• Energy Research
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A promising technology for renewable energy is energy piles used to heat and cool buildings. In this research, the effects of bio-cementation via microbially induced calcite precipitation (MICP) using mixed calcium and magnesium sources and the addition of fibres on the thermal conductivity of soil were investigated. Firstly, silica sand specimens were treated with cementation solutions containing different ratios of calcium chloride and magnesium chloride to achieve maximum thermal conductivity improvement. Three treatment cycles were provided, and the corresponding thermal conductivity was measured after each cycle. It was found that using 100% calcium chloride resulted in the highest thermal conductivity. This cementation solution was then used to treat bio-cemented soil samples containing fibres, including polyethylene, steel and glass fibres. The fibre contents used included 0.5%, 1.0% and 1.5% of the dry sand mass. The results show that the glass fibre samples yielded the highest thermal conductivity after three treatment cycles, and SEM imaging was used to support the findings. This research suggests that using MICP as a soil improvement technique can also improve the thermal conductivity of soil surrounding energy piles, which has high potential to effectively improve the efficiency of energy piles.

Chromium (Cr) exposure during gestation causes malformations in animal experiments. In this multicenter case-control study, we initially involved 130 orofacial clefts (OFCs) and 260 controls to assess the association between Cr concentration and risk for OFCs. Then, umbilical cord serum (49 vs. 119) and cord tissue (84 vs. 142) were used to validate the association between Cr and OFCs. We found that maternal serum Cr concentrations in OFC cases were significantly higher than those in controls. Compared with the lowest tertile of maternal serum Cr concentration, the highest tertile of Cr increased the risk for OFCs [OR = 2.14 (1.14-4.05)]. In the validation cohort of umbilical cord serum and tissue, higher concentrations of Cr were associated with increased risks for OFCs in a dose-dependent manner (all Ps for trends <0.05). Cr concentrations in maternal serum and cord serum showed a positive correlation. The Cr concentration in cord serum was inversely correlated with egg and milk consumption frequencies, and the Cr concentration in cord tissue was positively associated with indoor coal burning. In conclusion, prenatal Cr exposure is a risk factor for OFCs, and indoor coal burning during pregnancy may be one of the sources of Cr exposure.

Even with the benefits of sustainability and climate change reporting, there is limited information on how municipalities are reporting on performance for external stakeholders in comparison with private sector organizations. The purpose of this research was to gain an understanding of the current state of sustainability and climate change reporting at the local level and to investigate the extent to which municipalities across Ontario, Canada, report. We used content analysis to identify the presence or non-presence of information on the websites of 38 municipalities and analyzed the results using descriptive statistics. Our analysis showed that the sample municipalities were not widely reporting on sustainability or climate change performance. Also, we identified a gap between the number of plans and reports produced by sample municipalities, with the latter being less common, indicating a need for an improved evaluation of plan implementation. Further, we found that a provincial regulation that required municipalities to make their energy conservation and demand management plans public did not guarantee publication of the plan on a municipality’s website. This study contributes to the growing field of sustainability and climate change planning and reporting by local governments and offers empirical evidence specific to Ontario, Canada.

Pedestrian inertial navigation technology plays an important role in indoor positioning technology. However, low-cost inertial sensors in smart devices are affected by bias and noise, resulting in rapidly increasing and accumulating errors when integrating double acceleration to obtain displacement. The data-driven class of pedestrian inertial navigation algorithms can reduce sensor bias and noise in IMU data by learning motion-related features through deep neural networks. Inspired by the RoNIN algorithm, this paper proposes a data-driven class algorithm, RBCN-Net. Firstly, the algorithm adds NAM and CBAM attention modules to the residual network ResNet18 to enhance the learning ability of the network for channel and spatial features. Adding the BiLSTM module can enhance the network’s ability to learn over long distances. Secondly, we construct a dataset VOIMU containing IMU data and ground truth trajectories based on visual inertial odometry (total distance of 18.53 km and total time of 5.65 h). Finally, the present algorithm is compared with CNN, LSTM, ResNet18 and ResNet50 networks in VOIMU dataset for experiments. The experimental results show that the RMSE values of RBCN-Net are reduced by 6.906, 2.726, 1.495 and 0.677, respectively, compared with the above networks, proving that the algorithm effectively improves the accuracy of pedestrian navigation.

We researched China's climate and sustainable development goal with relevant and susceptible instruments capable of inducing and mitigating carbon emissions. Amidst the contributor to the global carbon emissions, China is caught in between mitigating its carbon emission and aiming towards placing its national contribution of emissions to the acceptable levels of 1.5 °C and below 2 °C. Following the intricacies surrounding China's sustainable development as it contains its economic and environmental performance, we adopt China's data of 1980 and 2018 with different scientific approaches (nonlinear autoregressive distributed lag (NARDL), dynamic ordinary least square test, and bootstrap Granger causality) with different instruments (such as economic growth, financial development, renewable energy, and innovation policies) to research China's sustainable development. For clear exposition and insight into our findings with policies attached, we draw a conclusion from the outcomes of the mentioned approaches. From NARDL and dynamic ordinary least squares (DOLS), we find that economic growth through economic activities is statistically significant in determining the trend (increase) of carbon emissions in China in both periods (short run and long run). However, other selected instruments (financial, renewable, and innovation policies) tend towards controlling and moderating the carbon emissions in China. Thus, China has good prospects to mitigate its carbon emissions if considered tailoring its policies towards favorable instruments. From bootstrap Granger causality, we find similar inferential results that support previous findings thereby confirming the positive implication of the selected instruments to China's sustainable development. Hence, the nexus that is established among the selected instruments clearly show the importance of technological innovation and renewable energy in mitigating carbon emissions.

The global energy demand is increasing due to climate changes and carbon usages. Accumulating evidences showed energy sources using offshore wind from the sea can be added to increase our consumption capacity in long term. In addition, building offshore wind farms can also be environmentally advantageous compared to onshore farms. The assessment of wind energy resources is crucial for the site selection of wind farms. Currently, short-term wind forecast models have been developed to predict the wind power generation. However, methods are needed to improve the forecasting accuracy for ever-changing weather data. So, we try to use deep learning methods to predict long-term wind energy for identifying potential offshore wind farms. The experimental results indicate that PredRNN++ prediction model designed from the spatiotemporal perspective is feasible to evaluate long-term wind energy resources and has better performance than traditional LSTM.

Fetkovich or Blasingame type rate decline analysis is a common and practical method to obtain reservoir parameters and evaluate well productivity. Pseudo-steady-state constant is an indispensable parameter for establishing these new type rate decline curves and works as a bridge linking conventional productivity and new type productivity. Refracturing is widely used to enhance tight oil wells’ productivity and improve their economic benefits, the pseudo-steady-state constant of refracturing horizontal wells has been presented in our previous research, but an in-depth discussion on the definition, accuracy, sensitivity, and application of this constant has not been conducted. It results in the insufficient understanding of the physical meaning, characteristics, and functions of pseudo-steady-state constant at present. In this study, taking the derived pseudo-steady-state constant for refracturing horizontal wells with fracture reorientation as an example, its accuracy was verified by an equivalent model presented in the literature, and the sensitivity of relevant key parameters on this constant was investigated. For the refracturing horizontal well defined in this study, pseudo-steady-state constant is independent of time, and related to fracture conductivity, fracture face damage, reorientation fracture number and permeability anisotropy. Results show that this constant decreases with the increase of fracture conductivity, but tends to remain unchanged when fracture conductivity increases to a certain extent. Meanwhile, this constant shows a positive correlation with fracture face damage and permeability anisotropy, but an inverse correlation with reorientation fracture number. Blasingame type rate decline curves of refracturing horizontal wells with fracture reorientation were also established, regarding as a practical application of this pseudo-steady-state constant and a concrete manifestation of its bridge-linking function. These type curves are directly conducive to the inversion of reservoir properties and fracturing parameters and the prediction of future productivity for refracturing horizontal wells. More importantly, this study is helpful to understand and strengthen the role and importance of pseudo-steady-state constant, and also beneficial to the establishment of new type rate decline curves of other similar models.

The surface morphology of rough fractures significantly affects the fluid flow and heat transfer characteristics in the fractures. A thermal-flow coupling model with specific geometric fractures was established to investigate the influence of surface morphology on the heat transfer characteristics of a single fracture. The effect of temperature on the physical properties of rocks and fluids was included in the study to reflect the actual situation more realistically. The research results show that the temperature of the fluid in the rough fracture is nonlinearly distributed along the flow direction and the higher the flow velocity, the higher the heat transfer efficiency. The fracture surface morphology has a significant impact on the heat transfer characteristics, and the surface fluctuation will greatly affect the flow velocity, causing the fluid temperature to change abruptly at the fracture surface. Under the same flow rate, with the increase of the fluctuation degree of the fracture surface and the fluctuation frequency, the larger the heat exchange area of the fracture surface, the stronger the heat exchange performance. The heat transfer efficiency of the fracture is directly related to the heat transfer area of the fracture, so even with the same permeability, the heat transfer performance of fractures with different surface topography is different.