Resource efficiency and circularity in the context of sustainability are rapidly gaining importance in the steel industry. One concept regarding circular economy is “repurposing”. In the context of this work, worn-out machine circular knives are used to produce new chisels for woodturning. The chisels can be extracted parallel or perpendicular to the rolling direction of the primary production process, resulting in an associated carbide orientation of the repurposed tool. The rolling direction, and therefore carbide alignment, will influence the wear resistance and the thermophysical properties, whereby the thermal conductivity will determine the temperatures at the tip of the chisel. Therefore, the thermal conductivity was investigated with the dynamic measurement method, where the specific heat capacity, density and thermal diffusivity of the extracted chisels and industrial reference chisels were measured separately. Moreover, the electrical resistivity was measured in order to calculate the electronic thermal conductivity according to the Wiedemann–Franz–Lorenz law. It was shown that all of these parameters exhibited different degrees of variability with rising temperature. In a detailed analysis, the thermal diffusivity could be identified as an essential parameter of thermal conductivity. By taking two conventional chisels with different chemical compositions and heat treatments into account, it can be seen that the microstructure determines the thermophysical properties. Considering the carbide direction, the chisels that were extracted parallel to the rolling direction showed differing thermophysical properties. Therefore, the carbide orientation is shown to play a significant role regarding the heat dissipation at the cutting edge, because differences, especially in the electronic thermal conductivity in the parallel and perpendicular extracted chisels, can be measured. In addition to the wear resistance factor, the thermal conductivity factor now also supports the removal of the repurposed chisels parallel to the rolling direction. Steel is a universally applicable material and therefore essential for today’s economy and society; for instance, the raw steel production figures have doubled in the last 20 years . With reAccording to Potting et al. , three dThe strategy used here belongs to the “Repurpose” category, in which an old and no-longer-usable product is converted into another product with a different application . This enThe service life of the extracted chisel depends mainly on the wear during woodworking. Wear describes the progressive loss of material from the surface of the base body in a tribological system, whereby the tribological system is determined by the base body in a relative motion to a counter body, sometimes in combination with an intermediate medium ,12. TherWhen relative motions in a tribological system occur, heat results from the energy lost due to friction between the base and the counter body. Due to the high rotational speed and the high thrust in woodworking, high temperatures are therefore generated at the cutting edge, which as a result is a decisive factor for the material selection.By combining these factors, the chisel underlies a coupled thermomechanical problem. Besides the wear resistance, the thermal conductivity is a further governing parameter which determines the temperature at the cutting edge. Higher conductivities are favourable because of an improved heat dissipation off the cutting edge. Furthermore, both parameters are strongly temperature-dependent and have to be taken into account when considering a tribological system, especially when a higher thermal conductivity lowers the operating temperature and thus results in a higher hardness and increased wear resistance during operation ,13. The In the scope of this work, the thermophysical properties of the repurposed woodturning chisel made out of a lederburitic cold work tool steel will be described and evaluated by comparing them with those of a conventional chisel made of a carbon martensitic tool steel and a performance chisel made of a high-speed steel as benchmark materials. In particular, the repurposed chisel can be taken out across and along the rolling direction, a decision which might influence the thermal conductivity and thus has to be studied. In particular, the influence of the carbide morphology regarding the chisel extraction, as well as the influence of the tempering behavior of the investigated alloys on the thermophysical properties, is of special interest. Due to technological challenges in the sample extraction campaign, this work investigates the thermal conductivity by two independent routes: namely, using the dynamic indirect measurement method and the Wiedemann–Franz–Lorenz law to shed light onto its the electronic component . In all Repurposed woodturning chisel blanks were taken out of the worn-out machine circular knives parallel and perpendicular to the carbide direction by laser cutting; the analogue of the primary hot-rolling and its transversal direction a,b showsThe blanks were then manufactured into ready-to-use chisel shapes “A 20” according to DIN 5144 . BenchmaAll materials were produced by casting, hot forming and a subsequent heat treatment, consisting of hardening and tempering according to industrial conditions .For metallographic investigations, samples were cut from the three chisels. The samples were ground with SiC paper and were subsequently polished with a diamond suspension with a mean grain size of 6, 3 and 1 µm.Microscopic images were obtained with a Vega 3 SHB SEM by TESCAN ORSAY HOLDING, a. s, Brno, Czech Republic, using secondary electron contrast with a voltage of 20 kV and a working distance of 10 mm. p, thermal diffusivity a and density ρ are measured in a temperature range of room temperature up to 500 °C (Equation (1)) ,9,108,9,2C, M6C and MC were present in a fully martensitic matrix. The cold-work tool steel D2 is characterized by eutectic carbides of the type M7C3, as shown by Kronenberg et al. [The investigated tool steels showed significant differences in their microstructure and resulting properties . The lowA more detailed analysis of the influences and resulting properties is therefore necessary and can be achieved with the help of the individual parameters of the thermal conductivity, namely the density, specific heat capacity and thermal diffusivity, and by consideration of the microstructure.7C3 carbides, which have a lower density than pure Fe [The temperature-dependent density of the four tool steels is displayed in  pure Fe . Only smWith respect to the two carbide-rich tool steels D2 and M2, a higher specific heat capacity can be detected. Due to the high content of nanometer-sized tempering carbides in high-speed steels, which are finely dispersed in the matrix , the scaThermal diffusivity is a central parameter of thermal conductivity and is defined as the time-dependent component. The thermal diffusivity is determined by factors that also influence the electrical resistivity and the specific heat capacity, because it reflects both electronic and phononic contributions and therefore scattering of those components. By contrasting Considering the thermal diffusivity, the tool steel L2 with the single-phase martensitic matrix showed the fastest heat transfer. As already mentioned, carbides act as impurity sources, thus scattering phonons and reducing the thermal conductivity and diffusivity ,10,23,28The M2 and D2 steels showed a significantly lower diffusivity at low temperatures due to increased scattering by carbides. In contrast to the alloy L2, the thermal diffusivity of the tool steels M2 and D2 was only slightly temperature-dependent. This result is due to the opposing influences of the decreasing effect of carbides and other scattering sources and enhancing influences of the matrix. It is well known that in high-alloyed matrices, the concentration of free electrons increases with temperature, enabling a higher thermal conductivity and diffusivity of the matrix ,21,29. ITo overcome the limitations in the extraction of laser-flash specimens from the chisels, an approach was used based on the electrical conductivity and the Wiedemann–Franz–Lorenz law. Thanks to the more accommodating sample geometry of the specimens for the determination of the electrical resistivity and the Seebeck coefficient, it was possible to measure the influence of the carbide orientation on these properties. This effectively enables a quantitative analysis of the thermal conductivity by means of the electronic part of the thermal conductivity. It is worth mentioning that both the resistivity and the electronic thermal conductivity are proportional to the number of conduction electrons in a metal ,31.The electrical resistivity is displayed in Of special interest is the influence of the carbide orientation of the D2 tool steel on electrical resistivity. It can be seen that the electronic thermal conductivity of the parallel extracted samples is higher than the conductivity of the samples that have been extracted perpendicularly. This behavior enables a comparison of these two conditions. A detailed view can be achieved by the aid of the electronic part of the thermal conductivity, according to Equation (4).Regarding the electronic part of the thermal conductivity, the alloy L2 showed the substantially highest values b. The noTherefore, the chisel should be extracted parallel to the rolling direction and carbide orientation in order to increase heat dissipation from the cutting edge and reduce the process temperatures during woodturning due to the increased heat flux. Besides thermal conductivity, other microstructural influences would make a parallel extraction preferable. Arguably, the impact energy will increase with a parallel extraction, because the crack has to pass for a longer distance along the carbides ,33. BesiThe current work describes a repurposing strategy implemented in the tool industry, where a worn-out machine circular knife was used in order to extract chisel blanks. These chisels were extracted parallel and perpendicular to the primary rolling direction, which resulted in a parallel and perpendicular carbide orientation of the chisels, respectively. Due to this, different properties were expected: the thermal conductivity was of special interest because of the expected high temperatures that occur during woodturning. Therefore, the thermal conductivity using the dynamic measurement method was measured, where the density, specific heat capacity and the thermal diffusivity were determined separately. Each parameter was therefore investigated as they vary with the measurement temperature, enabling a closer look at each factor individually. Unfortunately, it was not possible to extract samples parallel and perpendicular to the carbide direction out of the chisels for the laser-flash measurements. Therefore, an approach to calculate the electronic contribution to the thermal conductivity using the temperature-dependent measurement of the electrical resistivity and the Seebeck coefficient was used. In this way, the influence of the carbide alignment became measurable. By comparing the chisel material with the material of two conventional chisels, the thermal conductivity of the repurposed chisel was taken into perspective. It was possible to investigate the four tool-steels independently and in a temperature-dependent manner. All parameters were identified as temperature-dependent, making a detailed knowledge of the process temperature indispensable. Nevertheless, it was possible to discover the influences on the thermal conductivity for all investigated materials. The carbide precipitation was systematically identified as the governing factor on the scattering of heat carriers, namely phonons and electrons, reducing the thermal conductivity significantly. This behavior was measurable based on the thermal and electrical conductivity, respectively. Regarding the electrical thermal conductivity, it was possible to detect an influence of the carbide orientation on the thermal conductivity. Because of the free matrix path length, the heat carriers are able to dissipate the energy better from the cutting edge, indicating that the chisel extraction should be carried out parallel to the carbide orientation.Nevertheless, further studies regarding the wear resistance and the impact on energy consumption should be performed in future, enabling a final decision of the performance of re-purposed chisels.(i)Thermal conductivity depends on the microstructure of a material, which depends on the chemical composition and heat treatment.(ii)Due to the measurement temperature above the annealing temperature, the microstructure of an alloy changes, which is measurable in the thermophysical properties.(iii)The thermophysical properties are also dependent on the carbide morphology.(iv)Therefore, the removal of the repurposed chisel parallel to the rolling direction and, accordingly, also to the carbide orientation is to be preferred.In particular, the following observations could be made:


Artificial intelligence (AI) technology has huge scope in developing models to predict the survival rate of critically ill patients in the intensive care unit (ICU). The availability of electronic clinical data has led to the widespread use of various machine learning approaches in this field. Innovative algorithms play a crucial role in boosting the performance of models. This study uses a stacked ensemble model to predict mortality in ICU by incorporating the clinical severity scoring results, in which several machine learning algorithms are employed to compare the performance. The experimental results show that the stacked ensemble model achieves good performance compared with the model without integrating the severity scoring results, which has the area under curve (AUC) of 0.879 and 0.862, respectively. To improve the performance of prediction, two feature subsets are obtained based on different feature selection techniques, labeled as SetS and SetT. Evaluation performances show that the SEM based on the SetS achieves a higher AUC value (0.879 and 0.860). Finally, the SHapley Additive exPlanations (SHAP) analysis is employed to interpret the correlation between the risk features and the outcome. With the progress of medical technology and the arrival of aging society, the demand for critical care is increasing. The intensive care unit (ICU) is a vital department, in which patients with severe conditions will receive continuous, efficient, and intensive care to improve their health conditions. Generally, a critically ill patient requires special monitoring equipment and the support of multiple medical staff. The cost of patients in ICU is approximately 3.5 times that of patients in regular care . The regSeverity scoring system is a commonly used technique in clinical practice, in which the scores are calculated based on physiological assessment of patients. The Acute Physiology and Chronic Health Evaluation (APACHE) proposed by Knous  is a comArtificial intelligence technology has considerable scope in developing models to predict the survival rate of critically ill patients in the intensive care unit (ICU). The availability of electronic clinical data has led to the widespread use of various machine learning approaches in this field. Fortunately, the development of medical technology and the abundance of electronic records on monitoring indicators provide a reliable and rich source for the development of new mortality prediction methods. Medical Information Mart for Intensive Care 3 (MIMIC-3) is a freely available database that includes healthy data records from over forty thousand patients who stayed in the critical care units of the Beth Israel Deaconess Medical Center (BIDMC) between 2001 and 2012 . The rapThe combination of big data technology and clinical severity scoring approaches plays a crucial role in improving the quality of treatment , 11. AccA series of preprocessing procedures executed on the public clinical datasetIn terms of feature selection, the subset features (SetS) and the transformed feature set (SetT) are developedSeveral weak learners (AL) based on APACHE severity scoring results are learned using the machine learning methodsA mortality prediction model combined with APACHE severity score results is established by using stack integration techniqueTo interpret the correlation between features and the outcome, the SHAP analysis is employed to give the ranking of the importance featuresThe contributions of this study include the following:The rest of this research is organized as follows: materials and methods are given in In recent years, data-driven algorithms based on clinical big data have achieved good performance in clinical prediction. Logistic regression (LR) is a well-known linear model that can handle classification tasks. However, the nonlinear structure of the dataset often leads to poor performance in practice , 13. AppDue to various problems brought by clinical data, mortality prediction often faces significant challenges. To promote the development of clinical big data modeling, various international big data competitions on the theme of mortality prediction were held to boost the development of new algorithms for predicting mortality rate in ICU , 18. TheFeature selection is critical for developing a model to predict mortality in ICU. The risk of mortality in ICU varies with disease and depends on the monitoring values of different indicators. To determine the critical indicators based on the given monitoring dataset is a crucial factor in determining the prediction performance of the model. Feature selection based on correlation analysis  is one oThe interpretability of model results plays a key role in future prediction. However, in the field of artificial intelligence, models are often complex or built using an integrated approach, and it is not easy to evaluate the important features that have a significant impact on the results. Explainable AI (XAI)  is a burIn addition to promising accuracy, the ML-based model has strong model interpretabilityCompared to the single technique , stacked ensemble techniques can improve the prediction performance by weighting the various resultsSimilar to , the exiIn terms of the need for interpretability and the convenience of using computational software, several machine learning models are chosen as base and comparison models in this study. The motivation and focus of this work mainly come from the following points:The dataset used in this research is obtained from the 2020 WiDS (Women in Data Science) Challenge , which hClass imbalance: the imbalance of class features is a common problem in clinical data. The dataset is severely imbalanced, which includes survival case 83798 compared to the opposite case 7915. Dealing with class imbalance is an essential part of data preprocessing because it leads to poor performance of the modelMissing data (MD): the second problem is the high percentage of missing values. The number of cases that have no missing value is 300, which means that the missing problem exists for almost all cases. From the point of feature, the number of complete features is 32, and the value of the missing rate ranges from 0 to 0.92265. High dimension: there are 186 features in the original dataset, including 8 category features and 178 numerical features. Critically ill patients in ICU are likely to be monitored simultaneously with multiple monitoring devices for better information. This leads to the redundancy of electronic records. Another problem with high dimensions is the collinearity between features. For example, the value of d1_heart_min indicates the lowest heart rate in the first hour of unit stay, and h1_heart_min indicates the lowest heart rate in the first 24 hours of unit stay, which may have the same trend in the first 24 hours. The problem of high dimension makes modeling complicated and leads to feature interpretation difficultClinical data is often problematic for a variety of reasons. Some feature records are missing due to monitoring device faults. The extent to which patients participate in the monitoring process determines the validity of the monitoring data. In summary, the characteristics of the data are summarized as follows:U3 is the 75% quantile, and U1 is the 25% quantile. IQR represents the length of a random interval defined by two quantiles. The value is identified as an outlier when the value of the input is equal to 1, and the outliers are replaced by the median of the feature corresponding.From different perspectives, the structure of the dataset is complex, which poses challenges for modeling. In order to reduce the impact of various problems, some preprocessing work is needed to improve the quality of the data. It is noted that there are several features which have no contribution to the outcome, such as the features hospital_id, patient_id, and encounter_id. It is found that most continuous features do not have a normal approximation. We choose to delete the outliers based on the concept of quantile. The method used for handling outliers is proposed using the idea of the interquartile range (IQR) . The detMD is a common phenomenon in clinical data. The type of MD includes missing at random, missing at random completely, and not at random . MissingThe imbalanced structure of the dataset makes mortality prediction challenging. Handle problem of imbalance is a primary step for classification. There are two commonly used methods for handling the problem of imbalance. One is based on the random oversampling (undersampling) technique by increasing (decreasing) the number of minority samples to improve the effect of the algorithm . The othHigh-dimensional data exists in various fields, such as finance, biotechnology, and clinical research. Conventional approaches reach their limited performance due to the characteristic of high dimensionality. This section deals with high-dimensional features from two perspectives. One selects subset features using correlation analysis, and the other maps high-dimensional features into a lower space through linear transformation .The subset selection method is to retain the features with high correlation by performing a series of feature filtering and correlation selection on the original data. The detailed process is as follows: The chi-square test  is widelAi indicates the observed number, and the Ti indicates the theoretical number. The high value of statistics means a strong correlation between two features. The categorical features within the subset are selected through the χ2 test. The Pearson correlation coefficient is a widely used indicator that can describe the degree of linear correlation between two variables. The high value indicates the strong correlation between two variables. The subset features are selected according to some given threshold value based on the correlation matrix.The From the perspective of dimensionality reduction, the method used for feature selection is the transformation technology which maps the high-dimensional space to the low-dimensional space. The transformation includes linear and nonlinear, both of which aim to represent most of the information in the original feature with fewer features and remove the noise information. Techniques based on nonlinear transformations, such as t-distribution random neighborhood embedding (T-SNE) . PrincipThe widely used statistical technique is still principal component analysis (PCA), although improved versions based on PCA have been proposed in recent years. Due to the availability, conventional PCA techniques are used for dimension reduction in the following research.Ensemble learning is a commonly used technology, which combines several base models into an integrated model for the improvement of learning efficiency. Commonly used ensemble techniques include bagging, boosting, and stacking . Random The feature AHDP and AIDP represent two types of predicted probability for hospital and ICU deaths based on the APACHE 3 score and other covariates. Fortunately, the statistical test shows that both of them have significant effect on ICU mortality. It is essential to use the two features to improve model prediction. A natural idea is to integrate an ensemble model by using the corresponding probabilities of these two features. To improve the interpretability of the proposed SEM, LR, NB, RF, and XGB are employed to train weak classifiers, denoted as ALR, ANB, ARF, and AXGB. Finally, the proposed SEM model was constructed by integrating the base learner LR, NB, RF, XGB, and AXGB. For the future work, to improve the model performance, deep learning techniques such as CNN could be considered as the basic learner for building ensemble models. The framework of APACHE-based model is given in This section presents the experimental and analytical results of the proposed model. Several R packages  are usedClinical practice shows that age is an essential clinical indicator in ICU intensive care. The same diagnostic treatment programs or medical facilities have different effects on different age groups. As can be seen from There are two kinds of conventional approaches to deal with high-dimensional features. One is feature selection, which filters features with low relevance by retaining features with high relevance. The other is the dimensionality reduction technique, which replaces the original features with lower dimensionality by transforming the feature structure. To enhance the model performance, two feature sets are obtained based on the original feature sets, labeled as SetS and SetT. The detail information in terms of feature set are given in Two datasets are generated based on the feature set SetS and SetT. The experiments are executed on datasets, in which each dataset is divided into training data and test data using 70/30 split, and 5-fold cross-validation is performed on the training data. To evaluate the model performance, several evaluation indexes are used, such as F-score, AUC, recall, and confusion matrix . The conIn , APACHE As shown in The ensemble model takes LR, NB, RF, XGB, and AXGB as the base learner, in which AXGB is a weak learner based on the features AHDP and AIDP. Here, we choose the model AXGB as the AL in x-axis denotes the Shapley value, and the y-axis represents the features that are sorted in decreasing order of importance. Each row in the figure represents a feature, and the points in each row represent samples. The darker the color, the higher the Shapley value of the feature. As shown in The importance of features is characterized by their impact on response variables. Generally speaking, the greater the impact on the outcome feature, the more important the features are. In SHAP importance analysis explains which features contributed most to the mortality rate. In practice, risk at the same age may continuously change with other indicators. It is meaningful to determine the “threshold” at which the risk to the patient suddenly changes. SHAP dependency analysis is employed to explain the relationship between the value of the feature and the corresponding Shapley value.x-axis denotes feature values, the y-axis denotes the corresponding Shapley values, and the color denotes the interactive effect produced by the second feature. It can be seen from With the development of science and technology, intelligent diagnosis has attracted significant attention. Combining medical experience and scientific technology to improve the efficiency of diagnosis will benefit both medical workers and patients. The research proposes a novel stacked ensemble model that attempts to combine the conventional clinical severity scoring methods with machine learning techniques to improve mortality prediction in ICU. The proposed ensemble model SEM achieves good performance compared to LR, NB, RF, and XGB. The performance on the selected subset SetS gives an AUC of 0.879, while the transformed subset SetT gives 0.860. Compared to the model without integrating the APACHE 4 prediction results, the proposed model gives a higher AUC. Furthermore, SHAP analysis is employed to interpret the contribution of features and their significance.The performance and stability of the model are affected by various factors, such as data preprocessing, feature selection, and model structure. In the future work, the model could be constructed from the perspective of feature selection to explore the interpretation and interactivity between features and outcomes. In terms of method, taking the existing severity scoring results as prior information, combined with the machine learning algorithms to explore the mixture method may be an alternative strategy.  proposed


Improving the ergonomy of working environments is essential to reducing work-related musculo-skeletal disorders. We consider real-time ergonomic feedback a key technology for achieving such improvements. To this end, we present supportive tools for online evaluation and visualization of strenuous efforts and postures of a worker, also when physically interacting with a robot. A digital human model is used to estimate human kinematics and dynamics and visualize non-ergonomic joint angles, based on the on-line data acquired from a wearable motion tracking device. Poor ergonomics conditions in work environments may lead to serious work-related musculoskeletal disorders (WMSDs), including severe disabilities . The devClassic kinematics ergonomics evaluation tools such as RULA, REBA and OWAS ,7,8 use Recently, there has been much attention on improving the intuitiveness of ergonomics evaluation tools, as industrial operators should not be expected to have a background in ergonomics. Previous works have used digital human models (DHMs) alongside different types of visual cues for ergonomics evaluation: visualization of the human DHM with colored joints  or displThe data relevant to assessing ergonomics is high-dimensional, including kinematic and dynamic state variables related to posture and efforts. This high-dimensional data is difficult to interpret, even for experts. Physiological sensors such as surface EMG or EKG often measure critical information for ergonomics assessment, but they require considerable post-processing and are difficult to interpret promptly. They are mostly used for post-experimental analysis , while fHere, we focus on providing ergonomics visualization tools that are intuitive for most users—experts or otherwise—by using state-of-the-art methods for dimensionality reduction. We present a novel and potentially easy-to-use visualization tool for ergonomics assessment: Latent Ergonomics Maps (LEM). To construct the LEM, we project a given ergonomics score onto a 2D latent space that represents the human posture. In our previous work, we proposed to use latent representations to capture the human movement/posture . Here, wOnline visualization of joint angles and torques for ergonomic feedback. Given a human posture, the framework calculates the ergonomic assessment and an estimation of the human effort. This latter estimation is derived from inversion of the Lagrangian model using variables  extracted from a simulated DHM. This enables to quickly verify a body posture, captured online using a motion capture suit. This visualization consists of a DHM with color-coded visual cues that express specific locations and joints of the body postures that are particularly non-ergonomic, further facilitating the ergonomics assessment.Latent Ergonomic Maps(LEMs) for immediate overall 2D visual feedback on RULA and RULA-based (RULA-continuous) scores. The algorithm uses a state-of-the-art method for dimensionality reduction and generative network, namely Variational Auto-Encoders (VAE). VAE allows us to encode high dimension postures and to sample and decode variations of the same postures. The latter allows creating a LEM by sampling the latent space, decoding the posture, and applying ergonomic assessment to the posture.In this article, we present two supportive tools for the evaluation of strenuous efforts and postures:We demonstrate the feasibility of the proposed frameworks in two different scenarios: first, we demonstrate the feasibility of our visualization tool for typical industrial activities, such as walking, bending, and overhead manipulation; second, we showcase our tool during a physical human-robot interaction.DHMs typically have many degrees of freedom. A depiction of the human status, therefore, requires high-dimensional vectors, which in turn increase the computational cost of the ergonomics status evaluation . To reduDimensionality reduction techniques applied to human poses are also used in movement planning or prediction . This tyAmong dimensionality reduction techniques, autoencoders (AEs) and variational-AEs (VAEs) have high reconstruction ability and produce compact latent spaces. For this reason, they have been widely used for reducing the dimensionality of the human state and for movement generation . Dermy eTo plan appropriate collaborative actions, collaborative robots need to have an estimation of the current human posture ,29. TheyThe need to reduce musculoskeletal disorders in the industry leads to extensive research into measures of ergonomics. Many ergonomics assessment tools use the kinematic of a given worker to evaluate, and score a given task execution . For insPostural-based approaches, however, do not consider the dynamics properties of the human model nor any dynamics interactions with the environment. In many ergonomics scoring sheets, external forces and loads can be considered , but a more general approach considering efforts due to physical interaction with loads and robots is required to inform the human. For this reason, many works in human-robot collaboration use DHMs to estimate the status of human dynamics. Latella et al. ,36 estimIn this article, we present intuitive visualization tools to provide online feedback to industrial operators about the ergonomics of their movements. In the next sections, we present the methods and technologies used to build our tools  and thenThe main goal of this paper is to develop intuitive visualization tools to provide online ergonomic feedback to industrial operators. The system is designed to be applicable to diverse industrial scenarios in which a human operator may execute non-ergonomic movements, either working alone, or in interaction with a robot. The intuitive visualization tools enable the human to easily visualize the risks of their posture even if they are not ergonomic experts. The human operator could, visualize the latent maps and the DHM along with ergonomics suggestions on a display during a training session, where time can be dedicated to analyzing and improving the gestures and the postures associated with a workstation. Looking at the display is hardly possible during the regular work because the display could divert the attention of the worker and increase the risk of accidents; however, the display can be used for post-hoc analysis immediately after a gesture if the production time allows for it. The visual feedback could be also displayed to the worker using augmented reality glasses . InteresHere, we project the ergonomic information into a 2-dimensional latent space that is learned to represent the human posture. In previous work, we used latent representations to capture the human movement/posture . MoreoveOur DHM is a rigid multi-body system, similar to a humanoid robot, with anthropometrics properties of the human  to mimic their kinematic and dynamics. It has 66 segments and is based on the Xsens MVN model . Using sWe modeled the human spherical joints collected by the motion capture suit as a series of 3 one-dimensional revolute joints, where each DoF is controlled by a single actuator. The resulting DHM posture is represented by the 66 joints: the links are modeled with geometric shapes  scaled with the human height. The dynamic properties  are computed from anthropometric data available in the literature , assuminThe posture of the DHM is calculated using a one-to-one mapping from the original human poses, which are measured in our case by a human skeleton or motion tracking system. The position of the floating base of the DHM is calculated using the position of the pelvis, which is provided by the motion tracking system.The DHM enables the estimation of the human internal torques, as described in previous work . HoweverS is the actuation selection matrix due to the free-floating base and i-th link q¯¨+To estimate the motion ergonomics, we use scores based on kinematics information  and dynamics information (joint torques). The Rapid Upper Limb Assessment (RULA) tool  is oftenurations . It provFinally, we compute human joint torques using DHM, to account for the postural efforts and the effect of external wrenches acting on the human. To display the ergonomic assessments intuitively , we choose to visualize the local ergonomics scores in color-coded spheres attached to relevant body parts in the DHM, updated at each time step. The mapping from the ergonomic score to the colors follows existing recommendations when available: green is associated with low ergonomics scores and indicates low risks, red with high scores indicating high risks, and yellow with scores in between the two. Each ergonomic score (kinematic and dynamic) can be normalized between zero and one by using the maximum value for the specific task, computed in an offline calibration phase. Normalizing the values enables to highlight for a specific task the differences in the ergonomics while executing the same movement in different ways. We are aware that this normalization has some limitations . In future work, we want to overcome these limitations and integrate activity recognition  into ourLatent Ergonomics Maps (LEMs) project an ergonomics score on a 2-dimensional latent space that is trained to represent the human posture . Training the VAE aims at recovering values of the parameters The LEM is created in an offline phase, requiring a dataset of human movements and the definition of the ergonomics score. Training a LEM proceeds in two steps. The first step is to create and train a Variational Auto-Encoder (VAE) to represent the human postures. A schematic of the training procedure is shown We choose a relational VAE to define a loss function which represents both the reconstruction loss and a loss on the relation reconstruction :(2)L= and retargeted into the DHM  information about the current human pose: joint angles and link’s positions, orientations, velocities, and accelerations. Both the human’s and the robot’s sensors measurements are streamed online to the module, to visualize and simulate their movements in the simulation (Dart ) as showAn overview of the main modules used in the two scenarios is shown in The main interest of the second scenario is to use a DHM with visual clues of the joint torques and efforts, to account for the physical interaction between the human and the robot. The contact wrenches are input in the DHM in real-time, using the Franka robot’s sensor measurements, which enables a better representation of the ergonomics status of the human from the point of view of efforts.tanh and identity activations are used for the hidden layers and the output layer. The hyperparameters of the VAE are chosen based on the reconstruction error and the training time by grid search. To train the latent space, we perform first a pre-training with a large human motion dataset . For each point in the latent space we apply the decoder to reconstruct the original posture, we invert the normalization function and then we apply the ergonomic score to the estimated pose. The shape of the latent space is influenced by the choice of the activation functions, for instance, the sigmoid activation function maps the input to The trained encoder into a 2D point in the latent space. The result is a trace, i.e., a sequence of 2D points moving in the ergonomic map, representing the human motion and its ergonomics evaluation. Another example of the trace onto a LEM is shown in In the online phase, the LEM is used to visualize the overall ergonomics score of a posture. The DHM is used to display in real-time the ergonomics information associated with human movement. The real-time information about the human posture is used to update the DHM pose and compute the local ergonomics score, where “local” indicates the specific body location. Each score is calculated using the partial scores presented in the RULA table. In the case of RULA-C, these have continuous values because they are the result of polynomials that approximate the RULA values. The local ergonomic scores are then visualized on the DHM to provide intuitive feedback about the current posture. Specifically, we color some spheres placed at relevant joints  with colors that are proportional to the ergonomic score. The latter is normalized in the range In this article, we presented a set of tools for providing online ergonomics feedback to human workers during their activities, also when they physically interact with robots. A Digital Human Model is used to visualize, with color-coded spheres, the body areas and joints that are subject to efforts and non-ergonomic postures according to state-of-the-art ergonomics scores, such as RULA. Several plotting tools were also developed to provide an intuitive visualization of ergonomics scores associated with human movement.Our contribution, Latent Ergonomics Maps, are synthetic representations of the overall ergonomics scores projected onto a bidimensional latent space that maps human postures. The result is an intuitive color-coded map where the human posture is a point, a movement is a line, and their associated color is an estimation of the ergonomics score of choice. LEMs can be used for bio-feedback or self-correction, as a visual tool for teaching, or simply to inform the human. Their potential goes beyond the online feedback for the human, as they can be used to inform the robot as well, which can find applications in planning ergonomically optimal collaborative motions. Advantages of LEMs include their ease of interpretation also for non-experts, and the computational efficiency, enabling online feedback. A limitation of projecting the map on a 2D latent space, which is necessary for visualization purposes, is the information loss that may result from such a strong dimensionality reduction. However, the error is tolerable for ergonomics scores based on postural information, and otherwise acceptable if coupled with the visualization of efforts on the Digital Human Model.In the future, we plan to combine the prediction of intended movement  with LEM


Economic and social progress in the Republic of Korea resulted in an increased standard of living, which subsequently produced more waste. The Korean government implemented a volume-based trash disposal system that may modify waste disposal characteristics to handle vast volumes of waste efficiently. However, the inconvenience of having to purchase standard garbage bags on one’s own led to passive participation by citizens and instances of illegally dumping waste in non-standard plastic bags. As a result, there is a need for the development of automatic detection and reporting of illegal acts of garbage dumping. To achieve this, we suggest a system for tracking unlawful rubbish disposal that is based on deep neural networks. The proposed monitoring approach obtains the articulation points (joints) of a dumper through OpenPose and identifies the type of garbage bag through the object detection model, You Only Look Once (YOLO), to determine the distance of the dumper’s wrist to the garbage bag and decide whether it is illegal dumping. Additionally, we introduced a method of tracking the IDs issued to the waste bags using the multi-object tracking (MOT) model to reduce the false detection of illegal dumping. To evaluate the efficacy of the proposed illegal dumping monitoring system, we compared it with the other systems based on behavior recognition. As a result, it was validated that the suggested approach had a higher degree of accuracy and a lower percentage of false alarms, making it useful for a variety of upcoming applications. Economic and social progress in the Republic of Korea resulted in an enhanced standard of living, which subsequently led to enormous amounts of waste from enriching consumer goods. A significant societal issue is created by this rise in garbage levels, which also harms the environment . AdditioWaste eligible for volume-rate disposal corresponds to municipal waste generated by households and small enterprises. Standardized volume-based bags must be purchased to dispose of waste. As a motivation for minimizing a pollutant’s effect on health and the environment and an economic incentive to improve optimal waste disposal and increase knowledge of the citizens, the volume-based garbage disposal system aims to convey a need for the reduction of illegal garbage dumping and the cooperation and participation . The metWatchpersons or government officials patrol to find illegal dumping situations occasionally, but such efforts need a larger labor force in wide areas. The recently installed closed-circuit television (CCTV) in locations with a concentration of unlawful dumping contains video recordings. However, the lack of manpower to conduct ongoing surveillance or analyze every single film makes it difficult to bring charges for illegal dumping . AnotherRecently proposed methods combine deep-learning object detection technology widely in use with camera-based monitoring to monitor illegal dumping. The new approach can address the limitations of the existing methods requiring significant manpower and have the benefit of reducing unnecessary noise by enhancing false alarm rates. Min and Lee  proposedAs a result, several techniques exist to track unlawful dumping using object detection and video analysis technologies based on convolutional neural networks (CNNs), as well as detecting sensors. Nevertheless, Refs. ,7 considThis study suggests a strategy of augmented illegal dumping monitoring (AIDM) that determines the distance between the dumper’s wrist and the garbage bag. To estimate the dumper’s wrist joint, Single Person Pose Estimation, which is a method for estimating spatial dependence combinations between body parts, is required and is largely divided into a tree-structured graphical model ,11 and aWith improved detection performance, the proposed monitoring system for illegal dumping can reduce noises caused by unnecessary audio guidance due to the inaccuracies of the existing illegal dumping broadcasting system;Using the object detection model, YOLO can differentiate the standard bags that are legal for garbage dumping and the other non-standard bags. Also, the proposed technique can minimize errors of falsely recognizing dumping-like behavior as illegal dumping through OpenPose, which can extract the articulation points;Our suggested method tracks the objects throughout the entire video without the use of specifically designated observation zones to evaluate whether illegal dumping happened;By introducing the object tracking model DeepSORT, we give IDs to already dumped garbage and garbage held in a dumper’s hand and track the objects to detect illegal dumping, thus lowering the missed detection rate.The proposed method uses the OpenPose model  that canIn this Section, we discussed the need for an illegal dumping monitoring system and the goal of the study. In The monitoring system for illegal dumping that is presently in operation cannot decide on a dumping act itself for the illegality, and thus the impact is not as high as expected concerning the investment for the system implementation. Additionally, the systems that were recently designed using research on illicit dumping practices as the subject are highly susceptible to the probability of mistakenly associating suspicious behavior with unlawful dumping. As a result, we propose an improved monitoring system that identifies illegal dumping by classifying the types of garbage bags and estimating the distance of the dumper’s wrist to the garbage bag, as shown schematically in Deep-learning-based posture estimation is done in two ways: the top-down technique for first finding an area with a person in it and then determining the posture in the area, and the bottom-up method for estimating the posture from the characteristic points of a human body without finding a person. To predict the joints, we employed the bottom-up OpenPose model in this study. Fast joint extraction is possible with OpenPose, and if more individuals are added, the calculation volume does not considerably rise, making it appropriate for crowded areas .Based on a CNN, OpenPose infers characteristic points as joints and delineates them and uses VGGNet to enhance learning efficacy by extracting features of a wider area with fewer parameters. VGGNet creates a feature map ed below : (1)δt=βFinally, the model outputs pression  is used One of the areas of study in computer vision is object detection technology, which is employed to automatically operate and adjust particular devices. The detection involves classification and localization. In classification, a single object in the image is classified with class probabilities, and localization is a process of determining the location of the object. Object detection methods are largely divided into two-stage detectors and one-stage detectors. The two-stage detector conducts the localization and classification sequentially to obtain the results. In the first stage, the area where an object is likely to present is inferred quickly through the regional proposal. In the second stage, the classification identifies the type of object. The major models include Regions with CNN (R-CNN) , Fast R-Unlike the two-stage detector, which performs two processes sequentially, the one-stage detector produces results faster by conducting localization and classification concurrently. The main models include YOLO  and SingAfter dividing the input image into The conditional probability As shown above, the bounding box with the highest Multi-object tracking (MOT) ,32,33 isAs an extension of SORT, DeepSORT has object detection, the Kalman filter-based estimation as well as the matching cascade that uses a deep-learning feature Re-ID, and thus addresses the drawbacks of SORT, that is, unstable to occlusion or ID-switching . The KalIn this section, we describe the detailed procedure for designing the proposed monitoring system that detects illegal dumping based on the distance between the potential dumper’s wrist joints found using OpenPose and the garbage bag location obtained through YOLO and DeepSORT. The block diagram in I(t) of a possible dumper walking into the observation zone while holding the trash. To accomplish this, we input the given image to VGG-19 in OpenPose to generate a feature map, which is then used to generate a confidence map The articular points of the person’s wrist are retrieved from the video To identify the garbage bag held by the potential dumper, we employ the real-time object detection model YOLO to obtain the bounding box matching  are condAs described above, to determine the illegality of the garbage bag held by the potential dumper, we compute the Euclidean distance between the wrist joint coordinates As the final step, we check if the To assess the performance of the proposed illegal dumping monitoring method, we took into account eight scenarios that were similar to actual instances of illegal dumping, including garbage dumping by one hand, dumping by both hands, garbage dumping without bending the waist, and dumping yet to have occurred with the garbage in the dumper’s hand. We then gathered the data for these cases. Furthermore, to determine the performance against the existing garbage dumping monitoring techniques, we included the approach  that leaThe proposed illegal garbage dumping monitoring system was implemented by NVIDIA GeForce GTX 1060 Ti and Intel Core i7-8700 CPU. To train YOLOv4 for real-time object detection, we collected illegal dumping films for each situation using a Logitech C920 PRO HD. The dataset includes videos of the simulation of actual illegal dumping scenes, with 30 videos of about 10 s for each scenario.Commonly dumped garbage includes black plastic bags, white plastic bags, and paper bags containing general garbage, as well as volume-based bags that are recommended to be used. We selected four types of bags that are dumped the most, as shown in We used the average precision (AP) as a performance indicator for assessing the performance of the object detection model YOLOv4, which is trained on the different types of collected garbage bags. To denote the model’s performance as a single numerical value, we utilized the precision-recall curve and the accuracy to evaluate the confidence of the object identified by the model. Precision is the rate of the correctly detected objects among the detected objects, recall is the rate of the detected objects among all the objects that should be detected, and accuracy is the rate of the correctly detected objects among all the objects, as demonstrated below : (13)PreAs illustrated in the table, when the IoU is 0.5, the detection performance indicator, AP, for each class is mostly above 99%, while the average indicator, meanAP (mAP), for all classes is 99.38%, indicating that the model can classify all four objects with high accuracy. However, trashBLK indicates a lower precision than the other types of garbage bags due to the occasional false recognition of a person’s black hair or shoes.The data gathered for the evaluation has a total of four types of garbage bags previously described. As shown in The proposed AIDM determines illegal dumping based on the distance  and the transmission of audio warnings using human body detection sensors. Nevertheless, the effect is limited. As a result, numerous actions are necessary since unlawful dumping is constantly growing in the absence of strict prosecution. Therefore, this study suggested a deep neural network-based illegal dumping monitoring technique that can determine the distance between the dumper’s wrist and the garbage bag. The proposed technique retrieves the articular points of a dumper using OpenPose and identifies the type of garbage bag through the object detection model YOLO. Furthermore, to reduce false detection of illegal dumping, we introduced a method of tracking the IDs issued to the waste bags using the MOT model. The test results demonstrate that our approach of determining illegal dumping based on the distance of the actual dumper’s wrist to the garbage bag has better performance than other recently published methods based on behavior recognition or dumping zone designation. We expect the proposed method to be widely utilized in the future.


This paper introduces a novel theoretical model of ternary nanoparticles for the improvement of heat transmission. Ternary nanoparticles in a heat conductor are shown in this model. Ternary nanoparticles consist of three types of nanoparticles with different physical properties, and they are suspended in a base fluid. Analytical solutions for the temperature and velocity fields are found by using the Laplace transform approach and are modeled by using a novel fractional operator. As a result, the ternary nanoparticles are identified, and an improved heat transfer feature is observed. Further experimental research on ternary nanoparticles is being carried out in anticipation of a faster rate of heat transmission. According to the graphed data, ternary nanoparticles have greater thermal conductivity than that of hybrid nanoparticles. Moreover, the fractional approach based on the Fourier law is a more reliable and efficient way of modeling the heat transfer problem than the artificial approach. The researchers were driven to create a concept of existing nanoparticles in order to boost heat transfer, since there is a strong demand in the industry for a cooling agent with improved heat transfer capabilities. Channel flow is crucial in a wide variety of industrial applications, such as in chemical reactors in the research industry and heat exchangers in power plants. There are numerous associated applications wherever the continuous fluid phase shows non-Newtonian stream properties, even though many procedures of real-world significance can also be characterized as two-phase flows through Newtonian behaviors in both phases. The biochemical, biomedical, and food processing industries all provide numerous examples . On the Enhancing the rate of heat transmission of traditional base fluids is the main problem facing modern science and technology. To enhance heat performance and cooling systems, such as in the cooling of electronic devices, heat exchangers, and automotive cooling systems, with the greatest thermal performance, temperature reduction, precise working capability, and a long life span are required. As a result, scientists and researchers are intrigued the study of how solid particles transport heat in comparison to standard base fluids . After cHybrid nanoparticles are combination of two different nanoparticles with a base fluid. Babazadeh et al.  studied 2 nanoparticles were introduced by Mousavi et al. [Recently, researchers and scientists created a new class of nanoparticles by suspending three dissimilar types of nanoparticles in a pure fluid; this new class is known as ternary hybrid nanoparticles. Researchers have modified existing nanofluids in order to improve their thermal features, leading to the introduction of tri-hybrid nanoparticles, in response to the increased demand for cooling agents combined with the high thermal capabilities at the industrial level. As a result of this reasoning, more experimental studies have been carried out to improve the thermal properties of existing ternary nanoparticles by suspending three dissimilar types of solid nanoparticles in fluids ,19. Sahoi et al. . Raju eti et al.  discussei et al. .Fractional derivatives are essential in the mathematical modeling of practical events. It should be mentioned that fractional calculus is a topic from differentiation science, and L’Hopital introduced it in 1695. Fractional differential equations have recently gained much attention due to their numerous applications in the domains of engineering and physics. Therefore, learning how to generalize classical fluid models to fractional models is of interest for many analysts ,25. SomeAn important class of nanoparticles, that of mono-, hybrid, and ternary nanoparticles on a two-sided vertical plate, has not been studied while using a fractional operator. To describe the channel flow problem, the Prabhakar fractional derivative was used, as it included mono-, hybrid, and ternary nanoparticles. As a result, the primary goal was to discover analytical solutions for the energy and momentum by using the Laplace transform method. The researchers were driven to create a concept of existing nanoparticles in order to boost heat transfer, since there is a strong demand in the industry for a cooling agent with improved heat transfer capabilities. Three types of nanoparticles with different physical properties were suspended as ternary nanoparticles. In order to support the experimental results, a theoretical model for ternary nanoparticles is presented in this study. The relationship between the thermo-physical properties of ternary nanoparticles is given in 2) tri-hybrid nanoparticles, as shown in h. The channel is along the x-axis and is normal to the y-axis. At x-direction. A magnetic field of strength Let us consider an MHD convection flow through the microchannel of electrically conductive (Cu-Ag-TiO2) tri-hybrid nanoparticles causes an electromotive force, which yields a current. Simultaneously, the induced magnetic field is ignored because of the hypothesis of a very small Reynolds number. Moreover, the electromagnetic force changes the intensity of the electric flux [The flow of electrically conductive , we , we 3), By introducing dimensionless variables, we getThe dimensionless fundamental equations are obtained by substituting  into 1)1)–6) an an6) andThe dimensionless form of the momentum equation is as follows:The dimensionless form of the energy equation is as follows:The dimensionless form of the generalized Fourier law for thermal flux ,28 is asThe dimensionless forms of the associated conditions  are as fThis section deals with the solution of the temperature and velocity fields with the Laplace transform method.By applying the Laplace transform to Equations  and 10)10) with By introducing Equation  into EquThe general solution of Equation  with EquIt is important that Equation  can be wThe inverse Laplace transform cannot be found simply from the Laplace transform equation. As a result, Equation  is repreUsing the inverse Laplace transform of Equation , we haveBy taking the Laplace transform of Equation  with conFrom Equations , 22), a, a22), aThe inverse Laplace of Equation  can be oThe momentum equation is:The generalized Fourier law for thermal flux is as follows:ed as in ,28.We get the results for the temperature and velocity fields by using Equations  and 13)13).The dimensionless form of the momentum equation is:The dimensionless form of the generalized Fourier law for heat flux is:This section deals with the solutions of the temperature and velocity fields with the Laplace transform method.By applying the Laplace transform to Equation  with conThe general solution of Equation  with EquIt is important that previous Equation can be written in the equivalent form:The inverse Laplace transform cannot be found simply from the Laplace transform equation. As a result, Equation  is repreUsing the inverse Laplace transform of Equation , we haveBy applying the Laplace transform to Equation  with conFrom Equations , 35), a, a35), aThe inverse Laplace transform of Equation  can be oThis study focuses on the numerical solutions of fractional partial differential equations that appear in phenomena. An approach that uses Prabhakar fractional models with a Laplace transform is used to investigate unsteady MHD convective streams of incompressible viscous fluids in a moving frame with a non-Newtonian fluid in a turbulent channel with ternary nanoparticles and Prabhakar fractional derivatives on the boundary. Therefore, the thermal transport model is based on the generalized fractional Fourier law of the thermal flux. By using the Mathcad software, some of the physical implications of the flow characteristics were examined. Graphical representations of two components, velocity and temperature, were created. It was discovered that the thermal transport’s damping has a significant impact on the fluid’s temperature and velocity.The heat transfer properties of ternary nanoparticles are superior to those of fluids, mono-nanoparticles, and hybrid nanoparticles.It is evident that fractional modeling based on the Fourier law is efficient and suitable in comparison with replacement.The Prabhakar fractional approach is responsible for the better memory of the function due to the generalized Mittag–Leffer kernel of the three parameters.


Lens culinaris Medik.) is one of the major pulse crops cultivated worldwide. However, in the last decades, lentil cultivation has decreased in many areas surrounding Mediterranean countries due to low yields, new lifestyles, and changed eating habits. Thus, many landraces and local varieties have disappeared, while local farmers are the only custodians of the treasure of lentil genetic resources. Recently, the lentil has been rediscovered to meet the needs of more sustainable agriculture and food systems. Here, we proposed an image analysis approach that, besides being a rapid and non-destructive method, can characterize seed size grading and seed coat morphology. The results indicated that image analysis can give much more detailed and precise descriptions of grain size and shape characteristics than can be practically achieved by manual quality assessment. Lentil size measurements combined with seed coat descriptors and the color attributes of the grains allowed us to develop an algorithm that was able to identify 64 red lentil genotypes collected at ICARDA with an accuracy approaching 98% for seed size grading and close to 93% for the classification of seed coat morphology.The lentil ( Lens culinaris Medik.) is among the most commonly cultivated grain legumes and one of the oldest domesticated crops worldwide. While Canada, India, Turkey, and the United States are the main lentil-producing areas [The lentil  color space . Color aIn this paper, we developed a novel approach to overcome the drawbacks of the previous methods by introducing a pipeline fed by RGB  images. These were acquired in the laboratory under hybrid lighting systems with the use of available natural daylight, supplemented with artificial light to meet the required level of seed illumination. Our aim was to operate under the most common conditions in which in situ custodians of lentil germplasm work, enabling them to undertake a reliable and objective characterization of seed morphology of neglected lentil accessions. In this study, we used an off-the-shelf mobile phone camera to acquire the images of the 64 pigmented lentil genotypes comprising a collection of pigmented lentils received from the International Center for Agricultural Research in the Dry Areas (ICARDA-Lebanon). The images enabled us to recover very accurate information about lentil seed size and testa patterns exploiting a randomized circle detection strategy, local binary patterns descriptors, and XGBoost classifier, which were applied for the first time in seed morphology analysis.The proposed approach does not require a specific acquisition setup. Images can be taken from a camera mounted on off-the-shelf mobile devices. It only requires a “quasi” top view of the seeds (to avoid huge perspective deformations), a little radiometric contrast between the seeds and the background (seeds should be clearly visible in the images), and a sheet of graph paper pictured in a small area of the acquired images . The mobile phone can be handled by the user during the acquisition or placed on a support .The acquisition image campaign was carried out under hybrid lighting systems with the use of available natural daylight, sometimes supplemented with artificial light to meet the required level of seed illumination. The focus and iris parameters of the camera were set at fixed values.https://github.com/beppe2hd/unconstrainedLentils (accessed on 10 November 2022).The images were acquired from a camera of a smartphone positioned on a height-adjustable holding structure (camera support) on different days. The camera support was moved between acquisition campaigns, and we found that its repositioning contained a non-negligible error for the purposes of the evaluation process here described. This means that the images in some cases showed a different scaling factor that was handled by the algorithms. In In the present study, we analyzed the grains of 64 lentil genotypes received by ICARDA in Lebanon, including 48 varieties released in 19 different countries between 1984 and 2018, 9 germplasm accessions, and 7 elite breeding lines developed at ICARDA in Lebanon . For breThe input data for the computer vision analysis were 64 images picturing lentil seeds randomly positioned on a black surface. In each image, the number of lentil seeds varied. In total, the seeds in the images totaled 1408.Within each image, the seeds were separated well from each other, but in some cases, there was a superimposition among close seeds and partial occlusions occurred. The number of seeds randomly varied depending on the availability of the seed samples.Due to the unconstrained acquisition conditions, some seeds positioned out of the image center were not pictured in focus, and the images showed different radiometric values depending on the lighting conditions at the shot event.The proposed algorithmic pipeline  from the background had to be addressed in each input image. To exploit the curvature of edges on seeds, a circular shape detector was exploited for the scope. Circular shapes in the input images were detected using a randomized approach  that canA radiometric check was conducted to discard false circles containing large background areas .In the considered experiments, circular regions with more than 10% of background pixels were discarded. Moreover, a statistical check on detected radii was conducted, and values outside within two standard deviations were discarded too. To measure the average size of the seeds in the image, the estimation of the scaling factor in each image was first carried out. This allowed the system to transform the detected radii from pixel to mm. Each image contained a 1 mm graph paper pictured on top of it. The square shapes were detected by using edge extraction and contour following strategies, and the average square side in pixels was estimated. This allowed us to extract, image by image, the spatial coverage S of each pixel on the plane where the seeds were positioned and then obtain a measure in the real world for the object on that plane.Using a formula, the scale parameter S could be computed as S = ADV/EDV, where EDV was the estimated average measure (in pixels) of the squares on the graph paper, and ADV was the a priori known measure (in mm) of the square side .S. This way, the estimated radius of the lentil species in each input image was the outcome of the considered algorithmic step.This way, the seed measures were referred to as the measures of their orthogonal projection on the laying plane. It is worth noting that this was not a limitation because the final goal was to measure the radius of the sphere approximating a seed. The average radius in mm of seeds in each image was finally computed by multiplying the values in pixels with the computed scale factor According to the scientific literature, the classification for seed pattern includes 5 classes, namely, “absent”, “marbled”, “dotted”, “spotted”, and “complex” . HoweverThe patches extracted around the circles detected as described in the previous section were the input of the procedure. Each patch was processed independently from the image from which it was extracted. The first step addressed a 3-classes problem by analyzing texture patterns and color histograms. Each patch was classified as containing rare, sparse, and dense texture patterns. Seeds with rare texture did not contain specific patterns, or, if some blobs were present, they were few and localized in small regions. Each cluster could have a different size and shape. Sparse textures were characterized by a uniform distribution of patterns on the surface of the seed. In other words, there was no substantial texture difference among different regions of the seed.Finally, dense texture showed patterns covering the whole surface of the seed, and they looked like a large stain.Some examples of rare-, sparse-, and dense-textured seeds are reported in The algorithmic details of the methodological steps are detailed in the following steps. In the first step, the local binary pattern (LBP) descriptors  were comUsing the algorithm described in the previous sections, 1549 circles were detected in the 64 images of the provided dataset. After the radius check, 1136 patches were retained, and finally, after the radiometric check, 940 were definitively retained for further processing. The seeds pictured in the 64 images totaled 1270. This means that circle detection correctly retrieved more than 74% (940 × 100/1270) of seeds that were the input of the following processing steps. No false circles were detected. The missed circles were mainly due to defocusing and occluded edges (seeds too close to each other). The circle detection algorithm had thresholds concerning the minimum number of edge pixels to detect a circle. This parameter was set to avoid false circles . In S = 146 × 0.01765 = 5.16 mm. The absolute error E was then estimated as r is the actual radius of the seed, and E = abs (5 mm − 5.16 mm) = 0.16 mm.The average radius of detected circles was 146 pixels in the example, and then the size of the seed was computed as 146 × N = 64 is the number of input images.R-squared values were instead:RSS is the sum of squares of residuals, and TSS is the total sum of squares.The root mean square error and The accuracy of measurement was To assess the accuracy in pattern classification, all the 940 patches extracted from full images were annotated by a team of experts. The first experimental phase was devoted to validating the ability of the proposed pipeline to address the three classes of classification, i.e., to recognize the texture pattern of the seeds as rare, sparse, or dense. The method exploits LBP and histograms as features and XGBoost as the classifier. A square patch  was extracted around each of the 940 detected circles. The patches were labeled as follows: 635 seeds with rare patterns, 25 seeds with dense patterns, and 280 seeds with sparse patterns. Considering the size of the available dataset, the leave-one-out approach was used to test the performance of the proposed method. First, the three-class problem was considered.The results are reported as a confusion matrix in The second experimental phase dealt with the finer classification of lentil seed classes starting from the three-class coarse classification above. The 64 images used for the test contained 511 seeds classified as absent, 124 as spotted, 247 as dotted, 25 as marbled, and 33 as complex.To assess the algorithm based on morphological region analysis, the seeds containing rare textures and sparse patterns were processed. In Classification results concerning the sparse texture patterns were reported in the confusion matrix of In the third experimental phase, all the patches were classified according to the two-step scheme in In The seed size, color, and testa appearance of lentils are important grading factors influencing the nutritional quality  and markTo give evidence of the scientific contribution, it is worth noting that such an automatic classification of testa patterns was never addressed before. Previous attempts only concentrated on colour and size.For the first time, the color components of the seeds were not exploited for color grading, but they were combined with local binary features allowing the characterization of the testa patterns and their classification according to the prominent literature for lentil assessment. In addition, size measurements were not constrained by calibrated acquisition systems or seed handling facilities.Among the existing tools for seed measurement relying only on 2D measurements, the Belt and phenoSEED platforms represent a milestone for this research field. Anyway, according to the results reported in a related paper , experieOn the other hand, there exist platforms providing better accuracy than the proposed one , but theThe proposed pipeline can work in uncontrolled conditions (lighting and seed positioning); instead, it makes use of cameras mounted on off-the-shelf mobile devices, and it does not require any calibration or additional physical infrastructure. The only requirement, purely restricted to the size estimation, is to put a piece of graph paper in the acquired image of seeds. All the algorithmic steps do not rely on fixed thresholds, and unexpected situations are self-checked and controlled. This system works in a broad range of lighting conditions, but it can check if extreme conditions arise. Dark or saturated images are discarded at the beginning by checking lightness, as well as images not in focus are immediately notified to the user when squares on the graph paper are not visible enough. As described in the previous section, several checks were also performed in each algorithmic step to reduce uncertainty in measurement and classification  to acquire seeds and to provide it with an estimated seed size and testa class, thereby allowing users to perform fast and accurate lentil species cataloguing. On the other hand, an online web service could also be implemented to provide a labeling tool accessible from all over the world by uploading images of lentils seeds.Future works will deal with more stressful tests on more lentil genotypes and with different acquisitions per each genotype. This could lead to a systematic investigation of the possible classification bias  introduced by different devices. The resulting large database of images and data could finally bring deep-learning-based approaches for describing lentil morphology and differentiating between the genotypes.


Because of the high cost of experimental data acquisition, the limited size of the sample set available when conducting tissue structure ultrasound evaluation can cause the evaluation model to have low accuracy. To address such a small-sample problem, the sample set size can be expanded by using virtual samples. In this study, an ultrasound evaluation method for the primary α phase grain size based on the generation of virtual samples by a generative adversarial network (GAN) was developed. TC25 titanium alloy forgings were treated as the research object. Virtual samples were generated by the GAN with a fully connected network of different sizes used as the generator and discriminator. A virtual sample screening mechanism was constructed to obtain the virtual sample set, taking the optimization rate as the validity criterion. Moreover, an ultrasound evaluation optimization problem was constructed with accuracy as the target. It was solved by using support vector machine regression to obtain the final ultrasound evaluation model. A benchmark function was adopted to verify the effectiveness of the method, and a series of experiments and comparison experiments were performed on the ultrasound evaluation model using test samples. The results show that the learning accuracy of the original small samples can be increased by effective virtual samples. The ultrasound evaluation model built based on the proposed method has a higher accuracy and better stability than other models. For large titanium alloy forgings, because of their high cost, the metallographic analysis method with accompanying test specimens and sample dissection, as well as full-coverage ultrasonic testing and noise wave height threshold evaluation, is usually used for quality inspection and testing ,2,3. TitThe scattering of ultrasonic waves by the titanium alloy tissue structure is the foundation for performing the ultrasound evaluation. Blodgett  and YangLimited by the adjustable range and gradient of thermal processing parameters, test specimen processing cost, and other factors, the number of experimental samples is restricted in the ultrasound evaluation of metal structures. Yang et al. used one to four samples for validation ,8. Li usGenerating virtual samples is one of the main means to solve the problem of small samples. Poggio et al.  first prThe ultrasound evaluation of the grain size of the primary α phase is a typical small-sample problem. It involves the problem of low evaluation accuracy caused by the small scale of the sample set or insufficient sample information. In this study, to address the above problems, the appropriate virtual sample generation technology was selected according to the ultrasound evaluation characteristics of the grain size of primary α phase. Based on the real sample data information, an effective virtual sample screening mechanism was established to screen effective virtual samples. Furthermore, the effective virtual samples were used to expand the real sample set, and the support vector machine (SVM) regression method was adopted to establish a multiparameter ultrasonic prediction model of primary α phase grain size. The evaluation results were verified by a series of single ultrasonic feature fitting models without virtual samples and multiparameter ultrasonic prediction models using other algorithms.K, produced by a priori knowledge, is used to generate some Let the original training sample set be Virtual samples are divided into effective and invalid virtual samples. Only effective virtual samples can improve the small sample space  to increWhen building the virtual sample space, the prediction model than 10% .Poggio et al.  first inSampling-based VSG is a process of generating random variables. This process generally involves generating new examples from the specified distribution, such as using parameter distributions, including the Weibull distribution and Gaussian distribution, to improve the data distribution of multimodal small-sample datasets ,31. ThisInformation diffusion-based VSG is a method based on diffusion theory. This method is generally used to estimate the acceptable range of small-sample-set attributes, generate new samples through the diffusion function, and fill the sample information interval, so as to expand the sample set, such as mega-trend-diffusion  (MTD) anDeep-learning-based VSG is a method using a deep generative model. The core idea of this method is to simulate the data distribution of real samples to generate new false sample data. The common deep generative model is the generative adversarial network  (GAN).The first method has a relatively low computational cost, but, if there is high correlation between variables, this method is invalid. The second method depends on the extended range of each attribute. Moreover, it is computationally intensive, and the overall effect of each attribute is prone to be ignored. Therefore, this method is difficult to use for high-dimensional small datasets. The third method does not suffer from the disadvantages of the first two methods. In essence, it obtains the joint distribution between input and output variables from the historical dataset, establishes a mapping relationship between them and the real data distribution, and comprehensively considers the impact of all variables.According to the definition of virtual samples, the generation of virtual samples is divided into two processes. One is to generate The GAN is a deep-learning model, and it is one of the most promising methods of unsupervised learning on complex distributions in recent years. It is composed of a generator (G) and a discriminator (D). The generator learns the real data distribution, takes the random noise The GAN adopts a min-max objective to train two models with the following objective function:G and optimize D to maximize the discrimination accuracy of D. Then, D is fixed to optimize G to minimize the discrimination accuracy of D. If and only if The learning optimization method of GANs is to fix Assuming the real training sample set (1) The real training sample set Then, the binary coded real training sample set (2) The black-and-white image set is used as the input of the GAN to train the GAN model. The GAN model is saved when (3) The virtual image set is decoded and restored to the virtual sample set. The decoding formula isThe current virtual sample generation technology can produce a large number of virtual samples, but the validity of the virtual samples is low, and their direct use cannot meet the requirements. Hence, it is necessary to analyze and screen their validity. Taking “the added virtual samples can improve the accuracy of the evaluation model” as the validity criterion, the screening basis is established to screen the generated virtual samples.The optimization rate  indicator is used to measure the validity of virtual samples. The higher its value, the higher the validity of the virtual samples. Equation (4) is the calculation formula.Considering that the ultrasound evaluation model is to predict and evaluate the grain sizes of real samples, and the values of the real samples are limited by processing parameters, virtual samples beyond the range of real samples Algorithm 1: Effective virtual sample generation algorithm  non-linear measurement system. The central frequencies of the transmitting and receiving transducers were 2.5 MHz and 5 MHz, respectively.TC25 experimental material contains 168 effective samples, and each sample contains five ultrasonic characteristic values  and one primary α phase grain size value. For the 168 effective samples, the K-fold cross-validation method is used to test the model accuracy, in which K is 3, i.e., 118 effective samples are randomly selected as the training set and the remaining 50 effective samples are used as the test set.Each valid sample contains five ultrasonic characteristic values Virtual sample generation process.The TC25 real training sample set is preprocessed and normalized to the  interval to ensure the coding accuracy own in x , which iThe virtual sample image set (2)Virtual sample validity analysis and screening process.The optimal virtual sample set is generated by the (3)Modeling process.The real training sample set To verify the prediction model, the prediction accuracy of the real test samples in the model is set, i.e., the test accuracy value The This experiment was divided into two parts. In the first part, a standard function was used to verify the effectiveness of the proposed method. In the second part, the ultrasound evaluation model of the TC25 primary α phase grain size generated based on the GAN was constructed. The selected datasets were studied in detail, and the experimental results were analyzed.To verify the effectiveness of the method, a set of benchmark functions was used in the simulation. In this study, a nonlinear benchmark function is considered asFifty data points were selected as the original dataset, which was divided into a training dataset (30 samples) and a test dataset (20 samples). The binary coded training dataset is used to train a GAN to produce reasonable virtual samples. The architecture of the GAN is described in detail below. The generator is a five-layer neural network. Its input is a one-dimensional vector with a length of 100. The three hidden layers are multilayer perceptrons (MLPs) with 512 neurons in each MLP. The generator uses the leaky rectified linear unit (ReLU) as the activation function for all layers except for the last layer, which uses the tanh function for activation. The discriminator uses a four-layer neural network, and the two hidden layers are MLPs with 60 neurons in each MLP. The discriminator uses leaky ReLU as the activation function except that the last layer is activated by the sigmoid function. The generator and discriminator both use the Adam optimizer, and the learning rate of the optimizer is 0.0002.A standard dataset was introduced to verify the effectiveness of this method. First, the SVM regression model was constructed by using 30 original sample data. Then, the evaluation model based on the GAN was built by employing this method. Finally, the evaluation models established by virtual sample generation methods based on MTD and MD-MTD were compared.Effective virtual samples can fill the information interval between the original sample points and expand the limited and insufficient sample data to enhance the generalization ability of learning and reduce the error of the model.The virtual sample set is generated by multiple iterations of the GAN, and the impact of the virtual sample number on the evaluation model is observed. As The When the impact of different virtual sample generation methods on the evaluation model is analyzed, the number of virtual samples is assumed to be For the different virtual sample generation methods, the distribution of test sample values and predicted test sample values in the ultrasound evaluation model is shown in The proposed method was compared with traditional ultrasound evaluation models, such as the single ultrasonic parameter evaluation model, through a series of curve fitting and multi-ultrasonic parameter evaluation models obtained by different machine-learning methods.The least-squares method was used to establish the ultrasound evaluation model of a single ultrasonic characteristic parameter (sound velocity) and grain size. Here, The ultrasound evaluation model of multi-ultrasonic features and grain size was constructed by a multiple linear regression method and compared with the proposed method. Compared with the four traditional ultrasound evaluation models, the proposed model has a higher training accuracy, smaller MAPE, and greater stability.Small samples have low modeling accuracy because of the limited scale in the ultrasonic evaluation experiment of tissue structure. To address this problem, a method of ultrasound evaluation of the primary α phase grain size based on virtual samples generated by a GAN network was proposed. TC25 titanium alloy forgings were the research object. The experimental conclusions are as follows.(1) A GAN network was used to generate virtual samples, a virtual sample screening mechanism was introduced, and an ultrasound evaluation model was constructed using SVM regression. The experimental results show that the model has high accuracy and a small error.(2) The inclusion of virtual samples can better address small-sample problems, such as insufficient sample information or a small number of samples.(3) Compared with the traditional ultrasound evaluation model, the ultrasound evaluation method with the addition of virtual samples can improve the learning accuracy of the original small samples. Compared with evaluation models constructed using the MTD and MD-MTD virtual sample generation methods, the prediction data obtained by the proposed method are closer to the real sample values. Hence, the ultrasound evaluation model of the primary α phase grain size based on the virtual sample generation by a GAN network has a higher accuracy, a more-stable performance, and less error than other models.


The human tactile system is composed of multi-functional mechanoreceptors distributed in an optimized manner. Having the ability to design and optimize multi-modal soft sensory systems can further enhance the capabilities of current soft robotic systems. This work presents a complete framework for the fabrication of soft sensory fiber networks for contact localization, using pellet-based 3D printing of piezoresistive elastomers to manufacture flexible sensory networks with precise and repeatable performances. Given a desirable soft sensor property, our methodology can design and fabricate optimized sensor morphologies without human intervention. Extensive simulation and experimental studies are performed on two printed networks, comparing a baseline network to one optimized via an existing information theory based approach. Machine learning is used for contact localization based on the sensor responses. The sensor responses match simulations with tunable performances and good localization accuracy, even in the presence of damage and nonlinear material properties. The potential of the networks to function as capacitive sensors is also demonstrated. Soft robotic sensing technologies have shown incredible developments in the last decade . Their aFunctional materials used in the design of soft strain sensors include liquid conductors ,5,6, nanThe role of sensor morphology and its application for the processing and structuring of sensory cues is a commonly observed phenomenon in nature and often applied in robotic sensors . Two exaThis work demonstrates how 3D printing technology can improve the predictability of soft sensors and, hence, unlock model-based sensor optimization techniques . We firsThe complex sensory skins which we present are fabricated entirely using 3D printing, combining two soft and flexible materials—a piezoresistive sensing material and an elastomeric substrate—into a composite network. A Voladora NX+ pellet printer  is used to create the composite material , produciUsing additive manufacturing, the sensor networks can be produced within 20 min, which is significantly shorter than the time required for casting and curing silicone rubber elastomer sensor networks as reported by Thuruthel et al. . For theFor the piezoresistive sensing material, a styrene-based tri-block co-polymer TF5 ATL  with Shore hardness 50A is used. The material has a density of 0.88 g/cmformance . For the) method . In both) method .During printing, a temperature of 230 °C is used for the nozzle and 90 °C for the bed. The printing speed is 30 mm/s and the extrusion multiplier is set at the value of 10. The characterization of the two sensor networks is undertaken using a robotic arm to provide a series of controlled and precisely located 2 mm-deep presses across the surfaces of the two sensor morphologies: For each morphology, two of the eight sensors are chosen and their resistive response magnitudes to probing at two locations—Centre  and Offset —are displayed in The sign of the optimized morphology’s responses, as well as the magnitudes, can change with probe location: the outer (yellow) sensor is seen to increase in resistance when centrally probed, whilst decreasing in resistance during offset probing. Noting the proximity of the offset location to the tight cluster of sensors, and that the width of the channels in the printed samples lead to contact between adjacent sensors in this area, we hypothesize that this decrease in resistance is due to the deformation’s tendency to strengthen the connection between these channels, providing a path of lower impedance to ground regardless of which sensor is being sampled. Though not modeled by the simulation, changes in the response sign should not affect the performance of the network if the response is still a function of the length of the sensor fibers under deformation. The usage of information theory metrics is hence vital here, as it is infeasible to analytically model the response of these sensor networks. Additionally, this strengthening effect is expected to depend not only on the location and depth of the probing, but also the direction in which the probe moves. In this study, we consider only vertical deformations, but the robot’s additional degrees of freedom could be used in future work to incorporate directionality into the training data. The capacitances of the two networks are measured between pairs of sensors on opposite sides of the prints, marked by crosses in Given the thickness of the dielectric medium (0.4 mm) and the small surface areas between sensors, the limited magnitudes of the responses , this suggests that morphologies can be developed with lower error increase than a To compare the robustness of the two morphologies, better than the simulation, indicated by darker blue regions.The second damage scenario, in which the controller knows to neglect the damaged sensors, is presented in As predicted by the simulation, the effect of errors is more localized and less symmetric in the optimized morphology. With 25% of sensors damaged, The ability to easily fabricate complex resistive sensor morphologies through additive manufacturing enables us to optimize and tune the properties of sensory skins to particular applications, including areas of uniformly low error or particularly high redundancy. With measured error distributions demonstrating consistently similar patterns to those predicted by simulations, we have demonstrated the approach’s potential to minimize the previously large reality gap faced when optimizing such morphologies.The fabricated sensors can produce clean and repeatable tactile responses during measurements of resistance and capacitance, with minimal drift and overshoot. The combination of both sensitivity types shows promise in the design and production of sensors which are robust to significant damage, increasing redundancy with no corresponding increase in sensor complexity.The governing strain-dependent resistive response mechanism observed in the measured sensors is the same as that assumed by the simple simulator, with a number of additional effects observed to emerge from crosstalk and from the sensor channel width. Though not included in the model, the non-uniform responses that these produce are advantageous to the neural networks, allowing them to accurately infer the probed locations and to often outperform the predicted results. The crosstalk between the sensors and the non-linear responses could explain why the optimized sensor and grid sensor perform similarly in their robustness to damage. The uniqueness of these sensory signals to particular areas of the optimized morphology provides a promising route for further development of multi-touch flexible sensory skins. Furthermore, the simplicity of the digital fabrication enables a quick method of testing these real-world effects for inclusion in the optimization process, creating custom sensor morphologies that match the desired behaviors.N variables. The number of sensors in a grid is denoted by M is the number of sensors on each side. Each parameter corresponds to a point , increasing the joint entropy also results in an average increase in localization accuracy. The joint entropy of the simulated parametric model can be estimated by a short, continuous and uniform sampling of the response across the sensor area. We use a genetic algorithm for finding the optimal morphologies. On top of optimizing the joint entropy of the sensory skin, we add additional penalties on the cumulative area of sensor crossing and the sharpness of the shapes to obtain the right trade-off between information content, learnability and ease-of-fabrication. For this work, we test an optimized shape from the authors’ previous work  that is A Universal Robots UR5 robotic arm is used to characterize the sensor responses . A PLA pThe underside of each printed grid is secured onto 2 mm-thick EVA foam using VHB tape, and secured firmly to the table. The compressibility of this foam enables the probe to provide substantial local deformations to the grid, reproducing responses similar to those during use as a flexible skin on the surface of a soft material.To characterize the resistive responses, one end of each sensor is grounded and the other coupled to +5 V via a 1.2 k divider . The volThe capacitive responses of each of the two morphologies are demonstrated by sampling the capacitance across the thickness of the insulating material. This is recorded using a Keysight E4980AL LCR meter at 10 kHz, which sends measurements to a host PC with a ∼30 Hz sampling rate. Neural networks are used to predict the probed location on each sensor, given 8 inputs—one value from each sensor. To extract these values, the resistive responses of all 8 sensors are simultaneously recorded during the probing using the serially connected microcontroller. The time-series signals are filtered using total variation denoising (λ=10) ,36, in o