SYNTHESIS OF ZINC OXIDE/GRAPHENE OXIDE NANOCOMPOSITES AS ANTIBACTERIAL MATERIALS AGAINST STAPHYLOCOCCUS AUREUS AND ESCHERICHIA COLI

New materials with good antibacterial activity and less toxicity to other species have attracted numerous research interests. Modified Hummers method was used for preparing graphene oxide (GO). Zinc oxide/graphene oxide (ZnO/GO) nanocomposites were synthesized with three different ratios (0.5:1, 1:1, and 2:1) by solution precipitation method. The ZnO/GO nanocomposites were characterized by Fourier transform infrared spectroscopy, X–ray diffraction, Raman spectroscopy, Brunauer–Emmett–Tellerspecific surface area, and transmission electron microscopy image. The characterization results showed that ZnO nanoparticles with a mean size of 12–18 nm were randomly decorated on the surfaces and edges of GO sheets. ZnO/GO 1:1 with a high specific surface area of 65 m2/g was obtained. The antibacterial activity of ZnO, GO, and ZnO/GO was tested against Gram negative bacteria escherichia coli (E. coli) and Gram positive bacteria staphylococcus aureus (S. aureus) using well diffusion method. The test results confirmed that antibacterial activity of ZnO/GO was higher than that of GO and ZnO. Additionally, the ZnO/GO with the ratio of 1:1 is the strongest activity and more active against S. aureus than against E. coli and minimal inhibitory concentration (MIC) value of ZnO/GO 1:1 is 80 µg/mL for S. aureus and 160 µg/mL for E. coli. This novel nanocomposite could be used as a potential material for antimicrobial application

Graphene-Based Material for Fabrication of Electrodes in Dye-Sensitized Solar Cells

Graphene-based materials have been widely studied for the fabrication of electrodes in dye-sensitized solar cells (DSSCs). The use of graphene in the cathode is to reduce the amount of platinum (Pt), which in turn is expected to reduce the production cost of DSSCs. Additionally, in the structure of cathode, graphene acts as a supporting material to reduce the particle sizes of Pt and helps to maintain the high efficiency of DSSCs. For anodes, graphene can provide a more effective electron transfer process, resulting in the improvement of efficiency of DSSCs. In this chapter, the use of graphene-based materials for fabrication of cathodes and anodes in DSSCs, including platinum/reduced graphene oxide composite (Pt/rGO) and zinc oxide/reduced graphene oxide composite (ZnO/rGO) is discussed. The fabricated DSSCs were tested using current density-voltage (J-V) curves to evaluate the efficiency. The results of efficiency demonstrate that Pt/rGO is the potential material for fabrication of cathode in DSSCs, which helps to reduce the amount of Pt and maintain the high efficiency. The efficiency values of DSSCs fabricated from ZnO/rGO anodes show that the incorporation of reduced graphene oxide in the ZnO could improve the performance of DSSCs

A Study of SVC’s Impact Simulation and Analysis for Distance Protection Relay on Transmission Lines

This paper focuses on analyzing and evaluating impact of a Static Var Compensator (SVC) on the measured impedance at distance protection relay location on power transmission lines. The measured impedance at the relay location when a fault occurs on the line is determined by using voltage and current signals from voltage and current transformers at the relay and the type of fault occurred on the line. The MHO characteristic is applied to analyze impact of SVC on the distance protection relay. Based on the theory, the authors in this paper develop a simulation program on Matlab/Simulink software to analyze impact of SVC on the distance protection relay. In the power system model, it is supposed that the SVC is located at mid-point of the transmission line to study impact of SVC on the distance relay. The simulation results show that SVC will impact on the measured impedance at the relay when the fault occurs after the location of the SVC on the power transmission line

Application of PCR-DGGE method for identification of nematode communities in pepper growing soil: Ứng dụng phương pháp PCR-DGGE để định danh cộng đồng tuyến trùng trong đất trồng hồ tiêu

Soil nematodes play an important role in indication for assessing soil environments and ecosystems. Previous studies of nematode community analyses based on molecular identification have shown to be useful for assessing soil environments. Here we applied PCR-DGGE method for molecular analysis of five soil nematode communities (designed as S1 to S5) collected from four provinces in Southeastern Vietnam (Binh Duong, Ba Ria Vung Tau, Binh Phuoc and Dong Nai) based on SSU gene. By sequencing DNA bands derived from S5 community sample, our data show 15 species containing soil nematode, other nematode and non-nematode (fungi) species. Genus Meloidogyne was found as abundant one. The genetic relationship of soil nematode species in S5 community were determined by Maximum Likelihood tree re-construction based on SSU gene. This molecular approach is applied for the first time in Vietnam for identification of soil nematode communities.Tuyến trùng đất đóng vai trò chỉ thị quan trọng trong công tác đánh giá môi trường và hệ sinh thái đất. Các nghiên cứu trước đây đã cho thấy lợi ích của việc phân tích cộng đồng tuyến trùng đất bằng định danh sinh học phân tử đối với việc đánh giá môi trường đất. Ở đây, chúng tôi ứng dụng phương pháp PCR-DGGE dựa trên gene SSU để phân tích năm (ký hiệu từ S1 đến S5) cộng đồng tuyến trùng đất thuộc các vùng trồng chuyên canh cây hồ tiêu ở miền nam Việt Nam (Bình Dương, Bà Rịa Vũng Tàu, Bình Phước và Đồng Nai). Bằng cách giải trình tự các vạch của mẫu tuyến trùng S5, kết quả cho thấy cộng đồng tuyến trùng này có 15 loài gồm nhóm tuyến trùng đất, nhóm các loại tuyến trùng khác và nhóm không phải tuyến trùng (nấm) và trong đó Meloidogyne là giống ưu thế. Mối quan hệ di truyền của các các loài tuyến trùng đất thuộc cộng đồng S5 được xác định bằng việc thiết lập cây phát sinh loài Maximum Likelihood dựa trên gene SSU. Đây là nghiên cứu đầu tiên ở Việt Nam sử dụng kỹ thuật PCR-DGGE để phân tích các cộng đồng tuyến trùng đất trồng hồ tiêu

Optimizing Boiler Efficiency by Data Mining Teciques: A Case Study

In a fertilizer plant, the steam boiler is the most important component. In order to keep the plant operating in the effective mode, the boiler efficiency must be observed continuously by several operators. When the trend of the boiler efficiency is going down, they may adjust the controlling parameters of the boiler to increase its efficiency. Since manual operation usually leads to unex-pectedly mistakes and hurts the efficiency of the system, we build an information system that plays the role of the operators in observing the boiler and adjusting the controlling parameters to stabilize the boiler efficiency. In this paper, we first introduce the architecture of the information system. We then present how to apply K-means and Fuzzy C-means algorithms to derive a knowledge base from the historical operational data of the boiler. Next, recurrent fuzzy neural network is employed to build a boiler simulator for evaluating which tuple of input values is the best optimal and then automatically adjusting controlling inputs of the boiler by the optimal val-ues. In order to prove the effectiveness of our system, we deployed it at Phu My Fertilizer Plant equipped with MARCHI boiler having capacity of 76-84 ton/h. We found that our system have improved the boiler efficiency about 0.28-1.12% in average and brought benefit about 57.000 USD/year to the Phu My Fertilizer Plant

Status of the shore area from Tiengiang to Camau: causes of accumulation and erosion

The paper presents some results of the research programs which had been performed during 1996-1999 (“Studying of river-sea interaction in the mouth of Tien river” and KHCN.06.08). Based on these results the morphological schemes of the shore areas from Tiengiang to Camau were compiled; causes and mechanics of accumulation and erosion were also determined. These results may be used as scientific basis for forecasting the development of the shoreline, it will contribute to the management, protection and reasonable exploitation the shore areas

Application of the improved four-node element MISQ24 for geometrically nonlinear analysis of plate/shell structures

In this paper the smoothed strain based four-node flat element MISQ24 with driiling degrees of freedom is extended for geometrically nonlinear analysis of plate and shell structures. The von-Karman's large deflection theory and the Total Lagrangian (TL) approach are employed in the formulation of the elements to describe small strain geometric nonlinearity with large deformations using the first-order shear deformation theory (FSDT). The predictive capability of the present models is demonstrated by comparing the present results with analytical/experimental and other numerical solutions available in the literature. Numerical examples show that the presented formulations can prevent loss of accuracy in severely distorted meshes, and therefore, are superior to those of other quadrilateral elements with inplanes rotations

CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages

The driving factors behind the development of large language models (LLMs) with impressive learning capabilities are their colossal model sizes and extensive training datasets. Along with the progress in natural language processing, LLMs have been frequently made accessible to the public to foster deeper investigation and applications. However, when it comes to training datasets for these LLMs, especially the recent state-of-the-art models, they are often not fully disclosed. Creating training data for high-performing LLMs involves extensive cleaning and deduplication to ensure the necessary level of quality. The lack of transparency for training data has thus hampered research on attributing and addressing hallucination and bias issues in LLMs, hindering replication efforts and further advancements in the community. These challenges become even more pronounced in multilingual learning scenarios, where the available multilingual text datasets are often inadequately collected and cleaned. Consequently, there is a lack of open-source and readily usable dataset to effectively train LLMs in multiple languages. To overcome this issue, we present CulturaX, a substantial multilingual dataset with 6.3 trillion tokens in 167 languages, tailored for LLM development. Our dataset undergoes meticulous cleaning and deduplication through a rigorous pipeline of multiple stages to accomplish the best quality for model training, including language identification, URL-based filtering, metric-based cleaning, document refinement, and data deduplication. CulturaX is fully released to the public in HuggingFace to facilitate research and advancements in multilingual LLMs: https://huggingface.co/datasets/uonlp/CulturaX.Comment: Ongoing Wor