Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Decoupled RotLin Motor With Fuzzy Sliding Mode Control

In this paper, a new rotary-linear switched-reluctance motor with a magnetic decoupled structure is introduced. The rotational motion of the motor, managed by a sliding-mode controller, is coordinated with the proportional-derivation linear motion by using a fuzzy logic block. Consequently, both rotational and linear motions can be realized simultaneously. In addition, the decoupled magnetic paths of the motor in two directions are analyzed by finite-element method, with the force and torque measurements suggesting the feasibility of the motor. Both the simulation and experimental results show that the motor is totally decoupled in the linear and rotary directions. The linear movement and the rotation at the same time validate the effectiveness of the motor design and the proposed control method, with highly accurate responses for the two degrees of freedom movement.
Binding of a physiological substrate causes large-scale conformational reorganization in cytochrome P450 51

Sterol 14α-demethylases (CYP51s) are phylogenetically the most conserved cytochromes P450, and their three-step reaction is crucial for biosynthesis of sterols and serves as a leading target for clinical and agricultural antifungal agents. The structures of several (bacterial, protozoan, fungal, and human) CYP51 orthologs, in both the ligand-free and inhibitor-bound forms, have been determined and have revealed striking similarity at the secondary and tertiary structural levels, despite having low sequence identity. Moreover, in contrast to many of the substrate-promiscuous, drug-metabolizing P450s, CYP51 structures do not display substantial rearrangements in their backbones upon binding of various inhibitory ligands, essentially representing a snapshot of the ligand-free sterol 14α-demethylase. Here, using the obtusifoliol-bound I105F variant of Trypanosoma cruzi CYP51, we report that formation of the catalytically competent complex with the physiological substrate triggers a large-scale conformational switch, dramatically reshaping the enzyme active site (3.5–6.0 Å movements in the FG arm, HI arm, and helix C) in the direction of catalysis. Notably, our X-ray structural analyses revealed that the substrate channel closes, the proton delivery route opens, and the topology and electrostatic potential of the proximal surface reorganize to favor interaction with the electron-donating flavoprotein partner, NADPH–cytochrome P450 reductase. Site-directed mutagenesis of the amino acid residues involved in these events revealed a key role of active-site salt bridges in contributing to the structural dynamics that accompanies CYP51 function. Comparative analysis of apo-CYP51 and its sterol-bound complex provided key conceptual insights into the molecular mechanisms of CYP51 catalysis, functional conservation, lineage-specific substrate complementarity, and druggability differences.
Optimal Control Based on Quasi-Newton Method Considering Cooperation of Constrained Distributed Energy Resources

In this paper, load frequency control by optimal control algorithm using quasi-Newton method for microgrid introducing diesel generator and battery is discussed. In the proposed method, we consider microgrid model including diesel generator and battery. Also, saturation is taken into account as a constraint on the input to the model of diesel generator and battery. Then, a cost function considering the storage capacity constraint is formulated. Therefore, we propose an algorithm using the quasi-Newton method to determine inputs that minimize the formalized cost function. In addition to the quasi-Newton method, we propose an algorithm using Backward Sweep to match the terminal condition. Finally, we confirm the effectiveness of the proposed method through numerical simulations.
Modeling and Control Law Design for Rigid Body Dynamics by Conformal Geometric Algebra

Conformal geometric algebra (CGA) can simplify the dynamic model of the motion of rigid bodies by introducing the motor, the extended angular velocity bivector, and the extended torque bivector. Recently, CGA-based controller design methods have been proposed for a kinematic stabilization problem for the rigid body motion with 1-D rotation by means of the Lyapunov function for the motor. In this paper, more generalized result is shown and a controller for the stabilization of the dynamics is proposed.
Metagenomic Sequencing From Mosquitoes in China Reveals a Variety of Insect and Human Viruses

We collected 8,700 mosquitoes in three sites in China, which belonged to seven species. Their viromes were tested using metagenomic sequencing and bioinformatic analysis. The abundant viral sequences were detected and annotated belonging to more than 50 viral taxonomic families. The results were verified by PCR, followed by phylogenetic analysis. In the present study, we identified partial viral genes of dengue virus (DENV), a novel circovirus (CCV), densovirus (DNV), Japanese encephalitis virus (JEV), and Wuhan mosquito virus (WMV) in mosquitoes. Metagenomic analysis and PCR amplification revealed three DENV sequences, which were as homologous to the NS3 gene of DENV from Singapore isolated in 2005, with at least 91% nucleotide (nt) identity. Seven fragments of JEV encoding structural proteins were identified belonging to genotype I. They all shared high homology with structural protein genes of JEV isolated from Laos in 2009. The production of infectious virus particles of the newly isolated virus YunnanJEV2017-4 increased after passage from the BHK-21 cell line to the Vero cell line. Novel circovirus-related genes were identified and as being related to an unnamed gene of a mosquito circovirus (MCCV) sequence from the USA isolated in 2011, with at least 41% nt identity: this distant relationship suggests that the parent virus might belong to a novel circovirus genus. Additionally, numerous known viruses and some unknown viruses were also detected in mosquitoes from Yunnan province, China, which will be tested for propagation.
Probability-Weighted Robust Optimization for Distributed Generation Planning in Microgrids

Distributed generation (DG) has been rapidly integrated into microgrids. However, uncertain power generation of intermittent DG such as wind turbines brings challenges to the DG planning problem. This paper proposes a novel probability-weighted robust optimization (PRO) method to allocate DG units including microturbines and wind turbines in microgrids, aiming to maximize the total profit over a long-term planning horizon. First, probability-weighted uncertainty sets are proposed to model uncertainties including wind turbine output and load demand during a long-term planning horizon which can fully cover the uncertainty spectrum and accurately represent the uncertainty probability distribution. Then, the PRO method optimizes DG sizing and locating under the worst uncertainty cases considering their occurrence probabilities. Therefore, the planning decision is robust against any possible uncertainty realization. Besides, a modified column-and-constraint generation algorithm is developed to solve the PRO problem. Simulation results show that the DG planning obtained by the proposed method can achieve full operating robustness.
Cell Battery Emulator for Hardware-in-the-Loop BMS Test

The battery management system (BMS) measures cells voltage, temperature and battery current, acting as security controller, monitoring and reporting the state of the battery and also balancing it. It must be accurately validated and tested before to use. The HiL test is the better solution to test it in order to develop algorithms in a fast way, to test the BMS board using a secure and reproducible way, and to simulate extreme condition of lithium cells. This work introduces the HiL test system defining its specifications. The challenge of HiL test on BMS is to replicate the behaviour of the lithium cells without using them. For this purpose, a battery cell emulator board is proposed and analysed. Some SPICE simulations were conducted at design stage and the result were compared with the defined specifications. The battery cell emulator, being isolated from the main power supply, can be used in series connection simulating higher voltage batteries in the HiL test of BMSs.
Forecasting the usage of appliances of shared use: an analysis of simplicity over complexity

The current revolution of the Internet of Things around the world goes far beyond the goal of simply interconnecting devices and retrieving data from them. New challenges are appearing in relation to the data-flow architectures where Cloud-based initiatives are declining over the Edge computing paradigm. In this latter approach, the resources (i.e. devices involved) are optimized by processing the data as close as possible to the source of them. However, constrained devices still struggle embedding the computation that servers carry out in the Cloud. The case of load and devices usage forecasting is a particular example of this issue where efforts are being made to simplify the processing and device architecture towards reducing energy consumption in data flow and computation. The presented research focuses on the analysis of four typical forecasting models with different levels of complexity that predict the usage of several electrical coffee machines of shared use in office buildings. The results obtained draw on shedding light on the feasibility of embedding simple yet accurate probabilistic models on constrained devices with the aim of saving energy and costs on network infrastructure.
Design and Characteristic Analysis of a High-Speed Permanent Magnet Synchronous Motor Considering the Mechanical Structure for High-Speed and High-Head Centrifugal Pumps

This paper presents design guidelines for a high-speed permanent magnet synchronous motor (PMSM) for a 110 hp and 12 500 r/min centrifugal pump considering the mechanical and electromagnetic characteristics. We selected the size of the rotor based on the torque per unit rotor volume method. The rotor stress analysis was performed using the analytical method and the finite-element method. Based on these results, appropriate sleeve thickness and material were selected considering mechanical stability and electrical performance. The PMSM design was carried out by using the mechanically stable rotating body, and the model with excellent electromagnetic performance was selected based on the iron loss analysis and the eddy current loss analysis using the finite-element method. In addition, the stability of high-speed operation was confirmed by the critical speed analysis of the finally designed PMSM model. The high-speed PMSM verifies the performance through the manufacturing and test it was confirmed that there is no problem when applied to the actual centrifugal pump systems.
Positive Regard and Psychotherapy Outcome: A Meta-Analytic Review

This article meta-analytically reviews the research on the association between therapist positive regard (PR) and treatment outcome. The history of the construct of unconditional PR in client-centered theory and the efforts to clearly operationalize and measure this construct are reviewed. Several clinical examples are presented. The updated meta-analysis, which features expanded inclusion criteria and a larger number of studies (k = 64) than previous analyses, yielded a small positive association between PR and treatment outcome, g = . 28. To control for the repeated use of data sets and study samples within the database, a multilevel meta-analysis was adopted that indicated a stronger relation between PR and clinical outcome (g = 0.36). These analyses support PR’s standing as a significant component of the therapy relationship that leads to improved clinical outcomes. The article concludes with limitations of the research, patient contributions, diversity considerations, and recommendations for using PR in practice.
Design and Analysis of an Interior Permanent Magnet Linear Vernier Machine

A novel interior permanent magnet linear vernier machine (IPM-LVM) is proposed and analyzed in this paper. Compared to the existing surface-mounted permanent magnet linear vernier machine (SPM-LVM) and the SPM-LVM with Halbach arrays, the proposed machine can offer higher flux density in both the air-gap and armature iron core, which lead to a higher back electric motive force (back EMF) and higher thrust density under the same mover volume and heat capacity. Besides, it is proven that the proposed IPM-LVM also has a lower demagnetization risk compared to the SPM-LVM. Furthermore, in order to reduce the thrust ripple of the proposed machine, the three-sectional structure is adopted in this paper, which makes the proposed IPM-LVM exhibit high thrust density and low thrust ripple at the same time.
Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks

This paper presents a study on detecting cyber attacks on industrial control systems (ICS) using convolutional neural networks. The study was performed on a Secure Water Treatment testbed (SWaT) dataset, which represents a scaled-down version of a real-world industrial water treatment plant. We suggest a method for anomaly detection based on measuring the statistical deviation of the predicted value from the observed value. We applied the proposed method by using a variety of deep neural network architectures including different variants of convolutional and recurrent networks. The test dataset included 36 different cyber attacks. The proposed method successfully detected 31 attacks with three false positives thus improving on previous research based on this dataset. The results of the study show that 1D convolutional networks can be successfully used for anomaly detection in industrial control systems and outperform recurrent networks in this setting. The findings also suggest that 1D convolutional networks are effective at time series prediction tasks which are traditionally considered to be best solved using recurrent neural networks. This observation is a promising one, as 1D convolutional neural networks are simpler, smaller, and faster than the recurrent neural networks.
Approximating Certainty in Querying Data and Metadata

Metadata, such as mappings or constraints, is used in a variety of scenarios to facilitate query answering; these include data integration and exchange, consistent query answering, and ontology-based data access. A common feature of these scenarios is that data and metadata together produce multiple databases, and answers to queries must be certain, i.e., true in all such databases. This usually incurs prohibitively high complexity outside very restricted classes of queries such as conjunctive queries and their unions. To overcome this, we propose to approximate such query answering by reducing it to another scenario where multiple databases need to be taken into account, namely incomplete information in databases. For them, well-behaved approximation schemes exist for much larger classes of queries. We give a generic representation of query answering via incomplete data, and show how it works in the scenarios listed above. We use the connection to show how to effectively approximate several intractable query answering problems, and discuss differences between applying this framework under open and closed world semantics.
A Self-Organizing Tensor Architecture for Multi-view Clustering

In many real-world applications, data are often unlabeled and comprised of different representations/views which often provide information complementary to each other. Although several multi-view clustering methods have been proposed, most of them routinely assume one weight for one view of features, and thus inter-view correlations are only considered at the view-level. These approaches, however, fail to explore the explicit correlations between features across multiple views. In this paper, we introduce a tensor-based approach to incorporate the higher-order interactions among multiple views as a tensor structure. Specifically, we propose a multi-linear multi-view clustering (MMC) method that can efficiently explore the full-order structural information among all views and reveal the underlying subspace structure embedded within the tensor. Extensive experiments on realworld datasets demonstrate that our proposed MMC algorithm clearly outperforms other related state-of-the-art methods.
Incident Power Influence on Magnetoimpedance Element With Domain-Wall Resonance

We investigated the influence of the incident power on thin-film magnetoimpedance (MI) elements. When the incident power is relatively low, the impedance profile shows a typical frequency dependence, which can be explained by the theory based on the skin effect, eddy current loss, and ferromagnetic resonance. On the other hand, with the increase of the incident power, the impedance profiles show another impedance peak and large inductance decrease owing to domain-wall resonance (DWR) at a lower frequency. The peak height and inductance at 1 MHz increase with the applied field, and the resonant frequency shifts to a higher frequency. The impedance changes related to DWR strongly depend on the incident power. The field at which the impedance has a maximum value shifts to a lower field with the increase of the incident power. The dependence of the impedance peak values on the field reveals an optimal incident power to achieve a higher sensitivity for field detection. The obtained results indicate that the DWR enhances the sensitivity of thin-film MI elements at a relatively lower frequency, compared with the case without DWR; an incident power of approximately −10 dBm at 30 MHz was optimal in this paper.
Exploitation of road signalling for localization refinement of autonomous vehicles

In this paper we present the development of a framework for vehicle localization refinement based on the detection of traffic road signs. Leveraging on the detection capabilities of deep neural networks, the proposed architecture relies on the recognition of traffic signs to navigate a graph, in order to spatially localize the vehicle on the road. Knowing the internal camera parameters and the size of the road signals it is possible to refine the localization accuracy and propagate the information over time thanks to the application of a Kalman filter. The implemented solution demonstrates that the trajectory of the vehicle is more accurate, reducing the error when comparing the standard GPS information and the RTK positioning system.
Phase-Aware Speech Enhancement Based on Deep Neural Networks

Short-time frequency transform (STFT) is fundamental in speech processing. Because of the difficulty of processing highly unstructured STFT phase, most speech-processing algorithms only operate with STFT magnitude, leaving the STFT phase far from explored. However, with the recent development of deep neural network (DNN) based speech processing, e.g., speech enhancement and recognition, phase processing is becoming more important than ever before as a new growing point of DNN-based methods. In this paper, we propose a phase-aware speech enhancement algorithm based on DNN. Specifically, in the training stage, when incorporating phase as a target, our core idea is to transform an unstructured phase spectrogram to its derivative along the time axis, i.e., instantaneous frequency deviation (IFD), which has a similar structure with its corresponding magnitude spectrogram. We further propose to optimize both IFD and magnitude jointly in a multiobjective learning framework. In the test stage, we propose a postprocessing method to recover the phase spectrogram from the estimated IFD. Experimental results demonstrate the effectiveness of the proposed method.
Delay-Tolerant Data Traffic to Software-Defined Vehicular Networks With Mobile Edge Computing in Smart City

With the explosion in the number of connected devices and Internet of Things (IoT) services in smart city, the challenges to meet the demands from both data traffic delivery and information processing are increasingly prominent. Meanwhile, the connected vehicle networks have become an essential part in smart city, bringing massive data traffic as well as significant communication, caching, and computing resources. As the two typical services types in smart city, delay-tolerant and delay-sensitive traffic requires very different quality of service (QoS)/quality of experience (QoE), and could be delivered through the routes with different features to meet their QoS/QoE requirements with the lowest costs. In this paper, we propose a novel vehicle network architecture in the smart city scenario, mitigating the network congestion with the joint optimization of networking, caching, and computing resources. Cloud computing at the data centers as well as mobile edge computing at the evolved node Bs and on-board units are taken as the paradigms to provide caching and computing resources. The programmable control principle originated from the software-defined networking paradigm has been introduced into this architecture to facilitate the system optimization and resource integration. With the careful modeling of the services, the vehicle mobility, and the system state, a joint resource management scheme is proposed and formulated as a partially observable Markov decision process to minimize the system cost, which consists of both network overhead and execution time of computing tasks. Extensive simulation results with different system parameters reveal that the proposed scheme could significantly improve the system performance compared to the existing schemes.
Application of Gradient Learning Scheme to Pixel-Based Inversion for Transient EM Data

Traditional gradient descent inversion for transient electromagnetic (TEM) data is time and memory comsuming because the derivative matrices need to be computed repeatly. In this paper, we apply the Supervised Descent Method (SDM) into pixel-based inversion for TEM data. This method is based on the concept of gradient learning. In an offline stage, the average descent direction can be learned from a set of training data; and in an online stage, data inversion can be achieved by the learned descent directions without computing the derivative matrices. Numerical tests verify that this algorithm converges faster and is also more efficient. Moreover, SDM offers a more convinient way to incorporate prior information into inversion that could improve the efficiency of data interpretation.
Implementation of 16-Channel AMR Sensor Array for Quantitative Mapping of Two-Dimension Current Distribution

The advanced method of mapping magnetic field and retrieving 2-D current distribution is investigated in this paper. A 16-channel anisotropic magnetoresistance sensor array scanner was implemented to quickly map the magnetic field. The algorithm used for calculating the current density distribution is the 2-D Fourier transform with properly chosen spatial filters. The spatial resolution for resolving the current distribution is 2 mm with a 2.5 mm gap. The proposed system is promising for applications in non-destructive testing, such as foreign object detection, field interference mapping for mobile device, and open and short circuits in printed circuit board.