Buildings consume large amounts of energy and resources and have a significant impact on environment. In 2010 buildings accounted for 32% of total global final energy use and 19% of energy-related ...GHG emissions. This energy use and related emissions may double or potentially even triple by mid-century. China is the world's largest energy consumer and CO2 emitter. With the process of urbanization, China has been entering into a period of great prosperity for construction, about 1.6–2.0 billionm2 buildings are constructed each year, which accounts for about 40% of the world's total new buildings. It is predicted that China’s building energy use and related emissions will continue to increase over the next 15 years. Therefore, promoting green building development has become an urgent issue in China. However, green building is a highly complicated system engineering, its promotion needs evaluation standards as technical support. Many countries had issued a series of green building evaluation standards since 1990. Currently, there are some representative green building assessment schemes. Code for Sustainable Homes (CSH) has been fully implemented as the national authoritative standard of the UK since 2010. Leadership in Energy and Environmental Design (LEED) developed by the USA, was revised and updated regularly with the latest version issued in 2013. China enacted its national evaluation standard for green building in 2006, and revised the standard in 2014. Based on introducing the latest evaluation standards for green buildings in China, Britain and United States, the paper compared these standards from 5 aspects including energy-saving, water-saving, material-saving, site selection and the outdoor and indoor environmental quality. The comparison mainly focuses on evaluation methods and evaluation indicators in the three standard systems. Besides, the characteristics of each standard system were summarized and some suggestions for improving China’s evaluation standard for green building were proposed.
Solar‐driven conversion of CO2 into high value‐added fuels is expected to be an environmental‐friendly and sustainable approach for relieving the greenhouse gas effect and countering energy crisis. ...Metal sulfide semiconductors with wide photoresponsive range and favorable band structures are suitable photocatalysts for CO2 photoreduction. This review summarizes the recent progress on metal sulfide semiconductors for photocatalytic CO2 reduction. First, the fundamentals, mechanisms and some principles, like product selectivity, of photocatalytic CO2 reduction are introduced. Then, according to the elemental composition, the metal sulfide photocatalysts applied for CO2 reduction are classified into binary (CdS, ZnS, MoS2, SnS2, Bi2S3, In2S3,Cu2S, NiS/NiS2, and CoS2), ternary (ZnIn2S4, CdIn2S4, CuInS2, Cu3SnS4, and CuGaS2), and quaternary (Cu2ZnSnS4) systems, in which their crystal structures, photochemical characteristics, and photocatalytic CO2 reduction applications are systematically demonstrated. Especially, the diverse modification strategies for improving the activity and product selectivity of photocatalytic CO2 reduction on these metal sulfides are summarized. Finally, the current challenges and future directions for the development of metal sulfide photocatalysts for CO2 reduction are proposed. This review is expected to serve as a powerful reference for exploiting high‐efficiency metal sulfide photocatalysts for CO2 conversion and furthering related mechanism understanding.
Metal sulfide semiconductors present unique optical/electronic characteristics, which are advantageous for CO2 photoreduction. The advancements in binary, ternary, and quaternary metal sulfide photocatalysts in CO2 photoreduction are elaborately summarized, and the effects of various modification strategies on the reduction activity and product selectivity are highlighted, providing a reference for development of efficient metal sulfides for photocatalytic CO2 reduction to carbonaceous fuels.
Ozone (O3) pollution in the atmosphere is getting worse in many cities. In order to improve the accuracy of O3 prediction and obtain the spatial distribution of O3 concentration over a continuous ...period of time, this paper proposes a VAR-XGBoost model based on Vector autoregression (VAR), Kriging method and XGBoost (Extreme Gradient Boosting). China is used as an example and its spatial distribution of O3 is simulated. In this paper, the O3 concentration data of the monitoring sites in China are obtained, and then a spatial prediction method of O3 mass concentration based on the VAR-XGBoost model is established, and finnally its influencing factors are analyzed. This paper concludes that O3 features the highest correlation with PM2.5 and the lowest correlation with SO2. Among the measurement factors, wind speed and temperature are the most important factors affecting O3 pollution, which are positively correlated to O3 pollution. In addition, precipitation is negatively correlated with 8-hour ozone concentration. In this paper, the performance of the VAR-XGBoost model is evaluated based on the ten-fold cross-validation method of sample, site and time, and a comparison with the results of XGBoost, CatBoost (categorical boosting), ExtraTrees, GBDT (gradient boosting decision tree), AdaBoost (adaptive boosting), RF (random forest), Decision tree, and LightGBM (light gradient boosting machine) models is conducted. The result shows that the prediction accuracy of the VAR-XGBoost model is better than other models. The seasonal and annual average R2 reaches 0.94 (spring), 0.93 (summer), 0.92 (autumn), 0.93 (winter), and 0.95 (average from 2016 to 2021). The data show that the applicability of the VAR-XGBoost model in simulating the spatial distribution of O3 concentrations in China performs well. The spatial distribution of O3 concentrations in the Chinese region shows an obvious feature of high in the east and low in the west, and the spatial distribution is strongly influenced by topographical factors. The mean concentration is clearly low in winter and high in summer within a season. The results of this study can provide a scientific basis for the prevention and control of regional O3 pollution in China, and can also provide new ideas for the acquisition of data on the spatial distribution of O3 concentrations within cities.
•VAR model estimate the dynamic relationships of all endogenous variables.•VAR-tree model estimate the spatial and temporal distribution of O3.•The eight-tree model improve the accuracy of O3 change prediction.•VAR-XGBoost method solve the problem of spatial heterogeneity of the study object.
Unmanned aerial vehicles (UAVs) have stroke great interested both by the academic community and the industrial community due to their diverse military applications and civilian applications. ...Furthermore, UAVs are also envisioned to be part of future airspace traffic. The application functions delivery relies on information exchange among UAVs as well as between UAVs and ground stations (GSs), which further closely depends on aeronautical channels. However, there is a paucity of comprehensive surveys on aeronautical channel modeling in line with the specific aeronautical characteristics and scenarios. To fill this gap, this paper focuses on reviewing the air-to-ground (A2G), ground-to-ground (G2G), and air-to-air (A2A) channel measurements and modeling for UAV communications and aeronautical communications under various scenarios. We also provide the design guideline for managing the link budget of UAV communications taking account of link losses and channel fading effects. Moreover, we also analyze the receive/transmit diversity gain and spatial multiplexing gain achieved by multiple-antenna-aided UAV communications. Finally, we discuss the remaining challenge and open issues for the future development of UAV communication channel modeling.
Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy ...high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of Things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.
As the critical component of manufacturing systems, production scheduling aims to optimize objectives in terms of profit, efficiency, and energy consumption by reasonably determining the main factors ...including processing path, machine assignment, execute time and so on. Due to the large scale and strongly coupled constraints nature, as well as the real-time solving requirement in certain scenarios, it faces great challenges in solving the manufacturing scheduling problems. With the development of machine learning, Reinforcement Learning (RL) has made breakthroughs in a variety of decision-making problems. For manufacturing scheduling problems, in this paper we summarize the designs of state and action, tease out RL-based algorithm for scheduling, review the applications of RL for different types of scheduling problems, and then discuss the fusion modes of reinforcement learning and meta-heuristics. Finally, we analyze the existing problems in current research, and point out the future research direction and significant contents to promote the research and applications of RL-based scheduling optimization.
Past few years have witnessed the compelling applications of the blockchain technique in our daily life ranging from the financial market to health care. Considering the integration of the blockchain ...technique and the industrial Internet of Things (IoT), blockchain may act as a distributed ledger for beneficially establishing a decentralized autonomous trading platform for industrial IoT (IIoT) networks. However, the power and computation constraints prevent IoT devices from directly participating in this proof-of-work process. As a remedy, in this treatise, the cloud computing service is introduced into the blockchain platform for the sake of assisting to offload computational task from the IIoT network itself. In addition, we study the resource management and pricing problem between the cloud provider and miners. More explicitly, we model the interaction between the cloud provider and miners as a Stackelberg game, where the leader, i.e., cloud provider, makes the price first, and then miners act as the followers. Moreover, in order to find the Nash equilibrium of the proposed Stackelberg game, a multiagent reinforcement learning algorithm is conceived for searching the near-optimal policy. Finally, extensive simulations are conducted to evaluate our proposed algorithm in comparison to some state-of-the-art schemes.
Unmanned aerial vehicles (UAVs) may be used for providing seamless network coverage in urban areas for improving the performance of conventional cellular networks. Given the predominantly ...line-of-sight channel of drones, UAV-aided seamless coverage becomes particularly beneficial in case of emergency situations. However, a single UAV having a limited cruising capability is unable to provide seamless long-term coverage, multiple drones relying on sophisticated recharging and reshuffling schemes are necessary. In this context, both the positioning and the flight strategy directly affect the efficiency of the system. Hence, we first introduce a novel UAV energy consumption model, based on which an energy-efficiency-based objective function is derived. Second, we propose an energy-efficient rechargeable UAV deployment strategy optimized under a seamless coverage constraint. Explicitly, a two-stage joint optimization algorithm is conceived for solving both the optimal UAV deployment and the cyclic UAV recharging and reshuffling strategy. Our simulation results quantify the efficiency of our proposed algorithm.
Single-cell analysis is a valuable tool for dissecting cellular heterogeneity in complex systems
. However, a comprehensive single-cell atlas has not been achieved for humans. Here we use single-cell ...mRNA sequencing to determine the cell-type composition of all major human organs and construct a scheme for the human cell landscape (HCL). We have uncovered a single-cell hierarchy for many tissues that have not been well characterized. We established a 'single-cell HCL analysis' pipeline that helps to define human cell identity. Finally, we performed a single-cell comparative analysis of landscapes from human and mouse to identify conserved genetic networks. We found that stem and progenitor cells exhibit strong transcriptomic stochasticity, whereas differentiated cells are more distinct. Our results provide a useful resource for the study of human biology.
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