Enhancing PDF Malware Detection through Logistic Model Trees

Malware is an ever-present and dynamic threat to networks and computer systems in cybersecurity,and because of its complexity and evasiveness,it is challenging to identify using traditional signature-based detection approaches.The study article discusses the growing danger to cybersecurity that malware hidden in PDF files poses,highlighting the shortcomings of conventional detection techniques and the difficulties presented by adversarial methodologies.The article presents a new method that improves PDF virus detection by using document analysis and a Logistic Model Tree.Using a dataset from the Canadian Institute for Cybersecurity,a comparative analysis is carried out with well-known machine learning models,such as Credal Decision Tree,Naïve Bayes,Average One Dependency Estimator,Locally Weighted Learning,and Stochastic Gradient Descent.Beyond traditional structural and JavaScript-centric PDF analysis,the research makes a substantial contribution to the area by boosting precision and resilience in malware detection.The use of Logistic Model Tree,a thorough feature selection approach,and increased focus on PDF file attributes all contribute to the efficiency of PDF virus detection.The paper emphasizes Logistic Model Tree’s critical role in tackling increasing cybersecurity threats and proposes a viable answer to practical issues in the sector.The results reveal that the Logistic Model Tree is superior,with improved accuracy of 97.46%when compared to benchmark models,demonstrating its usefulness in addressing the ever-changing threat landscape.

Classifying Machine Learning Features Extracted from Vibration Signal with Logistic Model Tree to Monitor Automobile Tyre Pressure

Tyre pressure monitoring system(TPMS)is compulsory in most countries like the United States and European Union.The existing systems depend on pressure sensors strapped on the tyre or on wheel speed sensor data.A difference in wheel speed would trigger an alarm based on the algorithm implemented.In this paper,machine learning approach is proposed as a new method to monitor tyre pressure by extracting the vertical vibrations from a wheel hub of a moving vehicle using an accelerometer.The obtained signals will be used to compute through statistical features and histogram features for the feature extraction process.The LMT(Logistic Model Tree)was used as the classifier and attained a classification accuracy of 92.5%with 10-fold cross validation for statistical features and 90.5% with 10-fold cross validation for histogram features.The proposed model can be used for monitoring the automobile tyre pressure successfully.

M5 Model Tree to Predict Temporal Evolution of Clear-Water Abutment Scour

Scour is a natural phenomenon that is created by the rivers streams or the flood which brings about transferring or eroding of bed materials. To have accurate and safe erosion control structures design, maximum scour depth in downstream of the structures gains specific significance. In the current study, M5 model tree as remedy data mining approaches is suggested to estimate the scour depth around the abutments. To do this, Kayaturk laboratory data (2005), with different hydraulic conditions, are used. Then, the results of M5 model were also compared with genetic programming (GP) and pervious empirical results to investigate the applicability, ability, and accuracy of these procedures. To examine the accuracy of the results yielded from the M5 and GP procedures, two performance indicators (determination coefficient (R2) and root mean square error (RMSE)) were used. The comparison test of results clearly shows that the implementation of M5 technique sounds satisfactory regarding the performance indicators (R2 = 0.944 and RMSE = 0.126) with less deviation from the numerical values. In addition, M5 tree model, by presenting relationships based on liner regression, has good capability to estimate the depth of scour abutment for engineers in practical terms.

Predicting distant metastasis in nasopharyngeal carcinoma using gradient boosting tree model based on detailed magnetic resonance imaging reports

BACKGROUND Development of distant metastasis(DM)is a major concern during treatment of nasopharyngeal carcinoma(NPC).However,studies have demonstrated im-proved distant control and survival in patients with advanced NPC with the addition of chemotherapy to concomitant chemoradiotherapy.Therefore,precise prediction of metastasis in patients with NPC is crucial.AIM To develop a predictive model for metastasis in NPC using detailed magnetic resonance imaging(MRI)reports.METHODS This retrospective study included 792 patients with non-distant metastatic NPC.A total of 469 imaging variables were obtained from detailed MRI reports.Data were stratified and randomly split into training(50%)and testing sets.Gradient boosting tree(GBT)models were built and used to select variables for predicting DM.A full model comprising all variables and a reduced model with the top-five variables were built.Model performance was assessed by area under the curve(AUC).RESULTS Among the 792 patients,94 developed DM during follow-up.The number of metastatic cervical nodes(30.9%),tumor invasion in the posterior half of the nasal cavity(9.7%),two sides of the pharyngeal recess(6.2%),tubal torus(3.3%),and single side of the parapharyngeal space(2.7%)were the top-five contributors for predicting DM,based on their relative importance in GBT models.The testing AUC of the full model was 0.75(95%confidence interval[CI]:0.69-0.82).The testing AUC of the reduced model was 0.75(95%CI:0.68-0.82).For the whole dataset,the full(AUC=0.76,95%CI:0.72-0.82)and reduced models(AUC=0.76,95%CI:0.71-0.81)outperformed the tumor node-staging system(AUC=0.67,95%CI:0.61-0.73).CONCLUSION The GBT model outperformed the tumor node-staging system in predicting metastasis in NPC.The number of metastatic cervical nodes was identified as the principal contributing variable.

Impact Damage Testing Study of Shanxi-Beijing Natural Gas Pipeline Based on Decision Tree Rotary Tiller Operation

The North China Plain and the agricultural region are crossed by the Shanxi-Beijing natural gas pipeline.Resi-dents in the area use rototillers for planting and harvesting;however,the depth of the rototillers into the ground is greater than the depth of the pipeline,posing a significant threat to the safe operation of the pipeline.Therefore,it is of great significance to study the dynamic response of rotary tillers impacting pipelines to ensure the safe opera-tion of pipelines.This article focuses on the Shanxi-Beijing natural gas pipeline,utilizingfinite element simulation software to establish afinite element model for the interaction among the machinery,pipeline,and soil,and ana-lyzing the dynamic response of the pipeline.At the same time,a decision tree model is introduced to classify the damage of pipelines under different working conditions,and the boundary value and importance of each influen-cing factor on pipeline damage are derived.Considering the actual conditions in the hemp yam planting area,targeted management measures have been proposed to ensure the operational safety of the Shanxi-Beijing natural gas pipeline in this region.

基于i-Tree模型的行道树生态系统服务价值

行道树作为街道绿化系统的骨架,支撑着城市生态系统服务功能的正常运转。以武汉市临江大道中段为例,基于i-Tree构建行道树生态系统服务的综合评议模型,通过以货币值作为统一衡量标准分析演算研究地27种常见行道树及街道整体生态系统服务价值,旨在为行道树树种选择优化,以及街道景观与生态环境的提升提供参考。结果表明:1)在街道的植景搭配中,整体生态系统服务价值排序为:美学价值>能源节省价值>雨水截留价值>空气净化价值>生态固碳价值。2)研究地全部行道树年生态系统服务价值约131.8万元,平均1 km行道树年生态系统服务价值可达28.65万元,单株行道树年平均生态系统服务价值可达307.23元。3)行道树具有可观的生态系统服务价值,同时也存在明显种间差异,其中能源节省、美学价值和雨水截留价值在演算树种中价值量较高,空气净化和生态固碳价值量较低。因此,在街道行道树配置过程中,应将能源节省、美学价值和雨水截留价值作为首要参考因素,再结合街道属性综合考虑树种选择与应用,以构建功能全面的城市行道树生态系统服务结构,提高行道树整体的生态系统服务价值。

Geospatial Analysis of Urban Heat Island Effects and Tree Equity

In recent decades, Urban Heat Island Effects have become more pronounced and more widely examined. Despite great technological advances, our current societies still experience great spatial disparity in urban forest access. Urban Heat Island Effects are measurable phenomenon that are being experienced by the world’s most urbanized areas, including increased summer high temperatures and lower evapotranspiration from having impervious surfaces instead of vegetation and trees. Tree canopy cover is our natural mitigation tool that absorbs sunlight for photosynthesis, protects humans from incoming radiation, and releases cooling moisture into the air. Unfortunately, urban areas typically have low levels of vegetation. Vulnerable urban communities are lower-income areas of inner cities with less access to heat protection like air conditioners. This study uses mean evapotranspiration levels to assess the variability of urban heat island effects across the state of Tennessee. Results show that increased developed land surface cover in Tennessee creates measurable changes in atmospheric evapotranspiration. As a result, the mean evapotranspiration levels in areas with less tree vegetation are significantly lower than the surrounding forested areas. Central areas of urban cities in Tennessee had lower mean evapotranspiration recordings than surrounding areas with less development. This work demonstrates the need for increased tree canopy coverage.

Growth simulation and yield prediction for perennial jujube fruit tree by integrating age into the WOFOST model

Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter.The model was established using data collected from dedicated field experiments performed in 2016-2018.Simulated growth dynamics of dry weights of leaves,stems,fruits,total biomass and leaf area index(LAI) agreed well with measured values,showing root mean square error(RMSE) values of 0.143,0.333,0.366,0.624 t ha^-1 and 0.19,and R2 values of 0.947,0.976,0.985,0.986 and 0.95,respectively.Simulated phenological development stages for emergence,anthesis and maturity were 2,3 and 3 days earlier than the observed values,respectively.In addition,in order to predict the yields of trees with different ages,the weight of new organs(initial buds and roots) in each growing season was introduced as the initial total dry weight(TDWI),which was calculated as averaged,fitted and optimized values of trees with the same age.The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI.The modelling performance was significantly improved when it considered TDWI integrated with tree age,showing good global(R2≥0.856,RMSE≤0.68 t ha^-1) and local accuracies(mean R2≥0.43,RMSE≤0.70 t ha^-1).Furthermore,the optimized TDWI exhibited the highest precision,with globally validated R2 of 0.891 and RMSE of 0.591 t ha^-1,and local mean R2 of 0.57 and RMSE of 0.66 t ha^-1,respectively.The proposed model was not only verified with the confidence to accurately predict yields of jujube,but it can also provide a fundamental strategy for simulating the growth of other fruit trees.

Statistical Modeling with a Hidden Markov Tree and High-resolution Interpolation for Spaceborne Radar Reflectivity in the Wavelet Domain

With the increasing availability of precipitation radar data from space,enhancement of the resolution of spaceborne precipitation observations is important,particularly for hazard prediction and climate modeling at local scales relevant to extreme precipitation intensities and gradients.In this paper,the statistical characteristics of radar precipitation reflectivity data are studied and modeled using a hidden Markov tree(HMT)in the wavelet domain.Then,a high-resolution interpolation algorithm is proposed for spaceborne radar reflectivity using the HMT model as prior information.Owing to the small and transient storm elements embedded in the larger and slowly varying elements,the radar precipitation data exhibit distinct multiscale statistical properties,including a non-Gaussian structure and scale-to-scale dependency.An HMT model can capture well the statistical properties of radar precipitation,where the wavelet coefficients in each sub-band are characterized as a Gaussian mixture model(GMM),and the wavelet coefficients from the coarse scale to fine scale are described using a multiscale Markov process.The state probabilities of the GMM are determined using the expectation maximization method,and other parameters,for instance,the variance decay parameters in the HMT model are learned and estimated from high-resolution ground radar reflectivity images.Using the prior model,the wavelet coefficients at finer scales are estimated using local Wiener filtering.The interpolation algorithm is validated using data from the precipitation radar onboard the Tropical Rainfall Measurement Mission satellite,and the reconstructed results are found to be able to enhance the spatial resolution while optimally reproducing the local extremes and gradients.

Comparison of stem taper models for the four tropical tree species in Mount Makiling, Philippines

This study was conducted to evaluate the performance of six stem taper models on four tropical tree species, namely Celtis luzonica(Magabuyo),Diplodiscus paniculatus(Balobo), Parashorea malaanonan(Bagtikan), and Swietenia macrophylla(Mahogany) in Mount Makiling Forest Reserve(MMFR), Philippines using fit statistics and lack-of-fit statistics. Four statistical criteria were used in this study, including the standard error of estimate(SEE),coefficient of determination(R^2), mean bias( E),and absolute mean difference(AMD). For the lack-offit statistics, SEE, E and AMD were determined in different relative height classes. The results indicated that the Kozak02 stem taper model offered the best fit for the four tropical species in most statistics. The Kozak02 model also consistently provided the best performance in the lack-of-fit statistics with the best SEE, E and AMD in most of the relative height classes. These stem taper equations could help forest managers and researchers better estimate the diameter of the outside bark with any given height,merchantable stem volumes and total stem volumes of standing trees belonging to the four species of thetropical forest in MMFR.

Study on Model of Correlation Between Chemical Ele－ment Contents in Tree Rings and Soils near Tree Roots

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Complementary parametric probit regression and nonparametric classi?cation tree modeling approaches to analyze factors affecting severity of work zone weather-related crashes

Identifying risk factors for road traffic injuries can be considered one of the main priorities of transportation agencies. More than 12,000 fatal work zone crashes were reported between 2000 and 2013. Despite recent efforts to improve work zone safety, the frequency and severity of work zone crashes are still a big concern for transportation agencies. Although many studies have been conducted on different work zone safety-related issues, there is a lack of studies that investigate the effect of adverse weather conditions on work zone crash severity. This paper utilizes probit–classification tree, a relatively recent and promising combination of machine learning technique and conventional parametric model, to identify factors affecting work zone crash severity in adverse weather conditions using 8 years of work zone weatherrelated crashes (2006–2013) in Washington State. The key strength of this technique lies in its capability to alleviate the shortcomings of both parametric and nonparametric models. The results showed that both presence of traffic control device and lighting conditions are significant interacting variables in the developed complementary crash severity model for work zone weather-related crashes. Therefore, transportation agencies and contractors need to invest more in lighting equipment and better traffic control strategies at work zones, specifically during adverse weather conditions.

Tree shrew (Tupaia belangeri)as a novel laboratory disease animal model

The tree shrew （Tupaia belangeri） is a promising laboratory animal that possesses a closer genetic relationship to primates than to rodents. In addition, advantages such as small size, easy breeding, and rapid reproduction make the tree shrew an ideal subject for the study of human disease. Numerous tree shrew disease models have been generated in biological and medical studies in recent years. Here we summarize current tree shrew disease models, including models of infectious diseases, cancers, depressive disorders, drug addiction, myopia, metabolic diseases, and immune-related diseases. With the success of tree shrew transgenic technology, this species will be increasingly used in biological and medical studies in the future.

Development and evaluation of an individual tree growth and yield model for the mixed species forest of the Adirondacks Region of New York, USA

Background： Growth and yield models are important tools for forest planning. Due to its geographic location, topology, and history of management, the forests of the Adirondacks Region of New York are unique and complex. However, only a relatively limited number of growth and yield models have been developed and/or can be reasonably extended to this region currently. Methods： in this analysis, 571 long-term continuous forest inventory plots with a total of 10 - 52 years of measurement data from four experimental forests maintained by the State University of New York College of Environmental Science and Forestry and one nonindustrial private forest were used to develop an individual tree growth model for the primary hardwood and softwood species in the region. Species-specific annualized static and dynamic equations were developed using the available data and the system was evaluated for long-term behavior. Results： Equivalence tests indicated that the Northeast Variant of the Forest Vegetation Simulator （FVS-NE） was biased in its estimation of tree total and bole height, diameter and height increment, and mortality for most species examined. In contrast, the developed static and annualized dynamic, species-specific equations performed quite well given the underlying variability in the data. Long-term model projections were consistent with the data and suggest a relatively robust system for prediction. Conclusions： Overall, the developed growth model showed reasonable behavior and is a significant improvement over existing models for the region. The model also highlighted the complexities of forest dynamics in the region and should help improve forest planning efforts there.

An imputation/copula-based stochastic individual tree growth model for mixed species Acadian forests： a case study using the Nova Scotia permanent sample plot network

Background： A novel approach to modelling individual tree growth dynamics is proposed. The approach combines multiple imputation and copula sampling to produce a stochastic individual tree growth and yield projection system. Methods： The Nova Scotia, Canada permanent sample plot network is used as a case study to develop and test the modelling approach. Predictions from this model are compared to predictions from the Acadian variant of the Forest Vegetation Simulator, a widely used statistical individual tree growth and yield model. Results： Diameter and height growth rates were predicted with error rates consistent with those produced using statistical models. Mortality and ingrowth error rates were higher than those observed for diameter and height, but also were within the bounds produced by traditional approaches for predicting these rates. Ingrowth species composition was very poorly predicted. The model was capable of reproducing a wide range of stand dynamic trajectories and in some cases reproduced trajectories that the statistical model was incapable of reproducing. Conclusions： The model has potential to be used as a benchmarking tool for evaluating statistical and process models and may provide a mechanism to separate signal from noise and improve our ability to analyze and learn from large regional datasets that often have underlying flaws in sample design.

The research on the remote sensing＇s information tree model of the Nansha coral islets and reef＇s spatial structure

Based on the Nansha coral islets and reef's time-space attributes,and the intension and extension of the remote sensing information, the concept model and concept system of coral islets and reef are proposed.Then twin-tree remote sensing information model for different kinds of reef is constructed by using abstracted islets and reef's primitive, and the structure recognition system for coral islets and reef type is developed.

Modeling for Underground Cable Water Tree Growth Dynamics

Underground cable faults, whether transient or permanent, are traceable to in-sulation failure problems, most of which are water tree initiated. Insulation breakdown, which usually leads to costly power outages, may be prevented by taking pre-emptive actions. The most decisive pre-emptive action is one in which real-time tracking of water tree advancement within the cable insulation system is possible. Such pre-emptive actions, however, depend on accurate modeling of the phenomenon. Earlier water tree models are static in that they focused on the cable insulation property change at a time segment. Thus, they lack the properties needed for tracking water tree progress and for determining the onset of transient and permanent faults. This paper presents a new ap-proach to water tree modeling, focused on insulation degradation geometry in the form of parabolic expansion of water tree. We developed a dynamic model centered on the computation of the capacitance of a vented water tree as a function of time. The dynamic model accounts for the time-dependence of the radial growth of the water tree to track insulation degradation. The model was tested in predicting cross-linked polyethylene (XLPE) cable’s insulation lifespan. The result was found to be within the range of the recorded lifespan of field aged cables in the literature. Also, performance comparison with an earlier analytical model validated with COMSOL Hyperphysics software shows a signif-icant correlation between them.

Creating animal models, why not use the Chinese tree shrew ( Tupaia belangeri chinensis)？

The Chinese tree shrew （Tupaia belangeri chinensis） a squirrel-like and rat-sized mammal, has a wide distribution in Southeast Asia, South and Southwest China and has many unique characteristics that make it suitable for use as an experimental animal. There have been many studies using the tree shrew （Tupaia belangeri） aimed at increasing our understanding of fundamental biological mechanisms and for the modeling of human diseases and therapeutic responses. The recent release of a publicly available annotated genome sequence of the Chinese tree shrew and its genome database （www.treeshrewdb.org） has offered a solid base from which it is possible to elucidate the basic biological properties and create animal models using this species. The extensive characterization of key factors and signaling pathways in the immune and nervous systems has shown that tree shrews possess both conserved and unique features relative to primates. Hitherto, the tree shrew has been successfully used to create animal models for myopia, depression, breast cancer, alcohol-induced or non-alcoholic fatty liver diseases, herpes simplex virus type 1 （HSV-1） and hepatitis C virus （HCV） infections, to name a few. The recent successful genetic manipulation of the tree shrew has opened a new avenue for the wider usage of this animal in biomedical research. In this opinion paper, I attempt to summarize the recent research advances that have used the Chinese tree shrew, with a focus on the new knowledge obtained by using the biological properties identified using the tree shrew genome, a proposal for the genome-based approach for creating animal models, and the genetic manipulation of the tree shrew. With more studies using this species and the application of cutting-edge gene editing techniques, the tree shrew will continue to be under the spot light as a viable animal model for investigating the basis of many different human diseases.

Construction and analysis of tree models for chromosomal classification of diffuse large B-cell lymphomas

AIM: To construct tree models for classification of diffuse large B-cell lymphomas (DLBCL) by chromosome copy numbers, to compare them with cDNA microarray classification, and to explore models of multi-gene, multi-step and multi-pathway processes of DLBCL tumorigenesis. METHODS: Maximum-weight branching and distancebased models were constructed based on the comparative genomic hybridization (CGH) data of 123 DLBCL samples using the established methods and software of Desper et al . A maximum likelihood tree model was also used to analyze the data. By comparing with the results reported in literature, values of tree models in the classification of DLBCL were elucidated. RESULTS: Both the branching and the distance-based trees classified DLBCL into three groups. We combined the classification methods of the two models and classified DLBCL into three categories according to their characteristics. The first group was marked by +Xq, +Xp, -17p and +13q; the second group by +3q, +18q and +18p; and the third group was marked by -6q and +6p. This chromosomal classification was consistent with cDNA classification. It indicated that -6q and +3q were two main events in the tumorigenesis of lymphoma. CONCLUSION: Tree models of lymphoma established from CGH data can be used in the classification of DLBCL. These models can suggest multi-gene, multistep and multi-pathway processes of tumorigenesis. Two pathways, -6q preceding +6q and +3q preceding+18q, may be important in understanding tumorigenesis of DLBCL. The pathway, -6q preceding +6q, may have a close relationship with the tumorigenesis of non-GCB DLBCL.

An Improved Approach for Generating Test Cases during Model-Based Testing Using Tree Traversal Algorithm

During the model-based software testing process, test cases are generated from modeled requirements to conduct acceptance testing. However, existing approaches generate erroneous test cases, lack full coverage criteria and prototype tools. Therefore, the aim of this research is to develop an approach capable of reducing erroneous test case generation based on full coverage criteria and a prototype tool. The method employed was to develop a parser to extract information from the XMI file of a modeling diagram where a tree is constructed and a traversal operation executed on the nodes and edges to generate test cases. The results obtained from the proposed approach showed that 97.35% of the generated test cases were precise and comprehensive enough to conduct testing because 99.01% of all the nodes and edges were fully covered during the traversal operations.