Miracle-dz/UniFAR-FTUs huggingface dataset:
Sugarcane (Saccharum officinarum)-encoded microRNA Targets

15 Sugarcane Bacilliform Virus (SCBV) is considered an economically the most damaging 16 pathogen for sugarcane production worldwide. Three ORFs are characterized in a single 17 molecule of circular, ds-DNA genome of the SCBV, encoding for hypothetical protein (ORF1), 18 DNA binding protein (ORF2) and Polyprotein (ORF3). The study was aimed to predict and 19 comprehensively evaluate sugarcane miRNAs for the silencing of SCBV genome using in-silico 20 algorithms. Computational methods were used for prediction of candidate miRNAs from 21 sugarcane (S. officinarum L.) to silence the expression of SCBV genes through translational 22 inhibition by mRNA cleavage. Mature sugarcane miRNAs were retrieved and were assessed to 23 hybridization with the SCBV genome. A total of fourteen potential candidate miRNAs from 24
An Efficient Method for Computing Exact Delay-Margins of Large-Scale Power Systems

This letter proposes an efficient method for com-puting the delay-margins of large-scale power systems with single constant delay. The method computes the margin by identifying the minimal delay under which some eigenvalues are about to cross the imaginary axis from the left to the right half complex plane. Based on the Rekasius substitution, a new characterization for the crossing points is developed, followed by a method for determining the delay margins. The method only involves eigen-value decomposition on a low-order matrix whose dimension is independent of the system scale. Further leveraging the sparsity of power systems makes it computationally very attractive for practical large-scale system applications.
Online State-of-Charge Estimation for Lithium-Ion Batteries Considering Model Inaccuracies Under Time-Varying Current Conditions

Load currents of batteries undergo real-time change depending on the operations of battery-powered devices, such as acceleration, deceleration, and sudden braking of electric vehicles. With the variation in current magnitude, parameters in an equivalent circuit model of the battery change depending on its chemical characteristics. Therefore, accurately identifying model parameters offline for various sizes and patterns of load currents with few experiments is difficult. Besides, there is a model uncertainty due to the difference between the linearity of the model structure and the nonlinearity of the real battery; hence, an experimentally obtained open-circuit voltage (OCV)–state-of-charge (SOC) curve has difficulty in providing an accurate OCV value for the model. Consequently, these difficulties lead to inaccuracies in the model and SOC estimation. In this article, novel methods are proposed to reduce these inaccuracies under time-varying current conditions, where the magnitude of the load current changes in real time. These are (1) an OCV–SOC curve estimation for reducing the model uncertainty and (2) a current-adaptive extended Kalman filter algorithm for decreasing the effect of inaccurately identified model parameters. The experimental results show that the maximum-absolute-errors of the proposed SOC estimation were reduced to < 1.5%. Consequently, battery-powered applications can obtain highly accurate SOC values even the magnitude of the load current changes in real time.
A New Structure for PMG-Based WECSs With Battery Storage Systems

This paper develops and tests the performance of a new structure for grid-connected permanent magnet generator (PMG)-based wind energy conversion systems (WECSs) that have battery storage units (BSUs). The new structure is called the split dc-bus, and it is designed to reduce the pulsations in PMG developed torque and fluctuations in the power delivered to the grid. The reduction in PMG torque pulsations is achieved by employing a 5-level ac-dc power electronic converter (PEC) as the generator-PEC, whose dc outputs are processed by a two-port active dc-link. This active dc-link charges the BSUs and supplies the discharging PEC. The reduction of fluctuations in the delivered power is achieved by discharging the BSUs at point-of-common-coupling (PCC). The generator-side PEC, dc-link, grid-side PEC, and discharging PEC, are operated by a supervisor droop controller. The split-dc bus PMG-based WECS is implemented for performance testing under different wind speeds and levels of power delivery. Test results demonstrate reductions in PMG torque pulsations, along with reductions in the power delivered to the grid. These features of the split-dc bus PMG-based WECS are found to be complimented with a minor sensitivity to the changes in wind speed and changes in the power delivered to the grid.
An Interlinking Converter for Renewable Energy Integration Into Hybrid Grids

This letter proposes an interlinking converter architecture, which enables flexibly integrating renewable energy into hybrid grids. The proposed converter has one ac port, and two dc ports, offering a flexible solution to integrating various dc, and ac sources, which can also be versatilely configured as a dc–dc converter, a dc–ac inverter, or a dc–dc/ac multiport converter. The general concept of the architecture, its common-mode voltage analysis, and flexible operation modes are detailed in this letter. Experimental tests are performed on an example converter with a dedicated modulation strategy. The test results confirm the concept in terms of flexible conversion, high power density, low leakage currents, as well as controllable power flow.
Statistical Analysis of Brain Volume Changes in T1-Weighted Magnetic Resonance Images of Attention Deficit hyperactivity Disorder (ADHD) Based on Voxel-Based Morphometry

Attention Deficit hyperactivity disorder (ADHD), which is one of the most common abnormalities, usually appears at the early years of life. ADHD is usually observed with some structural changes at different regions of the brain cortex. Exploring those structural variations can have an important role in diagnosis and treatment of disease symptoms. In this paper, we propose a new framework for statistically exploring the main volume changes of the brain cortex in a group of 50 ADHD (combined-type) patients compared to controls. For this purpose, all the images are primarily registered with the ICBM152 template. Then, the gray-matter region of each image is segmented to obtain its corresponding probability map. To compute the local concentration/volume, the gray-matter probability map is smoothed by using a Gaussian filter with enough-large standard deviation. Next, through the hypothesis testing algorithm, the voxels with the significant volume change in all images of the ADHD group compared to controls are detected. Finally, the clusters with enough significant voxels are extracted as the regions with the significant volume changes due to ADHD. Finally, the exact position of every extracted cluster is obtained by using the parcellation atlas AAL2. Experimental results demonstrated significant volume increase in left medial frontal lobe, right cuneus, right rectus, right medial temporal gyrus, and left lingual gyrus. Also, there was significant volume decrease in right calcarine.
High Resolution and Polarization Independent Microwave Near-Field Imaging Using Planar Resonator Probes

Various near-field microwave imaging probes were developed previously for nondestructive testing of material structures. Sensitivity and resolution are key parameters for quantifying the efficacy of a given imaging probe. For polarized targets like cracks, the sensitivity of conventional aperture probes depends on the relative orientation between the probe’s electric-field vector and the target. In practice, target orientation is typically unknown. Furthermore, the imaging resolution of conventional aperture probes is limited to aperture dimensions. Obtaining imaging resolution in the order of few millimeters using conventional aperture probes requires operation at high frequencies, (above 30 GHz). In contrast, planar probes consisting of loops loaded with spiral resonators could provide resolution in the order of few millimeters while working at relatively low frequencies (below 1 GHz). Unlike conventional aperture probes, the radiated near-fields of these probes are characterized by two significant components oriented along orthogonal axes. Therefore, the probe intrinsically provides a polarization-independent response. In this article, the near-field imaging resolution of the planar resonator probe is further enhanced, and its polarization-independent response is demonstrated for the first time. The response of three planar probe prototypes of different dimensions is analyzed based on numerical simulation and experimental results. The performance of the probes is extensively evaluated by acquiring line-scans and 2D-images of metal and dielectric samples. The near-field images of the metal sample obtained using the planar probes working below 1 GHz are compared to the images obtained using aperture probes working at 24 GHz, 33 GHz, and 70 GHz. Furthermore, the probe imaging performance for a polarized target (i.e., narrow cuts) is compared to dual-polarized circular probe working at 24 GHz and open-ended coaxial probe working at 10 GHz. Overall, the proposed probe yielded more than twofold increase in the image signal-to-noise ratio (SNR) compared to all the probes utilized herein.
Multi-Instrument Automatic Music Transcription With Self-Attention-Based Instance Segmentation

Multi-instrument automatic music transcription (AMT) is a critical but less investigated problem in the field of music information retrieval (MIR). With all the difficulties faced by traditional AMT research, multi-instrument AMT needs further investigation on high-level music semantic modeling, efficient training methods for multiple attributes, and a clear problem scenario for system performance evaluation. In this article, we propose a multi-instrument AMT method, with signal processing techniques specifying pitch saliency, novel deep learning techniques, and concepts partly inspired by multi-object recognition, instance segmentation, and image-to-image translation in computer vision. The proposed method is flexible for all the sub-tasks in multi-instrument AMT, including multi-instrument note tracking, a task that has rarely been investigated before. State-of-the-art performance is also reported in the sub-task of multi-pitch streaming.
An ISO/IEC 7816-4 Application Layer Approach to Mitigate Relay Attacks on Near Field Communication

Near Field Communication (NFC) has become prevalent in access control and contactless payment systems, however, there is evidence in the literature to suggest that the technology possesses numerous vulnerabilities. Contactless bank cards are becoming commonplace in society; while there are many benefits from the use of contactless payments, there are also security issues present that could be exploited by a malicious third party. The inherently short operating distance of NFC (typically about 4 cm) is often relied upon as a means of ensuring intentional interaction on the user’s part and limiting attack vectors. However, NFC is particularly sensitive to relay attacks, which entirely negate the security usefulness of the short-range aspect of technology. The aim of this article is to demonstrate how standard hardware can be used to exploit the technology to carry out a relay attack. Considering the risk that relay attacks pose, a countermeasure is proposed to mitigate this threat. Our countermeasure yields a 100% detection rate in experiments undertaken – in which over 10,000 contactless transactions were carried out on a range of different contactless cards and devices. In these experiments, there was a false positive rate of 0.38% – 0.86%. As little as 1 in every 250 transactions were falsely classified as being the subject of a relay attack and so the user experience was not significantly impacted. With our countermeasure implemented, transaction time was lengthened by only 0.22 seconds.
Super-Resolution Range and Velocity Estimations With OFDM Integrated Radar and Communications Waveform

In the intelligent transportation system, it is an efficient way for the intelligent vehicle to use the integrated radar and communications system (IRCS) to obtain the range and velocity estimations of other vehicles and simultaneously communicate with other facilities, such as vehicles and base stations. In the IRCS, the transmitted waveform is the orthogonal frequency division multiplexing (OFDM) integrated radar and communications waveform that contains communications information. Due to the existence of communications information, the traditional range and velocity estimation methods in radar can not be directly utilized in the IRCS. Moreover, to improve the resolution of range and velocity estimations, the signal bandwidth and coherent processing interval (CPI) are usually required to be increased, which will result in the increase of system cost and the reduction of update rate. To solve these problems, a auto-paired super-resolution range and velocity estimation method is proposed by using the OFDM integrated radar and communications waveform. First, the communications information in the received signals is compensated. Then, the frequency smoothing is performed to reduce the correlation between the echoes reflected by different targets. Finally, the auto-paired super-resolution range and velocity estimations are obtained by exploiting the translational invariance structure of the underlying signal model in both frequency and pulse domains. Furthermore, Cramér-Rao bounds (CRBs) on range and velocity estimations with the OFDM integrated radar and communications waveform are derived. Several numerical examples are presented to verify the effectiveness of the proposed method.
Environment Sound Classification Based on Visual Multi-Feature Fusion and GRU-AWS

There are two major questions regarding Environmental Sound Classification (ESC). What is the best audio recognition framework, and what is the most robust audio feature? For investigating above problems, the Gated Recurrent Unit (GRU) network was used to analyze the effect of single features such as Mel Scale Spectrogram (Mel), Log-Mel Scale Spectrogram (LM), and Mel frequency cepstral coefficient (MFCC) as well as multi-feature about Mel-MFCC, LM-MFCC, and Mel-LM-MFCC (T-M) in this paper. The experiment results show that in the ESC tasks, multi-features are better than the single features in the same dimensions, and LM-MFCC has the strongest robustness. Meanwhile, reverse sequence MFCC (R-MFCC) and forward and reverse mixed sequence MFCC (FR-MFCC) were also proposed to study the effects of sequence changes on audio. The experiment results show that the sequence transformation of audio features has less influence on the recognition tasks. Furthermore, to investigate the ESC task further we introduced the attention weight similar model (AWS) in to the multi-feature. The AWS model allows different audio feature attention weights of the same sound to learn from each other. It makes the GRU-AWS model focus on the frame-level features more effectively. The experiment results show that the GRU-AWS gets excellent performance with a recognition rate of 94.3%, and it outperforms other state-of-the-art methods.
Refining Microservices Placement Employing Workload Profiling Over Multiple Kubernetes Clusters

As cloud-native computing is becoming the de-facto paradigm in the cloud field, Microservices Architecture has attracted attention from industries and researchers for agility and efficiency. Moreover, with the popularity of the IoT in the context of edge computing, cloud-native applications that utilize geographically-distributed multiple resources are emerging. In line with this trend, there is an increasing demand for microservices placement that selectively use optimal resources. However, optimal microservices placement is a significant challenge because microservices are dynamic and complex, depending on diversified workloads. Besides, generalizing workloads’ characteristics consisting of complex microservices is realistically challenging. Thus, microservices deployment with mathematically structured algorithms based on simulation is less practical. As an alternative, a microservices placement framework is required that can reflect the characteristics of workloads derived from empirical profiling. Therefore, in this research work, we propose a refinement framework for profiling-based microservices placement to identify and respond to workload characteristics in a practical way. To achieve this goal, we perform profiling experiments with selected workloads to derive delicate resource requirements. Then, we perform microservices placement with a greedy-based heuristic algorithm that considers application performance by using resource requirements derived from the profiled results. Finally, we verify the proposed concept by comparing the experimental results that use our work and those that don’t.
A Multistructure Multimode Three-Phase Dual-Active-Bridge Converter Targeting Wide-Range High-Efficiency Performance

A three-phase dual-active-bridge (3p-DAB) converter is an attractive topology for bidirectional power conversion in high-power applications. However, conduction and switching losses are two main loss mechanisms that severely affect its efficiency performance, and adoption of any single modulation scheme or topology cannot minimize these losses over a wide operating range. For this purpose, a reconfigurable topology of the 3p-DAB converter is proposed in this article that utilizes a reconfigurable and tunable resonant network to offer multiple degrees of freedom in minimizing conduction and switching losses over a wide range of operating conditions. The converter is designed such that for 40–100% of the rated output power, it operates as a tunable 3p-DAB resonant immittance converter with its output power controlled by varying switching frequency and tuning the resonant frequency of a resonant immittance network to track the switching frequency. Below 40% of the rated output power, the converter transforms to a tunable 3p-DAB series-resonant converter with its output power controlled by varying the impedance of a series-resonant network while keeping the switching frequency and phase shift constant. The combination of both operation modes jointly leads to wide-range zero circulating current and soft switching of all the switches and, hence, a wide-range high-efficiency performance, as validated by the experimental results.
A Novel DC Chopper Topology for VSC-Based Offshore Wind Farm Connection

Sudden increases in the dc-link voltage, caused by ac grid side voltage drops, can lead to potential damage in the equipment. Therefore, dc choppers are widely used in offshore wind power links to absorb the excess energy and keep the voltage within safe margins. This article investigates the existing chopper topologies and summarizes their advantages and disadvantages. On this basis, a novel dc chopper topology with both centralized and distributed resistances is proposed. It is a combination of two mainstream chopper topologies and gives the flexibility to tradeoff among the pros and cons of them. In this way, the proposed topology achieves great electromagnetic interference performance, precise power control, and very small system disturbance. In addition, the maximum power dissipation of the distributed resistance is reduced by 75%. The switching frequency and the loss of insulated gate bipolar transistors (IGBTs) are also decreased. Finally, the claimed attributes of the proposed topology are verified by test and simulation.
Sentiment Analysis for e-Payment Service Providers Using Evolutionary eXtreme Gradient Boosting

Online services depend primarily on customer feedback and communications. When this kind of input is lacking, the overall approach of the service provider can shift in unintended ways. These services rely on feedback to maintain consumer satisfaction. Online social networks are a rich source of consumer data related to services and products. Well developed methods like sentiment analysis can offer insightful analyses and aid service providers in predicting outcomes based on their reviews—which, in turn, enables decision-makers to develop effective strategic plans. However, gathering this data is more challenging on Arabic online social networks, due to the complexity of the Arabic language and its dialects. In this study, we propose an approach to sentiment analysis that combines a neutrality detector model with eXtreme Gradient Boosting and a genetic algorithm to effectively predict and analyze customers’ opinions of an e-Payment service through an Arabic social network. The proposed approach yields excellent results compared to other approaches. Feature analysis is also conducted on consumer reviews to identify influencing keywords.
Imaging Velocity Fields Analysis of Space Camera for Dynamic Circular Scanning

Compared with traditional push-broom imaging, the space camera that performs the new circular scanning imaging can effectively enlarge the imaging area and obtain high-resolution images. However, the imaging velocity fields of space camera will seriously deteriorate the image quality in the circular scanning imaging process. And the velocity fields are anisotropically distributed due to various factors, including attitude maneuvering, satellite precession, earth curvature, and earth rotation. Therefore, this paper proposes a mathematical model of the velocity field to analyze the effect of circular scanning imaging. Based on the principle of ray tracing, the expression of the instantaneous velocity field on the ground is first derived, and then the corresponding image velocity field is obtained by mapping the velocity vector to the focal plane. In addition, numerical simulations and image simulations are performed to analyze the anisotropy distribution of the velocity vectors. Moreover, the dynamic characteristics of the space camera are analyzed to evaluate the performance of this new imaging. The simulation results show that the higher velocity leads to worse imaging quality. The study of this paper provides guidance for attitude planning and imaging assessment for new remote sensing imaging of space cameras.
Exploiting Within-Channel Tunneling in a Nanoscale Tunnel Field-Effect Transistor

In this paper, using device simulations, we investigate electrical characteristics of a tunnel field-effect transistor (TFET) in which band-to-band tunneling (BTBT) occurs dominantly within the channel, rather than at source-channel junction. The within-channel BTBT is enabled by sharp band-bending induced by the dual material gate (DMG). The work-functions of two metal gates are chosen, such that the surface potential profile exhibits a distinct step at the DMG interface. Consequently, even under equilibrium condition, a high lateral electric field and an abrupt tunneling junction exist at the DMG interface. When a small gate voltage is applied, the inherent lateral electric field aids in creating an abrupt band alignment and obtaining a small tunneling width. As a result, an excellent average subthreshold swing is obtained in the proposed device. We have also investigated scaling of channel lengths in the proposed device and have demonstrated that within-channel tunneling can be exploited for channel lengths of 40nm and above. Furthermore, low drain threshold voltage and suppressed drain-induced barrier lowering can be obtained in the proposed device. Moreover, in contrast to conventional TFETs, electrical characteristics of the proposed device are less susceptible to source doping variations and shift in gate-edge with respect to the source-channel junction.