Context:Acid immersion of victim's body is one of the methods employed to subvert identification of the victim, and hence of the perpetrator. Being hardest and chemically the most stable tissue in the body, teeth can be an important forensic investigative medium in both living and nonliving populations. Teeth are also good reservoirs of both cellular and mitochondrial DNA; however, the quality and quantity of DNA obtained varies according to the environment the tooth has been subjected to. DNA extraction from acid-treated teeth has seldom been reported.Aims:The objectives of the present study were to assess the morphological changes along with DNA recovery from acid-immersed teeth.Materials and Methods:Concentrated hydrochloric acid, nitric acid, and sulfuric acid were employed for tooth decalcification. DNA was extracted on an hourly basis using phenol–chloroform method. Quantification of extracted DNA was done using a spectrophotometer.Results:Results showed that hydrochloric acid had more destructive capacity compared to other acids.Conclusion:Sufficient quantity of DNA was obtainable till the first 2 hours of acid immersion and there was an inverse proportional relation between mean absorbance ratio and quantity of obtained DNA on an hourly basis.

This paper proposes a novel, disturbance observerbased decentralized frequency control method for interconnected power systems. The method employs Extended Kalman Filter (EKF) as an observer to estimate inaccessible dynamic states of the system, including the total disturbance as one of the state variables. An optimal decentralized disturbance observer based controller (DOBC) is suggested for multi-area power systems that compensates the estimated disturbance and further based on minimizing the joint error energies of state estimation error and state tracking error provides its value to the controller for regulating the frequency variation. The proposed method is mathematically designed to be robust against parametric and non-parametric uncertainties. The efficacy and accuracy of the proposed control method is verified considering different types of practical operation scenarios. The results confirm the brilliant and superiority of the proposed method in controlling the frequency in power systems with high renewable shares.

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In this paper, a decentralized load frequency control (LFC) is proposed for the frequency regulation of multi-area power systems applying optimized integral sliding mode control (OISMC) scheme. A modified particle swarm optimization (MPSO) algorithm is utilised to optimize the control variables of the proposed controller for improving the performance of frequency response. A comprehensive model of a multi-area power system is developed where each area is considered to power from conventional sources, renewable energy resources (RERs), and electrical vehicle (EV) aggregators. Different types of interconnectors have been considered to tie different power regions. This system is subjected to random generation and loading conditions, as well as model uncertainties, which cause its frequency and area control error (ACE) to deviate from their nominal values. A decentralised OISMC is implemented for the proposed model to keep the frequency variation within the nominal range. Further, a validation study investigates the performance of the proposed control technique in MATLAB simulation environment to mimic the real power system operation. This study includes different test scenarios considering the dynamic events such as variations in load demand, amount of generated power, participation factor of EV reserve, system parameters, disturbances, and time delay. The simulated results support better frequency regulation with the suggested OISMC compared to other alternative control approaches.

Power quality issues are required to be addressed properly in forthcoming era of smart meters, smart grids and increase in renewable energy integration. In this paper, Deep Auto-encoder (DAE) networks is proposed for power quality disturbance (PQD) classification and its location detection without using complex signal processing techniques and complex classifiers. In this technique, Gabor filter is used to extract a set of general Gabor features from the convolution of PQD image. Subsequently, through sparse based DAE network, essential and optimal features are extracted and learnt which are used by a simple classifier (SoftMax) to classify the PQD type. Furthermore, the temporal information of the PQD is obtained using the PQD image to correctly locate the disturbance's initiating and terminating instants. The proposed DAE network has the benefits of Deep Learning-based networks in terms of automatic feature selection, but it requires smaller data sets. The issue of obtaining optimised, robust, and strong features from the PQD signal is thus resolved. Excellent classification accuracy of PQD is obtained with appropriate network parameter setting of the proposed DAE network. The proposed technique is compared with three other methods i.e. support vector machine (SVM), stacked auto-encoder (SAE) and principal component analysis (PCA) for PQD classification by implementing all the four techniques on python platform using the same data set. It has an overall classification accuracy of more than 97% at a signal to noise ratio (SNR) of 20dB, which is on the higher side when compared to other methods of PQD detection under noisy environment. Additionally, this method requires less computation time with the same data set than alternative approaches like SVM. Thus, the proposed method outperforms existing methods for PQD classification and detection of single disturbance and multi-disturbance in terms of greater accuracy and reduced computation complexity and computation time.INDEX TERMS Power quality monitoring, power quality disturbance, deep auto-encoders, optimal feature extraction, power quality event detection.The associate editor coordinating the review of this manuscript and approving it for publication was Nagesh Prabhu .