How to implement a knowledge graph completeness assessment with the guidance of user requirements

In the context of big data, many large-scale knowledge graphs have emerged to effectively organize the explosive growth of web data on the Internet. To select suitable knowledge graphs for use from many knowledge graphs, quality assessment is particularly important. As an important thing of quality assessment, completeness assessment generally refers to the ratio of the current data volume to the total data volume.When evaluating the completeness of a knowledge graph, it is often necessary to refine the completeness dimension by setting different completeness metrics to produce more complete and understandable evaluation results for the knowledge graph.However, lack of awareness of requirements is the most problematic quality issue. In the actual evaluation process, the existing completeness metrics need to consider the actual application. Therefore, to accurately recommend suitable knowledge graphs to many users, it is particularly important to develop relevant measurement metrics and formulate measurement schemes for completeness. In this paper, we will first clarify the concept of completeness, establish each metric of completeness, and finally design a measurement proposal for the completeness of knowledge graphs.

Classification of knowledge graph completeness measurement techniques

At present,although knowledge graphs have been widely used in various fields such as recommendation systems,question and answer systems,and intelligent search,there are always quality problems such as knowledge omissions and errors.Quality assessment and control,as an important means to ensure the quality of knowledge,can make the applications based on knowledge graphs more complete and more accurate by reasonably assessing the knowledge graphs and fixing and improving the quality problems at the same time.Therefore,as an indispensable part of the knowledge graph construction process,the results of quality assessment and control determine the usefulness of the knowledge graph.Among them,the assessment and enhancement of completeness,as an important part of the assessment and control phase,determine whether the knowledge graph can fully reflect objective phenomena and reveal potential connections among entities.In this paper,we review specific techniques of completeness assessment and classify completeness assessment techniques in terms of closed world assumptions,open world assumptions,and partial completeness assumptions.The purpose of this paper is to further promote the development of knowledge graph quality control and to lay the foundation for subsequent research on the completeness assessment of knowledge graphs by reviewing and classifying completeness assessment techniques.

Complete Hydatidiform Mole at Menopause: A Case Report from Panzi Hospital, Bukavu, Democratic Republic of Congo

Gestational trophoblastic disease is an abnormal proliferation of trophoblastic tissue during pregnancy. It occurs in women of childbearing age, although a few cases have also been observed in post-menopausal women, although it is extremely rare in the latter. Here we describe a rare case of complete hydatidiform mole in a 56-year-old female patient who presented with genital bleeding combined with nausea and vomiting and a gravid uterus 16 cm in height. The ultrasound findings and the increase in serum β-HCG to 182566.00 mIU/ml suggested a diagnosis of complete hydatidiform mole. Given the post-menopausal state and the future risk of post-molar gestational trophoblastic neoplasia, we opted for total hysterectomy without preservation of the adnexa via a transabdominal approach, followed by antimitotic treatment with methotrexate. The uterus measured 18.45 cm × 11.18 cm with intra cavitary vesicles. Microscopic examination showed chorionic villi of variable size and shape, most of which were dilated and oedematous, associated with trophoblastic cell proliferation and haemorrhage suggestive of complete benign hydatidiform mole. Follow-up showed a consistent decrease in serum β-HCG levels and no evidence of residual disease. A suspicion of gestational trophoblastic disease should be borne in mind when evaluating a patient with peri- or post-menopausal bleeding to avoid delay in diagnosis and treatment.

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.

基于CEEMDAN-AsyHyperBand-MultiTCN的短期风电功率预测

为减少风电功率短期预测误差,提高风电利用效率,提出一种基于经验模态分解和异步超参数优化的多层时间卷积网络(CEEMDAN-AsyHyperBand-MultiTCN)的短期风电功率预测方法。首先,确定序列分量的数量,并使用自适应噪声完备集合经验模态分解(CEEMDAN)对原始风电功率进行分解,构成训练数据集。其次,使用深度残差级联(DRnet)构建多层的时间卷积网络(TCN),并使用AsyHyperband算法对序列分量模型进行超参数寻优。最后,对序列分量分别进行预测,重构预测结果得到预测值。实验表明,该文提出的方法相比于其他方法能有效降低风电功率预测误差。

Distributed collaborative complete coverage path planning based on hybrid strategy

Collaborative coverage path planning(CCPP) refers to obtaining the shortest paths passing over all places except obstacles in a certain area or space. A multi-unmanned aerial vehicle(UAV) collaborative CCPP algorithm is proposed for the urban rescue search or military search in outdoor environment.Due to flexible control of small UAVs, it can be considered that all UAVs fly at the same altitude, that is, they perform search tasks on a two-dimensional plane. Based on the agents’ motion characteristics and environmental information, a mathematical model of CCPP problem is established. The minimum time for UAVs to complete the CCPP is the objective function, and complete coverage constraint, no-fly constraint, collision avoidance constraint, and communication constraint are considered. Four motion strategies and two communication strategies are designed. Then a distributed CCPP algorithm is designed based on hybrid strategies. Simulation results compared with patternbased genetic algorithm(PBGA) and random search method show that the proposed method has stronger real-time performance and better scalability and can complete the complete CCPP task more efficiently and stably.

Quality assessment of surgery for colorectal cancer:Where do we stand?

Quality assurance in surgery has been one of the most important topics of debate among colorectal surgeons in the past decade.It has produced new surgical standards that led in part to the impressive oncological outcomes we see in many units today.Total mesorectal excision,complete mesocolic excision(CME),and the Japanese D3 lymphadenectomy are now benchmark techniques embraced by many surgeons and widely recommended by surgical societies.However,there are still ongoing discrepancies in outcomes largely based on surgeon performance.This is one of the main reasons why many countries have shifted colorectal cancer surgery only to high volume centers.Defining markers of surgical quality is thus a perquisite to ensure that standards and oncological outcomes are met at an institutional level.With the evolution of CME surgery,various quality markers have been described,mostly based on measurements on the surgical specimen and lymph node yield,while others have proposed radiological markers(i.e.arterial stumps)measured on postoperative scans as part of the routine cancer follow-up.There is no ideal marker;however,taken together and assembled into a new score or set of criteria may become a future point of reference for reporting outcomes of colorectal cancer surgery in research studies and defining subspecialization requirements both at an individual and hospital level.

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.

Laparoscopic spleen-preserving total pancreatectomy for the treatment of low-grade malignant pancreatic tumors:Two case reports and review of literature

BACKGROUND Function-preserving pancreatectomy can improve the long-term quality of life of patients with benign or low-grade malignant tumors,such as intraductal papillary mucinous neoplasms(IPMNs)and mucinous cystic neoplasms.However,there is limited literature on laparoscopic spleen-preserving total pancreatectomy(LSpTP)due to technical difficulties.CASE SUMMARY Patient 1 was a 51-year-old male diagnosed with IPMN based on preoperative imaging,showing solid nodules in the pancreatic head and diffuse dilation of the main pancreatic duct with atrophy of the distal pancreas.We performed L-SpTP with preservation of the splenic vessels,and the postoperative pathology report revealed IPMN with invasive carcinoma.Patient 2 was a 60-year-old male with multiple cystic lesions in the pancreatic head and body.L-SpTP was performed,and intraoperatively,the splenic vein was injured and required ligation.Postoperative pathology revealed a mucinous cystic tumor of the pancreas with low-grade dysplasia.Both patients were discharged on postoperative day 7,and there were no major complications during the perioperative period.CONCLUSION We believe that L-SpTP is a safe and feasible treatment for low-grade malignant pancreatic tumors,but more case studies are needed to evaluate its safety,efficacy,and long-term outcomes.

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.

Some Statistical Aspects for Algerian Earthquake Catalogue

The scope of this study is to analyze some aspects of the Algerian earthquake catalogue between 1980 and 2009.Seismicity analysis is based on reliable compilation of earthquake catalogs obtained from different agencies.All intensities and magnitudes were converted to Ms magnitude using appropriate relationships.Dependent events were removed using adapted time and space windows.In addition,the completeness of the catalogue as a function of magnitude was determined from the standard deviation of occurrence rate plots,using the Stepp’s methodology.The remaining 2,016 independent earthquakes with Ms 2.2 were used to obtain various parameters(b-value,z-value)to characterize the temporal and spatial seismic activity for the entire northern part of Algeria.Finally,the obtained results are discussed to explain parameters variability.

Advanced cervix cancer patient with chemotherapy-induced grade IV myelosuppression achieved complete remission with cadonilimab:A case report

BACKGROUND The prognosis for patients with advanced metastatic cervix cancer(MCC)is poor,and this disease continues to pose a considerable therapeutic challenge.Despite the administration of first-line regimens consisting of cisplatin,paclitaxel,and bevacizumab,survival rates for patients with metastasis remain poor.The emergence of bispecific antibodies(BsAbs)offers a novel treatment option for patients diagnosed with MCC.CASE SUMMARY In this report,we present a patient with MCC who was treated with cadonilimab monotherapy at a dose of 6 mg/kg every two weeks after chemotherapy was proven to be intolerable.The patient exhibited a sustained complete response for a duration of 6 months,demonstrating an optimistic outlook.CONCLUSION This case illustrates the considerable efficacy of cadonilimab for treating advanced MCC.Therefore,BsAb therapy is a promising strategy for effectively treating patients with advanced MCC and should be considered as an option when patients are intolerant to standard chemotherapy.

基于ICEEMD-FastICA的滚动轴承故障诊断方法

针对滚动轴承早期故障特征信号提取困难的问题,提出了一种改进完备集成经验模态分解(Improved Complete Ensemble Empirical Mode Decomposition,ICEEMD)和独立分量分析(Independent Component Analysis,ICA)联合故障诊断方法。该方法利用峭度准则将经ICEEMD得到的固有模态分量(Intrinsic Mode Function,IMF)重构后结合快速独立分量分析(Fast Independent Component Analysis,FastICA)进行降噪解混,明显降低被测信号中的噪声,并且在故障特征频率处能量幅值取得最大值,便于辨识故障特征。通过试验研究分析,表明该方法可以明显降低噪声干扰,突出故障频率成分。和ICEEMD与包络谱结合的方法对比,信噪比提高了29.54%,能更准确地识别故障特征,达到对滚动轴承故障的判别需求,从而为轴承故障特征提取提供了一种新思路。

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.

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.

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.

2020年古冶M_(S)5.1地震序列微震检测及活动性研究

2020年7月12日河北省古冶(39.78°N,118.44°E)发生M_(S)5.1地震,震源深度10 km.本文通过对其周边500 km范围内的84个固定台站连续观测地震数据,采用基于图形处理器的模板匹配定位识别技术(GPU-M&L)对地震前后(2020年6月1日至2020年8月31日)共三个月的连续数据进行了微震检测和地震活动性分析.我们首先从台网目录的362个古冶地震序列事件中挑选了信噪比较高的118个地震作为模板,通过模板扫描和检测,获得共1017个地震,约为台网目录的2.8倍,然后运用双差定位方法(hypoDD)对669个地震事件进行了重定位.最后我们对古冶地震的事件序列进行时空分布、震源机制解以及b值空间分布等分析.结果显示古冶M_(S)5.1地震前,震中区周边地震活动性显著,地震前后地震序列分布优势方向为NE向,震源深度方向近直立分布;发震断裂为唐山—古冶断裂或其延伸部分,与1976年唐山M_(S)7.8地震同为唐山断裂带,发震断层走向基本一致.据此,我们推测古冶M_(S)5.1地震是1976年唐山M_(S)7.8地震后余震区应力调整所触发.

MASP-2、MACC1联合miR-17对结肠癌腹腔镜全结肠系膜切除术患者的预后评估价值

目的探讨甘露糖结合凝集素相关的丝氨酸蛋白酶2(MASP-2)、结肠癌转移相关基因1(MACC1)联合微小RNA-17(miR-17)对结肠癌腹腔镜全结肠系膜切除术(CME)患者的预后评估价值。方法选取2018年3月至2020年3月郑州市第七人民医院收治的80例结肠癌患者,均接受CME治疗。随访3 a,根据预后情况分组,对比不同预后患者血清MASP-2、MACC1、miR-17水平。分析影响预后的因素及MASP-2、MACC1、miR-17预测结肠癌CME患者的预后评估价值。结果结肠癌患者术后血清MACC1、miR-17水平低于术前,MASP-2水平高于术前(P<0.05)。随访3 a,80例结肠癌CME患者中19例被纳入预后不良组,60例被纳入预后良好组。预后不良组临床分期Ⅲ期占比及血清MACC1、miR-17水平均高于预后良好组,MASP-2水平低于预后良好组(P<0.05)。血清MACC1、miR-17是影响结肠癌CME预后的独立危险因素,MASP-2为保护因素(P<0.05)。血清MASP-2、MACC1、miR-17均可预测结肠癌CME患者的预后,三者联合预测价值最高(P<0.05)。结论结肠癌CME后患者血清MACC1、miR-17水平降低,MASP-2水平升高,上述指标均可作为结肠癌CME预后的预测指标,且三者联合的预后评估价值最高。

地铁车站BIM模型信息完备性审查研究

随着智能建造的发展,三维正向设计逐渐替代了传统二维设计,对设计成果的交付审查也将同步转换为对建筑信息模型的审查。地铁建筑设计区别于其他建筑具备较强的功能性,在实际设计交付成果审查中,会存在错审率、漏项高等问题。为解决该问题,本文依据相关国家标准,构建适用于地铁车站建筑信息模型完备性审查的本体模型,同时集成工业基础类(IFC 4.0)实例信息、计算机规则技术,实现模型完备性审查。以地铁车站的建筑单元为例,实现地铁车站模型在方案设计阶段的交付工作。该方法的提出有助于促进整个建筑行业的信息化发展。