Experimental Research of Compound Monitoring on Multiple Temporary Blocking Refracturing for Long-Section Horizontal Wells

As an important energy replacement block in China, the tight conglomerate oilfields in the Mahu area are difficult to develop and are characterized by strong heterogeneity, large horizontal stress differences, and undeveloped natural fractures. However, new development processes including temporary blocking diversion and large section-multiple clusters have been implemented on the oilfields in the past few years. In 2020, two adjacent horizontal wells in the MD well area experienced a poor fracturing development effect compared with the earlier wells in this area. Analysis suggests that the main reasons are water sensitivity of the reservoir, insufficient fracturing scale, and/or interference from the adjacent old wells. To ameliorate the problem, this study presents an experimental study of multiple temporary plugging and refracturing technology in long horizontal well sections, in combination with electromagnetic and microseismic monitoring. Results from the study show a great difference between the two monitoring techniques, which is attributed to their different detection principles. Interestingly, the combination of the two approaches provides a greater performance than either approach alone. As the fracturing fluid flow diversion is based on temporary plugging diversion and electromagnetic monitoring of fracturing fluid is advantageous in temporary plugging diversion monitoring, both approaches require further research and development to address complex situations such as multiple temporary plugging and refracturing in long intervals of adjacent older wells.

Malignancy risk of indeterminate mammographic calcification in symptomatic breast clinics

BackgroundTo explore the potential risk factors predicting malignancy in patients with indeterminate incidental mammographic microcalcification and to evaluate the short-term risk of developing malignancy.MethodsBetween January 2011 and December 2015, one hundred and fifty (150) consecutive patients with indeterminate mammographic microcalcifications who had undergone stereotactic biopsy were evaluated. Clinical and mammographic features were recorded and compared with histopathological biopsy results. In patients with malignancy, postsurgical findings and surgical upgrade, if any, were recorded. Linear regression analysis (SPSS V.25) was used to evaluate significant variables predicting malignancy. OR with 95% CIs was calculated for all variables. All patients were followed up for a maximum of 10 years. The mean age of the patients was 52 years (range 33–79 years).ResultsThere were a total of 55 (37%) malignant results in this study cohort. Age was an independent predictor of breast malignancy with an OR (95% CI) of 1.10 (1.03 to 1.16). Mammographic microcalcification size, pleomorphic morphology, multiple clusters and linear/segmental distribution were significantly associated with malignancy with OR (CI) of 1.03 (1.002 to 1.06), 6.06 (2.24 to 16.66), 6.35 (1.44 to 27.90) and 4.66 (1.07 to 20.19). The regional distribution of microcalcification had an OR of 3.09 (0.92 to 10.3), but this was not statistically significant. Patients with previous breast biopsies had a lower risk of breast malignancy than patients with no prior biopsy (p=0.034).ConclusionMultiple clusters, linear/segmental distribution, pleomorphic morphology, size of mammographic microcalcifications and increasing age were independent predictors of malignancy. Having a previous breast biopsy did not increase malignancy risk.

Continuous monitoring of aerial density and circadian rhythms of flying insects in a semi-urban environment

Although small in size, insects are a quintessential part of terrestrial ecosystems due to their large number and diversity. While captured insects can be thoroughly studied in laboratory conditions, their population dynamics and abundance in the wild remain largely unknown due to the lack of accurate methodologies to count them. Here, we present the results of a field experiment where the activity of insects has been monitored continuously over 3 months using an entomological stand-off optical sensor (ESOS). Because its near-infrared laser is imperceptible to insects, the instrument provides an unbiased and absolute measurement of the aerial density (flying insect/m3) with a temporal resolution down to the minute. Multiple clusters of insects are differentiated based on their wingbeat frequency and ratios between wing and body optical cross-sections. The collected data allowed for the study of the circadian rhythm and daily activities as well as the aerial density dynamic over the whole campaign for each cluster individually. These measurements have been compared with traps for validation of this new methodology. We believe that this new type of data can unlock many of the current limitations in the collection of entomological data, especially when studying the population dynamics of insects with large impacts on our society, such as pollinators or vectors of infectious diseases.

MD-SPKM: A set pair k-modes clustering algorithm for incomplete categorical matrix data

In order to solve the clustering problem with incomplete and categorical matrix data sets, and considering the uncertain relationship between samples and clusters, a set pair k-modes clustering algorithm is proposed (MD-SPKM). Firstly, the correlation theory of set pair information granule is introduced into k-modes clustering. By improving the distance formula of traditional k-modes algorithm, a set pair distance measurement method between incomplete matrix samples is defined. Secondly, considering the uncertain relationship between the sample and the cluster, the definition of the intra-cluster average distance and the threshold calculation formula to determine whether the sample belongs to multiple clusters is given, and then the result of set pair clustering is formed, which includes positive region, boundary region and negative region. Finally, through the selected three data sets and four contrast algorithms for experimental evaluation, the experimental results show that the set pair k-modes clustering algorithm can effectively handle incomplete categorical matrix data sets, and has good clustering performance in Accuracy, Recall, ARI and NMI.

A two-stage clustering-based cold-start method for active learning

The problem of initialization of active learning is considered in this paper. Especially, this paper studies the problem in an imbalanced data scenario, which is called as class-imbalance active learning cold-start. The novel method is two-stage clustering-based active learning cold-start (ALCS). In the first stage, to separate the instances of minority class from that of majority class, a multi-center clustering is constructed based on a new inter-cluster tightness measure, thus the data is grouped into multiple clusters. Then, in the second stage, the initial training instances are selected from each cluster based on an adaptive candidate representative instances determination mechanism and a clusters-cyclic instance query mechanism. The comprehensive experiments demonstrate the effectiveness of the proposed method from the aspects of class coverage, classification performance, and impact on active learning.

The Spatial Patterns of Service Facilities Based on Internet Big Data: A Case Study on Chengdu

In the context of the mid-late development of China’s urbanization, promoting sustainable urban development and giving full play to urban potential have become a social focus, which is of enormous practical significance for the study of urban spatial pattern. Based on such Internet data as a map’s Point of Interest (POI), this paper studies the spatial distribution pattern and clustering characteristics of POIs of four categories of service facilities in Chengdu of Sichuan Province, including catering, shopping, transportation, scientific, educational, and cultural services, by means of spatial data mining technologies such as dimensional autocorrelation analysis and DBSCAN clustering. Global spatial autocorrelation is used to study the correlation between an index of a certain element and itself (univariate) or another index of an adjacent element (bivariate); partial spatial autocorrelation is used to identify characteristics of spatial clustering or spatial anomaly distribution of geographical elements. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is able to detect clusters of any shape without prior knowledge. The final step is to carry out quantitative analysis and reveal the distribution characteristics and coupling effects of spatial patterns. According to the results, (1) the spatial distribution of POIs of all service facilities is significantly polarized, as they are concentrated in the old city, and the trend of suburbanization is indistinctive, showing three characteristics, namely, central driving, traffic accessibility, and dependence on population activity; (2) the spatial distribution of POIs of the four categories of service facilities is featured by the pattern of “one center, multiple clusters,” where “one center” mainly covers the area within the first ring road and partial region between the first ring road and the third ring road, while “multiple clusters” are mainly distributed in the well-developed areas in the second circle of Chengdu, such as Wenjiang District and Shuangliu District; and (3) there is a significant correlation between any two categories of POIs. Highly mixed multifunctional areas are mainly distributed in the urban center, while service industry is less aggregated in urban fringe areas, and most of them are single-functional or dual-functional regions.

Hierarchical coordinated anti-jamming channel access in clustering networks: a multi-leader multi-follower Stackelberg game approach

This paper mainly investigates the multi-user coordinated anti-jamming problem in clustering communication networks. In such kinds of networks, there exist multiple clusters and multiple users who communicate with their receivers simultaneously. Besides, a malicious jammer persistently attacks channels with wide-band and dynamic changing jamming signals. To cope with these challenges brought by the large-scale clustering network and the dynamic wide-band jamming, a hierarchical coordinated anti-jamming approach is proposed, and a multi-leader multi-follower Stackelberg game is introduced to model the anti-jamming problem. In detail, cluster heads act as leaders, and select available frequency bands to avoid jamming attacks, while users in each cluster act as followers and select corresponding channels distributedly and independently. Moreover, it is proved that there exist multiple Stackelberg equilibriums (SEs) in the proposed game. To obtain SEs, a hierarchical coordinated anti-jamming channel access (HCACA) algorithm is designed. Simulation results illustrate that the proposed approach is effective to cope with the dynamic wide-band jamming attacks. Furthermore, it is also depicted that the proposed approach outperforms the distributed anti-jamming comparative approach in terms of convergence speed.

BSO20: efficient brain storm optimization for real-parameter numerical optimization

Brain storm optimization (BSO) is an emerging global optimization algorithm. The primary idea is to divide the population into different clusters, and offspring are generated within a cluster or between two clusters. However, the problems of inefficient clustering strategy and insufficient exploration exist in BSO. In this paper, a novel and efficient BSO is proposed, called BSO20 (proposed in 2020). BSO20 pays attention to both the clustering strategy and the mutation strategy. First, we propose a hybrid clustering strategy, which combines two clustering strategies, i.e., nearest-better clustering and random grouping strategy. The size of the subpopulation clustered by two strategies is dynamically adjusted as the population evolves. Second, a modified mutation strategy is used in BSO20 to share information within a cluster or among multiple clusters to enhance the ability of exploration. BSO20 is tested on the problems of the 2017 IEEE Congress on Evolutionary Computation competition on real parameter numerical optimization. BSO20 is compared with several variants of BSO and two variants of particle swarm optimization, and the experimental results show that BSO20 is competitive.

Collective Motion and Self-Organization of a Swarm of UAVs: A Cluster-Based Architecture

This study proposes a collective motion and self-organization control of a swarm of 10 UAVs, which are divided into two clusters of five agents each. A cluster is a group of UAVs in a dedicated area and multiple clusters make a swarm. This paper designs the 3D model of the whole environment by applying graph theory. To address the aforesaid issues, this paper designs a hybrid meta-heuristic algorithm by merging the particle swarm optimization (PSO) with the multi-agent system (MAS). First, PSO only provides the best agents of a cluster. Afterward, MAS helps to assign the best agent as the leader of the nth cluster. Moreover, the leader can find the optimal path for each cluster. Initially, each cluster contains agents at random positions. Later, the clusters form a formation by implementing PSO with the MAS model. This helps in coordinating the agents inside the nth cluster. However, when two clusters combine and make a swarm in a dynamic environment, MAS alone is not able to fill the communication gap of n clusters. This study does it by applying the Vicsek-based MAS connectivity and synchronization model along with dynamic leader selection ability. Moreover, this research uses a B-spline curve based on simple waypoint defined graph theory to create the flying formations of each cluster and the swarm. Lastly, this article compares the designed algorithm with the NSGA-II model to show that the proposed model has better convergence and durability, both in the individual clusters and inside the greater swarm.