The study examines the behavior of the Mechanically Stabilized Earth (MSE) wall using available reinforcement materials in Danang, Vietnam. The MSE was reinforced by the self-fabricated galvanized steel grids using CB300V steel with 3 cm ribs. The backfill soil is a sandy clay soil from the local area with a low cohesion. A full-scale model with full instrumentation was installed to investigate the distribution of tensile forces along the reinforcement layers. The highest load that caused the wall to collapse due to internal instability (reinforcement rupture) was 302 kN/m2, which is 15 times the design load of 20 kN/m2. The failure surface within the reinforced soil had a parabolic sliding shape which is similar to the theoretical studies. At the failure load level, the maximum lateral displacement at the top of the wall facing was small (3.9 mm) which is significantly lower than the allowable displacements for the retaining wall. Furthermore, a numerical model using FLAC software was applied to simulate the performance of the MSE wall. The modeling results are in good agreement with the physical model. Thus, the self-fabricated galvanized steel grids could be confidentially used in combination with the local backfill soil for the MSE walls.

Burn injuries have profound implications for aesthetics, functionality, and patient survival. Umbilical cord lining mesenchymal stem cells (SCL-MSCs) have emerged as a potential therapeutic approach for burn wounds due to their easy isolation, favorable differentiation capabilities, and low immunogenicity. This study aimed to evaluate the growth characteristics of SCL-MSCs and their effectiveness in treating burn wounds. Mechanical isolation was employed to extract mesenchymal stem cells from the umbilical cord lining membrane. The isolated cells underwent characterization, including analysis of HLA-E, G, and DR expression, and assessment of immunogenicity against rabbit antigens using ELISA. A rabbit model was utilized to administer burn treatment. SCL-MSCs exhibited typical morphology of mesenchymal stem cells and demonstrated robust proliferation. These cells expressed CD90, HLA-E, and HLA-G markers while lacking HLA-DR expression. Intact cells and supercrushed cell fluid displayed minimal and unstable immunogenicity. SCL-MSCs exhibited remarkable differentiation potential toward fibroblast lineages, with differentiated fibroblasts showing elevated collagen production and efficient sheet formation. In the experimental rabbit model, burn wounds covered with SCL-MSC tegaderm material plates exhibited superior healing compared to controls. The stem cell material plate area demonstrated reduced inflammatory cell counts (P < 0.01) and increased fibroblast count, neovascularization, and mitotic index (P < 0.01). These findings highlight the potential of SCL-MSCs as a valuable resource for burn wound research and treatment.

This study proposes a novel hybrid machine learning approach for modeling of rainfall-induced shallow landslides. The proposed approach is a combination of an instance-based learning algorithm (k-NN) and Rotation Forest (RF), state of the art machine techniques that have seldom explored for landslide modeling. The Lang Son city area (Vietnam) is selected as a case study. For this purpose, a spatial database for the study area was constructed, and then, was used to build and evaluate the hybrid model. Performance of the model was assessed using Receiver Operating Characteristic (ROC), area under the ROC curve (AUC), success rate and prediction rate, and several statistical evaluation metrics. The results showed that the model has high performance with both the training data (AUC = 0.948) and the validation data (AUC = 0.848). The results were compared with those obtained from soft computing techniques i.e. Random Forest, J48 Decision Trees, and Multilayer Perceptron Neural Networks. Overall, the performance of the proposed model is better than those obtained from the above methods. Therefore, the proposed model is a promising tool for landslide modeling. The research result can be highly useful for land use planning and management in landslide prone areas.

In the present study, we isolated and screened thirty strains of GABA-producing lactic acid bacteria (LAB) from Indonesian traditional fermented foods. Two strains were able to convert monosodium glutamate (MSG) to GABA after 24 h of cultivation at 37oC based on thin layer chromatography (TLC) screening. 16S rDNA sequencing and proteomic identification using MALDI-TOF MS identified these two strains as Lactobacillus plantarum designated as L. plantarum FNCC 260 and L. plantarum FNCC 343. The highest yield of GABA production obtained from the fermentation of L. plantarum FNCC 260 was 809.2 mg/l of culture medium after 60 h of cultivation. Supplementation of 0.6 mM pyridoxal 5’-phosphate (PLP) and 0.1 mM pyridoxine led to the increase in GABA production to 945.3 mg/l and 969.5 mg/l, respectively. The highest GABA production of 1226.5 mg/l of culture medium was obtained with 100 mM initial concentration of MSG added in the cultivation medium. The open reading frame (ORF) of 1410 bp of the gadB gene from L. plantarum FNCC 260 encodes 469 amino acids with a calculated molecular mass of 53.57 kDa. The production of GABA via enzymatic conversion of monosodium glutamate (MSG) using purified recombinant glutamate decarboxylase (GAD) from L. plantarum FNCC 260 expressed in Escherichia coli was found to be more efficient (5-fold higher within 6 h) than the production obtained from fermentation. L. plantarum FNCC 260 could be of interest for the synthesis of GABA.