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.

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.

Completion of 6-mo isoniazid preventive treatment among eligible under six children:A cross-sectional study,Lagos,Nigeria

BACKGROUND Nigeria is one of the thirty high burden countries with significant contribution to the global childhood tuberculosis epidemic.Tuberculosis annual risk for children could be as high as 4%particularly in high tuberculosis(TB)prevalent communities.Isoniazid(INH)Preventive Therapy has been shown to prevent TB incidence but data on its implementation among children are scarce.AIM To determine the completion of INH among under six children that were exposed to adults with smear positive pulmonary TB in Lagos,Nigeria.METHODS This was a hospital-based retrospective cross-sectional review of 265 medical records of eligible children<6 years old enrolled for INH across 32 private hospitals in Lagos,Nigeria.The study took place between July and September 2020.Data was collected on independent variables(age,gender,type of facility,TB screening,dose and weight)and outcome variables(INH outcome and proportion lost to follow up across months 1-6 of INH treatment).RESULTS About 53.8%of the participants were female,95.4%were screened for TB and none was diagnosed of having TB.The participants’age ranged from 1 to 72 mo with a mean of 36.01±19.67 mo,and 40.2%were between the ages of 1-24 mo.Only 155(59.2%)of the 262 participants initiated on INH completed the six-month treatment.Cumulatively,107(41.0%)children were lost to follow-up at the end of the sixth month.Of the cumulative 107 loss to follow-up while on INH,largest drop-offs were reported at the end of month 2,52(49%)followed by 20(19%),17(16%),11(10.2%)and 7(6.5%)at months 3,4,5 and 6 respectively.The analysis showed that there was no significant association between age,gender,type of facility and completion of INH treatment(P>0.005).CONCLUSION This study demonstrated suboptimal INH completion rate among children with only 6 out of 10 children initiated on INH who completed a 6-mo treatment in Lagos,Nigeria.The huge drop-offs in the first 2 mo of INH calls for innovative strategies such as the use of 60-d INH calendar that would facilitate reminder and early engagement of children on INH and their caregivers in care and across the entire period of treatment.

High-temperature corrosion of phosphate completion fluid and corrosion inhibition method by membrane transformation

By analyzing the corrosion of phosphate completion fluid on the P110 steel at 170 °C, the high-temperature corrosion mechanism of phosphate completion fluid was revealed, and a corrosion inhibition method by membrane transformation was proposed and an efficient membrane-forming agent was selected. Scanning electron microscope (SEM) images, X-ray energy spectrum and X-ray diffraction results were used to characterize the microscopic morphology, elemental composition and phase composition of the precipitation membrane on the surface of the test piece. The effect and mechanism of corrosion inhibition by membrane transformation were clarified. The phosphate completion fluid eroded the test piece by high-temperature water vapor and its hydrolyzed products to form a membrane of iron phosphate corrosion product. By changing the corrosion reaction path, the Zn2+ membrane-forming agent could generate KZnPO4 precipitation membrane with high temperature resistance, uniform thickness and tight crystal packing on the surface of the test piece, which could inhibit the corrosion of the test piece, with efficiency up to 69.63%. The Cu2+ membrane-forming agent electrochemically reacted with Fe to precipitate trace elemental Cu on the surface of the test piece, thus forming a protective membrane, which could inhibit metal corrosion, with efficiency up to 96.64%, but the wear resistance was poor. After combining 0.05% Cu2+ and 0.25% Zn2+, a composite protective membrane of KZnPO4 crystal and elemental Cu was formed on the surface of the test piece. The corrosion inhibition efficiency reached 93.03%, which ensured the high corrosion inhibition efficiency and generated a precipitation membrane resistant to temperature and wear.

Design and feasibility analysis of a new completion monitoring technical scheme for natural gas hydrate production tests

As a prerequisite and a guarantee for safe and efficient natural gas hydrates(NGHs)exploitation,it is imperative to effectively determine the mechanical properties of NGHs reservoirs and clarify the law of the change in the mechanical properties with the dissociation of NGHs during NGHs production tests by depressurization.Based on the development of Japan’s two offshore NGHs production tests in vertical wells,this study innovatively proposed a new subsea communication technology-accurate directional connection using a wet-mate connector.This helps to overcome the technical barrier to the communication between the upper and lower completion of offshore wells.Using this new communication technology,this study explored and designed a mechanical monitoring scheme for lower completion(sand screens).This scheme can be used to monitor the tensile stress and radial compressive stress of sand screens caused by NGHs reservoirs in real time,thus promoting the technical development for the rapid assessment and real-time feedback of the in-situ mechanical response of NGHs reservoirs during offshore NGHs production tests by depressurization.

Passability test and simulation of sand control string with natural gas hydrates completion in large curvature hole

To meet the requirements of marine natural gas hydrate exploitation,it is necessary to improve the penetration of completion sand control string in the large curvature borehole.In this study,large curvature test wells were selected to carry out the running test of sand control string with pre-packed screen.Meanwhile,the running simulation was performed by using the Landmark software.The results show that the sand control packer and screen can be run smoothly in the wellbore with a dogleg angle of more than 20°/30 m and keep the structure stable.Additionally,the comprehensive friction coefficient is 0.4,under which and the simulation shows that the sand control string for hydrate exploitation can be run smoothly.These findings have important guiding significance for running the completion sand control string in natural gas hydrate exploitation.

Aquatic Medicine Knowledge Graph Completion Based on Hybrid Convolution

Aquatic medicine knowledge graph is an effective means to realize intelligent aquaculture.Graph completion technology is key to improving the quality of knowledge graph construction.However,the difficulty of semantic discrimination among similar entities and inconspicuous semantic features result in low accuracy when completing aquatic medicine knowledge graph with complex relationships.In this study,an aquatic medicine knowledge graph completion method(TransH+HConvAM)is proposed.Firstly,TransH is applied to split the vector plane between entities and relations,ameliorating the poor completion effect caused by low semantic resolution of entities.Then,hybrid convolution is introduced to obtain the global interaction of triples based on the complete interaction between head/tail entities and relations,which improves the semantic features of triples and enhances the completion effect of complex relationships in the graph.Experiments are conducted to verify the performance of the proposed method.The MR,MRR and Hit@10 of the TransH+HConvAM are found to be 674,0.339,and 0.361,respectively.This study shows that the model effectively overcomes the poor completion effect of complex relationships and improves the construction quality of the aquatic medicine knowledge graph,providing technical support for intelligent aquaculture.

Next Words Prediction and Sentence Completion in Bangla Language Using GRU-Based RNN on N-Gram Language Model

We use a lot of devices in our daily life to communicate with others. In this modern world, people use email, Facebook, Twitter, and many other social network sites for exchanging information. People lose their valuable time misspelling and retyping, and some people are not happy to type large sentences because they face unnecessary words or grammatical issues. So, for this reason, word predictive systems help to exchange textual information more quickly, easier, and comfortably for all people. These systems predict the next most probable words and give users to choose of the needed word from these suggested words. Word prediction can help the writer by predicting the next word and helping complete the sentence correctly. This research aims to forecast the most suitable next word to complete a sentence for any given context. In this research, we have worked on the Bangla language. We have presented a process that can expect the next maximum probable and proper words and suggest a complete sentence using predicted words. In this research, GRU-based RNN has been used on the N-gram dataset to develop the proposed model. We collected a large dataset using multiple sources in the Bangla language and also compared it to the other approaches that have been used such as LSTM, and Naive Bayes. But this suggested approach provides excellent exactness than others. Here, the Unigram model provides 88.22%, Bi-gram model is 99.24%, Tri-gram model is 97.69%, and 4-gram and 5-gram models provide 99.43% and 99.78% on average accurateness. We think that our proposed method profound impression on Bangla search engines.

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.