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Apr 19, 2024

Innovative frontiers: Post-quantum perspectives in healthcare and medical imaging

The growth of computer power is crucial for the development of contemporary information technologies. Artificial intelligence is a powerful instrument for every aspect of contemporary science, the economy, and society as a whole. Further growth in computing potential opens new prospects for biomedicine and healthcare. The promising works on quantum computing make it possible to increase computing power exponentially. While conventional computing relies on the formula with 2ⁿ bits, the simplified vision of quantum computer power is 2^N, where N is a number of logical qubits. With thousandfold or more improvements in computing performance, there will be realistic options for quick protein, genes and other organic molecules 3D fold discoveries, empowering pharmaceutics and biomedical research. Personalized blockchain-based healthcare will become a reality. Medical imaging and instant healthcare data analysis will significantly speed up diagnostics and treatment control. Biomedical digital twin usage will give useful tools to any healthcare practitioner, with options for intraoperative AR and VR micro-manipulations. Nanoscale intrabody bots will be instantly customized and AI-controlled. The smart environment will be enriched with multiple sensors and actuators, giving real control of the air, water, food, and physical health factors. All these possibilities are quickly achievable only in the case of realistic quantum computing options. Even with the ability to reach this stage, there will be questions for the stability of post-quantum society: privacy, ethical issues, and quantum computing control uncertainty. General solutions to these queries will give clues for post-quantum healthcare.

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Sept 3, 2024

Big data and supply chain crisis financial risk management: A bibliometric review

The presence of a crisis has consistently been an inherent aspect of the Supply Chain, mostly as a result of the substantial number of stakeholders involved and the intricate dynamics of their relationships. The objective of this study is to assess the potential of Big Data as a tool for planning risk management in Supply Chain crises. Specifically, it focuses on using computational analysis and modeling to quantitatively analyze financial risks. The “Web of Science—Elsevier” database was employed to fulfill the aims of this work by identifying relevant papers for the investigation. The data were inputted into VOS viewer, a software application used to construct and visualize bibliometric networks for subsequent research. Data processing indicates a significant rise in the quantity of publications and citations related to the topic over the past five years. Moreover, the study encompasses a wide variety of crisis types, with the COVID-19 pandemic being the most significant. Nevertheless, the cooperation among institutions is evidently limited. This has limited the theoretical progress of the field and may have contributed to the ambiguity in understanding the research issue.

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Sept 13, 2023

Fueling big data analytics for project success: Mediating role of knowledge sharing and innovation performance

Purpose: Drawing on the Resource Based View (RBV) and Dynamic Capabilities Theory (DCT), the study seeks to investigate the impact of Big Data Analytics (BDA) on Project Success (PS) through Knowledge Sharing (KS) and Innovation Performance (IPF). Design/Methodology: Survey data were collected from 422 senior-level employees in IT companies, and the proposed relationships were assessed using the SMART-PLS 4 Structural Equation Modeling tool. Findings: The results show a positive and significant indirect effect of big data analytics on project success through knowledge sharing. IPF significantly mediated the relationship between BDA and PS in IT companies. Originality/Value: This study is one of the first to consider big data analytics as an essential antecedent of project success. With little or no research on the interrelationship of big data analytics, knowledge sharing, innovation performance, and organizational performance, the study investigates the mediating role of knowledge sharing and innovation performance on the relationship between BDA and PS. Implications: This study, grounded in RBV and DCT, investigates BDA’s influence on PS through KS and IPF. Implications encompass BDA’s strategic role, KS and IPF mediation, and practical and research-based insights. Findings guide BDA integration, collaborative cultures, and sustained success.

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Nov 7, 2024

Research on innovation of basic accounting work in Universities in the Era of Big Data

With the rapid progress of cutting-edge technology, digital life has penetrated into all aspects of the public’s daily life and workplace work. As the foundation of financial work, the basic functions of accounting have also experienced the baptism of the era of big data. Big data not only brings challenges to traditional accounting functions, but also opens up new development space and promotes the continuous optimization of basic accounting functions. Based on this, this paper mainly discusses and analyzes how the basic accounting work of colleges and universities can carry out innovation under the background of big data era.

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Dec 18, 2023

Research on village classification and development based on particle swarm optimization model: Xiecun Township, Yuanping City, in Shanxi Province, China

China’s village development confronts substantial challenges, inefficient land use due to the scattered village layout, sluggish development of village industries, and lack of proper planning for village development and construction, etc. Scientific classification of villages and village-based policy is the key to address these challenges. This study employs cell phone signaling big data to construct a population flow model based on particle swarm optimization algorithm This model takes into account spatial driving force and social connection strength as dual conditions. Also, it analyzes the settlement redistribution optimization scheme under the model through empirical research on Xiecun Township of Yuanping City, Shanxi Province, in order to classify villages scientifically and apply different strategies to village development according to the classification results. The findings of the study indicate that the area characterized by the densest distribution of optimized particle clusters, with good land conditions and high economic income, are classified as “cluster-enhanced villages”. Regions with the densest distribution of the initial particle distance factor and the densest distribution of optimized particle clusters are closer to the urban centers, which are better positioned to take advantage of the city’s financial and technological resources and continuously improve the added value of agricultural products, so they are classified as “peri-urban integrated villages”. Areas characterized by sparse distribution of optimized particle clusters need to actively cultivate the advantageous and characteristic industries in the countryside, and bolster transportation conditions and the protection of local culture, so as to realize the green development of urban and rural regional economy. Such areas are classified as “characteristic conservation villages”. In areas without optimized particle cluster distribution or areas with extremely sparse distribution of optimized particle clusters, the rational layout of urban and rural settlement spatial organization should be vigorously promoted, with emphasis on the enhancement of ecological environment, and they are classified as “relocated and annexed villages”. According to the results of village classification, corresponding strategies to promote the development of different types of villages are proposed in terms of spatial optimization, industrial upgrading and planning.

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Nov 9, 2023

Impact of big data analytics in project success: Mediating role of intellectual capital and knowledge sharing

Purpose: This study empirically investigates the effect of big data analytics (BDA) on project success (PS). Additionally, in this study, the investigation includes an examination of how intellectual capital (IC) and (KS) act as mediators in the correlation between BDA and KS. Lastly, a connection between entrepreneurial leadership (EL) and BDA is also explored. Design/Methodology- Using a sample of 422 senior-level employees from the IT sector in Peru. The partial least squares structural equation modeling technique tested the hypothesized relationships. Findings- According to the findings, the relationship between BDA and PS is mediated by structural capital (SC) and relational capital (RC), and BDA demonstrates a positive and noteworthy correlation with PS. Furthermore, EL is positively associated with BDA in a significant manner. Practical implications- The finding of this study reinforce the corporate experience of BDA and suggest how senior levels of the IT sector can promote SC, RC, and EL. Originality/Value- This study is one of the first to consider big data analytics as an important antecedent of project success. With little or no research on the interrelationship of big data analytics, intellectual capital and knowledge sharing the study contributes by investigating the mediating role of intellectual capital and knowledge sharing on the relationship between big data analytics and project success.

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