Search Results (172)

Resol phenol–formaldehyde (PF) resin was modified with 2.5 and 5.0 wt% polymethylhydrosiloxane (PMHS). This study characterizes the modified resin and its subsequently fabricated glass fiber (GF)-reinforced composites (30–60 wt% GF). Formation of an organic–inorganic hybrid network, via reaction between Si-H groups of PMHS and hydroxyl (-OH) groups of the resol resin, was confirmed by FTIR and ¹H NMR. DSC and TGA/DTG revealed enhanced thermal stability for PMHS-modified resin: the decomposition temperature of Resol–PMHS 5.0% increased to 483 °C (neat resin: 438 °C), and char yield at 800 °C rose to 57% (neat resin: 38%). The 60 wt% GF-reinforced Resol–PMHS 5.0% composite exhibited tensile, flexural, and impact strengths of 145 ± 7 MPa, 160 ± 7 MPa, and 71 ± 5 kJ/m², respectively, superior to the unmodified resin composite (136 ± 6 MPa, 112 ± 6 MPa, and 51 ± 5 kJ/m²). SEM observations indicated improved fiber–matrix interfacial adhesion and reduced delamination. These results demonstrate that PMHS modification effectively enhances the thermo-mechanical properties of the PF resin and its composites, highlighting potential for industrial applications. Full article

In this study, relevant rice cultivation data were collected through a local survey, and the life cycle assessment (LCA) method was employed to quantify greenhouse gas (GHG) emissions from rice production on consolidated land in the Red River Delta (RRD). Systematic sampling was used in face-to-face interviews with 45 rice farming households in a representative commune of Hai Duong province. Specific GHG emissions were significantly higher in the summer crop (averaged at 11.4 t CO₂-eq/ha or 2.2 t CO₂-eq/t grain) than in the spring crop (6.8 t CO₂-eq/ha or 1.2 t CO₂-eq/t grain). Methane was a dominant GHG emitted from paddy fields, contributing 84% of the total emissions of CO₂-eq in the summer crop and 73% in the spring crop. Fertilizer use and N₂O emissions accounted for 9% of emissions in the summer crop and 16% in the spring crop. Energy consumption for machinery and irrigation added a further 4% and 8%, respectively. Annually, as of 2023, the rice production activities in the RRD release 7.3 Tg of CO₂-eq (100 years), a significant contribution to the national GHG emissions. GHG emissions under alternative scenarios of rice straw management were assessed. This study highlights the role of land consolidation in improving water management, which contributes to lowering emissions. Based on the findings, several mitigation measures could be identified, including improved irrigation practices, optimized fertilizer use, and the promotion of sustainable rice straw management practices. Full article

In this study, we present a novel surface-enhanced Raman scattering (SERS) substrate based on porous silicon microcavities (PSiMCs) decorated with silver nanoparticles (AgNPs) for ultra-sensitive molecule detection. This substrate utilizes a dual enhancement mechanism: the localized surface plasmon resonance (LSPR) of AgNPs and the optical resonance of the PSiMC structure, which together create intense electromagnetic hot spots and prolong photon–molecule interactions. The porous architecture provides a large surface area for uniform nanoparticle distribution and efficient analyte adsorption. The AgNP/PSiMC substrate demonstrates an impressive detection limit of 1.0 × 10⁻¹³ M for rhodamine101 and 1.0 × 10⁻¹⁰ M for methyl parathion, outperforming many previously reported SERS platforms. Furthermore, the substrate exhibits excellent signal uniformity (RSD ≈ 6.14%) and long-term stability, retaining over 50% signal intensity after 28 days. These results underscore the potential of AgNP/PSiMCs as highly efficient, reproducible, and scalable SERS platforms for trace-level chemical and environmental sensing applications. Full article

The precise identification and non-destructive measurement of structural features and defects in semiconductor wafers are essential for ensuring process integrity and sustaining high yield in advanced manufacturing environments. Unlike conventional measurement techniques, scanning acoustic microscopy (SAM) is an advanced method that provides detailed visualizations of both surface and internal wafer structures. However, in practical industrial applications, the scanning time and image quality of SAM significantly impact its overall performance and utility. Prolonged scanning durations can lead to production bottlenecks, while suboptimal image quality can compromise the accuracy of defect detection. To address these challenges, this study proposes LinearTGAN, an improved generative adversarial network (GAN)-based model specifically designed to improve the resolution of linear acoustic wafer images acquired by the breakthrough rotary scanning acoustic microscopy (R-SAM) system. Empirical evaluations demonstrate that the proposed model significantly outperforms conventional GAN-based approaches, achieving a Peak Signal-to-Noise Ratio (PSNR) of 29.479 dB, a Structural Similarity Index Measure (SSIM) of 0.874, a Learned Perceptual Image Patch Similarity (LPIPS) of 0.095, and a Fréchet Inception Distance (FID) of 0.445. To assess the measurement aspect of LinearTGAN, a lightweight defect segmentation module was integrated and tested on annotated wafer datasets. The super-resolved images produced by LinearTGAN significantly enhanced segmentation accuracy and improved the sensitivity of microcrack detection. Furthermore, the deployment of LinearTGAN within the R-SAM system yielded a 92% improvement in scanning performance for 12-inch wafers while simultaneously enhancing image fidelity. The integration of super-resolution techniques into R-SAM significantly advances the precision, robustness, and efficiency of non-destructive measurements, highlighting their potential to have a transformative impact in semiconductor metrology and quality assurance. Full article

The Mekong Delta, an important agricultural and economic hub in Vietnam, has suffered from severe water quality issues caused by both natural and anthropogenic forces. This paper aims to conduct a rational statistical approach to evaluate the current situation of surface water quality in the Mekong Delta, applying Factor Analysis (FA), Principal Component Analysis (PCA), and Agglomerative Hierarchical Clustering (AHC) to a database of 3117 samples collected by national and provincial monitoring stations. The results revealed significant contamination with organic pollutants (BOD5: 3.50–172.870 mg/L, COD: 6.493–472.984 mg/L), pesticides (e.g., DDTs: n.d to 1.227 mg/L), trace metals (As: 0.006–0.046 mg/L, Cr: n.d–1.960 mg/L), and microbial indicators (Coliforms: n.d–45,100 MPN/100 mL), often higher than the WHO drinking water threshold. PCA/AHC analysis identified the following five major pollution components: (1) organic pollution and sewage/industrial and deposited chemicals (PCA1—23.08% variance); (2) pesticide and agricultural runoff derived contamination with Hg (PCA2—15.44%); (3) microbial pollution of the water was found to correlate positively with Zn and Cu content (PCA3—8.90%); (4) salinity was found to mobilize As and Cr (PCA4—8.00%); (5) nutrient/microbial pollution presumably from agricultural and sewage inputs (PCA5—7.22%). AHC showed some spatial variability that grouped samples in urban/industrial (Cluster 1), rural/agricultural (Cluster 2), and a highly contaminated one, where water was toxic and presented with microbial and Cd contamination (Cluster 3). Levels of pesticides, Cr, and microbial pollution were higher than reported in previous Mekong Delta studies and exceeded regional trends. These results emphasize the importance of holistic water management strategies, including better wastewater treatment, pesticide control, sustainable farming, and climate-adaptive measures to reduce saltwater intrusion and safeguard drinking water quality for the Mekong Delta. Full article

The high-pressure die-casting (HPDC) process involves injecting molten light metal into a steel mold under high pressure, resulting in parts with excellent surface quality and precise dimensions. However, this process subjects the mold to thermal fatigue and mechanical stress, which can lead to damage over time. This study investigated the wear characteristics of two types of inserts made from different steel materials, SKD61 steel and FDAC steel, under HPDC conditions. A thorough approach that combined computer simulations, experiments, and 3D scanning was employed to analyze wear patterns and dimensional changes after up to 300 casting cycles. The results indicate that the SKD61 steel outperformed the FDAC steel in terms of wear resistance and dimensional stability. The maximum deposition values of the SKD61 mold were only 0.009 mm, which was only 25% compared to the FDAC mold, indicating a significantly higher wear resistance. These findings are crucial for selecting and enhancing insert materials in HPDC, ultimately leading to higher-quality and more efficient casting. Full article

The Na⁺/H⁺ antiporter (NHX) gene family plays a pivotal role in plant salt tolerance in regulating intracellular Na⁺ and H⁺ homeostasis. In this study, seven candidate OsNHX genes (OsNHX1 to OsNHX7) were identified in the rice genome and classified into three phylogenetic clusters (Vac, Endo, and PM) based on their predicted subcellular localization. Five OsNHX gene pairs (OsNHX1/OsNHX2, OsNHX1/OsNHX3, OsNHX1/OsNHX4, OsNHX2/OsNHX6, and OsNHX5/OsNHX6) were found to have arisen from dispersed duplication events and exhibited purifying selection, indicating functional conservation. Analysis of cis-regulatory elements (CREs) revealed a diverse range of elements associated with tissue-specific expression, hormone signaling, and stress responses, particularly to dehydration and salinity. Notably, CREs associated with tissue/organelle-specific expression and stress responses were the most abundant, suggesting a potential role for OsNHX genes in regulating growth, development, and stress tolerance in rice. Importantly, expression profiling revealed that OsNHX1, OsNHX2, OsNHX3, and OsNHX5 were upregulated under salt stress, with significantly higher expression levels in the salt-tolerant rice cultivar Pokkali compared to the salt-sensitive cultivar IR64. Our findings provide a comprehensive analysis of the evolutionary, structural, and functional features of the OsNHX gene family and highlights their critical role in rice salt tolerance, offering insights into potential applications for crop improvement. Full article

The structural and mechanical properties of dickite and nacrite are important in numerous scientific and industrial applications, but currently lacking in experimental data. To address this issue, numerical methods have been employed to quantify these properties. First, the interaction potential parameters for dickite and nacrite were determined using a relaxed fitting approach. Then, the properties were calculated with energy minimization. The results revealed that the interaction potential parameters and properties exhibit polytypic-specific dependence. When compared to available experimental and simulation data at zero pressure, such as lattice parameters, elastic constants, bulk modulus, and shear modulus, the calculated results are in good agreement, confirming the reliability of the numerical methods. In addition, properties challenging to determine experimentally, such as transverse and longitudinal wave velocities, dielectric properties, and piezoelectric constants, were also investigated, offering valuable contributions to the understanding of these kaolin polytypes. Full article

This study assessed the effects of fenobucarb (F) (1%, 10%, and 20% of the LC₅₀-96h value) on the brain cholinesterase (AChE) activity, food intake (FI), feed conversion rate (FCR), and growth of silver barb (Barbonymus gonionotus, Bleeker, 1849). It also assessed the AChE inhibition levels that cause the abnormal swimming, behavior, and mortality of silver barb and how the feeding regime affects the recovery rate of the AChE activity. The results showed that the brain AChE inhibition increased with the F concentrations. It peaked after nine hours, at 73.6% and 79.7% for the two highest concentrations, and then the AChE activity started to recover. After 96 h, the inhibition level was still 11.8% in the fish exposed to the two lowest concentrations and 30.5% in the fish exposed to the highest concentrations. Even when placed in clean water, the inhibition level in the fish that were exposed to the highest concentrations and only fed every third day was 32% after 14 days. Although there were no differences in the feed intake at any time, the fish exposed to F had a higher FCR and a lower specific growth rate and weight compared to the control fish at the later stages of the experiment. Thus, although the use of F in rice farming in the Mekong Delta may not lead to direct fish kills, it impacts the growth and health of the fish, which could have negative implications for wild fish populations and the long-term production of healthy fish in the Mekong Delta. Full article

The urgent need for sustainable, low-emission energy solutions has positioned proton exchange membrane fuel cells (PEMFCs) as a promising technology in clean energy conversion. Polysulfone (PSF) membranes with incorporated ionic liquid (IL) and hydrophobic polydimethylsiloxane-functionalized silica (SiO₂-PDMS) were developed and characterized for their potential application in PEMFCs. Using a phase inversion method, membranes with various combinations of PSFs, SiO₂-PDMS, and 1-butyl-3-methylimidazolium triflate (BMI.TfO) (1–10 wt%) were prepared and characterized to assess their morphology, porosity, wettability, ionic conductivity, and thermal stability. Incorporating IL significantly altered the membrane structure, increasing porosity and surface roughness, while SiO₂-PDMS enhanced IL retention, reducing leakage by up to 32%. Proton conductivity increased by up to 30 times compared to pure PSF, and membranes exhibited high hydrophilicity at optimal IL concentrations. This work highlights the potential of IL and silica-based membranes for practical applications in PEMFCs. Full article

The increasing prevalence of heart diseases has driven the development of automated arrhythmia classification systems using machine learning and electrocardiograms (ECGs). This paper presents a novel ensemble learning method for classifying multiple arrhythmia types using 12-lead ECG signals through a blending technique. The framework employs a predetermined meta-model from foundation models, while the remaining models serve as potential base estimators, ranked by accuracy. Using sequential forward selection and meta-feature augmentation, the system determines an optimal base estimator set and creates a meta-dataset for the meta-model, which is optimized through grid search with k-fold cross-validation. Experiments conducted with seven diverse machine learning algorithms (Adaptive Boosting, Extreme Gradient Boosting, Decision Trees, k-Nearest Neighbors, Logistic Regression, Random Forest, and Support Vector Machine) demonstrate that the proposed blending solution, utilizing an LR meta-model with three optimal base models, achieves a superior classification accuracy of 96.48%, offering an effective tool for clinical decision support. Full article

Coverage path planning describes the process of finding an effective path robots can take to traverse a defined dynamic operating environment where there are static (fixed) and dynamic (mobile) obstacles that must be located and avoided in coverage path planning. However, most coverage path planning methods are limited in their ability to effectively manage the coordination of multiple robots operating in concert. In this paper, we propose a novel coverage path planning model (termed Multi-ST) which utilizes the spiral-spanning tree coverage algorithm with intelligent reasoning and knowledge-based methods to achieve optimal coverage, obstacle avoidance, and robot coordination. In experimental testing, we have evaluated the proposed model with a comparative analysis of alternative current approaches under the same conditions. The reported results show that the proposed model enables the avoidance of static and moving obstacles by multiple robots operating in concert in a dynamic operating environment. Moreover, the results demonstrate that the proposed model outperforms existing coverage path planning methods in terms of coverage quality, robustness, scalability, and efficiency. In this paper, the assumptions, limitations, and constraints applicable to this study are set out along with related challenges, open research questions, and proposed directions for future research. We posit that our proposed approach can provide an effective basis upon which multiple robots can operate in concert in a range of ‘real-world’ domains and systems where coverage path planning and the avoidance of static and dynamic obstacles encountered in completing tasks is a systemic requirement. Full article

Due to its application as an anti-cancer drug, pazopanib (1) has attracted the interest of many researchers, and several studies on pazopanib synthesis have been reported over the years. This paper provides a novel route for synthesizing N,2,3-trimethyl-2H-indazol-6-amine (5), which is a crucial building block in the synthesis of pazopanib from 3-methyl-6-nitro-1H-indazole (6). By alternating between the reduction and two methylation steps, compound 5 was obtained in a yield comparable (55%) to what has been reported (54%). It is noteworthy that the last step of N²-methylation also yielded N,N,2,3-tetramethyl-2H-indazol-6-amine (5′) as a novel compound. Furthermore, the data presented in this paper can serve as a valuable resource for future research aimed at further refining the process of synthesizing pazopanib and its derivatives. Full article

Environmental changes and human impact can alter biodiversity in negative manners that affect mankind’s sustainable development. Particularly, such effects on inland waters are even more concerning, as those ecosystems play essential roles in sustaining human life as well as relevant wildlife. Thus, in this study, we investigated such effects on microbial diversity in inland waters in Vietnam using bacterial communities in sediment as indicators. To do this, we collected sediment samples from various locations in three respective regions (Red river in the north, Ba river in the central area, and Mekong delta flood zone in the south) using standard methods, extracted their total DNA, sequenced their V3-V4 16S rRNA gene fragments using an Illumina Miseq platform and analyzed the sequences to infer the diversity of the bacterial communities in the samples. These communities were compared in terms of richness (alpha diversity) and composition (beta diversity), and the correlations between their diversity levels and environmental factors, as well as human activities, were analyzed by using standard statistical tools. Our results showed that the communities were different from each other solely by region, in richness and critically in composition, although there were some communities distinctively different from all the others. Among environmental factors, only water salinity (and conductivity) had negative correlations with alpha-diversity indices of the bacterial communities, and phosphate concentration and turbidity had positive correlations, while the other factors had almost no correlations, which partially explains the region-specific diversity. These results also suggest that climate change incidences, especially sea intrusion, can have significant effects on microbial diversity in inland waters. The impact of human activities did not appear severe, solely affecting bacterial community richness, but not significantly affecting bacterial community composition. However, apparently reduced bacterial diversity in several sites with intense human impacts and distinct environmental conditions should be noted and deserve further investigation. Full article

Background and Objectives: Drug resistant epilepsy (DRE) is a major hurdle in epilepsy, which hinders clinical care, patients’ management and treatment outcomes. DRE may partially result from genetic variants that alter proteins responsible for drug targets and drug transporters in the brain. We aimed to examine the relationship between SCN1A, GABRA1 and ABCB1 polymorphism and drug response in epilepsy children in Vietnam. Materials and Methods: In total, 213 children diagnosed with epilepsy were recruited in this study (101 were drug responsive and 112 were drug resistant). Sanger sequencing had been performed in order to detect six single nucleotide polymorphisms (SNPs) belonging to SCN1A (rs2298771, rs3812718, rs10188577), GABRA1 (rs2279020) and ABCB1 (rs1128503, rs1045642) in study group. The link between SNPs and drug response status was examined by the Chi-squared test or the Fisher’s exact test. Results: Among six investigated SNPs, two SNPs showed significant difference between the responsive and the resistant group. Among those, heterozygous genotype of SCN1A rs2298771 (AG) were at higher frequency in the resistant patients compared with responsive patients, playing as risk factor of refractory epilepsy. Conversely, the heterozygous genotype of SCN1A rs3812718 (CT) was significantly lower in the resistant compared with the responsive group. No significant association was found between the remaining four SNPs and drug response. Conclusions: Our study demonstrated a significant association between the SCN1A genetic polymorphism which increased risk of drug-resistant epilepsy in Vietnamese epileptic children. This important finding further supports the underlying molecular mechanisms of SCN1A genetic variants in the pathogenesis of drug-resistant epilepsy in children. Full article