Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced NARX Neural Network Model for Specific Time-Series Prediction. Case Study: Battery State of Charge in Electric Vehicles

The paper presents an enhanced Nonlinear Autoregressive Exogenous (NARX) neural network model designed to address the complexities of specific time-series variables, enabling accurate predictions even for variables with different initial values. A serious problem that is not explicitly expressed in literature. A case study on predicting lithium-ion battery State of Charge (SOC) for electric vehicles (EVs) application is featured in this research. In fact, the battery SOC is a time-series variable, and its initial value can exhibit fluctuations due to diverse factors. The obtained simulation results demonstrate the capacity of the enhanced NARX neural network to provide satisfactory estimations, compared to conventional algorithm, even when dealing with different initial battery SOC values.
Simulation of ECG Using a Rabbit Cardiac Conduction Model with Dual-Pathway Atrioventricular Node

Combining a recently developed rabbit atrioventricular node model with a model of cardiac muscle response, we simulate synthetic ECG signals of rabbit heart. The cardiac conduction system model contains sinus node, internodal pathways, Bachmann’s bundle, atrioventricular node with dual pathway, and His-Purkinje system. The ECG model includes activation and recovery waves in atrial and ventricular muscles.This work presents simulations of well-known cardiac phenomena such as sinus tachycardia, atrial fibrillation, typical atrioventricular nodal reentrant tachycardia, and Wenckebach pattern formation.The proposed model provides a unique possibility to reproduce and observe cardiac conduction scenarios simultaneously as electrical signals inside the central part of the cardiac conduction system, including atrioventricular node and outside the body in the form of ECG, and can provide a better understanding of cardiac electrophysiological processes.
Exploiting Uncertainty: A Transformer-Based Multi-Disease Diagnostic Approach to Chest X-RAYS

In the field of medical image analysis, there is significant interest in developing a unified deep-learning framework for the automated detection of multiple diseases. However, the presence of vague annotations in multi-disease datasets hinders models from fully utilizing the available data. To address this, we propose a framework that considers both certain and uncertain labels to better model label dependencies. The methodology consists of three main steps: extracting visual features from medical images using convolutional neural networks, generating sample-specific semantic label embeddings, and utilizing a Transformer model to capture the complex associations between the visual features and semantic label embeddings. Our experiments on chest X-rays illustrate that this comprehensive approach outperforms methods that solely rely on definite labels.
Deep Transfer Learning to Predict Alzheimer Disease Status

Alzheimer disease (AD) is a significant global health concern, and early diagnosis is crucial for effective intervention. In this study, we explore the use of deep transfer learning convolutional neural networks to classify AD status based on brain magnetic resonance imaging (MRI) data. Specifically, we employ two pre-trained CNN architectures: VGG19 and DenseNet201 to extract features from MRI scans of the Kaggle AD dataset. The dataset used in this study includes a diverse collection of brain MRI scans, accurately classifying individuals into different categories, including those with AD at various stages such as mild, very mild, and moderate, as well as non-AD subjects. We employ these pre-trained CNN architectures to extract features from the Kaggle AD dataset. We employed the SMOTE technique to address the dataset’s class imbalance problem. We applied deep transfer learning to customize these pre-trained models for the AD classification task. Our results display the effectiveness of transfer learning in AD classification, achieving the following accuracies: VGG19 (0.93) and DenseNet201 (0.96), remarkably, the DenseNet201 model emerged as the top performer, achieving an accuracy of 0.96.
The Impact of the Weighted Features on the Accuracy of X-Platform’s User Credibility Detection Using Supervised Machine Learning

Social media represent a vital actor in our lives, often serving as a primary source of information, surpassing traditional sources. Among these platforms, the X-Platform, which used to be called Twitter, has emerged as a leading space for the exchange of opinions and emotions. In this study, we introduced a supervised machine learning system designed to detect user credibility in this influential platform. User credibility detection depends largely on the features of the users on the platform. Feature weighting plays a pivotal role in identifying the significance of each feature in a dataset. It can indicate irrelevant features, which can lead to better performance in classification problems. This study aims to highlight the impact of weighted features on the accuracy of X-Platform User Credibility Detection (XUCD) using supervised machine learning methods, such as Principal Component Analysis (PCA) and correlation-coefficient algorithms, and tree-based methods, such as (ExtraTressClarifier) to extract new weighted features in the dataset and then use them to train our model to discover their impact on the accuracy of user credibility detection issues. As a result, we measured the effectiveness of different feature-weighting methods on different dataset categories to determine which obtained the best detection accuracy. Experiments were conducted on real user profiles, and statistical and emotional information was extracted from a publicly available dataset called (ArPFN). The improvement in XUCD accuracy using different weighting methods was dependent on the method and dataset category used.
New Results on Input-output Decoupling of Boolean Control Networks

We provide new necessary and sufficient conditions (with low computational complexity) for the input-output decoupling problem of Boolean control networks. Instrumental in our approach, the introduction of a new concept relying on the construction of some input-output-decoupling matrices that have to satisfy some conditions to ascertain whether a given Boolean control network is input-output decoupled. A numerical example is provided to illustrate our theoretical developments.
A Multi-Channel, Low-Voltage, High-Frequency Programmable Electrical Stimulator for Sensory Feedback

Tactile sensory feedback using targeted transcutaneous electrical nerve stimulation (TENS) can enhance prosthesis embodiment, improve object manipulation, and alleviate phantom limb pain in amputees. However, conventional TENS devices used for sensory feedback rely on high voltage stimulation to overcome the large impedance of skin and require large, low-impedance electrodes. This hinders the development of a high-density electrical stimulator with many channels. In this work, we capitalize on the decreasing impedance of skin at high frequencies to develop a compact low voltage electrical stimulator that stimulates with a 10kHz biphasic square wave. The developed stimulator is integrated onto a 3x3 inch PCB, can multiplex programmable bipolar waveforms to any 2 electrodes in a 16 electrode array, has current monitoring with constant current control up to 3mA, and consumes at most 1.18W during normal operation. In this paper, we present a characterization of the stimulator, validate its behavior with an electrical model of the skin, and showcase notable abilities like arbitrary wave generation. Future work can integrate the presented device to create a wearable, high-density sensory feedback system.
Feedback Path Delay Attacks and Detection

The paper discusses delay injection attacks on regulator loops and suggests joint recursive prediction error identification of delay and dynamics for supervision and attack detection. The control system is assumed to be operated either in open- or closed-loop mode. It is shown why delay insertion in the feedback path before the user switches to closedloop operation is advantageous to disguise the attack. The detection performance is evaluated numerically for a linearized automotive cruise control feedback loop.
Intra-Body Wireless Power and Data Links for Biomedical Microsystems

Implantable biomedical microsystems are envisioned to offer precise sensing and interpretation of the conditions in the body, for long-term monitoring, early-stage disease detection and management. However, to enable the long-term operation and miniaturized form factor demanded from the implants, wireless powering and data acquisition have become the key challenge. In this paper, starting from the heterogeneous functionalities and requirements of the implantable microsystems, we will review and discuss the intra-body wireless power and data transmission mechanisms, their characteristics and applicability for heterogeneous implantable microsystems. After identifying the challenges faced by each power and data transmission mechanism, the design considerations, optimization and the power and data link integration will be analyzed.
A PMOS-based Programmable Stimulator with Adaptive Charge Pump for Cardiac Pacemakers

Conventional pacemaker stimulators adopt an NMOS-based voltage multiplier requiring higher gate drive voltage than the impulse voltage which is normally provided by an auxiliary charge pump. Such a scheme is neither good for power efficiency nor friendly to robust long-term operation due to higher voltage stress. This paper proposes a PMOS-based stimulator with adaptive regulation to enhance not only the power efficiency but also the reliability. Thanks to the PMOS-based scheme, the required driving voltage from the auxiliary voltage multiplier is much less than the NMOS-based counterpart. Designed with 3 operation modes, both the auxiliary voltage multiplier and main charge pump can adaptively adjust their voltage multiplication factor (1×, 2×, or 3× of the 2.8-V battery voltage) to improve the efficiency while offering an impulse range of 0.1 ~ 8.4 V with 0.1-V resolution. Designed in a 0.18-μm BCD process, the stimulator preserves a maximum power efficiency of 67.77% and a relative error range of 3.8~10%. The active area of the 2 stimulator channels is 1.62×1.03 mm2.
Annotating omics Data with sex and age of samples: Enabling powerful omics studies

There is increasing evidence that many molecular processes exhibit differences with age and sex. Such differences produce also differences in the insurgence and progression of many complex diseases. For instance, demographic data on the insurgence of comorbidities of mellitus diabetes, on the lethality of COVID-19, and on some cancers shows differences between sex and age groups. Therefore, the growing interest in such areas requires the management of related data as well as the development of algorithms and tools for the analysis. The availability of omics data annotated with metadata related to age and sex is mandatory for building the analysis pipeline. The number of databases containing data related to age and sex is henceforth growing. We here show some databases and tools storing such data. Finally, future research directions are highlighted.
Subtype-DCGCN: an unsupervised approach for cancer subtype diagnosis based on multi-omics data

Identifying cancer subtypes is an essential component of precision medicine, as it helps researchers develop more precise treatment methods and prevention strategies. Meanwhile, high-throughput sequencing technologies have produced a huge amount of multi-omics data for cancer patients and make it is practical to subtype cancers using multi-omics data. As existing cancer subtyping computational models based on multi-omics data could not effectively extended to weakly paired omics data, we proposed a novel unsupervised cancer subtyping model Subtype-DCGCN. Subtype-DCGCN uses Dual Contrast Graph Convolutional Networks guided by dual contrastive learning to lean low dimensional features for each type omics data, and with weighted average fusion Subtype-DCGCN could deal well with weakly paired multi-omics data. Extensive experiments on benchmark datasets showed that Subtype-DCGCN exhibited superior performance to other eight state-of-the-art similar methods in general to identify cancer subtypes. Moreover, tests on simulated datasets with varying missing rate showed that Subtype-DCGCN performed pretty well on weakly paired omics datasets.
A Framework for Cyber-Physical Simulation of Smart Grid

Growing digitalization of electric power systems requires modelling them as cyber physical systems. Given the size of such systems, from smart homes to high voltage transmission systems, robust and scalable cyber physical modelling frameworks are required. To this end, this paper presents a framework for modelling electric distribution systems as cyber-physical systems (CPS) using open-source software. Specifically, the Hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS) is used as the engine to combine Network Simulator3 (NS3) and python programs. Simulations are used to validate the flow of data between the simulators in the presented co-simulation framework. Discussion showing the need and value of a cyber physical modelling framework in power systems is also presented.
An Improved Machine Learning Algorithm for Stock Price Prediction

Twitter is being utilized to report and remark on market news. Currently, companies forecast stock prices using Twitter data. The paper discusses the merits and cons of Twitter. This paper investigates the effects of data quality, machine learning algorithm complexity, and the need for a realistic evaluation method on Twitter stock price forecasts. Proposed a hybrid algorithm for Twitter data forecasting. The proposed algorithm approach utilizes LSTM for feature extraction and RF for prediction. This Twitter investigation evaluated algorithms for predicting stock prices. A recent paper examined elements of stock price forecasting. This paper discusses the difficulties of predicting stock prices using tweets from Twitter, as well as ideas for improving the accuracy and robustness of machine learning models. The paper also uncovered limitations associated with using Twitter data to forecast stock prices.
Lung Cancer Accurate Detection Employing Machine Learning Stacking Approach and Relief Algorithm for Feature Selection Using Clinical Data

Lung cancer(LC) diagnosis is a critical health issue and an precise identification is expressively crucial for early cure. Different Artificial intelligence (AI) techniques particularly Machine learning (ML) techniques-based models have been proposed to detect Lung cancer. However, these models still have a lack of accuracy issue. To tackle the accuracy problem we proposed a stacking-based Machine Learning model for correct identification of LC. In designing the model ML algorithms are secondhand to diagnose LC using the Lung Cancer dataset. Feature selection(FS) algorithm Relief is incorporated to select appropriate features for effective training of the models. Further, the stacking approach is used to increase the predictive performance. The base classifiers NB, SVM, RF, AB, and KNN are trained selected features dataset and the meta classifier LR is selected for final prediction. Model performance is assessed using performance assessment metrics, and the data set is divided for training and validation using the held-out cross-validation (CV) approach. When accuracy was tested between the proposed model and baseline models, the suggested model SLC achieved a high accuracy. We suggest the suggested model (SLC) for the prediction of lung cancer in e- healthcare systems because of its excellent accuracy.
Asynchronous Wireless Network for Transmission of Neural Data from Thousands of Autonomous Microimplants

We introduce a large-scale wireless RF network concept for capturing neural data from spatially distributed autonomous microchip implants. Each sensor chip is assumed to be capable of event detection by transforming time-varying input signals to spike trains. Inspired by the brain’s information processing, we have developed a spectrally efficient, low-error rate asynchronous networking protocol based on a code-division multiple access method. We test the network performance with dozens of sub-millimeter-size silicon microchips in situ on laboratory testbench, complemented by large scale in silico simulations. Through an application example relevant to brain-computer interfaces, we simulate the wireless transmission of spiking data from eight thousand neurons in the primate cortex for accurate hand movement prediction in a cursor control task.
A Neural-ADC-Compatible Fully-Dynamic Lossless Adaptive Resolution Compression Technique for Energy-Constrained Bio-Signal Recording

This paper presents a lossless adaptive-resolution compression technique intended for energy-constrained implantable neural interface microsystems. The proposed method seamlessly integrates with the widely-adopted ADC-direct front-end architectures with minimal overhead and no need for architectural modification, making it universally-compatible. It maintains an always-Nyquist sampling rate and employs a single-bit quantizer to overcome the limitations of previously-reported adaptive sampling and adaptive resolution methods (e.g., missing short-time events, nonlinearity, power/area overdesign). It also features continuous DC variation calculation and adjustable hysteresis bands. The method is optimized and validated through MATLAB simulations, followed by synthesis and implementation in a standard 130nm CMOS process, resulting in an active area of 0.0529 mm2 and total power consumption of 1. 9µW @ 128kHz. Post-layout verification was conducted using prerecorded EEG and ECG datasets, demonstrating a minimum compression ratio of 83.75% of the theoretical maximum (i.e., $\frac{{{N_H}}}{{{N_L}}}$), highlighting its effectiveness for implantable neural recording devices.
Combining Relative and Absolute Learning Formulations to Predict Emotional Attributes From Speech

Predicting absolute scores is the most common speech-emotion recognition (SER) task when predicting emotional attributes (i.e., valence, arousal, and dominance). However, studies have shown that emotion has an ordinal nature where it is more reliable to establish a preference between speech samples (e.g., one sample is more positive than the other). This paper pursues a novel direction to combine absolute and relative learning formulations for SER. The proposed multitask formulation can simultaneously estimate preference between speech samples and predict their absolute score, providing a flexible tool to analyze emotional content in speech. Both tasks mutually complement each other, allowing the model to outperform SER systems that are exclusively trained to either predict absolute scores or estimate preferences. The multitask weights can be set according to the intended applications, prioritizing one task while slightly compromising the performance of the other task.
Characteristic Analysis of Ground Deformation in Chiba Prefecture Using Sentinel-1 Data

Ground deformation due to natural gas mining has been reported in the Chiba Prefecture. For example, in Yachimata City, the land subsided by approximately 40 cm from 1976 to 2020. In addition, reclaimed lands are found around Tokyo Bay, and periodic ground deformation monitoring is necessary. In this study, we utilized the short baseline subset (SBAS) method to determine the spatial tendency of the subsidence using Sentinel-1 to elucidate the characteristics of ground deformation in the Chiba Prefecture. The analysis was conducted using paired images of the ascending and descending orbits from January 2017 to December 2019, to determine the displacement velocity in each line-of-sight direction. Consequently, we quantitatively confirmed a large ground displacement in the Yachimata area. The estimated displacement velocity was evaluated using reference data, and the results showed that the characteristics of the variation were captured, although some residuals were observed.
Reinforcement Learning based optimal control with a probabilistic risk constraint

—This paper proposes a reinforcement learning (RL) technique to control a linear discrete-time system with a quadratic control cost while ensuring a constraint on the probability of potentially risky events. The proposed methodology can be applied to known and unknown system models with slight modifications to the reward (negative cost) structure. The problem formulation considers the average expected quadratic cost of the states and inputs over the infinite time horizon, and the risky events are modelled as a quadratic cost of the states at the next time step crossing a user-defined limit. Two approaches are taken to handle the probabilistic constraint under the assumption of known and unknown system model scenarios. For the known system model case, the probabilistic constraint is replaced with the Chernoff bound, while for the unknown system model case, the expected value of the indicator function of the occurrence of the risky event in the next time step is used. The optimal policy is derived from the observed data using an on-policy RL method based on an actor-critic (AC) architecture. Extensive numerical simulations are performed using a 2nd-order and a 4th-order system, and the proposed method is compared with the standard risk-neutral linear quadratic regulator (LQR).
AG1® Induces a Favorable Impact on Gut Microbial Structure and Functionality in the Simulator of Human Intestinal Microbial Ecosystem® Model

Modulation of the human gut microbiome has become an area of interest in the nutraceutical space. We explored the effect of the novel foundational nutrition supplement AG1® on the composition of human microbiota in an in vitro experimental design. Employing the Simulator of Human Intestinal Microbial Ecosystem (SHIME®) model, AG1® underwent digestion, absorption, and subsequent colonic microenvironment simulation under physiologically relevant conditions in healthy human fecal inocula. Following 48 h of colonic simulation, the gut microbiota were described using shallow shotgun, whole genome sequencing. Metagenomic data were used to describe changes in community structure (alpha diversity, beta diversity, and changes in specific taxa) and community function (functional heterogeneity and changes in specific bacterial metabolic pathways). Results showed no significant change in alpha diversity, but a significant effect of treatment and donor and an interaction between the treatment and donor effect on structural heterogeneity likely stemming from the differential enrichment of eight bacterial taxa. Similar findings were observed for community functional heterogeneity likely stemming from the enrichment of 20 metabolic pathways characterized in the gene ontology term database. It is logical to conclude that an acute dose of AG1 has significant effects on gut microbial composition that may translate into favorable effects in humans.
Rethinking impacts of tourism.

Current work on impacts of tourism has deficiencies which hamper the development of cumulative knowledge and the practical application of that knowledge. While much is known about the consequences of tourism for destination areas in a general sense, much less is known about the types of tourism which stimulate these changes and the contexts in which these changes occur. As a minimum, more comprehensive typologies of tourism are required, incorporating types of tourists, community characteristics, the nature of visitor-resident interactions and the role of culture brokers. More radically, it may be advisable to develop a research paradigm which acknowledges explicitly that tourism is usually only one among a number of agents of change impacting upon communities and that it is artificial to abstract tourism from this broader context.