Pruning Research Articles

Training Volunteers to Prune Recently Planted, Small Street Trees

AbstractBackgroundTrees in towns and cities provide many benefits, but also disservices such as risk and conflicts. Structural pruning of young trees can reduce future conflicts and risk as trees grow larger; it also can reduce future maintenance costs. Volunteers can perform important urban forestry tasks such as planting, watering, and conducting inventories. It was hypothesized that, with training, they could also learn to structurally prune young street trees.MethodsForty-seven volunteers in three cities in Massachusetts were trained to structurally prune trees. Twenty volunteers trained in a classroom lecture; twenty-seven trained with a hands-on approach. The volunteers’ performance was evaluated with a written exam andin situassessments of their ability to specify and explain pruning recommendations and make pruning cuts. Training type and covariates (e.g., volunteers’ familiarity with trees, number of branches) influence on volunteers’ performance were investigated.ResultsOn the assessment of volunteers’ ability to explain pruning recommendations, volunteers who received hands-on training achieved higher mean scores (79%) than volunteers who received classroom training (74%). All volunteers who received hands-on training did not leave a stub when making a reduction cut, but only 70% of volunteers who received classroom training did not leave a stub. Volunteers who received classroom training achieved higher scores on the exam (93%) than volunteers who received hands-on training (85%).ConclusionsResults suggest that with minimal training volunteers successfully learned structural pruning. This is an encouraging finding that may help municipal arborists accomplish more with limited urban forestry budgets.

Reinforced HDPE with optimized biochar content for material extrusion additive manufacturing: morphological, rheological, electrical, and thermomechanical insights

The development of efficient and sustainable composites remains a primary objective of both research and industry. In this study, the use of biochar, an eco-friendly reinforcing material, in additive manufacturing (AM) is investigated. A high-density Polyethylene (HDPE) thermoplastic was used as the matrix, and the material extrusion (MEX) technique was applied for composite production. Biochar was produced from olive tree prunings via conventional pyrolysis at 500 °C. Composite samples were created using biochar loadings in the range of 2.0–10.0 wt. %. The 3D-printed samples were mechanically tested in accordance with international standards. Thermogravimetric analysis (TGA) and Raman spectroscopy were used to evaluate the thermal and structural properties of the composites. Scanning electron microscopy was used to examine the fractographic and morphological characteristics of the materials. The electrical/dielectric properties of HDPE/biochar composites were studied over a broad frequency range (10–2 Hz–4 MHz) at room temperature. Overall, a laborious effort with 12 different tests was implemented to fully characterize the developed composites and investigate the correlations between the different qualities. This investigation demonstrated that biochar in the MEX process can be a satisfactory reinforcement agent. Notably, compared to the control samples of pure HDPE, biochar increased the tensile strength by over 20% and flexural strength by 35.9% when added at a loading of 4.0 wt. %. The impact strength and microhardness were also significantly improved. Furthermore, the Direct current (DC) conductivity of insulating HDPE increased by five orders of magnitude at 8.0 wt. % of biochar content, suggesting a percolation threshold. These results highlight the potential of C-based composites for the use in additive manufacturing to further exploit their applicability by providing parts with improved mechanical performance and eco-friendly profiles.Graphical

Dynamic changes of physicochemical parameters, antioxidant activity, organic acids, polyphenols, and volatile components in prune vinegar during fermentation

To investigate the quality variations of prune vinegar during fermentation, multivariate statistical analysis (MSA) was used in conjunction with high-performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GC-MS). The results showed that the pH decreased slightly, there was a rapid decline in sugar content, and a gradual increase in total acid content, which achieved stabilization at 3.68 g/100 mL. The content of organic acid increased, while the total phenol content decreased. The total flavonoid content did not change significantly. Furthermore, the antioxidant capacity initially decreased and then increased. Prune vinegar contains important phenolics such as rutin, resveratrol, salicylic acid, trans-ferulic acid, and (+)-catechin. In addition, the flavor of prune fruit vinegar underwent changes during the fermentation process. During the fermentation of fruit vinegar, large quantities of 2,3-butanediol, glacial acetic acid, nonanoic acid, ethyl acetate, ethyl heptanoate, ethyl caproate, and linalool were produced. These compounds give prune vinegar its distinctive fruity and floral aroma. This study provided a comprehensive framework for the efficient processing of prunes and a strategy for enhancing the quality characteristics of prune vinegar or other fermented products.

Unbalanced Distribution Network Cross-Country Fault Diagnosis Method with Emphasis on High-Impedance Fault Syndrome

Unusual fault scenarios can occur on the utility grid in a power system network. Cross-Country Faults (CCFs) connected to the High-Impedance Fault (HIF) syndrome are more prone to occur in forested areas due to thunderstorms, cyclones, and improper vegetation management and tree pruning. Finding and categorizing CCFs associated with HIF syndrome is a great challenge. This study employed the cross-correlation method to reconstruct the signals produced by CCFs with HIF, which were shown to be complicated, aperiodic, asymmetric, and nonlinear. A decreased sensitivity to random noise means that a given modification might not affect equally all component peaks. This allows for more precise signal recovery. The maximum voltage cross-correlation coefficients were carefully evaluated as distinguishing elements in the development of a suggested fault detection technique. The proposed concept was evaluated on a modified imbalanced IEEE 240 bus system under different case studies. These case studies cover a wide range of scenarios, such as the switching of a capacitor bank, feeder energization, and the effects of nonlinear loads under noisy conditions.

Tree based Regression Models for Predicting the Compressive Strength of Concrete at High Temperature

Predicting the compressive strength of concrete is a complicated process due to the heterogeneous mixture of concrete and high variable materials. Researchers have predicted the compressive strength of concrete for various mixes using soft computing models. In this research, compressive strength of concrete at high temperature with fly ash, super plasticizers, and fibre is predicted using three regression tree-based soft computing models (Random Forest, Random Tree, and Reduced-Error Pruning Tree (REP Tree)). The data used in this study is collected from the literature, and two-thirds of the total data is used for model training, while the remaining third is reserved for testing the prepared model. The model’s performance is evaluated based on scatter plots, variation plots, box plots, and prediction error rates, i.e., R, RMSE, and MAE. The results highlight the highest performance of the Random Forest model, with R of 0.9142; RMSE of 9.6285 MPa and MAE of 6.7931 MPa, outperforming the other competing models. Furthermore, the most influential parameter is determined using sensitivity analysis. Thus, the Random Forest model is the model that can be used for predicting the compressive strength of concrete at high temperatures.

Predictive modelling of nitrogen dioxide using soft computing techniques in the Agra, Uttar Pradesh, India

Nitrogen dioxide (NO2) is one of the air pollutants which aggravates the human health as well as causes environmental issues. It is more causes respiratory problems due to acid rains. Agra is a major tourist destination spot in India also similarly air pollution also increased growing urbanization and traffic reflux. The current study aims to predicted the NO2 episodes in the Agra city using soft computing models namely, M5P, Random Forest (RF), Group method of data handling (GMDH), Multivariate adaptive regression (MARS), Reduced error pruning tree (REP Tree) and Random tree (RT). The models were generated using 1116 observations, from 2015 to 2020 with input parameters such as Particulate matter (PM2.5), Nitrogen monoxide (NO), Oxides of nitrogen (NOX), Sulphur dioxide (SO2), Carbon monoxide (CO), Ozone (O), Benzene (Be), Toluene, Relative humidity (RH), Wind speed (WS), Wind direction (WD), Solar radiation (SR), Barometric pressure (BP) and Xylene. The performance of each model was evaluated based on the six statistical indices, namely correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), normalized root of mean squared relative error (NRMSE), Willmott's Index (WI), and Legates and McCabe's Index (LMI). The performance evaluation models results of study area, Box plot and Taylor's diagram indicated that M5P is the outperforming model among others with testing CC = 0.9543, RMSE = 5.8006, MAE = 3,9204, NRMSE = 0.1512, WI = 0.9744, and LMI = 0.7549. Based on the sensitivity analysis indicated that NOx is the most influential parameter followed by WD and CO. These results of study area can be helpful to understanding the air pollution causes, health issues, and future NO2 levels around the study area with useful results for air pollution monitoring policy and development.

The Combination of High-Light Efficiency Pruning and Mulching Improves Fruit Quality and Uneven Maturation at Harvest in Camellia oleifera

Camellia oleifera Abel is rich in oil, which is beneficial to human health. Seasonal drought and uneven canopy microclimate distributions result in low yields and picking efficiencies. To reveal the features of fruit development in the canopy and evaluate the impact of pruning on the canopy microclimate and fruit parameters, a field experiment was carried out in Zhuzhou, Hunan. The C. oleifera tree canopy was divided into four positions: inside the upper canopy (UI); inside the lower canopy (LI); outside the upper canopy (UO); and outside the lower canopy (LO). A combination of high-light efficiency tree pruning and mulching with camellia shells and weedproof film (CPM) was tested and compared with mulching (M). Compared with temperature and humidity, canopy light intensity is a key climate factor that affects the development of Camellia oleifera fruits. CPM improved the canopy light distribution and increased the light intensity inside the canopy of C. oleifera. There was no significant difference in the fruit cracking rate among the four canopy positions in the CPM treatment, which indicated that the fruit maturation period tended to be consistent across the canopy positions. Treatment with CPM resulted in 24.14% and 4.11% greater yield and oil content ratios, respectively, than the treatment with M. The fresh weight of the fruits from LO was the lowest, and the fatty acid accumulation was the lowest due to the poor light availability. CMP increased fruit quality, particularly in LI, which was 41.92%, 36.36%, 13.21%, and 12.79% greater in terms of fresh weight, yield, fresh seed rate, and oil content, respectively, than M. Unfortunately, there was no discernible difference in fatty acid components between CPM and M. Our research suggested that pruning combined with the mulching of organic material is an effective way to alleviate seasonal drought and uneven fruit maturation in C. oleifera trees, further increasing yield and fruit quality.

Tree form characteristics as criteria for tree species selection to improve pedestrian thermal comfort in street canyons: Case study of a humid subtropical city

The combined effect of tree characteristics on heat stress in different street canyons has been widely reported. However, there remains a lack of effective methods for improving pedestrian thermal comfort through the use of integrated knowledge of tree characteristics. Here, a tree species selection approach was developed for assessing the dynamic effects of tree forms and canyon geometries on pedestrian thermal comfort to address the issue of the right tree in the right street canyon. The leaf area density (LAD), crown diameter (CD), tree height (HT), and trunk height (TH) were used as criteria for determining tree species characteristics, and the weighted outdoor thermal comfort autonomy (OTCAw) was adopted as a quantitative index of thermal comfort variation over time. In a case study of Wuhan, China, we evaluated the thermal comfort enhancement potential of 45 tree forms in street canyons with varying aspect ratios and orientations from 7:00 to 21:00 by using a validated ENVI-met model. Accordingly, tree pruning strategies and 19 optimal tree forms were proposed for corresponding street canyons. Our study provides insight into the optimal street tree species for minimizing heat exposure of pedestrians under various urban canyon configurations.

Optimisation of sugar and solid biofuel co-production from almond tree prunings by acid pretreatment and enzymatic hydrolysis.

Almond pruning biomass is an important agricultural residue that has been scarcely studied for the co-production of sugars and solid biofuels. In this work, the production of monosaccharides from almond prunings was optimised by a two-step process scheme: pretreatment with dilute sulphuric acid (0.025M, at 185.9-214.1℃ for 0.8-9.2min) followed by enzyme saccharification of the pretreated cellulose. The application of a response surface methodology enabled the mathematical modelling of the process, establishing pretreatment conditions to maximise both the amount of sugar in the acid prehydrolysate (23.4kg/100kg raw material, at 195.7℃ for 3.5min) and the enzymatic digestibility of the pretreated cellulose (45.4%, at 210.0℃ for 8.0min). The highest overall sugar yield (36.8kg/100kg raw material, equivalent to 64.3% of all sugars in the feedstock) was obtained with a pretreatment carried out at 197.0℃ for 4.0min. Under these conditions, moreover, the final solids showed better properties for thermochemical utilisation (22.0MJ/kg heating value, 0.87% ash content, and 72.1mg/g moisture adsorption capacity) compared to those of the original prunings.

A Multiple Criteria Decision-Making Method Generated by the Space Colonization Algorithm for Automated Pruning Strategies of Trees

The rise of mechanical automation in orchards has sparked research interest in developing robots capable of autonomous tree pruning operations. To achieve accurate pruning outcomes, these robots require robust perception systems that can reconstruct three-dimensional tree characteristics and execute appropriate pruning strategies. Three-dimensional modeling plays a crucial role in enabling accurate pruning outcomes. This paper introduces a specialized tree modeling approach using the space colonization algorithm (SCA) tailored for pruning. The proposed method extends SCA to operate in three-dimensional space, generating comprehensive cherry tree models. The resulting models are exported as normalized point cloud data, serving as the input dataset. Multiple criteria decision analysis is utilized to guide pruning decisions, incorporating various factors such as tree species, tree life cycle stages, and pruning strategies during real-world implementation. The pruning task is transformed into a point cloud neural network segmentation task, identifying the trunks and branches to be pruned. This approach reduces the data acquisition time and labor costs during development. Meanwhile, pruning training in a virtual environment is an application of digital twin technology, which makes it possible to combine the meta-universe with the automated pruning of fruit trees. Experimental results demonstrate superior performance compared to other pruning systems. The overall accuracy is 85%, with mean accuracy and mean Intersection over Union (IoU) values of 0.83 and 0.75. Trunks and branches are successfully segmented with class accuracies of 0.89 and 0.81, respectively, and Intersection over Union (IoU) metrics of 0.79 and 0.72. Compared to using the open-source synthetic tree dataset, this dataset yields 80% of the overall accuracy under the same conditions, which is an improvement of 6%.

Estimating critical depth and discharge over sloping rough end depth using machine learning

Abstract This study uses machine learning (ML) to predict the end-depth structure's discharge and critical depth (yc). Linear regression, M5P, random forest, random tree, reduced error pruning tree, and Gaussian process (GP) are the ML methods used in this investigation. The findings indicate that the radial kernel function-based GP model is most suitable compared to other applied models with the lowest root-mean-square error = 0.0021, 0.007, normalized root-mean-square error = 0.0361, 0.0516 representing mean absolute error = 0.0015, 0.004 and the highest coefficient of correlation = 0.9912, 0.9916, Legates and McCabe's index = 0.8839, 0.9026 Willmott's index = 0.9956, 0.9956, and Nash Sutcliffe model efficiency = 0.9823, 09830 for yc for the end-depth structure (yc) and discharge (Q) with the testing stage, respectively. Results of the sensitivity study indicate that the friction coefficient is the most significant input variable compared to other parameters for predicting (yc) and flow running via the thickness model's last stage (Q) using this dataset.

The Effect of Coffee Canopy Pruning and Fertilization on Coffee Growth and Soil Physical Properties

Arabica coffee is primarily cultivated in agroforestry systems in Indonesia, but limited local knowledge and technology adoption hinder its productivity due to insufficient practices in coffee pruning management. This study aims to analyze variations in coffee canopy pruning (Pruning+Bending) management and the impact on plant growth and soil physical characteristics.. The experiment employed a split-plot experimental design and utilized the Fisher test (5%) to assess the treatment effects. The primary plot focused on coffee canopy pruning using two management options: (1) Pruning (PR) and (2) Bending (BN). The subplots included various types and doses of fertilizer treatments: (1) Control (F0), (2) Chicken manure (F1), (3) Chicken manure+NPK fertilizer (F2), and (4) NPK fertilizer (F3). Each experimental plot covered an area of 20x20 m and contained 50 coffee plants. Bending techniques represent alternative pruning methods, and, in general, they have a significant impact on improving several coffee parameters compared to total pruning. Regarding the soil’s physical properties, the bending technique exhibited a higher infiltration rate than pruning. The management approach of Bending+Chicken manure: NPK fertilizer (BNF2) enhanced various coffee parameters, resulting in an increased stem diameter of 4.79 cm, new shoot length of 471.20 cm, and chlorophyll content of 6.83 mg/g. Furthermore, this treatment increased soil organic carbon content by 7.51% and reduced bulk density to 0.58 g/cm. In conclusion, the bending technique wasproven to be more advantageous than pruning, especially when combined with chicken manure and NPK fertilizer for enhancing coffee management among farmers.

Production and characterization of cellulose acetate using olive tree pruning biomass as feedstock

AbstractOlive tree pruning (OTP) is one of the most abundant sources of biomass waste in the Mediterranean basin. This is especially relevant in southern Spain where olive oil production represents a large part of the economy. Olive tree prunings are mostly either burned or are spread in olive orchards as an organic amendment, or used for heat generation on a domestic scale. However, the lignocellulosic composition of OTP makes it a potential source of biopolymers, thus providing an excellent economic alternative for the olive oil sector. In this work, pretreated OTP fibers were subjected to an optimized alkaline treatment followed by a single‐step bleaching reaction with H2O2. Afterwards, the cellulose pulp was transformed chemically to obtain cellulose acetate. Noncellulosic components were removed effectively from OTP, thus obtaining a pulp highly purified in cellulose with 71% crystallinity and 355 °C maximum degradation temperature. Nevertheless, a very large amount of cellulose (ca. 50%) was eliminated throughout the process, especially during acid pretreatment, which was responsible for 38% solubilization. A similar level of acetylation and degree of substitution was obtained by using acetylation times in the range of 1 to 6 h. No large differences were observed in the infrared spectra and X‐ray diffractograms of the synthesized acetates. However, their thermal stability varied significantly with reaction time, evolving from a multistep degradation pattern to a single and sharp peak between 300 and 400 °C with increasing time. Thermogravimetric curves revealed that at least 5 h (preferably 6 h) were needed to obtain cellulose acetate from OTP with adequate thermal stability for further processing.

Integrated municipal management of waste from tree pruning and removal

Urban forests generate a significant amount of waste from leaf fall, pruning and removal of trees. This lignocellulosic material is typically used for composting, burned (with or without energy generation) or disposed in landfills. Despite its potential use for environmentally friendly purposes, access to the material is still limited, mainly due to the lack of knowledge about its generation and quantity, difficulties in sorting, and issues related to the transport and storage of this waste. Thus, this article characterizes an urban afforestation system, quantifies the generation of waste, and proposes an integrated municipal system for the use of this waste material. After describing the procedures and operations performed for tree pruning and removal, we report a case study of the city of Piracicaba, São Paulo State, Brazil, covering a period of 15 months. We adjusted empirical equations (Log Spurr, Spurr, and Schumacher-Hall models) to estimate the volume of waste produced, serving as a basis for the development of waste quantification methods. The city was estimated to generate an average of 180 tons of afforestation waste per month, composed mainly of leaves and thin branches (69%). Composting alone was considered an inefficient use of the material (about 31% of waste is not beneficially utilized). This material can be applied for the production of different products with high added value. Models to predict the generation of waste can contribute to the planning of municipal waste management. The Schumacher-Hall and Log Spurr models produced the best results. In general, the results indicated that adequate urban planning can help reduce waste disposal and burning by local governments. Additionally, carrying out the quantification of waste and exploring the use of new recovery measures of waste from urban afforestation can help support sustainable local development.

Synthesis and characterizations of conocarpus- and azadirachta-derived activated carbons as wastewater recycling material.

Water being the most important fluid supporting the life as well as industry is getting sparse and polluted day by day. Activated carbon (AC) can be utilized in various applications of significant environmental impact and sustainable living such as carbon dioxide sensing and capturing, air purification, and water recycling. However, in the wake of the recent corona pandemic which resulted in global lockdown and took the entire world by shock, a cost-effective and simple synthesis of such a useful material remains dire need of time. Therefore, this paper describes a simple and cost-effective synthesis of activated carbon (AC) of high porosity and surface area derived from the pruning of conocarpus and azadirachta trees. In reference to the study under consideration, alongside numerous others, a furnace was employed to synthesize activated carbon. However, our approach utilized a more conventional methodology wherein the environmental parameters were not optimized. In furnace-based procedures, factors such as temperature, pressure, and humidity are meticulously regulated, contrasting with the conventional methodologies where such parameters lack optimal control. Consequently, employing a furnace does not constitute a cost-effective approach for the physical activation of organic samples thus proving a furnace is not imperative for physical activation. The synthesis was carried out by physical activation in the form of carbonization followed by chemical activation with potassium hydroxide (KOH). The influence of activated carbon from each pruning over filtration of water containing industrial dye was investigated. Activation temperature and impregnation ratio of 600-800°C and 1:5 were selected respectively. X-ray diffraction patterns (XRD) for all AC samples indicted the appearance of broad peaks at 2θ value of 20-30° which confirms the presence of carbon in the sample. The physical morphology arrangement by SEM analysis showed uneven arrangement of pores of conocarpus which indicated higher iodine number and hence higher adsorption capacity of 442.13mg/g.

Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique

An accurate assessment of nitrate leaching is important for efficient fertiliser utilisation and groundwater pollution reduction. However, past studies could not efficiently model nitrate leaching due to utilisation of conventional algorithms. To address the issue, the current research employed advanced machine learning algorithms, viz., Support Vector Machine, Artificial Neural Network, Random Forest, M5 Tree (M5P), Reduced Error Pruning Tree (REPTree) and Response Surface Methodology (RSM) to predict and optimize nitrate leaching. In this study, Urea Super Granules (USG) with three different coatings were used for the experiment in the soil columns, containing 1 kg soil with fertiliser placed in between. Statistical parameters, namely correlation coefficient, Mean Absolute Error, Willmott index, Root Mean Square Error and Nash–Sutcliffe efficiency were used to evaluate the performance of the ML techniques. In addition, a comparison was made in the test set among the machine learning models in which, RSM outperformed the rest of the models irrespective of coating type. Neem oil/ Acacia oil(ml): clay/sulfer (g): age (days) for minimum nitrate leaching was found to be 2.61: 1.67: 2.4 for coating of USG with bentonite clay and neem oil without heating, 2.18: 2: 1 for bentonite clay and neem oil with heating and 1.69: 1.64: 2.18 for coating USG with sulfer and acacia oil. The research would provide guidelines to researchers and policymakers to select the appropriate tool for precise prediction of nitrate leaching, which would optimise the yield and the benefit–cost ratio.

Characteristics of Residence Time of the Torrefaction Process on the Results of Pruning Kesambi Trees

The excessive use of Kesambi (Schleichera oleosa) tree stems threatens the sustainability of Kesambi plants since it takes several decades for them to regenerate new stems. This research aims to determine the characteristics of torrefied Kesambi tree pruning. The used reactor has a diameter of 400 mm. An iron basket is positioned 100 mm above the reactor base, holding the material within an aluminum cylinder. The reactor temperature is maintained at 300°C using a K-type thermocouple sensor. A heater is placed near the reactor base and covered. The characteristics of the semi-charcoal biomass product are identified, including mass yield, water absorption capacity, moisture content (D3173, 2013); ash content (ASTM D1102-84. Standard Test Method for Ash in Wood, 2013); volatile matter (%) (ASTMD3175, 2011); and fixed carbon (%) (ASTM, 2013). The color of the leaves and the pruned Kesambi tree changes from brown to black as the residence time increases. The results of pruning the Kesambi tree at different torrefaction residence times indicate a decrease in mass yield with an increase in residence time, with the lowest mass yield observed at a residence time of 20 minutes. The water absorption capacity of torrefied Kesambi tree pruning material is found to be between 0.65% and 0.675%, or less than 1% and higher heating value (HHV) prediction 29.0750 MJ/kg.Keywords: Kesambi, Pruning, Residence, Time, Torrefaction

Benchmarking of Various Flexible Soft-Computing Strategies for the Accurate Estimation of Wind Turbine Output Power

This computational study explores the potential of several soft-computing techniques for wind turbine (WT) output power (kW) estimation based on seven input variables of wind speed (m/s), wind direction (°), air temperature (°C), pitch angle (°), generator temperature (°C), rotating speed of the generator (rpm), and voltage of the network (V). In the present analysis, a nonlinear regression-based model (NRM), three decision tree-based methods (random forest (RF), random tree (RT), and reduced error pruning tree (REPT) models), and multilayer perceptron-based soft-computing approach (artificial neural network (ANN) model) were simultaneously implemented for the first time in the prediction of WT output power (WTOP). To identify the top-performing soft computing technique, the applied models’ predictive success was compared using over 30 distinct statistical goodness-of-fit parameters. The performance assessment indices corroborated the superiority of the RF-based model over other data-intelligent models in predicting WTOP. It was seen from the results that the proposed RF-based model obtained the narrowest uncertainty bands and the lowest quantities of increased uncertainty values across all sets. Although the determination coefficient values of all competitive decision tree-based models were satisfactory, the lower percentile deviations and higher overall accuracy score of the RF-based model indicated its superior performance and higher accuracy over other competitive approaches. The generator’s rotational speed was shown to be the most useful parameter for RF-based model prediction of WTOP, according to a sensitivity study. This study highlighted the significance and capability of the implemented soft-computing strategy for better management and reliable operation of wind farms in wind energy forecasting.

Boosted Poisson regression trees: a guide to the BT package in R

Abstract Thanks to its outstanding performances, boosting has rapidly gained wide acceptance among actuaries. Wüthrich and Buser (Data Analytics for Non-Life Insurance Pricing. Lecture notes available at SSRN. http://dx.doi.org/10.2139/ssrn.2870308, 2019) established that boosting can be conducted directly on the response under Poisson deviance loss function and log-link, by adapting the weights at each step. This is particularly useful to analyze low counts (typically, numbers of reported claims at policy level in personal lines). Huyghe et al. (Boosting cost-complexity pruned trees on Tweedie responses: The ABT machine for insurance ratemaking. Scandinavian Actuarial Journal. https://doi.org/10.1080/03461238.2023.2258135, 2022) adopted this approach to propose a new boosting machine with cost-complexity pruned trees. In this approach, trees included in the score progressively reduce to the root-node one, in an adaptive way. This paper reviews these results and presents the new BT package in R contributed by Willame (Boosting Trees Algorithm. https://cran.r-project.org/package=BT; https://github.com/GiregWillame/BT, 2022), which is designed to implement this approach for insurance studies. A numerical illustration demonstrates the relevance of the new tool for insurance pricing.

Antioxidative, Metabolic and Vascular Medicinal Potentials of Natural Products in the Non-Edible Wastes of Fruits Belonging to the Citrus and Prunus Genera: A Review.

Diabetes mellitus and related metabolic and vascular impairments are notable health problems. Fruits and vegetables contain phenolics that are beneficial to metabolic and oxidative health and useful in preventing associated disease. Scientific evidence has shown that some bioactive phenolics are more abundant in the non-edible parts (especially the peels) of many fruits than in their respective edible tissues. Fruits belonging to the Citrus and Prunus genera are commonly consumed worldwide, including in South Africa, and their non-edible wastes (peel and seed) have been shown to have antioxidative, metabolic and vascular pharmacological potentials and medicinal phytochemistry. It is therefore imperative to evaluate the pharmacological actions and phytochemical properties of the non-edible wastes of these fruits and understand how they could potentially be of medicinal relevance in oxidative, metabolic and vascular diseases, including diabetes, oxidative stress, obesity, hypertension and related cardiovascular impairments. In the absence of a previous review that has concomitantly presented the medicinal potentials of fruits wastes from both genera, this review presents a critical analysis of previous and recent perspectives on the medicinal potential of the non-edible wastes from the selected Citrus and Prunus fruits in metabolic, vascular and oxidative health. This review further exposes the medicinal phytochemistry, while elucidating the underlying mechanisms through the fruit wastes potentiates their therapeutic effects. A literature search was carried out on "PubMed" to identify peer-reviewed published (mostly 2015 and beyond) studies reporting the antidiabetic, antioxidative, antihypertensive, anti-hyperlipidemic and anti-inflammatory properties of the non-edible parts of the selected fruits. The data of the selected studies were analyzed to understand the bioactive mechanisms, bioactive principles and toxicological profiles. The wastes (seed and peel) of the selected fruits had antioxidant, anti-obesogenic, antihypertensive, anti-inflammatory, antidiabetic and tissue protective potentials. Some phenolic acids and terpenes, as well as flavonoids and glycosides such as narirutin, nobiletin, hesperidin, naringin, naringenin, quercetin, rutin, diosmin, etc., were the possible bioactive principles. The peel and seed of the selected fruits belonging to the Citrus and Prunus genera are potential sources of bioactive compounds that could be of medicinal relevance for improving oxidative, metabolic and vascular health. However, there is a need for appropriate toxicological studies.