Search

Metallic Mg is an important strategic metal and its properties are greatly affected by impurities. Silicothermic reduction and electrolysis are the most used approaches to prepare metallic Mg. The products of these processes need to be further refined to obtain high-purity Mg metal. However, previous research has mainly focused on refining the crude Mg (CM) produced via silicothermic reduction, whereas no in-depth investigations have been conducted on refining the CM produced via electrolysis. Here, vacuum distillation was used to refine electrolytically produced CM. The content and morphological characteristics of the impurity elements in CM were studied via glow discharge mass spectrometry, mineral dissociation analysis, and electron probe microanalysis. The effect of different distillation temperatures and times on the quality of the refined Mg was investigated. The results show that the main impurity elements are Al, Fe, Si, Ti, Cr, S, Cl, and Ni. The content of impurities, such as Si, Al, Fe, Ni, Ti, and Cr, in the refined Mg is significantly reduced at a temperature of 1023 K and a time of 120 min, and the purity of the refined Mg reaches 99.99%, which meets the Mg9999 national standard for primary Mg ingots in China (GB/3499-2011).

In construction planning, decision making has a great impact on final project performance. Hence, it is essential for project managers to assess the construction planning and make informed decisions. However, disproportionately large uncertainties occur during the construction planning stage; in the worst case, reliable probability distributions of uncertainties are sometimes unavailable due to a lack of information before construction implementation. This situation constitutes a deep uncertainty problem, making it a challenge to perform a probability-based uncertainty assessment. The current study proposes a modeling approach that applies prediction intervals for construction planning via the integration of discrete-event simulation (DES), fuzzy C-means clustering (FCM), Bayesian regularization backpropagation neural networks (BRBNNs), and particle swarm optimization (PSO). The DES is used to perform data sampling of the construction alternatives and assess their performances under uncertainty. Based on the generated samples, the FCM, BRBNN, and PSO are integrated in a machine learning algorithm to model the prediction intervals that represent relationships between construction planning schemes, performances, and the corresponding uncertainties. The proposed approach was applied to a case project, with the results indicating that it is capable of modeling construction performance and deep uncertainties with a defined 95% confidence level and fluctuation within 1~9%. The presented research contributes a new and innovative option, using prediction intervals to solve deep uncertainty problems, without relying on the probability of the uncertainty. This study demonstrates the effectiveness of the proposed approach in construction planning.

Understanding the characteristics of the traditional village distribution contributes to the formulation of relevant protection and development strategies. We adopted a series of spatial analysis methods to investigate the characteristics of the traditional village distribution in China by using the watershed as the research unit. Moreover, we conducted quantitative and qualitative analyses of the relevant influencing factors affecting the distribution pattern using Geodetector and mathematical statistics. The findings indicate that traditional villages are distributed unevenly across watershed units. High–High clusters tend to occur at the boundaries of first-level watersheds. Traditional villages have a clear agglomeration trend in space, with a concentrated and contiguous distribution pattern based on the “core density area–ring-core expansion group–belt area”. The key factors affecting the traditional village distribution are annual precipitation, annual average temperature, and river density. The traditional village number has a clear inverted U-shaped relationship with the annual average temperature, river density, and road density. The study reveals the complex and various characteristics of the traditional village distribution and its influence mechanism and offers scientific advice for traditional villages’ future protection and development.

The building performance simulation (BPS) based on physical models is a popular method to estimate the expected energy-savings of energy-efficient building retrofitting. However, many buildings, especially the older building constructed several decades ago, do not have full access to complete information for a BPS method. Incomplete information generally comes from the information that is missing, such as the U-value of part building components, due to incomplete documentation or component deterioration over time. It also comes from the case-specific incomplete information due to different documentation systems. Motivated by the available big data of real-life building performance datasets (BPDs), a data-driven approach is proposed to support the decision-making of building retrofitting selections under incomplete information conditions. The data-driven approach constructed a Performance Modelling with Data Imputation (PMDI) with integrated backpropagation neural networks, fuzzy C-means clustering, principal component analysis, and trimmed scores regression. An empirical study was conducted on real-life buildings in Sweden, and the results validated that the PMDI method can model the performance ranges of energy-efficient retrofitting for family house buildings with more than 90% confidence. For a target building in Stockholm, the suggested retrofitting measure is expected to save energy by 12,017~17,292 KWh/year.

The current research focuses on the mechanism of the surfactant polyethylene glycol (PEG) in the preparation of magnesium hydroxide by electrolysis of a salt lake bischite aqueous solution. The samples were analyzed by a scanning electron microscope (SEM), X-ray diffraction (XRD), Fourier transform infrared spectrometer (FT-IR) and laser particle size analyzer. The characterization results show that PEG plays an important role in adjusting the growth mode and morphology of Mg(OH)2 crystals. The ether group of the PEG molecular chain and the hydroxyl group of Mg(OH)2 can be combined by a hydrogen bond, which provides a "template" for the growth of Mg(OH)2. At the same time, the difference in growth mode and morphology will also affect the economic performance of electrolytic reactions. When the PEG content reaches 0.4 g/L, the particle size of the product is uniform, which can well reduce the polarization of the electrode plate. The cell potential of electrolytic reaction is small, and the economic benefit is high. When the content of PEG is low, it has a low impact on the product and the economic benefits of electrolytic reaction. When the PEG content is higher than 0.4 g/L, the electrode reaction is hindered, resulting in an increase in cell potential.

Individual identification plays an important part in disease prevention and control, traceability of meat products, and improvement of agricultural false insurance claims. Automatic and accurate detection of cattle face is prior to individual identification and facial expression recognition based on image analysis technology. This paper evaluated the possibility of the cutting-edge object detection algorithm, RetinaNet, performing multi-view cattle face detection in housing farms with fluctuating illumination, overlapping, and occlusion. Seven different pretrained CNN models (ResNet 50, ResNet 101, ResNet 152, VGG 16, VGG 19, Densenet 121 and Densenet 169) were fine-tuned by transfer learning and re-trained on the dataset in the paper. Experimental results showed that RetinaNet incorporating the ResNet 50 was superior in accuracy and speed through performance evaluation, which yielded an average precision score of 99.8% and an average processing time of 0.0438 s per image. Compared with the typical competing algorithms, the proposed method was preferable for cattle face detection, especially in particularly challenging scenarios. This research work demonstrated the potential of artificial intelligence towards the incorporation of computer vision systems for individual identification and other animal welfare improvements.

Transit-oriented development (TOD) has been recognised as a sustainable planning approach and that is typically designed for a whole city. Individual land use characteristics and the causations have often been ignored. Therefore, the primary objective of this study was to explore the factors that influence the land use catchment area (LCA) characteristics at a station neighborhood level. First, it contributes a methodology to measure the LCA by introducing a new concept. The density gradient was introduced to generate the scale and compactness degree of each station. Second, it provides a theoretical framework for understanding the causes of different LCAs. The partial least squares (PLS) regression model was employed to explore the accessibility effects. By analysing density gradient curves, it reveals that stations grew to fit the negative exponential function. Regarding the scale and form degree of LCAs, the impact of accessibility before and after a station construction have been corroborated. Moreover, the effects of facilities function before construction, distance from main roads, and elevated stations have been emphasized. The results provide support for a more sophisticated concept of catchment area relating to land use at the level of an individual TOD station, while shedding light on the benefits of those engaged in the future design of TOD with due consideration of the local physical environments.

Construction contractors play a vital role in reducing the environmental impacts during the construction phase. To mitigate these impacts, contractors need to develop environmentally friendly plans that have optimal equipment, materials and labour configurations. However, construction plans with optimal environment may negatively affect the project cost and duration, resulting in dilemma for contractors on adopting low impacts plans. Moreover, the enumeration method that is usually used needs to assess and compare the performances of a great deal of scenarios, which seems to be time consuming for complicated projects with numerous scenarios. This study therefore developed an integrated method to efficiently provide contractors with plans having optimal environment–cost–time performances. Discrete-event simulation (DES) and particle swarm optimisation algorithms (PSO) are integrated through an iterative loop, which remarkably reduces the efforts on optimal scenarios searching. In the integrated method, the simulation module can model the construction equipment and materials consumption, the assessment module can evaluate multi-objective performances, and the optimisation module fast converges on optimal solutions. A prototype is developed and implemented in a hotel building construction. Results show that the proposed method greatly reduced the times of simulation compared with enumeration method. It provides the contractor with a trade-off solution that can average reduce 26.9% of environmental impact, 19.7% of construction cost, and 10.2% of project duration. The method provides contractors with an efficient and practical decision support tool for environmentally friendly planning.

As an important component of urban construction, the community sustainable construction is of great significance to the social development. Despite the importance of the social dimension of community sustainability, it has received relatively little attention when compared to the sustainability of environmental and economic dimensions. Based on the literature review, it is found that the social dimension of the sustainable community, such as residents’ psychological feelings, social capital, has a strong effect on the sustainability of communities. This paper, therefore, conducts an empirical study on the mediating role of emotion experience on the associations between social capital and life satisfaction. Research on the relationship between them can help to generate synergies and strengthen the effect of promoting community sustainable development. A total of 229 complete responses from residents in China is used for data analysis. Results from the multiple linear regression analysis indicate that social capital has the significant positive effect on life satisfaction, the influence of social capital on life satisfaction is partly mediated by positive and negative affect, the social capital improves the life satisfaction through promoting positive affect, while it enhances the life satisfaction through restraining the negative affect. Based on the relationship between them, the relevant recommendations for the measures on the sustainable community can be proposed.