Search Results (146)

The article thoroughly investigates the relationships between terrain features and tree measurements derived from aerial laser scanning (ALS) data and field surveys in a 1067-hectare forested area. A digital elevation model (DEM) was generated from ALS data, which was then used to derive additional layers such as slope, aspect, topographic position index (TPI), and landforms. The authors developed a mathematical procedure to determine the radii for the topographic position index. The canopy height model was created, and individual trees were segmented using a novel voxel aggregation method, allowing for the calculation of tree height and crown size. Accuracy assessments were conducted between ALS-derived data and field-collected data. Terrain variability within each forest unit was evaluated using characteristics such as standard deviation, entropy, and frequency. The relationships between tree height and the derived topographic features within forest subcompartments, as well as the correlation between the height yield map for the entire area and the TPI layer, were analysed. The authors found strong correlation between the topographic position index and tree heights in both cases. The presented remote-sensing-based methodology and the results can be effectively used in digital forest site mapping, complemented by field sampling and laboratory soil analyses, and, as final goal, in carbon stock assessment. Full article

Poverty remains a significant global challenge, particularly in severely impoverished areas where balancing eco-civilization and economic growth is crucial. This study aims to analyze livelihood assets, determine appropriate strategies, and establish an eco-compensation model based on ecological vulnerability in Longnan City. We developed a livelihood evaluation index system using the Sustainable Livelihoods Framework and entropy weight method to assess the vulnerable portfolio of livelihood assets. We examined poverty causes and proposed targeted alleviation measures. Additionally, we created an “Eco-Compensation Model of Longnan City” incorporating the Sloping Land Conversion Program, key industries exit compensation, and cross-provincial water compensation based on incentive and punitive mechanisms. Our findings reveal that severely impoverished areas in Longnan City are primarily in the central, southern, and northwestern regions. Poverty is driven by unfavorable natural conditions, frequent natural disasters, regional economic marginalization, inadequate infrastructure, and a limited agricultural structure. The evaluation shows that natural capital in the five areas is lower than the national average level. We propose targeted measures for different vulnerable livelihood portfolios. The eco-compensation model provides a scientifically calculated compensation standard, offering a crucial funding source for implementing targeted poverty alleviation strategies. Full article

Topography critically affects the occurrence of soil erosion, and computing slope spectrum information entropy (SSIE) allows for the convenient mirroring of the patterns of macroscopic topographic variation. However, whether SSIE can be effectively utilized for the quantitative assessment of soil erosion across various types of water-erosion areas and the specific methodology for its application remain unclear. This study focused on the quantitative relationship between SSIE, the slope length and slope steepness (LS) factor within various types of water-erosion areas across different spatial scales in China using multi-source geographic information data and technical tools such as remote sensing and geographic information systems. The results revealed (1) clear consistency in the spatial patterns of SSIE and the LS factor, which both displayed a distinct three-step distribution pattern from south to north. (2) The power model (Y = A·X^B) demonstrated a superior capacity to explaining the relationship between SSIE and the LS factors compared to the linear or exponential models, as evidenced by a higher coefficient of determination (R²). R² values of different evaluation units (second-grade water-erosion area, third-grade water-erosion area, 30 km × 30 km grid, and 15 km × 15 km grid) were 0.88, 0.88, 0.81, and 0.79, respectively. (3) Despite a range of variances across various spatial scale evaluation units and different types of water-erosion areas, no significant disparities were evident within the power model. These findings offer a new topographic factor that can be incorporated into models designed for the expedited evaluation of soil erosion rates across water-erosion areas. Information about the proximity of the SSIE to the LS factor is valuable for enhancing the practical utilization of SSIE in the quantitative evaluation of soil erosion. Full article

The detection performance of radar is significantly impaired by active jamming and mutual interference from other radars. This paper proposes a radio signal modulation recognition method to accurately recognize these signals, which helps in the jamming cancellation decisions. Based on the ensemble learning stacking algorithm improved by meta-feature enhancement, the proposed method adopts random forests, K-nearest neighbors, and Gaussian naive Bayes as the base-learners, with logistic regression serving as the meta-learner. It takes the multi-domain features of signals as input, which include time-domain features including fuzzy entropy, slope entropy, and Hjorth parameters; frequency-domain features, including spectral entropy; and fractal-domain features, including fractal dimension. The simulation experiment, including seven common signal types of radar and active jamming, was performed for the effectiveness validation and performance evaluation. Results proved the proposed method’s performance superiority to other classification methods, as well as its ability to meet the requirements of low signal-to-noise ratio and few-shot learning. Full article

Land suitability assessment, as an important process in modern agriculture, involves the evaluation of numerous aspects such as soil properties, climate, relief, hydrology and socio-economic aspects. The aim of this study was to evaluate the suitability of soils for wheat cultivation in the Gavshan region, Iran, as the country is facing the task of becoming self-sufficient in wheat. Various methods were used to evaluate the land, such as multi-criteria decision-making (MCDM), which is proving to be important for land use planning. MCDM and machine learning (ML) are useful for decision-making processes because they use complicated spatial data and methods that are widely available. Using a geomorphological map, seventy soil profiles were selected and described, and ten soil properties and wheat yields were determined. Three MCDM approaches, including the technique of preference ordering by similarity to the ideal solution (TOPSIS), gray relational analysis (GRA), and simple additive weighting (SAW), were used and evaluated. The criteria weights were extracted using Shannon’s entropy method. Random forest (RF) model and auxiliary variables (remote sensing data, terrain data, and geomorphological maps) were used to represent the land suitability values. Spatial autocorrelation analysis as a statistical method was applied to analyze the spatial variability of the spatial data. Slope, CEC (cation exchange capacity), and OC (organic carbon) were the most important factors for wheat cultivation. The spatial autocorrelation between the key criteria (slope, CEC, and OC) and wheat yield confirmed these results. These results also showed a significant correlation between the land suitability values of TOPSIS, GRA, and SAW and wheat yield (0.74, 0.72, and 0.57, respectively). The spatial distribution of land suitability values showed that the areas classified as good according to TOPSIS and GRA were larger than those classified as moderate and weak according to the SAW approach. These results were also confirmed by the autocorrelation of the MCDM techniques with wheat yield. In addition, the RF model showed its effectiveness in processing complex spatial data and improved the accuracy of land suitability assessment. In this study, by integrating advanced MCDM techniques and ML, an applicable land evaluation approach for wheat cultivation was proposed, which can improve the accuracy of land suitability and be useful for considering sustainability principles in land management. Full article

Successional patterns in lacustrine sand dunes along Lake Superior begin with grass-dominated plant communities leading to the establishment of Pinus banksiana Lamb. as initial forests. Using maximum entropy models, we predicted P. banksiana seedling and sapling patterns within the Grand Sable Dunes, Pictured Rocks National Lakeshore, USA, based on slope, aspect, forest basal area, and vegetation types. Across the different vegetation types, there were variable probabilities of seedling and sapling occurrence. For both seedlings and saplings, the higher likelihoods of occurrence were observed in coastal pine barrens vegetation types. P. banksiana regeneration is occurring in the Grand Sable Dunes in the absence of fire, with seedlings establishing and saplings being recruited in a variety of vegetation types. With the greatest probabilities in barrens, there is likely a relationship with seed source and canopy density. Understanding regeneration patterns in dune ecosystems is necessary to predict the future forest arrangement and colonization of P. banksiana into the dunes. These results contribute insights into the dynamics of plant communities in lacustrine dune systems, specifically the establishment of P. banksiana seedlings in various vegetation types. Continued forest establishment and increasing P. banksiana density will influence endangered species and non-native species management strategies for Pictured Rocks National Lakeshore. Full article

The exploration of catalysts for the oxygen evolution reaction (OER) with high activity and acceptable price is essential for water splitting to hydrogen generation. High-entropy materials (HEMs) have aroused increasing interest in the field of electrocatalysis due to their unusual physicochemical properties. In this work, we reported a novel FeCoNiMoZn-OH high entropy hydroxide (HEH)/nickel foam (NF) synthesized by a facile pulsed electrochemical deposition method at room temperature. The FeCoNiMoZn-OH HEH displays a 3D porous nanosheet morphology and polycrystalline structure, which exhibits extraordinary OER activity in alkaline media, including much lower overpotential (248 mV at 10 mA cm⁻²) and Tafel slope (30 mV dec⁻¹). Furthermore, FeCoNiMoZn-OH HEH demonstrates excellent OER catalytic stability. The enhanced catalytic performance of the FeCoNiMoZn-OH HEH primarily contributed to the porous morphology and the positive synergistic effect between Mo and Zn. This work provides a novel insight into the design of HEMs in catalytic application. Full article

Thermal events in lead-acid batteries during their operation play an important role; they affect not only the reaction rate of ongoing electrochemical reactions, but also the rate of discharge and self-discharge, length of service life and, in critical cases, can even cause a fatal failure of the battery, known as “thermal runaway.” This contribution discusses the parameters affecting the thermal state of the lead-acid battery. It was found by calculations and measurements that there is a cooling component in the lead-acid battery system which is caused by the endothermic discharge reactions and electrolysis of water during charging, related to entropy change contribution. Thus, under certain circumstances, it is possible to lower the temperature of the lead-acid battery during its discharging. The Joule heat generated on the internal resistance of the cell due to current flow, the exothermic charging reaction, and above all, the gradual increase in polarization as the cell voltage increases during charging all contribute to the heating of the cell, overtaking the cooling effect. Of these three sources of thermal energy, Joule heating in polarization resistance contributes the most to the temperature rise in the lead-acid battery. Thus, the maximum voltage reached determines the slope of the temperature rise in the lead-acid battery cell, and by a suitably chosen limiting voltage, it is possible to limit the danger of the “thermal runaway” effect. The overall thermal conditions of the experimental cell are significantly affected by the ambient temperature of the external environment and the rate of heat transfer through the walls of the calorimeter. A series of experiments with direct temperature measurement of individual locations within a lead-acid battery uses a calorimeter made of expanded polystyrene to minimize external influences. A hitherto unpublished phenomenon is discussed whereby the temperature of the positive electrode was lower than that of the negative electrode throughout the discharge, while during charging, the order was reversed and the temperature of the positive electrode was higher than that of the negative electrode throughout the charge. The authors relate this phenomenon to the higher reaction entropy change of the active mass of the positive electrode than that of the negative electrode. Full article

The stability of abandoned open-pit mine slopes and their ecological environment are threatened owing to their fragile, complicated, and uncertain characteristics. This study establishes a novel evaluation indicator system for enhancing mine design and environmental protection insight. The weights in the system are assigned using a combined method, which consists of the game theory, the interval analytic hierarchy process (IAHP), and the entropy weight method (EWM). The IAHP is optimized by the improved radial movement optimal (IRMO) algorithm and the simulated annealing (SA) algorithm to ensure calculation stability and efficiency. Meanwhile, a two-dimensional cloud model (TDCM) is developed to obtain the slope resilience level and visualize the result. This comprehensive evaluation method is applied to three abandoned mine slopes in the Yellow River Basin, and the results demonstrate that the method can provide crucial insights for rational mine slope stabilization and ecological restoration. Full article

High-entropy materials (HEMs) play a significant role in the electrocatalytic oxygen evolution reaction (OER) due to their unique properties. However, there are still challenges in the preparation of HEMs for OER catalysts. In this study, the FeCoNiMnCr catalyst is synthesized for the first time using the ultrasonic hydrothermal-sintering technique and exhibits excellent performance for OER electrocatalysis. There is an optimal ultrasonic hydrothermal time and power for achieving the best OER performance. The results demonstrate that the performance of FeCoNiMnCr catalysts prepared through ultrasonic hydrothermal sintering (US-FeCoNiMnCr) is significantly improved compared with the traditional hydrothermal-sintering method. The US-FeCoNiMnCr catalyst exhibits an overpotential of 228 mV at the current density of 10 mA cm⁻² and a Tafel slope as low as 45.39 mV dec⁻¹ in an alkaline medium. Moreover, the US-FeCoNiMnCr catalyst demonstrates remarkable stability in electrocatalytic OER with a minimal potential increase observed even after 48 h. This work not only provides valuable insights into high-entropy material synthesis, but also presents a powerful electrocatalyst for water electrolysis. Full article

This study introduces a novel nonlinear dynamic analysis method, known as beluga whale optimization–slope entropy (BWO-SlEn), to address the challenge of recognizing sea state signals (SSSs) in complex marine environments. A method of underwater acoustic signal recognition based on BWO-SlEn and one-dimensional convolutional neural network (1D-CNN) is proposed. Firstly, particle swarm optimization–slope entropy (PSO-SlEn), BWO-SlEn, and Harris hawk optimization–slope entropy (HHO-SlEn) were used for feature extraction of noise signal and SSS. After 1D-CNN classification, BWO-SlEn were found to have the best recognition effect. Secondly, fuzzy entropy (FE), sample entropy (SE), permutation entropy (PE), and dispersion entropy (DE) were used to extract the signal features. After 1D-CNN classification, BWO-SlEn and 1D-CNN were found to have the highest recognition rate compared with them. Finally, compared with the other six recognition methods, the recognition rates of BWO-SlEn and 1D-CNN for the noise signal and SSS are at least 6% and 4.75% higher, respectively. Therefore, the BWO-SlEn and 1D-CNN recognition methods proposed in this paper are more effective in the application of SSS recognition. Full article

Adaptability to all types of terrain changes, slopes, and course settings is a key aspect related to the coordinative ability that elite skiers possess. In recent years, several studies have analyzed coordinative aspects of different motor actions via the assessment of movement variability (MV), an indicator of the motor control that assesses movement regularity. The aims of this study were (a) to evaluate the influence of different slopes and slalom (SL) gate offsets on MV and performance and (b) to assess the relationship between MV and performance. Four SL courses were set: a flat-turned (FT), a steep-turned (ST), a flat-straighter (FS), and a steep-straighter (SS). Five elite alpine skiers (21.2 ± 3.3 years, 180.2 ± 5.6 cm, 72.8 ± 6.6 kg) completed several runs at maximum speed for each SL course. A total of 77 runs were obtained. The use of an IMU accelerometer attached to the lower back of skiers measured MV through entropy. The skiers’ performance was evaluated with the total time of each run. The one-way repeated measures analysis revealed that the steepness of the slope significantly increases skiers’ MV, concretely between FS and ST courses (p = 0.004). Differences at the 10% level have been found between FS and SS and FT and ST courses (p= 0.055 and p = 0.078, respectively). For a given slope, turned courses (FT and ST) tend to produce a higher MV. In addition, faster times correlate with lower MV (r = 0.587, p = 0.01). It has been observed that both steeper and turned courses produce greater MV and that the best performing skiers have lower MV. Determining MV through entropy can be used to assess skiers’ expertise regarding different types of slopes and gate offsets. Full article

Slope Entropy (SlpEn) is a novel method recently proposed in the field of time series entropy estimation. In addition to the well-known embedded dimension parameter, m, used in other methods, it applies two additional thresholds, denoted as δ and γ, to derive a symbolic representation of a data subsequence. The original paper introducing SlpEn provided some guidelines for recommended specific values of these two parameters, which have been successfully followed in subsequent studies. However, a deeper understanding of the role of these thresholds is necessary to explore the potential for further SlpEn optimisations. Some works have already addressed the role of δ, but in this paper, we extend this investigation to include the role of γ and explore the impact of using an asymmetric scheme to select threshold values. We conduct a comparative analysis between the standard SlpEn method as initially proposed and an optimised version obtained through a grid search to maximise signal classification performance based on SlpEn. The results confirm that the optimised version achieves higher time series classification accuracy, albeit at the cost of significantly increased computational complexity. Full article

Gully erosion poses severe problems for land degradation in several areas worldwide. This study aims to evaluate the accuracy and robustness of the maximum entropy (MaxEnt) method for assessing gully erosion susceptibility. We selected the catchment of the Mesima stream as the test site, which is situated in the southwest sector of the Calabria region (South Italy). An inventory map of gully erosion was realised and 12 predisposing factors, such as lithology, soil texture, soil bulk density, land use, drainage network, slope gradient, aspect, length–slope (LS), plan curvature, stream power index (SPI), topographic position index (TPI), and topographic wetness index (TWI), were selected to implement the dataset in the MaxEnt method. The accuracy and uncertainty of the method were tested by 10-fold cross-validation based on accuracy, kappa coefficient, and receiver operating characteristic curve (ROC) and related area under curve (AUC). The dataset was randomly divided into 10 equal-sized groups (folds). Nine folds (90% of the selected dataset) were used to train the model. Instead, the remaining fold (10% of the dataset) was used for testing the model. This process was repeated 10 times (equal to the number of the folds) and each fold was used only once as the validation data. The average of 10 repeated processes was performed to generate the susceptibility map. In addition, this procedure allowed the reliability of the susceptibility map to be assessed, in terms of variables, importance and role of predisposing factors selected, prediction ability, and accuracy in the assessed probabilities for each pixel of the map. In addition to exploiting the 10-fold cross-validation, the mean value and standard deviation for the probability estimates of each pixel were computed and reported in the susceptibility and uncertainty map. The results showed that the MaxEnt method has high values of accuracy (>0.90), of the kappa coefficient (>0.80), and AUC (>0.92). Furthermore, the achieved findings showed that the capacity of the method used for mapping gully erosion susceptibility is quite robust when the training and testing sets are changed through the 10-fold cross-validation technique. Full article

As global climate change and human activities increasingly influence our world, forest fires have become more frequent, inflicting significant damage to ecosystems. This study conducted measurements of combustible materials (moisture content ratio, ignition point, and calorific value) across 14 representative sites. We employed Pearson correlation analysis to ascertain the significant differences in combustible properties and utilized entropy methods to evaluate the fire resistance of materials at these sites. Cluster analysis led to the development of four combustible models. Using BehavePlus software, we simulated their fire behaviors and investigated the effects of wind speed and slope on these behaviors through sensitivity analysis. The results revealed notable differences in the moisture content ratios among different types of combustibles, especially in sites 2, 3, 8, 9, and 13, indicating higher fire risks. It was also found that while humus has a higher ignition point and lower calorific value, making it less prone to ignite, the resultant fires could be highly damaging. The Pearson analysis underscored significant variations in the moisture content ratios among different combustibles, while the differences in ignition points and calorific values were not significant. Sites 5 and 6 demonstrated stronger fire resistance. The simulations indicated that fire-spread speed, fireline intensity, and flame length correlate with, and increase with, wind speed and slope. Sensitivity analysis confirmed the significant influence of these two environmental factors on fire behavior. This study provides critical insights into forest fire behavior, enhancing the capability to predict and manage forest fires. Our findings offer theoretical support for forest fire prediction and a scientific basis for fire management decision-making. Full article