Search

Metagenomics is an important technique for discovering and screening significant bioactive compounds such as polyketide synthases, non-ribosomal peptide synthetases, antibiotics, and biocatalysts. The enormous potential of microbial diversity in coastal lagoons remains untapped because of the difficulty of culturing many microorganisms under laboratory conditions. Therefore, this study aimed to decipher the structural and functional diversity of microbial communities, including un-culturable species in lagoon sediments collected from Tam Giang, Nai, and Thi Nai lagoons, which were located in the central coast of Vietnam using metagenomics approach. The results showed that all three large lagoons exhibited a high diversity of fungal species among the eukaryotic species and a high diversity of bacterial and archaeal species. Among them, the most prominent phyla included Ascomycota (fungal phyla), Firmicutes and Proteobacteria (bacterial phyla), and Thaumarchaeota (archaeal phyla). Sequencing of the metagenome samples of microbiome in the three lagoons of Tam Giang, Nai, and Thi Nai using shotgun sequencing technology revealed the existence of 126 potential gene sequences from the Tam Giang lagoon, 346 sequences from the Nai lagoon, and 341 sequences from the Thi Nai lagoon. These sequences participated in various metabolic processes and belonged to more than 30 groups of biologically active compounds. Most of these genes were related to antibacterial, antifungal, antiviral, cancer cell inhibition, and antioxidant activities. This is the first study conducted on lagoon microorganisms in Vietnam, opening up prospects and potential in exploiting microorganisms and biologically active substances with important applications in life from the lagoon ecosystems of Vietnam in particular and the overall world in general.

This study presents a novel multi-level Stacking model designed to enhance the accuracy of customer churn prediction in the banking sector, a critical aspect for improving customer retention. Our approach integrates four distinct machine-learning algorithms – K-Nearest Neighbor (KNN), XGBoost, Random Forest (RF), and Support Vector Machine (SVM) – at the first level (Level 0). These algorithms generate initial predictions, which are then combined and fed into higher-level models (Level 1) comprising Logistic Regression, Recurrent Neural Network (RNN), and Deep Neural Network (DNN).

This study aims to improve the quality of vermicelli products by replacing a portion of rice flour with orange-fleshed sweet potato powder at four ratios ranging from 10 % (F1) to 25 % (F4) (5 % interval), while the control sample used 100 % rice flour (F0). The products were analyzed for quality and sensory characteristics. The analysis of five vermicelli production formulas revealed that the product made from formula F3, which contains OFSP powder and rice flour at a ratio of 20:80 %, had a relatively high amount of β-carotene (9.29 μg/g), a bright color (L∗ = 67.15, b∗ = 29.45), and a suitable hardness (46.79 g-force). The F3 sample had better cooking quality than the F0 sample, which was proved by the percentages of weight increase, volume increase, and dry matter loss of 59.88 %, 56.89 %, and 7.08 %, respectively. The estimated PRAL score of the product prepared from formula F3 was −1.45 (PRAL

Article Highlights - Camellia dalatensis and Camellia capitata are critically endangered species. - Habitat loss and deforestation threaten the survival of these Camellia species. - Vegetative propagation aids conservation of critically endangered Camellia species. - Rooting success depends on cutting types, growth regulators, and substrate mix. - Research aids conservation efforts for Vietnam’s rare and vulnerable flora. Abstract Camellia dalatensis (V. D. Luong, Ninh & Hakoda) and Camellia capitata (Orel, Curry & Luu) are classified as critically endangered (CR) by the IUCN. Conservation solutions for these two species have not yet been fully implemented, while wild populations are declining. This research on vegetative propagation examined the efficacy of indole-3-butyric acid (IBA in solution or in powdered activated charcoal), cutting types (terminal shoot or stem cuttings), and substrates for the growth of cuttings (sand-coir dust mixes) on rooting efficiency. The highest rooting of C. dalatensis was observed in terminal shoot cuttings at 1,000 ppm IBA, and 25% sand and 75% coir dust substrate. Maximum values obtained were survival 95.6%, rooting percentage 88.9%, number of roots per cutting 6.9, root length 6.2 cm, and rooting index 42.9. For C. capitata, superior rooting was obtained with semi-hardwood cuttings, at 1,500 ppm IBA, and equal parts of sand and coir dust as substrate. Camellia capitata was more difficult to root, with maximum values of 65.6% survival, 52.2% rooting, 2.8 roots per cutting, 2.9 cm root length, and rooting index of 8.1. The results of this study can be used to support further propagation and conservation of these two endangered species.

The present work aims to investigate the effect of foam-mat drying on drying rate and lycopene content of tomato powder using a three-level Box-Behnken experimental design of Response Surface Methodology (RSM). Three process parameters included egg albumin (EA) ranging from 3 to 9 % as a foaming agent, carboxymethyl cellulose (CMC) from 0.2 to 0.6 % as a foam stabilizer and drying temperatures (60, 70, and 80oC). The responses measured drying rate (DR) and lycopene content, which are the indication of drying process and product quality. Optimization of drying process using RSM and artificial neural network coupled genetic algorithm (ANN-GA) models has been also investigated. Foam mat dried tomato powder produced under optimal conditions was then used to prepare a nutritious soup powder with 4 designed recipes with other nutritious ingredients. The results showed that the ANN-GA model (network structure of 3-10-2) could predict and optimize better than the RSM model. The optimal conditions for foam-mat drying process were EA of 6.67 %, CMC of 0.381 %, and drying temperature of 70.6oC. These gave the DR and lycopene to be 3.004 g water/g dry matter/min and 392.8 μg/g, respectively. Validation optimal condition was performed and showed that the experimental values obtained were greatly close to the predicted values. From the 4 designed soup formulas, the macronutrient content in formula F2 met the range for AMDR with the percentage of calories from protein, lipid, and carbohydrate being 21.28 %, 20.28 %, and 58.44 %, respectively. It was proven that ANN-GA is a more reliable and robust predictive modelling tool for foam-mat tomato powder production optimization compared to RSM model. Also, the promising application of tomato powder in nutritious soup production also was shown in this study, which could further research in larger scale.

This study aims to optimize the parameters of the foam-mat drying to produce banana powder. Input parameters of the foam drying such as foaming agent (egg albumin) concentration used from 5 to 15 %, the foam stabilizer (maltodextrin) used from 1 to 3 % and the drying temperature varied between 60 and 80 °C. Factors with 3 levels are arranged according to the Box-Behnken design, which further modelled by RSM (response surface methodology) and ANN (artificial neural network), and optimized. The drying rate and total polyphenol content (TPC) of banana powder under the studied conditions were determined as the target output. The moisture, color, total polyphenol, antioxidant activity and some physical parameters of final fine powder were analyzed. Increasing temperature has increased the drying rate. In addition, increasing the concentrations of egg albumin and maltodextrin maintained the highest TPC and maximum drying rate. ANN model showed the higher forecasting capacity than that of RSM. Moreover, simultaneous optimization of two responses (TPC and drying rate) was selected to maximize the desired value at the concentration of albumin, maltodextrin and drying temperature of 11.86 %, 1.92 %, 74.94 °C, respectively, corresponding to the highest TPC value and drying rate of 1.31 mgGAE/g DW and 2.48 g water/g dry matter/min. At this condition, the drying time was recorded as 103 min. Validation of the optimal ratios showed that the experimental values of TPC and drying rate were in good agreement with the model predicted data. The moisture content and water activity of product were found to be 5.87 ± 0.07 % and 0.37 ± 0.01. The product had bright colors with L*, a* and b* values were measured as 86.4 ± 0.5, 1.75 ± 0.08 and 15.2 ± 0.3, respectively. The high DPPH radical scavenging activity was detected (53.5 %) with the water solubility index and water absorption index of banana powder was determined at value of 56.98 % and 5.17 g/g, respectively. Foam mat dried banana powder was well preserved in paper packaging with aluminum foil.

This study was conducted to optimize the foam-mat drying conditions to maximize quality [β-carotene and total polyphenol content (TPC)] and drying rate of “Gấc” aril powder by using two novel statistical techniques as Response Surface Methodology (RSM) and Artificial Neural Network (ANN) couple with Genetic Algorithm (GA). During production process, level of egg albumin (EA) used for foaming process and drying temperature mainly influenced the drying rate and content of antioxidant compounds in powder. ANN model of 3–10–3 showed more accuracy and faster prediction capacity than RSM model did. ANN-GA model predicted the optimal conditions to be 13.31 % EA, 0.26 % xanthan gum and drying temperature of 73.1 °C, with the drying rate of 1.89 g-water/g-dry matter/min, β-carotene content of 395.88 μg/g, TPC of 1.68 mgGAE/g. These results confirmed the suitability and promising of foam-mat drying for “Gấc” aril powder production, to be producing food ingredient containing highly bioactive compounds.

Drying fruit puree by the foam drying method has become popular due to its simplicity, low cost, short drying time, and low thermal degradation. The objective of the study was to investigate the effect of foaming conditions on foam properties (foam expansion, foam density) and content of β-carotene in lucuma powder using Box-Behnken design (BBD) with 3 factors and 3 levels, including water:lucuma ratio (1:1–3:1), egg albumin concentration (EA, 5–15 %), and xanthan gum (XG, 0.1–0.3 %). Response surface methodology (RSM) and artificial neural network (ANN) were used for model establishment. The results showed that as the EA increased, the foam volume increased significantly, while the foam density decreased. The ANN-coupled BBD model structure of 3–10-3 demonstrated a high level of accuracy in predicting the impact of foaming formulation on responses, with a coefficient of determination exceeding 0.99. The optimal conditions by stimulation multiple-objective RSM for lucuma foam-mat drying were achieved with a water:lucuma ratio, EA, and XG of 2.53:1, 10.8 %, and 0.22 %, respectively. Based on these ideal conditions, the foam density, foam expansion, and β-carotene content of the dried powder were found to be 0.23 ± 0.04 g/mL, 228 ± 2 %, and 237.1 ± 0.1 μg/g, respectively. The obtained experimental values were very close to the model-predicted results, with very low differences identified when the validation was performed. These findings provide information for controlling the drying process using an artificial model and further applying lucuma powder in various fields in the food industry.

The kinetic study of foam-mat drying to make lucuma powder was conducted at various drying temperatures (50–80oC) using thin layer drying and an artificial neural network (ANN) to forecast drying behavior. The physico-chemical properties of lucuma powder under these conditions were also analyzed. Among the six models, the parabolic model was chosen as the best appropriate model for describing the lucuma drying process with the highest correlation coefficient, the lowest RMSE and Chi-square. However, the ANN model (2-10-1) could predict the moisture ratio more accurately than the parabolic model, and it also presented the fittest with the actual experiment. As temperature increased from 50 to 80oC, effective moisture diffusivity increased from 1.05 × 10-9 to 1.67 × 10-9 m2/s, with an activation energy of 15.12 kJ/mol. Drying temperatures have a considerable impact on the quality of lucuma powder. When dried at 60oC, lucuma powder had a bright yellow color, retained its quality and excellent antioxidant activity compared to other temperatures evaluated simultaneously.

Imaging plays a central role in neuro-oncology including primary diagnosis, treatment planning, and surveillance of tumors. The emergence of quantitative imaging and radiomics provided an uprecedented opportunity to compile mineable databases that can be utilized in a variety of applications. In this review, we aim to summarize the current state of conventional and advanced imaging techniques, standardization efforts, fast protocols, contrast and sedation in pediatric neuro-oncologic imaging, radiomics-radiogenomics, multi-omics and molecular imaging approaches. We will also address the existing challenges and discuss future directions.

This article introduces a new design pattern that provides an optimal solution for the systematic development of AUV controllers. In this study, a hybrid control model is designed on the basis of the OOSEM (Object-Oriented Systems Engineering Method), combined with MDA (Model-Driven Architecture) concepts, real-time UML/SysML (Unified Modeling Language/Systems Modeling Language), and the UKF (unscented Kalman filter) algorithm. This hybrid model enables the implementation of the control elements of autonomous underwater vehicles (AUVs), which are considered HDSs (hybrid dynamic systems), and it can be adapted for reuse for most standard AUV platforms. To achieve this goal, a dynamic AUV model is integrated with the specializations of the OOSEM/MDA, in which an analysis model is clarified via a use-case model definition and then combined with HA (hybrid automata) to precisely define the control requirements. Next, the designed model is tailored via real-time UML/SysML to obtain the core control blocks, which describe the behaviors and structures of the control parts in detail. This design model is then transformed into an implementation model with the assistance of round-trip engineering to conveniently realize a controller for AUVs. Based on this new model, a feasible AUV controller for low-cost, turtle-shaped AUVs is implemented, and it is utilized to perform planar trajectory tracking.

Background: Digital transformation refers to applying digital technology in various fields of society. In the last 5 years, digital transformation has spread to most areas of social life, including education. However, research on digital transformation in education is still fragmented.Objectives: The aim of the study was to present a comprehensive review of studies on digital transformation in education using bibliometric analysis.Methods: We searched the Scopus database from inception to 1 January 2023 using the search terms ‘digital transformation’ AND ‘education’ within abstracts, keywords, or titles of journal articles or conference papers written in English. The retrieved articles were analysed using VOSviewer and Biblioshiny tools.Results: A total of 1329 relevant studies were retrieved. Although the first paper in this field was published in 1999, the number of publications has increased rapidly only in the past 4 years. The most influential countries in this field are the developed countries (Russian Federation, Germany, and the United States), but scholars from the developing countries (Indonesia and Thailand) are among the most productive. Papers on digital transformation are frequently published in journals with lower rankings within the Scopus database. Using VOSviewer for keyword co-occurrence analysis, we classified the research topics related to digital transformation in educa-tion into four main groups: digital transformation in higher education under the impact of the coronavirus disease pandemic 2019 pandemic, applying the technolo-gies of the Fourth Industrial Revolution to education, digitization and digital compe-tence in education in the context of digital transformation, and learning forms using technology (for example, e-learning, m-learning, and blended learning) in higher-education institutions.Conclusions: Four research trends related to digital transformation in education were identified. These trends may also change as digital transformation continues to develop.

In recent years, Science, Technology, Engineering, and Math (STEM) education has become important in many countries around the world. In this study a bibliometric analysis was applied to evaluate the scientific results of STEM education in the Association of Southeast Asian Nations (ASEAN) region, indexed in the Scopus database for the period 2000-2019. A total of 175 publications were taken from the Scopus database for analysis. Our main findings show that the trend of research in this field has shown a dramatic increase in scientific production in the last three years, when the published amount accounted for 67.43% of the collection. Authors with the most publications came from the top 10 universities and research institutes (accounting for 38.44%). Scholars in the ASEAN region tend to submit their works to Q3 and Q4 journals in the SCImago database. Diverse research with basic and cross-cutting topics focus on STEM education for undergraduate students, engineering education, and education computing.

Brain atrophy is correlated with risk of cognitive impairment, functional decline, and dementia. Despite a high infectious disease burden, Tsimane forager-horticulturists of Bolivia have the lowest prevalence of coronary atherosclerosis of any studied population and present few cardiovascular disease (CVD) risk factors despite a high burden of infections and therefore inflammation. This study (a) examines the statistical association between brain volume (BV) and age for Tsimane and (b) compares this association to that of 3 industrialized populations in the United States and Europe. This cohort-based panel study enrolled 746 participants aged 40-94 (396 males), from whom computed tomography (CT) head scans were acquired. BV and intracranial volume (ICV) were calculated from automatic head CT segmentations. The linear regression coefficient estimate β^T of the Tsimane (T), describing the relationship between age (predictor) and BV (response, as a percentage of ICV), was calculated for the pooled sample (including both sexes) and for each sex. β^T was compared to the corresponding regression coefficient estimate β^R of samples from the industrialized reference (R) countries. For all comparisons, the null hypothesis β T = β R was rejected both for the combined samples of males and females, as well as separately for each sex. Our results indicate that the Tsimane exhibit a significantly slower decrease in BV with age than populations in the United States and Europe. Such reduced rates of BV decrease, together with a subsistence lifestyle and low CVD risk, may protect brain health despite considerable chronic inflammation related to infectious burden.