Due to reducing the reliance of buildings on fossil fuels, Passive House (PH) is receiving more and more attention. It is important that integrated optimization of passive performance by considering energy demand, cost and thermal comfort. This paper proposed a set three-stage multi-objective optimization method that combines redundancy analysis (RDA), Gradient Boosted Decision Trees (GBDT) and Non-dominated sorting genetic algorithm (NSGA-II) for PH design. The method has strong engineering applicability, by reducing the model complexity and improving efficiency. Among then, the GBDT algorithm was first applied to the passive performance optimization of buildings, which is used to build meta-models of building performance. Compared with the commonly used meta-model, the proposed models demonstrate superior robustness with the standard deviation at 0.048. The optimization results show that the energy-saving rate is about 88.2% and the improvement of thermal comfort is about 37.8% as compared to the base-case building. The economic analysis, the payback period were used to integrate initial investment and operating costs, the minimum payback period and uncomfortable level of Pareto frontier solution are 0.48 years and 13.1%, respectively. This study provides the architects rich and valuable information about the effects of the parameters on the different building performance.

Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into “meta-features” to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%–31.6% for Case A and 16.2%–49.4% for Case B), generalization (improvement of 6.7%–29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%–34.1% for Case A and 1.8%–19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models.

Adjustment strategies including window ventilation and shading have important improvements in energy consumption, thermal and light environments, furthermore, the upper limit for improvement is affected by design parameters. However, studies incorporating adjustment strategies in the building design process are very limited. To address this research gap, we explore the effects of window ventilation and shading on building design performance from uncertainty analysis, sensitivity analysis, and multi-objective optimization. Furthermore, China's typical climate zones are compared given climate effects. Results indicate that (1) the uncertainty of total energy demand in the severe cold climate is most affected with the uncertainty increase rate being 32.0%, the uncertainty of thermal comfort ratio in the hot summer and cold winter climate and the hot summer and warm winter climate is most affected with the uncertainty increase rate being 16.3% and 14.0%, respectively. (2) the sensitivity analysis of the thermal comfort ratio is more sensitive to adjustment strategies than to total energy demand. The severe cold climate is more vulnerable than in other climates. (3) when multi-objective optimization is performed with maximum thermal comfort and minimum total energy demand when considering adjustment strategies, the severe cold climate has the greatest energy-saving potential (38.1%) and the hot summer and cold winter climate has the largest potential to improve thermal comfort (17.6%). More importantly, the light environment is within the comfort range from the daylight glare index, the illuminance, and illuminance uniformity ratios.

Evaluation of actual zero energy buildings (ZEBs) performance and identification of its regional characteristics are of great significance for similar future projects. Based on more than 400 cases in cold regions, this study compared the post-evaluation and drivers of ZEBs from China, the US and the European Union (EU). Results found that ZEB definition, energy drivers, standard, regional policies, technology adoption and their adoption ratio determine the energy performance of cases. Not all EU and China cases reach the net-zero energy target; however, most ZEBs in the US do. ZEBs in cold regions adopted multiple technologies to achieve high energy-efficiency. The adoption ratio of passive technologies is higher than that of active technologies, especially in China. The active technologies in cold regions are mainly seen in the application of advanced HVAC systems. Such application of renewable technologies exhibits strong regional characteristics; for example, the EU's carbon emission reduction policies promoted the use of biomass-based technologies. The analysis also found that energy policies greatly enhance the development of ZEBs. Overall, achieving zero energy is a process, not an endpoint and many projects need further refinement after occupancy.

In recent decades, environmental problems have enforced designers to estimate the level of environmental emission of building design and reduce their environmental impact. On the premise of ensuring indoor comfort, the cost-effectiveness of solutions for reducing the building's greenhouse gas has become a critical issue. Based on the Life Cycle Assessment (LCA), this paper establishes a building performance trade-off framework for indoor thermal comfort, economics, and environmental implication. This framework consists of four parts: the establishment of the optimization model; sensitivity analysis; obtain of Pareto frontier solutions, and decision-making analysis. Optimization variables involve envelope type and some envelope physical parameters. The “design variables-building performances” database is obtained by using building simulation software combined with the Latin hypercube sampling algorithm. Sensitivity analysis is used to extract the key factors affecting building performance. The designer can prioritize these key factors and it can reduce the uncertainty of building performance. A multi-objective optimization method coupling Gradient Boosted Decision Tree (GBDT) and non-dominated sorting genetic (NSGA-II) algorithm is proposed to seek the trade-off between three performances (obtain Pareto frontier solutions). The Pareto solution provides a more comprehensive reference for the preferences of different stakeholders, and the set of alternative solutions is further shrunk. Finally, take a specific residential building in China's cold climate zone as a showcase of the trade-off framework. According to the obtained Pareto frontier solution, the solution set is shrunk to a certain range, and the distribution ranges of Life Cycle Costs, the greenhouse gas emissions, and the annual thermal discomfort hour ratio are 122.3–137.1 USD/m2, 15.6–44.8 kg CO2/m2, and 19.1–25.2%, respectively. The trade-off framework adopts the order of objective Pareto optimal and then subjective preference selection, narrowing the scope of alternatives for designers and saving time-cost of decision-making.

Grasslands play important roles in ecosystem production and support a large farming and grazing industry. An accurate and efficient way is needed to estimate grassland health and production for monitoring and adjusting management to get sustainable products and other ecosystem services. Previous studies of grasslands have shown varying relationships between productivity and biodiversity, with most showing either a positive or a hump-shaped relationship where productivity peaks at intermediate diversity. In this study, we used airborne imaging spectrometry combined with ground sampling and eddy covariance measurements to estimate the spatial pattern of production and biodiversity for two sites of contrasting productivity in a southern Alberta prairie ecosystem. Resulting patterns revealed that more diverse sites generally had greater productivity, supporting the hypothesis of a positive relationship between production and biodiversity for this site. We showed that the addition of evenness to richness (using the Shannon Index of dominant species instead of the number of dominant species alone) improved the correlation with optical diversity, an optically derived metric of biodiversity based on the coefficient of variation in spectral reflectance across space. Similarly, the Shannon Index was better correlated with productivity (estimated via NDVI (Normalized Difference Vegetation Index)) than the number of dominant species alone. Optical diversity provided a potent proxy for other more traditional biodiversity metrics (richness and Shannon index). Coupling field measurements and imaging spectrometry provides a method for assessing grassland productivity and biodiversity at a larger scale than can be sampled from the ground, and allows the integrated analysis of the productivity-biodiversity relationship over large areas.

Sustainable development in the building sector requires the integration of energy efficiency and renewable energy utilization in buildings. In recent years, the concept of net zero energy buildings (NZEBs) has become a potential plausible solution to improve efficiency and reduce energy consumption in buildings. To achieve an NZEB goal, building systems and design strategies must be integrated and optimized based on local climatic conditions. This paper provides a comprehensive review of NZEBs and their current development in hot and humid regions. Through investigating 34 NZEB cases around the world, this study summarized NZEB key design strategies, technology choices and energy performance. The study found that passive design and technologies such as daylighting and natural ventilation are often adopted for NZEBs in hot and humid climates, together with other energy efficient and renewable energy technologies. Most NZEB cases demonstrated site annual energy consumption intensity less than 100 kW-hours (kWh) per square meter of floor space, and some buildings even achieved “net-positive energy” (that is, they generate more energy locally than they consume). However, the analysis also shows that not all NZEBs are energy efficient buildings, and buildings with ample renewable energy adoption can still achieve NZEB status even with high energy use intensity. This paper provides in-depth case-study-driven analysis to evaluate NZEB energy performance and summarize best practices for high performance NZEBs. This review provides critical technical information as well as policy recommendations for net zero energy building development in hot and humid climates.

This retrospective cohort study attempts to investigate pregnancy complications and adverse pregnancy outcomes in women of advanced maternal age (AMA). Data were extracted from electronic medical records system at West China Second University Hospital of Sichuan University from January 2013 to July 2016. The study cohort consisted 8 subgroups of women on 4 different age levels (20-29 years, 30-34 years, 35-39 years and ≥40 years) and 2 different parities (primiparity and multiparity). In the study period, 38811 women gave birth at our hospital, a randomized block was used to include 2800 women of singleton pregnancy >28 gestational weeks, with 350 patients in each subgroup. Maternal complications and fetal outcomes were collected and defined according to relevant guidelines. Confounding factors representing maternal demographic characteristics were identified from previous studies and analysed in multivariate analysis. There was an increasing trend for the risks of adverse pregnancy outcomes with increasing age, especially in AMA groups. Our study showed that AMA, primiparity, maternal overweight or obesity, lower educational level and residence in rural area increased pregnancy complications and adverse fetal outcomes. Increased professional care as well as public concern is warranted.

Speech neuroprostheses have the potential to restore communication to people living with paralysis, but naturalistic speed and expressivity are elusive1. Here we use high-density surface recordings of the speech cortex in a clinical-trial participant with severe limb and vocal paralysis to achieve high-performance real-time decoding across three complementary speech-related output modalities: text, speech audio and facial-avatar animation. We trained and evaluated deep-learning models using neural data collected as the participant attempted to silently speak sentences. For text, we demonstrate accurate and rapid large-vocabulary decoding with a median rate of 78 words per minute and median word error rate of 25%. For speech audio, we demonstrate intelligible and rapid speech synthesis and personalization to the participants pre-injury voice. For facial-avatar animation, we demonstrate the control of virtual orofacial movements for speech and non-speech communicative gestures. The decoders reached high performance with less than two weeks of training. Our findings introduce a multimodal speech-neuroprosthetic approach that has substantial promise to restore full, embodied communication to people living with severe paralysis.