COEL: A Web-based Chemistry Simulation Framework

The chemical reaction network (CRN) is a widely used formalism to describe macroscopic behavior of chemical systems. Available tools for CRN modelling and simulation require local access, installation, and often involve local file storage, which is susceptible to loss, lacks searchable structure, and does not support concurrency. Furthermore, simulations are often single-threaded, and user interfaces are non-trivial to use. Therefore there are significant hurdles to conducting efficient and collaborative chemical research. In this paper, we introduce a new enterprise chemistry simulation framework, COEL, which addresses these issues. COEL is the first web-based framework of its kind. A visually pleasing and intuitive user interface, simulations that run on a large computational grid, reliable database storage, and transactional services make COEL ideal for collaborative research and education. COEL's most prominent features include ODE-based simulations of chemical reaction networks and multicompartment reaction networks, with rich options for user interactions with those networks. COEL provides DNA-strand displacement transformations and visualization (and is to our knowledge the first CRN framework to do so), GA optimization of rate constants, expression validation, an application-wide plotting engine, and SBML/Octave/Matlab export. We also present an overview of the underlying software and technologies employed and describe the main architectural decisions driving our development. COEL is available at http://coel-sim.org for selected research teams only. We plan to provide a part of COEL's functionality to the general public in the near future.Comment: 23 pages, 12 figures, 1 tabl

Delay Line as a Chemical Reaction Network

Chemistry as an unconventional computing medium presently lacks a systematic approach to gather, store, and sort data over time. To build more complicated systems in chemistries, the ability to look at data in the past would be a valuable tool to perform complex calculations. In this paper we present the first implementation of a chemical delay line providing information storage in a chemistry that can reliably capture information over an extended period of time. The delay line is capable of parallel operations in a single instruction, multiple data (SIMD) fashion. Using Michaelis-Menten kinetics, we describe the chemical delay line implementation featuring an enzyme acting as a means to reduce copy errors. We also discuss how information is randomly accessible from any element on the delay line. Our work shows how the chemical delay line retains and provides a value from a previous cycle. The system's modularity allows for integration with existing chemical systems. We exemplify the delay line capabilities by integration with a threshold asymmetric signal perceptron to demonstrate how it learns all 14 linearly separable binary functions over a size two sliding window. The delay line has applications in biomedical diagnosis and treatment, such as smart drug delivery.Comment: 9 pages, 11 figures, 6 table

COEL: A Cloud-Based Reaction Network Simulator

Chemical Reaction Networks (CRNs) are a formalism to describe the macroscopic behavior of chemical systems. We introduce COEL, a web- and cloud-based CRN simulation framework, which does not require a local installation, runs simulations on a large computational grid, provides reliable database storage, and offers a visually pleasing and intuitive user interface. We present an overview of the underlying software, the technologies, and the main architectural approaches employed. Some of COEL’s key features include ODE-based simulations of CRNs and multicompartment reaction networks with rich interaction options, a built-in plotting engine, automatic DNA-strand displacement transformation and visualization, SBML/Octave/Matlab export, and a built-in genetic-algorithm- based optimization toolbox for rate constants. COEL is an open-source project hosted on GitHub (doi:10.5281/zenodo.46544), which allows interested research groups to deploy it on their own sever. Regular users can simply use the web instance at no cost at coel-sim.org. The framework is ideally suited for a collaborative use in both research and education

A Comparative Study of Reservoir Computing for Temporal Signal Processing

Reservoir computing (RC) is a novel approach to time series prediction using recurrent neural networks. In RC, an input signal perturbs the intrinsic dynamics of a medium called a reservoir. A readout layer is then trained to reconstruct a target output from the reservoir's state. The multitude of RC architectures and evaluation metrics poses a challenge to both practitioners and theorists who study the task-solving performance and computational power of RC. In addition, in contrast to traditional computation models, the reservoir is a dynamical system in which computation and memory are inseparable, and therefore hard to analyze. Here, we compare echo state networks (ESN), a popular RC architecture, with tapped-delay lines (DL) and nonlinear autoregressive exogenous (NARX) networks, which we use to model systems with limited computation and limited memory respectively. We compare the performance of the three systems while computing three common benchmark time series: H{\'e}non Map, NARMA10, and NARMA20. We find that the role of the reservoir in the reservoir computing paradigm goes beyond providing a memory of the past inputs. The DL and the NARX network have higher memorization capability, but fall short of the generalization power of the ESN

Improving PARMA Trailing

Taylor introduced a variable binding scheme for logic variables in his PARMA system, that uses cycles of bindings rather than the linear chains of bindings used in the standard WAM representation. Both the HAL and dProlog languages make use of the PARMA representation in their Herbrand constraint solvers. Unfortunately, PARMA's trailing scheme is considerably more expensive in both time and space consumption. The aim of this paper is to present several techniques that lower the cost. First, we introduce a trailing analysis for HAL using the classic PARMA trailing scheme that detects and eliminates unnecessary trailings. The analysis, whose accuracy comes from HAL's determinism and mode declarations, has been integrated in the HAL compiler and is shown to produce space improvements as well as speed improvements. Second, we explain how to modify the classic PARMA trailing scheme to halve its trailing cost. This technique is illustrated and evaluated both in the context of dProlog and HAL. Finally, we explain the modifications needed by the trailing analysis in order to be combined with our modified PARMA trailing scheme. Empirical evidence shows that the combination is more effective than any of the techniques when used in isolation. To appear in Theory and Practice of Logic Programming.Comment: 36 pages, 7 figures, 8 table

The use of image processing to determine cell defects in polycrystalline solar modules

This research aims to use image processingtodetermine cell defects in polycrystalline solar modules. Image processing is a process of enhancing images for differentapplications. One domain that seems to not yet utilise the use of image processing, is photovoltaics. An increased use of fossil fuels is damaging the earth and a call to protect the earth has resulted in the emergence of pollutant-free technologies such as polycrystalline photovoltaic (PV) cells, which are connected to make up solar modules. However, defects often affect the performance of PV cells and consequently solar modules. Electroluminescence (EL) images are used to examine polycrystalline solar (PV) modules to determine if the modules are defective. The main research question that this research addressed is“How can an image processing technique be used to effectively identify defective polycrystalline PV cells from EL images of such cells?“. The experimental research methodology was used to address the main research question. The initial investigation into the problem revealed that certain sectors within industry, as well as the Physics Department at Nelson Mandela University(NMU), do not currently utiliseimage processing when examining EL images of solar modules. The current process is a tedious, manual process whereby solar modules are manually inspected. An analysis of the current processes enabled the identification of ways in which to automatically examine EL images of solar modules. An analysis of literatureprovided a better understanding of the different techniques that are used to examine solar modules, and it was identified how image processing can be applied to EL images. Further analysis of literatureprovided a better understanding of image processing and how image classification experiments using Deep Learning (DL) as an image processing technique can be used to address the main research question. The outcome of the experiments conducted in this research weredifferentadaptive models(LeNet, MobileNet, Xception)that can classify EL images of PV cellsaccording to known standardsused by the Physics Department at NMU. The known standards yielded four classes; normal, uncritical, critical and very critical, which were used for the classification of EL images of PV cells. The adaptive models were evaluated to obtain the precision, recall and F1–scoreof the models.The precession, recall, and F1–score were required to determine how effective the models were in identifying defective PV cells from EL images.The results indicated that an image processing technique canbe used to identify defective polycrystalline PV cells from EL images of such cells. However, further research needs to be conducted to improve the effectiveness of the adaptive models

Shift-Symmetric Configurations in Two-Dimensional Cellular Automata: Irreversibility, Insolvability, and Enumeration

The search for symmetry as an unusual yet profoundly appealing phenomenon, and the origin of regular, repeating configuration patterns have long been a central focus of complexity science and physics. To better grasp and understand symmetry of configurations in decentralized toroidal architectures, we employ group-theoretic methods, which allow us to identify and enumerate these inputs, and argue about irreversible system behaviors with undesired effects on many computational problems. The concept of so-called configuration shift-symmetry is applied to two-dimensional cellular automata as an ideal model of computation. Regardless of the transition function, the results show the universal insolvability of crucial distributed tasks, such as leader election, pattern recognition, hashing, and encryption. By using compact enumeration formulas and bounding the number of shift-symmetric configurations for a given lattice size, we efficiently calculate the probability of a configuration being shift-symmetric for a uniform or density-uniform distribution. Further, we devise an algorithm detecting the presence of shift-symmetry in a configuration. Given the resource constraints, the enumeration and probability formulas can directly help to lower the minimal expected error and provide recommendations for system's size and initialization. Besides cellular automata, the shift-symmetry analysis can be used to study the non-linear behavior in various synchronous rule-based systems that include inference engines, Boolean networks, neural networks, and systolic arrays.Comment: 22 pages, 9 figures, 2 appendice