Mathematical Criteria Research Articles

COMBING: Clustering in Oncology for Mathematical and Biological Identification of Novel Gene Signatures.

Precision medicine is a paradigm shift in healthcare relying heavily on genomics data. However, the complexity of biological interactions, the large number of genes as well as the lack of comparisons on the analysis of data, remain a tremendous bottleneck regarding clinical adoption. In this paper, we introduce a novel, automatic and unsupervised framework to discover low-dimensional gene biomarkers. Our method is based on the LP-Stability algorithm, a high dimensional center-based unsupervised clustering algorithm. It offers modularity as concerns metric functions and scalability, while being able to automatically determine the best number of clusters. Our evaluation includes both mathematical and biological criteria to define a quantitative metric. The recovered signature is applied to a variety of biological tasks, including screening of biological pathways and functions, and characterization relevance on tumor types and subtypes. Quantitative comparisons among different distance metrics, commonly used clustering methods and a referential gene signature used in the literature, confirm state of the art performance of our approach. In particular, our signature, based on 27 genes, reports at least 30 times better mathematical significance (average Dunn's Index) and 25% better biological significance (average Enrichment in Protein-Protein Interaction) than those produced by other referential clustering methods. Finally, our signature reports promising results on distinguishing immune inflammatory and immune desert tumors, while reporting a high balanced accuracy of 92% on tumor types classification and averaged balanced accuracy of 68% on tumor subtypes classification, which represents, respectively 7% and 9% higher performance compared to the referential signature.

A mathematical extrapolation-based yield criterion and its application in modeling of burst pressure of defect-free straight pipes

To solve the problem of insufficient accuracy of the existing prediction models of burst pressure for defect-free straight pipes, a new yield criterion that can effectively reflect the deformation characteristics of pipeline materials is established by the mathematical extrapolation method, and a theoretical model of burst pressure for defect-free straight pipes is then derived. By comparing each yield criterion with the experimental data, the new yield criterion is found to be between the TSS criterion and the Tresca criterion, and is in good agreement with the experimental data. On this basis, a theoretical model for predicting the burst pressure of defect-free straight pipes is constructed by using this criterion, and the burst pressure under other yield criteria are also derived. The normalized burst pressures based on different yield criteria are given and compared with the full-size test data. It is found that the new yield criterion has the highest approximation degree to the test data since the average relative error is just 1.154% and the maximum error is no more than 10.801%. The model in this paper can meet the requirement of predicting the burst pressure of a defect-free straight pipe, which is of great significance to the structural design and safety assessment.

SeNPIS: Sequential Network Pruning by class-wise Importance Score

In the last decade, pattern recognition and decision making from images has mainly focused on the development of deep learning architectures, with different types of networks such as sequential, residual and parallel. Although the depth and size varies between models, they all have in common that they can contain multiple filters or neurons that are not important for the purpose of prediction, and that do negatively impact the size of the model and their inference times. Therefore, it is advantageous to use pruning methods that, while largely maintaining the initial performance of the classifier, significantly reduce its size and FLOPs. In parameter reduction, the decision rule is generally based on mathematical criteria, e.g. the amplitude of the weights, but not on the actual impact of the filter or neuron on the classifier performance for each of the classes. Therefore, we propose SeNPIS as a method that involves both filter and neuron selection based on a class-wise importance score, and network resizing to increase parameter reduction and FLOPs in sequential CNN networks. Several tests were performed to compare SeNPIS with other representative state-of-the-art methods, for the CIFAR-10 and Scene-15 datasets. It was found that for similar values of accuracy, and even in some cases with a slight increase in accuracy, SeNPIS significantly reduces the number of parameters by up to an additional 23.5% (i.e., a 51.05% reduction with SeNPIS versus a 27.53% reduction with Gradient) and FLOPs by up to an additional 26.6% (i.e., a 74.82% reduction with SeNPIS versus a 48.16% reduction with Weight) compared to the Weight, Taylor, Gradient and LRP methods.

Video analysis of a massive coiled spring transverse oscillations described by Fourier series

This work presents a low-cost and straightforward experiment to visualise Fourier synthesis using a coiled metallic spring whose weight is not negligible, oscillating in a vertical plane and fixed at both ends. A massive coiled spring still obeys the standard linear differential wave equation, but a new term accounting for the effect of gravity must be introduced in the equation. Hence, if the spring is plucked vertically at its centre so that its initial configuration is a triangle, the trapezoidal shape of the resulting oscillation, typical of a spring of negligible mass, is clearly distorted by the effect of gravity, when the amplitude of the oscillation moves away from its maximum value. The hypothesis that this distortion comes from some nonlinear effect produced by the larger pluck-length ratio used in the experiment was discarded when evaluating the magnitude of a mathematical criterion to estimate the nonlinear impact of oscillations with large amplitudes. As the spring’s tension is small (∼4 N, on average) and the frequencies of transverse waves are low (close to 1 Hz), oscillations could be filmed with a basic and cheap camera (60 fps), which makes it possible to carry out the experiment without the need to purchase equipment. In addition, as the frequencies involved are low, the oscillations could be reproduced and visualised in slow motion. The free software Tracker was used to collect the position data of the massive coiled spring at each instant and compare them with the related solution of the wave equation obtained by Fourier synthesis of the first six terms.

Denoised Jarque-Bera features-based K-Means algorithm for intelligent cooperative spectrum sensing

This paper aims to boost the blind perception performance of a cooperative spectrum sensing system in a noisy environment. A novel intelligent centralized-cooperative spectrum sensing technique based on new denoised features developed by the Jarque-Bera (JB) statistics test and the K-Means (KMS) clustering algorithm is proposed (JBKMS). The skewness and access kurtosis are high-order moments of JB statistics that are utilized to build denoised features and attenuate the noise level of sensing samples from secondary users. The new denoised features are named and formed by four mathematical criteria, including the difference between large and average JB (DLAJB) vector values; the difference between large and small JB (DLSJB) vector values; the ratio between large and small JB (RLSJB) vector values; and the ratio between large and trace JB (RLTJB) vector values. In comparison to other standard strategies, the proposed methods eliminate the computational complexity associated with threshold estimation and improve sensing performance by minimizing interference across KMS clusters. Then, the KMS method is used to classify the denoised features based on the presence or absence of the primary user in the noise channel in order to construct a well-trained classifier. Various energy features of random matrix theory based on the clustering algorithm are compared in this work. The results of the simulations demonstrate that the devised methods have superior detection performance in terms of probability of detection at low signal-to-noise ratio.

Curvilinear Flight Synthetic Aperture Radar (CF-SAR): Principles, Methods, Applications, Challenges and Trends

The research into curvilinear flight synthetic aperture radar (CF-SAR) is the inevitable result of the comprehensive practicality of SAR. The flight path of the SAR platform in real applications, which is highly nonlinear or curvy due to three-dimensional velocity and acceleration, cannot be described by the traditional uniform linear motion model. New mathematical models, signal characteristics, imaging algorithms, and system design criteria must be proposed and investigated for CF-SAR. This paper provides a comprehensive overview of CF-SAR. Firstly, the basic concept, unified model, and general signal characteristics of CF-SAR are defined, derived, and analyzed, respectively. Additionally, the advantages and drawbacks of current methodologies are reviewed. Discussions on the CF-SAR’s applications are presented from the perspective of typical platforms, new configurations, and advanced technologies, which are suitable means to fulfill the increasing user requirements. Finally, the challenges faced by CF-SAR are summarized, and some future trends for the study of CF-SAR are explored. Hopefully, this paper will serve as a reference for SAR researchers/engineers and stimulate the future development and actual application of CF-SAR.

A New Robust Method to Investigate Dynamic Instability of FTV for the Double Tripod Industrial Driveshafts in the Principal Parametric Resonance Region

The present work aims to design a robust method to detect and certify the deterministic chaos or ergodic process for the forced torsional vibrations (FTV) of a double tripod industrial driveshaft (DTID) in transition through the principal parametric resonance region (PPRR) which is considered by the researchers in the field as one of the most important resonance regions for the systems having parametric excitations. The DTID’s model for FTV considers the following effects: nonuniformities of inertial characteristics of the DTID’s elements, the harmonic torque excitation induced by the asynchronous electrical motor used for a heavy-duty grain mill, and the harmonic reaction torque generated by different granulation of the substance needed to be milled. Based on these aspects, a model of the FTV for the DTID was designed which was a modified, physically consistent model already used by the authors to investigate the FTV of automotive driveshafts (homokinetic transmission). For the DTID elements, the dynamic instability for nonstationary FTV in the PPRR using time–history analysis (THA) was analyzed—THA represents the phase portraits. Time–history analysis is a detection method for possible chaotic dynamic behavior for the nonstationary FTV (NFTV) in transition through PPRR. If this dynamic behavior was seen, a new robust method LEA–PM was created to certify and confirm the deterministic chaos for the NFTV of DTID. The new method, LEA–PM, is composed of the Lyapunov exponent’s approach (LEA) coupled with the Poincaré Map (PM) applied to the global system of differential equations that describe the FTV of DTID in the PPRR. This new robust method, which embeds LEA and PM, LEA–PM, establishes if the mechanical system has a deterministic chaotic dynamic behavior (strange attractor) or an ergodic dynamic process in this resonant region. LEA represents a new method that includes not only the maximal Lyapunov exponent method (MLEM) but also new mathematical criteria that is “the sum of all Lyapunov exponents has to be negative” which, coupled with MLEM, indicates the presence of deterministic chaos (strange attractors). THA–LEA–PM had been used for the NFTV of DTID computing the phase portraits, the Lyapunov exponents, and representing the Poincaré Maps of the NFTV for the DTID’s elements in transition through PPRR, founding deterministic chaos or ergodic dynamic behavior. Based on the obtained results, numerical simulations revealed the pitting manifestations of the DTID’s elements, typical for the geared systems transmission, mentioned recently in experimental data research for the homokinetic transmissions. Using the new robust method, THA–LEA–PM (time–history analysis coupled with LEA–PM) can be used in future research for chaotic dynamic analysis of DTID’s NFTV transition through superharmonic resonances, subharmonic resonances, combination resonances, and internal resonances. Time–history analysis as a detection method for chaos and LEA–PM as a certifying method for deterministic chaos can be integrated as a design tool for DTID’s FTV control of the homokinetic transmission.

Information-analytical System Supporting Marketing Decisions

The article discusses the information-analytical system supporting marketing decisions, which has paramount importance in marketing management. It represents a key facilitator in various marketing management processes. The paper proposes the main components of a marketing information system (MIS): an intelligent system of marketing decision or recommendation rules consisting of eccentric components, mathematical criteria, quality and priority criteria; request input/output system, which ensures that data received from the MIS is presented in the form required by the decision-maker (DM). The decision-maker, in addition to the complex marketing task of being responsible for making an effective economic decision, also adds to the burden of being able to evaluate the consequences that may result from the decision.

Solvable Criterion for the Contextuality of any Prepare-and-Measure Scenario

Starting from arbitrary sets of quantum states and measurements, referred to as the prepare-and-measure scenario, an operationally noncontextual ontological model of the quantum statistics associated with the prepare-and-measure scenario is constructed. The operationally noncontextual ontological model coincides with standard Spekkens noncontextual ontological models for tomographically complete scenarios, while covering the non-tomographically complete case with a new notion of a reduced space, which we motivate following the guiding principles of noncontextuality. A mathematical criterion, called unit separability, is formulated as the relevant classicality criterion – the name is inspired by the usual notion of quantum state separability. Using this criterion, we derive a new upper bound on the cardinality of the ontic space. Then, we recast the unit separability criterion as a (possibly infinite) set of linear constraints, from which we obtain two separate hierarchies of algorithmic tests to witness the non-classicality or certify the classicality of a scenario. Finally, we reformulate our results in the framework of generalized probabilistic theories and discuss the implications for simplex-embeddability in such theories.

Using system equalization principle to study the effects of multiple factors to the development of bee colony

In recent years, frequent outbreaks of bee colony collapse have seriously threatened food production and ecological balance. Many scholars have studied different factors and formed a consensus that multiple factors jointly cause the colony collapse. However, it is still a challenge to understand the effects of multiple factors on bee colony development due to the complex microcosmic mechanism of each factor. Based on the principle of system equilibrium, this paper models the macroscopic dynamics of bee colony with three core states: the stocks of food, the population of adult bees and the population of larval bees, and the internal and external factors have been abstracted as the model parameters. The mathematical criteria for the benign development of bee colonies are presented from the idea of system stability, and the effects of multiple factors are studied by critical parameter perturbation and Monte Carlo simulation. The results show that the population of adults is most likely to be influenced by multiple factors, that is to say, the observations related to adults, such as the total number of adults and the number/frequency of adults entering and leaving the nest, can more quickly reflect the health status of bee colony, which provides a theoretical basis for experimental studies. Meanwhile, this study reveals the effects of multiple factors on the development of bee colonies from the level of system equilibrium, which is of great significance for understanding the collapse of bee colonies in recent years. Further, according to monitoring the three core states, the trend changes of them could be used to diagnose the healthy problems of actual bee colonies.

ОПТИМИЗАЦИЯ РАСПОЛОЖЕНИЯ РЕГИОНАЛЬНЫХ ЦЕНТРОВ ТЕХНИЧЕСКОГО ОБСЛУЖИВАНИЯ И РЕМОНТА СЕЛЬСКОХОЗЯЙСТВЕННОЙ ТЕХНИКИ

The results of studies on optimizing the location of regional centers for maintenance and repair of agricultural machinery are presented. An original technique is proposed, the implementation of which includes three stages. The number of regional centers for maintenance and repair of agricultural machinery in the region, taking into account the relevant basic dealer standards, depending on the number of agricultural machinery in the region under consideration, was determined at the first stage.The second stage included the division of the considered territorial area into subareas, using the theory of sets. The elements of set theory made it possible to establish a one-to-one correspondence between the area under consideration as a territorial subject and a certain set as a mathematical object with certain mathematical properties. The elements of set theory made it possible to establish a one-to-one correspondence between the area under consideration as a territorial subject and a certain set as a mathematical object with certain mathematical properties. The representation of an area, which is a territorial unit, as a set, defined as a mathematical object, made it possible to abstract and proceed to the study of its physical properties using mathematical symbols and criteria. he values of the effective time for each district center included in the subdomain under consideration were determined at the final stage, and the choice of one district center within each subdomain under consideration was carried out according to the smallest value of the effective time. The coefficient of availability of serviced equipment for a particular district center, in which it was supposed to design a center for maintenance and repair of agricultural equipment, was used in calculating the effective time. It was determined that the travel time of a service center specialist - 1.8 hours was established. The optimal distance for finding technical service objects from the main or additional service centers - no more than 63 km was established.

A new criterion for vapour induced ice lens initiation in coarse-grained soils

Vapour migration and condensation into ice have been found to be the main reasons for frost heave in coarse-grained soils. The mechanism of this kind of frost heave is less understood than the traditional one due to condensation of liquid water. In particular, an accurate mathematical criterion is required to explain when and where the ice lens will form in coarse-grained soils. This study focuses on the migration of vapour from a mesoscale point of view. Mechanical equilibrium on the vapour-ice interface is discussed, and the rate of frost heave is acquired theoretically. A new criterion for ice lens initiation is proposed by analysing the forces acting at the vapour-ice interface. The influence of the overburden pressure and temperature gradient on the heaving rate is discussed. The new criterion provides a theoretical basis to understand frost heave in coarse-grained soils and can be used in numerical models for simulating frost damage in cold regions.

Stochastically Adaptive Control and Synchronization: From Globally One-Sided Lipschitzian to Only Locally Lipschitzian Systems

The mathematical framework of stochastically adaptive feedback control, which is generally applicable to significant problems in nonlinear dynamics such as stabilization and synchronization, has been previously established but only for systems whose vector fields satisfy the global Lipschitzian condition. Nonlinear dynamical systems arising from physical, chemical, or biological situations are typically described by vector fields that are only locally Lipschitzian. To generalize the mathematical theory of stochastically adaptive control to realistic systems is quite a challenging and formidable task. We meet this challenge by proving rigorously that stabilization and synchronization can be achieved with probability one for only locally Lipschitzian systems. The result holds not only for one-dimensional but also for any finite-dimensional white noises. Representative examples and an application to synchronization-based parameter identification are presented to illustrate the broad applicability of the developed mathematical criteria. Our successful relaxation of the mathematical condition from globally to locally Lipschitzian provides a rigorous guarantee of the stability of stochastically adaptive control in physical systems with significant implications to the design and realization of engineering control systems.

Computer Modeling of Grain Structure Formation during Quenching including Algorithms with Pre- and Post-Solidification

Simulation of the grain growth process, as a function of steel heat transfer conditions, is helpful for predicting grain structures of continuous cast steel products. Many authors have developed models based on numerical methods to simulate grain growth during metal solidification. Nevertheless, the anisotropic nature of grain structures makes necessary the employment of new mathematical methods such as chaos theory, fractals, and probabilistic and stochastic theories of simulation. The problem is significant for steelmakers to avoid defects in products and to control the steel microstructure during the continuous casting process. This work discusses the influence of nodal solidification times and computer algorithms on the dynamic formation of the chill, columnar, and equiaxed zones including physical phenomena such as nucleation and grain growth. Moreover, the model incorporates pre-nucleation and pre-growth routines in the original algorithm. There is a description of the influence of the mathematical parameter criteria and probabilities over the grain morphology obtained after solidification. Finally, an analysis of these algorithms elucidates the differences between these structures and those obtained from models considering only the solidification.

Hybrid energy harvesting for self-powered rotor condition monitoring using maximal utilization strategy in structural space and operation process

The energy harvesting technology is capable of harnessing the intrinsic rotating energy of the rotor system to realize self-powered rotor condition monitoring for the Internet of Things (IoT). It is promising to solve the issue of sustainable energy supply of the rotor monitoring system and achieve a self-powered IoT. In this work, we propose a novel maximal utilization strategy for piezoelectric-electromagnetic-triboelectric energy harvesting in a broad speed range and achieve the self-powered rotor condition monitoring system. The piezoelectric energy harvester (PEH), triboelectric nanogenerator (TENG), and electromagnetic energy harvester (EMH) are respectively arranged in the place where the system exhibits the maximum strain, the maximum contact area, and the maximum displacement, respectively, which can make full use of their characteristics in the structural space. The modulation boundaries (parts of TENG) render a more controllable dynamic behavior of the harvester, and realize the vibration and impact coordinated power generation mode, which can harvest more mechanical energy in the time domain. The theoretical mathematical model and working criteria of the proposed system are established and verified experimentally. In addition, the prototype can operate effectively in a wide speed range (0–1000 r/min) and it can charge a 100 μF capacitor to 5 V within 11 s. The self-powered rotor wireless temperature monitoring and self-powered wireless tire pressure monitoring are realized during the practical road tests. The maximum utilization strategy provides a new design methodology for hybrid energy harvesting, which has potential applications in intelligent driving and rotating machinery condition monitoring.

Mathematical Criteria for a Priori Performance Estimation of Activities of Daily Living Recognition.

Monitoring Activities of Daily Living (ADL) has become a major occupation to respond to the aging population and prevent frailty. To do this, the scientific community is using Machine Learning (ML) techniques to learn the lifestyle habits of people at home. The most-used formalism to represent the behaviour of the inhabitant is the Hidden Markov Model (HMM) or Probabilistic Finite Automata (PFA), where events streams are considered. A common decomposition to design ADL using a mathematical model is Activities–Actions–Events (AAE). In this paper, we propose mathematical criteria to evaluate a priori the performance of these instrumentations for the goals of ADL recognition. We also present a case study to illustrate the use of these criteria.

Optimization of Surround-View System Projection Parameters using Fiducial Markers

The paper is devoted to the problem of increasing quality of reproduction of the environment by mobile robot’s surround-view system, operating in the augmented reality mode. A variant of a surround-view system based on the cameras with over-lapping fields of view is being considered. A virtual model has been developed, it includes 3D-CAD models of a mobile robot and surrounding objects, as well as virtual models of cameras. The cross-platform integrated development environment "Unity" was chosen to implement the model. Methods for solving the problem of displaying the surrounding mobile robot space in the "third-person view" mode are determined. A mathematical criterion for assessing the quality of reproduction of the surrounding space is proposed. It is based on the comparison of points obtained from a virtual model with points obtained as a result of projection of images from virtual cameras. To obtain points, ArUco fiducial markers were used, providing an unambiguous comparison of points on the original and synthesized images. The dependence of the value of the objective function of the optimization problem on the projection parameters by the uniform search method are investigated. A method for automatic adaptation of projection parameters using fisheye lenses and stereo vision methods is proposed. Directions for further research are identified.

Temperature and NOx distribution characteristics of coal particles under high-temperature and low-oxygen environments simulating MILD oxy-coal combustion conditions

A diffusion-flamelet-based Hencken burner was used to produce moderate and intense low-oxygen dilution oxy-coal combustion (MILD-OCC) of coal particles under O2/CO2 environments with the coflow temperatures of 1000–1400 K and oxygen concentrations of 2.86–8.75%. The flame temperature distributions were measured by a R-type thermocouple coupled with temperature continuous recorder. The NOx was captured by a self-made air extraction sampling device and measured by UV spectrophotometer. Three influencing factors including fuel-air ratio of methane, secondary air and particle size of pulverized coal on temperature and NOx distributions were experimentally researched. It is found that increasing the fuel-air ratio and the volume of secondary air leads to lower peak temperature, more uniform temperature distribution, lower NOx and stronger NO-reburning effect in flame. To a certain extent, decreasing the particle size of pulverized coal causes lower ignition delay, higher release rate of volatiles and little change in NOx emissions, further reducing the particle size of pulverized coal results in higher NOx in flame. Moreover, the mathematical criterion of MILD-OCC state is deduced based on criterion of MILD coal combustion. The research has important reference value for in-depth understanding of MILD-OCC regime and the generation mechanism of fuel NOx.

A Unified Machine Reading Comprehension Framework for Cohort Selection.

Cohort selection is an essential prerequisite for clinical research, determining whether an individual satisfies given selection criteria. Previous works for cohort selection usually treated each selection criterion independently and ignored not only the meaning of each selection criterion but the relations among cohort selection criteria. To solve the problems above, we propose a novel unified machine reading comprehension (MRC) framework. In this MRC framework, we design simple rules to generate questions for each criterion from cohort selection guidelines and treat clues extracted by trigger words from patients' medical records as passages. A series of state-of-the-art MRC models based on BiDAF, BIMPM, BERT, BioBERT, NCBI-BERT, and RoBERTa are deployed to determine which question and passage pairs match. We also introduce a cross-criterion attention mechanism on representations of question and passage pairs to model relations among cohort selection criteria. Results on two datasets, that is, the dataset of the 2018 National NLP Clinical Challenge (N2C2) for cohort selection and a dataset from the MIMIC-III dataset, show that our NCBI-BERT MRC model with cross-criterion attention mechanism achieves the highest micro-averaged F1-score of 0.9070 on the N2C2 dataset and 0.8353 on the MIMIC-III dataset. It is competitive to the best system that relies on a large number of rules defined by medical experts on the N2C2 dataset. Comparing these two models, we find that the NCBI-BERT MRC model mainly performs worse on mathematical logic criteria. When using rules instead of the NCBI-BERT MRC model on some criteria regarding mathematical logic on the N2C2 dataset, we obtain a new benchmark with an F1-score of 0.9163, indicating that it is easy to integrate rules into MRC models for improvement.

Relationships between metabolic factors and Heart Rate Variability parameters. Prevention in occupational health.

The rhythmic functioning of the human body is influenced by many factors. The development of innovation in recent decades has made it possible to study heart rate variability. Objectives: (1) obtaining more accurate information than previously available to understand the complex effects on the human body, (2) logical exploration of the directions and magnitudes of variations in the metabolic factors and HRV parameters, (3) drawing attention to the importance of understanding these complex effects and to use them for prevention in occupational health. Methods: Using non-invasive methods of the HRV analysis, physiological measurements were taken at workplaces with the participation of people at work, both at rest in lying position and during work. In the present study, we focused on analysing the measurement data from groups where, due to the workplace conditions (e.g. chemical safety regulations), clinical laboratory test results were also available. Measurement results were evaluated using the SPSS software system and other advanced mathematical methods. Results: Of the more than 5,000 physiological measurements carried out at the workplace over nearly 20 years, 571 had measurement results that could be used to analyze changes in the individuals’ metabolism and heart rate. These analyses enabled us to assess the reported relationships, by taking into account (1) the combined changes in heart rate, blood pressure, blood glucose, cholesterol, triglyceride, body weight and HRV parameters, (2) the effects of age, years of service and life characteristics, and (3) the mathematical reliability criteria. On the basis of the available sample size and individual characteristics, groups with favorable and unfavorable conditions were formed. Differences in the data of these groups compared to each other and to the mean demonstrate the striking nature of the results. The reliability of the results was ensured by the mathematical methods used in the analysis. Conclusion: The simultaneous variations of metabolic syndrome factors and HRV parameters highlight the potential and the importance of using new methods developed through innovation.