Initial Parameters Research Articles

The relationship between vertebral compression rates and systemic inflammatory scores in osteoporotic vertebral fractures.

There are many scientific reports on systemic inflammation scores (SIS) associated with decreased bone mineral density in osteoporotic vertebral disease. However, there are no studies on the association of inflammation scores with the risk of collapse in osteoporotic vertebral collapse fractures. The aim of this study was to examine the correlation between the product of platelet and neutrophil counts (PPN), platelet/lymphocyte ratio (PLR), neutrophil/lymphocyte ratio (NLR), and systemic immune inflammation index (SII) derived from complete blood count analysis in cases of osteoporotic vertebral fractures and fracture severity based on vertebral collapse rates. This study is a retrospective analysis of a cohort of 50 patients aged 50 years or older who presented with osteoporotic vertebral fractures and underwent kyphoplasty at our clinic from 2018 to 2023. The study included both men and women. Computed tomography (CT) and magnetic resonance imaging (MRI) were used to diagnose and differentiate osteoporotic vertebral compression fractures from burst fractures and pathologic fractures. All compression rate measurements were performed with CT. The compression rate of the most affected vertebra (MAV-CR) was calculated. Groups were divided into two categories based on their compression rates: <50% and ≥50%. Initial PPN, PLR, NLR, and SII parameters were used as systemic inflammation scores. No statistically significant differences were found between MAV-CR groups in PPN, PLR, NLR, and SII parameters (p>0.05). No statistically significant correlation was observed between inflammation scores and MAV-CR groups (p>0.05). In this comparison, no significant difference was observed between the selected CBC parameters and the groups divided according to the compression rate (WBC: p=0.725, PC: p=0.069, NC: p=0.732, LC: p=0.513). ROC analysis was performed to analyze the diagnostic tests (AUC=0.372 for PPN, AUC=0.509 for PLR, AUC=0.525 for NLR, and AUC=0.435 for SII). None of the systemic inflammation scores had any predictive value for osteoporotic vertebral collapse fractures. Although it has been established in the scientific literature that systemic inflammation scores are associated with osteoporotic vertebral fractures, our analysis indicates no statistically significant correlation between the parameters of PPN, PLR, NLR, and SII and the severity of compression fractures in individuals with osteoporotic vertebral fractures. In this study, using systemic inflammation scores as a predictive test for the severity of osteoporotic vertebral fractures does not seem appropriate.

A simple solution to the Rietveld refinement recipe problem.

Rietveld refinements are widely used for many purposes in the physical sciences. Conducting a Rietveld refinement typically requires expert input because correct results may require that parameters be added to the fit in the proper order. This order will depend on the nature of the data and the initial parameter values. A mechanism for computing the next parameter to add to the refinement is shown. The fitting function is evaluated with the current parameter value set and each parameter incremented and decremented by a small offset. This provides the partial derivatives with respect to each parameter, along with information to discriminate meaningful values from numerical computational errors. The implementation of this mechanism in the open-source GSAS-II program is discussed. This new method is discussed as an important step towards the development of automated Rietveld refinement technology.

Study of bulk damage of high dose gamma irradiated p-type silicon diodes with various resistivities

The bulk damage of p-type silicon detectors caused by high doses of gamma irradiation has been studied. The study was carried out on three types of n+-in-p silicon diodes with comparable geometries but different initial resistivities. This allowed to determine how different initial parameters of studied samples influence radiation-induced changes in the measured characteristics. The diodes were irradiated by a Cobalt-60 gamma source to total ionizing doses ranging from 0.50 up to 8.28 MGy, and annealed for 80 minutes at 60 °C. The Geant4 toolkit for simulation of the passage of particles through matter was used to simulate the deposited energy homogeneity, to verify the equal distribution of total deposited energies through all the layers of irradiated samples, and to calculate the secondary electron spectra in the irradiation box. The main goal of the study was to characterize the gamma-radiation induced displacement damage by measuring current-voltage characteristics (IV), and the evolution of the full depletion voltage (V FD) with the total ionizing dose, by measuring capacitance-voltage characteristics (CV). It has been observed that the bulk leakage current increases linearly with total ionizing dose, and the damage coefficient depends on the initial resistivity of the silicon diode. The effective doping concentration and therefore V FD significantly decreases with increasing total ionizing dose, before starting to increase again at a specific dose. We assume that this decrease is caused by the effect of acceptor removal. Another noteworthy observation of this study is that the IV and CV measurements of the gamma irradiated diodes do not reveal any annealing effect.

Mathematical Modeling and Comparative Assessment of Centroid Based Leach Routing Protocol for Wireless Sensor Network

This research evaluates the performance of two key routing protocols, LEACH and an enhanced version of Centralized LEACH, in Wireless Sensor Networks (WSNs). The study sets initial simulation parameters, gathers data over numerous rounds, and presents results through diagrams. Additionally, it conducts a comparative analysis with existing research. In WSNs, nodes within transmission range communicate directly, while distant nodes rely on intermediaries. The study introduces hierarchical clustering with cluster heads for network efficiency. The number of cluster heads significantly impacts energy consumption .LEACH is explored, emphasizing its two phases: setup and steady-state. The setup phase involves autonomous cluster formation without central control. Cluster heads are selected based on thresholds and advertise their presence. Nodes then choose clusters based on signal strength. TDMA scheduling optimizes data transmission. In the steady-state phase, nodes follow a timetable to send data to cluster heads. Uneven node distribution among cluster heads may lead to varying data loads. Power control minimizes energy usage, and TDMA scheduling maximizes bandwidth use. Cluster heads forward aggregated data to the base station. The paper includes an energy model detailing communication components. Findings offer insights into LEACH and its enhanced variant, contributing to energy-efficient routing protocols in WSNs.

Optimasi Parameter Artificial Neural Network (ANN) Menggunakan Particle Swarm Optimization (PSO) Untuk Pengkategorian Nasabah Bank

Artificial Neural Network (ANN), also known as Jaringan Saraf Tiruan, is one of the methods commonly used for pattern recognition, classification, forecasting, and regression, depending on the problem or data used. While the results obtained are generally good, there are often issues with determining the initial parameters as the initial weights, which can lead to non-convergence of results. This is why a method is needed to optimize the ANN parameters to achieve better outcomes. Particle Swarm Optimization (PSO) was chosen as the method to optimize the ANN parameters (PSO-ANN). The best parameter values for PSO were predefined, with w (inertia weight) set to 0.8 and c1 and c2 (acceleration coefficients) set to 1.5. Subsequently, PSO-ANN was trained using a bank customer dataset to determine the categories of customers with credit problems or not. The results were compared with using ANN without parameter optimization. The obtained results showed an Accuracy rate of 82.6%, Precision of 91.1%, and Recall of 37.1%. This represents an improvement compared to the results of ANN without parameter optimization, which had an Accuracy rate of 80.1%, Precision of 89.5%, and Recall of 32.4%.

Solving systems of homogeneous linear algebraic equations in pricing problems of an alternative financial model

Subject. This article focuses on the problems of solving systems of homogeneous linear algebraic equations in the pricing problems of an alternative financial model. Objectives. The article aims to develop a non-trivial approach to solving the problem of pricing from the scratch in the construction of a national alternative financial model, which boils down to solving large homogeneous systems of linear algebraic equations, complicated by the fuzzy setting of initial parameters (initial data) within the framework of creating a State model of a socially oriented economy, guaranteed to ensure economic, military and other types of integrated security of the Russian Federation. Methods. For the study, we used tensorial notations involving the use of elements of linear algebra, including operations with matrices and vectors, the apparatus of the fuzzy set theory (interval arithmetic), as well as methods and principles of scientific research and complex logical analysis and modeling of pricing and technological processes. Results. The article shows that an effective non-zero (different from zero, i.e. classical, trivial) solution of systems of homogeneous algebraic equations in pricing problems of an alternative financial model from the scratch is possible if the initial parameters are specified by fuzzy sets, which significantly simplifies the procedure of the computational process and the volume of arithmetic operations. Conclusions. The results obtained can be used in structures involved in the creation and testing of financial and economic models both at the federal and the Russian Federation constituent entity levels, as well as in governmental and non-governmental organizations dealing with the issues of pricing, planning and logistics support for business entities not only in the Russian Federation, but also in other countries experiencing problems in the field of financing.

Prescribed-time tracking with prescribed performance for a class of strict-feedback nonlinear systems

In this paper, we will investigate the prescribed-time tracking with prescribed performance for a class of strict-feedback nonlinear systems. A control strategy will be proposed to achieve the tracking error constrain by prescribed performance bound (PPB) and converge to zero in prescribed-time. The PPB constrains not only the convergence rate, but also the maximum overshoot of the tracking error. The tracking error will converge to zero in prescribed-time. Such a prescribed-time is irrespective of both initial conditions and design parameters. First, by incorporating the PPB into the original system via a transformation, a equivalently transformed system is obtained, which is unconstrained and convenient for control design. Then, with the help of backstepping, a controller is recursively designed to guarantee that the tracking error satisfies the PPB all the time and converges to zero in prescribed-time. The key to designing the controller is that a fractional term is introduced when designing the virtual stabilizing function during each step of backstepping, which in turn decreases the Lyapunov function to origin within prescribed-time. Finally, two examples are presented to demonstrate the validity of our proposed control strategy.

Research on Optimization Design Based on Heliostat Field

This study aims to deeply explore the mathematical modeling and performance optimization of solar heliostat fields, in order to improve their energy output efficiency. The solar heliostat field is an innovative solar heating system that focuses solar radiation on the absorption tower through a reflector, thereby generating high-temperature heat energy. This article mainly discusses the performance analysis and parameter optimization of the heliostat field to achieve optimal performance while meeting the rated power requirements. A simulated solar model was established using a three-dimensional Cartesian coordinate system, taking into account the effects of solar altitude angle and direct normal irradiance (DNI). The model in this paper comprehensively considers the effects of shadow occlusion loss, cosine efficiency, atmospheric transmittance, collector truncation efficiency, and mirror reflectance on the mirror field. The specific values of each parameter were calculated using model algorithms such as the HFLCAL model, To provide a basis for accurate performance analysis and solve the problem of parameter optimization, this article defines an objective function aimed at improving the energy utilization efficiency of the heliostat field by adjusting the parameters of the reflector, in order to achieve optimal performance. The above parameters are determined by establishing a genetic algorithm to define constants and initial parameters, and initializing the overall layout of the heliostat field to calculate the optical efficiency of the heliostat. In each iteration, the optimal heliostat field layout can be recorded. The final optimal solution includes parameters such as the position coordinates of the absorption tower, heliostat size, installation height, number of heliostats, and heliostat position.

A calibration method for infrared measurements on building facades based on a WOA-BP neural network

As a critical method for analyzing the thermal properties of buildings, the audit results of infrared thermography are affected by the accuracy of temperature measurement. However, it is still a challenge to determine the actual temperature of the target surface due to various factors (e.g., object emissivity, ambient temperature, relative humidity, measurement distance, etc.). To this end, this paper proposes a robust temperature calibration model by utilizing a BP neural network trained with numerous sets of laboratory temperature measuring data, using the Whale Optimization Algorithm (WOA) to optimize the initial network parameters, and rigorously validating the model with field measurements. The comparison of WOA-BP with traditional BP, SVM, RBF, PSO-BP and GWO-BP confirms its superiority in terms of correction error and robustness, which can effectively solve the problems of temperature measurement deviations in building facades.

Neural network compensator-based robust iterative learning control scheme for mobile robots nonlinear systems with disturbances and uncertain parameters

Aiming at the problem of trajectory tracking control for mobile robot nonlinear systems with non-repetitive uncertain parameters, we propose a novel neural network compensator-based robust iterative learning control (NNRILC) scheme to achieve excellent tracking performance and uncertainty compensation. The NNRILC scheme consists of a parallel structure that includes a robust iterative learning control (RILC) term and a neural network (NN) compensator. The RILC term is used to ensure robust control performance and promise closed-loop stability. The compensator based on the neural network is employed to handle the uncertainties resulted from the nonlinear dynamics as well as the suppressed disturbances in the mobile robot nonlinear systems. Additionally, the fourth-order Runge-Kutta algorithm is employed to solve the state differential equation of the mobile robot. Consequently, The H∞ robust technique is used to construct an iterative learning control update rule to reduce the impact of disturbances. Meanwhile, the RILC scheme is designed to optimize the neural network initial parameters and weights of the NN compensator at each trial. Then the convergence of the proposed NNRILC scheme is analyzed, and the results are incorporated into the control update for the next process trial. Finally, the effectiveness of the proposed method is verified by mobile robot simulation.

New well water cut prediction using machine learning

Background: The drilling of new wells is one of the most effective geological and technical activities. In mature fields characterized by high production of reserves and high water availability, the selection of design points for drilling is a difficult task. Forecasting the parameters of new wells is possible by using geological and hydrodynamic models or analytical methods. In this paper, the authors propose the use of machine learning algorithms to predict the initial parameters of new wells based on an extensive set of geological and field data.&#x0D; Aim: The article describes the process of developing machine learning algorithms and demonstrates the performance indicators of a complex model. As part of this work, testing of machine learning algorithms was performed to predict the start-up water cut of potential candidates.&#x0D; Materials and methods: Within the framework of this work, various machine learning methods were applied on geological and technical field data.&#x0D; Results: The developed complex model showed acceptable convergence results based on classification and regression metrics, which indicates its applicability for predicting the start-up water cut of project wells.&#x0D; Conclusion: This method of predicting indicators is an alternative tool for predicting the start-up water cut of new wells, which makes it possible to clarify and supplement the forecast parameters calculated using a geological and hydrodynamic model or empirical dependencies of the initial water cut of new wells on geological parameters.

Nonlinear predictor‐feedback cooperative adaptive cruise control of vehicles with nonlinear dynamics and input delay

SummaryWe construct a nonlinear predictor‐feedback cooperative adaptive cruise control (CACC) design for homogeneous vehicular platoons subject to actuators delays, which achieves: (i) positivity of vehicles' speed and spacing states, (ii) string stability of the platoon, (iii) stability of each individual vehicular system, and (iv) regulation to the desired reference speed (dictated by the leading vehicle) and spacing. The design relies on a nominal, nonlinear adaptive cruise control (ACC) law that we construct, which guarantees (i)–(iv) in the absence of actuator delay, and nonlinear predictor feedback. We consider a classical (for ACC/CACC design) third‐order, nonlinear model subject to input delay, for the vehicles' dynamics. The proofs of the theoretical guarantees (i)–(iv) rely on derivation of explicit estimates on solutions (both during open‐loop and closed‐loop operation), capitalizing on the ability of predictor feedback to guarantee complete delay compensation after the dead‐time interval has elapsed, and derivation of explicit conditions on initial conditions and parameters of the nominal control law. We also present consistent simulation results, considering a platoon of ten vehicles, which validate the design developed.

Emulation of Quantitative Systems Pharmacology models to accelerate virtual population inference in immuno-oncology

Quantitative Systems Pharmacology (QSP) models are increasingly being applied for target discovery and dose selection in immuno-oncology (IO). Typical application involves virtual trial, a simulation of a virtual population of hundreds of model instances with model inputs reflecting individual variability. While the structure of the model and initial parameterisation are based on literature describing the underlying biology, calibration of the virtual population by existing clinical data is frequently required to create tumour and patient population specific model instances. Since comparison of a virtual trial with clinical output requires hundreds of large-scale, non-linear model evaluations, the inference of a virtual population is computationally expensive, frequently becoming a bottleneck. Here, we present novel approach to virtual population inference in IO using emulation of the QSP model and an objective function based on Kolmogorov-Smirnov statistics to maximise congruence of simulated and observed clinical tumour size distributions. We sample the parameter space of a QSP IO model to collect a set of tumour growth time profiles. We evaluate performance of several machine learning approaches in interpolating these time profiles and create a surrogate model, which computes tumor growth profiles faster than the original model and allows examination of tens of millions of virtual patients. We use the surrogate model to infer a virtual population maximising congruence with the waterfall plot of a pembrolizumab clinical trial. We believe that our approach is applicable not only in QSP IO, but also in other applications where virtual populations need to be inferred for computationally expensive mechanistic models.

Design and FPGA implementation of a hyperchaotic conservative circuit with initial offset-boosting and transient transition behavior based on memcapacitor

A charge-controlled memcapacitive conservative chaotic circuit with trigonometric function terms is constructed in this paper. By analyzing the characteristics of the infinite equilibrium point and the divergence of the system, it is proved that the system is conservative. Under the influence of system parameters, hyperchaotic and chaotic characteristics can be switched to each other. With the increase of parameters and initial values, the Lyapunov exponent is also greatly improved. In addition, the transient chaos phenomenon accompanied by the internal crisis is also found, which leads to the sudden change of the orbit state of the attractor, and different types of coexisting attractors in different regions of the basins are also found. Through further analysis of the phase diagram, bifurcation diagram and time-domain waveform, it is found that there are three initial offset-boosting behaviors with different characteristics in the system. Finally, the spectral entropy (SE) algorithm is used to compare the memcapacitive circuit complexity under different initial conditions and system parameters. We find that the SE complexity distribution map is consistent with the Lyapunov exponent trajectory, which further reflects the influence of system parameters and initial conditions on the dynamic characteristics of the system. On the platform of Cyclone IV E series, the FPGA main chip of EP4CE10F17C8N is used to complete the hardware implementation of the system. The FPGA implementation results are consistent with numerical simulation, and verify the realistic feasibility of the memcapacitive conservative chaotic circuit in practical application.

Analysis of Initial Design Parameters Affecting the Capacity of Shortened Tracks of the Marshalling Yard of a Freight Station

The article analyzes one of the current methods of determining the necessary track development of the marshalling yard of a freight station. Special attention is paid to the issue of identifying the initial design parameters that have a significant impact on the required number of shortened marshalling tracks. The complex multivariant task of determining the capacity of track development requires specification of the solution of some issues of this aspect.

Improved Selective Deep-Learning-Based Clustering Ensemble

Clustering ensemble integrates multiple base clustering results to improve the stability and robustness of the single clustering method. It consists of two principal steps: a generation step, which is about the creation of base clusterings, and a consensus function, which is the integration of all clusterings obtained in the generation step. However, most of the existing base clustering algorithms used in the generation step are shallow clustering algorithms such as k-means. These shallow clustering algorithms do not work well or even fail when dealing with large-scale, high-dimensional unstructured data. The emergence of deep clustering algorithms provides a solution to address this challenge. Deep clustering combines the unsupervised commonality of deep representation learning to address complex high-dimensional data clustering, which has achieved excellent performance in many fields. In light of this, we introduce deep clustering into clustering ensemble and propose an improved selective deep-learning-based clustering ensemble algorithm (ISDCE). ISDCE exploits the deep clustering algorithm with different initialization parameters to generate multiple diverse base clusterings. Next, ISDCE constructs ensemble quality and diversity evaluation metrics of base clusterings to select higher-quality and rich-diversity candidate base clusterings. Finally, a weighted graph partition consensus function is utilized to aggregate the candidate base clusterings to obtain a consensus clustering result. Extensive experimental results on various types of datasets demonstrate that ISDCE performs significantly better than existing clustering ensemble approaches.

Movement patterns during gait initiation in older adults with various stages of frailty: a biomechanical analysis

BackgroundGait initiation is challenging for older individuals with poor physical function, particularly for those with frailty. Frailty is a geriatric syndrome associated with increased risk of illness, falls, and functional decline. This study examines whether spatial and temporal parameters of gait initiation differ between groups of older adults with different levels of frailty, and whether fear of falling, and balance ability are correlated with the height of lifting the food during gait initiation.MethodsSixty-one individuals aged > 65 years, classified by Fried frailty phenotype, performed five self-paced gait initiation trials. Data was collected using a three-dimensional passive optical motion capture system, consisting of 10 cameras with the ability to perceive reflective markers, and two force plates. The total duration of gait initiation and the duration of its four sub-phases, the first step length, and the maximum foot clearance during the first step were derived, and compared statistically between groups. Additionally, an association analysis was conducted between foot clearance and fear of falling, and confidence in balance in older individuals.ResultsFrail individuals had significantly longer unloading durations, and total durations of gait initiation compared to non-frail older adults. Additionally, they had shorter first step lengths compared to non-frail older adults. Pre-frail older adults also showed shorter steps compared to the non-frail group. However, there were no significant differences between groups for the maximum foot clearance during the first step. Nevertheless, the maximum foot clearance of older individuals correlated significantly with their fear of falling and confidence in balance.ConclusionOlder adults with reduced physical function and signs of frailty mainly display longer duration of gait initiation and decreased first step length compared to non-frail older adults. The release phase is decreased as the double support phase is prolonged in frail patients. This information can guide the development of specialized exercise programs to improve mobility in this challenging motion between static and dynamic balance.

Thermal Decomposition Process of Fireproof Sealant Measured with Thermogravimetric and Fourier Transform Infrared Spectroscopy Analysis and Estimated Using Shuffled Complex Evolution

Fireproof sealing technology is widely used in industrial, commercial, and other public buildings, so the performance of fireproof sealing materials in high temperatures or fire environments must be taken into account as an important factor. Fireproof sealant is considered to be a highly effective adhesive for sealing and fireproofing purposes. To explore its thermal decomposition mechanism and estimate its pyrolysis behaviors, a series of thermogravimetric experiments from 10 K/min to 60 K/min coupled with Fourier transform infrared spectroscopy analysis technology were performed. The results indicated that the thermal decomposition of the fireproof sealant could be divided into three reactions: the degradation of ammonium polyphosphate, melamine, and acrylic acid. In addition, the pyrolysis behavior of the fireproof sealant was compared under two kinds of atmosphere (nitrogen and air). Furthermore, the initial kinetic parameters in the nitrogen atmosphere were calculated based on model-free methods including the Friedman, KAS, and Starink methods. The average activation energy of three reactions obtained by the three methods was 108.32 kJ/mol, 200.46 kJ/mol, and 177.10 kJ/mol, respectively, while these obtained parameters were hard to regenerate, the thermogravimetric curves were accurately based on the established pyrolysis reaction scheme, with the existence of clear deviations. Therefore, a global heuristic optimization algorithm, Shuffled Complex Evolution (SCE), was selected to optimize 14 parameters (including activation energies and the pre-exponential factors) and the optimized pyrolysis results agreed well with the experimental data, even at the extra heating rate, with the correlation coefficient for the mass loss and mass loss rate being reaching up to 0.9943 and 0.9019, respectively. The study indicated that the SCE algorithm showed an appropriate potential to estimate the pyrolysis behavior of an unknown thermogravimetric experiment group.

An automated gradient titration fluorescence methodology for high-resolution identification of aqueous two-polymer phase extraction conditions for single-wall carbon nanotubes

A significant advance in rate and precision of identifying the co-surfactant concentrations leading to differential extraction of specific single-wall carbon nanotube (SWCNT) species in aqueous two-polymer phase extraction experiments is reported. These gains are achieved through continuous titration of co-surfactant and other solution components during automated fluorescence measurements on SWCNT dispersions. The resulting fluorescence versus concentration curves display intensity and wavelength shift transitions traceable to the nature of the adsorbed surfactant layer on specific SWCNT structures at the (n,m) species and enantiomer level at high resolution. The increased precision and speed of the titration method resolve previously invisible complexity in the SWCNT fluorescence during the transition from one surfactant dominating the SWCNT interface to the other, offering insight into the fine details of the competitive exchange process. For the first time, we additionally demonstrate that the competitive process of the surfactant switch is direction independent (reversible) and hysteresis-free; the latter data effectively specifies an upper bound for the time scale of the exchange process. Titration curves are compared to literature results and initial advanced parameter variation is conducted for previously unreasonable to investigate solution conditions.

Modified Complex Robert-Bonamy calculations of line shape parameters for the CO2-H2O collision system

Modified Complex Robert-Bonamy calculations were made for the CO2-H2O collision system using an intermolecular potential comprised of quadrupole-dipole, quadrupole-quadrupole, Lennard-Jones atom-atom, induction, and London dispersion terms. The initial atom-atom parameters were obtained using combination rules and the other parameters from taken from the literature. The intermolecular potential was then adjusted to give results that agree with the measurements of Sung et al. [Can. J. Phys. 87,469-484, 2009]. The final potential results (pot 46) compared with the data of Sung et al. have an average percent difference of 0.07 and a standard deviation of 1.096 %. Calculations were then made for all transitions in the 00011-00001 band with J” from 0 to 120 at 13 temperatures from 200 to 3000 K. The speed dependence of the half-width and line shift were computed for use in more advanced line shape models. The temperature dependence of the half-widths and line shifts were determined using the double power-law (DPL) model of Gamache and Vispoel [JQSRT 217, 440-452, 2018].