Tree Structure Research Articles

Enhancing the content of phycoerythrin through the application of microplastics from Porphyridium cruentum produced in wastewater using machine learning methods

Microalgae can produce secondary metabolites like phycoerythrin (Phy). The effects of some microplastics (MPs), wastewater (WW), and light intensity (LI) parameters, including complex data sets, on Phy concentration from Porphyridium cruentum were investigated using machine learning methods in this study. Also, the deep learning (DL) model was developed to get the maximum phy concentration from the dataset. The dataset (232 data groups), including a feature set, polyethylene (PE), polypropylene (PP), polystyrene (PS), polyvinyl chloride (PVC), WW, LI, and an output variable, Phy, were randomly divided into training and test sets to create and evaluate the models. The highest experimental and predicted Phy concentrations were 52.3 mg/g and 58.32 mg/g in a scenario with 15% WW, 80 mg/L PE, PP, PS, and PVC, and a LI of 175 μmolm−2 s-1, respectively. The Pearson correlation coefficient (r) indicates a positive correlation between Phy and the variables PE (r = 0.35), PVC (r = 0.69), PP (r = 0.27), PS (r = 0.29), and LI (r = 0.22). However, variables such as WW (r = -0.05) have a weak correlation, and while PVC and PE showed the most significant effect on Phy concentration, WW had the lowest effect. Furthermore, LIME (local interpretable model-agnostic explanations) and SHAP (shapley additive explanations) provided us with important results for interpreting the random forest regression (RF) and DL models' predictions, respectively. The LIME and SHAP analyses suggest that the system with more PVC has a higher predicted Phy value. For WW, the reverse is true; higher WW values result in lower Phy predictions. Researchers were given the model explainability decision tree (DT) structure to study reactants' effects on output (Phy). In conclusion, the dye industry can use microalgae to treat WW contaminated with MPs while also producing high amounts of Phy using a DL model.

EFFT: An Event-Based Method for the Efficient Computation of Exact Fourier Transforms.

We introduce eFFT, an efficient method for the calculation of the exact Fourier transform of an asynchronous event stream. It is based on keeping the matrices involved in the Radix-2 FFT algorithm in a tree data structure and updating them with the new events, extensively reusing computations, and avoiding unnecessary calculations while preserving exactness. eFFT can operate event-by-event, requiring for each event only a partial recalculation of the tree since most of the stored data are reused. It can also operate with event packets, using the tree structure to detect and avoid unnecessary and repeated calculations when integrating the different events within each packet to further reduce the number of operations. eFFT has been extensively evaluated with public datasets and experiments, validating its exactness, low processing time, and feasibility for online execution on resource-constrained hardware. We release a C++ implementation of eFFT to the community.

Generating Global and Local Explanations for Tree-Ensemble Learning Methods by Answer Set Programming

Abstract We propose a method for generating rule sets as global and local explanations for tree-ensemble learning methods using answer set programming (ASP). To this end, we adopt a decompositional approach where the split structures of the base decision trees are exploited in the construction of rules, which in turn are assessed using pattern mining methods encoded in ASP to extract explanatory rules. For global explanations, candidate rules are chosen from the entire trained tree-ensemble models, whereas for local explanations, candidate rules are selected by only considering rules that are relevant to the particular predicted instance. We show how user-defined constraints and preferences can be represented declaratively in ASP to allow for transparent and flexible rule set generation, and how rules can be used as explanations to help the user better understand the models. Experimental evaluation with real-world datasets and popular tree-ensemble algorithms demonstrates that our approach is applicable to a wide range of classification tasks.

Abstract 4119052: Redefining the phenotypic continuum of cardiovascular disease using machine learning

Background: Current approaches that define cardiovascular disease rely on assignment of traditional diagnostic labels which may not reflect the natural continuum of phenotypic expression, influenced by genetic and environmental modifiers, or adequately identify groups with shared molecular mechanisms. Machine learning can leverage advances in genetic and imaging characterisation to reclassify phenotypic diversity along a continuum, from health to disease. Research Aims: Our study aims to build a tree-like classification of cardiovascular phenotypes in the community where branches represent subjects with shared features, ordered by their severity. Using dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM) as exemplar conditions with opposing phenotypes, we aim to demonstrate the interaction between genetic and environmental modifiers of disease expression. Methods: We analysed cardiovascular magnetic resonance (CMR) imaging, electrocardiogram (ECG), biomarkers and clinical data from participants in the UK Biobank. Using unsupervised learning of multiparametric data, we built a tree of phenotypic expression. We projected the risk of DCM and HCM cases, and associated rare and common variant risk, onto the tree structure. Results: Ten main branches were discovered, using data from 41,525 participants (51.7 % female, median age 65 yrs [IQR: 58, 70]). The extremities of branches 1 and 6 were enriched for DCM cases (p <0.05) and participants with reduced ejection fraction and high left ventricular volumes (p <0.05). Compared with subjects at the tree centre, distal participants in branch 1 had increased blood pressure (BP) and body mass index (BMI), whilst those in branch 6 were more likely to have high HbA1c levels and carry a pathogenic DCM variant (p <0.05). The extremity of branch 5 was enriched for HCM cases, rare sarcomeric variants (p = 0.004) and high polygenic risk (p <0.001). Whilst the ends of branches 3 and 9 had high polygenic risk for HCM, they were less likely to have elevated BP and BMI, and had reduced likelihood of HCM expression, compared with branch 5. A phenomapping model was trained to accurately project unseen subjects onto the tree (R 2 0.98). Conclusions: We present a tree-like continuum of cardiovascular phenotypes, providing a novel framework for mechanistic discovery and exploration of genotype-phenotype associations. Phenomapping of unseen subjects enables personalised estimation of genetic and cardiovascular risk.

Electrical Tree and Partial Discharge Characteristics of Silicone Rubber Under Mechanical Pressure

Silicone rubber (SIR) is a crucial insulating material in cable accessories, but it is also susceptible to faults. In practical applications, mechanical pressure from bending or shrinking can impact the degradation of SIR, necessitating the study of its electrical tree and partial discharge (PD) characteristics under such pressure. This work presents the construction of a test platform for electrical trees under varying pressures to observe their growth process. A high-frequency current transformer is used to measure PD patterns during tree growth, enabling analysis of the effect of PD on tree initiation and propagation under pressure. The experimental results demonstrate a significant decrease in tree inception probability and increase in PD inception voltage under pressure. The pressure also influences the tree structure and PD during the treeing process, where the longest tree with a branch-like structure appears under 800 kPa. The effect of pressure on electrical tree and PD characteristics can be attributed to changes in free volume, alterations in air pressure within the tree channels, and the affected charge accumulation.

A study of self-similar vector fields in bianchi type III spacetime via Rif tree approach

Abstract In this study, we use the Rif tree approach to explore self-similar vector fields in Bianchi type III spacetime. This work adopt a computer-based method to transform symmetry equations into an involutive form that is simplified and divides the integration problem into multiple cases, each represented as a tree structure. In some cases, the metric functions are subject to particular constraints. These conditions allow one to solve the system of equations governing self-similar symmetries and provide explicit formulations for the metrics and their corresponding self-similar vector fields. This approach is novel in that it covers not only the analysis of the direct integration strategy but also some metrics that are practically relevant. For a detailed investigation of the created models, stability and physical importance are also considered.

VRTree: Example‐Based 3D Interactive Tree Modeling in Virtual Reality

AbstractWe present VRTree, an example‐based interactive virtual reality (VR) system designed to efficiently create diverse 3D tree models while faithfully preserving botanical characteristics of real‐world references. Our method employs a novel representation called Hierarchical Branch Lobe (HBL), which captures the hierarchical features of trees and serves as a versatile intermediary for intuitive VR interaction. The HBL representation decomposes a 3D tree into a series of concise examples, each consisting of a small set of main branches, secondary branches, and lobe‐bounded twigs. The core of our system involves two key components: (1) We design an automatic algorithm to extract an initial library of HBL examples from real tree point clouds. These HBL examples can be optionally refined according to user intentions through an interactive editing process. (2) Users can interact with the extracted HBL examples to assemble new tree structures, ensuring the local features align with the target tree species. A shape‐guided procedural growth algorithm then transforms these assembled HBL structures into highly realistic, finegrained 3D tree models. Extensive experiments and user studies demonstrate that VRTree outperforms current state‐of‐the‐art approaches, offering a highly effective and easy‐to‐use VR tool for tree modeling.

Evaluation of Two-Dimensional DBH Estimation Algorithms Using TLS

Terrestrial laser scanning (TLS) has become a vital tool in forestry for accurately measuring tree parameters, such as diameter at breast height (DBH). However, its application in Mexican forests remains underexplored. This study evaluates the performance of five two-dimensional DBH estimation algorithms (Nelder–Mead, least squares, Hough transform, RANSAC, and convex hull) within a temperate Mexican forest and explores their broader applicability across diverse ecosystems, using published point cloud data from various scanning devices. Results indicate that algorithm accuracy is influenced by local factors like point cloud density, occlusion, vegetation, and tree structure. In the Mexican study area, the Nelder–Mead algorithm achieved the highest accuracy (R² = 0.98, RMSE = 1.59 cm, MAPE = 6.12%), closely followed by least squares (R² = 0.98, RMSE = 1.67 cm, MAPE = 6.42%), with different outcomes in other sites. These findings advance DBH estimation methods by highlighting the importance of tailored algorithm selection and environmental considerations, thereby contributing to more accurate and efficient forest management across various landscapes.

Evaluation of the impact of wood forest management operations on Amazon nut trees in forest concession areas in the Amazon

The Amazon rainforest holds has immense biodiversity and offers various possibilities for use. Moreover, there is an increasing need for development models that reconcile the rational use of forest resources with socio-economic development. Multiple-use forest management emerges as a legal mechanism for the sustainable use of forest resources. In this context, a forest use system that appears suitable for multiple-use management is one where timber is selectively harvested from forests that are also sources of Brazil nuts. However, the effects of logging management on forests with a high density of Brazil nut trees needs to be studied, as the damage resulting from logging operations on Bertholletia excelsa Bonpl. trees may impact the long-term maintenance of these nut stands. This study aimed to assess the impact of logging operations for timber production on Brazil nut trees to evaluate the compatibility of logging management with the management of native nut stands in a forest concession area in the Amazon. The study was conducted in two National Forests (Jamari and Jacundá) in the state of Rondônia. The damage from cutting and logging operations on the canopy structure of Brazil nut trees was quantified and assessed using photogrammetry with remotely piloted aircraft. The Jamari National Forest showed 4.3% canopy damage to Brazil nut trees, while the Jacundá National Forest showed 3.2% damage. The results indicated that the damage to the canopies of Brazil nut trees due to timber extraction was minimal, suggesting potential compatibility between logging management and the management of native nut stands. However, further studies are needed, especially those focused on planning the collection of Brazil nuts, since access to the nut stands is essential for effectively structuring management, ensuring that the full production potential is sustainably managed and generates income for the extractivists.

Evaluation of Tree Object Segmentation Performance for Individual Tree Recognition Using Remote Sensing Techniques Based on Urban Forest Green Structures

This study evaluated whether tree object segmentation using remote sensing techniques could be effectively conducted according to the green structures of urban forests. The remote sensing techniques used were handheld LiDAR and UAV-based photogrammetry. The data collected from both methods were merged to complement each other’s limitations. The green structures of the study area were classified into three types based on the distance between canopy trees and the presence of shrubs. The ability to individually classify trees within each of the three types of green structures was then evaluated. The evaluation method was to assess the success rate by comparing the actual number of trees, which were visually counted in the field, with the number of tree objects classified in the study. To perform semantic segmentation of tree objects, a preprocessing step was conducted to extract only the data related to tree structures from the data collected through remote sensing techniques. The preprocessing steps included data merging, noise removal, separation of DTM and DSM, and separation of green areas and structures. The analysis results showed that tree object recognition was not efficient when the green structures were complex and mixed, and the recognition rate was highest when only canopy trees were present, and the canopies did not overlap. Therefore, when observing in high-density areas, the semantic segmentation algorithm’s variables should be adjusted to narrow the object recognition range, and additional observations in winter are needed to compensate for areas obscured by leaves. By improving data collection methods and systematizing the analysis methods according to the green structures, the object recognition process can be enhanced.

BronchoTrack: Airway Lumen Tracking for Branch-Level Bronchoscopic Localization.

Localizing the bronchoscope in real time is essential for ensuring intervention quality. However, most existing vision-based methods struggle to balance between speed and generalization. To address these challenges, we present BronchoTrack, an innovative real-time framework for accurate branch-level localization, encompassing lumen detection, tracking, and airway association. To achieve real-time performance, we employ benchmark light weight detector for efficient lumen detection. We firstly introduce multi-object tracking to bronchoscopic localization, mitigating temporal confusion in lumen identification caused by rapid bronchoscope movement and complex airway structures. To ensure generalization across patient cases, we propose a training-free detection-airway association method based on a semantic airway graph that encodes the hierarchy of bronchial tree structures. Experiments on 11 patient datasets demonstrate BronchoTrack's localization accuracy of 81.72%, while accessing up to the 6th generation of airways. Furthermore, we tested BronchoTrack in an in-vivo animal study using a porcine model, where it localized the bronchoscope into the 8th generation airway successfully. Experimental evaluation underscores BronchoTrack's real-time performance in both satisfying accuracy and generalization, demonstrating its potential for clinical applications.

Characterizing the Structure of Online Conversations Across Reddit

The proliferation of social media platforms has afforded social scientists unprecedented access to vast troves of data on human interactions, facilitating the study of online behavior at an unparalleled scale. These platforms typically structure conversations as threads, forming tree-like structures known as ''discussion trees.'' This paper examines the structural properties of online discussions on Reddit by analyzing both global (community-level) and local (post-level) attributes of these discussion trees. We conduct a comprehensive statistical analysis of a year's worth of Reddit data, encompassing a quarter of a million posts and several million comments. Our primary objective is to disentangle the relative impacts of global and local properties and evaluate how specific features shape discussion tree structures. The results reveal that both local and global features contribute significantly to explaining structural variation in discussion trees. However, local features, such as post content and sentiment, collectively have a greater impact, accounting for a larger proportion of variation in the width, depth, and size of discussion trees. Our analysis also uncovers considerable heterogeneity in the impact of various features on discussion structures. Notably, certain global features play crucial roles in determining specific discussion tree properties. These features include the subreddit's topic, age, popularity, and content redundancy. For instance, posts in subreddits focused on politics, sports, and current events tend to generate deeper and wider discussion trees. This research enhances our understanding of online conversation dynamics and offers valuable insights for both content creators and platform designers. By elucidating the factors that shape online discussions, our work contributes to ongoing efforts to improve the quality and effectiveness of digital discourse.

Losengram: An effective demodulation frequency band selection method for rolling bearing fault diagnosis under complex interferences

Abstract Resonance demodulation is one of the most effective methods for rolling bearing fault diagnosis. However, the selection of the proper demodulation frequency band (DFB) has always been considered as a substantial challenge. Although many popular DFB selection methods have been developed, such as fast Kurtogram (FK), Protrugram, and Autogram, they would suffer unsatisfactory performance degradation when encountering random impulsive noise or cyclostationary noise. Therefore, this paper proposes a novel DFB selection method called Losengram to address this problem. In the proposed method, a robust sub-band indicator, localized square envelope spectrum kurtosis, is designed to evaluate the fault information in a sub-band. With this indicator, the interferences of random impulsive noise and cyclostationary noise could be suppressed well. Besides, in order to circumvent the various adverse effects incurred by the utilization of a multi-rate finite impulse response filter bank, a frequency-domain sub-band filtering strategy is presented to filter the divided sub-bands in a 1/3-binary tree structure. The effectiveness of the proposed method is tested on both simulated and experimental signals, and the results show that it has a superior performance than the FK, Protrugram, as well as Autogram.

A New Tree Quantum Key Agreement Protocol for Secure Multiparty Communication

The Tree Multiparty Quantum Key Agreement Protocol (TMQKAP) is introduced as a novel solution for secure quantum key agreement among multiple participants, specifically tailored for tree topologies. Based on the BB84 protocol, TMQKAP employs hierarchical tree structures and XOR operations to facilitate efficient and secure key generation. Key elements are exchanged among participants in an equitable manner, ensuring that each participant contributes equally to the generation of the shared key. The protocol demonstrates robust security, effectively defending against both external and internal attacks, and achieves a quantum efficiency of 1/2 (N −1), where N is the number of participants. Thorough security analysis and simulations show TMQKAP’s robustness against various attacks while maintaining high efficiency. Additionally, the protocol is readily implementable with current quantum technologies, utilizing single-photon transmission to facilitate secure key distribution.

Adaptive fuzzy neighborhood decision tree

Decision tree algorithms have gained widespread acceptance in machine learning, with the central challenge lying in devising an optimal splitting strategy for node sample subspaces. In the context of continuous data, conventional approaches typically involve fuzzifying data or adopting a dichotomous scheme akin to the CART tree. Nevertheless, fuzzifying continuous features often entails information loss, whereas the dichotomous approach can generate an excessive number of classification rules, potentially leading to overfitting. To address these limitations, this study introduces an adaptive growth decision tree framework, termed the fuzzy neighborhood decision tree (FNDT). Initially, we establish a fuzzy neighborhood decision model by leveraging the concept of fuzzy inclusion degree. Furthermore, we delve into the topological structure of misclassified samples under the proposed decision model, providing a theoretical foundation for the construction of FNDT. Subsequently, we utilize conditional information entropy to sift through original features, prioritizing those that offer the maximum information gain for decision tree nodes. By leveraging the conditional decision partitions derived from the fuzzy neighborhood decision model, we achieve an adaptive splitting method for optimal features, culminating in an adaptive growth decision tree algorithm that relies solely on the inherent structure of real-valued data. Experimental evaluations reveal that, compared with advanced decision tree algorithms, FNDT exhibits a simple tree structure, stronger generalization capabilities, and superior performance in classifying continuous data.

Genetic variation and population structure of the rice accessions maintained in the AfricaRice genebank using DArTseq

AbstractUtilizing the full potential of rice collections mainly depends on an in‐depth exploration and understanding of the vast diversity in its germplasm. The AfricaRice genebank holds the largest collection of rice germplasm originating from the African continent. In the present study, we comprehensively characterized a collection of 9013 accessions, including Oryza barthii A. Chev., Oryza glaberrima Steud., Oryza longistaminata A. Chev. & Roehr., Oryza sativa L. ssp. indica, and Oryza sativa L. ssp. japonica, for genetic diversity and population structure using genotyping‐by‐sequencing through DArTseq analysis. We identified 27,718 high‐quality single nucleotide polymorphism markers after the genotypic data were filtered. Based on the analyses, the collection has extensive genetic diversity, and the average genetic distance of the entire set was 0.267 (range 0.001–0.469), with 45.1% of pairs of accessions being highly distant and 40.1% moderately distant from each other. Neighbor‐joining tree, principal component, and Bayesian population structure analyses clustered the 9013 accessions into six groups, based roughly on their taxonomic and biological status. The first, second, and third groups consisted of accessions belonging to O. glaberrima, O. barthii, and O. longistaminata, respectively. The fourth, fifth, and sixth groups were improved‐indica, japonica, and traditional‐indica accessions, respectively. The highest value of genetic variance proportion (PhiPT) was found in the species group followed by groups based on cluster analysis and on Bayesian population structure at K = 6. These results allow us to better understand the genetic diversity present in 9013 rice accessions maintained in the AfricaRice genebank and offer a valuable tool for pre breeding, breeding, and further genetic applications.

ScImmOmics: a manually curated resource of single-cell multi-omics immune data.

Single-cell sequencing technology has enabled the discovery and characterization of subpopulations of immune cells with unique functions, which is critical for revealing immune responses under healthy or disease conditions. Efforts have been made to collect and curate single-cell RNA sequencing (scRNA-seq) data, yet an immune-specific single-cell multi-omics atlas with harmonized metadata is still lacking. Here, we present scImmOmics (https://bio.liclab.net/scImmOmics/home), a manually curated single-cell multi-omics immune database constructed based on high-quality immune cells with known immune cell labels. Currently, scImmOmics documents >2.9 million cell-type labeled immune cells derived from seven single-cell sequencing technologies, involving 131 immune cell types, 47 tissues and 4 species. To ensure data consistency, we standardized the nomenclature of immune cell types and presented them in a hierarchical tree structure to clearly describe the lineage relationships within the immune system. scImmOmics also provides comprehensive immune regulatory information, including T-cell/B-cell receptor sequencing clonotype information, cell-specific regulatory information (e.g. gene/chromatin accessibility/protein/transcription factor states within known cell types, cell-to-cell communication and co-expression networks) and immune cell responses to cytokines. Collectively, scImmOmics is a comprehensive and valuable platform for unraveling the heterogeneity and diversity of immune cells and elucidating the specific regulatory mechanisms at the single-cell level.

Evaluating Feature Impact Prior to Phylogenetic Analysis Using Machine Learning Techniques

The purpose of this paper is to describe a feature selection algorithm and its application to enhance the accuracy of the reconstruction of phylogenetic trees by improving the efficiency of tree construction. Applying machine learning models for Arabic and Aramaic scripts, such as deep neural networks (DNNs), support vector machines (SVMs), and random forests (RFs), each model was used to compare the phylogenies. The methodology was applied to a dataset containing Arabic and Aramaic scripts, demonstrating its relevance in a range of phylogenetic analyses. The results emphasize that feature selection by DNNs, their essential role, outperforms other models in terms of area under the curve (AUC) and equal error rate (EER) across various datasets and fold sizes. Furthermore, both SVM and RF models are valuable for understanding the strengths and limitations of these approaches in the context of phylogenetic analysis This method not only simplifies the tree structures but also enhances their Consistency Index values. Therefore, they offer a robust framework for evolutionary studies. The findings highlight the application of machine learning in phylogenetics, suggesting a path toward accurate and efficient evolutionary analyses and enabling a deeper understanding of evolutionary relationships.

Exploring the correlation between tree structure characteristics and carbon storage in historic gardens using TLS technology: a case study of Jian Xin Pavilions at Jingyi Park, Fragrant Hills Park

Historic gardens contain a greater number and variety of ancient trees, which are older, have unique forms, and larger volumes. These trees hold significant value in both natural ecosystems and cultural heritage. However, current research on the carbon sequestration value of ancient and non-ancient trees in historical gardens is relatively lacking. Based on the unique morphology and carbon storage estimation needs of ancient trees in historic gardens, this paper proposes a morphology-based point cloud single-tree segmentation method. This method can precisely extract the morphological structures of various tree species and accurately estimate their carbon storage. From the perspective of carbon sequestration, it evaluates the correlation between the structural characteristics and carbon storage of trees in historic gardens, as well as the potential changes in their carbon storage capacity.Using the Jing Yi Park’s Jian Xin Pavilions in Fragrant Hills Park as a case study, this method was applied to extract structural indicators of 116 ancient and non-ancient trees. The total carbon storage was found to be 19,171.13 kg, with an average carbon storage of 165.27 kg per tree. Among these, ancient trees accounted for 13,178.32 kg, or 68.74% of the total carbon storage. The study revealed that the correlation between tree age and carbon storage varied by species, and there were significant positive correlations between carbon storage and tree height, DBH, and canopy volume. Notably, there was a significant linear growth trend between DBH, canopy volume, and carbon storage. By 2030, the total carbon storage is projected to increase to 21,924.96 kg, with an annual average increase of 393.40 kg, representing a growth rate of 14.4%.The results indicate that studying the correlation between structural characteristics and carbon storage of aged trees in historical gardens can shed light on the important role of trees in sustainable carbon sequestration. The precise extraction of tree information through 3D digital technology and the prediction of carbon storage potential not only offer new perspectives for the conservation of cultural heritage in historical gardens, urban microclimate planning and design, and spatial management of carbon sinks and emissions but also have significant value for promoting the scientific management and protection of urban green spaces.

Pruned tree-structured temporal convolutional networks for quality variable prediction of industrial process

In real industrial processes, the rapid and accurate acquisition of quality variables is essential. Therefore, this paper proposes a pruned tree-structured temporal convolutional network (PT-TCN) for efficient and accurate variables prediction. First, a novel tree network is developed, utilizing dilated causal convolution blocks as nodes to avoid the loss of local information. Each node extracts distinct local information, and by concatenating all tree nodes, the network can capture a comprehensive range of temporal scales. Then, to avoid the increased complexity caused by the tree structure, we design an online two-stage pruning strategy to compress the tree network without introducing additional computations. During the training process, blocks are initially pruned based on the correlation assessment between quality variables and tree nodes. Subsequently, weight normalization layers are employed to evaluate the importance of output channels in blocks, thereby enabling intra-block channel pruning. The effectiveness of PT-TCN is verified on Tennessee Eastman benchmark process. In addition, experiments on the real zinc flotation process demonstrate that the proposed PT-TCN improves in R2 and MAE by 1.32% and 1.26% respectively in predicting quality variables, and it can reduce 91.8% parameters of the initial tree-structured TCN without sacrificing accuracy.