Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Cyclone Evolution with Hierarchical Features

The problem of tracking and visualizing cyclones is still an active area of climate research, since the nature of cyclones varies depending on geospatial location and temporal season, resulting in no clear mathematical definition. Thus, many cyclone tracking methods are tailored to specific datasets and therefore do not support general cyclone extraction across the globe. To address this challenge, we present a conceptual application for exploring cyclone evolution by organizing the extracted cyclone tracks into hierarchical groups. Our approach is based on extrema tracking, and the resulting tracks can be defined in a multi-scale structure by grouping the points based on a novel feature descriptor defined on the merge tree, so-called crown features. Consequently, multiple parameter settings can be visualized and explored in a level-of-detail approach, supporting experts to quickly gain insights on cyclonic formation and evolution. We describe a general cyclone exploration pipeline that consists of four modular building blocks: (1) an extrema tracking method, (2) multiple definitions of cyclones as groups of extrema, including crown features, (3) the correlation of cyclones based on the underlying tracking information, and (4) a hierarchical visualization of the resulting feature tracks and their spatial embedding, allowing exploration on a global and local scale. In order to be as flexible as possible, our pipeline allows for exchanging every module with different techniques, such as other tracking methods and cyclone definitions.
Pneumatic Bending Soft Actuator Coupling With Revolute Joint With Different Boundary Constraints

This article presents a study on pneumatic bending soft actuator (SA) coupling with revolute joint (RJ) with different boundary constraints (BCs), aimed to discover the bending performance of SA wearing on physical joint of human body as body joint actuators with different installation methods. First, a new pneumatic bidirectional bending SA is designed and fabricated, and its mechanical characteristic is obtained by experimental tests and fitting methods. Then, the mechanical characteristic is applied to the discrete bending model that approximates the continuous bending of the SA, and the kinetostatic analysis based on the principle of virtual work is conducted on the SA-RJ coupling system (SA-RJ CS) with fixing BC (FBC), hinging BC (HBC), and sliding BCs (SBCs). The kinetostatic equations are solved numerically, and thus the bending performance of the SA-RJ CS with different BCs is obtained. Results show the SA-RJ CS with SBCs performs better in bending angle and actuating torque than that with FBC or HBC. Therefore, it is suggested to install the SA of joint actuator on human body by SBCs. This study guides the application of pneumatic bending SA in rehabilitation exoskeleton. At present, we have used the axial SBC in rehabilitation exoskeleton.
Electrical Impedance Myography Measurements for Gesture Recognition Data Normalization

Electrical Impedance Myography (EIM) is a non-invasive method based on applying a low-intensity alternating current to muscle tissue and measuring the resulting voltage over these muscles in various frequencies, EIM has been recently used to recognize human hand gestures by measuring resistance (R) and reactance (X) changes during muscle contractions and relaxation. In hand gesture recognition, the level of muscle contraction and muscle shape typically varies within and between subjects for the same gesture; additionally, the subjects’ hands have different sizes and muscle shapes; normalization techniques are used to compensate for these differences and find a new specified data range from the existing data, this enhances the accuracy of the results and avoids the size effect. In this work, four normalization methods: Z-score, Min-Max, Decimal Scaling and Median & Median Absolute Deviation methods were applied to the multi-frequency EIM data measurement for 9 American sign language gestures. The result shows that the z-score is effective for normalizing EIM data with 100% training accuracy and 94.5% testing accuracy using an extreme learning machine classifier, the z-score method preserved a single value per muscle per patient; it helped to normalize across types of muscle and eliminated a possible confounding in the analysis variable. The results show that the z-score normalization is effective for hand gesture recognition using EIM data.
Fast Fourier transform based direct integration algorithm for the linear canonical transform

The linear canonical transform(LCT) is a parameterized linear integral transform, which is the general case of many well-known transforms such as the Fourier transform(FT), the fractional Fourier transform(FRT) and the Fresnel transform(FST). These integral transforms are of great importance in wave propagation problems because they are the solutions of the wave equation under a variety of circumstances. In optics, the LCT can be used to model paraxial free space propagation and other quadratic phase systems such as lens and graded-index media. A number of algorithms have been presented to fast compute the LCT. When they are used to compute the LCT, the sampling period in the transform domain is dependent on that in the signal domain. This drawback limits their applicability in some cases such as color digital holography. In this paper, a Fast-Fourier-Transform-based Direct Integration algorithm(FFT-DI) for the LCT is presented. The FFT-DI is a fast computational method of the Direct Integration(DI) for the LCT. It removes the dependency of the sampling period in the transform domain on that in the signal domain. Simulations and experimental results are presented to validate this idea.
E-RVP: An Initial Design Rule Violation Predictor Using Placement Information

This work proposes a machine learning model to predict Early Routing Violations, called E-RVP, using only the information available during the placement stage. Our learning model predicts violations of the first routing iteration, thus addressing the root of the problem since routing fixing is an iterative process that might introduce/shift violations to other nets or locations. The experimental results show good performance for the proposed learning architecture, with 0.93 recall and 0.97 precision, resulting in an F-score of 0.95. Additionally, we present a data augmentation strategy to help other machine learning approaches applied in the placement and routing steps of the physical design.
DVFS method of memory hierarchy based on CPU microarchitectural information

Due to limited battery power that devices have, an efficient DVFS (Dynamic Voltage and Frequency Scaling) algorithm is important. DVFS applies not only to the CPU, also the shared cache and memory associated with the CPU. The CPU and these memory hierarchy interact with each other, however, their DVFS methods are operated independently. Although there have been many discussions to reduce the memory access latency of CPU, the conventional DVFS technique does not relieve memory stall (the state during which CPU is stalled waiting for a memory access) effectively due to individual DVFS methods. In this article, we propose a new DVFS method, which alleviates the memory stall and reduces CPU power consumption utilizing microarchitectural information. Our proposed method identifies the layer on the memory hierarchy that mainly generates the memory stall and determines the optimal frequency of CPU and memory hierarchy that achieve a system-wide power optimization, without degrading the performance. In our experiments, our proposed method improved IPC (Instruction Per Cycle) by 11%, reduced MSPC (Memory Stall Per Cycle) by 26% and reduced power consumption by 5% compared to a conventional DVFS method.
Path Loss Measurement of Outdoor Wireless Channel in D-band

D-band (110–170 GHz) has received much attention in recent years due to its larger bandwidth. However, analyzing the loss characteristics of the wireless channel is very complicated at the millimeter-wave (MMW) band. Research on D-band wireless channels has been focused on indoor short-distance transmissions, with few studies looking at outdoor long-distance wireless channels. In this paper, we provide the design of the D-band outdoor long-distance transmission system, propose the outdoor line-of-sight (LOS) propagation measurements, and study the outdoor D-band propagation loss characteristics with distances up to 800 m. The path loss model uses the Floating Intercept (FI) and the Close-In (CI) model is established based on the least square method. In the CI model, the path loss exponent is greater than 2 and increases with frequency, while in the FI model, the path loss exponent has no apparent frequency dependence. The results show that D-band path loss in long-distance outdoor scenarios is greater than that in free space, indicating that the propagation condition is worse than in free space. The results show that both models have similar performance. Under this basis, the model with the smallest number of parameters would be the optimal choice. In addition, these results prospectively provide a theoretical model for designing and optimizing high frequency mm-wave propagation measurements at a distance of 200 m and beyond.
An Improved Space-Time Coding Scheme for FMCW MIMO-SAR

Multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) system, as a new SAR system, is expected to improve the performance of interferometric applications, ground moving target indication, wide-swath imaging, and three-dimensional imaging. Effective orthogonal waveform design and echo separation scheme are the key issues in the realization of MIMO-SAR system. Otherwise, the residual ambiguous energy introduced by incomplete separation will degrade the quality of SAR images. However, most schemes are designed for pulsed SAR systems, which are not necessarily compatible with continuous wave systems. This paper proposes an improved space-time coding scheme for FMCW MIMO-SAR, which avoids the performance degradation associated with the time-variant channel responses caused by the motion of the SAR platform. The echo separation performance of the proposed scheme is evaluated and compared with the conventional scheme through simulation experiments.
Unified Real-Time Simulation Method for DC/DC Conversion Systems Consisting of Cascaded Dual-Port Submodules

The dc/dc conversion systems are constructed from multiple cascaded dual-port submodules in series and/or parallel at both the input port and output port. The complexity of the series-parallel structures brings great challenges to real-time simulation. This article proposes a unified real-time simulation method of series-parallel dc/dc conversion systems, which unifies the input-series-output-series, input-series-output-parallel, input-parallel-output-parallel, and input-parallel-output-series structures. First, based on the nodal analysis method, the dual-port equivalent model of each submodule in the series-parallel dc/dc conversion system is obtained. Second, by cascading each submodule, the unified form of the system's dual-port equivalent model of the series-parallel dc/dc conversion system is constructed. For the series connection, the port is equivalent to Thevenin's equivalent circuit. For the parallel connection, the port is equivalent to Norton's equivalent circuit. The real-time simulation model of a specific series-parallel dc/dc conversion system can be obtained by combining the input/output port equivalent circuits. Besides, the high-frequency waveforms of the internal circuit can also be obtained through parallel calculation steps. The real-time simulation of the proposed unified model is carried out with a 250-ns time step on a Xilinx K-7 series field-programmable gate array, which verifies the accuracy and simulation efficiency of the unified model.
Frequency and Temperature-Based Noise Figure Modeling for GaAs HEMTs

The noise figure modeling in a high-frequency range for two GaAs-based HEMTs with different gate widths of $\mathbf{200}\ \boldsymbol{\mu} \mathbf{m}$ and $\mathbf{150}\ \boldsymbol{\mu} \mathbf{m}$ is shown in this work throughout a broad temperature range, e.g., −40 to 150°C. The typical noise characteristics have been modeled for the minimal noise figure (NFmin), association gain (Ga), and equivalent noise resistance (RN) at various frequencies and temperatures. Additionally, the noise-oriented parameters' temperature dependence was assessed. GaN HEMTs on various substrates and InP-and GaAs pHEMTs have noise-oriented features equivalent to the two GaAs FETs. The findings indicate that GaAs HEMTs have plenty of implications in operating LNAs at high temperatures.
Segmentation of Cell Nuclei in Microscopy Images using Modified ResUNet

Image segmentation is a branch of digital image processing and computer vision that classifies related regions or segments of an image. Hence image segmentation techniques canbe applied to detect and highlight the anomaly cell in the given image. In any of the segmentation problems, dice coefficient and the Intersection over Union (IoU) plays a major role than the accuracy as they give how perfectly the predicted output is matching the original ground truth masks. So, we have focusedon building a Deep Learning architecture which can yield betterdice loss and the IoU. We have designed an architecture by utilizing the concepts of traditional U-Net and residual neural networks. The proposed model has achieved better dice loss and the IoU scores. The proposed architecture has achieved 0.80 score of IoU and 0.82 score of dice coefficient which is better than some state of the art techniques.
SU‐FF‐T‐209: Electron Arc Therapy with An Elekta SL‐25 10 MeV Beam Using a Dedicated Short Applicator

Purpose: To implement the electron arc therapy with an Elekta SL‐25 10 Mev beam using a dedicated short applicator. Method and Materials:. All measurements were done on an Elekta SL‐25 with a 10 MeV electron beam. Radial PDD were characterized for 4 different cylindrical acrylic phantoms using TLDs and Kodak X‐Omat‐V radiographic films The phantom radius (di ) ranged between 5.1 and 15.2 cm, the total arc angles (α) varied between 60° and 160° and the number of monitor units (MU) per degree between 0.5 and 7. The field with at isocenter (w) was varied between 3.7 and 9.8 cm. Based on this data bank, an analytical model was developed for monitor unit (MU) calculation. This model estimates arc beam output at the depth of maximum dose (dmax) as a function of di and α for a given w. A verification of the model precision was done with a new cylindrical phantom. Results:Curve fitting of the complete set of beam output data with w fixed at 7.3 cm was done with an asymptotic relationship between the dose rate at d max and the inverse square of di . The dependence of the beam output on α was introduced by assuming an explicit function of α for each parameter of the model. Results show that the calculated beam output data is a good approximation for all measured data for all phantoms and arc angles: 89% of the calculated values are within ± 3% of the measured ones and all points are within a ± 5% error range. Conclusion: From our results, clinical implementation of the electron arc therapy technique is possible and should be facilitated by the use of our predictive model in the treatment planning process.
An element‐wise, locally conservative Galerkin (LCG) method for solving diffusion and convection–diffusion problems

An element‐wise locally conservative Galerkin (LCG) method is employed to solve the conservation equations of diffusion and convection–diffusion. This approach allows the system of simultaneous equations to be solved over each element. Thus, the traditional assembly of elemental contributions into a global matrix system is avoided. This simplifies the calculation procedure over the standard global (continuous) Galerkin method, in addition to explicitly establishing element‐wise flux conservation. In the LCG method, elements are treated as sub‐domains with weakly imposed Neumann boundary conditions. The LCG method obtains a continuous and unique nodal solution from the surrounding element contributions via averaging. It is also shown in this paper that the proposed LCG method is identical to the standard global Galerkin (GG) method, at both steady and unsteady states, for an inside node. Thus, the method, has all the advantages of the standard GG method while explicitly conserving fluxes over each element.
Environmental Factors Affecting Freshwater Snail Intermediate Hosts in Shenzhen and Adjacent Region, South China

Sound knowledge of the local distribution and diversity of freshwater snail intermediate hosts and the factors driving the occurrence and abundance of them is crucial to understanding snail-borne parasitic disease transmission and to setting up effective interventions in endemic areas. In this study, we investigated the freshwater snails, water quality parameters, physical characteristics of habitats, predators and competitors, and human activity variables at 102 sites during December 2018 and August 2019 in Shenzhen and adjacent areas in China. We used decision tree models and canonical correspondence analysis to identify the main environmental and biotic factors affecting the occurrence and abundance of snail species. A total of nine species of snail were collected throughout the study area, with Biomphalaria straminea, Sinotaia quadrata, and Physella acuta being the most predominant species. Our study showed that the most important variables affecting the abundance and occurrence of snail species were the presence of predators and competitors, macrophyte cover, chlorophyll-a, substrate type, river depth, and water velocity. In terms of human activities, snail species occurred more frequently and in larger numbers in water bodies affected by human disturbances, especially for sewage discharge, which may reduce the occurrence and abundance of snail predators and competitors. These findings suggest that proper management of water bodies to reduce water pollution may increase the abundance of snail predators and competitors, and should be considered in integrated snail control strategies in the study area.
Food State Recognition Using Deep Learning

Automated food detection and recognition methods have been studied to enhance end-user life. However, most existing research focused on food ingredient type recognition, with little work has been done for food ingredient state recognition. Successful recognition of food ingredient state plays a significant role in handling the food ingredient by an intelligent system. In this work, we propose a new novel cascaded multi-head approach based on deep learning to simultaneously recognize the state and type of food ingredients. We trained and evaluated the proposed approach on a benchmark dataset of food ingredient images with nine different food states and 18 food types. We compared the proposed approach with a non-cascaded deep learning approach. The cascaded approach shows improvement in food ingredient state recognition with 87% accuracy compared to 81% using a non-cascaded deep learning method. Our proposed method broadly applies to various tasks where food ingredient state recognition is essential, such as feeding elderly and disabled people and automating food recognition and preparation.
Galectin-1 from redlip mullet Liza haematocheilia:
 identification, immune responses, and functional characterization as pattern
 recognition receptors (PRRs) in host immune defense system

Galectins, a family of ß-galactoside-binding lectins, have emerged as soluble mediators in infected cells and pattern recognition receptors (PRRs) responsible for evoking and regulating innate immunity. The present study aimed to evaluate the role of galectin-1 in the host immune response of redlip mullet ( Liza haematocheilia ). We established a cDNA database for redlip mullet, and the cDNA sequence of galectin-1 (LhGal-1) was characterized. In silico analysis was performed, and the spatial and temporal expression patterns in gills and blood in response to lipopolysaccharide polyinosinic:polycytidylic acid, and Lactococcus garvieae were estimated via quantitative real-time PCR. Functional assays were conducted using recombinant protein to investigate carbohydrate binding, bacterial binding, and bacterial agglutination activity. LhGal-1 was composed of 135 amino acids. Conserved motifs (H-NPR, -N- and -W-E-R) within the carbohydrate recognition domain were found in LhGal-1. The tissue distribution revealed that the healthy stomach expressed high levels of LhGal-1. The temporal monitoring of LhGal-1 mRNA expression in the gill and blood showed its significant upregulation in response to immune challenges with different stimulants. rLhGal-1 exhibited binding activity in response to carbohydrates and bacteria. Moreover, the agglutination of rLhGal-1 against Escherichia coli was observed. Collectively, our findings suggest that LhGal-1 may function as a PRR in redlip mullet. Furthermore, LhGal-1 can be considered a significant gene to play a protective role in redlip mullet immune system
Design and Development of Integrated Map Database System to Store and Share Map Datasets

A map describes real world from a particular point of view, and there are many types of map objects. A map object may be either geometry or raster. In this paper, I present a design of map database system on the top of relational database management system. First, two types of objects node and edge are iintegrated into the system to be enable to handle graph maps. I also present a three dimensional map application using raster map. Integration of map and text is also presented.
Differential Allele-Specific Expression Revealed Functional Variants and Candidate Genes Related to Meat Quality Traits in B. indicus Muscle

Traditional transcriptomics approaches have been used to identify candidate genes affecting economically important livestock traits. Regulatory variants affecting these traits, however, remain under covered. Genomic regions showing allele-specific expression (ASE) are under the effect of cis-regulatory variants, being useful for improving the accuracy of genomic selection models. Taking advantage of the better of these two methods, we investigated single nucleotide polymorphisms (SNPs) in regions showing differential ASE (DASE SNPs) between contrasting groups for beef quality traits. For these analyses, we used RNA sequencing data, imputed genotypes and genomic estimated breeding values of muscle-related traits from 190 Nelore (Bos indicus) steers. We selected 40 contrasting unrelated samples for the analysis (N = 20 animals per contrasting group) and used a beta-binomial model to identify ASE SNPs in only one group (i.e., DASE SNPs). We found 1479 DASE SNPs (FDR ≤ 0.05) associated with 55 beef-quality traits. Most DASE genes were involved with tenderness and muscle homeostasis, presenting a co-expression module enriched for the protein ubiquitination process. The results overlapped with epigenetics and phenotype-associated data, suggesting that DASE SNPs are potentially linked to cis-regulatory variants affecting simultaneously the transcription and phenotype through chromatin state modulation.