Miracle-dz/UniFAR-FTUs huggingface dataset:
Diffusion Barrier Prediction of Graphene and Boron Nitride for Copper Interconnects by Deep Learning

The continuous scaling-down size of interconnects should be accompanied with ultra-thin diffusion barrier layers, which is used to suppress Cu diffusion into the dielectrics. Unfortunately, conventional barrier layers with thicknesses less than 4 nm fail to perform well. With the advent of 2D layered materials, graphene and hexagonal boron nitride have been proposed as alternative Cu diffusion barriers with thicknesses of ≈1 nm. However, defects such as vacancies may evolve into a Cu diffusion path, which is a challenging problem in design of diffusion barrier layers. The energy barrier of Cu atom diffused through a di-vacancy defect in graphene and hexagonal boron nitride is calculated by density functional theory. It is found that graphene offers higher energy barrier to Cu than hexagonal boron nitride. The higher energy barrier is attributed to the stronger interaction between Cu and C atoms in graphene as shown by charge density difference and Bader’s charge. Furthermore, we use the energy barriers of different vacancy structures and generate a dataset that will be used for machine learning. Our trained convolutional neural network is used to predict the energy barrier of Cu migration through randomly configured defected graphene and hexagonal boron nitride with $R^{2}$ of >99% for $4 \times 4$ supercell. These results provide guides on choosing between 2D materials as barrier layers, and applying deep learning to predict the 2D barrier performance.
A Review of Driving Simulation Technology and Applications

Driving simulation has become a very useful tool for vehicle design and research in industry and educational institutes. This paper provides a review of driving simulator components, including the vehicle dynamics model, the motion system, and the virtual environment, and how they interact with the human perceptual system in order to create the illusion of the driving. In addition, a sample of current state-of-the-art vehicle simulators and algorithms are described. Finally, current applications are discussed, such as driver-centered studies, chassis and powertrain design, and autonomous systems development.
Building a better baseline to estimate 160 years of avian population change and create historically informed conservation targets

Globally, anthropogenic land‐cover change has been dramatic over the last few centuries and is frequently invoked as a major cause of wildlife population declines. Baseline data currently used to assess population trends, however, began well after major changes to the landscape. In the United States and Canada, breeding bird population trends are assessed by the North American Breeding Bird Survey, which began in the 1960s. Estimates of distribution and abundance prior to major habitat alteration would add historical perspective to contemporary trends and allow for historically based conservation targets. We used a hindcasting framework to estimate change in distribution and abundance of 7 bird species in the Willamette Valley, Oregon (United States). After reconciling classification schemes of current and 1850s reconstructed land cover, we used multiscale species distribution models and hierarchical distance sampling models to predict spatially explicit densities in the modern and historical landscapes. We estimated that since the 1850s, White‐breasted Nuthatch (Sitta carolinensis) and Western Meadowlark (Sturnella neglecta) populations, 2 species sensitive to fragmentation of oak woodlands and grasslands, declined by 93% and 97%, respectively. Five other species we estimated nearly stable or increasing populations, despite steep regional declines since the 1960s. Based on these estimates, we developed historically based conservation targets for amount of habitat, population, and density for each species. Hindcasted reconstructions provide historical perspective for assessing contemporary trends and allow for historically based conservation targets that can inform current management.
A review of fault diagnosis methods for rotating machinery

As the core component of mechanical equipment, rotating machinery is subject to faults in tough working environment. Therefore, the effective fault diagnosis of rotating machinery has become a widely concerned topic. Numerous publications about fault diagnosis methods of rotating machinery have been proposed to technical reports, conference proceedings and academic journals. This paper attempts to review fault diagnosis approaches for rotating machinery. First, these methods of fault diagnosis could be divided into four categories, i.e., statistical analysis-based approach, signal processing-based approach, information fusion-based approach, and pattern recognition-based approach. Then, the basic idea of these ways and their disadvantage are briefly described. Finally, this article summarizes the trends developed in the future of fault diagnosis techniques and gives some new research direction. It is expected that this summary can offer a valuable references for boffins and provide the necessary foundation for the subsequent study of the fault diagnosis of rotating machinery.
Characteristics of pleomorphic adenomas, adenolymphomas, and malignant tumors of the salivary glands on color doppler ultrasonography and contrast enhanced ultrasonography.

OBJECTIVE To provide a comprehensive description of the quantitative and qualitative characteristics of pleomorphic adenomas, adenolymphomas, and malignant tumors of the salivary glands on color doppler ultrasonography and contrast-enhanced ultrasonography (CEUS). PATIENTS AND METHODS 64 patients with 35 pleomorphic adenomas, 24 adenolymphomas, and 12 malignant tumors were enrolled in this study. All patients were examined by color doppler ultrasonography and CEUS before operation. In color Doppler ultrasonography, degree of vascularity, peak systolic velocity (PSV) and the vascular resistance index (RI) were obtained. In CEUS, type of enhancement, rim enhancement and area of enhancement were assessed. After the time-intensity curves (TIC) were drawn, the time to peak enhancement (TTP), peak intensity (PI) and the time from peak to one half (TFP) were calculated for the tumors and surrounding salivary parenchyma. Postoperatively, histopathologic examination of surgical specimens was used as the gold standard. RESULTS Color Doppler ultrasonography showed no significant differences in PSV between tumors, significantly less adenolymphomas had Grade 0-1 vascularity compared to pleomorphic adenomas, and the RI was significantly lower in adenolymphomas compared to pleomorphic adenomas and malignant tumors. CEUS had acceptable diagnostic sensitivity, specificity, and accuracy for differential diagnosis of pleomorphic adenomas and adenolymphomas based on the diagnostic criteria of rim enhancement and slow wash-out rate, respectively. The sensitivity, specificity, and accuracy for differential diagnosis of malignant tumors based on the diagnostic criteria of an ill-defined enhancement margin, an enlarged enhancement area or a fast wash-in rate were also satisfactory. CONCLUSIONS Accurate diagnosis of salivary gland tumors in clinical practice can be increased using color Doppler ultrasound and CEUS in combination with a case history and other imaging.
SeqRepo: A system for managing local collections of biological sequences

Motivation Access to biological sequence data, such as genome, transcript, or protein sequence, is at the core of many bioinformatics analysis workflows. The National Center for Biotechnology Information (NCBI), Ensembl, and other sequence database maintainers provide methods to access sequences through network connections. For many users, the convenience and currency of remotely managed data are compelling, and the network latency is non-consequential. However, for high-throughput and clinical applications, local sequence collections are essential for performance, stability, privacy, and reproducibility. Results Here we describe SeqRepo, a novel system for building a local, high-performance, non-redundant collection of biological sequences. SeqRepo enables clients to use primary database identifiers and several digests to identify sequences and sequence alises. SeqRepo provides a native Python interface and a REST interface, which can run locally and enables access from other programming languages. SeqRepo also provides an alternative REST interface based on the GA4GH refget protocol. SeqRepo provides fast random access to sequence slices. We provide results that demonstrate that a local SeqRepo sequence collection yields significant performance benefits of up to 1300-fold over remote sequence collections. In our use case for a variant validation and normalization pipeline, SeqRepo improved throughput 50-fold relative to use with remote sequences. SeqRepo may be used with any species or sequence type. Regular snapshots of Human sequence collections are available. It is often convenient or necessary to use a computed digest as a sequence identifier. For example, a digest-based identifier may be used to refer to proprietary reference genomes or segments of a graph genome, for which conventional identifiers will not be available. Here we also introduce a convention for the application of the SHA-512 hashing algorithm with Base64 encoding to generate URL-safe identifiers. This convention, sha512t24u, combines a fast digest mechanism with a space-efficient representation that can be used for any object. Our report includes an analysis of timing and collision probabilities for sha512t24u. SeqRepo enables clients to use sha512t24u as identifiers, thereby seamlessly integrating public and private sequence sets. Availability SeqRepo is released under the Apache License 2.0 and is available on github and PyPi. Docker images and database snapshots are also available. See https://github.com/biocommons/biocommons.seqrepo.
Adaptive Interval Observer Design with Application to Wind Energy Converters

Estimation of state variables is a very important instrument for control system design. When the system has a nonlinear behavior that can be represented by a family of linear models whose parameters are arranged as interval vectors, it is useful to reckon with a different observer that is able to manage such models. In this contribution, the design of an adaptive observer for interval models is presented. The observer is used for the estimation of the state variables of a very large wind turbine, which cannot be assumed rigid and therefore the linear model changes with wind speed. The observer is also extended to estimate the effective wind speed. The interval modelling process is also included. Simulation of a high-definition model combined with the observer based on a middle-definition model yields satisfactory results. Thus, the observer provides useful values of state variables that can be used for vibration control.
Macroscopic and microscopic perspectives for adoption of technologies in the USA

Adoption of a new technology depends on many factors. Marketing, advertising, social interactions, and personal convictions are relevant features when deciding to adopt, or not, a new technology. Thus, it is very important to determine the relative weight of these factors when introducing a new technology. Here we discuss an agent based model to investigate the behavior of agents exposed to advertising and social contacts. Agents may follow the social pressure, or maybe contrarians, acting against the majority, to decide if they adopt or not a new technology. First, we solve analytically the model that relies on the above quoted factors. Then, we compare the theoretical results with empirical data concerning the adoption of innovations by American households during the 20th century. The analysis of the diffusion dynamics process is done either for the whole period, or by periods based on the so-called technical-economic paradigms, according to Freeman and Perez. Three different periods are considered: before 1920, from 1920 to 1970, and after 1970. We study the evolution of the model parameters for each technical-economic period. Finally, by adjusting the key parameters we are able to collapse all the data into a universal curve that describes all the adoption processes.
Space Microgrids: New Concepts on Electric Power Systems for Satellites

Electric Power Systems (EPSs) are of paramount importance in satellites. These systems are responsible for power generation, storage, delivery, and conditioning. A failure in an EPS may hinder all of the aforementioned functionalities, which, consequently, leads to a failure of the whole space mission. There are many technologies available in both industry and the literature of EPSs, where each has its own faulttolerance capabilities. On the other hand, the efficiency of EPSs is an important factor because it promotes having a higher power reserve, thus feeding payloads at all the necessary times. In this article, EPSs for satellite-based microgrids are reviewed, with more of a focus on their energy generation and storage, including their protection schemes. Moreover, sizing guidelines for energy generation and storage systems are also provided.
On adaptive control of a class of switched time-delay systems

This paper investigates robust adaptive control for a class of switched linear systems subject to time-varying delays and parametric uncertainties. Different switched adaptive control designs consisting of a switching law and adaptive laws are respectively proposed for the time-dependent switching case and for the state-dependent switching case. Solutions to multiple-mode differential Riccati equations and to multiple-mode algebraic Riccati equations are exploited to design the adaptive laws for the time-dependent and state-dependent switching settings. In addition, mode-dependent-dwell-time and min-switching strategies are considered to develop the switching laws. With the designed switched adaptive controller, global uniform ultimate boundedness of the switched closed-loop system is guaranteed.
Review and Trends in Electric Traction Motors for Battery Electric and Hybrid Vehicles

This paper presents an up-to-date review of design trends for electric traction motors of electric vehicles, mainly battery electric vehicles and full hybrid electric vehicles. The focus is on permanent magnet traction motors of the major car brands and includes vehicles from the last decade (from 2010 until today), including Jaguar, Tesla, Toyota, BMW, Nissan, General Motors and Audi. The main topic of this study is the electromagnetic motor design, but also mechanical features of the motor as well as trends in magnetic materials are discussed. Furthermore, key performance numbers are evaluated and compared with respect to power density, motor speed and field weakening performance. Finally, trends in traction motor design and performance are presented as well. The aim is to highlight the latest developments in electric traction motors and to provide an efficient overview of the state-of-the-art motor designs, performances, technical limitations, as well as future concepts and trends.
Improved Direct Torque Control for Open-Winding PMSM System Considering Zero-Sequence Current Suppression With Low Switching Frequency

This article proposes an improved direct torque control strategy for open-winding permanent magnet synchronous motor (OW-PMSM) with common dc bus for zero-sequence current (ZSC) suppression. The proposed strategy builds a novel voltage vectors (VVs) look-up table, which keeps the simple control structure of the conventional DTC (C-DTC) and effectively suppresses the ZSC. In each control period, two VVs are selected. One is selected based on torque and flux control mode in the C-DTC strategy with two switching phases conducting for the whole control period; the other is selected to produce a suitable zero-sequence voltage based on the ZSC control with an additional third switching phase conducting for a short period of time. Since the duty cycle generation controller only adjusts the conducting time of one switching phase and the duration time of VV for the torque and flux control is not affected, the proposed strategy has low switching frequency and wide modulation range. Experimental results validate that the proposed strategy has a favorable control performance.
Differential Network Analysis Reveals Molecular Determinants Associated with Blood Pressure and Heart Rate in Healthy Subjects

There is mounting evidence that subclinical nonpathological high blood pressure and heart rate during youth and adulthood steadily increase the risk of developing a cardiovascular disease at a later stage. For this reason, it is important to understand the mechanisms underlying the subclinical elevation of blood pressure and heart rate in healthy, relatively young individuals. In the present study, we present a network-based metabolomic study of blood plasma metabolites and lipids measured using nuclear magnetic resonance spectroscopy on 841 adult healthy blood donor volunteers, which were stratified for subclinical low and high blood pressure (systolic and diastolic) and heart rate. Our results indicate a rewiring of metabolic pathways active in high and low groups, indicating that the subjects with subclinical high blood pressure and heart rate could present latent cardiometabolic dysregulations.
Adaptive Fractional Delta-Amplitude Modulation Spectrogram:-An Improvised Method to Improve the Speech Intelligibility

To understand the speech signal in the presence of noise poses a great challenge in the communication environment. Various existing speech enhancement methods are developed to achieve satisfactory result, but processing the signal in case of worst signal to noise ratio introduce intelligibility degradation and signal distortion. To overcome these complexities, an adaptive Fractional delta-amplitude modulation spectrogram (adaptive FD-AMS) is proposed in this research to increase the quality of speech signal. The proposed adaptive FD-AMS is developed by modifying the FD-AMS with the adaptive parameter. However, the proposed speech enhancement method effectively extracts the features from the frames and eliminates the noise spectrum more efficiently. Accordingly, the extracted feature enables the DBN classifier to enhance the speech signal by generating the optimal mask. The fractional parameter is fine tune by FD-AMS features. However, the training process of classifier is achieved with the optimal mask. However, the proposed adaptive FD-AMS obtained better performance with the metrics, such as perceptual evaluation of speech quality (PESQ), and root mean square error (RMSE) with the values of 2.4034and 0.0172 using airport noise.
Parametric Analysis of Centralized DC Microgrids for Rural Electrification

Many solar photovoltaic (PV) based DC microgrids have been deployed in the recent past for supplying electricity facilities to the rural areas in developing regions. In this work, we assess various parameters that may impact the operational efficiencies of centralized DC microgrids. Primarily, we evaluate distribution losses in a typical system with varying critical system parameters such as a) distribution voltage b) distribution conductor size c) amount of load to be delivered at each connected rural house and d) system size (number of subscribing houses). The losses are evaluated using Newton-Raphson method modified for DC power flow and a general framework is given for loss evaluation in custom distribution architectures. Quantified system performance for a typical microgrid system from the perspective of distribution losses and maximal voltage dips will be highly useful for future solar PV-based microgrid installations.
Multi-LSTM Networks for Accurate Classification of Attention Deficit Hyperactivity Disorder from Resting-State fMRI Data

Attention deficit hyperactivity disorder (ADHD) is a widespread mental disorder among young children. Due to the complex pathological mechanisms and clinical symptoms, the diagnosis of ADHD is still challenging. In this paper, we propose a novel multi-network of long short term memory (multi-LSTM) for the identification of ADHD. The Gaussian mixture model (GMM) is introduced to cluster different regions of interests (ROIs) for feature selection. Then, the data augmentation and phenotypic information are used to further improve the classification performance. The simulation experiment demonstrates that the proposed model outperforms the state-of-the-art methods based on the multi-site ADHD-200 global competition dataset. It is anticipated that the proposed ROI-based clustering method and multi-LSTM model can provide valuable insights into the auxiliary diagnosis of ADHD from the rs-fMRI signal.
Torque Ripple Reduction of Outer Rotor Permanent Magnet Vernier Machine with Concentrated Winding

Permanent magnet vernier machines have considerable potential for low-speed direct-drive applications because of the high torque density at low speed. However, according to the theory of motor winding, concentrated windings have no weakening effect on higher odd-order harmonics, which leads to the result that the torque ripple of the vernier machine with concentrated windings is higher than that with distributed windings. Besides, due to the different permeance of iron and permanent magnets, the air gap flux density under consequent poles is asymmetric, which introduces even-order harmonics and thus makes phase back-EMF asymmetric. In this paper, the flux distribution, the back-EMF and torque characteristics of the permanent magnet vernier machine with concentrated windings are investigated with theoretical analysis and finite element analysis (FEA). The influence of several parameters of flux modulation poles (FMPs) are discussed to find the optimal coefficient combination, which aims to weaken higher odd-order harmonics. Furthermore, the effect of the rotor with axial segmentation is analyzed, which is utilized to reduce even-order harmonics in the back-EMF. FEA indicates that the proper FMP parameter selection and the axially segmented rotor can significantly suppress the higher harmonics and even-order harmonics in the back-EMF.
LoRa Architecture for V2X Communication: An Experimental Evaluation with Vehicles on the Move

The industrial development of the last few decades has prompted an increase in the number of vehicles by multiple folds. With the increased number of vehicles on the road, safety has become one of the primary concerns. Inter vehicular communication, specially Vehicle to Everything (V2X) communication can address these pressing issues including autonomous traffic systems and autonomous driving. The reliability and effectiveness of V2X communication greatly depends on communication architecture and the associated wireless technology. Addressing this challenge, a device-to-device (D2D)-based reliable, robust, and energy-efficient V2X communication architecture is proposed with LoRa wireless technology. The proposed system takes a D2D communication approach to reduce the latency by offering direct vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, rather than routing the data via the LoRa WAN server. Additionally, the proposed architecture offers modularity and compact design, making it ideal for legacy systems without requiring any additional hardware. Testing and analysis suggest the proposed system can communicate reliably with roadside infrastructures and other vehicles at speeds ranging from 15–50 km per hour (kmph). The data packet consists of 12 bytes of metadata and 28 bytes of payload. At 15 kmph, a vehicle sends one data packet every 25.9 m, and at 50 kmph, it sends the same data packet every 53.34 m with reliable transitions.