Asynchronous Model Research Articles

Quantitative analysis of the effects of iododeoxyuridine and ionising radiation treatment on the cell cycle dynamics of DNA mismatch repair deficient human colorectal cancer cells

DNA mismatch repair (MMR) is involved in processing DNA damage following treatment with ionising radiation (IR) and various classes of chemotherapy drugs including iododeoxyuridine (IUdR), a known radiosensitiser. In this study, the authors have developed asynchronous probabilistic cell cycle models to assess the isolated effects of IUdR and IR and the combined effects of IUdR + IR treatments on MMR damage processing. The authors used both synchronous and asynchronous MMR-proficient/MMR-deficient cell populations and followed treated cells for up to two cell cycle times. They have observed and quantified differential cell cycle responses to MMR damage processing following IR and IUdR + IR treatments, principally in the duration of both G1 and G2/M cell cycle phases. The models presented in this work form the foundation for the development of an approach to maximise the therapeutic index for IR and IUdR + IR treatments in MMR-deficient (damage tolerant) cancers.

First steps on asynchronous lattice-gas models with an application to a swarming rule

Lattice-gas cellular automata are often considered as a particular case of cellular automata in which additional constraints apply, such as conservation of particles or spatial exclusion. But what about their updating? How to deal with non-perfect synchrony? Novel definitions of asynchronism are proposed that respect the specific hypotheses of lattice-gas models. These definitions are then applied to a swarming rule in order to explore the robustness of the global emergent behaviour. In particular, we compare the synchronous and asynchronous case, and remark that a paradoxical phenomenon, the anti-alignment of particles, is no longer observed when a small but not infinitesimal amount of asynchronism is added.

Speech driven photo realistic facial animation based on an articulatory DBN model and AAM features

This paper presents a photo realistic facial animation synthesis approach based on an audio visual articulatory dynamic Bayesian network model (AF_AVDBN), in which the maximum asynchronies between the articulatory features, such as lips, tongue and glottis/velum, can be controlled. Perceptual Linear Prediction (PLP) features from audio speech, as well as active appearance model (AAM) features from face images of an audio visual continuous speech database, are adopted to train the AF_AVDBN model parameters. Based on the trained model, given an input audio speech, the optimal AAM visual features are estimated via a maximum likelihood estimation (MLE) criterion, which are then used to construct face images for the animation. In our experiments, facial animations are synthesized for 20 continuous audio speech sentences, using the proposed AF_AVDBN model, as well as the state-of-art methods, being the audio visual state synchronous DBN model (SS_DBN) implementing a multi-stream Hidden Markov Model, and the state asynchronous DBN model (SA_DBN). Objective evaluations on the learned AAM features show that much more accurate visual features can be learned from the AF_AVDBN model. Subjective evaluations show that the synthesized facial animations using AF_AVDBN are better than those using the state based SA_DBN and SS_DBN models, in the overall naturalness and matching accuracy of the mouth movements to the speech content.

Impact of the Konio pathway in the thalamocortical visual system: a modeling study

In the early visual system, regarding the detection of a visual event, motion information is pre-processed in the Magnocellular pathway, while it has been shown that the Koniocellular pathway [1] also plays an important role, providing a global analysis about such a kind of information processing. However, the functional interplay between these two parallel pathways remains partially understood. Previous works have attacked this question by studying the signals produced by the corresponding ganglion cells [1] and their elaboration at further steps [2], rather than proposing to model the underlying mechanisms at a mesoscopic level, i.e., focusing on the functional aspects of such dual processing. Neurobiological studies dedicated to the thalamocortical stage of the early visual system provide knowledge on particular characteristics of the system, namely: 1) the variety of cell types in the retina [2], inducing different pathways, 2) the variety of thalamocortical projections through focused vs diffuse efferences to the cortex [3], from core vs matrix (specific vs non-specific) thalamic nuclei, and 3) the variety of kinds of connectivity between thalamic, cortical and collicular areas (i.e., feedforward, feedback, shortcuts, driver and modulator information flows [4]). To figure out the impact of these multi-scale characteristics, we propose here a systemic approach at the structure level. To this end, we have developed a reduced bio-inspired distributed asynchronous model of the primitive mammal visual system, considering only motion event detection. This computational model is fed with natural image sequences, and is implemented as a large size distributed calculation [5] with thousands of computation units per structure. Thanks to the dual analysis integrating local and larger image cues, we test the system for the detection of specific dynamical patterns (which could be interpreted as, e.g., threats or targets). Our simulations aim at showing that these multi-scale interactions help improving the speed and/or quality of such critical tasks, including target selection and tracking. We expect this approach to propose an innovative answer to the interplay issues quoted here, generalizable to other visuo-motor functions. This also provides a platform that could be used as a testbed for new hypotheses. Further information flows to be included could correspond to the Parvocellular pathway (and its related functions). Other structures such as the pulvinar, or higher cortical areas, would also allow us to explore even more developed mechanisms and behaviors [3,6].

An energy-based free boundary asynchronous diffusion model for 3D warping of tissue dynamics

Soft tissue warping is one of the key technologies in dynamic simulation of many surgical procedures, such as image-guided surgery, surgical navigation and structural/lesion localization. Previously, we proposed a force asynchronous diffusion model (FADM) for soft tissue warping simulation. The FADM works well if a lesion or an anatomical object has a convex shape. However, in some cases, this convex assumption is invalid. In order to remove this significant limitation, this paper presents an energy-based free boundary asynchronous diffusion model (EBFBADM) with three unique features. First, we utilize hexahedral voxels to represent the physical model of the surface triangular mesh and a regional proliferation algorithm to remove invalid voxels, simplify computation and improve realness in warping simulation. Second, we adopt the concept of free boundary to simulate soft tissue geometric characteristics more precisely during the warping process. Finally, we optimize the process of asynchronous diffusion by using the mechanical energy of mass point to achieve realistic soft tissue warping effects. Experimental results have shown that the EBFBADM improves performance in both computation and realness in surgical simulation.

OTLA: A New Model for Online Teaching, Learning and Assessment in Higher Education

The study examined a new asynchronous model for online teaching, learning and assessment, called OTLA. It is designed for higher-education institutions and is based on LMS (Learning Management System) as well as other relevant IT tools. The new model includes six digital basic components: text, hypertext, text reading, lectures (voice/video), exercises (optional and compulsory) and a final computerised exam. It is accompanied by deep daily involvement of the course lecturer. The research was based on two samples of students studying in two colleges (n = 50, n = 46) who 1 2 participated in OTLA courses. All students were asked to answer the same questionnaire focused on three areas: the course components, characteristics of learning process and time investment. Besides, achievements of an OTLA course were compared to the same course in a to face format. The research reveals that according to students' views, the new model is very helpful for their studying process. Furthermore, achievements in an OTLA course were found to be equal or better in comparison to an equivalent face-to-face course. The study's results point out that there is a significant

The topology of distributed adversaries

Roughly speaking, a simplicial complex is shellable if it can be constructed by gluing a sequence of n-simplexes to one another along $$(n-1)$$ -faces only. Shellable complexes have been widely studied because they have nice combinatorial properties. It turns out that several standard models of concurrent computation can be constructed from shellable complexes. We consider adversarial schedulers in the synchronous, asynchronous, and semi-synchronous message-passing models, as well as asynchronous shared memory. We show how to exploit their common shellability structure to derive new and remarkably succinct tight (or nearly so) lower bounds on connectivity of protocol complexes and hence on solutions to the $$k$$ -set agreement task in these models. Earlier versions of material in this article appeared in the 2010 ACM Symposium on Principles of Distributed Computing (Herlihy and Rajsbaum 2010), and the International Conference on Distributed Computing (Herlihy and Rajsbaum 2010, doi: 10.1145/1835698.1835724 ).

The Development, Validity, and Reliability of Communication Satisfaction in an Online Asynchronous Discussion Scale

The purpose of this study was to identify dimensions of students’ communication satisfaction in an asynchronous discussion forum. An asynchronous discussion may be defined as text-based human-to-human communication via computer networks that provides a platform for the participants to interact with one another to exchange ideas, insights, and personal experiences. Two sets of samples (336 and 304 college students) served to explore and confirm the constructs of the Asynchronous Discussion Communication Satisfaction model proposed by this study. The model has been empirically validated via Exploratory factor analysis and confirmatory factory analysis. This study’s concept of communication satisfaction has been a relatively new one, which functions in academic settings as a facilitator of future investigations into the emotional reactions that students have while interacting in asynchronous discussion forums.

Maximum Likelihood Reconstruction for Ising Models with Asynchronous Updates

We describe how the couplings in an asynchronous kinetic Ising model can be inferred. We consider two cases: one in which we know both the spin history and the update times and one in which we know only the spin history. For the first case, we show that one can average over all possible choices of update times to obtain a learning rule that depends only on spin correlations and can also be derived from the equations of motion for the correlations. For the second case, the same rule can be derived within a further decoupling approximation. We study all methods numerically for fully asymmetric Sherrington-Kirkpatrick models, varying the data length, system size, temperature, and external field. Good convergence is observed in accordance with the theoretical expectations.

Asynchronous cellular automaton-based neuron: theoretical analysis and on-FPGA learning.

A generalized asynchronous cellular automaton-based neuron model is a special kind of cellular automaton that is designed to mimic the nonlinear dynamics of neurons. The model can be implemented as an asynchronous sequential logic circuit and its control parameter is the pattern of wires among the circuit elements that is adjustable after implementation in a field-programmable gate array (FPGA) device. In this paper, a novel theoretical analysis method for the model is presented. Using this method, stabilities of neuron-like orbits and occurrence mechanisms of neuron-like bifurcations of the model are clarified theoretically. Also, a novel learning algorithm for the model is presented. An equivalent experiment shows that an FPGA-implemented learning algorithm enables an FPGA-implemented model to automatically reproduce typical nonlinear responses and occurrence mechanisms observed in biological and model neurons.

Putting the “Tele-” in Telepractice

Telepractice is emerging as an effective service delivery model in speech-language pathology and audiology. Videoconferencing technology has continued to evolve and is now more widely available across a range of devices. These technologies support both synchronous and asynchronous service delivery models and may incorporate additional hardware and software. Practitioners who are establishing new telepractice programs, as well as those with more experience, must stay abreast of these technological changes. In this article, authors give an overview of ASHA's definition of telepractice and discuss synchronous versus asynchronous technology, hardware versus software, telepractice security concerns, and cloud-computing. Authors provide additional resources for these topics as well as for specific software programs used in support of telepractice.

Preservation of dynamic properties in qualitative modeling frameworks for gene regulatory networks

Mathematical modeling often helps to provide a systems perspective on gene regulatory networks. In particular, qualitative approaches are useful when detailed kinetic information is lacking. Multiple methods have been developed that implement qualitative information in different ways, e.g., in purely discrete or hybrid discrete/continuous models. In this paper, we compare the discrete asynchronous logical modeling formalism for gene regulatory networks due to R. Thomas with piecewise affine differential equation models. We provide a local characterization of the qualitative dynamics of a piecewise affine differential equation model using the discrete dynamics of a corresponding Thomas model. Based on this result, we investigate the consistency of higher-level dynamical properties such as attractor characteristics and reachability. We show that although the two approaches are based on equivalent information, the resulting qualitative dynamics are different. In particular, the dynamics of the piecewise affine differential equation model is not a simple refinement of the dynamics of the Thomas model

Breaks Strip Breakdown with the Parameter Identification Law Diagnosis Mouse Cage Asynchronous Motor Rotor

According to the three-phase squirrel-cage asynchronous motor rotor broken bar fault, presented with parameters identification method of asynchronous motor parameter identification to monitoring and diagnosis of rotor resistance variation of rotor bar breaking principle. Choose a three-phase squirrel-cage asynchronous motor in three working points of experiments, results show that the method is correct and feasible. This method is based on the three-phase squirrel-cage asynchronous motor steady state model parameter equation, using the method of least squares identification parameters, and consider the effects of temperature on the parameters, by the parameter variation in diagnosis of broken rotor bar fault. Advantages of simple scientific method, this method can also be used in conjunction with other methods and, on the three-phase squirrel-cage asynchronous motor rotor has no fault diagnosis.

Effects of community structure on the dynamics of random threshold networks

Random threshold networks (RTNs) have been widely used as models of neural or genetic regulatory networks. Network topology plays a central role in the dynamics of these networks. Recently it has been shown that many social and biological networks are scale-free and also exhibit community structure, in which autonomous modules are wired together to perform relatively independent functions. In this study we use both synchronous and asynchronous models of RTNs to systematically investigate how community structure affects the dynamics of RTNs with scale-free topology. Extensive simulation experiments show that RTNs with high modularity have more attractors than those RTNs with low modularity, and RTNs with smaller communities tend to have more attractors. Damage resulting from perturbation of initial conditions spreads less effectively in RTNs with higher modularity and RTNs with smaller communities. In addition, RTNs with high modularity can coordinate their internal dynamics better than RTNs with low modularity under the synchronous update scheme, and it is the other way around under the asynchronous update. This study shows that community structure has a strong effect on the dynamics of RTNs.

SYNCHRONOUS AND ASYNCHRONOUS MIGRATION IN ADAPTIVE DIFFERENTIAL EVOLUTION ALGORITHMS

The influence of synchronous and asynchronous migration on the per- formance of adaptive differential evolution algorithms is investigated. Six adaptive differential evolution variants are employed by the parallel migration model with a star topology. Synchronous and asynchronous migration models with various parameters settings were experimentally compared with non-parallel adaptive al- gorithms in six shifted benchmark problems of dimension D = 30. Three different ways of exchanging individuals are applied in a synchronous island model with a fixed number of islands. Three different numbers of sub-populations are set up in an asynchronous island model. The parallel synchronous and asynchronous migration models increase performance in most problems.

Scenario Analysis on Climate Change Impacts of Urban Land Expansion under Different Urbanization Patterns: A Case Study of Wuhan Metropolitan

Urban land expansion plays an important role in climate change. It is significant to select a reasonable urban expansion pattern to mitigate the impact of urban land expansion on the regional climate in the rapid urbanization process. In this paper, taking Wuhan metropolitan as the case study area, and three urbanization patterns scenarios are designed to simulate spatial patterns of urban land expansion in the future using the Partitioned and Asynchronous Cellular Automata Model. Then, simulation results of land use are adjusted and inputted into WRF (Weather Research and Forecast) model to simulate regional climate change. The results show that: (1) warming effect is strongest under centralized urbanization while it is on the opposite under decentralized scenario; (2) the warming effect is stronger and wider in centralized urbanization scenario than in decentralized urbanization scenario; (3) the impact trends of urban land use expansion on precipitation are basically the same under different scenarios; (4) and spatial distribution of rainfall was more concentrated under centralized urbanization scenario, and there is a rainfall center of wider scope, greater intensity. Accordingly, it can be concluded that decentralized urbanization is a reasonable urbanization pattern to mitigate climate change in rapid urbanization period.

A REDUCTION METHOD FOR BOOLEAN NETWORK MODELS PROVEN TO CONSERVE ATTRACTORS.

Boolean models, wherein each component is characterized with a binary (ON or OFF) variable, have been widely employed for dynamic modeling of biological regulatory networks. However, the exponential dependencse of the size of the state space of these models on the number of nodes in the network can be a daunting prospect for attractor analysis of large-scale systems. We have previously proposed a network reduction technique for Boolean models and demonstrated its applicability on two biological systems, namely, the abscisic acid signal transduction network as well as the T-LGL leukemia survival signaling network. In this paper, we provide a rigorous mathematical proof that this method not only conserves the fixed points of a Boolean network, but also conserves the complex attractors of general asynchronous Boolean models wherein at each time step a randomly selected node is updated. This method thus allows one to infer the long-term dynamic properties of a large-scale system from those of the corresponding reduced model.

Study on the Characteristics of Asynchronous Motor Vector Control

Asynchronous motor vector control is one of hot research problems. In this paper, according to the variable-frequency speed control principle, combining with Variable Voltage Variable Frequency (VVVF) vector control strategies, the asynchronous motor vector control simulation model is built based on the toolbox of Matlab/Simulink in order to study on the characteristics of asynchronous motor vector control. The dynamic characteristics including no-load, load, acceleration and deceleration were researched. The results verified the validity and feasibility of the proposed simulation model for synchronous motor vector control.

Bounded saturation-based CTL model checking

Formal verification is becoming a fundamental step of safety-critical and model-based software development. As part of the verification process, model checking is one of the current advanced techniques to analyze the behavior of a system. Symbolic model checking is an efficient approach to handling even complex models with huge state spaces. Saturation is a symbolic algorithm with a special iteration strategy, which is efficient for asynchronous models. Recent advances have resulted in many new kinds of saturation-based algorithms for state space generation and bounded state space generation and also for structural model checking. In this paper, we examine how the combination of two advanced model checking algorithms – bounded saturation and saturation-based structural model checking – can be used to verify systems. Our work is the first attempt to combine these approaches, and this way we are able to handle and examine complex or even infinite state systems. Our measurements show that we can exploit the efficiency of saturation in bounded model checking.

Synchronous and asynchronous Pareto-based multi-objective Artificial Bee Colony algorithms

Pareto-based multi-objective optimization algorithms prefer non-dominated solutions over dominated solutions and maintain as much as possible diversity in the Pareto optimal set to represent the whole Pareto-front. This paper proposes three multi-objective Artificial Bee Colony (ABC) algorithms based on synchronous and asynchronous models using Pareto-dominance and non-dominated sorting: asynchronous multi-objective ABC using only Pareto-dominance rule (A-MOABC/PD), asynchronous multi-objective ABC using non-dominated sorting procedure (A-MOABC/NS) and synchronous multi-objective ABC using non-dominated sorting procedure (S-MOABC/NS). These algorithms were investigated in terms of the inverted generational distance, hypervolume and spread performance metrics, running time, approximation to whole Pareto-front and Pareto-solutions spaces. It was shown that S-MOABC/NS is more scalable and efficient compared to its asynchronous counterpart and more efficient and robust than A-MOABC/PD. An investigation on parameter sensitivity of S-MOABC/NS was presented to relate the behavior of the algorithm to the values of the control parameters. The results of S-MOABC/NS were compared to some state-of-the art algorithms. Results show that S-MOABC/NS can provide good approximations to well distributed and high quality non-dominated fronts and can be used as a promising alternative tool to solve multi-objective problems with the advantage of being simple and employing a few control parameters.