VNUHCM Journal of Engineering and Technology

Latest Articles

1. Research article

   Effect of curing conditions on mechanical properties and sorption characteristics of concrete using sugarcane bagasse ash

   Dang V., Doan L., Quang Vu T., Dang Bao L., Luong Thi B., Chau Ngoc V.

   With the significant development in infrastructure and buildings, the demand for concrete is gradually increasing. However, with the trend of sustainable development, there are more and more researchers focusing on the application of industrial and agricultural by-products in concrete production. This study aims to evaluate the applicability of sugarcane bagasse ash (SCBA) in cement replacement considering the curing conditions. The cement was partially replaced by 5% of SCBA. The mechanical properties and sorption characteristics of concrete were investigated for up to 56 days. The results show that the ultrasonic pulse velocity and compressive strength of concrete specimens containing SCBA were lower than the reference concrete before 28 days, regardless of curing conditions. However, the ultrasonic pulse velocity, compressive strength, sorption characteristics, and apparent porosity of concrete incorporating SCBA were slightly higher than those of the reference concrete under tap water curing at 56 days. Additionally, ambient air curing significantly reduced the ultrasonic pulse velocity and compressive strength, as well as increased the sorption characteristics of concrete, especially concrete containing SCBA. As a consequence, the concrete specimens cured under ambient air condition showed lower performance than those under tap water condition.

   Published: 2024-10-15In press
2. Research article

   Elimination of PPG Signal Disturbances through Variational Mode Decomposition and Hilbert Transform

   Tùng Lưu Thai T., T., Phan K. D.

   The PPG signal presents considerable promise as a non-invasive technique across various applications. However, effectively utilizing this signal in real-world scenarios demands meticulous handling to identify and rectify disturbances within the photo-plethysmography (PPG) signal. Among the methodologies explored, integrating time-frequency spectra with a hybrid deep learning model, such as convolutional – long short term memory neural network model (CNN-LSTM), has emerged as a promising approach. Yet, prevalent methods often rely on Fourier-based algorithms for extracting time-frequency spectra, which are prone to energy leakage issues. To surmount this limitation, decomposition methods like Variational Mode Decomposition (VMD) coupled with the Hilbert transform offer a compelling solution. In this study, we propose a novel algorithm leveraging VMD and Hilbert transform to extract time-frequency spectra as features for a convolutional neural network model (CNN). Unlike studies employing Fourier-based time-frequency spectra and the hybrid CNN-LSTM model, this approach adopts a simpler architecture, relying solely on a CNN model. This simplicity owes to the efficacy of VMD and Hilbert transform in feature extraction, streamlining the computational process without sacrificing accuracy. Remarkably, our method yields high-performance outcomes, achieving accuracy, precision, and recall of 0.91, 0.95, 0.88, respectively on the MIMICIII dataset. These results underscore the robustness and effectiveness of our proposed methodology, offering promising avenues for enhanced utilization of the PPG signal in diverse biomedical applications. By amalgamating advanced signal processing techniques with deep learning models, our approach contributes to the advancement of non-invasive biomedical signal processing, potentially healthcare monitoring and diagnosis.

   Published: 2024-10-15In press
3. Research article

   Fabrication and Characterization of Hydrophobic Aerogels Containing Bacterial Cellulose Coated with Copper Species via Mild Reduction

   Lê Hà Nguyen H., Nguyen T., Nguyen T., Dang K., Nguyen K., Le K., Phan H.

   In this study, bacterial cellulose (BC) was coated with copper species via a room-temperature hydrazine-mediated reduction reaction of copper(II) acetate in a suspension phase of nata de coco, which was treated by freeze-drying, yielding hydrophobic lightweight aerogels. Structural and textural characteristics of the prepared aerogels were discovered using several techniques including X-ray diffraction (XRD), thermogravimetry analysis (TGA), water contact angle measurement and isothermal nitrogen physisorption. In detail, XRD results indicated the formation of the metallic copper phase in the aerogel while the high cellulose crystallinity was remained unchanged. No oxidized copper phases were detected in the material. Via the TGA profiles, the Cu loading was determined to be in the range from 3.9 to 13.4 wt.%, depending on the copper(II) acetate amount used for the reduction reaction. These values were generally lower than the theoretical Cu contents probably due to the unexpected Cu losses during the preparation procedure. In addition, increasing the Cu content in the BC aerogel led to a significant decrease in the specific surface area with the presence of Cu in the porous structure. On the other hand, as expected, the hydrophobicity of the BC aerogel was significantly enhanced with the Cu content. Indeed, the Cu-coated BC aerogels with high copper contents (> 6.2 wt.%) were hydrophobic, showing a large water-contact angle of up to 138°. Therefore, the resulting hydrophobic aerogels well interacted with water-immiscible organic solvents including diesel oil and cyclohexane with adsorption capacities varied from 20 to 30 g/g. The successful fabrication of the hydrophobic aerogels upon the simple surface modification of abundant bacterial cellulose with Cu species can introduce novel and efficient biomass-based material for the treatments of oil-based liquids in the aqueous environment.

   Published: 2024-10-15In press
4. Research article

   Computer vision for plastic bottle classification moving on conveyor

   Hanh L., Duy Cong V.

   Waste classification of used plastic bottles is one of the researches that have received much attention today. However, at present, this classification is still done manually by workers. In addition, there are also a number of studies that perform identification by traditional image processing methods. However, this does not bring about high identification efficiency because the shape of the plastic bottle is very complicated and it is also mixed in other types of waste. This paper will present a method to automatically recognize used water bottles when moving on a conveyor belt using machine learning techniques. This automatic identification method can assist workers in today's hard work of sorting waste. First, images of bottles are collected and preprocessed by conventional image processing methods, then these processed images are saved into a dataset for machine learning. The dataset is divided into 2 types of dirty bottles and clean bottles. The accuracy and processing time of recognition are verified experimentally. A 6-degree-of-freedom industrial manipulator with a camera fixed above the work area recognizes the coordinates of these plastic bottles and picks them up. Through experimental verification, accuracy of over 90% as well as identification time 200ms, the system proves to be applicable to the industrial environment of waste classification in the form of plastic bottles.

   Published: 2024-10-15In press
5. Research article

   Novel Chemo-Mechanical Production of Nanocellulose from Coconut Pith

   Do N., Nguyen C., Nguyen H., Mai P., Le T., Le P.

   Nanocellulose has been receiving tremendous attention due to its unique properties such as high strength, flexibility low density, biocompatibility, and natural hydrophilicity with numerous hydroxyl groups effortlessly modified for different purposes. Lignocellulosic biomass, especially coconut residues like coconut shells, coconut fibers, coconut husks, etc., has been effectively utilized for cellulose production in various sizes and morphologies such as microcrystalline cellulose, nanocellulose crystals, nanocellulose fibrils. In this study, novel chemo-mechanical production of nanocellulose from coconut pith (CP) is developed by implementing two-stage alkaline pretreatment, ball milling, acid hydrolysis, centrifugation, and dialysis. Nanocellulose obtained from CP is evaluated for its particle size distribution, zeta potential, chemical composition, crystalline and chemical structure. All parameters of ball-milling time, solid-to-liquid ratio, and hydrolysis duration greatly affect the formation of cellulose nanoparticles from CP as well as the stability of the particle system. Based on the experimental results, the cumulative intensity of particle size below 300 nm and in the range of 300 – 600 nm is respectively 40.93% and 59.08%, indicating the evident effectiveness of the developed chemo-mechanical approach in nanocellulose synthesis from cellulose-rich CP with an initial high holocellulose content of 49.07%. Remarkably, the crystallinity index of our nanocellulose from CP is as high as 72.08% compared to that from coconut husks. The present work demonstrates the potency of combining ball milling and acid hydrolysis in stable nanocellulose synthesis from CP and lays the foundation for further investigation to apply the formulated technique in industrial production.

   Published: 2024-10-15In press
6. Research article

   Effects of oligochitosan, oligoalginate, and microponic culture on the in vitro growth and ex vitro acclimatization of Hibiscus sagittifolius Kurz

   Vo P., Ngo M. P.

   Hibiscus sagittifolius Kurz is a valuable medicinal plant used in traditional oriental medicine. However, due to heavy exploitation and low seed germination rates in its natural habitat, the population of this tree species has significantly declined. Therefore, it is crucial to propagate Hibiscus sagittifolius Kurz. This study aimed to investigate the effects of various factors, such as oligochitosan, oligoalginate, and auxin pretreatment in microponic culture, on the growth process of propagating Hibiscus sagittifolius Kurz shoots. The results revealed that Murashige and Skoog’s medium supplemented with 10 mg/L of oligochitosan promoted shoot growth. Additionally, on medium supplemented with oligoalginate, plants had well-developed roots and larger leaves compared to the control samples. In the microponic system, shoots were cultured in a liquid medium without sucrose and vitamin supplementation. Plants pretreated with 10 mg/L of IBA or NAA in microponic conditions resulted in successful root development. Both the addition of oligochitosan and microponic culture enhanced seedling vitality when transferred to the nursery. Notably, seedlings pretreated with 10 mg/L of NAA in microponic culture exhibited a 100% survival rate, with 23 roots and a root length of 5.27 cm after 3 weeks of planting.

   Published: 2024-10-15In press
7. Review

   Dendritic cell-based vaccine against multiple myeloma

   Hoàng Mỹ D.

   Multiple myeloma is a disease related to the disorder of heterogeneous cell proliferation, characterized by uncontrolled growth of malignant plasma cells in the bone marrow. Cause of multiple myeloma is still not determined, however, environmental conditions, and genetic factors play a crucial role in the pathogenesis. Multiple myeloma is treated by (i) immunomodulatory drugs (IMiDs), which modulate the immune system to prevent the development of myeloma cells; (ii) glucocorticoids, which activate programmed cell death by inhibiting the expression of Bcl-xL and NF-ҡB, and (iii) proteasome inhibitors (PI), targeting the beta 5 subunit of the 20S proteasome (PSMB5). However, multiple myeloma remains incurable. Moreover, drug resistance is a significant challenge in the treatment of multiple myeloma. The development of drug resistance in multiple myeloma is influenced by genetic abnormalities, bone marrow microenvironment, and immune escape. Immunotherapy is promising for multiple myeloma treatment due to its ability to support immune recovery against cancer. Immunotherapy against multiple myeloma includes various approaches. Among these, dendritic cell-based vaccine is one of the potential directions in combination treatment, enhancing the survival rate in relapsed patients.

   Published: 2024-10-15In press
8. Research article

   A Modified 2-Dimensional Decentralized Interleaving Algorithm for Multilevel Multileg Converters

   Nguyễn Bảo A., Phan Q.,ốc Dũn., Gateau G.

   This paper proposes a modified algorithm to interleave the carrier signals in decentralized controller for multi-level multi-leg converter. Structure of these converters includes a number of semiconductor modules which are arranged in series and parallel to compose a matrix. The serial modules allow increasing the voltage levels of converter, while, the parallel modules allow increasing the working power of converter, therefore, the multi-level multi-leg converter can be applied in high-voltage high-power systems. The size of matrix can be changed by adding or removing the rows or columns at required positions while the system is working. The multi-level multi-leg converter can be controlled by a traditional algorithm which includes a central micro-controller using for all modules. However, this algorithm has a disadvantage when the system has a large amount of modules, at that time, the controller does not have enough resource to operate the algorithm. A decentralized algorithm has proposed to solve this problem, in this method, each module has its own controller to communicate and exchange the information with the horizontal and vertical neighboring modules over the communication channel. From the collected information, the controller of each module will calculate the phase angle of that module so that all carrier signals are interleaved successfully. This proposed algorithm also has a disadvantage, it cannot work correctly with matrices where number of rows and columns have non-one common divisor, they are called special matrices. This research focus on adjusting the algorithm so that it can work with all type of matrices including the special matrices. The ability of dynamical restructuring of system as well as the convergence speed of the algorithm are also considered and evaluated when some modules are added or removed. The research demonstrated the efficiency and the feasible by simulation results and experiment results. The proposed algorithm is simulated by the MATLAB/SIMULINK program and is experimented on micro-controller TMS320F28069.

   Published: 2024-10-15In press
9. Research article

   Deepwater drilling: challenges, evolutions of drilling practice, well design and lessons for Vietnam

   Nguyen M., Luu D., Huynh T., Le Nguyen N., Tran D.

   This paper provides an overview of the changes in deepwater and ultra-deepwater definitions, the evolutions in drilling rigs, well design, drilling and completion practices, and equipment to overcome many unique challenges and high drilling costs. Oil and gas drilling and development in deepwater and ultra-deepwater have been around for more than 60 years and become more and more important to the global petroleum industry. With each advancement in technology, we can explore and produce hydrocarbons further offshore, at deeper water depths, and thus the definitions of deepwater and ultra deepwater have been repeatedly rewritten. This segment of the petroleum industry has experienced the highest growth and number of innovations thanks to many giant deepwater discoveries turned into productive fields in the Gulf of Mexico, Brazil, North Sea, West Africa, and elsewhere throughout the world. With more and more wells drilled at ever increasing water depth, the drilling challenges, including both natural and operations hazards increase exponentially, which result in more and more stringent technical requirements for a safe operation. These are being met by the rapid and complicated evolution of BOPs and drillships over several generations, optimization of deepwater big bore well design, and applications of drilling and completion, marine navigation, ROV technologies and procedures, and sharing of lessons learned. This demonstrates the industry's continued and focused interest in the process of enabling resources extraction from this environment of deep and ultra-deep water, especially to combat the high risks and high investment costs for deep and ultra-deep water projects, in which drilling cost is the most significant share. In the second section, the paper looks at the hydrocarbon potentials of Vietnam deepwater and ultra deepwater, the current status, and the various challenges, and proposes required steps to enable Vietnam to tap in this vast unexplored hydrocarbon resources.

   Published: 2024-10-14In press
10. Research article

    GPS trajectory imputation: A hybrid approach combined clustering and GAIN-based algorithm

    Nguyen K., Hoang T., Tran N., Dang T., Thoai N.

    The advancement of computing power and the proliferation of big data have opened unprecedented avenues for the Intelligent Transportation Systems (ITS) community to extract valuable insights from Global Positioning System (GPS) trajectory data. However, the reality of real-world GPS trajectory data often lacks complete information due to various factors (e.g. detector damage, transmission loss, ...), thus posing significant challenges for trajectory analysis and operational efficiencies within transportation systems. To address this issue, time series data imputation techniques have emerged as critical solutions to accurately fill in missing data points. Existing imputation approaches can be classified into statistical methods and deep generative models. Significantly, within the domain of deep generative models, Generative Adversarial Imputation Networks (GAIN) have exhibited promise in the realm of data imputation. Nonetheless, their limited capacity to effectively handle time series data represents a notable limitation. Additionally, GPS trajectories, particularly those of buses, exhibit a distinctive characteristic wherein each vehicle is assigned to one or more predetermined routes, adding complexity to the data imputation process. In response to these challenges, this study proposes a novel hybrid imputation approach, Cluster-GRUI-GAIN, which integrates clustering techniques (e.g. KNN) with the enhanced generative adversarial imputation network, GRUI-GAIN. By combining the strengths of clustering and GAIN, our hybrid approach aims to enhance the accuracy of time series data imputation for GPS trajectories with diverse missing rates and significant gaps. Specifically, the GRUI-GAIN model within our proposed Cluster-GRUI-GAIN framework incorporates GRUI (GRU for Imputation) within the generator. This strategic integration enhances the model's ability to effectively handle missing data within time series, thereby bolstering the accuracy and reliability of imputations. Experimental evaluations on real-world dataset demonstrate that our proposed Cluster-GRUI-GAIN approach outperforms baseline methods in terms of time series imputation accuracy and offers robust and accurate imputations, making it well-suited for practical transportation applications.

    Published: 2024-10-07In press