Chimera states in a multi-weighted neuronal network

There are multiple types of interactions among neurons, each of which has a remarkable effect on the neurons' behavior. Due to the significance of chimeras in neural processes, in this paper, we study the impact of different electrical, chemical, and ephaptic couplings on the emergence of chimera. Consequently, a multi-weighted small-world network of neurons is considered. The simultaneous effects of two and three couplings are explored on the chimera and complete synchronization. The results represent that the synchronization is achieved in very small coupling strengths in the absence of chemical synapses. In contrast, without electrical synapses, the neurons only exhibit chimera behavior. In the three-weighted network, the synchronization is enhanced for special chemical coupling strengths. The network with directed links is also examined. The general behaviors of the directed and undirected networks are the same; however, the directed links lead to lower synchronization error

A Novel Highly Nonlinear Quadratic System: Impulsive Stabilization, Complexity Analysis, and Circuit Designing

This work introduces a three-dimensional, highly nonlinear quadratic oscillator with no linear terms in its equations. Most of the quadratic ordinary differential equations (ODEs) such as Chen, Rossler, and Lorenz have at least one linear term in their equations. Very few quadratic systems have been introduced and all of their terms are nonlinear. Considering this point, a new quadratic system with no linear term is introduced. This oscillator is analyzed by mathematical tools such as bifurcation and Lyapunov exponent diagrams. It is revealed that this system can generate different behaviors such as limit cycle, torus, and chaos for its different parameters' sets. Besides, the basins of attractions for this system are investigated. As a result, it is revealed that this system's attractor is self-excited. In addition, the analog circuit of this oscillator is designed and analyzed to assess the feasibility of the system's chaotic solution. The PSpice simulations confirm the theoretical analysis. The oscillator's time series complexity is also investigated using sample entropy. It is revealed that this system can generate dynamics with different sample entropies by changing parameters. Finally, impulsive control is applied to the system to represent a possible solution for stabilizing the system

A simple one-dimensional map-based model of spiking neurons with wide ranges of firing rates and complexities

This paper introduces a simple 1-dimensional map-based model of spiking neurons. During the past decades, dynamical models of neurons have been used to investigate the biology of human nervous systems. The models simulate experimental records of neurons’ voltages using difference or differential equations. Difference neuronal models have some advantages besides the differential ones. They are usually simpler, and considering the cost of needed computations, they are more efficient. In this paper, a simple 1-dimensional map-based model of spiking neurons is introduced. Sample entropy is applied to analyze the complexity of the model’s dynamics. The model can generate a wide range of time series with different firing rates and different levels of complexities. Besides, using some tools like bifurcation diagrams and cobwebs, the introduced model is analyzed

Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review

Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA. This virus has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Identifying COVID-19 from these medical images is extremely challenging as it is time-consuming, demanding, and prone to human errors. Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance. Among the AI methodologies, deep learning (DL) networks have gained much popularity compared to traditional machine learning (ML) methods. Unlike ML techniques, all stages of feature extraction, feature selection, and classification are accomplished automatically in DL models. In this paper, a complete survey of studies on the application of DL techniques for COVID-19 diagnostic and automated segmentation of lungs is discussed, concentrating on works that used X-Ray and CT images. Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL techniques is presented. Lastly, the challenges faced in the automated detection of COVID-19 using DL techniques and directions for future research are discussed

A systematic review of the data, methods and environmental covariates used to map Aedes-borne arbovirus transmission risk

BACKGROUND: Aedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for different Aedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used. METHODS: We searched on-line databases for predictive risk mapping studies for dengue, Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc.). RESULTS: We found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 176 papers published 2002-2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: (i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, (ii) regional models used to predict the spread of major epidemics between countries and (iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 31/144) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc.) and only 49% of studies assessed predictive performance via out-of-sample validation procedures. CONCLUSIONS: Here we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We identify key differences in mapping approaches between different arboviral diseases, discuss future research needs and outline specific recommendations for future arbovirus mapping

Developments in production of silica-based thermoluminescence dosimeters

This work addresses purpose-made thermoluminescence dosimeters (TLD) based on doped silica fibres and sol–gel nanoparticles, produced via Modified Chemical Vapour Deposition (MCVD) and wet chemistry techniques respectively. These seek to improve upon the versatility offered by conventional phosphor-based TLD forms such as that of doped LiF. Fabrication and irradiation-dependent factors are seen to produce defects of differing origin, influencing the luminescence of the media. In coming to a close, we illustrate the utility of Ge-doped silica media for ionizing radiation dosimetry, first showing results from gamma-irradiated Ag-decorated nanoparticles, in the particular instance pointing to an extended dynamic range of dose. For the fibres, at radiotherapy dose levels, we show high spatial resolution (0.1 mm) depth-dose results for proton irradiations. For novel microstructured fibres (photonic crystal fibres, PCFs) we show first results from a study of undisturbed and technologically modified naturally occurring radioactivity environments, measuring doses of some 10 s of μGy over a period of several months

Serology based disease status of Pakistani population infected with Hepatitis B virus

<p>Abstract</p> <p>Background</p> <p>The infection rate of hepatitis B virus is continuously increasing in Pakistan. Therefore, a comprehensive study of epidemiological data is the need of time.</p> <p>Methods</p> <p>A total of 1300 individuals were screened for HBV infection markers including HBsAg, anti-HBsAg, HBeAg and anti-HBcAg. The association of these disease indicators was compared with patients' epidemiological characteristics like age, socio-economic status and residential area to analyze and find out the possible correlation among these variables and the patients disease status.</p> <p>Results</p> <p>52 (4%) individuals were found positive for HBsAg with mean age 23.5 ± 3.7 years. 9.30%, 33.47% and 12% individuals had HBeAg, antibodies for HBsAg, and antibodies for HBcAg respectively. HBsAg seropositivity rate was significantly associated (<it>p </it>= 0.03) with the residing locality indicating high infection in rural areas. Antibodies titer against HBsAg decreased with the increasing age reflecting an inverse correlation.</p> <p>Conclusion</p> <p>Our results indicate high prevalence rate of Hepatitis B virus infection and nationwide vaccination campaigns along with public awareness and educational programs are needed to be practiced urgently.</p

Measurements of branching fraction ratios and CP-asymmetries in suppressed B^- -> D(-> K^+ pi^-)K^- and B^- -> D(-> K^+ pi^-)pi^- decays

We report the first reconstruction in hadron collisions of the suppressed decays B^- -> D(-> K^+ pi^-)K^- and B^- -> D(-> K^+ pi^-)pi^-, sensitive to the CKM phase gamma, using data from 7 fb^-1 of integrated luminosity collected by the CDF II detector at the Tevatron collider. We reconstruct a signal for the B^- -> D(-> K^+ pi^-)K^- suppressed mode with a significance of 3.2 standard deviations, and measure the ratios of the suppressed to favored branching fractions R(K) = [22.0 \pm 8.6(stat)\pm 2.6(syst)]\times 10^-3, R^+(K) = [42.6\pm 13.7(stat)\pm 2.8(syst)]\times 10^-3, R^-(K)= [3.8\pm 10.3(stat)\pm 2.7(syst]\times 10^-3, as well as the direct CP-violating asymmetry A(K) = -0.82\pm 0.44(stat)\pm 0.09(syst) of this mode. Corresponding quantities for B^- -> D(-> K^+ pi^-)pi^- decay are also reported.Comment: 8 pages, 1 figure, accepted by Phys.Rev.D Rapid Communications for Publicatio