Signal Perturbation Research Articles

Impact of temperature and relative humidity variations on coda waves in concrete

The microstructure of concrete can be affected by many factors, from non-destructive environmental factors through to destructive damage induced by transient stresses. Coda wave interferometry is a technique that is sensitive enough to detect weak changes within concrete by evaluating the ultrasonic signal perturbation compared to a reference state. As concrete microstructure is sensitive to many factors, it is important to separate their contributions to the observables. In this study, we characterize the relationships between the concrete elastic and inelastic properties, and temperature and relative humidity. We confirm previous theoretical studies that found a linear relationship between temperature changes and velocity variation of the ultrasonic waves for a given concrete mix, and provide scaling factors per Kelvin for multiple settings. We also confirm an anti-correlation with relative humidity using long-term conditioning. Furthermore, we explore beyond the existing studies to establish the relationship linking humidity and temperature changes to ultrasonic wave attenuation.

Deformable-boundary integral formulation for the solution of arbitrarily-forced acoustic wave equation

The propagation of perturbations in fluids is governed by an acoustic wave equation. This paper, first, introduces an arbitrarily-forced wave equation which, properly adapted, gives rise to equations describing specific phenomena of signal perturbation propagation in fluids (like, for instance, the Lighthill and Ffowcs-Williams and Hawkings equations for radiation and scattering). Then, its solution is determined through a novel boundary integral formulation based on the free-space Green function, which is applicable to fluid domains bounded by solid or porous deformable surfaces. Different versions of the proposed boundary integral formulation can be derived, depending on the frame of reference in which they are expressed. The numerical investigation begins with the comparison of the results obtained by the presented formulation against analytical solutions concerning both a pulsating solid sphere and a deformable porous surface that encloses pulsating sources. Then, the equivalence of the formulations expressed in different frames is examined for a bending and twisting non-lifting wing translating at different Mach numbers. Finally, the aeroacoustic field generated by a helicopter rotor model in forward flight is examined to assess the effect of the body deformation on the radiated noise and the accuracy of the numerical simulations by comparison with experimental data. The results of the numerical investigation have provided a comprehensive validation of the deformable-boundary integral formulation presented for the analysis of wave propagation in fluids, and confirmed its capability to study problems of engineering interest.

Observations and Numerical Simulations of the Effects of the Gamma Ray Burst 221009A on the Lower Ionosphere

AbstractThis paper investigates the impact of a powerful gamma ray burst (GRB) that occurred on 9 October 2022, on the Earth's environment using a very low frequency receiver (VLF) to probe the lower ionospheric region (the D region). In addition to the VLF data analysis, we employ numerical simulation through the Long Wavelength Propagation Capability code (LWPC) to derive the increase in the D− region electron density. Our results revealed discernible perturbations in amplitude and phase across all transmitter paths (NAA, DHO, ICV, and NSC) to the Algiers receiver persisting for 40 min. At the maximum of the signal perturbation, the LWPC simulation results showed a decrease in the mean new reference height h′ from 74 to 65.71 km, along with an increase in the sharpness factor β from 0.3 to 0.4875 km−1. Under these new conditions, the electron density increased from its ambient value (216.10 cm−3) to 33.7 103 cm−3.

A High-Speed Multichannel Electrochemical Impedance Spectroscopy System Using Broadband Multi-Sine Binary Perturbation for Retired Li-Ion Batteries of Electric Vehicles

Retired electric vehicle (EV) batteries are reused in second-life energy storage applications. However, the overall performance of repurposed energy storage systems (ESSs) is limited by the variability in the individual batteries used. Therefore, battery grading is required for the optimal performance of ESSs. Electrochemical impedance spectroscopy (EIS)-based evaluation of battery aging is a promising way to grade lithium-ion batteries. However, it is not practical to measure the impedance of mass-retired batteries due to their high complexity and slowness. In this paper, a broadband multi-sine binary signal (MSBS) perturbation integrated with a multichannel EIS system is presented to measure the impedance spectra for the high-speed aging evaluation of lithium-ion batteries or modules. The measurement speed is multiple times higher than that of the conventional EIS. The broadband MSBS is validated with a reference sinusoidal sweep perturbation, and the corresponding root-mean-square error (RMSE) analysis is performed. Moreover, the accuracy of the presented multichannel EIS system is validated by impedance spectra measurements of Samsung INR18650-29E batteries and compared with those measured using a commercial EIS instrument. A chi-squared error under 0.641% is obtained for all channels. The non-linearity of batteries has a significant impact on the quality of impedance spectra. Therefore, Kronig–Kramer (KK) transform validation is also performed.

The effect of electron backscatter and charge build up in media on beam current transformer signal for ultra-high dose rate (FLASH) electron beam monitoring

Objective. Beam current transformers (BCT) are promising detectors for real-time beam monitoring in ultra-high dose rate (UHDR) electron radiotherapy. However, previous studies have reported a significant sensitivity of the BCT signal to changes in source-to-surface distance (SSD), field size, and phantom material which have until now been attributed to the fluctuating levels of electrons backscattered within the BCT. The purpose of this study is to evaluate this hypothesis, with the goal of understanding and mitigating the variations in BCT signal due to changes in irradiation conditions. Approach. Monte Carlo simulations and experimental measurements were conducted with a UHDR-capable intra-operative electron linear accelerator to analyze the impact of backscattered electrons on BCT signal. The potential influence of charge accumulation in media as a mechanism affecting BCT signal perturbation was further investigated by examining the effects of phantom conductivity and electrical grounding. Finally, the effectiveness of Faraday shielding to mitigate BCT signal variations is evaluated. Main Results. Monte Carlo simulations indicated that the fraction of electrons backscattered in water and on the collimator plastic at 6 and 9 MeV is lower than 1%, suggesting that backscattered electrons alone cannot account for the observed BCT signal variations. However, our experimental measurements confirmed previous findings of BCT response variation up to 15% for different field diameters. A significant impact of phantom type on BCT response was also observed, with variations in BCT signal as high as 14.1% when comparing measurements in water and solid water. The introduction of a Faraday shield to our applicators effectively mitigated the dependencies of BCT signal on SSD, field size, and phantom material. Significance. Our results indicate that variations in BCT signal as a function of SSD, field size, and phantom material are likely driven by an electric field originating in dielectric materials exposed to the UHDR electron beam. Strategies such as Faraday shielding were shown to effectively prevent these electric fields from affecting BCT signal, enabling reliable BCT-based electron UHDR beam monitoring.

Research on strategy of load-side resonant soft-switching inverter based on interconnection and damping assignment-passivity based control

Soft-switching technologies can effectively solve the problem of switching losses caused by increasing switching frequency of grid-connected inverters. As a branch of soft-switching technologies, load-side resonant soft-switching is a hotspot for applications of high-frequency inverters, because it has the advantage of achieving soft-switching without using additional components. However, the traditional PI control strategy based on the linear model is prone to destabilization and non-robust dynamic performance when large signal perturbation occurs. In this paper, a novel Passivity-Based Control (PBC) method is proposed to improve the dynamic performance of load-side resonant soft-switching grid-connected inverter. Besides, the model based on the Port Controlled Hamiltonian (PCH) model of the soft switching inverter is carried out, and the passivity-based controller is designed based on the established model using the way of interconnection and damping assignmentpassivity based control (IDA-PBC). Both stable performance and dynamic performance of the load-side resonant soft-switching inverter can be improved over the whole operating range. Finally, a 750 W load-side resonant soft-switching inverter simulation model is built and the output performance is compared with the traditional PI control strategy under stable and dynamic conditions. The simulation results show that the proposed control strategy reduces the harmonic distortion rate and improves the quality of the output waveforms.

(Digital Presentation) Dynamic Thermal Detection of Water Electrolyzer with Dual-Layer Characteristic Temperature Based on IR

As the prevalence of water electrolysis systems continues to expand, increased attention has been placed on the dynamic thermal detection of the electrolyzer. Presently, the scope of dynamic thermal detection for water electrolysis systems is limited to solely the temperatures at the inlet and outlet. This study propounds a novel dual-layer characteristics temperature (DLCT) model for the purpose of water electrolyzer temperature monitoring. The DLCT model addresses two significant issues of the dynamic thermal detection of the WEE: characteristic temperature extraction difficulty and signal perturbation caused by auxiliary factors. The first layer of multi-gaussian distribution (MGD) regression in the DLCT model quantifies the sectional temperature of the electrolyzer surface and produces numerous potential characteristic temperatures (CT). These temperatures are subsequently assigned to each section and are then conveyed to the second layer of linear regression, which provides an incisive quantized temperature variation pattern for the electrolyzer surface temperature distribution. Importantly, the DLCT model requires no supplementary modifications to the water electrolysis system or the installation of temperature sensors within or on the surface. By employing the DLCT model during dynamic operation, the ability to monitor the temperature of the electrolyzer comprehensively is improved, the temperature distribution of the electrolyzer can be quantized and richer insights regarding the surface temperature distribution can be gained, promoting better thermal uniformity, as well as pin-pointing the potential hot spot. Hence this method can also be used for structure design to produce better uniformity. The dynamic temperature detection results under different operating conditions are analyzed, and it can be concluded that the operating parameters, especially the lye circulation flow, can greatly influence the temperature distribution uniformity of the electrolyzer significantly. In conclusion, the DLCT model effectively handles the two primary challenges in dynamic thermal detection of water electrolysis systems by providing global and sectional characteristic temperature readings, enabling electrolyzer to be monitored holistically. So that future improvement regarding both structural and operational optimization can be conducted in improving the thermal uniformity. Figure 1

DSteganoM: Deep steganography for motion capture data

Motion capture (mocap) data stores the skeleton movement of recorded objects or humans and is essential for various 3D applications, such as games, animations, virtual reality, surveillance and medical. The exploration of steganography using mocap data is still limited despite its wide-range of potential practical applications. Compared to image, audio, and video steganography, the challenges in mocap steganography arise from differences between the temporal lengths and topologies of the cover and secret data, non-zero residuals between mocap data sequences, non-repetitive movements in different chunks of the mocap data. Deep neural network (DNN) based hiding models could be a potential solution to address these challenges in mocap steganography. DNN-based steganography has been adopted in many recent works and has shown promising results in steganographic techniques in various domains, including image, audio and video; however, to date, no prior work has investigated the application of DNN models for hiding mocap data. In this paper, we investigate solutions to deal with the challenges in mocap steganography and propose a novel end-to-end deep neural network model, DSteganoM, to hide a sequence of skeletal motions into another. The proposed model is capable of learning and exploring the redundancy of spatial and temporal Euclidean positional information in each skeletal motion for the hiding task. In addition, by introducing upsampling and pooling mechanisms in our network architecture, the DSteganoM model also can hide mocap data of different topologies with different dimensions. Experimental results show that the proposed DSteganoM model outperforms other state-of-the-art models and can visually reconstruct the cover and secret with high resistance to signal perturbation. Our work bridges the research gap in applying deep steganography to address the challenges in mocap data hiding.

Effects of the Relative Dynamics of Ionospheric Irregularities and GPS Satellites on Receiver Tracking Loop Performance

AbstractTransionospheric satellite signals are exposed to perturbation, caused by irregularities generated in the ionosphere. However, the characteristics of the satellite motion can have an additional impact on signal perturbation, in addition to the effects of irregularity structures, that drift from west to east during geomagnetic quiet conditions. This paper reports the effect of Global Positioning System satellite geometry, on tracking loop performance of ground‐based receivers, during occurrence of ionospheric scintillations. Observations are made from station Calcutta (22.58°N, 88.38°E geographic; magnetic dip 34.54°), located near the northern crest of Equatorial Ionization Anomaly, during three different solar activity periods (March 2014, March 2015, and March 2022). Efforts have been made to study the correlation of east‐west component of satellite velocity at Ionospheric Pierce Point (IPP) with duration of loss‐of‐lock and rate of signal fading (<−10 dB), from ground scintillation pattern observations. Results of this study show 75%–78% correlation between duration of loss‐of‐lock and eastward component of satellite velocity, for all three period of observation. Subsequently, shorter duration of loss‐of‐lock has been observed corresponding to satellite velocity being westward. Signal fading rate is found to decrease with increasing satellite velocity, having median value of the fading rate cumulative distribution percentage, corresponding to satellite velocity of 16.69, 31.76, and 19.14 m/s respectively during March 2014, March 2015, and March 2022. Results of this study also indicate direction and component of satellite velocity at IPP to be a dominant cause of signal outage, even during periods of weak to moderate scintillations.

Reconstruction of Communication Signal in Wireless Networks Based on Perturbation Compression Perception

<p>With the maturity and development of new generation communication technology, the demand for wireless network communication quality is getting higher and higher. However, the existing wireless network data transmission models are greatly affected by the signal quality in the data transmission process, which seriously affects the quality of data transmission. To address the problem of signal perturbation affecting communication quality in wireless network communication, this study designs an improved perturbation compression reconstruction algorithm that can be used to process block-structured data based on the block sparse structure in the original signal, and constructs a single-user wireless network communication data transmission model based on this algorithm. The performance simulation test of the model shows that the MSE values of the model are lower than those of the conventional and perfect schemes used as comparisons under the same noise conditions and the transmitted compressed data length conditions. When the variance of Gaussian white noise is 0.125, the MSEs of the research design model, the conventional scheme and the perfect scheme are 0.031, 0.048, and 0.047, respectively. Experimental data show that the improved disturbance compressed sensing reconstruction algorithm can better reconstruct the compressed information and reduce the information loss caused by the disturbance in the compression process on the premise of removing the signal disturbance. This research result has reference significance for improving the signal quality of wireless network communication.</p> <p> </p>

BMP suppresses WNT to integrate patterning of orthogonal body axes in adult planarians.

Adult regeneration restores patterning of orthogonal body axes after damage in a post-embryonic context. Planarians regenerate using distinct body-wide signals primarily regulating each axis dimension: anteroposterior Wnts, dorsoventral BMP, and mediolateral Wnt5 and Slit determinants. How regeneration can coordinate perpendicular tissue axes without symmetry-breaking embryonic events is not fully understood. Here, we report that the planarian dorsoventral regulator bmp4 suppresses the posterior determinant wnt1 to provide patterning input to the anteroposterior axis. Double-FISH identified distinct anteroposterior domains within dorsal midline muscle that express either bmp4 or wnt1. Homeostatic inhibition bmp4 and smad1 expanded the wnt1 expression anteriorly, while elevation of BMP signaling through nog1;nog2 RNAi reduced the wnt1 expression domain and elevated bmp4 expression. Homeostatic BMP signal perturbation broadly affected anteroposterior identity as measured by expression of posterior Wnt pathway factors, and caused mislocalization of AP-regionalized pharynx progenitors, without strongly affecting expression domains of anterior regulators. Additionally, wnt1 inhibition elevated bmp4 expression in the tip of the tail. Therefore, dorsal BMP signals and posterior wnt1 mutually antagonize for patterning the tail. Furthermore, homeostatic bmp4 RNAi caused medial expansion of the lateral determinant wnt5 and reduced expression of the medial regulator slit. By contrast, nog1;nog2 RNAi restricted wnt5 expression. Double RNAi of bmp4 and wnt5 resulted in lateral ectopic eye phenotypes, suggesting bmp4 acts upstream of wnt5 to pattern the mediolateral axis. These results indicate bmp4 controls dorsoventral information and also, through suppression of Wnt signals, influences anteroposterior and mediolateral identity. Based on related functions across vertebrates and Cnidarians, Wnt and BMP cross-regulation could form an ancient mechanism for coordinating orthogonal axis patterning.

Multi‐Wavelength (L and S Band) Study of Ionospheric Irregularities From an Anomaly Crest Location

AbstractThis paper reports observation of moderate to intense scintillations, at S band of Indian Regional Navigation Satellite System (IRNSS). During current solar cycle 25, these occurrences are probably being reported for the first time, from a low‐latitude station Calcutta (22.58°N, 88.38°E geographic; magnetic dip 34.54°), located near the northern crest of Equatorial Ionization Anomaly. Efforts have been made to analyze irregularity dynamics at IRNSS S band, along with a comparative study at GNSS L1 to characterize occurrence and evolution of multiscale irregularity structures. Power spectral analysis technique has been applied on recorded C/NO, during scintillation patches, to measure east‐west zonal drift velocity. Efforts are also made to study intensity of signal perturbation at L5 frequency of IRNSS, compared to S band, when observed simultaneously, during period of ionospheric scintillation. Concurrent irregularity dynamics at L1, L5, and S band, particularly from a low‐latitude station, has not been extensively reported earlier. Data is recorded during vernal equinox (February–April 2022) of current solar cycle. Results of this study show occurrence of simultaneous night‐time scintillation at L1, L5, and S band. East‐west zonal drift velocity of irregularity, obtained from S band observations are found to decrease with the progress of time during 20–23 LT having a maximum value of 125 m/s. At GNSS L1, hourly average value of the velocity, is observed to maximize during 20–21 LT. Simultaneous observation of scintillation effects at L5 and S band of IRNSS led to loss‐of‐lock at L5, while no such occurrence was noted at S band.

Developmental control of noradrenergic system by SLITRK1 and its implications in the pathophysiology of neuropsychiatric disorders.

SLITRK1 is a neuronal transmembrane protein with neurite development-and synaptic formation-controlling abilities. Several rare variants of SLITRK1 have been identified and implicated in the pathogenesis of Tourette's syndrome, trichotillomania, and obsessive-compulsive disorder, which can be collectively referred to as obsessive-compulsive-spectrum disorders. Recent studies have reported a possible association between bipolar disorder and schizophrenia, including a revertant of modern human-specific amino acid residues. Although the mechanisms underlying SLITRK1-associated neuropsychiatric disorders are yet to be fully clarified, rodent studies may provide some noteworthy clues. Slitrk1-deficient mice show neonatal dysregulation of the noradrenergic system, and later, anxiety-like behaviors that can be attenuated by an alpha 2 noradrenergic receptor agonist. The noradrenergic abnormality is characterized by the excessive growth of noradrenergic fibers and increased noradrenaline content in the medial prefrontal cortex, concomitant with enlarged serotonergic varicosities. Slitrk1 has both cell-autonomous and cell-non-autonomous functions in controlling noradrenergic fiber development, and partly alters Sema3a-mediated neurite control. These findings suggest that transiently enhanced noradrenergic signaling during the neonatal stage could cause neuroplasticity associated with neuropsychiatric disorders. Studies adopting noradrenergic signal perturbation via pharmacological or genetic means support this hypothesis. Thus, Slitrk1 is a potential candidate genetic linkage between the neonatal noradrenergic signaling and the pathophysiology of neuropsychiatric disorders involving anxiety-like or depression-like behaviors.

Effect of carbonaceous reinforcements on anticorrosive and magnetic properties of Ni-Cu based composite coatings prepared by pulsed electrodeposition

Magnetic and anticorrosive properties of Ni-Cu alloy-based composite coatings prepared by pulsed electrodeposition reinforced with CNT (Ni-Cu-CNT), graphene (Ni-Cu-G), and diamond (Ni-Cu-D) have been assessed in the current work. The corrosion behavior of the composite coatings was evaluated in a 3.5% NaCl corrosive medium. Linear polarization test performed within ±10 mV with respect to open circuit potential (OCP) unveiled that graphene added coatings exhibited maximum polarization resistance (~13.1 kΩcm2) followed by the Ni-Cu-D (~9.1 kΩcm2), the Ni-Cu-CNT (~8.3 kΩcm2), and the Ni-Cu with a minimum resistance of ~4.2 kΩcm2. Electrochemical impedance spectroscopy tests performed with a signal perturbation of ±10 mV with respect to OCP and frequency sweep from 105 Hz to 10−2 Hz elicited maximum film resistance of ~32 kΩ cm2 for the Ni-Cu-G coating, which could be attributed to the high area to volume ratio, α ~134/m, of graphene nanoflakes inducing strong particle-matrix interaction and to the formation of micro galvanic cell between the graphene particles and the Ni-Cu matrix where the surface was covered predominantly with graphene, eventually lowering the corrosion rate to 5.91 MPY. For Ni-Cu-D coating, the uniform dispersion of diamond particles prohibits localized corrosion. In contrast, agglomeration of CNTs in Ni-Cu-CNT causes ~50% reduction in corrosion resistance compared to diamond reinforced coating. However, corrosion resistance of CNT grafted coating was around 2.5 times higher than Ni-Cu (~4.2 kΩcm2), which can be imputed to the filling of the pores, cracks, and crevices by CNT fibers. The hysteresis loops of Ni-Cu composite coatings traced by a vibrating sample magnetometer indicated that CNT incorporation enhanced the magnetic saturation to ~1.89 Am2/kg from ~0.84 Am2/kg (Ni-Cu) and maximum coercivity of ~110 Oe (~78.91 Oe for Ni-Cu). However, graphene and diamond addition has negligible effect on the magnetic properties of the coated systems. The enhanced anticorrosive and magnetic properties of proposed coating designs consisting of Ni-Cu matrix with carbonaceous reinforcements impart multifunctionality to the system. The marine industry can successfully adopt the proposed coating systems to protect propellers shafts, ship hulls, and specialty sensors.

Voltage Regulation of DC-DC Buck Converters Feeding CPLs via Deep Reinforcement Learning

Modeling accuracy of DC-DC converters may deviate largely in the presence of different variation levels of constant power loads (CPLs), hence is well acknowledged as a main hurdle for the design of advanced model-driven control strategies in the literature. Aiming to enhance the bus voltage regulation performance of DC-DC buck converters, a model-free deep reinforcement learning (DRL) control strategy is proposed in this brief. Firstly, a Markov Decision Process (MDP) model and a deep Q network (DQN) algorithm are utilized for the stabilization issue of the converter. Secondly, through a subgoal reward/penalty mechanism, the control objective and prescribed performance of the system are therefore guaranteed. Moreover, a specified action space is designed to match the switch speed of the switching element. As a distinguishable feature, the settling time under the proposed control scheme is significantly reduced in the occurrence of disturbance, resulting from the fast adaption ability of DRL. The simulation comparison results in reference to PI and fuzzy PI controllers demonstrate the efficacy and superiorities under large signal perturbation conditions.

A Deep Learning Architecture for Semantic Segmentation of Radar Sounder Data

During the last decades, radar sounders provided direct measurements (radargrams) of the Earth’s polar caps’ subsurface. Radargrams are of critical importance for a better understanding of glaciologic structures and processes of the ice sheet in the framework of climate change. This article aims to automatically extract information on basal boundary conditions given their substantial relevance for modeling the ice-sheet processes, such as the sliding. We introduce a novel automatic method based on deep learning to detect the basal layer and basal units in radargrams acquired in the inland of icy areas. Radargrams are segmented into englacial layers, bedrock, basal units, and noise-limited regions; the latter includes the echo-free zone (EFZ), thermal noise, and signal perturbation. The network is a U-Net with attention gates and the Atrous Spatial Pyramid Pooling (ASPP) module that automatically extract semantically meaningful features at different scales. Experimental results on two datasets acquired in north Greenland and west Antarctica by the Multichannel Coherent Radar Depth Sounder (MCoRDS3) indicate a high overall segmentation accuracy. The accuracy of basal ice and signal perturbation detection is high, and that of the other classes is comparable with the literature techniques based on handcrafted features. The results show the effectiveness of the proposed method in automatically extracting semantically meaningful features to segment radargrams and map the basal layer and basal units.

The impact of photospheric brightness field on exoplanetary transit timings and the TTV excess of HD 189733 b

ABSTRACT We consider the issue of excessive TTV noise observed for the exoplanet HD 189733 b. Trying to explain it through the host star photospheric activity, we model the stellar surface brightness as a random field, then characterize statistical properties of the resulting transit signal perturbation and compute individual corrections to transit timings uncertainties. We find that possible effect of the photospheric brightness field can explain only a minor portion (∼10 s) of the observed (∼70 s) TTV excess of HD 189733, suggesting that the rest should be attributed to other sources. Regarding the photospheric pattern, we place an upper limit of ∼0.01 on the combination εcellrcell, where εcell is the relative magnitude of brightness variations, and rcell is the geometric cellularity scale (relative to star radius).

Recombination facilitates genetic assimilation of new traits in gene regulatory networks.

A new phenotypic variant may appear first in organisms through plasticity, that is, as a response to an environmental signal or other nongenetic perturbation. If such trait is beneficial, selection may increase the frequency of alleles that enable and facilitate its development. Thus, genes may take control of such traits, decreasing dependence on nongenetic disturbances, in a process called genetic assimilation. Despite an increasing amount of empirical studies supporting genetic assimilation, its significance is still controversial. Whether genetic assimilation is widespread depends, to a great extent, on how easily mutation and recombination reduce the trait's dependence on nongenetic perturbations. Previous research suggests that this is the case for mutations. Here we use simulations of gene regulatory network dynamics to address this issue with respect to recombination. We find that recombinant offspring of parents that produce a new phenotype through plasticity are more likely to produce the same phenotype without requiring any perturbation. They are also prone to preserve the ability to produce that phenotype after genetic and nongenetic perturbations. Our work also suggests that ancestral plasticity can play an important role for setting the course that evolution takes. In sum, our results indicate that the manner in which phenotypic variation maps unto genetic variation facilitates evolution through genetic assimilation in gene regulatory networks. Thus, we contend that the importance of this evolutionary mechanism should not be easily neglected.

Fixed-Time Tracking Control of Wireless Power Transfer Systems with Disturbances and Current Constraints

This article investigates the tracking control of the wireless power transfer (WPT) system with disturbances and constraints. Using the generalized state-space averaging method, a small signal perturbation model of the system is obtained. To estimate the perturbation faster and more accurately, the fixed-time disturbance observer is introduced. Then a fixed-time disturbance observer-based controller (DOBC) is designed. In addition, to ensure the WPT current constraint not be violated, tan type Barrier Lyapunov Functions (BLFs) are added. To illustrate the robustness of the WPT system, a 100-watt WPT system is built and the closed system is proved to be effective. Experimental results show that the startup response time of the current is 4 ms without overshoot. Under loads and resonance capacitors changes, the WPT current can also converge to the reference value within 4 ms.

PHONEMeS: Efficient Modeling of Signaling Networks Derived from Large-Scale Mass Spectrometry Data.

Post-translational modifications of proteins play an important role in the regulation of cellular processes. The mass spectrometry analysis of proteome modifications offers huge potential for the study of how protein inhibitors affect the phosphosignaling mechanisms inside the cells. We have recently proposed PHONEMeS, a method that uses high-content shotgun phosphoproteomic data to build logical network models of signal perturbation flow. However, in its original implementation, PHONEMeS was computationally demanding and was only used to model signaling in a perturbation context. We have reformulated PHONEMeS as an Integer Linear Program (ILP) that is orders of magnitude more efficient than the original one. We have also expanded the scenarios that can be analyzed. PHONEMeS can model data upon perturbation on not only a known target but also deregulated pathways upstream and downstream of any set of deregulated kinases. Finally, PHONEMeS can now analyze data sets with multiple time points, which helps us to obtain better insight into the dynamics of the propagation of signals. We illustrate the value of the new approach on various data sets of medical relevance, where we shed light on signaling mechanisms and drug modes of action.