Analytical Prediction Of Homoclinic Bifurcations Following A Supercritical Hopf Bifurcation

An analytical approach to homoclinic bifurcations at a saddle fixed point is developed in this paper based on high-order, high-accuracy approximations of the stable periodic orbit created at a supercritical Hopf bifurcation of a neighboring fixed point. This orbit then expands as the Hopf bifurcation parameter(s) is(are) varied beyond the bifurcation value, with the analytical criterion proposed for homoclinic bifurcation being the merging of the pe riodic orbit with the neighboring saddle. Thus, our approach is applicable in any situation where the homoclinic bifurcation at any saddle fixed point of a dynamical system is associated with the birth or death of a periodic orbit. We apply our criterion to two systems here. Using approximations of the stable, post-Hopf periodic orbits to first, second, and third orders in a multiple-scales perturbation expansion, we find that, for both systems, our proposed analytical criterion indeed reproduces the numerically-obtained parameter values at the onset of homoclinic bifurcation very closely

Route Choice-based Socio-Technical Macroscopic Traffic Model

Human route choice is undeniably one of the key contributing factors towards traffic dynamics. However, most existing macroscopic traffic models are typically concerned with driving behavior and do not incorporate human route choice behavior models in their formulation. In this paper, we propose a socio-technical macroscopic traffic model that characterizes the traffic states using human route choice attributes. Essentially, such model provides a framework for capturing the Cyber-Physical-Social coupling in smart transportation systems. To derive this model, we first use Cumulative Prospect Theory (CPT) to model the human passengers' route choice under bounded rationality. These choices are assumed to be influenced by traffic alerts and other incomplete traffic information. Next, we assume that the vehicles are operating under a non-cooperative cruise control scenario. Accordingly, human route choice segregates the traffic into multiple classes where each class corresponds to a specific route between an origin-destination pair. Thereafter, we derive a Mean Field Game (MFG) limit of this non-cooperative game to obtain a macroscopic model which embeds the human route choice attribute. Finally, we analyze the mathematical characteristics of the proposed model and present simulation studies to illustrate the model behavior

Artificial Models for Determining Antenna Parameters for a Resonant Frequency

Abstract In this paper, two models are developed based on artificial intelligence which can be used to estimate the length, width and position of the radiating element which are the design parameters of square monopole antenna required to make it operate in a particular frequency band of 4.5 GHz and 8.9 GHz. All the antennas designed using these models gives a wideband of 4.5GHz -5 GHz. One of the two models were developed using Artificial neural network (ANN

Microsoft Word - 18

ABSTRACT Pain, which is an objective phenomenon, commonly originates from activation of primary nociceptive afferents by current or potential stimuli due to tissue damage and the processing of these activities within the nociceptive system. This is a complex experience of somatic mechanisms and psychological influence (affective & cognitive). Pain categorizes as nociceptive pain and neuropathic pain. Pain is a subjective condition that cannot be objectively measured; for this reason, self patient perspective is crucial. Neuropathic pain acts as an activation of pain pathway and it can occur due to the injury of peripheral nerves and posterior roots (peripheral neuropathic pain) and spinal cord and brain (central pain). Neuropathic pain is thought to a result of unique sensation, hence its makes more than sense to use verbal description for distinguishing neuropathic pain from tissue injury. For these reasons, screening and measurement tools were developed. Recently, several screening and measurement tools have been developed to discriminate the nociceptive and neuropathic pain. Screening tools (Doulear Neuropathique en 4 questions, ID-Pain) alert the clinician about possible presence of neuropathic pain mechanisms. Measurement tools assess the intensity of particular qualities of neuropathic pain. Both types of tools can improve measurement sensitivity within clinical trials and epidemiological research

Template-free synthesis of hexagonal ZnO disk and ZnO–Ag composite as potential photocatalyst

Stable hexagonal ZnO disk and ZnO–Ag composites with various percentages of Ag contents were prepared through a simple, easily reproducible and template free path, assisted by hexamine. Here, centrifugation speed and calcination temperature played a crucial role to segregate hexagonal disks of ZnO exclusively from some unwanted morphologies. The impregnation of Ag nanoparticles on ZnO surface was established after modifying the surface with oleic acid. Oleic acid played the role of anchor to hold Ag nanoparticles site-selectively on ZnO surface. Pristine ZnO and the composite both were tested for their light induced activities towards methylene blue dye degradation. The performance indicated that an optimized amount of Ag nanoparticles on ZnO matrix could maximize the performance, beyond which activity was suppressed. All the samples were thoroughly characterized using UV–Vis spectroscopy, Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), Thermogravimetric Analysis (TGA), Fourier Transform Infrared Spectroscopy (FTIR), X-ray Diffraction Analysis (XRD), X-ray Photoelectron Spectroscopy (XPS) and Brunauer–Emmett–Teller (BET) isotherm

Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine

The global healthcare sector continues to grow rapidly and is reflected as one of the fastest-growing sectors in the fourth industrial revolution (4.0). The majority of the healthcare industry still uses labor-intensive, time-consuming, and error-prone traditional, manual, and manpower-based methods. This review addresses the current paradigm, the potential for new scientific discoveries, the technological state of preparation, the potential for supervised machine learning (SML) prospects in various healthcare sectors, and ethical issues. The effectiveness and potential for innovation of disease diagnosis, personalized medicine, clinical trials, non-invasive image analysis, drug discovery, patient care services, remote patient monitoring, hospital data, and nanotechnology in various learning-based automation in healthcare along with the requirement for explainable artificial intelligence (AI) in healthcare are evaluated. In order to understand the potential architecture of non-invasive treatment, a thorough study of medical imaging analysis from a technical point of view is presented. This study also represents new thinking and developments that will push the boundaries and increase the opportunity for healthcare through AI and SML in the near future. Nowadays, SML-based applications require a lot of data quality awareness as healthcare is data-heavy, and knowledge management is paramount. Nowadays, SML in biomedical and healthcare developments needs skills, quality data consciousness for data-intensive study, and a knowledge-centric health management system. As a result, the merits, demerits, and precautions need to take ethics and the other effects of AI and SML into consideration. The overall insight in this paper will help researchers in academia and industry to understand and address the future research that needs to be discussed on SML in the healthcare and biomedical sectors