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ObjectivesTo estimate the healthcare costs associated with post-stroke dysphagia during acute hospitalisation and to identify factors influencing these costs in a tertiary hospital setting in Vietnam.DesignA cross-sectional study using clinical and billing data from hospital records.SettingThe study was conducted at the Neurology Center of Bach Mai Hospital, a tertiary care facility in Hanoi, Vietnam, between June 2020 and January 2022.ParticipantsA total of 951 patients aged ≥18 years with acute ischaemic stroke confirmed by CT or MRI were included. Dysphagia was assessed using the Gugging Swallowing Screen.Outcome measuresDirect healthcare costs during hospitalisation were collected from the hospital billing system and categorised as medications, diagnostic imaging, medical supplies, accommodation, food, procedures and laboratory tests. All costs were converted to USD. Associations between patient characteristics and total healthcare costs were analysed using generalised linear models (Gamma distribution with log link), applying robust standard errors.ResultsThe median treatment cost was 10.08 million VND (436.24 USD) in the dysphagia group vs 6.37 million VND (275.78 USD) in the non-dysphagia group. Costs increased with dysphagia severity, reaching 22.64 million VND (979.49 USD) among patients with severe dysphagia. In multivariate analysis, dysphagia was associated with a 21% increase in costs (exp(β) = 1.21; 95% CI 1.10 to 1.33; p<0.01). Other significant cost drivers included combined stroke lesions, National Institute of Health Stroke Scale score >14, pneumonia, prolonged hospitalisation and higher educational level.ConclusionsPost-stroke dysphagia substantially increases acute hospitalisation costs in Vietnam. Early screening, standardised management and preventive care for complications may improve outcomes and reduce costs.Trial registration numberThe study was registered on the Research Registry website (https://www.researchregistry.com/) under the unique identification number: researchregistry8203.

This prospective observational study aimed to assess the impact of behavioral therapy on dysphagia in patients with acute ischemic stroke undergoing nasogastric tube feeding. The study was conducted between June 2020 and May 2022 at the Neurological Center of Bach Mai Hospital, Vietnam, with a sample size of 230 patients divided into two groups: a normal and a behavioral therapy group. The normal therapy group received routine care and treatment based on standard protocols, while the behavioral therapy group underwent daily swallowing exercises for approximately 60 minutes. The Gugging Swallowing Screen (GUSS) was utilized to screen individuals with dysphagia, and the difference-in-differences (DID) method was adopted to estimate the effect of behavioral therapy on dysphagia patients. The study concluded that behavioral therapy improved dysphagia in patients with acute ischemic stroke undergoing nasogastric tube feeding. This study highlights the potential of behavioral therapy as an effective intervention for dysphagia rehabilitation in stroke patients.

In this work, the incorporation of halloysite nanotubes (HNTs) and polyphenylene ether-grafted maleic anhydride copolymer (FB) into acrylonitrile butadiene styrene (ABS)/polyphenylene oxide (PPO) blend has been investigated to enhance the compatibility and performance of the blends. ABS/PPO blend (AP) with the weight ratio of ABS/PPO = 70/30 with 5 wt% loading of FB and different weight contents of HNTs were elaborated by melt blending process, and their properties were studied. The SEM observation revealed that AP blend exhibited dispersed phases of PPO droplets with apparent gaps between PPO and ABS phases, indicating poor adhesion between the polymers in the blend. As the compatibilizer, FB effectively enhanced the inter-phase interaction, minimizing the formation of interfacial gaps, evidenced by the SEM images, leading to the adhesion between ABS, PPO, and HNTs phases. As a result, the mechanical performance of the AP blend was significantly improved with the addition of FB and HNTs. At 5 wt% of HNT, the AP/FB/HNT blend exhibited the tensile strength of 37.47 MPa and flexural strength of 59.09 MPa compared to 26.60 MPa and 39.45 MPa of neat AP, respectively. The glass transition temperature of the modified blends showed shifts compared to the neat AP, which indicates the improvement in miscibility of the modified-AP blends. The thermal stability and flame resistance of the blends were also enhanced with the introduction of HNTs and FB, evidenced by the V-1 rating in the UL-94 test and the increase in limiting oxygen index (LOI). These results demonstrated that the incorporation of HNTs and FB is a promising and straightforward method to improve the properties of ABS/PPO blends.

In this study, a novel recyclable, hydrophobicity foam with excellent oil/water separation based on polyurethane (PU) and polypropylene glycol (PPG) grafted stearic acid (PGGA) has been developed. PGGA was simply synthesized by the esterification of stearic acid and PPG, while the PU/PGGA foam was fabricated by the reaction of PPG and methyl diphenyl diisocynate (MDI) with the addition of PGGA. The PU/PGGA foam exhibits improved oil adsorption capacity, water rejection as well as oil/water selectivity with the increase of PGGA weight loadings. The presence of PGGA at high loading (≥ 5 wt%) reduces the pore size and porosity of PU/PGGA foam due to the high viscosity of PGGA preventing the foam formation reaction. As a result, the oil adsorption capacity of PU/PGGA foam is slightly neglected, nevertheless, the oil/water selectivity is significantly enhanced compared to the original PU foam. The result in this work suggests a simple and cost-effective method with potential in oil removal at large scale application.In this study, novel hydrophobicity agent (PGGA) is developed for enhancing the oil/water separation of polyurethane (PU) foam. At lower weight loading of PGGA (≤ 3 wt%), the PU/PGGA foam exhibits improvement in oil adsorption capacity, water rejection and oil/water separation. Above 5 wt% loading of PGGA, the oil adsorption of PU/PGGA foam is decreased, however, the oil/water selectivity were drastically improved. With this result, the PU/PGGA foam is highly potential for oil removal at large scale application.

Enhancing voltage of polyvinyl chloride (PVC)-insulated cable has been paid a great attention from researchers. Metal oxide nanoparticles such as: SiO 2 , ZnO, Al 2 O 3 have shown good promise to achieve this goal. However, the great challenge for nanocomposites preparation is the dispersibility and interfacial interaction between filler(s) and PVC matrix to obtain high mechanical and electrical performances of nanocomposites. In this study, two kinds of silane substances, (3-(trimethoxysilyl) propyl methacrylate (MPTS) and (3-aminopropyl) triethoxysilane (APTES)), were used as surface modification agents for γ -Al 2 O 3 nanoparticles (NPs). The silanes grafting on the surface of γ -Al 2 O 3 NPs was confirmed by Fourier transform infrared spectroscopy (FT-IR), thermogravimetric analysis (TGA) and X-ray diffraction (XRD). By field emission scanning electron microscopy (FESEM) observation, the interfacial interaction index and dynamic mechanical thermal analysis (DMTA) proved that the presence of silane remarkably improved the dispersibility, interfacial interaction and adhesion of Al 2 O 3 NPs with PVC matrix. Thanks to these improvements, the obtained PVC/Al 2 O 3 nanocomposites had much enhanced mechanical and electrical properties. At 0.75 wt% of loading content, MPTS-modified Al 2 O 3 nanoparticles incorporating into PVC matrix showed the high properties with 60.51 MPa of yield strength, 90.48 MPa of flexural strength and the maximum value of electrical breakdown strength ( E EB ) could reach up to 98.56 kV/mm. The nanocomposite based on PVC matrix reinforcing with γ -Al 2 O 3 NPs is a promising material for high-quality electric cable manufacturing field. Graphical abstract

Surface modification of metal oxide nanoparticles can enhance their dispersibility in polymer matrix. In this study, the zinc oxide (ZnO) nanoparticles (NPs) were organically modified by coupling agent as isopropyl tri (dioctyl phosphate) titanate (KR-12) with the initial content of 3 wt.% (in comparison with ZnO NPs weight). The characteristics of modified ZnO (m-ZnO) NPs, namely chemical changes, zeta potential, morphology, and thermal behavior, were evaluated using IR spectroscopy, DLS, SEM, and TGA methods, respectively. The obtained results suggested that ZnO NPs were modified successfully with KR-12 coupling agent. In comparison with the unmodified ZnO (u-ZnO) NPs, the m-ZnO NPs had additional functional groups and there were changes of some properties such as hydrophobic property, surface charge, and thermal stability of m-ZnO NPs. The m-ZnO NPs could disperse in epoxy resin better than the u-ZnO NPs. Therefore, the m-ZnO NPs improved the mechanical properties, chemical resistance, thermal stability, and anticorrosion protection ability of epoxy resin coating. The abrasion resistance and the adhesion of epoxy coating containing 2 wt.% m-ZnO NPs were increased about 40% and 54%, respectively. The bending resistance of epoxy resin was also enhanced, and the anticorrosion resistance of coatings was improved in the presence of m-ZnO nanoparticles.

Automated learning analytics is becoming an essential topic in the educational area, which needs effective systems to monitor the learning process and provides feedback to the teacher. Recent advances in visual sensors and computer vision methods enable automated monitoring of behavior and affective states of learners at different levels, from university to pre-school. The objective of this research was to build an automatic system that allowed the faculties to capture and make a summary of student behaviors in the classroom as a part of data acquisition for the decision making process. The system records the entire session and identifies when the students pay attention in the classroom, and then reports to the facilities. Our design and experiments show that our system is more flexible and more accurate than previously published work.

Fertilizer is applied widely to improve the productivity of plantations. Traditionally, fertilization is conducted in spring and/or in the early rainy season, and it is believed to support the growth of planted trees in the growing season. Little attention to date has been paid on identification of the optimal timing of fertilization and fertilizer dose. In this study, application of the fine root monitoring technique in identifying optimal fertilization timing for an Acacia plantation in Vietnam is described. The study used two fertilizer doses (100 and 200 g NPK/tree) and three fertilization timings (in spring; in the early rainy season; and based on the fine root monitoring technique to identify when the fine roots reach their growth peak). As expected fertilization timings significantly affected growth and above-ground biomass (AGB) of the plantation. Fertilization based on the fine root monitoring technique resulted in the highest growths and AGB, followed by fertilization in the early rainy season and then in spring. Applying fertilizer at 200 g NPK/tree based on the fine root monitoring technique increased diameter at breast height (DBH) by 16%, stem height by 8%, crown diameter (Dc) by 16%, and AGB by 40% as compared to early rainy season fertilization. Increases of 32% DBH, 23% stem height, 44% Dc, and 87% AGB were found in fertilization based on fine root monitoring technique compared to spring fertilization. This study concluded that forest growers should use the fine root monitoring technique to identify optimal fertilization timing for higher productivity.

Dengue fever (DF) represents a significant health burden in Vietnam, which is forecast to worsen under climate change. The development of an early-warning system for DF has been selected as a prioritised health adaptation measure to climate change in Vietnam. This study aimed to develop an accurate DF prediction model in Vietnam using a wide range of meteorological factors as inputs to inform public health responses for outbreak prevention in the context of future climate change. Convolutional neural network (CNN), Transformer, long short-term memory (LSTM), and attention-enhanced LSTM (LSTM-ATT) models were compared with traditional machine learning models on weather-based DF forecasting. Models were developed using lagged DF incidence and meteorological variables (measures of temperature, humidity, rainfall, evaporation, and sunshine hours) as inputs for 20 provinces throughout Vietnam. Data from 1997-2013 were used to train models, which were then evaluated using data from 2014-2016 by Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). LSTM-ATT displayed the highest performance, scoring average places of 1.60 for RMSE-based ranking and 1.95 for MAE-based ranking. Notably, it was able to forecast DF incidence better than LSTM in 13 or 14 out of 20 provinces for MAE or RMSE, respectively. Moreover, LSTM-ATT was able to accurately predict DF incidence and outbreak months up to 3 months ahead, though performance dropped slightly compared to short-term forecasts. To the best of our knowledge, this is the first time deep learning methods have been employed for the prediction of both long- and short-term DF incidence and outbreaks in Vietnam using unique, rich meteorological features. This study demonstrates the usefulness of deep learning models for meteorological factor-based DF forecasting. LSTM-ATT should be further explored for mitigation strategies against DF and other climate-sensitive diseases in the coming years.

As an innovative technology, the impedance-based technique has been extensively studied for the structural health monitoring (SHM) of various civil structures. The technique’s advantages include cost-effectiveness, ease of implementation on a complex structure, robustness to early-stage failures, and real-time damage assessment capabilities. Nonetheless, very few studies have taken those advantages for monitoring the health status and the structural condition of wind turbine structures. Thus, this paper is motivated to give the reader a general outlook of how the impedance-based SHM technology has been implemented to secure the safety and serviceability of the wind turbine structures. Firstly, possible structural failures in wind turbine systems are reviewed. Next, physical principles, hardware systems, damage quantification, and environmental compensation algorithms are outlined for the impedance-based technique. Afterwards, the current status of the application of this advanced technology for health monitoring and damage identification of wind turbine structural components such as blades, tower joints, tower segments, substructure, and the foundation are discussed. In the end, the future perspectives that can contribute to developing efficient SHM systems in the green energy field are proposed.