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Coronary artery disease (CAD) is a pathological condition that is often fatal and is the main cause of death throughout the world. Early detection of this disease is very important to avoid severe complications such as heart attacks and sudden death. This study employs artificial intelligence, specifically deep learning via Convolutional Neural Networks (CNNs), to enhance CAD detection. While CNN architectures like ResNet50V2 and MobileNetV2 exhibit satisfactory performance individually, they possess distinct strengths and weaknesses. ResNet50V2 requires significant computing resources, hindering its scalability, while MobileNetV2 struggles with extracting complex features from medical images. Therefore, this research aims to combine the EfficientNetV2B0, ResNet50V2, and MobileNetV2 using transfer learning techniques to enhance CAD detection. The methodology involves leveraging pre-trained models and fine-tuning them on a coronary artery disease dataset. Modified models, particularly EfficientNetV2B0 and MobileNetV2, achieve high accuracies of 94% and 86%, respectively, while ResNet50V2 yields 72%. However, combining the models boosts accuracy to 95%, addressing individual model limitations. The concatenated model demonstrates superior predictive capabilities, with more accurate predictions and fewer errors than individual models. [ABSTRACT FROM AUTHOR]

Advances in information technology (IT) and operation technology (OT) accelerate the development of manufacturing systems (MS) consisting of integrated circuits (ICs), modules, and systems, toward Industry 4.0. However, the existing MS does not support comprehensive identity forensics for the whole system, limiting its ability to adapt to equipment authentication difficulties. Furthermore, the development of trust imposed during their crosswise collaborations with suppliers and other manufacturers in the supply chain is poorly maintained. In this paper, a trust chain framework with a comprehensive identification mechanism is implemented for the designed MS system, which is based and created on the private blockchain in conjunction with decentralized database systems to boost the flexibility, traceability, and identification of the IC-module-system. Practical implementations are developed using a functional prototype. First, the decentralized application (DApp) and the smart contracts are proposed for constructing the new trust chain under the proposed comprehensive identification mechanism by using blockchain technology. In addition, the blockchain addresses of IC, module, and system are automatically registered to InterPlanetary File System (IPFS), individually. In addition, their corresponding hierarchical CID (content identifier) values are organized by using Merkle DAG (Directed Acyclic Graph), which is employed via the hierarchical content identifier mechanism (HCIDM) proposed in this paper. Based on insights obtained from this analysis, the trust chain based on HCIDM can be applied to any MS system, for example, this trust chain could be used to prevent the counterfeit modules and ICs employed in the monitoring system of a semiconductor factory environment. The evaluation results show that the proposed scheme could work in practice under the much lower costs, compared to the public blockchain, with a total cost of 0.002094 Ether. Finally, this research is developed an innovation trust chain mechanism that could be provided the system-level security for any MS toward Industrial 4.0 in order to meet the requirements of both manufacturing innovation and product innovation in Sustainable Development Goals (SDGs). [ABSTRACT FROM AUTHOR]

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Dentists usually observe the tertiary dentin formation after pulp capping treatment by comparing periapical radiograph before and after treatment visually. However many dentists find difficulties to observe tertiary dentin and also they can't measure exactly the thickness of the tertiary dentin. The aims of this study is to assist the dentists to measure the area of tertiary dentin and calculate the dentin formation using b-spline image processing. The dental radiograph of 38 patients of pulp capping in the Dental Hospital Universitas Muhammadiyah Yogyakarta, Indonesia. Each of patient visited dental hospital 3 times. First, the patient got an application of pulp capping material. Second, after one-week treatment and temporary restoration and the third, after more than one month with the composite as the final restoration. Every visited the patient take a radiograph. Dentist placed the dot from the patient's radiograph. The dots were combined and processed with digital image processing. The b-spline method changed the dot to one area. After the calculation, the dentist can see whether there was dentin formation which means it is one of the treatment success indicators. Dentist has the better view to measure the dentin formation by providing area value of its tertiary dentin thickness calculation. We compare the result to the program calculation using the b-spline method and visual observation from the dentist. This study indicated the thickness of tertiary dentin can be measured by this program with an accuracy of 94.2%. Therefore, dentist can make tertiary dentin thickness prediction from patient's radiograph. [ABSTRACT FROM AUTHOR]