Switching Events Research Articles

High-impedance fault detection method based on sparse data divergence discrimination in distribution networks

Due to the weak fault current characteristics, strong nonlinearity and uncertainty, and presence of similar transient features of switching events, it is difficult for traditional methods to accurately detect and identify high faults in a high-noise environment. This paper proposes a high-impedance fault detection method based on sparse data divergence discrimination. First, sensitive sparse principal component analysis is applied to extract the joint features contained in the measurement data. Then, according to the sparse data and singular covariance matrices, an improved Wasserstein divergence solution method is proposed to obtain the difference between data probability distributions before and during a fault. Finally, threshold and time criteria are applied to discriminate against high impedance faults in high-noise cases. The results show that the proposed method can accurately detect and identify high-impedance faults with good robustness to the fault inception angle, transition impedance, noise, and switching events.© 2017 Elsevier Inc. All rights reserved.

Quantitative Analysis of Isoform Switching in Cancer.

Over the past 8 years, multiple studies examined the phenomenon of isoform switching in human cancers and discovered that isoform switching is widespread, with hundreds to thousands of such events per cancer type. Although all of these studies used slightly different definitions of isoform switching, which in part led to a rather poor overlap of their results, they all leveraged transcript usage, a proportion of the transcript's expression in the total expression level of the parent gene, to detect isoform switching. However, how changes in transcript usage correlate with changes in transcript expression is not sufficiently explored. In this article, we adopt the most common definition of isoform switching and use a state-of-the-art tool for the analysis of differential transcript usage, SatuRn, to detect isoform switching events in 12 cancer types. We analyze the detected events in terms of changes in transcript usage and the relationship between transcript usage and transcript expression on a global scale. The results of our analysis suggest that the relationship between changes in transcript usage and changes in transcript expression is far from straightforward, and that such quantitative information can be effectively used for prioritizing isoform switching events for downstream analyses.

Non-volatile electrically programmable integrated photonics with a 5-bit operation

Scalable programmable photonic integrated circuits (PICs) can potentially transform the current state of classical and quantum optical information processing. However, traditional means of programming, including thermo-optic, free carrier dispersion, and Pockels effect result in either large device footprints or high static energy consumptions, significantly limiting their scalability. While chalcogenide-based non-volatile phase-change materials (PCMs) could mitigate these problems thanks to their strong index modulation and zero static power consumption, they often suffer from large absorptive loss, low cyclability, and lack of multilevel operation. Here, we report a wide-bandgap PCM antimony sulfide (Sb2S3)-clad silicon photonic platform simultaneously achieving low loss (<1.0 dB), high extinction ratio (>10 dB), high cyclability (>1600 switching events), and 5-bit operation. These Sb2S3-based devices are programmed via on-chip silicon PIN diode heaters within sub-ms timescale, with a programming energy density of sim 10,{fJ}/n{m}^{3}. Remarkably, Sb2S3 is programmed into fine intermediate states by applying multiple identical pulses, providing controllable multilevel operations. Through dynamic pulse control, we achieve 5-bit (32 levels) operations, rendering 0.50 ± 0.16 dB per step. Using this multilevel behavior, we further trim random phase error in a balanced Mach-Zehnder interferometer.

Stochastic switching of delayed feedback suppresses oscillations in genetic regulatory systems.

Delays and stochasticity have both served as crucially valuable ingredients in mathematical descriptions of control, physical and biological systems. In this work, we investigate how explicitly dynamical stochasticity in delays modulates the effect of delayed feedback. To do so, we consider a hybrid model where stochastic delays evolve by a continuous-time Markov chain, and between switching events, the system of interest evolves via a deterministic delay equation. Our main contribution is the calculation of an effective delay equation in the fast switching limit. This effective equation maintains the influence of all subsystem delays and cannot be replaced with a single effective delay. To illustrate the relevance of this calculation, we investigate a simple model of stochastically switching delayed feedback motivated by gene regulation. We show that sufficiently fast switching between two oscillatory subsystems can yield stable dynamics.

Optimal Threshold-Based High Impedance Arc Fault Detection Approach for Renewable Penetrated Distribution System

Detection of high impedance arcing faults (HIAFs) in an active distribution network is challenging due to limited fault current contribution from power electronic interfaced distributed energy resources. It may also happen that during the switching operation of any system, component like a transformer, capacitor bank, nonlinear load, and lines, the protection function may fail to maintain security. The secure operation of any detection algorithm depends on the proper selection of key parameters and threshold settings. To enhance the HIAF detection process, the residual current is preprocessed using OTET. The cumulative sum of the fault detection index from the energy coefficients of OTET is used to take a suitable decision. Optimal parameters of the basis function in OTET are obtained using particle swarm optimization algorithm with a wide variety of fault and no-fault datasets. Further, the optimal value of a drift parameter is designed using OTET investigations and utilized in the fault detection index calculation. The selection mechanism incorporated in the method increases both dependability and security. It can be used for grid-connected/islanded modes of the system, different switching events, and grounding connections. The method is also reliable for noise and harmonic contaminated signal and unbalanced system.

Stochastic Modeling of Beam Management in mmWave Vehicular Networks

Mobility management is a major challenge for the wide-spread deployment of millimeter-wave (mmWave) cellular networks. In particular, directional beamforming in mmWave devices renders high-speed mobility support very complex. This complexity, however, is not limited to system design but also the performance estimation and evaluation. Hence, some have turned their attention to stochastic modeling of mmWave vehicular communication to derive closed-form expressions characterizing the coverage and rate behavior of the network. In this article, we model and analyze the beam management for mmWave vehicular networks. To the best of our knowledge, this is the first work that goes beyond coverage and rate analysis. Specifically, we focus on a multi-lane divided highway scenario in which base stations and vehicles are present on both sides of the highway. In addition to providing analytical expressions for the average number of beam switching and handover events, we provide design insights for the network operators to fine-tune their network within the flexibility provided by the standard in the choice of system parameters, including the number of resources dedicated to channel feedback and beam alignment operations.

A Simple Switching-Event Dependent High-Frequency Sampling Method for Power Conversion System

Precise current and voltage sensing are challenging for digital control in high-frequency power conversion systems. Given that the power electronics circuitry is triggered repetitively by power device switching events, a switching-time dependent sampling method is proposed to realize an equivalent high-frequency signal sampling with a low-speed Analog Digital Converter (ADC) and digital controller. Two sampling modes are developed in the proposed methods in terms of sampling rates lower and higher than the digital controller clock frequency, respectively. A fast and simple signal reconstruction method is designed to ensure the feasibility in a low-cost processor. A series-resonant dual-active-bridge (SR-DAB) is taken as an example for the theoretical analysis and experimental verification. The resonant current and Miller plateau voltage in the topology are sampled to verify the potential of the proposed method for online parameter recognition of DC-DC converters and device behavior monitoring of power devices.

A Quasi-Z-Source Four-Switch Three-Phase Inverter With Null Vector Capability

In this paper, a novel structure for a four-switch three-phase inverter with the ability of creating a null vector is proposed. This structure utilizes a symmetrical quasi-Z-source network and generates zero output voltage during the shoot-through state, which is similar to the null vector in the conventional six- switch inverter. This vector is used for the remaining time for each switching period. This means that the proposed structure has several advantages over the conventional four-switch inverter, in which two active vectors with opposite directions are necessary for synthesizing a null vector. These two vectors increase the switching losses due to the current circulation through these active states. Moreover, the number of switching states increases because of applying two opposite vectors. However, this problem does not exist in the proposed topology. To decrease the output current distortion and to minimize the number of switching events per each cycle, a new switching pattern is proposed, too. Also, fluctuations of the two split capacitors in the dc-link are considered in the closed-loop modulation method, which balances the output voltages and decreases current THD by 0.7&#x0025;. To validate the proposed topology and the given pulse width modulation approach, its mathematical analyses are given here. Also, simulations and experimental verification have been performed on a 1 kW prototype inverter.

Detection of High Impedance Faults in Power Lines using Empirical Mode Decomposition with Intelligent Classification Techniques

An uninterrupted and well-managed energy distribution system requires an effective protection system against various faults and uncertainties. Power system networks have a built-in issue called High Impedance Faults (HIFs), which can't be detected by standard protection mechanisms. This paper deals with the aforementioned issue by combining a deep learning technique (DLT) with a signal decomposition method known as Empirical Mode Decomposition (EMD). This approach utilizes the EMD for the extraction of multiple features from the input current signal, and LSTM neural network is employed for the accurate classification of HIF signals. The comparison results with various EMD methods and various switching events are also performed. Finally, this paper proposes an approximate location identification of HIFs. The simulation results demonstrate the effectiveness of the proposed method for detection in various fault scenarios.

Correlated insulator collapse due to quantum avalanche via in-gap ladder states

The significant discrepancy observed between the predicted and experimental switching fields in correlated insulators under a DC electric field far-from-equilibrium necessitates a reevaluation of current microscopic understanding. Here we show that an electron avalanche can occur in the bulk limit of such insulators at arbitrarily small electric field by introducing a generic model of electrons coupled to an inelastic medium of phonons. The quantum avalanche arises by the generation of a ladder of in-gap states, created by a multi-phonon emission process. Hot-phonons in the avalanche trigger a premature and partial collapse of the correlated gap. The phonon spectrum dictates the existence of two-stage versus single-stage switching events which we associate with charge-density-wave and Mott resistive phase transitions, respectively. The behavior of electron and phonon temperatures, as well as the temperature dependence of the threshold fields, demonstrates how a crossover between the thermal and quantum switching scenarios emerges within a unified framework of the quantum avalanche.

Splicing Characterization and Isoform Switch Events in Human Keratinocytes Carrying Oncogenes from High-Risk HPV-16 and Low-Risk HPV-84.

Infection of epithelial cells with high-risk HPV (HR-HPV) types, followed by expression of virus oncogenic proteins (E5, E6, and E7), leads to genomic imbalance, suppression of tumor inhibitors, and induction of oncogenes. Low-risk HPV (LR-HPV) may slow the rate at which cervical cancer spreads to an invasive stage since co-infection with LR-HPV is linked to a decreased risk of future invasive cancer than infection with HR-HPV alone. We then propose that cancer-progressing changes may be distinguished through identifying the functional differences between LR-HPV and HR-HPV. Lentiviral strategies were followed to establish HaCaT cells with constitutive expression of HPV oncogenes. RNAseq experiments were designed to analyze the transcriptome modulations caused by each of the E5, E6, and E7 oncogenes of HPV-16 and HPV-84 in HaCaT cells. We identified enhanced RNA degradation, spliceosome, and RNA polymerase pathways related to mRNA processing. ATTS (alternative transcription termination site) was discovered to be more prevalent in cells with HPV-16E5 than HPV-84E5. In HPV-16E6-infected cells, ATTS gain was significantly higher than ATTS loss. Cells with HPV-16E7 had more isoforms with intron retention (IR) than those with HPV-84E7. We identified switches in ADAM10, CLSPN, and RNPS1 that led to greater expression of the coding isoforms in HR-HPV. The results of this work highlight differences between LR-HPV and HR-HPV in mRNA processing. Moreover, crucial cervical cancer-related switch events were detected.

An Event-Driven Real-Time Simulation for Power Electronics Systems Based on Discrete Hybrid Time-Step Algorithm

Real-time (RT) hardware-in-the-loop (HIL) simulation aims to speed up the validation process for power electronic systems (PES). The complex PESs with high switching frequency constitute some of the most challenging applications in RT-HIL. Conventional RT-HIL relies on adding extra expensive computing hardware to achieve sub-microsecond step-size, reducing errors caused by unavoidable sampling delays. This paper proposes a CPU-based event-driven real-time (EDRT) simulation framework by improving the algorithm rather than using additional hardware. The framework consists of two parts: 1) the synchronous-cycle event detection (SCED) sampling method, which eliminates the delay error by detecting switching events; 2) the discrete hybrid time-step (DHT) numerical algorithm, which combines variable and fixed step-size simulation to improve the calculation efficiency and uses the ideal model to improve the modeling accuracy. The proposed framework is applied to a power electronic transformer (PET) with 24 switches and a 20 kHz switching frequency as a simulated case. Comparing the proposed simulation results with experimental results and other simulation results, the proposed EDRT framework can achieve the same numerical accuracy as the offline simulation but only requires 1&#x002F;36 of the computation time. Furthermore, the hardware cost to achieve the same computational scale is reduced to 1&#x002F;20 of the conventional HIL.

Oversampling Techniques to Improve the Accuracy of Hardware-in-the-Loop Switching Models

Real-time Hardware-In-the-Loop implementations for power converters involve digital sampling of the on-off signals that are applied to the switches. The most basic way of considering it, is to process only one switch state during a simulation step. This usually causes an incorrect input reading because it can change during the step. The negative effects include the appearance of undesirable subharmonics and numerical inaccuracy. In this work, input signal oversampling is proposed. Thus, several switch states can be properly processed within the same simulation step. The question is how to use this additional information. This paper proposes two oversampling approaches: a sequential one, which is based on previous works, and a parallel implementation, which is the main contribution of the paper. The results obtained with these techniques are compared between them and with the model without oversampling, showing a significant increase in accuracy for both oversampling approaches around 98%. Although the accuracy is very similar in both methods, synthesis results for FPGA implementation are clearly better for the parallel method, which can be easily adapted to process multiple switching events. The final experimental results show a great subharmonic decrease up to 99%.

Battery state of charge based improved adaptive droop control for power management of a microgrid having large scale renewable generation

Power management in a standalone microgrid having large scale renewable generation is generally based upon droop control strategies. However, the renewable sources such as photovoltaics (PV), which are interfaced with microgrid through power electronic converters have low inertia. Because of low inertia, the transient power variation during switching events like change in load may be very large and may result into variation in voltage and frequency beyond permissible limits. In this paper an adaptive scheme for power management of a standalone microgrid is proposed, which is based on available state of charge (SoC) of the batteries to mitigate these issues. The proposed battery SoC based adaptive droop control for power management allows smooth transition of distributed energy resources (DERs) from maximum power point tracking (MPPT) mode to adaptive droop mode and vice versa. The transient behaviour is handled with the help of batteries and supercapacitor (SC), which is further improved with the help of fuzzy logic based proportional integral controller (FLC-PI). A significant drop in SC power during switching events is obtained with FLC-PI. The test microgrid is composed of multiple PV generation at different locations as primary source of generation, multiple batteries energy storage system (ESS) at different locations, fuel cell (FC), supercapacitor (SC) and a dynamic load.

Clonal Changes in the Pneumococcal Population Carried by Portuguese Children during Six Years of Private Use of the 13-Valent Pneumococcal Conjugate Vaccine: the Relative Contribution of Clonal Expansion, Clonal Emergence, and Capsular Switch Events

In Portugal, the 13-valent pneumococcal conjugate vaccine (PCV13) was available for private use from 2010 to 2015 and it was introduced in the National Immunization Program in 2015. We have reported that private use of PCV13 led to extensive serotype replacement and an increase in antimicrobial susceptibility among pneumococci carried by healthy children. We investigated which clonal changes concurred with these observations. A total of 657 pneumococcal strains, representative of a collection of 2,615 isolates, were genotyped by multilocus sequence typing (MLST). The isolates were recovered in 2009 to 2010 (pre-PCV13), 2011 to 2012 (early PCV13), and 2015 to 2016 (late PCV13) from children attending day care centers in two regions of Portugal (one urban, one rural). One-hundred seventy-one sequence types (STs) were identified, corresponding to 18 clonal complexes (CCs) and 58 singletons. Most CCs (n = 17) and several singletons (n = 16) were found in both regions, indicating that they were geographically disseminated. Clonal complexes expressing PCV13 serotypes in circulation in the late PCV13 period were a subset of the ones identified in the pre-PCV13 period and were often associated with antimicrobial resistance. Among those, the most frequent in both regions was CC179, a multidrug-resistant clone of serotype 19F. Serotype replacement, following PCV13 use, was mainly due to expansion of the susceptible lineages expressing non-PCV13 serotypes already in circulation in the pre-PCV13 period. The emergence of ST53, associated with serotype 8, a major cause of disease in several European countries, was observed in the rural region. Potential capsular switching events, unrelated to PCV13 use, were detected. This study improves our understanding of changes triggered by the private use of PCV13 in Portugal. IMPORTANCE Streptococcus pneumoniae (pneumococcus) is a major human respiratory pathogen linked with high morbidity, mortality, and health care-associated costs worldwide. This bacterium often colonizes asymptomatically healthy children. Colonization is a prerequisite for disease and is also essential for transmission between individuals. The 13-valent pneumococcal conjugate vaccine targets 13 of 101 capsular types of pneumococci described to date. This vaccine not only prevents pneumococcal disease but also impacts colonization by decreasing the carriage of vaccine serotypes. Consequently, serotype replacement occurs. The clonal changes occurring during serotype replacement may be due to various mechanisms, such as clonal expansion, emergence, extinction, or capsular switch (vaccine escape). This study shows that in Portugal, the use of PCV13 has led to significant changes in clonal composition and that these were mainly due to the clonal expansion of lineages expressing serotypes not included in the vaccine.

Streptococcus suis outbreak caused by an emerging zoonotic strain with acquired multi-drug resistance in Thailand.

Streptococcus suis is an emerging zoonotic swine pathogen which can cause severe infections in humans. In March 2021, an outbreak of S. suis infections with 19 confirmed cases of septicemia and meningitis leading to two deaths, occurred in Nakhon Ratchasima province, Thailand. We characterized the outbreak through an epidemiological investigation combined with Illumina and Nanopore whole genome sequencing (WGS). The source of the outbreak was traced back to a raw pork dish prepared from a single pig during a Buddhist ceremony attended by 241 people. WGS analysis revealed that a single S. suis serotype 2 strain belonging to a novel sequence type (ST) of the emergent Thai zoonotic clade CC233/379, was responsible for the infections. The outbreak clone grouped together with other Thai zoonotic strains from CC233/379 and CC104 in a global S. suis phylogeny and capsule switching events between serotype 2 zoonotic strains and serotype 7 porcine strains were identified. The outbreak strain showed reduced susceptibility to penicillin corresponding with mutations in key residues in the penicillin binding proteins (PBPs). Furthermore, the outbreak strain was resistant to tetracycline, erythromycin, clindamycin, linezolid and chloramphenicol, having acquired an integrative and conjugative element (ICE) carrying resistance genes tetO and ermB, as well as a transposon from the IS1216 family carrying optrA and ermA. This investigation demonstrates that multi-drug resistant zoonotic lineages of S. suis which pose a threat to human health continue to emerge.

A Non-Intrusive Load Monitoring Model for Electric Vehicles Based on Multi-Kernel Conventional Neural Network

With the widespread use of electric vehicles (EVs), the charging behavior of these resources has brought a large amount of load growth to the grid, leading to a series of problems such as increased peak valley load difference and line flow violation. Non-intrusive load monitoring (NILM) is a key technology that can be employed to monitor the multi-source load data information in the power grid and support the high-proportion access of electric vehicles. However, traditional NILM approaches are designed to identify the operation of household appliances and cannot be applied at the substation level directly due to frequent and intricate switching events of electrical equipment at this stage. In this paper, a NILM algorithm that can be applied for the monitoring of the charging behavior of electric vehicles at the substation level is proposed to support the high-proportion injection of distributed energy resources. The proposed approach employs a deep learning framework and a multi-kernel convolutional neural network (multi-kernel CNN) framework is used. The performance of the proposed method is verified on the self-organized datasets based on Pecan Street data and results showed that the obtained f1 score is over 90% for both the training sets and testing sets.

CODE SWITCHING DAN CODE MIXING PADA NOVEL CRYING WINTER KARYA MELL SHALIHA

The purpose of this study describes the types of code switching and code mixing in Mell Shaliha Crying Winter novel and the factors that cause code switching and code mixing. The method used in this research is qualitative method. This study uses a qualitative method. Data collection is done by observation and note-taking. The results of this study explain that code switching and code mixing events in the novel Crying Winter are found, namely internal and external code switching in the form of English into Indonesian and vice versa, Korean into Indonesian and the phenomenon of code mixing in the form of words, phrases, baster and clauses. The factors that influence the existence of code switching and code mixing are the participants, the situation, and the influence of the previous speech, and the aims of the conversation.

Phylogenomic analysis of Wolbachia genomes from the Darwin Tree of Life biodiversity genomics project.

The Darwin Tree of Life (DToL) project aims to sequence all described terrestrial and aquatic eukaryotic species found in Britain and Ireland. Reference genome sequences are generated from single individuals for each target species. In addition to the target genome, sequenced samples often contain genetic material from microbiomes, endosymbionts, parasites, and other cobionts. Wolbachia endosymbiotic bacteria are found in a diversity of terrestrial arthropods and nematodes, with supergroups A and B the most common in insects. We identified and assembled 110 complete Wolbachia genomes from 93 host species spanning 92 families by filtering data from 368 insect species generated by the DToL project. From 15 infected species, we assembled more than one Wolbachia genome, including cases where individuals carried simultaneous supergroup A and B infections. Different insect orders had distinct patterns of infection, with Lepidopteran hosts mostly infected with supergroup B, while infections in Diptera and Hymenoptera were dominated by A-type Wolbachia. Other than these large-scale order-level associations, host and Wolbachia phylogenies revealed no (or very limited) cophylogeny. This points to the occurrence of frequent host switching events, including between insect orders, in the evolutionary history of the Wolbachia pandemic. While supergroup A and B genomes had distinct GC% and GC skew, and B genomes had a larger core gene set and tended to be longer, it was the abundance of copies of bacteriophage WO who was a strong determinant of Wolbachia genome size. Mining raw genome data generated for reference genome assemblies is a robust way of identifying and analysing cobiont genomes and giving greater ecological context for their hosts.

High-Precision Mapping of Membrane Proteins on Synaptic Vesicles using Spectrally Encoded Super-Resolution Imaging.

The spatial resolution of single-molecule localization microscopy is limited by the photon number of a single switching event because of the difficulty of correlating switching events dispersed in time. Here we overcome this limitation by developing a new class of photoswitching semiconducting polymer dots (Pdots) with structured and highly dispersed single-particle spectra. We imaged the Pdots at the first and the second vibronic emission peaks and used the ratio of peak intensities as a spectral coding. By correlating switching events using the spectral coding and performing 4-9 frame binning, we achieved a 2-3 fold experimental resolution improvement versus conventional superresolution imaging. We applied this method to count and map SV2 and proton ATPase proteins on synaptic vesicles (SVs). The results reveal that these proteins are trafficked and organized with high precision, showing unprecedented level of detail about the composition and structure of SVs.