Thoracoscopic resection of giant left atrial appendage aneurysm: A case report

Left atrial appendage aneurysm(LAAA)was first reported in the 1960s.1 LAAA is a rare condition,with just over 100 congenital or acquired cases reported to date.2 LAAA is frequently diagnosed incidentally during echocardiography or computed tomography(CT)scans.Most patients with LAAA are asymptomatic,while a few exhibit nonspecific symptoms,such as dyspnea,palpitation,and chest tightness.Patients with LAAA frequently present with atrial arrhythmias and systemic thromboembolism,such as stroke or multiorgan infarctions,due to the formation of a left atrial appendage thrombus.3 The lesion may be cured using aneurysm resection.Considering it as the potential cause of atrial arrhythmias and thromboembolism,the lesion must be identified on time and cured using a suitable treatment approach.

Exploring the Spectacular Scenery of MAGQU ALONG NATIONAL HIGHWAY 317

May marks the beginning of the annual harvest of caterpillar fungus in Bachen County of Nagqu City,Xizang Autonomous Region.As the rainy season sets in,the vast grasslands receive frequent downpours.Regardless of whether you are in the downtown area or out on the grasslands,you can frequently witness stunning rainbows.

High Utility Periodic Frequent Pattern Mining in Multiple Sequences

Periodic patternmining has become a popular research subject in recent years;this approach involves the discoveryof frequently recurring patterns in a transaction sequence. However, previous algorithms for periodic patternmining have ignored the utility (profit, value) of patterns. Additionally, these algorithms only identify periodicpatterns in a single sequence. However, identifying patterns of high utility that are common to a set of sequencesis more valuable. In several fields, identifying high-utility periodic frequent patterns in multiple sequences isimportant. In this study, an efficient algorithm called MHUPFPS was proposed to identify such patterns. To addressexisting problems, three new measures are defined: the utility, high support, and high-utility period sequenceratios. Further, a new upper bound, upSeqRa, and two new pruning properties were proposed. MHUPFPS usesa newly defined HUPFPS-list structure to significantly accelerate the reduction of the search space and improvethe overall performance of the algorithm. Furthermore, the proposed algorithmis evaluated using several datasets.The experimental results indicate that the algorithm is accurate and effective in filtering several non-high-utilityperiodic frequent patterns.

Association RuleMining Frequent-Pattern-Based Intrusion Detection in Network

In the network security system,intrusion detection plays a significant role.The network security system detects the malicious actions in the network and also conforms the availability,integrity and confidentiality of data informa-tion resources.Intrusion identification system can easily detect the false positive alerts.If large number of false positive alerts are created then it makes intrusion detection system as difficult to differentiate the false positive alerts from genuine attacks.Many research works have been done.The issues in the existing algo-rithms are more memory space and need more time to execute the transactions of records.This paper proposes a novel framework of network security Intrusion Detection System(IDS)using Modified Frequent Pattern(MFP-Tree)via K-means algorithm.The accuracy rate of Modified Frequent Pattern Tree(MFPT)-K means method infinding the various attacks are Normal 94.89%,for DoS based attack 98.34%,for User to Root(U2R)attacks got 96.73%,Remote to Local(R2L)got 95.89%and Probe attack got 92.67%and is optimal when it is compared with other existing algorithms of K-Means and APRIORI.

Hybrid Recommender System Using Systolic Tree for Pattern Mining

A recommender system is an approach performed by e-commerce for increasing smooth users’experience.Sequential pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of transactions.This work will present the implementation of sequence pattern mining for recommender systems within the domain of e-com-merce.This work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender system.The feature selec-tion's objective is to pick a feature subset having the least feature similarity as well as highest relevancy with the target class.This will mitigate the feature vector's dimensionality by eliminating redundant,irrelevant,or noisy data.This work pre-sents a new hybrid recommender system based on optimized feature selection and systolic tree.The features were extracted using Term Frequency-Inverse Docu-ment Frequency(TF-IDF),feature selection with the utilization of River Forma-tion Dynamics(RFD),and the Particle Swarm Optimization(PSO)algorithm.The systolic tree is used for pattern mining,and based on this,the recommendations are given.The proposed methods were evaluated using the MovieLens dataset,and the experimental outcomes confirmed the efficiency of the techniques.It was observed that the RFD feature selection with systolic tree frequent pattern mining with collaborativefiltering,the precision of 0.89 was achieved.

Backward Support Computation Method for Positive and Negative Frequent Itemset Mining

Association rules mining is a major data mining field that leads to discovery of associations and correlations among items in today’s big data environment. The conventional association rule mining focuses mainly on positive itemsets generated from frequently occurring itemsets (PFIS). However, there has been a significant study focused on infrequent itemsets with utilization of negative association rules to mine interesting frequent itemsets (NFIS) from transactions. In this work, we propose an efficient backward calculating negative frequent itemset algorithm namely EBC-NFIS for computing backward supports that can extract both positive and negative frequent itemsets synchronously from dataset. EBC-NFIS algorithm is based on popular e-NFIS algorithm that computes supports of negative itemsets from the supports of positive itemsets. The proposed algorithm makes use of previously computed supports from memory to minimize the computation time. In addition, association rules, i.e. positive and negative association rules (PNARs) are generated from discovered frequent itemsets using EBC-NFIS algorithm. The efficiency of the proposed algorithm is verified by several experiments and comparing results with e-NFIS algorithm. The experimental results confirm that the proposed algorithm successfully discovers NFIS and PNARs and runs significantly faster than conventional e-NFIS algorithm.

Uncharted Territory: Frequent Relapsing, Steroid Sensitive Secondary Minimal Change Nephrotic Syndrome Cause by Solid Tumor of the Gastro-Esophageal Junction —(Case Presentation and Review of the Literature)

We reported a biopsy proved case of minimal change nephrotic syndrome in a 72-year-old patient. The minimal change nephrotic syndrome has been steroid sensitive, but the patient had 7 relapses over a span of 5 years. Each time the dose of steroid is tapered, a relapse of the nephrotic syndrome occurred. Eventually, the patient was complaining of dysphagia and difficulty swallowing. Hospital work-up with barium swallow, endoscopy, and CT of the chest, abdomen and pelvis, revealed a focal stenotic lesion with mild to moderate esophageal dysmotility 7/15/2022. A diagnosis of an ulcerating lesion with biopsy confirmed a neuro-endocrine carcinoma of the gastro-esophageal junction was entertained. The CT of the chest/abdomen/pelvis, 7/19/2022, has shown, an esophageal mass of 5.1 × 5.6 × 7 cm of the gastro-esophageal junction with ulceration. No evidence of spread beyond the esophagus and stomach. The histology revealed a poorly differentiated neuroendocrine tumor of the gastro-esophageal junction. The patient underwent several rounds of chemotherapy, radiation, and surgery culminating in tumor control. His nephrotic syndrome was resolved after the tumor has been controlled by surgery and chemotherapy.

Quantum Algorithm for Mining Frequent Patterns for Association Rule Mining

Maximum frequent pattern generation from a large database of transactions and items for association rule mining is an important research topic in data mining. Association rule mining aims to discover interesting correlations, frequent patterns, associations, or causal structures between items hidden in a large database. By exploiting quantum computing, we propose an efficient quantum search algorithm design to discover the maximum frequent patterns. We modified Grover’s search algorithm so that a subspace of arbitrary symmetric states is used instead of the whole search space. We presented a novel quantum oracle design that employs a quantum counter to count the maximum frequent items and a quantum comparator to check with a minimum support threshold. The proposed derived algorithm increases the rate of the correct solutions since the search is only in a subspace. Furthermore, our algorithm significantly scales and optimizes the required number of qubits in design, which directly reflected positively on the performance. Our proposed design can accommodate more transactions and items and still have a good performance with a small number of qubits.

基于HowNet语义相似度的FAQ研究

FAQ是网站提供在线帮助的主要手段。利用检索机制根据用户提出的问题建立一个候选问句集,利用知网研究了用户问句和候选问句之间的相似度,从中找出最相似的问句,并将相应答案返回给用户。实验表明,该方法提高了问句匹配的准确率。

Fast FP-Growth for association rule mining

In this paper, we propose an efficient algorithm, called FFP-Growth (shortfor fast FP-Growth) , to mine frequent itemsets. Similar to FP-Growth, FFP-Growth searches theFP-tree in the bottom-up order, but need not construct conditional pattern bases and sub-FP-trees,thus, saving a substantial amount of time and space, and the FP-tree created by it is much smallerthan that created by TD-FP-Growth, hence improving efficiency. At the same time, FFP-Growth can beeasily extended for reducing the search space as TD-FP-Growth (M) and TD-FP-Growth (C). Experimentalresults show that the algorithm of this paper is effective and efficient.

New algorithm of mining frequent closed itemsets

A new algorithm based on an FC-tree （frequent closed pattern tree） and a max-FCIA （maximal frequent closed itemsets algorithm） is presented, which is used to mine the frequent closed itemsets for solving memory and time consuming problems. This algorithm maps the transaction database by using a Hash table,gets the support of all frequent itemsets through operating the Hash table and forms a lexicographic subset tree including the frequent itemsets.Efficient pruning methods are used to get the FC-tree including all the minimum frequent closed itemsets through processing the lexicographic subset tree.Finally,frequent closed itemsets are generated from minimum frequent closed itemsets.The experimental results show that the mapping transaction database is introduced in the algorithm to reduce time consumption and to improve the efficiency of the program.Furthermore,the effective pruning strategy restrains the number of candidates,which saves space.The results show that the algorithm is effective.

LFU算法的ASIC实现

为ASIC实现内存管理中页面替换的LFU算法,基于TSMC公司0.18μm的单元库,用标准逻辑单元库实现LFU算法.通过研究一组数中的最小值检出和最小值地址检出的逻辑规律,简化逻辑,减小了电路规模.通过控制关键路径的级数,达到了减少延迟,提高运行速度的目的.给出了版图和静态时序分析结果.

Design of foundation structures of sea-crossing bridges on serviceability limit state

The target reliability indices of the foundation structures of sea-crossing bridges on the serviceability limit state （SLS） are different from those of common bridges due to their different surroundings. Consequently, three levels of the target reliability indices, which are 1.5, 2. 0 and 2. 3, respectively, for those structures on the SLS are suggested based on the Joint Committee on Structural Safety （JCSS） model code, and a new method of calibrating factors of live loads, which is based on the contribution ratio of tensile stresses of reinforcing bars produced by various loads to the maximum crack width of concrete, is proposed. Finally, the calibration of the reliability-based factors of the frequent value and the quasi-permanent value of live loads is conducted by the Joint Committee （JC） method through an actual design, and the indices are proved to be reasonable and the new method is proved to be feasible.

The Variation Characteristics of the Cool and Warm Summer of the Northeast China

By analyzing the features of cold and warm summer of the northeast China during 1961-2002,the results showed the time from 1960s to late 1970s was the phase of cold summer took place,and the time from 1980s to early 1990s was the phase of cold and warm summer alternately took place.After the middle and late period of 1990s,it was the concentrated occurrence period of warm summer.The cool and warm summer had the continuity and cluster occurrence characteristics.The frequency of the cool summer was more than the warm summer,and the abnormal degree of warm summer was stronger than the cool summer,and the influence scope was wide.The cool summer had 4 frequent occurrence centers,and the warm summer had 2 frequent occurrence centers,located at the mountain zone and the hills zone.Not only the cool summer was easy to appear,but also the warm summer was easy to happen in the west and the east of Heilongjiang province.Comparatively speaking,the cool summer was easier to appear in the Changbai Mountain area.

An Essay on the Double Nature of the Probability

Classical statistics and Bayesian statistics refer to the frequentist and subjective theories of probability respectively. Von Mises and De Finetti, who authored those conceptualizations, provide interpretations of the probability that appear incompatible. This discrepancy raises ample debates and the foundations of the probability calculus emerge as a tricky, open issue so far. Instead of developing philosophical discussion, this research resorts to analytical and mathematical methods. We present two theorems that sustain the validity of both the frequentist and the subjective views on the probability. Secondly we show how the double facets of the probability turn out to be consistent within the present logical frame.

航天侦察装备体系指标关联信息挖掘研究

航天侦察装备体系的体系效能和体系贡献率影响因素指标数量多,指标间关系复杂。如何识别关联,优选关键性能指标并明确性能指标与体系贡献率之间的关系是体系效能及贡献率评估问题的关键。通过MATLAB与STK联合仿真获取航天侦察装备底层指标数据,基于FP-Tree(Frequent Pattern-Tree)算法发现指标间关联信息,去除冗余,确定指标关联类型,并结合Marichal熵建立优化模型确定关键性能指标的贡献度。仿真实验结果表明,通过FP-Tree算法可挖掘航天侦察装备初始评价指标体系间的关联性及关联类型,确定指标贡献度并实现指标体系的精简。

Model test and numerical simulation on the dynamic stability of the bedding rock slope under frequent microseisms

Shake table testing was performed to investigate the dynamic stability of a mid-dip bedding rock slope under frequent earthquakes. Then, numerical modelling was established to further study the slope dynamic stability under purely microseisms and the influence of five factors, including seismic amplitude, slope height, slope angle, strata inclination and strata thickness, were considered. The experimental results show that the natural frequency of the slope decreases and damping ratio increases as the earthquake loading times increase. The dynamic strength reduction method is adopted for the stability evaluation of the bedding rock slope in numerical simulation, and the slope stability decreases with the increase of seismic amplitude, increase of slope height, reduction of strata thickness and increase of slope angle. The failure mode of a mid-dip bedding rock slope in the shaking table test is integral slipping along the bedding surface with dipping tensile cracks at the slope rear edge going through the bedding surfaces. In the numerical simulation, the long-term stability of a mid-dip bedding slope is worst under frequent microseisms and the slope is at risk of integral sliding instability, whereas the slope rock mass is more broken than shown in the shaking table test. The research results are of practical significance to better understand the formation mechanism of reservoir landslides and prevent future landslide disasters.

A Real-Time and Ubiquitous Network Attack Detection Based on Deep Belief Network and Support Vector Machine

In recent years, network traffic data have become larger and more complex, leading to higher possibilities of network intrusion. Traditional intrusion detection methods face difficulty in processing high-speed network data and cannot detect currently unknown attacks. Therefore, this paper proposes a network attack detection method combining a flow calculation and deep learning. The method consists of two parts: a real-time detection algorithm based on flow calculations and frequent patterns and a classification algorithm based on the deep belief network and support vector machine(DBN-SVM). Sliding window(SW) stream data processing enables real-time detection, and the DBN-SVM algorithm can improve classification accuracy. Finally, to verify the proposed method, a system is implemented.Based on the CICIDS2017 open source data set, a series of comparative experiments are conducted. The method's real-time detection efficiency is higher than that of traditional machine learning algorithms. The attack classification accuracy is 0.7 percentage points higher than that of a DBN, which is 2 percentage points higher than that of the integrated algorithm boosting and bagging methods. Hence, it is suitable for the real-time detection of high-speed network intrusions.

Clinical Observation on 57 Cases with Medical Amenorrhea Treated by Acupuncture

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Data-driven Product Functional Confguration:Patent Data and Hypergraph

The product functional confguration(PFC)is typically used by frms to satisfy the individual requirements of customers and is realized based on market analysis.This study aims to help frms analyze functions and realize functional confgurations using patent data.This study frst proposes a patent-data-driven PFC method based on a hypergraph network.It then constructs a weighted network model to optimize the combination of product function quantity and object from the perspective of big data,as follows:(1)The functional knowledge contained in the patent is extracted.(2)The functional hypergraph is constructed based on the co-occurrence relationship between patents and applicants.(3)The function and patent weight are calculated from the patent applicant’s perspective and patent value.(4)A weight calculation model of the PFC is developed.(5)The weighted frequent subgraph algorithm is used to obtain the optimal function combination list.This method is applied to an innovative design process of a bathroom shower.The results indicate that this method can help frms detach optimal function candidates and develop a multifunctional product.