Effects of pooling,specialization,and discretionary task completion on queueing performance

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.

Integrated design and control technology of liner completion and drilling for horizontal wells

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.

Hyperbolic hierarchical graph attention network for knowledge graph completion

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.

Non-Linear Matrix Completion

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.

Evaluation of Laboratory Request Forms Completion in a Tertiary Medical Laboratory of the Democratic Republic of Congo

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.

Seasonal variation and living alone are related to pulmonary rehabilitation non-completion

AIM: To identify baseline characteristics that independently predict pulmonary rehabilitation non-completion and compare these findings against the participant's reasons for non-completion. METHODS: Participants with chronic obstructive pulmonary disease(COPD) who attended a standardised twice weekly, eight week pulmonary rehabilitation program(located in the sub-tropics, latitude 27°29' South) between 2010 and 2012 were recruited. Thebaseline characteristics of program completers and non-completers were compared in a case-controlled design. Participants who attended < 12/16 sessions were classified as a non-completer. Non-completers(those who missed > 4 session of the program) were asked by one independent investigator to participate in a survey about their pulmonary rehabilitation experience. Baseline characteristics were assessed for differences between program completers and non-completers. The baseline characteristics included disease severity, exercise capacity, smoking history, participant's social support and the season when each participant commenced rehabilitation. Non-completers that agreed to participate in the survey were asked to indicate what personal factors or external factors contributed to their program non-completion. Comparisons of completers and non-completers baseline characteristics were performed using cross-tabulations and t-tests, with significant measures analysed in a multivariate binary logistic regression model. Non-completers survey responses were compared to the identified independent predictors using cross-tabulations.RESULTS: Twenty-six participants(23.4%) of the 111 participants with COPD [(mean ± SD) age was 67.4 ± 9.2 years and FEV1 54.6% ± 22.3%)], were classified as non-completers. Forty-five participants(40.5%) commenced pulmonary rehabilitation during winter. Thirty-six participants(32.4%) were living alone at program commencement. In the multivariate analysis(n = 111), only programs that commenced in winter(Exp B: 0.255, 95%CI: 0.090-0.727, P = 0.011) and participants that lived alone(Exp B: 2.925, 95%CI: 1.039-8.229, P = 0.042) were identified as independent predictors of program non-completion. Twenty participants of the twenty-six non-completers agreed to participate in the survey about their pulmonary rehabilitation experience. The reasons given for non-completion were grouped into: medical reasons(75%), other personal reasons(30%) and external barriers(45%), with ten non-completers reporting more than one reason.No participant reported living alone or that the program commenced during winter as a reason for noncompletion. There was no relationship between illness being the participant's reason for non-completion and the programs that commenced in winter(P = 0.135). CONCLUSION: Despite winter commencing programs and participants who lived alone being independent predictors of program non-completion, neither measure was reported by participants as a reason for noncompletion.

An Evaluation of "Dialogue Completion" in English Testing

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.

Novel Water-Based Drilling and Completion Fluid Technology to Improve Wellbore Quality During Drilling and Protect Unconventional Reservoirs

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.

Research and application of water jet technology in well completion and stimulation in China

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.

Intelligent Drilling and Completion:A Review

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.

Drilling and completion technologies of coalbed methane exploitation: an overview

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.

Is Minimizing Flow Completion Time the Optimal Way in Meeting Flow's Deadline in Datacenter Networks

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.

Tri-party deep network representation learning using inductive matrix completion

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.

A new mathematical model for horizontal wells with variable density perforation completion in bottom water reservoirs

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.

On Accelerated Singular Value Thresholding Algorithm for Matrix Completion

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.

Concise review of relaxations and approximation algorithms for nonidentical parallel-machine scheduling to minimize total weighted completion times

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.

The study of formulation and performance of formate drilling and completion fluid system

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.

Joint learning based on multi-shaped filters for knowledge graph completion

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.

5G Ultra-Dense Network Fingerprint Positioning Method Based on Matrix Completion

The problem of high-precision indoor positioning in the 5G era has attracted more and more attention.A fingerprint location method based on matrix completion(MC-FPL)is proposed for 5G ultradense networks to overcome the high costs of traditional fingerprint database construction and matching algorithms.First,a partial fingerprint database constructed and the accelerated proximal gradient algorithm is used to fill the partial fingerprint database to construct a full fingerprint database.Second,a fingerprint database division method based on the strongest received signal strength indicator is proposed,which divides the original fingerprint database into several sub-fingerprint databases.Finally,a classification weighted K-nearest neighbor fingerprint matching algorithm is proposed.The estimated coordinates of the point to be located can be obtained by fingerprint matching in a sub-fingerprint database.The simulation results show that the MC-FPL algorithm can reduce the complexity of database construction and fingerprint matching and has higher positioning accuracy compared with the traditional fingerprint algorithm.

A Singular Value Thresholding Based Matrix Completion Method for DOA Estimation in Nonuniform Noise

Usually,the problem of direction-of-arrival(DOA)estimation is performed based on the assumption of uniform noise.In many applications,however,the noise across the array may be nonuniform.In this situation,the performance of DOA estimators may be deteriorated greatly if the non-uniformity of noise is ignored.To tackle this problem,we consider the problem of DOA es-timation in the presence of nonuniform noise by leveraging a singular value thresholding(SVT)based matrix completion method.Different from that the traditional SVT method apply fixed threshold,to improve the performance,the proposed method can obtain a more suitable threshold based on careful estimation of the signal-to-noise ratio(SNR)levels.Specifically,we firstly employ an SVT-based matrix completion method to estimate the noise-free covariance matrix.On this basis,the signal and noise subspaces are obtained from the eigendecomposition of the noise-free cov-ariance matrix.Finally,traditional subspace-based DOA estimation approaches can be directly ap-plied to determine the DOAs.Numerical simulations are performed to demonstrate the effective-ness of the proposed method.