An Algorithm for Testing the Efficient Market Hypothesis

The objective of this research is to examine the efficiency of EUR/USD market through the application of a trading system. The system uses a genetic algorithm based on technical analysis indicators such as Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI) and Filter that gives buying and selling recommendations to investors. The algorithm optimizes the strategies by dynamically searching for parameters that improve profitability in the training period. The best sets of rules are then applied on the testing period. The results show inconsistency in finding a set of trading rules that performs well in both periods. Strategies that achieve very good returns in the training period show difficulty in returning positive results in the testing period, this being consistent with the efficient market hypothesis (EMH).     

An Affinity Chromatographic Reactor for Highly Efficient Turnover of Dissociable Cofactors

A conjugated enzyme system of alcool dehydrogenase and lactate dehydrogenase was immobilized in an ultrafiltration hollow fiber tube, which was inserted in a fine nylon tube to form a hollow-fiber-capillary reactor. To this reactor, the substrates, pyruvate and ethanol, were supplied continuously. The necessary cofactor, NAD, was supplied as a pulse for a short time. The retention time of NAD in the reactor, estimated from the response curve of lactate produced, was much longer than those of the other substrates and products because of the strong adsorption of NAD to the immobilized enzymes through affinity. Therefore, the reactor could produce lactate from pyruvate for a long time without any more NAD. As a typical case, when the enzyme concentration is sufficiently high, the estimated retention time of NAD was 50 times as long as those of other materials so that the NAD turnover obtained was 412,000. The effects of NAD pulse concentration and the immobilized enzyme concentration on the retention time of NAD and NAD turnover were investigated experimentally and theoretically.     

An Approximate EM Algorithm for Nonlinear Mixed Effects Models

In this article, we construct an approximate EM algorithm to estimate the parameters of a nonlinear mixed effects model. The iterative procedure can be viewed as an iterative method of moments procedure for estimating the variance components and an iterative reweighted least squares estimates for estimating the fixed effects. Therefore, it is valid without the normality assumptions on the random components. A computationally simple method of moments estimates of the model parameters are used as the starting values for our iterative procedure. A simulation study was conducted to compare the performances of the proposed procedure with the procedure proposed by Lindstrom and Bates (1990) for some normal models and nonnormal models.     

An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks

The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper.     

An Efficient, Nonlinear Stability Analysis for Detecting Pattern Formation in Reaction Diffusion Systems

Reaction diffusion systems are often used to study pattern formation in biological systems. However, most methods for understanding their behavior are challenging and can rarely be applied to complex systems common in biological applications. I present a relatively simple and efficient, nonlinear stability technique that greatly aids such analysis when rates of diffusion are substantially different. This technique reduces a system of reaction diffusion equations to a system of ordinary differential equations tracking the evolution of a large amplitude, spatially localized perturbation of a homogeneous steady state. Stability properties of this system, determined using standard bifurcation techniques and software, describe both linear and nonlinear patterning regimes of the reaction diffusion system. I describe the class of systems this method can be applied to and demonstrate its application. Analysis of Schnakenberg and substrate inhibition models is performed to demonstrate the methods capabilities in simplified settings and show that even these simple models have nonlinear patterning regimes not previously detected. The real power of this technique, however, is its simplicity and applicability to larger complex systems where other nonlinear methods become intractable. This is demonstrated through analysis of a chemotaxis regulatory network comprised of interacting proteins and phospholipids. In each case, predictions of this method are verified against results of numerical simulation, linear stability, asymptotic, and/or full PDE bifurcation analyses.     

An Efficient Automated Algorithm for Distinguishing Normal and Abnormal ECG Signal

ObjectiveThe present study aims to simulate an alarm system for online detecting normal electrocardiogram (ECG) signals from abnormal ECG so that an individual's heart condition can be accurately and quickly monitored at any moment, and any possible serious dangers can be prevented.Materials and methodsFirst, the data from Physionet database were used to analyze the ECG signal. The data were collected equally from both males and females, and the data length varied between several seconds to several minutes. The heart rate variability (HRV) signal, which reflects heart fluctuations in different time intervals, was used due to the low spatial accuracy of ECG signal and its time constraint, as well as the similarity of this signal with the normal signal in some diseases. In this study, the proposed algorithm provided a return map as well as extracted nonlinear features of the HRV signal, in addition to the application of the statistical characteristics of the signal. Then, artificial neural networks were used in the field of ECG signal processing such as multilayer perceptron (MLP) and support vector machine (SVM), as well as optimal features, to categorize normal signals from abnormal ones.ResultsIn this paper, the area under the curve (AUC) of the ROC was used to determine the performance level of introduced classifiers. The results of simulation in MATLAB medium showed that AUC for MLP and SVM neural networks was 89.3% and 94.7%, respectively. Also, the results of the proposed method indicated that the more nonlinear features extracted from the ECG signal could classify normal signals from the patient.ConclusionThe ECG signal representing the electrical activity of the heart at different time intervals involves some important information. The signal is considered as one of the common tools used by physicians to diagnose various cardiovascular diseases, but unfortunately the proper diagnosis of disease in many cases is accompanied by an error due to limited time accuracy and hiding some important information related to this signal from the physicians' vision leading to the risks of irreparable harm for patients. Based on the results, designing the proposed alarm system can help physicians with higher speed and accuracy in the field of diagnosing normal people from patients and can be used as a complementary system in hospitals.     

An Efficient Next Hop Selection Algorithm for Multi-Hop Body Area Networks

Body Area Networks (BANs) consist of various sensors which gather patient’s vital signs and deliver them to doctors. One of the most significant challenges faced, is the design of an energy-efficient next hop selection algorithm to satisfy Quality of Service (QoS) requirements for different healthcare applications. In this paper, a novel efficient next hop selection algorithm is proposed in multi-hop BANs. This algorithm uses the minimum hop count and a link cost function jointly in each node to choose the best next hop node. The link cost function includes the residual energy, free buffer size, and the link reliability of the neighboring nodes, which is used to balance the energy consumption and to satisfy QoS requirements in terms of end to end delay and reliability. Extensive simulation experiments were performed to evaluate the efficiency of the proposed algorithm using the NS-2 simulator. Simulation results show that our proposed algorithm provides significant improvement in terms of energy consumption, number of packets forwarded, end to end delay and packet delivery ratio compared to the existing routing protocol.     

An Efficient Algorithm for Computing Attractors of Synchronous And Asynchronous Boolean Networks

Biological networks, such as genetic regulatory networks, often contain positive and negative feedback loops that settle down to dynamically stable patterns. Identifying these patterns, the so-called attractors, can provide important insights for biologists to understand the molecular mechanisms underlying many coordinated cellular processes such as cellular division, differentiation, and homeostasis. Both synchronous and asynchronous Boolean networks have been used to simulate genetic regulatory networks and identify their attractors. The common methods of computing attractors are that start with a randomly selected initial state and finish with exhaustive search of the state space of a network. However, the time complexity of these methods grows exponentially with respect to the number and length of attractors. Here, we build two algorithms to achieve the computation of attractors in synchronous and asynchronous Boolean networks. For the synchronous scenario, combing with iterative methods and reduced order binary decision diagrams (ROBDD), we propose an improved algorithm to compute attractors. For another algorithm, the attractors of synchronous Boolean networks are utilized in asynchronous Boolean translation functions to derive attractors of asynchronous scenario. The proposed algorithms are implemented in a procedure called geneFAtt. Compared to existing tools such as genYsis, geneFAtt is significantly faster in computing attractors for empirical experimental systems.

Availability

The software package is available at https://sites.google.com/site/desheng619/download.     

An Evolutionary Firefly Algorithm for the Estimation of Nonlinear Biological Model Parameters

The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test.     

An In situ Proton Filter Covalent Organic Framework Catalyst for Highly Efficient Aqueous Electrochemical Ammonia Production

The electrocatalytic nitrogen reduction reaction (NRR) driven by renewable electricity provides a green synthesis route for ammonia (NH₃) production under ambient conditions but suffers from a low conversion yield and poor Faradaic efficiency (F.E.) because of strong competition from hydrogen evolution reaction (HER) and the poor solubility of N₂ in aqueous systems. Herein, an in situ proton filter covalent organic framework catalyst ( Ru-Tta-Dfp ) is reported with inherent Ruthenium (Ru) sites where the framework controls reactant diffusion by suppressing proton supply and enhancing N₂ flux, causing highly selective and efficient catalysis. The smart catalyst design results in a remarkable ammonia production yield rate of 2.03 mg h⁻¹ mg_cat⁻¹ with an excellent F.E. of ≈52.9%. The findings are further endorsed with the help of molecular dynamics simulations and control COF systems without in situ proton filter feasibility. The results point to a paradigm shift in engineering high-performance NRR electrocatalysts for more feasible green NH₃ production.     

Some Mathematical Models for Line Transect Sampling

ANDERSON and POSPAHALA (1970) investigated the estimation of wildlife population size using the belt or line transect sampling method and devised a correction for bias, thus leading to an estimator with interesting characteristics. This work was given a uniform mathematical framework in BURNHAM and ANDERSON (1976). In this paper we extend that mathematical framework to several different sampling models, and a number of interesting discrete probability distributions emerge.     

An Efficient Optimization Algorithm for Structured Sparse CCA,with Applications to eQTL Mapping

In this paper we develop an efficient optimization algorithm for solving canonical correlation analysis (CCA) with complex structured-sparsity-inducing penalties, including overlapping-group-lasso penalty and network-based fusion penalty. We apply the proposed algorithm to an important genome-wide association study problem, eQTL mapping. We show that, with the efficient optimization algorithm, one can easily incorporate rich structural information among genes into the sparse CCA framework, which improves the interpretability of the results obtained. Our optimization algorithm is based on a general excessive gap optimization framework and can scale up to millions of variables. We demonstrate the effectiveness of our algorithm on both simulated and real eQTL datasets.     

An Efficient,Box Shape Independent Non-Bonded Force and Virial Algorithm for Molecular Dynamics

Abstract

A notation is introduced and used to transform a conventional specification of the non-bonded force and virial algorithm in the case of periodic boundary conditions into an alternative specification. The implementation of the transformed specification is simpler and typically a factor of 1.5 faster than a conventional implementation. Moreover, it is generic with respect to the shape of the simulated system, i.e. the same routines can be used to handle triclinic boxes, truncated octahedron boxes etc. An implementation of this method is presented, and the speed achieved on various machines is given. Essence of the new method is that the number of calculations of image particle positions is strongly reduced during non-bonded force calculations.     

A Simple,Highly Efficient Plating Method for Trypanosomatids1

A simple technique for plating trypanosomatids includes use of plates with lower agar concentrations than those usually prescribed for plating bacteria and a simple system to prevent dehydration due to the long incubation time needed for formation of visible colonies. Consistently high plating efficiency was thus achieved. Colonies from Herpetomonas samuelpessoai and Crithidia deanei were clearly distinguishable from each other; their individual characteristics varied with plating conditions.     

Highly Efficient and Rapid Plant Regeneration in Citrus sinensis

Sweet orange (Citrus sinensis L Osbeck, var Nagpur) was explored for efficient multiple shoot regeneration and rooting in different media. The influence of phytohormones and carbon source on the in vitro morphogenesis of sweet orange epicotyl explants was investigated. Among the various concentrations and combinations of auxins (IAA and NAA) and cytokinins (BAP, Kn, Zn, and TDZ) tried, MT (Murashige and Tucker) medium fortified with benzylaminopurine (BAP) at 1 mg l^?1 without auxin had a strong promotive effect on shoot regeneration, and elucidated best morphogenic response from one-month-old etiolated epicotyl explants. A 100% regeneration frequency was obtained, and multiple shoots with an average of 8.24 shoots per explant were produced on all of the explants. Root formation was seen in response to all the three auxins viz. IBA, NAA and IAA, but the best response with rapid induction was observed under the influence of indole butyric acid (IBA) at 1 mg l^?1. Sucrose was observed to be at par with maltose as carbon source to support shoot regeneration. This study provided promising results, holds potential to be routinely employed for in vitro regeneration of important cultivars of Citrus spp, and can be incorporated for genetic transformation studies in citrus.     

Understanding and Eliminating Hysteresis for Highly Efficient Planar Perovskite Solar Cells

Through detailed device characterization using cross‐sectional Kelvin probe force microscopy (KPFM) and trap density of states measurements, we identify that the J–V hysteresis seen in planar organic–inorganic hybrid perovskite solar cells (PVSCs) using SnO₂ electron selective layers (ESLs) synthesized by low‐temperature plasma‐enhanced atomic‐layer deposition (PEALD) method is mainly caused by the imbalanced charge transportation between the ESL/perovskite and the hole selective layer/perovskite interfaces. We find that this charge transportation imbalance is originated from the poor electrical conductivity of the low‐temperature PEALD SnO₂ ESL. We further discover that a facile low‐temperature thermal annealing of SnO₂ ESLs can effectively improve the electrical mobility of low‐temperature PEALD SnO₂ ESLs and consequently significantly reduce or even eliminate the J–V hysteresis. With the reduction of J–V hysteresis and optimization of deposition process, planar PVSCs with stabilized output powers up to 20.3% are achieved. The results of this study provide insights for further enhancing the efficiency of planar PVSCs.