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In this study, the physiochemical properties of corn oil and its oxidative stability in an O/W emulsion were studied following short‐term (120 min) deep‐frying of chicken wing. The results showed that the levels of polyunsaturated fatty acids in corn oil decreased after frying. Furthermore, total polar compound content in frying oil was significantly increased to 11.3%. Fourier transform infrared spectra (FTIR) indicated that hydrolysis and oxidation reactions involving triglycerides occurred after frying. Additionally, the increased a* and b* values demonstrated that deep‐frying greatly enhanced the intensity of the red and yellow colors of corn oil. Frying reduced the oxidative stability of corn oil in an O/W emulsion as determined by the peroxide value and acid value. These findings indicated that short‐term deep‐frying of chicken wing deteriorated the quality of corn oil and decreased its oxidative stability in an O/W emulsion. Consumers should consider the potential hazards of food containing short‐term deep‐frying oil., Short‐term deep‐frying of chicken wing deteriorated the quality of corn oil and decreased its oxidative stability in an O/W emulsion. Consumers should consider the potential hazards of food containing short‐term deep‐frying oil.

In the new era of Industry 4.0, traditional manufacturing plants are forced to transform into smart factories with cyber-physical product creation systems. Robots are believed to be one of the key components of such systems. This paper presents an architecture of using cloud-based ubiquitous robotic systems for smart manufacturing of the customized product. A framework for designing a cloud-based ubiquitous robotic system (URS) is developed, which consists of the function, structure and behavior of a URS. After that, a procedure for the development of such a URS is provided. Finally, the implementation of a cloud-based ubiquitous robotic system for smart producing and assembly of a customized product shows that the proposed approach can achieve the goal of smart manufacturing of customized product by developing cloud-based ubiquitous robotic systems.

Integrated and collaborative manufacturing system develops as massive data are obtained by the Internet of Things (IoT) technology. However, the “trust tax” imposed on manufacturers during their countless collaborations with customers, suppliers, distributors, governments, service providers, and other manufacturers is very high. Blockchain is an emerging technology that can lead to more transparent, secure, and efficient transactions. It represents a new paradigm, as well as new thinking, of how data can be securely stored, integrated, and communicated among different stakeholders, organizations, and systems that unnecessarily trust each other. Blockchain is greatly useful for reducing the “trust tax,” especially beneficial for the small- and medium-sized enterprises that must tolerate much heavier trust tax than the established manufacturers. This paper investigates the blockchain-based security and trust mechanism and elaborates a particular application of blockchain for quality assurance, which is one of the strategic priorities of smart manufacturing. Data generated in a smart manufacturing process can be leveraged to retrieve material provenance, facilitate equipment management, increase transaction efficiency, and create a flexible pricing mechanism. The dairy industry is used to instantiate the value propositions of blockchain for quality assurance.

The monitoring of surface quality in machining is of great practical significance for the reliability and life of high-value products such as rocket, spacecraft and aircraft, particularly for their assembly interfaces of these products. Surface roughness is an important metric to evaluate the surface quality. The current research of online surface roughness prediction has the following limitations. The effect of the varying tool wear on the surface roughness is rarely considered in machining. In addition, the deteriorating trend of surface roughness and tool wear is different under variable cutting parameters. Prediction models trained under one set of cutting parameters fail when cutting parameters change. This paper proposes a surface roughness prediction method considering the varying tool wear under variable cutting parameters. A stacked autoencoder and long short-term memory network (SAE-LSTM) is designed as the basic surface roughness prediction model that uses tool wear conditions and sensor signals as the input. The transfer learning strategy is applied on SAE-LSTM such that the surface roughness online prediction under variable cutting parameters can be realized. Machining experiments for the assembly interface (Ti6Al4V material) of the aircraft’s vertical tail are conducted and the monitoring data are used to validate the proposed method. Ablations studies are carried out to evaluate the key modules of the proposed model. The experimental results show that the proposed method outperforms other models and well track the true surface roughness over time.

BackgroundDespite the survival benefits observed with immune checkpoint blockade (ICB) treatment—programmed cell death-1/programmed cell death ligand-1 (PD-1/PD-L1), many patients with cancer have not benefited from these agents because of impaired antigen presentation and other resistance mechanisms. To overcome resistance to checkpoint therapy, we designed bispecific antibodies (BsAbs) targeting CD89 and tumor antigens. We demonstrated their immunomodulatory efficacy as a separate treatment strategy or combined with immune checkpoint inhibitors.MethodsWe have previously generated a heterodimeric one-arm IgG1 Fc-based bispecific antibody. For animal efficacy studies, murine tumors in a humanized transgenic mice model were used to determine the effects of CD89-bispecific antibodies on antigen presentation and immune cell recruitment. The efficacy of the CD89 bispecific antibody against tumors resistant to pembrolizumab was evaluated in double-transgenic mice.ResultsBsAbs targeting CD89 on tumor-associated macrophages (TAMs) increased the ratio of M1:M2 and activated the antigen presentation, leading to increased cytotoxic T cell-mediated tumor regression. CD89-BsAbs also potentiated a combinational antitumor effect with PD-1/PD-L1 inhibitors and overcame the ICB resistance by augmenting cytotoxic T-cell infiltration and reshaping tumor immune microenvironment. In an hCD89/hPD-1 double transgenic mouse model engrafted with pembrolizumab-resistant B16-HER2 tumor cells, the HER2-CD89 bispecific antibody potently inhibited tumor growth.ConclusionsCD89 BsAbs targeting tumor antigens and TAMs controlled tumor growth in animal models by improving antigen presentation and T-cell infiltration. Our results suggest a general strategy for overcoming resistance to ICB.

In microbial research, the heterogeneity phenomenon is closely associated with microbial physiology in multiple dimensions. For now, A few studies were proposed in transcriptome and proteome analysis to discover the heterogeneity among single cells. However, microbial single cell metabolomics has not been possible yet. Herein, we developed a method, RespectM, based on discontinuous mass spectrometry imaging, which can detect more than 700 metabolites at a rate of 500 cells per hour. While ensuring the high throughput of RespectM, it integrates matrix sublimation, QC-based peak filtering, and batch correction strategies to improve accuracy. The results show that RespectM can distinguish single microbial cells from the blank matrix with an accuracy of 98.4%, depending on classification algorithms. Furthermore, to verify the accuracy of RespectM for distinguishing different single cells, we performed a classification test on Chlamydomonas reinhardtii single cells among allelic strains. The results showed an accuracy of 93.1%, which provides RespectM with enough confidence to perform microbial single cell metabolomics analysis. As we expected, untreated microbial cells will spontaneously undergo metabolic grouping coherence with genetic and biochemical similarities. Interestingly, the pseudo-time analysis also provided intuitive evidence on the metabolic dimension, indicating the cell grouping is based on microbial population heterogeneity. We believe that the RespectM can offer a powerful tool in the microbial study. Researchers can now directly analyze the changes in microbial metabolism at a single-cell level with high efficiency.

Mediator is a large modular protein assembly whose function as a coactivator of transcription is conserved in all eukaryotes. The Mediator complex can integrate and relay signals from gene-specific activators bound at enhancers to activate the general transcription machinery located at promoters. It has thus been described as a bridge between these elements during initiation of transcription. Here, we review recent studies on Mediator relating to its structure, gene specificity and general requirement, roles in chromatin architecture as well as novel concepts involving phase separation and transcriptional bursting. We revisit the mechanism of action of Mediator and ultimately put forward models for its mode of action in gene activation.

Plastic pollution has become one of the most pressing environmental issues today, leading to an urgent need to develop biodegradable plastics1-3. Polylactic acid (PLA) is one of the most promising biodegradable materials because of its potential applications in disposable packaging, agriculture, medicine, and printing filaments for 3D printers4-6. However, current biosynthesis of PLA entirely uses edible biomass as feedstock, which leads to competition for resources between material production and food supply7,8. Meanwhile, excessive emission of CO2 that is the most abundant carbon source aggravates global warming, and climate instability. Herein, we first developed a cyanobacterial cell factory for the de novo biosynthesis of PLA directly from CO2, using a combinational strategy of metabolic engineering and high-density cultivation (HDC). Firstly, the heterologous pathway for PLA production, which involves engineered D-lactic dehydrogenase (LDH), propionate CoA-transferase (PCT), and polyhydroxyalkanoate (PHA) synthase, was introduced into Synechococcus elongatus PCC7942. Subsequently, different metabolic engineering strategies, including pathway debottlenecking, acetyl-CoA self-circulation, and carbon-flux redirection, were systematically applied, resulting in approximately 19-fold increase to 15 mg/g dry cell weight (DCW) PLA compared to the control. In addition, HDC increased cell density by 10-fold. Finally, the PLA titer of 108 mg/L (corresponding to 23 mg/g DCW) was obtained, approximately 270 times higher than that obtained from the initially constructed strain. Moreover, molecular weight (Mw, 62.5 kDa; Mn, 32.8 kDa) of PLA produced by this strategy was among the highest reported levels. This study sheds a bright light on the prospects of plastic production from CO2 using cyanobacterial cell factories.

A reliable design of a composite structure needs to consider the failure of the composites. Hashin failure criterion is one of the most popular phenomenological models in engineering practice due to its simplicity of application. Although remarkable success has been achieved from the Hashin failure criterion, it does not always fit the experimental results very well. Over the past few years, a few experimental failure data have been collected. It would be of interest to leverage the existing data to improve the prediction of failure criteria. In this paper, we proposed to apply a framework that combines sparse regression with compressed sensing to discover failure criteria from data. Following the phenomenological failure models, we divided the failure of composites into tensile and compressive fiber modes, tensile and compressive matrix modes. Two examples were studied with the proposed framework. The first example was presented to demonstrate the capability of the framework. The data was generated by the Hashin failure criterion and added various magnitudes of noise. The proposed framework was implemented to discover the failure criterion from the noised data. For the second example, the proposed method was used to discover failure criteria from the experimental data which are collected from the first world wide failure exercise (WWFE I). Both examples show that the proposed method can discover the failure criteria accurately.

Background: CBX3 is a key gene that is involved in immune cell regulation, however, its prognostic values and its correlation with infiltrating lymphocytes in various cancers have not been clearly established. This study aims to investigate the role CBX3 in hepatocellular carcinoma (HCC).Methods: We first reviewed the expression of CBX3 in different cancers and adjacent tissues using oncomine database. Next, the authors focus on the expression of CBX3 in hepatocellular carcinoma. Therefore, the expression of CBX3 in hepatocellular carcinoma were analyzed through UALCAN online analysis website and the Human Protein Atlas (www.proteinatlas.org) website. In addition, we further found that CBX3 can be identified as an effective marker for the prognostic guidance of hepatocellular carcinoma according to the Kaplan-Meier plotter database and the Bioinformatics analysis online websites (www.aclbi.com). Next, we used the Bioinformatics analysis online websites to explore whether the expression level of CBX3 in liver cancer is related to the infiltration of certain immune cells. In addition, we also predicted the correlation between immune checkpoint and CBX3 in liver cancer.Results: The analysis results preliminarily show that CBX3 be expressed abnormally in many cancers, and CBX3 was significantly up-regulated in HCC. The high expression of CBX3 indicated survival outcomes and it showed a huge potential as a effective marker for the prognostic guidance of hepatocellular carcinoma. Furthermore, we found that CBX3 in liver cancer is related to the infiltration of certain immune cells, including CD4+ T cells, macrophages and B cells. In addition, the results showed HAVCR2 is most likely to become an effective immune checkpoint for HCC patients immunotherapy with high CBX3 expression.Conclusions: CBX3 is a potential diagnostic and prognostic marker in HCC and related to the infiltration of certain immune cells. It is expected to become a breakthrough point in immunotherapy in the future.

A sunscreen offers indispensable skin protection against UV damage and related skin diseases. However, due to the poor interfacial stability of sunscreen coatings on the skin, the synthetic ingredients in sunscreen creams easily fall off and enter aquatic environments, causing large ecological hazards and skin protection failure. Herein, we tackle this issue by introducing amyloid-like protein aggregates into a sunscreen to noticeably enhance the interfacial robustness of sunscreen coatings on the skin. The synthesis of such an agent to suppress sunscreen leakage can be achieved by manipulating the phase transition of bovine serum albumin (BSA) in a mild aqueous solution at room temperature. The resulting phase-transitioned BSA (PTB) aggregates effectively entrap the sunscreen ingredients to generate a uniform cream coating on the skin with robust amyloid-mediated interfacial adhesion stability. With continuous flushing in aquatic environments, such as salt water and seawater, this PTB-modified sunscreen (PTB sunscreen) coated on the skin maintains a retention ratio as high as >92%, which is 2-10 times higher than those of commercially available sunscreen products. The high retention ratio of the PTB sunscreen in aquatic environments demonstrates the great potential of amyloid-like protein aggregates in the development of leakage-free sunscreens with low ecosystem hazards and long-lasting UV protection in aquatic environments.

ConspectusProtein thin films (PTFs) with tunable structure and function can offer multiple opportunities in various fields such as surface modification, biomaterials, packaging, optics, electronics, separation, energy, and environmental science. Although nature may offer a variety of examples of high-level control of structure and function, e.g., the S layer of cells, synthetic alternatives for large-area protein-based thin films with fine control over both biological function and material structure are a key challenge, especially when aiming for facile, low-cost, green, and large-scale preparation as well as a further extension of function, such as the encapsulation and release of functional building blocks.Therefore, regarding the structure and function of PTFs, we will first briefly comment on the problems associated with PTF fabrication, and then, regarding the basis of our long-term research on protein-based thin films, we will summarize the new strategies that we have developed in recent years to explore and control the structure and function of PTFs for frontier research and practical applications.Inspired by naturally occurring protein amyloid fibrillization, we proposed the amyloid-like protein aggregation strategy to assemble proteins into supramolecular 2D films with extremely large sizes and enduring interfacial adhesion stability. This approach opened a new window for PTF fabrication in which the spontaneous interfacial 2D aggregation of protein oligomers instead of traditional 1D protofibril elongation directs the assembly of proteins. As a result, the film morphology, thickness, porosity, and function can be tailored by simply tuning the interfacial aggregation pathways.We further modified amyloid-like protein aggregation to develop chemoselective reaction-induced protein aggregation (CRIPA). It is well known that chemoselective reactions have been employed for protein modification. However, the application of such reactions in PTF fabrication has been overlooked. We initiated this new strategy by employing thiol-disulfide exchange reactions. These reactions are chemoselective toward proteins containing specific disulfide bonds with high redox potentials, resulting in amyloid-like aggregation and thin film formation. Functional proteins with immunity to such reactions can be encapsulated in thin films and released on demand without a loss of activity, opening a new avenue for the development of functional PTFs and coatings.Finally, the resultant amyloid-inspired PTFs, as a new type of biomimetic materials, provide a good platform for integration with various biomedical functions. Here, the creation of bioactive surfaces on virtually arbitrary substrates by amyloid-like PTFs will be discussed, highlighting antimicrobial, antifouling, molecular separation, and interfacial biomineralization activities that exceed those of their native protein precursors and synthetic alternatives.

The rapid growth of urbanization and population has aggravated the urban heat island (UHI) effect in urban agglomerations. However, because scholars have so far focused mainly on the magnitude of the UHI effect, there is still a lack of research on the quantitative evaluation of the relationship between urban expansion and the degree of the UHI effect from the urban agglomeration perspective. This paper analyzed the spatiotemporal characteristics and the interactive mechanism of the surface urban heat island footprint (SUHI FP) in the Yangtze River Delta urban agglomeration (YRDUA). The summer footprints (FPs) of 27 cities were extracted using a logistics model, and the temporal trend was estimated by a standard deviation ellipse (SDE). Furthermore, the authors used the classical machine-learning k-means algorithm to cluster the temperature attenuation curves to reveal development patterns in different cities. The results showed that the degree of FP expansion during the daytime was more apparent than at night, the area of urban growth positively correlated with a city’s population level, and from 2005 to 2018 (the period of the study), the spatial evolution for all cities showed an overall trend from east to west. These cities were divided roughly into three development patterns by clustering their 2018 temperature attenuation curves. These findings can provide a scientific basis for formulating effective land-use policies by giving a deeper understanding of the spatiotemporal changes in the SUHI FPs and their relationship with land cover in the YRDUA.

The application of intelligence to manufacturing has emerged as a compelling topic for researchers and industries around the world. However, different terminologies, namely smart manufacturing (SM) and intelligent manufacturing (IM), have been applied to what may be broadly characterized as a similar paradigm by some researchers and practitioners. While SM and IM are similar, they are not identical. From an evolutionary perspective, there has been little consideration on whether the definition, thought, connotation, and technical development of the concepts of SM or IM are consistent in the literature. To address this gap, the work performs a qualitative and quantitative investigation of research literature to systematically compare inherent differences of SM and IM and clarify the relationship between SM and IM. A bibliometric analysis of publication sources, annual publication numbers, keywords frequency, and top regions of research and development establishes the scope and trends of the currently presented research. Critical topics discussed include origin, definitions, evolutionary path, and key technologies of SM and IM. The implementation architecture, standards, and national focus are also discussed. In this work, a basis to understand SM and IM is provided, which is increasingly important because the trend to merge both terminologies rises in Industry 4.0 as intelligence is being rapidly applied to modern manufacturing and human–cyber–physical systems.

Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide. Studies have shown that bradykinin (BK) is highly expressed in liver cancer. We designed the novel BK receptor inhibitors J051-71 and J051-105, which reduced the viability of liver cancer cells and inhibited the formation of cancer cell colonies. J051-71 and J051-105 reduced cell proliferation and induced apoptosis in HepG2 and BEL-7402 cells, which may be due to the inhibition of the extracellular regulated protein kinase (ERK) signaling pathway. In addition, these BK receptor inhibitors reversed the cell proliferation induced by BK in HepG2 and BEL-7402 cells by downregulating B1 receptor expression. Inhibiting B1 receptor expression decreased the protein levels of p-ERK and reduced the malignant progression of HCC, providing a potential target for HCC therapy.

We report herein an unprecedented atroposelective dynamic kinetic resolution of Bringmann's lactones with C-nucleophiles. By the use of activated isocyanides as the reagent, a wide range of novel axially chiral oxazole-substituted biaryl phenols were accessed in high yields with high enantioselectivities. Key to the success of this process lies in the tandem atroposelective addition of isocyanides to the lactone substrate followed by a rapid cyclization, overcoming the challenge of stereochemical leakage induced by lactol formation.

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.

Phytochemical-enriched edible greens, sweet potato leaves (Ipomoea batatas L.), have become popular due to potential health benefits. However, the phytochemical contents in sweet potato leaves and their subsequent change over harvest stages and growth condition are mostly unknown. In this study, the anthocyanin profile and content in leaves of four sweet potato cultivars, i.e., white-skinned and white-fleshed Bonita, red-skinned and orange-fleshed Beauregard, red-skinned and white-fleshed Murasaki and purple-skinned and purple-fleshed P40, were evaluated. Fourteen anthocyanins were isolated and identified by HPLC-MSI/MS. The most abundant was cyanidin 3-caffeoyl-p-hydroxybenzoyl sophoroside-5-glucoside, which comprised up to 20% of the total anthocyanins. Of the young leaves (1st and 2nd slip cuttings), Bonita contained the highest anthocyanin content followed by P40. Of the mature leaves (vine stage), Beauregard had the greatest anthocyanin (592.5 ± 86.4 mg/kg DW) and total phenolic (52.2 ± 3 mg GAE/g DW). It should be noted that the lowest anthocyanin and total phenolic content of shoots were found in P40, while tubers of P40 contain the highest content of each. Furthermore, the increase in leaf anthocyanin content over the growth stages that was observed in three of the cultivars but not in P40. No significant difference of anthocyanin content was found in Beauregard leaves grown in the high tunnels when compared with that in the open field. This study demonstrated for the first time that anthocyanin levels were significantly changed in response to various growth stages but not high tunnel condition, indicating that the effect of anthocyanin biosynthesis in sweet potato leaves is highly variable and genotype specific.

Automated disassembly of End-of-Life (EoL) products can be difficult to implement due to uncertainties in their conditions. An automatic re-planning function is required to enable flexible adjustments of disassembly plans and thus increase disassembly efficiency. The re-planning function is able to detect subassemblies and separable components, and adjust disassembly sequences and directions when components interlock and are irremovable. This paper presents a two-pointer detection strategy to find detachable subassemblies very quickly. A summation operator and a list with two pointers are used to check the interferences between components in a minimum number of steps. Then, a ternary bees algorithm is proposed to identify new disassembly sequences and directions. The algorithm combines the merits of a greedy search and meta-heuristic techniques by using only three collaborative potential solutions and three concurrent operations. Experimental results show that the proposed approach is able to perform a rapid subassembly detection and sequence optimisation for a robotic disassembly task, thus allowing real-time re-planning.

Long noncoding RNA (lncRNA) urothelial carcinoma associated 1 (UCA1) has been reported to be highly expressed in many kinds of cancers. This meta-analysis summarized its potential prognostic value in digestive system malignancies. A meta-analysis was performed through a comprehensive search in PubMed, EMBASE, the Cochrane Library, Web of Science and Chinese National Knowledge Infrastructure (CNKI) for suitable articles on the prognostic impact of UCA1 in digestive system malignancies from inception to June 27, 2019. Pooled hazard ratios (HRs) with 95% confidence interval (95%CI) were calculated to summarize the effect. Sixteen studies were included in the study, with a total of 1504 patients. A significant association was observed between UCA1 abundance and poor overall survival (OS), and shorter disease-free survival (DFS) for patients with digestive system malignancies, with pooled HR of 2.07 (95%CI: 1.74-2.47), and of 2.50 (95%CI: 1.62-3.86). Subgroup analysis and sensitivity analysis suggested the reliability of our findings. It is suggested that UCA1 abundance may serve as a reliable predictive factor for poor prognosis in patients with digestive system malignancies.

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.

Task allocation (TA) problem is of critical importance in manufacturing industry, and determines the effectiveness and efficiency of advanced manufacturing systems. A proper TA approach can give an optimized arrangement of existing resources, enable manufacturing system's flexibility, thus improve both economic performance and social benefits. However, there is still no uniform analysis on TA to date, while it has been paid more attention from the view of manufacturing resource allocation. With the application of advanced information and manufacturing technologies, the TA process improved with intelligence or even smartness could respond to demand changes rapidly and maintain a good balance for supply-demand matching issues. In this paper, TA and its intelligent improvements are picked and investigated. The general workflow of TA is divided into six stages: task description and modelling, analysis and modelling of TA process, algorithm design and selection for TA, decision-making of TA, simulation, and task execution. Each stage is separately analyzed at first. In particular, the decision-making process of TA consists of two approaches: the traditional way of system-oriented process (SoP), and the task-oriented process (ToP). Researches show that the latter one can better suit current systems and their manufacturing environment. At last, future directions of TA are pointed out to make systems achieve much more intelligence.

Organizing multimodal Web pages into hot topics is the core step to grasp trends on the Web. However, the less-constrained social media generate noisy user-generated content, which makes a detected topic be less coherent and less interpretable. In this paper, we address this problem by proposing a coupled Poisson deconvolution to jointly handle topic detection and topic description. For the topic detection, the interestingness of a topic is estimated from the similarities refined by the description of topics; for the topic description, the interestingness of topics is leveraged to describe topics. Two processes cyclically detect interesting topics and generate the multimodal description of topics. This is the innovation of this paper, which just likes killing two birds with one stone. Experiments not only show the significantly improved accuracies for the topic detection but also demonstrate the interpretable descriptions for the topic description on two public data sets.

The rapid development of information and communication technologies (ICTs) and cyber–physical systems (CPSs) has paved the way for the increasing popularity of smart products. Context-awareness is an important facet of product smartness. Unlike artifacts, various bio-systems are naturally characterized by their extraordinary context-awareness. Biologically inspired design (BID) is one of the most commonly employed design strategies. However, few studies have examined the BID of context-aware smart products to date. This paper presents a structured design framework to support the BID of context-aware smart products. The meaning of context-awareness is defined from the perspective of product design. The framework is developed based on the theoretical foundations of the situated function–behavior–structure ontology. A structured design process is prescribed to leverage various biological inspirations in order to support different conceptual design activities, such as problem formulation, structure reformulation, behavior reformulation, and function reformulation. Some existing design methods and emerging design tools are incorporated into the framework. A case study is presented to showcase how this framework can be followed to redesign a robot vacuum cleaner and make it more context-aware. Keywords: Design method, Biologically inspired design, Context-awareness, Intelligent design

State-of-the-art technologies such as the Internet of Things (IoT), cloud computing (CC), big data analytics (BDA), and artificial intelligence (AI) have greatly stimulated the development of smart manufacturing. An important prerequisite for smart manufacturing is cyber–physical integration, which is increasingly being embraced by manufacturers. As the preferred means of such integration, cyber–physical systems (CPS) and digital twins (DTs) have gained extensive attention from researchers and practitioners in industry. With feedback loops in which physical processes affect cyber parts and vice versa, CPS and DTs can endow manufacturing systems with greater efficiency, resilience, and intelligence. CPS and DTs share the same essential concepts of an intensive cyber–physical connection, real-time interaction, organization integration, and in-depth collaboration. However, CPS and DTs are not identical from many perspectives, including their origin, development, engineering practices, cyber–physical mapping, and core elements. In order to highlight the differences and correlation between them, this paper reviews and analyzes CPS and DTs from multiple perspectives. Keywords: Cyber–physical systems (CPS), Digital twin (DT), Smart manufacturing, Correlation and comparison

With the advances in new-generation information technologies (New IT), such as internet of things (IoT), cloud computing, and big data, etc., the big data-driven smart manufacturing era is coming. The volume of data generated and collected in manufacturing process is explosively growing, and big data need to be transmitted from data resources to a fog or a cloud platform. However, some practical limitations, such as overfull bandwidth, and data loss, confine the promotion of smart manufacturing. The limiting capacity of current data communication technologies becomes the bottleneck for smart manufacturing systems. In this paper, fibre channel (FC) switch based on field programmable gate array (FPGA) is designed and implemented due to its high speed, low latency, and high performance transmission capacities. Categories of comparative experiments were conducted and a case study is presented, which indicate that the designed FC switch meets the need of big data transmission for smart manufacturing. Its advanced capacity of transmitting and processing big data opens a bright perspective for smart manufacturing.

l -Lactic acid is an important monomer of polylactic acid, a biodegradable plastic. In this study, we systematically investigated l -lactic acid fermentation by thermophilic Bacillus coagulans strains using the corncob residue (CCR), a widely available lignocellulosic material. Our results showed that 68.0 g/L l -lactic acid was obtained with a high yield of 0.85 g/g cellulose by B. coagulans strain H-1 after 36 h in batch fermentation. Strain H-1 could produce 79.1 g/L l -lactic acid with a yield of 0.76 g/g cellulose after 84 h in fed-batch fermentation. The results suggest that attractive lignocellulosic wastes such as CCR could be used in the efficient biomanufacture of l -lactic acid.

Background & Aims Genetic variability in the hepatitis B virus X gene (HBx) is frequently observed and is associated with hepatocellular carcinoma (HCC) progression. However, a genotype classification based on the full-length HBx sequence and the impact of genotypes on hepatitis B virus (HBV)-related HCC prognosis remain unclear. We therefore aimed to perform this genotype classification and assess its clinical impact. Methods We classified the genotypes of the full-length HBx gene through sequencing and a cluster analysis of HBx DNA from a cohort of patients with HBV-related HCC, which served as the primary cohort (n = 284). Two independent HBV-related HCC cohorts, a validation cohort (n = 171) and a serum cohort (n = 168), were used to verify the results. Protein microarray assay analysis was performed to explore the underlying mechanism. Results In the primary cohort, the HBx DNA was classified into 3 genotypes: HBx-EHBH1, HBx-EHBH2, and HBx-EHBH3. HBx-EHBH2 (HBx-E2) indicated better recurrence-free survival and overall survival for patients with HCC. HBx-E2 was significantly correlated with the absence of liver cirrhosis, a small tumor size, a solitary tumor, complete encapsulation and Barcelona Clinic Liver Cancer (BCLC) stage A-0 tumors. Additionally, HBx-E2 served as a significant prognostic factor for patients with BCLC stage B HCC after hepatectomy. Mechanistically, HBx-E2 is unable to promote proliferation in HCC cells and normal hepatocytes. It also fails to activate the Janus kinase 1 (JAK1)/signal transducer and activator of transcription 3 (STAT3)/STAT5 pathway. Conclusion Our study identifies a novel HBx genotype that is unable to promote the proliferation of HCC cells and suggests a potential marker to preoperatively predict the prognosis of patients with BCLC stage B, HBV-associated, HCC. Lay summary We classified a novel genotype of the full-length hepatitis B virus X gene (HBx), HBx-E2. This genotype was identified in tumor and nontumor tissues from patients with hepatitis B virus-related hepatocellular carcinoma. HBx-E2 could preoperatively predict the prognosis of patients with intermediate stage hepatocellular carcinoma, after resection.

The structure of Printed Circuit board (PCB) is very complicated because it consists of woven composites and custom defined conducting layers. To improve the reliability of PCB, it is critical to p...

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.

Digital twin (DT) is one of the most promising enabling technologies for realizing smart manufacturing and Industry 4.0. DTs are characterized by the seamless integration between the cyber and physical spaces. The importance of DTs is increasingly recognized by both academia and industry. It has been almost 15 years since the concept of the DT was initially proposed. To date, many DT applications have been successfully implemented in different industries, including product design, production, prognostics and health management, and some other fields. However, at present, no paper has focused on the review of DT applications in industry. In an effort to understand the development and application of DTs in industry, this paper thoroughly reviews the state-of-the-art of the DT research concerning the key components of DTs, the current development of DTs, and the major DT applications in industry. This paper also outlines the current challenges and some possible directions for future work.

International audience; The evolution from mass-produced to mass-customized and even personalized products, services, and product-service bundles leads to increasing complexity of operations management. These new realities challenge companies in both business-to-business and business-to-customer markets to compete not only on the traditional basis of cost, quality, and delivery time but also on the capabilities in managing this increasing operational complexity. The objective of this paper is to identify key challenges and opportunities in managing operations in complex customer-driven manufacturing covering the value network, requirements engineering, product configuration, and the production systems, as well as the opportunities for handling these through digitally-enabled methods and tools for materializing the Industry 4.0 vision of efficient lot-size-one productions.

Human-machine interaction (HMI) is a key technology for implementing smart manufacturing, which primarily focuses on the issues of communication, interaction, and cooperation between humans and machines. HMI has been widely studied in product lifecycle including product design, manufacturing, and service, but the efficiency and safety still cannot meet the new requirements for smart manufacturing along with new emerging technologies (e.g., big data, artificial intelligence, augmented reality, etc.). Digital twin (DT), as a new technology to realize cyber-physical integration, provides a new chance to address the above problems. Using a five-dimension DT model driven by real-time data, HMI will generate a more flexible interactive mode to improve machine efficiency and operation safety. In this paper, HMI in product lifecycle is investigated first, and the shortness and new requirements are analyzed, then a DT enhanced HMI framework is proposed to address the shortness.

The third wave of information technology (IT) competition has enabled one promising value co-creation proposition, Smart PSS (smart product-service systems). Manufacturing companies offer smart, connected products with various e-services as a solution bundle to meet individual customer satisfaction, and in return, collect and analyze usage data for evergreen design purposes in a circular manner. Despite a few works discussing such value co-creation business mechanism, scarcely any has been reported from technical aspect to realizing this data-driven manufacturer/service provider-customer interaction cost-effectively. To fill this gap, a novel hybrid crowd sensing approach is proposed, and adopted in the Smart PSS context. It leverages large-scale mobile devices and their massive user-generated/product-sensed data, and converges with reliable static sensing nodes and other data sources in the smart, connected environment for value generation. Both the proposed hybrid crowd sensing conceptual framework and its systematic information modeling process are introduced. An illustrative example of smart water dispenser maintenance service design is given to validate its feasibility. The result shows that the proposed approach can be a promising manner to enable value co-creation process cost-effectively. Funding Agencies|National Research Foundation (NRF), SingaporeSingapore National Research Foundation; Delta Electronics International (Singapore) Pte., Ltd., through the Corporate Laboratory@ University Scheme, Nanyang Technological University, Singapore [RCA-16/434]

Green material optimal-selection in product design is a key issue for realizing sustainable manufacturing. In order to improve the accuracy and efficiency for green material optimal-selection in product, a new method driven by digital twin (DT) is proposed. In this method, an optimal evolvement model which is an evolving and high-fidelity digital twin of the physical product is first established. Then the collected cyber and physical data are fused. Based on the mode and fused data, the performance of green materials selection is simulated and evaluated. Finally, a case study on laptop design is given out to illustrate the proposed method.

In the big data era, a variety of new data can be collected from various sources and analyzed through different techniques to support the decision-making regarding product customization. Based on a thorough investigation of relevant data, this paper presents a new customization framework that is greatly driven by data. The framework includes three interrelated components: data module, design module, and service module. It is intended to promote smart customization in terms of product designing, manufacturing, pricing, marketing, and service. A case study of product customization in the footwear industry is presented to instantiate the implementation of the proposed framework.

Data acquisition of manufacturing equipment and process is the foundation to achieve intelligent shop-floor. Due to the instantaneity and suddenness of sudden small-volume data (SSVData), sensitive perception and high frequency acquisition are necessary. However, continuous high frequency acquisition will result in a large amount of data, which brings great difficulties to data storage and analysis. Meanwhile, the existing low frequency acquisition for conventional large-volume data (CLVData) cannot meet the requirements of SSVData. Therefore, this paper systematically analyzes the differences and relationship between CLVData and SSVData, and investigates state-of-the-art of both in shop-floor. Then a novel variable frequency sampling method is proposed, which solves the contradiction between low frequency acquisition for CLVData and high frequency acquisition for SSVData.