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Circular RNAs (circRNAs) have shown pivotal regulatory roles in multiple human ocular diseases, including age-related cataract (ARC). Here, we explored the role of circRNA mitogen-activated protein kinase kinase kinase 4 (circMAP3K4, hsa_circ_0078619) in ARC pathology and its associated mechanism. The expression of RNAs and proteins was examined by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blot assay. Cell viability, senescence, proliferation, and apoptosis were analyzed by 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, senescence-associated-β-galactosidase (SA-β-Gal) staining, 5-ethynyl-20-deoxyuridine (EdU) assay, and flow cytometry. The oxidative stress status of SRA01/04 cells was analyzed using the commercial kits. The interaction between microRNA-193a-3p (miR-193a-3p) and circMAP3K4 or phospholipase C delta 3 (PLCD3) was verified by dual-luciferase reporter assay, RNA immunoprecipitation (RIP) assay, and RNA-pull down assay. CircMAP3K4 was significantly down-regulated in ARC patients and H2O2-induced SRA01/04 cells. H2O2 treatment restrained the viability and proliferation and promoted the senescence, apoptosis, and oxidative stress of SRA01/04 cells, and circMAP3K4 overexpression protected SRA01/04 cells from H2O2-induced dysfunction. MiR-193a-3p was a direct target of circMAP3K4, and circMAP3K4 overexpression-mediated protective effects in H2O2-induced SRA01/04 cells were largely reversed by the accumulation of miR-193a-3p. MiR-193a-3p interacted with the 3’ untranslated region (3’UTR) of PLCD3, and PLCD3 knockdown largely overturned miR-193a-3p silencing-induced protective effects in H2O2-induced SRA01/04 cells. CircMAP3K4 up-regulated the expression of PLCD3 via sponging miR-193a-3p in SRA01/04 cells. In conclusion, circMAP3K4 protected SRA01/04 cells from H2O2-induced dysfunction in ARC through mediating miR-193a-3p/PLCD3 axis.

Viral diseases caused severe yield losses and quality decline of sweet potato. To identify viruses that are infecting sweet potato in fields in Beijing, we used small RNA deep sequencing and PCR or RT-PCR and found seven RNA viruses and four DNA viruses, including an unrecorded virus, broad bean wilt virus 2. Sweet potato leaf curl virus and sweet potato feathery mottle virus had the highest incidence (88% of tested samples). Coinfection with sweet potato viruses was common in Beijing, and most samples were simultaneously infected by four or more virus species with different genomic types. These results indicated that potyviruses and DNA viruses were prevalent in sweet potato in Beijing, which will be useful for detection and control of viral diseases. This is the first comprehensive report of sweet potato viruses in Beijing. We expected that targeted detection of viruses in sweet potato seedlings will increase the number detected and thus inform management decisions of sweet potato seedlings.

An in-situ synthesized WC/TaC reinforced nickel-based composite coatings have been fabricated on AISI 1045 mild carbon steel substrate by laser cladding of powder mixture of Ni60/WC with Ta2O5 and graphite. The microstructure and hardness distribution of WC/TaC/Ni60 composite coating were investigated by scanning electron microscopy (SEM) and X-ray diffraction (XRD) techniques. Results show that the microstructure of the coating has a metallurgical bonding with substrate and is mainly composed of γ-Ni dendrites, W2C, M7C3 carbides, and dispersed TaC particles. The hardness HV of WC/TaC/Ni60 composite coating has been enhanced to a value similar to 9650 MPa, which is 1.3 times higher than that of Ni60 coating. Measurement results demonstrate that these improvements are attributed to the presence of in-situ synthesized TaC particles, microstructure change and phase variations.

In this paper, the high-pressure common rail system of the marine diesel engine is taken as case study to establish a real-time simulation model of the high-pressure common rail system that can be used as the controlled object of the control system. On the premise of ensuring accuracy, the real-time simulation should also respond quickly to instructions issued by the control system. The development of the real-time simulation is based on the modular modeling method, and the high-pressure common rail system is divided into submodels, including the high-pressure oil pump, common rail tube, injector, and mass conversion. The submodels are built using the “surrogate model” method, which is mainly composed of MAP data and empirical formulas. The data used to establish the real-time simulation are not only from the empirical research into the high-pressure common rail system, but also from simulations of the high-pressure common rail system undertaken in AEMSim. The data obtained from this real-time simulation were compared with the experimental data to verify the model. The error in fuel injection quality is less than 5%, under different pressures and injection durations. In order to carry out dynamic verification, the PID control strategy, the model-based control strategy, and the established real-time simulation are all closed-loop tested. The results show that the developed real-time simulation can simulate the rail pressure wave caused by cyclic injection according to the control signal, and can feedback the control effect of different control strategies. Through verification, it is clear that the real-time simulation of the high-pressure common rail system can depict the rail pressure fluctuation caused by each cycle of fuel injection, while ensuring the accuracy and responsiveness of the simulation, which provides the ideal conditions for the study of a rail pressure control strategy.

Thermal dimensional stability is an important issue for many space structures. An extremely low thermal deformation composite platform is designed with the prototype application in the satellite payload-mounting platform. The platform was achieved through using composite material to provide near-zero coefficient of thermal expansion (CTE) and isolating the residual thermal expansion by flexible structure connection to obtain the entire stability rather than the traditional thermal control technology, which is energy-consuming. Firstly, the design of systematical scheme with near-zero CTE is investigated, by which the extremely low CTE components of satellite structure were developed. Then, a flexible connecting method is proposed to further minimize the thermally induced deformation through “deformation isolation”. The effectiveness of this method is demonstrated by finite element analyses and further verified by physical Composite Fiber Reinforced Plastics (CFRP) prototype. The experiment results of the prototype recorded a 90% reduction measured by theodolites and projection moire methods. The thermally induced pointing accuracy is dramatically decreased from 72.6″ down to 3.6″ in the flexible connection compared with the rigid connection, while the panel wrapping displacement reduced from 1.013 mm to 0.104 mm. The result proves that this method is effective to the thermal deformation isolation so that it has an extraordinary potential to engineering practical application.

Three-dimensional laser scanning has become an increasingly popular measurement method in industrial fields as it provides a non-contact means of measuring large objects, whereas the conventional methods are contact-based. However, the data acquisition process is subject to many interference factors, which inevitably cause errors. Therefore, it is necessary to precisely evaluate the accuracy of the measurement results. In this study, an error-ellipsoid-based uncertainty model was applied to 3D laser radar measurement system (LRMS) data. First, a spatial point uncertainty distribution map was constructed according to the error ellipsoid attributes. The single-point uncertainty ellipsoid model was then extended to point–point, point–plane, and plane–plane situations, and the corresponding distance uncertainty models were derived. Finally, verification experiments were performed by using an LRMS to measure the height of a cubic object, and the measurement accuracies were evaluated. The results show that the plane–plane distance uncertainties determined based on the ellipsoid model are comparable to those obtained by actual distance measurements. Thus, this model offers solid theoretical support to enable further LRMS measurement accuracy improvement.

The influence of component errors on the final error is a key point of error modeling of computer numerical control (CNC) machine tool. Nevertheless, the mechanism by which the errors in mechanical parts accumulate to result in the component errors and then impact the final error of CNC machine tool has not been identified; the identification of this mechanism is highly relevant to precision design of CNC machine. In this study, the error modeling based on the Jacobian-torsor theory is applied to determine how the fundamental errors in mechanical parts influence and accumulate to the comprehensive error of single-axis assembly. First, a brief introduction of the Jacobian-torsor theory is provided. Next, the Jacobian-torsor model is applied to the error modeling of a single-axis assembly in a three-axis machine center. Furthermore, the comprehensive errors of the single-axis assembly are evaluated by Monte Carlo simulation based on the synthesized error model. The accuracy and efficiency of the Jacobian-torsor model are verified through a comparison between the simulation results and the measured data from a batch of similar vertical machine centers. Based on the Jacobian-torsor model, the application of quantitative sensitivity analysis of single-axis assembly is investigated, along with the analysis of key error sources to the synthetical error ranges of the single-axis assembly. This model provides a comprehensive method to identify the key error source of the single-axis assembly and has the potential to enhance the tolerance/error allocation of the single axis and the whole machine tool.

Thermal deformation is one of the main contributors to machining errors in machine tools. In this paper, a novel approach to build an effective thermal error model for a machining center is proposed. Adaptive vector quantization network clustering algorithm is conducted to identify the temperature variables, and then one temperature variable is selected from each cluster to represent the same cluster. Furthermore, a non-linear model based on output-hidden feedback Elman neural network is adopted to establish the relationship between thermal error and temperature variables. The output-hidden feedback Elman network is adopted to predict the thermal deformation of the machining center. Back propagation (BP) neural network is introduced for comparison. A verification experiment on the machining center is carried out to validate the efficiency of the newly proposed method. Experimental verification shows that the adaptive vector quantization network clustering algorithm and output-hidden feedback Elman neural network is an accurate and effective method.

This paper proposes a method to accurately predict thermal errors in spindles by applying experimental modifications to preliminary theoretical models. First, preliminary theoretical models of the temperature field and the thermal deformation are built via mechanism analysis, which is based on the size of the spindle and the parameters of the bearing. Then, thermal basic characteristics tests are conducted at two different initial temperatures. Finally, the results of the thermal basic characteristic tests are evaluated, and the preliminary theoretical model is modified to obtain the final model. A simulation of axial thermal deformation under different speeds is conducted by finite element analysis. It shows that the relationship between the axial thermal deformation and the speed is approximately linear. The model is validated via some experiments on the spindle of a numerical control lathe. The results indicate that the proposed model precisely predicts the spindle’s temperature field and multi-degree of freedom thermal errors.

The establishment of an academic platform is crucial for graduate education. This paper explores and practices the conference from four aspects: enhancing the critical thinking ability of graduate students, writing and oral expression skills, communication skills and the enthusiasm of graduate students to participate in the conference. This paper mainly has the following contents: Organizational Link Design, Teacher-student participation model research, Review indicator system and measurement research, Incentive design, Design of academic exchange platform. In the conference participation mode, the teacher and the students participate together, and the students are the main participants of the conference; in the evaluation index system and the measurement research, the combination of qualitative and quantitative research is adopted. In qualitative evaluation, it evaluates from six aspects: the topic selection, verbal ability, article normativeness, article innovation, written expression ability and communication ability. In quantitative evaluation, it is reflected in the final “level” of the paper. In the design of the incentive method, based on Herzberg's two-factor theory and Skinner's reinforcement theory. In the design of the academic communication platform, the combination of online and offline is adopted. The results showed that the design for the academic conference is successful. However, it should be enhanced on paper reviewing process when the next meeting is prepared because some graduate students are not so much experienced on reviewing manuscripts.

Thermal errors are the major contributor to the dimensional errors of a workpiece in precision machining. Error compensation technique is a cost-effective way to reduce thermal errors. Accurate modeling of errors is a prerequisite of error compensation. In this paper, a thermal error model was proposed by using projection pursuit regression (PPR). The PPR method improves the prediction accuracy of thermal errors in the computer numerical control (CNC) turning center. A thermal error compensation system was developed based on the PPR model, and which has been applied to the CNC turning center in daily production. The results show that the thermal drift in workpiece diameter has been reduced from 34 to 5 μm.

Based on drilling, logging and geochemical data from 32 wells, the depositional and tectonic evolution of the Tangyuan fault depression were studied and the gas accumulation conditions and gas reservoir types were analyzed. Based on interpretation of 2D and 3D seismic data, a structure map of each layer was plotted and the trap types developed in various structural layers were found and confirmed. The source rock of the Tangyuan fault depression was mainly developed in the Cretaceous and in the Eogene Wuyun Formation, Xin'ancun Formation, Dalianhe Formation Member 1 and Baoquanling Formation Member 1. The reservoirs are of similar age and include the Eogene Wuyun Formation, Xin'ancun Formation, Dalianhe Member 2 and Baoquanling Member 2. The caprock was developed in the Dalianhe and Baoquanling Member 1. There are three types of source-reservoir-caprock assemblages present which include self-generating & self-preserving, lower-generating & upper-preserving and new-bed-generating & old-bed-preserving. Drilling has demonstrated that there are two types of gas reservoirs, a composition lithologic-structural gas reservoir and a lithologic gas reservoir. With reference to the seismic data and known reservoir data in adjacent areas, it is estimated that there may also be structural gas reservoirs and stratigraphic gas reservoirs. Key words: Reservoir formation conditions, gas reservoir type, structure evaluation, Tangyuan fault depression

Thermal errors are the largest contributor to the dimensional errors of a workpiece in precision machining. The error compensation technique is an effective way of reducing thermal errors. Accurate modeling of errors is a key part of error compensation. The thermal errors of a machine tool can be treated as the superposition of a series of thermal error modes. In this paper, five key temperature points of a turning center were obtained based on the thermal error mode analysis. A thermal error model based on the five key temperature points was proposed by using genetic algorithm-based back propagation neural network (GA-BPN). The GA-BPN method improves the accuracy and reduces computational cost for the prediction of thermal deformation in the turning center. A thermal error real-time compensation system was developed based on the proposed model. An experiment was carried out to verify the performance of the compensation system. The experimental results show that the diameter error of the workpiece reduced from about 27–10 μm after implementation of the compensation.

Cutting forces in traditional machining processes solely originates from the contact points on the cutting tool and workpiece. Therefore comprehensive mechanistic modeling of the machining process offers a means for realizing a sensorless cutting force monitoring system. This paper presents the progressive development of a sensorless compensation system for cutting force-induced error, whereby a learning and intelligent computer system is established, based on machining mechanics modeling and a reference compensation system. Experiences from normal machining sessions of new cutting tools and workpieces are modeled progressively and incorporated into the system. Finally with ample experience available, a full-fledged sensorless system is developed as a stand-alone solution. The sensorless system is economical, convenient, reliable and efficient. Administered on a CNC face milling machine, the model demonstrated exceptional performance and robustness.

The thermal deformations of a CNC machine tool spindle are the major contributor of thermal error. It is very significant both theoretically and practically to study how to accurately simulate the thermal error of the spindle. Firstly, this paper proposes a method for computing the coefficient of convection heat transfer of the spindle surface by referencing the theory on computing the coefficient of convection heat transfer of a flat plate when air flows along it. Secondly, the temperature field and thermal errors of the spindle are dynamically simulated under the actions of thermal loads using the finite element method. Thirdly, the characteristics of heat flow and thermal deformation within the spindle are analyzed according to the simulation results. Fourthly, the selection principle of thermal key points, which are indispensable for building a robust thermal error model, is provided based on the thermal error sensitivity technology. At last, a verification experiment is implemented on a CNC turning center, and the results show the simulation results are satisfying to replace the experiment results for further studies.

Based on the synthetic analysis of thermal error source on grinding machine and the time domain locality property of wavelet transform, a new WNN (Wavelet neural networks) method is proposed to fulfil the thermal error modelling of grinding machine. The structure and algorithm of the WNN is also proposed then. Finally, hardware system of thermal error compensation is presented, and it is tested during abrasive machining. The experimental results shows that WNN has a quicker convergence speed compared with other neural networks, which improved the machining accuracy of grinding machine effectively.

To improve batch shaft parts machining accuracy and efficiency,machining error distribution law is obtained through analysis of the machining data of batch shaft parts.A mathematical model of machining error is built by using Newton interpolation.Macro program is used to realize real-time error compensation according to the serial number of work piece and the cutting position.To avoid incomplete analysis of error factors,they are comprehensively considered in this compensation method,including cutting force,thermal deformation,tool wear,programming,measurement and adjustment,and machine geometry.The relationship of error factors and error distribution law is analyzed.The results show that cutting force is the main factor affecting single shaft error distribution,tool wear is the main factor affecting batch shafts error distribution,and thermal error is the main factor causing distortion of error distribution law.Practice shows that the maximum machining error is reduced from 60 μm to 4 μm,a compensation of 93.3%,by using this error compensation method,while the machining efficiency is improved by 13% as a result of reducing the measurement and adjustment time.