Recently, it is concentrated on the advance technologies that are the source of the electronic banking in the interbank competition. Electronic banking provides an important competition advantage to the banks in terms of time, location and cost. Demand on web based banking products increased as a result of expanding customer focused service understanding, decreasing attainability cost, its competitive market structure and consumers’ wish to reach banking products fast, effective, productive way. The banking industry is one of the fastest sectors realizing the developments and changes in the field of technology innovation.
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The efficiency and usability of the proposed platform are demonstrated through a series of experiments using various metrics. A proof of concept that integrates a legacy fish farm system with the Hyperledger Fabric blockchain is implemented on top of the proposed architecture. Diverse processes of the fish farm are executed automatically by using the smart contract to reduce the risk of error or manipulation. The designed platform aims to provide fish farmers with secure storage for preserving the large amounts of agriculture data that cannot be tampered with. This paper proposes a blockchain-based fish farm platform to ensure agriculture data integrity. The blockchain can be a possible solution to the analyst’s problem of uncertain data quality from prior data manipulation since it ensures data have not been inappropriately manipulated or at the very least documents what changes have been made by specific individuals. Agriculture data are known to be messy, especially from combine yield monitors, and analysts are concerned with the validity of data, especially given that other people may have impacted data quality at various steps along the data path. In recent years, heightened interest in agriculture data has arisen since the commercialization of precision agriculture technology. Internet of Things (IoT) has opened up a new dimension for smart farming and agriculture because of the natural feature that makes it possible to assign tasks made by a user or that transfers agriculture data obtained through sensors to producers for analysis on various terminal devices. The findings encourage brand managers to seek greater word–deed alignment, to achieve the most significant social benefits. Two single-paper meta-analyses confirm the theoretical value of transparency as a signal. The advertising response variables exhibit greater sensitivity for poor reputation brands, suggesting a transparency-repairing mechanism. Perceived empowerment mediates the relationship, with downstream effects on behavioral intentions. The data indicate causal relationships between transparency signals and brand integrity, and this robust effect persists across studies, independent of the level of product complexity-to-design.
Four experiments compare transparency signaling effects across various levels of product complexity-to-design and reputation. Drawing on signaling theory, this study investigates the signaling effect of brand transparency in marketing communications on brand integrity perceptions, in the timely context of advertising for consumer-ideated new products. Persuasion literature suggests that consumers increasingly seek brand integrity, yet this advertising outcome remains underexplored, despite the clear need for managerial guidance from academic research.
The neural network potentials can be used to further investigate the behavior of each phase and their interaction. The mechanism of transformation is also observed for each phase pair. The calculated triple points are 8.67 GPa and 1058 K for Ti and 5.04 GPa and 988.35 K for Zr, close to their experimentally observed values.
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Transitions between each of the phase pairs are observed in dynamic simulation and, using calculations of the Gibbs free energy, both potentials are shown to accurately predict the experimentally observed phase transformation temperatures and pressures over the entire phase diagram. This implementation allows the potential to reproduce density functional theory results including elastic and plastic properties, phonon spectra, and relative energies of each of the three phases at classical molecular dynamics (MD) speeds. The potentials are constructed based on the rapid artificial neural network (RANN) formalism which bases its structural fingerprint on the modified embedded atom method. Here we present new neural network potentials capable of accurately modeling the transformations between the α, β, and ω phases of titanium(Ti) and zirconium (Zr), including accurate prediction of the equilibrium phase diagram.