Statistical Model Research Articles

Neural Mechanism of Musical Pleasure Induced by Prediction Errors: An EEG Study.

Musical pleasure is considered to be induced by prediction errors (surprise), as suggested in neuroimaging studies. However, the role of temporal changes in musical features in reward processing remains unclear. Utilizing the Information Dynamics of Music (IDyOM) model, a statistical model that calculates musical surprise based on prediction errors in melody and harmony, we investigated whether brain activities associated with musical pleasure, particularly in the θ, β, and γ bands, are induced by prediction errors, similar to those observed during monetary rewards. We used the IDyOM model to calculate the information content (IC) of surprise for melody and harmony in 70 musical pieces across six genres; eight pieces with varying IC values were selected. Electroencephalographic data were recorded during listening to the pieces, continuously evaluating the participants' subjective pleasure on a 1-4 scale. Time-frequency analysis of electroencephalographic data was conducted, followed by general linear model analysis to fit the power-value time course in each frequency band to the time courses of subjective pleasure and IC for melody and harmony. Significant positive fits were observed in the β and γ bands in the frontal region with both subjective pleasure and IC for melody and harmony. No significant fit was observed in the θ band. Both subjective pleasure and IC are associated with increased β and γ band power in the frontal regions. β and γ oscillatory activities in the frontal regions are strongly associated with musical rewards induced by prediction errors, similar to brain activity observed during monetary rewards.

Eelgrass (Zostera marina) Trait Variation Across Varying Temperature-Light Regimes

Seagrass trait variation, which results from both local genetic adaptation and phenotypic plasticity, has important effects on ecosystems. Physical drivers underlie these processes and are important determinants of trait variation. Despite this, few studies examine multivariate predictive relationships between sets of physical drivers and sets of seagrass traits. Here, we use redundancy analysis to define this relationship for eelgrass Zostera marina, using traits that represent bed structure, morphology, and physiology and physical drivers that emphasize light conditions and temperature variability on different time scales. We found a relationship between plant size (i.e., leaf length and width, rhizome width, number of leaves) and shoot density that dominated the trait variation. Specifically, as temperatures became warmer, more variable, and light was less limiting, plants became smaller (shorter, narrower, and fewer leaves, thinner rhizomes) but beds became denser. Plant biomass (leaf area index), which increased with decreasing temperature variability and bottom light, further refined this relationship. Overall, temperature variability (i.e., daily temperature range, heat accumulation, time in the optimal temperature range, and tidal and meteorological variability), as well as bottom light, were important predictors of eelgrass traits. We further identified three distinct temperature-light regimes across which traits differed; these included the cool low variability temperature regime with low light, the warm high variability temperature regime with high light, and the intermediate case between these endpoints. Our study identifies specific temperature and light drivers that define certain eelgrass traits and provides a multivariate statistical model that can be used to predict eelgrass trait values from known physical conditions.

Current water contact and Schistosoma mansoni infection have distinct determinants: a data-driven population-based study in rural Uganda

Water contact is a key element of the system of human-environment interactions that determine individual exposure to schistosome parasites and, in turn, community transmission. Yet, there is a limited understanding of the complexity of water contact. We characterised patterns and determinants of water contact within the large-scale SchistoTrack study on 2867 individuals aged 5-90 years in Eastern and Western Uganda, employing Bayesian variable selection and advanced statistical modelling. We found a 15-year gap between the population-level peak in water contact (age 30) and infection (age 15) with practically no correlation (ρ = 0.03) between individual-level water contact and infection. Adults had higher water contact than children, and 80% of individuals with water contact lived within 0.43 km of water bodies. Domestic water contact was most common for children and women, while occupational water contact was most common for men. Water contact was positively associated with older age, fishing or fish mongering occupations, the number of water sites, and type (beach/pond/swamp), and lower village-level infection prevalence. Only older age and fishing were positively, though inconsistently, associated with infection status/intensity. By providing profiles of at-risk groups, and suitable water contact metrics, our research opens avenues for spatially-targeted interventions and exposure monitoring in endemic countries.

Crop-raiding by wildlife and cropland abandonment as feedback from nature-based solutions: lessons from case studies in China and Nepal

Abstract Conservation efforts under the nature-based solutions (NbS) framework aim at better management of ecosystems and improvement of human well-being. Policies targeting forest-based livelihoods align well with the NbS principles, but their social-ecological outcomes are often confounded by complex human-environment interactions. In this study, we identify one major feedback effect of the ecosystem dynamic on people’s livelihoods based on datasets collected from two study areas in China and Nepal. Our methodology integrates satellite remote sensing, household surveys, and statistical models to investigate households’ cropland abandonment decisions under the influence of crop-raiding by wildlife. Results show that cropland parcels that have experienced crop-raiding are more likely to be abandoned in the following years. The more damage the crops have suffered on a given parcel, the more likely it is that the parcel will be abandoned. Parcels in proximity to natural forests, farther away from the house location, and with poorer access to paved roads bear a higher risk of being abandoned. These effects are robust and consistent after controlling for multiple parcel features and household characteristics at different levels and using the dataset from each study area separately. We conclude that policymakers need to consider this undesirable feedback of the ecological system to the livelihoods of local people to better achieve co-benefits for ecosystems and human society.

Applying acoustic telemetry, vessel tracking and fisher knowledge to investigate and manage fisher-shark conflict at Lord Howe Island, Australia

Fisher-shark conflict is occurring at Lord Howe Island, Australia due to high levels of Galapagos shark (Carcharhinus galapagensis) depredation (where sharks consume hooked fish) and bycatch. Depredation causes costly loss of target catch and fishing gear and increased mortality of target species, and sharks can be injured or killed when bycaught. This study applied acoustic telemetry and vessel tracking from 2018 to 2021 to identify; (1) how the movements of 30 tagged sharks and activity of six fishing vessels overlapped, and (2) where key ‘hotspots’ of overlap occurred. Fisher surveys were also conducted to collect information about mitigating shark interactions. Residency index analysis indicated that three sharks tagged at a fish waste dumping site had markedly higher residency. Core home ranges of sharks overlapped with higher fishing activity at four ‘hotspots’. Statistical modelling indicated positive linear effects of fishing activity and bathymetric complexity on shark detections and tagged sharks were present for 13% of the total time that vessels were fishing close to acoustic receivers. Spatio-temporal overlaps between shark movements and fishing activity could potentially have occurred because sharks learned to associate fishing vessels with food (i.e. hooked fish) and because fishers and sharks utilise highly productive shelf edge areas, however more research is needed to investigate these relationships. Fishers reported that rotating fishing areas and reducing time at each location, fishing deeper than 100 m, and using electric reels and lures instead of bait, reduced bycatch and depredation. The integrated approach used here identified practical methods for reducing fisher-shark conflict, improving socio-economic outcomes for fishers and conservation prospects for this unique shark population.

Exact Expressions for Kullback-Leibler Divergence for Univariate Distributions.

The Kullback-Leibler divergence (KL divergence) is a statistical measure that quantifies the difference between two probability distributions. Specifically, it assesses the amount of information that is lost when one distribution is used to approximate another. This concept is crucial in various fields, including information theory, statistics, and machine learning, as it helps in understanding how well a model represents the underlying data. In a recent study by Nawa and Nadarajah, a comprehensive collection of exact expressions for the Kullback-Leibler divergence was derived for both multivariate and matrix-variate distributions. This work is significant as it expands on our existing knowledge of KL divergence by providing precise formulations for over sixty univariate distributions. The authors also ensured the accuracy of these expressions through numerical checks, which adds a layer of validation to their findings. The derived expressions incorporate various special functions, highlighting the mathematical complexity and richness of the topic. This research contributes to a deeper understanding of KL divergence and its applications in statistical analysis and modeling.

Selecting a Time-Series Model to Predict Drinking Water Extraction in a Semi-Arid Region in Chihuahua, Mexico

As the effects of global climate change intensify, it is increasingly important to implement more effective water management practices, particularly in arid and semi-arid regions such as Meoqui, Chihuahua, situated in the arid northern center of Mexico. The objective of this study was to identify the optimal time-series model for analyzing the pattern of water extraction volumes and predicting a one-year forecast. It was hypothesized that the volume of water extracted over time could be explained by a statistical time-series model, with the objective of predicting future trends. To achieve this objective, three time-series models were evaluated. To assess the pattern of groundwater extraction, three time-series models were employed: the seasonal autoregressive integrated moving average (SARIMA), Prophet, and Prophet with extreme gradient boosting (XGBoost). The mean extraction volume for the entire period was 50,935 ± 47,540 m3, with a total of 67,233,578 m3 extracted from all wells. The greatest volume of water extracted has historically been from urban wells, with an average extraction of 55,720 ± 48,865 m3 and a total of 63,520,284 m3. The mean extraction volume for raw water wells was determined to be 20,629 ± 19,767 m3, with a total extraction volume of 3,713,294 m3. The SARIMA(1,1,1)(1,0,0)12 model was identified as the optimal time-series model for general extraction, while a “white noise” model, an ARIMA(0,1,0) for raw water, and an SARIMA(2,1,1)(2,0,0)12 model were identified as optimal for urban wells. These findings serve to reinforce the efficacy of the SARIMA model in forecasting and provide a basis for water resource managers in the region to develop policies that promote sustainable water management.

FORMULATION AND EVALUATION OF LULICONAZOLE NANOEMULGEL USING BOX-BEHNKEN DESIGN APPROACH

Objective: The present study was aimed to develop and assess a Luliconazole-loaded nano emulgel for topical application. Methods: Nanoemulsion of Luliconazole was prepared by ultrasonication method. A pseudo-ternary phase diagram was constructed to determine the ideal ratio of oil and the surfactant/co-surfactant mixture for nanoemulsion preparation. The Box Behnken statistical design was utilized to optimize the nanoemulsion. The optimized batch of nanoemulsion was incorporated into the 1% Carbopol gel as nanoemulgel. It was evaluated for various parameters like globule size, zeta potential, pH, spreadability, viscosity, drug content, drug release, ex vivo permeation study, in vivo animal skin irritation study, and histopathology studies. Results: The optimized formulation showed a globule size of 130.5 nm and entrapment efficiency of 80% and the values were found to be within±5% of predicted values, indicating the suggested statistical model was significant at 95% of confidence interval. The zeta potential of the formulation was found to be-22.1 mV, indicating enhanced stability of the formulation. Transmission Electron Microscopy (TEM) images revealed that the formulation had a smooth surface texture with a mean globule size meeting the nanoscale size range. The drug release study demonstrated a sustained release pattern for the formulation, with a maximum release of 74.93±0.8% over 8 h. The formulated gel exhibited appreciable ex vivo permeability. An in vivo skin irritation test on Wister rats showed no signs of skin irritation from the formulation. The histopathological examination further confirmed that the formulation was dermatologically safe, exhibiting no toxicity or irritation. Conclusion: The results of the present study concluded that Luliconazole-loaded nanoemulgel could be a potential topical drug delivery approach for the management of fungal infections.

Impact of Natural Disasters on House Prices: A Global Analysis and Policy Implications

This study addresses the limited research on how natural disasters affect house prices by analyzing data from 117 countries between 2000 and 2018, using a panel regression approach to assess these impacts. The results reveal that natural disasters tend to decrease house prices, a finding that holds strong across multiple robustness checks. Specifically, geological disasters are found to have the most significant negative impact on property values. The study also distinguishes between the effects of small and large disasters, with both showing a clear downward effect on house prices. These insights carry important implications for policymakers and investors, as lower house prices in disaster-prone areas may indicate reduced consumption and investment due to the wealth effect, potentially leading to wider economic downturns. To support these regions, economic policymakers might consider measures such as low-tax policies or quantitative easing. The findings also highlight the urgent need for governments to address climate change impacts on the housing market by adopting sustainable technologies and renewable energy sources. Keywords Natural Disasters, House Pricing, Climate Change, Statistical model

Marine steel corrosion prediction and zonation using feature extraction and machine learning in the seas around China

To reduce the losses caused by the marine corrosion of steel, it is important to establish a prediction model to determine the corrosion rate of steel in depth-varying aggressive marine environments. The use of statistical feature extraction methods and machine learning modeling for marine steel corrosion prediction and zoning in the seas around China is investigated. In this study, 856 samples were collected. Mean and standard deviation were selected as environmental characteristics and corrosion loss time-varying relationships were log-transformed. Subsequently, four main supervised machine learning (ML) algorithms including Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), and XGBoost were explored for predicting corrosion loss in different depth-varying marine exposure zones. The GB model showed the best prediction accuracy and generalization ability with MSE, RMSE, MAE, and R2 values of 0.08, 0.43, 0.19, and 0.92, respectively. The spatial and temporal distribution of corrosion loss and zoning map in the seas around China were obtained. According to the corrosion zoning map of the splash zone, the South China Sea has a higher degree of corrosion, particularly in its northwestern region.

Rotating Bending Fatigue Durability of Wrought 304 Stainless Steel: A Comparative Study of Surface-Treated vs. Untreated Specimen

The rotating bending fatigue test, which is a common durability test for industrial components, was employed in this study to examine the impact of the surface treatment procedure on the material's performance under room temperature. The studied material in this research is a wrought stainless steel (SS) 304 alloy that has undergone a specialized surface treatment to enhance the fatigue properties of this unique material. The fatigue endurance of untreated materials was 405 MPa (65% of untreated specimen’s yield strength) on 5 million runout cycles, while the surface-treated samples exhibited 545 MPa (85% of treated specimen’s yield strength), a significantly greater value compared to the untreated ones (even up to 10 million runout cycles). The experimental results demonstrated that the surface-treated samples exhibited no signs of cracking. Instead, all failures were attributed to plastic bending, particularly at higher stress levels. Furthermore, the samples displayed a limited range of stress levels, spanning from endurance to plastic bending conditions. This finding suggests that the hypothesis of an increase in stiffness in the surface layer is more pronounced than in the material interior. It also indicates that the surface layer material was not cracked under the maximum stress level, while the layer beneath it experienced plastic deformation. A comparison has been made between the Basquin equation and a statistical machine learning model (for a limited amount of data) to incorporate the impact of surface treatment on predicting fatigue lifetime, and the random forest regressor model would be able to predict the lifetime with the accuracy of R2= 87% which was much higher than the classical model.

Likert Scale Variables in Personal Finance Research: The Neutral Category Problem

Personal finance research often utilizes Likert-type items and Likert scales as dependent variables, frequently employing standard probit and ordered probit models. If inappropriately modeled, the “neutral” category of discrete dependent variables can bias estimates of the remaining categories. Through the utilization of hierarchical models, this paper demonstrates a methodology that accounts for the econometric issues of the neutral category. We then analyze the technique through an empirical exercise relevant to personal finance research using data from the National Financial Capability Study. We demonstrate that ignoring the “neutral” category bias can lead to incorrect inferences, hindering the progression of personal finance research. Our findings underscore the importance of refining statistical modeling techniques when dealing with Likert-type data. By accounting for the neutral category, we can enhance the reliability of personal finance research outcomes, fostering improved decision-relevant insights.

Grey Relation System Applications in Multi-Attribute Decision-Making and Forecasting in Financial Management

Objective: The objective of the paper is to verify the Grey relation analysis methods and compare them with known models in financial management. The first part is devoted to MADM (multi-attribute decision methods) and the second one to grey forecasting methods. Method: Localization GRA and globalization GRA multi-criteria models are described and compared with the TOPSIS method. The forecasting GRA(1,1) model based on cumulative data is described, derived and compared with the linear growth model. The models are verified on the data set. Results and Discussion: The globalisation model based on a pair-wise comparison of alternatives is different, and the ranking of alternatives is fully different in comparison with the TOPSIS model. It was found that the localisation GRA model and TOPSIS, both constructed on similarity measures, present similar and comparable results. So, the grey relation approach considers the uncertainty of input data. The forecasting model GRA(1,1) is computed and compared with the growth model. Results show that the growth model is the trendy model, and the non-trendy series is overestimated or underestimated, so the GRA model looks more robust. Research Implications: The problem of imprecise data is investigated. And it is a realistic condition for practical decision-making. The problem of lack of data is often a phenomenon in decision-making. Traditional statistical methods cannot be applied. Therefore, grey relation methodology is one approach to overcoming the problem of imprecise data and lack of data. Uncertainty and lack of data problems are topics important for research and the managerial community. Originality/Value: Sometimes, in financial decision-making, high-quality data or long-term time series is not at our disposal, and on the other hand, decisions have to be made. This problem is often neglected and solved by not-relevant traditional statistical models. Therefore, methods that allow the solving of such problems are useful. It was shown that a grey relation methodology for multicriteria financial performance evaluation and forecasting shows more robust and stable results and could be more relevant for imprecise data or short time series data in financial decision-making problems.

Mapping soil moisture across the UK: assimilating cosmic-ray neutron sensors, remotely sensed indices, rainfall radar and catchment water balance data in a Bayesian hierarchical model

Abstract. Soil moisture is important in many hydrological and ecological processes. However, data sets which are currently available have issues with accuracy and resolution. To translate remotely sensed data to an absolute measure of soil moisture requires mapped estimates of soil hydrological properties and estimates of vegetation properties, and this introduces considerable uncertainty. We present an alternative methodology for producing daily maps of soil moisture over the UK at 2 km resolution (“SMUK”). The method is based on a simple linear statistical model, calibrated with 5 years of daily data from cosmic-ray neutron sensors at ∼ 40 sites across the country. The model is driven by precipitation, humidity, a remotely sensed “soil water index” satellite product and soil porosity. The spatial variation in the parameter describing the soil water retention (and thereby the response to precipitation) was estimated using daily water balance data from ∼ 1200 catchments with good coverage across the country. The model parameters were estimated by Bayesian calibration using a Markov chain–Monte Carlo method, so as to characterise the posterior uncertainty in the parameters and predictions. The approach reduces uncertainty by integrating multiple data sources, all of which have weaknesses but together act as a better constraint on the true soil moisture. The model explains around 70 % of the variance in the daily observations with a root-mean-square error of 0.05 m3 m−3, better than results from more complex process-based models. Given the high resolution of the inputs in time and space, the model can predict the very detailed variation in soil moisture which arises from the sporadic nature of precipitation events, including the small-scale and short-term variations associated with orographic and convective rainfall. Predictions over the period 2016 to 2023 demonstrated realistic patterns following the passage of weather fronts and prolonged droughts. The model has negligible computation time, and inputs and predictions are updated daily, lagging approximately 1 week behind real time.

Quantifying chemomechanical weakening in muscovite mica with a simple micromechanical model

In response to gradual nanoindentation, the surface of muscovite mica deforms by sudden stochastic nanometer-scale displacement bursts. Here, the statistics of these displacement events are interpreted using a statistical model previously used to model earthquakes to understand how chemically reactive environments alter the surface properties of this material. We show that the statistics of nanoindentation displacement bursts in muscovite mica are tuned by chemomechanical weakening in a manner similar to how the statistics of model events are tuned by a mechanical weakening parameter that describes how easily system-spanning cracks can be nucleated. Because the predictions of this model are independent of any surface defects or structural details, these results suggest this simple model can be universally used to describe chemomechanical weakening in many systems prone to slip avalanches on a wide range of spatio-temporal scales.

Asteroid material classification based on multi-parameter constraints using artificial intelligence

Material types of asteroids provide key clues to their evolutionary history and contained resources. The Gaia mission has released extensive low-resolution spectral observation data of small Solar System bodies. However, methods for classifying asteroids based on low-resolution space-based spectra are still inadequate, and do not fully leverage the complementary features of spectra and multiple intrinsic attributes of asteroids to achieve precise material classification. Our goal is to propose a method with a higher generalization accuracy for asteroid material classification by integrating multi-source information, identifying optimal feature combinations for model inputs, and deepening the understanding of relationships among asteroid parameters. The effective asteroid photometric, physical, and orbital parameters were screened using the information gain ratio and Spearman's rank correlation coefficient. Then, artificial intelligence techniques were employed to combine asteroid spectra with the selected various parameters for six-class material classification. By comparing five machine learning models, we identified network structures with higher validation accuracy and stable generalization performance. Meanwhile, feature ablation experiments were conducted to determine the input parameter combinations suitable for different scenarios. Finally, based on the statistical results and model outputs, the constraint relationships among asteroid parameters were visualized and analyzed. The proposed AsterRF model achieved a validation accuracy of 92.2<!PCT!>, an improvement of approximately 7.8 percentage points compared to existing methods that use only spectra. V-type asteroids exhibited the highest classification accuracy, followed by A-type and D-type. X-type asteroids had the lowest precision and recall, and were easily confused with C-type. The model generally showed higher classification confidence for S-type asteroids. The top five attributes that the model focused on are the phase slope parameter (G), orbital type, albedo, H magnitude, and effective diameter. Additionally, the correlations between asteroid materials and other parameters were generally below 0.4. Incorporating optimal asteroid parameter combinations can significantly enhance classification accuracy based on spectra. A dual-channel network that processes spectra and parameter inputs separately, and employs a self-attention mechanism for feature fusion is effective in combining multi-source asteroid information. Both the statistical correlations and model performance-based importance rankings of parameters contribute to understanding the constraint relationships among asteroid attributes.

When Automated Assessment Meets Automated Content Generation: Examining Text Quality in the Era of GPTs

The use of machine learning (ML) models to assess and score textual data has become increasingly pervasive in an array of contexts including natural language processing, information retrieval, search and recommendation, and credibility assessment of online content. A significant disruption at the intersection of ML and text are text-generating large-language models (LLMs) such as generative pre-trained transformers (GPTs). We empirically assess the differences in how ML-based scoring models trained on human content assess the quality of content generated by humans versus GPTs. To do so, we propose an analysis framework that encompasses essay scoring ML-models, human and ML-generated essays, and a statistical model that parsimoniously considers the impact of type of respondent, prompt genre, and the ML model used for assessment model. A rich testbed is utilized that encompasses 18,460 human-generated and GPT-based essays. Results of our benchmark analysis reveal that LLMs and transformer pretrained language models (PLMs) more accurately score human essay quality as compared to CNN/RNN and feature-based ML methods. Interestingly, we find that LLMs and transformer PLMs tend to score GPT-generated text 10-20% higher on average, relative to human-authored documents. Conversely, traditional deep learning and feature-based ML models score human text considerably higher. Further analysis reveals that even though the LLMs and transformer PLMs are exclusively fine-tuned on human text, they more prominently attend to certain tokens appearing only in GPT-generated text, possibly (in part) due to familiarity/overlap in pre-training. Our framework and results have implications for text classification settings where automated scoring of text is likely to be disrupted by generative AI.

An empirical model of the kinetics of hydrogen-induced cracking in pipeline steel, using statistical distribution models and considering microstructural characteristics and hydrogen diffusion parameters

This study proposes an empirical model to predict the kinetics of hydrogen-induced cracking (HIC) growth rate in pipeline steels based on experimentally measured hydrogen diffusion parameters and spatial distribution of microstructural features previously identified to have a role on HIC kinetics. In the experimental work, the HIC was induced by electrochemical cathodic charging and the crack growth was monitored by ultrasonic inspection. Optical and scanning electron microscopy were used to determine the spatial distribution parameters of non-metallic inclusions, and the ferrite grain and second phase characteristics. The hydrogen microprint technique used to visualize hydrogen diffusion path in the microstructure and the hydrogen diffusion parameters were determined by hydrogen permeation tests. Results show that NMI shape affects HIC nucleation sites, using student's t-distribution, while ferrite grain characteristics affect HIC growth rates, with X70–2 and X56 steel plates recorded highest HIC growth rate. The Log-Normal distribution model, supported by statistical analysis, effectively predicts HIC growth rates compared with Weibull and Gamma distribution models.

Spatio-Temporal Analysis of Net Anthropogenic Phosphorus Inputs (NAPIs) and Their Impacts in Ningxia Hui Autonomous Region Using Monte Carlo Simulations and Sensitivity Analysis

This study employed the Net Anthropogenic Phosphorus Inputs (NAPI) model to assess the impact of human activities on phosphorus input in a watershed, analyzing county-level statistical data and NAPI model parameters from 1991 to 2020. The Monte Carlo method was used for a quantitative analysis of the model parameters’ effects on each NAPI component and the overall simulation results. The sensitivity index method identified each component’s sensitive parameters. The study found that the lowest NAPI value was 454 kg/(km2·a) in 1991 and the highest was 1336 kg/(km2·a) in 2003. NAPI in Ningxia showed an overall upward trend from 1991 to 1999, a slight decrease from 1999 to 2003, and a slight increase from 2003 to 2020, with fertilizer being the main contributing factor, accounting for 77.4% of the total input. On a spatial scale, NAPI in Ningxia was significantly correlated with land use patterns, showing higher values in the northern and southern regions compared to the central part. The NAPI values derived from Monte Carlo simulations with appropriate parameters ranged from −24.83% to 31.49%. The study highlighted the net food and feed imports component as having the highest uncertainty, impacting simulation results within a range of −23.89% to 53.98%. It was observed that the larger a component’s proportion in the NAPI model, the more sensitive its parameters, with the phosphorus fertilizer (Pfer) component’s parameters being notably more sensitive than those of the food/feed phosphorus input and the non-food phosphorus input (Pnf) components. These findings can inform phosphorus pollution control policies in Northwest China, while the selection of sensitive parameters provides a useful reference for future NAPI research in other regions.

Analysis of Quantitative Estimates of the Greenhouse Gases Net Flow in the Russian Land Use Sector

A comparative analysis of the actual greenhouse gas net runoff by the Russian land use sector has been carried out, which showed that depending on the methodologies and approaches used, as well as the type of initial data, information on the absorptive capacity can be aggregated into three groups: 1) studies based on process (inverse) modeling and VNIILM methodology; 2) studies with statistical net runoff modeling and approaches regulated by the IPCC on the basis of the state forest registry; 3) studies based on dynamic global vegetation models (DGVM) and ROBUL methodology, verified by the IPCC, but using as input data information from the state forest registry. An assumption is made that the existing official Russian ROBUL methodology, which coincides with the IPCC methodology, currently reflects a conservative estimate of the absorption capacity of the Russian land use sector and can be adjusted with a potential increase to 35-45% of the current values due to additional accounting of reserve forests in the calculations, as well as the results of the state forest inventory and a new state forest registry with a high degree of spatial resolution.