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Irrigation is one of the steps that is very crucial in a high-quality endodontic treatment. Hence, irrigant with good substantivity is essential which must not only be effective for the dissolution of the organic tissues but also effectively eliminate bacterial contamination. The aim of the study was to investigate and compare the antimicrobial efficacy of gentamicin, amoxicillin, and metronidazole (GAM) antibiotic solution, chitosan and their combination (GAMC), and analyze their sustained release property. Mueller–Hinton agar medium was inoculated with E. faecalis. The medicaments were then poured at the center of the plate in the prepared wells and incubated at 37°C. Antibacterial property of each medicament was evaluated by measuring the diameter of the zone of inhibition at the end of 48 hours. The substantivity of GAM antibiotic solution and the GAMC was checked using an ultraviolet spectrophotometer. The GAMC demonstrated the strongest antimicrobial activity and good sustained-release properties. Distilled water showed no activity, and chlorhexidine acted as the positive control. Conclusion: The combination of gentamicin, amoxicillin, and metronidazole (GAM) solution with chitosan (GAMC) can be used as an alternative intracanal irrigant as it was found to be a potent antibacterial agent.

Polarized small-angle neutron scattering has been used to measure the local magnetic structure of writable thin-film perpendicular media with a granular CoCrPt-SiOx recording layer. By exploiting the cross terms between the nuclear and magnetic scattering, we are able to probe simultaneously both the grain structure and the subgranular magnetic structure of the recording layer, which has a thickness of only 15 nm and which is embedded within a full perpendicular media structure including soft underlayer. Two models are used to analyze the data, one analytical and the other a numerical approach based on transmission electron microscopy measurements of the grains. Both models show that the recording layer consists of ferromagnetically ordered core regions that are smaller in extent than the corresponding grains and allow a direct, quantitative comparison of these two length scales. [ABSTRACT FROM AUTHOR]

The optimal placement of distributed generation (DG) is a critical task for distribution companies in order to keep the distribution network running smoothly. The optimal placement of DG units is an optimization problem. In this paper, minimization of the voltage deviation from flat voltage is considered as an objective function. The self-adaptive Lévy flight-based Jaya algorithm is used as an optimization technique to determine the best location and size of distributed generation units. In the MATLAB environment, the proposed algorithm was implemented on IEEE 15 and PG and E 69 bus distribution systems. According to the simulation results, distribution networks can supply more quality power to customers by minimizing the voltage deviation from the flat voltage profile if the DG units are properly placed and sized.

This paper examines the application of multilayered hard coatings on aluminum substrates to improve their mechanical, anticorrosion, and tribological properties. In the present study, graded coatings with alternate layers of chrome carbide-nickel chrome (75:25) and tungsten carbide-cobalt (88:12) are applied on aluminum substrates for improvement in mechanical and tribological properties. High-velocity liquid fuel (HVLF) spray technique is employed to coat the materials on the aluminum substrate. Nanosized Cr3C2-NiCr and WC-Co powders with particle sizes ranging from 300 to 1000 nm are employed for coating on aluminum substrates. The tribological properties are tested on a pin-on-disk tribometer under lubricated conditions. It is found that graded coating with alternate layers of Cr3C2-NiCr and WC-Co have shown good tribological properties. The hardness has improved, with coating strength being in acceptable range for application in automotive engines. The corrosion resistance of multilayered coating is excellent and in line with Cr3C2-NiCr coating. The effects of number of coating layers of graded coating on tribological and mechanical properties are found to be significant. [ABSTRACT FROM AUTHOR]

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. Distorted sinusoidal signals are generated in MATLAB with various noise and harmonic levels. In this paper, a decision tree classifier is used to predict the zero crossing point in a distorted signal based on input features like slope, intercept, correlation and Root Mean Square Error (RMSE). Decision tree classifier model is trained and tested in the Google Colab environment. As per simulation results, it is observed that decision tree classifier 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.

Short-term electric power load forecasting is a critical and essential task for utilities of the electric power industry for proper energy trading and that enable the independent system operator to operate the network without any technical and economical issues. In this paper, machine learning model such as linear regression model is used to forecast the active power load one hour and one day ahead. Real time active power load data to train and test the machine learning model is collected from a 33/11 kV substation located in Telangana State, India. Based on the simulation results, it is observed that linear regression model can forecast the load with less mean absolute error i.e. 0.042 with training data and 0.045 with testing data in comparison with support vector regressor model for an hour ahead operation. Whereas in the case of the day ahead operation, linear regression model can forecast the load with less mean absolute error i.e. 0.055 with training data and 0.057 with testing data in comparison with support vector regressor model. A platform independent web application is developed to help the operators of the 33/11 kV substation which is located in Godishala, Telangana State, India.

Electrical load forecasting study is required in electric power systems for different applications with respect to the specific time horizon, such as optimal operations, grid stability, Demand Side Management (DSM) and long-term strategic planning. In this context, machine learning and data analytics models represent a valuable tool to cope with the intrinsic complexity and especially design future demand-side advanced services. The main novelty in this paper is that the combination of a Recurrent Neural Network (RNN) and Principal Component Analysis (PCA) techniques is proposed to improve the forecasting capability of the hourly load on an electric power substation. A historical dataset of measured loads related to a 33/11 kV MV substation is considered in India as a case study, in order to properly validate the designed method. Based on the presented numerical results, the proposed approach proved itself to accurately predict loads with a reduced dimensionality of input data, thus minimizing the overall computational effort.

Abstract An iterative method based on Shapley Value Cooperative Game Theory is proposed for the calculation of local marginal price (LMP) for each Distributed Generator (DG) bus on a network. The LMP value is determined for each DG on the basis of its contribution to reduce loss and emission reduction, which is assessed using the Shapley Value approach. The proposed approach enables the Distribution Company (DISCO) decision-maker to operate the network optimally in terms of loss and emission. The proposed method is implemented in the Taiwan Power Company distribution network 7 warnings consisting of 84 buses and 11 feeders in the MATLAB environment. The results show that the proposed approach allows DISCO to operate the network on the basis of its priority between the reduction of active power loss and emission in the network

Weather forecasting is an essential task in any region of the world for proper planning of various sectors that are affected by climate change. In Warangal, most sectors, such as agriculture and electricity, are mainly influenced by climate conditions. In this study, weather (WX) in the Warangal region was forecast in terms of temperature and humidity. A radial basis function neural network was used in this study to forecast humidity and temperature. Humidity and temperature data were collected for the period of January 2021 to December 2021. Based on the simulation results, it is observed that the radial basis function neural network model performs better than other machine learning models when forecasting temperature and humidity.

Chitosan is a biopolymer with numerous biological properties such as antibacterial, anti-inflammatory, and wound healing. Chitosan also stimulates cell proliferation and osteogenesis and thus used as a scaffold material in tissue engineering. The physical proprieties of chitosan such as biocompatibility and biodegradability give promising results in periodontal therapies. This review gives an updated explanation of the applications of chitosan in dentistry and periodontics. Furthermore, the review demonstrates the actions of chitosan in detail and its role in the regeneration of periodontal structures.

Keloids are unorganized proliferation of fibrous tissue, usually arising from a site of injury, due to an aberrant healing process. Clinically, it presents as an ugly scar tissue on the skin and shows genetic predilection. They cause esthetic, physical, and psychological disturbances in the affected individuals. Such patients require special precautions during the normal surgical procedures. Keloid treatment is prone to a high degree of resistance and recurrence. In this article, one such a case is reported along with a review of the literature discussing the nature of the lesion, treatment options, and the recommended precautions.

Aim: This in vitro study was done to evaluate and compare the frictional forces produced by a passive self-ligating bracket (SLB) and two types of elastomeric ligatures (unconventional elastomeric ligatures [UELs] and conventional elastomeric ligatures [CELs]) on a conventional bracket used with four types of wires. Materials: In this in vitro study, 8 numbers of conventional preadjusted edgewise appliance stainless steel brackets and 4 numbers of passive SLBs were tested. Four types of archwires and two types of ligatures were tested. Brackets were divided into three groups – Group I, Group II, and Group III. All the three groups were further divided into four subgroups based on different archwires. Methods: Custom-made mounting jig was specially constructed for this study – upper member and lower member. After the samples were mounted, traction test was conducted using universal testing machine and readings were tabulated for all specimens. The variables in each group were subjected to one-way analysis of variance. Results: All the three groups were tested for its total friction, static friction, and kinetic friction. SLBs showed a static mean little lower and had kinetic mean little greater than conventional bracket with unconventional module. Conclusion: UELs on conventional brackets and SLBs are able to produce lower frictional force when compared with CELs on conventional brackets.