Idiopathic nodular glomerulosclerosis in a chronic marijuana user; a case report and review of the literature

Background: Nodular glomerulosclerosis is a characteristic histological finding of diabetic nephropathy (DN) with thickened glomerular basement membrane (GBM) and hyalinized arterioles. Idiopathic nodular glomerulosclerosis (ING), a rare distinct clinicopathologic entity, is the term used to denote classic DN confirmed by light microscopy, immuno-fluorescence, and electron microscopy in the absence of diabetes mellitus (DM). ING has been linked to heavy tobacco smoking, chronic hypertension, obesity and insulin resistance. Its association with marijuana use is unknown. Case Presentation: We report a case of biopsy-proved ING in the absence of pre-existing history of DM and heavy smoking. This report addresses the possible accentuation of tobacco use risk by marijuana. Conclusions: This report addresses the possible accentuation of tobacco use risk by marijuana. © 2017 The Author(s); Published by Society of Diabetic Nephropathy Prevention

Dynamic Sliding Mode Control of DC-DC Converter to Extract the Maximum Power of Photovoltaic System Using Dual Sliding Observer

This paper concerns the maximum power extraction of a photovoltaic generator system (PGS). The PGS consists of single photovoltaic (PV) cells. To improve the efficiency of a PGS, it is necessary to work within its maximum power point (MPP). In a PGS, output power is dependent on solar irradiance and the operating temperature and, therefore, MPP would be varied. To address this problem, a converter should be placed after the PGS and a smooth control signal should be used to adjust its duty cycle. The other challenge of a total system, i.e., PGS and converter, is the uncertainty involved. To overcome this uncertainty, a dynamic sliding mode control (SMC) can be used to regulate the smooth duty cycle. The low-pass integrator before the system can remove the chattering in dynamic SMC. However, due to the integrator, the states of the system increase and, hence, we propose a dual sliding observer (DSO) to estimate this added state. For a reliable comparison with the conventional SMC, the same proposed DSO can be applied in both dynamic and conventional SMC. The provided comparison shows the effectiveness of dynamic SMC in chattering suppression and real implementation with respect to conventional SMC

Higher Order Sliding Mode Control of MIMO Induction Motors: A New Adaptive Approach

In this paper the objective is to force the outputs of nonlinear nonaffine multi-input multi-output (MIMO) systems to track those of a linear system with the desired properties. The approach is based on designing higher order sliding mode controller (HOSMC) with the definition of a new proportional-integral (PI) sliding surface. To this end, a linear state feedback with an adaptive switching gain (ASG) is applied to the nonlinear MIMO systems. Therefore, the switching gain can increase or decrease based on the system conditions. Then, the chattering is completely removed using a combination of HOSMC and ASG. Moreover, the proposed procedure is independent from the upper bound of the matched uncertainty, which is in the direction of system inputs. The finite time convergence to the sliding surface is also proved, which provides an invariance property in finite time. Note that invariance is the most important property of SMC. Finally, the general model of MIMO induction motors (IM) is used to address and to verify the proposed controller.The authors wish to express their gratitude to the Basque Government, through the project EKOHEGAZ II (ELKARTEK KK-2023/00051), to the Diputación Foral de Álava (DFA), through the project CONAVANTER, to the UPV/EHU, through the project GIU20/063, and to the MobilityLab Foundation (CONV23/14. Proy. 16) for supporting this work

Pitch Control of Wind Turbine Blades Using Fractional Particle Swarm Optimization

To achieve the maximum power from wind in variable-speed regions of wind turbines (WTs), a suitable control signal should be applied to the pitch angle of the blades. However, the available uncertainty in the modeling of WTs complicates calculations of these signals. To cope with this problem, an optimal controller is suitable, such as particle swarm optimization (PSO). To improve the performance of the controller, fractional order PSO (FPSO) is proposed and implemented. In order to construct this approach for a two-mass WT, we propose a new state feedback, which was first applied to the turbine. The idea behind this state feedback was based on the Taylor series. Then, a linear model with uncertainty was obtained with a new input control signal. Thereafter, the conventional PSO (CPSO) and FPSO were used as optimal controllers for the resulting linear model. Finally, a comparison was performed between CPSO and FPSO and the fuzzy Takagi–Sugeno–Kang (TSK) inference system. The provided comparison demonstrates the advantages of the Taylor series with combination to these controllers. Notably, without the state feedback, CPSO, FPSO, and TSK fuzzy systems cannot stabilize WTs in tracking the desired trajectory

Probabilistic G-Metric space and some fixed point results

In this note we introduce the notions of generalized probabilistic metric spaces and generalized Menger probabilistic metric spaces. After making our elementary observations and proving some basic properties of these spaces, we are going to prove some fixed point result in these spaces

Estimation of Phytoplankton Chlorophyll-a Concentration in the Western Basin of Lake Erie Using Sentinel-2 and Sentinel-3 Data

Worldwide phenomena called algae bloom has been recently a serious matter for inland water bodies. Temporal and spatial variability of the bloom makes it di cult to use in-situ monitoring of the lakes. This study aimed to evaluate the potential of Sentinel-3 Ocean and Land Colour Instrument (OLCI) and Sentinel-2 Multispectral Instrument (MSI) data for monitoring algal blooms in Lake Erie. Chlorophyll-a (Chl-a) related products were tested using NOAA-Great Lakes Chl-a monitoring data over summer 2016 and 2017. Thematic water processor, fluorescence line height/maximum chlorophyll index (MCI) and S2 MCI, plug-in SNAP were assessed for their ability to estimate Chl-a concentration. We processed both Top of the Atmosphere (TOA) reflectance and radiance data. Results show that while FLH algorithms are limited to lakes with Chl-a < 8 mg m-3, MCI has the potential to be used effectively to monitor Chl-a concentration over eutrophic lakes. Sentinel-3 MCI is suggested for Chl-a > 20 mg m-3 and Sentinel-2 MCI for Chla > 8 mg m-3. The different Chl-a range limitation for the MCI products can be due to the different location of the maximum peak bands, 705 and 709 for MSI and OLCI sensors respectively. TOA radiances showed a signi cantly better correlation with in situ data compared to TOA reflectances which may be related to the poor pixel identi cation during the process of pixel flagging affected by the complexity of Case-2 water. Our fi nding suggests that Sentinel-2 MCI achieves better performance for Chl-a retrieval (R2 = 0.90). However, the FLH algorithms outperformed showing negative reflectance due to the shift of reflectance peak to longer wavelengths along with increasing Chl-a values. Although the algorithms show moderate performance for estimating Chl-a concentration; this study demonstrated that the new satellite sensors, OLCI and MSI, can play a signi ficant role in the monitoring of algae blooms for Lake Erie

Adaptive fuzzy pole placement for stabilization of non-linear systems

A new approach for pole placement of nonlinear systems using state feedback and fuzzy system is proposed. We use a new online fuzzy training method to identify and to obtain a fuzzy model for the unknown nonlinear system using only the system input and output. Then, we linearized this identified model at each sampling time to have an approximate linear time varying system. In order to stabilize the obtained linear system, we first choose the desired time invariant closed loop matrix and then a time varying state feedback is used. Then, the behavior of the closed loop nonlinear system will be as a linear time invariant (LTI) system. Therefore, the advantage of proposed method is global asymptotical exponential stability of unknown nonlinear system. Because of the high speed convergence of proposed adaptive fuzzy training method, the closed loop system is robust against uncertainty in system parameters. Finally the comparison has been done with the boundary layer sliding mode control (SMC)