Seawater desalination using air gap membrane distillation-an experimental study on membrane scaling and cleaning

The connection between operating temperature and membrane scaling/cleaning during an air gap membrane distillation (AGMD) process of seawater has been systematically elucidated in this study. Experimental and mathematically simulated data demonstrate the profound influences of feed salinity and membrane scaling on water flux at various operating temperatures. Feed salinity exerted significant impacts on water flux at high operating temperatures because of aggravated polarization effects. Membrane scaling and the subsequent membrane cleaning efficiency were also strongly affected by operating temperatures. Indeed, membrane scaling was more severe and occurred at a lower water recovery when operating at 60-50 °C (feed-coolant temperature) compared to that at 35-25 °C. Moreover, membrane cleaning with fresh water and vinegar was less effective for the membrane scaled at 60-50 °C compared to 35-25 °C. Finally, membrane cleaning using vinegar was much more efficient than fresh water. Given the availability of vinegar at household level, vinegar cleaning can potentially be a low cost and readily accessible approach for MD maintenance for small scale seawater desalination applications in remote coastal communities

Relative Positional Encoding for Speech Recognition and Direct Translation

Transformer models are powerful sequence-to-sequence architectures that are capable of directly mapping speech inputs to transcriptions or translations. However, the mechanism for modeling positions in this model was tailored for text modeling, and thus is less ideal for acoustic inputs. In this work, we adapt the relative position encoding scheme to the Speech Transformer, where the key addition is relative distance between input states in the self-attention network. As a result, the network can better adapt to the variable distributions present in speech data. Our experiments show that our resulting model achieves the best recognition result on the Switchboard benchmark in the non-augmentation condition, and the best published result in the MuST-C speech translation benchmark. We also show that this model is able to better utilize synthetic data than the Transformer, and adapts better to variable sentence segmentation quality for speech translation.Comment: Submitted to Interspeech 202

Effect of Polypyrrole on the Electrical, Dielectric and Mechanical Properties of Waterborne Epoxy Coatings

In this context, conducting composite based on waterborne epoxy system and polypyrrole (PPy) was investigated. The polypyrrole was synthesized by chemical oxidation polymerization. Its morphology and chemical structure were confirmed by using field emission scanning electron microscopy (FESEM) and Fourier transform infrared spectroscopy (FTIR). Then, PPy was well-dispersed in the epoxy coating and had a good compatibility with the matrix. The effects of PPy on dielectric, electrical and mechanical properties of epoxy/PPy composites was examined. The dielectric constant and electrical conductivity of the coatings increased with addition of PPy fillers. Over to 15 wt. % of PPy loading, the volume resistivity of samples slightly decreased from 6.7 × 1010 to 1.5 × 1010 Ω cm. In contrast, the presence of PPy diminished both impact and abrasion resistance of the epoxy/PPy composites, down to 160 kg cm and 10.2 L/mil, respectively, but they stayed acceptable for the coatings. The results reveal that the epoxy containing polypyrrole is suitable for various electrical and dielectric applications

Cardiovascular Disease Risk Factor Patterns and Their Implications for Intervention Strategies in Vietnam

Background. Data on cardiovascular disease risk factors (CVDRFs) in Vietnam are limited. This study explores the prevalence of each CVDRF and how they cluster to evaluate CVDRF burdens and potential prevention strategies. Methods. A cross-sectional survey in 2009 (2,130 adults) was done to collect data on behavioural CVDRF, anthropometry and blood pressure, lipidaemia profiles, and oral glucose tolerance tests. Four metabolic CVDRFs (hypertension, dyslipidaemia, diabetes, and obesity) and five behavioural CVDRFs (smoking, excessive alcohol intake, unhealthy diet, physical inactivity, and stress) were analysed to identify their prevalence, cluster patterns, and social predictors. Framingham scores were applied to estimate the global 10-year CVD risks and potential benefits of CVD prevention strategies. Results. The age-standardised prevalence of having at least 2/4 metabolic, 2/5 behavioural, or 4/9 major CVDRF was 28%, 27%, 13% in women and 32%, 62%, 34% in men. Within-individual clustering of metabolic factors was more common among older women and in urban areas. High overall CVD risk (≥20% over 10 years) identified 20% of men and 5% of women—especially at higher ages—who had coexisting CVDRF. Conclusion. Multiple CVDRFs were common in Vietnamese adults with different clustering patterns across sex/age groups. Tackling any single risk factor would not be efficient

RMDM: A Multilabel Fakenews Dataset for Vietnamese Evidence Verification

In this study, we present a novel and challenging multilabel Vietnamese dataset (RMDM) designed to assess the performance of large language models (LLMs), in verifying electronic information related to legal contexts, focusing on fake news as potential input for electronic evidence. The RMDM dataset comprises four labels: real, mis, dis, and mal, representing real information, misinformation, disinformation, and mal-information, respectively. By including these diverse labels, RMDM captures the complexities of differing fake news categories and offers insights into the abilities of different language models to handle various types of information that could be part of electronic evidence. The dataset consists of a total of 1,556 samples, with 389 samples for each label. Preliminary tests on the dataset using GPT-based and BERT-based models reveal variations in the models' performance across different labels, indicating that the dataset effectively challenges the ability of various language models to verify the authenticity of such information. Our findings suggest that verifying electronic information related to legal contexts, including fake news, remains a difficult problem for language models, warranting further attention from the research community to advance toward more reliable AI models for potential legal applications.Comment: ISAILD@KSE 202