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Autoclave is the most important equipment in the composite curing process, and its real-time condition has a direct impact on the quality of composite materials. Therefore, rapid and precise fault diagnosis reasoning is of great significance for the autoclave. To address the shortage of signed directed graph (SDG)-based fault diagnosis method, this paper proposes a fault diagnosis method based on digital twin (DT) enhanced SDG for autoclave. Firstly, the SDG model of autoclave temperature control system is constructed, and the model is improved and enhanced by pre-fault transition state identification, fuzzy confirmation of node states, and simplification of potential branch circuits by using DT. The effectiveness of the method in this paper is verified by fault diagnosis based on SDG and DT-SDG methods respectively. The experimental results show that the method proposed in this paper can improve the speed and resolution of fault diagnosis by reducing the number of potential fault propagation paths and the number of inferences. [ABSTRACT FROM AUTHOR]

Pictet-Spengler reaction is one of the main ways to synthesize spiroindolones, which widely exists in natural products and pharmaceutical agents. We accidentally discovered bovine serum albumin (BSA) had catalytic activity for Pictet-Spengler reaction previously. After a series of exploratory experiments around the catalytic process, it was found that some amino acids played a key role. It is worth noting that, Pictet-Spengler reaction catalyzed by amino acids has been rarely described. Herein, we report the cysteine-catalyzed Pictet-Spengler reaction between tryptamine and isatin under metal-free and convenient conditions. The catalytic performance of amino acid was optimized through screening reaction conditions such as time, temperature, solvent, molar ratio and catalyst amount and a series of spiroindolones can be obtained in moderate to excellent yields. The present investigation featured relatively mild reaction conditions with good functional group tolerance and provided an alternative method for preparation of spiroindolones. [ABSTRACT FROM AUTHOR]

The spatiotemporal distribution of intangible cultural heritage in Fujian Province, China, and the factors that influence it were explored using multiple spatial scales. The samples include five batches of Chinese national-level and six batches of Chinese provincial-level intangible cultural heritage items, totaling 554. The samples involve city-scale, county-scale, and traditional dwellings, and the analysis uses various methods, such as gravity migration theory and the GeoDetector model. The results show that, in terms of historical timescales, the moving trajectory of the center of gravity of the intangible cultural heritage distribution in Fujian Province involves reciprocation from north to south, and the center moves south in time from the pre-Qin period to the modern period. Spatially, intangible cultural heritage in Fujian Province forms an overall aggregate pattern, among which traditional skills fall into an aggregate pattern; five categories, including traditional drama, fall into an aggregate-random pattern; and four categories, including traditional medicine, fall into a random pattern. The traditional art category and other intensive distribution areas are located in coastal areas, and the traditional dance category is scattered in northwestern Fujian Province. Experiments on the factors influencing intangible cultural heritage distribution were conducted at the city and county levels. At the city level, human geography factors, such as the resident population, urbanization rate, and gross domestic product, have a greater influence on distribution. At the county level, the influence of physical geographical factors clearly increases. The dominant factors for coastal cities are the normalized difference vegetation index (NDVI), average precipitation, and coastline, and that for inland cities is elevation. From the perspective of categories, the dominant factors of traditional music and Quyi are human geography factors, and those of other types of intangible cultural heritage are the synergistic effects of physical geography and human geography. The conclusions of this study are helpful for systematically protecting and developing the intangible cultural heritage industry and improving research theories and methodological systems of the spatiotemporal distribution and mechanism influencing intangible cultural heritage. [ABSTRACT FROM AUTHOR]

Membrane proteins are critical to biological processes and central to life sciences and modern medicine. However, membrane proteins are notoriously challenging to study, mainly owing to difficulties dictated by their highly hydrophobic nature. Previously, we reported QTY code, which is a simple method for designing water-soluble membrane proteins. Here, we apply QTY code to a transmembrane receptor, histidine kinase CpxA, to render it completely water-soluble. The designed CpxAQTY exhibits expected biophysical properties and highly preserved native molecular function, including the activities of (i) autokinase, (ii) phosphotransferase, (iii) phosphatase, and (iv) signaling receptor, involving a water-solubilized transmembrane domain. We probe the principles underlying the balance of structural stability and activity in the water-solubilized transmembrane domain. Computational approaches suggest that an extensive and dynamic hydrogen-bond network introduced by QTY code and its flexibility may play an important role. Our successful functional preservation further substantiates the robustness and comprehensiveness of QTY code. Membrane proteins play essential roles in biological processes but are very difficult to study due to their hydrophobic nature. Here, the authors apply the QTY code, a method for designing water soluble membrane proteins, to histidine kinase CpxA to render it completely water-soluble and preserve the native molecular function. [ABSTRACT FROM AUTHOR]

In this paper we prove that isometries with respect to the Kobayashi metric between certain domains having boundary points at which the boundary is infinitely flat extend continuously to the boundary. The strategy is to reestablish the Gehring-Hayman-type Theorem for these complex domains. Furthermore, the regularity of boundary extension map is given. [ABSTRACT FROM AUTHOR]

In recent years, as a promising way to realize digital transformation, digital twin shop-floor (DTS) plays an important role in smart manufacturing. The core feature of DTS is the synchronization. How to implement and maintain the synchronization is critical for DTS. However, there is still a lack of a common definition for synchronization in DTS. Besides, a systematic synchronization mechanism for DTS is strongly needed. This paper first summarizes the definition and requirements of synchronization in DTS, to clarify the understanding of synchronization in DTS. Then, a 5M synchronization mechanism for DTS is proposed, where 5M refers to multi-system data, multi-fidelity model, multi-resource state, multi-level state, and multi-stage operation. As a bottom-up synchronization mechanism, 5M synchronization mechanism for DTS has the potential to support DTS to achieve and maintain physical-virtual state synchronization, and to realize operation synchronization of DTS. The implementation methods of 5M synchronization mechanism for DTS are also introduced. Finally, the proposed synchronization mechanism is validated in a digital twin satellite assembly shop-floor, which proves the effectiveness and feasibility of the mechanism. [ABSTRACT FROM AUTHOR]

Rechargeable aluminum ion batteries (RAIBs) are a very attractive option for large-scale energy storage thanks to their promising theoretical capacity, high energy density, low cost, abundant earth resources, and environmental friendliness. While the cathode materials chosen and prepared are so essential for the electrochemical performance of RAIBs that extensive efforts and research have been done. In this study, the electrochemical performances of RAIBs were optimally improved by the chemical polymerization of triphenylamine to obtain polytriphenylamine (PTPAn) as the cathode material. The polymerization process improved the spatial reticulate structure of triphenylamine, gained a three-dimensional mesh-like nanostructure, which provided more chemical reaction sites and ion reaction channels, greatly increased the specific surface area, and accelerated the electrochemical reaction kinetics. On this basis, a stable discharge-specific capacity of around 137.4 mAh g−1 was achieved at high current densities of 1 A g−1 for the PTPAn cathode, and the Coulombic efficiency was maintained at about 99% after the life of 500 cycles. The understanding and appreciation of the charging and discharging working principle of PTPAn material as RAIBs cathode, meantime, were deepened by a multitude of ex-situ experiments. These findings are anticipated to serve as the cornerstone for the subsequent development of large-scale RAIBs systems for energy storage that use organic polymers as the cathode material. [ABSTRACT FROM AUTHOR]

Underwater adhesive proteins secreted by organisms greatly inspires the development of underwater glue. However, except for specific proteins such as mussel adhesive protein, barnacle cement proteins, curli protein and their related recombinant proteins, it is believed that abundant common proteins cannot be converted into underwater glue. Here, we demonstrate that unfolded common proteins exhibit high affinity to surfaces and strong internal cohesion via amyloid-like aggregation in water. Using bovine serum albumin (BSA) as a model protein, we obtain a stable unfolded protein by cleaving the disulfide bonds and maintaining the unfolded state by means of stabilizing agents such as trifluoroethanol (TFE) and urea. The diffusion of stabilizing agents into water exposes the hydrophobic residues of an unfolded protein and initiates aggregation of the unfolded protein into a solid block. A robust and stable underwater glue can thus be prepared from tens of common proteins. This strategy deciphers a general code in common proteins to construct robust underwater glue from abundant biomass. Underwater adhesive proteins greatly inspired the development of underwater glue but except for specific proteins it is believed that abundant common proteins cannot be converted into underwater glue. Here, the authors demonstrate, using bovine serum albumin as model protein, that unfolded common proteins exhibit high affinity to surfaces and strong internal cohesion via amyloid-like aggregation in water. [ABSTRACT FROM AUTHOR]

The modern complicated manufacturing industry and smart manufacturing tendency have imposed new requirements on the scheduling method, such as self-regulation and self-learning capabilities. While traditional scheduling methods cannot meet these needs due to their rigidity. Self-learning is an inherent ability of reinforcement learning (RL) algorithm inhered from its continuous learning and trial-and-error characteristics. Self-regulation of scheduling could be enabled by the emerging digital twin (DT) technology because of its virtual-real mapping and mutual control characteristics. This paper proposed a DT-enabled adaptive scheduling based on the improved proximal policy optimization RL algorithm, which was called explicit exploration and asynchronous update proximal policy optimization algorithm (E2APPO). Firstly, the DT-enabled scheduling system framework was designed to enhance the interaction between the virtual and the physical job shops, strengthening the self-regulation of the scheduling model. Secondly, an innovative action selection strategy and an asynchronous update mechanism were proposed to improve the optimization algorithm to strengthen the self-learning ability of the scheduling model. Lastly, the proposed scheduling model was extensively tested in comparison with heuristic and meta-heuristic algorithms, such as well-known scheduling rules and genetic algorithms, as well as other existing scheduling methods based on reinforcement learning. The comparisons have proved both the effectiveness and advancement of the proposed DT-enabled adaptive scheduling strategy. [ABSTRACT FROM AUTHOR]

Ni3S2 nanosheets dual-doped with manganese, cobalt elements on conductive nickel foam (MC-Ni3S2) had been synthesized by two steps hydrothermal and directly applied as the electrode for high-performance electrochemical Supercapacitors (SCs). A plausible mechanism is proposed to explain the formation of the manganese, cobalt elements dual-doped Ni3S2 nanosheets. SCs electrodes based on the MC-Ni3S2 nanosheets are characterized and exhibit a specific capacitance of 2922 F g−1 (365.3 mAh g−1) at 2 A g−1. After 2000 cycles, it exhibited an excellent cycling stability owing to the unique nanosheets architecture providing fast ion/electron transfer. The fabricate approach may offer a new strategy for the synthesis of other mix transition metal sulfide for applications in supercapacitor. [ABSTRACT FROM AUTHOR]

The effect of crystallography on transgranular chloride-induced stress corrosion cracking (TGCISCC) of arc welded 304L austenitic stainless steel is studied on >300 grains along crack paths. Schmid and Taylor factor mismatches across grain boundaries (GBs) reveal that cracks propagate either from a hard to soft grain, which can be explained merely by mechanical arguments, or soft to hard grain. In the latter case, finite element analysis reveals that TGCISCC will arrest at GBs without sufficient mechanical stress, favorable crystallographic orientations, or crack tip corrosion. GB type does not play a significant role in determining TGCISCC cracking behavior nor susceptibility. TGCISCC crack behaviors at GBs are discussed in the context of the competition between mechanical, crystallographic, and corrosion factors. [ABSTRACT FROM AUTHOR]

Nowadays, most of the manufacturing industries rely on effective shop floor schedules to improve productivity and to optimize the makespan, production cost, tardiness, etc., which are usually considered in traditional scheduling problems. Recently, in response to the global initiatives for sustainability in manufacturing industries, an increasing number of shop floor schedules have taken energy consumption into account. However, few research works have considered traditional objectives, energy consumption, and other sustainability factors simultaneously in a shop floor schedule. In this paper, a many-objective optimization model for a flexible job shop scheduling problem considering makespan, total energy consumption, and three other indicators is formulated. Then, an improved electromagnetism-like mechanism algorithm is proposed to find the optimal or near-optimal solutions. Finally, a real-life case study is conducted to evaluate the proposed model and the algorithm. The results show that the many-objective model is effective for reducing energy consumption and improving sustainability in the shop floor. [ABSTRACT FROM AUTHOR]

The performance of CNC spindle directly affects the machining accuracy and processing speed of the machine tool. The time-varying environment and complex working conditions increase difficulty of thoroughly assessing the spindle relying on experimental test. Therefore, this paper proposes a comprehensive framework for assessing the performance of the spindle driven by digital twin. First, a multi-domain modeling method is used to establish a spindle twin model, and then an index system for assessing spindle performance is constructed combining experimental test and simulation. A multi-index fusion assessment that combines subjective and objective weights is presented to construct the performance assessment level, which comprehensively assesses the performance of the spindle from multiple dimensions such as vibration, temperature, and stiffness. Finally, the JSC180A electric spindle is selected to verify the proposed method. The conclusion that the electro-spindle is operating in good condition is consistent with the on-site inspection, which verifies the effectiveness of the proposed method and the feasibility of its on-site application. [ABSTRACT FROM AUTHOR]

As the new generation of information technology, blockchain is characterized by a set of unique qualities and features in data storage and computing infrastructure, such as data traceability, data security, decentralized consensus, and decentralized process execution. These characteristics make it to be an important enabling technology for smart manufacturing, especially in product lifecycle management (PLM). In this paper, key features of blockchain are summarized, and its development history is analyzed first. Then, the challenges of PLM in data management and data security among multi-stakeholders are investigated, and a potential way to address these challenges by employing blockchain is discussed. An assessment framework is designed to evaluate the viability of applying blockchain in specific PLM scenarios. The application or potential application of blockchain in each section of PLM is studied in detail. Finally, both technical and non-technical challenges of applying blockchain in PLM are pointed out to achieve more effective applications. [ABSTRACT FROM AUTHOR]

Purpose: Disitamab vedotin (RC48) is an HER2-directed antibody–drug conjugate, emerging as an effective strategy for cancer therapy, which not only enhances antitumor immunity in previous animal models but also improves clinical outcomes for patients such as with gastric cancer, urothelium carcinoma, and HER2 low-expressing breast cancer. Here, we explore the combination therapeutic efficacy of this novel HER2-targeting ADC with immune checkpoint inhibitors in a human HER2-expressing syngeneic breast cancer model. Methods: The human HER2+ cancer cell line is constructed by stable transfection and individual clones were isolated by single-cell sorting. Flow cytometry was performed to determine its binding activity. Cytotoxic effect was determined using an MTT assay with the supplement of RC48. Human PD-1 transgenic mice were used to analyze the in vivo antitumor effects of the ADC and its combination therapy with PD-1/PD-L1 antibody. Results: The combination of RC48 and PD-1/PD-L1 immune checkpoint inhibition significantly enhanced tumor suppression and antitumor immunity. Tumor rejection in the synergistic groups was accompanied by massive T cell infiltration and immune marker activation. Furthermore, the combination therapy promoted immunological memory formation in the tumor eradication animals, protecting them from tumor rechallenge. Conclusion: A novel HER2-targeting ADC combined with immune checkpoint inhibitors can achieve remarkable effects in mice and elicit long-lasting immune protection in a hHER2+ murine breast cancer model. This study provides insights into the efficacy of RC48 therapeutic activity and a rationale for potential therapeutic combination strategies with immunotherapy. [ABSTRACT FROM AUTHOR]

As the Internet of Things continues to grow and thrive, more and more data are consumed by machines for intelligent analysis. How to simultaneously support fast machine vision analysis and obtain high-quality reconstructed images to serve human vision has become a problem that needs to be solved. We propose a fast face image compression scheme based on a Joint Super-Resolution Network (JSRNet), where images are encoded hierarchically to support machine vision and human vision. To support fast machine vision tasks, we compress the downsampled version of the input image at the encoding side and implement base layer decoding at the decoding side with a lightweight super-resolution network, thus effectively reducing the codec time. To obtain high-quality reconstructed images, we design a content transfer module to transfer the visual information generated by the base layer to the enhancement layer. The joint optimization of the base layer and the enhancement layer also allows them to better cooperate to achieve better performance. We verify the performance of this scheme on the facial landmark detection task, and the experimental results show that our method saves more bit-rate and achieves more accurate facial landmark detection results with faster codec time than baselines. The reconstructed images are more in line with human visual characteristics, and the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) are also improved significantly. [ABSTRACT FROM AUTHOR]

Recently, artificial intelligence (AI) technology receives extensive attention in the manufacturing field. As the core technology, it generates considerable interest among smart manufacturing and Industry 4.0 strategy. Product lifecycle management (PLM) copes with various kinds of engineering, business, and management activities concerning a product throughout its whole lifecycle—from the inception of an intangible concept through the recycling of a finished product. In the context of smart manufacturing, this paper reviews various theories, algorithms, and technologies of AI to different stages of PLM (i.e., product design, manufacturing, and service). A structured roadmap is presented to navigate the future research and application of AI in PLM. This paper also discusses the opportunities and challenges of applying AI for PLM. [ABSTRACT FROM AUTHOR]

Endowing mesophilic microorganisms with high-temperature resistance is highly desirable for industrial microbial fermentation. Here, we report a cold-shock protein (CspL) that is an RNA chaperone protein from a lactate producing thermophile strain (Bacillus coagulans 2–6), which is able to recombinantly confer strong high-temperature resistance to other microorganisms. Transgenic cspL expression massively enhanced high-temperature growth of Escherichia coli (a 2.4-fold biomass increase at 45 °C) and eukaryote Saccharomyces cerevisiae (a 2.6-fold biomass increase at 36 °C). Importantly, we also found that CspL promotes growth rates at normal temperatures. Mechanistically, bio-layer interferometry characterized CspL's nucleotide-binding functions in vitro, while in vivo we used RNA-Seq and RIP-Seq to reveal CspL's global effects on mRNA accumulation and CspL's direct RNA binding targets, respectively. Thus, beyond establishing how a cold-shock protein chaperone provides high-temperature resistance, our study introduces a strategy that may facilitate industrial thermal fermentation. [ABSTRACT FROM AUTHOR]

In the future smart manufacturing, both of sensor-based environment in shop floors and cloud-based environment among more and more enterprises are deployed gradually. Various distributed and separated manufacturing facilities are as collaborative cloud services, integrated and aggregated with their real-time information. It provides opportunities for the distributed and collaborative manufacturing operations across lots of distributed but networked enterprises on demand with enough flexibility. To this end, the scheduling problem and its result of those collaborative services for distributed manufacturing operations play an important role in improving manufacturing utilization and efficiency. In this paper, we put forward the hypernetwork-based models introducing the thought of graph coloring and an artificial bee colony algorithm based method for this scheduling problem. Three groups of experiments are carried out respectively to discuss therein different situations of distributed and collaborative manufacturing operations, i.e., in a private cloud, in a public cloud, and in a hybrid cloud. Some future studies with further consideration of collaboration equilibrium, dynamic control and data-based intelligence, are finally pointed out in the conclusion. [ABSTRACT FROM AUTHOR]

Manufacturing resources sharing and collaboration has become the inevitable trend to optimize the operation of manufacturing industry. The development of various advanced manufacturing systems (AMSs) and new-generated information technologies (New ITs) prompt the emergence of various industrial Internet platforms. In this context, it provides a brand new opportunity for enterprises to conduct manufacturing resources sharing and collaboration in the form of manufacturing services, namely, manufacturing services collaboration (MS-collaboration). However, the relevant research on MS-collaboration under the environment of industrial Internet platforms is still insufficient. Therefore, in this paper, the evolution from manufacturing collaboration to MS-collaboration toward industrial Internet platforms is analyzed at first, and the derived environment characteristics and challenges of MS-collaboration are discussed accordingly. After exploring the connotation of MS-collaboration, its implementation framework and corresponding enabling technologies are proposed. A 3D Printing case is presented to illustrate the application of the proposed methods. Finally, in order to promote the application of an industrial Internet platform, stimulate more enterprises to participate in, and conduct MS-collaboration in it, several future research issues are pointed out. [ABSTRACT FROM AUTHOR]

Numerous optimization problems exist in the product and process design, engineering, and planning, as well as production management, and the evolutionary algorithms (EAs) have been proved to be effective optimization methods to solve these problems. However, how to choose the appropriate EA is one of the key issues due to the variety of EAs and lack of experience. Under this circumstance, firstly, a novel EA recommendation system framework is designed and proposed, and the implementation of the EA recommendation method is also described in this paper. Then, computational experiments are implemented to demonstrate the effectiveness and accuracy of the proposed method on the basis of 14 typical benchmark problems and 20 classical EAs. Finally, a case study regarding permutation flow shop scheduling is also carried out to test its effectiveness and accuracy. The proposed recommendation method provides a new way to select the appropriate EAs scientifically and reasonably to solve a given optimization problem. [ABSTRACT FROM AUTHOR]

Currently, the inspection and supervision of animal ingredients relies primarily upon specific amplification-dependent methods, whose efficiency and accuracy are being seriously challenged by the increasing diversity and complexity of meat products. High-throughput sequencing (HTS) technology was employed to develop an alternative method to detect animal-derived ingredients in meat products. A custom-built database containing 2,354 complete mitochondrial genomic sequences from animals, an identification analysis pipeline based on short-sequence alignment, and a web-based server were built to facilitate this detection. The entire process, including DNA extraction, gene amplification, and sequencing, was established and optimized for both marker gene (part of the CYTB gene)-based detection and total DNA-based detection. Using simulated samples containing various levels of pig, cattle, sheep, chicken, rabbit, and mice ingredients, the detection capability and accuracy of this method were investigated. The results of this study indicated that the method is capable of detecting animal components in meats that are present at levels as low as 1%. Our method was then tested using 28 batches of real meat products such as raw meat slices, raw meat mince, cooked dried meat, cooked meat sausage, and other supermarket samples, with a traditional qPCR method as the control. The results demonstrated an accuracy of 97.65% for the qualitative detection method, which indicate that the developed method is reliable for the detection of animal components. The method is also effective for the identification of unknown food samples containing mixed animal components, which suggests a good future in application. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have obtained high accuracy results for pedestrian re-identification in the past few years. There is always a trade-off between high accuracy and computational time in CNNs. Training CNN is always very difficult as it may take a long time to produce high accuracy results. To overcome this limitation, a novel method parallel stochastic gradient descent (PSGD) is proposed to train a five-hierarchical parallel CNNs that is designed according to pedestrian attributes. Moreover, the momentum correction and adaptive adjustment of learning rate are applied during training process and the time interval for updating parameters is inspected during optimization of parameters selection. The results of this paper prove the effectiveness of proposed PSGD that successfully decreases the training process by five times and surpasses the state-of-the-art methods of pedestrian re-identification in terms of both accuracy and time. The minimum reported running time of the proposed method is 8.7 s which is minimum among all other state-of-the-art methods. These promising results show the efficiency and performance of the proposed model. [ABSTRACT FROM AUTHOR]

During the past years, a number of smart manufacturing concepts have been proposed, such as cloud manufacturing, Industry 4.0, and Industrial Internet. One of their common aims is to optimize the collaborative resource configuration across enterprises by establishing platforms that aggregate distributed resources. In all of these concepts, a complete manufacturing system consists of distributed physical manufacturing systems and a platform containing the virtual manufacturing systems mapped from the physical ones. We call such manufacturing systems platform-based smart manufacturing systems (PSMSs). A PSMS can therefore be regarded as a huge cyber-physical system with the cyber part being the platform and the physical part being the corresponding physical manufacturing system. A significant issue for a PSMS is how to optimally schedule the aggregated resources. Multi-agent technology provides an effective approach for solving this issue. In this paper we propose a multi-agent architecture for scheduling in PSMSs, which consists of a platform-level scheduling multi-agent system (MAS) and an enterprise-level scheduling MAS. Procedures, characteristics, and requirements of scheduling in PSMSs are presented. A model for scheduling in a PSMS based on the architecture is proposed. A case study is conducted to demonstrate the effectiveness of the proposed architecture and model. [ABSTRACT FROM AUTHOR]

This paper aims to introduce the concept of cloud manufacturing (CMfg) in the injection molding industry. The CMfg platform for injection molding enterprises is built to improve the sharing, circulation and integration of the injection molding resources. With the implementation of the Internet of Things technologies in the traditional injection molding shop, the real-time manufacturing information of resources can be accurately captured and the entire molding process becomes more visible and traceable. The virtual machining service of the injection molding machine is encapsulated as a cloud service that published into the platform for on-demand use. When task orders are published, through the presented task-driven proactive service discovery method, competent services can be quickly found. The custom-oriented evaluation method based on technique for order preference by similarity to ideal solution is designed to help the demanders to find satisfying services according to their customized criteria. Since the task orders arrive dynamically, after these orders are assigned to the specified machine, a real-time order dispatching mechanism is developed to provide an optimal scheduling plan for the cloud service. Finally, the proposed framework and methods are illustrated by a numerical simulation. [ABSTRACT FROM AUTHOR]

Due to the wide usage of digital devices and easy access to the edge items in manufacturing industry, massive industrial data is generated and collected. A data-driven smart production line (SPL), which is a basic cell in a smart factory, is derived primarily. This paper studies the data-driven SPL and its common factors. Firstly, common factors such as integration, data-driven, service collaboration, and proactive service of SPL are investigated. Then, a data-driven method including data self-perception, data understanding, decision-making, and precise control for implementing SPL is proposed. As a reference, the research of the common factors and the data-driven method could offer a systematic standard for both academia and industry. Moreover, in order to validate this method, this paper presents an industrial case by taking an energy consumption forecast and fault diagnosis based on energy consumption data in a prototype of LED epoxy molding compound (EMC) production lines for example. [ABSTRACT FROM AUTHOR]

A series of Nb-doped (Ce,Cr)xO2-Nb2O5 mixed oxides with varying (Ce,Cr)xO2/Nb2O5 mass ratio were prepared by a co-precipitation method and evaluated for the catalytic performance of eliminating 1,2-dichloroethane (DCE). The results indicate that there exists a strong synergistic effect between acid sites and redox species in (Ce,Cr)xO2-Nb2O5 improving the catalytic activity for DCE oxidation. Appropriate Nb doping could promote the high dispersion and the interaction of metal oxides in the (Ce,Cr)xO2-Nb2O5 catalysts, resulting in the formation of more Cr6+ species with strong oxidizing ability and excellent mobility of oxygen species from bulk to surface to create more active sites for DCE deep oxidation. The (Ce,Cr)xO2-Nb2O5 catalysts with (Ce,Cr)xO2/Nb2O5 ratios of 2/1~1/2 exhibit excellent catalytic activity and durability for DCE degradation in dry air as well as benzene or water vapor, and less chlorinated byproduct is produced during the degradation of DCE. [ABSTRACT FROM AUTHOR]

The current study on digital factory (DF) meets some problems, such as disconnected manufacturing sites, independent digital models, isolated data, and non-self-controlled applications. In order to move current situation of DFs forward towards smart manufacturing, this paper attempts to present an overview of current digital situation of factories, and propose a systematical framework of cyber-physical integration in factories, with consideration of the concept of digital twin and the theory of manufacturing service. Particularly, the proposed framework includes four key issues, i.e., (a) fully interconnected physical elements integration, (b) faithful-mirrored virtual models integration, (c) all of elements/flows/businesses-covered data fusion, and (d) data-driven and application-oriented services integration. The corresponding implementable solutions of these four key issues are discussed in turn. As a reference, this paper is promising to bridge the gap in factories from current digital situation to smart manufacturing, so as to effectively facilitate their smart production. [ABSTRACT FROM AUTHOR]