This study aimed to assess the knowledge level of street food vendors on hygiene in the Tamale metropolis in the Northern Region of Ghana. The study employed the health belief model as the theoretical basis. Quantitatively, the study employed a descriptive cross-sectional study design to examine the microbial load of street-cut fruits and assess the knowledge and practice of vendors of cut fruits on personal and food hygiene in the study setting. The population consists of cut and vented pawpaw, watermelon, and street fruit vendors registered with the health directorate in the Tamale Metropolis. A convenient sampling technique was used to select 113 respondents for the study. The Yamane formula was used to determine the sample size to select one hundred and thirteen participants (113) out of one hundred and fifty-eight street fruit vendors in the Tamale Metropolis. The main instrument for data collection was a questionnaire. A questionnaire had close-ended questions which were developed using a 'Yes' and 'No' response, and a four-point Likert-type scale ranging from 1=Strongly Disagree (SD), 2=Disagree (D), 3=Agree (A) and 4= Strongly Agree (SA). The data were analysed using descriptive statistics (frequency, percentages, means and standard deviation). The findings revealed that the overall knowledge level of respondents is low. The findings also indicate that vendors do not control the rate at which their customers touch their vended fruits. It is recommended that Street fruit vendors and handlers be educated on fruit hygiene practices through engagement by the Health Directorate Unit of Tamale Metropolis and the Ministry of Health. To keep consumers safe, the Tamale Metropolitan Assembly must strictly enforce compliance with regulations on operation permits and health clearance certificates. Metropolitan sanitation officers must regularly monitor fruit vendors to ensure compliance with goods.

With the rapid growth of online communication platforms, the identification and management of toxic comments have become crucial in maintaining a healthy online environment. Various machine learning approaches have been employed to tackle this problem, ranging from traditional models to more recent attention-based transformer networks. This paper aims to compare the performance of attention-based transformer networks with several traditional machine learning methods for toxic comments classification. We present an in-depth analysis and evaluation of these methods using a common benchmark dataset. The experimental results demonstrate the strengths and limitations of each approach, shedding light on the suitability and efficacy of attention-based transformers in this domain.

Poor irrigation water quality due to oil spillage on surface water can result in food insecurity, health and economic challenges. This paper investigated the effect of total petroleum hydrocarbon (TPHC) and lead (Pb) on irrigation water quality in the oil spill prone area of the Niger Delta region of Nigeria. Water samples were taken from five different sections labelled A, B, C, D, and E along the Choba River, in Rivers State, Nigeria. Sections B, C, D and E were direct industrial effluent discharge points while section A was without direct industrial effluent discharge. Standard methods were employed in the water sampling and analysis. Suitability of Choba river water for irrigation was assessed by comprehensive pollution index (CPI) that incorporated salinity, sodicity, and permeability hazard potentials as well as the specific toxicity hazard potentials of TPHC and Pb. Results showed that all primary water parameters except pH were within the Food and Agriculture Organization (FAO) guidelines. The pH was low, ranging between 4.48 and 5.6. TPHC values for four out of the five samples were greater than the 10mg/l guideline as recommended by the Directorate of Petroleum Resources for surface water. TPHC for the four samples ranged between 14.52 and 174.32mg/l. The parameters with the most impact on CPI include EC, PI and TPHC with TPHC having the most impact. Water samples from sections A, B and E with CPI values 0.14, 0.37, and 0.8 respectively were classified in the clean, sub clean and slightly polluted categories respectively, while water samples from sections C and D with CPI values greater than 1 range from moderately to heavily polluted and not suitable for agricultural irrigation. Only water sample A was found suitable for irrigation.

Background: Currently, the DRL quantity in mammography are evaluated in terms of mean glandular dose (MGD). Since the MGD cannot be measured directly, it can be obtained by calculation using the equation (D=K*g*c*s). In previous studies, the conversion factor g was calculated by Monte Carlo simulation and is not reported from actual measurements. In this study, we focused on the g-factor, which is a conversion factor to the MGD at 50% glandularity, and attempted to measure it using a nanoDot dosimeter to see if it can be used in mammography. Methods: The nanoDot dosimeters were inserted in a PMMA phantom at depths ranging from 0 cm to 6 cm in 1 cm increments, and measurements were made in three HVLs of 0.3 mmAl, 0.35 mmAl, and 0.4 mmAl HVL. The g-factor was calculated from the nanoDot dosimeter values using a conversion equation. Results and Discussion: The measured g-factors for all the HVLs were in close agreement with those of Dance et al. The values of the previous studies did not include the backscatter factor, which may have underestimated the MGD. The difference was smaller for the 0.4 mm Al. Compared to the other HVLs, the 0.4 mm Al was measured without a compression plate, which may have been influenced by the presence or absence of a compression plate. Conclusion: The nanoDot dosimeters were used to calculate g-factors. The results agreed with those of previous studies within uncertainty. This indicates that nanoDot dosimeters can be used in the mammography field.