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This paper presents the numerical approaches for sensitivity analysis (SA) and its application in the modeling effort of a modular multilevel converter (MMC). A review of the state-of-the-art techniques in the sensitivity analysis is provided, with a special focus on the numerical approaches, followed by the sensitivity analysis of an MMC. To further reduce the computational cost per model evaluation in parametric uncertainty quantification (P-UQ) of the MMC, this paper also proposes a simplified model with a minimum number of power modules for P-UQ analysis without introducing any further uncertainties in the modeling and simulation.

The second part of the multi-objective design and optimization with parametric and model-form uncertainty quantification (MDO with P&MF-UQ) is dedicated to incorporating MF-UQ into the MDO framework. MF-UQ is used to estimate the error due to modeling inaccuracies and to validate the mathematical models used in the MDO framework. In this approach, the sensitivity index introduced in the first part of the article is modified too so that it is a quantitative measure of system design robustness with regards to modeling inaccuracies as well as the manufacturing variability in the design of systems with multiple performance functions. The optimum design solution is finally realized by exploring the Pareto Front of the enhanced performance space, where the model-form error associated with each design is used to modify the estimated performance measures and the parametric sensitivity of each design point is considered to discern between cases and help identify the most parametrically robust of the Pareto-optimal design solutions. To demonstrate the benefits of incorporating uncertainty quantification analysis into the design optimization from a more practical standpoint, design of a robust high-efficiency high-power-density 1.25 kW Vienna-type rectifier is used as a case study to compare the theoretical analysis with a comprehensive experimental validation. It is shown that the final design selected using the MDO with P&MF-UQ reduces the design sensitivity and system loss by 33% and 5%, respectively, compared to the optimal design selected using the conventional MDO.

Modeling and design with parametric and model-form uncertainty quantification is an alternative to conventional model-based design approaches as it improves the existing modeling practice and validates the model used in the design of power converters. However, in the case of modular multilevel converters (MMCs), uncertainty quantification, as the main step in this design methodology, becomes challenging due to the inherent complexity and sheer size of such units. In this article, these limitations are discussed and a systematic study for developing a simplified testbed for uncertainty quantification of an MMC is presented. To this end, first sensitivity analysis is conducted to identify the key parameters whose tolerances contribute the most to the parametric uncertainty of the selected design variables. Second, the effect of increasing the number of power cells in each arm on the estimated total uncertainty, and thus the predictive capability of the MMC simulation models for medium- and high-voltage applications is studied. A simplified testbed for model validation of the power cell in the design of an MMC is developed accordingly. The development of this simplified testbed allows validating the models used and estimating uncertainties in the design with less computational cost and hardware prototyping.

Introduction. Given the limited number of candidaemia studies in Iran, the profile of yeast species causing bloodstream infections (BSIs), especially in adults, remains limited. Although biochemical assays are widely used in developing countries, they produce erroneous results, especially for rare yeast species.Aim. We aimed to assess the profile of yeast species causing BSIs and to compare the accuracy of the Vitek 2 system and 21-plex PCR.Methodology. Yeast blood isolates were retrospectively collected from patients recruited from two tertiary care training hospitals in Tehran from 2015 to 2017. Relevant clinical data were mined. Identification was performed by automated Vitek 2, 21-plex PCR and sequencing of the internal transcribed spacer region (ITS1-5.8S-ITS2).Results. In total, 137 yeast isolates were recovered from 107 patients. The overall all-cause 30-day mortality rate was 47.7 %. Fluconazole was the most widely used systemic antifungal. Candida albicans (58/137, 42.3 %), Candida glabrata (30/137, 21.9 %), Candida parapsilosis sensu stricto (23/137, 16.8 %), Candida tropicalis (10/137, 7.3 %) and Pichia kudriavzevii (Candida krusei) (4/137, 2.9 %) constituted almost 90 % of the isolates and 10 % of the species detected were rare yeast species (12/137; 8.7 %). The 21-plex PCR method correctly identified 97.1 % of the isolates, a higher percentage than the Vitek 2 showed (87.6 %).Conclusion. C. albicans was the main cause of yeast-derived fungaemia in this study. Future prospective studies are warranted to closely monitor the epidemiological landscape of yeast species causing BSIs in Iran. The superiority of 21-plex PCR over automated Vitek 2 indicates its potential clinical utility as an alternative identification tool use in developing countries.

Background: Candida albicans remains the main cause of candidiasis in most clinical settings. Available drugs for candidiasis treatment have many side effects. In this work, novel nitroglycerin derivatives were synthesized and their cytotoxic and antifungal effects evaluated against fluconazole susceptible and resistant clinical C. albicans isolates. Methods: This experimental study was performed in Tehran University of Medical Sciences and Baqiatallah University of Medical Sciences, Tehran, Iran between Feb to Dec 2019. The in vitro activities of two novel nitroglycerin derivatives (1b and 2b) against 25 clinical fluconazole-susceptible and resistant C. albicans isolates and four standard C. albicans strains were determined according to CLSI reference M27-A3 documents. The cytotoxicity of chemical compounds was investigated near the SNL76/7 cells by colorimetric assay. Real-time PCRs were performed to evaluate the alterations in the regulation of ERG11 and CDR1 genes under nitroglycerin derivatives-treated and untreated conditions. Results: The derivatives 1b and 2b exhibited potent antifungal activity against C. albicans isolates; MICs and MFCs varied from 18 μg/ml to 72 μg/ml and 36 μg/ml to 144 μg/ml, respectively. The cell viability evaluation demonstrated that both chemical compounds are safe within 24h. The nitroglycerin derivatives were able to reduce the transcription level of CDR1 and ERG11 genes in all susceptible and resistant C. albicans isolates. Conclusion: Considering the potential and efficacy of these compounds against clinical C. albicans isolates, the complementary in vivo and clinical trials should be investigated.

Zahra Rafat,1 Seyed Jamal Hashemi,1,2 Keyhan Ashrafi,3 Iraj Nikokar,4 Alireza Jafari,5 Abbas Rahimi Foroushani,6 Behrad Roohi,7 Zeinab Borjian Boroujeini,1 Niloofar Rashidi,1 Niki Najar-Shahri4 1Department of Medical Parasitology and Mycology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran; 2Food Microbiology Research Center, Tehran University of Medical Sciences, Tehran, Iran; 3Department of Medical Microbiology, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran; 4Laboratory of Microbiology and Immunology of Infectious Diseases, Paramedicine Faculty, Guilan University of Medical Sciences, Langeroud, Iran; 5Urology Research Center, Razi Hospital, Guilan University of Medical Sciences, Rasht, Iran; 6Department of Statistics and Epidemiology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran; 7Department of Medical Mycology, Mazandaran University of Medical Sciences, Sari, IranCorrespondence: Seyed Jamal Hashemi Tel +98 21 4293 3150Fax +98 21 88951392Email sjhashemi@tums.ac.irIntroduction: Fungal respiratory infections are being recognized with increasing frequency in parallel with an expanding population of immunocompromised patients. In most cases, colonization is the first step in the progression to pulmonary fungal infection. This study was designed to evaluate the distribution of fungal elements in the respiratory tract of symptomatic patients hospitalized in pulmonary units.Methods: This descriptive cross-sectional study was carried out over a period of two years, from October 2017 to October 2019 in Guilan province, located in Iran’s northern region. In the current study, bronchoalveolar lavage or sputum specimens were collected. All samples were analyzed by direct microscopy using KOH 10% and culture. Fungal identification was accomplished by internal transcribed spacer (ITS) and beta-tubulin sequencing. Also, in patients suspected of invasive pulmonary aspergillosis, BAL specimens were tested for galactomannan (GM) antigen.Results: A total of 384 lung specimens (192 bronchoalveolar lavage (BAL) and 192 sputum samples) were obtained from symptomatic patients hospitalized in pulmonary units. Of these, 137 (35.67%) were positive in direct examination and culture. Among the 137 positive cases, most isolates were from male patients 86 (62.77%) and most of them were between 46 and 72 years. Candida albicans (37.22%) and Candida tropicalis (21.89%) represent the two most commonly isolated species in the current study. Cough (94.16%), dyspnea (81.02%), purulent sputum (62.04%) and weight loss (56.2%) were the predominant symptoms and tuberculosis (24.81%), chemotherapy (21.89%) and diabetes mellitus (19.70%) were the predominant underlying conditions. Also, 5 cases of invasive pulmonary aspergillosis and 1 case of mucormycosis were diagnosed.Conclusion: Candida albicans was the most common fungal species isolated from symptomatic patients hospitalized in pulmonary units. Tuberculosis, chemotherapy and diabetes mellitus were important underlying conditions for pulmonary fungal colonization and/or infection.Keywords: pulmonary fungal infection, bronchoalveolar lavage, sputum, respiratory tract, Iran

In this paper, a novel single-dimensional parametric deformable model based on calculus of variations is proposed for automatic QRS detection in electrocardiogram (ECG) signal. It is inspired by the active contours, conventionally used for image segmentation. Primarily, the Shannon envelop of the ECG signal is obtained by a preprocessing algorithm. The proposed parametric deformable model includes a chain of consecutive points, randomly spread along the temporal domain. These points should be collected at local maxima of the Shannon envelop. For this purpose, the proposed energy functional consists of internal and external energy terms. By minimization of the former, all model points are pushed towards the peaks of their probability density function, while by minimization of the later, every peak of the probability density function is fitted on the corresponding local maximum of the Shannon envelop. The whole energy functional is minimized, in the light of the Euler–Lagrange equation, using the gradient descend and finite difference methods. After convergence of the deformation process, for R-peak detection, it is sufficient to extract the point clusters of the optimal deformable model. Experimental results demonstrated superior/comparable performance of the proposed algorithm compared to a number of well-known methods.

This article presents an enhanced modeling framework for the design of power electronics converters and systems. This methodology provides the means to assess and quantify the predictive accuracy of power converter models, a capability that is attained by the proper identification, characterization, and quantification of the different, and often numerous, sources of uncertainty in the modeling and simulation process. For illustrative purposes, an in-depth example is presented using a power electronics building block (PEBB) as the modeling test bed, demonstrating how, through this formal modeling procedure-and taking into consideration the uncertainties in the system-the model in question can predict the variable of interest within a known range with a given level of confidence, without requiring heuristic safety factors. This predicted range can be used as the required design margin when the selected output is a design variable, evincing the inherent potential of having a less conservative and more reliable power converter using the design methodology with uncertainty quantification.

This paper presents a design methodology with uncertainty quantification to estimate the required margin for two main design variables in a modular multilevel converter (MMC). In this methodology, the minimum required design margins are calculated by quantifying all sources of uncertainty in the modeling and simulation of MMCs. To this end, an enhanced modeling framework is presented to take into account the parametric uncertainty (PU) that results from manufacturing variability, and model-form uncertainty (MFU) that results from inherent inaccuracies of the models used in the design process. In this paper, sensitivity analysis (SA) is used to guide the modeling effort and minimize the number of uncertain parameters required for inclusion in uncertainty quantification. Besides, a simplified testbed for model validation of the MMC is developed. This testbed is used for conducting low-power validation experiments and Monte-Carlo simulations to estimate PU and MFU, respectively.

This paper presents the design of a Power Electronics Building Block (PEBB) based on 1.7 kV SiC MOSFET power modules. The PEBB is an H-bridge converter module that can be cascaded to construct multilevel converters. Novel designs including gate driver with Rogowski shortcircuit protection, powerful distributed controller, isolated A/D sensor with high common-mode noise rejection, double-side cooling mechanical layout have been achieved. The paper begin with the evolution of PEBB architectures and the specifications of the designed PEBB. It then presents detailed design considerations and solutions of all the critical components. Finally, experimental results demonstrate the excellent performance of the individual components and the PEBB as a unity in various operation modes.

The Medium Voltage dc (MVDC) distribution is actively being sought as the eventual structure for the next-generation shipboard electrical power systems and the Modular Multilevel Converter (MMC) has become a preferred choice for MVDC systems. However, the fault response is a big challenge for both the shipboard MVDC system and the MMC. A fault current control is proposed to work with MMCs composed of full-bridge Power Electronics Building Blocks (PEBBs). The converter is able to fast clear the fault current when a dc shortcircuit fault happens and easily restore the service after the fault is isolated. The fault response is verified by both simulation results and experimental results from a three-phase SiC PEBBs based MMC prototype.

The performance of power electronic converters is sensitive to main design variables such as topology, control algorithm, and modulation scheme as well as details such as manufacturing variability. In this paper, sensitivity index is introduced as an indicator of design quality to measure converter robustness in the presence of manufacturing uncertainty. This paper presents the design and optimization of a Vienna rectifier that converts variable frequency 115 V AC voltage into 340 V DC voltage to illustrate the multi-objective optimization with parametric uncertainty quantification. The primary design target is to maximize converter efficiency and robustness within given size limitations and operation requirements.

The deviation of real system behavior from the predictions made from modeling and simulation is inevitable due to variations in different model input parameters as well as inaccurate modeling. The potential sources of uncertainty in modular multilevel converters (MMCs) is significant in medium- and high-voltage applications where each arm consists of several power electronics building blocks (PEBBs) connected in series. Therefore, assessing the predictive proficiency of the model, in the presence of various uncertainties, is critical in gaining confidence in modeling and simulation results. This paper investigates the predictive capability of MMC simulation models when adding more PEBBs. The relationship between the total uncertainty in modeling and simulation, and the number of PEBBs in each arm is presented for different model outputs. The results reveal an interesting feature of MMC — despite the fact that the number of potential sources of uncertainty increases by adding more PEBBs in each arm, the total uncertainty in the prediction of a system response quantity remains the same or decreases, depending on the selected model output response.

The Modular Multilevel Converter (MMC) is a promising converter choice for Medium Voltage DC (MVDC) distribution systems, where SiC devices can be readily adopted taking full advantage of this semiconductor's properties. Specifically, 1.7 kV SiC MOSFETs are used to build 1kV modules operating at 100 kHz, thus eliminating the need to use multiple modules to generate a high equivalent switching frequency. Furthermore, a full-bridge configuration is used to take advantage of the additional control and energy storage capabilities that it offers when compared to conventional half-bridge modules. The full in-depth design, controls, and testing of the MMC prototype for MVDC distribution systems is presented in this paper, including among others: component selection, control algorithms, control hardware implementation, precharge and discharge circuits, and protection scheme. Experimental results are presented to demonstrate the proposed SiC-based converter.

Background: Human papilloma viruses (HPV) have been detected in several types of cancers. Over the last few years, a possible correlation between HPV infection and colon cancers has been suggested. The aim of this study was to assess the presence of HPV-DNA in colon adenocarcinomas and adenomas to describe the relationship of HPV infection with these pathologic features. Materials and Methods: The study included formalin-fixed and paraffin-embedded tissue samples of 70 patients with colon adenocarcinoma and 30 patients with adenoma (as study group) and 30 tumor adjacent tissues (as control). HPV-DNA was purified and first amplified through MY09/MY11 and GP5+/GP6+ primers. Subsequently, for more precision, another PCR was performed using PGMY09/11 L1 consensus primer system. Results: All tested adenocarcinomas and adenomas as well as normal tumor adjacent tissues were negative for all types of HPV in two PCR assays. Conclusion: Our results do not support the relationship between HPV infection and colon carcinoma or adenoma. Attributing a role to the HPV in the etiology of colon carcinogenesis will require further studies.

This paper predicts the behavior of a high-switching frequency SiC-based modular power converter based on low-power validation experiments. Switching model of the modular power converter is developed and low-power validation experimental results are provided. Different sources of uncertainties causing the deviation of the modeling and simulation results from the real system behavior are identified, characterized, and quantified via verification and validation analysis using the switching model and low-power validation experiments. A regression-based model is then used to extrapolate the estimated model form uncertainty (MFU) to the full-power condition where there is no experimental data available.

A discrepancy between simulation and experimental results is inevitable due to their different sources of uncertainty, which are not usually incorporated at an early design stage. Therefore, design margins are used for design variables in order to generate reliable modeling and simulation-based designs. These margins are generally estimated using heuristic safety factors. In this paper, a design margin calculation procedure is proposed to minimize the allocated margin of the selected design specification when designing a converter in order to better utilize the converter's components. This procedure is based on probabilistic modeling and simulation, which take into account different sources of uncertainties.