scholarly journals Neural Matrix and Its Role in Preoperative Evaluation of Partial Epilepsy

2017 ◽  
Vol 3 (4) ◽  
pp. 246-256 ◽  
Author(s):  
Jingzhan Wu ◽  
Mingming Zhou
Keyword(s):  
Neural Network ◽  
Preoperative Evaluation ◽  
Neural System ◽  
Partial Epilepsy ◽  
Close Attention ◽  
Global Workspace ◽  
The Matrix ◽  
The Neural Networks ◽  
Processing Mechanism

The network characteristic of the central neural system has been widely accepted as a basic fabric form. However, the matrix characteristics of neural network are still not fully understood. If we ignore the matrix characteristics of the neural networks and just pay close attention to its connection mode, we are likely to fall into the theory of mechanical reductionism. This can lead to a problem in representing consciousness in a disadvantageous situation. It can also be a barrier to further improving the global workspace theory. Incomplete elucidation of the mechanisms of consciousness representation can also affect the assessment of the surgical outcome of partial epilepsy with conscious injury. Therefore, this paper reviews the epistemological development of neuroscience. We will initially describe the matrix characteristics of the neural system and their significance to the information processing mechanism, and further explore the role of neural matrix in identifying cases of partial epilepsy with little effect on the resection of the lesion.

Role of Artificial Neural Network for Prediction of Gait Parameters and Patterns

2022 ◽  
pp. 427-439
Author(s):  
Kamalpreet Sandhu ◽  
Vikram Kumar Kamboj
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Human Locomotion ◽  
Neural System ◽  
Learning Ability ◽  
Ann Model ◽  
Gait Parameters ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Walking is very important exercise. Walking is characterized by gait. Gait defines the bipedal and forward propulsion of center of gravity of the human body. This chapter describes the role of artificial neural network (ANN) for prediction of gait parameters and patterns for human locomotion. The artificial neural network is a mathematical model. It is computational system inspired by the structure, processing method, and learning ability of a biological brain. According to bio-mechanics perspective, the neural system is utilized to check the non-direct connections between datasets. Also, ANN model in gait application is more desired than bio-mechanics strategies or statistical methods. It produces models of gait patterns, predicts horizontal ground reactions forces (GRF), vertical GRF, recognizes examples of stand, and predicts incline speed and distance of walking.

Role of Artificial Neural Network for Prediction of Gait Parameters and Patterns

2020 ◽  
pp. 124-135
Author(s):  
Kamalpreet Sandhu ◽  
Vikram Kumar Kamboj
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Human Locomotion ◽  
Neural System ◽  
Learning Ability ◽  
Ann Model ◽  
Gait Parameters ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Walking is very important exercise. Walking is characterized by gait. Gait defines the bipedal and forward propulsion of center of gravity of the human body. This chapter describes the role of artificial neural network (ANN) for prediction of gait parameters and patterns for human locomotion. The artificial neural network is a mathematical model. It is computational system inspired by the structure, processing method, and learning ability of a biological brain. According to bio-mechanics perspective, the neural system is utilized to check the non-direct connections between datasets. Also, ANN model in gait application is more desired than bio-mechanics strategies or statistical methods. It produces models of gait patterns, predicts horizontal ground reactions forces (GRF), vertical GRF, recognizes examples of stand, and predicts incline speed and distance of walking.

Extracellular matrix: the gatekeeper of tumor angiogenesis

2019 ◽  
Vol 47 (5) ◽  
pp. 1543-1555 ◽  
Author(s):  
Maurizio Mongiat ◽  
Simone Buraschi ◽  
Eva Andreuzzi ◽  
Thomas Neill ◽  
Renato V. Iozzo
Keyword(s):  
Extracellular Matrix ◽  
Tumor Angiogenesis ◽  
Signaling Cascades ◽  
Cancer Growth ◽  
Cell Behavior ◽  
Metastatic Progression ◽  
Surface Receptors ◽  
The Matrix ◽  
Multiple Signaling

Abstract The extracellular matrix is a network of secreted macromolecules that provides a harmonious meshwork for the growth and homeostatic development of organisms. It conveys multiple signaling cascades affecting specific surface receptors that impact cell behavior. During cancer growth, this bioactive meshwork is remodeled and enriched in newly formed blood vessels, which provide nutrients and oxygen to the growing tumor cells. Remodeling of the tumor microenvironment leads to the formation of bioactive fragments that may have a distinct function from their parent molecules, and the balance among these factors directly influence cell viability and metastatic progression. Indeed, the matrix acts as a gatekeeper by regulating the access of cancer cells to nutrients. Here, we will critically evaluate the role of selected matrix constituents in regulating tumor angiogenesis and provide up-to-date information concerning their primary mechanisms of action.

scholarly journals Tax Fraud Detection through Neural Networks: An Application Using a Sample of Personal Income Taxpayers

2019 ◽  
Vol 11 (4) ◽  
pp. 86 ◽  
Author(s):  
César Pérez López ◽  
María Delgado Rodríguez ◽  
Sonia de Lucas Santos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Income Tax ◽  
Fraud Detection ◽  
Personal Income ◽  
Personal Income Tax ◽  
Tax Returns ◽  
The Neural Networks ◽  
Tax Fraud ◽  
Efficiency Rate

The goal of the present research is to contribute to the detection of tax fraud concerning personal income tax returns (IRPF, in Spanish) filed in Spain, through the use of Machine Learning advanced predictive tools, by applying Multilayer Perceptron neural network (MLP) models. The possibilities springing from these techniques have been applied to a broad range of personal income return data supplied by the Institute of Fiscal Studies (IEF). The use of the neural networks enabled taxpayer segmentation as well as calculation of the probability concerning an individual taxpayer’s propensity to attempt to evade taxes. The results showed that the selected model has an efficiency rate of 84.3%, implying an improvement in relation to other models utilized in tax fraud detection. The proposal can be generalized to quantify an individual’s propensity to commit fraud with regards to other kinds of taxes. These models will support tax offices to help them arrive at the best decisions regarding action plans to combat tax fraud.

scholarly journals The Interactions between Polyphenols and Microorganisms, Especially Gut Microbiota

Antioxidants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 188
Author(s):  
Małgorzata Makarewicz ◽  
Iwona Drożdż ◽  
Tomasz Tarko ◽  
Aleksandra Duda-Chodak
Keyword(s):  
Intestinal Bacteria ◽  
Research Data ◽  
Bioactive Metabolites ◽  
Human Organism ◽  
Comprehensive Knowledge ◽  
Bidirectional Relationship ◽  
The Matrix ◽  
Intestinal Pathogens ◽  
The Impact

This review presents the comprehensive knowledge about the bidirectional relationship between polyphenols and the gut microbiome. The first part is related to polyphenols’ impacts on various microorganisms, especially bacteria, and their influence on intestinal pathogens. The research data on the mechanisms of polyphenol action were collected together and organized. The impact of various polyphenols groups on intestinal bacteria both on the whole “microbiota” and on particular species, including probiotics, are presented. Moreover, the impact of polyphenols present in food (bound to the matrix) was compared with the purified polyphenols (such as in dietary supplements) as well as polyphenols in the form of derivatives (such as glycosides) with those in the form of aglycones. The second part of the paper discusses in detail the mechanisms (pathways) and the role of bacterial biotransformation of the most important groups of polyphenols, including the production of bioactive metabolites with a significant impact on the human organism (both positive and negative).

scholarly journals What is beyond the edges? Gated communities and their role in China’s desire for harmonious cities

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Badiaa Hamama ◽  
Jian Liu
Keyword(s):  
Economic Growth ◽  
Real Estate ◽  
Urban Form ◽  
Gated Communities ◽  
Urban Fabric ◽  
Chinese Cities ◽  
The Matrix ◽  
Process Of Urbanization ◽  
New Urbanization

Abstract During the rapid process of urbanization in post-reform China, cities assumed the role of a catalyst for economic growth and quantitative construction. In this context, territorially bounded and well delimited urban cells, globally known as ‘gated communities’, xiaoqu, continued to define the very essence of Chinese cities becoming the most attractive urban form for city planners, real estate developers, and citizens alike. Considering the guidelines in China’s National New Urbanization Plan (2014–2020), focusing on the promotion of humanistic and harmonious cities, in addition to the directive of 2016 by China’s Central Urban Work Conference to open up the gates and ban the construction of new enclosed residential compounds, this paper raises the following questions: As the matrix of the Chinese urban fabric, what would be the role of the gated communities in China’s desire for a human-qualitative urbanism? And How to rethink the gated communities to meet the new urban challenges? Seeking alternative perspectives, this paper looks at the gated communities beyond the apparent limits they seem to represent, considering them not simply as the ‘cancer’ of Chinese cities, rather the container of the primary ingredients to reshape the urban fabric dominated by the gate.

scholarly journals A Neural Network-Inspired Matrix Formulation of Chemical Kinetics for Acceleration on GPUs

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2710
Author(s):  
Shivam Barwey ◽  
Venkat Raman
Keyword(s):  
Neural Network ◽  
Chemical Kinetics ◽  
Graphics Processing Units ◽  
Source Term ◽  
Turbulent Flames ◽  
Peak Performance ◽  
Computational Time ◽  
Detailed Chemical Kinetics ◽  
The Matrix ◽  
Artificial Neural

High-fidelity simulations of turbulent flames are computationally expensive when using detailed chemical kinetics. For practical fuels and flow configurations, chemical kinetics can account for the vast majority of the computational time due to the highly non-linear nature of multi-step chemistry mechanisms and the inherent stiffness of combustion chemistry. While reducing this cost has been a key focus area in combustion modeling, the recent growth in graphics processing units (GPUs) that offer very fast arithmetic processing, combined with the development of highly optimized libraries for artificial neural networks used in machine learning, provides a unique pathway for acceleration. The goal of this paper is to recast Arrhenius kinetics as a neural network using matrix-based formulations. Unlike ANNs that rely on data, this formulation does not require training and exactly represents the chemistry mechanism. More specifically, connections between the exact matrix equations for kinetics and traditional artificial neural network layers are used to enable the usage of GPU-optimized linear algebra libraries without the need for modeling. Regarding GPU performance, speedup and saturation behaviors are assessed for several chemical mechanisms of varying complexity. The performance analysis is based on trends for absolute compute times and throughput for the various arithmetic operations encountered during the source term computation. The goals are ultimately to provide insights into how the source term calculations scale with the reaction mechanism complexity, which types of reactions benefit the GPU formulations most, and how to exploit the matrix-based formulations to provide optimal speedup for large mechanisms by using sparsity properties. Overall, the GPU performance for the species source term evaluations reveals many informative trends with regards to the effect of cell number on device saturation and speedup. Most importantly, it is shown that the matrix-based method enables highly efficient GPU performance across the board, achieving near-peak performance in saturated regimes.

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