Stochastic Data Research Articles

Crafting efficient blockchain adoption strategies under risk and uncertain environments

Risk and uncertainty are crucial factors in decision-making processes, especially when integrating emerging technologies into essential systems like supply chains. Failing to adequately consider significant risks can disrupt supply chain operations, leading to a loss of competitive edge and causing financial and reputational damage. On the other hand, the complex nature of new technology environments, differing viewpoints among stakeholders, and the challenges of interpreting data introduce a variety of uncertainties in decision-making. In this study, we conduct a thorough examination of how blockchain strategies can be applied within supply chain frameworks. Our analysis utilizes data-driven network decision-making models that are refined to effectively manage uncertainty and risk. These models take into account aspects such as supply chain dynamics and technological factors. Importantly, we meld risk considerations with our models to tackle efficiency shortfalls, while also accounting for uncertainty caused by ambiguous and stochastic data environments. By applying and assessing these models in a real-world case study of the oil and gas industry, our research uncovers insightful observations. Specifically, we find that adopting a localization strategy presents specific risks, while a single-use strategy yields significant efficiency improvements.

Dynamic event-triggered mean-square consensus of positive multi-agent systems via impulsive control under false data injection attacks

This paper investigates the mean-square impulsive consensus problem of positive multi-agent systems (MASs) under stochastic sensor false data injection attacks (FDIA). False data injection attacks are mortal to a positive system since the system positivity could be destroyed with ease (by tampering with the sampled positive state value to a negative one, for instance). To deal with this issue, a data compensator is proposed which preprocesses deliberately distorted state values to reduce their adverse impacts on the positivity of the positive MASs. On the other hand, a novel sampled-data-based dynamic event-triggered mechanism (SDB-DETM) is tailored to generate impulsive control time sequences, save control costs, and promise a desired control performance. A maximum event-trigger time interval is introduced as a substitute for the assumption of the average time interval when considering the randomness of FDIA. Furthermore, the dynamics of the auxiliary dynamic variable are improved to adapt the novel SDB-DETM with mixed signals. A sufficient and necessary condition of the positivity of the positive MASs under FDIA is presented. The commonly used positive constraints on the error system are removed. By constructing a quadratic Lyapunov function, a sufficient condition of the mean-square consensus of the positive MASs is derived. Two numerical examples are provided to illustrate the effectiveness of theoretical results.

Polynomial chaos expansion of SAR and temperature increase variability in 3 T MRI due to stochastic input data

Objective. Numerical simulations are largely adopted to estimate dosimetric quantities, e.g. specific absorption rate (SAR) and temperature increase, in tissues to assess the patient exposure to the radiofrequency (RF) field generated during magnetic resonance imaging (MRI). Simulations rely on reference anatomical human models and tabulated data of electromagnetic and thermal properties of biological tissues. However, concerns may arise about the applicability of the computed results to any phenotype, introducing a significant degree of freedom in the simulation input data. In addition, simulation input data can be affected by uncertainty in relative positioning of the anatomical model with respect to the RF coil. The objective of this work is the to estimate the variability of SAR and temperature increase at 3 T head MRI due to different sources of variability in input data, with the final aim to associate a global uncertainty to the dosimetric outcomes. Approach. A stochastic approach based on arbitrary Polynomial Chaos Expansion is used to evaluate the effects of several input variability’s (anatomy, tissue properties, body position) on dosimetric outputs, referring to head imaging with a 3 T MRI scanner. Main results. It is found that head anatomy is the prevailing source of variability for the considered dosimetric quantities, rather than the variability due to tissue properties and head positioning. From knowledge of the variability of the dosimetric quantities, an uncertainty can be attributed to the results obtained using a generic anatomical head model when SAR and temperature increase values are compared with safety exposure limits. Significance. This work associates a global uncertainty to SAR and temperature increase predictions, to be considered when comparing the numerically evaluated dosimetric quantities with reference exposure limits. The adopted methodology can be extended to other exposure scenarios for MRI safety purposes.

3D model of the El Hornito pluton, Sierras Pampeanas of Argentina: Evidence of hybridization in the re-used feeder channels for emplacement in the ductile-brittle level

A comprehensive structural, petrological, and geophysical study of the 385 ± 2 Ma Devonian El Hornito Pluton (EHP) in the Sierra Grande de San Luis, Argentina, is presented. We establish that the EHP results from the intrusion of three main magma batches. The three-dimensional shape of the pluton was modeled using stochastic litho-constrained gravity data inversion and was complemented with a field geology survey. Results suggest a shallow-rooted, irregularly shaped pluton with a mean depth of 1.5 km, 16 km long and 4 km deep with a volume of 217 km³. Two areas of negative residual Bouguer anomaly reveal the existence of two root zones interpreted to indicate feeder channels. One of the feeders does not have a surface expression, but the other coincides with the central part of the pluton. A hot mafic magma intruded into and rejuvenated a crystallizing granite mush and the thermal perturbations triggered local convection with the development of an ascending magma plume developing mixing/mingling structures. Pluton emplacement was controlled by a shear zone at the boundary between two major metamorphic units. Emplacement is evidenced by the location of the two root zones along the interpreted contacts of the metamorphic zones, and the shear zone along this basement anisotropy provided the space into which magma batches were emplaced. Internal and external structures indicate that space for pluton emplacement was generated by a combination of tectonism with ballooning and stoping assisted by floor depression, which were the most effective processes during the final emplacement of the magma. This was possible due to the contrasting rheological responses of the host rocks.

The efficiency of China's export trade with Africa and its influence mechanism

AbstractThis paper calculates the trade efficiency of China's exports to Africa by considering trade inefficiency factors, such as Chinese aid to Africa and Chinese contracted projects. Based on a stochastic frontier gravity model and data for China's exports to 38 African countries from 2010 to 2020, this study draws the following conclusions. First, trade inefficiencies, such as China's aid and contracted projects, play a positive role in promoting China's export trade to Africa. Second, the efficiency of China's export trade to Africa varies significantly in different regions, among which countries with high export trade efficiency are concentrated in North and South Africa. Third, the trade potential and trade expansion space of China's exports to Africa vary significantly among different product sectors, which provides much space for promoting trade policy. We highlight that China's government should develop differentiated regional trade policies due to regional heterogeneity in terms of export trade efficiency between China and African countries.

Chance constrained directional models in stochastic data envelopment analysis

We construct a new family of chance constrained directional models in stochastic data envelopment analysis, generalizing the deterministic directional models and the chance constrained radial models. We prove that chance constrained directional models define the same concept of stochastic efficiency as the one given by chance constrained radial models and, as a particular case, we obtain a stochastic version of the generalized Farrell measure. Finally, we give some examples of application of chance constrained directional models with stochastic and deterministic directions, showing that inefficiency scores obtained with stochastic directions are less or equal than those obtained considering deterministic directions whose values are the means of the stochastic ones.

Cooperative Adaptive Cruise Control in the Presence of Communication and Radar Stochastic Data Loss

Cooperative Adaptive Cruise Control in the Presence of Communication and Radar Stochastic Data Loss

Efficient estimation of true fixed-effects stochastic frontier models

Fixed-effects modeling has become the method of choice in several panel data settings, including models for stochastic frontier analysis. A notable instance of stochastic frontier panel data models is the true fixed-effects model, which allows disentangling unit heterogeneity from efficiency evaluations. While such a model is theoretically appealing, its estimation is hampered by incidental parameters. This note proposes a simple and rather general estimation approach where the unit-specific intercepts are integrated out of the likelihood function. We apply the theory of composite group families to the model of interest and demonstrate that the resulting integrated likelihood is a marginal likelihood with desirable inferential properties. The derivation of the result is provided in full, along with some connections with the existing literature and computational details. The method is illustrated for three notable models, given by the normal-half normal model, the heteroscedastic exponential model, and the normal-gamma model. The results of simulation experiments highlight the properties of the methodology.

A hybrid stochastic data envelopment analysis and decision tree for performance prediction in retail industry

Assessing the retail industry's efficiency is pivotal for economic growth and corporate productivity. This study employs a novel approach, utilizing a regression-based Stochastic Data Envelopment Analysis (SDEA) model, Balanced Scorecard (BSC), and Decision Tree. The integration of these methods is a pioneering effort in the retail sector. This is a data-driven decision-making framework, aiding managers in predicting efficient and inefficient Decision-Making Units (DMUs). Results from a case study in 44 retail store chains in Iran indicate that the accuracy of the SDEA model is 99%. The Decision Tree highlights low branch efficiency due to a low customer count, a unique finding in comparison to prior studies.

Lending business models and FinTechs efficiency

The aim of the study is to analyse which managerial issues can be considered the main efficiency drivers for all Italian FinTechs engaged in lending. We measure their efficiency in the period 2020–2022 via Stochastic Data Envelopment Analysis. The main determinants seem to be ROA and cost-to-income ratio; this means that the ability to control both the business risk level and costs is crucial for FinTechs’ managers and other players interested in M&A deals in this industry. The results are useful for FinTechs, other financial players, regulators and supervisors in defining homogeneous rules in the lending sector.

Environmental performance evaluation in the forest sector: An extended stochastic data envelopment analysis approach

This study addresses the global concern about undesirable outputs in the Forest Sector. We propose two innovative models, namely a directional weak disposable DEA model and an extended stochastic DEA model, to measure environmental efficiency. These models make a significant contribution to the field by specifically assessing the uncertain environmental efficiency of the forest sector. We validated our proposed models by conducting an empirical application using the United Nations Economic Commission for Europe (UNECE) forest sector dataset. The study examines important outputs such as above ground biomass stock, export unit prices of industrial roundwood, wood removals (desirable outputs), and CO2 emissions from wildfires (undesirable output). The results demonstrate that our novel stochastic weak disposability DEA model outperforms traditional approaches when the second scenario is applied. Specifically, the average technical efficiency (TE) score decreases to 0.92, and the number of efficient units reduces to 27, representing an approximate improvement of 55 %. Furthermore, the reduction rate of CO2 emissions is 4.09 % lower than the benchmark. Hence, our extended novel stochastic weak disposability DEA approach enhances the assessment of efficiency and inefficiency in decision-making units, contributing to the mitigation of risk and uncertainty. It also improves overall environmental performance in forest management.

Data Validation Algorithm Based on Vector Rank Analysis

In the process of solving the problem of managing large technical systems, the data obtained from various measuring devices are processed by known methods. On the basis of their analysis, acceptable solutions are formed, and as a result of the choice, the best is made. Some of the data are parametrized and are stochastic, i.e. they are random variables. However, the information for making management decisions must be strictly deterministic. Therefore, the main task of stochastic data processing is to obtain deterministic invariants suitable for use as information in the decision-making process. The article presents an algorithm for verifying data that allows you to determine which type they belong to: Gaussian or non-Gaussian. The results of this test will make it possible to make the right choice of mathematical apparatus for obtaining deterministic invariants. The scientific novelty of the algorithm lies in the fact that the mathematical apparatus of the algorithm is developed within the framework of vector rank analysis. Its essence lies in the fact that a sample is made from the "general population" of available data, on which the average and standard are determined. Then a part of the data taken from the "general population" is added to this sample, and the average and standard are determined again. Such a procedure for increasing the sample continues until the "general population" is completely exhausted. Next, the normalized dependence of the mean and standard values on the sample size is constructed. At the same time, if the dependence has a pronounced tendency to stabilize, then the data belong to the Gaussian type. In another case, they are considered non-Gaussian. The efficiency of the algorithm has been confirmed in the framework of studies of a significant number of samples of data on the power consumption of various large technical systems.

A firm-specific Malmquist productivity index model for stochastic data envelopment analysis: an application to commercial banks

In the data envelopment analysis (DEA) literature, productivity change captured by the Malmquist productivity index, especially in terms of a deterministic environment and stochastic variability in inputs and outputs, has been somewhat ignored. Therefore, this study developed a firm-specific, DEA-based Malmquist index model to examine the efficiency and productivity change of banks in a stochastic environment. First, in order to estimate bank-specific efficiency, we employed a two-stage double bootstrap DEA procedure. Specifically, in the first stage, the technical efficiency scores of banks were calculated by the classic DEA model, while in the second stage, the double bootstrap DEA model was applied to determine the effect of the contextual variables on bank efficiency. Second, we applied a two-stage procedure for measuring productivity change in which the first stage included the estimation of stochastic technical efficiency and the second stage included the regression of the estimated efficiency scores on a set of explanatory variables that influence relative performance. Finally, an empirical investigation of the Iranian banking sector, consisting of 120 bank-year observations of 15 banks from 2014 to 2021, was performed to measure their efficiency and productivity change. Based on the findings, the explanatory variables (i.e., the nonperforming loan ratio and the number of branches) indicated an inverse relationship with stochastic technical efficiency and productivity change. The implication of the findings is that, in order to improve the efficiency and productivity of banks, it is important to optimize these factors.

Changes in efficiency and physical size of container ports: An integration of genetic matching and stochastic data envelopment analysis

Benchmarking container ports of different physical sizes and figuring out the relationship between size and efficiency is complex due to the heterogeneous environment. In dealing with heterogeneity, the selection bias is often overlooked. Therefore, this study proposes an integrated multivariate genetic matching and stochastic DEA algorithm to evaluate the efficiency of container ports. It paves the way for container ports that differ in a selected feature, such as size, to be benchmarked using well-balanced clusters. Thus, the port managers can identify the most similar-featured peers in benchmarking with DEA or alternative models to acquire robust estimates without dependence on a longitudinal data set. The results of the model applied to international container ports imply the increase in size impacts efficiency negatively, while connectivity does positively, which contradicts the commonly held perception of stakeholders that the larger the container ports, the more efficient. That is, well-managed small container ports can also be as efficient. Therefore, it is concluded that the results of integrating genetic matching into the performance measurement provide beneficial inferences. In future research, clustering the observed container ports according to a specific feature and balancing clusters with multivariate genetic matching can provide valuable insights into the industry.

Data-driven discovery of stochastic dynamical systems with [formula omitted]-stable Lévy noise based on residual networks

Identifying evolution laws of complex systems from massive data has been a challenging issue in the cross field of stochastic dynamics and data science with greater and greater concern in recent years. So far, significant progress has been made in dealing with cases where differential equations are driven by Gaussian noise, yet work for non-Gaussian cases remains great room for growth. In this paper, a data-driven approach based on deep residual networks (ResNets) is proposed to extract governing laws of stochastic dynamical systems perturbed by non-Gaussian α-stable Lévy noise. Specifically, we firstly separate the non-Gaussian noise from drift term for subsequent extraction according to the fundamental theory of differential equations. For the noise part, the Lévy jump measure and noise intensity are estimated via some properties of α-stable distribution. Next the residual network is employed to identify drift. Numerical results of systems of 1 to 3 dimensions are given to demonstrate the feasibility of this method. It is necessary to emphasize here that the contribution of this method is mainly manifested in its effective resolution of the identification for complex non-Gaussian stochastic dynamical systems via cutting-edge machine learning techniques.

Применение методов машинного обучения для анализа показателей на финансовых рынках

The results of research in the field of application of various machine learning (ML) methods for the analysis and forecasting of financial time series are presented. The purpose of the study is to assess the suitability of ML algorithms for solving the problem of analyzing and predicting the state of financial markets. The source data was public data on Apple shares for the period from May 2002 to June 2020. As part of the study, machine learning algorithms were developed using stochastic analysis and data was prepared with subsequent preprocessing for better learning algorithms, as shown in the example of matrix formation for the boosting algorithm. The area of forecasting financial time series was studied and algorithms of boosting, fully connected neural network, recurrent neural network, neural networks with long short-term memory, convolutional neural network were applied. Based on the trained algorithms, a comparative analysis was carried out on a sample of financial instruments from one sector

Computation of mRNA Number Distributions in Interactive Pathway Stochastic Gene Transcription Model

Gene expression is a stochastic process characterized by fluctuations in the amount of mRNA in individual cells of an isogenic cell population. The fluctuation can be characterized by the mRNA number distribution Pm (t) , which denotes the probability of producing m copies of mRNA molecules in one cell at time t. For a given stochastic gene transcription model, how to solve the corresponding Pm (t) exact expression has been a hot research topic in this field. In the existing work, most of the results are solved for Pm (t) in the steady state case or in a specific parameter region, which affects us to fully understand the kinetic effects of different stochastic gene regulation patterns on the distribution of mRNA quantities. In this thesis, we explore the computation of Pm (t) for the cross-talk pathway model and give its exact expression under arbitrary system parameter conditions. The interactive signaling pathway model is successfully applied to elaborate stochastic transcription data of inducible genes in response to environmental changes. This work provides a kinetic theoretical basis for us to further investigate the interactive signaling pathway regulatory mechanism of gene transcription.

Statistical Analysis of Ice Load on Icebreaker Ship Based on Stochastic Ice Fields

Accurately assessing ice loads is a fundamental issue in the field of structural design for ships in ice-covered regions. In this paper, we conducted research on extreme ice load estimation for icebreaking ships, combining stochastic theory with numerical simulation. Firstly, using sea ice data from the Arctic region of the United States National Snow and Ice Data Center, a stochastic ice field model was established under Arctic sea ice conditions using non-parametric estimation and the rejection sampling method, and ice field data were generated stochastically. Then, based on the stochastic ice field data, a three-dimensional numerical model of the interaction between the ice field and the ship hull was established, and the reliability of the numerical model was verified by experimental results. Finally, based on the numerical model of the interaction between the ice field and the ship hull, asymptotic methods were used to study the extreme ice load estimation in different parts of the ship hull, revealing the variation law of the extreme ice load in different parts of the ship hull. This study provides basic theory and technical support for the structural design of ships in polar regions and has engineering application value.

Parameter identification algorithm for ship manoeuvrability and wave peak model based multi-innovation stochastic gradient algorithm use data filtering technique

This paper addresses the issue of identifying ship motion parameters and wave peak frequency. Utilising the Euler discretisation principle, we establish a discrete-time auto-regressive moving average model with exogenous input (ARMAX) for the ship-wave system. Furthermore, we develop a filtering-based stochastic gradient algorithm for the system by applying filtering techniques and auxiliary model identification idea. A filtering-based multi-innovation stochastic gradient algorithm, utilizing the multi-innovation identification theory, was developed to enhance the convergence rate and accuracy of parameter identification. This approach was found to be more effective than the filtering-based stochastic gradient algorithm. Simulation results validate the efficacy of the proposed algorithm in parameter identification.

Geoinformation modeling of radioactive contamination of territories on the example of mines of the “ShidGSK” mining and processing plant

The aim of the study was to model the territorial distribution and statistical assessment of migration through the trophic chain of naturally occurring radionuclides released into the environment as a result of uranium ore mining. Statistical, geoinformation and experimental research methods were used in the study. Interpolation of the results of volumetric activity of natural radionuclides in environmental components and development of spatial models of their territorial distribution were performed using the ArcGIS software; statistical processing of modelling results and development of mathematical models of migration of natural radionuclides between environmental components were performed using ArcGIS Geostatistical Analyst software. The paper substantiates the choice of the method of geostatistical modelling of the territorial distribution of volumetric activity of natural radionuclides 234U, 238U, 210Po, 210Pb, 226Ra in soils and plants, which allows modelling the values of probabilistic indicators of radioecological contamination in the absence of a sufficient array of initial actual research results. Based on the analysis of the data obtained, the methodology of mathematical modelling of migration of natural radionuclides between soil and plant parts was further developed, which will facilitate consideration of the specifics of migration of natural radionuclides through the trophic chain and help in determining the level of radioecological hazard to the environment. The developed territorial models allow one to obtain stochastic data for their further analytical processing and visualizing radioecological hazard zones. Based on the developed models, zones of increased radioecological hazard within the existing sources of radioactive contamination were identified.