Search Results (55)

Amid fossil-fuel depletion and worsening environmental impacts, battery electric vehicles (BEVs) are pivotal to the energy transition. Energy management in BEVs relies on accurate motor efficiency maps, yet real-time onboard control demands models that balance fidelity with computational cost. To address map inaccuracy under real driving and the high runtime cost of 2-D interpolation, we propose a driving-cycle-aware, physically interpretable quadratic polynomial-surface framework. We extract priority operating regions on the speed–torque plane from typical driving cycles and model electrical power $P_e$ as a function of motor speed $n$ and mechanical power $P_m$. A nested model family (M3–M6) and three fitting strategies—global, local, and region-weighted—are assessed using $R^2$, RMSE, a computational complexity index (CCI), and an Integrated Criterion for accuracy–complexity and stability (ICS). Simulations on the Worldwide Harmonized Light Vehicles Test Cycle, the China Light-Duty Vehicle Test Cycle, and the Urban Dynamometer Driving Schedule show that region-weighted fitting consistently achieves the best or near-best ICS; relative to Global fitting, mean ICS decreases by 49.0%, 46.4%, and 90.6%, with the smallest variance. Regarding model order, the four-term M4 $\left(+P_m^2\right)$ offers the best accuracy–complexity trade-off. Finally, the region-weighted fitting M4 $\left(+P_m^2\right)$ polynomial model was integrated into the vehicle-level economic speed planning model based on the dynamic programming algorithm. In simulations covering a 27 km driving distance, this model reduced computational time by approximately 87% compared to a linear interpolation method based on a two-dimensional lookup table, while achieving an energy consumption deviation of about 0.01% relative to the lookup table approach. Results demonstrate that the proposed model significantly alleviates computational burden while maintaining high energy consumption prediction accuracy, thereby providing robust support for real-time in-vehicle applications in whole-vehicle energy management. Full article

Drug delivery from cylindrical tablets of arbitrary dimensions is discussed here, using the analytical solution of diffusion equation. Utilizing dimensionless quantities, we show that the release profiles are determined by a unique parameter, represented by the aspect ratio of the cylindrical formulation. Fractional release curves are presented for different values of the aspect ratio, covering a range of many orders of magnitude. The corresponding release profiles lie in between the two opposite limits of release from thin slabs and two-dimensional radial release, pertinent to the cases of thin and long cylinders, respectively. In a quest for a part of the delivery process closer to a zero-order release, the release rate is calculated, which is found to exhibit the typical behavior of purely diffusional release systems. Two simple fitting formulae, containing two parameters each, are considered to approximate the infinite series of the exact solution: The stretched exponential (Weibull) function and a recently suggested expression interpolating between the correct time dependencies at the initial and final stages of the process. The latter provides a better fitting in all cases. The variation of the fitting parameters with the aspect ratio of the device is presented for both fitting functions. We also calculate the characteristic release time, which is found to correspond to an amount of fractional release between 64% and around 68% depending on the cylindrical aspect ratio. We discuss how the last quantities can be used to estimate the drug diffusion coefficient from experimental release profiles and apply these ideas to published drug delivery data. Full article

Satellite remote sensing is an important approach for monitoring lake water environments. However, in regions with frequent cloud and rainfall, optical remote sensing imagery often suffers from extensive data gaps caused by cloud cover, rainfall, and sun glint, which severely limit its continuity and reliability for long-term monitoring. To address this issue, this study uses Lake Taihu—a typical eutrophic lake located in a cloudy and rainy region—as a case study and systematically compares four representative gap-filling methods: Kriging Interpolation, Savitzky–Golay (SG) Filtering, Data Interpolating Empirical Orthogonal Functions (DINEOF), and the Data Interpolating Convolutional Auto Encoder (DINCAE). The results show that traditional methods retain some accuracy under low missing-data conditions (for Kriging: R = 0.84, RMSE = 7.85 μg/L; for SG Filtering: R = 0.88, RMSE = 6.67 μg/L), but tend to produce over-smoothing or distorted estimations in cases of extensive gaps or highly dynamic environments. In contrast, both DINEOF and DINCAE capture the spatiotemporal variability of chlorophyll-a more effectively, maintaining relatively high accuracy and robustness even when the missing rate exceeds 60% (for DINEOF: R = 0.84, RMSE = 6.91 μg/L; for DINCAE: R = 0.79, RMSE = 8 μg/L). Based on the optimal algorithm, a seamless long-term dataset of chlorophyll-a concentration covering Lake Taihu can be constructed, providing a solid data foundation for eutrophication trend analysis and algal bloom early warning. This study demonstrates the effectiveness of integrating statistical and deep learning approaches for lake water color remote sensing data reconstruction, offering important implications for enhancing continuous monitoring of lake water environments and supporting ecological management decisions. Full article

The chlorophyll-a (Chl-a) concentration, a critical parameter for characterizing marine primary productivity and ecological health, plays a vital role in providing ecological environment monitoring and climate change assessment while serving as a core retrieval product in ocean color remote sensing. Currently, more than ten ocean color satellites operate globally, including China’s HY-1C, HY-1D and HY-1E satellites. However, significant spatial data gaps exist in Chl-a concentration retrieval from satellites because of cloud cover, sun-glint, and limitation of sensor swath. This study aimed to systematically enhance the spatiotemporal integrity of ocean monitoring data through multisource data merging and reconstruction techniques. We integrated Chl-a concentration datasets from four major sensor types—Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Ocean and Land Color Instrument (OLCI), and Chinese Ocean Color and Temperature Scanner (COCTS)—and quantitatively evaluated their global coverage performance under different payload combinations. The key findings revealed that single-sensor 4-day continuous observation achieved effective coverage levels ranging from only 10.45–26.1%, while multi-sensor merging substantially increased coverage, namely, homogeneous payload merging provided 25.7% coverage for two MODIS satellites, 41.1% coverage for three VIIRS satellites, 24.8% coverage for two OLCI satellites, and 37.1% coverage for three COCTS satellites, with 10-payload merging increasing the coverage rate to 55.4%. Employing the Data Interpolating Empirical Orthogonal Functions (DINEOFS) algorithm, we successfully reconstructed data for China’s ocean color satellites. Validation against VIIRS reconstructions indicated high consistency (a mean relative error of 26% and a linear correlation coefficient of 0.93), whereas self-verification yielded a mean relative error of 27% and a linear correlation coefficient of 0.90. Case studies in Chinese offshore and adjacent waters, waters east of Mindanao Island and north of New Guinea, demonstrated the successful reconstruction of spatiotemporal Chl-a dynamics. The results demonstrated that China’s HY-1C, HY-1D, and HY-1E satellites enable daily global-scale Chl-a reconstruction. Full article

This research develops a reliable methodology for estimating minimum temperature distribution in agricultural areas, focusing on frost conditions threatening crop production. The data was collected across the plain of Krya Vrysi in Central Macedonia. The approach uses linear regression equations between daily minimum temperatures from a central station and 12 autonomous temperature sensors with data loggers. Statistical analysis covered winter 2023–2024, with 2025 validation showing exceptional predictive capability—R² values of 0.97–0.99 and RMSE of 0.34–0.58 °C. Spatial interpolation employed the Radial Basis Function with thin plate splines, effective for agricultural microclimatic interpolation. This methodology provides an operational frost prediction tool, enabling targeted interventions, reducing production losses and enhancing agricultural resilience. Full article

Arid mountainous regions are vulnerable to extreme hydrological events such as floods and droughts. Providing accurate and continuous rainfall records with no gaps is crucial for effective flood mitigation and water resource management in these and downstream areas. Satellite data and geospatial interpolation can be employed for this purpose and to provide continuous data series. However, it is essential to thoroughly assess these methods to avoid an increase in errors and uncertainties in the design of flood protection and water resource management systems. The current study focuses on the mountainous region in northern Oman, which covers approximately 50,000 square kilometers, accounting for 16% of Oman’s total area. The study utilizes data from 279 rain gauges spanning from 1975 to 2009, with varying annual data gaps. Due to the limited accuracy of satellite data in arid and mountainous regions, 51 geospatial interpolations were used to fill data gaps to yield maximum annual and total yearly precipitation data records. The root mean square error (RMSE) and correlation coefficient (R) were used to assess the most suitable geospatial interpolation technique. The selected geospatial interpolation technique was utilized to generate the spatial distribution of annual maxima and total yearly precipitation over the study area for the period from 1975 to 2009. Furthermore, gamma, normal, and extreme value families of probability density functions (PDFs) were evaluated to fit the rain gauge gap-filled datasets. Finally, maximum annual precipitation values for return periods of 2, 5, 10, 25, 50, and 100 years were generated for each rain gauge. The results show that the geostatistical interpolation techniques outperformed the deterministic interpolation techniques in generating the spatial distribution of maximum and total yearly records over the study area. Full article

Snow cover, as a critical component of the cryosphere, serves as a vital water resource for arid regions in Northwest China. The Qilian Mountains (QLM), situated on the northeastern margin of the Tibetan Plateau, function as an important ecological barrier and water conservation area in western China. This study presents the first high-resolution historical snow cover product developed specifically for the QLM, utilizing a multi-level snow classification algorithm tailored to the complex topography of the region. By employing Landsat satellite data from 1986–2024, we constructed a comprehensive 39-year snow cover dataset at a resolution of 30 m. A dual adaptive cloud masking strategy and spatial interpolation techniques were employed to effectively address cloud contamination and data gaps prevalent in mountainous regions. The spatiotemporal characteristics and driving mechanisms of snow cover changes in the QLM were systematically analyzed using Sen–Theil trend analysis and Mann–Kendall tests. The results reveal the following: (1) The mean annual snow cover extent in the QLM was 15.73% during 1986–2024, exhibiting a slight declining trend (−0.046% yr⁻¹), though statistically insignificant (p = 0.215); (2) The snowline showed significant upward migration, with mean elevation and minimum elevation rising at rates of 3.98 m yr⁻¹ and 2.81 m yr⁻¹, respectively; (3) Elevation-dependent variations were observed, with significant snow cover decline in high-altitude (>5000 m) and low-altitude (2000–3500 m) regions, while mid-altitude areas remained relatively stable; (4) Comparison with MODIS data demonstrated good correlation (r = 0.828) but revealed systematic differences (RMSE = 12.88%), with MODIS showing underestimation in mountainous environments (Bias: −8.06%). This study elucidates the complex response mechanisms of the QLM snow system under global warming, providing scientific evidence for regional water resource management and climate change adaptation strategies. Full article

Large convolution kernels offer better performance advantages. They can cover a wider area and capture a broader range of spatial information in a single convolution operation. This is of great importance when dealing with tasks that have significant spatial variations. However, increasing the kernel size brings substantial memory and computational costs to deep convolutional neural networks. The computational complexity becomes unimaginable. Therefore, we proposed a learnable kernel element position convolution using the symmetric Whittaker–Shannon interpolation function (WSIPC). We also performed channel-level pruning (CP) on this large convolutional neural network to achieve network compression. Specifically, WSIPC permits any number of kernel elements. The positions of non-zero elements are learned in a gradient-based manner. We made use of the normal distribution of effective receptive fields to reduce computation, parameter complexity, and improve classification performance. This method achieved the best performance on the large-kernel ConvNeXt. CP used the scaling factor in the Layer Normalization (LN) of the ConvNeXt network as a proxy for channel selection. During training, it automatically identified and pruned unimportant channels in wide and large network models. As a result, it produced a streamlined model with comparable accuracy. This model was more compact in terms of model size, runtime memory, and computational operations. Full article

Aerosol transport flux LiDAR was used to observe heavy pollution events in Luohe City during January 2022 and was combined with monitoring data of ground meteorological parameters and conventional pollutants to analyze the vertical optical properties of aerosols, transport sources, and causes of heavy pollution. Two pollution events (January 2nd–5th and 13th–20th, 2022) were effectively monitored and divided into four pollution phases according to PM_2.5 concentrations and relative humidity (RH). The results showed that all ground PM_2.5/PM₁₀ values were above 0.5 throughout the pollution, indicating a predominance of fine particulate matter. Analysis of the vertical distribution of aerosol flux LiDAR data showed that the inversion layer was distributed below 1 km; the vertical profile of extinction coefficient showed that all the pollution events were dominated by local emissions, while the contribution of regional transmission during the January 2nd to 5th was also quite prominent; kriging interpolation results showed that this pollution covered the most central and eastern regions of China during January 2022. The flux LiDAR monitoring results showed that there were three main transmission channels of PM_2.5: east (Zhoukou, Lu–Wan–Yu–Su junction), northeast (Lu–Yu junction), and southeast (YRD). The analysis of the clustered backward trajectories, potential source contribution function (PSCF), and concentration-weighted trajectory (CWT) models showed that the potential transmission sources of PM_2.5 were mainly in junction zones of Lu–Wan–Yu–Su as well as Shaanxi Province, with PSCF values above 0.7 and CWT values above 70 μg/m³. This study could provide a scientific basis for the prevention and control of local pollution. Full article

Tongzhou District is designated as a city sub-center with 33.3% forest cover, representing significant ecological value for Beijing. However, this extensive forest area has lacked detailed conservation measures, leading to inefficient resource utilization over the years. Therefore, determining the various maintenance measures for the different areas is very important. This study focused on exploring the relationship between the soil nutrient structure and vegetation indices in the area to develop a more precise plan for forest maintenance. This study collected 163 sample points in the four zones of Tongzhou district, including electrical conductivity, acidity and alkalinity, bulk density, soil organic matter, total nitrogen, available nitrogen, total phosphorus, total potassium, available potassium, available phosphorus, as well as vegetation characteristics such as richness, coverage, and height. The normalized difference vegetation index, difference vegetation index, ratio vegetation index, green light vegetation index, and soil-adjusted vegetation index were calculated by remote sensing images. To test the spatial distribution of soil nutrient construction and the relationship between soil and vegetation indices using the spatial interpolation method and Pearson correlation analysis, the results showed that: (1) The soil organic matter and total nitrogen were extremely low (1.282 and 0.461 g/kg). In contrast, the available and total potassium was extremely high (227.994 mg/kg and 16.866 g/kg); (2) High-value areas of available and total potassium are in the northern area, the available and total phosphorus in the central area, and the pH in the northeast area, with overall neutral-to-alkaline conditions; (3) The mean of coverage is 72.120, with high-value areas concentrated in northern parts of the central areas. While the overall coverage is extensive, height varies significantly (3.300–479.867), and high-density vegetation is limited to the northern part of the central area; (4) Vegetation height shows a significant negative correlation with total potassium and a significant positive correlation with pH values. We suggest that it is necessary to properly retain fallen leaves and dead grass in the forest to increase the organic matter content of the soil, apply more organic fertilizers, and supplement nitrogen fertilizers. In Tongzhou District, potassium fertilizer application should be reduced, particularly in the northeast and northern areas, to prevent excess fertility. In the central area, phosphorus fertilizer application should also be controlled, while in alkaline areas, fertilizer use should be optimized, and lime should be added to improve pH. Compost or humic acid can improve the soil’s ability to absorb and release phosphorus, thereby enhancing plant phosphorus uptake and increasing vegetation height and coverage. This study only analyzed spatial changes without further examining soil layer differences at varying depths and the effects of soil microorganisms. In the future, soil fertility in various depths and the functionality and diversity of soil microorganisms are worth further exploring. Full article

One of the important distribution areas of stone pine (Pinus pinea L.), a native tree species of the Mediterranean Basin in Türkiye, is the Kozak Basin. Pine nut production plays an important role in the livelihood of the rural people of the Kozak Basin. However, in recent years, as a result of mining activities, climate change, and damage caused by the alien invasive species, the western conifer seed bug (Leptoglossus occidentalis Heidemann 1910 (Hemiptera; Coreidae), the decrease in cone and seed yield in the basin has reached significant dimensions. This process has caused the local people’s income sources to decrease. In this study, land use and land cover (LULC) changes and population changes in the Kozak Basin were discussed during the process, where changing forest land functions, especially economic effects, triggered vulnerable communities due to various factors such as climate change and insect damage. LULC classes of the Kozak Basin and their changes in three time periods are presented using the maximum likelihood method. In addition, the exponential population growth rates of the local people in three different time periods were calculated and these rates were interpolated in the spatial plane with a Kriging analysis. In conclusion, the responses of vulnerable communities to the cone and seed yield decline in the Kozak Basin are manifested by LULC changes and migration from the basin. Therefore, in the management of P. pinea areas, the creation of regulations within the framework of sustainability understanding regardless of ownership difference, stakeholder participatory approach management, close monitoring of ecological events occurring in the basin, awareness of vulnerable communities, and alternative livelihoods can be supported. Full article

This work explores the numerical translation of the weak or integral solution of nonlinear partial differential equations into a numerically efficient, time-evolving scheme. Specifically, we focus on partial differential equations separable into a quasilinear term and a nonlinear one, with the former defining the Green function of the problem. Utilizing the Green function under a short-time approximation, it becomes possible to derive the integral solution of the problem by breaking it into three integral terms: the propagation of initial conditions and the contributions of the nonlinear and boundary terms. Accordingly, we follow this division to describe and separately analyze the resulting algorithm. To ensure low interpolation error and accurate numerical Green functions, we adapt a piecewise interpolation collocation method to the integral scheme, optimizing the positioning of grid points near the boundary region. At the same time, we employ a second-order quadrature method in time to efficiently implement the nonlinear terms. Validation of both adapted methodologies is conducted by applying them to problems with known analytical solution, as well as to more challenging, norm-preserving problems such as the Burgers equation and the soliton solution of the nonlinear Schrödinger equation. Finally, the boundary term is derived and validated using a series of test cases that cover the range of possible scenarios for boundary problems within the introduced methodology. Full article

The Doppler band compensated by the keystone transform (KT) is limited. Therefore, it needs to be used in conjunction with the Doppler ambiguity compensation function to correct the range migration (RM) caused by maneuvering targets with Doppler ambiguity. However, the KT implemented by sinc interpolation suffers from significant performance loss at boundaries of compensation Doppler bands. Additionally, in a multi-target scenario, KT implementation methods occupy high complexity when the Doppler range of targets spans over two compensation Doppler bands. To address the aforementioned issues, this study presents a variable Doppler starting point keystone transform (VDSPKT) method, where a new form of ambiguity compensation function is constructed, turning the Doppler starting point of the compensation band in KT variable. Firstly, the position of the compensation Doppler band is changed from fixed to adjustable as needed, enhancing the flexibility of KT. Crucially, the connection points of the compensation Doppler bands in sinc interpolation are reset, avoiding performance loss at their boundaries. Also, the compensation band is adjusted to cover the narrow Doppler frequency range caused by targets, significantly improving computational efficiency. Finally, the simulation and real data experiments demonstrate that the proposed approach effectively addresses the performance degradation and high computational complexity of KT in the aforementioned scenarios, resulting in a computational load reduced by approximately 50% compared to traditional methods. Full article

Satellite-based remote sensing enables the quantification of tropospheric NO₂ concentrations, offering insights into their environmental and health impacts. However, remote sensing measurements are often impeded by extensive cloud cover and precipitation. The scarcity of valid NO₂ observations in such meteorological conditions increases data gaps and thus hinders accurate characterization and variability of concentration across geographical regions. This study utilizes the Empirical Orthogonal Function interpolation in conjunction with the Extreme Gradient Boosting (XGBoost) algorithm and dense urban atmospheric observed station data to reconstruct continuous daily tropospheric NO₂ column concentration data in cloudy and rainy areas and thereby improve the accuracy of NO₂ concentration mapping in meteorologically obscured regions. Using Chengdu City as a case study, multiple datasets from satellite observations (TROPOspheric Monitoring Instrument, TROPOMI), near-surface NO₂ measurements, meteorology, and ancillary data are leveraged to train models. The results showed that the integration of reconstructed satellite observations with provincial and municipal control surface measurements enables the XGBoost model to achieve heightened predictive accuracy (R² = 0.87) and precision (RMSE = 5.36 μg/m³). Spatially, this approach effectively mitigates the problem of missing values in estimation results due to absent satellite data while simultaneously ensuring increased consistency with ground monitoring station data, yielding images with more continuous and refined details. These results underscore the potential for reconstructing satellite remote sensing information and combining it with dense ground observations to greatly improve NO₂ mapping in cloudy and rainy areas. Full article

Satellite sea surface temperature (SST) images are valuable for various oceanic applications, including climate monitoring, ocean modeling, and marine ecology. However, cloud cover often obscures SST signals, creating gaps in the data that reduce resolution and hinder spatiotemporal analysis, particularly in the waters near Taiwan. Thus, gap-filling methods are crucial for reconstructing missing SST values to provide continuous and consistent data. This study introduces a gap-filling approach using the Double U-Net, a deep neural network model, pretrained on a diverse dataset of Level-4 SST images. These gap-free products are generated by blending satellite observations with numerical models and in situ measurements. The Double U-Net model excels in capturing SST dynamics and detailed spatial patterns, offering sharper representations of ocean current-induced SST patterns than the interpolated outputs of Data Interpolating Empirical Orthogonal Functions (DINEOFs). Comparative analysis with buoy observations shows the Double U-Net model’s enhanced accuracy, with better correlation results and lower error values across most study areas. By analyzing SST at five key locations near Taiwan, the research highlights the Double U-Net’s potential for high-resolution SST reconstruction, thus enhancing our understanding of ocean temperature dynamics. Based on this method, we can combine more high-resolution satellite data in the future to improve the data-filling model and apply it to marine geographic information science. Full article