Adaptive Neuro Fuzzy Technique for Speed Control of Six-Step Brushless DC Motor

The brushless DC motors with permanent magnets (PM-BLDC) are widely used in a miscellaneous of industrial applications. In this paper, The adaptive neuro fuzzy inference system (ANFIS) controller for Six-Step Brushless DC Motor Drive is introduced. The brushless DC motor’s dynamic characteristics such as torque , current , speed, , and inverter component voltages are showed and analysed using MATLAB simulation. The  propotional-integral (PI) and fuzzy system controllers  are developed., based on designer’s test and error process and experts. The  experimential and hardware resuts for the inverter- driver circuits are presented. The simulation results using MATLAB simulink are conducted to validate the proposed (ANFIS) controller’s robustness and high performance relative to other controllers

Comparative Analysis of Three-Phase PV Grid Connected Inverter Current Control Schemes in Unbalanced Grid Conditions

Recently, the regulation of photovoltaic inverters, effectively under imbalanced voltages on the grid, has been crucial for the operation of grid-connected solar systems. In this regard, determining the output current reference is an integral aspect of managing a solar inverter with an unbalanced voltage. Based on evaluations of Instantaneous Active-reactive Control (IARC), Positive Negative Sequence Control (PNSC), Balanced Positive Sequence Control (BPSC), and Average Active-reactive Control (AARC), this paper proposes a novel variable-current-t-reference calculation method for minimizing power fluctuations and current harmonics. The controller is employed to regulate both constructive and destructive sequences inside a static framework, therefore enhancing dynamic performance and facilitating the selection of suitable controls in the presence of significant network defects. This study also suggests comparing the four MPPT methodologies examined (fuzzy logic, current only, Incremental Conductance, and Perturb & Observe) to maximize energy output. The simulation results efficiently validate the suggested computation approach that is presented in the current reference.Peer reviewe

Operation of Grid-Connected PV System With ANN-Based MPPT and an Optimized LCL Filter Using GRG Algorithm for Enhanced Power Quality

The expanding use of photovoltaics (PV) as a green energy resource has been rising in these years, mostly due to the possibility of being incorporated with traditional power systems, to meet the world’s energy needs and reduce carbon emissions. However, providing green electricity from this renewable generator is frequently vulnerable to power quality (PQ) disruptions resulting from the PV’s intermittent nature and other factors associated with the electric grid, power converters, and linked loads. These disruptions need to be reduced to keep the investigated system’s PQ from deteriorating. The investigated system includes PV, DC-DC, and DC-AC converters, filter, power grid, and control schemes. If the DC-DC converter is not managed, a deviation from the maximum power point (MPP) extrapolated from the PV system will take place. In order to maximize the energy harvested from the PV system by managing the DC-DC converter, this research developed two MPP tracking (MPPT) algorithms: artificial neural networks (ANN) and cuckoo search (CS). Additionally, a design and implementation for a shunt active power filter (LCL) using genetic algorithm and GRG is provided to lower the injected total harmonic distortion (THD) and thereby enhance the PQ. To achieve the smallest size of the LCL components, the generalized reduced gradient (GRG) was the best compared to genetic algorithms GA. The results of the simulation showed that ANN performed better at tracking maximum power than CS. With the designed LCL, the THD is reduced by 99.78% compared to without a filter. To verify the simulation’s findings, a practical configuration is implemented

Design, synthesis, in vitro anticancer, molecular docking and SAR studies of new series of pyrrolo[2,3-d]pyrimidine derivatives

Abstract The current study involves the design and synthesis of a newly synthesized pyrrolo[2,3-d]pyrimidine derivatives to contain chlorine atoms in positions 4 and 6 and trichloromethyl group in position 2 using microwave technique as a new and robust approach for preparation of this type of pyrrolo[2,3-d]pyrimidine derivatives. The chemical structure of the synthesized pyrrolo[2,3-d]pyrimidine derivatives 3–19 was well-characterized using spectral and elemental analyses as well as single-crystal X-ray diffraction. All compounds were tested in vitro against seven selected human cancer cell lines, namely, MCF7, A549, HCT116, PC3, HePG2, PACA2 and BJ1 using MTT assay. It was found that compounds 14a, 16b and 18b were the most active toward MCF7 with IC50 (1.7, 5.7, and 3.4 μg/ml, respectively) relative to doxorubicin (Dox.) (26.1 μg/ml). Additionally, compound 17 exerted promising cytotoxic effects against HePG2 and PACA2 with IC50 (8.7 and 6.4 μg/ml, respectively) relative to Dox. (21.6 and 28.3 μg/ml, respectively). The molecular docking study confirmed our ELISA result which showed the promising binding affinities of compounds 14a and 17 against Bcl2 anti-apoptotic protein. At the gene expression level, P53, BAX, DR4 and DR5 were up-regulated, while Bcl2, Il-8, and CDK4 were down-regulated in 14a, 14b and 18b treated MCF7 cells. At the protein level, compound 14b increased the activity of Caspase 8 and BAX (18.263 and 14.25 pg/ml) relative to Dox. (3.99 and 4.92 pg/ml, respectively), while the activity of Bcl2 was greatly decreased in 14a treated MCF7 (2.4 pg/ml) compared with Dox. (14.37 pg/ml). Compounds 14a and 14b caused cell cycle arrest at the G1/S phase in MCF7. Compounds 16b and 18b induced the apoptotic death of MCF7 cells. In addition, the percentage of fragmented DNA was increased significantly in 14a treated MCF7 cells

Multiport Converter Utility Interface with a High-Frequency Link for Interfacing Clean Energy Sources (PV\Wind\Fuel Cell) and Battery to the Power System: Application of the HHA Algorithm

The integration of clean energy sources (CESs) into modern power systems has been studied using various power converter topologies. The challenges of integrating various CESs are facilitated by the proper design of multi-port power converter (MPPC) architecture. In this study, a brand-new two-stage MPPC is suggested as a solution to the intermittent nature and slow response (SR) of CESs. The suggested system combines a DC\DC and a DC\AC converter and storage unit, and the suggested circuit additionally incorporates a number of CESs (PV\wind\fuel cell (FC)). This article discusses the power management and control technique for an integrated four-port MPPC that links three input ports (PV, wind, and FC), a bidirectional battery port, and an isolated output port. One of the recent optimization techniques (Harris Hawk’s algorithm) is applied to optimize the system’s controller gains. By intelligently combining CESs with complementary characteristics, the adverse effects of intermittency are significantly mitigated, leading to an overall enhancement in system resilience and efficiency. Furthermore, integrating CESs with storage units not only addresses SR challenges but also effectively combats intermittent energy supply. The proposed system exhibits improved dynamic capabilities, allowing it to efficiently distribute excess energy to the load or absorb surplus energy from external sources. This dual functionality not only optimizes system operation but also contributes to a reduction in system size and cost, concurrently enhancing reliability. A comprehensive investigation into operational principles and meticulous design considerations are provided, elucidating the intricate mechanics of the suggested MPPC system. Employing MATLAB/Simulink, the proposed architecture and its control mechanisms undergo rigorous evaluation, affirming the feasibility and efficacy of this innovative system

Paeonol Attenuates Hepatic Ischemia/Reperfusion Injury by Modulating the Nrf2/HO-1 and TLR4/MYD88/NF-κB Signaling Pathways

Hepatic ischemia/reperfusion (HIR) is the most common type of liver injury following several clinical situations. Modulating oxidative stress and inflammation by Nrf2/HO-1 and TLR4/MYD88/NF-κB pathways, respectively, is involved in alleviating HIR injury. Paeonol is a natural phenolic compound that demonstrates significant antioxidant and anti-inflammatory effects. The present study explored the possible protective effect of paeonol against HIR injury and investigated its possible molecular mechanisms in rats. Rats were randomly divided into four groups: sham-operated control, paeonol-treated sham-operated control, HIR untreated, and HIR paeonol-treated groups. The results confirmed that hepatic injury was significantly aggravated biochemically by elevated serum levels of alanine transaminase and aspartate transaminase, as well as by histopathological alterations, while paeonol reduced the increase in transaminases and alleviated pathological changes induced by HIR. Additionally, paeonol inhibited the HIR-induced oxidative stress in hepatic tissues by decreasing the upraised levels of malondialdehyde and nitric oxide and enhancing the suppressed levels of reduced glutathione and superoxide dismutase activity. Furthermore, paeonol activated the protective antioxidative Nrf2/HO-1 pathway. The protective effect of paeonol was associated with inhibiting the expression of the inflammatory key mediators TLR4, MYD88, NF-κB, and TNF-α. Finally, paeonol inhibited the increased mRNA levels of the pro-apoptotic marker Bax and enhanced the reduced mRNA levels of the anti-apoptotic marker Bcl-2. Taken together, our results proved for the first time that paeonol could protect against HIR injury by inhibiting oxidative stress, inflammation, and apoptosis

Expression of Reactive Oxygen Species–Related Transcripts in Egyptian Children With Autism

The molecular basis of the pathophysiological role of oxidative stress in autism is understudied. Herein, we used polymerase chain reaction (PCR) array to analyze transcriptional pattern of 84 oxidative stress genes in peripheral blood mononuclear cell pools isolated from 32 autistic patients (16 mild/moderate and 16 severe) and 16 healthy subjects (each sample is a pool from 4 autistic patients or 4 controls). The PCR array data were further validated by quantitative real-time PCR in 80 autistic children (55 mild/moderate and 25 severe) and 60 healthy subjects. Our data revealed downregulation in GCLM, SOD2, NCF2, PRNP , and PTGS2 transcripts (1.5, 3.8, 1.2, 1.7, and 2.2, respectively; P < .05 for all) in autistic group compared with controls. In addition, TXN and FTH1 exhibited 1.4- and 1.7-fold downregulation, respectively, in severe autistic patients when compared with mild/moderate group ( P = .005 and .0008, respectively). This study helps in a better understanding of the underlying biology and related genetic factors of autism, and most importantly, it presents suggested candidate biomarkers for diagnosis and prognosis purposes as well as targets for therapeutic intervention