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The modular multilevel converter (MMC) has become popular for medium-voltage applications. During MMC operation, unavoidable circulating currents flow through its arms and cause extra losses in the converter system. To generate a reference for circulating current, different current injection methods to control the circulating current have been reported. The conventional current injection techniques used so far relies on a mathematical model that excludes the effect of arm inductor voltages in its design. For high-voltage applications with many submodules (SMs), the above exclusion does not make much difference. But for medium-voltage applications with few SMs, the applied arm inductor voltages may be significant for a given dc-link voltage. In this article, inductor voltages are included in the proposed circulating current model, and a new reference for circulating current is obtained resulting in reduced circulating current through MMC arms. The proposed method is analyzed in detail on both seven-level and nine-level three-phase MMC, where advantages of the 2 N + 1 level phase disposition pulsewidth modulation (PD-PWM) scheme are retained. The simulation study is carried out in PLECS blockset to show the reduction in the rms magnitude of circulating current. The validation of the proposed technique in a lab-scale hardware prototype shows the effectiveness of the proposed method.

Many stresses induce the accumulation of unfolded and misfolded proteins in the endoplasmic reticulum, a phenomenon known as ER stress. In response to ER stress, cells initiate a protective response, known as unfolded protein response (UPR), to maintain cellular homeostasis. The UPR sensor, inositol-requiring enzyme 1 (IRE1), catalyzes the cytoplasmic splicing of bZIP transcription factor-encoding mRNAs to activate the UPR signaling pathway. Recently, we reported that pretreatment of Arabidopsis thaliana plants with tunicamycin, an ER stress inducer, increased their susceptibility to bacterial pathogens; on the other hand, IRE1 deficient mutants were susceptible to Pseudomonas syringae pv. maculicola (Psm) and failed to induce salicylic acid (SA)-mediated systemic acquired resistance. However, the functional relationship of IRE1 with the pathogen and TM treatment remains unknown. In the present study, we showed that bacterial pathogen-associated molecular patterns (PAMPs) induced IRE1 expression; however, PAMP-triggered immunity (PTI) response such as callose deposition, PR1 protein accumulation, or Pst DC3000 hrcC growth was not altered in ire1 mutants. We observed that IRE1 enhanced plant immunity against the bacterial pathogen P. syringae pv. tomato DC3000 (Pst DC3000) under ER stress. Moreover, TM-pretreated ire1 mutants were more susceptible to the avirulent strain Pst DC3000 (AvrRpt2) and showed greater cell death than wild-type plants during effector-triggered immunity (ETI). Additionally, Pst DC3000 (AvrRpt2)-mediated RIN4 degradation was reduced in ire1 mutants under TM-induced ER stress. Collectively, our results reveal that IRE1 plays a pivotal role in the immune signaling pathway to activate plant immunity against virulent and avirulent bacterial strains under ER stress.

The plant pathogenic bacterium Pseudomonas syringae pv. tomato DC3000 (Pst DC3000) has become a paradigm to investigate plant-bacteria interactions due to its ability to cause disease in the model plant Arabidopsis thaliana. Pst DC3000 uses the type III secretion system to deliver type III secreted effectors (T3SEs) directly into the plant cytoplasm. Pst DC3000 T3SEs contribute to pathogenicity by suppressing plant defense responses and targeting plant’s physiological processes. Although the complete repertoire of effectors encoded in the Pst DC3000 genome have been identified, the specific function for most of them remains to be elucidated. Among those effectors, the mitochondrial-localized T3E HopG1, suppresses plant defense responses and promotes the development of disease symptoms. Here, we show that HopG1 triggers necrotic cell death that enables the growth of adapted and non-adapted pathogens. We further showed that HopG1 interacts with the plant immunity-related protein AtNHR2B and that AtNHR2B attenuates HopG1- virulence functions. These results highlight the importance of HopG1 as a multi-faceted protein and uncover its interplay with AtNHR2B.

Nowadays, the classification of medical images has become an essential part of identifying the disease. Among various existing critical diseases, identification of breast cancer has now come up with the topic of investigation. To identify the affected regions of the images, a deep learning-based approach has got wide attention for decades. Convolutional neural networks (CNN) among all deep learning techniques proved their best efficiency in this field. In this paper, one improved CNN-based approach has been proposed to classify the breast cancer images obtainable from the standard PatchCamelyon (PCam) benchmark dataset. It is available for free from the website link https://www.kaggle.com/c/histopathologic-cancer-detection/data. In the improved model, various existing layers like convolutional, ReLU, pooling, fully connected have been added as well as modified for better efficiency and efficacy of the algorithm. Further to be added that Adam optimizer has been used here with cross entropy as a loss function. This improved model has been compared with two recent CNN-based approaches applied to medical datasets. The comparative outcomes suggest strong improvements in terms of classification accuracy (probably 2–3%) and computational time of validation loss for both training and testing data over existing models.

Clustering-based image segmentation got wide attention for decades. Among various existing clustering techniques, K-means algorithm gained popularity for its better outcome. But the drawback of this algorithm can be found, when it is applied to noisy medical images. So, modification of the standard K-means algorithm is highly desired. This paper proposes an improved version of K-means algorithm called as (IKM) to get more effective and efficient outcomes. The efficiency of the algorithm depends on the speed of forming the clusters. So, in the proposed approach, new idea has been applied to find the minimum distance to generate the clusters. The proposed IKM algorithm has been applied to the set of noisy medical images, and the segmented outcomes have been evaluated by the standard quality measurement metrics, namely Peak-Signal-to-Noise-Ratio (PSNR) and structural similarity index measurement (SSIM). The outcomes have also been compared with the Watershed algorithm for showing the betterment of the proposed approach.

Image segmentation problem has been solved by entropy-based thresholding approaches since decades. Among different entropy-based techniques, fuzzy entropy (FE) got more attention for segmenting color images. Unlike grayscale images, color images contain 3-D histogram instead of 1-D histogram. As traditional fuzzy technique generates high time complexity to find multiple thresholds, so recursive approach is preferred. Further optimization algorithm can be embedded with it to reduce the complexity at a lower range. An updated robust nature-inspired evolutionary algorithm has been proposed here, named improved differential evolution (IDE) which is applied to generate the near-optimal thresholding parameters. Performance of IDE has been investigated through comparison with some popular global evolutionary algorithms like conventional DE, beta differential evolution (BDE), cuckoo search (CS), and particle swarm optimization (PSO). Proposed approach is applied on standard color image dataset known as Berkley Segmentation Dataset (BSDS300), and the outcomes suggest best near-optimal fuzzy thresholds with speedy convergence. The quantitative measurements of the technique have been evaluated by objective function’s values and standard deviation, whereas qualitative measures are carried out with popular three metrics, namely peak signal-to-noise ratio (PSNR), structural similarity index measurement (SSIM), and feature similarity index measurement (FSIM), to show efficacy of the algorithm over existing approaches.

Modular Multilevel Converters (MMC) are known for their high modularity and good quality output power. The problems of circulating current and capacitor voltage balancing for the MMC have been widely studied in available literature but little work is done towards grid current control with fast dynamic response. Conventional current controllers use PI or PR controllers which are slow and thus degrade transient performance of the MMC. Recently reported Model Predictive Control (MPC) methods for MMC aim to control the grid current but MPC suffers from computational burden and difficulties in adjusting of weights. Space Vector based Hysteresis Current Controllers (SCHCC) have been well established for drive applications. In this work, an SVHCC has been developed for a grid connected MMC system. Simulation studies show fast current control for cases of sudden change of active power reference and current reference. The controller exhibits quick tracking of the current reference in all cases. The proposed method has been discussed for a MMC system with three submodules, but it may be extended to any number of submodules and can be easily implemented in a digital controller with less computational effort.

Model Predictive Control (MPC) is rapidly making inroads into power electronics converter control due to its simple and intuitive design and fast transient performance. However issues like heavy computational burden, tedious weights selection process and lack of fixed switching frequency seriously inhibit its application for large systems like the Modular Multilevel Converter (MMC). To tackle this problem, this article proposes a unique simplified two-stage MPC strategy for the Modular Multilevel Converter (MMC) using a simple search algorithm. The proposed technique falls under the category of Modulated MPC. One of the major advantages of this method includes fixed switching frequency for MMC with $2\mathrm{N}+1$ levels in the phase voltage, which is absent in many other MPC techniques in literature. Moreover the circulating current suppression and the output current control are decoupled. The capacitor voltage balancing is carried out using the simple reduced switching frequency type of sorting. It has lower computational complexity and ensures simplified digital implementation. The simulation results carried out using PLECS for 10 SM/0.7 MW MMC connected to a passive load validate the performance of this proposed algorithm.

The modular multilevel converter (MMC) used in medium and high voltage applications due to its several advantages related to scalability and modularity. An MMC consists of several sub-modules (SMs) which house several capacitors in each arm of each phase. For the working of MMC, the voltage across these capacitors needs to be measured. High voltage applications demand more number of SMs, which needs more capacitors and its voltage sensing devices for operation. In this paper, a discrete time adaptive observer (DTAO) is designed for the estimation of capacitor voltages of SMs by taking the discrete model of an MMC. It handles the uncertainties due to the modeling error and parasitic dynamics and makes the design robust. This design approach is very simple as it serves the engineer to choose the design parameter in an easy way. This method is also compared with discrete time sliding mode observer (DTSMO) approach.

Hexokinase1 (HXK1) is an Arabidopsis glucose sensor that has a variety of roles during plant growth and devlopment, including during germination, flowering, and senescence. HXK1 also acts as a positive regulator of plant immune responses. Previous research suggested that HXK1 might influence plant immune responses via responses to glucose. Plant immune responses are governed by two main pathways: PAMP-triggered immunity (PTI) and effector-triggered immunity (ETI). PTI involves the recognition of Pathogen-Associated Molecular Patterns (PAMPs) and leads to increased callose formation and accumulation of pathogenesis response (PR) proteins. ETI acts in response to effectors secreted by Gram-negative bacteria. During ETI, the membrane-localized protein RPM1-interacting protein 4 (RIN4) becomes phosphorylated in reponse to interactions with effectors and mediates the downstream response. In this study, the effects of glucose on plant immune responses against infection with Pseudomonas syringae pv. tomato DC3000 and other P. syringae strains were investigated in the presence and absence of HXK1. Infiltration of leaves with glucose prior to infection led to decreases in bacterial populations and reductions in disease symptoms in wild-type Arabidopsis plants, indicating that glucose plays a role in plant immunity. Both PTI and ETI responses were affected. However, these effects were not observed in a hxk1 mutant, indicating that the effects of glucose on plant immune responses were mediated by HXK1-related pathways.

The modular multilevel converter (MMC) has found a place in medium and high voltage applications because of its several advantages over the multilevel topologies. It comprises of several capacitors in each phase leg and their voltages need to be measured for its reliable operation. More the number of sub-modules (SMs); more is the number of required voltage measurement devices. The use of more number of physical voltage measurement devices makes the system costly and more complex in terms of hardware implementation. A unique capacitor voltage estimation technique is proposed in this paper, using a variable gain discrete time sliding mode observer (VGDTSMO) technique. These variable gains present in sliding mode observer make it simpler for implementation. This is a generalized method that can be scalable to any number of phases and SMs. The VGDTSMO technique is validated by a mathematical proof of error convergence and simulation results are provided to substantiate the theory. The results are compared with existing literature on discrete time sliding mode observer (DTSMO) based capacitor voltage estimation of an MMC.

Entropy-based thresholding techniques are quite popular and effective for image segmentation. Among different entropy-based techniques, minimum cross-entropy thresholding (MCET) has received wide attention in the field of image segmentation. Considering the high time complexity of MCET technique for multilevel thresholding, recursive approach to reducing its computational cost is highly desired. To reduce the complexity, further optimization techniques can be applied to find optimal multilevel threshold values. In this paper, a novel improved particle swarm optimization (IPSO)-based multilevel thresholding algorithm is proposed to search the near-optimal MCET thresholds. The general PSO algorithm often suffers from premature convergence problem which has been addressed in the IPSO by decomposing a high-dimensional swarm into several one-dimensional swarms, and then premature convergence is removed from each one-dimensional swarm. The proposed technique is applied to the set of grayscale images, and the experimental results infer that it produces better MCET optimal threshold values at a higher and faster convergence rate. The qualitative and quantitative results are compared with existing optimization techniques like modified artificial bee colony, Cuckoo search, Firefly, particle swarm optimization, and genetic algorithm. It has been observed that the proposed technique performs better in terms of producing better fitness value, less CPU time as quantitative measurements, and effective misclassification error, peak signal-to-noise ratio, feature similarity index measurement, complex wavelet structural similarity index measurement values as qualitative measurements compared to other considered state-of-the-art methods.

Most secretory and membrane proteins are properly folded in the endoplasmic reticulum (ER) before being transferred to their functional destinations. Physiological and pathological stresses induce unfolded and misfolded protein accumulation in the ER, termed as ER stress. Under ER stress, cells initiate a protective response to maintain cellular homeostasis, which is referred as unfolded protein responses. Although protein processing in the ER has been known to regulate cell lifespan and disease, few evidences that prove the role of ER stress in plant immunity have been reported. We investigated the interaction between ER stress and pathogenicity in Arabidopsis by utilizing the N-glycosylation inhibitor, tunicamycin (TM) as an ER stress inducer. TM induced the accumulation of PR1 (pathogenesis-related protein 1) and callose in plant leaves, which are markers for PAMP-triggered immunity (PTI) activation. However, TM pre-treatment increased susceptibility of Arabidopsis to all bacterial pathogens tested. Moreover, TM resulted in cell death of plant leaves with an additive effect to hypersensitive response by bacterial effector proteins, suggesting TM-induced cell death is independent of the effector-triggered immunity. These results imply that TM-induced ER stress weakens overall immune system of plant not a specific immune pathway, probably via disruption of post-translational modification of immune-related proteins in the ER and subsequent cell death by apoptosis or autophagy. This study provides proves for the distinct suppressive effect of ER stress on the plant immune system.

In eukaryotes, RNA silencing, mediated by small interfering RNAs, is an evolutionarily widespread and versatile silencing mechanism that plays an important role in various biological processes. Increasing evidences suggest that various components of RNA silencing pathway are involved in plant defense machinery against microbial pathogens in Arabidopsis thaliana. Here, we show genetic and molecular evidence that Arabidopsis SDE5 is required to generate an effective resistance against the biotrophic bacteria Pseudomonas syringae pv. tomato DC3000 and for susceptibility to the necrotrophic bacteria Erwinia caratovora pv. caratovora. SDE5, encodes a putative mRNA export factor that is indispensable for transgene silencing and the production of trans-acting siRNAs. SDE5 expression is rapidly induced by exogenous application of phytohormone salicylic acid (SA), methyl jasmonate (MeJA), phytopathogenic bacteria, and flagellin. We further report that SDE5 is involved in basal plant defense and mRNA export. Our genetic data suggests that SDE5 and Nonexpressor of PR Gene1 (NPR1) may contribute to the same SA-signaling pathway. However, SDE5 over-expressing transgenic plant exhibits reduced defense responsive phenotype after flagellin treatment. Taken together, these results support the conclusion that SDE5 contributes to plant innate immunity in Arabidopsis.

The Modular Multilevel Converter (MMC) that consists of several submodules (SMs) in its structure generates high quality output voltages. The controller of the MMC requiring all the SM capacitor voltages to be sensed makes it expensive and less reliable in terms of field implementation. In practical scenario, Sliding Mode Observer (SMO) can be used as a robust estimation technique in sensing the SM capacitor voltages. This paper proposes a Discrete Time Sliding Mode Observer (DTSMO) that estimates the capacitor voltages of all the SMs of an MMC based on discrete time model approach. This generalized technique can be suitably adopted for a MMC with any phases and any number of submodules. The proposed observer is simulated for medium voltage application in MATLAB/Simulink and results show its effectiveness in estimation. The problem of dissimilar initial charging among SM capacitors is also studied and the observer exhibits stable performance in such conditions. The MMC structure with this proposed observer can eliminate a large number of sensors particularly in high voltage applications.

The Modular Multilevel Converter (MMC) has found considerable use in medium voltage applications like MVDC in recent times. Circulating currents that do not appear at the output and flow between the phase legs, pose serious concern causing unnecessary converter losses. Several methods of circulating current control have been proposed and they are based on simplified average models of the MMC. Such models are well suited for high voltage systems where there are large number of Sub-Modules (SMs). But, for medium voltage MMC using fewer SMs, circulating current control can be better managed by considering more accurate model. This paper proposes an improved model that includes the inductor voltage in its design. In this approach, the circulating current reference is first obtained and then it is used to choose a correct switching redundancy to control the circulating current. The proposed work uses a phase leg based PD 2N+1 modulation scheme that has inherently better harmonic performance compared to conventional arm-based modulation. Through simulation studies, it is proved here that the rms circulating current gets reduced resulting in reduced conduction loss. The proposed work is applicable for MVDC applications which involve fewer number of SMs.

Presently, the prediction of share is a challenging issue for the research community as share market is a chaotic place. The reason behind it, there are several factors such as government policies, international market, weather, performance of company. In this article, a model has been developed using long short term memory (LSTM) to predict the share price of DLF group. Moreover, for the experimental purpose the data of DLF group has been taken from yahoo financial services in the time duration of 2008 to 2018 and the recurrent neural network (RNN) model has been trained using data ranging from 2008 to 2017. This RNN based model has been tested on the data of year 2018. For the performance comparison purpose, other linear regression algorithms i.e. k-nn regression, lasso regression, XGboost etc has been executed and the proposed algorithm outperforms with 2.6% root mean square error.

As novel absorber materials are developed and screened for their photovoltaic (PV) properties, the challenge remains to reproducibly test promising candidates for high-performing PV devices. Many early-stage devices are prone to device shunting due to pinholes in the absorber layer, producing "false-negative" results. Here, we demonstrate a device engineering solution toward a robust device architecture, using a two-step absorber deposition approach. We use tin sulfide (SnS) as a test absorber material. The SnS bulk is processed at high temperature (400 °C) to stimulate grain growth, followed by a much thinner, low-temperature (200 °C) absorber deposition. At a lower process temperature, the thin absorber overlayer contains significantly smaller, densely packed grains, which are likely to provide a continuous coating and fill pinholes in the underlying absorber bulk. We compare this two-step approach to the more standard approach of using a semi-insulating buffer layer directly on top of the annealed absorber bulk, and we demonstrate a more than 3.5× superior shunt resistance Rsh with smaller standard error σRsh. Electron-beam-induced current (EBIC) measurements indicate a lower density of pinholes in the SnS absorber bulk when using the two-step absorber deposition approach. We correlate those findings to improvements in the device performance and device performance reproducibility.

The endoplasmic reticulum (ER) is a well-characterized protein folding mechanism in eukaryotic organisms. Many secretory and membrane proteins are folded in the ER before they are translocated to their functional destination. Various conditions, such as biotic, abiotic, or physiological stresses, lead to the accumulation of unfolded and misfolded proteins in the ER, resulting in ER stress. In response to ER stress, cells initiate a protective response called the unfolded protein response (UPR) to maintain cellular homeostasis. Previous studies suggest that inositol-requiring kinase 1 (IRE1) is a universal ER stress sensor in yeast, mammals, and plants. IRE1-mediated splicing of UPR transducers, such as HAC1, XBP1, and bZIP60, triggers the UPR in yeast, mammals, and plants, respectively. In mammals, activated transcription factor 6 and double stranded RNA-activated protein kinase-like ER kinases are involved in the UPR. In plants, the additional UPR transducers bZIP28 and bZIP17 are activated by Golgi-localized S1P and S2P proteases. Subsequently, these UPR transducers are exported to the nucleus and upregulate the expression of UPR-responsive genes encoding BiP, calreticulin, calnexin, protein disulfide isomerase, and glucose-regulated protein 94 to decrease the amount of misfolded proteins and induce endoplasmic reticulum-associated degradation. In plants, the UPR signaling pathway plays an important role in ER homeostasis and normal biological processes; however, the molecular mechanisms of the UPR in plants remain poorly understood. This paper provides an overview of the regulatory and signaling mechanisms of the UPR across kingdoms. In addition, the emerging role of the UPR in plant physiology and defense response will be discussed.

Space Vector Modulation (SVM) has already shown its effectiveness in multi-level converter applications. An innovative sample based SVM technique has been adopted here for the first time for the Modular Multi-level Converter (MMC). This proposed technique uses mapping of the original reference vector in the multilevel space vector structure to a two level hexagon in order to find out a generalized PWM technique for any n-level MMC. The switching vectors found in the two level SVM structure are escalated to actual switching vectors to generate gating pulses using simple logic. Computational complexity is reduced by adopting a generalized algorithm which is even effective for high voltage applications where number of submodule can increase substantially. The gating pulses are generated for the submodules in a manner that the voltage across the inductor is actively controlled and hence, better control on the output voltage is achieved. A desktop simulation study based on 9-level MMC is carried out for validation purpose. The main focus of this paper is on the novel SVM technique and hence, ideal dc sources are used for submodules for proving the above mentioned PWM technique.

Space vector modulation (SVM) is well established for conventional multilevel topologies and it can be very useful for MMC owing to its superior harmonic performance. This paper proposes a dual SVM strategy which is a generalized approach and has same number of steps for any level converter. This technique is based on mapping of the actual reference vector to a two level mapped reference vector and finding of dwell times of corresponding equivalent two level switching vectors. Once these are obtained, equivalent switching vectors are transformed to the actual switching vectors in the multilevel space vector structure using simple predetermined transformation relations. Here, two SVMs are used to modulate the upper and lower arms. The problem of circulating current is also included in the analysis and a circulating current controller is used for compensating the dc and second harmonic arm currents. This circulating current controller uses both PI and resonant controller to control the dc and second harmonic currents. This work focuses on the simple generalized digital implementation of SVM for MMC and hence ideal submodules (SMs) are chosen with fixed DC voltages.

N-glycosylation of proteins in the endoplasmic reticulum (ER) and Golgi apparatus is essential for protein posttranslational modification in plant cells. Although the Nglycosylation in the ER and Golgi apparatus has been known to regulate protein quality control, salt stress and cellulose biosynthesis, few evidences related to the roles of Nglycosylation in plant immunity have been reported. Arabidopsis thaliana mutants defective in N-glycosylation, namely, staurosporin and temperature sensitive 3a and 3b (stt3a and stt3b) of oligosaccharyltransferase subunit and complex glycan 1 (cgl1) in Golgi apparatus were used in this study. Results showed that Arabidopsis mutant stt3a was more susceptible against Pseudomonas syringae pv. tomato DC3000 (Pst) and Erwinia carotovora subsp. carotovora (ECC). In addition, stt3a was less resistant against Pst harboring effector protein AvrRpm1 compared to the wild type plant. However stt3b showed less significant changes in susceptibility against these three bacterial strains. These results infer the functions of STT3B in N-glycosylation that STT3B is likely supplementary to the main STT3A. Flg22-induced PR1 accumulation and callose deposition were blocked in Nglycosylation mutants, implying that N-glycosylation is involved in the PAMP-triggered immunity. However, Nglycosylation was not absolutely required for AvrRpm1- and AvrRpt2-triggered immunity. STT3A-binding proteins were searched in order to understand the role of STT3A in plant immunity. Ubiquitin-conjugating enzyme E2s, UBC7 and UBC13, were found to be binding proteins of STT3A by yeast two-hybrid assay.

This Paper represents a multi-level image thresholding approach based on the normalized index value of image and probability of pixel intensities. One new objective function proposed, which is the multiplication of normalized index value and probability, to obtain the scenario. This multiplication measure is then optimized to obtain the thresholds of the image. In order to solve an optimization problem, Differential Evolution (DE) as a meta-heuristic approach is used, which results a fast and accurate convergence towards the optimal solution. The performance of DE is compared to other well-known optimized algorithms like Particle swarm optimization (PSO), Genetic Algorithms (GA). The outcomes of images are compared with Kapur entropy, Tsalli entropy and Otsu method, both visually and statistically for establishing the perceptible difference in image.

Secondary phase segregation is hypothesized to have detrimental impacts on Cu 2 ZnSnS 4 (CZTS) thin-film solar cells. In this study, we demonstrate the potential of using kinetic stabilization to inhibit phase decomposition in CZTS. By growing CZTS films at low temperature, we achieve a kinetically stabilized alloy with an expanded solid solution window in the pseudoternary CuS-ZnS-SnS phase diagram. Using X-ray absorption spectroscopy, we study the structural evolution and stability of this metastable alloy upon annealing. For near-stoichiometric samples, we observe a continuous emergence of short-range order toward crystalline CZTS that is nearly complete after a 1-min anneal at 450 °C. For Zn-rich samples, we detect precipitation of ZnS upon annealing, which suggests that the excess Zn exists as cation antisite defects in metastable CZTS.