Themodel Parameters Research Articles

Research on wireless channel model based on improved ray tracing algorithm that considers multiple diffuse scattering and employs pyramid-shaped ray tubes as the carrier

This paper extensively utilizes fine three dimensional environmental data obtained from laser point clouds. Based on theories such as geometrical optics and effective roughness theory, a deterministic wireless channel model is established, which integrates higher-order diffuse scattering. This model is referred to as the ray tracing fusion with higher-order diffuse scattering model. To expedite the collision calculation between rays and the scene, this paper introduces a combined approach of voxelization and signed distance field, resulting in a remarkable 16-fold improvement in computational speed. Moreover, aiming to balance accuracy and efficiency, the paper systematically analyzes the optimization computation parameters of the model. Finally, the proposed model is validated using measurement data in the frequency range of 1 GHz to 6 GHz in mountainous terrain. The results indicate that the predicted outcomes of the proposed model have an accuracy within 6 dB compared to the measurement results, and are superior to ITU-R P.1546, which is an international standard recommended by the International Telecommunication Union for modeling electromagnetic wave propagation in undulating terrain. This provides necessary technical support for network planning and optimization.

Investigation of an optimal control strategy for a cholera disease transmission model with programs

Cholera is a disease of poverty affecting people with inadequate access to safe water and basic sanitation. Conflict, unplanned urbanization and climate change all increase the risk of cholera. In this article, an optimal control deterministic mathematical model of cholera disease with cost-effectiveness analysis is developed and analyzed considering both direct and indirect contact transmission pathways. The model qualitative behaviors, such as the invariant region, the existence of a positive invariant solution, the two equilibrium points (disease-free and endemic equilibrium), and their stabilities (local as well as global stability) of the model are studied. Moreover, the basic reproduction number of the model is obtained. We also performed sensitivity analysis of the basic parameters of the model. Then an optimal control problem is designed with a control functional having five controls: vaccination, treatment, environment sanitation and personal hygiene, and water quality improvement program. We examined the existence and uniqueness of the optimal controls of the system. Through the implementation of Pontryagin's maximum principle, the characterization of the optimal controls optimality system is established. The numerical simulation results the integrated control strategies demonstrated that strategy 2, 7, and 12 are effective programs to combat cholera disease from the community. Based on the local circumstances, available funds, and resources, it is recommended to the government stakeholders and policymakers to execute any one of the three integrated intervention programs.

A convexity-constrained parameterization of the random effects generalized partial credit model.

An alternative closed-form expression for the marginal joint probability distribution of item scores under the random effects generalized partial credit model is presented. The closed-form expression involves a cumulant generating function and is therefore subjected to convexity constraints. As a consequence, complicated moment inequalities are taken into account in maximum likelihood estimation of the parameters of the model, so that the estimation solution is always proper. Another important favorable consequence is that the likelihood function has a single local extreme point, the global maximum. Furthermore, attention is paid to expected a posteriori person parameter estimation, generalizations of the model, and testing the goodness-of-fit of the model. Procedures proposed are demonstrated in an illustrative example.

Symmetric teleparallel gravity with variable deceleration parameter

In this paper, our investigation focuses on the cosmic history in modified [Formula: see text] gravity, a different theory that describes the gravitational force using a non-metricity scalar. The equations of the theory are solved by assuming a parametric form for the deceleration parameter. The parameters of the model are estimated by using the Cosmic Chronometer dataset. A number of parameters, like the deceleration parameter, whose signature changes with redshift and shows typical behavior in late-time cosmology, are subject of our analysis. We also show the evolution of other parameters, such as the equation of state parameter and energy density. The parametrization approach used here in [Formula: see text] modified gravity provides a better description of the late-time cosmic evolution without dark energy.

Pedestrian safety behavior modeling method in human-vehicle-intelligence shared road space

In the context of the development of autonomous vehicles sharing road resources with traditional traffic participants in the future, modeling the safe behavior of pedestrians in the shared road space between people, vehicles and intelligence is crucial for the simulation development and reliability and safety testing of autonomous vehicles. To meet this demand, the safe movement behavior of pedestrians is first analyzed, and on this basis, the traditional pedestrian social force model is improved to construct a momentum model for the interactive movement of pedestrians with other pedestrians, traditional vehicles and autonomous vehicles in the shared road space; then, the safety parameters of the model are calibrated using a data set of human-vehicle interactive movement and a genetic algorithm, and a typical shared road space scenario is built to verify the effectiveness of the model. The simulation results show that the model can simulate the real safe movement behavior of pedestrians, and has better simulation effects when used in tasks such as human-vehicle-intelligence shared road space simulation.

Enhancing the data processing speed of a deep-learning-based three-dimensional single molecule localization algorithm (FD-DeepLoc) with a combination of feature compression and pipeline programming

Three-dimensional (3D) single molecule localization microscopy (SMLM) plays an important role in biomedical applications, but its data processing is very complicated. Deep learning is a potential tool to solve this problem. As the state of art 3D super-resolution localization algorithm based on deep learning, FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing, even though it has greatly improved the data processing throughput. In this paper, a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM. This new algorithm uses the feature compression method to reduce the parameters of the model, and combines it with pipeline programming to accelerate the inference process of the deep learning model. The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy, which can realize real-time processing of [Formula: see text] pixels size images. The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering, and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm.

Efficient Data Collection Strategy for Modeling the Dynamics of Floating Robotic Vehicles Using Neural Networks

As the hydrodynamic model of a surface vehicle is highly nonlinear and of high dimension, an extensive set of experiments is required to estimate the parameters of the model. Typically, the dynamic behavior of a surface vehicle is investigated through experimental tests such as planar motion mechanisms or free running tests in specialized test facilities. However, the cost of such tests is very high, and it is challenging to design effective and efficient test conditions, especially when the model is represented by numerous parameters such as neural networks. In this paper, we propose an efficient data collection strategy for modeling the dynamics of a floating robotic vehicle using neural networks. For generalized modeling, it is important to collect diverse data by exploring all the reachable state space of the vehicle. To achieve this, a data collection policy is built based on sampling-based planning toward reducing the uncertainty of the neural network-based model. The proposed method allows for collecting more comprehensive data than other commonly used random-based data collection policies. We show experimentally that the proposed method enables more accurate modeling of both fully actuated and under-actuated floating robotic vehicles, as demonstrated by the effective execution of various tasks.

Cost analysis of technological vs. conventional upper limb rehabilitation for patients with neurological disorders: an Italian real-world data case study.

Most patients suffering from neurological disorders endure varying degrees of upper limb dysfunction, limiting their everyday activities, with only a limited number regaining full arm use. Robotic and technological rehabilitation has been demonstrated to be a feasible solution to guarantee an effective rehabilitation to recover upper limb performance or to prevent complications of upper limb immobility. However, there is currently a lack of studies which analyze the sustainability of robotic and technological rehabilitation by comparing its costs to conventional rehabilitation pathways. Since technology-based and conventional rehabilitation of the upper limb have been demonstrated to have comparable efficacy when the rehabilitation dose is matched, our study concentrates on a cost minimization analysis. The aim of the study is to compare the costs of a "mixed" rehabilitation cycle, which combines conventional and technology-based treatments (the latter delivered with a single therapist supervising several patients), with a cycle of purely conventional treatments. This has been done by developing a cost model and retrospectively analyzing the costs sustained by an Italian hospital which has adopted such a mixed model. A sensitivity analysis has been done to identify the parameters of the model that have the greatest influence on cost difference and to evaluate their optimal values in terms of efficiency of mixed rehabilitation. Finally, probabilistic simulations have been applied to consider the variability of model parameters around such optimized values and evaluate the probability of achieving a given level of savings. We found a cost difference of 49.60 € per cycle in favor of mixed rehabilitation. The sensitivity analysis demonstrated that, in the situation of the hospital under investigation, the parameter having the largest influence on the cost difference is the number of robotic treatments in a mixed rehab cycle. Probabilistic simulations indicate a probability higher than 98% of an optimized mixed rehabilitation cycle being less expensive than a pure conventional one. Through a retrospective cost analysis, we found that the technology-based mixed rehabilitation approach, within a specific organizational model allowing a single physiotherapist to supervise up to four patients concurrently, allowed cost savings compared to the conventional rehabilitation model.

When two chains meet: Optimizing the design of blockchain-enabled supply chain networks

Applications of the blockchain technology are rapidly emerging across sectors. By redefining information flow, blockchain technology promises to instill trust among supply chain members, improve transaction efficiency, thus reshaping the structure and configuration of supply chains. The practical exploratory use of blockchain has shown immense potential in supply chain management. In order to explore the new changes brought by blockchain technology to supply chain network design issues, this study focuses on the supply chain network design problem in the context of blockchain technology. Specifically, we consider a supply chain network comprised of a number of distribution centers (DCs) and retailers. The operation of this physical network is supported by blockchain-based information infrastructure. As the interrelated relationship between these two networks, it is necessary to optimize them simultaneously to improve the profitability and information exchange level. We propose an objective function based on previous studies to maximize the profits of the entire supply chain network, with decision variables including DC locations, retailer assignment, inventory replenishment policy, and blockchain adoption level. At the same time, to characterize the interrelation between the two networks, the demand and certain cost parameters of the model become endogenous depending on the blockchain adoption level, which significantly increases the complexity of the solution algorithm. We reformulate the model and use the polymatroid cutting plane approach to address this issue. We conducted calculations and numerical analysis. The computational results show that the proposed method can solve practically sized problems, and integrating blockchain design reduces the system-wide cost. We discuss managerial insights based on a range of numerical studies and solve a case study using publicly available instance data.

Finite temperature fermionic condensate and energy–momentum tensor in cosmic string spacetime

Here we analyze the expectation value of the fermionic condensate and the energy–momentum tensor associated with a massive charged fermionic quantum field with a nonzero chemical potential propagating in a magnetic-flux-carrying cosmic string in thermal equilibrium at finite temperature T. The expectation values of the fermionic condensate and the energy–momentum tensor are expressed as the sum of vacuum expectation values and the finite temperature contributions coming from the particles and antiparticles excitation. The thermal expectations values of the fermionic condensate and the energy–momentum tensor are even periodic functions of the magnetic flux with period being the quantum flux, and also even functions of the chemical potential. Because the analyses of vacuum expectation of the fermionic condensate and energy–momentum tensor have been developed in literature, here we are mainly interested in the investigation of the thermal corrections. In this way we explicitly study how these observable behaves in the limits of low and high temperatures, and also for points near the string. Besides the analytical discussions, we included some graphs that exhibit the behavior of these observable for different values of the physical parameters of the model.

Pulmonary epithelial wound healing and the immune system. Mathematical modelling and bifurcation analysis of a bistable system

Respiratory diseases such as asthma, acute respiratory distress syndrome (ARDS), influenza or COVID-19 often directly target the epithelium. Elevated immune levels and a ‘cytokine storm’ are directly associated with defective healing dynamics of lung diseases such as COVID-19 or ARDS. The infected cells leave wounded regions in the epithelium which must be healed for the lung to return to a healthy state and carry out its main function of gas-exchange. Due to the complexity of the various interactions between cells of the lung epithelium and surrounding tissue, it is necessary to develop models that can complement experiments to fully understand the healing dynamics. In this mathematical study we model the mechanism of epithelial regeneration. We assume that healing is exclusively driven by progenitor cell proliferation, induced by a chemical activator such as epithelial growth factor (EGF) and cytokines such as interleukin-22 (IL22). Contrary to previous studies of wound healing, we consider the immune system, specifically the T effector cells TH1, TH17, TH22 and Treg to strongly contribute to the healing process, by producing IL22 or regulating the immune response. We therefore obtain a coupled system of two ordinary differential equations for the epithelial and immune cell densities and two functions for the levels of chemicals that either induce epithelial proliferation or recruit immune cells. These functions link the two cell equations. We find that to allow the epithelium to regenerate to a healthy state, the immune system must not exceed a threshold value at the onset of the healing phase. This immune threshold is supported experimentally but was not explicitly built into our equations. Our assumptions are therefore sufficient to reproduce experimental results concerning the ratio TH17/Treg cells as a threshold to predict higher mortality rates in patients. This immune threshold can be controlled by parameters of the model, specifically the base-level growth factor concentration. This conclusion is based on a mathematical bifurcation analysis and linearization of the model equations. Our results suggest treatment of severe cases of lung injury by reducing or suppressing the immune response, in an individual patient, assessed by their disease parameters such as course of lung injury and immune response levels.

Epicyclic oscillations and particle collision with trajectories around quantum corrected black holes

The present study deals with the orbital, oscillatory motion, and trajectories of test particles around quantum-corrected black holes. The angular momentum within specific energy is calculated to discuss stable circular orbits. The effective potential through Hamiltonian is calculated around quantum-corrected black holes. The trajectories scenario around particles of considered black hole solutions are discussed. Further, we provide the mathematical formulas for both latitudinal and radial frequencies. The primary properties of test particle quasi-periodic oscillations in stable circular orbits in the black hole’s equatorial plane are investigated. We provide a Markov Chain Monte Carlo analysis of the restrictions on quantum-corrected black hole attributes. We analyze QPOs detected in X-ray binaries, namely the cores of the galaxies Sgr A∗, M82 X-1, microquasars XTE J1550-564, GRS 1915+105 and GRO J1655-40. Additionally, the process of Periastron precession is examined. We show that the velocity of particles around black holes is strongly dependent on the parameters of the model. Notably, the pictorial behavior that describes our results feasibility.

A new modelling method for satellite solar panel and its correlation verification

AbstractIn satellite design and manufacturing, due to the influence of material properties, sandwich structure and equivalent algorithm, there is usually a significant deviation between the theoretical model and the actual physical prototype. To solve the issue, a new finite element model of satellite sailboard based on sandwich theory is constructed. The key parameters of the model are iteratively optimized by Lagrange optimization algorithm to improve its accuracy and reliability. The Lansos method is used to simulate the satellite sailboard model, and the comparison is made with the actual test model. The results show that the error of the modified model in the first six orders of frequency is less than 5%, which meets the modal correlation standard, and the modal assurance criterion (MAC) value is higher than 0.85. Compared with the uncorrected model, the first six frequency errors are reduced by 0.1%, 0.9%, 0.7%, 2.0%, 1.1% and 5.5%, respectively. At the same time, the MAC value of each mode increased by 1.9%, 3.2%, 1.2%, 2.6%, 13.1% and 27.1%, respectively. These findings show that the revised sailboard model is closer to the test model in terms of frequency and modal characteristics, while providing a higher level of accuracy and reliability.

СИСТЕМНО-ОБЪЕКТНОЕ ИМИТАЦИОННОЕ МОДЕЛИРОВАНИЕ МИКРОКОНТРОЛЛЕРА ATMEL ATMEGA 168

The paper presents the process of developing a simulation model, including a description of the functionality of themicrocontroller, determination of the main characteristics and parameters of the model, as well as the selection of tools for its creation. The paper presents a detailed description of the developed simulation model, including its architecture, main components and interfaces. The results provide a valuable contribution to the field of education by providing educational materials and tools for teaching students how to use the Atmel ATmega 168 microcontroller.

On the Anniversary of the Academician of the National Academy of Sciences of Belarus Stanislav Ivanovich Grib

Stanislav Ivanovich Grib, Doctor of Agricultural Sciences, Professor, an Honored Scientist of the Republic of Belarus, a Laureate of the State Prize of the Republic of Belarus, an Academician of the National Academy of Sciences (NAS) of Belarus, a Foreign Member of the Russian Academy of Sciences and the National Academy of Agrarian Sciences of Ukraine, a talented Belarusian scientist in the field of plant breeding, known in the country and abroad, the leader of the Belarusian scientific school in the area of breeding and seed production, an experienced organizer of agricultural science, turned 80 years old on August 6, 2024. He was the first in Belarus to scientifically substantiate and implement in practice a new scientific direction, that is, breeding of intensive varieties of grain crops with a yield of 10-12 t/ha of grain; developed methods for broadening genetic diversity and identifying genotypes that combine high productivity and good product quality with tolerance to biotic and abiotic environmental factors; substantiated the parameters of the model of intensive varieties; created and introduced into production systems of highly productive, resource-saving varieties of barley, triticale, and spring wheat. The results of his research have been published in more than 700 scientific works, including 12 monographs, books and textbooks. Stanislav Ivanovich is the author of eight inventions and 89 varieties of agricultural plants. Academician S.I. Grib, the leader of the scientific school of plant breeding in Belarus, trained four doctors and twelve candidates of science. S.I. Grib's achievements have been recognized abroad. He was elected a foreign member of the Russian Academy of Sciences and the National Academy of Agrarian Sciences of Ukraine, an Honorary Professor of the Siberian Branch of the Russian Academy of Sciences, a member of the European Association of Plant Breeders (EUCARPIA), and an Honorary Doctor of the Belarusian Agricultural Academy (BSAA). Stanislav Ivanovich was awarded the Order of the Badge of Honor, a diploma of the Supreme Council of the BSSR, medals and diplomas of the Exhibition of Economic Achievements, honorary diplomas of the National Academy of Sciences of Belarus, the Ministry of Agriculture and Food of the Republic of Belarus, and the Scientific and Practical Center of the National Academy of Sciences of Belarus for Agriculture.

Intermediate inflation in a generalized non-minimal derivative coupling model

In this work, we consider intermediate inflation in the context of the generalized non-minimal derivative coupling (GNMDC) model. In the GNMDC model, inflation is driven by a canonical scalar field that is coupled not only to gravity but also to the derivative of the scalar field. The model introduces new dynamics and features during the inflationary epoch. We find inflationary solutions with a power law scalar field for the power law coupling function. Additionally, we determine the inflaton potential that generates intermediate expansion of the scale factor. We also discuss the background equations in the high friction limit and derive constraints on the parameters of our model. Furthermore, we investigate the cosmological perturbations in the slow roll approximation within the GNMDC model, and we calculate the scalar and tensor spectral index and the tensor-to-scalar ratio during intermediate inflation. We compare the results of this model with observational data that can be used to test the model using cosmic microwave background radiation data. Overall, we establish conditions for the inflaton potential that ensure the continuation of accelerated expansion during slow roll inflation. We numerically analyze the power spectrum and spectral index for scalar and tensor modes in intermediate inflation in the high friction limit, and we use Planck 2018 data to obtain constraints on the parameters of the model. We demonstrate that intermediate inflation in the GNMDC model is successful in evaluation and explanation of background and perturbation quantities using observational data.

Dynamics of the age composition of the red king crab males in the Barents Sea with account of probability molt

The aim of the research was improvement of the algorithm of age determination for red king crab males in the Barents Sea, by taking into account the molting probability.Materials were the data of research surveys and fisheries monitoring obtained in 2001–2022 and combined in 54 data sets, as well tagging data published in open press.As results the previous algorithm which allowed establish the age composition of the red king crab males catches was improved. The parameters of the model of the dependence of the probability of molting on the width of the carapace, which was established earlier according to the tagging data, were changed. The modified model allowed to improve the adequacy of the estimates by reducing the values of the approximation error. Compared to the previous estimates of the age composition, the account of the probable molting history did not lead to a significant revision of the results: all noticeable generations remained the same as before, the time for recording a generation in catches however increased from 10 to 12 years. The parameters of the proposed model are the closest to those for the Kikhchik subpopulation of the red king crab off the Western Kamchatka. The contradiction associated with the discrepancy in the assessment of the maximum age of both the generation and individual specimens is discussed.Practical value. The obtained results can be applied for the red king crab stocks assessment with the help of cohort models.

Method of reference models for synthesis of intellectual systems of nonlinear dynamic objects identification

The paper is devoted to resolving the contradiction between the accuracy of modeling nonlinear dynamic objects and the speed of models building under conditions of limited computing resources. The purpose of the work is to reduce the time for building models of nonlinear dynamic objects with continuous characteristics while ensuring a given modeling accuracy. This goal is achieved by further developing the method of synthesing intelligent systems based on the superposition of pre-trained reference models in the form of neural networks reflecting the basic properties of the object. The scientific novelty of the work novelty consists in the development of a method for identifying nonlinear dynamic objects in the form of neural networks with time delays based on a set of pre-trained neural network models that reflect the basic properties of the subject area. In contrast to the traditional approach based on pre-trained neural networks the developed method allows building models of lower complexity and with shorter training time while ensuring the required accuracy. To determine the initial parameters of the model, expressions based on the superposition of reference models in the form of neural networks are proposed. The practical usefullness of the work consists in the development of an algorithm for the method of reference models for training neural networks with time delays in the tasks of identifying nonlinear dynamic objects with continuous characteristics, which can significantly reduce the training time of neural networks without losing the accuracy of the model. The value of the study lies in determining the area of effective use of the proposed method, namely, in the availability of a sufficient amount of qualitative data for the building of reference models. Insufficient data or poor data quality can significantly reduce the accuracy of reference models and, as a result, significantly reduce the training time of the target model.

Study on the Weibull distribution function-based stochastic damage evolution law for uniaxial compression in high-performance concrete with full aeolian sand

The issues of environmental degradation due to natural sand overextraction are becoming increasingly severe. In desert regions, utilizing aeolian sand as a substitute for natural sand in construction projects facilitates the resource economization and the effective management of desertification. The concrete industry is expected to significantly contribute to this objective, as high-performance concrete with full aeolian sand (FA-HPC) has demonstrated excellent mechanical properties. However, current researches have yet to establish a rational damage evolution law for uniaxial compression in FA-HPC from the perspective of damage characteristics. In this study, uniaxial compression tests were conducted on FA-HPC with different mixture proportions. Simultaneously, the stress-strain behavior was examined, and acoustic emission (AE) signals were collected and localized. Based on the Weibull distribution function-based stochastic damage evolution law for uniaxial compression, regression analyses of the stress-strain and AE cumulative ringing count for FA-HPC were performed. Damage evolution model for effective stress and damage variable were established and validated with experimental data to confirm their applicability. The scale and shape parameters of the model were predicted using the mixture proportions as independent variables. The results show that the rationality of the damage evolution law assumption was validated through AE source localization. The goodness of fit values for damage variable and effective stress equation range from 0.87 to 0.98, with the ascending segment and the vicinity of the peak point closely matching the experimental data, indicating that the Weibull distribution function-based stochastic damage model for uniaxial compression is applicable for FA-HPC. The predicted values of the damage evolution model parameters based on the mixture proportions align well with the measured values, achieving a goodness of fit of 0.99, thereby guiding the study of FA-HPC's compressive characteristics prior to the formation of primary cracks. Notably, there remains a need to propose corrective equations to enhance the correlation between regression curves and experimental data in the late failure stages of FA-HPC in future research, achieving precise predictions by considering the interactions of multiple factors.

A new extension of Burr-Hatke exponential distribution with engineering and biomedical applications

In this study, the Topp-Leone family of distribution approach was used to modify the Burr Hatke Exponential distribution to provide adequate fits for some engineering and health data which previous existing distributions in the family of Burr Hatke Exponential have failed to do. The new distribution improves the robustness of Burr Hatke Exponential distribution by making it capable of modeling emerging new world complex data with varying features, possesses greater capacity and flexibility to model lifetime data, has better goodness of fit. Some mathematical properties of the derived distribution such as quantile function, moments, order statistics, entropies, etc were obtained and discussed. Some non-Bayesian estimation approaches like maximum likelihood estimation (ML), maximum Product spacing estimation (MPSE), Least squares estimation (LS), weighted least squares estimation (WLS), Cramer-von-Mises estimation (CVM), Anderson-Darling estimation (AD), and right-tailed Anderson Darling estimation (RTAD), as well as Bayesian method under independent gamma priors were adopted to estimate the parameters of the model and the various methods proved efficient. From the simulation results, the bias and root mean squared error for the parameters are relatively small and the become smaller as the sample size becomes larger. This shows convenience and improved estimation accuracy. We further constructed a regression model using the proposed distribution. Extensive simulation studies were used to determine the efficiency of the method in both the estimation of its parameters and that of the regression model. The TL-BHE regression was fitted on censored CD4 count data of HIV/AIDs patients. The competitiveness, applicability, and usefulness of the model were demonstrated using datasets and the results validate the theoretical findings. The results indicate that the TL-BHE distribution achieved better results than the baseline distribution and other variants of the classical distributions.