Statistical Stability Research Articles

Statistical abundance and stability of carbon nanostructures by combined condensation-annealing molecular dynamics simulations

Molecular dynamic (MD) simulations of the Combined Condensation and Annealing (CCA) of free carbon atoms was used to investigate the statistical abundance and stability of neutral carbon nanostructures for a cluster size between 2 and 54 atoms. During these CCA MD-simulations carbon atoms are submitted to a condensation/heating phase, a CTR phase at a prescribed annealing temperature and a cooling phase. Numerical experiments showed that the determination of the statistical abundances requires at least 100 ns condensation/heating phase, 100 ns CTR phase at the annealing temperature and 100 ns cooling phase. The clusters obtained by CCA MD-simulations are dominated by linear structures for n = 2–5, mono-ring for n = 6–15, multi-ring structures for n = 16–18, 2D graphene-like structure for sizes in the range n = 19–29 and cage-like structure above n = 30. Open cage structures are obtained at temperature around 2000 K, while closed cage structures dominate at 3000 K. For even larger temperature, i.e., 4000 K, the dominant structures are 2D and 3D multi-ring structures, 2D graphene-like structure remaining quite significant. The resulting bond energy versus cluster size variation is in good agreement with those obtained by other approaches.

Adaptive Coding and Bit-Power Loading Algorithms for Underwater Acoustic Transmissions

Underwater acoustic channel (UAC) is featured as fast time-varying characteristic, and challenges the transmission designs. To countermine the time variation effect, we propose an adaptive design for orthogonal frequency division multiplexing (OFDM) transmission systems by utilizing the long-term stability of the second-order statistics of the channel state information (CSI). We derive the analytical expression of signal-to-interference-plus-noise-ratio (SINR) at each subcarrier to reach the target error performance based on the statistical information of the CSI. Thereafter, a new adaptive coding and bit-power loading algorithm with low computational complexity is proposed to pursuit the highest achievable bit rate with fixed error rate. The validity of SINR calculations and the effectiveness of the proposed adaptive algorithm are demonstrated under various conditions, wherein both simulated and measured channels have been tested.

New information technologies in the estimation of the third type systems

In the middle of the 20th century, W. Weaver, one of the founders of information theory, propose a hypothesis about special systems of the third type (living systems). In the article, W. Weaver directly points out the inability to describe living systems within the framework of determinism and stochastics. However, no one even tried to study the third type systems from these positions over the past 70 years. 20 years ago, we proved the Eskov-Zinchenko effect in the form of a lack of statistical stability of samples of human movement parameters. A new mathematical apparatus is now proposed to accurately describe the behavior of such systems. It is based on an analogue of Heisenberg’s uncertainty principle.

Ранговый классификатор состояний природной среды

This paper discusses the main features associated with the development and research of the device based on the methods of intelligent technology for assessing the state of the natural environment. It should be noted that natural and technical objects, as well as the processes occurring in them, are characterized by high complexity and dynamism, and a significant part of these processes has not yet been fully studied and formalized. Therefore, one of the most important areas of data analysis in this area is the use of artificial neural networks in information and measurement systems. In the works of scientists from various countries, the high efficiency of the use of artificial neural networks in solving individual data processing problems in the classification of environmental conditions is shown. The proposed approach is based on methods of nonparametric statistics using rank criteria and will allow for intelligent analysis of data on key environmental indicators, such as hydrometeorological data on the level of pollution and composition of air, soil, maximum permissible emissions of harmful substances, environmental monitoring of anomalies, and others. Static, dynamic, integral, and generalized models of classification of environmental conditions are presented. Further research plans suggest evaluating the impact of sample size on statistical sensitivity, statistical stability, and areas of confident/uncertain recognition, as well as building a decision support system for detecting the G-effect, and considering an adaptive approach to constructing an evaluation matrix.

Integrating additional knowledge into the estimation of graphical models.

Graphical models such as brain connectomes derived from functional magnetic resonance imaging (fMRI) data are considered a prime gateway to understanding network-type processes. We show, however, that standard methods for graphical modeling can fail to provide accurate graph recovery even with optimal tuning and large sample sizes. We attempt to solve this problem by leveraging information that is often readily available in practice but neglected, such as the spatial positions of the measurements. This information is incorporated into the tuning parameter of neighborhood selection, for example, in the form of pairwise distances. Our approach is computationally convenient and efficient, carries a clear Bayesian interpretation, and improves standard methods in terms of statistical stability. Applied to data about Alzheimer's disease, our approach allows us to highlight the central role of lobes in the connectivity structure of the brain and to identify an increased connectivity within the cerebellum for Alzheimer's patients compared to other subjects.

On the Statistical Stability of Families of Attracting Sets and the Contracting Lorenz Attractor

We present criteria for statistical stability of attracting sets for vector fields using dynamical conditions on the corresponding generated flows. These conditions are easily verified for all singular-hyperbolic attracting sets of $$C^2$$ vector fields using known results, providing robust examples of statistically stable singular attracting sets (encompassing in particular the Lorenz and geometrical Lorenz attractors). These conditions are shown to hold also on the persistent but non-robust family of contracting Lorenz flows (also known as Rovella attractors), providing examples of statistical stability among members of non-open families of dynamical systems. In both instances, our conditions avoid the use of detailed information about perturbations of the one-dimensional induced dynamics on specially chosen Poincaré sections.

The Statistical Fragility of Platelet-Rich Plasma in Rotator Cuff Surgery: A Systematic Review and Meta-analysis

Background: The practice of evidence-based medicine relies on objective data to guide clinical decision-making with specific statistical thresholds conveying study significance. Purpose: To determine the utility of applying the fragility index (FI) and the fragility quotient (FQ) analysis to randomized controlled trials (RCTs) evaluating the utilization of platelet-rich plasma (PRP) in rotator cuff repairs (RCRs). Study Design: Systematic review and meta-analysis. Methods: RCTs pertaining to the utilization of PRP in surgical RCRs published in 13 peer-reviewed journals from 2000 to 2020 were evaluated. The FI was determined by manipulating each reported outcome event until a reversal of significance was appreciated. The associated FQ was determined by dividing the FI by the sample size. Results: Of the 9746 studies screened, 19 RCTs were ultimately included for analysis. The overall FI incorporating all 19 RCTs was only 4, suggesting that the reversal of only 4 events is required to change study significance. The associated FQ was determined as 0.092. Of the 43 outcome events reporting lost to follow-up data, 13 (30.2%) represented lost to follow-up >4. Conclusion: Our analysis suggests that RCTs evaluating PRP for surgical RCRs may lack statistical stability with only a few outcome events required to alter trial significance. Therefore, we recommend the reporting of an FI and an FQ in conjunction with P value analysis to carefully interpret the integrity of statistical stability in future comparative trials. Clinical Relevance: Clinical decisions are often informed by statistically significant results. Thus, a true understanding of the robustness of the statistical findings informing clinical decision-making is of critical importance.

Statistical stability indices for LIME: Obtaining reliable explanations for machine learning models

Nowadays we are witnessing a transformation of the business processes towards a more computation driven approach. The ever increasing usage of Machine Learning techniques is the clearest example of such trend. This sort of revolution is often providing advantages, such as an increase in prediction accuracy and a reduced time to obtain the results. However, these methods present a major drawback: it is very difficult to understand on what grounds the algorithm took the decision. To address this issue we consider the LIME method. We give a general background on LIME then, we focus on the stability issue: employing the method repeated times, under the same conditions, may yield to different explanations. Two complementary indices are proposed, to measure LIME stability. It is important for the practitioner to be aware of the issue, as well as to have a tool for spotting it. Stability guarantees LIME explanations to be reliable therefore a stability assessment, made through the proposed indices, is crucial. As a case study, we apply both Machine Learning and classical statistical techniques to Credit Risk data. We test LIME on the Machine Learning algorithm and check its stability. Eventually, we examine the goodness of the explanations returned.

Statistical properties of physical-like measures* *SG is supported by NSFC 11831001 and NSFC 11771025. JY is partially supported by CNPq, FAPERJ, and PRONEX of Brazil and NSFC 11871487 of China.

In this paper we consider the semi-continuity of the physical-like measures for diffeomorphisms with dominated splittings. We prove that any weak-* limit of physical-like measures along a sequence of C 1 diffeomorphisms {f n } must be a Gibbs F-state for the limiting map f. As a consequence, we establish the statistical stability for the C 1 perturbation of the time-one map of three-dimensional Lorenz attractors, and the continuity of the physical measure for the diffeomorphisms constructed by Bonatti and Viana.

Probabilistic prediction of short term wind power considering temporal and spatial dependence of prediction error

The short-term probabilistic prediction of wind power has the characteristics of spatial dependence and time-series dependence. Considering the two characteristics at the same time can improve the prediction level. In this paper, a probabilistic short-term wind power prediction model considering the temporal and spatial dependence of prediction error is proposed. Considering the coupling relationship between the two properties, the NWP(Numerical Weather Prediction) wind speed point prediction error in the historical period is hierarchical clustering, and the empirical distribution model is used to fit the probability distribution of the error under different wind conditions; the cumulative empirical distribution probability value corresponding to the NWP wind speed at the time to be predicted is bootstrap sampling; under the given confidence level, the possible wind speed at each time point to be predicted in the short term is calculated The power fluctuation range of the generator. The test results show that this method can ensure the statistical significance and fitting stability of the sub sample set at the same time, and improve the classification accuracy of the days to be predicted. Compared with considering a single property, the result of probability prediction performs better on multiple evaluation indexes.

Methods for long-term forecasting of water availability in spring floods (r. Arpa – p. Jermuk)

The article discusses the main physical and geographical factors, affecting the runoff of spring floods in the Arpa rivers catchment in the station Jermuk. Also the article discusses the development of a methodology for long-term forecasting of runoff volume of spring flood (WIV–VI) of river Arpa, station Jermuk. The study used data of water discharges of Arpa river catchment (station Jermuk), air temperature, precipitation, reserve water in snow at meteostation Jermuk. A linear correlation was also revealed between the values of the annual runoff and runoff of spring floods in Arpa river catchment, which can be used to predict the annual runoff. To predict the volume of spring flood runoff, regression method and obtained multivariate correlation dependencies. Assessment of statistical significance and stability the proposed models showed their «satisfactory» quality and the possibility of using in the practice of engineering and hydrological forecasts.

Bernoulli's Number One Solution for Stochastic Equilibrium

The present research studies the numerical methods for resolving Bernoulli’s divisive statistical stability, because it is difficult to find analytical solutions for the largest number of stochastic divisive calculations. We found a new way for Bernoulli by adding white noise. Price measurement was achieved with a variety of selected models, and we realized through the solutions that the larger the (n) we find, the more we find the direct solution that approaches zero, and the error rate that approaches zero. The difference between a numerical solution and a direct solution has been observed.

МОДЕЛЬ QSPR ДЛЯ ПРОГНОЗА ОКТАНОВЫХ ЧИСЕЛ УГЛЕВОДОРОДОВ РЯДА АЛКЕНОВ ПО ТОПОЛОГИЧЕСКИМ ХАРАКТЕРИСТИКАМ МОЛЕКУЛ

Relevance Modeling, optimization and design of processes in the technology of oil refining and organic synthesis, as well as the use of new chemical and technological processes involv ing hydrocarbon systems with the calculation of physical and chemical properties (РCS) of liquids and gases. The accuracy of calculations plays an observed role in the calculations of oil refining and organic synthesis processes, as well as approximate reactor processes, fractionation and heat exchange equipment. Despite the availability of РCS databases, the search for adequate initial data takes a long time at the stage of the entire development or design process, since the РCS of many hydrocarbons remains unknown. Aim of research To develop a quantitative Structure-Property Ratio (QSPR) model for octane hydrocarbons of a series of alkenes. Research methods To predict the octane numbers of normal and substituted alkenes — components of catalytic cracking, a nonlinear multivariate regression model Quantitative Structure-Property Relationship (QSPR) is proposed. The objects of the study were 30 hydrocarbons of a number of alkenes, selection in the basic and test samples, made randomly using computer data of physical and chemical properties. The model associates a set of descriptors with the octane numbers — the topological characteristics of their molecular graphs: the Wiener index, the Randich index and the magnitude of the quadratic dependence of bone structure, which affect the octane numbers and reflect the main structural and chemical factors, such as the length and branching of the carbon skeleton, and sensitive parameters. molecules. Results The adequacy of the models was confirmed by statistical data processing, so the coefficient of determining the models is 0.856. For the quality characteristics of the QSPR model, the multiple correlation coefficient r = 0.925 was calculated, which suggested a force relationship between the proposed topological characteristics of hydrocarbon molecules and their octane numbers. To assess the statistical stability of the model, a correlation correction was used. The maximum absolute and relative errors for octane number sampling tests are 4.0 units and 4.1 %, respectively. The statistical calculation, which makes it possible to judge the adequacy of the predicted indicators, their compliance with the reference data, is the standard regression error of 6.5 units. The small value of the standard error of the regression in comparison with the values of the dependence of the applicable adequacy of the proposed model. The model adequately uses the octane numbers of linear and branched alkenes and can be used to predict the octane numbers of gasoline components.

Statistical models of network traffic

The article considers the issues of statistical modeling of traffic in telecommunication networks with packet switching. The simulation results are used in the development of network technical condition management systems, in particular, diagnostics, troubleshooting and network configuration management. The peculiarities of congestion control of separate network segments are emphasized. With improper analysis the overload condition can be mistaken for equipment failure. Therefore, control and elimination of congestion is a statistical task. The concept of end-to-end network diagnostics is considered. This concept provides for effective assessment of the quality of functioning of all network components taking into account their interrelationships. The main issues are the interaction of equipment, inefficient configuration, improper network organization and user operation. Methods of traffic statistical control characteristics based on perforated and marker bucket algorithms are analyzed. A feature of these algorithms is the formation of a strict output stream at a rate that does not depend on the non-uniformity of the input stream. The possibility of improving the token bucket algorithm by adapting to changes in the statistical characteristics of traffic is shown. To solve this problem, statistical mathematical models of network traffic are built. Data traffic circulating in telecommunication networks by packet switching has self-similar (fractal) properties. The self-similar process retains its properties when considered at different time scales (invariance to scale changes). The degree of statistical stability of the process with multiple scaling is determined by the Hirst parameter (the self-similarity parameter). Graphs of statistical characteristics of low-speed and high-speed data traffic are obtained. Their comparative analysis is carried out.

Impact of Surgical Margin Status on Survival in Gastric Cancer: A Systematic Review and Meta-Analysis.

BackgroundIt is inconclusive whether R1 margin determined by postoperative pathological examination indicates worse long-term survival in gastric cancer (GC) patients after curative intent resection (CIR). Hence, we aimed to systematically pool the conflicting evidence to fill this gap.MethodsThe present study was performed according to the published protocol and Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. Published studies examining the impact of microscopic margin status on overall survival (OS) and 5-year OS rate in GC were systematically searched in PubMed, Embase, and Cochrane Library databases. RevMan 5.3 was used to conduct statistical analysis, and the Grading of Recommendations, Assessment, Development, and Evaluations approach was used to assess the certainty of evidence for each outcome.ResultsTwenty-three retrospective cohort studies including 19 992 patients were analyzed. The pooled hazard ratio for OS of 14 studies was 2.06 (95% confidence interval [CI]: 1.61–2.65, low certainty), indicating that R1 margin predicted inferior OS. Subgroup and sensitivity analyses upheld the statistical stability of this finding. The pooled odds ratio (OR) of 14 studies was .21 (95% CI: .17–.26, moderate certainty), demonstrating that the presence of R1 margins was associated with a poorer 5-year OS rate. Sensitivity analyses and most of the subgroup analyses confirmed this finding, except the “esophagogastric junction (EGJ) cancers” subgroup, which included two studies with a pooled OR of .41 (95% CI: .10–1.61).ConclusionR1 margin detected by pathological examination might exhibit a high correlation with poorer OS and 5-year OS rate in GC (except EGJ cancers) patients who underwent CIR. To figure out the effect of R1 margin on survival of different stages and histological types need prospective studies with large sample sizes and standardized methods. What is the best treatment for R1 margin patients also need more in-depth and special research.

Метод підвищення продуктивності корелограмного оцінювання спектральної густини потужності випадкових сигналів

The method of increase of productivity of estimation of spectral density of power (SDP) of random signals at the expense of reduction of computational complexity of procedures of digital processing of investigated signals is offered in the work. The choice and substantiation of the methodology of digital evaluation of SDP signals is performed. It is proved that the correlogram methodology of SDP estimation under the condition of the same volume of processed signal samples is characterized by higher quality factor in comparison with direct estimation methods. At the same time, the application of the fast Fourier transform (FFT) technique to calculate the autocorrelation function of the signal creates conditions for improving the performance of correlogram methods for estimating SDP. Therefore, the correlogram methodology of fast digital estimation of SDP of random signals is applied in the work. A high-performance method for determining the autocorrelation function of the signal has been developed. In this paper, it is proposed to use the fast digital correlation theorem in order to accelerate the definition of the autocorrelation function. A recurrent relation is obtained to determine the discrete Fourier transform (DFT) of a subsequence in which a number of samples are updated. The proposed method requires two DFT and one reverse DFT. The synthesis of the correlation window function, which provides the maximum asymptotic rate of decline of the side lobes of the signal power spectrum for a given level of side lobes relative to the level of the main lobe of the spectrum. An expression for estimating the SDP of a random signal is obtained, according to which the statistical stability of the estimation is provided by averaging by processing segments, and correlation weighing allows to control the level of lateral parasitic petals of the spectrum of the studied signal. The research of productivity of the developed method of correlogram estimation of SDP of signals is executed. An expression is obtained to determine the coefficient of productivity, which takes into account the number of multiplication operations in the implementation of correlogram estimation of SDP. The results of the analysis proved that the proposed method makes it possible to significantly reduce the time for determining the SDP of random signals and to ensure the mode of operation of digital spectrum analyzers in a real time scale.

Long Baseline Tightly Coupled DGNSS Positioning with Ionosphere-Free Inter-System Bias Calibration

Based on the statistical stability of the inter-system bias (ISB), we propose a tightly coupled Differential Global Navigation Satellite System (DGNSS) positioning method by using ionosphere-free combination for the long baseline applications. The proposed method is compatible with the traditional Radio Beacon (RBN) base station implementation. The tightly coupled DGNSS positioning method is utilized at the long baseline rover by eliminating the effect of ionosphere delay with ionosphere-free (IF) based differential ISB calibration. The improved positioning model strength can be obtained with the proposed method when compared with the traditional loosely coupled method, particularly under the satellite-deprived environment. GNSS datasets of different baselines were collected to test the proposed method. The results of the ISB stability show that the ISB has long-term stability and needs to be calibrated when the receiver is rebooted. The positioning results show that when compared with the IF-based loosely coupled method, the IF-based tightly coupled DGNSS method based on ISB calibration can obtain better positioning performance of accuracy and continuity within 240 km baselines.

Intelligent decision-making in Smart Food Industry: Quality perspective

Fog-Cloud computing empowered Internet of Things (IoT) technology has conceptualized the ideology of Industry 4.0. Inspired by this, the food industry 4.0 presents a unique concept for determining food quality in real-time. Conspicuously, the current research provides an IoT-based smart framework for evaluating the food-quality parameters in restaurants and food outlets. IoT technology is primarily utilized to gather data that can explicitly affect food quality within a food serving environment. Such data is analyzed using the Bayesian Modeling Technique on the Fog-Cloud platform to derive a unanimous metric in terms of Probability of Food Grade (PoFG). Also, Food Grade Assessment Scale (FGAS) is quantified to assess real-time food-oriented parameters in the ambient environment of food-outlets and restaurants. Furthermore, a 2-player game-theoretic model is proposed for food quality-oriented decision services by monitoring officials and food managers. For evaluation purposes, the presented model is deployed over a challenging dataset comprising of nearly 42,410 instances. The comparative simulations were carried out with state-of-the-art methodologies, which demonstrated the dominance of the presented model in terms of Data assessment efficacy, Statistical classification analysis, Decision-making efficiency, Reliability, and Stability.

Метод оценивания частотно-временных параметров сигналов в системе одновременно и независимо функционирующих генераторов.

The issues of assessing the current frequency of each of the total of simultaneously and independently functioning generators within the electronic system are considered. The most common case is considered, in which the deviation of the true values of the nominal parameters of generators from the estimated values of these parameters has an unknown distribution law, and random deviations of frequency from the true nominal value are subject to the normal distribution law with the corresponding variance. The proposed method of obtaining parameters estimates of both independently functioning generators (true nominal frequency values and long-term instability, accidental deviation of frequency caused by short-term instability of the generator) is based on the measurement of the signals phases of each of the generators during several measuring intervals. The first step in obtaining estimates is to line the relationship between deviations of generator parameters from nominal values and the random deviation of the generator frequency by deviation of the measured phase value from the intended nominal value. This allows the formation of a system of linear algebraic equations relative to unknown values of frequency par deviations and relative instability, random deviation of frequency. Minimally necessary number of measuring intervals estimated in the materials of the article. The method proposed in the article is a further generalization of the previously proposed method of statistical frequency stabilization of the system of simultaneously and independently functioning generators, in which, based on finding the extremum of the multidimensional likelihood function, formed under the assumption of a normal distribution law for each of the arguments, the current duration of the measurement time interval, the nominal frequency and the relative instability of each of the generators. The suggested generalization is as follows: formation of an overdetermined system of equations, the unknowns in which are the deviation of the duration of the measurement time interval from the nominal value, the deviation of the values​​of the nominal frequencies of each of the generators from the corresponding assumed values, the deviation of the values ​​of the relative instabilities of each of the generators from the corresponding assumed values; formation of a system of normal equations, based on the results of the solution of which, a joint assessment of the duration of each of the measurement time intervals, frequency deviations and relative instabilities of each of the generators from the corresponding assumed values ​​is carried out; measurements of the signal phases of each of the generators at each time interval of the measurement. The proposed generalization allows us to abandon the following assumptions: exactly known values ​​of the nominal frequency and relative instability of each of the generators; about the known distribution law of the deviation of the values​​of the specified parameters from the assumed values. At the same time, the use of the proposed method leads to the need to measure the phases of the signals of each of the generators not in one, but in several measurement intervals. On the basis of numerical modeling, an increase in the accuracy of estimating the duration of time intervals of measurements and, accordingly, the frequency of generators is analyzed. It is shown that the refinement of the values ​​of the relative frequency instability of each of the generators does not lead to a practically significant increase in the accuracy of estimates of the current values ​​of the generator frequency. A transition to a simplified likelihood function is proposed, in which only the duration of the measurement time interval and the nominal frequency of each of the generators are estimated.

The Statistical Fragility of Hamstring Versus Patellar Tendon Autografts for Anterior Cruciate Ligament Reconstruction: A Systematic Review of Comparative Studies

Background: Evidence-based medicine utilizes data to inform clinical decision making, despite the ability of a small number of outcome reversals to change statistical significance. P values are common measurements of statistical significance that possess inherent flaws. The inclusion of the fragility index (FI) and fragility quotient (FQ) may provide a clearer conveyance of statistical strength. Purpose/Hypothesis: The purpose was to examine the statistical stability of studies comparing hamstring tendon and bone–patellar tendon–bone autografts in primary single-bundle anterior cruciate ligament reconstruction with independent tunnel drilling. We hypothesized that the findings of these studies are vulnerable to a small number of outcome event reversals, often fewer than the number of patients lost to follow-up. Study Design: Systematic review. Methods: Comparative studies and randomized controlled trials (RCTs) published in 10 leading orthopaedic journals between 2000 and 2020 were analyzed. Statistical significance was defined as a P value ≤.05. FI for each outcome was determined by the number of event reversals necessary to alter significance. FQ was calculated by dividing the FI by the respective sample size. Results: Of the 1803 studies screened, 643 met initial search criteria, with 18 comparative studies ultimately included for analysis, 8 of which were RCTs. A total of 114 outcomes were examined. Overall, the mean (interquartile range) FI and FQ were 3.77 (2-4) and 0.040 (0.016-0.055), respectively. The FI was less than the number of patients lost to follow-up for 76.3% of outcomes. Conclusion: Studies examining graft choice for anterior cruciate ligament reconstruction may not be as statistically stable as previously thought. Comparative studies and RCTs are at substantial risk for statistical fragility, with few event reversals required to alter significance. The reversal of <4 outcome events in a treatment group can alter the statistical significance of a given result; this is commonly fewer than the number of patients lost to follow-up. Future comparative study analyses might consider including FI and FQ with P values in their statistical analysis.