Cut Trees Research Articles

Comparison of Cut-to-Length Harvesting Methods in Tree Plantations in Brazil

The aim of this research was to determine the most productive tree-cutting methods, and the factors influencing them, in flat and sloping terrains in tree plantations in Brazil. The study utilized drone-captured video material from harvesting operations in eucalyptus and pine plantations. In both terrains, two cutting methods were compared, differing in the felling to the side method used: either at the edge or inside of the harvesting front. In addition, on flat terrain, the efficiency of forward felling was studied in relation to the aforementioned cutting methods. In sloping terrain, the machines were also equipped with a winch assistance system. The time study data of the harvesting work were processed using a video analysis tool developed for the research. The output data of the cut trees were collected with the automatic measuring system of the harvester. Statistical tests were used to determine the most productive cutting methods by analyzing differences in productivity. With an average tree size of 0.3 m3, cutting productivity was 45 m3/E0h in pine cuttings and 55 m3/E0h in eucalyptus cuttings. The average cutting productivity on flat terrain was about 11 m3/E0h higher than on sloping terrain, mainly due to the time spent attaching the winch assistance system, which was a necessary phase of the work on sloping terrain. The research results suggest that it would be most productive to use sideways felling inside the harvesting front method. However, the need for further research is evident, if we want to precisely identify the factors and work phases in the tree-cutting cycle affecting differences in the productivity of the harvesting chain in tree plantations.

The novel hierarchical clustering approach using self‐organizing map with optimum dimension selection

AbstractIntroductionData clustering is an important field of machine learning that has applicability in wide areas, like, business analysis, manufacturing, energy, healthcare, traveling, and logistics. A variety of clustering applications have already been developed. Data clustering approaches based on self‐organizing map (SOM) generally use the map dimensions (of the grid) ranging from 2 × 2 to 8 × 8 (4–64 neurons [microclusters]) without any explicit reason for using the particular dimension, and therefore optimized results are not obtained. These algorithms use some secondary approaches to map these microclusters into the lower dimension (actual number of clusters), like, 2, 3, or 4, as the case may be, based on the optimum number of clusters in the specific data set. The secondary approach, observed in most of the works, is not SOM and is an algorithm, like, cut tree or the other.MethodsIn this work, the proposed approach will give an idea of how to select the most optimal higher dimension of SOM for the given data set, and this dimension is again clustered into the lower actual dimension. Primary and secondary, both utilize the SOM to cluster the data and discover that the weight matrix of the SOM is very meaningful. The optimized two‐dimensional configuration of SOM is not the same for every data set, and this work also tries to discover this configuration.ResultsThe adjusted randomized index obtained on the Iris, Wine, Wisconsin diagnostic breast cancer, New Thyroid, Seeds, A1, Imbalance, Dermatology, Ecoli, and Ionosphere is, respectively, 0.7173, 0.9134, 0.7543, 0.8041, 0.7781, 0.8907, 0.8755, 0.7543, 0.5013, and 0.1728, which outperforms all other results available on the web and when no reduction of attributes is done in this work.ConclusionsIt is found that SOM is superior to or on par with other clustering approaches, like, k‐means or the other, and could be used successfully to cluster all types of data sets. Ten benchmark data sets from diverse domains like medical, biological, and chemical are tested in this work, including the synthetic data sets.

A new genus and species of Platypodini pinhole borers from South America (Coleoptera, Curculionidae, Platypodinae)

I describe Amplioscapus new genus for Amplioscapus mirabilis new species represented by 164 specimens collected from tropical forests of the Amazon Basin and Guiana Shield. The new genus differs from all other Neotropical Platypodini genera in the antennae being inserted at the upper margin of the eyes in females; in the extraordinarily expanded antennal scape with dense long setae; in the numbers of transverse rugae on the protibiae; and in the numbers of small teeth on the anterior margin of the impression at the distal ends of the metanepisternum and metaventrite. The new pinhole borer genus is most similar to Megaplatypus Wood; in addition to the characters mentioned previously, the new genus differs from all known Megaplatypus in the pair of large mycangia on the female pronotum and the strongly alternating lengths of the elytral interstriae of males. Most individuals were taken from low elevations. Host records for specimens taken from cut trees show the new species to be a host generalist. The vast majority of specimens were captured by flight intercept traps, Malaise traps or canopy fogging, underlining the importance of passive trapping for tropical insect biodiversity surveys.

Evaluation of Fire Protection System Implementation in A Production Area of Oil and Gas Company, East Java

The oil and gas industry is an industry with a high risk of fire. This is because this industry involves several types of substances that cause pressure and have the potential to cause leaks and spills. Industry's inability to detect these conditions can cause fires. Therefore, it is necessary to evaluate the implementation of fire protection systems in the industry. This research was conducted using a checklist method for fulfilling requirements according to appropriate regulations. The results of this assessment show that the company has fulfilled regulations in accordance with each system with a percentage above 75%. Some suggestions that can be considered for improvement include replacing expired fire extinguishers so that they are ready for use, providing APAR signs or symbols at each location point, providing a cover or placing the fire extinguisher outside in a roofed area, equip each hydrant point with instructions for use, carry out hydrant maintenance regarding the completeness and cleanliness of the hydrant box, and update evacuation route signs that are no longer legible and cut trees or branches that cover the signs.

Generalized cut trees for edge-connectivity

We present three cut trees of graphs, each of them giving insights into the edge-connectivity structure. All three cut trees have in common that they are defined with respect to a given binary symmetric relation R on the vertex set of the graph, which generalizes Gomory-Hu trees. Applying these cut trees, we prove the following:•A pair of vertices {v,w} of a graph G is pendant if λ(v,w)=min⁡{d(v),d(w)}. Mader showed in 1974 that every simple graph with minimum degree δ contains at least δ(δ+1)/2 pendant pairs. We improve this lower bound to δn/24 for every simple graph G on n vertices with δ≥5 or λ≥4 or vertex connectivity κ≥3, and show that this is optimal up to a constant factor with regard to every parameter.•Every simple graph G satisfying δ>0 has O(n/δ)δ-edge-connected components. Moreover, every simple graph G that satisfies 0<λ<δ has O((n/δ)2) cuts of size less than min⁡{32λ,δ}, and O((n/δ)⌊2α⌋) cuts of size at most min⁡{α⋅λ,δ−1} for any given real number α≥1.•A cut is trivial if it or its complement in V(G) is a singleton. We provide an alternative proof of the following recent result of Lo et al.: Given a simple graph G on n vertices that satisfies δ>0, we can compute vertex subsets of G in near-linear time such that contracting these vertex subsets separately preserves all non-trivial min-cuts of G and leaves a graph having O(n/δ) vertices and O(n) edges.

Fuel treatments in shrublands experiencing pinyon and juniper expansion result in trade-offs between desired vegetation and increased fire behavior

BackgroundNative pinyon (Pinus spp.) and juniper (Juniperus spp.) trees are expanding into shrubland communities across the Western United States. These trees often outcompete with native sagebrush (Artemisia spp.) associated species, resulting in increased canopy fuels and reduced surface fuels. Woodland expansion often results in longer fire return intervals with potential for high severity crown fire. Fuel treatments are commonly used to prevent continued tree infilling and growth and reduce fire risk, increase ecological resilience, improve forage quality and quantity, and/or improve wildlife habitat. Treatments may present a trade-off; they restore shrub and herbaceous cover and decrease risk of canopy fire but may increase surface fuel load and surface fire potential. We measured the accumulation of surface and canopy fuels over 10 years from ten sites across the Intermountain West in the Sagebrush Steppe Treatment Evaluation Project woodland network (www.SageSTEP.org), which received prescribed fire or mechanical (cut and drop) tree reduction treatments. We used the field data and the Fuel Characteristic Classification System (FCCS) in the Fuel and Fire Tools (FFT) application to estimate surface and canopy fire behavior in treated and control plots in tree expansion phases I, II, and III.ResultsIncreased herbaceous surface fuel following prescribed fire treatments increased the modeled rate of surface fire spread (ROS) 21-fold and nearly tripled flame length (FL) by year ten post-treatment across all expansion phases. In mechanical treatments, modeled ROS increased 15-fold, FL increased 3.8-fold, and reaction intensity roughly doubled in year ten post-treatment compared to pretreatment and untreated controls. Treatment effects were most pronounced at 97th percentile windspeeds, with modeled ROS up to 82 m min−1 in mechanical and 106 m min−1 in prescribed fire treatments by 10 years post-treatment compared to 5 m min−1 in untreated controls. Crown fire transmissivity risk was eliminated by both fuel treatments.ConclusionsWhile prescribed fire and mechanical treatments in shrublands experiencing tree expansion restored understory vegetation and prevented continued juniper and pinyon infilling and growth, these fuel treatments also increased modeled surface fire behavior. Thus, management tradeoffs occur between desired future vegetation and wildfire risk after fuel treatments.

Modelling of snow interception on a Japanese cedar canopy based on weighing tree experiment in a warm winter region

AbstractSnow interception by tree canopy affects the water and energy budgets. Several snow interception models have been proposed to estimate the temporal change of intercepted snow on the tree canopy from meteorological data. However, most models are based on a few observational results at limited sites or some conceptual understandings and assumptions; they still need more observational evidence and more detailed descriptions in the parameterization model for snow accumulation and snow unloading response to various meteorological conditions. A weighing tree experiment that measures the intercepted snow on the cut Japanese cedar trees was conducted in Tokamachi, Japan, to investigate the relationship between meteorological conditions and the change of intercepted snow. The results showed that the interception efficiency, which shows the ratio of intercepted snow to the total precipitation, increased with increasing air temperature in the range from −4.2 to 0°C due to increased adhesion and cohesion of snow. The maximum interception capacity was not present in observations such as an air temperature range. The unloading of intercepted snow from the canopy induced by snowmelt was related to air temperature, solar radiation, and intercepted snow amount. The snow unloading caused by wind‐blowing intercepted snow off the tree, started to develop at wind speeds exceeding 0.8 m s−1. The wind‐induced snow unloading rate coefficient increased as the wind speed increased. This study proposed a new parameterization model of snow interception based on these observations. It is the only snow interception model to use solar radiation in the snow unloading parameterization. It found that simulated and observed temporal changes of intercepted snow correlated well. Nevertheless, there were significant errors between the simulated and observed intercepted snow on several snowfall events because the model could not accurately assess the occurrence of snow unloading due to snowmelt.

Green synthesis of silver nanoparticles using Eucommia ulmoides leaf extract for inhibiting stem end bacteria in cut tree peony flowers.

Tree peony ( Paeonia suffruticosa Andr.) is a popular cut flower among ornamental plants. However, its short vase life severely hinders the production and application of cut tree peony flowers. To extend the postharvest longevity and improve the horticultural value, silver nanoparticles (Ag-NPs) was applied for reducing bacterial proliferation and xylem blockage in cut tree peony flowers in vitro and in vivo. Ag-NPs was synthesized with the leaf extract of Eucommia ulmoides and characterized. The Ag-NPs aqueous solution showed inhibitory activity against bacterial populations isolated from stem ends of cut tree peony 'Luoyang Hong' in vitro. The minimum inhibitory concentration (MIC) was 10 mg L-1. Compared with the control, pretreatments with Ag-NPs aqueous solution at 5 and 10 mg L-1 for 24h increased flower diameter, relative fresh weight (RFW), and water balance of tree peony 'Luoyang Hong' flowers. Additionally, malondialdehyde (MDA) and H2O2 content in pretreated petals were lower than the control during the vase life. The activities of superoxide dismutase (SOD) and catalase (CAT) in pretreated petals were lower than that of the control at the early vase stage and higher at the late vase life. Furthermore, pretreatments with Ag-NPs aqueous solution at 10 mg L-1 for 24h could reduce bacterial proliferation in the xylem vessels on the stem ends by confocal laser scanning microscope (CLSM) and scanning electron microscope (SEM). Overall, pretreatments with green synthesized Ag-NPs aqueous solution effectively reduced bacteria-induced xylem blockage of cut tree peony, resulting in improved water uptake, extended vase life, and enhanced postharvest quality. Therefore, this technique can be used as a promising postharvest technology in the cut flower industry.

Порівняння 3D-моделей об’єктів, побудованих за матеріалами наземного лазерного сканування

The purpose of the work was to assess the accuracy and functionality of the 3D model obtained as a result of the 3D scanning and post-processing. The article discusses 3D modeling methods and their characteristics. The procedure for working with a 3D scanner is studied and its principle of operation is given. Scan data was post-processed to create 3D models of varying accuracy. The equipment used for the scanning is a FARO Focus Laser S150 terrestrial laser scanner. SCENE was used for post-processing. The object for scanning with a ground laser scanner was six identical logs of wood. The functionality and accuracy of the models are determined. Three-dimensional scanning of the research objects was carried out from six stations to obtain a full-fledged point cloud. At each station, the device was installed in the working position on the horizontal level using the inclinometer readings. Scanning was performed after selecting the appropriate shooting mode. It was taken into account that during scanning, no object can touch the mirror unit, because during operation, the scanner rotates clockwise by 180 degrees. Scanning in this study was carried out in color, so the scanner rotated 360 degrees. Based on ground laser scanning data, 3D models of cut tree trunks of the same size were built. All models are built with different accuracy for comparison and determination of the optimal one in terms of reliability and load. The obtained research results, as well as the technique itself, can be used in forestry by forestry and various structures related to wood analysis. Also, if we consider only our industry, the research data can be useful for the cadastre of park territories, etc. In the course of the conducted research, 3D models with different accuracies and memory volumes were obtained, which can be used to solve the relevant tasks. The functionality of using models consists in choosing the number of polygons that will affect the amount of memory. It should be noted that the functionality of the models lies in the convenience of their use on any device, compared to point clouds, which are demanding on the hardware component of a computer. The accuracy of the models is high in all cases. This makes it possible to use them to solve many problems, taking into account the needs. So, we can conclude that the use of models is relevant due to high accuracy and functionality.

A DNA barcode reference library for CITES listed Malagasy Dalbergia species.

On Madagascar, the illegal and unsustainable exploitation and illegal international trade of Dalbergia (rosewood) precious woods remain a serious conservation problem. Members of this genus are at high risk of extinction as a consequence of logging, mining, and slash and burn agriculture. Morphological identification of these Malagasy species is difficult in the absence of flowers and fruits, especially in the case of cut trees, sawn wood, and finished product. In this study, we use molecular barcoding to identify the Dalbergia species with the intent to contribute to the control of their illegal trade. Thirty-six Dalbergia samples representing 12 Malagasy species of which 11 have high commercial value, were collected to test the efficacy of a region of the plastid genome (rbcL) and a nuclear-transcribed ITS for barcoding. These widely used markers, as well as DNA barcoding gaps, "best match" and "best close match" approaches, and the neighbor-joining method were employed. All samples were amplified and sequenced using the two markers. Using a single locus, the "best match" and "best close match" approaches revealed that ITS has high discriminatory power within the tested Malagasy species. The combination of rbcL + ITS revealed 100% species discrimination. This study confirms that ITS alone and in combination with chloroplast barcode rbcL allow non-ambiguous identification for the 12 species studied. The results contribute to the development of DNA barcoding as a useful tool to identify Malagasy Dalbergia and suggest that the approach developed should be expanded to all 56 potentially exploited species in reference to international CITES requirements and the sustainable management of valuable resources.

Growth and functional leaf traits of coppice regrowth of Bertholletia excelsa during an El Niño event in the central Amazon

ABSTRACT The most severe drought of this century in the Amazon rainforest, which was caused by El Niño, occurred from 2015 to 2016. With a focus on the ecophysiology of the regrowth of the Brazil nut tree, Bertholletia excelsa, it was investigated how the progression of the drought of 2015-2016 affected the physiological traits of the coppice regrowth of B. excelsa. The experiment was carried out in a ten-year-old plantation of Brazil nut trees, which had been subjected to thinning and coppice regrowth two years earlier. In the sprouts grown on the stumps of cut trees, the following treatments were applied: (T1) thinning to one sprout per stump; (T2) thinning to two sprouts per stump, and (T3) maintenance of three sprouts per stump. Thinning treatments did not alter the growth and ecophysiological traits of the Brazil nut tree sprouts, though the phosphorus content of the leaves was higher in T1. However, the progression of the drought in 2015-2016 negatively affected the growth (height) and gas exchange of sprouts of all treatments. In addition, an increase of around 37% was observed in the intrinsic water-use efficiency. Concerning photochemical performance, no alterations were observed. Therefore, drought stress promoted a negative effect on sprout growth and affected traits related to the photosynthesis of the B. excelsa sprouts independently of the number of sprouts per stump.

A novel CO2-EOR potential evaluation method based on BO-LightGBM algorithms using hybrid feature mining

The main application of carbon capture, utilization, and storage (CCUS) in oil-and-gas field development engineering is carbon-dioxide enhanced oil recovery (CO2-EOR). However, accurate and rapid assessment of its application potential remains a major challenge. Traditional methods almost always evaluate whether a reservoir is suitable for the CO2-EOR technology by customizing static screening indicators and establishing threshold ranges. That is irrefutably time consuming, laborious, and subjective. In this study, a novel CO2-EOR potential assessment method based on hybrid feature mining and a light gradient boosting machine using Bayesian optimization (BO-LightGBM) is proposed. The fair cut tree and synthetic minority oversampling technique (FCT-SMOTE) were used to correct the missing and unbalanced oilfield data. This has ensured that the model has a balanced high classification accuracy for each type of reservoir. Hybrid feature selection was used to analyze oilfield data to improve the learning ability of the model and improve the calculation efficiency. To achieve multitasking scalability and rapidity, BO-LightGBM was adopted as a classifier for judging the CO2-EOR potential of reservoirs, and a regressor for predicting EOR factor. The proposed classification model has a prediction accuracy of 98.7%, and the proposed regression model has a root mean square error (RMSE) of 1.72, coefficient of determination (R2) of 0.95, and mean absolute percentage error (MAPE) of 18%. Overall, the CO2-EOR potential assessment method proposed in this study has the advantages of high accuracy, scalability, and speed. In addition, it should be emphasized that using static weight indicators to evaluate CO2-EOR potential may not be appropriate. Rather, machine learning (ML) methods to evaluate the application potential of CO2-EOR is recommended. This is because ML methods can divide the scope of multiple factors simultaneously, providing more intelligent and accurate results.

Anomaly Detection for Hydroelectric Generating Units by Fast Robust Random Cut Forest with Fast Feature Selection Considering Characteristics of Operating Data and Random Cut Trees

This paper proposes anomaly detection for hydroelectric generating units by Fast Robust Random Cut Forest with a fast feature selection method by considering characteristics of operating data and Random Cut Trees. Hydroelectric generating units are renewable electric generating sources for electricity supply. Therefore, it is crucial to accurately detect anomalies of hydroelectric generating units. Moreover, effective features for anomaly detection should be selected to reduce operation costs of anomaly detection services. The proposed anomaly detection method can detect anomalies approximately thirty times faster than the conventional Robust Random Cut Forest with an accuracy comparative to that of the conventional method. The proposed feature selection method can select effective features almost fifty times faster than the conventional feature selection method.

Reduced Robust Random Cut Forest for Out-of-Distribution Detection in Machine Learning Models

Most machine learning-based regressors extract information from data collected via past observations of limited length to make predictions in the future. Consequently, when input to these trained models is data with significantly different statistical properties from data used for training, there is no guarantee of accurate prediction. Consequently, using these models on out-of-distribution input data may result in a completely different predicted outcome from the desired one, which is not only erroneous but can also be hazardous in some cases. Successful deployment of these machine learning models in any system requires a detection system, which should be able to distinguish between out-of-distribution and in-distribution data (i.e. similar to training data). In this paper, we introduce a novel approach for this detection process using a Reduced Robust Random Cut Forest (RRRCF) data structure, which can be used on both small and large data sets. Similar to the Robust Random Cut Forest (RRCF), RRRCF is a structured, but a reduced representation of the training data sub-space in form of cut trees. Empirical results of this method on both low and high-dimensional data showed that inference about data being in/out of training distribution can be made efficiently and the model is easy to train with no difficult hyper-parameter tuning. The paper discusses two different use-cases for testing and validating results.

Cutting branches of trees and using opportunities of them

Taking into account that in Uzbekistan, great attention is being paid to the establishment of new gardens and vineyards, the expansion of the area of forests, the greening of roads and avenues, and the amount of pruning works will also increase. As a result, it is expected that the size of the branches formed because of their shaping and pruning will increase, and this bushy tree requires additional processing and consideration of the possibilities of their targeted use in order to prevent the accumulation of branches. In article it is analyzed the formed volumes of pruning trees, foreign experience in processing and using them. According to the analysis, it was revealed that in the coming years in the Republic the volume of cut branches of trees increases by 2 times and there is a need to use them as firewood with cutting them 100-400 mm long. Based on this, the development of an effective device for chopping cut tree branches in the required size in order to use them as firewood in areas is relevant.

Localized impact of livestock settlements on vegetation patterns in fir forests of Kashmir Himalaya

Among the many global drivers of ecosystem degradation, the long-term impact of livestock settlements on vegetation patterns and composition, and is one of the greatest hurdles to successful forest ecosystem restoration. In order to identify the ecological implications of human land use changes on vegetation patterns and composition, the current study was conducted for the first time in Fir Forests of Kashmir Himalaya, to illustrate how vegetation responds to livestock settlements. We took samples from forest types both near and far from livestock settlements (ALS and NLS, respectively). To collect data on forest types, we employed a systematic random sampling strategy. After evaluation of the important value index (henceforth IVI) for each plant species along with anthropogenic data of forests, we analyzed data using ordination and cluster analysis techniques. On comparative analysis, the lesser values for diversity indices have been found for at the forest types near livestock settlement. It was discovered that 36% species were unique to away from livestock settlements forest type and 18% species in near to livestock settlements forest type, but that maximum 46% of species were common to all forest types studied. Species composition was unevenly distributed along with various anthropogenic factors, according to the canonical correspondence analysis. Forests near livestock settlements were also discovered to affect species abundance and diversity by promoting the growth of weedy species. The abundant growth of weedy flora such as Cirsium falconeri, Polygonum aviculare, and Urtica dioica around livestock settlement, results in alterations of plant community composition. The findings revealed that cut trees contributed 16% of reported density at ALS forest type and considerably higher values 35% at NLS forest type, respectively. Findings of the study could be used to develop effective livelihood options and management policies for long-term forest harvest impacts in the Himalayas, resulting in the sustainable use of local forest reserves.

Weighted Gene Correlation Network Analysis (WGCNA) of Arabidopsis Somatic Embryogenesis (SE) and Identification of Key Gene Modules to Uncover SE-Associated Hub Genes.

Somatic embryogenesis (SE), which occurs naturally in many plant species, serves as a model to elucidate cellular and molecular mechanisms of embryo patterning in plants. Decoding the regulatory landscape of SE is essential for its further application. Hence, the present study was aimed at employing Weighted Gene Correlation Network Analysis (WGCNA) to construct a gene coexpression network (GCN) for Arabidopsis SE and then identifying highly correlated gene modules to uncover the hub genes associated with SE that may serve as potential molecular targets. A total of 17,059 genes were filtered from a microarray dataset comprising four stages of SE, i.e., stage I (zygotic embryos), stage II (proliferating tissues at 7 days of induction), stage III (proliferating tissues at 14 days of induction), and stage IV (mature somatic embryos). This included 1,711 transcription factors and 445 EMBRYO DEFECTIVE genes. GCN analysis identified a total of 26 gene modules with the module size ranging from 35 to 3,418 genes using a dynamic cut tree algorithm. The module-trait analysis revealed that four, four, seven, and four modules were associated with stages I, II, III, and IV, respectively. Further, we identified a total of 260 hub genes based on the degree of intramodular connectivity. Validation of the hub genes using publicly available expression datasets demonstrated that at least 78 hub genes are potentially associated with embryogenesis; of these, many genes remain functionally uncharacterized thus far. In silico promoter analysis of these genes revealed the presence of cis-acting regulatory elements, “soybean embryo factor 4 (SEF4) binding site,” and “E-box” of the napA storage-protein gene of Brassica napus; this suggests that these genes may play important roles in plant embryo development. The present study successfully applied WGCNA to construct a GCN for SE in Arabidopsis and identified hub genes involved in the development of somatic embryos. These hub genes could be used as molecular targets to further elucidate the molecular mechanisms underlying SE in plants.

Construction of Prediction Model for Atrial Fibrillation with Valvular Heart Disease Based on Machine Learning.

Valvular heart disease (VHD) is a major precipitating factor of atrial fibrillation (AF) that contributes to decreased cardiac function, heart failure, and stroke. Stroke induced by VHD combined with atrial fibrillation (AF-VHD) is a much more serious condition in comparison to VHD alone. The aim of this study was to explore the molecular mechanism governing VHD progression and to provide candidate treatment targets for AF-VHD. Four public mRNA microarray datasets were downloaded and differentially expressed genes (DEGs) screening was performed. Weighted gene correlation network analysis was carried out to detect key modules and explore their relationships and disease status. Candidate hub signature genes were then screened within the key module using machine learning methods. The receiver operating characteristic curve and nomogram model analysis were used to determine the potential clinical significance of the hub genes. Subsequently, target gene protein levels in independent human atrial tissue samples were detected using western blotting. Specific expression analysis of the hub genes in the tissue and cell samples was performed using single-cell sequencing analysis in the Human Protein Atlas tool. A total of 819 common DEGs in combined datasets were screened. Fourteen modules were identified using the cut tree dynamic function. The cyan and purple modules were considered the most clinically significant for AF-VHD. Then, 25 hub genes in the cyan and purple modules were selected for further analysis. The pathways related to dilated cardiomyopathy, hypertrophic cardiomyopathy, and heart contraction were concentrated in the purple and cyan modules of the AF-VHD. Genes of importance (CSRP3, MCOLN3, SLC25A5, and FIBP) were then identified based on machine learning. Of these, CSRP3 had a potential clinical significance and was specifically expressed in the heart tissue. The identified genes may play critical roles in the pathophysiological process of AF-VHD, providing new insights into VHD development to AF and helping to determine potential biomarkers and therapeutic targets for treating AF-VHD.

Relative Effectiveness of Four Different Guards In Preventing Beaver Cutting of Urban Trees

Beavers are expanding into cities as they recolonize their historic range. While they increase the ecological functioning of urban green areas, human-beaver conflicts occur. Public support to deal with conflicts has shifted from population to forage control. Tree guards are becoming popular with management personnel in North America and Europe to reduce damage to valuable trees. The problem is that this management technique has not been studied. We inventoried the tree guard types in use in natural and manicured river parks in the City of Saskatoon, Canada, determined their adherence to an installation protocol by measuring guard dimensions, and assessed the relative effectiveness of guards in protecting trees from beaver cutting. The inventory revealed that four types of tree guards are in use, ranging from light gauge chicken wire to heavy gauge chain link fencing. Overall, 11% of the trees with guards that we inventoried were cut by beavers, but variation among guard types was observed. Less than 10% of trees with type i and ii guards were beaver cut whereas 17% of trees with types iii and iv guards were beaver cut. Fewer trees were cut when there was adherence to installation protocol, regardless of guard type. Cut trees with guard types i, iii and iv experienced both minor and major damage whereas cut trees with guard type ii experienced only minor damage. The study results have implications for developing effectiveness and implementation monitoring plans for tree guards as part of an overall beaver management plan.

Study on Forest Management Plan Based on Forecast of the Carbon Sequestration and Comprehensive Value

Carbon sequestration by the forest and its products can help to reduce carbon dioxide. However, when trees age, they will release the carbon accumulated in the body. So it raises the problem that how to make trees into products at the appropriate amount and time and make an effective forest management plan. Firstly, "DBH-Biomass-carbon sequestration" model is developed to calculate the amount of carbon sequestration of a tree at different ages. Then we predict the total carbon sequestration of a forest in the next 100 years, assuming that the age distribution of a forest obeys a normal distribution, and the best cutting time for a tree is when its marginal carbon sequestration is zero. Besides carbon sequestration, we introduce ecological, economic, and social dimensions to evaluate the comprehensive value of forests, and use the multi-objective planning model to obtain the optimized annual deforestation plan which take ecological, economic, and social dimensions into account. Finally, we select Tianshan Spruce Forest to apply the above models. The results show that the carbon sequestration of spruce began to decline after the age of 21. compare two management plans, one is to cut down spruce trees older than 21 years old every year and the other is to neither cut nor plant, we find that the forest carbon sequestration amount in 100 years for plan one and two is 4571t and 1728t, respectively.