Cell Invasion Dynamics into a Three Dimensional Extracellular Matrix Fibre Network

The dynamics of filopodia interacting with the surrounding extracellular matrix (ECM) play a key role in various cell-ECM interactions, but their mechanisms of interaction with the ECM in 3D environment remain poorly understood. Based on first principles, here we construct an individual-based, force-based computational model integrating four modules of 1) filopodia penetration dynamics; 2) intracellular mechanics of cellular and nuclear membranes, contractile actin stress fibers, and focal adhesion dynamics; 3) structural mechanics of ECM fiber networks; and 4) reaction-diffusion mass transfers of seven biochemical concentrations in related with chemotaxis, proteolysis, haptotaxis, and degradation in ECM to predict dynamic behaviors of filopodia that penetrate into a 3D ECM fiber network. The tip of each filopodium crawls along ECM fibers, tugs the surrounding fibers, and contracts or retracts depending on the strength of the binding and the ECM stiffness and pore size. This filopodium-ECM interaction is modeled as a stochastic process based on binding kinetics between integrins along the filopodial shaft and the ligands on the surrounding ECM fibers. This filopodia stochastic model is integrated into migratory dynamics of a whole cell in order to predict the cell invasion into 3D ECM in response to chemotaxis, haptotaxis, and durotaxis cues. Predicted average filopodia speed and that of the cell membrane advance agreed with experiments of 3D HUVEC migration at r[superscript 2] > 0.95 for diverse ECMs with different pore sizes and stiffness.Singapore. National Research Foundation (Singapore-MIT Alliance for Research and Technology)National Science Foundation (U.S.). Science and Technology Center and Emergent Behaviors of Integrated Cellular Systems (Grant EFRI-0735997)National Science Foundation (U.S.). Science and Technology Center and Emergent Behaviors of Integrated Cellular Systems (Grant STC-0902396)National Science Foundation (U.S.). Science and Technology Center and Emergent Behaviors of Integrated Cellular Systems (Grant CBET-0939511

Toward a Unified Performance and Power Consumption NAND Flash Memory Model of Embedded and Solid State Secondary Storage Systems

This paper presents a set of models dedicated to describe a flash storage subsystem structure, functions, performance and power consumption behaviors. These models cover a large range of today's NAND flash memory applications. They are designed to be implemented in simulation tools allowing to estimate and compare performance and power consumption of I/O requests on flash memory based storage systems. Such tools can also help in designing and validating new flash storage systems and management mechanisms. This work is integrated in a global project aiming to build a framework simulating complex flash storage hierarchies for performance and power consumption analysis. This tool will be highly configurable and modular with various levels of usage complexity according to the required aim: from a software user point of view for simulating storage systems, to a developer point of view for designing, testing and validating new flash storage management systems

Incremental embodied chaotic exploration of self-organized motor behaviors with proprioceptor adaptation

This paper presents a general and fully dynamic embodied artificial neural system, which incrementally explores and learns motor behaviors through an integrated combination of chaotic search and reflex learning. The former uses adaptive bifurcation to exploit the intrinsic chaotic dynamics arising from neuro-body-environment interactions, while the latter is based around proprioceptor adaptation. The overall iterative search process formed from this combination is shown to have a close relationship to evolutionary methods. The architecture developed here allows realtime goal-directed exploration and learning of the possible motor patterns (e.g., for locomotion) of embodied systems of arbitrary morphology. Examples of its successful application to a simple biomechanical model, a simulated swimming robot, and a simulated quadruped robot are given. The tractability of the biomechanical systems allows detailed analysis of the overall dynamics of the search process. This analysis sheds light on the strong parallels with evolutionary search

A System-of-Systems Framework for Performance Assessment in Complex Construction Projects

Performance inefficiency is a critical challenge facing the construction industry. Despite the efforts made in the existing body of literature, an integrated theory of performance assessment facilitating a bottom-up understanding of the dynamic behaviors, uncertainties, and interdependencies between the constituents in construction projects is still missing. The traditional paradigm for performance assessment z is mainly based on a reductionism perspective, in which construction projects are identified as monolithic systems. However, complex construction projects are systems-of-systems. Systems-of-systems have unique traits that are different from those of monolithic systems. Failure to investigate construction projects as systems-of-systems has led to theoretical and methodological limitations in the creation of integrated tools and techniques for better assessment of performance in complex construction projects. To address these theoretical and methodological limitations, a system-of-systems framework is proposed as a theoretical lens and methodological structure toward creation of tools and techniques for integrated performance assessment of complex construction projects. Two principles (i.e., base-level abstraction and multi-level aggregation) are used to develop the proposed framework. The proposed framework facilitates a bottom-up evaluation of the dynamic behaviors, uncertainties, and interdependencies between the constituents in construction projects. The capabilizties of the proposed framework show its potential in addressing the limitations pertaining to the traditional frameworks for performance assessment. Hence, it can be adopted and tested by researchers to advance the body of knowledge and create integrated theories of performance assessment in complex construction projects

Building analytical three-field cosmological models

A difficult task to deal with is the analytical treatment of models composed by three real scalar fields, once their equations of motion are in general coupled and hard to be integrated. In order to overcome this problem we introduce a methodology to construct three-field models based on the so-called "extension method". The fundamental idea of the procedure is to combine three one-field systems in a non-trivial way, to construct an effective three scalar field model. An interesting scenario where the method can be implemented is within inflationary models, where the Einstein-Hilbert Lagrangian is coupled with the scalar field Lagrangian. We exemplify how a new model constructed from our method can lead to non-trivial behaviors for cosmological parameters.Comment: 11 pages, and 3 figures, updated version published in EPJ

Learning over Knowledge-Base Embeddings for Recommendation

State-of-the-art recommendation algorithms -- especially the collaborative filtering (CF) based approaches with shallow or deep models -- usually work with various unstructured information sources for recommendation, such as textual reviews, visual images, and various implicit or explicit feedbacks. Though structured knowledge bases were considered in content-based approaches, they have been largely neglected recently due to the availability of vast amount of data, and the learning power of many complex models. However, structured knowledge bases exhibit unique advantages in personalized recommendation systems. When the explicit knowledge about users and items is considered for recommendation, the system could provide highly customized recommendations based on users' historical behaviors. A great challenge for using knowledge bases for recommendation is how to integrated large-scale structured and unstructured data, while taking advantage of collaborative filtering for highly accurate performance. Recent achievements on knowledge base embedding sheds light on this problem, which makes it possible to learn user and item representations while preserving the structure of their relationship with external knowledge. In this work, we propose to reason over knowledge base embeddings for personalized recommendation. Specifically, we propose a knowledge base representation learning approach to embed heterogeneous entities for recommendation. Experimental results on real-world dataset verified the superior performance of our approach compared with state-of-the-art baselines

Develop a Cyber Physical Security Platform for Supporting Security Countermeasure for Digital Energy System

The paper develops a cyber physical system (CPS) security platform for supporting security countermeasures for digital energy systems based on real-time simulators. The CPS platform provides functions that trainers or trainees can be able to operate and test their scenarios with a state-of-the-art integrated solution running at a real-time simulator. Those integrated solutions include energy systems simulation software and communication systems simulation/emulation software. The platform provides practical “hand-on-experiences” for participants and they are able to test, monitor and predict behaviors of both systems at the same time. The platform also helps achieve training’s objectives that meet skilled requirements for the future generation in both smart energy systems evaluation and cyber physical security fields. In particular, we present the CPS platform’s architecture and its functionalities. The developed CPS platform has also been validated and tested within different simulated threat cases and systems.©2022 Mike Mekkanen, Tero Vartiainen, Duong Dang. This work is licensed under a Creative Commons Attribution 4.0 International License.fi=vertaisarvioitu|en=peerReviewed

Integrating reinforcement-learning, accumulator models and motor-primitives to study action selection and researching in monkeys.

This paper presents a model of brain systems underlying reaching in monkeys based on the idea that complex behaviors are built on the basis of a repertoire of motor primitives organized around specific goals (in this case, arm\u27s postures). The architecture of the system is based on an actor-critic reinforcement-learning model, enhanced with an accumulator model for action selection, capable of selecting sensorimotor primitives so as to accomplish a discrimination reaching task that has been used in physiological studies of monkeys\u27 premotor cortex. The results show that the proposed architecture is a first important step towards the construction of a biologically plausible integrated motor-primitive based model of the hierarchical organization of mammals\u27 sensorimotor systems