Ultra-Robust and High-Performance Rotational Triboelectric Nanogenerator by Bearing Charge Pumping

As an emerging technology to convert environmental high-entropy energy into electrical energy,triboelectric nanogenerator(TENG)has great demands for further enhancing the service lifetime and output performance in practical applications.Here,an ultra-robust and high-performance rotational triboelectric nanogenerator(R-TENG)by bearing charge pumping is proposed.The R-TENG composes of a pumping TENG(P-TENG),an output TENG(O-TENG),a voltage-multiplying circuit(VMC),and a buffer capacitor.The P-TENG is designed with freestanding mode based on a rolling ball bearing,which can also act as the rotating mechanical energy harvester.The output low charge from the P-TENG is accumulated and pumped to the non-contact O-TENG,which can simultaneously realize ultralow mechanical wear and high output performance.The matched instantaneous power of R-TENG is increased by 32 times under 300 r/min.Furthermore,the transferring charge of R-TENG can remain 95%during 15 days(6.4×10^(6)cycles)continuous operation.This work presents a realizable method to further enhance the durability of TENG,which would facilitate the practical applications of high-performance TENG in harvesting distributed ambient micro mechanical energy.

Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

In-phase and out-of-phase spin pumping effects in Py/Ru/Pysynthetic antiferromagnetic structures

The spin pumping effect in magnetic heterostructures and multilayers is a highly effective method for the generationand transmission of spin currents. In the increasingly prominent synthetic antiferromagnetic structures, the two ferromagneticlayers demonstrate in-phase and out-of-phase states, corresponding to acoustic and optical precession modes. Withinthis context, our study explores the spin pumping effect in Py/Ru/Py synthetic antiferromagnetic structures across differentmodes. The heightened magnetic damping resulting from the spin pumping effect in the in-phase state initially decreaseswith increasing Py thickness before stabilizing. Conversely, in the out-of-phase state, the amplified damping exceeds thatof the in-phase state, suggesting a greater spin relaxation within this configuration, which demonstrates sensitivity to alterationsin static exchange interactions. These findings contribute to advancing the application of synthetic antiferromagneticstructures in magnonic devices.

Experimental Analysis of Radial Centrifugal Pump Shutdown

Centrifugal pumps are widely used in the metallurgy,coal,and building sectors.In order to study the hydraulic characteristics of a closed impeller centrifugal pump during its shutdown in the so-called power frequency and frequency conversion modes,experiments were carried to determine the characteristic evolution of parameters such as speed,inlet and outlet pressure,head,flow rate and shaft power.A quasi-steady-state method was also used to further investigate these transient behaviors.The results show that,compared to the power frequency input,the performance parameter curves for the frequency conversion input are less volatile and smoother.The characteristic time is longer and the response to shutdown is slower.The quasi-steady-state theoretical head-flow curves match the experimental head-flow curves more closely at low flow rates when the frequency conversion input is considered.Moreover,in this case,the similarity law predicts the hydraulic performance more accurately.

Simulation Method and Feature Analysis of Shutdown Pressure Evolution During Multi-Cluster Fracturing Stimulation

Multistage multi-cluster hydraulic fracturing has enabled the economic exploitation of shale reservoirs,but the interpretation of hydraulic fracture parameters is challenging.The pressure signals after pump shutdown are influenced by hydraulic fractures,which can reflect the geometric features of hydraulic fracture.The shutdown pressure can be used to interpret the hydraulic fracture parameters in a real-time and cost-effective manner.In this paper,a mathematical model for shutdown pressure evolution is developed considering the effects of wellbore friction,perforation friction and fluid loss in fractures.An efficient numerical simulation method is established by using the method of characteristics.Based on this method,the impacts of fracture half-length,fracture height,opened cluster and perforation number,and filtration coefficient on the evolution of shutdown pressure are analyzed.The results indicate that a larger fracture half-length may hasten the decay of shutdown pressure,while a larger fracture height can slow down the decay of shutdown pressure.A smaller number of opened clusters and perforations can significantly increase the perforation friction and decrease the overall level of shutdown pressure.A larger filtration coefficient may accelerate the fluid filtration in the fracture and hasten the drop of the shutdown pressure.The simulation method of shutdown pressure,as well as the analysis results,has important implications for the interpretation of hydraulic fracture parameters.

Pumpingline管道预制与安装研究——以2023 SH 二次配Pumping-line工程项目为例

本文以中芯南方集成电路制造有限公司负责的2023?SH?二次配Pumping-line工程项目为例,首先对Pumping line管道预制与安装流程进行了简单介绍。在此基础上,本文围绕机台Pumping line会勘、Pumping line管道测量绘图作业、Pumping line管道的预制与现场安装作业要素、机台Monve-in定位后Hook up相关作业要素等展开分析,希望为相关从业人员提供一定的参考。

Comparative Study of the Types of Photovoltaic Panels Used in Solar Pumping Systems in Dry Tropical Zones: Case of Adamawa Region in Cameroon

In the dry tropical zone where access to water is increasingly difficult for populations, solar pumping units are increasingly installed to provide water to population. In the local market, there are essentially two types of solar panels, namely monocrystalline and polycrystalline. However, the part of the local market is more dominated by the polycrystalline panel. In this work, comparative studies are carried out in order to characterize the two types of solar panels with regard to local constraints. Tests were carried out over the course of the sun to establish the performance of each type. The panels used have the same electrical characteristics and are connected to loads with same characteristics. Under the set operating conditions, the monocrystalline panel presents more performance than the polycrystalline panel. Although the local market is dominated by the polycrystalline panel, dust deposition tests on the surface of the panels show that the performance of the polycrystalline panel is more affected compared to the performance of the monocrystalline panel.

Adoption of Solar Pumping Systems by Vegetable Farmers in Niayes Agro-Ecological Zone of Senegal: Adoption as a Sequential Process

This article examines the determinants of the adoption of solar pumping systems (PV) by vegetable farmers in the Niayes area of Senegal. To measure the determinants, we used a sequential logit model to translate the adoption process from becoming aware of solar pumping systems to testing them, i.e. using them at least once, and then continuing to use them over time. The results show that the main variables affecting awareness of the use of solar pumping systems (PV) are age, marital status, experience, access to credit, the farmer’s knowledge of climate change, the farmer’s origin in the Thiès region and length of time in the Niayes area. The first use of PVs is influenced by factors such as the size of the plot, the distance of the plot from the main road or from the market. Finally, the decision to adopt or continue use is influenced by gender, experience, household size and access to credit. Surprisingly, access to credit does not affect the first use of solar pumping systems, but plays a key role in their continued use.

Using the curve moment and the PSO-SVM method to diagnose downhole conditions of a sucker rod pumping unit

Downhole working conditions of sucker rod pumping wells are automatically identified on a computer from the analysis of dynamometer cards. In this process, extraction of feature parameters and pattern classification are two key steps. The dynamometer card is firstly divided into four parts which include different production information according to the ＂four point method＂ used in actual oilfield production, and then the moment invariants for pattern recognition are extracted. An improved support vector machine （SVM） method is used for pattern classification whose error penalty parameter C and kernel function parameter g are optimally chosen by the particle swarm optimization （PSO） algorithm. The simulation results show the method proposed in this paper has good classification results.

Multiple fault diagnosis of down-hole conditions of sucker-rod pumping wells based on Freeman chain code and DCA

It is important to achieve continuous, stable and efficient pumping well operation in actual oilfield operation. Down-hole pumping well working conditions can be monitored in real-time and a reasonable production scheme can be designed when computer diagnosis is used. However, it is difficult to make a comprehensive analysis to supply efficient technical guidance for operation of the pumping well with multiple faults of down-hole conditions, which cannot be effectively dealt with by the common methods. To solve this problem, a method based on designated component analysis （DCA） is used in this paper. Freeman chain code is used to represent the down-hole dynamometer card whose important characteristics are extracted to construct a designated mode set. A control chart is used as a basis for fault detection. The upper and lower control lines on the control chart are determined from standard samples in normal working conditions. In an incompletely orthogonal mode, the designated mode set could be divided into some subsets in which the modes are completely orthogonal. The observed data is projected into each designated mode to realize fault detection according to the upper and lower control lines. The examples show that the proposed method can effectively diagnose multiple faults of down-hole conditions.

Fault diagnosis for down-hole conditions of sucker rod pumping systems based on the FBH-SC method

Dynamometer cards are commonly used to analyze down-hole working conditions of pumping systems in actual oil production. Nowadays, the traditional supervised learning methods heavily rely on the classification accuracy of the training samples. In order to reduce the errors of manual classification, an automatic clustering algorithm is proposed and applied to diagnose down-hole conditions of pumping systems. The spectral clustering （SC） is a new clustering algorithm, which is suitable for any data distribution. However, it is sensitive to initial cluster centers and scale parameters, and needs to predefine the cluster number. In order to overcome these shortcom- ings, we propose an automatic clustering algorithm, fast black hole-spectral clustering （FBH-SC）. The FBH algo- rithm is used to replace the K-mean method in SC, and a CritC index function is used as the target function to automatically choose the best scale parameter and clus- tering number in the clustering process. Different simulation experiments were designed to define the relationship among scale parameter, clustering number, CritC index value, and clustering accuracy. Finally, an example is given to validate the effectiveness of the proposed algorithm.

Few-shot working condition recognition of a sucker-rod pumping system based on a 4-dimensional time-frequency signature and meta-learning convolutional shrinkage neural network

The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.

Spin pumping by higher-order dipole-exchange spin-wave modes

Spin pumping(SP)and inverse spin Hall effect(ISHE)driven by parametrically-excited dipole-exchange spin waves in a yttrium iron garnet film have been systematically investigated.The measured voltage spectrum exhibits a feature of the field-induced transition from parallel pumping to perpendicular pumping because of the inhomogeneous excitation geometry.Thanks to the high precision of the SP-ISHE detection,two sets of fine structures in the voltage spectrum are observed,which can correspond well to two kinds of critical points in the multimode spin-wave spectrum for magnetic films.One is the q=0 point of each higher-order dispersion branch,and the other is the local minimum due to the interplay between the dipolar and exchange interactions.These fine structures on the voltage spectrum confirm the spin pumping by higher-order dipole-exchange spin-wave modes,and are helpful for probing the multimode spin-wave spectrum.

A cladding-pumping based power-scaled noise-like and dissipative soliton pulse fiber laser

We report a high-average-power noise-like pulse(NLP) and dissipative soliton(DS) pulse fiber laser. Average power as high as 4.8 W could be obtained at the fundamental mode-locked repetition rate. The NLP can also be transformed into a more powerful DS mode-locking state by optimizing the polarization and losses of intra-cavity pulses in the nonlinear polarization evolution regime. The operation mode between the NLP and DS can be switched, and the laser output performance in both modes has been studied. The main advantage of this work is switchable high-power operation between the NLP and DS. In comparison with conventional single-mode NLP fiber lasers, the multi-function high-power optical source will greatly push its application in supercontinuum generation, coherence tomography, and industrial processing.

Factors of Pumping Capacity in Some Different Types of Channel Brownian Pumps

The motion of particles in different channel Brownian pumps can be described by Langevin equations,and the pumping capacity is a useful indicator to demonstrate the strength of a pump’s transportation ability.Via the simulation,there is always an optimal value of temperature and unbiased external force for different pumps which make the concentration ratio between the right tube and left tube derive its maximum and minimum in two asymmetric tubes respectively.Besides,the concentration ratio will keep 1 regardless of radius,temperature or magnitude of force in the tube in a symmetric tube.To obtain more information about pumping capacity,exploring the average probability current(APC) of tubes in different conditions is necessary.Results indicate that as the concentration ratio is 1,the change of the APC when x_(0)=0 is similar to that when x_(0)=π.Also,when the concentration ratio is more than 1,there are optimal values of temperature,radius and magnitude of force where the APC gains a maximum,and the maximum decreases as the concentration in the right tube increases when x_(0)=0.

Design and Operation of Municipal Rain and Sewage United Pumping Station

In the process of designing a municipal rainwater and sewage pumping station,it is necessary to accurately grasp the design points,reasonably determine the scale of the rainwater and sewage pumping station,scientifically select the location and layout,and select materials reasonably.After completing the process design of the municipal rainwater and sewage pumping station and putting it into operation,in order to ensure the normal and stable operation of the pumping station,it is necessary to focus on strengthening the operation and management of diving equipment,cleaning equipment,and electromechanical equipment.The author analyzes the key points and specific design path of the process design of municipal rainwater and sewage pumping stations,and puts forward effective strategies for operation management,hoping to contribute to the scientific design and stable operation of municipal rainwater and sewage pumping stations.

Petri网语言的Pumping引理

Petri网语言是Petri网理论的重要组成部分,也是系统行为分析的一种重要的工具.Petri网语言的Pumping引理反映了Petri网语言的共性,可用来证明某些语言不是Petri网语言.已经证明,当一个Petri网语言可被某个有界Petri网产生时,此语言是正规语言,因此,正规语言的Pumping引理对此语言是有效的,但正规语言的Pumping引理并不适用于所有的Petri网语言.文中给出了一种Petri网语言的Pumping引理,证明其对任意无空标注的Petri网语言都有效,并且正规语言的Pumping引理是此引理的一种特殊形式.利用此Pumping引理可以证明某些语言是不能由Petri网产生的.

Studies on the Proton Pumping Activity of H^+-ATPase in Tonoplast Vesicles of Populus euphratica

Tonoplast-enriched vesicles were prepared from suspension-cultured Populus euphratica Oliv. cells by differential centrifugation and discontinuous sucrose density gradient centrifugation. The properties of the proton pumping activity of H+-ATPases in tonoplast vesicles were studied by acridine orange fluorescent quenching measured at 22 degreesC. The proton pumping activity of ATPase was ATP-dependent with apparent Michaelis-Menten Constant (K-m) for ATP about 0.65 mmol/L. The optimal pH for H+-ATPases activity was 7.5. The proton pumping activity of H+-ATPase could be initiated by some divalent cations, Mg2+ being highly efficient, much more than Fe2+; and Ca2+, Cu2+ and Zn2+ were inefficient under the experimental condition. The proton translocation could be stimulated by halide anions, with potencies decreasing in the order Cl- > Br- > I- > F-. The proton pumping activity was greatly inhibited by N-ethylmaleimide (NEM), N, N'-dicyclohexylcarbodiimide (DCCD), NO3- and Bafilomycin A(1), but not by orthovanadate and azide. These results demonstrated that the H+-ATPase in the tonoplast of Populus euphratica belonged to vacuolar type ATPase. This work was the first time that tonoplast-enriched vesicles were isolated from Populus euphratica cells.

Effects of proton pump inhibitors on inflammatory bowel disease:An updated review

Inflammatory bowel disease(IBD)is believed to be caused by various factors,including abnormalities in disease susceptibility genes,environmental factors,immune factors,and intestinal bacteria.Proton pump inhibitors(PPIs)are the primary drugs used to treat acid-related diseases.They are also commonly prescribed to patients with IBD.Recent studies have suggested a potential association between the use of certain medications,such as PPIs,and the occurrence and progression of IBD.In this review,we summarize the potential impact of PPIs on IBD and analyze the underlying mechanisms.Our findings may provide insights for conducting further investigations into the effects of PPIs on IBD and serve as an important reminder for physicians to exercise caution when prescribing PPIs to patients with IBD.

Pumping引理的Petri网描述──Petri网语言属型的一组判定条件

本文通过Ｐｕｍｐｉｎｇ引理在Ｐｅｔｒｉ网的变迁节引发序列中的反映，揭示了对应的Ｐｅｔｒｉ网结构内涵，从而给出了判定一个标准Ｐｅｔｒｉ网产生的语言分别为正规语言或上下文无关语言的充要条件．这样，就可以从网的结构直接判断其语言属型．