This article presents the results of studying Newtonian fluids using squeeze - tack testing methods at different speeds and sample thicknesses to evaluate the influence of sample thickness and tack ...speed on the rheological properties of Newtonian fluids. The results show that the greater the laminate thickness, the lower its resistance to peeling. Squeeze speed has virtually no effect on the tensile yield stress in most cases. For colloidal materials, the squeeze thickness should be studied at less than 2 mm. Research can be developed with squeeze thicknesses of 0.5 mm, 1 mm, 1.5 mm and 2 mm.
Xanthan is a natural polysaccharide in powder form, created through the fermentation of sugar by the bacteria Xanthomonas campestris. In construction, Xanthan has been researched and applied in road ...and bridge construction materials, viscosity adjusting additives in selfcompacting concrete... and in construction materials technology in general, Xanthan is used as a viscosity modifier, thickener... in concrete. In this study, the applicability of Xanthan in concrete was preliminarily evaluated. Through the results of squeeze - tack experiments to evaluate the rheological behavior of Xanthan pastes, the author conducted a compression test with concrete using Xanthan additive (2%), evaluating the change in strength of concrete when using Xanthan. The results show that Xanthan can be used in concrete to improve adhesion to the mold and increase the ability to easily peel off the mold. At the same time, using Xanthan in concrete with a content of 2% also contributes to increasing the compressive strength of concrete with an increase of 7%.
The selective expression of CD137 on cells of the immune system (e.g., T and DC cells) and oncogenic cells in several types of cancer leads this molecule to be an attractive target to discover cancer ...immunotherapy. Therefore, specific antibodies against CD137 are being studied and developed aiming to activate and enhance anti-cancer immune responses as well as suppress oncogenic cells. Accumulating evidence suggests that anti-CD137 antibodies can be used separately to prevent tumor in some cases, while in other cases, these antibodies need to be co-administered with other antibodies or drugs/vaccines/regents for a better performance. Thus, in this work, we aim to update and discuss current knowledge about anti-cancer effects of anti-CD137 antibodies as mono- and combined-immunotherapies.
Vaginal candidiasis is frequent in women of reproductive age. Accurate identification Candida provides helpful information for successful therapy and epidemiology study; however, there are very ...limited data from the Vietnam have been reported. This study was performed to determine the prevalence, species distribution of yeast causing vaginal discharge and antifungal susceptibility patterns of Candida albicans among symptomatic non-pregnant women of reproductive age. Vaginal discharge samples were collected from 462 women of reproductive age in Hanoi, Vietnam between Sep 2019 and Oct 2020. Vaginal swabs from these patients were examined by direct microscopic examination (10% KOH). CHROMagarTM Candida medium and Sabouraud dextrose agar supplemented with chloramphenicol (0.5 g/l) were used to isolate yeast, and species identification was performed using morphological tests and molecular tools (PCR and sequencing). Antifungal susceptibility testing was determined according to the Clinical and Laboratory Standards Institute guidelines (M27-A3 and M27-S4). The prevalence of vaginal yeast colonization in non-pregnant women was 51.3% of 462 participants. Nine different yeast species were identified. Among these isolates, C. albicans (51.37%) was the most frequent, followed by C. parapsilosis (25.88%), C. glabrata (11.37%), C. tropicalis (4.31%), C. krusei (3.92%), C. africana (1.57%), Saccharomyces cerevisiae (0.78%), C. nivariensis (1 isolates, 0.39%), and C. lusitaniae (1 isolates, 0.39%), respectively. Among C. albicans, all 46 isolates were 100% susceptible to micafungin, caspofungin, and miconazole. The susceptibility rates to amphotericine B, 5-flucytosine, fluconazole, itraconazole and voriconazole were 95.65, 91.30, 91.30, 82.61 and 86.95%, respectively. The prevalence of VVC among symptomatic non-pregnant women of reproductive age in Vietnam was higher than many parts of the world. The high frequency of non-albicans Candida species, which were often more resistant to antifungal agents, was a notable feature. Resistance rates of vaginal C. albicans isolates to antifungal agents was low. Our findings suggest that continued surveillance of changes in species distribution and susceptibility to antifungals should be routinely screened and treated.
Monitoring agricultural soil organic carbon (SOC) has played an essential role in sustainable agricultural management. Precise and robust prediction of SOC greatly contributes to carbon neutrality in ...the agricultural industry. To create more knowledge regarding the ability of remote sensing to monitor carbon soil, this research devises a state-of-the-art low cost machine learning model for quantifying agricultural soil carbon using active and ensemble-based decision tree learning combined with multi-sensor data fusion at a national and world scale. This work explores the use of Sentinel-1 (S1) C-band dual polarimetric synthetic aperture radar (SAR), Sentinel-2 (S2) multispectral data, and an innovative machine learning (ML) approach using an integration of active learning for land-use mapping and advanced Extreme Gradient Boosting (XGBoost) for robustness of the SOC estimates. The collected soil samples from a field survey in Western Australia were used for the model validation. The indicators including the coefficient of determination (R2) and root - mean – square - error (RMSE) were applied to evaluate the model's performance. A numerous features computed from optical and SAR data fusion were employed to build and test the proposed model performance. The effectiveness of the proposed machine learning model was assessed by comparing with the two well-known algorithms such as Random Forests (RF) and Support Vector Machine (SVM) to predict agricultural SOC. Results suggest that a combination of S1 and S2 sensors could effectively estimate SOC in farming areas by using ML techniques. Satisfactory accuracy of the proposed XGBoost with optimal features was achieved the highest performance (R2 = 0.870; RMSE = 1.818 tonC/ha) which outperformed RF and SVM. Thus, multi-sensor data fusion combined with the XGBoost lead to the best prediction results for agricultural SOC at 10 m spatial resolution. In short, this new approach could significantly contribute to various agricultural SOC retrieval studies globally.
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•Binary classification map and DPGS played crucial roles.•Multi-sensor data fusion enabled more precise SOC prediction than single sensors.•The potential predictor variables derived from S-1 and S-2 data were extracted.•The Extreme Boosting regression (XGBoost) functioned best in SOC retrieval.•Soil Adjusted Vegetation Index (SAVI) was the most influential feature.
The efforts of this study aimed to evaluate the feasibility of the nanotubular halloysites in weathered pegmatites (NaHWP) for removing heavy metals (i.e., Cd2+, Pb2+) from water. Furthermore, two ...novel intelligent models, such as teaching-learning-based optimization (TLBO)-artificial neural network (ANN), and TLBO-support vector regression (SVR), named as TLBO-ANN and TLBO-SVR models, respectively, were proposed to predict the Cd2+ and Pb2+ absorption efficiencies from water using the NaHWP absorbent. Databases used, including 53 experiments for Pb2+ absorption and 56 experiments for Cd2+ absorption from water, under the catalysis of different conditions, such as initial concentration of Pb2+ and Cd2+, solution pH, adsorbent weight, and contact time. Subsequently, the TLBO-ANN and TLBO-SVR models were developed and applied to predict the efficiencies of Cd2+ and Pb2+ absorption from water, aiming to evaluate the role as well as the effects of different conditions on the absorption efficiencies using the NaHWP absorbent. The standalone ANN and SVM models were also taken into consideration and compared with the proposed hybrid models (i.e., TLBO-ANN and TLBO-SVR). The results showed that the NaHWP detected in a Kaolin mine (Vietnam) with 70% nanotubular halloysites is a potential adsorbent for water treatment to eliminate heavy metals from water. The two novel hybrid models proposed, i.e., TLBO-ANN and TLBO-SVR, also yielded the dominant performances and accuracies in predicting the Cd2+ and Pb2+ absorption efficiencies from water, i.e., RMSE = 1.190 and 1.102, R2 = 0.951 and 0.957, VAF = 94.436 and 95.028 for the TLBO-ANN and TLBO-SVR models, respectively, in predicting the Pb2+ absorption efficiency from water; RMSE = 3.084 and 3.442, R2 = 0.971 and 0.965, VAF = 96.499 and 96.415 for the TLBO-ANN and TLBO-SVR models, respectively, in predicting the Cd2+ absorption efficiency from water. Furthermore, the validation results also demonstrated these findings in practice through 23 experiments with the accuracies of 98.3% and 98.37% for the TLBO-ANN and TLBO-SVR models, respectively, in predicting the Pb2+ absorption efficiency from water; the accuracies of 98.3% and 97.46% for the TLBO-ANN and TLBO-SVR models, respectively, in predicting the Cd2+ absorption efficiency from water. Besides, solution pH was evaluated as the most critical parameter that can be adjusted to enhance the performance of the absorption of the heavy metals in this study. By using the NaHWP absorbent and the novel proposed intelligent models developed, heavy metals can be eliminated entirely from water, providing pure water/clean freshwater without any risk of adverse health effects for the short term or long term.
•Nanotubular halloysites from weathered pegmatites was used for water treatment.•The catalysis of different conditions were implemented to remove heavy metals.•The TLBO-ANN and TLBO-SVR were proposed for water treatment.•Solution pH should be adjusted to improve heavy metals absorption efficiency.•Heavy metals can be eliminated entirely using the proposed material and models.
Text summarization is a widely-researched problem among scholars in the field of natural language processing. Multiple techniques have been proposed to help tackle this problem, yet some of these ...methodologies may still exhibit limitations such as the requirements for large training datasets, which might not always be possible, but more importantly, the lack of interpretability or transparency of the model. In this paper, we propose using meta-learning algorithm to train a deep learning model for extractive text summarization and then using various explanatory techniques such as SHAP (Shapley, 1953), linear regression (Lederer, 2022), decision trees (Fürnkranz, 2010), and input modification to gain insights into the model’s decision making process. To evaluate the effectiveness of our approach, we will compare it to other popular natural language processing models like BERT (Miller, 2019) or XLNET (Yang et al., 2020) using the ROUGE metrics (Lin, 2004). Overall, our proposed approach provides a promising solution to the limitations of existing methods and a framework for improving the explainability of deep learning models in other natural language processing tasks.
•Bi-LSTM and Meta-learning produce better results compared to other popular models.•Meta-learning can be utilized to improve performance in data-scarce scenarios.•Use SHAP, regression, decision tree, and text mutation to enhance interpretability.
The natural wetland areas in Vietnam, which are transition areas from inland and ocean, play a crucial role in minimizing coastal hazards; however, during the last two decades, about 64% of these ...areas have been converted from the natural wetland to the human-made wetland. It is anticipated that the conversion rate continues to increase due to economic development and urbanization. Therefore, monitoring and assessment of the wetland are essential for the coastal vulnerability assessment and geo-ecosystem management. The aim of this study is to propose and verify a new deep learning approach to interpret 9 of 19 coastal wetland types classified in the RAMSAR and MONRE systems for the Tien Yen estuary of Vietnam. Herein, a Resnet framework was integrated into the U-Net to optimize the performance of the proposed deep learning model. The Sentinel-2, ALOS-DEM, and NOAA-DEM satellite images were used as the input data, whereas the output is the predefined nine wetland types. As a result, two ResU-Net models using Adam and RMSprop optimizer functions show the accuracy higher than 85%, especially in forested intertidal wetlands, aquaculture ponds, and farm ponds. The better performance of these models was proved, compared to Random Forest and Support Vector Machine methods. After optimizing the ResU-Net models, they were also used to map the coastal wetland areas correctly in the northeastern part of Vietnam. The final model can potentially update new wetland types in the southern parts and islands in Vietnam towards wetland change monitoring in real time.
Endothelium dysfunction and decrease of incretin effects occur early in type 2 diabetes mellitus and these changes contribute to diabetic cardiovascular complications such as atherosclerosis, thick ...intima-media, coronary, and peripheral arterial diseases. In patients with diabetes, the femoral artery is a site of a high incidence of injury in peripheral vascular diseases, and atherosclerotic changes may appear earlier in the femoral artery compared to the carotid artery. This study was conducted to determine the prevalence of increased femoral artery intima-media thickness (IMT) and atherosclerotic plaque and their correlation with serum glucagon-like peptide-1 (GLP-1) levels in newly-diagnosed patients with type 2 diabetes mellitus.
A cross-sectional study was conducted on 332 patients with nT2D in the National Endocrinology Hospital, Vietnam from January 2015 to May 2018. IMT was measured by Doppler ultrasound and GLP-1 by enzyme-linked immunosorbent assay (ELISA). All data were analyzed with SPSS version 26 for Windows (SPSS Inc, Chicago, IL).
Prevalence of thick femoral artery IMT and atherosclerotic plaque was 38.2 and 22.3%, respectively. There was a relationship between IMT and age, waist to hip ratio (WHR), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting GLP-1, high sensitive CRP (hsCRP) and 24-hour microalbuminuria secretion (24-h MAUS). The fasting serum GLP-1 (fGLP-1) levels were reduced significantly in patients with thickness and atherosclerosis femoral artery (p = 0.001). After adjusting with other related factors, namely, DBP and estimated glomerular filtration rate (eGFR), whilst hsCRP and 24-h MAUS showed a significantly positive correlation to IMT (Standardized B and p of 0.242, 0.004 and 0.178, 0.043, respectively), fGLP-1 showed a significantly negative correlation to IMT (Standardized B = -0.288, p = 0.001).
Among n2TD, the percentage for femoral artery thick IMT and atherosclerosis was 38.2% and 22.3% respectively, and serum GLP-1 was negatively correlated with thick IMT and atherosclerosis.
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