Robust Tree Research Articles

Organizing planning knowledge for automated vehicles and intelligent transportation systems

AbstractIntelligent Transportation Systems (ITS) are crucial for developing fully automated vehicles. While significant progress has been made with advanced driver assistance systems and automation technology, challenges remain, such as improving traffic information, enhancing planning and control systems, and developing better decision‐making capabilities. Despite these hurdles, the potential benefits of ITS are so many that its challenges have attracted substantial industrial investment and research groups interested in the automated driving field. In this work, a methodology based on state space search for planning knowledge integration is proposed. The main goal of the proposal is to provide a planning system with the necessary information to perform properly any task related to lateral and longitudinal control, path following, trajectory generation, arbitration and behavior execution by localizing the vehicle with respect to a high‐level road plan. To this end, this research compares cutting‐edge methods for rapidly finding the K nearest neighbor in relatively high dimensional road plans constructed from the traffic information stored in a high definition map. During the experimentation phase, promising real‐time results have been obtained for fast KNN algorithms, leading to a robust tree index‐based methodology for decision making in self‐driving vehicles combining path planning, trajectory tracking, trajectory creation, knowledge aggregation and precise vehicle control.

Bursts of Rapid Diversification, Dispersals Out of Southern Africa, and Two Origins of Dioecy Punctuate the Evolution of Asparagus.

The genus Asparagus arose ∼9 to 15 million years ago (Ma), and transitions from hermaphroditism to dioecy (separate sexes) occurred ∼3 to 4 Ma. Roughly 27% of extant Asparagus species are dioecious, while the remaining are bisexual with monoclinous flowers. As such, Asparagus is an ideal model taxon for studying the early stages of dioecy and sex chromosome evolution in plants. Until now, however, understanding of diversification and shifts from hermaphroditism to dioecy in Asparagus has been hampered by the lack of robust species tree estimates for the genus. In this study, a genus-wide phylogenomic analysis including 1,726 nuclear loci and comprehensive species sampling supports two independent origins of dioecy in Asparagus-first in a widely distributed Eurasian clade and then in a clade restricted to the Mediterranean Basin. Modeling of ancestral biogeography indicates that both dioecy origins were associated with range expansion out of southern Africa. Our findings also reveal several bursts of diversification across the phylogeny, including an initial radiation in southern Africa that gave rise to 12 major clades in the genus, and more recent radiations that have resulted in paraphyly and polyphyly among closely related species, as expected given active speciation processes. Lastly, we report that the geographic origin of domesticated garden asparagus (Asparagus officinalis L.) was likely in western Asia near the Mediterranean Sea. The presented phylogenomic framework for Asparagus is foundational for ongoing genomic investigations of diversification and functional trait evolution in the genus and contributes to its utility for understanding the origin and early evolution of dioecy and sex chromosomes.

The life care annuity: enhancing product features and refining pricing methods

AbstractThe state-of-the-art proposes life care annuities, that have been recently designed as variable annuity contracts with Long-Term Care payouts and Guaranteed Lifelong Withdrawal Benefits. In this paper, we propose more general features for these insurance products and refine their pricing methods. We name our proposed product “GLWB-LTC”. In particular, as to the product features, we allow dynamic withdrawal strategies, including the surrender option. Furthermore, we consider stochastic interest rates, described by a Cox–Ingersoll–Ross process. As to the numerical methods, we solve the stochastic control problem involved by the selection of the optimal withdrawal strategy through a robust tree method, which outperforms the Monte Carlo approach. We name this method “Tree-LTC”, and we use it to estimate the fair price of the product, as some relevant parameters vary, such as, for instance, the entry age of the policyholder. Furthermore, our numerical results show how the optimal withdrawal strategy varies over time with the health status of the policyholder. Our findings stress the important advantage of flexible withdrawal strategies in relation to insurance policies offering protection from health risks. Indeed, the policyholder is given more choice about how much to save for protection from the possible disability states at future times.

Extraction of Oil from Azadirachta indica and Moringa stenopetala Seeds and Evaluation of its Physicochemical Properties

Azadirachta indica and Moringa stenopetala trees have been regarded as underutilized, tropical plants, fast-growing, drought-tolerant, robust, oleaginous, and evergreen perennial trees growing widely in various regions of Ethiopia. Almost every part of these plants (i.e., roots, stems, foliage, seeds, and barks) can be used as food additives and as raw materials for pharmaceuticals, cosmetics, soap, and biofuel processing industries. This study aimed at the extraction and characterization of oil from A. indica and M. stenopetala seeds using the solvent method. The Box-Behnken Design was employed in the experimental design and result analysis. The particle size (0.2, 0.5, 0.8 mm), solvent-to-solute ratio (3:1, 6:1, 9:1), and extraction time (2, 5, 8 hrs) were experimental variables with three levels of low, medium, and high, whereas, the extraction temperature was kept uniform. Seventeen experiments were conducted for each species thereby developing the quadratic models with a P-value < 0.0001(significant). The quality and adequacy of the models were evaluated by analysis of variance (ANOVA) at 5% least significant difference. Results of the physicochemical determination of oils were triplicated and obtained as mean ± standard deviation. The determined physicochemical properties of A. indica and M. stenopetala seed oils were kinematic viscosity, specific gravity, pH value, refractive index, acid value, free fatty acid, saponification value, iodine value, and peroxide value. The obtained experimental results showed that the extracted oils from A. indica and M. stenopetala seeds exhibit good oil quality, and hence, they can be employed for commercial and industrial purposes, and the generation of renewable energy (biofuel).

New insights into the phylogeny of Neogastropoda aided by draft genome sequencing of a volutid snail

AbstractNeogastropoda is a large order of predominantly marine gastropod molluscs, typically predatory or parasitic on other animals. It includes over 16,000 species representing a large post‐Cretaceous radiation, but the internal phylogenetic relationships of contained taxa are far from resolved, with inconsistent results from nuclear genes, mitogenomes, and morphology. One major issue in reconstructing the molecular phylogeny is the lack of high‐quality sequences for early‐diverging families and superfamilies such as Volutidae (Volutoidea). Here, we examine the superfamily‐level phylogenetic relationships in Neogastropoda, aided by newly sequenced draft genome and mitogenome of the volutid snail Fulgoraria chinoi from the deep sea off Japan. The genome of F. chinoi is relatively large at 1.54 Gb but exhibits low genome heterozygosity; over 54% of this genome constitutes of repeat contents, and we find evidence for active insertion of transposable elements, particularly LINEs and SINEs. A total of 50,792 protein‐coding genes (PCGs) were predicted from the nuclear genome, and a circular mitogenome was also assembled and annotated. Our phylogenetic analyses using mitogenomes recovered each neogastropod superfamily as monophyletic, though also revealed inconsistent phylogenetic signals within superfamilies. Phylogenetic reconstructions using the PCGs resulted in a robust tree from different models and data matrices, recovering Volutoidea as the earliest diverging superfamily (among those for which comparable data is available) within a monophyletic Neogastropoda. Dated phylogenetic analysis revealed an early Cretaceous radiation of Neogastropoda, congruent with the fossil record. Our study provides a robust internal evolutionary framework for the speciose but genomically undersampled Neogastropoda, and expands the available genomic resources for this order. Genomic data for key missing lineages such as Mitroidea, Olivoidea, and Cancellariidae are much sought in the future for a full understanding of Neogastropoda evolution.

Morphological traits of fruits and seeds of Ziziphus tree species growing in different land uses in Ethiopia

The practice of gathering and utilizing Ziziphus tree fruits serves as a key strategy to enhance nutrition and livelihoods employed by rural communities across various regions worldwide. Despite a burgeoning interest in and comprehension of their significance, there remains a dearth of information concerning the morphological attributes of both fruits and seeds essential for bolstering resistance against pests and diseases through breeding efforts. In this regard, comprehensive data pertaining to fruit dimensions (length, width, weight, and maturity level) and seed characteristics (length, width, and 100-seed weight) across three distinct land use types (LUT)—farmland (FL), home garden (HG), and roadside (RS)—were systematically gathered to ascertain the variability in traits among Ziziphus tree species in the Bosset and Bati districts of Ethiopia. Significant disparities in fruit and seed morphological traits were evident among different populations inhabiting the aforementioned LUTs. Notably, the most substantial mean measurements for fruit length (16 mm), width (18 mm), and weight (28 g), as well as seed width (6 mm), were documented within the farmland setting of Bosset. The highest mean seed length (7 mm) and 100-seed weight (5 g) were observed within farmland and home garden environments in Bati. Furthermore, a significant and positive correlation was determined between fruit length and width (r = 0.78), alongside the weight of ten fruits and width (r = 0.65). Ffruit maturity levels exhibited a negative correlation with weight but not with length and width dimensions. Similarly, seed length and width demonstrated a significant correlation (r = 0.88), while 100-seed weight exhibited a modest correlation with seed dimensions. Overall, findings suggest that the Bosset district boasts superior morphological traits, thereby indicating its potential for harboring robust stands and candidate trees conducive to selection for breeding programs aimed at enhancing resistance against insect pests and diseases.

Dynamics and timing of diversification events of ciliated eukaryotes from a large phylogenomic perspective

Ciliophora, an exceptionally diverse lineage of unicellular eukaryotes, exhibits a remarkable range of species richness across classes in the ciliate Tree of Life. In this study, we have acquired transcriptome and genome data from 40 representative species in seven ciliate classes. Utilizing 247 genes and 105 taxa, we devised a comprehensive phylogenomic tree for Ciliophora, encompassing over 60 % of orders and constituting the most extensive dataset of ciliate species to date. We established a robust phylogenetic framework that encompasses ambiguous taxa and the major classes within the phylum. Our findings support the monophyly of each of two subphyla (Postciliodesmatophora and Intramacronucleata), along with three subclades (Protocruzia, CONTHREEP, and SAPML) nested within Intramacronucleata, and elucidate evolutionary positions among the major classes within the phylum. Drawing on the robust ciliate Tree of Life and three constraints, we estimated the radiation of Ciliophora around 1175 Ma during the middle of the Proterozoic Eon, and most of the ciliate classes diverged from their sister lineage during the latter half of this period. Additionally, based on the time-calibrated tree and species richness pattern, we investigated net diversification rates of Ciliophora and its classes. The global net diversification rate for Ciliophora was estimated at 0.004979 species/Ma. Heterogeneity in net diversification rates was evident at the class level, with faster rates observed in Oligohymenophorea and Spirotrichea than other classes within the subclades CONTHREEP and SAPML, respectively. Notably, our analysis suggests that variations in net diversification rates, rather than clade ages, appear to contribute to the differences in species richness in Ciliophora at the class level.

Refining the predictive value of preoperative apparent diffusion coefficient (ADC) by whole-tumor analysis for facial nerve outcomes in vestibular schwannomas.

Apparent diffusion coefficient (ADC) in MRI has been shown to correlate with postoperative House-Brackmann (HB) scores in patients with vestibular schwannoma despite limited methodology. To rectify limitations of single region of interest (ROI) sampling, we hypothesize that whole-tumor ADC histogram analysis will refine the predictive value of this preoperative biomarker related to postoperative facial nerve function. Of 155 patients who underwent resection of vestibular schwannoma (2014-2020), 125 patients were included with requisite clinical and radiographic data. After volumetric analysis and whole-tumor ADC histogram, regression tree analysis identified ADC cutoff for significant differences in HB grade. Outcomes were extent of resection, facial nerve function, hospital length of stay (LOS), and complications. Regression tree analysis defined three quantitative ADC groups (× 10-6 mm2/s) as high (> 2248.77; HB 1.7), mid (1468.44-2248.77; HB 3.1), and low (< 1468.44; HB 2.3) range (p 0.04). The mid-range ADC group had significantly worse postoperative HB scores and longer hospital LOS. Large tumor volume was independently predictive of lower rates of gross total resection (p <0.0001), higher postoperative HB score (p 0.002), higher rate of complications (p 0.04), and longer LOS (p 0.003). Whole-tumor histogram yielded a robust regression tree analysis that defined three ADC groups with significantly different facial nerve outcomes. This likely reflects tumor heterogeneity better than solid-tumor ROI sampling. Whole-tumor ADC warrants further study as a useful radiographic biomarker in patients with vestibular schwannoma who are considering surgical resection.

Coevolutionary Algorithm for Building Robust Decision Trees under Minimax Regret

In recent years, there has been growing interest in developing robust machine learning (ML) models that can withstand adversarial attacks, including one of the most widely adopted, efficient, and interpretable ML algorithms—decision trees (DTs). This paper proposes a novel coevolutionary algorithm (CoEvoRDT) designed to create robust DTs capable of handling noisy high-dimensional data in adversarial contexts. Motivated by the limitations of traditional DT algorithms, we leverage adaptive coevolution to allow DTs to evolve and learn from interactions with perturbed input data. CoEvoRDT alternately evolves competing populations of DTs and perturbed features, enabling construction of DTs with desired properties. CoEvoRDT is easily adaptable to various target metrics, allowing the use of tailored robustness criteria such as minimax regret. Furthermore, CoEvoRDT has potential to improve the results of other state-of-the-art methods by incorporating their outcomes (DTs they produce) into the initial population and optimize them in the process of coevolution. Inspired by the game theory, CoEvoRDT utilizes mixed Nash equilibrium to enhance convergence. The method is tested on 20 popular datasets and shows superior performance compared to 4 state-of-the-art algorithms. It outperformed all competing methods on 13 datasets with adversarial accuracy metrics, and on all 20 considered datasets with minimax regret. Strong experimental results and flexibility in choosing the error measure make CoEvoRDT a promising approach for constructing robust DTs in real-world applications.

Unveiling IoT Customer Behaviour: Segmentation and Insights for Enhanced IoT-CRM Strategies: A Real Case Study.

In today's competitive landscape, achieving customer-centricity is paramount for the sustainable growth and success of organisations. This research is dedicated to understanding customer preferences in the context of the Internet of things (IoT) and employs a two-part modeling approach tailored to this digital era. In the first phase, we leverage the power of the self-organizing map (SOM) algorithm to segment IoT customers based on their connected device usage patterns. This segmentation approach reveals three distinct customer clusters, with the second cluster demonstrating the highest propensity for IoT device adoption and usage. In the second phase, we introduce a robust decision tree methodology designed to prioritize various factors influencing customer satisfaction in the IoT ecosystem. We employ the classification and regression tree (CART) technique to analyze 17 key questions that assess the significance of factors impacting IoT device purchase decisions. By aligning these factors with the identified IoT customer clusters, we gain profound insights into customer behaviour and preferences in the rapidly evolving world of connected devices. This comprehensive analysis delves into the factors contributing to customer retention in the IoT space, with a strong emphasis on crafting logical marketing strategies, enhancing customer satisfaction, and fostering customer loyalty in the digital realm. Our research methodology involves surveys and questionnaires distributed to 207 IoT users, categorizing them into three distinct IoT customer groups. Leveraging analytical statistical methods, regression analysis, and IoT-specific tools and software, this study rigorously evaluates the factors influencing IoT device purchases. Importantly, this approach not only effectively clusters the IoT customer relationship management (IoT-CRM) dataset but also provides valuable visualisations that are essential for understanding the complex dynamics of the IoT customer landscape. Our findings underscore the critical role of logical marketing strategies, customer satisfaction, and customer loyalty in enhancing customer retention in the IoT era. This research offers a significant contribution to businesses seeking to optimize their IoT-CRM strategies and capitalize on the opportunities presented by the IoT ecosystem.

Application of Decision Tree and Machine Learning in New Energy Vehicle Maintenance Decision Making

Abstract Several incidents of electric vehicle combustion across various regions in China have brought the safety concerns associated with new energy vehicles into sharp focus within public discourse. Addressing these concerns, this paper explores maintenance decision-making for new energy vehicles through the application of decision trees and machine learning techniques. Initially, the study analyzes how decision trees and machine learning are employed in crafting maintenance decisions for these vehicles. It involves collecting data through internet searches, followed by statistical analyses and preprocessing to set the groundwork for further inquiry. Furthermore, the research advances by developing and refining decision tree models, which facilitate the integration of fault diagnosis and maintenance decision-making processes for new energy vehicles. This effort culminates in the establishment of a robust decision tree model specifically designed for the maintenance of new energy vehicles, which is subsequently evaluated through a detailed case study. The results are presented: the approximation degree of new energy vehicle fault diagnosis based on the decision tree model is 90.58%, 90.67%, 88.09%, 91.28%, and 90.19% at the significant level of α taking the value of 0.01, 0.05, 0.100, respectively. This study provides theoretical guidance on the application of decision trees for the diagnosis and repair of faults in new energy vehicles so that the development of new energy vehicles in China can be ensured.

Development of a technology tree using patent information

A technology tree is used to represent technological attributes, such as the function, purpose, and components of a technology, and has been regarded as a valuable tool for investigating a technology in detail. However, most existing approaches to developing a technology tree considerably rely on experts; thus, the evaluation is subjective and does not reflect rapidly changing technological attributes under the paradigm of convergence. This study proposes the use of patent information to develop a robust technology tree. Subsequently, technological attributes were extracted in terms of key functions, and their relationships were analyzed to form a hierarchy. For the analysis, a series of methods were employed: clustering analysis to group patents into similar content, semantic analysis to identify key technological attributes in each group, and association-rule mining to define the relationships between the technological attributes. The proposed approach was then applied to a furniture manufacturing process to validate its applicability. Providing meaningful information on the technological structure will help improve the understanding of a target technology and further support research and development planning.

The robust scheme for intrusion detection system in Internet of Things

Machine learning and deep learning-based anomaly intrusion detection systems (IDSs) have become prevalent in securing IoT networks due to their ability to monitor traffic and detect zero-day attacks. However, recent studies highlight the high vulnerability of these models to adversarial attacks, in which minor input perturbations can significantly decrease the detection accuracy. Although many studies have focused on adversarial attack and defense techniques for deep learning, machine learning, particularly decision trees, has received limited attention. In this study, we aim to assess the efficacy of the robust decision tree in adversarial IoT environments. Our first experiments reveal the robust decision tree’s sensitivity to the offset parameter. We thus propose a statistical approach to auto-select the offset value, enhancing model stability across varying attack offsets. Then, we present a robust scheme for IDSs in IoT environments. This approach employs the enhanced robust decision tree and a tabular deep learning model to detect and classify a range of cyber attacks. Our evaluation results on three popular IDS datasets—IoTID20, CIC-IDS-2017, and BOT-IoT—demonstrate that our proposed approach is robust under various adversarial attack conditions and achieves a consistent accuracy of over 95% in classifying different attack types.

Branching the limits: Robust 3D tree reconstruction from incomplete laser point clouds

Accurate individual tree reconstruction based on laser point clouds is vital for precise biomass estimation, virtual geographic environment modeling, and simulation. However, mobile laser 3D scanning systems often capture tree point clouds obscured by leaves, resulting in missing or incomplete branch data, posing significant challenges to detailed tree reconstruction. In this paper, a novel method for fine-grained 3D tree model reconstruction from incomplete point clouds is proposed. This approach addresses the structural reconstruction of trees, tackling two main problems: reconstructing the main trunk and branches based on data completeness. Initially, a 3D morphological algorithm is employed to separate the main trunk and branches in the point cloud. Next, branch point clouds are node-aggregated using clustering, and the trunk’s skeleton point is computed using a multi-scale curve fitting method. To account for incomplete branch point clouds, the Alpha shape is calculated and used as a constraint for the growth model of the L-system, enabling the automatic generation of branch structures. Finally, a morphologically constrained multi-level trunk fusion method is utilized to achieve complete tree model reconstruction. To validate the effectiveness of this method, five trees with varying structures and levels of complexity were selected for structural reconstruction and compared with state-of-the-art (SOTA) methods. Experimental results evince that the method delineated in this study exhibits an aptitude for adeptly approximating tree nodes, even in scenarios characterized by incomplete point cloud data. This approach transcends the reconstruction accuracy manifested by SOTA algorithms specific to three-dimensional tree modeling, simultaneously demonstrating a pronounced resilience to noise and enhanced robustness.

More appropriate DenseNetBL classifier for small sample tree species classification using UAV-based RGB imagery

To effectively classify tree species within datasets characterized by limited samples, we introduced a novel approach named DenseNetBL, founded upon the fusion of the DenseNet architecture and a pivotal bottleneck layer. This bottleneck layer, encompassing a compact convolutional component, played a central role in our methodology. The evaluation of DenseNetBL was conducted under varying conditions, encompassing small-sample tree species data, extensive remote sensing datasets, and state-of-the-art classifiers. Furthermore, a quantitative assessment was executed to extract tree species areas. This was achieved by quantifying pixel areas within manually delineated tree species maps and classifier-generated counterparts. The findings of our study indicated that, in scenarios devoid of pre-trained weights, DenseNetBL consistently outperformed its DenseNet counterpart with equivalent layer numbers. In the realm of small-sample situations, both the Swin Transformer and Vision Transformer exhibited inferior performance when juxtaposed with DenseNet and DenseNetBL. Remarkably, among the shallow architectures, DenseNet33BL showcased superior aptitude for small-sample tree species classification, culminating in the most commendable results (Overall Accuracy (OA) = 0.901, Kappa = 0.892). Conversely, the Vision Transformer yielded the least favorable classification outcomes (OA = 0.767, Kappa = 0.708). The amalgamation of DenseNet33BL and simple linear iterative clustering emerged as the optimal strategy for attaining robust tree species area extraction results across two prototypical forests. In contrast, DenseNet121 exhibited suboptimal performance in the same forests, attaining the least satisfactory tree species area extraction results. These comprehensive findings underscore the efficacy of our DenseNetBL approach in addressing the challenges associated with small-sample tree species classification and accurate tree species area extraction.

A Straightforward Bifurcation Pattern-Based Fundus Image Registration Method.

Fundus image registration is crucial in eye disease examination, as it enables the alignment of overlapping fundus images, facilitating a comprehensive assessment of conditions like diabetic retinopathy, where a single image's limited field of view might be insufficient. By combining multiple images, the field of view for retinal analysis is extended, and resolution is enhanced through super-resolution imaging. Moreover, this method facilitates patient follow-up through longitudinal studies. This paper proposes a straightforward method for fundus image registration based on bifurcations, which serve as prominent landmarks. The approach aims to establish a baseline for fundus image registration using these landmarks as feature points, addressing the current challenge of validation in this field. The proposed approach involves the use of a robust vascular tree segmentation method to detect feature points within a specified range. The method involves coarse vessel segmentation to analyze patterns in the skeleton of the segmentation foreground, followed by feature description based on the generation of a histogram of oriented gradients and determination of image relation through a transformation matrix. Image blending produces a seamless registered image. Evaluation on the FIRE dataset using registration error as the key parameter for accuracy demonstrates the method's effectiveness. The results show the superior performance of the proposed method compared to other techniques using vessel-based feature extraction or partially based on SURF, achieving an area under the curve of 0.526 for the entire FIRE dataset.

Robust decision trees for the multi-mode project scheduling problem with a resource investment objective and uncertain activity duration

In this paper, we advocate the use of robust decision trees for a multi-mode resource constrained project scheduling problem with uncertain activity duration and a resource investment objective, coming from an industrial assembly line scheduling application. This work takes place in the context of multi-stage optimization where uncertainty is revealed progressively across a succession of decision time points. In a robust decision tree, a node represents a robust partial schedule from the time origin to a specific decision time point. At this point, the decision maker has access to some information, which partitions the uncertainty scenario set, yielding for each scenario subset a child node and an associated extended partial robust schedule up to the next decision point. Considering that the level of uncertainty is lowered, the new partial schedule is less conservative and improves the robustness guarantee. However, since all accessible information may not be relevant, we turned the information selection part into an optimization problem. An algorithm is proposed to solve the robust decision tree problem. Experimentation is provided to study the influence of decision tree parameters as well as highlighted recommendations. The interest of the decision tree is shown through an experimental comparison with classical approaches of the literature on benchmark instances and industrial instances.

Prediction of Urban Trees Planting Base on Guided Cellular Automata to Enhance the Connection of Green Infrastructure

Urbanization and climate change pose significant challenges to urban ecosystems, underscoring the necessity for innovative strategies to enhance urban green infrastructure. Tree planting, a crucial aspect of green infrastructure, has been analyzed for optimized positioning using data metrics, priority scoring, and GIS. However, due to the dynamic nature of environmental information, the accuracy of current approaches is compromised. This study aims to present a novel approach integrating deep learning and cellular automata to prioritize urban tree planting locations to anticipate the optimal urban tree network. Initially, GIS data were collated and visualized to identify a suitable study site within London. CycleGAN models were trained using cellular automata outputs and forest mycorrhizal network samples. The comparison validated cellular automata’s applicability, enabled observing spatial feature information in the outputs and guiding the parameter design of our 3D cellular automata system for predicting tree planting locations. The locations were optimized by simulating the network connectivity of urban trees after planting, following the spatial-behavioral pattern of the forest mycorrhizal network. The results highlight the role of robust tree networks in fostering ecological stability and cushioning climate change impacts in urban contexts. The proposed approach addresses existing methodological and practical limitations, providing innovative strategies for optimal tree planting and prioritization of urban green infrastructure, thereby informing sustainable urban planning and design. Our findings illustrate the symbiotic relationship between urban trees and future cities and offer insights into street tree density planning, optimizing the spatial distribution of trees within urban landscapes for sustainable urban development.

Action of the plant-based essential oil-derived compound Taxol for improvising drought tolerance in Eucalyptus by modulating the VIT1 channel protein: a cutting-edge computational approach.

Background: Drought poses a significant threat to the growth and survival of woody plants, especially Eucalyptus grandis, which is known for its slow and steady growth. Understanding the physiological and molecular responses of E. grandis to abiotic stress is essential for developing strategies to improve its drought resistance. This study focuses on the potential vulnerability of E. grandis during the initial months of root system proliferation and investigates the role of the essential oil-derived compound Taxol in enhancing its drought resistance. Methodology: A comprehensive analysis was performed on various aspects of E. grandis, including morphological features, photosynthetic rates, pigment concentrations, nitrogenous components, and lipid peroxidation. Furthermore, the study examined the accumulation of soluble carbohydrates, proline, and antioxidant enzymes as part of the tree's response to drought stress. Molecular docking and molecular dynamics simulations were conducted to determine the binding affinity of Taxol, an essential oil derived from Taxus brevifolia, with the VIT1 protein in E. grandis. Results: E. grandis displayed remarkable resilience to drought by accumulating vast reserves of soluble carbohydrates, proline, and antioxidant enzymes. The essential oil-derived compound Taxol exhibited a strong binding affinity with the VIT1 protein (-10.23kcal/mol), suggesting its potential role in enhancing the tree's drought resistance. Conclusion: This study reveals the pivotal role of Taxol in augmenting the resilience of E. grandis against drought stress and improving its therapeutic oil properties. Emphasizing the tree's inherent tolerance during its susceptible early stages is crucial in promoting sustainable agriculture and forestry practices. The findings underscore the importance of advanced scientific research in uncovering the concealed capabilities of robust trees like E. grandis as we continue our pursuit of a sustainable future.

Robust Scan Registration for Navigation in Forest Environment Using Low-Resolution LiDAR Sensors.

Automated forest machines are becoming important due to human operators' complex and dangerous working conditions, leading to a labor shortage. This study proposes a new method for robust SLAM and tree mapping using low-resolution LiDAR sensors in forestry conditions. Our method relies on tree detection to perform scan registration and pose correction using only low-resolution LiDAR sensors (16Ch, 32Ch) or narrow field of view Solid State LiDARs without additional sensory modalities like GPS or IMU. We evaluate our approach on three datasets, including two private and one public dataset, and demonstrate improved navigation accuracy, scan registration, tree localization, and tree diameter estimation compared to current approaches in forestry machine automation. Our results show that the proposed method yields robust scan registration using detected trees, outperforming generalized feature-based registration algorithms like Fast Point Feature Histogram, with an above 3 m reduction in RMSE for the 16Chanel LiDAR sensor. For Solid-State LiDAR the algorithm achieves a similar RMSE of 3.7 m. Additionally, our adaptive pre-processing and heuristic approach to tree detection increased the number of detected trees by 13% compared to the current approach of using fixed radius search parameters for pre-processing. Our automated tree trunk diameter estimation method yields a mean absolute error of 4.3 cm (RSME = 6.5 cm) for the local map and complete trajectory maps.