Evaluation of Production of Digital Twins Based on Blockchain Technology

A blockchain, as a form of distributed ledger technology, represents the unanimity of replication, synchronization, and sharing of data among various geographical sites. Blockchains have demonstrated impressive and effective applications throughout many aspects of the business. Blockchain technology can lead to the advent of the construction of Digital Twins (DTs). DTs involve the real representation of physical devices digitally as a virtual representation of both elements and dynamics prior to the building and deployment of actual devices. DT products can be built using blockchain-based technology in order to achieve sustainability. The technology of DT is one of the emerging novel technologies of Industry 4.0, along with artificial intelligence (AI) and the Internet of Things (IoT). Therefore, the present study adopts intelligent decision-making techniques to con-duct a biased analysis of the drivers, barriers, and risks involved in applying blockchain technologies to the sustainable production of DTs. The proposed model illustrates the use of neutrosophic theory to handle the uncertain conditions of real-life situations and the indeterminate cases evolved in decision-makers’ judgments and perspectives. In addition, the model applies the analysis of Multi-criteria Decision Making (MCDM) methods through the use of ordered weighted averaging (OWA) and the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) to achieve optimal rankings for DT production providers based on consistent weighted decision-maker’s judgments in order to maintain and to assure sustainability. An empirical study is applied to the uncertain environment to aid decision-makers in achieving ideal decisions for DT providers with respect to various DT challenges, promoting sustainability and determining the best service providers. The Monte Carlo simulation method is used to illustrate, predict, and forecast the importance of the weights of decision-makers' judgments as well as the direct impact on the sustainability of DT production

An IoT model for supporting global governmental lockdown scenarios: investigating comparative lockdown strategies and assessing generic perception of pandemic response

We propose an integrated IoT model to blend IoT technologies, neutrosophic theory and AHP to handle uncertain conditions of real-life situations and aid decision-makers with systematic and optimum decisions. In our case study, four ranked scenarios are assigned the appropriate IoT technology generated to support the government and competent authorities in the pandemic outbreak to prevent growing risks. Our study is based on the decision-makers’ judgments that need to be expanded with more experts in the various aspects of government and competent authorities. The integrated IoT model provides a balance between the restart of economic life and COVID-19 outbreaks

Saudi-KAU Coupled Global Climate Model: Description and Performance

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An IMU-based traffic and road condition monitoring system

This paper presents a new type of wireless platform designed for real-time traffic estimation and road surface monitoring. The sensor platform is built around a 32-bit ARM Cortex M4 micro-controller and a LSM9DS0 Inertial Measurement Unit module. This platform is mainly designed for probe vehicles and can be easily installed in a vehicle equipped with a USB charger. The hardware architecture design and software programming system of the proposed platform are introduced as well as its cost evaluation from the first generation to the third generation. The article then demonstrates some applications of such a platform in smart cities, including trajectory estimation and road condition monitoring. All design files have been uploaded and shared in an open science framework, and can be accessed from https://osf.io/524y9/?view_only=85859a345a7b429fbb8fe194966daa5b. It is licensed under the GNU General Public License v3.0. Keywords: Wireless sensor platform, Embedded system, Real-time traffic estimation, Road surface monitorin

Chipset Nanosensor Based on N‐Doped Carbon Nanobuds for Selective Screening of Epinephrine in Human Samples

Chipset nanosensor design and fabrication are important for healthcare research and development. Herein, a functionalized chipset nanosensor is designed to monitor neurotransmitters (i.e., epinephrine (EP)) in human fluids. An interdigitated electrode array (IDA) is functionalized by N-doped carbon nanobud (N-CNB) and N-doped carbon nanostructure (N-CNS). The surface morphology of N-CNB shows the formation of nanotubular-like branches on sheets and micrometer-size tubes. The N-CNS design consists of the formation of aggregated sheets and particles in nanometer size. The irregular shape formation provides surface heterogeneity and numerous free spaces between the stacked nanostructures. N-atoms ascertain highly active N-CNS with multifunctional active centers, electron-rich charged surface, and short distance pathway. The N-CNB/IDA exhibits the best performance for EP signaling with high sensitivity and selectivity. The N-CNB/IDA sensing performance for EP detection indicates the successful design of a highly selective and sensitive assay with low detection limit of 0.011 × 10−6 m and a broad linear range of 0.5 × 10−6 to 3 × 10−6 m. The N-CNB/IDA exhibits a high degree of accuracy and reproducibility with RSD of 2.7% and 3.9%, respectively. Therefore, the chipset nanosensor of N-CNB/IDA can be used for on-site monitoring of EP in human serum samples and further used in daily monitoring of neuronal disorders