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A huge amount of diverse data is generated in the Internet of Things (IoT) because of heterogeneous devices like sensors, actuators, gateways and many more. Due to assorted nature of devices, interoperability remains a major challenge for IoT system developers. The purpose of this study is to use mapping techniques for converting relational database (RDB) to resource directory framework (RDF) for the development of ontology. Ontology helps in achieving semantic interoperability in application areas of IoT which results in shared/common understanding of the heterogeneous data generated by the diverse devices used in health-care domain.

To overcome the issue of semantic interoperability in healthcare domain, the authors developed an ontology for patients having cardio vascular diseases. Patients located at any place around the world can be diagnosed by Heart Experts located at another place by using this approach. This mechanism deals with the mapping of heterogeneous data into the RDF format in an integrated and interoperable manner. This approach is used to integrate the diverse data of heart patients needed for diagnosis with respect to cardio vascular diseases. This approach is also applicable in other fields where IoT is mostly used.

Experimental results showed that the RDF works better than the relational database for semantic interoperability in the IoT. This concept-based approach is better than key-based approach and reduces the computation time and storage of the data.

The proposed approach helps in overcoming the demerits of relational database like standardization, expressivity, provenance and supports SPARQL. Therefore, it helps to overcome the heterogeneity, thereby enabling the semantic interoperability in IoT.

The world is shifting towards the fourth industrial revolution (Industry 4.0), symbolising the move to digital, fully automated habitats and cyber-physical systems. Industry 4.0 consists of innovative ideas and techniques in almost all sectors, including Smart health care, which recommends technologies and mechanisms for early prediction of life-threatening diseases. Cardiovascular disease (CVD), which includes stroke, is one of the world’s leading causes of sickness and deaths. As per the American Heart Association, CVDs are a leading cause of death globally, and it is believed that COVID-19 also influenced the health of cardiovascular and the number of patients increases as a result. Early detection of such diseases is one of the solutions for a lower mortality rate. In this work, early prediction models for CVDs are developed with the help of machine learning (ML), a form of artificial intelligence that allows computers to learn and improve on their own without requiring to be explicitly programmed.

The proposed CVD prediction models are implemented with the help of ML techniques, namely, decision tree, random forest, k-nearest neighbours, support vector machine, logistic regression, AdaBoost and gradient boosting. To mitigate the effect of over-fitting and under-fitting problems, hyperparameter optimisation techniques are used to develop efficient disease prediction models. Furthermore, the ensemble technique using soft voting is also used to gain more insight into the data set and accurate prediction models.

The models were developed to help the health-care providers with the early diagnosis and prediction of heart disease patients, reducing the risk of developing severe diseases. The created heart disease risk evaluation model is built on the Jupyter Notebook Web application, and its performance is calculated using unbiased indicators such as true positive rate, true negative rate, accuracy, precision, misclassification rate, area under the ROC curve and cross-validation approach. The results revealed that the ensemble heart disease model outperforms the other proposed and implemented models.

The proposed and developed CVD prediction models aims at predicting CVDs at an early stage, thereby taking prevention and precautionary measures at a very early stage of the disease to abate the predictive maintenance as recommended in Industry 4.0. Prediction models are developed on algorithms’ default values, hyperparameter optimisations and ensemble techniques.

This paper aims to apply the doctrine of siyasah shariyyah to a policy proposal in the area of monetary economics, namely, the Grondona system of conditional currency convertibility, which has been proposed as a practical means of resisting the economic instability caused by the present-day fiat money system.

The paper uses library research to review the literature relevant to the Grondona system, and examines the extent to which its operations conform to the principle of siyasah shariyyah, thereby encouraging Maslahah, i.e. the public interest.

It has been found that the Grondona system conforms to the philosophy of siyasah shariyyah because it promotes public welfare in a number of ways. First, it is based on the fundamental principle of Prophet Yusuf’s/Joseph (peace be upon him) economic planning, which is accumulating reserves of primary commodities during times of plenty and releasing those reserves of commodities during periods of scarcity. Second, it provides the necessary linkage between the monetary world and the real economy. Third, it could be implemented in parallel with the existing monetary system by using the national currency. Fourth, it would help the least developed countries of the world, which mainly depend on exports of primary commodities (mostly agricultural).

Because of the chosen research approach, this research study is theoretical in nature. Therefore, researchers are encouraged to evaluate the system from economic perspective based on simulation for the purpose of possible implementation.

The paper includes important implications for the policymakers in the Organization of Islamic Cooperation countries for the possible implementation of Grondona system.

This paper fulfils an identified need to apply the philosophy of siyasah shariyyah to the area of monetary economics.

In the developing field of nano-materials synthesis, copper oxide nanoparticles (NPs) are deemed to be one of the most significant transition metal oxides because of their intriguing characteristics. Its synthesis employing green chemistry principles has become a key source for next-generation antibiotics attributed to its features such as environmental friendliness, ease of use and affordability. Because they are more environmentally benign, plants have been employed to create metallic NPs. These plant extracts serve as capping, stabilising or hydrolytic agents and enable a regulated synthesis as well.

Organic chemical solvents are harmful and entail intense conditions during nanoparticle synthesis. The copper oxide NPs (CuO-NPs) synthesised by employing the green chemistry principle showed potential antitumor properties. Green synthesised CuO-NPs are regarded to be a strong contender for applications in the pharmacological, biomedical and environmental fields.

The aim of this study is to evaluate the anticancer potential of CuO-NPs plant extracts to isolate and characterise the active anticancer principles as well as to yield more effective, affordable, and safer cancer therapies.

This review article highlights the copper oxide nanoparticle's biomedical applications such as anticancer, antimicrobial, dental and drug delivery properties, future research perspectives and direction are also discussed.

Serious concerns about the stability of the international financial systems have arisen recently, resulting from the mounting inflation rates and the accompanying procedures to control them. Consequently, this study aims at examining empirically the impact of inflationary pressures/shocks on the stability of banking sectors.

The study adopts a dynamic GMM models and exploits a sample of 188 banks operating in 14 MENA economies, over the period 1999–2021.

This research finds that high inflation does indeed harm bank financial stability and deteriorates banks credit risk. Furthermore, the examination of the impact of interaction terms between inflation and bank-specific and institutional quality variables shows that better capitalisation levels, higher liquidity buffers, larger asset size, greater market power, foreign ownership and overall political stability, all can counterbalance the impact of inflationary pressures on MENA banks financial stability.

In addition to empirically revealing how inflationary shocks can deteriorate financial stability, the main novelty of this research is examining how the interactions between inflation on one hand, and bank-specific and institutional quality on the other, affect bank stability.

This paper aims to investigate the heat transfer in porous channels.

Finite element method is used to simulate the heat transfer in porous channels.

The number and width of channels play a key role in determining the heat transfer of the porous channel. The heat transfer is higher around the channel legs. Smaller base height is better to get higher heat transfer capability.

This study represents the original work to investigate heat transfer in a porous domain having multiple channels.

This study aims to investigate the role of e-service quality and e-trust for achieving e-loyalty among digital library users in the digital economy. The current study examined the mediation effect of e-trust in the connection between e-service quality and e-loyalty.

Cross-sectional design was applied for the purpose of data collection and empirical findings of the study. Survey method was used for the purpose of data collection from 783 online digital libraries users.

Results reveal that e-service quality positively predicts e-trust in digital economy. Moreover, in digital economy e-trust predicts the e-loyalty. The findings also reveal that e-trust mediates the relationship between e-service quality and e-loyalty links.

The finding of study suggested that individual level e-trust have a strong effect on e-loyalty in digital economy. Individual level aspects in term of e-service quality have a direct effect on e-trust to improve their e-loyalty. The finding indicated that digital libraries users in future will be more loyal toward e-service quality providers. The results are useful for the management of digital libraries and academia for future. This is the first study that includes e-service quality, e-trust and e-loyalty in the context of digital economy.

Blockchain technology is one of the major contributors to supply chain sustainability because of its inherent features. However, its adoption rate is relatively low due to reasons such as the diverse barriers impeding blockchain adoption. The purpose of this study is to identify blockchain adoption barriers in sustainable supply chain and uncovers their interrelationships.

A three-phase framework that combines machine learning (ML) classifiers, BORUTA feature selection algorithm, and Grey-DEMATEL method. From the literature review, 26 potential barriers were identified and evaluated through the performance of ML models with accuracy and f-score.

The findings reveal that feature selection algorithm detected 15 prominent barriers, and random forest (RF) classifier performed with the highest accuracy and f-score. Moreover, the performance of the RF increased by 2.38% accuracy and 2.19% f-score after removing irrelevant barriers, confirming the validity of feature selection algorithm. An RF classifier ranked the prominent barriers and according to ranking, financial constraints, immaturity, security, knowledge and expertise, and cultural differences resided at the top of the list. Furthermore, a Grey-DEMATEL method is employed to expose interrelationships between prominent barriers and to provide an overview of the cause-and-effect group.

The outcome of this study can help industry practitioners develop new strategies and plans for blockchain adoption in sustainable supply chains.

The research on the adoption of blockchain technology in sustainable supply chains is still evolving. This study contributes to the ongoing debate by exploring how practitioners and decision-makers adopt blockchain technology, developing strategies and plans in the process.

The purpose of this study was to analyze the heat transfer in a square porous cavity that has a solid block placed at its center. The prime focus of this study is to investigate the effect of size of the square solid block and other physical parameters on the heat transfer rate from the hot surface into the porous medium. The left vertical surface of cavity is maintained at a hot temperature and the right vertical surface at a cool temperature, T_c. The finite element method is used to simplify the governing equations and is solved iteratively. It is noted that the size of the solid block plays a vital role in dictating the heat transfer from the hot surface to porous medium.

The current work is based on finite element formulation of a square porous cavity that has a solid square block placed at its center. Governing equations were solved iteratively.

The size of the solid block has a pronounced effect on the heat transfer behavior inside the porous cavity.

This study highlights the heat transfer due to a conducting square solid block at mid of porous cavity.