Utilization of Computer Software in Posting Transactions to Ledger Accounts in the Teaching of Financial Accounting in Tertiary Institutions in the North-East Nigeria

The study determined the extent of utilization of computer software in posting transactions to ledger accounts in teaching financial accounting in tertiary institutions in the North-East, Nigeria. The survey method was used for the study and the instrument for data collection was Computer Software Utilization Questionnaire (CSUQ). The Cronbach Alpha Coefficient was used to determine the level of reliability of the instrument, with the test yielding reliability coefficient of 0.99. The tools for analysis of data were the mean and standard deviation (S.D.) for the research question and ANOVA for testing the hypothesis. It was found that the accounting lecturers utilized computer software in posting transactions to ledger accounts, to a moderate extent. It was recommended that the proprietors of the institutions should provide appropriate facilities in the institutions and the lecturers should endeavour to improve their software utilization skills, among others. Keywords: utilization, computer software, posting transactions, ledger accounts, teaching, and financial accounting

Evaluation of Physicochemical and Antibacterial Properties of Ethanolic and Gel Extracts of Common Wireweed (Sida acuta Burm.f.)

Man has used medicinal plants as remedies for several human diseases for centuries. This paper therefore evaluates the physicochemical and antibacterial properties of ethanolic and gel extracts of common wireweed (Sida acuta Burm.f.) using appropriate standard techninques. The filtrate was concentrated to a semi-solid mass (extract) and the antibacterial activity of the extract against Staphylococcus aureus, Staphylococcus epidermidis, Pseudomonas aeruginosa, and Escherichia coli were assessed using the agar well diffusion technique. The gels have good homogeneity, and spreadability, and are easily washable. The pH ranges between 5.8 and 6.3 and is within the normal skin pH range (4.0-6.8). The extrudability ranges from 45 to 70%. The viscosity of the gels is between 6178.6 mPas and 59,343 mPas and they are shear thinning systems. The gels prepared using Sida acuta ethanolic extract have antibacterial activity that is comparable to that of the extract alone. Gells formulated using carbopol were comparable to those prepared using HPMC

Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

PRODUCTION AND PHYSICOCHEMICAL EVALUATION OF VINEGAR PRODUCED FROM PINEAPPLE AND PAWPAW FRUITS WITH THEIR PEELS

Vinegar is the product made from the conversion of ethyl alcohol to acetic acid by a genus of bacteria, Acetobacter. The major aim of the work is to produce vinegar with locally grown pineapple and pawpaw fruits with their peels. Production was carried out using the homemade scale process fermentation and analysis was carried out at various days of fermentation which includes 6th, 10th and 28th days of fermentation. The analysis carried out includes pH, titrable acidity, alcohol content, specific gravity and temperature. The final product was sensory evaluated. The result showed an increase in the physicochemical parameters analysed as the fermentation period increases with the 28thday of the vinegar production giving a pH range of from 3.9-7.8 for the four samples, temperature ranges between 27.7-30.7oC and titrable acidity ranging from 0.018-0.422%. Their alcohol content % ranges from 5.0-7.0% and their specific gravity ranging from 0.229-0.476.The parameters of the sensory analysis includes appearance, aroma, mouth feel, taste, thickness and overall acceptability and the result showed a score range of 5-7.5 for appearance, 5-8 for aroma, 5-7 for mouth feel, 5-8 for taste, 4,5-7 for thickness, 5-8 for overall acceptability on the nine-point Hedonic scale. The results of this work showed that there is a low titrable acidity formed hence giving room for more optimization methods to be carried out so as to improve the quality of the produced vinegar