Dynamic Tree Research Articles

Fuzzy Association Rules Mining based on Type-2 Fuzzy Sets over Data Stream

Fuzzy set theory aims to solve the problem of mining more valuable human reasoning knowledge from quantitative databases, and to narrow the gap between computer and human understanding. Fuzzy Frequent Itemset (FFI) mining, which is very important in fuzzy association rule mining, converts quantitative values into language, and finds fuzzy frequency itemset according to the defined membership function. In the traditional fuzzy frequent pattern mining method, only the language item with the largest scalar base is considered, and the uncertainty of the membership function is not considered. At the same time, it is mainly considered for the fixed data set. This paper proposes a Fuzzy Frequent Pattern mining algorithm based on the Type-2 Fuzzy Set (T2FS) theory of the data stream. Under the T2FS theory, the data stream is dynamically divided based on the sliding window method, and the ambiguity is quickly found from the numerical data stream. In order to reduce the search space and improve over-mining, this paper designs a dynamic compressed lists structure and trees method. Under different minimum support degrees and sliding window sizes, many experiments have been carried out, and the experiments have verified the efficiency and effectiveness of the designed method in terms of running time and memory usage. The results can also contribute to exchange the information and preferences among the people who cooperate in decision-making.

Modelling the Tree Height, Crown Base Height, and Effective Crown Height of Pinus koraiensis Plantations Based on Knot Analysis

Taking 1735 Pinus koraiensis knots in Mengjiagang Forest Farm plantations in Jiamusi City, Heilongjiang Province as the research object, a dynamic tree height, effective crown height, and crown base height growth model was developed using 349 screened knots. The Richards equation was selected as the basic model to develop a crown base height and effective crown height nonlinear mixed-effects model considering random tree-level effects. Model parameters were estimated with the non-liner mixed effect model (NLMIXED) Statistical Analysis System (SAS) module. The akaike information criterion (AIC), bayesian information criterion (BIC), −2 Log likelihood (−2LL), adjusted coefficient (Ra2), root mean square error (RMSE), and residual squared sum (RSS) values were used for the optimal model selection and performance evaluation. When tested with independent sample data, the mixed-effects model tree effects-considering outperformed the traditional model regarding their goodness of fit and validation; the two-parameter mixed-effects model outperformed the one-parameter model. Pinus koraiensis pruning times and intensities were calculated using the developed model. The difference between the effective crown and crown base heights was 1.01 m at the 15th year; thus, artificial pruning could occur. Initial pruning was performed with a 1.01 m intensity in the 15th year. Five pruning were required throughout the young forest period; the average pruning intensity was 1.46 m. The pruning interval did not differ extensively in the half-mature forest period, while the intensity decreased significantly. The final pruning intensity was only 0.34 m.

Memory effect and phase transition in a hierarchical trap model for spin glasses.

We introduce an efficient dynamical tree method that enables us to explicitly demonstrate the thermoremanent magnetization memory effect in a hierarchical energy landscape. Our simulation nicely reproduces the nontrivial waiting-time and waiting-temperature dependences in this nonequilibrium phenomenon. We further investigate the condensation effect, in which a small set of microstates dominates the thermodynamic behavior in the multilayer trap model. Importantly, a structural phase transition of the multilayer tree model is shown to coincide with the onset of the condensation phenomenon. Our results underscore the importance of hierarchical structure and demonstrate the intimate relation between the glassy behavior and structure of barrier trees.

Toward Concurrent Video Multicast Orchestration for Caching-Assisted Mobile Networks

Multicasting does not work well for video applications such as video on demand, where individual users request video content at discrete and unpredictable moments. In this paper, we study a concurrent video multicast orchestration solution for the caching-assisted and centrally controlled mobile networks. The proposed solution strives to obtain rapid video prefetch and effective traffic reduction. By rapid video prefetch, user devices can cache required contents in advance, which can effectively improve the streaming fluency in unsteady wireless environments, especially the highly mobile vehicular environment. We introduce a maximum-rate concurrent multicast (MRCM) problem whose solution is intended to maximize the total video delivery rate by a concurrent multicast manner, and prove that the MRCM problem is NP-hard. Then, we propose a dynamic concurrent multicast tree construction algorithm and a global concurrent multicast tree optimization algorithm to approximately solve the MRCM problem in polynomial time. We also introduce a cache competition mechanism that strives to maximize the total cache hit rate when the cache space is insufficient. Performance evaluations show that compared with the existing cache-assisted multicast delivery solutions, the proposed solution significantly reduces the video delivery duration and the traffic stress of backhaul links.

Dynamic PRA Prospects for the Nuclear Industry

This review paper highlights approaches and tools available to the nuclear industry for dynamic probabilistic risk assessment (DPRA) using dynamic event trees. DPRA is an emerging methodology that has advantages as compared to traditional, static PRA predominantly owing to the addition of time dependent modeling. Traditional PRAs predefine events and outcomes into Event Trees (ET) and Fault Trees (FT), that are coupled with various combinations of Initiating Events (IE), Top Events (TE), branches, end states and sequences. A more complete depiction of the system and accident progression behavior can be quantified using DPRA to account for dynamic events such as those involving human actions. This paper discusses the strengths and needs of existing DPRA tools to align with the risk informed methodology currently used in the nuclear industry. DPRA is evolving during an exciting time in the nuclear industry with emerging advanced reactor designs also coming on the scene. Advanced nuclear (Gen IV) designs often incorporate passively safe systems that have less readily available data for traditional PRA due to their limited operating history. DPRA is a promising methodology that can address this challenge and demonstrate to the regulatory bodies and public that advanced designs operate within safety margins. In this light, the paper considers the historical role of PRA in the nuclear industry and motivation for considering dynamic PRA models. An introduction to the differences inherent in DPRA and how it complements and enhances existing PRA approaches is discussed. Additionally, a review of research from U.S national laboratories and universities features recent DPRA tool advancements that could be applied in the nuclear industry. These DPRA approaches and tools are summarized and examined to thoughtfully provide a path forward to best leverage existing research and integrate DPRA into advanced reactor design and analysis.

Development of a dynamic event tree (DET) to analyze SBO accident in VVER-1000/V446 nuclear reactor

Probabilistic safety assessment (PSA) is the main approach currently applied in safety assessment of nuclear power plants to calculate the core damage frequency (CDF). The conventional event tree/fault tree methodology, adopted in current PSA studies, has weaknesses such as nonrealistic modeling of operator and recovery actions, inability for functional reliability modelling of passive safety systems, using predetermined timing and order of accident sequences without any interaction of system responses, insensitivity of the system failure to uncertainties in the process physics and consequently its output is not comprehensive for safety considerations, risk management and decision making tasks. In this study to overcome these bottlenecks, a dynamic approach based on dynamic event tree (DET) is applied to calculate the CDF for the station blackout (SBO) accident in VVER-1000/V446 nuclear reactor. To implement the DET, deterministic and probabilistic models are developed using the plant data and the characteristics of the SBO accident. These models are then run simultaneously to investigate the time of operator actions, AC power supply recovery, and the availability of safety systems on the accident progression and consequences. The results show that the value of CDF calculated with DET is equal to 1.97e-6 (yr−1). The results also illustrates that the DET can provide useful insights concerning potential risk contributors and the effectiveness of operator and recovery actions.

Mental Calculation Drives Reliable and Weak Distant Connectivity While Music Listening Induces Dense Local Connectivity.

The online version contains supplementary material available at 10.1007/s43657-021-00027-w.

Risk analysis virtual ENvironment for dynamic event tree-based analyses

Conventional Event-Tree (ET) based methodologies are extensively used as tools to perform reliability and safety assessment of complex and critical engineering systems. One of the disadvantages of these methods is that timing/sequencing of events and system dynamics is not explicitly accounted for in the analysis. In order to overcome these limitations several techniques, also known as Dynamic Probabilistic Risk Assessment (DPRA), have been developed. Monte-Carlo (MC) and Dynamic Event Tree (DET) are two of the most widely used DPRA methodologies to perform safety assessment of Nuclear Power Plants (NPP). Since 2012, the Idaho National Laboratory (INL) is developing its own tool to perform Dynamic PRA: RAVEN (Risk Analysis and Virtual ENvironment). RAVEN has been designed in a high modular and pluggable way to enable easy integration of different programming languages (i.e., Python, C++) and coupling with other application including, among the others, several thermal–hydraulic and severe accident codes (e.g., RELAP5-3D, MELCOR, MAAP5, TRACE, etc.). RAVEN is aimed to provide a framework/container of capabilities for engineers and scientists to analyze the response of systems, physics and multi-physics, employing advanced numerical techniques and algorithms. Moreover, RAVEN models stochastic events, such as components failures, and performs uncertainty quantification (UQ). Such stochastic modeling is employed by using sampling strategies among which both MC and DET algorithms, which are going to be employed in this paper. In addition, RAVEN processes the large amount of data generated by sampling the physical models using data-mining based algorithms and risk assessment techniques. This paper provides an overview of the DET methodologies that have been deployed within the RAVEN framework, showing the potential of such techniques for the analysis of complex systems. A brief background of classical methodologies and their limitation is also reported and represent the motivation for the deployment of such dynamic technique. In addition, results from a pressurized water reactor loss of coolant accident scenario, using RELAP5-3D as physical model, are reported.

Damage assessment for large-scale surface warship systems using a dynamic location damage tree model quantified based on the multilevel Monte Carlo simulation

The multiple functions of large-scale surface warship systems involve the main system, subsystems and important components with complex logical relationships, and some components require a number of redundant components to maintain their high reliability. Moreover, the information about the ship structure, system and equipments cannot be fully determined in practical naval battlefield, especially locations of some key components. Therefore, it is difficult to accurately perform damage assessment of large-scale surface warships using traditional fault tree models. Monte Carlo simulation (MCS) is the most robust method for damage assessment, but it is computationally demanding. To address the issue, a new method with high efficiency and accuracy is proposed herein. In the proposed method, a dynamic damage tree (DLDT) model is developed to describing complex ship systems with redundant and uncertain information, and the multilevel Monte Carlo (MLMC) simulation is modified to reduce the computational cost of MCS. The modified MLMC is capable of obtaining the damage probabilities of various components, subsystems and top events for different numbers of anti-ship weapons. Results of numerical validation demonstrate that the computational cost of the proposed MLMC is much less than that of MCS while achieving the same accuracy level. By combining DLDT and MLMC, the proposed method provides a reference for efficiently and accurately assessing the functional damage to large-scale surface warships.

A Dynamic Tree Algorithm for Peer-to-Peer Ridesharing Matching

On-demand peer-to-peer ridesharing services provide flexible mobility options and are expected to alleviate congestion by sharing empty car seats. An efficient matching algorithm is essential to the success of a ridesharing system. The matching problem is related to the well-known dial-a-ride problem, which also tries to find the optimal pickup and delivery sequence for a given set of passengers. In this paper, we propose an efficient dynamic tree algorithm to solve the on-demand peer-to-peer ridesharing matching problem. The dynamic tree algorithm benefits from given ridesharing driver schedules and provides satisfactory runtime performances. In addition, an efficient pre-processing procedure to select candidate passenger requests is proposed, which further improves the algorithm performance. Numerical experiments conducted in a small network show that the dynamic tree algorithm reaches the same objective function values of the exact algorithm, but with shorter runtimes. Furthermore, the proposed method is applied to a larger size problem. Results show that the spatial distribution of ridesharing participants influences the algorithm performance. Sensitivity analysis confirms that the most critical ridesharing matching constraints are the excess travel times. The network analysis suggests that small vehicle capacities do not guarantee overall vehicle-kilometer travel savings.

Feature Fusion Approach for Temporal Land Use Mapping in Complex Agricultural Areas

Accurate temporal land use mapping provides important and timely information for decision making for large-scale management of land and crop production. At present, temporal land cover and crop classifications within a study area have neglected the differences between subregions. In this paper, we propose a classification rule by integrating the terrain, time series characteristics, priority, and seasonality (TTPSR) with Sentinel-2 satellite imagery. Based on the time series of Normalized Difference Water Index (NDWI) and Vegetation Index (NDVI), a dynamic decision tree for forests, cultivation, urban, and water was created in Google Earth Engine (GEE) for each subregion to extract cultivated land. Then, with or without this cultivated land mask data, the original classification results for each subregion were completed based on composite image acquisition with five vegetation indices using Random Forest. During the post-reclassification process, a 4-bit coding rule based on terrain, type, seasonal rhythm, and priority was generated by analyzing the characteristics of the original results. Finally, statistical results and temporal mapping were processed. The results showed that feature importance was dominated by B2, NDWI, RENDVI, B11, and B12 over winter, and B11, B12, NDBI, B2, and B8A over summer. Meanwhile, the cultivated land mask improved the overall accuracy for multicategories (seven to eight and nine to 13 during winter and summer, respectively) in each subregion, with average ranges in the overall accuracy for winter and summer of 0.857–0.935 and 0.873–0.963, respectively, and kappa coefficients of 0.803–0.902 and 0.835–0.950, respectively. The analysis of the above results and the comparison with resampling plots identified various sources of error for classification accuracy, including spectral differences, degree of field fragmentation, and planting complexity. The results demonstrated the capability of the TTPSR rule in temporal land use mapping, especially with regard to complex crops classification and automated post-processing, thereby providing a viable option for large-scale land use mapping.

A Practical Index Structure Supporting Fréchet Proximity Queries among Trajectories

We present a scalable approach for range and k nearest neighbor queries under computationally expensive metrics, like the continuous Fréchet distance on trajectory data. Based on clustering for metric indexes, we obtain a dynamic tree structure whose size is linear in the number of trajectories, regardless of the trajectory’s individual sizes or the spatial dimension, which allows one to exploit low “intrinsic dimensionality” of datasets for effective search space pruning. Since the distance computation is expensive, generic metric indexing methods are rendered impractical. We present strategies that (i) improve on known upper and lower bound computations, (ii) build cluster trees without any or very few distance calls, and (iii) search using bounds for metric pruning, interval orderings for reduction, and randomized pivoting for reporting the final results. We analyze the efficiency and effectiveness of our methods with extensive experiments on diverse synthetic and real-world datasets. The results show improvement over state-of-the-art methods for exact queries, and even further speedups are achieved for queries that may return approximate results. Surprisingly, the majority of exact nearest-neighbor queries on real datasets are answered without any distance computations.

Discovery of network motifs based on induced subgraphs using a dynamic expansion tree

Biological networks are powerful representations of topological features in biological systems. Finding network motifs in biological networks is a computationally hard problem due to their huge size and abrupt increase of search space with the increase of motif size. Motivated by the computational challenges of network motif discovery and considering the importance of this topic, an efficient and scalable network motif discovery algorithm based on induced subgraphs in a dynamic expansion tree is proposed. This algorithm uses a pruning strategy to overcome the space limitation of the static expansion tree. The proposed algorithm can identify large network motifs up to size 15 by significantly reducing the computationally expensive subgraph isomorphism checks. Further, the present work avoids the unnecessary growth of patterns that do not have any statistical significance. The runtime performance of the proposed algorithm outperforms most of the existing algorithms for large network motifs.

Prediction of branch growth using quantile regression and mixed-effects models: An example with planted Larix olgensis Henry trees in Northeast China

Branch is an essential component that supports foliage for photosynthesis, and its size appears as a major determinant for log grading. Forest managers have always been facing a challenge to maximize tree increment and timber quality; hence, linking the dynamic relationship between the branch and tree growth will be beneficial for crucial stand managerial decisions (pruning, thinning, and other silvicultural practices). In this study, branch growth models were developed using 77 destructively sampled Korean larch trees (Larix olgensis Henry) from several plantations in the Northeastern area of China. A modified Mitscherlich function that includes dynamic tree height and branch height was used to predict the growth of the branch’s diameter and length. The jackknifing technique was utilized to evaluate five alternative modeling approaches: (1) fixed-effects model; (2) calibrated mixed-effects model; (3) three-quantile regression method; (4) five-quantile regression method; and (5) nine-quantile regression method. The results showed that the prediction performance of both calibrated mixed-effects and quantile regression approaches outperformed the fixed-effects model, in which the calibrated mixed-effects provided better performance than quantile regression. In addition, three sampling strategies and various number of branches (1 to 8 branches per sample tree) were used for calibrating the mixed-effect and quantile regression model. The three sampling strategies brought relatively similar prediction performance, specifically for the larger sample size. Model performance improved as the sample size increased, but gains in performance decreased gradually. Overall, a combination of Type I sampling strategy and at least five sample branches per tree was recommended as a win–win solution to minimize the required measurement cost without sacrificing the model’s predictive accuracy.

PhyloPrimer: a taxon-specific oligonucleotide design platform.

Many environmental and biomedical biomonitoring and detection studies aim to explore the presence of specific organisms or gene functionalities in microbiome samples. In such cases, when the study hypotheses can be answered with the exploration of a small number of genes, a targeted PCR-approach is appropriate. However, due to the complexity of environmental microbial communities, the design of specific primers is challenging and can lead to non-specific results. We designed PhyloPrimer, the first user-friendly platform to semi-automate the design of taxon-specific oligos (i.e., PCR primers) for a gene of interest. The main strength of PhyloPrimer is the ability to retrieve and align GenBank gene sequences matching the user’s input, and to explore their relationships through an online dynamic tree. PhyloPrimer then designs oligos specific to the gene sequences selected from the tree and uses the tree non-selected sequences to look for and maximize oligo differences between targeted and non-targeted sequences, therefore increasing oligo taxon-specificity (positive/negative consensus approach). Designed oligos are then checked for the presence of secondary structure with the nearest-neighbor (NN) calculation and the presence of off-target matches with in silico PCR tests, also processing oligos with degenerate bases. Whilst the main function of PhyloPrimer is the design of taxon-specific oligos (down to the species level), the software can also be used for designing oligos to target a gene without any taxonomic specificity, for designing oligos from preselected sequences and for checking predesigned oligos. We validated the pipeline on four commercially available microbial mock communities using PhyloPrimer to design genus- and species-specific primers for the detection of Streptococcus species in the mock communities. The software performed well on these mock microbial communities and can be found at https://www.cerealsdb.uk.net/cerealgenomics/phyloprimer.

Metatheory.jl: Fast and Elegant Algebraic Computation in Julia with Extensible Equality Saturation

We introduce Metatheory.jl: a lightweight and performant general purpose symbolics and metaprogramming framework meant to simplify the act of writing complex Julia metaprograms and to significantly enhance Julia with a native term rewriting system, based on state-of-the-art equality saturation techniques, and a dynamic first class Abstract Syntax Tree (AST) pattern matching system that is dynamically composable in an algebraic fashion, taking full advantage of the language's powerful reflection capabilities. Our contribution allows to perform general purpose symbolic mathematics, manipulation, optimization, synthesis or analysis of syntactically valid Julia expressions with a clean and concise programming interface, both during compilation or execution of programs.

PROGRAMMING DATA STRUCTURE ALGORITHMS IN PYTHON

The article discusses ways of programming algorithms for complex data structures in Python. Knowledge of these structures and the corresponding algorithms is necessary when choosing the best methods for developing various software. When studying the subject "Algorithms and Data Structures", it is important to understand the essence of data structures. This is due to the fact that manipulating a data structure to fit a specific problem requires an understanding of the essence and algorithms of this data structure. Examples of programming algorithms related to dynamic lists and binary search trees in the currently widely used Python language are given. The algorithms for traversing the graph in depth and breadth are optimally and clearly implemented using the Python dictionary.

A Review of Dynamic Tree Behaviors: Measurement Methods on Tree Sway, Tree Tilt, and Root–Plate Movement

Urban forest ecosystems are being developed to provide various environmental services (e.g., the preservation of urban trees) to urban inhabitants. However, some trees are deteriorated asymptomatically without exhibiting an early sign of tree displacement, which results in a higher vulnerability under dynamic wind loads, especially during typhoon seasons, in the subtropical and tropical regions. As such, it is important to understand the tilt and sway behaviors of trees to cope up with the probability of tree failure and to improve the efficacy of tree management. Tree behaviors under wind loads have been broadly reviewed in the past literature, yet thorough discussions on the measurement methods for tree displacement and its analysis of broadleaf specimens are lacking. To understand the behavioral pattern of both broadleaf and conifer species, this paper presents a detailed review of sway behavior analysis from the perspectives of the aerial parts of the individual tree, including tree stem, canopy, and trunk, alongside a highlighted focus on the root–plate movement amid the soil-root system. The analytical approaches associated with the time-space domain and the time-frequency domain are being introduced. In addition to the review of dynamic tree behaviors, an integrated tree monitoring framework based on geographic information systems (GIS) to detect and visualize the extent of tree displacement using smart sensing technology (SST) is introduced. The monitoring system aims to establish an early warning indicator system for monitoring the displacement angles of trees over the territory of Hong Kong’s urban landscape. This pilot study highlights the importance of the monitoring system at an operational scale to be applicable in the urban areas showcasing the practical use of the Internet of Things (IoT) with an in-depth understanding of the wind-load effect toward the urban trees in the tropical and subtropical cities.

Dynamic tree branch tracking for aerial canopy sampling using stereo vision

Maintaining the massive forests of the forestry industry requires physical samples from the tree canopy to enable breeding, genetics research and monitoring of diseases. An autonomous Unmanned Aerial Vehicle (UAV) canopy sampling solution requires locating and tracking a highly unstructured, moving object against a dynamic, cluttered background. The developed algorithm utilises stereo vision techniques to reconstruct a 3D point cloud of the target branch and subsequently estimate the intercept point location. The optimal parameters are selected by quantifying the accuracy against ground truth obtained from motion capture. Since wind will likely affect the tree canopy during sampling, the branch will exhibit some sway motion which can be modelled as simple harmonic motion. Hence, a linear Kalman filter is formulated to estimate the sway parameters. With the branch 1m from the camera, the average error is 41mm for sway amplitudes between 50mm and 250mm with a plain background. When tested with a cluttered background, the reduced contrast results in higher standard deviations. Overall, the developed Kalman filter significantly reduces the intercept point error standard deviation, averaging 17% and 33% reductions for plain and cluttered backgrounds respectively. Subsequent outdoor testing verified the capability of the developed algorithm to estimate the branch trajectory to well within the maximum gripper opening of 115mm.

Kinodynamic planning with reachability prediction for PTL maintenance robot

This article presents a novel method—Dynamic Environment Rapid Search Tree—for power transmission line maintenance robot, which is based on the learned field function of reachability and the genetic best-first policy. Dynamic Environment Rapid Search Tree uses a priori information to optimize the node selection strategy in the rigid–flexible coupling environment with slight perturbation and generates the joint path in the configuration space. While the search tree rapidly extends toward the goal configuration, it effectively avoids the obstacles and greatly reduces the expansion of the irrelevant region. Finally, the joint trajectory considering the dynamic constraints and cost function is given, which provides the reference positions and torques for the robot controller. Traditional planning algorithms are compared with our proposed method under two different operation modes, and the planner is demonstrated on the robot under real settings. The experiment results verify the feasibility and adaptiveness of the proposed algorithm and planner, even in slightly and continuously varying environment.