Search Results (112)

Hydrochar is a carbon-rich material produced through the hydrothermal carbonization (HTC) of wet biomass such as sewage sludge. Its nitrogen content is a critical quality parameter, influencing its suitability for use as a soil amendment and its potential environmental impacts. This study develops a high-accuracy ensemble machine learning framework to predict the nitrogen content of hydrochar derived from sewage sludge based on feedstock compositions and HTC process conditions. Four ensemble algorithms—Gradient Boosting Regression Trees (GBRTs), AdaBoost, Light Gradient Boosting Machine (LightGBM), and eXtreme Gradient Boosting (XGBoost)—were trained using an 80/20 train–test split and evaluated through standard statistical metrics. GBRT and XGBoost provided the best performance, achieving R² values of 0.993 and 0.989 and RMSE values of 0.169 and 0.213 during training, while maintaining strong predictive capabilities on the test dataset. SHAP analyses identified nitrogen content, ash content, and heating temperature as the most influential predictors of hydrochar nitrogen levels. Predicting nitrogen behaviour during HTC is environmentally relevant, as the improper management of nitrogen-rich hydrochar residues can contribute to nitrogen leaching, eutrophication, and disruption of aquatic biogeochemical cycles. The proposed ensemble-based modelling approach therefore offers a reliable tool for optimizing HTC operations, supporting sustainable sludge valorisation, and reducing environmental risks associated with nitrogen emissions. Full article

Predicting the compressive strength of concrete is essential for engineering design and quality assurance. Traditional empirical formulas often fall short in capturing complex multi-factor interactions and nonlinear relationships. This study employs an interpretable machine learning framework using Gradient Boosting Trees, Random Forest, and Backpropagation Neural Networks to predict concrete compressive strength. Bayesian optimization was employed for hyperparameter tuning, and SHAP analysis was used to quantify feature contributions. Based on 223 sets of compression test data, this study systematically compared the predictive performance of the five models. Results demonstrate that the CatBoost model achieved the best results, R² of 0.9388, RMSE of 2.7131 MPa, and MAPE of 5.45%, outperforming other models. SHAP analysis indicated that cement content had the greatest impact on strength, followed by water content, water reducer, fly ash, and aggregates, with notable interactive effects between factors. Compared to the empirical formula in the current industry standard Specification for Mix Proportion Design of Ordinary Concrete, the CatBoost model showed higher accuracy under specific raw material and curing conditions, with MAPE values of 2.94% and 5.96%, respectively. The optimized CatBoost model, combined with interpretability analysis, offers a data-driven tool for concrete mix optimization, balancing high precision with practical engineering applicability. Full article

Koelreuteria paniculata is an amenity landscape tree whose seed extracts and cold-pressed oil are proven biopesticides and biodiesel feedstocks. However, the residual seed cake phytochemical profile has not been systematically assessed or evaluated for multifunctionality across pesticidal, fertilizing, and nutritional domains. Therefore, the aim of this study was to perform a comprehensive chemotyping of K. paniculata seed cake and evaluate its potential for use as a biopesticide, biofertilizer, and feed additive, contributing to sustainable and circular agricultural systems. Detailed analyses of the defatted seed cake included moisture, crude protein, crude ash, crude fat, and crude fiber determination, as well as amino acid and fatty acid composition determination, supplemented with HPLC and antioxidative capacity investigation. Results delivered a comprehensive chemotyping of K. paniculata seed cake, revealing a nutrient-rich profile with moderate protein (20.01%), substantial monounsaturated fatty acids (75.8%, mainly eicosenoic and oleic), and significant phenolic content, including ellagic acid, rutin, catechin, and gallic acid. Antioxidant assays (DPPH and ABTS) confirmed moderate radical scavenging activity, indicating that bioactivity is retained after cold-press extraction. These compositional and functional traits highlight the potential of the seed cake as a raw material for natural biopesticides, biofertilizers, and value-added agro-industrial products. However, due to its unusual fatty acid profile and possible anti-nutritional factors, feed applications should proceed with caution and be preceded by targeted safety evaluations. Full article

The main advantage of using biomass for energy generation is the reduction in carbon dioxide emissions. For a fast reduction effect, it is important to use biomass characterised by an annual growth cycle. These may be fallen leaves. The fuel properties of the leaves can change during the growth period. These changes can result from both the natural growth process and environmental factors—particulate matter adsorption. The main objective was to determine changes in the characteristics of leaves and needles during the growth period (from May to October). Furthermore, to determine the effect of adsorbed particulate matter, the washing process was carried out. Studies were carried out for three tree species: Norway maple, horse chestnut and European larch. Proximate and ultimate analysis was performed and mercury content was determined. During the growth period, beneficial changes were observed: an increase in carbon content and a decrease in hydrogen and sulphur content. The unfavourable change was a significant increase in ash content, which caused a decrease in calorific value. The increase in ash content was caused by adsorbed particulate matter. They were mostly absorbed by the tissues of the needle and leaves and could not be removed by washing the surface. Full article

Urochloa brizantha (Brizantha) is cultivated under varying altitudinal and management conditions. Twelve full-sun (monoculture) plots and twelve shaded (silvopastoral) plots were established, proportionally distributed at 170, 503, 661, and 1110 masl. Evaluations were conducted 15, 30, 45, 60, and 75 days after establishment. The conservation and integration of trees in silvopastoral systems reflected a clear anthropogenic influence, evidenced by the preference for species of the Fabaceae family, likely due to their multipurpose nature. Although the altitudinal gradient did not show direct effects on soil properties, intermediate altitudes revealed a significant role of CaCO₃ in enhancing soil fertility. These edaphic conditions at mid-altitudes favored the leaf area development of Brizantha, particularly during the early growth stages, as indicated by significantly larger values (p < 0.05). However, at the harvest stage, no significant differences were observed in physiological or productive traits, nor in foliar chemical components, underscoring the species’ high hardiness and broad adaptation to both soil and altitude conditions. In Brizantha, a significant reduction (p < 0.05) in stomatal size and density was observed under shade in silvopastoral areas, where solar radiation and air temperature decreased, while relative humidity increased. Nonetheless, these microclimatic variations did not lead to significant changes in foliar chemistry, growth variables, or biomass production, suggesting a high degree of adaptive plasticity to microclimatic fluctuations. Foliar ash content exhibited an increasing trend with altitude, indicating greater efficiency of Brizantha in absorbing calcium, phosphorus, and potassium at higher altitudes, possibly linked to more favorable edaphoclimatic conditions for nutrient uptake. Finally, forage quality declined with plant age, as evidenced by reductions in protein, ash, and In Vitro Dry Matter Digestibility (IVDMD), alongside increases in fiber, Neutral Detergent Fiber (NDF), and Acid Detergent Fiber (ADF). These findings support the recommendation of cutting intervals between 30 and 45 days, during which Brizantha displays a more favorable nutritional profile, higher digestibility, and consequently, greater value for animal feeding. Full article

Accurate prediction of the coal abrasive index (AI) is critical for optimizing coal processing efficiency and minimizing equipment wear in industrial applications. This study explores tree-based machine learning models; Random Forest (RF), Gradient Boosting Trees (GBT), and Extreme Gradient Boosting (XGBoost) to predict AI using selected coal properties. A database of 112 coal samples from the KwaZulu-Natal Coalfield in South Africa was used. Initial predictions using all eight input properties revealed suboptimal testing performance (R²: 0.63–0.72), attributed to outliers and noisy data. Feature importance analysis identified calorific value, quartz, ash, and Pyrite as dominant predictors, aligning with their physicochemical roles in abrasiveness. After data cleaning and feature selection, XGBoost achieved superior accuracy (R² = 0.92), outperforming RF (R² = 0.85) and GBT (R² = 0.81). The results highlight XGBoost’s robustness in modeling non-linear relationships between coal properties and AI. This approach offers a cost-effective alternative to traditional laboratory methods, enabling industries to optimize coal selection, reduce maintenance costs, and enhance operational sustainability through data-driven decision-making. Additionally, quartz and Ash content were identified as the most influential parameters on AI using the Cosine Amplitude technique, while calorific value had the least impact among the selected features. Full article

The Mixed Ombrophyllous Forest (MOF), inserted in the Atlantic Forest biome, is of great ecological value, with deficient management strategies. In this context, sustainable management helps to promote the regeneration and growth of individual trees and control others, while maintaining the natural forest structure. This study therefore aimed to discuss opportunities and limitations of biochar, produced from two species from the MOF, which are currently only utilized to a limited extent in the study area in southern Brazil. A slow pyrolysis process at a lab scale was designed, biochar was produced, and key properties were analyzed from Hovenia dulcis Thunb. (chosen as an invasive species) and Mimosa scabrella Benth. (chosen as a native, fast-growing species), including branches and stems. The results showed that branches of Mimosa scabrella (BMS) had the highest biochar yield (30.32 ± 0.3%) and the highest electrical conductivity (415.08 ± 24.75 mS cm⁻¹). Stems of Mimosa scabrella (SMS) showed the highest higher heating value (HHV—31.76 ± 0.01 MJ kg⁻¹), lower heating value (LHV—31.03 ± 0.01 MJ kg⁻¹), and energy yield (49.1%), while the branches of Hovenia dulcis (BHD) showed the lowest values. For the elemental analysis, SMS showed the best results, with the highest amount of fixed carbon (78.62 ± 0.22%) and carbon content (85.87 ± 0.083%), and consequently the lowest amount of ash (3.52 ± 0.08%). BHD showed a better water-holding capacity (303.26 ± 15.21%) and higher pH value (7.65 ± 0.14). The investigations conducted on the biochar from both species indicate a strong suitability of these woods for producing high-quality biochar. Full article

Predicting the flexural behavior of fiber-reinforced ultra-high-performance concrete (UHPC) remains a significant challenge due to the complex interactions among numerous mix design parameters. This study presents a machine learning-based framework aimed at accurately estimating the modulus of rupture (MOR) of UHPC. A comprehensive dataset comprising 566 distinct mixtures, characterized by 41 compositional and fiber-related variables, was compiled. Seven regression models were trained and evaluated, with Random Forest, Extremely Randomized Trees, and XGBoost yielding coefficients of determination (R²) exceeding 0.84 on the test set. Feature importance was quantified using Shapley values, while partial dependence plots (PDPs) were employed to visualize both individual parameter effects and key interactions, notably between fiber factor, water-to-binder ratio, maximum aggregate size, and matrix compressive strength. To validate the predictive performance of the machine learning models, an independent experimental campaign was carried out comprising 26 UHPC mixtures designed with varying binder compositions—including supplementary cementitious materials such as fly ash, ground recycled glass, and calcium carbonate—and reinforced with mono-fiber (straight steel, hooked steel, and PVA) and hybrid-fiber systems. The best-performing models were integrated into a hybrid neural network, which achieved a validation accuracy of R² = 0.951 against this diverse experimental dataset, demonstrating robust generalizability across both material and reinforcement variations. The proposed framework offers a robust predictive tool to support the design of more sustainable UHPC formulations incorporating supplementary cementitious materials without compromising flexural performance. Full article

Forest wood biomass is a key renewable resource for advancing energy transition and mitigating climate change. This review analyzes the physicochemical properties of forest biomass from major European tree species to assess their suitability for bioenergy applications. This study encompasses key parameters, such as moisture content, ash content, volatile matter, fixed carbon, elemental composition, bulk density, and energy content (HHV and LHV). This review analyzed data from 43 publications and extracted 140 records concerning the physicochemical properties of the most common European forest species used for bioenergy. The most commonly represented species were Quercus robur, Eucalyptus spp., and Fagus sylvatica. Moisture content, referring to fresh matter, ranged from 5% to 65%; ash content, referring to a dry basis, ranged from 0.2% to 3.5%; and higher heating value (HHV), referring to dry matter, ranged from 17 to 21 MJ kg⁻¹. This study highlights variability among species and underscores the importance of standardizing biomass characterization methods and the scarcity of data on bulk density and other key logistical parameters. These findings emphasize the need for consistent methodologies and species-specific selection strategies to optimize sustainability and efficiency in forest biomass utilization for bioenergy. Full article

(1) Background: Carob tree (Ceratonia siliqua L.) pulp is of great interest nowadays due to its nutritional benefits and diverse utilization in the food process. The nutritional and antioxidant properties of carob pulp in the Mediterranean have been assessed in several studies. Still, few studies have combined, within the same work, a comprehensive analysis of the chemical composition of carob pulp from fruits of natural populations across different countries of the Mediterranean basin, while also incorporating new research areas. (2) Methods: In the present work, we evaluated the nutritional value, total phenolic compounds, and antioxidant activity of carob pulp derived from wild populations of carob trees from three Mediterranean countries: Lebanon, Spain, and Morocco; (3) Results: All assessed bromatological characteristics, with the exception of ash and fiber content, revealed significant differences in the carob pulp from the three countries under study. High variability was observed for the total polyphenols ranging between 5.05 mg/g and 12.70 mg/g. Sucrose was the predominant sugar quantified ranging between 13.70 g/100 g and 28.10 g/100 g. The lipid content was low (0.26–0.36%). The moisture content of carob pulp ranges between 4.36% and 6.40%. Carob pulp presented a rich composition in fiber, with an average of 35.87%. The ash content was between 2.52% and 3.28%. The percentage of the protein content of the carob pulp ranged between 4.40 and 5.52, with an average carbohydrate value of 74.71%; (4) Conclusions: Spanish wild carob pulp samples offered higher carbohydrates contents and values for sucrose, fructose, and glucose, polyphenol content, and antioxidant activity, whereas Moroccan samples had higher values of carbohydrates and in concrete, the monosaccharides fructose and glucose showed higher contents in proteins and lipids. In contrast, Lebanese samples exhibit a high content of the disaccharide sucrose. These findings could be exploited in breeding programs to improve varieties that balance both the agronomical quality and nutritional values of carob pulp. Full article

Forest stands are developing in changeable climate conditions that influence stand health and consequently assortment quality. Narrow-leaved ash is strongly affected by dieback because of new fungal diseases. The main aim of this study was to determine the quantity and quality of produced wood assortments in dieback-affected narrow-leaved ash stands. Based on the study results, the average tree value increased with tree diameter and partially decreased with tree crown defoliation degree. The healthy (crown defoliated up to 25%) and 3A (crown defoliated from 61 to 80%) trees had significantly higher average tree values (EUR/m³) compared to the significantly defoliated 3B trees (crown defoliated from 81 to 99%) and dead trees (100% defoliated crown). The influence of stand age and share of narrow-leaved ash in stand volume were confirmed as factors influencing the average tree value. Wood chips quality remained the same regardless of tree crown defoliation degree. Based on the significance influence of the tree crown defoliation degree on the average tree value, current assortment tables should be expanded in order to achieve more accurate expected values. Full article

Grazing is the primary human-induced disturbance affecting giant panda (Ailuropoda melanoleuca) habitats and has a severe impact on the long-term sustainability of the giant panda population. To address the lack of quantitative studies on grazing’s impact on habitat quality, we selected China’s most heavily grazed giant panda nature reserve. Utilizing the Maxent model and stoichiometric analysis, we investigated habitat quality degradation caused by grazing and quantified changes in bamboo nutritional quality and soil physicochemical properties. The results indicate that grazing has significantly reduced the suitable habitat area for giant pandas from 101.87 km² to 80.64 km². Specifically, high-suitability habitats declined by 14.14%, moderate-suitability habitats declined by 22.70%, and low-suitability habitats declined by 22.88%. Grazing has forced pandas to move to higher altitudes (2650–3057 m) with taller (12–20 m) trees, denser (28–55 plants) shrubs, and sparser (30–69%) bamboo. Additionally, the soil water content has decreased, while soil bulk density, total N, available N, and pH have significantly increased. Reductions in crude protein and ether extract, along with increased crude fiber and ash, have lowered bamboo’s nutritional value and palatability. This study elucidates how grazing degrades giant panda habitat quality and provides a scientific basis for its conservation management. Full article

Arbutus unedo (strawberry tree) is a small Mediterranean tree capable of vigorous regrowth after disturbances like fire. Traditionally used for biomass fuel, its bark and branches hold potential for higher-value products through ecovalorization into liquid mixtures that could replace petroleum-based materials. This study aimed to explore the chemical composition of various components of Arbutus unedo and to produce a liquefied material from its internal (IB) and external bark (EB). Chemical compositions of internal and external bark were determined using TAPPI standards including ash, extractive content, lignin, and cellulose. Metal cations were analyzed by ICP. Liquefaction of bark was optimized in a PARR reactor, evaluating factors such as particle size, temperature, and time, and the best polyols were monitored by FTIR-ATR. Polyurethane foams were made with internal and external bark materials liquefied by polymerization with isocyanate, a catalyst, and water as a blowing agent. Results showed that EB has a higher extractive and lignin content, while IB contains more cellulose. Liquefaction yields were higher for IB (74%) than EB (68%), with IB yielding polyols that produced stronger and more resilient foams with higher compressive strength and modulus of elasticity. Mechanical properties of the foams were influenced by the NCO/OH ratio and catalyst levels. Overall, the internal bark demonstrated superior performance for foam production, highlighting its potential as an eco-friendly alternative to petroleum-derived materials. Full article

Polarimetric measurement has been proven to be of great importance in various applications, including remote sensing in agriculture and forest. Polarimetric full waveform LiDAR is a relatively new yet valuable active remote sensing tool. This instrument offers the full waveform data and polarimetric information simultaneously. Current studies have primarily used commercial non-polarimetric LiDAR for tree species classification, either at the dominant species level or at the individual tree level. Many classification approaches combine multiple features, such as tree height, stand width, and crown shape, without utilizing polarimetric information. In this work, a customized Multiwavelength Airborne Polarimetric LiDAR (MAPL) system was developed for field tree measurements. The MAPL is a unique system with unparalleled capabilities in vegetation remote sensing. It features four receiving channels at dual wavelengths and dual polarization: near infrared (NIR) co-polarization, NIR cross-polarization, green (GN) co-polarization, and GN cross-polarization, respectively. Data were collected from several tree species, including coniferous trees (blue spruce, ponderosa pine, and Austrian pine) and deciduous trees (ash and maple). The goal was to improve the target identification ability and detection accuracy. A machine learning (ML) approach, specifically a decision tree, was developed to classify tree species based on the peak reflectance values of the MAPL waveforms. The results indicate a re-substitution error of 3.23% and a k-fold loss error of 5.03% for the 2106 tree samples used in this study. The decision tree method proved to be both accurate and effective, and the classification of new observation data can be performed using the previously trained decision tree, as suggested by both error values. Future research will focus on incorporating additional LiDAR data features, exploring more advanced ML methods, and expanding to other vegetation classification applications. Furthermore, the MAPL data can be fused with data from other sensors to provide augmented reality applications, such as Simultaneous Localization and Mapping (SLAM) and Bird’s Eye View (BEV). Its polarimetric capability will enable target characterization beyond shape and distance. Full article

Fraxinus griffithii C. B. Clarke is introduced from Taiwan to Thailand by the Royal Project Foundation beginning of the 1980s for highland rehabilitation. To improve the growth traits and tree form characteristics, a tree breeding program for this species has been initiated. In the present study, we evaluated the among seed sources variations in growth traits (diameter at 1.3 m above the ground [DBH] and tree height) and wood traits (basic density [BD], modulus of elasticity [MOE], modulus of rupture [MOR], and compressive strength parallel to the grain [CS]) of 10-year-old F. griffithii trees originated from 15 seed sources planted in highland areas of Thailand. The mean values of measured trees were 7.25 cm in DBH, 11.59 m in tree height, 0.76 g/cm³ in basic density, 9.74 GPa in MOE, 100.78 MPa in MOR, and 38.46 MPa in CS, respectively. Broad-sense heritability ranged from 0.13 to 0.16 in growth traits and 0.02 to 0.85 in wood traits. As a result of principle component analysis and cluster analysis, 15 seed sources were classified into three groups. Of the three groups, one showed good performance in both growth and wood traits. Significant phenotypic and genetic correlations were found between growth traits and between wood traits. However, no significant correlations were found between growth and wood traits. Based on the results, it is concluded that progeny with good performance of both growth and wood traits can be produced from the combinations of superior seed sources tested in the present study. Full article