Exploring high‐efficiency and stable halide perovskite‐based photocatalysts for the selective reduction of CO2 to methane is a challenge because of the intrinsic photo‐ and chemical instability of ...halide perovskites. In this study, halide perovskites (Cs3Bi2Br9 and Cs2AgBiBr6) were grown in situ in mesoporous TiO2 frameworks for an efficient CO2 reduction. Benchmarked CH4 production rates of 32.9 and 24.2 μmol g−1 h−1 with selectivities of 88.7 % and 84.2 %, were achieved, respectively, which are better than most reported halide perovskite photocatalysts. Focused ion‐beam sliced‐imaging techniques were used to directly image the hyperdispersed perovskite nanodots confined in mesopores with tunable sizes ranging from 3.8 to 9.9 nm. In situ X‐ray photoelectronic spectroscopy and Kelvin probe force microscopy showed that the built‐in electric field between the perovskite nanodots and mesoporous titania channels efficiently promoted photo‐induced charge transfer. Density functional theory calculations indicate that the high methane selectivity was attributed to the Bi‐adsorption‐mediated hydrogenation of *CO to *HCO that dominates CO desorption.
Halide perovskites (Cs3Bi2Br9, Cs2AgBiBr6) are grown in situ in a mesoporous titania framework for efficient CO2 reduction reaction (CO2RR). A benchmarked production rate of CH4 (32.9 and 24.2 μmol g−1 h−1) is achieved with selectivity values of 88.7 % and 84.2 %, respectively. In situ X‐ray photoelectronic spectroscopy and Kelvin probe force microscopy reveal that the inner surface built‐in electric field between the perovskite nanodots and mesoporous titania channels can efficiently promote photo‐induced charge transfer.
With the advances in new-generation information technologies, especially big data and digital twin, smart manufacturing is becoming the focus of global manufacturing transformation and upgrading. ...Intelligence comes from data. Integrated analysis for the manufacturing big data is beneficial to all aspects of manufacturing. Besides, the digital twin paves a way for the cyber-physical integration of manufacturing, which is an important bottleneck to achieve smart manufacturing. In this paper, the big data and digital twin in manufacturing are reviewed, including their concept as well as their applications in product design, production planning, manufacturing, and predictive maintenance. On this basis, the similarities and differences between big data and digital twin are compared from the general and data perspectives. Since the big data and digital twin can be complementary, how they can be integrated to promote smart manufacturing are discussed.
With the developments and applications of the new information technologies, such as cloud computing, Internet of Things, big data, and artificial intelligence, a smart manufacturing era is coming. At ...the same time, various national manufacturing development strategies have been put forward, such as Industry 4.0 , Industrial Internet , manufacturing based on Cyber-Physical System , and Made in China 2025 . However, one of specific challenges to achieve smart manufacturing with these strategies is how to converge the manufacturing physical world and the virtual world, so as to realize a series of smart operations in the manufacturing process, including smart interconnection, smart interaction, smart control and management, etc. In this context, as a basic unit of manufacturing, shop-floor is required to reach the interaction and convergence between physical and virtual spaces, which is not only the imperative demand of smart manufacturing, but also the evolving trend of itself. Accordingly, a novel concept of digital twin shop-floor (DTS) based on digital twin is explored and its four key components are discussed, including physical shop-floor, virtual shop-floor, shop-floor service system, and shop-floor digital twin data. What is more, the operation mechanisms and implementing methods for DTS are studied and key technologies as well as challenges ahead are investigated, respectively.
Nowadays, along with the application of new-generation information technologies in industry and manufacturing, the big data-driven manufacturing era is coming. However, although various big data in ...the entire product lifecycle, including product design, manufacturing, and service, can be obtained, it can be found that the current research on product lifecycle data mainly focuses on physical products rather than virtual models. Besides, due to the lack of convergence between product physical and virtual space, the data in product lifecycle is isolated, fragmented, and stagnant, which is useless for manufacturing enterprises. These problems lead to low level of efficiency, intelligence, sustainability in product design, manufacturing, and service phases. However, physical product data, virtual product data, and connected data that tie physical and virtual product are needed to support product design, manufacturing, and service. Therefore, how to generate and use converged cyber-physical data to better serve product lifecycle, so as to drive product design, manufacturing, and service to be more efficient, smart, and sustainable, is emphasized and investigated based on our previous study on big data in product lifecycle management. In this paper, a new method for product design, manufacturing, and service driven by digital twin is proposed. The detailed application methods and frameworks of digital twin-driven product design, manufacturing, and service are investigated. Furthermore, three cases are given to illustrate the future applications of digital twin in the three phases of a product respectively.
The state-of-the-art technologies in new generation information technologies (New IT) greatly stimulate the development of smart manufacturing. In a smart manufacturing environment, more and more ...devices would be connected to the Internet so that a large volume of data can be obtained during all phases of the product lifecycle. Cloud-based smart manufacturing paradigm facilitates a new variety of applications and services to analyze a large volume of data and enable large-scale manufacturing collaboration. However, different factors, such as the network unavailability, overfull bandwidth, and latency time, restrict its availability for high-speed and low-latency real-time applications. Fog computing and edge computing extended the compute, storage, and networking capabilities of the cloud to the edge, which will respond to the above-mentioned issues. Based on cloud computing, fog computing, and edge computing, in this paper, a hierarchy reference architecture is introduced for smart manufacturing. The architecture is expected to be applied in the digital twin shop floor, which opens a bright perspective of new applications within the field of manufacturing.
Tumor cells with stemness (stem‐cell) features contribute to initiation and progression of hepatocellular carcinoma (HCC), but involvement of long noncoding RNAs (lncRNAs) remains largely unclear. ...Genome‐wide analyses were applied to identify tumor‐associated lncRNA‐DANCR. DANCR expression level and prognostic values of DANCR were assayed in two HCC cohorts (China and Korea, n = 135 and 223). Artificial modulation of DANCR (down‐ and overexpression) was done to explore the role of DANCR in tumorigenesis and colonization, and tumor‐bearing mice were used to determine therapeutic effects. We found that lncRNA‐DANCR is overexpressed in stem‐like HCC cells, and this can serve as a prognostic biomarker for HCC patients. Experiments showed that DANCR markedly increased stemness features of HCC cells to promote tumorigenesis and intra‐/extrahepatic tumor colonization. Conversely, DANCR knockdown attenuated the stem‐cell properties and in vivo interference with DANCR action led to decreased tumor cell vitality, tumor shrinkage, and improved mouse survival. Additionally, we found that the role of DANCR relied largely on an association with, and regulation of, CTNNB1. Association of DANCR with CTNNB1 blocked the repressing effect of microRNA (miR)−214, miR‐320a, and miR‐199a on CTNNB1. This observation was confirmed in vivo, suggesting a novel mechanism of tumorigenesis involving lncRNAs, messenger RNAs, and microRNAs. Conclusions: These studies reveal a significance and mechanism of DANCR action in increasing stemness features and offer a potential prognostic marker and a therapeutic target for HCC. (Hepatology 2016;63:499–511)
Smart manufacturing is increasingly becoming the common goal of various national strategies. Smart interconnection is one of the most important issues for implementing smart manufacturing. However, ...current solutions are not tended to realize smart interconnection in dealing with heterogeneous equipment, quick configuration and implementation, and online service generation. To solve the issues, industrial Internet-of-Things hub (IIHub) is proposed, which consists of customized access module (CA-Module), access hub (A-Hub), and local service pool (LSP). A set of flexible CA-Modules can be configured or programed to connect heterogeneous physical manufacturing resources. Besides, the IIHub supports manufacturing services online generation based on the service encapsulation templates and also supports quick configuration and implementation for smart interconnection. Furthermore, related smart analysis and precise management have the potential to be achieved. Finally, a prototype is given to illustrate the functions of the proposed IIHub, and to show how IIHub realizes smart interconnection.
Based on micro-level enterprise panel data from 2005 to 2014, a slack-based global data envelopment analysis (DEA) model and a global Malmquist-Luenberger index model are used to calculate the ...environmental governance efficiency (EGE) of China's iron and steel enterprises (ISEs). Then, the different effects of two types of environmental regulation on EGE of China's ISEs are analysed. The results show that the overall level of EGE for China's ISEs has remained low over the past 10 years. The total factor environmental governance efficiency (TFEGE) presents a decreasing trend from 2005 to 2014, and the decline in TFEGE is mainly attributed to the technical progress change index (GTPCH). Moreover, the bootstrap DEA method is used for bias correction, and the correction efficiency values are all within the confidence interval, improving the accuracy of EGE evaluation. The regression analysis results show that different types of environmental regulations exert heterogeneous effects on TFEGE. The relationship between market incentive environmental regulation and TFEGE has an inverted U-shaped, implying that although market incentive environmental regulation may improve the TFEGE in the short term, continuously increasing this intensity would inhibit it. However, command control environmental regulation and TFEGE have a positive but not significant relationship.
In order to realize the full-scale sharing, free circulation and transaction, and on-demand-use of manufacturing resource and capabilities in modern enterprise systems (ES), Cloud manufacturing ...(CMfg) as a new service-oriented manufacturing paradigm has been proposed recently. Compared with cloud computing, the services that are managed in CMfg include not only computational and software resource and capability service, but also various manufacturing resources and capability service. These various dynamic services make ES more powerful and to be a higher-level extension of traditional services. Thus, as a key issue for the implementation of CMfg-based ES, service composition optimal-selection (SCOS) is becoming very important. SCOS is a typical NP-hard problem with the characteristics of dynamic and uncertainty. Solving large scale SCOS problem with numerous constraints in CMfg by using the traditional methods might be inefficient. To overcome this shortcoming, the formulation of SCOS in CMfg with multiple objectives and constraints is investigated first, and then a novel parallel intelligent algorithm, namely full connection based parallel adaptive chaos optimization with reflex migration (FC-PACO-RM) is developed. In the algorithm, roulette wheel selection and adaptive chaos optimization are introduced for search purpose, while full-connection parallelization in island model and new reflex migration way are also developed for efficient decision. To validate the performance of FC-PACO-RM, comparisons with 3 serial algorithms and 7 typical parallel methods are conducted in three typical cases. The results demonstrate the effectiveness of the proposed method for addressing complex SCOS in CMfg.
Audio-based automatic speech recognition (A-ASR) systems are affected by noisy conditions in real-world applications. Adding visual cues to the ASR system is an appealing alternative to improve the ...robustness of the system, replicating the audiovisual perception process used during human interactions. A common problem observed when using audiovisual automatic speech recognition (AV-ASR) is the drop in performance when speech is clean. In this case, visual features may not provide complementary information, introducing variability that negatively affects the performance of the system. The experimental evaluation in this study clearly demonstrates this problem when we train an audiovisual state-of-the-art hybrid system with a deep neural network (DNN) and hidden Markov models (HMMs). This study proposes a framework that addresses this problem, improving, or at least, maintaining the performance when visual features are used. The proposed approach is a deep learning solution with a gating layer that diminishes the effect of noisy or uninformative visual features, keeping only useful information. The framework is implemented with a subset of the audiovisual CRSS-4ENGLISH-14 corpus which consists of 61 h of speech from 105 subjects simultaneously collected with multiple cameras and microphones. The proposed framework is compared with conventional HMMs with observation models implemented with either a Gaussian mixture model or DNNs. We also compare the system with a multi-stream HMM system. The experimental evaluation indicates that the proposed framework outperforms alternative methods under all configurations, showing the robustness of the gating-based framework for AV-ASR.
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