Particle Swarm Optimization to solve Economic Dispatch considering Generator Constraints

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Predicting The Strength Properties of Self Healing Concrete Using Artificial Neural Network

An extensive simulation program is used in this study to discover the best ANN model for predicting the compressive strength of concrete with respect to the percentage of mineral admixture and percentage of crystalline admixture. To accomplish this, an experimental database of 100 samples is compiled from the literature and utilized to find the best ANN architecture. The main aim of this paper was to predict the strength properties of self-healing concrete (SHC) with crystalline admixture and different mineral admixtures using an artificial neural network (ANN). The samples, 100 in Number, with different mixes, were analyzed after 28 days of curing of the samples. ANN was fed with the experimental data containing four input parameters: mineral admixture (MA), percentage of mineral admixture (PMA), Percentage of crystalline admixture (PCA), and type of exposure (TE). Correspondingly, strength (Fc) was the output parameter. The experimental data showed a good correlation with the values predicted by ANN. In conclusion, ANN could be used to accurately evaluate SHC strength characteristics

Zero Crossing Point Detection in a Distorted Sinusoidal Signal Using Decision Tree Classifier

Zero-crossing point detection in a sinusoidal signal is essential in the case of various power systems and power electronics applications like power system protection and power converters controller design. In this paper, 96 data sets are created from a distorted sinusoidal signal based on MATLAB simulation. Dis- torted sinusoidal signals are generated in MATLAB with various noise and harmonic levels. In this pa- per, a decision tree classi er is used to predict the zero crossing point in a distorted signal based on input fea- tures like slope, intercept, correlation and Root Mean Square Error (RMSE). Decision tree classi er model is trained and tested in the Google Colab environment. As per simulation results, it is observed that decision tree classi er is able to predict the zero-crossing points in a distorted signal with maximum accuracy of 98.3 % for noise signals and 100 % for harmonic distorted signals

Nicotinamide Inhibits Alkylating Agent-Induced Apoptotic Neurodegeneration in the Developing Rat Brain

BACKGROUND: Exposure to the chemotherapeutic alkylating agent thiotepa during brain development leads to neurological complications arising from neurodegeneration and irreversible damage to the developing central nerve system (CNS). Administration of single dose of thiotepa in 7-d postnatal (P7) rat triggers activation of apoptotic cascade and widespread neuronal death. The present study was aimed to elucidate whether nicotinamide may prevent thiotepa-induced neurodegeneration in the developing rat brain. METHODOLOGY/PRINCIPAL FINDINGS: Neuronal cell death induced by thiotepa was associated with the induction of Bax, release of cytochrome-c from mitochondria into the cytosol, activation of caspase-3 and cleavage of poly (ADP-ribose) polymerase (PARP-1). Post-treatment of developing rats with nicotinamide suppressed thiotepa-induced upregulation of Bax, reduced cytochrome-c release into the cytosol and reduced expression of activated caspase-3 and cleavage of PARP-1. Cresyl violet staining showed numerous dead cells in the cortex hippocampus and thalamus; post-treatment with nicotinamide reduced the number of dead cells in these brain regions. Terminal deoxynucleotidyl transferase (TdT)-mediated dUTP nick end-labeling (TUNEL) and immunohistochemical analysis of caspase-3 show that thiotepa-induced cell death is apoptotic and that it is inhibited by nicotinamide treatment. CONCLUSION: Nicotinamide (Nic) treatment with thiotepa significantly improved neuronal survival and alleviated neuronal cell death in the developing rat. These data demonstrate that nicotinamide shows promise as a therapeutic and neuroprotective agent for the treatment of neurodegenerative disorders in newborns and infants

Improvement of Dynamic Performance in SEIG WECS by Using ANFIS Controller

A new hybrid controller for the Self-Excited Induction Generator (SEIG) driven by the Wind Energy Conversion Scheme (WECS) was proposed in this paper. The dynamic stability of the control grid is essential for both user protection and system performance. There must be a full grasp of the effects of power system volatility to research and govern power systems. The suggested control systems were examined using frequency-domain approaches that focused on the nonlinear design of a device that is subjected to severe faults on a related bus, which was tested using time-domain strategies. In this paper, a novel 3-level inverter is designed and controlled by the ANFIS control for the Dynamic Response of the system at the load side. The ANFIS approach can be used to regulate a self-excited induction generator in this study. The design incorporates wind power to give on-grid electricity access. SEIGs are used to power wind turbines in this project, which generates alternating current (AC) for the grid. The system model uses a rotor reference frame and dynamic vector control for the machine reference model. Wind power voltage and active power are controlled by an ANFIS controller in the converter. The ANFIS controller's performance is evaluated in all abnormal scenarios, including the worst-case scenario. System modeling and simulation in Simulink-Matlab allow it to be used in SEIG configurations. Wind power system quality and stability are both improved by the ANFIS control unit, according to simulation results

Non-Zero Crossing Point Detection in a Distorted Sinusoidal Signal Using Logistic Regression Model

Non-Zero crossing point detection in a sinusoidal signal is essential in case of various power system and power electronics applications like power system protection and power converters controller design. In this paper 96 data sets are created from a distorted sinusoidal signal based on MATLAB simulation. Distorted sinusoidal signals are generated in MATLAB with various noise and harmonic levels. In this paper, logistic regression model is used to predict the non-zero crossing point in a distorted signal based on input features like slope, intercept, correlation and RMSE. Logistic regression model is trained and tested in Google Colab environment. As per simulation results, it is observed that logistic regression model is able to predict all non-zero-crossing point in a distorted signal

Non-Zero Crossing Point Detection in a Distorted Sinusoidal Signal Using Logistic Regression Model

Non-Zero crossing point detection in a sinusoidal signal is essential in case of various power system and power electronics applications like power system protection and power converters controller design. In this paper 96 data sets are created from a distorted sinusoidal signal based on MATLAB simulation. Distorted sinusoidal signals are generated in MATLAB with various noise and harmonic levels. In this paper, logistic regression model is used to predict the non-zero crossing point in a distorted signal based on input features like slope, intercept, correlation and RMSE. Logistic regression model is trained and tested in Google Colab environment. As per simulation results, it is observed that logistic regression model is able to predict all non-zero-crossing point in a distorted signal

Dynamic mechanical characterization of epoxy composite reinforced with areca nut husk fiber

Natural fiber polymer composites are gaining focus as low cost and light weight composite material due to the availability and ecofriendly nature of the natural fiber. Fiber composites are widely used in civil engineering, marine and aerospace industries where dynamic loads and environmental loads persist. Dynamic analysis of these composites under different loading and environmental conditions is essential before their usage. The present study focuses on the dynamic behavior of areca nut husk reinforced epoxy composites under different loading frequencies (5 Hz, 10 Hz and 15 Hz) and different temperatures (ranging from 28◦C to 120◦C). The effect of loading and temperature on storage modulus, loss modulus and glass transition temperature was analyzed. Increase in storage modulus is observed with increase in loading frequency. The storage modulus decreases with increase in temperature. The glass transition temperature of the composite is determined to be 105◦C. The elastic modulus calculated from the DMA data is compared with three point bending tes

Influence of health education on knowledge, attitude, and practices toward organ donation among dental students

OBJECTIVES: Knowledge, attitudes, and behaviors are essential factors in fostering an environment that positively influences organ donation rates. Thus, the present study aimed to assess the impact of intervention (classroom education) on knowledge, attitude, and practices on organ donation. MATERIALS AND METHODS: A questionnaire-based interventional study was conducted among 112 dental house surgeon students, Hyderabad. A 27-item self-administered questionnaire was distributed to students as a pretest and collected back after completion. Then, a session on organ donation was delivered in a lecture hall setting instilling the basic facts about organ donation. Posttests using the same questionnaire were filled after the intervention and 2 weeks later. RESULTS: Responses on knowledge obtained from the subjects showed significant changes in several key areas from baseline to postintervention and at follow-up. More than 50% of study subjects had a positive attitude regarding organ donation. There was a significant increase in the number of subjects who pledged/signed to donate an organ (before - 14.3%, postintervention - 50%, and at follow-up - 60.7%; P < 0.05). Pairwise comparison revealed a significant increase in the mean knowledge, attitude, and practice scores at postintervention and at follow-up of 2 weeks in comparison to the baseline scores. Female subjects and subjects following Hindu religion had good knowledge, positive attitude, and good practice. CONCLUSION: The one brief educational intervention had significantly increased perceived knowledge of organ donation and positively influenced attitude and practices to organ donation among dental students