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.

Eating at home remains very much ingrained in Ghanaian culture but rapid urbanization coupled with busy lifestyle and advancement in technology has greatly changed the way of life of many Ghanaians. These changes have altered the tradition of cooking and eating at home. The study focused on the determinants of consumer patronage of street foods in the Koforidua Metropolis. The target population comprised customers that patronize the street foods in Koforidua Metropolis. From the target population, 197 consumers were selected using convenience. A structured self-administered questionnaire was utilized to gather the required data. The data collected were coded and analyzed with the help of SPSS-23. The findings revealed that food characteristics and social status determines consumers patronage of street food. It became evident that age (r=0.261, p<0.01), age (r=-0.318, P<0.01), educational level (r=0.144, P<0.05) and occupation (r=-0.477, P<0.01) of consumers has a significant influence on the decision and patronage of street food. The study concluded that food characteristics and social factors are major determinants of consumers patronage of street foods. It is recommended that Food and Drug Authority (FDA), other stakeholders, and street food vendors work cooperatively to establish laws that capture the distinctive and diverse foods sold on the street and their various preparation, storage, and sale methods in order to ensure that food preparation and sales are safe and hygienic.

The pharmaceutical industry is undergoing a profound transformation driven by advancements in Information Technology (IT), with Enterprise Resource Planning (ERP) systems playing a pivotal role in reshaping operations. These systems offer integrated solutions that streamline key business processes, such as production, inventory management, supply chain optimization, regulatory compliance, and data integration, contributing significantly to operational efficiency and organizational agility. This paper explores the evolution and impact of ERP systems within the pharmaceutical sector, highlighting their contributions to overcoming the industry’s inherent challenges, including complex regulatory requirements, the need for accurate and real-time data, and the demand for supply chain resilience. The integration of cloud-based ERP solutions, the incorporation of emerging technologies like Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT), and enhanced data analytics capabilities have revolutionized pharmaceutical IT. These advancements not only reduce operational costs, improve forecasting accuracy, and enhance collaboration but also ensure compliance with stringent global regulations, such as Good Manufacturing Practices (GMP) and FDA guidelines. Moreover, ERP systems have been instrumental in managing the pharmaceutical supply chain, ensuring product traceability, and improving inventory control and order fulfillment processes. This manuscript examines how ERP systems enable pharmaceutical companies to maintain high standards of product quality, improve decision-making, and ensure the safety and efficacy of drugs through robust tracking and auditing mechanisms. A case study of a pharmaceutical company that implemented an ERP system demonstrates the tangible benefits, including increased operational efficiency, improved compliance rates, and enhanced customer satisfaction. However, despite the clear advantages, challenges such as customization complexities, data integration issues, and resistance to change remain. As the pharmaceutical industry continues to evolve, ERP systems will remain a cornerstone of digital transformation, facilitating smarter decision-making, better resource management, and enhanced collaboration across global operations. This paper also identifies future trends, including the potential of AI and blockchain technologies in further strengthening ERP systems and transforming the pharmaceutical landscape.

Sentiment analysis, a vital aspect of natural language processing, involves the application of machine learning models to discern the emotional tone conveyed in textual data. The use case for this type of problem is where businesses can make informed decisions based on customer feedback, identify the sentiments of their employees, and make decisions on hiring or retention, or for that matter, classify a text based on its topic like whether it is about a particular subject like physics or chemistry as is useful in search engines. The model leverages a sequential architecture, transforms words into dense vectors using an Embedding layer, and captures intricate sequential patterns with two Long Short-Term Memory (LSTM) layers. This model aims to effectively classify sentiments in text data using a 50-dimensional embedding dimension and 20 % dropout layers. The use of rectified linear unit (ReLU) activations enhances non-linearity, while the SoftMax activation in the output layer aligns with the multi-class nature of sentiment analysis. Both training and test accuracy were well over 80%.