Interpolation Of Data Research Articles

Uncertainty analysis of Altantic salmon fish scale’s acoustic impedance using 30 MHz C-Scan measurements

Understanding the biomechanics of fish scales is crucial for their survival and adaptation. Ultrasonic C-scan measurements offer a promising tool for non-invasive characterization, however, existing literature lacks uncertainty analysis while evaluating acoustic impedance. This article presents an innovative integration of uncertainty into the analytical framework for estimating stochastic specific acoustic impedance of salmon fish scale through ultrasonic C-scans. In this study, the various types of uncertainties arising due to variation in biological structures and aging, measurement errors, and analytical noises are combined together in the form of uncertain reflectance. This uncertain reflectance possesses a distribution which is derived using a theory of waves by assuming suitable stochasticity in wavenumber. This distribution helps in development of a stochastic-specific acoustic impedance map of the scales which demonstrates the possible deviations of impedance from mean value depending on uncertainties. Furthermore, maximal overlap discrete wavelet transform is employed for efficient time–frequency deconvolution and Kriging for spatial data interpolation to enhance the robustness of the impedance map, especially in scenarios with limited data. The framework is validated by accurately estimating the specific acoustic impedance of well-known materials like a pair of target medium (polyvinylidene fluoride) and reference medium (polyimide), achieving over 90% accuracy. Moreover, the accuracy of the framework is found superior when compared with an established approach in the literature. Applying the framework to salmon fish scales, we obtain an average specific acoustic impedance of 3.1 MRayl along with a stochastic map visualizing the potential variations arising from uncertainties. Overall, this work paves the way for more accurate and robust studies in fish scale biomechanics by incorporating a comprehensive uncertainty analysis framework.

Prognostication of zones with variable physical and mechanical characteristics of soil massifs

Purpose. The use of geographic information systems, in particular Google Earth Pro and Golden Software Surfer, to predict and visualize changes in the geological structure and create 3D models of built-up areas in the central part of Kyiv. Methodology. The research methodology included several stages and an integrated approach to the analysis of man-made soils in the city of Kyiv. Field studies were carried out with soil sampling in the building area for laboratory analyzes with the degree of concentration of man-made sediments. Modeling and data analysis with the creation of 3D models of man-made soils in the Surfer software package with interpolation of field and laboratory research data into GIS to analyze the distribution of man-made soils. The results of the study were obtained. The results of the study emphasize the importance of an integrated approach to the analysis and management of man-made soils in the urban environment, which contributes to improving the safety and efficiency of construction processes in Kyiv. Based on the collected data, 3D models of man-made soils were created, which made it possible to visualize their distribution and assess the risks associated with construction in dense urban areas. Originality. A methodology for studying the distribution of geotechnogenic soils depending on their concentration has been developed. The dependencies of the distribution of pollution in depth on the building area of the Podilskyi district of Kyiv have been obtained. The use of geographic information systems for forecasting the distribution and volumes of soils with variable physical and mechanical characteristics is proposed. Using Google Earth Pro cellular data and creating a 3D model allows you to analyze the concentration of pollution by man-made soils in an urbanized area. Practical value. The results of the study allow for a better understanding of the geological structure of man-made soils and their impact on the bearing capacity of building foundations. This helps to avoid possible problems with the stability of buildings and increases the safety of building structures. The use of geographic information systems and 3D modeling makes it possible to conveniently and accurately predict the distribution of man-made soils on construction sites, which contributes to the optimization of design solutions and efficient use of resources.

Hydrogeologic Framework Model‐Based Numerical Simulation of Groundwater Flow and Salt Transport and Analytic Hierarchy Process‐Based Multi‐Criteria Evaluation of Optimal Pumping Location and Rate for Mitigation of Seawater Intrusion in a Complex Coastal Aquifer System

AbstractA series of hydrogeologic framework model (HFM)‐based steady‐ and transient‐state numerical simulations is performed first using a coupled subsurface flow‐transport numerical model to analyze groundwater flow and salt transport in an actual three‐dimensional complex coastal aquifer system before and during groundwater pumping. A series of analytic hierarchy process (AHP)‐based multi‐criteria evaluations is then performed applying a multi‐criteria decision‐making approach to determine optimal pumping location and rate for a new pumping well in the complex coastal aquifer system during groundwater pumping. The complex coastal aquifer system is composed of six anisotropic fractured porous geologic media (five rock formations and one fault) and three isotropic porous geologic media (three soil formations) and shows high geometric irregularity and significant heterogeneity and anisotropy of the nine geologic media. Results of the steady‐state numerical simulations show successful model calibration with 26 measured groundwater levels and two observed seawater intrusion front lines. The latter two are determined by spatial interpolation and extrapolation of electrical conductivity logging data and electrical resistivity survey data, respectively. Based on the status and prospect of necessary water uses and available groundwater resources, the field observations of groundwater and seawater intrusion, and the analyses of the steady‐state numerical simulation after the model calibration, six candidate pumping locations are selected for the new pumping well. In addition, from six preliminary individual transient‐state numerical simulations, maximum pumping rates at the six candidate pumping locations are calculated first, and a set of six incremental candidate pumping rates is then assigned at each of the six candidate pumping locations. Results of the transients‐state numerical simulations show that groundwater flow and salt transport are spatially and temporally changed, and seawater intrusion is further intensified by groundwater pumping. In addition, the magnitudes of such spatial and temporal changes and intensification are significantly different depending on the candidate pumping locations and rates. Results of the steady‐ and transient‐state numerical simulations also show that both complexity (geometric irregularity, heterogeneity, and anisotropy including the fault) and topography have significant effects on the spatial distributions and temporal changes of groundwater flow and salt transport in the coastal aquifer system before and during groundwater pumping. In addition, results of statistical estimations of the mesh Peclet and Courant numbers confirm acceptabilities of minimizing numerical dispersion in the steady‐ and transient‐state numerical simulations. Based on the analyses of the transient‐state numerical simulations, eight multiple criteria are chosen to judge, prioritize, and rank the six candidate pumping locations and six candidate pumping rates for optimal pumping. Results of the multi‐criteria evaluations determine the optimal pumping location and rate for the new pumping well among the six candidate pumping locations and six candidate pumping rates. In addition, results of consistency checks confirm consistencies of judgments in the multi‐criteria evaluations.

Theoretical Modeling and Surface Roughness Prediction of Microtextured Surfaces in Ultrasonic Vibration-Assisted Milling

Textured surfaces with certain micro/nano structures have been proven to possess some advanced functions, such as reducing friction, improving wear and increasing wettability. Accurate prediction of micro/nano surface textures is of great significance for the design, fabrication and application of functional textured surfaces. In this paper, based on the kinematic analysis of cutter teeth, the discretization of ultrasonic machining process, transformation method of coordinate systems and the cubic spline data interpolation, an integrated theoretical model was established to characterize the distribution and geometric features of micro textures on the surfaces machined by different types of ultrasonic vibration-assisted milling (UVAM). Based on the theoretical model, the effect of key process parameters (vibration directions, vibration dimensions, cutting parameters and vibration parameters) on tool trajectories and microtextured surface morphology in UVAM is investigated. Besides, the effect of phase difference on the elliptical shape in 2D/3D ultrasonic elliptical vibration-assisted milling (UEVAM) was analyzed. Compared to conventional numerical models, the method of the cubic spline data interpolation is applied to the simulation of microtextured surface morphology in UVAM, which is more suitable for characterizing the morphological features of microtextured surfaces than traditional methods due to the presence of numerous micro textures. The prediction of surface roughness indicates that the magnitude of ultrasonic amplitude in z-direction should be strictly limited in 1D rotary UVAM, 2D and 3D UEVAM due to the unfavorable effect of axial ultrasonic vibration on the surface quality. This study can provide theoretical guidance for the design and fabrication of microtextured surfaces in UVAM.

Analysis Of The Influence Of Domestic Investment, Foreign Direct Investment, Popu-lation, Crime Rate, And Unemployment Rate On Economic Growth In Deli Serdang Regency: ARDL Model Approach

The aim of development in a country or region is to improve the welfare and prosperity of society in a fair and equitable manner, one of which is by increasing sustainable economic growth. The main objective of this study is to analyze the effect of domestic investment, foreign investment, population, crime and unemployment on economic growth in Deli Serdang Regency. The data analysis method used is the Autoregressive Distributed Lag (ARDL) approach in the time series data model and the observation time span of this study starts from 2010 to 2021 where data interpolation will be carried out in quarterly. According to the results of the analysis, the variables domestic investment and foreign direct investment have a positive significant effect on economic growth. Meanwhile, the variables of population, crime and unemployment have a negative significant impact on economic growth in the long run. However, in the short run, the variables of direct investment, foreign investment, population and crime have a positive significant impact on economic growth. On the other hand, only the number of unemployed has a negative significant effect on economic growth. Based on the results of this study, it is appropriate for the Government of Deli Serdang Regency to optimize programs that are oriented towards increasing investment both domestic and foreign, suppressing the rate of population growth and the number of unemployed and maintaining regional security in the Deli Serdang Regency area.

An Interpolation and Prediction Algorithm for XCO2 Based on Multi-Source Time Series Data

Carbon satellites are an important observation tool for analyzing ground carbon emission. From the perspective of the Earth’s scale, the spatiotemporal sparse characteristics of raw data observed from carbon satellite requires the accurate interpolation of data, and based on only this work, people predict future carbon emission trends and formulate appropriate management and conservation strategies. The existing research work has not fully considered the close correlation between data and seasons, as well as the characteristics accumulated over a long time scale. In this paper, firstly, by employing extreme random forests and auxiliary data, we reconstruct a daily average CO2 dataset at a resolution of 0.25°, and achieve a validated determination coefficient of 0.92. Secondly, introducing technologies such as Time Convolutional Networks (TCN), Channel Attention Mechanism (CAM), and Long Short-Term Memory networks (LSTM), we conduct atmospheric CO2 concentration interpolation and predictions. When conducting predictive analysis for the Yangtze River Delta region, we train the model by using quarterly data from 2016 to 2020; the correlation coefficient in summer is 0.94, and in winter it is 0.91. These experimental data indicate that compared to other algorithms, this algorithm has a significantly better performance.

Combination of Fourier and CRS-Based Reconstruction Algorithms in Land Seismic Data

ABSTRACT. The reconstruction based on the partial CRS stacking operator presents higher signal-to-noise ratio and better continuity of events. However, irregularly sampled land data often introduce errors in the CRS attributes, creating artifacts that contaminate the seismic data. Recently, the combination of Fourier and CRS-based reconstruction algorithms has significantly solved these problems. The approach consists of applying a Fourier-based interpolation method as a regularization operator to the original data and then the CRS attributes are searched in the reconstructed data. The CRS attributes determined in this way are more accurate and they can be applied in two different forms, either in the interpolation and regularization of the original data or in the denoising of the Fourier-based reconstructed data. We propose to compare the combination of the Fourier-based interpolation methods MWNI and MPFI with the CRSbased interpolation method in order to evaluate which is the best preconditioner of the prestack data to search the CRS attributes. We applied the proposed flowcharts combining the interpolation methods mentioned above to the land seismic data from the Tacutu basin, which is vintage data with very low fold and noisy. The reconstructed data obtained by the combinations show significant improvements compared to the data reconstructed using the algorithms separately, in other words, the weaknesses and limitations of each method are overcome when they are applied in combination. The MWNI+CRS combination flow produces the best results, with the stacked section of reconstructed data showing better noise removal, enhancement of coherent events, better definition and continuity of steeply dipping events.

Interpolation of environmental data using deep learning and model inference

The temporal resolution of environmental data sets plays a major role in the granularity of the information that can be derived from the data. In most cases, it is required that different data sets have a common temporal resolution to enable their consistent evaluations and applications in making informed decisions. This study leverages deep learning with long short-term memory (LSTM) neural networks and model inference to enhance the temporal resolution of climate datasets, specifically temperature, and precipitation, from daily to sub-daily scales. We trained our model to learn the relationship between daily and sub-daily data, subsequently applying this knowledge to increase the resolution of a separate dataset with a coarser (daily) temporal resolution. Our findings reveal a high degree of accuracy for temperature predictions, evidenced by a correlation of 0.99 and a mean absolute error of 0.21 °C, between the actual and predicted sub-daily values. In contrast, the approach was less effective for precipitation, achieving an explained variance of only 37%, compared to 98% for temperature. Further, besides the sub-daily interpolation of the climate data sets, we adapted our approach to increase the resolution of the Normalized difference vegetation index of Landsat (from 16 d to 5 d interval) using the LSTM model pre-trained from the Sentinel 2 Normalized difference vegetation index—that exists at a relatively higher temporal resolution. The explained variance between the predicted Landsat and Sentinel 2 data is 70% with a mean absolute error of 0.03. These results suggest that our method is particularly suitable for environmental datasets with less pronounced short-term variability, offering a promising tool for improving the resolution and utility of the data.

Updating knowledge of vegetation belts on a complex oceanic island after 20 years under the effect of climate change

Climate change is causing major changes in terrestrial ecosystems and biomes around the world. This is particularly concerning in oceanic islands, considered reservoirs of biodiversity, even more in those with a significant altitudinal gradient and high complexity in the vegetation they potentially harbour. Here, in Tenerife (Canary Islands), we have evaluated the changes in potential vegetation belts during the last 20 years by comparing them with a previous study. Considering the intimate linkage between vegetation and climate, we used a methodology based on phytosociological knowledge, ordination techniques and geostatistics, using multivariate spatial interpolations of bioclimatic data. This has allowed us to spatially detect the variations experienced by eight vegetation units during the last 20 years and incorporating a set of vulnerability metrics. New bioclimatic and vegetation cartography are provided according to the current scenario studied (1990–2019). Our results indicate that summit vegetation, humid laurel forest and thermo-sclerophyllous woodland are the habitats that have experienced a very high area loss and mismatch index, strong changes, if we consider that we are only comparing a period of 20 years. Simultaneously, the more xeric vegetation belts, the dry laurel forest and the pine forest would have benefited from this new warmer and drier climate, by gaining area and experiencing strong upward movements. These changes have not been spatially uniform, indicating that the elevational gradient studied not explain completely our results, showing the influence of the complex island topography. Effective landscape management should consider current remnants, transition capacity and movement limitations to better understand current and future vegetation responses in a global change context.

Bagging Algorithm Based on Possibilistic Data Interpolation for Brain-Computer Interface

Recently, brain-computer interfaces (BCIs) and brain-machine interfaces have garnered the attention of researchers. Based on connections with external devices, external computers and machines can be controlled by brain signals measured via near-infrared spectroscopy (NIRS) or electroencephalograph devices. Herein, we propose a novel bagging algorithm that generates interpolation data around misclassified data using a possibilistic function, to be applied to BCIs. In contrast to AdaBoost, which is a conventional ensemble learning method that increases the weight of misclassified data to incorporate them with high probability to the next datasets, we generate interpolation data using a membership function centered on misclassified data and incorporate them into the next datasets simultaneously. The interpolated data are known as virtual data herein. By adding the virtual data to the training data, the volume of the training data becomes sufficient for adjusting the discriminate boundary more accurately. Because the membership function is defined as a possibility distribution, this method is named the bagging algorithm based on the possibility distribution. Herein, we formulate a bagging-type ensemble learning based on the possibility distribution and discuss the usefulness of the proposed method for solving simple calculations using NIRS data.

PROBABLE APPEARANCE OF THE ANCIENT RUS TRANSFORMATION CHURCH IN THE VILLAGE OF SPAS ACCORDING TO KNOWN ARCHIVE, ICONOGRAPHIC AND HYPOTHETICAL MATERIALS

The currently available materials are outlined and systematized, which can serve as the basis for a proposal to reproduce the original appearance of the now defunct Church of the Transfiguration in the village. Spas in the Sambir region, which are divided into historiographical, hypothetical and reflective. Value levels of the mentioned materials were determined based on the method of mutual agreement. Repetitive data on the architectural features of the building are considered more reliable. Further, this method was developed in the first stage of crystallization of the reproduction concept - a critical analysis of available materials. The next stage was the selection of data for reproduction, which is divided into historiographic, interpolation and architectural data. In particular, the peculiarities of the light context of the creation of the monument are emphasized, which falls on the day of the spread of Eastern Christian mystical practices related to the concept of Hesyham. Therefore, attention was paid to the experience of the contemporaneous sacred construction of the Byzantine area. On the basis of the preparatory works carried out above for the complex scientific analysis of the available materials, a proposal was determined for the reproduction of the appearance of the primary building of the Transfiguration temple in the village. Spas in Sambir region. The following characteristic features have been determined for it: brick masonry with stone inserts and a stone foundation, the presence of a central recess on each of the facades, and the absence of lateral recesses. Double windows in the central spans, four windows in the cupola, etc.

Soft-landing dynamics of a type of four-legged space lander

Analyzing the landing dynamics response is essential for enhancing the success rate of surface explorations on celestial bodies like Mars, the moon, and asteroids. This paper develops a comprehensive dynamics model for a space lander equipped with four sets of buffer legs. Each set comprises a primary strut, two secondary struts, and a footpad. The compression force of the buffer struts is determined through interpolation of real experimental data, while the contact force between the footpad and the ground is calculated using Archimedes law for granular media. The Lagrange equation of the second kind is employed to construct the dynamical model for the lander with minimal degrees of freedom. The proposed dynamics model is validated through experiments involving touchdowns of a single legged lander and a four legged lander. Moreover, key physical quantities such as the buffer force, energy absorption and body acceleration are computed for both 1-2-1 and 2-2 landing configurations. The results indicate that the buffer strut is capable of absorbing approximately half of the impact energy, leading to a 50-fold reduction in the shock acceleration of the main body in both landing configurations. However, it is observed that the maximum compression of the buffer strut is greater in the 1-2-1 landing configuration than in the 2-2 landing configuration. Furthermore, these simulations can be completed within seconds, enabling extensive simulations for the design and optimization of soft landing processes.

A Daily High-Resolution Sea Surface Temperature Reconstruction Using an I-DINCAE and DNN Model Based on FY-3C Thermal Infrared Data

The sea surface temperature (SST) is one of the most important parameters that characterize the thermal state of the ocean surface, directly affecting the heat exchange between the ocean and the atmosphere, climate change, and weather generation. Generally, due to factors such as the weather, satellite scanning orbit range, and satellite sensor malfunction, there are large areas of missing satellite remote sensing SST data, greatly reducing data utilization. In this situation, how to use effective data or avenues to rebuild missing SST data has become a research hotspot in the field of ocean remote sensing. Based on the SST data from an FY-3C visible and infrared radiometer with a spatial resolution of 5 km (FY-3C VIRR), an improved data interpolation convolutional autoencoder (I-DINCAE) was used to reconstruct the missing SST data. Through cross-validation, the accuracy of the reconstruction results was quantitatively evaluated with an RMSE of 0.36 °C and an MAE of 0.24 °C. The results showed that the I-DINCAE algorithm outperformed the original DINCAE algorithm greatly. For further optimization, a deep neural network (DNN) was chosen to adjust the error between the reconstructed SST and the in situ data. The RMSE of the final adjusted SST and in situ data is 0.466 °C, and the MAE is 0.296 °C. Compared to the in situ data, the accuracy of the adjusted data has shown a significant improvement over the reconstructed data. This method successfully applies deep-learning technology to the reconstruction of SST data, achieving the full coverage and high accuracy of SST products, which can provide more reliable and complete SST data for marine scientific research.

Investigations of a functional version of a blending surface scheme for regular data interpolation

This paper describes an implementation and tests of a blending scheme for regularly sampled data interpolation, and in particular studies the order of approximation for the method. This particular implementation is a special case of an earlier scheme by Fang for fitting a parametric surface to interpolate the vertices of a closed polyhedron with n-sided faces, where a surface patch is constructed for each face of the polyhedron, and neighbouring faces can meet with a user specified order of continuity. The specialization described in this paper considers functions of the form z=f(x,y) with the patches meeting with C2 continuity. This restriction allows for investigation of order of approximation, and it is shown that the functional version of Fang's scheme has polynomial precision.

Generalized multilevel B-spline approximation for scattered data interpolation in image processing

This paper proposes a Generalized Multilevel B-spline Approximation (GMBA) method, which addresses scattered data interpolation problems in image processing. Mathematically, the GMBA provides a better solution for the B-spline control lattice by superimposing identical level B-splines compared with traditional Multilevel B-spline Approximation (MBA). Specifically, the GMBA allows the spacing of next control lattice to be subdivided arbitrarily or remained unchanged, which is determined by a predefined spacing set or the current error level. These improvements bring higher approximation accuracy and more flexibility for algorithm design to avoid over-fitting. In this paper, basic GMBA algorithm and its refined algorithm are compiled for image processing. Finally, six relevant cases are involved to test the GMBA, including surface approximation, image enlargement, image completion, and Salt-and-Pepper (SAP) noise removal. The experimental results show that the GMBA has better performance than the MBA in surface approximation and image processing, performs comparatively fast with the best performance on more than half of the standard test images compared with traditional algorithms, and has partially better performance even than deep learning algorithms. The GMBA can effectively recover meaningful details in images contaminated with even extremely high SAP noise level (up to 99%).

Derivation of Landslide Rainfall Thresholds by Geostatistical Methods in Southwest China

Derivation of Landslide Rainfall Thresholds by Geostatistical Methods in Southwest China

An intelligent BIM-enabled digital twin framework for real-time structural health monitoring using wireless IoT sensing, digital signal processing, and structural analysis

Structural health monitoring (SHM) of civil infrastructure is critically important due to its direct influence on public safety and economic activities. Exiting Building Information Modeling (BIM)-based SHM systems often use offline data processing techniques to analyze and visualize structural health data. Despite some of them adopting Internet of Things (IoT) to enable real-time sensor data collection, sensor data quality still remains uncertain due to a lack of sensor signal preprocessing in those existing systems. Additionally, the IoT-based SHM systems often disregard structural analysis domain knowledge, which is important for accurate and precise SHM. Therefore, there is still a need to improve existing systems and practices by enabling more efficient and reliable data collection and processing as well as providing more representative SHM data visualization in BIM. As such, this paper proposes an intelligent BIM-enabled digital twin (DT) framework that integrates wireless IoT sensing and communication, digital signal processing (DSP), and structural analysis domain knowledge. The proposed system (1) leverages IoT sensing and wireless communication to enable autonomous and real-time SHM sensor data collection and transmission, (2) applies and compares multiple DSP techniques to preprocess the sensor data/signals, and (3) innovatively embraces structural analysis expertise into structural behavior visualization in BIM by performing sensor data interpolation for enabling the visualization of structural behaviors at different locations of a structural element/component. The proposed BIM-enabled DT framework was demonstrated and tested for monitoring and visualizing the structural deformations of critical structural components using a prototyped structural frame subject to bending forces. The developed framework could be used and extended for any structural elements (such as beams, columns, trusses, slabs, arches, bracings, walls, footings, foundations, and girders, among others) and could be applied to any kind of structure. Experimental results showed that the proposed framework could effectively monitor and intuitively visualize the structural deformations under different load configurations with a high DT updating frequency of 5 Hz. The innovation of this study is reflected by integrating structural analysis expertise with IoT-enabling sensing data analytics in order to improve the representativeness of real-time structural behavior visualization in BIM and to advance the DT-based SHM systems in a faster and more adaptive direction. Ultimately, this paper contributes to the body of knowledge by developing a generic and easily extendable BIM-enabled DT framework for SHM with high sensor data quality and improved visualization to advance the existing practices of BIM-based SHM for civil infrastructure asset management.

Cosmological transition epoch from gamma-ray burst correlations

The redshift zt and the jerk parameter jt of the transition epoch are constrained by using two model-independent approaches involving the direct expansion of the Hubble rate and the expansion of the deceleration parameter around z=zt. To extend our analysis to high-redshifts, we employ the Amati, Combo, Yonetoku and Dainotti gamma-ray burst correlations. The circularity problem is prevented by calibrating these correlations through the Bézier interpolation of the updated observational Hubble data. Each gamma-ray burst data set is jointly fit with type Ia supernovae and baryonic acoustic oscillations through a Monte Carlo analysis, based on the Metropolis-Hastings algorithm, to obtain zt, jt and the correlation parameters. The overall results are compatible with the concordance model with some exceptions. We also focus on the behaviors of the dark energy, verifying its compatibility with a cosmological constant, and the matter density Ωm and compare them with the expectations of the concordance paradigm.

Evaluating Seasonal Variations in Human Contact Patterns and Their Impact on the Transmission of Respiratory Infectious Diseases.

Human contact patterns are a key determinant driving the spread of respiratory infectious diseases. However, the relationship between contact patterns and seasonality as well as their possible association with the seasonality of respiratory diseases is yet to be clarified. We investigated the association between temperature and human contact patterns using data collected through a cross-sectional diary-based contact survey in Shanghai, China, between December 24, 2017, and May 30, 2018. We then developed a compartmental model of influenza transmission informed by the derived seasonal trends in the number of contacts and validated it against A(H1N1)pdm09 influenza data collected in Shanghai during the same period. We identified a significant inverse relationship between the number of contacts and the seasonal temperature trend defined as a spline interpolation of temperature data (p = 0.003). We estimated an average of 16.4 (95% PrI: 15.1-17.5) contacts per day in December 2017 that increased to an average of 17.6 contacts (95% PrI: 16.5-19.3) in January 2018 and then declined to an average of 10.3 (95% PrI: 9.4-10.8) in May 2018. Estimates of influenza incidence obtained by the compartmental model comply with the observed epidemiological data. The reproduction number was estimated to increase from 1.24 (95% CI: 1.21-1.27) in December to a peak of 1.34 (95% CI: 1.31-1.37) in January. The estimated median infection attack rate at the end of the season was 27.4% (95% CI: 23.7-30.5%). Our findings support a relationship between temperature and contact patterns, which can contribute to deepen the understanding of the relationship between social interactions and the epidemiology of respiratory infectious diseases.

Interpolation of wind lidar data supported by MLP

To address the issue of invalid values in the measured wind speed data of the wind lidar, a data interpolation experiment was conducted by using the wind speed data outputted by the wind lidar as the base data source. Based on data preprocessing, the interpolation model was constructed by integrating MLP and the mean wind speed shear index. The accuracy of the interpolated wind speed data was evaluated. The experimental results show that the proposed interpolation method for wind lidar data achieved high accuracy, with a root mean square error of less than 0.75 m/s and an average relative error of less than 3.5%. This approach enhances the reliability and integrity of wind lidar data, providing a reference for the use of wind lidar data in wind resource assessment.