A calibration framework toward model generalization for bacteria concentration estimation in water resource recovery facilities.

Reduced bacteria concentrations in wastewater is a key indicator of the efficacy of water resource recovery facilities (WRRFs). However, monitoring the presence of bacterial concentrations in real time at each stage of the WRRF is challenging as it requires taking and processing water samples offline. Although few studies have been proposed to predict bacterial concentrations using data-driven models, generalizing these models to unseen data from different WRRFs remains challenging.

Fractional-Order Modeling of Arterial Compliance in Vascular Aging: A Computational Biomechanical Approach for Investigating Cardiovascular Dynamics.

The goal of this study is to investigate the application of fractional-order calculus in modeling arterial compliance in human vascular aging. A novel fractional-order modified arterial Windkessel model that incorporates a fractional-order capacitor (FOC) element is proposed to capture the complex and frequency-dependent properties of arterial compliance. The model's performance is evaluated by verifying it using data collected from three different human subjects, with a specific focus on aortic pressure and flow rates.

EEG Epileptic Data Classification Using the Schrodinger Operator's Spectrum.

Epilepsy is a common brain disorder characterized by recurrent, unprovoked seizures which affects over 65 million people. Visual inspection of Electroencephalograms (EEG) is common for diagnosis; however, it requires time and expertise. Therefore, an accurate computer-aided epileptic seizure diagnosis system would be valuable.

Seizure Onset Localization in Focal Epilepsy using intracranial-EEG data and the Schrodinger Operator's Spectrum.

Epilepsy is a neurological disorder characterized by recurrent, unprovoked seizures that vary from short attention failure to convulsions. Despite its threats and limitations, existing medications target only specific types of seizures while up to 33% of epileptic conditions are drug-resistant. The best available treatment is surgical resection or neurostimulation and both require accurate localization of the Seizure Onset Zone.

Synergy of Small Antiviral Molecules on a Black-Phosphorus Nanocarrier: Machine Learning and Quantum Chemical Simulation Insights.

Favipiravir (FP) and Ebselen (EB) belong to a broad range of antiviral drugs that have shown active potential as medications against many viruses. Employing molecular dynamics simulations and machine learning (ML) combined with van der Waals density functional theory, we have uncovered the binding characteristics of these two antiviral drugs on a phosphorene nanocarrier. Herein, by using four different machine learning models (i.

A learning-based image processing approach for pulse wave velocity estimation using spectrogram from peripheral pulse wave signals: An study.

Carotid-to-femoral pulse wave velocity (cf-PWV) is considered a critical index to evaluate arterial stiffness. For this reason, estimating Carotid-to-femoral pulse wave velocity (cf-PWV) is essential for diagnosing and analyzing different cardiovascular diseases. Despite its broader adoption in the clinical routine, the measurement process of carotid-to-femoral pulse wave velocity is considered a demanding task for clinicians and patients making it prone to inaccuracies and errors in the estimation.

Parameter Sensitivity and Experimental Validation for Fractional-Order Dynamical Modeling of Neurovascular Coupling.

Modeling neurovascular coupling is very important to understand brain functions, yet challenging due to the complexity of the involved phenomena. An alternative approach was recently proposed where the framework of fractional-order modeling is employed to characterize the complex phenomena underlying the neurovascular. Due to its nonlocal property, a fractional derivative is suitable for modeling delayed and power-law phenomena.

A Multiple Linear Regression Model for Carotid-to-Femoral Pulse Wave Velocity Estimation Based on Schrodinger Spectrum Characterization.

In this paper, a multiple linear regression model for estimating the Carotid-to-femoral pulse wave velocity (cf-PWV) from a single non-invasive peripheral pulse wave, namely blood pressure or photoplethysmography, is proposed. The training and testing datasets were extracted from in-silico, publicly available, pulse waves and hemodynamics data. The proposed model relies on a preprocessing and features extraction steps, which are performed using a semi-classical signal analysis (SCSA) method.

A novel method for Magnetic Resonance Spectroscopy lipid signal suppression using Semi-classical signal analysis and Bidirectional Long short-term memory.

Magnetic resonance spectroscopy (MRS) is a non-invasive method that enables the analysis and quantification of brain metabolites, which provide useful information about the neuro-biological substrates of brain function. Lactate plays a pivotal role in the diagnosis of various brain diseases. However, accurate lactate quantification is generally difficult to achieve due to the presence of large lipid peaks resonating at a similar spectral position.

Human Hypertension Blood Flow Model Using Fractional Calculus.

The blood flow dynamics in human arteries with hypertension disease is modeled using fractional calculus. The mathematical model is constructed using five-element lumped parameter arterial Windkessel representation. Fractional-order capacitors are used to represent the elastic properties of both proximal large arteries and distal small arteries measured from the heart aortic root.

Towards Characterization of the Complex and Frequency-dependent Arterial Compliance based on Fractional-order Capacitor.

Arterial compliance is a vital determinant of the ventriculo-arterial coupling dynamic. Its variation is detrimental to cardiovascular functions and associated with heart diseases. Accordingly, assessment and measurement of arterial compliance are essential in the diagnosis and treatment of chronic arterial insufficiency.

Combining Machine Learning and Blind Estimation for Central Aortic Blood Pressure Reconstruction.

Central blood pressure is a vital signal that provides relevant physiological information about cardiovascular diseases risk factors. The standard clinical protocols for measuring these signals are challenging due to their invasive nature. This makes the estimation-based methods more convenient, however, they are usually not accurate as they fail to capture some important features of the central pressure waveforms.

Automatic Detection of Epileptiform EEG Discharges based on the Semi-Classical Signal Analysis (SCSA) method.

In this paper we utilize a signal processing tool, which can help physicians and clinical researchers to automate the process of EEG epileptiform spike detection. The semi-classical signal analysis method (SCSA) is a data-driven signal decomposition method developed for pulse-shaped signal characterization. We present an algorithm framework to process and extract features from the patient's EEG recording by deriving the mathematical motivation behind SCSA and quantifying existing spike diagnosis criterion with it.

Fractional-order model representations of apparent vascular compliance as an alternative in the analysis of arterial stiffness: anstudy.

. Recent studies have demonstrated the advantages of fractional-order calculus tools for probing the viscoelastic properties of collagenous tissue, characterizing the arterial blood flow and red cell membrane mechanics, and modeling the aortic valve cusp. In this article, we present novel lumped-parameter equivalent circuit models for apparent arterial compliance using a fractional-order capacitor (FOC).

A Position Weight Matrix Feature Extraction Algorithm Improves Hand Gesture Recognition.

Recent advances in the biomedical field have generated a massive amount of data and records (signals) that are collected for diagnosis purposes. The correct interpretation and understanding of these signals present a big challenge for digital health vision. In this work, Quantization-based position Weight Matrix (QuPWM) feature extraction method for multiclass classification is proposed to improve the interpretation of biomedical signals.

Schrödinger Spectrum Based PPG Features for the Estimation of the Arterial Blood Pressure.

In this paper, photoplethysmogram (PPG) features are combined with supervised machine learning algorithms to estimate arterial blood pressure (ABP). Three algorithms for the estimation of cuffless ABP using PPG signals are compared. Since PPG signals are measured non-invasively, this method guarantees an individuals comfort while not omitting important ABP information.

Fractional-Order SEIQRDP Model for Simulating the Dynamics of COVID-19 Epidemic.

Coronavirus disease (COVID-19) is a contagious disease caused by a newly discovered coronavirus, initially identified in the mainland of China, late December 2019. COVID-19 has been confirmed as a higher infectious disease that can spread quickly in a community population depending on the number of susceptible and infected cases and also depending on their movement in the community. Since January 2020, COVID-19 has reached out to many countries worldwide, and the number of daily cases remains to increase rapidly.

Assessment of Fractional-Order Arterial Windkessel as a Model of Aortic Input Impedance.

Fractional-order Windkessel model is proposed to describe the aortic input impedance. Compared with the conventional arterial Windkessel, the main advantage of the proposed model is the consideration of the viscoelastic nature of the arterial wall using the fractional-order capacitor (FOC). The proposed model, along with the standard two-element Windkessel, three-element Windkessel, and the viscoelastic Windkessel models, are assessed and compared using in-silico data.

QuPWM: Feature Extraction Method for Epileptic Spike Classification.

Epilepsy is a neurological disorder ranked as the second most serious neurological disease known to humanity, after stroke. Inter-ictal spiking is an abnormal neuronal discharge after an epileptic seizure. This abnormal activity can originate from one or more cranial lobes, often travels from one lobe to another, and interferes with normal activity from the affected lobe.

Two-Element Fractional-Order Windkessel Model to Assess the Arterial Input Impedance.

Arterial system is completely coupled with the heart, such that the contractile state of the left ventricle and its produced central blood pressure (the pressure in the aorta) are in tune with the arterial mechanical properties. This study investigates the use of fractional-order capacitor and resistor elements to expose, and estimate the main arterial mechanical properties. We propose a simple two-element fractional-order Windkessel model that is able to capture the real aortic impedance dynamic for different cardiac physiological states.

Feature Generation and Dimensionality Reduction using the Discrete Spectrum of the Schrödinger Operator for Epileptic Spikes Detection.

Epilepsy is a neurological disorder classified as the second most serious neurological disease known to humanity, after stroke. Magnetoencephalography (MEG) is performed to localize the epileptogenic zone in the brain. However, the detection of epileptic spikes requires the visual assessment of long MEG recordings.

Hybrid model for efficient prediction of poly(A) signals in human genomic DNA.

Polyadenylation signals (PAS) are found in most protein-coding and some non-coding genes in eukaryotes. Their accurate recognition improves understanding gene regulation mechanisms and recognition of the 3'-end of transcribed gene regions where premature or alternate transcription ends may lead to various diseases. Although different methods and tools for in-silico prediction of genomic signals have been proposed, the correct identification of PAS in genomic DNA remains challenging due to a vast number of non-relevant hexamers identical to PAS hexamers.

Sparse Reconstruction of Glucose Fluxes Using Continuous Glucose Monitors.

A new technique for estimating postprandial glucose flux profiles without the use of glucose tracers is proposed. A sparse vector space representation is first found for the space of plausible glucose flux profiles using sparse encoding. A Lasso formulation is then used to estimate the glucose fluxes that combines (1) known patient model parameters; (2) the vector space of plausible glucose flux profiles; (3) continuous glucose monitor measurements taken during the meal; (4) amount of insulin injected; (5) amount of meal carbohydrates; and (6) an estimate of the initial conditions.

A New ROI-Based performance evaluation method for image denoising using the Squared Eigenfunctions of the Schrödinger Operator.

In this paper a new Region Of Interest (ROI) characterization for image denoising performance evaluation is proposed. This technique consists of balancing the contrast between the dark and bright ROIs, in Magnetic Resonance (MR) images, to track the noise removal. It achieves an optimal compromise between removal of noise and preservation of image details.