Interpolation Technique Research Articles

Neuromuscular Adaptations in Endurance-Trained Male Adolescents Versus Untrained Peers: A 9-Month Longitudinal Study.

Neuromuscular function is considered as a determinant factor of endurance performance during adulthood. However, whether endurance training triggers further neuromuscular adaptations exceeding those of growth and maturation alone over the rapid adolescent growth period is yet to be determined. The present study investigated the concurrent role of growth, maturation, and endurance training on neuromuscular function through a 9-month training period in adolescent triathletes. Thirty-eight 13- to 15-year-old males (23 triathletes [~6 h/week endurance training] and 15 untrained [<2 h/week endurance activity]) were evaluated before and after a 9-month triathlon training season. Maximal oxygen uptake (V̇O2max) and power at V̇O2max were assessed during incremental cycling. Knee extensor maximal voluntary isometric contraction torque (MVCISO) was measured and the voluntary activation level (VAL) was determined using the twitch interpolation technique. Knee extensor doublet peak torque (T100Hz) and normalized vastus lateralis (VL) electromyographic activity (EMG/M-wave) were also determined. VL and rectus femoris (RF) muscle architecture was assessed using ultrasonography. Absolute V̇O2max increased similarly in both groups but power at V̇O2max only significantly increased in triathletes (+13.8%). MVCISO (+14.4%), VL (+4.4%), and RF (+15.8%) muscle thicknesses and RF pennation angle (+22.1%) increased over the 9-month period in both groups similarly (p < 0.01), although no changes were observed in T100Hz, VAL, or VL EMG/M-wave. No changes were detected in any neuromuscular variables, except for coactivation. Endurance training did not induce detectible, additional neuromuscular adaptations. However, the training-specific cycling power improvement in triathletes may reflect continued skill enhancement over the training period.

Trajectory Planning Research Based on Rapidly-exploring Random Trees (RRT) Algorithm and Modified Forms

With the rapid advancement of computer technology, robots have become indispensable in various fields. Trajectory planning plays a crucial role in optimizing robot performance by enhancing work efficiency and reducing operational costs. While the widely used Rapidly-exploring Random Trees (RRT) algorithm is effective in path planning, it often generates non-optimal paths with poor smoothness. By incorporating elements from RRT-Connect and reverse optimization, the algorithm can efficiently navigate through intricate and dynamic surroundings. Furthermore, the utilization of cubic polynomial interpolation in optimizing path curves adds a layer of sophistication to the trajectory planning process. This interpolation technique allows for the creation of smoother and more natural path trajectories, reducing jerky movements and enhancing overall motion control precision. The paths produced by the enhanced RRT algorithm demonstrate increased continuity and visual attractiveness, crucial for tasks necessitating accurate and elegant robotic motions. The improved algorithm showcases reduced path length and time consumption, as well as enhanced path smoothness. Particularly, relative to the traditional RRT approach, the time consumption has dropped by 69.15% and the median duration of the paths has fallen by 21.11%. These improvements signify a substantial advancement in trajectory planning for robots, offering more efficient and smoother paths for robotic operations across various applications.

Soil Fertility Mapping Using GIS in Sirur Sub-Watershed of Kundagol Taluk of Dharwad District, Karnataka, India

Aims: To identify available nutrient constraints in soils and to generate soil fertility maps using remote sensing and GIS of Sirur sub-watershed in northern dry zone of Karnataka. Place and Duration of Study: The study was carried out in the Sirur sub-watershed (4D5B5j) located in Kundagol taluk of Dharwad district, Karnataka during 2022-24. Methodology: Soil samples (601) from Sirur sub-watershed in northern dry zone of Karnataka were drawn at 320 m x 320 m grid interval and assessed for fertility parameters. Analytical data was interpreted and statistical parameters like range, mean, standard deviation and coefficient of variation were calculated for each parameter. Soil fertility maps were prepared for each parameter using interpolation technique. Results: Soils were neutral to alkaline in reaction and non-saline. Soil organic carbon content was low to medium. Available nitrogen and phosphorus were low to medium, available potassium was high and sulphur was medium to high. Regarding available micronutrients, iron, copper and manganese were sufficient in the soils whereas Zn was deficient in about half of the sub-watershed area and B content was medium to high. Conclusion: The major constraints in sub-watershed are low content of OC, poor status of available N, P2O5 and Zn indicating their immediate attention for sustained crop production.

A GIS-based Approach to Spatially Explicit Mean Monthly Areal Rainfall Estimation of Krishna District, Andhra Pradesh, India

Rainfall analysis is pivotal for understanding hydrological systems, crucial for effective water resource management. In this study spatial rainfall analysis is carried out for Krishna District of Andhra Pradesh using ArcGIS software. Rainfall data for a period of 30 years (1992-2021) was collected from the Directorate of Economics and Statistics, Vijayawada, Andhra Pradesh. Utilizing the spatial interpolation techniques in Geographic Information System (GIS) software, mean monthly areal rainfall was found using the three methods viz., Arithmetic mean, Thiessen’s polygon and isohyet method. This study utilized Geographic Information Systems (GIS) to visualize spatial patterns of rainfall across Krishna district. To account for missing data in the rainfall record, the normal ratio method was implemented. Spatial maps were generated to depict mean monthly areal rainfall variations. The analysis yielded mean monthly areal rainfall for Krishna district as 84.72 mm, 85.22 mm and 83.97 mm using the arithmetic mean, Thiessen polygon and Isohyet methods, respectively. Isohyet method is considered to be more accurate and rainfall is the basic data required for all hydrological analysis as an input layer for runoff generation, water resources planning, evapotranspiration studies, crop planning, flood estimation and water budget estimations etc.

MODELING STOCHASTIC ADVERSE EFFECTS OF CBN 2023 REDESIGNED NAIRA NOTES POLICY ON RURAL FARMERS IN NIGERIA

The recent Central Bank of Nigeria (CBN) 2023 redesigned naira notes is of good benefits to strengthen the economy of the country by checking counterfeiting and hoarding of large volume of banknotes by the public. Despite all the efforts made by the CBN for citizens to enjoy the benefits of this implementation, most rural farmers were faced with adverse effects of uncertainties in the production and marketing of their agricultural produce due to lack of redesigned new naira notes in circulation. The adverse effects of these uncertainties are modeled as Advanced Stochastic Time-Delay Differential Equation (ASTDDE). The modeled equation is solved using Extended Second Derivative Block Backward Differentiation Formulae Method (ESDBBDFM) without the use of interpolation techniques in the evaluations of the delay term and noise term. In comparing the numerical results of this method with other existing methods in literature, the newly developed mathematical expressions for the evaluations of the delay term and the noise term in solving ASDDEs with the discrete schemes of ESDBBDFM gives better results for step number than step numbers and 3 by producing Least Minimum Absolute Random Error (LMARE) in a Lower Computational Processing Unit Time (LCPUT) faster than other existing methods that applied interpolation techniques in evaluations of the delay term and the noise term.

Robust detection of ductile fracture by acoustic emission data-driven unsupervised learning

The quantitative assessment of the formability of bulk metallic materials remains challenging, primarily due to considerable difficulties in accurately identifying the initiation of fractures. This study introduces a novel methodology for detecting the onset of ductile fracture by using acoustic emission signals. Unlike traditional methods that rely on standard characteristics, this approach utilizes acoustic emission signals that are depicted through vivid and direct feature images. These images represent short-term and relative energies, which are termed as energy allocation maps. These maps are generated utilizing a novel signal processing technique that employs the maximal overlap discrete wavelet transform for multi-resolution analysis. In this study, a parameter known as accumulative relative energy is developed to illustrate the global energy distribution across resolutions, while another parameter, normalized short-term energy, is designed to capture the local energy distribution of resolutions. The natural neighbor interpolation technique is subsequently employed to derive the energy allocation map. A set of stacked autoencoders is accordingly configured to transform this map into a singular value that represents the condition of the material. Consequently, a ductile fracture indicator is proposed to identify the onset of fracture. The accuracy and efficacy of the proposed method are validated through partial experiments. The study examines four bulk metallic materials, providing insights into their deformation capabilities under compression.

Mapping of Atmospheric Corrosion of Steel in Southeast India Using Inverse Distance Interpolation Technique

Reinforced concrete and steel structural elements undergo premature degradation and lose strength primarily due to corrosion. Corrosion is an electrochemical phenomenon and its severity depends on several environmental factors. Experimental data on the corrosion of steel is important for making engineering decisions toward improving the service life of civil infrastructures. No recent dataset on atmospheric steel corrosion in an Indian coastal environment is found to be available in the literature, and hence this research attempts to address some of the literature gap. This paper presents an experimental study conducted to determine the corrosion rate of TMT, high-chromium steel, and stainless steel rebars exposed in actual coastal and inland regions for a period of 1 y. The site locations were located in southeastern parts of Tamil Nadu state in India. Based on the first-year corrosion rate, the corrosion rate after extended exposure (10 y) was determined based on ISO 9224 recommendations. The atmospheric steel corrosion map of Tamil Nadu state was developed using inverse distance interpolation technique. Microstructural studies indicated the formation of lepidocrocite (γ-FeOOH) phase composition in the rust products collected from coastal regions.

The Impact of Missing Continuous Blood Glucose Samples on Machine Learning Models for Predicting Postprandial Hypoglycemia: An Experimental Analysis

This study investigates how missing data samples in continuous blood glucose data affect the prediction of postprandial hypoglycemia, which is crucial for diabetes management. We analyzed the impact of missing samples at different times before meals using two datasets: virtual patient data and real patient data. The study uses six commonly used machine learning models under varying conditions of missing samples, including custom and random patterns reflective of device failures and arbitrary data loss, with different levels of data removal before mealtimes. Additionally, the study explored different interpolation techniques to counter the effects of missing data samples. The research shows that missing samples generally reduce the model performance, but random forest is more robust to missing samples. The study concludes that the adverse effects of missing samples can be mitigated by leveraging complementary and informative non-point features. Consequently, our research highlights the importance of strategically handling missing data, selecting appropriate machine learning models, and considering feature types to enhance the performance of postprandial hypoglycemia predictions, thereby improving diabetes management.

An Enhanced Mapping Interpolation ISPH–FVM Coupling Method for Simulating Two-Phase Flows with Complex Interfaces

In this paper, an enhanced mapping interpolation ISPH–FVM coupling method is developed to improve the accuracy and stability of two-phase flows simulation. Similar to the original ISPH–FVM coupling method, the coupling framework between the numerical schemes of ISPH and FVM is established at the overlapping region of the ISPH particles and the FVM grids. Although the original ISPH–FVM coupling method can effectively predict movement and deformation of the two-phase interface, it is difficult to ensure the mass conservation in the flow domain during the information transfer between ISPH particles and FVM grids, thus weakening the numerical accuracy of interaction between different fluid phases. To improve the mapping interpolation accuracy and ensure the mass conservation, a volume fraction correction scheme combined with the particle approximate interpolation technique is introduced to form an enhanced mapping interpolation ISPH–FVM coupling method. The effect of surface tension is assessed by the continuum surface force (CSF) model. Several 2D/3D numerical cases are given to verify the efficiency and robustness of the present ISPH–FVM coupling method. Numerical results demonstrate that the enhanced mapping interpolation developed in the ISPH–FVM coupling method can reproduce the two-phase flow with complex interfaces effectively.

Evaluating the performance of various interpolation techniques on digital elevation models in highly dense forest vegetation environment

A Digital Elevation Model (DEM) is a numerical representation of the Earth's topography using an array of cells or pixels. DEMs are important for various purposes such as hydrological studies and environmental monitoring in situations where there is a need to assess land use changes, deforestation, and disaster response. In this study, various spatial interpolation methods, such as the Inverse distance weighting (IDW) technique, Natural Neighbor, and Triangulated Irregular Network (TIN) methods, were compared to evaluate the performance in generating accurate and reliable DEMs. We also considered factors such as computational efficiency and ease of implementation to determine the most suitable method. The precision of the generated DEMs was assessed through metrics such as root mean square error (RMSE) and mean absolute bias (MAB). The predicted elevation models were compared with elevation values obtained through ground surveys. Furthermore, a raster agreement analysis was used to compare the elevation values recorded by the two data sources, the USGS 3D Elevation Program (USGS 3DEP) and Unmanned Aerial Vehicle (UAV). The analysis revealed that TIN method demonstrated the highest level of agreement at 92%, while the binning technique showed inconsistency in predicting the elevation values based on the data source. In regions with high forest cover, the results were less accurate due to the limited number of last returns available for developing the DEM. This study also found that TIN recorded the fastest processing time per square acre. The presence of vegetation significantly influences the quantity of Light Detection and Ranging (LiDAR) pulses capable of reaching the forest floor, subsequently impacting the quantity of points available for the creation of a DEM.

Water quality assessment of Bheemasandra Lake, South India: A blend of water quality indices, multivariate data mining techniques, and GIS.

An integrated approach combining water quality indices (WQIs), multivariate data mining, and geographic information system (GIS) was employed to examine the water quality of Bheemasandra Lake, located adjacent to a sewage treatment plant (STP) in Tumakuru city, India. The analysis of 22 lake water samples, examined before and after the monsoons, revealed that the physicochemical parameters namely - electrical conductivity, biochemical oxygen demand, turbidity, total dissolved solids, ammoniacal nitrogen, nitrates, phosphates, magnesium, total hardness, total alkalinity, and calcium - exceeded the acceptable limits stipulated by national and international standards. The Canadian Council of Ministers of the Environment WQI (pre-monsoon: 25.3; post-monsoon: 33.9) and weighted arithmetic WQI (pre-monsoon: 3398; post-monsoon: 2093) designated the water as unsafe for drinking. Irrigation WQIs (sodium adsorption ratio, sodium percentage, residual sodium carbonate, magnesium hazard, permeability index, and potential salinity) implied water's suitability for irrigation. However, electrical conductivity indicated otherwise. Industrial WQIs (Larson-Skold Index, Langelier Index, Aggressive Index, and Puckorius Scaling Index) illustrated scaling propensity and the chloride sulfate mass ratio alluded galvanic corrosion potential. Hierarchical cluster analysis gathered 22 sampling points into two clusters (cluster 1: relatively lower polluted regions; cluster 2: highly polluted regions) for each season based on similarities in water features. Principal component analysis extracted four (79.07% cumulative variance) and six (87.14% cumulative variance) principal components before and after the monsoons, respectively. These components identified the primary pollution sources as urban sewage and natural lithological processes. WQI maps, created using the inverse distance weighted interpolation technique, enhanced the visualization of spatial-temporal variations. This study highlights the dire consequences of urbanization, STP pollution, and sewage management failures, necessitating that concerned authorities should implement policies and measures to curb the negative impacts on the environment and public health.

Possibility and quality assessment in seafloor modeling relative to the sea surface using hybrid data

Possibility and quality assessment in seafloor modeling relative to the sea surface using hybrid data

Derivation of Landslide Rainfall Thresholds by Geostatistical Methods in Southwest China

Derivation of Landslide Rainfall Thresholds by Geostatistical Methods in Southwest China

Random Forest Spatial Interpolation Techniques for Crop Yield Estimation at District Level

General Crop Estimation Surveys (GCES) based on Crop Cutting Experiments (CCEs) are conducted for estimation of crop yield following random sampling approach for almost all major crops. About 13 lakh CCEs are conducted every year which has now increased rapidly due to the Pradhan Mantri Fasal Bima Yojana (PMFBY) which is yield based insurance scheme. As suggested by Ministry of Agriculture and Farmers’ Welfare (MoA&amp;FW), this number needs to be reduced drastically by developing sampling procedures based on the use of advanced technologies and advanced survey techniques for crop yield estimation. In this study, an attempt has been made to develop crop yield estimation procedures using Random Forest Spatial Interpolation (RFSI) technique including the spatial variables like spatial distance and nearest neighbours as covariates. RFSI is one of the most adaptable and user-friendly interpolation techniques, as well as one of the fastest in large training datasets. Estimates of yield of wheat were obtained for all the six tehsils of Barabanki district using the estimator under stratified two stage sampling technique. The district level estimates were also obtained by pooling area under wheat crop in each tehsil along with the district level estimate of crop yield, estimate of variance, estimate of standard error (SE) and percentage SE (%SE) of these estimates were also computed in order to make comparison. The results of this study suggest that the estimates derived using RFSI are comparable to kriging and superior to inverse distance weighting (IDW) for the prediction of yield at unknown locations using distance and nearest neighbours.

Geotechnical correlation field-informed and data-driven prediction of spatially varying geotechnical properties

Geotechnical measurements are often limited, leading to the use of interpolation techniques for interpreting spatial variations in geotechnical properties from sparse geo-data. Traditional geostatistical methods suffer from significant computational complexity. On the other hand, data-driven approaches often lack integration with geotechnical domain knowledge, potentially oversimplifying or complicating predictions related to the spatial variability of geotechnical properties. This study introduces a novel framework that combines geotechnical knowledge with data-driven methods to model inherent soil spatial variability incorporating Geotechnical Correlation Field (GCF) that reflects domain knowledge. The GCF, influenced by Autocorrelation Function (ACF) types and Scale of Fluctuation (SOF), provides a flexible basis for accurately representing spatially varying geotechnical properties. Using a large synthetic database comprising known ACF types and SOFs, we constructed a series of specialized neural networks. These networks identify random field parameters at different sites based on sparse data, and the estimated parameters can be directly used to calculate GCFs for a given site. The performance of the proposed method is validated using a set of synthetic data and a real case history in New Zealand. The results demonstrate the model can accurately predict random field parameters for irregularly spaced geo-data, even with limited information. Significantly, the GCFs offer improved physical interpretations and enhance the performance of subsurface modeling. The computational complexity of this method is independent of the number of soil cells, making it highly efficient and scalable.

Discontinuous Galerkin finite element method for dynamic viscoelasticity models of power‐law type

AbstractLinear viscoelasticity can be characterized by a stress relaxation function. We consider a power‐law type stress relaxation to yield a fractional order viscoelasticity model. The governing equation is a Volterra integral problem of the second kind with a weakly singular kernel. We employ spatially discontinuous Galerkin methods, symmetric interior penalty Galerkin method (SIPG) for spatial discretization, and the implicit finite difference schemes in time, Crank–Nicolson method. Further, in order to manage the weak singularity in the Volterra kernel, we use a linear interpolation technique. We present a priori stability and error analyses without relying on Grönwall's inequality, and so provide high quality bounds that do not increase exponentially in time. This indicates that our numerical scheme is well‐suited for long‐time simulations. Despite the limited regularity in time, we establish suboptimal fractional order accuracy in time as well as optimal convergence of SIPG. We carry out numerical experiments with varying regularity of exact solutions to validate our error estimates. Finally, we present numerical simulations based on real material data.

GANSharp: High-definition image reconstruction using generative adversarial networks

In the realm of digital imaging, enhancing low-resolution images to high-definition quality is a pivotal challenge, particularly crucial for applications in medical imaging, security, and remote sensing. Traditional methods, primarily relying on basic interpolation techniques, often result in images that lack detail and fidelity. GANSharp introduces an innovative GAN-based framework that substantially improves the generator network, incorporating adversarial and perceptual loss functions for enhanced image reconstruction. The core issue addressed is the loss of critical information during down-sampling processes. To counteract this, we proposed a GAN-based method leveraging deep learning algorithms, trained using sets of both low- and high-resolution images. Our approach, which focuses on expanding the generator network’s size and depth and integrating adversarial and perceptual loss, was thoroughly evaluated on various benchmark datasets. The experimental results showed remarkable outcomes. On the Set5 dataset, our method achieved a PSNR of 34.18 dB and a SSIM of 0.956. Comparatively, on the Set14 dataset, it yielded a PSNR of 31.16 dB and an SSIM of 0.920, and on the B100 dataset, it achieved a PSNR of 30.51 dB and an SSIM of 0.912. These results were superior or comparable to those of existing advanced algorithms, demonstrating the proposed method’s potential in generating high-quality, high-resolution images. Our research underscores the potency of GANs in image super-resolution, making it a promising tool for applications spanning medical diagnostics, security systems, and remote sensing. Future exploration could extend to the utilization of alternative loss functions and novel training techniques, aiming to further refine the efficacy of GAN-based image restoration algorithms.

Simulation method for multichromatic light spots in mosaic aperture telescopes with large image planes during deployment

In optical systems, diffraction limits significantly impact spot simulations. This study addresses this problem by applying the Fourier transform to calculate spots in imaging systems. Typically, a 1 mm image plane suffices; however, mosaic aperture telescopes with notable wavefront discontinuities require an approximately 10 mm simulation image plane. This necessitates high sampling rates for pupils, posing challenges for conventional methods. Our model overcomes this challenge by leveraging an interpolation technique to align multiwavelength spots on a uniform image plane grid, thus effectively analyzing spot translation and spreading in imaging systems with diffraction limits.

Simulation of controlled modulus column installation using a large deformation finite element analysis

Controlled modulus columns (CMC) are widely used in ground improvement techniques where grout columns are constructed by penetrating an auger into the ground using the displacement approach. Numerical simulation of this problem is challenging owing to the large soil deformations associated with installation. Small strain-based finite element analysis procedures available in commercial finite element modeling software are unsuitable for simulating this problem because they cannot simulate large soil deformations around the auger during column installation. Hence, this study presents a numerical approach based on the finite element method to simulate the installation of CMCs. The numerical modeling technique adopted in this study considers large soil deformations around columns during penetration, and the proposed approach is based on remeshing and interpolation techniques. This method was developed using the Python development environment (PDE) within the ABAQUS/standard finite element program. It can penetrate columns below the ground surface without causing large mesh distortions. In this study, silty sand-type soil was considered; hence, the constitutive behavior was simulated using the Mohr–Coulomb criteria. The load-carrying capacity of a controlled modulus column was predicted and compared with an empirical equation based on the cone penetration resistance. The influence zone and failure mechanism of the soil during installation were determined by considering the stress distribution and soil flow pattern around the column.

Analysing Spatial and Temporal Rainfall Variability of Southern Rajasthan using GIS Approach

This study focuses on analysing the behavioral patterns of rainfall in southern Rajasthan, particularly in reference to its amount and variation over time. The study made use of standard meteorological weeks (SMW) and historic weekly rainfall records. IDW (Inverse Distance Weighting) interpolation technique within the ArcGIS platform was used for spatial analysis, creating spatial variation maps. It was found that significant rainfall occurred from Standard Meteorological Week (SMW)-22 to SMW-42. The weekly mean rainfall across the study area during the monsoon period ranged from 2.1 to 68.4 mm, with corresponding standard deviations ranging from 9.3 to 79.7 mm. Notably, the standard deviation was often higher than the mean, indicating substantial variability in weekly rainfall. Trend analysis using the Mann-Kendall test revealed that most stations in the study area exhibited no significant trend in annual rainfall data. Similarly, weekly rainfall data showed a trend in SMW-30 for 44 stations, while all other weekly data series did not exhibit significant trends, except for SMW-30. Stationarity was generally accepted for most weekly data series, except for SMW-30 and SMW-32, which showed non-stationary behavior. However, homogeneity tests indicated homogeneity in weekly rainfall data series for almost all selected stations. Overall, the study highlighted the significant variability in rainfall patterns in southern Rajasthan, with no significant trend in annual rainfall, except during the peak monsoon period (SMW-30). These findings contribute to a better understanding of regional rainfall behavior, which is crucial for water resource management and agricultural planning in the region.