SOCIALSCI JOURNAL

Communities of Digital Libraries

Received: 05-Apr-2022, Manuscript No. to social-22-63196; Editor assigned: 07-Apr-2022, Pre QC No. to social-22-63196(PQ); Reviewed: 22-Apr-2022, QC No. to social-22-63196(R); Revised: 26-Apr-2022, Manuscript No. to social-22-63196; Published: 03-May-2022

Description

The results of scientific experiments and research work carried out by individuals or organizations are published in various types of scientific publications, including books, chapters, journals, articles, reference books, reference book entries, and shared with the scientific community. One aspect of these documents is content and the other is metadata. You can use scientific document metadata to improve mutual collaboration, find people with common interests and research papers, and find scientific documents in the right areas.

Infectious diseases pose a public health risk and require effective disease prevention strategies. Digital health monitoring technology offers new opportunities to improve disease prevention, detection, tracking, reporting and analysis. However, in addition to concerns about the effectiveness of these technologies in achieving public health goals, there are also concerns about the ethics, legality, safety, and sustainability of digital surveillance technologies.

Public libraries need to address the needs of the communities that serve them in order to remain relevant, but with the rapid evolution of information technology, it is particularly difficult to assess those needs. This study explores needs assessments to understand user communities, library services, and potential sources of information to determine user needs in terms of space and services. This study used a mixed approach approach that included semi-structured interviews, survey ratings, and big data analysis of social network sites (SNS).

Using post-operative computer tomography data and a library of digital dental implant components, we created a custom healing stent that fits into a connected implant abutment without the need for a clear abutment impression. After segmenting the dental implant and importing it into a computer-assisted design software program, we aligned the appropriate digital implant components to the dental implant. Healing stents were then virtually designed, quickly prototyped, and converted into biocompatible, sterile materials using standard experimental methods. The method presented provides the physician with the opportunity to place a healing stent in the second stage of transplantation without the need to manufacture a stent or make an impression during the procedure.

Despite this possibility, the integration of digital technology tools into mental health systems was relatively inadequate. It identifies barriers and realization factors for integrating digital technology into mental health systems and integrates qualitative evidence aimed at classifying them into context domains at the individual, organizational, and system levels.

Conclusion

During the COVID 19 pandemic, social care organizations had to make a sudden switch to e-health solutions to continue to support vulnerable groups. Qualitative data from three cases show that the adoption of these digital technologies evolved differently more than a year after the outbreak of the pandemic. Current research aims to shed light on the processes that lead to such differences. The first organizations, in particular, resisted the large-scale deployment of digital technology. Second, they faced disagreements about the role of digital technology in care. And the third organization struggled, but managed the wider and more successful adoption of digital technology.

The use of digital twins in smart factories, which model the actual state of the manufacturing system through simulation with real-time updates, manifests itself in increased productivity, reduced costs and energy consumption. The rapid increase in customer demands has allowed factories to adapt quickly and shorten product lifecycles. Traditional modeling and simulation approaches are not suitable for such scenarios. As a possible solution, we propose a general data-driven framework for automatic generation of simulation models as the foundation of smart factories’ digital twins. The novelty of the proposed framework lies in the data-driven approach, leveraging advances in machine learning and process mining techniques, as well as continuous model improvement and validation.

Acknowledgement

None

Conflict of Interests

The author has nothing to disclose and also state no conflict of interest in the submission of this manuscript.