Perspectives and Challenges in Doctoral Research—Selected Papers from the 11th Edition of the Scientific Conference of the Doctoral Schools of “Dunărea de Jos” University of Galati (SCDS-UDJG)

A special issue of Inventions (ISSN 2411-5134).

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 9352

Special Issue Editors


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Guest Editor
Head of Laboratory of Computations and Modeling in Applied Mechanics, Department of Applied Mechanics, "Dunarea de Jos" University of Galati, 800008 Galați, Romania
Interests: renewable energy; marine engineering; offshore technologies
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Special Issue Information

Dear Colleagues,

This is the third SI dedicated to the Scientific Conferences of the Doctoral Schools from “Dunărea de Jos” University of Galaţi; the first two concerned the 9th and 10th editions in 2021 and 2022.

We are seeking submissions for the 11th edition of the Scientific Conference of the Doctoral Schools from “Dunărea de Jos” University of Galaţi. The objective of the 2023 Conference is to provide a common forum for perspectives and challenges in doctoral research, allowing researchers to convene and share state-of-the-art developments in their field. At this conference, our institution aims to promote excellence and set up partnerships and collaborative relationships through the exchange of knowledge and expertise.

As in the previous editions, the conference invites oral and poster presentations in sections related to the main domains of doctoral research at UDJG. Workshops, exhibition stands, and social activities are also included, all with the aim of developing and improving the network of the doctoral schools.

Recommended topics include (but are not limited to) the following:

  1. Advanced research in mechanical and industrial engineering;
  2. Progress in food science and bio-resources engineering;
  3. Advances in engineering and management in agriculture and rural development;
  4. Advanced research in electrical/electronic engineering, system engineering and information technologies;
  5. Future of eco-nanotechnologies, functional materials and coatings chemistry;
  6. Electrochemistry in life sciences.

All the papers presented at the conference and accepted for publication in this Special Issue will receive a 50% discount on the APC.

Prof. Dr. Eugen Rusu
Prof. Dr. Gabriela Rapeanu
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Inventions is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • PhD students
  • excellence in research
  • young researchers
  • encouraging performance

Published Papers (5 papers)

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Research

20 pages, 8290 KiB  
Article
Numerical Study of a Model and Full-Scale Container Ship Sailing in Regular Head Waves
by Andreea Mandru, Liliana Rusu, Adham Bekhit and Florin Pacuraru
Inventions 2024, 9(1), 22; https://doi.org/10.3390/inventions9010022 - 12 Feb 2024
Viewed by 1176
Abstract
In the present study, the added resistance, heave, and pitch of the KRISO Container Ship (KCS) in waves, at both model scale and full scale, are predicted numerically in regular head waves, for four wavelengths and three wave heights. The ISIS-CFD viscous flow [...] Read more.
In the present study, the added resistance, heave, and pitch of the KRISO Container Ship (KCS) in waves, at both model scale and full scale, are predicted numerically in regular head waves, for four wavelengths and three wave heights. The ISIS-CFD viscous flow solver, implemented in the Fidelity Fine Marine software provided by CADENCE, was employed for the numerical simulations. The spatial discretization was based on the finite volume method using an unstructured grid. The unsteady Reynolds-averaged Navier–Stokes (RANS) equations were solved numerically, with the turbulence modeled by shear stress transport (k-ω) (SST). The free-surface capturing was based on the volume-of-fluid method. The computed solutions were validated through comparisons with towing test data available in the public domain. To predict the uncertainties in the numerical solution, a systematic grid convergence study based on the Richardson extrapolation method was performed for a single wave case on three different grid resolutions. Specific attention was given to the free-surface and wake flow in the propeller plane. The purpose was to compare the numerical results from the model- and full-scale tests to examine the scale’s effect on the ship’s performance in regular head waves. The comparison between the model scale and full scale showed obvious differences, less accentuated for the free-surface topology and clearly observed in terms of boundary layer formation in the propeller’s vicinity. Full article
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20 pages, 8410 KiB  
Article
The Potential of Lakes for Extracting Renewable Energy—A Case Study of Brates Lake in the South-East of Europe
by Eugen Rusu, Puiu Lucian Georgescu, Florin Onea, Victoria Yildirir and Silvia Dragan
Inventions 2023, 8(6), 143; https://doi.org/10.3390/inventions8060143 - 09 Nov 2023
Viewed by 1310
Abstract
The aim of this work is to provide some details regarding the energy potential of the local wind and solar resources near the Galati area (south-east of Romania) by considering the performances of a few recent technologies. Based on 22 years of ERA5 [...] Read more.
The aim of this work is to provide some details regarding the energy potential of the local wind and solar resources near the Galati area (south-east of Romania) by considering the performances of a few recent technologies. Based on 22 years of ERA5 data (2001–2022), a picture concerning the renewable energy resources in the Brates Lake area is provided. Comparing the wind and solar resources with in situ and satellite data, a relatively good agreement was found, especially in regards to the average values. In terms of wind speed conditions at a hub height of 100 m, we can expect a maximum value of 19.28 m/s during the winter time, while for the solar irradiance the energy level can reach up to 932 W/m2 during the summer season. Several generators of 2 MW were considered for evaluation, for which a state-of-the-art system of 6.2 MW was also added. The expected capacity factor of the turbines is in the range of (11.71–21.23)%, with better performances being expected from the Gamesa G90 generator. As a next step, several floating solar units were considered in order to simulate large-scale solar projects that may cover between 10 and 40% of the Brates Lake surface. The amount of the evaporated water saved by these solar panels was also considered, being estimated that the water demand of at least 3.42 km2 of the agricultural areas can be covered on an annual scale. Full article
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29 pages, 5796 KiB  
Article
Harnessing Deep Convolutional Neural Networks Detecting Synthetic Cannabinoids: A Hybrid Learning Strategy for Handling Class Imbalances in Limited Datasets
by Catalina Mercedes Burlacu, Adrian Constantin Burlacu, Mirela Praisler and Cristina Paraschiv
Inventions 2023, 8(5), 129; https://doi.org/10.3390/inventions8050129 - 16 Oct 2023
Cited by 1 | Viewed by 2034
Abstract
The aim of this research was to develop and deploy efficient deep convolutional neural network (DCNN) frameworks for detecting and discriminating between various categories of designer drugs. These are of particular relevance in forensic contexts, aiding efforts to prevent and counter drug use [...] Read more.
The aim of this research was to develop and deploy efficient deep convolutional neural network (DCNN) frameworks for detecting and discriminating between various categories of designer drugs. These are of particular relevance in forensic contexts, aiding efforts to prevent and counter drug use and trafficking and supporting associated legal investigations. Our multinomial classification architectures, based on Attenuated Total Reflectance Fourier-Transform Infrared (ATR-FTIR) spectra, are primarily tailored to accurately identify synthetic cannabinoids. Within the scope of our dataset, they also adeptly detect other forensically significant drugs and misused prescription medications. The artificial intelligence (AI) models we developed use two platforms: our custom-designed, pre-trained Convolutional Autoencoder (CAE) and a structure derived from the Vision Transformer Trained on ImageNet Competition Data (ViT-B/32) model. In order to compare and refine our models, various loss functions (cross-entropy and focal loss) and optimization algorithms (Adaptive Moment Estimation, Stochastic Gradient Descent, Sign Stochastic Gradient Descent, and Root Mean Square Propagation) were tested and evaluated at differing learning rates. This study shows that innovative transfer learning methods, which integrate both unsupervised and supervised techniques with spectroscopic data pre-processing (ATR correction, normalization, smoothing) and present significant benefits. Their effectiveness in training AI systems on limited, imbalanced datasets is particularly notable. The strategic deployment of CAEs, complemented by data augmentation and synthetic sample generation using the Synthetic Minority Oversampling Technique (SMOTE) and class weights, effectively address the challenges posed by such datasets. The robustness and adaptability of our DCNN models are discussed, emphasizing their reliability and portability for real-world applications. Beyond their primary forensic utility, these systems demonstrate versatility, making them suitable for broader computer vision tasks, notably image classification and object detection. Full article
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20 pages, 6047 KiB  
Article
Quantification of Statins in Pharmaceutical Products Using Screen-Printed Sensors Based of Multi-Walled Carbon Nanotubes and Gold Nanoparticles
by Ramona Oana Roșca, Alexandra Virginia Bounegru and Constantin Apetrei
Inventions 2023, 8(5), 111; https://doi.org/10.3390/inventions8050111 - 30 Aug 2023
Viewed by 2521
Abstract
This study describes the use of electrochemical sensors to detect and quantify several statins (rosuvastatin and simvastatin) in pharmaceutical products. Two types of commercially screen-printed sensors were used and compared: one based on carbon (SPCE) and the other modified with gold nanoparticles and [...] Read more.
This study describes the use of electrochemical sensors to detect and quantify several statins (rosuvastatin and simvastatin) in pharmaceutical products. Two types of commercially screen-printed sensors were used and compared: one based on carbon (SPCE) and the other modified with gold nanoparticles and multi-walled carbon nanotubes (SPE/GNP-MWCNT). Cyclic voltammetry was employed for determination. The AuNP-MWCNTs/SPCE sensor outperformed the SPCE sensor, displaying excellent electrochemical properties. It demonstrated high sensitivity with low limits of detection (LOD) and quantification (LOQ) values: 0.15 µM and 5.03 µM, respectively, for rosuvastatin and 0.30 µM and 1.01 µM, respectively, for simvastatin. The sensor had a wide linear range of 20–275 µM for rosuvastatin and 50–350 µM for simvastatin. Using the AuNP-MWCNTs/SPCE sensor, rosuvastatin and simvastatin were successfully quantified in pharmaceutical products. The results were validated towards producer-reported values (standardized drugs) and a conventional analysis method (FTIR). The sensor exhibited excellent stability, reproducibility, and analytical recovery ranging from 99.3% to 106.6% with a low relative standard deviation (RSD) of less than 1%. In conclusion, the AuNP-MWCNTs/SPCE sensor proved to be a reliable and sensitive tool for detecting and quantifying statins in pharmaceutical products. Its superior electrochemical properties, low LOD and LOQ values, wide linear range, and high analytical recovery make it a promising choice for pharmaceutical quality control. Full article
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16 pages, 4712 KiB  
Article
A Comparative Assessment of Homomorphic Encryption Algorithms Applied to Biometric Information
by Georgiana Crihan, Marian Crăciun and Luminița Dumitriu
Inventions 2023, 8(4), 102; https://doi.org/10.3390/inventions8040102 - 11 Aug 2023
Cited by 2 | Viewed by 1323
Abstract
This paper provides preliminary research regarding the implementation and evaluation of a hybrid mechanism of authentication based on fingerprint recognition interconnected with RFID technology, using Arduino modules, that can be deployed in different scenarios, including secret classified networks. To improve security, increase efficiency, [...] Read more.
This paper provides preliminary research regarding the implementation and evaluation of a hybrid mechanism of authentication based on fingerprint recognition interconnected with RFID technology, using Arduino modules, that can be deployed in different scenarios, including secret classified networks. To improve security, increase efficiency, and enhance convenience in the process of authentication, we perform a comparative assessment between two homomorphic encryption algorithms, the Paillier partial homomorphic algorithm and the Brakerski–Gentry–Vaikuntanathan fully homomorphic encryption scheme, applied to biometric templates extracted from the device mentioned above, by analyzing factors such as a histogram analysis, mean squared error (MSE), peak signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), the number of pixel change rate (NPCR), the unified average changing intensity (UACI), the correlation coefficient, and average encryption time and dimension. From security and privacy perspectives, the present findings suggest that the designed mechanism represents a reliable and low-cost authentication alternative that can facilitate secure access to computer systems and networks and minimize the risk of unauthorized access. Full article
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