Molecular property prediction is essential for drug screening and reducing the cost of drug discovery. Current approaches combined with deep learning for drug prediction have proven their viability. Based on the previous deep learning networks, we propose the Molecular Information Fusion Neural Network (MIFNN). The features of MIFNN are as follows: (1) we extracted directed molecular information using 1D-CNN and the Morgan fingerprint using 2D-CNN to obtain more comprehensive feature information; (2) we fused two molecular features from one-dimensional and two-dimensional space, and we used the directed message-passing method to reduce the repeated collection of information and improve efficiency; (3) we used a bidirectional long short-term memory and attention module to adjust the molecular feature information and improve classification accuracy; (4) we used the particle swarm optimization algorithm to improve the traditional support vector machine. We tested the performance of the model on eight publicly available datasets. In addition to comparing the overall classification capability with the baseline model, we conducted a series of ablation experiments to verify the optimization of different modules in the model. Compared with the baseline model, our model achieved a maximum improvement of 14% on the ToxCast dataset. The performance was very stable on most datasets. On the basis of the current experimental results, MIFNN performed better than previous models on the datasets applied in this paper. The discovery of new drugs or the reuse of drugs is a popular task in biochemistry. Prediction of drug effectiveness or toxicity based on molecular properties plays an important role in this task. In recent years, with the development of machine learning, especially the emergence of deep learning ,2, many Schneider’s studies  have shoLater, Durant et al.  proposedThe comparison in classification performance between various descriptors and molecular fingerprints is still a relatively ambiguous situation. The study by Mayr et al.  demonstrIn the previous study of Tseng et al. , an atteAlthough these models and methods have made some progress, many problems are still worthy of further exploration and research. One of the critical problems is the performance gap between different molecular descriptors and molecular fingerprints. There are some differences between molecular descriptors and molecular fingerprints, and it is necessary to make them more complementary. Another problem worth discussing is optimizing the structure of deep learning networks for feature extraction. With the development and advancement of deep learning, the effectiveness and efficiency of feature extraction are advancing continuously. Taherkhani’s study  found thTherefore, we focus on molecular fingerprints and molecular descriptors that contain more abundant molecular information. We combine both in a multimodal way to obtain complete information or features. Through the reasonable design of a deep learning network structure, we can achieve better feature extraction and avoid the occurrence of the overfitting phenomenon. This paper aims to achieve the above goals by designing the Molecular Information Fusion Neural Network (MIFNN). Two different patterns of molecular information are extracted from two different networks as feature information, and two parts of feature information are fused into the classification module to get the final classification results.In MIFNN, we use two convolution networks with different dimensions to extract the characteristics of molecular information. In the one-dimensional convolution network, we process the molecular orientation information proposed by Yang et al.  and add In the two-dimensional convolution network, the Morgan fingerprint is extracted. This is because there are many common digits in Morgan fingerprint; thus, the data can be spliced into a two-dimensional map to obtain complete feature information. For the classifier module, we use the particle swarm optimized support vector machine (PSO-SVM). This is the PSO algorithm optimization of the traditional SVM. The PSO algorithm is an adaptive algorithm based on alpha stable distribution and dynamic fractional calculus proposed by Deng . After sOur MIFNN has two unique advantages: (1) feature extraction methods with different dimensions are used, and splicing and fusion methods are used to obtain more comprehensive molecular features; (2) the bi-LSTM mechanism and attention module are used for feature extraction to obtain complete molecular information. This can provide good support for subsequent prediction and classification, as well as achieve better results; (3) we choose the PSO support vector machine as the classifier, which can obtain more accurate classification results without easy overfitting . We exteAccording to the characteristics of molecules, an MIFNN structure for molecular screening is proposed in this paper. The whole network structure can be divided into two parts. The first part is the molecular feature extraction module, which includes two subnetworks, namely, the Molecular Directed Information Feature Extraction Network (MDIFEN) and the Morgan Fingerprint Feature Extraction Network (MFFEN). MDIFEN can process the molecular directed information obtained through the transmission information network and enrich the information contained in the extracted features by the attention model. MFFEN is used to flatten the molecular Morgan fingerprint and send it to 2D-CNN for feature extraction. The second part is responsible for the fusion and classification of the features obtained by the molecular feature extraction module to achieve the purpose of molecular screening. The complete structure of the network model is shown in The extraction and description of drug molecular information should be as comprehensive as possible and have wide applicability. To improve the performance of our network structure, we chose the molecular directed information and molecular Morgan fingerprint . The speThe molecular directed information based on the directed message passing neural network (D-MPNN) structure  places iThe whole feature extraction part of MIFNN consists of MDIFEN and MFFEN, where MDIFEN is used to extract 300 bit molecular directed information, while MFFEN is used to process 2048 bit molecular Morgan fingerprints.MDIFEN includes the embedding layer, 1D convolution layer, mean pooling layer, bi-LSTM layer, concatenation layer, and attention module. We added a bi-LSTM layer to the traditional 1D-CNN to prevent gradient disappearance and gradient explosion. LSTM alone cannot deal with the sequential information flow well, and the molecular orientation information we use in MDIFEN has strict requirements for the order in the information sequence. Therefore, we use bi-LSTM, which has achieved good results in the NLP field, to optimize our structure. The extraction network of molecular directed information is shown in LSTM is a variant of traditional RNN, and its specific structure is shown in As shown in According to the previous statement, the bi-LSTM structure is used, with bidirectional hidden information forward After bi-LSTM is processed, it is sent to the attention module for the final processing of feature extraction. The calculation process of this module is as follows:In MFFEN, the 2048 bit molecular Morgan fingerprint is first converted into a 32 × 64 matrix as input, and 2D convolution is used for convolution calculation throughout the process. The pooling layer is also used for average pooling, and the padding is all ‘SAME’. Throughout the model network operation, the learning rate is set to 0.001, the Adam optimizer is used, the number of iterations is 100, and the dropout is 0.5 for training and 1.0 for testing. The network structure of the Morgan fingerprint feature extraction network is shown in Before features of different dimensions are fused, the data need to be flattened to one dimension. The flattening operation can be expressed asIn this module, a fusion layer is adopted first to connect the previously extracted features containing different information . When thThe adopted fusion strategy is feature fusion, a mid-level fusion between data fusion and decision fusion. It avoids both a large amount of computation and a large amount of information detail loss. The fusion is performed by using a concatenated fusion, where the feature vectors from the previous 1D-CNN and 2D-CNN spread are concatenated. The fusion equation isWith the help of the flattening and feature fusion strategy, the model can obtain more comprehensive and accurate evaluation results. The fused vectors are fed into the FC layer.We add an FC layer after the feature fusion layer to integrate various features extracted from different convolutional kernels in the previous layers to achieve the final classification. The implementation of the FC layer isEach feature value We use the dropout layer to improve the generalization ability and robustness of the model. The dropout parameter set here is 0.5. Its output is used as the final extracted feature and transmitted to the classification layer.SVM is a sparse and robust classifier that uses the hinge loss function to compute the empirical risk and adds a regularization term to the solution system to optimize the structural risk . It is oAt the same time, SVM as a traditional machine learning classifier has been improved continuously in recent years, with PSO-SVM representing one of the SVM variants. The PSO algorithm is used to optimize the kernel parameter It is worth mentioning that, when using SVM as the classification layer to perform the final classification, the result obtained is the distance from the experimental sample to the hyperplane, which is the basis for SVM classification . However program .The message passing neural network (MPNN) is a network structure that operates on graph G . It usesThere are The D-MPNN is a new network structure that improves the MPNN described above . This neUnlike the formula mentioned by MPNN, directed hidden states  follows :(25)ipqsIt is worth mentioning that  formula :(27)cpq0We chose the message function Note that the addition of According to the process mentioned above, the messaging phases of D-MPNN can be summarized in the following order:For The molecular directed passing information we need is obtained at this point by summing the hidden information. This information is further processed in the subsequent network to extract features and contribute to the subsequent classification .(36)DPIMorgan fingerprints, which are circular and topological fingerprints, are obtained by adapting the standard Morgan algorithm . They caThese fingerprints bring some advantages: (1) fast computation, (2) no need for predefinition (can represent an infinite number of different molecular features), (3) contain chiral information, with each element in the fingerprint representing a specific substructure, (4) can be easily analyzed and interpreted, (5) can be modified accordingly to different needs, etc. These fingerprints are originally designed to search for molecular features related to activity rather than substructure searches . They caStep.1. Atom initialization. Assign an integer identifier to each atom.Step.2. Iterative update. Take each heavy atom as the center and merge it in the surrounding circle of heavy atoms until the specified radius is reached.Step.3. Feature generation. Perform operations on substructures and generate a list of features.In contrast to ECFPs, which capture precise substructure details, functional class fingerprints (FCFPs) are more generalized, allowing the same class of functional groups to be used as a feature structure. Both features can be implemented in RDKit using ‘GetMorganFingerprint’. In this research, the RDKit package in Python is used for the extraction of binary molecular Morgan fingerprints .We set the number of bits of the desired molecular fingerprint to 2048 before running the code. When obtaining the corresponding fingerprint, we transformed it from one-dimensional information (2048 × 1) to two-dimensional information (32 × 64) to facilitate further extraction and classification of features. The specific acquisition steps are illustrated in In this section, we conduct extensive comparative experimental studies on the proposed MIFNN model to validate the method’s effectiveness. The CPU of the device we use is Intel (R)_ Core (TM)_ 9-10920x, the graphics card is gtx3080, and the system is Ubuntu 16.04.The data used in this paper are filtered according to publicly available datasets that are more used in the field and contain thousands to more than 100,000 drug molecules with different structures. Detailed statistical information on all datasets is listed in It is worth mentioning that, because the amount of data expressed as valid in these datasets is small, and two types of labeled data appear extremely unbalanced as a dichotomy, the receiver operating characteristic (ROC) curve is used to judge the network performance. The ROC is a curve based on a series of different dichotomies , with the true positive rate as the vertical coordinate and the false positive rate as the horizontal coordinate. The AUC (area under curve) is defined as the area under the ROC curve. We use the AUC value as the evaluation standard of the model. When the AUC value is larger, the classification effect is better. The calculation formulas of the horizontal and vertical coordinates of the ROC curve are as follows:To verify the performance of our MIFNN, we set up two control experiments in different directions. The first was to compare the complete MIFNN model with other baseline models with good results in this field. The second was to simplify MIFNN to verify the optimization effect of the modified algorithm and network structure.We first compared our model with several different baseline models. The baseline models used in this section included the model mentioned in MoleculeNet, the model Chemprop proposed by Yang et al., and the model proposed by Mayer et al. It is worth mentioning that, to make the verification results more convincing, we chose the model that performed best against the same dataset in MoleculeNet. The specific comparison results are shown in The models in MolNet had different optimal solutions for different datasets. However, they had one thing in common with other baseline models, i.e., the use of a single dimension of molecular information. Our MIFNN uses two different dimensions of molecular information for feature extraction. This led to our classification effect outperforming the baseline models. Our MIFNN model had the best performance in all datasets except SIDER.As mentioned above, MIFNN is composed of various modules. To verify the functions of various modules, we set up control experiments. The performance of the complete MIFNN was verified by simplifying the model. The simplified model included (1) using a convolution network of a single dimension in the feature extraction part of molecular information and using a simple classifier model such as FFN for comparison, (2) removing the bidirectional LSTM layer and attention module from the complete MIFNN model, and (3) comparing the traditional SVM with PSO-SVM. The specific comparison results are shown in We conducted a series of comparative experiments based on the D-MPNN network in the citation. By comparing the experimental results in the citation and the specific experimental results of our MIFNN, we can see that the AUC values obtained from our network were improved by 5.6%, 6.6%, 3.5%, 16.9%, and 6.4% for HIV, BACE, Tox21, ToxCast, and ChEMBL. In general, the optimization of the model can be considered more obvious when the AUC value increased by 0.03 or more.In the ClinTox and BBBP datasets, the AUC values obtained by our network were 0.1%, and 1.6% higher than the highest values of other models, respectively. This kind of situation can be considered a slight optimization, but the improvement effect was not obvious. Lastly, on SIDER, our model did not work as well as other traditional models.By observing In general, in the eight datasets, we performed a complete comparison with the current common baseline models with good results. Moreover, a comparative experiment was performed for the optimization points of the model to verify that our optimization method was effective. In the four datasets, MIFNN was much better than the best baseline model, while it was slightly better than the baseline model in three datasets, and only slightly lower than the Chemprop model in one dataset ; however, the difference was very small. This shows that MIFNN is better than the best baseline model. A more representative advantage is that the MIFNN model structure of each dataset was roughly the same, whereas, in the previous methods, the optimal model of different datasets was different. This shows that our method is more universal and can better complete the screening task of different drug molecular datasets.This paper presented a relatively detailed comparison of molecular property prediction models based on fixed descriptors, molecular fingerprints, and our proposed fusion features through multiple experiments on eight public datasets. Our future work will constitute two directions. The first is to try to add a residual module or a self-attention mechanism module to the classification model to obtain more accurate classification results. The continuous development of deep learning will provide us with the optimal structure to optimize our network and make it more comfortable with unbalanced datasets. The second is how to expand the training set by discovering new molecular descriptors or generating virtual fingerprint information for the problem of the small number of datasets during training.


When dealing with complex functional brain networks, group analysis still represents an open issue. In this paper, we investigated the potential of an innovative approach based on PARAllel FActorization (PARAFAC) for the extraction of the grand average connectivity matrices from both simulated and real datasets. The PARAFAC approach was solved using three different numbers of rank-one tensors (PAR-FACT). Synthetic data were parametrized according to different levels of three parameters: network dimension (NODES), number of observations (SAMPLE-SIZE), and noise (SWAP-CON) in order to investigate the way they affect the grand average estimation. PARAFAC was then tested on a real connectivity dataset, derived from EEG data of 17 healthy subjects performing wrist extension with left and right hand separately. Findings on both synthetic and real data revealed the potential of the PARAFAC algorithm as a useful tool for grand average extraction. As expected, the best performances in terms of FPR, FNR, and AUC were achieved for great values of sample size and low noise level. A crucial role has been revealed for the PAR-FACT parameter, revealing that an increase in the number of rank-one tensors solving the PARAFAC problem leads to an increase in FPR values and, thus, to a worse grand average estimation. Investigating the functional mechanisms underlying the complexity of the human brain still represents a challenging issue in modern biomedicine. The representation of the central nervous system as a complex network made its own way in traditional neuroscience, being validated at both structural and functional levels by anatomical studies and noninvasive neuroimaging techniques . An analBrain networks can be derived from anatomical or physiological observations, resulting in structural and functional networks, respectively . While sGroup level characterization of brain networks can give useful insights on the behavior of such a complex network within a given population. Group analysis can be used in neuroscience to investigate specific physiological or pathological conditions or to determine the existence of a common pattern, that is, the grand average, elicited by the same experimental paradigm in different subjects’ brain circuits. Such a problem implicitly requires an effective way to integrate information from multiple subjects, as well as handling the intrinsic inter-subject variability. t-test across subjects. Grand average connections were identified by means of their p-value: a connection exists if the p-value associated to it in the statistical comparison is below the significance threshold. Since statistical comparison was repeated for each connection in the network, a family-wise error rate correction was applied in order to reduce type I errors due to multiple comparisons [Many different approaches have been proposed over the years, but there is currently no consensus on a univocal analysis pipeline for the extraction of the grand average connectivity pattern. In  Toppi etparisons . In factparisons , resultiPester et al.  proposedEspecially in the last decades, the PARAFAC algorithm proved to be a useful tool for the analysis of multidimensional biomedical data. In , MiwakeiIn this work, we investigated the potential of a PARAFAC-based approach to extract the grand average from both synthetic and real connectivity datasets. Synthetic data mimic PDC matrices of different subjects under the same experimental condition, parametrized according to different levels of (i) number of subjects in the group (SAMPLE-SIZE), (ii) inter-subject variability (SWAP-CON), and (iii) number of nodes (NODES). The way the number of required factors for PARAFAC application (PAR-FACT) affects the final performance was also investigated. The algorithm was then tested on a real dataset made by PDC matrices of When dealing with autoregressive representation of multivariate time series, Partial Directed Coherence (PDC)  was demorocesses . By applSquared versions of PDC in its different normalizations are usually adopted, due to higher stability and accuracy .A tensor is a multidimensional array. More formally, an PARAFAC is a decomposition technique that factorizes an l tensor . AccordiAs a consequence, the PARAFAC decomposition of a rank By switching the error term to the left side of Equation (8), als eijd . For theThe solution to the problem in Equation (11) can be found by means of the alternating least squares (ALS) method. The key idea is to fix all factor matrices except for one in order to optimize for the non-fixed matrix and then repeat this step for every matrix, until some stopping criterion is satisfied . If an eg matrix . By lettA generic connectivity dataset Since ion of X .In order to identify the grand average pattern For the purpose of this study, the minimization problem in Equation (11) has been substituted byPARAFAC algorithm was tested on different synthetic datasets representing a set of connectivity matrices obtained from a group of participants for a specific experimental condition. Generation of synthetic datasets composed of different connectivity matrices (one per subject) obtained as modified versions of a predefined ground-truth matrix representing the grand average. The percentage of modification was modulated in the study and the ground-truth was changed at each repetition of the simulation process;Application of the PARAFAC algorithm to synthetic datasets in order to factorize the whole data matrix as a sum of rank-one tensors  was then carried out to evaluate PARAFAC performances in terms of false positive and false negative rates , each obtained as randomly modified version of a predefined ground-truth matrix. Modifications of the original ground-truth matrix consisted in the displacement of connections between different positions in the matrix. The density of the network was fixed to False Positives (FP), defined as the total number of null connections in the ground truth labeled as not-null in the grand average;True Positives (TP) defined as the total number of not-null connections in both the grand average and ground truth;False Negatives (FP), defined as the total number of not-null connections in the ground truth labeled as null in the grand average;True Negatives (TN) defined as the total number of null connections in both the grand average and ground truth.In dealing with the identification of the grand average connectivity pattern, PARAFAC accuracy was quantified by means of false positive and false negative rates. The comparison between the extracted rank-one tensor representing the grand average and the imposed ground-truth network provided four different parameters :False PoFor the evaluation of PARAFAC performances, we used:In order to have a synthetic measure including both parameters, we also considered the Area Under the receiver operating characteristic Curve (AUC) .Three separate three-way repeated measures Analyses of Variance (ANOVAs) were applied considering as main within factors SAMPLE-SIZE . This step allows us to remove input spurious connections due to volume–conduction phenomena, which will corrupt the final estimated pattern. The binarized PDC patterns of the overall subjects, obtained for extension task  and frequency bands, were properly arranged in a data matrix When considering the human brain as a complex network, degree distribution offers useful insight in describing the role of different brain areas. Since information flow within the brain is bidirectional, for each node As shown in Equation (23), when dealing with adjacency matrices, the joint degree distribution is nothing but the sum of output and input degree of each vertex . Such a For each NODES level, a In order to describe such an effect, in Results of the 3-way repeated measures ANOVA on FNR are summarized in vels see .vels see .PARAFAC algorithm was applied to the EEG dataset collected in a group of sholding . Grand aPARAFAC decomposition on real data confirmed what synthetic data analysis pointed out: increasing the number of PAR-FACT from 2 to 5 lead to different grand average estimations. In particular, using 5 led to an increase in the number of false positives, testified by the fact that almost all the electrodes showed the same high degree. As showed in Instead, the use of two factors allowed us to discriminate the role of different scalp areas during the execution of each task. Electrodes on sagittal lines close to the imaginary conjunction between inion and nasion  showed the highest degree. Electrodes in the hemisphere contralateral to the moved hand showed, in general, a greater degree.In this work we exploited the potentiality of a PARAFAC-based approach to extract the common mode pattern from both synthetic and real connectivity datasets.For synthetic data, ANOVA tables and boxplot representations in As expected, noise corruption and sample size have opposite effects: low levels of noise allowed for better estimation of the grand average pattern, whereas the opposite occurs for the sample size. Considering the noise corruption as the effect of the inter-subject variability and the number of subjects as the number of realizations of a noisy stochastic process, the search for the grand average pattern can be interpreted as the calculation of the “expected” pattern within the population. Because noise is assumed to be uncorrelated from the signal of interest (white process), under linearity assumptions the expected value for the sum between signal and noise is nothing but the summation of the expected value for the signal and the expected value for the noise. The expected value for a white noise process approaches zero as the number of realizations of such a process approaches the infinitive, thus justifying results about the effects of sample size on the estimated common pattern.These considerations specifically hold when the effect of the inter-subject variability can be modeled as the superimposition of white noise to the grand average network. This is only true when the inter-subject variability is due to random processes related to experimental and technical factors. Depending on the situation, the inter-subject variability could be better represented as a variation in cognitive strategies carried on by the individuals or an activation of different brain circuits in response to the same cognitive task. In such a scenario, PARAFAC decomposition is not successful and thus advanced modeling procedures should be applied. The idea could be to set up a multisubject network  or to coOur findings also point out that the number of required factors significantly affects the grand average estimation. When dealing with data modeling, different empirical approaches have been proposed to determine the proper number of factors for the PARAFAC model that better suits the original dataset ,32,33. TIt has been empirically proved that when the number of required factors draw close to the rank of the original data matrix, each rank-one tensor approximates the specific pattern of each subject . In the loadings , not eacFindings on synthetic data lead us to investigate both validity and robustness of the PARAFAC algorithm on a real dataset. As expected, the use of a greater number of factors led to an increase in the number of false positives. In fact, five-factor scalp maps show no involvement of specific scalp areas, but a general activation of all the included electrodes. Two-factor scalp maps allow the discrimination of the role of different scalp areas in the execution of the two motor tasks. The extracted grand average patterns show a higher degree on central and fronto-central lines electrodes mainly located in the hemisphere contralateral to the moved hand, which is expected according to the physiological EEG activity/reactivity during motor tasks which involves mainly electrodes above the contralateral sensorimotor cortex . Although it has been widely used for data fitting and tensor decomposition problems, this is the first time, to the best of our knowledge, in which PARAFAC has been used in EEG-based functional connectivity context. In particular, we systematically investigated PARAFAC performances in the reconstruction of a grand average pattern from a set of connectivity matrices estimated in a group of subjects. The study conducted on simulated data, properly modulated according to factors typical of experimental studies in the network science field , not only demonstrated the brilliant performances of PARAFAC in group analysis, but also provided guidelines for its application in different contexts. In fact, the results obtained would help other researchers to set a PARAFAC number of factors and to define the appropriate sample-size of their studies in order to obtain such high performances.In this paper, we applied PARAFAC algorithm to networks obtained by means of PDC estimator; however, the results could likely be extended to other connectivity approaches, and not only those related to the causality concept. As further development, we suggest comparing PARAFAC to other tensor decomposition techniques or, more generally, to different methods  to find the best approach for this purpose. Future works should also assess PARAFAC performances on real data in different neuroscience contexts since in this work we limited the investigation to networks underlying simple motor tasks.


The electroless copper plating of textiles, which have been previously printed with a catalyst, is a promising method to selectively metallise them to produce high-reliability e-textiles, sensors and wearable electronics with wide-ranging applications in high-value sectors such as healthcare, sport, and the military. In this study, polyester textiles were ink-jet printed using differing numbers of printing cycles and printing directions with a functionalised copper–silver nanoparticle catalyst, followed by electroless copper plating. The catalyst was characterised using Transmission Electron Microscopy (TEM) and Ultraviolet/Visible (UV/Vis) spectroscopy. The electroless copper coatings were characterised by copper mass gain, visual appearance and electrical resistance in addition to their morphology and the plating coverage of the fibres using Scanning Electron Microscopy (SEM). Stiffness, laundering durability and colour fastness of the textiles were also analysed using a stiffness tester and Launder Ometer, respectively. The results indicated that in order to provide a metallised pattern with the desired conductivity, stiffness and laundering durability for e-textiles, the printing design, printing direction and the number of printing cycles of the catalyst should be carefully optimised considering the textile’s structure. Achieving a highly conductive complete copper coating, together with an almost identical and sufficiently low stiffness on both sides of the textile can be considered as useful indicators to judge the suitability of the process. In the last few decades, interest in electronic textiles (e-textiles) and wearable electronics has significantly increased, which has stimulated development in these areas. Electronic components such as sensors, batteries or lights can be embedded into fabrics to add functionalities or decorative effects. This added value resulting from the integration of fabrics with electronics has been (and continues to be) beneficial to a wide range of applications, including, but not limited to, medical and healthcare, fashion, military, and workwear . TextileAmong the methods to coat textiles with a conductive layer, electroless plating is a promising one due to its industrial feasibility, low cost, deposit uniformity and high conductivity . In thiscore–Agshell NPs) can be performed in two steps: the first step is the synthesis of core Cu NPs, and the second step is the reduction of silver in the presence of Cu NPs. The reduction of silver from its salt is achieved by a displacement reaction where Cu0 is oxidised and donates an electron to Ag+, reducing it from its salt. As a result, the surface atoms of Cu NPs are replaced by silver [In the case of non-conductive materials, for the electroless deposition reaction to initiate, the substrate needs to be activated . The mosy silver ,37,38.core–Agshell NP-based inks for ink-jet printing [To produce conductive tracks for e-textiles using electroless copper plating, the copper coating must be deposited on a certain area of the textile and not cover the whole surface. To achieve this, the catalyst should be deposited only on the required areas of the substrate. This selective deposition of the catalyst can be obtained using ink-jet printing ,40, screprinting ,47,48. Iprinting .core–Agshell NPs. The developed catalyst was analysed using Ultraviolet/Visible (UV/Vis) spectroscopy and Transmission Electron Microscopy (TEM). The catalyst was then deposited selectively by ink-jet printing onto a polyester textile in different printing conditions. These variable conditions included the number of printing cycles  and the printing direction. The effect of the catalyst printing conditions on the subsequent electroless copper coatings was investigated regarding their visual appearance, copper mass gain, morphology, coverage and electrical resistance besides stiffness, laundering durability and the colour fastness of the respective metallised textiles. To realise this aim, Scanning Electron Microscopy (SEM), multimeter, stiffness tester and Launder Ometer were used.In our previous work , we inve2. Ends per centimetre (EPC) and picks per centimetre (PPC) were 69 and 30, respectively. The chemicals for preparation of the Cu–Ag NP catalyst were purchased from Sigma-Aldrich , including copper (II) acetate monohydrate (98%), poly solution , hydrazine  and silver nitrate . An ammonia solution (30% ROTIPURAN®), sodium hydroxide  and ethanol  were obtained from Carl Roth . Proprietary chemistry including Circuposit Conditioner 3323A used to pre-treat the textiles and Circuposit 3350-1 used to prepare the electroless copper electrolyte were obtained from A-Gas Electronic Materials.The textile used in this study was Polyester Crepe de Chine White which was obtained from Whaleys Ltd. . This was a plain weave polyester with a thickness of 0.32 mm and mass/unit area of 120 g/mFor printing the catalyst, a small format thermal ink-jet BREVA printer with a printing stage of 15 cm × 15 cm was used. Standard empty HP45 cartridges were filled with the Cu–Ag NP catalyst and loaded into the printer. Ink cartridge settings were as follows: voltage of 13.3 V and pulse width of 2.7 µs.Cu–Ag NP colloids were synthesised in the following steps: (i) synthesis of Cu NPs; (ii) modification of the Cu NPs with polyacrylic acid (PAA); and (iii) formation of an Ag shell on the stabilised Cu NPs. The first two steps were carried out following the same procedure described in our previous work  to prepa3 in 2.5 M NH4OH was slowly injected drop by drop into the stirred Cu NP-PAA colloid. Following the addition of the first drop of the Ag solution, the Ag complex was directly reduced by the Cu atoms and Ag deposited on the surface of Cu NPs. This led to a colour change from the red wine  to brown red and deep black (characterised to Cu–Ag NPs). Cu–Ag NP colloids were initially synthesised in different Ag concentrations of 0.25–5 at.%, and following their analysis, the one with 5 at.% concentration was selected as the catalyst for this study.To prepare the Ag shell in step (iii), a freshly prepared solution of 0.1 M AgNOThe initial printing image designed for the characterisation of deposited electroless copper coatings is depicted in Subsequently, each pair of simultaneously printed samples were placed into an electroless copper electrolyte at 46 °C for 30 min. The electrolyte was made up using Circuposit 3350-1 according to the supplier’s guideline. The textile samples were then rinsed under running water for 5 min followed by drying in the oven overnight.In order to find the appropriate Ag concentration to protect Cu NPs, optical spectra were collected using a Hitachi U-2001 UV/Vis spectrometer from the diluted Cu–Ag NP colloids in different Ag concentrations . To characterise the structure, size and distribution of the NPs in the Cu–Ag NP catalyst, a TEM technique was employed. For TEM analysis, a few drops of the dispersed Cu–Ag NP catalyst were pipetted onto a holey carbon film on a 200-mesh copper grid placed on top of a filter paper. This was followed by air-drying overnight, and then the grid was placed in the FEI Talos F200X TEM instrument  equipped with a super X EDS. The operating voltage was 200 kV and Velox software  was used to obtain the data.To measure the copper mass gain of the textiles, the mass of each piece of textile was measured once after cutting (in 8 cm × 4 cm) and then after electroless copper plating and drying in the oven overnight. The morphology of the copper coatings as well as the degree of the fibres’ coverage were characterised using a ZEISS GEMINI 500 VP SEM . To have a better insight into the conductivity of the metallised patterns from application point of view and in order to compare their performance in a more practical way, electrical resistance of the patterns was measured using a multimeter. The resistance was measured on the furthest corners of each dumbbell shape :2010 standard  in a Laucore–Agshell NPs as the main product.During the synthesis of Cu–Ag NP colloids using the method applied in this study, Ag was deposited on Cu NPs as a result of a galvanic replacement happening spontaneously after Ag ions came into contact with the Cu NPs in the solution. Subsequently, while Cu atoms were oxidised into ions and dissolved in the solution, Ag ions were reduced into atoms and deposited on the surface of the Cu NPs. The sufficiently slow injection rate of the Ag precursor into the Cu NP colloid produced a low Ag ion concentration around the Cu NPs to ensure collision and nucleation of the Ag atoms on the Cu NPs. Therefore, our method of synthesis was expected to synthesise Cucore-Agshell NPs. Similar results have been reported in other studies [In order to determine the appropriate Ag concentration to protect Cu NPs against oxidation and to understand the architecture of the synthesised Cu–Ag NPs, UV/Vis spectroscopy was carried out on the diluted Cu–Ag NP colloids.  studies ,53,54,55One of the aims of this work was the formation of the Ag shell on Cu NPs to protect Cu NPs from air oxidation. Therefore, the oxidation behaviour of the synthesised Cu–Ag NPs was investigated for up to 96 h after their synthesis and compared with the behaviour of pure Cu NPs. The synthesised Cu–Ag NP catalyst was analysed using TEM to determine its structure in addition to the size and distribution of the Cu and Ag NPs . As it iThe location of the plasmon absorption bands of metal NPs depends on their size and shape, whereas for the core-shell NPs, it also depends on the thickness of the shell . The obsCopper mass gain and appearance of the textiles after electroless plating are important indicators to compare the ability of catalysts  to facilitate electroless plating. In the case of our study, the aim of these analyses was to determine the effect of catalyst NP loading (different numbers of printing cycles) and printing direction on the deposited electroless copper coatings. In ink-jet printing of a catalyst on textiles, metal NP loading is one of the most important parameters influencing different properties of the final metallised patterns. In the case of underloading, as the catalyst particles do not locate close enough to each other, a continuous electroless copper coating will not grow a,b. In aRegarding the effect of printing direction, the copper mass gain of the samples printed with a catalyst at the same number of cycles in different directions was comparable after electroless plating . This reIt is worth mentioning that not all the spreading happened perpendicular to the printing direction, and some ink spread in the printing direction. This type of spreading was more obvious on patterns printed in the warp direction as a result of the higher density of the warp yarns. This resulted in the copper plated spreading in the printing direction for the patterns printed in the warp direction  was examined under a magnifying counting glass a. The teSelectively metallised textiles should be multifunctional and comfortable to wear to be able to serve as wearable electronics for different purposes. Stiffness is one of the most important characteristics of the textiles, which influences their flexibility, drape and handle properties. The bending resistance of the textiles is an indicator of their stiffness. To determine this, bending lengths of the metallised textiles printed with the Cu–Ag NP catalyst at different numbers of printing cycles and directions were measured using a stiffness tester. The results were compared with the ones for a control sample and are depicted in The metallised textiles printed with the Cu–Ag NP catalyst at different conditions were washed at different numbers of cycles. After each washing cycle, the electrical resistance on the face and back side of the metallised samples were measured using a multimeter. The aim was to observe the changes in their electrical resistance as well as the colour fastness to washing following each washing cycle. The results are shown in The laundering durability results confirmed the necessity of protecting the conductive tracks  in practical applications, against corrosion and wear damage resulting from the water stress, temperature, and mechanical stress of washing cycles. There are a variety of ways of protecting the electroless copper deposit including coating it with a polymer layer. Acrylate resin, polyurethane resin and polydimethylsiloxane have been proved to protect the copper plated tracks on the polyester textiles from wear resistance without affecting the track’s conductivity .The ISO standard Grey Scales were used to visually assess the colour change on the washed samples and the colour staining on the white multifibre fabrics from the metallised samples after each washing cycle. The results are listed in In this study, a functionalised the Cu–Ag NP catalyst was successfully synthesised and used as an ink-jet printing ink for the selective catalysation of the polyester textiles. The presence of a core-shell structure in the synthesised catalyst was proven to protect copper from oxidation. Different printing conditions were applied during ink-jet printing of the catalytic ink including varying the number of printing cycles and printing directions to see their effect on the final properties of the metallised textiles. Increasing the number of printing cycles enhanced the electroless copper mass gain in both printing directions. Although the textiles printed with the catalyst at the same number of printing cycles had similar electroless copper mass gain, the conductivities were lower on the ones printed in the weft direction, especially at lower numbers of printing cycles. This was probably due to the textile’s structure leading to higher total ink spreading on textiles printed with the catalyst in the weft direction. It was found that ink spreading can be minimised by careful selection of the textile’s structure and printing conditions including printing design, printing direction and number of printing cycles.Due to the lower weft cover factor compared to the warp one, stiffness of the metallised samples was lower on the weft way at the same number of printing cycles. As a lower weft cover factor leads to the deeper absorption of the catalytic ink by fibres, the weft way stiffness was similar on the face and back side of the metallised samples regardless of the number of printing cycles. On the other hand, for the warp way metallised samples, stiffness was very different between the face and back side of the metallised samples at the lower number of printing cycles. However, it became similar at the higher printing cycles. Independent of the printing direction, metallised samples printed with the catalyst at five cycles had highly conductive, complete electroless copper coatings on their back side. For all numbers of printing cycles and directions, conductivity of the metallised samples decreased after each washing cycle. While the decrease was moderate for the initial washing cycles, it became more significant for the higher number of washing cycles. The colour staining grade was considered as Good (grade 4/5) for all the metallised samples while the lowest grade of the colour change only occurred at the increased number of washing cycles .For the first time, the results of this study indicate the importance of understanding the textiles’ structure including the yarns thickness, density and the textiles cover factor in the warp and weft ways and how these influence the properties of the metallised textiles, such as conductivity, stiffness and laundering durability. Therefore, for each specific textile, printing conditions should be optimised to deposit the catalyst in such a way that can provide a metallised pattern with the desired properties to be suitable to serve as e-textiles.


In order to determine the site of origin (SOO) in outflow tract ventricular arrhythmias (OTVAs) before an ablation procedure, several algorithms based on manual identification of electrocardiogram (ECG) features, have been developed. However, the reported accuracy decreases when tested with different datasets. Machine learning algorithms can automatize the process and improve generalization, but their performance is hampered by the lack of large enough OTVA databases. We propose the use of detailed electrophysiological simulations of OTVAs to train a machine learning classification model to predict the ventricular origin of the SOO of ectopic beats. We generated a synthetic database of 12-lead ECGs  by running multiple simulations from the most typical OTVA SOO in 16 patient-specific geometries. Two types of input data were considered in the classification, raw and feature ECG signals. From the simulated raw 12-lead ECG, we analyzed the contribution of each lead in the predictions, keeping the best ones for the training process. For feature-based analysis, we used entropy-based methods to rank the obtained features. A cross-validation process was included to evaluate the machine learning model. Following, two clinical OTVA databases from different hospitals, including ECGs from 365 patients, were used as test-sets to assess the generalization of the proposed approach. The results show that V2 was the best lead for classification. Prediction of the SOO in OTVA, using both raw signals or features for classification, presented high accuracy values (>0.96). Generalization of the network trained on simulated data was good for both patient datasets  and presented better values than using exclusively real ECGs for classification (accuracy of 0.84 and 0.76 for each dataset). The use of simulated ECG data for training machine learning-based classification algorithms is critical to obtain good SOO predictions in OTVA compared to real data alone. The fast implementation and generalization of the proposed methodology may contribute towards its application to a clinical routine. In structurally healthy hearts, ventricular tachycardia (VT) occurs primarily as a consequence of abnormal ectopic foci in the ventricles, overtaking sino-atrial activation and leading to premature ventricular complexes. The most common type of idiopathic ventricular arrhythmias originates from the outflow tract, and shows a high incidence in young population . For thiIt is common to obtain recordings of the focal VT in the form of an electrocardiogram (ECG) prior to a radiofrequency ablation (RFA) procedure, which contain important information related to the OTVA and its origin. It is known that the majority of OTVAs originates from the RVOT (70–80%) . CliniciWe propose the application of machine learning (ML) techniques on ECGs from OTVA patients to guide their treatment. The use of ML and deep learning (DL) algorithms to learn from ECG data and provide predictions is becoming very popular in the medical field . One parML and DL techniques rely on the quality of training datasets, which should represent the target population and be balanced. In the particular case of OTVAs, there are different locations for the SOOs , and other co-variables such as the ventricular anatomy, its orientation, or the presence of pathological tissue (scar) that affect the ECG morphology. Since there are not large public labelled databases of OTVA patients available (largest in the order of 350 cases), the solution could be the use of computational models, e.g., digital twins  to buildIn this paper, we propose the use of ML models trained with large synthetic datasets of simulated ECG data obtained from biophysical electrophysiology simulations of OTVAs on digital twins. We present results on the prediction of SOO using different approaches in which we train ML models with a virtual population of synthetic simulated data, validating them with real clinical datasets from different centers, also evaluating combinations of synthetic and real data for training and validation. We also evaluated the best precordial leads and the signal features that produced a better LVOT/RVOT classification. This approach in which all the simulations have been performed beforehand and only ML models are used to predict the SOO of new patients permits its translation to daily clinical routine.We have developed a computational pipeline to build and validate our approach, from clinical data to final ML-based predictions, summarized in We included two different clinical ECG datasets from OTVA patients for the validation of the proposed approach. The first dataset consisted of 31 ECGs (DS-31) that were prescribed for catheter ablation procedure at the Hospital Clinic, Barcelona. All patients underwent a electro-anatomical mapping study by CARTO three navigation system  with a 3.5 mm irrigated tip catheter . During the procedure, 12-lead surface ECG and intracardiac recordings were obtained and displayed by an electrophysiology data acquisition system . Ablation was considered successful if the targeted OTVA was eliminated and it was noninducible after isoproterenol infusion. The site where RFA application eliminated the OTVA was considered the SOO and was labelled and saved in the electroanatomical mapping data for validation purposes. The study was approved by the local ethics committee and written informed consent was obtained from all participants. The second dataset was an open-source 12-lead ECG database of 334 OTVA patients (DS-334) published by μm. Every element was labeled according to its cellular properties as, endocardial, midmyocardial or epicardial cells. As described in In this work, we constructed ventricular digital twins from 16 different biventricular geometries built from patient-specific computed tomography (CT) scans. Each model was represented by a volumetric 3D mesh made of hexahedral elements with an average resolution of 400 We performed data homogenization to facilitate the data processing by the ML algorithm and a better comparison of the results. In particular, we classified all the ECGs  according to the SOO provided in the work by We chose support vector machines (SVM) to classify patient arrhythmias as a function of the SOO. An SVM is a well-known learning algorithm  that hasSince all ECG signals were conveniently normalized and aligned, segmented QRS complexes could be directly treated as patterns and also as feature vectors, where the pseudo-features correspond to the ECG amplitude at each time point. Given that the changes in the voltage convey the most important information of the ECG, we simply consider that the down-sampled raw signal  is a set of features that represent the data at specific time points around the R-peak. Therefore, after studying the cumulative variance of the principal components, the signals were down-sampled to 10 samples see .We used this down-sampled raw signal representation to determine the best lead combinations as well as the most important lead in terms of classification. This exploration was carried out exhaustively, that is, for each one of the possible lead combinations . Therefore, a SVM classification model was trained with the corresponding lead combinations and then evaluated with the two test datasets. To obtain the feature vector of a lead combination we simply concatenate the corresponding signal leads (increasing the number of dimensions associated to the classification tasks). Finally, we computed the accuracy distribution associated to each lead by considering all the accuracy obtained from any lead combination that contain that particular lead.extra − trees classifier models. In this kind of forests, the importance of the features are computed as the mean and standard deviation of accumulation of the impurity decrease within each tree (entropy based) and it is provided by the fitted model. The feature importance is specially interesting for raw signals as each feature covers a short time interval of the beat, so that the most important features correspond with the time intervals used by the classifier that better explain its predictions.The signal low dimension representation can also be used to determine what part of the signal is the most important in terms of classification. To assess this issue, we calculate a feature importance ranking based on Previous studies have reported a decrease in generality when using the raw ECG trace as opposed to ECG-derived features . To addrend − QRSstart) and the localization of the precordial transition. The precordial transition was computed via retrieving the signed maximum of each precordial lead and selecting the first lead where the QRS complex changed polarity, codified as a decimal point value within zero (V1) and one (V6). Secondly, eight per-lead features are extracted. These comprise the polarity of the lead’s signed maximum , the lead’s maximum and minimum voltage, its absolute maximum voltage (signed and unsigned), the lead’s amplitude and its area . Finally, three comparative features were computed: the signed maximum voltage of the difference between the leads, the area of the difference between the leads and the cross-correlation between the leads.The signal-based features consisted in the computation of several markers from the raw ECG. Firstly, two all-lead features were considered: the QRS′ total activation time  on various sub-samples of the training set. Then, predictions are made by majority voting from the trees. Similar methods like bagging and random forest develop each decision tree from a bootstrap sample of the training set, while the extra − trees algorithm fits each decision tree on the whole training set. Furthermore, similarly to the random forest method, the extra − trees model will randomly sample the features at each split point of a decision tree. However, random forest uses a greedy algorithm to select an optimal split point, while the extra − trees model selects a split point at random. All the features extracted from the simulated dataset (DS-2496) were ranked according with the results of the extra − trees classifier. A variance analysis was also conducted to evaluate the minimum number of features that optimizes the classification performance.When working with the 356-featured training set, the selection of the best features for classification has been carried out using r models . The modxi and xj) and performing a linear combination of the two yi or yj) was fixed through the λ parameter: yi was adopted if λ > 0.5, and yj was selected otherwise.In order to improve the performance of the classification task, the simulated dataset was augmented using mixup, as described by xi, and the labels yi were the ground-truth SOO (either LVOT vs. RVOT or the nine finer SOO sublocations). For the purposes of this work, α = 5 and β = 1.5 were selected as hyperparameters. The λ parameter was saved to be used as sample weight in the classification process. In the case of the finer sublocations, and to avoid issues with labels corresponding to distant sublocalizations , mixup was only applied when xi and xj were neighboring segments in a spatial sense, as can be seen in In this work, we employed single 12-lead QRS complexes as samples To evaluate the degree of generalization achieved by the SVM models and to exploit the datasets used in this work, we considered the following scenarios See :As we manage four datasets, namely DS-2496 , DS-7488 , DS-334 , and DS-31 , the test-set(s) used for the assessment of each learning scenario are those not employed in training. Furthermore, cross validation (CV) techniques (folds = 5) in the domain of the training set are also included. Accuracy values obtained for the DS-334 and DS-31 datasets were computed as balanced accuracy. More information about the classification of the main Scenarios (confusion matrix and accuracy per class) is attached in the Using the down-sampled simulated raw data (10 equidistant samples from the voltage ECG traces), we explored which were the lead combinations that showed a better performance for ECG classification. A total of 4,095 different models were trained using all the possible lead combinations. extra − trees model. As can be seen, the most important features were signal-based features extracted from the V2 and V3 leads. In addition, the panel on the right shows the accuracy scores obtained by the SVM-classifier by varying the number of features used in the training set. The calculated scores are the accuracy from a cross validation process (folds = 5) and the predictions of the datasets employed for model testing .Scenario 1, where only simulated signals were considered for training, presents the highest accuracy values in the classification. Raw down-sampled signals were able to classify the clinical signals of the DS-334 with an accuracy of 0.86, whereas the DS-31 presented lower values (0.71). Results did not improve significantly when we made use of augmented simulated data for training (DS-7488). The use of signal features also presented good classification values. Although the accuracy slightly decreased in the DS-334 with respect to the raw data , the accuracy for dataset DS-31 was considerably higher . As with the case of raw data, augmented feature data barely increased the accuracy values. Furthermore, as shown in the cumulative variance plot, classification with only 10 features was also performed. Results showed that a classification using the best 10 features, determined in Scenario 2, which was calculated using only real data for training, served as a good comparison of the classification performance of the simulated data versus the real data. It can be seen that, when using signals from different clinical datasets for training and classification, the accuracy in the prediction of the SOO decreases significantly, being inferior to the values of Scenario one in all cases. Scenario 3, on its behalf, used a mix of simulated and real data for training. All the accuracies (using raw data and featured data) surpassed the values of the Scenario 2, showing that the use of simulated data in the training can considerably improve the classification results.In this work, we have presented a methodology to automatically classify ECGs from patients that suffer OTVAs by a ML model purely trained with synthetic data from biophysical simulations carried out on ventricular digital twins. To this end, we trained SVMs classifiers that were able to determine the SOO of the arrhythmia, differentiating between LVOT and RVOT. We have validated the method with two clinical datasets acquired in different clinical centers. In particular, we show that this method can predict the SOO with an accuracy of 0.86 in a clinical database of 334 patients, and 0.84 in a second clinical database of 31 patients, without the need of performing any manual analysis on the ECG signals. This is key, since other algorithms in the literature require an electrophysiologist to extract a considerable number of the features from the 12-lead ECG signals and perform several calculations on them to predict the SOO . FurtherOur analysis on the use of different combinations of signals to train the model and predict the SOO pointed out that V2 was the signal that convey more information followed by V3. This is in agreement with the results already reported in a few clinical studies . A more The obtained classification results had a similar level of accuracy than clinical algorithms used in the SOO prediction . From thOne of the most remarkable results of this work is that the use of simulated ECGs for training not only predicts the OTVA SOO with good accuracy, but it even surpasses the performance of the databases trained with real data, especially when they are used with a different database. This is due to the higher variability of the simulated data, which also presents less bias towards acquisition instruments or protocol. Consequently, as it is shown in Scenario 3, the addition of simulated data to real databases can improve the prediction of the SOO in any dataset when compared with the results obtained by training with only real data (Scenario 2). These results support the use of simulated signals for improving the performance of ML classifiers, as it has been done previously for atrial fibrillation  or cardiIt is important to note that the designed ML-based pipeline does not require any further complex and time-consuming simulations, unless there is a need to update the model with additional data. That is one of the main limitations of physics-based approaches, in which the construction of the patient digital twin, and the computation of electrophysiology simulations is complex and requires hours to days to produce the results . This maEven though our method shows promising results when compared to existing solutions, it presents some limitations. First of all, to build the database, digital twin models must be faithful representations of patients, and the biophysical simulations have to be properly calibrated to produce realistic simulations that provide ECGs comparable to those recorded in clinical practice . OtherwiIn addition, although we include variability in our simulations , simulated data still is highly dependent on the initial conditions of the model. Increasing the number of the simulations, and varying additional parameters  could reduce the bias that the simulated data may present. The use of more anatomies could help to cover a wider range of anatomical variability. In this study we used 16 patient-specific anatomies that presented considerable differences in shape and volume, but including a greater inter-subject variability could also improve the simulated ECG data. However, the computational time necessary to build or extend the simulated database can increase considerably.Finally, we have not explored the classification of the SOO in the nine sublocations . The available datasets did not produced enough well-labeled data, and some of these sublocations were underrepresented . This same limitation was present in other works that used similar datasets . Richer We have shown a computational approach to predict the SOO of idiopathic ventricular tachycardia originated in the ventricular outflow tract. The method, that relies in biophysical simulation and machine learning techniques, is able to differentiate between LV or RV origin of the ectopic beat with an accuracy of 0.86 in a clinical database of 334 patients, and 0.84 in a second clinical database of 31 patients.Since all the simulated training set was generated offline, the presented methodology could be transferred to a clinical environment, avoiding the need of time consuming tasks such as building computational models of the heart and performing electrophysiology simulations. Nevertheless, the simulated signals  achieved high performance in the test sets (DS-344 and DS-31), demonstrating the viability to produce good classification models for real data. Moreover, the methodology is not dependent on the expertise of the electrophysiologist, and it is consistent between cases, which could provide an additional tool to electrophysiologist to plan RFA interventions of this type of tachycardia. Future works will focus on improving the accurate determination of the exact SOO of the tachycardia within the ventricles, especially in the outflow tract sublocations.


Floods are the most frequent type of natural disaster. Flash floods are one of the most common types of floods, caused by rapid and excessive rainfall. Normally, when a flash flood occurs, the water of the upstream river increases rapidly and flows to the downstream watersheds. The overflow of water increasingly submerges villages in the drainage basins. Flash flood early warning systems are required to mitigate losses. Water level monitoring stations can be installed at upstream river areas. However, telemetry stations face several challenges because the upstream river areas are far away and lack of public utilities . This research proposes hybrid effortless resilient operation stations, named HERO stations, in the flash flood early warning system. The HERO station was designed and developed with a modular design concept to be effortlessly customized and maintained. The HERO station adapts its working operation against the environmental changes to maintain a long working period with high data sensing accuracy. Moreover, the HERO station can switch its communication mode between the centralized and decentralized communication modes to increase availability. The network of the HERO stations has already been deployed in the northern part of Thailand. It results in improvements of the telemetry station’s availability. The HERO stations can adapt to environmental changes. The flash flood early warning messages can be disseminated to the villagers to increase the flood preparation time and to reduce flash flood damage. A flash flood is a major natural disaster that creates significant loss ,2,3. It A flash flood early warning system is designed to mitigate the loss. The early warning system’s goal is to give enough time for villagers in the risk area to save themselves before the flash flood impact, which is known as ‘golden time’ or ‘preparing time’ . The flaHowever, the traditional flash flood early warning system faces several challenges. Accurate flood situation estimates are the key to the flash flood early warning system . Flash fFurthermore, the telemetry station should work for a long time without an electric power line connection. Solar panels and batteries are commonly used in telemetry stations. The data transmission frequency is then adjusted to be suitable to maintain the flooding incident prediction accuracy with low power energy consumption ,18,19. HIn addition, the early warning chain must be complete to guarantee that the warning messages can reach the villagers in the risk areas. On the technical side, the early warning chain often experiences power outages and interruptions to the telecommunication systems. During a natural disaster, the communications networks might be damaged . AlthougThis research proposes hybrid effortless resilient operation stations, named HERO stations, in the flash flood early warning system. The HERO station was designed and developed with a modular design concept to be effortlessly customized and maintained. It adapts its working operation against the environmental changes to maintain a long working period with high accuracy in data sensing. Moreover, the HERO station can switch between centralized and decentralized communication modes. In the centralized communication mode, the HERO stations transmit the sensing data to the database in the central server. The water level data are represented on a website. The village leaders can decide on flooding incident preparation from the information on the website. In the decentralized communication mode, the HERO station can directly communicate with other HERO stations to examine the flash flood situation without the centralized server using short message services (SMS). In addition, the flash flood warning messages are also disseminated to village leaders directly using SMS text messaging to sustain the early warning chain. This network of HERO stations has already been deployed in the northern part of Thailand.Compared to the related works in the telemetry station design, the telemetry stations ,21,22 haIn contrast, the HERO telemetry stations are practically used in rural flood risk areas where the GSM communication is unstable and there is no Wi-Fi coverage. The HERO station design also focuses on the practical maintenance of the villagers. The modular design concept is also applied in HERO station architecture.https://www.pyflood.com, accessed on 11 April 2022) has already applied for flood risk areas. Moreover, the HERO station with an adaptive operation mode algorithm for switching operation modes and communication modes can be applied to disaster early warning systems such as tsunamis, volcanic eruptions, and wildfires. The modular design concept can also be applied to the sensor node development.The contribution of this research is the modular design of the telemetry station, the adaptive operation mode algorithm, and the design of the decentralized and centralized communication mode switching. This network of HERO stations has already been deployed in the northern part of Thailand. The website for flash flood early warning system . Practically, the water discharges have been collected from field surveys. The surveyors examine the river’s behavior by measuring the water discharge related to each stage of the river water level. The surveying process is time-consuming to collect most of the river water level stages, requiring about one year to manage the river’s low-flow, high-flow, and normal-flow. Then, the hydrograph, rating curve, can be plotted using a water level-discharge relationship. As shown in In , Q repreQ represents the water discharge . h is the water level measured by the telemetry station. Equation  demonstr   Generally, the flash flood early warning system consists of two main parts, which are (1) the telemetry stations and (2) the central server, as displayed in In traditional telemetry station design, the water levels are measured using the float level gauge, as shown in The new model of telemetry stations are proposed in this paper. They have been designed and invented to decrease maintenance requirements. Water levels are measured using an ultrasonic sensor to avoid contact with the water surface. Nevertheless, there are still issues since a telemetry station should be installed in the upstream river area (for early warning flash flood) where there is no electrical power. A solar panel and a battery system are required as the primary energy sources. However, the solar panel system cannot generate adequate power for the telemetry station in the rainy season. Moreover, the internet signal is unstable when the telemetry station is installed far from the city.In this paper, the telemetry station was designed with a modular design concept to enhance effortless maintenance and improve the working time of the telemetry stations. A self-adaptive operation mode is proposed to adopt the telemetry station’s battery against the environmental changes while keeping high sensing data accuracy. Moreover, a hybrid communication mode is also proposed to keep the early warning chain during internet connection disruptions using SMS to communicate between the telemetry stations to validate the flash flood situation and to disseminate warning messages to the villagers in the flood risk area.The proposed hybrid effortless resilient operation stations, called HERO stations, have been designed to work as telemetry stations in flash flood early warning systems. The telemetry stations should work for a long period of time. The availability of the telemetry station should be high, even though the telemetry station is installed in the upstream river area where there is no electrical power or telephone communication lines. The solar panel and battery component, along with wireless communication module, should be applied along with the other sensor modules. Therefore, the modular design concept is applied in the HERO station. The modules can independently be created, modified, and replaced. The modularity of the HERO station brings effortless maintenance to the flash flood early warning system and rural villagers can diagnose and repair the station with minimum effort. The modular architecture of the HERO station is described in As mentioned above, the telemetry stations are responsible for being the flash flood early warning system’s observers. They have been installed at the rural upstream river, where there is no electricity or telephone communication lines. Their energy source is a battery charged by a solar panel. Practically, there are problems with a lack of energy and hardware component failure issues. These issues result in the telemetry stations not operating well. The telemetry stations require maintenance and repair periodically. However, they are installed in remote areas, and villagers with technical expertise are lacking. The maintenance and repair costs for rural telemetry stations are prohibitive. Thus, the modularity concept is designed for minimum maintenance effort ,29,30. TThe HERO station consists of six essential components: (1) a solar panel and a control charger, (2) electronic circuit boards (main board), (3) a battery, (4) sensor devices , (5) water proof container (case), and (6) housing connected to the bridge, as shown in The electronic circuit boards have been designed following the modularity concept. There are eleven small electronic circuit modules: (1) input/output controller (I/O controller), (2) communication controller, (3) electric power controller, (4) water level sensor , (5) voltage/current sensor, (6) weather sensor, (7) RTC , 8) rain gauge, (9) mobile communication modules, (10) memory, and (11) display, as shown in INITIAL: When the HERO station starts, the I/O controller performs sensor connection checking by looking at its module interconnection table  and sendOPERATION: The I/O controller performs sensor operation readings periodically, and the frequency of this can be adjusted. The data from each sensor module is collected in the I/O controller, which reads the sensor data of the module registered in its module connection list. A time division is applied to each module communication. If the module cannot respond within the operational time limit, it is then deleted from the I/O controller’s module connection list, with the I/O controller assuming that the module is broken. Thus, the I/O controller will not communicate with that module again in the next operation.UPDATE: The I/O controller updates its module connection list systematically. The I/O performs initial step to recheck the module connection. In terms of maintenance, if a new module is connected to the system, the HERO station automatically registers the new module.The HERO station requires minimum maintenance effort. Each sensor module can be added to or removed from the station without reprogramming or reconfiguration. For example, when the weather sensor module is malfunctioning, it cannot send data to the I/O controller. The I/O controller then skips data processing from the malfunctioning sensor. The modular design makes the HERO station resilient to its minor failures. This procedure can be explained as follows:The HERO station consists of four sensor devices for measuring the water level and other environmental data. This section shows the part list of the sensor devices.Water Level Sensor: The maxbotic MB7066 ultrasonic sensor is used for measuring the distance between the telemetry station and the water surface. The maximum range of the sensor is 10 m. The sampling frequency is 10 Hz. The ultrasonic sensor sends the data to the I/O controller via an analog port.Rain Gauge: The David rain collection 6463 rain gauge sensor is used for measuring rainfall. During operation, the device can automatically drain water out. The maximum rainfall measured is 100 mm/h. The rain gauge sensor sends an interrupt signal to the I/O Controller via a digital port.Weather Sensor: The BME280 weather sensor is used for measuring temperature, humidity, and air pressure. The measured data are sent to the I/O Controller via an I2C bus.Station Energy Status: The IC MAX 471 is used for measuring the voltage of the HERO stations battery. The power consumption of the HERO station circuit is also measured.The critical issue with the HERO stations for flash flood early warning systems is energy management. Because the HERO stations installation location has no electrical power line, the HERO station is designed to use its charged battery as its primary energy. To increase the lifespan, the HERO station battery is charged using a solar panel via a control charger circuit. Equation  shows thThe specification of the battery used for the HERO station is 12 V. DC, 21 AH, sealed lead-acid battery. The battery does not require distilled water, so it is maintenance free. It also supports high recharging numbers. It can be used for up to five years in the right environment . A 40-W.The telemetry stations are usually installed at the upstream river, with no communication line for data transmission to the central server. The HERO station transmits the data through its wireless cellular communication module. The wireless communication module uses a UC20-G chip, which supports radio wave frequency at 800/850/900/1900/2100 MHz (3G UMTS) and 850/900/1800/1900 MHz (2G GSM). The UC20 chip provides a maximum data communication in HSPA+ of 14.4 Mbps for downlink and 5.57 Mbps for uplink . The funThe HERO stations are installed in rural areas. It is designed to be portable and securely installed on concrete bridge rails. The dimensions of the HERO station are 40 cm × 30 cm × 40 cm, excluding the solar panel. The solar panel is on the top of the HERO station to reduce the temperature of the component box during the daytime. This expands the lifetime of the electronic components. The HERO station, including the solar panel, is placed in a guarding cage for physical security reasons. The rain gauge container is located on the outside of the guarding cage to measure rain levels effectively. The HERO station uses an ultrasonic sensor to measure the water level. The HERO station hangs over the bridge rail, where there is no obstruction beneath the ultrasonic sensor. There are 18 HERO stations installed in Phayao province, Thailand. The areas in which the HERO stations have been installed are flash flood risk areas. This research focuses on two HERO stations located along a river. To measure the water level without contacting the water’s surface, the HERO station uses an ultrasonic sensor device, which emits a sound that humans cannot hear. It will also determine the time when the sound reflection returns and calculate the distance between the ultrasonic sensor and the water’s surface. W) calculation. Y is the distance from the HERO station base to the zero points of the surveyed river cross-section which are (a) and (b) in X is the distance measured by the ultrasonic sensor. For calibrating, the calculated distances are compared with the staff gauge, which is installed beside the bridge to measure the water level. The photo image of the installed staff gauge is also shown in Equation  explainsHowever, the ultrasonic sensor data may come with noise due to the actual floating objects in the river. Therefore, the HERO station filters the noise of sensing data using a moving average filter. The moving average is a simple low pass filter process ,35,36. TIn the case of a hardware component failure, the HERO station can continually operate. For example, if the RTC is broken, the HERO station can continue to send data packets to the central server. In this case, the data will be timestamped using the server time instead of the RTC. Moreover, if any sensor module is broken, the unmeasured data are not included within the data packet transmitted to the central server. Therefore, because of the missing data, the central server will be informed that the sensor has failed. Then, the maintenance process is started by volunteer villagers living close to the station. A diagnostic fault check is performed to identify the failed module. In the HERO station’s modular design, each module has an LED status indicator. The LED status indicator lights green when the module is working properly. If there is no green LED light or the LED status indicator blinks to show an error code, the module should be repaired. The broken module can be replaced in plug-and-play manner, with the volunteer villagers unplugging and delivering the broken module to the research lab, where a working replacement module will be released. During this maintenance process, the HERO station can continue to operate with no idle time.It is necessary to install the HERO station for flash flood early warning system at a point upstream, where there is no power electric transmission line connection. The solar panel and battery are vital energy sources for the HERO station operation. The HERO station can effectively manage power consumption by adjusting for suitable data transmission frequency according to the water changes. The adaptive operation modes are designed for the HERO station to perform highly accurate data sensing over a long working period.Algorithm 1: Main AlgorithmThe HERO station can adapt its operation between active and sleep mode. In the active, or transmission operation, mode, the HERO station measures and collects the sensor data from its sensors and then transmits sensor data to the central server to analyze and decide whether or not to disseminate the warning message to the villagers. While in sleep mode, the HERO station only measures and collects sensor data, but there is no data transmission to the central server in order to preserve energy consumption. To adapt operation, the HERO station analyzes the water level data to determine if it is similar to the previous data over a long-time span, which means there is no flash flood. Then, the HERO station continues its sleep mode period. However, when the water level changes dramatically, the HERO station works in active mode and continues transmitting the sensed data to the central server, which is explained in Algorithm 1. The water level threshold cannot be used as a constant because in each season the average water level is different. Therefore, data analysis is required to estimate the water level situation, which is explained in Algorithm 2. Then, the active and sleep mode adaptation is explained in Algorithm 3.INIT state. Then, the mobile communication radio is on and searches for the network (telephone network). If the WAIT_NETWORK.ACK is not Available, the HERO station waits to retry and returns to INIT state again. The HERO station searches for the network until a response is given. When the WAIT_NETWORK.ACK is Available, the HERO station calls Sensing  procedure and sends a data packet to test the central server in the TEST_SERVER state. If the HERO station receives a response from the central server (Server.Ack), the HERO station enters CENTRALIZED_MODE state. If the data transmission fails but the central server returns Server.Ack in time, the HERO station will return to the INIT state again. On the other hand, if the HERO station does not receive a response from the central server three times, the HERO station will enter into DECENTRALIZED_MODE state to prevent the HERO station from using too much power to transmit data while unable to contact the central server. To switch communication mode back to the centralized mode, the HERO station uses a timer  explains the operation of the HERO station. The process begins with the HERO station turning on Algorithm 3: Sleep and Active Mode AdaptationEWMA control chart. EWMA chart or exponentially weighted moving average chart is weighted the data in decreasing order. The recent data are high weighted while the most distant data contribute a less weighted. As such, the boundary values can be adjusted based on a series of previous water level data ,,TA) and reliable . AlthougThe HERO station is designed to perform hybrid communication modes to guarantee that the warning message will be disseminated. Under normal circumstances, the HERO stations work in the centralized communication mode. They periodically sense the data and transmit it to the central server to analyze the flood situation. The flood incident warning message is disseminated through the website and social media . Whenever a flood occurs at the same time as a communication problem, the HERO station can switch to the decentralized communication mode. Based on the water data sensed by the HERO station and its neighbor stations, the HERO station analyzes the water situation and decides to disseminate the flood incident warning messages directly to the village leaders through the SMS network.The network of HERO stations works as the decision support system in flash flood incidents for the village leader. Village leaders require to receive spatial-temporal data on river water level and the amount of water falling in the rain within a given time and area. Working as the decision support system as shown in FSM) of the HERO station’s communication mode switching. The operation begins with the initial state (INIT) in which the station starts the mobile communication module service to wait for the network connection. Then, the state turns to WAIT NETWORK.ACK, meaning the station waits for the acknowledgement message to return from the mobile communication gateway. If the acknowledgement message does not return within the prescribed time (ack time), the mobile communication module service will be stopped and the status will change to WAIT FOR RETRY state. The HERO station then has to wait for wait time seconds and returns to the initial state again. However, if the HERO station receives the acknowledgement message back from the communication network gateway, meaning the HERO station is connected to the network, the state changes to TEST SERVER state. During this state, the HERO station sends the test packet to the central server to check the connection. If no acknowledgement message returns within the specified time (test server time), the station will turn itself state back to TEST SERVER state and re-sends the test packet to the central server. This server connection test will be tried three times (DECENTRALIZED MODE). Otherwise, the station performs in a centralized communication mode, and the counter parameter will be reset to zero. Moreover, the HERO station will automatically reset (HW reset) twice a day to maintain stability. This procedure performs the HERO station’s state return to the initial state.The HERO station commonly works in the centralized communication mode with a good internet connection. The sensed data will be collected and transmitted to the central server through a mobile data network. The data packet details are shown in During communication failures, when the HERO station cannot contact the central server and OR the stakeholders cannot access the early warning website, the HERO station will switch to operate in the decentralized communication mode. Algorithm 4 demonstrates how the HERO station works in this mode. The sensed data are collected and their status determined, as explained in Algorithm 2. If the data status is equal to zero, the HERO station only stores sensed data within an external drive. Otherwise, the HERO station sends the request message to ask for the neighbor’s (NTS) status. If the HERO station receives a message from NTS, the HERO calculates voting weight  is the value setting for the HERO station to check the situation of flooding in decentralized mode. Usually, The HERO station checks the situation of water level data every sensing data. For example, when Equation  shows thr), the warning message will be directly disseminated to the village leader through the SMS network. If there is no response from any neighbor, the warning weight , dissemination of the warning message before the flood , parameter impact, and energy efficiency.The HERO stations have been deployed since 2018 in Mueang District, Phayao Province, Northern Thailand, to track water levels, which are collected and transferred for storage in the database on the control server every 5 min. STI21 and STI22 were installed on bridges located upstream and downstream of the same river. The water level data, together with the river velocity and a cross-section of the river collected from the field survey, was used to plot the rating curve graph of STI21 and STI22, as shown in In the experimental area, two HERO stations called STI21 and STI22 during the flood incident in August 2019. Local authorities have confirmed the data sets for their accuracy and will be used for simulation in this paper. The graph shows the water level data from time 0 to 4000 min (approximately three days). At the 1000th min, the water level measured by STI21 increased dramatically. At the same time, the water level measured by STI22 increases at the 1500th min. It means the significant water volume has occurred upstream, and this water volume will come along the river to the village located downstream within 500 min (about 8 and 1/2 h). This golden time helps the villagers prepare for the coming flood and reduce losses.The objective of the simulation is to assess the HERO station’s efficiency in terms of operation mode adaptation, data accuracy, and energy lifetime. STI21 and STI22, respectively. The X-axis displays the total simulation time in minutes, and the Y-axis shows the water level. The graph shows the operation mode of each interval time using the colored band. The results show that the HERO station primarily operates in the sleep mode during normal situations. This causes the HERO station to reduce total energy consumption and expand overall battery operation lifetime. However, during heavy rain , the HERO station mainly operates in active mode.The HERO station switches between transmission and sleep operation modes according to the water situation in centralized communication mode. The aim is to save the lifetime of battery consumption. The accuracy of the water level data is the main issue that has to be considered during the flood incident. During active mode, the actual water level is sensed and transmitted to the central server. However, under normal conditions, the water level data are measured and stored in the HERO station. Consequently, the central server will use previous data to analyze and disseminate the water situation. The data checking period . Then, the HERO station will disseminate an SMS about the serious water situation to alert the community leaders. As seen in In the decentralized communication mode, the HERO station is not able to communicate with the central server. Thus, the HERO station must determine the water situation and disseminate the warning message. In the centralized communication mode, the HERO station continuously transmits data to the central server. The village leaders and villagers can monitor the river water level and rainfall on the website. The flood incident warning messages are sent to the village leaders when the water level exceeds the warning threshold via the central server. The village leaders investigate the flood incident using the river water level and rainfall. Then, the village leaders can decide to warn the villagers in the flood risk area before the flood comes.In the Internet communication failure in the flooding area, the adaptive operation mode in the HERO station switches its communication mode to a decentralized communication mode. The decentralized communication mode starts when the HERO station cannot contact the central server. If the water level is out of bounds, warning messages are sent to the village leaders via mobile network using SMS. The proposed voting with a warning weight algorithm can reduce false-positive errors for flood incident warnings.N) and the amount of power used in sleep mode  in the centralized communication mode compared with that in the decentralized communication mode. The graph also compares energy usage with the traditional telemetry station. The results show that the HERO station operating in the centralized and decentralized communication mode uses less energy than an traditional telemetry station, at 38% and 42%, respectively.This paper proposes the HERO station, which was designed and implemented using three concept designs (1) the modular design, (2) the adaptive operation mode, and (3) the hybrid communication mode. The HERO station was designed and developed with a modular design concept to be effortlessly customized and maintained. It can reduce maintenance costs and make HERO stations highly available for flash flood EWS. The adaptive operation mode allows the HERO station to improve data accuracy while maintaining energy efficiency. The hybrid communication mode also means the HERO station can disseminate flash flood early warning messages to villagers even in Internet communication failures. The HERO station can adapt its operation and communication modes to environmental changes. The results show that even if the HERO station works mostly in sleep mode, sensing data errors from receiving the missing water level data are acceptable (MAE =