Samra Kousar | Biological Sensors | Best Researcher Award

Best Researcher Award

Samra Kousar β€” Centre of Excellence in Molecular Biology, Pakistan

Samra Kousar
Affiliation Centre of Excellence in Molecular Biology
Country Pakistan
Scopus ID 35976155100
Documents 9
Citations 121
h-index 4
Subject Area Biological Sensors
Event Global Sensor Awards
ORCID 0000-0002-5388-3498

Samra Kousar is a researcher affiliated with the Centre of Excellence in Molecular Biology in Pakistan, with a research profile associated with the subject area of Biological Sensors. Her scholarly identity is represented through a Scopus Author ID and an ORCID identifier, providing persistent mechanisms for distinguishing and documenting research contributions across academic information systems.[1] [2]

The Best Researcher Award recognition presented through the Global Sensor Awards framework is associated with research activity in sensing technology and related scientific disciplines. This article provides a structured academic recognition profile based on the supplied researcher-identification and affiliation information, while avoiding unsupported claims concerning publication counts, citation metrics, or individual research achievements.

Abstract

This academic recognition profile presents Samra Kousar, affiliated with the Centre of Excellence in Molecular Biology, Pakistan, in connection with the Best Researcher Award under the Global Sensor Awards. The identified subject area is Biological Sensors, a field concerned with the development and application of sensing approaches involving biological systems, biomolecular recognition, biological materials, and related analytical technologies. The profile records the researcher’s supplied institutional affiliation, country, Scopus Author ID, ORCID identifier, and award context. Scopus and ORCID provide complementary systems for scholarly identity and research-output identification.[1] [2]

Keywords

  • Samra Kousar
  • Best Researcher Award
  • Biological Sensors
  • Biosensing
  • Molecular Biology
  • Sensing Technology
  • Centre of Excellence in Molecular Biology
  • Global Sensor Awards
  • Scopus
  • ORCID

Introduction

Biological sensors, commonly described within the broader field of biosensing, combine biological recognition mechanisms with physical or chemical transduction and signal-processing approaches. Such systems can support the detection or analysis of biological, chemical, or environmental targets and are relevant to areas including biomedical research, diagnostics, biotechnology, food analysis, and environmental monitoring.

The integration of molecular biology with sensing technologies has contributed to the development of analytical platforms that seek to improve target recognition, measurement, selectivity, and practical deployment. Research profiles associated with this area may therefore span interdisciplinary boundaries between molecular biology, biotechnology, analytical science, materials science, electronics, and sensor engineering.

Samra Kousar’s supplied academic profile places her within this interdisciplinary context through the stated subject area of Biological Sensors and affiliation with the Centre of Excellence in Molecular Biology in Pakistan. The researcher’s Scopus and ORCID identifiers provide reference points for scholarly identity verification and further examination of documented research outputs.[1] [2]

Research Profile

Samra Kousar is identified as being affiliated with the Centre of Excellence in Molecular Biology in Pakistan. The supplied research subject area is Biological Sensors, indicating an academic context that connects biological or molecular systems with sensing and analytical technologies.

Research Contributions

The supplied information identifies Biological Sensors as the principal subject area associated with this recognition profile. Within this field, research contributions can encompass biological recognition elements, biomolecular interactions, biosensing platforms, signal transduction, analytical detection, and the integration of biological components with sensor architectures.

Because no individual publication titles, experimental datasets, patents, research projects, or specific technological developments were supplied for this profile, no individual contribution is attributed here beyond the documented subject-area classification. This approach keeps the recognition article limited to information that can be supported by the supplied researcher identifiers and institutional details.

The Scopus and ORCID records can provide pathways for examining the researcher’s documented scholarly output and identity information. Such records should be consulted directly when evaluating specific publications, collaborations, affiliations, or research themes.[1] [2]

Publications

No publication list was supplied as part of the input data for this article. Accordingly, specific articles, journals, publication years, citation counts, and DOI identifiers are not listed to avoid attributing publications to the researcher without supporting bibliographic information.

For authoritative publication-level verification, the researcher’s Scopus Author Profile and ORCID record may be consulted. These resources can be used to identify scholarly works associated with the supplied researcher identifiers.[1] [2]

No DOI citation is included in this section because no specific publication or DOI was provided in the source information. Adding an unverified DOI could incorrectly associate another researcher’s work with this profile.

Research Impact

Research in Biological Sensors can have interdisciplinary relevance because sensing systems may connect biological analysis with quantitative measurement and technological instrumentation. Depending on the specific research application, biological sensing approaches may contribute to laboratory analysis, health-related detection, environmental observation, food and agricultural analysis, and biotechnology.

For this recognition profile, quantitative research-impact indicators such as documents, citations, and h-index were not supplied. They are therefore recorded as unavailable rather than estimated or inferred. Direct consultation of the relevant scholarly databases is appropriate when current bibliometric measurements are required.[1]

Award Suitability

The Best Researcher Award profile identifies Samra Kousar with the subject area of Biological Sensors and an institutional affiliation at the Centre of Excellence in Molecular Biology. These supplied details establish a direct connection between the recognition profile and the broader scientific area represented by the Global Sensor Awards.

The documented researcher identifiers also provide mechanisms for scholarly identity verification. The Scopus Author ID can be used to locate the corresponding author record within Scopus, while the ORCID identifier provides a persistent researcher identifier that can be used to connect scholarly activities and affiliations.[1] [2]

A complete assessment of research achievements for any academic recognition would ordinarily require examination of relevant publications, research contributions, citation records, collaborations, innovation outputs, and other documented evidence. Such information is not included in the supplied input and is therefore not independently characterized in this article.

Conclusion

Samra Kousar is presented in this academic recognition profile as a researcher affiliated with the Centre of Excellence in Molecular Biology, Pakistan, with Biological Sensors identified as the relevant subject area. The profile records the supplied Scopus Author ID, ORCID identifier, institutional affiliation, country, and association with the Best Researcher Award under the Global Sensor Awards.

The article deliberately distinguishes supplied identification information from bibliometric or publication-level evidence that was not provided. Scopus and ORCID links are included to support further verification of the researcher’s scholarly identity and documented research record.[1] [2]

References

  1. Elsevier. (n.d.). Scopus author details: Samra Kousar, Author ID 35976155100. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=35976155100
  2. ORCID. (n.d.). ORCID record: Samra Kousar, ORCID iD 0000-0002-5388-3498. ORCID.
    https://orcid.org/0000-0002-5388-3498
  3. Global Sensor Awards. (n.d.). Global Sensor Awards. Official event website.
    https://globalsensorawards.com/

Li-Chung Pien | Biological Sensors | Best Researcher Award

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

Prof. Guanghao Huang | Optical sensors Awards | Best Researcher Award

Prof. Guanghao Huang | Optical sensors Awards | Best Researcher Award

Prof. Guanghao Huang, Qingdao University, China

Dr. Guanghao Huang is an Assistant Professor at the Institute for Future, Qingdao University, where he has served since April 2020. He holds a Ph.D. in Electronics Engineering from Pusan National University, South Korea, and previously earned an M.S. in Cogno-Mechatronics Engineering from the same institution. He also holds a B.Eng. in Electronics Engineering from Jilin Institute of Chemical Technology. Dr. Huang’s research focuses on integrated optical sensors, artificial intelligence, brain-computer interfaces (BCI), and functional near-infrared spectroscopy (fNIRS). He has been recognized with multiple best paper awards and a national scholarship from the Chinese Embassy in South Korea. His work has led to several patents in optical waveguide technologies and polarization devices.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award – Dr. Guanghao Huang

Dr. Guanghao Huang exemplifies research excellence in the fields of integrated optical sensors, brain-computer interfaces (BCI), artificial intelligence, and functional near-infrared spectroscopy (fNIRS). With a robust academic trajectory from Pusan National University and professional experience spanning both academia and industry, Dr. Huang is currently serving as an Assistant Professor at the Institute For Future, Qingdao University.

πŸŽ“ Education

  • πŸ§‘β€πŸŽ“ Ph.D. in Electronics Engineering, Pusan National University, South Korea (2015.03 – 2020.02)

  • πŸ“˜ M.S. in Cogno-Mechatronics Engineering, Pusan National University, South Korea (2012.03 – 2014.08)

  • πŸ’» B.Eng. in Electronics Engineering, Jilin Institute of Chemical Technology, China (2006.09 – 2010.06)

πŸ’Ό Work Experience

  • πŸ§‘β€πŸ« Assistant Professor, Institute for Future, Qingdao University, China (2020.04 – Present)

  • πŸ”¬ Research Associate, Department of Electronics Engineering, Pusan National University (2014.08 – 2015.03)

  • πŸ› οΈ Engineer, Beijing Rijin Automotive Systems Co., Ltd (2010.03 – 2012.01)

πŸ† Patents & Achievements

  • πŸ”§ Apodized Gratings for Polymeric Waveguide Tunable Wavelength Filters (Patent No. 10-1759727, 2017)

  • πŸ”§ Integrated Optic Polarization Splitter via Total Internal Reflection (Patent No. 10-2018368, 2017)

  • πŸ”§ Broadband Integrated Optic Polarization Splitters (Patent No. 10-1923956, 2018)

πŸ₯‡ Awards & Honors

  • πŸ‡¨πŸ‡³ 2018: National Excellent Self-funded Scholarship (Excellence Award), Chinese Embassy in South Korea

  • πŸ… 2017: Best Paper Award, 24th Optoelectronics & Optical Communications Conference

  • πŸ… 2016: Best Paper Award, 23rd Optoelectronics & Optical Communications Conference

  • πŸ… 2015: Best Paper Award, 24th Photonics Conference

  • πŸ… 2013: Best Paper Award, 20th Optoelectronics & Optical Communications Conference

PublicationΒ Top Notes:

Development and Characterization of an Asymmetric MZI Temperature Sensor Using Polymer Waveguides for Extended Temperature Measurement Scopes

Physiological Noise Filtering in Functional Near-Infrared Spectroscopy Signals Using Wavelet Transform and Long-Short Term Memory Networks