Tree Pattern Research Articles

An Evolutive Frequent Pattern Tree-based Incremental Knowledge Discovery Algorithm

To understand current situation in specific scenarios, valuable knowledge should be mined from both historical data and emerging new data. However, most existing algorithms take the historical data and the emerging data as a whole and periodically repeat to analyze all of them, which results in heavy computation overhead. It is also challenging to accurately discover new knowledge in time, because the emerging data are usually small compared to the historical data. To address these challenges, we propose a novel knowledge discovery algorithm based on double evolving frequent pattern trees that can trace the dynamically evolving data by an incremental sliding window. One tree is used to record frequent patterns from the historical data, and the other one records incremental frequent items. The structures of the double frequent pattern trees and their relationships are updated periodically according to the emerging data and a sliding window. New frequent patterns are mined from the incremental data and new knowledge can be obtained from pattern changes. Evaluations show that this algorithm can discover new knowledge from evolving data with good performance and high accuracy.

An Investigation into the Effect of Backup Coat Sands on Investment Casting Products

Investment casting is a well-known method for producing precise and finished products. The shell-making process takes a long time and consumes a large amount of coating sands. On the wax pattern tree, the ceramic coating sands are used as a primary coat and several backup coats. Zirconia sand is used for primary coating on a shell in this work. Backup coat sands are made up of fused silica and alumina silica. Colloidal silica is used as a binding material in the formation of all shells. In the work, the effect of fused silica and alumina-silica sand as a backup coat on casting properties was determined. The experimental work evaluated casing properties such as microstructures, grain size measurement, gas porosity, and shrinkages. On the dried, de-waxed, and fired shell, the porosity of the shell mold was determined for the permeability level. Alumina-silica sand has a porosity of 27 to 31 percent, whereas fused silica has a porosity of 22 to 25 percent. It produces better casting properties in alumina–silica casting products than fused silica. Casting grain sizes range from 22 to 38 microns. The grain size of alumina-silica casting is finer than that of fused silica shell casting. Gas porosity and shrinkage in the casting were found to be moderate in all castings.

Pitiriasis Rosea: Manifestasi Klinis Dan Tatalaksana

Pitiriasis rosea (PR) is a papulosquamous disorder with appearance of multiple, discrete patchesof skin rash in a distinctive pattern over the trunk and limbs. Epidemiologic studies on PR in Africa showed 2,2% to 4,8% dermatologist patient have a PR. The incidence is 170 cases per 100,000 persons per year and It typically affects persons 10 to 35 years of age. The clinical presentation of PR can be a single larger lesion, measuring about 2 to 10 centimetres, usually precedes the widespread rash for up to two weeks. This initial lesion, also known as the 'herald patch', most commonly appears on the trunk, and then the secondary eruption occurs. Lesions are similar to the herald patch but smaller. Their distribution commonly follows the skin cleavage lines in what is referred to as the 'Christmas tree pattern'. The risk factor of PR such as COVID-19 infection, herpes virus reactivation, environmental factors, and pregnancy factors. Treatment is aimed at controlling symptoms and consists of antihistamines, antibiotics or corticosteroids. In some cases, acyclovir can be used to treat symptoms and reduce the length of disease. Ultraviolet phototherapy can also be considered for severe cases

RHPTree—Risk Hierarchical Pattern Tree for Scalable Long Pattern Mining

Risk patterns are crucial in biomedical research and have served as an important factor in precision health and disease prevention. Despite recent development in parallel and high-performance computing, existing risk pattern mining methods still struggle with problems caused by large-scale datasets, such as redundant candidate generation, inability to discover long significant patterns, and prolonged post pattern filtering. In this article, we propose a novel dynamic tree structure, Risk Hierarchical Pattern Tree (RHPTree), and a top-down search method, RHPSearch, which are capable of efficiently analyzing a large volume of data and overcoming the limitations of previous works. The dynamic nature of the RHPTree avoids costly tree reconstruction for the iterative search process and dataset updates. We also introduce two specialized search methods, the extended target search (RHPSearch-TS) and the parallel search approach (RHPSearch-SD), to further speed up the retrieval of certain items of interest. Experiments on both UCI machine learning datasets and sampled datasets of the Simons Foundation Autism Research Initiative (SFARI)—Simon’s Simplex Collection (SSC) datasets demonstrate that our method is not only faster but also more effective in identifying comprehensive long risk patterns than existing works. Moreover, the proposed new tree structure is generic and applicable to other pattern mining problems.

Reliable Keyword Query Interpretation on Summary Graphs

The semantic gap between keyword queries and search intents behind them motivates intensive studies on keyword query interpretation, which aims to interpret a keyword query to structured queries (a.k.a. patterns) representing most possibly relevant search intents. However, there still lacks of study on an important issue: how to guarantee the patterns are "reliable", which means the structured queries can be evaluated as really existing results. In this paper, we regard the reliability as a new metric for ranking patterns, and present a keyword query interpretation approach to find both reliable and relevant pattern trees on an arbitrary summary graph of underlying data. Specifically, we firstly propose a reliability estimation model to measure how possibly a pattern tree can be evaluated as a nonempty result set by statistics under reasonable assumptions. Secondly, we develop constrained top-k search algorithms that guarantee to return the optimal pattern trees for a specific keyword query. Moreover, to improve the efficiency of online search, we also design elaborate indexes, search heuristics and pruning strategies. Lastly, we perform comprehensive experiments on two real-world datasets, DBpedia and Yago, with both QALD-9 queries and random queries. The observations indicate our approach improves the accuracy and overall quality of top-k results significantly.

Changes in phenological patterns of a tropical Atlantic Forest tree: implications for conservation

Changes in phenological patterns of a tropical Atlantic Forest tree: implications for conservation

Analysing changes in spatial point patterns: A proposal using data from a forest thinning experiment

Spatial patterns reveal critical features at the individual and community levels. However, how to evaluate changes in spatial characteristics remains largely unexplored. We assess spatial changes in spatial point patterns by augmenting current statistical functions and indices. We fitted functions to describe unmarked and marked (tree size) spatial patterns using data from a large-scale silvicultural experiment in southern Chile. Furthermore, we compute the mingling index to represent spatial tree diversity. We proffer the pair correlation function difference before and after treatment to detect changes in the unmarked-point pattern of trees and the semivariogram-ratio to evaluate changes in the marked-point pattern. Our research provides a quantitative assessment of a critical aspect of forest heterogeneity: changes in spatial unmarked and marked-point patterns. The proposed approach can be a powerful tool for quantifying the impacts of disturbances and silvicultural treatments on spatial patterns in forest ecosystems.

Christmas tree pattern in age related cataracts

A 66-year-old male presented in eye outpatient department for insidious onset-colored halos for a month. He, being a known case of Parkinsonism, was on treatment with Syndopa for the past 5 years. His visual acuity was OU 6/18–6/12. His anterior segment showed multiple polychromatic needle-shaped crystals traversing in all directions, located in the anterior nucleus of the lens in the right eye [Figure 1], the same being minimally visible in the left eye lens [Figure 2].Figure 1: Right eye showing several polychromatic needle crystals occupying anterior nucleus of inferior part of lens giving it a decorated Christmas tree patternFigure 2: Left eye shows very few needle crystals in the center of lensIt was diagnosed as Christmas tree cataract (CTC), though an appropriate systemic association could not be demonstrated. From a review of such CTCs, mechanisms postulated for its occurrence vary from study to study.[1] While few studies suggest the opacities to be cholesterol crystals,[2] few say that they were most likely made of cystine crystals due to an age-related aberrant breakdown of crystallins induced by elevated Ca++ levels.[3] One study postulated the diffraction of light from parallel-sided stacks of fused cell membranes to be the cause for the striking polychromatic luster.[4] CTC is very well associated with myotonic dystrophy (DM), wherein premature cataract (<55 years) with polychromatic crystals of the CTC in posterior lens capsule.[5] CTC has been identified as the first sign of DM, developing into full-blown symptoms of myotonia after 7–14 years.[6] An exhaustive search of PubMed literature did not show any association between CTCs and Parkinsonism or Syndopa usage. As noted, in the reported patient, the presence of CTC in anterior nucleus and in elderly age of 66 years does not fit into DM. CONCLUSION The Christmas tree cataract in this patient is probably age related. Had it been diagnosed in a young patient, the Ophthalmologist should take up the responsibility to refer promptly to a Neurophysician to rule out the possibility of DM. Declaration of patient consent The authors certify that they have obtained all appropriate patient consent forms. In the form the patient (s) has/have given his/her/their consent for his/her/their images and other clinical information to be reported in the journal. The patients understand that their names and initial s will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.

Tree spatial pattern and mortality prediction in burned patches of Dahurian larch (

Background Fire-caused tree mortality and spatial pattern are crucial for evaluating forest dynamics and developing management prescriptions. Aims We investigated direct fire effects on spatial distribution and mortality of Dahurian larch (Larix gmelinii Rupr.) and assessed the Ryan and Amman (R–A) model performance and the importance of immediate mortality predictors. Methods We analysed spatial patterns of fire-killed and surviving trees of three size classes in plots that burned at low- to high-severity using pair-correlation functions and tree mortality with the R–A model and generalised linear mixed models. Key results The mixed-severity fire caused strong density-dependent mortality and more aggregated surviving tree patterns at short distances. The R–A model generally performed acceptably, and crown scorch and bole char height were critical predictors determining post-fire tree mortality. Conclusions Fire-caused tree mortality and spatial patterns are controlled primarily by spatial variation in tree size and biological and structural characteristics. The prediction biases of the R–A model arose primarily from the intrinsic traits of Dahurian larch and the imbalanced dataset. Fine-scale neighbourhood density might be a fundamental priority for fire management and restoration. Implications This study could possibly improve mechanistic understanding of spatial pattern development and tree mortality in similar fire-prone conifer forests.

Dynamic Support Range based Rare Pattern Mining over Data Streams

Rare itemset mining is a relatively recent topic of study in data mining. In certain application domains, such as online banking transaction analysis, sensor data analysis, and stock market analysis, rare patterns are patterns with low support and high confidence that are extremely interesting when compared to frequent patterns. Numerous applications generate large amounts of continuous data streams. We require efficient algorithms capable of processing data streams in order to analyze them and find unique patterns. The strategies developed for static databases cannot be used to data streams. As a result, we require algorithms created expressly for data stream processing in order to extract critical unique patterns. Rare pattern mining is still in its infancy, with only a few ways available. To address this is developed the Dynamic Support Range-based Hybrid-Eclat Algorithm (DSRHEA), an Eclat-based technique for mining unique patterns from a data stream using bit-set vertical mining with two item-based optimizations. The detected patterns are kept in a prefix-based rare pattern tree that uses double hashing to maintain the unusual pattern in the data stream. Testing showed that the proposed method did well in terms of how long it took to run ,how many rare patterns it made and accuracy.

Analysis of Intelligent Energy Saving Strategy of 4G/5G Network Based on FP-Tree

With the large-scale deployment of 5G network of communication operators, there are more and more 5G devices, and the power consumption of mobile network surges. This paper uses the improved FP-Tree (Frequent Pattern Tree) to mine association rules for the service load of 4G/5G base stations of operators. It reduced the number of times of traversing the data set when generating candidate frequent and complex itemsets, and fully considers the problem of low utilization of equipment resources caused by ”Peak Valley Efect”, so as to improve the accuracy and operation efciency of the model. It greatly improves the energy-saving potential of the equipment. The evaluation of the operator’s 4G/5G network KPI, KQI and other indicators shows that the network performance is stable and the user perception is basically unchanged.

Fuzzy Pattern Tree Evolution Using Grammatical Evolution

A novel approach to induce Fuzzy Pattern Trees using Grammatical Evolution is presented in this paper. This new method, called Fuzzy Grammatical Evolution, is applied to a set of benchmark classification problems. Experimental results show that Fuzzy Grammatical Evolution attains similar and oftentimes better results when compared with state-of-the-art Fuzzy Pattern Tree composing methods, namely Fuzzy Pattern Trees evolved using Cartesian Genetic Programming, on a set of benchmark problems. We show that, although Cartesian Genetic Programming produces smaller trees, Fuzzy Grammatical Evolution produces better performing trees. Fuzzy Grammatical Evolution also benefits from a reduction in the number of necessary user-selectable parameters, while Cartesian Genetic Programming requires the selection of three crucial graph parameters before each experiment. To address the issue of bloat, an additional version of Fuzzy Grammatical Evolution using parsimony pressure was tested. The experimental results show that Fuzzy Grammatical Evolution with this extension routinely finds smaller trees than those using Cartesian Genetic Programming without any compromise in performance. To improve the performance of Fuzzy Grammatical Evolution, various ensemble methods were investigated. Boosting was seen to find the best individuals on half the benchmarks investigated.

Considering Structural and Vocabulary Heterogeneity in XML Query: FPTPQ and Holistic Evaluation.

The integration of XML data sources which have different schemas/DTD can originate structural and vocabular heterogeneity. In this context, it is difficult to write satisfiable queries. As a solution, many Information Systems focus on building approximate evaluation techniques for exact queries. As a project, we build flexible and preference XML query languages and associated evaluation algorithms. In this paper, we propose the Flexible Preference Tree Pattern Query (FPTPQ), a new TPQ that allows multiple items/names (resp. paths) for the same node, in order to integrate (resp. to locate) all the different instances of the database nodes. The FPTPQ enable to have preference nodes and ordering operators among label items and paths. We also provide a holistic algorithm that evaluates the FPTPQ and capitalises the preferences to determine the best available solutions. Illustrations and experimentations are realized to show the effectiveness of our solutions.

Spatiotemporal dynamics of encroaching tall vegetation in timberline ecotone of the Polar Urals Region, Russia

Previous studies discovered a spatially heterogeneous expansion of Siberian larch into the tundra of the Polar Urals (Russia). This study reveals that the spatial pattern of encroachment of tree stands is related to environmental factors including topography and snow cover. Structural and allometric characteristics of trees, along with terrain elevation and snow depth were collected along a transect 860 m long and 80 m wide. Terrain curvature indices, as representative properties, were derived across a range of scales in order to characterize microtopography. A density-based clustering method was used here to analyze the spatial and temporal patterns of tree stems distribution. Results of the topographic analysis suggest that trees tend to cluster in areas with convex surfaces. The clustering analysis also indicates that the patterns of tree locations are linked to snow distribution. Records from the earliest campaign in 1960 show that trees lived mainly at the middle and bottom of the transect across the areas of high snow depth. As trees expanded uphill following a warming climate trend in recent decades, the high snow depth areas also shifted upward creating favorable conditions for recent tree growth at locations that were previously covered with heavy snow. The identified landscape signatures of increasing tall vegetation, and the effects of microtopography and snow may facilitate the understanding of treeline dynamics at larger scales.

Generating 2m fine-scale urban tree cover product over 34 metropolises in China based on deep context-aware sub-pixel mapping network

Contrast to the global forest, few trees live in cities but contribute significantly to urban environment and human health. However, the classical satellite-derived land cover/forest cover products with limited resolution are not fine enough for the identification of urban tree, which is usually appeared in small size and intersected with infrastructure. To relieve the dilemma, this study developed an urban tree specific sub-pixel mapping (SPM) architecture with deep learning approach, which aimed to generate 2m fine-scale urban tree cover product from 10 m Sentinel-2 images for large-scale area of 34 metropolises in China. The proposed approach has remarkable reconstruction ability for delineating the contextual characteristic of the urban tree patterns, and reliable generalization ability to large-scale area. In addition, this study creates a large-volume urban tree cover dataset (UTCD) with 0.13 billion urban tree samples at 2 m resolution, which fills the deficiency of standard dataset in urban tree cover research field. Quantitative analysis of our products was conducted on two typical study sites of Beijing and Wuhan. The results show that our products recover averagely more than 58.72% of urban tree covers that have been underestimated in the existing land cover/forest cover products, and outperforms the state-of-the-art approach both visually and quantitatively, by averagely 11.31% improvement in overall accuracy. From our annual products during 2016–2020, we found an evolution characteristic of urban tree cover: it is more stable in developed cities like Beijing, while more fluctuated in developing cities like Wuhan, and the alteration are usually concentrated at the outer-ring of downtown, which may be caused by the municipal planning and the land development of real estate industry.

Optimizing neighborhood-based stand spatial structure: Four cases of boreal forests

Over the past fifty years, societies have placed increasing demands on forests, and their use has shifted gradually from wood production to the diversified benefits and functions of ecosystem services. The effects of neighborhood-based structural characteristics on regulating growth and promoting sustainability have therefore drawn much attention. However, direction for managing natural mixed forests using neighborhood-based indexes are still not clear. Thus, a tree-level harvest planning tool that considers four neighborhood-based structural indexes (species mingling, diametric differentiation, horizontal spatial pattern and crowdedness of trees) while concurrently recognizing other operational constraints, was developed using simulated annealing algorithm. The approach was applied to four 1-ha mapped stands in northeast China, namely a natural larch forest (NLF), a natural birch forest (NBF), a natural secondary forest (SEF), and a Korean pine broad-leaved forest (KBF). The results indicated that the tree-level harvest optimization tool improved the objective function values by approximately 78.33% of NLF, and 134.96% of NBF, and 156.70% of SEF and 252.95%, respectively. The optimal harvest intensities for partial cutting activities varied from 22.16% (SEF) to 26.07% (NBF) of the standing volume. In evaluating the four neighborhood-based structural indexes, both species mingling and crowdedness have the highest priority to be adjusted in structure-based forest management. Our results demonstrated that that the commonly used neighborhood-based structural indexes could be employed to control the spatial layout of potential harvest trees, in turn may be conducive to regulate the growth and stability of forests.

Morphological Responses of a Light-Demanding Alstonia scholaris and a Shade-Tolerant Eusideroxylon zwageri to the Air Humidity and Light Intensity

The response of tropical trees to the change of light intensity has been reported to be varied among different species. Some reports argued that the growth was increasing parallel to the increasing of light intensity, but other reports mentioned that the sensitivity to the light intensity was depending on the species. Another environmental factor that has been scientifically proven to affect tree growth is humidity. While humidity itself also directly affected by the light intensity in the forest ecosystems. Therefore, it is possible that the growth pattern of trees under different light intensities is also affected by air humidity under the canopy. This research aimed to study the growth response of a light-demanding Alstonia scholaris and a shade-tolerant Eusideroxylon zwageri to the different levels of air humidity and light intensity. The experiment was conducted in Jambi, Indonesia from April to November 2019. The experiment was carried out using split plot design with factorial treatments. The main plot was the air humidity with three levels and the sub plots was light intensity with five levels. Four replicates were applied. In general, the A. scholaris tends to be more sensitive to the humidity and light intensity compared to E. zwageri. In particular, A. scholaris tends to be more sensitive to the light intensity while, E, zwageri is more sensitive to the humidity. However, there is also strong indication that the effects of light intensity to the growth, especially for A. scholaris, was affected by the humidity level.

Effects of silvicultural selection treatments on spatial pattern and dynamics in a Pinus palustris Mill. woodland

Natural disturbance-based silviculture emphasizes harvest methods that emulate the timing and structural changes of natural disturbances. Longleaf pine woodlands are ecologically important ecosystems of the southeastern U.S. that support high biodiversity. Options for multi-aged silviculture include individual tree and group selection methods to promote regeneration—the latter method may be modified by retention of reserve trees. To explore the extent to which selection methods are congruent with natural disturbance regimes, we evaluated how treatments in mature second-growth longleaf pine woodlands affected overstory structure, pattern, and dynamics, and we made comparisons to an old-growth longleaf stand. In 2010, stands were harvested using individual tree selection, group selection, or group selection with reserves. We compared treatment effects on spatial pattern of residual trees, recruitment of trees into the 10 cm diameter class (hereafter “recruitment”), and tree mortality 8 years after harvest. Basal area and residual volume were similar among treatments, but the individual tree selection treatment had lower density and a unimodal rather than bimodal diameter distribution compared to other treatments. Group selection and group selection with reserves increased spatial aggregation, compared to individual tree selection which reduced aggregation. Recruitment was similar across treatments but usually occurred near existing trees in the group selection treatments and was further from existing trees in the individual tree selection treatment. Tree mortality primarily occurred as single trees rather than tree groups for all treatments. These results indicate that natural disturbance-based treatments vary in their effects on overstory spatial pattern and alter forest dynamics. In longleaf pine woodlands, the rationale for individual tree selection emphasizes maintaining a continuous input of needles as fine fuels for control of resprouting hardwoods with prescribed fire. This method, may simplify overstory spatial structure and alter forest dynamics after initial harvest. Maintenance of vertical and horizontal complexity is a central tenet of natural disturbance-based management, thus attention to spatial pattern must be given when individual tree selection methods are used. In longleaf pine woodlands, natural disturbance-based techniques such as group selection with reserves may better mimic spatial patterns seen in old-growth stands while preserving continuity of fine fuels.

Risk model of financial supply chain of Internet of Things enterprises: A research based on convolutional neural network

The emergence of the financial supply chain provides assistance for small, medium and micro enterprises in the supply chain through a secured credit model based on real trade. Moreover, in the multi-level structure of the financial supply chain of the Internet of Things enterprise, there are information barriers and information islands. Besides, data is often not transmitted smoothly, and the intermediate offline process is complicated. What is worse, the efficiency is low, and the verification cost is high. Therefore, based on supply chain finance, an evolutionary risk model is constructed in this paper. Firstly, the income matrix of the regulatory risk model is established, and the convolutional neural network used will pool the training data to the maximum and set the local corresponding normalization layer. With the help of the evolutionary risk theory, the dynamic equation of the financial supply chain is obtained, forming the dynamic path and abnormal model of strategy selection. Then, a compact pattern tree is added to the knowledge granularity method to mine data anomalies. Finally, an experimental platform is built to verify the effectiveness of the method proposed in this paper, and experiments are performed on the accuracy of model evolution conditions, abnormal data identification, and abnormal numerical examples. The experimental results prove that the algorithm in this paper is consistent with the set parameters, and the effect is significantly higher than other comparison methods. The experimental mining time and the comparison method are shortened by 6∼13S. The research results obtained from this paper solve the problem that the decision-making of supply chain finance and the supervision and review of supply chain enterprise are complex, which improves the characteristics identification of supply chain platform, and provides reference suggestions for financial institutions and supply chain platforms.

People Prefer Greener Corridors: Evidence from Linking the Patterns of Tree and Shrub Diversity and Users’ Preferences in Lisbon’s Green Corridors

Despite its importance as a component of urban green spaces, as far as we are aware no study has focused on plant diversity in urban green corridors (GCs). Therefore, this study aimed at: (i) characterizing tree and shrub communities in Lisbon’s GCs and (ii) assessing whether GCs’ users value trees and shrubs. We counted Lisbon’s GCs users in the same places where we assessed the tree and shrub community. Along the nine GCs, we observed trees and shrubs belonging to 70 species, distributed across 35 families with most (≥50%) species and plants being trees, exotic, pollinated by insects, with fruit dispersion by animals, evergreen leaves, and producing dry fruits. Most GCs had a similar number of users (20–30 users h−1 survey−1) except for those of Central and Ribeirinho, which were more frequented (60 and 100 users h−1 survey−1, respectively). Most users (≥50%) were adults, walking accompanied, and performing leisure activities. Finally, the number of users was shown to be influenced by: (i) tree and shrub relative abundance, confirming that users preferred greener corridors; and (ii) function(s), showing that users preferred the most multifunctional GCs (i.e., GCs fulfilling ecological, cultural, and recreational functions). Our data suggest that Lisbon’s GCs favor more the inclusion of citizens than ecological functionality and resilience.