2022 Vol.28

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    Research progress on catalytic oxidation for the removal of volatile organic compounds
    JIAO Zheng, WU Minghong
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (1): 1-18.   DOI: 10.12066/j.issn.1007-2861.2350
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    The sources and types of typical volatile organic compounds (VOCs) are reviewed in this paper. The impact of the category of nanocatalysts and their preparation methods is discussed, and the key parameters controlling nanocatalyst performance, such as particle size, structure, and morphology, are evaluated. Current research trends on the development of supported noble- and transition-metal oxide catalysts to remove halocarbons, aromatic hydrocarbons, oxygenated organic compounds, aliphatic hydrocarbons, and nitrogen- and sulfur-containing compounds are then examined. Moreover, the effects of carbon monoxide and water vapor on the catalytic oxidation of VOCs are investigated. Finally, perspectives on the development of methods to achieve the catalytic oxidation of VOCs are presented.

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    Synthesis of Ce-doped Co3O4  nanoflowers with rich oxygen vacancies based on MOF template method for enhancing gas sensing performance
    HE Yongchao, LI Fei, YAN Bingjun, HE Xinhua, PU Xianjuan, NING Zhukai, CHENG Lingli, JIAO Zheng
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (1): 19-30.   DOI: 10.12066/j.issn.1007-2861.2284
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    Co3O4  nanomaterials have low sensitivity and long response/recovery time in gas sensor applications. Ce-doped Co-based metal organic framework (MOF) precursors were prepared by a simple solvothermal method and Ce-doped Co3O4  nanoflowers were then successfully synthesised by heat treatment. The morphology and composition of the materials were analysed by X-ray diffraction (XRD), scanning electron microscope (SEM), X-ray photoelectron spectroscope (XPS), energy dispersive spectroscope (EDS), and other characterization methods. The results indicated that Ce doping could effectively change the oxygen distribution and increase the number of oxygen vacancies in Co3O4 . The sensor made of this material exhibited an excellent sensing performance. At an operating temperature of 190 °C, the response to 100×10-6 n-butanol could reach 87.79 and the calculated theoretical detection limit could reach 122×10-9.

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    Effects of diesel fuel and Cd pollution on enzyme activity and microbial biomass in the soil
    HE Zichen, HU Xuefeng, LU Siwen, ZHAO Jinglong, LAN Guojun, LI Mei, ZHANG Weijie
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (1): 31-39.   DOI: 10.12066/j.issn.1007-2861.2233
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    A pot experiment was conducted to study the effects of pollutants of diesel fuel and Cd alone or in combination on the activities of urease (UR) and dehydrogenase (DE) and microbial biomass in non-polluted paddy soil collected from an urban setting in Shanghai. The experiment included seven treatments: control (CK), low content of diesel fuel (CYL), high content of diesel fuel (CYH), low content of Cd (CdL), high content of Cd (CdH), combination of high content of diesel fuel and Cd (CYH+CdH), and combination of high content of diesel fuel, Cd and crop seedlings (CYH+CdH+P). The activity of UR was significantly raised when the soil was polluted with CYH and Cd for the treatments of CYH and CYH + CdH. The maximum UR for the two treatments was 5.25 and 2.63 times that of CK, respectively. The microbial biomass for the treatments was also correspondingly raised to some extent. Conversely, the activity of DE in the soil for the CYL, CYH and CYH + CdH treatments was significantly inhibited. The influence of Cd on DE activity in the soil was relatively mild. DE was activated even when the soil was polluted with CdL.

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    Remediation of Cd-Zn-Cu contaminated river sediment by typical herbaceous plants
    SHEN Jiayi, GAO Mingjing, LIU Yu, HUANG Xun, GUO Hao, ZHANG Xinying
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (1): 40-48.   DOI: 10.12066/j.issn.1007-2861.2260
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    A typical black-odor urban river sediment in Shanghai was assessed in the study. Six characteristic herbaceous plants, namely Trifolium repens L., Festuca arundinacea, Lolium perenne L., Elymus dahuricus Turcz., Amaranthus hypochondriacus L., and Medicago sativa L., were used as experimental plants in the phytoremediation of Cd-Zn-Cu contaminated river sediment. The tolerance to heavy metals, enrichment and transport features, and remediation efficiencies of the six plants in the river sediment were explored. The results showed that, except for Trifolium repens L., the plants had good tolerance to heavy metals in the sediment. For Cd and Zn, the accumulation ability of Lolium perenne L. was higher, and the content of Zn in the plant was 707.69 mg/kg. The accumulation ability of Elymus dahuricus Turcz. to Cu was relatively higher. The accumulation of heavy metals in the plants was primarily within the roots, except for the accumulation of Zn in Medicago sativa L. The bioaccumulation factor of Cd and Zn in Festuca arundinacea and Lolium perenne L. was greater than 1. Furthermore, the Cd, Zn and Cu extraction efficiencies of Festuca arundinacea and Lolium perenne L. exceeded 7%, 6% and 2%, respectively, which suggested stronger phytoextraction potentiality of the Cd-Zn-Cu contaminated river sediment.

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    Ammonia nitrogen release from surface sediment of a reservoir in East China
    ZHU Yiping, GAO Peiyue, ZHAO Yiying, WANG Feifei, HUANG Xin, LI Huaizheng
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (1): 49-56.   DOI: 10.12066/j.issn.1007-2861.2331
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    To determine the contribution of ammonia nitrogen release from the surface sediments of reservoirs, the total nitrogen and ammonia nitrogen contents in surface sediments ($<$10 cm), overlying water, and interstitial water in typical areas of a reservoir were analyzed via on-site sampling of a reservoir in East China combined with an ammonia nitrogen release test, which revealed the temporal and spatial distribution characteristics of nitrogen in the reservoir sediments. The results showed that the ammonia nitrogen and total nitrogen contents in the reservoir showed clear seasonal changes, and the total nitrogen concentration in the surface sediment was higher than that in the summer. The total nitrogen concentration in the surface sediment was higher than that in the overlying water, and the ammonia nitrogen diffusion flux in the sediment was 0.18$\sim$0.53 mg/(m$^{2}\cdot $d), which was one order of magnitude lower than that of a heavily polluted reservoir. During the thermodynamic distribution of ammonia nitrogen in the sediment-interstitial water-overlying water, the surface sediment had an adsorption effect on ammonia nitrogen, which could buffer the sudden ammonia nitrogen pollution problem to a certain extent; however, the strong disturbance of the water flow could also promote the diffusion of ammonia nitrogen in the sediment to the water body in a short time.

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    Degradation characteristics of biodegradation of tetrabromobisphenol A by the novel aerobic strain W1-2
    YANG Shuxian, HU Xing
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (1): 57-66.   DOI: 10.12066/j.issn.1007-2861.2267
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    A novel strain, namely W1-2, was acclimatised and isolated from aerobic activated sludge in a mineral salt medium with tetrabromobisphenol A (TBBPA). Strain W1-2 was identified as Pseudomonas sp. according to the 16S rDNA sequence. After 5 days of aerobic incubation, the degradation rate of 10 mg/L TBBPA was 91.4% at 30 ℃, pH=7, and 150 r/min. The temperature, rotation speed, pH and TBBPA concentration could influence the degradation rate, among which pH was the most influential parameter. The optimal conditions for bacterial degradation and the growth of strain W1-2 were 150 r/min, 30~35 ℃, initial TBBPA concentration of 10 mg/L, and pH=8 according to a single-factor experiment. Strain W1-2 had an excellent degradation ability at high levels of TBBPA(30 mg/L) and under micro-oxygen conditions (0 r/min) without the support of other carbon sources. This study on the degradation characteristics of strain W1-2 provides a new perspective to explore the microbial removal of TBBPA in aerobic environments.

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    Copper-ion-doped vanadium-based coordination polymers for high-performance hybrid supercapacitors
    GAO Yun, ZHI Chuanwei, LIU Tongxin, LÜ Liping
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (1): 67-79.   DOI: 10.12066/j.issn.1007-2861.2221
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    The microspheres of copper-ion-doped vanadium-based coordination polymers (V-Cu-HHTP) with diameters of approximately 1.5 μm are prepared through two steps of microwave treatment. The introduction of Cu$^{2+}$ is achieved by cation exchange and is assumed to improve electronic conductivity and provide the synergic effect derived from the bimetallic feature of the vanadium-based coordination polymers. Results show that the V-Cu-HHTP exhibit good specific capacitance and cycle stability when used as electrode materials in supercapacitors. More specifically, V-Cu-HHTP show a capacitance of287 F$\cdot$g$^{-1}$ at 1 A$\cdot$g$^{-1}$ and have a 98.6% capacitance retention of 10 A$\cdot$g$^{-1}$ after 3 000 charging--discharging cycles. In comparison, the V-HHTP electrode shows a lower specific capacitance of 227 F$\cdot$g$^{-1}$ at 1 A$\cdot$g$^{-1}$ with a 94.2% capacitance retention of 10 A$\cdot$g$^{-1}$. An asymmetric supercapacitor is assembled with the V-Cu-HHTP as a cathode and activated carbon (AC) as an anode (denoted as V-Cu-HHTP//AC). The assembled V-Cu-HHTP//AC device can achieve a potential window of 1.6 V, and the energy density is as high as 44.1 Wh$\cdot$Kg$^{-1}$ when the power density is 795.0 W$\cdot$Kg$^{-1}$. We attribute these excellent electrochemical properties to the following. First, the bimetal-based coordination polymer provides an excellent synergistic effect derived from the two metallic elements. Second, Cu doping improves the electronic conductivity and structural stability of the vanadium-based coordination polymers. The porous characteristics of V-Cu-HHTP provide numerous active sites to the electrode, thus leading to improved energy storage properties.

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    Characteristics of nitrogen-containing components within submicron particles in Shanghai
    ZHANG Yuying, CHEN Hao, FENG Jialiang
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (1): 80-90.   DOI: 10.12066/j.issn.1007-2861.2232
    Abstract1262)   HTML2)    PDF(pc) (2728KB)(117)       Save

    The formation, transformation, transportation, and deposition of inorganic and organic nitrogen within fine particles is critical in the nitrogen cycle. To understand the concentrations, compositions, and seasonal variations of the nitrogen-containing components within submicron particles (PM$_{1}$) in Shanghai, PM$_{1}$ samples were collected in Shanghai during 2017—2018 using a high-volume sampler. The concentrations of water-soluble ions and water-soluble organic nitrogen (WSON) were measured using ion chromatography and UV/Vis photometry. The annual average concentrations of NH$_{4}^{+}$-N, NO$_{3}^{-}$-N, and WSON within PM$_{1}$ in Shanghai were 1.79, 0.97, and 0.41 μg/m3 respectively. NH$_{4}^{+}$-N showed the highest contribution (56 %) to the water-soluble total nitrogen (WSTN), followed by NO$_{3}^{-}$-N (31 %), while the annual contribution of WSON to WSTN was 13 %. The concentrations of the nitrogen-containing components within PM$_{1}$ in Shanghai were highest in winter and lowest in summer. However, the contributions of NH$_{4}^{+}$-N, NO$_{3}^{-}$-N, and WSON to WSTN differed. The contribution of NH$_{4}^{+}$-N to WSTN showed a small seasonal variation, and a distinct trend of higher in winter (38 %) and lower in summer (18 %) was observed for the contribution of NO$_{3}^{-}$-N, while the contribution of WSON to WSTN was the highest in summer (22 %) and lowest in winter (8 %). Positive matrix factorisation (PMF) showed that secondary formation and the burning of biomass contributed 48 % of the WSON within PM$_{1}$ in Shanghai, with coal combustion contributing 11 %, secondary formation from biogenic volatile organic compounds 20 %, and emissions from kitchens and vehicles 21 %. The sources of WSON varied significantly according to season.

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    Adsorption and transport properties of the lithium ion in a covalent organic framework/carbon nanotube composite by molecular simulation
    XU Yi, CUI Zhiyuan, WU Fan, YUAN Bin
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (1): 91-101.   DOI: 10.12066/j.issn.1007-2861.2224
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    In this study, the adsorption and transport properties of the lithium ion (Li$^{+})$ in a covalent organic framework/carbon nanotube composite (COF@CNT) are investigated through molecular simulation. The adsorption sites and sequence of Li$^{+}$ are defined and the corresponding adsorption energy is obtained. In addition, apparent change in the morphology of the COF@CNT is identified. When saturated adsorption is reached, the volumetric change rate of the COF@CNT is only 0.25. Simultaneously, the average voltage is maintained at greater than 2.00 V, and the theoretical capacity reaches as high as1402.47 mAh/g. Finally, the electronic conductivity of Li$^{+}$ inside the COF@CNT exceeds that in a pure CNT. The results of this study can provide a theoretical basis for the practical application of these systems.

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    Synthesis of functionalised CMK-3 ordered mesoporous carbon materials and their humidity sensitivity performance
    ZHANG Zhiwei, FAN Yu, MA Zhiheng, LI Runlong, HAO Chenran, XU Jiaqiang
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (1): 102-110.   DOI: 10.12066/j.issn.1007-2861.2262
    Abstract705)   HTML9)    PDF(pc) (9644KB)(111)       Save

    Mesoporous materials are widely used in several fields, such as energy storage, catalysis, and humidity sensors. In this study, ordered mesoporous silicon SBA-15 is used as a template to prepare CMK-3 mesoporous carbon materials with specific surface areas greater than 1 000 m2/g using a nanocasting method. The carbon materials are then modified by carboxyl functionalisation using ammonium persulfate. Fourier transform infrared spectroscopy (FTIR), transmission electron microscopy (TEM), small-angle X-ray diffraction (XRD), and Brunauer-Emmet-Teller (BET) nitrogen adsorption and desorption methods are used to characterise the materials. Results show that both before and after functionalisation, CMK-3 mesoporous carbon materials have favourable ordered mesoporous structures. Using a quartz crystal microbalance (QCM) test platform, the humidity sensing performances of the materials are tested. Results show that after carboxyl functional modification, the materials exhibit a high response to different humidity environments, and their humidity response is clearly enhanced. Particularly in a 97% RH high-humidity environment, a remarkable response of as high as 1 600 Hz is obtained, which proves beneficial to the application of CMK-3 humidity sensitive materials in high-humidity environments.

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    Effects of Nb on hydrogen diffusion in Fe-13Cr-6Al-2Mo alloys
    MA Xingxing, CHEN Yexin, ZHANG Huawei
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (1): 111-120.   DOI: 10.12066/j.issn.1007-2861.2236
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    In this study, the diffusion behaviors of hydrogen atoms in Fe-13Cr-6Al-2Mo and Fe-13Cr-6Al-2Mo-0.5Nb alloys were studied by the electrochemical hydrogen permeation method. The effects of 0.5% Nb (mass fraction) added to the alloy on the diffusion behavior of hydrogen atoms in the Fe-13Cr-6Al-2Mo alloy were analyzed. Results showed that the grain of the alloy was refined, and a large amount of fine Fe$_2$Nb phase was dispersed in the Fe-13Cr-6Al-2Mo alloy when 0.5% Nb was added. The results also showed that 0.5% Nb greatly reduced the apparent diffusion coefficient of hydrogen in the Fe-13Cr-6Al-2Mo alloy and increased the diffusion activation energy of hydrogen in the alloy by 50.3%. The Nb atom effectively reduced the diffusion rate of hydrogen in the FeCrAlMo-based alloy by increasing the hydrogen trap concentration in the alloy.

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    Effect of molybdenum disulfide-graphene oxide nanohybrids on anticorrosive waterborne polyurethane acrylate coatings
    WANG Jiangyu, GUO Xiaofeng, SHI Lei, CHEN Liquan, WANG Xu, LIU Liqi
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (1): 121-131.   DOI: 10.12066/j.issn.1007-2861.2249
    Abstract3830)   HTML14)    PDF(pc) (19280KB)(167)       Save

    Metal corrosion considerably affects the global economy; thus, strategies for the prevention of metal corrosion have gained considerable research and industrial attention. To develop one such strategy, herein, molybdenum disulfide-graphene oxide (MoS$_{2}$-GO) nanohybrids is synthesized by a simple method. The as-synthesized MoS$_{2}$-GO nanohybrids are added to waterborne polyurethane acrylates (WPUA) to prepare anticorrosive MoS$_{2}$-GO/WPUA coatings. Firstly, silane-functionalized molybdenum disulfide (A-MoS$_{2}$) via simple covalent functionalization with 3-aminopropyltriethoxysilane (APTES) is obtained. Secondly, MoS$_{2}$-GO nanohybrids are synthesized by a simple method in N, N-dimethylformamide (DMF). Finally, different amounts of the MoS$_{2}$-GO nanohybrids (0, 0.2%, 0.4%, 0.6%, 0.8%, 1.0%) are added to WPUA to prepare MoS$_{2}$-GO/WPUA coatings. The results show that the MoS$_{2}$-GO nanohybrids are synthesized successfully. The surface of the 0.4% MoS$_{2}$-GO/WPUA coating consisting the 0.4% MoS$_{2}$-GO sample is smooth. The contact angle is 99.67$^{\circ}$ and $\vert Z\vert _{0.01{\rm Hz}}$ is 3.19$\times $10$^{7}\Omega \cdot $cm2 after 28 days of immersion in 3.5% NaCl. The Nyquist plot shows a semicircle and the phase angle diagram has a single peak close to 90$^{\circ}$, indicating high corrosion resistance.

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    Swelling characteristics of a mixture of crushed granite and bentonite under the condition of alkali-thermo coupling
    QIN Aifang, HU Hongliang
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (1): 132-144.   DOI: 10.12066/j.issn.1007-2861.2238
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    During the life of a high-level radioactive waste repository, the decay heat of nuclear waste and alkaline pore water produced by the aging of concrete affect the working performance of the buffer material. To study the effects of alkali-thermo coupling on the swelling characteristics of buffer materials, NaOH solution is used in this study to simulate alkaline pore water, and a water bath pot is used to provide a constant temperature solution, where the solution is continuously circulated in a self-developed corrosion-resistant consolidator. The evolution curves of the swelling force are obtained through swelling force experiments under concentrations of 0.1, 0.5, and 1.0 mol/L and temperatures of 25 $^\circ$C and 50 $^\circ$C. Results showed that at the same temperature, the maximum and final swelling force of the sample decreased with an increase in the solution concentration. In addition, at the same concentration of alkaline solution infiltration, the maximum and final swelling force of the sample decreased with an increase in the granite mixing rate. Under the same granite mixing rate and same infiltration solution, the maximum and final swelling force of the sample decreased with an increase in temperature. The attenuation degree of swelling force increased with increases in solution concentration and temperature. For the mixture of crushed granite and bentonite, under the same temperature and same infiltration solution, the attenuation degree of swelling force decreased with an increase in the granite mixing rate.

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    Thermodynamic analysis of a fluid-saturated porous thermo-elastic symmetric plane
    ZHU Yuanyuan, YANG Xiao, WU Haitao
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (1): 145-156.   DOI: 10.12066/j.issn.1007-2861.2264
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    To address problems related to geometric nonlinearity and the local thermal equilibrium, the thermodynamic characteristics of an incompressible fluid-saturated porous thermo-elastic half-plane subjected to surface temperature loadings are studied. First, a mathematical model of the problem of geometric nonlinearity is established based on the porous media theory. Then, a synthetic numerical computation method is presented to simulate the numerical results of the problem. Here, the differential quadrature method and second-order backward difference scheme are applied to discretize the mathematical model in the spatial and time domains, respectively. In addition, the Newton-Raphson iterative method is used to solve nonlinear algebraic equations and to present the numerical results of the problem. The method presented in this study is proven to be effective and reliable, where its advantages include a small calculated amount and high accuracy. Finally, the thermodynamic characteristics of the fluid-saturated porous thermo-elastic half-plane subjected to surface temperature loadings are studied, and the effects of material parameters and geometric nonlinearity on the dynamic characteristics are considered in detail.

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    Inner-relation modelling with memory networks in aspect-based sentiment analysis
    ZHANG Ke, ZHANG Wenjun, ZHU Yunwen, XING Yixue
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (1): 157-169.   DOI: 10.12066/j.issn.1007-2861.2265
    Abstract1385)   HTML11)    PDF(pc) (2643KB)(106)       Save

    Aspect-based sentiment analysis (ABSA) predicts the sentiment polarity of particular entities in given sentences. Studies show that the most effective methods use features obtained by modelling entities and their contexts with attention to sentiment prediction. However, these methods calculate the attention weights of contexts by using mean vectors for target entities. In addition, these methods cannot highlight the importance of individual words in the text of whole sentences. Therefore, this study proposes an aspect-based sentiment analysis method that can model the inner relations with memory networks, where networks can learn more effective contextual representations. First, gated recurrent units (GRUs) are used to embed the distributed representations of aspect words and words in sentences. Then, a long short-term memory network (LSTM) takes the distributed representation as input to increase the weights of entities according to the attention-based contextual relationship. Finally, through a query mechanism, aspect-based sentiment polarity is obtained. The proposed model is tested on the open-source datasets Semeval-2014 and Semeval-2016, with results showing that the proposed method is effective and the accuracy is higher than that of the baselines.

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    Incorporating article information for sentiment analysis of news comments
    YANG Yipu, ZHU Yonghua, GAO Haiyan, GAO Wenjing
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (1): 170-178.   DOI: 10.12066/j.issn.1007-2861.2252
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    News comments reflect people's opinions or sentiments toward news events. Therefore, analysis of news comments is potentially useful for many applications. Traditional methods of sentiment analysis focus on the contents of comments while ignoring the influence of news topics and semantics information from news articles. This study proposes a sentiment analysis approach using support vector machine and $K$-means clustering that considers the impact of news articles on the sentiments of news comments. Experimental results on a news comment dataset demonstrate the effectiveness of our proposed method.

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    Skeleton-based action recognition by manifold assumption
    PENG Yaxin, ZHAO Qian
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (2): 179-200.   DOI: 10.12066/j.issn.1007-2861.2316
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    Skeletal data are obtained by encoding the spatial geometricposition of the action, which can prevent the interference ofredundant background information. It is one of the commonly useddata types in the field of action recognition. The existing reviewof action recognition related to skeletal data is mainly dividedinto the classical skeletal data representation and the applicationof skeletal action recognition based on deep learning. Compared withthe action recognition methods based on the traditional Euclideanmetric, manifolds provide an important mathematical tool for abetter study of nonlinear structures. However, there is still a lackof summaries about action recognition from skeletal data using themanifold assumption. Therefore, starting from the four steps ofskeleton representation -- trajectory temporal alignment, actionsequence characterization, and action classification -- thisarticle systematically summarizes the action recognition work basedon the manifold assumption, and compares the performance of eachwork on the benchmark datasets. Finally, according to the currentdevelopment trend of action recognition, further improvement of themanifold assumption in thedirection of action recognition is prospected.

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    An equilibrium analysis of commuting considering location of parking slot and walking cost in a two-to-one network
    ANG Tingcai, JIANG Rui
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (2): 201-214.   DOI: 10.12066/j.issn.1007-2861.2363
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    Commuters from two different residence places drive to the same working place through two completely non-overlapping highways with bottlenecks and share a parking space. Based on the classical bottleneck model, we introduced competition for parking spaces between the two groups of commuters, constructed a function about location of parking slot and established the departure time choice model considering walking cost. Based on the start time and end time of the rush hour of the two groups, the equilibrium state can be classified into four situations. Considering the time of on time arrival, the four situations can be further classified into 14 sub-situations. We have derived the departure pattern and arrival pattern of the commuters in these sub-situations. Finally, considering the scenario that two groups of commuters have the same value of time and the bottleneck capacities on the two highways are also the same, numerical examples have been presented to show how the equilibrium state changes with walking speed and commuter numbers.

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    Traffic bottleneck induced by vehicles entering residential areas and its effect on road capacity
    SHENG Zhe, ZHOU Wenhai, GAO Qingfei, DONG Liyun
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (2): 215-225.   DOI: 10.12066/j.issn.1007-2861.2292
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    Vehicles decelerate in advance when entering a residential area, forming traffic bottlenecks. Especially in evening peaks, vehicles returning to residential areas will significantly reduce road capacity. This paper introduces anticipation time to reflect the deceleration behavior of vehicles before entering the residential area based on the velocity-dependent randomization (VDR) model. Both the one-way, single-lane system and the two-way, two-lane system are studied in this paper. The simulation results show that there is a critical injection probability when the entry probability is large enough. When the injection probability is greater than the critical value, a traffic platform appears. The critical injection probability and its corresponding flux value decrease with increasing the entry probability. When the injection probability is less than the critical value, the flux in the two-way, two-lane system is greater than that in the one-way, single-lane system. There are three bottlenecks in the two-way, two-lane system: the entrance to the residential area and the upstream and downstream ends of the road. When the entrance to the residential area is not close to the ends of the road, the influence of the entrance location on road capacity is negligible.

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    An algorithm for solving the permutation indeterminacy problem of frequency-domain ICA based on speech energy ratio
    ANG Zhiqiang, WANG Tao, JIN Zhiwen
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (2): 226-237.   DOI: 10.12066/j.issn.1007-2861.2239
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    With the development of artificial intelligence & internet of things (AIoT) and the rapid advancement of hardware technology, an increasing number of smart speakers are becoming a part of people's lives. Human-computer interaction has also witnessed a shift from remote control to voice control. However, the audio signals recorded by the microphone in a device usually contain considerable noise and interfering voices. Therefore, separation needs to be performed on the signals recorded by the microphones. Frequency-domain independent component analysis (ICA) is a commonly used separation technique, but it faces the permutation indeterminacy problem, i.e., the separated components from Source 1 are classified into a channel for Source 2, whereas the separated components from Source 2 are classified into a channel for Source 1, which greatly deteriorates the separation performance. To address this issue, we proposed an algorithm based on the speech energy ratio, which effectively improved the separation performance. The separation performance was tested on the Signal Separation Evaluation Campaign (SiSEC) and Computational Hearing in Multisource Environments (CHiME) datasets. The results showed that the proposed algorithm outperformed existing algorithms, and a good separation performance for mixed signals could be maintained even in an environment with strong reverberations.

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    Time difference of arrival localization based on an improved salp swarm algorithm
    MA Yiming, SHI Zhidong, ZHAO Kang, GONG Changlei, SHAN Lianhai
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (2): 238-249.   DOI: 10.12066/j.issn.1007-2861.2237
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    To address the nonlinear optimization problem of indoor time difference of arrival (TDOA) location estimation, an improved salp swarm algorithm (SSA) is proposed to search target locations. An improved fitness function is constructed by selecting the optimal master base station so that the fitness function can better reflect the quality of the solution, thereby enhancing search accuracy. The approximate solution is introduced into an initial salp population to simplify global exploration, and the convergence speed of the algorithm is accelerated in the early stage. An adaptive following strategy is used to update follower locations to solve the problem of low efficiency in local exploitation, which accelerates the algorithm convergence speed in the later stage. Simulation results show that the TDOA localization technology based on the improved SSA has higher localization accuracy and faster convergence speed than other meta-heuristic algorithms.

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    Just-noticeable distortion model based on colour complexity and structure tensor
    WANG Chuang, WANG Yongfang, LIAN Junjie
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (2): 250-260.   DOI: 10.12066/j.issn.1007-2861.2276
    Abstract1793)   HTML9)    PDF(pc) (13888KB)(92)       Save

    The just noticeable distortion (JND) threshold refers to the minimum distortion at which eyes can perceive. JND can be used to remove visual redundancy derived from image or video compression. Considering that JND models do not make full use of colour features and structural information, this study proposes a JND model based on colour complexity and structure tensor. First, the colour complexity is estimated and it is used to calculate the visual weight values related to the sensitivity of human eyes. Then the estimated colour complexity is combined with the contrasting masking effect to improve the accuracy of the model. Next, utilising the local structure tensor to represent local features, the modulation factor is established to calculate the visual redundancy of irregular regions. Finally, the colour complexity structure tensor based JND (CSJND) model is estimated by combining the colour-complexity-based JND model and structure tensor modulation factor. Experimental results show that the proposed CSJND model can acquire a noticeably lower peak-signal-to-noise ratio as compared with some existing JND models while also achieving the same subjective perceptual quality. This is more consistent with human visual perception. The proposed CSJND model can also calculate the JND thresholds more accurately.

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    Image matting based on deep learning
    WANG Rongrong, XU Shugong, HUANG Jianbo
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (2): 261-269.   DOI: 10.12066/j.issn.1007-2861.2287
    Abstract1546)   HTML44)    PDF(pc) (5423KB)(430)       Save

    Image editing technology, which is widely used in the post-production of film and television and in daily life, is based on image matting. In this study, an image matting network based on deep learning which estimates the value of each pixel by inputting the original image and trimap is proposed. Based on the original down- and up-sampling network and to address the problem of slow network convergence caused by the large difference between matting dataset pictures, batch normalisation (BN) is applied after each convolution layer in this study. In the normalisation layer, the input data are normalised to speed up the convergence of the model. This enables the update direction of the parameters to be more consistent with the overall characteristics of the dataset. Because the edge of the object should be carefully considered in the matting task, a deformable convolution layer is used instead of the custom convolution layer. The deformable convolution layer can adaptively learn the shape of the convolution kernel according to different input data, effectively expand the range of the receptive field, and improve the prediction effect in detailed image parts.

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    Chinese nested named entity recognition based on hierarchical tagging
    JIN Yanliang, XIE Jinfei, WU Dijia
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (2): 270-280.   DOI: 10.12066/j.issn.1007-2861.2283
    Abstract1712)   HTML27)    PDF(pc) (1410KB)(209)       Save

    Chinese named entity recognition plays a critical role in Chinese information processing. In Chinese information text, many named entities contain nested entities. However, most recent studies have focused solely on the recognition of flat entities, which cannot fully capture the boundary information between nested entities. In this study, a hierarchical tagging method is used for nested named entity recognition (NNER), in which each layer of entity recognition is parsed into a separate task, and a gated filtering mechanism is used to promote information exchange between layers. Experiments are conducted on the public NNER corpus of the People's Daily from 1998 to verify the effectiveness of the model. Experimental results show that the F1 value of this method on the People's Daily dataset reach 91.41% without using external resource dictionary information. Thus, the method is shown to improve the recognition of Chinese nested named entities.

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    Multi-label label-specific feature selection based on graph Laplacian
    WU Zhejun, HUANG Rui
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (2): 281-290.   DOI: 10.12066/j.issn.1007-2861.2243
    Abstract3909)   HTML6)    PDF(pc) (794KB)(109)       Save

    Multi-label feature selection, which can effectively removeredundant features and improve classification performance, has become an effective solution for the problem of "curse of dimensionality". However, existing multi-label feature selection methods select the same features for all labels without considering the intrinsic relation between labels and features. In fact, each label has label-specific features that reflect the specific attributes of the label. A feature selection method called multi-label label-specific feature selectionbased on graph Laplacian (LSGL) is proposed in this study. LSGL first obtains alow-dimensional embedding of instances for each class label based on Laplacianeigenmaps. Next, it obtains a projection matrix that can project samples from adata space to manifold embedding space through sparse regularization. It thendetermines the label-specific features of the corresponding class label bycoefficient analysis of the matrix. Finally, the label-specific featuresare used for classification. Experimental results of multi-label featureselection andclassification on five public multi-label datasets showed the effectiveness of the proposed algorithm.

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    First-principles calculation of defects and mechanical properties of tungsten/graphane/tungsten as a first wall material
    GUO Shun, ZHANG Zhaochun, XIE Yaoping, GUO Haibo
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (2): 291-303.   DOI: 10.12066/j.issn.1007-2861.2240
    Abstract1249)   HTML7)    PDF(pc) (9998KB)(126)       Save

    A Tokamak is a primary device used to obtain energy from controlledthermonuclear fusion. The first wall material inside the device isthe key to its stable operation. Tungsten metal is widely used as afirst wall material, but the helium atoms derived from the fusionreaction generate helium bubbles and point defects after they enterthe tungsten crystal, which critically affect the stability of thefirst wall material. Therefore, we designed for the first time atungsten/graphane/tungsten system as a first wall material. Resultsof first-principles calculations showed that the interface of thetungsten/graphane/tungsten system can capture helium atoms andvacancies as well as promote recombination between self-interstitialtungsten atoms and vacancies, thereby reducing the defect density ofthe tungsten metal. An elastic constant calculation showed that thepresence of a graphane layer could increase the Cauchy pressurevalue ($C'$) and anisotropy factor ($A$) of the tungsten metal,indicating that the ductility of the material was improved andcracks were not likely to occur. The mechanical modulus of thetungsten/graphane/tungsten material decreased under the sametemperature. Using the quasi-harmonic Debye model to calculate theGibbs free energy ($G^\ast$), heat capacity at constant volume($C_\mathrm V$), entropy ($S$), and other thermodynamic functionsshowed that the thermodynamic stability of thetungsten/graphane/tungsten material decreasedas compared with that of the pure tungsten metal.

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    Controllable synthesis of ZnO with different structures and its effect on gas sensitivity
    LI Shen, ZHOU Diwen, HE Xinhua, LI Fei, NING Zhukai, JIAO Zheng
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (2): 304-313.   DOI: 10.12066/j.issn.1007-2861.2285
    Abstract1835)   HTML9)    PDF(pc) (13569KB)(148)       Save

    The micromorphology of a material is the main factor that influencesits gas sensitivity. Applications of low-dimensional structuralmaterials are greatly limited because of agglomeration issues duringreactions. The performance of nanomaterials can be improved bydesigning and synthesising multilevel structures. Materialmorphology was mainly adjusted by controlling the hydrothermal time,the concentration of anionic surfactant citrate ions and hydroxideions. The ZnO nanomaterial synthesised under the controlledhydrothermal conditions had three types of morphologies,pompon-like, flaky-flower-shaped, and buns-like. The pompon-like ZnOshowed good gas sensitivity. At 340 $^\circ$C, itsgas-sensitive response to 100$\times10^{-6}$ n-butanol was 238,which was 2.12 times higher than that in case of ethanol, showinggood selectivity. In addition, by investigating the growth andsensing mechanisms of the multistage structure of ZnO, insights wereprovided for designing high-performance gas sensitive materialsusing multilevelarchitecture of ZnO.

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    Improved approach to detect small sample target based on remote sensing image
    LI Chengfan, ZHAO Junjuan
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (2): 314-323.   DOI: 10.12066/j.issn.1007-2861.2352
    Abstract1485)   HTML19)    PDF(pc) (6200KB)(188)       Save

    In this study, an improved convolutional neural network (CNN)approach is proposed to detect small sample targets from remotesensing images. The approach is designed to address the two issuesof small target samples and the unbalanced distribution of groundobject samples with respect to target detection of small samples byremote sensors. In the proposed method, first, $K$-nearest neighbor(kNN) regression is adopted to extract the features of each pointand convolution layer to construct the local neighborhood. Second,all local features are aggregated by maximum pooling layer in CNN torepresent global features. Subsequently, the full connection layerand scaled exponential linear unit (SELU) activation function areapplied to calculate the probability corresponding to each categoryfor classification. Finally, the proposed approach is tested andevaluated on hyperspectral imager remote sensing images datasets.Experimental results show that the proposed improvements to the CNNmodel fuse fully local features and result in the effectiverecognition and detection of small sample targets from remotesensing image with high accuracy while maintaining thenonlocal diffusion capabilities of information.

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    Frequency characteristics of magnetic coupling resonance superconducting wireless power transmission
    DAI Peng, HAN Shulun, ZHOU Difan, GUO Yanqun, CAI Chuanbing
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (2): 324-332.   DOI: 10.12066/j.issn.1007-2861.2320
    Abstract1333)   HTML3)    PDF(pc) (2722KB)(107)       Save

    Traditional wireless power transmission systems have lowtransmission efficiency under low-frequency conditions, limitingtheir application in low-frequency and high-power wireless powertransmission scenarios, such as electric vehicles and underwatersubmarines. In this study, a solution was developed to solve thisproblem using high-temperature superconducting coils instead ofcopper coils. The transmission characteristics of the wireless powertransmission systems with superconducting and copper coils atdifferent operating frequencies were investigated throughtheoretical analysis and experimental verification. The resultsshowed that the superconducting coils had a lower resistance and ahigher quality factor than the copper coils. In addition, thesuperconducting coils had unique advantages under low-frequencyconditions. It was demonstrated that the use of superconductingcoils in wireless power transmission systems operating at lowfrequencies could significantly improve the output power andtransmission efficiency of the system.

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    Implementation of charge qubits in ultra-strong coupling regime and quantum-state transfer
    YU Jing, ZHOU Mo, HUANG Tangyou, HAO Minjia, CHEN Xi
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (2): 333-346.   DOI: 10.12066/j.issn.1007-2861.2345
    Abstract1194)   HTML5)    PDF(pc) (1951KB)(88)       Save

    The Cooper-pair box (CPB) capacitively coupled to an LC resonatorwas considered in a superconducting quantum circuit that permittedthe high adjustability of light-matter interactions. The deep-strongcoupling (DSC) and ultra-strong coupling (USC) regimes could beobtained by increasing the impedance of the LC resonator anddecreasing the Josephson energy of the qubit. In this regard, atwo-qubit circuit, as a coherent mediator with a promising degree ofnoise immunity, was used to transfer quantum states between pairs ofTransmon qubits. This study provided new insights into USC regimesin light-matter interaction systems. Furthermore, it contributed tothe fields of quantum control, quantum simulation, and quantuminformation processing with superconducting quantum circuits.

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    Strong decay of ${\Lambda}_{\bf c}\textbf{(2880)}^{+}$ as 2D-wave excitations
    LI Yang, ZHANG Ailin
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (2): 347-356.   DOI: 10.12066/j.issn.1007-2861.2324
    Abstract1166)   HTML36)    PDF(pc) (1851KB)(256)       Save

    The strong decay of $\Lambda _{c} (2880)^+$ in the $^3P_0 $model was investigated. The decay widths and the ratio of branchingfractions of $\Lambda _{c} (2880)^+$ were calculated. Thenumerical results showed that $\Lambda _{c} (2880)^+$ may be a2D-excited $\Lambda _{{c}2}\big(\frac{3}{2}^+\big)$ with$J^P\!=\!\frac{3}{2}^+$. $n_\rho \!=\!1$ and $l_\lambda\!=\!2$belonged to the radial $\rho $-mode excitation and the orbital$\lambda $-mode excitation, respectively. The ratio of branchingfractions was $R={\it\Gamma} (\Lambda _{c} (2880)^+\to \Sigma_{c} (2520)\pi )$/${\it\Gamma} (\Lambda _{c} (2880)^+\to\Sigma _{c} (2455)\pi )=0.16$. The total decay width was${\it\Gamma} _{total} = 18.53$ MeV. $\Lambda _{c} (2880)^+$could also be a 2D-excited $\Lambda _{{c}2}^{'}\big(\frac{3}{2}^+\big)$ with $J^P=\frac{3}{2}^+$,$n_\lambda =1$ and $l_\lambda=2$, indicating the radial $\lambda$-mode excitation and the orbital $\lambda $-mode excitation. Inthis study, ${\it\Gamma} _{total} =1.69$ MeV, and $R={\it\Gamma}(\Lambda _{c} (2880)^+\to \Sigma _{c} (2520)\pi)$/${\it\Gamma} (\Lambda _{c} (2880)^+\to \Sigma _{c}(2455)\pi )=0.10$.

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    Materials informatics—data-driven materials research and development
    ZHANG Tongyi
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (3): 357-360.   DOI: 10.12066/j.issn.1007-2861.2370
    Abstract2096)   HTML198)    PDF(pc) (587KB)(395)       Save
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    High-precision data acquisition method based on Jaya optimization and calibration
    ZHANG Hesheng, JIAO Peng, HU Qirui, CAI Jiangqian, HU Shunbo, CAO He, OUYANG Qiubao
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (3): 361-371.   DOI: 10.12066/j.issn.1007-2861.2372
    Abstract1822)   HTML31)    PDF(pc) (1195KB)(122)       Save

    Materials genome engineering (MGE) integrates high-throughput experiments, high-throughput computations, databases, and artificial intelligence to accelerate the development of advanced materials. However, a reliable and effective method to acquire data from experimental equipment is yet to be identified in MGE. Because the calibration data of high-precision data acquisition systems are not synchronized in terms of time, a linear model is used in this study as a model for data processing parameters, and the value displayed by the device is used as the real value to construct the objective function to optimize the data processing parameters. The Jaya optimization algorithm is used to realize the optimization search of processing parameters. Based on the data acquisition of the equipment temperature as an example, a high-precision data acquisition system is constructed and verified experimentally. The experimental results show that using the optimized model parameters, the average error of data acquisition is only 0.13 $^\circ$C, and the maximum accuracy is 99.89%. Compared with the non-optimized model parameters, the average error reduced by 63.20%, which significantly improves the data acquisition accuracy.

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    Material data named entity recognition based on matching contextual lexical words and graph convolution
    CHEN Qian, WU Xing
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (3): 372-385.   DOI: 10.12066/j.issn.1007-2861.2377
    Abstract1848)   HTML25)    PDF(pc) (1294KB)(199)       Save

    Literature pertaining to materials contain abundant information regarding data mining using machine learning and natural language processing, which is currently being investigated extensively. Named entity recognition (NER) is first performed when mining and extracting information from data such that the data can be used efficiently. As vector representation cannot solve multiple meanings of words, and models often extract contextual features while disregarding global features, a named entity recognition method based on matching contextual lexical words and graph convolution is proposed herein. First, the contextual dynamic features of text is obtained using XLNet; second, the contextual and global features are obtained using a long short-term memory network and a graph convolutional network (GCN) combined with contextual lexical words of the text, respectively. Finally, a sequence of labels is output via a conditional random field. The model is validated using two different datasets. Experimental results of the material data show that the precision, recall, and F1 score are 90.05%, 88.67%, and 89.36%, respectively, which effectively improve the named entity recognition accuracy.

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    Constructing a material-domain knowledge graph based on natural language processing
    WEI Xiao, WANG Xiaoxin, CHEN Yongqi, ZHANG Huiran
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (3): 386-398.   DOI: 10.12066/j.issn.1007-2861.2380
    Abstract2178)   HTML39)    PDF(pc) (6137KB)(290)       Save

    Determining how to combine material-domain knowledge with the machine learning method is an urgent problem in materials intelligence. As an efficient knowledge-organization method, knowledge graphs (KGs) can effectively represent, organize, and reasoning material-domain knowledge so as to improve the intelligence level of machine-learning algorithms for materials. In this paper, we study natural language processing (NLP)-based knowledge-acquisition methods for materials and propose a joint extraction method comprising the material entity relationship based on bidirectional-gated recurrent unit-graph neural network-conditional random field (Bi-GRU-GNN-CRF) and a material-processing knowledge-extraction method based on the improved TextRank algorithm. Using the proposed knowledge-acquisition method, we acquire material-domain knowledge such as material entities, relationships, and technological processes from patents, papers, and other types of texts. The experimental results show that the proposed knowledge acquisition method has good accuracy and recall, which can effectively improve the knowledge coverage of the material KGs. The knowledge coverage of the material KGs constructed based on proposed method reaches 80%, which provides more comprehensive knowledge support for materials research and development. We also construct the domain KGs of special non-modulated steel, an aluminum matrix composite material, and a thermal-barrier ceramic-coating material, and the results further verify the potential of using material knowledge maps in materials research and development.

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    Database for materials genome engineering
    YUE Xichao, FENG Yan, LIU Jian, YU Yeyong, XI Kangjie, QIAN Quan
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (3): 399-412.   DOI: 10.12066/j.issn.1007-2861.2388
    Abstract2519)   HTML58)    PDF(pc) (6883KB)(394)       Save

    Materials data are multi-source, heterogeneous, and high-dimensional. Acquiring diverse and complex materials data as well as establishing a dedicated database for materials genome engineering (MGE) is the foundation for realizing data-driven new materials design. Herein, the materials genome database platform is introduced in terms of its system architecture, implementation, and deployment on a supercomputer. It is based on several core technologies, such as normalized representation of materials data, machine-learning modeling and model cross-domain deployment, machine learning under data privacy protection, and a materials database to a knowledge base using a knowledge graph. Finally, based on an anti-perovskite negative expansion material as an example, the entire application process of the MGE database platform from data curation to machine learning modeling followed by inverse design, in addition to a final experimental validation are discussed comprehensively herein.

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    Blockchain based data copyright protection and combinatorial auction
    XU Yuqin, QIAN Quan
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (3): 413-426.   DOI: 10.12066/j.issn.1007-2861.2376
    Abstract1706)   HTML16)    PDF(pc) (3954KB)(87)       Save

    A data copyright protection and combinatorial auction system was designed and implemented based on blockchain, digital watermark and sealed bids combinatorial auction. First, a smart contract was applied to store the copyright data and their transaction record via a decentralized technique known as blockchain, which could achieve consensus between each node who did not trust each other. Consequently the system could be rendered more trustworthy. Moreover, a digital watermark was a unique code hidden in data, which could be used to prove the ownership of copyright. The results showed that the watermark module used in the system barely altered original data and was efficient when embedding and extracting watermark. Finally, a combinatorial auction algorithm was designed to select an optimal bid combination automatically to implement the value exchange of data copyright.

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    Kalman filter based method for processing small noisy sample data
    LIU Fen, FAN Hongqiang, LÜ Tao, LI Qian, QIAN Quan
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (3): 427-439.   DOI: 10.12066/j.issn.1007-2861.2379
    Abstract1859)   HTML18)    PDF(pc) (1783KB)(140)       Save

    A small sample noisy data processing method based on Kalman filter and extended Kalman filter has been proposed. The core idea was to establish a system model using physical models or empirical formula, then used the system model to predict the model data, and finally used the observation data to correct the model data and achieve the effect of smoothing data noise. Experimental results showed that when using the autoregressive integrated moving average (ARIMA) model and random forest (RF) model to predict the corrosion weight gain of weather steel BC500, the coefficient of determination $R^{2}$ was increased by an average of 6.4% after Kalman filter denoising, while the $R^{2}$ was increased by an average of 4.9% after extended Kalman filter. These results verified the effectiveness of the proposed methods.

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    Ensemble learning of polypropylene-composite aging data
    WU Xing, GAO Jin, DING Peng
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (3): 440-450.   DOI: 10.12066/j.issn.1007-2861.2382
    Abstract1594)   HTML9)    PDF(pc) (4113KB)(183)       Save

    Aging experiments conducted on polypropylene composites have long durations, and a limited number of samples can be collected in a single experiment. As a result, traditional machine-learning approaches have a low prediction accuracy. To address these issues, we present an ensemble learning prediction based on virtual sample generation (VSG). To generate valid virtual samples of aging data for polypropylene composites, we first adopted the Gaussian mixed model (GMM) method and then used the generated data set to build an ensemble-learning prediction model comprising the random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost) algorithms. The LightGBM and CatBoost algorithms in the ensemble learning model demonstrate the best performance on the test data; the mean square errors are 0.001 3 and 0.000 1, respectively, which are 0.4 and 0.2 higher than those of the RF algorithm and XGBoost algorithm, respectively. This study's aging VSG and ensemble learning approach for polypropylene composites can not only successfully overcome the long experimental times and insufficient number of data samples acquired in a single experiment but outperforms a single machine-learning algorithm.

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    Regression modeling and multi-objective optimization for small sample scattered data
    YAO Yu, HU Tao, FU Jianxun, HU Shunbo
    Journal of Shanghai University(Natural Science Edition)    2022, 28 (3): 451-462.   DOI: 10.12066/j.issn.1007-2861.2387
    Abstract2368)   HTML22)    PDF(pc) (5003KB)(257)       Save

    Regression modeling on small-sample scattered data poses certain challenges. In this study, the Gaussian process is used to model regression, and maximum likelihood estimation is performed to learn the hyperparameters of the kernel function. The regression results, i.e., the mean and variance of the objective function, are calculated and predicted from the posterior. Combining the results with the multi-objective optimization of variance, the uncertainty of material reverse design can be estimated. Experimental verifications are conducted on 1215MS non-quenched and tempered steel and three-point bending concrete datasets. The results show that for the three-point bending concrete, 50% of the experimental data are within the 95% confidence interval of the prediction, and the Gaussian process regression (GPR) model can measure the uncertainty of the scattered small-sample data more effectively and yield reasonable predictions. For the 1215MS dataset, a non-dominated genetic algorithm with an elite strategy is used to perform multi-objective optimization based on the GPR model. The mechanical properties of the material and the corresponding variance are used as optimization objectives, and the optimal mechanical properties are considered while considering the effect of uncertainties on the experimental results. The optimal Pareto solution set is obtained, which is subsequently used as candidate points for the next experiment to assist material design and preparation optimization.

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