Although some extended studies about the short-term behavior of NSM FRP strengthened beams have been carried out, there is a lack of knowledge about the behavior of this kind of strengthening under sustained loads and high service temperatures. Electromechanical impedance method formulated from measurements obtained from PZT patches gives the ability for monitoring the performance and changes experienced by these strengthened beams at a local level, which is a key aspect considering its possible premature debonding failure modes. This paper presents an experimental testing program aimed at investigating the long-term performance of a concrete beam strengthened with a NSM CFRP laminate. Long term performance under different levels of sustained loading and temperature conditions is correlated with EMI signatures processed using Linear Mixed-effects models. These models are very powerful to process datasets that have a multilevel or hierarchical structure as those yielded by our tests. Results have demonstrated the potential of these techniques as health monitoring methodology under different conditions in an especially complex problem such as NSM-FRP strengthened concrete structures. Strengthening of reinforced concrete (RC) beams using near surface mounted (NSM) fiber-reinforced polymer (FRP) strips and bars has gained greater interest and increased field applications in recent years. In comparison with the more extended methodology of FRP strengthening based on externally bonded reinforcement (EBR), NSM FRP technique presents several advantages such as better anchorage capacity and better protection from accidental damage as those due to, for instance, vandalism and environmental effects ,2,3. AddExtensive studies about the short-term behaviour of NSM FRP strengthened beams have been carried out ,5. HowevThe application of smart piezoelectric transducers (PZT) for the structural health monitoring (SHM) of FRP composite RC structures is a relatively innovative method since some studies have been already developed in this area ,12,13,14There are mainly two main detection approaches based on the use of PZTs, wave propagation and electromechanical impedance (EMI). In wave propagation , the strThis paper aims to contribute to the study of the behavior of RC beams strengthened with NSM FRP strips under the combined action of sustained loading and temperature variations which, to the knowledge of the author, has not been carried out up to date. For it, an experimental testing program aimed at investigating the long-term performance of a concrete beam strengthened with a NSM CFRP laminate is performed. The program has been implemented specifically to address the performance of NSM FRP strengthening systems under different sustained load levels and at elevated temperatures. EMI method will be used for monitoring the performance and changes of this strengthened beam. The EMI signatures are recorded using FRP reinforcement and concrete surface bonded PZT sensors. However, the correlation of the long term performance under different levels of sustained loading and temperature conditions with EMI signatures is not easy, considering the complexity of the captured experimental data and the coupling between mechanical and temperature effects in the impedance measurements. A suitable analysis tool should be used to get a successful interpretation of the data. Linear Mixed-effects Models (LMMs) ,19 as a s) and that of PZT sensor (Za) can induce an electrical impedance of PZT, which is the reciprocal of the admittance (Y). EMI technique was firstly developed by Liang et al. [In the EMI technique, the same PZT is used as actuator and sensor in such a way that the structure is excited with the sensor using the converse piezoelectric effect and the mechanical response is captured by the same PZT using the direct piezoelectric effect. The mechanical impedance of the host structure , CCD (correlation coefficient deviation), and MAPD (mean absolute percentage deviation) can be used.To characterize the behavior of the NSM-FRP strengthening system under sustained loading and different conditions of temperature, a reinforced concrete specimen strengthened with this system was used. The beam was loaded with a four-point bending test.c = 30 MPa, Ec = 26 GPa, fct = 3 MPa; (b) Steel: fy = 500 MPa, Es = 210 GPa; (c) CFRP: ffu = 2500 MPa, Ef = 170 GPa.The material properties of the concrete, the reinforcement steel, and the FRP were the following: (a) Concrete: fThe test set-up and instrumentation are shown in For the EMI tests, eight PZT patches  lower than 2 × 10−16 was computed, which indicates a significant departure from the normal distribution in the residuals of the model.Normal distribution of the residuals  was also verified by the Shapiro–Wilk test when all results from the experimental tests were combined. A Non-normality is also confirmed by the quantile-quantile plot (Q-Q plot) . The strp-values for all tests considering all sensors. The interpretation of p-value is similar to the Shapiro-Wilk test. Results demonstrate that, except for test 32, our data follow a log-normal distribution and, therefore, the LMM analysis will be made using logRMSD. As a sample and for comparison with Because of the non-normality of the residuals, we will work with a log-transform of the response to check if this condition is improved. Kolmogorov-Smirnov test will allow to know if the data have a significant departure from log-normality. As previously commented, we can perform analyses across all measurements. However, results from the analysis could vary between sensors since different types of sensors and different locations  were used in the test. Therefore, the intersensor variability should be taken into account when analyzing the data to avoid inaccurate results. For the analysis, four groups of sensors were considered according to p-value of the residuals for the four groups of sensors as well as for all sensors considered jointly.Normal distribution of the four groups was checked with the Lilliefors-corrected Kolmogorov-Smirnov test. This test was used instead of Shapiro–Wilk test because this last became enough conservative when applied in this case. Considering that the normality of residuals assumption is the one that is least important for LMM analysis, since these models are robust even in the absence of normality in the data, Kolmogorov test is considered as valid. Histograms and Q-Q plots confirm these results .Finally, the linearity and homocedasticity of the logRMSD function are checked such as required by the model. All statistical calculations were carried out using the statistical software “R” version 1.3.1093 . Mixed lLMMs enable to estimate fixed effects while properly taking into account the random variance associated with different participants, in this case the different sensors and experimental tests.p-values. Boxplots have been constructed for all sensors and for each of the groups of sensors In general, the observed pattern for all groups of sensors according to the pairwise analysis across the tests is very similar in agreement with the boxplots, except for some differences which will be commented next;(b)p-values appears when heating of the specimen is performed. That high contrast is also shown when the specimen returns to the environmental temperature once its heating is interrupted. Therefore, high temperature variations are perfectly identified with p-values.It is clear that the highest contrast in the (c)For the first tests which were performed under a sustained load of 8 kN (tests 3 to 10), there is not a significant difference between consecutive tests except for test 10 when sensors of groups 1, 2 and 4 are considered. This test was the longest test of all those tests carried out under 8 kN and this same conclusion was derived from the boxplots.(d)When a new sustained load test of 8 kN is performed after the heating and subsequent cooling of the beam, the groups of sensors fail to detect a significant difference in RMSD coefficient, except for group 2 between 13 and 14.(e)For the sustained load tests under 9.3 kN, no significant difference was detected. The same occurs for the sustained tests under 13.7 kN. In this case, only a clear variation is identified for those sensors bonded to FRP close to the midspan when the beam is initially loaded up to 13.7 kN.(f)p-value in comparison with other previous values, except those due to heating/cooling may be a symptom of severe damage in the structure as the experimental tests demonstrated.The last load increment until reaching 19.6 kN shows a clear deterioration of the specimen clearly identified by the internal sensors. The much lower From The available research about the behavior of concrete beams strengthened with NSM-FRP systems when subjected to different levels of sustained loads and at ambient and elevated temperature is very limited or practically inexistent. However, its prediction is essential taking into account the sudden and brittle nature of the most common failure modes for this type of strengthening. Because of it, EMI technique based on high frequency impedance spectra has been used. This technique is also particularly sensitive to temperature variations. To give a suitable interpretation of the experimental impedance measurements, strain and temperature FBG sensors together with LMM analysis model have been used.For the study, an extensive experimental programme has been carried out during more than 1.5 years on a concrete beam strengthened with a NSM-FRP laminate. In the work, three main difficulties have been addressed. On the one hand, the application of high levels of load which will introduce mechanical damage into the structure. On the other hand, the application of a load sustained along the time and the possible induced creep strains; and, finally, the consideration of elevated temperature variations which will influence on the performance of the strengthened beam. The complexity of these three aspects together with the hierarchical structure of the monitored data along the experimental programme using PZT based EMI method in different regions require of some methodologies able to address them suitably.Linear mixed models provide an appropriate framework for the analysis of data which are likely to be correlated across the variability of an experimental programme as those derived from the monitoring of a structure along time and under different conditions. By using a linear mixed model, accounting for repeated measures among PZT sensors, as well as sensor level variance, it was found how the mechanical and thermal effects affected on RMSD metrics. Variations of temperature experienced by the beam are filtered from those variations due to mechanical damage. Additionally, the mechanical performance of the beam under sustained load is also collected by LMMs.LMMs are becoming increasingly popular as a data analysis method in other scientific areas and researchers are encouraged to apply them rather than other analysis tools for structural monitoring purposes since they allow for the consideration of different phenomena in a direct and joint way giving a global vision about the structural performance but considering simultaneously the individuality of the sensors as well as of the different monitored stages.p-values derived from a pairwise analysis can be assumed as quantitative indicators of the severity of the experienced changes.Usually, in the proposed impedance-based methods to separate temperature variations from structural damage, temperature compensation techniques based on the use as a pattern of impedance measurements obtained from the structure in various thermal levels are applied. These techniques are applicable in a lab framework but, however, its application on real structures is more limited because of the lack of a reference pattern to do the compensation. In the proposed method the analysis is performed in a direct way using as a support FBG measurements and LLMs. Although this methodology is mainly qualitative, The lack of published works about this topic in the literature make difficult its comparison with other proposed approaches. Because of it, one of the topics to be covered in the near future should be the comparison of the proposed procedure with machine learning approaches since these methods are able to detect hidden patterns from monitored data. In the same way, another possible topic to be addressed in the far future is about the study of the performance of this type of structures under the effect of high loading rates, which is a still hardly explored topic.


Dynamic causal modeling (DCM) is a widely used tool to estimate the effective connectivity of specified models of a brain network. Finding the model explaining measured data is one of the most important outstanding problems in Bayesian modeling. Using heuristic model search algorithms enables us to find an optimal model without having to define a model set a priori. However, the development of such methods is cumbersome in the case of large model-spaces. We aimed to utilize commonly used graph theoretical search algorithms for DCM to create a framework for characterizing them, and to investigate relevance of such methods for single-subject and group-level studies. Because of the enormous computational demand of DCM calculations, we separated the model estimation procedure from the search algorithm by providing a database containing the parameters of all models in a full model-space. For test data a publicly available fMRI dataset of 60 subjects was used. First, we reimplemented the deterministic bilinear DCM algorithm in the ReDCM R package, increasing computational speed during model estimation. Then, three network search algorithms have been adapted for DCM, and we demonstrated how modifications to these methods, based on DCM posterior parameter estimates, can enhance search performance. Comparison of the results are based on model evidence, structural similarities and the number of model estimations needed during search. An analytical approach using Bayesian model reduction (BMR) for efficient network discovery is already available for DCM. Comparing model search methods we found that topological algorithms often outperform analytical methods for single-subject analysis and achieve similar results for recovering common network properties of the winning model family, or set of models, obtained by multi-subject family-wise analysis. However, network search methods show their limitations in higher level statistical analysis of parametric empirical Bayes. Optimizing such linear modeling schemes the BMR methods are still considered the recommended approach. We envision the freely available database of estimated model-spaces to help further studies of the DCM model-space, and the ReDCM package to be a useful contribution for Bayesian inference within and beyond the field of neuroscience. In recent years of neuroscience, increasing attention is drawn toward brain connectivity studies. Non-invasive techniques, like functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), have gained popularity for this purpose. Dynamic causal modeling (DCM) is a continuously developing Bayesian framework for estimating effective neuronal connectivity between brain regions. It models neuronal signal alteration underlying the fMRI or EEG data for predicting network connectivity, their modulations and the effects of experimental inputs, while physiological parameters of the measured data are also accounted for. Initially, DCM was a hypothesis-driven method, useful to compare a small number of models to test neurobiologically relevant questions . Then, we construct topological alternatives to this network by adding or subtracting edges between network nodes. Finally, we evaluate the set of alternatives based on some approximation of the model evidence, for example, Akaike Information Criterion (AIC) or Bayesian Information Criterion (BIC) to select the best ones for the next iteration. In DCM variational free-energy generally gives a better approximation of the log evidence, and is preferred over AIC and BIC Penny, . For BayIn the case of DCM, every model search method attempts to find the model with the highest evidence based on fMRI data. As DCM models can be represented as graphs, some commonly used heuristic network optimization algorithms can easily be adapted to search through the DCM model-space. The computational difficulties limit the possibilities to develop searching methods for DCM. In the literature, only a few optimization methods are available to search for the best fitting model. Pyka et al.  investigpost-hoc model selection to find an optimal solution in a more analytic approach. The advantage of BMR is that it assumes that all models are evaluated around the same minima of free-energy. However, the approximation of model evidence is not known exactly for DCMs, as BMR assumes that the reduced posterior parameter distributions are Gaussian, which might not be appropriate due to nonlinearities in the model  algorithm , each corresponding to a hypothesis about the underlying network, and assign each model alternative to one of these families. In this setup model search algorithms may prove useful if they can recover properties of the winning family.Considering the topological complexity of DCM models and the underlying neurobiological modeling, as well, the high computational demand of DCM still limits the efficiency to explore large model-spaces. The algorithm for model inversion, used by a variational Laplace scheme to estimate the physiological and connection parameters of the causal model, is computationally costly. This is especially true for DCM for fMRI, because they integrate the neuronal and the hemodynamic states of each region, as well. As an obvious solution to speeding up DCM by graphical processing units, GPU-enhanced calculations was accomplished previously for fMRI  around the system's resting point. These time-dependent dynamics can be expressed as the differential equation below:DCM for fMRI models neural interactions between brain regions of a specified network. At any time point, the state of neuronal activity of each region depends on its neural state f point. Jacobian matrices A, B and C are parameters of the three different kinds of neuronal interactions modeled by DCM: the endogenous connectivity of the network, the modulatory effects of external input on the connections and the direct or driving effects of the stimuli (input) on the regions, respectively. These parameters define the model topology describing the inter-regional connections, and the external stimuli induced regional activity alterations and regional interaction modulations.where the network dynamics are computed around the h of network regions, where Θ are the parameters of the neuronal and the hemodynamic models. Because of the nonlinearities in the Balloon-model, the differential state equations need to be integrated numerically that can be extremely demanding on computational power.The temporal neuronal activation needs to be combined with a modality-specific forward model to explain regional BOLD (blood oxygen level dependent) fMRI responses. DCM for fMRI uses a hemodynamic model based on the Balloon-Windkessel  model  algorithm optimizes the maximum a posteriori (MAP) estimate of the free model parameters  Matlab toolbox, evaluating large amounts of models requires unmanageable processor time. To overcome this limitation we reimplemented the deterministic bilinear DCM12 algorithm (build v6225) in the R package ReDCM. . The whole estimation procedure of variational Laplace and the DCM forward models for fMRI  is available in ReDCM. The R programming language provided us a feasible environment to implement the Bayesian estimation framework, data analysis methods and model search algorithms as well. Besides DCM for fMRI, ReDCM also implements BOLD signal simulation, Bayesian model selection using fixed effects statistics or random effects with Gibbs-sampling  increases hyper-exponentially, depending on the number of regions and experimental inputs. As the parameter priors can be expressed as binary variables (connected or not connected), the number of possible bilinear models can be computed with a simple expression:In the case of a neural network, the number of all mathematically possible models , the second adds the number of possible modulatory effects of inputs (B) and the third counts the direct effects on each region (C). We subtracted the number of regions from terms related to the A matrix, because self-connections always represent self-inhibitory effects which need to be estimated.where C matrix so that only the “Task” condition is used as driving inputs for each region, while “Pictures” and “Words” are used for modulatory effects. In accordance with Zeidman et al. . In the group-level PEB analysis several effects are modeled similarly to a general linear modeling scheme. Most importantly the commonalities, or main effect among all subjects and the LI to model subject variability. Handedness, gender and age are also included to capture variance no interest. Detailed description of the experiment is found in Seghier et al. .A, B, and C matrices as directed cyclic graph structures, we can describe the topological difference of any two models by their Hamming-distance. The Hamming-distance of two models is defined as the number of different graph edges that describe their connectivity.Taking advantage of the ReDCM implementation and high performance computing, we estimated all models of the constrained model-space, and organized the estimated parameters of all models into an easy to handle data table. Description of this database is provided in A, B, and C matrices define the model-space, searching through it is actually about finding the optimal model with the combination of connectivity parameters that yields the highest evidence of DCM model estimation on the fMRI data. The comparison of model search algorithms is based on two main factors: the difference between the estimated Fe of the found model and the best fitting model of the model-space , and also the number of models estimated until convergence is reached . Based on the applied search method the number of models considered can still be relatively high. However, replacing DCM computations with looking up records from the already estimated model-space, as depicted in Search algorithms aim to find the optimal model with the combination of connectivity parameters that yield the highest evidence of DCM model estimation on the fMRI data. We adapted three different model search algorithms to DCM and characterized their performance. As the connectivity parameters of Another way to improve model search efficiency is to omit models from the current iteration of the algorithm, that differs from previously computed DCM models only in parameters that don't effectively change model evidence based on the Bayesian estimation procedure. Thus, we created optimized versions of the following model search algorithms that skip DCM models that adds or subtracts parameters that are receding or exceeding a definite posterior parameter probability in previously estimated models. Doing this we ensure that connections with high (or low) average probability don't get deleted (or added) by any alternative models. This search space reduction method is outlined with green on Our greedy search algorithm is based on the Greedy Equivalence Search described by Ramsey et al. . It starThe optimized algorithm, denoted as GES', takes the posterior parameter probabilities into account when generating the set of models to be estimated. Connection parameters can be removed from the model only if their mean posterior probability is below 0.9. With this modification we fix all connections that have a significant impact on model evidence.A more general case of GES considers the models as vectors of binary connectivity parameters, rather than graph structures. Similarly, the Hamming-distance based Greedy Search (GHD) starts with a randomized initial model and estimates every model that is at most 1 Hamming-distance far from that model. At the next stage, the model with the highest evidence is selected to repeat the procedure. When no further winning model is found, the algorithm terminates. The idea behind this algorithm is that it is assumed that the models around the best fitting model also have high model evidence  are represented as a set of attributes whose combinations describe the individual. These attributes are combined or swapped between individuals in a population of candidates and simulate genetic operations, like crossovers or mutations, to create more viable individuals. In our case, the models are the individuals and its connectivity parameters are the attributes.The genetic algorithm we use is described in details by Pyka et al. . The genp < 0.3 is replaced with a new model that is mutated from the population.Similarly to previously specified search methods, our modified GA' algorithm is also informed by posterior parameter probabilities. Each model that contains connections with average probability post-hoc model search results can be compared with topological search methods.Two routes of individual-level model search were followed. In the first one we performed search among separately estimated DCM models using the adapted algorithms. In this scheme we can compare search efficiency of the GES, GHD, and GA algorithms on the DCM model-space. Another approach involves using BMR to derive model evidence and posterior parameter estimates from the fully connected model. In this BMR model-space the same search methods can be applied. Additionally, We analyzed each adapted graph-based model search method from the aspects of model fit (Fe), graph structure of the found model (Hd from the best model), and the number of estimated models (N) until the algorithm converges. Note that the GES and GHD procedures are inherently deterministic methods, and always find the same model with the same initialization. However, randomized initialization allows us to measure search performance more accurately. The stochastic methods such as GA, has a different convergence point each run regardless of the initial set of models. Consequently, we derived the efficiency of the implemented search algorithms from 20 consecutive runs for each subject's data, and assessed the model search characteristics by computing the results' mean and standard deviation.The model families should be created to correspond to hypotheses about network structure attributes that are of interest by the experimenter. In case of BMS, both the null hypothesis and alternative hypotheses are compared to each other to make inference from the family that most likely to describe the structure of the network.8 = 256 models assigned to each base models, as there are eight free parameters found in the A matrix. We then performed a random effects analysis (RFX) of family-wise comparison along the group of ten subjects using the same set of models.In case of our dataset three meaningful separation of the model-space can be made  matching with the highest percentage determines the model family we assign model search results to. When more base models shares the connectivity of the found model, then corresponding families share the model accordingly. It is possible that the found model will show commonalities with more than one model family to some extent. Finally, we summarize model search results for all subjects to determine accuracy to recover family properties.post-hoc model selection, and uses BMR to evaluate a large number or nested PEB models. As reduced posterior probability can be analytically derived from linear models, BMR is safe to use in conjunction with PEB methods. Thus, using BMR estimated model evidence as decision criterion we compared the implemented topological search methods against the automatic search used in the SPM software. We then show the connectivity parameters for both commonalities and LI that are likely to contribute to the PEB model on the group-level.For group-level analysis of DCM data the currently recommended procedure is to perform a linear PEB analysis of a fully connected network estimated for all subjects. A common tool to discover network structure on the group-level is based on the same greedy algorithm as The reimplemented model estimation procedure in ReDCM achieves a significant improvement in computational performance. We compared the variational iteration runtime of ReDCM and DCM12 by estimating the 18 test models by six different scan length  by three models of varying model size . −4. Precision differences originate from different low level software libraries used for numerical methods.As ReDCM is intended to be an exact reimplementation of the DCM algorithm, we did not perform validation using the simulated data. Comparing posterior parameter estimates we found that the average difference between the two versions is lower than 1*10dFe = Fe0−Fe, where Fe0 denotes the free-energy of the best model, and (2) the topological distance by Hd. Based on the estimated model-space of 10 subjects the dFe and Hd shows a significant, but moderate correlation with a coefficient of r = 0.3(p < < 0.001). This is partly in line with the hypothesis of Pyka et al. (r = 0.45(p < < 0.001). The main reason could be that with BMR every model is evaluated around the same minima of free-energy, which is the full model of the model-space.Running ReDCM on computers 4 * 48 CPU cores, we were able to estimate the full model-space of 10 subjects each containing 65,536 models in 12 days. We organized the estimated parameters of all models into an easy to handle data table. Description of this database is provided in a et al. , that thResults for model search in the DCM model-space are shown in Using the implemented modifications to the original methods we managed to exploit DCM's ability to estimate every connection strength parameter. For each of the three search procedures, the implemented modifications succeed to improve efficiency. Simply fixing parameters based on previously estimated model posteriors the optimized GA' method found the best model multiple times and reached an average of 7.37 dFe. Furthermore, each optimized algorithm need to compute significantly less models until convergence.post-hoc model search implemented in SPM. Results are shown on the right side of post-hoc method finds the models closest to the best model in the model-space estimated by BMR. As an example Searching through the model-space using BMR allows us to compare the developed methods to the post-hoc model selection algorithms performed similarly between 75 and 99% accuracy to match task-based modulation patterns of the winning families.As a step toward group-level analysis, family-level inference is drawn from groups of models over the population. In many cases the models need to be estimated for selecting the winning family. Model search methods may be useful to recover the properties of families. We compared methods for searching among nested PEB models using the automatic greedy method  groups or families around the tested connectivity patterns. In this study we showed that topological model search methods can successfully identify patterns of the winning model family constructed by a priori hypothesis about network structure.The model search algorithms we used to find the best model are based on iteratively changing the structure of the models to improve model fitting. This approach assumes that models close to the best one in Fe are also close in structure (or Hamming-distance in this case). This assumption holds to some extent, but it is still unclear how free-energy and Hamming-distance are distributed over an arbitrary dataset and how these characteristics are related to each other. We found that these methods can be applied to achieve useful models, although they are not always reliable. A limitation of using topological search algorithms, generally used for graph discovery, is the questionable generalizability for any input data or different model structures.post-hoc model selection, described in the work by Friston and Penny  methods while searching for the optimal model structure on the subject-level. Furthermore, each algorithm performs well in finding models that share properties described by the winning family in a family-wise model selection scheme. However, topological methods reveal their limitations in searching through nested PEB models, which confirms BMR to be the currently recommended way to optimize group-level models. To develop and test model search methods efficiently, we separated DCM computations from generating model alternatives, replacing model inversion with time efficient database lookups. We share model-space data used in this study, and the ReDCM R package, which reimplements deterministic bilinear DCM to support high-performance computing facilities. We hope that our work will help further studies of the DCM model-space and the ReDCM package will be a useful contribution for Bayesian inference in the field of neuroscience.In summary, we characterized three graph theory based model search algorithms adapted for DCM and compared their efficiency based on free-energy difference and Hamming-distance relative to the best model in the model-space and the number of estimated models during search. We included the BMR-based https://aranyics.github.io/ReDCM/. The data table contains model description and DCM parameter estimation results for each model in the model-space. The current version of the ReDCM R package and source codes can be downloaded from the GitHub repository of the package: https://github.com/aranyics/ReDCM/releases. The implemented algorithm is based on DCM12 from the v6225 build of the SPM toolbox.The generated database of parameter estimates and model search results supporting the conclusions of this article are available at SA and ME took part in study conceptualization and development of model search methods. ReDCM software was developed by SA. GO managed hardware resources and data storage for model-space data. SA, MN, and ME contributed to writing the original manuscript and visualization. EB and ME was responsible for funding acquisition and project supervision. All authors revised and approved the submitted manuscript. All authors contributed to the article and approved the submitted version.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.


Glass fibre is the most widely used material for reinforcing thermoplastic matrices presently and its use continues to grow. A significant disadvantage of glass fibre, however, is its impact on the environment, in particular, due to the fact that glass fibre-reinforced composite materials are difficult to recycle. Polyamide 6 is an engineering plastic frequently used as a matrix for high-mechanical performance composites. Producing polyamide monomer requires the use of a large amount of energy and can also pose harmful environmental impacts. Consequently, glass fibre-reinforced Polyamide 6 composites cannot be considered environmentally friendly. In this work, we assessed the performance of a road cycling pedal body consisting of a composite of natural Polyamide 11 reinforced with lignocellulosic fibres from stone-ground wood, as an alternative to the conventional glass fibre-reinforced Polyamide 6 composite . We developed a 3D model of a pedal with a geometry based on a combination of two existing commercial choices and used it to perform three finite-element tests in order to assess its strength under highly demanding static and cyclic conditions. A supplementary life cycle analysis of the pedal was also performed to determine the ecological impact. Based on the results of the simulation tests, the pedal is considered to be mechanically viable and has a significantly lower environmental impact than fully synthetic composites. Presently, 95% of all reinforced composites contain glass fibre, generating more than 7 billion dollars annually ,2. CompoNatural fibres are less expensive, lighter, and less dense than synthetic fibres such as glass or carbon; hence, they are more attractive economically . These aThermoplastic matrices have been reinforced with fibres from cellulose-rich plants such as flax, hemp, jute, kenaf, and sisal . Such fiReplacing synthetic fibres with natural fibres is one approach, but it is not the only way to obtain more sustainable composites. Another approach is to replace petroleum-based plastic matrices with others from renewable sources such as proteins, polysaccharides, lipids and polyesters from plants or microbes—or with materials obtained from renewable sources . These pIn this work, we adopted a case study methodology to assess mechanical performance and industrial viability in a bicycle pedal consisting of a fully bio-based composite compared to a pedal made from conventional materials. The composite materials examined were bio-based Polyamide 11 reinforced with lignocellulosic fibres from stone-ground wood (SGW) and a synthetic polyamide reinforced with glass fibre. A 3D model of the pedal was generated by using a hybrid design based on two different commercial brands. The main piece of the modelled pedal was assigned the bio-based composite, and its mechanical performance was assessed under static and cyclic loads through computer simulations. In order to assess the sustainability of the production of the bio-based material in relation to its commercial counterpart, which requires careful investigation ,35, bothThe primary aims of this work were to validate theoretically the technical viability of a fully bio-based product and to quantify the environmental benefits of its use considering a holistic life cycle. The novelty of this work is the assessment of mechanical performance of an environmentally friendly composite material, and an approach to advantageously replace composites based on technical, non-generalist matrices. Due to the fact that one of the greatest weaknesses of natural fibre-reinforced composites is their mechanical properties, we examined their mechanical performance under static conditions; we also performed a novel investigation of the fatigue behaviour of the bio-based product under cyclic loadings. In studying natural reinforced composites, it is common to analyse the thermomechanical properties of the materials to predict their behaviour, but not to perform fatigue tests; therefore, analysing the fatigue behaviour of the bio-based composite is also an innovative contribution of the present research. We consider this study extension essential to assure designers and engineers that these more sustainable and environmentally friendly materials can be reliably used in new products and applications. Consequently, the hypothesis of this research is that, for some applications, bio-based composites can replace synthetic composites, without a loss in performance and offering several important environmental benefits.The remaining sections of this paper are structured as follows: The experimental design began with selecting the specific test target . For this purpose, we examined products used by sports professionals typically manufactured from composites reinforced with synthetic fibres, where the plastic matrix is an engineering plastic that can withstand relatively high mechanical stress. The aim was not to simply select a conventional product but to confirm that a bio-based composite was a viable choice for developing industrial products capable of fulfilling relatively demanding application requirements. The product selected for the analysis was an automatic pedal for road bicycles.In terms of design, the geometry and constituent material of a product can influence factors such as its processability and its disposal at the end of its life cycle ,38. TherThe bio-based material used in the simulations was Polyamide 11 (PA11) reinforced with 60 wt% natural fibres from SGW. In a previous study, this bio-based composite was found to have similar properties to polypropylene reinforced with 20–30% of glass fibre (S-Glass and C-Glass); however, the increased density of the bio-based polyamide resulted in slightly worse mechanical performance in the natural composite . The syn6 cycles. Considering a pedalling cadence of 80 rpm, the force value was obtained from the average power value measured for professional cyclists on a mountain stage of the Tour of Italy, which was 235W [Once the characteristics of the bio-based composite were assigned to the 3D prototype for the bio-based pedal, three finite-element numerical analyses were performed using SolidWorks . A first static test was performed to simulate cleat engagement. No maximum force for this event had seemingly been reported, where the cyclist, with both hands on the handlebar and one foot on the ground—or already on the other pedal—fits the cleat on the pedal. This led us to assume a hypothetical abuse of the pedal by applying a force of 500 N to engage the cleat. A second static test was performed to simulate the highest effective force acting on the pedal during pedalling. The force exerted by a cyclist on a pedal changes over each rotation cycle of the connecting rod and can be separated into an effective pedalling force and an ineffective force along the direction of the rod ,45. A nuwas 235W . Whereaswas 235W ,54. Sincwas 235W —and PA11was 235W ,57, the The environmental and commercial implications of the bio-based material were compared with those of the commercial composite in an LCA by using the sustainability module in SolidWorks 2020 under the assumption that the pedal would be manufactured and recycled in Europe. The geometry used was that of the hybrid pedal’s main body, which was assumed to consist of the materials shown in As discussed above, the test case was the main body of an automatic bicycle pedal. This type of pedal enables more efficient pedalling than the classical model by virtue of a pedal–shoe interface that results in more effective pedalling force during each rotation of the connecting rod. Automatic pedals are widely used among professional road cyclists and extensively used by recreational bikers. The cyclist’s shoe fits the pedal through a piece called a “cleat” that is screwed onto the shoe. Generally, the pedal components in contact with the cleat are made of a composite reinforced with synthetic fibres, whereas the cleat consists of non-reinforced thermoplastic material .Unlike commercial models, the main body of the hybrid pedal consisted of a bio-based PA11 + 60% SGW composite rather than glass fibre-reinforced PA6 as in amateur pedals. The main body of an automatic pedal is the component under the greatest loading during pedalling; therefore, it was selected as the component to be simulated. The locking part could be made from the same material; in addition, all other components were made of the same materials as those used in automatic pedals for amateur cycling.As stated in Engaging the cleat requires the cyclist to apply a slight force over a highly specific zone in the front of the pedal body a. When tThe loaded surface differs between the cleat engagement and pedalling operations because, in the latter, the cleat is in contact with the central part of the pedal’s main body. Due to the fact that the cleat is anchored to the pedal, the surface over which the force is exerted is independent of the rotation angle of the part. In contrast, the applied force and effective force are dependent on the rotation angle of the connecting rod. As stated in 6 cycles were applied, with cyclical stresses of constant amplitude, a stress ratio equal to 0, and a single event (SolidWorks parameters). The new finite-element analysis was conducted by applying a load of 165 N to the central portion of the designed pedal’s main body, which is the average pedalling force measured on a mountain stage of the Tour of Italy at a cadence of 80 rpm [6 cycles, the mechanical properties of the material will not deteriorate substantially, so it is possible to interpret the results as the material having infinite life. As can be seen in 6 cycles with negligible impact, since no point on the mesh has a load factor lower than unity. In fact, the minimum load factor obtained in the analysis is above 5, indicating that the material properties were effectively unchanged. Therefore, it can be concluded that the part would be able to withstand a much higher number of cycles; this is further evidenced by the fact that conservative values were used in the analysis.As the load is applied cyclically every pedal stroke, the fatigue behaviour of the pedal’s main body during pedalling is another valuable subject for analysis. This fatigue analysis is actually critical since, at 80 rpm , a cyclist can generate 4800 full turns of the connecting rod in an hour. In other words, with the static analysis carried out previously, it would be insufficient to characterize and validate the response of the bio-based composite applied to the geometry of the pedal’s main body. To standardise the fatigue analysis, 10f 80 rpm . As stat3) synthetic glass fibre was replaced with a lower density (1.34 g/cm3) natural alternative (SGW fibres). As a result, the bio-based material was expected to be more environmentally friendly than the synthetic material. The order of magnitude of the potential gains in environmental impacts was estimated using the sustainability module in SolidWorks 2020, to compare the two types of material through LCA. Like similar commercial software tools, SolidWorks performs weighted computations and does not account for important design and manufacturing characteristics, which limits its usefulness [It is understood that a fully bio-based composite should have a lesser environmental impact than that of a conventional synthetic composite. In this study, the thermoplastic matrix of the bio-based composite was obtained from the castor bean, a ricin oil-rich plant; in addition, the typical high-density . PA6 production has a strong impact on CO2 emissions and glass fibre increases the impact slightly. Likely due to the release of various gaseous pollutants during the glass fibre production process, the emission factor of this material is penalised, since the energy consumption is not higher, as seen in Based on the carbon footprint data in 2 emissions because the equivalent amount of CO2 is absorbed during new plant growth; in addition, the consumption energy for PA11 production is lower than that for any other polyamide [Due to the fact that PA11 is not included in the database of the sustainability module of SolidWorks, and the fact that this software does not allow the creation of new entries, the material was assumed to have the same properties as PA6, and the pedal volume was altered to have the same density as PA11. This was another conservative assumption since PA11 production does not increase atmospheric COolyamide . As a reThe energy consumption results of the LCA showed a similar trend to the carbon footprint results. Thus, the bio-based composite was the most environmentally friendly option see , and alsThe benefit in terms of carbon footprint and energy consumption is significant, in contrast to other recent research in which SGW-reinforced PA11 did not represent a great advantage . It can The superior material properties of glass fibre-reinforced composites are well known. Depending on the nature of the glass fibres, their size, and their orientation in the thermoplastic matrix, a composite can have good mechanical, chemical, and electrical insulation properties in addition to low weight and cost. However, glass fibres are known to pose serious environmental problems arising from the low recyclability of their widely used composites. In response, scientists are developing new procedures to increase their recycling or replace the reinforcing material for easier reuse. Both areas of research are advancing at a promising pace.Natural fibres constitute an effective alternative to synthetic raw materials such as glass fibres. Natural fibres are less expensive, lighter, and less dense than synthetic fibres; in addition, they are biodegradable, and hence offer environmental and economic benefits. Mechanically, however, natural fibres are less resistant to impact; they are also more hydrophilic than synthetic materials. These material properties are important for glass fibre-reinforced composites.In this work, we used finite-element simulations to estimate the mechanical suitability of a bicycle pedal part made from a fully bio-based composite consisting of a natural polyamide (PA11) matrix reinforced with 60 wt% lignocellulosic fibre from SGW. The simulation results indicate the product is mechanically feasible. Based on the nature of the finite-element simulations, it is advisable to assess the performance of the pedal under real-life conditions.From an environmental standpoint, the preliminary analysis of the life cycle of the product showed considerable benefits in terms of carbon footprint and energy consumed. Specifically, the estimates indicated reductions in the impact of the carbon footprint and energy consumption of more than 40% with the use of the bio-based composite.Therefore, it is theoretically demonstrated that fully bio-based technical composites can replace synthetic composites in certain applications, and that the environmental benefits can be high. Future improvements in the mechanical properties of PA11 + 60%SGW would enable more widespread use and increase its environmental benefits.


Single-pixel imaging techniques as an alternative to focal-plane detector arrays are being widely investigated. The interest in these single-pixel techniques is partly their compatibility with compressed sensing but also their applicability to spectral regions where focal planes arrays are simply not obtainable. Here, we show how a phased-array modulator source can be used to create Hadamard intensity patterns in the far-field, thereby enabling single-pixel imaging. Further, we successfully illustrate an implementation of compressed sensing for image reconstruction in conditions of high noise. In combination, this robust technique could be applied to any spectral region where spatial light phase modulators or phased-array sources are available. This measured power is proportional to the overlap integral between the imaged scene and the chosen mask. By measuring the overlap integral for a wide range of different masks, it is possible, using various algorithms, to infer the image of the scene. These single-pixel approaches are useful at wavelengths where detector arrays are expensive or problematic in other ways, or where the enhanced time resolution of the single-pixel detector gives other detection modalities2 such as depth-resolved imaging5, microscopy7, Raman imaging9, to name a few. Furthermore, by appropriate choices of mask and associated reconstruction algorithms, it is possible to implement compressed sensing techniques12 to recover a high-quality image in fewer measurements than a more traditional raster-scanned approach.Traditional digital imaging systems use a lens to form an image of the scene on a focal-plane detector array, with each element of the array recording the corresponding intensity of the image pixel. Single-pixel imaging systems are an alternative to this where the detector array is replaced by a spatial light modulator which applies a mask to the image and then a large-area, single-pixel detector subsequently measures the total optical power transmitted by this mask13. In both cases, choice of mask design and reconstruction algorithms are subject to the same considerations.Single-pixel imaging systems can be configured in two distinct ways. In the first approach, the object can be flood (uniformly) illuminated and the backscattered light is imaged onto the plane of the spatial light modulator where the mask is applied. The reflected/transmitted light from the modulator is subsequently detected. Secondly, that same mask can be used to structure the illuminating light. The resulting backscatter of the target from the modulated light is measured by the detector directly. In this latter configuration, the approach has much in common with the technique commonly referred to as computational ghost imaging16. These masks are frequently implemented using a digital micro-mirror device (DMD) which works in reflection to apply binary intensity masks to the incident light with an update rate of less than 0.1 msec. However, inherent in the binary nature of the masks is that 50% of the light is lost. Another widely used spatial light modulator technology is based on a liquid crystal layer on a silicon backplane (LC-SLM) which can be electrically addressed to create spatially varying phase masks. Unlike the DMD, which is inherently binary in nature, the LC-SLM can typically create 128 different phase delays, in the 0-2In many single-pixel imaging systems to date, the chosen basis for the masks has been the Hadamard basis due to it being a complete orthogonal set17. The GS algorithm is applied in its original formulation to generate holograms for projection on the LC-SLM. We assess the suitability of the Hadamard pattern sources  for those that contribute the least in terms of noise and/or uncertainty. This choice helps achieve the focus of this research optimally, i.e. to identify the best combination of individual elements for compressive sensing in poor SNR conditions.Rather than creating a binary intensity mask in the image plane, the phase-only LC-SLM can act as a diffractive optical element. This allows the system to create computer generated holograms that can create chosen intensity distributions in the far-field. In principle, nearly all of the illumination light can be diffracted into the desired pattern, overcoming the 50% loss of the DMD. One approach for calculating the required spatial form of the required diffractive pattern encoded on the LC-SLM is the Gerchberg–Saxton (GS) algorithmIn this work, we use the Gerchberg–Saxton algorithm to calculate the required spatial phase distributions that can be programmed on a LC-SLM to create Hadamard intensity patterns in the far-field. These Hadamard patterns are used to sequentially illuminate an object and the power backscattered by the object is measured. Knowing both the mask patterns and the measured power allows the image to be reconstructed. In addition, we also successfully illustrate compressed sensing with a selected subset of the pattern set to reconstruct the image, gaining the advantage of faster imaging and/or robustness in low signal-to-noise ratio (SNR) conditions.We note here that although we demonstrate our approach in the optical regime, this approach is also suited to phased-array sources, such as those possible in the radar regime.17, to create the Hadamard patterns in the far-field plane.As illustrated in Fig. 17, the phase mask for the SLM is calculated and superposed onto the incident beam. Once encoded on the SLM, the hologram is propagated by fast Fourier transformation and plane wave decomposition to simulate the propagation through the optical system .As the modulator is used in a diffractive mode, a phase-grating is added to the kinoform so that the resulting Hadamard pattern is shifted to the first-order where it is selected from the other diffracted orders in the Fourier-plane with an aperture. The chosen order is propagated through a series of lenses into the far-field and into two optical paths—one with an sCMOS camera and the other with a single-pixel imaging system . The PMT is positioned to collect the light backscattered from the object. This measurement is proportional to the overlap integral between the projected Hadamard pattern and the object. The sCMOS camera plays no role in the image acquisition but is included as a diagnostic tool to confirm the far-field illumination pattern. The overall experimental system is similar to that reported in Toninelli et al.16. The Hadamard matrices used in this research are 32-by-32 square arrays of binary patterns, forming a complete orthonormal set of 1024 patterns. Historically, Hadamard matrix-based image encoding was developed to decompose an image into its Hadamard transforms as a method of image compression for reducing the bandwidth required for data transmission15. As natural images have a sparse representation in the Hadamard basis, measurement in this basis allows sharp image reconstruction with fewer measurements than pixels, a form of compressed sensing. In the experiment, the maximum spatial frequency of the Hadamard intensity patterns is limited in accordance with the diffraction limit. The scale of the Hadamard pattern is set accordingly to not exceed this limit.Although in principle, we could use GS to generate any orthogonal pattern basis, here we choose to use the Hadamard basis (instead of others such as the Fourier basis) since the application of this basis to compressed sensing has been heavily studied and understood. Additionally, the advantages of Hadamard patterns are assessed and detailed in a comparison with the Fourier-basis by Zhang et al.I from 1024 patterns19:Using a sequence of mentclass2pt{minimInitially, we considered two approaches to image reconstruction. To create an image, we used the signals measured by the PMT and the knowledge of (1) the ideal Hadamard patterns and (2) calculated holograms . Initial testing showed a better image reconstruction and contrast using the measured signals and the idealised patterns, and therefore, became the choice for all subsequent image reconstruction. This also led to the advantage of not requiring additional computational resources, as would be needed for modelling the propagation from the SLM to the far-field.In ideal conditions, with unlimited resources and time, reconstruction of the object can be done over innumerable iterations of the full pattern set, averaging the individual images to overcome noise. Indeed, we established a “ground truth” image using precisely this approach, averaging 20 and single-pixel imaging in the near-infrared wavelengths for microscopy22. The compressive sensing method in the telescopic system uses both arms to reduce the sampling rate and achieve high resolution image reconstruction. The disadvantages, however, could be seen when atmospheric turbulence induces noise in the measured signal. Similarly, Denk et al.22 showed compressed sensing applied for single-pixel based microscopy using multiple, balanced Germanium photodiodes. In comparison, our method detailed here uses a single PMT for signal measurement and remains robust in poor SNR conditions . The compressed sensing is implemented using only a subset of the full pattern set to reconstruct the image. The subset itself is chosen based on the contribution of the pattern to the image reconstruction. This is quantified in terms of the measured signal strength. Note that this approach places no reliance upon image libraries or image priors and can be applied to arbitrary images.In practical situations, a more realistic scenario is that of a higher noise level and with constraints on the number of measurements that can be obtained within a given time or total illumination dose. For such cases, compressed sensing  is an option. Different approaches to compressed sensing have been illustrated in the past such as complementary compressive sensing in a telescopic systemm pixels.Given a fixed number of measurements, the resulting image reconstruction can be enhanced by concentrating these measurement on the subset of the patterns most significant to the image. In our investigations we initiate each image acquisition using a full measurement set of patterns and then use the signals from this set to establish which subset of patterns are most important for defining the image. We then use the remaining measurement resource to repeatedly measure the signals and hence suppress the noise for these important patterns. For example, the same number of measurements required for a further 5 iterations of the full pattern set can instead be used for 20 iterations of the 25% set. After every iteration, to measure the quality of the images reconstruction we calculated the RMS error  that the quality of our patterns formed in the far-field of the modulator allow us to recreate the scene from backscattered light and (2) the method’s robustness to facilitate a compressed sensing approach that yields an image improvement in high noise conditions.We have shown a successful implementation of single-pixel imaging using a phase-only modulator to create Hadamard intensity patterns in the far-field of the modulator. Using these patterns to illuminate the object, we measure the backscattered light and show image reconstruction through an iterative process under ideal conditions of complete measurement sets and high SNR. We also confirm the advantages of compressed sensing in achieving lower image RMS error and its applicability in lower SNR conditions. This approach to single-pixel imaging using spatial phase modulators to create Hadamard intensity patterns in the far-field is applicable to any imaging system where a spatial phase modulator or phased array source is available, including potentially in the radio frequency regime.23.The data underlying the results presented in this paper  are accessible in the following repository


This study analyzed the sensitivity between fund flow and fund performance with Korean funds, whether there would be a difference in the sensitivity between environmental, social and governance (ESG) funds and non-ESG funds, and whether there was a difference in sensitivity according to the type of past fund performance (positive and negative). The main results of the analysis are as follows. First, the analysis of the fund flow–performance correlation of Korean funds revealed that they had a negative (−) correlation and the ESG did not affect fund flow. Analysis of the difference in sensitivity between fund flow and performance volatility revealed that there was a negative (−) correlation regardless of the performance measuring method and ESG. Finally, the comparison of fund flow and performance sensitivity according to the type of past fund performance revealed that despite consistent asymmetry, there was little difference in sensitivity asymmetry between ESG funds and non-ESG funds. The results reveal that, unlike the expectation that investors in Korean ESG funds would focus more on non-financial properties like the purpose of investment than on profit, they attach the same importance to fund performance. There are many institutional changes currently taking place, one of which is environmental, social and governance (ESG). International interest in ESG investment is increasing as the importance of ESG management is emphasized. Consequently, ESGRecently, major domestic and foreign companies and financial institutions have increased the importance of the issue of ESG bonds. There have been many discussions about the origin and definition of ESG. More specifically, ESG is a concept that began with the fact that non-financial factors, in which we had little interest, might shake the essence of the issue’s subject.To examine it with stricter criteria, it is defined as bonds issued based on Green Bond Principles (GBP), Social Bond Principles (SBP), and Sustainable Bond Guideline (SBG) announced by the International Capital Market Association (ICMA) and those issued based on The Climate Bond Standards (CBS) announced by the Climate Bond Initiative (CBI).The concept of ESG that had first exhibited an influence on the stock market is spreading fast in the bond market as well, starting in 2020. The most question and concern is “What is the difference between non-ESG bonds and ESG bonds?” The conclusion is the generalization of new bonds. Finally, with global paradigm change, ESG bonds will become common while non-ESG bonds will not be common anymore. Instead of the decrease in credit spread of ESG bonds or becoming excellent bonds, it is possible to see a phenomenon in which credit spread expands as demand for non-ESG bonds decreases.ESG investment has evolved into an essential element. The size of the ESG bond issues has increased noticeably at home and abroad. In 2015, there were only $80.7 billion in global ESG bonds. In 2020, there were $789.8 billion in ESG bonds. This was almost a 10-fold increase. The weight of responsible funds considering ESG is still lower for bonds than stocks. However, ESG bond numbers are increasing at a significant rate. Responsible investments, which have been limited to stocks, are made actively through bonds. In the ESG bond market, as well as the issue of bonds to raise specific funds , the method of adjusting the weight of the inclusion in the portfolio considering the ESG performance of the issuer draws attention for the same reasons.When compared to the global ESG market, the Korean ESG bond market is still in an early stage in terms of size, diversity, and investor base. It needs the power to experience growth. And yet, as the National Pension amended the fund management principle in November 2019, it expanded and applied ESG investment to all asset classes. Hence, Korean investors’ interest in ESG investment has gradually expanded.This study is primarily concerned with ESG funds. An analysis of the impacts of the setting of ESG on the monetary flow of the Korean funds will be conducted. ESG and non-ESG funds will be compared to examine the relative difference in the sensitivity between fund flow and fund investment performance. The focus will be placed on the method for measuring the fund performance, as well as the type of fund’s past performance. The focus will also be placed on describing the factors with significant positive impacts.The rest of this article is structured as follows. Section “Literature Review and Hypotheses” summarizes the previous studies of correlations between ESG funds and fund flows. It also presents the empirical hypotheses to be dealt with in this study along with the grounds. Section “Reference Data and Research Methods” describes the characteristics of the samples and the main variables used in the empirical analysis. Section “Result of Empirical Analysis” presents the main results of the empirical analysis, and section “Conclusion” draws a conclusion and provides suggestions.It is difficult to clearly define the concept of ESG investment. It is difficult to accurately classify ESG by subject and to examine the history of ESG investment, its definition differs depending on the culture, religion, values, and belief. By the occasion, it is used in various names, such as Socially Responsible Investment (SRI), Responsible Investment (RI), and ethical investment. The SRI forum in the United States and Europe does not clearly define SRI. Consequently, these terms are used in diverse and mixed ways.The global ESG investment began from an ethical/religious motivation to exclude specific industries  in the 1920s. In the 1960s–1970s, responsible investments in the public interest became revitalized. As social interest in global warming, human rights issues, and corporate corruption increased, its meaning and concept evolved. Entering the 1970s, South Africa’s apartheid policy triggered the SRI of institutional investors. In the 1980s, as large accidents took place , environmental issues have drawn more attention. Since 2000, when the Principles for Responsible Investment were enacted by the UN PRI, there have been international public debates. There has also been a revitalization in ESG investments centered around the Pension and Funds Audit Bureau.The sales of ESG bonds in South Korea began with the issue of green bonds by the Export–Import Bank in 2013. As the demand for investment in ESG bonds increased in the global financial market , which set out for expanding investment assets with responsible investment, Korean paper issuers also set out to expand the base of investors through the issue of ESG bonds. The size of the issue of ESG bonds in Won has increased, thanks to the issue of these bonds by many private companies .ESG bonds began being issued in Won in May of 2018, much later than the ESG bonds that were first issued in foreign currency in 2013. The Korean Development Bank issued green bonds worth 300 billion Won. Later, Shinhan Bank and Korea Southern Power issued green bonds in Won. And in February of 2019, IBK and Woori Bank issued sustainable bonds in Won. Woori Card issued a social bond in Won for the first time as a financial company specializing in the loan business.The domestic ESG market centered around green bonds in the earlier stage has recently expanded to social bonds and sustainable bonds. In particular, since sustainable bonds can use the funds raised in eco-friendly investments (green bonds) and investments to solve social problems , there is an advantage in terms of versatility. Consequently, the size of the issue has tended to expand. In addition, ESG bonds centering around the bonds in a foreign currency  are highly preferred by foreign investors in the global market. In 2018, the Korean Development Bank issued bonds in Won. The number of Won-issued bonds has increased in earnest since 2019.In this investigation, we will examine the advantages and disadvantages of the investors and issuers of ESG bonds, when compared to non-ESG bonds. The investors have advantages , since it is possible to check fund uses. In contrast, the disadvantages include low liquidity and the clear performance of cumulative returns. Issuers have advantages . However, when compared to non-ESG bonds, there are also risks .ESG investments emphasize prioritizing the maximization of the return on investment for customers and beneficiaries as a trustee’s duty. The purpose of the investment has been changed from investing in good companies to investing in good companies with bright prospects. The question of whether or not profits can be created in investing in ESG funds continues.ESG investments aim to increase profitability in priority; however, contributing to social responsibility and capital market fidelity is also a fundamental goal. No conclusions have been drawn concerning whether or not it is possible to achieve non-financial values simultaneously . Of course, the direction investors want is to consider non-financial values in investments.There are various opinions in the studies that verified the correlation between ESG investment and financial performance. According to the result of the survey conducted by 2 analyzed about 2,000 research papers that described the correlations of ESG factors with companies’ financial performance and reported that 48% concluded the correlation between ESG and financial performance to be positive; 11%, to be negative; and 23%, to be neutral.Friede et al. (2015)The IMF Global Financial Stability Report In conclusion, there is a lack of ground for judging the performance of ESG investment to be positive or negative. Most countries all over the world have not prepared an environment in which non-financial factors can be measured, and assessment institutions’ appraisal methodologies have not been settled. Especially, since in Korea, ESG investment is in its beginning stage, so it is too early to mention its effects.Investors’ choice of funds generally depends on fund performance and risk appetite. In other words, rational investors would compose portfolios based on the past performance of the fund. Thus, rational investors can predict a positive (+) correlation between the past return of the fund and the fund flow . In addiHowever, it is expected that ESG fund investors  would be relatively less sensitive to past performance or volatility .Hypothesis 1: The fund flow–performance sensitivity would be weaker in ESG funds than in non-ESG funds.ESG investors decide on investment, actively reflecting non-financial factors such as corporate social responsibility (CSR) activities. Consequently, ESG investors are expected that it would be unlikely that ESG investors decide short-term investment curtailment  even if the financial performance of the fund is poor . Thus, iHypothesis 2: The fund flow-negative fund performance sensitivity would be weaker in ESG funds than in non-ESG funds.There are conflicting claims on the correlation between fund flow and fund performance volatility. According to In particular, since positive feedback trading may be accompanied by a short-term volatility increase, the increase in the volatility of the fund performance and fund flow may have a positive (+) correlation.Hypothesis 3: The fund flow–performance volatility sensitivity would be weaker in ESG funds than in non-ESG funds.The disposition effect, an irrational investment behavior, may differ depending on the fund performance for several reasons. First, if a positive fund performance is realized, investors would realize the profits (sell-off) earlier as the fund performance volatility increases. Thus, a negative (−) correlation is expected to exist between the fund flow and the volatility. On the contrary, if a negative fund performance is realized, they would keep holding it as the fund performance volatility increases. Thus, a positive (+) correlation is expected between the fund flow and the volatility. Therefore, the correlation between the fund flow and fund performance volatility will appear asymmetric, according to the type of the fund performance. However, the asymmetric sensitivity of the ESG funds would be weaker than that of the non-ESG funds.Hypothesis 4: The fund flow-volatility sensitivity would be asymmetric according to fund performance, and the asymmetry would be weaker in ESG funds than in non-ESG funds.The reference dataset used in this study includes funds classified as ESG funds in the fund-related materials provided by the Korean Fund Ratings (KFR). This information was matched with non-ESG funds with the fund characteristics most similar to them . The anaOf all the funds since 2015, 52 non-ESG funds had similar characteristics to those of the experimental group. The ESG funds were matched to extract them as a control group. The extraction process is presented in Return), the average of the market-adjusted return  is the size of the assets operated by the fund at the end of Month According to the previous studies, there are various definitions of fund flow and methods for measuring that. In this study, the net change in fund assets is defined as fund flows. The fund flow that reflects money inflow and outflow is calculated using Eq. (3) .A comparison of ESG fund investors with non-ESG fund investors to determine whether or not they had a relatively weaker fund flow–investment performance sensitivity was assumed through the regression equation in Eq. (4).Flowi,t is money flows in Month t of Fund i measured with Eq. (3) above presented; Returni,   is the average of the monthly rate of return of Fund i from t-1 month through t-12 months (or that of market-adjusted return or CAPM-adjusted return); dummy variable is indicated as Where, To analyze whether or not ESG funds are less sensitive to negative returns than non-ESG funds, we conducted the regression in Eq. (5).R+ and R− are dummy variables having a value of 1 if the monthly rate of return of the fund (or market-adjusted return or CAPM-adjusted return) is a positive (+) value or a negative (−) value, respectively. In Eq. (5), regression coefficients β1 and β2 represent the sensitivity of the fund flow to the positive (+) average raw return in comparison with that of the previous year and the sensitivity of the fund flow to the negative (−) average raw return for non-ESG fund, respectively. The regression coefficients β3 and β4 represent the sensitivity of the fund flow to the positive (+) average raw return and the sensitivity of the fund flow to the negative (−) average raw return for ESG funds, respectively.Where: The correlation between the fund performance volatility and fund flow is analyzed in Eq. (6).Voli,  represents the standard deviation of the monthly rate of return of Fund i from month t-1 through months t-12 (or the market-adjusted return or CAPM-adjusted return). Finally, according to the fund’s past performance, the difference between the asymmetry of the sensitivity between the fund performance volatility and the fund flow and the asymmetry of the sensitivity between the ESG funds and non-ESG funds is estimated in Eq. (7).Where: 1 and β2 represent the fund flow sensitivity to the volatility of the rate of return of non-ESG funds that realized a positive (+) average raw return in comparison with that of the previous year and the fund flow sensitivity to the volatility of the rate of return of the non-ESG funds that realized a negative (−) average raw return, respectively.Where regression coefficients βThe empirical analysis yielded several important results. First, the whole fund flow–performance sensitivity was analyzed. This included investigating whether or not ESG fund investors had weaker fund flow–performance sensitivity than non-ESG fund investors. Eq. (4) was used to test this; the results are presented in A regression equation was used to test Hypothesis 1. A dummy variable was added to show whether or not it is an ESG fund. The regression equation included return rate volatility and fund characteristics. The results are presented in Fund volatility had significant positive (+) impacts on the dependent variable in Model (2), where fund volatility was measured by the raw return volatility. Model (6) measured fund volatility with the CAPM-adjusted return volatility. It had a significant negative (−) impact in Model (4), which measured it with a market-adjusted return. In other words, the raw return and CAPM-adjusted return result in an increase in fund flow with larger volatility. The market-adjusted return yields an increase in fund flow with smaller volatility. This result suggests that fund flow–performance sensitivity may differ depending on the method used to measure fund performance.We applied Eq. (5) to test Hypothesis 2. Hypothesis 2 focused on whether or not the sensitivity between the fund flow and fund performance would be asymmetric, according to the type of the fund’s past performance . This asymmetric sensitivity would also be weaker in ESG funds than in non-ESG funds. The results are summarized in In Models (2) and (6), we examined the interactions between raw return and CAPM-adjusted return by including a dummy variable ESG. The results reveal a negative (−) impact on the dependent variable when it had a positive (+) value and a significantly positive (+) impact when it had a negative (−) value. This means that when the fund’s past performance is positive, the fund flow sensitivity decreases in the ESG funds. When it is negative, the sensitivity increases. We examined the interaction between the market-adjusted return and the ESG dummy variable in Models (3) and (4). In reverse, when it had a positive (+) value, there was a significantly positive (+) impact on the dependent variable. When it had a negative (−) value, there was a significantly negative (−) impact. Thus, the results are inconsistent.In summary, the ESG fund shows asymmetric sensitivity between fund flow and fund performance according to the type of the fund’s past performance. With a positive fund performance, the ESG fund sensitivity is much higher . In addiThe previous analysis showed a similar result when the interaction variable between performance volatility and the ESG dummy was added to the entire sample. In It turned out that, of the variables representing the fund properties, the size variable was found to negatively affect the dependent variable. This means that the smaller the fund size, the lower the fund flow sensitivity becomes.The result of the Hypothesis 4 test on the difference in volatility according to fund flow sensitivity and the quality of fund performance is summarized in Rational investors make investments based on risk appetite and the fund’s past performance. In other words, they expect a positive (+) correlation between the fund’s previous returns and the fund flow. However, ESG fund investors reflect upon other non-financial factors , rather than just on the fund’s previous returns. Thus, it is expected that ESG fund investors would have weaker fund performance–fund flow sensitivity than non-ESG fund investors.This study analyzed the sensitivity between Korean fund flow and fund performance. We examined whether there were differences in the sensitivity between ESG funds and non-ESG funds. We also examined the sensitivity according to the type of fund’s past performance . In addition, this study analyzed the correlation between the fund flow–performance volatility to check the fund flow–past performance interaction and asymmetry and the difference in the sensitivity between ESG and non-ESG funds.The primary results of the empirical analysis are as follows. First, it is noted that there is a negative (−) correlation between Korean fund flow and fund performance. Furthermore, unlike what was expected, in ESG funds, there was no impact on fund flow. This implies that fund flow is more sensitive when Korean funds have a poor return. Whether it is ESG or not does not affect the fund flow. On the other hand, the fund volatility showed different sensitivity according to the method used to measure the fund’s performance.Second, the analysis of the asymmetry between fund flow and performance sensitivity, according to the type of the fund’s past performance, revealed that the fund flow–performance interaction had a negative (−) correlation. This was the case regardless of the quality of the fund’s performance. This result was not expected. Hence, the interaction between the fund’s past performance and the ESG dummy variable yielded asymmetric sensitivity. The sensitivity of the ESG funds was a little higher when the performance was positive. In the meantime, the analysis of the difference in the sensitivity between fund flow and fund performance volatility, measured by dividing the funds into ESG and non-ESG funds, revealed that fund flow and fund performance volatility had a negative (−) correlation, regardless of the method used for measuring fund performance and whether or not it was an ESG fund. Therefore, the lower the investors’ fund performance volatility, the lower their risk awareness and the higher their expected performance. As a result, the less the monetary outflow of the fund becomes. The analysis of the entire sample with the interaction variable between the fund performance volatility and the ESG dummy added yielded negative (−) interactions with fund flow. This result shows that investors are more sensitive to the price than to the purpose of the ESG investment.Finally, the comparison of fund flow–performance volatility sensitivity according to the type of fund’s past performance reveals that it is consistently asymmetric, according to the quality of the fund’s performance. However, it is noted that there is almost no difference between the ESG fund sensitivity asymmetry and the non-ESG fund sensitivity asymmetry.The results deviate from the previous expectations that Korean ESG fund investors would focus more on non-financial properties  than on returns. The general investors’ choice of ESG fund is to choose nice companies with good performance, instead of simply investing in good companies. Thus, ESG fund sellers and operators should not overlook the fact that ESG fund investors also attach importance to fund performance. Hence, they should attract investors through developing various ESG fund products that can continue to create and maintain high performance. This means that the screening of the environment, governance, and social responsibility has not been working when investors choose a fund. This proves that there is still a lack of awareness of ESG despite that receives global attention.ESG fund selling and management companies to develop ESG fund products that can maintain and create a high performance without overlooking the fact that ESG fund investors, of course, attach importance to fund performance as well in the early stages of ESG funds. However, it would be necessary to induce investors to use related screening as a factor supplementing financial performance by making them more interested in social agendas such as the environment (climate change), fair society, shared growth, ethics, and morality. Since the differing effect of capital inflow according to the type of screening is a kind of the clientele effect, it is necessary to develop ESG fund products with a variety of screening.It would be necessary to examine the relationship between the ESG fund performance and the fund flow once again in the future and check if there is any change if the data are accumulated as ESG funds become more generalized after the awareness of ESG grows further, and investors become more interested in the practical properties of ESG funds.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.DK: conceptualization and investigation. YK: data curation and resources. IK: formal analysis, writing – original draft, and methodology. DK and YK: writing – review and editing. All authors have read and agreed to the published version 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.