Transfer Function Parameters Research Articles

Added value of an atmospheric circulation pattern‐based statistical downscaling approach for daily precipitation distributions in complex terrain

AbstractReliable prediction of heavy precipitation events causing floods in a world of changing climate is crucial for the development of appropriate adaption strategies. Many attempts to provide such predictions have already been conducted but there is still much potential for improvement left. This is particularly true for statistical downscaling of heavy precipitation due to changes present in the corresponding atmospheric drivers. In this study, a circulation pattern (CP) conditional downscaling to the station level is proposed which considers occurring frequency changes of CPs. Following a strict circulation‐to‐environment approach we use atmospheric predictors to derive CPs. Subsequently, precipitation observations are used to derive CP conditional cumulative distribution functions (CDFs) of daily precipitation. Raw precipitation time series are sampled from these CDFs. Bias correction is applied to the sampled time series with quantile mapping (QM) and parametric transfer functions (PTFs) as methods being tested. The added value of this CP conditional downscaling approach is evaluated against the corresponding common non‐CP conditional approach. The performance evaluation is conducted by using Kling–Gupta Efficiency (KGE), root mean squared error (RMSE), and mean absolute error (MAE) metrics. In both cases the applied bias correction is identical. Potential added value can therefore only be attributed to the CP conditioning. It can be shown that the proposed CP conditional downscaling approach is capable of yielding more reliable and accurate downscaled daily precipitation time series in comparison to a non‐CP conditional approach. This can be seen in particular for the extreme parts of the distribution. Above the 95th percentile, an average performance gain of +0.24 and a maximum gain of +0.6 in terms of KGE is observed. These findings support the assumption of conserving and utilizing atmospheric information through CPs can be beneficial for more reliable statistical precipitation downscaling. Due to the availability of these atmospheric predictors in climate model output, the presented method is potentially suitable for downscaling precipitation projections.

Optimal metaheuristic state-dependent parameter proportional-integral-plus control: Alternative to gain-scheduled controller for control of a nonlinear continuous stirred tank reactor

The continuous stirred-tank reactors have complex dynamic behavior. In the reactor, the residual concentration decreases during an exothermic chemical reaction. In this transition proper cooling is essential to stabilize the reaction, and to prevent reactor overheating. This paper presents the data-driven metaheuristic state-dependent parameter proportional-integration-plus (M-SDP-PIP) control as an alternative of gain-scheduled control for adjusting the coolant temperature of the reactor. In fact, the parameters of the discrete transfer function were identified in the non-minimal state space using state-dependent parameter which is a nonlinear method; and weighting matrixes of linear quadratic servomechanism cost function of the optimal controller were achieved by metaheuristic methods, genetic algorithm, and particle swarm optimization, according to the minimization of the integral absolute error index and energy consumption. The proposed controller was compared to gain-scheduled and non-gain-scheduled controllers in the closed-loop condition in terms of tracking performance. The results show particle swarm optimization algorithm converge to the same optimal solution as genetic algorithm using fewer number of function evaluation, so that proposed controller has more effective offset-free servo-regulatory performance than other control schemes. In addition, it is simpler and more comprehensible because it requires no auxiliary equations and models to determine and manage the control gain, as opposed to the gain-scheduled structure.

Ability of Black-Box Optimisation to Efficiently Perform Simulation Studies in Power Engineering

Abstract In this study, the potential of the so-called black-box optimisation (BBO) to increase the efficiency of simulation studies in power engineering is evaluated. Three algorithms (“Multilevel Coordinate Search” (MCS) and “Stable Noisy Optimization by Branch and Fit” (SNOBFIT) by Huyer and Neumaier and “blackbox: A Procedure for Parallel Optimization of Expensive Black-box Functions” (blackbox) by Knysh and Korkolis) are implemented in MATLAB and compared for solving two use cases: the analysis of the maximum rotational speed of a gas turbine after a load rejection and the identification of transfer function parameters by measurements. The first use case has a high computational cost, whereas the second use case is computationally cheap. For each run of the algorithms, the accuracy of the found solution and the number of simulations or function evaluations needed to determine the optimum and the overall runtime are used to identify the potential of the algorithms in comparison to currently used methods. All methods provide solutions for potential optima that are at least 99.8% accurate compared to the reference methods. The number of evaluations of the objective functions differs significantly but cannot be directly compared as only the SNOBFIT algorithm does stop when the found solution does not improve further, whereas the other algorithms use a predefined number of function evaluations. Therefore, SNOBFIT has the shortest runtime for both examples. For computationally expensive simulations, it is shown that parallelisation of the function evaluations (SNOBFIT and blackbox) and quantisation of the input variables (SNOBFIT) are essential for the algorithmic performance. For the gas turbine overspeed analysis, only SNOBFIT can compete with the reference procedure concerning the runtime. Further studies will have to investigate whether the quantisation of input variables can be applied to other algorithms and whether the BBO algorithms can outperform the reference methods for problems with a higher dimensionality.

Effectiveness of butylphthalide on cerebral autoregulation in ischemic stroke patients with large artery atherosclerosis (EBCAS study): A randomized, controlled, multicenter trial.

Finding appropriate drugs to improve cerebral autoregulation (CA) in patients with acute ischemic stroke (AIS) is necessary to improve prognosis. We aimed to investigate the effect of butylphthalide on CA in patients with AIS. In this randomized controlled trial, 99 patients were 2:1 randomized to butylphthalide or placebo group. The butylphthalide group received intravenous infusion with a preconfigured butylphthalide-sodium chloride solution for 14 days and an oral butylphthalide capsule for additional 76 days. The placebo group synchronously received an intravenous infusion of 100 mL 0.9% saline and an oral butylphthalide simulation capsule. The transfer function parameter, phase difference (PD), and gain were used to quantify CA. The primary outcomes were CA levels on the affected side on day 14 and day 90. Eighty patients completed the follow-up (52 in the butylphthalide group and 28 in the placebo group). The PD of the affected side on 14 days or discharge and on 90 days was higher in the butylphthalide group than in the placebo group. The differences in safety outcomes were not significant. Therefore, butylphthalide treatment for 90 days can significantly improve CA in patients with AIS.Trial registration: ClinicalTrial.gov: NCT03413202.

Middle cerebral artery dynamic cerebral autoregulation is impaired by infarctions in the anterior but not the posterior cerebral artery territory in patients with mild strokes.

The aim of this study was to ascertain whether dynamic cerebral autoregulation (CA) in the middle cerebral artery (MCA) is disturbed by cerebral infarctions outside the MCA territory. We estimated transfer function parameters gain and phase from simultaneous recordings of spontaneous oscillation in blood pressure and MCA cerebral blood flow velocity in 10 consecutive patients with isolated anterior cerebral artery (ACA) infarctions and in 22 consecutive patients with isolated posterior cerebral artery (PCA) infarctions. All ACA infarctions were in the motor, premotor, or supplementary motor cortex areas and presented with pronounced leg hemiparesis. Twenty-eight age- and sex-matched healthy subjects served as controls. Compared to controls, phase was significantly reduced in the MCA ipsilateral to the lesion site and in the contralateral MCA (unaffected hemisphere) in the very low (0.02-0.07 Hz) and low (0.07-0.15 Hz) frequency ranges in the ACA infarctions but not in the PCA infarctions. Gain was reduced only in the very low frequency range in the MCA contralateral to the ACA lesion site. Systemic factors were unrelated to phase and gain results. Bilateral impairment of MCA dynamic CA in patients with a unilateral ACA infarction is frequent.

Correction of precipitation measurement for weighing precipitation gauges in a glacierized basin in the Tianshan Mountains

Precipitation is one of the most important climatological data for global hydrothermal cycle and climate change. The accuracy of precipitation data not only directly affects the hydrological processes, but also plays an important role in the climate and hydrology at regional and global scales. According to the in situ datasets, the precipitation measurement in automatic weather stations for Geonor T-200B was corrected by the World Meteorological Organization Solid Precipitation Intercomparison Experiment (WMO-SPICE) transfer functions. The parameters of transfer functions were tested and recalibrated by the local datasets. The results showed that the transfer functions showed better performance after recalibrating parameters by the local datasets. The root-mean-square error (RMSE) and mean bias decreased by an average of 34% and 42%, respectively. The corrected snowfall increased by 7% (14 mm) at the test station. Then, the new parameters were used in other automatic weather stations to correct precipitation, and it was found that solid precipitation was underestimated by 13% on the glacier surface affected by wind speed. Moreover, according to the corrected precipitation datasets observed in automatic weather stations and national meteorological stations, the precipitation–altitude relationship in the Urumqi River Basin was analyzed. The annual precipitation gradient was 115 mm km-1, and the maximum seasonal altitude occurred in summer with a value of 35 mm km-1 and in autumn with the lowest value of 1 mm km-1. When considering precipitation on the glacier surface, the yearly precipitation gradient was increased with the value of 158 mm km -1 in 2019.

Wavelet-artificial neural network to predict the acetone sensing by indium oxide/iron oxide nanocomposites

This study applies a hybridized wavelet transform-artificial neural network (WT-ANN) model to simulate the acetone detecting ability of the Indium oxide/Iron oxide (In2O3/Fe2O3) nanocomposite sensors. The WT-ANN has been constructed to extract the sensor resistance ratio (SRR) in the air with respect to the acetone from the nanocomposite chemistry, operating temperature, and acetone concentration. The performed sensitivity analyses demonstrate that a single hidden layer WT-ANN with nine nodes is the highest accurate model for automating the acetone-detecting ability of the In2O3/Fe2O3 sensors. Furthermore, the genetic algorithm has fine-tuned the shape-related parameters of the B-spline wavelet transfer function. This model accurately predicts the SRR of the 119 nanocomposite sensors with a mean absolute error of 0.7, absolute average relative deviation of 10.12%, root mean squared error of 1.14, and correlation coefficient of 0.95813. The In2O3-based nanocomposite with a 15 mol percent of Fe2O3 is the best sensor for detecting acetone at wide temperatures and concentration ranges. This type of reliable estimator is a step toward fully automating the gas-detecting ability of In2O3/Fe2O3 nanocomposite sensors.

Series PIDA Controller Design for IPDT Processes

This paper discusses optimal design of the series proportional–integral–derivative–accelerative (PIDA) controller for integral-plus-dead-time (IPDT) plants. The article starts with the design of disturbance reconstruction and compensation based on proportional-derivative-accelerative (PDA) stabilizing controllers. It shows that by introducing positive feedback by a low-pass filter from the (limited) output of the stabilizing PDA controller, one gets disturbance observer (DOB) for the reconstruction and compensation of input disturbances. Thereby, the DOB functionality is based on evaluating steady-state controller output. This DOB interpretation is in full agreement with the results of the analysis of the optimal setting of the stabilizing PDA controller and of its expanded PIDA version with positive feedback from the controller output. By using the multiple real dominant pole (MRDP) method, it confirms that the low-pass filter time constant in positive feedback must be much longer than the dominant time constant of the stabilized loop. This paper also shows that the constrained PIDA controller with the MRDP setting leads to transient responses with input and output overshoots. Experimentally, such a constrained series PIDA controller can be shown as equivalent to a constrained MRDP tuned parallel PIDA controller in anti-windup connection using conditional integration. Next, the article explores the possibility of removing overshoots of the output and input of the process achieved for MRDP tuning by interchanging the parameters of the controller transfer function, which was proven as very effective in the case of the series PID controller. It shows that such a modification of the controller can only be implemented approximately, when the factorization of the controller numerator, which gives complex conjugate zeros, will be replaced by a double real zero. Neglecting the imaginary part and specifying the feedback time constant with a smaller approximative time constant results in the removal of overshoots, but the resulting dynamics will not be faster than for the previously mentioned solutions. A significant improvement in the closed-loop performance can finally be achieved by the optimal setting of the constrained series PIDA controller calculated using the performance portrait method. This article also points out the terminologically incorrect designation of the proposed structure as series PIDA controller, because it does not contain any explicit integral action. Instead, it proposes a more thorough revision of the interpretation of controllers based on automatic reset from the controller output, which do not contain any integrator, but at the same time represent the core of the most used industrial automation. In the end, constrained structures using automatic reset of the stabilizing controller output can ensure a higher performance of transient responses than the usually preferred solutions based on parallel controllers with integral action that, in order to respect the control signal limitation, must be supplemented with anti-windup circuitry. The excellent properties of the constrained series PIDA controller are demonstrated by an example of controlling a thermal process and proven by the circle criterion of absolute stability.

Augmented reality system based on full-color holographic optical elements lens

Holographic optical element (HOE) lens is an imaging element fabricated through recording wavefront by interference. Because of its advantages of small form factor and wavelength, angle selectivity and arbitrary wavefront formation, it has a good application prospect in augmented reality display. To make the system more compact, the HOE lens is adopted as an off-axis optical element. At the same time, according to diffraction principle, its wavelength response is more sensitive than those of traditional refractive and reflective optical elements. Thus the fabrication and design of a full-color HOE lens is a challenge to optimizing the free-space head-up display system. To systematically analyze the HOE imaging system, the conjugate relation between the object and image is derived by scalar diffraction theory. Then the Gaussian conjugate imaging equation is obtained and the off-axis aberration of distortion and astigmatism in the HOE imaging system are analyzed. In addition, A head-up display with field of view (FOV) of 18° and eyebox of 10 mm is simulated and its imaging process is visualized through the geometric optics method of <i> <b>k</b> </i>-vector diagram and ray-tracing. A full-color HOE lens with high diffraction efficiency is fabricated by interference. Its average peak diffraction efficiency is 56.7%, reaching a high level in the world. A prototype of augmented reality system is established by integrating laser pico-projectior with HOE lens. The experimental results of distortion effect and astigmatism effect of the system are obtained, which are consistent with the simulation results. The modulation transfer function (MTF) parameter of the system is measured, and its definition basically meets the requirements of the human eyes for resolution. The aberration of the system is analyzed and the optimization method is proposed. To optimize the monochromatic image quality, an extra cylindrical lens is added to ensure the same optical power of meridian and sagittal plane to eliminate the astigmatism. Besides, a freeform wavefront is designed by the geometric construction method and forms a freeform HOE to deal with the distortion problem. The local recording freeform wavefront can be calculated by the imaging equation. When full-color HOE is applied to the display system, the images of three channels may separate in the space because of their different reconstruction wavelengths and angles. We propose a pre-compensation method of recording process to solve this problem. If these above-mentioned problems can be solved, due to its good image uniformity, sufficient field angle and eyebox area, the head-up display based on HOE lens with extra optical power will have a better application in augmented reality technology.

Integral-proportional derivative controller tuning rules obtained from optimum responses for set-point tracking of unstable processes with time delay

This paper introduces the design of integral–proportional derivative (I-PD) controllers for unstable first-order plus time-delay and integrating-unstable first-order plus time-delay processes. Minimization of the error based on integral performance criteria is performed to derive analytical rules using curve fitting techniques. Obtained analytical formulas may be used to determine the necessary tuning parameters of the I-PD controller from the known plant transfer function parameters. Advantages of the introduced tuning of I-PD controllers are shown by comparisons with other existing ones in terms of unit step responses, measurement noise, control signals, perturbations in plant transfer function parameters, and integral performance indices. Results have shown that some significant advantages have been achieved with the introduced tuning of I-PD controllers when compared to others in the literature.

Automatic Regionalization of Model Parameters for Hydrological Models.

Parameter estimation is one of the most challenging tasks in large-scale distributed modeling, because of the high dimensionality of the parameter space. Relating model parameters to catchment/landscape characteristics reduces the number of parameters, enhances physical realism, and allows the transfer of hydrological model parameters in time and space. This study presents the first large-scale application of automatic parameter transfer function (TF) estimation for a complex hydrological model. The Function Space Optimization (FSO) method can automatically estimate TF structures and coefficients for distributed models. We apply FSO to the mesoscale Hydrologic Model (mHM, mhm-ufz.org), which is the only available distributed model that includes a priori defined TFs for all its parameters. FSO is used to estimate new TFs for the parameters "saturated hydraulic conductivity" and "field capacity," which both influence a range of hydrological processes. The setup of mHM from a previous study serves as a benchmark. The estimated TFs resulted in predictions in 222 validation basins with a median NSE of 0.68, showing that even with 5years of calibration data, high performance in ungauged basins can be achieved. The performance is similar to the benchmark results, showing that the automatic TFs can achieve comparable results to TFs that were developed over years using expert knowledge. In summary, the findings present a step toward automatic TF estimation of model parameters for distributed models.

Optimal integral–proportional derivative controller design for input load disturbance rejection of time delay integrating processes

One of the commonly encountered type of processes in industry is integrating systems. For this type of processes, tuning formulas to calculate optimally integral–proportional derivative controller parameters to reject input load disturbance, which is an important problem in process control, are presented here. Simple tuning formulas are provided to set the integral–proportional derivative controller parameters for rejection of input load disturbance of integrating plus first order plus dead time and double integrating plus first order plus dead time processes. Time-weighted integral performance indices are exploited to derive optimal formulas for input load disturbance rejection. Achieved formulas for integral–proportional derivative tuning contain only process transfer function parameters. To show benefits and usefulness of the suggested design approach, simulation examples are imparted. Furthermore, comparisons to some available design approaches are given to reveal the superior performance of the suggested design method. In addition, proposed design method is experimentally validated by controlling a cart position.

Maximum sensitivity (Ms)-based I-PD controller design for the control of integrating processes with time delay

Integrating processes, whose one or more poles are located at the origin, are common in the process industry. This paper focuses on maximum sensitivity (Ms)-based control of these types of processes. Integral–proportional derivative (I-PD) controllers are designed by exploiting the direct synthesis method for different forms of integrating processes. The suggested design approach is based on comparing the characteristic equation of the closed-loop system, which comprises the integrating system and I-PD controller with a lead/lag filter, with the desired characteristic equation. Simple and analytical adjusting rules are followed to determine the parameters of the I-PD controller and the lead/lag filter according to desired robustness specified by maximum sensitivity (Ms). The formulas provided contain process transfer function parameters and a tuning parameter that is used for setting the desired Ms. The benefits of the proposed technique are demonstrated by simulation examples and a real-time application of cart position control on an experimental set-up. Comparisons with some reported proportional–integral–derivative (PID) and I-PD design techniques are presented to demonstrate the advantages of the proposed design method more evidently.

Inverse transfer function identification for high-frequency pressure probes using M-sequence pressure generators

Accurate unsteady pressure measurement is important for the development and improvement of turbomachinery. The miniature pressure probe can adapt to the accurate measurement of pressure in a tight space under harsh working conditions, but the presence of the pressure transmission line makes the probe frequency response characteristics poor, and even causes the pressure signal to be distorted. In this paper, a novel Maximum-length sequence (M-sequence) pressure generator was developed, which generated pulsating pressure signals with wide frequency range and large amplitude pressure fluctuations at a fixed speed, and a time-domain parametric probe inverse transfer function identification method based on finite impulse response (FIR) adaptive filter using the generator was proposed. Four probes with different geometric parameters were fabricated. The probe inverse transfer function obtained by the FIR adaptive filter was compared with that obtained by the traditional non-parametric method to verify the accuracy of the identification method. The pressure at the pressure tap of the probes were reconstructed from the pressure measured by the probe and the inverse transfer function, which was in good agreement with the pressure measured by the reference sensor.

An Explicit Parametrization of Closed Loops for Spatially Distributed Controllers With Sparsity Constraints

In this article,we study the linear time-invariant state-feedback controller design problem for distributed systems. We follow the recently developed system level synthesis (SLS) approach and impose locality structure on the resulting closed-loop mappings; the corresponding controller implementation inherits this prescribed structure. In contrast to existing SLS results, we derive an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">explicit</i> (rather than implicit) parameterization of all achievable stabilized closed-loops. This admits more efficient IIR representations of the temporal part of the closed-loop dynamics, and it allows for the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {H}_2$</tex-math></inline-formula> design problem with closed-loop spatial sparsity constraints to be converted to a standard model matching problem, with the number of transfer function parameters scaling linearly with the closed-loop spatial extent constraint. We illustrate our results with two applications: consensus of first-order subsystems and the vehicular platoons problem. In the case of first-order consensus, we provide analytic solutions and further analyze the architecture of the resulting controller implementation. Results for infinite extent spatially invariant systems are presented to provide insight to the case of a large but finite number of subsystems.

Methodology for applying control theory in modeling the processes of centrifugal enrichment of mineral raw materials

ABSTRACT The article discusses an approach aimed at the formation of a methodology for applying the principles and models used in the automatic control theory in relation to the processes of enrichment of mineral raw materials. The problem solved in the article is posed as follows, try to represent the process of gravitational enrichment, namely centrifugal concentration, in the form of a transfer function and try to find its parameters. The paper proposes an approach to modeling centrifugal concentrators based on transfer functions, using averaged values of output characteristics. Proposal of a mechanism for determining the parameters of transfer functions and their calculation based on experimental data. To check the presented calculations, the MatLAB software tool with the Simulink package was used. As a result, the transfer function of the KC-CVD6 apparatus used in the gold deposit was found and satisfactory results were obtained. The work uses methods of identification of automatic systems, methods of system analysis, as well as methods of mathematical modeling, namely the numerical solution of differential equations, implemented in the MatLAB environment with the Simulink package. A comparison with the classical regression model was made; as a result, the modeling error was reduced by two times. The simulation error for the concentrate yield was less than 1%.

Error Correction Method of TIADC System Based on Parameter Estimation of Identification Model

The performance of analog-to-digital converters (ADCs) has reached a bottleneck due to the limitations of the manufacturing process and testing environment. Time-interleaved ADC (TIADC) technology can increase the sampling rate without changing the resolution. However, channel mismatch severely degrades the dynamic performance of the TIADC system. For the channel mismatch problem of TIADC, most of the current solutions have preconditions, such as eliminating only some kind of error or increasing the complexity of the hardware. A few methods can estimate multiple errors without changing the hardware circuit. To improve the dynamic performance of the TIADC system, on the basis of an in-depth study of the channel mismatch error of TIADC, according to the system identification theory, an identification model is designed to characterize the frequency characteristics of TIADC. Using the system observation data, the transfer function parameters of the system are recursively estimated. By constructing and verifying the identification model of the TIADC system, and then through the frequency domain correction method, a digital compensation filter is established to complete the error correction of the system. The test results of the four-channel TIADC high-speed data acquisition system show that the actual input and output characteristics of the test system are consistent with the nature of the identification model. The four channels of the TIADC system are provided by four sub-channels of two AD9653 chips, and the highest sampling rate of a single channel is 125MSPS. For sinusoidal input signals from 20 MHz to 150 MHz, the sampling system can achieve a signal-to-noise ratio (SNR) above 56.8 dB and spurious free dynamic range (SFDR) above 69.7 dB. The dynamic performance of the sampling system is nearly equivalent to that of its sub-ADC; the feasibility of the model identification method and the effectiveness of error correction are verified in simulation and experiment.

Artificial neural networks with conformable transfer function for improving the performance in thermal and environmental processes

This research proposes a novel transfer function based on the hyperbolic tangent and the Khalil conformable exponential function. The non-integer order transfer function offers a suitable neural network configuration because of its ability to adapt. Consequently, this function was introduced into neural network models for three experimental cases: estimating the annular Nusselt number correlation to a helical double-pipe evaporator, the volumetric mass transfer coefficient in an electrochemical reaction, and the thermal efficiency of a solar parabolic trough collector. We found the new transfer function parameters during the training step of the neural networks. Therefore, weights and biases depend on them. We assessed the models applied to the three cases using the determination coefficient, adjusted determination coefficient, and the slope-intercept test. In addition, the MSE for the training set and the whole database were computed to show that there is no overfitting problem. The best-assessed models showed a relationship of 99%, 97%, and 95% with the experimental data for the first, second, and third cases. This novel proposal made reducing the number of neurons in the hidden layer feasible. Therefore, we show a neural network with a conformable transfer function (ANN-CTF) that learns well enough with less available information from the experimental database during its training.

The Impact of Serial Remote Ischemic Conditioning on Dynamic Cerebral Autoregulation and Brain Injury Related Biomarkers.

ObjectiveRecent studies have demonstrated the positive roles of remote ischemic conditioning (RIC) in patients with cerebrovascular diseases; however, the mechanisms remain unclear. This study aimed to explore the effect of serial RIC on dynamic cerebral autoregulation (dCA) and serum biomarkers associated with brain injury, both of which are related to the prognosis of cerebrovascular disease.MethodsThis was a self-controlled interventional study in healthy adults. The RIC was conducted twice a day for 7 consecutive days (d1–d7) and comprised 4 × 5-min single arm cuff inflation/deflation cycles at 200 mmHg. All participants underwent assessments of dCA ten times, including baseline, d1, d2, d4, d7, d8, d10, d14, d21, and d35 of the study. Blood samples were collected four times (baseline, d1, d7, and d8) immediately after dCA measurements. The transfer function parameters [phase difference (PD) and gain] were used to quantify dCA. Four serum biomarkers associated with brain injury, ubiquitin C-terminal hydrolase-L1, neuron-specific enolase, glial fibrillary acidic protein, and S100β were tested.ResultsTwenty-two healthy adult volunteers (mean age 25.73 ± 1.78 years, 3 men [13.6%], all Asian) were enrolled in this study. Bilateral PD values were significantly higher since four times of RIC were completed (d2) compared with PD values at baseline (left: 53.31 ± 10.53 vs. 45.87 ± 13.02 degree, p = 0.015; right: 54.90 ± 10.46 vs. 45.96 ± 10.77 degree, p = 0.005). After completing 7 days of RIC, the significant increase in dCA was sustained for at least 28 days (d35, left: 53.11 ± 14.51 degree, P = 0.038; right: 56.95 ± 14.57 degree, p < 0.001). No difference was found in terms of different serum biomarkers related to brain injury before and after RIC.ConclusionThe elevation in dCA was detected immediately after four repeated times of RIC, and 7-day consecutive RIC induced a sustained increase in dCA for at least 28 days and did not affect blood biomarkers of brain injury in healthy adults. These results will help us to formulate detailed strategies for the safe and effective application of RIC in patients with cerebrovascular disease.

Geoelectric studies of the Kozloduy nuclear power plant region, Bulgaria

The task of the work was geoelectrical studies using variations of the magnetotelluric (MT) field of the Kozloduy nuclear power plant (KNPP) region and the integration of its results with other geological and geophysical knowledge. This paper presents the determined interpretation parameters of the MT field. The KNPP is located on the right bank of the Danube River in close proximity to the river. This fact, together with the location of electrified railways determined the unique network of locations of observation points for MT field variations. Based on the analysis of Earthquake Catalogs of Bulgaria and international seismicity databases, a map of the seismicity of nuclear power plant areas was built. Over the past 50 years, about 750 earthquakes (mainly south of KNPP) have been recorded at a distance of 40—80 km from the KNPP. Two magnetotelluric stations GEOMAG-02 were used at measurement sites, but equipment for recording electrical channels was available only for one station (due to the lack of another set of non-polarizable electrodes). The MT field variations were observed at 21 points, which are located on the territory with sides approximately 30—35 km from east to west and 40—50 km from north to south. For all observation points on the profile, only the parameters of the vertical magnetic transfer function (VMPF) were determined, in the form of the real (Cu) and imaginary (Cv) parts of the induction vector. The steadily induction vector was defined for periods from 10—20 to 4900—10 800 s. For most points it was possible to estimate the values Cu, Cv with an error of 0.02—0.04 and AzCu, AzCv 3—5°. The analysis showed the presence of anomalous behavior of Cu, Cv in different intervals of periods at some points. In the shortest (about 20 s) and longest periods (600 to 1000 s), the Cu directions completely coincide and indicate the presence of anomalous conductivity of the quasi-longitudinal strike to the west of the study area. This behavior of the Cu vector is in good agreement with power isohypsum strike of the Cenozoic deposits. At intermediate periods of 50—200 s, the behavior of Cu is more complex. Approaching the zone of high seismicity, the direction of the Cu differs from the previous ones by almost 90°. On the Geoelectrical sections, obtained as a result of 1D inversions of MTS curves at 4 points located in the southern part of the region, anomalous layers are identified (ρ about 10 ohm · m, the depth of the center of the object is 15—20 km). It can be assumed that well-conducting objects in the Earth’s crust of the region, apparently, prevent the propagation of seismic waves from nearby earthquakes to the north towards the KNPP.