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In order to promote the simultaneous development of art creation and modern information technology, this paper studies the application of digital technology in the improvement of art creation. Moreover, this paper combines image analysis technology and computer simulation technology to improve point cloud digital technology and build a human-computer interaction system for art creation based on digital technology. The system uses the visual computer to communicate, gives instructions through actions, and uses the computer’s visual dynamic tracking technology to make the computer recognize the instructions and information sent by the user so as to provide visual feedback. In addition, this visual interactive art creation application uses the real-time nature of the camera to capture real images of interaction between the environment and the work and then interacts the captured images with virtual bubbles. Finally, this paper studies the method proposed in this paper through case analysis. The research results show that digital technology can effectively promote the development of art creation.

Objectives: Atherosclerosis (AS) remains a major contributor to death worldwide. This study sought to explore the role of Krüppel-like factor 7 (KLF7) in AS lesions via regulating glucose metabolic reprogramming (GMR) in macrophages. Methods: AS mouse and cell models were established via high-fat-diet feeding and oxidized low-density lipoprotein (ox-LDL) induction. KLF7, histone deacetylase 4 (HDAC4), miR-148b-3p, and nuclear receptor corepressor 1 (NCOR1) expressions in aortic tissue and cells were detected via reverse transcription quantitative polymerase chain reaction or Western blotting. Parameters of AS lesions and mouse metabolism were detected via hematoxylin-eosin, oil red O, and Masson staining, assay kits, glucose tolerance test, and enzymatic analysis. Peritoneal macrophages of mice were isolated and cellular metabolism was detected via Seahorse metabolic flux analysis, assay kits, ELISA, and Western blotting. Bindings among KLF7, HDAC4, microRNA (miR)-148b-3p, and NCOR1 were testified via the dual-luciferase assay and chromatin immunoprecipitation assay. Results: KLF7 was poorly expressed in AS mice and ox-LDL-induced RAW264.7 cells. KLF7 overexpression attenuated AS lesions and rescued metabolic abnormities in AS mice, and reduced glucose intake and GMR in ox-LDL-induced RAW264.7 cells. Mechanically, KLF7 bound to the HDAC4 promoter to activate HDAC4. HDAC4 reduced H3 and H4 acetylation levels in the miR-148b promoter to inhibit miR-148b-3p and promote NCOR1 transcription. HDAC4 downregulation abolished the protective role of KLF7 overexpression in AS mice and ox-LDL-induced RAW264.7 cells via the miR-148b-3p/NCOR1 axis. Conclusion: KLF7 bound to the HDAC4 promoter to activate HDAC4, inhibit miR-148b-3p via reducing acetylation level, and promote NCOR1 transcription, thereby limiting GMR in macrophages and alleviating AS lesions.

Objective: The primary purpose of this study was to explore the safety of peripheral intravenous catheter (PIVC) replacement every 96 h compared to that of clinically indicated catheter removal. Methods: A prospective, single-blind, randomized controlled trial was conducted. A random number table method was used. Six hundred patients treated with PIVC intravenous infusion in 10 nursing units of a hospital from September to October 2019 were selected. Sixty were collected from each nursing unit, including 30 in the clinically indicated replacement group and 30 in the routine replacement group. The incidence of phlebitis, catheter-related infection (CRI), occlusion, infiltration, and any form of infusion therapy failure were compared between the two groups. SPSS 23.0 software was used. Results: The dwelling times of PIVC in the clinically indicated replacement group and routine replacement group were significantly different (hours) (83.62 ± 50.08, 69.75 ± 25.54, t = 3.021, p = 0.003). The incidence of any form of infusion therapy failure (RR = 4.448, 95% CI: 3.158–6.265, p Conclusion: Replacement of PIVC every 96 h is safer than clinically indicated.

Abnormal relative position and attitude offset may cause powered supports to collide during movement, affecting the stability of a longwall operation. This study proposed a collision detection method for virtual powered support groups in the undulating strata condition. First, a support workspace was developed by defining key points, and the Denavit–Hartenberg (DH) matrix coordinate system of powered supports was established to solve the key points. Then, based on the intersection rule of plane line segments, collision detection between powered supports was realized by calculating the coordinates of the key points. According to actual coal face data, a virtual simulation platform is established, and the sequential movement of support groups on the virtual floor was realized. Finally, contrast experiments of different virtual floor models were conducted, and the Mesh floor, which is closer to the actual coal seam, was selected for subsequent virtual simulation. The collision detection experiments of powered supports were conducted in both virtual and physical environments, and the results validated the feasibility of the collision detection method. This method improves the virtual simulation degree of longwall coal mining, and provides simulation reference for the attitude adjustment of actual powered support groups before moving.

Caspase recruitment domain 6 (CARD6) was initially implicated in the immune system and oncogenesis, which has also been emerged to play an important role in cardio-metabolic diseases. Nevertheless, the potential role of CARD6 on macrophage activation remains unknown. In the present study, we observed a decreased CARD6 expression in bone marrow derived macrophages (BMDMs) and mouse peritoneal macrophages (MPMs) isolated from ApoE deficiency mice and administrated with OX-LDL, which were tested by RT-PCR and western bolt analysis. Moreover, the immunofluorescence co-staining revealed that a weaker immunoreactivity of CARD6 was found and primary located in cytoplasm of macrophages induced by OX-LDL. Phenotypically, loss-of-function of CARD6 dramatically increased pro-inflammatory M1 macrophage but decreased resolving M2 macrophage markers expression. Additionally, CARD6 knockdown significantly promoted cholesterol uptake but attenuated cholesterol efflux, which lead to increased foam cell formation. Mechanistically, a downregulated AMP-activated protein kinase (AMPK) expression was required for the promoted effect of CARD6 knockdown on macrophage activation. Taken together, these results suggest that CARD6 protects against macrophage activation partially through activation of AMPK-dependent mechanism.

Studies have probed the function of microRNA (miR)-16-5p in the progression of atherosclerosis (AS), while the regulatory function of exosomal miR-16-5p from macrophage on AS remains largely unknown. This study commits to exploring the efficiency of exosomal miR-16-5p from macrophage on AS via modulating mothers against decapentaplegic homolog 7 (SMAD7).Macrophages were cultured and transfected with miR-16-5p antagomir, then, the exosomes from macrophages were extracted. The AS mouse model was established, and miR-16-5p or SMAD7 expression in AS mice was detected. Thereafter, the effects of macrophage-derived exosomes, miR-16-5p or SMAD7 on serum inflammatory response, oxidative stress response, pathological changes and apoptosis in AS mice were observed by immunohistochemical and biochemical analysis. Finally, the binding relation between miR-16-5p and SMAD7 was examined.MiR-16-5p was elevated while SMAD7 was depleted in AS mice. Macrophage-derived exosomes aggravated AS progression via facilitating inflammatory response and oxidative stress, exacerbating pathological changes and increasing cell apoptosis in AS mice; while downregulation of miR-16-5p reversed the exacerbation of AS progression by macrophage-derived exosomes in AS mice. MiR-16-5p targeted SMAD7, and the down-regulated SMAD7 reversed the impacts of depleted miR-16-5p on AS progression.Exosomal miR-16-5p from macrophages aggravates AS progression via downregulating SMAD7 expression. This study provides novel therapeutic targets for AS treatment from the animal level.

The mechanisms that link hyperandrogenism and insulin (INS) resistance (HAIR) to the increased miscarriage rate in women with polycystic ovary syndrome (PCOS) remain elusive. Previous studies demonstrate that increased uterine and placental ferroptosis is associated with oxidative stress-induced fetal loss in a pre-clinical PCOS-like rat model. Here, we investigated the efficacy and molecular mechanism of action of the antioxidant N-acetylcysteine (NAC) in reversing gravid uterine and placental ferroptosis in pregnant rats exposed to 5α-dihydrotestosterone (DHT) and INS. Molecular and histological analyses showed that NAC attenuated DHT and INS-induced uterine ferroptosis, including dose-dependent increases in anti-ferroptosis gene content. Changes in other molecular factors after NAC treatment were also observed in the placenta exposed to DHT and INS, such as increased glutathione peroxidase 4 protein level. Furthermore, increased apoptosis-inducing factor mitochondria-associated 2 mRNA expression was seen in the placenta but not in the uterus. Additionally, NAC was not sufficient to rescue DHT + INS-induced mitochondria-morphological abnormalities in the uterus, whereas the same treatment partially reversed such abnormalities in the placenta. Finally, we demonstrated that NAC selectively normalized uterine leukemia inhibitory factor, osteopontin/secreted phosphoprotein 1, progesterone receptor, homeobox A11 mRNA expression and placental estrogen-related receptor beta and trophoblast-specific protein alpha mRNA expression. Collectively, our data provide insight into how NAC exerts beneficial effects on differentially attenuating gravid uterine and placental ferroptosis in a PCOS-like rat model with fetal loss. These results indicate that exogenous administration of NAC represents a potential therapeutic strategy in the treatment of HAIR-induced uterine and placental dysfunction.

A reliable braking system is an important guarantee for safe operation of mine hoist. In order to make full use of the monitoring data in the operation process of mine hoist, identify the operation status of the hoist, and further carry out fault diagnosis on it, the deep learning method was introduced into the fault diagnosis of the hoist, and a fault diagnosis method of hoist braking system based on convolution neural network has been proposed. Firstly, the working principle and fault mechanism of disc brake and its hydraulic station in hoist braking system are analyzed, and the monitoring parameters of this study are determined; then, based on massive monitoring data, the convolutional neural networks (CNN) is established, the one-dimensional signal collected by the sensor is transformed into two-dimensional image for coding, the neural network is trained by gradient descent method, and the network structure parameters are modified according to the training results. Finally, the fault diagnosis model is compared and verified by using the sample set based on the traditional back propagation neural network (BP) and CNN. The results show that the accuracy of CNN is higher than that of BP, and the accuracy rate can reach 99.375% after reducing the involvement between samples. This method can make full use of the monitoring data for diagnosis, without subjective intervention of experts, and improve the accuracy of diagnosis.

BACKGROUND: The aim of this study was to observe the effect of Sacubitril/Valsartan on cardiac function and remodeling in patients with heart failure with reduced ejection fraction (HFrEF). METHODS: A total of 120 patients with HFrEF were selected and given standard heart failure treatment according to the 2017 ACC/AHA/HFSA guidelines. Regardless of whether patients had taken Angiotensin-Converting Enzyme Inhibitors/Angiotensin Receptor Blockers (ACEI/ARB) medications previously, after admission they were treated with the minimum effective dose of Sacubitril/Valsartan according to their blood pressure reading. Baseline clinical data were recorded and patients were followed up at 1, 3, 6, 9, and 12 months post discharge, during which time the dose of Sacubitril/Valsartan was gradually increased to the maximum tolerated dose (dose range 25-200 mg/twice daily). During follow-up, N-terminal pro-brain natriuretic peptide (NT-proBNP), left ventricular ejection fraction (LVEF), left ventricular end diastolic diameter (LVEDD), left ventricular end systolic diameter (LVESD), and left atrium diameter (LAD) were monitored; a 6-minute walking test and Kansas City Cardiomyopathy Questionnaire (KCCQ) scores were recorded; and adverse reactions were collected. RESULTS: Over the course of the 12-month follow-up, the plasma concentrations of NT-proBNP were significantly reduced compared with the baseline, and the longer the follow-up time, the lower the NT-proBNP levels were (P

In view of the problems in traditional monitoring methods of hydraulic support in fully mechanized mining face, such as non intuitive display of monitoring information, difficult transmission of monitoring data, and unsatisfactory monitoring effect, a unity3d virtual monitoring method based on real-time running data driven and network cooperation technology is proposed. Taking the monitoring of hydraulic support in a fully mechanized mining face as an example, firstly, the real-time running data of the support is used to drive the model movement, and the visual monitoring of the hydraulic support is realized under the unity3d environment; Then, considering the limitation of advanced explosion-proof computer configuration of underground centralized control center, a distributed virtual monitoring method of hydraulic support group is proposed based on the optimization of unity3d built-in network structure and dynamic allocation of monitoring tasks on consistency hash algorithm, which reduces the data monitoring amount and network transmission amount of single monitoring host, and realizes the real and virtual synchronous monitoring of hydraulic support group; Finally, the distributed virtual monitoring system is built, and the method is verified by experiments. The experimental results show that compared with the traditional monitoring method, this method can not be affected by the bad working face environment. On the basis of greatly reducing the amount of data transmission, it can realize the visual display of the attitude of the hydraulic support group.

In order to solve the problem that it is difficult to extract the working attitude of fully mechanized coal mining equipment in real-time in the existing monitoring methods, a method for constructing the working space of fully mechanized coal mining equipment based on virtual reality is proposed. First, the key information points were patched on the hydraulic support and scraper conveyor model, and the space-time kinematics relationship between the fully mechanized coal mining equipment and the 3D geological model was built in the virtual reality engine Unity3d; Then the initial working curves of the hydraulic support and scraper conveyor were extracted, and the reliability of the method was verified by virtual experiments. Finally, the support space of hydraulic support and the working space of fully mechanized coal mining equipment are constructed by using the key point marking method. The results show that the working space constructed by this method can reflect the working trajectory of the equipment more realistically. This method provides a theoretical basis for extracting the working attitude of fully mechanized coal mining equipment in the fully mechanized coal mining face.

Coal machinery equipment plays an important role in the process of coal mining. It is of great significance for its safe, stable, reliable and healthy operation to carry out the research on data monitoring methods of coal machinery equipment. Aiming at the problems of repeated construction of monitoring and diagnosis system of coal machine equipment, serious “information island” phenomenon and poor expansion of transmission network, this study puts forward the data monitoring method of coal machine equipment based on the perception of Internet of Things (IoT), and focuses on the key technologies of data acquisition, data transmission, data processing and application of coal machine equipment, and establishes the overall structure of coal machinery equipment monitoring based on IoT. In the sensing layer, a cooperative information acquisition system for key components of coal machinery equipment based on ZigBee short-distance wireless communication technology is designed to realize real-time data acquisition. In the network layer, a long-distance wireless data transmission and reception platform based on 4G technology is established, and Message Queuing Telemetry Transport (MQTT) communication protocol is used to send the data to the cloud, the interface is reserved for future communication technology, the real-time transmission and storage of data are realized. In the middleware layer, the processing methods of heterogeneous data are proposed, and the preprocessing technology of big data is studied to realize data sharing. In the application layer, the application direction of coal machine equipment data monitoring is proposed. Based on the interactive technology, a humanized and visual fault monitoring and diagnosis platform is established, which verifies the above theories and methods. This study not only realizes the effective monitoring of coal machine equipment data, but also promotes the sharing of coal machine information, which provides a new idea for the construction of intelligent mine.

This article takes the shape of the wok as the research object, explores the factors that affect the effective heat absorption efficiency of the pot outer surface, and proposes an analysis method of the wok shape based on ANSYS combustion reaction. The three-dimensional solid modeling of the fluid region that simulates combustion is based on the shape of the pot body, and then the simulation of combustion of methane and air mixed in the fluid area is performed based on ANSYS software. Constantly change the morphological parameters of the pot body, the bottom surface radius of the pot body and the arc radius of the bottom surface edge, and repeat the test to obtain the fluid temperature field distribution and the outer surface temperature field distribution results of the pot. And then calculate the coverage of the outer surface of the pot in different temperature ranges, and analyze the experimental results. The results show that both the bottom surface radius and the bottom arc radius of the morphological parameters of the pot can affect the heating efficiency of the pot outer surface. When a single variable is increased, the surface of the pot body has a higher adsorption effect on high-temperature gas, and the higher the proportion of the high-temperature region, the higher the heat absorption efficiency of the surface of the pot body. This method provides a basis for improving the thermal efficiency of the wok on the basis of saving resources and shortening the engineering cycle.

In this study, a real-time remote monitoring and fault diagnosis method has been developed based on the Internet of Things (IoT) frame perception, and successfully applied to a mine hoist system. The proposed method combines the sensor technology, online monitoring technology, wireless transmission technology, and fault diagnosis technology. The basic structure of the traditional IoT comprises a perception layer, a network layer, and an application layer, the proposed structure contains an additional middleware layer between the network layer and the application layer. This four-layer system is used in a mine hoist remote monitoring and fault diagnosis framework to process heterogeneous multi-source information. The sensors and parameters are connected in the perception layer, the characteristic parameters are obtained using the configuration software, and the mine local area network is saved to the data server, thereby synchronizing real-time data in the local area network. The network layer utilizes mature Internet and long-distance wireless transmission communication technologies, whereas the middleware layer comprises of a Service-Oriented Architecture (SOA)-based IoT data processing framework that integrates the multi-source heterogeneous data. Further, the fault diagnosis method is analyzed and verified based on the gray association rules. In the application layer, a human-computer interface is used for the remote monitoring and diagnosis of the mine hoist and to provide the diagnosis results as feedback to the user. The results using the aforementioned analyses are applied to the remote monitoring and diagnosis of a mine hoist system. In this study, experimental tests are conducted in this study to significantly improve the fault monitoring, diagnostic capabilities, and reliability of the mine hoist system, indicating the good application good prospects of the proposed method.

The performance of the brake system is directly related to the safety and reliability of the mine hoist operation. Mining the useful fault information in the operation of a mine hoist brake system, analyzing the abnormal parts and causes of the equipment, and making accurate early prediction and diagnosis of hidden faults are of great significance to ensure the safe and stable operation of a mine hoist. This study presents a fault diagnosis method for hoist disc brake system based on machine learning. First, the monitoring system collects the information of the hoist brake system, extracts the fault features, and pretreats it by SPSS (Statistical Product and Service Solutions). This work provides data support for fault classification. Then, due to the complex structure of the hoist brake system, the relationship between the fault factors often has a significant impact on the fault. Considering the correlation between the fault samples and the attributes of each sample data, the C4.5 decision tree algorithm is improved by adding Kendall concordance coefficient, and the improved algorithm is used to train the sample data to get the decision tree classification model. Finally, the fault sample of the hoist brake system is trained to get the algorithm model, and then the fault diagnosis rules are generated. The state of the brake system is judged by classifying the data. Experiments show that the improved C4.5 decision tree algorithm takes the relativity of conditional attributes into account, has a higher diagnostic accuracy when processing more data, and has concise and clear fault classification rules, which can meet the needs of hoist fault diagnosis.

The mini-LED as the backlight of field sequential color LCD (FSC-LCD) enables high contrast, thin volume, and theoretically tripled light efficiency and resolution. However, color breakup (CBU) induced by a relative speed between an observer and the display severely limits the application of FSC-LCDs. Several driving algorithms have been proposed for CBU suppression, but their performance depends on image content. Moreover, their performance plateaus with increasing image segment number, preventing taking advantage of the massive segments introduced by mini-LEDs. Therefore, this study proposes an image content-adaptive driving algorithm for mini-LED FSC-LCDs. Deep learning-based image classification accurately determines the best FSC algorithm with the lowest CBU. In addition, the algorithm is heterogeneous that the image classification is independently performed in each segment, guaranteeing minimized CBU in all segments. We perform objective and subjective validation. Compared with the currently best algorithm, the proposed algorithm improves the performance in suppressing CBU by more than 20% using two evaluation metrics, supported by experiment-based subjective evaluation. Mini-LED FSC-LCDs driven by the proposed algorithm with outstanding CBU suppression can be ideal for display systems requiring high brightness and high resolution, such as head-up displays, virtual reality, and augmented reality displays.

With regards to the low degree of digitization, lack of real geological terrain, and low degree of automation in the cutting process of the traditional virtual fully mechanized mining face, we studied the key technologies of virtual operation and cutting path planning of the shearer on the three-dimensional (3D) roof and floor based on the virtual reality engine (Unity3D). Firstly, the virtual 3D coal seam was constructed through the 3D geological coordinate data of the mine. On this basis, the shape function of the scraper conveyor with the adaptive configuration on the floor was constructed to obtain the combined operation of the virtual shearer and the scraper conveyor. The movement of the shearer&rsquo, s walking and height-adjustment was then, analyzed. A strategy for automatic height-adjustment based on the adjustment of the direction of the drum movement is hence, proposed to control the cutting path of the shearer. Finally, different experimental schemes were simulated in the developed prototype system after which each of the schemes was evaluated using the fuzzy comprehensive evaluation method. The results show that the proposed strategy for trajectory control can improve the accuracy and stability of the shearer&rsquo, s motion trajectory. In Unity3D, the pre-selected schemes and digital and visual planning of coal production are previewed ahead of time, the whole production process can be mapped synchronously in the production process. It is also obtained that the virtual preview and evaluation of the production process can provide some guidance for actual production.

Background: The study of the development and evaluation of self-management intervention among patients with acute myocardial infarction (AMI) undergoing percutaneous coronary intervention (PCI) is lacking, especially in China. Aim: To examine the effects of a nurse-led individualized self-management program (NISMP) on health behaviors, control of cardiac risk factors, and health-related quality of life (HRQoL) among patients with AMI undergoing PCI. Methods: The quasi-experimental design included a convenience sample of 112 participants recruited from a tertiary hospital in China. The participants were assigned to the control group ( n = 56) or the intervention group ( n = 56). The intervention group underwent the NISMP, which includes six group-based education sessions, a face-to-face individual consultation, and 12-month telephone follow-ups. Data were collected at baseline and at the end of the 12-month program using the Health Promotion Lifestyle Profile, the Risk Factors Assessment Form, and the Short Form 36-item Health Survey. Results: The baseline sociodemographic and clinical characteristics of the two groups were comparable ( p > 0.05). After the 12-month intervention, the health behaviors and HRQoL of the participants in the intervention group had significantly improved ( p < 0.05 for both) compared to those of the control group. Compared to the control group, the participants in the intervention group also reported significantly better control of cardiac risk factors including smoking (χ2 = 4.709, p = 0.030), low-density lipoprotein (χ2 = 4.160, p = 0.041), body mass index (χ2 = 3.886, p = 0.049) and exercise (χ2 = 10.096, p = 0.001). Conclusion: The NISMP demonstrated positive effects on health behaviors, control of cardiac risk factors, and HRQoL among Chinese patients with AMI undergoing PCI.

Benign paroxysmal positional vertigo is characterized by recurrence, which exposes patients to repeated vertigo attacks. Vitamin D deficiency has been found to be a risk factor in benign paroxysmal positional vertigo, although effect of its elimination on recurrence reduction remains unknown. To determine the effect of vitamin D supplementation in preventing recurrence of benign paroxysmal positional vertigo patients with vitamin D deficiency using a meta-analysis study. We searched and retrieved relevant articles from several databases, then used the Cochrane evaluation system or Methodological Index for Non-Randomized Studies (MINORS) to assess the quality of studies. We adopted risk-ratio (RR) with corresponding 95% confidence interval (CI) to determine effect sizes, and further performed statistical analyses under a randomized- or fixed-effects model. Seven studies, comprising 602 and 731 participants in the case and control group respectively, met our inclusion criteria, and were therefore included in the meta-analysis. Assessment based on Cochrane evaluation system or MINORS revealed that most of the studies had high quality. Moreover, the randomized- model revealed that the vitamin D supplementation group had a lower recurrence rate than the control group which did not accepted vitamin D supplementation (RR = 0.41, 95% CI = 0.26–0.65, p

The existing automatic control program and its parameters for three machines in a fully mechanized Coal Mining face are static and simplex and are therefore inadequate for satisfying the complex and dynamic environment of underground coal mines. To overcome this problem, a collaborative mathematical model is established that includes the effects of a dynamic environment. A virtual reality collaborative planning simulator with methods for the three machines is also proposed based on a multi-agent system. According to the dynamic characteristics of the environment, equipment, and technologies, a fully mechanized Unity3D simulator (FMUnitySim) is designed in terms of multiple factors and multiple dimensions. The factors affecting the coordinated operation of the three machines are analyzed and modeled. The communication modes, coordination, and redundant sensing process among multiple agents, which include the shearer agent and the scraper conveyor agent, are also investigated in detail. Using this system, the key parameters of the three machines can be planned and adjusted online to design and distinctly observe the corresponding collaborative simulations of coordinated operation with multiple perspectives and in real time. Tests of different maximum shearer haulage speeds for regular or reverse transporting coal are designed; their key parameters, including the average shearer haulage speed, average follower distance, and average scraper conveyor load, are planned and simulated using FMUnitySim. The optimal parameter combination is obtained by analyzing and comparing the simulation results. The proposed FMUnitySim offers an effective means and theoretical basis for the rapid planning and safe automatic production of a fully mechanized Coal Mining face.

We optimise a new liquid crystal mixture with wide nematic range, high birefringence (Δn), high resistivity, moderate dielectric anisotropy (∆ε) and relatively low rotational viscosity for augmented reality display applications. High Δn and large Δε allow a thin cell gap (d ≈ 1.5 μm) to be employed in a reflective liquid-crystal-on-silicon (LCoS) device to achieve 2π phase change at 5 V and 2.87 ms average phase-to-phase response time at 40°C without complicated overdrive circuitry. Such a fast response time enables an LCoS panel to achieve 240 Hz frame rate for field sequential colour operation to avoid image flickering.

Rough set is a new data analysis theory. It is often used to deal with fuzzy and uncertain problems. Attribution reduction is the key step in obtaining the knowledge by utilizing rough set. An improved heuristic reduction algorithm of attribute significance is proposed in the study based on analyzing the classic knowledge acquisition method of rough set theory. The algorithm corrects the discernibility matrix and redefines the calculation method of attribute importance. Then it fuses the both, gets the core by using the revised method of discernibility matrix and calculates the attribute importance by using the weighted method and then the algorithm is applied to extract the rules of the hoist fault diagnosis. Verified by the experiment, using the algorithm, it can excavate high reliability diagnosis rules from existing history diagnosis knowledge and expert knowledge. This method can provide reasonable basis for fault diagnosis.

Ring-opening polymerizations of γ-glycidoxypropyltrimethoxysilane (GPTMS) were carried out by using multi-metal cyanide (MMC) catalyst and the synthesized homopolymer was a comb-shaped polymer with regular structure. The structure of the polymer obtained (P-GPTMS) was characterized by FTIR, 1H-NMR, and 29Si-NMR spectroscopy, and the molecular weight and its distribution were analyzed by size-exclusion chromatography with multi-angle laser light scattering (SEC-MALLS). The thermal behavior of P-GPTMS was investigated by differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). P-GPTMS with high molecular weight (Mn = 18,000–80,000) and narrow molecular weight distribution (1.10–1.35) were synthesized when the dosage of MMC catalyst was 0.1% and polymerization temperature was 130 °C. The molecular weight of the product could be adjusted by controlling the polymerization time. The Tg of P-GPTMS is in the range of −34 to −30 °C. On the basis of the TGA data, the decomposition rate of P-GPTMS reached its peak at 374.14 °C and the entire decomposition stopped at 600 °C.