Surrogate Data Research Articles

Ab initio investigation of magnetic properties of metal doped ZnO-Buckyball structures

Magnetic properties of metal-doped ZnO buckyball ‘X@(ZnO-bb)’ structure have been investigated using first-principle density functional theory. Different structures of ZnO Buckyball, with the dopant atom X(Where X = Al, Ag, Fe, and Co) have been studied. To understand if the magnetic properties are arising fully due to the dopant atoms or because of the structural variation or intrinsic defects, the Zn and O atom is placed at the center of ZnO-bb, instead of other dopants. This study claims a novel ZnO-bb structure whose magnetic properties are reported for the first time, to the best of our knowledge. The change in the design of the structure, as well as the doping atom, led to variations in the magnetic properties of ZnO. A small amount of magnetic moment was observed in the ZnO-bb with non-magnetic dopant atoms like Al and Ag. Furthermore, a comparatively large amount of magnetic moments were observed with magnetic atoms like Co and Fe. The presence of a small amount of magnetic moment in the X@(ZnO-bb) structure unwraps a scope to further study this novel structure with different dopant atoms for various applications. A possible mechanism for the initiation of structure-based magnetism is also discussed. When the Zn or O atoms are placed at the center of ZnO-bb, a small amount of magnetic moment is observed in the O@(ZnO-bb) structure. This work systematically studies the magnetic properties in the ZnO-bb structure, which can be used for various applications like analog and digital data storage, magnetic therapy, and devices used in medical fields like MRI, etc.

A Machine Learning Workflow to Support the Identification of Subsurface Resource Analogs

Identifying subsurface resource analogs from mature subsurface datasets is vital for developing new prospects due to often initial limited or absent information. Traditional methods for selecting these analogs, executed by domain experts, face challenges due to subsurface dataset's high complexity, noise, and dimensionality. This article aims to simplify this process by introducing an objective geostatistics-based machine learning workflow for analog selection. Our innovative workflow offers a systematic and unbiased solution, incorporating a new dissimilarity metric and scoring metrics, group consistency, and pairwise similarity scores. These elements effectively account for spatial and multivariate data relationships, measuring similarities within and between groups in reduced dimensional spaces. Our workflow begins with multidimensional scaling from inferential machine learning, utilizing our dissimilarity metric to obtain data representations in a reduced dimensional space. Following this, density-based spatial clustering of applications with noise identifies analog clusters and spatial analogs in the reduced space. Then, our scoring metrics assist in quantifying and identifying analogous data samples, while providing useful diagnostics for resource exploration. We demonstrate the efficacy of this workflow with wells from the Duvernay Formation and a test scenario incorporating various well types common in unconventional reservoirs, including infill, outlier, sparse, and centered wells. Through this application, we successfully identified and grouped analog clusters of test well samples based on geological properties and cumulative gas production, showcasing the potential of our proposed workflow for practical use in the field.

Deriving community vulnerability indices by analyzing multi-resolution space-borne data and demographic data for extreme weather events in global cities

Climate change in the Anthropocene is cited as one of the main reasons for extreme weather events around the world. There is a need for rapid vulnerability assessment models that may be deployed in near-real time to assist stake holders in driving the required mitigation and adaptation efforts to such events, especially in the Global South. We present an approach to derive Community Vulnerability Index (CVI) to flooding by using an integrated geospatial computational model and illustrate the methodology for six global cities. Time-series of multi-resolution space-borne optical and synthetic aperture radar (SAR) data were analyzed to derive land cover metrics from before and after flood imagery for delineating and mapping the land cover and the spatial extent of flash floods. A texture-based analysis was employed on the SAR data to delineate the spatial extent of flash floods, and this was integrated with the land cover metrics derived from the optical Sentinel-2 (Level 2A) orthorectified surface reflectance imagery. Land cover classification based on supervised image classification yielded a minimum overall accuracy of 71% when only SAR data was available after floods, and increased to 87% with the availability of cloudless Sentinel-2 data. A weighted index method, using weights derived through Factor Analysis, was used to assign appropriate weights to the composite variables derived from satellite datasets and demographic datasets for developing the CVI. The model was developed and tested on global cities with contrasting economies and validated by cross checking results with reliable open source surrogate data. The model provides an effective, adaptable, and cost-effective approach for vulnerability assessment and may provide information for policy makers to design appropriate resilience and adaptation efforts.

Development of an Optoelectronic System for Recording and Processing Signals From Fiber-optic Sensors

Natural disasters, in particular earthquakes, can damage bridges, tunnels, dams, high-rise buildings, railways, oil platforms, buildings, and pipelines with catastrophic humanitarian, social, economic, and environmental consequences. Thus, automatic assessment of damage to industrial and civil construction structures after an earthquake in real-time or on request is necessary for early emergency response, effective preparation of rescue plans, and mitigation of these catastrophic consequences. The article presents the use of a system of distributed fiber-optic sensors based on Bragg fiber arrays, which will allow periodic measurements and monitoring of the condition of structures, assessing trends in its technical condition and thereby facilitating the identification of possible threats. The given research presents assembling and testing optoelectronic systems to record and process signals from fiber-optic sensors. The main optoelectronic systems to record and process the signals from fiber-optic sensors are light source controllers and optical power detectors. There was an assembled controller diagram, which apart from the light source includes the current source for its adequate operation, as well as the systems necessary for stabilizing its working point. The scheme was modeled for specifying nominal and maximum operation criteria. Construction has been designed in a way, that the light source controller includes structures of the current regulation and stabilization SLED (super luminescent diode) and temperature stabilization. Apart from that, there was assembled the microsystem of the optical power detector additionally to the light detector, which includes the microsystems of intensification and filtration of the signal measured, processing analog data into digital form, and microcontroller, used for preliminary data analysis. Data of optoelectronic systems diagram to record and process the signals from fiber-optic sensors has high response speed, low noise level, and sufficient progress. type your abstract here.

Statistical Approaches to Identify Pairwise and High-Order Brain Functional Connectivity Signatures on a Single-Subject Basis.

Keeping up with the shift towards personalized neuroscience essentially requires the derivation of meaningful insights from individual brain signal recordings by analyzing the descriptive indexes of physio-pathological states through statistical methods that prioritize subject-specific differences under varying experimental conditions. Within this framework, the current study presents a methodology for assessing the value of the single-subject fingerprints of brain functional connectivity, assessed both by standard pairwise and novel high-order measures. Functional connectivity networks, which investigate the inter-relationships between pairs of brain regions, have long been a valuable tool for modeling the brain as a complex system. However, their usefulness is limited by their inability to detect high-order dependencies beyond pairwise correlations. In this study, by leveraging multivariate information theory, we confirm recent evidence suggesting that the brain contains a plethora of high-order, synergistic subsystems that would go unnoticed using a pairwise graph structure. The significance and variations across different conditions of functional pairwise and high-order interactions (HOIs) between groups of brain signals are statistically verified on an individual level through the utilization of surrogate and bootstrap data analyses. The approach is illustrated on the single-subject recordings of resting-state functional magnetic resonance imaging (rest-fMRI) signals acquired using a pediatric patient with hepatic encephalopathy associated with a portosystemic shunt and undergoing liver vascular shunt correction. Our results show that (i) the proposed single-subject analysis may have remarkable clinical relevance for subject-specific investigations and treatment planning, and (ii) the possibility of investigating brain connectivity and its post-treatment functional developments at a high-order level may be essential to fully capture the complexity and modalities of the recovery.

Spiking Neurons with Neural Dynamics Implemented Using Stochastic Memristors

AbstractImplementing and integrating spiking neurons for neuromorphic hardware realization conforming to spiking neural networks holds great promise in enabling efficient learning and decision‐making. The spiking neurons, however, may lack the spiking dynamics to encode the dynamical information in complex real‐world problems. Herein, using filamentary memristors from solution‐processed hexagonal boron nitride, this study assembles leaky integrate‐and‐fire spiking neurons and, particularly, harnesses the common switching stochasticity feature in the memristors to allow key neural dynamics, including Poisson‐like spiking and adaptation. The neurons, with the dynamics fitted via hardware‐algorithm codesign, suggest a potential in realizing spike‐based neuromorphic hardware capable of handling complex problems. Simulation of an autoencoder for anomaly detection of time‐series real analog and digital data from physical systems is demonstrated, underscoring its promising prospect in applications, especially, at the edges with limited computation resources, for instance, auto‐pilot, manufacturing, wearables, and Internet of things.

Doppler Echocardiographic Phenotypes in Suspected ‘Severe’ Aortic Stenosis: Matrix-Based Approach to Diagnosis and Management

Among patients with suspected severe aortic stenosis (AS), Doppler echocardiographic (DE) data are often discordant, and further analysis is required for accurate diagnosis and optimal management. In this study, an automated matrix-based approach was applied to an echocardiographic database of patients with AS that identified 5 discrete echocardiographic data patterns, 1 concordant and 4 discordant, each reflecting a particular pathophysiology/measurement error that guides further workup and management. A primary/discovery cohort of consecutive echocardiographic studies with at least 1 DE parameter of severe AS and analogous data from an independent secondary/validation cohort were retrospectively analyzed. Parameter thresholds for inclusion were aortic valve area (AVA) <1.0cm2, transaortic mean gradient (MG) ≥ 40mmHg, and/or transaortic peak velocity (PV) ≥ 4.0m/sec. Doppler velocity index (DVI) was also determined. Logic provided by an in-line SQL query embedded within the database was used to assign each patient to 1 of 5 discrete matrix patterns, each reflecting 1 or more specific pathophysiologies. Feasibility of automated pattern-driven triage of discordant cases was also evaluated. In both cohorts, data from each patient fitted only 1 data pattern. Of the 4,643 primary cohort patients, 39% had concordant parameters for severe AS and DVI <0.30 (pattern 1); 35% had AVA < 1.0cm2, MG<40mm Hg, PV < 4 m/sec, DVI < 0.30 (pattern 2); 9% had MG≥ 40mmHg and/or PV ≥ 4 m/sec, DVI > 0.30 (pattern 3); 10% had AVA < 1.0cm2, MG<40mmHg, PV < 4 m/sec, DVI >0.30 (pattern 4); and 7% had MG> 40mmHg and/or PV ≥ 4 m/sec, AVA > 1.0cm2, DVI < 0.30 (pattern 5). Findings were validated among the 387 secondary cohort patients in whom pattern distribution was remarkably similar. Matrix-based pattern recognition permits automated in-line identification of specific pathophysiology and/or measurement error among patients with suspected severe AS and discordant DE data.

Impact of the US Accelerated Approval for New Anticancer Drugs on Time to Verification of Benefit and Regulatory Approval in the EU and Japan.

The accelerated approval (AA) program in the USA has succeeded in expediting the regulatory approval of new cancer drugs based on surrogate endpoint data. It is unclear whether the AA program promotes overall drug development, including verification of the clinical benefit, as the verification of drugs granted AA often takes long time. To determine the impact of the AA program on overall drug development, the time required for verification of clinical benefits was compared between anticancer drugs that initially received AA and those that received regular approval (RA). It was found that anticancer drugs that were approved under the AA program took longer time for verification, suggesting that the program may delay the start of a confirmatory study, and there may be room for speeding up the process. In addition, discordance was found in the pivotal study between the USA and the EU and the USA and Japan for obtaining the indication for which AA was granted in the USA and a delay in the start of the confirmatory study for the AA indication was considered to lead to a delay in approval in the EU and Japan. Early initiation of confirmatory studies for AA indications is recommended to reduce the time that patients receive drugs with unproven benefit in the USA, as well as to deliver innovative new drugs to patients earlier in the EU and Japan.

1688O Informative censoring of surrogate end-point data in FDA-approved cancer drugs

1688O Informative censoring of surrogate end-point data in FDA-approved cancer drugs

Fixed-station and glider-based passive acoustic monitoring reveals spatiotemporal spawning dynamics of Atlantic cod and their potential interaction with offshore wind energy

Atlantic cod, which produce sounds associated with courtship behaviors and are overfished in the US, are potentially vulnerable to disturbance from offshore wind energy (OWE) construction and operation during their spawning period. We used a combination of fixed-station and glider-based passive acoustic monitoring methods to evaluate the spatiotemporal spawning dynamics of Atlantic cod and assess their potential interaction with OWE at the extreme southern extent of the species’ range in Southern New England waters of the Western North Atlantic Ocean. Generalized linear modeling of call presence and rates suggests that spawning was concentrated in November and December and was greatest near the new and full moons. The results from both fixed-station and glider-based methods suggest that spawning overlaps with planned OWE construction in time and space. Comparison of these temporal spawning dynamics in Southern New England to analogous PAM data from the geographically separated Massachusetts Bay winter-spawning Atlantic cod subpopulation revealed that the seasonality of inferred spawning was the same between the two regions. By leveraging multiple methods and datasets, this work demonstrates the effectiveness of PAM for understanding spawning phenology of and potential disturbance to an economically important fishery.

육계농장의 ICT장비가 생산한 빅데이터 분석을 활용한 질병발생예측모델(DOPM) 개발연구

Since 2014, the government has promoted the ICT equipment support project for livestock farmers as an alternative system to address the rapid aging of livestock farmers and sharp decline in labor force due to population decline. This project aims to realize more precise and scientific smart livestock farming through the analysis of big data produced by ICT equipment in real time. Prior to the introduction of ICT equipment, farmers relied on their long-term experience in farm operations to implement emergency measures by using a sudden increase in the number of dead birds or sudden change in the amount of water consumption data as an indicator of the occurrence of disease. However, damage and loss to farmers have not been reduced with these measures. Therefore, this study aims to address the farmers' consistent desire to develop a disease outbreak prediction model (DOPM) that can be easily utilized in the field and has high precision. The current study developed a disease outbreak prediction model (DOPM) that can find a singularity (golden time) in the data even if only one type of ICT data (Water &amp; Feed) is properly monitored and analyzed. We found that if the time when a sharp decrease (more than 100% decrease from the previous day), confirmed through the daily feed intake data for two consecutive days (golden time = DOPM), is set as the start of the disease outbreak and preventive measures are taken quickly, this model can be used to minimize farm losses and damages better than the existing method that relies on analog data and experience. Additionally, the DOPM was confirmed to be the fastest, most sensitive, and most accurate model in the field poultry farms. As a result, we have found a method to interpret non-verbal signals of the chickens with the transformed ICT-Data in the AI-age.

Complexity Measures for EEG Microstate Sequences: Concepts and Algorithms

EEG microstate sequence analysis quantifies properties of ongoing brain electrical activity which is known to exhibit complex dynamics across many time scales. In this report we review recent developments in quantifying microstate sequence complexity, we classify these approaches with regard to different complexity concepts, and we evaluate excess entropy as a yet unexplored quantity in microstate research. We determined the quantities entropy rate, excess entropy, Lempel–Ziv complexity (LZC), and Hurst exponents on Potts model data, a discrete statistical mechanics model with a temperature-controlled phase transition. We then applied the same techniques to EEG microstate sequences from wakefulness and non-REM sleep stages and used first-order Markov surrogate data to determine which time scales contributed to the different complexity measures. We demonstrate that entropy rate and LZC measure the Kolmogorov complexity (randomness) of microstate sequences, whereas excess entropy and Hurst exponents describe statistical complexity which attains its maximum at intermediate levels of randomness. We confirmed the equivalence of entropy rate and LZC when the LZ-76 algorithm is used, a result previously reported for neural spike train analysis (Amigó et al., Neural Comput 16:717–736, https://doi.org/10.1162/089976604322860677, 2004). Surrogate data analyses prove that entropy-based quantities and LZC focus on short-range temporal correlations, whereas Hurst exponents include short and long time scales. Sleep data analysis reveals that deeper sleep stages are accompanied by a decrease in Kolmogorov complexity and an increase in statistical complexity. Microstate jump sequences, where duplicate states have been removed, show higher randomness, lower statistical complexity, and no long-range correlations. Regarding the practical use of these methods, we suggest that LZC can be used as an efficient entropy rate estimator that avoids the estimation of joint entropies, whereas entropy rate estimation via joint entropies has the advantage of providing excess entropy as the second parameter of the same linear fit. We conclude that metrics of statistical complexity are a useful addition to microstate analysis and address a complexity concept that is not yet covered by existing microstate algorithms while being actively explored in other areas of brain research.

Independent of technology and generating curiosity – paper as an age-resistant data store medium

Abstract. Humankind is known to be curious and inquisitive. Even if the code were lost, future individuals would be eager to find out what kind of message a printed paper offers. This was already the case when hieroglyphs were deciphered. Printed paper does not need additional equipment to be recognized as a data carrier. Therefore, paper as an analogue data store medium provides an interesting redundancy to deliver important information to future generations. The presentation deals with topics of age resistance of paper and inks as well as recommendations of how to empower printed or written paper to be a long-term data store. Different ageing conditions, different inks, different printing processes, and different fibre sources reveal the opportunities and threats. Data obtained during a 2-year-long project together with BASE (Bundesamt für die Sicherheit der nuklearen Entsorgung – Federal Authority for the Safety of Nuclear Waste Management) on the long-term stability of paper (Langzeitbeständigkeit von Papier – Labest Papier) emphasize the feasibility (Arbeitsstandberichte AP1–AP3 (literature, typical damage, recommendations), unpublished data). The ageing mechanisms of paper are known, as are countermeasures. This leads to standards and recommendations for the use of paper for documents. ISO 9706 (1994) is the main proponent of the direction that longevity can be ensured solely via the composition of the paper and the initial situation at the beginning of ageing. DIN 6738 (2007), on the other hand, takes as its basis the approach of assessing the physical ageing of the paper based on artificial ageing and the strength losses observed in the process. Other standards vary the approaches somewhat or mix them in part, for which the lecture presents a comparison table. The usability of a document depends on two essential factors. Firstly, the residual strength after an ageing process must be high enough for the document to be usable. Secondly, the information written on it must still be legible or at least recognizable (ISO 11798, 2023). However, although certain fibre resources that are recommended by different standards dealing with the long-term storage of documents as well as some fibre sources are denied, we must consider that all fibre sources undergo an ageing process. Different combinations of fibre and ink will lead to different results after natural or artificial ageing. This does not necessarily mean that only the best combination is able to fulfil the requirements related to transport information to an address in the far future. Albeit less strong or with higher optical changes, even by current standards, a rejected fibre source may nevertheless be able to keep the printed information over a long period of time. Paper has been a proven reliable information carrier for approx. 1000 years as the oldest known paper document archived in Palermo is from 1109 CE. It is reasonable to expect a reliability for many more years because the “Ebers” papyrus (worldwide longest, complete, and best-preserved papyrus scroll) stored at the University of Leipzig is already 3500 years old. Compared to modern information storage media such as digital media or microfilm, paper also has the advantage of a long migration time and that no reading or decoding device is necessary. Taking this into account, paper documents are predestined to play a major role in record, knowledge, and memory (RK&amp;amp;M) strategies, as described by Pescatore and Palm (2023). Finally, it remains to say that there can never be an intactness in the sense of being completely untouched. Paper, however, is a material consisting of natural polymers that prove their longevity in the world's oldest tree, a 9500-year-old spruce in Sweden.

Wrangling Whole Mixtures Risk Assessment: Recent Advances in Determining Sufficient Similarity.

Human health risk assessments for complex mixtures can address real-world exposures and protect public health. While risk assessors typically prefer whole mixture approaches over component-based approaches, data from the precise exposure of interest are often unavailable and surrogate data from a sufficiently similar mixture(s) are required. This review describes recent advances in determining sufficient similarity of whole, complex mixtures spanning the comparison of chemical features, bioactivity profiles, and statistical evaluation to determine "thresholds of similarity". Case studies, including water disinfection byproducts, botanical ingredients, and wildfire emissions, are used to highlight tools and methods. Limitations to application of sufficient similarity in risk-based decision making are reviewed and recommendations presented for developing best practice guidelines.

Dynamic state of low-Reynolds-number turbulent channel flow.

We numerically study the dynamic state of a low-Reynolds-number turbulent channel flow from the viewpoints of symbolic dynamics and nonlinear forecasting. A low-dimensionally (high-dimensionally) chaotic state of the streamwise velocity fluctuations emerges at a viscous sublayer (logarithmic layer). The possible presence of the chaotic states is clearly identified by orbital instability-based nonlinear forecasting and ordinal partition transition network entropy in combination with the surrogate data method.

A novel IoT-based framework with Prognostics and Health Management and short term fire risk assessment in smart firefighting system

In recent years, “smart firefighting” becomes a popular research topic which aims at improving the efficiency of fire protection and safety. However, in existing smart firefighting systems: (1) state and analog firefighting IoT data is not fully utilized; (2) fire risk assessment requires a long time to update; (3) few existing works can recognize faults and forecast potential risks with both temporal and spatial information from multiple IoT sensors. Therefore, a novel framework is proposed to solve the above problems as follows: (1) Short-term Fire Risk Assessment (SFRA) is proposed by considering the working status, maintenance and rectification performance of firefighting facilities based on IoT state data; (2) Prognostics and Health Management (PHM) algorithms are applied in the fire safety field for the first time to detect faults and forecast potential risks with temporal and spatial information with IoT analog data from single or multiple sensors. A case study is carried out in a building in Shanghai and a smart firefighting system is deployed with the edge computing gateway. The experimental results of the SFRA and PHM are presented and analyzed in details. The performance evaluation and comparison results are presented with multiple metrics. The reliability of the proposed framework is discussed. The case study concluded that the proposed framework extends the utilization of IoT data, improves the efficiency of fire risk assessment and enhances the accuracy of faults detection and potential risk forecast. Moreover, the deployed firefighting system improves the data privacy and computational efficiency.

Depositional environment and hydrocarbon exploration potential based on sedimentary facies and architectural analysis of the Upper Cretaceous Shendi Formation in Musawwarat-Naga area, Shendi-Atbara Basin, Sudan

This study examines both vertical and lateral facies changes as well as petrographic characteristics of the outcropping section of the Upper Cretaceous Shendi Formation in the Musawwarat-Naga area with a view of interpreting the depositional environment, provenance, and tectonic setting that prevailed during the deposition of the sediments. Field studies revealed the presence of eight different sedimentary lithofacies. The predominant is the trough cross-bedded sandstone facies (St), representing 75% of the total succession. Four architectural elements were recognized: sandy bedforms (SB), channel fill (CH), gravelly bars and bedforms (GB), and overbank fines (OF). Moreover, the mean value of paleocurrent directions was 285.3°, suggesting a southeasterly located source area. The majority of the sandstones were classified as quartz arenites with subordinate sublithic-arenites. Ternary plots of quartz-feldspar-lithic fragments (QFL) revealed that the tectonic setting is predominantly within the interior craton. It can be suggested that the sediments were sourced from the southeast region and associated with a braided, proximal, fluvial depositional environment. The results of this study provide outcrop analog data that might be useful in exploring hydrocarbons. Specifically, it can enhance the understanding and prediction of the proximal fluvial depositional styles in the subsurface deposits in the Shendi-Atbara Basin and similar rift basins in Sudan and Africa.

Group Surrogate Data Generating Models and similarity quantification of multivariate time-series: A resting-state fMRI study

Advancements in non-invasive brain analysis through novel approaches such as big data analytics and in silico simulation are essential for explaining brain function and associated pathologies. In this study, we extend the vector auto-regressive surrogate technique from a single multivariate time-series to group data using a novel Group Surrogate Data Generating Model (GSDGM). This methodology allowed us to generate biologically plausible human brain dynamics representative of a large human resting-state (rs-fMRI) dataset obtained from the Human Connectome Project. Simultaneously, we defined a novel similarity measure, termed the Multivariate Time-series Ensemble Similarity Score (MTESS). MTESS showed high accuracy and f-measure in subject identification, and it can directly compare the similarity between two multivariate time-series. We used MTESS to analyze both human and marmoset rs-fMRI data. Our results showed similarity differences between cortical and subcortical regions. We also conducted MTESS and state transition analysis between single and group surrogate techniques, and confirmed that a group surrogate approach can generate plausible group centroid multivariate time-series. Finally, we used GSDGM and MTESS for the fingerprint analysis of human rs-fMRI data, successfully distinguishing normal and outlier sessions. These new techniques will be useful for clinical applications and in silico simulation.

Revisiting nonlinearity of heart rate variability in healthy aging

Aging is commonly regarded as a physiological process in which the dynamic complexity of physiological time series and organ systems is gradually lost. This notion is derived from the identification of a decline of nonlinear measures with the advance of aging. However, additional research on cardiovascular control studied through heart rate variability (HRV), i.e., the instantaneous changes in heart rate, shows that despite the constriction of its statistical distribution, the nonlinear organization remains present in advanced age. Here, we used surrogate data testing to investigate the presence of nonlinear information in HRV time series from a publicly available database of 1121 healthy human subjects from 18 to 92 years old. We also studied the influence of basic clinical features, such as sex, body mass index (BMI), and mean heart rate (HR), on such nonlinear information. We found that the percentage of nonlinear time series after 30 years of age diminishes significantly (p < 0.01). Furthermore, larger BMI and HR are associated with the presence of more linear information in HRV, while the female sex is associated with the manifestation of nonlinear information. This work provides a common background for the contextualized interpretation of nonlinear testing and shows that the nonlinear content of HRV time series diminishes through aging.

Estimation in nonparametric functional-on-functional models with surrogate responses

We construct an estimator for the regression operator of a functional response variable using surrogate data, given a functional random variable. The almost complete uniform convergence rate of the estimator is then established. Finally, to demonstrate the predictive utility and superiority of the estimator when dealing with incomplete data, we apply the methodology to both simulated data and meteorological data.