Large-scale modern exhibition venues are more sensitive to uneven foundation settlements, where the spatial distribution of the compressive modulus of the bearing layer is essential in controlling foundation deformations. Conventional engineering survey boreholes provide only a small number of precise compressive modulus geotechnical test values, whereas in-situ testing can provide numerous random cone penetration values. To integrate the data of indoor and in-situ tests, a Bayesian spatial interpolation method of compression modulus is proposed in this study. Our research was conducted as follows. Based on the data accuracy of geotechnical engineering investigation, test data were divided into hard and soft data. A spatial random function was then used to describe the spatial variability of the compression modulus. Next, maximum entropy theory was applied to analyze the uncertainty of the soft data. Based on Bayesian theory, a random field interpolation method was then established to estimate the posterior distribution of the compression modulus of unknown points. Finally, to verify the effectiveness of the proposed method, a Bayesian spatial interpolation method was applied to the spatial variability analysis of the compressive modulus of silty clay in the shallow bearing layer ②$_1$ of Shanghai National Convention and Exhibition Center. Compared with the ordinary Kriging interpolation method, the proposed Bayesian method can integrate multi-source survey data for spatial interpolation with greater accuracy.
Design changes represent one of the main risk factors that affects the performance and safety of prefabricated building projects. Multi-stakeholder participation in prefabricated component supply chains involves a complex and changeable system, and information integration of each participant is difficult. This in turn makes it difficult for the project to achieve systematic and effective design change risk management and control. Based on the induction of design change risks and risk-causing factors, a method that combines a structural equation and multi-agent simulation model is proposed in this study. Management strategies are first proposed based on the identification of risk-inducing factors. The actual operational effects of these management strategies are then simulated to achieve an optimal allocation of resources. Based on a prefabricated construction project in Nanxiang Town, Shanghai, an ontology instance library is established, and the proposed management strategies by empirical analysis of the structural equation model are simulated through the multi-agent simulation model. The effects of the strategies on project cost and time performance are evaluated, and the feasibility and effectiveness of the model are verified.
The literature related to molecular design and performance prediction of high-temperature resistant transparent polyimide at home and abroad in recent years was studied, and the common molecular designs of high-temperature resistant transparent polyimide: introduction of trifluoromethyl, alicyclic structure, non-coplanar structure, bulk side groups, and inorganic materials were summarized, and the research on the application of molecular dynamics simulation and machine learning methods in the performance prediction of polyimide was analyzed. Finally, the molecular design and performance prediction of high-temperature resistant transparent polyimide were summarized and prospected.
In this paper, the impact of corporate social responsibility (CSR) goodwill on market demand was considered. For the cases of Nash non-cooperative game, manufacturer-led Stackelberg game, retailer-led Stackelberg game, and cooperative game, differential game models were constructed and the optimal level of CSR effort for the manufacturer and the retailer, the optimal trajectory of CSR goodwill, and the optimal value of profit was studied. Also, the correctness of the theoretical derivations was verified through numerical simulations, and some important results were obtained. First, the optimal subsidy rate of CSR cost is related to marginal profit and is unaffected by other factors. Second, compared with the Nash non-cooperative game, in the case of Stackelberg game, the leader subsidizes the CSR cost of the follower when the marginal profit of the leader is greater than half the marginal profit of the follower. Moreover, the optimal level of CSR effort for the leader remains unchanged, while the optimal level of CSR effort for the follower increases, so the profits improve for both the manufacturer and the retailer. Third, compared to the Stackelberg game, in the case of cooperative games, the optimal level of CSR effort improves for both the manufacturer and the retailer, and the profit also increases for the supply chain system.
Because of the diverse compositions of asphalt pavement damage, multiple cases of damage that are inspected using the same pavement condition index (PCI) may yield different damage combinations. When multiple types of damage coexist but the degree of damage is similar, obtaining targeted maintenance measures with PCI and determining the predominant damage (i.e., most severe road damage with the maximum deduction value) are challenging. Therefore, this study considers PCI analysis and an existing preventive maintenance decision-making method to clarify those sections in which the predominant damage was not well-targeted during inspection and proposes a supplementary approach to make more appropriate conservation decisions. Based on detection and maintenance data of urban roads in Shanghai from over the past five years, a sequential clustering method is used to classify road sections according to their PCI levels. The composition of and difference in pavement damage at different levels are analyzed. Then, for those sections with multiple cases of damage and no significant damage differences, road sections that historically reflect proper preventive maintenance are then selected based on whether effective preventive maintenance can be implemented. Finally, two back propagation (BP) neural network models for preventive maintenance decisions are established and compared based on the effective maintenance road sections. The main differences between the two models are the compositions of pavement damage. The results showed that when the PCI levels were high (84.4~93.0 points), the degrees of damage were very similar and the predominant damage was not represented. Of the two BP neural network models, Model 2,which considered multiple damage components, showed a higher decision accuracy. Specifically, its decision accuracy with the test set reached 86.20%. This was significantly better than that of Model 1 (58.50%), which considered only the predominant damage. Combining the BP neural network and traditional decision tree method can help to optimize decision-making processes related to asphalt pavement and improve the selection of maintenance measures.
To study the nonlinear deformation problem of functionally graded plates, this study uses the S-R decomposition theorem combined with the updated comoving coordinate system method and meshless Galerkin method to derive a discrete equation for solving three-dimensional geometric nonlinear problems. The meshless method is programmed by MATLAB. The nonlinear bending problem of the functionally graded plate is first solved, and the effects of the volume fraction index and width-thickness ration on the bending of plates are studied. Results are compared with existing results, and the rationality of solving the large deformation problem of functionally graded plates using the three-dimensional S-R meshless method is verified.
To conduct an experimental study of the flexural theory of hybrid steel and fibre reinforced plastic (FRP) reinforced beams, the finite element software ABAQUS was used to model and nonlinearly analyse existing hybrid FRP/steel reinforced test beams. The effects of FRP bar type, concrete strength, and the equivalent reinforcement ratio on the bending performance of hybrid FRP/steel reinforced beams were then studied. Finite element results revealed that the stress expression of FRP bars could be obtained and a formula for the bearing capacity of hybrid FRP reinforced beams could be derived. Finally, experimental data were used to verify the correctness of the formula. Results showed that the equivalent reinforcement ratio had the most significant effect on the bearing capacity and deformation performance of hybrid FRP bars beams, followed by FRP bar type. The strength of concrete had a certain effect on the bearing capacity but had little effect on stiffness. Based on the stress expression of FRP bars in this study, the flexural capacity of hybrid FRP reinforced beams were accurately calculated.
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.
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.
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.
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}$, indicating high corrosion resistance.
Currently, new robotic technology is widely used in pipeline maintenance and inspection. Robots made of soft materials have been developed and used in pipeline inspection to overcome the limitations of rigid in-pipe robots and improve maneuverability. The turning control of soft robots in pipelines is a great challenge, owing to the various specifications and branches of pipelines. A soft robot for pipes with small diameters was developed in this study to solve this problem using a kinematic model. Based on this model, the flexible turning strategy of robots in T-branch pipes was established. Finally, the effectiveness and accuracy of the turning strategy were verified using experiments. The proposed turning strategy can effectively improve the mobility and intelligence of soft in-pipe robots in T-branch pipes.
In this study, the diffusion behaviors of hydrogen atoms in Fe-13Cr-6Al-2Mo and Fe-13Cr-6Al-2Mo-0.5Nb alloys were studied by the electrochemical hydrogen permeation method. The effects of 0.5% Nb (mass fraction) added to the alloy on the diffusion behavior of hydrogen atoms in the Fe-13Cr-6Al-2Mo alloy were analyzed. Results showed that the grain of the alloy was refined, and a large amount of fine Fe$_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.
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.
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.
This study investigates the asymptotic behaviors of a solution to time-space fractional partial differential equation with the fractional Laplacian, where the time fractional derivative is in the sense of Caputo, with the order $\alpha\in(1,2)$. By using the properties of the Fox $H$-function and Young's inequality, gradient estimates and large time behavior of the solution are obtained.
Owing to the temperature field generated by early-hydration heat, mass concrete causes temperature stress cracking, affecting structural safety and normal use. The accuracy of the thermal parameters of concrete affects the accuracy of the concrete temperature-field calculations. Based on the hydration reaction of cementitious materials in concrete, theory of chemical reaction kinetics, and experimental data for cement hydration heat from different fly ash additives, a formulation of the hydration heat model for concrete was developed, considering the effects of fly ash and temperature. The model could accurately predict the heat release and temperature rise of the concrete hydration reaction with age, and the prediction results were in good agreement with the experimental data.
The formal spinel oxides AB$_{2}$O$_{4}$ can have 5 329 configurations by substituting A and B with 73 elements. The first-principles method was applied to calculate the formation energies and lattice constants of 5 329 spinel oxides. To develop efficient machine learning (ML) methods, centre-environment (CE) feature models were proposed to construct the input variables of the ML methods containing local composition and structure information. Based on the first-principles computational data, random forest algorithm was used to develop an ML model to predict the formation energies and lattice constants of spinel oxides. By comparing the formation energies of hypothetical and experimental structures predicted by ML, 361 new and more stable spinel oxides were discovered. The “good” and “bad” stabilisation elements were disscussed, which helped in guiding theexperimental synthesis of novel stable spinel oxides.
This study considerd the impacts of responsibility cost and quality improvement on supply-chain enterprises' decisions and profits. Stackelberg decision models ofnon-, one-way-, and two-way-cooperation were constructed based on a structure of a cross-border E-commerce dual-channel supply chain composed of an overseas supplier and a domestic retailer. In addition, the pricing strategy, quality selection decision, and profit were analysed using comparison under different cooperation scenarios. The results revealed that the overseas supplier's wholesale prices of domestic retail channels were reduced in three cooperation scenarios, and was the lowest in the two-way-cooperation of the overseas supplier and domestic retailer. In the one-way-cooperation, the profit of the overseas supplier or the domestic retailer could be increased, and the Pareto improvement couldn't be achieved. In the two-way-cooperation, the Pareto improvement could be achieved, but the space of cooperation was limited.
