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Type 2 diabetes mellitus (T2DM) increases the risk of neurological diseases, yet how brain oscillations change as age and T2DM interact is not well characterized. To delineate the age and diabetic effect on neurophysiology, we recorded local field potentials with multichannel electrodes spanning the somatosensory cortex and hippocampus (HPC) under urethane anesthesia in diabetic and normoglycemic control mice, at 200 and 400 days of age. We analyzed the signal power of brain oscillations, brain state, sharp wave associate ripples (SPW-Rs), and functional connectivity between the cortex and HPC. We found that while both age and T2DM were correlated with a breakdown in long-range functional connectivity and reduced neurogenesis in the dentate gyrus and subventricular zone, T2DM further slowed brain oscillations and reduced theta-gamma coupling. Age and T2DM also prolonged the duration of SPW-Rs and increased gamma power during SPW-R phase. Our results have identified potential electrophysiological substrates of hippocampal changes associated with T2DM and age. The perturbed brain oscillation features and diminished neurogenesis may underlie T2DM-accelerated cognitive impairment. [ABSTRACT FROM AUTHOR]

A novel scheme for electric field-induced strong terahertz (THz) emission by beating two filamented spatial-Gaussian laser beams (with intensities 10 14 W cm - 2 ) in the pre-existing underdense magnetized plasma is proposed. The coupling between the nonlinear density and nonlinear velocity of the plasma electrons results in the strong nonlinear current which drives the efficient THz radiation in the presence of the D.C. electric and magnetic fields. The contribution of the electron temperature on the THz generation has been investigated. The modulation index also makes a significant contribution to the enhancement of the nonlinear current density and normalized THz amplitude. With the help of a magnetic field, D. C. electric field, D.C. drift speed, and modulation index, one can easily tune the THz radiations for medical and astronomical applications. The normalized amplitude of emitted THz radiation is observed to vary with the externally applied electric field, magnetic field, normalized frequency, drift velocity of electrons, thermal velocity of electrons, temperature of plasma electrons, and modulation index of incident laser beams. The frequency of the emitted THz radiation (corresponding to 25.0 kG ) is found to lie in the frequency range of molecular rotational and vibrational spectra of deoxyribonucleic acid. As a result, emitted THz radiation in the present scheme can play an important role in developing a new technique to distinguish between healthier and non-healthier tissues of human beings. The emitted THz radiation shows sensitive behavior toward water particles. Due to this, emitted THz radiation can also be used to detect water at the surface of the earth as well as on other celestial bodies. [ABSTRACT FROM AUTHOR]

Chan Zhang,1– 3,&ast; Yanhui Wang,1,3,4,&ast; Mengjie Li,1,4 Pengpeng Niu,1,2 Shuo Li,1,2 Zhuopeng Hu,5 Changhe Shi,1,2 Yusheng Li1– 3 1Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, 450000, People’s Republic of China; 2NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, Zhengzhou, Henan, People’s Republic of China; 3Henan Neurological Function Detection and Regulation Center, Zhengzhou, Henan, 450000, People’s Republic of China; 4The Academy of Medical Sciences of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China; 5The First Bethune Clinical Medical College of Ji Lin University, Changchun, Jilin, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yusheng Li, Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, 450000, People’s Republic of China, Tel +86-18236145799, Fax +86-371-66862134, Email fccliyusheng@zzu.edu.cnBackground: Phase-amplitude coupling (PAC) between the phase of low-frequency signals and the amplitude of high-frequency activities plays many physiological roles and is involved in the pathological processed of various neurological disorders. However, how low-frequency and high-frequency neural oscillations or information synchronization activities change under chronic central hypoxia in OSA patients and whether these changes are closely associated with OSA remains largely unexplored. This study arm to elucidate the long-term consequences of OSA-related oxygen deprivation on central nervous system function.Methods: : We screened 521 patients who were clinically suspected of having OSA at our neurology and sleep centers. Through polysomnography (PSG) and other clinical examinations, 103 patients were ultimately included in the study and classified into mild, moderate, and severe OSA groups based on the severity of hypoxia determined by PSG. We utilized the phase-amplitude coupling (PAC) method to analyze the modulation index (MI) trends between different frequency bands during NREM (N1/N2/N3), REM, and wakefulness stages in OSA patients with varying severity levels. We also examined the correlation between the MI index and OSA hypoxia indices.Results: Apart from reduced N2 sleep duration and increased microarousal index, the sleep architecture remained largely unchanged among OSA patients with varying severity levels. Compared to the mild OSA group, patients with moderate and severe OSA exhibited higher MI values of PAC in the low-frequency theta phase and high-frequency beta amplitude in the frontal and occipital regions during N1 sleep and wakefulness. No significant differences in the MI of phase-amplitude coupling were observed during N2/3 and REM sleep. Moreover, the MI of phase-amplitude coupling in theta and beta bands positively correlated with hypoxia-related indices, including the apnea-hypopnea index (AHI) and oxygenation desaturation index (ODI), and the percentage of oxygen saturation below 90% (SaO2< 90%).Conclusion: OSA patients demonstrated increased MI values of theta phase and beta amplitude in the frontal and occipital regions during N1 sleep and wakefulness. This suggests that cortical coupling is prevalent and exhibits sleep-stage-specific patterns in OSA. Theta-beta PAC during N1 and wakefulness was positively correlated with hypoxia-related indices, suggesting a potential relationship between these neural oscillations and OSA severity. The present study provides new insights into the relationship between neural oscillations and respiratory hypoxia in OSA patients.Keywords: phase-amplitude coupling, PAC, modulation index, MI, obstructive sleep apnea, polysomnography, EEG

In this paper, with the development of an intelligent power system idea, sustainable energy sources were increasingly deployed, including transmission and distribution systems networks. As a result, optimal use of cascaded H-bridge inverter topologies (MLIs) and power distribution operations is critical for long-term power generation. Traditionally, selective harmonics reduction models must be performed to achieve the optimal switching frequency of multilevel inverters. This research aims to determine the switching frequency for wind-incorporated multilevel inverters to reduce overall harmonic components used in grid applications. This research adds towards the best possible solution by employing multiple newly established adaptive optimization techniques: MNSGA-II and salp swarm. The well-known genetic algorithm and particle swarm optimization are used for the wind-tied multilevel inverters optimization issue. Seven-level, eleven-level, and fifteen-level MLIs were employed to reduce overall harmonic distortion. The reliability and convergence rate of simulated data with various modulation indices for seven-, eleven-, and fifteen-level MLIs are obtained and compared. Models are developed based on MATLAB Simulink and are used to validate quantitative measurements.

In this paper, with the development of an intelligent power system idea, sustainable energy sources were increasingly deployed, including transmission and distribution systems networks. As a result, optimal use of cascaded H-bridge inverter topologies (MLIs) and power distribution operations is critical for long-term power generation. Traditionally, selective harmonics reduction models must be performed to achieve the optimal switching frequency of multilevel inverters. This research aims to determine the switching frequency for wind-incorporated multilevel inverters to reduce overall harmonic components used in grid applications. This research adds towards the best possible solution by employing multiple newly established adaptive optimization techniques: MNSGA-II and salp swarm. The well-known genetic algorithm and particle swarm optimization are used for the wind-tied multilevel inverters optimization issue. Sevenlevel, eleven-level, and fifteen-level MLIs were employed to reduce overall harmonic distortion. The reliability and convergence rate of simulated data with various modulation indices for seven-, eleven-, and fifteen-level MLIs are obtained and compared. Models are developed based on MATLAB Simulink and are used to validate quantitative measurements. [ABSTRACT FROM AUTHOR]

The objective and automatic detection of the sleep–wake cycle (SWC) stages is essential for the investigation of its physiology and dysfunction. Here, we propose a machine learning model for the classification of SWC stages based on the measurement of synchronization between neural oscillations of different frequencies. Publicly available electrophysiological recordings of mice were analyzed for the computation of phase–amplitude couplings, which were then supplied to a multilayer perceptron (MLP). Firstly, we assessed the performance of several architectures, varying among different input choices and numbers of neurons in the hidden layer. The top performing architecture was then tested using distinct extrapolation strategies that would simulate applications in a real lab setting. Although all the different choices of input data displayed high AUC values (>0.85) for all the stages, the ones using larger input datasets performed significantly better. The top performing architecture displayed high AUC values (>0.95) for all the extrapolation strategies, even in the worst-case scenario in which the training with a single day and single animal was used to classify the rest of the data. Overall, the results using multiple performance metrics indicate that the usage of a basic MLP fed with highly descriptive features such as neural synchronization is enough to efficiently classify SWC stages. [ABSTRACT FROM AUTHOR]

The 5G system generally requires the transmission of significant amount of information in allocated frequency bands. Besides, 5G channel presents two major problems for wireless communications, namely time dispersion, due to the phenomenon of multipath propagation, as well as frequency selectivity. So, improving the reliability of a wireless communication system for 5G requires the use of more suitable modulation techniques and more performance in the wireless context. Among the modulations that can be considered for this kind of application, we find the CPM (continuous phase modulation) scheme. This class of modulations is robust to phase drifts and nonlinear distortions generated by the amplifier when it is pushed to its maximum output power. In this paper, we propose a non-coherent receiver insensitive to fluctuations in the modulation index and capable of offering better performance on 5G channels. A detailed description of CPFSK signal is performed. The blind equalizer was introduced in order to combat inter symbol interference of the 5G channel as well as to make the transceiver insensible to the variation of the modulation index. The obtained results show the robustness, efficiency and low complexity of the proposed system over 5G channel. [ABSTRACT FROM AUTHOR]

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It is a crucial basis to improve the performance of neural network by constructing an appropriate activation function. This paper proposes a novel modulation window radial basis function neural network (MW-RBFNN) with an adjustable activation function. In this MW-RBFNN, a raised cosine radial basis function (RC-RBF) is adaptively modulated by an exponential function, and served as a shape-tunable activation function of MW-RBFNN. Compared with the basic RC-RBF neural network, the approximating ability of MW-RBFNN is improved due to its shape-tunable activation function. Besides, the computation of MW-RBFNN is far less than that of Gaussian radial basis function neural network (GRBFNN) because the MW-RBFNN is compactly supported. The training algorithm of MW-RBFNN is provided and its approximating ability is proved. Moreover, the regulation mechanism of the modulation index for the NN's performance is proved and the regulating algorithm of the modulation index in MW-RBFNN is given. The computational complexity of MW-RBFNN is also analyzed. Five typical application cases are presented to illustrate the effectiveness of this proposed MW-RBFNN. [ABSTRACT FROM AUTHOR]

This paper proposes a modified structure of reduced switch count H-Bridge Multilevel inverter (MLI) which comes with an advantages of higher voltage level, lower switching loss and lower number of components. This developed topology of MLI can generate 15-level output voltage with only three number of DC voltage sources and ten power devices. In order to generate the higher voltage level in analogy to the conventional Multilevel inverter asymmetrical input voltage sources are used which reduced the number of power devices input voltage sources and driver circuits. To mitigate the voltage and current harmonics from the output of the MLI JAYA algorithm is employed. This algorithm resulted 2.5% total harmonic distortion (THD) for three phase system and 5.2% THD for single phase system by developing suitable switching angles with rapid acceleration and reduces the computational burden. The efficacy of the algorithm has been justified by the comparison study with most popular swarm based algorithms known as particle swarm optimization (PSO) and Genetic algorithm (GA). The performance of the reduced MLI has been studied by developing a prototype for single phase and three phase in both MATLAB Simulink environment and experimental setup.

This paper presents a technique of harmonic minimization from output voltage waveform of a reduced switch Multilevel Inverter (MLI) through an efficient bio inspired metaheuristic algorithm called Black widow optimization (BWO). The proposed reduced switch 13- level MLI scheme uses a single Photovoltaic (PV) source which can be suitable for grid integration. The proposed BWO algorithm minimizes the Total Harmonic Distortion (THD) of output voltage with low operational time compared to other existing nature based algorithms considering large searching area. The weighted THD (WTHD) of the output voltage is also minimized in order to reduce the effect of lower order harmonics from the output voltage in a greater extent. The convergence rate and level of accuracy of BWO algorithm is compared with two different bio inspired algorithms for justification. The MLI operation is carried out with fundamental frequency, minimizing the switching stress while lowering the power loss compared to PWM or Sinusoidal PWM switching schemes. A single PV panel with multi winding flyback converter is used for medium voltage application through reduced switch MLI, serving the purpose of both isolation and reduction of energy sources. Simulation and experimental analysis are carried out with online control technique using a three phase 13-level reduced MLI to validate the proposed concept on a practical system.

This paper introduces a hybrid multilevel inverter (MLI) with reduced switch count, which can generate higher output voltage level with minimum number of DC input sources. The operation of this proposed MLI is carried out with unequal DC sources to achieve the desired output voltage level. The reduced MLI output voltage is set to minimal total harmonic distortion (THD) with the help of modified JAYA (MJAYA) algorithm. A JAYA algorithm with improved steps by adopting an accelerating parameter has been proposed in this research work to obtain a faster convergence of the objective function. The MJAYA algorithm has provided the suitable switching angles for the proposed three-phase 15-level MLI and reduced the output voltage THD to 2.23%, which satisfies the standard set by IEEE-519. To prove the efficiency of this proposed modified algorithm, the comparative analysis is carried out through MATLAB program and Simulink tool using common JAYA and modified particle swarm optimisation algorithms. The performance and productivity of the proposed MLI have been investigated through simulation and experimental setups. [ABSTRACT FROM AUTHOR]

A scheme of generating millimeter‐wave signals with 16 times frequency using a cascaded Mach–Zehnder modulator (MZM) is proposed. Optical delay lines (ODLs) are used to place between cascaded MZMs, two MZMs work at the maximum transmission point. By controlling the modulation index of the MZM and the delay of the ODL, 0‐order, ±8 $\pm 8$‐order, and ±12 $\pm 12$‐order sidebands can be generated. The optical phase shifter and attenuator are adjusted to eliminate the 0‐order sideband, and the photodetector output generates a frequency 16‐tupling millimeter wave (MMW). In the simulation platform, a 160‐GHz MMW signal can be obtained by taking a 10‐GHz radio frequency signal as the driving signal of two MZMs. The optical sideband suppression ratio is 29.92 dB, and the radio frequency sideband suppression ratio is 24.89 dB. Further, the effect of system performance on optical devices under nonideal conditions is also discussed. [ABSTRACT FROM AUTHOR]

The objective and automatic detection of the sleep–wake cycle (SWC) stages is essential for the investigation of its physiology and dysfunction. Here, we propose a machine learning model for the classification of SWC stages based on the measurement of synchronization between neural oscillations of different frequencies. Publicly available electrophysiological recordings of mice were analyzed for the computation of phase–amplitude couplings, which were then supplied to a multilayer perceptron (MLP). Firstly, we assessed the performance of several architectures, varying among different input choices and numbers of neurons in the hidden layer. The top performing architecture was then tested using distinct extrapolation strategies that would simulate applications in a real lab setting. Although all the different choices of input data displayed high AUC values (>0.85) for all the stages, the ones using larger input datasets performed significantly better. The top performing architecture displayed high AUC values (>0.95) for all the extrapolation strategies, even in the worst-case scenario in which the training with a single day and single animal was used to classify the rest of the data. Overall, the results using multiple performance metrics indicate that the usage of a basic MLP fed with highly descriptive features such as neural synchronization is enough to efficiently classify SWC stages.

This paper investigates the performance of Permanent Magnet Synchronous Motors (PMSM) in terms of torque ripples, a critical aspect affecting their operational efficiency and stability. Torque ripples in AC motors are primarily attributed to the switching action of the inverter Insulated Gate Bipolar Transistors (IGBTs), resulting in current fluctuations that directly influence motor torque. Mitigating these ripples is crucial for enhancing motor performance and reducing mechanical stress. This study focuses on utilizing multilevel inverters to minimize torque ripples by smoothing the motor current through harmonic elimination. Three different types of inverters, namely 2-level, 3-level, and 5-level inverters, are simulated to assess their impact on torque ripple reduction. The torque ripples were obtained from two methods where direct simulation is a method chosen and in second method, the average torque is subtracted from instantaneous torque samples. Stator currents are analyzed in the alpha-beta frame to evaluate the effectiveness of each inverter configuration in producing smoother current profiles and subsequently reducing torque ripples. Additionally, simulations are conducted under varying load torque conditions to assess the robustness and applicability of the proposed methodology across a wide range of operating conditions.