Sleep quality among college students is affected by numerous factors. Previous studies have linked sleep quality to family dynamics as well as personality. However, little is known about the mechanisms underlying this relationship. The aim of this study is to incorporate a moderated mediation model to explore the big five personality traits in mediating the relationship between family dynamics and sleep quality and whether this indirect relationship is modified by only-child status among Chinese undergraduate students. Survey data were collected from a cross-sectional study conducted in Shandong, China and 1022 undergraduate students aged 18–24 were recruited. The mediation and moderated mediation modeling analyses were carried out with the software SPSS PROCESS macro. After controlling for gender and grade, mediation analysis indicated that conscientiousness and agreeableness of the big five personality traits partially mediated the link between family dynamics and sleep quality , and moderation analysis found only-child status acted as a moderator in the relationship between family dynamics and the agreeableness of the big five personality traits . Results showed that family dynamics positively predicted sleep quality both directly and indirectly via the conscientiousness and agreeableness of big five personality traits. Only-child status moderated the indirect association between family dynamics and agreeableness of big five personality traits. The current study advanced our understanding of the mechanism underlying the connection between family dynamics and sleep quality and helped to develop intervention measures to improve sleep quality of college students. Over the past few decades, the Big Five personality structure model has been extensively studied and proved to have cross-assessor and cross-cultural stability. It has been widely accepted at the dimensional level by personality psychologists. It has been the most influential human ethics model around the world . Five peFamily has a significant impact on individual growth and physical and mental health. In the research of family system, it is usually studied in two ways. One is to explain the family system through family function, the other is to use the family dynamic characteristics to measure the internal characteristics of the family system. Family dynamics emphasizes the dynamic process of family internal interaction and can present a unique method of interaction among family members . The stuCollege students are important reserve talents for national construction, and it has always been the goal of society to promote their physical and mental health. College students’ sleep quality is closely associated with their health and personal development . A good There are various factors influencing the sleep quality of college students. Numerous studies have explored the relationship between family-related factors and sleep quality, as well as the relationship between Big Five personality and sleep quality. In terms of family-related factors, studies have shown that only-child status, family economic status, and mother’s education level are related to the sleep quality of college students . In the To fill these gaps, the present study built a moderated mediation model to explore the mediation and the moderation effects of big five personality traits in the association between family dynamics and sleep quality. The purpose of this study is twofold. First, it aimed to examine the mediating roles of big five personality traits in the association between family dynamics and sleep quality. We hypothesized that all aspects of the big five personality would significantly mediate this relationship. Second, this study explored the moderating effect of only-child status. The hypothesized model is shown in The survey was conducted in Shandong province, China from 20 March to 31 March 2021. Stratified cluster sampling was used to ensure the diversity of participants. According to the geographical distribution characteristics of Shandong Province, two universities were selected from the four regions of West Shandong, South Shandong, Central Shandong, and East Shandong. An online questionnaire was adopted in this survey. Investigators were recruited and standardized training was given to the investigators. Digital questionnaires were established and saved online. Investigators excluded participants who had long-term use of sedative-hypnotic drugs such as diazepam as well as other sleep disorders through questioning and then forwarded to participants by investigators through the link of the “Wenjuanxing” widely used in China. The survey was conducted at the end of each class. Participants clicked the link and received the questionnaires, which took approximately 10–20 min to complete. In the end, questionnaires were collected automatically when participants clicked the submit button. A total of 1022 questionnaires were collected and 963 were valid for analyses. Sociodemographic characteristics information included Gender, Ethnicity, Grade, Major, Location of their home, Whether only-child status, and Family type see . The surThe SSFD  consistsPersonality traits were assessed with the Chinese Big Five Personality Inventory brief version (CBF-PI-B) , includiThe Pittsburgh Sleep Quality Index (PSQI)  is a selIn this study, SPSS 26.0  was used for statistical analyses and the significance level was set at 0.05. First, the Harman single-factor test was used to perform factor analysis on all variables combined in the questionnaire. The results of this study showed that 24.36% of the variation was explained by the first principal component, which was below the critical value (40%), indicating that there was no common method bias effect among the variables measured. Second, the data distribution is examined and found to be non-normal. According to Preacher et al. , the anaFirst of all, through literature review, we found differences in sleep quality of college students by gender  and gradThe relationship between family dynamics, big five personality traits, PSQ, and whether only child is shown in p < 0.001) (see As showed in 001) see  and indi001) see  after co001) see .p < 0.001)  (see The test results of SPSS macro PROCESS 3.1 on the moderated mediation effects are shown in 001) see  and indi001) see . Namely,309) see . The verOur investigation focused on family dynamics, sleep quality, and big five personality traits. By constructing a moderated mediation model, we found that family dynamics positively predicted sleep quality both directly and indirectly via the conscientiousness and agreeableness of big five personality traits. In addition, only-child status moderated the indirect association between family dynamics and agreeableness of big five personality traits.First of all, a preliminary correlation analysis was performed on all variables in this study. It is interesting that family dynamics was positively associated with all the big five personality traits except neuroticism. Familial influences on neuroticism and education in the UK Biobank showed that no effect of family environment on neuroticism was found, which is consistent with the findings of the present study . AnotherSecondly, consistent with our hypothesis, this study indicates that family dynamics has a positive impact on sleep quality among college students. The study of family dynamics originated from Systemic Family Therapy (SFT), which is one of the branches of family therapy that has developed rapidly in the past 40 years. It is characterized by the use of system theory, cybernetics, information theory, and game theory to explain family structure and planning treatment techniques . With thFurthermore, it is indicated that conscientiousness and agreeableness among the big five personality traits partially mediated the impact of family dynamics on sleep quality, which partially supported our hypotheses. Family dynamics have a very important impact on the mental health and personality development of college students . The higFinally, this study proved the moderation effect of only-child status on the indirect association between family dynamics and agreeableness. In other words, the indirect effect of family dynamics on agreeableness from an only-child family was stronger compared with those from non-only-child family. This finding was in line with the viewpoint that there is a difference in agreeableness between the only-child group and the non-only-child group . ChildreThere are still some limitations that need to be resolved in future studies. First, the current cross-sectional study cannot determine the causal relationship between family dynamics and sleep quality. Future research could adopt a longitudinal design or experiments to explore the causal relationship between family dynamics and sleep quality through aggregating cross-sectional design and a multilevel linear model or manipulation of independent variables and intermediary variables. Second, only Chinese undergraduates were included in this study. The results can only be extended to the age group of 18–24 years old and cannot represent the entire population. Future research should consider a much wider age range of the population to see if this pattern of results can replicate in non-college-student samples as well.Despite limitations, the results of the current study have important practical implications. One of the important research implications of this study may be that it is the first study to examine the mechanism among family dynamics, big five personality, and sleep quality. Family dynamics could positively predict sleep quality directly and indirectly via conscientiousness and agreeableness of the big five personality traits and whether only child moderated the indirect relationship between agreeableness and sleep quality. Examining personality traits may help build a knowledge base that can be used to map phenotypes associated with individual sleep differences and identifying sleep-related phenotypes may help to examine the underlying genetic background and physiological mechanisms .This study involved 963 Chinese undergraduates and examined the mediating role of the big five personality in the relationship between family dynamics and sleep quality, and the moderating role of only-child status. Based on our findings, it is helpful to develop intervention measures to improve sleep quality of college students, and by improving sleep quality, it can also improve academic performance and overall health of college students.


Central venous port catheters (CVPCs) are commonly employed for long-term chemotherapy. One of the rare complications associated with CVPCs is catheter fracture and further embolization of the fragmented segment into the heart. The most common site of embolization is the superior vena cava-right atrium (RA) junction. However, infrequently, the catheter may embolize further distally into the right ventricle (RV) and beyond making the fragmented tips difficult to access directly with a snare. Here, we report a case wherein both the catheter tips were lodged in the RV cavity forming a loop in the RA. This necessitated the use of a modified technique to retrieve the fragment percutaneously. Every interventionalist should be well versed with the techniques to retrieve foreign materials from the venous and arterial circulation, as well as the right and left heart -3. CentrA 51-year-old female, a known case of carcinoma breast on regular chemotherapy, was found to have a resistance in infusing fluid through the CVPC. A subsequent chest X-ray showed a fracture of the CVPC and dislodgement of the fragment into the RA-RV with a loop. The patient was referred to our center for retrieval of the fragment.On fluoroscopy, the CVPC was fractured with a dislodged fragment. Both the tips of the fragment were in the RV forming a loop in the right atrium Figure . Right fThe JR catheter was used to cross the loop with the wire and was then withdrawn Figure . Then, tEmbolization of fractured fragments of CVPCs has been well described in the literature. However, these are relatively rare occurrences. The most common location of the fracture described in the literature is the anastomotic site between the ports and catheter, likely due to the constant shear stress at that location . DislodgThough in most cases direct snaring is feasible, cases like the present one wherein the tips are not easily accessible present a challenge. Cheng et al. have described the use of wire loops, pigtail catheters, and dormia baskets for catheter retrievals in such situations . There hPercutaneous retrieval of dislodged catheters is safe and effective with a success rate of >90%. Surgical intervention has been resorted to in only rare situations. In the 92-patient series of Cheng et al., there were only two failed procedures . In anotAnatomical challenges in snaring occur when the dislodged fragments are in relatively inaccessible locations, such as deep in the RV and proximal pulmonary vasculature. In such cases, similar to the present one, improvised techniques using routinely available endovascular hardware can lead to procedural success even in the most difficult of cases.


With the rapid development of the computer and sensor field, inertial sensor data have been widely used in human activity recognition. At present, most relevant studies divide human activities into basic actions and transitional actions, in which basic actions are classified by unified features, while transitional actions usually use context information to determine the category. For the existing single method that cannot well realize human activity recognition, this paper proposes a human activity classification and recognition model based on smartphone inertial sensor data. The model fully considers the feature differences of different properties of actions, uses a fixed sliding window to segment the human activity data of inertial sensors with different attributes and, finally, extracts the features and recognizes them on different classifiers. The experimental results show that dynamic and transitional actions could obtain the best recognition performance on support vector machines, while static actions could obtain better classification effects on ensemble classifiers; as for feature selection, the frequency-domain feature used in dynamic action had a high recognition rate, up to 99.35%. When time-domain features were used for static and transitional actions, higher recognition rates were obtained, 98.40% and 91.98%, respectively. Human activity recognition (HAR) is to recognize the actions performed by a person through a series of observations of the person and their surroundings. To improve user experience, HAR has been widely used in medical care, social security, behavior analysis, traffic safety and other industries. In the field of health care, HAR has been successfully applied to behavior monitoring , observaHuman activity classification methods mainly include vision system-based technology and wearable sensor system-based technology. Vision system-based technology appeared earlier than wearable sensor-based technology. In the past few decades, many effective research methods have been proposed. Its core processing stages mainly include data preprocessing, object segmentation, feature extraction and classifier implementation . HoweverThe wearable sensor system can measure several human physiological parameters and send data to the cloud platform with the help of smartphones. The cloud system can perform multiple tasks, such as data cleaning, data storage and data analysis. Under the electronic health monitoring system, doctors can see the patient’s data in real-time, especially for disease monitoring, prevention and treatment  to make At present, the main challenge to deal with the HAR problem is still the problem of motion characteristics. Time-domain features  are wideAnother challenging problem is the recognition of switching movements. Because the incidence of posture switching is low and the duration is shorter than other basic physical activities , most prThis study compared the effects of three groups of features  on the classification of various actions and then quantitatively analyzed the recognition performance of different actions under different feature subsets and learning classifiers.The main contributions are as follows:(1) According to the different dynamic characteristics of various actions, a classification and recognition model of human daily actions and switching actions is proposed. The model adopts different features to classify different actions, which avoids the problem that the unified features cannot represent multi-class action information and reduces the number of features involved in classification.(2) The oversampling technology was used to eliminate the influence of unbalanced datasets and then transition actions were classified and identified as basic actions to improve the flexibility of this method in practical applications. Experiments compared the recognition performance of different actions under different feature subsets and multiple classifiers. The results show that the classification effect of transition actions and dynamic actions on support vector machines was the best, while the classification effect of static actions on ensemble classifiers was the best.The rest of the paper is arranged as follows: HAR machine learning methods for smartphones equipped with inertial sensors can be divided into shallow algorithms (such as support vector machine (SVM), decision tree (DT), random forest (RF), K-nearest neighbor (KNN)) and deep algorithms. The main difference between a shallow algorithm and a deep algorithm lies in the way of feature extraction, that is, manual extraction or automatic extraction . In addiAt present, the extracted features in the HAR system are divided into three categories: time feature, frequency feature and time–frequency feature . When usFor studies that focus on long-term activity monitoring, ignoring transition activities has little impact on the results, but, for the recognition of short-term actions, transition actions cannot be ignored. A lot of work on transition activities regard transition activities as a new activity similar to basic activities. Thien Huynh et al.  used SVMAt present, most of the existing works aim to extract unified features for actions, which has great drawbacks. Unified features should take into account multiple actions, which leads to an increase in the number of features. On the contrary, when the number of features is reduced, the classification effect is reduced. Most of the existing works aim to select classifiers empirically and there is no comparison of classifiers for action classification. At the same time, there are relatively few studies on transition movement. To solve the above problems, this paper divided the actions, compared the extracted features on different classifiers and analyzed the features and models that were more conducive to action classification.Traditional methods to extract features for basic actions are shown in The action recognition model of multi-features and -classifiers proposed in this paper is shown in (1) In addition to basic actions, we considered transition actions as basic action recognition.(2) Since the number of transition samples was much smaller than the basic action, we balanced the dataset.(3) As the action data came from a continuous sequence of complex actions, first, the basic action was segmented. The action segmentation method derives from our team’s previous research results . The int(4) Compared with the traditional method, which only uses a single time-domain and frequency-domain feature for classification, we fully considered the behavior characteristics of different actions, reasonably divided the action types and extracted different types of features for different actions for model training.(5) According to the features extracted from different actions, the training model obtained the optimal feature type and the optimal classifier and the results were directly used in the test set to obtain the optimal classification results.The actions of the whole dataset were divided into two subsets; 70% of the data were selected to generate training data and the rest were selected to test. Then, the training set was used to train the model and the generated training model was used for the prediction of the test set. The features extracted from the training model are shown in Generally, the sensor data collected in the real scene inevitably contain noise. The median filter with core 5 and butterworth low-pass filter with a cut-off frequency of 20 Hz is usually used for noise reduction. Because 99% of its energy is contained below 15 Hz , this raAccording to the actions described in the dataset and the duration of each action, the average duration of transition action was 3.7 s, while the basic activity was about 20.1 s. The signals collected by a volunteer were extracted and the data of 12 actions (6 basic actions and 6 transition actions) were statistically analyzed see . The speTransition activities are different from basic daily activities. The duration of transition activities is very short, the length of duration is not fixed and the sample size of transition actions is smaller than that of other basic actions. Therefore, to classify and identify the transition action as the basic action, it is necessary to oversample the transition data or under-sample the basic action data. It is pointed out that the oversampling method leads to an overfitting problem, while the under-sampling method leads to the loss of key information.The SMOTE technology, proposed in reference , is wideTo make the extracted features more suitable for the types of actions, different features were selected for training for different action training sets. At the same time, the number of single category samples of transition actions was expanded, which was roughly consistent with the number of single category samples of basic actions. Therefore, we could divide the original data into three categories: dynamic, static, and transition and extract features.The specific features in Six new sets of data  were obtained by deriving the original six-axis data. Secondly, the Euclidean norm was obtained from the original acceleration and angular velocity data to obtain two new groups of data. Therefore, a total of 14 sets of data were obtained. The sliding window has a good recognition effect on a single action, which effectively avoids the problem that the extracted features cannot well represent the real characteristics of the data due to different action lengths. To solve this problem, this paper applied an overlapping time window to slice the filtered data according to the window length. In our work, the length of each window was set to 2.56 s and the overlap was set to 50%, intercepting the action signal. Nine frequency-domain features and five time-domain features were extracted for each of the 14 groups of data in each window, so that 126 frequency-domain features and 70 time-domain features could be obtained for each sliding window. The time-domain and frequency-domain features were extracted by each window in the paper see .The type of action, the number of features and the quality of raw data affect the effect of the classifier. On the premise of determining the training set and features, the selection of a classifier directly affects the effect of the whole classification. In machine learning, classifiers are divided into linear and nonlinear categories according to model parameters and input characteristics. The linear classifier has good interpretability and low computational complexity; the disadvantage is that the fitting effect of the model is relatively weak. The nonlinear classifier has a strong fitting ability; the shortcomings are insufficient data, easy overfitting, high computational complexity and poor interpretability. Based on the advantages and disadvantages of the above classifiers, this paper selected five classifiers for model training, namely, linear discriminant analysis (LDA) and KNN among the linear classifiers and DT , SVM 4949 and enThe experimental data of this paper were obtained from the dataset of “human activity recognition using smartphones” in the UCI  machine learning repository, which is publicly available . For thiThe experimental platform was mounted on Intel (R) Core (TM) i5-6200u@2.30 GHz on a Dell computer; the operating system was Windows 10 and MATLAB programming language tools were used. For activity recognition, indicators such as accuracy, recall, accuracy, F1 score, ROC curve and AUC value were intensively applied. It is proved that these evaluation methods are feasible.To evaluate the performance of the model, this paper used four evaluation indexes: accuracy, accuracy, recall and F1 score .Simple actions with long duration and repeatability are collectively referred to as human basic actions, as shown in The overall recognition rate of six types of actions is reported in The classification effect of frequency-domain feature information of six basic actions on SVM is further given in The segmentation of various actions is a key problem in this study. The action segmentation method adopts the previous research results of the team. The segmentation of basic actions is shown in The experimental results in Whatever the characteristics, SVM was the best classifier to recognize dynamic actions, with a recognition accuracy of more than 99%. Transition actions were also classified best on SVM, with a recognition accuracy of 91.98%. DT was the worst classifier for the classification of two types of actions because DT is prone to overfitting and ignores the correlations among data see . BecauseThere was a big gap between static action and the other two kinds of action. This kind of action had the best classification effect in integrated learning, up to 98.40%. The bagging method in ensemble learning was used in this paper, which mainly reduced the variance and improved stability through repeated training. Ensemble learning classification uses the combination of multiple learners, which helps to reduce the risk of falling into the local optimal solution, to improve the overall generalization performance. In addition, the combination of multiple learners also helps to expand the hypothesis interval and may learn better approximation. Therefore, static actions achieved the best classification effect on this classifier.The performance of specific 12 types of action features on different classifiers are shown According to the analysis in The superiority of the proposed method is reflected by comparing the previous research results. The proposed model was tested on the UCI dataset, so the selected comparative study was also carried out on the UCI dataset. A bar chart comparison of recall rate identified under different characteristics of three specific actions is shown in A3, the frequency-domain feature of single action, had the highest recognition rate. By comprehensively analyzing the overall accuracy and recall rate of single-action recognition, we can infer that the effect of frequency-domain features was the best see a and thiThe bar graph comparison of the recall rate of specific action recognition in a static action is reported in The classification recall rate of multiple transition actions under different features is shown in Summarizing the above analysis see , it was In this study, based on the built-in sensor data of smartphones, human behavior was classified and identified. Firstly, the transitional actions between human continuous actions were balanced as samples and identified as basic actions. Secondly, the basic actions were divided more carefully and different characteristics were extracted from the processed transition actions; then, the classification and recognition were compared on multiple learning classifiers. The experiments show that the oversampling of transitional actions was conducive to action recognition. Secondly, the ensemble learning classifier had better classification performance for static action features and SVM showed better classification performance for dynamic action features with large fluctuations. In addition, dynamic actions could obtain better classification results under a small number of frequency-domain features and several common time-domain features of static actions and transition actions could obtain a good recognition rate, which shows the importance of feature engineering for action classification and the necessity of classification processing. The performance measures used to evaluate the classification techniques included accuracy, precision, recall and F1 score. At the same time, the classification and comparison of each type of action also included performance indicators such as precision, recall and F1 score. In the future, we will explore the applicability of this method on other datasets and will continue to explore other methods to improve the recognition performance of transition actions.This paper only used the SMOTE method, as the balanced sample method is too traditional and single. In addition, there is a widespread phenomenon of class imbalance in natural datasets, which represents a great test for the sample balance method. In the future, a more systematic sample balance method will be adopted to further study the influence of the sample balance method on transition action recognition.


Five-axis flank milling has been applied in industry as a relatively new method to cut spiral bevel gears (SBGs) for its flexibility, especially for the applications of small batches and repairs. However, it still has critical inferior aspects compared to the traditional manufacturing ways of SBGs: the efficiency is low, and the machining accuracy may not ensure the qualified meshing performances. To improve the efficiency, especially for cutting non-ferrous metals, this work proposes an approach to simultaneously cut the tooth surface and tooth bottom by a filleted cutter with only one pass. Meanwhile, the machining accuracy of the contact area is considered beforehand for the tool path optimization to ensure the meshing performances, which is further confirmed by FEM (finite element method). For the convenience of the FEM, the tooth surface points are calculated with an even distribution, and the calculation process is efficiently implemented with a closed-form solution. Based on the proposed method, the number  of the tool path is reduced, and the contact area is qualified. Both the simulation and cutting experiment are implemented to validate the proposed method. Spiral bevel and hypoid gears are significant components of power transmission systems used in automobiles, helicopters, etc. Spiral bevel gears perform rotation about intersecting axes, while hypoid gears rotate about crossed axes. Both spiral bevel gears and hypoid gears can be manufactured in the same way. In the following article, we only mention SBGs for the convenience. SBGs are mainly cut by three conventional approaches, face milling ,2, face Unlike the conventional approaches, computer numerical control (CNC) milling has also been introduced as a new technology to cut SBGs on general CNC machine tools. Although CNC milling has a lower production rate than the conventional approaches, it takes advantage of cutting SBGs for small batches, prototypes and repairs . In addition, since the rigidity of CNC milling machine tools is poorer than the conventional gear machine tools, the cutting efficiency is limited to the application of CNC milling to the gears with the hard material. This limitation is not a serious problem for the non-ferrous metals. Hence, it would be beneficial to find the efficient way for CNC milling of non-ferrous gears.With the recent development of CNC milling technologies, CNC milling of SBGs becomes an advanced technology applied in some gear manufacturers. Many researchers have studied the manufacturing of SBGs with CNC milling. Tsiafis et al.  studied End milling is a mode of CNC milling to remove the material around the midst of the cutter’s flat end. DMG-Mori’s gearMILL  offers SFlank milling and the manufacturing of SBGs are two different disciplines. Exksting research on flank milling is mainly about the minimization of machining errors. The corresponding issues include envelope surface ,23,24,25In this work, a filleted end mill cutter is used to simultaneously cut the tooth surface and tooth bottom with only one pass, and this method improves the machining efficiency by avoiding the extra pass to cut the tooth bottom, which is necessary for the methods in ,53. In aThe 3D model of an SBG with 33 teeth is shown in ed to as ,53.When a pair of SBGs are meshing to transfer power during the process of two tooth surfaces engaging with each other, they contact at different points when they are treated as rigid bodies. Practically, they contact at small ellipses around the contact points due to the deformation of the tooth surface. The contact paths are formed by connecting these points, and the contact area is generated by joining these contact ellipse. Once both tooth surfaces of a pair of gear drive are obtained, the contact path and area can be calculated by TCA . For a gSince the tooth surface is a complex 3D surface, it is difficult to define the ideal contact path directly. Alternatively, it can be defined according to the gear blank. As shown in  surface . We have surface : 1)Z on the contact path, the principal curvature and principal direction at this point can be calculated by Equation d [d alonEquation .(6)μ1,2In conclusion, for a set of data points The corresponding optimization model is replaced byIn order to ensure the good contact performances of the tooth surface after flank milling, the contact analysis of the tooth surface based on the FEM can be carried out, so the three-dimensional model of the tooth surface is needed. In order to facilitate the modeling in an efficient way, tooth surface points should be generated with an approximately even distribution . Although and Assume that the cutter surface is expressed as pproach) ,31 as(1proach) [r=ocHowever, for some special cases, Equations  and 11)11) are nAs shown in Combining with this geometric characteristic and envelope condition, we can obtain the contact points on the toroidal part, which will be described next.As shown in Considering the envelope theory, the envelope condition can be written asAssume that Equation  can be rAccording to the Equations  and 16)16), n2 cBy substituting Equation  into EquThe envelope surface can be calculated by using the above two methods, but in some cases, one method may be more suitable than the other.h method is used, more contact points (blue points) will be obtained. If the discrete h method is used, fewer contact points (blue points) will be obtained. If the discrete In According to the data in Table 1, combined with the tool path planning method and closed-form modeling method proposed in this paper, the optimization model is established according to Equation . For thiquations  and 11)μi is choAn SBG represented by the parameters in In the experiment, five slots were machined, the calculated tool path was imported into CATIA, and the reasonable advance and retreat tool path was set. Finally, the complete tool path of this experiment was obtained. The simulation processing was verified in Mastercam, as shown in The simulation results show that the tooth surface errors in the meshing area are between −0.01 and 0.01 mm, while the tooth surface errors in the top and root area are relatively large.The corresponding NC code is calculated by using the simulation software, and the actual processing experiment is carried out on the five-axis NC precision engraving machine GR200V A15SH manufactured by Beijing Jingdiao Technology Co., Ltd. . The gear material is 7075 aluminum alloy and the five tooth slots model of SBG is obtained, as shown in The tooth profile errors of the machined tooth surface were measured by CMM (coordinate measuring machine). According to Gleason’s standard, 5 × 9 grid points are planned on the gear shaft section, and the tooth profile errors are calculated with the grid midpoint as the reference point, as shown in Equation .(18)δ=(The tooth profile errors of concave and convex sides of tooth surfaces are shown in According to the tooth surface geometry and its meshing utilization, each side of the tooth surface of SBGs is cut by the five-axis flank milling with only one pass. A filleted end mill cutter is applied to implement the tool path planning. Due to the special requirements of gears in meshing performances, the contact area is accurately machined by the cutter flank side with a tangency condition between the cutter envelop surface and tooth surface along the contact path. The fillet part is directly machined by the cuter bottom. The tooth surface of flank milling can be obtained by two different explicit calculation methods. In some cases, one method may be more suitable than the other. By optimizing the tool path, the accuracy of tooth surface and contact performance can be improved. The machining efficiency is improved because each side of the tooth surface is machined by one pass rather than multiple passes.In order to verify the effectiveness and authenticity of the method proposed in this paper, contact simulation analysis and flank milling experiments were carried out, respectively. The simulation results show that there is a slight edge contact between the gear and the pinion, and the contact trace is slightly closer to the inner diameter of the gear, but the overall contact area is basically towards the tooth direction. Theoretically, the contact trace should be located in the middle of the tooth surface. However, for the following two reasons, the results shown in The contact trace of SBGs are sensitive to the topological morphology of the tooth surface. Small changes in the tooth surface may cause changes in the position and orientation of the contact trace.The research content of this paper aims to adopt an efficient flank milling method to ensure low tooth surface error near the contact area, which is not a real research method to fully realize the pre-control of meshing performances.Further research is needed to realize the pre-control of meshing performances of SBG based on five-axis flank milling. The experimental results show that the tooth surface error of the meshing area obtained by NC simulation is less than 10 For some cases, the high machining accuracy is required for not only the contact area, but also the other areas. For those cases, multiple passes are needed to machine the convex or concave sides of the tooth surface, and some further machining processes might also be needed to accurately machine the fillet part of tooth surface. Furthermore, future work about practical machining, measurement, and error control would also be valuable.


The density of the jaw bone has an inhomogeneous distribution and, even within partial volumes, varies more or less strongly from the size of typical implants. Consequently, the primary stability of implants using conventional techniques can only ever be determined postimplantation. The aim of the present case report is to present digital volume tomography (DVT) osteodensitometry as a procedure for assessing the primary stability preimplantation and to evaluate its benefits.  An orthopantomogram (OPT) and vertical sections, including bone density measurement, were obtained from a DVT in the course of preimplantological diagnosis. Conventional implant planning and densitometric simulation were performed on this basis.  Densitometric simulation enabled assessment of the bone density at the interface to the implant body preimplantation. This procedure provides not only an overall value (averaged density) but also allocation of bone areas with different densities to the adjacent areas on the implant surface. This then allows the implant with the best possible macroscopic geometry and optimum position to be selected, with the aim of maximising primary stability. In the present case, the maximum torques during insertion confirmed the densitometric values recorded in advance.  DVT osteodensitometry enables selection of an implant optimised to bone density distribution. At the same time, the point at which loading occurs can already be defined at the planning stage, thanks to the predictable primary stability. A standard integration of DVT osteodensitometry in the algorithms of implant planning software thus seems desirable. There are a variety of technologies using Cone Beam-Computer Tomography (CB-CT) scan and CAD/CAM to measure implant stability: cutting resistance analysis (CRA), reverse torque test (RTT), resonance frequency analysis (RFA), and impact hammer method .The standard methods in implantology—torque measurement during insertion , 3 and iThe use of DVT osteodensitometry opens up the possibility of a predictive method –11. WithThe following case shows an example of a densitometrically planned implantation from the planning stage to prosthetic rehabilitation and to the 3-year follow-up.A 55-year-old female patient presented with the request for a fixed restoration in the left mandible. She was in good general condition and had no systemic preexisting diseases. The periodontal indices were inconspicuous. There was a slight narrowness in the lower jaw front. Previous patient's dental history was an abutment loss of tooth 36 with shortening of the existing bridge to the distal of the crowned tooth 34. In this area, there was evidence of a completely healed region with moderate atrophy of the alveolar ridge .Following comprehensive patient history, diagnosis, and consultation, the patient decided in favour of an implant-borne restoration in the form of all-ceramic crowns. Likewise, the insufficient crowns on 33 and 34, which were on teeth worth preserving, should also be replaced.The aim of preimplantological diagnosis was to ensure the best possible selection of implants for the local bone with regard to the expected primary stability and thus the planning of the time of loading.Intraoral scans  of the initial situation were taken and a DVT created . From these, a level-optimised OPT and a vertical section in the region of implants to be placed were also generated using Hounsfield unit measurement Figures .After importing the DVT data, including Hounsfield values, in an implant planning software , the densitometric-supported planning for the guided surgery process was completed .In addition to the usual determination of implant position, length, and diameter, a densitometric-based simulation could also be created using this data/software combination. This provides visualisation of which parts of the implant body come into contact with areas of different bone density.Apart from limited and restricted flexible parameters, such as implant length and diameter due to anatomical conditions, this additional information now allows the macrostructure of the implant body to be evaluated in relation to its effect on the (primary) stability in the bone site and selected accordingly.For this, it was also an advantage to have an implant system available that provides various implant bodies with the same instrumentarium and the same prosthetic interface . The system selected in the present case allows selection between three different types of implant body , as there was mainly soft bone present towards the buccal side at the implant position Figures  and 6. TImmediate loading was excluded due to the predominantly D3/D4 bone density, and unloaded healing was planned. According to planning, the guided surgery template was fabricated in the dental laboratory using the implant software, and the drill protocol to be used was printed and provided for treatment.Treatment was performed using electronically controlled intraosseous anaesthesia  with approximately 0.8 ml Ultracain DS-Forte osteocentral in the implant insertion region . This avThe intraoral scan for the long-term temporary restoration (LTT) was taken immediately after insertion Figures , and 10.Filling of small bone defects in the cervical areas of the implant using intraoperatively harvested host bone chips and closure using sutures  completed the treatment Figures  and 12.The patient explicitly expressed her satisfaction with the treatment procedure at the check-up on the following day and reported discreet initial wound pain.2 abutments , which are also custom milled.Based on the intraoral scan from the day of treatment and the DVT data, which was matched in the implant planning software, the LTT was then digitally planned in the form of two single crowns . The temTi bases with PMMA, with the required transgingival emergence profile for contouring the soft tissue, were fabricated as gingival formers following implant exposure and fitted five months after insertion. Both implants proved to be optimally osseointegrated and exhibited ISQ values of 72 and 75 . X-ray images and sound on percussion affirmed the clinical findings.2 abutments on Ti bonding bases  were finally screwed in position using the recommended torque of 20 Ncm for fitting. The insufficient crowns on teeth 33 and 34, which were due to be renewed, were removed, and the teeth built up at the same appointment. The opportunity was also used to take an X-ray  desired during the course of treatment, the temporary restoration was only fitted two months later . The cusan X-ray .After final preparation of teeth 33 and 34, a renewed intraoral mandibular scan was created for fabricating the final restoration following completion of the aligner treatment. In addition to the prepared teeth 33 and 34, the scan also included the 35 and 36 abutments.2  and characterised .The final crowns were designed virtually in the dental laboratory in the CAD process  and thenAfter a total treatment time of approximately 18 months, including aligner treatment, the final restoration of the support zone could be completed using crowns on the teeth and implants 33, 34, 35, and 36 Figures . The croA final X-ray check was also taken at the same appointment .The patient still comes regularly for a check-up. The latest check OPG three years after implant insertion shows an unchanged stable bone level . The cliThe present case demonstrates how implant planning and treatment using modern digital methods for diagnosis, planning, and fabrication lead to treatment reliability and predictable results. DVT-based planning, with osteodensitometry analysis and insertion simulation, plays a decisive role in this. It allows case-by-case selection and optimum positioning of the implants. In this way, it is possible for the first time, using objective data and their imaging, to be able to evaluate osseointegration conditions already at the planning stage and to make the correct decision for the time at which to load the implants.Any changes to the planning, such as the required aligner treatment after beginning treatment in the present case, could be implemented in the digital workflow without problem based on the intraoral scan. Flapless technique can be used when the minimal keratinized tissue height of 2 mm has remained .Any prosthetic adjustments to the masticatory function due to the long-term temporary restorations could also be undertaken. The selected approach proved flexible, including with the decision for a specific type of implant body . The decision can still even be made at the moment of insertion after preparation is complete. In contrast to parallel forms, the tapered form selected in the present case still allows compression of the bone to be varied by slightly adjusting the insertion depth to create better primary stability .The next stage in the ongoing development of implantological planning software could consequently be in the inclusion of DVT-based osteodensitometry in the algorithm. The programme would then make far-reaching suggestions for implant positions, insertion , and suitable type of implant body. This could possibly be further optimised using artificial intelligence—just like the suggestions of the planning software.It is self-evident that treatment decisions are ultimately made by the implantologists, if necessary following a consultation meeting together with the patient. It would seem preferable that this can be explained even more clearly regarding possible treatment options using imaging of the further advanced software. “Implantology must become simpler”! If this dogma is followed, we expect in future informed patients who, following a visit to an implantological centre, are highly motivated and look forward to their implantological rehabilitation, thanks to appropriate explanation and reliably predicted treatment schedule.EDI Journal within the framework of a study into the reliability of a certain implant design (SICtapered) [Note: some photographs in this case were unspecifically, for clarification of an example of a treatment procedure, already published in the magazine tapered) .


Surface texture information plays an important role in the cognition and manipulation of an object. Vision and touch are the two main methods for extracting an object’s surface texture information. However, vision is often limited since the viewing angle is uncertain during manipulation. In this article, we propose a fine surface texture detection method based on a stochastic resonance algorithm through a novel solid–liquid composite flexible tactile sensor array. A thin flexible layer and solid–liquid composite conduction structure on the sensor effectively reduce the attenuation of the contact force and enhance the sensitivity of the sensor. A series of ridge texture samples with different heights , different widths , but the same spatial period (2 mm) of ridges were used in the experiment. The experimental results prove that the stochastic resonance algorithm can significantly improve the signal characteristic of the output signal of the sensor. The sensor has the capability to detect fine ridge texture information. The mean relative error of the estimation for the spatial period was 1.085%, and the ridge width and ridge height, respectively, have a monotonic mapping relationship with the corresponding model output parameters. The sensing capability to sense a fine texture of tactile senor surpasses the limit of human fingers. The tactile sensor can provide useful contact information for robot feedback and control to better complete manipulation tasks in a complex environment. Existing research on tactile sensors has achieved monitoring of static contact information, such as temperature, curvature, and contact force ,2,3, butSome existing researches use multi-array tactile sensors and fast Fourier transform  technology to obtain characteristic signals and estimate the surface texture information of objects, which had certain limitations. In 2011, C.M. Oddo et al. ,11 develAnother commonly used method is using the tactile signal obtained by the designed tactile sensor for machine learning to classify and identify the surface texture of the object. In 2012, in order to achieve the ability of human fingertips to detect texture, Jeremy A. Fishel et al. ,19 propoTo sum up, research on texture detection needs more extensive and systematic investigations. FFT processing directly on the tactile signals can quickly and easily obtain texture feature information, such as the spatial period and ridge height of the ridge texture. However, due to the limited performance of most simple-structured tactile sensors to discern the tiny structural variation, FFT processing does not perform well in situations of fine texture detection. Thus, a tactile sensor with a higher sensitivity is needed. However, the capability of the existing simple tactile sensor to detect weak force changes is still far behind that of humans . AnotherIn the process of detecting fine textures, the signals generated by the tactile sensor are weak and may even be submerged in system noise, such as touch action noise, circuit noise, etc., resulting in its characteristics being difficult to detect. The stochastic resonance algorithm  is a commonly used weak signal detecting method, which adds random noise to the weak signal to improve the detectability of the target signal or enhance the accuracy of information interpretation . ThroughIn this article, we use a 2 × 2 solid–liquid composite flexible tactile sensor array to obtain tactile information. A method of fine texture detection based on SR is put forward. By adding random noise to the output signal of the tactile sensor and inputting the mixed signal to a bistable SR model, we obtain tactile signals with strong signal characteristics and figure out the corresponding texture information through numerical analysis. The presence of the flexible layer will lead to a decrease in the transmission efficiency of the contact force, and the thicker the flexible layer, the greater the attenuation of the contact force ,29. It iThe rest of this article is organized as follows. The tactile sensor device is presented in In this article, we propose a 2 × 2 piezoresistive solid–liquid composite flexible sensor array to detect fine texture based on previous research in the laboratory . PiezoreThe structure of the sensing cell is shown in The sensor fabrication process is shown in sp, the ridge height rh, and the ridge width rw. The tactile sensor array with sensing cell spaced d is integrated on the prosthetic fingertip and slides horizontally across the surface of the texture sample at a certain speed v from left to right. The two adjacent sensing cells successively contact the ridge of the sample and are both affected by normal force F so that the sensing element inside them generate corresponding voltage signals. Since the ridges are uniformly distributed on the sample surface, these signals have a period of T; in other words, they have a principal frequency of prinf. Moreover, the two adjacent sensing cells will successively generate the same signal for the same ridge. The time interval between the two signals is ∆t. It can be easily figured out that the spatial period sp and the ridge width rw are related to the characteristic parameter prinf × ∆t, which isn is the ratio of the ridge width to the spatial period in the ridge texture, which can be obtained from the time domain information of the signal. The ridge height rh is related to the amplitude information A of the principal frequency of the signal [The following is the sensing method for texture detection based on the designed tactile sensor. As shown in e signal .rh less than 10 μm (this height is close to the detection limit of a human finger). The signal generated when the tactile sensor array slides over the ridge texture surface is mainly composed of two-state voltage signals, which correspond to the positions of the ridges and grooves, respectively. However, because the height difference between ridges and grooves is small and the detection capability of the tactile sensor array is limited by its sensitivity, the difference between the two signal states is not obvious, and the signals may even be submerged by system noise. Therefore, this article mainly discussed a method to enhance signal characteristics of weak bistable signals to improve the detection capability of the tactile sensor array.This article focuses on the detection capability of the tactile sensor array for fine ridge textures whose ridge height The bistable system stochastic resonance method is chosen to enhance the signal characteristic of the tactile signals. When the tactile sensor array slides over the surface of the object, it will sense the surface texture information of the object and transmit signals in time through a limited number of sensing cells. Especially in the process of fine texture detection, the generated tactile signals have two states, and its characteristic is weak. The bistable system stochastic resonance method can effectively enhance some deterministic characteristics of weak tactile signals, which is suitable for the investigation of fine texture information detection.rh of 0.9 μm at a certain speed v, the time-domain signal generated has a weak periodicity, as shown in As mentioned in V(x) is the nonlinear bistable potential function, ands(t) is the periodic tactile signal; Γ(t) is the input Gaussian white noise with intensity D and 0 mean value. K is the normalized transformation factor of the input mixed signal. By comparing the frequency range of the generated tactile signal and the intensity . The determined bistable system is shown in In this article, a bistable system stochastic resonance model based on the Langevin equation is established, which isThe experimental loading platform for the object surface texture detection experiment is shown in x-axis and y-axis are equal. When the tactile sensor starts from the initial position and slides across the sample surface along the x-axis direction that is perpendicular to the ridge texture at a certain speed, we can obtain the time interval ∆t of the signals generated by adjacent sensing cells for the same ridge. The positive x-axis is selected previously as the sliding direction of the tactile sensor; thus, the signals generated by the sensing cells 1#, 3# and 2#, 4# form a signal group with time-domain correlation, respectively and the two signal groups are in a control relationship with each other. Selected the signal group  as the experimental result to validate the method proposed in this article.The main experimental process is as follows. As shown in t can be easily obtained through correlation analysis with the processing result of the signal generated by sensing cell 1#. The frequency-domain signal obtained by FFT processing contains the principal frequency information prinf and its amplitude information A. The difference between the two states of the time-domain signal is obvious, but there is still a certain amount of noise that interferes with the acquisition of texture information. Passed the randomly intercepted signal segment through a software-designed threshold comparator with a threshold of 1 V to obtain a state-unified signal, as shown in n of the ridge width to the spatial period in the ridge texture. The texture feature information of the corresponding sample can be figured out by combining the parameter results obtained above.The processing flow of the raw output signal of sensing cell 3# is shown in rk, which depends on the noise distribution and intensity. Therefore, we can easily obtain the time interval through correlation analysis. Comparing Signals processed by SR exhibit a stronger signal characteristic in both time and frequency domains. Combining the information obtained above, we can figure out the spatial period and ridge height information of the texture according to the method proposed in n is shown in n. However, because the SR method has certain randomness, the high-level part of the signal cannot accurately represent the ridge width, and the errors of the calculated ratio are large. However, it can be seen from n. Thus, the ridge width of the ridge texture sample can be obtained from the output signal.The ridge height detection experiment of the texture samples is as follows. From the SR-processed signal output by tactile cell 3#, as shown in In the existing research on fine texture detection using tactile technology, HB Muhammad et al.  successfMore strikingly, we have completed the detection task of fine textures with fewer data sets based on a sensor array with a simple structure and a simple method. In 2012, Jeremy A. Fishel et al.  achievedIn more detail, the noticeable difference in mind caused by external stimulus difference in human finger touches can be used to evaluate the fine texture detection capability of the sensor. In 2012, Kadir Beceren et al.  validateIn this article, a flexible solid–liquid composite tactile sensor array developed by our laboratory was used to systematically investigate the performance of tactile sensors in fine ridge texture detection through a bistable system stochastic resonance model, including estimation of the spatial period and recognition of ridge height and ridge width. Our experiment used several fine ridge texture samples to provide a weak periodic stimulus to validate the detection capability of the tactile sensor array. The results show that this method can significantly improve the signal characteristic of the output signal. Regarding the detection of texture information, the results show that the mean relative error of the estimation for the spatial period is 1.085%, and the ridge width and ridge height, respectively, have a monotonic mapping relationship with the corresponding model output parameters. Compared with the human finger experiment, the tactile sensor has a similar capability to sensing fine textures, which shows that the sensor designed in the laboratory has a very promising application prospect.In future work, the following aspect can be improved and expanded on the work of this article: explore the accurate estimation method of ridge width and ridge height, consider the self-adaptability of the model parameters of the stochastic resonance bistable system, and extend the fine texture detection method to non-periodic ridge texture.


Extraskeletal osteosarcoma is an uncommon and high-grade soft tissue malignancy. The incidence is even lower when the lung and pulmonary artery are the primary site. The purpose of this report is to present the radiological features of this neoplasm in a 52-year-old man. In our case, contrast-enhanced CT and 3D-CT reconstruction clearly showed the primary lesion and its invasion into surrounding tissues. Although wide local excision of the primary tumor is the treatment of choice, local recurrence and metastasis rates remain high, and this progression can be clearly shown on CT and SPECT/CT examinations. Primary osteosarcoma occurring in the lung and pulmonary artery simultaneously is a rare mesenchymal malignancy. Compared to conventional osteosarcoma in the bone, extraskeletal osteosarcoma has an older average onset age. The clinical features are typically nonspecific; patients often present with an enlarging and painful soft tissue mass in the chest. From a pathology point of view, this tumor is characterized by tumor cells and osteoid matrix observed under the microscope. The clinical and radiological findings are typically nonspecific, but we present a case of this tumor that showed radiographic evidence of calcification on chest CT. According to the documented literature, up to 50% of extraskeletal osteosarcoma can produce calcification or osteoid matrix , 2.A 51-year-old man was admitted to the hospital with a diagnosis of a left lung nodule for 1 month. The patient reported a history of cough and hemoptysis 1 month previously, at which time a chest CT scan showed a nodule in the left lung hilum accompanied by calcification , and Pre-research Foundation of Second Affiliated Hospital of Soochow University (SDFEYBS1904 and SDFEYQN1816).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.The handling editor declared a shared affiliation, though no other collaboration with the authors at the time of the review.


The analysis of variance, and mixed models in general, are popular tools for analyzing experimental data in psychology. Bayesian inference for these models is gaining popularity as it allows to easily handle complex experimental designs and data dependence structures. When working on the log of the response variable, the use of standard priors for the variance parameters can create inferential problems and namely the non-existence of posterior moments of parameters and predictive distributions in the original scale of the data. The use of the generalized inverse Gaussian distributions with a careful choice of the hyper-parameters is proposed as a general purpose option for priors on variance parameters. Theoretical and simulations results motivate the proposal. A software package that implements the analysis is also discussed. As the log-transformation of the response variable is often applied when modelling response times, an empirical data analysis in this field is reported.The online version contains supplementary material available at 10.1007/s11336-021-09769-y. The analysis of variance (ANOVA) is a popular tool for analyzing experimental data in psychology as in many other research fields. The assumptions underpinning the standard ANOVA are rather restrictive as response variables may not be normally distributed Micceri, , sample For these reasons, ANOVA analyses are often conducted in the more general framework of mixed models, either linear, nonlinear or linear but specified on a transformation of the response variable  distribution as default prior for the variance components of linear mixed models. Endowed with suitably selected hyper-parameters, GIG priors lead to results virtually equal to those obtained adopting currently default choices when the problem of back-transforming quantities estimated on the log-scale is not involved and guarantee correct inferences when it is. The GIG is a flexible family of three parameters distributions with positive support that encompasses several well-known special cases . More importantly, they allow for simple expressions of the conditions on prior parameters that guarantee the existence of posterior moments; eventually, their conjugacy with the normal allows for the implementation of fast Gibbs sampling algorithms to explore the posterior distributions of interest.This work builds upon earlier contributions of Fabrizi and Trivisano ; 2016 buThe structure of the paper is as follows. Section In this section, we first introduce a general specification for the linear mixed model on the log-scale along with a basic result conditional on the variance components. Then, we shortly describe the GIG distribution that will be considered in further analyses.n-dimensional vector of strictly positive responses q different variances related to the random components Let us consider a The introduced model is fairly general. All standard one and multi-ways ANOVA models as well as mixed models suitable for the analysis of repeated measures with both nested and crossed effects  for In this paper, we assume a GIG prior for the variance components. In general, a random variable y Gelman . For mory Gelman .l columns, without loss of generality. Consequently, we note that s-th variance component of the random effect. Eventually, In this section, we study the existence of moments for the posterior distributions of fined in  and 4),,\documen}p(y~|y) . As antir-th, with (i)(ii)(iii)If the normal linear mixed model in the log scale  is consiSee appendix. Few comments on Theorem i) parallels the result by Fabrizi and Trivisano (r is multiplied by the leverage associated with Statement (rivisano  for the iii), the existence of its posterior moments is related only to the term i), the quantity As far as the posterior predictive distribution, it concerns, i.e., case , half-t  do not contain the exponential term in question. Other priors such as the inverse gamma  of Theorem m of the columns of The constraints on mentclass2pt{minimt priors  and 8)  \documenIn this section, we consider two issues related to practical implementation. In Sect. In Section r, the order of posterior moments for which we need to impose the existence. In principle, a priori we would set r we are interested in, with the aim of avoiding priors with exceedingly light tails. In practice, it is advisable to set c can be found in Section S1 in the supplementary material. In short, choices of The lower bounds in Theorem r to exist for all the The existence conditions stated in Theorem In general, the advice is to fix the parameter As expected, constraints on the existence of posterior moments lead to priors with light tails for the variance components. In order to avoid excessively informative priors, we propose a weakly informative strategy for the selection of remaining parameters. To illustrate our heuristic, let us work on the notable special case where Chaloner  proposesDaniels, . If botho et al.  show thaThe resulting density is a function of the product K function:When  density  can be s}ϕ by Ye  within t}ϕ by Ye .The strategy can be summarized as:Bayesian Lasso and is characterized by a spike in 0. In general, the degree of shrinkage determined by the prior can be increased setting We note that under the described setting, if the nd Brown  as priornd Brown  and Fabrnd Brown  already The main difference between Griffin and Brown , FrühwirAn appealing characteristic of the adoption of GIG priors  and 8)  8) for tIn this section, we present two simulation exercises focused on simple models specified on the logarithm of the response variable. In the first place, we consider a special case of  where \d(i)The posterior means of (ii)The posterior means of (iii)The posterior means of (iv)conditional Bayes predictors in which A  Zellner , we can entclass1pt{minimaBias, root mean square error (RMSE), frequentist coverage and average interval width are reported for estimators of k and In the first simulation exercise, we generate m different estimates for To jointly evaluate the j-th true group specific expectation k.More in detail we define the quantities:In Tables Moving to results about group means Tables  and S2, IG prior for variance components: probably it is not an appropriate choice in cases of variance components near to 0, as often happens in log-transformed data.In the supplementary material, results about the frequentist properties of credible intervals are reported for In Section S3 of the supplementary material, results about this simulation setting under three further prior settings are presented. The first two explore the sensitivity of posterior with respect to different choices of the GIG scale parameter er prior . The thier prior . ResultsAs far as the second simulation exercise, we mentioned above is concerned, the results  show that different priors on the variance components do not induce remarkable changes on the estimation of a regression coefficient, whereas the problems affecting the moments of Several applications in psychology and cognitive sciences have as central output the time requested to perform some tasks. By definition, times are positive numbers and often show a positively skewed distribution: for these reasons, it is common to analyze their logarithmic transformations.The data we use to apply our methodologies were originally collected by Gibson and Wu  in orderThis dataset has been analyzed also by Sorensen and Vasishth , that prk if the observation is related to the item k and 0 otherwise; on the other hand, j if the observation is related to subject j and 0 otherwise.More technically, the design matrix As a consequence, the rank deficiency of i) in Theorem Hyper-parameters n priors  and 8)  \documenmentclass2pt{minimIn Table settings , 17) an an\documsettings  are repoWe note that the issues that affect posterior moments of functionals in the original data scale are masked by the moderately large sample size. In fact, there are no clear symptoms of the fact that posterior results obtained under inverse gamma priors are theoretically meaningless, since they are MCMC estimates of integrals that are analytically not finite, as already noted in the simulation section. We also note that the inverse gamma prior with parameters both equal to 1 can be a largely informative prior for variances when their actual value is near to 0, as it often happens in the analysis of log-transformed data. In this application, the variance components   \documenmentclass2pt{minimSee Section S6 in the Supplementary material. The previous result allows to extend the existing conditions for moments of functional studied in Theorem The log is a special case of the Box-Cox family of transformations  can emerge, the extension of the main results to this case is also discussed. To help the practical implementation in this case, we provide Stan code useful to specify the proposed GIG priors, allowing to fit models that include correlated random effects.In the supplementary material, the following information is reported. In Section S1, we complement the discussion on the choice of prior specification for the hyper-parameters Supplementary material 1 (pdf 845 KB)Below is the link to the electronic supplementary material.


Stretchable electronics is a new innovation and becoming popular in various fields, especially in the healthcare sector. Since stretchable electronics use less printed circuit boards (PCBs), it is expected that the environmental performance of a stretchable electronics-based device is better than a rigid electronics-based device that provides the same functionalities. Yet, such a study is rarely available. Thus, the main purpose of this research is to perform a comparative life cycle analysis of stretchable and rigid electronics-based devices. This research combines both the case study approach and the research review approach. For the case study, a cardiac monitoring device with both stretchable and rigid electronics is used. The ISO 14044:2006 standard's prescribed LCA approach and ReCiPe 2016 Midpoint (Hierarchist) are followed for the impact assessment using the SimaPro 9.1 software. The LCA results show that the stretchable cardiac monitoring device has better environmental performance in all eighteen impact categories. This research also shows that the manufacturing process of stretchable electronics has lower environmental impacts than those for rigid electronics. The main reasons for the improved environmental performance of stretchable electronics are lower consumption of raw material as well as decreased energy consumption during manufacturing. Based on the LCA results of a cardiac monitoring device, the study concludes that stretchable electronics and their manufacturing process have better environmental performance in comparison with the rigid electronics and their manufacturing process. The production and consumption of electrical and electronic equipment (EEE) in the European Union (EU) are on the rise Eurostat . Due to As new fields of applications for stretchable electronics continue to emerge, such as wearable smart textiles and medical/health-monitoring devices, the market for stretchable electronics is expected to grow rapidly. Amid the COVID-19 crisis, research indicates that the global stretchable electronics market will reach $2.6 billion by 2027  Analysis: Collecting the inventory data like raw materials, energy consumed, environmental releases, etc., associated with each life cycle stage. The sources for this inventory data include different LCA databases, scientific literature, reports from public authorities, etc.Life Cycle Impact Assessment (LCIA): Modeling of inventory data collected in LCA software package and translating the environmental emissions into environmental impact categories using the characterization factors of the LCA method selected.Interpretation: Analyzing the results of impact assessment; identifying different environmental hot spots; and making recommendations to minimize the environmental impacts.Life Cycle Assessment (LCA) is a useful method to identify and quantify the environmental impacts of a product, process, or activity. Comparative LCA can be carried to compare the environmental impacts of two or more products that have similar functionality. To perform the proposed comparative LCA, this research combines both the case study approach and the research review approach. Here, a case study of a cardiac monitoring device is used to compare the environmental impacts of devices based on stretchable and rigid electronics with a special focus on the manufacturing processes for the PCBs. A cardiac monitoring device was chosen since it is readily available and is representative of a large number of physiological monitoring devices. The conclusions drawn from the analysis of the manufacturing process should be analogous to a class of related products using stretchable electronics. For the impact assessment, the LCA approach prescribed by the ISO 14044:2006 standard is used (ISO ReCiPe 2016 Midpoint (Hierarchist) is used as the impact assessment method. The commercial LCA software package used for this study was SimaPro 9.1 .The rigid cardiac monitoring device consists of a monitor and a strap as shown in Fig. The raw material for the rigid cardiac monitoring device is extracted from nature. The manufacturing phase involves the production of individual components of the monitor and the strap, as well as their assembly. The manufacturing of the cardiac monitor includes injection molding of ABS to form the outer casing, manufacturing of the PCB from printed wiring boards (PWB) and semiconductor devices using surface-mount technology (SMT), and drawing of metallic wires for sensor electrode. The SMT process for the manufacturing of the PCB consists of the deposition of solder paste on the PWB, an inspection of the solder paste deposition, mounting of components, an inspection of component mounting, and finally, a reflow step, see Fig. The stretchable cardiac monitoring device , and end of life phases.The complete lifecycle inventory data for the rigid and stretchable cardiac monitoring devices are available in Appendix of this paper. Appendix also includes the recycling and incineration data for both devices, as well as the incineration data for the PCB. In cases where data are not readily available in the Ecoinvent 3 database, other sources are sought and assumptions are made. Those exceptional cases and additional assumptions are described in the subsections below.The life cycle inventory (LCI) data for raw materials for both the products are taken from the Ecoinvent 3 database whenever available. For the materials where the data are unavailable, the LCI data from previously published works are used. For instance, the LCI data for silver nitrate in a rigid device are taken from the study by Bafana et al. . For LCIThe data related to manufacturing processes, such as injection molding and wire drawing in the rigid device, are available in the Ecoinvent 3 database. The LCI data for roll-to-roll printing in strap fabrication are not available in the database or any existing LCA studies. Hence, only the electricity and the raw materials consumed in this step are considered. For the manufacturing processes of the stretchable device, the energy and compressed air consumed, calculated from the equipment rating, are considered due to the unavailability of their LCI data in any database or previous studies.It has been stated earlier that this study does not include the use phase due to the fact that the use phase in this case primarily involves the energy consumed from the batteries used in both devices, which is identical.In the case of the rigid device, it is assumed that the ABS and metallic wires are recycled. The strap, PCB, and battery are incinerated as hazardous industrial waste. Similarly, in the case of the stretchable device, the materials, such as PDMS, polyethylene (PE), and Galinstan, are recycled, while the PCB and battery are incinerated. The emissions to air due to incineration of the PCB in both devices are taken from the report of the US Environmental Protection Agency . For theRecipe 2016 Midpoint (Hierarchist) is chosen as the impact assessment method due to its strong relation to the environmental flows and a relatively low level of uncertainty . The copper, along with silver used in the strap, is responsible for 9% and 21% of metal depletion, respectively. The ABS casing is another noticeable contributor to fossil depletion (12%), water depletion (7%), and climate change (7%).In the stretchable cardiac monitoring device, the PCB accounts for more than an 85% share in all the impact categories. PDMS causes 7% of the emissions in ozone depletion. LMA contributes 6% each in terrestrial ecotoxicity and metal depletion. The battery has a 5% share in each of the areas of ozone depletion, human toxicity, terrestrial ecotoxicity, marine ecotoxicity, and metal depletion. The contribution of polyethylene is negligible in all the impact categories (less than 1%).Looking deeper into the manufacturing phase and considering the time required to manufacture each device, the consumed energy and compressed air used are presented in Tables It is evident from Tables From this LCA study, it is clear that the stretchable cardiac monitoring device causes lesser environmental impacts than the rigid cardiac monitor device. The main reason behind this is that the stretchable device consumes approximately six (6) times lesser amount of raw material for PCB and nearly seven (7) times lesser electricity in manufacturing than the rigid device.From the interpretation of LCA results, it is clear that the environmental performance of the cardiac monitoring device is influenced by the raw materials and the energy consumed in their manufacturing process. A sensitivity analysis, included in Appendix section, is carried out to determine how the impact assessment results vary if the raw material, electricity, and compressed air inputs are changed. Each input is varied by + 10% and − 10%, and the changes in impact categories are calculated with reference to the baseline. The sensitivity analysis clearly shows that the variation of the input of PCB material in both devices has a comparatively higher influence on all the impact categories. For instance, in the rigid device, the variation of ± 10% in PCB material amount showed a change of ± 8% in the terrestrial ecotoxicity impact category. Similarly, in the stretchable device, the variation of ± 10% in PCB material input showed a change of ± 7.9% in the climate change impact category.A comparative life cycle assessment of stretchable and rigid electronics-based cardiac monitoring devices is conducted to establish which device is better from an environmental point of view. Furthermore, this research compares the environmental impacts of the manufacturing processes of stretchable and rigid electronics when applied to cardiac monitoring device as a representative electronic device.This study concludes that the stretchable electronics-based device causes significantly lower environmental impacts in comparison with the rigid electronics-based device in all eighteen (18) impact categories defined by the Recipe 2016 Midpoint (Hierarchist) method. The improved environmental performance of the stretchable electronics-based device is primarily linked to the lower usage of raw materials due to innovative methods, such as the utilization of LMA to connect electronic components in and to the PCB reducing the required amount of raw materials.The study also concludes that the manufacturing process of stretchable electronics has lower environmental impacts. The higher environmental impact of the manufacturing phase of rigid electronics can be attributed to a higher amount of electricity consumed due to manufacturing steps like the reflow phase, injection molding, and roll-to-roll printing.This study adopted some simplifications due to a lack of field and research data. One of the simplifications implemented is the exclusion of the transportation phase. Since the stretchable device is in the prototype phase, the supply chain of this device is not in place, and therefore, no data are available.Furthermore, the review presented in this paper indicates that there is very little work on the LCA of stretchable electronics. In this respect, this work is novel but also in its infancy. Further research, as well as more LCA studies, is needed to strengthen the results of this research.


The electrochemical gradients established across cell membranes are paramount for the execution of biological functions. Besides ion channels, other transporters, such as exogenous pore-forming toxins, may present ionic selectivity upon reconstitution in natural and artificial lipid membranes and contribute to the electrochemical gradients. In this context, we utilized electrophysiology approaches to assess the ionic selectivity of the pore-forming toxin lysenin reconstituted in planar bilayer lipid membranes. The membrane voltages were determined from the reversal potentials recorded upon channel exposure to asymmetrical ionic conditions, and the permeability ratios were calculated from the fit with the Goldman–Hodgkin–Katz equation. Our work shows that lysenin channels are ion-selective and the determined permeability coefficients are cation and anion-species dependent. We also exploited the unique property of lysenin channels to transition to a stable sub-conducting state upon exposure to calcium ions and assessed their subsequent change in ionic selectivity. The observed loss of selectivity was implemented in an electrical model describing the dependency of reversal potentials on calcium concentration. In conclusion, our work demonstrates that this pore-forming toxin presents ionic selectivity but this is adjusted by the particular conduction state of the channels. E. fetida, which inserts very stable channels in membranes containing sphingomyelin  is Ca2+ concentration in the bulk solution, and N is the total number of lysenin channels inserted into the membrane.Assessing the evolution of the membrane voltage when variable populations of fully conducting and sub-conducting channels coexist in the membrane requires estimating n process , for whiThe combination of the last two equations leads to a simplified formula for the membrane voltage:2+ concentration. To verify this hypothesis, we measured the variation of the membrane voltage created by KCl gradients upon successive Ca2+ addition. We utilized buffered KCl (50 mM) support electrolyte and created a membrane voltage of ~22.4 mV by adding KCl to the grounded reservoir (for a concentration ratio of 4.1). The addition of Ca2+ up to 25 mM to both reservoirs decreased the membrane voltage in a concentration-dependent manner, as predicted by Equation (5)  . The plo2+ ions to the channel, which will change the electrostatic properties and the energy landscape. However, this may require direct binding to the selectivity filter to annihilate its function. Another hypothesis, which we consider more attractive, is that the channel's conformational changes during the transition from the open to sub-conducting state [Ionic selectivity is a salient feature of ion channels, largely utilized for establishing fundamental physiological processes in all living systems. Our work demonstrates that lysenin channels also present ionic selectivity and that the permeability is different for monovalent anions and cations. Although the permeability ratios are much smaller than what is typically encountered for many ion channels, the selectivity function may lead to the development of large resting potentials, comparable to the ones measured for cells. Consequently, this may be further exploited to modulate the membrane voltage of natural membrane systems. Lysenin reconstitution into artificial membrane systems  may provide the means to create electrochemical gradients to be utilized for energy production and control of metabolic processes in synthetic cell-like systems. The ability of lysenin to attain stable sub-conducting states allowed us to investigate its selectivity to monovalent ions, which was significantly diminished. To explain the loss of this function, one may consider it an effect originating in the binding of Cang state  lead to The selectivity function presented by lysenin, together with the high transport rate and exquisite regulation by voltage and ligands, closely resembles the salient features of ion channels. It is not clear why a pore-forming toxin meant to kill the target cells is endowed with such remarkable functionalities, which are rather descriptive of ion channels. In this respect, the hypothesis that lysenin is part of a defense system is not fully supported by its unusual characteristics. Future extended work is needed to better understand the ionic permeability of lysenin channels in open and sub-conducting states, identify conformational changes induced by divalent ions, elucidate their physiological role, and develop applications emerging from its unique biophysical properties.