Determinants of Capital Adequacy of Nigerian Banks

A reliable banking system in developing economies like Nigeria is vital for economic progress as it facilitates the flow of funds to productive investment sectors. The capital adequacy requirement of banks is a crucial feature of the stability of the banks globally. Because of its importance, we have examined the antecedents to capital adequacy. We have used the data set of ten leading banks of Nigeria from 2007 to 2017. Our results indicate that ROA and loan to total assets are significantly associated with capital adequacy. However, we found that nonperforming loans and size are negatively associated with the capital adequacy. Our results do not support the association between macroeconomics variables and capital adequacy. Therefore, we recommend that all banking entities should reserve sufficient cash and cash equivalents as a percentage of deposits and apply aggressive risk management practices to reduce the magnitude of nonperforming loans. This study was restricted to one country. Future studies can be carried out in other countries. A comparative data set of more than one country may bring further insight into the phenomenonKeywords: Capital adequacy ratio, banks-specific determinants, macroeconomic determinants, Nigeria

Growth of multi-walled carbon nanotubes on platinum

In this paper, Multi-Walled Carbon Nanotubes were grown on a surface of a substrate that consists of a quartz piezoelectric substrate with titanium under layer and platinum electrodes. The Carbon Nanotubes (CNT) was grown using thermal CVD with Iron Nitrate as the catalyst. The growth of the carbon nanotubes was carried out at a temperature of 800°C with hydrogen as the process gas and benzene as the hydrocarbon. Characterization of the as grown CNT was done using Scanning Electron Microscope (SEM) and Raman Spectroscopy. The Raman spectroscopy was carried out on a selected area of 100micron by 100 micron and the peaks of the D-band, G-band and the second order modes were observed from the Raman spectra. Image j image processing software was also used for the extraction of the diameter of the nanotube in which the average diameter was computed to be 46nm

Implementation of Design for Safety (DfS) in Construction in Developing Countries: A Study of Designers in Malaysia

Design for Safety (DfS) is a concept that emphasises eliminating health and safety hazards to construction workers in the design phase. However, despite the importance of DfS implementation, there are limited studies on DfS in developing countries, including Malaysia. This research, therefore, investigates DfS implementation among design professionals in the Malaysian construction industry through a questionnaire survey. The response was analysed by conducting descriptive analyses and inferential statistical tests. The findings revealed a high implementation of DfS practices among designers parallel with having high awareness of DfS concept and a positive attitude towards DfS implementation. However, the engagement in DfS professional training is low, despite the fact that the designers showed a high interest in DfS professional training. While the findings revealed limited association between the implementation of DfS practices and designers’ professional body membership, designers’ professional role, and the size of designers’ organisation, the findings also showed that DfS awareness and DfS training were associated with greater implementation of DfS practices.  Furthermore, the design professionals perceive DfS education, client’s influence and DfS legislation as being the most important factors that affect DfS implementation in Malaysia. This study adds to the current DfS body of knowledge by providing deeper insights into the current state of designer awareness, education training, influencing factors, and DfS engagement, especially when DfS legislative framework is in place. Such findings could serve as a guide for other countries in the event of future developments related to DfS implementation

A Recent Approach towards Fluidic Microstrip Devices and Gas Sensors: A Review

This paper aims to review some of the available tunable devices with emphasis on the techniques employed, fabrications, merits, and demerits of each technique. In the era of fluidic microstrip communication devices, versatility and stability have become key features of microfluidic devices. These fluidic devices allow advanced fabrication techniques such as 3D printing, spraying, or injecting the conductive fluid on the flexible/rigid substrate. Fluidic techniques are used either in the form of loading components, switching, or as the radiating/conducting path of a microwave component such as liquid metals. The major benefits and drawbacks of each technology are also emphasized. In this review, there is a brief discussion of the most widely used microfluidic materials, their novel fabrication/patterning methods

A new mathematical model for mapping indoor environment

This paper presents a mathematical model as a new approach to object mapping, the system is proscribed to indoor and applied to approach a landmark. The contribution of this paper is to propose a new mathematical model for object mapping, the landmark is captured at varying distant points, the Scale invariant Feature Transform (SIFT) to extract object options, at the side of their uncertainty, from camera sensors. The (SIFT) features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection, which is suitable for our application. As image options do not seem to be noise-free, the error analysis of the landmark positions and a preprocessing to obtained information which is incorporated into a model, using curve fitting techniques. Predictions createdby our model square measure well and correlate with experimental knowledge. This has eliminated correspondence Problem also known as a data association problem

Derivation of load peak voltage, power consumption and potential energy management in a thyristor controlled Marx impulse generator for capacitor discharge application

Calculation of the load peak voltage, potential energy and power consumption of a Marx impulse generator, as a function of time, are presented. The equations are generalized and can be used to the design of any type of n-stage Marx impulse generator. The results were validated for a thyristor controlled Marx impulse generator with a maximum number of stages of 10 and 3 kV input DC voltage, which used 1 MΩ resistors and 33 nF capacitors in its topology

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