Keywords: equations : differentielles ; equations : lineaires ; elements : finis Reference Record created on 2005-11-18, modified on 2016-08-08

Numerical solution partial differential equations in science and engineering

Background 
Neuroergonomics combines neuroscience with ergonomics to study human performance using recorded brain signals. Such neural signatures of performance can be measured using a variety of neuroimaging techniques, including functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), and electroencephalography (EEG). EEG has an excellent temporal resolution, and EEG indices are highly sensitive to human brain activity fluctuations.


Objective 
The focus of this systematic review was to explore the applications of EEG indices for quantifying human performance in a variety of cognitive tasks at the macro and micro scales. To identify trends and the state of the field, we examined global patterns among selected articles, such as journal contributions, highly cited papers, affiliations, and high-frequency keywords. Moreover, we discussed the most frequently used EEG indices and synthesized current knowledge regarding the EEG signatures of associated human performance measurements.


Methods 
In this systematic review, we analyzed articles published in English (from peer-reviewed journals, proceedings, and conference papers), Ph.D. dissertations, textbooks, and reference books. All articles reviewed herein included exclusively EEG-based experimental studies in healthy participants. We searched Web-of-Science and Scopus databases using specific sets of keywords.


Results 
Out of 143 papers, a considerable number of cognitive studies focused on quantifying human performance with respect to mental fatigue, mental workload, mental effort, visual fatigue, emotion, and stress. An increasing trend for publication in this area was observed, with the highest number of publications in 2017. Most studies applied linear methods (e.g., EEG power spectral density and the amplitude of event-related potentials) to evaluate human cognitive performance. A few papers utilized nonlinear methods, such as fractal dimension, largest Lyapunov exponent, and signal entropy. More than 50% of the studies focused on evaluating an individual’s mental states while operating a vehicle. Several different methods of artifact removal have also been noted. Based on the reviewed articles, research gaps, trends, and potential directions for future research were explored.


Conclusion 
This systematic review synthesized current knowledge regarding the application of EEG indices for quantifying human performance in a wide variety of cognitive tasks. This knowledge is useful for understanding the global patterns of applications of EEG indices for the analysis and design of cognitive tasks.

/pdf/applications-of-eeg-indices-for-the-quantification-of-human-4g2zk39z2r.pdf

Applications of EEG indices for the quantification of human cognitive performance: A systematic review and bibliometric analysis.

Biomathematics has become one of the most significant areas of research as a result of interdisciplinary study. Chronic diseases sometimes referred to as non-communicable and communicable diseases, are conditions that develop over an extended period as a result of different factors like genetics, lifestyle, and environment. The most important common types of disease are cardiovascular, alcohol, cancer, and diabetes. More than three-quarters of the world’s (31.4 million) deaths occur in low- and middle-income nations, which are disproportionately affected by different infections. Fractional Calculus is a prominent topic for research within the discipline of Applied Mathematics due to its usefulness in solving problems in many different branches of science, engineering, and medicine. Recent researchers have identified the importance of mathematical tools in various disease models as being very useful to study the dynamics with the help of fractional and integer calculus modeling. Due to the complexity of the underlying connections, both deterministic and stochastic epidemiological models are founded on an inadequate understanding of the infectious network. Over the past several years, the use of different fractional operators to model the problem has grown, and it is now a common way to study how epidemics spread. Recently, researchers have actively considered fractional calculus to study different diseases like COVID-19, cancer, TB, HIV, dengue fever, diabetes, cholera, pine welts, smoking and heart attacks, etc. With the help of fractional operator, we modified a mathematical model for the dynamical transmission, analysis, treatment, vaccination, and precaution leveling necessary to mitigate the negative impact of illness on society in the long run, overcoming the memory effect without defining or considering others parameters. In this review paper, we considered all the recent studies based on the fractional modeling of infectious and non-infectious diseases with different fractional operators such as Caputo, Caputo Fabrizio, ABC, and constant proportional with Caputo, etc. This review paper aims to bring all the information together by considering different fractional operators and their uses in the field of infectious disease modeling. The steps taken to accomplish the goal were developing a mathematical model, identifying the equilibrium point, figuring out the minimal reproductive number, and assessing the stability around the equilibrium point. For future direction, we consider the cancer model to study the growth cells of cancer and the impact of therapy to control infections. An equilibrium solution and an analysis of the behavior dynamics of the cell spread with treatment in the form of chemotherapy were obtained. The simulation shows that the population of cancer cells is influenced by the pace of cancer cell growth with the Caputo fractional derivative. The acquired results show how effective and precise the suggested approach is in helping to better understand how chemotherapy works. Chemotherapy medications have been found to increase immunity against particular cancer by reducing the number of tumor cells. Further, we suggested some future work directions with the help of the new hybrid fractional operator. Our innovative methodology might have significant effects on global stakeholders, policymakers, and national health systems. The current strategies for controlling outbreaks and the vaccination and prevention policies that have been implemented would benefit from a more accurate representation of the dynamics of contagious diseases, which necessitates the development of highly complex mathematical models. Microorganisms, interactions between individuals or groups, and environmental, social, economic, and demographic factors on a broader scale are all examples. 

A review on epidemic models in sight of fractional calculus

Comparative analysis of different characteristics of automatic sleep stages.

The origin of the COVID-19 pandemic has given overture to redirection, as well as innovation to many digital technologies. Even after the progression of vaccination efforts across the globe, total eradication of this pandemic is still a distant future due to the evolution of new variants. To proactively deal with the pandemic, the health care service providers and the caretaker organizations require new technologies, alongside with improvements in existing related technologies, Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning in terms of infrastructure, efficiency, privacy, and security. This paper provides an overview of current theoretical and application prospects of IoT, AI, cloud computing, edge computing, deep learning techniques, blockchain technologies, social networks, robots, machines, privacy, and security techniques. In consideration of these prospects in intersection with COVID-19 pandemic, we reviewed the technologies within the broad umbrella of AI-IoT technologies in the most concise classification scheme. In this review, we illustrated that AI-IoT technological applications and innovations have most impacted the field of healthcare. The essential AI-IoT technologies found for healthcare were fog computing in IoT, deep learning, and blockchain. Furthermore, we highlighted several aspects of these technologies and their future impact with a novel methodology of using techniques from image processing, machine learning and differential system modeling. 

Artificial Intelligence and Internet of Things (AI-IoT) Technologies in Response to COVID-19 Pandemic: A Systematic Review

One of the most challenging research areas in the field of biomedical signal is feature extraction and classification of electroencephalogram (EEG) signal for normal and epileptic patients. Epileptic seizures are manifestations of epilepsy and epileptic seizures are disclosures of epilepsy. This paper illustrates the use of wavelet transform (WT) used for feature extraction of EEG signals and the classifiers used are Artificial Neural Network (ANN) and Support Vector Machine (SVM). For feature extraction the EEG signal is decomposed using Daubechies wavelet. Performances of classifiers are based on the parameters such as Accuracy, Sensitivity and Specificity.

Wavelet Transform for Classification of EEG Signal using SVM and ANN

Historically, progress in automation systems has been motivated by the need for collaborative robots in the manufacturing sector. There is a need for technological adoption in the healthcare sector. Robots are the best options to help and assist doctors in healthcare activities. The healthcare sector’s lack of focus on technological innovations has created new opportunities to enhance service robots that can assist patients in dealing with their illnesses and providing better solutions. Humanoid robots can benefit healthcare by increasing the patient’s satisfaction level. They can also perform precise surgery in tiny rooms and transport hazardous substances. This study conducted a systematic literature review by extracting articles from three databases. This paper highlights the critical role of humanoid Robots in the healthcare sector. This article also discusses the future trends of humanoid robots for the health sector.

Humanoid robot in healthcare: A Systematic Review and Future Research Directions

Estimation of Parameter of Fractional Order Covid -19 SIQR Epidemic Model.

Nowadays, most analytical instruments in modern laboratories are computerized, partly owing to the rapid development of advanced micro-electronic technology. Digitalized spectroscopic data can be exported from these instruments very easily for subsequent signal processing. Raman Spectroscopy is widely recognized as powerful, nondestructive techniques for characterizing materials. The key to realize the qualitative and quantitative analysis is data processing and analysis. But signals in Raman spectral analysis often have noise, which greatly influences the achievement of accurate analytical results. The de-noising of Raman spectral is an important part of de-noising. Wavelet functions are localized both time and frequency (or scale) and in time, via dilations and translations of the mother wavelet, respectively, both time and frequency information are maintained after transformation. This paper presents wavelet based de-noising method for Raman Spectral data of Sr 2+ modified PMN-PZT and compared the results with Daubechies, Coiflet, Symlet.

/pdf/raman-spectral-data-de-noising-based-on-wavelet-analysis-2midvfx6a7.pdf

Raman Spectral Data De-noising based on Wavelet Analysis

In this article, a new conformable fractional anisotropic diffusion model for image denoising is presented, which contains the spatial derivative along with the time-fractional derivative. This model is a generalization of the diffusion model (Welk et al. in Scale space. Springer, Berlin, pp 585–597, 2005) with forward–backward diffusivities. The proposed model is very efficient for noise removal of the noisy images in comparison to the classical anisotropic diffusion model. The numerical experiments are performed using an explicit scheme for different-different values of fractional order derivative $$\alpha $$
 . The experimental results are obtained in terms of peak signal to noise ratio (PSNR) as a metric.

Fractional derivative based nonlinear diffusion model for image denoising

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