In an effort to refine transcatheter vascular interventions, radial artery access has moved more distally at the anatomical snuffbox. Here, more challenges appear as the artery is smaller, more angulated, and more difficult to palpate. Including ultrasound guidance as a mandatory step during puncture may encourage more operators to switch to this approach. In the femoral approach, ultrasound guidance is strongly recommended because of bleeding complications, whereas in the proximal  radial approach, the role of ultrasound remains optional, and in current practice, almost all cases are performed by palpation of the pulse only. However, in distal radial access, the situation is different because the artery differs in caliber and position, and imaging can help the operator for a clean puncture, especially since repeated punctures are not only painful but also any hematoma formation leads to the complete compression of the artery and failure of access. The aim of this review is to investigate the rationale of vascular ultrasound during distal radial access and to establish some techniques and anatomical landmarks for the ultrasonographic exploration of the dorsal area of the hand. Distal radial access (DRA) has recently gained global popularity as an alternative access route for vascular procedures. With this approach, the hand is pronated, and the puncture is performed at the level of anatomical snuffbox where the artery seems to have approximately the same caliber, a superficial approachable trajectory but with a different puncture learning curve. Among the benefits of DRA are the low risk of entry-site bleeding complications and the low rate of radial artery occlusion (RAO) . AntegraThe anatomical snuffbox is defined as the triangular depression in the dorsum of the hand, forming a hollow space when the thumb is fully extended. It is bordered by the abductor pollicis longus and extensor pollicis brevis laterally and the extensor pollicis longus medially. The scaphoid and trapezium carpal bones compose the floor. The distal radial artery, cephalic vein, and superficial branches of the radial nerve are all contained within the snuffbox; therefore, caution should be taken when gaining access . This isThe purpose of this article is to present our single-center learning curve experience with ultrasound-guided DRA, explain some relevant technical considerations, and provide a step-by-step protocol for performing DRA using sonographic and anatomical integration, ensuring safe access and maximizing technical success. The puncture site and the anatomy of the distal radial artery and its surrounding structures are illustrated in There are two sites where the pulse of the radial artery in the dorsum of the hand may be palpated and vascular access could be attempted . The firDespite the short accessible length, shallow depth, and limited diameter of the RA in the snuff box with complex surrounding structures, distal radial access through the snuff box has recently been performed ,7,10,11.With the advent of DRA, Kiemeneij et al., who first reported the success rate of this technique in 2017 (89%) , publishThe probe is placed on the wrist at the level of the anatomical snuffbox. The visualization of the vessels can be improved by adjusting the depth on the ultrasound machine to the minimum depth and adjusting the gain. The examiner’s hand should rest partially on the patient, and only light pressure should be applied to the soft tissues. The vigorous pressing of the transducer leads to the compression of the artery. The artery is visualized as an anechoic or dark circle that can be distinguished from veins based on the pulsation of the vessel. Manual compression, pulsed wave Doppler, and colour flow Doppler are additional tools that ease the detection of the artery. The transverse  ultrasound view is easy to obtain and is best for identifying veins and arteries and their orientation to each other. The long-axis  ultrasound view is technically more difficult to obtain, but the entire needle is continuously imaged, ensuring accurate intraluminal placement. At our institution, the longitudinal method is preferred for cannulation. During the early stages of US use, a learning curve is to be expected; however, this should not dissuade operators from using this valuable know-how. Basic expertise in PRA is needed before moving to DRA but no prior US experience is mandatory. One common error is that once the needle punctures the skin, it is no longer helpful to inspect the wrist. The operator should instead watch the ultrasound screen and move the probe to search for the needle tip.The key anatomical structures that should be identified in the anatomical snuffbox are illustrated in A step-by-step description of the puncture technique is shown in As discussed earlier, the distal radial artery may be punctured proximally to the tendon of the extensor pollicis longus muscle in the anatomical snuffbox or distally to it at the dorsum of the hand. An open metal needle for a clean anterior puncture is recommended in order to avoid touching the periosteum. This also avoids spasm and other puncture-related complications such as hematoma or dissection. As the artery is very superficial, immediately under the skin, even a small hematoma can compress the artery and cause further attempts to be more difficult to perform. The vessel can be punctured as long as there is Doppler blood flow visible. In the video, a 21-gauge needle was used for puncturing the desired artery, and 5–6 French sheaths were passed through a 0.018-inch guidewire. No palpation of the artery is required, and the operator watches the US screen. The needle can be inclined more than 45 degrees. In some cases the guidewire may encounter resistance due to tortuosity; leaving the needle free rather than fixing it with the hand may help overcome the acute angles, or even coronary guidewires may be used. A spasmolytic cocktail which consisted of 200–250 micrograms of nitrate and 2.5 mg verapamil was introduced through the sheath together with 2500–5000 IU of unfractionated heparin. Hemostasis was achieved using a combination of one StatSeal Disc  with elastic bandage compression (3–4 h). Alternatively, there are dedicated compression devices as well , panel Bp < 0.001; left: 2.18 ± 0.39 mm vs. 2.47 ± 0.45 mm, p < 0.001) [p = 0.001), and the difference was significant in both genders [In general, a difference of 10–20% exists between the proximal and distal segments of the radial artery. However, this difference does not always apply, and there are some variations between studies, populations, ethnicities, sex, constitution, etc. In order to ensure the safety of the procedure and to reduce the risk of RAO, it is recommended that all patients undergo preoperative US to determine the diameter of the distal radial artery and select an appropriate radial artery sheath because the puncture success rate is positively correlated with the diameter of the distal radial artery . Moreove< 0.001) . Female < 0.001) . Among t< 0.001) . A study< 0.001) . A study genders .To summarize, the diameter and structure of the distal radial artery can vary significantly, and it is difficult to predict the actual diameter of the artery. Besides sex and small bodies, other comorbidities such as diabetes mellitus can affect the integrity of the radial artery’s wall. There is an overall 2+ mm diameter among all reported populations which safely allows 6-French catheters to be used. With the availability of thinner sheaths by various manufacturers, the same size sheath may be used for DRA by keeping the outer diameter to 6-French and increasing the inner diameter. In order to ensure the safety of the procedure and to reduce the risk of RAO, it is recommended that all patients undergo preoperative ultrasonography to determine the diameter of the distal radial artery and select an appropriate radial artery sheath. Another indirect but not-so-reliable method is to rely on how well and strong the pulse feels when palpated. Finally, the differences between the aforementioned studies could also be explained by the variability in defining the exact place where it is measured (snuffbox versus dorsum of the hand) and the method of measurement .As with any technique, learning distal radial access is associated with a learning curve. We have analyzed the impact of the learning curve in our high-volume catheterization center, in terms of the number of attempts and the puncture time . Figure Our center experience reached 1240 consecutive patients who underwent DRA between 2019–2021, all of them under US guidance . The extensive results of the registry were published in a multicenter study ,27. ThisIn the only study comparing ultrasound-guided DRA versus tactile-guided DRA, ultrasound guidance shifted DRA success from 87% to 97% . The stuFinally, the role of US in DRA can be extended to postoperative evaluation for RAO  or for the imaging of DRA vascular complications. US scanning can easily detect pseudoaneurysms and arteriovenous fistulas or check the patency of the artery during follow-up . The majTo conclude, it is worth remembering that the transradial approach and devices have improved over the last 28 years, and it is logical to expect that the distal access technique will undergo similar maturation. Almost without a doubt, the method will continue to improve and be condensed even further. The question of whether ultrasound-guided distal radial access should be routinely implemented and performed in everyday practice is still open, but in our view, that is a question well worth pursuing.US guidance is a powerful tool for mastering DRA, reducing complications, and maximizing technical success. For more complex percutaneous procedures that require larger catheters (7–8 French), US can also defer small-diameter and/or pathological radial arteries, thus avoiding unnecessary and painful puncture attempts.


The reproducibility of computational knee joint modeling is questionable, with models varying depending on the modeling team. The influence of model variations on simulation outcomes should be investigated, since knowing the sensitivity of the model outcomes to model parameters could help determine which parameters to calibrate and which parameters could potentially be standardized, improving model reproducibility. Previous sensitivity analyses on finite element knee joint models have typically used one model, with a few parameters and ligaments represented as line segments. In this study, a parameter sensitivity analysis was performed using multiple finite element knee joint models with continuum ligament representations. Four previously developed and calibrated models of the tibiofemoral joint were used. Parameters of the ligament and meniscus material models, the cartilage contact formulation, the simulation control and the rigid cylindrical joints were studied. Varus-valgus simulations were performed, changing one parameter at a time. The sensitivity on model convergence, valgus kinematics, articulating cartilage contact pressure and contact pressure location were investigated. A scoring system was defined to categorize the parameters as having a “large,” “medium” or “small” influence on model output. Model outcomes were sensitive to the ligament prestretch factor, Young’s modulus and attachment condition parameters. Changes in the meniscus horn stiffness had a “small” influence. Of the cartilage contact parameters, the penalty factor and Augmented Lagrangian setting had a “large” influence on the cartilage contact pressure. In the rigid cylindrical joint, the largest influence on the outcome parameters was found by the moment penalty parameter, which caused convergence issues. The force penalty and gap tolerance had a “small” influence at most. For the majority of parameters, the sensitivity was model-dependent. For example, only two models showed convergence issues when changing the Quasi-Newton update method. Due to the sensitivity of the model parameters being model-specific, the sensitivity of the parameters found in one model cannot be assumed to be the same in other models. The sensitivity of the model outcomes to ligament material properties confirms that calibration of these parameters is critical and using literature values may not be appropriate. Computational knee joint models can give valuable insight into the mechanics of the knee joint and have increased in popularity over the last few decades . HoweverSensitivity analyses on finite element models of the knee joint were previously performed, focusing on different model aspects, including material model properties e.g., , ligamenThis study presents a parameter sensitivity analysis on multiple finite element knee joint models with continuum ligament representations to explore the importance of parameter sensitivity in patient-specific modeling of the knee joint. Parameters of the ligament and menisci, tibiofemoral cartilage contact formulation, rigid cylindrical joint (RCJ), and simulation control settings were studied to obtain a broad understanding of model sensitivity.https://simtk.org/projects/openknee) and the Natural Knee Data repository (specimen du02) . The full description of the model development and calibration workflow is available on https://simtk.org/projects/abi_knee_models (Documents section), and is summarized here.Four tibiofemoral joint finite element models were used , which wThe segmentations of the bones, cartilages, ligaments and menisci were obtained from the Open Knee(s) dataset for the Open Knee(s) specimens, and segmented from MRI data for the Natural Knee Data specimen. Full-length bone meshes of the femur, tibia, and fibula were initially predicted using statistical shape models . These bFor each model parameter changed, a simulation was run, bringing the tibiofemoral joint into contact, applying 0.5 body weight of axial load, then applying varus-valgus moments of 40 Nm . SimulatThe parameters of interest included ligament and meniscus material properties, parameters of the sliding elastic tibiofemoral cartilage contact, simulation control parameters, and parameters of the RCJs . One parThe anterior and posterior cruciate ligaments (ACL & PCL) and the medial and lateral collateral ligaments (MCL & LCL) were studied. To change the ligament attachment condition, the original number of nodes involved in the superior and inferior ligament-bone rigid tied contact Set 1,  was halvTo analyze the sensitivity of the models to the parameters of interest, the model convergence, valgus kinematics, peak contact pressure, and location of peak contact pressure were studied. The six DOF kinematics during the varus and valgus simulations were calculated, but only the varus-valgus kinematics were used for analysis.The peak contact pressure was calculated at a time step to which all simulations converged. This time step differed per parameter of interest and had to be larger than 2.25 . If a simulation did not converge past a timestep of 2.25, this simulation was excluded from the analysis. The contact pressures were analyzed on the articulating surfaces of the tibial cartilages, and the medial and lateral tibial cartilages were analyzed separately.To obtain the peak contact pressure, the faces in the tibial cartilage articulating surface with a contact pressure larger than 0 N/mm were selected and ordered from lowest to highest contact pressure. The average of the top 10% of the faces with the highest contact pressure was calculated as the peak contact pressure. The location of peak contact pressure was calculated by taking the average location of the faces included in the top 10%, to avoid any unusually high stresses from single deformed elements .The influence of changing the parameters of interest on the outcome parameters was analyzed and compared to the original simulations . To quanThe root mean square error (RMSE) between the valgus kinematics in the original simulation and the simulation of interest was calculated. The RMSE was calculated over all converged time steps of the simulation of interest. Only the time steps for which both the simulation of interest and the original simulation converged were taken into account. Since the time steps at which the model converged can be different between the original and the simulation of interest, the valgus kinematics of the original simulation were interpolated between converged time steps. The average RMSE of the simulations of the parameter of interest was calculated for the varus and valgus simulations separately.The percentage difference in contact pressure of the simulation of interest compared to the original simulation was calculated. The percentage was calculated for the medial and lateral tibial cartilage separately, after which the average was obtained. The average percentage difference of the simulations of the parameter of interest was calculated for the varus and valgus simulations separately.The peak contact pressure location was quantified by calculating the distance between the peak contact pressure location of the simulation of interest and the original simulation. The distances were calculated for the medial and lateral tibial cartilages separately, after which the average was obtained. The average distance of the simulations of the parameter of interest was calculated for the varus and valgus simulations separately.To compare between the four outcome parameters, the quantitative results were scored on a scale from 0 to 3 . The avehttps://simtk.org/projects/abi_knee_models (Documents section).For each parameter of interest, all simulations’ convergence, valgus kinematics, peak contact pressures, and peak contact pressure location results were plotted. Examples of these plots are shown in The influence of the ligament prestretch factor on the four outcome parameters was different between the four ligaments . In geneDifferences in sensitivities to ligament Young’s modulus on outcome parameters were found between the four models , for exaFor all models and all ligaments, except for ligament attachment condition of the PCL and MCL of model oks001, the change in ligament attachment condition had up to a medium influence on convergence, valgus kinematics, contact pressure location, and contact pressure . The conThe influence of the meniscus horn stiffness on convergence, valgus kinematics, contact pressure location, and peak contact pressure was small.With the Augmented Lagrangian contact formulation enabled, the area of contact pressure decreased, resulting in high contact pressure values . For exaFor all models, the simulations exploring the sensitivity of the auto penalty and search radius parameters did not converge far enough to start the varus and valgus simulations. The simulations with the two-pass parameters disabled did converge and produced anatomically unrealistic outcomes such as cartilage penetration with no contact pressures present at the penetrating areas.Changing the Quasi-Newton update method to BFGS or Broyden instead of the original Full Newton resulted in models du02 and oks006 not converging, whereas the other two models converged with negligible changes to valgus kinematics, contact pressure location and peak contact pressure. Up to a small influence of changing the displacement tolerance on outcome parameters was found in all models, except for model oks001, for which no influence was found.When changing the RCJ moment penalty, all models except for model oks003 failed to converge far enough to start the varus and valgus simulations. Not all of the simulations of model oks003 converged entirely either. However, when the model converged, up to a small influence on the other outcome parameters was found. The RCJ force penalty and gap tolerance had up to a small influence on the outcome parameters. RCJ angular tolerance had up to a small influence on convergence and valgus kinematics for all models. Larger influences were found on the peak contact pressure (rated 0.75 to 1.5) and contact pressure location (rated 1 to 2).• The parameters resulting in converging simulations with the largest influence on the model outcomes were the ligament prestretch factor, ligament Young’s modulus, and parameters of the articulating cartilage contact formulation.• For the majority of parameters, the sensitivity was model-specific. Therefore, the sensitivity of the parameters found in one model cannot be assumed to be the same in other models.• The sensitivity of the model outcomes to ligament material properties confirms that calibration of these parameters is critical and using literature values may not be appropriate.In this study, the sensitivity of the convergence, valgus kinematics, peak contact pressure, and peak contact pressure location of four knee finite element models to various modeling parameters was studied to explore the importance of parameter sensitivity in patient-specific modeling of the knee joint. To summarize, the main findings from this study are:Differences in sensitivity between the models were found in the majority of modeling parameters studied. This indicates that the sensitivity of modeling parameters is model-dependent. For some parameters the trend of the influence on model outcomes was similar between the models. Knee joint finite element model sensitivity analyses reported in the literature typically present data from one model. Since the sensitivity to modeling parameters was found to be model-dependent in the majority of parameters studied, the sensitivities of the parameters found in one model cannot be assumed to be the same in other models.As expected, the parameters with the greatest influence on the valgus kinematics included the ligament material properties. Often, literature values are used for the ligament material properties, however, our results suggest a critical need to calibrate ligament properties, across a range of conditions using suitable, preferably specimen-specific, data. In light of these results, literature values should only be used as starting points for model calibration. An influence on the valgus kinematics was often accompanied by an influence on the peak contact pressure and its location. This was expected since a different orientation of the bones will influence the area of the cartilages in contact, and therewith the peak contact pressure. The parameters had a variable, model dependent, effect on convergence, reflecting the influence of the parameter on the stability of the model. The model-specific sensitivity to convergence could be due to differences in mesh topology, which is one of the few model-specific aspects, whereas most other settings were the same between the models. Typically, model convergence is not discussed in papers on finite element knee joint modeling, even though it is a major problem to overcome in model development.Our findings confirm that the material properties of the ligaments are sensitive parameters that influence the model outcomes. This is in agreement with previous studies, where the sensitivity of the model ligament prestretch factors  on the kinematics  and joinFor most models and most ligaments, the ligament attachment condition had up to a medium influence on the model outcomes. A sensitivity analysis by The meniscus horn stiffness had a small effect on the outcome parameters. In a study by The penalty factor of the cartilage contact formulation influenced the model outcomes, which was expected since the penalty factor provides control of the contact traction forces and therewith penetration distance of the contact surfaces [FEBio user manual v3.0 ]. Enablihttps://simtk.org/projects/openknee) and resulted in an acceptable contact. The search radius is a scaling factor of a dimensional cut-off value to select point to facet projections included in the contact calculations. Due to curvature in the meshes, multiple distances could be calculated per point [FEBio user manual v3.0  than the default FEBio value (1.0). The original search radius value was copied from the Open Knee(s) model  parameters. Changing these values could have potentially improved convergence.https://simtk.org/projects/openknee) model and were a lot higher than the values investigated in the sensitivity analysis.Of the RCJ parameters, the largest influence on the outcome parameters was found by the moment penalty parameter, which caused convergence issues. The RCJ moment penalty represents the torsional stiffness of the RCJ. The original parameter values were based on the RCJ values in the Open Knee(s) (The LCL prestretch factor had a larger influence on the valgus kinematics in the varus compared to the valgus simulations, whereas the effect was opposite for the MCL prestretch factor. This was due to the LCL often being slack in the valgus simulation and the MCL often being slack in the varus simulation. This finding shows that the influence of the ligament parameter studied is dependent on the simulations performed. Therefore, performing sensitivity analyses for simulations of multiple DOF of the knee joint would be encouraged, whereas in this study the scope of the analysis was limited to varus-valgus simulations.The qualitative rating scores used in this study were established specifically for this sensitivity study. The score that is assigned to a certain outcome parameter range influences the qualitative results directly, and a different scoring system is likely to affect the results. However, this method provided a means to be able to compare between outcome parameters. The models converged at different time steps, which means different loads were applied at the analyzed point in the simulation, which could have influenced the results. For analysis, the average rating of the varus and valgus simulations was taken, which might have influenced the conclusions due to different influences of the parameter in the two simulations.This study is also limited to the modeling parameters that were investigated. Many parameters were not included, which potentially influences the outcome parameters. For example, in a previous study, the geometry and material properties of cartilage and the menisci influenced the joint pressure, the contact area, and the compressive load . Other mThe four models included in this study were obtained using the same model development and calibration workflows. Sensitivity analyses might be dependent on these workflows, for example, on the FE solver software used. In this sensitivity analysis, the contact formulation and the RCJ parameters are specific to FEBio. To investigate the sensitivity of modeling parameters on models developed in other FE solver software and obtained with different workflows, further sensitivity analyses should be performed.In this study, we gained insight into the influence of multiple knee joint finite element modeling parameters on model outcomes. The sensitivity of the model parameters was found to be model-specific. This indicates that the sensitivity of the parameters found in one model cannot be assumed to be the same in other models. To best understand the important parameters on a patient-specific basis, we suggest a sensitivity analysis to be performed for each model. The sensitivity of the model outcomes to ligament material properties confirms that calibration of these parameters is critical and using literature values may not be appropriate. Knowing the sensitivity of the multiple parameters can help with deciding which model parameters should have a model-specific or subject-specific value and which can be adequately captured using a generic value which could potentially be standardized improving model reproducibility. It is important to understand the influence of the parameters on the model outcomes, even if these influences are small, since all influences add up and could affect the conclusions drawn from the model.


Heterogeneous cyberattacks against industrial control systems (ICSs) have had a strong impact on the physical world in recent decades. Connecting devices to the internet enables new attack surfaces for attackers. The intrusion of ICSs, such as the manipulation of industrial sensory or actuator data, can be the cause for anomalous ICS behaviors. This poses a threat to the infrastructure that is critical for the operation of a modern city. Nowadays, the best techniques for detecting anomalies in ICSs are based on machine learning and, more recently, deep learning. Cybersecurity in ICSs is still an emerging field, and industrial datasets that can be used to develop anomaly detection techniques are rare. In this paper, we propose an unsupervised deep learning methodology for anomaly detection in ICSs, specifically, a lightweight long short-term memory variational auto-encoder (LW-LSTM-VAE) architecture. We successfully demonstrate our solution under two ICS applications, namely, water purification and water distribution plants. Our proposed method proves to be efficient in detecting anomalies in these applications and improves upon reconstruction-based anomaly detection methods presented in previous work. For example, we successfully detected 82.16% of the anomalies in the scenario of the widely used Secure Water Treatment (SWaT) benchmark. The deep learning architecture we propose has the added advantage of being extremely lightweight. Industrial control systems are used to manage and supervise industrial processes in critical infrastructures. Due to the adoption of internet technologies, ICSs are increasingly being targeted by cyberattacks . This shAn ICS is a cyber-physical system (CPS), that is characterized by its high degree of complexity . It typiThe detection of anomalies in CPSs concerns the identification of unusual system behaviors. The central issue with anomaly detection is to be able to distinguish between normal behaviors and behaviors that are potentially disadvantageous or even dangerous . AlthougThe detection of anomalies in CPSs is difficult due to several challenges. When the size of a CPS and its internal dependencies increase, complexity often reaches a point where it is no longer trivial to identify anomalous system behavior. Typically, multimodal monitoring sensors are mixed with actuator states. Water treatment and water distribution applications often involve hundreds of sensors and actuators. For this reason, it is important to develop anomaly detection techniques that can cope with data from various types of sensors and actuators. Another challenge is temporal dependency of sensor and actuator signals. When various plant processes are interdependent, timing plays an important role. This increases the need for solutions that take the temporal nature of sensor signals into account. The use cases we are targeting include intrusion detection, and systems health monitoring, as well as event detection in sensor networks.We successfully propose the adaption of a variational auto-encoder (VAE) with a long short-term memory (LSTM)  network Our methodology is evaluated against data collected from the SWaT and WADI testbeds, built at the Singapore University of Technology and Design for cybersecurity research . The SWaAn in-depth description of the methodology is presented in The recognition and prevention of unforeseen events in smart environments has caught the attention of the research community in recent years . AnomalyClassically, researchers often detected anomalies by directly analyzing data patterns in a reduced space  or by deDetecting anomalous system behaviors in ICSs is a common problem in smart industrial applications. A variety of different anomaly detection algorithms exist that have been applied in this application domain. The classical anomaly detection algorithms include one-class support vector machines (OC-SVM)  and prinIn , researcReconstruction-based anomaly detection approaches very recently began to be applied in the industrial application domain ,26,27,30An AE is a representative example of a reconstruction-based approach. It is implemented as a connected network with an encoder and a decoder . For theIn addition to default AE architectures, VAEs have the potential to abstract over the diverseness of sensor and actuator data by observing statistics of expected distributions . The motFor the detection of anomalies in industrial sensor and actuator signals, solutions are needed that take into account the time dependency of the signals. LSTM networks are a suitable choice to capture long- and short-term dependencies of sequential time-series data. There are several advantages of using LSTM networks in comparison to classical approaches . An LSTM-based anomaly detector is expected to have higher representational performance and the memory consumption is expected to be significantly lower when tracking long-term dependencies of sequential time-series data . AdditioIn , the autUnlike previous works, the methodology proposed in the work, our LW-LSTM-VAE architecture, is a combination of a VAE with an LSTM network, specifically designed for the purpose of anomaly detection in ICSs. Our proposed methodology has the additional advantage of being extremely compact. The LSTM network is used to model the temporal dependencies between sensor and actuator signals. The VAE is used to obtain a more robust model that captures the underlying statistics of the data.This section provides an overview of related datasets, addressing anomalies in industrial operations. Unlike datasets that are generally considered for the application of anomaly detection algorithms, the availability of datasets that contain sensor and actuator data from ICSs is limited . In the Remote terminal units (RTU) have been used to monitor the gas pipeline. RTUs are electronic devices that can connect physical devices  to SCADA automation systems. They transfer telemetry data to the systems and/or change the physical state of connected objects based on control messages received from the SCADA system. The dataset has been used to evaluate the ability of learning algorithms to identify data injection attacks. Similarly, the Water Storage Tank  dataset The Center for Cyber Security Research, iTrust , built sAnother dataset originates from the BATtle of the Attack Detection ALgorithms (BATADAL)  competitThis section presents the methodology proposed in this work. A visualization of the methodology, including the processing pipeline, is shown in In the first phase, the input phase, a dataset is preprocessed. Our methodology involves several preprocessing steps, namely, feature selection, feature normalization, and window extraction.Feature selection is performed for the individual features of an input dataset since the features do not contribute equally to the anomaly detection task. A feature might be redundant  or the distribution of feature values in the training set differs greatly from the distribution of feature values in the testing set. Feature selection is important because it greatly influences the performance of the anomaly detector.In the feature normalization step, the individual feature values are normalized. Feature normalization has a significant effect on the anomaly detection performance because machine learning algorithms are highly sensitive to varying degrees of feature magnitudes. After feature selection and feature normalization are performed on the input data, windows are extracted from the training and testing sets of the respective datasets. The window extraction step divides time-series data into smaller time-series sub-sequences  by sliding a fixed size window across the data. A window then consists of multiple samples that are in sequential order over time. Creating windows of samples enables our methodology to cope with the temporal dependency of the sequential time-series data. The window extraction process is further described in In the reconstruction phase, our LW-LSTM-VAE neural network architecture attempts to reconstruct the individual input windows. The intuition behind this is that the algorithm learns to model normality, such that it is able to effectively reconstruct windows that resemble normal data patterns, but it fails at reconstructing windows that resemble anomalous data patterns. After reconstruction, the original input window and the reconstructed window serve as input for the final anomaly detector phase. Further details about the LW-LSTM-VAE architecture are provided in In the anomaly detector phase, the deviation between an input window and a reconstructed window is computed. The deviation  is used as a measure for normality of a respective window and directly serves as an anomaly score. If the reconstruction error exceeds a defined reconstruction error threshold, a window is classified anomalous, otherwise it is classified normal. Details about the computation of the reconstruction error are provided in This section describes the foundations of auto-encoders and how sparse auto-encoders (SAE) can be used for reconstruction-based anomaly detection. The anomaly detector presented in this work is based on an AE architecture; therefore, it is important to highlight the fundamental working principles.Z along with the input data space X is defined. An encoder function E relates both spaces by representing a mapping from the input space X into the latent space Z  loss function is applied during training by gradient descent iterations. The MSE loss function is given by:X to Z . To address this problem of latent space irregularity, the AE architecture is extended by the means of variational inference. A VAE trains the encoder in such a way that it returns a distribution over the latent space instead of a single data point . To enabThe regularization term reduces the distance between the two distributions The complete loss function of our methodology is composed of the reconstruction loss and the regularization loss given by:The optimal parameters of the neural network model are obtained by minimizing the loss function:The reparameterization trick is necessary to make the ELBO loss function differentiable . The latThe equation is modified by the reparameterization trick:rainable . The proZ. The encoder consists of an LSTM layer and two dense layers. The LSTM layer encodes the window, including the temporal dependency between the samples in a window, into a compressed representation. The dense layers map the compressed representation of the input into the latent vectors Z is then passed to the decoder to reconstruct the original input window. The decoder consists of an LSTM layer and a dense layer. The LSTM layer in the decoder reconstructs the samples of a window by taking their temporal dependency into account.This section presents our proposed LW-LSTM-VAE neural network architecture. Our LW-LSTM-VAE is an adaption of a VAE that has been extended using an LSTM network. The LSTM network enables the architecture to cope with the temporal dependency of sensor and actuator signals in sequential time-series data. Our LW-LSTM-VAE neural network architecture attempts to reconstruct the individual windows. The intuition behind this is that the algorithm learns to model normality, such that it can effectively reconstruct windows that resemble normal data patterns, but fails at reconstructing windows that resemble anomalous data patterns. The reconstruction mechanism involves an encoder and a decoder. The encoder performs dimensionality reduction by compressing an input window into a reduced representation in latent space Equation . The latIf the application is more to classify fixed length sequences, 1D CNNs can usually be trained much faster and perform better. However, in industrial applications, determining an appropriate sequence length is not trivial, as can be seen from the various sequence lengths proposed in previous work. In cases where the data has an unknown length, it is more intuitive to use RNNs than to try to include CNNs. For this reason, we have focused our research on the practical application of LSTMs.Z is sufficient to capture relevant statistics of normal system behavior.The models we propose require only an extremely small number of model parameters. The main reason for this is that our models make use of LSTM layers in the encoder and decoder; thus, the number of model parameters does not increase with the number of samples in an input window. In the case of fully dense AEs or 1D CNNs, the samples in an input window are usually concatenated; thus, the number of model parameters significantly grows with increasing window sizes. The number of hidden layers used in our models is also significantly smaller compared to the majority of deep-learning methods reported in previous work. In addition, our investigations indicated that a relatively small dimensionality of the latent representation Similar to the default loss function of a VAE, the loss function of our architecture comprises a reconstruction term and a regularization term. The regularization term is given by Equation . The reci-th sample and w is the window size. During inference, our LW-LSTM-VAE architecture classifies each window to be either normal or anomalous. The entire time-series dataset consists of k samples. By sliding a fixed-size window across the entire time series, This section describes how the time-series data are converted. The time series is converted because a suitable data format is required such that it can be further processed by our proposed neural network architecture. Another reason is the temporal dependency between sequential data, which needs to be preserved such that anomalous data patterns can be effectively recognized. The time-series data are converted into individual feature vectors  using the sliding window method . A windoX denotes the features of a particular sample, and This section describes how the deviation  between an input window and a reconstructed window is computed. The reconstruction error serves directly as an anomaly score. It measures the degree to which a window resembles normal data patterns. First, the MSE over each feature  in a window is computed. Second, the mean over the entire window is computed. By computing the MSE over the features of a sample, the individual deviations of each feature are taken into account. This results in an anomaly score for each sample in a window. The degree to which the entire window is anomalous is then measured by taking the mean over the individual reconstruction errors of the samples. The reconstruction error of a window is then given by:This section describes how the reconstruction error threshold is chosen. The reconstruction error threshold directly serves as a decision function to differentiate between normal and anomalous data.After model training, the reconstruction errors for the training windows are computed. Only the reconstruction errors obtained from the training windows are used to set the reconstruction error threshold. Since the reconstruction error threshold setting has a large impact on the performance of the anomaly detector, we employ two different ways of setting the reconstruction error threshold: (a) the r and then taking the value from the ordered list that corresponds to that rank. The ordinal rank r is given by:The The standard deviation from the mean is computed by taking the mean This section presents the experimental setup. The SWaT  and WADIThe SWaT testbed is a scaled-down but fully operational industrial water purification plant. The SWaT testbed was coordinated with the Public Utility Board of Singapore in 2016. SWaT is capable of producing five gallons of drinking water per minute and it mimics the functionalities of real systems in this field. The processes of the SWaT testbed are divided according to their respective operational function. Process P1 manages the supply and storage of the water to be processed. In P2, the water undergoes pretreatment and is tested for quality. P3 consists of an ultrafiltration process in which unwanted substances are filtered out. In P4, the remaining chlorine is removed by dechlorination. In P5, inorganic impurities are reduced by reverse osmosis. Finally, in P6, the purified water is stored for distribution. For our experiments, specifically the neural network parameter optimization of our deep-learning architecture, sensor, and actuator data recorded over seven days of continuous operation is used. Evaluation of our models is performed on the remaining four days of logged data, which include the 36 different attack scenarios. The raw network logs that were provided with the dataset have not been considered.The WADI testbed can be considered an extension of the SWaT testbed. Although the WADI testbed is similar to the SWaT testbed, it contains components such as analyzers, booster pumps, and chemical dosing systems . Table 3Due to the scale of the numerous pipelines used in real water distribution systems, there can be many possible occasions for anomalies, such as water leakage, congestion, or malicious actuator control. Therefore, scientists built the WADI testbed to be able to simulate attacks and to enable the development of methodologies that can detect these attacks. The training set consists of data that were collected with the WADI testbed for 14 days under normal operating conditions. The testing set was collected during the last 2 days of operation and includes various attacks scenarios. Similarly to the attacks that were carried out on the SWaT testbed, the attacks on the WADI testbed are reflected in the dataset by modified sensor and actuator values and targeted various attack points and operational processes. Additional information about the attacks is provided in the original publication .The WADI testbed is divided into three processes. In P1-W, water delivered from the SWaT testbed is extracted and stored in two tanks. Based on a preset demand pattern that simulates water consumption, P2-W controls the distribution from the storage tanks and six consumer tanks. In P3-W, the water is recycled and returned to P1-W.This section describes the process of feature selection and data preprocessing for our experiments. Feature selection and data preprocessing is necessary to allow for the removal of unwanted data such that the dataset contains more valuable information after the preprocessing steps. Our proposed reconstruction-based method aims at the reconstruction of the system behavior during normal operation. This is possible if the training data are representative of the testing data. The system’s states and transitions between the states that appear in the training data should also appear in the testing data . Only the samples that were generated by simulating attacks should have a different distribution. For this reason, following the suggestions of , featurew = 2, 4, 8, 16, 32. Following [w = 4 samples resulted in the best performing instances.This section presents the hyperparameter configurations that were used for the experiments conducted. ollowing , our invAdditionally, we varied the latent and intermediate layer dimensionality in the range of 16–128. To study the effect of the latent and intermediate layer dimensionality, we provide an ablation experiment including a small version of our architecture, which we refer to as LW-LSTM-VAE-S, and a medium version, which we refer to as LW-LSTM-VAE-M. In the case of the LW-LSTM-VAE-M model, we increased the dimensionality of the latent as well as the intermediate layers and included an additional LSTM layer in the decoder. The LW-LSTM-VAE neural network architectures were optimized using the Adam optimizer with early stopping enabled. The LSTM layers of the architecture use the rectified linear unit (ReLU) activation function, while the dense output layers use the linear activation function. Experiments were conducted using both the This section presents the anomaly detection performance metrics used for the evaluation of the anomaly detection on the SWaT and WADI datasets. The anomaly detection performance is mainly reported using the F1-score performance metric. The SWaT and WADI datasets are highly unbalanced in their class distribution. Thus, the performance metrics reported for our method have to be suitable for unbalanced data. For the evaluations conducted in this work, the anomaly detection performance is reported using the precision (Pre), recall (Rec), and F1-score (F1) metrics that are given by:This section presents our results on the SWaT and WADI datasets, respectively. To place the achieved results in perspective, we also report all previous works that reported on the used datasets. The results are listed in In the following, we present the results of our ablation experiment. The performances of our LW-LSTM-VAE-S and LW-LSTM-VAE-M models, taking into account the choice of the reconstruction error threshold, are listed in In the following, we compare the performance of our solution to previous work that reported on the widely used SWat and WADI benchmarks. With In , Li et aThe discrepancy in the performance of our method compared to previous work is due to several reasons. In , the autOverall, the results for the WADI benchmark are worse than for the SWat benchmark, as listed in Our proposed models do have a significant advantage in terms of memory requirements and training time, because our models only require an extremely small amount of model parameters. Additionally, our method does not involve concatenating the individual features of an input window. In the case of fully dense DNNs or 1D CNNs, the concatenation of the individual features results in significantly larger layer sizes. Instead, our models use LSTM layers that have higher representational power for time-series data. The efficiency of training our LW-LSTM-VAE neural network architecture is shown in The detection performance of our proposed method ranks among the best-performing algorithms proposed in previous work. Although not achieving the best recognition performance, we highlight its unique combination of advantages. Based on variational inference, our proposed method learns to capture the variations of sensor and actuator signals. By assuming a simple underlying probabilistic model to describe the data, we enable better latent space organization compared to default AE architectures. We extended the models generalization capabilities and prevented overfitting, such that a more robust anomaly detector is obtained. Additionally, our algorithm is capable of intrusion detection given only past or current data observations. Discrete and continuously valued sensor and actuator signals are treated equally, such that the architecture can easily be adapted to other anomaly detection scenarios. The extremely low memory requirements also point towards future research avenues. Our proposed architecture can potentially be deployed on embedded systems, enabling anomaly detection in other application domains, such as smart buildings or smart homes, where storage requirements and energy consumption play an important role.In this work, we propose a reconstruction-based multivariate time-series anomaly detection solution based on our lightweight long short-term memory variational auto-encoder (LW-LSTM-VAE) architecture. Our solution aims at learning the underlying statistics of sensor and actuator data patterns. Our solution is successful in detecting deviations from normal data patterns, such that anomalous system behaviors of CPS are recognized. We considered two real-world datasets for the evaluations conducted. These enable the focus on two applications, anomaly detection in water treatment facilities , and anomaly detection in water distribution facilities . Our solution outperforms the majority of methods suggested in previous work in terms of the reported performance metrics. Additionally, our solution is extremely lightweight because the model parameter and training time requirements are extremely low.


With the vigorous rise of online third-party recycling platforms, dual-channel recycling has become the primary recycling mode in the reverse supply chain (RSC). However, as the main body of recycling, consumers have a significant impact on the recycling process, and their behavioral preferences are rarely considered in the pricing decision of the reverse recycling supply chain. Based on the dual-channel RSC, this paper considers the competition among channels. It introduces the loss aversion behavior preference of consumers to establish a dual-channel RSC composed of remanufacturers and online and offline recyclers. This study aims to analyze the impact of consumers’ loss aversion behavior on the recycling pricing and profit of each node in the green RSC and discuss the decision of recyclers under consumers’ loss aversion behavior. The results show that the deeper consumers’ aversion to the loss of recycling price, the lower the recycling price of dual-channel recyclers will be, which will be more conducive to the increase in the profit of online recyclers. However, the profit of remanufacturers will be reduced, and the total amount of recycling will decline. This paper considers the impact of consumer loss aversion behavior on dual-channel reverse supply chain pricing decisions based on prospect theory. It provides references for chain members to set recycling prices to increase people’s enthusiasm for recycling and the amount of recycled scrap, contributes to the cause of resource conservation and environmental protection, and improves the economic efficiency of recycling enterprises. With the rapid development of the social economy and the continuous expansion of consumer demand, the problems of resource shortage and environmental pollution are becoming increasingly severe. Green development has become a global consensus. In addition, the diversification of market demand, scientific and technological innovation, and other factors make product updating and iteration faster and faster, and its life cycle also shows a reverse trend of shortening. In particular, electronic products produce more and more waste products . By 2021The traditional supply chain management is limited to fully utilizing resources within the supply chain. However, it needs to consider how to recycle the waste products of the whole supply chain and whether the supply process uses resources rationally, so the green supply chain has come into being. In order to promote the development of green supply chains, scholars, practitioners, and scientists from different fields have conducted research on how to promote the development of green supply chains. How to maximize the benefits of the supply chain members is a significant research problem to promote the development of a green supply chain, so many experts and scholars have researched the pricing decision problem of a reverse supply chain. For example, Wu et al. constructed a Stackelberg game model between recycling centers and third-party recyclers under centralized and decentralized decision-making to optimize recycling centers’ pricing and service decisions to obtain their optimal and maximum profits . Shan etThe above literature mainly studies recycling pricing decisions regarding recyclers’ behaviors without considering the influence of consumers’ behaviors. As one of the recycling subjects, consumers have an important influence on the behavioral preferences of reverse supply chain recycling and, therefore, gradually attract extensive attention from scholars. For example, Li and Wu et al. considered consumer preferences and service levels when designing a two-channel reverse supply chain multilevel network . They foWhen consumers face uncertain trading decisions, they often have expectations about prices and compare realized prices with reference points. Numerous studies in psychology and economics have shown that decision-makers are keener on minimizing losses than maximizing gains relative to reference points ,16,17,18(1)Compared with the existing literature, this paper studies the dual-channel recycle pricing from the perspective of competition based on the dual-channel reverse supply chain (RSC) and constructs a Stackelberg game model. It pays more attention to the impact of competition between actors on pricing and profits, enriches the management scenario of RSC pricing decisions, and provides management suggestions for enterprise development.(2)When studying the pricing decision of an RSC, most existing literature regards the decision-making subject of the supply chain as entirely rational. The research on consumers’ behavioral preferences has yet to be entirely carried out. Based on this, this paper considers the loss aversion behaviour of consumers in the pricing decision of dual-channel RSC, further enriching the research of RSC and making it closer to reality.The above studies have significantly contributed to developing a green supply chain. Many scholars discuss the pricing decision of a reverse supply chain from the perspective of dual-channel recycling and fully prove that consumer behaviour has an important impact on the pricing decision of the supply chain. However, relevant researchers need to be more explicit about the mechanism of pricing decisions and the main body profit of dual-channel reverse supply chain under consumer loss aversion. Based on a dual-channel reverse supply chain, this paper constructs a utility function considering the loss aversion behaviour of consumers, discusses the amount of recycling in each channel, and analyzes the influence of changes in the degree of consumer loss aversion on the recycling pricing and profit of enterprises at each node of the reverse supply chain when the reference point of loss aversion is certain. Finally, it discusses how the profit of each node will change when consumers refer to different loss aversion reference points under the condition of a specific loss aversion coefficient. Compared with the existing studies, this paper makes some innovations and improvements in the following aspects:The dual-channel RSC studied in this paper comprises an online third-party recycling platform and an offline recycler, as shown in In this paper, we study the Stackelberg game led by the remanufacturer under decentralized decision-making, where the remanufacturer is the leader, and the online third-party recycling platform and offline recyclers are the followers and competitors of each other. In this scenario, the remanufacturer is dominant in the RSC. It makes the decision first, while the online and offline recyclers make the corresponding decisions based on the manufacturer’s actions. The remanufacturer first decides the recycling transfer price The relevant parameters involved in this paper are shown in Considering the audience size of the online recycling channel and the fact that consumers need to deal with more significant uncertainty when recycling online, we hypothesized that consumers are more susceptible to loss aversion in the online recycling channel and developed a loss-averse consumer value assessment model. For simplicity, this study only considers a group of electronic products of the same type, brand, and degree of wear and tear, according to Chen and Feng et al. ,23.Assumption 1: The expected utility function is established. It is assumed that each consumer’s evaluation of waste products in the market is heterogeneous and uniformly distributed in a continuous range Assumption 2: Based on the fact that the recycling price of one unit of the used products in the offline recycling channel is Assumption 3: Considering the relatively low operating cost of online platforms and online bidding mechanism, Feng et al. pointed out that the online recycling price of electronic products is generally higher than that of offline channels, that is, pa>pb . Based oecycling .Then, the loss aversion valuation of the product introduced in the online recycling channel is shown in As loss aversion is reference dependent, we set the consumer’s reference position to Assumption 4: Prospect theory suggests that the decision reference point determines the decision maker’s attitude toward risk. The heterogeneity of consumers determines that they will show different risk preference tendencies in the face of losses; therefore, this paper assumes that different consumers refer to different loss aversion reference points (LARPs) for the same recyclables.(1)As shown in (2)As shown in (3)As shown in  pH−μ−v0 . In addi pH−μ−v0 .We analyzed consumer utility based on different dual-channel recycling prices and defined the choice of consumer recycling channels as follows: choose the recycling channel that provides more efficient use to maximize consumer utility. If the utility of both recycling channels is negative, consumers do not participate in recycling. When the utilities of the two recycling channels are equal, consumers choose to recycle offline due to loss aversion to the price of the online recycling channel. The comparison of the utility of online and offline recycling channels is shown in In summary, referring to Ma and Matsui et al., assume that the recycling quantity of waste products is linearly distributed with the price, and we get the recycling volume of the online channel:The recycling volume in the offline channel isIn the recycling process, online and offline recyclers deliver the recycled scrap to the remanufacturer, so the total amount of scrap recycled by the remanufacturer is equal to the sum of online and offline recycling: In this paper, we study the Stackelberg game led by the remanufacturer under decentralized decision-making, where the remanufacturer is the leader, and the online third-party recycling platform and offline recyclers are the followers and competitors of each other. In this scenario, the remanufacturer is in the dominant position in the RSC and makes the first decision, and the online and offline recyclers then make the corresponding decisions based on the manufacturer’s actions. That is, the remanufacturer first decides the recycling transfer price of the used product The loss aversion two-channel recycling model isThe online profit function isThe offline profit function isThe remanufacturer’s profit function isIn this paper, inverse regression is used to solve the optimal decision strategy of each party in the game. By calculating the first and second derivatives of Equations (1) and (2) concerning As The solution isTake the first derivative with respect to Substituting Proposition 1: Offline channel recycling volumeis positively correlated with LARP. Online channel recycling volumeand remanufacturer recycling volumeare negatively correlated with LARPProof: Proposition 1 shows that different consumers hold different levels of LARPs for the same salvage value. With the change in LARPs, the amount of offline channel recycling increases, while the amount of online channel recycling decreases, leading to a decrease in the total amount of recycling, i.e., the amount of recycling by the remanufacturer. For the same recycled product, at the same time, and in the same space, when consumers rely on a higher reference point, they are more likely to have loss aversion to the online recycling channel and turn to the offline recycling channel, which leads to a decrease in the online recycling volume and a corresponding increase in the offline recycling volume. In addition, compared with the actual recycling price, a higher reference point of loss aversion will lead to a stronger loss aversion of consumers, and the total recycling quantity, i.e., the remanufacturer’s recycling quantity, will be reduced.Proposition 2: The optimal recycling pricefor the online channel and the optimal recycling transfer pricefor the remanufacturer is positively correlated with the LARP. The optimal recycling pricefor the offline channel is negatively correlated with the LARP. The optimal recycling price A for the offline channel is positively correlated with the LARP M.Proof: Proposition 2 shows that a change in the LARP increases the optimal recycling price for the online channel and the optimal recycling transfer price for the remanufacturer and decreases the optimal recycling price for the offline channel. When consumers have higher LARPs for the same recyclables, online channel recyclers increase the channel recycling volume by raising the recycling price. Remanufacturers increase the total recycling volume by raising the recycling transfer price. At the same time, offline channel recyclers reduce the recycling price when consumers shift from online to offline recycling, resulting from the supply chain parties seeking to maximize benefits. Therefore, online and offline recyclers should pay close attention to the recovery market and consumers’ aversion to the loss of recovery price, stabilize the recovery price, actively improve the corporate image, and increase consumers’ willingness to recover to increase the amount of recovery and expand corporate profits.In order to study the effects of different loss aversion coefficients on the optimal recycling price, recycling volume, and profit of each party in a two-channel reverse supply chain, a numerical example of two-channel reverse supply chain pricing is given in this part of the paper, and Mathematica is used to analyze the sensitivity of the relevant parameters. By collecting the relevant literature, a broad range of parameters, such as the transportation cost of online recycling, trouble cost caused by the inconvenience of offline recycling, and loss aversion reference point, are obtained ,26,27. MTo ensure that each decision variable is non-negative, the loss aversion coefficients in this section are discussed in the range of The variation curves of recycling transfer prices, and online and offline recycling prices with different loss factors are shown in Therefore, companies seek to maximize profits. When consumers unilaterally demand that losses are minimized, this leads to a loss of profit and efficiency for the company and the entire supply chain, resulting in a smaller business and a lower price for the final recycled product.As seen in In summary, with the aggravation of consumers’ loss aversion, the recovery of online channels will gradually increase, while offline channels will decrease progressively. However, due to the intensified competition between channels, the online and offline recovery prices will eventually become stable.From In summary, greater consumer loss aversion is conducive to lower recycling prices and increased profits and recycling volumes for online recyclers. However, it will lead to a decrease in the recycling volume and gain for offline recyclers and a reduction in the overall supply chain recycling volume.When consumers have different LARPs for the same recycled product, we analyzed the impact of the change in LARPs on the profits of the parties in the RSC in (1)Online and offline recycling channels will lower the recycling price to optimize profits when customers use the same LARP for the same recycled goods. However, the overall volume of recycling will fall. As consumers’ loss aversion deepens, the recycling prices of dual-channel recyclers show a decreasing trend of change, while the recycling transfer prices of remanufacturers maintain a stable trend. Competitive behaviour exists between online and offline recycling channels in the recycling process. When the online recycling price decreases, offline recyclers will also adopt a lower recycling price to maximize profits. Therefore, recycling companies in a competitive market environment can improve their service level to enhance their strength as a “price war” is not desirable.(2)When consumers refer to different LARPs for the same recycled product, although the recycling volume, optimal recycling price, and profit of each node enterprise have changed, the range of change is small. The deepening of consumers’ loss aversion will reduce recycling volume by recyclers and result in a reduction in the total recycling volume as well. As a result, companies should actively improve their image and expand their credibility while lowering recycling prices, such as using advertising and policies to guide consumers to increase their willingness to recycle and their awareness of sustainability and promote the growth of recycling volume.(3)Regardless of the loss aversion coefficient change or LARP, each node enterprise’s profit change trend in the RSC is basically similar to the changing trend in recycling. Therefore, in the recycling process of waste products, online and offline recycling channels should reduce the degree of competition between channels, stabilize their recycling prices, strive to improve the amount of recycling, and effectively increase the revenue of each channel in the RSC. The deeper the loss aversion of consumers to recycling prices, the more favorable the profit increase of online recyclers, but the profit increase of offline recyclers is less, and the profit of remanufacturers is reduced. Therefore, when recycling, enterprises should pay attention to consumers’ loss aversion to recycling prices by understanding the recycling market situation, stabilizing the recycling price, and actively improving the corporate image to increase consumers’ willingness to recycle in order to increase the volume of corporate recycling and expand corporate profits.Based on the background of socially sustainable development and the rapid development of Internet technology, online third-party recycling platforms are booming, and dual-channel recycling has become the primary recycling mode in the RSC. However, there is still the problem that consumers will have loss aversion when facing uncertain recycling prices. To solve this problem, this paper introduces the loss aversion coefficient and LARPs and constructs a loss aversion consumer model of dual-channel RSC based on channel competition. The research finds that:The hypotheses of this study still need further research. First, this paper supposes that the remanufacturer can take all recyclables from recyclers. If the remanufacturer’s demand is uncertain, what impact will consumers’ loss aversion have on it? Secondly, different types of recycled goods (such as basic waste and second-hand luxury goods) have different residual values. How will consumers’ loss aversion change when recycling, and how will the profits of each node in the RSC change? Therefore, the future study can comprehensively consider the influence of consumers’ loss aversion on the pricing decisions of each node when the remanufacturer does not fully accept the recyclers’ recyclables and faces different types of recyclables.


The zebrafish eleutheroembryo model is increasingly used to assess the toxicity and developmental adverse effects of xenobiotics. However, the actual exposure is seldom measured (poorly accessible), while a predictive model could estimate these concentrations. The predictions with a new eleutheroembryo physiologically based pharmacokinetic (PBPK) model have been evaluated using datasets obtained from literature data for several bisphenols. The model simulated the toxicokinetics of bisphenols A (BPA), AF, F, and S through the eleutheroembryo tissues while considering the body and organ growth. We further improved the predictions by adding dynamic flows through the embryo and/or its chorion, impact of experimental temperature, metabolic clearance, and saturation of the absorption by Bayesian calibration. The model structure was determined using the BPA dataset and generalized to the other bisphenols. This model revealed the central role of the chorion in the compound uptake in the first 48 h post-fertilization. The predictions for the BPA substitutes estimated by our PBPK model were compared to available toxicokinetics data for zebrafish embryos, and 63% and 88% of them were within a twofold and fivefold error intervals of the corresponding experimental values, respectively. This model provides a tool to design new eleutheroembryo assays and evaluate the actual exposure.The online version contains supplementary material available at 10.1007/s11356-022-22741-2. The zebrafish eleutheroembryo (zfe) model offers many attractive advantages and has emerged as a relevant alternative vertebrate model in (eco)toxicology. At the regulatory level, specific OECD test guidelines (TG) based on zfe assays have been recently adopted either to determine acute toxicity  OCDE  or endocNew approach methodologies could be complemented to the traditional toxicology to refine the risk-based decision-making approaches and are receiving increased regulatory acceptance  OR (danio*)) AND (bisphenol*) AND ((Juvenile) OR (larva*) OR (embryo*) OR (eleuthero*))).” A total of 209 articles were identified. The papers were selected when (i) at least one concentration was measured both in the embryos, and (ii) the embryo age was no older than 120 hpf at the time of first dosing. Thirteen papers were selected for our study: 10 for BPA   of the experimental device and the evaporation of the compound in the air  were modeled with dynamic flows:The binding of the chemical to the polymer walls (Q is the quantity (ng) fixed to polymer or in air, Fpolymer is the exchange rate between water and the polymer (μL/h/mm2), Spw is the surface of exchanges between polymer and water (mm2), Cwater is the concentration in water (mM), Ppw is the polymer:water partition coefficient (mm−1), Fair is the exchange rate between water and the air (μL/h/mm2), Saw is the surface of exchanges (mm2), and Paw is the air:water partition coefficient (no dimension).where Regarding the biometrics, the equations describing the organs’ growth were modified to consider the impact of the experimental temperature using Arrhenius’ formula . The volumes of the liver, gut, muscle, skeleton, eye, brain, heart, skin, and other tissues were computed as follows:Water temperature also impacts the timing of the organogenesis in addition to the growth rate; hence, the time of growth initiation of the organs was also corrected to integrate the effect of the temperature:Vk(t) is the volume of the organ k at time t, Vembryo(120) is the volume of the embryo at 120hpf, Kgk is the organ growth rate at the reference temperature, τk is the time of growth initiation of the organ k  is null ), and tfec is the time of fecundation. TA, TR, and T are, respectively, the Arrhenius temperature, the reference temperature of the organ growth , and the temperature of the assay, expressed in Kelvin. The yolk consumption rate constant was also multiplied by Arrhenius’ formula. The total volume of the zfe at time t was computed as the sum of the volumes of all the organs and the yolk.where Ci concentration in the organ or the yolk i, nmol/μL or mm3) were assumed to be, at any time, proportional to the concentration in the water (Cwater) suggesting an instantaneous equilibrium between the tissues and the medium. However, the actual diffusion is a slower process. To better describe the influx of the chemical in the zfe, a flow was added to mimic the diffusion of the substance. The uptake was assumed to increase with the body surface area from fecundation to the end of the experiment as follows:In the initial model  is a constant: the exchange rate between the water and the embryo per zfe body surface area. Vi and Vembryo are the volumes of the organs (μL or mm3) and the zfe (including the yolk sac) respectively, Qi is the quantity in the organ or the yolk (nmol) and Pi : water is the tissue (or yolk):water partition coefficient.where The chorion acts as an additional barrier .To better take into account the animal-physiology and molecule-specific kinetic, three modifications were tested by comparing the model predictions and the observations, namely, the chorion effect on the exchange rate, absence or saturation of the metabolic clearance, and influx saturation with the dose level.Uptake, metabolism, and disposition of organic xenobiotics have been demonstrated in zfe, highlighting it is metabolically competent, with most of the oxidative processes, esterase, and ADH/ALDH activities starting to occur around 96 hpf :Kmet is the metabolic clearance.where Finally, to face the wide range of tested dose levels, a saturation process for the tissue uptake of bisphenols was added to the model with:50 is the half-saturation concentration as described in Bertin et al.  and the ontogeny  were set to the ones previously published  were estimated using the QSAR approach embedded in the VIVD model  to refine the partition coefficient values using information given by the TK observations as previously performed for the zfe  using the variance-based Sobol method , the absorption saturation constant (Sat50), the metabolic clearance (Kmet), and the scaling factor (fpc). The parameters (Sat50 and/or Kmet) not influencing the compound kinetics were fixed to null or high values. The prior distributions of the estimated parameters were assumed to be uniform  test N° 236. Simulation details are provided in the supporting information document (§5. Simulation details in SI).The BPA model structure and flow rates (pre-and post-hatch) were used to evaluate the model generalizability to other BPA substitutes. The compound-specific parameter, scaling factor of the partition coefficients, was calibrated for each molecule based on chemical concentration data in the zfe and water as their values could not be retrieved from literature data Table .https://www.gnu.org/software/mcsim/)  Bois  and R veThe PBPK model was calibrated with the BPA dataset to select the most appropriate structure. Before and after the calibration phase on the BPA TK data, a SA of the model was performed Figs , and S1BPaw to a very low value. Using this assumption, the simulation of BPA water concentrations was run under every experimental condition of literature  and the obtained predictions of BPA water concentrations were compared to observed concentrations , depending on the plastic:water exchange surface and, consequently, on the size of the volume of the wells. Therefore, for poorly volatile molecules as the bisphenols of interest, the model could be simplified by removing the equation for volatilization Eq.  or by seons Fig. .Table Kmet) and the absorption saturation constant (Sat50), when included in the model, were estimated to values that suggest that these processes do not influence the total BPA concentrations over the range of the tested dose levels . The current TK data report the total concentration in the zfe only; therefore, metabolism estimation might be uncertain. Indeed, from the SA results, Kmet seems to have a significant impact on the liver concentration only  than the quantity in the tissues (numerator)) and a change in the proportion between organs with a low partition coefficient fpc, and both the pre- and post-hatch flows , indicating an overprediction of the QSAR models . Nevertheless, these long-term exposure residuals suggest also that some underlying mechanisms are not included in the model. This discrepancy could be due to some neglected biological processes, such as but not restricted to, progressive permeabilization and nonspecific binding to the chorion, gills increase in functionality   decreased with age by a 3-factor, from the earliest treatment (1hpf) to the last treatment (120hpf). The time post-dosing to reach Cmax (tmax) slightly decreased during the pre-hatching period (from 39.5 to 38.9 h post-dosing), while it increased after hatching (from 16.3 to 22.0 h post-dosing). This result is in line with the physiological changes occurring during the zfe development: the permeability of the zebrafish slightly changes during the pre-hatching period with a permeability that is increasing from the 6-somite stage (c.a. 12 hpf)  could not be accurately estimated from the zfe in vivo kinetics when using a custom experimental design. This finding could, at least partially, explain the wide range of observed BCF found in zebrafish embryo literature, with a BPA BCF ranging from 1.44 to 643, depending on the initial concentration, duration exposure, and age of embryo  , no clear difference in the whole-body concentrations could be evidenced (less than 5% difference within each group).In the present study, the hatching was assumed to occur at 48 hpf. Considering the variability of the experimental conditions in the included studies, it would be of interest to set the hatching time as a parameter to be estimated by the model and per molecule and experiment in future updates. Indeed, to the biological intragroup variability, intergroup variability in hatching time can be observed as some molecules could directly interfere with the hatching process . However, some slight differences in diffusion parameters could exist between each form; then, further in vitro experiments using these analogs would be helpful to determine more accurately these parameters. Here, the chorion crossing was considered to be similar among all of the bisphenols due to their structural similarities. The predictions obtained using the flow between water/chorion/embryo initially calibrated using the BPA data give accurate predictions for the other bisphenols  and water/embryo (post-hatch) were fixed to the values obtained for BPA  and geometric standard deviation (σ) were calibrated for the analogs since they are compound-specific parameters (Table Only the partition coefficient corrector (fpc are summarized in Table fpc) allows refining the value of the partition coefficients with the TK data obtained in ZFE .The tissue:water partition coefficient corrected using the Pi:water) coefficients of each bisphenol were in the same order of magnitude except for BPS. Indeed, BPS has a lower log Kow and a higher solubility than the other tested bisphenols, suggesting that the observed discrepancy in partition coefficients would be due to different physicochemical behaviors.The corrected partition coefficients  could be performed.The data heterogenicity is a known source of bias when modeling, but it could also give precious information about the model’s robustness. In the substitute dataset, the outlier value corresponds to the data obtained with radiolabeled BPS and radio-HPLC measurement  clearly shows that zfe internal exposure to BPF was higher than exposure to BPA (about + 30–50%) and exposure to BPF (about + 70–100%), while the internal exposure to BPS was lower than 2% of the exposure to other analogs Table , Fig. 8.The present model includes several features enabling the prediction of saturation and metabolic processes, and low permeability of the chorion to heavy molecular mass molecules, but it would be also advisable to investigate to which extent the refined zfe-PBPK model can be generalized to other classes of xenobiotics.Notwithstanding, this updated PBPK model provides a relevant tool to design eleutheroembryo assays and to better apprehend the fate of xenobiotics in these assays thereby reinforcing the interest of the zfe model and related assays OCDE , 2021 fo


Although the full vaccination rate of South Korea compared to other countries, concerns about the effectiveness of the vaccine are growing as new COVID variants such as Alpha, Beta, Gamma, Delta, and Omicron appear over time. In this study, we collected Twitter data in South Korea that contained keywords like vaccines after the outbreak of the Omicron variant from 27 November 2021 to 14 February 2022. First, we analyzed the relationship between potential keywords associated with vaccination after the appearance of the Omicron variant in Twitter using network analysis. Second, we developed an efficient model for predicting the emotion of speech regarding vaccination after the COVID-19 Omicron variant pandemic by using deep learning algorithms. We constructed sentiment analysis models regarding vaccination after the COVID-19 Omicron pandemic by using five algorithms . The results confirmed that KoBERT showed the best performance (71%) in all predictive performance indicators . It is necessary to prepare measures to alleviate the negative factorss of the public about vaccination in the long-term pandemic situation and help the public recognize the efficacy and safety of vaccination by using big data based on the results of this study. COVID-19 was first reported in Wuhan, China, in 2019, and it is still ongoing worldwide as of May 2022. The World Health Organization (WHO) has declared COVID-19 a “pandemic,” the sixth and highest risk rating, for the third time in history, after Hong Kong flu and swine flu, due to its rapid transmission rate and strong fatality rate . As the Vaccination may reduce the chance of virus transmission because it can directly protect vaccinated people and inhibit viral shedding . In addiThe Omicron variant was first discovered in the Republic of South Africa in November 2021, and it was subsequently classified by the WHO as a variant of concern for COVID-19 on 26 November 2021 . FurtherNews media are criticized for lowering credibility and causing confusion due to reckless reporting caused by excessive competition for breaking news and provocative reporting that promotes fear and hatred. Conversely, social network service (SNS) helps users establish social relationships between them through interaction in the internet space. It also influences the opinions and decision-making of the public directly and indirectly. Since users candidly express their opinions on social media , it is aIn this study, we collected Twitter data in South Korea that mentioned vaccines after the outbreak of the Omicron variant from 27 November 2021 to 14 February 2022. First, we analyzed the relationship between potential keywords associated with vaccination after the appearance of the Omicron variant in Twitter using network analysis. Second, we developed the best model for predicting the emotion of speech regarding vaccination after the COVID-19 Omicron variant pandemic by using deep learning algorithms, such as support vector machine (SVM), recurrent neural networks (RNNs), long short-term memory models (LSTMs), bidirectional encoder representations from transformers (BERT), and Korean BERT (KoBERT).The data collection period was determined using Google Trends. In Google Trends, when the number of searches is closer to 100, it is interpreted as higher public interest. The data collection period was set from 27 November 2021 (when the volume of searching the Omicron variant began to increase rapidly) to 14 February 2022 because the COVID-19 Omicron variation continued to spread . The datNumbers, alphabetical characters, and special characters were removed from the collected data using KoNLPy, a Python library to process Korean natural language. Likewise, we study extracted noun morphemes through Korean morpheme analysis using okt.nouns in the KoNLPy module and removed posts on topics that did not agree with the objective of this study. We study treated words that appeared frequently but were not necessary for the study as stopwords . Then, we judged slang and newly coined words  as expressions of emotions, so they were not excluded in the data cleanup process and included in the data for analysis. Lastly, we recruited experimental groups (each group was composed of five subjects) and labeled the emotion felt from the collected data as negative (–1), neutral (0), or positive (1). The final data were generated by (1) merging the five datasets after labeling and (2) designating the label found most frequently for each sentence as the final emotion. The schematic diagram of this study is presented in The term frequency–inverse document frequency (TF-IDF) weighting model is a way to evaluate the importance of a word in a document for text mining. A word with a higher TF-IDF weight is more likely to determine the subject or meaning of the document it belongs to, and this can be used as a yardstick to extract keywords .Topic modeling is one of the text mining techniques that stochastically extract topics from documents or texts in documents . Topic mNetwork analysis is also called semantic network analysis. It analyzes by constructing a keyword network to understand the structure of the concepts and symbols in sentences . It treaCONCOR analysis is an analysis method for deriving relationship patterns between nodes and is used for structural analysis of relationships in subgroups existing in the network . This anWe constructed sentiment analysis models regarding vaccination after the COVID-19 Omicron pandemic by using five algorithms . The order of these algorithms refers to the temporal order of development. This study could analyze and interpret factors, which improved the performance of the high-dimensional computational models that overcome the shortcomings of the previous general models and those of the increased model evaluation scores by developing NLP models using five algorithms because classification models had different aspects in processing natural language. Lastly, we selected the model with the highest performance after comparing and analyzing the predictive performance  of the developed five NLP models. Then, we designed models that could predict emotions by converting negative, neutral, and positive emotions into percentages when an arbitrary sentence was entered.The SVM algorithm is a non-stochastic binary linear classification model that determines the category in the new data will fall into based on the given dataset, which belongs to either of the two categories, and defines a decision boundary, which is a baseline for classification between categories . This moA RNN is a type of artificial neural network, and it is specialized in processing ordered information like natural language . An RNN LSTM is a model that improves gradient vanishing, gradient exploding, and long-term dependency problems of RNNs. Although LSTM has the characteristics of an RNN, it is different in that it can adjust values by attaching cells, called gates, to the input, forget, and output parts of the RNN. A forget gate decides whether to save previous-state information, an input gate decides whether to save new input information, and an output gate controls the output of an updated cell. The structure of LSTM is presented in BERT, developed by Google, is a pre-trained language model, and it performs well in various natural language processing fields, such as question and answer and sentence classification. ELMo  and GPT KoBERT was developed to overcome the limitations in analyzing Korean language of the existing BERT. It learned a large corpus consisting of millions of Korean sentences collected from Wikipedia and news. It improved performance by more than 2.6% using only 27% of the previous amount by applying the data-based tokenization technique to reflect the characteristics of irregular changes in Korean . AlthougCONCOR analysis derived four appropriate similarity clusters . The cluWe set the learning environment to 100 epochs with a batch size of 32 and 20% Test_dataset. We evaluated the performance of the KoBERT-based sentiment prediction model by entering new speech sentences. Twitter, a social media platform, has more than 320 million users worldwide and generates more than 500 million Tweets per day . The infThe results of topic modeling and CONCOR analysis showed that public opinions about vaccine effectiveness and breakthrough infection were formed on Twitter. Topic 2 of LDA topic modeling (vaccine) and cluster 3 of CONCOR analysis (vaccine effectiveness and the need for vaccine research) derived the same keywords such as effect, Pfizer, vaccine booster, prevention, antibody, and research. It is believed that it is because of people’s fear due to the occurrence of COVID-19 variants such as Delta variant and Omicron variant, as well as the decrease in vaccine efficacy according to the time elapsed after the primary series of the COVID-19 vaccines. Although Ouh et al.  reportedIn particular, breakthrough infection was derived from topic 4 of LAD topic modeling and cluster 2 of CONCOR analysis, where the breakthrough infection was defined as a confirmed COVID-19 case after 14 days from the day of completing vaccination . These rWe compared the predictive performance of the five sentiment classification models and confirmed that their accuracy was different according to the order of algorithm’s advancement . Especially, we confirmed that the difference in predictive performance between the SVM and RNN was the largest in this study because the SVM focused on binary classification and the SVM classification model was not linked to neural network computation. On the other hand, among deep learning models, the predictive accuracy of BERT was 5% higher than that of the RNN or LSTM. It was because the pre-trained natural language processing performed better in classifying actual speech. In other words, BERT showed faster and more accurate sentiment classification performance than the RNN and LSTM that were trained with a limited dataset in this study because BERT had more sophisticated embedding and induced more efficient computational processing than the RNN and LSTM, which were based on pre-training and computation. Moreover, in this study, KoBERT had 5% better predictive performance than BERT. It could be because although BERT and KoBERT are pre-training-based modes, the structure of the pre-trained text corpus and the corresponding tokenizer were optimized for learning Korean, and they performed better when the language is “Korean.” It is required to conduct more studies using BERT and KoBERT to understand the predictive performance of pre-trained-based models in actual speech including newly coined words.Another finding of this study was that there was little difference in predictive performance between the RNN and LSTM. The LSTM is developed by improving gradient vanishing, gradient exploding, and long-term dependency problems of the RNN. Many previous studies –39 reporThe importance of this study was to analyze people’s opinions about COVID-19 vaccination after the emergence of the COVID-19 Omicron variant in South Korea by conducting topic modeling and semantic network analysis using Twitter data. We also proved that pre-trained language models such as KoBERT greatly improved performance compared to the conventional RNN and LSTM, and presented basic data for the development of a future sentiment predictive model by comparing the performance of five sentiment classification models using unstructured data, which were meaningful results. The limitations of this study were as follows: First, Twitter limits the characters of the posting to 140. It has the advantage of maintaining the conciseness of the message, but at the same time, it may make it difficult to understand the message due to the use of abbreviations. Second, the results of this study could not be generalized and interpreted as general characteristics for the COVID-19 Omicron variant because this study used Tweets generated only for 3 months. It is necessary to continue monitoring because the Omicron variant is still ongoing. Also, in future studies, additional analysis including not only Twitter but also other social media is needed. Third, since the young generation mainly uses Twitter, the results of this study may not reflect the characteristics of the elderly who do not use Twitter. Fourth, the y-class of this study data  showed the characteristics of unbalanced data. Therefore, the unbalanced distribution of the y-class could play a key role in increasing the loss in processes such as weight selection in the learning process of the five NLP models. Particularly, there were more negative or neutral expressions than positive expressions due to the nature of an infectious disease or disaster—COVID-19. Fifth, due to the nature of the colloquial-oriented Twitter, the accuracy of predicting neutral or positive expressions is not high. It is believed that Tweets containing a negative characteristic could be classified confidently because they included strong negative meanings such as slang and direct wording . However, positive Tweets often contained neutral expressions, so it could make classification difficult. Future studies are needed to evaluate algorithms that can overcome the limitations associated with unbalanced data in embedding and weight selection calculation to improve the performance of NLP models that analyze colloquial speech such as Twitter.We performed topic modeling on Twitter users regarding the vaccination after the emergence of the COVID-19 Omicron variant by using Twitter data. The results confirmed that expectation, distrust, and anxiety regarding the efficacy of the vaccination coexisted: “Omicron,” “vaccine,” “vaccine inequality,” and “breakthrough infection.” Moreover, we developed sentiment classification models using five algorithms. The results confirmed that KoBERT showed the best performance (71%) in all predictive performance indicators . It is necessary to prepare measures to alleviate the negative factors of the public about vaccination in the long-term pandemic situation and help the public recognize the efficacy and safety of vaccination by using big data based on the results of this study.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The study was conducted according to the guidelines of the Declaration of Helsinki.GE, SY, and HB conceived the study and drafted the manuscript. GE collected the data. GE and SY performed the statistical analyses. HB critically reviewed and edited the manuscript. All authors contributed to data analysis, drafting, and revising the manuscript; gave final approval of the version to be published, have agreed on the journal to which the article has been submitted, and agreed to be accountable for all aspects of the work.