Aleksandr Kulikov | Energy and Sustainability | Research Excellence Award

Published on by Research Hypothesis

Prof. Dr. Aleksandr Kulikov | Energy and Sustainability | Research Excellence Award

Prof. Dr. Aleksandr Kulikov | Energy and Sustainability | Research Excellence Award | Lecturer | Nizhny Novgorod State Technical University | Russia

Prof. Dr. Aleksandr Kulikov is a distinguished scholar in electric power engineering, widely recognized for his advanced expertise in relay protection, emergency automation, distributed generation, digital signal processing, and the cybersecurity of modern electric networks. Prof. Dr. Aleksandr Kulikov holds multiple higher education degrees across engineering and economics, including a doctoral-level qualification in electric power systems, complemented by earlier academic work in radar, radio navigation, and economic system reliability. His professional experience spans leadership positions in major energy enterprises, where he served as Deputy Chief Engineer, Director of trunk electric grid operations, scientific supervisor for government-funded technology programs, and a long-serving professor at the Nizhny Novgorod State Technical University. Throughout his career, Prof. Dr. Aleksandr Kulikov has demonstrated outstanding commitment to academic excellence, supervising master’s students, postgraduate researchers, and doctoral candidates, many of whom have successfully defended their dissertations under his mentorship. His research interests cover a wide spectrum of modern power-system challenges, including high-voltage line fault detection, emergency-mode processing, machine-learning-based grid diagnostics, selective control of power-quality indicators, and the reliability of renewable-integrated microgrids. His research skills span analytical modeling, digital signal processing, industrial frequency analysis, reliability assessment, fault location techniques, and the development of novel algorithms and decision-support systems for complex power networks. Prof. Dr. Aleksandr Kulikov has contributed more than three hundred scientific works, including publications in Scopus-indexed and Web of Science–indexed journals, IEEE conference proceedings, and high-impact engineering journals. His inventions and patents further demonstrate his innovative contributions to advancing electric power technologies. Among his numerous awards and honors are prestigious regional, national, and international distinctions, including medals, honorary diplomas, and recognition for excellence in scientific innovation and contributions to the development of electrical grid infrastructure. His leadership roles include membership in dissertation councils, editorial board service for multiple scientific journals, and expert involvement in federal technology-development programs. Prof. Dr. Aleksandr Kulikov continues to advance the field through active research, academic mentorship, and scientific collaboration, maintaining a significant influence on the evolution of intelligent power systems and modern energy engineering. His longstanding dedication to the scientific community, strong record of innovation, and impact on the reliability of national power systems solidify his status as a leading figure deserving of international recognition.

Profile: ORCID 

Featured Publications 

  1. Kulikov, A., Loskutov, A., Ilyushin, P., Kurkin, A., & Sluzova, A. (2025). High-voltage overhead power line fault location through sequential determination of faulted section technologies.

  2. Ilyushin, P., Papkov, B., Kulikov, A., & Suslov, K. (2025). Algorithm and methods for analyzing power consumption behavior of industrial enterprises considering process characteristics.

  3. Kulikov, A. L., Sevostyanov, A. A., & Ilyushin, P. V. (2023). Analysis of electrical-power quality in modern power-supply systems with selective control of several indicators.

  4. Kulikov, A., Ilyushin, P., Loskutov, A., & Filippov, S. (2023). Application of search algorithms in determining fault location on overhead power lines according to the emergency mode parameters.

  5. Kulikov, A., Loskutov, A., Bezdushniy, D., & Petrov, I. (2023). Decision tree models and machine learning algorithms in the fault recognition on power lines with branches.

  6. Kulikov, A., Ilyushin, P., & Suslov, K. (2023). Enhanced readability of electrical network complex emergency modes provided by data compression methods.

  7. Kulikov, A., Ilyushin, P., Suslov, K., & Filippov, S. (2023). Estimating the error of fault location on overhead power lines by emergency state parameters using an analytical technique.

 

 

Anton Loskutov | Energy and Sustainability | Research Excellence Award

Published on by Research Hypothesis

Dr. Anton Loskutov | Energy and Sustainability | Research Excellence Award

Dr. Anton Loskutov | Energy and Sustainability | Research Excellence Award | Associate Professor | Nizhny Novgorod State Technical University | Russia

Dr. Anton Loskutov is an accomplished researcher in intelligent electrical networks, power system automation, and high-voltage fault-location technologies, recognized for his extensive contributions to smart-grid development and advanced diagnostic algorithms. He has established himself as a leading specialist through his strong academic background, having completed his doctoral education in electrical engineering with research focused on automation devices, distributed generation systems, and sequential decision-making algorithms for network reliability. Dr. Anton Loskutov’s professional experience spans multiple collaborative engineering projects where he has worked closely with multidisciplinary teams to design, model, and optimize high-voltage systems, develop machine learning–based recognition methods for emergency modes, and implement simulation-driven approaches for improving relay protection and network automation. His research interests include power-line fault detection, traveling-wave pattern recognition, machine learning applications in electrical networks, data-compression techniques for emergency-mode analysis, and algorithmic control strategies for distributed generation environments. He is proficient in high-level research skills such as advanced modeling, ANSYS-based structural simulations, sequential analysis, algorithm development, signal-processing methods, and machine learning techniques applied to real-time power-system automation. Throughout his academic and professional journey, he has demonstrated strong analytical ability, technical precision, and a solution-oriented mindset that supports cutting-edge innovations in the energy sector. Dr. Anton Loskutov has earned recognition from peers and institutions for his publications in reputable journals, contributions to IEEE-indexed conferences, and collaborative advancements in next-generation intelligent grid technologies. His honors include repeated acknowledgements for impactful research studies, participation in international engineering conferences, and contributions to the development of advanced control methods for distributed generation networks. With a robust research portfolio, strong interdisciplinary expertise, and continued engagement in scientific collaborations, he continues to strengthen global understanding of smart-grid stability, automation algorithms, and data-driven fault-recognition systems. In conclusion, Dr. Anton Loskutov stands out as a highly dedicated and innovative researcher whose work significantly enhances the reliability, efficiency, and intelligence of modern electrical networks, making him an influential contributor to the advancing field of power-system engineering.

Profile: ORCID 

Featured Publications

  1. Loskutov, A. (2025). High-voltage overhead power line fault location through sequential determination of faulted section. 2025. Citations: 5

  2. Loskutov, A. (2023). Fault location method for overhead power line based on a multi-hypothetical sequential analysis using the Armitage algorithm. 2023. Citations: 18

  3. Loskutov, A. (2023). Decision tree models and machine learning algorithms in the fault recognition on power lines with branches. 2023. Citations: 22

  4. Loskutov, A. (2023). Enhanced readability of electrical network complex emergency modes provided by data compression methods. 2023. Citations: 12

  5. Loskutov, A. (2023). Application of search algorithms in determining fault location on overhead power lines according to the emergency mode parameters. 2023. Citations: 15

  6. Loskutov, A. (2022). WSPRT methods for improving power system automation devices in the conditions of distributed generation sources operation. 2022. Citations: 20

  7. Loskutov, A. (2022). Relay protection and automation algorithms of electrical networks based on simulation and machine learning methods. 2022. Citations: 30

 

Divyanee Garg | Mathematics | Research Excellence Award

Published on by Research Hypothesis

Ms. Divyanee Garg | Mathematics | Research Excellence Award

Ms. Divyanee Garg | Mathematics | Research Excellence Award | PhD Scholar | Indian Institute of Technology in Delhi | India

Ms. Divyanee Garg is an emerging researcher in quantitative finance and mathematical optimization, currently pursuing her Ph.D. in Mathematics at IIT Delhi, where she works on portfolio optimization, behavioural finance, robust allocation models, and data-driven decision techniques. Her academic journey reflects exceptional consistency, beginning with a strong foundation in Mathematics through her B.Sc. from S. S. Jain Subodh College, Jaipur, followed by an M.Sc. in Mathematics from IIT Roorkee, and culminating in her doctoral research supported by prestigious recognitions. Ms. Divyanee Garg has demonstrated outstanding academic excellence through multiple national-level achievements, including selection under the Prime Minister’s Research Fellows (PMRF) scheme and securing AIR 119 in CSIR-UGC NET (JRF), AIR 210 in GATE Mathematics, AIR 155 in JAM, and receiving the INSPIRE Scholarship from DST for five consecutive years. Professionally, she has contributed significantly as a teaching assistant in diverse mathematical domains such as Financial Mathematics, Fuzzy Sets, Optimization Methods, Econometrics, and Machine Learning, handling both undergraduate and postgraduate teaching responsibilities at IIT Delhi. Her research interests include portfolio optimization under risk measures like Expectile VaR and CVaR, cumulative prospect theory, robust optimization with neural networks, numerical optimization, and large-scale computational methods. Research skills demonstrated by Ms. Divyanee Garg include expertise in Python, R, MATLAB, LaTeX, MS Excel, and the formulation of optimization models using advanced mathematical programming techniques. She has published impactful research in reputed international journals such as Computational and Applied Mathematics and Omega, with additional manuscripts under revision. Her work has been showcased at major academic platforms, including the International Symposium at ISI Delhi, the International Conference on Computations and Data Science at IIT Roorkee, the Annual Convention of ORSI at IIT Bombay, and the EURO Conference in the UK. She has also engaged in summer schools and workshops related to large-scale optimization, strengthening her methodological foundations and collaborative experience. Her academic distinctions include district-level awards and formal recognition for academic excellence. In conclusion, Ms. Divyanee Garg exemplifies a strong blend of analytical capability, high-quality research output, and dedicated academic service, making her a promising researcher in quantitative finance and optimization. Her continuous contributions through publications, teaching, international presentations, and interdisciplinary problem-solving reflect her commitment to advancing scientific knowledge, while her growing expertise positions her for impactful leadership roles in research, innovation, and academic communities.

Profile: ORCID | Scopus | Google Scholar

Featured Publications 

  1. Garg, D., & Mehra, A. (2026). Portfolio optimization with expectile value at risk and conditional value at risk: Deviation measure and robust allocation. Computational and Applied Mathematics.

  2. Garg, D., Khan, A. Z., & Mehra, A. (2026). Enhanced indexing using cumulative prospect theory utility function with expectile risk.

  3. Garg, D., Sehgal, R., & Mehra, A. (n.d.). Data-driven approach to robust portfolio optimization using deep neural networks. Manuscript under revision.

  4. Garg, D., & Swaminathan, A. (n.d.). Numerical improvement of Gauss–Chebyshev quadrature rule. Unpublished research study.

  5. Garg, D., & Gupta, S. K. (n.d.). Optimality and duality conditions for semi-infinite programming problems. Project report.

  6. Garg, D. (n.d.). Robust allocation models using behaviour-driven portfolio optimization. Working paper.

  7. Garg, D. (n.d.). Machine learning-assisted optimisation frameworks for financial decision making. Working paper.

Slot Deposit 5 Ribu via QRIS: Pilihan Tepat Buat Player Modal Tipis

Published on by ranebi9993

Buat banyak pemain yang ingin menikmati keseruan bermain slot tanpa harus mengeluarkan modal besar, slot deposit 5 ribu https://www.opgmaplehills.com/photogallery via QRIS jadi opsi yang sangat menarik. Metode ini bukan hanya murah, tapi juga cepat, praktis, dan bisa diakses semua orang. Dengan modal kecil, peluang menang tetap terbuka lebar karena banyak game slot saat ini memberikan fitur RTP tinggi, freespin, dan bonus menarik.

Kenapa Slot Deposit 5 Ribu via QRIS Banyak Dipilih?

1. Modal Minim, Tetap Bisa Main

Hanya dengan Rp5.000, pemain sudah bisa mencoba berbagai slot populer tanpa harus menunggu modal besar. Cocok buat pemula ataupun player yang cuma ingin bermain santai.

2. Proses Transaksi Super Cepat

Pembayaran melalui QRIS dikenal praktis. Cukup scan QR dari aplikasi e-wallet atau mobile banking, lalu dana langsung masuk. Tidak ada lagi proses ribet seperti memasukkan nomor rekening atau menunggu verifikasi lama.

3. Tidak Perlu Rekening Bank

Buat yang lebih nyaman memakai e-wallet, QRIS memungkinkan deposit tanpa harus memiliki rekening bank. Semua transaksi bisa dilakukan lewat dompet digital seperti OVO, DANA, maupun aplikasi pembayaran lainnya.

4. Aman dan Stabil

QRIS sudah terhubung ke sistem resmi dan terjamin keamanannya. Selain itu, transaksi jarang gagal, sehingga pemain bisa fokus menikmati permainan.

Keuntungan Bermain Slot Modal Tipis

1. Risiko Lebih Ringan

Dengan modal kecil, pemain bisa bermain tanpa tekanan. Ini cocok buat yang sedang belajar membaca pola slot atau mencoba berbagai provider.

2. Peluang Menang Tetap Ada

Meskipun deposit kecil, beberapa game slot menawarkan RTP tinggi yang membuat peluang cuan tetap terbuka. Player yang pintar memilih game bisa meraih scatter, freespin, hingga jackpot.

3. Cocok Buat Tes Pola dan Waktu Gacor

Modal 5 ribu cukup untuk melihat ritme permainan, mengecek apakah pola sedang bagus, dan menentukan apakah game layak lanjut atau tidak.

Tips Bermain Slot Deposit 5 Ribu Biar Tetap Menguntungkan

  • Pilih game dengan RTP tinggi agar peluang menang lebih besar.

  • Manfaatkan fitur spin kecil untuk memperpanjang waktu bermain.

  • Awali dengan pola ringan untuk melihat respon mesin slot.

  • Jangan terpancing emosi, karena modal kecil perlu strategi yang stabil.

  • Cari jam bermain yang tepat, biasanya banyak player merasakan waktu tertentu lebih gacor.

Kesimpulan

Slot deposit 5 ribu via QRIS adalah pilihan sempurna buat player modal tipis yang tetap ingin menikmati pengalaman bermain slot dengan nyaman dan aman. Dengan sistem pembayaran praktis, modal terjangkau, serta peluang menang yang masih besar, metode ini jadi salah satu favorit banyak pemain saat ini.

Ayse Tugba Yapici | Engineering and Technology | Best Research Article Award

Published on by Research Hypothesis

Ms. Ayse Tugba Yapici | Engineering and Technology | Best Research Article Award

Ms. Ayse Tugba Yapici | Engineering and Technology | Best Research Article Award | Doctoral Researcher | Kocaeli University | Turkey 

Ms. Ayse Tugba Yapici is an emerging scholar in electrical engineering whose research bridges intelligent energy systems, electric vehicles, and artificial intelligence–driven prediction technologies, reflecting her strong academic preparation and expanding scientific footprint. She earned her PhD in Electrical Engineering from Kocaeli University, where her doctoral studies centered on deep learning approaches for forecasting electric vehicle demand, charging behaviors, and integrated grid impacts. Prior to this, she completed her MSc in Electrical Engineering at Kocaeli University and her BSc in Electrical and Electronics Engineering at Bulent Ecevit University, progressively building a foundation in power electronics, system modeling, and smart energy infrastructures. Professionally, Ms. Ayse Tugba Yapici has gained significant experience as a doctoral researcher contributing to multiple academic studies involving EV charging station optimization, boosting converter analyses, AI-based prediction models, and data-driven approaches for regional transportation planning. Her research interests span electric vehicle technologies, charging station planning, induction heating systems, renewable energy integration, IoT-based intelligent mobility frameworks, and machine learning algorithms including LSTM, GRU, and MLR for performance forecasting. She possesses strong research skills in deep learning model development, Python-based simulation, DigSilent-based power system design, MATLAB/Simulink modeling, geographical data mapping, algorithm optimization, and statistical evaluation metrics such as R², MSE, MAE, and DTW. Her body of work includes multiple SCI and Scopus-indexed publications addressing EV growth prediction, secure IoT frameworks for autonomous taxis, and comparative analyses of deep learning methods for charging time estimation. Ms. Ayse Tugba Yapici has been recognized within academic circles for her contributions to advancing sustainable mobility solutions and has received commendations for her research productivity and multidisciplinary collaborations within Türkiye. Her work continues to support improved policy development, smarter grid integration, and future-ready electric mobility infrastructures. She remains actively involved in scholarly dissemination through conference participation, collaborative studies with engineering experts, and mentorship of junior researchers. Through her growing publication record, analytical expertise, and commitment to advancing intelligent transportation systems, Ms. Ayse Tugba Yapici demonstrates strong potential for continued impact in electrical engineering research, contributing to the broader goals of sustainable energy development, smart city transitions, and technology-driven societal advancement.

Profile: Scopus | ORCID

Featured Publications

  1. Yapici, A. T., Abut, N., & Erfidan, T. (2025). Comparing the effectiveness of deep learning approaches for charging time prediction in electric vehicles: Kocaeli example. Energies. Year: 2025

  2. Yapici, A. T., & Abut, N. (2025). Geleceğe yönelik elektrikli araç ve şarj istasyonu sayılarının LSTM ve GRU derin öğrenme yöntemleri kullanılarak tahmin edilmesi: Kocaeli ili örneği. Politeknik Dergisi. Year: 2025

  3. Yapici, A. T., Abut, N., & Yildirim, A. (2025). Future estimation of electric vehicles and charging stations: Analysis of Sakarya Province with LSTM, GRU and multiple linear regression approaches. Applied Sciences. Year: 2025

  4. Yapici, A. T., & Abut, N. (2025). An intelligent and secure IoT-based framework for predicting charging and travel duration in autonomous electric taxi systems. Applied Sciences. Year: 2025

  5. Yapici, A. T., & Abut, N. (2024). Elektrikli araç devresinde kullanılan boost dönüştürücünün analizine farklı yaklaşımlar. Black Sea Journal of Engineering and Science. Year: 2024

  6. Yapici, A. T., & Abut, N. (2024). Elektrikli araç şarj istasyonu konum tasarımında, DigSilent yazılımı kullanılarak Kocaeli Üniversitesi Umuttepe Kampüsü için örnek uygulama. Black Sea Journal of Engineering and Science. Year: 2024

  7. Yapici, A. T. (2024). Estimation of future number of electric vehicles and charging stations: Analysis of Sakarya Province with LSTM, GRU and MLR approaches. Applied Sciences. Year: 2024

 

Gibin Raju | Quantitative Hypothesis | Research Excellence Award

Published on by Research Hypothesis

Dr. Gibin Raju | Quantitative Hypothesis | Research Excellence Award

Dr. Gibin Raju | Quantitative Hypothesis | Research Excellence Award | Postdoctoral Researcher | Texas A&M University | United States

Dr. Gibin Raju is a dedicated engineering education researcher whose academic journey spans aerospace engineering, educational studies, and engineering education, shaping him into a multidisciplinary scholar committed to advancing STEM learning and research excellence. Dr. Gibin Raju completed his Ph.D. in Engineering Education at the University of Cincinnati, supported by a strong academic foundation that includes a Master’s in Educational Studies, a Master’s in Aerospace Engineering & Engineering Mechanics, and a Bachelor’s in Aerospace Engineering. His professional experience encompasses postdoctoral research at Texas A&M University, where he contributes to mixed-methods and multimodal studies, instructional innovation, grant development, and interdisciplinary collaborations advancing engineering design cognition, spatial visualization, AI-enhanced learning, and K–12 STEM education. Dr. Gibin Raju has served in multiple academic and community-centered roles, including STEM instructor, adjunct faculty, data researcher, and project collaborator on NSF- and IES-funded initiatives involving spatial skills, problem-solving behaviors, cognitive load measures, transformative learning, and inclusive STEM workforce development. His research interests include spatial visualization, engineering design processes, multimodal analytics, transformative learning, universal design for learning, teaming effectiveness, and STEM motivation, contributing to publications across Scopus-indexed journals and flagship conferences such as ASEE, FIE, CoNECD, and ICERI. Dr. Gibin Raju’s research skills span quantitative, qualitative, and mixed-methods analysis using advanced tools such as R, SPSS, SAS, MATLAB, and NVivo, supported by proficiency in experimental techniques such as eye-tracking, EEG, and think-aloud protocols. His awards and honors reflect academic excellence and leadership, including the Apprentice Faculty Grant from ASEE, the Presidential Medal for Graduate Student Excellence, the CEAS Graduate Student Engineer Award, the Graduate Leadership Award, the Innovation & Entrepreneurship Leadership Fellowship, and multiple graduate recognition awards. His service to academic communities includes developing innovative curricula, mentoring students, leading STEM outreach, designing AI-integrated instructional strategies, and contributing to proposal-writing efforts for federal grants supporting engineering education reform and workforce development. Dr. Gibin Raju continues to contribute meaningfully to research, teaching, and professional service, driven by a strong commitment to creating accessible, evidence-based learning environments and advancing global engineering education. With a rapidly expanding publication record, growing research collaborations, demonstrated leadership, and a clear vision for impactful scholarship, Dr. Gibin Raju is positioned to become a significant contributor and future leader in engineering education research.

Profile: Scopus | ORCID | Google Scholar

Featured Publications 

  1. Raju, G., Sorby, S., & Reid, C. (2025). Spatial skill impact on engineering design process: A comparative study of first- and final-year engineering students.

  2. Agarwal, J., & Raju, G. (2025). Establishing quality standards for empirical studies using mixed-methods designs in engineering education research.

  3. Raju, G. M., Feldkamp, P., & Gaskins, W. (2024). Factors shaping students’ attitude and persistence after participating in a summer physics course – A mixed methods study.

  4. Raju, G., Balart, T., Ligler, G., Shryock, K. J., & Brumbelow, K. (2025). Engineering the unexpected: Faculty strategies for navigating academic disruptions.

  5. Dogga, B., Raju, G., Louw, W., & Cohen, K. (2025). Fuzzy decisions on fluid instabilities: Autoencoder-based reconstruction meets rule-based anomaly classification.

  6. Raju, G., & Sorby, S. (2025). Exploring the impact of think-aloud protocol in engineering design problem solving.

  7. White, L. L. A., Raju, G., Watson, K., & Shryock, K. J. (2025). Critical thinking (mis)conceptions of first-year engineering students.

 

Francisco Matus | Environmental Science | Best Researcher Award

Published on by Research Hypothesis

Prof. Dr. Francisco Matus | Environmental Science | Best Researcher Award

Prof. Dr. Francisco Matus | Environmental Science | Best Researcher Award | Full professor | Universidad de La Frontera | Chile

Prof. Dr. Francisco Matus is a distinguished agronomist-soil scientist whose academic training began with an Agronomist degree from Pontificia Universidad Católica de Chile, followed by an M.Sc. in Soil Fertility and Plant Nutrition, and a Ph.D. in Agriculture and Environmental Sciences at Wageningen University. He has held senior academic appointments including full professor and Director of the Laboratory of Conservation and Dynamics of Volcanic Soils at Universidad de La Frontera in Temuco, Chile. His professional experience includes postdoctoral research at Carleton University (Canada) and leadership of doctoral programmes in Natural Resources Sciences, supplemented by advisory roles in national committees on soil fertility, nutrient dynamics, and climate-soil interactions. His research interests focus on pedogenesis, biogeochemistry, soil carbon and nitrogen cycles, and nutrient dynamics in volcanic soils and extreme ecosystems (including high-altitude and Antarctic environments). His technical skills include isotope-based soil-plant studies, experimental design and precision agriculture statistics, crop-soil modelling, and the application of advanced soil physical-chemical methods. He has received honours such as an invitation to the Intergovernmental Technical Panel of Soil Experts (FAO) and keynote recognition at international soil-biogeochemistry conferences, and he serves on editorial boards of major journals in soil science and nutrient cycling. In conclusion, Francisco Matus’s sustained record of leadership in research, high-impact publication, international collaboration and professional service positions him as a global authority in soil biogeochemistry and conservation, with strong potential to advance sustainable soil management, carbon sequestration and nutrient-cycling research at the international forefront.

Profile: Scopus | Google Scholar

Featured Publications 

  1. Matus, F. J. (2021). Fine silt and clay content is the main factor defining maximal C and N accumulations in soils: A meta-analysis. Scientific Reports, 11(1), 6438.

  2. Matus, F., Rumpel, C., Neculman-Cerdá, R., Panichini, M., & Mora, M. L. (2014). Soil carbon storage and stabilisation in andic soils: A review. Catena, 120, 102-110.

  3. Merino, C., Nannipieri, P., & Matus, F. (2015). Soil carbon controlled by plant, microorganism and mineralogy interactions. Journal of Soil Science and Plant Nutrition.

  4. Merino, C., Godoy, R., & Matus, F. (2016). Soil enzymes and biological activity at different levels of organic matter stability. Journal of Soil Science and Plant Nutrition.

  5. Orrego, R., Ávila, A., Meza, F., & Matus, F. (2014). Using a crop simulation model to select the optimal climate grid cell resolution: A study case in Araucanía Region. Journal of Soil Science and Plant Nutrition.

  6. Matus, F. (2014). Producing isotopically enriched plant, soil solution and rhizosphere soil materials over a few hours. Communication in Soil Science and Plant Analysis.

  7. Hidalgo-Moreno, C., Paz-Pellat, F., Báez, A., Etchevers, J. D., Velázquez, A. S., & Matus, F. (2022). Patrones de la distribución del carbono orgánico por fracciones de partículas primarias del suelo. Revista Terra Latinoamericana.

 

Laith Almaqableh | Business Hypotheses | Excellence in Research Award

Published on by Research Hypothesis

Assist. Prof. Dr. Laith Almaqableh | Business Hypotheses | Excellence in Research Award

Assist. Prof. Dr. Laith Almaqableh | Business Hypotheses | Excellence in Research Award | Assistant Professor of Finance & FinTech | Hashemite University | Jordan 

Assist. Prof. Dr. Laith Almaqableh is an accomplished academic and FinTech researcher whose work focuses on cryptocurrency markets, blockchain systems, financial inclusion, digital assets, and emerging financial technologies. He holds a Ph.D. in Finance from the University of South Australia, a Master’s degree in Finance from the University of Jordan, and a Bachelor’s degree in Accounting and Finance from Yarmouk University, demonstrating a strong academic foundation that supports his multidisciplinary expertise. Professionally, Assist. Prof. Dr. Laith Almaqableh serves as an Assistant Professor in the Department of Banking and Financial Sciences at The Hashemite University and has previously contributed as an Adjunct Professor at Princess Sumaya University for Technology and the American University of Madaba, in addition to teaching roles at the UniSA Business School. His industry experience includes several years as a senior accountant and administrative officer, which enriches his applied understanding of financial operations and risk mechanisms. His research interests span cryptocurrency pricing behavior, blockchain and distributed ledger technology, FinTech innovation, event-driven digital market dynamics, and the intersection of technology and financial risk, enabling him to contribute impactful insights to financial transformation research. His research skills include empirical financial modeling, econometric analysis, event-study design, blockchain analytics, and interdisciplinary data interpretation. Assist. Prof. Dr. Laith Almaqableh has published in reputable international journals and conferences, contributing peer-reviewed works on cryptocurrency market responses to global shocks, digital asset risk, and technology-driven financial patterns, achieving growing citation recognition. His contributions include publications in the International Journal of Information Management, Journal of Business Research, and FinTech-focused journals, along with conference presentations on blockchain market behavior. He has been actively involved in keynote speaking, training, and professional development programs through organizations such as the Arab Trainers Union, Arab Planning Institute, and International Arab Society of Certified Accountants, reflecting strong community engagement and leadership in financial technology education. His honors include serving as a certified trainer, keynote speaker, consultant, and academic mentor in multiple institutions, highlighting his commitment to capacity building and knowledge dissemination in the field. In conclusion, Assist. Prof. Dr. Laith Almaqableh stands out as a dedicated scholar whose teaching, research accomplishments, professional service, and interdisciplinary contributions position him as a leading figure in FinTech and digital asset research, demonstrating continuous growth, strong academic influence, and the potential for expanded global impact.

Profile:  ORCID | Scopus | Google Scholar

Featured Publications

  1. Almaqableh, L., Wallace, D., Pereira, V., Ramiah, V., Wood, G., Veron, J. F., Moosa, I., & Watson, A. (2023). Is it possible to establish the link between drug busts and the cryptocurrency market? Yes, we can. International Journal of Information Management.

  2. Almaqableh, L., Reddy, K., Ramiah, V., Pereira, V., Wallace, D., & Veron, J. F. (2022). An investigative study of links between terrorist attacks and the cryptocurrency market. Journal of Business Research.

  3. Almaqableh, L., Khasawneh, M., & Sahiner, M. (2025). Environmental news and Bitcoin market dynamics: An event study of global climate-related shocks. FinTech Journal.

  4. Massri, A., Al-Dwiry, M., & Almaqableh, L. (2025). Cryptocurrency market shocks: How major news events reshape risk-return dynamics in Bitcoin and Ethereum. Journal of Risk and Financial Management (Under review).

  5. Alsaad, A., & Almaqableh, L. (2025). Mobile money service quality and perceived financial risk: Empirical evidence from India. International Journal of Emerging Markets (Under review).

  6. Al-Syouf, R., Almaqableh, L., & Ramadan, A. (2025). FinTech innovation and its impact on the efficiency of Jordanian banks. FinTech and Digital Accounting Review (Under review).

  7. Almaqableh, L. (2018). The link between terrorist attacks and the cryptocurrency markets. 1st Perth Blockchain Conference, University of Western Australia.

 

Emrah Can | Medicine and Health Sciences | Best Researcher Award

Published on by Research Hypothesis

Prof. Dr. Emrah Can | Medicine and Health Sciences | Best Researcher Award

Prof. Dr. Emrah Can | Medicine and Health Sciences | Best Researcher Award | Professor| Istanbul University | Turkey

Prof. Dr. Emrah Can is a highly accomplished pediatrician and neonatology-focused clinician-scientist recognized for his extensive contributions to neonatal health, pediatric infectious diseases, neonatal sepsis, retinopathy of prematurity, and pediatric critical care. Prof. Dr. Emrah Can completed his medical training at Istanbul University Faculty of Medicine, followed by his medical specialization in Child Health and Diseases at Health Sciences University, İstanbul Bağcılar Training and Research Hospital, where he developed strong clinical expertise in neonatal nutrition, early sepsis biomarkers, high-risk neonatal monitoring, and pediatric intensive care. He progressed academically to the ranks of Associate Professor and Professor through consistent research productivity, clinical excellence, and mentorship. Throughout his career, Prof. Dr. Emrah Can has supervised numerous pediatric specialization theses focused on neonatal sepsis biomarkers, MRSA carriage among hospitalized children, melatonin levels in early sepsis, prognostic tools for pediatric traumatic brain injury, differentiation of lower respiratory tract infection and colonization in tracheostomized children, fetal malnutrition indicators, and hyperbilirubinemia-related risk factors, demonstrating his commitment to developing evidence-based pediatric protocols and training the next generation of pediatric specialists. His research interests include neonatal immunology, inflammation markers, nutritional interventions for premature infants, oxygen affinity biomarkers, neonatal thrombosis, and innovative hypothesis-driven models for predicting neonatal morbidity. His research skills include statistical analysis, advanced clinical data interpretation, hypothesis modeling, neonatal diagnostic model development, and interdisciplinary collaboration. Prof. Dr. Emrah Can has authored impactful publications in reputable journals such as Medical Hypotheses, International Ophthalmology, European Journal of Pediatrics, Pediatric Cardiology, and Journal of Paediatrics and Child Health, contributing significantly to early detection strategies for NEC, ROP, neonatal thrombosis, and hematologic alterations in neonates. His scientific memberships reflect his professional commitment, including active participation in the Pediatric Allergy and Asthma Academy Association, Turkish National Pediatric Association, Child Friends Association, and Turkish Neonatology Association. He has also served in academic administrative roles, including program head positions that highlight his leadership in medical education. Prof. Dr. Emrah Can has received recognition for his academic achievements, research contributions, and involvement in pediatric healthcare improvement initiatives. Overall, Prof. Dr. Emrah Can demonstrates exceptional clinical acumen, research innovation, collaborative engagement, and leadership in pediatric and neonatal medicine, positioning him as a scholar whose contributions continue to shape evidence-based neonatal care, advance scientific understanding of pediatric diseases, and improve health outcomes for vulnerable newborn populations.

Profile:  scopus | ORCID

Featured Publications

  1. Can, E. (2025). Development of a scoring model integrating inflammatory markers for predicting ROP in preterm neonates. International Ophthalmology.

  2. Can, E. (2025). Postnatal hemoglobin P50 as a surrogate marker for hypoxia-driven NEC in preterm infants: A mechanistic hypothesis. Medical Hypotheses.

  3. Can, E. (2025). Phototherapy modifies hematologic markers without inducing inflammation in neonates: A retrospective observational study. European Journal of Pediatrics.

  4. Can, E. (2025). Could parabiotics safely enhance immune maturation and mitigate NEC/sepsis in preterm neonates? Medical Hypotheses.

  5. Can, E. (2025). Early haemoglobin oxygen affinity as a hypothesis-generating marker for retinopathy of prematurity risk in preterm infants. Journal of Paediatrics and Child Health.

  6. Can, E. (2025). Neonatal portal vein thrombosis: A case series from a tertiary NICU. Pediatric Cardiology.

 

Nader Genedy | Medical Hypotheses | Best Researcher Award

Published on by Research Hypothesis

Dr. Nader Genedy | Medical Hypotheses | Best Researcher Award

Dr. Nader Genedy | Medical Hypotheses | Best Researcher Award | CRF Cardiology | University Hospital of Wales | United Kingdom 

Dr. Nader Genedy is an accomplished clinician–researcher whose work spans metabolic medicine, cardiology, geriatric care, and osteoporosis, integrating advanced academic training with extensive frontline clinical experience. Dr. Nader Genedy completed his medical degree at the University of Alexandria and progressed through multiple postgraduate qualifications, including MRCGP, MRCP(UK), MRCP(I), and specialist certification in acute internal medicine, enabling him to contribute meaningfully to both primary and specialist healthcare environments. Professionally, Dr. Nader Genedy serves as a GP with Special Interest in Osteoporosis and Geriatric Medicine, a Senior Research Fellow in Cardiology, and a Metabolic Medicine and Chemical Pathology Registrar, combining metabolic bone services, lipidology, endocrine disorders, and cardiovascular risk research with high standards of patient-centred care. His responsibilities extend to complex elderly care, DXA interpretation, risk-modelling analysis, and participation in multidisciplinary teams addressing metabolic and cardiovascular disorders. His research interests encompass atherosclerotic cardiovascular disease risk prediction, familial hypercholesterolaemia modelling, metabolic bone disease, cardiometabolic ageing, and biochemical diagnostics, with notable contributions to clinical trials such as RSA-PACE and metabolic therapeutics studies. Dr. Nader Genedy possesses advanced research skills, including statistical modelling, Cox regression, calibration analysis, SPSS/R processing, clinical dataset evaluation, and quality-improvement methodology, in addition to curriculum design, postgraduate teaching, and specialist question writing for MRCPUK examinations. His scholarly work includes conference presentations, co-authorship of national hyperlipidaemia management tools, and mentorship for postgraduate medical research candidates. Awards and honours recognising his excellence include the Bill Richmond First Prize from HEART UK, the Unsung Hero Award from the Inherited Metabolic Disease Service, multiple GREATix commendations for exemplary clinical service, and a patient feedback award for outstanding metabolic medicine contributions. Through his integrated roles in clinical care, research leadership, and academic assessment, Dr. Nader Genedy consistently demonstrates a strong commitment to advancing evidence-based practice, enhancing clinical education standards, and improving outcomes for complex elderly and cardiometabolic populations. In conclusion, Dr. Nader Genedy’s multifaceted expertise, dedication to research innovation, and ongoing contributions to both healthcare systems and academic scholarship establish him as a highly impactful medical professional whose work continues to influence patient care, clinical research, and postgraduate medical education.

Profile:  ORCID 

Featured Publications 

  1. Genedy, N., & Zouwail, S. (2025). ApoB/LDL-C discordance as a predictor of atherosclerotic cardiovascular disease in genetically confirmed heterozygous familial hypercholesterolemia: A hypothesis-generating cohort study. Journal of Clinical Lipidology.

  2. Genedy, N. (2024). Risk-modelling approaches for residual ASCVD in familial hypercholesterolaemia: An integrative clinical dataset analysis. Cardiometabolic Research Insights.

  3. Genedy, N. (2024). Metabolic bone disease assessment in primary care: Evaluating DXA utilisation and therapeutic optimisation. Journal of Osteoporosis and Geriatric Medicine.

  4. Genedy, N. (2023). Clinical implications of biochemical variability in lipidology clinics: A service-evaluation study. International Journal of Metabolic Medicine.

  5. Genedy, N. (2023). Post-CABG haemodynamic assessment: Inter-operator variability between USCOM and transthoracic echocardiography. Cardiovascular Clinical Methods Review.

  6. Genedy, N. (2022). Optimising hyperlipidaemia pathways: A quality-improvement initiative across multidisciplinary teams. British Journal of Clinical Pathway Innovation.

  7. Genedy, N. (2022). Clinical learning through biochemical case commentary: A structured model for postgraduate teaching. Medical Education Practice Journal.