Results Of Experiments Research Articles

Biomass C-doped three-dimensional Bi2WO6 for enhanced visible-light-driven photodegradation of diclofenac and rhodamine B

Photocatalysis as a green and efficient pollutant degradation technology, displaying great potential in environmental purification. By one step hydrothermal method in this study, flower-like Bi2WO6 with a large specific surface area was obtained, and the preparation of biomass carbon modified Bi2WO6 with sorghum stalks was also proposed to construct a series (BWO/x%C). The composites were effectively characterized in terms of several aspects of structure and properties. With the dyes rhodamine B and diclofenac as typical organic pollutants, the photocatalytic activity of the proposed photocatalysts was evaluated. Under visible light irradiation, the removal of rhodamine B by BWO/4%C reached 97.27% within 50 min and that of diclofenac reached 96.19% within 120 min, which were significantly favorable to single-phase Bi2WO6. The superiority of this system BWO/4%C is mainly due to its large specific surface area (33.51 m2/g), which provides additional reaction sites for contaminants. The UV–Vis DRS findings indicated that the modified biomass C reduced the band gap of Bi2WO6 and improved the availability of Bi2WO6 to visible light, and the separation efficiency of the electron and hole pairs of Bi2WO6 was improved due to the strong electron transfer ability of biomass C. The analyzing results of free radical trapping experiments confirmed that h+ and •O2- are the main active substances involved in the photodegradation reaction. The results of five cycle experiments exhibited that the degradation rate of rhodamine B still reached higher than 90%, confirming the good stability of the prepared material. Finally, a feasible degradation mechanism was proposed for the degradation of rhodamine B and diclofenac. It offers new ideas for the development of composite nano-photocatalytic materials targeting difficult-to-degrade organic pollutants.

Enhanced photocatalytic hydrogen production performance of g-C3N4 with rich carbon vacancies

Hydrogen energy is globally recognized as one of the clean and renewable chemical energy sources. Development of photocatalysts that can efficiently utilize sunlight for photocatalytic hydrogen production is an urgent mission at present. In this study, g-C3N4 with abundant pore structure and carbon defects for photocatalytic hydrogen generation was prepared by heat treatment of g-C3N4 using ammonium carbonate. The results of photocatalytic hydrogen evolution experiments demonstrate that the 5NHC sample (2 g g-C3N4 was mixed with 5.0 g ammonium carbonate) exhibits the highest hydrogen precipitation rate (972.3 μmol g-1h−1), which is 4.29 times higher than that of the reference g-C3N4 (183.6 μmol g-1h−1). Moreover, the 5NHC sample shows excellent structural stability during the hydrogen production cycling experiments, confirmed by the experimental results. Cr(VI) reduction results further demonstrate excellent photocatalytic performance of 5NHC, which is 1.54 times higher than that of the reference g-C3N4. The defective energy levels induced by carbon vacancies will lead to a rapid separation of photoinduced charge pairs, which will result in boosted photocatalytic performance. This work provides an effective strategy for development of highly performance g-C3N4.

The identification of metabolites from gut microbiota in coronary heart disease via network pharmacology

Although the gut microbial metabolites exhibit potential effects on coronary heart disease (CHD), the underlying mechanism remains unclear. In this study, the active gut microbial metabolites acting on CHD and their potential mechanisms of action were explored through a network pharmacological approach. We collected a total of 208 metabolites from the gutMgene database and 726 overlapping targets from the similarity ensemble approach (SEA) and SwissTargetPrediction (STP) database, and ultimately identified 610 targets relevant to CHD. In conjunction with the gutMGene database, we identified 12 key targets. The targets of exogenous substances were removed, and 10 core targets involved in CHD were eventually retained. The microbiota–metabolites–targets–signalling pathways network analysis revealed that C-type lectin receptor signalling pathway, Lachnospiraceae, Escherichia, mitogen-activated protein kinase 1, prostaglandin-endoperoxidase synthase 2, phenylacetylglutamine and alcoholic acid are notable components of CHD and play important roles in the development of CHD. The results of molecular docking experiments demonstrated that AKT1-glycocholic acid and PTGS2-phenylacetylglutamine complexes may act on C-type lectin receptor signalling pathways. In this study, the key substances and potential mechanisms of gut microbial metabolites were analysed via network pharmacological methods, and a scientific basis and comprehensive idea were provided for the effects of gut microbial metabolites on CHD.

Metal nanoparticles to improve the heat resilience in wheat (Triticum aestivum L.)

This study evaluated the efficacy of phytogenic silver and zinc nanoparticles in improving heat resilience in various wheat varieties. The silver and zinc nanoparticles were synthesized using plant leaf extract and characterized using various techniques. Four wheat varieties (DBW187, Black Wheat, DBW 50, and PBW 621) were subjected to field trials. The random block design was used, and nanoparticles in different concentrations were applied at various growth stages and morphologically, and yield parameters were recorded. UV–vis spectroscopy spectral analysis showed peaks for Ag nanoparticles at 420 nm wavelength and Zn nanoparticles at 240 and 350 nm wavelength, depicting the preliminary confirmation of nanoparticle synthesis. Electron microscopic analysis (TEM and SEM) provided morphological insights and confirmed synthesis of fine-sized particle mostly in a range between 10 and 60 nm. Energy dispersive x-ray analysis confirmed the elemental composition of the synthesized nanoparticles, with Ag and Zn elements detected in their respective samples. It also confirmed the oxide nature of synthesized ZnNPs. Dynamic light scattering analysis provided size distribution profiles, indicating average sizes of approximately 61.8 nm for Ag nanoparticles and 46.5 nm for Zn nanoparticles. The concentrations of Ag and Zn nanoparticles in the samples were found to be 196.3 ppm and 115.14 ppm, respectively, through atomic absorption spectroscopic analysis. Fourier transform infrared spectroscopy analysis revealed characteristic functional groups present in the nanoparticles. The results of field experiments established that Ag nanoparticles at 75 ppm concentration exhibited the most significant enhancements in plant growth. Conversely, Zn nanoparticles at a 100 ppm concentration demonstrated the most substantial improvements in the growth and yield of heat-stressed wheat varieties. The study concludes that optimized concentrations of silver and zinc nanoparticles can effectively improve heat stress resilience in wheat. These findings are promising to enhance abiotic stress resilience in crops.

Immunoinformatics-guided recombinant polypeptide-based enzyme-linked immunosorbent assay for seromonitoring of laboratory animals for minute virus of mice and Kilham rat virus.

Subclinical infection of laboratory animals with one or more of several pathogens affects the results of experiments on animals. Monitoring the health of laboratory animals encompasses routine surveillance for pathogens, including several viruses. This study aimed to explore the development of an alternative assay to the existing ones for detecting infection of mice and rats with the parvoviruses minute virus of mice (MVM) and Kilham rat virus (KRV), respectively. Full-length VP2 and NS1 proteins of these parvoviruses, besides fragments containing multiple predicted epitopes stitched together, were studied for serological detection. The optimal dilution of full-length proteins and antigenic regions containing predicted epitopes for coating, test sera, and conjugate was determined using a checkerboard titration at each step. The assays were evaluated vis-à-vis commercially available ELISA kits. The results showed that an engineered fusion of fragments containing multiple predicted MVM VP2 and NS1 epitopes was better than either of the full-length proteins for detecting antibodies in 90% of the tested sera samples. For KRV ELISA, full-length VP2 was better compared to other individual recombinant protein fragments or combinations thereof for the detection of antibodies in sera. This report is the first description of an ELISA for KRV and an improved assay for MVM. Importantly, our assays could be exploited with small volumes of sera. The results also demonstrate the utility of immunoinformatics-driven polypeptide engineering in the development of diagnostic assays and the potential to develop better tests for monitoring the health status of laboratory animals.

Modelling of iron oxide reduction with hydrogen in a small fixed bed

The need to reduce the environmental impact of the steel industry necessitates the development of new production routes that drastically reduce the emissions of carbon dioxide. A potential solution is to change the reductant from carbon monoxide to hydrogen. In the development of such new ironmaking technologies, it is important to understand the iron oxide reduction kinetics under the novel conditions. In this work, a mathematical model of a small fixed-bed reactor was developed using the key assumptions of the Shrinking Core Model to describe the kinetics of the reactive network and mass transport phenomena involved. The model was found to be robust and to perform in a consistent way when subjected to changes in the model parameters and conditions. It was demonstrated to qualitatively reproduce the results of reduction experiments in a small-scale laboratory reactor. Preliminary analysis indicated a promising potential to adapt the model quantitatively to experimental results.

MUC1 promotes RIF by regulating macrophage ROS-SHP2 signaling pathway to up-regulate inflammatory response and inhibit angiogenesis.

To explore the effect of MUC1 on recurrent implantation failure (RIF) and its molecular mechanism. Bioinformation analysis was used to find possible molecular mechanisms of specific genes in the pathogenesis of RIF. The number of M1 and M2 macrophages was measured by flow cytometry. Immunohistochemical staining and western blotting were used to detect the expression of related proteins. Angiogenesis capacity was measured by cell tube-formation assay. Bioinformatics analysis results suggest that MUC1 may play an important role in RIF. The results of flow cytometry showed that compared with NC group, M1 macrophages increased significantly and M2 macrophages decreased significantly in MUC1 OE group. The results of immunohistochemical staining showed that MUC1 could inhibit the expression of VEGF. Western blotting results showed that MUC1 could significantly increase the expression of P22, P47, gp91, p-TBK1, IFNγ and IL-1β, and decrease the expression of p-SHP2, p-PI3K, p-mTOR, HIF1α and VEGF. After the addition of ROS inhibitor and PI3K inhibitor, the effect of MUC1 on the above proteins was eliminated. The results of tube formation experiments showed that MUC1 could inhibit vascular formation. As a promising biomarker for the diagnosis of RIF, MUC1 can promote RIF by regulating macrophage ROS-SHP2 signaling pathway to up-regulate inflammatory response and inhibit angiogenesis.

Study of the mechanism by gentiopicroside protects against skin fibroblast glycation damage via the RAGE pathway

The occurrence of nonenzymatic glycosylation reactions in skin fibroblasts can lead to severe impairment of skin health. To investigate the protective effects of the major functional ingredient from Gentianaceae, gentiopicroside (GPS) on fibroblasts, network pharmacology was used to analyse the potential pathways and targets underlying the effects of GPS on skin. At the biochemical and cellular levels, we examined the inhibitory effect of GPS on AGEs, the regulation by GPS of key ECM proteins and vimentin, the damage caused by GPS to the mitochondrial membrane potential and the modulation by GPS of inflammatory factors such as matrix metalloproteinases (MMP-2, MMP-9), reactive oxygen species (ROS), and IL-6 via the RAGE/NF-κB pathway. The results showed that GPS can inhibit AGE-induced damage to the dermis via multiple pathways. The results of biochemical and cellular experiments showed that GPS can strongly inhibit AGE production. Conversely, GPS can block AGE-induced oxidative stress and inflammatory responses in skin cells by disrupting AGE-RAGE signalling, maintain the balance of ECM synthesis and catabolism, and alleviate AGE-induced dysfunctions in cellular behaviour. This study provides a theoretical basis for the use of GPS as an AGE inhibitor to improve skin health and alleviate the damage caused by glycosylation, showing its potential application value in the field of skin care.

Forward modelling of synthetic-aperture radar (SAR) backscatter during lake ice melt conditions using the Snow Microwave Radiative Transfer (SMRT) model

Abstract. Monitoring of lake ice is important to maintain transportation routes, but in recent decades the number of in situ observations have declined. Remote sensing has worked to fill this gap in observations, with active microwave sensors, particularly synthetic-aperture radar (SAR), being a crucial technology. However, the impact of wet conditions on radar and how interactions change under these conditions have been largely ignored. It is important to understand these interactions as warming conditions are likely to lead to an increase in the occurrence of slush layers. This study works to address this gap using the Snow Microwave Radiative Transfer (SMRT) model to conduct forward-modelling experiments of backscatter for Lake Oulujärvi in Finland. Experiments were conducted under dry conditions, under moderate wet conditions, and under saturated conditions. These experiments reflected field observations during the 2020–2021 ice season. Results of the dry-snow experiments support the dominance of surface scattering from the ice–water interface. However, conditions where layers of wet snow are introduced show that the primary scattering interface changes depending on the location of the wet layer. The addition of a saturated layer at the ice surface results in the highest backscatter values due to the larger dielectric contrast created between the overlying dry snow and the slush layer. Improving the representation of these conditions in SMRT can also aid in more accurate retrievals of lake ice properties such as roughness, which is key for inversion modelling of other properties such as ice thickness.

A hybrid numerical model for coupled hydro-mechanical analysis during CO2 injection into heterogeneous unconventional reservoirs

This paper presents a hybrid numerical model for simulating compressible multiphase multicomponent transport in deformable, heterogeneous unconventional reservoirs. A fully implicit theoretical framework for coupled hydromechanical behaviour is established, in which the flash calculation is avoided. To preserve the local mass conversation, an element based finite volume method is used to spatially discretize mass conversation equations. Linear momentum equilibrium equation is discretized with finite element method. Two numerical approaches are coupled through sharing the same shape functions. An implicit mid-interval backwards difference algorithm is used to handle the temporal derivative. Reliability and efficiency of the developed model have been examined by verification tests against available analytical or numerical solutions. The coupled hydromechanical processes in heterogeneous reservoirs were simulated. The results of numerical experiments show that oscillatory problem for saturation in heterogeneous media can be eliminated, demonstrating better performance of developed model for heterogeneous reservoir simulations. Reservoir performance is significantly shaped by its heterogeneity through controlling fluid flow and time for CO2–CH4 displacement. Simulation results show that the time required for completion of CO2–CH4 displacement increases by about 250 days when considering heterogeneity effect. In conclusion, this study offers a valuable tool for facilitating the development of CO2-enhanced unconventional gas recovery.

The Effect of Smartphones Media to Improve Critical Thinking Skills Student of Elementary School

21st-century learning is oriented towards digital lifestyles. Smartphones are a digital technology that many students already have. Smartphones can be used in learning activities. Smartphones have features that can be used for learning activities. Smartphones can make students more actively engaged in the learning process. This study aims to improve the critical thinking skills of elementary school students through the use of smartphones. This research is a quantitative study using Quasi-experimental method. The research design used is the non-equivalent control group design where the sample consists of 43 students. Research data were collected using a multiple-choice test of critical thinking with 21 questions on the human respiratory system material with 5 aspects of thinking questions. The results showed a significant difference between the experiment and control group. This means that learning using smartphones can improve critical thinking skills. It can be concluded that learning becomes more enjoyable with the use of smartphones because students are given time to ask questions, argue about the results of experiments recorded using smartphones, so that students can improve their critical thinking skills.

Optimal control of a semiclassical Boltzmann equation for charge transport in graphene

An ensemble optimal control problem governed by a semiclassical space-homogeneous Boltzmann equation for charge transport in graphene is formulated and analyzed.The control mechanism is an external time-dependent electric field having the purpose of driving the material density of electrons in graphene to follow a desired trajectory. For this purpose, an ensemble cost functional with quadratic H1 costs is considered.Well-posedness of the control problem is proved, and the characterization of optimal controls by an optimality system is discussed. This system is approximated by a discontinuous Galerkin scheme and solved using a nonlinear conjugate gradient method. Results of numerical experiments are presented that demonstrate the ability of the proposed control framework to design control fields.

EXPRESS CALCULATION OF OXYGEN REGIME IN WASTEWATER FACILITIES

Problem statement. The efficiency of biological water purification depends significantly on the concentration of dissolved oxygen in the volume of the bioreactor. But during the operation of bioreactors, the oxygen regime changes, so it is important to predict the oxygen concentration for different modes of operation of treatment plants. Simplified mathematical models are used to calculate the oxygen regime, but these models are designed for typical bioreactors. An important task is the development of effective multivariate numerical models for evaluating the oxygen regime in reactors of different geometric shapes, which differ from “classical” reactors. The purpose of the article. Development of a CFD model for operational evaluation of the oxygen regime in bioreactors. Methodology. For mathematical modeling of the oxygen regime in the bioreactor, a two-dimensional mass transfer equation is used, which takes into account the place of oxygen supply from the aerator located in the building, the hydrodynamics of the flow in the bioreactor, and the diffusion process. The potential motion model was used to solve the hydrodynamic problem. Numerical integration of modeling equations is carried out by using finite-difference splitting schemes. At each splitting step, the difference equations are solved using explicit schemes. This allows a simple computer implementation of the numerical model. Scientific novelty. A multifactor two-dimensional CFD model was developed, which allows for a quick assessment of the oxygen regime in a bioreactor used for wastewater treatment. Practical significance. The developed CFD model and computer code can be used at the stage of designing or re-engineering bioreactors in order to find the optimal location of aerators in the structure. Conclusions. The software implementation of the developed numerical model was carried out. The results of a computational experiment on the study of the wastewater treatment process in a sedimentation tank are presented.

ASSESSMENT OF THE ENERGY FACILITY IMPACT ON ENVIRONMENTAL POLLUTION

Problem statement. The task of assessing the influence of the boiler house on atmospheric air pollution is under consideration. Carrying out such an assessment is very important when locating boiler houses near residential areas. Since the weather conditions for each region are characterized by a change in the direction of the wind, the intensity of atmospheric diffusion, the appearance of a calm, it is important to have information about the formation of pollution zones in sedimentary zones. To solve this important problem, it is very important to use mathematical modeling, since it is currently impossible to create a sufficient number of observation posts near the objects of the fuel and energy complex. The purpose of the article. Numerical analysis of the influence of the boiler house on the intensity of atmospheric air pollution under different weather conditions. Methodology. The 3D equation of convective-diffusion transport is used to estimate the pollution zones formed in the atmospheric air when it is emitted from the boiler house pipe. The model takes into account atmospheric stratification, impurity emission intensity, wind profile, wind speed. Numerical integration of the equation of convective-diffusion transport of impurities is carried out using finite-difference schemes. The modeling equation is split into an equation for the convective transport of the pollutant and an equation for the dispersion of the pollutant due to atmospheric diffusion. The equation describing the change in the concentration of the impurity in the atmospheric air as a result of the emission of the pollutant from the pipe is solved separately. At the next stage, finite-difference schemes are built that allow solving the splitting equation. Scientific novelty. On the basis of the constructed numerical model, the zones of atmospheric air pollution during the emission of CO from the pipe of the boiler house are determined. A numerical model is proposed that allows forecasting atmospheric air pollution for different weather conditions within the framework of one program package. Practical significance. On the basis of the proposed numerical model, the forecasting of the zones of chemical pollution of the atmospheric air in the settlement zone in which the boiler house is located was carried out. The results of the computational experiment are presented.

Demand-adapted train timetabling with coupling-decoupling operations on a bidirectional intercity railway line

Many factors need to be considered when designing the timetable of intercity railway lines, where the passenger demand is variable and unbalanced along time in both directions of the line. This paper introduces a new multi-objective nonlinear integrated optimization model for designing full-day train timetables incorporating coupling-decoupling operations and rolling stock assignment on a bidirectional railway line. The model explicitly considers train capacity constraints and oversaturation while minimizing the total passenger waiting time and operating costs. To solve the problem, a hybrid algorithm governed by a genetic algorithm is proposed. Before evaluating the fitness of each individual, to complete the definition of each chromosome, a truncated branch and cut algorithm is used to solve a capacity and train unit allocation subproblem. A good-quality initial population is partially generated using the spatiotemporal synchronous coupling algorithm. The proposed approach is applied to a real case using the Shanghai-Hangzhou intercity railway line. The results of different experiments show the effectiveness of the model and algorithm, both in terms of passenger convenience and cost savings.

Temperature-insensitive cascaded micro-ring resonator silicon photoelectric sensor based on wavelength interrogation for HSA detection

Silicon-based optical biosensors have garnered significant interest since their inception. They are, however, susceptible to temperature fluctuations in a controlled environment. In order to address this issue, we provide a solution including the utilization of a silicon-on-insulator (SOI) cascaded micro-ring resonators (CMRR) biosensor that is designed to be insensitive to temperature changes. It consists of two ring resonators exposed to the reference solution and analyte, respectively, their radius and waveguide width are designed to meet the temperature-insensitivity criterion of the CMRR sensor. The results of experiments indicate that by real-time monitoring of the envelope peak drift in the transmission spectrum of the sensor, the bulk refractive index (RI) sensitivity is 2940.71 nm/RIU, the limit of detection (LOD) is 3.1×10−5 RIU, and the temperature sensitivity is less than 40 pm/℃. Furthermore, the sensor was applied for HSA detection, with a calculated LOD of 18.09 ng/mL. The effect of temperature on HSA detection is much smaller than that of a CMRR sensor without athermal design. The sensor demonstrates high sensitivity and temperature insensitivity, and the absence of negative thermo-optic coefficient materials or supplementary temperature control mechanisms simplifies its practical applications. It can be made by a large array to detect different type protein molecules for high throughput multiparameter analysis.

Supervised graph contrastive learning for cancer subtype identification through multi-omics data integration.

Cancer is one of the most deadly diseases in the world. Accurate cancer subtype classification is critical for patient diagnosis, treatment, and prognosis. Ever-increasing multi-omics data describes the characteristics of the patients from different views and serves as complementary information to promote cancer subtype identification. However, omics data generally have different distributions and high dimensions. How to effectively integrate multiple omics data to classify cancer subtypes accurately is a challenge for researchers. This work proposes a method integrating multi-omics data based on supervised graph contrast learning (MCRGCN) to classify cancer subtypes. The method considers the unique feature distribution of each omics data and the interaction of different omics data features to improve the accuracy of cancer subtype classification. To achieve this, MCRGCN first constructs different sample networks based on the multi-omics data of the samples. Then, it puts the omics data and adjacency matrix of the sample into different residual graph convolution models to get multi-omics features of the samples, which are trained with a supervised comparison loss to maintain that the sample features of each omics should be as consistent as possible. Finally, we input the sample features combining multi-omics features into a classifier to obtain the cancer subtypes. We applied MCRGCN to the invasive breast carcinoma (BRCA) and glioblastoma multiforme (GBM) datasets, integrating gene expression, miRNA expression, and DNA methylation data. The results demonstrate that our model is superior to other methods in integrating multi-omics data. Moreover, the results of survival analysis experiments demonstrate that the cancer subtypes identified by our model have significant clinical features. Furthermore, our model can help to identify potential biomarkers and pathways associated with cancer subtypes.

Formulation of a cement-based grout characterizing controllable gelation time and its modification mechanism

Achieving a coordinated balance between pumpability, setting time, and compressive strength is essential for the effective implementation of grouting tasks. This facet also poses a significant challenge in the formulation of cement-based rapid-setting grout. Therefore, precise control over gelation time, while concurrently preserving the pumpability and mechanical strength of cement grout, unquestionably enhances its engineering feasibility. In pursuit of this objective, the current study executed a factorial experiment, assigning alkanolamine, fluoride, aluminum sulfate, and citric acid as experimental factors. Subsequently, through an experiment involving changes to the experimental setup, mathematical predictive models for diverse performance parameters were established to determine the optimal formula. Moreover, to elucidate the hydration process of the proposed grout, a comprehensive investigation was conducted using factor effect analysis, isothermal calorimetry, X-ray diffraction (XRD), and scanning electron microscopy (SEM). Finally, by drawing on the results of the above-mentioned experiments and theoretical analyses, the modification mechanisms of the admixtures on the performance of the matrix were deduced. This paper introduces a novel approach to optimize the overall performance of rapid-setting grout, providing a new material solution for grouting jobs.

Magnetic toroidal dipole resonance terahertz wave biosensor based on all-silicon metasurface

Optical sensing is crucial in many applications and has received widespread attention in recent years. Transmission spectroscopy with narrow linewidth resonances is indispensable for improving metasurface sensing performance. However, the narrower linewidth results in a smaller resonant modulation range, which seriously hinders the application of metasurfaces in sensing detection. We designed an all-silicon metasurface sensor. The structural layer of the metasurface works together with the substrate layer. Multiple magnetic ring dipoles are generated in the structural layer and multiple magnetic dipoles are generated in the substrate layer. The two work together to stimulate sharp Fano resonance, with a quality factor of up to 272.84. Based on the characteristics of metasurface, when applied to sensing and detection, the theoretical sensitivity can reach 59.64 GHz/RIU. Metasurface samples were successfully prepared using photolithography and dry etching. After characterizing the transmission spectrum of the prepared metasurface, the actual quality factor reached 76.8. At the same time, the metasurface sensor was used to detect bovine serum albumin (BSA) solutions of different concentrations. Based on the test results, it is calculated that the detection limit of the metasurface sensor for detecting BSA can reach 0.027 mg/mL, and the detection sensitivity can reach 9.7 GHz/(ng/mm2). The results of theoretical simulation and biological experiments show that the sensor has good sensing performance. This work is of great significance to the research of all-silicon metasurfaces and the application of metasurface sensors.

The preferences of people in Australia to respond and engage with advertisements to promote reproductive health: Results of a discrete choice experiment

ObjectivesThe health of people prior to pregnancy impacts pregnancy outcomes and childhood health, making the preconception period an important time to optimise health behaviours. Low awareness of the importance of this issue is a recognised barrier to achieving good preconception health. Public health messaging can help to address this barrier. MethodsA discrete choice experiment to assess the preferences of people of reproductive age for a health promotion advertisement for preconception health was conducted. Attributes of the advertisement image, title, additional text content and positioning, and the location of advertisement were assessed by fitting a mixed logit model to the choices made. ResultsThree hundred and thirty-four responses were obtained, from people of reproductive age, both planning and not planning a pregnancy, in Australia. Participants placed most importance on the image, and the location in which they saw the advertisement. An image of adult and baby hands was preferred to adult hands only, and healthcare settings were preferred to more general media locations such as advertising online or on public transport. Preference was also given to the advertisement title of “Healthy you, Healthy baby”, closely followed by “Are you ready for pregnancy?”. The location and content of additional text did not significantly impact engagement with the advertisement. ConclusionThe image and title on the advertisement, and the locations in which they are placed were the most significant features to impact engagement with a health promotion advertisement for preconception health. This can inform health promotion efforts for preconception health.