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A real-time calculation model discretized by the cellular automata (CA) method was developed for the numerical simulation of AZ31 magnesium alloy microstructure evolution during recrystallization (RX). The RX processes under different strains were simulated, also, variations in morphologies of recrystallization grains are discussed. The results of numerical simulation were compared with those of experiment analysis, and the microstructure obtained by CA was found to well agree with the actual pattern obtained by EBSD (Electron Backscattered Diffraction) analysis. The numerical simulation technique provides a flexible way of predicting the recrystallization of deformation microstructure evolution.

The hydrogen-passivated N chain zigzag silicon carbide nanoribbons (N-ZSiCNRs) are indexed by their width N (the number of zigzag Si–C chains across the nanoribbon). Based on first-principles GW and Bethe–Salpeter equation (BSE) approaches, we investigated the quasiparticle band structures, exciton level structures and optical absorption spectra of the ultra-narrow N-ZSiCNRs with N = 2–3. It is found that the GW band gap of 3-ZSiCNR is 0.804 eV, which is more than two times larger than the HSE06 band gap (0.401 eV). The GW band gap of 2-ZSiCNR is 2.911 eV, which is also almost more than two times larger than the HSE06 band gap (1.621 eV). These results indicate that for 1-dimensional structure materials, HSE06 approaches underestimate the band gap of the system. The GW + BSE calculations demonstrate that the optical absorption spectra of the N-ZSiCNRs are dominated by edge-state-derived excitons with large binding energy, composed of a characteristic series of exciton states. It is found that the edge-state excitons of N-ZSiCNR belong to charge-transfer excitons, where the excited electron is confined to a Si edge while the hole is located on a C edge. The exciton binding energy increases with decreasing width N, which indicates that the quantum confinement effect enhances with decreasing width N. The excitons in 2-ZSiCNR can have a binding energy up to 1.78 eV. In addition, the exciton level structure and wave function are classified. It is very interesting to find a relationship between the node of the exciton wave functions and the incoming polarization light exciton excitation. For example, in the longitudinal optical absorption spectra, if the exciton whose wave function possesses an odd number of nodes is optically active, then the exciton whose wave function possesses an even number of nodes is optically inactive. In contrast, in the transverse optical absorption spectra, the exciton whose wave function possesses an odd number of nodes is optically inactive, while the exciton whose wave function possesses an even number of nodes is optically active.

Industrial cloud robotics (ICRs), which is proposed to integrate the distributed industrial robots (IRs) resources to provide ICRs services at any place, has been attracted great attention due to the characteristics of convenient access, cheaper computing cost, more convenient network resources, etc. Meanwhile, in manufacturing industry, the energy-efficient issue, which means minimize the amount of energy resources to achieve a given output level in manufacturing process, is also gradually paid great attention by academia, industry and government. Currently, ICRs plays a crucial role in production. The implementation of energy-efficient manufacturing for ICRs will significantly decrease the energy consumption on the premise of normal production process, and also have remarkable effect on energy-saving and emission-reduction in manufacturing industry. In this context, the energy condition perception and big data analysis of ICRs are the essential procedure to achieve the aforementioned goals. A novel system architecture which mainly focuses on distributed energy condition perception and big data analysis for ICRs is built. Based on the perceptive data of ICRs related to energy consumption, a big data analysis model combined with the manufacturing status of ICRs is proposed, and the relationship between the big data and the analysis model is presented. Through the data analysis model, we can analyze the energy consumption fluctuation characteristic of ICRs operating state, count the energy consumption of the product related to different production phases, predict the health status of ICRs, as well as the trend of energy consumption associated with their operations. A case study is implemented to demonstrate the effectiveness of the proposed system and approaches.

The contamination of antibiotic resistance genes (ARGs) caused by abuse of antibiotics has attracted more and more attention. Due to their low price, tetracyclines and sulfonamides are widely used. The plenty of residual tetracyclines and sulfonamides is discharged into wastewater treatment plant (WWTPs), with consequent ARGs pollution. To understand the current status of ARGs contamination and removal efficiency, we summarized the distribution and spread mechanism of tetracyclines and sulfonamides ARGs, and further emphasized the ARGs removal efficiency across different treatment technologies. Based on the current knowledge and lack of ARGs, future work were proposed, such as investigating ARGs contamination in WWTPs, improving ARGs removal technologies, exploring spread mechanisms of ARGs.近年来,抗生素滥用造成的抗性基因(ARGs)污染问题引起了人们的关注.四环素及磺胺类抗生素由于价格低廉被广泛使用,大量残留的四环素和磺胺通过各种途径进入污水处理厂,并进一步导致ARGs的污染.为深入了解四环素和磺胺类ARGs的污染及治理现状,本研究对污水处理厂中四环素和磺胺类ARGs的分布情况及传播机制进行了综述,并重点讨论了不同污水处理工艺对ARGs的去除效果.在此基础上,从加大污水处理厂ARGs污染调查、改进污水处理工艺以及探讨ARGs传播机制等方面进行了展望.

Condition sensing and understanding for CNC machine tools is an effective means to find hidden faults and to make their diagnosis. In this paper, an intelligent perception system of CNC machine tools is designed and implemented based on human-machine collaboration. With the application of wearable and mobile smart devices, such as Google Glasses and mobile phones, the system makes the information acquisition and data analysis capabilities of site-operator improved further more. A light deep learning model MobileNetV2 is used in this system, which can identify the key parts of the machine tools observed by the site-operator with the Google Glass. The various sensing data of the machine tools can be displayed visually via the screen of the Google Glass, so that site-operator can monitor them more conveniently. Finally, a fault response method for CNC machine tools based on human-machine collaboration is presented. The method improves the sensing capability and responding speed of site-operator by the collaboration of the smart glasses.

Cloud manufacturing is a new kind of manufacturing paradigm, in which geographically distributed various manufacturing resources belonging to different enterprises are encapsulated as manufacturing services (service providers) for providing users (service consumers) with services and users are able to find, select, and trade with MSs via Networks on a cloud manufacturing platform. To solve the trust issues between service providers and service consumers, a trust mechanism based on blockchain(BC) for services' information was proposed. Based on the decentralized, distributed storage and non-tamperable of BC, using public BC and consortium BC to construct a trusted mechanism for cloud manufacturing service platforms. Public BC is high credible, consortium BC is efficient and has larger capacity. The trusted mechanism can ensure that the source and storage of the service information is trusted, to solve the problem that the cloud manufacturing platform service data is untrustworthy and easily falsified. It can provide trusted choices to service consumers while operating efficiently and at low cost.

In order to predict the thermal error of the CNC machine tool motorized spindle more precisely, this paper analyzes the mechanism of motorized spindle temperature field and thermal deformation. A thermal error exponential model of the motorized spindle is proposed in terms of pattern recognition and mechanism analysis. Firstly, the thermal characteristics of the motorized spindle are analyzed, and the exponential mathematical model of the motorized spindle temperature field and thermal error is derived. Then, optimizing the parameters of the exponential model by model-based parameter estimation method. Finally, the performance of the exponential model is verified on a CNC machine tool. The results show that the method is precise in predicting the temperature filed and thermal deformation of the motorized spindle during the heating and cooling process.

Carbon fiber has excellent properties such as high specific strength and specific modulus, and is widely used in aerospace and civil industries. The quality of carbon fiber precursor (CFP) is one of the important factors affecting the quality of carbon fiber. Identification of critical process parameters plays a vital role in improving the stability of the CFP production and optimization of process parameters. However, the production of the CFP involves nearly 200 process parameters, and there are highly non-linear relationships between quality and process parameters, and interactions between process parameters, which makes the identification of critical process parameters much harder. This paper aims to uncover the critical process parameters based on the extreme gradient boosting (XGBoost) approach because of the great advantages of XGBoost algorithm in solving the problems above. In this paper, a XGBoost-based model is proposed to classify the quality level of the CFP based on actual process monitoring data, and the critical process parameters are identified simultaneously. The performance of XGBoost-based classification model is compared to two traditionally classification model, Support Vector Machine (SVM) and Random Forest (RF), in terms of classification accuracy. In addition, this paper uses the Maximum Information Coefficient to validate the critical process parameters uncovered by the proposed model. It is demonstrated that the proposed method can identify the critical process parameters correctly and intuitively.

As a key factor for making decisions for production scheduling, manufacturing capability has been attracted great attention by academia and industries. With the increasing customized and personalized needs, modern manufacturing enterprises need to fabricate products at low cost, at right time, and at high effectiveness. Therefore, it is necessary to optimize the manufacturing system in order to improve productivity and the competitiveness of enterprises. The analysis and evaluation of manufacturing capabilities play an extremely important role in the optimal scheduling of production. In this paper, the manufacturing capability of a job shop is investigated. The indicator system is built for evaluating the manufacturing capability of a job shop and an evaluation method is proposed based on the combination of the entropy weight method and the grey relational analysis. Finally, a case study is used to validate the method.

A new stable transition-metal trihydride (ScH

With the development of information technology and network technology, as well as sensing technology, more and more data are collected from the processing of producing and machining in industrial sites. Industrial data are usually with noises and errors because the interference of ambient and the fault of sensors. Data preprocessing is necessary for analyzing further. The local polynomial regression is usually used to data denoising because of its characters of simplicity and flexibility, but the smoothing parameter of this algorithm need to be manually set by trial and error. In this paper, an adaptive parameter optimization method is used to calibrate its parameter, that is, the multi-indicator fusion method is used to fuse the root mean square error (RMSE), signal-to-noise ratio (SNR) and smoothness (r) of the denoised signal into a composite evaluation indicator. The smoothing parameter corresponding to the minimum value of the indicator is the optimal smoothing parameter of the denoising algorithm. A data set which is collected from monitoring the temperature field and thermal error of a heavy-duty CNC machine tool is used to validate the method.

To analyze the potential of fatty liver index (FLI) and several obesity indices and to explore which index is best for predicting nonalcoholic fatty liver disease (NAFLD) in Chinese postmenopausal women.A cross-sectional study was conducted in 680 Chinese postmenopausal women. NAFLD was defined as a hepatic steatosis observed on liver ultrasonography in the absence of a second cause. Odds ratio and corresponding 95% confidence interval (CI) between hepatic steatosis and FLI as well as different obesity indices were evaluated by Binary Logistic regression model. Receiver operating characteristic curve and area under curve (AUC) were used to compare the ability of predicting hepatic steatosis between FLI and obesity indices.The upper values of all indices were significantly associated with the presence of hepatic steatosis (all p0.01) after the adjustment for potential confounders. The largest AUC [0.85 (0.82-0.88), 95% CI, p0.01] was observed for FLI, followed by the frequently used obesity indices.FLI is closely associated with the presence of hepatic steatosis in Chinese postmenopausal women. Compared to the obesity indices frequently used, FLI is a better surrogate marker for predicting the presence of hepatic steatosis in Chinese postmenopausal women.

Thermal error is one of the main factors that affect the machining precision of heavy CNC machine tools. Through the establishment of thermal error model based on temperature field monitoring, the compensation of thermal error is an effective method to improve the machining precision. Because the mechanism of the thermal error is complex, now a data driven modeling method is usually used to establish a thermal error model. However, traditional data driven modeling methods need to choose different key temperature points according to different working conditions to model in order to improve prediction accuracy and generalization. Therefore, it is difficult to get the thermal error model with strong generalization in different working conditions. Convolution neural network is a kind of deep neural network that can automatically extract feature of input data without artificial construction. A thermal error prediction model based on convolution neural network is proposed and the generalization is improved. The experimental results show that this method can accurately predict the thermal error of the Z axis in different working conditions, and validate the effectiveness of the modeling method.

The dynamic model for the vehicle-CRTS III slab track system is established subjected to uniform seismic excitation, and the calculation program with MATLAB is compiled and verified. The influences of track parameters, seismic intensity, and running speed of the vehicle on the dynamic responses of the system and the vehicle running safety are analyzed. The results show that (1) the track parameters have certain influence on the dynamic responses of the system, and the seismic intensity and the running speed of the vehicle have important influence on the vehicle running safety; (2) the derailment coefficient is highly sensitive to seismic intensity, and the wheel load reduction rate is also highly sensitive to the running speed of the vehicle.

Battery lifetime prediction is a promising direction for the development of next-generation smart energy storage systems. However, complicated degradation mechanisms, different assembly processes, and various operation conditions of the batteries bring tremendous challenges to battery life prediction. In this work, charge/discharge data of 12 solid-state lithium polymer batteries were collected with cycle lives ranging from 71 to 213 cycles. The remaining useful life of these batteries was predicted by using a machine learning algorithm, called symbolic regression. After populations of breed, mutation, and evolution training, the test accuracy of the quantitative prediction of cycle life reached 87.9%. This study shows the great prospect of a data-driven machine learning algorithm in the prediction of solid-state battery lifetimes, and it provides a new approach for the batch classification, echelon utilization, and recycling of batteries.

The shielding property of cylinder with circular, square, and equilateral triangle holes was investigated by finite element analysis (FEA). The hole area (S hole) plays an important role in magnetic circuit on the surface of cylinder. When S hole is less than the critical area (S H), cylinder with three shapes of holes obtained the same remanent magnetization inside, indicating that the shielding property is unaffected by the shape of the hole. Hence, high-permeability material is the major path of the magnetic field. On the condition of S hole > S H, the sequence of the shielding property is equilateral triangle > square > circular, resulting from magnetoresistance of leakage flux in air dielectric. Besides, the anisotropy of shielding property caused by hole structural differences of the cylinder is evaluated. We find that a good shielding effectiveness is gained in the radial direction, compared with the axis direction. This research focuses on providing a theoretical support for the design of magnetic shield and improvement on the magnetic shielding ability.

With the increasing reliance on global sourcing and the growth in the likelihood of disruptive incidents, today’s supply networks are more prone to unexpected natural and man-made disruptive events. In order to alleviate the losses caused by these disruptive events, when a large-scale event disrupts multiple suppliers simultaneously, a single or several critical suppliers should be selected from the disrupted ones to assist them to recover their production as soon as possible. The selection of these recovery suppliers is of great importance in the recovery process of the entire supply network. Thus, this paper proposes a recovery supplier selection method from the view of the supply network structure. Firstly, a tripartite graph-based supply model is proposed to depict a two-stage supply network, which consists of multiple manufacturers and suppliers as well as the diverse product supply-demand interdependence connecting them. To measure the impacts caused by supplier disruptions and to evaluate the effectiveness of recovery supplier decisions, two supply network performance metrics reflecting product supply availability are also given. Then, the recovery supplier selection problem is described as a combinatorial optimization problem. To solve this problem, a heuristic algorithm, with enhanced variable neighborhood search (EVNS) is designed based on the general framework of a variable neighborhood search. Finally, experiments based on a real-world supply network are conducted. The experimental results indicate that the proposed method is applicable and effective.

In the production process, cutting parameters greatly affect the production cost and energy consumption, so it is very important for manufacturers to optimize cutting parameters. In this paper, an improved particle swarm optimization (PSO) is presented to optimize cutting parameters for minimizing carbon emissions, production cost and processing time in multi-pass milling. First, a multi-objective optimization model of cutting parameters is established with number of milling passes as one of decision variables. Then, an improved adaptive simulated annealing particle swarm optimization (IAPSOSA) is proposed to obtain the optimal solution of cutting parameters. At last, a case study is given to illustrate that the proposed method is effective to optimize cutting parameters for economic and environmental benefits.

The Multiple traveling salesmen problem (MTSP) is a complex combinatorial optimization problem, which is an extension of the well-known traveling salesmen problem (TSP). Compared to TSP, MTSP is more suitable to model real-life problems. In this paper, an open path multi-depot multiple traveling salesmen problem (OPMDMTSP) is studied. For the problem studied, two different objectives are considered: minimizing the total cost of all sales staff and minimizing the longest travel length. For the OPMDMTSP, an improved partheno-genetic algorithm (IPGA) is proposed in this paper. In IPGA, a new selection operator that combining roulette selection and elitist selection is implemented. In addition, a more comprehensive mutation operation that introduces the propagation mechanism of invasive weed optimization algorithm is used. Extensive experiment that compares the proposed method with some state of the art methods shows that the IPGA is outperform other methods in terms of both solution quality and convergence ability.

Images lack of light often show the characteristics of low visibility, low contrast and high noise, due to limited imaging equipment. To address the problem, inspired by Retinex theory, we propose a two-stage method called Multi-Scale Illumination-Guided Low-Light Image Enhancement Network (denoted as MSIGEN). In the first stage, we employ an enhancement module to achieve low-light image enhancement, including a Decom-Net for decomposing into illumination and reflectance, an Adjust-Net for illumination adjustment, a Restore-Net for the illumination fusion map guided reflectance restoration. Secondly, the enhancement module is introduced to refine the initial images to effectively remove visual defects amplified and ensure the naturalness of the image. In addition, extensive experiment results demonstrate the advantages of our method and the effect of this method has reached the state-of-the-art.

The recent application of computer vision to disassembly and remanufacturing has pushed forward this field significantly. In disassembling the laptop, we find that the existing positioning screw methods such as hough transform and template matching were not suitable for black laptops, so we propose a new method to solve this problem. We use RCF network to locate the screw hole contour, and make two improvements according to the characteristics of small screw hole size: 1. A feature integration module is added to RCF network to increase multi-scale features.2. A modulating factor is added to the loss function to enhance the attention to the indistinguishable pixels. Our approach has shown good results on black laptops and has higher ODS and OIS than the original RCF network and the existing method.

ObjectiveWaist-to-height ratio (WHtR) has been reported to be more strongly associated with cardiometabolic risk factors among non-obese individuals than BMI and waist circumference (WC). A body shape index (ABSI) and body roundness index (BRI) have been proposed recently to assess obesity-related disorders or mortalities. Our aim was to compare the ability of ABSI and BRI with that of WHtR to identify cardiometabolic risk factors in Chinese adults with normal BMI and WC.DesignReceiver-operating characteristic curves and areas under the curve (AUC) were employed to evaluate the ability of the indices (WHtR, BRI, ABSI) to identify metabolic risk factors and to determine the indices’ optimal cut-off values. The value of each index that resulted in maximization of the Youden index (sensitivity + specificity – 1) was defined as optimal. Differences in the AUC values between the indices were also evaluated.SettingIndividuals attending a voluntary health check-up in Beijing, China, July–December 2015, were recruited to the study.SubjectsNon-obese adults (n 1596).ResultsAmong both genders, ABSI exhibited the lowest AUC value for identifying each risk factor among the three indices; the AUC value of BRI for identifying each risk factor was very close to that of WHtR, and no significant differences were observed between the AUC values of the two new indices.ConclusionsWhen evaluating cardiometabolic risk factors among non-obese adults, WHtR was a simple and effective index in the assessment of cardiometabolic risk factors, BRI could be used as an alternative body index to WHtR, while ABSI could not.

A mixed two- and three-dimensional model is developed to simulate the dynamic track-tunnel-soil interaction in underground railway with ballastless track taking into account the moving train comprising a number of carriages, the track irregularities, and interaction among various components of the system. The effects of different track irregularities on the environmental vibration generated by underground railway with direct fixation and floating slab tracks are investigated. The results show that random track irregularities have large effects on the environmental vibration compared with perfectly smooth track condition. For direct fixation tracks, the environmental vibration induced by the irregularity of short wavelength is more significant than that induced by the irregularity of medium wavelength. However, for floating slab tracks, the environmental vibration induced by the irregularity of short wavelength is less significant than that induced by the irregularity of medium wavelength. The track irregularity samples measured by track geometry measuring car should therefore be used together with the short-wavelength random track irregularity for accurate prediction of the environmental vibration induced by underground railway with direct fixation track.

Circadian clocks have evolved independently in all three domains of life, and fitness benefits of a functional clock have been demonstrated in experimental genotypes in controlled conditions. Still, little is known about genetic variation in the clock and its fitness consequences in natural populations from heterogeneous environments. Using Wyoming populations of the Arabidopsis relative Boechera stricta as our study system, we demonstrate that genetic variation in the clock can occur at multiple levels: means of circadian period among populations sampled at different elevations differed by less than 1 h, but means among families sampled within populations varied by as much as 3.5 h. Growth traits also varied among and within populations. Within the population with the most circadian variation, we observed evidence for a positive correlation between period and growth and a negative correlation between period and root-to-shoot ratio. We then tested whether performance tradeoffs existed among families of this population across simulated seasonal settings. Growth rankings of families were similar across seasonal environments, but for root-to-shoot ratio, genotype × environment interactions contributed significantly to total variation. Therefore, further experiments are needed to identify evolutionary mechanisms that preserve substantial quantitative genetic diversity in the clock in this and other species.

Background/Aims: Diabetes mellitus and pre-diabetes are closely associated with visceral obesity. Visceral adiposity index (VAI) is a novel sex-specific index, indirectly expressing visceral adipose function. Our aim was to determine the associations of VAI with dysglycemia (the combination of diabetes and pre-diabetes) and to compare the predictive ability for dysglycemia between VAI and traditional obesity indices. Methods: We performed a cross-sectional analysis of the data of 2,754 Chinese community-dwelling people who participated in the health checkup. Sex-specific VAI tertile cut-off points were used as follows: 1.70, 2.77 in males and 0.98, 1.75 in females. Binary logistic regression models were performed to estimate the association of the higher values of all the obesity indices with pre-diabetes and diabetes. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was applied to compare the predictive potential for dysglycemia among the obesity indices. Results: VAI was the only index significantly associated with both pre-diabetes and diabetes in both sexes after adjusting for potential confounders. The results of ROC analysis and AUC showed that VAI possessed the largest AUC, followed by other obesity indices. Conclusions: Higher VAI values are positively associated with the presence of pre-diabetes and diabetes in Chinese adults.

A new topologically fluorinated armchair single-walled silicon carbide nanotube ((n,n)SWSiCNT) with one fluorine per unit cell adsorbed on (n,n)SWSiCNT (F–Si-(n,n)SWSiCNT), where the F atoms are adsorbed on top of the Si atoms to form an infinitely straight line of F atoms (F-Line) along the tube axis, has been predicted via first principles density functional theory (DFT) and nonequilibrium Green’s function method, as well as ab initio molecular dynamic (MD) simulations. The DFT calculations demonstrate that the F–Si-(n,n)SWSiCNT structures can be spontaneously formed. Ab initio molecular dynamics (MD) show that the F–Si-(n,n)SWSiCNT structures are stable at room temperature. It was found that except for F–Si-(2,2)SWSiCNT, which is a nonmagnetic metal, all F–Si-(n,n)SWSiCNTs are spin-semiconductors with long-ranged ferromagnetic spin ordering along the tube axis. Even more excitingly, the ferromagnetism of the F-(n,n)SWSiCNT survives at room temperature. This is to say, the F–Si-(n,n)SWSiCNT is a room-temperature metal-free ferromagnetic spin-semiconductor. Moreover, the simulations of F–Si-(n,n)SWSiCNT as a field-effect transistor (FET) show that the F–Si-(n,n)SWSiCNT FET can provide completely spin-polarized currents with reversible spin-polarization direction by applying a gate voltage. Thus, F–Si-(n,n)SWSiCNTs may open new routes towards practical nanoelectronics and optoelectronics as well as spintronic devices based on SWSiCNT-based materials. In addition, it was demonstrated that F atoms topologically adsorbed on (n,n)SWSiCNT bisect sp2-like bonding networks of (n,n)SWSiCNT, creating Klein and zigzag π-edge states at each side of the F-Line. It is such Klein and zigzag π-edge states that lead to the unexpected room-temperature ferromagnetism.

Due to the rail-bridge thermal interaction, the high additional axial force in continuously welded rails on continuous bridges may lead to rail buckling or breaking. However, there is little research on the influence of the location of the fixed bearing of continuous beam on the additional force of rail. In order to study the influence of bridge bearing arrangement on the additional longitudinal force of CWR, the thermal interaction model is established for rail, and simple and continuous beams considering nonlinear stiffness and the methods are proposed to determine the locations of fixed bearings of continuous beams corresponding to the maximum additional forces in rail reaching minimum values. Multiple continuous beams with several different lengths and simple beams with three types of bearing arrangements are taken into account to find the effect laws of the locations of the fixed bearings of continuous beams on the maximum additional forces in rail. The results show that as long as the same number of continuous beams, the ratios of the distances of adjacent two fixed bearings to the distance between the two fixed bearings of the simple beams neighbour to the first and last continuous beams respectively are approximately equal to each other. Furthermore the appropriate locations of the fixed bearings of continuous beams are recommended. The results can guide designing the location of the fixed bearing of continuous railway bridge while reducing the additional axial force in continuously welded rails due to bridge thermal effect.

Due to the temperature change of bridges, there is a great additional force in continuously welded rails on continuous bridges. Laying rail expansion regulators is an effective measure to reduce the additional force. The nonlinear finite element model is presented for a continuously welded rail track with a rail expansion regulator resting on the embankment and simple and continuous beams, considering the temperature change of the bridge. Then, a method is proposed to determine the locations of the rail expansion regulator and the fixed bearing of the continuous beam, corresponding to the maximum additional forces of rail reaching minimum values. Their appropriate matching locations are recommended based on the obtained influence laws of any locations of the rail expansion regulator and the fixed bearing of the continuous beam on the maximum additional forces of rail. The results can provide the theoretical basis for the design of the rail expansion regulator and the fixed bearing of long-span continuous bridges.

With the increasingly fierce market competition, manufacturing enterprises have to continuously improve their competitiveness through their collaboration and labor division with each other, i.e. forming manufacturing enterprise collaborative network (MECN) through their collaboration and labor division is an effective guarantee for obtaining competitive advantages. To explore the topology and evolutionary process of MECN, in this paper we investigate an empirical MECN from the viewpoint of complex network theory, and construct an evolutionary model to reproduce the topological properties found in the empirical network. Firstly, large-size empirical data related to the automotive industry are collected to construct an MECN. Topological analysis indicates that the MECN is not a scale-free network, but a small-world network with disassortativity. Small-world property indicates that the enterprises can respond quickly to the market, but disassortativity shows the risk spreading is fast and the coordinated operation is difficult. Then, an evolutionary model based on fitness preferential attachment and entropy-TOPSIS is proposed to capture the features of MECN. Besides, the evolutionary model is compared with a degree-based model in which only node degree is taken into consideration. The simulation results show the proposed evolutionary model can reproduce a number of critical topological properties of empirical MECN, while the degree-based model does not, which validates the effectiveness of the proposed evolutionary model.

At present, rail transit is developing rapidly in the world, and this means new and changing requirements for the training of talents in railway engineering experiments. Given the current problems of limited laboratory/field instruments for railway engineering experimentsand the safety/administrative difficulties of going to the frontline of railway lines to teach railway engineering experiemnts in the field, the Department of Railway Engineering of Central South University tried to introduce virtual reality (VR) technology to teach students experiments in the field of railway engineering. Through the virtualized experimental methods, students can carry out railway engineering experiments such as; vehicle wheel pair off-axis experiments, track geometry and position detection, etc by immersive means. It was observed that after performing virtual simulation experiments, students appeared conversant in subsequent field experiments. Thus, VR greatly improves the teaching efficiency of railway engineering experiments.

Thermal error of CNC machine tools is one of the main factors affecting the machining accuracy. The data-driven method for thermal error modeling is an effective and efficient, but they have some flaws, such as poor accuracy, bad robustness, and etc. because of having no quite enough data set and imbalanced data set. In this paper, a new method based on transfer learning for thermal error modeling is presented for solving the issue of imbalanced data set. The dataset of monitoring the temperature field of the machine tools includes monitoring data of three kinds of operating conditions, namely stopping, idling, and machining. When the fewer idling data is used to train a model, the larger stopping data are introduced as train aids. Transfer learning is adopted to fully learn the common characteristics of the two different working conditions, which can effectively solve the problem of imbalanced dataset. The experimental results prove that our method have better performance than other methods trained only with limited idling data.

It is a trend for robots to replace human in industrial fields with the increment of labor cost. Mobile robots are widely used for executing tasks in harsh industrial environment. It is an important problem for mobile robots to plan their path in unknown environment. The ordinary deep Q-network (DQN) which is an efficient method of reinforcement learning has been used for mobile robot path planning in unknown environment, but the DQN generally has low convergence speed. This paper presents a method based on Double DQN (DDQN) with prioritized experience replay (PER) for mobile robot path planning in unknown environment. With sensing its surrounding local information, the mobile robot plans its path with this method in unknown environment. The experiment results show that the proposed method has higher convergence speed and success rate than the normal DQN method at the same experimental environment.

Solid-state electrolyte batteries are considered as one of the promising next generation power sources. A novel cross-linked nanocomposite polymer electrolyte (CNPE) based on poly (propylene oxide)-poly (ethylene oxide)-poly (propylene oxide) triblock main chains and surface-modified SiO2 nanoparticles are prepared to establish a super flexible and stable polymer framework. The CNPE exhibits high ionic conductivity of 1.32 mS cm−1 at 20 °C and its electrochemical stability window reaches up to 6.5 V (versus Li+/Li). The favorable Li deposition behavior provided by CNPE films is identified by the stripping/plating test at a current density of 2.4 mA cm−2 for 1700 h. The CNPE films possess excellent mechanical stability (highly stretchable with ultimate elongation of 700%). Li/CNPE/LiFePO4 batteries deliver an initial discharge capacity of 160 mAh g−1 at 0.2 C, which is 94.1% of the theoretic capacity, and maintain the high specific capacity of 148 mAh g−1 at 0.5 C. This work provides a new approach toward designing high performance electrolyte for lithium metal batteries.

Lithium metal batteries (LMBs) have been regarded as the most promising battery system for the next-generation high energy density batteries due to the extremely high specific capacity of the lithium (Li) metal anode. Unfortunately, the commercial application has been hindered by sharp dendrite Li growth and infinite relative volume change during the repeated charge/discharge process, which lead to severe safety issues and poor cyclability of the battery. Herein, a novel composite Lithium anode is fabricated via thermally infusion molten Li into three-dimensional (3D) nickel foam (NF) framework decorated by lithiophilic Co3O4 nanosheet arrays. The universalized growth of Co3O4 nanosheet on the Ni foam can provide superior wettability between the molten Li and Ni foam, and facilitate more convenient infusion process (5 s). Benefitting from uniform distribution of Li in the 3D host and homogeneous Li+ flux on the surface of the electrode, excellent Li stripping/plating performance (1000 h at 3 mA cm−2 and 275 h at 5 mA cm−2) and dendrite-suppressed morphology are achieved for the Li–Co3O4/NF composite anode. When utilized in full cells paired with a LiFePO4 cathode, the Li–Co3O4/NF composite anode demonstrates greater cyclability and rate performance than bare Li anode. As a result, the obtained Co3O4/NF demonstrates excellent electrochemical performance as a lithium host skeleton for advanced Li metal battery.

The spinel lithium titanate (Li4Ti5O12, LTO) has attracted special attention for its low cost, stable structure and little safety issue. However, LTO displays a low electronic conductivity, which seriously impedes its practical applications at remarkable rate capability. Herein, to improve the electrochemical properties of LTO, this research constructed a novel composite anode by mixing the GaSn liquid metal (LM) nanoparticles with LTO. The composite anode’s phase structure and morphology were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM). The electrochemical data indicate that LM-LTO electrode delivers a better rate capability (52% retention from 10 mA/g to 500 mA/g) as well as long cyclic stability (76% retention after 1000 cycles at 200 mA/g). Therefore, this work opens a new avenue for fabricating better rate performance and safer lithium ions battery.

Aims: A pilot survey shows that primary nocturnal enuresis (PNE) prevalence has increased significantly during the past decade in Mainland China. Whether it is related to the delay of elimination communication (EC) is unclear. This study retrospectively investigated the influence of delayed EC on the PNE prevalence in children and adolescents in mainland China. Methods: A cross-sectional study of PNE prevalence was performed by distributing 19 500 anonymous self-administered questionnaires to parents in five provinces of mainland China from July 2017 to October 2017. The questionnaires included sociodemographic data, family caregivers’ information, and details about the disposable diapers (DD) usage, EC commencement date, psychological disorders, lower urinary tract symptoms, and family history of PNE in children and adolescents. The 2017 PNE prevalence was compared with that of 2006 in Mainland China. Results: The total response rate was 97.04% (18 631 of 19 500) and 92.39% (18 016 of 19 500) qualified for statistical analysis. The PNE prevalence in 2017 has increased significantly compared to that of 2006 (7.30% vs 4.07%, P < 0.001). The PNE prevalence in children with EC starting before 6 months of age was significantly lower than those who start after 12 months of age. The longer DD were used and the later the beginning of EC, the higher the PNE prevalence was found. Conclusions: The PNE prevalence in Mainland China has increased significantly during the past 10 years. A longer use of DD and later onset of EC may be risk factors for PNE.

The joint moment provides specific information of human motion. It plays an important role as an advanced interfacing technology in robot assistant systems for elderly and disabled people. The surface electromyography (sEMG) signals are usually affected by the adjacent muscles. And muscle tendon units in the same muscle show different activation characteristics with different movement patterns. It is significant to calculate the contribution degree of signals from multi-channels to different movements. In this paper, the wrist joint moment, in particular the flexion and extension of wrist (WFE), is estimated by a novel approach that combines muscle synergy theory with musculoskeletal model. sEMG signal and joint angle of WFE were collected and input to the estimation model to calculate the joint moment. Experiments on five healthy subjects have demonstrated that, the estimation result of the proposed approach is more accurate with higher average correlation coefficient (CC) and lower normalized root-mean-square error (NRMSE) between estimated moment and reference moment.

Bamboo charcoal is a high-quality biochar, with a large surface area, well-developed pores, and high mechanical strength. Therefore, it is one of the best choices of microbial immobilization carrier. In this study, the optimal preparation condition was examined for microorganisms immobilized on bamboo charcoal by the orthogonal test. The degradation effects of estrogen nonylphenol were compared between the bacteria immobilized on bamboo charcoal and free bacteria, and then feasibility of the reuse of immobilized bacteria was investigated. The results showed that lots of degrading bacteria could adhere to the surface and internal pores of bamboo charcoal. The optimum conditions for the preparation of immobilized microorganisms were as follows: 30 ℃, pH=7, 35-mesh bamboo charcoal. The degradation rate of nonylphenol was in good agreement with the first order kinetics equation. When the initial concentrations of nonylphenol were 30, 50, 80 and 100 mg·L竹炭是一种优质生物质炭,不仅比表面积大,孔隙发达,而且机械强度高,是微生物固定化载体的最佳选择之一.本文采用正交试验确定了竹炭固定化微生物的最佳制备条件,对比了竹炭固定菌和游离菌对水中类雌激素壬基酚的降解效果,并考察了竹炭固定菌的重复利用性.结果表明: 固定化后降解菌大量地附着在竹炭表面及内部孔隙中,其最佳制备条件为温度30 ℃、pH=7、竹炭粒径35目.壬基酚的降解符合一级动力学方程,在不同的壬基酚初始浓度下(30、50、80、100 mg·L

Cloud Manufacturing (CMfg) is an emerging service-oriented manufacturing paradigm in the last few years. This innovative manufacturing paradigm provides users with a platform in which distributed geographically manufacturing resources are encapsulated as Manufacturing Services (MSs). And also in this platform these MSs can autonomously interact with users and independently make decisions whether they help users to finish their tasks. Usually one task needs several MSs to work together to finish it. In this paper, a bipartite network is used to depict the relationship between tasks and MSs and a multi-player game model is put forward to model the behavioral strategies of MSs in this CMfg environment. A profit allocation mechanism is presented to encourage the cooperative behaviors of MSs in the process of task completion. At last, Agent-Based Modeling (ABM) is used to model and simulate of the mutual relation of MSs and develop a multi-agent system with Repast. The simulating result shows that the effective profit allocation can urge the emergence of cooperative behaviors.

With the advent and swift development of advanced network and information technology, various advanced manufacturing paradigms are rapidly emerged. A new kind of service-oriented network manufacturing paradigm, named Cloud Manufacturing (CMfg) is paid attraction by industries and academia. In this CMfg environment, the different kinds of manufacturing resources are encapsulated as manufacturing services (MSs), which are able to provide users with various machining/producing services and also can independently make decisions by themselves. Hence users can select different manufacturing services to help them finish their tasks together. In this paper, two different preferential selection mechanisms are used to select MSs, namely the local preferential and the global preferential selection. Therefore, there are two different relationship structures between tasks and MSs to be formed and a bipartite network is used to describe this relationship structures. For exploring the effect of the relationship structures on cooperative behaviors, the Stag Hunt game is used to describe their behavioral strategies in the processing of their repeated interactions. Lastly the agent-based modeling and simulating are used to analyze the cooperative behaviors of MSs in the process of repeated game with Repast. The simulating and analyzing result shows that different relationship structures are influenced on the emergence of cooperative behaviors in the CMfg environment.

Recently, a kind of service-oriented network manufacturing, cloud manufacturing (CMfg), has been emerged. In a CMfg environment, geographically distributed various manufacturing resources are encapsulated as different manufacturing services with the capability of independent making-decision, which are able to provide users with various services to help them to finish manufacturing tasks. Usually one manufacturing task needs several manufacturing services to work together. Apparently mutual cooperation between manufacturing services plays a crucial part in a CMfg environment. It can ensure service quality and reduce the interaction costs between manufacturing services in the process of collaborative manufacturing. Thus, the building of cooperative relations is an important issue of CMfg. In this paper, to promote the establishment of cooperative relations between manufacturing services and make manufacturing services get the high profit, a behavior learning mechanism is proposed to support the action selection of manufacturing services during the process of repeated interaction. The results show that the relationship of cooperation between manufacturing services can be established by using this mechanism. Moreover, the willingness of cooperation (WOC) of manufacturing services and the profit that is generated by behaviors have an effect on the emergence of cooperative behaviors of manufacturing services.

With the increasing occurrence of haze during the summer, the physicochemical characteristics and toxicity differences in PM2.5 in different seasons are of great concern. Hangzhou is located in an area that has a subtropical monsoon climate where the humidity is very high during both the summer and winter. However, there are limited studies on the seasonal differences in PM2.5 in these weather conditions. In this test, PM2.5 samples were collected in the winter and summer, the morphology and chemical composition of PM2.5 were analyzed, the toxicity of PM2.5 to human bronchial cells BEAS-2B was compared, and the correlation between PM2.5 toxicity and the chemical composition was discussed. The results showed that during both the winter and summer, the main compounds in the PM2.5 samples were water-soluble ions, particularly SO4 2−, NO3 −, and NH4 +, followed by organic components, while heavy metals were present at lower levels. The higher the mass concentration of PM2.5, the greater its impact on cell viability and ROS levels. However, when the mass concentration of PM2.5 was similar, the water extraction from the summer samples showed a greater impact on BEAS-2B than that from the winter samples. The cytotoxicity of PM2.5 was closely associated with heavy metals and organic pollutants but less related to water-soluble ions.

The classic job shop scheduling problem mainly focuses on one kind of manufacturing resources, such as machine tools, and etc. But the job shop scheduling in practical production activities always needs to consider the constraints of different manufacturing resources. In this paper, a double-resource flexible job shop scheduling problem (DFJSSP) is presented. Both machines and workers are considered in the process of job shop scheduling in this DFJSSP. And a genetic algorithm (GA) is used to solve this problem, in which a new well designed three-layer chromosome encoding method has been adopted and some effective crossover and mutation operators are designed. Finally, a case study is used to validate the method.

In the process of online monitoring the temperature measuring points of CNC machine tools, the sensing data are usually missing or abnormal because of sensor or transmission failure. To recover the abnormal temperature measuring points, a recovery method based on compressed sensing is proposed. The method uses K-SVD algorithm to carry out dictionary learning with the large amount of temperature monitoring data to obtain the over-complete dictionary, and then the mutual coherence is used to constructs the observation matrix that is suitable for the data type. Finally the OMP algorithm is used to realize the recovery of the temperature measuring points. In this paper, the method is applied to the recovery of spindle temperature measuring points of heavy CNC machine tools. Experiments under different working conditions show that the method can achieve good recovery results.