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This paper presents an innovative general structure for a single-loop Sigma-Delta (Σ Δ) modulator that combines low-distortion and noise-coupled techniques to achieve high-resolution for low-power applications. The low-distortion technique adjusts the signal transfer function of the proposed structure to unity, while the noise-coupled technique enhances the order of quantization noise-shaping at the modulator output. These techniques were incorporated into the design to increase the modulator order without requiring additional operational amplifiers during its circuit implementation, resulting in a low-power modulator compared to similar structures. To reduce the number of required amplifiers, a second-order infinite impulse response (IIR) filter was used in the modulator loop filter, in place of two integrators. To assess the proposed structure's performance, a third-order modulator sample was implemented and simulated for speech recognition applications, specifically, digital hearing aids, using 180 nm complementary metal oxide semiconductor (CMOS) technology. The results indicate that for a third structure with a sampling rate of 64, an input sine signal of −6 dBFS and a sampling frequency of 2.56 MHz, the signal to noise plus distortion (SNDR) was 81.9 dB, and the dynamic range (DR) was 88 dB for a 20 KHz bandwidth. The modulator's power consumption was 126.9 μ W. Circuit and system-level simulations demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Accurately predicting the dynamics of complex systems governed by partial differential equations (PDEs) is crucial in various applications. Traditional numerical methods such as finite element methods (FEMs) offer precision but are resource‐intensive, particularly at high mesh resolutions. Machine learning–based surrogate models, including graph neural networks (GNNs), present viable alternatives by reducing computation times. However, their accuracy is significantly contingent on the availability of substantial high‐fidelity training data. This paper presents innovative multifidelity GNN (MFGNN) frameworks that efficiently combine low‐fidelity and high‐fidelity data to train more accurate surrogate models for mesh‐based PDE simulations, while reducing training computational cost. The proposed methods capitalize on the strengths of GNNs to manage complex geometries across different fidelity levels. Incorporating a hierarchical learning strategy and curriculum learning techniques, the proposed models significantly reduce computational demands and improve the robustness and generalizability of the results. Extensive validations across various simulation tasks show that the MFGNN frameworks surpass traditional single‐fidelity GNN models. The proposed approaches, hence, provide a scalable and practical solution for conducting detailed computational analyses where traditional high‐fidelity simulations are time‐consuming. [ABSTRACT FROM AUTHOR]

Background: Identification and precise localization of the liver surface and its segments are essential for any surgical treatment. An algorithm of accurate liver segmentation simplifies the treatment planning for different types of liver diseases. Although liver segmentation turns researcher's attention, it still has some challenging problems in computer-aided diagnosis. Objective: This study aimed to extract the potential liver regions by an adaptive water flow model and perform the final segmentation by the classification algorithm. Material and Methods: In this experimental study, an automatic liver segmentation algorithm was introduced. The proposed method designed the image by a transfer function based on the probability distribution function of the liver pixels to enhance the liver area. The enhanced image is then segmented using an adaptive water flow model in which the rainfall process is controlled by the liver location in the training images and the gray levels of pixels. The candidate liver segments are classified by a Multi-Layer Perception (MLP) neural network considering some texture, area, and gray level features. Results: The proposed algorithm efficiently distinguishes the liver region from its surrounding organs, resulting in perfect liver segmentation over 250 Magnetic Resonance Imaging (MRI) test images. The accuracy of 97% was obtained by quantitative evaluation over test images, which revealed the superiority of the proposed algorithm compared to some evaluated algorithms. Conclusion: Liver segmentation using an adaptive water flow algorithm and classifying the segmented area in MRI images yields more robust and reliable results in comparison with the classification of pixels. [ABSTRACT FROM AUTHOR]

In tills paper a second-order low distortion Sigma-Delta Modulator (EAM) with utilization of Comparator-Based Switched-Capacitor (CBSC)-based IIR filter, is explored. The advantages of this new structure are justified by the reduction of power and area. For this purpose IIR filter block can be made with single CBSC gain stage that has the same accuracy and performance of second-order filter with two Op-amps. The design of CBSC gain stages is intended to minimize the power consumption, and maximize Modulator performance. As shown in the simulation result, for a 20-KHz signal bandwidth, the modulator achieves a dynamic range of 70.2 dB and a peak signal-to-noise and distortion ratio (SNDR) of 68 dB with an over-sampling ratio of 64. In addition it consumes only 198/xW from a 1.8-V power supply at 2.56 MS/s. [ABSTRACT FROM AUTHOR]

Introduction Congenital heart defects (CHD) are one of the most common congenital anomalies, and their association with assisted reproductive technology (ART) is controversial in different populations. The purpose of this study was to evaluate this association and to provide information about the necessity of specialized echocardiography during pregnancy with ART. Methods This retrospective study was performed on all pregnancies conceived by ART and referred for fetal echocardiography to the Rasoul Akram and Akbar Abadi hospitals in Tehran, Iran. A total of 109 patients were enrolled in the study (56 in the ART group and 53 in the non-ART). Two-dimensional and color Doppler echocardiography were performed on all patients to identify heart problems and anomalies and medical records of the patients were reviewed. The outcome was considered the presence of functional and structural heart defects on echocardiography. Results The study groups were similar in terms of maternal age and GA. The ART group consisted of 31 singletons (55%) and 25 multiples (45%). All pregnancies in the non-ART group were singletons. Following in vitro fertilization (33%), ovulation induction (25%) was the next most used method. The findings of echocardiography were one atrial septal defect (ASD) in ART and one in non-ART, six ventricular septal defects (VSD) in ART and three in non-ART, and one ASD and VSD in the ART group. These structural abnormalities showed no difference in the two groups (P value = 0.58). There was no significant difference in rhythm between the two groups (P = 0.51). Echocardiographic indices of both groups did not differ statistically except in the TR-PG index (P value = 0.02). Conclusions The structural defects of the two groups were not different, and no heart dysfunction was observed in ART fetuses. There was no association between ART and CHD in our study. We concluded that echocardiography by pediatric cardiologists is not necessary for these fetuses., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Rasouli et al.)