A Machine Learning-Assisted Numerical Predictor for Compressive Strength of Geopolymer Concrete Based on Experimental Data and Sensitivity Analysis

Geopolymer concrete offers a favourable alternative to conventional Portland concrete due to its reduced embodied carbon dioxide (CO2) content. Engineering properties of geopolymer concrete, such as compressive strength, are commonly characterised based on experimental practices requiring large volumes of raw materials, time for sample preparation, and costly equipment. To help address this inefficiency, this study proposes machine learning-assisted numerical methods to predict compressive strength of fly ash-based geopolymer (FAGP) concrete. Methods assessed included artificial neural network (ANN), deep neural network (DNN), and deep residual network (ResNet), based on experimentally collected data. Performance of the proposed approaches were evaluated using various statistical measures including R-squared (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE). Sensitivity analysis was carried out to identify effects of the following six input variables on the compressive strength of FAGP concrete: sodium hydroxide/sodium silicate ratio, fly ash/aggregate ratio, alkali activator/fly ash ratio, concentration of sodium hydroxide, curing time, and temperature. Fly ash/aggregate ratio was found to significantly affect compressive strength of FAGP concrete. Results obtained indicate that the proposed approaches offer reliable methods for FAGP design and optimisation. Of note was ResNet, which demonstrated the highest R2 and lowest RMSE and MAPE values

A comparison for donor-acceptor interactions between E(PH3)2 and NHEMe ligands (E = C to Pb) of W(CO)5 complexes using energy decomposition analysis method with natural orbitals for chemical valence theory

Quantum chemical calculations at the BP86/TZ2P+ level of theory are performed for a comparison of density functional theory (DFT) between tetrylones [(CO)5W-{E(PH3)2}] (W5-EP2) and tetrylenes [(CO)5W-{NHEMe}] (W5-NHEMe) when E = C to Pb. The EDA-NOCV results suggest that the W-E bond dissociation energies (BDEs) in tetrylone complexes increase from the lighter to the heavier homologues. The W-E bond dissociation energies (BDEs) trend in W5-EP2 comes from the increase in (CO)5W←E(PH3)2 donation and strong electrostatic attraction, and that the ligands E(PH3)2 (EP2) are strong s-donors and very weak π-donors. The W-E BDEs trend in tetrylene complexes W5-NHEMe is opposite to that of the W5-EP2 complexes which decrease from the lighter to the heavier homologues. The NHEMe ligands are strong s-donors and weak π-acceptors. NOCV pairs were used in a description of the chemical bond between the W(CO)5 fragment and the ligands in the transition-metal complexes and the results indicated that the NOCV pairs lead to very valuable description of the bonding situation of the fragment-ligand bond in complexes. Keywords. Density functional theory; Bond dissociation energies (BDEs); Energy decomposition analysis (EDA); Natural Orbitals for Chemical Valence (NOCV)

Structural Variations and Chemical Bonding in Platinum Complexes of group 14 heavier Tetrylene Homologues (Germylene – Plumbylene)

We computationally investigated the structures of Pt(II) complexes containing the heavier homologues of germylene, stannylene, and plumbylene (called heavier tetrylenes) [PtCl2-{NHEMe}] (Pt-NHE) with E = Ge – Pb using density functional theory (DFT) calculations at the BP86 level with the various basis sets def2-SVP, def2-TZVPP, and TZ2P+. The bonding situation of complexes was analyzed with charge and energy decomposition analysis methods. The results of bonding analysis showed that NHEMe ligands exhibit donor-acceptor bonds with the s lone pair electrons of heavier NHEMe donated into the vacant orbital of the metal fragment and the Pt-E bonds have PtCl2←NHEMe strong s-donation. The divalent heavier tetrylenes(II) have some degree of role as the divalent heavier tetrylones(0) character with the ligand can retain the two lone pairs at E atom. Current efforts in experimental laboratories are directed towards the synthesis of tetrylenes Pt(II) complexes from natural products, so the results in this study will provide properties and chemical bonding of complexes investigated for experimental researches

Spatiotemporal and Socioeconomic Risk Factors for Dengue at the Province Level in Vietnam, 2013-2015: Clustering Analysis and Regression Model.

Dengue is a serious infectious disease threat in Vietnam, but its spatiotemporal and socioeconomic risk factors are not currently well understood at the province level across the country and on a multiannual scale. We explore spatial trends, clusters and outliers in dengue case counts at the province level from 2011-2015 and use this to extract spatiotemporal variables for regression analysis of the association between dengue case counts and selected spatiotemporal and socioeconomic variables from 2013-2015. Dengue in Vietnam follows anticipated spatial trends, with a potential two-year cycle of high-high clusters in some southern provinces. Small but significant associations are observed between dengue case counts and mobility, population density, a province's dengue rates the previous year, and average dengue rates two years previous in first and second order contiguous neighbours. Significant associations were not found between dengue case counts and housing pressure, access to electricity, clinician density, province-adjusted poverty rate, percentage of children below one vaccinated, or percentage of population in urban settings. These findings challenge assumptions about socioeconomic and spatiotemporal risk factors for dengue, and support national prevention targeting in Vietnam at the province level. They may also be of wider relevance for the study of other arboviruses, including Japanese encephalitis, Zika, and Chikungunya

Spatiotemporal analysis of historical records (2001-2012) on dengue fever in Vietnam and development of a statistical model for forecasting risk

Background: Dengue fever is the most widespread infectious disease of humans transmitted by Aedes mosquitoes. It is the leading cause of hospitalization and death in children in the Southeast Asia and western Pacific regions. We analyzed surveillance records from health centers in Vietnam collected between 2001–2012 to determine seasonal trends, develop risk maps and an incidence forecasting model. Methods: The data were analyzed using a hierarchical spatial Bayesian model that approximates its posterior parameter distributions using the integrated Laplace approximation algorithm (INLA). Meteorological, altitude and land cover (LC) data were used as predictors. The data were grouped by province (n = 63) and month (n = 144) and divided into training (2001–2009) and validation (2010–2012) sets. Thirteen meteorological variables, 7 land cover data and altitude were considered as predictors. Only significant predictors were kept in the final multivariable model. Eleven dummy variables representing month were also fitted to account for seasonal effects. Spatial and temporal effects were accounted for using Besag-York-Mollie (BYM) and autoregressive (1) models. Their levels of significance were analyzed using deviance information criterion (DIC). The model was validated based on the Theil’s coefficient which compared predicted and observed incidence estimated using the validation data. Dengue incidence predictions for 2010–2012 were also used to generate risk maps. Results: The mean monthly dengue incidence during the period was 6.94 cases (SD 14.49) per 100,000 people. Analyses on the temporal trends of the disease showed regular seasonal epidemics that were interrupted every 3 years (specifically in July 2004, July 2007 and September 2010) by major fluctuations in incidence. Monthly mean minimum temperature, rainfall, area under urban settlement/build-up areas and altitude were significant in the final model. Minimum temperature and rainfall had non-linear effects and lagging them by two months provided a better fitting model compared to using unlagged variables. Forecasts for the validation period closely mirrored the observed data and accurately captured the troughs and peaks of dengue incidence trajectories. A favorable Theil’s coefficient of inequality of 0.22 was generated. Conclusions: The study identified temperature, rainfall, altitude and area under urban settlement as being significant predictors of dengue incidence. The statistical model fitted the data well based on Theil’s coefficient of inequality, and risk maps generated from its predictions identified most of the high-risk provinces throughout the country

A quantum chemical computation insight into the donor-acceptor bond interaction of silver complexes with tetrylene

We computationally investigate the nature of chemical bonding from linear to bent structures of N-heterocyclic carbene-analogues of silver complexes (called tetrylene) AgCl-NHEMe (Ag-NHE) with E = C – Pb using quantum chemical calculations at the BP86 level with the various basis sets def2-SVP, def2-TZVPP, and TZ2P+. The geometry calculations find that the equilibrium structures of Ag-NHE system show major differences in the bonded orientation of NHPb ligand in Ag-NHPb compared with NHE ligands the slighter homologues Ag-NHE (E = C - Sn). The bond dissociation energy results show that the Ag-carbene bond in Ag-NHC is a strong bond and decreases from the slighter to the heavier homologues. The EDA-NOCV results indicate that the ligand NHE in complexes is strong s-donors and very weak π donor. The NOCV pairs of the bonding show small π-back donation from the Ag to the NHEMe ligands. Keywords. N-heterocyclic tetrylene, bond dissociation energy, quantum chemical calculations, bonding analysis

Building the hospital event-based surveillance system in Viet Nam: a qualitative study to identify potential facilitators and barriers for event reporting

Introduction: Hospitals are a key source of information for the early identification of emerging disease outbreaks and acute public health events for risk assessment, decision-making, and public health response. The objectives of this study were to identify potential facilitators and barriers for reporting of unusual events from the curative sector to the preventive medicine system in Viet Nam. Methods: In 2016, we conducted 18 semi-structured in-depth interviews and 9 focus group discussions with representatives from the curative and preventive medicine sectors in four provinces. We transcribed the interviews and focus group discussions and conducted a thematic analysis of the factors that appeared to affect public health event reporting. Results: We identified five major themes. Firstly, the lack of a legal framework to guide reporting meant there was an over-reliance on internal procedures. Secondly, participants reported the importance of an enabling environment to facilitate reporting such as leadership support and having focal points for reporting. Thirdly, potential benefits for reporting were seen such as support during outbreaks and receiving feedback. Fourthly, some challenges prohibited timely reporting such as not perceiving reporting to be the task of the curative sector and hesitancy to report without laboratory confirmation. Finally, the limited resources and specialist capacities in remote areas hindered timely detection and reporting of unusual events. Discussion: This study identified potential opportunities to promote the detection and reporting of unusual events from health care workers to the public health sector, and thus improving the overall health security system in Viet Nam and beyond