Miracle-dz/UniFAR-FTUs huggingface dataset:
Parametrically Excited Nonlinear Two-Degree-of -Freedom Systems with Repeated Natural Frequencies

The method of normal forms is used to study the nonlinear response of two-degree-of-freedom systems with repeated natural frequencies and cubic nonlinearity to a principal parametric excitation. The linear part of the system has a nonsemisimple one-to-one resonance. The character of the stability and various types of bifurcation including the formation of a homoclinic orbit are analyzed. The results are applied to the flutter of a simply supported panel in a supersonic airstream.
Biomimetic sonar recognizes objects using binaural information

An active sonar using Polaroid electrostatic transducers positioned at the end of a robot arm is described that adaptively changes its location and configuration in response to the echoes it observes in order to recognize an object. The sonar mimics biological echo-location systems, such as those employed by bats and dolphins, in that there is a center transmitter flanked by two adjustable receivers, the sonar has rotational and translational mobility, and the echo processing contains elements that have been observed in the mammalian auditory system. Using information in the echoes, the sonar translates in a horizontal plane and rotates in pitch and yaw to position an object at a standard location within the beam patterns. The transmitter points at the object to maximize the incident acoustic intensity and the receivers rotate to maximize the echo amplitude and the bandwidth, and to minimize the echo-producing region. This procedure results in a unique echo vector associated with each object at a given ob.
A fiber optic probe for measurement of an autonomic dysreflexia event on SCI patients

Autonomic Dysreflexia (AD) is an inappropriate response of the sympathetic nervous system that often occurs in individuals with spinal cord injury (SCI ) at or above the sixth thoracic vertebrae (T6) level when a noxius stimulus is applied below the level of injury. An AD event can be put into motion by something as simple as an ingrown toenail or a full bladder, with symptoms such as headache, elevated blood pressure, reduced heart rate, decreases in blood flow below the level of injury, and in extreme cases, stroke. We have developed a quantitative method of measuring skin oxygen levels during AD using a fiber optics based probe. Two such probes were located above and below the injury level (on the patient forearm and thigh respectively) and were connected to a dual channel spectrophotometer. Oxygen saturation was calculated using the reflectance spectra and an algorithm based on melanin and hemoglobin absorption. We found that during an AD event, the amount of oxygen in the skin below the injury level drops by as much as 40%, while above the injury level skin oxygenation remains constant. Additionally, we observed elevated persperation levels below the injury level. We hypothesize that the combination of AD-related ischemia with pressure related ischemia and increased perspiration places individuals with injury level at T6 or above at significant risk for developing a pressure sore below the injury site.
A parametric model for clustering single-cell mutation data

Clustering tumor single-cell mutation data has formed an important paradigm for deciphering tumor subclones and evolutionary history. This type of data may often be heavily complicated by incompleteness, false positives and false negatives errors. Despite to the fact that several computational methods have been developed for clustering binary mutation data, their applications still suffer from degraded accuracy on large datasets or datasets with high sparsity. Therefore, more effective methods are sorely required. Here, we propose a novel method called CBM for reliably Clustering Binary Mutation data. CBM formulates the binary mutation data under a probabilistic framework through parameterizing false positive errors, false negative errors, presence probability distribution of subclones and their binary mutation profiles. To cope with the difficulty of optimizing discrete parameters, Gibbs sampling for mixtures is employed to iteratively sample cell-to-cluster assignments and cluster centers from the posterior. Extensive evaluations on simulated and real datasets demonstrate CBM outperforms the state-of-the-art tools in different performance metrics such as ARI for clustering and accuracy for genotyping. CBM can be integrated into the pipeline of reconstructing tumor evolutionary tree, and detecting subclones using CBM can be employed as a pre-text task of tumor subclonal tree inference, which will significantly improve computational efficiency of phylogenetic analysis especially on large datasets. CBM software is freely available at https://github.com/zhyu-lab/cbm.
On the asymptotics of a class of two-dimensional Fourier integrals

We derive the asymptotic expansion of a class of two-dimensional Fourier integrals that typically arise in perturbative treatments of the interaction of the quantized electromagnetic field with atoms and other quantum systems. The principal difficulty that prevents the application of standard methods is the fact that the integrand has a saddle point in the corner of the domain of integration.
Identification of the 1400 and 1600 A features observed in the ultraviolet spectra of DA white dwarfs

The lambda1600 absorption feature in the spectra of cool DA white dwarf stars has been identified as a resonance broadening of Ly.cap alpha. due to the hydrogen quasi molecule. Likewise, the lambda1400 absorption feature in the spectra of cool and moderately warm DA white dwarf stars appears to be due to a Ly.cap alpha. satellite line arising from the hydrogen ion quasi molecule. The strength of both features is gravity sensitive and therefore promises to be an excellent indicator of surface gravity.
A Multi-Beam Array Antenna Employing Simple 4 × 2 Beam-Forming Network Integrating Magic-T and Hybrid Coupler

This paper presents a multi-beam array antenna using a simple 4×2 beam-forming network (BFN). The BFN consists of a magic-T, hybrid coupler, and two single-pole double-throw (SPDT) switches. Simulated results confirm the BFN concept with good impedance matching and high port isolation. When an antenna array is connected with the proposed BFN, four beam patterns can be obtained. The multi-beam radiation patterns (sum, difference, and two tilted beams at – 15° and + 18°) are experimentally verified using a 5.8-GHz prototype.
The Analysis of Experiment Video on Cognitive Conflict-Based Teaching Materials to Enhance Momentum-Impulse Concepts Understanding

Video analysis experiments can be used to construct physics concepts and equations. Integrating video analysis experiments into cognitive conflict learning allows students to increase their understanding of concepts. Some research results show that students need clarification about momentum and impulse material, low conceptual understanding, and misconceptions occur. The problem of misconceptions can hinder student learning progress. One solution to overcome these problems is the application of physics teaching materials based on cognitive conflict integrated with real experiment video analysis. This study aims to analyze the effectiveness of these teaching materials in improving students’ understanding of the concept of momentum and impulse. This study used a quasi-experimental method with a nonequivalent control group design. The sample consisted of 72 high school students with two sample classes, the experimental and the control classes, which were selected using the cluster random sampling technique. The instrument in this study was a concept test that was valid and reliable. Data were analyzed using Mann-Whitney test nonparametric statistics with the help of IBM SPSS Statistic 25. The results obtained by Sig. Asim < 0.05, in the rejection area of H0, means that teaching materials influence students’ understanding of concepts on momentum and impulse. Thus, conflict-based teaching materials integrating real experiment video analysis have been effective in increasing students’ conceptual understanding.
A syntagmatic analysis of antonym co-occurrences in Chinese: contrastive constructions and co-occurrence sequences

Antonyms co-occur far more frequently than we might expect to by chance alone. However, few studies have adopted a corpus-based approach to examine antonym co-occurrences in Chinese, and disyllabic antonyms in Chinese have long been ignored in the literature. Therefore, this study turns to the Chinese Gigaword Corpus and includes both monosyllabic and disyllabic pairs for analysis, aiming to identify contrastive constructions in which antonyms in Chinese often co-occur and to examine antonym co-occurrence sequences in Chinese. First, it is found that most functional classes of antonymy in Chinese are associated with specific constructions. Second, a positive antonym in Chinese (e.g., dui ‘right’) usually precedes its negative (e.g., cuo ‘wrong’) counterpart when they co-occur, and the notion of positivity needs to be defined in a broad sense. Third, the behaviour of an antonym pair in Chinese can be influenced by its morphosyllabic structure and some language-external factors such as cognitive functions a.
Identification of personalized driver genes for individuals using graph convolution network

The correct identification of the driver genes that lead to cancer development is essential for understanding the mechanisms of cancer and developing drugs to treat it. Currently, most computational methods for identifying cancer driver genes are based on a cohort of patients. However, due to the heterogeneity of cancers, patients diagnosed with the same cancers may have different genomic characteristics and present varied clinical symptoms. It requires devising effective methods to identify personalized cancer driver genes in an individual. This work developed a novel method to predict personalized cancer driver genes of a single sample based on graph convolution networks, namely pDriverGCN. pDriverGCN constructed a mutant gene-sample heterogeneous network according to the known driver genes of samples. Then it employed two separate graph convolution network models to learn feature representations for genes and samples by gathering the features of themselves and their neighbors. Finally, pDriverGCN used the feature representations to reconstruct the association matrix between genes and samples through a linear correlation coefficient decoder. We apply our model to identify personalized driver genes of samples on the TCGA datasets. The experimental results show that our model outperforms state-of-the-art methods being evaluated at both population and individual levels.
Deep Learning in Colonoscopies: Improving Small Polyps Recognition Rate

Deep Learning in automatic video colonoscopy processing may result in missing small polyps or detecting them with low confidence. We conducted a study to demonstrate that we can overcome such problems by fitting the dimensions of the input image to the dimensions of the input layer of a trained Mobilenet Deep Learning network and by grouping polyps in classes according to their size. The results show that it is possible to substantially improve the confidence rates and we propose a scheme to increase small polyps detection in real-time video colonoscopies based on two trained Mobilenet networks.
Evaluation of Bag-of-Words Classifiers for COVID-19 Detection from Cough Recordings

The paper assesses the efficiency of bag-of-words classifiers for reliable detection of Covid-19 from cough recordings. The effect of using two distinct encoding strategies and variable codebook dimensions is evaluated in terms of Area Under Curve (AUC), accuracy, sensitivity, and specificity. Three distinct feature extraction procedures are tested, followed by a Support Vector Machine (SVM) classifier. Experiments conducted on two cough recordings datasets indicate that sparse encoding yields best performances, showing robustness against feature type and codebook dimension.
SNEP-DB: An integrated database to associate genomic and pathological aspects of psychiatric disorders

Psychiatric disorders are widespread throughout the world. Large-scale genome-wide association studies (GWAS) have revealed numerous SNPs associated with a risk for psychiatric disorders. However, the biological mechanisms by which the SNPs contribute to the development of the disease are unknown. Systematical evaluation of the association between the SNPs and various quantitative phenotypes such as levels of gene expression and pathological markers can be a useful approach to identifying the underlying mechanisms. To facilitate this approach, we developed a database named Stanley Integrated Neurogenomic Pathology Database, SNEP-DB, that includes 4,070 neuropathology marker data, gene expression data, and SNPs from samples of the Stanley Medical Research Institute (SMRI) brain collections. Moreover, the database allows users to explore association analysis results from the SMRI brain data integrated with the GWAS results from recent large-scale studies. By exploiting the database, we have identified 43,417 associations between SNPs and levels of neuropathology markers, so-called markerQTLs. We have also identified 77,410 gene-level eQTLs and 129,980 transcript-level eQTLs. We expect SNEP-DB will enable users to explore the mechanisms underlying major psychiatric disorders by identifying significant associations between disease-associated SNPs and various quantitative phenotypes. (Database URL: http://bidas.kaist.ac.kr/snep/)
Comparison of Food Calorie Measurement Using Image Processing and Machine Learning Techniques

This paper investigates the accuracy of the different Machine Learning algorithms used for calorie estimation through food image processing. Moreover, the most popular food image datasets and calorie calculation techniques will be compared. The conducted comparative study considers the accuracy of ML algorithms and the features of training and testing food image datasets. It utilizes the research efforts in the last five years to propose the most reliable combination of techniques and data to develop an efficient food image processing system. In terms of Machine Learning algorithms, the comparative study results showed that convolutional neural network (CNN) and support vector machine (SVM) algorithms are the most reliable machine learning algorithms for food image processing. Moreover, the most suitable food image datasets for training and testing food image processing ML algorithms are Food-101, UEC-Food100, and ECUSTFD. And in terms of the calorie estimation techniques, the mathematical model technique was the most efficient approach to calculating the food calorie content. The implication of this study is to guide developers to improve the current food image processing applications by using reliable algorithms, datasets, and computation strategies.
LoRA Gateway Coverage and Capacity Analysis in Urban Area For IoT Smart Gas Meter Demand

Today, the deployment of LoRa gateway in Indonesia uses only traditional methods by predicting the coverage and capacity without using formulas related to LoRa gateway performance or considering area type (urban, suburban or rural). This paper conducts the study and research for coverage and capacity analysis of LoRa Gateway to cover all sensor device demands. The formula is calculated to save more resources and optimize the performance of the LoRa gateway covering all demands needed for Smart Gas Meter devices in Urban areas. The location taken as a model is Jakarta City. In order to obtain the number of gateways to cover all smart gas meter demands, this paper uses the Okumura-Hata formula to obtain coverage prediction for each gateway. It uses time on air and interval concepts to determine capacity per interval times. The calculation result is about 4 LoRa gateways need to be deployed, and the interval of transmission data is about once per 35 minutes to cover all Smart Gas Meter devices in Jakarta.
Pretraining Transformers for TCR-pMHC Binding Prediction

The knowledge concerning antigen presentation by the main histocompatibility complex (MHC) to T-cell receptor (TCR) and TCR binding specificity can facilitate the application of T-cell immunity in modern medicine, such as tumor immunotherapy and drug and vaccine design cases. With the development of high-throughput sequencing technology and artificial intelligence, data-driven approaches can be employed to help understand the rules of TCR-pMHC binding. Simulating the biological binding process of TCRs and pMHCs, we propose a novel pipeline, pMTattn, using transfer learning based on an attention mechanism for TCR-pMHC binding prediction. During the pretraining stage, partner-specific training strategies can capture useful local binding features. In the fine-tuning stage, an attention block is employed to aggregate the TCR encoding and pMHC encoding information, forming a better global TCR-pMHC representation. Visualization experiments indicate that the pMTattn model focuses more on the voxels near the binding sites of pMHCs and TCRs. This key observation effectively supports our hypothesis that attention is critical for TCR-pMHC binding prediction. In addition, on an independent test set, the area under the precision-recall curve (AUPR) and the area under the receiver operating characteristic curve (AUC) are improved from 0.533 to 0.583 and from 0.830 to 0.866, respectively, by pMTattn compared to those of the state-of-the-art model. Simultaneously, we also explore the influences of different sequence lengths and dataset differences on the model effect, and pMTattn exhibits better robustness than other models. These results suggest that pMTattn has the ability to be used as an adjunct tool for screening and discovering neoantigens.
Stability of Ion Exchange Membranes in Electrodialysis

During electrodialysis the ion exchange membranes are affected by such factors as passage of electric current, heating, tangential flow of solution and exposure to chemical agents. It can potentially cause the degradation of ion exchange groups and of polymeric backbone, worsening the performance of the process and necessitating the replacement of the membranes. This article aims to review how the composition and the structure of ion exchange membranes change during the electrodialysis or the studies imitating it.
The Phasebook: Building Complex Masks via Discrete Representations for Source Separation

Deep learning based speech enhancement and source separation systems have recently reached unprecedented levels of quality, to the point that performance is reaching a new ceiling. Most systems rely on estimating the magnitude of a target source, either directly or by computing a real-valued mask to be applied to a time-frequency representation of the mixture signal. A limiting factor in such approaches is a lack of phase estimation: the phase of the mixture is most often used when reconstructing the estimated time-domain signal. We propose to estimate phase using "phasebook", a new type of layer based on a discrete representation of the phase difference between the mixture and the target. We also introduce "combook", a similar type of layer that directly estimates a complex mask. We present various training and inference schemes involving these representations, and explain in particular how to include them in an end-to-end learning framework. We also present an oracle study to assess upper bounds on performance for various types of masks using discrete phase representations. We evaluate the proposed methods on the wsj0-2mix dataset, a well-studied corpus for single-channel speaker-independent speaker separation, matching the performance of state-of-the-art mask-based approaches without requiring additional phase reconstruction steps.
Stability of general plasma equilibria - I formal theory

A method is described for the detailed investigation of electrostatic instabilities in real experimental geometries. These have frequently been discussed in the plane slab model, and modifications of it, but the present work includes all geometrical effects from the outset. The starting point is the collisionless Boltzmann equation with the approximation that the scale length of the equilibrium is long compared to the ion gyro radius. The main interest is in perturbations of low frequency but of arbitrary wavelength, which may be comparable to the ion Larmor radius. Thus several instabilities such as drift wave, flute or trapped particle, come within the scope of the theory. Expressions are first obtained for the contribution to the charge density produced by an arbitrary electrostatic perturbation affecting particles whose unperturbed orbits are (i) trapped between magnetic mirrors; (ii) circulating around closed field lines; (iii) tracing out a magnetic surface. Together with Poisson's equation these expressions lead, via the appropriate Nyquist contours, to stability criteria valid for arbitrary equilibria. Finally it is shown how this method leads to a differential equation whose solution will determine the stability of an experimental configuration such as the multipole.
Calculation of x-ray spectra for nanocrystalline materials

The calculation of two-theta x-ray diffraction profiles for multimillion atom computer generated nanocrystalline (nc) Ni systems is presented. Peak profile analysis is performed using a standard Williamson-Hall analysis. The derived mean grain size and root-mean-square inhomogeneous strain quantities are compared to similar quantities obtained by direct structural investigation and visualization of the nc atomic configurations. Debye-Waller factors are also calculated from the theoretical x-ray spectra and compare well with those derived from the phonon properties of the nc system.
QSPR study on nematic transition temperatures of thermotropic liquid crystals based on DFT-calculated descriptors

The individual correlations between nematic transition temperatures and the molecular descriptors derived from density functional theory (DFT) method are investigated. Significant quantitative structure–property relationships (QSPRs) for TN of a set of 42 theromtropic liquid crystal were established through principle component analysis (PCA) and multiple linear regression (MLR). A promising six-descriptor linear model with good statistical fitting and predicting capability were developed with the help of DFT. The model is based on EHOMO, ELUMO, μ, S, W and X. The reliability of this model is clear from its correlation coefficient (R2 = 0.95) and cross-validation coefficient (R2CV = 0.93).
An Analysis of Operator Splitting Techniques in the Stiff Case

Operator splitting methods are commonly used in many applications. We focus here on the case where the evolution equations to be simulated are stiff. We will more particularly consider the case of two operators: a stiff one and a nonstiff one. This occurs in numerous application fields (e.g., combustion, air pollution, and reactive flows). The classical analysis of the splitting error may then fail, since the chosen splitting timestep ? t is in practice much larger than the fastest time scales: the asymptotic expansion ? t? 0 is therefore no longer valid. We show here that singular perturbation theory provides an interesting framework for the study of splitting error. Some new results concerning the order of local errors are derived. The main result deals with the choice of the sequential order for the operators: the stiff operator must always be last in the splitting scheme.