The quality of water has deteriorated due to urbanization and the occurrence of urban stormwater runoff. To solve this problem, this study investigated the pollutant reduction effects from the geometric and hydrological factors of green infrastructures (GIs) to more accurately design GI models, and evaluated the factors that are required for such a design. Among several GIs, detention basins and retention ponds were evaluated. This study chose the inflow, outflow, total suspended solids (TSS), total phosphorus (TP), watershed area, GI area (bottom area in detention basins and permanent pool surface area in retention ponds), and GI volume (in both detention basins and retention ponds) for analysis and applied both ordinary least squares (OLS) regression and multiple linear regression (MLR). The geometric factors do not vary within each GI, but there may be a bias due to the number of stormwater events. To solve this problem, three methods that involved randomly extracting data with a certain range and excluding outliers were applied to the models. The accuracies of these OLS and MLR models were analyzed through the percentage bias (PBIAS), Nash-Sutcliffe efficiency (NSE), and RMSE-observations standard deviation ratio (RSR). The results of this study suggest that models which consider the influent concentration combined with the hydrological and GI geometric parameters have better correlations than models that consider only a single parameter. The water quality of stormwater runoff has rapidly deteriorated due to urbanization. Consequently, stormwater runoff introduces significant quantities of nonpoint-source pollutants into water bodies in the regions surrounding urban areas. As a result, different types of stormwater best management practices (BMPs), which are referred to as green infrastructure (GI) and low-impact development (LID), have been developed in order to address stormwater runoff and its reduction in quality. For instance, Yu et al.  presenteConstructed stormwater wetlands (CSWs) are a common form of stormwater BMP. However, the plug flow assumptions in first order (the k-C* model) decay kinetics do not accurately reflect the real fluid dynamics in CSWs . Real flout) of retention ponds tended to decrease as the permanent pool volume increased, but that the volume divided by the mean annual storm runoff volume at the site was not significant for fewer than 10. However, even with multiple linear regression (MLR), the relationship between the pool volume and water quality reduction could not be accurately expressed. A report by the Environmental Protection Agency (EPA) [in) and other parameters, including the GI geometric and hydrological factors. In the case of retention ponds, the greater the permanent pool volume is, the smaller the variability in the discharge concentrations and the better the performance for individual events with a large runoff volume. A significant relationship was not observed in the data among the detention time, depth of banks and TSS removal.Barrett  investigcy (EPA)  indicatecy (EPA) , howevercy (EPA)  evaluatecy (EPA)  recognizAccording to previous studies, the stormwater treatment performance in wetlands, a type of storage GIs, is determined primarily by measuring the inflow or hydraulic loading rate (HLR) and the detention time, both of which are influenced by the storm intensity, runoff volume, and surface area and volume of GIs ,9,10,11.These studies analyzed GI performance based on influent concentration alone, and did not consider design characteristics, such as GI geometric and hydrological factors themselves. Most previous models for designing GIs were developed considering only the influent and effluent concentrations. However, these models encounter difficulties when the concentration range is outside the design configuration, such as extrapolation beyond the contaminant concentrations that have been previously measured. In reality, many factors affect the GI performance, but which parameters exert a greater influence remains unclear. Additionally, no quantitative evaluations are available, and previous studies predicted only the stormwater runoff effluent concentration as the influent concentration, while excluding other parameters.The aim of this study is to assess how GI design factors, such as GI geometric and hydrological factors, influence performance, compared to influent concentration. Hence, the purpose of this study is to investigate how geometric, hydrological, and influent concentration factors of GIs influence the effluent concentration. Through model evaluations of detention basins and retention ponds, this study uncovers which parameter combination exhibits the best performance for each GI, and a better model is found by comparing the developed model with a model that only considers the influent concentration. The results of this study are expected to provide insight into the influencing factors in the design of GIs.The detention basin and retention pond data were acquired from the BMP database, and the influent flow rates and geometric factors of the two GIs were sorted. Essentially, only the stormwater events listed that include the GI geometry , hydrological factors , watershed area, inflow concentration and outflow concentration were used. Ultimately, data from a total of twelve detention basins and eight retention ponds were used . BecauseNot all stormwater events collected from the BMP database were used; only those occurring when the GI was operating properly were chosen. To satisfy this criterion, the inflow rate must be greater than the outflow rate, and the inflow concentration must be greater than the outflow concentration . All datThe aim of this study is to assess if the influent concentration is dominant in the design of detention basins and retention ponds as GIs. The reason for choosing traditional techniques (detention basins/retention ponds) among GIs is that the quantity of stormwater monitoring data about these techniques is the largest in the BMP database. In the stormwater BMP/LID database, the number of observed TP is enough to apply statistical analysis, but enough orthophosphate (SRP) data is not available. Therefore, this study applied TP rather than SRP.Sampling without replacement was performed a thousand times for each combination of parameters used in the model. Each combination of parameters required the inflow concentration. In addition, when only one element was added, one of the following was included: the hydraulic loading rate (HLR), inflow rate (Q), detention time (T), area (A), or ratio of the area to the watershed area (A/WA). When two additional factors were added, the GI geometry, watershed area (WA), or A/WA was included, along with a hydrological factor, such as the inflow rate (Q) or detention time (T). Excluding the OLS regression model, which considers only the inflow concentration, a total of 14 combinations were established to evaluate the model. Cluster sampling is a method in which all elements are sampled by selecting a specific cluster after creating clusters according to certain criteria. Each cluster encompasses a naturally occurring group. However, this approach is less accurate than simple random sampling because the elements which are not organized into clusters during the cluster selection phase are not included in the sampling scheme . To compThe MCS method involved dividing the population into clusters and then randomly extracting the number of similar stormwater events that were possible for each cluster. The detention basins were extracted by setting the number of events as close as possible for each of the 12 GIs; the eight retention ponds were extracted in the same way. This sampling method is possible only when the GI geometry and the watershed area are separate. In contrast, the hydrological factors and influent concentrations were not separate because they are not constants in a given GI. This approach is suitable for stratified sampling or modified stratified sampling excluding outliers a.Stratified sampling produces a representative sample when the sample to be collected from the data is not homogeneous, requiring a layered sampling approach. This method samples each layer by splitting the layers into subcollections that have more uniform populations, and more reliable estimates for each sample are obtained if the data are divided by stratification. This sampling technique obtains an estimate of the population in each stratum when each variable is more accurate. Layered sampling gives relatively accurate and informative data on the sample b. The reAs shown in The EPA  report iOLS regression is a type of generalized linear modeling that can be used to model a single response variable on at least an interval scale . The leaMLR identifies the effects of the interaction between certain variables independent of the variation in the dependent variable in order to determine the independent variables that best predict or explain the most variance in the dependent variable. In MLR, a dependent variable is related to two or more independent variables, as follows:The magnitude of the relationship between one predictor and the criterion varies as a function of at least one other predictor in an interaction. Generally, one predictor is considered to be a focal predictor, while all other predictors multiplied by the focal predictor are moderators, which are assumed to influence the relationship between the focal predictor and the criterion  . This inWhen outliers exist, linear regression analysis using the least squares method is not suitable. In a robust regression model, outliers are not excluded, and a robust analysis is performed and is not affected if outlier data exist. Robust regression, as the name implies, is a more robust version of least squares regression compared to other regression techniques. When outliers exist, they provide regression coefficient estimates. However, in the absence of outliers, MLR coupled with OLS exhibits better performance than robust regression.The robust regression model assigns a weight to each data point. The bisquare weight function uses the least squares method to iteratively reweight the data. Robust fitting was conducted with a default tuning constant of 4.685 and the bisquare weight function, which is also called the biweight function, as follows:unbiased ,22,23,24In this study, MATLAB software was used to analyze the data. Four regression models  were created, and model evaluations were performed using the ‘fitlm’ function in the Regression Learner Toolbox.This study normalized the data to evaluate the performance of the four models. The RSR is a ratio based on the observed standard deviation of the root mean square error (RMSE), a commonly used error index statistic. The RSR varies from an optimal value of zero  to positive infinity. A lower RSR value (close to zero) indicates good model performance with a low RMSE ,26,27,28The PBIAS represents the average trend in which the value of the simulated data is greater or less than the value of the observed data, thereby reflecting the relative error from the measured value. The optimal PBIAS value is zero, and the closer the value is to zero, the more accurate the model simulation is. Positive values indicate that the model exhibits an underestimation bias, and negative values conversely indicate an overestimation bias ,29. The The NSE normalizes the relative magnitude of the residual variance between the observed value and the model-simulated value, thereby indicating how well a plot of the two data values fits along the 1:1 line . The optWhen designing the GI model, 14 combinations of parameters were created using the GI geometric and hydrological factors, as well as the inflow concentration. The GI geometric factors included the area (A), volume (V), and depth (D), while the hydrological factors included the inflow rate (Q), hydraulic loading rate (HLR), and detention time (T). The watershed area (WA) and the ratio of the GI area to the watershed area (A/WA) were also entered as combination parameters. Among the GI geometric factors, ‘A’ describes the bottom area of a detention basin and the permanent surface area of the pool in a retention pond. The TSS and TP removal performances in the model evaluations of the detention basins are both “good” and “acceptable”. The removal performance of the detention basins is well represented by each of the MLR models for most parameter combinations. The numbers along the x axes in the figures denote the combination numbers of the parameters listed in The PBIAS TP removal performance values of all the models (except the MLR-R model) in the detention basins are similar to the PBIAS TSS removal performance values in the detention basins. The MLR-R model PBIAS is the closest to zero, indicating a “good” performance. Some of the simulated values of the MLR-R model are overestimated, which is different to the other models . In contThe TSS and TP removal performances in the retention pond model evaluations are “acceptable” and “poor”, but some of the parameter combinations achieve “good” levels. The TSS removal performance in the retention ponds has a larger variation than that in the detention basins. The PBIAS levels of TSS removal in the retention ponds are “acceptable” and “poor” since the results are lower than −20% . The RSRThe PBIAS range of values pertaining to the TP removal performance in retention ponds includes some positive values, which indicates that the models exhibit an underestimation bias . The TP In comparison to the PBIAS values of the other models, the MLR-R model PBIAS is close to zero in both the detention basins and the retention ponds. Among the MLR models, the MLR-I models RSR and NSE perform better. Some of the MLR-R model RSR and NSE values exhibit worse performance than those of the OLS model among the 14 combinations of parameters. The MLR-I model performs well in the three model evaluations compared with the OLS model.As shown in Unlike the PBIAS of the TSS removal performance in detention basins , there iIn the case of combination No. 8 regarding the TP removal performance in retention ponds , the MLRThe difference between a detention basin and a retention pond is the existence of a permanent pool, i.e., the former does not have a permanent pool, whereas the latter (being wet ponds) does have a permanent pool. The performance results in retention ponds are more different from the OLS results than those in detention basins. Certain types of BMPs, such as wet ponds, can reduce runoff through evaporation and infiltration. The removal of pollutants contributes to reducing pollutant loading while improving effluent quality . Figure As shown in Combinations No. 2 and No. 11 in Three sampling methods, namely, MCS, SS and SS (no outliers), were used to reduce the bias of stormwater events per GI through the removal of TSS and TP from two GIs, i.e., detention basins and retention ponds. This study applied the OLS model (including only the influent concentration) and the MLR, MLR-I and MLR-R models, which included other parameters. Although the detention basins exhibited better model performance evaluations than the retention ponds, the correlations of the parameters showed clearer results in the retention ponds. For both detention basins and retention ponds, the hydrological factors of inflow rate and detention time were positively correlated with GI geometric factors. Combinations with both hydrologic and GI geometric factors showed more of a gap with OLS results than those including only one factor. Among several the GI geometric parameters, area and volume included in the combination were positively correlated with the two GIs. The two GIs were also positively correlated with the watershed area. In TSS removal of detention basins, combinations of the bottom area included hydrological factors and the ratio of the bottom area to the watershed area had more difference than other combinations. Therefore, the bottom area was more relevant than other GI geometric parameters in TSS removal of detention basins. In TSS and TP removal of retention ponds, combinations which included depth had smaller gap with OLS results than other combinations with GI geometric factors. But in TSS and TP removal of detention basins, combinations with depth produced different OLS results, similarly to other combinations with GI geometric factors. Unlike detention basins, there was a negative correlation with depth in the retention ponds. This study demonstrates that parameters other than the influent and effluent concentrations are correlated with the treatment performance of detention basins and retention ponds. The combinations with both GI geometric and hydrological parameters showed greater differences from the OLS model than the combinations with only one parameter in addition to the influent concentration. To design the optimal GI model, this study proposed that it would be better to consider other parameters in addition to the influent concentration. For future study, it is necessary to account for the underlying soil condition in the analysis. The type of underlying soil is one of the most important factors involved in determining the performance of a stormwater basin. A basin with a soil with high permeability performs better than one with a less permeable soil. Mechanisms such as adhesion and sedimentation were not considered in combination with the soil type when analyzing the data.


The maintenance of serial order information is a core component of working memory (WM). Many theoretical models assume the existence of specific serial order mechanisms. Those are considered to be independent from the linguistic system supporting maintenance of item information. This is based on studies showing that psycholinguistic factors strongly affect the ability to maintain item information, while leaving order recall relatively unaffected. Recent language-based accounts suggest, however, that the linguistic system could provide mechanisms that are sufficient for serial order maintenance. A strong version of these accounts postulates serial order maintenance as emerging from the pattern of activation occurring in the linguistic system. In the present study, we tested this assumption via a computational modeling approach by implementing a purely activation-based architecture. We tested this architecture against several experiments involving the manipulation of semantic relatedness, a psycholinguistic variable that has been shown to interact with serial order processing in a complex manner. We show that this activation-based architecture struggles to account for interactions between semantic knowledge and serial order processing. This study fails to support activated long-term memory as an exclusive mechanism supporting serial order maintenance. The ability to maintain serial order information is a core component of verbal working memory (WM). Mechanisms involved in the maintenance of serial order  have been considered to be independent from those involved in the maintenance of item information . This assumption is supported by different lines of research. Studies examining the impact of psycholinguistic factors, such as lexicality, on verbal WM performance typically observe effects on item recall, with minimal effects on serial order recall . In the experimental condition, the fifth item was semantically related to the triplet in the first half of the list. Compared to the control condition, the authors observed an increase of migration errors of the fifth item toward earlier serial positions, that is, towards the semantically related triplets of words. The authors assumed that since the semantically related triplets pre-activated, the semantically related target , this target should have a higher activation level in the experimental condition , where θ and σ′ are two free parameters. If the selected item’s activation value (without the noise added during the first step) is above the retrieval threshold, the item is correctly recalled. Otherwise, an omission is produced. It must be pointed out that this implementation assumes response suppression as being always applied during the first step of retrieval, regardless of whether an omission had been produced during the second step. This choice of implementation by Norris et al.  by the number of times items have been recalled, regardless of their serial position .The pattern of transposition errors in the Poirier et al.  study waEstimation of the model’s basic parameters was performed using a simulated annealing algorithm . Figure This drop in performance over positions 4 and 5 in the experimental condition is explained by the pattern of serial order errors, displayed in Fig. One may argue that these results merely reflect the property of the model only for the specific set of parameters we found using the simulated annealing algorithm. Instead, there might be some configurations of parameters that could reproduce the results observed by Poirier et al. . We explThis study used a computational modeling approach to investigate the hypothesis that serial order maintenance results from patterns of activation in long-term memory. The model successfully captured the overall impact of semantic relatedness effect on WM performance. However, it failed to predict the specific influence of semantic relatedness on the processing of serial order information. The model failed to predict the absence of semantic relatedness effects on order-recall performance, and instead predicted better order-recall performance for semantically related items. In addition, while human participants tend to erroneously recall an item at the position of its semantic neighbors, the computational model produced transposition errors involving migrations towards unrelated items.Basically, the problem lies in the architecture's lack of dissociation between serial order information and the activation in long-term memory. Simulation of Dataset #1 showed that the increased level of activation of items led to better memory for order information, contrary to what is usually found in the literature (Ishiguro & Saito, More generally, maintenance of serial order information via a primacy gradient in long-term memory is problematic for several reasons. First, it is not clear how the model would perform simple tasks, such as rehearsal/refreshing(Barrouillet, Bernardin, & Camos, Many theoretical models of WM postulate an independence between the nature of the representational codes involved in maintenance of serial order information and those involved in item information. This relative independence is explicitly assumed by positional models of WM (Burgess & Hitch, This study fails to support the idea that serial order information is maintained via item-relative activation in the linguistic system. At the same time, this study does not discard the possibility that maintenance of serial order could be completely constrained by the statistical regularities learned from language exposure (Schwering & MacDonald, This study looked at the range of plausible mechanisms involved in the temporary retention of serial order information. Through a computational modeling approach, we demonstrated that maintenance of serial order information via a primacy gradient of activation in long-term memory is implausible. Whether serial order is coded via independent serial order mechanisms or directly through the statistical regularities occurring in language processing or both remains to be formally established.


We report a method for the phase reconstruction of an ultrashort laser pulse based on the deep learning of the nonlinear spectral changes induce by self-phase modulation. The neural networks were trained on simulated pulses with random initial phases and spectra, with pulse durations between 8.5 and 65 fs. The reconstruction is valid with moderate spectral resolution, and is robust to noise. The method was validated on experimental data produced from an ultrafast laser system, where near real-time phase reconstructions were performed. This method can be used in systems with known linear and nonlinear responses, even when the fluence is not known, making this method ideal for difficult to measure beams such as the high energy, large aperture beams produced in petawatt systems. Creating the short pulse durations used in these interactions requires the pulse to have a large spectral bandwidth along with having minimal phase shifts between the frequencies. If spectral phase differences are introduced to the pulse, significant changes to the temporal structure of the pulse can occur. The change in temporal structure could affect physical interactions, either due to a decrease in peak intensity or through complex temporal profiles introducing non-trivial interactions when used in an experiment. While knowing the temporal profile is essential for many applications, measuring the temporal profile of a high energy beam in situ is non-trivial.Short pulse duration lasers , rely on changing the phase of the initial pulse by a known amount and monitoring how that affects the nonlinear interaction14. Commonly, a second harmonic mechanism is used for the nonlinear effect but other nonlinearities, such as the effects originating from the third-order term of the nonlinear electric susceptibility, 19. While these techniques have been highly successful, in situ measurements of high energy beams are practically difficult.A common method to gain this additional information is to measure the spectrum after a nonlinear interaction inside of some media. The nonlinear interaction is typically used to encode the phase information into a signal measurable by a square-integrable detector. Techniques such as intensity auto-correlation and frequency resolved optical gating (FROG) rely on measuring a nonlinearity induced from the interaction between two or more pulses20. The nonlinear change in index takes the form of 21. If these effects are negligible, then the GNLSE is able to be solved analytically, taking the form of a nonlinear temporal phase shift, E(z) is the electric field after propagating through a material of thickness z. The total amount of nonlinearity of the system is described by the B-integral, which is the integral of the nonlinear phase shift accumulated through self-phase modulation. Under the approximation that the temporal profile is constant during propagation, the B-integral simplifies to Another nonlinearity that can be used for pulse measurement is self-phase modulation (SPM). SPM is a nonlinear optical effect that occurs due to an intensity dependent index of refraction called the optical Kerr effect, which is a 16. Specifically, the phase reconstruction does not require information about the peak power of the pulse and does not have a direction of time ambiguity (i.e. which side of the pulse is the leading edge of the pulse).Since the spectral change between the initial and SPM spectrum are directly dependent on the temporal intensity profile. This nonlinear relationship between the initial and SPM spectra can be utilized to obtain the additional information required to reconstruct both the initial spectral phase and peak power. While SPM in dispersionless media has not been explicitly proven to uniquely find the phase for all laser pulses , it has been shown it is able to accurately and robustly solve for the phase in a wide variety of laser pulses17. Iterative phase retrieval methods have seen success for many-cycle laser pulses, however relying on the analytical solutions to the GNLSE limits the application to systems with negligible material dispersion. For broadband laser systems, even a small amount of material dispersion can substantially alter the way the spectrum changes from SPM, requiring the GNLSE to be solved numerically.One benefit of SPMs ability to measure both the initial spectral phase and peak power is that by combining the peak power reconstruction with knowledge of the spatial profile of the laser pulse, the peak intensity of the laser pulse can also be calculated. With the usage of a high damage threshold dielectric as a nonlinear media, self-phase modulation can be performed with high intensity beams, enabling in situ pulse reconstructions of the beam for high energy beams. Commonly, the analytical solution for the GNLSE has been used in an Gerchberg-Saxton style iterative phase reconstruction algorithm, in which the phase is reconstructed from the measured spectra of subsequent thin dielectric plates23. Deep learning can directly learn nonlinear relationships between various features within data and map them to the desired target variables24. Since the information is present in the data used in the iterative phase reconstruction algorithms, deep learning can be used to directly learn the transformation between the data and the reconstructions without the need for the iterative algorithms. Since deep learning methods bypass the need for an iterative algorithm during reconstruction, deep learning approaches can be significantly faster than their iterative counterparts, enabling real-time reconstructions. Problems involving self-phase modulation have already been shown to be well suited for usage of neural networks, as neural networks have been applied to both solve the NLSE25 and to solve for the peak power and pulse duration of a soliton transversing an optical fiber26.Deep learning based algorithms recently have shown great promise for ultrafast laser pulse reconstructions. Deep learning has been applied to other pulse measurement techniques, such as SHG FROG and D-Scan, where neural networks replaced iterative algorithmsIn this study, we demonstrate a technique to perform a robust deep learning reconstruction of the spectral phase and peak fluence of ultrafast laser pulses, which enables phase reconstruction to occur even if material dispersion is non-negligible. The reconstruction is based using neural networks trained to extract the initial phase and fluence from the relative shapes of the laser spectrum before and after self-phase modulation through a dielectric medium. The training for the neural networks is done by generating a large simulated dataset of various spectra and phases before and after self-phase modulation. By training the neural network on data generated using the GNLSE, which includes effects such as material dispersion, self-steepening, and the Raman response, the neural network is able to account of these effects in its reconstruction.The technique is based on predicting the phase of the initial laser pulse, as shown in Fig. For the broadband simulated spectra, two separate neural networks were trained on randomly generated simulated pulses. Both networks were designed to make predictions off of the spectral measurements of the initial pulse and the pulse after SPM, with one network used to predict the initial phase of the pulses and the other network used to predict the initial fluence of the pulses. After training, 20,000 pulses withheld from the training data were run through the networks to test the accuracy of the reconstructions for previously unseen data. A set of example reconstructions are given in Fig. Comparing the reconstructed fluence to its known value provides a way to measure the accuracy of the neural network’s predictions on the physical values we are trying to predict. For entclass1pt{minimaBy combining the results from the fluence neural network with the results of the phase neural network, the temporal intensity profile can be reconstructed, including the direction of time. Using the peak temporal intensity as an estimate of the intensity reconstruction error, which has an mean intensity reconstruction error of Even in the presence of noise, accurate reconstructions can be obtained. In Fig. In additional to validating the technique on broadband simulated data, we also utilized this method to reconstruct the phase of a experimental Ti:Sapphire laser pulse. This experiment was performed on a commercially available, 1 kHz repetition-rate laser system (Spectra-Physics Solstice ACE) with an energy of 6.6 mJ, beam diameter of 12 mm, central wavelength of 800 nm, and FTL pulse duration of 34 fs. The output of the laser was characterized using an SHG FROG. The nonlinear media, 8 mm of fused silica, is oriented at Brewster’s angle such that the effective propagation length after taking into account refraction is 9.6 mm. The collimated laser beam has a peak fluence of 11.7 mJ   cmTo predict the initial phase of the laser, a new neural network was trained. While the network trained on simulated broadband pulse performed well in the previous section it has limited application to the experimental system due to the training data for that network having a bandwidth Due to the laser having The reconstruction of the experimental neural network for two separate pulses is shown in Fig. We have shown an inexpensive and easy to experimentally implement method for measuring the temporal intensity profile of the laser pulse by utilizing self-phase modulation. While two separate neural networks were trained to predict the phase for the initial pulse, this was only done to simplify the data generation process. Since a neural network is only able to predict pulses of similar structure to what it was trained on, a specifically tailored network was designed for the broadband simulated pulses and many-cycle experimental pulses. Since the majority of experimental systems output laser spectrum that are relatively consistent in spectral width this constraint does not cause significant limitations when the method is applied to experimental systems.27, which often measure the beam using a low energy sampling of the beam. When combined with the knowledge of the focal spot of an experimental system, this information would enable peak intensity of the focal spot to be calculated.Due to relying only on the localized intensity of the pulse and requiring no scanning, single-shot intensity profile characterization can be performed in-situ for a collimated beam of with energies ranging from the milli-Joule to Joule level without requiring attenuation of the beam. With the usage of a imaging spectrometer and sampling the beam at various spatial locations, a 3-dimensional intensity mapping of the beam could be created. These capabilities make this technique ideal for large aperture multi-petawatt laser systems such as ZEUSWhen applied to experimental data, as shown in Fig. While the assumption of a constant laser spectrum and pulse energy was able to be assumed for the experimental results discussed, environmental effects could cause a long timescale spectral drift, invalidating this assumption. For a laser system where this is a concern, a network architecture where the initial spectrum is given as an input to the neural network can be used instead of including it as an assumption of the neural network. An example of such a network is the neural networks used for the simulated few-cycle sections of this manuscript, whic has been shown to able to account for a wide range of spectral profiles and pulse energies.OPCPA laser systems have enabled high power large bandwidth laser systems in the mid-infrared (MIR) wavelength regions. In these regions traditional silicon-based detectors no longer work, meaning forcing a reliance on more expensive InGaAs detectors. While this technique was demonstrated for wavelengths from a Ti:Sapphire laser system, the technique could be applied to the wavelengths in the MIR region. Scaling to other wavelengths would only require knowledge of material properties of the nonlinear media used along with retraining of the neural network. Being able to reconstruct the phase from only two spectral measurements enables the phase information to be readily obtainable from a field auto-correlator, meaning phase information could be reconstructed from a single power diode. Field auto-correlators are already commonly used for techniques such as Fourier transform infrared spectroscopy, making this technique simple to implement into such systems.The ability for self-phase modulation to spectrally broaden a pulse is utilized in many pulse compression techniques to generate a few-cycle laser pulses. Modifying the neural network to predict the spectral phase after self-phase modulation would enable the reconstruction of the temporal profile after pulse compression. With this modification, the same system could be used to generate and characterize a few-cycle laser pulse.The presented pulse measurement technique shows a general technique of measuring the intensity profile of a laser pulse in single-shot applications using inexpensive and readily available components, only requiring a piece of glass and a spectrometer. By using a fully connected neural network phase reconstruction based on the generalized nonlinear Schrödinger equation is able to be done, which includes material dispersion, delayed Raman effect, and self-steepening. Since material dispersion is included in the modeling this technique is able to be used to characterize broadband laser pulses in real-time. With minor modifications, this technique enables measuring the fluence and spectral phase of the pulse across the wavefront, enabling measuring variance in the temporal profile across the beam for large aperture beams that are common at facilities such as ZEUS.Training of the neural network occurs by running many examples of the problem through the neural network and updating the parameters of the network to find the optimum weights for predicting the desired target values. Once trained, neural networks can be used to predicted the phase of pulses similar to the training data. While neural networks can excel in extract information from data similar to what it was trained on, prediction on data vastly different from the training data can drastically hurt the performance of the network. Due to the quasi-infinite range of laser pulses possible, generating a single generalized network is non-trivial, as one would be required to ensure all possible laser spectrum and phases are present in the training data. While a single neural network may not be generalize to a complete range of spectral bandwidths, the method of generating a neural neural network still holds.In this work, we focus on two systems of interest and train neural networks separately for each system. One system is purely simulation based and simulates a wide range of broadband spectral shapes and initial phases. The second system uses simulations to model an experimental Ti:Sapphire laser. For the experimental system, due to the stability of the laser spectra the initial spectrum is able to be assumed to reduce the number of input features into the neural network.28. PyNLO numerically models material dispersion, self-steepening, and the delayed Raman response. The central frequency of the simulations was set to 374.0 THz, which is the central frequency of Ti:Sapphire lasers. The material properties were based on the values for fused silica, which is a common optical glass that is able to be obtained with high optical quality and is well characterized. The material dispersion was modeled by using the second, third, and fourth order expansion curves of the Sellmeier equation for fused silica, which are 36.1 fsTo generate sufficiently large data sets for training neural networks, numerical simulations of a wide range of ultrafast laser pulses with varying phases were produced. The nonlinear propagation was modeled with PyNLO, a python based 1-dimensional GNLSE solver using the split-step Fourier methodTo ensure a representative set of phases and spectrum were present, the training data was generated from a randomly generated vector. The vector has a Gaussian envelope applied in the temporal and then spectral domains, generating a pulse with a random spectrum and spectral phase. The temporal envelope used to generate the data is 30 fs. The spectral envelope used has a width of 40 THz centered on 374 THz.29. The resulting simulations had a B-integral ranging from  0.65 to  4.25, with an average B-integral of 2.23.After the temporal and spectral envelopes have been applied, the peak fluence is set by randomly sampling from a uniform distribution spanning the range of 16.2 mJ cmThe initial phase and initial spectrum were interpolated to 40 linearly space bins spanning a frequency range of 120 THz centered on 374 THz. The SPM spectrum was interpolated onto a linearly spaced vector with 100 bins and spanning the frequency range of 300 THz centered on 374 THz. After interpolation the area under the curve (i.e. the energy) for both the initial and SPM spectrum were normalized to unity to ensure the neural network is learning from the relative shape changes of the spectra. The initial and SPM spectra are then combined to create the feature vector that the neural network is trained on. A total of 1,830,000 samples were generated for the training and validation sets, with an additional 20,000 samples generated for the test set. An example pulse generated using this method is shown in Fig. The neural network models were trained on a dataset of 1,830,000 generated samples, initially with The input features to the neural network are the interpolated initial and SPM spectra, with a total of 140 features. The individual features of the input tend to be right-skewed, with a majority of events taking smaller scalar values and a small minority occurring in higher regions. In order to correct this we first take the log of the input features and then normalize them, by subtracting the mean and dividing by the standard deviation. The target variables are also normalized in the same fashion. Transforming the data through this process ensures all features are on the same scale.Separate networks were trained to reconstruct the initial phase and the fluence of the pulse. The phase neural networks were trained with the targets being the initial phase of the pulse interpolated to the same 40 length frequency grid as the initial spectrum. The fluence networks were trained with the only target being the fluence of the initial pulse. All networks were implemented in Keras with a Tensorflow backend and trained on NVIDIA TITAN X GPUs.All training samples were augmented with small amounts of Gaussian noise, 31.Building and training neural networks requires one to set many values, called hyperparameters, a priori. Hyperparameters include the number of layers, the number of nodes per layer, the kinds of activation functions, the learning rates, and the dropout rates. Dropout is a randomization procedure used during training that turns off different connections in the neural network, forcing the network to learn a more general solution which also helps avoid overfitting32, a Python software library which is compatible with Keras and other modern deep learning libraries, and has been used to effectively optimize neural networks in various scientific applications (e.g.34).In the experiments, the hyperparameters were optimized using SHERPALeveraging SHERPA, a large suite of 500 models were explored using a Bayesian optimization algorithm. The Bayesian search has the advantage of learning a distribution over the hyperparameters of the network architecture, in relation to the task to be optimized. By employing this procedure we are able to evaluate a large space of possible models and test many configurations. To demonstrate the efficacy of the hyperparameter search, we compare the resulting model against an initially proposed baseline model. The baseline architecture is shown in Table In total, 500 network architectures were explored with differing hyperparameters for both the phase and fluence neural networks. The final architectures from the hyper-parameter search are shown in Table Following the hyperparameter search, the best performing phase and fluence networks were evaluated using tenfold cross validation. During tenfold cross validation the data is randomly partitioned into ten distinct folds. Each network is then trained on nine of the folds and tested on the remaining one, and the process is repeated ten times. The mean and the standard deviation of the performance (error bars) can then be computed over the ten experiments. The results from tenfold cross validation are presented in Fig. The simulations used to generated the training data were based on the methods discussed in the Broadband Simulated Pulse Reconstruction portion of the Methods section. Due to the dominant phase terms of the pulse being group delay dispersion (GDD) and third-order dispersion (TOD), the phase was modeled primarily as a Taylor series expansion, with random GDD, TOD, and forth-order (FOD) phase terms. The dispersion coefficient were generated from a normal distribution with the standard deviation of 10A neural network was then trained on 432 thousand pulses total using a 80/20 split for the training and validation data sets. Due to the assumption of a single initial laser spectrum, the features used in training only needed to be based on the SPM spectrum. Both the broadened spectrum and the initial phase were interpolated to the range from 330 THz to 418 THz binned 100 linearly spaced bins. The network consisted of 8 layers with a width of 200 and was trained using a Gaussian noise of 0.1, learning rate off 0.001, drop out rate of 0.1 over 200 epochs.Supplementary Information.Supplementary Video S1.


Migration of an encapsulated leukemia HL60 cell through sudden contractions in a capillary tube is investigated. An HL60 cell is initially encapsulated in a viscoelastic shell fluid. As the cell-laden droplet moves through the sudden contraction, shear stresses are experienced around the cell. These stresses along with the interfacial force and geometrical effects cause mechanical deformation which may result in cell death. A parametric study is done to investigate the effects of shell fluid relaxation time, encapsulating droplet size and contraction geometries on cell mechanical deformation. It is found that a large encapsulating droplet with a high relaxation time will undergo low cell mechanical deformation. In addition, the deformation is enhanced for capillary tubes with narrow and long contraction. This study can be useful to characterize cell deformation in constricted microcapillaries and to improve cell viability in bio-microfluidics. Microfluidics has emerged as a very stable and robust platform for droplet-based bio-microfludic systems and biomechanical applications such as cell deposition , cell enResearchers have performed several experiments and used different methods to measure localized or whole-cell mechanical deformation under different conditions . These tIn bio-microfluidics, the geometry of the microchannel and the presence of different obstacles within the microcapillary may affect the flow pattern and change cell mechanical characteristics. These geometrical constraints enhance the shear stresses around the cell and cause cell deformation, which may lead to cell death. Thus, understanding the effects of microchannel geometry on the deformation of cell-laden droplets is of interest and provides researchers an insight into the physics of cell migration through microcapillaries and in the design of an appropriate microfluidic device for single-cell studies. Bento et al.  measuredRheology of the fluid is another important property of the fluid in characterizing flow behavior, especially in biological flows where fluids exhibit non-linear viscoelastic behavior. In these types of viscoelastic fluids, the mechanical stress has a non-linear relationship with the strain rate. The large polymeric molecules in viscoelastic fluids often show elastic behavior due to the stretching and coiling of the polymeric chains, which enhance the complexity of the flow and significantly enrich the flow behavior . PolymerThe migration of an encapsulated HL60 cell through sudden contraction of a microfluidic channel is shown schematically in ectively . Both ceThe HL60 cell is shown to be more viable with less deformability compared to other leukemia cell types such as Neutrophil and Jurkat . Thus, t3,25,26. T3. Our pol103 kg/m3. The shesibility . The int=52 mN/m .p, u, and A, L, I, and A) represents the deformation of polymer molecules within the viscoelastic fluids in different directions, and is used to calculate the viscoelastic extra tensor  and (2). Microchannel walls are treated with no-slip boundary conditions, and a symmetry boundary condition is used at the centerline. Constant pressure is set as the boundary condition at the outlet. A uniform Cartesian grid is employed, and a grid convergence study is done to determine the minimum grid size required to reduce the spatial discretization error below a threshold value. It is found that a computational grid containing 128 cells in the radial direction and 640 cells in the axial direction are sufficient to reduce the spatial error for all flow configurations. Therefore, this mesh is used in all the results presented in this paper.The numerical method is validated with experiments of Zhang  on drop periment  after thperiment  are showThe migration of an encapsulated cell can be described in three different stages: (I) before contraction, (II) within the contraction, and (III) after the contraction . The celRelaxation time, defined as the time taken by the polymers in the viscoelastic fluid to return from the deformed state to its initial equilibrium state, is an important property of viscoelastic fluids which can influence the flow structure in bio-microfluidics. The fluids with high relaxation times get relaxed and returned to their initial state at a longer pace. Detailed simulations are performed to investigate the effects of shell fluid’s relaxation time on the deformation of an encapsulated HL60 cell and the dynamics of the encapsulating droplet. Shape evolution of both encapsulating droplet and the HL60 cell at different locations of the microchannel are shown for two different relaxation times A and The deformation of the cell membrane is quantified by the relative cell membrane area  and the narrowness of the cross-section in the constriction area (R). The effects of these two properties on deformation are studied for an encapsulated HL60 cell. The constriction in the middle of the channel dictates the flow structure and can significantly change the shape of the encapsulating droplet and deformation of the cell. This constriction restricts encapsulating droplet movement as it migrates downstream the channel. The two important geometrical properties which characterize the constricted microchannel are the length of the constriction (Another geometry characteristic that has a big influence on cell deformation is the constriction length. Simulations show that for a short constriction, the cell is under shear stress for a short period of time and therefore this small shear stress cannot threaten its life. As a result, the cell undergoes a low mechanical deformation a, which  channel b. FigureA parametric study has been performed to investigate the influence of shell fluid relaxation time, the encapsulating droplet size and contraction geometry on the deformation of an encapsulated HL60 cell passing through sudden contractions within a microcapillary. A three-phase front-tracking method is used to simulate the flow dynamics of encapsulated cells in the microchannel and to investigate the deformation of an encapsulated HL60 cell during its passage through the contractions of a microcapillary.Shell fluid rheology is found to help reduce cell deformation. In particular, in the case of an HL60 cell covered with a high polymeric shell fluid, the cell undergoes low mechanical stresses which help it remain its spherical shape. Membrane area of an HL60 cell covered with a low relaxation time shell fluid (The effects of two main geometrical characteristics including the constriction narrowness and length were also studied for an HL60 cell encapsulated by a viscoelastic shell fluid. The simulations suggested that short constrictions cannot have a significant influence on cell mechanical properties and the cell remains almost spherical. In the case of a long constriction, the cell experiences high shear stresses for a long time which affects its membrane shape. The narrow constriction also significantly impact cell dynamics, which threatens the cell viability.In other words, to have a minimum cell deformation, the constriction size needs to be short and wide enough for the cell to freely pass through the microchannel. A small encapsulating droplet with high relaxation time helps reduce the deformation rate.


The prediction of system parameters is important for understanding the dynamic behavior of composite structures or selecting the configuration of laminated carbon in carbon-based composite (CBC) structures. The dynamic nature of CBC structures allows the representation of system parameters as modal parameters in the frequency domain, where all modal parameters depend on the carbon fiber orientations. In this study, the variation in the system parameters of a carbon fiber was derived from equivalent modal parameters, and the system parameters at a certain carbon fiber orientation were predicted using the modal information at the reference carbon fiber orientation only and a representative curve-fitted function. The target CBC structure was selected as a simple rectangular structure with five different carbon fiber orientations, and the modal parameters were formulated based on a previous study for all modes. Second-order curve-fitted polynomial functions were derived for all possible cases, and representative curve-fitting functions were derived by averaging the polynomial coefficients. The two system parameters were successfully predicted using the representative curve-fitting function and the modal information at only the reference carbon fiber orientation, and the feasibility of parameter prediction was discussed based on an analysis of the error between the measured and predicted parameters. Carbon-based composite (CBC) materials are widely applied in various industries owing to their high strength-to-weight ratio as well as the development of mass producibility ,2,3. SevMechanical systems can be modeled with mechanical components, i.e., mass, damper, and spring, if they are a linear system ,21. The Here,  and (3) ,31.(2)cHere, ith mode (ith mode are expressed in Equations (8) and (9), respectively.Here, ith mode, respectively; ith mode, respectively. Here, The characteristics of the CBC structure are associated closely with the carbon fiber orientation, and the system parameters shown in Equations (8) and (9) should be addressed by considering the anisotropic mechanical properties. If the carbon fibers in the CBC structure are assumed to be aligned at a certain angle (The system parameters in Equations (10) and (11) can be formulated using a combination of coefficients for both the carbon fiber and polymer matrix. In a published paper , the senith mode. Here, The equivalent system parameters can be predicted using Equations (12) and (13). If the system parameters at us study , as follThe equivalent stiffness coefficient is a series combination of the stiffness coefficients of the carbon fiber and polymer matrix (see Equation (5)), and it can be assumed that the stiffness coefficient is maximum at the reference angle  and (15), respectively, can be approximately expressed by the curve-fitted function from all the carbon fiber orientation sets and the estimated fitting curves were defined as The increase or decrease in the equivalent system parameters are not always started from the reference angle ortional . The ratAs shown in Equations (12) and (15), the equivalent system parameters, the viscous damping coefficient, and stiffness coefficient of the CBC structure can be directly predicted from the curve-fitting functions CBC specimens were prepared based on five different angles of the carbon fiber, namely, The modal parameters of the CBC specimens were identified using the experimental impact test technique. The frequency response functions (FRFs) between the impact point and response locations were obtained using an impact hammer  and uniaxial accelerometers . The impact force was assigned at #4, and the response accelerations were measured at #1–#7, as shown in A frequency range between 10 (Hz) and 4096 (Hz) was selected, and ten times the average of the measured FRFs was used to eliminate noise in the FRFs of the CBC specimens. The constraint condition of the CBC specimens was set as free–free with rubber bands, and all data were recorded using an eight-channel data-acquisition device (Test.Lab/Siemens/Germany) ,31. The The system parameters were first predicted by selecting ines see  in the mThe nodal lines showed the same direction in all three bending modes; however, two directional orthogonal nodal lines were observed in two torsional modes, as shown in Using the selected angle sequence for predicting the ratio of the system parameters, viscous damping coefficient, and stiffness coefficient, the rearranged variation of the ratios can be illustrated as shown in The curve-fitted functions, The system parameters were predicted directly using the corresponding curve-fitted polynomial functions  did not significantly affect the prediction of the system parameters. In other words, if all measured parameter sets for the possible carbon fiber orientations are well prepared in the CBC structure, then the prediction of the system parameters can be regarded as redundant. However, the system parameters calculated from the representative fitted function should be considered when predicting the system parameters at any carbon fiber orientation. The representative functions see  are the If the representative fitted functions (The limitations of the proposed method can be briefly discussed from a practical perspective. The first is the possibility of a representative curved-fitting function for the system parameters. The representative curve-fitted function is valid owing to a similar variation trend in the system parameters for the five modes of interest. If the mode number is extended to a higher frequency range, then a similar variation in the system parameters cannot be guaranteed. If the variation in the system parameter shows a completely different trend at higher modes, then the simple averaged approximated functions cannot be represented at the appropriate resonance frequency. The second limitation is that the representative function depends significantly on the configuration of the CBC structure. In practice, the CBC structure may be designed using a configuration other from the current simple rectangular shape such that the resonance frequencies and the corresponding mode shapes may be altered from the current results. Subsequently, the selection of The prediction of the system parameters for a CBC structure was formulated using curve-fitted polynomial functions as well as modal information at the reference carbon fiber orientation only. For the prepared simple rectangular CBC structure with five different carbon fiber orientations, all modal parameters were taken from previous reports, and the variation in the system parameters was expressed using second-order curve-fitted polynomial functions. Because the two system parameters, namely, the structural stiffness and the modal damping coefficient, were rearranged based on the parameter value order in each mode, the variation trend for all modes of interest decreased as the rearranged carbon fiber orientation


The increasing demand for reconstructions of concrete structures and the wide availability on the market of surface protective products and systems could lead to misunderstandings in the decision of the most effective solution. Surface protectors have become increasingly widespread in recent years in concrete restoration interventions thanks to their properties: they are able to protect the substrate from aggressive agents and consequently extend the useful life of the structures. The aim of this article is first of all to present the surface protective treatments available on the market, outlining their strengths and weaknesses. Subsequently, a characterization of seven different commercial coatings for reinforced-concrete structures is provided, taking into account chemical nature, fields of use and effectiveness, both in terms of physic and elastic performance and resistance to aggressive agents that undermine the durability of the treated concrete elements. In recent years, the concept of sustainability has gained ground in the construction sector, and a particular attention has been paid to the durability of the structures ,2. In faThe penetration of aggressive external agents such as carbon dioxide or chlorides is what is mainly responsible for the deterioration of reinforced-concrete structures ,6,7. ForThe aim of this paper is to outline the main protective surface treatments by analyzing their characteristics and to define, through a careful analysis of the properties, the selection criteria for each protective product or system in relation to the field of use and the expected performance. (a)Polymeric and cementitious coatings(b)Hydrophobic impregnations(c)Pore-blocking treatmentsTo date, the surface treatments can be divided in three different macrocategories, in accordance with European Standard EN 1504-2 :(a)PolymThe surface coatings are defined as continuous films able to act as a physical obstacle to limit the penetration of aggressive substances into concrete elements. There are basically two different types of surface coatings: the polymer-based and cementitious protective coatings. The first ones promote the formation of a dense polymeric film with a thickness of about 0.1–1 mm on the concrete surface while the second ones realize a low-permeability cement-based layer with a thickness of about 2–10 mm. Traditional polymeric coatings have always been widely used in the construction sector. The main categories of polymeric coatings are epoxy resins ,19,20, a(a)The use of polymers strongly reduces the microcracks formation in the coatings, reducing the penetration of aggressive substances;(b)The pore structure of polymer-containing cementitious mortars is finer than that of traditional cement-based composites, further improving the impermeability of coatings;(c)The low-elastic modulus of the cementitious coatings enhances their crack-bridging capability, ensuring high performances even in the presence of cracked concrete.Cementitious coatings are cement-based mortars manufactured with a huge number of polymers  able to improve the mechanical properties , the chemical resistance and the impermeability of cement mortars. The protective effects of cementitious coatings on concrete structures can be summarized as follow:Emerging solutions for concrete protection based on alkali-activated materials and geopolymers show rapid setting times, excellent bond strength and durability, low water and chloride permeability and high freeze-thaw resistances. Moreover, these new coatings also possess an electrolytic conductivity, which can allow them to act as a protective layer for concrete and skin sensor for structural health monitoring. To date, this kind of coating is still in the experimental phase , but it is extremely promising and close to being widely used on real reinforced-concrete structures. Hydrophobic impregnations perform their task of avoiding the penetration of aggressive substances in liquid phase into the concrete by increasing the contact angle (the surface is considered hydrophobic when the contact angle is greater than 90° ) withoutImpregnating coatings  are able to block the capillary pores of the concrete and consequently improve the impermeability of buildings ,29. GeneThe choice of the ideal surface treatment strongly depends on the purpose of the work: protection against the ingress of aggressive substances into concrete and protection against the physicomechanical actions that occur during the service life of a structure or both. The following are the main properties of the surface treatments commercially available for concrete protection.First of all, it must be stated that the mechanical performance of the substrate cannot be improved in relation to the performance of the treatment, since the latter is not able to modify the porosity of the concrete; however, it is able to prevent the deterioration of concrete performance. In this regard, the compressive strength is not a noteworthy parameter in the examination of the treatments. A more important parameter for the surface treatments is the bonding strength; good adhesion to the substrate ensures its long-term performance and protection of concrete . ExperimAnother important indicator of the effectiveness of a treatment is the abrasion resistance, since it is able to evaluate of the service life of surface-treated concrete under repetitive traffic loadings ,39. ManyThe crack-bridging ability is the property of the coatings to cover cracks formed in the concrete substrate , keepingThe resistance of the treatment against possible alterations promoted by the environment in which they are located or by the tasks they must perform is very important for the success of a protection intervention. Research showed that temperature and ultraviolet radiation highly affected the efficiency of surface treatments. Vries  studied In most cases, the success of the protection is related to the durability of the concrete-surface treatment systems ,49. SincIn a concrete structure, chlorides can penetrate into the cement matrix and initiate degradation phenomena due to diffusion under the influence of a concentration gradient, migration in an electrical field or absorption due to a capillary action . In most2, calcium-silicate-hydrate (C-S-H) and CO2 to form CaCO3, silica-rich C-S-H and amorphous silica gel [2 and the reactivity of CO2 with the concrete. They depend on the pore system of hardened concrete and the exposure condition [Especially in urban or industrialized areas, the problem of carbon dioxide penetration is relevant. Carbonation is a chemical reaction between Ca(OH)lica gel ; it is alica gel ,61. The ondition . Many stondition ,64,65.Although the treatments cannot fulfil the functions performed by the air-entraining additives against the phenomena related to freezing and thawing cycles, they can contribute to making the concrete substrate more resistant to cold climate by preventing the critical moisture content from being reached. Both the acrylic treatments and the polymer-modified cementitious coatings have a good behavior against the destructive phenomena induced by the freezing and thawing cycles; by contrast, the behavior of silane-based coatings has not yet been well defined and is debated by the scientific community: Basheer et al.  stated tIn order to provide a complete analysis able of evaluating some of the aspects previously described, 7 different commercial treatments  have beeThe pot life is defined as the maximum time during which a coating material supplied as separate components (product A and product B or powder and water) should be used after the components have been mixed together. This parameter was evaluated at room temperature by taking into account the longest period of time which the consistency met the requirements related to the laying of the coating. Results reported in The adhesion tests were carried out on samples before and after thermal cycles in presence of deicing salts. In particular, after the curing of concrete and the application of the protective coating, a core drilling is carried out that affects both the substrate and the protective one. Then, a plug is applied, and finally, at a speed of 0.05 MPa/s, the tear test is performed on one half of samples to evaluate the adhesion and the failure type. On the other hand, the other specimens were subjected to 50 thermal cycles  before the adhesion test. The resistance to carbon dioxide diffusion is an important parameter in the choice of a coating. To avoid any design errors or the impossibility of carrying out the restoration work in a workman-like manner, it is possible to integrate the missing concrete cover thickness by choosing a suitable protective. In light of this, EN 1504-2  sets a m2 greater than 300 m), epoxy-bitumen and acrylic elastomeric-based treatments have a resistance to CO2 penetration of just over 50 m. At the same time, in this study, the E and PMC are characterized by an Sd CO2 close to 100 m of equivalent air thickness. On the other hand, authors want to highlight that unpublished experimental results on polymer-modified cementitious coatings demonstrated that the Sd CO2 is strongly influenced by the type and dosage of polymer used in manufacturing the coating. In particular, it is possible to obtain carbon dioxide resistances up to those of polyurethane-based coatings. The results, first of all, highlight that all the protectives tested comply with the limits prescribed by EN 1504-2  for the In terms of water vapor permeability , the bes2h0.5. Results reported in 2h0.5), while A and AE treatments evidence a capillary absorption coefficient close to the standard limits of 0.1 kg/m2h0.5. By contrast, the waterproofing ability of polymer-modified cementitious coatings are lower with respect to the epoxy-based treatments, even if it is necessary to demonstrate that this property is also strongly affected by the type and dosage of polymer used in manufacturing the coating. In particular, a proper composition allows one to reach extremely low capillary absorption coefficients.Aggressive substances for the cement matrix are often transported by water by capillary absorption. For this reason, the EN 1504-2  standardThe ability to cover the cracks that occur in the substrate to avoid the penetration of aggressive agents turns out to be a fundamental parameter in the choice of a protective. The graph reported in In addition to the static crack-bridging ability, the capability of the coatings to take up the elongation resulting from the periodic movement of the crack sides was evaluated for different crack widths. After 1000 cycles with a maximum crack width of 500 μm, AE and P appear to be sound, while the A and PMC systems limit their dynamic ability to cover cracks of about 150 μm . On the In relation to the weight loss connected to a forced abrasion (EN 5470-1 ) listed The evaluation of the resistance to make contact with chemical agents is an important parameter closely related to the intended applications of the coating; due to these peculiarities, some protective products have not been tested because their low resistance against chemicals is commonly known . Table 6The analysis of the parameter of resistance to negative hydrostatic pressure becomes of primary importance for waterproofing underground structures such as retaining walls. For this purpose, not all the tested coatings are suitable; therefore, the test in compliance with EN 8298-8  was condThe fire resistance is a fundamental properties of building materials, and it was evaluated by determining the ignitability of products by direct flame impingement and the production of smoke and flaming droplets. Experimental results reported in All the samples were exposed to wetting and drying cycles and UV radiation in accordance with EN 1062-11  to deter(a)2 penetration and a proper water absorption; they usually do not have excellent crack-bridging capabilities and, due to their conformation, are not suitable for withstanding mechanical stress or contact with aggressive chemical agents. They can be used to protect concrete in structures not fully immersed in water or in contact with aggressive chemicals; finally, elastomeric acrylic coatings embrace a slightly wider market share given the crack-bridging properties that make them suitable for applications where an elasticity of the coating is required, such as the protection of residential buildings.Water-based acrylic and water-based acrylic elastomeric coatings are the best systems when it is necessary to maintain the original texture and/or a specific aesthetic finish is desired. Acrylics have an excellent resistance to CO(b)Polyurethane and epoxy resin-based coatings, whether they are epoxy, epoxy-polyurethane or epoxy-bituminous can be used to protect concrete structures subject to continuous contact with water and aggressive chemicals. The main applications of this family of coatings are the protection a of reinforced-concrete structures subjected to severe environmental aggressions such as sewage collectors, purification plants (settling and aeration tanks), and bridge decks. The limits of epoxy protectives are the high resistance to vapor diffusion which could compromise the durability of coatings applied on wet substrates and the reduced crack-bridging capability that preclude their use on cracked concretes. The EB, EP and P, on the contrary, shows good crack-bridging ability, which makes them suitable for applications where a good ability to cover cracks is required, such as waterproofing of canals or containment tanks.(c)Polymer-modified cementitious coatings have excellent resistance to steam penetration and crack-bridging ability, as well as a high resistance to hydraulic pressure. They can be applied in the protection of both new concrete structures or during restoration works. The high waterproofing properties make these products suitable for the protection of reinforced-concrete structures such as tanks, fountains, wells, flower boxes or in any case of structures in continuous contact with water without extremely aggressive chemical agents. Their elasticity makes these protective products suitable for under-tile applications typical of the restoration works of projecting structures such as terraces and balconies. Therefore, it is necessary to highlight that the main properties of these coatings are strongly related to the type and dosage of polymer used during their manufacturing.In light of the scientific literature review and tests performed on protective products, the following conclusions can be drawn: