Interpolation Method Research Articles

A Method for the Spatial Interpolation of EEG Signals Based on the Bidirectional Long Short-Term Memory Network.

The precision of electroencephalograms (EEGs) significantly impacts the performance of brain-computer interfaces (BCI). Currently, the majority of research into BCI technology gives priority to lightweight design and a reduced electrode count to make it more suitable for application in wearable environments. This paper introduces a deep learning-based time series bidirectional (BiLSTM) network that is designed to capture the inherent characteristics of EEG channels obtained from neighboring electrodes. It aims to predict the EEG data time series and facilitate the conversion process from low-density EEG signals to high-density EEG signals. BiLSTM pays more attention to the dependencies in time series data rather than mathematical maps, and the root mean square error can be effectively restricted to below 0.4μV, which is less than half the error in traditional methods. After expanding the BCI Competition III 3a dataset from 18 channels to 60 channels, we conducted classification experiments on four types of motor imagery tasks. Compared to the original low-density EEG signals (18 channels), the classification accuracy was around 82%, an increase of about 20%. When juxtaposed with real high-density signals, the increment in the error rate remained below 5%. The expansion of the EEG channels showed a substantial and notable improvement compared with the original low-density signals.

NUMERICAL SIMULATION OF UNSTEADY SIGNAL TRANSDUCTION DURING THE FORMATION OF INVADOPODIA USING LEVEL SET METHOD

The finger-like protrusions formed by an invasive cancer cell known as invadopodia is actively observed recently because of this formation on the cancer invasion. The signal on the interface which is stimulated upon contact between epidermal growth factor receptor and ligand, is investigated to be the start of invadopodia formation. In this research, a model of invadopodia formation with signal variable is formulated in two dimensions. The plasma membrane is assumed to be free boundary. The signal is in an unsteady state. The equation of signal is represented by heat-like equation with time-dependent boundary condition. Trigonometry types of the boundary condition which are sine and cosine function are tested to identify the most suitable boundary condition represented the free boundary. The plasma membrane is considered as zero level set function. The membrane moves by the velocity of the signal inside the cell. To handle the free boundary problem, the level set method combining features of ghost method, interpolation and extrapolation method is applied to solve the model numerically. Our result shows that the free boundary is moved at different positions and seen to move inward meaning that the boundary has shrunk. Cosine function is discovered to fit the boundary conditions since the boundary solutions are stable across the time. The computation of the signal density profiles displayed highest density on the membrane.

Predicting the entire pyrolysis process of representative charring material infiltrated with kerosene using an improved artificial neural network

It is crucial to predict the entire pyrolysis process of charring materials infiltrated with flammable liquids to provide guidance for reducing the fire hazards and implementing numerical simulations of fire accidents caused by the leakage of flammable liquids. In the present study, pyrolysis data obtained from the thermogravimetric experiments were preprocessed using linear interpolation and data normalization methods. The processed data set was then used to train the artificial neural network (ANN) framework to predict the pyrolysis behaviors of typical charring material (Mongolian Scots pine) infiltrated with typical flammable liquid (kerosene) under three different conditions: (1) pyrolysis at a constant mass fraction of kerosene with multiple heating rates; (2) pyrolysis at multiple mass fractions of kerosene with a constant heating rate; and (3) pyrolysis at multiple mass fractions of kerosene and multiple heating rates. The ANN, with the double hidden layer structure (6−3), was capable to well predict the entire pyrolysis behaviors of Mongolian Scots pine under all the three scenarios. Besides, five data transformation function (multiplication, exponential function with base e, logarithmic function with base e, square root, and exponentiation function) were applied to the present three input variables (temperature, mass fraction of kerosene, and heating rate) to generate new input variables in order to extract more characteristics among the pyrolysis data. Therein, the ANN utilizing the data transformation functions of multiplication and exponential function with base e exhibited the best predictive ability. Among the three input variables in the ANN (temperature, mass fraction of kerosene, and heating rate), temperature was identified as the most sensitive one to the prediction performance, followed by mass fraction of kerosene, with heating rate being the least sensitive.

Tomato Recognition Method Based on the YOLOv8-Tomato Model in Complex Greenhouse Environments

Tomatoes are a critical economic crop. The realization of tomato harvesting automation is of great significance in solving the labor shortage and improving the efficiency of the current harvesting operation. Accurate recognition of fruits is the key to realizing automated harvesting. Harvesting fruit at optimum ripeness ensures the highest nutrient content, flavor and market value levels, thus maximizing economic benefits. Owing to foliage and non-target fruits obstructing target fruits, as well as the alteration in color due to light, there is currently a low recognition rate and missed detection. We take the greenhouse tomato as the object of research. This paper proposes a tomato recognition model based on the improved YOLOv8 architecture to adapt to detecting tomato fruits in complex situations. First, to improve the model’s sensitivity to local features, we introduced an LSKA (Large Separable Kernel Attention) attention mechanism to aggregate feature information from different locations for better feature extraction. Secondly, to provide a higher quality upsampling effect, the ultra-lightweight and efficient dynamic upsampler Dysample (an ultra-lightweight and efficient dynamic upsampler) replaced the traditional nearest neighbor interpolation methods, which improves the overall performance of YOLOv8. Subsequently, the Inner-IoU function replaced the original CIoU loss function to hasten bounding box regression and raise model detection performance. Finally, the model test comparison was conducted on the self-built dataset, and the test results show that the mAP0.5 of the YOLOv8-Tomato model reached 99.4% and the recall rate reached 99.0%, which exceeds the original YOLOv8 model detection effect. Compared with faster R-CNN, SSD, YOLOv3-tiny, YOLOv5, and YOLOv8 models, the average accuracy is 7.5%, 11.6%, 8.6%, 3.3%, and 0.6% higher, respectively. This study demonstrates the model’s capacity to efficiently and accurately recognize tomatoes in unstructured growing environments, providing a technical reference for automated tomato harvesting.

Real-time detection system for polishing metal surface defects based on convolutional feature concentration and activation network

This study aims to improve the accuracy and efficiency of detecting defects on polished metal surfaces by developing a new detection system. A network model explicitly designed for detecting defects on polished metal surfaces and a convolutional feature concentration and activation network (CFCANet) are proposed in this paper. This model significantly enhances the recognition of tiny defects by introducing a small-target detection head, ensuring high-precision detection results. In addition, the feature concentration and activation (FCA-C2f) module proposed in this study enhances the model’s sensitivity to anisotropic features, thereby improving defect detection accuracy. The content-aware reassembly of features (CARAFE) upsampling algorithm is used instead of traditional nearest-neighbour interpolation methods to effectively preserve detailed information and improve the quality and efficiency of upsampling. By optimising the lighting conditions and using composite light source illumination technology, the probabilities of missed detections and false alarms can be reduced. Combined with the CFCANet detection network, the defect detection performance of the proposed method on polished metal surfaces is effectively enhanced. To validate the effectiveness of the proposed method, a new dataset for detecting defects on polished metal surfaces, PMS-DET, was constructed in this study and validated on the NEU-DET dataset. Experimental results show that CFCANet effectively improves the defect detection accuracy on polished metal surfaces, achieving a mAP0.5:0.95 value of 42.4 % on the PMS-DET dataset, an increase of 11.9 %. The model parameters are reduced by 6.7 %, and the detection speed is improved by 28.1 %. Compared with existing detection models, this research method demonstrates significant improvements in detection accuracy, model size and computational efficiency, especially regarding GFLOPs and detection speed, proving its potential application value in practical industrial scenarios.

Evaluation of the Pollution by the Potentially Toxic Elements in the Topsoils of a Semi-Industrial Zone, Northwestern Iran

ABSTRACT Various industries, including cement production, petrochemical processing, and power plants, exert significant environmental impacts on their surroundings. The close proximity of these industries within the study area underscores the critical need to investigate soil pollution of potentially toxic elements and elucidate possible pollution sources and associated risks. The present study involved taking 44 topsoil (0–10 cm) samples of Arsenic (As), Cadmium (Cd) Cobalt (Co), Chromium (Cr), Copper (Cu), Nickel (Ni), Lead (Pb), and Zinc (Zn) concentrations, which their mean concentration were found to be 11.29, 0.46, 15.89, 67.36, 44.36, 79.93, 8.50 and 68.27 mg kg−1, respectively. Comparison with the baseline values stated in local and international soil quality guidelines has indicated higher As, Cd, Cr, Cu, and Ni concentrations. The soil pollution indices of the geoaccumulation index, pollution index, pollution load index, and enrichment factor pollution have evaluated the pollution status of the study area to be uncontaminated to moderately contaminated, moderately polluted, polluted, deficient enrichment to moderate enrichment classes, respectively. Ecological and health risks that involved the application of the single index of ecological risk, potential ecological risk, hazard quotient, hazard index, and cancer risk have revealed that the study area was ecologically in a low-risk state with an average potential ecological risk value of 90.38, while carcinogenic (10−6-10−4) and non-carcinogenic risks (>1) were also found to be in a tolerable range for human health. Possible pollution sources were identified using multivariate statistics and further confirmed and visualized by the spatial distribution maps obtained from the deterministic interpolation method of inverse distance weighting. These analyses have suggested that the soil pollution which its existence was confirmed by the related indices and comparisons, to the potentially toxic elements of Cr and Ni can be possibly attributed to the petrochemical and power plants, to Cd could be appointed to cement plant, and to Cu could be associated with agricultural activities. It has also been found that arsenic was determined to be a common pollutant among the three main industries.

Assessment and Source Analysis of Heavy Metal Pollution in Farmland Soil Around Tin Mine

Studying the status and source analysis of heavy metal pollution in farmland in typical mining and processing areas is an important prerequisite for promoting farmland soil ecological restoration and farmland protection in concentrated mining areas. In this study, the heavy metal content of farmland soil around a mining area in southwest China was detected, and the pollution status, distribution law, health risks, and sources of heavy metals were studied by using the land accumulation index method, comprehensive pollution index method, kriging interpolation method, health risk assessment method, and PMF receptor model on the sampling data. The results showed that the mean values of eight heavy metals in farmland soil except Ni exceeded the local soil background values, and the results of the ground accumulation index evaluation showed that Cd and Hg were extremely polluted； Pb and As showed medium pollution-heavy pollution； and Cr, Zn, Ni, and Cu were lightly polluted. In the health risk assessment, oral ingestion was the main exposure route posing a health risk to the human body； the main element that constituted non-carcinogenic health risks was As, and the carcinogenic risks were from As and Cd. PMF model analysis showed that the contribution rate of weathering natural sources of iron-bearing ore was 28.02%, and the main factors were Ca and Fe. The contribution rate of agricultural sources was 3.02%, and the main factors were Pb and As. The contribution rate of industrial and atmospheric deposition composite sources was 33.09%, and the main factor was Hg. The contribution rate of the parent material source was 17.27%, and the main factor was Ca. The contribution rate of mining activities such as mining and smelting was 18.60%, and the main factors were Zn and Cd.

Hybrid intelligent and numerical methods to estimate the transmission coefficients of rectangular floating breakwaters

ABSTRACT Breakwaters are used to reduce incoming wave energy at harbors and shorelines. This paper presents a comparison of novel two-dimensional hybrid intelligent models for the idealization of the effects of waves on the performance of moored rectangular floating breakwaters (FBs). Fluid structure interactions (FSIs) were idealized by airy-type monochromatic regular waves generated in a numerical wave tank. The coupled Volume of Fluid-Fast Fictitious Domain (VOF-FFD) interpolation method was used to evaluate FB motions. Different forms of Least Squares Support Vector Machine Methods (LSSVMs) that utilized 183 data streams were used to model FB performance for different wave height-to-water depth ratios, dimensional aspect ratios, and specific length-to-water depth ratios. Of those, 80% were used to train the model and 20% to test it. Parametric studies have shown that during training a Least Squares Support Vector Machine Method-Bat Algorithm (LSSVM-BA) with R2 = 0.8725, MAE = 0.0276, and RMSE = 0.0488 presents the most appropriate model for the evaluation of FB performance. Notwithstanding this, during testing a Least Squares Support Vector Machine Method-Cuckoo Search (LSSVM-CS) Algorithm with corresponding values of 0.6841, 0.0519, and 0.0708 performs better.

Spatial Models of Solar and Terrestrial Radiation Budgets and Machine Learning: A Review

Currently, spatial modeling is of particular relevance as it enables the understanding of the patterns and spatial variability of an event, the monitoring and prediction of the spatial behavior of a variable, the optimization of resources, and the evaluation of the impacts of a phenomenon of interest. Research carried out recently on variables related to solar energy budgets has been of special relevance due to its applications and developments in machine learning (ML) and deep learning (DL). These algorithms are crucial to improve the efficiency, precision, and applicability of remote sensing, allowing greater decision making with more reliable and timely data. Thus, this work proposes a systematic and rigorous methodology for searching research articles about the latest advances and contributions related to the modeling of radiative energy budgets using novel techniques and algorithms in some of the most relevant international scientific databases (Scopus, ScienceDirect, ResearchGate). Search parameters were applied using tracking methods through various filters, specific classifiers, and Boolean operators. The results allowed for an analysis of search trends and citations in the last 5 years related to the topic of interest and the number of most relevant articles for this research, analyzed through specialized metrics and graphs. Additionally, this methodology was classified into four categories of importance for refined and articulated searches in this evaluation: (i) according to the applied interpolation methods, (ii) according to the remote sensors used, (iii) according to the applications in different fields of knowledge. As a relevant fact and with an essentially prospective purpose, a subchapter of this review was dedicated to the latest advances and developments applied to (iv) spatial modeling of terrestrial radiation through ML, this method being a tool that opens multiple alternatives for data processing and analysis in the development and achievement of objectives in the field of geotechnologies. A quantitative comparison was conducted on the predictive performance results between the classification/regression algorithms found in the studies explored in this review. The evaluation confirmed the existence of a persistent shortage of studies in recent years within the geotechnologies field, particularly concerning the comparison of spatial distribution modeling techniques developed and implemented through ML for incident solar and terrestrial radiation. Therefore, this work provides a synthesis and analysis of the most used and novel techniques in the modeling of solar energy budgets, their limitations, and biggest challenges.

On the optimality of voronoi‐based column selection

AbstractWhile there exists a rich array of matrix column subset selection problem (CSSP) algorithms for use with interpolative and CUR‐type decompositions, their use can often become prohibitive as the size of the input matrix increases. In an effort to address these issues, in earlier work we developed a general framework that pairs a column‐partitioning routine with a column‐selection algorithm. Two of the four algorithms presented in that work paired the Centroidal Voronoi Orthogonal Decomposition (CVOD; Centroidal Voronoi Tessellation Based Proper Orthogonal Decomposition Analysis, 2003, 137–150) and an adaptive variant (adaptCVOD) with the Discrete Empirical Interpolation Method (DEIM; SIAM J. Sci. Computer. 38 (2016), no. 3, A1454–A1482). In this work, we extend this framework and pair the CVOD‐type algorithms with any CSSP algorithm that returns linearly independent columns. Our results include detailed error bounds for the solutions provided by these paired algorithms, as well as expressions that explicitly characterize how the quality of the selected column partition affects the resulting CSSP solution. In addition to examples involving matrix approximation, we test several of our partition‐based constructions on tasks commonly encountered in model order reduction (MOR).

Multimodal multiobjective differential evolution algorithm based on enhanced decision space search

Multimodal multiobjective optimization problems (MMOPs) have attracted extensive research interest. These problems are characterized by the presence of multiple equivalent optimal solutions in the decision space, all corresponding to the same optimal values in the objective space. However, effectively finding a high-quality and evenly distributed Pareto sets (PSs) remains a challenge for researchers. This paper introduces a multimodal multiobjective differential evolution algorithm based on enhanced decision space search (MMODE_EDSS). By adopting two types of strategies to enhance the decision space search capability, the algorithm generates multiple high-quality non-dominated solutions. In the early stages of evolution, neighborhood information is used to enhance search capabilities, while in the later stages, data interpolation methods following clustering are employed for searching. Moreover, to improve the overall population distribution, an environmental selection mechanism based on dual-space crowding distance is adopted. The effectiveness of the proposed algorithm, MMODE_EDSS, is evaluated by comparing it with eight state-of-the-art multimodal multiobjective evolutionary algorithms (MMOEAs). Experimental results confirm the significant advantages of MMODE_EDSS.

Leak detection in water distribution networks based on deep learning and kriging interpolation method

ABSTRACT The burgeoning growth of urban areas has escalated the necessity for efficient and precise leak detection in water distribution networks. Automatic detection methods based on deep learning are a state-of-the-art research topic. In this paper, a methodology that combines deep learning and data imaging is proposed. The framework employs pressure monitoring data and is anchored on the following three pillars: (1) the generation of a comprehensive dataset, encompassing one year of leak-free demand data derived from Fourier Series analysis and monitoring pressure under normal and leak conditions, (2) the transformation of pressure time series into images using kriging interpolation, (3) establishing convolution neural network (CNN) and evaluating its performance of abnormal identification. The effectiveness of the proposed methodology is assessed in different image sets under various leak conditions. The findings reveal that this method meets dependable and effective outputs for leak detection, with the deep learning model achieving a high true positive rate (TPR) of 98% and an area under the curve (AUC) of 94%. This study provides invaluable information for strategic action planning and the enhancement of water loss management protocols, especially in situations where water utilities and regulatory authorities grappling with limited budgets and diminishing revenues.

A novel efficient calculation method for rotor aeroacoustic characteristics based on multiple aerodynamic surfaces source model

Based on the simplified acoustic source model of multiple aerodynamic surfaces, an efficient calculation method for rotor aeroacoustic characteristics has been established. Firstly, the aerodynamic characteristics database of the airfoil is obtained based on the Computational Fluid Dynamics (CFD) method. The inflow environment of each blade element is calculated using the free wake method, and the distribution of chord-wise loading is obtained by combining the established database. Subsequently, based on the F1A equation, a simplified formula for loading noise with pressure difference as a parameter is derived. The thickness noise is obtained from the blade shape and operating conditions, and the blade surface mesh is constructed by using the adaptive curvature distribution strategy of airfoil points. The validation is carried out by comparing with experimental values in the hover and forward flight states. Then, A hybrid weighted interpolation scheme is proposed based on inverse distance weighted interpolation (IDW) and bidirectional linear interpolation method, which is suitable for capturing blade/vortex interaction (BVI) noise. A set of parameters suitable for engineering applications is obtained through the parametric sensitivity analysis. The computational time and memory usage are reduced to 1/47 and 1/4 of the original amount, respectively. Finally, Using the above method, the influence of rotational speeds (ω) on rotor aeroacoustic characteristics are studied. The results show that ω and sound pressure level (SPL) are linearly related and the SPL decreases by approximately 5 dB for every 0.1 Ω decrease in ω. The rotational speed ω≈90 %Ω can effectively suppress rotor aeroacoustic levels, benefiting the stealth design of helicopters.

Multichannel Adaptive Data Mixture Augmentation for Graph Neural Networks

Graph neural networks (GNNs) have demonstrated significant potential in analyzing complex graph-structured data. However, conventional GNNs encounter challenges in effectively incorporating global and local features. Therefore, this paper introduces a novel approach for GNN called multichannel adaptive data mixture augmentation (MAME-GNN). It enhances a GNN by adopting a multi-channel architecture and interactive learning to effectively capture and coordinate the interrelationships between local and global graph structures. Additionally, this paper introduces the polynomial–Gaussian mixture graph interpolation method to address the problem of single and sparse graph data, which generates diverse and nonlinear transformed samples, improving the model's generalization ability. The proposed MAME-GNN is validated through extensive experiments on publicly available datasets, showcasing its effectiveness. Compared to existing GNN models, the MAME-GNN exhibits superior performance, significantly enhancing the model's robustness and generalization ability.

Downscaling sea surface height and currents in coastal regions using convolutional neural network

Downscaling is the process to obtain high-resolution data from low-resolution data. Recently statistical models using convolutional neural networks have gained popularity for fast downscaling of environmental fields, while their application to the coastal sea surface height and currents is lacking. This research aims to downscale sea surface height and depth-averaged current to a resolution of hundreds of meters in coastal regions with dynamic shorelines using convolutional neural networks. Hourly outputs over one year from a physical numerical model for a coastal region in the German Bight are used as the low-resolution input and high-resolution ground truth for the network. The results show that the network effectively reconstructs sea surface height and current in the region to a resolution of hundreds of meters with a scale factor of 16 or even 64, and accurately traces the moving sea surface and shorelines. The global mean absolute error and root mean square error for the sea surface height are found to be less than 0.03 m and 0.07 m, respectively, and for the current less than 0.03 m/s and 0.05 m/s, respectively. These values are around ten times smaller than those obtained from interpolation methods including nearest neighbor, bilinear and bicubic. The network also effectively replicates the distribution of high-resolution data. The errors in the reconstructed time average, 1st percentile and 99th percentile are significantly smaller than those from interpolation methods, especially for the current. These results highlight the ability of the network to downscale sea surface height and currents in regions with complex shorelines, and have implications for downscaling other coastal fields and shoreline tracking.

Simulation of Groundwater Potential Zone Using Geographic Information System (GIS) Through Related Factors

Groundwater is the primary water source for irrigating and drinking in dry seasons. This work was examined to assess groundwater potential area underlain by basement complex with GIS based approach of selected locations in Osogbo. The soil samples were taken and soil textural and permeability analyses were done in the laboratory. Supervised classification analysis was done on LandSAT imagery to generate Land use and a lineament map, surface analyst algorithm was applied on ASTER dam to generate elevation map. Hydraulic conductivity and overburden thickness maps were generated from the Borehole data parameters. Interpolation method was used to generate spatially spread clay, silt, sand, rainfall and hydraulic conductivity. Multi-criteria analysis was used to generate groundwater potential zone map in the study area. Specific factors were considered for groundwater potential zone. Reclassification was done on each of the considered factors and AHP model was used to assign weight to all the factors. The results of the various factors responsible for groundwater potential include lineament density 0-0.0028; sand 67.2-89.60%, silt 9.00-30.00%, clay 0.17-2.80%, permeability 0.000010-0.00002, rainfall 1304.02-1469.06mm/yr, hydraulic conductivity 0.21-0.96m/min, land use consisting of degraded forest 48.65%, riparian forest 13.05%, built up 38.29%, and water body 0.001%. However, ground water potential of the study area was assessed and classified into fair, good and very good with the coverage of 2%, 80% and 18% respectively. Conclusively, the groundwater recharge potential is very good at Ofatedo, Gbonmi and Oke-Oro locations, good at Awosuru, Gbodofon and Dagbolu locations, but fair at Mallam Tope location in Osogbo

Complex Emotion Dynamics Contribute to the Prediction of Depression: A Machine Learning and Time Series Feature Extraction Approach.

Emotion dynamics have demonstrated mixed ability to predict depressive symptoms and outperform traditional metrics like the mean and standard deviation of emotion reports. Here, we expand the types of emotion dynamic features used in prior work and apply a machine learning algorithm to predict depression symptoms. We obtained seven ecological momentary assessment (EMA) studies from previous work on depression and emotion dynamics (N = 890). These studies measured self-reported sadness, positive affect, and negative affect 5 to 10 times per day for 7 to 21days (schedule varied across studies). These data were fed through a feature extraction routine to generate hundreds of emotion dynamic features. A gradient boosting machine (GBM) using all available emotion dynamics features was the best of all models assessed. This model's out-of-sample prediction (R 2 pred) for depression severity ranged from .20 to .44 depending on EMA interpolation method and samples included in the analysis. It also explained significantly more variance than a benchmark model of individuals' mean emotion ratings over the assessment period, R 2 pred = .089. Comprehensive feature mining of emotion dynamics obtained during EMA may be necessary to identify processes that predict depression symptoms beyond mean emotion ratings.

Hyper-reduced-order model for estimating convection heat transfer coefficients of turbine rotors

Forced convection heat transfer occurs inside the steam turbine, and it is crucial for thermal and strength analysis to accurately estimate the convection heat transfer coefficients of the turbine rotor. This paper presents a hyper-reduced-order model for efficiently estimating the forced convection heat transfer coefficients of a steam turbine rotor. Unlike the full-order finite element method, the hyper-reduced-order model uses the discrete empirical interpolation method to approximate the domain integration of transient heat conduction simulations. This approach greatly reduces the degrees of freedom of numerical integration, thus substantially improving the computational efficiency of forward heat conduction problems. The hyper-reduced-order model and Levenberg–Marquardt algorithm are combined to minimize temperature errors between the numerical simulation results and remote sensor measurements and iteratively estimate the heat transfer coefficients of forced convection in the rotor. The accuracy and efficiency of the proposed model are verified through a transient heat conduction simulation. Moreover, the effects of the initial guess values and measurement errors are investigated to demonstrate the universality and robustness of the proposed approach in determining the convection heat transfer coefficient. Compared with the full-order finite element model, the proposed hyper-reduced-order model can increase the efficiency of forward heat conduction simulations of the turbine rotor by more than 10 times, and it only takes 0.87 s computer runtime in single transient step simulation, and then rapidly estimate the forced convection heat transfer coefficients. Furthermore, the performance of the proposed approach is insensitive to the initial guess values, and it exhibits great robustness under measurement error. The mean errors are 4.78 %, 2.67 % and 10.51 % when the initial values are 0.015 mW·mm−3·°C−1, 0.05 mW·mm−3·°C−1 and 0.15 mW·mm−3·°C−1 respectively. When there is no measurement error, the estimated error of the convection heat transfer coefficients is 2.67 %, and even if the measurement error reaches 5 %, and the accuracy loss is only 5.57 %. Consequently, this approach is highly suitable for estimating convection heat transfer coefficients during the dynamic operation of steam turbines, and this research provides reliable guidance for the optimization design and safe operation of steam turbines.

A time window averaging method to mitigate the impact of shell growth trends on Tridacna δ18O records

Tridacna shells have become valuable archives for high-resolution paleoclimate reconstructions due to their daily-to-annual bands. The δ18O, influenced by temperature and salinity, serves as a key geochemical proxy for Tridacna. Traditionally, linear interpolation has been the go-to method for creating evenly spaced Tridacna δ18O timeseries. However, the accuracy of this method can be influenced by the prominent negative shell growth trends. Given that Tridacna shells exhibit significantly faster growth rates during the juvenile stage than in adulthood, constant sampling intervals may lead to overly dense sampling during the juvenile phase, which could potentially introduce errors in the linearly interpolated Tridacna δ18O record. In this study, we proposed a time window averaging method to mitigate the influence of growth trends in Tridacna δ18O records from the northern South China Sea (SCS). The time window averaging method calculates the average of measured Tridacna δ18O data within a time window, whose length is determined by the sampling resolution for each year and the expected temporal resolution. Pseudo-Tridacna experiments, using the newly developed proxy system model for Tridacna δ18O, were conducted to assess the accuracy of both interpolation and averaging methods. Results indicate that the averaging method outperforms the interpolation method, especially in seasonality estimation. The pseudo-Tridacna experiments recommend employing the averaging method for monthly Tridacna δ18O records when sampling density exceeds ∼20 samples/year. Two real Tridacna δ18O records from the northern SCS demonstrate consistency with pseudo-Tridacna experiments, supporting the efficacy of the averaging method in reducing bias in Tridacna δ18O records associated with growth trends. Our results show the potential of the time window averaging method to improve Tridacna-based paleoclimate reconstructions.

Improved mapping of heavy metals in agricultural soils using machine learning augmented with spatial regionalization indices

The accurate spatial mapping of heavy metal levels in agricultural soils is crucial for environmental management and food security. However, the inherent limitations of traditional interpolation methods and emerging machine-learning techniques restrict their spatial prediction accuracy. This study aimed to refine the spatial prediction of heavy metal distributions in Guangxi, China, by integrating machine learning models and spatial regionalization indices (SRIs). The results demonstrated that random forest (RF) models incorporating SRIs outperformed artificial neural network and support vector regression models, achieving R2 values exceeding 0.96 for eight heavy metals on the test data. Hierarchical clustering for feature selection further improved the model performance. The optimized RF models accurately predicted the heavy metal distributions in agricultural soils across the province, revealing higher levels in the central-western regions and lower levels in the north and south. Notably, the models identified that 25.78 % of agricultural soils constitute hotspots with multiple co-occurring heavy metals, and over 6.41 million people are exposed to excessive soil heavy metal levels. Our findings provide valuable insights for the development of targeted strategies for soil pollution control and agricultural soil management to safeguard food security and public health.