Moving Window Method Research Articles

Improving the estimation of the Boyce index using statistical smoothing methods for evaluating species distribution models with presence‐only data

Species distribution models (SDMs) underpin a wide range of decisions concerning biodiversity. Although SDMs can be built using presence‐only data, rigorous evaluation of these models remains challenging. One evaluation method is the Boyce index (BI), which uses the relative frequencies between presence sites and background sites within a series of bins or moving windows spanning the entire range of predicted values from the SDM. Obtaining accurate estimates of the BI using these methods relies upon having a large number of presences, which is often not feasible, particularly for rare or restricted species that are often the focus of modelling. Wider application of the BI requires a method that can accurately and reliably estimate the BI using small numbers of presence records. In this study, we investigated the effectiveness of five statistical smoothing methods (i.e. thin plate regression splines, cubic regression splines, B‐splines, P‐splines and adaptive smoothers) and the mean of these five methods (denoted as ‘mean') to estimate the BI. We simulated 600 species with varying prevalence and built distribution models using random forest and Maxent methods. For training data, we used two levels for the number of presences (NPtrain: 20 and 500), along with 2 × NPtrain and 10000 random points (i.e. random background sites) for each modelling method. We used the number of presences at four levels (NPbi: 1000, 200, 50 and 10) to investigate its effect, together with 5000 random points to calculate the BI. Our results indicate that the BI estimates from the binning and moving window methods are severely affected by the decrease of NPbi, but all the estimates of the BI from smoothing‐based methods were almost always unbiased for realistic situations. Hence, we recommend these methods for estimating the BI for evaluating SDMs when verified absence data are unavailable.

Size Dependency of Elastic and Plastic Properties of Metallic Polycrystals Using Statistical Volume Elements

We present an efficient approach to evaluate the size dependency of elastic and plastic properties of metallic polycrystalline materials. Specifically, we consider different volume fractions of ferrite and martensite phases for the construction of three macroscopic domains. Statistical Volume Elements (SVEs) of different sizes are extracted from these domains using the moving window method. Linear and Crystal Plasticity (CP) simulations provide elastic and plastic properties of the SVEs such as the bulk and shear moduli, yield strength, and hardening modulus. We use a variation-based criterion to determine the Representative Volume Element (RVE) size of these properties. This RVE size corresponds to a size beyond which the given property can be idealized as homogeneous. We also use anisotropy indices and an additional RVE size criterion to determine the size limits beyond which these properties can be idealized as isotropic. Numerical results show that the plastic properties often reach their homogeneity and isotropy limits at larger sizes compared to elastic properties. This effect is more pronounced for the hardening modulus compared to the yield strength.

Minimum area of primitive broad-leaved Korean pine forest community based on biomass

Based on the survey data of nine primitive broad-leaved Korean pine forest plots ranging from 1 to 10.4 ha in Heilongjiang Province, this study used the moving window method and GIS technology to analyze the variation characteristics of the spatial distribution pattern of forest biomass in each plot. We explored the minimum area that can reflect the structural and functional characteristics of the primitive broad-leaved Korean pine forest, and used computer simulation random sampling method to verify the accuracy of the minimum area. The results showed that: (1) Through the analysis of the spatial distribution raster map of biomass deviation in the plots at various scales of 10 − 100 m, there is a minimum area (0.64 ha) for the critical range of biomass density variation in the primitive broad-leaved Korean pine forest. This minimum area based on biomass density can indirectly reflect the comprehensive characteristics of productivity level per unit area, structure, function, and environmental quality of the primitive broad-leaved Korean pine forest community. (2) Using computer simulation random sampling, it was found that only by sampling in a specific plot larger than or equal to the minimum area can equivalent or similar results be achieved as random sampling within the plot, indicating that the minimum area determined by the moving window method is accurate. (3) The minimum area determined in this paper is an excellent indicator reflecting the complexity of community structure, which can be used for comparing changes in community structure and function before and after external disturbances, and has a good evaluation effect. This minimum area can also be used as a basis for scientific and reasonable setting of plot size in the investigation and monitoring work of broad-leaved Korean pine forests in this region, thereby achieving the goals of improving work efficiency and saving work costs.

The regional characteristics of the occurrence rate of ionospheric anomalies in 2011–2018

This paper presents the regional characteristics of the occurrence rate of ionospheric anomalies of total electron content derived from global position system (GPS) observations. The area of concern spans 70° of longitude, from 70° E to 140° E, and 40° of latitude, from 15° N to 55° N. We introduced the 27 days moving window method to automatically detect anomalies that are caused by various sources. The spatial, diurnal, seasonal, and yearly distribution of the occurrence rate of extreme anomalies in 2011–2018 were provided. The spatial distribution of the occurrence rates of positive and negative anomalies had clear patterns in latitude and it did not change much in longitude at a certain local time over this area. The occurrence rates of positive and negative anomalies at the middle latitudes had significantly different diurnal variations from those at low latitudes. The maximum and minimum of the occurrence rates of positive anomalies occurred at about 17:00 local time (LT) and about 13:00 LT over the region from 36° N to 45° N. The maximum and minimum of the occurrence rates of negative anomalies in the same area occurred at about 07:00 LT and 18:00 LT over the region from 36° N to 45° N. There was no clear pattern in the seasonal variations of occurrence rates of positive and negative anomalies over [70° E-140° E, 15° N-55° N] in 2011–2018. The mean O/N2 had low values in summer and comparable high values in other seasons in 2015, and the O/N2 may not be the main reason for irregular seasonal characteristics of occurrence rate of anomalies. The occurrence rates of positive and negative anomaly in the year when the F10.7 index is relatively low, such as 2018, may be larger than the occurrence rates in the year with relatively large F10.7 index, such as 2014.

Monitoring resilience in bursts

The possibility to anticipate critical transitions through detecting loss of resilience has attracted attention in many fields. Resilience indicators rely on the mathematical concept of critical slowing down, which means that a system recovers more slowly from external perturbations when it gets closer to tipping point. This decrease in recovery rate can be reflected in rising autocorrelation and variance in data. To test whether resilience is changing, resilience indicators are often calculated using a moving window in long, continuous time series of the system. However, for some systems, it may be more feasible to collect several high-resolution time series in short periods of time, i.e., in bursts. Resilience indicators can then be calculated to detect a change of resilience between such bursts. Here, we compare the performance of both methods using simulated data and showcase the possible use of bursts in a case study using mood data to anticipate depression in a patient. With the same number of data points, the burst approach outperformed the moving window method, suggesting that it is possible to downsample the continuous time series and still signal an upcoming transition. We suggest guidelines to design an optimal sampling strategy. Our results imply that using bursts of data instead of continuous time series may improve the capacity to detect changes in resilience. This method is promising for a variety of fields, such as human health, epidemiology, or ecology, where continuous monitoring can be costly or unfeasible.

Optimizing Rural Landscape Planning and Design: A Random Forest Algorithm Approach for Sustainable Development

This study addresses challenges in rural planning amid economic growth and the implementation of rural revitalization policies. The aim is to enhance the integration of cultural and ecological elements in rural areas, combating issues such as the fading village atmosphere and incomplete agricultural chains. The research focuses on optimizing the random forest algorithm to explore innovative approaches to landscape planning and design for rural human settlements. Using the moving window method, the study computes two-dimensional and three-dimensional landscape indices in surrounding villages of Beijing, conducting a multi-scale analysis of the living environment. Power function fitting indicates an optimal window size of approximately 700 meters for studying the relationship between art patterns and three-dimensional landscape patterns in the rural area. The findings offer insights into improving rural living environments through effective landscape planning and design influenced by artistic modes.

A landscape pattern gradient analysis method coupling wavelet algorithm for balancing development and protection activities of the coastal zone: A case study

Due to the economic importance and ecological vulnerability of coastal areas, the optimization of spatial patterns is important for the sustainable development of coastal zones. In this study, a new landscape pattern index gradient analysis method coupled with wavelet algorithm (GA-WA) was explored to balance the development and protection activities in the coastal zone. First, based on the classification results of remote sensing images, the gradient detection of the coastal zone was carried out using the cumulative moving window method, and the calculation of the landscape pattern index was carried out using Fragstats software to obtain the gradient change curve of the landscape pattern index. Secondly, the wavelet algorithm was applied to micro-analyze the above gradient change curves, and the results of multiscale analysis of the landscape pattern index in the study area were obtained. Finally, it is demonstrated how this method can help in practical decision making. It was found that:(1) The landscape pattern index gradient analysis method coupled with wavelet algorithms offers a new approach to balance the development and protection of coastal zones. This approach presents an innovative method for optimizing the ecological pattern of coastal zone landscapes from the perspective of macro–micro combination, which is not only applicable to coastal zones but also can be extended to other strip corridor landscapes.(2) The gradient analysis of the coastal zone landscape pattern index provides results at multiple spatial scales, which is superior to the analysis at a single spatial scale. The results of the gradient analysis method are finer and more stable, which solves the uncertainty problem of traditional landscape pattern index analysis. There is a significant difference between the landscape indices of the overall landscape and the gradient landscape, in which the AREA_MN index of the ecological land has the largest difference, reaching 110.18%.(3) Wavelet coefficient variance diagrams, real component contour diagrams, and modulus squared diagrams reveal the characteristics, cycles, and patterns of landscape dynamics changes, as well as the distribution at different gradient window scales. These tools help to identify the driving factors and development trends of landscape dynamics changes and solve problems of functional positioning in undeveloped areas, as well as the transformation of microscopic small parcels within developed areas. By analyzing regular cycles of landscape index changes, it is possible to determine the functional positioning of land and to position human intervention through peaks to guard against risky gradient windows.

Adaptive soft sensor using stacking approximate kernel based BLS for batch processes

To deal with the highly nonlinear and time-varying characteristics of Batch Process, a model named adaptive stacking approximate kernel based broad learning system is proposed in this paper. This model innovatively introduces the approximate kernel based broad learning system (AKBLS) algorithm and the Adaptive Stacking framework, giving it strong nonlinear fitting ability, excellent generalization ability, and adaptive ability. The Broad Learning System (BLS) is known for its shorter training time for effective nonlinear processing, but the uncertainty brought by its double random mapping results in poor resistance to noisy data and unpredictable impact on performance. To address this issue, this paper proposes an AKBLS algorithm that reduces uncertainty, eliminates redundant features, and improves prediction accuracy by projecting feature nodes into the kernel space. It also significantly reduces the computation time of the kernel matrix by searching for approximate kernels to enhance its ability in industrial online applications. Extensive comparative experiments on various public datasets of different sizes validate this. The Adaptive Stacking framework utilizes the Stacking ensemble learning method, which integrates predictions from multiple AKBLS models using a meta-learner to improve generalization. Additionally, by employing the moving window method—where a fixed-length window slides through the database over time—the model gains adaptive ability, allowing it to better respond to gradual changes in industrial Batch Process. Experiments on a substantial dataset of penicillin simulations demonstrate that the proposed model significantly improves predictive accuracy compared to other common algorithms.

Virtual Sensing of Key Variables in the Hydrogen Production Process: A Comparative Study of Data-Driven Models.

Hydrogen is an ideal energy carrier manufactured mainly by the natural gas steam reforming hydrogen production process. The concentrations of CH4, CO, CO2, and H2 in this process are key variables related to product quality, which thus need to be controlled accurately in real-time. However, conventional measurement methods for these concentrations suffer from significant delays or huge acquisition and upkeep costs. Virtual sensors effectively compensate for these shortcomings. Unfortunately, previously developed virtual sensors have not fully considered the complex characteristics of the hydrogen production process. Therefore, a virtual sensor model, called "moving window-based dynamic variational Bayesian principal component analysis (MW-DVBPCA)" is developed for key gas concentration estimation. The MW-DVBPCA considers complicated characteristics of the hydrogen production process, involving dynamics, time variations, and transportation delays. Specifically, the dynamics are modeled by the finite impulse response paradigm, the transportation delays are automatically determined using the differential evolution algorithm, and the time variations are captured by the moving window method. Moreover, a comparative study of data-driven virtual sensors is carried out, which is sporadically discussed in the literature. Meanwhile, the performance of the developed MW-DVBPCA is verified by the real-life natural gas steam reforming hydrogen production process.

Enhancing Rural Revitalization Through Optimized Landscape Planning and Design: Insights from Beijing's Surrounding Villages

With the rapid economic development and the proposal of rural revitalization policies, the development of rural planning and construction has also received more and more attention, and the content of rural planning and construction practice is gradually expanding, but what follows is the integration of the regional cultural environment and the natural ecological environment is not high enough, and the phenomenon of disappearing village atmosphere, incomplete agricultural industrial chain, weakened functions, and incompatible ecological development has repeatedly occurred. Gradually, the village living environment has also been threatened, and the landscape planning and design of the rural living environment conveyed by different artistic modes and meanings has brought new opportunities for the realization of the rural revitalization strategy. This research takes the optimization of random forest algorithm as the core, and deeply explores the new ideas and methods of art mode and landscape planning and design of rural human settlements. This study mainly takes the surrounding villages of Beijing as an example and uses the moving window method to calculate the two-dimensional and three-dimensional landscape indices in this rural area. The multi-scale relationship of the living environment was analyzed. The power function fitting results of the window variation coefficient of the landscape index show that the optimal window size for studying the relationship between art patterns and three-dimensional landscape patterns is about 700 M.

Impacts of Spatio-Temporal Changes in Anthropogenic Disturbances on Landscape Patterns in the Nandu River Basin, China

We explored the characteristics of landscape pattern (LP) changes in the Nandu River Basin (NRB) and its dynamic response mechanism to anthropogenic disturbance (AD). This is important for ecological protection and for land use decision-making in the basin in the context of the construction of a free trade port. Land use and land cover change (LULCC) data of 1990, 2000, 2010, and 2020 were analyzed with the help of the LP index, moving window method, hemeroby index, geo-information atlas, and geographic information system (GIS) spatial analysis to reveal the dynamic changes in LP characteristics in the NRB. Furthermore, this paper discusses into the correlation between LP and AD. The results indicate that over the past three decades, the NRB showed a staggered LP, dominated by forestland and cropland, whose total area has decreased by 25.27 km2 and 62.75 km2, respectively. On the other hand, the built-up land increased by 91.37%. The overall landscape fragmentation, landscape patch homogeneity, and landscape diversity have increased in the NRB. AD is the main reason for the dramatic changes in the LP of urban agglomerations centered in Haikou city. The area of minor disturbance (over 34%) occupies the largest proportion of the land, followed by higher (about 13~25%) and moderate disturbances (about 17~22%). The area of minor and strong disturbances has increased significantly, whereas those of moderate and higher disturbances have significantly decreased. The spatial distribution pattern of AD is gradually increasing from the southwest (natural landscape) to the northeast (human landscape). A transition in areas of low disturbance levels to higher levels is obvious. The area of the atlas that has transitioned from “minor → lighter disturbance (12)” and “higher → minor disturbance (41)” changed changed most significantly during 2000 to 2010 and from 2010 to 2020, occupying 26.79% and 11.99% of the transfer atlas, respectively. All regions encountering disturbances were significantly correlated with the largest patch index (LPI) from 1990 to 2020. The overall AD in the NRB has increased, especially in Haikou. Urbanization is the main factor for an increase in AD. The ecological and environmental management and monitoring in the basin need to be strengthened.

A multi-subsystem collaborative Bi-LSTM-based adaptive soft sensor for global prediction of ammonia-nitrogen concentration in wastewater treatment processes

Ammonia-nitrogen concentration is a key water quality indicator, which reflects changes in pollutant components during wastewater treatment processes. The timely and accurate detection results contribute to optimizing control and operational management of wastewater treatment plants (WWTPs), but current detection methods only focus on the effluent location. This paper proposes a multi-subsystem collaborative Bi-LSTM-based adaptive soft sensor to achieve the global prediction of ammonia-nitrogen concentration. Firstly, the wastewater treatment process is divided into several independent subsystems depending on the reaction mechanism, and the variable selection is performed using mutual information. Subsequently, the bidirectional long short-term memory network (Bi-LSTM) is employed to construct a model for predicting ammonia-nitrogen concentration within each subsystem, and the outputs between neighboring subsystems are incorporated as a set of new variables added into the training dataset to strengthen their connection. Finally, to address performance degradation caused by environmental factors, a probability density function (PDF)-based dynamic moving window method is proposed to enhance the robustness. The effectiveness and superiority of the proposed soft sensor are validated in the Benchmark Simulation Model no. 1 (BSM1). The experimental results demonstrate that the proposed soft sensor can accurately predict the global ammonia-nitrogen concentration in the face of different weather conditions including sunny, rainy, and stormy days. This study contributes to the stable operation of WWTPs with higher treatment efficiency and lower economic costs.

Volatility spillovers across Russian oil and gas sector. Evidence of the impact of global markets and extraordinary events

This paper provides empirical evidence on the evolution of the impact of external shocks on the volatility connectedness of Russian oil and gas companies’ stock prices. We examine how the spillover structure has changed following the outbreak of COVID-19, as well as under the impact of the 2022–2023 events.We have extended the Diebold–Yilmaz approach to calculate conditional connectivity measures adjusted for the influence of global factors and unexpected shocks caused by extraordinary events.We use an augmented vector autoregressive model (VARX) to evaluate the adjusted conditional connectivity measures. The dynamics of connectivity indices are analyzed using the moving window method for VAR and VARX models, as well as using a dynamic model with time-varying coefficients (TVP-VAR). The comparison of adjusted and unadjusted connectivity measures allows us to draw conclusions about the impact of changes in foreign financial markets as well as shocks caused by extraordinary events on connectedness structure.We found that measures of conditional connectivity change much more slowly than unconditional ones. Our results support the hypothesis that essential part of the fluctuation of Russian oil and gas connectedness indices can be explained by exposure to unexpected shocks and global economic and political factors. Accordingly, the transformation of the network structure of risk transfer occurs quite smoothly, without sharp jumps and takes a considerable amount of time.

Carbon and water relationships change nonlinearly along elevation gradient in the Qinghai Tibet Plateau

The influence of terrain elevation on carbon and water ecosystem services, mediated by heat and energy redistribution, has significant implications for the Qinghai Tibet Plateau (QTP). Renowned as the “Third Pole of the Earth” and the “Water Tower of Asia,” the QTP boasts the planet’s most extensive elevation range. However, understanding the intricate effects of the elevation gradient (EG) on carbon and water services and their interrelationships remains limited. In this study, we deploy the moving window method (MWM), the generalized additive model (GAM), and the structural equation model (SEM) to explain the dynamics of ecosystem service relationship across the EG. Our analysis, spanning 2000 to 2018, reveals annual mean values of carbon storage (CS) at 50.45 tC/ha and water yield (WY) at 246.14 mm for the QTP. The results highlight the importance of the carbon–water trade-off and the pronounced CS reduction in the south-central plateau under varying EG, alongside the harmonized interactions in the northeast and southern sectors. GAM outcomes show a non-linear association between ecosystem service relationship and EG. With elevational ascent, the correlation between the digital elevation model (DEM) and CS transitions from positive to negative, and the ecosystem service relationship transitions from synergy to trade-off. In the QTP, the explanatory force of inherent meteorological factors in shaping CS and WY surpasses that of all socio-economic factors, with the latter wielding greater influence over ecosystem service relationship dynamics. The insights from this investigation provide a foundation for integrating intricate terrain gradients into strategies aimed at alleviating potential adverse ramifications for human well-being.

Research on Monitoring Technology and Analysis of Urban Green Space Ecology and Environment

Urban green land plays an active role in the process of improving urban ecological environment and the harmony between human and nature. With the economic development, industrial progress and the improvement of people's living standard, urban greenness as the regulator of urban environment has been generally concerned. The dynamic monitoring of urban green land is conducive to scientific and effective urban management, and provides scientific basis and evaluation criteria for urban green land system planning. In this paper, we use remote sensing as the observation means to systematically carry out the research of urban green land ecological environment monitoring technology and analysis. Firstly, it introduces the pre-processing techniques of remote sensing data for urban monitoring, and selects the area method, grid cell method, buffer zone method and moving window method as the key techniques for spatial measurement of urban greenness spaces, and finally evaluates the ecological effects of urban green land with the greenness environment index model of urban buildings. This paper provides the key techniques and evaluation process of urban green space remote sensing in a more systematic way, which will play an important role in garden city evaluation, ecological city evaluation, garden planning, smart city and ecological city support, urban building planning, and sponge map city construction.

A Framework to Quantify Riverine Dissolved Inorganic Nitrogen Exports under Changing Land-Use Patterns and Hydrologic Regimes

Riverine dissolved inorganic nitrogen (DIN), when elevated by human activities (e.g., land-use change), can accelerate the nitrogen cycle and downstream dispersal. However, estimating DIN export coefficients for individual land-use types can be complex due to mosaic land-use patterns and interactions between fertilizers and hydrological processes. We propose a framework that integrates an empirical model, a moving-window method, and an elasticity method to quantify seasonal DIN export coefficients for each land use in the Shixi Creek catchment, southeast China. Our model showed good agreement with field observations according to root mean square error and a normalized objective function. The export coefficients of farmland and forest were the highest (9.16 mg L−1) and lowest (2.91 mg L−1) ones, resulting in annual DIN exports, respectively, for farmland and forests of 1951 kg km−2 yr−1 and 619 kg km−2 yr−1, respectively. Urbanization was a dominant factor influencing DIN export; the export coefficient of built-up areas showed the highest elasticity and highest uncertainty, with abrupt fluctuations from dry to wet years. Our framework revealed the complex role of built-up areas in nitrogen export. Our results can shed light on how to improve riverine N management in a catchment by considering the interactive effects of climate and land use.

Evolution Characteristics of Landscape Patterns and the Response of Surface Runoff in a Rapid Urbanization Area: Focus on the Chang–Zhu–Tan Metropolitan Area of China

With the acceleration of urbanization, the disturbance to urban landscape patterns causes changes to urban surface runoff and increases the risk of urban waterlogging. We studied the response relationship between landscape pattern change and surface runoff in the Chang–Zhu–Tan metropolitan area for the period from 2000 to 2020, analyzing the driving factors that affected surface runoff. The influence of landscape pattern evolution on surface runoff was studied using the SCS-CN flow generation model, the moving window method, and Pearson’s analysis. The analysis showed that between 2000 and 2020, the forest area decreased, while the impermeable land area increased substantially. At the same time, the landscape spread degree (CONTAG) decreased, whereas the landscape fragmentation degree (DIVISION) increased, and the Shannon diversity index (SHDI) and landscape shape index (LSI) increased. The surface runoff in the main urban areas of Chang–Zhu–Tan increased substantially. The results showed that surface runoff is negatively correlated with SHDI, LSI, and DIVISION but displays a positive relationship to CONTAG. Soil texture and precipitation had the greatest impact on surface runoff. This study emphasizes the importance of landscape pattern evolution to surface runoff in rapidly developing metropolitan areas in terms of reducing surface runoff, alleviating urban waterlogging and preventing regional floods. Our research additionally seeks to optimize the landscape pattern of the Chang–Zhu–Tan metropolitan area.

Active disturbance rejection control strategy for PEMFC oxygen excess ratio based on adaptive internal state estimation using unscented Kalman filter

The effectiveness of gas flow control will significantly affect the output performance of the PEMFC, however, the gas state inside the cathode is difficult to observe due to the tightness and safety requirements. To achieve effective control of PEMFC in the presence of parameter bias, an improved active disturbance rejection control (IADRC) strategy based on the adaptive unscented Kalman filter (AUKF) is proposed in this paper. A dynamic model of the air supply system with seven state variables was developed, capable of describing the non-linear characteristics of the system, and a semi-empirical air compressor model was designed to match the actual operating characteristics based on the experimental data set. Based on the standard UKF algorithm, an adaptive process was designed using the moving window method to dynamically adjust the noise characteristics to reduce the state estimation bias caused by changes in system structural parameters during service. Finally, the algorithmic structure of the ADRC was optimized to achieve an accurate estimation of the total system perturbation using cascaded extended state observers (ESO), thus achieving a control effect with the advantages of less overshoot and response speed based on fewer tuning parameters. Simulation results show that the proposed strategy can effectively improve the system response and robustness to structural parameters, and has great control effects under real road conditions.

State-of-charge of lithium-ion battery based on equivalent circuit model – Relevance vector machine fusion model considering varying ambient temperatures

Ambient temperature significantly impacts battery characteristics and state-of-charge (SOC) accuracy, so it is crucial to estimate SOC under various temperatures. Most circuit models of lithium-ion batteries have poor temperature adaptability, especially at low temperatures. To solve this challenge, this paper proposes a fusion model of the Equivalent Circuit Model-Relevance Vector Machine to enhance SOC estimation under various temperatures. First, a second-order equivalent circuit model based on the forgetting factor recursive least squares is built to obtain the SOC value. Second, build a data-driven battery model based on the relevance vector machine (RVM) algorithm with a moving window method, and the SOC value is obtained based on the RVM model and the unscented Kalman filter algorithm. The weights of the models are merged using the Bayesian principle. The experiments verify that the fusion model accurately estimates SOC for different types of batteries under variable ambient temperature environments and exhibits strong robustness.

Evaluation of Biodiversity Maintenance Capacity in Forest Landscapes: A Case Study in Beijing, China

Understanding how landscape pattern changes impact forest biodiversity conservation and ecosystem management is crucial. This study evaluated the biodiversity maintenance capacity (BMC) of forest landscapes in Beijing, China from 2005 to 2020 based on habitat quality and carbon sink. For this, the moving window method was employed to compute landscape indices that depict variations in landscape patterns, including intermixing, connectivity, diversity, and compactness. Lastly, the relationship between landscape pattern changes and the BMC of forest landscapes was investigated using a combination of spatial correlation analysis and geographic weighted regression measurement models. The results showed the following. (1) The average BMC increased from 0.798 to 0.822. Spatially, 84.14% of the areas experienced an improvement, mainly in the mountainous region. In contrast, a decrease was observed in 4.03% of the areas, primarily concentrated in the transition zone between mountains and suburban plain. (2) The landscape pattern changed dramatically from 2005 to 2020. Landscape intermixing and compactness decreased slightly by 11.45% and 7.82%, while landscape connectivity and diversity increased significantly by 64.28% and 55.44%, respectively. (3) The BMC’s global Moran’s I values in 2005 and 2020 were 0.711 and 0.782, respectively, signifying a spatial bipolar agglomeration pattern. (4) Among the four selected landscape indices, the compactness was found to be the most critical factor. It attained a positive contribution to forests with high BMC, but had a negative impact on forests with low BMC. The results could provide a reference for planners coordinating forest management and biodiversity conservation.