Cristina Veres

@umfst.ro

Faculty of Engineering and Information Technology / Department of Industrial Engineering and Management
George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures

Cristina Veres


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https://researchid.co/cristina.veres

RESEARCH, TEACHING, or OTHER INTERESTS

Management of Technology and Innovation, Engineering, Multidisciplinary
46

Scopus Publications

Scopus Publications

  • Machine learning in sustainable fiber-reinforced polymers: a bibliometric and critical assessment
    Maria Tanase, Roland Bolboaca, Cristina Veres
    Polymer Bulletin, 2026
    The growing demand for sustainable materials has accelerated the development of bio-based fiber-reinforced polymers (BFRPs) as environmentally friendly alternatives to conventional composites. However, the intrinsic variability of natural fibers and the complexity of processing–structure–property relationships pose significant challenges for material design and optimization. In this context, machine learning (ML) has emerged as a powerful data-driven approach for predicting material behaviour and accelerating the development of sustainable composites. This paper presents a comprehensive bibliometric and critical review of ML applications in BFRPs. Publication trends, influential contributors, collaboration networks, and thematic evolution are analyzed to map the development of this interdisciplinary research field. The review further examines the most frequently used ML algorithms, targeted material properties, and investigated bio-fiber systems. Particular attention is given to methodological practices, data limitations, and model validation strategies. The analysis reveals a rapid increase in ML-based studies, with artificial neural networks, support vector machines, and tree-based methods dominating the literature. While promising predictive capabilities have been demonstrated, challenges related to data quality, model interpretability, and generalization remain. The study concludes with recommendations for future research directions to enhance the reliability, transparency, and sustainability impact of ML-assisted BFRP development.
  • A Correlation-Driven, Process-Oriented Framework for Vibro-Acoustic Comfort Assessment in Special-Purpose Vehicle Cabins
    Bianca-Mihaela Cășeriu, Cristina Veres, Maria Tănase, Petruța Blaga
    Processes, 2026
    The evaluation of vibro-acoustic comfort in vehicle cabins is frequently limited by fragmented treatment of noise and vibration indicators and by the absence of structured, reproducible assessment frameworks. This study proposes an advanced, correlation-driven and process-oriented methodology for vibro-acoustic comfort evaluation, designed to support systematic analysis and decision-making across varying vehicle operating conditions. The proposed framework is formulated as a sequential process comprising experimental data acquisition, signal preprocessing, statistical correlation analysis, and decision-oriented interpretation. The framework was experimentally validated on five special-purpose armored platforms under both stationary and dynamic operating regimes, with repeated measurement trials to ensure robustness. Interior and exterior sound pressure levels, together with vibration-related parameters, are experimentally measured under stationary and dynamic operating regimes. Pearson correlation coefficients are employed to quantify interdependencies among vibro-acoustic variables and identify dominant contributors affecting comfort-related conditions. The results indicate statistically significant correlations between interior noise levels and selected vibration indicators, revealing distinct correlation patterns associated with different operating states. Based on these findings, correlation strength was classified as weak (|r| < 0.3), moderate (0.3 ≤ |r| < 0.6), and strong (|r| ≥ 0.6), enabling structured contributor ranking. The primary contribution of this work consists in elevating correlation analysis from a descriptive statistical technique to a formalized assessment process suitable for integration into predictive modeling and optimization workflows. The framework provides a transferable methodological structure, validated within the investigated vehicle category.
  • Machine-Learning Algorithm and Decline-Curve Analysis Comparison in Forecasting Gas Production
    Dan-Romulus Jacota, Cristina Roxana Popa, Maria Tănase, Cristina Veres
    Processes, 2026
    This study utilizes machine-learning algorithms to reinterpret existing datasets originally plotted using Decline-Curve Analysis (DCA), aiming to enhance predictive accuracy without requiring new field-data acquisition. Historical production records were compiled: monthly oil/gas rates, bottom-hole pressures, and cumulative productions, which were fitted to Arps equations via least-squares optimization, and key decline parameters, such as initial rate, nominal decline rate, and hyperbolic exponent, served as input data. Four machine-learning models were trained and validated: Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Linear Regression (LR), using 80/20 train–test splits and 5-fold cross-validation. Models were evaluated using Mean Squared Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2). The ANN emerged as the best-performing method, achieving near-unity predictive accuracy (R2 ≈ 1) on the independent test set, with low error values (MSE = 0.0012 Ncm2/month2, RMSE = 0.035 Ncm/month, MAE = 0.028 Ncm/month) for oil production rates. Similar levels of accuracy were obtained for gas rates and pressures. These results reflect the strong and highly regular relationships present in the dataset analyzed rather than an exact zero-error fit. The multi-layer architecture of the ANN effectively captured the nonlinear interactions between Arps parameters and transient flow regimes, outperforming the empirical and physics-constrained approaches. Linear regression yielded strong results (R2 = 0.98, RMSE = 0.15 Ncm/month) but faltered in high-decline scenarios, failing to model exponential tails accurately. SVM exhibited the highest deviations (RMSE = 0.42 Ncm/month, R2 = 0.89), attributable to kernel sensitivity in sparse, noisy decline data. RF provided intermediate performance (R2 = 0.97). This ANN-driven approach redefines decline analysis by automating parameter tuning and uncertainty quantification, reducing forecasting errors by 85% versus classical Arps methods.
  • The Effects of Physical Therapy in the Rehabilitation of Motor Delays in Children with Down Syndrome: A Systematic Review
    Dan Alexandru Szabo, Adina Stoian, Cristina Veres, Heidrun Adumitrachioaie, Carmen Pârvu, et al.
    Journal of Clinical Medicine, 2026
    Background/Objectives: This study’s primary goals are to evaluate the effects of physical therapy on motor delays in children with Down Syndrome, identify the most successful interventions, look at current trends in the field, and suggest future directions for clinical and research development by reviewing the scientific literature published over the past ten years. Methods: Using reputable databases, including PubMed, ScienceDirect, CENTRAL (Cochrane Central Register of Controlled Trials), PEDro (Physiotherapy Evidence Database), Web of Science, and NIH, an electronic search of scholarly literature was carried out between January and April 2025. To organise the findings and select the most pertinent papers, a search strategy was required. Results: The studies analysed provide a complex picture of how different types of physical therapy interventions affect children and adolescents with Down syndrome. Conclusions: Physical therapy interventions suggest greater effectiveness during the early stages of motor development in children with Down Syndrome; however, the evidence, based on six heterogeneous studies, remains moderate and does not support definitive recommendations. In clinical practice, physical therapists are advised to design individualised programmes that address specific needs, utilising traditional therapies, online training, or movement stimulation techniques, and to systematically monitor their outcomes.
  • The Role of Leadership in Lean Healthcare Transformation: A Mixed-Methods Study
    Cristina Veres, Mircea Stoian, Dan-Alexandru Szabo, Manuela Rozalia Gabor
    Journal of the Knowledge Economy, 2026
    This study explores the efficacy of Lean Management principles within healthcare settings, focusing on waste reduction and process efficiency. By integrating Lean methodologies, healthcare facilities aim to optimize operations and improve patient outcomes. Our analysis underscores the critical role of leadership in spearheading Lean initiatives, which are essential for fostering a culture of continuous improvement and operational excellence in healthcare. Utilizing a mixed-method approach, the study incorporates a literature review and quantitative analysis of data collected from multiple public and private healthcare institutions. Correlation analysis identified relationships among various types of waste. Kruskal–Wallis tests indicated significant differences in waste perception based on seniority for waiting time and overproduction. Regression analysis explained 20.1% of the variance in the use of waste reduction methods, with age, Lean Management knowledge, gender, and managerial position as significant predictors. These findings highlight the importance of leadership and targeted training for the successful implementation of Lean Management practices in healthcare. This study contributes original insights by quantitatively identifying key leadership and demographic factors that influence Lean Management adoption in Romanian healthcare institutions, providing a structured framework for understanding waste reduction across varied healthcare environments. Our findings emphasize the role of targeted leadership strategies and demographic adaptability as critical drivers in achieving waste reduction and operational efficiency through Lean principles.
  • Innovative Approaches to Acoustic Comfort in Vehicles: Experimental Assessment and Strategic Noise Reduction Solutions
    Petruța Blaga, Bianca-Mihaela Cășeriu, Cristina Veres
    Applied Sciences Switzerland, 2026
    This study presents a rigorous experimental investigation of in-cabin acoustic comfort across a heterogeneous set of road and special-purpose vehicles. Interior noise measurements were conducted on a total of 35 vehicles, comprising five vehicles from each of seven operational categories, grouped according to RNTR-2 regulations into three distinct vehicle classes: N1, N2, and N2G. The adopted research methodology ensures a unified, phenomenological, and experimental approach to the assessment of interior vehicle acoustics, enabling consistent data acquisition and comparative analysis across vehicle classes. Measurements were performed under both stationary and dynamic operating conditions using Class 1 precision instrumentation. The experimental results reveal systematic differences in acoustic performance between vehicle classes. While N1 and N2 vehicles generally comply with recommended comfort thresholds, N2G special-purpose vehicles exhibit significantly elevated interior noise levels, reaching up to 90 dBA during dynamic operation, together with increased variability at higher engine regimes. These findings highlight the influence of vehicle architecture, operational conditions, and mission-oriented design constraints on vibro-acoustic behavior. Passive noise control solutions based on advanced sound-absorbing and sound-insulating materials were further evaluated, demonstrating interior noise reductions of up to 10 dBA. The scientific contribution of this work lies in the establishment of a unified, reproducible methodology that enables direct cross-category comparison of in-cabin acoustic comfort while explicitly integrating special-purpose vehicles into a comfort-oriented analytical paradigm. By moving beyond regulatory compliance toward a multidimensional interpretation of acoustic comfort, the study provides a robust foundation for vehicle design optimization and supports the future development of dedicated comfort assessment standards.
  • Artificial Intelligence in Biomedical 3D Printing: Mapping the Evidence
    Maria Tănase, Cristina Veres, Dan-Alexandru Szabo
    Journal of Manufacturing and Materials Processing, 2025
    This study provides an integrated synthesis of Artificial Intelligence (AI) applications in Biomedical 3D Printing, mapping the conceptual and structural evolution of this rapidly emerging field. The bibliometric analysis, based on 229 publications indexed in the Web of Science Core Collection (2018–2025) and visualised in CiteSpace, identifies three interconnected research domains: AI-driven design and process optimisation, data-assisted bioprinting for tissue engineering, and the development of smart and adaptive materials enabling 4D functionalities. The results highlight a clear progression from algorithmic control of additive manufacturing parameters toward predictive modelling, deep learning, and autonomous fabrication systems. Leading contributors include China, India, and the USA, while journals such as Applied Sciences, Polymers, and Advanced Materials act as major dissemination platforms. Emerging clusters around “4D printing”, “deep learning”, and “shape memory polymers” indicate a shift toward intelligent, sustainable, and personalised biomanufacturing. In addition, a qualitative synthesis of the most influential papers complements the bibliometric mapping, providing interpretative depth on the scientific core driving this interdisciplinary evolution. Overall, the study reveals the consolidation of a multidisciplinary research ecosystem in which computational intelligence and biomedical engineering converge to advance the next generation of adaptive medical fabrication technologies.
  • Polymeric Materials in Biomedical Engineering: A Bibliometric Mapping
    Cristina Veres, Maria Tănase, Dan-Alexandru Szabo
    Polymers, 2025
    This study offers an integrated synthesis of polymeric materials in biomedical engineering, revealing four major and interlinked research domains: tissue engineering and regenerative medicine, drug delivery and nanomedicine, wound healing and antimicrobial applications, and advanced fabrication through 3D/4D printing and bioprinting. Across these areas, hydrogels, biodegradable composites, and stimuli-responsive polymers emerge as the most influential material classes. The analysis highlights substantial progress in extracellular matrix–mimetic scaffolds, smart drug delivery systems with controlled release, multifunctional wound dressings integrating antimicrobial and healing functions, and patient-specific constructs produced via additive manufacturing. Despite these advances, recurring challenges persist in long-term biocompatibility and safety, scalable and reproducible fabrication, and regulatory standardisation. The results point toward a convergence of bioactivity, manufacturability, and clinical translation, with hybrid natural–synthetic systems and personalised polymeric designs defining the next phase of biomedical polymer innovation.
  • Green Composites in Additive Manufacturing: A Combined Review and Bibliometric Exploration
    Maria Tănase, Cristina Veres
    Journal of Manufacturing and Materials Processing, 2025
    This review provides a comprehensive analysis of recent developments in the additive manufacturing of green composites, with a particular focus on their mechanical behavior. A bibliometric analysis of 482 research articles indexed in the Web of Science Core Collection and published between 2015 and 2025 reveals a sharp increase in publications, with dominant contributions from countries such as China, India, and the United States, as well as strong collaboration networks centered on materials science and polymer engineering. Thematic clustering highlights a growing emphasis on natural fiber reinforcement, biodegradable matrices, and performance optimization. Despite these advances, few studies have combined bibliometric analysis with a technical evaluation of mechanical performance, leaving a gap in understanding the relationship between research trends and material or process optimization. Building on these insights, the review synthesizes current knowledge on material composition, print parameters, infill design, and post-processing, identifying their combined effects on tensile strength, stiffness, and durability. The study concludes that multi-variable optimization—encompassing fiber-matrix compatibility, print architecture, and thermal control—is essential to achieving eco-efficient and high-performance green composites in additive manufacturing.
  • Sustainable Shell Structures: A Bibliometric and Critical Review of Buckling Behavior and Material-Efficient Design Strategies
    Cristina Veres, Maria Tănase
    Applied Sciences Switzerland, 2025
    Sustainable shell structures are thin, curved systems such as domes, vaults, and cylindrical shells that achieve strength and stability primarily through membrane action, allowing significant material savings. Their sustainability lies in minimizing embodied energy and CO2 emissions by using less material, integrating recycled or bio-based components, and applying optimization strategies to extend service life and enable reuse or recycling, all while maintaining structural performance and architectural quality. This review critically examines the state-of-the-art in sustainable shell structures, focusing on their buckling behavior and material-efficient design strategies. Integrating bibliometric analysis with thematic synthesis, the study identifies key research trends, theoretical advancements, and optimization tools that support structural efficiency. Emphasis is placed on recent developments in composite and bio-based materials, imperfection-sensitive buckling models, and performance-based design approaches. Advanced computational methods, including finite element analysis, machine learning, and digital twins, are highlighted as critical in enhancing predictive accuracy and sustainability outcomes. The findings underscore the dual challenge of achieving both structural stability and environmental responsibility, while outlining research gaps and future directions toward resilient, low-impact shell construction.
  • STRATEGIC IGNORANCE: THE PARADIGM OF UNVEILING THE HIDDEN MOTIVES BEHIND MANAGERIAL BLIND SPOTS
    Bahareh Abedin, Cristina Veres, Manuela Rozalia Gabor
    Economics Innovative and Economics Research Journal, 2025
  • Lean Management Framework in Healthcare: Insights and Achievements on Hazardous Medical Waste
    Adela Dana Ciobanu, Alexandru Ozunu, Maria Tănase, Adrian Gligor, Cristina Veres
    Applied Sciences Switzerland, 2025
  • A Bibliometric Review of 3D-Printed Functionally Graded Materials, Focusing on Mechanical Properties
    Cristina Veres, Maria Tănase
    Machines, 2025
  • Sustainable Universities: A Bibliometric and Thematic Analysis in Higher Education
    Cristina Veres, Maria Tănase, Ioan Bogdan Bacos, Mihaela Kardos
    Sustainability Switzerland, 2025
  • A Bibliometric and Systematic Review of the Use of Recycled Composite Materials with an Emphasis on the Mechanical Performance of Structures
    Cristina Veres, Maria Tănase
    Materials, 2025
  • Change Management - A Multi-Criteria Decision-Making Approach Using the Electre Method
    Mihai Daniel Anitei, Cristina Veres
    Lecture Notes in Networks and Systems, 2025
  • Lean Management - A Prerequisite for Digitization in the Automotive Industry
    Cristina Veres, Loredana Maria Păunescu, Mihai Anitei
    Lecture Notes in Networks and Systems, 2025
  • Utilizing Modern Management Methods in Pathology Services: A Case Study
    Aurora Maria Popa, Liviu Marian, Ovidiu Cotoi, Cristina Veres
    Lecture Notes in Networks and Systems, 2025
  • Employer Perspectives on Bridging Skills Gaps in Romania's Labor Market
    Cristina Veres, Mihaela Kardos, Daniela Stefanescu, Mihai Daniel Anitei
    Lecture Notes in Networks and Systems, 2025
  • A Comprehensive Review of Ergonomics and Human Factors Engineering for Enhancing Comfort and Efficiency in Transport Systems
    Petruța Blaga, Bianca-Mihaela Cășeriu, Bogdan Bucur, Cristina Veres
    Systems, 2025
  • Analyzing Urban Air Quality Perceptions: Integrating Socio-Demographic Patterns with Sensor-Based Measurements Using Regression Model and Multidimensional Scaling
    Cristina Veres, Ioan-Bogdan Bacos, Maria Tănase, Manuela Rozalia Gabor
    Sustainability Switzerland, 2025
  • Motor Control Disorders (Balance and Coordination) Among Older People: A Systematic Review with the Assistance of EndNote Reference Management Software
    Szabo Dan-Alexandru, Pârvu Carmen, Veres Cristina, Bunea Miruna Maria, Mocanu George Dănuţ
    Lecture Notes in Networks and Systems, 2025
  • Impacts of Air Pollution on Community Well-Being in the Tourism Sector: A Comprehensive Literature Review
    Ioan Bogdan Bacos, Cristina Veres, Petru Alexandru Curta, Manuela Rozalia Gabor, Flavia Dana Oltean
    Sustainability Switzerland, 2024
  • Integrated Analytical, Numerical, and Statistical Analysis of Buckling Behavior in Steel Cylindrical Silos with Corrugated Walls
    Maria Tănase, Alexandra Ileana Portoacă, Manuela Rozalia Gabor, Cristina Veres
    Processes, 2024
  • Quantitative Analysis of Predictors of Acoustic Materials for Noise Reduction as Sustainable Strategies for Materials in the Automotive Industry
    Bianca-Mihaela Cășeriu, Manuela-Rozalia Gabor, Petruța Blaga, Cristina Veres
    Applied Sciences Switzerland, 2024
  • Economic Effects of Idea Generation and Idea Management System in Automotive Industry: a Quantitative Case Study for Romania
    Cristina Veres, Sebastian Cândea, Manuela Rozalia Gabor, Laura Elly Naghi
    Journal of the Knowledge Economy, 2024
  • Green Innovation for Carbon Footprint Reduction in Construction Industry
    Argeime López-Malest, Manuela Rozalia Gabor, Mirela Panait, Alina Brezoi, Cristina Veres
    Buildings, 2024
  • Quality Management in Romanian Cultural Institutions: A Comprehensive Analysis
    Claudia Chibelean, Cristina Veres, Miklos-Marius Veres, Daniela Stefanescu, Dan-Alexandru Szabo
    Lecture Notes in Networks and Systems, 2024
  • Delving into Educators’ Views on Vocational Education and Training (VET): An In-Depth Study
    Cristina Veres, Miklos-Marius Veres, Sorin Cristian Albu, Dan-Alexandru Szabo, Claudia Chibelean, et al.
    Lecture Notes in Networks and Systems, 2024
  • New Approaches in Medical Rehabilitation: A Literature Review on the Use and Importance of Technology in Rehabilitation
    Dan-Alexandru Szabo, Cristina Veres, Carmen Pârvu, Hunor Tamas Gyurka, Andi Stoica, et al.
    Lecture Notes in Networks and Systems, 2024
  • Unveiling Managerial Perspectives on the Essential Role of Lean Implementation in Romanian Healthcare
    Cristina Veres, Flaviana Rotaru, Miklos-Marius Veres, Sorina Moica, Dan-Alexandru Szabo, et al.
    Lecture Notes in Networks and Systems, 2024
  • The Role and Importance of Using Sensor-Based Devices in Medical Rehabilitation: A Literature Review on the New Therapeutic Approaches
    Dan Alexandru Szabo, Nicolae Neagu, Silvia Teodorescu, Mihaela Apostu, Corina Predescu, et al.
    Sensors, 2023
  • A Perspective on the Romanian National System of Vocational Training: Factors, Challenges and Solutions
    Marius Veres, Cristina Veres
    Lecture Notes in Networks and Systems, 2023
  • Streamlining Production by Implementing a Control Center System
    Alexandrina-Simona Pitea, Dumitrita-Ioana Apafaian, Cristina Veres
    Lecture Notes in Networks and Systems, 2023
  • Smart Production Concept and Improvements in Automotive Industry
    Cristina Veres, Roxana-Diana Dobrau
    Lecture Notes in Networks and Systems, 2022
  • Sustainability Strategy Methodology to Increase Brand Value
    Michele Wong, Mihai Daniel Anitei, Cristina Veres
    Lecture Notes in Networks and Systems, 2022
  • Effects of Using Combined Approach of Quality Circles and 7 Steps Method in Automotive Industry. A Case Study
    Sebastian Candea, Cristina Veres, Petruta Blaga, Emil Nutiu
    Lecture Notes in Networks and Systems, 2022
  • Externalities of lean implementation in medical laboratories. Process optimization vs. adaptation and flexibility for the future
    Simona Andreea Apostu, Valentina Vasile, Cristina Veres
    International Journal of Environmental Research and Public Health, 2021
  • Conceptual model for introducing lean management instruments
    Cristina Veres
    Procedia Manufacturing, 2020
  • Lean six sigma in the energy service sector: A case study
    Mihnea-Dorin Bloj, Sorina Moica, Cristina Veres
    Procedia Manufacturing, 2020
  • Case study regarding the implementation of one-piece flow line in automotive company
    Apafaian Dumitrita Ioana, Egri Diana Maria, Veres Cristina
    Procedia Manufacturing, 2020
  • The Integration of the Blended Learning Concept into Employee Training as a Factor in Shifting Mentalities towards the Industry 4.0 Approach
    Sorina Moica, Alina Gherendi, Cristina Veres, Theodor Moica
    Proceedings of 2019 8th International Conference on Industrial Technology and Management Icitm 2019, 2019
  • Design of Smart Forestry Cableway
    Daniel Lates, Sorina Moica, Cristina Veres, Alexandru Pozdirca, Elena Harpa
    Procedia Manufacturing, 2019
  • The contribution of lean manufacturing tools to changeover time decrease in the pharmaceutical industry. A SMED project
    Al-Akel Karam, Marian Liviu, Veres Cristina, Horea Radu
    Procedia Manufacturing, 2018
  • Case study concerning 5S method impact in an automotive company
    Cristina Veres (Harea), Liviu Marian, Sorina Moica, Karam Al-Akel
    Procedia Manufacturing, 2018
  • Case Study Concerning Effects of Japanese Management Model Application in Romania
    Cristina Veres, Liviu Marian, Sorina Moica
    Procedia Engineering, 2017