In today’s era, monitoring the health of the manufacturing environment has become essential in order to prevent unforeseen repairs, shutdowns, and to be able to detect defective products that could incur big losses. Data-driven techniques and advancements in sensor technology with Internet of the Things (IoT) have made real-time tracking of systems a reality. The health of a product can also be continuously assessed throughout the manufacturing lifecycle by using Quality Control (QC) measures. Quality inspection is one of the critical processes in which the product is evaluated and deemed acceptable or rejected. The visual inspection or final inspection process involves a human operator sensorily examining the product to ascertain its status. However, there are several factors that impact the visual inspection process resulting in an overall inspection accuracy of around 80% in the industry. With the goal of 100% inspection in advanced manufacturing systems, manual visual inspection is both time-consuming and costly. Computer Vision (CV) based algorithms have helped in automating parts of the visual inspection process, but there are still unaddressed challenges. This paper presents an Artificial Intelligence (AI) based approach to the visual inspection process by using Deep Learning (DL). The approach includes a custom Convolutional Neural Network (CNN) for inspection and a computer application that can be deployed on the shop floor to make the inspection process user-friendly. The inspection accuracy for the proposed model is 99.86% on image data of casting products. The manufacturing and production industry has undergone a fundamental transformation over the past century. Strategies such as Total Quality Management (TQM), Six Sigma, Lean, and Zero-Defect Manufacturing have pushed for higher yields while lowering costs. More recently, approaches such as Industry 4.0, Cyber-Physical Systems and Smart Manufacturing are allowing an interconnected shop floor environment with developments in robotics and automation. While larger enterprises have the economic prowess to implement transformative changes to the industry, Small and Medium-sized Enterprises (SMEs) are often unable to do so. When it comes to overall Quality Management (QM), SMEs face challenges due to the following reasons: there is a lack of information about the importance of QM, they face resource constraints, and there is an inadequacy in standards for SMEs ,2. ArtifQuality inspection is a planned and organized process in which the state of the product is assessed by examination, measurement, testing, gauging, or comparison to determine if it conforms to desired specifications . In mostTo that end, this paper proposes a twofold approach to the quality inspection process. First, a DL algorithm based on a custom Convolutional Neural Network (CNN) architecture is constructed for defect detection, and then a shop floor tool is designed to deploy the inspection model. The rest of the paper is structured as follows. It would be remiss to discuss quality inspection, a crucial component of the QC process, without providing a context about the health of the entire manufacturing environment. There are two major areas of health monitoring in manufacturing. The first concerns the system’s health, ensuring that the machinery and equipment are functioning satisfactorily. The second area pertains to monitoring the product’s health throughout its lifecycle. For the system, PHM deals with component-level and system-level health monitoring. On the product side, QC techniques are relied upon to guarantee the health and quality of the product. Prognostics and Health Management (PHM) is a discipline that monitors the system’s health, detects failures, diagnoses failures, and predicts the Remaining Useful Life (RUL) of components . Using tWith the rise in modern manufacturing systems and the development of highly complex products, quality management has taken an essential role in organizational planning and strategies . QualityThe traditional quality improvement process is cyclical—it involves generating inspection plans, implementing the plans, and checking the results . SimilarPrecision: The decisions made should be well-informed, to ensure that there are no biases or errors.Validity: Decisions made must be valid and must not differ if the product were to be available for use.Reliability: There must be consistency in the decisions made—repeatability and reproducibility. The decision process should not require recalibration.Robustness: The decision-making must demonstrate versatility in detecting different types of defects.Rapidness: The process must be quick and must be able to act before any more defective products are produced.The inspection process is an important decision process in the manufacturing/production system . AccordiNote that the above characteristics are desired from all inspection processes regardless of whether it is conducted by either human operators or by some form of automation.Present: Present the product for inspection.Search: Examine and analyze the product for possible flaws/defects.Decision: Assess the flaws/defects and determine if it falls out of the desired specifications.Action: Accept or reject the item based on the decision.An important type of quality inspection in manufacturing is visual inspection. Operators visually assess the state of the product at different stages and decide whether it can be moved on to the next process. Sinclair  suggesteSimilarly, Wang and Drury  characteAny inspection process or system requires some form of human action. There cannot be a system that is entirely automated or manual . InspectTask factors refer to the manual and physical aspects of the inspection task. The task itself can affect the operator and influence their performance. Environmental factors can also significantly impact the outcome of visual inspection. Factors such as temperature, humidity, lighting, etc. can make the environment unsuitable which in turn influences the operator’s ability to conduct the inspection. Operator or individual factors refer to features such as an operator’s physical and mental attributes. Physical attributes could be an operator’s vision, visual acuity, gender, etc. Mental attributes could be their state of mind, aptitude, personality, biases, etc. Organizational factors concern the administration and management under which the inspection process takes place. It also includes the organizational importance given to quality inspection and visual inspection, training provided, etc. Social factors include relationships that the operator has with their peers and management, whether communication in their working environment is effective or not, and the other aspects of the social environment in which the inspection task occurs. A synopsis of all the factors that affect the visual inspection process based on ,21 is prThe manufacturing process of casting usually involves pouring liquefied metal into the cavity of a mold that is of the desired shape . There a(a)Sand Casting—most metal types(b)Investment Casting—most metal types(c)Resin Shell Molding—Primarily Iron and Copper(d)Gravity Die Casting—Primarily Aluminum, Zinc, Magnesium, Copper, and some of their alloys(e)Low-Pressure Die Casting—Primarily Aluminum and Magnesium(f)High-Pressure Die Casting—Primarily Aluminum, Magnesium, and Zinc(g)Squeeze Casting—Primarily AluminumSome of the types of casting processes are listed with the types of materials that are used :(a)Sand Patternmaking—Designing and preparing a patternPreparing the mold that is approximately the same shape/size as the desired patternIdentifying the material to be used in casting Liquefying the material in a furnacePouring the liquefied metal into the cavity of the moldOpening the mold to access the castingFettling—removing excess material, surface cleaning, and finishingHeat treatment based on requirementsFinal inspectionMost of the casting processes listed in The quality inspection of casting products is the most critical step in determining whether the product is acceptable for use or must be rejected and scrapped/reworked. There are a few types of inspections for casting products: visual inspection, dimensional inspection, metallurgical inspection, chemical and physical inspection, and other methods involving Non-Destructive Testing (NDT) . While mThis visual inspection process for casting generally involves the examination of the product by an operator or a group of operators. Operators look for surface defects, cracks, tears, molding flaws, scabs, blowholes, runouts, adhesions, and various other types of defects . Many ofOwing to the numerous factors that can affect an operator during the visual inspection process, data-driven approaches are being used increasingly to detect defective products. While traditional ML methods often require domain knowledge in the feature generation or feature engineering process, DL methods can automatically select and learn abstract features . DL methChang et al.  apply a There are several works that have implemented image-based quality inspection methods for defect detection in manufacturing products. He et al.  use CNN performs well in detecting defects, i.e., shows high accuracy, precision, recall, etc.,minimizes the factors affecting the visual inspection process, andallows documentation of decisions made.These research works achieve good performance on real-world data but do not provide a holistic approach to deploying DL-based models to the shop floor. While most of the works that apply AI to visual inspection focus on improving the model performance, very few of them take into consideration the various factors involved in the visual inspection process. An even fewer attempt to minimize these factors in their proposed methods. Factors outlined in Considering the above requirements, we propose Smart Quality Inspection—an AI-based approach to the visual inspection process and demonstrate a use case on a benchmark image dataset from a casting process.The Smart Quality Inspection (SQI) approach aims to improve model performance and address several factors that affect the visual inspection process. By automating the inspection process to an extent, the effects of many of the task factors, environmental factors, and individual factors can be controlled. Stage 1: Manufacturing product arrives at the inspection area:In the first stage, the product from the assembly line is brought to the inspection area. The item is placed in a designated location to allow the inspection process to begin.Stage 2: Product image is capturedIn this stage, a high-quality camera is used to capture images of the product undergoing inspection. The lighting conditions and distance from the product are measured based on the product size and camera equipment in use.Stage 3: Image preprocessingIt is identified if grayscale or color images would be appropriate based on the availability of computational resources and desired precision and accuracy of predictions. Any augmentation or transformation is done at this stage—flips, shears, rotation, shifts, whitening, contrast adjustment, etc.Stage 4: CNN-based defect detectionA custom CNN architecture is used to detect defects in images. The architecture has the versatility to handle different types of images with just a small number of changes. The model is trained on images of defective products and non-defective products to learn the necessary feature representations. The defect detection model is built into an application that can be used on the shop floor to make the inspection process trouble-free.Stage 5: Decision stage—accept/reject the productThe operator inspects the product using the defect detection algorithm and instantaneously receives the inspection results from the computer application. Based on the results, a decision is made whether to accept or reject the product.Stage 6: Document results in the inspection logThe results of the inspection process are input into the SQI shop floor application and are automatically stored in a spreadsheet.The dataset used in this paper is from Pilot Technocast, an SME that manufactures casting products in Gujarat, India. The data has been made available publicly by Ravirajsinh Dabhi . The datx is the input to the neuron.The AI model chosen for the inspection task is a CNN with a custom architecture. The network is constructed using a set of Convolutional Layers (Conv2D), Max Pooling Layers (MaxPooling2D), Activation Functions, and Dense Layers. The convolution operation performed by the Conv2D layer is a dot product of the ‘kernel’ and the image. The MaxPooling2D layer creates a pooled feature map by reducing the parameters involved. The max pooling layer consists of a technique called zero-padding that involves adding zeros to the edges of the image before the next convolution operation. This preserves any features that are generated at the edges of the images. In the proposed model, we use a kernel of size 3 function . EquatioThe model also uses the Adaptive Moment Estimation (Adam) optimizer  and the Input Layer: The input layer is the raw image either in grayscale or Red-Green-Blue (RGB) format with  as its dimensions. This 300 Rescaling Operation: Neural networks generally perform better when the inputs are normalized. The channel coefficients for images are in the  range, which is high. Higher numeric values may be computationally more expensive and could affect performance. For the casting data, we rescale the inputs to the  range by using a 1/255 scaling factor.Convolution Layer (conv2d): The first of the three convolution layers has 448 parameters with the data as  shaped array.Max-Pooling (max_pooling2d): The pooling layer is useful in reducing the number of dimensions of the data. Pooling not only reduces the consumption of computing resources but also improves overall performance . Max-pooConvolution Layer (conv2d_1): The second convolution layer has 4640 parametersMax-Pooling (max_pooling2d_1): Like the max_pooling2d, this layer is aimed at optimizing the feature space from  to .Convolution Layer (conv2d_2): The third convolution layer has 18496 parameters with an input shape of .Max-Pooling (max_pooling2d_2): This max-pooling layer further reduces the dimensions of the feature map from  to  by selecting the maximum value of elements from every pool.Flatten Layer (flatten): The pooled feature map is transformed from 3 dimensions to a 1-dimensional vector. This layer essentially collapses all the input into a single dimension.Dense Layer (dense and dense_1): The dense and dense_1 layers from the model are geared towards the classification task. In general, a dense layer is a fully connected layer—every input and output neuron have a connection. The dense layer uses a ReLU  activatiA brief explanation of the different layers of the model is provided below:The aim of this application is to enable a hassle-free inspection process on the shop floor. Images of the product from the assembly line can be uploaded into the application, and the CNN model will inspect the product to determine whether it is defective or acceptable, thereby failing or passing inspection respectively. Additionally, the SQI application allows the operator to document the findings in the inspection log. Information such as product identifiers, machine identifiers, the result of the inspection, and additional remarks can be stored in the inspection log. Stages 1–3 of the SQI method involve receiving the product, capturing product images, and preprocessing images. The casting data considered in our work consists of images that were captured under stable lighting and with a Canon EOS 1300D camera . AdditioThe training dataset consists of 6633 images, out of which 5307 images were used purely for training the model and the rest 1326 files were used in the validation set to tune the model. To optimize the performance of the model and usage of computational resources, we use the ‘Autotune’ option from the Tensorflow library. As noted previously, the ReLU activation functions were used in the Conv2D and Dense layers, and the loss function considered was a sparse-categorical cross-entropy loss. The Adam optimizer was used to compile the model, but it is worth noting that other optimization methods were also tested. The Root Mean Squared Propagation (RMSProp) and Nesterov-accelerated Adaptive Moment Estimation (Nadam) were also explored but the Adam optimizer narrowly outperformed them on the casting data. The training phase was set to run for a maximum of 20 epochs but was completed in 13 epochs. This is due to the inclusion of the early stopping criteria as a safeguard against overfitting . We use The performance of the model is evaluated on test data of 715 images. Overall, the model achieves an accuracy of 99.86% and outperforms all the other existing models from published works. We can also note that there was only one product that erroneously failed inspection when it was actually an acceptable product. If we look at the confusion matrix in In the case of quality inspection, False Positives are regarded as Producer’s risk and False Negatives are regarded as Consumer’s risk. Producer’s risk is the error of rejecting a good-quality product, and Consumer’s risk is the error of accepting a bad-quality product. In manufacturing, the aim is to reduce or minimize consumer’s risk while producer’s risk is acceptable to some extent. Based on the evaluation of 715 images, our proposed Smart Quality Inspection approach shows that there is no risk to the consumer, i.e., no defective products have been incorrectly accepted by the model.Using the shop floor application for SQI, we can inspect the casting products. To demonstrate the application’s functionalities, we inspect a defective product and an acceptable product. This paper addresses the area of product health monitoring from the perspective of the quality inspection process. The monitoring of system health and product health in manufacturing are reviewed. The steps involved in the quality inspection and visual inspection process are discussed, and key factors that affect the visual inspection process are analyzed. The casting operation is reviewed and the process of visual inspection of casting products is examined. Based on the challenges involved in visual inspection, the AI-based approach of Smart Quality Inspection (SQI) is proposed. A custom CNN model for SQI is designed and implemented on casting product images. The model achieves a high accuracy of 99.86% in inspecting casting products. The accuracy and F-1 score for the model are the highest compared to all the published works on the dataset. Additionally, a shop floor application is also developed to make the inspection process hassle-free. The goal of the application is to minimize as many factors affecting the inspection process as possible. The effects of many of the operator or individual factors, social factors, and organizational factors are minimized with AI-based inspection. Even some of the task factors and environmental factors are controlled. For instance, in an automated inspection system, environmental factors such as time of the day and shift duration would have no impact on the AI model’s performance. The application also allows the quality inspector to document their findings from the inspection process and store it in an inspection log.We do believe that there are areas in which the proposed work can be improved upon. The defects from the casting products could be classified into different types: blowholes, surface blemishes, cracks, adhesions, etc. A formal categorization of defects could be undertaken before classifying them. Localized detection of defects is another feature that can be incorporated into SQI. Localized defect detection identifies the exact location of defects and will return a probability of a defect occurring at that location. This feature is a work in progress, but at this time the results are not satisfactory. So far, the proposed SQI method has been tested on images of casting products. Some of the environmental conditions during the data collection were not in our control. For example, we had no control over the lighting setup to capture the images nor were we able to select the camera equipment used. This is the case with most publicly available datasets. On the other hand, there is limited real-time accessibility to factories where one can perform experiments. In an ideal scenario, this system can be set up directly on the assembly line by automatically taking a feed of product images. This would automate the inspection process even further and reduce any remaining factors that might affect an inspector’s performance. Data from the inspection logs of the SQI tool could also be studied using various Natural Language Processing (NLP) techniques. NLP methods are gaining importance in manufacturing, especially in maintenance.


Early in life, infants exhibit motor overflow, which can be defined as the generation of involuntary movements accompanying purposeful actions. We present the results of a quantitative study exploring motor overflow in 4-month-old infants. This is the first study quantifying motor overflow with high accuracy and precision provided by Inertial Motion Units. The study aimed to investigate the motor activity across the non-acting limbs during goal-directed action. To this end, we used wearable motion trackers to measure infant motor activity during a baby-gym task designed to capture overflow during reaching movements. The analysis was conducted on the subsample of participants (n = 20), who performed at least four reaches during the task. A series of Granger causality tests revealed that the activity differed depending on the non-acting limb and the type of the reaching movement. Importantly, on average, the non-acting arm preceded the activation of the acting arm. In contrast, the activity of the acting arm was followed by the activation of the legs. This may be caused by their distinct purposes in supporting postural stability and efficiency of movement execution. Finally, our findings demonstrate the utility of wearable motion trackers for precise measurement of infant movement dynamics. From very early on, infants undertake countless motor activities despite having limited control over their bodies. Acting on objects and moving around is one of the limited ways to learn about the environment and explore it. As motor behavior enables emerging cognitive and social skills, studying infant motor abilities is a significant part of understanding human development ,2. For iBefore the age of 4 months, infants make their first attempts at reaching , which iOne of the areas of motor development that can benefit from applying novel technologies with high precision and accuracy is the phenomenon of motor overflow, i.e., the production of involuntary movements that accompany goal-directed actions . Althougith time ,16,17,18Indeed, research on motor overflow has been mostly based on manual coding and binary classification of overflow movement . HoweveFurthermore, no studies utilizing wearable data to measure motor overflow were found. An information source that uses sensor data rather than solely manual coding is much more trustworthy, as it additionally enables quantification and inspection of the magnitude of the signal. Moreover, motion trackers enable the detection of even small changes in infants’ movements that are impossible to register with the naked eye. Therefore, studying motor activity in infancy calls for methods with high accuracy, enabling insight into infants’ spontaneous movements. In the current study, the motor behavior of infants was registered with wearable motion trackers. IMUs are a suitable research method especially in infant studies, as they can take the form of light and small sensors that can be placed on the wrists and ankles, making them comfortable but still providing excellent temporal resolution and high precision. Sensors consist of accelerometers, which can be used to investigate physical activity by monitoring changes in motor velocity, gyroscopes capturing the orientation of movement, and magnetometers registering changes in the Earth-magnetic field, enabling the recording of motor behavior in three dimensions.In the current study, we investigated 4-month-old infants’ motor activity in the acting and non-acting limbs during spontaneous, purposeful reaching. Our main goal was to quantify the movement across the non-acting limbs during goal-directed actions and investigate the temporal distribution of motor overflow by comparing it with the motor activity of the acting limb. To this end, we first recorded infants’ limb movements using wearable motion trackers and collected detailed parameters of movement acceleration. Secondly, each reaching movement was detected and manually annotated based on the video recording. It allowed us to take a deeper look into motor behavior in the arms and legs and investigate co-activation that would traditionally be considered overflow. We expected to observe (1) differences in the temporal distribution of the motor activity between the non-acting arm and the legs and (2) diverse relationships between activation of the acting arm and the non-acting limbs depending on reaching movement.In total, 47 infants and their families took part in this study. The infants—19 girls and 28 boys—were born between the 35th and 42nd week of gestation. Participants’ age ranged from 3.9 to 4.9 months The data in this analysis come from a longitudinal study of infant-caregiver dyadic social interactions with wearable movement sensors. The experimental task analyzed here was part of a series of tasks lasting around an hour, during which caregiver–infant dyads played different games with different sets of toys . These games varied in their task demands. All of them, except for the baby-gym task, which was predominantly the last one, were counterbalanced. The parent was offered to take a break between tasks to ensure the infant’s well-being. For the current analysis, we focus on movements produced during one game.The experimental task involved a baby-gym, which is a useful apparatus in infant testing . The tasThe task was recorded with three remote-controlled CCTV color cameras in HD quality recording at 25 FPS, which were placed in three corners of the room. During each experimental task, an experimenter operated the camera that captured an infant best and then manipulated it during the task remotely from a separate room, which included moving the camera and zooming in and out. Movement data were recorded using 7 wearable, wireless motion trackers  predominantly at 60 Hz  and an Awinda station receiver (Xsens Technologies B.V.). Each sensor was a rectangular shape (47 × 30 × 13 mm) and weighed 16 g. The dynamic accuracy was from 0.75 degrees RMS (roll/pitch) to 1.5 degrees RMS (heading) and accuracy time synchronization was ≤10 μs. The sensors were placed on the head, both arms, both legs, and two on the torso . The motThe goal-directed reaching movements were manually annotated in ELAN 6.3 (2022) . The reaTo test the reliability of reaching movement annotation, a subset of participants  and replacing them with NaNs. The threshold of 150 greatly exceeded the inter-quartile range analysis threshold . The rest of the missing values did not exceed the proportion of 18% per sensor ).After pre-processing, the sensor data and manual coding had to be synchronized, as sensors and cameras had to be turned on separately; consequently, there was a short delay between the data. The synchronization was performed based on the isolated activity, i.e., infant’s leg kicks, and followed the procedure from Laudańska et al. . First, Further analysis of the sensor data was conducted on a subsample of participants who produced at least four reaching movements (n = 20).The analyses were conducted in Python  using ingrangercausalitytests function from statsmodels package [The main goal of the analysis was to investigate the relationship between the activity generated by the goal-directed movement with the co-activation of other limbs. To this end, a series of Granger causality tests  were per package . This fud [d was as follows:x.The effect sizes were calculated with Cohen’s d , which hd . The forThe Granger causality test is applied to determine whether information from one time series can provide insight into forecasting another time series. This measure is mainly applied to economic studies; however, it has also been used for human acceleration data  and infaadfuller function from statsmodels package [past values of one time series can be helpful in predicting future values of another one. Therefore, to perform Granger causality, we need to identify a maximum lag, i.e., the longest time period for prediction [A Granger causality test is recommended to be performed under certain conditions. Firstly, each time series should be stationary . Station package . The tes package . Grangerediction . To thisediction  frequentediction . The maxediction , was appA sample of 20 infants during the baby-gym task made a total of 297 reaches  was tested against an analogous times series of the non-acting arm . The resNext, a Granger causality test was performed between the acting arm and the averaged acceleration of legs for all unimanual reaching movements (n = 184) . The resNext, the reaching instances were analyzed within particular reaching types. To this end, the acceleration of the acting and the non-acting arm and legs during only a certain type of reaching movement was collapsed and averaged.The activity of limbs for reaches that ended with a grasp (n = 89) can be found in Finally, The main purpose of this research was to study the distribution of motor activation across limbs during purposeful reaches and investigate whether the acceleration of the acting arm is related to the patterns of activation in the non-acting limbs.The reaching abilities varied substantially among participants, which might be expected in 4-month-olds who are in the transition period toward the acquisition of reaching. Half of the infants in the total sample made less than four reaches and over When all reaching movements were considered, the activity of the acting arm was found to be related to the other arm and the legs, which indicates that the movement of the non-acting arm was not random and dependent on the goal-directed action. However, differences in the temporal distribution were registered, as the non-acting arm was ’leading’ the acting arm, whereas the acting arm was a ‘leader’ to the activity of the legs. Therefore, it can be concluded that the activation in the non-acting arm was on average before the goal-directed movement, whereas the legs predominantly activated after the onset of the reaching movement. This suggests that the movements of the non-acting limbs could have a functional explanation in the context of a developing motor system. The temporal sequence of activation of the subsequent body parts could be related to the maximization of reaching performance, supported by findings on the relationship between postural control and reaching movements in new reachers . ReachinFurther analysis across the reaching types provided additional insight into the motor activity of the non-acting limbs. Even though there was a relationship between the activation of limbs for all reaching movements, hardly any relationship was found for particular reaching types, except for unsuccessful reaching movements, during which the acting arm was ’leading’ the legs. The difference in temporal relationship underlines differences in those reaching types, and the activation of the non-acting limbs could again illustrate the diverse patterns of postural adjustments that had to be made while executing each reaching movement. However, it is possible that the amount of the reaching movements of certain types was insufficient to test the relationship.Furthermore, investigating motor activity in new reachers provides valuable insight into how this skill is developed, which was reflected here in the multiplicity of the reaching types. In previous studies , the anGiven the portability, mobility, low hardware cost, small size, and low weight, wearable IMU technology has tremendous potential in infant studies in both laboratory and natural environments. First, it enables conducting day-long and continuous recordings of spontaneous movements of infants . Second,In our study, the majority of participants did not display any inconvenience related to wearing sensors. After the experimental session, the caregivers were asked whether they observed any differences in the infant’s motor behavior as a result of wearing IMUs. Only two parents from the final sample reported changes in behavior—for the great majority of infants, the sensors did not affect their performance. Furthermore, the recording of reaching actions was the last task in a session of several interaction games with caregivers, so infants had around 40–60 min of previous experience with wearing equipment to get used to it. Experimenters had a live view of the infant’s performance during the session through CCTV cameras and corrected the placement of sensors between tasks if they were loose or displaced. They were also making sure that the sensors were put in the same place on each limb and that the Velcro bands that held the sensors on the wrists and ankles were tightly adjusted. In addition, the caregivers were instructed not to interfere with the sensors’ placement and instead to inform the experimenter if any sensor placement corrections were necessary. In addition, the data used in the present paper were based on manual annotation of reaching movements and the coders only marked episodes when the sensors were placed correctly. Finally, in our previous validation paper , we showParticipants before performing the baby-gym task took part in a series of other tasks lasting around 40–60 min. Consequently, there is a possibility that the order of the task influenced the participants’ performance or that the infants were too tired to contribute to the current task fruitfully. On the other hand, the baby-gym task was the only one that the infant was performing alone (without being in interaction with the parent); therefore, it was required that the infant felt most comfortable in the new place. Secondly, the manual coding and the sensor data had to be synchronized, which was carried out based on the isolated activity . Consequently, the synchronization process could potentially cause inaccuracy in data alignment; however, the procedure applied in the current project was found to be useful in other research . FinallyFindings from the current study provide opportunities for further research. Most importantly, the development of motor skills over time can be explored by comparing the data obtained at the age of 4 months with later time points. Additionally, it might be investigated to see if the co-activation of limbs at 4 months predicts the development of fine and gross motor skills at later stages. Finally, exploration of activity in the non-acting limbs during reaching at 6 and 9 months can help to establish a developmental trajectory of motor overflow.Wearable sensor systems in general can be applied to the clinical field as a health monitoring tool in infancy (see a review of sensor systems ). MoreovThis is the first study investigating motor overflow with wearable motion trackers. In conclusion, in the current study, we used wearable motion trackers to measure motor activity across limbs during goal-directed reaching movements of 4-month-old infants. The findings from a series of Granger causality tests conducted between the acting arm and the non-acting arm and legs indicate diversified patterns of activation under certain conditions. When all reaching movements were analyzed, the activity of the acting arm and the non-acting limbs were related; however, the movement of the non-acting arm was followed by the acting arm, whereas the relationship was the opposite for the activation of the legs. The temporal differences in activation could be explained by diverse needs for postural adjustment to maintain body stability, indicating the purpose behind the movement patterns of the non-acting limbs. The effects were predominantly insignificant when only particular types of reaching movements were considered. The results support further analysis of the co-activation of limbs, as the movement patterns had major differences depending on the type of purposeful reaching and the non-acting limb.


Here, this multi-objective optimal affinity approach is presented, together with a virtual drug discovery pipeline for its practical implementation. When applied to finding pH-specific drug candidates, it combines protonation state-dependent structure and ligand preparation with high-throughput virtual screening. We employ this pipeline to characterize a set of MOR agonists identifying a morphine-like opioid derivative with higher predicted binding affinities to the MOR at low pH compared to neutral pH. Our results also confirm existing experimental evidence that NFEPP, a previously described fentanyl derivative with reduced side effects, and recently reported Opioids are essential pharmaceuticals due to their analgesic properties, however, lethal side effects, addiction, and opioid tolerance are extremely challenging. The development of novel molecules targeting the  The discovery of opium to treat severe pain has been one of the most important achievements in ancient medicine, although manufactured opium preparations in later history had often led to treatment failures due to the fluctuating concentration of the active ingredient. Friedrich Sertürner conducted analytical experiments which in 1804/1805 led to the discovery of the active ingredient of opium: morphine  be the equilibrium concentration of free target protein, [L] the respective concentration of unbound ligand (opioid), and [PL] the concentration of opioids which are bound to the MOR in equilibrium. The binding process can be expressed by a reaction equationIn view of this history, it is surprising why the paradigm of modern drug development, especially when looking for a lead compound (similar to how heroin was a lead compound), still optimizes towards the strongest possible effect, i.e. the lowest possible dose of active ingredient to be administered. In this questionable spirit, a strong affinity of the searched-for lead compound with regard to its target protein is the main goal of current drug screening approaches. In the case of pain relief, the target protein addressed by the active ingredients is the f. This function f depends on the coordinates of the atoms of the ligand molecule with regard to the atomic coordinates of the respective structure T(P) of the target protein. Estimating the affinity of a given ligand to a target protein is equivalent to finding the global minimum of a scoring functionoptimal affinity approach one then aims to find ligands Having identified a target protein, virtual drug screening methods can be used to find ligands with high affinity to the target and are becoming increasingly important for efficient drug discovery , 4. In oL that minimize f are the ones that maximize the free energy difference One of the most often used scoring functions is given by the (Gibbs) free energy difference mentclass2pt{minime” e.g. –7). Seve. Seve\doe”  affinity to the MOR, and it is even more potent and cheaper to produce than heroin. However, it has the same lethal side effects and fentanyl is one of the causes of the opioid crisis in the USA . Due to Summing up, the conventional drug screening approaches for finding a new lead compound are mainly based on maximizing the binding affinity of the protein-ligand system. However, also other factors can influence the efficacy. We therefore propose a novel approach, broadening this view:Instead of investigating In the literature, several cases have been documented, where the environmental conditions can have an influence on the protein-ligand system. For instance, in  it has bnot the solution. One solution could be given by the following new paradigm: The pain relief effect can be caused by an opioid that binds to the MOR in inflamed tissue, whereas the lethal side effects are primarily caused by opioids that bind to MOR in healthy tissue. The difference between inflamed and healthy tissue is e.g. given by the pH value of the chemical environment of the MOR. Inflamed tissue has a low (acidic) pH, healthy tissue has a neutral pH value. The three-dimensional structure of the MOR has a different conformation at low pH than at neutral pH, i.e., the target structure also depends on pH. And also the opioid (ligand) may change its chemical properties and structure with changing pH.For example, in the context of the above-mentioned fentanyl, we previously exploited the surrounding pH value of the target protein to design a novel drug: Here, the wanted effect (pain relief), as well as the unwanted, potentially lethal effects, are both caused by binding of the opioid to the MOR. A primarily high binding affinity  of the searched-for ligand with regard to the MOR is therefore multi-objective optimal affinity approach then seeks for ligands minimize the affinity for maximize the affinity for On the basis of this knowledge we propose to change the paradigm in  drug screening by including the effect of the environment already in the primary search for a lead structure. Our approach proposed in this article represents the environment Solving multi-criteria optimization problems means exploring the set of possible ligands that satisfy . In genementclass2pt{minimIn this article, we will not go into the details of how to find algorithms for solving the multi-objective optimization  in the emulti-objective optimal affinity approach can be implemented in practice. We have built a flexible and high-throughput capable differential virtual screening and docking pipeline. The pipeline is general, i.e. it can handle diverse characterizations of the environment by different parameters. However, it is herein explained for our guiding example, the case of MOR-agonists at acidic and neutral pH, see Fig. Protonation state generation and Molecular dynamics: The protonation state of the target structure is generated at neutral and acidic pH using PROPKA ).As one of the basic steps of the computational experiment presented herein, we generated neutral and acidic environment MOR structures by neutralization or protonation of its side-chains. While we used the experimentally determined conformation of the MOR for the neutral docking scenario, we performed subsequent MD simulation of the acidic protonation state so that the differential protonation of the amino acid side chains can exert its effect on the conformation. Additionally, we prepared ligands for pH-specific docking by predicting their protonation state in a neutral or acidic environment. While on the one hand this approach can yield pH-specific ligands by changes in binding affinity due to direct interactions between the  ligand and differentially protonated side-chains, also the protonation state-related conformational changes to the binding cavity can affect pH-specific binding. It is important to note, however, that prediction of both the The multi-objective optimal affinity approach and its implementation via a differential docking pipeline are general in the sense that can be utilized independent from the application to finding pH-specific agonists for the MOR presented. It will apply whenever drug design is based on several partially conflicting objectives. These objectives may result from desiring different ligand-target interactions at different environmental conditions (as discussed herein) but can also be used to describe other conflicting aims like maximizing binding affinity versus minimizing side effects or toxicity e.g. associated with binding to other targets, for example. Additionally, a multi-objective optimal affinity approach can serve as a drug discovery pipeline in the search for dual inhibitors or polypharmacological molecules, in which it is the goal to identify drugs that bind multiple targets with high affinity , 60. By In current drug discovery, most molecules identified in primary screenings and prioritized after lead optimization studies fail at a later stage in the drug development process. One of the main reasons for this high drop-out rate are safety issues at the preclinical or even clinical stage, putting a huge financial risk on drug development programs. Thus, early identification of chemical compounds and subclasses at risk to cause unwanted and severe side effects is key to prevent such extremely expensive pitfalls. By performing multi-objective optimal affinity optimization in virtual screening approaches, i.e. prioritizing molecules with a high binding affinity for the target structure(s) in the desired, diseased environment, while deprioritizing molecules with high binding affinity for the target structure(s) in the undesired, healthy environment, the selection of candidate molecules with a good safety profile can already be implemented at the initial screening process. With the expected increase in computing power and the development of faster search algorithms in predicting binding affinities, this approach can be further extended to multiple targets, e.g. key regulatory proteins or enzymes of essential cellular processes known to bear safety concerns. Ultimately, performing multi-objective affinity optimization in virtual drug discovery should contribute to the identification and development of safer, more specific drugs at higher pace.


Securing critical infrastructures and manufacturing plants in the Industrial-Internet-Of-Things and Industry 4.0 is a challenge today due to the increased number of attacks against automation and SCADA systems. These systems were built without any security considerations in mind, so the evolution towards interconnection and interoperability makes them vulnerable in the context of exposing data to the outside network. Even though new protocols are considering built-in security, the widely spread legacy standard protocols need to be protected. Hence, this paper attempts to offer a solution for securing the legacy insecure communication protocols based on elliptic curve cryptography while fulfilling the time constraints of a real SCADA network. Elliptic curve cryptography is chosen to respond to the low memory resources available for the low level devices of a SCADA network , and also because it can achieve the same level of security as other cryptographic algorithms using smaller sizes for the secure keys. Furthermore, the proposed security methods have the purpose of assuring that the data transmitted between entities of a SCADA and automation system are authentic and confidential. The experimental results showed good timing performance for the cryptographic operations executed on Industruino and MDUINO PLCs, demonstrating that our proposed concept is deployable for Modbus TCP communication in a real automation/SCADA network on existing devices from the industry. SCADA (Supervisory Control and Data Acquisition) and automation systems are in charge of controlling vital infrastructures for the well-functioning of our society, such as health systems, transportation systems, water, energy, economy and national security. A cyber-attack conducted on critical infrastructures can lead to the disruption of normal processes and even physical damage, having a major impact on the end users of the provided services. Stuxnet , the FloAutomation and SCADA systems are evolving towards Industry 4.0 and IIoT concepts. Legacy systems and solutions have received the most coverage. Currently, at hierarchical levels up to the PLC, legacy protocols are implemented with a prominent usage of Modbus, and, when Ethernet is present, Modbus TCP. Even new PLC equipment has to cope with smarter measuring devices and other legacy solutions that have only Modbus, S7, etc. Horizontally, the PLC level in the industry is communicated through Modbus TCP, Profinet, Ethernet/IP, etc., and yet only a few are using OPC UA. PLC-SCADA communication follows mostly legacy patterns, either legacy Ethernet-based protocols, or centralized OPC UA servers converting the same protocols on the lower side. Although the industrial tendency is to use OPC UA, where specifications including security are being continuously researched, the industry has to secure legacy protocols-based communications. Currently, with the lack of available and proper security solutions at the protocol level, the industry is struggling to achieve important Industry 4.0 objectives.In the abovementioned context, the authors propose an approach for legacy communication protocols to secure the transferred data between lower levels of an industrial system architecture based on elliptic curve cryptographic methods and an Optiga Trust X security controller. The proposed concept is highlighted for the widely spread Modbus TCP legacy communication protocol, and the entire solution is developed and evaluated on devices used in industry for automation and SCADA systems. In addition, our contribution is emphasized by the technological readiness level and by the possibility of deploying the proposed concept, which provides the means for assuring authenticity and confidentiality on legacy communication protocols other than Modbus TCP.We design and implement a solution for securing Modbus TCP communication within automation and SCADA systems based on the Elliptic Curve Digital Signature Algorithm (ECDSA) and Elliptic Curve Integrated Encryption Scheme (ECIES) as elliptic curve cryptographic methods, and use Optiga Trust X as a security controller.We implement our security solution using open-source C language libraries, which were adapted and optimized to fit low memory resources and timing constraints of a real SCADA network. In addition, we built and configured a cryptographic library to allow the integration of various elliptic curves.We integrate within our solution an open-source software library for Optiga Trust X to perform ECC key generation and execution of cryptographic operations corresponding to an authentication cryptographic scheme based on ECDSA.We integrate and deploy our approach on Industruino PLCs and MDUINO PLC, devices used in industry. Moreover, we integrate the security chip Optiga Trust X within the experimental setup as an additional element for providing secure key storage and fast execution of the cryptographic operations.We measure, in the experimental setup, the duration of each cryptographic operation and evaluate for each cryptographic method the security level introduced and the latency added by our approach. In addition, we perform a security analysis which addresses the security protection introduced by the proposed concept against MITM and replay attacks.The major contributions of this paper can be summarized as follows:The paper describes the relevant literature studies for the subject of this paper in The literature addresses critical infrastructure security from various perspectives, starting with the analysis of vulnerabilities of legacy structures towards providing security guidelines to be applied at different levels of architecture up to attempts of providing solutions for specific issues raised as a result of exposing the SCADA and automation systems to the outside world in the context of evolution towards IIoT. In this context, a study by Embed Security  showed dWith respect to vulnerabilities, SCADA and automation systems are liable to attacks on communication protocols, hardware devices and software components. One important aspect to be considered when analyzing vulnerabilities of SCADA and automation systems is related to PLC (programmable logic controller) security, and more specifically, to payload and firmware attacks . AttackeThe authors in  emphasizFurthermore, the authors from  highlighSecuring SCADA and automation systems is addressed by the literature by proposing various methods to mitigate the identified vulnerabilities. Worth mentioning are the challenges which are introduced by Industry 4.0, such as providing real-time data access, methods to allow the integration of a high amounts of sensors and devices in a secure manner and live tools for error identification . With reWith respect to intrusion detection mechanisms, the approach presented by  collecteAnother direction in research in respect to the IIoT concept is the addition of new security mechanisms in critical infrastructure architecture, such as blockchains ,29, digiElliptic curve cryptography (ECC) is an alternative to classical cryptographic mechanisms, such as RSA, since it offers the same security level for smaller key sizes, as shown in , and it With respect to digital signatures, authors from  proposedIn summary, to the best of our knowledge, our work is among the first studies to provide a solution based on ECC and Optiga Trust X for securing legacy communication protocols, such as Modbus TCP, while complying with the time constraints and low memory resources requirements of SCADA and automation systems. To our knowledge, only two research papers have approached ECC integration with Modbus TCP. The first paper , our ownIn order to address the security challenges posed by the requirements of IIoT, to assure the horizontal and vertical interoperability of systems, the authors approach has the goal of presenting a solution based on ECC for securing the network communication between devices from the lower levels of an operational technology architecture. Moreover, the intent of the paper is to demonstrate the integration of the proposed solution on equipment related to the industrial context through experiments. Concerning the approached legacy communication protocol, the authors chose Modbus TCP for implementing the proposed concept, since it has no security in place even though it is prevalent in industry. Nevertheless, the proposed concept can be extended to other legacy communication protocols, such as DNP3, S7, etc. Securing legacy protocols-based communication and deploying security mechanisms on devices with low memory resources and low computational costs while allowing real-time data access inside PLC-SCADA communication are the major security challenges addressed by the proposed concept in the context of achieving Industry 4.0 objectives. Furthermore, our work aims to address MITM, cyphertext attacks and replay attacks by providing a solution to ensure the authenticity, integrity and confidentiality of exchanged messages within an automation and SCADA network.As described in As an option to enhance the security of the proposed concept, for each system element, the authors implement an Optiga Trust X device in order to emphasize the advantages of using a security controller, such as secure key storage, and the fast execution of cryptographic operations. For each proposed method, the latency introduced by each cryptographic operation is evaluated along with the duration of each chosen elliptic curve.In order to provide a solution for data authenticity between a master and a slave within an automation/SCADA network, ECDSA is selected with a focus on the usability of a hardware security accelerator in terms of the concerns of the level of security introduced. As an authentication scheme, the ECDSA was chosen due to its smaller key sizes than other digital signatures, and its performance against cyber-attacks. The authors selected Optiga Trust X as a secure cryptographic microprocessor due to its capabilities in terms of secure key storage, a crypto engine, low computational costs and easy integration with low memory devices, since it has been optimized for IIoT. It also provides an efficient power management, which enables the possibility of configuring the power consumption of the chip in a specific range. Furthermore, this device has a lower acquisition cost than more complexes devices, such as trusted platform modules. A lower cost represents an advantage when analyzing the upgrading of a legacy SCADA and automation system with numerous devices. Worth mentioning is that the security module is designed as a turnkey, delivered with four ECC key slots from which the first one is assigned to the unique ECC key pair generated during production of the chip. The private key of the security module is stored in its built-in tamper-protected non-volatile memory (NVM), while the public key is used to generate an X.509 endorsement certificate which can be used to prove the authenticity of the chip. The remaining key slots can be used to securely store the private keys generated for performing cryptographic operations, such as for computing a digital signature and encryption or decryption operations. Therefore, having the private keys stored in the Optiga’s secured memory, an attacker will not be able to retrieve the private key and impersonate the issuer of the digital signature.Step1: Generate the key pair  using the security chip for each system element.Step2: Store kDevicePrivECC into the secure memory of the security chip.Load kDevicePubECC of the slave device in the security chip associated with the master device.Load kDevicePubECC of the master device in the security chip associated with the slave device.Step3: Load kDevicePubECC for each device into the security chip of the other master or slave device by performing an exchange of public keys, as follows:The Optiga Trust X offers the possibility to select NIST P-256 and NIST P-384 curves for generating the ECC key pair. For the proposed concept, the NIST P-256 was chosen with SHA-256 as a hash function for the ECDSA method. The authors did not address the method of interchanging the public key generated for each system element, but mention the requirements for having the necessary inputs available for the digital signature algorithm. Therefore, the following steps are required to be performed in a third-party secure environment for the first time when the system elements are connected:The same steps are required to be performed for each system element that is an active participant in the communication over Modbus TCP in order to achieve mutual authentication inside the SCADA network. Nevertheless, the solutions for establishing trust for multiple participants may differ in respect to the architecture of the network and are not addressed in this paper.Step1: The master computes a hash over the message m using SHA256 primitive.Step2: The master signs the hashed message m with the secret key kMasterPrivECC computing a digital signature S of a 512-bit length.Step3: The obtained signature is added as an extension of the message m and sent over the Modbus TCP insecure channel to the slave device.Step4: The slave device receives the message m and the signature S in one frame and computes the hash over the message m.Step5: The resulted hash and the kMasterPubECC are used by the slave device to verify the computed signature.If the verification is successful, the slave device accepts the request and further communication with the master device.If the signature is invalid, the slave device rejects the request and aborts the communication.Step6: The result of the signature verification can lead to two options:With the key pairs generated and the public keys exchanged, at system startup, it is required that each master or slave device loads the public key of the other from the non-secure memory of its associated security chip. The principles of the cryptographic scheme are described in The same protocol is applicable when the slave initiates a request, or in case it sends a response by performing the same steps over the message in order to allow the master to verify the identity of the slave. In this way, the protocol allows bidirectional communication between the master and each slave, and can offer protection against MITM attacks. The ECDSA algorithm can be implemented and deployed on the system of interest without having an Optiga Trust X security chip having, as a base, the same principles for the cryptographic authentication scheme. However, since all the cryptographic operations will have to be executed by each device instead of the security chip, one must analyze the aspects related to the computational costs introduced and options for secure storage of the secret keys.The ECIES is a hybrid encryption scheme that uses ECC-based asymmetric cryptography and symmetric cyphers for providing encryption of the sensitive data in order to assure its confidentiality. Furthermore, the hybrid encryption scheme supports key generation, key derivation, key exchange mechanisms and message authentication. The integrated encryption scheme has various implementation methods, even though it was standardized by ANSI, IEEE, SECG and ISO/IEC. A comparison of the requirements and implementation guidelines detailed by each standard is presented in , with thNIST P-160 and NIST P-192 curves are chosen for elliptic key pair generationSHA-256 is chosen as a hashing functionAES-128 in CBC mode is used.The proposed concept aims to prove the integration of ECIES on low level devices of a SCADA network in order to enhance their security and to offer a method for assuring confidentiality of data transmitted over an insecure channel. The cryptographic primitives and algorithms for performing the integrated encryption scheme were selected by the authors based on two reasons. The first is related to limited resources on the automation devices, while the second aims to satisfy the minimum security requirements in respect to known vulnerable attacks. For each operation, the duration and performance is evaluated. The relic toolkit cryptographic library offers the possibility of selecting several elliptic curves for key generation and it allows ECIES configuration. Therefore, the selected options and additional considerations for ECIES configuration based on the relic toolkit library is listed as follows, which is used for deploying the proposed concept:Step1: The master generates an ephemeral key pair  using the ECC-based selected elliptic curve.Generate a shared secret s using DH algorithm based on the master ephemeral private key and slave public key.Generate the keys kEnc and kMac using KDF2 based on the shared secret s.Encrypt the message m using AES-128 in CBC mode having as input the symmetric key kEnc.Compute HMAC-SHA256 over the encrypted message c using the secret key kMac as input.Concatenate the encrypted message c with the resulted keyed hash h obtaining the output message cyphertextEcies.Send a message to the slave device composed of cyphertextEcies and master ephemeral public key kMasterPubEcies.Step2: To encrypt the plaintext message m, the master device needs as input the ephemeral private key kMasterPrivECIESC, the slave’s public key kSlavePubECC. The encryption scheme contains several cryptographic operations which must be executed by the master device, as follows:Retrieve from cyphertextEcies the encrypted message c and the corresponding keyed hash h.Reproduce the shared secret s using DH algorithm based on the master ephemeral public key and slave private key.Generate a symmetric key kEnc and a secret key kMac using KDF2 based on the shared secret s.Compute HMAC-SHA256 over the received encrypted message c using the secret key kMac obtaining a keyed-hash h∗.Compare the resulted keyed-hash h∗ with the one received as input h. If they match the slave will perform further the decryption of the message. Otherwise, the slave will reject the message since there is a failure in message authentication code verification.Decrypt the message c using the AES-128 in CBC mode having as input the symmetric key kEnc.Process the original message m obtained after a successful decryption process.Step3: To decrypt the received message cyphertextEcies and obtain the original message m, the slave device needs as input master ephemeral public key kMasterPubEcies and its private key kSlavePrivECC. The decryption scheme contains several cryptographic operations which must be executed by the slave device, as follows:Additionally, relic toolkit implementation of ECIES uses KDF2 as a key derivation function, HMAC-SHA256 as a message authentication code function and Diffie-Hellman (DH) as a key agreement function. In order to attain secure key management, the authors recommend that the generation is carried out in a secure environment by a third party for each device. Starting with the assumption that each device knows the others public key, and that this key is stored encrypted in the device’s memory or in a security chip, the steps of the proposed algorithm are shown in Considering the same protocol, the bidirectional communication between a master and a slave can be encrypted using ECIES in order to respond to the need of protecting sensitive data, offering confidentiality and integrity. Even if an attacker intercepts the message, the original data transmitted cannot be recomputed without having access to the ECC keys used by the algorithm. To secure the communication channel between the two devices, first the authentication procedure must take place, followed by the integrated encryption procedure. In order to be resistant against replay attacks, a timestamp needs to be added to each transmitted message (must be known by the receiver or received by both parties) for both the authentication scheme and hybrid encryption scheme.In order to deploy our proposed concept on actual devices used in industry within a SCADA and automation system architecture, we chose two Industruino devices and one MDUINO device. The Industruino IND.I/O D21G and Industruino PROTO D21G are two controllers with capabilities for industrial and automation projects based on a ATSAMD21G microcontroller with 256 KB flash memory and a 32 KB SRAM. MDUINO PLC 21+ is used in industrial automation projects, such as for water treatment, being open-source hardware with ATmega2560 as a microcontroller, 256 KB flash memory and 8 KB SRAM. In terms of clock speed, the Industruino devices run at 48 Mhz, while the MDUINO device has a clock speed of 16 MHz. All three devices run open-source C language code, being fully compatible with ARDUINO IDE and having dedicated open-source libraries, which is an advantage from the perspectives of cost and customization possibilities. Additionally, they have integrated communication interfaces used in the industry, such as RS-232, I2C, RS-485, Ethernet and the Modbus protocol. In order to emulate an industrial communication network, we chose Modbus TCP as the communication protocol between the devices used in the experimental setup. Therefore, the physical support in communication is realized through Ethernet.Furthermore, three Shield2Go OPTIGA TRUST X SLS 32 AIA were added as security chips to the experimental setup for implementing cryptography functions and secure key storage for each device part of the authentication cryptographic scheme. Each security chip was connected through I2C to each one of the Modbus nodes. The security chip was designed for securing IoT infrastructures, as presented in , where OArduino Optiga Trust X library for integration of Optiga Trust X .MsgModbus for Modbus TCP protocol .Modbus TCP Industrial Shields library for Modbus TCP protocol custom made for MDUINO PLC RELIC library for cryptographic operations .In order to achieve the intended functionality of the hardware setup and to allow the evaluation of the latency introduced by each cryptographic operation, several open-source C language libraries were used, as follows:To integrate the Optiga Trust X software stack  with theMsgModbus was chosen due to its capability to send/receive messages on Modbus TCP, acting as either a master or as a slave. More than that, it has a scalable design following the Modbus specification, which allows integration with other C language software libraries. MsgModbus was updated by incorporating a functional application layer responsible for handling modified messages with an additional payload and the integration of cryptographic functions.For the MDUINO device, the Modbus TCP library from Industrial Shields was used as it was custom built for this equipment, following the Modbus specifications. This library was modified in a similar manner to the MsgModbus library, since they have the same goals in deploying the system concept.The RELIC library was selected due to its flexible configuration and its scalability in terms of building efficient custom cryptographic toolkits, allowing for the implementation of different security solutions. More than that, it supports the configuration of a variety of elliptic curves over both primary and binary fields, including the ones specified by NIST, among which are NIST P-160, NIST P-192 and NIST P-256. Additionally, the cryptographic library implements cryptographic protocols such as ECDSA, ECIES, ECDH and AES together with block cyphers, along with multiple-precision integer arithmetic, bilinear maps and related extension fields. Its performance was evaluated by ,58,59, wThe RELIC library supports build configurations with a reduced number of cryptographic operations for various hardware architectures, including for low memory microprocessors such as ATMEGA2560. The low memory configuration uses assembly instruction, which supports the efficient execution of complex mathematical operations from a memory resources perspective. Additionally, in terms of assembly mathematics, we chose to use binary elliptic curves on a 160-bit with right-to-left width-w NAF as the point multiplication method and Interleaving of w-(T)NAF as the simultaneous point multiplication method. Therefore, for the MDUINO device, we used the low memory configuration with the options mentioned above. For the Industruino devices, which have more memory resources at their disposal, we configured and built the RELIC library without using assembly mathematics in order to allow for the selection of several primary field elliptic curves for key generation with a size higher than 160 bit, and also for deploying the ECIES-based proposed concept. Additionally, we configured the primary elliptic curve left-to-right width-w NAF as the point multiplication method and Interleaving of w-(T)NAF as the simultaneous point multiplication method. The NAF-based multiplication methods were selected due to their efficiency for hardware devices with memory constraints, as presented by .To evaluate the proposed concept for both the authentication scheme and the hybrid encryption scheme, we measured the duration of each cryptographic operation executed by Optiga Trust X and by the devices selected for the experimental setup, which can be used in a real automation/SCADA system. In addition, we analyzed the latency introduced by the cryptographic functions necessary for an exchange of Modbus TCP messages over a secure channel in contrast with the real-time requirements of the industrial system. Furthermore, the security strength introduced by the proposed concept and the protection against cyber-attacks are analyzed and discussed. However, the security level introduced by the proposed concept is discussed first in order to allow for a better comparison of each ECC curve used. As defined by NIST in , the secThe duration of each cryptographic operation associated with the authentication scheme performed by Optiga Trust X are shown in The measurements for ECDSA functions based on the RELIC library were conducted with the purpose of demonstrating the advantages of using a crypto processor in respect to latency and security strength. Furthermore, when comparing the duration of the ECDSA functions for the signing and verifying executed by the MDUINO device for a 160-bit ECC curve, as shown in In respect to the hybrid encryption scheme, the measurements results from Having a larger message for either sending or receiving messages on Modbus TCP will require an additional time on the communication network, so when choosing the key size, one must consider all characteristics, including security strength introduced versus added latency. For the authentication scheme concept based on the ECDSA algorithm, the additional size of the original message m consists of the signature S, which is double that of the key size. Therefore, for the operation executed by Optiga Trust X with an ECC key size of 256 bit, the additional size of the original transmitted Modbus TCP message will be 512 bit. If an authentication method based on the RELIC library is used instead, the additional size will be 384 bit for the 192-bit key size and 320 bit for the 160-bit key size. For the hybrid encryption scheme, the size of the output message will vary based on the key size length, as observed from With respect to known cyber-attacks, we can argue that the proposed concept offers protection against MITM and replay attacks. In a scenario of communication between a master and a slave, an intruder can intercept the message m, change its content and send it to the intended party, as described in This paper presents an elliptic curve cryptography-based working solution for securing legacy communication protocols, such as Modbus TCP, with emphasis on its integration in lower-level industrial devices used within a real SCADA and automation system architecture. Additionally, it emphasizes the advantages of using Optiga Trust X as a secure cryptographic microprocessor instead of a software-based only solution, which is a hardware device designed specifically for addressing IoT requirements.Two methods are proposed for securing the Modbus TCP communication channel between two devices to respond to the needs of assuring authenticity, integrity and confidentiality. The first method uses Optiga Trust X as a security chip for deploying the ECDSA authentication scheme. One of the main advantages of the Optiga Trust X is the secure storage of the ECC keys, which brings an advantage from a security perspective. In addition, as presented in the paper, compared to a software-based solution, the Optiga Trust X offers a much lower computational cost with a higher security level due to the capability of a fast execution of cryptographic operations. Nevertheless, the ECDSA software-based authentication scheme fulfills the time constraints requirements, but raises security concerns in respect to efficient methods of secure storing for the ECC keys. The hybrid encryption scheme ECIES encrypts the communication over the Modbus TCP channel in order to assure confidentiality and integrity for the exchanged messages. As presented, for the chosen ECC curves, the computational cost increases based on the key size, along with the security level introduced. Therefore, NIST P-192 has a higher computational cost and a higher security level than NIS P-160, but both are within the acceptable communication time for Modbus TCP. In order to obtain a secure trusted channel, the authentication procedure must first take place, and then the hybrid encryption scheme. Nevertheless, the two methods can be independently used considering the requirements of the targeted SCADA and automation system architecture. With respect to protection attacks, the analysis shows that both methods are resistant against targeted types of attacks.The paper demonstrated, through measurements, that the proposed concept fulfills the time constraints of a real SCADA network for Modbus TCP communication while, being integrated into and deployed on Industruino and MDUINO PLCs. Additionally, it proves that security algorithms can be implemented on existing devices from the industry without the need for replacing them with new devices that have built-in security mechanisms, which is a costly and invasive action. Considering these aspects, the proposed concept has real potential to be deployed in the industry among critical infrastructures and adopted for securing other communication protocols.


This work describes the development of a fast and accurate machine learning (ML) 3D U-Net dose engine, trained with Monte Carlo (MC) radiation transport simulations, to calculate the dose in rat patients treated in Microbeam Radiation Therapy (MRT) preclinical studies at the Imaging and Medical Beamline at the Australian Synchrotron. Digital phantoms are created based on CT scans of sixteen rats and are augmented to obtain enough anatomical data. Augmented variations of the digital phantoms are then used to simulate with Geant4 the energy depositions of an MRT beam inside the phantoms with 15% (high-noise) and 2% (low-noise) statistical uncertainty. The high-noise MC simulations are used for ML model training and validation, while the low-noise ones for testing. The results show that the ML dose engine provides a satisfactory dose description in the tumor target and generates the dose maps in less than one second.Microbeam radiation therapy (MRT) utilizes coplanar synchrotron radiation beamlets and is a proposed treatment approach for several tumor diagnoses that currently have poor clinical treatment outcomes, such as gliosarcomas. Monte Carlo (MC) simulations are one of the most used methods at the Imaging and Medical Beamline, Australian Synchrotron to calculate the dose in MRT preclinical studies. The steep dose gradients associated with the 50 In recent years, an increasing number of studies investigating fast dose predictions for radiotherapy treatment planning with GPU algorithms  and deepThis study presents an accelerated development process suitable for preclinical treatments where available training data are limited. Here, our development process is applied to a novel treatment technique, Microbeam Radiation Therapy (MRT) , which p between . Several between , gliosar between ,17,18,19 between ; thus, a between ,14,21.For clinically available treatments, delivered treatment plans and computed dose distributions in previous patients can often be used as training data; however, such data does not exist for novel treatments and preclinical studies. Instead, frequently evolving phantom designs and irradiation scenarios require new training data for ML models to be calculated on a semiregular basis. This renders the development of ML models unviable for many novel treatments.Recent proof-of-concept studies deploying ML models to estimate the doses for MRT ,23 rely This study shows that dose estimations with satisfactory accuracy can be obtained within milliseconds with the developed ML model, even with a small amount of training data available, by implementing data augmentation techniques and training the models with relatively low-statistics (15% noise) MC data, which are significantly faster to acquire.The rest of this paper is structured as follows. In this work, an existing Geant4 simulation ,27 was abased on ,27.The Geant4 prebuilt electromagnetic physics constructor EmStandardPhysics Option 4  is adoptlization ,27 to deFor each geometrical configuration , the simulation was repeated twenty times with different random seeds. Then, the results of the repeated simulations were used to calculate the mean value and standard error of the energy deposition in each voxel.The digital phantoms of the rat heads used in this work are based on CT scans of a total of 16 rats, two weeks after implanting 9 L gliosarcoma cells  sourced X-axis, which coincides with the beam direction. The Hounsfield Units (HU) from the CT scan are used to manually categorize the phantom voxels into three material classes: air , water  (G4_AIR ) and bon (G4_AIR ). Finall (G4_AIR ) is plac , rotating around the center of the brain (±10 degrees around each axis) and scaling the size of voxels isotropically in the three dimensions (factor 0.8–1.2). With this method, a total of 6500 simulation data samples are produced: 4569 samples (generated with rats number 1–10) for training, 1431 samples (generated with rats number 11–13) for validation and 500 samples (generated with rats number 14–16) for testing.In addition to the high-noise test samples, three low-noise test samples were simulated at the actual tumor positions shown in  for testThe statistical uncertainties compared with the high-noise datasets are significantly lower in these treatment test samples. The ML model is the same as in our previously published study  and is bThe input of the model comprises the 96 × 16 × 16 density matrix of the phantom within the prediction volume, indicated with red lines in the schematic. Both the material density (input) and energy deposition (output) matrices are normalized to the range A compression path using strided convolution layers with a following decompression path using blocks of 3D upsampling followed by two convolution layers achieves a multiscale extraction of relevant geometric features from the input to subsequently predict the energy deposition in each voxel. Skip connections between the compression and the decompression path allow bypassing deeper model layers, allowing features of each compression level to be used for the prediction and to avoid vanishing gradients .Two independent ML models are trained for the peak and valley energy deposition predictions, respectively. For each of them, an individual search for an optimal hyperparameter configuration is conducted by evaluating the performance on the validation data.i of the target region, with a total number n of voxels. Training is stopped when the MAE computed on the validation data does not improve anymore for at least 30 epochs. The models of the respective epoch which achieve the lowest MAE on the validation data are used for obtaining the predictions for the test cases and for the corresponding comparisons.In the scope of this study, the number of convolution filters per convolution layer, the batch size and the learning rate are varied in the optimization. For this, different neural networks with respective settings are trained. For the training, the Adam optimizer  is used,In the search for optimal hyperparameters, the MAE computed on the validation data is used as the main measure of comparison.Due to the shifts and rotations used for data augmentation, several data samples exhibit clinically less relevant features, such as large proportions of the spine or auditory canal, which are both not subject to MRT treatments under current preclinical protocols at the Australian Synchrotron. Voxels with bone material especially lead to large MAE values, as the energy depositions are larger within bone structures compared with the brain. To allow for a more outlier-robust comparison of the ML model performance on the training, validation and test datasets, not only the average MAE but also the resulting boxplots are analyzed, which contain more information about the distribution of deviations.Much of the deviation of the ML predictions from high-noise MC data results from statistical fluctuations of the MC simulations themselves. An accurate voxelwise prediction of energy deposition of high-noise MC simulation data is not only not desired but would also mean poor generalization. Instead, an estimate of the mean value of the underlying energy deposition distribution is desired, which would match an MC simulation of the same scenario with less statistical uncertainty. In order to investigate if the ML model is capable to interpolate the high-noise data, the smooth ML energy deposition predictions are compared with low-noise MC simulation data as well as with the high-noise MC simulation data, relative to their standard error.In the case of an unbiased prediction of the values for each voxel, it is expected that 68% of values lie within one standard deviation  is compThe best average MAE on the validation data in dependence on the different hyperparameter settings, together with the corresponding MAE on the training data, are shown for the valley model in The optimal peak and valley ML models are used to predict all high-noise training, validation and test data samples to assess the overall performance and generalization. The averaged fractions of voxels with deviations smaller than 1 The training loss is observed to be higher than the validation loss, at least for the chosen peak prediction model, and this tendency is visible for multiple valley prediction models in In The two examples of the test data with the respective lowest agreement between ML prediction and MC simulation are shown in The energy deposition predictions for the three treatment cases described in The respective fractions of voxels with a difference in terms of relative dose By predicting both the peak and valley doses, the biologically important PVDR can also be calculated with the ML model see . As an eUsing the ML models of the peak and valley regions, it is possible to assess the impact of hypothetical treatment planning using the ML models. During preclinical rat treatment, according to the method defined in ,27, the This study presents an essential step in advancing fast dose prediction models for MRT treatment planning. Although trained on relatively high-noise MC data with mean statistical fluctuations of 5% (15%) in the peak  region, the developed ML model exhibits dose deviations of under 3% compared with low-noise MC simulation data for most voxels (>95%) in the tumor volume in the three exemplary treatment cases under study. The prediction of a dose distribution (using a preprocessed density matrix as input) takes approximately 50 ms, which is significantly faster than the currently fastest calculation method, which takes approximately 30 min . Batch pWhile the achieved dose prediction accuracy is of more than 3% within nearly all of the tumor volume for both the peak (100.0% of voxels) and valley (>95.5% of voxels), its dosimetric performance may be improved outside the target tumor (close to air cavities and bone structures), increasing the training set with a larger number of CT scans from rats. The difference of the ML model performance between the peak and the valley dose predictions may be partly explained by the different noise in the used training MC data. Nevertheless, the ML model predictions agree very well, even when compared with the low-noise MC data. Only around 20% of the voxels of the full phantom, or around 4% of the voxels in the tumor volume, deviate by more than 3%, although the model was trained with on average 5% and 15% noisy data for peaks and valleys, respectively. This gives us confidence that the model generalizes well and learns a very good approximate of the underlying function despite the noise.While the presented ML training method and the achieved results provide a very promising outlook for future studies, important limitations of the study need to be stated. The ML model is trained only on a single set of beam characteristics, such as energy profile, divergence and fixed MRT field size. In addition, the used target phantoms exhibit only limited variation, as irradiations were performed only from the top of the skull. Another limitation, which probably is common when developing ML dose engines for radiotherapy treatments at the preclinical stage, is the small number of CT scans that are available; therefore, it was necessary to augment the data artificially. Another implication arising from the relatively small number of data points, especially the three independent test subjects, is that individual features of each of them contribute strongly to the final results. While this is notable in the reported results, the performances on the training, validation and test subjects were found to be statistically comparable between those data points. Regarding the presented model trained only on the limited number of CT scans available, we believe that this shows a sufficient degree of generalization. Regarding future studies, the success of the approach should be reproduced and validated with a larger number of test subjects with a larger degree of variation to show the generalization capability of such a model more significantly. When comparing the performances on test and training data, small systematic biases occur, which tend to hinder the accurate prediction of the valley doses around air cavities and bones, especially outside the tumor. Still, despite this limitation, the dose prediction of the ML dose engine is satisfactory and, when applied to a clinical environment, the ML dose engine should generate even better results than shown here, thanks to the availability of a larger training dataset, which would translate to better predictive power of the ML model. Another limitation is that the ML dose engine needs to be adapted and, at least, partially retrained when applying it to other cases .Our results indicate that it is feasible to train ML prediction models with satisfactory accuracy using relatively high-noise MC training data. The clear advantage of using high-noise MC simulations is the acceleration of the training of the ML dose engine when applied to spatially fractionated therapies such as MRT, for which MC simulations are usually very time-consuming. The high-noise MC samples used in this study in the training and validation are acquired with 1/40th of the simulation time of the low-noise MC simulations. The significantly reduced execution times of the high-noise MC training data actually allows for easy adaptation to different preclinical conditions. Investigating the percentage of voxels exhibiting a dose deviation of less than one standard deviation and comparing it with the expectation of 68% allows for a meaningful interpretation of smooth ML predictions. This is more difficult when only using measures such as the MAE, which is mainly driven by the deviation between ML prediction and MC simulation caused by the statistical uncertainty of the data.An aspect for future studies would be the quantitative investigation of the dependency of the dose prediction accuracy of the ML dose engine on the statistical uncertainty of the training data, which was not investigated in the scope of this study. In this work, the presented high-noise MC simulation data were chosen as exemplary.In future extensions of the developed ML model, a considerable additional benefit could be achieved by increasing the training set and including larger prediction volumes, especially out-of-field, for a better estimation of doses to organs at risk (OAR) around the tumor, which is currently not considered in preclinical treatments at the Australian Synchrotron.This study presents the first successful application of an ML model for MRT dose prediction in a preclinically relevant scenario. The training data comprise high-noise MC data, which are much faster to acquire than low-noise MC data. The resulting ML predictions are smooth and do not exhibit the noise present in the MC data. A comparison with low-noise test data shows that the predicted doses are accurate within 3% for at least 77.6% of all predicted voxels (at least 95.9% of voxels containing tumors) in the case of the valley dose prediction and for at least 93.9% of all predicted voxels (100.0% of voxels containing tumors) in the case of the peak dose prediction. The ML model seems to generalize well, even if we use training MC data with relatively high statistical uncertainty (15%).The findings of this study allow for an optimistic outlook for the development of ML models to quickly predict doses for preclinical and especially spatially fractionated treatments, which usually require long MC simulation times. Future studies will translate the findings to other MRT treatment settings, including different beam modalities, conformal irradiations and new target phantoms.


Bochdalek hernias (BHs) are rare, and the presentation, diagnosis, and management of them can be complex. We present a 70-year-old man presenting with left flank pain who underwent a successful laparoscopic repair of BH with mesh placement. One of the many achievements of anatomist Vincent Bochdalek was to describe a peculiar hernia that arose as an error of diaphragmatic development. It is known as the ‘Bochdalek hernia' (BH), this congenital diaphragmatic hernia is the protrusion of tissue through a defect in the lumbocostal triangle of the diaphragm, also known as the foramen of Bochdalek. This foramen is a result of the improper fusion of the septum transversum and the pleuroperitoneal folds during the development of the hernia in gestation .A 70-year-old man presented with increasing pain in the left flank on a background of a known BH. The hernia was discovered five years before and managed non-operatively as the patient lived rurally and had been asymptomatic. Contemporary imaging via computed tomography (CT) demonstrated that the hernia was now larger, and that there was a 15 mm defect in the posterior hemidiaphragm on the left side, with herniation of intra-abdominal fat, most likely from the retroperitoneum . The herGiven the patient was having ongoing back and left flank pain, likely due to strangulation of the herniated intra-abdominal fat, a laparoscopic diaphragmatic hernia repair of the foramen of Bochdalek was performed. Following the establishment of pneumoperitoneum and safe laparoscopic port placement, the short gastric vessels were divided to enter the lesser sac. The lateral and superior borders of the spleen were mobilised to improve the exposure of the diaphragm. The hernia was identified; the main findings were a small defect approximately 3 cm × 3 cm over the left dome of the diaphragm behind the left kidney, containing fat , which wThe incidence of BH in infants is approximately 1 in 4000 . In contAsymptomatic BH occurs in approximately 0.17% of the adult population . OtherwiThe diagnosis of BH is challenging due to its rarity and its non-specific presentation in adulthood .Diagnosis may be obtained from plain radiographs or CT. A chest X-ray may demonstrate gas and organs above the diaphragm, whereas a CT is accurate in identifying diaphragmatic defects and signs of ischaemia . The usuDue to its presentation, delayed and missed diagnosis are common and can result in lethal complications. Once discovered, patients should undergo surgical repair as soon as possible, as mortality and complications are as high as 32% in emergency surgery, whereas the mortality rate for elective surgery is less than 3%. This makes early diagnosis vital in reducing the morbidity and mortality of the disease .The main complications of BH are obstruction, strangulation, perforation, and other coexisting anomalies. It was noted that up to 18% of cases were complicated by life-threatening strangulation [In the literature, both transthoracic and transabdominal approaches to surgical repair have led to similar outcomes . LaparotGiven the potential complications, this study aims to educate surgeons about BH, especially since it is an uncommon diagnosis that can present non-specifically. Surgeons should consider a lower threshold for operating on these hernias in the elective setting to prevent the occurrence of avoidable complications.