Find Academic Supervisors

Christopher C. JobesView profile

Professor

• Mechanical Engineering
• Engineering Mechanics
• Finite Element Analysis
• +7 more

Christopher C. Jobes is a Professor in the Department of Mechanical Engineering at Geneva College, with a Ph.D. in Engineering from West Virginia University (1987). He also holds M.S. and B.S. degrees in Mechanical Engineering from West Virginia University (1985) and Geneva College (1983, 1982), respectively. Fields of Instruction: Mechanical Engineering & Engineering Mechanics Research Interests: Machine Component Design, Kinematics of Mechanisms, Finite Element Methods, Control Systems Design Professional Certification: Registered Professional Engineer (Pennsylvania) His recent work focuses on Mining Equipment Safety , including proximity detection systems, vibration control, and electromagnetic interference mitigation. He has collaborated extensively with NIOSH on safety protocols for underground mining machinery. Key awards include: Excellence in Scholarship Award, Geneva College (2020-2021) Silver Chairman’s Award (2014, Federal Executive Board of Pittsburgh) Outstanding Scientific Paper Nominee (2013, Charles C. Shepard Science Award) NIOSH/HHS Excellence in Communication (2012) Jobes serves as faculty advisor for Geneva College’s SAE Baja Racing Team and has over 69 publications with 35 first authorships in peer-reviewed journals and conferences.

Osama A. MohammedView profile

Professor

• Power Systems
• Smart Grid
• Energy Cyber Physical Systems
• +9 more

Dr. Osama A. Mohammed is a Distinguished Professor and Associate Dean of Research at the College of Engineering and Computing, Florida International University (FIU), where he also serves as Director of the Energy Systems Research Laboratory (ESRL) in the Department of Electrical and Computer Engineering. He has been a faculty member since 1983 and is internationally recognized for his pioneering work in power systems, smart grids, and energy cyber-physical systems. His educational background includes: B.S. in Electrical Engineering, Zagazig University, Egypt (1977) M.S. in Electrical Engineering, Virginia Tech (1981) Ph.D. in Electrical Engineering, Virginia Tech (1983) Dr. Mohammed's research is centered on advancing energy systems through innovation in power electronics, electric drives, computational electromagnetics, and smart grid technologies. He emphasizes real-world applications, student involvement, and interdisciplinary collaboration. His work bridges academia and industry, focusing on resilient, secure, and efficient energy infrastructure. His extensive publication record—over 750 papers—demonstrates a strong trend toward intelligent control, cybersecurity in energy systems, real-time simulation, and optimization of hybrid AC/DC microgrids. Many of his recent works integrate AI and machine learning for predictive maintenance, demand response, and digital twin applications in power systems. His scientific awards and recognitions include: Fellow of the National Academy of Inventors (2022) IEEE Fellow (1996) ACES Fellow (2006) IEEE PES Cyril Veinott Award (2010) Multiple Best Paper Awards in IEEE Transactions Top 2% Scientist Worldwide (Stanford, 2020) Dr. Mohammed is a dedicated mentor, having supervised 34 PhD graduates and over 60 master’s students, many of whom now hold prestigious positions in academia and industry. He has secured over $30 million in research funding from DoE, ONR, NSF, and industry. His leadership extends to major multi-university consortia, including the $12.2M DoE SEEDS Center. He has developed key research labs at FIU, such as the Smart Grid Test-Bed and Power Electronics Laboratory, providing unparalleled hands-on experience for students.

Robert JacksonView profile

Professor

• Tribology
• Friction
• Wear
• +11 more

Robert Jackson is the Albert Smith Jr. Professor in the Department of Mechanical Engineering at Auburn University’s Samuel Ginn College of Engineering. He holds a Ph.D., M.S., and B.S. in Mechanical Engineering from the Georgia Institute of Technology and is a leading researcher in tribology, contact mechanics, friction, wear, and lubrication. He serves as Editor-in-Chief of the ASME Journal of Tribology and leads a research group focused on multiscale modeling, electrical contact reliability, and sustainable biolubricants. His research interests include: tribology, friction, wear, surface engineering, surface fatigue, lubrication, nano-lubricants, surface texturing, and multiphysics modeling. He applies these to challenges in electric vehicles, aerospace systems, and biomedical devices. His work integrates experimental validation with finite element and statistical modeling to understand contact behavior across scales. His recent publications focus on electrical erosion in EV bearings, nanoparticle-enhanced greases, mixed lubrication models, and biolubricants from waste oil. These works reflect a strong trend toward sustainable engineering, advanced materials, and predictive modeling in mechanical systems. His interdisciplinary approach bridges mechanical, materials, and electrical engineering. ASME Fellow Fellow, Society of Tribologists and Lubrication Engineers (STLE) Ralph Beard Memorial Academic Award Editor-in-Chief, ASME Journal of Tribology Dr. Jackson advises graduate students such as Jack Janik and collaborates with researchers on projects involving electrical connectors, solenoid valves, and biolubricant development. His lab engages in both fundamental contact mechanics and applied industrial problems. He leads the tribology minor and student sections of ASME and STLE at Auburn.

Silvia Salinas BlemkerView profile

Professor

• Biomechanics
• Musculoskeletal Diseases
• Multi-scale Modeling
• +5 more

Dr. Silvia Salinas Blemker is a Professor of Biomedical Engineering at the University of Virginia, with a courtesy appointment in Mechanical and Aerospace Engineering. Her research focuses on understanding muscle structure-function relationships through computational and experimental models to develop treatments for musculoskeletal diseases. She earned her undergraduate and Master’s degrees in Biomedical Engineering from Northwestern University and a PhD in Mechanical Engineering from Stanford University. Her Multiscale Muscle Mechanophysiology (M3) Lab investigates skeletal muscle mechanics, regeneration, and disease mechanisms, particularly in conditions like muscular dystrophy and aging. The lab’s work integrates AI-driven MRI analysis, finite element modeling, and clinical collaborations to improve patient outcomes. Key projects include developing the FDA-approved Springbok Analytics tool for muscle health assessment and advancing understanding of muscle regeneration through agent-based modeling. Dr. Blemker has received prestigious awards such as the National Academy of Inventors Fellowship (2024) and the American Society of Biomechanics’ Goel Award (2024). She teaches courses in biomechanics and motion analysis. Her research has been featured in innovations like biomechanics-based injury prevention systems for athletes and novel imaging techniques for neuromuscular disease monitoring.

Giuseppe QuarantaView profile

Associate Professor

• Structural Monitoring and Control
• Sensing systems for structural monitoring
• Dynamic identification
• +9 more

Giuseppe Quaranta is an Associate Professor in the Department of Structural and Geotechnical Engineering at Sapienza University of Rome, Faculty of Civil and Industrial Engineering. He is actively engaged in teaching Construction Techniques and Construction Process Management, and maintains regular office hours for students. His research focuses on structural monitoring, dynamic identification, seismic protection systems, and AI applications in civil engineering. Department: Department of Structural and Geotechnical Engineering Faculty: Faculty of Civil and Industrial Engineering Institution: Sapienza University of Rome Email: giuseppe.quaranta@uniroma1.it Dr. Quaranta's research interests span structural monitoring and control, sensing systems, dynamic identification, diagnostics of civil structures, passive vibration control devices, structural concrete, and the analysis and design of reinforced concrete and composite steel-concrete structures. His work integrates computational modeling, experimental validation, and data-driven approaches, particularly using artificial intelligence and machine learning for structural health monitoring and seismic assessment. He investigates energy harvesting using piezoelectric materials for powering wireless sensors in smart infrastructure. The recent publications of Dr. Quaranta demonstrate a strong trend toward energy-based seismic engineering, machine learning for structural performance prediction, nonlinear dynamics of isolation systems, and advanced modal identification techniques. His work combines theoretical modeling, numerical simulation, and experimental testing, with applications to bridges, buildings, and historical structures like the Leaning Tower of Pisa and the Colosseum. Dr. Quaranta is involved in the research project titled "Smart technologies and decision support tools for the assessment of deteriorating reinforced concrete infrastructures in seismic areas at territorial scale," which aligns with his expertise in corrosion hazard mapping, seismic resilience, and smart monitoring systems. While student advising is implied through research collaboration, specific names of advisees are not listed in the provided materials. Smart technologies and decision support tools for the assessment of deteriorating reinforced concrete infrastructures in seismic areas at territorial scale His research team conducts experimental and computational studies on structural systems, including dynamic monitoring of iconic structures such as the Leaning Tower of Pisa and the Colosseum. The lab focuses on developing and applying advanced signal processing techniques, nonlinear system identification methods, and AI-driven tools for structural health monitoring and seismic risk assessment.

Mingjun ZhongView profile

Lecturer

• Machine Learning
• Computational Statistics
• Non-intrusive Load Monitoring
• +12 more

Dr. Mingjun Zhong is a Lecturer in the School of Natural and Computing Sciences at the University of Aberdeen. His research focuses on machine learning and computational statistics with applications in healthcare, energy systems, and medical imaging. He is actively engaged in teaching and academic service, including editorial roles in prominent journals. Position: Lecturer Institution: University of Aberdeen School: School of Natural and Computing Sciences Email: mingjun.zhong@abdn.ac.uk Dr. Zhong's research interests center on probabilistic and statistical machine learning methodologies applied to real-world data. He works on healthcare data analysis, non-intrusive load monitoring (NILM), spectroscopy, EEG/fMRI, and energy disaggregation. His methodological expertise includes variational inference, Markov chain Monte Carlo, Bayesian matrix factorization, and deep learning. He has developed lightweight and efficient neural network models for applications in medical imaging and smart grids. The most recent publications reflect a strong trend in applying advanced machine learning techniques—particularly deep learning, self-supervised learning, and capsule networks—to diverse domains such as medical diagnostics, energy disaggregation, and clinical decision support. There is a clear emphasis on developing efficient, interpretable, and robust models for real-world deployment, often addressing challenges like class imbalance, domain adaptation, and data scarcity. Scientific recognition includes: Fellow of the Higher Education Academy (FHEA) Associate Editor, Neural Processing Letters Review Editor, Frontiers in Applied Mathematics and Statistics Regular reviewer for top-tier journals and conferences in AI and machine learning Grant reviewer for multiple funding bodies Dr. Zhong advises a number of students, as evidenced by co-authorships on numerous publications. His research is supported by academic collaborations and likely external grants, though specific funding details are not mentioned. He teaches courses in Robotics, Machine Learning, and Knowledge Representation and Reasoning, contributing significantly to the curriculum in computing sciences. He is involved in interdisciplinary research, particularly through projects like ARCHERY (Artificial intelligence to Revolutionise the patient Care pathway in Hip and knEe aRthroplastY), which integrates AI into orthopaedic care. His work bridges computer science, statistics, and domain-specific applications, demonstrating a strong commitment to impactful, application-driven research.

• Structural Health Monitoring
• Non-Destructive Testing and Evaluation
• Stress Wave Research
• +12 more

Professor Wing Chiu is a faculty member in the Department of Mechanical & Aerospace Engineering at Monash University, Faculty of Engineering. He is actively engaged in research and is accepting PhD students. His work spans structural mechanics, failure analysis, and advanced monitoring systems. His research interests include Structural Health Monitoring , Non-Destructive Testing and Evaluation , Stress Wave Research , Composite Structures , Fatigue and Failure Analysis , and Noise and Vibration Control . He applies these to real-world engineering challenges, particularly in aerospace and critical infrastructure. The recent publications highlight a consistent focus on smart sensing, acoustic emission, and data-driven modeling for structural integrity. His team employs advanced materials like relaxor-ferroelectrics and machine learning techniques such as deep learning encoder-decoder networks and feature fusion architectures to enhance monitoring accuracy and automation. Professor Chiu leads multiple externally funded research projects, including those on autonomous structural health monitoring, durable UAV antennas, and digital-physical asset assessment. He also conducts industrial consulting through private practice, applying his expertise in structural mechanics and vibration analysis. He is involved in laboratory and field-based research, supervising graduate students and collaborating with national and international partners. His lab focuses on sensor development, signal processing, and multi-physics modeling for real-time structural assessment.

Wenyi YanView profile

Professor

• Mechanical Engineering
• Materials Science
• Railway Engineering
• +7 more

Prof. Wenyi Yan is a Professor in the Department of Mechanical & Aerospace Engineering at Monash University. His career includes research fellowships in Europe, roles at multiple Australian universities, and leadership in over 14 Australian Research Council (ARC) grants since 2006. He has supervised 19 PhD and 5 MSc graduates, currently mentoring 12 PhD students. Education: B.Eng. (Hons) from Beijing University of Aeronautics and Astronautics (1989), M.Sc. (1992), and Ph.D. (1995) from Tsinghua University. Research focuses on material behavior under cyclic loading, railway infrastructure optimization, laser cladding repair, and additive manufacturing. Key areas include fracture mechanics, tribology, and computational modeling for rail systems. Notable projects: Development of machine learning tools for structural analysis (2025–2028), ARC Hub for Smart Process Design (2024–2029), and rail component repair using laser cladding. His work addresses UN SDGs related to sustainable infrastructure and innovation. Awards: While no specific prizes are listed, his extensive grants and publications (3 book chapters, 176 journals, 68 conferences; h-index 42) reflect his impact. Current grants total over 10 projects, emphasizing industry collaboration with Rio Tinto, ANSTO, and others.

Antonis StylianouView profile

Associate Professor

• Computational Biomechanics
• Musculoskeletal Modeling
• Multibody Dynamics
• +3 more

Antonis Stylianou is an Associate Professor in the Department of Mechanical Engineering at the University of Missouri - Kansas City (UMKC). He holds a Ph.D. in Mechanical Engineering from the University of Kansas (2004), along with multiple master's degrees in Mathematics (2012) and Mechanical Engineering (2000), and a B.S. in Mechanical Engineering (1998). His primary affiliation is within the School of Science and Engineering, where he focuses on computational biomechanics, musculoskeletal modeling, and multibody dynamics. Dr. Stylianou's research emphasizes Elbow joint mechanics and ligament deficiency modeling Virtual surgical simulators for pelvic surgery error prevention Pediatric knee anatomy and ACL injury risk analysis Musculoskeletal models of human motion Finite element analysis of cartilage contact pressures His work bridges computational methods with clinical applications, including developing tools for orthopedic surgeons and training paradigms for surgical techniques. Recent studies have explored surgeon kinematics during retropubic trocar insertion and posteromedial rotatory incongruity of the elbow. Teaching responsibilities include advanced dynamics, biomechanics, and experimental methods courses. His contributions underscore the integration of engineering principles with medical challenges, particularly in orthopedic and surgical contexts.

• Machine Learning
• Reinforcement Learning
• Causal Machine Learning
• +7 more

Dr. Bevan Smith is a Senior Lecturer at the School of Mechanical, Industrial & Aeronautical Engineering at the University of the Witwatersrand (Wits), where he also completed his BIng(Mech), MSc(Eng), and PhD. His research bridges machine learning and engineering systems, with a focus on optimization through simulation and AI. His research interests include: Machine Learning and Reinforcement Learning Causal Machine Learning and Causal Discovery Graph Neural Networks Explainable AI (XAI) Digital Twins Application of AI in Education and Industrial Systems His recent publications demonstrate a strong trajectory in applying causal inference and machine learning to both industrial optimization (e.g., inventory management, air quality monitoring) and educational challenges (e.g., at-risk student prediction, online video interventions). He utilizes simulation-based methods and counterfactual reasoning to evaluate treatment effects and improve decision-making. His work spans journals in industrial engineering, artificial intelligence in education, and computational applications. Notable scientific contributions include: Developing AI-driven solutions for low-cost air quality monitoring (AI_r) Evaluating X-Learner performance under confounding and non-linearity Applying Pearl’s counterfactual framework to assess explanation validity Designing personalized interventions for engineering students using model-agnostic explanations Dr. Smith has contributed to curriculum development through the creation of online educational videos and has explored structural design in mechanical engineering, notably in carbon fibre swingarm development. While no formal advising or grant information is available, his work reflects an interdisciplinary approach combining mechanical engineering principles with cutting-edge AI methodologies. He is actively publishing and remains engaged in both technical and educational research domains.

Philip NakashimaView profile

Associate Professor

• Transmission Electron Microscopy
• Quantitative Convergent-Beam Electron Diffraction
• Electron Crystallography
• +15 more

Philip Nakashima is an Associate Professor in the Department of Materials Science & Engineering within the Faculty of Engineering at Monash University. He is an active researcher with a PhD in Physics from the University of Western Australia (2002) and has over 25 years of experience in advanced transmission electron microscopy (TEM) and quantitative convergent-beam electron diffraction (QCBED). He is currently accepting PhD students and is involved in cutting-edge research in materials characterization and quantum information technology. His research focuses on the development and application of advanced electron microscopy techniques to study the structure, bonding, and properties of materials such as metals, alloys, ceramics, and nanostructures. Key areas include quantitative CBED, electron crystallography, digital image restoration, noise quantification, and multi-parameter optimization. He has made seminal contributions to understanding chemical bonding in aluminum and has extensive experience in high-performance computing for materials analysis. His most recent publications demonstrate a strong trend toward integrating machine learning with materials design, particularly for magnesium alloys, while maintaining core expertise in electron diffraction and microscopy. He continues to publish in high-impact journals such as Science , Physical Review Letters , and Acta Materialia . Philip Nakashima has received several prestigious awards for his research excellence: John Sanders Medal (2012) : Awarded by the Australian Microscopy and Microanalysis Society for excellence in electron microscopy techniques. Barry Inglis Medal (2011) : Awarded by Australia’s National Measurement Institute for outstanding achievement in measurement research. The Cowley-Moodie Award (2006) : Recognizing research excellence in electron microscopy in the physical sciences. He has been a visiting researcher at the ARC Future Fellowship (2012–2016) and is currently an Associate Investigator in the Quantum Information Technology project (2023–2027). He teaches advanced crystallography to undergraduate and postgraduate students and has been invited to lecture at international schools on electron and quantum crystallography. His research involves collaboration with leading scientists in Australia and internationally, and he leads work on advanced microscopy for materials engineering applications.

Gabriel PereiraView profile

Assistant Professor

• Critical Algorithm Studies
• Computer Vision
• Surveillance Studies
• +5 more

Gabriel Pereira is Assistant Professor in AI & Digital Culture at the University of Amsterdam (UvA), based at the Media Studies department and the Institute for Logic, Language and Computation (ILLC). His primary affiliation is with the Natural Language Processing & Digital Humanities (NLP&DH) group. Pereira's research focuses on the critical study of data and algorithms, particularly as they intersect with vision, images, and surveillance systems. He brings an interdisciplinary approach that combines Science and Technology Studies (STS), critical algorithm studies, platform studies, and artistic interventions to examine how computational systems shape social relations and power structures. Pereira's research interests center on critical perspectives of computer vision and algorithmic governance, with particular attention to how these technologies operate in contexts of surveillance, platform labor, and digital culture. His work critically examines hegemonic computer vision systems that reinforce surveillance capitalism while exploring alternative ways of seeing and engaging with technology. He has conducted extensive research on Automated License Plate Recognition systems, WhatsApp disruptions in Brazil, and the political economy of smart surveillance projects like Curitiba's 'Digital Wall.' His methodology combines qualitative research with practice-based inquiry, including arts-based and interventionist approaches that challenge dominant technological narratives. His publication trends reveal a consistent focus on critical examinations of algorithmic systems, with growing attention to resistance practices, decolonial perspectives, and situated knowledge from the Global South. The articles show a trajectory from analyzing specific surveillance technologies toward broader theoretical frameworks for understanding algorithmic antagonisms and developing critical technical practices. His work consistently bridges theory and practice, often collaborating with artists and activists to create interventions that make visible the hidden operations of algorithmic systems. Independent Research Fund Denmark International Postdoc grant Deviant Practice Grant (Van Abbemuseum) Pereira actively organizes academic and artistic events, serving as Secretary of the Association of Internet Researchers and participating in the Tierra Común network. He co-organized the Con/Crit/Tec residency in São Paulo that brought together over 80 transdisciplinary researchers to explore alternative geographies of digital consciousness. His collaborative approach extends to numerous grant-funded projects examining platform labor, algorithmic surveillance, and critical data literacy. Pereira emphasizes organizing as an essential element of research work, facilitating knowledge exchange between academic, artistic, and activist communities. He is affiliated with multiple research collectives including the Center for Arts, Design and Social Research (CAD+SR), where he serves as a Researcher in Residence. His practice-based projects like 'Future Movement Future – REJECTED,' 'Algorithmic Sea,' and 'History of _rt' demonstrate his commitment to using artistic interventions as critical research methods. These projects often involve collaborations with artists, technologists, and activists to create alternative ways of engaging with and understanding algorithmic systems.

• Electrical Power Systems
• Analysis, Planning, Operation and Economics of Electrical Systems
• Renewable Energy Integration
• +12 more

Enrique Lobato Miguélez is a Full Professor at the Higher Technical School of Engineering (ICAI) of Comillas Pontifical University in Madrid, Spain, where he has been a faculty member since June 1, 1998. His research is conducted at the Technological Research Institute (IIT), focusing on electrical power systems, with a strong emphasis on renewable integration, energy storage, and island grid stability. His research interests include the analysis, planning, operation, and economics of electrical systems, with specific expertise in frequency-power control, voltage control, electricity markets, and the sustainability of insular systems. He has made significant contributions to the integration of renewable energy sources and the application of storage elements in power systems. The recent publications highlight a consistent focus on optimization, control, and integration challenges in modern power systems, particularly in islanded and renewable-rich environments. Key themes include unit commitment with frequency constraints, robust design of under-frequency load shedding, battery energy storage in wind farms, and data-driven modeling for stability enhancement. His work often combines theoretical modeling with practical applications for industry partners like Red Eléctrica de España, Naturgy, and Endesa. Scientific Awards: Eolo annual Innovation Award in May 2016 awarded by the Wind Energy Business Association AEE Distinción Honorífica a la mejor Tesis Doctoral en Ingeniería «Voltage control design of wind energy harvesting networks» por Elena Sáiz Professor Lobato has supervised several PhD students, including M. Rajabdorri, I. Saboya, and E. Saiz. He has led numerous research and consulting projects funded by the European Commission, Spanish government agencies, and private companies such as Iberdrola, Endesa, and Viesgo, covering topics like grid integration of renewables, energy storage, and market design. He has also organized major scientific events such as the ICREPQ conferences and conducted training courses for industry professionals. He has conducted research stays at Sophia University in Tokyo and University College Dublin, and his work has resulted in a book on island power systems and over 60 journal publications.

Muhammet Fethi GüllüView profile

Associate Professor

• Reinforced Concrete Structures
• Earthquake Engineering
• Structural Mechanics
• +3 more

Muhammet Fethi Güllü is an Associate Professor at Harran University's Civil Engineering Department, specializing in reinforced concrete structures, earthquake engineering, and structural mechanics. He has contributed to numerous nonlinear finite element modeling studies and seismic performance evaluations of RC buildings. PhD from Boğaziçi University (2019) MSc in Civil Engineering from Boğaziçi University (2013) BSc in Civil Engineering from Istanbul Technical University (2009) His research focuses on advanced modeling of structural components, including shear walls, T-beams, and columns, with emphasis on seismic resilience and retrofitting techniques using FRP materials. He has published extensively in journals like Structures , Buildings , and Journal of Earthquake Engineering . Recent works include experimental and analytical investigations of retrofitted T-beams (2024), machine learning applications for RC building characteristics (2024), and studies on infill wall configurations (2023). He actively participates in international conferences such as the World Conference on Earthquake Engineering. As a principal investigator, he leads research projects on earthquake performance evaluation of existing buildings, RC column behavior, and FRP retrofitting. Collaborative efforts include a Turkish-Japanese survey of earthquake-damaged RC buildings (2024).

Mohammad PoursinaView profile

Associate Professor

• Dynamic Modeling and Simulation of Multibody Systems
• Robotics and Robotic Rehabilitation
• Artificial Intelligence and Control Systems
• +4 more

Mohammad Poursina is an Associate Professor at the Department of Engineering Sciences , University of Agder, Norway. His research spans multibody dynamics , robotics , and celestial mechanics , with a focus on dynamic modeling, control systems, and biomechatronics. He has held prior academic roles at the University of Arizona and Rensselaer Polytechnic Institute. Ph.D. in Mechanical Engineering, Rensselaer Polytechnic Institute (2011) M.Sc. and B.Sc. in Mechanical Engineering, University of Tehran (2006, 2003) His work addresses complex systems through interdisciplinary approaches, including robotic rehabilitation , flexible manipulators , and molecular dynamics . Recent publications highlight advancements in trajectory optimization , VR/AR surgical training , and asteroid system modeling . He serves on the editorial boards of Multibody System Dynamics and Journal of Computational and Nonlinear Dynamics , and has organized ASME conference sessions. Key research groups: Artificial Intelligence, Biomechatronics, and Collaborative Robotics Projects: Reality-Connected Machine Simulation of Heavy Machinery (RealSim)