This study integrated the theory of planned behaviour (TPB), the norm activation model (NAM), and the stimulus–organism–behaviour–consequences theory (SOBC) to determine how external (subjective injunctive norm, subjective descriptive norm, and perceived behavioural control) and internal stimuli (ascription of responsibility, awareness of consequences) stimulate organisms (attitude towards energy saving and personal norms), which in turn drives behavioural responses (energy-saving intentions and behaviours) and their consequences (energy-saving habits). A sample of 1514 residents of five large cities in Vietnam and a multiple linear regression analysis were used to test the hypothesised model. The results show that external stimuli positively shaped a favourable energy-saving attitude, while internal stimuli aroused individuals’ personal norms. In addition, energy-saving intention, behaviours, and habits were serial mediators impacted by both internal and external stimuli. The results also indicate that a long-term orientation positively moderated the relationship between energy-saving intention, behaviours, and habits, but collectivism only moderated the nexus between energy-saving behaviours and habits. These findings imply that policymakers should focus on conveying information related to energy conservation among surrounding people, increasing citizens’ awareness of the consequences, personal responsibilities, moral obligations regarding saving energy, and should not neglect the informative role of cultural values in energy conservation practices.
The test study area is the Miocene reservoir of Nam Con Son Basin, offshore Vietnam. In the study we used unsupervised learning to automatically cluster hydraulic flow units (HU) based on flow zone indicators (FZI) in a core plug dataset. Then we applied supervised learning to predict HU by combining core and well log data. We tested several machine learning algorithms. In the first phase, we derived hydraulic flow unit clustering of porosity and permeability of core data using unsupervised machine learning methods such as Ward’s, K mean, Self-Organize Map (SOM) and Fuzzy C mean (FCM). Then we applied supervised machine learning methods including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Boosted Tree (BT) and Random Forest (RF). We combined both core and log data to predict HU logs for the full well section of the wells without core data. We used four wells with six logs (GR, DT, NPHI, LLD, LSS and RHOB) and 578 cores from the Miocene reservoir to train, validate and test the data. Our goal was to show that the correct combination of cores and well logs data would provide reservoir engineers with a tool for HU classification and estimation of permeability in a continuous geological profile. Our research showed that machine learning effectively boosts the prediction of permeability, reduces uncertainty in reservoir modeling, and improves project economics.
Gold nano particles (GNPs) concentration dependence of the energy transfer occurs between the fluorophores and GNPs is investigated. In the case of theses pairs, GNPs can enhance or quench the fluorescence of fluorophores depending upon the relative magnitudes of two energy transfer mechanisms: i) the plasmonic field enhancement at the fluorophores emission frequencies (plasmon coupled fluorescence enhancement) and ii) the localized plasmon coupled Forster energy transfer from fluorescent particles to gold particles, which quenches the fluorescence. The competition of these mechanisms is depending on the spectral overlap of fluorophores and GNPs, their relative concentration, excitation wavelength. Simple two branches surface plasmon polariton model for GNPs concentration dependence of the energy transfer is proposed. The experimental data and theoretical results confirm our findings.
Objective: To determine the prevalence and risk factors for malnutrition in children with congenital heart disease (CHD) attending a cardiology clinic in Vietnam. Methods: Cross‐sectional survey of all children with CHD aged <5 years old at Children’s Hospital #1 (March‐August 2012). Data included weight (W), height (H), age (A), gender, cardiac diagnosis, severity of CHD, corrective surgery status, birth order, preterm delivery, low‐birth‐weight (LBW), breastfeeding initiation and cessation, age at weaning, parent education and income stability. Patients with genetic syndromes, dysmorphic features, or neurologic disabilities were excluded (n=9). Nutrition status was classified using WHO standards. Results: Participants were 410 children; 153 had previous corrective surgery. The rate of stunting (H/A 蠄‐2SD) was 153/410 (37.4%) and that of wasting (W/H 蠄‐2SD) was 136/410 (33.3%). These rates did not differ according to surgery status (P>0.05). Risk factors for stunting included preterm delivery, LBW, mother’s income stability, and father’s education, (P<0.05). Risk factors for wasting included age 6‐12 mo, LBW, breastfeeding cessation at <6 mo and weaning at <6 mo (p<0.05). Conclusion: Malnutrition rates are high in this cohort of Vietnamese children with CHD. Modifiable factors (breastfeeding and age at weaning) play a role. Prospective studies on nutritional status in this CHD patient population are needed to inform intervention studies. Grant Funding Source: Support from the Abbott Fund
Abstract. Phosphine-free selenium precursor solutions have been prepared by heating at temperatures ranging from 160 °C to 240 °C and studied by means of infrared absorption spectroscopy. The colloidal CdSe nanocrystals (NCs) synthesized from all those solutions by the wet chemical method. The influence of heating temperature on the chemical reactivity of selenium precursor and its role on the optical and vibrational properties of CdSe NCs are discussed in details. Their morphology, particle size, structural, optical and vibrational properties were investigated using transmission electron microscopy, X-ray diffraction, UVVis, fluorescence and Raman spectroscopy, respectively.
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