Activities of the practice teaching organization and vocational teaching facilities in collaboration between the vocational school and units employing

The aim of this research is to evaluate the current level of preparation in the organization of practical training as well as the facilities that are available for practical vocational training. The collaboration in education between businesses and vocational schools is an effective strategy. As a result of the rapid transformation that has taken place in the socioeconomic context for professional skills and practical training among employees, a new educational strategy is required to address these demands in order to meet the needs of the workforce. A quantitative method was applied in this research. There were 570 individuals who were chosen at random. According to the findings, the majority of instructors and technicians possessed regulations for conducting practical teaching activities as well as suitable facilities and equipment for the purpose of vocational training. This study presents a number of suggestions for improving the standard of educational institutions as well as the professional growth opportunities available to teachers and lecturers. In addition, facility management and maintenance as well as optimize the instructional facilities and equipment are required

Evaluating model teacher education and training at Vietnam's universities of technology and education

The traditional educational paradigm has become outdated as a result of changes in both the cultural and socioeconomic setting. A more sustainable and acceptable teacher is needed in education. A quantitative study was conducted. 95 administrators and teachers at the University of Technology and Education participated in this study with the aim of analyzing the current status of the teacher education model at the institution. According to the findings of the study, the model for educator preparation has been put into practice primarily through the processes of planning, organizing and directing activities related to educator preparation as well as inspecting and assessing the quality of education. The outcomes of the study indicate that it is essential to design educational programs that are appropriate for the present context. In particular, the study suggests that one of the most important steps towards achieving success is to incorporate technology into teaching methods. The process of educating teachers with the right degree of expertise and skills should be emphasized by educators and policymakers by developing relationships with other educational institutions and allowing teachers to participate in internships

Management and monitoring of air and water pollution by using GIS technology: Research article

The need for a green clean living environment is increasing today, with the boom of the socioeconomic development, educational level. However, the environmental pollution becomes an alerted global issue due to the large amount of wastes discharged making this need to be not easily met at the moment. Greenhouse gas emission mainly from energy, transport and agricultural land use is causing climate change because of their long atmospheric lifetime and trapping the heat in the atmosphere. Harmful effects and damages caused by environment pollution and climate change are unpredictable. It was reported that every year millions of people die because of fine particles when exposing to air pollution and other millions die from water-born diseases. Management and monitoring of air and water pollution by using GIS technology is an effective method. The measured data can be obtained continuously, quickly and accurately at stations in any regions even with complex terrain. This helps reduce the required number of employees, manage automatically and continuously a large number of data.Ngày nay nhu cầu về một môi trường sống xanh, sạch đang gia tăng, với sự bùng nổ của phát triển kinh tế - xã hội và trình độ dân trí. Tuy nhiên, ô nhiễm môi trường đang trở thành một vấn đề cảnh báo toàn cầu do số lượng lớn các chất thải được xả ra môi trường làm cho nhu cầu này không dễ dàng được đáp ứng tại thời điểm này. Phát thải khí nhà kính chủ yếu là từ sử dụng năng lượng, giao thông vận tải và đất nông nghiệp đang gây ra biến đổi khí hậu vì thời gian tồn tại của cúng dài và giữ nhiệt trong khí quyển. Các ảnh hưởng xấu và thiệt hại gây ra bởi ô nhiễm môi trường và biến đổi khí hậu là không thể đoán trước. Thông tin báo cáo chỉ ra rằng mỗi năm có hàng triệu người chết vì hít các hạt bụi mịn khi tiếp xúc với ô nhiễm không khí; và hàng triệu người khác chết vì bệnh do nước sinh ra. Quản lý và giám sát ô nhiễm không khí và nước bằng cách sử dụng công nghệ GIS là một phương pháp hiệu quả. Các dữ liệu đo có thể được lấy liên tục, nhanh chóng và chính xác tại các trạm ở bất kể khu vực nào, ngay cả nơi có địa hình phức tạp. Điều này giúp làm giảm số lượng lao động cần thiết, quản lý tự động và liên tục một số lượng lớn dữ liệu

Machine Learning-Based Digital Twin for Predictive Modeling in Wind Turbines

Wind turbines are one of the primary sources of renewable energy, which leads to a sustainable and efficient energy solution. It does not release any carbon emissions to pollute our planet. The wind farms monitoring and power generation prediction is a complex problem due to the unpredictability of wind speed. Consequently, it limits the decision power of the management team to plan the energy consumption in an effective way. Our proposed model solves this challenge by utilizing a 5G-Next Generation-Radio Access Network (5G-NG-RAN) assisted cloud-based digital twins’ framework to virtually monitor wind turbines and form a predictive model to forecast wind speed and predict the generated power. The developed model is based on Microsoft Azure digital twins infrastructure as a 5-dimensional digital twins platform. The predictive modeling is based on a deep learning approach, temporal convolution network (TCN) followed by a non-parametric k-nearest neighbor (kNN) regression. Predictive modeling has two components. First, it processes the univariate time series data of wind to predict its speed. Secondly, it estimates the power generation for each quarter of the year ranges from one week to a whole month (i.e., medium-term prediction) To evaluate the framework the experiments are performed on onshore wind turbines publicly available datasets. The obtained results confirm the applicability of the proposed framework. Furthermore, the comparative analysis with the existing classical prediction models shows that our designed approach obtained better results. The model can assist the management team to monitor the wind farms remotely as well as estimate the power generation in advance

Synthesis of Flower-like Silver Nanostructures on Silicon and Their Application in Surface-enhanced Raman Scattering

To enhance the intensity of surface-enhanced Raman scattering (SERS), production of metal nanostructures with sharp points, lying side by side at the nanometer level plays an extremely important role. In this paper, we report on a manufacturing process in which the silver nanoparticles with the flower-like shape have been fabricated. Such silver nanoparticles have been fabricated by chemical deposition of silver particles on silicon wafers, using a solution of hydrofluoric acid (HF), silver nitrate (AgNO3) and ascorbic acid (AsA) in water, at room temperature. During the manufacturing we found that only when the concentrations of AgNO3 and AsA are appropriate, the flower-like silver nanoparticles will form. Note that while other authors mainly made flower-like silver nanoparticles in the form of suspensions, we have created flower-like silver nanoparticles with cabbage-shape on a silicon surface. The ensembles of flower-like silver nanoparticles above were used as SERS substrates to detect crystal violet (CV) in low concentrations and good results were obtained

A surrogate-assisted measurement correction method for accurate and low-cost monitoring of particulate matter pollutants

Air pollution involves multiple health and economic challenges. Its accurate and low-cost monitoring is important for developing services dedicated to reduce the exposure of living beings to the pollution. Particulate matter (PM) measurement sensors belong to the key components that support operation of these systems. In this work, a modular, mobile Internet of Things sensor for PM measurements has been proposed. Due to a limited accuracy of the PM detector, the measurement data are refined using a two-stage procedure that involves elimination of the non-physical signal spikes followed by a non-linear correction of the responses using a multiplicative surrogate model. The correction layer is derived from the sparse and non-uniform calibration data, i.e., a combination of the measurements from the PM monitoring station and the sensor obtained in the same location over a specified (relatively short) interval. The device and the method have been both demonstrated based on the data obtained during three measurement campaigns. The proposed correction scheme improves the fidelity of PM measurements by around two orders of magnitude w.r.t. the responses for which the post-processing has not been considered. Performance of the proposed surrogate-assisted technique has been favorably compared against the benchmark approaches from the literature

Chemical composition and source apportionment of PM _2.5 in urban areas of Xiangtan, central south China

Xiangtan, South China, is characterized by year-round high relative humidity and very low wind speeds. To assess levels of PM2.5, daily samples were collected from 2016 to 2017 at two urban sites. The mass concentrations of PM2.5 were in the range of 30⁻217 µg/m3, with the highest concentrations in winter and the lowest in spring. Major water-soluble ions (WSIIs) and total carbon (TC) accounted for 58⁻59% and 21⁻24% of the PM2.5 mass, respectively. Secondary inorganic ions (SO42−, NO3−, and NH4+) dominated the WSIIs and accounted for 73% and 74% at the two sites. The concentrations of K, Fe, Al, Sb, Ca, Zn, Mg, Pb, Ba, As, and Mn in the PM2.5 at the two sites were higher than 40 ng/m3, and decreased in the order of winter > autumn > spring. Enrichment factor analysis indicates that Co, Cu, Zn, As, Se, Cd, Sb, Tl, and Pb mainly originates from anthropogenic sources. Source apportionment analysis showed that secondary inorganic aerosols, vehicle exhaust, coal combustion and secondary aerosols, fugitive dust, industrial emissions, steel industry are the major sources of PM2.5, contributing 25⁻27%, 21⁻22%, 19⁻21%, 16⁻18%, 6⁻9%, and 8⁻9% to PM2.5 mass

IDENTIFICATION AND ANTIMICROBIAL ACTIVITY OF ACTINOMYCETES STRAINS ISOLATED FROM SAMPLES COLLECTED IN THE COASTAL AREA OF HUE, DA NANG AND QUANG NAM PROVINCES, VIETNAM

Microorganisms are of particular interest because of their ability to synthesize high-value secondary compounds and provide us with novel and diverse chemical structures. The most common source of antibiotics is Actinomycetes which provide around two-third of naturally occurring antibiotics, including many of medical importance. In this study, 81 strains of actinomycetes were isolated from 145 samples including: sediments, sponges, soft corals, echinoderms and starfish collected from three sea areas of Vietnam: Hue, Da Nang and Quang Nam. The strains were fermented in A+ medium and fermentation broths were extracted 5 times with ethyl acetate. The extracts were evaporated under reduced  pressure to yield crude extracts. Quantitative assay was used to determine MIC (Minimum inhibitory concentration) of extract against 7 reference strains. From the results of screening, Seven strains of actinomycetes that have the highest biological activity (Code: G244, G246, G261, G266, G278, G280 and G290) were chosen to be identified by morphological and phylogenetic based on 16S rRNA gene sequences. The results showed that 6 strains G246, G261, G266, G278, G280 and G290 belonged to the genus Streptomyces; and the strain G244 belonged to the genus Micromonospora. In particular, strains G244, G278, G280 were resistant 5/7 strains of microorganisms test, with values  MICs from 2 µg/mL to 256 µg/mL; and three strains G261, G266, G290 showed the inhibitory effect towards 4/7 strains of microorganisms test, with respective values MICs from 2 µg/mL to 256 µg/mL. Moreover, six of the seven selected strains were highly resistant to yeast Candida albicans ATCC10231 with MIC values from 2 µg/mL to 256 µg/mL. These results indicated that marine Actinomycetes in Vietnam are also a potential source to find bioactive substances