Fujian University of Technology

The age of big data is now coming. But the traditional data analytics may not be able to handle such large quantities of data. The question that arises now is, how to develop a high performance platform to efficiently analyze big data and how to design an appropriate mining algorithm to find the useful things from big data. To deeply discuss this issue, this paper begins with a brief introduction to data analytics, followed by the discussions of big data analytics. Some important open issues and further research directions will also be presented for the next step of big data analytics.

In recent years, with the rapid development of Internet technology, online shopping has become a mainstream way for users to purchase and consume. Sentiment analysis of a large number of user reviews on e-commerce platforms can effectively improve user satisfaction. This paper proposes a new sentiment analysis model-SLCABG, which is based on the sentiment lexicon and combines Convolutional Neural Network (CNN) and attention-based Bidirectional Gated Recurrent Unit (BiGRU). In terms of methods, the SLCABG model combines the advantages of sentiment lexicon and deep learning technology, and overcomes the shortcomings of existing sentiment analysis model of product reviews. The SLCABG model combines the advantages of the sentiment lexicon and deep learning techniques. First, the sentiment lexicon is used to enhance the sentiment features in the reviews. Then the CNN and the Gated Recurrent Unit (GRU) network are used to extract the main sentiment features and context features in the reviews and use the attention mechanism to weight. And finally classify the weighted sentiment features. In terms of data, this paper crawls and cleans the real book evaluation of dangdang.com, a famous Chinese e-commerce website, for training and testing, all of which are based on Chinese. The scale of the data has reached 100000 orders of magnitude, which can be widely used in the field of Chinese sentiment analysis. The experimental results show that the model can effectively improve the performance of text sentiment analysis.

Abstract Researches on flexible thermoelectric materials usually focus on conducting polymers and conducting polymer-based composites; however, it is a great challenge to obtain high thermoelectric properties comparable to inorganic counterparts. Here, we report an n-type Ag 2 Se film on flexible nylon membrane with an ultrahigh power factor ~987.4 ± 104.1 μWm −1 K −2 at 300 K and an excellent flexibility (93% of the original electrical conductivity retention after 1000 bending cycles around a 8-mm diameter rod). The flexibility is attributed to a synergetic effect of the nylon membrane and the Ag 2 Se film intertwined with numerous high-aspect-ratio Ag 2 Se grains. A thermoelectric prototype composed of 4-leg of the Ag 2 Se film generates a voltage and a maximum power of 18 mV and 460 nW, respectively, at a temperature difference of 30 K. This work opens opportunities of searching for high performance thermoelectric film for flexible thermoelectric devices.

This research paper focuses on a water quality prediction model which requires high-quality data. In the process of construction and operation of smart water quality monitoring systems based on Internet of Things (IoT), more and more big data are produced at a high speed, which has made water quality data complicated. Taking advantage of the good performance of long short-term memory (LSTM) deep neural networks in time-series prediction, a drinking-water quality model was designed and established to predict water quality big data with the help of the advanced deep learning (DL) theory in this paper. The drinking-water quality data measured by the automatic water quality monitoring station of Guazhou Water Source of the Yangtze River in Yangzhou were utilized to analyze the water quality parameters in detail, and the prediction model was trained and tested with monitoring data from January 2016 to June 2018. The results of the study indicate that the predicted values of the model and the actual values were in good agreement and accurately revealed the future developing trend of water quality, showing the feasibility and effectiveness of using LSTM deep neural networks to predict the quality of drinking water.

The preparation, characterization, fluorescence (FL) and electrochemiluminescence (ECL) of graphite-like carbon nitride nanoflake particles (g-C(3)N(4) NFPs) and nanoflake films (g-C(3)N(4) NFFs) have been reported. Highly water-dispersible g-C(3)N(4) NFPs with a height of ~5 to 35 nm and a lateral dimension of ~40 to 220 nm have been extracted from bulk g-C(3)N(4) materials by chemical oxidation. New, stable and defined g-C(3)N(4) NFFs can be easily obtained by drying NFPs on certain hydrophilic substrates such as glass or electrode surfaces. Both g-C(3)N(4) NFPs and g-C(3)N(4) NFFs have good FL activities, i.e. they can give strong blue light (435 nm) emission under UV light (365 nm) excitation. The as-prepared g-C(3)N(4) NFFs on a glassy carbon electrode exhibit strong non-surface state ECL activity in the presence of reductive-oxidative coreactants, including dissolved oxygen (O(2)), hydrogen peroxide (H(2)O(2)) and peroxydisulfate (S(2)O(8)(2-)) and give rise to blue light emission (435 nm), which is the same as the wavelength of FL. The non-surface state ECL mechanisms of the g-C(3)N(4) NFF-coreactant systems have been studied and discussed in detail.

A new type of deacetylated cellulose acetate (DA)@polydopamine (PDA) composite nanofiber membrane was fabricated by electrospinning and surface modification. The membrane was applied as a highly efficient adsorbent for removing methylene blue (MB) from an aqueous solution. The morphology, surface chemistry, surface wettability, and effects of operating conditions on MB adsorption ability, as well as the equilibrium, kinetics, thermodynamics, and mechanism of adsorption, were systematically studied. The results demonstrated that a uniform PDA coating layer was successfully developed on the surface of DA nanofibers. The adsorption capacity of the DA@PDA nanofiber membrane reached up to 88.2 mg/g at a temperature of 25 °C and a pH of 6.5 after adsorption for 30 h, which is about 8.6 times higher than that of DA nanofibers. The experimental results showed that the adsorption behavior of DA@PDA composite nanofibers followed the Weber's intraparticle diffusion model, pseudo-second-order model, and Langmuir isothermal model. A thermodynamic analysis indicated that endothermic, spontaneous, and physisorption processes occurred. Based on the experimental results, the adsorption mechanism of DA@PDA composite nanofibers was also demonstrated.

As a fast emerging topic, van der Waals (vdW) heterostructures have been proposed to modify two-dimensional layered materials with desired properties, thus greatly extending the applications of these materials. In this work, the stacking characteristics, electronic structures, band edge alignments, charge density distributions and optical properties of blue phosphorene/transition metal dichalcogenides (BlueP/TMDs) vdW heterostructures were systematically studied based on vdW corrected density functional theory. Interestingly, the valence band maximum and conduction band minimum are located in different parts of BlueP/MoSe2, BlueP/WS2 and BlueP/WSe2 heterostructures. The MoSe2, WS2 or WSe2 layer can be used as the electron donor and the BlueP layer can be used as the electron acceptor. We further found that the optical properties under visible-light irradiation of BlueP/TMDs vdW heterostructures are significantly improved. In particular, the predicted upper limit energy conversion efficiencies of BlueP/MoS2 and BlueP/MoSe2 heterostructures reach as large as 1.16% and 0.98%, respectively, suggesting their potential applications in efficient thin-film solar cells and optoelectronic devices.

Covalent triazine-based frameworks (CTFs) with a graphene-like layered morphology have been controllably synthesized by the trifluoromethanesulfonic acid-catalyzed nitrile trimerization reactions at room temperature via selecting different monomers. Platinum nanoparticles are well dispersed in CTF-T1, which is ascribed to the synergistic effects of the coordination of triazine moieties and the nanoscale confinement effect of CTFs. CTF-T1 exhibits excellent photocatalytic activity and stability for H2 evolution in the presence of platinum under visible light irradiation (λ ≥ 420 nm). The activity and stability of CTF-T1 are comparable to those of g-C3 N4 . Importantly, as a result of the tailorable electronic and spatial structures of CTFs that can be achieved through the judicial selection of monomers, CTFs not only show great potential as organic semiconductor for photocatalysis but also may provide a molecular-level understanding of the inherent heterogeneous photocatalysis.

Background: Multiphoton microscopy (MPM) offers a feasible approach for the biopsy in clinical medicine, but it has not been used in clinical applications due to the lack of efficient image processing methods, especially the automatic segmentation technology. Segmentation technology is still one of the most challenging assignments of the MPM imaging technique. Methods: The MPM imaging segmentation model based on deep learning is one of the most effective methods to address this problem. In this paper, the practicability of using a convolutional neural network (CNN) model to segment the MPM image of skin cells in vivo was explored. A set of MPM in vivo skin cells images with a resolution of 128×128 was successfully segmented under the Python environment with TensorFlow. A novel deep-learning segmentation model named Dense-UNet was proposed. The Dense-UNet, which is based on U-net structure, employed the dense concatenation to deepen the depth of the network architecture and achieve feature reuse. This model included four expansion modules (each module consisted of four down-sampling layers) to extract features. Results: Sixty training images were taken from the dorsal forearm using a femtosecond Ti:Sa laser running at 735 nm. The resolution of the images is 128×128 pixels. Experimental results confirmed that the accuracy of Dense-UNet (92.54%) was higher than that of U-Net (88.59%), with a significantly lower loss value of 0.1681. The 90.60% Dice coefficient value of Dense-UNet outperformed U-Net by 11.07%. The F1-Score of Dense-UNet, U-Net, and Seg-Net was 93.35%, 90.02%, and 85.04%, respectively. Conclusions: The deepened down-sampling path improved the ability of the model to capture cellular fined-detailed boundary features, while the symmetrical up-sampling path provided a more accurate location based on the test result. These results were the first time that the segmentation of MPM in vivo images had been adopted by introducing a deep CNN to bridge this gap in Dense-UNet technology. Dense-UNet has reached ultramodern performance for MPM images, especially for in vivo images with low resolution. This implementation supplies an automatic segmentation model based on deep learning for high-precision segmentation of MPM images in vivo.

Abstract The drug‐loaded polyvinyl alcohol (PVA)/chitosan (CS) composite nanofibers intended to be used as matrix for transdermal drug delivery were fabricated by electrospinning, and then crosslinked through glulataraldehyde (GA). The morphology, chemical structure, thermal behavior, mechanical properties, hydrophilicity and drug release properties of drug‐loaded PVA/CS composite nanofibers before and after crosslinking were characterized. The results showed that the morphology of PVA/CS composite nanofibers was not been destroyed in both crosslinking and in vitro drug release process. The Young's modulus, tensile strength, thermal properties and hydrophobicity of crosslinked PVA/CS composite nanofibers significantly increased in comparison with those of PVA/CS (without crosslinking) due to the formation of crosslinking network structure. In vitro release studies showed that crosslinked PVA/CS composite nanofibers had lower drug release rate and smaller amount of drug burst release than that of PVA/CS. According to release exponent “ n ”, the release of ampicillin sodium from crosslinked PVA/CS composite nanofibers fit to the Fickian diffusion mechanism. Those results demonstrate the potential utilization of crosslinked PVA/CS composite nanofibers as a transdermal drug delivery system.

Energy efficiency and energy balancing are crucial research issues as per routing protocol designing for self-organized wireless sensor networks (WSNs). Many literatures used the clustering algorithm to achieve energy efficiency and energy balancing, however, there are usually energy holes near the cluster heads (CHs) because of the heavy burden of forwarding. As the clustering problem in lossy WSNs is proved to be a NP-hard problem, many metaheuristic algorithms are utilized to solve the problem. In this paper, a special clustering method called Energy Centers Searching using Particle Swarm Optimization (EC-PSO) is presented to avoid these energy holes and search energy centers for CHs selection. During the first period, the CHs are elected using geometric method. After the energy of the network is heterogeneous, EC-PSO is adopted for clustering. Energy centers are searched using an improved PSO algorithm and nodes close to the energy center are elected as CHs. Additionally, a protection mechanism is also used to prevent low energy nodes from being the forwarder and a mobile data collector is introduced to gather the data. We conduct numerous simulations to illustrate that our presented EC-PSO outperforms than some similar works in terms of network lifetime enhancement and energy utilization ratio.

Secure real-time data about goods in transit in supply chains needs bandwidth having capacity that is not fulfilled with the current infrastructure. Hence, 5G-enabled Internet of Things (IoT) in mobile edge computing is intended to substantially increase this capacity. To deal with this issue, in this article, we design a new efficient lightweight blockchain-enabled radio frequency identification (RFID)-based authentication protocol for supply chains in 5G mobile edge computing environment, called lightweight blockchain-enabled RFID-based authentication protocol (LBRAPS). LBRAPS is based on bitwise exclusive-or (XOR), one-way cryptographic hash and bitwise rotation operations only. LBRAPS is shown to be secure against various attacks. Moreover, the simulation-based formal security verification using the broadly-accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) tool assures that LBRAPS is secure. Finally, it is shown that LBRAPS has better trade-off among its security and functionality features, communication and computation costs as compared to those for existing protocols.

Arrays of ultrasmall and uniform carbon nanodots (CDs) are of pronounced interest for applications in optical devices. Herein, we describe a low-temperature calcination approach with rather inexpensive reactants. After glucose molecules had been loaded into the pores of metal-organic frameworks (MOFs), well-defined CD arrays were produced by heating to 200 °C. The size and spacing of the CDs could be controlled by the choice of templating MOF: HKUST-1, ZIF-8, or MIL-101. The sizes of the obtained CDs were approximately 1.5, 2.0, and 3.2 nm, which are close to the corresponding MOF pores sizes. The CD arrays exhibited interesting photophysical properties, including photoluminescence with tunable emission and pronounced nonlinear optical (NLO) effects. The NLO properties of the obtained CD arrays were significantly different from those of a CD suspension, thus indicating the existence of collective phenomena.

The Fourth Industrial Revolution (Industry 4.0) is reshaping the construction industry and bringing it into an intelligent construction era. Emerging technologies, such as the Building Information Modelling, Internet of Things, big data, cloud computing, and artificial intelligence, have penetrated into all stages of the building life cycle and play a significant role. However, the major issue of intelligent construction is integrating multiple technologies to create more potential opportunities rather than their fragmented application. Considering the various special characteristics of the construction industry and the high heterogeneity of these technologies, their integration in the construction industry is challenging and requires in-depth investigations. This paper summarizes the Industry 4.0-related technologies involved in the construction industry based on an analysis of the characteristics of the industry. Further, this study presents a framework of a cyber-physical system to integrate these technologies and improve the overall capabilities of construction organization and management. A case study of the Xiong'an citizen service center is introduced to verify the technological feasibility and preliminary implementation effect of the proposed framework. As forward-looking research, the significance of this paper may also to inspire more efforts in this field.

Recently, wireless sensor network (WSN) has drawn wide attention. It can be viewed as a network with lots of sensors that are autonomously organized and cooperate with each other to collect, process, and transmit data around targets to some remote administrative center. As such, sensors may be deployed in harsh environments where it is impossible for battery replacement. Therefore, energy efficient routing is crucial for applications that introduce WSNs. In this paper, we present an energy efficient routing schema combined with clustering and sink mobility technology. We first divide the whole sensor field into sectors and each sector elects a Cluster Head (CH) by calculating its members' weight. Member nodes calculate energy consumption of different routing paths to choose the optimal scenario. Then CHs are connected into a chain using the greedy algorithm for intercluster communication. Simulation results prove the presented schema outperforms some similar work such as Cluster-Chain Mobile Agent Routing (CCMAR) and Energy-efficient Cluster-based Dynamic Routing Algorithm (ECDRA). Additionally, we explore the influence of different network parameters on the performance of the network and further enhance its performance.

Introducing multiclusters and multiligands (mm) in a well-defined array will greatly increase the diversity of metal–organic frameworks (MOFs). Here, a series of porous mm-MOFs constructed from a pillared-layer and pore-space partition (PL-PSP) have been achieved. FJU-6 with {Co3}-cluster-based sheets and {Co6}-cluster-based pillars exhibits new (3,9,12)-connected llz topology. By using the substituted analogues of the ligands and metal ions, seven isoreticular mm-MOFs (FJU-6-X, X = PTB, TATB, Me-INA, F-INA, NDC, BrBDC, Ni) have been synthesized with the adjustable BET surface areas ranging from 731 to 1306 m2/g as well as the adsorption capacity of CO2 increasing by 77%. The C2H2/CO2 mixture can be effectively separated in the breakthrough experiments in the fixed bed filled with solid FJU-6-TATB at ambient temperature. In all, integrating pillared-layer strategy and pore-space partitioning is effective at constructing mm-MOFs with multivariate environments for the optimization of gas adsorption and separation.

Edge computing is an important tool for smart computing, which brings convenience to data processing as well as security problems. In particular, the security of data storage under edge computing has become an obstacle to its widespread use. To solve the problem, the mechanism combing blockchain with regeneration coding is proposed to improve the security and reliability of stored data under edge computing. Our contribution is as follows. 1) According to the three-tier edge computing architecture and data security storage requirements, we proposed hybrid storage architecture and model specifically adapted to edge computing. 2) Making full use of the data storage advantages of edge network devices and cloud storage servers, we build a global blockchain in the cloud service layer and local blockchain is built on the terminals of the Internet of things. Moreover, the regeneration coding is utilized to further improve the reliability of data storage in blockchains. 3) Our scheme provides a mechanism for periodically validating hash values of data to ensure the integrity of data stored in global blockchain.

The CO2 solubilities (including CO2 Henry's constant) in physical- and chemical-based ILs/DESs and the COSMO-RS models describing these properties were comprehensively collected and summarized. The summarized results indicate that chemical-based ILs/DESs are superior to physical-based ILs/DESs for CO2 capture, especially those ILs have functionalized cation and anion, and superbase DESs; some of the superbase DESs have higher CO2 solubilities than those of ILs; the best physical- and chemical-based ILs, as well as physical- and chemical-based DESs are [BMIM][BF4] (4.20 mol kg−1), [DETAH][Im] (11.91 mol kg−1), [L-Arg]-Gly 1:6 (4.92 mol kg−1) and TBD-EG 1:4 (12.90 mol kg−1), respectively. Besides the original COSMO-RS mainly providing qualitative predictions, six corrected COSMO-RS models have been proposed to improve the prediction performance based on the experimental data, but only one model is with universal parameters. The newly determined experimental results were further used to verify the perditions of original and corrected COSMO-RS models. The comparison indicates that the original COSMO-RS qualitatively predicts CO2 solubility for some but not all ILs/DESs, while the quantitative prediction is incapable at all. The original COSMO-RS is capable to predict CO2 Henry's constant qualitatively for both physical-based ILs and DESs, and quantitative prediction is only available for DESs. For the corrected COSMO-RS models, only the model with universal parameters provides quantitative predictions for CO2 solubility in physical-based DESs, while other corrected models always show large deviations (> 83%) compared with the experimental CO2 Henry's constants.

OBJECTIVE: To demonstrate whether abnormal glucose metabolism (diabetes and prediabetes) is associated with increased risk for cancer in the Chinese population and to identify factors that modify the risk of cancer among individuals with abnormal glucose metabolism. METHODS: Between 2011 and 2012, 259 657 community-dwelling adults, aged 40 years and older, were recruited from 25 centers across mainland China to participant in the baseline survey of the REACTION study, with follow-up investigations performed 3, 5, and 10 years later. Detailed questionnaires about lifestyles, physical and biochemical measurement, bio-samples including serum, urine, and whole blood for DNA extraction were collected for all the participants. RESULTS: The mean ± standard deviation (SD) age of this cohort was 57 ± 10 years. And the prevalence of pre-existing and newly diagnosed diabetes was 10.32% and 10.57%, respectively. A total of 4511 prevalent cancer cases (988 men and 3523 women) were identified, the prevalence was 1.79. Compared to those with normal glucose metabolism, men with diabetes had a significantly lower adjusted prevalence ratio (PR) of stomach cancer (PR: 0.38, 95% CI: 0.16-0.89), and women with diabetes had significantly higher adjusted PRs of cancer of all sites (PR: 1.36, 95% CI: 1.20-1.56), and cancer of the breast (PR: 1.56, 95% CI: 1.21-2.00), the endometrium (PR: 1.58, 95% CI: 1.16-2.15), and the thyroid (PR: 1.53, 95% CI: 1.03-2.27). CONCLUSION: The multi-center REACTION study has captured a broad range of data on physical, psychological and metabolic function as well as health status, biochemical and lifestyle information in 259 657 adults from the general population across the China.

With the increasing capacity of new energy in the power system, new energy cannot provide support for the system frequency directly. This characteristic of new energy affects the frequency stability of the power system. Therefore the control strategy of a virtual synchronous generator (VSG) is proposed to improve the frequency stability of the system. An adaptive virtual inertia control strategy based on an improved bang-bang control strategy for a micro-grid is presented. On one hand, it can make full use of the variability of virtual inertia to reduce dynamic frequency deviation. On the other hand, the steady-state interval of frequency and the steady-state inertia are set to improve the system frequency stability. Then the stability analysis of the value range of the virtual inertia is performed by the small signal model of the VSG for the micro-grid. Meanwhile, the ranges of virtual inertia and steady-state inertia are determined. Finally, Matlab/Simulink is applied to accomplish simulation experiments to compare various virtual inertia control strategies. The results indicate the effectiveness of the proposed strategy.