In this paper,we established a class of parallel algorithm for solving low-rank tensor completion problem.The main idea is that N singular value decompositions are implemented in N different processors for each slice ...In this paper,we established a class of parallel algorithm for solving low-rank tensor completion problem.The main idea is that N singular value decompositions are implemented in N different processors for each slice matrix under unfold operator,and then the fold operator is used to form the next iteration tensor such that the computing time can be decreased.In theory,we analyze the global convergence of the algorithm.In numerical experiment,the simulation data and real image inpainting are carried out.Experiment results show the parallel algorithm outperform its original algorithm in CPU times under the same precision.展开更多
Porters play a crucial role in hospitals because they ensure the efficient transportation of patients,medical equipment,and vital documents.Despite its importance,there is a lack of research addressing the prediction ...Porters play a crucial role in hospitals because they ensure the efficient transportation of patients,medical equipment,and vital documents.Despite its importance,there is a lack of research addressing the prediction of completion times for porter tasks.To address this gap,we utilized real-world porter delivery data from Taiwan University Hospital,China,Yunlin Branch,Taiwan Region of China.We first identified key features that can influence the duration of porter tasks.We then employed three widely-used machine learning algorithms:decision tree,random forest,and gradient boosting.To leverage the strengths of each algorithm,we finally adopted an ensemble modeling approach that aggregates their individual predictions.Our experimental results show that the proposed ensemble model can achieve a mean absolute error of 3 min in predicting task response time and 4.42 min in task completion time.The prediction error is around 50%lower compared to using only the historical average.These results demonstrate that our method significantly improves the accuracy of porter task time prediction,supporting better resource planning and patient care.It helps ward staff streamline workflows by reducing delays,enables porter managers to allocate resources more effectively,and shortens patient waiting times,contributing to a better care experience.展开更多
This study introduces a novel methodology and makes case studies for anomaly detection in multivariate oil production time-series data,utilizing a supervised Transformer algorithm to identify spurious events related t...This study introduces a novel methodology and makes case studies for anomaly detection in multivariate oil production time-series data,utilizing a supervised Transformer algorithm to identify spurious events related to interval control valves(ICVs)in intelligent well completions(IWC).Transformer algorithms present significant advantages in time-series anomaly detection,primarily due to their ability to handle data drift and capture complex patterns effectively.Their self-attention mechanism allows these models to adapt to shifts in data distribution over time,ensuring resilience against changes that can occur in time-series data.Additionally,Transformers excel at identifying intricate temporal dependencies and long-range interactions,which are often challenging for traditional models.Field tests conducted in the ultradeep water subsea wells of the Santos Basin further validate the model’s capability for early anomaly identification of ICVs,minimizing non-productive time and safeguarding well integrity.The model achieved an accuracy of 0.9544,a balanced accuracy of 0.9694 and an F1-Score of 0.9574,representing significant improvements over previous literature models.展开更多
The goal of point cloud completion is to reconstruct raw scanned point clouds acquired from incomplete observations due to occlusion and restricted viewpoints.Numerous methods use a partial-to-complete framework,direc...The goal of point cloud completion is to reconstruct raw scanned point clouds acquired from incomplete observations due to occlusion and restricted viewpoints.Numerous methods use a partial-to-complete framework,directly predicting missing components via global characteristics extracted from incomplete inputs.However,this makes detail re-covery challenging,as global characteristics fail to provide complete missing component specifics.A new point cloud completion method named Point-PC is proposed.A memory network and a causal inference model are separately designed to introduce shape priors and select absent shape information as supplementary geometric factors for aiding completion.Concretely,a memory mechanism is proposed to store complete shape features and their associated shapes in a key-value format.The authors design a pre-training strategy that uses contrastive learning to map incomplete shape features into the complete shape feature domain,enabling retrieval of analogous shapes from incomplete inputs.In addition,the authors employ backdoor adjustment to eliminate confounders,which are shape prior components sharing identical semantic structures with incomplete inputs.Experiments conducted on three datasets show that our method achieves superior performance compared to state-of-the-art approaches.The code for Point-PC can be accessed by https://github.com/bizbard/Point-PC.git.展开更多
Multi-modal knowledge graph completion(MMKGC)aims to complete missing entities or relations in multi-modal knowledge graphs,thereby discovering more previously unknown triples.Due to the continuous growth of data and ...Multi-modal knowledge graph completion(MMKGC)aims to complete missing entities or relations in multi-modal knowledge graphs,thereby discovering more previously unknown triples.Due to the continuous growth of data and knowledge and the limitations of data sources,the visual knowledge within the knowledge graphs is generally of low quality,and some entities suffer from the issue of missing visual modality.Nevertheless,previous studies of MMKGC have primarily focused on how to facilitate modality interaction and fusion while neglecting the problems of low modality quality and modality missing.In this case,mainstream MMKGC models only use pre-trained visual encoders to extract features and transfer the semantic information to the joint embeddings through modal fusion,which inevitably suffers from problems such as error propagation and increased uncertainty.To address these problems,we propose a Multi-modal knowledge graph Completion model based on Super-resolution and Detailed Description Generation(MMCSD).Specifically,we leverage a pre-trained residual network to enhance the resolution and improve the quality of the visual modality.Moreover,we design multi-level visual semantic extraction and entity description generation,thereby further extracting entity semantics from structural triples and visual images.Meanwhile,we train a variational multi-modal auto-encoder and utilize a pre-trained multi-modal language model to complement the missing visual features.We conducted experiments on FB15K-237 and DB13K,and the results showed that MMCSD can effectively perform MMKGC and achieve state-of-the-art performance.展开更多
Background The potential of exercise as a concurrent therapy for actively treated primary tumors has been suggested by emerging preclinical and observational studies.However,clinical trials regarding this question are...Background The potential of exercise as a concurrent therapy for actively treated primary tumors has been suggested by emerging preclinical and observational studies.However,clinical trials regarding this question are scarce.Therefore,we conducted a randomized controlled trial investigating the effects of aerobic or resistance exercise concomitant to neoadjuvant chemotherapy(NACT)on tumor size.Methods In the BENEFIT study(German title:Bewegung bei neoadjuvanter chemotherapie zur verbesserung der fitness),patients with breast cancer scheduled for NACT were randomly assigned to supervised resistance training(RT,n=60)or aerobic training(AT,n=60)twice weekly during NACT or to a waitlist control group(WCG,n=60).The primary outcome,“change in tumor size”,as well as the secondary clinical outcomes pathologic complete response(pCR),type of surgery(breast conserving/mastectomy),axillary lymph node dissection(ALND,yes/no),premature discontinuation of chemotherapy(yes/no),and relative dose intensity(RDI)were derived from clinical records.Due to the highly skewed distribution,the primary outcome was categorized.Multiple(ordinal)logistic regression analyses were performed.Results Overall,there was no significant difference in post-intervention tumor size between RT or AT and WCG.However,there was a significant effect modification by hormone receptor(HR)status(P_(interaction)=0.030).Among patients with HR+tumors,results suggest a beneficial effect of AT on tumor shrinkage(odds ratio(OR)=2.37,95%confidence interval(95%CI):0.97‒5.78),on pCR(OR=3.21,95%CI:0.97‒10.61);and on ALND(OR=3.76,95%CI:0.78‒18.06)compared to WCG.The effects of RT were slightly less pronounced.For HR−subtypes,beneficial effects on RDI were found for AT(OR=3.71,95%CI:1.20‒11.50)and similarly for RT(OR=2.58,95%CI:0.88‒7.59).Both AT and RT had favorable effects on premature discontinuation of chemotherapy(OR(no vs.yes)=2.34,95%CI:1.10‒5.06),irrespective of tumor receptor status.Conclusion While there was no significant effect on the primary outcome in the overall group,aerobic and resistance exercise concomitant to NACT seem to beneficially affect tumor shrinkage and pCR,reduce the need for ALND among patients with HR+breast cancers,and prevent low RDI among patients with HR–breast cancers.These results warrant confirmation in further trials.展开更多
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
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
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
For safe and efficient development of the sour gas reservoirs of the Cambrian Longwangmiao Fm in the Anyue Gas Field,the Sichuan Basin,and reduction of safety barrier failures and annulus abnormal pressure which are c...For safe and efficient development of the sour gas reservoirs of the Cambrian Longwangmiao Fm in the Anyue Gas Field,the Sichuan Basin,and reduction of safety barrier failures and annulus abnormal pressure which are caused by erosion,corrosion,thread leakage and improper well completion operations,a series of studies and field tests were mainly carried out,including optimization of well completion modes,experimental evaluation and optimization of string materials,sealing performance evaluation of string threads,structural optimization design of downhole pipe strings and erosion resistance evaluation of pipe strings,after the technical difficulties related with the well completion in this reservoir were analyzed.And consequently,a set of complete well completion technologies suitable for HTHP(high temperature and high pressure)and highflowrate gas wells with acidic media was developed as follows.First,optimize well completion modes,pipe string materials and thread types.Second,prepare optimized string structures for different production allocation conditions.And third,formulate well completion process and quality control measures for vertical and inclined wells.Field application results show that the erosion of high-flowrate production on pipe strings and downhole tools and the effect of perforation on the sealing performance of production packers were reduced effectively,well completion quality was improved,and annulus abnormal pressure during the late production was reduced.This research provides a reference for the development of similar gasfields.展开更多
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 w...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.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 exploration and development of oil and gas resources in the Sichuan Basin is shifting to the deeper strata.The deep and ultra-deep wells in deep carbonate gas reservoirs are faced with many difficulties,such as mu...The exploration and development of oil and gas resources in the Sichuan Basin is shifting to the deeper strata.The deep and ultra-deep wells in deep carbonate gas reservoirs are faced with many difficulties,such as multiple pressure systems,poor drillability,ultra-high pressure,ultra-high temperature and high sulfur content,which bring great challenges to drilling and completion engineering.In order to ensure the smooth exploration and development of deep carbonate oil and gas resources in the Sichuan Basin,the overall concept of combiningfield practice and technological research is followed.During the 13th Five-Year Plan,a batch of ultra-deep wells(well depth about 8000 m)have been drilled fast and safely(such as Well SYX133 in the Shuangyushi structure),and great progresses have been achieved in the experimental research of the support technologies in drilling and completion of deep and ultra-deep wells.And the achievements are as follows.First,the optimization of non-standard well structure,combined with the application of precisely managed pressure drilling technology and under-pressure plugging technology,lays a foundation for the optimized fast drilling and the safe reaching of geological targets.Second,a high-efficiency customized PDC drill bit is comprehensively optimized,popularized and applied,which effectively improves the average rate of penetration(ROP)in difficult-to-drill formations.Third,the development and application of drillingfluids(e.g.being resistant to high temperature of 200 C and anti-composite brine)and active under-pressure plugging technology effectively reduces downhole complexities while drilling the complex for-mations,such as high temperature and high pressure brine and circulation loss.Fourth,when precisely managed pressure drilling and precisely managed pressure cementing methods are applied in the strata with a narrow density window,multiple pressure systems and pressure sensitivity,the average drilling and completionfluid loss is reduced by more than 90%,and the complexity treatment time is cut down by more than 85%,and the cementing quality pass rate is increased by more than 20%.Fifth,the integrated application of ROP improvement technologies and tools(e.g.air drilling)results in a great reduction of drilling cycle and cost.It is concluded that the experimental research achievements provide basic support for the fast development of natural gas resources in the Anyue Gas Field,Sichuan Basin,and the great discovery of the Permian and Devonian natural gas resources in the northwestern Sichuan Basin.What's more,in order to adapt to the development of the deep natural gas(over 9000 m)in the Sichuan Basin during the 14th Five-Year Plan,it is necessary to speed up researches on drilling and completion tech-nologies in nine aspects,e.g.high-temperature downhole tools and workingfluid.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
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
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
基金Supported by National Nature Science Foundation(12371381)Nature Science Foundation of Shanxi(202403021222270)。
文摘In this paper,we established a class of parallel algorithm for solving low-rank tensor completion problem.The main idea is that N singular value decompositions are implemented in N different processors for each slice matrix under unfold operator,and then the fold operator is used to form the next iteration tensor such that the computing time can be decreased.In theory,we analyze the global convergence of the algorithm.In numerical experiment,the simulation data and real image inpainting are carried out.Experiment results show the parallel algorithm outperform its original algorithm in CPU times under the same precision.
基金supported by National Taiwan University Hospital Yunlin Branch Project NTUHYL 110.C018National Science and Technology Council,Taiwan.
文摘Porters play a crucial role in hospitals because they ensure the efficient transportation of patients,medical equipment,and vital documents.Despite its importance,there is a lack of research addressing the prediction of completion times for porter tasks.To address this gap,we utilized real-world porter delivery data from Taiwan University Hospital,China,Yunlin Branch,Taiwan Region of China.We first identified key features that can influence the duration of porter tasks.We then employed three widely-used machine learning algorithms:decision tree,random forest,and gradient boosting.To leverage the strengths of each algorithm,we finally adopted an ensemble modeling approach that aggregates their individual predictions.Our experimental results show that the proposed ensemble model can achieve a mean absolute error of 3 min in predicting task response time and 4.42 min in task completion time.The prediction error is around 50%lower compared to using only the historical average.These results demonstrate that our method significantly improves the accuracy of porter task time prediction,supporting better resource planning and patient care.It helps ward staff streamline workflows by reducing delays,enables porter managers to allocate resources more effectively,and shortens patient waiting times,contributing to a better care experience.
文摘This study introduces a novel methodology and makes case studies for anomaly detection in multivariate oil production time-series data,utilizing a supervised Transformer algorithm to identify spurious events related to interval control valves(ICVs)in intelligent well completions(IWC).Transformer algorithms present significant advantages in time-series anomaly detection,primarily due to their ability to handle data drift and capture complex patterns effectively.Their self-attention mechanism allows these models to adapt to shifts in data distribution over time,ensuring resilience against changes that can occur in time-series data.Additionally,Transformers excel at identifying intricate temporal dependencies and long-range interactions,which are often challenging for traditional models.Field tests conducted in the ultradeep water subsea wells of the Santos Basin further validate the model’s capability for early anomaly identification of ICVs,minimizing non-productive time and safeguarding well integrity.The model achieved an accuracy of 0.9544,a balanced accuracy of 0.9694 and an F1-Score of 0.9574,representing significant improvements over previous literature models.
基金National Key Research and Development Program of China,Grant/Award Number:2020YFB1711704。
文摘The goal of point cloud completion is to reconstruct raw scanned point clouds acquired from incomplete observations due to occlusion and restricted viewpoints.Numerous methods use a partial-to-complete framework,directly predicting missing components via global characteristics extracted from incomplete inputs.However,this makes detail re-covery challenging,as global characteristics fail to provide complete missing component specifics.A new point cloud completion method named Point-PC is proposed.A memory network and a causal inference model are separately designed to introduce shape priors and select absent shape information as supplementary geometric factors for aiding completion.Concretely,a memory mechanism is proposed to store complete shape features and their associated shapes in a key-value format.The authors design a pre-training strategy that uses contrastive learning to map incomplete shape features into the complete shape feature domain,enabling retrieval of analogous shapes from incomplete inputs.In addition,the authors employ backdoor adjustment to eliminate confounders,which are shape prior components sharing identical semantic structures with incomplete inputs.Experiments conducted on three datasets show that our method achieves superior performance compared to state-of-the-art approaches.The code for Point-PC can be accessed by https://github.com/bizbard/Point-PC.git.
基金funded by Research Project,grant number BHQ090003000X03。
文摘Multi-modal knowledge graph completion(MMKGC)aims to complete missing entities or relations in multi-modal knowledge graphs,thereby discovering more previously unknown triples.Due to the continuous growth of data and knowledge and the limitations of data sources,the visual knowledge within the knowledge graphs is generally of low quality,and some entities suffer from the issue of missing visual modality.Nevertheless,previous studies of MMKGC have primarily focused on how to facilitate modality interaction and fusion while neglecting the problems of low modality quality and modality missing.In this case,mainstream MMKGC models only use pre-trained visual encoders to extract features and transfer the semantic information to the joint embeddings through modal fusion,which inevitably suffers from problems such as error propagation and increased uncertainty.To address these problems,we propose a Multi-modal knowledge graph Completion model based on Super-resolution and Detailed Description Generation(MMCSD).Specifically,we leverage a pre-trained residual network to enhance the resolution and improve the quality of the visual modality.Moreover,we design multi-level visual semantic extraction and entity description generation,thereby further extracting entity semantics from structural triples and visual images.Meanwhile,we train a variational multi-modal auto-encoder and utilize a pre-trained multi-modal language model to complement the missing visual features.We conducted experiments on FB15K-237 and DB13K,and the results showed that MMCSD can effectively perform MMKGC and achieve state-of-the-art performance.
基金supported by an intramural proof of concept grant of the NCT Heidelberg.
文摘Background The potential of exercise as a concurrent therapy for actively treated primary tumors has been suggested by emerging preclinical and observational studies.However,clinical trials regarding this question are scarce.Therefore,we conducted a randomized controlled trial investigating the effects of aerobic or resistance exercise concomitant to neoadjuvant chemotherapy(NACT)on tumor size.Methods In the BENEFIT study(German title:Bewegung bei neoadjuvanter chemotherapie zur verbesserung der fitness),patients with breast cancer scheduled for NACT were randomly assigned to supervised resistance training(RT,n=60)or aerobic training(AT,n=60)twice weekly during NACT or to a waitlist control group(WCG,n=60).The primary outcome,“change in tumor size”,as well as the secondary clinical outcomes pathologic complete response(pCR),type of surgery(breast conserving/mastectomy),axillary lymph node dissection(ALND,yes/no),premature discontinuation of chemotherapy(yes/no),and relative dose intensity(RDI)were derived from clinical records.Due to the highly skewed distribution,the primary outcome was categorized.Multiple(ordinal)logistic regression analyses were performed.Results Overall,there was no significant difference in post-intervention tumor size between RT or AT and WCG.However,there was a significant effect modification by hormone receptor(HR)status(P_(interaction)=0.030).Among patients with HR+tumors,results suggest a beneficial effect of AT on tumor shrinkage(odds ratio(OR)=2.37,95%confidence interval(95%CI):0.97‒5.78),on pCR(OR=3.21,95%CI:0.97‒10.61);and on ALND(OR=3.76,95%CI:0.78‒18.06)compared to WCG.The effects of RT were slightly less pronounced.For HR−subtypes,beneficial effects on RDI were found for AT(OR=3.71,95%CI:1.20‒11.50)and similarly for RT(OR=2.58,95%CI:0.88‒7.59).Both AT and RT had favorable effects on premature discontinuation of chemotherapy(OR(no vs.yes)=2.34,95%CI:1.10‒5.06),irrespective of tumor receptor status.Conclusion While there was no significant effect on the primary outcome in the overall group,aerobic and resistance exercise concomitant to NACT seem to beneficially affect tumor shrinkage and pCR,reduce the need for ALND among patients with HR+breast cancers,and prevent low RDI among patients with HR–breast cancers.These results warrant confirmation in further trials.
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.
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.
基金Project supported by the National Manjor S&T Project“Development Demonstration Project of Large Carbonate Gas Field in Longgang Area,the Sichuan Basin”(No.2011ZX05047-02)Ph.D Station S&T Research Project of PetroChina Southwest Oil and Gas Field Company“Study on String Mechanics concerning to the Integrity of HTHP High-yield Gas Wells in the Longwangmiao Fm Reservoir”(No.20140305-11).
文摘For safe and efficient development of the sour gas reservoirs of the Cambrian Longwangmiao Fm in the Anyue Gas Field,the Sichuan Basin,and reduction of safety barrier failures and annulus abnormal pressure which are caused by erosion,corrosion,thread leakage and improper well completion operations,a series of studies and field tests were mainly carried out,including optimization of well completion modes,experimental evaluation and optimization of string materials,sealing performance evaluation of string threads,structural optimization design of downhole pipe strings and erosion resistance evaluation of pipe strings,after the technical difficulties related with the well completion in this reservoir were analyzed.And consequently,a set of complete well completion technologies suitable for HTHP(high temperature and high pressure)and highflowrate gas wells with acidic media was developed as follows.First,optimize well completion modes,pipe string materials and thread types.Second,prepare optimized string structures for different production allocation conditions.And third,formulate well completion process and quality control measures for vertical and inclined wells.Field application results show that the erosion of high-flowrate production on pipe strings and downhole tools and the effect of perforation on the sealing performance of production packers were reduced effectively,well completion quality was improved,and annulus abnormal pressure during the late production was reduced.This research provides a reference for the development of similar gasfields.
基金Supported by The Prince Charles Hospital Foundationthe Queensland Health’s Health Practitioner Research Scheme
文摘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.
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 Research Council of Science and Technology Major Project“Sichuan Basin Large-scale Carbonate Cas Fiekd Development Demonstration Project”(No.:2016ZX05052).
文摘The exploration and development of oil and gas resources in the Sichuan Basin is shifting to the deeper strata.The deep and ultra-deep wells in deep carbonate gas reservoirs are faced with many difficulties,such as multiple pressure systems,poor drillability,ultra-high pressure,ultra-high temperature and high sulfur content,which bring great challenges to drilling and completion engineering.In order to ensure the smooth exploration and development of deep carbonate oil and gas resources in the Sichuan Basin,the overall concept of combiningfield practice and technological research is followed.During the 13th Five-Year Plan,a batch of ultra-deep wells(well depth about 8000 m)have been drilled fast and safely(such as Well SYX133 in the Shuangyushi structure),and great progresses have been achieved in the experimental research of the support technologies in drilling and completion of deep and ultra-deep wells.And the achievements are as follows.First,the optimization of non-standard well structure,combined with the application of precisely managed pressure drilling technology and under-pressure plugging technology,lays a foundation for the optimized fast drilling and the safe reaching of geological targets.Second,a high-efficiency customized PDC drill bit is comprehensively optimized,popularized and applied,which effectively improves the average rate of penetration(ROP)in difficult-to-drill formations.Third,the development and application of drillingfluids(e.g.being resistant to high temperature of 200 C and anti-composite brine)and active under-pressure plugging technology effectively reduces downhole complexities while drilling the complex for-mations,such as high temperature and high pressure brine and circulation loss.Fourth,when precisely managed pressure drilling and precisely managed pressure cementing methods are applied in the strata with a narrow density window,multiple pressure systems and pressure sensitivity,the average drilling and completionfluid loss is reduced by more than 90%,and the complexity treatment time is cut down by more than 85%,and the cementing quality pass rate is increased by more than 20%.Fifth,the integrated application of ROP improvement technologies and tools(e.g.air drilling)results in a great reduction of drilling cycle and cost.It is concluded that the experimental research achievements provide basic support for the fast development of natural gas resources in the Anyue Gas Field,Sichuan Basin,and the great discovery of the Permian and Devonian natural gas resources in the northwestern Sichuan Basin.What's more,in order to adapt to the development of the deep natural gas(over 9000 m)in the Sichuan Basin during the 14th Five-Year Plan,it is necessary to speed up researches on drilling and completion tech-nologies in nine aspects,e.g.high-temperature downhole tools and workingfluid.
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 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.
