Dynamic Parameters Research Articles

Approaching Pharmacological Space: Events and Components.

The pharmacological space comprises all the dynamic events that determine the bioactivity (and/or the metabolism and toxicity) of a given ligand. The pharmacological space accounts for the structural flexibility and property variability of the two interacting molecules as well as for the mutual adaptability characterizing their molecular recognition process. The dynamic behavior of all these events can be described by a set of possible states (e.g., conformations, binding modes, isomeric forms) that the simulated systems can assume. For each monitored state, a set of state-dependent ligand- and structure-based descriptors can be calculated. Instead of considering only the most probable state (as routinely done), the pharmacological space proposes to consider all the monitored states. For each state-dependent descriptor, the corresponding space can be evaluated by calculating various dynamic parameters such as mean and range values.The reviewed examples emphasize that the pharmacological space can find fruitful applications in structure-based virtual screening as well as in toxicity prediction. In detail, in all reported examples, the inclusion of the pharmacological space parameters enhances the resulting performances. Beneficial effects are obtained by combining both different binding modes to account for ligand mobility and different target structures to account for protein flexibility/adaptability.The proposed computational workflow that combines docking simulations and rescoring analyses to enrich the arsenal of docking-based descriptors revealed a general applicability regardless of the considered target and utilized docking engine. Finally, the EFO approach that generates consensus models by linearly combining various descriptors yielded highly performing models in all discussed virtual screening campaigns.

Collective oscillations in a three-dimensional spin model with non-reciprocal interactions

Abstract We study the onset of collective oscillations at low temperature in a three-dimensional spin model with non-reciprocal short-range interactions. Performing numerical simulations of the model, the presence of a continuous phase transition to global oscillations is confirmed by a finite-size scaling analysis, yielding values of the exponents β and ν compatible with both the three-dimensional XY and Ising equilibrium universality classes. By systematically varying the interaction range, we show that collective oscillations in this spin model actually result from two successive phase transitions: a mean-field phase transition over finite-size neighborhoods, which leads to the emergence of local noisy oscillators, and a synchronization transition of local noisy oscillators, which generates coherent macroscopic oscillations. Using a Fokker–Planck equation under a local mean-field approximation, we derive from the spin dynamics coupled Langevin equations for the complex amplitudes describing noisy oscillations on a mesoscopic scale. The phase diagram of these coupled equations is qualitatively obtained from a fully-connected (mean-field) approximation. This analytical approach allows us to clearly disentangle the onset of local and global oscillations, and to identify the two main control parameters, expressed as combinations of the microscopic parameters of the spin dynamics, that control the phase diagram of the model.

Machine learning approaches for improving atomic force microscopy instrumentation and data analytics

Atomic force microscopy (AFM) is a part of the scanning probe microscopy family. It provides a platform for high-resolution topographical imaging, surface analysis as well as nanomechanical property mapping for stiff and soft samples (live cells, proteins, and other biomolecules). AFM is also crucial for measuring single-molecule interaction forces and important parameters of binding dynamics for receptor-ligand interactions or protein-protein interactions on live cells. However, performing AFM measurements and the associated data analytics are tedious, laborious experimental procedures requiring specific skill sets and continuous user supervision. Significant progress has been made recently in artificial intelligence (AI) and deep learning (DL), extending into microscopy. In this review, we summarize how researchers have implemented machine learning approaches so far to improve the performance of atomic force microscopy (AFM), make AFM data analytics faster, and make data measurement procedures high-throughput. We also shed some light on the different application areas of AFM that have significantly benefited from applications of machine learning frameworks and discuss the scope and future possibilities of these crucial approaches.

COMSOL-Based Simulation of Microwave Heating of Al2O3/SiC Composites with Parameter Variations

The process of microwave heating involves the coupling of multiple physical phenomena, specifically including the distribution of the electromagnetic field in the resonant cavity. The electromagnetic effect generates heat and encourages the transfer of heat inside the material. In this numerical study, a 3D computer model of microwave heating of Al2O3/SiC composites using a multimode microwave heating chamber was established based on the simulation software COMSOL Multiphysics 5.6, and the symmetry treatment of the model was carried out, which effectively reduced the amount of model calculations and accurately analyzed the microwave heating characteristics of the samples. The analysis of the microwave heating characteristics of the sample was mainly preformed from the perspective of the electric field distribution in the resonant cavity, the sample heating rate and the sample heating uniformity, and then it was determined how the microwave source power and sample mold selection affect the temperature and electric field parameters of the sample. After experimental verification, the error between the simulation results and the temperature parameters obtained from the actual experiments is less than 2%. This study contributes to further understanding the heating behavior of Al2O3/SiC composites in complex multimode microwave heating environments and can be used to control the dynamic parameters during microwave heating in order to improve the heating rate and heating uniformity of samples.

Remote control of the technical condition of metro trains using seismological tools

As urban road congestion increases and people shift to using the subway, the safety of under-ground transportation becomes a priority. Therefore, it is necessary to establish a monitoring sys-tem for subway trains to detect malfunctions that occur during operation. The article proposes an engineering seismology method for remote monitoring of the technical condition of metro trains based on the analysis of micro seismic vibrations. This method allows remote control of the dynamic parameters of each train and their deviations from the norm. The authors demonstrate how a seismic recorder placed in a building adjacent to a metro station can be used to monitor changes in the amplitude-frequency spectrum of vibro seismic recordings. It is used to determine the arrival and departure times of trains, and it is shown that observations are best made for departures due to their more pronounced signal amplitude. The recorded signal of a metro train should fall within a certain frequency range, which is determined based on its average values, and any deviation of the observed signal spectrum from this norm indicates changes in the train's condition. The proposed approach not only helps improve passenger safety, but also reduces the risk of emergencies associated with technical malfunctions of rolling stock. In the future, it is possible to develop software equipped with a database of statistical data, which will allow even more efficient control of metro operations and prompt response to any deviations, thus ensuring a high level of safety and comfort for passengers.

Graph-enabled spatio-temporal transformer for ionospheric prediction

The ionosphere, crucial for satellite navigation and radio communication, is highly sensitive to solar and geomagnetic activities. Traditional ionospheric prediction models often struggle to capture its complexities. This paper introduces a Graph-Enabled Spatio-temporal transformer (GEST) model, designed to predict the Total electron content (TEC) in the ionosphere with higher accuracy. The GEST model innovatively integrates six dynamic space weather parameters, including the Kp index, and refines grid distances and weights in the Global ionospheric maps (GIM). Leveraging the efficiency of the Transformer architecture in processing spatio-temporal data, the GEST model adapts to varying ionospheric conditions and provides superior precision. Our evaluations, based on data from the Center for orbit determination in Europe (CODE), demonstrate that the GEST model significantly outperforms traditional methods and other intelligent algorithms such as conv-LSTM. It shows marked improvements in Mean absolute error (MAE), Root mean square error (RMSE), Pearson correlation coefficient (CC), and Mean absolute percentage error (MAPE), particularly during periods of intense solar and geomagnetic activity. These advancements highlight the GEST model’s substantial contributions to the field of ionospheric prediction technology, offering a robust tool for enhancing the reliability of satellite-based systems.

Diversity and transition of periodic motion of a periodically excited soft-impacting machinery

Dynamics of a periodically excited vibro-impact system with soft impacts is investigated. Essential features of period-one multi-impact motion group and correlated transition characteristics in low-frequency range are discussed in detail by the way of two-parameter bifurcation space providing qualitative domains for different periodic motions. The main focus is given to the effect of sensitive parameters including constraint stiffness k 0, clearance threshold b, and damping parameter ζ on the system response. The low-frequency characteristics in the finite-dimensional parameter space are particularly explored. It is found that the increase of k 0 induces multi-type bifurcation of period-one double-impact symmetrical motion, which induces a rich variety of periodic motions, and period-one multi-impact motion group orbit primarily exist in the small-clearance b and low-frequency ω zone. Based on the evolution irreversibility of adjacent period-one multi-impact orbit, the mechanism of singularies appearing in pairs and two different transition zones (hysteresis and liguliform zones) is studied, the result of which provides a theoretical reference value for the common low-frequency vibration instability phenomenon in the field of mechanical engineering. For small-damping coefficient ζ, period-one multi-impact motion has a large quantity, and the main bridge for the transition of adjacent period-one multi-impact motion is liguliform zone, which embraces period-one multi-impact asymmetrical motion and period-n multi-impact subharmonic motion and a certain chaotic zone. For large-damping coefficient ζ, the amount of period-one multi-impact motion group is reduced, and the main bridge for the transition of adjacent period-one multi-impact motion is hysteresis zone, where adjacent period-one multi-impact orbits can coexist according to initial conditions. As designing and renovating impact mechanical equipment, the reasonable matching law of dynamic parameters can be determined through two-parameter bifurcation space, which is conducive to making the system work in stable periodic motion and obtaining larger instantaneous impact velocity.

Establishment of IGF-1 and IGFBP-3 continuous reference percentiles from data of healthy children using three kinds of immunoassay systems

Background and aimsAppropriate continuous reference intervals (RIs) for serum insulin-like growth factor 1 (IGF-1) and insulin-like growth factor-binding protein 3 (IGFBP-3) are important for diagnosing growth hormone deficiency or excess. Material and methodsWe retrospectively reviewed serum IGF-1 and IGFBP-3 levels in Korean children aged 0–17 years who were diagnosed as healthy during a short stature workup in the outpatient clinics of three hospitals. IGF-1 and IGFBP-3 levels were measured using various immunoassays, including Liaison XL for IGF-1, an immunoradiometric assay (IRMA) for IGFBP-3 (n = 5522), and Immulite 2000 (n = 3036) and cobas e801 (n = 314). We established RIs from the 2.5th to 97.5th percentile RI curves using the lambda–mu–sigma (LMS) method for each sex group. ResultsPediatric serum continuous IGF-1 and IGFBP-3 reference percentiles by LMS method were found to be immunoassay method-dependent, but aligned relatively well with the manufacturers’ RIs. IGFBP-3 levels displayed notable discrepancies among the different immunoassay methods. ConclusionAge- and sex-specific pediatric LMS based continuous reference intervals are method dependent and they should be calculated for dynamic parameters that show variations throughout childhood.

DFT insights into the nonlinear optical properties of azulene-stilbene dyads for photonics and optoelectronics

The present study aims to investigate the nonlinear optical characteristics of a series of azulene-stilbene chromophores through the utilization of density functional theory (DFT). Various acceptor substitution techniques were used to enhance first, second, and third order static and dynamic polarizabilities of the parent azulene-stilbene dyad (AS). The natural transition orbitals and UV-Visible absorption spectra were analyzed using time dependent-density functional theory (TD-DFT). Theoretical computations were performed to analyze the structural, electronic, and charge transfer properties of all designed compounds. The energy gap separating the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) of the designed moieties was observed to decrease in the new compounds compared to the parent compound. Analysis of the nonlinear optical (NLO) data among the designed compounds (AS1-AS10) revealed that AS2, AS3, AS4, and AS10 exhibit promising second and third order NLO characteristics, with AS6 exhibits suitability as a second order NLO material and AS8 and AS9 display third order NLO responses. The computed static and dynamic NLO parameters and maximum absorption wavelength closely match the experimentally reported data in the literature, suggesting these derivatives are excellent candidates for photonic and optoelectronic applications in the future.

Profiles and some dynamic parameters of a layered inhomogeneous elliptical galaxy

Several new models of a layered inhomogeneous elliptical galaxy (EG) having the shape either a triaxial ellipsoid or an oblate or prolate spheroid and consisting of baryonic mass and dark matter with different laws of density distribution — profiles. Based on these models, some key dynamic parameters of the EG were determined: gravitational (potential) energy and rotational kinetic energy, total surface brightness, total luminosity, and velocity dispersion depending on the distance to the EG center. The relationships between the important dynamic parameters of the galaxy have been established: “mass-dimensions”, “mass-velocity dispersion”, “size-dispersion speeds–luminosity” (surface brightness). Evolutionary scenarios for the formation of EG were studied according to these models. The results obtained were applied to sixty model EGs with parameters exactly matching those that actually exist and are presented in the form of tables.

Anti-icing transparent coatings modified with bi- and tri-functional octaspherosilicates for photovoltaic panels

In recent years, to progress towards carbon emissions reduction, photovoltaic technology has become one of the most significant methods of generating electricity using renewable sources. However, the accumulation of ice and snow during the winter season affects the decrease in the power generation efficiency of photovoltaic modules. As a promising solution, coatings that exhibit anti-icing properties can be used. To date, no efficient ice-phobic coating has been developed for use on photovoltaic panels. In this paper development of transparent silicone-epoxy coatings modified with bi- and tri-functionalized octaspherosilicates was presented. The used organosilicone-based modifiers reveal both types of functional groups: one increases the surface properties and the second compatibility with the polymer matrix. The icephobic properties were discussed by determining an ice adhesion (IA) and a freezing delay time of water droplets (FDT). The coating exhibiting the highest anti-icing performance achieved the IA of 102 kPa and the FDT of 210 min, a 43 % reduction and a 70 times increase in value compared to the unmodified coating, respectively. The performed study included also the characterization of hydrophobic properties: water contact angle (WCA) at room temperature and below 0°C, contact angle hysteresis(CAH), and roll-off angle (RoA) as well as surface roughness and coatings thickness. In addition, the correlations between them and ice adhesion/freezing delay time were determined. It was found that roughness as well as dynamic parameters of the wettability of CAH and RoA have a significant effect on the obtained IA values. Moreover, the developed coatings can be potentially applied to photovoltaic panels, since the conducted modification did not affect the optical properties of the investigated coatings.

Impact of chaplygin-like equation of state on Joule–Thomson expansion and tidal forces of AdS black holes

This study examines a recently hypothesized black hole. We study the Joule–Thomson coefficient, the inversion temperature and also the isenthalpic curves in the Ti−Pi plane. The Joule–Thomson coefficient, the inversion curves and the isenthalpic curves are discussed in AdS black holes surrounded by Chaplygin dark fluid. In T−P plane, the inversion temperature curves and isenthalpic curves are obtained with different parameters. Next, we explore the radial time-like geodesics that leads us to explore the tidal force effects for a radially in-falling particle in such black hole spacetime. We also numerically solve the geodesic deviation equation for two nearby radial geodesics for a freely falling particle. Our analysis shows that contrary to the Schwarzschild spacetime, the tidal forces do not become zero at spatial infinity due to the lack of asymptotic flatness because of the presence of a non-zero cosmological constant. The geodesic separation profile shows an oscillating trend and depends on the dynamic spacetime parameters q,B and Λ.

Associations of static and dynamic iris parameters in healthy Chinese individuals: the Handan Eye Study.

To determine the associations between the demographic factors (age and sex) and physiological dynamic iris changes and explore the associated factors for iris cross-sectional area (IA) change in healthy Chinese individuals. This cross-sectional study included individuals aged ≥40 years with an open angle and underwent anterior segment optical coherence tomography under light and dark conditions from the follow-up cohort of the Handan Eye Study. Ocular data from the right eye were analyzed. The Pearson correlation coefficient was used to assess the relationship between age and iris parameters, including iris thickness (IT), IA, and iris curvature (IC), as well as the pupil diameter (PD) in the dark, and their changes from light to dark conditions. Linear regression analysis was performed to identify the potential factors associated with IA change. The final analysis included 465 healthy individuals. PD in dark, IA change and PD change decreased with age (P < 0.001), whereas IC increased with age (P < 0.001). IT and IT change were smaller, and IC was larger in women than that in men (P = 0.021, 0.007, and 0.010, respectively). Older age (P = 0.041), larger lens thickness (P = 0.013), larger IC change (P < 0.001), and smaller PD change (P < 0.001) were significantly associated with a smaller IA change. Our study demonstrated the associations of static and dynamic iris parameters in healthy Chinese individuals. The findings provided a possible explanation for the higher prevalence of primary angle closure disease in elderly and female populations.

Flux-quench induced dynamical quantum phase transitions in an extended XY spin-chain

Dynamical quantum phase transitions (DQPTs) induced by flux-quench in an extended transversed XY spin-chain have been investigated in this paper. We discussed the conditions for the appearance of DQPTs, and the different regions of the flux quench restricted by strength of transverse field were given. The Loschmidt echo, rate function, geometric phase, as well as dynamical topological order parameter (DTOP) have been calculated, which consistently verified the emergence of DQPTs.

Modal parameters and damage evaluation of steel fiber reinforced concrete under flexural cyclic loadings

Structures may be subjected to static and dynamic loads during their service life. When it comes to the dynamic spectrum, wind action, ocean waves and seismic loads are key examples and are responsible for cracking evolution and material degradation along their lifespan. Therefore, the fatigue behavior of cementitious materials has been receiving great attention due to cyclic loading that can be of natural occurrence or induced by human activity lately. The present research aims to investigate the evolution of the modal parameters of steel fiber reinforced concretes under flexural cyclic load and to evaluate how the mechanical degradation can be monitored from a non-destructive impact modal testing over the cycles. The dynamic properties of the material, including natural frequencies, vibration modes and damping ratios, were tracked to assess the level of fatigue damage using modal testing techniques. Hence, it was possible to evaluate the material mechanical degradation as a function of the dynamic parameters. From the dynamic test, it was possible to evaluate the presence of damage, which was corroborated by the observed stiffness degradation. The first flexural mode and the following second, third and sixth vibration modes were more affected along the cycles. Finally, the proposed non-destructive methodology was effective in assessing the mechanical decay of cementitious composites.

Performance analysis of optimized equilibrium optimizer algorithm in coherent free-space optical communication system

Sensor-less adaptive optics (SLAO) is commonly employed in coherent free-space optical communication (CFSOC) system due to its capacity to mitigate the impact of atmospheric turbulence. The control algorithm determines the SLAO system's capacity to efficiently recorrect wavefront aberrations. In this study, we apply an Optimized Equilibrium Optimizer (OEO) algorithm as a control algorithm to the CFSOC system, the dynamic parameters and search framework of the Equilibrium Optimizer (EO) are modified to enhance the algorithm's resilience in strong atmospheric turbulence. Both theoretical analysis and simulation results demonstrate that the OEO algorithm exhibits exceptional calibration performance and robustness, effectively enhancing ME and reducing BER. In the simulation, the Stochastic Parallel Gradient Descent (SPGD) algorithm takes 10 times longer to achieve the same performance as the OEO algorithm. Meanwhile, the OEO algorithm and the other 6 algorithms were simulated with strong turbulence. Results demonstrated that the OEO algorithm outperforms other algorithms. The OEO algorithm is suitable for SLAO systems to enhance the communication quality of the CFSOC systems.

Weightless Model Predictive Control for Permanent Magnet Synchronous Motors with Extended State Observer

Traditional model predictive torque control (MPTC) predicts the torque and flux values for the next time step and selects the voltage vector that minimizes the cost function as the optimal vector to apply to the inverter. This control approach is straightforward and allows for multi-objective control, but it has some issues in terms of the dynamic steady-state performance and parameter robustness. Therefore, this paper proposes a weightless model predictive control method based on an extended state observer (ESO). By designing an improved ESO to observe and compensate for motor parameter disturbances in real time, and employing a novel 2-D switching table and voltage vector sector selection diagram, the method evaluates three out of eight voltage vectors based on the torque and stator flux error signals. This reduces the computational load while increasing the number of candidate voltage vectors. Finally, a cost function without weighting factors is designed to lower the computational complexity. The simulation results show that the proposed new control method effectively reduces the torque and flux ripple and improves the current waveform compared to traditional MPTC.

Seismic response prediction for buildings with sliding live loads using neural networks

The seismic response of primary structures (PS) can be significantly influenced by live loads, particularly when these loads consist of stacked bodies that are capable of sliding during seismic events. Conventional design methods often fail to account for the energy dissipation resulting from such sliding, leading to overly conservative structural estimates. This study examines the effects of sliding live loads on the seismic response of a PS, where two rigid bodies, referred to as secondary bodies (SBs), are stacked as live loads on a PS. The interaction between the lower SB and the PS, as well as between the SBs, is modelled using Coulomb's friction model. The governing equations are solved using the fourth-order Runge-Kutta method. Seismic Zones III and V from IS 1893:2016 are considered as hazard levels. A parametric investigation explores the impact of varying dynamic parameters of both the PS and SBs on the seismic response. The results demonstrate significant energy dissipation within the stack due to sliding, which, if not considered, results in conservative displacement estimates. The study also evaluates how friction coefficients, mass ratios, and excitation levels affect the stack's energy dissipation capacity. A novel method to compute the modified primary structural period (Tnew) for design purposes is proposed, and an artificial neural network (ANN) model is developed, achieving a high prediction accuracy (R2 = 99 %). Sensitivity analysis is performed to assess the influence of input parameters on the output. This research highlights the need to include sliding loads in seismic design.

Enhanced permeability prediction in porous media using particle swarm optimization with multi-source integration

Accurately and efficiently predicting the permeability of porous media is essential for addressing a wide range of hydrogeological issues. However, the complexity of porous media often limits the effectiveness of individual prediction methods. This study introduces a novel Particle Swarm Optimization-based Permeability Integrated Prediction model (PSO-PIP), which incorporates a particle swarm optimization algorithm enhanced with dynamic clustering and adaptive parameter tuning (KGPSO). The model integrates multi-source data from the Lattice Boltzmann Method (LBM), Pore Network Modeling (PNM), and Finite Difference Method (FDM). By assigning optimal weight coefficients to the outputs of these methods, the model minimizes deviations from actual values and enhances permeability prediction performance. Initially, the computational performances of the LBM, PNM, and FDM are comparatively analyzed on datasets consisting of sphere packings and real rock samples. It is observed that these methods exhibit computational biases in certain permeability ranges. The PSO-PIP model is proposed to combine the strengths of each computational approach and mitigate their limitations. The PSO-PIP model consistently produces predictions that are highly congruent with actual permeability values across all prediction intervals, significantly enhancing prediction accuracy. The outcomes of this study provide a new tool and perspective for the comprehensive, rapid, and accurate prediction of permeability in porous media.

Assessment of the effects of winter wheat scattering on SAR backscatter for soil moisture estimation based on a radiative transfer model

ABSTRACT This study evaluated the potential of the Single Scattering Radiative Transfer (SSRT) model coupled with the Oh model to retrieve surface soil moisture (SSM) in two drip-irrigated wheat fields in the Tensift al Haouz plain, over three growing seasons (2016–2019). This research focuses on evaluating isotropic scattering and Rayleigh scattering by calibrating the coupled SSRT_Oh model, using data gathered on the calibrated field. The data includes measured SSM at 5 cm depth, Leaf Area Index (LAI) and vegetation height (H). The aim is to fit the extinction coefficient through comparing simulated and Sentinel-1 backscatter, at VV and VH polarizations. The validated field data were used to retrieve SSM. Calibration revealed the best retrieval for Rayleigh scattering, at VV polarization, with an RMSE of 1.25 dB, a correlation coefficient (r) of 0.86, and a bias of 0.1 dB, whereas, the isotropic approach yielded r, RMSE and bias values of 0.83, 1.37 dB and −0.01 dB, respectively. Rayleigh’s performance remained unchanged throughout the inversion process for SSM retrieval, at VV polarization, with r of 0.87 against 0.86 for isotropic scattering. To enhance our findings, the introduction of dynamic extinction parameter and exclusive use of Sentinel-1 data to describe vegetation could improve SSM retrieval.