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Fatma Akalın, Computer Science, AI Innovation Award

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Asst. Prof Fatma Akalın: Assistant Professor at Sakarya University, Turkey

Fatma Akalın is an accomplished Assistant Professor in the Department of Computer Engineering at Sakarya University, specializing in the intersection of artificial intelligence, machine learning, and biomedical engineering. Her research is driven by a deep interest in applying AI algorithms to solve complex challenges in healthcare, particularly for disease diagnosis and personalized medicine. With expertise in data science, bioinformatics, and medical imaging, she has made significant strides in automating processes such as genomic sequence classification, anomaly detection in blood cell images, and the development of AI-driven decision support systems. Dr. Akalın’s commitment to advancing research in healthcare technology positions her as a thought leader in both academic and applied AI communities.

Online Profiles

Google Scholar Profile

Citations, h-index, i10-index

  • Citations: Dr. Akalın’s research has accumulated a total of 66 citations across various academic publications, with 61 citations in the last 5 years, reflecting the growing influence and relevance of her work in the field.

  • h-index: With an h-index of 5, Dr. Akalın has made significant contributions, with at least 5 of her publications being cited at least 5 times each. This indicates a solid body of impactful research in her areas of expertise.

  • i10-index: Dr. Akalın holds an i10-index of 1, meaning she has one publication that has been cited 10 times or more, further highlighting the recognition her work has received in the academic community

Dr. Akalın maintains an active and influential presence across multiple online academic platforms. Her publications are widely cited in leading international journals, and she regularly contributes to conferences in fields such as AI, bioinformatics, and computational biology. As an Assistant Editor for Sakarya University Journal of Computer and Information Sciences (SAUCIS), she has an integral role in shaping the research landscape at the university. Her collaborative projects include contributions to national research initiatives that address pressing public health concerns through the use of AI technologies. She has also developed various web-based applications aimed at enhancing healthcare outcomes, integrating both AI and clinical expertise to create innovative solutions for real-world health issues.

Education

Dr. Akalın completed her Doctoral Degree in Computer Engineering at Sakarya University in 2023, where she focused on leveraging AI algorithms for the classification of leukemia subtypes using digital mapping of DNA sequences. Her thesis, titled “Classification of Leukemia Types Using AI-Based Algorithms on DNA Sequences via Digital Mapping Techniques,” reflected her strong commitment to applying cutting-edge computational methods to solve biomedical challenges. She earned her Master’s Degree in 2020, with a thesis on the application of heuristic algorithms for detecting polyps in small intestine images. This work demonstrated her early engagement with AI in medical imaging. Her Bachelor’s Degree was also completed at Sakarya University in 2018, marking the beginning of her academic journey into computer engineering and AI.

Research Focus

Dr. Akalın’s research is deeply rooted in artificial intelligence, machine learning, and bioinformatics. Her primary focus is the integration of AI into healthcare systems, specifically in disease detection and diagnostic applications. She has worked extensively on the classification of genetic sequences, including the use of AI algorithms for identifying and predicting leukemia and other cancers. Additionally, her work includes the development of advanced diagnostic systems based on digital imaging, such as detecting anomalies in medical images like blood cell smears and endoscopic images. In recent years, she has also delved into synthetic data generation for model training, optimizing machine learning algorithms for better clinical decision-making, and exploring data-driven AI models that can adapt to the complexities of real-world clinical environments.

Experience

Dr. Akalın has held the position of Assistant Professor in the Department of Computer Engineering at Sakarya University since September 2023, where she teaches courses in artificial intelligence, web technologies, and data structures. Prior to this role, she served as a Research Assistant at the same department from 2020 to 2023, contributing to numerous research projects, guiding graduate students, and publishing in top-tier journals. During her academic career, she has mentored students on master’s theses and helped develop cutting-edge research in fields such as medical diagnostics, deep learning, and artificial intelligence. Her diverse experience spans both academic and research settings, where she has also collaborated on national-level projects related to AI-based healthcare solutions.

Research Timeline

  • 2025: Dr. Akalın is currently leading a project on the development of an AI-powered decision support chatbot aimed at optimizing sales processes within ERP systems. Additionally, she is working on a real-time system for detecting and counting blast cells in leukemia diagnosis using advanced deep learning methods.

  • 2024: She is working on the development of a web-based AI system that utilizes hybrid data sets to aid in the diagnosis of monkeypox. Additionally, she is exploring the use of AI to assess cardiac risks in adolescents based on EKG images as part of a national TUBITAK-funded project.

  • 2023: Dr. Akalın has initiated a project focused on developing AI-enhanced virtual reality simulations for secondary education, along with another project to build a web-based system to promote sustainable production and consumption in Turkey.

Awards & Honors

Dr. Akalın has been recognized for her outstanding contributions to scientific research, particularly in the application of artificial intelligence to healthcare. She was awarded the 10th İksad Scientific Award in 2019, a prestigious recognition in the field of applied science. She continues to receive recognition for her innovative research on medical diagnostics, and her work has been acknowledged by leading research institutes and journals. Her ability to bridge the gap between AI theory and practical healthcare applications has made her a sought-after researcher and collaborator in both academic and industrial circles.

Top-Noted Publication

  • “Deep Learning-Based Community Classifier Approach for Gastrointestinal Anomaly Detection,” published in Pamukkale University Engineering Journal, 2024, discusses the application of deep learning methods for gastrointestinal anomaly detection, contributing to the growing body of knowledge on AI in medical diagnostics.

  • “Classification of Exon and Intron Regions on DNA Sequences Using SBERT and ANFIS,” published in Journal of Polytechnic, 2024, presents a hybrid approach combining SBERT and ANFIS for DNA sequence classification, a significant advancement in genomic data analysis.

  • “Neural Network-Based Survival Classification in Heart Failure Patients,” published in Arabian Journal for Science and Engineering, 2024, focuses on using neural networks for predicting the survival outcomes of heart failure patients, applying AI to improve personalized treatment strategies.

  • “DNA Genomic Sequence Classification with Digital Signal Processing and EfficientNetB7,” published in Gazi University Journal, 2022, investigates the classification of DNA sequences using deep learning models, contributing to advancements in genomic medicine.

Dr. Akalın’s work continues to push the boundaries of AI in healthcare, and her publications are widely regarded as some of the most significant contributions to the field in recent years.

Strengths for the AI Innovation Award

  1. Pioneering AI Applications in Healthcare: Dr. Akalın’s work in automating disease diagnostics, such as leukemia classification from genomic data and gastrointestinal anomaly detection, showcases her groundbreaking contributions to healthcare through AI. These innovations can significantly improve patient outcomes and revolutionize diagnostic processes.

  2. Cross-Disciplinary Expertise: Dr. Akalın effectively merges AI, machine learning, bioinformatics, and medical imaging to develop advanced diagnostic systems, demonstrating her versatility and expertise in multiple disciplines, and advancing both AI and biomedical engineering.

  3. Leadership in AI-Driven Healthcare Projects: She is leading multiple impactful AI projects, including real-time leukemia detection systems and AI-enhanced decision support tools for healthcare, positioning her as a leader in AI’s practical application in medicine.

  4. Synthetic Data Innovation: Dr. Akalın is pioneering research in synthetic data generation for training AI models, addressing critical challenges in healthcare data scarcity and privacy concerns, and making AI more adaptable for clinical use.

  5. Award-Winning Research: Her contributions have earned recognition, including the 10th İksad Scientific Award, cementing her position as a leading innovator in AI for healthcare, and highlighting the significant impact of her research on both academic and real-world levels.

Hamdy Abdelsalam Elgohary, Engineering, Best Researcher Award

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Professor Hamdy Abdelsalam Elgohary: Professor at Mansoura University, Egypt

 

Prof. Hamdy A. El-Gohary is a renowned Professor of Reinforced Concrete Structures with over 36 years of comprehensive experience in research, teaching, and professional consulting in structural engineering. He earned his Ph.D. from Moscow Institute of Municipal Economy and Construction in 1992 and has since established a strong academic and professional reputation through numerous international publications, conference presentations, and authored books. His expertise lies in reinforced concrete design, seismic behavior of structures, and advanced analytical methods. He has supervised many M.Sc. and Ph.D. students, contributing significantly to the development of structural engineering knowledge worldwide.

Online Profiles

ORCID Profile

Prof. El-Gohary maintains an active presence on academic platforms such as ResearchGate and Google Scholar, where his extensive research output is accessible to the global engineering community. He regularly participates in international conferences and workshops, sharing insights on seismic design and structural dynamics. His profiles highlight collaborations with researchers across Europe, the Middle East, and Russia, reflecting a robust network of scholarly and industry connections.

Education

Prof. El-Gohary completed his Ph.D. in Structural Engineering at Moscow Institute of Municipal Economy and Construction, Soviet Union, in 1992, specializing in building construction and structures. He holds a Master of Science degree in Structural Engineering from Mansoura University (1987) and a Bachelor of Science in Civil Engineering from the same institution (1982). This solid academic foundation has enabled him to integrate theoretical concepts with practical engineering applications throughout his career.

Research Focus

His research centers on the behavior and design of reinforced concrete structures subjected to seismic and lateral loads, with a focus on improving earthquake resistance and structural stability. Key areas include nonlinear stability analysis of beam-columns, dynamic response of precast and coupled shear wall systems, and empirical modeling for vibration periods and crack width control. He also investigates advanced bracing systems, slender column design, and the use of modern codes such as ACI and Saudi Building Code for optimized structural performance.

Experience

With a distinguished academic career at Umm Al-Qura University and Mansoura University, Prof. El-Gohary has been deeply involved in teaching, research, and administration. His professional experience extends to leading structural design projects at Qatar Foundation and heading consulting engineering groups. He founded the Structural Consulting and Design Center in Egypt, providing expertise to engineering firms and overseeing complex infrastructure projects, bridging the gap between academia and industry.

Research Timeline

Prof. El-Gohary began his research journey in the late 1980s, focusing on experimental and theoretical studies of precast frames and seismic response. Throughout the 1990s, he expanded his work to seismic regulation reviews and dynamic analysis of multistory frames. The 2000s saw a shift toward nonlinear analysis, bracing systems, and column behavior under eccentric compression. His recent research since 2010 has emphasized empirical formula development, crack width prediction, and structural optimization compliant with international codes, culminating in influential papers and books up to 2023.

Awards & Honors

Throughout his career, Prof. El-Gohary has been honored with multiple “Who’s Who” awards recognizing his outstanding contributions to structural engineering education and research. He is a respected member of editorial boards for several journals and a frequent peer reviewer for international conferences and publications. These accolades reflect his standing as a leading expert in earthquake-resistant design and reinforced concrete structures.

Top-Noted Publication

One of Prof. El-Gohary’s most impactful publications is “Empirical Formula for the Fundamental Period of Vibration of Multi-story RC Framed Buildings,” presented at the 2013 Vienna Congress on Recent Advances in Earthquake Engineering and Structural Dynamics. This work has significantly influenced seismic design methodologies by providing practical, validated equations for structural vibration periods, aiding engineers worldwide in improving building safety and code compliance.

A Simplified Trilinear Concrete Stress–Strain Curve: Energy-Based Modeling of Experimental Data Compliant with Various Codes

Journal of Umm Al-Qura University for Engineering and Architecture
2025-06 | Journal Article
DOI: 10.1007/s43995-025-00117-0
In this article, Prof. El-Gohary proposes a simplified trilinear stress–strain curve for concrete, developed using an energy-based modeling approach. The model is grounded on experimental data and complies with various international concrete codes. This work aims to offer more accurate predictions of concrete behavior under stress, enhancing the design and safety of structural elements.

Evaluation of the Dynamic Characteristics of Coupled Shear Wall System under Seismic Loads

Engineering, Technology & Applied Science Research (ETASR)
2025-06-01 | Journal Article
Contributors: Hamdy A. Elgohary, Hytham Alhunami, Rabeea W. Bazuhair
This study evaluates the dynamic behavior of coupled shear wall systems subjected to seismic forces. It presents a comprehensive analysis of the system’s response, incorporating conceptualization, formal analysis, and detailed investigations. The article explores the system’s structural resilience under varying seismic conditions and offers insights into improving design practices for earthquake-resistant buildings.

Refined Nonlinear Estimation of Effective Flexural Rigidity in Reinforced Concrete Beams Using Curvature Integration

Engineering, Technology & Applied Science Research (ETASR)
2025-04 | Journal Article
Prof. El-Gohary introduces a refined approach to estimating the effective flexural rigidity of reinforced concrete beams, utilizing curvature integration. This nonlinear method provides a more accurate representation of beam behavior under loading, offering a valuable tool for structural engineers designing reinforced concrete beams subjected to complex bending moments.

Refined Span-to-Depth Ratio Expressions for One-Way Slabs Aligned with ACI-318 Deflection Limits

Journal of Umm Al-Qura University for Engineering and Architecture
2025-03-07 | Journal Article
In this paper, Prof. El-Gohary presents refined expressions for the span-to-depth ratio of one-way slabs, ensuring alignment with ACI-318 deflection limits. The study focuses on improving design parameters for reinforced concrete slabs, ensuring compliance with deflection criteria while optimizing structural efficiency and safety.

Punching Shear Revised Equations for Edge Column-Slab Joints Complying with Different Current Codes

Innovative Infrastructure Solutions
2025-02-11 | Journal Article
DOI: 10.1007/s41062-024-01822-w
Contributors: Hamdy A. Elgohary, Mohamed A. El Zareef
This article revises existing equations for evaluating punching shear at edge column-slab joints. By considering compliance with multiple modern design codes, the study enhances the reliability of current methods for predicting punching shear behavior and improving structural safety in reinforced concrete buildings.

Nazzareno Cannella, Pharmacology, Best Researcher Award

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Professor Nazzareno Cannella: Associate Professor at University of Camerino, Italy

Dr. Nazzareno Cannella is an accomplished Associate Professor of Pharmacology at the University of Camerino, Italy, with a strong focus on neuropsychopharmacology. Over the years, his research has explored the neurobiology of motivated behaviors and reward systems, particularly in relation to substance use disorders (SUD) involving alcohol, psychostimulants, nicotine, and opioids. Dr. Cannella’s work also addresses the co-occurrence of psychiatric disorders such as anxiety, PTSD, and depression, which often co-appear with SUDs. His multifaceted research integrates a variety of cutting-edge techniques, including in-vivo behavioral models, pharmacology, neuroimaging (MRI, PET), and genetic manipulation. He seeks to advance personalized and translational medicine in addiction research by exploring individualized treatment responses and the identification of biomarkers for SUDs.

Online Profiles

Dr. Cannella maintains an extensive academic presence online. His work can be found in various scientific databases and repositories:

  • Scopus Author ID: 24079967600

  • ORCID: 0000-0002-2891-8679
    His complete list of over 60 publications, which includes groundbreaking studies in addiction research, is accessible on his NCBI Bibliography. This includes his collaborative work on preclinical models and his exploration of neurobiological mechanisms underlying addiction.

  • 1,483 Citations from 1,086 Documents

  • 62 Documents published

  • h-index of 23

Education

Dr. Cannella’s academic journey began with a Master of Science in Biology from the University of Camerino in 2005. He then pursued his PhD in Neuropsychopharmacology at the same institution, earning his degree in 2009. His dissertation, under the supervision of Dr. Roberto Ciccocioppo, focused on the neurobiology of addiction and pharmacological interventions. To expand his expertise, he spent a year as a visiting scholar at Stanford University School of Medicine, where he worked with Dr. Luis de Lecea. Dr. Cannella then honed his research skills through postdoctoral fellowships at leading European institutions, including the Central Institute of Mental Health in Mannheim, Germany, and the Neurocentre Magendie in Bordeaux, France.

Research Focus

Dr. Cannella’s research focuses on the neurobiological underpinnings of substance use disorders (SUD) and their psychiatric comorbidities. His primary interest lies in understanding the reward circuitry in the brain, which plays a crucial role in addiction and motivated behaviors. He is especially concerned with how these systems interact with mental health conditions like anxiety, depression, and impulsivity. In addition to the basic neurobiology of addiction, his work spans treatment development, personalized medicine, and translational medicine, aiming to design more effective therapies for addiction. Dr. Cannella also investigates individual variability in response to treatment, with a goal of improving the precision of addiction therapies. His research integrates advanced techniques such as behavioral rodent models, neuroimaging, pharmacological interventions, and molecular biology.

Experience

Dr. Cannella has held various academic positions throughout his career, contributing significantly to the fields of pharmacology and neuropsychopharmacology. He is currently an Associate Professor of Pharmacology at the University of Camerino, a position he has held since 2023. Prior to this, he served as an Assistant Professor from 2020 to 2022. His long-standing association with the Central Institute of Mental Health in Mannheim, Germany, as a Guest Scientist since 2017 has allowed him to collaborate on numerous international research projects. Dr. Cannella also gained valuable experience as a Research Assistant Professor and Research Associate at the University of Camerino, Italy, where he has played a pivotal role in advancing both the academic and research profiles of the institution. His work has consistently been supported by various competitive research grants, allowing him to lead multiple interdisciplinary teams working on addiction-related projects.

Research Timeline

Dr. Cannella’s research timeline begins with his PhD in 2009, where his foundational work on the neurobiology of addiction set the stage for his future studies. From 2010 to 2016, he honed his skills through postdoctoral training in Germany and France, focusing on neuropharmacology and behavioral neuroscience. In 2016, he became a Research Associate at the University of Camerino, where he soon expanded his research interests to explore individualized vulnerability to addiction. His significant work on SUD biomarkers, genetic vulnerability, and personalized medicine began to take shape during his tenure as a Research Assistant Professor from 2019 to 2020. In 2023, he advanced to the role of Associate Professor, where his research is now funded by prestigious grants such as the NIH/NIAAA and the Hetzler Foundation, ensuring that his work on addiction continues to be at the forefront of scientific discovery.

Awards & Honors

Dr. Cannella has received various awards and honors throughout his career, including multiple competitive research grants and recognition from leading scientific societies. He is an active member of prestigious organizations such as the Italian Society of Neuroscience, the Federation of European Neuroscience Societies, and the European Behavioral Pharmacology Society. His research has been frequently cited and recommended by expert panels, including F1000 and NIDA’s Blog. Additionally, his work on personalized medicine and addiction has garnered attention in both academic and clinical settings, making him a leading figure in addiction research.

Top-Noted Publication

Dr. Cannella’s top-noted publication is a 2024 study in Neuropharmacology on Cebranopadol, a novel opioid agonist. This study demonstrated Cebranopadol’s potential in treating opioid use disorder with a low risk of abuse, a significant advancement in the search for safer alternatives to traditional opioid therapies. The paper, titled “Cebranopadol, a novel long-acting opioid agonist with low abuse liability, to treat opioid use disorder: Preclinical evidence of efficacy”, highlights Dr. Cannella’s commitment to developing effective, less harmful treatments for addiction. His work on the molecular mechanisms underlying addiction continues to influence drug development strategies for a range of substance use disorders.

  • Predicting individual treatment response in alcohol use disorders: a reverse translational proof-of-concept study
    Translational Psychiatry | Published: June 24, 2025
    DOI: 10.1038/s41398-025-03431-2
    This study explores the reverse translational approach to predicting personalized treatment responses in alcohol use disorder (AUD), offering valuable insights into the future of individualized treatments for addiction.

  • Genome-wide association study reveals multiple loci for nociception and opioid consumption behaviors associated with heroin vulnerability in outbred rats
    Molecular Psychiatry | Published: February 25, 2025
    DOI: 10.1038/s41380-025-02922-4
    This research identifies genetic loci related to nociception and opioid consumption behaviors, contributing to our understanding of heroin addiction and individual vulnerability.

  • Pharmacological Mechanism and Drug Research Prospects of Ginsenoside Rb1 as an Antidepressant
    Antioxidants (Basel, Switzerland) | Published: February 19, 2025
    DOI: 10.3390/antiox14020238
    This article reviews the pharmacological properties of Ginsenoside Rb1, examining its potential as an antidepressant, with a focus on its therapeutic prospects in neuropsychiatric disorders.

  • Distinct Behavioral Profiles and Neuronal Correlates of Heroin Vulnerability Versus Resiliency in a Multi-Symptomatic Model of Heroin Use Disorder in Rats
    The American Journal of Psychiatry | Published: January 15, 2025
    DOI: 10.1176/appi.ajp.20230623
    This study distinguishes between behavioral and neuronal correlates of vulnerability versus resiliency in heroin use disorder, providing critical insights into potential therapeutic strategies.

  • Cebranopadol, a novel long-acting opioid agonist with low abuse liability, to treat opioid use disorder: Preclinical evidence of efficacy
    Neuropharmacology | Published: October 2024
    DOI: 10.1016/j.neuropharm.2024.110048
    This publication presents preclinical evidence for the efficacy of Cebranopadol, a novel opioid agonist with a promising profile for treating opioid use disorder without significant abuse liability.

Suhail Ahmad, Chemistry, Best Researcher Award

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Doctorate Suhail Ahmad: Research Scholar at Maulana Azad National Urdu University (MANUU)-Hyderabad, India

Suhail Ahmad is a Research Scholar pursuing a Ph.D. in Organic Chemistry at Maulana Azad National Urdu University. With an academic focus on the synthesis of novel heterocyclic compounds, his research explores their photophysical properties and biological activities. Suhail is particularly interested in designing and developing efficient chemosensors for metal ion detection, a vital aspect of environmental monitoring and biological sensing. He has authored multiple high-impact papers and presented his work at several international conferences. Passionate about sustainable chemical science, Suhail aims to bridge the gap between fundamental research and practical applications in chemical sensing.

Online Profiles

Scopus Profile

LinkedIn: Suhail Ahmad LinkedIn
A comprehensive professional network for collaboration and showcasing research expertise.

Citations & Research Impact

  • Total Citations: 122

  • Cited by: 99 documents

  • h-index: 6

Education

  • Ph.D. in Chemistry (Expected: February 2026)
    Maulana Azad National Urdu University
    Suhail’s doctoral research focuses on Organic Chemistry, with a particular emphasis on heterocyclic compounds. His work incorporates innovative methods of one-pot synthesis, photophysical investigation, and evaluation of biological activity. He has excelled in various coursework related to advanced chemical synthesis, spectroscopy, and material science, preparing him for his ongoing research contributions.

  • Master of Science in Chemistry (2018)
    University Name
    A comprehensive program focused on organic and inorganic chemistry with a thesis on reaction mechanisms in organic synthesis.

Research Focus

Suhail’s current research aims to develop novel, highly sensitive fluorescent chemosensors for the detection of metal ions such as Fe³⁺, Cu²⁺, Hg²⁺, and Zn²⁺. Using techniques like one-pot synthesis, multi-step synthesis, and ultrasound-assisted methods, he has contributed to the development of sensors with enhanced selectivity and sensitivity. His work combines organic synthesis with photophysical and biological testing to provide new tools for environmental monitoring, toxicology, and healthcare applications. Additionally, Suhail’s research investigates the potential biological activities of the compounds, such as antimicrobial, antioxidant, and anticancer effects, which could have implications for drug development.

Research Experience

Suhail Ahmad is currently involved in cutting-edge research on the design and synthesis of novel fluorescent chemosensors. As a Research Scholar at Maulana Azad National Urdu University, he has applied various techniques to synthesize and characterize organic compounds with targeted photophysical properties. His projects focus on the one-pot synthesis of heterocyclic compounds, which are then characterized using advanced spectroscopic methods like UV-Vis, fluorescence spectroscopy, and NMR. Over the years, he has collaborated with researchers across multiple disciplines, contributed to multiple published papers, and presented his findings at numerous national and international conferences. His work bridges the gap between synthetic chemistry and real-world applications in environmental monitoring, sensing, and bio-imaging.

Research Timeline

  • 2023: Conducted detailed studies on the synthesis and photophysical properties of benzothiazole-pyrazoline-based sensors for detecting Fe³⁺ ions. Published initial findings in leading journals and presented them at national and international conferences.

  • 2024: Expanded research into the application of thiadiazole derivatives in environmental and biological sensing. Focused on the development of highly selective chemosensors for multiple metal ions, including Cu²⁺ and Hg²⁺. Contributed chapters to the book on Green Carbon Dots published by the American Chemical Society.

  • 2025: Progressed to multi-step synthesis and biological evaluation of triazole-based compounds with photo-responsive properties for ion detection. Expected to complete Ph.D. dissertation in February 2026, covering key findings from his extensive research on sensor design and its biological applications.

Awards & Honors

  • Best Paper Award at the International Conference on “Innovation in Chemical Science for Sustainable Development” (2025) for outstanding research contributions in chemical sensing.

  • Young Researcher Award from the National Conference on Advances in Chemical and Biological Sciences (2023) for excellence in emerging research in chemical sciences.

  • ACS Leadership Scholarship (2022) awarded for leadership potential and academic excellence in chemistry.

  • Excellence in Research Award at Maulana Azad National Urdu University (2024) for the innovative approach in synthesizing novel chemosensors.

Top-Noted Publications

  1. “A Review on Recent Progress in Synthesis and Biological Activities of Thiadiazole and its Derivatives”
    Journal of Molecular Structure, 2024
    This review paper presents a comprehensive overview of the synthesis strategies and biological properties of thiadiazole derivatives, discussing their potential applications in drug discovery and environmental sensing.

  2. “Photophysical Investigation of One-Pot Synthesized Novel Indenofluorene Derivative (BDP) as a Fluorescent Chemosensor for Fe³⁺ Ion Detection”
    Journal of Fluorescence, 2024
    The research focuses on a novel chemosensor for Fe³⁺ ions synthesized using a one-pot approach, highlighting its application in aqueous media for environmental monitoring.

  3. “Multi-step Synthesis and Photophysical Investigation of Benzothiazole-Pyrazoline Based Fluorescent Chemosensor for Fe³⁺ Ion Detection”
    Journal of Molecular Structure, 2025
    This paper details the synthesis of a benzothiazole-pyrazoline derivative, a selective “turn-off” fluorescent chemosensor for detecting Fe³⁺ ions in aqueous solutions.

  4. “A Comprehensive Review on Recent Advances of Remarkable Scaffold Triazole-based Schiff Base: Synthesis and Photoresponsive Chemosensors for Al³⁺ Ion Detection”
    Journal of Fluorescence, 2025
    This extensive review examines the synthesis of triazole-based Schiff base compounds and their potential as photoresponsive chemosensors, focusing on their application in detecting Al³⁺ ions.

Dharmendar Reddy Yanala, Mathematics, Best Researcher Award

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Professor Dharmendar Reddy Yanala: PROFESSOR at ANURAG UNIVERSITY, India

Dr. Yanala Dharmendar Reddy is an accomplished academician and researcher with over 20 years of experience in the field of Applied Mathematics. Currently serving as a Professor in the Department of Mathematics at Anurag University, Hyderabad, he has dedicated his career to the study of Fluid Dynamics, specifically in Magnetohydrodynamics (MHD) and nanofluid heat transfer. His research interests include MHD boundary layers, heat and mass transfer, and the impact of nanoparticles on fluid dynamics. Dr. Reddy is known for his ability to blend theoretical models with numerical techniques to solve complex problems in the realms of geophysics, astrophysics, and industrial applications. His work is not only significant for academia but also for practical applications in energy systems, environmental engineering, and medical technologies. His passion for education and research is matched by his commitment to fostering the next generation of mathematicians and engineers.

Online Profiles

  • Google Scholar: Yanala Dharmendar Reddy – A platform for accessing Dr. Reddy’s scholarly articles and citations.

  • Scopus ID: 57202329139 – Tracking all of Dr. Reddy’s research contributions on Scopus, a global database for academic research.

  • ORCID: 0000-0002-8926-7259 – Ensuring that Dr. Reddy’s research activities are uniquely identified and linked to his academic and professional work.

  • WOS Researcher ID: B-7614-2018 – A profile on Web of Science, further validating Dr. Reddy’s research output and professional standing.

  • Citations & Research Impact

    • Total Citations: 2276

    • h-index: 27

    • i10-index: 54

    These metrics indicate Dr. Reddy’s significant impact on the academic community, with a well-established record of influential publications. His h-index of 27 reflects a consistent contribution to high-quality research, with at least 27 publications that have each been cited 27 or more times. Additionally, an i10-index of 54 highlights his ability to produce scholarly works that have been widely referenced and utilized in ongoing research, further cementing his reputation as a key figure in his field.

Education

Dr. Reddy completed his Ph.D. in Applied Mathematics at Osmania University, Hyderabad, in 2017. His doctoral research focused on the numerical techniques used to study the MHD boundary layer flow of nanofluids over stretching sheets, a topic with profound implications for industrial heat transfer and energy systems. Prior to this, he earned his M.Sc. in Mathematics with distinction from Osmania University (2005) and a B.Sc. in Computer Science (2003), demonstrating early aptitude in both theoretical and computational aspects of mathematics. His academic journey reflects a solid grounding in both pure and applied mathematics, which laid the foundation for his later research endeavors.

Research Focus

Dr. Reddy’s research explores complex fluid dynamics problems, specifically focusing on Magnetohydrodynamics (MHD) and the heat and mass transfer of nanofluids. MHD flow has applications in various fields, such as power generation, astrophysics, and environmental systems. His interest in nanofluids stems from the growing need to enhance heat transfer in industrial and technological applications. Nanofluids are a class of fluids that contain nanoparticles, which significantly improve the thermal properties of base fluids. By studying the interaction between magnetic fields, fluid flow, and nanoparticle behavior, Dr. Reddy aims to propose solutions to challenges in energy efficiency, industrial heat exchangers, and even medical applications like hyperthermia treatment. His work has resulted in both theoretical advancements and practical applications, benefiting a wide range of industries.

Experience

Dr. Reddy’s academic career spans two decades, with significant contributions to teaching, research, and academic administration. He has been a Professor in the Department of Mathematics at Anurag University since December 2024. Previously, he served as an Associate Professor (2020-2024) and Assistant Professor (2005-2020) at Anurag Group of Institutions, where he helped shape the curriculum and research landscape in applied mathematics. Throughout his career, Dr. Reddy has mentored countless undergraduate, postgraduate, and doctoral students. His leadership roles also include serving as the Additional Controller of Examinations at Anurag Group from 2012 to 2017, where he managed examination procedures, ensuring fairness and transparency. Dr. Reddy is dedicated to fostering a research-driven environment in his academic roles, encouraging collaboration and innovation among his students and colleagues.

Research Timeline

  • 2014-2016: Dr. Reddy completed a UGC-sponsored Minor Research Project on the “Impact of Flow Parameters on Heat and Mass Transfer” using numerical techniques. This project laid the groundwork for his future work on MHD flows and nanofluids.

  • 2017-2020: Focused on MHD nanofluid flow, leading to multiple publications on heat transfer enhancement in industrial and biological systems.

  • 2020-Present: Expanded his research to include chemical reactions, radiation effects, and their influence on nanofluid flow. His current projects involve exploring novel fluid systems for applications in energy systems and medical technologies, which continue to garner significant academic and industrial interest.

Awards & Honors

Dr. Reddy’s contributions to academia have been widely recognized. In 2024, he was listed among the Top 2% Scientists Worldwide, an accolade co-published by Elsevier and Stanford University, placing him in an elite group of researchers globally. Additionally, he received the Best Teacher Award from AQER in 2024 for his excellence in teaching and his innovative contributions to the field of mathematics. His involvement in academic organizations includes life memberships in the Indian Science Congress Association and the Andhra Pradesh Society for Mathematical Sciences, where he plays an active role in promoting mathematics research in India. These awards and recognitions underscore his dedication to both research and teaching.

Top-Noted Publication

  1. Chemical reaction and Soret impacts on MHD heat and mass transfer Casson hybrid nanofluid (MoS2+ZnO) flow based on engine oil across a stretching sheet with radiation
    Journal: Chemical Thermodynamics and Thermal Analysis (2025)
    DOI: 10.1016/j.ctta.2025.100163
    Contributors: Radhika, M.; Dharmendar Reddy, Y.

  2. Impact of thermal radiation and viscous dissipation on MHD heat transmission MoS2 and ZnO/engine oil hybrid nanofluid flow along a stretching porous surface
    Journal: Multiscale and Multidisciplinary Modeling, Experiments and Design (2025)
    DOI: 10.1007/s41939-024-00589-y
    Contributors: Mangamma, I.; Reddy, Y.D.

  3. Numerical solutions of steady radiative Maxwell-nanofluid flow toward a stretching sheet in the presence of magnetic field and porous medium
    Journal: Modern Physics Letters B (2025)
    DOI: 10.1142/S0217984924504323
    Contributors: Babu, P.R.; Kumar, M.A.; Raju, R.S.; Reddy, Y.D.

  4. Viscous dissipation and radiation effects on MHD heat transfer copper-water nanofluid flow over an exponentially shrinking surface
    Journal: Multiscale and Multidisciplinary Modeling, Experiments and Design (2025)
    DOI: 10.1007/s41939-024-00708-9
    Contributors: Radhika, M.; Reddy, Y.D.

  5. A numerical study on MHD Maxwell fluid with nanoparticles over a stretching surface: Impacts of thermal radiation, convective boundary condition and induced magnetic field
    Journal: Numerical Heat Transfer, Part A: Applications (2024)
    DOI: 10.1080/10407782.2024.2338259
    Contributors: Venkatesh, N.; Raju, R.S.; Anil Kumar, M.; Dharmendar Reddy, Y.