Performance evaluation of logarithmic spiral search and selective mechanism based arithmetic optimizer for parameter extraction of different photovoltaic cell models.

The imperative shift towards renewable energy sources, driven by environmental concerns and climate change, has cast a spotlight on solar energy as a clean, abundant, and cost-effective solution. To harness its potential, accurate modeling of photovoltaic (PV) systems is crucial. However, this relies on estimating elusive parameters concealed within PV models. This study addresses these challenges through innovative parameter estimation by introducing the logarithmic spiral search and selective mechanism-based arithmetic optimization algorithm (Ls-AOA). Ls-AOA is an improved version of the arithmetic optimization algorithm (AOA). It combines logarithmic search behavior and a selective mechanism to improve exploration capabilities. This makes it easier to obtain accurate parameter extraction. The RTC France solar cell is employed as a benchmark case study in order to ensure consistency and impartiality. A standardized experimental framework integrates Ls-AOA into the parameter tuning process for three PV models: single-diode, double-diode, and three-diode models. The choice of RTC France solar cell underscores its significance in the field, providing a robust evaluation platform for Ls-AOA. Statistical and convergence analyses enable rigorous assessment. Ls-AOA consistently attains low RMSE values, indicating accurate current-voltage characteristic estimation. Smooth convergence behavior reinforces its efficacy. Comparing Ls-AOA to other methods strengthens its superiority in optimizing solar PV model parameters, showing that it has the potential to improve the use of solar energy.

Sequential graph convolutional network and DeepRNN based hybrid framework for epileptic seizure detection from EEG signal.

Automated epileptic seizure detection from ectroencephalogram (EEG) signals has attracted significant attention in the recent health informatics field. The serious brain condition known as epilepsy, which is characterized by recurrent seizures, is typically described as a sudden change in behavior caused by a momentary shift in the excessive electrical discharges in a group of brain cells, and EEG signal is primarily used in most cases to identify seizure to revitalize the close loop brain. The development of various deep learning (DL) algorithms for epileptic seizure diagnosis has been driven by the EEG's non-invasiveness and capacity to provide repetitive patterns of seizure-related electrophysiological information. Existing DL models, especially in clinical contexts where irregular and unordered structures of physiological recordings make it difficult to think of them as a matrix; this has been a key disadvantage to producing a consistent and appropriate diagnosis outcome due to EEG's low amplitude and nonstationary nature. Graph neural networks have drawn significant improvement by exploiting implicit information that is present in a brain anatomical system, whereas inter-acting nodes are connected by edges whose weights can be determined by either temporal associations or anatomical connections. Considering all these aspects, a novel hybrid framework is proposed for epileptic seizure detection by combined with a sequential graph convolutional network (SGCN) and deep recurrent neural network (DeepRNN). Here, DepRNN is developed by fusing a gated recurrent unit (GRU) with a traditional RNN; its key benefit is that it solves the vanishing gradient problem and achieve this hybrid framework greater sophistication. The line length feature, auto-covariance, auto-correlation, and periodogram are applied as a feature from the raw EEG signal and then grouped the resulting matrix into time-frequency domain as inputs for the SGCN to use for seizure classification. This model extracts both spatial and temporal information, resulting in improved accuracy, precision, and recall for seizure detection. Extensive experiments conducted on the CHB-MIT and TUH datasets showed that the SGCN-DeepRNN model outperforms other deep learning models for seizure detection, achieving an accuracy of 99.007%, with high sensitivity and specificity.

Optimizing AVR system performance via a novel cascaded RPIDD2-FOPI controller and QWGBO approach.

Maintaining stable voltage levels is essential for power systems' efficiency and reliability. Voltage fluctuations during load changes can lead to equipment damage and costly disruptions. Automatic voltage regulators (AVRs) are traditionally used to address this issue, regulating generator terminal voltage. Despite progress in control methodologies, challenges persist, including robustness and response time limitations. Therefore, this study introduces a novel approach to AVR control, aiming to enhance robustness and efficiency. A custom optimizer, the quadratic wavelet-enhanced gradient-based optimization (QWGBO) algorithm, is developed. QWGBO refines the gradient-based optimization (GBO) by introducing exploration and exploitation improvements. The algorithm integrates quadratic interpolation mutation and wavelet mutation strategy to enhance search efficiency. Extensive tests using benchmark functions demonstrate the QWGBO's effectiveness in optimization. Comparative assessments against existing optimization algorithms and recent techniques confirm QWGBO's superior performance. In AVR control, QWGBO is coupled with a cascaded real proportional-integral-derivative with second order derivative (RPIDD2) and fractional-order proportional-integral (FOPI) controller, aiming for precision, stability, and quick response. The algorithm's performance is verified through rigorous simulations, emphasizing its effectiveness in optimizing complex engineering problems. Comparative analyses highlight QWGBO's superiority over existing algorithms, positioning it as a promising solution for optimizing power system control and contributing to the advancement of robust and efficient power systems.

Biophysics-Guided Lead Discovery of HBV Capsid Assembly Modifiers.

Hepatitis B virus (HBV) is the leading cause of chronic liver pathologies worldwide. HBV nucleocapsid, a key structural component, is formed through the self-assembly of the capsid protein units. Therefore, interfering with the self-assembly process is a promising approach for the development of novel antiviral agents. Applied to HBV, this approach has led to several classes of capsid assembly modulators (CAMs). Here, we report structurally novel CAMs with moderate activity and low toxicity, discovered through a biophysics-guided approach combining docking, molecular dynamics simulations, and a series of assays with a particular emphasis on biophysical experiments. Several of the identified compounds induce the formation of aberrant capsids and inhibit HBV DNA replication in vitro, suggesting that they possess modest capsid assembly modulation effects. The synergistic computational and experimental approaches provided key insights that facilitated the identification of compounds with promising activities. The discovery of preclinical CAMs presents opportunities for subsequent optimization efforts, thereby opening new avenues for HBV inhibition.

A MATLAB simulation-based analytical study of energy, exergy, and cost benefits in jet-impinged protrusion-roughened double pass solar air collector.

This study analyzes the performance and cost-effectiveness of a protrusion-roughened jet-impinged double-pass solar air collector (PRJDPSAC) within a Reynolds number (Re) range of 2500 to 22,500. Examining jet slot parameters, i.e., the jet height ratio (Hjp/Dhd = 0.11-0.44), stream-wise pitch ratio (Xjp/Dhd = 0.44-1.32), and span-wise pitch ratio (Yjp/Dhd = 0.44-1.32), the model demonstrates enhanced energy conversion, minimizes losses, improves efficiency, and brings positive economic impact, making it a promising solution for diverse applications including drying processes, livestock facilities, remote accommodations, and HVAC system pre-heating. The examination incorporates advanced MATLAB simulations to assess energy-exergy performance and cost viability. At lower Re values, both energy ([Formula: see text]) and exergy ([Formula: see text]) efficiencies increase uniformly; however, stabilization and decline occur at higher Re values. The maximum [Formula: see text] for the PRJDPSAC is 4.38% under a temperature rise parameter of 60 × 10-3 Km2/W for obtaining optimum values of Xjp/Dhd = 1.32, Hjp/Dhd = 0.22, and Yjp/Dhd = 1.32, which is 31% higher than that of the smooth double-pass solar air collector (DPSAC). Economic benefits are significant for PRJDPSAC within mair (0.01-0.07 kg/s), but above 0.07 kg/s, the DPSAC becomes more cost-effective. Integrating simulation and experimental data, the study highlights MATLAB's effectiveness for solar energy system analysis and optimization, reinforcing the practicality of the proposed collector design.

A comparison of nocturnal upper trapezius muscle activity between chronic neck pain patients with sleep disturbance and healthy participants.

BACKGROUND: Sleep disturbances frequently occur in patients with chronic neck pain. In these patients, upper trapezius muscle dysfunction is observed during sleep. This study aimed to evaluate the trapezius muscle activity during sleep among patients with chronic neck pain and sleep disturbances for comparison with healthy subjects.  STUDY DESIGN: Cross-sectional study. METHODS: Patients with chronic neck pain and healthy subjects participated in the study. Two overnight polysomnography recordings were conducted for each subject. Surface electromyography was utilized to record the nocturnal activity of the right and left upper trapezius muscles throughout the night. The nocturnal upper trapezius activity recording was divided into the following parts: wakefulness, rapid eye movement sleep (REM), and non-rapid eye movement sleep (NREM). The nocturnal activity during NREM sleep was further divided into three parts (stage I NREM sleep, stage II NREM, and stage III NREM. Normalization of EMG signals was performed. The normalized value of nocturnal activity was derived for analysis. RESULTS: Among 15 patients with chronic neck pain and 15 healthy subjects, statistically significant differences were observed in the nocturnal activity of the upper trapezius. Compared to healthy subjects, the nocturnal activity of the upper trapezius was significantly higher during wakefulness, REM sleep, and NREM II and III sleep in patients with chronic neck pain and sleep disturbances. CONCLUSION: There was higher nocturnal upper trapezius activity in patients with chronic neck pain compared to healthy controls. The findings suggest a possible pathophysiological mechanism that may relate to chronic neck pain. TRIAL REGISTRATION: CTRI/2019/09/021028.

Remote Loading: The Missing Piece for Achieving High Drug Payload and Rapid Release in Polymeric Microbubbles.

The use of drug-loaded microbubbles for targeted drug delivery, particularly in cancer treatment, has been extensively studied in recent years. However, the loading capacity of microbubbles has been limited due to their surface area. Typically, drug molecules are loaded on or within the shell, or drug-loaded nanoparticles are coated on the surfaces of microbubbles. To address this significant limitation, we have introduced a novel approach. For the first time, we employed a transmembrane ammonium sulfate and pH gradient to load doxorubicin in a crystallized form in the core of polymeric microcapsules. Subsequently, we created remotely loaded microbubbles (RLMBs) through the sublimation of the liquid core of the microcapsules. Remotely loaded microcapsules exhibited an 18-fold increase in drug payload compared with physically loaded microcapsules. Furthermore, we investigated the drug release of RLMBs when exposed to an ultrasound field. After 120 s, an impressive 82.4 ± 5.5% of the loaded doxorubicin was released, demonstrating the remarkable capability of remotely loaded microbubbles for on-demand drug release. This study is the first to report such microbubbles that enable rapid drug release from the core. This innovative technique holds great promise in enhancing drug loading capacity and advancing targeted drug delivery.

Sign language recognition using the fusion of image and hand landmarks through multi-headed convolutional neural network.

Sign Language Recognition is a breakthrough for communication among deaf-mute society and has been a critical research topic for years. Although some of the previous studies have successfully recognized sign language, it requires many costly instruments including sensors, devices, and high-end processing power. However, such drawbacks can be easily overcome by employing artificial intelligence-based techniques. Since, in this modern era of advanced mobile technology, using a camera to take video or images is much easier, this study demonstrates a cost-effective technique to detect American Sign Language (ASL) using an image dataset. Here, "Finger Spelling, A" dataset has been used, with 24 letters (except j and z as they contain motion). The main reason for using this dataset is that these images have a complex background with different environments and scene colors. Two layers of image processing have been used: in the first layer, images are processed as a whole for training, and in the second layer, the hand landmarks are extracted. A multi-headed convolutional neural network (CNN) model has been proposed and tested with 30% of the dataset to train these two layers. To avoid the overfitting problem, data augmentation and dynamic learning rate reduction have been used. With the proposed model, 98.981% test accuracy has been achieved. It is expected that this study may help to develop an efficient human-machine communication system for a deaf-mute society.

Synthesis and evaluation of highly potent HBV capsid assembly modulators (CAMs).

Chronic hepatitis B virus (HBV) infection remains a major global health burden. It affects more than 290 million individuals worldwide and is responsible for approximately 900,000 deaths annually. Anti-HBV treatment with a nucleoside analog in combination with pegylated interferon are considered first-line therapy for patients with chronic HBV infection and liver inflammation. However, because cure rates are low, most patients will require lifetime treatment. HBV Capsid Assembly Modulators (CAMs) have emerged as a promising new class of compounds as they can affect levels of HBV covalently closed-circular DNA (cccDNA) associated with viral persistence. SAR studies around the core structure of lead HBV CAM GLP-26 (Fig. 1B) was performed and led to the discovery of non-toxic compound 10a displaying sub-nanomolar anti-HBV activity. Advanced toxicity and cellular pharmacology profiles of compounds 10a were also established and the results are discussed herein.

A challenging case of pancreaticogastric anastomotic stricture in a patient with surgically altered anatomy.

Video 1EUS-guided gastro-jejunostomy using a lumen-apposing metal stent to access the surgical pancreaticogastric anastomotic site and perform EUS-guided de novo pancreatico-gastrostomy for the management of a postsurgical pancreaticogastric anastomotic stricture with pancreatic insufficiency.

Quad-Band Metamaterial Perfect Absorber with High Shielding Effectiveness Using Double X-Shaped Ring Resonator.

This study assesses quad-band metamaterial perfect absorbers (MPAs) based on a double X-shaped ring resonator for electromagnetic interference (EMI) shielding applications. EMI shielding applications are primarily concerned with the shielding effectiveness values where the resonance is uniformly or non-sequentially modulated depending on the reflection and absorption behaviour. The proposed unit cell consists of double X-shaped ring resonators, a dielectric substrate of Rogers RT5870 with 1.575 mm thickness, a sensing layer, and a copper ground layer. The presented MPA yielded maximum absorptions of 99.9%, 99.9%, 99.9%, and 99.8% at 4.87 GHz, 7.49 GHz, 11.78 GHz, and 13.09 GHz resonance frequencies for the transverse electric (TE) and transverse magnetic (TM) modes at a normal polarisation angle. When the electromagnetic (EM) field with the surface current flow was investigated, the mechanisms of quad-band perfect absorption were revealed. Moreover, the theoretical analysis indicated that the MPA provides a shielding effectiveness of more than 45 dB across all bands in both TE and TM modes. An analogous circuit demonstrated that it could yield superior MPAs using the ADS software. Based on the findings, the suggested MPA is anticipated to be valuable for EMI shielding purposes.

Atomistic Study for the Tantalum and Tantalum-Tungsten Alloy Threshold Displacement Energy under Local Strain.

The threshold displacement energy (TDE) is an important measure of the extent of a material's radiation damage. In this study, we investigate the influence of hydrostatic strains on the TDE of pure tantalum (Ta) and Ta-tungsten (W) alloy with a W content ranging from 5% to 30% in 5% intervals. Ta-W alloy is commonly used in high-temperature nuclear applications. We found that the TDE decreased under tensile strain and increased under compressive strain. When Ta was alloyed with 20 at% W, the TDE increased by approximately 15 eV compared to pure Ta. The directional-strained TDE (Ed,i) appears to be more influenced by complex ⟨i j k⟩ directions rather than soft directions, and this effect is more prominent in the alloyed structure than in the pure one. Our results suggest that radiation defect formation is enhanced by tensile strain and suppressed by compressive strain, in addition to the effects of alloying.

Reduction of Radar Cross Section by Adopting Symmetrical Coding Metamaterial Design for Terahertz Frequency Applications.

This work focused on the novel and compact 1-bit symmetrical coding-based metamaterial for radar cross section reduction in terahertz frequencies. A couple of coding particles were constructed to impersonate the elements '0' and '1', which have phase differences of 180°. All the analytical simulations were performed by adopting Computer Simulation Technology Microwave Studio 2019 software. Moreover, the transmission coefficient of the element '1' was examined as well by adopting similar software and validated by a high-frequency structure simulator. Meanwhile, the frequency range from 0 to 3 THz was set in this work. The phase response properties of each element were examined before constructing various coding metamaterial designs in smaller and bigger lattices. The proposed unit cells exhibit phase responses at 0.84 THz and 1.54 THz, respectively. Meanwhile, the analysis of various coding sequences was carried out and they manifest interesting monostatic and bistatic radar cross section (RCS) reduction performances. The Coding Sequence 2 manifests the best bistatic RCS reduction values in smaller lattices, which reduced from -69.8 dBm2 to -65.5 dBm2 at 1.54 THz. On the other hand, the monostatic RCS values for all lattices have an inclined line until they reach a frequency of 1.0 THz from more than -60 dBm2. However, from the 1.0 THz to 3.0 THz frequency range the RCS values have moderate discrepancies among the horizontal line for each lattice. Furthermore, two parametric studies were performed to examine the RCS reduction behaviour, for instance, multi-layer structures and as well tilt positioning of the proposed coding metamaterial. Overall it indicates that the integration of coding-based metamaterial successfully reduced the RCS values.

Experimental investigation and optimization of dimple-roughened impinging jet solar air collector using a novel AHP-MABAC approach.

The effect of the flow and geometric parameters of a dimple-roughened absorber plate on the enactment of solar air collectors (SACs) with air-impinged jets was investigated in this study. The performance-defining criteria (PDCs) of a jet-impinged dimple-roughened SAC (JIDRSAC)-forced convection airflow system are significantly affected by variations in the system's control factors (CFs), such as the arc angle (αaa) ranging from 30° to 75°, dimple pitch ratio (pd/Dh) ranging from 0.269 to 1.08, and dimple height ratio (ed/Dh) ranging from 0.016 to 0.0324. The constant parameters of the jet slot are a stream-wise pitch ratio (Xi/Dhd) is 1.079, a span-wise pitch ratio (Yi/Dhd) is 1.619, and a jet diameter(Di/Dhd) is 0.081. Based on the combined approach of the analytic hierarchy process and multi-attributive border approximation area comparison (AHP-MABAC), the Reynolds number (Re) = 15,000, αaa = 60°, pd/Dh = 0.27, and ed/Dh = 0.027 depicted the best alternative (A-9) set among 16 alternatives to deliver the optimal performance of the JIDRSAC. The jet impingement pass compared to the smooth pass, the Nusselt number increased by 2.16-2.81, and friction factor increased by 3.35-5.95, and JIDRSAC was compared to the jet impingement pass, exhibiting an enhancement in Nusselt number and friction factor in the range of 0.55-0.80 and 0.05-0.15, respectively. In addition, sensitivity analysis is used to examine the ranking's stability and reliability in relation to the PDC weights.

Exploring the photo-thermal conversion behavior and extinction coefficient of activated carbon nanofluids for direct absorption solar collector applications.

This work aims to explore the optical and thermal conversion characteristics of activated carbon-solar glycol nanofluids with various volume fractions namely 0.2, 0.4, and 0.6%, respectively. Kigelia africana leaves were synthesized into porous activated carbon nanomaterials by using the high-temperature sintering process and the pyrolysis process in a muffle furnace. The experimental investigation was carried out with different nanofluid concentrations by using the solar simulator. Nanofluids were heated with the assistance of a solar simulator test system and the convection/conduction heat loss was decreased by using the glass as an insulating material around the test section. Prepared nanofluid with 0.6 vol% activated carbon augmented the thermal conductivity by 14.36% at 60°C. The maximum temperature difference of 10°C was attained at 0.6% volume concentrations of nanofluid as compared with base fluid (solar glycol). In addition, maximum receiver efficiency of 94.51% was attained at 0.6% volume fractions of activated carbon-based nanofluid compared with solar glycol thru a light radiation time of 600 s. Moreover, activated carbon-based nanofluid exhibited significantly higher absorption efficiency as the majority of the radiation was absorbed by the nanofluid. It is concluded that activated carbon-based nanofluids could be a suitable low-cost highly stable material for developing working fluid for direct absorbance solar collector-based applications.

Assessment of Dietary Exposure to Ochratoxin A in Lebanese Students and Its Urinary Biomarker Analysis.

The present study investigated the dietary and urinary OTA occurrence among 44 Lebanese children. Relying on HPLC-FLD analysis, OTA was found in all the urine samples and in 46.5% and 25% of the 24 h duplicate diet and dinner samples, respectively. The means of OTA levels in positive samples were 0.32 ± 0.1 ng/g in 24 h diet, 0.32 ± 0.18 ng/g in dinner and 0.022 ± 0.012 ng/mL in urines. These values corresponded to margin of exposure (MOE) means of 7907 ± 5922 (neoplastic) and 2579 ± 1932 (non-neoplastic) calculated from positive 24 h diet, while 961 ± 599 (neoplastic) and 313 ± 195 (non-neoplastic) calculated from the urine. Since the MOE levels for the neoplastic effect were below the limit (10,000), a major health threat was detected and must be addressed as a health institutions' priority. Besides, the wide difference between PDIs and MOEs calculated from food and urine suggests conducting further OTA's toxicokinetics studies before using urine to measure OTA exposure.

Oral simvastatin combined with narrowband UVB for the treatment of psoriasis: A randomized controlled trial.

Psoriasis is a common chronic skin condition, which is an immune-related hyperproliferative disorder. Among the different treatments for psoriasis, statins have been found to reduce the severity of the disease. Accordingly, fluvastatin and simvastatin are known to have anti-inflammatory effects by inhibiting inflammatory cytokines and lymphocyte function. Narrowband ultraviolet B (NB-UVB) is known as an effective and safe modality for psoriasis treatment. In this double blind, randomized controlled trial, we investigated the efficacy and safety of adding simvastatin to NB-UVB phototherapy in patients with psoriasis. Forty-eight patients with psoriasis undergoing NB-UVB phototherapy were randomly divided into placebo groups; one received oral simvastatin, and the other received a placebo for 12 weeks. Psoriasis severity was assessed with the Psoriasis Area and Severity Index (PASI) and Dermatology Life and Quality Index (DLQI). Both groups showed a significant decline in PASI score after 6 and 12 weeks compared to the baseline. The differences in reducing PASI score and DLQI between the two groups were not significant neither at week sixth nor 12th. In addition, DLQI decreased significantly in the placebo group at week 12th. In contrast with previous studies, we did not find any additional effects for oral simvastatin5 in treating psoriasis with NB-UVB. Also, an insignificant difference in the improvement of quality of life between both groups was ascertained.

A Rare Case of Tumoral Scleromyxedema.

Scleromyxedema is an uncommon disease, affecting the skin mainly and other internal organs sometimes, characterized by fibroblasts proliferation, fibrosis, and mucous deposition in the absence of thyroid disorder. It is associated with monoclonal gammopathy in most cases. We are reporting a case with a rare presentation of tumoral scleromyxedema in the neck, with a mass mimicking other tumoral lesions, highlighting the importance of diagnosis and histopathologic correlation.

Myristic acid based imidazoline derivative as effective corrosion inhibitor for steel in 15% HCl medium.

There is a high demand of effective and eco-friendly corrosion inhibitor for industrial applications. In an attempt to prepare a benign and effective corrosion inhibitor for acidizing purpose, an imidazoline derivative, N-(2-(2-tridecyl-4,5-dihydro-1H-imidazol-1-yl)ethyl)tetradecanamide (NTETD) was synthesized from myristic acid and diethyleneamine. The characterization of the newly synthesized compound was done using 1H NMR, FTIR, and elemental analysis techniques. NTETD was examined as a corrosion inhibitor for low carbon steel in 15% HCl solution using weight loss, electrochemical impedance spectroscopy (EIS), potentiodynamic polarization (PDP), linear polarization (LPR), scanning electron microscope (SEM), energy dispersive spectroscopy (EDAX), atomic force spectroscopy (AFM), and X-ray photoelectron spectroscopy (XPS) techniques. It was found that, the optimum concentration of NTETD is 300 mg/L. With this concentration, inhibition efficiency above 93% is achievable. Results from PDP show that, NTETD acted as a mixed type corrosion inhibitor but with principal effect on cathodic corrosion half reactions. The calculated value of the adsorption-desorption equilibrium constant (1.015 × 103) reveals a strong bonding between NTETD molecules and the steel surface. The EDAX, FTIR, and XPS results confirm the adsorption of NTETD molecules on the steel surface. SEM and AFM results agree with experimental findings that NTETD is effective in corrosion mitigation of steel in 15% HCl solution. The possible corrosion inhibition mechanism by NTETD has been proposed.

Non-invasive fetal ECG extraction using discrete wavelet transform recursive inverse adaptive algorithm.

BACKGROUND: Fetal heart activity adds significant information about the status of the fetus health. Early diagnosis of issues in the heart before delivery allows early intervention and significantly improves the treatment. OBJECTIVE: This paper presents a new adaptive filtering algorithm for fetal electrocardiogram (FECG) extraction from the maternal abdominal signal, known in literature as abdominal electrocardiogram (AECG) signal. Fetal QRS complex waves will be identified and extracted accurately for fetal health care and monitoring purposes. METHODS: We use discrete wavelet transform recursive inverse (DWT-RI) adaptive filtering algorithm for this objective. Thoracic maternal electrocardiogram (MECG) is used as a reference in the proposed algorithm and FECG components are extracted from AECG signal after suppressing the MECG projections. The proposed algorithm is compared to other typical adaptive filtering algorithms, least mean squares (LMS), recursive least squares (RLS), and recursive inverse (RI). RESULTS: Fetal QRS waveforms successful identification and extraction from AECG signal is evaluated objectively and visually and compared to other algorithms. We validated the proposed algorithm using both synthetic data and real clinical data. CONCLUSIONS: The proposed algorithm is capable of extracting fetal QRS waveforms successfully from AECG and outperforms other adaptive filtering algorithms in terms of accuracy and positive predictivity.