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3D printing (3DP) has emerged as a disruptive manufacturing technology that benefits both the manufacturing and educational sectors. The emergence of low-cost and affordable 3D printers has facilitated the widespread use of 3DP technology, particularly in higher education institutions (HEIs) in developing countries. Despite its promising benefits, 3DP is still poorly adopted in developing countries. While research has explored the use and adoption of 3DP in education within developed nations, little research has been conducted on the integration of 3DP to capitalize on its benefits in developing countries. Therefore, in this study, we aim to investigate the perspectives of students and faculty members in higher education (HE) regarding 3DP integration in the classroom. A survey was conducted with 307 participants (272 students and 35 faculty members) at 12 Vietnamese universities where 3DP education is offered. We found that a misaligned curriculum, lack of training for faculty members, the high cost of materials and equipment, English language barriers, and limited student exposure to 3DP technology were significant obstacles to 3DP education. Several recommendations have been provided to address these challenges; for instance, faculty members and industry professionals need collaboration to ensure that the curriculum is current and that faculty members receive adequate training to teach 3DP effectively. This study provides guidelines for faculty members and policymakers to reinforce 3DP education for sustainable development in developing countries.

In Industry 4.0, technology and innovation constitute a driver for enhancing firms’ performance, particularly for small and medium-sized enterprises (SMEs). Our study aims to examine whether that statement is correct for SMEs in a developing country. We developed a generic model and employed multiple regression techniques, including ordinary least-squares, robust standard errors and weighted least squares, to test this hypothesis and address heteroskedasticity. Using a micro-level dataset including 11,630 SMEs in Vietnam’s logistics sector, an emerging logistics market in a developing country, our results reveals that the effects of technology on firm performance may differ depending on particular metrics of this performance. Furthermore, not all forms of innovation significantly affect SME performance. Control variables such as “firm’s age”, “firm’s size”, “state ownership”, “education of the manager” and “foreign activities” also play a significant role in SME performance, underscoring the importance of both internal capabilities and external technological elements.

Abstract Background Paranasal sinus mucoceles are benign cystic lesions characterized by mucous accumulation that can lead to significant complications due to erosion of sinus walls and compression of adjacent structures. This retrospective study aims to compare the clinical characteristics and treatment outcomes of primary and secondary mucoceles. Methods We reviewed medical records of 68 patients treated for mucoceles at the Ear, Nose, and Throat Hospital in Ho Chi Minh City, Vietnam, from June 2022 to June 2024. Advanced diagnostic imaging techniques, such as computed tomography (CT) and magnetic resonance imaging (MRI), were utilized in the management of these lesions. Statistical analysis was conducted using chi-square and t-tests to compare the characteristics and outcomes between the two groups. Results The study found that primary mucoceles often presented with less severe symptoms compared to secondary mucoceles, which occur post-surgery or post-trauma and are associated with more severe symptoms and complications. Primary mucoceles were most frequently located in the frontal-ethmoid region (47.4%), while secondary mucoceles had a similar distribution but were more prevalent following previous surgical interventions. Endoscopic sinus surgery was the primary treatment for both groups, with secondary mucoceles frequently requiring more complex surgical interventions. Postoperative improvement was significant in both groups, though secondary mucoceles exhibited slower recovery and higher rates of residual minor visual impairments. Conclusion The comparison between primary and secondary paranasal sinus mucoceles reveals critical differences in clinical profiles and treatment outcomes. Understanding these differences is essential for optimizing management strategies and improving prognosis for patients with these challenging conditions. Further research is needed to explore long-term treatment effects and the efficacy of different surgical approaches.

This study conducts a rigorous comparative analysis between two cutting-edge instance segmentation methods, Mask R-CNN and YOLOv8, focusing on stomata pore analysis. A novel dataset specifically tailored for stomata pore instance segmentation, named PhenomicsStomata, was introduced. This dataset posed challenges such as low resolution and image imperfections, prompting the application of advanced preprocessing techniques, including image enhancement using the Lucy-Richardson Algorithm. The models underwent comprehensive evaluation, considering accuracy, precision, and recall as key parameters. Notably, YOLOv8 demonstrated superior performance over Mask R-CNN, particularly in accurately calculating stomata pore dimensions. Beyond this comparative study, the implications of our findings extend across diverse biological research, providing a robust foundation for advancing our understanding of plant physiology. Furthermore, the preprocessing enhancements offer valuable insights for refining image analysis techniques, showcasing the potential for broader applications in scientific domains. This research marks a significant stride in unraveling the complexities of plant structures, offering both theoretical insights and practical applications in scientific research.

In this experimental study, efforts were undertaken to augment the overall efficiency of a dual-fuel engine. This present study was conducted in three steps. In the initial phase, Aluminium oxyhydroxide (AlO(OH)) was synthesized and analyzed using UV–visible spectroscopy, XRD (X-ray diffraction), Thermogravimetric (TG) analysis, and Differential Scanning Calorimeter (DSC). In the second part of the study, the impact of AlO(OH) NP dosage on the performance of a producer gas-powered diesel engine was investigated. To optimize adequate AlO(OH) NPs addition, three working fluids are prepared by dissolving NPs in dairy scum oil methyl ester (DiSOME) biodiesel ranging from 20 to 60 ppm and varied in steps of 20. In the next phase, the present study examined the effect of 60 ppm of various NPs, including multi-walled carbon nanotubes (MWCNT), Aluminum oxide (Al2O3), and AlO(OH) on the combustion and emission characteristics of a 1-cylinder 4-stroke direct injection diesel engine operating in dual fuel mode using a combination of DiSOME and producer gas. The study concluded that DiSOME-PG operation with 60 ppm AlO(OH) and without nano-addition resulted in decreased BTE by 2.9 % and 14.6 % respectively compared to diesel-supported dual fuel operation. To the extent that exhaust levels are concerned, AlO(OH) addition to the DiSOME-PG combination lowers hydrocarbon (HC) and carbon monoxide (CO) emissions than identical fuel amalgamation without AlO(OH) NP. It is noticed that the retarded combustion related to the DiSOME-producer gas mixture can be improved with NP addition. The DiSOME-producer gas functioning with NPs addition is the individuality of this current effort.

Medicinal plants, such as safflower (Carthamustinctorius), are essential in both conventional and modern healthcare. This study evaluatesthe reliability of leaf image classification for differentiating safflower germplasm accessions, despite the challenge posed by similar leaf structures across varieties. Traditional classification methods can be time-consuming and error prone, underscoring the need for fine-grained classification techniques. To address this, we introduce a novel comprehensive leaf database of safflower varieties that is meticulously curated by experts. This database can be used to support future research. We evaluate state-of-the-art deep learning methods for classifying safflower varieties and propose a novel approach using a Vision Transformer (ViT) model with an optimal leaf structure feature (OLSF). The OLSF, calculated as the average response of a multidirectional Gabor filter bank and optimized with the structural similarity index measure (SSIM), enhances complex leaf features, such as veins, texture, and frequency variations, to enhance the classification performance of deep learning models. The experimental results reveal that the OLSF-ViT model achieves excellent accuracy scores of 100 %, 99.05 %, and 89.65 % on the Folio, UCI Leaves, and JNUSafflower datasets, respectively. These findings demonstrate that leaf image analysis is an effective and affordable tool for investigating the phenotypic diversity among safflower cultivars. This study highlights the potential of OLSF-ViT in automatic plant classification, and the results can be used in plant science, herbal medicine, and biodiversity conservation.

This study aims to thoroughly investigate the linguistic impact of emojis and emoticons in modern written communication. This work attempts to shed light on these digital symbols’ linguistic structure, social implications, and evolution through a multidisciplinary research method. A mixed-methods approach was used in the research, integrating interdisciplinary viewpoints with quantitative and qualitative analyses. A vast amount of data was gathered from various digital communication channels, such as email exchanges, messaging apps, and social media. A sizable collection of text messages with emojis and emoticons was assembled. Emotional cons and emojis were categorized using linguistic analysis, breaking down their structure into visual components and repeating patterns. Their grammatical and syntactic effects on written language were also evaluated in this investigation. The findings indicate that using emojis in writing positively correlates with emotional expressiveness. This implies that when people wish to express emotions and subtlety in their messages, they typically utilize more emoticons. They enhance digital etiquette, reduce misunderstandings, improve emotional connection, and foster cross-cultural understanding. Emoticons are a global visual language that helps people communicate, create empathy, and navigate digital communication.

Recently, because of serious global challenges including the consumption of energy and climate change, there has been an increase in interest in the environmental effect of port operations and expansion. More interestingly, a strategic tendency in seaport advancement has been to manage the seaport system using a model which balances environmental volatility and economic development demands. An energy efficient management system is regarded as being vital for meeting the strict rules aimed at reducing the environmental pollution caused by port facility activities. Moreover, the enhanced supervision of port system operating methods and technical resolutions for energy utilisation also raise significant issues. In addition, low-carbon ports, as well as green port models, are becoming increasingly popular in seafaring nations. This study comprises a comprehensive assessment of operational methods, cutting-edge technologies for sustainable generation, storage, and transformation of energy, as well as systems of smart grid management, to develop a green seaport system, obtaining optimum operational efficiency and environmental protection. It is thought that using a holistic method and adaptive management, based on a framework of sustainable and green energy, could stimulate creative thinking, consensus building, and cooperation, as well as streamline the regulatory demands associated with port energy management. Although several aspects of sustainability and green energy could increase initial expenditure, they might result in significant life cycle savings due to decreased consumption of energy and output of emissions, as well as reduced operational and maintenance expenses.

The transition from fossil fuels to more clean, affordable, and sustainable energy resources is of strategic importance for developing Asian countries like Vietnam, in which solar energy is an attractive option. The government of Vietnam is implementing various policy measures and has substantial investment plans to accelerate the national spread of solar energy. In this regard, location optimization for solar power installation is a vital strategic decision that needs to be analysed in-depth to harness solar power. This study aims to determine suitable areas for solar power installation in a case study in Vietnam. For the decision-making that influences solar plant site selection, while some criteria can be evaluated based on a numerical database, numerous qualitative criteria must be under experts’ judgements in linguistic terms and can be represented through grey numbers. To deal with the complex nature of the prioritization problem posed by the coexistence of multiple factors, this study proposes a combination of the Data Envelopment Analysis (DEA) method, Grey Analytic Hierarchy Process (G-AHP), and Grey Technique for Order Preference by Similarity to Ideal Solution (G-TOPSIS). With the utilized integrated approach, both quantitative and qualitative effective criteria are considered to evaluate locations. DEA is used in the first phase to select high-efficiency locations based on various measurable criteria. In the second phase, these locations are further assessed in terms of qualitative criteria: technical, economic, environmental, and socio-political factors. For this evaluation, G-AHP is used for criteria weighting, and G-TOPSIS ranks the locations. While the proposed approach is expected to help save costs and resources towards determining hot spots for solar power plants installation all around the country, it is recommended that governments and local authorities take into account suitability policies and support mechanisms for sustainable solar energy development.

Abstract n‐Type organic electrochemical transistors (OECTs) and organic field‐effect transistors (OFETs) are less developed than their p‐type counterparts. Herein, polynaphthalenediimide (PNDI)‐based copolymers bearing novel fluorinated selenophene‐vinylene‐selenophene (FSVS) units as efficient materials for both n‐type OECTs and n‐type OFETs are reported. The PNDI polymers with oligo(ethylene glycol) (EG7) side chains P(NDIEG7‐FSVS), affords a high µC* of > 0.2 F cm−1 V−1 s−1, outperforming the benchmark n‐type Pg4NDI‐T2 and Pg4NDI‐gT2 by two orders of magnitude. The deep‐lying LUMO of −4.63 eV endows P(NDIEG7‐FSVS) with an ultra‐low threshold voltage of 0.16 V. Moreover, the conjugated polymer with octyldodecyl (OD) side chains P(NDIOD‐FSVS) exhibits a surprisingly low energetic disorder with an Urbach energy of 36 meV and an ultra‐low activation energy of 39 meV, resulting in high electron mobility of up to 0.32 cm2 V−1 s−1 in n‐type OFETs. These results demonstrate the great potential for simultaneously achieving a lower LUMO and a tighter intermolecular packing for the next‐generation efficient n‐type organic electronics.

The primary objective of this investigation is to examine the impact of rib configuration on flow behavior and heat transfer characteristics in a spiral tube with longitudinal ribs, through numerical simulations. Different cross-sectional shapes, including semicircular, V-shaped, trapezoidal, and rectangular ribs, are analyzed to assess the fluid flow, heat transfer, and exergetic efficiency of the tube. The findings indicate that incorporating longitudinal ribs of different cross-sectional forms substantially improves the fluid flow and thermal efficiency of the spirally coiled tube. An increase in rib depth leads to an accompanying rise in the Nusselt number, with improvements ranging from 18.4% to 27.9% for V-shaped and trapezoidal ribs, respectively. The spiral tube with rectangular shaped ribs exhibits the highest Nusselt number. Additionally, when the rib depth increases from 0.02 to 0.12 for the spiral tube with rectangular-shaped ribs, the THPP (Thermohydraulic performance parameter) experiences a significant increase of 38.1%. Also, the number of ribs (Nr) in the longitudinally ribbed spirally coiled tube has a crucial impact on its performance. Increasing the number of ribs results in higher heat transfer coefficients and Nusselt numbers for all tested rib shapes. However, the inclusion of more ribs leads to increased exergy destruction across all scenarios.

Insects can have a significant impact on biodiversity, ecology, and the economy. Certain insects, such as aphids, caterpillars, and beetles, feed on plant tissues, including leaves, stems, and fruits. They can cause direct damage by chewing on the plant parts, resulting in holes, defoliation, or stunted growth. This can weaken the plant and affect its overall health and productivity. Therefore, the aim of this research was to develop a model system that can identify insects and track their behavior, movement, size, and habits. We successfully built a 3D monitoring system that can track insects over time, facilitating the exploration of their habits and interactions with plants and crops. This technique can assist researchers in comprehending insect behavior and ecology, and it can be beneficial for further research in these areas.

Transforming data into appropriate formats is crucial because it can speed up the training process and enhance the performance of classification algorithms. It is, however, challenging due to the complicated process, resource-intensive and preserved meaning of the data. This study proposes new approaches to building knowledge representation models using word-embedding and ontology techniques, which can transform text data into digital data and still keep semantic/context information of themselves in order to enhance modeling data later. To evaluate the effectiveness of the built models, a classification framework is proposed and performed on a public real dataset. Experimental results show that the constructed knowledge representation models contribute significantly to the performance of classification methods.

Tree species inventories, particularly of poorly known dry forests, are necessary to protect and restore them in degraded landscapes. The present research has been conducted to compare taxonomic diversity and community composition in four dry forests (DF) categories with different standing volume levels: very low (DFV), low (DFP), medium (DFM) and high (DFR). This quantitative assessment of taxonomic diversity, forest structure and species composition were obtained from 103 sample plots (0.1 ha each). The regeneration potential of trees was assessed in 515 subplots (4 m × 4 m) located within the 103 plots. A total of 1,072 trees representing 87 species belonging to 37 families were recorded in 10.3 ha of total sampled area. The ranges of diversity indices observed in the four forest types were: Margalef's (5.44–8.43), Shannon-Wiener (1.80–2.29), Simpson diversity (0.76–0.87) and evenness (0.32–0.35). The regeneration potential of rare and threatened species Dalbergia oliveri, Hopea recopei, Dalbergia bariensis, Sindora siamensis, Parashorea stellata was observed to be poor. Conversely, Cratoxylon formosum, Shorea obtusa, Dipterocarpus tuberculatus, Dipterocarpus obtusifolius, Terminalia alata, Shorea siamensis and Xylia xylocarpa were the most dominant species at the seedling and sapling stage, showing a strong potential for regeneration. Overall, this study provides useful information on tree species diversity and composition for tropical dry forests which can be used as baseline data to develop incoming plans for forest management and conservation in Vietnam's Central Highlands Region.

This study conducts a rigorous comparative analysis between two cutting-edge instance segmentation methods, Mask R-CNN and YOLOv8, focusing on stomata pore analysis. A novel dataset specifically tailored for stomata pore instance segmentation, named PhenomicsStomata, was introduced. This dataset posed challenges such as low resolution and image imperfections, prompting the application of advanced preprocessing techniques, including image enhancement using the Lucy-Richardson Algorithm. The models underwent comprehensive evaluation, considering accuracy, precision, and recall as key parameters. Notably, YOLOv8 demonstrated superior performance over Mask R-CNN, particularly in accurately calculating stomata pore dimensions. Beyond this comparative study, the implications of our findings extend across diverse biological research, providing a robust foundation for advancing our understanding of plant physiology. Furthermore, the preprocessing enhancements offer valuable insights for refining image analysis techniques, showcasing the potential for broader applications in scientific domains. This research marks a significant stride in unraveling the complexities of plant structures, offering both theoretical insights and practical applications in scientific research. [ABSTRACT FROM AUTHOR]

In the era of Industry 4.0, companies must quickly adapt to market and technological changes to remain competitive. While monolithic software systems are still common, Process- Driven Applications (PDA) offer flexible, sustainable, and integrable solutions, relying on Business Process Model and Notation (BPMN) for process modeling. However, BPMN lacks formal semantics, leading to potential errors during process execution. This paper introduces a verification model that integrates Colored Petri Nets (CPN) into the PDA development lifecycle to improve process verification. By converting BPMN models into CPN, the method allows for early detection of errors such as deadlocks and inconsistencies. A real-world, loan application, case study demonstrates that the BPMN + CPN approach reduces errors by 80%, significantly enhancing the reliability and robustness of PDA systems. [ABSTRACT FROM AUTHOR]

In model predictive control (MPC), methods of linear offset free MPC are well established such as the disturbance model, the observer method and the state disturbance observer method. However, the observer gain in those methods is difficult to define. Based on the drawbacks observed in those methods, a novel algorithm is proposed to guarantee offset-free MPC under model-plant mismatches and disturbances by combining the two proposed methods which are the proposed Recursive Kalman estimated state method and the proposed Disturbance-Kalman state method. A comparison is made from existing methods to assess the ability of providing offset-free MPC onWood-Berry distillation column. Results shows that the proposed offset free MPC algorithm has better disturbance rejection performance than the existing algorithms.

Abstract In response to challenges from the COVID-19 pandemic and climate change to achieve the goal of ensuring sustainable economic growth, offshore wind power development not only provides a clean and sustainable source of energy but also provides opportunities for economic growth and job creation. Offshore wind energy projects have been promptly suggested in Vietnam due to policy advancement, with the country's excellent wind resources. The success of an offshore wind energy project is decided mainly by choosing the best location for offshore wind power station (OWPS) construction, which is a complex multicriteria decision-making (MCDM) problem with the coexistence of conflicting factors. There is a problem with incomplete decision information use and information loss during the decision-making process, and it is easy to overlook the interaction difficulty in a fuzzy environment. To address the complex nature of the prioritization problem posed, this study proposes a hybrid MCDM framework combining the spherical fuzzy analytical hierarchy process (SF-AHP) and weighted aggregated sum product assessment (WASPAS). SF-AHP is used in the first stage to determine the significance levels of OWPS evaluation criteria. WASPAS is then utilized to rank locations of OWPS. A comprehensive set of evaluation criteria developed based on the concept of sustainable development has been recognized by reviewing the literature review and interviewing experts to practice the two-stage MCDM model. A real case study for Vietnam is conducted to test the effectiveness of the proposed method. The best location schemes have been determined by using the decision framework. The results of the sensitivity analysis and a comparison analysis demonstrate that the decision framework is practical and robust. The proposed methodology can be used to attain a decision-making process at the regional level for offshore wind farm planning and coastal development, and the study results encourage the establishment of renewable energy development policies.

Vietnam has enormous potential for solar power thanks to its favorable geographical location. In recent years, the Vietnamese government has strived to develop renewable energy. The objective of this paper is to measure the operational efficiency of solar photovoltaic (PV) power plants using data envelopment analysis (DEA) with the epsilon-based measure (EBM) model. The utilization of the EBM model is due to its advantage to integrate radial and non-radial measurements to get a more exact estimate of relative efficiency. Using the merits of the EBM model, a case study of 18 decision-making units (DMUs) in Vietnam is presented. In the proposed model, three input variables are selected, which are capital cost, installed capacity, solar irradiation, while energy production and consumer density are considered as output variables. Following that, the technique for order of preference by similarity to ideal solution (TOPSIS) method is used to assess the validity and applicability of results. The results illustrate that two methods reach common rankings, in which the priority rankings of the best performing DMUs are very similar. This shows that the applied models are robust in nature. This paper offers significant materials that serve as practical and timelier solutions for decision-makers in the operating and management strategies of the solar energy industry, also a critical guideline in many related decision-making problems for any other industries around the world.

The shape and pose estimation of a human body are essential for human behavior analysis, sports and medical analysis, and virtual reality. Although 2D image data are much easier to acquire than 3D scan data, the estimation accuracy using 2D images is still far below the 3D scanning methods’ ones. In this paper, we propose a 2D image-based human body estimation method appropriate for body shape and size measurement. The proposed method uses SMPL (Skinned Multi-Person Linear model), a human body model, and rich correspondences of Dense-Pose network so named Dense-Pose2SMPL, which estimates the SMPL parameters estimation by minimizing the re-projection error of the correspondences between the pixels in the human image and 3D surface points in SMPL. The previous SMPL parameter estimation methods use the sparse joint correspondence so that they show very limited performance in shape estimation. We compare the body measurement accuracy of Dense-Pose2SMPL with a single human image with SMPLify, a joint-based estimation method, and DecoMR, a Dense-Pose-based neural network regression method. The experiments results show a dramatic improvement of Dense-Pose2SMP over SMPLify and DecoMR. In the circumference estimation error, over 30 percent decrease for the overweight and underweight and 10 to 20 percent decrease on average. We also provide an analysis of the effects of the input conditions: the subject’s BMI level, pose, clothing style, and camera viewpoint. A-pose, side (profile) camera views and minimal and tight clothing style show higher accuracy than other conditions. Finally, the extension with multiple (two) images can give around 15 percent more improvement in body size estimation accuracy over the single image-based method.