—similar to literature values. Simulated gentamicin concentrations varied with respect to postnatal age and bodyweight. (4) Conclusions: A 5 mg/kg/24 h dosage regimen yielded simulated gentamicin concentrations with respect to age and birthweight similar to those previously reported in the literature to be safe and efficacious, confirming its appropriateness. Full article

Integrating artificial intelligence (AI) in open and distance learning (ODL) necessitates comprehensive frameworks to evaluate its educational implications. Existing models lack a robust multilayered analysis of AI’s impact on educational outcomes in ODL. This study introduces a Multilayered Process Framework designed to predict academic performance in ODL and enhance inclusivity, aligning with UNESCO’s 2030 educational goals. The current research employed structural equation modelling (SEM) to analyse the impact of AI adoption, focusing on the initial layers of the process framework. Preliminary validation of the SEM framework showed a good model fit, with a Chi-square/df ratio of 2.34, Root Mean Square Error of Approximation (RMSEA) of 0.045, and Comparative Fit Index (CFI) of 0.97, indicating the model’s effectiveness in capturing the complexities of AI impacts on student outcomes. This framework provides a structured, multilayered approach to understanding AI’s role in education, facilitating the development of equitable and accessible AI-driven educational technologies. It lays the foundational work for expanding research into predictive analytics with a support vector machine (SVM), aiming to universalise quality education and ensure global educational equity. This study highlights the practical implications for integrating AI in educational settings and suggests future research directions to enhance the adaptability and effectiveness of AI-driven educational interventions. Full article

This study investigates the performance of YOLOv8, a Convolutional Neural Network (CNN) architecture, for multi-crop classification in a mixed farm with Unmanned Aerial Vehicle (UAV) imageries. Emphasizing hyperparameter optimization, specifically batch size, the study’s primary objective is to refine the model’s batch size for improved accuracy and efficiency in crop detection and classification. Using the Google Colaboratory platform, the YOLOv8 model was trained over various batch sizes (10, 20, 30, 40, 50, 60, 70, 80, and 90) to automatically identify the five different classes (sugarcane, banana trees, spinach, pepper, and weeds) present on the UAV images. The performance of the model was assessed using classification accuracy, precision, and recall with the aim of identifying the optimal batch size. The results indicate a substantial improvement in classifier performance from batch sizes of 10 up to 60, while significant dips and peaks were recorded at batch sizes 70 to 90. Based on the analysis of the obtained results, Batch size 60 emerged with the best overall performance for automatic crop detection and classification. Although the F1 score was moderate, the combination of high accuracy, precision, and recall makes it the most balanced option. However, Batch Size 80 also shows very high precision (98%) and balanced recall (84%), which is suitable if the primary focus is on achieving high precision. The findings demonstrate the robustness of YOLOv8 for automatic crop identification and classification in a mixed crop farm while highlighting the significant impact of tuning to the appropriate batch size on the model’s overall performance. Full article

Sustainability and the quest for a more robust construction material cannot be divorced from each other. While Portland cement has revolutionized the construction sector, its environmental toll, particularly in greenhouse gas emissions and global warming, cannot be ignored. Addressing this dilemma requires embracing alternatives like geopolymer cement/geopolymer binder (GPC/GPB). Over the last few decades, considerable strides have been achieved in advancing GPC as a sustainable construction material, including its utilization in pavement construction. Despite these advances, gaps still exist in GPC optimal potential in pavement construction, as most studies have concentrated on specific attributes rather than on a comprehensive evaluation. To bridge this gap, this review adopts a novel, holistic approach by integrating environmental impacts with performance metrics. To set the stage, this review first delves into the geopolymer concept from a chemistry perspective, providing an essential broad overview for exploring GPC’s innovations and implications in pavement applications. The findings reveal that GPC not only significantly reduces greenhouse gas emissions and energy consumption compared to Portland cement but also enhances pavement performance. Further, GPC concrete pavement exhibits superior mechanical, durability, and thermal properties to ensure its long-term performance in pavement applications. However, challenges to GPC utilization as a pavement material include the variability of raw materials, the need for suitable hardeners, the lack of standardized codes and procedures, cost competitiveness, and limited field data. Despite these challenges, the process of geopolymerization presents GPC as a sustainable material for pavement construction, aligning with Sustainable Development Goals (SDGs) 3, 9, 11, and 12. Full article

There has been a growing concern about the influence of inclusive leadership on employee engagement in the telecommunications industry. The telecommunications industry is undergoing rapid technological advancements, leading to increased competition and pressure on employees. Creating an inclusive workplace in such a dynamic environment is essential to attracting and retaining talented professionals and driving eco-innovation for sustainable development in Industry 4.0. Despite the importance of inclusive leadership, there is dearth of studies exploring the influence of inclusive leadership on employee engagement in the telecommunications industry. Hence, this study sought to examine the influence of inclusive leadership on employee engagement in selected telecommunication companies in Namibia. A qualitative approach was adopted, where face-to-face interviews were conducted using a semi-structured interview guide. The participants (22 executives) were selected for interviews from two telecommunication companies in Namibia using a purposive sampling method. Data collection involved recording and transcribing participant interviews using Microsoft Word. Thematic analysis was conducted using NVivo 20 software. Results show the importance of empathy and non-hierarchical leadership behaviour as the most critical factor influencing team engagement and discretionary effort in the telecommunications industry in Namibia. Meanwhile, clear career advancement pathways, work autonomy, cross-departmental collaboration, and supervisory support are major contributors to employee engagement for innovative performance and sustainability in the telecommunications industry. Full article

This study assesses the efficacy of the Heliosat-2 algorithm for estimating solar radiation, comparing its outputs against ground measurements across seven distinct countries: the Netherlands, Spain, Japan, Namibia, South Africa, Saudi Arabia, and India. To achieve this, the study utilizes two distinct satellite data sources—Himawari-8 for Japan and Metosat Second Generation-MSG for the rest of the countries—and spanning the time between January 2022 and April 2024. A robust methodology for determining albedo parameters specific to Heliosat-2 was developed. During cloudy days, the estimates provided by Heliosat-2 generally exceeded the ground measurements in all of the countries. Conversely, on clear days, there was a tendency for underestimation, as indicated by the median values of the mean bias (MB) across most of the countries. The Heliosat-2 model slightly underestimates daily radiation values, with a median MB ranging from −27.5 to +10.2 W·m⁻². Notably, the median root mean square error (RMSE) on clear days is significantly lower, with values ranging from 24.8 to 108.7 W·m⁻², compared to cloudy days, for which RMSE values lie between 75.3 and 180.2 W·m⁻². In terms of R² values, both satellites show strong correlations between the estimated and actual values, with a median value consistently above 0.86 on a monthly scale and over 92% of daily data points falling within ±2 standard deviations. Full article

The new generation of satellite hyperspectral (HS) sensors provides remarkable potential for regional-scale mineralogical mapping. However, as with any satellite sensor, mapping results are dependent on a typically complex correction procedure needed to remove atmospheric, topographic and geometric distortions before accurate reflectance spectra can be retrieved. These are typically applied by the satellite operators but use different approaches that can yield different results. In this study, we conduct a comparative analysis of PRISMA, EnMAP, and EMIT hyperspectral satellite data, alongside airborne data acquired by the HyMap sensor, to investigate the consistency between these datasets and their suitability for geological mapping. Two sites in Namibia were selected for this comparison, the Marinkas-Quellen and Epembe carbonatite complexes, based on their geological significance, relatively good exposure, arid climate and data availability. We conducted qualitative and three different quantitative comparisons of the hyperspectral data from these sites. These included correlative comparisons of (1) the reflectance values across the visible-near infrared (VNIR) to shortwave infrared (SWIR) spectral ranges, (2) established spectral indices sensitive to minerals we expect in each of the scenes, and (3) spectral abundances estimated using linear unmixing. The results highlighted a notable shift in inter-sensor consistency between the VNIR and SWIR spectral ranges, with the VNIR range being more similar between the compared sensors than the SWIR. Our qualitative comparisons suggest that the SWIR spectra from the EnMAP and EMIT sensors are the most interpretable (show the most distinct absorption features) but that latent features (i.e., endmember abundances) from the HyMap and PRISMA sensors are consistent with geological variations. We conclude that our results reinforce the need for accurate radiometric and topographic corrections, especially for the SWIR range most commonly used for geological mapping. Full article

This study highlights the adsorption efficiency of A. erioloba Seed Nanoparticles (AESNs) in the removal of ibuprofen from water. Ibuprofen is one of the most commonly used drugs in the world and often makes its way into aquatic resources through improper disposal. The AESNs (adsorbents) were extracted from the A. erioloba seed pod via dewaxing, alkali treatment, bleaching, and acid hydrolysis to obtain nanoparticles. These nanoparticles were characterized by scanning electron microscopy (SEM) analysis. An ibuprofen solution model was prepared via the dissolution of water–methanol at a ratio of 9:1. A calibration curve was prepared with a standard solution of the ibuprofen in a concentration range of 0.001–0.010 mg/L. The effect of dosage, pH, time, and temperature in each of the prepared ibuprofen concentrations was determined. Fourier–transform infrared spectroscopy (FTIR) was used to determine functional groups, and SEM was used to study the morphology, size, and surface structure of the nanoparticles. UV spectroscopy determined the concentration of ibuprofen after the interaction with the AESN in different parameters, and X-ray diffraction (XRD) was used to determine the crystal structure of the AESN. The Langmuir and Freundlich isotherm models, as well as Brunauer–Emmett–Teller (BET) methods, were applied to optimize the conditions for maximum adsorption and elucidate the surface area of AESNs and the behaviour of AESN as an adsorbent. The BET results indicate that the surface area was found to be 0.7313 m²/g and the pore size was 0.001148 cm³/g. The R² of 0.77 and 0.3710 results indicate that they do not favor both Langmuir and Freundlich isotherm models. The use of a low ibuprofen concentration, i.e., a low dosage, in this study resulted in positive results. Full article