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.

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.

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.

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.

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.

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.

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.

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.

RotatS:temporal knowledge graph completion based on rotation and scaling in 3D space

As the research of knowledge graph(KG)is deepened and widely used,knowledge graph com-pletion(KGC)has attracted more and more attentions from researchers,especially in scenarios of in-telligent search,social networks and deep question and answer(Q&A).Current research mainly fo-cuses on the completion of static knowledge graphs,and the temporal information in temporal knowl-edge graphs(TKGs)is ignored.However,the temporal information is definitely very helpful for the completion.Note that existing researches on temporal knowledge graph completion are difficult to process temporal information and to integrate entities,relations and time well.In this work,a rotation and scaling(RotatS)model is proposed,which learns rotation and scaling transformations from head entity embedding to tail entity embedding in 3D spaces to capture the information of time and rela-tions in the temporal knowledge graph.The performance of the proposed RotatS model have been evaluated by comparison with several baselines under similar experimental conditions and space com-plexity on four typical knowl good graph completion datasets publicly available online.The study shows that RotatS can achieve good results in terms of prediction accuracy.

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.

中国石化超深层钻完井关键技术挑战及展望

超深层是中国石化油气勘探开发的重要增储上产领域。针对超深层勘探开发需求和高温高压及复杂地质环境带来的工程挑战,中国石化持续开展关键技术攻关,在钻完井基础理论和方法、钻完井装备、高温井筒工作液、耐高温井下工具仪器和完井测试等方面取得关键技术进展,保障了120口超8 000 m油气井的安全钻探,钻深能力突破9 000 m,助推塔里木盆地顺北大型超深油气田建设、四川盆地海相特深层油气勘探开发突破,为保障超深层油气勘探开发提供了有力的工程技术支撑。随着油气勘探开发不断向更深层推进,技术升级转型需求日益迫切,需进一步夯实理论研究基础,突破耐高温关键工具仪器和井筒工作液,加快推进钻完井工程技术向数智化转型,为保障深层、超深层油气高效勘探开发和加快技术升级转型提供工程技术支撑。

机构投资者持股会助推企业“漂绿”吗--基于重污染企业社会责任报告披露的实证研究

以中国A股上市的455家重污染企业为样本,探究机构投资者与企业“漂绿”的关系。实证结果表明,机构投资者持股比例越高,企业发布社会责任报告的可能性越大,但报告内容披露的完整程度会降低,即机构投资者持股助推企业“漂绿”。机制分析表明,机构投资者持股可以通过提升公司盈利能力,助推企业“漂绿”,但通过降低公司负债水平来抑制企业“漂绿”。进一步分析发现,2016年绿色金融政策发布后,机构投资者持股对企业“漂绿”的助推效果减弱;机构投资者持股对国企“漂绿”行为的助推效果不明显。建议提升对公司绿色治理的监督力度,促使公司减少“漂绿”行为,更好地维护投资者的利益和社会效益。

超声心动图在评估完全型肺静脉异位引流合并限制型与非限制型房间隔交通中的应用价值分析

目的 总结超声心动图在评估完全型肺静脉异位引流(TAPVC)合并限制型与非限制型房间隔交通中的应用价值。方法 对2020年1月至2022年6月在首都医科大学附属北京安贞医院经外科手术确诊的49例TAPVC患者的超声心动图及临床资料进行回顾性分析。设定超声心动图测量房间隔交通≤5 mm为限制型房间隔交通,将患者分为限制组(23例)和非限制组(26例)。分别于术前及术后1、3个月行超声心动图检查,测量左心室内径(LVD)、右心室内径(RVD)、三尖瓣反流峰值压差(PTR)。分析并比较2组患者术前、术后心室内径和PTR的变化。结果 49例患者术前超声心动图诊断均与手术诊断一致,符合率100%。术前,限制组LVD、LVD/RVD比值明显低于非限制组,而PTR、RVD则明显高于非限制组,差异均有统计学意义(均P<0.01)。术后1、3个月2组PTR、RVD和LVD/RVD比值比较差异均无统计学意义(均P>0.05),限制组LVD小于非限制组[(19.3±4.2)mm比(22.3±4.6)mm、(22.6±3.8)mm比(25.9±3.7)mm],差异均有统计学意义(均P<0.05)。结论 超声心动图是诊断TAPVC的首选及准确的检查方法,早期诊断有利于尽早手术治疗,并能有效评价心脏容量改变及肺动脉高压下降情况,具有较高的临床应用价值。

川西地区深层致密砂岩气氮气钻完井试验及发展建议

深层低渗致密砂岩气藏基质黏土胶结物含量高、孔喉细小、低渗透率、低孔隙度、强亲水,采用常规水基工作液勘探开发易产生储层伤害,且伤害后渗透率难以恢复。氮气钻完井技术可从根本上避免钻井液中的固液相侵入引起储层损害,有效保护储层,提高单井产量。文章回顾了近年来氮气钻完井技术开发深层致密砂岩气的实践应用情况,从储层保护、及时发现气层、自然建产、提供地质甜点依据、为气藏富集规律确定提供辅助依据五个方面系统评价该项技术开发深层致密砂岩气的效果,同时针对井眼轨迹控制、井壁失稳及井下岩爆诱发卡钻、钻杆完井和井位及井段优选四个方面阐述了将来氮气钻完井技术的研究方向,以期为四川盆地须家河组致密砂岩气及国内外其他相似气藏的高效开发提供借鉴。

完整经验的基本涵义、生成机制及其教育意蕴

完整经验具有与经验同一的动态性、生成性等本质特征,蕴含着过程取向的完整观。完整经验的生成应以生活经验与课程经验的交互为基础,以认知经验与社会经验的融合为条件,以反省经验到审美经验的升华为关键。基于完整经验的幼儿园教育变革,在课程设置方面,要体现经验的多样性、连续性、过程性和整合性,注重经验向反省经验、审美经验的进阶与升华,应整体考虑生活经验、社会经验和审美经验之于幼儿学习与发展的价值。在教学过程中,应提供适宜的支架,珍视幼儿生活经验的独特价值,创设真实的问题解决情境。

“三全育人”视域下应用型本科土建类专业实践教学改革路径思考--以齐齐哈尔工程学院土木工程专业为例

高等教育是专业教育,构建“大思政”格局,将思想政治教育工作贯穿教育教学全过程,实现高等教育“三全育人”新局面,离不开专业教育主阵地。该文结合“新工科”理念,以应用型本科齐齐哈尔工程学院土木工程专业学生实践能力和创新能力培养为主线,从“三全育人”视域下土木工程专业实践教学现状进行分析,提出专业实践教学改革路径,将“三全育人”融入土木工程专业实践教学中,不仅可以提高学生专业技能水平,还可以增强学生的爱国意识,增强民族自豪感和自信心,具备一定的工程伦理意识和科学素养,坚定今后从事土木工程专业相关领域工作的信念。

分层一次爆破成井精确延时爆破参数研究

为了研究精确延时微差起爆对一次成井分层爆破效果的影响,理论计算了层内孔间微差时间,利用LS-DYNA软件,采用JH-2岩石模型,模拟了大直径深孔一次成井爆破中的精确延时分层起爆,分析了井筒岩石的损伤演化过程,试验验证了延期时间参数。结果表明,层间采用18 ms延期时间时,爆破效果最佳。结合理论分析和数值模拟结果确定了一次成井的延期时间方案。井筒的成形大致相似,其特征截面面积相似度在83.4%~96.6%之间。通过理论分析、数值模拟和现场试验获得了一次爆破成井工程的精确延时微差分层爆破方法,具有实际应用价值。