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Objective: Osteosarcoma (OS) is the most common primary malignant tumor of bone. Patients with localized osteosarcoma are routinely treated with chemotherapy and surgery. However, many patients eventually relapse after these treatments. Therefore, it is important and urgent to identify better therapeutic strategies. Hedgehog-GLI is responsible for the development of bone and tumorigenesis. Aberrant activation of GLI-2 is correlated with various malignancies including OS. Methods: RT-QPCR and western blot were performed to detect the expression of GLI-2 among human osteosarcoma cell lines U2OS, SaOS and human osteoblast cells HOB-c. siRNA or overexpression method were used to knock down/overexpress GLI-2 or β-catenin and MTS formazan generation method were applied to study the function of GLI-2 and β-catenin in the proliferation of OS cells. Results: We showed that GLI-2 is highly expressed in osteosarcoma cell lines. Knockdown of GLI-2 by siRNA decreases osteosarcoma cell proliferation. Further, we showed that knockdown of GLI-2 can decrease the protein level of β-catenin, and β-catenin depletion by siRNA could decrease the proliferation of OS cells. Interestingly, overexpression of β-catenin in GLI-2 knockdown cells partially increased proliferation. Conclusion: These findings suggest Hh/GLI-2-Wnt/β-catenin crosstalk is required for osteosarcoma cell proliferation. GLI-2 may play a role in the regulation of the expression of β-catenin in OS cells and β-catenin may function as downstream effector of crosstalk between Hh and Wnt signaling. GLI-2 may be exploited as a therapeutic target for the treatment of osteosarcoma patients.

Cloud manufacturing (CMfg) is a new networked manufacturing mode based on the big data and the Internet of Thing technologies, which can dynamically and flexibly allocate the manufacturing resources on demand. All the current researches and applications on CMfg focused on the factory manufacturing schema, the field manufacturing schema has not been concerned. Field manufacturing refers to the geographically dispersed manufacturing resources are arranged to different locations specified by customers to execute their manufacturing tasks, such as the assembly, measurement and maintenance of large equipments, this type of manufacturing is getting more and more applications with the increase of product complexity and the trend of manufacturing industry servicizing. At present, the massive complicated field manufacturing tasks bring urgent needs on the dynamic organization and global planning of the dispersed manufacturing resources, which can be achieved by the network technology. To this end, this paper studies the scheduling of field manufacturing resources in CMfg environment. Firstly, the detailed process of the field service resource scheduling in CMfg environment (FSRS-CMfg) is designed based on analyzing the practical field manufacturing process. After presenting the assumptions and constraints of field manufacturing, the optimization model for the FSRS-CMfg problem is established, in which the overall quality of service (QoS) is used as the optimization objective and its evaluation indicators and aggregation method are elaborately designed. Then, to solve the above optimization model, the encoding and decoding methods are designed and the discrete search operators are developed. For improving the solving speed and accuracy, a multi-population competitive-cooperative grey wolf optimizer (MPCCGWO) is built to avoid trapping in local optimum. Finally, two experiments are carried out to verify the correctness and effectiveness of the proposed model and algorithm. Results show that the overall QoS of the field manufacturing resource scheduling scheme can be markedly improved based on the FSRS-CMfg model, and MPCCGWO possesses higher convergence precision than the commonly-used intelligent optimization algorithms without obvious increase of time consumption, so it is suitable for solving the problems with large solution spaces such as the FSRS-CMfg problem.

The Voronoi-based random honeycomb cylindrical shell with specific mesostructures was constructed programmatically and additively manufactured on material of polylactide (PLA). Then, a new PLA random honeycomb-filled double circular tube (PLA-RHFDCT) was proposed. The mechanical performance of the PLA-RHFDCTs in terms of deformation mode, crushing response and energy absorption was studied experimentally by carrying out the quasi-static axial compression loading. A parametric study was performed to evaluate the influence of tube sizes and the mesostructures of honeycomb cores on the crushing resistance and energy absorption capacity of the PLA-RHFDCTs. Results indicate that the cell configuration dominates the deformation modes of PLA honeycomb cylindrical structure. For the PLA-RHFDCTs, the deformation and the bearing capacity are mainly determined by the wall thicknesses of tubes. The introduction of PLA honeycomb core enhances the mechanical performance and affects the localized buckling of the PLA-RHFDCTs due to both the in-plane and out-of-plane crushing resistance of honeycomb core structures. Compared with the parameter of cell irregularity, the relative density of honeycomb cylindrical cores exhibits a higher influence on the energy absorption performance of the PLA-RHFDCTs, though the relatively low crushing strength of PLA honeycomb cylindrical structures.

Thermal error has become a key reason hindering machine tool’s thermal stability improvement. The error compensation is carried out from the view of error mechanism of spindle systems to increase the thermal stability of machine tools. The hysteresis effect of the thermal expansion is revealed with theoretical modeling of error mechanism, and long-term memory characteristics of thermal error on historical thermal information are demonstrated. Then the applicability of long short term memory (LSTM) neural networks for the training of the error model is proved. The variational mode decomposition (VMD) decomposes error data into several inherent modal function (IMF) components to remove the coupling effect of high- and low-frequency data, improving the robustness and generalization capability of the error model. Moreover, the hyper-parameters of LSTM neural networks are optimized with grey wolf (GW) algorithms to remove the sensitivity of the predictive performance to its hyper-parameters. Finally, error models are trained with VMD-GW-LSTM neural networks, VMD-LSTM neural networks, and recurrent neural networks (RNNs). To verify the effectiveness of compensation methods, the error compensation and machining were performed at different working conditions. The results show that compensation rates of the VMD-GW-LSTM network model are 77.78%, 75.00%, and 77.78% for Sizes 1, 2, and 3, respectively. Moreover, the predictive performance and compensation performance of the VMD-GW-LSTM network model is far better than that of VMD-LSTM network and RNN models.

Taijiquan practice exercises control the functional connectivity of cognitive control networks in the elderly. Cognitive impairment is one of the most common problems affecting older people. In this study, to examine whether Taijiquan practice exercises could regulate the connection between mind control and the dormant function of the cognitive control network in the elderly. Participants in the exercise group practiced Taijiquan and 12 weeks while the control group received basic health education. Taijiquan exercise movement intervention work, frequency and duration included Fuzzy control theory, ranges from 2 business days a week to 5 weeks a year, 5 meetings per week. Results show that synthesis has two outcome areas: (1) mental health and behavior, and (2) physical health and function. This increase is associated with an improvement in low frequency memory function, which is exactly 5 slow in the overall Taijiquan exercise group. Therefore, Taijiquan practice in the adolescent population has been systematically reviewed and evaluated in intervention studies to determine how these measures are applied to fuzzy control theory and the outcome of comprehensive interventions. The results of synthetic data based on the Fuzzy Control Theory Show that Taijiquan practice actively improves physical health and function, and that its effects on mental health and behavior are mostly decisive improvements in research reports.

The residual properties of the ball screw raceway after whirling milling are the critical factors affecting the performance of the workpiece. Comprehensive research has been conducted to investigate the residual properties (including the residual stress and full-width half-maximum (FWHM)) of the ball screw raceway in dry machining. More importantly, two kinds of machine learning methods including the neural network (NN) and support vector machine (SVM) have been proposed based on experimental data to predict the residual properties of the ball screw raceway. In the method of NN, two representative methods including the back-propagation neural network (BPNN) and radial basis function neural network (RBFNN) have been employed. The model verifications show that the average absolute relative errors of the residual stress perpendicular to the cutting direction and FWHM are very small by using the method of machine learning. The comparative analysis of the predictive models shows that SVM has a better prediction performance than NN. The accurate prediction of residual properties by SVM can provide technical support for the optimization of machining parameters and improve the performance of ball screw.

CoS2 nanoparticles embedded in Al2O3 nanosheets (CoS2 NP/Al2O3 NSs) have been designed and fabricated using a controllable hydrothermal process followed by a simple low-temperature sulfurization step. The as-prepared CoS2 NP/Al2O3 NSs display combined properties of high nanoporosity, thin thickness and good structural stability. When used as an electrocatalyst for the hydrogen evolution reaction (HER), the composite demonstrates high catalytic activity, including a small overpotential of ∼53 mV, a small Tafel slope of 50.9 mV dec−1 and remarkable stability. Moreover, the CoS2 NP/Al2O3 NSs have a promising lithium-storage capability with high specific capacity (∼1150 mA h g−1 at 100 mA g−1 in the first cycle) and enhanced cycling stability (coulombic efficiency of around 96% for 150 cycles). The facile strategy used to synthesize the unique architecture could be expanded to the preparation of other transition metal sulfides for the HER and lithium ion batteries (LIBs).

Cellular materials under high-velocity impact have highly localized deformation with the cells layer-wise collapse, which is usually characterized by the propagation of shock wave. Researches have shown that the shock wave speed is strongly dependent on the impact velocity, but the effect of the meso-structural and base-material parameters is unclear. In this study, the dynamic material parameters of closed-cell foams are investigated with cell-based finite element models. The one-dimensional velocity distribution along the loading direction is used to capture the propagation of shock front. The shock wave speed is thus determined and it exhibits a linear relationship with impact velocity when the impact velocity is high enough. The difference between the shock wave speed and the impact velocity is a dynamic material parameter of cellular material and the effect of meso-structural and base-material parameters on this dynamic material parameter is investigated with dimensional analysis. An expression of the dynamic material parameter with respect to the relative density and the base-material parameters is obtained. It shows that the dynamic material parameter intensively relies on the relative density and increases linearly with the relative density. The investigation of dynamic stresses with the aid of a shock model shows that the initial crushing stress increases in a power-law tendency with the increase of relative density. As a result, a stress–strain relation involving the relative density of material cellular and the yield stress and density of base material is obtained for the closed-cell foams considered. The effects of hardening behaviors of cell-wall material and gas trapped within cells are also considered. It is found that the dynamic material parameter exhibits nearly linear increase with the increase of hardening parameters of base material and initial gas pressure, while the dynamic initial crushing stress is independent of the strain-hardening parameter and the entrapped gas pressure but increases with the strain-rate hardening parameter linearly. These findings may be helpful for guiding the crashworthiness design of cellular materials and structures.

Carbon dots (CDs) hold great promise as a class of fluorophores for bioimaging. Here, we report a simple solvothermal approach to prepare photoluminescent CDs derived from the antineoplastic drug etoposide. The CDs show photoluminescence ranging from 365 to 650 nm when excited at 360 nm. When excited at 405, 488 and 543 nm, strong blue, green and red fluorescence can be detected. Our experiments reveal that the CDs can penetrate into the cells in a short time and remain emissive, even at low temperature. Also, optical signals can be detected from mice after hypodermic or intravenous injection. We demonstrated that the CDs can serve as a low-cytotoxicity biological imaging agent for bioimaging in vitro and in vivo.

Shan Jiang,1 Rongrong Zhu,1 Xiaolie He,1 Jiao Wang,1 Mei Wang,1 Yechang Qian,2 Shilong Wang1 1Tenth People’s Hospital, School of Life Science and Technology, Tongji University, 2Department of Respiratory Disease, Baoshan District Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai, People’s Republic of China Abstract: Curcumin (Cur) is a promising photosensitizer that could be used in photodynamic therapy. However, its poor solubility and hydrolytic instability limit its clinical use. The aim of the present study was to encapsulate Cur into solid lipid nanoparticles (SLNs) in order to improve its therapeutic activity. The Cur-loaded SLNs (Cur-SLNs) were prepared using an emulsification and low-temperature solidification method. The functions of Cur and Cur-SLNs were studied on the non-small cell lung cancer A549 cells for photodynamic therapy. The results revealed that Cur-SLNs induced ~2.27-fold toxicity higher than free Cur at a low concentration of 15 µM under light excitation, stocking more cell cycle at G2/M phase. Cur-SLNs could act as an efficient drug delivery system to increase the intracellular concentration of Cur and its accumulation in mitochondria; meanwhile, the hydrolytic stability of free Cur could be improved. Furthermore, Cur-SLNs exposed to 430 nm light could produce more reactive oxygen species to induce the disruption of mitochondrial membrane potential. Western blot analysis revealed that Cur-SLNs increased the expression of caspase-3, caspase-9 proteins and promoted the ratio of Bax/Bcl-2. Overall, the results from these studies demonstrated that the SLNs could enhance the phototoxic effects of Cur. Keywords: photodynamic therapy, curcumin, solid lipid nanoparticles, drug delivery, reactive oxygen species

Thermal deformation of large computer numerical control (CNC) gear profile grinding machine tools, which causes the position changes of grinding point between a grinding wheel and workpiece in X and Y directions, affects the machining accuracy significantly. In view of this situation, a theoretical model of thermal deformation based on the theories of thermal expansion of metallic materials, extensional axis of beam, non-uniform temperature distribution of Euler-Bernoulli beam, and Kirchhoff assumption of plane section is proposed according to the complex structure and machining process of the machine tools. To verify the accuracy and the reliability of the model, an experimental system and a platform for thermal deformation are established on a large CNC gear profile grinding machine tool, and the experimental data of temperature and displacement is measured and analyzed. Compared with theoretical value of displacement, the measurement data indicates that the relative errors are no more than 5 %. The proposed theoretical model and the adopted theories, technologies, and methods have great guidance significance and practical value to improve the accuracy of the machine tools effectively.

Glioblastoma is difficult to cure due to the high risks of invasion and metastasis. Although curcumin has shown beneficial effects against malignant glioma cells, its poor properties have limited its application. The aim of this study was to design a nanocarrier for curcumin to exert sufficient anti-migration and anti-invasion effects on A172 cells. Curcuminloaded layered double hydroxide nanoparticles (Cur/LDH NPs) were synthesized, and the physicochemical properties were characterized using transmission electron microscopy (TEM), zeta potential, Fourier transform infrared spectrometry (FTIR) and X-ray powder diffraction (XRD). Then, Cur/LDH NPs were applied to A172 cells to assess the anti-migration and anti-invasion effects, cellular uptake, autophagy activity, and the related mechanisms. The prepared Cur/LDH NPs were 119±34 nm in size and exhibited a drug-loading efficiency of 21.31±1.83%. Compared with LDH and Cur alone, the Cur/LDH NPs significantly reduced cell migration and invasion when applied at low doses (0.1 and 0.5 μg/ml), and these effects were associated with a down-regulation of PI3K/AKT/mTOR signaling pathway. The Cur/LDH NPs were easily taken up by A172 cells and were mainly located in lysosomes and mitochondria in a time-dependent manner. Importantly, autophagy, which can be triggered by the inhibition of mTOR and by nanomaterials, was detected distinctly in A172 cells exposed to Cur/LDH NPs via the autophagic marker LC3A/B. In addition, the expression levels of Atg5-Atg12 and LAMP-1 were increased in the Cur/LDH NPs groups, and autophagic vacuoles were observed via TEM. Our work suggests that autophagy can promote the anti-migration and anti-invasion effects of Cur/LDH NPs against glioblastoma.

Xiaolie He,* Yanjing Zhu,* Mei Wang, Guoxin Jing, Rongrong Zhu, Shilong Wang Research Center for Translational Medicine at East Hospital, School ofLife Science and Technology, Tongji University, Shanghai, People’sRepublicof China *These authors contributed equally to this work Abstract: Major depression is a complex neuropsychiatric disorder with few treatment approaches. The use of nontargeted antidepressants induced many side effects with their low efficacy. A more precise targeting strategy is to develop nanotechnology-based drug delivery systems; hence, we employed solid lipid nanoparticles (SLNs) to encapsulate HU-211 and curcumin (Cur). The antidepressant effects of the dual-drug nanoparticles (Cur/SLNs-HU-211) for major depression treatment were investigated in corticosterone-induced cellular and animal models of major depression. Cur/SLNs-HU-211 can effectively protect PC12 cells from corticosterone-induced apoptosis and can release more dopamine, which may be associated with the higher uptake of Cur/SLNs-HU-211 shown by cellular uptake behavior analysis. Additionally, Cur/SLNs-HU-211 significantly reduced the immobility time in forced swim test, enhanced fall latency in rotarod test, and improved the level of dopamine in mice blood. Cur/SLNs-HU-211 can deliver more Cur to the brain and thus produce a significant increase in neurotransmitters level in brain tissue, especially in the hippocampus and striatum. The results of Western blot and immunofluorescence revealed that Cur/SLNs-HU-211 can significantly enhance the expression of CB1, p-MEK1, and p-ERK1/2. Our study suggests that Cur/SLNs-HU-211 may have great potential for major depression treatment. Keywords: major depression, curcumin, HU-211, solid lipid nanoparticles, dopamine

Cellular materials under high loading rates have typical features of deformation localization and stress enhancement, which have been well characterized by one-dimensional shock wave models. However, under moderate loading rates, the local stress–strain curves and dynamic response of cellular materials are still unclear. In this paper, the dynamic stress–strain response of cellular materials is investigated by using the wave propagation technique, of which the main advantage is that no pre-assumed constitutive relationship is required. Based on virtual Taylor tests, a series of local dynamic stress–strain history curves under different loading rates are obtained by Lagrangian analysis method. The plastic stage of local stress-strain history curve under a moderate loading rate presents a crooked evolution process, which demonstrates the dynamic behavior of cellular materials under moderate loading rates cannot be characterized by a shock model. A unique dynamic stress–strain state curve of the cellular material is summarized by extracting the critical stress–strain points just before the unloading stage on the local dynamic stress–strain history curves. The result shows that the dynamic stress–strain states of cellular materials are independent of the initial loading velocity but deformation-mode dependent. The dynamic stress–strain states present an obvious nonlinear plastic hardening effect and they are quite different from those under quasi-static compression. Finally, the loading-rate and strain-rate effects of cellular materials are investigated. It is concluded that the initial crushing stress is mainly controlled by the strain-rate effect, but the dynamic densification behavior is velocity-dependent.

We have developed a novel eco-friendly approach involving hydrazine hydrate-assisted hydrothermal cutting followed by functionalization with poly (ethylene imine) (PEI) for fabricating highly fluorescent graphene quantum dots from coffee grounds. The PEI-functionalized graphene quantum dots exhibit enhanced band-edge photoluminescence with single exponential decay. Their preliminary applications in bioimaging and sensing of heavy metals have been demonstrated.

Tool wear monitoring is critical for ensuring product quality and productivity. This article presents a novel tool wear prediction model based on improved least squares support vector machine method, combined with leave-one-out technique and Nelder–Mead technique. Leave-one-out is applied to tune the regularization factor and radial basis function kernel parameter of least squares support vector machine for enhancing the global search ability. Nelder–Mead is applied to raise the local search ability. The optimized least squares support vector machine based tool wear prediction model is constructed by learning the highly nonlinear correlationships between tool cutting conditions and actual tool wear. The effectiveness of the proposed prediction model is validated by experiments. Compared with particle swarm optimization algorithm-based least squares support vector machine and basic least squares support vector machine, Nelder–Mead-leave-one-out-based least squares support vector machine demonstrates a better performance in prediction accuracy, generalization, robustness, and convergence. The average accuracy obtained in tests for tool wear prediction is above 97%. This model provides theoretical basis for the machining condition configuration in the actual processing.

Resin infusion (RI) process has been widely used for manufacturing composite parts. The variation of preform thickness brings great difficulty to the three-dimensional simulation of the filling stage. To accurately simulate the preform thickness change and resin flow during resin infusion, precise preform compaction models and dynamically changing geometry models need to be adopted. At present, resin flow is usually considered as two-dimensional and simple compaction models are employed to simplify the simulation, which degrades the prediction accuracy seriously. In this paper, general equations to describe the resin flow in the changing thickness cavity are developed, and the viscoelastic model is adopted which can fully express the dynamic characteristics of the preform compaction. To avoid solving the coupled resin flow/preform deformation equations directly, the volume of fluid method and the dynamic mesh model are employed to implement the tracking of the flow front and updating of cavity geometry model. The resin storage and release induced by porosity variations are adjusted by a master-slave element method to ensure mass conservation. Two simulation examples are carried out to demonstrate the capability of the above approach. The applicability of the approach on arbitrary complex domains and sequential injection strategy is also verified.

A virtual Taylor impact of cellular materials is analyzed with a wave propagation technique, i.e. the Lagrangian analysis method, of which the main advantage is that no pre-assumed constitutive relationship is required. Time histories of particle velocity, local strain, and stress profiles are calculated to present the local stress–strain history curves, from which the dynamic stress–strain states are obtained. The present results reveal that the dynamic-rigid-plastic hardening (D-R-PH) material model introduced in a previous study of our group is in good agreement with the dynamic stress–strain states under high loading rates obtained by the Lagrangian analysis method. It directly reflects the effectiveness and feasibility of the D-R-PH material model for the cellular materials under high loading rates.

The critically important at-a-station hydraulic geometry (AHG) relationships relate hydraulic variables (depth, width, or velocity) to discharge in power law form. The recently discovered at-many-stations hydraulic geometry (AMHG) states that AHG exponents and coefficients are strongly correlated, removing one parameter and lending AMHGs to remote sensing discharge estimation. Despite the excitement, there is an urgent need to clarify AMHG's geomorphological significance for different hydraulic variables. Using data from 57 rivers in the U.S. and perturbation experiments, we show that the width-AMHG is weak, arising mainly from the mathematical construct involving the exponent in both the regressor and the regressand. In contrast, the depth-AMHGs result from geomorphological coevolution. The similar-time-mean condition, as argued for in the literature, is sufficient but not necessary for rating curve convergence and in turn AMHG, for depth and velocity. The predictive accuracy of AMHGs, whose coefficient of determination is unit dependent, is similar to the flow percentile-based downstream hydraulic geometry.

Wide Electrostatic Precipitator (having plate-to-plate spacing 400 mm or more) is one of a promising ways to improve existing ESPs, yet its large-scale application has been limited because of potential collection efficiency reduction. This article focus on study the electrohydrodynamic flow inside the ESP and the particle collection efficiency by using Particle Image Velocimetry and Electrical Low Pressure Impactor respectively when several different electrode geometries were applied, in order to increase wide ESP efficiency. Results showed that geometry with juxtaposed high voltage wires provided best performance of the wide ESP (both in collection efficiency and running cost) among our tests.

In this article, max–min ant colony optimization algorithm is proposed to determine how to allocate jobs and schedule tools with the objective of minimizing the makespan of processing plans in flexible manufacturing system. To expand the application range of max–min ant colony optimization algorithm, tool movement policy is selected as the running mode of flexible manufacturing system, which assumes that tools are shared among work centers and each operation is allowed to be machined by different kinds of tools. In the process of converting this scheduling problem into traveling salesman problem, disjunctive graph is modified to possess more than one path between each neighbor node. Besides providing practical methods of initializing pheromone, selecting node and calculating pheromone increment, max–min ant colony optimization algorithm employs the pheromone updating rule in max–min ant system to limit pheromone amount in a range, of which the upper and lower boundaries are updated after each iteration by formulations involving the current optimal makespan, the average number of optional tools and parameters. Finally, different sizes of processing plans are randomly generated, through which max–min ant colony optimization algorithm is proved effectively to tackle early stagnation and local convergence and thus obtains better solution than ant colony optimization algorithm and bidirectional convergence ant colony optimization algorithm.

Celtis sinensis Pers. (Chinese hackberry), belonging to the family Ulmaceae, is widely used as a street tree or landscape plant because of its longevity and aesthetic growth habit. Additionally, C. sinensis is of economic importance due to its medicinal properties. Roots and bark of the plant can be used in natural medicine for the treatment of lumbago, measles, tumor, etc (Zhang et al. 2016). In July 2019, symptoms of leaf spot were observed on C. sinensis in Yuanshan national forest park of Zibo, Shandong Province, China (36.48°N, 117.84°E). We surveyed more than 500 square meters of forest area, and more than 80% of the acreage was affected with the leafspot disease. Symptoms on infected leaves appeared as regular round or oval spots, colored in yellow with brown borders, which coalesced into larger spots as the disease progressed. To investigate the cause, 20 leaves of infected tissues were cut into ~2 mm pieces and surface disinfected with 75% ethanol for 30 s, rinsed three times with sterile deionized water. These were air dried and placed on potato dextrose agar (PDA) and incubated at 25℃ for 5 to 7 days. A minimum of 15 isolates were obtained and cultures were initially white, gradually becoming gray green to dark after 1 week, producing copious amounts of gray aerial mycelium. Three representative single isolates were used for molecular identification, which were verified based on the amplification of DNA sequences of internal transcribed spacer region, translation elongation factor 1 alpha and beta-tubulin genes, using the primers ITS1/ITS4 (White et al. 1990), EF1-728F/EF1-986R (Carbone and Kohn 1999), and BT-2a/BT-2b (Glass and Donaldson 1995), respectively. The sequenced genes (GenBank accession no. MT367874, MT385087, MT374083) exhibited 99.63% (Identity=545/547), 99.00% (Identity=297/300), and 100.00% (Identity=451/451) homology with the corresponding genes of type specimen of Botryosphaeria dothidea strain CBS110302 (GenBank accession no. AY259092, AY573218, EU673106), respectively. Morphological and molecular results showed that the isolates were B. dothidea (Slippers et al. 2014; Zhai et al. 2014). Pathogenicity was confirmed using five living, healthy C. sinensis plants with three leaves were wound inoculated with mycelial plugs (about 4 mm in diameter) of B. dothidea from a 7-day-old culture grown on PDA, while inoculated with sterile PDA plugs on the same leaves were served as negative controls. All the plants were covered by plastic sheeting and keep high relative humidity by adding water in time. Seven days later, all inoculated leaves appeared as round dark brown spots, which were larger than observed in the field. The pathogenicity test was repeated three times. No symptoms were observed on negative controls. Fungi re-isolated from inoculated leaves were confirmed as B. dothidea on the basis morphology and molecular characterization as described above. To our knowledge, this is the first report on the presence of B. dothidea affecting C. sinensis plants in China. This discovery is important to ensure the sustainable production of C. sinensis, an important landscaping and medicinal tree.

Gear profile grinding accuracy is often restricted by geometric errors of the rotary worktable. This paper proposes a novel approach to improve the geometric accuracy of the worktable based on single-axis motion measurement and the actual inverse kinematic model (IKM). The volumetric error model of gear profile grinding machines is first established based on homogeneous transformation matrices (HTMs) and machine kinematic chains, and the actual IKM based compensation method is then proposed to offset the position-dependent geometric errors (PDGEs) of the worktable. It directly gives explicit analytical expressions of compensated motion commands for efficient compensation. Besides, a novel PDGEs identification approach based on single-axis motion measurement with a double ball bar (DBB) is presented for the worktable. This approach has improved identification accuracy and stronger robustness as the interference error of non-targeted axes is avoided. The influence of the installation errors of the workpiece ball on measurement results and the PDGEs on gear grinding errors were discussed. The experiments verified the effectiveness of the identification approach, and the geometric accuracy tests of the worktable validated the compensation method.

The macroscopic mechanical performances and the mesostructural deformation mechanisms of random open-cell polymer foams subjected to quasi-static loading were investigated. Three typical materials, namely, polyamide 6 (PA6), polyamide 12 (PA12), and polylactide (PLA), used in the additive manufacturing techniques, were considered as the base materials of open-cell polymer foams. Polymer foam specimens with predesigned mesostructural configurations and different relative densities were examined under quasi-static compression. The experimental results showed that the deformation modes and mechanical responses of the polymer foams exhibited a strong dependence on the properties of bulk materials and the fabrication process. It was found that elastic buckling dominated the failure of the PA12 and PA6 foams, whereas the failure of the PLA foams was dominated by strut collapse and the debonding of the relatively weak interlayers. The PA6 and PA12 foams, with low relative densities, could significantly recover from their compacted states, whereas the PLA foams were crushed without distinct rebounding. The elastic modulus, plateau stress, and specific energy absorption of the fabricated foams were quantified by using scaling laws as a function of the relative density. A revised statistical constitutive model was proposed for the random open-cell polymer foams under compression and it was confirmed that the revised model could accurately capture the whole crushing behavior of the foams.

As a crucial component of the spent fuel, the stainless steel thin-walled tube would be cut into segments with dozens of millimeters in length during the closed fuel cycle. The high ductility and malleability of the stainless steel material bring great challenges to shear tools. Thus, it is worthwhile to investigate the tube shearing process. However, currently, few research has involved the tube shearing process and no readily available model could be employed to predict accurately the tube shearing. To study the shearing process of tubes, an improved Gurson-Tvergaard-Needleman model was proposed to investigate the shearing process of tubes. The results show that the predicted shear forces match well with the experimental data. Moreover, the tube shearing process could be simplified into two cases in the closed fuel cycle, i.e. shearing tubes without a support block (case I) and shearing tubes with a support block (case II). By comparing the shearing process and damage evolution between the case I and case II, it is concluded that the support block plays a significant role in the shearing process and accelerates the damage evolution.

3D micro-scale model of fibrous media with controllable structural parameters (such as fiber diameter, solid volume fraction and cell configuration) was constructed by using Voronoi algorithm in this study. Compared with the fibrous media model having layer-structured topology, the Voronoi-based model can well characterize the penetration behavior between fibers and layer connections. The gas–solid flow characteristics in fibrous media under different structural and operating parameters were examined numerically and semi-analytically. The results show that the pressure drop of fibrous media between numerical results and experimental values in literature is not always consistent. A new pressure drop correlation for fibrous media is proposed. Additionally, numerical results of the filtration efficiency show similar tendencies to the results that are calculated by the semi-analytical model. For submicron particles, the Stokes-Cunningham drag law combined with Saffman’s lift force and Brownian force can accurately predict the capture action dominated by the combination of diffusion and inertia. The deviation of filtration efficiencies between numerical results and theoretical predictions increases with increase of the Peclet number. For micron particles, the semi-analytical model-determined filtration efficiency is generally higher than numerical results which adopting the spherical drag law and considering the gravity effect. Moreover, a threshold value of the Stokes number that determines the deviation between numerical and semi-analytical approaches is presented. When the Stokes number is greater than the threshold, a relatively small deviation can be achieved. It is concluded that the new pressure drop correlation can provide guidance for macro-scale model to study the filtration performance. As well, the correlation calculating of filtration efficiency between numerical simulation and semi-analytical model is obtained by using the Peclet number and the Stokes number as criteria.

To increase the machining accuracy of worm gear machines, the thermal error compensation was carried out from the view of X-axis's error mechanism of worm gear machines. The spatial motion error of the worm hob caused by X-axis's positioning error is derived, and then the necessity for the reduction of the positioning error is proved. The memory behaviors of the thermal error are revealed, and finally, the applicability of self-recurrent wavelet (SRW) neural networks to the error modeling is demonstrated. Then the genetic algorithm (GA) is used to optimize the number of neurons in the wavelet and product layers and weights of SRW neural networks, and the error models are established with the GA-SRW, SRW, and back propagation (BP) neural networks. The results show that the predictive performance of the error models based on GA-SRW, SRW, and BP neural networks decreases in turn. Moreover, the precision improvement ratios for the average normal error of tooth surfaces of the GA-SRW neural network error model are more than 21.8% and 43.7% compared to the SRW and BP neural networks error models, respectively. Besides, the predictive performance of the GA-SRW neural network model is better than that of the regression models.

As an important branch of production scheduling, flexible job-shop scheduling problem (FJSP) is difficult to solve and is proven to be NP-hard. Many intelligent algorithms have been proposed to solve FJSP, but their key parameters cannot be dynamically adjusted effectively during the calculation process, which causes the solution efficiency and quality not being able to meet the production requirements. Therefore, a self-learning genetic algorithm (SLGA) is proposed in this paper, in which genetic algorithm (GA) is adopted as the basic optimization method and its key parameters are intelligently adjusted based on reinforcement learning (RL). Firstly, the self-learning model is analyzed and constructed in SLGA, SARSA algorithm and Q-Learning algorithm are applied as the learning methods at initial and later stages of optimization, respectively, and the conversion condition is designed. Secondly, the state determination method and reward method are designed for RL in GA environment. Finally, the learning effect and performance of SLGA in solving FJSP are compared with other algorithms using two groups of benchmark data instances with different scales. Experiment results show that the proposed SLGA significantly outperforms its competitors in solving FJSP.

The thermal contact conductance (TCC) between balls and bearing rings is predicted by proposing the TCC model between the curved and spherical surfaces. The Weierstrass-Mandelbrot function is applied to characterize the microscopic topography of surfaces. The fractal contact model is constructed by considering the elastic, elastic-plastic, and plastic deformations of microprotrusions, and then an effective contact coefficient is built to modify the fractal contact parameters. Finally, the TCC modeling method is proposed by considering the elastic and plastic TCCs of different contacting microprotrusions with different sizes. The experiments of TCCs were conducted under different conditions to verify the validity of the proposed model. The results show that the predicted TCCs fit well with measured values and that the predictive accuracy can be improved greatly by considering the effective contact coefficient. Then the effect of critical factors on TCCs is discussed, and the TCC increases with the external load, and increases with the fractal dimension D and decreases with the scaling constant G. The TCC of the dynamic lubricated ball bearing is much greater than that of the static lubricated ball bearing and the TCC of the static lubricated ball bearing is much greater than that of the static non-lubricated ball bearing.

To improve the machining accuracy of machine tool, thermal error modeling and compensation methods are proposed from the view of formation mechanism of thermally-induced error of ball-screw linear axis with fixed-fixed installation. The relationships among such factors as heat generation, heat dissipation, thermal stress, temperature rise, thermal expansion, and the resulting thermal error are explored. The thermal error is formulated as the product of an amplitude function with temperature of screw shaft as its independent variable and a cosine function with axial position as its independent variable. The independent variables of thermal error model are temperature of screw shaft, axial position, and the running time. The closed-loop iterative thermal behavior modeling method is proposed to determine accurate temperature of screw shaft by updating rate of heat generation, convective coefficient, thermal contact resistance (TCR), lubricant viscosity, and preload in each sub-step. To demonstrate the modeling method, the thermal errors are predicted and compensated at different working conditions. The results show that the repetitive positioning error is reduced by more than 45%, 36%, and 19% compared to that without the compensation, with pitch compensation and multivariate linear regression analysis (MLRA) compensation, respectively.

Honeycomb structure, which presents potential ability in energy absorption and impact resistance, has advantages of simplicity, lightweight, easy manufacturing and designability. In this study, honeycomb structure panel is expanded to cylindrical shell by adopting the rolled-up algorithm and the crushing behaviors of the randomly honeycomb cylindrical shell (RHCS) structures are investigated under axial loading. The influence of geometrical and topological parameters, i.e. the thickness-to-diameter ratio, cell irregularity, relative density on the deformation evolution and energy absorption performance is systematically performed. It is found that the deformation modes of RHCS structures are significantly affected by thickness-to-diameter ratio and cell irregularity. Moreover, the rate-independent, rigid–plastic hardening (R–PH) idealisation with two parameters is adopted to characterize the crushing responses of RHCS structures and the parameters are quantified as power-law relations with respect to relative density. Further, the crushing behaviors of density-graded RHCS structures with continuously varying in density are carried out under different impact velocities. Results indicated that the introduction of density gradients can strongly affect the deformation mode, crushing force and energy absorption performance of density-graded RHCS structures. Combining with analysis of crushing front propagation, the theoretical determination of crushing responses of the density-graded RHCS under different loading rates is presented based on R–PH model. The results of predicted crushing force-time histories of density-graded RHCS structures show versatile agreement with that of finite element values at both impact and support ends. Additionally, the energy absorption capacity that is related to deformation mechanisms of density-graded RHCS structures is investigated.

This paper proposes a novel classified compensation method considering frequency response to eliminate the transmission chain error (TCE) of gear hobbing machines. Based on error source analysis and Discrete Fourier Transform (DFT), the measured TCE was firstly decomposed into different error components, including the integer harmonic, the non-integer harmonic and the other. A classified compensation theory was then presented, in which the Measurement System Error Compensation (MSEC) and the software axis were used to compensate the first two types of error components, respectively. Then, the frequency response identification for the C-axis control system was performed, and the final compensation values were calculated and integrated into the classified compensation process. The experimental results on a YS3120-type gear hobbing machine showed that the peak-to-peak value of TCE was reduced by 85.5% and that both the integer and non-integer harmonic errors were effectively compensated. It lays the foundation for gear precision hobbing.

Five-axis CNC machining of sculptured surfaces plays a significant role in aerospace industry, such as blisk machining. The key issue involved is the determination of cutter posture in tool path planning. As the interference will cause the cutter to intrude the workpiece or machine tool and then seriously damage the cutter or machine tool, cutter posture must be well planned to avoid interference. Meanwhile, as the machined surface quality will be deteriorated inevitably when chatter arises, chatter must be eliminated as well. In this paper, posture accessibility and stability diagram (PASD) is firstly constructed by identifying interference- and chatter-free cutter postures based on geometric analysis and machining dynamic analysis. Furthermore, as surface roughness is an important characteristic to evaluate the surface properties of workpieces and should be generally minimized, its prediction model is also established for optimization. As is well known, surface roughness is mainly affected by cutter deflection due to inevitable cutter deformation force (CDF). By analyzing the relationship between surface roughness and maximum CDF, a novel surface roughness prediction model is proposed from a new viewpoint, i.e., the maximum CDF. Compared with the traditional prediction models considering only geometry, the proposed prediction model is much more straightforward, and the prediction results are more accurate (the average prediction error is only about 9.0%). This study reveals that the cutter posture has a great effect on surface roughness, which implies that surface roughness can be optimized from a new perspective (cutter posture). Finally, based on the proposed PASD and surface roughness prediction model, a new cutter posture optimization algorithm is proposed to minimize surface roughness. The algorithm considers both geometrical and dynamical constraints (interference and chatter) simultaneously, and it is verified by machining experiments under different cutter postures. According to the validation experiments, the proposed algorithm can effectively avoid the interference and chatter while minimizing the surface roughness (the optimized Ra is only 0.3589 μm compared to normal one 0.5476 μm). To the authors’ best knowledge, it is the first time to optimize cutter posture by considering the following three aspects simultaneously: avoiding interference, eliminating chatter and reducing surface roughness. It will greatly improve the machined surface properties, while optimizing the utilization of the machine tool and cutter (extending their service life by avoiding various potential damages). This also will provide a new perspective that optimizes the machining process of complex and precise parts from cutter posture, which is of great significance to aviation and aerospace industries.

The data-driven thermal error compensation method of high-speed and precision five-axis machine tools was proposed based on the homogeneous transformation. The compensation component was obtained by analyzing the error transmission chain of machine tools and was expressed as the transmission matrix consisting of thermal error terms of linear axes and spindle system according to the differential movement of the compensation axis. From the view of thermal error generation mechanism, the thermal error of the linear axis was formulated as the product of the polynomial function with time as its independent variable and the polynomial function with position as its independent variable. Then the thermal errors of the linear axes were decomposed and identified from the measured positioning error. The thermal error of the spindle system was identified by the thermal characteristic experiments. The compensation component in each direction is calculated by substituting the identified thermal error terms of the linear axes and spindle system into the data-driven thermal error model. Finally, to demonstrate the effectiveness of the proposed method, the thermal error at a new working condition was predicted, and then the error compensation and actual machining were carried out. The results show that the machining error is reduced by more than 85% and 37% with the present error compensation compared with that without compensation and that without traditional error compensation, respectively. This research sheds new light on both the generation mechanism and the compensation method of the thermally-induced error of five-axis machine tools.

Currently, no model could be used to readily predict the TCR of baring outer ring/bearing housing interface, then a thermal contact conductance (TCC) model between cylindrical surfaces with transition fits is constructed. The topography is described by the fractal geometry to characterize the multiscale and self-affinity behaviors and two stochastic variables are introduced into the fractal function to guarantee the random and disordered features. Then the contact states of contact points are analyzed, and a new fractal inner contact model is established. An effective contact factor is introduced to modify the contact parameters. Eventually, the predictive model of TCCs is constructed by summing up the TCCs of all the contacting protrusions with different scales. To demonstrate the validity of the TCC model, the measuring setup of TCCs was developed, and then the tests were conducted. The results show that the average deviations between the measured data and predicted TCCs are 10.25% and 8.73% for SS304 and carbon steel, respectively. When the effective contact factor is not considered, the average deviations between the predicted TCCs and measured data are 25.47% and 20.61% for SS304 and carbon steel, respectively. Then the necessity to introduce the effective contact factor is verified. For SS304, the prediction accuracy of the present model is 15.22%, 56.36%, and 31.55% higher than that of the present model without effective contact factor, M-T model, and Zou model, respectively. For carbon steel, the prediction accuracy of the present model is 11.88%, 63.76%, and 34.87% higher than that of the present model without effective contact factor, M-T model, and Zou model, respectively. Finally, the effects of the surface roughness, the protrusion top radius, the effective contact factor, and the transition fit on TCC are discussed. The results show that TCC decreases with surface roughness. The TCC with different protrusion top radii is greater than that with identical top radius. The TCC increases with the effective contact factor slowly and then the growth rate increases rapidly. Besides, the TCC increases with transition fit sharply at the early stage, and then increases slowly for both SS304 and carbon steel.

Whirling milling is a promising machining process that couples the tools and workpiece motions while both conventional turning and milling are carried out, which is widely used for machining precision screw parts made of hard materials, such as titanium alloy, quenched steel, etc. The challenge is due to the complex kinematics can lead to the varying tool-workpiece engagement and undeformed chip geometries which affect significantly the mechanics, quality and productivity. The existing studies are mainly based on the simplification of process mechanism to study the mechanics and quality however, the material removal mechanism and influences of cutting parameters are still not well understood. This paper presents an analytical approach to investigate the material removal mechanism in whirling milling, thus to predict the undeformed chip geometry, material removal rate (MRR), cutting forces and form errors. The varying tool-workpiece engagement geometry along the cutting trajectory is identified to model the varying undeformed chip geometry including instantaneous chip thickness, cross-section area and tool-workpiece contact length. The form errors including circularity error, scallop height and surface roughness are defined and predicted as a function of tools and workpiece motion, position and dimension parameters. The whirling milling experiments were conducted to validate the analytical modeling approach with the largest error 11.3% and average error 13.0% for force and surface roughness prediction, respectively. The influences of cutting parameters on surface roughness and MRR are finally analyzed to explore the potential of productive cutting conditions for whirling milling.

Remanufacturing is deemed to be an effective method for recycling resources, achieving sustainable production. However, little importance of remanufacturing has been attached in PLM. Surely, there are many problems in implementation of the remanufacturing strategy, such as inability to effectively reduce uncertainty, lack of product multi-life-cycle remanufacturing process tracking management, lack of smart enabling technology application in the full lifecycle that focusing on multi-life-cycle remanufacturing. After analyzing the reasons, through integrating smart enabling technologies, a new PLM paradigm focusing on the multi-life-cycle remanufacturing process: Big Data driven Hierarchical Digital Twin Predictive Remanufacturing (BDHDTPREMfg) is proposed. And the definition of BDHDTPREMfg is proposed. A big data driven layered architecture and the hierarchical CPS-Digital-Twin(CPSDT) reconfiguration control mechanism of BDHDTPREMfg are respectively developed. Then, this paper presents an application scenario of BDHDTPREMfg to validate the feasibility and effectiveness. Based on the above application analysis, the benefits of penetrating BDHDTPREMfg into the entire lifecycle are demonstrated. The summary of this paper and future research work is discussed in the end.

The geometric error, which will significantly affect grinding accuracy of the gear form grinding machine tool, should be compensated. This paper proposes a crucial geometric error compensation method towards gear grinding accuracy enhancement, and it can directly obtain simplified explicit analytical expressions of compensated motion commands for more efficient and effective compensation. Firstly, an actual forward kinematic model is established by rapid reconstruction of the geometric error modules of motion axes. Next, the conventional actual inverse kinematic model (IKM) directing at tool posture error compensation is developed to calculate analytical expressions of compensated motion commands. Then, the material removal mechanism of gear form grinding process is considered to construct the geometric error- tooth surface posture error model, which directly reveals the mapping rules between the geometric error and the tooth surface errors. On this basis, the crucial error elements regarding tooth profile and helix deviations are identified based on global sensitivity analysis, and the compensated expressions obtained by the actual IKM are further simplified and modified directly towards gear accuracy enhancement. Finally, the crucial error identification and compensation process was verified by both numerical analysis and machining tests. The results show that tooth profile deviations will reduce by more than 80% theoretically but only by 43.27% in fact due to other errors, and it proves the feasibility and effectiveness of the proposed method.

Hydrogen sulfide (H2S), as an endogenous signaling molecule in mammals, shows a variety of biological effects. Cystathionine gamma-lyase (CSE)/H2S pathway has been implicated in scavenging reactive oxygen species (ROS) in the mammalian cells. Therefore, we first investigated the regulatory effects of exogenously applied hydrogen peroxide (H2O2) on CSE expression in the mammalian cells. African green monkey kidney fibroblastlike cells (COS-7 cells) or human embryonic kidney 293 cells (HEK 293 cells) were transfected with CSE promoter-luciferase reporter constructs and treated with H2O2 of 1, 5, and 10 microM for 0.5 and 1.5 h at 37 degrees C. The transfected cells were assayed for firefly luciferase activities normalized by Renilla luciferase activity. Human lung adenocarcinoma cells (A549 cells) or human liver cancer cells (SMMC-7721 cells) were treated with H2O2 of 1, 5, and 10 microM for 0.5 and 1.5 h at 37 degrees C, and were then harvested and analyzed by Western blotting and quantitative RT-PCR. Our results showed that the treatment of a medium concentration (5 microM) of H2O2 at a longer time (1.5 h) upregulated CSE expression in the mammalian cells at the levels of the promoter, message RNA, and protein. Collectively, exogenously applied H2O2 can not only markedly affect CSE mRNA and protein expression, but also can affect the CSE promoter activity in the mammalian cells. Our observations indicate that that exogenous H2O2 can upregulate the expression of the CSE gene in the mammalian cells, which will provide the possibility of the scavenging effect of the CSE gene indirectly on ROS in the mammalian cells. However, the regulatory mechanism involved in the effects of exogenously applied H2O2 on CSE expression in the mammalian cells need be further studied.

Glioblastoma (GBM) is the most malignant and lethal glioma in human brain tumors and contains self-renewing, tumorigenic glioma stem cells (GSCs) that contribute to tumor initiation, therapeutic resistance and further recurrence. In this study, we combined in vitro cellular efficacy with in vivo antitumor performance to evaluate the outcome of an etoposide (VP16) loaded layered double hydroxide (LDH) nanocomposite (L-V) on human GSCs. The effects on GSC proliferation and apoptosis showed that loading with LDH could significantly sensitize GSCs to VP16 and enhance the GSC elimination. Further qPCR and western blot assays demonstrated that L-V could effectively attenuate GSC related pluripotency gene expression and reduce the cancer stemness. An in vivo GSC xenograft mice model showed that L-V can overcome drug resistance, eradicate GSCs, sharply decrease the stemness and reverse the epithelial-mesenchymal transition (EMT). RNA-seq analysis elucidated that L-V plays a vital role by down-regulating the PI3K/AKt/mTOR expression and activating the Wnt/GSK3β/β-catenin signaling pathway, hence leading to GSC stemness loss and greatly enhancing the GSC targeting effect. Taken together, this study demonstrated the outstanding performance of L-V reversing the drug resistance of GSCs, thus providing a novel strategy for clinical translation application of nanomedicine in malignant glioma chemotherapy.

Cervical cancer is the fourth most common cancer in women worldwide, and existing treatments cause severe side effects and great burdens. Thus, the development of safe, inexpensive therapeutic agents is necessary. Curcumin (Cur), a well-known natural product, exerts promising anti-cancer activities against various cancer types. However, its therapeutic efficacy is severely restrained due to rapid degradation, poor aqueous solubility, and low bioavailability. The objective of this study was to investigate the therapeutic potential of novel curcumin-loaded TPGS/F127/P123 mixed polymeric micelles (Cur@NPT100) for cervical cancer treatment. The Cur@NPT100 exhibited an average size of approximately 19 nm, a zeta potential of around -4 mV, a drug loading of 8.18 ± 0.36%, and an encapsulation efficiency of 79.38 ± 4.65%. Unlike free Cur, Cur@NPT100 are readily dispersed in aqueous medium, showing enhanced stability and a sustained release profile over a 6-day period. In vitro cell culture experiments revealed that TPGS/F127/P123 mixed polymeric micelles (NPT100) based nanocarriers substantially promoted the selective cellular uptake of Cur into HeLa cells rather than by non-cancerous NIH3T3 cells, inducing higher cytotoxicity and greater apoptosis and significantly increasing the percentage of cells arrested at the G2/M phase of the cell cycle. Additionally, the Cur@NPT100 facilitated more Cur accumulation in the mitochondria and decreased the mitochondrial membrane potential. In addition, western blot assays demonstrated that Cur@NPT100 were more potent than free Cur at activating the mitochondria-mediated apoptosis pathway. In vivo results further confirmed that Cur@NPT100 exhibited a much higher antitumor efficacy than free Cur and had excellent biocompatibility. In conclusion, Cur@NPT100 might be an effective therapeutic agent for cervical cancer.

Flute grinding is a critical process in end-mill manufacturing. The wheel geometry and position during the grinding process govern the flute profile and determine the flute parameters (i.e. rake angle, core radius, and flute width). Accuracy of these parameters should be ensured to obtain optimal cutting performance. For a given wheel geometry, the current technologies can only determine the wheel position for the desired rake angle and core radius without consideration of the flute width. This disadvantage seriously restricts the improvement of machining quality and the flexibility of flute grinding. To cope with this problem, this paper presents a novel method of five-axis flute grinding using standard 1V1/1A1 wheels to ensure the accuracy of the three flute parameters. Based on the theories of analytic geometry and envelope, equations for calculating the flute parameters are first derived. Afterwards, a system of nonlinear equations is created to calculate the wheel position for flute grinding. The validity of the proposed method is verified by 3D simulations and machining experiments. Lastly, the sensitivity of the valid flute width with respect to the wheel geometric parameters is further investigated in this work.

This communication describes a new strategy to fabricate a nanogel layer around magnetic nanoparticles by surface free-radical polymerization triggered by the cascade reaction of urate oxidase and horseradish peroxidase, which showed high loading capacity, pH-responsive drug release and low cytotoxicity.

In this paper, a new Ni-P-Si 3 N 4 nanowire composite coating has been successfully prepared on AZ31 Mg substrate through electroless deposition technique. The effect of Si 3 N 4 nanowire concentration in the plating bath on the surface morphology, hardness and wear behavior of the composite coatings have been investigated. The results show that when the concentrations of Si 3 N 4 nanowire is 1.5 g/L, the morphology of composite coating appears the fine nodular structure. Moreover, the Si 3 N 4 nanowire is uniformly dispersed in the coating at the 1.5 g/L concentrations of Si 3 N 4 nanowire. But when the concentrations of Si 3 N 4 nanowire in bath over 1.5 g/L, the coatings surface morphology become roughness and some pores appear on the coating surface because of the agglomeration of Si 3 N 4 nanowire. As seen from the experiments results, the microhardness of the composite coating were significantly increased to about 790HV 200 as plating, the friction coefficient and wear weight loss of the composite coating is both decreased to the 1/6 of conventional Ni-P electroless coating. These improvements have been attributed to the dispersion strengthening effect of Si 3 N 4 nanowire.

Cell migration and invasion are integral to lung cancer metastasis. In this study, we investigated the combination of traditional chemotherapy and a layered double hydroxide (LDH) carrier as a new strategy for the inhibition of migration and invasion. To investigate the characteristics and possible mechanisms of VP16-LDH [the Mg-Al/LDH containing etoposide (VP16)], we used several experimental techniques, such as transmission electron microscopy (TEM) and fluorescent microscopy. The TEM, X-ray diffraction (XRD) and zeta potential results indicated that VP16 binds well with LDH, with an average size of 70 nm, and the drug delivery system was confirmed to have the desired quality of slow release by the in vitro release test results. Fluorescent images showed that the cellular uptake of VP16-LDH was a caveolae-mediated and energy-dependent process. Moreover, A549 cells treated with VP16-LDH (5 μg/ml, 10 μg/ml) demonstrated significant inhibition of cell migration and invasion compared with the cells treated with free VP16 at the same concentration. The inhibition of AKT, mTOR and STAT3 phosphorylation and p-β-catenin up-regulation in VP16-LDH-treated cells revealed a possible molecular mechanism via the mTOR/AKT and STAT pathways, through which VP16-LDH had a stronger inhibitory effect on migration than the drug alone.

The development of new generation information technology has brought opportunities for industrial production model innovation. Especially, the cloud computing, Internet of Things, and big data technology are widespread applied in industrial fields. Based on this tendency, a service-oriented networked manufacturing model called cloud manufacturing (CM) was proposed in 2010. In order to realize this manufacturing model, one of the key technologies is how to achieve the discovery of manufacturing service which has not found a suitable solution. In this paper, a manufacturing service discovery framework based on agent is provided. The architecture consists of two parts: one is that manufacturing task agent and manufacturing service agent based on the expansion of the object model, and the other one is task and service matching process knowledge base. Furthermore, a structural matching method is proposed to implement the static parameters matching of task agent and service agent, and a multi-agent system bid mechanism is built to accomplish the dynamic parameters matching of the two agents. A simulation environment based on JADE has been properly developed. The simulation shows that the discovery method can effectively achieve the manufacturing service discovery in CM environment, which provides technical support for the cloud manufacturing service platform development.