Review on the impact of cell phone radiation effects on green plants.

The aim of this review is to assess the impact of cell phone radiation effects on green plants. Rapid progress in networking and communication systems has introduced frequency- and amplitude-modulated technologies to the world with higher allowed bands and greater speed by using high-powered radio generators, which facilitate high definition connectivity, rapid transfer of larger data files, and quick multiple accesses. These cause frequent exposure of cellular radiation to the biological world from a number of sources. Key factors like a range of frequencies, time durations, power densities, and electric fields were found to have differential impacts on the growth and development of green plants. As far as the effects on green plants are concerned in this review, alterations in their morphological characteristics like overall growth, canopy density, and pigmentation to physiological variations like chlorophyll fluorescence and change in membrane potential etc. have been found to be affected by cellular radiation. On the other hand, elevated oxidative status of the cell, macromolecular damage, and lipid peroxidation have been found frequently. On the chromosomal level, micronuclei formation, spindle detachments, and increased mitotic indexes etc. have been noticed. Transcription factors were found to be overexpressed in many cases due to the cellular radiation impact, which shows effects at the molecular level.

Emerging Vanadium-Doped Cobalt Chloride Carbonate Hydroxide for Flexible Electrochromic Micro-Supercapacitor: Charged-State Prediction from RGB Input by ANN Model.

Multifunctional devices integrated with electrochromic and supercapacitance properties are fascinating because of their extensive usage in modern electronic applications. In this work, vanadium-doped cobalt chloride carbonate hydroxide hydrate nanostructures (V-C3H NSs) are successfully synthesized and show unique electrochromic and supercapacitor properties. The V-C3H NSs material exhibits a high specific capacitance of 1219.9 F g-1 at 1 mV s-1 with a capacitance retention of 100% over 30 000 CV cycles. The electrochromic performance of the V-C3H NSs material is confirmed through in situ spectroelectrochemical measurements, where the switching time, coloration efficiency (CE), and optical modulation (∆T) are found to be 15.7 and 18.8 s, 65.85 cm2 C-1 and 69%, respectively. A coupled multilayer artificial neural network (ANN) model is framed to predict potential and current from red (R), green (G), and blue (B) color values. The optimized V-C3H NSs are used as the active materials in the fabrication of flexible/wearable electrochromic micro-supercapacitor devices (FEMSDs) through a cost-effective mask-assisted vacuum filtration method. The fabricated FEMSD exhibits an areal capacitance of 47.15 mF cm-2 at 1 mV s-1 and offers a maximum areal energy and power density of 104.78 Wh cm-2 and 0.04 mW cm-2, respectively. This material's interesting energy storage and electrochromic properties are promising in multifunctional electrochromic energy storage applications.

Instability and fault analysis of arc plasma using advanced signal processing methods.

This study focuses on the instability and fault analysis of transferred arc plasma, utilizing advanced signal processing methods. Transferred arc plasma systems find significant applications in various industries, including material processing, metallurgy, and waste management. However, the occurrence of instabilities and fault events can severely impact system performance and reliability. To address instabilities in arc plasma, various conditions were experimented. The operating parameters, such as arc voltage, arc current, acoustic, optical, and spectroscopic signals, were simultaneously recorded at a higher sampling rate. The proposed approach employs advanced signal processing methods, such as the Lyapunov exponent, fast-Fourier transform, short-time-Fourier transform, and power spectral density, to analyze the characteristics and instabilities of the transferred arc plasma process. By capturing and analyzing signals from multiple sensors, it becomes possible to identify deviations, irregularities, and fault patterns that arise during plasma operation. The outcomes of this research will have significant implications for the optimization and control of transferred arc plasma processes. By identifying and characterizing instabilities due to fault events at an early stage, system operators can take timely corrective actions, preventing potential damage and improving the overall system efficiency.

Understanding perception of active noise control system through multichannel EEG analysis.

In this Letter, a method is proposed to investigate the effect of noise with and without active noise control (ANC) on multichannel electroencephalogram (EEG) signal. The multichannel EEG signal is recorded during different listening conditions such as silent, music, noise, ANC with background noise and ANC with both background noise and music. The multiscale analysis of EEG signal of each channel is performed using the discrete wavelet transform. The multivariate multiscale matrices are formulated based on the sub-band signals of each EEG channel. The singular value decomposition is applied to the multivariate matrices of multichannel EEG at significant scales. The singular value features at significant scales and the extreme learning machine classifier with three different activation functions are used for classification of multichannel EEG signal. The experimental results demonstrate that, for ANC with noise and ANC with noise and music classes, the proposed method has sensitivity values of 75.831% ( p<0.001 ) and 99.31% ( p<0.001 ), respectively. The method has an accuracy value of 83.22% for the classification of EEG signal with music and ANC with music as stimuli. The important finding of this study is that by the introduction of ANC, music can be better perceived by the human brain.

Head movement immune active noise control with head mounted moving microphones.

The active headrest is an important application of active noise control (ANC) where the occupant gets a quiet zone during use. However, its performance degrades due to head movement, which is an important issue. Unlike conventional active headrest algorithm, in the present paper, the two error microphones are attached to a head attachment such as a hat or band so that both error microphones can move along with the head, keeping the relative distance of ears and the microphones intact. The two secondary loudspeakers are kept near to the head, facing the ears. The performance of the active headrest was studied through real-time experimentation considering head movement. The innovation in this paper is that the ANC algorithm uses signals of moving error microphones to tune the controller without re-estimating the secondary path transfer functions. Since, the phase error of secondary path estimates at different head positions remains within ±90°, the secondary paths estimated once, when the head is positioned at the center, can be used for other head positions. This is established through real-time experiments using dSpace 1104 ACE system (dSpace, Germany).

Co-existing malakoplakia and xanthogranulomatous pyelonephritis of kidney: Two different spectrum of same disease process.

Xanthogranulomatous pyelonephritis (XGP) and malakoplakia (MKP) are chronic inflammatory condition of kidney characterized by infiltration of inflammatory cells. We are presenting a rare case of concomitant XGP and MKP in the same kidney. This signifies that the two are different spectrums of the same disease process.

Non-invasive detection of the freezing of gait in Parkinson's disease using spectral and wavelet features.

In this study, we have analyzed the accelerometer data recorded during gait analysis of Parkinson disease patients for detecting freezing of gait (FOG) episodes. The proposed method filters the recordings for noise reduction of the leg movement changes and computes the wavelet coefficients to detect FOG events. Publicly available FOG database was used and the technique was evaluated using receiver operating characteristic (ROC) analysis. Results show a higher performance of the wavelet feature in discrimination of the FOG events from the background activity when compared with the existing technique.

A nonlinear active noise control algorithm for virtual microphones controlling chaotic noise.

In active noise control (ANC) systems, virtual microphones provide a means of projecting the zone of quiet away from the physical microphone to a remote location. To date, linear ANC algorithms, such as the filtered-x least mean square (FXLMS) algorithm, have been used with virtual sensing techniques. In this paper, a nonlinear ANC algorithm is developed for a virtual microphone by integrating the remote microphone technique with the filtered-s least mean square (FSLMS) algorithm. The proposed algorithm is evaluated experimentally in the cancellation of chaotic noise in a one-dimensional duct. The secondary paths evaluated experimentally exhibit non-minimum phase response and hence poor performance is obtained with the conventional FXLMS algorithm compared to the proposed FSLMS based algorithm. This is because the latter is capable of predicting the chaotic signal found in many physical processes responsible for noise. In addition, the proposed algorithm is shown to outperform the FXLMS based remote microphone technique under the causality constraint (when the propagation delay of the secondary path is greater than the primary path). A number of experimental results are presented in this paper to compare the performance of the FSLMS algorithm based virtual ANC algorithm with the FXLMS based virtual ANC algorithm.