Empirical Model Research Articles

Empirical and Machine Learning Models for Soil Erosion Risk Assessment: A Case Study of Tsageri Municipality, Georgia

Soil erosion caused by water is one of the most common causes of land degradation worldwide. Within framework of this research soil erosion risk in Tsageri municipality, Georgia was evaluated using Revised Universal Soil Loss Equation (RUSLE) and a machine learning-based Random Forest (RF) model. Open access digital datasets and field observations collected in 2023-2024, which included visually identified erosion areas and GPS-recorded data on the presence or absence of erosion, were utilized in modeling process. Data processing and modeling conducted using ArcGIS Pro 3.0 and RStudio software. According to RUSLE results, 39.7% of the study area falls under the very low erosion risk zone, and 20.7% is in the very high risk zone. The RF model results indicated that 16.5% of the territory is under very low risk of erosion and 13.9% - very high risk. It was observed that RUSLE model tends to overestimate erosion rates on steep, forested slopes, while the RF model, by incorporating additional variables, provided more accurate prediction. These findings suggest that combining RUSLE with machine learning improves soil erosion risk assessment, particularly in complex landscapes such as in Tsageri municipality. Future researche should focus on testing additional variables to refine the modeling process further and enhance predictions. The generated digital thematic maps offer valuable insights for understanding the spatial dynamics of soil erosion within the study area, analyzing the factors driving the process and developing effective mitigation strategies.

How Do We Identify Anthropogenic Allee Effects in the Wildlife Trade?

ABSTRACTThe harvest and sale of wildlife can drive species to extinction when consumers are willing to pay high prices for the last harvested individuals of a very rare species, a phenomenon known as the anthropogenic Allee effect (AAE). Because demand for rarity is an inherent human desire, the AAE has the potential to affect a wide range of exploited species across several geographic regions. Here, we assess the current extent of empirical evidence for the AAE, how such evidence has been measured, and how this evidence interfaces with existing models of the AAE. We find substantial gaps in the empirical evidence base for the AAE and suggest that this deficit prevents assessment of the AAE in species extinctions. We provide a framework for generating empirical evidence that can identify when the AAE is likely occurring or has the potential to occur in the future, and recommend directions for both empirical and theoretical modeling research designed to strengthen our ability to forecast the ecological and market conditions that result in an AAE.

Optimizing food waste anaerobic digestion in Kuwait: Experimental insights and empirical modelling using artificial neural networks.

This study investigates, for the first time, the anaerobic digestion of food waste in Kuwait to optimize methane production through a combination of artificial neural network (ANN) modelling and continuous reactor experiments. The ANN model, utilizing eight hidden neurons and a 70-20-10 split for training, validation and testing sets, yielded mean squared error values of 0.0056, 0.0048 and 0.0059 and coefficient of determination (R²) values of 0.9942, 0.9986 and 0.9892, respectively. Methane percentages in biogas were predicted using six parameters: biomass type, pH, organic loading rate (OLR), hydraulic retention time (HRT), temperature and reactor volume. To validate the ANN results, continuous reactor experiments were conducted under an OLR of 3 kg VS m⁻³ d⁻¹ and HRT of 20 days at varying temperatures (35°C, 40°C, 45°C, 50°C and 55°C). The experiments demonstrated optimal methane production in the mesophilic range, with ANN predictions closely aligning with experimental data up to 45°C. However, deviations were observed at higher temperatures, particularly under thermophilic conditions beyond 50°C. This study provides novel insights into waste-to-energy initiatives in Kuwait and highlights the potential of integrating computational models with empirical data to enhance biogas production processes.

Risk for nitrogen leaching after application of solid manure in autumn differs between manure types

Manure application performed in autumn requires regulation due to the risk of nitrogen (N) leaching during winter. On the other hand, application of manure in spring can pose some practical problems depending on the risk for N immobilization and the difficulty in ploughing heavy clay soils during this time of year. The risk for leaching is likely to differ between different soil texture and manure characteristics. In this study, we sought to estimate the risk for nitrogen leaching depending on the carbon/nitrogen ratio (C/N) of the manure, time for manure application, and soil type. We combined 3-year lysimeter experiments with 2-year field plot experiments and laboratory soil incubations and compared results with those from an empirical model. We compared effects of manure application on two soils (silty clay and loamy sand) and solid manures with different representing C/N ratios. In incubations, only manure with a C/N-ratio below 14 contributed with significant amounts of leachable N during the first months after application. In the lysimeter study, N leaching was unaffected by the timing of the application of the manure with C/N-ratio 18. However, regarding manure with C/N-ratio 10, N leaching was elevated with 10–15 kg N per ha after manure application in October compared to November and March on both soil types. The mineral N analyses from soil profiles in the field experiments showed a similar pattern, however, the increase was higher on the loamy sand compared to the silty clay. The ammonia emissions did not differ between manure types, but were on average 24% of applied NH4-N after application in October when air temperature was on average 12°C compared to only 3% of applied NH4-N in November and March when air temperature was on average 5°C. Similar to lysimeter results, the modelled N leaching was higher the earlier the manure application occurred in autumn. However, the model also predicted increased leaching for the manure with a high C/N-ratio, and a smaller effect on leaching on clay soil. Considering this, the model calculations overestimated the leaching effects from manure with a high C/N-ratio and underestimated the leaching effects on clay soil.

Laser-Induced Decomposition and Mechanical Degradation of Carbon Fiber-Reinforced Polymer Subjected to a High-Energy Laser with Continuous Wave Power up to 120 kW

Carbon fiber-reinforced polymer (CFRP), noted for its outstanding properties including high specific strength and superior fatigue resistance, is increasingly employed in aerospace and other demanding applications. This study investigates the interactions between CFRP composites and high-energy lasers (HEL), with continuous wave laser powers reaching up to 120 kW. A novel automated sample exchange system, operated by a robotic arm, minimizes human exposure while enabling a sequence of targeted laser tests. High-speed imaging captures the rapid expansion of a plume consisting of hot gases and dust particles during the experiment. The research significantly advances empirical models by systematically examining the relationship between laser power, perforation times, and ablation rates. It demonstrates scalable predictions for the effects of high-energy laser radiation. A detailed examination of the damaged samples, both visually and via micro-focused computed X-ray tomography, offers insights into heat distribution and ablation dynamics, highlighting the anisotropic thermal properties of CFRP. Compression after impact (CAI) tests further assess the residual strength of the irradiated samples, enhancing the understanding of CFRP’s structural integrity post-irradiation. Collectively, these tests improve the knowledge of the thermal and mechanical behavior of CFRP under extreme irradiation conditions. The findings not only contribute to predictive modeling of CFRP’s response to laser irradiation but enhance the scalability of these models to higher laser powers, providing robust tools for predicting material behavior in high-performance settings.

Characteristics and development of steepland gullies in the dry valleys of Southwest China

In semi-arid and arid areas, gully erosion is one of the most destructive forms of erosion and causes serious land degradation and resource destruction. Steepland gullies are widely distributed in the dry valleys of southwest China, and their formation is one of the main causes of soil erosion and the destruction of sloping farmland in the region. Previous research on the development of steepland gullies is limited, and further study is needed. In this study, 11 steepland gullies at various stages of development located in Guobu Village, Xide County, Liangshan, Sichuan Province, were selected for investigation using a digital elevation model (DEM) derived from unmanned aerial vehicle data as the primary data source. These data had a spatial resolution of 0.1 m. Fundamental parameters such as the gully length, width, depth, area, and volume were extracted from the remote sensing data. Other characteristic parameters, including the coefficient of main and tributary gullies, vertical gradient, gully elongation, and gully openness, were also investigated. The results indicate a significant linear positive correlation between the gully’s degree of openness and elongation as the gully’s length, width, and depth increase. Furthermore, the vertical gradient and coefficient of main and tributary gullies exhibit power-law relationships with these gully dimensions. The development of steepland gullies was divided into infancy, youth, maturity, and old age based on the use of the gully length as an ergodic indicator in space-for-time substitution. The morphological characteristics of these different stages were quantitatively analyzed, and a proposed mechanism for how the evolution of the gullies proceeds was developed. An empirical model of volume–length erosion was established to investigate the development process of steepland gullies in the dry valleys. It has been observed that the development law of steepland gullies is essentially consistent with the very active stage of typical gully formation, suggesting that steepland gully may represent the initial stage of gully development. The results show that these steepland gullies have their origin in high-intensity rainfall events that are accompanied by the formation of steps and drop water. The effects of gravity erosion and hydraulic erosion then cause the gullies to expand rapidly, forming gullies with a large head and a small tail before they gradually stabilize. The results of this study will help with the understanding of the formation and evolution of steepland gullies and will be of practical significance for the prevention of gully erosion and the protection of sloping farmland in the dry valley region of southwest China.

Duration Models for Subduction Earthquakes

ABSTRACT An empirical model for the significant duration of acceleration for subduction zone earthquakes is developed based on the normalized Arias intensity. The data set is a subset from the Next Generation Attenuation-Subduction database (Bozorgnia and Stewart, 2020). The functional form of the duration model uses an additive model for the source, path, and site terms with three lognormally distributed random effects for the source, path, and site. The remaining aleatory variability is modeled by a power-normal distribution. There are two parts of the duration model: a model for the D5–75 duration and a model for the D5–X/D5–75 ratio, which can be combined with the D5–75 model to estimate the DY–X for any interval. The D5–75 model includes regionalized coefficients for Japan, New Zealand, South America, and Taiwan. The large-magnitude (M >7) scaling of the D5–75 model is much weaker than the recent duration model by Bahrampouri et al. (2021). The residuals of the D5–75 model are negatively correlated with the lnPGA residuals with a correlation coefficient of −0.50 such that above-average peak ground acceleration values are associated with below-average duration values.

An empirical model of carbon-ion relative biological effectiveness based on the linear correlation between radiosensitivity to photons and carbon ions.

To develop an empirical model to predict carbon ion (C-ion) relative biological effectiveness (RBE). 

Approach. We used published cell survival data comprising 360 cell line/energy combinations to characterize the linear energy transfer (LET) dependence of cell radiosensitivity parameters describing the dose required to achieve a given survival level, e.g. 5% (D5%), which are linearly correlated between photon and C-ion radiations. Based on the LET response of the metrics D5% and D37%, we constructed a model containing four free parameters that predicts cells' linear quadratic model (LQM) survival curve parameters for C-ions, αCand βC, from the reference LQM parameters for photons, αXand βX, for a given C-ion LET value. We fit our model's free parameters to the training dataset and assessed its accuracy via leave-one out cross-validation. We further compared our model to the local effect model (LEM) and the microdosimetric kinetic model (MKM) by comparing its predictions against published predictions made with those models for clinically relevant LET values in the range of 23-107 keV/μm. 

Main Results. Our model predicted C-ion RBE within ±7%-15% depending on cell line and dose which was comparable to LEM and MKM for the same conditions. 

Significance. Our model offers comparable accuracy to the LEM or MKM but requires fewer input parameters and is less computationally expensive and whose implementation is so simple we provide it coded into a spreadsheet. Thus, our model can serve as a pragmatic alternative to these mechanistic models in cases where cell-specific input parameters cannot be obtained, the models cannot be implemented, or for which their computational efficiency is paramount.

Cloud point extraction of heavy metal ions for wastewater remediation: an equilibrium and kinetic study

Abstract Surfactants offer a promising alternative for the efficient and environmentally friendly removal of organic pollutants and toxic heavy metal ions from various media. Their high efficiency and environmental compatibility make them a valuable option for remediation efforts. This study focuses on the cloud point extraction (CPE) of ions from aqueous solutions using biodegradable nonionic surfactants combined with ionic surfactants instead of chelating agents. Phase diagrams of binary surfactant/water systems were first constructed. The effects of salt, inorganic contaminants, and ionic surfactants on the cloud point (Tc) were then investigated. At temperatures above the cloud point, two distinct phenomena were observed and monitored over time: phase separation and phase clarification. The kinetic process was studied using the Turbiscan Lab Expert. Extraction results were evaluated based on four responses: extraction yield (E%), residual concentrations of solute (Xs,w) and surfactant (Xt,w) in the dilute phase, and volume fraction of coacervate at equilibrium (Φ C). Empirical modelling gives a satisfactory agreement between experimental and calculated values. The capacity of CPE to simultaneously remove an organic pollutant and a toxic heavy metal was demonstrated.

Assessing the Impact and Cost-Effectiveness of Exposome Interventions on Alzheimer’s Disease: A Review of Agent-Based Modeling and Other Data Science Methods for Causal Inference

Background: The exposome (e.g., totality of environmental exposures) and its role in Alzheimer’s Disease and Alzheimer’s Disease and Related Dementias (AD/ADRD) are increasingly critical areas of study. However, little is known about how interventions on the exposome, including personal behavioral modification or policy-level interventions, may impact AD/ADRD disease burden at the population level in real-world settings and the cost-effectiveness of interventions. Methods: We performed a critical review to discuss the challenges in modeling exposome interventions on population-level AD/ADRD burden and the potential of using agent-based modeling (ABM) and other advanced data science methods for causal inference to achieve this. Results: We describe how ABM can be used for empirical causal inference modeling and provide a virtual laboratory for simulating the impacts of personal and policy-level interventions. These hypothetical experiments can provide insight into the optimal timing, targeting, and duration of interventions, identifying optimal combinations of interventions, and can be augmented with economic analyses to evaluate the cost-effectiveness of interventions. We also discuss other data science methods, including structural equation modeling and Mendelian randomization. Lastly, we discuss challenges in modeling the complex exposome, including high dimensional and sparse data, the need to account for dynamic changes over time and over the life course, and the role of exposome burden scores developed using item response theory models and artificial intelligence to address these challenges. Conclusions: This critical review highlights opportunities and challenges in modeling exposome interventions on population-level AD/ADRD disease burden while considering the cost-effectiveness of different interventions, which can be used to aid data-driven policy decisions.

Purchase efficiency in Dutch youth care: locally least squares frontier method applied to municipality data

AbstractIn this paper, we present an empirical model to analyse the efficiency of youth care by local government, especially with regard to purchasing policies. Locally least squares is applied to data from 352 Dutch municipalities operating in 2021. The outcomes reveal significant variations in the cost efficiency related to purchasing policies among municipalities. For all municipalities, the average cost efficiency is 84%. However, the corresponding standard errors of cost efficiency vary between 2.6% and 14.8% with a mean of 8.6% implying that only a limited number of municipalities are able to achieve efficiency gains with a high degree of certainty. Open House outsourcing and a framework contract without intermediate access are the most influential instruments on cost efficiency. Other features such as the duration of the contract and collaboration with other municipalities appear to have only a modest effect.

Machine learning prediction of the unconfined compressive strength of controlled low strength material using fly ash and pond ash.

The sustainable use of industrial byproducts in civil engineering is a global priority, especially in reducing the environmental impact of waste materials. Among these, coal ash from thermal power plants poses a significant challenge due to its high production volume and potential for environmental pollution. This study explores the use of controlled low-strength material (CLSM), a flowable fill made from coal ash, cement, aggregates, water, and admixtures, as a solution for large-scale coal ash utilization. CLSM is suitable for both structural and geotechnical applications, balancing waste management with resource conservation. This research focuses on two key CLSM properties: flowability and unconfined compressive strength (UCS) at 28days. Traditional testing methods are resource-intensive, and empirical models often fail to accurately predict UCS due to complex nonlinear relationships among variables. To address these limitations, four machine learning models-minimax probability machine regression (MPMR), multivariate adaptive regression splines (MARS), the group method of data handling (GMDH), and functional networks (FN) were employed to predict UCS. The MARS model performed best, achieving R2 values of 0.9642 in training and 0.9439 in testing, with the lowest comprehensive measure (COM) value of 1.296. Sensitivity analysis revealed that cement content was the most significant factor with obtaining R = 0.88, followed by water (R = 0.82), flowability (R = 0.79), pond ash (R = 0.78), curing period (R = 0.73), and fine content (R = 0.68), with fly ash (R = 0.55) having the least impact. These machine learning models provide superior accuracy compared to traditional methods, particularly in handling complex interactions between mix components. The proposed models offer a practical approach for predicting CLSM performance, supporting sustainable construction practices and the efficient use of industrial byproducts. The novelty of this study lies in the development of precise design equations for evaluating UCS, promoting both practical applicability and environmental sustainability.

Coupled infectious disease and behavior dynamics. A review of model assumptions.

To comprehend the dynamics of infectious disease transmission, it is imperative to incorporate human protective behavior into models of disease spreading. While models exist for both infectious disease and behavior dynamics independently, the integration of these aspects has yet to yield a cohesive body of literature. Such an integration is crucial for gaining insights into phenomena like the rise of infodemics, the polarization of opinions regarding vaccines, and the dissemination of conspiracy theories during a pandemic. 
We make a threefold contribution. First, we introduce a framework to describe models coupling infectious disease and behavior dynamics, delineating four distinct update functions. Reviewing existing literature, we highlight a substantial diversity in the implementation of each update function. This variation, coupled with a dearth of model comparisons, renders the literature hardly informative for researchers seeking to develop models tailored to specific populations, infectious diseases, and forms of protection.
Second, we advocate an approach to comparing models' assumptions about human behavior, the model aspect characterized by the strongest disagreement. Rather than representing the psychological complexity of decision-making, we show that "influence-response functions'' allow one to identify which model differences generate different disease dynamics and which do not, guiding both model development and empirical research testing model assumptions. 
Third, we propose recommendations for future modeling endeavors and empirical research aimed at selecting models of coupled infectious disease and behavior dynamics. We underscore the importance of incorporating empirical approaches from the social sciences to propel the literature forward.

Effect of temperature on transport index of fish oil-based drilling mud in high pressure high temperature well: a hybrid model approach

Diesel Oil-based drilling mud used in the oil and gas industry causes severe environmental and public health issues due to its high toxicity, especially in high-pressure, high-temperature (HPHT) wells. Therefore, easily biodegradable synthetic oil-based mud with high drilling performance is essential. Fish oil is a bio-oil known for its non-toxicity, high lubricity, thermal stability, and biodegradability. Effective hole cleaning is crucial for drilling performance and is ensured by the rheological properties of drilling mud. While rheological parameter prediction can be performed using an artificial neural network (ANN) based model, such models often fail to provide accurate predictions for unseen data. In contrast, empirical models are more robust and facilitate generalization. This study aims at assessment of the suitability of invert emulsion fish oil-based drilling mud (IEFOBDM) in HPHT wells through evaluation of hole cleaning performance (HCP) using a hybrid approach that combines an ANN with nonlinear regression (NLR) for temperatures ranging from 40 °C to 80 °C.Firstly, an ANN model was developed considering mud weight, temperature, and shear rate. Secondly, the NLR model was used in the prediction of rheological parameters, namely, Yield point (YP) and Plastic viscosity (PV), based on the predicted shear stress from the ANN model. Finally, the performance metrics of the ANN-NLR rheological model were evaluated using the root mean square error (RMSE) and correlation coefficient (R2) and comparison made with the Single ANN rheological model. The results showed the outperformance of ANN-NLR model over the ANN model for rheological parameter prediction, with R2 values of (0.99) for YP and (1) for PV. The predicted PV and YP values were then used for evaluation of transport index (TI) for HCP analysis. The effect of temperature on TI analysis showed this with increase in temperature, TI also increased, indicating the proposed mud providing good HCP under HPHT conditions.

Analysis of the influence of macroeconomic factor on long-term and short-term fluctuation of the Indonesian stock exchange (BEI) using the error correction method (ECM) approach

This research aims to empirically test the influence of the short-term equilibrium relationship and long-term equilibrium relationship of economic growth, interest rate, inflation, and exchange rate on IHSG (IDX Composite) using empirical Error Correction Model (ECM) data from 2000 to 2023. During the observation period, the macroeconomic variables showed no significant influence on the movement of the IHSG (IDX Composite) in the short-term equilibrium relationship, whereas in the long-term equilibrium relationship almost all variables, namely economic growth, interest rate, and exchange rate, had a significant influence on the IHSG ( IDX Composite), while inflation has no short or long term effect on the IHSG.

Maximizing the Hesperidin Extraction Using Supercritical Carbon Dioxide and Ethanol: Theoretical Prediction and Experimental Results

Agroindustrial waste can be valorized towards the obtaining of several products such as pigments, proteins, fibers, and polyphenolic compounds with antioxidant capacity. Orange peel waste is a promising source of polyphenolic compounds such as hesperidin. However, conventional extraction techniques present some environmental limitations such as high solvent consumption and high wastewater generation. Supercritical fluid extraction (SFE) has emerged as a green extraction technique due to the low use of solvent. The aim of this study was to maximize the hesperidin extraction based on theoretical predictions of the operating conditions from empirical thermodynamic models using SFE with carbon dioxide (CO2) as a solvent. The theoretical conditions were validated experimentally on a semi-pilot scale. The extracts were evaluated in terms of hesperidin content, total polyphenol content, and antioxidant capacity. Thermodynamic prediction of the operating conditions showed that the ethanol used as a co-solvent promotes hesperidin extraction. The optimum operating conditions were 25 °C, 80 bar, and a volumetric co-solvent concentration of 10%. The validation of the operating conditions resulted in a final hesperidin concentration of 11.5 ± 0.03 g/kg of orange peel waste. The experimental results were 30.26-times higher using 10% vol of ethanol than the extraction of hesperidin with pure ethanol as a co-solvent. The total polyphenol content and antioxidant capacity resulted in 831.92 ± 40.01 mg Galic acid/100 g orange peel waste, 15.41± 0.91 EC50/mL, and 5.31 ± 0.67 µMolTrox/100 g orange peel. Finally, the prediction of operating conditions from empirical thermodynamic models such as the Peng–Robinson equation of state with some modifications (Stryjek Vera) for solid–gas equilibrium solubility calculations, allows for maximizing the content of the polyphenolic compounds using SFE.

Modal Frequency Shifts Measured on Xinjiang Bridge Excited by Traveling Vehicles

To excite sufficient vibrations of bridges for more effective modal experiments to take place, traveling vehicles were utilized in many bridge field inspection campaigns in history. However, the dynamics of a bridge subjected to the traveling vehicles are of changed resonant properties compared with those of the same structure with the ambient excitation in nature. Therefore, the field modal experiments employing vehicle excitations might unfavorably lead to shifted measurement results of low-order natural frequencies for long-span bridges of large mass. Using Xinjiang Bridge (a single-tower pre-stressed concrete cable-stayed bridge with spans [Formula: see text]) as the engineering background, this paper validates this technical notion based on reliable physical experiments and numerical simulations and seeks a practical approach to deal with the issue. It is found that the maximum deviation between the low-order modal frequencies measured on Xinjiang Bridge for the vehicle excitation case and those measured for the ambient excitation case (the accurate values) reaches 0.65[Formula: see text]Hz, and the deviation between the lower limit of the usual vehicle excitation’s energy band and the modal frequency measured (factor 1) and the traveling vehicles’ speed (factor 2) are the two key factors influencing the modal frequency shifts. An empirical model to compensate for modal frequencies’ deviations is formulated considering the two key factors accordingly, which is of high-practical significance in future applications of the modal experiments using vehicle excitations to other long-span bridges.

Biomass yield potential, feedstock quality, and nutrient removal of perennial buffer strips under continuous zero fertilizer application

Abstract. Perennial-based buffer strips have been promoted as having the potential for improving ecosystem services from riparian areas while producing biomass as livestock feed or as a bioenergy feedstock. Both biomass production and nutrient removal of buffer strips are substantially influenced by the vegetation types for the multipurpose perennial buffers. In this 2016–2019 study in western Illinois, two perennial cropping systems, including forage crops composed of cool-season grass mixtures (forage system) and bioenergy crops made up of warm-season grass mixtures (bioenergy system), were used to establish buffer strips for assessing biomass production, feedstock quality, nutrient removal, and buffer longevity. Treatments for this study reflecting agronomic practices included (1) two harvests occurring in summer (at anthesis) and fall (after complete senescence) and (2) one harvest in fall for the forage system (two-cut vs. one-cut forage) and (3) one fall harvest for the bioenergy system (one-cut bioenergy). Successively harvesting without any fertilizer input resulted in a yield decline in forage biomass over 3 years by approximately 30 % (6.3 to 4.4 Mg DM ha−1 (dry matter) with a rate of 1.0 Mg ha−1 yr−1) in the two-cut forage and by 35 % (4.9 to 3.2 Mg DM ha−1 with a rate of 0.9 Mg ha−1 yr−1) in the one-cut forage systems. The feed quality also decreased over the years by showing declined rates of 12.9 (crucial protein), 0.9 (calcium), 0.7 (copper), and 1.3 g kg−1 DM yr−1 (zinc). Empirical models predicted enteric CH4 emissions from cattle ranged from 225.7 to 242.6 g per cow per day based on the feed nutritive values. In contrast, bioenergy biomass yield increased by 27 % from 4.9 to 6.7 Mg DM ha−1 with a consistent quality (cellulose of ∼ 397.9 g kg−1; hemicellulose of ∼ 299.4 g kg−1), corresponding to the increased total theoretical ethanol yield from 1.8 × 103 to 2.4 × 103 L ha−1 (∼ 33 % increase). Annual nutrient removals of N, P, K, Ca, and Mg were significantly higher in the forage systems (e.g., two-cut: 52.6–106.9 kg N ha−1; one-cut: 44.5–84.1 kg N ha−1) than those in the bioenergy system (e.g., 25.9–34.4 kg N ha−1); however, the removal rate declined rapidly over 3 years (e.g., ∼ 49 % reduction) as the annual biomass yield declined in the forage systems. This on-farm field study demonstrated the potential of the perennial crop used as buffer strip options for biomass production and buffer sustainability at the edge of the field.

Adapting and Verifying the Liming Index for Enhanced Rock Weathering Minerals as an Alternative Liming Approach

Acidic soils limit plant nutrient availability, leading to deficiencies and reduced crop yields. Agricultural liming agents address these issues and are crucial for deploying silicate amendments used in enhanced rock weathering (ERW) for carbon sequestration and emission reduction. Grower recommendations for liming agents are based on the liming index (LI), which combines the neutralizing value (NV) and fineness rating (FR) to predict a mineral’s acidity neutralization relative to pure calcite. However, the LI was originally developed for carbonate minerals, and its applicability to silicates remains uncertain, with studies often yielding inconclusive results on soil carbon and liming efficiency. This study aims to evaluate the liming efficiency of silicates. We determined the LI of five candidate ERW minerals (basalt, olivine, wollastonite, kimberlite, and montmorillonite) and compared them to pure calcite. Post-NV acid digestion, we characterized the minerals and soils, applying nonparametric statistical tests (Wilcoxon, Kendall) to correlate liming results with LI, dosage, and amendment methods. We developed an empirical model incorporating mineralogy and kinetics to explain silicate behavior in liming, considering soil, climate, and crop factors.

Pull-out behavior and failure mechanism of steel expansion anchors in high-performance steel fiber reinforced concrete(HPSFRC)

Past research on the pull-out performance of anchors mainly focuses on normal concrete(NC) concretes. New design methods are needed for the pull-out performance of anchors in the concretes of new cementitious materials with high strength and toughness. Accordingly, the pull-out behavior of torque-controlled expansion anchor(TCE) in high-performance steel fiber reinforced concrete(HPSFRC) concrete with different fiber content and installation conditions(embedment depth and anchor diameter) was investigated. The focus was on the failure modes, crack morphology, and load-displacement behavior(including pull-out load-bearing capacity, anchorage stiffness, and anchorage toughness). The addition of fibers leads to increased load-bearing capacity and toughness, reduced stiffness, and all failure modes exhibited ductile cracking and sufficient tensile ductility. Both diameter and depth had a positive impact on load-bearing capacity. A broader database based on literature and this study was analyzed for further exploration of the effects of fibers. The accuracy of predicting load-bearing capacity was compared between two empirical models(concrete capacity design-CCD and Toth models) and the proposed modified model. The CCD model greatly underestimates capacity, and the Toth model is unsuitable for high fiber content(exceeds 2.0 %) HPSFRC scenarios. The modified model showed the best bearing capacity prediction for anchors on HPSFRC concretes, with a mean ratio of predicted values to experimental values reaching 1.01–1.08 and a coefficient of dispersion below 15.0 %.