Power Values Research Articles

The utility of wearable electroencephalography combined with behavioral measures to establish a practical multi-domain model for facilitating the diagnosis of young children with attention-deficit/hyperactivity disorder

Abstract Background A multi-method, multi-informant approach is crucial for evaluating attention-deficit/hyperactivity disorders (ADHD) in preschool children due to the diagnostic complexities and challenges at this developmental stage. However, most artificial intelligence (AI) studies on the automated detection of ADHD have relied on using a single datatype. This study aims to develop a reliable multimodal AI-detection system to facilitate the diagnosis of ADHD in young children. Methods 78 young children were recruited, including 43 diagnosed with ADHD (mean age: 68.07 ± 6.19 months) and 35 with typical development (mean age: 67.40 ± 5.44 months). Machine learning and deep learning methods were adopted to develop three individual predictive models using electroencephalography (EEG) data recorded with a wearable wireless device, scores from the computerized attention assessment via Conners’ Kiddie Continuous Performance Test Second Edition (K-CPT-2), and ratings from ADHD-related symptom scales. Finally, these models were combined to form a single ensemble model. Results The ensemble model achieved an accuracy of 0.974. While individual modality provided the optimal classification with an accuracy rate of 0.909, 0.922, and 0.950 using the ADHD-related symptom rating scale, the K-CPT-2 score, and the EEG measure, respectively. Moreover, the findings suggest that teacher ratings, K-CPT-2 reaction time, and occipital high-frequency EEG band power values are significant features in identifying young children with ADHD. Conclusions This study addresses three common issues in ADHD-related AI research: the utility of wearable technologies, integrating databases from diverse ADHD diagnostic instruments, and appropriately interpreting the models. This established multimodal system is potentially reliable and practical for distinguishing ADHD from TD, thus further facilitating the clinical diagnosis of ADHD in preschool young children.

Annihilators of power values of generalized skew derivations on Lie ideals in prime rings

Annihilators of power values of generalized skew derivations on Lie ideals in prime rings

Numerical study on aerodynamics of small scale horizontal axis wind turbine with Weibull analysis.

This paper presents a computational fluid dynamics (CFD) analysis and blade element momentum (BEM) analysis of horizontal small-scale wind turbines under various parameters. Random airfoils such as the NACA 0012, NACA 0018, NACA 4412, S1016, S1210, S1223, and SC20402 are chosen, and the glide ratio is analysed. A Reynolds number of 100,000 is considered because of the small wind turbine design. The foil with the highest glide ratio is selected. Various notations are used to represent the chord and the twist angle of the blade, and the blade with the best chord and twist angle is chosen. The lift, drag, and power coefficients and power curves are analysed via the BEM. The power curves are evaluated for different air densities, and a higher value of power is obtained. Finally, via CFD, the SST model for turbulence is analysed for various angles of attack via Ansys Fluent for different air densities. The results are satisfactory compared with those of the CFD and BEM analyses. The Weibull distribution is analysed to understand the required frequencies for various wind speeds so that the installation of wind turbines can be made convenient in preferred areas with suitable wind properties.

Machine learning for forecasting initial seizure onset in neonatal hypoxic-ischemic encephalopathy.

This study was undertaken to develop a machine learning (ML) model to forecast initial seizure onset in neonatal hypoxic-ischemic encephalopathy (HIE) utilizing clinical and quantitative electroencephalogram (QEEG) features. We developed a gradient boosting ML model (Neo-GB) that utilizes clinical features and QEEG to forecast time-dependent seizure risk. Clinical variables included cord blood gas values, Apgar scores, gestational age at birth, postmenstrual age (PMA), postnatal age, and birth weight. QEEG features included statistical moments, spectral power, and recurrence quantification analysis (RQA) features. We trained and evaluated Neo-GB on a University of California, San Francisco (UCSF) neonatal HIE dataset, augmenting training with publicly available neonatal electroencephalogram (EEG) datasets from Cork University and Helsinki University Hospitals. We assessed the performance of Neo-GB at providing dynamic and static forecasts with diagnostic performance metrics and incident/dynamic area under the receiver operating characteristic curve (iAUC) analyses. Model explanations were performed to assess contributions of QEEG features and channels to model predictions. The UCSF dataset included 60 neonates with HIE (30 with seizures). In subject-level static forecasting at 30 min after EEG initiation, baseline Neo-GB without time-dependent features had an area under the receiver operating characteristic curve (AUROC) of .76 and Neo-GB with time-dependent features had an AUROC of .89. In time-dependent evaluation of the initial seizure onset within a 24-h seizure occurrence period, dynamic forecast with Neo-GB demonstrated median iAUC = .79 (interquartile range [IQR] .75-.82) and concordance index (C-index) = .82, whereas baseline static forecast at 30 min demonstrated median iAUC = .75 (IQR .72-.76) and C-index = .69. Model explanation analysis revealed that spectral power, PMA, RQA, and cord blood gas values made the strongest contributions in driving Neo-GB predictions. Within the most influential EEG channels, as the preictal period advanced toward eventual seizure, there was an upward trend in broadband spectral power. This study demonstrates an ML model that combines QEEG with clinical features to forecast time-dependent risk of initial seizure onset in neonatal HIE. Spectral power evolution is an early EEG marker of seizure risk in neonatal HIE.

A Hybrid Data Assimilation and Dynamic Mode Decomposition Approach for Xenon Dynamic Prediction of Nuclear Reactor Cores

In this paper, a dynamic prediction scheme that combines the data assimilation method and dynamic mode decomposition (DMD) is brought out for the prediction of the whole-core power distribution under xenon oscillations within the HRP1000 reactor. The DMD is used to predict the power values over the nodes where in-core detectors exist, and predicted power is then extended to the whole core using data assimilation methodologies, e.g. the inverse distance–based data assimilation method. In the data assimilation stage, the selection of the background physical field and the regularization factor under different noise levels is investigated. A series of numerical experiments, based on the HPR1000 proof of feasibility of the coupling scheme, is conducted under low noise levels or low prediction step sizes. Finally, the optimal application conditions and the prediction performance of the coupling scheme in different noise levels are analyzed for practical engineering usage.

A hybrid piezoelectric-electromagnetic body energy harvester: Design and experiment validation

Abstract The rapid advancement of electronic devices and wireless sensors has heightened the demand for energy sustainability and portable power solutions. This paper proposes a hybrid piezoelectric-electromagnetic energy harvesting (HP-EEH) structure designed to enhance the efficiency of kinetic energy conversion within the human body. Firstly, the structure and principle of the energy capture device are introduced, and the electromechanical coupling model of the energy harvester is derived using Hamilton's principle. Furthermore, the system is numerically simulated, and the voltage output characteristics of the piezoelectric unit and the electromagnetic unit are analyzed by using the finite element analysis software. Finally, the experimental setup of the (HP-EEH) is constructed, and the voltage output characteristics are tested for different swinging angles and positions. The results show that two parts of energy can be captured simultaneously under ultra-low-frequency motion conditions. At a swing angle of 50 degrees, the piezoelectric and electromagnetic units achieved maximum output power values of 14.96 µW at 0.8 Hz and 10.4 µW at 1.2 Hz, respectively. Incorporating the output power of the electromagnetic unit aims to address the power consumption requirements of low-power devices better.

Investigation of H-mode density limit in mixed protium–deuterium plasmas at JET with ITER-like wall

Analysis of comparable discharges fuelled by either deuterium or protium reveals a clear relationship between the isotope mass and the H-mode density limit. Notably, the density limit is significantly lower in protium, showing a reduction of up to 35 % compared to identical deuterium plasma conditions. Within mixed H-mode density limit (HDL) plasmas, the maximum achievable density, or H-mode density limit, decreases with increasing protium concentration, denoted as cH. For instance, the highest corresponding maximum Greenwald fraction (fGW) of about 1.02 was observed in the pulse with the lowest cH value of 4.4 %. This fGW decreases to 0.96 at cH = 48 %. The average atomic mass, A¯, of the plasma species decreases in these pulses from the value of 1.96 (cH = 4.4 %) down to 1.52(cH = 48 %). Interestingly, the maximum achievable density appears to be largely unaffected by the applied power value, regardless of whether deuterium or protium is used, as well as under mixed H/D fuelling conditions.Additionally, the measured Greenwald fractions are agreed with a heuristic model based on the SOL pressure threshold of an MHD instability, as proposed by Goldston. This comparison, especially concerning the model’s dependence on isotopic mass, shows full consistency between the measured and predicted Greenwald fractions.

Effect of ultrasonic vibrations on mass efficiency and microstructure of laser direct deposition Inconel 718 superalloy

Laser direct deposition (LDD) is widely used to repair and manufacture high-value industrial components. However, it faces various defects, such as porosity, cracks, non-uniform microstructure, lack of fusion, keyhole phenomenon, element segregation, and undesirable secondary phases. A method to manage these defects is to concurrently apply ultrasonic vibrations (USV) during the LDD process. This study investigates the effect of USV on the mass efficiency and microstructure of LDD Inconel 718 superalloy to understand how incorporating USV can change the performance and structural integrity of single passes produced using the LDD process. For this purpose, USV is applied to a substrate during the LDD process. The resulting samples are characterized and analyzed using optical microscopy, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. The results indicate that applying USV below a threshold power value increases mass deposition by over 25%, while exceeding this threshold reduces it. Attention to this threshold power value is crucial for determining the process parameters, including laser power and speed. Additionally, USV transforms the microstructure from columnar to equiaxed and increases subgrain formation. This implementation also enhances the cooling rate, significantly decreasing the Laves phase by over 30% in all process parameters.

EEG-Characteristics of Aesthetic Perception and Evaluation of Artworks During a Museum Visit: А Neuroaesthetic Study

Twenty-eight people (30–70 years old, 12 men, 16 women, artists and non-artists) participated in a comparative neuroaesthetic study in the conditions of real-life visit to M. Vrubel mono-exhibition (Russian Museum, St. Petersburg). During the visit to the exhibition (usually lasted for around 60 min), EEG of participants was recorded. The subjects were looking at the paintings for 30 seconds to 3 min and evaluated the subjective aesthetic “attractiveness” of the paintings by a series of button presses (from 1 to 10). Were analyzed EEG spectral power in the α1 (8–10 Hz)-, α2 (10–13 Hz)-, β1 (13–18 Hz)-, β2 (13–30 Hz)-frequency bands during viewing the most famous Vrubel paintings (“Bogatyr”, “Swan Princess”, “Swan”, “Sitting Demon”, “Flying Demon”, “Pan”, etc.) and event-related EEG synchronization/desynchronization in relation to the subjective emotional and aesthetic evaluation of these paintings. Professional artists showed lower spectral power values in α1 (leads F3, C3, T4, Pz) and α2 (F3, Fz, F4, C3, Cz, C4, P3, Pz, P4)-frequency bands in frontal, central, and parietal regions. The artists were also found to have lower power values in β1,2-frequency bands in frontal (F3, F4, C3) regions and higher power values in occipital (O1, O2 – β1, β2) and posterior temporal (β2) cortical regions compared to the group of subjects with no special artistic education. Moreover, artists decision-making about the high emotional-aesthetic attractiveness of paintings was accompanied by an increase in event-related EEG synchronization for 11.5–27 Hz in frontal and central cortical areas over 580–360 ms before giving the response, compared to non-artists, whereas low emotional-aesthetic evaluation was characterized by 9–27 Hz EEG desynchronization, which started 60 ms before the giving the response and lasted up to 440 ms after it, in the posterior temporal and parietal regions. The differences in frontal cortical areas may indicate a higher engagement of the reward system during the perception of aesthetically pleasing paintings, and the differences in parietal and posterior temporal areas may indicate a continuing visual synthesis (more sustained visual attention) during the perception of subjectively less attractive paintings in artists compared to non-artists.

High‐contrast QPSK pattern recognition device consisting of a 4×$\times$4 MMI coupler

AbstractPattern recognition of optical label is utilized not only in packet switches but also in the field of data security due to its wide range of applications. In particular, pattern recognition using waveguide devices is highly valued because it eliminates the need for encoder processing. However, existing methods suffer from the problem of degraded contrast ratio because half of the input power are emitted from non‐target waveguides. In this article, a method is proposed to improve the contrast ratio employing a 44 multi‐mode interference (MMI) coupler with multiple input signals. The theoretical results reveal that the proposed method improves the contrast ratio from 3.0 dB to 9.5 dB and dB for the three‐input and four‐input cases, respectively, for quadrature phase‐shift keying (QPSK)‐modulated signal. Furthermore, numerical simulation was performed through the three dimension finite‐difference time‐domain (3D‐FDTD) method, and the proposed scheme successfully discriminated each QPSK pattern. The power value at all ports closely matches the theoretical value, and the ratios of 9.5 dB and 30.0 dB are numerically obtained.

Individualized optimal strategy in team pursuit for track cycling

In track cycling, performance in the team pursuit depends on the mechanical and physiological abilities of each member of the team, but also on the choice of racing strategy. Athletes must cover the 4000 m of the race, sharing the effort between them in successive relays. This raises the question of the optimum strategy. We propose a method for resolving this question by coupling a mechanical model of the race to physiological models (digital twins) of the athletes. The mechanical model enables one to predict a theoretical finishing time for a given strategy, while the physiological model enables one to determine whether or not a given strategy is feasible. By coupling the two models and using numerical optimization, an optimal strategy for a given team can then be predicted. Simplified team composition case studies are explored. For each case studied, an optimal strategy to maximize performance is obtained and composed of a set of three variables: relay lengths, power values for each relay, and the starting order of cyclists. The proposed method can be used for real athletes and extended to other disciplines.

Reset transition in HfO2-Based memristors using a constant power signal

Memristors, also known as resistive switching devices, have great potential for applications in memory and neuromorphic systems. Understanding the switching mechanisms is crucial since ReRAM memories need to operate at high frequencies. It is known the reset transition is dominated by the conductive filament Joule heating. We have studied the reset transition in TiN/Ti/HfO2/W metal–insulator–metal memristors by applying constant power signals to different initial filament thicknesses, which were obtained using different initial low resistance states. The results show that power value controls the reset times, decreasing when the power is increased. On the other hand, larger resistances lead to faster reset transitions. These measurements have allowed us to obtain a value of the thermal resistance of the conductive filament. Moreover, we have observed the reset times as a function of the initial resistance and the power lies on a common plane, which allows us to estimate the transition time by fixing an initial resistance and a power value.

A Novel Interpolation Method for Soil Parameters Combining RBF Neural Network and IDW in the Pearl River Delta

Soil fertility is a critical factor in agricultural production, directly impacting crop growth, yield, and quality. To achieve precise agricultural management, accurate spatial interpolation of soil parameters is essential. This study developed a new interpolation prediction framework that combines Radial Basis Function (RBF) neural networks with Inverse Distance Weighting (IDW), termed the IDW-RBFNN. This framework initially uses the IDW method to apply preliminary weights based on distance to the data points, which are then used as input for the RBF neural network to form a training dataset. Subsequently, the RBF neural network further trains on these data to refine the interpolation results, achieving more precise spatial data interpolation. We compared the interpolation prediction accuracy of the IDW-RBFNN framework with ordinary Kriging (OK) and RBF methods under three different parameter settings. Ultimately, the IDW-RBFNN demonstrated lower error rates in terms of RMSE and MRE compared to direct RBF interpolation methods when adjusting settings based on different power values, even with a fixed number of data samples. As the sample size decreases, the interpolation accuracy of OK and RBF methods is significantly affected, while the error of IDW-RBFNN remains relatively low. Considering both interpolation accuracy and resource limitations, we recommend using the IDW-RBFNN method (p = 2) with at least 60 samples as the minimum sampling density to ensure high interpolation accuracy under resource constraints. Our method overcomes limitations of existing approaches that use fixed steady-state distance decay parameters, providing an effective tool for soil fertility monitoring in delta regions.

Reducing Peak Power in a Multiple Load System by Delaying the Activation of Electrical Loads Using a Filter Based on a PI Controller

In a power grid with multiple two-state loads, the total power can vary over a significant range. This results in the inability to supply this system from a low-power source. The solution is an algorithm that shapes energy demand depending on its availability. For this purpose, a new load distribution method is proposed based on introducing a buffer between the temperature controller output and the heater and filtering the load using a master Proportional–Integral (PI) controller. The aim of the work was to evaluate the quality of the developed algorithm for limiting power peaks in the power grid. The research was conducted on a model of the Creep Test Laboratory with 389 heaters simulated in MATLAB Simulink R2023b. The algorithm was tested with various settings of the master controller parameters. By experimentally adjusting these parameters, a ten-fold reduction in peak power was achieved. The standard deviation for the L1 phase was reduced from 7.6 kW to 0.6 kW. Similar results were obtained for phases L2 and L3. The tested control system tracked changes in the average power value by changing the number of loads switched on and by frequency-modulating the signal when the change was less than the power of a single load. It was demonstrated that the controlled delayed switching of electrical loads can modify the shape of the total electric power without affecting their operation. The proposed solution features a low computational complexity, which allows its implementation in various systems.

DETERMINING THE ROLE OF SPATIAL ABILITY IN PERFORMING LUNAR PHASE CHANGE TASK USING BRAIN ACTIVITY ANALYSIS

Many students have difficulty understanding the concept of lunar phase changes (LPCs) due to spatial ability problems such as perspective-taking (PT) and mental rotation (MR). Therefore, this study aimed to compare brain activity during PT and MR tasks while performing the LPC task to determine the involvement of PT and MR. This study measured brain waves using EEG in 20 participants while solving the 3 tasks. First, the power values in the theta band of all cortical areas showed a significant difference between MR and LPC tasks. Second, in the strategy execution section with a statistically significant difference, the occipital lobe and limbic system were mainly active during the PT task, whereas the frontal lobe was mainly active during the LPC task. Third, for strategy execution, during MR and LPC tasks, the frontal lobe, temporal lobe, and limbic system were all activated to significantly different degrees. Therefore, both PT and MR, particularly PT, are required to solve the LPC task. Moreover, for students who have difficulty learning LPC, it is necessary to recognize the need for spatial ability, such as PT ability, and establish an appropriate teaching strategy. Keywords: brain activation, electroencephalography, lunar phase change, mental rotation, perspective-taking

РАДІАЦІЙНИЙ СТАН ЗОНИ ВІДЧУЖЕННЯ В 2023 РОЦІ ЗА РЕЗУЛЬТАТАМИ РАДІАЦІЙНО-ЕКОЛОГІЧНОГО МОНІТОРИНГУ

The article presents results of radiation and environmental monitoring in the Exclusion Zone for the year 2023, after de-occupation of the territory, which was carried out in accordance with the current regulation of work, with the exception of objects on which it is temporarily impossible to carry out work due to the lack of safe access. In 2022, due to the full-scale invasion of russian federation, radiation monitoring of the environment was interrupted, monitoring of radiation-hazardous objects and environmental components of the Exclusion Zone was not carried out. Implementation of priority measures to restore the radiation and environmental monitoring system in the Exclusion Zone, after occupation by russian troops, made it possible to resume the work of the automated radiation monitoring system points, to create an additional network of measurement of gamma radiation indicators in the Exclusion Zone, to restore work of the central analytical laboratory and to restore the work of aspiration units. Thus, the enterprise resumed obtaining the necessary data on the dose rate of gamma radiation, information on the radiation state of the surface layer of the atmosphere, radiation state of surface and underground waters, radiation state of landscapes and places of residence of self-settlers. According to the results of radiation and environmental monitoring in the Exclusion Zone in 2023, important analytical data were obtained: - Maximum power values of the ambient dose equivalent of gamma radiation are characteristic of the territories in the northern and western 'traces' of radioactive fallout; - Radiation state of the Exclusion Zone air environment and its dynamics during 2023 were determined by the nature of surface contamination of the territory, man-made and biological factors, as well as meteorological conditions. Control levels exceeding by 2.5 times of 137Cs volumetric activity in the air was recorded in Chornobyl; - Surface waters remain an important route of radionuclide removal beyond the Exclusion Zone. Removal of 90Sr from Pripyat River in the area of Chornobyl in 2023 was at a level that was 2.4 times higher than the average value for the last 5 years; - As a result of observations of the radiation state of underground waters, it was determined that the average value of radionuclide contamination of eocene and cenomanian-lower cretaceous water-bearing complexes does not exceed 1 Bq/m3 for 137Cs and 5.7 Bq/m3 for 90Sr; - Monitoring results at landscape landfills indicate that the most contaminated component of the environment is the top layer of soil cover; - Radiation survey results of unauthorized residence places of the population in the Exclusion Zone showed that over the years there is an increase in the intake of 90Sr in the food products of 'self-settlers'.

Network Coding Schemes and Cooperative Communication Systems Amplify and Forward in Outdoor Environments

Fading is a phenomenon of decreasing signal strength during transmission, caused by changes in environmental conditions or physical obstacles in the propagation path. Fading can cause interference in signal reception, such as distortion or loss of information, especially in Non-Line of Sight (NLoS) paths, where signals must pass through many paths or obstacles such as buildings, trees, or other objects. To eliminate fading interference, a cooperative communication system is created to reduce the effects of fading in wireless networks. The cooperative communication system works by utilizing other nodes in sending signals to the destination. The relay protocol used in this study is amplify and forward, where the signal to be sent will be amplified and forwarded to the destination by the relay node. A network coding scheme is added to the cooperative communication system to improve network performance. This scheme encodes each piece of information with the XOR technique. The parameters used in measuring the network coding scheme in the cooperative communication system are BER and throughput. Viewed from the BER value, the conventional cooperative communication system has better performance compared to the cooperative communication system with network coding. Cooperative communication system scheme without NC BER value is 0 when the transmit power value is -19 dBm or Gain 32. While for the cooperative communication system scheme with network coding BER value is 0 when transmit power of -9.25 dBm. Throughput system with network coding has increased by 17% compared to the conventional system.

An optimal approach for active power reserve of a de-loaded PV generator operating in coordination with synchronous generators

The de-loading operation of PVs, where PVs are operated with an active power reserve has picked up the interest of the researchers in the last few years. However, in an integrated system, where the PV generators are operated in coordination with the synchronous generators, the estimation of the amount of the active power reserve of the PV generator is crucial. A very high reserve will cause an excessive economical loss and a very low reserve will deteriorate the frequency regulation capability. Therefore, in this paper, the cost function of an integrated system is minimised using the particle swarm optimisation algorithm, and the amount of power reserve of the PV generator and operating power values of synchronous generators are obtained as a result of the minimisation. Further, the frequency response of the system for the obtained power reserve value is investigated by suddenly adding the load in the system. The lowest and highest system costs of 2521.87 and 4474.98 $/h are obtained for the irradiance 1000 and 400 W/m2 respectively, and upon a significant load change, the frequency deviation limits to 0.4513 for a reserve value of 29.0712 MW. System modelling and validation have been carried out in the SIMULINK toolbox of the MATLAB software.

PENGARUH EKSTRAK BUAH BELIMBING WULUH (AVERRHOA BILIMBI L.) TERHADAP DAYA TETAS TELUR IKAN GURAMI

Carp is one of commodities freshwater many developed by the cultivator of the fish, as the demand for a high market, maintenance easy and princes relatively stable, think nice and nutrition content high. One alternative to address the problem is the use of extract fruit carambola wuluh. Of which it has infected a fungus saprolegnia sp. Marinated to extrac the fruit of the leatherback wuluh. The concentration of an extract to the leatherback wuluh that is used is identification ppm 4.000 to the density of the 50 egg. The research uses the experimental methods random design it is the complete (RAL) with 4 treatment and 3 preparation of test questions so that apol corporate secretary ronald naggol there were a total of 12 of the experiment. Treatment P1 (time of soaking 10 minutes), treatment P2 (time of soaking 15 minutes), treatment P3 (time of soaking 20 minutes), treatment P4 (in the absence of soaking / control). The research results show that the addition of an extract the fruit of the leatherback wuluh to exert an influence real (P<0,05) against hatch power has risen and other eggs and concern only the survival of roasted gouramy fish with a long time of soaking an extract the fruit of the leatherback wuluh as longaudience with the value of hatch power has risen and other eggs as much as (96.67%). and the level of concern only the survival of (97.93%)

Brian effects of electroacupuncture on sleep quality and cognitive function in patients with insomnia related to cerebral infarction

To observe the effects of electroacupuncture (EA) on sleep quality, sleep structure, and cognitive function in patients with insomnia related to cerebral infarction, and to explore the brain effect mechanism of EA on insomnia related to cerebral infarction. Thirty-six patients with insomnia related to cerebral infarction were randomly divided into an EA group (18 cases, 1 case was eliminated and 1 case dropped out) and a sham acupuncture group (18 cases, 1 case was eliminated and 2 cases dropped out). Both groups received conventional treatment for cerebral infarction. The EA group received EA at Sishencong (EX-HN 1) with continuous waves at a frequency of 2 Hz, at an intensity tolerable to the patient. The sham acupuncture group received sham acupuncture at non-acupoints with the same EA parameters but electrical stimulation was interrupted after 30 s. Both groups were treated for 20 min each session, once daily, 5 days per week, for a total of 4 weeks. Pittsburgh sleep quality index (PSQI), Montreal cognitive assessment-basic (MoCA-B) scores, and short-term memory (STM) encoding test accuracy and average reaction time were observed before and after treatment in the two groups. Polysomnography (PSG) was used to evaluate sleep structure, and electroencephalogram (EEG) data were collected to observe the standardized power value of the Theta frequency band before and after treatment. Compared with before treatment, PSQI score was decreased and MoCA-B score was increased in the EA group after treatment (P<0.001); the EA group had lower PSQI score and higher MoCA-B score than those in the sham acupuncture group (P<0.001, P<0.01). Compared with before treatment, STM encoding test accuracy was increased and average reaction time was shortened in the EA group after treatment (P<0.01, P<0.001); sleep efficiency (SE) was increased (P<0.01), total sleep time (TST), REM, and N3 stages were prolonged (P<0.01, P<0.05), and sleep latency (SL) was shortened (P<0.01). The standardized power value of the Theta frequency band in EEG channels F3, C3, C4, O1 and O2 was decreased (P<0.05). After treatment, the EA group had higher STM encoding test accuracy, shorter average reaction time (P<0.05), higher SE (P<0.01), longer TST, REM and N3 stages (P<0.01), and shorter SL (P<0.01) than those in the sham acupuncture group. There was no statistically significant difference in the standardized power value of the Theta frequency band between the two groups (P>0.05). EA could regulate sleep quality and structure in patients with insomnia related to cerebral infarction, and improve cognitive function, possibly related to the reduction of slow-wave activity in EEG.