Particle diffusion Monte Carlo (PDMC)

General expressions for anisotropic particle diffusion Monte Carlo (PDMC) in a d-dimensional space are presented. The calculations of ground state energy of a helium atom for solving the many-body Schrödinger equation is carried out by the proposed method. The accuracy and stability of the results are discussed relative to other alternative methods, and our experimental results within the statistical errors agree with the quantum Monte Carlo methods. We also clarify the benefits of the proposed method by modeling the quantum probability density of a free particle in a plane (energy eigenfunctions). The proposed model represents a remarkable improvement in terms of performance, accuracy and computational time over standard MCMC method. © 2019 Walter de Gruyter GmbH, Berlin/Boston

Ultracold Lattice Gas Automata for Single Trapped Ion Interacting with a Laser Field

In the same context of lattice gauge theory, a notion of cellular automaton (CA) is considered as a description of the underlying dynamics of fields evaluated on a discrete lattice. The explorative approach of this work is initiated by taking the automata evolution as an elementary description of fields dynamics on one side and pivotal role of locality on other side to connect the notion of states and transformations to density operators in quantum electrodynamics. In order to parameterize the definition of proposed model in the context of lattice field theory the nonlinear interaction is introduced with neighboring cells. In this paper, analytical and explicit expression of atomic density matrix for single trapped ion interacting with laser field based on cellular automata dynamics are discussed. The definition is further explored by introducing some probabilistic theories as the theoretical framework then it is concluded that the graph of cellular automata in continuum time limit of the CA dynamics leads to Maxwell-Bloch equation

Ultracold Lattice Gas Automata for Single Trapped Ion Interacting with a Laser Field

In the same context of lattice gauge theory, a notion of cellular automaton (CA) is considered as a description of the underlying dynamics of fields evaluated on a discrete lattice. The explorative approach of this work is initiated by taking the automata evolution as an elementary description of fields dynamics on one side and pivotal role of locality on other side to connect the notion of states and transformations to density operators in quantum electrodynamics. In order to parameterize the definition of proposed model in the context of lattice field theory the nonlinear interaction is introduced with neighboring cells. In this paper, analytical and explicit expression of atomic density matrix for single trapped ion interacting with laser field based on cellular automata dynamics are discussed. The definition is further explored by introducing some probabilistic theories as the theoretical framework then it is concluded that the graph of cellular automata in continuum time limit of the CA dynamics leads to Maxwell-Bloch equation

Lorentz Excitable Lattice Gas Automata (LELGA) for optimization of Lennard-Jones atomic cluster size up to N≤383

From the perspective of cellular-automaton and by exploiting the interactions between travelling and localized waves the Lorentz Excitable Lattice Gas Automata (LELGA) is proposed and applied to the optimization of Lennard-Jones (LJ) atomic clusters. Our approach exploits the fact that a reaction-diffusion phenomenon with self-localized excitations cells which behave like quasi-particles, could be potentially used to implement dynamical computation. As an example of collision-based computation in reaction-diffusion systems based on cellular automata we provided our experimental results for unbiased optimization of Lennard-Jones (LJ) atomic clusters. We also demonstrated that the proposed method has successfully located the known global minima of LJ clusters for configuration space size of N ≤ 383

Statistical complexity of boolean cellular automata with short-term reaction-diffusion memory on a square lattice

Memory is a ubiquitous phenomenon in biological systems, in which the present system state is not entirely determined by the current conditions but also depends on the time evolutionary path of the system. Specifically, many phenomena related to memory are characterized by chemical memory reactions that may fire under particular system conditions. These conditional chemical reactions contradict the extant approaches for modeling chemical kinetics and have increasingly posed significant challenges to mathematical modeling and computer simulation. Along these lines, we can imagine a memory module contributing to cell therapy or the synthetic differentiation of certain cells in a certain fashion after experiencing a brief stimulus. We demonstrate that information processing properties of cellular automata (CAs) can be controlled by a signal composed of excitation pulses. We discuss how cellular memory can be incorporated into more complex systems like CAs to understand the controlling of information processing performed by a medium with the use of a pulse signal propagated from a number of control cells. In this paper, we also investigate the potential application of cellular computation for constructing pseudorandom number generators (PRNGs). Furthermore, the PRNG scheme based on CAs with reaction-diffusion memory is proposed for its capability of generating ultrahigh-quality random numbers. However, the quality bottleneck of a practical PRNG lies in the limited cycle of the generator. To close the gap between the pure randomness generation and the short period, we propose and implement a memory algorithm based on a reaction-diffusion process in a chemical system for Boolean CAs. This scheme is characterized by a tradeoff between, on one hand, the rate of generation of random bits and, on the other hand, the degree of randomness that the series can deliver. These successful applications of the memory modeling framework suggest that this innovative theory is an effective and powerful tool for studying memory processes and conditional chemical reactions in a wide range of complex biological systems. This result also opens a new perspective to apply CAs as a computational engine for the robust generation of pure random numbers, which has important applications in cryptography and other related areas. © 2019, Complex Systems Publications, Inc. All rights reserved

Phenolic Compounds and Antioxidant Activity of Dried Peel of Iranian Pomegranate

Background: Literature review shows that there are not sufficient data about polyphenolic compounds of peel of Iranian pomegranate. So, this work was mainlyundertaken to determine phenolic compounds and antioxidant activity of dried peel of Iranian pomegranate. Methods: Pomegranate fruits were obtained from mature fruits grown in Saveh, Iran and the Pomegranate Peel (PP) were dried with three different methods. Powders of PP were extracted with four different solvents, using a soxhlet apparatus. The compounds of PP extracts were analyzed by High Performance Liquid Chromatography (HPLC). Then, yield percentage and Radical Scavenging Activity (RSA) were determined. Statistical analysis was performed using the SAS 9.1 software. Results: Different ranges of tannic acids, testosterone and α-estradiol, estriol, estrone, cyanidin 3-glucoside, cyanidin3,5-diglucoside, pelargonidin 3-glucoside, pelargonidin 3,5diglucoside, and delphinidin 3-glucoside were identified. Both the highest yield percentage (18.820±0.661) as well as the highest RSA percentage (63.862±0.376) were obtained from the ethanol showing significant (p0.05) relationship with yield of extraction and also antioxidant activity of the PP extracts. Conclusion: HPLC analysis identified some various phenolic compounds in Iranian PP extract showing considerable antioxidant activities. Although drying method showed no relation with yield of extraction and also antioxidant activity of the PP extracts, but type of solvent was effective on yield of extraction and type of extracted compounds of PP

Can vitamin E supplementation affect obesity indices? A systematic review and meta-analysis of twenty-four randomized controlled trials

Background: Several mechanisms have been proposed for the effect of vitamin E on weight loss. Yet various interventional studies with wide ranges of doses and durations have reported contradictory results. Methods: Cochrane Library, PubMed, Scopus, and Embase databases were searched up to December 2020. Meta-analysis was performed using random-effect method. Effect size was presented as weighted mean difference (WMD) and 95 confidence interval (CI). Heterogeneity was evaluated using the I2 index. In order to identification of potential sources of heterogeneity, predefined subgroup and meta regression analyses was conducted. Results: A total of 24 studies with 33 data sets were included. There was no significant effect of vitamin E on weight (WMD: 0.15, 95 CI: �1.35 to 1.65, P = 0.847), body mass index (BMI) (WMD = 0.04, 95 CI: �0.29 to 0.37, P = 0.815), and waist circumference (WC) (WMD = �0.19 kg, 95 CI: �2.06 to 1.68, P = 0.842), respectively. However, subgroup analysis revealed that vitamin E supplementation in studies conducted on participants with normal BMI (18.5�24.9) had increasing impact on BMI (P = 0.047). Conclusion: There was no significant effect of vitamin E supplementation on weight, BMI and WC. However, vitamin E supplementation might be associated with increasing BMI in people with normal BMI (18.5�24.9). © 2021 Elsevier Ltd and European Society for Clinical Nutrition and Metabolis

Machine learning based voice analysis in spasmodic dysphonia: An investigation of most relevant features from specific vocal tasks

Adductor-type spasmodic dysphonia (ASD) is a task-specific speech disorder characterized by a strangled and strained voice. We have previously demonstrated that advanced voice analysis, performed with support vector machine, can objectively quantify voice impairment in dysphonic patients, also evidencing results of voice improvements due to symptomatic treatment with botulinum neurotoxin type-A injections into the vocal cords. Here, we expanded the analysis by means of three different machine learning algorithms (Support Vector Machine, Naïve Bayes and Multilayer Percept), on a cohort of 60 ASD patients, some of them also treated with botulinum neurotoxin type A therapy, and 60 age and gender-matched healthy subjects. Our analysis was based on sounds produced by speakers during the emission of /a/ and /e/ sustained vowels and a standardized sentence. As a conclusion, we report the main features with discriminatory capabilities to distinguish untreated vs. treated ASD patients vs. healthy subjects, and a comparison of the three classifiers with respect to their discriminating accurac

Machine learning based voice analysis in spasmodic dysphonia: an investigation of most relevant features from specific vocal tasks

Adductor-type spasmodic dysphonia (ASD) is a task-specific speech disorder characterized by a strangled and strained voice. We have previously demonstrated that advanced voice analysis, performed with support vector machine, can objectively quantify voice impairment in dysphonic patients, also evidencing results of voice improvements due to symptomatic treatment with botulinum neurotoxin type-A injections into the vocal cords. Here, we expanded the analysis by means of three different machine learning algorithms (Support Vector Machine, Naïve Bayes and Multilayer Percept), on a cohort of 60 ASD patients, some of them also treated with botulinum neurotoxin type A therapy, and 60 age and gender-matched healthy subjects. Our analysis was based on sounds produced by speakers during the emission of /a/ and /e/ sustained vowels and a standardized sentence. As a conclusion, we report the main features with discriminatory capabilities to distinguish untreated vs. treated ASD patients vs. healthy subjects, and a comparison of the three classifiers with respect to their discriminating accuracy