Search Results (7,672)

Remnant explosive devices are a deadly nuisance to both military personnel and civilians. Traditional mine detection and clearing is dangerous, time-consuming, and expensive. And routine production and testing of explosives can create groundwater contamination issues. Remote detection methods could be rapidly deployed in vegetated areas containing explosives as they are known to cause stress in vegetation that is detectable with hyperspectral sensors. Hyperspectral imagery was employed in a mesocosm study comparing stress from a natural source (drought) to that of plants exposed to two different concentrations of Royal Demolition Explosive (RDX; 250 mg kg⁻¹, 500 mg kg⁻¹). Classification was accomplished with the machine learning algorithms Support Vector Machine (SVM), Random Forest (RF), and Least Discriminant Analysis (LDA). Leaf-level plant data assisted in validating plant stress induced by the presence of explosives and was detectable. Vegetation indices (VIs) have historically been used for dimension reduction due to computational limitations; however, we measured improvements in model precision, recall, and accuracy when using the complete range of available wavelengths. In fact, almost all models applied to spectral data outperformed their index counterparts. While challenges exist in scaling research efforts from the greenhouse to the field (i.e., weather, solar lighting conditions, altitude when imaging from a UAV, runoff containment, etc.), this experiment is promising for subsequent research efforts at greater scale and complexity aimed at detecting emerging contaminants. Full article

This study explores the relationship between forest culture (FC) and sustainable consumption by applying the means-end chain (MEC) theory. Compared with general products, FC products are consumed from a value consumption perspective, and their benefits have varying impacts on individuals and society. This study uses MEC theory to link the preferred attributes of FC with the expected benefits and pursued values (PVs) of FC. We (1) identified the indicators of the expected benefits of PVs and preferred activities (PAs) of FC through factor analysis, (2) examined the relationships between these factors using MEC theory, and (3) validated the factors through structural equation modeling (SEM). We surveyed 1700 Koreans to explore how FC benefits, values, and activities relate to consumer behavior. Factor analysis divided PVs into symbolic, social, and consumption values and PAs into tourism–exploration, cultural–artistic, and living–leisure activities of FC. According to SEM analysis, the contributing characteristics of FC affect the PVs and, in turn, the PAs of FC, yielding an acceptable model fit (GFI > 0.9). Thus, the concrete attitudes of consumers toward FC were categorized via abstract concepts, which influenced their practical and behavioral attitudes. In conclusion, FC products should be developed with a focus on value consumption. Full article

Conservation strategies that use sustainable use of natural resources through green-labelled markets generally do not recognize the legal sale of wild meat as appropriate due to potential overexploitation and zoonotic disease risks. Wildlife hunting is important to the livelihoods of rural communities living in tropical forests for protein and income. Wildlife management plans in the Peruvian Amazon permit hunting of wild meat species for subsistence and sale at sustainable levels, that include peccaries, deer, and large rodents. These species have fast reproduction making them less vulnerable to overhunting than other species. This study assessed the sustainability of a wildlife management plan. Populations of species were estimated using camera traps and distance transect surveys, and sustainability analysis used hunting pressure from community hunting registers. Interviews were conducted to understand hunters, perceptions of the management plan. Long-term time-series showed increases in collared peccary (3.0 individual/km² to 5.41 individual/km²) and white-lipped peccary (3.50 individual/km² to 7.00 individual/km²) populations and short-term time series showed a decline in paca populations from 8.5 individual/km² to 3.01 individual/km². The unified harvest analysis showed permitted species populations were greater than 60% of their carrying capacities and hunted at less than 40% of their production, which shows sustainable hunting. The wildlife management plan achieved its general objective of sustainable hunting and improving livelihoods. The broader question is whether sustainable wildlife use plans that allow Amazonian communities to sell limited amounts of wild meat can be a way to change illegal wild meat trade to a legal, green labelled trade with added value. Full article

Accurate prediction of the height of water-conducting fissure zone (HWCFZ) is an important issue in coal water control and a prerequisite for ensuring the safe production of coal mines. At present, the prediction model of HWCFZ has some issues such as poor prediction accuracy. Based on the widely collected measured data of the HWCFZ in different coal mines in northern Shaanxi Province, China, the HWCFZ in shallow-buried coal seams is categorized into two types, i.e., typical shallow-buried coal seams and near-shallow-buried seams, according to the different depths of burial and base-loading ratios. On the basis of summarizing the research results of the previous researchers, three factors, namely, mining thickness, coal seam depth, and working length, were selected, and the data of the height of the water-conducting fissure zone in the study area were analyzed by using a multivariate nonlinear regression method. Subsequently, each group of the data was randomly divided into training data and validation data with a ratio of 70:30. Then, the training data were used to build a neural network model (BP), random forest model (RF), a hybrid integration of particle swarm optimization and the support vector machine model (PSO-SVR), and a hybrid integration of genetic algorithm optimization and the support vector machine model (GA-SVR). Finally, the test samples were used to test the model accuracy and evaluate the generalization ability. Accordingly, the optimal prediction model for the typical shallow-buried area and near-shallow-buried area of Jurassic coal seams in northern Shaanxi was established. The results show that the HWCFZ for the typical shallow-buried coal seam is suitable to be determined by the multivariate nonlinear regression method, with an accuracy of 0.64; the HWCFZ for near-shallow-buried coal seams is suitable to be predicted by the two-factor PSO-SVR computational model of mining thickness and the burial depth, with a prediction accuracy of 0.84; and machine learning methods are more suitable for near-shallow-buried areas, dealing with small-scale data and discrete data. Full article

In recent years, the demand for forest products has remained high, which, in turn, has intensified competition for timber exports. The Russian Far East is a region with one of the largest forest areas in the country; however, the competitiveness of the Far Eastern Federal District (FEFD) in wood forest product exports remains an open question. The purpose of this study is to assess and compare the competitiveness of the timber industry in the FEFD using a comprehensive competitiveness index. In this study, international trade indices were calculated on the basis of export and import data on wood forest products. Then, the indices were weighted by the methods of entropy weight and coefficient of variation. Finally, the two methods were combined, and a comprehensive competitiveness index of the Russian region’s timber industry was derived. The results show that the FEFD maintains competitiveness in the wood processing industry. The calculation results for the competitiveness of the woodworking industry will help to strengthen the attractiveness of trade in the Far Eastern Federal District and will contribute to the strengthening of positions in the domestic market and the expansion of trade relations of the FEFD in the international market. All of this will form new trade chains, which, in turn, will have a positive impact on the economic development of both the region itself and the countries that have trade relations with the FEFD in the sphere of export and import of wood products. Full article

Loblolly pine plantations have long been cultivated primarily for timber production due to their rapid growth and economic value. However, these forests are now increasingly acknowledged for their important role in mitigating climate change. Their dense canopies and fast growth rates enable them to absorb and store substantial amounts of atmospheric carbon dioxide. By integrating sustainable management practices, these plantations can maximize both timber yield and carbon sequestration, contributing to global efforts to reduce greenhouse gas emissions. Balancing timber production with vital ecosystem services, such as carbon storage, demands carefully tailored management strategies. This study examined how the timing of thinning—specifically early thinning at 17 years and late thinning at 32 years—impacts biomass accumulation, carbon storage capacity, and carbon sequestration rates in loblolly pine plantations located in northern Iran. Two thinning intensities were evaluated: normal thinning (removal of 15% basal area) and heavy thinning (removal of 35% basal area). The results demonstrated that thinning significantly improved biomass, sequestration rates and carbon storage compared to unthinned stands. Early thinning proved more effective than late thinning in enhancing these metrics. Additionally, heavy thinning had a greater impact than normal thinning on increasing biomass, carbon storage, and sequestration rates. In early heavy-thinned stands, carbon storage reached 95.8 Mg C/ha, which was 63.0% higher than the 58.8 Mg C/ha observed in unthinned 32-year-old stands. In comparison, early normal thinning increased carbon storage by 41.3%. In late heavy-thinned stands, carbon storage reached 199.4 Mg C/ha, which was 29.0% higher than in unthinned stands of the same age (154.6 Mg C/ha at 52 years). In contrast, late normal thinning increased carbon storage by 13.3%. Similarly, carbon sequestration rates in unthinned stands were 1.84 Mg C/ha/yr at 32 years and 2.97 Mg C/ha/yr at 52 years. In comparison, 32-year-old stands subjected to normal and heavy thinning had sequestration rates of 2.60 and 2.99 Mg C/ha/yr, respectively, while 54-year-old normally and heavily thinned stands reached 3.37 and 3.83 Mg C/ha/yr, respectively. The highest carbon storage was concentrated in the stems for 52–58% of the total. Greater thinning intensity increased the proportion of carbon stored in stems while decreasing the contribution from foliage. These results indicate that heavy early thinning is the most effective strategy for maximizing both timber production and carbon sequestration in loblolly pine plantations. Full article

Biochar is produced from a wide variety of feedstocks (algal biomass, forest, agricultural and food residues, organic fraction of municipal waste, sewage sludge, manure) by thermochemical conversion. In general, it is a dark, porous material with a large surface area, low density, high cation exchange capacity, and alkaline pH. By reducing the content of harmful substances in the soil, the application of biochar increases the activity, number, and diversity of microorganisms and improves plant growth in contaminated areas. The aim of the review was to explore the advantages and drawbacks of biochar use in soil bioremediation. General issues such as methods of biochar production, its physical and chemical properties, and various applications are presented. As biochar is an efficient adsorbent of heavy metals, the review focused on its benefits in (I) soil bioremediation, (II) improvement of soil parameters, (III) reduction of metal toxicity and bioaccumulation, (IV) positive interaction with soil microorganisms and soil enzymatic activity, and (V) promotion of plant growth. On the other hand, the potential risks of biochar formulation and utilization were also discussed, mainly related to the presence of heavy metals in biochar, dust hazard, and greenhouse gases emission. Full article

This study investigates the long-run relationship between forest footprint, which shows the amount of forest area needed for pulp, industrial wood, firewood and timber, and forest products as an environmental indicator. Forest footprint, forest product exports, forest product production, forest areas, biomass consumption, and urbanization variables are used in the analyses with annual data for the period 2000–2017 for selected European Union (EU) countries. As a result of the cointegration analyses, there is a long-run relationship between the variables. According to the results of coefficient estimation, it is concluded that forest product exports and urbanization have a decreasing effect on forest ecological footprint, while forest area, forest product production, and biomass consumption have an increasing effect. According to the Method of Moments Quantile Regression (MMQR) estimation results, it is concluded that forest product exports have a decreasing effect on forest footprint in all quantiles in the analysis period. The production of forest products is determined as the variable with the highest negative impact on the forest’s ecological footprint. The effect of urbanization is calculated as positive, but it is the variable with the lowest impact together with forest area. Biomass consumption is found to significantly reduce the forest footprint. In view of the aforementioned findings, it is recommended that efforts be made to promote high-value added, sustainable, and environmentally friendly production processes in forest products exports. This is considered to be a key strategy to reducing the ecological footprint of forests. Full article

The continuous development of satellite imagery, coupled with advancements in machine learning technologies, allows detailed mapping of terrestrial landscapes. This study evaluates the classification performance of tree typologies using Sentinel-2 and PRISMA data, focusing on central Italy’s different areas. The purpose is to assess the role of spectral and spatial resolution in land cover classification, contributing to forest management and conservation efforts. Random Forest Classifier was applied to classify tree typologies across two study areas: the Roman Coastal region and the Lake Vico Basin. Ground truth (GT) data, collected from a trial citizen survey campaign, were used for training and validation. PRISMA datasets, particularly when processed with PCA, consistently outperformed Sentinel-2. The PRISMA PCA dataset achieved the highest overall accuracy with 71.09% for the Roman Coastal region and 87.15% for the Lake Vico Basin, emphasizing the value of spectral resolution. However, Sentinel-2 showed comparative strength in spatially heterogeneous areas. Tree typologies with more uniform distribution, such as hazelnut and chestnut, achieved higher classification accuracy compared to mixed-species forests. The study assesses that Sentinel-2 remains a viable alternative where spatial resolution is critical also considering the limited PRISMA images’ availability. Moreover, the work explores the potential of combining satellites and accurate GT for improved land cover mapping. Full article

Increased tree species diversity can promote forest production by reducing intra-specific competition and promoting an efficient unitization of resources. However, questions remain on whether and how mixed stands affect the dynamics of intra–annual xylem formation in trees, especially in subtropical forests. In this study, we randomly selected 18 trees from a monoculture of 63-year-old Masson pine (Pinus massoniana) growing in pure stands and mixed them with 39-year-old Castanopsis hystrix in Pinxiang, southern China. A total of 828 microcores were collected biweekly throughout the growing season from 2022 to 2023 to monitor the intra-annual xylem formation. Cell production started in early March and ended in late December and lasted about 281 to 284 days. Xylem phenology was similar between mixed and pure stands. During both seasons, the Masson pine in mixed stands showed higher xylem production and growth rates than those in pure stands. The Masson pine in mixed stands produced 45–51 cells in 2022 (growth rate of 0.22 cells day⁻¹) and 35–41 cells in 2023 (0.17 cells day⁻¹). Growth rate, and not growth seasons, determined the superior xylem growth in the mixed stands. Our study shows that after 39 years of management, Masson pine and C. hystrix unevenly aged mixed stands have a significant positive mixing effect on Masson pine xylem cell production, which demonstrates that monitoring intra-annual xylem growth dynamics can be an important tool to evaluate the effect of species composition and reveal the mechanisms to promote tree growth behind the mixing effect. Full article

Predicting biomass gasification gases is crucial for energy production and environmental monitoring but poses challenges due to complex relationships and variability. Machine learning has emerged as a powerful tool for optimizing and managing these processes. This study uses Bayesian optimization to tune parameters for various machine learning methods, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LightGBM), Elastic Net, Adaptive Boosting (AdaBoost), Gradient-Boosting Regressor (GBR), K-nearest Neighbors (KNN), and Decision Tree (DT), aiming to identify the best model for predicting the compositions of CO, CO₂, H₂, and CH₄ under different conditions. Performance was evaluated using the correlation coefficient (R), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Relative Absolute Error (RAE), and execution time, with comparisons visualized using a Taylor diagram. Hyperparameter optimization’s significance was assessed via t-test effect size and Cohen’s d. XGBoost outperformed other models, achieving high R values under optimal conditions (0.951 for CO, 0.954 for CO₂, 0.981 for H₂, and 0.933 for CH₄) and maintaining robust performance under suboptimal conditions (0.889 for CO, 0.858 for CO₂, 0.941 for H₂, and 0.856 for CH₄). In contrast, K-nearest Neighbors (KNN) and Elastic Net showed the poorest performance and stability. This study underscores the importance of hyperparameter optimization in enhancing model performance and demonstrates XGBoost’s superior accuracy and robustness, providing a valuable framework for applying machine learning to energy management and environmental monitoring. Full article

Biomass is the primary source of renewable energy in Poland. Its share in renewable energy production in Poland has decreased in recent years, but it still maintains a nearly 70% share. Poland has extensive forest and straw resources, such as pellets, which can be used for stable biomass production. The main objective of this research was to understand the potential of plant biomass production for energy purposes in Poland and other European Union (EU) countries in terms of sustainable development. The period of analysis covered 2000–2022. Secondary data from Statistical Poland and Eurostat were used. The primary research method was the Augmented Dickey–Fuller (ADF) test, which aimed to check the stationarity of stable biomass. Moreover, we calculated the Vector Auto-Regressive (VAR) model, which was used to develop the forecast. The indigenous production of solid biomass in 2022 decreased to 363,195 TJ, while in 2018, it was 384,914 TJ. Our prognosis confirms that biomass will increase. The prognosis based on the VAR model shows an increase from 365,395 TJ in 2023 to 379,795 (TJ) in 2032. Such countries as France, Germany, Italy, Spain, Sweden, and Finland have a bigger potential for solid biomass production from forests because of their higher area. As a result, Poland’s biomass production competitiveness is varied when compared to other EU nations; it is lower for nations with a large forest share and greater for those with a low forest cover. The two main benefits of producing solid biomass are its easy storage and carbon dioxide (CO₂) neutrality. The main advantage is that solid biomass preserves biodiversity, maintains soil fertility, and improves soil quality while lowering greenhouse gas emissions and environmental pollutants. The ability to leave added value locally and generate new jobs, particularly in troubled areas, is the largest social advantage of sustained biomass production. Full article

Background: The forest products industry plays a significant role in global carbon emissions, highlighting the need for sustainable practices to address the climate crisis. Reverse logistics (RL), focusing on the return, reuse, and recycling of materials, offers a promising approach to decarbonizing supply chains. However, its application within forest products supply chains remains underexplored. Methods: This study conducts a review of the literature on RL, its environmental implications, and its potential to reduce carbon emissions in forest products supply chains. Key areas examined include greenhouse gas reduction, waste management, and the promotion of circular economy principles. Additionally, the study evaluates case studies and models that integrate RL practices into forest-based industries. Results: The findings reveal that RL can significantly reduce greenhouse gas emissions by optimizing transportation routes, minimizing waste, and extending product life cycles. Incorporating these practices into forestry operations reduces the environmental impact and aligns with sustainable forestry goals. The study identifies gaps in current research, particularly regarding empirical data and the scalability of RL solutions. Conclusions: RL represents a critical strategy for decarbonizing forest products supply chains and advancing sustainable development. Future research should focus on developing standardized methodologies, enhancing technological integration, and fostering policy support to maximize its impact. These steps are essential to fully leverage RL as a tool for mitigating climate change and promoting a circular economy. Full article

In contemporary business environments, manufacturing companies must continuously enhance their performance to ensure competitiveness. Material feeding systems are of pivotal importance in the optimization of productivity, with attendant improvements in quality, reduction of costs, and minimization of delivery times. This study investigates the selection of material feeding methods, including Kanban, line-storage, call-out, and kitting systems, within a manufacturing company. The research employs six machine learning (ML) algorithms—logistic regression (LR), decision trees (DT), random forest (RF), support vector machines (SVM), K-nearest neighbors (K-NN), and artificial neural networks (ANN)—to develop a multi-class classification model for material feeding system selection. Utilizing a dataset comprising 2221 materials and an 8-fold cross-validation technique, the ANN model exhibits superior performance across all evaluation metrics. Shapley values analysis is employed to elucidate the influence of pivotal input parameters within the selection process for material feeding systems. This research provides a comprehensive framework for material feeding system selection, integrating advanced ML models with practical manufacturing insights. This study makes a significant contribution to the field by enhancing decision-making processes, optimizing resource utilization, and establishing the foundation for future studies on adaptive and scalable material feeding strategies in dynamic industrial environments. Full article

Soil is one of the most important factors of agricultural productivity, directly influencing crop growth, water management, and overall yield. However, inefficient soil moisture monitoring methods, such as manual observation and gravimetric in rural areas, often lead to overwatering or underwatering, wasting resources and reduced yields, and harming soil health. This study offers a digital twin approach for soil moisture measurement, integrating real-time physical data, virtual simulations, and machine learning to classify soil moisture conditions. The digital twin is proposed as a virtual representation of physical soil designed to replicate real-world behavior. We used a multispectral rotocam, and high-resolution soil images were captured under controlled conditions. Physically based rendering (PBR) materials were created from these data and implemented in a game engine to simulate soil properties accurately. Image processing techniques were applied to extract key features, followed by machine learning algorithms to classify soil moisture levels (wet, normal, dry). Our results demonstrate that the Soil Digital Twin replicates real-world behavior, with the Random Forest model achieving a high classification accuracy of 96.66% compared to actual soil. This data-driven approach conveys the potential of the Soil Digital Twin to enhance precision farming initiatives and water use efficiency for sustainable agriculture. Full article