Frequency Parameters Research Articles

Efficacy of Ayurvedic Medication on Anxiety-induced Migraine: A Case Study

Anxiety-induced Migraine is a complex and debilitating condition where physiological and psychological stress trigger severe headache episodes. Throbbing pain which is typical of Migraine headache presented with distressing symptoms of anxiety, significantly impacts the quality of life for sufferers. The relationship between stress and Migraine is bidirectional; not only can anxiety provoke Migraine, but the chronic pain and disruption caused by Migraine can also heighten anxiety levels, creating a vicious cycle. In Ayurveda, Ardhavabhedaka is mentioned with the characterization of headache in half of the head (hemicrania) which is the main feature of Migraine. A 29-year-old man with complaints of headache visited Shalakya Tantra OPD of the Institute of Teaching and Research in Ayurveda, Jamnagar for his Anxiety-induced Migraine. He was treated with Laghu Sutashekhara Rasa, Brihata Dashmoola Taila Nasya, Tablet Anzee (combination of Tagar, Ashwagandha, Brahmi, and Sankhapusphi) and Rasayana Yoga. There was a significant improvement in all three parameters of duration, severity, and frequency of headache and the most important was a reduction in frequency of headaches. The combination of medications shows improvement in the signs and symptoms of Migraine with Anxiety. This study recommends the importance of clinical protocol and evaluation with the clinical study for evaluating efficacy and safety.

Design and implementation of deep neural network hardware chip and its performance analysis

The artificial neural network (ANN) with a single layer has a limited capacity to process data. Multiple neurons are connected in the human brain, and the actual capacity of the brain lies in the interconnectedness of multiple neurons. As a specified generalization of ANN deep learning makes use of two or more hidden layers, which implies that a greater number of neurons are required to construct the model. A network that has more than one hidden layer, also known as two or more hidden layers, is referred to as a deep neural network, and the process of training such networks is referred to as deep learning. The research article focuses on the design of a multilayer or deep neural network presented for the target field programmable gate array (FPGA) device spartan-6 (xc6stx4-2t9g144) FPGA. The simulation is carried out using Xilinx ISE and ModelSim software. There are two hidden layers in which (2×1) multiplexer blocks are utilized for processing twenty neurons into the output of ten neurons in the first hidden layer and demultiplexers (1×2) and vice versa. The hardware utilization is estimated on FPGA to compute the performance of the deep neural hardware chip based on memory, flip flops, delay, and frequency parameters. The design is scalable and applicable to various FPGA devices, which makes the work novel. FPGA-based neuromorphic hardware acceleration platform with high speed and low power for discrete spike processing on hardware with great real-time performance.

ДИНАМИКА ВАРИАБЕЛЬНОСТИ СЕРДЕЧНОГО РИТМА ПОСЛЕ СИЛОВЫХ ТРЕНИРОВОК С ОГРАНИЧЕНИЕМ КРОВОТОКА У ЛИЦ С МЕТАБОЛИЧЕСКИМ СИНДРОМОМ

We know that autonomic disfunction is due to metabolic pathologies. The same time, physical activity has beneficial effects on metabolic health and heart rate variability (HRV). But there is no consensus about the effect of trainings with weights on HRV in the scientific literature. Purpose: To asses the impact of low-intensity power trainings with blood flow restrictions on HRV of men with metabolic syndrome (MS). Materials and methods: Influence of different weight training modes on 60 men with MS was analyzed. The participants were divided into three groups: low-load resistance training with blood flow restriction (LLBFR; n = 20), high-intensity resistance training without blood flow restriction (HI; n = 20), low-load resistance training without blood flow restriction (LL; n = 20). Trainings were conducted twice a week for 12 weeks. Time and frequency indicators were assessed before and after the investigation. Results: After the investigation, there were an increase in SDNN for the LLBFR group (p = 0.000; Cohen’s d = 0.69), for the HI group (p = 0.000; Cohen’s d = 0.83), for the LL group (p = 0.016; Cohen’s d = 0.22), an increase in RMSSD for the LLBFR group (p = 0.000; Cohen’s d = 0.67), for the HI group (p = 0.000; Cohen’s d = 0.87), for the LL group (p = 0.027; Cohen’s d = 0.26), as well as an increase in pNN50 for the LLBFR group (p = 0.000; Cohen’s d = 0.58), for the HI group (p = 0.000; Cohen’s d = 0.53), for the LL group (p = 0.079; Cohen’s d = 0.14). Statistically significant changes (p ˂ 0.05) were also observed for the frequency parameters of HRV for the LLBFR and HI groups. Conclusions: Resistance training, including in combination with blood flow restriction, has a positive effect on HRV in men with MS.

Cutting-edge Bioinformatics strategies for synthesizing Cyclotriazadisulfonamide (CADA) analogs in next-Generation HIV therapies

Cyclotriazadisulfonamide (CADA) is a macrocyclic compound known for its unique mechanism in inhibiting HIV infection by downregulating the CD4 T-cell receptor, a crucial entry point for the virus. Unlike other antiretrovirals, CADA exhibits activity against a wide range of HIV strains, as all HIV variants require CD4 binding for infection. Furthermore, CADA has shown a synergistic effect with clinically approved anti-HIV drugs, offering potential for enhanced therapeutic strategies (Vermeire & Schols, [65]). One proposed mechanism for CADA’s inhibition of the CD4 receptor involves blocking the gates of the Sec61 channel, thereby preventing its translocation. However, CADA suffers from poor solubility and bioavailability. To address this, the study aimed to design CADA analogs with improved binding to the Sec61 channel, enhanced bioavailability, and reduced toxicity. The analogs were designed using SeeSAR, with Avogadro and Meeko used for 3D configuration and pseudoatom placement, respectively. AutoDock Vina version 1.2.4 was employed to predict the binding energies of these analogs. Of the 113 analogs designed, 93 demonstrated a more negative binding energy to the Sec61 channel compared to CADA. Structure-binding energy analyses were done to the top-binding analogs to show favorable structural modifications. Enzyme-ligand interactions were analyzed to elucidate the forces contributing to these binding energies. Additionally, 33 of the 113 analogs were deemed bioavailable using a bioavailability criteria specific for macrocycles. Toxicity predictions using PASS Online and StopTox identified analogs JGL023, JGL024, JGL032, and JGL047 as potential drug candidates. Molecular dynamics simulations using Gromacs-2020.4 revealed that JGL023 and JGL032 exhibited the most favorable binding to the Sec61 channel, as determined by evaluating ligand and residue flexibility, compactness, contact frequency, motion pathways, free energy, and other relevant parameters. Synthetic routes for these four analogs were proposed for future studies. The results of this study offer a new perspective on developing drugs to inhibit HIV entry.

Probability analysis of compressor blade vibration based on radial basis function extremum neural network method

To enhance the transient response and modeling accuracy of compressor blade vibration characteristics in time domain dynamic reliability analysis, a method called the radial basis function extremum neural network (RBFENN) is proposed. This method combines the radial basis function neural network (RBFNN) with the extreme response surface method (ERSM). By considering the time domain and frequency domain characteristics of unsteady aerodynamic excitation on blades affected by multi-row stator/rotor interference, the stress response distribution of the blades is determined through deterministic analysis. The RBFENN mathematical model is established using RBFNN to address the high nonlinearity in surrogate modeling. The ERSM is used to process the transient problem of motion reliability analysis, taking extreme values into account throughout the response process. In evaluating the vibrational reliability of the compressor blades, the maximum stress is considered as the primary focus of the study, taking into account the stochastic nature of aerodynamic excitation frequency, rotational velocity, and material parameters. It is observed that the unsteady aerodynamic excitation induces higher-order harmonic resonances in the blade velocity margin, resulting in increased stress at the leading edge of the blade root. This stress distribution follows a normal distribution pattern. A comparison among Monte Carlo methods, ERSM, and RBFENN demonstrates the superiority of the proposed RBFENN method in terms of computational accuracy and efficiency. This study introduces the RBFENN method as a learning-based approach for blade vibration reliability design, enriching the theory of mechanical reliability.

Multimode long-wave approximation for a viscoelastic coating subject to antiplane shear

A general asymptotic approach involving multimode long-wave approximations is illustrated by a 2D time-harmonic scalar problem for the dynamic antiplane shear of a viscoelastic coating. For the first time, a 1D equation of motion with the coefficients depending on frequency parameters is derived. The associated dispersion relation also seems to be a fresh development approximating its exact counterpart near the vicinities of all the cut-off frequencies. As might be expected, the developed formulation is not valid for short wavelength patterns. At the same time, as it is shown for a δ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\delta $$\\end{document}-type loading, it proves to be robust for various scenarios dominated by long-wave response.

Study on the effect mechanism of vibration excitation parameters on bolt loosening based on an implicit monitoring method

This study focuses on investigating the mechanism of bolt loosening under harmonic vibration. An implicit monitoring method of bolt loosening based on strain detection is proposed. By conducting simulations on a PCB (Printed Circuit Board) structure with fastening bolts base on the implicit method, a three-stage peculiarity for bolt loosening is established under harmonic vibration. Under different tightening torques, distinct effecting trends of the two excitation parameters of excitation amplitudes and frequencies on the strain response are indicated comprehensively. The comprehensive effect mechanism of the excitation amplitudes and frequencies on bolt loosening is investigated, and the results reveal that when the tightening torque is small, the excitation amplitude has a main impact on the strain RMS, while when the tightening torque is larger, the excitation frequency plays a major role. A bolt loosening peculiarity based on strain monitoring is provided for different excitation frequencies, amplitudes accompanied with different tightening torques under harmonic vibration. In practical application, the RMS value of the strain response of the monitored point is calculated and then compared with the three-stage loosening peculiarity to determine whether the bolt is loosen or not. The simulation analysis and test verification on a four-bolt connection structure are conducted based on the implicit method under harmonic vibration, and the good consistency is shown. Valuable insights for the detection and prevention of bolt loosening are provided, which have important engineering reference significance.

Clinical Application of Micro-Movement of Head by Vibraimage for Psychological Traits Study

The authors have conducted various research studies in medical care, anti-aging, psychosomatic medicine, health, and sports. The VibraImage project has recently begun. Using a 70-second video of the subject’s eye, face, and head, the micro-movement and vibration parameters of frequency and amplitude are determined. The vestibulo-ocular reflex (VOR) is involved in the mechanism. These data can suggest mental attractiveness or charm. The obtained results include 10 items: aggression, stress, tension, suspicion, stability, leadership, vitality, self-control, inhibition, and nervousness. The most attractive person is referred to as a “charismatic” person, and the project is therefore named the “charisma judge evaluation.

Heart Rate Variability for Supplementing Withdrawal Assessment in Patients with Opioid Dependence: An Exploratory Study.

Management of opioid dependence requires titrating medication doses based on withdrawal symptoms, but its clinical assessment presents challenges when it comes to subjective reporting. This study aimed to find out the relationship between heart rate variability (HRV) and opioid withdrawal in patients with opioid dependence. Three groups of adult males were recruited: (a) patients with opioid dependence undergoing inpatient detoxification, (b) patients with opioid dependence stabilized on buprenorphine-based opioid substitution treatment, and (c) healthy controls. Frequency and time-domain parameters of HRV were used in the analysis. The opioid withdrawal was assessed using the Subjective Opiate Withdrawal Scale (SOWS). Resting heart rate was found to be significantly different across the three groups (higher in patients stabilized on buprenorphine than the other two groups). In time-domain parameters, the detoxification group had the highest beat-to-beat variability. In frequency-domain parameters, the total power was highest for the detoxification group and lowest for the opioid substitution treatment group. In contrast, the relative power of frequency bands (very low, low, and high) did not vary across the groups at baseline. The SOWS had a weak negative correlation with root mean square of successive differences (RMSSD) in the opioid substitution group and did not have any relationship with HRV parameters in the detoxification group. This exploratory study did not find HRV parameters to be robustly associated with subjective withdrawal, except for a negative association with the beat-to-beat variability among patients on opioid substitution treatment. This study adds to information on HRV in patients with opioid dependence.

A Comparative Investigation of Different Ionospheric Models to Predict the MUF Parameter During Severe Geomagnetic Storm on 17th March 2015.

The present work aimed to make a comparative investigation between three different ionospheric models: IRI-2020, ASAPS and VOACAP. The purpose of the comparative study is to investigate the compatibility of predicting the Maximum Usable Frequency parameter (MUF) over mid-latitude region during the severe geomagnetic storm on 17 March 2015. Three stations distributed in the mid-latitudes were selected for study; these are (Athens (23.50o E, 38.00o N), Jeju (124.53o E, 33.6o N) and Pt. Arguello (239.50o W, 34.80o N). The daily MUF outcomes were calculated using the tested models for the three adopted sites, for a span of five-day (the day of the event and two days preceding and following the event day). The calculated datasets were compared for each location with the observed daily MUF values. In general, the findings show that the three investigated models gave good outcomes compared to the observed values for all selected stations. The comparative investigation results of the three tested models corresponding to the observed MUF values during the storm event revealed that the IRI -2020 Model indicate a clear impact of the geomagnetic storm on the predicted MUF values during the day of event. Similarly, for ASAPS Model, the storm's impact is clear on both the day of the event and the subsequent day, in contrast, the VOACAP model showed almost no impact of the geomagnetic storm on the observed MUF values throughout the entire study period for event 17 March 2015.

The Impact of Geomagnetic Storms on the Ionospheric Critical Frequency in the Northern and Southern Mid-Latitude Hemisphere Regions

In this work, the impact of different geomagnetic storm events on the plasma-sphere layer (ionosphere layer) over the northern and southern hemisphere regions was investigated during solar cycle 23. To grasp the influence of geomagnetic storms on the behavior and variation of the critical frequency parameter of the F2 ionospheric layer (foF2), five geomagnetic storms (classified as great, severe, and strong), with Disturbance storm time (Dst) values <-100 nT were chosen. Four stations located in different mid-latitude regions in northern and southern hemispheres were designated, the northern stations are: Millstone Hill (42.6° N, 288.50° W) and Rome (41.90° N, 12.50° E) and the southern stations are: Port Stanley (-51.60° S, 302.10° W) and Grahamstown (-33.30° S, 26.50° E). The findings of this study showed that during events of 16 July 2000 and 24 August 2005, the negative storms cause a noticeable reduction in the values of the foF2 parameter at the northern hemisphere stations compared to those at the southern hemisphere. These outcomes are consistent with the results of the examining the variation of D(foF2) and the electron density depletion during the tested event times at all stations except in Rome, where minor enhancements in foF2 value were observed during the August 24 2005 storm. During equinox storm events occurring on March 31 and November 6 2001, a noticeable negative impact of storms was observed across all stations. However, at Millstone Hill and Port Stanley stations, the results showed a slight positive storm impact during the October 21, 2001event.

Multicomponent signals interference detection exploiting HP-splines frequency parameter

Multicomponent signals play a key role in many application fields, such as biology, audio processing, seismology, air traffic control and security. They are well represented in the time-frequency plane where they are mainly characterized by special curves, called ridges, which carry information about the instantaneous frequency (IF) of each signal component. However, ridges identification usually is a difficult task for signals having interfering components and requires the automatic localization of time-frequency interference regions (IRs). This paper presents a study on the use of the frequency parameter of a hyperbolic-polynomial penalized spline (HP-spline) to predict the presence of interference regions. Since HP-splines are suitably designed for signal regression, it is proved that their frequency parameter can capture the change caused by the interaction between signal components in the time-frequency representation. In addition, the same parameter allows us to define a data-driven approach for IR localization, namely HP-spline Signal Interference Detection (HP-SID) method. Numerical experiments show that the proposed HP-SID can identify specific interference regions for different types of multicomponent signals by means of an efficient algorithm that does not require explicit data regression.

Meta-analysis of the acoustic adaptation hypothesis reveals no support for the effect of vegetation structure on acoustic signalling across terrestrial vertebrates.

Acoustic communication plays a prominent role in various ecological and evolutionary processes involving social interactions. The properties of acoustic signals are thought to be influenced not only by the interaction between signaller and receiver but also by the acoustic characteristics of the environment through which the signal is transmitted. This conjecture forms the core of the so-called "acoustic adaptation hypothesis" (AAH), which posits that vegetation structure affects frequency and temporal parameters of acoustic signals emitted by a signaller as a function of their acoustic degradation properties. Specifically, animals in densely vegetated "closed habitats" are expected to produce longer acoustic signals with lower repetition rates and lower frequencies (minimum, mean, maximum, and peak) compared to those inhabiting less-vegetated "open habitats". To date, this hypothesis has received mixed results, with the level of support depending on the taxonomic group and the methodology used. We conducted a systematic literature search of empirical studies testing for an effect of vegetation structure on acoustic signalling and assessed the generality of the AAH using a meta-analytic approach based on 371 effect sizes from 75 studies and 57 taxa encompassing birds, mammals and amphibians. Overall, our results do not provide consistent support for the AAH, neither in within-species comparisons (suggesting no overall phenotypically plastic response of acoustic signalling to vegetation structure) nor in among-species comparisons (suggesting no overall evolutionary response). However, when considering birds only, we found weak support for the AAH in within-species comparisons, which was mainly driven by studies that measured frequency bandwidth, suggesting that this variable may exhibit a phenotypically plastic response to vegetation structure. For among-species comparisons in birds, we also found support for the AAH, but this effect was not significant after excluding comparative studies that did not account for phylogenetic non-independence. Collectively, our synthesis does not support a universal role of vegetation structure in the evolution of acoustic communication. We highlight the need for more empirical work on currently under-studied taxa such as amphibians, mammals, and insects. Furthermore, we propose a framework for future research on the AAH. We specifically advocate for a more detailed and quantitative characterisation of habitats to identify frequencies with the highest detection probability and to determine if frequencies with greater detection distances are preferentially used. Finally, we stress that empirical tests of the AAH should focus on signals that are selected for increased transmission distance.

A Study of The Geographical Information System (GIS) based Energy Station Identification and Frequency Analysis Using ETA

The energy corporation entity recently pays maximum attention to the environmental and social safe guards associated with its operations. In developing countries mainly suffers to balance the gap between generation and demand for effective operation by the energy utilities. An inadequate transmission capacity, non-uniform location of energy stations may lead to worst consequences when the occurrences of fault and may end up with black out. To overcome the above situation and to improve the stability of such system, In this research proposes a Geographical Information System (GIS) based identification of location of the uncertainty power stations and also a technique called Islanding Scheme to protect the severely affected system by measuring its frequency parameters. The severely disturbed large interconnected power system is divided into number of subsystems with two or more generating buses feeding its nearby loads by disintegrating the tie lines. The sample 50 bus test case system taken from Tamilnadu Electricity Board (TNEB), INDIA to analyze and simulate the results using Electrical Transient Analyzer Program (ETAP) and Geographical Information System (GIS). In this paper, ETAP and GIS effective software tools used for identify the energy station in various locations and analyzing the frequency status of different buses for better operation in electric utilities and thus ensure better management, enhance the improved stability by mapping the data and outage management.

Fourier Neural Operator Networks for Solving Reaction–Diffusion Equations

In this paper, we used Fourier Neural Operator (FNO) networks to solve reaction–diffusion equations. The FNO is a novel framework designed to solve partial differential equations by learning mappings between infinite-dimensional functional spaces. We applied the FNO to the Surface Quasi-Geostrophic (SQG) equation, and we tested the model with two significantly different initial conditions: Vortex Initial Conditions and Sinusoidal Initial Conditions. Furthermore, we explored the generalization ability of the model by evaluating its performance when trained on Vortex Initial Conditions and applied to Sinusoidal Initial Conditions. Additionally, we investigated the modes (frequency parameters) used during training, analyzing their impact on the experimental results, and we determined the most suitable modes for this study. Next, we conducted experiments on the number of convolutional layers. The results showed that the performance of the models did not differ significantly when using two, three, or four layers, with the performance of two or three layers even slightly surpassing that of four layers. However, as the number of layers increased to five, the performance improved significantly. Beyond 10 layers, overfitting became evident. Based on these observations, we selected the optimal number of layers to ensure the best model performance. Given the autoregressive nature of the FNO, we also applied it to solve the Gray–Scott (GS) model, analyzing the impact of different input time steps on the performance of the model during recursive solving. The results indicated that the FNO requires sufficient information to capture the long-term evolution of the equations. However, compared to traditional methods, the FNO offers a significant advantage by requiring almost no additional computation time when predicting with new initial conditions.

Parenteral nutrition dependence and growth in pediatric patients with intestinal failure following transition to blenderized tube feedings: A case series.

Human milk and amino acid (AA) formulas are correlated with a shorter duration of parenteral nutrition (PN) dependence for infants with intestinal failure (IF). Literature to guide feeding practices beyond infancy in this population is limited. We aimed to assess PN dependence, growth patterns, and stool frequency in pediatric patients with IF who transitioned from AA or hydrolyzed formula to blenderized tube feedings (BTFs). We performed a retrospective review among children with IF observed at Boston Children's Hospital from January 2014 to January 2019. Inclusion criteria were receipt of BTF for ≥3 months at a volume of ≥200 ml/day and ≥2 outpatient visits during the study period. Patients who received BTF in combination with another formula or food purees were excluded. Twelve children met criteria. Eleven had a small bowel resection with mean residual small bowel length of 51 ± 47 cm. Two retained their ileocecal valve (ICV), and eight had colonic resection. All patients were dependent on PN with mean (SD) energy intake of 51 ± 21 kcal/kg/day. After transition to BTF, three patients (25%) achieved enteral autonomy, and seven (58%) had a reduction in PN energy intake. Anthropometric data and stool frequency were generally unchanged. The transition from AA or hydrolyzed formula to BTF was associated with a substantial reduction in PN support in 12 children with IF. Stool frequency and growth parameters were not significantly changed. Our findings suggest that the use of BTF in older children with IF should be considered.

Fundamental effects of array density and modulation frequency on image quality of diffuse optical tomography.

Diffuse optical tomography (DOT) provides three-dimensional image reconstruction of chromophore perturbations within a turbid volume. Two leading strategies to optimize DOT image quality include, (i) arrays of regular, interlacing, high-density (HD) grids of sources and detectors with closest spacing less than 15mm, or (ii) source modulated light of order ∼100MHz. However, the general principles for how these crucial design parameters of array density and modulation frequency may interact to provide an optimal system design have yet to be elucidated. Herein, we systematically evaluated how these design parameters effect image quality via multiple key metrics. Specifically, we simulated 32 system designs with realistic measurement noise and quantified localization error, spatial resolution, signal-to-noise, and localization depth of field for each of ∼85000 point spread functions in each model. We found that array density had a far stronger effect on image quality metrics than modulation frequency. Additionally, model fits for image quality metrics revealed that potential improvements diminish with regular arrays denser than 9mm closest spacing. Further, for a given array density, 300MHz source modulation provided the deepest reliable imaging compared to other frequencies. Our results indicate that both array density and modulation frequency affect the spatial sampling of tissue, which asymptotically saturates due to photon diffusivity within a turbid volume. In summary, our results provide comprehensive perspectives for optimizing future DOT system designs in applications from wearable functional brain imaging to breast tumor detection.

Experimental study of a gas-coupled pulse tube cold finger with both active piston and cold inertance tube as phase shifters for 8 K applications

Stirling-type pulse tube cryocoolers (SPTCs) working at temperatures below 8 K represent advantages in very long wave infrared detection, terahertz detection, etc. It is essential for SPTCs to obtain reasonable distribution of impedance to achieve good cooling performance. However, adjusting the distribution of impedance to an optimal value is hard as temperature decreases. It is critical for muti-stage SPTCs working at temperatures below 8 K to arrange appropriate phase shifter at each stage. This paper introduces a gas-coupled pulse tube cold finger with active piston and cold inertance tube as phase shifters for 8 K applications. The cold finger consists of pulse tube 1 (PT1) which works at liquid hydrogen temperatures and pulse tube 2 (PT2) which operates at liquid helium temperatures. Active piston is the phase shifter of PT1. It could adjust the impedance to any value theoretically and make the cold finger highly adaptable. The phase shifter of PT2 is cold inertance tube and gas reservoir. Cold inertance tube and gas reservoir has simple structure which makes it suitable for space applications. The impact of frequency and operating parameters of active piston on no-load temperature is investigated by experiments. A lowest no-load temperature of 5.16 K is achieved in experiments. The effect of temperature distribution of cold finger on cooling capacity at 8 K is also researched. A cooling capacity of 74 mW at 8 K can be obtained with the electric input power of 177.5 W and a pre-cooling capacity of 9.1 W/70 K.

Influence of Opening Time Interval of Gate Signals on Suppression of Horizontally Polarized Signal of Infrared Pulsed Laser.

Since the beginning of the 21st century, infrared Nd:YVO4 pulsed lasers have been widely applied, especially in some actual industrial processes. In the working process of a laser-aided etching device, the "match-head" effect must be effectively controlled by suppressing the first giant pulse for a solid-state Q-switched laser. In the process of optimizing the infrared Nd:YVO4 pulsed laser by adjusting the slope parameters of the radio frequency (RF) modulation to suppress the first giant pulse, it has been found that an abnormal horizontally polarized emission with a very short time appears before the formal vertically polarized emission when the gate signal is artificially started. Actually, abnormal horizontally polarized emissions will bring some unexpected machining traces during the production process and even greater dangers. The experimental results show that with the increase in the slope duration of an RF signal, the existence time of abnormal output horizontally polarized light will be shortened, and the horizontal giant pulse and vertical giant pulse are well suppressed. When the slope duration is greater than 0.18 ms, both horizontal and vertical giant pulses will disappear. The horizontally polarized light can be thoroughly suppressed when the slope duration is greater than 13.7 ms. Compared with the method of adding a polarizer to eliminate abnormal output horizontally polarized light, this method does not add elements in the laser, ensuring that the laser volume is relatively small, and does not affect the quality of the normal output laser. The research conclusion is thought to be of great practical significance, especially for processing transparent materials.

Optimal Capacitance Design for IRS Aided Wireless Power Transfer for Sustainable IoT Communication

ABSTRACTIntelligent reflecting surface (IRS) is a cutting‐edge technique that can significantly improve wireless propagation. It can efficiently utilize wireless power transfer to enable sustainable Internet‐of‐Things (IoT) transmission by reconfiguring the incident signal from the active transmitter. However, the flexibility of capacitance tuning in the IRS system, which controls underlying reflections, is often overlooked. The effective capacitance design in the IRS system can provide a new degree of freedom in the IoT communication system, which can enable additional performance gain in the received power. To achieve this, a novel IRS circuital optimization model is proposed in this work. It incorporates various electrical parameters of the meta‐surface unit cell for improved IoT‐enabled communication. The proposed optimization model provides an optimal capacitance as a function of phase shift (PS), which is controlled by IRS, incident frequency, and other IRS electrical parameters. This optimal capacitance is then used to define the received power. The convexity of the optimization problem is proved, and the global optimal capacitance is obtained for received power maximization. Our simulations show that the proposed optimization model outperforms the existing constructive interference‐based optimal PS method, for which the capacitance is first calculated. Finally, the analytical results are numerically validated with several optimal design insights.