Miracle-dz/UniFAR-FTUs huggingface dataset:
A Field-Circuit Co-Simulation Method for Antenna and RF Front-Ends with DGTD Solver

Due to the complex structure of array antenna and nonlinear microwave circuit system, the co-simulation of antenna structure and back-end microwave circuit will have multi-scale problems, which greatly affects the simulation efficiency. To solve the above problem, an antenna equivalent model extraction method based on the electromagnetic pulse effect is proposed in this paper first. When the load port of antenna is open, open circuit voltage can be obtained at the antenna receiving port. When the load port is short-circuited, the short-circuit current can also be obtained at the antenna receiving port. According to Davinan's theorem, the complex antenna structure is equivalent to a circuit in series between the signal source and the equivalent impedance. By using fitting function to approximate the resistance curve of antenna in frequency domain, all parameters of fitting function can be solved, and then the expression of impedance function can be obtained. The high-order fitting function in the frequency domain is transformed into the time domain by inverse Fourier transform, and the time domain partial differential equation is discretized by the forward difference method to solve the current density at the antenna port. The next step is to get the current density of the IC (integrated circuit) ports. In this step, the integrated circuit port current can be solved by using the voltage-current relationship such as equivalent lumped-charge model and Ohm's law. Finally, the current density information of antenna port and integrated circuit ports is introduced into Maxwell equation tested by DGTD (discontinuous Galerkin time domain) method through the continuity condition of the field to form a field-circuit co-simulation format. Then several examples of RF field-circuit co-simulation including antenna model can be further proved that the proposed method simplifies the simulation process and the simulation results consistent with commercial simulation software, which shows the effectiveness and accuracy of the proposed method. The method proposed in this paper will provide a convenient way for the co-simulation of complex models.
Distortion-Independent Pairwise Underwater Image Perceptual Quality Comparison

Ranking underwater images according to their quality is a key indicator comparing the performance of different methodologies and therefore is critical in the field of instrumentation and measurement. Perceiving differences in the quality of underwater images is a challenging task that has received relatively less attention, primarily due to mixed distortion, diversified image content, and the absence of high-quality reference images. Thus, based on the pairwise underwater image quality voting data, we develop a novel ternary classification transformer to predict a quality comparison of underwater images without reference. This is the first attempt to model the quality discrimination of an image pair. The proposed model combines the perception of convolutional neural networks and transformer encoder to explore local quality features and visual perceptual connections between different patch tokens. Experimental results reveal that the proposed pairwise underwater image quality comparison (PUIQC) scheme predicts noticeable quality differences correlating well with subjective perception. The quantification of complex distortions in underwater images compared to other learning-based methods is a compelling feature of this technology. It delivers competitive results in ranking the different enhancement outputs. In addition, we reveal the self-attention of local quality features within the two images and capture their responsive contribution to the quality decision, which explains the underlying subjective quality-sensitive mechanism during image quality comparison.
Enhancing Performance of Interior Permanent Magnet Motors Using Novel Stator Slot Designs

This paper presents a novel design for an interior permanent magnet (IPM) motor to reduce cogging torque and torque ripple for applications requiring high-performance variable speed drives, electric vehicles (EVs), and servo drives. The proposed design incorporates a higher number of stator slots combined with a V-shaped rotor configuration. Two types of rectangular stator slots were employed to house the rectangular stator winding, leading to a more sinusoidal radial flux distribution at the airgap and thus improving the overall torque performance. The maximum torque and per ampere (MTPA) control strategy was employed to optimize the output power for both models. To evaluate the effectiveness of the proposed design, a comparative study was conducted between the novel model and the conventional model while maintaining the same rotor configuration for both models. Different simulations were carried out using finite-element analysis under identical electrical and magnetic loading conditions. Various aspects, including radial flux density at the airgap, static torque, back electromotive force (EMF), cogging torque, d- and q-axis inductances, saliency ratio, iron loss, output torque, and torque ripple, were analyzed using 2-D finite element analysis. The results confirm that the proposed model achieves higher output torque, improved torque density, and reduced torque ripple compared to conventional designs.
Mid-Long-Term Prediction of Surface Seawater Organic Carbon in the Southern South China Sea Based on Multi-Applicability CNN-LSTM Prediction Model

The organic carbon pool is a crucial component of the ocean carbon cycle. The study of organic carbon distribution and interannual variability in the land-sea interface can contribute to understanding the global ocean carbon cycle and ecological effects in the context of the Anthropocene and help achieve the Sustainable Development Goals (SDGs). At present, there has been a certain amount of research on the source and flux of carbon in the ocean carbon cycle, but the prediction of marine carbon is still in its infancy. In this paper, a CNN-LSTM deep learning model that takes into account spatio-temporal features was used to make a 5-year mid-long-term rolling prediction of particulate organic carbon (POC) and yellow matter (CDOM) using MODIS Level 2 semimonthly synthetic data from the official website of NASA from January 2002 to June 2020. The model uses chlorophyll-a data to adjust the parameters. The results showed that the model could also be applied to the mid-long-term rolling prediction of POC and CDOM. The model was capable of accurately predicting POC and CDOM over periods of three and two years, respectively (R > 0.5). Meanwhile, the 5-year trends of the predicted and actual values were verified using the least squares method and the Mann-Kendall trend test. The results showed that the predicted and actual values of sea surface POC and CDOM in 2015–2020 showed an overall upward trend. The surface-level POC and CDOM in the ocean are considered to be related to primary production. The mid-long-term prediction of surface seawater organic carbon in the southern South China Sea helps humans explore the regional characteristics of organic carbon in the coral reef waters of the South China Sea and study the changing trend of surface seawater organic carbon.
Metavirome Analysis and Identification of Midge-Borne Viruses from Yunnan Province, China, in 2021

Midges are widely distributed globally and can transmit various human and animal diseases through blood-sucking. As part of this study, 259,300 midges were collected from four districts in Yunnan province, China, to detect the viral richness and diversity using metavirome analysis techniques. As many as 26 virus families were detected, and the partial sequences of bluetongue virus (BTV), dengue virus (DENV), and Getah virus (GETV) were identified by phylogenetic analysis and PCR amplification. Two BTV gene fragments, 866 bps for the VP2 gene of BTV type 16 and 655 bps for the VP5 gene of BTV type 21, were amplified. The nucleotide sequence identities of the two amplified BTV fragments were 94.46% and 98.81%, respectively, with two classical BTV-16 (GenBank: JN671907) and BTV-21 strains (GenBank: MK250961) isolated in Yunnan province. Furthermore, the BTV-16 DH2021 strain was successfully isolated in C6/36 cells, and the peak value of the copy number reached 3.13 × 107 copies/μL after five consecutive BHK-21 cell passages. Moreover, two 2054 bps fragments including the E gene of DENV genotype Asia II were amplified and shared the highest identity with the DENV strain isolated in New Guinea in 1944. A length of 656 bps GETV gene sequence encoded the partial capsid protein, and it shared the highest identity of 99.68% with the GETV isolated from Shandong province, China, in 2017. Overall, this study emphasizes the importance of implementing prevention and control strategies for viral diseases transmitted by midges in China.
The Phoenix Project: A Novel About IT, DevOps, and Helping Your Business Win

Bill is an IT manager at Parts Unlimited. It's Tuesday morning and on his drive into the office, Bill gets a call from the CEO. The company's new IT initiative, code named Phoenix Project, is critical to the future of Parts Unlimited, but the project is massively over budget and very late. The CEO wants Bill to report directly to him and fix the mess in ninety days or else Bill's entire department will be outsourced. With the help of a prospective board member and his mysterious philosophy of The Three Ways, Bill starts to see that IT work has more in common with manufacturing plant work than he ever imagined. With the clock ticking, Bill must organize work flow streamline interdepartmental communications, and effectively serve the other business functions at Parts Unlimited. In a fast-paced and entertaining style, three luminaries of the DevOps movement deliver a story that anyone who works in IT will recognize. Readers will not only learn how to improve their own IT organizations, they'll never view IT the same way again.
Long-Term Prognosis of Different Subtypes of Left Ventricular Noncompaction Cardiomyopathy Patients: A Retrospective Study in China

Left ventricular noncompaction (LVNC) is a heterogeneous cardiomyopathy that can be classified into different subtypes based on morphologic and functional features. However, the prognosis of the dilated and isolated subtypes of non-pediatric LVNC remains unknown. We retrospectively studied 101 patients with LVNC diagnosed at Peking Union Medical College Hospital from 2006 to 2022 using the Jenni criteria of transthoracic echocardiography. The patients were grouped into those with dilated LVNC (n = 64) or isolated LVNC (n = 37), and 88 patients (54 with dilated LVNC and 34 with isolated LVNC) were followed up successfully. The primary outcome was major adverse cardiovascular events (a composite of cardiovascular mortality, heart failure, severe ventricular arrhythmia, and systolic embolism). The median follow-up time was 5.24 years. The incidence of major adverse cardiovascular events was 43.2%; patients with dilated LVNC had a higher risk (adjusted hazard ratio, 4.43; 95% confidence interval, 1.24–15.81; p = 0.02) than those with isolated LVNC. None of the isolated LVNC patients had cardiovascular deaths or severe ventricular arrhythmias. The risk of systemic embolism was similar between patients with dilated and isolated LVNC. Our findings indicate that transthoracic echocardiography is a useful tool for classifying LVNC into subtypes with distinct clinical outcomes. Dilated LVNC is associated with a poor prognosis, while the isolated subtype is probably a physiological condition.
Applications of Remote Sensing and GIS to Modeling Fire for Vegetative Restoration in Northern Arizona

An accurate fire model is a useful tool in predicting the behavior of a prescribed fire. Simulation of fire requires an extensive amount of data and can be accomplished best using GIS applications. This paper demonstrates integrative procedures of using of ArcGIS™, ERDAS Imagine™, GPS, and FARSITE© to predict prescribed fire behavior on the Kaibab-Paiute Reservation. ArcGIS was used to create a database incorporating all variables into a common spatial reference system and format for the FARSITE model. ArcGIS Spatial Analyst was then used to select optimal burn sites for simulation. Our predictions will be implemented in future interagency efforts towards vegetative restoration on the reservation.
Adiponectin Modulates Smooth Muscle Cell Morpho-Functional Properties in Murine Gastric Fundus via Sphingosine Kinase 2 Activation

Adipokines are peptide hormones produced by the adipose tissue involved in several biological functions. Among adipokines, adiponectin (ADPN) has antidiabetic and anti-inflammatory properties. It can also modulate food intake at central and peripheral levels, acting on hypothalamus and facilitating gastric relaxation. ADPN exerts its action interacting with two distinct membrane receptors and triggering some well-defined signaling cascades. The ceramidase activity of ADPN receptor has been reported in many tissues: it converts ceramide into sphingosine. In turn, sphingosine kinase (SK) phosphorylates it into sphingosine-1 phosphate (S1P), a crucial mediator of many cellular processes including contractility. Using a multidisciplinary approach that combined biochemical, electrophysiological and morphological investigations, we explored for the first time the possible role of S1P metabolism in mediating ADPN effects on the murine gastric fundus muscle layer. By using a specific pharmacological inhibitor of SK2, we showed that ADPN affects smooth muscle cell membrane properties and contractile machinery via SK2 activation in gastric fundus, adding a piece of knowledge to the action mechanisms of this hormone. These findings help to identify ADPN and its receptors as new therapeutic targets or as possible prognostic markers for diseases with altered energy balance and for pathologies with fat mass content alterations.
Underground hyperspectral outcrop scanning for automated mine‐face mapping: The lithium deposit of Zinnwald/Cínovec

The inherent complexity of underground mining requires highly selective ore extraction and adaptive mine planning. Repeated geological face mapping and reinterpretation throughout mine life is therefore routine in underground mines. Hyperspectral imaging (HSI) has successfully been applied to enhance geological mapping in surface mining environments, but remains a largely unexplored opportunity in underground operations due to challenges associated with illumination, wet surfaces and data corrections. In this study, we propose a workflow that paves the way for the operational use of HSI in active underground mines. In a laboratory set‐up, we evaluated different hyperspectral sensors and lighting set‐ups as well as the effect of surface moisture. We then acquired hyperspectral data in an underground mine of the Zinnwald/Cínovec Sn‐W‐Li greisen‐type deposit in Germany. These data were corrected for illumination effects, back‐projected into three dimensions and then used to map mineral abundance and estimate Li content across the mine face. We validated the results with handheld laser‐induced breakdown spectroscopy. Despite remaining challenges, we hope this study will help establish hyperspectral sensors in the extractive industry as a means to increase the volume and efficiency of raw material supply, advance digitalisation, and reduce the environmental footprint and other risks associated with underground mining.
Online Junction Temperature Estimation for SiC MOSFETs Using Drain Voltage Falling Edge Time

The junction temperature is vital for the reliability evaluation and health management in silicon carbide (SiC) MOSFET applications. This article proposes a novel method to estimate the junction temperature under actual operating conditions, using the drain voltage falling edge time ( ${t_{f,\text {edge}}}$ ) as temperature-sensitive electrical parameters (TSEPs). Experimental results show that the proposed circuit can estimate the junction temperature with good linearity, high-temperature sensitivity, and load current independence. Furthermore, the proposed method is verified with the aged SiC MOSFET after the power cycling test. It shows a tiny impact of the bond wire degradation on the junction temperature estimation accuracy.
Decoding Hip Muscle Activation: A Comparative Electromyographic Analysis of Turn-Out Bent Knee Pulse and Single-Leg Banded Glute Bridge Exercises in Healthy Female Subjects

Individuals with lower extremity injuries and back pain may exhibit weakness and stiffness in important muscles such as the gluteus maximus and external hip rotators. To aid clinicians in understanding the impact of exercises on factors like stability, endurance, and strength, electromyography (EMG) examination can be employed to monitor muscle activation. In this investigation, the EMG activity of the gluteus maximus and medius were compared between two exercises: the turn-out bent knee pulse and the single-leg banded glute bridge. The study enrolled a group of 64 healthy young women, aged 19 to 24 years. The raw data collected were standardized and represented as a percentage of maximum voluntary isometric contraction (%MVIC). To assess the reliability of the EMG recordings, the test–retest analysis was performed using the intraclass correlation coefficient (ICC3,1). Statistical analysis involved conducting a one-way ANOVA to compare the EMG amplitudes between the two exercises. Remarkably, the results demonstrated a significantly higher EMG signal amplitude during the single-leg banded glute bridge exercise (mean ± SD: 90 ± 28% MVIC) when compared to the turn-out bent knee pulse exercise (mean ± SD: 70 ± 15% MVIC) (F = 16.584, p = 0.001). The study found that the single-leg banded glute bridge exercise had a significantly higher EMG signal amplitude compared to the turn-out bent knee pulse exercise. This suggests that the single-leg banded glute bridge exercise may be more effective in strengthening the gluteus maximus and medius muscles. Overall, this study highlights the importance of targeted muscle training in rehabilitation protocols and the use of EMG examination to monitor muscle activation.
Wavelength Selection Considerations for Optical Wireless Positioning Systems

Indoor Positioning Systems act as an important technology to provide real-time location estimation, enabling a large variety of industrial applications. In this context, Optical Wireless Positioning employs the propagation characteristics of optical signals as a means to calculate an accurate and precise position. While Visible Light Positioning focuses on the use of LED lighting to support both positioning and illumination simultaneously, Infrared-based systems also offer viable positioning solutions featuring distinct advantages and drawbacks over their visible light counterparts. The selection of the used wavelength thus proves to be an important design consideration for an Optical Wireless Positioning system. This work summarises the main differences and trade-offs fundamental to the selection of the wavelength, and compares them in order to make an informed decision on the selection.
Presentation of the Morris F. Collen Award to Robert S. Ledley, DDS

Robert S. Ledley, DDS The following description, written by Daniel Masys, provides a historical perspective of the Morris F. Collen Award. “The American College of Medical Informatics is an honorary society established to recognize those who have made sustained contributions to the field. Since its founding in 1984, 170 fellows have been-elected to the College. Its highest award, for lifetime achievement and contributions to the discipline of medical informatics, is the Morris F. Collen Award. Dr. Collen's own efforts as a pioneer in the field stand as the embodiment of creativity, intellectual rigor, perseverance, and personal integrity. And so it is that the College gives its highest recognition to those whose attainments have, throughout their careers, substantially advanced the science and art of medical informatics. ”1 The College is pleased to announce that Robert S. Ledley, DDS, is the 1998 recipient of the Morris F. Collen Award. Dr. Robert Ledley is a great inventor, scholar, scientist, teacher, and family man. He is witty, clever with words, and has a tremendous sense of humor. Although humble and shy in many ways, he is highly confident of his ability to achieve the impossible. He recognizes that he can invent almost anything. Today Dr. Robert Ledley is Professor of Radiology, Physiology and Biophysics and the Director of the National Biomedical Research Foundation at Georgetown University Medical Center. He was born about 60 years ago in Flushing, New York, where he lived his early years and went to school. He earned a doctorate in dental surgery from New York University in 1948, and a master's degree in mathematical physics from Columbia University the next year. In 1950, two years later, he married Terry Ledley. When they first met, he boldly told Terry he was going to “push forward the frontiers of scientific knowledge. ” So, …
Cross-domain synergy: Leveraging image processing techniques for enhanced sound classification through spectrogram analysis using CNNs

In this paper, the innovative approach to sound classification by exploiting the potential of image processing techniques applied to spectrogram representations of audio signals is reviewed. This study shows the effectiveness of incorporating well-established image processing methodologies, such as filtering, segmentation, and pattern recognition, to enhance the feature extraction and classification performance of audio signals when transformed into spectrograms. An overview is provided of the mathematical methods shared by both image and spectrogram-based audio processing, focusing on the commonalities between the two domains in terms of the underlying principles, techniques, and algorithms. The proposed methodology leverages in particular the power of convolutional neural networks (CNNs) to extract and classify time-frequency features from spectrograms, capitalizing on the advantages of their hierarchical feature learning and robustness to translation and scale variations. Other deep-learning networks and advanced techniques are suggested during the analysis. We discuss the benefits and limitations of transforming audio signals into spectrograms, including human interpretability, compatibility with image processing techniques, and flexibility in time-frequency resolution. By bridging the gap between image processing and audio processing, spectrogram-based audio deep learning gives a deeper perspective on sound classification, offering fundamental insights that serve as a foundation for interdisciplinary research and applications in both domains.
Deployment of phytoremediation at the 317/319 area at Argonne National Laboratory - East

The 317 and 319 Areas are located on the extreme southern end of the ANL-E site, immediately adjacent to the DuPage County Waterfall Glen Forest Preserve. The 317 Area is an active hazardous and radioactive waste processing and storage area. In the late 1950s, liquid waste was placed in the unit known as the French Drain. Since that time, this waste has migrated into underlying soil and groundwater. The principal environmental concern in the 317 Area is the presence of several VOCs in the soil and groundwater and low levels of tritium in the groundwater beneath and down gradient of the site. The 319 Area Landfill and French Drain are located immediately adjacent to the 317 Area. The principal environmental concern in the 319 Area is the presence of radioactive materials in the waste mound, in the leachate in the mound, and in the shallow groundwater immediately down gradient of the landfill. Several interim actions have already been implemented in this area, to reduce the VOC and tritium releases from these areas, as the result of the Resource Conservation and Recovery Act (RCRA) Facility Investigation (RFI) conducted from December 1994 through September 1996.