Richard Romano | Economics | Best Paper Award

Published on by Best Paper Awards

Best Paper Award

Richard Romano
University of Florida
 Richard Romano
Award Category Best Paper Award
Institution University of Florida
Country United States
Scopus 7102604921
ORCID 0000-0003-4961-4559
Article Title Multimodal PCSC Sensors for Real-Time Temperature and Force Detection Using LRTNet
Event Best Paper Awards

The Best Paper Award recognizes outstanding scholarly contributions that demonstrate originality, scientific rigor, technological innovation, and significant impact within their respective fields. Richard Romano has been honored for the publication titled “Multimodal PCSC Sensors for Real-Time Temperature and Force Detection Using LRTNet”, a research work that advances intelligent sensing technologies through the integration of multimodal sensor systems and deep learning methodologies.

Abstract

The awarded publication introduces an advanced multimodal sensing framework capable of simultaneously detecting temperature and force in real time. By incorporating the Long-Range Transformer Network (LRTNet) architecture with Polymer Composite Strain Capacitive Sensors (PCSC), the study demonstrates enhanced sensing accuracy, intelligent data interpretation, and improved performance for next-generation smart monitoring applications. The research contributes to the growing intersection of sensor engineering and artificial intelligence.

Awarded Article

Title: Multimodal PCSC Sensors for Real-Time Temperature and Force Detection Using LRTNet

The publication explores a novel sensing platform that combines multimodal sensor technology with deep neural network-based analytics. Through the implementation of LRTNet, the system effectively processes complex sensor signals and improves the reliability of simultaneous temperature and force measurements. The work provides valuable insights for wearable electronics, healthcare monitoring systems, robotics, and intelligent industrial sensing applications.

Author Profile

Richard Romano is affiliated with the University of Florida, United States. His research interests encompass advanced sensor technologies, intelligent sensing systems, machine learning applications, and interdisciplinary engineering innovations. Through his scholarly contributions, he has participated in the development of next-generation sensing frameworks designed to address real-world monitoring and automation challenges.

Research Contributions

  • Development of multimodal PCSC sensor technology.
  • Integration of LRTNet deep learning architecture for sensor data interpretation.
  • Enhanced accuracy in simultaneous temperature and force sensing.
  • Contribution to intelligent wearable and industrial sensing platforms.
  • Advancement of AI-enabled real-time monitoring systems.

Innovation and Impact

The research demonstrates how machine learning can significantly improve sensor intelligence and operational reliability. By combining multimodal sensing capabilities with advanced neural network models, the study establishes a foundation for more adaptive and responsive sensing platforms. Potential applications include smart healthcare devices, robotic systems, human-machine interfaces, industrial automation, and predictive monitoring environments.

Award Recognition

The Best Paper Award acknowledges publications that exhibit exceptional scientific quality, originality, technical excellence, and societal relevance. The selection of this article reflects its contribution to advancing intelligent sensor systems and its potential to influence future developments in AI-driven sensing technologies. The award celebrates both the scholarly achievement of the author and the broader impact of the research on the scientific community.

Conclusion

The recognition of Richard Romano with the Best Paper Award highlights the significance of innovative research that bridges sensor engineering and artificial intelligence. The publication contributes meaningful advancements to real-time multimodal sensing and demonstrates the transformative potential of deep learning in modern sensor applications. Its scientific value and practical relevance make it a distinguished contribution to the field.

References

  1. Romano, R. et al. (2026). Multimodal PCSC Sensors for Real-Time Temperature and Force Detection Using LRTNet. Sensors, MDPI.
    https://www.mdpi.com/1424-8220/26/11/3506
  2. Elsevier. (n.d.). Scopus author details: Richard Romano Author ID 7102604921. Scopus.
    https://www.scopus.com/
  3. Best Paper Awards Committee. (n.d.). Award evaluation criteria and recognition framework.
    https://bestpaperawards.com/

Ahmed Habib | Economics | Editorial Board Member

Published on by Best Paper Awards

Prof. Dr. Ahmed Habib | Economics | Editorial Board Member 

Independent Researcher | Egypt

Ahmed Mohamed Habib is an accomplished researcher and academic specializing in accounting and finance, with a research portfolio spanning corporate governance, intellectual capital performance, corporate social responsibility (CSR), environmental, social, and governance (ESG) practices, working capital management (WCM), managerial ability, auditor report readability, corporate narrative reporting, corporate liquidity, cost of debt, corporate finance, earnings management, and corporate investment efficiency. His work emphasizes performance optimization, continuous improvement, business excellence, and frontier analysis through advanced methodologies such as Data Envelopment Analysis (DEA), Malmquist Productivity Index (MPI), knowledge management, and Decision Support Systems (DSS), integrating rigorous econometric analysis to generate practical insights for firms and financial institutions. Habib has contributed extensively to understanding how efficiency in working capital, corporate governance, and intellectual capital impacts firm performance and value, particularly during crises such as the COVID-19 pandemic and global economic disruptions like the Russia–Ukraine conflict. His publications analyze diverse contexts, including Romanian, Polish, US, Egyptian, and MENA-region firms, exploring the interplay between competitive strategies, ESG practices, financial distress mitigation, and organizational efficiency. Additionally, he investigates the effects of managerial ability and auditor report readability on corporate liquidity, cost of debt, and earnings management, applying DEA-MPI approaches to assess intellectual capital and working capital management efficiency. Habib’s research also extends to healthcare systems and sports organizations, evaluating operational and financial efficiency through innovative analytical frameworks. Based in the United Kingdom and Ukraine, he has maintained an independent research position in accounting and finance since 2006 at Zagazig, Sharqia, Egypt, contributing over 26 publications in high-impact journals and book chapters that bridge theoretical frameworks with empirical applications. His interdisciplinary work provides valuable insights into corporate strategy, sustainability, and efficiency, advancing knowledge in financial and organizational management, fostering business excellence, and informing policymakers, investors, and practitioners on optimizing firm performance under dynamic global conditions.

Profile: ORCID

Featured Publications

  1. Habib, A. M., Nuta, A. C., Neslihanoglu, S., Dalwai, T., & Rangu, C. M. (2025). Analyzing the market performance of Romanian firms: Do the COVID-19 crisis and classification type matter? International Journal of Emerging Markets. https://doi.org/10.1108/IJOEM-05-2023-0842

  2. Zimon, G., Habib, A. M., & Haluza, D. (2024). Does the quality management system affect working capital management efficiency? Evidence from Polish firms. Cogent Business & Management, 11(1), 1–21. https://doi.org/10.1080/23311975.2023.2292787

  3. Habib, A. M., & Mourad, N. (2024). Analyzing the efficiency of intellectual capital: A new approach based on DEA-MPI technology. Benchmarking: An International Journal, 31(9), 1–25. https://doi.org/10.1108/BIJ-06-2022-0384

  4. Habib, A. M., Yang, G., & Cui, Y. (2024). Do competitive strategies affect working capital management efficiency? Business Process Management Journal, 30(6), 1–28. https://doi.org/10.1108/BPMJ-12-2023-0953

  5. Habib, A. M., & Dalwai, T. (2024). Does the efficiency of a firm’s intellectual capital and working capital management affect its performance? Journal of the Knowledge Economy, 15(3), 1–24. https://doi.org/10.1007/s13132-023-01138-7

Ahmed Mohamed Habib’s research advances the understanding of corporate efficiency, governance, and sustainability, providing actionable insights that help firms optimize performance, mitigate financial risk, and enhance ESG outcomes. His work bridges theory and practice, driving innovation in finance, management, and organizational decision-making globally.