Plant electrophysiology carries a strong potential for assessing the health of a plant. Current literature for the classification of plant electrophysiology generally comprises classical methods based on signal features that portray a simplification of the raw data and introduce a high computational cost. The Deep Learning (DL) techniques automatically learn the classification targets from the input data, overcoming the need for precalculated features. However, they are scarcely explored for identifying plant stress on electrophysiological recordings. This study applies DL techniques to the raw electrophysiological data from 16 tomato plants growing in typical production conditions to detect the presence of stress caused by a nitrogen deficiency. The proposed approach predicts the stressed state with an accuracy of around 88%, which could be increased to over 96% using a combination of the obtained prediction confidences. It outperforms the current state-of-the-art with over 8% higher accuracy and a potential for a direct application in production conditions. Moreover, the proposed approach demonstrates the ability to detect the presence of stress at its early stage. Overall, the presented findings suggest new means to automatize and improve agricultural practices with the aim of sustainability. Such a mechanism helps them react and adapt to environmental changes that manifest with variations of plant electrical potential9. Such studies demonstrate the potential of the monitored plant electrical response to enable an automated plant health alert system that could optimize today’s agricultural practice regarding yield and sustainability.In recent years, by using machine learning techniques, several studies have revealed the existence of signal patterns in the monitored plant electrophysiology that could identify the plant’s health status in the presence of either biotic or abiotic stimuli9 explore plant electrophysiological signals acquired outside a laboratory, i.e., under typical greenhouse growing conditions, which brings a more direct impact on the everyday agricultural routine. They rely on traditional machine learning models using local signal features extracted from relatively small windows of several seconds to 30 min. One of their main aims was to study the signal characteristics and the temporal extent to which these characteristics could discriminate the state when the plant is growing in normal conditions from the stressed state caused by an applied stimulus, such as drought, nutrient deficit, or pest attack. They report accuracies of more than 80% for distinguishing these two classes.However, few of these studiesNevertheless, calculating features could be time-consuming and introduce an important computational cost. Moreover, as a single feature portrays a specific aspect of the signal, it is an important simplification of the raw data.5.Deep Neural Networks (DNNs) have taken a dominant place in the classification field with the emergence of novel deeper architectures and access to high computing power. In addition to their high performance, another advantage of the Deep Learning (DL) methods is the ability to automatically learn classification targets from the raw input data without needing a preceding preprocessing step to generate features14. For instance, the use of Convolutional Neural Networks (CNNs) is the prevalent method for automated and accurate detection and classification of diverse plant diseases17. However, this diagnostic-assisting methodology is mainly based on digital images, not a measure obtained directly from the plant and, therefore, could detect the presence of the disease once the symptoms are visible.Among numerous fields of application, DL techniques possess an emerging but substantial impact on improving and optimizing current agricultural practices, from production monitoring and management to robust decision support6 used two-dimensional (2D) CNNs applied to images representing the Visual Rhythm of the acquired signal18. Due to the limited data, the related results were not as satisfactory. More recently, another approach proposes the application of 1D CNNs to augmented data obtained from the original recordings using a Conditional Generative Adversarial Network5. With this approach, the accuracy for identifying salt tolerance in wheat seedlings reached approximately 93%. Nevertheless, as previously stated, both of these studies explore signals acquired in controlled laboratory conditions using a Faraday cage.The DL techniques have been scarcely explored for classifying the electrophysiological signal recorded from the plants. One of the reported studies in this fieldThe present study aims to apply DL techniques to electrophysiology signals acquired from tomato plants growing in typical production conditions, i.e., greenhouse, to explore the ability of these advanced end-to-end classification methodologies to identify the presence of stress caused by the nitrogen deficit in the provided nutrition solution. An additional objective of the presented study is to compare the DL techniques, in terms of accuracy and inference time, against the current state-of-the-art (SOA) approaches based on more classical machine learning algorithms that require precalculated features.Solanum Lycopersicum) from the commercial variety Admiro (De Ruiter) growing in a soilless manner using coconut fiber substrate. In the present study, all methods complied with the relevant guidelines and regulations. In particular, the variety used in our research is not genetically modified and does not represent a risk to the environment.The experiment for plant data collection was conducted by Agroscope, it took place at the research station in Conthey (Switzerland) starting on the 12th of July 2019. It included 16 tomato plants  and eight pairs of electrodes. As described in the previous studiesThe collected data consists of time series of univariate samples expressing the electrophysiology of each plant throughout 15 days of recordings. The first four days, corresponding to the period before the deprivation of nitrogen, represent each plant’s normal, pre-stimulus state, whereas the ten days after the application of the stimulus the stressed state.For a more straightforward presentation, the plants from the described experiment will be further referred to with their identifiers, namely B0–B7 and C0–C7, where each letter denotes the recording device, whereas the number represents the respective channel.Initial visual data inspection showed that different segment lengths of the recorded signal portray the normal and the stressed state differently, as presented in Fig. 19 presents an extensive evaluation of the performance of nine different and commonly used DL architectures for time-series classification applied on 81 specific baseline datasets. Hence, this study indicates the DL architectures most likely to achieve high accuracy for the data considered in the presented work. Among the nine, four architectures providing the highest accuracy for the largest number of the studied datasets were chosen to be tested. The following subsection briefly describes these four architectures and their implementation.A recent study20. The used implementation integrates a dropout technique21 at each layer for regularization and rectified linear unit (ReLU)22 as the activation function preventing gradient saturation when the network gets deep. The network ends with a SoftMax layer to output the classification confidences for each class.The Multi-layer perceptron (MLP) is a feed-forward architecture consisting of fully-connected layers with non-linear activation functions23. FCN is typically used in the semantic segmentation task, where they aggregate two paths: the first, called the downsampling path, performs the extraction of data information and their interpretation, whereas the second, the upsampling one, serves for localization23.The fully-convolutional network (FCN) resides only on locally connected layers, decreasing the number of parameters to tune; therefore, such architecture requires less time to train24 and a ReLU activation function.As the carried-out task is classification, the used implementation encloses only the downsampling path that extracts data features at different levels of abstraction. The final classification is done with a dense layer. Moreover, the network represents a union of several blocs, where each integrates a convolutional layer followed by a batch normalization layer25 it’s a specific type of network formed from residual blocks, each incorporating skip connections to move over various, which enables the extension of the maximum depth of DNNs without degrading the accuracy level of the model. Such architecture allows the layers to learn the identity function considerably easier and to find an alternative path for the gradient flow, which addresses the problem of vanishing gradient in very deep structures.Residual Network architecture (ResNet)The ResNet used in this work stacks three residual blocks followed by a global average pooling layer and a SoftMax layer.26.The Encoder for time series is a CNN-based architecture that was established with the aim of building an encoder network able to learn representations that are generalizable for different types of data that were not used for the training27, a parametric rectified linear unit (PReLU) activation28, and a dropout layer, as represented in in Fig. In the used implementation, the CNN consists of three convolutional blocks with max-pooling layers between them. Each of these blocks is formed by a 1D convolution, followed by an instance normalization layer29 on a machine with a GPU NVIDIA Quadro RTX 5000, CUDA 11.4, Windows 10 Enterprise, using tensorflow-gpu 2.5.0 and Python version 3.7.7.Before training the described DL architectures, the stored raw signal was filtered at 50 Hz and 100 Hz to eliminate potential power-source perturbance. The application was carried out using the official implementation provided on the respective Github repository9, the data separation was done with respect to the plants. Namely, the samples from the plants B0, B5, and C5 formed the test set, whereas the samples of the remaining 13 plants constituted the train set. This choice of plants was randomly made.Following the usual practice, the dataset was initially divided into a train set used for building the model and a test set for evaluating the model. To avoid eventual bias in the evaluationFour days of recordings were taken from each plant to build these two sets. The first two represent the period of normal growing conditions, i.e., the normal state, whereas the remaining 48 h portray the stage when visual symptoms appeared due to the lack of nitrogen—the stressed state.Within the first step of this study, preliminary modeling with the four architectures presented previously was performed to identify the most suitable algorithm, in terms of performance and accuracy, for the classification of the given plant electrophysiological data.As previously described, all these architectures differ between them and involve a different number of parameters and layers. Hence, one should expect that the time each one would take to train is different. To make a fair comparison, the time for training for all four models was limited to 1 h and 30 min.Signal windows with a length of 4 s were the input of each architecture, as initial exploration has shown that several seconds provide enough information to discriminate the plant states. Additionally, in preparatory analyses, similar metrics were obtained for windows of length 1 s, 8 s, and 16 s. Given that the sampling frequency is 500 Hz, each window consists of 2000 samples of the raw filtered signal.Normalization type. Through a visual exploration of the data, it was observed that the time series of each plant represents a different range of values, independently of the stressed state. Therefore, using the data without normalization could engender learning of spurious relations between the scale of a plant and the target. Another reason to normalize the data is to get better numerical stability in the training. As in the previous step, the window length used for this analysis was 4 s. The following approaches were compared:No scaling. To have a baseline of how the model would learn without normalization, the first approach involved training with input windows without any preprocessing after filtering.Scaling per plant array. A typical way of normalizing numeric data is scaling the input values in the range between 0 and 1, using that data’s global min and max values. The scaling employing the extrema values represents the min-max normalization. Before performing this normalization on an individual plant array, the extrema for that plant were first calculated. Scaling per window. Another way to normalize the data is to employ the min-max normalization method on each input window individually. Such scaling would modify the structure of the time series but could also help the network not to overfit regardless of the temporal evolution of the time series. Additionally, this approach does not require knowledge about the time series data outside the given window. This normalization was done in all windows representing the data of a plant.Subtract mean per window. The scaling performed individually on each window modifies its range and variance, which could be an important discriminative feature of the time series. To study this, another normalization approach involved only subtracting the mean value from each respective window without scaling it. In a similar manner as in the previous case, the normalization was performed for the entire data of a plant.Window length. The length of the signal sample is closely related to the extent of the discriminative information provided by the data. Consequently, the choice of window length affects the model’s performance. The selection of window lengths to explore was based on the visual data observations previously stated  was performed. As the dataset encloses 16 plants, a total of 16 distinct models were built. The training set for each model included 15 different plants, and the model was evaluated on the remaining one. Prior to this analysis, the signal of each plant underwent the simplification transform described previously.8 for classifying plant electrophysiological signal based on features extracted locally from the raw data. This approach uses the XGBoost30 algorithm to build the classification model.The DL-based classifier was compared with the recently proposed approach by Najdenovska et al.8. Following preliminary studies, the highest accuracy was obtained for 15 windows with a length of 60 s, which aligns with previously reported findings8.The features-extraction step requires choosing the length of the window in which the features will be calculated. The number of windows regulating the extent of the feature space is also a parameter to be selected for optimizing the classification outcome. In total, 34 features representing temporal, frequency, or time-frequency signal characteristics were calculated in each window8.To make a reliable performance comparison, the chosen SOA approach was built and evaluated on the same train and test set as the end-to-end DL-based one. The XGBoost parameters, such as the number of trees, the max tree depth, the regularization terms lambda and alpha, and the subsampling fraction, were tuned using a custom cross-validation process where each fold was represented by the samples calculated from one plant forming the training set, as described by Najdenovska et al.Besides the accuracy, another metric to compare these approaches is the inference time that would allow for assessing if it is feasible to predict in real-time with these models. The inference processing for Neural Networks is similar to the training as they process data in batches, which are batches of windows in the present study. These batches are processed at once for both training and inference processing. However, for training, they are all used to perform one step. Current practice often takes advantage of this to optimize the inference time when performing a prediction for more than one window, as passing one batch of N windows through the Neural Networks is faster than passing N batches of one window. Regarding the SOA approach, along with the employment of the XGBoost model, the features’ calculation is also part of the inference process.The framework for building the classifier was set following the results for different tested scenarios.Although 1 h and 30 min might appear as a short interval to train a DL model, the performed preliminary training was not a computing-demanding task, which allowed all four models, based on a different architecture to converge within that interval. Moreover, the encoder-based model could complete one epoch for the given time, while the other three went up to two. In fact, the encoder-based model involved a considerably larger number of parameters to learn. Table The pronounced differences between the training and testing accuracy of the built models Table  indicateThe data standardization analyses demonstrated that different types of normalization affect the model performance differently. Table Models built with windows of lengths four times smaller and four times larger than the length taken in the previous analyses (4 s) gave similar results but without achieving better classification accuracy than that obtained with the length initially tested (4 s). Such results, provided in more detail in Table However, an improvement of around 2% was observed with windows of length 30 s downsampled by a factor of 3 (Table The use of the simplified signal considerably reduces the models’ tendency to overfit. Namely, as shown in Table The proposed approach for combining the prediction confidence improved testing accuracy by around 10% when using the predictions of the 10 previous samples. The combination with 1000 samples provided even higher accuracies, around 99%. The results are more detailed in Table The combination employing the mean function for both sequence lengths resulted in similar accuracies to the combination with the median. Hence, as the prediction distribution is not expected to be skewed, the combination with the mean will be used in the further analyses.The LOOCV analysis demonstrated stable training accuracy across all 16 models, averaging 83.21% with a standard deviation lower than 1%. Nevertheless, high variability with a standard deviation of around 12% was observed across the testing accuracies, engendering a difference of around 6% between the mean training and testing prediction. Table The combination of 1000 predictions was also performed for each individual case, which improved the average testing accuracy by around 10%. However, the outcome for B3, B4, and C6 did not present a notable improvement.To analyze the behavior of the built end-to-end DL-based classifier for different stress stages, it was applied to each individual full-length electrophysiological signal recorded from the test plants. Figure One could observe that the model performs well for the time intervals used for training. In contrast, the model often fails to identify the stressed state for the period during early stress and the beginning of the appearance of visual symptoms. Such observations are more evident on the plot showing the thresholded confidence in Fig. These findings suggest that plants react differently at different stages of stress. Hence, the model should be trained on data representing more extended time intervals to learn a broader range of variabilities characterizing stress.An additional model was therefore built applying the same workflow established in this study that was used for the previous model. The new model was trained using 216 h from each plant, where half represented the normal state and the other half the stressed state.As expected, the model built on extended signal recordings could more accurately predict the presence of stress throughout its different stages. Namely, the confidence for predicting the stressed state is higher than 0.5 for the main part of the recordings acquired after applying the stressor. The confidence for predicting stress during the normal state is also increased but generally remains below 0.5. Figure Generally, with the built model on an extended length of recordings, it is possible to detect the presence of stress before it is very pronounced or visually apparent. With the resulting prediction confidence, the beginning of the stressed state could be considered the first peak of its temporal evolution that is higher than a given threshold above 0.5. For a threshold value set to 0.65, the presence of stress for plants B0 and B5 is detected 1 to 2 days before these plants showed visual symptoms related to the nitrogen deficiency Fig. . In the 8, predicts the stress on the testing plants with an accuracy of 79.27%, which is similar to previously reported findings by the same authors for classification of plant electrophysiology8. However, comparing it to the results from this study summarized in Table The model built with the XGBoost algorithm applied on signal features extracted from the electrophysiological recordings, as proposed by Najdenovska et al.The inference time for both approaches was computed for a single window and within a batch of 64 windows, as reported in Table In GPU using a batch of 64 windows, the prediction with the DL-based model takes 0.00057 s. The prediction with the XGBoost for 15 windows of 60 s, which are the parameters used for this experiment, takes 0.0156 s if the sample is predicted alone. If instead, it is predicted in a batch of 64 samples, it takes only 0.00028 s per sample. Hence, the time for performing the prediction only is similar to the DL-based approach. However, as the calculation of the features of 15 windows takes 3.73 s, the total inference time of the SOA method is considerably increased as the preprocessing step dominates it.This work presents an end-to-end DL-based approach, using the encoder architecture for time series, that highly accurately classifies plant electrophysiological signal acquired within the scope of studying the effect of nitrogen deficiency on tomato plants growing in typical greenhouse conditions. The developed workflow directly explores the raw acquired signal, eliminating the need for a preprocessing step calculating signal features, which is required by the current SOA techniques based on classical machine learning algorithms.Applied on a simplified signal representing 10 times less granularity than the original signal stored at 500 Hz, the proposed DL methodology predicts the presence of stress due to lack of nitrogen with an accuracy of around 88%. Without prior knowledge regarding the frequency band that potentially represents the information with a more discriminative power, we initially aimed to explore the raw signal at the original sampling rate. However, the model built on the simplified signal leads to an improved prediction regarding the initial models built on the original raw data. Additionally, using the downsampled smoothed signal considerably reduces the models’ tendency to overfit, which is represented by the large difference between the training and the testing accuracy in the previous cases. These findings suggest that the signal variations at high frequencies, potentially representing noise, portray high variability between plants, which perturbs the learning of the model. Along with the proposed methodology for simplifying the data, the granularity of the simplified signal was empirically established, but future studies could investigate further the frequencies to identify those that portray the patterns discerning the different states.The inter-plant variabilities are predominantly evident from the LOOCV analysis, showing large differences across the individual plant testing accuracies. Additionally, in less than 20% of the cases, the respective model, although with high training accuracy, performed a poor prediction. These differences could be due to the individual plant reaction to the applied stressor, closely related to its latent biological and health predisposition. Namely, while most plants would endure critical stress caused by the nitrogen deficiency after a particular time, some could be more resilient or would need a longer time interval to present the same reaction. Nevertheless, as the training accuracies remain stable across all 16 models, we can assume that the observed variabilities are immersed through the learning phase. Hence, a larger number of plants should be further considered allowing the model to learn and enclose a broader spectrum of these variabilities.The normalization of the data compensates for the range-based inter-plant variabilities. Moreover, the chosen standardization methodology is dependent only on a given window’s values, without needing to know the values range and evolution of the entire recorded time series. Hence, it enables a more straightforward application of the developed workflow for predicting the plant status in actual growing conditions. The assessed inference time of order of ms fortifies the possibility for such an application.The combination of the prediction confidence for the last subsequent samples leads to a substantial increase in the model’s accuracy. The prediction is improved by around 10% for a sequence of only 10 samples, whereas for 1000 samples, it reaches 99%. Nevertheless, the system would need more time for a large sequence to realize the change from one state to another. For instance, given that the selected window length is 16 s, the prediction delay for a combination of 1000 samples is 4.44 h, whereas for 10 samples is only 2.66 min. However, as the plant’s reaction to the nitrogen deficiency is a relatively slow process, detecting the presence of stress after four and a half hours could still be acceptable for the growers if the accuracy of that prediction is crucial.31. Moreover, confidence-based fusion has been employed within classification frameworks for different physiological signals33. However, different from these studies, our method uses the output from the same algorithm but for subsequent samples relying on the assumption that the likelihood of quick variation between the states is very low. Consequently, this approach is more straightforward than the usual combination of outputs from different learners.The fusion decision based on previous outputs to improve the prediction accuracy is a concept introduced previously. The literature proposes numerous techniques to this end, mainly used for combining the output of different single learners in an ensemble model incorporating the advantages of the individual learners, therefore achieving better performanceThe presented findings also show the strong potential for detecting the presence of stress in its early stage before the plant manifests evidently the lack of nitrogen. Such detection was notably more achievable for models trained on extended recordings intervals that allow the model to learn different plant reactions concerning different stress intensities. Early stress detection would potentially mitigate the damage and require fewer resources to increase and preserve the crop’s health. The threshold of 0.65 detecting the first confidence peak is selected empirically for the plants forming the used test set in this study. However, it could be further studied for an extended set of plants to establish a value that better generalizes inter-plant variabilities regarding the reaction to the applied stressor.8 by achieving higher accuracy of around 8% without applying the combination of the predictions, and 177 to 6544 times faster inference processing, depending on the configuration of the accelerator and the batch size. This important difference is due to the DL-based approach’s absence of the features-extracting process. However, it is worth noting that the time required to calculate the features depends on how computationally demanding they are. Furthermore, reducing the feature set by selecting the most discriminative ones could also decrease the inference time for this preprocessing step. Nevertheless, the ranking provided by the XGBoost gain measure indicates that among the three most discriminative features, one is related to wavelet decomposition, which takes considerably longer to compute than all other features. Hence, for the studied data, reducing the feature space to the set enclosing the most discriminative ones would not sustainably change the inference time for their extraction. However, the discriminatory information is closely related to the applied stimulus, i.e., the reaction it provokes in plants. Therefore, the inference time for the SOA approach could be much lower when classifying electrophysiological data acquired from plants that undergo different stress. On the other hand, as DL algorithms can learn complex patterns and relationships that may not be immediately apparent to traditional techniques, they could be more suitable for classifying plant electrophysiological data enclosing different stresses caused by multiple distinct stimuli and, therefore, portrayed by different signal patterns that, for traditional machine learning techniques, would require the extraction of numerous sets of features to represent them.The proposed methodology outperforms the current SOA approach in the field based on more classical machine learning techniquesThe calculation of the features predominates the inference time of the SOA approach. However, as the features represent the information on which the model is built, their extraction is essential for performing the prediction. Moreover, the SOA approach employs a normalization methodology requiring the knowledge of the full-length signal, and not only the given window, to deduce the needed extreme values, which deployment for plant state prediction in everyday agricultural practice is more challenging. An approximation of the plant range could potentially be established, but such an approach would introduce a bias.The proposed approach should further be validated for an extended set of plants, which would also enlarge the generalizability of the built model. Additionally, given that the studied data portrays highly discriminative information, the developed framework could be extended toward a multi-class classification predicting different stages of stress caused by a lack of nitrogen. Moreover, to assess the universality of the presented findings, the framework could be further applied to different types of stressors or different crops.Overall, the present work provides a new tool for fast and accurate identification of nitrogen deficit in commercial tomato plants based on the raw recorded electrophysiological signal, outperforming the current literature and allowing stress detection at early stages. Moreover, the proposed methodology possesses strong potential for a direct application in production conditions, enabling new paths toward an automated agricultural practice.


As psychological diseases become more prevalent and are identified as the leading cause of acquired disability, it is essential to assist people in improving their mental health. Digital therapeutics (DTx) has been widely studied to treat psychological diseases with the advantage of cost savings. Among the techniques of DTx, a conversational agent can interact with patients through natural language dialog and has become the most promising one. However, conversational agents' ability to accurately show emotional support (ES) limits their role in DTx solutions, especially in mental health support. One of the main reasons is that the prediction of emotional support systems does not extract effective information from historical dialog data and only depends on the data derived from one single-turn interaction with users. To address this issue, we propose a novel emotional support conversation agent called the STEF agent that generates more supportive responses based on a thorough view of past emotions. The proposed STEF agent consists of the emotional fusion mechanism and strategy tendency encoder. The emotional fusion mechanism focuses on capturing the subtle emotional changes throughout a conversation. The strategy tendency encoder aims at foreseeing strategy evolution through multi-source interactions and extracting latent strategy semantic embedding. Experimental results on the benchmark dataset ESConv demonstrate the effectiveness of the STEF agent compared with competitive baselines. Mental disorders have a higher lifetime prevalence and have a greater influence on people's quality-adjusted life expectancy . AccordiDue to the limited access to treatment and the increasing expenditures on healthcare, it is critical to develop digital health solutions , 5. DigiDTx products are generally delivered via smartphones or computers, which offers patients more convenience and privacy. In particular, DTx products on smartphones can be multilingual. Thus, DTx has the potential to address the inadequacy of psychological treatment access. Patients suffering from major depressive disorder (MDD) frequently struggle to apply what they learn in therapy or lose motivation to do what their therapists assign them to do. DTx can help patients with MDD keep practicing their skills and improve their ability to move away from negative thoughts. At the same time, DTx can provide therapists with a wealth of additional information about their patients' daily lives. With the help of DTx, clinicians can adjust the treatment and communicate with patients online in real time, intervening as needed .One of the most promising technologies for these DTx products is conversational agents. Conversational agents utilize natural language processing technologies to provide supplemental treatment or track adherence with patients. The advantages of conversational agents in mental health include giving people who require psychological counseling 24/7 access to treatment resources . ConversResearch shows that patients with severe symptoms are more likely to keep having a conversation with the conversational agent if they get emotional messages while they communicate , 17. HowIntroducing emotion into conversation systems has been widely studied since the early days. The emotional chatting machine (ECM)  was a noSeveral emotion conversation datasets have also been built based on social context. Medeiros and Bosse , collectESC) task was defined by Liu et al. . The first stage requires the supporter  to identify the user's problem, followed by properly selecting a support strategy to comfort the user for the second stage. Finally, the supporter should provide suggestions to evoke a positive mental state.To fully focus on the emotional support for conversational agents, the emotional support conversation , mentioned in MISC , D-2(Distinct-2), BLEU , and R-L in MISC  to indicFluency: Which of the following responses is more fluent and easier to understand? (2) Identification: Which answer is more accurate about your situation and more helpful in identifying your problem? (3) Comforting: Which answer makes you feel more comfortable? (4) Suggestion: Which answer between the two candidates gives advice that contains specific methods that are more useful to you than the other one? (5) Overall: Generally speaking, which of these two forms of emotional support do you prefer overall?We adopted the questionnaire  mentioneIn xi, yi are spoken by the seeker and the supporter, respectively, i denotes the index of round, and n−1 denotes the round number of history conversation. In addition to D, inputs for the ESC task also include historical strategy sequence S = {s1, s2, …, sn−1}, the seeker's last utterance with m words B = {b1, b2, …, bm}, and a psychological problem with p words C = {c1, c2, …, cp}. Hence, the goal of this task is to generate a supportive response conditioned on the dialog history D, history strategies S, the seeker's last utterance B, and the seeker's psychological problem C.Emotional support conversation is a generation task, and we can define this task as below. Given a sequence of utterances in dialog history Blenderbot-small  is an opEnc is the encoder, CLS is the start token, and SEP is the separation token between two utterances.where In the ESC task, the supporter chooses a different strategy to comfort the seeker based on the seeker's different mental conditions, which indicates that the seeker's mental states are important. We exploit COMET  to captuM is the sequence of user's mental states, relxAttr is one of the pre-defined relations in COMET, and ui is the utterance of the seeker. The relation xAttr in COMET denotes how the person might be described in an event (utterances). Note that the outputs of COMET are a series of emotion-related synonyms, and we select the first result as emoi.where Furthermore, we also use our common encoder to represent the sequence of historical mental states obtained from COMET.He is the representation of historical mental states and hei is the hidden state of the encoder. Similarly, we can feed the seeker's last utterance to obtain the seeker's current mental state emoB using COMET. The representation where Hg, we continue to employ the common encoder:According to Liu et al. , each coMotivated by the study by Peng et al. , we propFuse is the fusion kernel, Relu is non-linear transformation, ◦ denotes the element-wise product, and the wf, bf are learnable parameters.where t1, t2, …, tq}, each strategy embedding can be formulated asIt is essential that the ESC system chooses an appropriate strategy based on the seeker's mental states and generates a strategy-constrained response. Inspired by DialogEIN , we propEt denotes the strategy embedding lookup table and ei indicates the embedding of the i-th strategy category. We initialize the strategy embedding randomly and tune them during the model training. The dimension of strategy embedding is the same as the representations of dialog history and mental states for exploring the interaction from them. Thereafter, we use the strategy embedding to construct the representation of history strategies, S, denoted aswhere esi denotes the history strategy embedding for the i-th utterance.where To capture the evolution of a support strategy, a multi-head attention module is applied. Based on DialogEIN, we modify the multi-head attention module asHD is the query, He is the key, and Es is the value of the self-attention mechanism. Hs indicates the tendency information of strategy explicitly and contains the interaction information of historical strategies and mental states. We add the residual of query HD to Hs to ensure it sustains semantic information.where MHA stands for the multi-head attention module, Hs. By combining the distribution and strategy embedding, we derive latent strategy representation Thereafter, we train a multi-class classifier to predict the response strategy distribution for fully using strategy tendency information sp is the strategy probability distribution prediction, and  is the embedding set of the strategy label.where multi-classifier is a multi-layer perceptron, For conversational agents, the decoder learns a continuous space representation of a phrase that preserves both the semantic and syntactic structure of the utterance. To generate a supportive response, we fully integrate all kinds of information from the above-mentioned source. In MISC , the croCross − attn is the cross-attention module.where O is the hidden states of the decoder and The architecture of our model has two tasks: predict the strategy and generate the response. In this study, we directly adopted the same objective from MISC to train our model.Lr is the loss of generated response, nr is the length of generated response, Ls is the loss of predicting strategy label, s′ is the ground truth of the strategy label, and L is the combined objective to minimize.where Our experiments were conducted on the ESConv dataset, following the MISC division of the ESconv dataset for 9882/1235/1235 samples for the training, validation, and testing of partitions. We fine-tuned STEF agent based on the blender-bot small with the size of 90M parameters. The maximum length of the input sequence for the common encoder is 512, and the dimension of all hidden embeddings is 512. We set the training batch size and evaluating batch size to 8 and 16, respectively, to fit GPU memory and the dropout rate to 0.1. Following the previous work, we employed linear warm-up in 120 warm-up steps. We also employed AdamW as an optimizer, which builds upon the Adam optimizer and incorporates weight decay to improve the performance of regularization. The number of epochs (10 to 40) and initial learning rate (5e-4 to 5e-6) were also tuned. We evaluated perplexity for each checkpoint on the validation set, finally selecting the one corresponding to the lowest perplexity as the trained model. We used one GPU of the NVIDIA Tesla V100 to train the STEF agent, and the overall training time was 1.5 h. During training, we observed that the STEF agent trained for 20 epochs with a learning rate of 2e-5 showed the best performance based on perplexity.After training, we evaluated the model on the test dataset through two dimensions: automatic evaluation and human evaluation. In automatic evaluation, our model was compared to the baseline in terms of the accuracy of the predicted strategy and common LM metrics of generated responses. In human evaluation, we recruited 10 annotators and asked them to complete questionnaires. Each questionnaire includes two responses generated by our model and another model separately. The annotator compared the two responses on five aspects  and annotated the better one. A total of 64 samples were selected from the test set for response generation, and two other models were compared to ours.We compared our model with several baseline models: Transformer, MoEL , MIME 2, BlenderAs above mentioned in the procedure, we recruited 10 volunteers to complete the questionnaire. To assist the volunteer in acting as the support seeker as effectively as possible, each sample in the questionnaire includes information on a mental problem description and dialog history. The volunteer was asked to label the generated response with the “win" label when they thought the generated response was superior to the other response. At last, we made a statistical analysis of these questionnaires from three aspects . The human evaluation results in ACC metric of strategy prediction is lower than the competitor baseline. The seeker's mental states affect the selection of support strategies. Even when faced with a small mental problem, the seeker's mental state still changed with different support strategies. The support strategy is an alternative in different ESC stages; consequently, the ground truth of the support strategy is not unique. The human evaluation results can demonstrate that the strategy tendency encoder can construct the strategy evolution for the whole conversation and predict the appropriate support strategy to improve the performance.The automatic evaluation result and human evaluation result reveal that the STEF agent has a promising performance of support though the Case 1, our model first expresses its understanding of the seeker's description and then explains the benefits of talking with a friend. The response of our model is better than MISC's response, as shown in In Case 2, our model first affirms that the problem the seeker faces is difficult and provides the seeker with empathetic encouragement. Compared to the response our model generated, the MISC's response is not reasonable in this scenario, particularly in the DTx area. Users of DTx products are aware the conversational agent will be with them 24/7; hence, the expression will make them feel ridiculous, reducing the reliability of DTX products even further.In The two cases above have also been evaluated by annotators, and their feedback demonstrates that our model is able to comprehend the seeker's mental state, choose an appropriate strategy, and generate a supportive response. Compared to the ground truth, the generated responses of our model have the same effect on the seeker.The cases demonstrate that the STEF agent can show understanding and comfort the users. The STEF agent can be employed in the DTx solution to provide a personalized response based on the various symptoms of patients. The strategy of the STEF agent can be replaced or supplemented with more professional mental counseling skills. Thereafter, the STEF agent in the DTx platform can utilize recorded dialog to be more helpful and professional. Furthermore, the STEF agent can utilize the translation technologies to provide multilingual service for patients from all over the world.This paper proposes a novel conversational agent with a concentration on historical support strategies and the fusion of the seeker's mental states. We proposed the strategy tendency encoder to obtain the tendency of support strategies and the emotional fusion mechanism to gain the influence of historical mental states. Experiments and analysis demonstrate that the STEF agent achieves promising performance. However, we find that our results tend to include content that commonly appears in many samples . The results show a lack of diversity and are unable to show personalization, which is insufficient in ESC. There are other limitations, including those as follows: (1) The available data are inadequate, and the support strategy must be annotated. It is costly to train crowd workers to annotate the vast amount of data. (2) The support strategy should be more alternative in each phase. How to evaluate whether the strategy is appropriate is worth exploring. For future studies, we plan to improve the STEF agent based on the above limitations.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.QW developed the conversational agent, conducted the experiment, analyzed the data, created all figures, and wrote the manuscript. SP contributed to the research by providing critical feedback and editing the manuscript. ZZ created all figures and also conducted experiments. XH supervised the entire research process and providing guidance throughout. CD, LH, and PH contributed to the research by providing critical feedback. All authors contributed to the article and approved the submitted version.


In this paper, a structural health monitoring (SHM) system is proposed to provide automatic early warning for detecting damage and its location in composite pipelines at an early stage. The study considers a basalt fiber reinforced polymer (BFRP) pipeline with an embedded Fiber Bragg grating (FBG) sensory system and first discusses the shortcomings and challenges with incorporating FBG sensors for accurate detection of damage information in pipelines. The novelty and the main focus of this study is, however, a proposed approach that relies on designing an integrated sensing-diagnostic SHM system that has the capability to detect damage in composite pipelines at an early stage via implementation of an artificial intelligence (AI)-based algorithm combining deep learning and other efficient machine learning methods using an Enhanced Convolutional Neural Network (ECNN) without retraining the model. The proposed architecture replaces the softmax layer by a k-Nearest Neighbor (k-NN) algorithm for inference. Finite element models are developed and calibrated by the results of pipe measurements under damage tests. The models are then used to assess the patterns of the strain distributions of the pipeline under internal pressure loading and under pressure changes due to bursts, and to find the relationship of strains at different locations axially and circumferentially. A prediction algorithm for pipe damage mechanisms using distributed strain patterns is also developed. The ECNN is designed and trained to identify the condition of pipe deterioration so the initiation of damage can be detected. The strain results from the current method and the available experimental results in the literature show excellent agreement. The average error between the ECNN data and FBG sensor data is 0.093%, thus confirming the reliability and accuracy of the proposed method. The proposed ECNN achieves high performance with  In order to meet safety and environmental regulations, it is vital to ensure the reliability of operating gas and oil pipelines. The failure of a large number of pipelines in several countries in recent years has led to big losses in human lives, destruction of residential and industrial buildings, and has caused complicated environmental hazards . These dThodi et al.  pointed It is critically important to provide effective and suitable SHM systems for structures, especially dangerous structures that would cause catastrophic loss of life if their failure occurred suddenly without warning. The main use of SHM systems is to diagnose the health and safety of structures over time through the collection of structural health datasets from sensors installed in the structure and analysis using assistant algorithms to predict the remaining life of the structure. Therefore, catastrophic accidents can be prevented before they happen by detecting the different stages of the damage over time prior to a potential failure ,8,9,10Several studies have been reported on SHM methods for pipelines. Morison  introducOnce Fiber-Optic (FO) Sensors appeared in the mid-1970s, they became highly valued by relevant research departments in various countries. The United States is the country with the earliest and the highest level of research on FO sensors, and its progress in military and civilian applications is very rapid. In terms of military applications, their research and development mainly concentrates on using FO sensors for underwater detection, FO sensors for aviation monitoring, FO gyroscopes, and FO sensors for nuclear radiation detection. In the mechanical and civil engineering fields, FO sensors are mainly used to monitor important parameters such as current, voltage, and temperature of the power system, and to monitor stress changes in bridges, pipelines, and also important buildings.Numerous studies have been carried out on pipelines using FO sensors to estimate the effects of pipeline damage using both experiments and numerical prediction methods. Nikles and Briffod  introducTo develop a non-slippage FO, the bonding and point fixation methods were investigated experimentally and the critical effective sensing length for long-gauge fiber was studied . A combiAccording to the background of the literature presented, most of the conventional non-destructive testing (NDT) techniques are not applicable for current research work. It has been shown that the conventional methods do not provide the necessary information about either the current or future performance of composite pipelines systems and are not suitable for either new or old composite pipelines systems. In this case, FO sensors could be an effective alternative. In this study, a novel monitoring approach is developed to detect damage at an early stage in composite pipelines. The proposed approach relies on designing an integrated sensing-diagnostic SHM system with the capability to detect damage in composite pipelines at an early stage. This is achieved by implementing an artificial intelligence (AI)-based algorithm combining deep learning and other efficient machine learning methods using an Enhanced Convolutional Neural Network (ECNN) without retraining the model. The ultimate goal of the research is to provide pipeline operators with a continuous, real-time, active warning system for the detection of damages in composite pipelines using distributed strain sensors.The methodology used in this research aims for damage detection and safety assessment of composite pipeline structure via a highly effective and reliable approach, and then integrates this approach as a practical SHM which is reliable for old and new piping system operation.The aim is to investigate the feasibility of developing and operating a SHM system for the early detection of damage occurring in composite pipelines. The flowchart of the proposed method is shown in (1)Establish an understanding of the FBG sensor characteristics in a composite structure made of BFRP composite pipe.(2)Assess the effect of damage occurring in the BFRP composite pipeline on the displacement response from the dynamic signal for every FBG, including on dispersion, attenuation, and scattering.(3)Develop a sensory health monitoring platform for early detection of damage and damage classification of the composite pipeline.(4)Propose a novel AI-based SHM that integrates with the FBG sensors to provide damage classification in composite pipelines.(5)Suggest a hybrid approach to improve the accuracy of automatic identification of damage in pipelines by combining deep learning and machine learning in a new algorithm, known in this research as ECNN using a hybrid CNN + k-NN algorithm.(6)Verify the effectiveness and accuracy of the proposed AI algorithm based on the four indexes that include the true-positive rate (TPR), true-negative rate (TNR), false-positive rate (FPR), and false-negative rate (FNR), the accuracy rate (P), regression rate (R) and F1-score (F) can be determined for proposed AI algorithm.More specifically, this paper intends to:In recent years, there have been dramatic increases in composite material use in pipelines. This due to their unique advantages in mechanical properties, particularly specific strength and specific stiffness, as well as better fatigue resistance and damage tolerance capabilities.The specific characteristic of the damage is that the three components of FRP composites  do not fail at the same time. The most frequent type of damage is fatigue damage which is caused by cyclic loading effects. In this type of damage, the failure is caused by reductions in the residual strength in a part of a composite structure, depending on their type and size . There aThere are three regions in which failure mechanisms occur in composite structures: the matrix (I), matrix–fiber interface (II), and fiber (III). These are illustrated in The FEM of a composite pipeline is established by ANSYS software, and then both free meshing and tetrahedron meshing are applied. The size of the minimum element and element type of the composite pipeline are selected as The damage is modeled by reducing the stiffness of the pipeline. An enlarged view of damaged areas (marked in purple) in the pipeline is shown in The FBG sensor is a type of non-destructive test used in situ on a structure to measure the generated vibration applied to the structure due to external excitation sources. When the vibration source is environmental loads , then the structures are subjected to loads expressed as ambient oscillation or vibration.The ambient excitation force applied in this work is numerically simulated by MATLAB (−0.4~0.4 N) as shown in The 1st, 2nd, 3rd, and 4th frequency mode shapes of the intact pipeline are listed in As shown in By applying the stress–strain relation  for a pipeline under hydrostatic internal pressure  is reflected, and the remaining light passes the grating:The FBG sensors that have been developed can be used for detecting damage in the pipelines. These include FBG-based pressure sensors for finding the point of leakage. The mechanisms of these sensors depend on, when a leak initially occurs, the pressure drop in a pipeline in either direction of the leakage point. Determining the point of leakage depends on the time taken for the wave to reach the FBG-based pressure sensors. The advantages and possible challenges of the FBG sensor system are as follows:FBG sensors for pipelines can not only detect damage but also provide early warning of hazardous pipeline events that occur along the pipeline. They have features not found in traditional sensing technology. The key FBG sensor technologies are described as follows: The ability to measure the temperature and strain of thousands of points with a single fiber is very important for the inspection of slender structures ;(1)Reliable monitoring and locating of small and slow leakages of gas, oil, heating, etc.;(2)The ability to identify man-made pipeline damage and provide real-time alarm and positioning with a very high accuracy rate and very low false alarm rate;(3)The system can provide an intelligent alarm function based on the geographic information system (GIS) platform for monitoring requirements;(4)Continuous distributed measurement along the line without blind spots, with self-diagnosis function, real-time detection and localization of damage by the detection sensor system;(5)One optical cable can perform temperature strain detection and communicate at the same time;(6)High-temperature and low-temperature resistance, long-distance testing, centralized ground signal processing;(7)Passive, intrinsically explosion-proof, especially suitable for use in flammable and explosive environments;(8)Good anti-corrosion and anti-interference performance;(9)Long-term stability and measurement accuracy which are not affected by the loss of transmission fiber.The sensor cannot gauge the hoop strain at a certain point until it is closely adhered to the outer wall surface of the pipeline. In this work, due to the advantages of FBG distributed sensing, a single sensor with a multi-point monitoring system is used to monitor external strain changes. The FBG distributed sensor is installed on the outer wall surface of the pipeline with arbitrary orientation (From oils see b. Nt canThe proposed sensor sensitivity can be determined by assuming that the relationship between the pipe hoop strain And From Equation (12): The relationship between the pipe hoop strain The relationship between the strain and the wavelength of the grating in this band can be approximated as:Therefore, the relationships between the center wavelength of the grating, the hoop strain As shown in (1)Use of the CNN softmax layer, which is known in this research as TCNN.(2)Using the last hidden CNN layer (before the softmax layer) to obtain the neural codes that are passed to a k-NN which compares them to the prototypes of the training set using the Euclidean distance in order to obtain the most likely class, known in this research as ECNN.(3)Using the k-NN directly on the raw data without any representation learning.In the Because of its ease of implementation and high efficiency, the k-NN algorithm is considered one of the most successful in past SHM applications. The method of k-NN application is based on searching for (1)Integer k should be assigned first.(2)According to dataset distribution, the optimized k is determined so that we can iterate the integer number to be the best k that results in the highest accuracy.(3)The damage datasets pass through min-max normalization and z-score standardizations, as shown in Equations (23) and (24).(4)This is because when a pair of features is inputted, the k-NN searches the nearest k-pair of features using Euclidean distance on the same scale.As the target of this new algorithm, the sensor displacement datasets and TFS maps before and after damage are used as training features for damage detection in the composite pipe k-NN damage prediction datasets with the following steps:The performance of k-NN results depends on the effectiveness of the method to measure the distance between the model datasets feature and new test inputs. The Euclidean method is the optimal method usually used for distance calculations between test and trained data. We measure the distance along a straight line from point The connection architecture of the proposed CNN layer is presented in The fully connected layers in the CNN architecture are located as shown in ith output map in layer ith output map in layer Using weights One important stage of the neural network is the activation function that gives it the required nonlinearity, where a neural network becomes simple linear without it .Softmax is used for multi-classification problems, where it normalizes the neural network’s output to fit between “0” and “1”. It is applied to represent the certainty “probability” in the network output. The expression of the softmax activation function is given as Equation (27):c, jth is the region, and lth is the number of pooling layers:The pooling layer is usually arranged between sequential convolution layers. It is used to reduce the feature maps’ locative size. This is also called undersampling, by which network overfitting can be controlled. The operations that can be used for undersampling are maximum pooling and average pooling. It can be expressed as the average pooling feature of the pooling layer in Equation (28), assuming the pooling size is In this section, we present some experimental verifications of the proposed approach by applying the present technique to an experimental dataset adapted from Wang et al. . They usThe vibration experiments were performed on the cantilever CFRP pipes shown in The vibration strains values at In the last layer of the CNN, the learned feature maps are flattened into one vector by a fully connected layer and the expected output is extracted. In this paper, the displacement response evaluation problem for undamaged and damaged pipes is a regression problem. Therefore, a softmax activation function or k-NN algorithm is adopted in the full connection layer, by which a vector value denoting the displacement response is outputted. As discussed in The prediction of damaged pipeline displacement can be described through the following steps in pipeline .As shown in In general, for all indexes , using k-NN over the input datasets resulted in a lower average accuracy than the TCNN configuration. The hybrid approach ECNN achieved better results than the TCNN or k-NN. As a general conclusion, the proposed ECNN approach consistently outperformed the TCNN and k-NN for all indexes.To estimate the proposed ECNN performance in damage identification of BFRP composite pipelines, three indicators were calculated during the training process. The accuracy rate (As shown in As seen in Based on the response of the FBG sensor system of the damaged composite pipeline, a novel AI-based algorithm is proposed that combines deep learning and machine learning utilizing ECNN without modifying the training stage for the displacement response prediction of the composite pipeline. The proposed architecture replaces the softmax layer in TCNN with a k-NN algorithm for inference. The proposed ECNN model was divided into two networks: a response and frequency network. The frequency network converges to the shape mode of the pipeline, and the response network is a feedback network for predicting the displacement response a long pipeline. A composite pipeline made of the BFRP was analyzed using an FEM to simulate the displacement of the pipeline. Three damage levels were introduced to validate the effectiveness of the proposed approach. The training data were generated by the FEM. From the results, it can be concluded that the proposed AI-based model can effectively predict the displacement response of composite pipelines, and works much faster than in terms of computational time of the traditional FEM. The results show that replacing softmax with k-NN significantly outperforms the TCNN architecture in terms of accuracy. The proposed method achieved satisfactory performance, with the values of


In general, the athletes with higher positions on the ranking list presented a higher level of postural stability in both tests, which is also confirmed by the normalized values. However, for all variables of postural stability, no statistically significant correlations with sports ranking were observed. Higher values of Spearman’s rank correlation coefficients were found for the test performed in the one-foot standing test compared to the two-foot test. The results obtained indicate that particular attention in badminton training should be paid to the development of the level of postural stability in order to improve sports performance.The main aim of this study was to determine the relationships between postural stability and the place in the ranking of badminton players. The study examined 10 elite players from Polish national badminton team. The scope of the study included basic somatic characteristics, such as body height, body weight, BMI, and training experience. A Microgate GYKO inertial sensor system was used to assess the postural stability of athletes. Using Spearman’s rank correlation, cause-and-effect relationships between the place in the sports ranking and the analyzed variables characterizing postural stability were recognized. Depending on the distribution and homogeneity of variance, the significance of differences in variables that characterize postural stability between players of different sports skill levels (two groups) was calculated. The Student’s  One of the most important coordination motor skills is the ability to maintain balance, which plays a key role because of the significant impact on maintaining a vertical body posture, as well as being essential when performing complex arbitrary movements . A high Static balance is most often defined as the ability to maintain the projection of the body’s center of gravity within the support area . For theAnother issue is the choice of tools and methods for measuring static and dynamic balance. In population studies ,1 tests The above disadvantages and limitations forced the researchers to search for alternative tools to measure postural stability. It seems that different types of accelerometers can be an ideal solution. They are relatively inexpensive, small in size and do not require complicated software . The useThe importance of balance as an essential training element for badminton players, in preventing injuries and improving sports performance, has been highlighted by many authors. Moving around the court requires players to get to the shuttlecock as quickly as possible, while maintaining good balance and keeping the body under control. The aim of this report was to determine possible relationships between selected indicators of postural stability and the sports ranking of elite badminton players. Furthermore, differentiation of postural stability indices was also sought, depending on the sports skill level of badminton players.On the basis of the current state of knowledge in the addressed research problem, the following research hypotheses were adopted:Cause-and-effect relationships will be observed between the athletes’ position in the sports ranking and selected indicators of postural stability.Due to the nature of the game of badminton, higher correlation coefficients will be observed between the players’ position in the sports ranking and the postural stability test performed with one foot than in standing with both feet.Higher levels of postural stability indices will be observed in athletes ranked higher in the sports ranking lists.It is likely that no statistically significant differences in postural stability will be observed between the results of badminton players classified into two groups by sport level.The study group consisted of 10 males, players from the Polish National Badminton Team with an average training experience of 12.80 ± 2.74 years. In the 3 months prior to the study, all the athletes qualified for the study did not report any serious injuries, such as ankle or knee joint injury, chronic ligament dislocation, or other injuries to the lower limb. They also did not report neurological problems related to balance disorders. The research was carried out during the starting period during the players’ preparatory camp grouping. All badminton players participating in the training camp who consented to the study were covered. Thus, it was a purposive selection.The observed population was a group of players competing in top-rank national and international tournaments. Analysis of the level of achievement of these badminton players reveals that all of them had won the highest trophies in national competitions, whereas some of them had participated in the Olympic Games and world championships; therefore, the competitors were elite Polish badminton players.The tests and anthropometric measurements were performed in accordance with the Declaration of Helsinki. The examinations were approved by the Bioethics Committee at the Regional Medical Chamber in Kraków, Poland .The participants performed all the tests barefoot, in the same room, between 11 a.m. and 2 p.m. Postural stability tests were conducted in a separate room providing peace and quiet for the badminton player being tested. The tests were conducted at a temperature of about 22°C providing thermal comfort. All postural stability tests were performed by the first author of the report. Before the test, the players did not perform warm-ups or other physical activity. During the examination, the athletes had their feet placed straight, with no rotation in the talocrural joint. Feet were spread to the width of the hips, whereas upper limbs were freely positioned along the torso. Immediately after the completion of this test, the contestants performed a one-legged test. They chose the dominant lower limb, whereas the other limb was bent in the knee joint at an angle of about 90°, with the upper limbs freely positioned along the torso. The Waterloo Footedness Questionnaire-Revised (WFQ-R) was used to determine the dominant limb . During Somatic characteristics were measured using the Martina technique. These included the following variables: body height (b-v), GYKO high (b-T1), and body mass . Measurements of somatic characteristics were taken by an experienced person employed by the “Motoric Laboratory.” The focus of the analysis was on the following variables that characterize postural stability 2:2): The 95% ellipse of confidence is the ellipse that contains approximately 95% of the points of the trajectory.Area (mm2): The Convex Hull is the smallest polygon that contains all the points of the trajectory.Area Convex Hull : The ML length is the total distance in the medio-lateral direction given as the sum of the absolute distances between two consecutive points in the ML direction.Length AP : The AP length is the total distance in the anteroposterior direction given as the sum of the absolute distances between two consecutive points in the AP direction.Mean Distance: This is the mean distance from the midpoint of the trajectory.Mean Distance ML (mm): This is the mean distance from the midpoint of the medio-lateral trajectory.Mean Distance AP (mm): This is the mean distance from the midpoint of the antero-posterior trajectory.RMS Mean Distance: This is the dispersion of the distance (root mean square). In this case, as the points are centered on the mean, it is equivalent to the Standard Deviation.RMS Distance ML (mm), AP (mm): This is the dispersion of the distance (root mean square). In this case, as the points are centered on the mean, it is equivalent to the Standard Deviation.Mean Velocity: This is the mean travel velocity of the trajectory.Mean Velocity ML (mm/s): This is the mean travel velocity of the trajectory in medio-lateral direction.Mean Velocity AP (mm/s): This is the mean travel velocity of the trajectory in antero-posterior direction.www.pzbad.pl).The sports skill level of the tested players was determined based on classification lists drawn up by the Polish Badminton Association .n = 5) and low-level group (n = 5). The basis for the division of players into two groups (depending on the sports level) was the current sports ranking (the list of the Polish Badminton Association) and the subjective classification of players proposed by the two coaches of the national team (purposive selection).The whole material was divided into two groups according to the sports skill level: high-level group  of 0.71 was obtained for: Length AP (mm), Mean Velocity AP (mm/s), then for: Length (mm), Mean Velocity (mm/s)—0.59. The mean effect size was found for Convex Hull Area (mm2) and Area . No effect size was found for variables: Mean Distance (mm), Mean Distance AP (mm), Mean Distance ML (mm), RMS Distance (mm), RMS Distance AP (mm), and RMS Distance ML (mm).Basic statistical characteristics of parameters characterizing postural stability for the 2-ft test performed in groups depending on sports skill level are presented in 2), Length (mm), Mean Velocity (mm/s). The standardized values for these variables were ca. −0.70 SD.Regardless of the statistical analysis of the significance of mean differences, the analysis of standardized intergroup differences of analyzed parameters of postural stability provides interesting information. Such a methodological approach allowed for the analysis of the differentiation within all the tested properties (measured in different units). Analysis of the system of standardized differences revealed that a higher level of all parameters is presented by higher-ranked badminton players. In the case of the 2-ft standing test, the effect of the sports skill level on the results was the most pronounced: Length AP (mm), Mean Velocity AP (mm/s). A relatively large variation, in favor of the group with higher sports skill level, was also obtained for: Area . Basic statistical characteristics of parameters characterizing postural stability for the one-foot test performed by both groups depending on sports skill level are presented in In sports training, the aim should be to recognize mutual cause-and-effect relationships between its type, somatic, energetic, coordination, and mental aptitudes and the development of the results achieved. The effectiveness of badminton playing depends on many combinations of factors which affect the player during the whole training process . An adeqThe high level of balance allows badminton players to use all their muscle strength and speed in a variety of categories of techniques such as smash, clear, and drop shot . During The analysis of the effective playing time revealed that the energy is largely fueled by aerobic pathways (around 60–70%), while around 30% of the energy is generated from anaerobic processes . The effModern technological developments increasingly allow the use of various types of accelerometers  to measure postural stability. Particularly after the cost of such tools was reduced and wireless technology was used, they became popular in scientific research. The ICCs reliability results of postural stability measurements obtained with accelerometers are good and are usually above 0.75 . The GYKSo far, most research has been devoted to the comparison of the level of selected coordination skills between athletes practicing different sports and non-athlete peers and athletes at different sports skill levels. In this area of research, the greatest achievements concern comparisons of reaction time. The review of the results indicates that badminton players had shorter reaction times compared to those from non-athlete control groups . FurtherIn our study, we also compared selected variables characterizing postural stability between badminton players with different sports skill level. For both analyzed samples, the differences in mean results were statistically insignificant. However, analysis of the system of standardized differences revealed that a higher level of all parameters was presented by players from the group with a higher sports skill level. The calculated Cohen effect size should be regarded as medium for most variables. These regularities are particularly noticeable for the test performed on the dominant limb. Research on the displacements of the center of gravity (COG) of eight high-level athletes (belonging to the top three teams of the Badminton Championship in Japan) and eight amateur badminton players playing in university clubs was carried out by We believe that in the training programs of badminton players, special attention should be paid to the formation of balance. The training structure should include various types of static and dynamic balance exercises. Balance exercises should take into account different positions , the ground , and be conducted with eyes open and with the removal of visual feedback.It is necessary to study the effects of basic postural stability training in badminton players of different training seniority as well as sports level. Such studies should be conducted in different age groups in both sexes.Further studies are needed in groups with much larger numbers of individuals.Postural stability studies should be conducted with consideration of different starting positions, with eyes open or closed and on a stable or unstable surface.The use of alternative measurement tools for assessing balance should also be considered .The results presented in the study lead to the following conclusions:Spearman’s rank correlation coefficients indicate the cause-and-effect relationships between the ranking of badminton players and postural stability indices. These relationships are particularly noticeable for the one-foot test performed on the dominant limb.A higher level of postural stability is observed by badminton players classified higher on the ranking lists.The results obtained indicate that particular attention in badminton training should be paid to the development of the level of postural stability in order to improve sports performance.Further research should be conducted for different training groups and sports skill levels in order to confirm the effect of balance training on the effectiveness of playing badminton.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.The studies involving human participants were reviewed and approved by Bioethics Committee at the Regional Medical Chamber in Kraków, Poland . The patients/participants provided their written informed consent to participate in this study.JJ, GL, and MŻ contributed to conception and design of the study. JJ and MŻ organized the database. JJ and GL performed the statistical analysis. JJ, KW, and PP wrote the first draft of the manuscript and wrote the sections of the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Current railroad customs clearance systems are problematic in that the movement of trains is occasionally restricted for extended periods during inspections to verify cargo integrity at customs clearance. Consequently, significant human and material resources are consumed to obtain customs clearance to the destination, considering different processes exist for cross-border trade. Therefore, we developed a cross-border blockchain-based non-stop customs clearance (NSCC) system to address these delays and reduce resource consumption for cross-border trains. The integrity, stability, and traceability of blockchain technology are used to construct a stable and reliable customs clearance system to address these problems. The proposed method connects diverse trade and customs clearance agreements in a single blockchain network, which ensures integrity and minimal resource consumption, and includes railroads, freight vehicles, and transit stations in addition to the current customs clearance system. The integrity and confidentiality of customs clearance data are protected using sequence diagrams and the blockchain to strengthen the resilience of the NSCC process against attacks; the blockchain-based NSCC system structurally verifies the attack resilience based on matching sequences. The results confirm that the blockchain-based NSCC system is time- and cost-efficient compared with the current customs clearance system and offers improved attack resilience. Cross-border freight transport by rail needs to undergo customs clearance at the border. The clearance process is time-consuming and requires considerable human and material resources. Furthermore, the rail freight agreements between different countries differ from each other , with seThe existing customs clearance process transmits customs clearance data using the server–client method, and human resources directly validate cargo integrity. However, NSCC transmits data in a P2P system, with cargo integrity easily checked using the hash function. In addition, compared to the existing customs clearance system, which requires trains to stop at stations, NSCC improves efficiency because it inspects cargo integrity while the train is underway, making this operation the first example of NSCC procedure using blockchain for international rail freight transportation. In addition to resolving the problems associated with the current customs clearance system, the issue of data integrity while the data are being transferred from customs clearance can also be structurally resolved. Furthermore, a blockchain can be effectively applied to other domains because the developed blockchain-based NSCC system offers resilience to common attack scenarios and structural advantages. By utilizing blockchain-based NSCC, a plan was proposed to ensure cyber resilience against attacks targeting station and train nodes that participated in existing customs clearance procedures. In this study, we experimented with the cyber resilience environment that blockchain structurally provides and applied it to existing customs procedures, which enabled us to confirm the structural advantages of blockchain-based NSCC by constructing attack–response scenarios.The remainder of this article is organized as follows: The structural resilience and consensus process of a blockchain is briefly described in In this study, we develop a blockchain-based NSCC system and demonstrate its structural attack resilience using a blockchain, its consensus algorithm, and an attack sequence diagram created with MITRE ATT&CK. In this section, we discuss relevant prior studies and present a method that applies to this study.In this section, we describe the structural resilience of a blockchain, including the structural benefits of using a blockchain with the NSCC system, as well as the background of this study. A blockchain is a distributed ledger technology in which every node connected to the blockchain network owns the same ledger and is structurally resilient to data forgery, denial of service (DoS) , and avaOne drawback of blockchain technology is that it cannot be utilized in various applications currently in operation in the industry. At present, efforts are ongoing in numerous domains to introduce blockchain technology into conventional industries; however, introducing a blockchain structurally is problematic. Incorporating a blockchain in conventional industries is time-consuming because of the high initial introduction cost. Moreover, an attack can target a blockchain network while it is transmitting, receiving, and distributing data when blockchain technology and older systems are combined. However, in this study, we assume that trains, freight cars, and customs clearance stations comprise one IoT node environment. Moreover, we utilize the structural advantages of a blockchain to demonstrate resilience to various attacks targeting industrial networks. In addition, we identify methods to utilize blockchain technology in various industrial domains.In this study, we develop a blockchain-based NSCC system. An integrated higher-level decision-making process is necessary because the NSCC system is a new consensus body created by the collaboration of numerous consensus bodies. To choose the best blockchain consensus algorithm for NSCC, we compare four primary consensus algorithms: proof of work (PoW), proof of activity (PoA), proof of stake (PoS), and delegated PoS (DPoS). To construct a blockchain-based NSCC system, this study chooses the PoS consensus algorithm and obtains consensus from each nation and consultative body. Railway cooperation organizations, such as the Organization for Cooperation of Railways (OSJD)  and the The MITRE organization created MITRE ATT&CK in response to the expansion of the influence of and harm inflicted by cross-border cyberattacks ,13,14. TIn this study, we build a blockchain-based NSCC system attack and defense scenario and demonstrate its attack resilience by applying attack scenarios and sequences created using matrices of MITRE ATT&CK. In conventional security studies, the attack life cycle is depicted as a sequence diagram. We demonstrate the attack resilience of the NSCC developed in this study using this attack sequence diagram. Attack sequence diagrams are frequently used in academic research to support and demonstrate the reliability of networks and systems. By employing a DoS attack against the voice-over-internet protocol  environmIn this study, an attack sequence diagram is established and used for process analysis to demonstrate the attack resilience of the developed blockchain-based NSCC system. A sequence is constructed in accordance with the basic NSCC process, while the attack life cycle is developed by selecting a random attack point in the sequence.The border NSCC system operates in areas in the vicinity of stations located on the borders between different countries. The system is implemented by configuring the blockchain network and enabling data transmission between trains and transit stations that belong to different networks. As shown in The process developed in this study enables trains to proceed through customs clearance without stopping between transit stations; in addition, the attack resilience is structurally demonstrated. To proceed through Station 2 in country B, a train that already passed through Station 1 communicates the information required for NSCC. After receiving it, Station 2 checks the integrity of the data by comparing it to the hash value ,43 of thThe operation of the NSCC system is described in The NSCC process is divided into five individual steps, as shown in (Step 1) Enter and transact: A train node approaches a station node by this process to conduct the NSCC process. The customs clearance data (raw data) are processed by the train node and sent to the station node for customs clearance. This process employs a security network  that utilizes the base station of each country [ country ,49.(Step 2) Receive and hash: Data from the train node are relayed to the station node, which hashes the data using a hash function. The calculated hash value is compared and validated in Step 3. The hash function to be used at this point is chosen from SHA-256 [ SHA-256  or Kecca SHA-256  and appl(Step 3) Compare: The station node compares the hash value of the hashed data with that of the initial customs clearance data generated when the cargo was initially loaded. The hash value uploaded to the distributed storage is currently compared with that produced by the station node based on the transaction recorded in the blockchain. The results of the comparison are broadcast in Step 4.(Step 4) Broadcast: The success of the NSCC process is determined by comparing the hash value produced by the station node to that in the distributed storage. Subsequently, the train node decides on whether to proceed. If the hash value of the distributed storage that already exists differs from that generated in the relevant station node, the train proceeds in accordance with the existing customs clearance procedures. If the two hash values correspond, indicating that no irregularities exist with the data or cargo, the train node passes through without stopping. The passing information is broadcast to other stations and train nodes.(Step 5) Dashboard: A dashboard displays the NSCC-related data. The visualized data can be examined and subsequently analyzed. The corresponding dashboard of each node allows users to view information about the blockchain network and hardware resources.Potential attack points for each node, component, interface, and layer that constitute NSCC are shown in For instance, if the station in This section describes the organizational structure of the blockchain network, and the manner data are sent to and received from the network via an existing railway network. The structure of the blockchain-based NSCC network is depicted in The network for NSCC based on a blockchain, shown schematically in The number of connected nodes is also changed if NSCC is applied to customs clearance. The network includes the customs clearance nodes from 29 OSJD and 51 OTIF member countries, assuming that the present customs clearance offices in border areas are participating (as of December 2022) . As moreA summary of each process depicted in A sequence diagram for basic customs clearance is defined and systematically discussed in The basic flow of the developed blockchain-based NSCC is shown in The sequence diagram shown in Given that a blockchain is a platform that offers integrity and reliability, the NSCC process can be conducted by relying on these features of a blockchain for customs clearance in trains and transit stations. As presented in The attack–response sequence diagram for the potential attack points of the blockchain-based NSCC system, which is the main concept of this research, is presented in this section. The entire sequence demonstrates that the blockchain structurally has attack resilience, with an attacker performing an attack sequence against a victim and the victim responding in correspondence with the sequence. As defined in Blockchain-based NSCC technology is robust and has attack resilience in terms of the availability of customs clearance. In this sequence, an attacker targets Station A with a DoS  attack. Attack sequence: One of the potential attack points indicated in Corresponding sequence: As the train node cannot receive permission to pass through transit Station A, it sends a request to other nearby transit station nodes for customs clearance. When transit Station B receives a request for customs clearance, the train is granted permission to pass through Station B, and the train is processed for passage through transit Station A in accordance with the existing customs clearance sequence.Analysis and discussion: A flowchart based on the attack–response scenarios for DoS attacks is shown in In terms of the data integrity and reliability of customs clearance, the developed blockchain-based NSCC is attack-resilient and robust. In this sequence, an attacker conducts a spoofing attack  targetinAttack Sequence: Corresponding sequence: Transit Station A analyzes the distributed storage data for inconsistencies and compares them to transactions [sactions  on its oAnalysis and discussion: An attack that targets the routing database occurs when a transit station is proceeding with verification. The distributed storage and blockchain network transactions contain the data needed for verification, and any data inconsistencies can be determined in the event of an attack directed against the distributed storage. In this case, the blockchain network is accessed to verify data because every participating node has the same ledger. The blockchain platform structurally ensures integrity, reliability, and traceability because all participating nodes share the same ledger. These features of the blockchain can be used to safely conduct the data verification process.In terms of data integrity and customs clearance resilience, NSCC based on blockchain technology is attack-resilient and robust. In this sequence, an attacker targets Station A using advanced persistent threat (APT)  and backAttack sequence: The attacker is based at potential attack point C, as shown in Corresponding sequence: The train that is refused customs clearance sends its request for permission to pass through to nearby transit Station B. The customs clearance data are checked at transit Station B, which responds with the necessary permission for customs clearance. Furthermore, data sync to transit Station B is requested to restore the blockchain transaction data of transit Station A, which is falsified. To recover the transaction data of transit Station A and conduct its ledger sync process, transit Station B and other transit stations transfer the entire blockchain data to transit Station A, which can re-participate in the customs clearance process.Analysis and discussion: Root access can be hijacked using numerous methods. In this section, we describe the simulation of the environment in In this study, the NSCC environment was constructed using SUMO, Docker, and Ethereum, with the final experimental results of the NSCC derived based on the experimental results of each component. SUMO makes it possible to simulate the navigation of a given road network by single vehicles in response to a given traffic demand. The simulation is purely microscopic: each vehicle is modeled explicitly, has its own route, and navigates the network individually. Therefore, we used SUMO to derive the travel route and the timing of the train on the railroad. In addition, NSCC was implemented using Docker to configure the train and station as one node, while Geth was used to implement the blockchain network. Station and Train nodes are implemented as a single Docker container; considering they are implemented on a single local machine for experimentation, they all have the same IP address and different port numbers. However, in a real environment, all nodes have different IP addresses and port numbers. Each node was composed of Geth nodes to connect to the Ethereum network; in the case of the Train node, the SUMO client was run inside the Docker container to serve as a train in this experiment. Each Docker container communicates using the IP address and port number. During the communication process, the Docker container transmits and receives data, interlocks with the Ethereum network, and propagates transactions and blocks. In this study, using SUMO, the NSCC was tested for a simulation involving trains moving between Kazakhstan and Mongolia. This study developed a solution to the problem of time delays caused by the need to obtain customs clearance, consumption of human and material resources, and reliability and integrity of customs clearance of the existing customs clearance system. The system utilizes a Bitcoin-derived blockchain . A blockThe average processing times for Step 1 to Step 4 are listed in Using these features of a blockchain network and software, we created a “blockchain-based border NSCC” system in this study and tested its resilience against attacks. The main contributions of this study and the differences between the developed NSCC and current customs clearance systems are discussed below.Time: The current customs clearance process is time-consuming because individuals have to directly inspect customs clearance items and cargo. However, with NSCC, customs clearance can be completed in as little as 1 h if the validity of the customs documents is not questionable.Resource: In the current customs clearance system, people directly participate in customs clearance and personally inspect the goods and cargo. However, resource consumption is minimal because the accuracy of the customs data is verified by machine. Customs clearance is conducted by verifying the integrity using the hash value of the data, which is broadcast to the blockchain network.Integrity: Data integrity is safeguarded by the distributed ledger technology used in the blockchain. However, data forgery and tampering can occur because documents are stored in a database and written by hand in the current customs clearance system.Reliability: The current customs clearance system assumes that the people participating in customs clearance are reliable. However, the blockchain-based NSCC system can structurally ensure reliability.Transparency: The blockchain-based NSCC guarantees that the customs clearance process remains transparent. The participation of each of the member countries in verification and customs clearance enables transparent data management. However, the transparency of the current customs clearance process cannot be ensured because of possible threats by malicious attackers.Traceability: The current customs system tracks data to documents and databases. However, the blockchain-based NSCC uses a distributed storage and blockchain network to track every step of the continuous customs clearance process from shipment to unloading.The comparison in In this study, we developed a blockchain-based NSCC system intended as a new customs clearance mechanism with structural robustness and attack resilience. As demonstrated in This study was conducted based on an IoT-based network environment. Further research on IoT and artificial intelligence (AI)-based block seals and smart container capabilities  for inspThis study involved developing a blockchain-based NSCC system and structurally demonstrating its resilience to cyberattacks. Cyberattacks aimed at legacy systems can occur because the NSCC system combines a blockchain with the legacy customs clearance system. However, an attack–response sequence diagram was used to demonstrate that cyberattack resilience can be secured by employing the integrity, reliability, and traceability features of the blockchain.Compared to the current customs clearance methods, the blockchain-based NSCC system excels in terms of integrity, security, and reliability. Reducing the time required for customs clearance can improve the performance of the freight transportation sector using railroads for cross-border trade. Consequently, the developed customs clearance method uses fewer materials and people overall. This study demonstrated the versatility of blockchain technology and its implications for maritime and aviation trade and the customs clearance system for cross-border railroad transport.This work demonstrated the compatibility of blockchain with traditional systems. Future research could employ trade domains, such as land, sea, and air. The blockchain-based NSCC system proposed in this study can also be improved using IoT and AI-based object recognition systems to verify cargo integrity. Furthermore, M&S of the NSCC system can be conducted based on the created attack–response sequence diagram presented in