Model Of States Research Articles

Spectral wave modeling of bimodal sea states at laboratory and coastal scales

A downshift of the wind wave peak frequency was observed in a wind wave tunnel when irregular long paddle-waves (i.e. generated mechanically with a plane wave-maker) are added in the sea state. The 3rd generation spectral wave model, TOMAWAC, is used to assess the extent at which this peak frequency downshift can take place at prototype scale in bimodal sea-state conditions involving swell and wind wave systems. Several parameterizations of the modeled physical processes are selected to numerically reproduce the laboratory experiments in the first place. Then, the model performances are further inquired in reproducing coastal observations during a specific event combining a wind wave and a swell system. Overall, a good agreement is obtained between the simulations and the observations both at laboratory and coastal scale. In particular, a set of parameterizations combining one of the latest developments in spectral wave models for the whitecapping dissipation and the nonlinear 4-wave interactions reveals high performances in reproducing the observations. Lastly, based on the performances of this latter set of parameterizations, a generic numerical domain with typical coastal scale dimensions is created to inquire the occurrence of the downshift at prototype scale. This last study reveals a wind wave peak period shift from 5 s without swell to more than 6.5 s with a 2 m high swell.

Understanding Flow Experience in Video Learning by Multimodal Data

Video-based learning has successfully become an effective alternative to face-to-face instruction. In such situations, modeling or predicting learners’ flow experience during video learning is critical for enhancing the learning experience and advancing learning technologies. In this study, we set up an instructional scenario for video learning according to flow theory. Different learning states, i.e., boredom, fit (flow), and anxiety, were successfully induced by varying the difficulty levels of the learning task. We collected learners’ electrocardiogram (ECG) signals as well as facial video, upper body posture and speech data during the learning process. We proposed classification models of the learning state and regression models to predict flow experience by utilizing different combinations of the data from the four modalities. The results showed that the model performance of learning state recognition was significantly improved by the decision-level fusion of multimodal data. By using the selected important features from all data sources, such as the standard deviation of normal to normal R-R intervals (SDNN), high-frequency (HF) heart rate variability and mel-frequency cepstral coefficients (MFCC), the multilayer perceptron (MLP) classifier gave the best recognition result of learning states (i.e., mean AUC of 0.780). The recognition accuracy of boredom, fit (flow) and anxiety reached 47.48%, 80.89% and 47.41%, respectively. For flow experience prediction, the MLP regressor based on the fusion of two modalities (i.e., ECG and posture) achieved the optimal prediction (i.e., mean RMSE of 0.717). This study demonstrates the feasibility of modeling and predicting the flow experience in video learning by combining multimodal data.

Interplay between biochemical processes and network properties generates neuronal up and down states at the tripartite synapse.

Neuronal up and down states have long been known to exist both in vitro and in vivo. A variety of functions and mechanisms have been proposed for their generation, but there has not been a clear connection between the functions and mechanisms. We explore the potential contribution of cellular-level biochemistry to the network-level mechanisms thought to underlie the generation of up and down states. We develop a neurochemical model of a single tripartite synapse, assumed to be within a network of similar tripartite synapses, to investigate possible function-mechanism links for the appearance of up and down states. We characterize the behavior of our model in different regions of parameter space and show that resource limitation at the tripartite synapse affects its ability to faithfully transmit input signals, leading to extinction-down states. Recovery of resources allows for "reignition" into up states. The tripartite synapse exhibits distinctive "regimes" of operation depending on whether ATP, neurotransmitter (glutamate), both, or neither, is limiting. Our model qualitatively matches the behavior of six disparate experimental systems, including both in vitro and in vivo models, without changing any model parameters except those related to the experimental conditions. We also explore the effects of varying different critical parameters within the model. Here we show that availability of energy, represented by ATP, and glutamate for neurotransmission at the cellular level are intimately related, and are capable of promoting state transitions at the network level as ignition and extinction phenomena. Our model is complementary to existing models of neuronal up and down states in that it focuses on cellular-level dynamics while still retaining essential network-level processes. Our model predicts the existence of a "final common pathway" of behavior at the tripartite synapse arising from scarcity of resources and may explain use dependence in the phenomenon of "local sleep." Ultimately, sleeplike behavior may be a fundamental property of networks of tripartite synapses.

RETRACTED ARTICLE: Research on reliability evolutionary optimization of relays device based on Multi-Markov model

As a critical component of the present day power system, a few studies have been conducted on the optimization and future prediction for quality reliability of relay protection devices during their production stage of intelligent testing. Therefore, in this study, a Markov model of multimodal hierarchical spatial states is proposed (1) to calculate the stationary probability and comprehensive availability of different spatial state transfers (2) to predict the quality reliability of the device during production intelligence testing and (3) to infer and calculated the integrated availability of the relay protection device for its future operation during testing. Statistical results from the theoretical findings of this study are highly relevant when compared to the actual operation of relay protection device systems. The correctness of the model calculation method proposed in this study is verified, which provides a feasible method for reliability assessment of relay protection device intelligence tests. In addition, it was also found that the failure rate of the device internal module CPU has a relatively large impact on the comprehensive availability. It is therefore recommended to focus on CPU module detection and timely replacement during the maintenance cycle of on-site operations and maintenance. Finally, the CPU module failure rate threshold for production intelligence testing was amended, which is a valuable indicator for the evolutionary optimization of quality problems in actual smart manufacturing and testing.

Characterization of paramagnetic states in an organometallic nickel hydrogen evolution electrocatalyst

Significant progress has been made in the bioinorganic modeling of the paramagnetic states believed to be involved in the hydrogen redox chemistry catalyzed by [NiFe] hydrogenase. However, the characterization and isolation of intermediates involved in mononuclear Ni electrocatalysts which are reported to operate through a NiI/III cycle have largely remained elusive. Herein, we report a NiII complex (NCHS2)Ni(OTf)2, where NCHS2 is 3,7-dithia-1(2,6)-pyridina-5(1,3)-benzenacyclooctaphane, that is an efficient electrocatalyst for the hydrogen evolution reaction (HER) with turnover frequencies of ~3,000 s−1 and a overpotential of 670 mV in the presence of trifluoroacetic acid. This electrocatalyst follows a hitherto unobserved HER mechanism involving C-H activation, which manifests as an inverse kinetic isotope effect for the overall hydrogen evolution reaction, and NiI/NiIII intermediates, which have been characterized by EPR spectroscopy. We further validate the possibility of the involvement of NiIII intermediates by the independent synthesis and characterization of organometallic NiIII complexes.

Method of diagnosing some diseases of the neuro-muscular system and features of data processing in software

Electromyostimulation is a method of restorative treatment based on electrical stimulation of nerves and muscles. The electric current, which is used in electrical stimulation to obtain induced muscle contractions, is characterized by a large number of different parameters. However, not every possible option of electrical stimulation is highly effective. To solve the task of diagnosing some diseases of the neuromuscular system, it is important to organize the software by analyzing the parameters of the evoked potentials. Therefore, the object of research is the processes of skeletal muscle contraction under the influence of natural electrical pulses of the nervous system or under the influence of external electrical stimulation. The subject of research is models describing the processes in muscles during contraction and methods of data processing. In the course of the study, such research methods as mathematical modeling methods and methods of processing medical and biological data were used. The paper examines the experimental strength-duration dependence of skeletal muscle and obtained mathematical models for the normal state of the neuromuscular apparatus and different degrees of denervation. On the basis of electrodiagnosis of a patient with impaired motor functions, the dynamics of changes in the patient's condition and the effectiveness of treatment were traced. Based on the results of the study, an analysis of the parameters of the evoked potentials of the stimulation electromyogram during adaptive electrostimulation was carried out in order to control its effectiveness or establish a diagnosis in some diseases of the neuromuscular system. This made it possible to develop a method for correcting errors in the interpretation of one of the quality parameters and increase the reliability of the diagnosis. The obtained results can be used in the improvement of technical devices for electrostimulation therapy, as well as control of the effectiveness of rehabilitation procedures.

Modeling Excited States of Molecular Organic Aggregates for Optoelectronics.

Light-driven phenomena in organic molecular aggregates underpin several mechanisms relevant to optoelectronic applications. Modeling these processes is essential for aiding the design of new materials and optimizing optoelectronic devices. In this review, we cover the use of different atomistic models, excited-state dynamics, and transport approaches for understanding light-activated phenomena in molecular aggregates, including radiative and nonradiative decay pathways. We consider both intra- and intermolecular mechanisms and focus on the role of conical intersections as facilitators of internal conversion. We explore the use of the exciton models for Frenkel and charge transfer states and the electronic structure methods and algorithms commonly applied for excited-state dynamics. Throughout the review, we analyze the approximations employed for the simulation of internal conversion, intersystem crossing, and reverse intersystem crossing rates and analyze the molecular processes behind single fission, triplet-triplet annihilation, Dexter energy transfer, and Förster energy transfer.

Bipolar Switching Properties of the Transparent Indium Tin Oxide Thin Film Resistance Random Access Memories.

In this study, the bipolar switching properties and electrical conduction behaviors of the ITO thin films RRAM devices were investigated. For the transparent RRAM devices structure, indium tin oxide thin films were deposited by using the RF magnetron sputtering method on the ITO/glass substrate. For the ITO/ITOX/ITO/glass (MIM) structure, an indium tin oxide thin film top electrode was prepared to form the transparent RRAM devices. From the experimental results, the 102 On/Off memory ratio and bipolar switching cycling properties for set/reset stable states were found and discussed. All transparent RRAM devices exhibited the obvious memory window and low set voltage for the switching times of 120 cycles. The electrical transport mechanisms were dominated by the ohmic contact and space charge limit conduction (SCLC) models for set and reset states. Finally, the transmittances properties of the transparent ITO/ITOX/ITO RRAM devices for the different oxygen growth procedures were about 90% according to the UV-Vis spectrophotometer for the visible wavelength range.

Code and Data: Optimized Stochastic Representation of Soil States Model Uncertainty of WRF (v4.2) in the Ensemble Data Assimilation System

Code and Data: Optimized Stochastic Representation of Soil States Model Uncertainty of WRF (v4.2) in the Ensemble Data Assimilation System

A Helmholtz Energy Equation of State for trans-1,1,1,4,4,4-Hexafluoro-2-butene [R-1336mzz(E)] and an Auxiliary Extended Corresponding States Model for the Transport Properties

A Helmholtz Energy Equation of State for trans-1,1,1,4,4,4-Hexafluoro-2-butene [R-1336mzz(E)] and an Auxiliary Extended Corresponding States Model for the Transport Properties

Methodology for Evaluating the Performance Data of Practical Honeycomb Fairing

The quality criteria for fairing used to protect antenna devices (AD) of radar stations (RS) from environmental influences are the requirements for ensuring and maintaining strength and protective properties with minimal insertion loss of the transmitted electromagnetic wave (EMW). It is important to assess the influence of manufacturing technology on these parameters, identifying narrow critical zones on the product with minimum strength characteristics and maximum EMW insertion loss. Calculation models of the stress state in a three-layer sandwich are presented with the identification of critical places from a given type of loading. A method for flaw detection of fairings is presented, taking into account the influence of the dynamics of changes in the maximum allowable threshold of EMW losses, which is extremely necessary for analyzing the technological process and correcting strength calculations.

Reorientation processes of tilted skyrmion and spiral states in a bulk cubic helimagnet Cu2OSeO3

We present a systematic study of tilted spiral states obtained theoretically within the classical Dzyaloshinskii model for magnetic states in cubic non-centrosymmetric ferromagnets. Such tilted spirals are shown to stabilize under the competing effect of cubic and exchange anisotropies inherent to cubic helimagnets. By focusing on the internal structure of these spirals and their field-driven behaviour for different aspect ratios of the anisotropy coefficients, we are able to capture the main features of the experimental findings in a bulk cubic helimagnet Cu2OSeO3and to make a step further towards a complete quantitative model of this chiral magnet. In particular, we show that for strong anisotropy values (which experimentally correspond to low temperatures near zero) there exist an angular separation between the conical and tilted spirals, i.e., the conical spiral flips into a tilted state and immediately composes some finite angle with respect to the field direction. As the anisotropy ratio decreases, such a transition between two spiral states becomes almost continuous and corresponds to higher temperatures at the experiments. In addition, we investigate the field-driven reorientation of metastable skyrmion lattices induced by the competing anisotropies, which may be responsible for some peculiarities at the experimental phase diagrams of Cu2OSeO3.

A scale-spanning integrative modeling strategy to study structure, dynamics, and function of vital molecular machines.

Life is motion driven through cellular processes carried out by molecular machines. To understand and manipulate such cellular processes, scale-spanning knowledge of structure, dynamics, and function of molecular machines is needed. This knowledge cannot be obtained by one method alone. Therefore, I developed a strategy that combines ab initio structure prediction, molecular dynamics (MD) simulation, and quantum chemical calculations with data from structure resolving experiments to investigate the assembly and functional cycle of molecular machines from the electron up to the molecular level. First, experimental data is converted into static structural models of different mechanistic states of molecular machines using bioinformatics methods. Second, active sites are refined at sub-Å resolution and intermediate states inaccessible by experiments are identified employing QM/MM simulations. Third, the obtained refined snapshots are merged to map the dynamics of the processes in their native environment using MD simulations. The strategy provides insights into the interplay between local processes like chemical reactions at the active sites and global conformational changes driving cellular function. These insights lead to mechanistic hypotheses and key functional residues that motivate targeted experiments, like mutagenesis studies, to validate the structural dynamic models. This generally applicable strategy was applied e.g. to obtain insights into ATP‑hydrolysis driven protein recycling by the proteasome [Schweitzer et al. (2016) PNAS; Hung et al. (2022) Nature Communications] or the assembly of proteins involved in photosynthesis like photosystem II [Zabret et al. (2021) Nature Plants] or vesicle inducing protein in plastids Vipp1 [Gupta et al. (2021) Cell].

Computational assessment of molecular mechanisms underlying hERG K+ channel conduction and affinity for drug binding.

During drug development, one of the most significant regulatory concerns is drug-induced cardiotoxicity in the form of abnormal heart rhythms known as arrhythmias. Detrimental drug side effects are often linked to the block of the cardiac ion channel KV11.1, also known as hERG, responsible for a significant repolarizing K+ current, IKr, in cardiac myocytes. Blockage of the channel conductance can lead to prolongation of the cardiac action potential and the QT interval on the surface electrocardiogram, which may result in deadly arrhythmias. hERG channel blockade is a leading cause for drug withdrawal from the drug development pipeline or marketplace. However, not all hERG-blocking and QT-prolonging drugs lead to a high risk for arrhythmia, and there is still no robust and accurate way to distinguish between safe and unsafe hERG blockers. One proposed mechanism is that preferential high-affinity drug binding to hERG inactivated conformation is correlated with its high arrhythmogenic risk. Here, we construct and validate wild-type and mutant hERG channel models in different conformational states based on cryo-EM structures using Rosetta comparative modeling. Using molecular dynamics (MD) simulations, we assessed the ability of these hERG models to conduct ions, interact with drugs, and observed results consistent with published literature. Ultimately, such computer-generated data can be used in conjunction with experimental data to construct state-dependent functional kinetic models of drug-induced hERG channel blockade. The presented model will provide a basis for a new-generation multi-scale model of cardiac safety pharmacology, explicitly incorporating drug modulation of hERG channel gating and thus allowing accurate predictions of drug-induced cardiotoxicity from drug chemical structures.

Investigation and modeling of trap states in ambipolar organic field-effect transistor

This work presents a compact analytical model for charge carrier transport in an ambipolar OFET (a-OFET) based on trap-limited conduction (TLC). The model accounts for tail traps in the band gap of the organic semiconductor (OSC) in order to analyse the transistor behaviour in above threshold regime. For the sub-threshold operation of ambipolar OFET, both deep and interface trap states are accounted for, thus accurately describing the transistor behaviour comprising of drift and diffusion elements respectively. A detailed description of surface potential and field-effect mobility for both the electron and hole carriers, in consequence to these trap states has been presented. Individual current components, obtained as a result of modeling tail, deep and interface trap states are combined using the hyperbolic tangent transition function and are in good agreement with the available experimental data. • This work presents modeling of ambipolar OFET using trap-limited conduction (TLC) approach. • Presented work includes modeling of ambipolar current in both above and below threshold regime, by considering tail, deep and interface acceptor and donor-like states in organic semiconductor. • Subthreshold modeling includes both drift and diffusion components. • Dependence of the mobility of electrons and holes on the characteristic temperatures of the distributions of both tail and deep acceptor and donor-like states has been studied. • The model accurately describes the transfer characteristics of ambipolar OFET.

Artificial intelligence-guided discovery of gastric cancer continuum

BackgroundDetailed understanding of pre-, early and late neoplastic states in gastric cancer helps develop better models of risk of progression to gastric cancers (GCs) and medical treatment to intercept such progression.MethodsWe built a Boolean implication network of gastric cancer and deployed machine learning algorithms to develop predictive models of known pre-neoplastic states, e.g., atrophic gastritis, intestinal metaplasia (IM) and low- to high-grade intestinal neoplasia (L/HGIN), and GC. Our approach exploits the presence of asymmetric Boolean implication relationships that are likely to be invariant across almost all gastric cancer datasets. Invariant asymmetric Boolean implication relationships can decipher fundamental time-series underlying the biological data. Pursuing this method, we developed a healthy mucosa → GC continuum model based on this approach.ResultsOur model performed better against publicly available models for distinguishing healthy versus GC samples. Although not trained on IM and L/HGIN datasets, the model could identify the risk of progression to GC via the metaplasia → dysplasia → neoplasia cascade in patient samples. The model could rank all publicly available mouse models for their ability to best recapitulate the gene expression patterns during human GC initiation and progression.ConclusionsA Boolean implication network enabled the identification of hitherto undefined continuum states during GC initiation. The developed model could now serve as a starting point for rationalizing candidate therapeutic targets to intercept GC progression.

Axisymmetric Stress State of Adhesive Joint of a Circular Patch with a Plate Weakened by a Circular Cut-out

Adhesive lap joints are widely used in modern structures. Known analytical mathematical models of the stress state of lap joints describe the joints of straight beams or cylindrical coaxial pipes. It is assumed that the stress state of these structures depends on only one coordinate. The study of the stress state of plates with defects, which are reinforced by patches, in most cases requires the use of at least two-dimensional mathematical models. In this work, it is shown that the axial symmetry of the plate, the cut-out, the patch, and the applied load makes it possible to reduce the problem to a one-dimensional problem in the polar coordinate system. An adhesive lap joint with circular symmetry is considered for the first time. The stress-strain state of the structure is described in an analytical form. Comparison of the results of calculating the stress state of the joint with the results of finite element modelling showed good adequacy of the proposed mathematical model.

Algorithm for interpreting a full-scale experiment on a special-purpose communications network segment

Subject of research: the possibility of using the relationship revealed in the course of the field experiment between performance indicators and conflict assessments of organizational and technical systems on the example of the communication network to determine the degree of conflict of their states.
 The purpose of research: the need to develop methods that allow us to classify the systems under study according to the degree of conflict, based on the indicators of their effectiveness.
 Objects of research: mathematical models of the special-purpose communication network, taking into account the conflict relations between the elements of such organizational and technical systems.
 Methods and objects of research: the method of system analysis, coupled with the allocation of types of relations between the elements of the systems under study in terms of conflict theory; the method of field experiment on a special purpose communication network segment, based on which the analysis of the data obtained and the identified dependencies is carried out.
 Results of research: descriptive models of states in which such systems with their inherent performance indicators can be found are given; an interpretation algorithm is developed, which includes the knowledge base formed during the field experiment, which revealed the relationship between the performance indicators of systems and estimates of the conflicts of their graph models. An example of the algorithm's work on one of the communication network configurations considered in the course of the full-scale experiment is given, which shows how the classification of the systems' states by the degree of conflict can be performed.

Study on angle-only relative navigation for unmanned aerial vehicle formation in GPS-denied environment

To satisfy the autonomous relative navigation demand of unmanned aerial vehicle (UAV) formation in a GPS-denied environment, an angle-only relative navigation algorithm is proposed. First, the relative states in 3-dimension space are decoupled into relative motion along the horizontal plane and altitude by applying the relative azimuth between the north-east-down global frame and the body-fixed horizontal frame belonging to UAVs. Second, a relative dynamic and measurement model considering the measurement deviation of sensors are established. This solves the problem of colored noise in a majority of sensors used in practical engineering. Third, a consensus constraint model of relative states is established by introducing a geometric constraint among formation members. Additionally, a consensus-extended Kalman filter is designed. Finally, the angle-only relative navigation method is validated using standard Monte Carlo simulations. The simulation results show that all the states of the system are observable when the observability criterion is satisfied. The relative position accuracy achieved using gyros with a drift rate of 50°/h, accelerometers with 1 mg deviation, and optical cameras with 1° Gaussian white noise is within 10 m (3σ). Additionally, the method proposed in this study can be used to estimate the sensor measurement deviations effectively. Furthermore, it can be potentially applied to improve the absolute navigation accuracy of UAVs.

Continuous voluntary community care services for older people in China: Evidence from Wuhu.

China has limited formal care services and weak unpaid informal care support for older people, which has caused a care service shortage for them. Voluntary community care services are thus a type of formal care service that aims to meet older people's unmet care needs. However, the continuity of such voluntary community care services is important for the degree to which these unmet care needs of older people can be satisfied. Therefore, this study examines what motivates volunteers to provide voluntary community care services for older people in China. It argues that providing continuous voluntary community care services can be motivated by the interaction of volunteers' internal and external motivations. This study employs the grounded theory approach, including open coding, axial coding, selective coding, and saturation testing, and derives the data from 15 semi-structured interviews with volunteers from September to December 2021 in Wuhu, China. The analysis identifies three internal motivations (altruism, social interaction, and self-fulfillment) and three external motivations (social support, standardized management, and relevant benefits) as well as the interaction between them as factors that impact volunteers' willingness to offer continuous voluntary community care services for older people. The study's findings highlight the impact of continuous volunteering on society, which is significant to provide voluntary community care services for older people. It thus contributes to the development of China's care policy and future care supply services as well as serves as a reference for care development models in other welfare states, particularly in places where both formal and informal care are underdeveloped such as China.