Thin-plate Spline Research Articles

INCORPORATION AND RELEASE OF HAMAMELIS VIRGINIANA IN “SCAFFOLDS” PRECURSOR MATRIX: AN APPROACH WITH THE “THIN PLATE SPLINE” INTERPOLATION METHOD

In recent years, tissue engineering has been developing methodologies to potentialize the regeneration of injured tissues, such as using biomaterials to obtain scaffolds performing the controlled release of drugs. The polymers polyhydroxy butyrate (PHB) and chitosan (CHI) have been used in the production of matrices applied to scaffold production. This study aims to produce and evaluate matrices containing different proportions of PHB and CHI using the compression molding technique. These matrices can form scaffolds after drug incorporation of Hamamelis virginiana (HV). In order to predict the swelling of the matrices, the Thin Plate Spline Interpolation method (TPSIM) was used to generate three-dimensional data fitted, showing the influence of time and concentration variables on drug absorption. Results show that the percentage of CHI in the samples determines the swelling degree of the matrices. According to scanning electron microscopy analyses, increasing this polymer's quantity modifies the matrix's morphology, making it more heterogeneous. The sample with 50% by weight composition of CHI showed better-swelling results and samples loaded with HV demonstrated drug release ability. Thus, the obtained matrices have great potential to work as scaffolds and drug delivery systems, and therefore, they are promising products for application in tissue engineering.

Geometric Morphometrics Can Predict Postoperative Visual Acuity Changes in Patients With Epiretinal Membrane: A Retrospective Study.

To investigate the efficacy of the geometric morphometrics method for the evaluation of retinal deformation in patients with epiretinal membrane (ERM) and determine whether the degree of deformation can serve as a predictive factor for postoperative visual outcome. We retrospectively evaluated data from 29 eyes of 29 patients with primary ERM. Preoperative optical coherence tomography images were compared with images of their normal fellow eyes using the geometric morphometrics thin-plate spline technique. Conventional parameters such as retinal layer thickness and previously reported indices were also measured. The correlation between the preoperative parameters and visual acuity was evaluated. Statistical comparisons were performed using a paired t-test, and associations between the optical coherence tomography image parameters and visual acuity were determined using Spearman's rank correlation coefficient. Bending energy, which was calculated using geometric morphometrics, was significantly associated with visual acuity as well as conventional optical coherence tomography parameters and previously reported indices. Multiple regression analysis showed that bending energy was an independent predictive factor for postoperative visual acuity changes. The geometric morphometrics method is an effective approach for evaluating the severity of ERM and predicting the efficacy of surgery. Geometric morphometrics can effectively evaluate retinal deformation in eyes with epiretinal membrane.

Energy-Efficient Post-Failure Reconfiguration of Swarms of Unmanned Aerial Vehicles

In this paper, the reconfiguration of swarms of unmanned aerial vehicles after simultaneous failures of multiple nodes is considered. The objectives of the post-failure reconfiguration are to provide collision avoidance and smooth energy-efficient movement. To incorporate such a mechanism, three different failure recovery algorithms are proposed namely thin-plate spline, distance- and time-optimal algorithms. These methods are tested on six swarms, with two variations on failing nodes for each swarm. Simulation results of reconfiguration show that the execution of such algorithms maintains the desired formations with respect to avoiding collisions at run-time. Also, the results show the effectiveness concerning the distance travelled, kinetic energy, and energy efficiency. As expected, the distance-optimal algorithm gives the shortest movements, and the time-optimal algorithm gives the most energy-efficient movements. The thinplate spline is also found to be energy-efficient and has less computational cost than the other two proposed methods. Despite the suggested heuristics, these are combinatorial in nature and might be hard to use in practice. Furthermore, the use of the regularization parameter λ in thin-plate spline is also investigated, and it is found that too large values on λ can lead to incorrect locations, including multiple nodes on the same location. In fact, it is found that using λ = 0 worked well in all cases.

Enhanced Attention-Based Encoder-Decoder Framework for Text Recognition

Recognizing irregular text in natural images is a challenging task in computer vision. The existing approaches still face difficulties in recognizing irregular text because of its diverse shapes. In this paper, we propose a simple yet powerful irregular text recognition framework based on an encoder-decoder architecture. The proposed framework is divided into four main modules. Firstly, in the image transformation module, a Thin Plate Spline (TPS) transformation is employed to transform the irregular text image into a readable text image. Secondly, we propose a novel Spatial Attention Module (SAM) to compel the model to concentrate on text regions and obtain enriched feature maps. Thirdly, a deep bi-directional long short-term memory (Bi-LSTM) network is used to make a contextual feature map out of a visual feature map generated from a Convolutional Neural Network (CNN). Finally, we propose a Dual Step Attention Mechanism (DSAM) integrated with the Connectionist Temporal Classification (CTC) - Attention decoder to re-weights visual features and focus on the intra-sequence relationships to generate a more accurate character sequence. The effectiveness of our proposed framework is verified through extensive experiments on various benchmarks datasets, such as SVT, ICDAR, CUTE80, and IIIT5k. The performance of the proposed text recognition framework is analyzed with the accuracy metric. Demonstrate that our proposed method outperforms the existing approaches on both regular and irregular text. Additionally, the robustness of our approach is evaluated using the grocery datasets, such as GroZi-120, WebMarket, SKU-110K, and Freiburg Groceries datasets that contain complex text images. Still, our framework produces superior performance on grocery datasets.

Segmentation of Bruch's Membrane in Retinal OCT With AMD Using Anatomical Priors and Uncertainty Quantification.

Bruch's membrane (BM) segmentation on optical coherence tomography (OCT) is a pivotal step for the diagnosis and follow-up of age-related macular degeneration (AMD), one of the leading causes of blindness in the developed world. Automated BM segmentation methods exist, but they usually do not account for the anatomical coherence of the results, neither provide feedback on the confidence of the prediction. These factors limit the applicability of these systems in real-world scenarios. With this in mind, we propose an end-to-end deep learning method for automated BM segmentation in AMD patients. An Attention U-Net is trained to output a probability density function of the BM position, while taking into account the natural curvature of the surface. Besides the surface position, the method also estimates an A-scan wise uncertainty measure of the segmentation output. Subsequently, the A-scans with high uncertainty are interpolated using thin plate splines (TPS). We tested our method with ablation studies on an internal dataset with 138 patients covering all three AMD stages, and achieved a mean absolute localization error of 4.10 μm. In addition, the proposed segmentation method was compared against the state-of-the-art methods and showed a superior performance on an external publicly available dataset from a different patient cohort and OCT device, demonstrating strong generalization ability.

Some enhancements to DeepKriging

With an increased volume of spatial data, conventional spatial prediction methods have encountered significant challenges in handling complex data structures while maintaining statistical and computational efficiency. Recently, a spatial neural network method called DeepKriging has been proposed, which utilizes a set of basis functions as an embedded input to capture spatial information. In this study, we enhance this approach by using an ordered set of multi‐resolution thin plate spline basis functions, which offers ease of implementation and alleviates the challenges associated with basis function allocation, particularly when the data locations are highly irregular. The proposed method requires only the selection of the number of basis functions based on a validation dataset. In addition, we propose a robust version of DeepKriging, which is resistant to outliers. Several simulation experiments are conducted to show the advantages of our method over conventional statistical methods and the original DeepKriging approach. Finally, we apply the proposed method to a PM2.5 dataset in Taiwan.

Fine Beam Tracking Using Spatio–Temporal Interpolation in Wireless Power Transfer Systems

The use of transmit beamforming in a radio-frequency wireless power transfer (WPT) system can improve the energy transmission efficiency by concentrating the transmit power in the angle of departure (AoD). Optimal beamforming requires information regarding the AoD or the channel between the transmitter and receiver. A previously proposed WPT system continually estimates the optimal AoD using custom-designed pilot signals, which are known as beam-steered pilot signals and are transmitted periodically at every frame. Herein, we propose a spatio–temporal interpolation technique to improve the accuracy of beam tracking for such a WPT system. Specifically, thin-plate spline interpolation is adopted in the space domain to overcome the limited angular resolution of the beam-steered pilot signals in a three-dimensional channel. In the time domain, polynomial interpolation is performed to track the changing AoD during consecutive frames. Numerical results show that the proposed technique can improve the WPT efficiency as well as the mean square error performance of the beamforming weight vector.

A dataset of time series of climate variables in the karst areas of Southwest China from 1951 to 2014

The areas with karst topography in Southwest China have a fragile ecological environment and the ecosystem there is vulnerable to climate change and human activities. Due to the influence of the karst topography, the spatial distribution of weather stations in this area is uneven which, together with the slight difference of meteorological observation time series of each observation station and the limited number of stations, makes it difficult for the observed data to be used in the study on the realationship between terrestrial ecosystems and climate change. In this study, we used the local smooth thin plate spline function from the ANUSPLIN software version 4.3, combining with the Shuttle Radar Topography Mission (SRTM) digital elevation model, to spatially interpolate four monthly climatic variables (i.e. temperature, precipitation, sunshine percentage, and wet days with daily precipitation <0.1 mm). In this way, we finally obtained three sets of gridded data in different formats with a resolution of 1km. The error statistics show that the error of the interpolation results is relatively low, especially with a high accuracy of the temperature interpolation. The gridded data of the four climate variables can truly reflect the spatial distribution of climates in the karst areas. Further analyses show that from 1951 to 2014, the distribution of temperature and precipitation showed a decreasing trend from the southeast to the northwest. The overall change of temperature showed an upward trend, and the change trend of precipitation was not significant. The distribution of sunshine percentage gradually decreased from the middle to the two sides, and the sunshine percentage showed an overall decline trend. The distribution patterns of wet days are inversely related to altitudes. This dataset can provide data support for the regional research on climate, the relationship between vegetation, rocky desertification and climate change, the relationship between land use and land cover changes, as well as the climate–driven terrestrial ecological model simulations.

Development of an Adaptive Aero-Propulsive Performance Model in Cruise Flight – Application to the Cessna Citation X

To accurately predict the amount of fuel needed by an aircraft for a given flight, a performance model must account for engine and airframe degradation. This paper presents a methodology to identify an aero-propulsive model to predict the fuel flow of an aircraft in cruise, while considering initial modeling uncertainties and performance variation over time due to degradation. Starting from performance data obtained from a Research Aircraft Flight Simulator, an initial aero-propulsive model was identified using different estimation methods. The estimation methods studied in this paper were polynomial interpolation, thin-plate splines, and neural networks. The aero-propulsive model was then structured using two lookup tables: one lookup table reflecting the aerodynamic performance, and another table reflecting the propulsive performance. Subsequently, an adaptative technique was developed to locally and then globally, adapt the lookup tables defining the aero-propulsive model using flight data. The methodology was applied to the Cessna Citation X business jet aircraft, for which a highly qualified level D research aircraft flight simulator was available. The results demonstrated that by using the proposed aero-propulsive performance model, it was possible to predict the aerodynamic performance with an average relative error of 0.99%, and the propulsive performance with an average relative error of 3.38%. These results were obtained using the neural network estimation method.

Daily High-Resolution Temperature and Precipitation Fields for the Contiguous United States from 1951 to Present

Abstract In this paper, a new set of daily gridded fields and area averages of temperature and precipitation is introduced that covers the contiguous United States (CONUS) from 1951 to present. With daily updates and a grid resolution of approximately 0.0417° (nominally 5 km), the product, named nClimGrid-Daily, is designed to be used particularly in climate monitoring and other applications that rely on placing event-specific meteorological patterns into a long-term historical context. The gridded fields were generated by interpolating morning and midnight observations from the Global Historical Climatology Network–Daily dataset using thin-plate smoothing splines. Additional processing steps limit the adverse effects of spatial and temporal variations in station density, observation time, and other factors on the quality and homogeneity of the fields. The resulting gridded data provide smoothed representations of the point observations, although the accuracy of estimates for individual grid points and days can be sensitive to local spatial variability and the ability of the available observations and interpolation technique to capture that variability. The nClimGrid-Daily dataset is therefore recommended for applications that require the aggregation of estimates in space and/or time, such as climate monitoring analyses at regional to national scales. Significance Statement Many applications that use historical weather observations require data on a high-resolution grid that are updated daily. Here, a new dataset of daily temperature and precipitation for 1951–present is introduced that was created by interpolating irregularly spaced observations to a regular grid with a spacing of 0.0417° across the contiguous United States. Compared to other such datasets, this product is particularly suitable for monitoring climate and drought on a daily basis because it was processed so as to limit artificial variations in space and time that may result from changes in the types and distribution of observations used.

Global Thin Plate Spline Differential Quadrature as a Meshless Numerical Solution for Two Dimensional Viscous Burgers’ Equation

Global Thin Plate Spline Differential Quadrature as a Meshless Numerical Solution for Two Dimensional Viscous Burgers’ Equation

A combined deformable model and medical transformer algorithm for medical image segmentation

Deep learning–based segmentation models usually require substantial data, and the model usually suffers from poor generalization due to the lack of training data and inefficient network structure. We proposed to combine the deformable model and medical transformer neural network on the image segmentation task to alleviate the aforementioned problems. The proposed method first employs a statistical shape model to generate simulated contours of the target object, and then the thin plate spline is applied to create a realistic texture. Finally, a medical transformer network was constructed to segment three types of medical images, including prostate MR image, heart US image, and tongue color images. The segmentation accuracy of the three tasks achieved 89.97%, 91.90%, and 94.25%, respectively. The experimental results show that the proposed method improves medical image segmentation performance.Graphical abstract

HRLT: a high-resolution (1 d, 1 km) and long-term (1961–2019) gridded dataset for surface temperature and precipitation across China

Abstract. Accurate long-term temperature and precipitation estimates at high spatial and temporal resolutions are vital for a wide variety of climatological studies. We have produced a new, publicly available, daily, gridded maximum temperature, minimum temperature, and precipitation dataset for China with a high spatial resolution of 1 km that covers a long-term period (1961 to 2019). It has been named the HRLT, and the dataset is publicly available at https://doi.org/10.1594/PANGAEA.941329 (Qin and Zhang, 2022). In this study, the daily gridded data were interpolated using comprehensive statistical analyses, which included machine learning methods, the generalized additive model, and thin plate splines. It was based on the 0.5∘ × 0.5∘ gridded dataset from the China Meteorological Administration, together with covariates for elevation, aspect, slope, topographic wetness index, latitude, and longitude. The accuracy of the HRLT daily dataset was assessed using observation data from meteorological stations across China. The maximum and minimum temperature estimates were more accurate than the precipitation estimates. For maximum temperature, the mean absolute error (MAE), root mean square error (RMSE), Pearson's correlation coefficient (Cor), coefficient of determination after adjustment (R2), and Nash–Sutcliffe modeling efficiency (NSE) were 1.07 ∘C, 1.62 ∘C, 0.99, 0.98, and 0.98, respectively. For minimum temperature, the MAE, RMSE, Cor, R2, and NSE were 1.08 ∘C, 1.53 ∘C, 0.99, 0.99, and 0.99, respectively. For precipitation, the MAE, RMSE, Cor, R2, and NSE were 1.30 mm, 4.78 mm, 0.84, 0.71, and 0.70, respectively. The accuracy of the HRLT was compared to those of three other existing datasets, and its accuracy was either greater than the others, especially for precipitation, or comparable in accuracy, but with higher spatial resolution or over a longer time period. In summary, the HRLT dataset, which has a high spatial resolution, covers a longer period of time and has reliable accuracy.

The Application of Fast Multipole Boundary Element Method to the Calculation of Slamming Load of Three-Dimensional Elastic Sphere

An efficient fluid-structure interaction (FSI) method is established to quickly and accurately calculate the fluid load of three-dimensional (3D) elastic structure in the process of water slamming. In this method, the boundary element method (BEM) and computational structural dynamics (CSD) are coupled by thin plate spline (TPS) interpolation method and flow field reconstruction. In order to improve the calculation efficiency, the fast multipole algorithm is introduced into the BEM, the finite element mesh of the wetted part of the structure is directly used as the flow field boundary, and the structural dynamic response is analyzed by the direct integration method of state equation based on modal superposition. The numerical results are compared with the results of Nisewanger’s sphere water entry test and the existing literature, which proves the accuracy of the numerical calculation method established in this paper. The effects of time-domain iteration step size, mesh size, slamming velocity, fluid density and structural elastic effect on water slamming load are studied. The results show that the slamming velocity and fluid density have a great influence on the simulation results; the iteration step size has little influence on the accuracy of simulation calculation, and the mesh size has a certain influence on the accuracy of simulation calculation; elastic effect reduces the peak value of slamming load, considering elastic effect is necessary for accurate calculation of slamming load.

VCFN: Virtual Cloth Fitting Try-On Network

Virtual cloth fitting network has an increasing demand with a growing online shopping trend to map target clothes on reference subject. Previous research depicts limitations in the generation of promising deformed clothes on the wearer's body while retaining the design features of cloth-like logo, text, and wrinkles. The proposed model first learns thin-plate spline transformations to warp images according to body shape, followed by a try-on module. The former model combines deformed cloth with a rendered image to generate a composition mask and outputs target body without blurry clothes while preserving critical requirements of the wearer. Experiments are performed on the Zalando dataset and the model produces fine richer details and promised generalized results.

Associations between Per- and Polyfluoroalkyl Substances (PFAS) and Cardiometabolic Biomarkers in Adults of Czechia: The Kardiovize Study.

Even though there is evidence of decreasing trends of per- and polyfluoroalkyl substances (PFAS) in Czechia, there are still major sources of PFAS pollution. Regarding the still-inconsistent results of the relationship between cardiometabolic health and PFAS, the present study sought to determine the association between PFAS levels and the presence of cardiometabolic biomarkers, including blood pressure and dysglycemia drivers in the Czech population. A cross-sectional study with 479 subjects (56.4% women, median: 53 years, range: 25-89) was conducted. Four PFAS were measured in serum: perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA), and perfluorooctane sulfonate (PFOS). The associations between natural log (ln)-transformed PFAS and cardiometabolic biomarkers were assessed through generalized additive models using linear regression and smoothing thin plate splines, adjusted for potential confounders. There were positive and significant (p < 0.05) associations between the ln-transformed PFOA and glucose (β = 0.01), systolic (β = 0.76) and diastolic blood pressure (β = 0.65); total cholesterol (β = 0.07) and LDL-c (β = 0.04); and PFOS with glucose (β = 0.03), BMI (β = 2.26), waist circumference (β = 7.89), systolic blood pressure (β = 1.18), total cholesterol (β = 0.13), and HDL-c (β = 0.04). When significant, the correlations of PFNA and PFDA were negative. Of the four PFAS, only PFOA and PFOS showed a positive association, even in serum levels not as high as the values from the literature.

Automatic lung tumor segmentation from CT images using improved 3D densely connected UNet.

Accurate lung tumor segmentation has great significance in the treatment planning of lung cancer. However, robust lung tumor segmentation becomes challenging due to the heterogeneity of tumors and the similar visual characteristics between tumors and surrounding tissues. Hence, we developed an improved 3D dense connected UNet (I-3D DenseUNet) to segment various lung tumors from CT images. The nested dense skip connection adopted in the I-3D DenseUNet aims to contribute similar feature maps between encoder and decoder sub-networks. The dense connection used in encoder-decoder blocks also encourages feature propagation and reuse. A robust data augmentation strategy was employed to alleviate over-fitting based on a 3D thin plate spline (TPS) algorithm. We evaluated our method on 938 lung tumors from three datasets consisting of 421 tumors from the Cancer Imaging Archive (TCIA), 450 malignant tumors from the Lung Image Database Consortium (LIDC), and 67 tumors from the private dataset. Experiment results showed an excellent Dice similarity coefficients (DSC) of 0.8316 for the TCIA and LIDC and 0.8167 for the private dataset. The proposed method presents a strong ability in lung tumor segmentation, and it has the potential to help radiologists in lung cancer treatment planning. Framework of the proposed lung tumor segmentation method.

Towards realistic dynamic topography from coast to offshore by incorporating hydrodynamic and geoid models

One of the major factors preventing utilization of realistic sea-level data is that various sources are referred to inconsistent vertical reference datum (VRD). This study presents a methodology for deriving instantaneous and realistic sea level data from the coastal to offshore areas of the Baltic Sea, by synergizing different sources of sea level data (tide-gauge (TG), hydrodynamic model (HDM) and satellite altimetry (SA)). The key component being the geoid that links the VRD of these different sources.HDMs are known to be a representable source of sea level data, however their VRD is often undisclosed. Therefore, they are often spatially and temporally biased with respect to in-situ data. This study demonstrates that by using geoid-referenced TG data that represents ‘realistic’ sea level it is possible to correct HDM, by deriving and applying a spatial bias correction. Three interpolation methods (linear, thin plate spline regression, and inverse distance weighted) are examined to derive the corrected HDM. The results showed an improvement with respect to TG data, and the annual mean dynamic topography of corrected HDM was improved by a factor of almost 1.5. Examination of the Sentinel-3A satellite along-track data also confirms the corrected HDM data to be more accurate. The methodology applied also identified problematic locations of HDM, SA and geoid data along with unreliable TG. This accuracy and conformity in sea level data are urgently required for a comprehensive understanding of climate change, marine engineering and navigation applications, which can be achieved by adapting the uniquely defined geoid for the vertical reference datum.

A comprehensive comparison of facial skin hydration based on capacitance and conductance measurements in Chinese women.

The aim of this study was to compare the data of conductance and capacitance measurements of facial skin hydration and to evaluate and discuss the advantages and disadvantages of the different approaches. We measured skin capacitance (Corneometer® CM 825) and skin conductance (Skicon-200EX®) on 30 pre-defined facial sites of 125 Chinese women, resulting in 3750 readings per device. The data were analysed and compared, and continuous colour maps were generated on a 3D avatar for capacitance, conductance, relative difference (Δ%) and correlation (R-value) by interpolating between the individual readings and converting the values to colours. This visualization allows a better interpretation of the results. The complexity of facial skin hydration is revealed by this approach. The similarities and discrepancies in the facial hydration maps are clearly apparent. Due to the superiority of the Skicon in measuring high hydration levels, differences in skin hydration were evident on the forehead compared with the Corneometer maps, which may be related to the more superficial measurement of the Skicon within the stratum corneum. Conversely, a greater understanding of the complexity of facial skin hydration in the nasolabial fold was obvious when using the Corneometer. The best congruence between the instruments was found at two specific but separated facial areas, one around the inner eye region and the other one on a line between the nasolabial sulcus and the oblique, lateral jaw. Interestingly, the data were not normally distributed for both instruments and they had opposite skews. All facial clusters were statistically different from each other (p< 0.001), except the cheek and jaw for the Skicon. Larger than expected percentage coefficients of variance were found for the Corneometer on some facial sites that might be explainable by differences in stratum corneum physiology and biochemistry. Corneometer values of 48 AU and Skicon values of 132 μS were taken as the cutoff for normally hydrated facial skin. Both devices have their advantages and disadvantages suggesting that bio-instrumental measurement of skin hydration is actually more complicated than commonly thought and that the different facial zones and the use of multiple instrumentation have not been adequately considered.

The Spatial Modification of the Non-Linear Effects of Ambient Oxides of Nitrogen during Pregnancy on Birthweight in a South African Birth Cohort

Background Birthweight is strongly associated with infant mortality and is a major determinant of infant survival. Several factors such as maternal, environmental, clinical, and social factors influence birthweight, and these vary geographically, including across low, middle, and economically advanced countries. The aim of the study was to investigate the geographical modification of the effect of oxides of nitrogen exposure on birthweight adjusted for clinical and socio-demographic factors. Methods Data for the study was obtained from the Mother and Child in the Environment birth cohort study in Durban, South Africa. Pregnant females were selected from public sector antenatal clinics in low socioeconomic neighborhoods. Land use regression models were used to determine household level antenatal exposure to oxides of nitrogen (NOx). Newborns were assessed at the local hospitals by doctors or qualified midwives. Six hundred and seventy seven births were analysed, using the geoadditive model with Gaussian distribution and identity link function. Continuous covariates were modelled by thin-plate regression splines and smoothing was estimated by the generalized cross validation. Findings A total of 677 births were analyzed. The newborns in the cohort had mean birthweight of 3106·5g (standard deviation (SD):538·2g and maternal mean age was 26 years (SD: 5·7). The mean prenatal exposure to NOx was found 31·5µg/m3 with standard deviation of 8·6 µg/m3. A spatially modified NOx exposure-related effect on birthweight was found across two geographic regions in Durban. Prenatal exposure to NOx was also found to have a non-linear effect on the birthweight of infants. The geoadditive model adjusted for maternal age, weight gain, and body mass index showed significant non-linear influences. Conclusion The study suggested that incorporating spatial variability is important to understand and design appropriate policies to reduce air pollution in order to prevent risks associated with birthweight and child development.