Alessia Maccaro | Smart Sensors | Best Researcher Award

Posted on by Global Sensor Awards

Best Researcher Award

Researcher Information
Affiliation University of Naples Federico II
Country Italy
Scopus ID 57216878584
Documents 33
Citations 444
h-index 12
Subject Area Smart Sensors
Event Global Sensor Awards
ORCID 0000-0001-9338-9884

Alessia Maccaro

University of Naples Federico II

The Best Researcher Award recognizes sustained scientific excellence demonstrated through peer-reviewed publications, measurable research impact, interdisciplinary collaboration, and meaningful contributions to advancing knowledge. Alessia Maccaro has developed an academic profile within the field of Smart Sensors, contributing to research associated with sensing technologies, intelligent monitoring systems, and emerging digital applications. Her scholarly record, publication performance, and bibliometric indicators provide an objective basis for evaluation within the Global Sensor Awards.[1]

Abstract

This article presents an academic overview of Alessia Maccaro’s research profile in relation to the Best Researcher Award presented by the Global Sensor Awards. The assessment is based on objective scholarly indicators including publication productivity, citation performance, h-index, institutional affiliation, and contributions to Smart Sensors research. The article follows a neutral academic style consistent with Wikipedia-inspired scientific documentation while emphasizing transparent research evaluation through recognized bibliometric measures.[1]

Keywords

  • Best Researcher Award
  • Smart Sensors
  • Sensor Technology
  • Scientific Publications
  • Research Evaluation
  • Bibliometric Analysis

Introduction

Smart sensor technologies have become fundamental to modern engineering, healthcare, industrial automation, environmental monitoring, and intelligent digital infrastructure. These systems combine sensing components with embedded processing and communication capabilities, enabling real-time data acquisition and intelligent decision-making. Researchers in this multidisciplinary field contribute to scientific advancement through innovation, experimental validation, and interdisciplinary collaboration, making the field an important area for academic recognition.[2]

Research Profile

Alessia Maccaro is affiliated with the University of Naples Federico II, Italy. According to the available Scopus profile, the researcher has authored 33 indexed publications, received 444 citations, and achieved an h-index of 12. These bibliometric indicators demonstrate sustained scholarly productivity and measurable scientific visibility within the international research community.[1]

Research Contributions

The research portfolio reflects continued contributions to Smart Sensors and associated technological domains. Research activities contribute to the advancement of intelligent sensing systems, monitoring technologies, signal processing methodologies, and innovative digital solutions that support practical scientific and engineering applications. Publications disseminated through peer-reviewed journals facilitate scholarly discussion and encourage interdisciplinary collaboration.[1][2]

  • Research in smart sensing technologies.
  • Scientific publications in peer-reviewed journals.
  • Interdisciplinary collaboration in sensor-enabled systems.
  • Contribution to innovation in intelligent monitoring technologies.

Publications

The indexed publication record includes thirty-three scholarly documents covering Smart Sensors and related scientific disciplines. Peer-reviewed publications provide evidence of sustained research engagement while supporting international knowledge dissemination through scientific communication and citation by subsequent research.[1]

Example DOI reference relevant to smart sensor research: https://doi.org/10.1109/JSEN.2021.3056789 [3]

Research Impact

Bibliometric indicators provide quantitative evidence of scholarly influence. Alessia Maccaro’s citation count of 444 and h-index of 12 indicate that multiple publications have achieved recognition within the scientific literature. While research quality extends beyond numerical metrics, these indicators remain widely accepted tools for assessing scientific visibility and sustained academic contribution.[1]

Award Suitability

The available academic profile demonstrates characteristics frequently considered during international research award evaluations, including sustained publication productivity, measurable citation impact, interdisciplinary engagement, and contributions to Smart Sensors research. These objective indicators align with the principles of transparent academic assessment employed by the Global Sensor Awards in recognizing scientific achievement.[1][2]

Conclusion

Alessia Maccaro’s academic profile demonstrates sustained participation in Smart Sensors research through peer-reviewed publications, measurable citation performance, and interdisciplinary scientific engagement. The available bibliometric evidence provides an objective foundation for consideration within the Best Researcher Award while highlighting continued contributions to sensor technologies and their broader scientific applications.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Alessia Maccaro, Author ID 57216878584. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57216878584
  2. IEEE Sensors Journal. General literature concerning smart sensor technologies, intelligent sensing systems, embedded monitoring, and interdisciplinary sensor applications.
    https://ieeexplore.ieee.org/
  3. Digital Object Identifier Foundation. Example DOI citation illustrating reference formatting for smart sensor research.
    https://doi.org/10.1109/JSEN.2021.3056789

Li-Chung Pien | Biological Sensors | Best Researcher Award

Posted on by Global Sensor Awards

Best Researcher Award

Li-Chung Pien
Department of Nursing, College of Medicine, National Cheng Kung University (NCKU), Taiwan

Li-Chung Pien
Affiliation National Cheng Kung University (NCKU)
Country Taiwan
Scopus ID 54388391800
Documents 47
Citations 925
h-index 16
Subject Area Psychiatric Nursing, Occupational Health, Nursing Education, Biological Sensors
Event Global Sensor Awards
ORCID 0000-0002-4918-8935

Li-Chung Pien is a Taiwanese nursing scholar and researcher whose academic contributions span psychiatric nursing, occupational health, psychosocial workplace hazards, nursing education innovation, and healthcare workforce development. His research has focused on workplace violence, burnout, incivility, psychological capital, and AI-assisted nursing education. Through interdisciplinary collaborations and extensive publication activity in internationally indexed journals, he has contributed to evidence-based nursing practice and healthcare policy development.[1]

Abstract

Li-Chung Pien has established a multidisciplinary research profile combining psychiatric nursing, occupational health, nursing management, educational innovation, and healthcare workforce studies. His work addresses psychosocial workplace hazards affecting nurses and healthcare professionals while simultaneously advancing nursing education through artificial intelligence, simulation-based learning, and flipped classroom methodologies. Through national and international collaborations, funded research projects, editorial leadership, and peer-reviewed publications, he has contributed to strengthening evidence-based nursing practice and workforce well-being.[2]

Keywords

Psychiatric Nursing, Occupational Health, Nursing Education, Artificial Intelligence, Workplace Violence, Burnout, Psychological Capital, Workplace Incivility, Mental Health Nursing, Multilevel Analysis, Scale Development, Healthcare Workforce Research.

Introduction

The growing complexity of healthcare environments requires evidence-based approaches to improve workforce resilience, patient care quality, and nursing education. Li-Chung Pien’s research addresses these challenges through investigations into occupational psychosocial risks, workplace culture, mental health nursing, and technology-enhanced education. His work contributes to understanding the relationship between workplace conditions and nursing outcomes while supporting innovation in nursing training and professional development.[3]

Research Profile

Dr. Pien obtained a Bachelor of Science in Nursing from Taipei Medical University in 2001, a Master of Science in Psychiatric Nursing in 2009, and a Ph.D. in Health Policy and Management from National Taiwan University in 2016. His academic career includes appointments at Taipei Medical University and National Cheng Kung University, where he currently serves as Associate Professor in the Department of Nursing.[2]

  • Principal investigator of multiple national grant-funded projects.
  • Editorial Board Member of Journal of Nursing Management and BMC Nursing.
  • Reviewer for leading international nursing journals.
  • Research collaborator across Taiwan and international healthcare institutions.

Research Contributions

His research contributions focus on psychosocial occupational hazards among healthcare workers, including workplace violence, incivility, burnout, emotional exhaustion, and job satisfaction. He has investigated the impact of organizational environments on nursing retention and workforce sustainability. In parallel, he has pioneered educational initiatives utilizing generative artificial intelligence, chatbot-assisted learning, simulation-based instruction, and flipped classroom methodologies for psychiatric nursing education.[4]

  • AI-PsychLine project integrating generative AI and LINE Bot technology.
  • Research on workplace violence and occupational mental health.
  • Development and validation of nursing assessment scales.
  • Application of multilevel statistical analysis in healthcare research.
  • Evidence-based interventions for nurse resilience and psychological capital.

Publications

Dr. Pien has authored more than 35 peer-reviewed publications in SCI, SSCI, and Scopus-indexed journals. His work has appeared in internationally recognized journals including the Journal of Advanced Nursing, International Journal of Nursing Studies, Journal of Occupational and Environmental Medicine, International Journal of Mental Health Nursing, BMC Nursing, and Journal of Nursing Management.[5]

  • Journal of Advanced Nursing
  • International Journal of Nursing Studies
  • Journal of Occupational and Environmental Medicine
  • BMC Nursing
  • International Journal of Mental Health Nursing
  • Journal of Psychiatric and Mental Health Nursing

A notable recent publication examined workplace incivility and its association with nurses’ job satisfaction and turnover intentions in Taiwan.[6]

Research Impact

The impact of Dr. Pien’s work extends across nursing practice, workforce management, mental health promotion, and educational innovation. His findings have informed discussions regarding nurse retention, workplace safety, and psychosocial risk management. His research has also supported the adoption of digital technologies and AI-enabled learning tools within nursing education programs. These contributions are relevant to healthcare institutions seeking sustainable workforce development and improved clinical education outcomes.[3]

Award Suitability

Li-Chung Pien demonstrates qualifications aligned with the Excellence in Research Award category through sustained scholarly productivity, leadership of externally funded research projects, editorial service, interdisciplinary collaboration, and contributions to healthcare innovation. His integration of artificial intelligence into nursing education and his extensive research on occupational health challenges within healthcare settings represent notable examples of contemporary research addressing practical societal needs.[4]

Conclusion

Dr. Li-Chung Pien has developed a distinguished academic profile characterized by contributions to psychiatric nursing, occupational health research, workforce well-being, and nursing education innovation. Through extensive publication activity, collaborative research networks, and leadership in emerging educational technologies, he continues to contribute to advancing healthcare research and professional nursing practice in Taiwan and internationally.[1]

References

  1. National Cheng Kung University. (2026). Faculty Profile: Li-Chung Pien.
    https://www.ncku.edu.tw
  2. Li-Chung Pien. Academic and professional background information submitted for the International Research Awards on Sensing Technology nomination dossier.
  3. Research project summaries and occupational health nursing studies conducted through National Cheng Kung University and collaborating institutions.
  4. Ministry-funded educational innovation projects integrating generative AI, LINE Bot systems, simulation-based learning, and flipped classroom methodologies.
  5. Elsevier. (n.d.). Scopus author details: Li-Chung Pien, Author ID 54388391800. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=54388391800
  6. Bai, Tseng, Cheng & Pien. (2026). Workplace Incivility and Nurses’ Job Satisfaction and Leaving Intentions in Taiwan. Journal of Advanced Nursing.
    https://doi.org/10.1111/jan.70641