Aljaz Hojski | Vision Sensing | Best Researcher

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Dr. Aljaz Hojski | Vision Sensing | Best Researcher

Dr. Aljaz Hojski | Vision Sensing | Cadre doctor at Universitätspital Basel | Switzerland

Dr. Aljaz Hojski is a highly respected thoracic surgeon and clinical researcher, currently affiliated with Universitätspital Basel. With a strong focus on surgical innovation and patient-centered care, his contributions in minimally invasive thoracic procedures and oncological surgery have gained widespread recognition across academic and clinical communities. His medical background is complemented by an extensive portfolio of scientific publications, collaborative research initiatives, and active peer-review responsibilities in high-impact journals. A committed academician and practicing consultant, Dr. Hojski is known for bridging the gap between clinical application and evidence-based research, especially in lung cancer management, thoracic trauma, and postoperative pain optimization.

Academic Profile:

ORCID

Scopus

Education:

Dr. Hojski obtained his foundational medical education at the University of Ljubljana, where he developed a keen interest in thoracic medicine and surgical procedures. His education included comprehensive training in general medicine, with progressive specialization in thoracic surgery during his clinical rotations and postgraduate residency programs. Throughout his academic journey, he emphasized scientific inquiry alongside clinical excellence, engaging in laboratory-based research and hospital-based surgical trials. This dual focus on science and surgery established a strong platform for his later contributions to applied clinical research and international collaborations in minimally invasive thoracic techniques.

Experience:

Dr. Hojski currently serves in a senior consultant role within the Department of Thoracic Surgery at Universitätspital Basel, a leading center for cardiothoracic care and research in Europe. He is actively involved in surgical planning, patient care, and mentoring junior clinicians. In addition to his clinical duties, he contributes to institutional and multicenter research protocols aimed at improving perioperative outcomes and refining surgical strategies. His professional experience spans diverse domains including advanced thoracoscopic resections, surgical pain management, and postoperative complication risk stratification. Dr. Hojski’s extensive collaborations with multidisciplinary teams, including radiologists, anesthesiologists, and oncologists, have enabled the successful translation of academic research into clinical best practices.

Research Interest:

Dr. Hojski’s primary research interests include thoracic oncology surgery, 3D imaging and surgical planning, postoperative pain control strategies, and risk prediction in lung resection patients. He has been an investigator and co-investigator on several funded research projects focused on optimizing pain therapy following minimally invasive lung operations, and the development of advanced imaging tools for segmental lung function assessment. His research also extends into clinical outcome analysis, where he contributes to developing predictive models for surgical complications and evaluating the effectiveness of new procedural technologies. His interdisciplinary approach enables him to align clinical insight with scientific rigor in solving real-world surgical challenges.

Awards:

Dr. Hojski has been nominated for several recognitions in the field of medical science and thoracic surgery, reflecting his continued impact on both clinical advancement and scientific contribution. His research output and leadership have earned him invitations to present at international symposia, while his peer-reviewed publications and service as a reviewer demonstrate his influence in academic publishing. He remains committed to excellence in both operative care and medical scholarship, making him a compelling nominee for awards that celebrate high-impact contributions to science and medicine.

Selected Publications:

  • Estimating Postoperative Lung Function Using Three-Dimensional Segmental HRCT-Reconstruction: A Retrospective Pilot Study on Right Upper Lobe Resections, 2025, 60 citations

  • Perioperative Intravenous Lidocaine in Thoracoscopic Surgery for Improved Postoperative Pain Control: A Randomized, Placebo-Controlled, Double-Blind, Superiority Trial, 2024, 85 citations

  • Planning Thoracoscopic Segmentectomies with 3-Dimensional Reconstruction Software Improves Outcomes, 2025, 45 citations

  • A Risk Score to Predict Postoperative Complications in Patients with Resectable Non-Small Cell Lung Cancer, 2025, 50 citations

Conclusion:

Dr. Aljaz Hojski represents the ideal candidate for prestigious international research recognition, owing to his consistent contributions to thoracic surgery, clinical research, and interdisciplinary innovation. Through a well-balanced integration of surgical expertise, scientific research, and professional leadership, he has advanced both patient care and academic knowledge in thoracic medicine. His published works continue to shape protocols and influence best practices within surgical communities globally. As a forward-looking clinician-scientist, Dr. Hojski is well-positioned to lead future developments in thoracic healthcare and surgical outcomes research, making him a deserving nominee for awards that honor excellence in clinical and academic medical sciences.

 

 

Ms. Preeti Shakya | Gas Sensor | Best Researcher Award

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Ms. Preeti Shakya | Gas Sensor | Best Researcher Award 

Ms. Preeti Shakya, Malaviya National Institute of Technology Jaipur, India

Preeti Shakya is a dedicated researcher in nanotechnology, currently affiliated with the Materials Research Centre at Malaviya National Institute of Technology (MNIT) Jaipur. She specializes in nanoscale materials and device design, with a particular focus on gas sensor development using MEMS technology and advanced 2D materials. Her research expertise spans synthesis and characterization of nanomaterials such as graphene oxide (GO), reduced graphene oxide (rGO), MoS₂, MoSe₂, WSe₂, and CNT-based composites. Proficient in advanced characterization techniques including FTIR, UV-Vis spectroscopy, BET surface area measurement, FE-SEM, HR-TEM, and XPS, she actively contributes to the development of next-generation sensing devices. Preeti holds a B.Tech in Electronics and Communication Engineering from Rajasthan Technical University, an M.Tech in Nanotechnology from University College of Engineering, RTU Kota, and is pursuing a Ph.D. at MNIT Jaipur. Her research interests extend to gas sensors, supercapacitors, batteries, nanofabrication, and the development of novel intelligent material systems. Passionate about advancing gas sensor technology, she is committed to creating innovative solutions that have a lasting impact on the field.

Professional Profile:

ORCID

SCOPUS

Summary of Suitability for Best Researcher Award  

Preeti Shakya demonstrates a strong background in nanotechnology, particularly in the development of gas sensors using MEMS technology and advanced nanomaterials. Her expertise in synthesis, fabrication, and characterization of nanoscale materials, along with her proficiency in advanced research software and instrumentation, makes her a strong contender for the Best Researcher Award.

📚 Education

  • 🎓 Doctor of Philosophy (Ph.D.) – Materials Research Centre, Malaviya National Institute of Technology Jaipur (CGPA: 8.1)
  • 🎓 M.Tech (Nanotechnology) – University College of Engineering, RTU Kota (79%)
  • 🎓 B.Tech (Electronics and Communication Engineering) – Rajasthan Technical University (75.75%)
  • 🏫 All India Senior Secondary School Examination (2011) – R.B.S.E (72.92%)
  • 🏫 All India Secondary School Examination (2009) – C.B.S.E (61.61%)

💼 Work Experience

🔬 Nanotechnology Researcher – Specializing in:

  • Gas sensor development using MEMS technology
  • Synthesis of nanomaterials (GO, rGO, MoS₂, MoSe₂, WSe₂, CNT, rGO-CNT composites)
  • Electronic devices (Gas Sensors, Supercapacitors, Batteries)
  • Advanced characterization techniques (FTIR, UV-Vis, FE-SEM, HR-TEM, XPS, Raman spectroscopy)
  • Nanofabrication & research instrumentation development

🏆 Achievements, Awards & Honors

🌟 Recognized researcher in nanotechnology with expertise in advanced materials
🏅 Published research in gas sensors and sensing materials
🎖️ Contributions to MEMS-based gas sensor development using 2D materials
🏆 Active participation in national & international research projects

Publication Top Notes:

Charge storage kinetics of interconnected MnO<sub>2</sub> nano-needles/reduced graphene oxide composite for high energy density quasi-solid-state sodium ion asymmetric supercapacitor

Unraveling the Pseudocapacitive Charge Storage Mechanism of NiCo<sub>2</sub>O<sub>4</sub> Nanoflakes for Advanced Quasi Solid-State Hybrid Supercapacitor

Electrochemical Study of Reduced Graphene Oxide for Supercapacitor Application

Exploring Eco-friendly Nanocellulose-Based Hydrogel Membranes as Flexible and Biocompatible Electrolyte in Supercapacitors

Ultrathin and Flexible Gas Sensor Based on Monolayer Graphene for Environmental Monitoring

 

Prof. Dr. Weidong Jiao | Smart Detection | Best Researcher Award

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Prof. Dr. Weidong Jiao | Smart Detection | Best Researcher Award 

Prof. Dr. Weidong Jiao, Zhejiang Normal University, China

Dr. Weidong Jiao was born in Wafangdian, Liaoning, China, in 1970. He received his B.E. and M.E. degrees in Safety Engineering and Mechanical Engineering from Gansu University of Technology in 1992 and 2001, respectively, and earned his Ph.D. in Mechanical Engineering from Zhejiang University in 2004. From 2004 to 2009, he served as a Professor in the Mechanical Engineering Department at Jiaxing University. Since 2013, he has been a Professor at the School of Engineering, Zhejiang Normal University. Dr. Jiao has authored over 100 research articles and holds approximately 20 invention patents. His research focuses on smart testing and signal processing, mechanical dynamics, and condition monitoring and fault diagnosis of mechanical equipment. He also serves as an Editor for the Journal of Vibration, Measurement & Diagnosis.

Professional Profile:

SCOPUS

Summary of Suitability for Best Researcher Award – Prof. Weidong Jiao

Prof. Weidong Jiao is a highly qualified candidate for the Best Researcher Award, based on his extensive contributions to mechanical engineering, fault diagnosis, and intelligent signal processing. His strong research background, innovative work, and leadership in academia make him a worthy contender for this prestigious recognition.

🎓 Education:

  • B.E. in Safety Engineering – Gansu University of Technology, Lanzhou (1992)
  • M.E. in Mechanical Engineering – Gansu University of Technology, Lanzhou (2001)
  • Ph.D. in Mechanical Engineering – Zhejiang University, Hangzhou (2004)

💼 Work Experience:

  • Professor, Mechanical Engineering Department, Jiaxing University (2004–2009)
  • Professor, School of Engineering, Zhejiang Normal University (Since 2013)

🏆 Achievements & Contributions:

  • 📚 Published over 100 research articles
  • 🔬 Invented approximately 20 innovations
  • 🛠️ Expertise in smart testing, signal processing, mechanical dynamics, condition monitoring, and fault diagnosis
  • 📝 Editor of Journal of Vibration, Measurement & Diagnosis

🏅 Awards & Honors:

  • 🎖️ Recognized for contributions in mechanical engineering and diagnostics
  • 🏅 Honored for advancements in fault diagnosis and condition monitoring
  • 🔍 Acknowledged for outstanding research and academic contributions in mechanical dynamics

Publication Top Notes:

Compact multiphysics coupling modeling and analysis of self-excited vibration in maglev trains

Deep learning in industrial machinery: A critical review of bearing fault classification methods

Recursive prototypical network with coordinate attention: A model for few-shot cross-condition bearing fault diagnosis

Double attention-guided tree-inspired grade decision network: A method for bearing fault diagnosis of unbalanced samples under strong noise conditions

Cross-Conditions Fault Diagnosis of Rolling Bearing Based on Transitional Domain Adversarial Network