Pooling,unpooling/specialization,and discretionary task completion are typical operational strategies in queueing systems that arise in healthcare,call centers,and online sales.These strategies may have advantages and...Pooling,unpooling/specialization,and discretionary task completion are typical operational strategies in queueing systems that arise in healthcare,call centers,and online sales.These strategies may have advantages and disadvantages in different operational environments.This paper uses the M/M/1 and M/M/2 queues to study the impact of pooling,specialization,and discretionary task completion on the average queue length.Closed-form solutions for the average M/M/2 queue length are derived.Computational examples illustrate how the average queue length changes with the strength of pooling,specialization,and discretionary task completion.Finally,several conjectures are made in the paper.展开更多
Aiming at the problems of large load of rotation drive system,low efficiency of torque transmission and high cost for operation and maintenance of liner steering drilling system for the horizontal well,a new method of...Aiming at the problems of large load of rotation drive system,low efficiency of torque transmission and high cost for operation and maintenance of liner steering drilling system for the horizontal well,a new method of liner differential rotary drilling with double tubular strings in the horizontal well is proposed.The technical principle of this method is revealed,supporting tools such as the differential rotation transducer,composite rotary steering system and the hanger are designed,and technological process is optimized.A tool face control technique of steering drilling assembly is proposed and the calculation model of extension limit of liner differential rotary drilling with double tubular strings in horizontal well is established.These results show that the liner differential rotary drilling with double tubular strings is equipped with measurement while drilling(MWD)and positive displacement motor(PDM),and directional drilling of horizontal well is realized by adjusting rotary speed of drill pipe to control the tool face of PDM.Based on the engineering case of deep coalbed methane horizontal well in the eastern margin of Ordos Basin,the extension limit of horizontal drilling with double tubular strings is calculated.Compared with the conventional liner drilling method,the liner differential rotary drilling with double tubular strings increases the extension limit value of horizontal well significantly.The research findings provide useful reference for the integrated design and control of liner completion and drilling of horizontal wells.展开更多
Utilizing graph neural networks for knowledge embedding to accomplish the task of knowledge graph completion(KGC)has become an important research area in knowledge graph completion.However,the number of nodes in the k...Utilizing graph neural networks for knowledge embedding to accomplish the task of knowledge graph completion(KGC)has become an important research area in knowledge graph completion.However,the number of nodes in the knowledge graph increases exponentially with the depth of the tree,whereas the distances of nodes in Euclidean space are second-order polynomial distances,whereby knowledge embedding using graph neural networks in Euclidean space will not represent the distances between nodes well.This paper introduces a novel approach called hyperbolic hierarchical graph attention network(H2GAT)to rectify this limitation.Firstly,the paper conducts knowledge representation in the hyperbolic space,effectively mitigating the issue of exponential growth of nodes with tree depth and consequent information loss.Secondly,it introduces a hierarchical graph atten-tion mechanism specifically designed for the hyperbolic space,allowing for enhanced capture of the network structure inherent in the knowledge graph.Finally,the efficacy of the proposed H2GAT model is evaluated on benchmark datasets,namely WN18RR and FB15K-237,thereby validating its effectiveness.The H2GAT model achieved 0.445,0.515,and 0.586 in the Hits@1,Hits@3 and Hits@10 metrics respectively on the WN18RR dataset and 0.243,0.367 and 0.518 on the FB15K-237 dataset.By incorporating hyperbolic space embedding and hierarchical graph attention,the H2GAT model successfully addresses the limitations of existing hyperbolic knowledge embedding models,exhibiting its competence in knowledge graph completion tasks.展开更多
Background: The inadequacy in the completeness of the Laboratory Request Form (LRF) has been reported as one of the major sources of errors during the pre-analytical step of laboratory analysis. To prevent the occurre...Background: The inadequacy in the completeness of the Laboratory Request Form (LRF) has been reported as one of the major sources of errors during the pre-analytical step of laboratory analysis. To prevent the occurrence of such errors, this study aimed at assessing the level of completeness of LRFs. Methods: A retrospective analysis of laboratory request forms was conducted at the Clinical Biology Laboratory of the Kinshasa University Clinic, DR Congo, between November 2021 to May 2022. The LRFs were evaluated according to the completeness of all sections including administrative data of the patient, data of physician who ordered the test, relevant patient’s clinical data and data of the biological sample. Results: From a total of 2842 LRFs evaluated, none was fully completed with all required information. Particularly, patient’s clinical data including the medical history, provisional diagnosis and current treatment, were the most absent in 99% LRFs. However, two sections related to patient’s ID and prescribed test were informed in 100% LRFs. Conclusion: The results of this preanalytical audit can serve as an improvement opportunity focused on strengthening awareness about complete filling of LRF.展开更多
Current methods for predicting missing values in datasets often rely on simplistic approaches such as taking median value of attributes, limiting their applicability. Real-world observations can be diverse, taking sto...Current methods for predicting missing values in datasets often rely on simplistic approaches such as taking median value of attributes, limiting their applicability. Real-world observations can be diverse, taking stock price as example, ranging from prices post-IPO to values before a company’s collapse, or instances where certain data points are missing due to stock suspension. In this paper, we propose a novel approach using Nonlinear Matrix Completion (NIMC) and Deep Matrix Completion (DIMC) to predict associations, and conduct experiment on financial data between dates and stocks. Our method leverages various types of stock observations to capture latent factors explaining the observed date-stock associations. Notably, our approach is nonlinear, making it suitable for datasets with nonlinear structures, such as the Russell 3000. Unlike traditional methods that may suffer from information loss, NIMC and DIMC maintain nearly complete information, especially in high-dimensional parameters. We compared our approach with state-of-the-art linear methods, including Inductive Matrix Completion, Nonlinear Inductive Matrix Completion, and Deep Inductive Matrix Completion. Our findings show that the nonlinear matrix completion method is particularly effective for handling nonlinear structured data, as exemplified by the Russell 3000. Additionally, we validate the information loss of the three methods across different dimensionalities.展开更多
Patients with complete spinal cord injury retain the potential for volitional muscle activity in muscles located below the spinal injury level.However,because of prolonged inactivity,initial attempts to activate these...Patients with complete spinal cord injury retain the potential for volitional muscle activity in muscles located below the spinal injury level.However,because of prolonged inactivity,initial attempts to activate these muscles may not effectively engage any of the remaining neurons in the descending pathway.A previous study unexpectedly found that a brief clinical round of passive activity significantly increased volitional muscle activation,as measured by surface electromyography.In this study,we further explored the effect of passive activity on surface electromyographic signals during volitional control tasks among individuals with complete spinal cord injury.Eleven patients with chronic complete thoracic spinal cord injury were recruited.Surface electromyography data from eight major leg muscles were acquired and compared before and after the passive activity protocol.The results indicated that the passive activity led to an increased number of activated volitional muscles and an increased frequency of activation.Although the cumulative root mean square of surface electromyography amplitude for volitional control of movement showed a slight increase after passive activity,the difference was not statistically significant.These findings suggest that brief passive activity may enhance the ability to initiate volitional muscle activity during surface electromyography tasks and underscore the potential of passive activity for improving residual motor control among patients with motor complete spinal cord injury.展开更多
To evaluate the validity of "dialogue completion" item in the language testing, this article explores three contradictions by analyzing aspects of its characters, content, validity and reliability. The findi...To evaluate the validity of "dialogue completion" item in the language testing, this article explores three contradictions by analyzing aspects of its characters, content, validity and reliability. The finding is that "dialogue completion" patten has unpredicted answer and should be avoided in the testing as much as possible.展开更多
The efficient exploration and development of unconventional oil and gas are critical for increasing the self-sufficiency of oil and gas supplies in China.However,such operations continue to face serious problems(e.g.,...The efficient exploration and development of unconventional oil and gas are critical for increasing the self-sufficiency of oil and gas supplies in China.However,such operations continue to face serious problems(e.g.,borehole collapse,loss,and high friction),and associated formation damage can severely impact well completion rates,increase costs,and reduce efficiencies.Water-based drilling fluids possess certain advantages over oil-based drilling fluids(OBDFs)and may offer lasting solutions to resolve the aforementioned issues.However,a significant breakthrough with this material has not yet been made,and major technical problems continue to hinder the economic and large-scale development of unconventional oil and gas.Here,the international frontier external method,which only improves drilling fluid inhibition and lubricity,is expanded into an internal-external technique that improves the overall wellbore quality during drilling.Bionic technologies are introduced into the chemical material synthesis process to imitate the activity of life.A novel drilling and completion fluid technique was developed to improve wellbore quality during drilling and safeguard formation integrity.Macroscopic and microscopic analyses indicated that in terms of wellbore stability,lubricity,and formation protection,this approach could outperform methods that use typical OBDFs.The proposed method also achieves a classification upgrade from environmentally protective drilling fluid to an ecologically friendly drilling fluid.The developed technology was verified in more than 1000 unconventional oil and gas wells in China,and the results indicate significant alleviation of the formation damage attributed to borehole collapse,loss,and high friction.It has been recognized as an effective core technology for exploiting unconventional oil and gas resources.This study introduces a novel research direction for formation protection technology and demonstrates that observations and learning from the natural world can provide an inexhaustible source of ideas and inspire the creation of original materials,technologies,and theories for petroleum engineering.展开更多
In recent years, rapid progress in the use of high pressure water jets (HPWJ) has been made in oil and gas well drilling, completion, and stimulation; and good results have been achieved in field applications. Advan...In recent years, rapid progress in the use of high pressure water jets (HPWJ) has been made in oil and gas well drilling, completion, and stimulation; and good results have been achieved in field applications. Advances in technologies and developments of well completion and stimulation with hydrajet are reviewed in this paper. Experiments were conducted to study the characteristics of abrasive water jetting and to optimize jet parameters, which can provide methods for the well completion and hydrajet fracturing. Deep-penetrating hydrajet perforating can create a 2-3 m clean hole with a diameter of 20-35 mm. Multilayer hydrajet fracturing is a process whereby multiple layers are stimulated in a single run without using mechanical packers, thereby reducing operation procedure and risk. Multilateral radial wells can be drilled using hydraulic jetting up to 100 m in length. The technique to remove sand particles and plugs with rotating self-resonating cavitating water jets in horizontal wellbores has been developed and oilfield-tested, which shows promising, cost effective prospects.展开更多
The application of artificial intelligence(AI)has become inevitable in the petroleum industry.In drilling and completion engineering,AI is regarded as a transformative technology that can lower costs and significantly...The application of artificial intelligence(AI)has become inevitable in the petroleum industry.In drilling and completion engineering,AI is regarded as a transformative technology that can lower costs and significantly improve drilling efficiency(DE),In recent years,numerous studies have focused on intelligent algorithms and their application.Advanced technologies,such as digital twins and physics-guided neural networks,are expected to play roles in drilling and completion engineering.However,many challenges remain to be addressed,such as the automatic processing of multi-source and multi-scale data.Additionally,in intelligent drilling and completion,methods for the fusion of data-driven and physicsbased models,few-sample learning,uncertainty modeling,and the interpretability and transferability of intelligent algorithms are research frontiers.Based on intelligent application scenarios,this study comprehensively reviews the research status of intelligent drilling and completion and discusses key research areas in the future.This study aims to enhance the berthing of AI techniques in drilling and completion engineering.展开更多
Coalbed methane(CBM)drilling and completion technologies(DCTs)are signifcant basis for achieving efcient CBM exploration and exploitation.Characteristics of CBM reservoirs vary in diferent regions around the world,the...Coalbed methane(CBM)drilling and completion technologies(DCTs)are signifcant basis for achieving efcient CBM exploration and exploitation.Characteristics of CBM reservoirs vary in diferent regions around the world,thereby,it is crucial to develop,select and apply the optimum DCTs for each diferent CBM reservoir.This paper frstly reviews the development history of CBM DCTs throughout worldwide and clarifes its overall development tendency.Secondly,diferent well types and its characteristics of CBM exploitation are summarized,and main application scopes of these well types are also discussed.Then,the key technologies of CBM drilling(directional drilling tools,measurement while drilling,geo-steering drilling,magnetic guidance drilling,underbalanced drilling and drilling fuids),and the key technologies of CBM completion(open-hole,cavity and under-ream completion,cased-hole completion,screen pipe completion and horizontal well completion)are summarized and analyzed,it is found that safe,economic and efcient development of CBM is inseparable from the support of advanced technologies.Finally,based on the current status of CBM development,the achievements,existing challenges and future prospects are summarized and discussed from the perspective of CBM DCTs.展开更多
In modern datacenters, the most common method to solve the network latency problem is to minimize flow completion time during the transmission process. Following the soft real-time nature, the optimization of transpor...In modern datacenters, the most common method to solve the network latency problem is to minimize flow completion time during the transmission process. Following the soft real-time nature, the optimization of transport latency is relaxed to meet a flow's deadline in deadline-sensitive services. However, none of existing deadline-sensitive protocols consider deadline as a constraint condition of transmission.They can only simplify the objective of meeting a flow's deadline as a deadline-aware mechanism by assigning a higher priority for tight-deadline constrained flows to finish the transmission as soon as possible, which results in an unsatisfactory effect in the condition of high fan-in degree. It drives us to take a step back and rethink whether minimizing flow completion time is the optimal way in meeting flow's deadline. In this paper, we focus on the design of a soft real-time transport protocol with deadline constraint in datacenters and present a flow-based deadline scheduling scheme for datacenter networks(FBDS).FBDS makes the unilateral deadline-aware flow transmission with priority transform into a compound centralized single-machine deadlinebased flow scheduling decision. In addition, FBDS blocks the flow sets and postpones some flows with extra time until their deadlines to make room for the new arriving flows in order to improve the deadline meeting rate. Our simulation resultson flow completion time and deadline meeting rate reveal the potential of FBDS in terms of a considerable deadline-sensitive transport protocol for deadline-sensitive interactive services.展开更多
Most existing network representation learning algorithms focus on network structures for learning.However,network structure is only one kind of view and feature for various networks,and it cannot fully reflect all cha...Most existing network representation learning algorithms focus on network structures for learning.However,network structure is only one kind of view and feature for various networks,and it cannot fully reflect all characteristics of networks.In fact,network vertices usually contain rich text information,which can be well utilized to learn text-enhanced network representations.Meanwhile,Matrix-Forest Index(MFI)has shown its high effectiveness and stability in link prediction tasks compared with other algorithms of link prediction.Both MFI and Inductive Matrix Completion(IMC)are not well applied with algorithmic frameworks of typical representation learning methods.Therefore,we proposed a novel semi-supervised algorithm,tri-party deep network representation learning using inductive matrix completion(TDNR).Based on inductive matrix completion algorithm,TDNR incorporates text features,the link certainty degrees of existing edges and the future link probabilities of non-existing edges into network representations.The experimental results demonstrated that TFNR outperforms other baselines on three real-world datasets.The visualizations of TDNR show that proposed algorithm is more discriminative than other unsupervised approaches.展开更多
Horizontal wells are commonly used in bottom water reservoirs,which can increase contact area between wellbores and reservoirs.There are many completion methods used to control cresting,among which variable density pe...Horizontal wells are commonly used in bottom water reservoirs,which can increase contact area between wellbores and reservoirs.There are many completion methods used to control cresting,among which variable density perforation is an effective one.It is difficult to evaluate well productivity and to analyze inflow profiles of horizontal wells with quantities of unevenly distributed perforations,which are characterized by different parameters.In this paper,fluid flow in each wellbore perforation,as well as the reservoir,was analyzed.A comprehensive model,coupling the fluid flow in the reservoir and the wellbore pressure drawdown,was developed based on potential functions and solved using the numerical discrete method.Then,a bottom water cresting model was established on the basis of the piston-like displacement principle.Finally,bottom water cresting parameters and factors influencing inflow profile were analyzed.A more systematic optimization method was proposed by introducing the concept of cumulative free-water production,which could maintain a balance(or then a balance is achieved)between stabilizing oil production and controlling bottom water cresting.Results show that the inflow profile is affected by the perforation distribution.Wells with denser perforation density at the toe end and thinner density at the heel end may obtain low production,but the water breakthrough time is delayed.Taking cumulative free-water production as a parameter to evaluate perforation strategies is advisable in bottom water reservoirs.展开更多
An accelerated singular value thresholding (SVT) algorithm was introduced for matrix completion in a recent paper [1], which applies an adaptive line search scheme and improves the convergence rate from O(1/N) for SVT...An accelerated singular value thresholding (SVT) algorithm was introduced for matrix completion in a recent paper [1], which applies an adaptive line search scheme and improves the convergence rate from O(1/N) for SVT to O(1/N2), where N is the number of iterations. In this paper, we show that it is the same as the Nemirovski’s approach, and then modify it to obtain an accelerate Nemirovski’s technique and prove the convergence. Our preliminary computational results are very favorable.展开更多
A class of nonidentical parallel machine scheduling problems are considered in which the goal is to minimize the total weighted completion time. Models and relaxations are collected. Most of these problems are NP-hard...A class of nonidentical parallel machine scheduling problems are considered in which the goal is to minimize the total weighted completion time. Models and relaxations are collected. Most of these problems are NP-hard, in the strong sense, or open problems, therefore approximation algorithms are studied. The review reveals that there exist some potential areas worthy of further research.展开更多
Formate drilling and completion fluid system is a new type of clean organic salt brine system which has been developed from inorganic salt brine drilling fluid system. It is beneficial to protecte and find hydrocarbon...Formate drilling and completion fluid system is a new type of clean organic salt brine system which has been developed from inorganic salt brine drilling fluid system. It is beneficial to protecte and find hydrocarbon reservoir. Due to the solid free system, the damage of solid phase particles on reservoir, especially low permeability oil and gas layer, can be greatly eliminated, at the same time, drilling fluid and completion fluid have greater compatibility. It will avoid that precipitation which is not compatible with drilling and completion fluid and generates damages on reservoir. And because mud cake of the solid free system is thin and resilient, it is conductive to improve cementing quality greatly. Experiments show that the formate drilling and completion system has good rheological property, strong inhibition ability, good lubricating performance, good compatibility with reservoir rocks and formation water at high temperature.展开更多
To solve the problem of missing many valid triples in knowledge graphs(KGs),a novel model based on a convolutional neural network(CNN)called ConvKG is proposed,which employs a joint learning strategy for knowledge gra...To solve the problem of missing many valid triples in knowledge graphs(KGs),a novel model based on a convolutional neural network(CNN)called ConvKG is proposed,which employs a joint learning strategy for knowledge graph completion(KGC).Related research work has shown the superiority of convolutional neural networks(CNNs)in extracting semantic features of triple embeddings.However,these researches use only one single-shaped filter and fail to extract semantic features of different granularity.To solve this problem,ConvKG exploits multi-shaped filters to co-convolute on the triple embeddings,joint learning semantic features of different granularity.Different shaped filters cover different sizes on the triple embeddings and capture pairwise interactions of different granularity among triple elements.Experimental results confirm the strength of joint learning,and compared with state-of-the-art CNN-based KGC models,ConvKG achieves the better mean rank(MR)and Hits@10 metrics on dataset WN18 RR,and the better MR on dataset FB15k-237.展开更多
文摘Pooling,unpooling/specialization,and discretionary task completion are typical operational strategies in queueing systems that arise in healthcare,call centers,and online sales.These strategies may have advantages and disadvantages in different operational environments.This paper uses the M/M/1 and M/M/2 queues to study the impact of pooling,specialization,and discretionary task completion on the average queue length.Closed-form solutions for the average M/M/2 queue length are derived.Computational examples illustrate how the average queue length changes with the strength of pooling,specialization,and discretionary task completion.Finally,several conjectures are made in the paper.
基金Supported by the Project of National Natural Science Foundation of China(52234002,42230814)。
文摘Aiming at the problems of large load of rotation drive system,low efficiency of torque transmission and high cost for operation and maintenance of liner steering drilling system for the horizontal well,a new method of liner differential rotary drilling with double tubular strings in the horizontal well is proposed.The technical principle of this method is revealed,supporting tools such as the differential rotation transducer,composite rotary steering system and the hanger are designed,and technological process is optimized.A tool face control technique of steering drilling assembly is proposed and the calculation model of extension limit of liner differential rotary drilling with double tubular strings in horizontal well is established.These results show that the liner differential rotary drilling with double tubular strings is equipped with measurement while drilling(MWD)and positive displacement motor(PDM),and directional drilling of horizontal well is realized by adjusting rotary speed of drill pipe to control the tool face of PDM.Based on the engineering case of deep coalbed methane horizontal well in the eastern margin of Ordos Basin,the extension limit of horizontal drilling with double tubular strings is calculated.Compared with the conventional liner drilling method,the liner differential rotary drilling with double tubular strings increases the extension limit value of horizontal well significantly.The research findings provide useful reference for the integrated design and control of liner completion and drilling of horizontal wells.
基金the Beijing Municipal Science and Technology Program(No.Z231100001323004).
文摘Utilizing graph neural networks for knowledge embedding to accomplish the task of knowledge graph completion(KGC)has become an important research area in knowledge graph completion.However,the number of nodes in the knowledge graph increases exponentially with the depth of the tree,whereas the distances of nodes in Euclidean space are second-order polynomial distances,whereby knowledge embedding using graph neural networks in Euclidean space will not represent the distances between nodes well.This paper introduces a novel approach called hyperbolic hierarchical graph attention network(H2GAT)to rectify this limitation.Firstly,the paper conducts knowledge representation in the hyperbolic space,effectively mitigating the issue of exponential growth of nodes with tree depth and consequent information loss.Secondly,it introduces a hierarchical graph atten-tion mechanism specifically designed for the hyperbolic space,allowing for enhanced capture of the network structure inherent in the knowledge graph.Finally,the efficacy of the proposed H2GAT model is evaluated on benchmark datasets,namely WN18RR and FB15K-237,thereby validating its effectiveness.The H2GAT model achieved 0.445,0.515,and 0.586 in the Hits@1,Hits@3 and Hits@10 metrics respectively on the WN18RR dataset and 0.243,0.367 and 0.518 on the FB15K-237 dataset.By incorporating hyperbolic space embedding and hierarchical graph attention,the H2GAT model successfully addresses the limitations of existing hyperbolic knowledge embedding models,exhibiting its competence in knowledge graph completion tasks.
文摘Background: The inadequacy in the completeness of the Laboratory Request Form (LRF) has been reported as one of the major sources of errors during the pre-analytical step of laboratory analysis. To prevent the occurrence of such errors, this study aimed at assessing the level of completeness of LRFs. Methods: A retrospective analysis of laboratory request forms was conducted at the Clinical Biology Laboratory of the Kinshasa University Clinic, DR Congo, between November 2021 to May 2022. The LRFs were evaluated according to the completeness of all sections including administrative data of the patient, data of physician who ordered the test, relevant patient’s clinical data and data of the biological sample. Results: From a total of 2842 LRFs evaluated, none was fully completed with all required information. Particularly, patient’s clinical data including the medical history, provisional diagnosis and current treatment, were the most absent in 99% LRFs. However, two sections related to patient’s ID and prescribed test were informed in 100% LRFs. Conclusion: The results of this preanalytical audit can serve as an improvement opportunity focused on strengthening awareness about complete filling of LRF.
文摘Current methods for predicting missing values in datasets often rely on simplistic approaches such as taking median value of attributes, limiting their applicability. Real-world observations can be diverse, taking stock price as example, ranging from prices post-IPO to values before a company’s collapse, or instances where certain data points are missing due to stock suspension. In this paper, we propose a novel approach using Nonlinear Matrix Completion (NIMC) and Deep Matrix Completion (DIMC) to predict associations, and conduct experiment on financial data between dates and stocks. Our method leverages various types of stock observations to capture latent factors explaining the observed date-stock associations. Notably, our approach is nonlinear, making it suitable for datasets with nonlinear structures, such as the Russell 3000. Unlike traditional methods that may suffer from information loss, NIMC and DIMC maintain nearly complete information, especially in high-dimensional parameters. We compared our approach with state-of-the-art linear methods, including Inductive Matrix Completion, Nonlinear Inductive Matrix Completion, and Deep Inductive Matrix Completion. Our findings show that the nonlinear matrix completion method is particularly effective for handling nonlinear structured data, as exemplified by the Russell 3000. Additionally, we validate the information loss of the three methods across different dimensionalities.
基金supported by the Fundamental Research Funds for Central Public Welfare Research Institute,No.2020CZ-5(to WS and GS)the National Natural Science Foundation of China,No.31970970(to JSR)Fundamental Research Funds for the Central Universities,No.YWF-23-YG-QB-010(to JSR)。
文摘Patients with complete spinal cord injury retain the potential for volitional muscle activity in muscles located below the spinal injury level.However,because of prolonged inactivity,initial attempts to activate these muscles may not effectively engage any of the remaining neurons in the descending pathway.A previous study unexpectedly found that a brief clinical round of passive activity significantly increased volitional muscle activation,as measured by surface electromyography.In this study,we further explored the effect of passive activity on surface electromyographic signals during volitional control tasks among individuals with complete spinal cord injury.Eleven patients with chronic complete thoracic spinal cord injury were recruited.Surface electromyography data from eight major leg muscles were acquired and compared before and after the passive activity protocol.The results indicated that the passive activity led to an increased number of activated volitional muscles and an increased frequency of activation.Although the cumulative root mean square of surface electromyography amplitude for volitional control of movement showed a slight increase after passive activity,the difference was not statistically significant.These findings suggest that brief passive activity may enhance the ability to initiate volitional muscle activity during surface electromyography tasks and underscore the potential of passive activity for improving residual motor control among patients with motor complete spinal cord injury.
文摘To evaluate the validity of "dialogue completion" item in the language testing, this article explores three contradictions by analyzing aspects of its characters, content, validity and reliability. The finding is that "dialogue completion" patten has unpredicted answer and should be avoided in the testing as much as possible.
基金supported by the National Natural Science Foundation of China Youth Science Fund Project(52004297)China Postdoctoral Innovative Talent Support Program(BX20200384)。
文摘The efficient exploration and development of unconventional oil and gas are critical for increasing the self-sufficiency of oil and gas supplies in China.However,such operations continue to face serious problems(e.g.,borehole collapse,loss,and high friction),and associated formation damage can severely impact well completion rates,increase costs,and reduce efficiencies.Water-based drilling fluids possess certain advantages over oil-based drilling fluids(OBDFs)and may offer lasting solutions to resolve the aforementioned issues.However,a significant breakthrough with this material has not yet been made,and major technical problems continue to hinder the economic and large-scale development of unconventional oil and gas.Here,the international frontier external method,which only improves drilling fluid inhibition and lubricity,is expanded into an internal-external technique that improves the overall wellbore quality during drilling.Bionic technologies are introduced into the chemical material synthesis process to imitate the activity of life.A novel drilling and completion fluid technique was developed to improve wellbore quality during drilling and safeguard formation integrity.Macroscopic and microscopic analyses indicated that in terms of wellbore stability,lubricity,and formation protection,this approach could outperform methods that use typical OBDFs.The proposed method also achieves a classification upgrade from environmentally protective drilling fluid to an ecologically friendly drilling fluid.The developed technology was verified in more than 1000 unconventional oil and gas wells in China,and the results indicate significant alleviation of the formation damage attributed to borehole collapse,loss,and high friction.It has been recognized as an effective core technology for exploiting unconventional oil and gas resources.This study introduces a novel research direction for formation protection technology and demonstrates that observations and learning from the natural world can provide an inexhaustible source of ideas and inspire the creation of original materials,technologies,and theories for petroleum engineering.
基金The authors express their appreciation to the National Natural Science Foundation of China (No.50774089)the High-tech Research and Development Program of China (No.2007AA09Z315) for the fi nancial support of this work
文摘In recent years, rapid progress in the use of high pressure water jets (HPWJ) has been made in oil and gas well drilling, completion, and stimulation; and good results have been achieved in field applications. Advances in technologies and developments of well completion and stimulation with hydrajet are reviewed in this paper. Experiments were conducted to study the characteristics of abrasive water jetting and to optimize jet parameters, which can provide methods for the well completion and hydrajet fracturing. Deep-penetrating hydrajet perforating can create a 2-3 m clean hole with a diameter of 20-35 mm. Multilayer hydrajet fracturing is a process whereby multiple layers are stimulated in a single run without using mechanical packers, thereby reducing operation procedure and risk. Multilateral radial wells can be drilled using hydraulic jetting up to 100 m in length. The technique to remove sand particles and plugs with rotating self-resonating cavitating water jets in horizontal wellbores has been developed and oilfield-tested, which shows promising, cost effective prospects.
基金support of the National Key Research and Development Project of China(2019YFA0708300)National Science Fund for Distinguished Young Scholars of China(52125401)National Natural Science Foundation of China(L1924060)。
文摘The application of artificial intelligence(AI)has become inevitable in the petroleum industry.In drilling and completion engineering,AI is regarded as a transformative technology that can lower costs and significantly improve drilling efficiency(DE),In recent years,numerous studies have focused on intelligent algorithms and their application.Advanced technologies,such as digital twins and physics-guided neural networks,are expected to play roles in drilling and completion engineering.However,many challenges remain to be addressed,such as the automatic processing of multi-source and multi-scale data.Additionally,in intelligent drilling and completion,methods for the fusion of data-driven and physicsbased models,few-sample learning,uncertainty modeling,and the interpretability and transferability of intelligent algorithms are research frontiers.Based on intelligent application scenarios,this study comprehensively reviews the research status of intelligent drilling and completion and discusses key research areas in the future.This study aims to enhance the berthing of AI techniques in drilling and completion engineering.
基金supported by the Youth Scientific and Technological Innovation Team Foundation of Southwest Petroleum University(2019CXTD09)the Program of Introducing Talents of Discipline to Chinese Universities(111 Plan)(D18016).
文摘Coalbed methane(CBM)drilling and completion technologies(DCTs)are signifcant basis for achieving efcient CBM exploration and exploitation.Characteristics of CBM reservoirs vary in diferent regions around the world,thereby,it is crucial to develop,select and apply the optimum DCTs for each diferent CBM reservoir.This paper frstly reviews the development history of CBM DCTs throughout worldwide and clarifes its overall development tendency.Secondly,diferent well types and its characteristics of CBM exploitation are summarized,and main application scopes of these well types are also discussed.Then,the key technologies of CBM drilling(directional drilling tools,measurement while drilling,geo-steering drilling,magnetic guidance drilling,underbalanced drilling and drilling fuids),and the key technologies of CBM completion(open-hole,cavity and under-ream completion,cased-hole completion,screen pipe completion and horizontal well completion)are summarized and analyzed,it is found that safe,economic and efcient development of CBM is inseparable from the support of advanced technologies.Finally,based on the current status of CBM development,the achievements,existing challenges and future prospects are summarized and discussed from the perspective of CBM DCTs.
基金supported in part by the Fundamental Research Funds for the Central Universities under Grant No.2014JBM011 and No.2014YJS021in part by NSFC under Grant No.62171200,61422101,and 62132017+2 种基金in part by the Ph.D.Programs Foundation of MOE of China under Grant No.20130009110014in part by "NCET" under Grant No.NCET-12-0767in part by China Postdoctoral Science Foundation under Grant No.2015M570028,2015M580970
文摘In modern datacenters, the most common method to solve the network latency problem is to minimize flow completion time during the transmission process. Following the soft real-time nature, the optimization of transport latency is relaxed to meet a flow's deadline in deadline-sensitive services. However, none of existing deadline-sensitive protocols consider deadline as a constraint condition of transmission.They can only simplify the objective of meeting a flow's deadline as a deadline-aware mechanism by assigning a higher priority for tight-deadline constrained flows to finish the transmission as soon as possible, which results in an unsatisfactory effect in the condition of high fan-in degree. It drives us to take a step back and rethink whether minimizing flow completion time is the optimal way in meeting flow's deadline. In this paper, we focus on the design of a soft real-time transport protocol with deadline constraint in datacenters and present a flow-based deadline scheduling scheme for datacenter networks(FBDS).FBDS makes the unilateral deadline-aware flow transmission with priority transform into a compound centralized single-machine deadlinebased flow scheduling decision. In addition, FBDS blocks the flow sets and postpones some flows with extra time until their deadlines to make room for the new arriving flows in order to improve the deadline meeting rate. Our simulation resultson flow completion time and deadline meeting rate reveal the potential of FBDS in terms of a considerable deadline-sensitive transport protocol for deadline-sensitive interactive services.
基金Projects(11661069,61763041) supported by the National Natural Science Foundation of ChinaProject(IRT_15R40) supported by Changjiang Scholars and Innovative Research Team in University,ChinaProject(2017TS045) supported by the Fundamental Research Funds for the Central Universities,China
文摘Most existing network representation learning algorithms focus on network structures for learning.However,network structure is only one kind of view and feature for various networks,and it cannot fully reflect all characteristics of networks.In fact,network vertices usually contain rich text information,which can be well utilized to learn text-enhanced network representations.Meanwhile,Matrix-Forest Index(MFI)has shown its high effectiveness and stability in link prediction tasks compared with other algorithms of link prediction.Both MFI and Inductive Matrix Completion(IMC)are not well applied with algorithmic frameworks of typical representation learning methods.Therefore,we proposed a novel semi-supervised algorithm,tri-party deep network representation learning using inductive matrix completion(TDNR).Based on inductive matrix completion algorithm,TDNR incorporates text features,the link certainty degrees of existing edges and the future link probabilities of non-existing edges into network representations.The experimental results demonstrated that TFNR outperforms other baselines on three real-world datasets.The visualizations of TDNR show that proposed algorithm is more discriminative than other unsupervised approaches.
文摘Horizontal wells are commonly used in bottom water reservoirs,which can increase contact area between wellbores and reservoirs.There are many completion methods used to control cresting,among which variable density perforation is an effective one.It is difficult to evaluate well productivity and to analyze inflow profiles of horizontal wells with quantities of unevenly distributed perforations,which are characterized by different parameters.In this paper,fluid flow in each wellbore perforation,as well as the reservoir,was analyzed.A comprehensive model,coupling the fluid flow in the reservoir and the wellbore pressure drawdown,was developed based on potential functions and solved using the numerical discrete method.Then,a bottom water cresting model was established on the basis of the piston-like displacement principle.Finally,bottom water cresting parameters and factors influencing inflow profile were analyzed.A more systematic optimization method was proposed by introducing the concept of cumulative free-water production,which could maintain a balance(or then a balance is achieved)between stabilizing oil production and controlling bottom water cresting.Results show that the inflow profile is affected by the perforation distribution.Wells with denser perforation density at the toe end and thinner density at the heel end may obtain low production,but the water breakthrough time is delayed.Taking cumulative free-water production as a parameter to evaluate perforation strategies is advisable in bottom water reservoirs.
文摘An accelerated singular value thresholding (SVT) algorithm was introduced for matrix completion in a recent paper [1], which applies an adaptive line search scheme and improves the convergence rate from O(1/N) for SVT to O(1/N2), where N is the number of iterations. In this paper, we show that it is the same as the Nemirovski’s approach, and then modify it to obtain an accelerate Nemirovski’s technique and prove the convergence. Our preliminary computational results are very favorable.
基金the National Natural Science Foundation of China (70631003)the Hefei University of Technology Foundation (071102F).
文摘A class of nonidentical parallel machine scheduling problems are considered in which the goal is to minimize the total weighted completion time. Models and relaxations are collected. Most of these problems are NP-hard, in the strong sense, or open problems, therefore approximation algorithms are studied. The review reveals that there exist some potential areas worthy of further research.
文摘Formate drilling and completion fluid system is a new type of clean organic salt brine system which has been developed from inorganic salt brine drilling fluid system. It is beneficial to protecte and find hydrocarbon reservoir. Due to the solid free system, the damage of solid phase particles on reservoir, especially low permeability oil and gas layer, can be greatly eliminated, at the same time, drilling fluid and completion fluid have greater compatibility. It will avoid that precipitation which is not compatible with drilling and completion fluid and generates damages on reservoir. And because mud cake of the solid free system is thin and resilient, it is conductive to improve cementing quality greatly. Experiments show that the formate drilling and completion system has good rheological property, strong inhibition ability, good lubricating performance, good compatibility with reservoir rocks and formation water at high temperature.
基金Supported by the National Natural Science Foundation of China(No.61876144)。
文摘To solve the problem of missing many valid triples in knowledge graphs(KGs),a novel model based on a convolutional neural network(CNN)called ConvKG is proposed,which employs a joint learning strategy for knowledge graph completion(KGC).Related research work has shown the superiority of convolutional neural networks(CNNs)in extracting semantic features of triple embeddings.However,these researches use only one single-shaped filter and fail to extract semantic features of different granularity.To solve this problem,ConvKG exploits multi-shaped filters to co-convolute on the triple embeddings,joint learning semantic features of different granularity.Different shaped filters cover different sizes on the triple embeddings and capture pairwise interactions of different granularity among triple elements.Experimental results confirm the strength of joint learning,and compared with state-of-the-art CNN-based KGC models,ConvKG achieves the better mean rank(MR)and Hits@10 metrics on dataset WN18 RR,and the better MR on dataset FB15k-237.