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Abstract The assessment of natural radionuclides in soil samples from artisanal gold mine sites in Iperindo, Nigeria was conducted using different analysis methods. Soil samples collected across three locations and within a control area were analyzed using well-calibrated gamma spectroscopy fitted with a sodium iodide detector. The average activity concentrations (ranges) of 238U, 232Th, and 40 K were found to be 61.55 ± 13.97 (33.40–87.00) Bqkg−1, 72.65 ± 16.83 (41.90–105.10) Bqkg−1, and 1134.99 ± 142.24 (720.50–1722.10) Bqkg−1, respectively for the mining areas and 15.26 ± 6.96 (10.90–20.10) Bqkg−1, 21.46 ± 9.35 (17.40–25.30) Bqkg−1, and 381.04 ± 51.27 (280.30–511.70) Bqkg−1, respectively for the control locations. The average activity concentrations of the radionuclides assessed were above the worldwide permissible limits for the Iperindo mining areas and below the worldwide average for the control areas. The investigation also assessed various radiological parameters, encompassing the radium equivalent, dose rate, annual effective dose rate, internal and external hazard indices, alpha and gamma indices, annual gonadal equivalent, and excess lifetime cancer risk. The recorded values for mine locations were 252.83 Bqkg−1, 119.98 nGyh−1, 0.15 mSvy−1, 0.85 Bqkg−1, 0.68 Bqkg−1, 0.31 Bqkg−1, 1.89 Bqkg−1, 850.23 μSvy−1, and 0.52 $$\times$$ × 10−3, respectively. Except for the radium equivalent, external and internal hazard risks, and the alpha index, which were notably below the recommended threshold, all the other radiological parameters exceeded the globally advised limits. All the radiological parameters from the control areas were below the worldwide average values.

The increasing penetration of the internet and new media technologies has created more avenues for the participation of people, formerly known as media audiences, in the news production process through a practice termed citizen journalism. News about events, people and places break on-the-go as reported by individuals with smartphones, internet connection and social media accounts. However, citizen journalism has promoted sensationalism because sharing of emotion-provoking content is fast becoming a popular culture on social media, thereby posing ethical challenges that call for regulation. This study investigated sensationalism as an ethical issue in citizen journalism among undergraduates in selected universities in Ogun State, Nigeria. It was anchored in social responsibility media theory. The population of this study comprised 53,749 full-time undergraduates of Federal University of Agriculture, Abeokuta; Tai Solarin University of Education, Ijagun; and Babcock University, Ilishan-Remo all in Ogun State, Nigeria. A multistage sampling technique was adopted to select 381 respondents studied using a self-structured questionnaire. Results revealed that a majority of the undergraduates participated in citizen journalism; they mostly shared entertainment content; they were motivated by entertainment, appeal, and controversies in sharing sensational content; but they perceived sensationalism to be ethically wrong. The study recommended media literacy and fact-checking as antidotes to sensationalism in digital media.

In this paper, we present the Academic Results Datasets Predictor (ARDP), for missing academic results datasets, based on chi-squared expected calculation, positional clustering, progressive approximation of relative residuals, and positional averages of the data in a sampled population. Academic results datasets are data originating from inside academic institutions’ results repositories. It is a technique designed specifically for predicting missing academic results. Since the whole essence of data mining is to elicit useful information and gain knowledge-driven insights into datasets, ARDP positions data explorer at this advantageous perspective. ARDP is committed to solve missing academic results dataset problems more quickly over and above what currently obtains. PARD is computed by leveraging on the averages of neighbouring values. The predictor was implemented using Python, and the results show that it is admissible in a minimum of up to 85 percent accurate predictions of the sampled cases. It has been verified that ARDP shows a tendency toward greater precision in providing the best solution to the problems of predictions of missing academic results datasets in universities.

Purpose: Metal artifacts cause to increase in the uncorrected dose evaluation during radiotherapy planning. This study aimed to evaluate the probable difference of the dose parameters calculated by the Treatment Planning System (TPS) in radiotherapy of head and neck cancer before and after metal artifact correction in Neusoft-Philips Corporation Computed Tomography (CT) images. Materials and Methods: In the present study, the radiotherapy planning of the head and neck cancer from thirty patients was first performed on the CT default images with dental implants. The same processes were applied after performing a body standard metal kernel on the CT images to reduce the metal artifacts. The Gross Tumor Volume (GTV) and Organ At Risks (OARs) were contoured on the CT slices. The dosimetric parameters (mean, minimum, and maximum) for the GTV and OARs (eyes and spinal cord) were obtained for both sets of CT images (defaults and filtered). Wilcoxon signed-rank test was used to calculate the probability dose variations between the two sets. Results: There are significant differences in several dose parameters between the default and filtered CT images (P-value < 0.05). These dosimetric parameters are related to the GTV (mean dose), spinal cord (minimum and mean doses), right eye (maximum dose), as well as left eye (mean dose). The average range of dose differences between the default and filtered images was obtained; 1.12%-3.11% for the GTV as well as 0.22%-12.05% for the OARs. Conclusion: Based on the results, the body standard metal kernel can cause a significant difference in several dosimetric parameters of GTV and OARs during the radiotherapy of head and neck cancer. Therefore, it is recommended to make a metal artifact correction on CT images for accurate dose calculation before designing a treatment plan.

Apart from the rapid ionospheric response to geomagnetic forcing originating from the Sun during extreme space weather events, forcing from the lower atmosphere below still exerts a significant influence on the ionosphere during quiet-time conditions. This study examines the ionospheric response of the equatorial ionization anomaly (EIA) in the American sector to the combined influence of the cascades of sudden stratospheric warming (SSW) events and the geomagnetic storms that coexisted with them during the period of January–March 2016. We adopted a multi-instrument and multi-modeling approach with the study locations spanning ±40° geomagnetic latitudes. Our results showed a hemispheric asymmetry in the total electron content and change in total electron content (ΔTEC) distribution with higher enhancement clearly visible in the Northern Hemisphere in comparison to the Southern Hemisphere (NH). Semidiurnal signatures were observed in both ΔTEC and equatorial electrojet parameters for some days. The double-peak zonal mean zonal wind amplitude days supported the formation of the reverse fountain effects. The different SSW peak temperature days also showed either positive or negative ionospheric response. Generally, orientation of the prompt penetration electric field (PPEF) and their strengths at either daytime or nighttime played a weak role in the ionosphere response during some of the geomagnetic storms. The negative and positive ionospheric responses under geomagnetic storm conditions were ascribed to changes in the composition of the thermosphere, prompt penetration electric field (PPEF), and traveling atmospheric disturbances (TADs).

Abstract Background Our purpose was to evaluate the application of volumetric histogram parameters obtained from conventional MRI and apparent diffusion coefficient (ADC) images for grading the meningioma tumors. Results Tumor volumetric histograms of preoperative MRI images from 45 patients with the diagnosis of meningioma at different grades were analyzed to find the histogram parameters. Kruskal-Wallis statistical test was used for comparison between the parameters obtained from different grades. Multi-parametric regression analysis was used to find the model and parameters with high predictive value for the classification of meningioma. Mode; standard deviation on post-contrast T1WI, T2-FLAIR, and ADC images; kurtosis on post-contrast T1WI and T2-FLAIR images; mean and several percentile values on ADC; and post-contrast T1WI images showed significant differences among different tumor grades (P < 0.05). The multi-parametric linear regression showed that the ADC histogram parameters model had a higher predictive value, with cutoff values of 0.212 (sensitivity = 79.6%, specificity = 84.3%) and 0.180 (sensitivity = 70.9%, specificity = 80.8%) for differentiating the grade I from II, and grade II from III, respectively. Conclusions The multi-parametric model of volumetric histogram parameters in some of the conventional MRI series (i.e., post-contrast T1WI and T2-FLAIR images) along with the ADC images are appropriate for predicting the meningioma tumors’ grade.

Objective: To explore the effect of metformin on therapeutic outcomes in cancer patients undergoing radiotherapy. Methods: A literature search of the electronic databases was conducted to retrieve articles that investigated the effect of metformin on radiotherapy. Timeframe of electronic search ranged from the inception of each database to May 2020. These databases include PubMed, Embase and Scopus. Results: From an initial search of 142 articles, and after the removal of duplicates as well as applying the inclusion and the exclusion criteria, 20 articles were finally included in this systematic review. Results from the reviewed clinical studies showed promising therapeutic effect of the combination of metformin with radiotherapy. Metformin has promising therapeutic effects as an adjuvant with radiotherapy. Conclusions: The use of metformin as an adjuvant with radiotherapy should be treated with caution, as further studies investigating its optimal dose as well as molecular mechanisms are still needed.

Objective: The objective of this study was to evaluate the thyroid glands' radiation dose and the risk of thyroid cancer induction from head or neck computed tomography (CT) examinations. Methods: In a prospective study, we evaluated all participants of all ages and sex referred for Head or Neck CT Scan at the University College Hospital, Ibadan and Me Cure Healthcare Limited, Ibadan, Oyo State, Nigeria. Thyroid radiation dose was estimated with impact scan calculator, and real-time dose measurement with thermoluminescent badge dosimeters (TLDs). Data were analysed and P < 0.05 was considered statistically significant. Results: One hundred and sixty-three participants (128 adults and 35 children) participated in the study. In most participants (74%), the tube voltage was 120 kVp. The estimated median thyroid gland dose by the imPACT scan calculator was 4.95 mGy (range = 1.20–30.0 mGy) and 4.40 mGy (range = 3.0–5.10 mGy), while the real-time dose measured by the TLD was 4.79 mGy (range = 1.73–96.7 mGy) and 2.33 mGy (range = 1.20–3.73 mGy) at Centre A and B, respectively. The estimated median thyroid cancer risk was 2.88 × 10−6 (maximum range of 52 × 10−6) at centre A and a median value of 3.20 × 10−6 with a cancer risk estimate that may reach 17.9 × 10−6 recorded at centre B, compared to a cumulative thyroid cancer risk of 0.12 × 10−5 among the general Nigerian population. Conclusions: Scanner specifications and technique may significantly contribute to variations seen in thyroid radiation doses. There may be a need to optimise centre protocols and apply dose reference levels for head and neck CT examinations to reduce thyroid cancer risk in Nigeria.

Abstract Aim Despite remarkable theoretical evidence of positive outcomes of patient and family‐centred care, it is rarely performed in the intensive care setting. The aim of this review was to assess the barriers to patient and family‐centred care among healthcare providers, patients and family members in adult intensive care units. Design A systematic review of both qualitative and quantitative studies. Methods The search strategy sought for published peer‐reviewed research papers limited to English language from conception to 2018. The review protocol was registered in the CRD Prospero database (CRD42018086838). Literature search was carried out in four databases: EMBASE, Cochrane Library, PubMed and Scopus where keywords “barriers,” “patient and family centered care,” “patient‐centered care” and “intensive care unit” appeared in any part of the reference. Hand search of reference lists of identified papers was also done to capture all pertinent materials. Each study was assessed by three independent reviewers against the inclusion criteria. Evidence was graded according to sampling quality, quantity and measurement of intended outcomes. Screening of studies and citations resulted in seven studies that were included in the analysis. Results Barriers to patient and family‐centred care broadly fall under four categories; lack of understanding of what is needed to achieve patient and family‐centred care, organizational barriers, individual barriers and interdisciplinary barriers.

Using micro-level data from three constituencies in Ghana, which are cases of high, average and low turnout respectively, I assess whether voters’ perceptions of the cost of voting (resource and time) can explain such variation in voter turnout. Results suggest that in Ghana, such individual perceptions of the cost associated with voting do not help in explaining variance in voter turnout at the constituency level: Across the different levels of turnout, there is little to no variance in voters’ perceptions. I find that the high positive perceptions of the electoral processes across high, average, and low turnout constituencies are not only due to the activities of the electoral management body but among others, the adjustments by citizens to the process based on their experiences from past elections.

The recent evolution of the novel COVID-19 pandemic posed unprecedented challenges for the global community and researchers alike without reference literature or prior prediction approaches applied to that context, leading to its great impact on the economies of different countries. This work aims to address these challenges by predicting the spread of COVID-19 in Nigeria. In order to carry out this research, raw data on COVID-19 pandemic in Nigeria was gotten from John Hopkins University repository through Github as submitted by the Nigeria Center for Disease Control (NCDC) through the World Health Organization (WHO). Python, along with its packages such as Numpy, SciPy, Pandas, Matplotlib, and Plotly, was used to process and visualize the data for this model. Google Colab; a Jupyter notebook environment that run on the cloud was employed too. The SIR (Susceptible, Infected, Recovered or Removed) population model, which is commonly employed for modelling epidemics, was used to predict the spread of the pandemic in Nigeria. The machine learning Python packages used in the analysis included Pandas for data analysis and manipulation, Numpy for executing mathematical and logical operations, and SciPy for operations such as regression, minimization, and Fourier series. The model developed in this study predicted a peak of the pandemic between June and August, 2020, and a flattening of the curve in January 2021; the predicted dates were accurate when compared to real-world data. The outcome of the study provides better insight (for instance; rate of spread in response to certain conditions and corresponding mortality or survival rate) on the future of the COVID-19 pandemic; yielding more information that will help in planning for the future through evidenced correctness and reliability. The work present the recovery coefficient rate (β), the transmission coefficient (γ) and, the basic reproduction ratio (R0) values for the data under consideration that indicates a reliable model for predictions. [ABSTRACT FROM AUTHOR]

Background: The benefits of adherence to optimal infant and young child feeding (IYCF) to both the mothers and their infants below two years are well documented. However, compliance to optimal IYCF practices has been noted to vary in different settings. This study sought to establish factors influencing mothers’ adherence to optimal infant and young child feeding practices for babies below two years in Mpigi town council- Mpigi District. Methods: The study was a cross-sectional study carried out among 264 mothers of babies between six months to two years of age attending postnatal care units of health facilities in Mpigi town council, Uganda: Mpigi Health Center (HC) IV and Kyaali HC III. Purposive sampling method was used to select the health centers while simple random sampling was then used to select the sample from the selected centers. A self-administered questionnaire was used to collect data. Data entry and analysis was performed using SPSS version 16. Results: 264 participants were invited to participate in the study, 100% of these fully completed the survey. The majority of the mothers were aged 20 to 34 years (80.3%). After scoring each participant using the four characteristics which included: initiation of breastfeeding within the first hour following birth, exclusive breastfeeding up to 6 months followed by continued breastfeeding with appropriate complementary foods upto 2 years and beyond, the majority of the participants were adherent (79.6%) to IYCF practices while 20.4% were non adherent to IYCF practices. Conclusion: A good estimate of adherence to optimal IYCF practices was revealed in this study. Sustaining well-established policies to support IYCF programmes is recommended to maintain optimal IYCF practices.

Computational complexity is a characteristic of almost all Lesk-based algorithms for word sense disambiguation (WSD). In this paper, we address this issue by developing a simple and optimized variant of the algorithm using topic composition in documents based on the theory underlying topic models. The knowledge resource adopted is the English WordNet enriched with linguistic knowledge from Wikipedia and Semcor corpus. Besides the algorithm’s eficiency, we also evaluate its efectiveness using two datasets; a general domain dataset and domain-specific dataset. The algorithm achieves a superior performance on the general domain dataset and superior performance for knowledge-based techniques on the domain-specific dataset.