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The quest for global ranking has motivated higher education institutions (HEIs) in Vietnam to increase the number of international students in their enrolments. However, little is known about experiences of current international students in Vietnamese HEIs. This article offers a mosaic of international student experiences (ISE) in a Vietnamese-taught course offered by a Vietnamese university. The data show that teaching staffs play a crucial role in the satisfaction of international students, however, services and physical facilities are areas that need significant improvement. It also discusses other interactive aspects of ISE before presenting the case of an international student with disability to show how international mobility, aspiration, and personal determination can navigate him through a journey that was nothing short of extraordinary. This article is also to answer the recent scholarly call for a more geographically diverse and transformation-centred ISE literature, which is believed to remain relevant in the future.

Student mobility has become a global phenomenon with an upward trend in the past two decades, and Asian countries are becoming important receiving countries. Such phenomenon has been increasingly investigated producing a growing body of literature on Asia-bound international student mobility. However, while some countries stand in the spotlight of international student scholarship, other lesser known destinations remain underrepresented. This article provides an exploratory effort to examine factors that attract international students to Vietnam, a country which has not been visible on international student mobility landscape as a receiving destination. The study employs push-pull factors framework and a mixed methods approach combining qualitative and quantitative methods (n = 94) to examine the push-pull factors that influence the decision of international students to choose Vietnam as the destination country and a Vietnamese higher education institution as host institution. Research findings show that major push factors include aspirations for new experiences, home country economic outlook and pessimistic job prospects at home. Regarding pull factors, at national level, bright economic prospect is the top pull factor attracting international students to a developing country like Vietnam, and at institutional level, the university's reputation and its Vietnamese Studies and language programmes are the most important pull factors attracting international students to the university to study. Based on the research results, recommendations are proposed for higher education institutions in Vietnam and other developing Asian countries to attract international students with implications against the context of the ongoing COVID-19 pandemic.

Thiobencarb is an herbicide component extensively applied for weed control. In this study, the mixed culture of Pseudomonas sp. Th1 and Cupriavidus oxalaticus Th2 was determined for degradability towards thiobencarb at several conditions. The degradation of thiobencarb at 50 µM by the mixture of these strains in the medium without any co-substrate was 48.5±5.5% for 24 hours, increased to 78.0% with the addition of 0.5 mg/L ammonium sulfate and 0.5 mg/L succinate in to the medium. Moreover, the mixed culture could degrade the compound at quite ranges of NaCl concentrations, pH and temperatures. To increase the degradation of the compound in a commercial herbicide, bacteria were immobilized in a matrix consisting of polyvinyl alcohol and sodium alginate. The average degradation of thiobencarb in an herbicide named Satunil 60E by immobilized bacteria was 25.8% higher than that of non-immobilized bacteria at a cell density of 1.0×107 CFUs/mL.

Background: Houttuynia cordata Thunb. has long been widely used as a daily vegetable and traditional medicine. The flavonoid component of H. cordata has plenty of pharmacological effects, such as antibacterial, anti-inflammatory, and antioxidant. In this study, we applied the aqueous two-phase system (ATPS) combined with ultrasonic extraction for extracting H. cordata leaves. Methods: We optimized the extraction process to improve the extraction efficiency of the two flavonoids, hyperin and quercitrin, by Surface Method Response - Central Composite Design (RSM-CCD). Next, we investigated the antibacterial ability of H. cordata ATPS extract from optimal conditions against two bacterial strains, Cutibacterium acnes and Staphylococcus epidermidis. Results: The results showed that using 10% (NH4)2SO4 and 35% ethanol for ATPS extraction resulted in the highest hyperin and quercitrin contents. From the RSM-CCD results, the optimal extraction conditions were determined to be ultrasonic extraction at 50 °C for 30 min, giving results consistent with the predicted model and obtaining hyperin and quercitrin contents at 1.5681 ± 0.0114 and 4.6225 ± 0.0327 mg/g, respectively.Furthermore, ATPS extract has excellent antibacterial activity with a minimum inhibitory concentration (MIC) value of 250 μg/mL on both C. acnes and S. epidermidis. This MIC is significantly lower than the H. cordata ultrasound-assisted (UA) extract, with MICs of 1500.00 and 156.25 μg/mL on C. acnes and S. epidermidis, respectively. In addition, the results from the disk diffusion assay also showed that ATPS extraction has superior internal antibacterial activity with a zone of inhibition diameter at 250 μg/mL of 8.67 ± 1.15 and 5.00 ± 2.00 mm. Meanwhile, those of UA extract on C. acnes is 5.67 ± 1.53 mm (at 1500 μg/mL), and on S. epidermidis is 1.34 ± 0.58 mm (at 156.25 μg/mL). Conclusion: To sum up, our research highlights the potential of H. cordata ATPS extracts as the starting material for topical preparations for effectively treating acne.

Mangan dioxit (MnO2) được tổng hợp trực tiếp trên nền graphen oxit (GO). Chúng tôi nghiên cứu quá trình khử điện hoá vật liệu α-MnO2/GO bằng hai kỹ thuật chính là von-ampe vòng (CV) và điện thế thời gian (i-T). Các mẫu vật liệu được đặc trưngằng phổ FT-IR, phổ Raman, diện tích bề mặt và phân tích tổng trở điện hoá. Cấu trúc α-MnO2 được xác định thông qua phổ FT-IR và phổ Raman tại các bước sóng kích thích dao động 507 và 642,5 cm–1. Vật liệu khử α-MnO2/ErGOCV và α-MnO2/ErGOi-T có tổng trở điện hoá thấp (0,188 và 0,147 kΩ) và diện tích bề mặt lớn gấp 1,7 lần so với vật liệu α-MnO2/GO. Các tính ưu việt của vật liệu composite α-MnO2/ErGO thúc đẩy khả năng chuyển điện tử và tăng cường hoạt tính xúc tác điện hoá của sensor oxy hoá khử trên bề mặt của điện cực đã được biến tính. Ở các điều kiện thí nghiệm thích hợp, phương pháp LS-AdCSV sử dụng điện cực biến tính α-MnO2/ErGO có thể áp dụng để phân tích đồng thời chloramphenicol và tinidazole. Hàm lượng chloramphenicol trong thuốc mỡ tra mắt xác định trên điện cực này có giá trị đúng với số liệu ghi trên nhãn.

Time series is acknowledged as one of the most common crucial data types in our daily lives. Among the time series mining tasks, rule discovery is important to provide valuable knowledge that brings us a profound insight view of relationships between different objects through time. One challenge is that when the number of objects and their lengths increase, it easily leads to a combinatorial explosion. Therefore, we propose a temporal inter-object association rule mining algorithm, NPTR, to discover new informative temporal inter-object association rules from time series and overcome the challenge with parallelization. Another remarkable point is that NPTR defines a concurrent approach by performing the frequent pattern mining process and rule mining one simultaneously. From the experiments on real-world data, NPTR returns the rules exactly with less time and memory costs than others do. Those rules can be further utilized for other tasks such as prediction, classification, and clustering.

Reinforced concrete (RC) transfer slabs (or beams) are common support structures for high-rise or multi-use buildings. The structure serves as a new base for the upper zones and transfers considerable loads to the foundation; hence, the RC transfer slab necessitates a massive thickness design, as well as the use of high-strength material, and should be constructed without cracks. In this paper, the temperature rise of the massive RC transfer slab controlled by embedded cooling pipes was predicted by the Ansys program, and the numerical prediction was verified by recorded data of the temperature rise of the massive RC transfer slab. Regarding to the temperature rise, both prediction data and recorded data showed that, initially, the temperature at the bottom surface and the core of the RC transfer slab rose fast, resulting in a substantial temperature differential (20.4 °C) with the top surface, and, during the cooling stage, the top surface then retained heat and cooled more slowly than the bottom surface; moreover, the model fitted quite well with the measurement at the peak temperature. Regarding cooling pipe operation, within the first four days, it was recommended that the flow rate of 30 °C cooling water was set as high as 26.5 L/m (liters/minute), and this number was subsequently increased to 30 L/m when the temperature differential within the concrete peaked at 20 h.

A course-level early final study status prediction task is to predict as soon as possible the final success of each student after studying a course. It is significant because each successful course accomplishment is required for a degree. Further, early predictions provide enough time to make necessary changes for ultimate success. This article aims at an effective solution to this task. Different from the existing works, we resolve the task in a more practical context. First, the temporal aspects of the task and its data are considered. For the task, historical datasets are used to support the task on current ones. For the data, both labeled and unlabeled data before the midterm break are used. Second, our solution examines assessment data for the task, and thus, requires less data collection cost and effort over time. Third, we propose a semisupervised learning method, ST_OS, to obtain a better prediction model because ST_OS handles data insufficiency when exploiting all the labeled and unlabeled data. Moreover, ST_OS combines Self-Training and Tri-Training to create a resulting ensemble model in an effective semisupervised learning process with local learning for each selected unlabeled instance. Above all, the task is addressed in a general manner for different course types. As a result, our solution outperforms several existing supervised and semisupervised learning ones with higher Accuracy and F-measure. Therefore, it can be used as a forecasting tool before their courses end. More activities can be then improved to help the students complete the courses successfully.

This paper reports polymer composites′ microstructure and dielectric properties based on an epoxy resin matrix containing different amounts (from 2–10 wt. %) of barium strontium titanate nanoparticles. (Ba,Sr)TiO3 was prepared using the hydrothermal method, showing an average particle size of 100 nm. The composites′ thermal properties and morphology were characterized using thermogravimetric analysis, differential scanning calorimetry, and scanning electron microscopy. Our studies show that the (Ba,Sr)TiO3 nanoparticles were well dispersed in the epoxy resin matrix thanks to controlled silane functionalization. Accordingly, the glass transition temperature (Tg) value of the nanocomposites was found to be 159.0 °C at 10 wt. % (Ba,Sr)TiO3 loading and is higher than the neat epoxy resin Tg (Tg=154.7 °C). Moreover, the dielectric composite properties were investigated comprehensively via a wide range of frequencies from 10 Hz to 100 kHz. The results indicate that (Ba,Sr)TiO3 nanopowders and the surface modifying filler play a crucial role in the significant increase of the dielectric constants, approximately 1.5 times at the optimum added amount (Ba,Sr)TiO3. The limited functionalization of (Ba,Sr)TiO3 nanoparticles by the silane affords to control the nanocomposite dielectric loss. Furthermore, the nanocomposites also exhibited good thermal stability. [ABSTRACT FROM AUTHOR]

Abstract The experimental program investigated concrete with a large amount of fly ash (FA) with silica fume (SF) to replace Portland cement on the results of semi-adiabatic test, compressive strength test, and the rapid chloride permeability test (RCPT). The replacement ratios of cement by a combination of FA and SF were 30%, 35%, and 40% by mass. The percentages of SF to replace cement were 0%, 4%, and 8% by mass. Three different water-to-binder ratios (W/B) of 0.34, 0.36, and 0.38 were also investigated. Multiple linear regression was applied to construct the predicted equations (models) for the semi-adiabatic temperature rise test and the compressive strength test. Models were assessed statistically and were used to solve the concrete mixture design optimization problems. The mixture with W/B of 0.36, 31% FA, and 5% SF was found to optimally satisfy the multi-objective problem: 28-day compressive strength of 50 MPa, low heat of hydration, and very low chloride penetrability classification. Field test on the actual wind turbine foundation of the optimal mixture revealed the maximum temperature rise was 74.8 °C and the maximum temperature differential was 21.9 °C.

Abstract Particulate matter (PM) accounts for millions of premature deaths in the human population every year. Due to social and economic inequality, growing human dissatisfaction manifests in waves of strikes and protests all over the world, causing paralysis of institutions, services and circulation of transport. In this study, we aim to investigate air quality in Ecuador during the national protest of 2019, by studying the evolution of PM2.5 (PM ≤ 2.5 µm) concentrations in Ecuador and its capital city Quito using ground based and satellite data. Apart from analyzing the PM2.5 evolution over time to trace the pollution changes, we employ machine learning techniques to estimate these changes relative to the business-as-usual pollution scenario. In addition, we present a chemical analysis of plant samples from an urban park housing the strike. Positive impact on regional air quality was detected for Ecuador, and an overall − 10.75 ± 17.74% reduction of particulate pollution in the capital during the protest. However, barricade burning PM peaks may contribute to a release of harmful heavy metals (tire manufacture components such as Co, Cr, Zn, Al, Fe, Pb, Mg, Ba and Cu), which might be of short- and long-term health concerns.

Nowadays, teaching and learning activities in a course are greatly supported by information technologies. Forums are among information technologies utilized in a course to encourage students to communicate with lecturers more outside a traditional class. Free-styled textual posts in those communications express the problems that the students are facing as well as the interest and activeness of the students with respect to each topic of a course. Exploiting such textual data in a course forum for course-level student prediction is considered in our work. Due to hierarchical structures in course forum texts, we propose a solution in this paper which combines a deep convolutional neural network (CNN) and a loss function to extract the features from textual data in such a manner that more correct recognitions of instances of the minority class which includes students with failure can be supported. In addition, other numeric data are examined and used for the task so that all the students with and without posts can be predicted in the task. Therefore, our work is the first one that defines and solves this prediction task with heterogeneous educational data at the course level as compared to the existing works. In the proposed solution, Random Forests are suggested as an effective ensemble model suitable for our heterogeneous data when many single prediction models which are random trees can be built for many various subspaces with different random features in a supervised learning process. Experimental results in an empirical evaluation on two real datasets show that a heterogeneous combination of textual and numeric data with a Random Forest model can enhance the effectiveness of our solution to the task. The best accuracy and F-measure values can be obtained for early predictions of the students with either success or failure. Such better predictions can help both students and lecturers beware of students’ study and support them in time for ultimate success in a course.

In educational data mining, student classification is an important and popular task by predicting final study status of each student. In the existing works, this task has been considered in many various contexts at both course and program levels with different learning approaches. However, its real-world characteristics such as temporal aspects, data imbalance, data overlapping, and data shortage with sparseness have not yet been fully investigated. Making the most of deep learning, our work is the first one addressing those challenges for the program-level student classification task. In a simple but effective manner, convolutional neural networks (CNNs) are proposed to exploit their well-known advantages on images for temporal educational data. As a result, the task is resolved by our enhanced CNN models with more effectiveness and practicability on real datasets. Our CNN models outperform other traditional models and their various variants on a consistent basis for program-level student classification.

This study explores the adsorption of doxycycline (DOX) from aqueous solutions onto biochar derived from banana peel, which was prepared using a potassium hydroxide activation method (KOH-BPB). The biochar properties were characterized based on morphology, surface area (SBET of 710.241 m² g-1), functional groups, and surface charge (pHPZC = 7.7). Parameters, including initial pH, DOX concentration, and ionic strength, that influenced the DOX adsorption capacity of KOH-BPB were examined. Adsorption equilibrium of DOX on KOH-BPB was assessed through four isothermal models: the Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich models. The obtained data were most compatible with the Langmuir model (R² = 0.9879). KOH-BPB has a maximum DOX absorption capacity of 121.95 mg g-1 which exceeds that of many comparable absorbents. The maximum DOX removal was 96.7% at pH 6, a DOX concentration of 20 mg L-1, and a KOH-BPB dose of 1.0 g L-1. These findings reveal that biochar from banana peel effectively removes antibiotic residues from water. This study provides a potential, low-cost, and environmentally friendly adsorbent. [ABSTRACT FROM AUTHOR]

In this study, Fe3O4@TiO2 was synthesized by a facile precipitation–impregnation method and applied as a magnetic photocatalyst for degrading synthetic and actual textile dyeing wastewater in the presence of peroxymonosulfate (PMS). In Rhodamine B (RhB) degradation, the removal efficiency reached 99.86% (for RhB) and 93.67% (for color) under optimal operating conditions of 80 mg L–1 RhB, 0.5 g L–1 PMS, 0.5 g L–1 Fe3O4@TiO2, at pH 6, and after 30 min of UVA irradiation. The influence levels of anions on the RhB removal and decolorization were in the order of Cl− < SO42− < CO32− < PO43−. In addition, the Fe3O4@TiO2 composite exhibited good applicability for treating other dyes (e.g., 99.6% of tartrazine and 60.3% of methylene blue after 30 min) in water and actual textile dyeing wastewater with high color (70.3% after 5 min) and COD removal (71.4% after 120 min). The radical scavenging tests indicated the role of reactive oxygen species in the order of O2•− ∼ 1O2 > h+ > SO4•− > HO• for RhB degradation but O2•− > h+ > 1O2 > SO4•− > HO• for decolorization. These results suggest the potential application of PMS activation using Fe3O4@TiO2 in the photocatalytic treatment of textile and dyeing wastewater with the dominant role of O2•−. [ABSTRACT FROM AUTHOR]

The tumour microenvironment provides signals to support leukaemic stem cells (LSC) maintenance and chemoresistance. Here, the authors show that disrupting niche-associated signalling by inhibiting receptor-mediated endocytosis with a dynamin GTPase inhibitor overcomes chemoresistance of LSC.

The application of robot-assisted laparoscopic techniques is new and generates numerous benefits for patients. Here, we summarise the experience of our first series through 52 cases of prostate cancer treated by robot-assisted radical prostatectomy (RARP) in the Department of Urology of Binh Dan Hospital, from December 2016 to September 2017, to study the learning curves of this procedure. In this clinical comparative study, 52 patients diagnosed with prostate cancer (clinical stage T1 to T3) received RARP with and without nerve sparing as well as standard pelvic lymphadenectomy. Patients were divided into 4 groups according to their surgeon (surgeons A, B, C, and D, with 22, 12, 10, and 8 patients, respectively) for comparison. Research variables were cancer stage, preand postoperative prostate-specific antigen (PSA) serum levels, Gleason scores, lymph node metastasis, estimated blood loss, surgery time, urinary incontinence, hospital stay, and complications. Mean age, PSA, and stage of cancer were statistically similar (p>0.3). Operative times were 194.55, 269.17, 236.00, and 306.88 min, respectively (p

Agilent’s innovative EMR-Lipid and Captiva EMRLipid sorbents efficiently replace the traditional QuEChERS d-SPE clean-up products in selective lipid removal from fatty matrices, thus improving instrumental analytical reproducibility, reliability, and long-term use. These products have dual functionality; a hydrophobic interaction between the sorbent with long aliphatic lipid chains of the matrices, which allows for complete lipid retention, and a size exclusion property that does not retain analytes, thus this maximizes, in principle, analyte recovery in any sample. As most of the analytes under study were polar or not highly nonpolar and had a relatively large size, the examination of small-sized nonpolar or less polar compounds is necessary to check for partial retention by the sorbents and if any, further precautions should be taken when using these sorbents. These queries are answered in our present communication concerning the analytes Trifluralin (logP: 5.27), Fipronil (logP: 4.0), and Clenbuterol (logP: 2.63).