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The objective of this research article is to analyze the performance of the Autoregressive Integrated Moving Average (ARIMA) model and the 'earlyR' statistical model in predicting epidemic outbreaks using COVID-19 data of India. The results of this analysis can help take preventive actions and restrict the spread of the epidemic with the help of projected data. The 'projections' module was utilized to generate the epidemic path by aligning the available COVID-19 data from India, distribution of the time interval between successive cases and reproduction number (R0) of the corresponding regions with the 'earlyR' epidemic statistical model. The values of (p, d and q) were obtained utilizing the 'auto.arima' function, and an ARIMA time series model was created using the 'forecast' module to forecast future infected occurrences. The 'earlyR' epidemic model yielded a median projected value with an inaccuracy of 35.1 %, while the ARIMA model had a mean error of -1.9 %. A comparison of these methods indicates that the ARIMA model is a superior method compared to the 'earlyR' epidemic model in terms of accuracy. [ABSTRACT FROM AUTHOR]

Signal processing based research was adopted with Electroencephalogram (EEG) for predicting the abnormality and cerebral activities. The proposed research work is intended to provide an automatic diagnostic system to determine the EEG signal in order to classify the brain function which shows whether a person is affected with schizophrenia or not. Early detection and intervention are vital for better prognosis. However, the diagnosis of schizophrenia still depends on clinical observation to date. Without reliable biomarkers, schizophrenia is difficult to detect in its early phase and hence we have proposed this idea. In this work, the EEG signal series are divided into non-linear feature mining, classification and validation, and t-test integrated feature selection process. For this work, 19-channel EEG signals are utilized from schizophrenia class and normal pattern. Here, the datasets initially execute the splitting process based on raw 19-channel EEG into 6250 sample point's sequences. With this process, 1142 features of normal and schizophrenia class patterns can be obtained. In other hand, 157 features from each EEG patterns are utilized based on Non-linear feature extraction process where 14 principal features can be identified in terms of considering the essential features. At last, the Deep Learning (DL) technique incorporated with an effective optimization technique is adopted for classification process called a Deep Convolutional Neural Network (DCNN) with mayfly optimization algorithm. The proposed technique is implemented into the platform of MATLAB in order to obtain better results and is analyzed based on the performance analysis framework such as accuracy, Signal to Noise Ratio (SNR), Mean Square Error, Normalized Mean Square Error (NMSE) and Loss. Through comparison, the proposed technique is proved to a better technique than other existing techniques. [ABSTRACT FROM AUTHOR]

LaFeO3 nanocrystalline ferrites were synthesized through sol-gel method in different size distributions and the effect of finite size on magnetic properties is investigated. Results of magnetization and Mössbauer measurements show that superparamagnetism and weak ferromagnetic behavior in some of the size distributions. The origin of the superparamagnetism is from fine particles similar to ferromagnetic single domains and the weak ferromagnetism comes from surface spin disorder caused by Dzyaloshinskii-Moriya interaction. The magnetic ground state of LaFeO3 nanoparticles differs from that of bulk, and the ground state is dictated by the finite size effect because density of states depends on the dimensionality of the sample. [ABSTRACT FROM AUTHOR]

Resistance and magneto-resistance of nanocrystalline SrFe1-xTixO3-δ (x=0 to 0.3) systems Ti4+ are investigated. Substitution of non magnetic Ti4+ stabilizes the single phase tetragonal crystal structure with I4/mmm space group and Ti introduces bond disorder as well as site disorder. With increase in Ti content, the disorder increase leading to localization and large change in resistivity is observed in all the systems. In undoped system, the increase in resistivity is associated with the magnetic transition. All the Ti doped samples show large change in resistivity much above the magnetic ordering temperature and three dimensional Variable Range Hopping mechanism seems to explain the transport and magneto-transport behavior in these systems. [ABSTRACT FROM AUTHOR]

The magnetic ground state of single phase tetragonal crystal structure with I4/mmm space group SrFe1–xTixO3–δ (x=0.2 and 0.3) is investigated from 2K to 300 K. Strong irreversibility is observed in zero-field-cooled (ZFC) and field-cooled DC magnetization curves. Arrott plots show the absence of spontaneous magnetization (MS) down to 2K, ruling out the possibility of long range ferromagnetic order. Neutron diffraction measurements carried out at H=0, 7 T (field cooled) at several temperatures above and below the T* (temperature at which MZFC(T) is maximum) do not show any additional peaks and also no difference in intensity rules out, both the long range antiferromagnetic and ferromagnetic orders. Hence, the combined study of dc magnetization and neutron diffraction results reveals cluster spin glass behavior in SrFe1–xTixO3–δ (x=0.2 and 0.3). [ABSTRACT FROM AUTHOR]

Ilmenite (FeTiO3) nanoparticles with different size distributions were prepared through a sol-gel auto-ignition method. As-prepared powder is annealed at moderate temperatures to get nanocrystalline FeTiO3, and structural, micro-structural, and magnetization characterizations are carried out. Magnetization vs temperature shows antiferromagnetic ordering around 52 K and below 15 K a weak ferromagnetic order develops. The Curie-Weiss law is well obeyed above the Néel temperature (TN) for all samples. Hysteresis curves show finite coercivity below the peak temperature and none at room temperature. M-H curves for all the samples cooled with a bias magnetic field show exchange bias below TN which is interpreted due to the finite size and surface effects. The exchange bias is attributed to weak ferromagnetism due to surface spin disorder (Dzyaloshinskii-Moriya interaction) and ferromagnetic interaction (double exchange) due to mixed valence states of Fe ions. [ABSTRACT FROM AUTHOR]

Fe2O3 nanoparticles are embedded in amorphous SiO2 matrix by coprecipitation method with varying concentrations. Conditions are optimized to get almost monodispersed Fe2O3 nanoparticles with high chemical stability. Microstructure of synthesized nanoparticles is well characterized and found that Fe2O3 is in nanocrystalline form and embedded uniformly in amorphous SiO2 matrix. Enhanced surface reactivity is found for nanoparticles which influences physical properties of the SiO2 supported Fe2O3 system due to adsorption. In oxide nanoparticles, significant number of defect sites at the surface is expected but when supported medium such as SiO2 it reduces this defect concentration. Field- and temperature-dependent magnetisation studies on these samples show superparamagnetic behaviour. Superparamagnetic behaviour is seen in all the concentration systems but the coercivity observed in the lower concentration systems is found to be anomalous compared to that of higher concentrations. The observed magnetic behaviour comes from either unsaturated bond existing due to the absence of anions at the surface of nanoparticles or reconstruction of atomic orbitals taking place at interface of Fe2O3- SiO2 system. [ABSTRACT FROM AUTHOR]