Non-local interaction in discrete Ricci curvature-induced biological aggregation.

We investigate the collective dynamics of multi-agent systems in two- and three-dimensional environments generated by minimizing discrete Ricci curvature with local and non-local interaction neighbourhoods. We find that even a single effective topological neighbour suffices for significant order in a system with non-local topological interactions. We also explore topological information flow patterns and clustering dynamics using Hodge spectral entropy and mean Forman-Ricci curvature.

Predicting odor from vibrational spectra: a data-driven approach.

This study investigates olfaction, a complex and not well-understood sensory modality. The chemical mechanism behind smell can be described by so far proposed two theories: vibrational and docking theories. The vibrational theory has been gaining acceptance lately but needs more extensive validation.

Session-independent subject-adaptive mental imagery BCI using selective filter-bank adaptive Riemannian features.

The brain-computer interfaces (BCIs) facilitate the users to exploit information encoded in neural signals, specifically electroencephalogram (EEG), to control devices and for neural rehabilitation. Mental imagery (MI)-driven BCI predicts the user's pre-meditated mental objectives, which could be deployed as command signals. This paper presents a novel learning-based framework for classifying MI tasks using EEG-based BCI.

Progress in the Optimization of Compositional Design and Thermomechanical Processing of Metastable β Ti Alloys for Biomedical Applications.

Over the past few years, significant research and development in the manufacturing industry related to the medical field has been done. The aim has been to improve existing biomaterials and bioimplants by exploring new methods and strategies. Beta titanium alloys, known for their exceptional strength-to-modulus ratio, corrosion resistance, biocompatibility, and ease of shaping, are expected to play a crucial role in manufacturing the next generation of biomedical equipment.

A Meditation Based Cognitive Therapy (HMBCT) for Primary Insomnia: A Treatment Feasibility Pilot Study.

Previous research has indicated a critical need for cost-effective alternative therapies. The present pilot study aimed to evaluate a novel, cost-effective therapy for treating insomnia. The study employed a randomized controlled trial with two groups: therapy and control.

Neural response to sad autobiographical recall and sad music listening post recall reveals distinct brain activation in alpha and gamma bands.

Although apparently paradoxical, sad music has been effective in coping with sad life experiences. The underpinning brain neural correlates of this are not well explored. We performed Electroencephalography (EEG) source-level analysis for the brain during a sad autobiographical recall (SAR) and upon exposure to sad music.

A Discrete-Time Event-Driven Near-Optimal Second-Order SMC for Multirobotic System Formation Prone to Network Uncertainties.

In this article, we propose a novel stochastic event-driven near-optimal sliding-mode controller design for addressing the consensus of a multiagent system in a network. The system is prone to external disturbances and network uncertainties, such as losses and delays of data packets. The randomness of network uncertainties introduces stochasticity in the system.

Uncertainty Compensator and Fault Estimator-Based Exponential Supertwisting Sliding-Mode Controller for a Mobile Robot.

This work proposes a novel event-triggered exponential supertwisting algorithm (ESTA) for path tracking of a mobile robot. The proposed work is divided into three parts. In the first part, a fractional-order sliding surface-based exponential supertwisting event-triggered controller has been proposed.

Adaptive Integral Sliding Mode Control Using Fully Connected Recurrent Neural Network for Position and Attitude Control of Quadrotor.

This article proposes an adaptive integral sliding mode control (ISMC) strategy for quadrotor control that ensures faster and finite-time convergence along with chattering attenuation. Quadrotor dynamics are assumed to be unknown because of the high degree of parametric uncertainties, including external disturbances. The equivalent control law obtained by ISMC consists of quadrotor dynamics and, thus, cannot be applied to the quadrotor.

Online Nash Solution in Networked Multirobot Formation Using Stochastic Near-Optimal Control Under Dynamic Events.

This article proposes an online stochastic dynamic event-based near-optimal controller for formation in the networked multirobot system. The system is prone to network uncertainties, such as packet loss and transmission delay, that introduce stochasticity in the system. The multirobot formation problem poses a nonzero-sum game scenario.

An Online Event-Triggered Near-Optimal Controller for Nash Solution in Interconnected System.

This article proposes a real-time event-triggered near-optimal controller for the nonlinear discrete-time interconnected system. The interconnected system has a number of subsystems/agents, which pose a nonzero-sum game scenario. The control inputs/policies based on proposed event-based controller methodology attain a Nash equilibrium fulfilling the desired goal of the system.

Short-term enhancement of cognitive functions and music: A three-channel model.

Short-term effects of music stimulus on enhancement of cognitive functions in human brain are documented, however the underlying neural mechanisms in these cognitive effects are not well investigated. In this study, we have attempted to decipher the mechanisms involved in alterations of neural networks that lead to enhanced cognitive effects post-exposure to music. We have investigated the changes in Electroencephalography (EEG) power and functional connectivity of alpha band in resting state of the brain after exposure to Indian classical music.

Entropically driven controlled release of paclitaxel from poly(2-ethyl-2-oxazoline) coated maghemite nanostructures for magnetically guided cancer therapy.

Theranostic nanostructures serve a dual purpose of therapy and diagnosis. A major fraction of these are based on polymer coated magnetic nanostructures of iron oxides (magnetite and maghemite), owing to the efficient drug loading capacity of polymer shells and enhanced magnetic contrast effects of the iron oxide core. In the current work we are proposing poly(2-ethyl-2-oxazoline) coated linear thermoresponsive nanostructures of maghemite (γ-FeO) for potential application in targeted cancer therapy.

Preparation and characterization of maghemite nanoparticles from mild steel for magnetically guided drug therapy.

Maghemite (γ-Fe O ) nanoparticles for therapeutic applications are prepared from mild steel but the existing synthesis technique is very cumbersome. The entire process takes around 100 days with multiple steps which lack proper understanding. In the current work, maghemite nanoparticles of cuboidal and spheroidal morphologies were prepared from mild steel chips by a novel cost effective oil reduction technique for magnetically guided intravascular drug delivery.

Automatic Facial Expression Recognition System Using Deep Network-Based Data Fusion.

This paper presents a novel automatic facial expressions recognition system (AFERS) using the deep network framework. The proposed AFERS consists of four steps: 1) geometric features extraction; 2) regional local binary pattern (LBP) features extraction; 3) fusion of both the features using autoencoders; and 4) classification using Kohonen self-organizing map (SOM)-based classifier. This paper makes three distinct contributions.

Near-Optimal Controller for Nonlinear Continuous-Time Systems With Unknown Dynamics Using Policy Iteration.

This paper presents a single-network adaptive critic-based controller for continuous-time systems with unknown dynamics in a policy iteration (PI) framework. It is assumed that the unknown dynamics can be estimated using the Takagi-Sugeno-Kang fuzzy model with arbitrary precision. The successful implementation of a PI scheme depends on the effective learning of critic network parameters.

Quantum neural network-based EEG filtering for a brain-computer interface.

A novel neural information processing architecture inspired by quantum mechanics and incorporating the well-known Schrodinger wave equation is proposed in this paper. The proposed architecture referred to as recurrent quantum neural network (RQNN) can characterize a nonstationary stochastic signal as time-varying wave packets. A robust unsupervised learning algorithm enables the RQNN to effectively capture the statistical behavior of the input signal and facilitates the estimation of signal embedded in noise with unknown characteristics.

Control of an optimal finger exoskeleton based on continuous joint angle estimation from EMG signals.

Patients suffering from loss of hand functions caused by stroke and other spinal cord injuries have driven a surge in the development of wearable assistive devices in recent years. In this paper, we present a system made up of a low-profile, optimally designed finger exoskeleton continuously controlled by a user's surface electromyographic (sEMG) signals. The mechanical design is based on an optimal four-bar linkage that can model the finger's irregular trajectory due to the finger's varying lengths and changing instantaneous center.

Variable-gain controllers for nonlinear systems using the T-S fuzzy model.

This correspondence proposes two novel control schemes with variable state-feedback gain to stabilize a Takagi-Sugeno (T-S) fuzzy system. The T-S fuzzy model is expressed as a linear plant with nonlinear disturbance terms in both schemes. In controller I, the T-S fuzzy model is expressed as a linear plant around a nominal plant arbitrarily selected from the set of linear subsystems that the T-S fuzzy model consists of.

On adaptive learning rate that guarantees convergence in feedforward networks.

This paper investigates new learning algorithms (LF I and LF II) based on Lyapunov function for the training of feedforward neural networks. It is observed that such algorithms have interesting parallel with the popular backpropagation (BP) algorithm where the fixed learning rate is replaced by an adaptive learning rate computed using convergence theorem based on Lyapunov stability theory. LF II, a modified version of LF I, has been introduced with an aim to avoid local minima.