Miracle-dz/UniFAR-FTUs huggingface dataset:
Time-Domain Calculation of Sound Propagation in Lined Ducts with Sheared Flows

A recent application of the time-domain equivalent of the classical acoustic impedance condition, i.e., the particle displacement continuity equation, to numerical simulations of a flow-impedance tube in the time domain yielded reasonably good results with uniform mean flows. The present paper extends this application to include sheared mean-flow effects on sound propagation over acoustically treated walls. To assess the prediction improvements with sheared flows, especially at relatively high Mach numbers, numerical simulations of the NASA Langley Research Center flow-impedance tube are carried out at actual conditions. Calculations are realized for mean-flow peak velocities as high as Mach 0.5 at various frequencies. Results are compared with those obtained with uniform mean flows and experimental data. It is shown that solutions that were not attainable previously with uniform flows at high Mach numbers can now be obtained with the help of the no-slip conditions of sheared background flows at the wall.
Nuclear Division Index (NDI) in Peripheral Blood Lymphocytes Exposed to X-Rays and Its Interferences in the Biodosimetry of Medical Personnel Occupationally Exposed to Ionizing Radiation

The response to X-Rays by Nuclear Division Index (NDI) of peripheral blood lymphocytes was performed from a study group of general population. Two cell culture were initiated for each blood sample, being experimentally exposed at doses varying from 0,25 to 3 Gy. The NDI calculated revealed slight correlation to the gender-lower values for women. For occupationally exposed personnel to ionizing radiation, the average values of NDI, analyzed by gender or profession, were not correlated with micronucleus (MN/CBMN) test results. Beside gender, medication or health status related to lower NDI should be investigated, as literature provides.
An In-Depth Correlative Study Between DRAM Errors and Server Failures in Production Data Centers

Dynamic Random Access Memory (DRAM) errors are prevalent and lead to server failures in production data centers. However, little is known about the correlation between DRAM errors and server failures in state-of-the-art field studies on DRAM error measurement. To fill this void, we present an in-depth data-driven correlative analysis between DRAM errors and server failures, with the primary goal of predicting server failures based on DRAM error characterization and hence enabling proactive reliability maintenance for production data centers. Our analysis is based on an eight-month dataset collected from over three million memory modules in the production data centers at Alibaba. We find that the correctable DRAM errors of most server failures only manifest within a short time before the failures happen, implying that server failure prediction should be conducted regularly at short time intervals for accurate prediction. We also study various impacting factors (including component failures in the memory subsystem, DRAM configurations, types of correctable DRAM errors) on server failures. Furthermore, we design a machine-learning-based server failure prediction workflow and demonstrate the feasibility of server failure prediction based on DRAM error characterization. To this end, we report 14 findings from our measurement and prediction studies.
Long-term recurrence prediction models for NICE based on machine learning and China national stroke screening data

There are few long-term recurrent risk prediction models specifically designed for high risk non-disabling ischemic cerebrovascular events (HR-NICE). Based on the continuous national stroke screening cohort data (from 2015 to 2018), we identify and choose the features of HR-NICE patients, to develop stroke risk classification models using machine learning algorithms. Compared with the commonly used clinical scoring tools, our models can improve the prediction performance significantly. The framework developed in this paper can increase the accuracy of recognizing patients with HR-NICE in the long term.
The single-server queue with independent GI/G and M/G input streams

This paper studies the single-server queueing system with two independent input streams: a GI/G and an M/G stream. A new proof is given of an old result which shows how this system can be transformed into an equivalent ‘single input stream’ GI/G/1 queue, and methods to study that equivalent system numerically are given. As part of the numerical analysis, algorithms are given to compute the moments and the distribution function of busy periods in the M/G/1 queue, and of other related busy periods. Special attention is given to the single-server queue with independent D/G and M/G input streams. This work is to be used in the modeling of real-time computer systems, which can often be described as a single-server queueing system with independent D/G and M/G input streams, see for example Ott (1984b).
Iterative Solution of Large Linear Systems.

Read more and get great! That's what the book enPDFd iterative solution of large linear systems david m young will give for every reader to read this book. This is an on-line book provided in this website. Even this book becomes a choice of someone to read, many in the world also loves it so much. As what we talk, when you read more every page of this iterative solution of large linear systems david m young, what you will obtain is something great.
A framework for associating structural variants with cell-specific transcription factors and histone modifications in defect phenotypes

Structural Variations (SVs) naturally occur in healthy human populations and account for more genomic difference than Single Nucleotide Variations (SNVs). However, because of the low prevalence in gene coding regions and the ubiquity in the intergenic regions, determining SV association for defect phenotypes remains challenging. We developed a framework that classifies five categories of SVs using defect associated genes along with cell-specific histone modifications (HM) concurrently with transcription factor binding sites (TFBS). We completed a comprehensive structural variation analysis of 17 family trios consisting of healthy paternal and maternal genomes along with the non-syndromic Orofacial cleft (OFC) child genomes which are publicly available from the Kids First Data Resource Portal. For the integrative analysis, we used ChIP-seq data from Neural Crest and Mesenchymal Stem Cell types from ENCODE along with TFBS taken from JASPAR. We found that the OFC children had elevated regulatory SVs when classified with our framework, aligning with prior studies regarding the complex non-syndromic phenotype. This result supports the use of a defect-specific gene paralog lists integrated with HMs and TFBS to identify plausible SV regions connected to a defect phenotype.
Design and Research of a New Quadruped Mechanical Single-leg Prototype

As an important and basic component of a quadruped robot interacting with the external environment, the mechanical leg determines the core application performance of a robot such as walking speed, load capacity, and operational adaptability. Therefore, research on mechanical leg has important practical value and theoretical significance. This paper develops a new quadruped mechanical single-leg prototype, which has a novel knee motor arrangement and transmission mechanism. The novel transmission mechanism consists of a bevel gear train, a ball screw mechanism and a four-bar linkage. To be better promoted and used, the transmission mechanism is modeled and analyzed, and the influence of several important parameters of the mechanism on the torque and speed of motor is studied. Finally, the mechanical properties of the bearing structures are analyzed, and the results show that the structural strength can meet the design requirements.
Pola Viz Reveals Microglia Polarization at Single Cell Level in Alzheimer’s Disease

Microglia are macrophages residing in the brain and spinal cord and responsible for neurological immune defense. A growing number of studies point out the importance of microglia’s role in Alzheimer’s disease (AD) pathology. Like macrophages in other tissues, microglia are polarized to M1/M2 states, Ml inhibiting cell inflammatory and causing tissue damage and M2 resolving inflammation and repairing damaged tissue. Thanks to scRNA-seq technology, single cell gene expression data sets for microglia have become available. However, examining the polarization of microglia at single cell level to study their association with AD is difficult for reasons such as complexity (too many genes, too many cells), expression dropouts, etc. We propose a scRNA-seq data visualization framework, namely, Pola Viz that can associate the polarization states of microglia with gene regulatory pathways that are potentially responsible for defining M1/M2 states or transition between them. For its two-dimensional placement of single cells, the x-axis depicts each cell’s M1/M2 or its transition score and the y-axis denotes what we call, the pathway “route” scores designed to assign the degree of contribution of the concerned pathway routes to each cell’s M1/M2 state. We conducted two case studies involving publicly available AD single cell mRNA datasets, one from human brain and one from mouse brain. Our method identified pathway routes that are highly correlated with known functional roles of microglia. It also discovers similarities between mice and human AD microglia activation such as T cell receptor signaling pathway, cell cycle, NF-kappa B signaling pathways, all highly disturbed at late stage of AD. Our method provides a new way of examining the M1/M2 polarization of microglia at single cell level
Unified Fine-Grained Biomedical Entity Recognition as a Combination of Boundary Detection and Sequence Generation

Biomedical Named Entity Recognition (BioNER) is a critical component of biomedical information extraction. NER is more challenging in the biomedical domain because of fine-grained entity types and more common nested and discontinuous entity forms. However, none of the BioNER datasets contains a large amount of all three entity forms, including flat, nested, and discontinuous. Not to mention that there is a unified BioNER model for dealing with the above three entity forms simultaneously. Methods in the public domain only focus on identifying text spans and ignore distinguishing fine-grained entity types. To address these issues, we propose a unified framework based on our own BioNER dataset CCNER, which innovatively models the BioNER task as a combination of boundary recognition and sequence generation. CCNER is a comprehensive and fine-grained BioNER dataset, where the proportion of discontinuous, nested, and flat entities in the dataset is 8.9%, 52.6%, and 38.5%, respectively. Meanwhile, it includes five fine-grained entity types. Our proposed framework includes two modules which are boundary detection and entity generation. In the boundary detection module, we propose a sample-based span representation method to determine fine-grained entity boundaries better. Finally, we conduct experiments on four datasets and achieve competitive results1.1Code is available at https://github.com/lx-hit/BioNER.
Modelling of Critical Success Factors Affecting the Adoption of Enterprise Resources Planning: A mediated Model

This research aimed to examine the influence of critical success factors (CSFs) on the adoption of ERP from three perspectives: organizational, environmental, and technological, and whether the effect of these factors differs under different levels of firm size. Research data were collected from managers in manufacturing firms in Jordan through an online survey. 370 respondents were analyzed by using PLS-SEM. The findings revealed that support from top management, employee IT skills, and cost had no impact on adopting an ERP system, while providing support, competitive pressure, compatibility, relative advantage, and complexity significantly impacted the adoption. In addition, the impact of these factors varied across different levels of firm size.
Energetic (∼ 1 to 50 MeV) protons associated with Earth‐directed coronal mass ejections

During the period from January through mid‐May, 1997, four large Earth‐directed CMEs were observed by the Large Angle Spectroscopic Coronograph (LASCO). These CMEs were associated with long‐lasting fluxes of >1.6 MeV protons detected by the Energetic and Relativistic Nuclei and Electron instrument (ERNE). However, the magnitudes of energetic proton events differed dramatically on different occasions. In strong proton events, production of 10‐50 MeV protons started during expansion of the coronal Moreton wave in the western hemisphere of the Sun. The new SOHO observations suggest that potentialities of CMEs to produce energetic particles in the interplanetary medium crucially depend on the previous evolution of the explosion below ∼2R⊙. Forecasting of the near‐Earth >10 MeV proton intensity requires multiwavelength observations of the early phase of an event particularly the Extreme‐ultraviolet Imaging Telescope (EIT) observations.
Effect of Pressure Reactivity Index Calculation Settings on the Range of the Optimal Cerebral Perfusion Pressure

The most important task in patients with traumatic brain injury is to prevent the secondary injury. Ensuring an adequate cerebral perfusion has a pivotal role in this concept. One of the approaches in determination of the individual target CPP is computation of the optimal CPP (CPPopt) using the pressure reactivity index (PRx). The PRx index represents the correlation between mean arterial pressure and intracranial pressure. In order to determine the effect of parameters of PRx calculation on the range of CPPopt values, two window lengths (60 s and 300 s) and two critical values of PRx (0.2 and 0.3) were selected. As a result, the choice of the parameters caused differences in the CPPopt range, and the time evaluated as spent in the critical region of cerebral autoregulation. These values could affect the clinician's decision-making in patient treatment if the CPPopt is used at the bedside.
Continuous assessment in a large first-year engineering mechanics course: the effect of participation and performance in compulsory and voluntary assessments on final grades

This paper presents a study on continuous assessment (CA) in a first-year engineering course at a South African University. CA is widely used to encourage active learning and keep students up to date with work. The use of CA has largely been shown to result in an improvement in student performance when compared to the use of final examinations only. This study showed that performance in low-stakes, formative automatically assessed online quizzes had a moderate positive correlation with the semester grade, and weak positive correlation with overall final results. This indicates that the assessments are performing well in keeping students up to date with work, and thus preparing them for higher stakes, summative assessments. The ability to use CA results to flag students early in the semester with a medium to high-risk of not achieving exam entrance or failing the module was noted. Students that participated in additional voluntary CA activities showed a significant improvement in success in the module. Student perceptions identified the CA quizzes as overall the most beneficial activity to improving performance; comments on reasons for this align to the instructive nature of this assessment technique.
Mossbauer-effect study of the hyperfine field distributions in the residual amorphous phase of Fe-Cu-Nb-Si-B nanocrystalline alloys

Amorphous Fe73.5-xCu1Nb3+xSi13.5B9 (x=0 and 1.5) alloys were isothermally heat treated at 550 degrees C in a vacuum for up to 16 h. 57Fe transmission Mossbauer spectra revealed a lower rate of crystallization for x=1.5. The annealing time dependences of the hyperfine parameters and the different behaviours of the intergranular component in both samples were studied via three-dimensional projections of the hyperfine field distributions. Segregation of Fe, Si and also B, from which the crystalline phases have been created, lead to a depletion of these elements in the residual amorphous phase. As a consequence, the number of Fe atoms with Nb, Cu and possibly B atoms in their nearest surroundings increased, giving rise to changes in the chemical and topological short-range order. The amount of the residual amorphous phase as well as its average hyperfine field decreased whereas the standard deviation of the hyperfine field distribution increased with increasing annealing time.
Smart face detection and recognition in Pose variant images using Alexnet CNN compare accuracy with SVM

Many face recognition tasks have been carried out in controlled environments, compared to controlled environments face recognition in uncontrolled environments like pose variant images is the most challenging task. The main aim of this work is to recognize the faces in pose variant images with different arbitrary rotations using two different machine learning algorithms Alexnet convolutional neural network (ACNN) and Support Vector Machine (SVM) are trained with 20 samples of pose variant images dataset each, totally 40 samples to recognize the person. : From Matlab simulation results ACNN has achieved better accuracy(96%) compared to SVM(89%). Attained Significance value (P! O.000) using SPSS analysis. From this study it shows that ACNN has higher accuracy than SVM.
Flexural Strengthening of RC Columns with Near Surface Mounted FRP or Stainless Steel Reinforcement

The paper presents the results of a comprehensive experimental program aiming to provide a fundamental understanding of the behavior of reinforced concrete (RC) columns under simulated seismic loading, strengthened in flexure (of crucial importance in capacity design) with different types and configurations of near-surface mounted (NSM) reinforcing materials. The role of various parameters, such as carbon or glass fiber-reinforced polymers (FRP) versus stainless steel, configuration and amount of NSM reinforcement, confinement via local jacketing and type of bonding agent, is examined, by comparison of the lateral load versus displacement response characteristics. The results demonstrate that NSM FRP and stainless steel reinforcement is a viable solution towards enhancing the flexural resistance of reinforced concrete columns subjected to seismic loads. This is especially the case when the retrofitting scheme combines epoxy-bonded NSM bars with local confining jackets, provided in this study with textile-reinforced mortars (TRM).
Multi-MedVit: a deep learning approach for the diagnosis of COVID-19 with the CT images

The grim situation of novel coronavirus pneumonia 2019 (COVID-19) and its terrible spreading speed have already constituted a severe risk to human life, so it is ultimately essential to rapidly and accurately diagnose for COVID-19 pneumonia. Based on this study’s 746 lung CT images, we propose Multi-MedVit, a novel auxiliary COVID-19 diagnosis framework based on the multi-input Transformer. We compare Multi-MedVit with state-of-the-art deep learning methods, such as CNN, VGG16, and ResNet50. Multi-MedVit outperformed the other methods on the benchmark dataset and proved that multiscale data input for data augmentation helped enhance model stability. Based on an interpretable analysis of the input and output of Multi-MedVit, we found that with the support of the training set data, the model has been possible to accurately focus on the lesion area for diagnosis of COVID-19 without expert annotations, which can provide initial references containing more potential information to doctors more precisely and fleetly.
Co-Optimization of DNN and Hardware Configurations on Edge GPUs

The ever-increasing complexity of both Deep Neural Networks (DNN) and hardware accelerators has made the co-optimization of these domains extremely complex. Previous works typically focus on optimizing DNNs given a fixed hardware configuration or optimizing a specific hardware architecture given a fixed DNN model. Recently, the importance of the joint exploration of the two spaces drew more and more attention. Our work targets the co-optimization of DNN and hardware configurations on edge GPU accelerators. We propose an evolutionary-based co-optimization strategy by considering three metrics: DNN accuracy, execution latency, and power consumption. By combining the two search spaces, a larger number of configurations can be explored in a short time interval. In addition, a better tradeoff between DNN accuracy and hardware efficiency can be obtained. Experimental results show that the co-optimization outperforms the optimization of DNN for fixed hardware configuration with up to 53% hardware efficiency gains with the same accuracy and inference time.
Automated Extraction of ABAC Policies from Natural-Language Documents in Healthcare Systems

The healthcare system is a distributed collaborative system and the sensitivity of the medical data is one of the most important requirements. Preventing unauthorized access to healthcare information and data sharing security in the healthcare environment are critical processes that affect the credibility of the system. To achieve this goal and to meet the requirements of the healthcare system, access control is an important measure to realize the safe sharing of resources. The attribute-based access control (ABAC) model meets the complex security requirements of large and complex systems and provides a dynamic, flexible and scalable solution. The main obstacle to deploying ABAC is the precise development of ABAC policies. Manually developing access control policies is tedious, time-consuming and error prone. Most systems have high-level requirement specifications, which are written in natural language. These natural language (NL) documents have the intended access control policies for the systems. In this paper, we propose a new approach towards extracting policies from natural language documents. By fully taking advantage of Bidirectional Encoder Representations from Transformers (BERT) and Semantic role labeling (SRL), we are able to correctly identify access control policy (ACP) sentences with an average F1 score of 85% and correctly extract rules with an average F1 score of 72%, which outperforms the state-of-the-art and leads to a performance improvement of 7% and 2% respectively over the previously reported results.
Simulation-Supported Engineering Curriculum

This study outlines a systematic approach to simulation-supported teaching and learning across the undergraduate engineering curriculum. Simultaneous use of finite element analysis and computational fluid dynamics tools to visualize and teach mechanical engineering topics and simulation assignments to facilitate learning has been examined in five thermo-fluids courses in the past seven years. The outcomes of the same pedagogical and assessment approaches applied in different courses, using different simulation software, and led by different instructors, demonstrated increased engagement, study time, and ultimately, confidence. The significance of this educational method is in bringing the digital engineering process into the curriculum, increasing the time students invest in studying, ensuring access to real-world experiences for all learners, and creating a roadmap for curricular design and assessment easily transportable across science and engineering disciplines. Furthermore, a simulation-based approach to learning does not depend on access to laboratory facilities and funding that can reach a limited number of students. It encourages students' spirit of inquiry and ultimately leads to professional development opportunities beyond the classroom setting.