Computer Simulation Experiments Research Articles

Requirements for Documenting Electrical Cell Stimulation Experiments for Replicability and Numerical Modeling∗.

Thorough documentation of biological experiments is necessary for their replicability. This becomes even more evident when individual steps of in vitro wet-lab experiments are to be incorporated into computer simulation models. In the highly interdisciplinary field of electrical stimulation of biological cells, not only biological but also physical aspects play a crucial role. Simulations may help to identify parameters that influence cells and thereby reveal new insights into mechanisms of the cell biological system. However, missing or misleading documentation of the electrical stimulation step within wet-lab experiments may lead to discrepancies between reported and simulated electrical quantities. In addition, this threatens the replicability of electrical stimulation experiments. Thus, we argue that a minimal set of information is needed to enable a translation of electrical stimulation experiments of biological cells into computer simulation experiments and to support replicability. This set includes detailed information about the electronic devices and components, their set-up as well as the applied stimulus and shall be integrated into an existing guideline for cell biological experiments. Ideally, the documentation should also contain measured properties of the cellular and experimental environment. Furthermore, a realization of our proposed documentation requirements within electronic lab notebooks may provide a crucial step toward a more seamless integration of wet-lab data into simulations. Based on two exemplary studies, we demonstrate the relevance of our claim.

Widely Linear Complex-Valued Diffusion Subband Adaptive Filter Algorithm

The adaptive algorithms applied to distributed networks are usually real-valued diffusion subband adaptive filter algorithms. However, it cannot be used for processing the complex-valued signals. In this paper, a novel augmented complex-valued diffusion normalized subband adaptive filter (D-ACNSAF) algorithm is proposed for distributed estimation over networks. In order to deal with the noncircular complex-valued signals, the D-ACNSAF algorithm uses the widely linear model for a diffusion network. Due to the second-order statistical properties of signal, the D-ACNSAF algorithm can process the circular and non-circular complex-valued signals simultaneously. Moreover, the stability and mean-square steady-state analysis of the proposed algorithm are derived based on the spatial–temporal energy conservation principle. Computer simulation experiments on complex-valued system identification and prediction show that the proposed algorithm has better performance (lower mean-square deviation and faster convergence rate) than diffusion complex least-mean-square and diffusion augmented complex least-mean-square algorithms. And the simulation results are consistent with the analysis results.

Molecular mechanisms involved in drug-induced liver injury caused by urate-lowering Chinese herbs: A network pharmacology study and biology experiments.

As an important part of the comprehensive treatment methods, the urate-lowering Chinese herbs could provide favorable clinical effects on hyperuricemia in its ability to invigorate spleen and remove dampness. Owing to the long-term duration, it brought up the potential adverse reactions (ADRs) and concerns about the drug-induced liver injury from these herbs. To address this problem, the bioinformatics approaches which combined the network pharmacology, computer simulation and molecular biology experiments were undertaken to elucidate the underlying drug-induced liver injury molecular mechanisms of urate-lowering Chinese herbs. Several electronic databases were searched to identify the potential liver injury compounds in published research. Then, the putative target profile of liver injury was predicted, and the interaction network was constructed based on the links between the compounds, corresponding targets and core pathways. Accordingly, the molecular docking simulation was performed to recognize the representative compounds with hepatotoxicity. Finally, the cell experiments were conducted to investigate the biochemical indicators and expression of the crucial protein that were closely associated with liver injury. In conclusion, the current research revealed that the compounds with potential liver injury including diosgenin, baicalin, saikosaponin D, tetrandrine, rutaecarpine and evodiamine from urate-lowering Chinese herbs, could lead to decline the survival rate of L-02 cell, increase the activities of aspartate aminotransferase (AST), alanine aminotransferase (ALT), lactate dehydrogenase (LDH) and alkaline phosphatase (ALP) in cell-culture medium, enhance the expression of p-p38/p38, while the p38 inhibitor could achieve the trend of regulating and controlling liver injury. These research findings bring further support to the growing evidence that the mechanism of the liver injury induced by the compounds from urate-lowering Chinese herbs may be associated with the activation of p38α.

Regional-surface-based registration for image-guided neurosurgery: effects of scan modes on registration accuracy.

The conventional surface-based method only registers the facial zone with preoperative point cloud, resulting in low accuracy away from the facial area. Acquiring a point cloud of the entire head for registration can improve registration accuracy in all parts of the head. However, it takes a long time to collect a point cloud of the entire head. It may be more practical to selectively scan part of the head to ensure high registration accuracy in the surgical area of interest. In this study, we investigate the effects of different scan regions on registration errors in different target areas when using a surface-based registration method. We first evaluated the correlation between the laser scan resolution and registration accuracy to determine an appropriate scan resolution. Then, with the appropriate resolution, we explored the effects of scan modes on registration error in computer simulation experiments, phantom experiments and two clinical cases. The scan modes were designed based on different combinations of five zones of the head surface, i.e., the sphenoid-frontal zone, parietal zone, left temporal zone, right temporal zone and occipital zone. In the phantom experiment, a handheld scanner was used to acquire a point cloud of the head. A head model containing several tumors was designed, enabling us to calculate the target registration errors deep in the brain to evaluate the effect of regional-surface-based registration. The optimal scan modes for tumors located in the sphenoid-frontal, parietal and temporal areas are mode 4 (i.e., simultaneously scanning the sphenoid-frontal zone and the temporal zone), mode 4 and mode 6 (i.e., simultaneously scanning the sphenoid-frontal zone, the temporal zone and the parietal zone), respectively. For the tumor located in the occipital area, no modes were able to achieve reliable accuracy. The results show that selecting an appropriate scan resolution and scan mode can achieve reliable accuracy for use in sphenoid-frontal, parietal and temporal area surgeries while effectively reducing the operation time.

The Mid-Twentieth Century Baby Boom and the Role of Social Influence. An Agent-Based Modelling Approach

Around the middle of the 20th century, most Western countries experienced a surge in birth rates, called the Baby Boom. This boom was unexpected at the time and the underlying mechanisms are still not entirely clear. It was characterized by high levels of inter- and intra-country variability in fertility, as some regions even experienced fertility decline during the Boom. In this paper, we suggest that social influence processes, propelling a shift towards two-child families, might have played an important role in the observed changes in fertility. Interactions in social networks can lead new types of childbearing behaviour to diffuse widely and thereby induce changes in fertility at the macro level. The emergence and diffusion of a two-child norm resulted in homogenization of fertility behaviour across regions. Overall, this led to a reduction of childlessness and thus an increase of fertility, as more people aspired to have at least two children. Yet, in those regions where larger family sizes were still common, the two-child norm contributed to a fertility decline. To explore the role of social influence with analytical rigor, we make use of agent-based computational modelling. We explicate the underlying behavioural assumptions in a formal model and assess their implications by submitting this model to computational simulation experiments. We use Belgium as a case study, since it exhibited large variability in fertility in a relatively small population during the Baby Boom years. We use census data to generate realistic starting conditions and to empirically validate the outcomes that our model generates. Our results show that the proposed mechanism could explain an important part of the variability of fertility trends during the Baby Boom era.

A Fast Sparse Recovery Algorithm for Compressed Sensing Using Approximate l₀ Norm and Modified Newton Method.

In this paper, we propose a fast sparse recovery algorithm based on the approximate l0 norm (FAL0), which is helpful in improving the practicability of the compressed sensing theory. We adopt a simple function that is continuous and differentiable to approximate the l0 norm. With the aim of minimizing the l0 norm, we derive a sparse recovery algorithm using the modified Newton method. In addition, we neglect the zero elements in the process of computing, which greatly reduces the amount of computation. In a computer simulation experiment, we test the image denoising and signal recovery performance of the different sparse recovery algorithms. The results show that the convergence rate of this method is faster, and it achieves nearly the same accuracy as other algorithms, improving the signal recovery efficiency under the same conditions.

An improved two-point calibration method for stereo vision with rotating cameras in large FOV

ABSTRACTAt present, binocular stereo vision is gradually being applied for 3D coordinate measurements in large fields of view (FOVs). In this study, a binocular stereo vision system with fixed and non-zooming cameras in a large FOV is constructed, in which cameras can rotate horizontally and vertically. All intrinsic parameters except the focal length of the cameras are set to default values and these focal lengths are calibrated offline in advance. Only the pitch angle and yaw angle of each camera need to be obtained during rotation. Therefore, we present a novel calibration method by using two control points and transform the imaging model of the pitch and yaw angles into a quadratic equation of the tangent value of the pitch angle so that the closed-form solutions of the pitch and yaw angles can be obtained. Computer simulation and real experiments demonstrate the effectiveness of the proposed method.

ZVS-Operation of LLC Resonant Inverter with Phase Limit Control for Induction furnace

This paper presents an LLC resonant inverter with phase limit control to guarantee zero voltage switching (ZVS) operation and protect switching devices from spike current during the heating process. The output power is controlled by using the asymmetrical duty cycle (ADC) modulation. With phase limit control, the non-ZVS operation and spike current caused by a change of duty cycle with fixed frequency and load Curie’s temperature can be eliminated. The proposed method is confirmed through computer simulation and hardware experiment. The experimental results are provided with the heating of a 300 g of Tin from room temperature until melting at 232 ˚C.

Pseudo-random Trajectory Scanning Suppresses Motion Artifacts on Gadoxetic Acid-enhanced Hepatobiliary-phase Magnetic Resonance Images.

Purpose:Hepatobiliary-phase (HBP) MRI with gadoxetic acid facilitates the differentiation between lesions with and without functional hepatocytes. Thus, high-quality HBP images are required for the detection and evaluation of hepatic lesions. However, the long scan time may increase artifacts due to intestinal peristalsis, resulting in the loss of diagnostic information. Pseudo-random acquisition order disperses artifacts into the background. The aim of this study was to investigate the clinical applicability of pseudo-random trajectory scanning for the suppression of motion artifacts on T1-weighted images including HBP.Methods:Our investigation included computer simulation, phantom experiments, and a clinical study. For computer simulation and phantom experiments a region of interest (ROI) was placed on the area with motion artifact and the standard deviation inside the ROI was measured as image noise. For clinical study we subjected 62 patients to gadoxetic acid-enhanced hepatobiliary-phase imaging with a circular- and a pseudo-random trajectory (c-HBP and p-HBP); two radiologists graded the motion artifacts, sharpness of the liver edge, visibility of intrahepatic vessels, and overall image quality using a five-point scale where 1 = unacceptable and 5 = excellent. Differences in the qualitative scores were determined using the two-sided Wilcoxon signed-rank test.Results:The image noise was higher on the circular image compared with pseudo-random image (101.0 vs 60.9 on computer simulation image, 91.2 vs 67.7 on axial, 95.5 vs 86.9 on reformatted sagittal image for phantom experiments). For clinical study the score for motion artifacts was significantly higher with p-HBP than c-HBP imaging (left lobe: mean 3.4 vs 3.2, P < 0.01; right lobe: mean 3.6 vs 3.4, P < 0.01) as was the qualitative score for the overall image quality (mean 3.6 vs 3.3, P < 0.01).Conclusion:At gadoxetic acid-enhanced hepatobiliary-phase imaging, p-HBP scanning suppressed motion artifacts and yielded better image quality than c-HBP scanning.

An optimization and simulation approach to emergency stations relocation

This paper suggests and compares three median-type location models to determine how to optimally relocate existing emergency medical stations. It also describes a detailed computer simulation model used to evaluate the locations proposed by each model in terms of several indicators of system performance. The simulation model was calibrated using real data from a health care provider in the Slovak Republic. The results of the computer simulation experiments suggest that the simple p-median model can significantly improve the accessibility of urgent health care to patients.

Using distortion correction to improve the precision of camera calibration

In this paper, we proposed a method to improve the precision of camera calibration through distortion correction. Before calibrating parameters of the camera, we first corrected the distortion of the camera lens in an independent way. Using a specific virtual template, we were able to calculate the ideal projection coordinates in the camera’s image plane and the distortion correction value of the feature points in the template. Taking these feature points as samples, we corrected the distortion of all pixels in the camera’s image plane based on compressed sensing technology. After completing the correction of camera distortion, we established linear equations to estimate the camera’s intrinsic and extrinsic parameters. Finally, the high precision calibration of the camera can be realized. The computer simulation experiment results show that the precision and stability of this calibration method is better than that of the other method. In the practical experiment, we adopted the binocular vision measurement system to prove the precision of the calibration method, and the results show that for the same vision measurement system, using this calibration method significantly reduces measurement error compared to the traditional methods as the maximum error is only 0.074 mm. Which indicates this method is effective for improving the precision of calibration.

Effect of computer-simulated experiment on senior secondary school students' anxiety level and performance in practical physics in Owerri Municipal, Imo State, Nigeria

Effect of computer-simulated experiment on senior secondary school students' anxiety level and performance in practical physics in Owerri Municipal, Imo State, Nigeria

МИКРО- И МАКРОМОДЕЛИ ПРОЦЕССА ИНФОРМАЦИОННОГО ВЗАИМОДЕЙСТВИЯ В СОЦИАЛЬНЫХ СИСТЕМАХ

The processes occurring in social systems are now increasingly being studied by methods of systems analysis and mathematical modeling. First of all, it concerns the processes of information dissemination in the social environment. Understanding the patterns of information exchange in the social system allows you to monitor information background, predict the dynamics of opinion formation, assess the level of use of social communication channels and, as a result, develop effective information management decisions and take measures to counteract malicious information campaigns. There is a need for scientifically based approaches to conducting research in social systems of various sizes, obtaining and interpreting data characterizing the participants in information exchange. The developed mathematical models of the process of information interaction that are based on two approaches to the description of social systems - at the micro and macro levels- have been submitted for consideration. By carrying out computer simulation experiments, the qualitative and quantitative coincidence of the results of calculating the awareness and distribution of the spectrum of opinions obtained using these models has been proved.

Copolymers of Diketopyrrolopyrrole and Benzothiadiazole: Design and Function from Simulations with Experimental Support

Alternating block copolymers consisting of diketopyrrolopyrrole and benzothiadiazole electron acceptor units linked together via aromatic five-membered donor heterocycles are studied using a combination of computer simulation techniques and experiments. Four copolymers are modeled starting from their monomers to stacked macromolecules: with two different linkers—thiophene or furan, connecting electron-withdrawing core units—and two different alkyl substituents at lactam nitrogens of diketopyrrolopyrrole—linear dodecyl and branched 2-octyldodecyl chains. In our experiments, we aim at characterization of the optical and electrochemical properties of two copolymers with branched side chains differing in the linker, since as the literature survey shows the data published on these copolymers are very sparse. These properties can be easily interpreted and later compared with theoretical predictions. The results of simulations supported by experiments show that monomers of these polymers have very similar electr...

Study on Sampling Based Discrete Nie-Tan Algorithms for Computing the Centroids of General Type-2 Fuzzy Sets

General type-2 fuzzy logic systems (GT2 FLSs) have become a hot topic in current academic field. Computing the centroids of general type-2 fuzzy sets (also called type-reduction) is a central block in GT2 FLSs. Recent studies prove the continuous Nie-Tan (CNT) algorithms to be actually an accurate approach to calculate the centroids of interval type-2 fuzzy sets (IT2 FSs). This paper compares the sum operation in discrete NT algorithms and the integral operation in CNT algorithms. According to the alpha-planes representation theory of general type-2 fuzzy sets (GT2 FSs), both the discrete and continuous NT algorithms can be extended to compute the centroids of GT2 FSs. Four computer simulation experiments indicate that, when the centroid type-reduced sets and defuzzified values of GT2 FSs are solved, to properly increase the number of sampling points of primary variable can make the results of discrete NT algorithms exactly approach to the accurate benchmark CNT algorithms. Furthermore, the computation efficiency of sampling based discrete NT algorithms is much higher than the CNT algorithms.

Active Coefficient Detection Maximum Correntropy Criterion Algorithm for Sparse Channel Estimation Under Non-Gaussian Environments

In this paper, a kind of active coefficient detection (ACD)-based maximum correntropy criterion (MCC) algorithm is proposed to estimate a sparse multi-path channel under the non-Gaussian environments. The proposed ACD-based MCC algorithms are realized by developing an active coefficient detection mechanism, which can distinguish the active taps within the sparse channels and find out the position and the number of active taps. Therefore, only the active taps coefficient is updated while the trivial channel coefficients are set to be zeros. Various computer simulation experiments are carried out to investigate the performance of the proposed ACD-based MCC algorithms under different impulsive noises. The achieved simulation results prove that the proposed ACD-based MCC algorithms are effective and outperform the previous adaptive filtering algorithms for the sparse channel estimation with regard to both the convergence and the estimation error.

A Fast Feature Selection Method Based on Coefficient of Variation for Diabetics Prediction Using Machine Learning

Diabetes has become a prevalent metabolic disease nowadays, affecting patients of all age groups and large populations around the world. Early detection would facilitate early treatment that helps the prognosis. In the literature of computational intelligence and medical care communities, different techniques have been proposed in predicting diabetes based on the historical records of related symptoms. The researchers share a common goal of improving the accuracy of a diabetes prediction model. In addition to the model induction algorithms, feature selection is a significant approach in retaining only the relevant attributes for the sake of building a quality prediction model later. In this article, a novel and simple feature selection criterion called Coefficient of Variation (CV) is proposed as a filter-based feature selection scheme. By following the CV method, attributes that have a data dispersion too low are disqualified from the model construction process. Thereby the attributes which are factors leading to poor model accuracy are discarded. The computation of CV is simple, hence enabling an efficient feature selection process. Computer simulation experiments by using the Prima Indian diabetes dataset is used to compare the performance of CV with other traditional feature selection methods. Superior results by CV are observed.

Discrete-Time ZND Models Solving ALRMPC via Eight-Instant General and Other Formulas of ZeaD

Repetitive motion planning and control (RMPC) of redundant robot manipulators is a fundamental and important problem widely existing in industrial manufacturing. In this paper, the acceleration-level RMPC (ALRMPC) is studied and solved in a discrete-time manner. For solving this problem, a new ALRMPC scheme with feedback control term is derived and presented at first. Then, by adopting Lagrange’s undetermined multipliers method and zeroing neural dynamics (ZND), a continuous-time ZND model, which is based on the new ALRMPC scheme, is developed and proposed. Besides, an eight-instant general formula with high precision is constructed, proposed and analyzed. By using this eight-instant general formula and other multiple-instant Zhang et al discretization (ZeaD) formulas to discretize the continuous-time ZND model, four discrete-time ZND (DTZND) models for solving ALRMPC are thus obtained. Finally, theoretical analyses and computer simulation experiment results further substantiate the effectiveness and accuracy of the proposed DTZND models.

A Novel Methodology for HYIP Operators’ Bitcoin Addresses Identification

Bitcoin is one of the most popular decentralized cryptocurrencies to date. However, it has been widely reported that it can be used for investment scams, which are referred to as high yield investment programs (HYIP). Although from the security forensic point of view it is very important to identify the HYIP operators’ Bitcoin addresses, so far in the open technical literature no systematic method which reliably collects and identifies such Bitcoin addresses has been proposed. In this paper, a novel methodology is introduced, which efficiently collects a large number of the HYIP operators’ Bitcoin addresses and identifies them based upon a novel analysis of their transactions history. In particular, a scraping-based method is first proposed which is able to collect more than 2,000 HYIP operators’ Bitcoin addresses from the Internet thus providing a large number of the HYIPs’ samples. Second, a supervised machine learning technique, which classifies, whether or not, specific Bitcoin addresses belong to the HYIP operators, is introduced and its performance is evaluated. The proposed classification method is based upon two novel approaches, namely the rate conversion technique that mitigates the effect of Bitcoin price volatility and the sampling technique that reduces the computational amount without sacrificing the classification performance. By employing close to 30,000 real Bitcoin addresses, extensive performance evaluation results obtained by means of computer simulation experiments have shown that the proposed methodology achieves excellent performance, i.e., 95% of the HYIP addresses can be correctly classified, while maintaining a false positive rate less than 4.9%. In order to further validate the proposed classifier’s ability to detect the HYIP operators’ Bitcoin addresses, our designed classifier has been tested against a recently published list of the HYIP addresses maintaining its excellent detection accuracy by achieving a 93.75% success rate.

V2V and V2I Communications for Traffic Safety and CO2 Emission Reduction: A Performance Evaluation

In this paper, we consider a special scenario where connected (V2V and V2I) vehicular technologies are used to alert motorists when they approach a hazardous zone, such as a low visibility area, and recommend proper speeds. We present the principles of the proposed safety driving system and compare the performance of V2V and V2I communications in terms of road safety effectiveness and network communication efficiency. This performance analysis is based on extensive computer simulation experiments by adapting the iTetris platform under various scenarios. We also explore, via simulations, whether CHAA systems, based on V2V and V2I communications can potentially contribute towards eco-driving by reducing Carbon Dioxide (CO2) emissions. Our simulation results showed that our alerting system, based on V2I communication, yields better message reception rate and better safety efficiency compared to a V2V alert system. The results also show that the proposed CHAA system can contribute as well towards reduced CO2 emissions by promoting speed harmonization.