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In this paper, energy-preserving schemes based upon the discrete gradient are used to numerically solve the semilinear wave equation under periodic boundary conditions, Dirichlet boundary conditions and Neumann boundary conditions. Both the integral form and the evolutionary behaviour of the energy depend on the associated boundary conditions. The wave equations are semi-discretized in different manners for different boundary conditions such that the Hamiltonian functions of the resulting semi-discrete Hamiltonian systems are the discrete analogue of the original energy. Furthermore, the Hamiltonian functions of the resulting semi-discrete systems have similar evolutionary behaviours as the original energy. It is noted that the semi-discrete system exhibits an oscillatory structure. Subsequently, the semi-discrete approximation of the energy evolution as well as the oscillatory structure is passed to the fully-discrete problem by applying extended discrete gradient formula in the temporal direction. Numerical experiments are carried out to show the effectiveness of the new schemes. [ABSTRACT FROM AUTHOR]

This paper presents the rationale for incorporating engineering design into project-based laboratory learning. To ensure an effective and efficient pedagogy for the new laboratory format, we placed the emphasis of the pedagogical framework on constructivist learning for deep laboratory learning, and integrated experiential learning cycle with cyclic engineering design to formulate a sequential instruction and formative assessment methodology. The implementation of the pedagogy was exemplified using a case study of a concrete distillation design consisting of conceptualizing the design, reasoning the adequacy and experiment-based validation of the design correlations, and verifying the final design as per experimental observations. The impact of the novel lab format on student learning experience was surveyed and compared to that of a traditional laboratory. The survey results revealed that the project-based laboratory with design resulted in an improved learning experience in addressing high-level learning outcomes and engineering skills. Evidence of the survey also suggested that the sequential instruction and formative assessment methodology was effective with every stage of the experiential learning and formative assessment essential for the successful and efficient implementation of the project-based laboratory learning. • Project-based laboratory (PBL) with design promotes deep laboratory learning. • Integration of experiential learning with design for an effective instruction pedagogy. • A practical distillation design case exemplifies implementation of the novel pedagogy. • PBL with design improves students' learning experience in laboratory learning. [ABSTRACT FROM AUTHOR]

A novel superhydrophobic electro-conductive coating with liquid metal (Ga 90 In 10 ) and ZnO nano-petals is fabricated by integrating the methods of spraying, crystal growth and low surface energy modification. The results indicate that the paper-based surface could be superhydrophobic in cold environment and successfully realize uni-directional ice driving with the help of thermal gradient. We anticipate that the combination of excellent superhydrophobic performance of the composite structure with inherent conductive advantage of liquid metal will find a vast application prospect. [ABSTRACT FROM AUTHOR]

• Paper proposed traffic prediction based on vehicle detection from aerial images. • Predictors (road characteristics and image timestamps) were captured from aerial images. • The model proposes a coefficient of transformation (day to year) for image-based counts. • Predicted Image-based AADTs are close to the observed traffic counts. Around the world, metropolitan areas invest in infrastructure for traffic data collection, albeit focusing on highway networks, thus limiting the amount of data available on inner-city roads. For this purpose, various modelling techniques have been developed to interpolate traffic counts spatially and temporally across an entire road network. However, the predictive accuracy of these models depends on the quality and coverage of traffic count data. In this study, we extend the power of spatio-temporal interpolation models with vehicle detection from aerial images, developing a new approach to estimate Annual Average Daily Traffic (AADT) across all roads in an urban area. Using Google aerial images, we extracted the number of vehicles on a road segment and treated these values as observed traffic counts collected over a short period of time. This information was used as input and merged with traffic count data at stations with longer record lengths to predict traffic on all urban roads. This approach was compared against a hold-out sample of roads with observed traffic count data and images, indicating an R-squared (R2) = 90% and RMSE = 7675 between predicted and observed daily traffic counts and R2 = 58% and RMSE = 18918 between observed and predicted AADT. The higher prediction accuracy for daily traffic indicates the power of the proposed method for predicting daily values from images; while the lower accuracy of AADT prediction stresses the need for longer-term data to achieve accurate annual averages based on counts derived from images. [ABSTRACT FROM AUTHOR]

Abstract: Increasing evidences have shown that dissolved organic components are responsible for the significant C and N exports from terrestrial ecosystems to the surrounding aquatic ecosystems and very sensitive to CO2 enrichment. However, there is still a lack of direct evidence about CO2-led effects on these components at the ecosystem scale, especially in wetlands. We, therefore, simultaneously investigated the contents of dissolved organic carbon (DOC) and dissolved nitrogen (DN) in the surface water and soil layer in a paddy field under FACE facility in Eastern China. Elevated CO2 significantly increased the contents of DOC and DN in the surface water by 18.0% and 14.3% on average. Elevated CO2 also increased DOC content in the soil, but decreased DN content. The contents of DOC and DN in the soil–water interface of 0–1cm soil layer were on average 22.4% and 47.5% higher than in the 5–15cm soil layer. Besides, significant higher DOC and DN contents existed in the soil porewater than in the surface water. Due to multiple drainage regime and rainstorm-induced runoffs in rice cropping regions, CO2-led DOC and DN increments in the surface water may increase C and N exports from paddies to the surrounding aquatic ecosystems under future climate patterns. [Copyright &y& Elsevier]

Dispersion modelling is an effective tool to estimate traffic-related fine particulate matter (PM 2.5) concentrations in near-road environments. However, many sources of uncertainty and variability are associated with the process of near-road dispersion modelling, which renders a single-number estimate of concentration a poor indicator of near-road air quality. In this study, we propose an integrated traffic-emission-dispersion modelling chain that incorporates several major sources of uncertainty. Our approach generates PM 2.5 probability distributions capturing the uncertainty in emissions and meteorological conditions. Traffic PM 2.5 emissions from 7 a.m. to 6 p.m. were estimated at 3400 ± 117 g. Modelled PM 2.5 levels were validated against measurements along a major arterial road in Toronto, Canada. We observe large overlapping areas between modelled and measured PM 2.5 distributions at all locations along the road, indicating a high likelihood that the model can reproduce measured concentrations. A policy scenario expressing the impact of reductions in truck emissions revealed that a 30% reduction in near-road PM 2.5 concentrations can be achieved by upgrading close to 55% of the current trucks circulating along the corridor. A speed limit reduction of 10 km/h could lead to statistically significant increases in PM 2.5 concentrations at twelve out of the eighteen locations. [Display omitted] • A traffic-emission-dispersion model is developed within the R-LINE framework. • Trucks emit half of traffic PM 2.5 and contribute most to emission uncertainty. • Modelled PM 2.5 levels match measurements at locations least affected by background. • Traffic variability is the main driver of uncertainty. [ABSTRACT FROM AUTHOR]

This article proposes a new integral evolution formula of boundary value problem for wave equations of the form u t t (x , t) + L (x , D) u (x , t) = f (x , t). By introducing the operator functions, e.g., ϕ -functions, and using the Duhamel's principle, a compact integral evolution formula is established for inhomogeneous wave equations. The derivation is based on Duhamel's principle and the theory of operational calculus. [ABSTRACT FROM AUTHOR]

• A two-step modal emission model for HEVs was developed using real-driving emissions. • The HEV can achieve over 50% CO 2 and CO emission reduction in on-road tests. • The model was applied to large real-driving datasets from Toronto and Beijing. • Emission reductions were lower for Toronto, which has more aggressive driving. • The study highlights the conditions where HEV can yield the most emission benefits. This study tested exhaust emissions and fuel consumption for a hybrid electric vehicle (HEV) in real-world conditions using a portable emissions measurement system (PEMS). A gradient boosting model was developed to predict the electric motor's operation and emissions using only vehicle kinematic data. The model was applied to estimate the potential emission reductions that would be achieved with HEVs compared to conventional vehicles, using two large real-driving activity datasets collected in Greater Toronto and Metropolitan Beijing. The emission reductions estimated for Toronto were 21.6%, 31.3%, and 53.0% for CO 2 , CO, and NO x , and 41.0%, 28.9%, and 68.5% for Beijing. We observed higher emission reductions for CO 2 and NO x under low power demand vehicle operations, which occur more frequently in Beijing, while more aggressive driving was noted in Toronto, leading to smaller estimated benefits of HEVs. Compared to previous studies, our explainable gradient boosting model improved prediction accuracy and robustness substantially by achieving an average Pearson correlation of 0.741 from cross-validation. This study goes beyond an analysis of HEV emissions from engine and motor operations by applying real-world driving data from two large metropolitan areas to the model. By doing so, a novel investigation of the traffic situations, roads, and driving behaviours that yield the highest emission benefits for HEVs was conducted. [ABSTRACT FROM AUTHOR]

• Logic NOT and AND gates with a footprint of 1.2 × 1.2 μm2 is designed. • The respond time of our devices is less than 0.25 ps. • The devices keep functional when pixel side length varies from 112 to 125 nm. • Both gates have good tolerance for Input phase difference. We have designed high-speed ultra-compact all-optical NOT and AND gates operating at 1550 nm on silicon-on-insulator (SOI) by a multi-objetice particle swarm optimized inverse-design method. The two gates have a similar four-port structure based on an extremely small area of 1. 2 × 1. 2 µm2. The ultra-small size of device leads to a short respond time less than 0.25 ps. Benefiting from the global optimization of our inverse-design method, our devices have a good tolerance on phase changes of input light. The not gate could keep working while the phase difference of input ranges from 0 to 0.3 π. The situation for AND gate is −0.25 to 0.75 π. Also the maximum contrast ratio is 7.16 dB for the NOT gate and 3.98 for the AND gate, respectively. Moreover, we demonstrate that the design method could have tolerance for small changes in device geometry, which means that our logic gates could remain functional while the pixel side length ranges from 112 to 125 nm. The tiny size, fast response and high robustness make our devices promising for future photonic-integrated circuits. [ABSTRACT FROM AUTHOR]

[Display omitted] • A year-long campaign measured ultrafine particle and black carbon concentrations. • Land use regression models developed using land use parameters. • Deep learning models developed using digital satellite view images. • Combining land use regression and deep learning models resulted in best performance. • Residential instability was associated with elevated pollution concentrations. Concentrations of outdoor ultrafine particles (UFP; <0.1 µm) and black carbon (BC) can vary greatly within cities and long-term exposures to these pollutants have been associated with a variety of adverse health outcomes. This study integrated multiple approaches to develop new models to estimate within-city spatial variations in annual median (i.e. average) outdoor UFP and BC concentrations as well as mean UFP size in Canada's two largest cities, Montreal and Toronto. We conducted year-long mobile monitoring campaigns in each city that included evenings and weekends. We developed generalized additive models trained on land use parameters and deep Convolutional Neural Network (CNN) models trained on satellite-view images. Using predictions from these models, we developed final combined models. In Toronto, the median observed UFP concentration, UFP size, and BC concentration values were 16,172pt/cm3, 33.7 nm, and 1225 ng/m3, respectively. In Montreal, the median observed UFP concentration, UFP size, and BC concentration values were 14,702pt/cm3, 29.7 nm, and 1060 ng/m3, respectively. For all pollutants in both cities, the proportion of spatial variation explained (i.e., R2) was slightly greater (1–2 percentage points) for the combined models than the generalized additive models and a greater (approximately 10 percentage points) than the deep CNN models. The Toronto combined model R2 values in the test set were 0.73, 0.55, and 0.61 for UFP concentrations, UFP size, and BC concentration, respectively. The Montreal combined model R2 values were 0.60, 0.49, and 0.60 for UFP concentration, UFP size, and BC concentration models respectively. For each pollutant, predictions from the combined, deep CNN, and generalized additive models were highly correlated with each other and differences between models were explored in sensitivity analyses. Predictions from these models are available to support future epidemiological research examining long-term health impacts of outdoor UFPs and BC. [ABSTRACT FROM AUTHOR]

Significant efforts have been made to assess the impact of tillage regimes on crop yields and/or greenhouse gas (GHG) emissions across single crop growing season. However, few studies have quantified the impact across a whole rotation cycle in multiple cropping systems. Utilizing on a long-term tillage experiment with the rice–wheat rotation system in East China, we examined the GHG emissions under different tillage practices with or without crop straw incorporation. Results showed that compared to the no-straw control, straw incorporation increased wheat yield by 28.3% ( P < 0.05), irrespective of tillage practices, but had no significant effect on rice yield. Although straw incorporation did not significantly affect CH 4 emissions during the wheat season and N 2 O emissions during the whole rice–wheat cycle, it significantly stimulated CH 4 emissions by 98.8% ( P < 0.01) during the rice season. Also, there were no significant differences in CH 4 and N 2 O emissions between tillage practices during the wheat season. Compared to plowing, rotary tillage increased CH 4 emissions significantly by an average of 38.8% ( P < 0.01) but had no significant impacts on N 2 O emissions during the rice season. Across the rotation cycle, annual yield-scaled global warming potential of CH 4 and N 2 O emissions under no-tillage plus rotary tillage was 26.8% ( P < 0.01) greater than that of rotary tillage plus plowing with or without straw incorporation. Significant interactions between soil tillage and straw management practices were found on annual GHG emissions, but not on crop yields. Together, these results indicate that plowing in the rice season plus rotary tillage in the wheat season may reduce GHG emissions while increasing crop yield in rice–wheat cropping areas. [ABSTRACT FROM AUTHOR]

Studies on greenhouse gas (GHG) emissions from paddy field have primarily focused on the post-transplanting period, however, recent researches raise new concerns about GHGs emission from rice nursery. In this study, CH 4 and N 2 O fluxes were determined from different nurseries under major rice cropping systems in China. The tested nurseries included flooded nursery (FN), moist nursery (MN) and dry nursery (DN). Methane emissions from FN were significantly higher than those from MN and DN under all the rice cropping systems. When comparing with FN, MN decreased total CH 4 emissions by 74.2%, 72.1% and 49.6% under the rice–upland rotation cropping system (RUR), and the double rice cropping system for the early rice (EDR) and the late rice (LDR), respectively. DN decreased CH 4 emissions by 99.2%, 92.0%, 99.0% and 78.6% compared to FN under the single rice cropping system (SR), RUR, EDR and LDR, respectively. When comparing with FN, MN and DN increased N 2 O emissions by 58.1–134.1% and 28.2–332.7%, respectively. Ultimately, compared with FN across the cropping systems, MN and DN decreased net global warming potentials (GWPs) of CH 4 and N 2 O by 33–68% and 43–86%, respectively. The mitigating effect of MN and DN on total GWPs varied greatly across the systems, ranging from 30.8% in the LDR to 86.5% in the SR. Chinese actual emission from rice nurseries was reduced to 956.66 × 10 3 t CO 2 eq from the theoretical estimate of 2242.59 × 10 3 t CO 2 eq if under the flooded nursery scenario in 2012. Taking into account the large rice nursery area (2032.52 × 10 3 ha) in China, the results of this study clearly indicate the importance to estimate and mitigate GHGs emission from flooded rice nursery. Being effective to reduce GHG emissions and increase rice yield, dry nursery technique is a promising candidate for climate smart rice cropping. [ABSTRACT FROM AUTHOR]

Kitchen wastes (KW) have been widely investigated for bio-ethanol production, while no study utilizes KW as ethanol source to stimulate the methanogenic communities to perform direct interspecies electron transfer (DIET), since the excess acidity contained after the biological ethanol-type fermentation pretreatment (BEFP) can seriously inhibit the DIET-based syntrophic metabolism. In this study, a strategy that utilized waste activated sludge (WAS) as co-substrate to relieve the excess acidity after BEFP during anaerobic co-digestion (AcoD) was proposed. The results showed that, under the mixed ratio of 1:2 and 1:5 (KW:WAS, volume ratio), both methane production and organic compound removal evidently increased, compared with that treating the sole WAS. Conversely, under the other mixed ratios (sole KW, 5:1, 2:1 and 1:1), no methane but the evident hydrogen production was detected, and syntrophic metabolism of organic acids and alcohols was prevented. Three-dimensional excitation emission matrix (3D-EEM) analysis showed that the protein-like organic compounds contained in both KW and WAS were effectively degraded. Furthermore, the maximum methane production potential from WAS during AcoD (260.5 ± 4.1 and 264.3 ± 2.7 mL/g-COD) was higher than that treating sole WAS (250.8 ± 0.1 mL/g-COD). Microbial community analysis showed that, some genera capable of metabolizing the complex organic compounds with the reduction of the elemental sulfur or equipped with the electrically conductive pili were specially enriched during AcoD under the mixed ratio of 1:2 and 1:5. They might proceed DIET with methanogens, such as Methanosarcina and Methanospirillum species, to maintain the syntrophic metabolism effective and stable, since the abundance of both Methanosarcina and Methanospirillum species evidently increased. Image 1 • Kitchen wastes (KW) as bio-ethanol source were used to establish DIET. • Waste activated sludge (WAS) relieved excess acidity in KW after ethanol production. • Degradation of both KW and WAS was enhanced during anaerobic co-digestion. • Some sulfur-reducing genera and genera equipped with e-pili were specially enriched. • Abundance of both Methanosarcina and Methanospirillum significantly increased. [ABSTRACT FROM AUTHOR]