Purpose: Rotor stability and meandering are key mechanisms determining and sustaining cardiac fibrillation, with important implications for anti-arrhythmic drug development. However, little is yet known on how rotor dynamics are modulated by variability in cellular electrophysiology, particularly on kinetic properties of ion channel recovery.Methods: We propose a novel emulation approach, based on Gaussian process regression augmented with machine learning, for data enrichment, automatic detection, classification, and analysis of re-entrant biomarkers in cardiac tissue. More than 5,000 monodomain simulations of long-lasting arrhythmic episodes with Fenton-Karma ionic dynamics, further enriched by emulation to 80 million electrophysiological scenarios, were conducted to investigate the role of variability in ion channel densities and kinetics in modulating rotor-driven arrhythmic behavior.Results: Our methods predicted the class of excitation behavior with classification accuracy up to 96%, and emulation effectively predicted frequency, stability, and spatial biomarkers of functional re-entry. We demonstrate that the excitation wavelength interpretation of re-entrant behavior hides critical information about rotor persistence and devolution into fibrillation. In particular, whereas action potential duration directly modulates rotor frequency and meandering, critical windows of excitability are identified as the main determinants of breakup. Further novel electrophysiological insights of particular relevance for ventricular arrhythmias arise from our multivariate analysis, including the role of incomplete activation of slow inward currents in mediating tissue rate-dependence and dispersion of repolarization, and the emergence of slow recovery of excitability as a significant promoter of this mechanism of dispersion and increased arrhythmic risk.Conclusions: Our results mechanistically explain pro-arrhythmic effects of class Ic anti-arrhythmics in the ventricles despite their established role in the pharmacological management of atrial fibrillation. This is mediated by their slow recovery of excitability mode of action, promoting incomplete activation of slow inward currents and therefore increased dispersion of repolarization, given the larger influence of these currents in modulating the action potential in the ventricles compared to the atria. These results exemplify the potential of emulation techniques in elucidating novel mechanisms of arrhythmia and further application to cardiac electrophysiology. Self-sustaining patterns of aberrant excitation in the heart, re-entries, are the cause of dangerously accelerated heartrates (tachycardia) and complete losses of synchronized action (fibrillation) , Emulation is a powerful technique for greatly reducing the computational cost associated with exploration of parameter variability in complex models that are time-intensive to simulate, with a history in climate modeling  spreads by tissue coupling with associated constant D, kinetics of cellular excitation and repolarization are encoded in the Iion term, and the external supply of stimulus is represented by Istim. For the description of excitation and repolarization kinetics we selected the reduced Fenton–Karma model , allowed by the high-order convergence of Fourier spectral methods. The accuracy of the numerical simulations was verified in one-dimensional cables by halving the time and space integration steps. This resulted in <1% change in conduction velocity, even in the low excitability limit.Simulation software was written in Mo et al. . Brieflyo et al.  is used o et al. , therefou = ucrit at this location), a second stimulus is provided to one whole quadrant on the wave's back side. With appropriate selection of ucrit (here ucrit = 0.05), excitation can propagate in one cardinal direction and not the other, resulting in the generation of a rotor tip in the center of the domain, at the corner of the second stimulated region . The first stimulus acts at one edge of the domain, generating a planar wave. Then, when the waveback of this wave has reached the middle of the domain , as measures of the critical size of tissue substrate required to support such a rotor, and the complexity of its trajectory, respectively. Additionally, we created several virtual probes throughout the domain at regular intervals, that measured the time course of the membrane potential at their location. The power spectrum of each of these signals was averaged, and then used to calculate the standard organization index using the main four peaks in power spectrum (OI4), as well as the dominant frequency (DF). These provide a measure of the level of chaos present in the rotor dynamics, and the effective rate of induced tachycardia, respectively. Each of these biomarkers, along with more detail regarding their calculation, is provided in Table For biomarkers, we measured steady-state CV and APD prior to the establishment of re-entry, given that they are key properties in determining the cardiac wavelength. These were measured using the S1 stimulus, and so refer to the CV and APD of planar waves in the tissue. We term these the “tissue-level” biomarkers, given that they can be experimentally measured at this scale. For stable rotors, we recorded the greatest distance between any two points on the rotor's trajectory (after it had been given a chance to stabilize), and the organization index derived from the main peak in power spectrum (OIθ) that defines the mean of the process (in the absence of data) at any point in the parameter space, θ, and a covariance function, k that defines the covariance between any two points in the parameter space, θ and θ′. For the basic forms of these functions, we select for the mean function a linear trend, and for the covariance function the Matern-5/2 covariance,θ and θ′ become more correlated as the two points in parameter space become closer, but with this measure of “closeness” defined such that each dimension in the parameter space can contribute differently (encoded by the choice of li's). The values of these li's are determined during the training process, and thus the method automatically determines the relative importance of each variable in θ toward the output being emulated (known as automatic relevance determination). Here σ controls the overall amount of variance (and covariance) of the process, and σn the noise in the data . In the case of emulation, the data is output from a deterministic computer simulation and so technically σn = 0, but its inclusion can regularize the process and we here do not assume σn = 0.For emulation, we make use of Gaussian process (GP) regression, as introduced for the emulation of computer models by Sacks et al. . GP regrl = , σ and σn, the likelihood of generating the training data with the corresponding GP may be analytically calculated  is stored during the training process. Emulator predictions are thus extremely rapid, and the optimization problem involved with training the emulator can be greatly accelerated by the use of derivative information, which can be calculated at very little additional computational cost , in total we perform 80 million emulated runs of the two-dimensional FK model. The necessity of emulation in performing such analyses for cardiac electrophysiological models is clear.We again use LHS to improve the overall coverage over the parameter space in calculating these expectations, and choose a large in silico data allows us to rigorously explore how well the effects of electrophysiological variability on rotor inducibility and maintenance are captured by the tissue-level biomarkers that can be more readily measured, the steady-state values of CV and APD. The large extent of variability in model parameters resulted in a corresponding amount of variation in CV and APD, with a good spread across the space of these biomarkers, as illustrated in Figure Our generated Especially predictable are the cases where no re-entry could be induced, suggesting that APD and CV are generally sufficient in themselves for describing the inducibility of re-entry. This includes both the case where CV is very low and excitation fails to properly propagate, and the case where APD is too high and thus the critical length is too long for the spiral wave's tip to successfully rotate before colliding with the simulation boundary. A faster CV decreases the critical APD value beyond which rotors fail to form, because the wavetip travels further while waiting for tissue to recover its excitability. This agrees well with the known importance of wavelength in defining the critical length of re-entrant paths , and the rate of repolarization (via gso). Well-defined regions of the parameter space that correspond to the different behaviors can be clearly observed, but no boundaries can be drawn to separate the different classes in the top right of the parameter space. This issue is largely addressed by combining the “fibrillatory” and “transient rotor” classes together into a single “chaotic” rotor class, after which SVM classification successfully identifies the different regions and attains an accuracy of 96% on the data not used for training. The classifier model is thus appropriate for use in defining boundaries for partitioned emulation.Figure Despite very complex dependencies of the spatial biomarkers upon the values of these two channel densities, our partitioned emulation approach is able to very successfully capture the response surfaces implied by the data Figure , allowinTraining of the set of partitioned emulators required less than seven minutes, but the cost of generating the 2,000 simulations for use as training and test data required more than 2,000 h of computational time . The time required for the emulator to generate predicted biomarker values at a set of 2,000 additional points randomly sampled across the parameter space was 0.23 s, indicating a speedup of eight orders of magnitude when considering serial implementation. Even making good use of supercomputer architecture, the speedup was about six orders of magnitude, indicating the power of emulation for studies of variability in cardiac electrophysiology models. These immense savings offered by the emulator allow further simulations to be generated almost immediately, enabling analyses such as the one that follows for a larger number of variable parameters.Given the cost of generating the initial data used to construct our partitioned emulators, a natural question is how classifier and emulator performance depend upon the amount of training data used. In terms of accurately predicting the classes of the data reserved for testing, the strong performance of the SVM classifier remained even when using much less data for training Figure . Howevergfi) and only very slightly impacted by the conductance of the slow outward current (gso). APD depends strongly on the strength of the slow outward current, but importantly only above some critical level of fast inward current density. This effect arises because only sufficiently strong excitations are able to drive the AP upstroke beyond the activation threshold of the model's equivalent of the Ca2+ current. “Incomplete” excitations that fail to significantly activate this current result in very short APs, the duration of which are not strongly affected by either gfi or gso.The emulated surfaces for the biomarkers defining excitation wavelength, CV, and APD, demonstrate intuitive responses, to an extent, to variability in the two considered current densities Figure . CV depe1 biomarkers. Specifically, the tightening of circular trajectories results in a decrease in core size until trajectories become epicycloidal, at which point sizes start increasing again, peaking for the case of traveling helices that collide with the boundary before paths become hypocycloidal. OI1 has a maximal value for the circular paths, decreased for epicycloidal and hypocycloidal trajectories, with severely reduced values for traveling helices and the high-excitability cases where trajectories meander much more.As the conductance of the fast inward current is increased, a shift of rotor trajectories from circular to epicycloidal, then hypocycloidal paths is well established  the dependence on either of the two parameters depends strongly upon the value chosen for the other. Thus we must consider variability in all properties of interest at once, in order to ensure that the conclusions drawn are not significantly biased by the specific set of base parameter values chosen. This is one of the advantages we achieve in the following section via emulation.Several interesting observations can be made from the initial data presented up to this point. Figure in silico data  to be used for training the classifier model and emulator, in order to attain the best performance possible when the emulator is then used outside of the dataset to calculate the main effects of the parameters.The previous section demonstrated the importance of allowing different electrophysiological properties to vary simultaneously, in order to properly understand how the effects of variation in these different properties (including the effects of drug treatments) together determine re-entrant behavior. However, when our full set of electrophysiological properties are all allowed to vary, we lose the ability to use forward simulation to properly explore the parameter space due to its high dimensionality. Here the SVM classifier model and associated emulators become invaluable tools, to enrich the data and to make the calculation of the main effects of each parameter possible, thus allowing us to quantify how each individual ionic property contributes to the type of arrhythmia that develops, as well as its relative severity. We select the majority of the Although the predictions of the emulators and classifier model they depend upon cannot be simply visualized when these many parameters are varied, the test data not involved in the training process can be used to evaluate their performance. The increased dimensionality makes both the classification and emulation problems significantly more difficult, but the SVM classifier still achieves an accuracy of 81%. Emulation performance is also diminished as compared to the lower-dimensional problem, but remains good enough that the emulator is suitable for the efficient and automatic extraction of hidden data trends Figure . Use of Using our emulators to rapidly evaluate Equation (3), we quantify using mean effects how the different parameters controlling excitability and its recovery impact upon the important features of our simulated re-entries. We re-iterate that these insights are not the same as would be obtained by simply varying each individual parameter in turn, but instead represent the overall effects of the parameters upon variability in the others. This improves the generalizability of the conclusions we draw in the face of both physiological variability and uncertainty in the most appropriate base values of the parameters in a model.gfi) largely impacts CV by modulating upstroke velocity and AP amplitude, as well as regulating APD owing to the effect discussed previously where only sufficiently strong excitations are able to fully activate the slow inward current , as the faster propagation causes the rotor to attempt to rotate more quickly (increased DF), promoting tip-wake interactions. The risk of breakup (lower OI4) is however reduced, as in these situations the rotor tip makes longer linear runs along lines of conduction block. Note, however, that both the complexity of re-entry and the risk of breakup are reduced  for decreasing values of gfi, in spite of this implying a reduction of the excitation wavelength. This corresponds newly to situations of weak excitability, where the slow inward current can never activate and the APD rate-dependence is significantly lost, preventing repolarization heterogeneities that can lead to wave breakup.Figure 1), and risk of breakup (lower OI4).The discussion above additionally applies to fast inward current inactivation (1) show no significant dependence on recovery of excitability. More remarkably, the risk of breakup (lower OI4) actually increases in this case, as the slower recovery of excitability lengthens the timing window in which the fast inward channels are only partially recovered and weak excitations can be triggered. These weak excitations fail to completely activate the slow inward current, creating large APD discrepancies across the tissue and thus an increased repolarization heterogeneity and hence fibrillation. Importantly, this result challenges the hypothesis that increased ERP may be protective in all cases against fibrillation by increasing the excitation wavelength . Notably, the inactivation of the slow inward current  increases APD, resulting in longer critical lengths and slower re-entrant DF. Variability in repolarization exerts stronger control and with more monotonic trends over these biomarkers as compared with excitability. ERP then modulates the overall size and angular velocity of these trajectories, which for reduced repolarization  translates into a smaller complexity of rotor trajectories (increased OI1) and reduced risk of fibrillatory and other chaotic behaviors (increased OI4), nicely fulfilling the excitation wavelength hypothesis.Variability in the primary repolarization current modulates APD and by extension ERP, determining the availability of excitable tissue for a spiral wave's tip as it rotates. A weaker repolarization  prolongs the AP plateau and therefore APD/ERP, increasing the critical size and decreasing the DF of re-entry and exerting a more dominant effect on the shape of rotor trajectories (increased OI1) compared to the repolarization currents. However, when the conductance of the slow outward current is increased, a sharp decrease in rotor stability is observed (reduced OI4), followed by an approximately level trend, which contradicts the excitation wavelength hypothesis given the increased ERP. The de-stabilizing effect of increased conductance of the slow inward current can be explained by the increased rate-dependence of the tissue, exacerbating spatial dispersion of repolarization that may arise and thus promoting wave breakup. In the context of rotor stability, this control over rate dependence is seen here to be more important than the current's control over APD, explaining some of the failure in using APD and CV to predict the class of re-entrant behavior that has been previously discussed.The effects described above are largely mirrored by the slow inward current, corresponding to its role in opposing repolarization. An increased current density , although our classifier automatically takes the simultaneous variability in all parameters into account.gfi, when excitation is too weak, all formed rotors remain stable as the slow inward current is not fully activated, diminishing the rate-dependence of the tissue. As the density of the fast inward current increases, the region of chaotic re-entrant behaviors determined by the balance between the slow inward and outward currents (which together primarily determine APD except when excitability is too weak) shifts significantly. This confirms that APD/ERP does not serve as a suitable predictor for the risk of fibrillation, and given the complex dependency on excitability we see here and in the main effects , the resulting classification maps display all of the same key behaviors , a function of both the recovery of excitability and the rate of repolarization. These results agree with the relative success of anti-arrhythmics that prolong ERP in treating ventricular tachycardias . Increased slow inward current was consistently associated with wave breakup, but increased slow outward current failed to show the opposite effect. This strongly suggests that it is not physiological variability in APD/ERP that controls breakup, and instead implicates the importance of slow inward currents in defining the extent of rate-dependence in the tissue and hence promoting dispersion of repolarization. In fact, we do note that some treatments that decrease ERP by antagonizing slow inward currents can trigger a reversion from ventricular fibrillation by reducing spatial dispersion of repolarization (Kimura et al., 2+ currents play a greater role in modulating rate dependence as compared to the more triangular APs in the atria (Sánchez et al., Reduced sodium channel availability by slow recovery of the fast inward channels emerged as a significant promoter of chaotic behavior like wave breakup, by increasing the aforementioned dispersion of repolarization associated to differential activation of slow inward currents. Such a mechanism is indeed expected to be of greater importance in the ventricles, where CaOur findings with regard to the stability of functional re-entries were further supported and refined by the predictions of our SVM classifier model. After identifying the important ionic properties underlying risk of wave breakup, visualization of the classifier's predictions confirmed the importance of slow inward current density, the existence of critical windows of excitability, and the inability to rely upon APD or excitation wavelength as biomarkers for the risk of wave breakup. Furthermore, the pro-arrhythmic potential of slowed recovery of excitability was suggested to be of stronger importance for rotor cores that move via a pattern of a long run followed by a tight rotation, potentially differentiating the cases discussed above where class Ic anti-arrhythmics may or may not induce fibrillation in the ventricles.2+ and Na+ current densities as the most important parameters (Liberos et al., Importantly, each of these observations on properties of re-entry and susceptibility to breakup have been obtained while considering variability across a large number of electrophysiological properties. The emergence of these behaviors across significant variation in the parameters suggests that they are in fact core electrophysiological behaviors, and not simply limited to the regime implied by the base parameter values considered in the FK model. Promisingly, a recent study using a biophysically-detailed model to explore variability in ionic current conductances with regard to the meander of rotor-driven re-entries in the atria also identified the CaMethodologically, our work is probably the most comprehensive computational investigation to date of the complex interplay between cardiac excitability and refractoriness in modulating rotor-driven arrhythmic behavior and susceptibility to breakup, comprising more than 5,000 forward simulations of long-lasting arrhythmic episodes, further enriched to 80 million electrophysiological scenarios by the innovative application of emulation to this field. Despite the existence of clear bifurcations and chaotic regimes, our classification and emulation approaches proved capable of predicting with good accuracy the nature of rotor behavior and important spatial biomarkers characterizing rotor-driven re-entry, even with a relatively large number of electrophysiological properties allowed to vary. The highly complex and non-monotonic response surfaces for spatial biomarkers of re-entry presented in this study further illustrate that the dynamics of arrhythmia and fibrillation in cardiac tissue cannot be predicted by simple fits to the data such as linear regression. On the other hand, these are accurately and automatically captured by the use of emulation, also serving to eliminate representation bias in data analysis and interpretation. Altogether, our work exemplifies a synergistic combination of supercomputing, machine learning, and advanced statistical methods, pushing the frontiers of big data applications for investigations on cardiac electrophysiology.Due to our specific focus on rotor dynamics, we have used here the reduced FK model (Fenton and Karma, In conclusion, we have demonstrated how emulation can be adapted to models that govern the complex spatiotemporal dynamics of re-entry in cardiac electrophysiology. We have used the great reduction in computational cost offered by emulation in order to further explore how variability in tissue excitability, repolarization, and post-repolarization refractoriness all affect whether rotor-driven re-entries are electrophysiologically supported, the likelihood that they exhibit wave breakup and the severity of the arrhythmias that they induce. This variability analysis did not require the fixing of key model parameters to specific values, making the results much more generalizable. This type of approach is of especial relevance in cardiac electrophysiology, where parameter variability is known to be important, and has a significant effect on the interpretation of both experimental and modeling studies.All authors contributed to the development of the presented methodology. BL, CD, and AB-O implemented the methodology and performed simulations. BL, KB, PB, and AB-O performed analysis of the results. All authors contributed to the drafting and refinement of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer RC and handling editor declared their shared affiliation at the time of the review.


In this paper, we propose a novel approach to undertake the colorimetric camera characterization procedure based on a Gaussian process (GP). GPs are powerful and flexible nonparametric models for multivariate nonlinear functions. To validate the GP model, we compare the results achieved with a second-order polynomial model, which is the most widely used regression model for characterization purposes. We applied the methodology on a set of raw images of rock art scenes collected with two different Single Lens Reflex (SLR) cameras. A leave-one-out cross-validation (LOOCV) procedure was used to assess the predictive performance of the models in terms of CIE XYZ residuals and  Accurate recording of color is one of the fundamental tasks in many scientific disciplines, such as chemistry, industry, medicine, or geosciences to name just a few. Color measurement is a crucial aspect in archaeology and specifically in rock art documentation ,2. The cThe appropriate description of color is not a trivial issue in cultural heritage documentation ,4. ColorTo avoid damage into the pigment, direct contact measurements with colorimeters or spectrophotometers on painted rock art panels are not allowed. Instead, indirect and noninvasive methods for color determination are required. Thus, the use of digitization techniques with conventional digital cameras to support rock art documentation is becoming more and more frequent ,6,7,8.The color information obtained from digital images can be easily captured, stored, and processed. The drawback of such color information lays on the response recorded by the sensor, which is not strictly colorimetric. The RGB responses do not satisfy the Luther–Ives condition so that RGB data are not a linear combination of CIE XYZ coordinates . If the A widely accepted approach to establish the mathematical relationships between the original RGB data and well-defined independent color spaces is the procedure of digital camera characterization . DiffereThe colorimetric characterization of digital cameras based on polynomial models is an appropriate starting point; they are widely accepted, mathematically simpler and require smaller training sets and less computing time ,21. PrevN-dimensional functions, with multivariate predictors (input variables) in each dimension [In this work, we introduce a novel approach for documenting rock art paintings based on a Gaussian process (GP) model. GPs are natural, flexible nonparametric models for imension ,25. The imension .GP models are formulated and estimated within a Bayesian framework, and all inference is based on the multivariate posterior distribution. Computing the posterior distribution is often difficult, and for this reason, different computation approaches can be used. The Markov chain Monte Carlo (MCMC) is a sampling method that provides a sample of the joint posterior distribution of the parameters ,28.The GP model results were compared to the common approach based on polynomial regression models. The main advantage of nonparametric over parametric models is their flexibility ,30. In aThe goodness of fit and predictive performance of the models are assessed by analyzing the adjustment residuals and the leave-one-out cross-validation (LOOCV) residuals. The quality of the characterized image is also evaluated in terms of colorimetric accuracy by means of color differences among observed and fitted colors using the CIE framework. In addition, we evaluate the induced noise into the output image after the characterization which is recognized as a drawback for some image applications such as image matching or pattern recognition. The induced noise is evaluated by computing and comparing the coefficients of variation of digital values between the original and output images.The working area is a rock art scene located in Shelter II of the Cova dels Cavalls site in the county of Tirig, province of Castelló (Spain). This cave is part of one of the most singular rock art sites of the Mediterranean Basin in the Iberian Peninsula, which is listed by UNESCO as a World Heritage since 1998 .The images were taken using two different SLR digital cameras, a Sigma SD15 and a Fujifilm IS PRO. The images contain the hunting scene located in the central part of this emblematic archaeological site. Parameters such as focal, exposure time, aperture, and ISO were controlled during the photographic sessions for both cameras. Photographs were taken in the raw format under homogeneous illumination conditions.®X3 sensor of 2640 × 1760 pixels, which makes it a true trichromatic digital camera [The main difference between the Fujifilm IS PRO and the Sigma SD15 cameras is their integrated sensors. The Fujifilm incorporates a 12 megapixels Super CCD imaging sensor, with resolution of 4256 × 2848 pixels and a color filter array (CFA) with a Bayer pattern. The use of CFA implies that the color registered in every individual pixel is not acquired directly but as a result of interpolation between channels. On the other hand, the Sigma carries a three-layer CMOS Foveonl camera . The maiThe output RGB digital values registered by the camera depend on three main factors: the sensor architecture, the lighting conditions, and the object being imaged. Even assuming the same object and lighting conditions, other factors can still produce different RGB responses within and across scenes. Some elements such as the internal color filters or user settings  can modify the output digital values. As a result, the original RGB data registered by the sensor cannot be used rigorously for the quantitative determination of color, and native RGB camera color spaces are said to be device dependent. A way to transform the signal captured by the camera sensor into a physicaly-based, device-independent color space is by means of camera characterization . CIE XYZ coordinates can be obtained by transforming the spectral data using well-known CIE formulae .To visualize the tristimulus coordinates, it is necessary to perform a final transformation of the CIE XYZ values into the sRGB color space, which is compatible with most digital devices. This transformation is carried out based on the technical recommendations published by the International Electrotechnical Commission . Thus, tOnce the digital camera is colorimetrically characterized, it can be used for the rigorous measurement of color simulating a colorimeter . Using aThe main aim of camera characterization is to find the mapping function between the RGB color values and the CIE XYZ tristimulus coordinates:Commonly, this multivariate mapping function is divided into independent functions for each each single XYZ tristimulus value. In this paper, we propose a Gaussian process (GP) to estimate these functions, with different model parameters, A GP is a stochastic process which defines the distribution over a collection of random variables ,25. The D inputs variables (predictors) I the i and j  for an acceptable color difference so that it is imperceptible by human vision. This concept is known as ”Just Noticeable Difference” (JND). A good reference is found in the Metamorfoze guideline, which employs the CIE76 color difference formula, and establishes a color accuracy of 4 CIELAB units .The radiometric response of a digital camera is the outcome of a number of factors, such as electromagnetic radiation, sensor electronics, the optical system, and so forth ,53,54,55The nonlinear transformation functions in camera characterization models modify the input data which are themselves affected by noise. In the camera characterization, noise is transferred from the original image to the characterized image and transformed in different ways. In this paper, the analysis of noise is carried out by comparing the coefficients of variation in the original and the characterized images. The noise assessment was conducted on four selected patches of the color checker .For model assessment we processed the CIE XYZ residuals and the Thus, given the results achieved using the GP, a notable improvement can be observed compared with the common second-order polynomial models, that is, a higher adjustment correlation coefficient and a greater predictive capacity were achieved. An improvement in the predictive capacity  implies a better model generalization, that is, a better capacity for interpolation and extrapolation. This is a key aspect in the characterization procedure, as the output digital image is the result of the application of the model established.The Equation ) obtaineUnder a strict colorimetric criterion, the average and median values for It is clearly observed that the main improvement is in the maximum AB units .Moreover, the number of patches with g the GP . Thus, tg the GP  and ΔEab* values , which mThe use of the LOOCV procedure in this study is twofold: it allows an overall model checking as well as analyzing patches used in the camera characterization at an individual level. Values for Particularly, there are eight patches with the highest rChecker a as wellrChecker b. The worChecker .Thus, the nature and colorimetric characteristics of the color patches used as training sample set have an effect on the overall accuracy of the model used . The colPrevious research show that a proper selection of the patches, such as the skin tone colors, provides suitable results for camera characterization procedure applied in rock art paintings ,60. In S®X3 sensor, whereas the Fujifilm IS PRO carries a Super CCD sensor. The values of the variation coefficients were computed and compared between the input RGB image and the output sRGB characterized image for the two mathematical transformations. The pixel variability evaluation was conducted in a reduced group of ColorChecker patches with homogeneous reflectance .The two cameras used in this study have different built-in sensors. The Sigma SD15 camera incorporates a FoveonMoreover, as the degree of the polynomial model used can affect the results achieved in terms of noise, we included the comparison of the variation coefficients for the linear model as well . Our out®X3 sensor is greater than noise in the SuperCCD ; and the hunting scene (lower left corner) . For eacBetter results are observed in the images characterized with the Fujifilm IS PRO. The color differences en color g. A limi support g. On therization c. TherefIt can be seen that the images have been successfully characterized, independently of the regression model used. All details in the characterized images present the same ranges of colors, as well as homogeneous lighting for both cameras cf. b,d,f,h.The use of digital images to support cultural heritage documentation techniques has undergone unprecedented advance in the last decades. However, the original RGB data provided by digital cameras cannot be used for rigorous color measurement and communication. To face the lack of colorimetric rigor of the input RGB data recorded by the sensor, it is necessary to conduct a colorimetric camera characterization; alternatively, color profiles can also be used.In this paper, the experimental assessment of a GP model has been carried out, and compared with a common second-order polynomial model. Although both regression models yielded good results, the use of the GP provides an improvement in colorimetric terms and fits better to the original raw RGB data. The lowest CIE XYZ residual values achieved for the adjustment and ®X3 but not in the Super CCD. Thus, the correct choice of the digital camera is a key factor to be taken into consideration in the camera characterization procedure.Additionally, the GP regression model has been tested on two SLR digital cameras with different built-in sensors to analyze the performance of the model in terms of pixel variability. The noise errors achieved show that similar results were obtained regardless the regression model used. However, the results also reveal that the induced noise highly depends on the camera sensor, which is clearly significant in the FoveonIt is observed that the camera characterization procedure allows clear identification of the different pigments used in the scene, a proper separation from the support, the achievement of more accurate digital tracings, and accurate color measurement for monitoring aging effects on pigments. This methodology proves to be highly applicable not only in cultural heritage documentation tasks, but in any scientific and industrial discipline where a correct registration of the color is required.


A proximity warning system to detect the presence of a worker/workers and to warn heavy equipment operators is highly needed to prevent collision accidents at construction sites. In this paper, we developed a robust construction safety system (RCSS), which can activate warning devices and automatically halt heavy equipment, simultaneously, to prevent possible collision accidents. The proximity detection of this proposed system mainly relies on ultra-wideband (UWB) sensing technologies, which enable instantaneous and simultaneous alarms on (a) a worker’s personal safety ) device and (b) hazard area device ). This system also communicates with electronic control sensors (ECSs) installed on the heavy equipment to stop its maneuvering. Moreover, the RCSS has been interfaced with a global positioning system communication unit (GCU) to acquire real-time information of construction site resources and warning events. This enables effective management of construction site resources using an online user interface. The performance and effectiveness of the RCSS have been validated at laboratory scale as well as at real field (construction site and steel factory). Conclusively, the RCSS can significantly enhance construction site safety by pro-actively preventing collision of a worker/workers with heavy equipment.  Occupational injuries occurring in the construction sector are a global issue, and the construction industry is a hazardous sector in which occupational accidents frequently occur . In the In recent years, technological advancement and the latest tool development have paved a way for the development of active proximity warning systems (PWS). In this regard, wireless sensor networks have gained high popularity and have shown high potential for real applications in the construction field because of low-cost and high efficiency . For insGenerally, PWSs ensure collision safety using transmitters  mounted on the heavy vehicles to detect in-range receivers (carried by workers) and an alert interface . HoweverFor proximity warning at non-line-of-sight (NLoS), GPS and UWB (Ultra-Wideband) sensors have shown high applicability because of their high accuracy and suitability for outdoor applications . UWB offThe main purpose of this study is implementing a robust construction safety system (RCSS) which activates an advanced warning device and automatic equipment control in order to prevent collision accidents from workers in hazardous positions. The RCSS utilizes a RFID sensor, around view monitor (AVM), and UWB sensor to recognize the workers and alert them to hazard situations using an alarm device. This system can automatically control the construction equipment when necessary. In addition, the RCSS was interfaced with a GPS technology and aimed at managing resources accessing construction sites. The rest of this paper is organized as follows. In Mine Safety and Health Administration (MSHA) studied more than 7500 surface mine accidents occurring between 1987 and 1996, and there were more than 120 fatalities caused by accidents. The most significant cause was the involvement of either moving or stationary power haulage machinery because of the associated blind spots of vehicles and non-availability of active warning systems . The higRecently, many researchers have focused on the installation of RFID and magnetic field detection for providing real-time safety against collision accidents of vehicles at construction sites and mining areas ,24,25,26An advance system  with theSimilarly, GPS is a technology which has been extensively adopted to prevent equipment related accidents at construction sites and surface mines. GPS-based proximity and warning systems have shown high potential for reducing collision accidents in work zones . Neito iFor the prevention of collision accidents at construction sites, a pro-active proximity warning system to detect the presence of worker/workers, performing their duties in the close vicinity of heavy equipment, and to warn the operator is highly needed. Such proximity detection and warning systems can significantly enhance construction site safety by pro-actively preventing the collision of a worker with heavy equipment. Much research has been conducted to prevent hazardous collision accidents between workers and heavy equipment in construction sites ,38,39,40In this study, we developed a RCSS that combines RFID, computer vision, UWB, and GPS technology. It monitors the movement of heavy equipment as well as workers’ movement at constantly dynamic construction sites to prevent potential accidents. The overall concept of the RCSS is shown in The present study, for real demonstration, is limited to RCSS installation on excavators and forklifts, but it can be extended to all other construction equipment. Overall, communication in the RCSS is based on three types of communication protocols: i) UWB, ii) CAN-bus, and iii) GPS for onsite interaction of individual variables of a construction site . Details of communication decorum for the communication between various components are summarized in (a)Continuous and simultaneous monitoring of workers and construction equipment at construction sites;(b)Real-time warning to workers, equipment operators, and entire working space as the level of risk increases;(c)Automatic controlling of construction equipment before the occurrence of a collision accident.Major applications of the RCSS are as follows:The proposed system is robust in the sense it can be applied as a single unit or its sub-systems  can be applied separately, depending upon the requirements of construction sites. This section describes the operational scenarios and implementation process for each sub-system.In the GRMS, the lowest layer is implemented data collection and a communication device. A commercial communication device (GCU) has been adopted. In this device, GPS was chosen as the location tracking technology for construction equipment. The GCU uses two types of communication interfaces. The CAN-bus interface is for communication with the construction equipment or UMCU (in UCAS), and the LTE interface is for communication with the management server. The APS is the main information management tool in the GRMS for general managers. The managers can access and manage information of construction resources via web browsers, including internet explorer (Microsoft) or chrome web browser (Google). The APS enables all the construction managers to log into a portal site and immediately obtain the necessary information for safety planning of a construction site. The APS enables input of field resources as list, real-time visualization of the tracked equipment in the monitoring window, signaling of upcoming hazards to the resources, statistics of event information, and history management of resources. Visualization technology of event data and location tracking of equipment enables managers to monitor the progress of the construction resources and have a more intuitive understanding of the safety status in the construction site. The APS data architecture is depicted in The UCAS and GRMS consist of six devices: UOPU, PPU, ZAU, UMCU, ECS, and GCU. In this research, we selected commercial proximity warning sensors  as the real-time worker detection and alarm devices. These devices can be classified based on their functions and contribution in the UCAS.Proximity warning devices: The proximity warning devices include UOPU, PPU, ZAU, and UMCU devices based on UWB and CAN bus interfaces. The UMCU can be mounted on the construction equipment using a flexible magnetic foot. The PPU can be mounted on the helmet of a construction worker and includes an alarm function. The appropriate warning distance can be set via the UOPU. The ZAU is installed on the construction site and provides both audible (speaker) and visual (LED) warning information when a detection signal is received from the PPU or UMCU. The ZAU is used to alert resources approaching hazard zones. The UOPU and UMCU communicate to each other using CAN bus. The UMCU interacts with the PPU or ZAU via the UWB communication. Whenever, either the PPU or ZAU receives a signal in a hazard zone, they automatically transmit the signals to the UMCU. The UMCU finally communicates with the UOPU. This real-time and sequential communication results in simultaneous triggering of an alert in all involved devices such as the PPU, ZAU, and UOPU. Thus, this system enhances the safety of the entire work zone by pro-actively generating an alert using proximity warning devices and effectively reduces collision accidents.ECS: In addition to UMCU communication with UPOU upon receival of a communication signal from the PPU or ZAU and also communicates with the ECS. The ECS activates the Electronic Control Unit (ECU) to control the equipment instantaneously. The basic techniques for hydraulic control of equipment was developed previously by the authors . HoweverGCU: The UCAS is integrated with the resource management system through the GCU. The GCU is installed on all equipment present at a construction site, and it collects location and operation information of the heavy equipment, and alarm data, through a CAN-bus interface with the UMCU. The GCU transmits the collected data to the remote management server through the LTE or Wi-Fi interface. The ACAS consists of three main modules: i) an AVM module for visual monitoring, ii) RFID module for automatic detection of workers, and iii) AMCU module for equipment control. The ACAS was developed in a previous study , and theAVM module: This module consists of four fisheye cameras, one AVM, and an AOPU. The fisheye cameras were mounted around the construction equipment  to detect approaching workers. The AVM was mounted on the cabin of the construction equipment and allows the operator to see the surroundings view.RFID module: The RFID module includes four RFID antennas and RFID tags. A RFID tag is deployed to each worker in the construction site. An RFID antenna was installed on the side or back of the excavator and emits radio frequency signals. The RFID antenna recognizes the RFID tags of workers approaching the excavator.AMCU module: The operation of the equipment can be automatically controlled using an AMCU module. An OFCV is connected to the solenoid valve of the heavy equipment that controls the flow of oil for equipment operation. The heavy equipment can be automatically stopped using the OFCV. As mentioned in the UCAS, an excavator can be controlled using the ECS, which is backed in this operation by the ECU. In addition, each module including an OAU is combined in an AMCU.GCU: Like the UCAS, the ACAS is integrated with the GRMS through the GCU. However, only the operation and location information of heavy equipment is collected in this system. In order to collect additional information, it must be linked with an AMCU. This scope is not included in this study. All modules except for the GCU are connected to the AMCU by cables. The test of recognition rates for one-to-one UWB communication between the UMCU and another PPU in an outdoor environment were conducted by varying the distances between these two objects . The recFor the cases when separation distances were 5 m and 10 m, the recognition rates were found to be 99.4%, which means that 497 times out of 500, the PPU was efficiently detected by the UMCU. The recognition rate slightly decreased to 98.6 % when the separation distance between the UMCU and PPU was increased to 20 m. When the separation distance between the PPU and UMCU was maintained at 30 m the recognition rate was found to be 98.4 %, which is still in an acceptable range . From thIn order to check the performance of the warning zone (10 m), we performed two separate experiments under different conditions: (a) stationary UMCU and moving PPU, and (b) stationary PPU and moving UMCU. The setup of these tests is shown in In this experiment, the real distance was obtained directly from the marked lines and compared with those obtained from the UWB software. The UMCU and PPU tests set the alarm position to 10 m and evaluated the accuracy of the position measurement. The UMCU was connected with a ZAU and its position was fixed. The static test of an approaching PPU to a stationary UMCU provides the detection area of the UMCU at various approaching rotation angles. The UMCU was installed using a tripod at the origin. The UWB signal was transmitted to record the distance at which recognition was successful, and distance against each detection point was recorded. (a) Stationary UMCU and moving PPUIn this test, the UMCU was held stationary at a height of 2.4 m . Against(b) Stationary PPU and moving UMCUIn this trial, an UMCU was installed on the forklift. Once again, the UMCU was fixed at a height of 2.4 m from the ground. A mannequin wearing a helmet with a PPU was placed 30 m from the forklift, and the forklift approached the mannequin at a constant speed (10 km/h). The results of this test have been summarized in The effectiveness on heavy machinery in actual field environments was checked at (a) construction site and (b) industrial factory (steel factory). In the following, each of the field experimentation sites has been explained separately.(a) Construction site (Hwado-Yangpyeong expressway construction)After completing experimentation at laboratory scale, this study performed harsh field tests to further verify the applicability of the proposed total system mainly focusing on the UCAS and GRMS in an actual construction environment. For this purpose, the UMCU, PPU, UOPU, ZAU, ECS, and GCU were deployed at Hwado-Yangpyeong expressway construction site. At this construction site, a 4.92 km road was built including 3.92 km of a road tunnel. The challenging nature of this construction site provided an ideal dynamic environment to test the performance of the proposed system in construction site conditions. The long-term monitoring program started in July 2018 and continued until the end of December 2018. From the menu tool box, the second menu is for real-time monitoring, with shows GUI in the form of Daum maps. The third box in the menu box is related to resource management. This menu shows the operation time as well as the event status of equipment and workers using their device’s IDs, PPU and UMCU. The histories of the equipment and workers can be downloaded as Excel files shown in b. After The interface of this system has also been provided as a mobile website. In this interface, it is possible to send event occurrence messages to the construction site managers, so that the manager can remotely check the safety status at construction sites using this MAPS. Screen shots of the MAPS are shown in (b) Industrial factory application (steel factory)Along with real field application at a construction site, we tested a sub-system of the proposed system at an industrial factory (steel factory) located in Pohang, South Korea. As mentioned above, our proposed system can be implemented as a total system or its sub-systems can be adopted separately. Usually, the use of forklifts is very limited at construction sites, therefore, we selected an industrial factory comprising mainly of forklifts as the main heavy machinery. In this field application, we implemented only a sub-system (UCAS) of our entire system. Presently, the focus of the construction industry is to minimize accidents and occupational injuries at construction sites. The main purpose of this study was to determine if proximity sensing technology in the construction environment is feasible and to enhance the safety of the construction site by preventing collision with heavy equipment. For this goal, a robust construction safety system (RCSS) was developed by integrating resource management, a UWB-based proximity warning system, electronic machine control techniques, and GPS technologies to prevent collisions by heavy equipment at construction sites. The proposed system can be applied as a single unit or its sub-systems (AVM-based collision avoidance system (ACAS), an UWB-based collision avoidance system (UCAS), and a GPS-based resources management system (GRMS)), individually. In order to verify the system, this research investigated the feasibility and technical soundness of the proposed system mainly focusing on a UCAS and GRMS. The proposed real-time resource management, proximity warning, and machine control technology have proven to be effective in aiding the safety needs at a dynamic construction site. The UWB-based main control units can effectively detect the presence of workers approaching heavy equipment. Warning devices installed on excavators and dump trucks or forklifts successfully activated alerts in harsh field construction environments. Recent research of proximity warning systems have been performed by many researchers and this research has argued that the appropriate use of advanced proximity sensing technologies could be effective in preventing collision accidents. Conventional proximity warning technologies use RF technology to detect workers and activate alerts, monitor heavy machineries using GPS technology, manage light equipment using cameras, or operate individual systems for surrounding site resources. The proposed system can operate various technologies such as proximity warning using RFID, UWB, camera, and real-time position tracking using GPS technology as a single integrated construction safety system based on a GRMS. In addition, it is possible to apply sub-systems according to the conditions of the construction site. In addition, the RCSS can record safety data and important event data of equipment efficiently, and the collected data can be used for construction site safety planning.Despite the various advantages listed above, the RCSS has the following limitations: workers and construction equipment must be installed and the specific conditions of the construction site can significantly affect the performance of the system. For example, RF technology can be affected by temperature, humidity, and ambient radio waves. Therefore, future research will analyze how these environmental factors affect the system, and it will include the efforts to improve the system.


For the first time, temperature-dependent conductivities at constant water content for a series of solid polyelectrolyte complexes with varying mixing ratios of anionic poly(sodium 4-styrene sulfonate) and poly are presented. For water absorption, the samples are first equilibrated at an ambient temperature and at fixed relative humidity (RH). During the conductivity measurements, the so achieved water content of the samples is kept constant. At all of the hydration levels, the dc conductivities of the hydrated polyelectrolyte complexes (PEC) display Arrhenius behavior with activation enthalpies that are significantly lower than those of dry complexes. The activation enthalpy decreases linearly with water content. The lower activation enthalpies in case of hydrated as compared to dried complexes are attributed to a lowering of the energy barriers for ion motion. Finally, it is shown that the temperature-dependent conductivity spectra at constant water content obey the time-temperature superposition principle. Additionally, temperature-dependent conductivities at constant water content are compared to data sets determined in a separate study with constant RH at all of the temperatures. For the latter case, the influence of the type of alkali ion is also considered. Using the broad variety of data sets, the influences of water content and temperature on the conductivity mechanism can be separated from each other. Initiated by the work of Bungenberg de Jong and Kruyt , polymerDuring the past years progress has also been made in the theoretical understanding of PEM and PEC by developing and applying different types of computer simulation techniques ,13. Holm−12 S cm−1 to 10−7 S cm−1. Work on other PEM materials followed [Systematic studies on the ion transport in PEM and PEC systems are still rare. Durstock and Rubner  were thefollowed ,16,17. Afollowed  and morefollowed  showed tfollowed . NeverthOn the other hand, systematic investigations of the influence of polyion stoichiometry on the ion transport properties are feasible for PEC, since a predefined amount of small ions can be incorporated via controlling of the mixing ratio of polyanions to polycations. In spite of the advantage of known stoichiometry, only a few studies have been made to explore the ion dynamics of PEC, see for example the review in Reference . In the + ions are dominating the ion transport [+ or Cs+ as counterions showed that therein hydrated alkali ions are the major conducting species [T-dependent conductivities reveal the activation enthalpy of the macroscopic charge transport, which in turn allows for the interpreting of the underlying microscopic ion dynamics.The lack of extensive studies on the ion dynamics in precipitated PEC is probably due to the low dc conductivity of dry PEC. Imre et al. have reported conductivity data of dry PEC and concluded that Naransport ,27. Late species . Moreove species , the aut species . T-depenx MPSS·(1 − x) PDADMAC throughout the text. “M” stands for Na or Cs, respectively. The samples were allowed to absorb water during equilibration in an environment of defined relative humidity (RH). We have used conductivity spectroscopy to determine the conductivity of different compositions of these humid polyelectrolytes complexes as a function of temperature.For the study presented here, we have prepared several compositions of polyelectrolyte complexes from poly and poly(styrene sulfonate) abbreviated as One possibility of studying the temperature dependence of the ion dynamics of hydrated PEC is to vary the temperature while keeping the RH of the environment constant. These results will be referred to as results from the “cRH-series”. However, when interpreting data of the cRH-series, one has to consider the following aspect: If a sample is equilibrated at a particular temperature in an atmosphere of constant RH and then subjected to heating or cooling, the water content in the sample does not necessarily remain fixed, although the relative humidity of the environment is kept constant. The reason for this is that the absolute amount of water in the atmosphere varies with temperature, even though the relative humidity remains constant. Therefore, the water content in PEC at a particular RH might also vary with temperature. Hence, the results of the cRH-series will be mainly presented in the part “T-dependent spectra recorded at a fixed water content (denoted as cWC-series). For the cWC-series, the samples were first equilibrated at ambient temperature in an environment of fixed relative humidity in which the PEC samples can absorb water. During the temperature-dependent conductivity measurements, this water constant was maintained by isolating the sample within a closed sample cell from the outer atmosphere.To separate the effect of temperature on the ion motion from the combined effect of temperature and water content within the samples, the focus in the following sections will be on x NaPSS∙(1 − x) PDADMAC PEC at constant water content. In addition to the comparison of the absolute conductivity values, the variation of the activation enthalpy as a function of PEC composition and water content is also discussed. Furthermore, an effort has been made to understand the combined effects of water content and temperature on the ion dynamics by scaling of the temperature-dependent conductivity spectra to a master curve.In this contribution we present for the first time temperature-dependent conductivity spectra of hydrated The anionic polyelectrolyte poly(sodium 4-styrene sulphonate)  was purchased from Acros organics  as a solid white powder. This powder was dissolved in ultrapure water and dialyzed against ultrapure water (filter cut-off: 25 to 30 nm pore size) in order to filter out the short chains and other possible contaminations. After dialysis, the solution was freeze-dried to obtain pure NaPSS.CsPSS was prepared from NaPSS by an ion exchange technique as explained in Reference . The cat−1. After completion of the dialysis, the complexes were freeze-dried. For conductivity measure ments, cylindrical pellets were prepared from approximately 0.05 g of powdered sample by applying a pressure of 12.5 kN cm−2 for 2 min. The so obtained pellets are shown in For complex formation, appropriate amounts of 0.05 M aqueous solutions of PDADMAC were added dropwise into a 0.05 M aqueous solution of NaPSS or CsPSS, respectively, and stirred constantly during the complexation reaction. After the complexation, the precipitated complex and the remaining water/salt solution were dialyzed against ultrapure water in order to get rid of the free salt that formed during the complexation reactions. The dialysis was continued until the conductivity of the exchanged water fell below 2 μS cmThe two opposite faces of the cylindrical pellets were sputtered with gold. The pressed samples were equilibrated in an atmosphere of fixed relative humidity. Our previous results from in situ conductivity measurements under humid conditions show that an equilibration time of one week was sufficient to obtain reproducible results . Hence, + ions.The stoichiometry of the solid state PEC is given by the ratio of polycations to polyanions. A PEC composition specified as 0.6 NaPSS·0.4 PDADMAC implies that per 0.6 mol monomers of the polyion PSS there are 0.4 mol monomers of the polycation PDADMA. When considering all of the charged groups on the different polyions together, the above composition yields an intrinsic charge compensation of polyion groups of 80%. By contrast, 20% of the charged groups on the polymers (here PSS) are extrinsically charge compensated by NaThe investigation of the ion dynamics is performed by frequency-dependent conductivity spectroscopy. This technique has the advantage that the dc conductivity can be extracted from the measured spectra, even in the presence of blocking electrodes. The applied broadband method not only allows for the studying of the macroscopic ion transport, but also of elementary steps of the ion movements . In the In order to investigate the influence of varying outer conditions on the conductivity of PEC, temperature-dependent admittance studies at constant relative humidity (cRH) were carried out inside a constant climate chamber . The measurements were performed at a relative humidity of 46%, 55% and 64%, respectively, which was always fixed during the temperature variation between 298 K and 333 K. Measurements beyond these ranges of temperature and relative humidity were not possible due to condensation of water vapor inside the chamber. The measurements were performed with an impedance analyzer , over a frequency range of 5 Hz to 13 MHz.For the investigation of PEC samples with constant water content (cWC-series), a closed cylindrical sample cell was used. Temperature-dependent conductivity spectra (between 0.1 Hz and 3 MHz) at fixed water content were determined using an ALPHA-AN-high resolution analyzer equipped with a Quatro cryosystem from Novocontrol Technologies . Measurements were performed in a so-called sandwich conformation where the sample was placed between a lower and an upper electrode. In case of temperature-dependent conductivity measurements in the cWC-series, the sample was first placed onto the lower electrode within an open sample holder and was kept for humidification inside of a glove box at room temperature. The relative humidity inside the glove box was maintained by appropriate saturated salt solutions as reported elsewhere ,31. Afted/A. A denotes the surface area of the gold electrodes and d is the sample thickness measured after sputtering. The bulk resistance was obtained by fitting an equivalent circuit, consisting of a constant phase element connected in parallel with a resistor, to the experimental impedance data. The dc conductivity was obtained by multiplying the inverse of the bulk resistance with the cell constant. More details about the data analysis were already presented in References. [The specific conductivity is obtained by multiplying the experimentally determined admittance with the cell constant, erences. ,28.cwater, as a weight fraction determined by gravimetric analysis for x NaPSS∙(1 − x) PDADMAC PEC samples equilibrated at different RH values (RH < 90%). The values reported here were determined from three consecutive measurements (each separated by a 24 h gap), which showed basically the same mass for the samples with deviations only in 4th or 5th decimal.As the water content plays a crucial role for the conductivity of polyelectrolyte complexes, we first determined the water content in polyelectrolyte complexes equilibrated at varying relative humidity and at ambient temperature. The range of composition reported here refers only to those complexes with an excess of alkali ions, which are subject of this study. cwater and the relative humidity of the environment. A deviation from linearity only occurs at RH > 90% as shown in Reference [In the presented RH range the water content in the complexes does not depend on PEC composition, but remains constant within experimental error. eference . Hence, The index “eq” has been added to distinguish the relative humidity at which the PEC samples were equilibrated at ambient temperature in the cWC-series from the RH-values of the cRH-series, where the relative humidity of the surrounding air was kept constant at each temperature.a is determined by the arithmetic mean of the water content at different compositions and it is found to be: a = 0.230 ± 0.005. It refers to the relative humidity and the weight fraction of water within the complex both being given in %.The value of the proportionality factor + or Cs+) in x MPSS∙(1 − x) PDADMAC PEC on the water content in PEC is displayed in In the following sections, this value is now used to convert all of the measured eq ≥ 40% show a linear dependence on RHeq. However, our current results at even lower RH reveal that the conductivity is lower than expected from the extrapolation of conductivity data obtained at RHeq ≥ 40%. This deviation from linearity at lower relative humidity was not evident and subsequently not reported earlier, possibly due to the limited RH range investigated [stigated .eq > 40%. Here, the conductivity is high enough so that data can be obtained even well below ambient temperature. The semi-log plot eq at all of the temperatures.B and found to decrease with temperature. In fact, as shown in B varies linearly with the inverse temperature. This is an important new finding which leads to deeper insights into the influence of water molecules in PEC even well beyond ambient temperature, and it will be discussed in detail later.The slope of these straight lines is termed Apart from the spectra taken at low temperature and low humidity, we also observe a decrease in conductivity at frequencies lower than those of the dc regime. This low-frequency part of the spectra can be attributed to electrode polarization effects that are caused by the presence of blocking electrodes. The frequency at which these polarization effects become visible in the spectra decreases with decreasing temperature and moves out of the experimental frequency window at low temperatures.At frequencies higher than those of the dc regime, the conductivity increases monotonously with frequency. Moreover, for a particular composition, not only the dc plateau moves to higher conductivity values with increasing temperature, but also the onset of dispersion shifts towards higher frequency.From comparison of The spectral shapes as well as the influence of temperature seen in the spectra are very similar to those of other ion-conducting materials. The conductivity spectra recorded at intermediate and high frequencies provide important insights into the ion motion. The dc conductivity reflects macroscopic transport, whereas the data at higher frequencies are determined by ionic motions on shorter time and length scales. In the following section, we will first discuss the dc conductivity and its activation enthalpy and then move to the discussion of the more localized dynamics probed in the dispersive regime of the conductivity spectra.xNaPSS = 0.55 are displayed in The validity of the Arrhenius Equation was shown for completely dry PEC in Reference ,32. Arrhy-axis. Here, we show for the first time that in all of the cases the dc conductivity of humid PEC follows the Arrhenius law.Following the Nernst-Einstein Equation, which yields a proportionality between the diffusion coefficient and eq. In other words, the difference between the isothermal conductivities of samples with different water contents increases with decreasing temperature. Analogous trends are also found in the Arrhenius plots of data for the cRH-series, see x-labels RHeq and cwater correspond to the measurement series with constant water content, whereas the label RH corresponds to data taken at constant RH.In Equation (2), eq or cwater.The activation enthalpies determined at constant water content (filled squares) decrease linearly with increasing RHxNaPSS = 0.55. Extrapolation of our data presented in eq = 0% yields ΔHdc = 1.02 eV. One might speculate that the further lowering of RHeq below 16% could result in a sharp increase of the activation enthalpy, which finally reaches the value of the completely dry case, implying strong deviations from the straight line presented in eq value and to which extent such a change might be visible is currently still an open question. Possible deviations from the plotted linear relation could be related to structural changes of the PEC matrix, as well as with changes in the mechanism governing the ion dynamics. This important finding of a significant deviation between the measured and the extrapolated value of the activation enthalpy for dried PEC will be addressed in more detail in the section “Discussion”.For comparison, the activation enthalpy for completely dried PEC from Reference  is also The activation enthalpies for the cWC-series, also shown in In this subsection, we will analyze the conductivity spectra probing the ion dynamics on different time and length scales. Earlier scaling studies on hydrated PEC exposed to different RH values at a fixed temperature showed that the amount of absorbed water does not influence the number density of mobile ions. Instead, only the ion mobility is changed by water absorption . Here wedc can be expressed by a function σ′/σdc = F(ν/ν0) in which ν0 is an individual scaling parameter for each conductivity isotherm. The scaling function, F, is independent of temperature. An appropriate choice of ν0 for each curve is necessary to superimpose spectra measured at different T to a master curve. Summerfield scaling, often found to be valid for ion-conducting materials [0 are proportional to each other. Summerfield scaling can therefore be expressed byFor a variety of materials including semiconductors ,34, fullaterials ,34 can bThe validity of the time-temperature superposition principle implies that in humidified PEC the basic conductivity mechanism does not change with temperature. Ionic motions on short length and time scales as well macroscopic transport on long timescales are accelerated simultaneously with an increasing temperature. Furthermore, the applicability of the Summerfield scaling approach shows that neither the number density of mobile ions nor the number of available ion sites change with temperature. Only the ion mobility increases with temperature.T-dependent spectra of humidified PEC found in this work are in contrast to earlier results obtained for dried PEC. For dried x NaPSS∙(1 − x) PDADMAC PEC, deviations from Summerfield scaling were reported by Imre et al. [Finally, it should be noted that the scaling relations of the e et al. .The interpretation of data determined at fixed relative humidity is not straight forward because the water content in the PEC varies with temperature. In other words, the data taken at constant RH are always influenced by a combined effect of temperature and water content. Nevertheless, the results obtained in the cRH-series also contribute to the understanding of the ion transport mechanism in humid PEC, especially as we can compare the influence of the type of alkali cations present in the PEC.The conductivity data of the cRH-series show similarities to the results of the cWC-series. The general shape of the spectra is for example very similar to the data of the cWC series, see Also, in the Arrhenius representations shown in T- and RH-range of the cRH-study, where only three activation enthalpies could be determined, does not allow for more detailed conclusions,When looking at the activation enthalpies determined from T-range could be superimposed onto a master curve using Summerfield scaling. This implies that also in PEC measured at constant RH, the number density of mobile ions or ionic sites do not vary with temperature and that temperature influences the ionic movements on different time and length scales in the same way.But not only the dc conductivities in the cRH- and the cWC-studies are similarly influenced by temperature variation. The influence of temperature on the conductivity spectra of the cRH-series is also similar as in the cWC-study. In all of the investigated systems (cRH-series and cWC-series) we also find that the conductivity increases with PSS content. An increase of the conductivity of PEC with PSS content has been published before ,28,32. T+ or Na+ ions. Such differences have already been discussed for room temperature data in Reference [x CsPSS∙(1 − x) PDADMAC than in x NaPSS∙(1 − x) PDADMAC.For the cRH-series we also see differences between the PEC, which either contain Cseference . NeverthxPSS > 0.5, Cs+ ions move faster than Na+ ions. As the amount of small counterions is defined by the PEC composition, which is the same for PEC with Na+ and Cs+, the reason for the different mobilities might be due to a varying size of the water shells for both types of cations. Comparison of + being a smaller ion as compared to Cs+ has a higher charge density  than Cs+. Consequently, Na+ ions possess a larger hydration shell as compared to Cs+ ion. This conclusion is in accordance with the radius of hydrated alkali ions as published in Reference [+ ions might be that according to Reference [Additionally, at constant RH the PEC with CsPSS not only show higher dc conductivities but they also have lower activation enthalpies than those with NaPSS. These findings strengthen the conclusion put forward in References ,29, viz.eference . Anothereference  larger aeference  and in veference .Under same external conditions PEC with NaPSS absorb more water than PEC with CsPSS.At fixed temperature and humidity, CsPEC show a higher conductivity than NaPEC.The dc conductivity increases as a function of temperature, humidity and/or water content.The dc conductivity obtained at constant water content and at constant RH increases with increasing PSS content.The temperature dependence of the dc conductivities of all humid PEC follows the Arrhenius law.The activation enthalpy values for humid PEC are significantly lower than those of completely dried complexes as reported in Reference . For theThe time-temperature superposition principle is valid for the conductivity spectra of humidified PEC and the conductivity spectra can be scaled with the help of the Summerfield scaling.The major findings we obtain on the basis of our systematic temperature-dependent conductivity study at fixed relative humidity and at fixed water content are as follows:T-dependent conductivity measurements in both of our measurement series presented here, now provide the first direct evidence that the presence of water molecules indeed reduces the activation enthalpies.These findings together allow for detailed conclusions about the ion transport in PEC. On the basis of room temperature conductivity studies, De et al. have already shown that water molecules absorbed in humid PEC not only hydrate the ions, but also facilitate the ion motion via screening the electrostatic interactions between polyion-polyion and polyion-cation, and thereby lowering the barriers in the energy landscape of the migrating ions ,29. ThisAssuming that water exists in two different sites, i.e., hydration water of the ions and water molecules absorbed by PEC that do not participate in hydration of the counterions (matrix water), we can use the above findings to interpret our temperature-dependent data. The activation enthalpies reported here show that the matrix water molecules lower the energy barriers for the moving ion/hydration shell complexes. This effect gets stronger with increasing humidity at which the samples were equilibrated, as shown in In Reference , which wB that is obtained by linear regression of lnB depends linearly on 1/T implying that T-dependent spectra for humid PEC with constant water content, we can now for the first time confirm the reduction in the activation enthalpy for the ion transport. Here, eference , we haveersus RH ,29, see as found . The ∆Hdcwater, which is proportional to Alternatively, the water content The microscopic origin for the lowering of activation enthalpy could be explained by the fact that due to absorption of water by PEC at elevated relative humidity, the activation barriers for the ion transport are lowered. The lowering effect of activation enthalpy is more prominent with an increasing relative humidity. The difference between T-dependent. For dried PEC two possible reasons were given to explain the deviations from Summerfield scaling for PEC with an excess of PSS [T. The latter effect can be caused by temperature-induced changes of the chain conformation or a higher local mobility of chain segments in the polyelectrolyte network resulting in a blocking of some pathways for the Na+ ion transport.Another significant difference between humid and dried PEC is also seen in the scaling relations for the respective temperature-dependent conductivity spectra. In both cases scaling is possible, which shows that the time-temperature-superposition principle is fulfilled for dried and hydrated PEC. However, in contrast to humidified PEC where Summerfield scaling is applicable, in dried PEC of the same composition, Baranowskii/Cordes scaling ,43 was es of PSS . Either PEC implies that the number density of ions or ionic sites does not change with temperature. Additionally, the mechanism of ion movements on local scales (probed by the dispersive conductivity above the dc plateaus) as well as macroscopic transport (probed by the dc plateau) is not influenced by temperature. The effect of temperature is simply to be seen as an acceleration of the ion dynamics. The reason why we observe Summerfield scaling in humid PEC might be due to the fact that the absorbed water releases all of the alkali ions present in the PEC, even at lower temperatures. Therefore, no increase in the number density of mobile ions is detected with temperature. In addition, the absorbed water further softens the complete PEC network which leads to generally faster dynamics resulting from structural rearrangements. This could be the reason why no pathways for the ion transport are blocked when the temperature is changed.By contrast, the validity of Summerfield scaling in hydrated If proton transport dominated the dc conductivity, one would expect that the conductivity in the cRH-series should increase with temperature because of the additional absorption of water into the PEC.T-dependent, indicating that hydrated alkali ions are the major charge carriers.An increasing water content in the PEC should go hand in hand with an increasing number of mobile charge carries, which would result in deviations from the Summerfield scaling concept. By contrast, the number density of mobile ions is not PEC prepared from CsPSS show under same conditions a higher conductivity than PEC with NaPSS. This also confirms that the alkali ions in their hydrations shell determine the ion transport properties.The scaling results on PEC with constant RH presented in this work also strengthen the conclusions of References ,29, that− ions. This will be topic of a forthcoming paper.This work has focused on PEC, which have an excess of PSS and therefore alkali ions. It would now be interesting to analyze the ion dynamics in humid PEC with an excess of PDADMAC and therefore of Cl