Infant and Young Child Feeding Practices Differ by Ethnicity of Vietnamese Mothers

Background: Limited studies have examined ethnic variation in breastfeeding and complementary feeding practices in developing countries. This study investigated ethnic variation in feeding practices in mothers with children 0–23 months old in Vietnam. Methods: We used data on 1875 women who came from the ethnic majority, Kinh (n = 989, randomly sampled from 9875 surveyed Kinh mothers, 10 % from each province) and three ethnic minorities: E De-Mnong (n = 309), Thai-Muong (n = 229) and Tay-Nung (n = 348). Ethnic minorities were compared with the Kinh group using logistic regression model. Results: Prevalence of breastfeeding initiation within an hour of birth was 69 % in Thai-Muong, but ~50 % in other ethnicities. In logistic regression, the prevalence of breastfeeding within one hour was lower in Tay-Nung (OR: 0.54; 95 % CI: 0.38, 0.77) than the majority Kinh. Prevalence of exclusive breastfeeding under 6 months was 18, 10, 17, and 33 % in Kinh, Thai-Muong, Tay-Nung, and E De-Mnong, respectively; compared to the majority Kinh, the prevalence was lower in Thai-Muong (OR: 0.42; 95 % CI: 0.25, 0.71) and higher in E De-Mnong (OR: 1.99; 95 % CI: 1.04, 3.82). Overall prevalence of bottle feeding in Thai-Muong and E De-Mnong (~20 %) was lower than in Kinh (~33 %): Thai-Muong (OR: 0.50; 95 % CI: 0.37, 0.68) and E De-Mnong (OR: 0.69; 95 % CI: 0.50, 0.95). Compared with Kinh (75 %), fewer ethnic minority children received minimum acceptable diets (33 % in Thai-Muong, 46 % in E De-Mnong, and 52 % in Tay-Nung; P \u3c 0.05). Prevalence of minimum acceptable diet (met both dietary frequency and diversity) was lower in Thai-Muong (OR: 0.23; 95 % CI: 0.11, 0.46), Tay-Nung (OR: 0.52; 95 % CI: 0.39, 0.69), and E De-Mnong (OR: 0.55; 95 % CI: 0.33, 0.89) than the majority Kinh. Conclusions: Breastfeeding practices were suboptimal and differed by ethnicity, which suggests need for tailored interventions at multiple levels to address ethnic-specific challenges and norms. Complementary feeding practices were less optimal among ethnic minorities compared to Kinh, which suggests need for broad intervention including improved food availability, accessibility, and security

Bayesian Active Learning With Abstention Feedbacks

We study pool-based active learning with abstention feedbacks where a labeler can abstain from labeling a queried example with some unknown abstention rate. This is an important problem with many useful applications. We take a Bayesian approach to the problem and develop two new greedy algorithms that learn both the classification problem and the unknown abstention rate at the same time. These are achieved by simply incorporating the estimated average abstention rate into the greedy criteria. We prove that both algorithms have near-optimality guarantees: they respectively achieve a ${(1-\frac{1}{e})}$ constant factor approximation of the optimal expected or worst-case value of a useful utility function. Our experiments show the algorithms perform well in various practical scenarios.Comment: Poster presented at 2019 ICML Workshop on Human in the Loop Learning 2019 (non-archival). arXiv admin note: substantial text overlap with arXiv:1705.0848

One-pot preparation of alumina-modified polysulfone-graphene oxide nanocomposite membrane for separation of emulsion-oil from wastewater

In recent years, polysulfone-based nanocomposite membranes have been widely used for contaminated water treatment because they comprise properties such as high thermal stability and chemical resistance. In this study, a polysulfone (PSf) nanocomposite membrane was fabricated using the wet-phase inversion method with the fusion of graphene oxide (GO) and alumina (Al2O3) nanoparticles. We also showed that GO-Al2O3 nanoparticles were synthesised successfully by using a one-pot hydrothermal method. The nanocomposite membranes were characterised by Fourier transform infrared (FT-IR), scanning electron microscopy (SEM), nitrogen adsorption-desorption isotherms, energy-dispersive X-ray spectroscopy (EDX), thermogravimetric analysis (TGA), and water contact angle. The loading of GO and Al2O3 was investigated to improve the hydrophilic and oil rejection of the matrix membrane. It was shown that by using 1.5 wt.% GO-Al2O3 loaded in polysulfone, ~74% volume of oil was separated from the oil/water emulsion at 0.87 bar and 30 min. This figure was higher than that of the process using the unmodified membrane (PSf/GO) at the same conditions, in which only ~60% volume of oil was separated. The pH, oil/water emulsion concentration, separation time, and irreversible fouling coefficient (FRw) were also investigated. The obtained results suggested that the GO-Al2O3 nanoparticles loaded in the polysulfone membrane might have potential use in oily wastewater treatment applications

Designing a novel heterostructure AgInS₂@MIL-101(Cr) photocatalyst from PET plastic waste for tetracycline degradation

Semiconductor-containing porous materials with a well-defined structure could be unique scaffolds for carrying out selective organic transformations driven by visible light. We herein introduce for the first time a heterostructure of silver indium sulfide (AgInS(2)) ternary chalcogenide and a highly porous MIL-101(Cr) metal–organic framework (MOF) synthesised from polyethylene terephthalate plastic waste. Our results demonstrate that AgInS(2) nanoparticles were uniformly attached to each lattice plane of the octahedral MIL-101(Cr) structure, resulting in a nanocomposite with a high distribution of semiconductors in a porous media. We also demonstrate that the nanocomposite with up to 40% of AgInS(2) doping exhibited excellent catalytic activity for tetracycline degradation under visible light irradiation (∼99% tetracycline degraded after 4 h) and predominantly maintained its performance after five cycles. These results could promote a new material circularity pathway to develop new semiconductors that can be used to protect water from further pollution

Structural assessment based on vibration measurement test combined with an artificial neural network for the steel truss bridge

Damage assessment is one of the most crucial issues for bridge engineers during the operational and maintenance phase, especially for existing steel bridges. Among several methodologies, the vibration measurement test is a typical approach, in which the natural frequency variation of the structure is monitored to detect the existence of damage. However, locating and quantifying the damage is still a big challenge for this method, due to the required human resources and logistics involved. In this regard, an artificial intelligence (AI)-based approach seems to be a potential way of overcoming such obstacles. This study deployed a comprehensive campaign to determine all the dynamic parameters of a predamaged steel truss bridge structure. Based on the results for mode shape, natural frequency, and damping ratio, a finite element model (FEM) was created and updated. The artificial intelligence network’s input data from the damage cases were then analysed and evaluated. The trained artificial neural network model was curated and evaluated to confirm the approach’s feasibility. During the actual operational stage of the steel truss bridge, this damage assessment system showed good performance, in terms of monitoring the structural behaviour of the bridge under some unexpected accidents.This research was funded by FCT/MCTES through national funds (PIDDAC) from the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under the reference UIDB/04029/2020, and from the Associate Laboratory Advanced Production and Intelligent Systems ARISE, under the reference LA/P/0112/2020, as well as financial support of the project research “B2022-GHA-03” from the Ministry of Education and Training. And The APC was funded by ANI (“Agência Nacional de Inovação”) through the financial support given to the R&D Project “GOA Bridge Management System—Bridge Intelligence”, with reference POCI-01-0247-FEDER069642, which was cofinanced by the European Regional Development Fund (FEDER) through the Operational Competitiveness and Internationalisation Program (POCI)