Kaemika app, Integrating protocols and chemical simulation

Kaemika is an app available on the four major app stores. It provides deterministic and stochastic simulation, supporting natural chemical notation enhanced with recursive and conditional generation of chemical reaction networks. It has a liquid-handling protocol sublanguage compiled to a virtual digital microfluidic device. Chemical and microfluidic simulations can be interleaved for full experimental-cycle modeling. A novel and unambiguous representation of directed multigraphs is used to lay out chemical reaction networks in graphical form

Engineering Education and Research Using MATLAB

MATLAB is a software package used primarily in the field of engineering for signal processing, numerical data analysis, modeling, programming, simulation, and computer graphic visualization. In the last few years, it has become widely accepted as an efficient tool, and, therefore, its use has significantly increased in scientific communities and academic institutions. This book consists of 20 chapters presenting research works using MATLAB tools. Chapters include techniques for programming and developing Graphical User Interfaces (GUIs), dynamic systems, electric machines, signal and image processing, power electronics, mixed signal circuits, genetic programming, digital watermarking, control systems, time-series regression modeling, and artificial neural networks

Memristor models for machine learning

In the quest for alternatives to traditional CMOS, it is being suggested that digital computing efficiency and power can be improved by matching the precision to the application. Many applications do not need the high precision that is being used today. In particular, large gains in area- and power efficiency could be achieved by dedicated analog realizations of approximate computing engines. In this work, we explore the use of memristor networks for analog approximate computation, based on a machine learning framework called reservoir computing. Most experimental investigations on the dynamics of memristors focus on their nonvolatile behavior. Hence, the volatility that is present in the developed technologies is usually unwanted and it is not included in simulation models. In contrast, in reservoir computing, volatility is not only desirable but necessary. Therefore, in this work, we propose two different ways to incorporate it into memristor simulation models. The first is an extension of Strukov's model and the second is an equivalent Wiener model approximation. We analyze and compare the dynamical properties of these models and discuss their implications for the memory and the nonlinear processing capacity of memristor networks. Our results indicate that device variability, increasingly causing problems in traditional computer design, is an asset in the context of reservoir computing. We conclude that, although both models could lead to useful memristor based reservoir computing systems, their computational performance will differ. Therefore, experimental modeling research is required for the development of accurate volatile memristor models.Comment: 4 figures, no tables. Submitted to neural computatio

Design and VHDL Modeling of All-Digital PLLs

International audienceIn this paper, a VHDL model of a second-order alldigital phase-locked loop (ADPLL) based on bang-bang phase detectors is presented. The developed ADPLL is destined to be a part of a distributed clock generators based on networks of the ADPLL. The paper presents an original model and architecture of a digital multi-bit phase-frequency detector (PFD), and describes in details the VHDL modeling of metastability issues related with asynchronous operation of the digital PFD. This particular architecture of the digital PHD is required by the synchronised operation of the ADPLL network in the context of distributed clock generator. The whole ADPLL model have been validated by purely behavioral (VHDL) and mixed simulation, in which the digital PFD detector was represented by its transistorlevel model

Insights from computational modelling and simulation towards promoting public health among African countries

One of the problems associated with some African countries is the increasing trend of road mortality as a result of road fatalities. This has been a major concern. The negative impacts of these on public health cannot be underestimated. An issue of concern is the high record of casualties being recorded on an annual basis as a result of over-speeding, overtaking at dangerous bends, alcohol influence and non-chalant attitude of drivers to driving. The aim of this research is to explore and adapt the knowledge of finite state algorithm, modeling and simulation to design and implement a novel prototype of an advanced traffic light system towards promoting public health among African countries. Here, we specify and built a model of an advanced wireless traffic control system, which will help complement existing traffic control systems among African countries. This prototype is named Advanced Wireless Traffic Control System (WPDTCS). We developed this model using an event-driven programming approach. The technical details of the model were based on knowledge adapted from the Finite State Automation Transition algorithm. It is expected that the AWTCS will promote the evolution of teaching in modeling, simulation, public safety by offering trainees an advanced pedagogical product. It will also permit to strengthen the collaboration of knowledge from the fields of Computer Science, Public health, and Electrical Engineering. Keywords: public health, public safety, modelling , simulation, pr

Data-driven learning how oncogenic gene expression locally alters heterocellular networks

Developing drugs increasingly relies on mechanistic modeling and simulation. Models that capture causal relations among genetic drivers of oncogenesis, functional plasticity, and host immunity complement wet experiments. Unfortunately, formulating such mechanistic cell-level models currently relies on hand curation, which can bias how data is interpreted or the priority of drug targets. In modeling molecular-level networks, rules and algorithms are employed to limit a priori biases in formulating mechanistic models. Here we combine digital cytometry with Bayesian network inference to generate causal models of cell-level networks linking an increase in gene expression associated with oncogenesis with alterations in stromal and immune cell subsets from bulk transcriptomic datasets. We predict how increased Cell Communication Network factor 4, a secreted matricellular protein, alters the tumor microenvironment using data from patients diagnosed with breast cancer and melanoma. Predictions are then tested using two immunocompetent mouse models for melanoma, which provide consistent experimental results