Rajesh Kumar Samala | Engineering | Innovative Research Award

Published on by Forensic Scientist Awards

Innovative Research Award

Rajesh Kumar Samala
Visvesvaraya College of Engineering and Technology, India

Rajesh Kumar Samala
Affiliation Visvesvaraya College of Engineering and Technology
Country India
Scopus ID 57204032308
Documents 20
Citations 109
h-index 5
Subject Area Engineering
Event International Forensic Scientist Awards
ORCID 0000-0002-0324-4436

Rajesh Kumar Samala is an engineering researcher whose indexed scholarly work spans nanotechnology, additive manufacturing, machine learning, Internet of Things applications, and technology-assisted analysis. His Scopus record comprises 20 documents, 109 citations, and an h-index of 5. [1] The research profile demonstrates an interdisciplinary orientation in which computational methods and engineering systems are applied to contemporary technical and applied research questions.

Abstract

Rajesh Kumar Samala’s research record reflects work across engineering technologies with particular emphasis on computational intelligence, advanced manufacturing, nanotechnology, and connected systems. His recent publications address nanoparticle toxicology, deep-learning-based tool wear prediction, healthcare-related statistical assessment, and machine-learning-driven energy optimization in wireless sensor networks. [2] [3] These studies illustrate the application of quantitative and computational approaches to engineering and technology-oriented problems.

Keywords

  • Engineering research
  • Nanoparticle toxicology
  • Deep learning
  • Additive manufacturing
  • Internet of Things

Introduction

Contemporary engineering research increasingly combines experimental investigation with machine learning, networked technologies, and data-driven modelling. Samala’s indexed publications reflect this broader development through studies addressing both emerging materials and computational engineering applications. The documented research covers publications from 2024 and 2025, indicating recent engagement with interdisciplinary engineering topics. [1]

Research Profile

The research profile is characterized by the integration of engineering analysis and computational techniques. Work on additive manufacturing applies deep learning to tool wear prediction, while another study examines crossbreed clustering for energy optimization in wireless sensor networks. [3] Research concerning nanoparticle properties and toxicological effects extends the profile toward materials-related engineering and technology assessment. [2]

Research Contributions

  • Investigates relationships between nanoparticle characteristics and toxicological effects.
  • Applies deep learning to tool wear prediction in additive manufacturing.
  • Explores machine learning and IoT methods for energy optimization.
  • Contributes to interdisciplinary engineering research involving data-driven methodologies.

Publications

Selected indexed publications include research on nanoparticle toxicology, additive manufacturing, healthcare analysis, and wireless sensor networks. The 2025 article on nanoparticle properties and toxicological effects was published in Proceedings on Engineering Sciences. [2] The 2025 study on deep learning and tool wear prediction appeared in Progress in Additive Manufacturing. [3]

Research Impact

The available Scopus metrics record 20 documents, 109 citations, and an h-index of 5. [1] These bibliometric indicators provide a quantitative view of indexed research visibility and should be interpreted in relation to publication age, field-specific citation practices, and database coverage.

Award Suitability

The documented research themes correspond to an Innovative Research Award context through their emphasis on emerging technologies, computational approaches, and interdisciplinary engineering applications. The publication record provides identifiable evidence of research activity in areas including nanotechnology, additive manufacturing, machine learning, and IoT-enabled systems. [2] [3]

Conclusion

Rajesh Kumar Samala’s indexed research profile presents a multidisciplinary engineering portfolio combining computational intelligence, advanced manufacturing, nanotechnology, and connected systems. The documented publications and Scopus metrics provide a concise evidence base for recognizing his continuing contribution to contemporary engineering research.

References

  1. Elsevier. (n.d.). Scopus author details: Rajesh Kumar Samala, Author ID 57204032308. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57204032308
  2. Samala, R. K., et al. (2025). Exploring the Interrelationship Between Nanoparticle Properties and Their Toxicological Effects. Proceedings on Engineering Sciences.
    https://doi.org/10.24874/PES07.03A.009
  3. Samala, R. K., et al. (2025). Enhancing Tool Wear Prediction with Deep Learning Models in Additive Manufacturing Processes. Progress in Additive Manufacturing.
    https://doi.org/10.1007/s40964-025-01372-2
  4. Samala, R. K., et al. (2024). Assessment of Primary Lung Cancer Survival Rates in Relation to the Number of Thoracoscopic Lobectomies Performed in Hospitals. Onkologia I Radioterapia.
  5. Samala, R. K., et al. (2024). Machine Learning and Internet of Things Driven Energy Optimization in Wireless Sensor Networks through Crossbreed Clustering. Journal of Intelligent Systems and Internet of Things.
    https://doi.org/10.54216/JISIoT.130204
  6. Elsevier. (n.d.). Scopus indexed publication records associated with Author ID 57204032308. Scopus.

Elijah Gbenga Osunkentan | Engineering | Innovative Research Award

Published on by Forensic Scientist Awards

Innovative Research Award

Elijah Gbenga Osunkentan
Federal University of ABC

Elijah Gbenga Osunkentan
Affiliation Federal University of ABC
Country Brazil
Scopus ID 60381839900
Documents 1
Subject Area Engineering
Event International Forensic Scientist Awards
Google Scholar ID 8mrS_zkAAAAJ

Elijah Gbenga Osunkentan is associated with engineering research focused on electrical power systems, microgrids, fault detection, transmission-line analysis, and intelligent computational methods. The supplied publication record indicates work involving statistical voltage-based islanding detection and machine-learning approaches for identifying electrical faults. These themes connect conventional power-system analysis with data-driven techniques for improving the monitoring and diagnosis of modern electrical networks. [1]

Abstract

The research profile of Elijah Gbenga Osunkentan centers on engineering applications in electrical power systems. Documented work includes passive islanding detection in microgrids, transmission-line fault detection using Support Vector Machines (SVM) and Artificial Neural Networks (ANN), and fault-location methods using Long Short-Term Memory (LSTM) neural networks. These studies represent complementary approaches to system monitoring, classification, and intelligent fault diagnosis. [1] [2] [3]

Keywords

Microgrids; islanding detection; power systems; fault detection; transmission lines; SVM; ANN; LSTM; intelligent fault location; electrical engineering.

Introduction

Modern electrical networks require dependable methods for detecting abnormal operating conditions and locating faults. Microgrids introduce additional operational considerations because distributed generation can alter system behavior during islanded and grid-connected states. Machine-learning techniques can complement established analytical approaches by supporting classification and pattern recognition. The supplied research record addresses these engineering challenges through statistical and intelligent computational methods. [1] [2]

Research Profile

Osunkentan’s documented research combines statistical signal interpretation with machine-learning models for electrical-system diagnosis. The reported areas include passive islanding detection, transmission-line fault classification, and intelligent fault-location techniques. This combination reflects an interdisciplinary engineering approach in which computational models are applied to practical power-system monitoring problems. [1] [2] [3]

Research Contributions

  • Development of a statistical voltage-based approach for passive islanding detection in microgrids. [1]
  • Comparative investigation of SVM and ANN techniques for transmission-line fault detection. [2]
  • Application of LSTM neural networks to intelligent transmission-line fault-location analysis. [3]

Publications

The supplied publication information identifies three relevant research outputs. The 2026 article, “An efficient and cost-effective statistical voltage-based method for passive islanding detection in microgrids,” appears in Discover Electronics 3(1), article 132. A 2025 conference contribution examines SVM and ANN for transmission-line fault detection, while another work addresses transmission-line fault location using LSTM neural networks. [1] [2] [3]

Research Impact

The available record documents research activity across journal, conference, and preprint-oriented channels. The stated Scopus record contains one document; citation and h-index values were not supplied and are therefore not inferred here. The research themes have practical relevance to power-system monitoring because islanding detection and fault diagnosis address operational reliability and system protection requirements. [1]

Award Suitability

The documented research aligns with an Innovative Research Award category through its focus on statistical analysis, machine-learning methods, microgrid operation, and intelligent fault diagnosis. The supplied record provides identifiable research outputs and methods that can be considered in an academic recognition assessment. Final award decisions remain subject to the applicable evaluation criteria and review process of the International Forensic Scientist Awards. [4]

Conclusion

Elijah Gbenga Osunkentan’s supplied research record demonstrates a focused engineering interest in electrical-system diagnostics, microgrid islanding detection, transmission-line fault identification, and neural-network-based fault location. The combination of statistical and intelligent methods provides a coherent basis for documenting his research profile within an innovation-oriented academic recognition context.

References

  1. Osunkentan, E. G. (2026). An efficient and cost-effective statistical voltage-based method for passive islanding detection in microgrids. Discover Electronics, 3(1), 132.
  2. Osunkentan, E. G., dos Santos, R. C., & Da Silva, A. M. (2025). Comparative Analysis of SVM and ANN for Fault Detection in Transmission Lines. 2025 16th IEEE International Conference on Industry Applications (INDUSCON).
  3. Silva, A. M. (n.d.). An Effective Intelligent Method for Fault Location in Transmission Lines Using LSTM Neural Networks. SSRN 5928668.
  4. International Forensic Scientist Awards. (n.d.). International Forensic Scientist Awards.
    forensicscientist.org
  5. Elsevier. (n.d.). Scopus author details: Elijah Gbenga Osunkentan, Author ID 60381839900. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=60381839900
  6. Google Scholar. (n.d.). Elijah Gbenga Osunkentan publication and citation record.
    Google Scholar Profile

Guangyao Li | Engineering | Innovative Research Award

Published on by Forensic Scientist Awards

Innovative Research Award

Guangyao Li
Harbin Institute of Technology, China

Guangyao Li
Affiliation Harbin Institute of Technology
Country China
Scopus ID 58371333200
Documents 38
Citations 244
h-index 9
Subject Area Engineering
Event International Forensic Scientist Awards
ORCID 0000-0003-3343-1889

Guangyao Li is an engineering researcher affiliated with Harbin Institute of Technology whose documented research record includes work on wireless power transfer (WPT), inductive power transfer, compensation networks, power electronics, and wireless charging systems. The available bibliographic record lists 38 documents, 244 citations, and an h-index of 9. His recent publications address practical challenges including frequency switching, compensation topology, circulating-current suppression, output regulation, misalignment tolerance, and wireless charging for unmanned aerial vehicles.

Abstract

Guangyao Li’s research profile is centered on engineering problems associated with wireless and inductive power transfer. Recent publications demonstrate attention to compensation topology design, controllable power delivery, system stability, and operating conditions encountered in practical WPT applications. His 2026 work includes a dual-frequency three-coil topology with constant-current and constant-voltage outputs and zero-voltage switching, as well as an inductive power transfer system designed for misalignment-tolerant UAV charging. Additional research examines circulating currents in segmented dynamic wireless power transfer systems and bidirectional inductive power transfer control.

Keywords

  • Wireless power transfer
  • Inductive power transfer
  • Power electronics
  • Compensation networks
  • Wireless charging

Introduction

Wireless power transfer research seeks to improve the efficiency, controllability, reliability, and application range of contactless energy-transfer systems. Within this field, compensation networks and control strategies strongly influence operating frequency, power regulation, switching conditions, and tolerance to changes in coupling. Li’s recent publications address these engineering considerations through circuit topologies and control approaches reported in peer-reviewed journals.

Research Profile

The documented profile comprises 38 Scopus-indexed documents, 244 citations, and an h-index of 9. These bibliometric indicators provide a quantitative description of the indexed publication record and should be interpreted in relation to field, publication age, database coverage, and citation practices. [1] His research activity is particularly associated with power-transfer systems and engineering methods for regulating electrical energy delivered through inductive coupling.

Research Contributions

Recent work describes a dual-frequency switchable LCC-S-S and S-S-S compensated three-coil topology supporting constant-current and constant-voltage outputs with zero-voltage switching. [2] Another study proposes an L-S/N inductive power-transfer system using a lightweight receiver, primary-side tuning, and constant-current control for UAV wireless charging under misalignment conditions. [3] Further research investigates induced circulating currents in segmented dynamic WPT systems and output regulation in bidirectional inductive power transfer. [4] [5]

Publications

  • A Dual-Frequency Switchable LCC-S-S and S-S-S Compensated Three-Coil Topology with CC/CV Outputs and ZVS Operation for WPT Applications. Electronics, 2026. [2]
  • An L-S/N IPT System with a Lightweight Receiver and Primary-Side Tuning and Constant-Current Control for Misalignment-Tolerant UAV Wireless Charging. Electronics, 2026. [3]
  • Analysis and Suppression of Induced Circulating Currents in Segmented DWPT Systems With a Primary-Side LCC Compensation Network. IEEE Transactions on Power Electronics, 2026. [4]
  • Enhancing Output Stability in Bidirectional Inductive Power Transfer Using Switching Controlled Capacitors and Dual-Phase-Shift Control. IEEE Transactions on Industry Applications, 2026. [5]

Research Impact

The available citation record indicates measurable scholarly visibility, with 244 citations and an h-index of 9 in the supplied Scopus profile data. [1] The publication themes also show continuity around practical WPT engineering, including power regulation, switching operation, misalignment tolerance, dynamic charging, and bidirectional energy transfer. The significance of individual contributions should be assessed through their technical results, independent citations, and subsequent adoption rather than bibliometric indicators alone.

Award Suitability

For the Innovative Research Award, the documented research record provides evidence of recent work addressing technically specific challenges in wireless and inductive power-transfer systems. The publications cover circuit topology development, control methods, switching behavior, output stability, and application-oriented wireless charging. These documented areas can be considered in an award evaluation alongside the complete research record, independent scholarly impact, and the stated criteria of the International Forensic Scientist Awards.

Conclusion

Guangyao Li’s documented research profile combines indexed scholarly output with recent publications focused on wireless power-transfer technologies and associated power-electronic control challenges. His 2026 publications provide identifiable examples of work on compensation structures, switching control, system stability, and misalignment-tolerant charging, establishing a clear research focus within engineering.

References

  1. Elsevier. (n.d.). Scopus author details: Guangyao Li, Author ID 58371333200. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=58371333200
  2. Li, Guangyao, et al. (2026). A Dual-Frequency Switchable LCC-S-S and S-S-S Compensated Three-Coil Topology with CC/CV Outputs and ZVS Operation for WPT Applications. Electronics.
    https://doi.org/10.3390/electronics15184285
  3. Li, Guangyao, et al. (2026). An L-S/N IPT System with a Lightweight Receiver and Primary-Side Tuning and Constant-Current Control for Misalignment-Tolerant UAV Wireless Charging. Electronics.
    https://doi.org/10.3390/electronics15184245
  4. Li, Guangyao, et al. (2026). Analysis and Suppression of Induced Circulating Currents in Segmented DWPT Systems With a Primary-Side LCC Compensation Network. IEEE Transactions on Power Electronics.
    https://doi.org/10.1109/TPEL.2025.3650608
  5. Li, Guangyao, et al. (2026). Enhancing Output Stability in Bidirectional Inductive Power Transfer Using Switching Controlled Capacitors and Dual-Phase-Shift Control. IEEE Transactions on Industry Applications.
    https://doi.org/10.1109/TIA.2025.3603528

Guangzhen Si | Engineering | Innovative Research Award

Published on by Forensic Scientist Awards

Innovative Research Award

Guangzhen Si
Affiliation Zhejiang University of Technology
Country China
Scopus ID 57192688905
Documents 17
Citations 359
h-index 6
Subject Area Engineering
Event International Forensic Scientist Awards

Guangzhen Si
Zhejiang University of Technology, China

Guangzhen Si is a researcher affiliated with Zhejiang University of Technology, China, whose documented scholarly profile is associated with engineering research. The available bibliographic record lists 17 documents, 359 citations, and an h-index of 6 in Scopus. [1] These indicators provide a quantitative view of the researcher’s indexed publication and citation activity.

Abstract

This academic recognition profile presents the documented research record of Guangzhen Si in engineering. The available Scopus information identifies 17 indexed documents, 359 citations, and an h-index of 6. [1] A 2026 conference paper further records Si as a co-author of research on multi-scale dynamic adaptive attention mechanisms for cross-domain specific emitter identification, indicating engagement with computational and signal-processing research themes. [2]

Keywords

Engineering; adaptive attention mechanisms; specific emitter identification; cross-domain learning; computational research; research impact; bibliometrics.

Introduction

Guangzhen Si is affiliated with Zhejiang University of Technology and is represented in the Scopus database under author ID 57192688905. [1] The available record places the research profile within Engineering and provides bibliometric indicators that can be used to describe indexed scholarly activity.

Research Profile

The documented research profile combines engineering-oriented scholarly output with work involving computational methods. The reported Scopus indicators of 17 documents and 359 citations, together with an h-index of 6, provide measurable evidence of indexed research activity. [1]

Research Contributions

A recorded 2026 conference contribution titled MAAB: Multi-scale Dynamic Adaptive Attention Mechanisms for Cross-Domain Specific Emitter Identification lists Guangzhen Si among its authors. The work addresses adaptive attention mechanisms in the context of cross-domain specific emitter identification, illustrating an application of machine-learning approaches to an engineering research problem. [2]

Publications

The documented publication record includes the following 2026 conference paper:

  • MAAB: Multi-scale Dynamic Adaptive Attention Mechanisms for Cross-Domain Specific Emitter Identification. H. Zou, M. Wang, J. Wang, G. Si. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (LNICST), 2026. [2]

Research Impact

The Scopus record reports 359 citations across 17 documents, with an h-index of 6. [1] These figures describe citation activity within the indexed database and should be interpreted in relation to publication year, field-specific citation practices, and database coverage.

Award Suitability

For the International Forensic Scientist Awards, the available record provides identifiable evidence of engineering research activity, indexed publications, citation impact, and a recent contribution involving adaptive computational methods. [1] [2] Final award assessment should consider the complete nomination materials, verified publication record, originality, and the criteria established by the award organizers.

Conclusion

Guangzhen Si’s documented scholarly profile reflects engineering research activity supported by indexed publication and citation indicators. The 2026 conference contribution on adaptive attention mechanisms provides a specific example of current research involvement, while the reported Scopus metrics offer a quantitative description of the broader indexed record. [1] [2]

References

  1. Elsevier. (n.d.). Scopus author details: Guangzhen Si, Author ID 57192688905. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57192688905
  2. Zou, H., Wang, M., Wang, J., & Si, G. (2026). MAAB: Multi-scale Dynamic Adaptive Attention Mechanisms for Cross-Domain Specific Emitter Identification. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (LNICST).
  3. Zhejiang University of Technology. Institutional research and academic information concerning engineering research activities.
  4. International Forensic Scientist Awards. Award information and nomination resources.
    forensicscientist.org