Evaluation of Methods for Estimating Fractal Dimension in Motor Imagery-Based Brain Computer Interface

A brain computer interface BCI enables direct communication between a brain and a computer translating brain activity into computer commands using preprocessing, feature extraction, and classification operations. Feature extraction is crucial, as it has a substantial effect on the classification accuracy and speed. While fractal dimension has been successfully used in various domains to characterize data exhibiting fractal properties, its usage in motor imagery-based BCI has been more recent. In this study, commonly used fractal dimension estimation methods to characterize time series Katz's method, Higuchi's method, rescaled range method, and Renyi's entropy were evaluated for feature extraction in motor imagery-based BCI by conducting offline analyses of a two class motor imagery dataset. Different classifiers fuzzy k-nearest neighbours FKNN, support vector machine, and linear discriminant analysis were tested in combination with these methods to determine the methodology with the best performance. This methodology was then modified by implementing the time-dependent fractal dimension TDFD, differential fractal dimension, and differential signals methods to determine if the results could be further improved. Katz's method with FKNN resulted in the highest classification accuracy of 85%, and further improvements by 3% were achieved by implementing the TDFD method

"'Asianness Under Construction:' The Contours and Negotiation of Panethnic Identity/Culture among Interethnically Married Asian Americans."

Based on life-history interviews of interethnically married U.S.-raised Asians, this article examines the meaning and dynamics of Asian American interethnic marriages, and what they reveal about the complex incorporative process of this “in-between” racial minority group into the U.S.. In particular, this article explores the connection between Asian American interethnic marriage and pan-Asian consciousness/identity, both in terms of how panethnicity shapes romantic/ marital desires of individuals and how pan-Asian culture and identity is invented and negotiated in the process of family-making. My findings indicate that while strong pan-Asian consciousness/ identity underlies the connection among intermarried couples, these unions are not simply a defensive effort to “preserve” Asian-ethnic identity and cultur against a society that still racializes Asian Americans, but a tentative and often unpremeditated effort to navigate a path toward integration into the society through an ethnically based, albeit hybrid and reconstructed identity and culture, that helps the respondents retain the integrity of “Asianness.

Including Total EGFR Staining in Scoring Improves EGFR Mutations Detection by Mutation-Specific Antibodies and EGFR TKIs Response Prediction

Epidermal growth factor receptor (EGFR) is a novel target for therapy in subsets of non-small cell lung cancer, especially adenocarcinoma. Tumors with EGFR mutations showed good response to EGFR tyrosine kinase inhibitors (TKIs). We aimed to identify the discriminating capacity of immunohistochemical (IHC) scoring to detect L858R and E746-A750 deletion mutation in lung adenocarcinoma patients and predict EGFR TKIs response. Patients with surgically resected lung adenocarcinoma were enrolled. EGFR mutation status was genotyped by PCR and direct sequencing. Mutation-specific antibodies for L858R and E746-A750 deletion were used for IHC staining. Receiver operating characteristic (ROC) curves were used to determine the capacity of IHC, including intensity and/or quickscore (Q score), in differentiating L858R and E746-A750 deletion. We enrolled 143 patients during September 2000 to May 2009. Logistic-regression-model-based scoring containing both L858R Q score and total EGFR expression Q score was able to obtain a maximal area under the curve (AUC: 0.891) to differentiate the patients with L858R. Predictive model based on IHC Q score of E746-A750 deletion and IHC intensity of total EGFR expression reached an AUC of 0.969. The predictive model of L858R had a significantly higher AUC than L858R intensity only (p = 0.036). Of the six patients harboring complex EGFR mutations with classical mutation patterns, five had positive IHC staining. For EGFR TKI treated cancer recurrence patients, those with positive mutation-specific antibody IHC staining had better EGFR TKI response (p = 0.008) and longer progression-free survival (p = 0.012) than those without. In conclusion, total EGFR expression should be included in the IHC interpretation of L858R. After adjusting for total EGFR expression, the scoring method decreased the false positive rate and increased diagnostic power. According to the scoring method, the IHC method is useful to predict the clinical outcome and refine personalized therapy

Mentoring in palliative medicine in the time of covid-19: a systematic scoping review : Mentoring programs during COVID-19.

IntroductionThe redeployment of mentors and restrictions on in-person face-to-face mentoring meetings during the COVID-19 pandemic has compromised mentoring efforts in Palliative Medicine (PM). Seeking to address these gaps, we evaluate the notion of a combined novice, peer-, near-peer and e-mentoring (CNEP) and interprofessional team-based mentoring (IPT) program.MethodsA Systematic Evidence Based Approach (SEBA) guided systematic scoping review was carried out to study accounts of CNEP and IPT from articles published between 1st January 2000 and 28th February 2021. To enhance trustworthiness, concurrent thematic and content analysis of articles identified from structured database search using terms relating to interprofessional, virtual and peer or near-peer mentoring in medical education were employed to bring together the key elements within included articles.ResultsFifteen thousand one hundred twenty one abstracts were reviewed, 557 full text articles were evaluated, and 92 articles were included. Four themes and categories were identified and combined using the SEBA's Jigsaw and Funnelling Process to reveal 4 domains - characteristics, mentoring stages, assessment methods, and host organizations. These domains suggest that CNEP's structured virtual and near-peer mentoring process complement IPT's accessible and non-hierarchical approach under the oversight of the host organizations to create a robust mentoring program.ConclusionThis systematic scoping review forwards an evidence-based framework to guide a CNEP-IPT program. At the same time, more research into the training and assessment methods of mentors, near peers and mentees, the dynamics of mentoring interactions and the longitudinal support of the mentoring relationships and programs should be carried out

Energy applications of ionic liquids

Ionic liquids offer a unique suite of properties that make them important candidates for a number of energy related applications. Cation–anion combinations that exhibit low volatility coupled with high electrochemical and thermal stability, as well as ionic conductivity, create the possibility of designing ideal electrolytes for batteries, super-capacitors, actuators, dye sensitised solar cells and thermoelectrochemical cells. In the field of water splitting to produce hydrogen they have been used to synthesize some of the best performing water oxidation catalysts and some members of the protic ionic liquid family co-catalyse an unusual, very high energy efficiency water oxidation process. As fuel cell electrolytes, the high proton conductivity of some of the protic ionic liquid family offers the potential of fuel cells operating in the optimum temperature region above 100 °C. Beyond electrochemical applications, the low vapour pressure of these liquids, along with their ability to offer tuneable functionality, also makes them ideal as CO2 absorbents for post-combustion CO2 capture. Similarly, the tuneable phase properties of the many members of this large family of salts are also allowing the creation of phase-change thermal energy storage materials having melting points tuned to the application. This perspective article provides an overview of these developing energy related applications of ionic liquids and offers some thoughts on the emerging challenges and opportunities

An Integrated Approach To Face Authentication And Facial Pose Estimation For A User-Wheelchair Interface

This work presents an interface which is intended to be used by disabled and elderly users to pilot an electric powered wheelchair (EPW) using their facial poses. The main objectives of this thesis is to introduce an original integrated approach of a two-factor face authentication module to formulate head gesture based interface 9HGI. The HGI was thoroughly developed which can perform both identity verification and EPW navigation control via human face data

On the Post Hoc Explainability of Optimized Self-Organizing Reservoir Network for Action Recognition

This work proposes a novel unsupervised self-organizing network, called the Self-Organizing Convolutional Echo State Network (SO-ConvESN), for learning node centroids and interconnectivity maps compatible with the deterministic initialization of Echo State Network (ESN) input and reservoir weights, in the context of human action recognition (HAR). To ensure stability and echo state property in the reservoir, Recurrent Plots (RPs) and Recurrence Quantification Analysis (RQA) techniques are exploited for explainability and characterization of the reservoir dynamics and hence tuning ESN hyperparameters. The optimized self-organizing reservoirs are cascaded with a Convolutional Neural Network (CNN) to ensure that the activation of internal echo state representations (ESRs) echoes similar topological qualities and temporal features of the input time-series, and the CNN efficiently learns the dynamics and multiscale temporal features from the ESRs for action recognition. The hyperparameter optimization (HPO) algorithms are additionally adopted to optimize the CNN stage in SO-ConvESN. Experimental results on the HAR problem using several publicly available 3D-skeleton-based action datasets demonstrate the showcasing of the RPs and RQA technique in examining the explainability of reservoir dynamics for designing stable self-organizing reservoirs and the usefulness of implementing HPOs in SO-ConvESN for the HAR task. The proposed SO-ConvESN exhibits competitive recognition accuracy

A Contactless Visitor Access Monitoring System

This project presents a contactless visitor access monitoring in small premises which implemented deep learning model in face recognition, develop the graphical user interface (GUI) for new visitor registration and visitor identification. Five stages of monitoring process are designed in the contactless visitor access monitoring (CVAM) GUI, the first step is to give instructions to the admin user regarding the monitoring process, the second step is to perform face recognition, the third step is to scan the body temperature, the fourth step is to perform mask detection on the visitor, and the final stage is to record visitor access time. Another visitor registration (VisReg) GUI is designed to register new visitors into the system. In VisReg, admin user is required to pre-process face images with MTCNN technique and generate new classifier with a ResNet pre-trained model. The contactless visitor access monitoring process is demonstrated. The face recognition gives an accuracy of 82%, while the mask detection gives an accuracy of 95% when tested with the validation dataset. It can be concluded that the visitor monitoring process can be carried out in a contactless way to eliminate the close contact between the security officers, receptionist, and visitors

Parameters Estimation in Topological Kernel Bayesian ART using Multi-objective Particle Swarm Optimization

Potentials in Topological Kernel Bayesian Adaptive Resonance Theory (TKBA) are advocated by the data specific parameters: kernel bandwidth σcim in correntropy induced metric (CIM) and kernel bandwidth σkbr in kernel Bayes' rule (KBR). This paper proposes a new calibration mechanism to implement Multi-objective Particle Swarm optimization (MOPSO) for parameters estimation in TKBA with the intention of searching for optimal values of parameters σcim and σkbr. Calibration mechanism is designed based on the measure of robustness, adaptability and the quality of the learned topological network. Two case studies has been empirically carried out using UCI real world dataset. Experiment results in case study I provide proof-of-concept of the proposed calibration mechanism. Case study II compares MOPSO calibrated TKBA with manual calibrated TKBA in terms of classification performance. Experiment results shows that MOPSO calibrated TKBA is able to enhance the capabilities of TKBA

Two-factor face authentication: Topographic Independent Component Analysis (TICA) and Multispace Random Projection (MRP)

A two-factor face authentication method is presented by using Topographic Independent Component Analysis (TICA) as feature extractor in conjunction with Multispace Random Projection (MRP). Instead of using face alone to access the system, a permissible access requires a pair of genuine face and valid token in authentication mechanism. The formulation is revocable but not reversible and makes replacement effortlessly, hence, offers security reinforcement. Evaluations on TICA as feature extractor before and after MRP have been conducted in the forms of ROC curve, FAR, FRR and scores distribution. The performance of the system has been evaluated under a number of scenarios (normal, stolen token, and stolen face) using Facial Recognition Technology face images