Search

Multi-source observations are used to comprehensively analyze the Doppler weather radar characteristics of strong storms and the formation mechanisms of surface damaging wind induced by two strong downbursts, occurred in Shandong on 2 June 2017 and 6 August 2017. It's found that two damaging wind events occurs under strong synoptic scale weather system forcing and favorable meso-scale environmental conditions, the relatively isolated supercell storm on 2 June (6·2 supercell) and strong single storm on 6 August (8·6 strong single storm) fiercely develop into series of downbursts, which leads to the occurrence of large-scale damaging winds. As two strong downbursts induced by 6·2 supercell storm and 8·6 strong single storm evolves, the vertically integrated liquid water content radar parameter first rises and then quickly plunges down. During 6·2 supercell storm, a powerful downburst descends sharply, causing the mesocyclone's top and bottom to shoal and its thickness to decrease. The occurrence of two strong downbursts are accompanied by obvious radar characteristics including reflective factor core rapid decline, high value area of radial velocity at low elevation, strong divergence at bottom, remarkable mid altitude radial convergence and severe divergence at upper-level. 6·2 supercell storm is characterized by intense rotation. Its mesocyclone lasts for a long time and extends deeply both upwards and downwards. Additionally, there are arc-shaped inflow notches and hook echoes at low levels of 6·2 supercell storm. 8·6 strong single storm is characterized by significant low-level convergence. Besides, there is a convergence zone at or near the surface formed by the outflow of the strong downburst and the inflow in front of 8·6 strong single storm. Among all the formation mechanisms of two damaging winds induced by two strong downbursts, the negative buoyancy effect of two storms is basically equivalent, but the cold pool outflow effect is more evident for 6·2 supercell storm, and the downward transport momentum effect is more significant for 8·6 strong single storm. The Nansun Station of Weifang locating right ahead of 8·6 strong single storm's approaching direction, therefore the front divergence flow from the strong downbursts is preferable superimposed on the fast moving homodromous storm, indicating the speed of the front divergence flow better superimposes on the speed of the storm itself, which is crucial for the occurrence of 37.0 m·s-1 extreme wind.

The integration of renewable energy into the power grid poses significant challenges for optimization and scheduling of the power system. In recent years, methods based on deep reinforcement learning have surpassed traditional methods on the high complexity and long-term decision-making of power system optimization and scheduling. However, faced with the inherent uncertainty of renewable energy generation and the different optimization objectives in power system, the deep reinforcement learning methods are unable to effectively address them. This paper proposes a method that combines meta reinforcement learning with multi-agent reinforcement learning to solve the multi-objective two-stage robust optimization of wind/PV/thermal power system. We conducts optimization and scheduling experiments on the IEEE39 bus system. The results indicate that our method not only enhances the robustness of the scheduling strategy, but also outperforms baseline methods in terms of convergence, diversity, and uniformity of the Pareto frontier.

Objective·To investigate the relation between urinary excretion of protein-bound uremic toxins (PBUTs) and upper urinary tract calculus.Methods·Residents aged 18‒80 years in the community of Haitou, Danzhou city in Hainan Province were recruited. Basic information and diet for the last 3 d of the subjects were recorded. Their fasting sera and 24-hour urine samples were collected, and they also underwent ultrasound examination of kidneys and ureters. The subjects with upper urinary calculi detected by ultrasound or a clear history of upper urinary calculi were selected as the calculus group, and the others as the non-calculus group. The biochemical indicators related to the formation of calculus in blood and urine were detected, and the levels of PBUTs, including indoxyl sufate (IS), indole-3-acetic acid (IAA), and p-cresol sulfate (PCS) in blood and urine, as well as oxalic acid and citric acid in urine were detected by high-performance liquid chromatography. The related factors of upper urinary tract calculus formation were analyzed by multivariate Logistic regression. The correlations of urine PBUTs with urine uric acid, oxalic acid, and citric acid were analyzed by Spearman correlation test.Results·A total of 117 participants were screened out with 54 people in the calculus group and 63 people in the non-calculus group. There were no significant differences between the two groups in terms of gender, age, serum indicators, and prevalence of complications such as hypertension, diabetes, and hyperuricemia/gout. The 24-hour urine pH, calcium, uric acid, and chlorine in the calculus group were significantly higher than those in the non-calculus group (all P

IntroductionResidential load forecasting is a challenging task due to the random fluctuations caused by complex correlations and individual differences. The existing short-term load forecasting models usually introduce external influencing factors such as climate and date. However, these additional information not only bring computational burden to the model, but also have uncertainty. To address these issues, we propose a novel multi-level feature fusion model based on graph attention temporal convolutional network (MLFGCN) for short-term residential load forecasting.MethodsThe proposed MLFGCN model fully considers the potential long-term dependencies in a single load series and the correlations between multiple load series, and does not require any additional information to be added. Temporal convolutional network (TCN) with gating mechanism is introduced to learn potential long-term dependencies in the original load series. In addition, we design two graph attentive convolutional modules to capture potential multi-level dependencies in load data. Finally, the outputs of each module are fused through an information fusion layer to obtain the highly accurate forecasting results.ResultsWe conduct validation experiments on two real-world datasets. The results show that the proposed MLFGCN model achieves 0.25, 7.58% and 0.50 for MAE, MAPE and RMSE, respectively. These values are significantly better than those of baseline models.DiscussionThe MLFGCN algorithm proposed in this paper can significantly improve the accuracy of short-term residential load forecasting. This is achieved through high-quality feature reconstruction, comprehensive information graph construction and spatiotemporal features capture.

Abstract With the continuous transformation of the global power system, ensuring the flexibility and stability of the power system has become a direction that the global energy industry has been striving for. This puts forward higher requirements for load forecasting, especially high‐precision short‐term residential load forecasting. However, the high volatility and uncertainty of electric consumption make this problem challenging. Existing methods usually focus on capturing temporal correlations and ignore the underlying spatial correlations in historical loads, which is crucial for accurate forecasting. In this paper, we propose a novel spatial–temporal gated fusion network (STGNet) for short‐term residential load forecasting including not only individual residential load but also aggregated load. By seeking both adaptive neighbors and temporal similarity neighbors, we construct a dynamic graph based on a data‐driven method. On this basis, we provide an adaptive graph gated fusion mechanism to capture fine‐grained node‐specific spatial dependence more accurately and comprehensively. Finally, a bidirectional gated recurrent unit (BiGRU) is utilized to learn the temporal dependence of load data. Extensive experiments on real‐world datasets are conducted and demonstrate the superiority of STGNet over the existing approaches.

Accurate and reliable medium- and long-term load forecasting is crucial for the rational planning and operation of power systems. However, existing methods often struggle to accurately extract and capture long-term dependencies in load data, leading to poor predictive accuracy. Therefore, this paper proposes a medium- and long-term residential load forecasting method based on FEDformer, aiming to capture long-term temporal dependencies of load data in the frequency domain while considering factors such as electricity prices and temperature, ultimately improving the accuracy of medium- and long-term load forecasting. The proposed model employs Discrete Cosine Transform (DCT) for frequency domain transformation of time-series data to address the Gibbs phenomenon caused by the use of Discrete Fourier Transform (DFT) in FEDformer. Additionally, causal convolution and attention mechanisms are applied in the frequency domain to enhance the model’s capability to capture long-term dependencies. The model is evaluated using real-world load data from power systems, and experimental results demonstrate that the proposed model effectively learns the temporal and nonlinear characteristics of load data. Compared to other baseline models, DCTformer improves prediction accuracy by 37.5% in terms of MSE, 26.9% in terms of MAE, and 26.24% in terms of RMSE.

Abstract In this study, a mechanical model suitable for deepwater test string was proposed. An analysis of the dynamic response of the test string under different frequencies, different water depths and different fluctuation amplitudes was carried out by using the finite element method based on the change in the internal pressure and axial force measured. The results of the analysis showed that the response parameters (maximum stress and maximum deformation) tended to be stable after one period of fluctuation in the axial force and half a period of fluctuation in the internal pressure, respectively. When a sine waveform fluctuation in the internal pressure and axial force occurred, the response parameters increased with an increase in the amplitude of the fluctuation and increased with an increase in the frequency of fluctuation, and the amplitude of variation decreased with an increase in the fluctuation period. Under fluctuation in the axial force, the response parameter decreased with an increase in the water depth. The response parameter decreased first and then increased with an increase in the water depth when the fluctuation in the internal pressure occurred with a sine waveform. The maximum deformation and stress of the test string always changed with a change in the load when the fluctuation in the internal pressure and axial force had a sine waveform, and the test string under a load with a sine waveform was prone to periodic fatigue failure. The relevant conclusions provide a basis for the analysis and prevention of fatigue failure in test strings.

Graph contrastive learning has demonstrated significant superiority for collaborative filtering. These methods typically use augmentation technology to generate contrastive views, and then train graph neural networks with contrastive learning as an auxiliary task. Although these methods are very effective, they do not consider using contrastive learning from the perspective of user–item interaction. As a result, they do not fully leverage the potential of contrastive learning. It is well known that contrastive learning can maximize the consistency of positive pairs and minimize the agreement of negative pairs. Collaborative filtering expects high consistency between users and the items they like and low consistency between users and the items they dislike. If we treat the items that users like as positive examples and the items they dislike as negative examples, contrastive learning can work very well with the goal of collaborative filtering. Based on the above understanding, we propose a new objective function called DCL loss, which improves graph collaborative filtering from the perspective of user–item interaction when Directly using Contrastive Learning. Extensive experiments have shown that when a model adopts DCL loss as its objective function, both its recommendation performance and training efficiency exhibit significant improvements.

A mechanical model for offshore casing cutting based on the field application of a mechanical cutting device in the South China is presented in this paper. The proposed model includes the calculation of the piston acting on the cutter and the calculation of the cutting torque and wellhead driving torque. The influence of structural parameters on cutting extension distance, cutting torque, wellhead driving torque, and the proportion of cutting torque to wellhead driving torque are analyzed. The required extension distance is related to piston displacement and cutter face angle, the cutter face angle and cutting depth (water depth) have obvious influence on the cutting torque and wellhead driving torque, and the drilling revolution affects the inertia torque and wellhead driving torque. Since the proportion of cutting torque to wellhead driving torque decreased with the increase in the cutting depth, we cannot determine whether the casing cutting is completed based on the sudden decrease in the wellhead driving torque with cutting depths greater than 800 m. The numerical simulation results of the cutting tool cutting the casing show that if the revolution speed is too high, the initial cutting melting of the casing may affect the cutting force, and it is recommended to increase the rake angle as much as possible within a certain range. The field example shows the limitation of judging casing cutting by a sudden drop in torque.

Lipopolysaccharide (LPS) is an essential component of the outer wall of gram-negative bacteria and can induce a strong inflammatory response in the body. Acyloxyacyl hydrolase (AOAH) is a highly conserved host lipase expressed in a variety of cells, including neutrophils, macrophages and proximal tubular epithelial cells. AOAH can inactivate LPS by selectively removing the secondary acyl chains from the lipid A moiety of gram-negative bacterial LPS, which alleviates inflammation and tissue damage, promotes the recovery from the immune tolerance and regains normal responsiveness. It plays an essential role in host defenses against infection. Moreover, AOAH has been shown in vivo and in vitro for a variety of enzymatic activities including phospholipase, lysophospholipase and diacylglycerol lipase. Researches increasingly show that AOAH plays a critical role in various noninfectious diseases such as allergic asthma, psoriasis, chronic pelvic pain and other autoimmune diseases and chronic inflammatory diseases in addition to infections. Besides, DING Feng Research Group, Department of Nephrology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine found that AOAH was capable of attenuating renal fibrosis via proximal renal tubular epithelial cells CD74 signaling pathway. Given the increasingly diverse role of AOAH in diseases, this article reviews its sources, structure, effects and possible mechanisms in diseases in order to provide reference for future research on its roles in diseases and new lines of investigation into disease pathogenesis and novel therapeutic targets.

Dynamic monitoring and quantitative estimation of forage crop growth are of great importance to the large-scale production of forage crop. UAV remote sensing has the advantages of high resolution, strong flexibility and low cost. In recent years, it has developed rapidly in the field of forage crop growth monitoring. In order to clarify the development status of forage crop growth monitoring and find the development direction, first, methods of UAV crop remote sensing monitoring were briefly described from two aspects of data acquisition and processing. Second, three key technologies of forage crop including canopy information extraction, spectral feature optimization and forage biomass estimation were described. Then the development trend of related research in recent years was analyzed, and it was pointed out that the number of papers published on UAV remote sensing forage crop monitoring showed an overall trend of rapidly increasing. With the rapid development of computer information technology and remote sensing technology, the application potential of UAV in the field of forage crop monitoring has been fully explored. Then, the research progress of UAV remote sensing in forage crop growth monitoring was described in five parts according to sensor types, i.e., visible, multispectral, hyperspectral, thermal infrared and LiDAR, and the research of each type of sensor were summarized and reviewed, pointing out that the current researches of hyperspectral, thermal infrared and LiDAR sensors in forage crop monitoring were less than that of visible and multispectral sensors. Finally, the future development directions were clarified according to the key technical problems that have not been solved in the research and application of UAV remote sensing forage crop growth monitoring: (1) Build a multi-temporal growth monitoring model based on the characteristics of different growth stages and different growth years of forage crops, carry out UAV remote sensing monitoring of forage crops around representative research areas to further improve the scope of application of the model. (2) Establish a multi-source database of UAV remote sensing, and carry out integrated collaborative monitoring combined with satellite remote sensing data, historical yield, soil conductivity and other data. (3) Develop an intelligent and user-friendly UAV remote sensing data analysis system, and shorten the data processing time through 5G communication network and edge computing devices. This paper could provide relevant technical references and directional guidelines for researchers in the field of forage crops and further promote the application and development of precision agriculture technology.

Currently, the use of fixed empirical parameters combined with traditional algorithms to construct train parameter prediction models for estimating train energy consumption is often used. However, due to changes in train related parameters, there is a significant error between the estimated results of this method and the actual values, which cannot reflect the true state of train operation. It affects the subsequent maintenance and repair of trains, thereby affecting the safety of the entire subway operation system. To address the above issues, a train traction power flow model based on digital twin technology is proposed. The swarm intelligence optimization algorithm is not used to correct model parameters. By correcting the deviation of train passenger load, accurate prediction of train energy consumption can be achieved. The experimental results show that the power flow model constructed by the research institute can reduce the power error of single interval trains by over 11.91%. The average DC voltage error of the train power flow model after parameter correction is 0.19%. The average DC current error is 12.07%. The root mean square error range of passenger capacity for similar day neural network models is [0.0521,0.0811]. The experimental results indicate that the model constructed by the research institute can accurately predict train energy consumption and ensure the safety of the subway operation system.

Fatigue of miners is caused by intensive workloads, long working hours, and shift-work schedules. It is one of the major factors increasing the risk of safety problems and work mistakes. Examining the detection of miner fatigue is important because it can potentially prevent work accidents and improve working efficiency in underground coal mines. Many previous studies have introduced feature-based machine-learning methods to estimate miner fatigue. This work proposes a method that uses electroencephalogram (EEG) signals to generate topographic maps containing frequency and spatial information. It utilizes a convolutional neural network (CNN) to classify the normal state, critical state, and fatigue state of miners. The topographic maps are generated from the EEG signals and contrasted using power spectral density (PSD) and relative power spectral density (RPSD). These two feature extraction methods were applied to feature recognition and four representative deep-learning methods. The results showthat RPSD achieves better performance than PSD in classification accuracy with all deep-learning methods. The CNN achieved superior results to the other deep-learning methods, with an accuracy of 94.5%, precision of 97.0%, sensitivity of 94.8%, and F1 score of 96.3%. Our results also show that the RPSD–CNN method outperforms the current state of the art. Thus, this method might be a useful and effective miner fatigue detection tool for coal companies in the near future.

Aspect-based sentiment analysis (ABSA) is a popular topic for natural language processing,in which opinion target extraction and sentiment polarity classification of opinion target are one of the basic subtasks of ABSA.However,few studies directly extract the opinion targets of specific emotional polarity,especially the negative emotion opinion targets with more potential value.A new ABSA subtask--negative emotion opinion target extraction (NE-OTE) is proposed,and a BiLSTM-CRF model based on attention mechanism and character and word mixture embedding (AB-CE) is proposed.On the basis of bi-directional long short-term memory (BiLSTM) learning textual semantic information and capturing long distance bi-directional semantic dependency,through the attention mechanism,the model can better pay attention to the key parts in the input sequence and capture the implied characteristics related to the opinion target and its emotional tendency.Finally,the CRF layer can be used to predict the optimal tag sequence at the sentence level,so as to extract the negative emotional opinion target.This paper builds three NE-OTE task datasets based on the mainstream ABSA task baseline datasets and conducts extensive experiments on these datasets.Experimental results show that the model proposed in this paper can effectively identify the target of negative emotional opinions,and is significantly better than other baseline models,which verifies the effectiveness of the method proposed in this paper.

During the operation of drilling rigs, bolts are subjected to significant loads, rendering bolt groups vulnerable to failure due to uneven load distribution. This study investigates the multi-bolt load distribution characteristics of eight-gear and four-gear symmetric transmission mechanisms in drilling rigs. The spring stiffness method is utilized to analyze multi-bolt load distribution. A finite element model of the bolted connection is developed by integrating the structural shape and transmission mechanism. The model’s accuracy is validated through a rack strain test under various loads. Based on the finite element analysis results, this study proposes an evaluation method for bolted connections using relative deformation difference. The impacts of bolt pitch, end distance, preload, and thickness of the derrick connection plate under different load positions on the connection are examined. This paper presents a methodology and conclusions that can inform the design of bolted connections for heavy-duty drive systems.

To solve the problem of large delay and small capacity of tactical cooperative network data link, in order to meet the requirements of low delay and large capacity in tactical network, a threshold adaptive access mechanism based on multi-channel simultaneous reception is proposed based on tactical targeting network technologies (TTNT). A layered open architecture is adopted in this mechanism, and the physical layer adopts multi-channel simultaneous receiving technology based on fast frequency hopping to realize virtual full-duplex transmission mode. The MAC layer adopts threshold adaptive access technology based on channel load statistics, which can realize fast access of burst service. Compared with SR-ATMA, CSMA, TDMA and other access mechanisms, it has higher channel utilization and lower access delay, and can be well applied to the tactical network with the characteristics of burst service.

Abstract Conventional maintenance mode for the traction power supply system (TPSS) is to perform scheduled regular maintenance activities for power supply equipment, while such maintenance mode may result in undue maintenance tasks and low efficiency due to different degradation processes of different sorts of equipment. To address this problem, this paper introduces a preventive opportunistic maintenance (POM) method for TPSS based on equipment reliability. Firstly, a POM model is established by considering the equipment reliability degradation process based on Weibull distribution. Then, by considering the total power outage time in the planned operation cycle of TPSS as the optimization objective, the optimal maintenance scheme of TPSS is formulated by iterative method of maintenance strategies. The proposed method is verified by introducing practical maintenance strategies and fault record data of the traction transformer, circuit breaker and disconnector in an actual TPSS of a railway administration. Results show that the presented method can make full use of the existing fault data to develop a POM scheme for TPSS. It can improve maintenance efficiency and reduce power outage time, providing guidance to formulate scientific maintenance strategies for TPSS.

Through a review of the literature on surface treatment of superhydrophilic implants and its clinical application, this paper discusses the shortening of load time, the improvement of the planting success rate and its long-term effect. Additionally, attention should be paid to the nonindication of superhydrophilic implants and issues requiring attention. The literature review showed that healthy patients could carry out an early load 21 days after implantation of superhydrophilic implants, and the load could be completed as soon as 6 weeks after implantation with superhydrophilic short implants when the residual alveolar bone height of the posterior dental area was repaired. Even if the residual alveolar bone density of the patient is low, the application of superhydrophilic implants can shorten the healing period to 8 weeks. Notably, some studies have reported that superhydrophilic implants have no significant effect on patients with a history of radiotherapy and the use of anticoagulants. Because the adhesion of the superhydrophilic implant to the bacteria is also improved to some extent, it is very important to prevent the use of antibiotics when using the superhydrophilic implant. Finally, this paper discusses and anticipates the future research direction of superhydrophilic implants: longer periodic follow-up and more in-depth molecular mechanism studies.

Introducing negative items into sentences can shift the polarity of emotional words and leads to misclassification. Therefore, dealing with the negative item is indispensable to the analysis of the polarity of tweets. This paper first uses the combination of Conjunction Analysis (CA) technology and Punctuation Mark Identification (PMI) technology to detect negation cue and its scope. Besides, we propose the OL-DAWE model, which uses Data Augmentation(DA) technology to generate opposed tweets according to the original tweet. The model extends the training data set, and test data set and learns the original and opposed sides of the tweet in the training module. When predicting the polarity of tweets, the OL-DAWE model considers the positive degree (negative degree) of the original tweet and the negative degree (positive degree) of its opposed tweet. We conduct experiments on two real-world data sets. We prove the effectiveness of our combined technology in negation processing and show that the OL-DAWE model in the polarity sentiment analysis of tweets is better than the baseline for its simplicity and high efficiency.

Abstract Fault frequency of catenary is related to meteorological conditions. In this work, based on the historical data, catenary fault frequency and weather-related fault rate are introduced to analyse the correlation between catenary faults and meteorological conditions, and further the effect of meteorological conditions on catenary operation. Moreover, machine learning is used for catenary fault prediction. As with the single decision tree, only a small number of training samples can be classified correctly by each weak classifier, the AdaBoost algorithm is adopted to adjust the weights of misclassified samples and weak classifiers, and train multiple weak classifiers. Finally, the weak classifiers are combined to construct a strong classifier, with which the final prediction result is obtained. In order to validate the prediction method, an example is provided based on the historical data from a railway bureau of China. The result shows that the mapping relation between meteorological conditions and catenary faults can be established accurately by AdaBoost algorithm. The AdaBoost algorithm can accurately predict a catenary fault if the meteorological conditions are provided.

A 3D printing photosensitive resin structure has many advantages, such as good corrosion resistance, flexibility, low yield, and superior deformability; thus, it has been widely used in several fields.In this study, a new photosensitive resin structure was designed and built using 3D printing method.The new photosensitive resin structure is mainly used as a damping element in shockabsorbing and -isolating composite structures.First, load-displacement curves were obtained by compression experiments using the universal testing machine controlled via a microcomputer, and the equivalent elastic modulus of the specific points in the structure was calculated.Dynamic loading tests were conducted using the fatigue machine.Moreover, the hysteresis loops under different frequencies between 5 Hz and 20 Hz were obtained.The equivalent damping ratio was calculated on the basis of the hysteresis loops.The static and dynamic calculation models were built on the basis of the finite element method.After the calculations, the numerical results and the test data under the same conditions were compared.The numerical results agreed well with the test data, thereby verifying the feasibility of the finite element models.Furthermore, the influences of different geometric parameters, such as slot width, inner arc, outer arc, and thickness, on the equivalent elastic modulus of the specific location and equivalent damping ratio of photosensitive resin were investigated by the finite element method.When the 3D printing photosensitive resin structure is subjected to force, it maintains a stiffness output and small deformation along the longitudinal direction; however, the lateral displacement can be enlarged.The 3D printing photosensitive resin structure has a good capability to reduce vibration and resist deformation.The results provide references for the future research of the static and dynamic characteristics and engineering application of the 3D printing photosensitive resin structure.

Cryo-electron microscopy (cryo-EM) has been widely used to reveal the structures of proteins at atomic resolution. One key challenge is that almost all proteins are predominantly adsorbed to the air-water interface during standard cryo-EM specimen preparation. The interaction of proteins with air-water interface will significantly impede the success of reconstruction and achievable resolution. Here, we highlight the critical role of impenetrable surfactant monolayers in passivating the air-water interface problems, and develop a robust effective method for high-resolution cryo-EM analysis, by using the superstructure GSAMs which comprises surfactant self-assembled monolayers (SAMs) and graphene membrane. The GSAMs works well in enriching the orientations and improving particle utilization ratio of multiple proteins, facilitating the 3.3-Å resolution reconstruction of a 100-kDa protein complex (ACE2-RBD), which shows strong preferential orientation using traditional specimen preparation protocol. Additionally, we demonstrate that GSAMs enables the successful determinations of small proteins (

Conventional methods cannot accurately predict buildup rate. In this study, an algorithm was developed to predict the geometric deflection rate of the tool considering the influence of the radial displacement of a combined bearing. Three-point geometry was used to calculate the buildup rate and its relationship with the radial displacement of the combined bearing. The relation between the maximum spindle deflection and the load of the combined bearing was also determined by treating the spindle as a statically indeterminate beam. To consider the internal structural parameters of the combined bearing, a mathematical relationship was obtained between its load and radial displacement with the Stribeck maximum rolling element load formula and Palmgren roller bearing displacement formula. The influence of the structural parameters of the combined bearing on the buildup rate was analyzed, and the results showed that the buildup rate can be increased by increasing the thickness and pre-compression of the disc spring and decreasing its diameter ratio and the radial clearance of the self-aligning roller bearing. An experimental device was used to obtain test results with different maximum spindle deflection values. In particular, the error was less than 1% when the maximum spindle deflection was more than 4 mm.

Lubricant leakage will inevitably occur during the working process of wellbore trajectory control tools. Even including the lubricant compensation system, serious leakage will still cause lacks lubrication of the internal mechanical structure as well as electronic system damaged by external infiltration fluid, especially when it comes to battery sub and other electronic equipment. Seal system leakage prediction method was presented based on the assumption of steady gap flow. It is assumed that there is a constant gap between the lip seal and the rotating shaft, the gap height is determined by oil film thickness, and the length of the gap was determined by the contact analysis using the Mooney–Rivlin constitutive model. The analysis results show that the contact length between the primary seal lip and the rotary shaft is about 0.1 mm under the condition of ensuring the contact between the deputy seal lip and the rotary shaft. The overall lubricant leakage finite element analysis model was established, and the relationship between the internal lubricant pressure of the tool and the total leakage was obtained. The results of analysis indicate that under the internal pressure of 0.03 MPa, the lubricant leakage is approximately 6 mL/h, which was verified by experiments.

Objective To investigate the expression of Ras-related proteins in brain 34 (RAB34) regulated by miR-130b in pancreatic cancer. Methods We collected 22 paired specimens of primary pancreatic cancer and adjacent normal tissues specimens. Immunohistochemistry, Western blot and qRT-PCR were employed to detect the expression level of RAB34. Meanwhile, the expression of miR-130b was detected by qRT-PCR. Cell transfection method was used to discover the regulation of miR-130b on RAB34 gene. The luciferase reporter assay was applied to identify whether RAB34 was directly targeted by miR-130b. Results Compared with adjacent tissues specimens, miR-130b was significantly downregulated and its target gene RAB34 was upregulated in pancreatic cancer tissues. Overexpression of miR-130b remarkably inhibited the expression of RAB34. Moreover, the dual luciferase assay revealed that RAB34 was directly targeted by miR-130b. Conclusion RAB34 might have a considerable potential in prognosis identification of pancreatic cancer and be regulated by miR-130b.

Studying of probability distribution types of fatigue failure for GH4133 disc material,through the empirical distribution function and the average failure rate function methods to deal with the fatigue failure data. Based on the obtained data information,hypothesizing disk material fatigue failure probability distribution is the normal distribution, the lognormal distribution or the three parameter Weibull distribution. By means of parameter estimation for the three distributions,then using the Kolmogorov test of goodness fit method,to test the overall fitting effect of this distribution function. And to plot failure rate curves which distribution has been determined compare with the average failure rate curves. Comparing the data of each group which is under the same temperature with different strain ratio and the same stain ratio with different temperature then come to conclusion that the lognormal distribution is best fatigue failure distribution function for GH4133 disk material.