Miracle-dz/UniFAR-FTUs huggingface dataset:
Night blood pressure variability, brain atrophy, and cognitive decline

Background Although blood pressure variability (BPV) has emerged as a novel risk factor for Alzheimer's disease, few studies have examined the effects of night BPV on brain structure and function. This study investigated the association of night BPV with brain atrophy and cognitive function changes. Methods The analysis included 1,398 participants with valid ambulatory blood pressure (BP) monitoring at baseline and both baseline and 4-year follow-up brain magnetic resonance images who were recruited from the Korean Genome and Epidemiology Study. Participants underwent a comprehensive neuropsychological test battery. BPV was derived from ambulatory BP monitoring and calculated as a standard deviation (SD) of 24-h and daytime and nighttime BP. Results During the median follow-up of 4.3 years, increased SD of night systolic or diastolic BP was an indicator of total brain volume reduction, while daytime BPV or night average BP was not associated with total brain volume changes. High SD of night systolic BP was associated with reduced gray matter (GM) volume, independent of average night BP, and use of antihypertensive drugs. It also was associated with a reduction of temporal GM volume, mostly driven by atrophy in the left entorhinal cortex and the right fusiform gyrus. In cognitive performance, high variability of night systolic BP was associated with a decrease in visual delayed recall memory and verbal fluency for the category. Conclusion Increased night BPV, rather than night mean BP, was associated with reduced brain volume and cognitive decline. High night BPV could be an independent predictor for rapid brain aging in a middle-aged population.
The early-stage intermediate

Abstract: The multistep polypeptide chain folding model presented in this chapter involves several intermediates, the first of which is called the early-stage (ES) intermediate. This intermediate is assumed to be defined solely on the basis of the backbone conformation and does not take side chains into account. The geometric principles that guide the backbone alignment process and its quantitative influence on the structural arrangement of the folded chain can be expressed by means of a contingency table, linking known structural motifs to specific polypeptide sequences. The basic unit of this algorithm is the tetrapeptide, and the corresponding ES conformational subspace is assumed to consist of seven types of motifs. This limited subspace represents a subset of the full conformational space (i.e., the Ramachandran plot). The volumetric structure of the ES intermediate corresponds to the output of the early folding stage and, simultaneously, provides input for further stages of the folding process.
Relative Hilbert scheme of points

Let $D$ be a smooth divisor on a non singular surface $S$. We compute Betti numbers of the relative Hilbert scheme of points of $S$ relative to $D$. In the case of $\PP^2$ and a line in it, we give an explicit set of generators and relations for the cohomology groups of this space.
TMP: Meta-path based Recommendation on Time-Weighted Heterogeneous Information Networks

Heterogeneous information network is a new efficient way of representing data in current recommender systems. It provides rich side information that can be added to deal with the data sparsity problem and produces better recommendations. However, current HINs ignore the time influence on relationships, and the widely used meta-path in HIN fails to capture the temporal changes of users’ preferences. In this paper, we extend current HIN and meta-path with time attributes through introducing a time deviation matrix, which can distinguish users’ past and recent behaviors. Moreover, we propose a time-weighted meta-path-based recommendation method (TMP) to predict the ratings of users on items, which use the matrix factorization idea of Funk-SVD and combine predicting results from different meta-paths through a weight learning method. To optimize the recommendation, we use user and item biases to address those items which are durably popular and those users who have stable preferences. Experimental results on dataset show the effectiveness of our approach.
A cross-sectional study of psychiatric morbidity and quality of life among participants utilizing the preventive health-care services of a tertiary hospital

Background: The burden of mental disorders has been increasingly recognized and 450 million people globally are suffering from mental illness. Mental–physical comorbidity has adverse effects on the overall outcome. Research is scarce with regard to mental health screening in the context of “preventive health care” in India. Thus, the study aimed to identify the prevalence of mental illness and the effect on quality of life (QOL) among participants attending preventive health-care unit (PHCU). Settings and Design: This was a cross-sectional study conducted in PHCU of a tertiary hospital in Puducherry. Materials and Methods: All consecutive participants (>18 years) attending PHCU were included in the study. The Standard for Clinicians' Interview in Psychiatry (SCIP) screening module, Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) Level 1 Cross-Cutting Symptom Measure, Mini-Cog, and Quality of Life Enjoyment and Satisfaction Questionnaire were administered. Relevant diagnostic modules of SCIP were applied to participants who screened positive on SCIP. Data analysis was performed using SPSS (version 17.0). Results: The mean age of participants was 43.38 ± 13.99 years. Of 203 participants enrolled, 28.1% screened positive and 26.1% were confirmed to have psychiatric disorder. About 4% screened positive for cognitive impairment. Most commonly diagnosed disorders were alcohol use disorder and major depressive disorder. The prevalence of depressive disorder was higher in patients with physical disorder. Participants with psychiatric disorder alone or with both psychiatric and physical disorders had significantly poorer QOL (F = 27.13; P < 0.001). Conclusion: One-fourth of the participants attending preventive health-care services were found to have psychiatric disorders. The presence of psychiatric disorder was associated with significantly poorer QOL. This highlights the importance of routine mental health screening in this setting.
3D high speed characterization of phase objects using the transport of intensity equation

In this work we will extend the traditional TIE setup of phase retrieval of a phase object through axial translation of the CCD by employing a tunable lens (TL-TIE). This setup is also extended to a 360° tomographic 3D reconstruction through multiple illuminations from different angles by rotating the phase object. Finally, synchronization between the CCD, and the tunable lens is employed using a reconfigurable hardware to automate the 3D 360° tomographic reconstruction process.
A quantile regression approach for modelling a Health-Related Quality of Life Measure

Objective . The aim of this study is to propose a new approach for modeling the EQ-5D index and EQ-5D VAS in order to explain the lifestyle determinants effect using the quantile regression analysis. Methods . Data was collected within a cross-sectional study that involved a probabilistic sample of 1,622 adults randomly selected from the population register of two Health Authorities of Bologna in northern Italy. The perceived health status of people was measured using the EQ-5D questionnaire. The Visual Analogue Scale included in the EQ-5D Questionnaire, the EQ-VAS, and the EQ-5D index were used to obtain the synthetic measures of quality of life. To model EQ-VAS Score and EQ-5D index, a quantile regression analysis was employed. Quantile Regression is a way to estimate the conditional quantiles of the VAS Score distribution in a linear model, in order to have a more complete view of possible associations between a measure of Health Related Quality of Life (dependent variable) and socio-demographic and determinants data. This methodological approach was preferred to an OLS regression because of the EQ-VAS Score and EQ-5D index typical distribution. Main Results. The analysis suggested that age, gender, and comorbidity can explain variability in perceived health status measured by the EQ-5D index and the VAS.
Recursive Matrix Decomposition Methods and Applications in Wireless Communication

Matrix decomposition methods such as the Cholesky and the QR decomposition arise in several applications in signal processing for multiple-input, multiple-output (MIMO) communication systems. The computational complexity of regular Cholesky and QR solvers is known to be $\mathcal{O}\left( {{N^3}} \right)$. To reduce this, several recursive algorithms at both column- and block-levels have been proposed in the literature. In this paper, we utilize one such recursive structure in Cholesky and QR decompositions for matrices with entries from the field of complex numbers, which results in a level of complexity reduction. The use of the considered techniques is discussed in the context of a MIMO decoder. In particular, the utility of proposed methods is illustrated in a MIMO successive interference cancellation based detector. Simulation results are provided to substantiate the performance of a detector under two different antenna and receiver configurations.
Finite-Time Adaptive Event-Triggered Control for Robot Manipulators With Output Constraints

This brief investigates the tracking control of robot manipulators with output constraints. A novel finite-time adaptive event-triggered command filtered backstepping scheme is given, in which the command filters are utilized to avoid the direct differentials of virtual control signal and the compensation mechanism is used to eliminate filtering errors. The barrier Lyapunov functions are employed to ensure the outputs are constrained in the predefined region, and the finite-time control with fraction-power signals can help the closed-loop system get better convergence rate and steady-state performance. Moreover, the relative threshold-event-triggered mechanism is used to reduce the resource waste and communication burden effectively. A simulation model of 2-DOF robot manipulator is employed to demonstrate the proposed algorithm.
Convolutional Neural Network-based ECG Classification on PYNQ-Z2 Framework

An electrocardiogram (ECG) contains vital information to diagnose and monitor conditions affecting the heart. There are many types of ECG signals depicting heart conditions. Manual classification of these vital signals is an error-prone and time-consuming process. Furthermore, accurate and automated online classification of ECG signals on an Edge platform has been a challenging task owing to the complexity of the inference models. In this paper, we propose a deep convolutional neural network-based method to classify five classes of ECG beats. The model is deployed on a PYNQ-Z2 board. The Analog Discovery Kit reproduces a bioelectrical representation of ECG for each class. The classification task is performed using the proposed model on the PYNQ-Z2 board to achieve an accuracy of 95.6% and an F1-score of 95.61% with lesser parameters. The proposed architecture shows improved performance as compared to state-of-the-art models.
Development of Intelligent Cradle for Infant Monitoring System

Sleep Apnea is a syndrome that causes one to experience shortness of breath while asleep. It occurs in persons of all ages but is a high risk for infants, as their immature respiration system often leads to inconsistent breathing patterns. Infant apnea, referred to as Sudden Infant Death Syndrome (SIDS), is an extensive condition in which infants fail to breathe, fatal. It is necessary to develop a system capable of monitoring an infant’s vitals and detecting and rectifying sleep apnea and the occurrence of SIDS. The developed intelligent cradle for Infant monitoring aims to protect infants from the health risks of sleep apnea and map algorithms in real-time. The system uses a Pulse rate sensor to detect Heart rate as a baseline to monitor an infant’s breath. If the system detects a symptom of a possible apnea event - an alarm sounds, alerting the caretaker, and signals the electromechanical vibrators to produce vibrations to re-activate the infant’s breath. As soon as an infant’s breathing becomes normal, the physical stimulus is withdrawn. The system also consists of a PIR sensor to monitor the physical movement of the infants, a moisture sensor to sense whether the infant has urinated, temperature and humidity sensors to monitor surrounding temperature and humidity. These sensors are connected to simulation software, which follows an algorithm to process the sensory data. The data is transferred to an android application (via a cloud setup) where the user (doctor/caretaker) can track the infant’s vitals.
Automatic intestinal content classification using transfer learning architectures

Investigation of anomalies in capsule endoscopy (CE) is affected by an impairment of the mucosal frames with bubbles, debris, intestinal fluid, foreign objects, and chyme (food) etc., which can lead to a higher false-positive rate during manual and computer-aided analysis. An automatic intestinal content classification can help in checking the reliability and efficacy of computer-aided anomaly detection for CE frames. This paper presents three transfer learning (TL) architectures namely VGG16, InceptionResNetV2, and ResNet50V2 for automatic intestinal content classification using 1,67,486 and 140 CE patches and frames. A comparative analysis of the TL architectures has been done through various evaluation metrics like accuracy, precision, recall, specificity, loss, area-under-curve (AUC) and F1-score, test set evaluation, and feature maps. ResNet50V2 performed best among the three architectures and achieved an accuracy, precision, recall, specificity, and F1-score up-to 94.15%, 94.73%, 93.17%, 95.08%, and 93.95% respectively for CE frames. All three architectures efficiently classified ‘dirty’ test set CE frames and outperformed in comparison to the existing state-of-the-art works.
DBSCAN-R: A Machine Learning Approach for Routing in Opportunistic Networks

Opportunistic networks(OppNets) are a subset of mobile IoT networks in which no direct connection can be established between a message’s source and its destination node. Instead, routing occurs through a collection of intermediate mobile nodes. The lack of a direct connection, along with the mobile nature of the nodes makes routing in OppNets a challenge. This paper aims to utilize machine learning to automate the routing process in Opportunistic networks. The proposed model is a context-aware protocol called DBSCAN-R. DBSCAN-R utilizes DBSCAN(Density-Based Clustering of Application with Noise), an unsupervised soft clustering algorithm to make routing choices. Four dynamic network parameters are chosen to use as features for clustering in DBSCAN. DBSCAN-R outperforms 3 benchmark algorithms i.e Epidemic routing, ProPHET routing, and MaxProp routing when comparing the delivery success rate, average hop count, overhead ratio, and messages dropped.
Stochastic control theory : dynamic programming principle

This book offers a systematic introduction to the optimal stochastic control theory via the dynamic programming principle, which is a powerful tool to analyze control problems. First we consider completely observable control problems with finite horizons. Using a time discretization we construct a nonlinear semigroup related to the dynamic programming principle (DPP), whose generator provides the Hamilton-Jacobi-Bellman (HJB) equation, and we characterize the value function via the nonlinear semigroup, besides the viscosity solution theory. When we control not only the dynamics of a system but also the terminal time of its evolution, control-stopping problems arise. This problem is treated in the same frameworks, via the nonlinear semigroup. Its results are applicable to the American option price problem. Zero-sum two-player time-homogeneous stochastic differential games and viscosity solutions of the Isaacs equations arising from such games are studied via a nonlinear semigroup related to DPP (the min-max principle, to be precise). Using semi-discretization arguments, we construct the nonlinear semigroups whose generators provide lower and upper Isaacs equations. Concerning partially observable control problems, we refer to stochastic parabolic equations driven by colored Wiener noises, in particular, the Zakai equation. The existence and uniqueness of solutions and regularities as well as Ito's formula are stated. A control problem for the Zakai equations has a nonlinear semigroup whose generator provides the HJB equation on a Banach space. The value function turns out to be a unique viscosity solution for the HJB equation under mild conditions. This edition provides a more generalized treatment of the topic than does the earlier book Lectures on Stochastic Control Theory (ISI Lecture Notes 9), where time-homogeneous cases are dealt with. Here, for finite time-horizon control problems, DPP was formulated as a one-parameter nonlinear semigroup, whose generator provides the HJB equation, by using a time-discretization method. The semigroup corresponds to the value function and is characterized as the envelope of Markovian transition semigroups of responses for constant control processes. Besides finite time-horizon controls, the book discusses control-stopping problems in the same frameworks.
Deep learning approach on surface EEG based Brain Computer Interface

In this work we analysed the application of con-volutional neural networks in motor imagery classification for the Brain Computer Interface (BCI) purposes. To increase the accuracy of classification we proposed the solution that combines the Common Spatial Pattern (CSP) with convolutional network (ConvNet). The electroencephalography (EEG) is one of the modalities we try to use for controlling the prosthetic arm. Therefor in this paper we exploited the subject dependent approach and show results for models trained individually for a particular subject. Although the ConvNets are design to work directly with EEG data, presented approach of joining CSP with ConvNet shows increase in accuracy of movement classification. In average, our approach resulted in ∼80% accuracy.
Automatic Detection of Prostate Cancer Systemic Lesions Based on Deep Learning and 68Ga-PSMA-11 PET/CT

The identification of lesions is critical for the diagnostic evaluation of prostate cancer. 68Ga-PSMA-11 PET/CT is a specific imaging for prostate cancer. However, this is extremely challenging considering the large number of lesions of varying size and uptake that may be distributed in various anatomical settings with different backgrounds throughout the body. In this paper, we propose a deep learning approach for automatic detection of whole-body prostate cancer lesions on PSMA imaging. We established and evaluated our model on the 68Ga-PSMA-11 PET/CT image dataset of 107 patients with metastatic prostate cancer, and finally obtained Precision, Recall and F1-score of 82.9%, 100% and 90.6%, respectively, on the independent test set. Preliminary tests confirmed the potential of our method for disease detection on a systemic scale. Increasing the amount of training data can further improve the performance of the proposed deep learning method.
Attention-based Battery Parameter Augmentation Technique to Alleviate Limited Data Challenges

Lithium-ion batteries have proven to be a reliable and energy dense source for most of the commercial consumer electronics devices. Increase in the number of battery-powered Electric Vehicles (EVs) have further fueled the need to produce energy-dense batteries to improve the range of these vehicles. State-of-Charge computation techniques have amassed considerable interest among battery researchers. Precise State-of-Charge values help to asses the remaining range of an EV. Unfortunately, access to proprietary battery datasets have restricted research in this area. Furthermore, open access datasets lack the heterogeneity needed to emulate real-world battery discharge profiles. This paper aims to resolve challenges posed by limited data by employing an attention-based mechanism to produce battery synthetic datasets. The introduced scheme was used to produce synthetic Capacity, Voltage & Current datasets. We employ Mean Absolute Error (MAE) and Mean Squared Error (MSE) metrics to evaluate the efficacy of this data augmentation approach. The implementation of this technique is available at this URL: https://github.com/vageeshmaiya/Attention-based-Synthetic-Battery-Data-Augmentation-Technique
A Networked Control Scheme of Residential Microgrid for China Remote Areas

A set of complete residential microgrid (MG) control scheme for China remote areas is proposed in the paper. Firstly, a residential MG, which mainly includes distributed generation (DG), energy storage (ES), controllable load (CL), key load (KL), and control system, is proposed. Considering some communication security issues, main control systems including prediction system (PS), energy management system (EMS), and DGs control system are then introduced. By the above design, the residential MG can provide reliable electricity for local residents. Finally, simulations in MATLAB indicate the feasibility of proposed scheme.
Silicon-Based Mode-Order Converters Using Etching Slots on the Circular Waveguide

We develop a silicon-based mode-order converter scheme using etching slots on the circular waveguide that can achieve the mode-order conversion from fundamental TE0 mode to higher-order $\mathrm{TE}_{1} \sim \mathrm{TE}_{3}$ modes in compact sizes and good performances. © 2021 The Author(s)
Investigating COVID-19 Vaccine Messaging in Online Social Networks using Artificial Intelligence

Safe and effective vaccination is leading the recovery from the COVID-19 pandemic. Despite the urgency of full vaccination that prevents serious illness, a state of vaccine messaging augmented by disinformation campaigns in online social networks has emerged. Several studies have established a link between social media activity and vaccine messaging, and most platforms are actively removing vaccine disinformation. The objective of this study is to apply a validated Artificial Intelligence (AI) framework to extract, analyze and synthesize themes, emotions and emotion transitions associated with COVID-19 vaccine messaging in online social networks. We applied the framework on approximately 400,000 COVID-19 vaccine-related posts and conversations on two social media platforms, Twitter and Reddit, from March 2020 to September 2021. The results of this study are threefold, firstly, the discovery of a minority of implied anti-vaccine themes on infertility, microchips, gene editing and fetal cells that have remained undetected. Secondly, the discovery of six themes that capture a majority of the vaccine messaging namely, social lockdown measures, frontline healthcare providers, side effects, vaccine distribution, breakthrough infections and vaccine efficacy on variants. Thirdly, the variety and intensity of emotions expressed since the start of the pandemic, and comparatively negative emotions being expressed in recent months. We anticipate the findings of our study will contribute towards improved vaccine messaging as the world returns to a new normal from the COVID-19 pandemic.