Statistical Curve Fitting Research Articles

Topological indices and patterns in iron telluride networks

This paper explores the complex interplay between topological indices and structural patterns in networks of iron telluride (FeTe). We want to analyses and characterize the distinct topological features of (FeTe) by utilizing an extensive set of topological indices. We investigate the relationship that these indicators have with the network’s physical characteristics by employing sophisticated statistical techniques and curve fitting models. Our results show important trends that contribute to our knowledge of the architecture of the (FeTe) network and shed light on its physiochemical properties. This study advances the area of material science by providing a solid foundation for using topological indices to predict and analyses the behavior of intricate network systems. More preciously, we study the topological indices of iron telluride networks, an artificial substance widely used with unique properties due to its crystal structure. We construct a series of topological indices for iron telluride networks with exact mathematical analysis and determine their distributions and correlations using statistical methods. Our results reveal significant patterns and trends in the network structure when the number of constituent atoms increases. These results shed new light on the fundamental factors that influence material behavior, thus offering a deeper understanding of the iron telluride network and may contribute to future research and engineering of these materials.

Optimizing Piezoresistive MEMS Pressure Sensor on a Double Cross Beam Silicon Diaphragm With Statistical Curve-Fitting and Optimization Techniques

Optimizing Piezoresistive MEMS Pressure Sensor on a Double Cross Beam Silicon Diaphragm With Statistical Curve-Fitting and Optimization Techniques

Using deep learning–derived image features in radiologic time series to make personalised predictions: proof of concept in colonic transit data

ObjectivesSiamese neural networks (SNN) were used to classify the presence of radiopaque beads as part of a colonic transit time study (CTS). The SNN output was then used as a feature in a time series model to predict progression through a CTS.MethodsThis retrospective study included all patients undergoing a CTS in a single institution from 2010 to 2020. Data were partitioned in an 80/20 Train/Test split. Deep learning models based on a SNN architecture were trained and tested to classify images according to the presence, absence, and number of radiopaque beads and to output the Euclidean distance between the feature representations of the input images. Time series models were used to predict the total duration of the study.ResultsIn total, 568 images of 229 patients (143, 62% female, mean age 57) patients were included. For the classification of the presence of beads, the best performing model (Siamese DenseNET trained with a contrastive loss with unfrozen weights) achieved an accuracy, precision, and recall of 0.988, 0.986, and 1. A Gaussian process regressor (GPR) trained on the outputs of the SNN outperformed both GPR using only the number of beads and basic statistical exponential curve fitting with MAE of 0.9 days compared to 2.3 and 6.3 days (p < 0.05) respectively.ConclusionsSNNs perform well at the identification of radiopaque beads in CTS. For time series prediction our methods were superior at identifying progression through the time series compared to statistical models, enabling more accurate personalised predictions.Clinical relevance statementOur radiologic time series model has potential clinical application in use cases where change assessment is critical (e.g. nodule surveillance, cancer treatment response, and screening programmes) by quantifying change and using it to make more personalised predictions.Key Points• Time series methods have improved but application to radiology lags behind computer vision. Colonic transit studies are a simple radiologic time series measuring function through serial radiographs.• We successfully employed a Siamese neural network (SNN) to compare between radiographs at different points in time and then used the output of SNN as a feature in a Gaussian process regression model to predict progression through the time series.• This novel use of features derived from a neural network on medical imaging data to predict progression has potential clinical application in more complex use cases where change assessment is critical such as in oncologic imaging, monitoring for treatment response, and screening programmes.

Deep learning approaches for prediction of adiabatic temperature rise of concrete with complex mixture constituents

Temperature control and crack prevention of mass concrete are significant to the safety and durability of structures such as dams and long-span bridges. Adiabatic temperature rise is the key issue in thermal and crack risk analysis of mass concrete. Due to the substantial diversity of admixtures influencing the hydration process in modern concrete, it is challenging to develop a comprehensive analytical model to predict adiabatic temperature rise based on theoretical frameworks. As a data-driven approach, deep learning (DL) provides a potential solution for predicting the properties of complex materials in recent years. DL model based on artificial neuron network (ANN) and recurrent neuron network (RNN) were employed in this study to predict adiabatic temperature rise considering 15 variables as input parameters. The determination coefficient (R2) of the prediction results given from ANN and RNN was 0.7616 and 0.8542, respectively. Segmentation technique and bidirectional deep (BD) neuron network algorithm were implemented to improve the performance of RNN. It was demonstrated that BD-RNN provided better results with promising statistical parameters (R2 = 0.9245) and curve fitting of adiabatic temperature rise profiles. Through optimization and k-fold cross-validation, an evolutionary DL model was developed for reliable prediction of adiabatic temperature rise of concrete with complex admixtures.

ANN-based LoRaWAN Channel Propagation Model

LoRaWAN wireless communication channels are often impacted by noise and interference over long-range causing loss of a received signal. One of the main drawbacks of using existing propagation models is less accurate as these models in designing the communication link are tailored to simplify the estimation. In this paper, an artificial intelligent real time path loss model is proposed. It is capable of processing complex variables over a short period of time. Providing it with enough data, the model is able to learn channel behavior and predict the path loss accurately. Results of the model are benchmarked against classical statistical curve fitting models where RMSE values are also compared and indicating that the artificial intelligent model has better accurate prediction.

Keyphrases Concentrated Area Identification from Academic Articles as Feature of Keyphrase Extraction: A New Unsupervised Approach

The extraction of high-quality keywords and sum-marising documents at a high level has become more difficult in current research due to technological advancements and the expo-nential expansion of textual data and digital sources. Extracting high-quality keywords and summarising the documents at a high-level need to use features for the keyphrase extraction, becoming more popular. A new unsupervised keyphrase concentrated area (KCA) identification approach is proposed in this study as a feature of keyphrase extraction: corpus, domain and language independent; document length-free; utilized by both supervised and unsupervised techniques. In the proposed system, there are three phases: data pre-processing, data processing, and KCA identification. The system employs various text pre-processing methods before transferring the acquired datasets to the data processing step. The pre-processed data is subsequently used during the data processing step. The statistical approaches, curve plotting, and curve fitting technique are applied in the KCA identification step. The proposed system is then tested and evaluated using benchmark datasets collected from various sources. To demonstrate our proposed approach’s effectiveness, merits, and significance, we compared it with other proposed techniques. The experimental results on eleven (11) datasets show that the proposed approach effectively recognizes the KCA from articles as well as significantly enhances the current keyphrase extraction methods based on various text sizes, languages, and domains.

The Potential of Landsat NDVI Sequences to Explain Wheat Yield Variation in Fields in Western Australia

Long-term maps of within-field crop yield can help farmers understand how yield varies in time and space and optimise crop management. This study investigates the use of Landsat NDVI sequences for estimating wheat yields in fields in Western Australia (WA). By fitting statistical crop growth curves, identifying the timing and intensity of phenological events, the best single integrated NDVI metric in any year was used to estimate yield. The hypotheses were that: (1) yield estimation could be improved by incorporating additional information about sowing date or break of season in statistical curve fitting for phenology detection; (2) the integrated NDVI metrics derived from phenology detection can estimate yield with greater accuracy than the observed NDVI values at one or two time points only. We tested the hypotheses using one field (~235 ha) in the WA grain belt for training and another field (~143 ha) for testing. Integrated NDVI metrics were obtained using: (1) traditional curve fitting (SPD); (2) curve fitting that incorporates sowing date information (+SD); and (3) curve fitting that incorporates rainfall-based break of season information (+BOS). Yield estimation accuracy using integrated NDVI metrics was further compared to the results using a scalable crop yield mapper (SCYM) model. We found that: (1) relationships between integrated NDVI metrics using the three curve fitting models and yield varied from year to year; (2) overall, +SD marginally improved yield estimation (r = 0.81, RMSE = 0.56 tonnes/ha compared to r = 0.80, RMSE = 0.61 tonnes/ha using SPD), but +BOS did not show obvious improvement (r = 0.80, RMSE = 0.60 tonnes/ha); (3) use of integrated NDVI metrics was more accurate than SCYM (r = 0.70, RMSE = 0.62 tonnes/ha) on average and had higher spatial and yearly consistency with actual yield than using SCYM model. We conclude that sequences of Landsat NDVI have the potential for estimation of wheat yield variation in fields in WA but they need to be combined with additional sources of data to distinguish different relationships between integrated NDVI metrics and yield in different years and locations.

Comparison of Fracture Delineation Methods in Anteroposterior Pelvic Radiographs

Pelvic fractures are very difficult to detect due to the visual complexity of the pelvic bone. Pelvic fracture occurs less frequently, only when there is a high energy event such as fall from a height or vehicle collision. In elder people and in osteoporosis patients even a low energy incident may cause fracture. The paper includes the comparison of three different fracture detection methods – GLCM and ANN based, Statistical curve fitting and classifier based and finally statistical curve fitting and ANN based method.

Nodal distance distributions in cluster flight spacecraft network

The high‐speed flight of cluster flight spacecraft modules increases the uncertainty of network topology. In order to better design the orbital of the cluster flight spacecraft and improve the performance of cluster flight spacecraft network (CFSN), this paper studies the nodal distance distributions (NDDs). First, based on twin‐satellites mode, the mobility model of nodes is established. And then by adopting empirical statistical method and curve fitting method, the solution of the nodal distance density function in the CFSN is obtained. Second, the probability density function is applied to the model distance‐dependent path loss, and the probability density of path loss under the model is analyzed. Finally, the distributions of maximum and minimum distance among multiple nodes and the threshold range of nodal connection distance are derived, and the probabilistic connectivity matrix of any time slot in the orbital hyper‐period and spatial–temporal evolution graphs under different thresholds is obtained. The analytical results verify the feasibility of the model and the approximate solution of the NDDs in this paper. This method also provides a theoretical reference for the nodal connection in irregular wireless networks.

Epidemiological analysis on 1 052 cases of COVID-19 in epidemic clusters

Objective: To understand the epidemiological characteristics of the cases of COVID-19 epidemic clusters, and explore the influence of family factors and social factors such as group activities on the spread of the disease. Methods: The data of cases of COVID-19 epidemic clusters from 19 January, 2020 to 25 February, 2020 were collected from the official platforms of 36 cities in 6 provinces in China. Descriptive statistical methods, χ(2) test and curve fitting were used to analyze the epidemiological characteristics of the clustered cases. Results: By 25 February, 2020, the data of 1 052 cases in 366 epidemic clusters were collected. In these clustered cases, 86.9%(914/1 050) occurred in families. Among the 1 046 cases with gender information, 513 were males (49.0%) and 533 were females (51.0%). The cases were mainly young adults between 18 and 59 years old, accounting for 68.5% (711/1 038). In the 366 epidemic clusters , the clusters in which the first confirmed cases with the history of sojourn in Wuhan or Hubei accounted for 47.0%(172/366). From 19 January to 3 February, 2020, the first confirmed cases with Wuhan or Hubei sojourn history accounted for 66.5%. From 4 to 25 February, the first confirmed cases who had Wuhan or Hubei sojourn history accounted for only 18.2%. The median of interval between the first generation case onset and the second generation case onset was 5 (2-8) days. The median of onset- diagnosis interval of the initial cases was 6 (3-9) days, and the median of onset-diagnosis interval of the secondary cases was 5 (3-8) days. Conclusions: Epidemic clusters of COVID-19 were common in many cities outside Wuhan and Hubei. Close contact in family was one of the main causes for the spread of household transmission of the virus. After 4 February, the epidemic clusters were mainly caused by the first generation or second generation cases in local areas, and the time for diagnosis became shorter.

Splitting tensile strength of natural aggregates, recycled aggregates and brick chips concrete

This paper presents an experimental study and analysis of the splitting tensile strength (ft) and relationship between splitting tensile strength and compressive strength (f′c) of concrete made of three types of coarse aggregates; namely crushed brick chips (CBC), natural stone aggregates (SA) and recycled concrete aggregates (RCA). Four concrete mixtures with different target strengths were prepared and for each mixture, six cylindrical samples were subjected to split-cylinder tests and another six cylindrical samples were subjected to compression tests at 28 d. Raw test data were then analysed according to the American Concrete Institute (ACI) 214 and the average tensile strength obtained was compared with the relevant compressive strength based on statistical curve fitting. This study shows that while the ACI equation and equations suggested by other authors for the correlation between splitting tensile strength and compressive strength are applicable for SA concrete, they are not quite applicable for concrete made with CBC or RCA. Thus, new empirical equations are derived to predict the splitting tensile strength from concrete compressive strength for concrete made with CBC and RCA.

Color Shift Failure Prediction for Phosphor-Converted White LEDs by Modeling Features of Spectral Power Distribution with a Nonlinear Filter Approach.

With the expanding application of light-emitting diodes (LEDs), the color quality of white LEDs has attracted much attention in several color-sensitive application fields, such as museum lighting, healthcare lighting and displays. Reliability concerns for white LEDs are changing from the luminous efficiency to color quality. However, most of the current available research on the reliability of LEDs is still focused on luminous flux depreciation rather than color shift failure. The spectral power distribution (SPD), defined as the radiant power distribution emitted by a light source at a range of visible wavelength, contains the most fundamental luminescence mechanisms of a light source. SPD is used as the quantitative inference of an LED’s optical characteristics, including color coordinates that are widely used to represent the color shift process. Thus, to model the color shift failure of white LEDs during aging, this paper first extracts the features of an SPD, representing the characteristics of blue LED chips and phosphors, by multi-peak curve-fitting and modeling them with statistical functions. Then, because the shift processes of extracted features in aged LEDs are always nonlinear, a nonlinear state-space model is then developed to predict the color shift failure time within a self-adaptive particle filter framework. The results show that: (1) the failure mechanisms of LEDs can be identified by analyzing the extracted features of SPD with statistical curve-fitting and (2) the developed method can dynamically and accurately predict the color coordinates, correlated color temperatures (CCTs), and color rendering indexes (CRIs) of phosphor-converted (pc)-white LEDs, and also can estimate the residual color life.

Design Index and Model for Controlling the Permanent Deformation of the Granular Base Course

This study proposes a design index and model for controlling the permanent deformation of the granular base course for asphalt pavements by adopting two types of graded macadam. Three levels of confining pressure stresses and different stress ratios were set as the test loading conditions. The permanent deformation shakedown characteristics of the graded macadam were examined by conducting triaxial repeated load testing based on shakedown theory. Statistical regression and curve fitting were applied to the test results to overcome the limitation on the number of repeated loads. In accordance with the fitting results, the testing curves were extended to analyze the limit stresses for the shakedown under different shakedown statuses of the permanent deformation of the aggregates. A new design model was established for controlling the permanent deformation of the granular base course in zone A (plastic shakedown status). The permanent deformation of granular base courses of typical asphalt pavement structures was calculated by adopting the layer-wise strain summation and employing the evaluation model currently adopted by the United States Mechanical-Empirical Pavement Design Guide for the permanent deformation of granular base courses. Consequently, the critical standard values of the permanent deformation and the permanent strain of granular base courses were determined. The stress ratio is recommended as a design index for controlling the permanent deformation of the granular base course. The control model of the regression formula between the stress ratio and the number of axial loading was originally created based on laboratory tests.

A trajectory planning method for sprawling robot inspired by a trotting animal

Motion coordination is a key factor affecting the stability and efficiency of robots. Such coordination involves the intra-limb coordination between each motion stage of a single leg and the inter-limbs coordination among all legs. The motion behaviors of animals, which have developed over a long period of evolution, offer good prototypes for the motion control of robots. In this paper, the locomotion of a quadruped sprawling animal (i.e., gecko) trotting on a horizontal surface was observed, and the feet trajectories around the hip on the same side were analyzed by statistical analysis and curve fitting. Results showed that feet on stance phase maintain the same speed to satisfy the requirement of inter-limb coordination. In swing phase, feet speed decreases from the moving speed to zero at the beginning and increases from zero to the desired speed in the next step at the end. The feet accomplish the transition from the backmost to the foremost position along a smooth curve that can be fit by a cosine function, which has good kinematic and dynamic characters. A foot trajectory planning method for robots was proposed based on the experiment results, which offered references not only for the foot trajectory planning of robots to realize intra-limb coordination but also for the inter-limbs coordination between stance feet during locomotion. Finally, the motion of a sprawling robot is simulated by using the bio-inspired trajectories to show the performance and benefits of the proposed method.

Inspecting lens collars for defects using discrete cosine transformation based on an image restoration scheme

Automated cutting process may produce superficial defects such as scratches and depressions on lens collars which are caused by cutter offset or scrap accumulation. Additionally, electroplating defects, such as dots and uneven electroplating occurs if the surface is rough or contaminated with foreign matters. As the inclined camber of lens collar contributes to external appearances of a single-lens camera, customers extremely concern about its surface quality. Relying on human inspection to ensure the quality is time-consuming, labor-intensive and produces occupational injuries. Therefore, the implementation of auto-inspection can help overcome these problems. The inspection system is composed of charge coupled device (CCD), coaxial light and motor. After an image is taken, it goes through a segmentation sub-function to obtain a region of interesting (ROI). Since the texture of inclined camber of a lens collar is statistically distributed, an image restoration sub-function based on discrete cosine transformation (DCT) is used to map the texture onto high-energy components on a spectrum. They are then compressed by a notch-rejecting filter. In contrast with the defects, the grey value of texture is limited within a certain range. Statistical process control binarisation, curve fitting and binary large object (blob) analysis are used to highlight defects.

Exploration of the Intraoperative Motor Evoked Potential.

Prospective study. The objectives of the study was to (1) seek a relation between motor evoked potential (MEP) and corresponding cervical cord function in cervical compression myelopathy (CCM) and (2) explore a high-sensitive MEP range that can predict the intraoperative monitoring change ahead in cervical spine surgery. There have been lots of controversies concerning the application of transcranial MEP in cervical spine surgery. We prospectively investigate 86 consecutive patients with CCM who underwent posterior laminoplasty or laminectomy from December 2012 to September 2014. The 18-point modified Japanese Orthopedic Association (mJOA) score and intraoperative MEP were used for neurological and electrophysiological assessment. Statistical correlation analysis and curve fitting were used to definite the relationship between MEP and corresponding cervical cord function. And a novel concept of high-sensitive MEP range was firstly addressed for predicting the intraoperative monitoring change ahead in CCM. Our results showed that the preoperative mJOA score of lower extremity presented a significant correlation with MEP parameters in CCM, and the correlation was expressed in an exponential relationship. The monitoring change in CCM often appeared at a high-sensitive MEP range (amplitude <159 μV or latency >36.1 ms). In addition, the high-sensitive MEP ranges not only included MEP degeneration but also a larger number of MEP improvement cases. Intraoperative MEP may imply an exponential correlation with the corresponding cervical cord function in CCM. And we first characterize a high-sensitive MEP range which may indicate high risk for the impending monitoring change during cervical cord decompression and we must watch more closely.

Energy transfer model and its applications of ultrasonic gas flow-meter under static and dynamic flow rates.

Most of the ultrasonic gas flow-meters measure the gas flow rate by calculating the ultrasonic transmission time difference between the downstream and upstream. Ultrasonic energy attenuation occurs in the processes of the ultrasonic generation, conversion, transmission, and reception. Additionally, at the same time, the gas flow will also affect the ultrasonic propagation during the measurement, which results in the ultrasonic energy attenuation and the offset of ultrasonic propagation path. Thus, the ultrasonic energy received by the transducer is weaker. When the gas flow rate increases, this effect becomes more apparent. It leads to the measurement accuracy reduced, and the measurement range narrowed. An energy transfer model, where the ultrasonic gas flow-meter under without/with the gas flow, is established by adopting the statistical analysis and curve fitting based on a large amount of experimental data. The static sub model without the gas flow expresses the energy conversion efficiency of ultrasonic gas transducers, and the dynamic sub model with the gas flow reflects the energy attenuation pattern following the flow rate variations. The mathematical model can be used to determine the minimum energy of the excitation signal for meeting the requirement of specific measurement range, and predict the maximum measurable flow rate in the case of fixed energy of excitation signal. Based on the above studies, a method to enhance the excitation signal energy is proposed under the output power of the transmitting circuit being a finite value so as to extend the measurement rage of ultrasonic gas flow-meter.

Estimation of the Postmortem Interval by Measuring Blood Oxidation-reduction Potential Values

Accurate estimation of the postmortem interval (PMI) is an important task in forensic practice. In the last half-century, the use of postmortem biochemistry has become an important ancillary method in determining the time of death. The present study was carried out to determine the correlation between blood oxidation-reduction potential (ORP) values and PMIs, and to develop a three-dimensional surface equation to estimate the PMI under various temperature conditions. A total of 48 rabbits were placed into six groups and sacrificed by air embolism. Blood was obtained from the right ventricle of each rabbit, and specimens were stored at 10°C, 15°C, 20°C, 25°C, 30°C, and 35°C. At different PMIs (once every 4 h), the blood ORP values were measured using a PB-21 electrochemical analyzer. Statistical analysis and curve fitting of the data yielded cubic polynomial regression equations and a surface equation at different temperatures. Result: The results showed that there was a strong positive correlation between the blood ORP values at different temperatures and the PMI. This study provides another example of using a three-dimensional surface equation as a tool to estimate the PMI at various temperature conditions.

Evaluation of Interpolants in Their Ability to Fit Seismometric Time Series

This article is devoted to the study of the ASARCO demolition seismic data. Two different classes of modeling techniques are explored: First, mathematical interpolation methods and second statistical smoothing approaches for curve fitting. We estimate the characteristic parameters of the propagation medium for seismic waves with multiple mathematical and statistical techniques, and provide the relative advantages of each approach to address fitting of such data. We conclude that mathematical interpolation techniques and statistical curve fitting techniques complement each other and can add value to the study of one dimensional time series seismographic data: they can be use to add more data to the system in case the data set is not large enough to perform standard statistical tests.

Input-Sensitive Profiling

In this article we present a building block technique and a toolkit towards automatic discovery of workload-dependentperformance bottlenecks. From one or more runs of a program, our profiler automatically measures how the performance of individual routines scales as a function of the input size, yielding clues to their growth rate. The output of the profiler is, for each executed routine of the program, a set of tuples that aggregate performance costs by input size. The collected profiles can be used to produceperformance plots and derive trend functions by statistical curve fitting techniques. A key feature of our method is the ability toautomatically measure the size of the input given to a generic code fragment: to this aim, we propose an effective metric for estimating the input size of a routine and show how to compute it efficiently. We discuss several examples, showing that our approach can reveal asymptotic bottlenecks that other profilers may fail to detect and can provide useful characterizations of the workload and behavior of individual routines in the context of mainstream applications, yielding several code optimizations as well as algorithmic improvements. To prove the feasibility of our techniques, we implemented a Valgrind tool called aprof and performed an extensive experimentalevaluation on the SPEC CPU2006 benchmarks. Our experiments show that aprof delivers comparable performance to otherprominent Valgrind tools, and can generate informative plots even from single runs on typical workloads for mostalgorithmically-critical routines.