The Thurstonian item response theory (IRT) model allows estimating the latent trait scores of respondents directly through their responses in forced-choice questionnaires. It solves a part of problems brought by the traditional scoring methods of this kind of questionnaires. However, the forced-choice designs may still have their own limitations: The model may encounter underidentification and non-convergence and the test may show low test reliability in simple test designs . To overcome these weaknesses, the present study applied the Thurstonian IRT model and the Graded Response Model to a different test format that comprises both forced-choice blocks and Likert-type items. And the Likert items should have low social desirability. A Monte Carlo simulation study is used to investigate how the mixed response format performs under various conditions. Four factors are considered: the number of traits, test length, the percentage of Likert items, and the proportion of pairs composed of items keyed in opposite directions. Results reveal that the mixed response format can be superior to the forced-choice format, especially in simple designs where the latter performs poorly. Besides the number of Likert items needed is small. One point to note is that researchers need to choose Likert items cautiously as Likert items may bring other response biases to the test. Discussion and suggestions are given to construct personality tests that can resist faking as much as possible and have acceptable reliability. Personality tests are widely used in personnel selection situations, yet the authenticity and validity of the results are controversial. Conventional personality tests, which often use multidimensional Likert-type scales, may lead to many kinds of response biases, such as the halo effect and impression management  models in recent years. One of them is the Thurstonian IRT model of Brown  and BrowNevertheless, the forced-choice designs may still have their own limitation and the Thurstonian IRT model can only solve part of problems of ipsative data. An accurate recovery of trait scores and parameters based on the model largely depends on the test design, especially on the number of constructs measured . For blocks of only two items, respondents are asked to choose the one that best describes them. When there are more than two statements in a block, respondents are required to rank the items or select two items that are “most like me” and “least like me,” respectively. In a forced-choice test in which one block is composed of two matched statements, if the traditional scoring method is used, the score of a respondent in a dimension equals the number of statements he/she chooses measuring that dimension. The scoring method is similar when each block consists of more than two items. Thus, for any individual, the sum of his/her scores is a fixed value, which generates A number of studies have indicated that ipsative data make trouble for the explanation and analysis of the scores for the relative nature of scores Hicks, . In addiMore important, the ipsative scoring distorts reliability estimates Meade, . The ipsAs a solution to the problems brought by ipsative data, Brown and Maydeu-Olivares  proposedIn spite of these results, the studies have one thing in common: their measurement instruments are also composed of a number of subtests, for example, 16 dimensions in the CCSQ and 32 subscales in the Occupational Personality Questionnaire 32. A stable and accurate estimation of the Thurstonian IRT model entails some requirements specific to the forced-choice format. Brown and Maydeu-Olivares  discusseThe recommendations of Brown and Maydeu-Olivares  are for The force-choice test format may also pose a problem to the identification of the Thurstonian IRT model. For most forced-choice designs, to identify a Thurstonian IRT model, it generally suffices to set all variances of latent traits and the uniqueness of one item per block to 1. But when the test measures only two traits with blocks of two items, the factor loading of the first item in each trait should be set to its true values  and the CCSQ. In these tests, each item has to be rated on a five-point Likert scale, while all statements are further grouped in quads and the rater also are required to indicate which of each set of four items is most true and least true of the target subject. The scale scores were derived by summing the ipsative item scores and the normative item scores. In this article, we use different statements as Likert items and forced-choice items, which shortens the test length, and the Likert items should have low social desirability for the resistance of the test to faking. We also use IRT models for scoring and estimation instead. More important, both IMC and CCSQ still have 16 dimensions but we investigate the performance of this combined format when a fewer number of traits are measured.About the model, we use the combination of the Thurstonian IRT model and the Graded Response Model .The combined model is in Figure a given by the test is the sum of the information provided by all binary outcomes and Likert items contributing to the measurement of trait ηa, that is,Therefore, the total information on trait ηIa(η) is the total information provided by all binary outcomes contributing to the measurement of trait ηa. a.In the Equation (1), SE of the estimated score The A Monte Carlo simulation study was performed and the forced-choice blocks presented are presented in the form of item pairs in which the two items measure different traits.Thirty-two conditions were examined in this simulation study by crossing the following four factors: (a) the number of traits (2 or 5); (b) test length, expressed as the ratio of the number of all questions in the test to the number of traits ; (c) the percentage of Likert items ; (d) the proportion of pairs composed of items keyed in opposite directions (0 or 20%), which means the ratio of the number of pairs in which comparisons are between opposite directions to the total number of Likert items and the blocks. It should be noted that all the Likert items were positively worded and there were equal numbers of Likert items per trait. Under all conditions, the latent traits were all normally distributed, the correlation between the traits was set to 0.2, and the proportion of negative statements was about 30% of all statements.In test designs measuring two traits with Likert items, no factor loadings needs constraints when the model is analyzed. But in pure forced-choice designs, the designs with 0% of Likert items, measuring two traits, the factor loading of the first item in each trait still needs to be set to its true value for model identification.The true item parameters were drawn from a uniform distribution: between −0.8 and 0.8 for intercepts (μ) and between 0.45 and 0.9 for absolute values of factor loadings (λ).Then, according to the test design conditions, items from different traits were matched yielding forced-choice pairs while a certain percentage of Likert items were retained to construct 32 versions of tests.ti as the latent utility associated with Item i. For a forced-choice block, the difference of the latent utilities between two paired items could be computed for each subject. The differences were then transformed into a dichotomous variable y1 according to whether the difference values are <0. If the difference is <0, y1 is equal to 0. If not, y1 is 1. The variable y1 represents the responses to the forced-choice block. For a Likert-type item, the distribution of utilities for each item from all participants could be approximated to a normal distribution. According to the probabilities of the distribution, the latent utilities of each item could be transformed into a rating scale from 1 to 5. Then, the Likert rating scores can be obtained.Finally, the responses from subjects were generated corresponding to the different test designs. The latent scores were subject to a standard normal distribution and true uniquenesses were all fixed at one. Specifically, use Mplus to generate latent trait scores (η) and errors (e) for each subject. According to the Thurstonian IRT model of Brown and Maydeu-Olivares , each itUsing R and Mplus, a total of 100 replications were obtained for each condition and the sample size was 2,000 observations for all conditions.To compare the performance of the combined model under different conditions, three aspects of the model were investigated, that is, model convergence, the accuracy of parameter estimation, and the precision of latent trait recovery.In the simulation study of Brown and Maydeu-Olivares , the conSEs. The index illustrates the deviation of the estimated value of the parameter from its true value, and a smaller RMSE shows that the item parameter recovery is more accurate. This can be calculated by Equation (4).For all conditions investigated, the root-mean-square error (RMSE) was used to assess the accuracy of the estimation of parameters  and their To evaluate the trait recovery, actual reliability described in Brown and Maydeu-Olivares  was usedTable SEs under the four conditions with two traits measured and no Likert items. A test without Likert items means that it is purely a forced-choice questionnaire and analyzed by the Thurstonian IRT model. However, the similar things occurred when the test included only 20% of Likert items with two traits measured and no pairs composed of items keyed in opposite directions and short length. Table SEs were larger than 10 under these conditions.It should be particularly specified that, in addition to the low convergence rates, there were also several extreme estimated SEs were smaller than 10, for the subsequent analysis.To avoid the effect of those estimated results that were unacceptable on the following analysis, the present study selected those replications that converged successfully and for which all estimated SE estimates. For the 32 conditions, the table in the SEs. For a more intuitive description, Figures SEs corresponding to each type of parameters.The accuracy of parameter estimation was evaluated for both parameter estimates and Figure SEs of thresholds and factor loadings of forced-choice items, and Figure SEs of factor loadings of Likert items and intertrait correlations under different conditions. Generally, the trends in the precision of estimated SEs were very similar to the trends of estimated parameters in Figures SEs of thresholds and factor loadings of forced-choice items and intertrait correlations, especially in the designs with fewer traits, shorter length, or no pairs composed of items keyed in opposite directions. It also appeared that the accuracy of most estimated SEs changed little among designs with different percentage of Likert items, except the factor loadings' SEs for both types of items. With regard to the estimation accuracy of loadings' SEs of Likert items, its variation trend was exactly the same as that of Likert item loading estimates. The only difference between the tendency of loadings' SEs of forced-choice items and that of forced-choice item loadings estimates was the substantial decrease in its RMSE-values when the percentage of Likert items was increased from 20 to 40%. However, taken as a whole, 20% of Likert items also seemed sufficient to improve the estimation precision of the SEs of most parameters.Figure The average actual reliabilities estimated under all 32 conditions are shown in Table As can be seen from Figures The present study integrates the Thurstonian IRT model and the Graded Response Model for a test format which combines the forced-choice questionnaire with the Likert scale. We found that the forced-choice format performed poorly under certain conditions, which is consistent with previous research findings, while the mixed response format was better, especially when the number of traits measured was small.Does the mixed response format help to solve the problems of forced-choice designs? From the simulation, we found a remarkable improvement in the accuracy of parameter and true score recovery of the combined model, compared with those of the Thurstonian IRT model. In addition, the average convergence rate of the combined model was demonstrably greater than that of the Thurstonian IRT model and the IRT model obtained some extreme estimated standard errors in some designs measuring two traits. These indicate the model estimation of the mixed test format is more stable. Most important, the difference between the two models is remarkable under the simple test designs , where the performance of the Thurstonian IRT model is unacceptable. But when the test measures more traits, has more items or includes pairs composing of opposite keyed items, the performance of the two models become similar. Further, the model may need less constraints when Likert items are added into the test.The findings prove that Likert items provide the model estimation with the complete information on the absolute latent trait scores. Accordingly, the latent trait scale can be easily defined and the information is sufficient for the model to produce a stable and accurate estimation, particularly when the test measures only a small number of traits. From this view, the combined model only needs fewer constraints than the IRT model for model identification. In conclusion, the mixed test format does provide a solution for low reliability and model identification problem of the forced-choice format under certain conditions.One possible object to the conclusion is that we eliminated replications whose estimated standard errors were larger than 10 to avoid the influence of extreme values. Choosing 10 as the cut-off point is out of the following considerations. If we use the relative criterion, such as the standard score of 3 or greater as a cut-off point, we should screen each type of standard errors. But extreme values usually occur only in standard errors of some parameters. Then we may delete more replications than we should, making the estimation results seem precise but actually are not. Thus, the absolute criterion seems more appropriate. However, there is no generally accepted absolute criterion to judge whether the estimated standard errors were in a normal range. Accordingly, we counted the number of replications which had at least one estimated standard error was larger than 1, 5, 10, 20, or 50, respectively. We found that using 10 as a cut-off point, we would not delete too many replications and could get the right tendency of the results, as seen in the section of results. But no matter what, obtaining extreme estimated standard errors has already proved the unstable model estimation of the forced-choice designs.How many Likert items does the test need? The simulation results indicated that 20% of Likert items in the test almost provide sufficient information for stable and accurate estimation in the conditions where the Thurstonian IRT model performed poorly. Among these conditions, the most complex one is that the test measures two traits and has a total of 20 questions (the ratio of the number of questions to the number of traits is 10:1). In this test, 20% of Likert items equals only four Likert items and each trait has two of them. Besides, when the number of traits measured is large or the test length is relatively long, the forced-choice questionnaire can perform well and there is no need to add Likert items. Hence, the number of these items needed in certain designs is not large.The most obvious limitation of this research is about the resistance to response biases of the mixed test formats. Although the number of Likert items needed is small, they may bring different types of response biases to the test. The present study is to explore an available solution for problems which forced-choice design may encounter in application, which is from the aspect of statistical models and model estimation. Things are more complex than this in practice. Researchers should choose Likert items with great cautions. To reduce the probability of faking and social desirability responding, we emphasize that items with low social desirability should be considered as Likert items. The items may be those simple and objective questions Skinner, . There hThe study also has some other limitations. The forced-choice tests have various formats because the forced-choice blocks can have different sizes. The mixed response format as presented here uses only the simplest forced-choice format, blocks composed of two items. Besides, the factor loadings of Likert items in our simulation were drawn from a uniform distribution ranging from 0.45 to 0.95. Accordingly, the conclusion of the current study cannot be generalized to the conditions where the Likert items have low factor loadings. It is quite possible that the results may be different if each forced-choice block has more than two items or the factor loadings of Likert items are smaller. Researchers interested in this test format and scoring method could investigate how the mixed response format performs in these conditions. A final point to note is that the present study is a simulation study to compare model performance among different conditions. Future study is also required to examine the results in empirical examples.Furthermore, there exist other IRT models for scoring forced-choice tests, such as the multi-unidimensional pairwise-preference (MUPP) model  and statements measuring each dimension, founded on psychological theories and other previous literature.The second step is to assess the social desirability of each statement for the resistance to faking.The third step is to determine which type of tests should be used. It is recommended that the test format combining the Likert scale and the forced-choice questionnaire should be used if the test only measures a small number of traits. When the number of traits is large, researchers could choose either the integrated test or the forced-choice test.The fourth step is to construct the test. If researchers choose the combined test format, they should be quite cautious to choose items as Likert items to avoid response biases as much as possible. To resist faking, researchers should first consider items with lowest social desirability in each dimension. Then they should also use other approaches of diminishing other response biases to construct Likert items according to the situation. In most conditions, 20% of Likert items are sufficient for the test. The remaining items are used to design the forced-choice blocks, i.e., two items from different traits are matched on the social desirability to form pairs, and it's better to include some blocks composed of items keyed in opposite directions in the test. Alternatively, if the traditional forced-choice test is chosen, researchers should refer to the rules of test construction in Brown and Maydeu-Olivares .YX contributes the most to the article. HYL is the corresponding author who organizes and helps conducting the analysis. HL helps provide useful suggestions on modeling and revising the article.This article is supported by National Natural Science Foundation of China (31571152), the Fundamental Research Funds for the Central Universities, Beijing Advanced Innovation Center for Future Education, and Special Found for Beijing Common Construction Project (019-105812).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.


Masseter hypertrophy (MH) is one of the uncommon conditions that swelling can be seen in the angular mandibular region of the face. The etiology of MH includes several factors, and various treatment methods are mentioned in the literature. Botulinum toxin type A application is most commonly used for the treatment because of its less invasive feature. As a surgical method, some treatment alternatives that aim to reduce muscle mass or reshape the bone tissue in the angular region are considered. In this case report, a 21-year-old male patient with unilateral masseter hypertrophy on the right side is presented. After the patient was diagnosed with MH, botulinum toxin treatment in two sessions at one-month intervals was done. Since the reduction in muscle volume was not in satisfactory dimensions after the botulinum toxin application, the masseter was reduced on the right side through an intraoral approach. At the same time, bone enlargements on each side of the angulus mandibula were reshaped and smoothened through an extraoral retro mandibular approach. Clinical and radiographic evaluation of the patient revealed more aesthetic and symmetrical appearance in the regular controls. Clostridium botulinum and when injected into a muscle causes interference with the neurotransmitter mechanism, producing selective paralysis and subsequent atrophy of the muscle [Masseter hypertrophy (MH) is an uncommon condition that can cause aesthetic and functional problems. Aesthetic problems consist of prominent masseter muscle in the face, rectangular face shape, and wide mandibular angle. Patients may suffer psychological issues due to an unattractive look . Differee muscle –5. Resule muscle –6.A 22-year-old male patient applied to our clinic for painless asymmetric swelling on the right side of the face for five years . The hisBotulinum toxin type A  was supplied as a freeze-dried powder of 100 units and was reconstituted with 2 ml of sterile saline solution, giving a concentration of 100 units. Percutaneous intramuscular injection of botulinum toxin type A was performed to the hypertrophic muscle using 2 ml syringe with 25G needle. 75 units of botulinum toxin type A was injected equally into five points at the centre of the lower third of the masseter muscle . DetermiThe patient underwent surgery involving bilateral resection of mandibular angles and unilateral resection of the masseter muscle through intraoral and extraoral submandibular approaches. Under general anaesthesia, on the right side of the patient, an intraoral incision was made supraperiostally, slightly lateral to the external oblique line, and extended mandibular first molar region. The anterior portion of the masseter muscle was exposed, and the inner belly of the muscle was removed by the method described by Beckers . The intThere are many factors in the etiology of masseter hypertrophy, such as tensions and clenching caused by emotional stress and parafunctional habits. It is observed that none of the etiologic factors in the literature are present in our patient such as dental attrition, and therefore, we can refer to this condition as idiopathic masseter hypertrophy. Especially in the diagnosis of the clinical situation in unilateral hypertrophy, the differential diagnosis of head and neck soft tissue pathologies should be made , 7. BefoThere is not yet a standard protocol in the literature about the treatment of masseter hypertrophy. When treatment is planned, the patient's expectations and physical findings should be evaluated thoroughly. In cases where masseter hypertrophy causes appositional changes in the bone, the success rate with botulinum toxin treatment alone is low, and the surgical option should be considered. Further experimental and clinical studies are necessary to estimate the success of surgical treatment in the short and long term.


This manuscript describes the design considerations, implementation, and laboratory validation of an odor sensing module whose purpose is to support people that suffer from incontinence. Because of the requirements expressed by the affected end-users the odor sensing unit is realized as a portable accessory that may be connected to any pre-existing smart device. We have opted for a low-cost, low-power consuming metal oxide based gas detection approach to highlight the viability of developing an inexpensive yet helpful odor recognition technology. The system consists of a hotplate employing, inkjet-printed p-type semiconducting layers of copper(II) oxide, and chromium titanium oxide. Both functional layers are characterized with respect to their gas-sensitive behavior towards humidity, ammonia, methylmercaptan, and dimethylsulfide and we demonstrate detection limits in the parts-per-billion range for the two latter gases. Employing a temperature variation scheme that reads out the layer’s resistivity in a steady-state, we use each sensor chip as a virtual array. With this setup, we demonstrate the feasibility of detecting odors associated with incontinence. Avoiding social insecurity and shame;Unconfined participation in social life;Appropriate or improved care;Easing the burden of caregivers.Physical disabilities and aging can cause the loss of control of the bladder and intestinal functions. Affected persons often cannot perceive the odors related to their own excretions. Besides, many are unable to verbalize explicitly the need of nursing care . One theHowever, it is fundamental that advances in technological capabilities are tailor-made to the needs of end-users to ensure acceptance of innovations. As a result, an iterative research process has been adapted during development of the odor sensing system to prevent rejection of possible technology-based solutions by the affected person. For the results presented here, we have adopted a setting- and user-centered approach, which includes the early and continuous involvement of affected people, considering their living and care environment during the process, as well as the empirical examination and evaluation. These key principles are at the heart of the research and development efforts presented here. The needs of potential groups of users was analyzed by applying established research methods ,3,4,5,6 In the past and currently, extensive research and development efforts have been and are aimed at developing so-called electronic noses . Main apIn our contribution, we do not aim for emulating the human olfactory sense, whose working principle relies on the binding of odorant molecules to highly specific receptors in the human nose . InsteadFrom this list it becomes apparent that sulfidic molecules and amines play a central role for the odor perceived by the human olfactory system in the relevant scenarios. Furthermore, the gas composition for situations where an odor linked to urine or feces occurs differ from odors, which in turn should make it possible to reliably detect the associated situations. The interviews of different user groups yielded as the most important issue for the users the need to inconspicuously provide information about occurrence of incontinence related odors directly to the user and if desired also to a supporting person . Therefore, the sensor system is supposed to give discrete notification to mobile, active, and socially participating people who use it whenever other people might perceive an odor related to urine or feces. The novel system has been designed according to the end-user needs and in close collaboration with them.To achieve this, we have established a concept that relies on using any pre-existing smart device of the user and an odor detection unit as accessory for the smart device. The concept of the overall system is depicted in On the one hand, this approach allows the use of the smart device’s large computing power as well as all the human-machine interfaces state-of-the-art mobile devices offer, including apps, messenger services, and acoustic and tactile signaling. On the other hand, the sensor device might be fastened at arbitrary locations and independent of the smart device  within the communication range of the wireless protocol used.2 :en class .To comply with user requirements, the battery-powered odor sensing component’s runtime without charging should at least match that of the mobile phone and, therefore, we have performed a power consumption test by recording the total current usage of a complete measurement cycle. We have also performed experiments to test our steady-state claim. To this end, we have produced another sensing chip using CuO and CTO as gas sensitive layer. We have performed an independent evaluation of the transient behavior of the gas sensitive layer upon temperature change using a Prema 5017 Digital Multimeter  and a data acquisition rate of eight samples per second. Each temperature step had a duration of 60 s and we have determined the minimum time necessary to reach steady-state values.In order to test the robustness of our protocol, further investigate our steady-state approach, and check a stable operation, we have used the system to record a transient response towards exposure to varying levels of methylmercaptan from 1 ppm–5 ppm. The device performance of the prototype has been tested using well-defined gas mixtures produced by our custom-built apparatus to simulate real-world conditions in the lab . Taking Next, we have tested further gases according to an analysis of the odorous components occurring in feces and urine. We measure the response to gas concentrations close to the head space concentrations detected in real samples ,52,53—i.In a final step, we have evaluated whether or not the information collected by the sensor module is able to distinguish the gas matrices associated with the target odors. We have exposed the system to atmospheres generated by pure samples of water, urine, and feces. Finally, we have collected data during the repeated exposure of the sensor module towards 10 min in laboratory air, above a water reservoir, a urine reservoir, and a feces reservoir.The time-resolved evolution of the sensing layer’s resistivity for the different temperatures within a measurement cycle during the validation experiments is shown in Even though the baseline resistivity of CuO and CTO differ by more than one order of magnitude when compared to the layers of the prototype, the results may be applied to the respective layers used in the prototype device without loss of generality for both, CuO and CTO. As can be seen in Furthermore, the results obtained in determining the layer’s resistivity with the time-to-digital conversion are equivalent to that of more sophisticated equipment such as the Keithley 2700, which we use in our fully automated gas measurement apparatus . As a reThe total current consumption of the device during a complete measurement cycle is visualized in The total current consumption of the device exceeds that of the individual hotplate chips, which is due to the electronics design, which dissipates large parts of the excess current in other parts of the electronics. Because of the electronic circuitry, a considerable share of the total current consumption is lost in individual components of the electronics. Most notably, we have to employ an IC component to step-up the battery voltage due to the requirements of the sensor´s heater. 3SH. Accordingly, the resistance of the p-type semiconducting metal oxides increases upon exposure towards CHG/R0 exceeding 1.05, i.e., RG/R0 > 1.05, thus taking into account the baseline stability and noise of the resistivity reading. From the measurement data presented in The assessment of the capabilities of both layers to detect low levels of the most relevant trace gases with high reliability has been done by exposing the prototype system to head space concentrations reported in literature. The results also highlight the important influence of water vapor on the surface processes, which effects the layer’s response even though the influence is much weaker as compared to n-type semiconducting layers. We define the limit of detection towards a specific gas species as the concentration that causes a response RAnalysis of gas sensitive performance also highlights the potential for an increase in selectivity by using different temperatures. The changes in sensor response as a function of the layer’s temperature are as high as 80% in our experiments and The collected data during the repeated exposure of the sensor module towards laboratory air, above a water reservoir, a urine reservoir, and a feces reservoir is shown in It shows, as expected, that humidity levels play a pivotal role in the resistivity of the metal oxide layers. The sensor data form the device have been treated according to the description in 3, CH3SH, and (CH3)2S in relevant concentration ranges. An LDA shows that in near real-world situations the detection of target situations appears possible. Still, important issues need to be resolved prior to a field deployment. Currently, the detection limit is close to the target gas concentrations as encountered in real world samples. So, while the human odor threshold is still several orders of magnitude away, employing a device based on the technology presented here as an aiding device for stoma patients or elderly people suffering from incontinence seems possible. We have presented the design, development, and characterization of a portable, battery-powered odor sensing module that may be connected via Bluetooth low energy to any mobile device. Using low-power consuming MEMS devices with a metal oxide sensing layers placed on a hotplate, a runtime of more than two days has been achieved, which is about the utilization time of current smart devices without intermediate charging. The gas sensitive layers employed are capable of detecting relevant trace gases such as NHHowever, in order to successfully test the device in the field, we estimate that the sensor response needs to be improved by about one order of magnitude, which is feasible when using, e.g., catalysts. Additionally, the necessary detection limits will crucially depend on the location of the sensor in respect to the odor-producing source. To this end, one may place the device next to possible odor sources or employ pumping systems to provide the device with samples with high concentrations of the analyte. In this regard, we suggest that the mobile device will allow for a positioning close to the origin of the odor, thus providing relatively high target gas concentrations. Furthermore, the use of pre-concentrators is a possibility to increase the sensor signal and lower the detection limit. Adding highly selective elements, such as low-power consuming, room temperature electrochemical cells  or impleThe signal analysis may also have the potential to be considerably improved beyond using LDA and employing advanced, non-linear techniques such as multilayer perceptron (MLP). The employed algorithms will also have to be able to cope with the issue of changes in concentration associated with positioning of the device. Furthermore, the reproducible performance and calibration of various sensor modules will have to be investigated. We aim to deal with these issues based on the results presented in this paper. On top of that, non-technical issues such as user-acceptance, privacy concerns, and trust in new technologies in ambient assisted living settings will have to be investigated. Our current results constitute promising intermediate results aimed at addressing the issue of personal care and demonstrates the feasibility of employing low-cost, metal oxide-based sensing technology in a demanding measurement situation.


Single-cell mRNA sequencing (scRNA-seq) is a powerful method to identify and classify cell types and reconstruct differentiation trajectories within complex tissues, such as the developing human cortex. scRNA-seq data also enables the discovery of cell type-specific marker genes and genes that regulate developmental transitions. Here we provide a brief overview of how scRNA-seq has been shaping the study of human cortex development, and present ShinyCortex, a resource that brings together data from recent scRNA-seq studies of the developing cortex for further analysis. ShinyCortex is based in R and displays recently published scRNA-seq data from the human and mouse cortex in a comprehensible, dynamic and accessible way, suitable for data exploration by biologists. New technologies to sequence the transcriptomes of single cells is having an enormous impact on developmental biology research  that biologists can use to explore the gene expression patterns from these publications.Recently, these methods have been applied to the developing human and mouse cortex, which has led to remarkable progress in understanding the molecular signatures that define cell states within these tissues  to identify the molecular signatures that mark radial glia cells located in the outer sub-ventricular zone  . Spheroid fusion led to the formation of forebrain-like organoids and interneurons migrated from the ventral to the dorsal region, providing information on brain-region interactions. These authors also performed single-cell transcriptomics  on both ventral and dorsal forebrain spheroids individually and we compiled the dorsal forebrain data into the ShinyCortex application.Telley et al. analyzed the transcriptome of single cells  isolated from multiple time points from the developing mouse neocortex . ShinyCortex can be used to visualize the transcript level of any gene of interest as a function of cell types or over pseudotime, and it allows the user to explore the expression correlations between genes. Generally, the user of ShinyCortex first chooses the dataset that he/she wants to explore . The application is then divided into four sections. The first panel plots the distribution of gene expression values of any gene of interest according to the cell type assignment from each study, and options include box, violin, or scatter plots  for each dataset was obtained directly through the authors. Person's correlation coefficients between genes were calculated with r base. All interactive plots were generated with plotly and ggplot2 R packages. Correlation heatmaps were generated with the gplot R package. The pseudotime plots use a generalized additive model (GAM) implemented with the mgcv R package and level of confidence interval (0.95 by default). Pseudotime heatmaps were produced with the stats R package.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The handling Editor declared a shared affiliation, though no other collaboration, with one of the authors BT. The other 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.


In our context, where the two kinds of agents are buyers and sellers, our study shows that not only do their interactions bring economic benefits for the agents directly involved, but they also contribute to the stability of the market. Our results help to explain the surprising coexistence of the two forms of market in the distinctive Boulogne sur Mer Fish Market.Centralized markets are often considered more efficient than bilateral exchanges because information is public and the same for all the agents. On decentralized markets, where the information is private, the influence of trust on the market outcome has been underlined by many authors. We present an empirical study of the distinctive Boulogne-sur-Mer Fish Market (where both buyers and sellers can choose to trade by either bidding or bargaining), focused on the interactions between agents. Our approach is inspired by studies of  How do social interactions influence the outcome of economic exchanges? This question, which has generated long-lasting debate between economists and sociologists , is begiWhile it is clear that human beings rely on cooperation with others for their survival , economiwho exchanges with whom?  indicate whether or not the seller i and the buyer j have interacted during the selected period, and the weighted bipartite matrix, B, of elements From the previously described database, we build a network of interactions which can be coded into a For this work, we have chosen not to include a weighting for the links, which would take into account information about the prices or the quantities exchanged. This is certainly a simplifying hypothesis, but it also serves another purpose. As this market is very heterogeneous  this assumption is needed to prevent the information about social interactions from being blurred by the different buying/selling capacities of the agents involved. Previous results  confirm different customers (suppliers) that a seller (buyer) has in the chosen period. The strength of an agent, . Some of them are generalists, having (many) contacts with their counterparts, while others are specialists, having contacts mainly with generalist counterparts and rarely with specialists. Therefore, nestedness is not only different from a random structure of interactions, but also different from another common ordered state described by a block type matrix, which would indicate a niche structure of the market.extinction curve or isocline of perfect nestedness (IPN) that can be analytically determined as a function of the number of rows, n, the number of columns, m, and the density of contacts, ϕ [K , shows a triangular shape, with all its zero and non-zero elements on either side of a curve called tacts, ϕ  of the bnesting index based on robustness of the network [Several indicators aimed at quantifying nestedness coexist in the ecological literature. They are based on different properties of a nested system, and have different advantages and drawbacks. In order to avoid bias, here we measure the nestedness of both sub-markets using four well known indices, each one targeting a different property of the nested matrix. We use the indices proposed in the NED  package, network  exploitsloyalty matrix based on the weighted bipartite matrix B:An indication of trust may be obtained by measuring the frequency of contacts between any two agents. We define a Li, j = 1 if seller i and buyer j only transact between themselves and Li, j ≪ 1 if seller i and buyer j transact much less between themselves than with others agents in the same period.Note that simple bipartite matrix, K, and the weighted bipartite matrix, B, both in a global and in a detailed time scale, by studying the networks obtained for both sub-markets, using not only the data integrated over the whole period (18 months) but also differentiating between seasons and between days. This multi-scale study is necessary because data integrated over the whole period lead to matrices of larger size, allowing for the study of the statistical properties of the system, while zooming in on chosen periods  allows to retrieve information about the structure of interactions that could have been washed out by the coarse-graining procedure.We analysed the properties of the Focusing on the structure of the interactions between agents brings to light valuable information about the agents’ behavior in each sub-market.The degree of a seller (buyer) indicates the number of different customers (suppliers) this agent has.exploration phase of the buyers. In fact, when buyers enter this sub-market they diversify their suppliers before choosing their preferred partners. This exploration phase will appear later through the comparison between degree and strength distributions.Conversely, the sellers’ degree distributions (bottom) of the two sub-markets are very different. The bilateral negotiation sub-market shows a left-skewed distribution, with a maximum of around 60 customers, showing that about half of the sellers have many different customers, while for the auction market the distribution seems quite uniform with large fluctuations. This reflects the fact that in the auction market, sellers cannot influence the transaction prices once the minimum price for their goods has been fixed, while in the bilateral sub-market, they can adapt their strategy to attract more customers.The strength of a seller (buyer) measures the number of contacts that this agent has had during the period studied (with the same or different counterparts). In order to understand the following graphs, a rough estimation of the maximum number of transactions is helpful. In these markets there are typically 200 sellers and 100 buyers, and the full period covers 18 months, which is about 450 working days (the market does not operate on Sundays). So a seller who was present on every market day and dealt with all the buyers every day would have a degree of The comparison of sellers’ strength distributions in the two sub-markets shown in Buyers’ strength distributions, which are monotonously decreasing with degree, do not show any differences between the sub-markets, except for the fact that the tail is much longer in the bilateral negotiation sub-market  to be meaningful. In spite of this limitation, which is true for both sub-markets, the important result here is the clear qualitative difference between the shapes of these two cumulative distributions.Interestingly, the cumulative distributions of loyalty in these sub-markets (right panels) clearly reveal qualitatively different behavior. In the auction market, there are two different regimes: the cumulative number of pairs having low and average loyalty values varies slowly until a critical value where the probability of finding pairs with a higher loyalty decreases very fast. In the bilateral market, on the contrary, there is no such characteristic critical value. This lack of characteristic scale suggests that this cumulative distribution could be fitted by a power law (with a finite-size cut-off). However, the data range is too short for the actual value of the exponent  are much higher than those typical of mutualistic ecosystems, which are much sparser. K of both sub-markets for the whole period studied. They are ordered so as to bring to light the nested structure (if any). Both types of agent (buyers and sellers) appear ordered by decreasing degree, with the maximum degree on the top-left corner. The resulting structure clearly shows that the interactions are not randomly distributed in a uniform way in either of these sub-markets.In mutualistic ecosystems, the observed nested structure of the bipartite matrices describing the network is known to contribute to their robustness and stability . We haveblock type adjacency matrix, which would correspond to a market segmented into “niches” of interaction among groups of buyers and sellers who only interact between themselves. Although the existence of some groups of specialized buyers and sellers cannot be excluded, they are not dominant in the structure of interactions of these markets.All the nesting indices show an important degree of nestedness in both sub-markets, thus revealing the existence of generalist buyers and sellers along with some specialists of both types who mainly interact with generalists , agents of one type (either buyers or sellers) are removed in order, starting from those of highest degree, and the agents of the other type who are left without any contacts are considered to disappear from the market (secondary extinctions). The attack tolerance curve (ATC) gives the fraction of agents of one type that remain in the market as a function of the fraction of agents of the other type who are removed. The extreme opposite attack strategy, called increasing degree removal (IDR), consists in the same procedure, but now the agents to be removed are chosen in increasing degree order. This attack strategy is less sensitive to the network ordering [We have also studied daily matrices using the nesting index based on robustness described in . This inordering  (see S2 We can clearly observe that for the DDR strategy (full symbols), the ATC of the bilateral market decreases faster than the ATC for the auction market as buyers are removed. The area between the ATCs corresponding to the two attack strategies, which is proportional to the nesting index, is larger in the case of the bilateral sub-market . This means that when high degree buyers are removed from the market, most of the sellers may still find customers, while as sellers disappear, buyers are immediately affected. This effect is dominant in the auction sub-market, where a removal of about 50% of the generalist buyers affects the sellers very little. This observation suggests that the auction market is more robust to fluctuations than the negotiated market, which could be an argument in favour of auction.As the density of contacts in these sub-markets is much higher than in mutualistic ecosystems, the sensitivity of this nesting indicator is diminished. However it should be noted that in all cases, the nesting coefficient (cf. the area between the IDR and DDR curves) is much larger than the one corresponding to randomized matrices (in the insets) of the same characteristics, thus indicating again that the structure of interactions is not random.We have performed a data-based study of the interaction networks of the two coexisting sub-markets  of the Boulogne-sur-Mer Fish Market, searching for stylized facts characterizing agents’ behavior and market structure. By applying tools drawn from the study of mutualistic ecosystems, we observe that both sub-markets are nested, although typical market densities are much higher than those observed in mutualistic ecosystems. It is noteworthy that the densities of contacts corresponding to the whole period and to daily data are quite different. The density is quite high for both markets when the whole period database is analyzed , but it is much lower, and with a reverse tendency, for daily matrices (10% and 20% respectively), thus affecting the nesting indices. In other words, at a daily level, the proportion of pairs trading on the auction market is larger than in the bilateral market: however, when data are analyzed for the whole period, the bilateral sub-market has more trading pairs. While it is easy to understand the aggregation of dense matrices producing a denser matrix (the auction case), how can we explain that the aggregation of sparse matrices might lead to an even denser one? This paradoxical result can be understood by recalling that the degree distribution of buyers in the bilateral sub-market exhibits a longer tail than in the auction market, revealing that a “search phase” takes place every time a new buyer arrives in the market, thus increasing the density of the matrix aggregated over the whole period.K matrices of the bilateral sub-market are larger than the corresponding matrices of the auction sub-market and comparable in size to the K matrices obtained for the whole period. This means that each day, the affluence in the bilateral sub-market is higher (the corresponding matrices are larger) but the number of trading pairs is smaller (the corresponding matrices are sparser). The observed fact that the K matrix corresponding to the whole period of the bilateral sub-market (obtained by overlapping daily matrices) is denser than the corresponding daily matrices necessarily implies that in this sub-market, the trading pairs are mainly different from day to day, thus increasing the number of ′1s′ in the aggregated matrix.Moreover, we observe that the daily The ATC curves reveal that the auction market is the more robust. Almost 50% of the high degree buyers can be removed in daily matrices without significantly affecting the fraction of sellers that still have at least one contact. Certainly, this does not mean that removing those buyers is innocuous for the auction market, as it may affect volumes and prices. The centralized structure of the information helps the crossing of supply and demand. The adjustment will be performed by the prices. For the negotiated market, removing the same fraction for buyers leaves a larger fraction of sellers without any contact, thus revealing the importance of social relationships between agents. As sellers are fixed for the day, if their loyal buyers are absent, due to market frictions , it is costly for the sellers to match new customers.The defined loyalty index measures the proportion of contacts between a given pair of agents with respect to the total amount of contacts of both, as an indicator of the trust existing in that pair. Its cumulative distribution shows two regimes for the auction sub-market: it decreases slowly for low values of loyalty, until a critical value beyond which it decreases very quickly. On the contrary, the distribution of loyalty in the bilateral sub-market looks like a power law with no typical scale of the loyalty index.It is worth recalling that the results discussed here are based on networks whose links only take into account social behaviour, disregarding the prices and the quantities exchanged for each pair. As stated in the Methods section, this assumption, based on the results of previous work which show that on average, prices and quantities are not a discriminant of sub-markets, is necessary to study the role of social contacts in the formation of trust. Weighting the links with the information on prices and quantities exchanged may blur the phenomenon we want to measure. In this very heterogeneous market where agents have very different trading capacities, if for example, a big and a small buyer both buy from the same seller, the fact of weighting the links by the quantities exchanged or the price paid might lead to the weight of the corresponding interactions becoming comparable even if the big buyer interacts only rarely with the seller while the small buyer comes to trade repeatedly with him. The next step will be to build networks which include the information about price and quantity, which, along with the results presented here, may bring new information about different aspects of this system. This work is in process.In spite of the widely accepted view that auction markets are more efficient than bilateral ones, the case of the Boulogne-sur-Mer Fish Market, where there is no signal of quality for the goods, shows that both market organizations can coexist without one eclipsing the other. Our study provides a measurement of social interactions which sheds light on how they reinforce trust in the bilateral market, thus resulting in mutual benefit for the traders. This mutual benefit seems to compensate for the lack of information and the cost of linking. Our analysis also provides empirical evidence supporting the performance of the auction market, which is more resistant to the extreme case of the absence of high degree buyers. By focusing on social interactions, we can measure the global properties characterizing each sub-market which help to explain their observed coexistence. Our results suggest that the advantages offered by each sub-market help to increase the stability of the whole.S1 AppendixDescription of a perfectly nested mutualistic ecosystems.(ZIP)Click here for additional data file.S2 AppendixDescription of different nestedness indices.(ZIP)Click here for additional data file.S3 AppendixIn this supplement we give the nestedness values obtained with different indices for the global period along with details of the daily analyzed matrices and their nestedness index using NIR. We also provide more examples as a complement of the results shown in Figs (ZIP)Click here for additional data file.


The risks involved in nighttime driving include drowsy drivers and dangerous vehicles. Prominent among the more dangerous vehicles around at night are the larger vehicles which are usually moving faster at night on a highway. In addition, the risk level of driving around larger vehicles rises significantly when the driver’s attention becomes distracted, even for a short period of time. For the purpose of alerting the driver and elevating his or her safety, in this paper we propose two components for any modern vision-based Advanced Drivers Assistance System (ADAS). These two components work separately for the single purpose of alerting the driver in dangerous situations. The purpose of the first component is to ascertain that the driver would be in a sufficiently wakeful state to receive and process warnings; this is the driver drowsiness detection component. The driver drowsiness detection component uses infrared images of the driver to analyze his eyes’ movements using a MSR plus a simple heuristic. This component issues alerts to the driver when the driver’s eyes show distraction and are closed for a longer than usual duration. Experimental results show that this component can detect closed eyes with an accuracy of 94.26% on average, which is comparable to previous results using more sophisticated methods. The purpose of the second component is to alert the driver when the driver’s vehicle is moving around larger vehicles at dusk or night time. The large vehicle detection component accepts images from a regular video driving recorder as input. A bi-level system of classifiers, which included a novel MSR-enhanced KAZE-base Bag-of-Features classifier, is proposed to avoid false negatives. In both components, we propose an improved version of the Multi-Scale Retinex (MSR) algorithm to augment the contrast of the input. Several experiments were performed to test the effects of the MSR and each classifier, and the results are presented in experimental results section of this paper. The ultimate goal of our study is to increase driver’s safety by alerting the driver when driving under non-ideal conditions using a vision-based ADAS. Advanced Drivers Assistance (ADAS) is slowly reaching technological maturity. There are already many ADAS models that use the video driving recorder as a sensor, for example, for detecting pedestrians . A few mThe danger level of driving any vehicle in non-ideal environments is greatly raised when the driver’s attention level is sub-optimal ,7,8,9,10In previous studies for detecting drowsiness using the driver’s eyes ,7,8,9,10Experimental results using six videos of five different drivers show that we could achieve an average accuracy of 94.26%, which is comparable to the results of previous studies. In previous studies for detecting vehicles at night ,14, cameThe large vehicle detector component first uses the rear-light detection method proposed by Wu and Lee  to find An infrared camera is chosen to capture driver’s upper body image during night-time driving. The condition of the eyes are used to determine the level of focus attention of the driver. After an MSR operation is used to improve the contrast of the image, a trained LBP-based Ada-boost classifier is then applied to do a quick initial detection of the locations of the eyes; the requirement for this stage of processing is that the eyes must be fully opened and the driver is facing directly ahead, as shown in The regions containing the eyes are then passed to a pupil detection algorithm, which seeks to determine whether the driver’s eyes are open. This is done by using a heuristic of first inverting the video within these regions then thresholded so that reflection of the infrared lights reflected by the pupils would appear as black surrounded by white pixels, as shown in If the heuristic fails to detect both pupils, as shown in R represent the distance between the eyes when facing front, and θ approximates the angle the head rotation. The rotation angle θ, is approximated by the following equation: R is the distance between the eyes when the driver’s head is facing toward the camera, and Rcos(θ) is the distance between eyes measured by the camera image when the head is turned away from the camera.However, if the driver’s pupil can be detected, then the locations and the distance between the detected eyes and the distance between them are used to calculate the relative angle of focus of the driver, as shown in This information can be helpful to determine if the driver’s attention is on the road ahead. In As shown by the flowchart in This results in an initial estimation are the locations of large vehicles. However, because false negatives are possible, in order to catch these false negatives, a rectified MSR based on  is used W is the weighting function, I is the input image, and M is the convolution mask. The inner expression can be written as:k is used to control the shape of function. Because the Log function quickly climbs beyond the value of 1 as shown in For the purpose of improving the contrast for nighttime driving video, the Sigmoid function is used to replace the Log function in the original MSR algorithm, in order to minimize overexposure. The original MSR function can be written as:G is the Gaussian function, and a is used to control the weighting. Finally the result after MSR is combined with the original image for the final result X:tW is the weight used to combine the images.The weighting function we used is:A convolutional neural network is composed of one or more convolution layers plus fully connected layers , but also include weighting and pooling layers. This structure allows convolutional neural network to make use of: (1) nonlinear units (ReLU), (2) dropouts that are used during training to selectively dismiss individual neural unit in order to avoid overfitting the model, and (3) overlap pooling  in orderWe performed an experiment to select a suitable CNN structure to extract feature for our purposes before choosing the AlexNet CNN structure. The first experiment compares the performance of the features from two pre-trained CNN architectures using the ImageNet data: the VggNet  architecA second experiment was designed to test whether using other classification features that also calculates the features from the entire image region, such as the Hog and the LBP features, would perform better than the AlexNet CNN-based features. The experiment we performed used only 486 of the 23,066 images and using features such as LBP , HoG 2626 and AlL is the luminance, σL is the Gaussian-smoothed L, and λ is the contrast factor. The AKAZE features seeks to improve the speed of the KAZE by trying to calculate the scale space faster. The difference between KAZE and AKAZE descriptors from the patented SURF and SIFT descriptors is that KAZE and AKAZE calculate the main direction of the feature point for building nonlinear scale space.The patent-free KAZE feature, introduced by Alcantarilla et al. , uses thUnlike the CNN-based features, KAZE and AKAZE features have to perform key-points detection first before extracting feature descriptions in each detected key-point in the sample space. They extract disordered sets of local small blocks from which descriptors are calculated from the key-points. These descriptors are then grouped into local clusters using a clustering algorithm, such as the K-means algorithm, where each cluster center is treated as a vocabulary word for a visual dictionary, and a term characterized by a clustering center corresponds to the formation of a code word. However, KAZE is relatively slow due to the calculation for nonlinear scale space, but the AKAZE feature is faster. We performed experiments to test KAZE against AKAZE by using them as features in BoF classifiers in the task of classifying large vehicle types using different dictionary sizes (number of bags in BoF). The purpose of these experiment was to determine an appropriate dictionary size for both KAZE and AKAZE BoF classifiers. However, as the graphs in A total of eight experiments were set up to test the performance and accuracy of these two modules. The first five experiments were set up to test the large vehicle detector module according to the order of the flowchart in The first experiment seeks to test the performance of the LBP-based Adaboost classifier, which is the first-pass classifier according to the flowchart for the large vehicle detector module. The positive training samples include several types of large vehicles, such as buses, trucks and trailers, for a total of 10,420 positive images. The negative training samples include images of small sedans and empty street lights, for a total of 12,646 images, so the total of number images is 23,066 images. Two LBP-based Adaboost classifiers were trained, one to the 21st stage, and the other to only the 20th stage. The second experiment sought to test the effectiveness of our Sigmoid-based MSR vs. the traditional Log-based MSR as preprocessors when used in the second-stage classifiers. We used a total of 200 images of large and small vehicles taken at night. The plots of classification miss rate vs. ratio of training data to overall data are generated for the classifier using the AlexNet CNN-based features, and the classifier using KAZE features. The third experiment sought to test the effectiveness of the BoF classifier using the KAZE vs. the AKAZE features. We first separated the training set into four classes: bus, truck, trailer, and standard-sized sedan. The training set include 8532 bus images, 1466 truck images, 352 trailer images, and 3117 sedan images, for a total of 13,467 images. In the fourth experiment we wanted to validate our choice of using CNN-features for the classifier, so we compared the AlexNet CNN-features against similar feature-based types that also calculate features using the entire image, such as the HOG and LBP  featuresThese feature vectors are then used to train each SVM for classifying large and small vehicles at the ratios of 10%, 50% and 90% of all data as training data. In the fifth experiment, we used the images from the third experiment plus 10% additional images taken during sub-ideal conditions, such as during rainshowers, and preprocessed each one with our proposed Sigmoid-based MSR. Each of these images is a cleanly cropped image containing only a single vehicle. These images then used to compare the performance of classification of the CNN features-based classifier against the KAZE features-based and AKAZE features-based BoF classifiers in separating large vehicles from the small vehicles. The processing time was taken for each image in order to calculate the average execution time to process each image. The average processing time are decomposed into the time used for feature extraction, the time used for SVM training, and the time used for SVM classification. The average processing time, in seconds, for each classifier are listed in In addition to the processing time, the classification accuracy is also determined for each combination of MSR and classifier in this experiment. We measured the performance by splitting the images into different ratios of training and testing data; from 10% to 90% of images were allocated to training, and the rest for testing. The results for different ratios are shown in y-axis is the miss rate, and the x-axis are simply the percentage of the total number of images mis-classified as large vehicles by the classifiers.Therefore, in the sixth experiment, we deliberately chose images that resemble vehicles but actually were street lights and signs. These images, an example of which is shown in These images were not in the training set of these classifiers, since they are neither large vehicle types nor sedans. Again, these images were first pre-processed with Sigmoid-based MSR before training and classification. As it can be seen in The results of the above experiments confirmed our flowchart for the large vehicle detector module. After the initial detection and extraction of tail-lights, the size of each region-of-interest is determined by assuming a large vehicle would be contained within each pair of tail-lights. This assumption could either result in a clean bounding box containing just the vehicle itself, in which case the Adaboost LBP classifier helps in the identification, and the false negatives can be caught using the CNN-features based classifier. However, if the bounding box contains other misleading features, such as those in The last experiment was designed to test the accuracy of the driver drowsiness detection system. Six infrared video sequences taken at 20 fps of five different drivers were obtained, as shown in The results show that for these six videos, their accuracies ranges between 88% and 98%, with the average of 94.26%, without validating the eyes while tracking. These results show a good promise that the driver would be alerted when and if drowsiness occurs.In this paper, two components to augment the capabilities of vision-based ADAS at night were proposed. We also proposed a modified MSR which would improve the contrast of night-time videos without overexposure and can help improve classifying accuracy. The first component seeks to improve the driver’s safety by alerting the driver when he or she becomes too distracted or drowsy to process warnings. The second component seeks to alert the driver when large vehicles come near the driver’s vehicle. Both components use our modified MSR as a preprocessor. The first component was designed to evaluate driver drowsiness at night by tracking the driver’s eyes and detecting the presence of the pupils using an infrared camera. The second component uses the images from a standard driving video recorder to differentiate vehicles by size. It locates the tail-lights, and use them as hints to extract regions, each of which should contain a single vehicle. In this component, a trained Ada-boost classifier is used to quickly detect large vehicles. The identified large vehicles then are tracked immediately in a CPU process independent of the secondary classifiers which are designed to catch false negatives from the first classifier. The negatives of the first classifier, which are assume to contain false negatives, are first enhanced by a Sigmoid-based MSR before being processed by two types of classifiers, one of which—the KAZE features-based classifier—uses a key-points detection-based approach. The second type—the CNN feature-base classifier—computes a feature vector directly using the entire region. Several experiments were designed to test each stage of the flowcharts of these two components. The results of these experiments together show that the false negatives of the LBP-based Adaboost classifier can be caught by the secondary classifiers. Our experiments also shows that while the CNN-features-based classifier is the preferred classifier if the objects have relatively clean backgrounds, for classifying images containing easy-to-misidentify features, a key-point based classifier like the KAZE BoF can perform better. Also, in order to test the classification accuracy under sub-ideal driving conditions, at least one of the experiments included images taken while driving under bad conditions, such as rainshowers. From the results of these experiments, the classification accuracy results show that these classifiers can be trained to perform their designed classification tasks even under bad driving conditions. Future researches include combining different features into a single classifier and define the interactions between these two components.


To implement steel bridge maintenance, especially that related to fatigue damage, it is important to monitor bridge deformations under traffic conditions. Bridges deform and rotate differently under traffic load conditions because their structures differ in terms of length and flexibility. Such monitoring enables the identification of the cause of stress concentrations that cause fatigue damage and the proposal of appropriate countermeasures. However, although bridge deformation monitoring requires observations of bridge angle response as well as the bridge displacement response, measuring the rotation angle response of a bridge subject to traffic loads is difficult. Theoretically, the rotation angle response can be calculated by integrating the angular velocity, but for field measurements of actual in-service bridges, estimating the necessary boundary conditions would be difficult due to traffic-induced vibration. To solve the problem, this paper proposes a method for determining the rotation angle response of an in-service bridge from its angular velocity, as measured by a inertial measurement unit (IMU). To verify our proposed method, field measurements were conducted using nine micro-electrical mechanical systems (MEMS) IMUs and two contact displacement gauges. The results showed that our proposed method provided high accuracy when compared to the reference responses calculated by the contact displacement gauges. In steel bridge maintenance, countermeasures for fatigue damage are important because such damage is hard to detect and may cause sudden brittle failure. In order to conduct appropriate maintenance for fatigue damage in a steel bridge, it is important to monitor bridge deformation, which consists of the displacement response and the rotation angle response, against traffic loads because such deformation is the primary source of the stress concentrations that cause fatigue damage ,2,3. FigHerein, due to their compact and inexpensive nature, the use of micro-electro-mechanical system (MEMS) sensors to measure responses against the external forces of traffic loading, wind force, and seismic force are proposed ,6,7,8,9.In order to monitor bridge deformations against actual traffic loads, bridge displacement measurements are essential, and many studies have explored methods for conducting such measurements using accelerometers ,19,20,21Hou et al.  proposedIn another study considering gravitational acceleration measurements, it was thought that the rotation angle could be calculated from gravitational acceleration changes caused by the MEMS accelerometer tilt . HoweverIn theory, a bridge rotation angle response can be calculated from the integration of its angular velocity, as measured by an inertial measurement unit (IMU). However, the integration process would require numerical integration of the initial condition, which would be difficult to achieve on an in-service bridge that is constantly vibrating due to vehicle traffic. Furthermore, since sensor noise and quantization errors, due to the analog-to-digital (A/D) conversion process, are inevitably included in the angular velocity measured by an IMU (particularly at low frequencies), and those errors can be expected to degrade the integrated results .In the present study, in order to analyze bridge rotation angle responses, field measurements were first conducted on an actual in-service bridge using nine MEMS IMUs and two contact displacement gauges. Next, based on the characteristics of the bridge rotation angle response, a method was proposed by which we could calculate the bridge rotation angle response from the measured angular velocity. Finally, by comparing the rotation angle calculated from the measured angular velocity using the proposed method with the rotation angle determined from two contact displacement gauges, the effectiveness of the proposed method was verified. The results obtained using our proposed method were found to be highly accurate.Field measurements of an actual in-service bridge were performed in order to analyze the bridge rotation angle response characteristics and propose a method to determine rotation angle from measured angular velocity. The test bridge, which is a mixed steel-concrete structure equipped with three traffic lanes, is a 38-m-long single-span bridge see  equippedWe began by installing a MEMS IMU at the longitudinal center of the lower flange of G4 using a magnet. Two contact displacement gauges were also fixed at the same location in order to verify the accuracy of the rotation angle values obtained from the measured angular velocity. This experimental setup is shown in In order to precisely detect vehicle entry and exit times using the measured acceleration responses, eight MEMS IMUs were attached to the vertical stiffeners on both longitudinal edges of the four main girders. These IMUs also allowed us to determine the travel lanes used when vehicles passed over the bridge. The MEMS IMU experimental setup located at the vertical stiffener of the exit side of G5 is shown in By comparing the bridge displacement and bridge rotation angle responses under traffic load conditions in both the time and frequency domains, the bridge rotation angle response under traffic loads can be analyzed.The 25 s displacement response measured by the contact displacement gauges at the lower flange center of G4 is shown in The bridge rotation angle response for 25 s calculated from the displacement responses measured by the two contact displacement gauges at G4 is shown in The displacement response in Details of the displacement response spectrum shown in The displacement response spectrum in As described above, the rotation angle response is largely classified into forced displacement and free vibration displacement responses. In addition, when no vehicle is travelling on the bridge, the bridge rotation angle rotates about the zero-axis. Using these characteristics, the bridge rotation angle response is determined from the measured angular velocity. The basic concept of our proposed method will now be described. First, precise vehicle entry and exit times are detected using acceleration responses recorded by MEMS IMUs at the vertical stiffeners on both longitudinal edges of the main girders. Second, by assuming that the bridge is rotating at its free vibration frequency before and after the vehicle travels over the bridge, the initial and terminal conditions of the rotation angle response can be estimated. Finally, the angular velocity of the bridge during vehicular passage is integrated into the rotation angle response using the initial and terminal estimates produced above.Free and forced vibration regions are separated by precisely detecting vehicle entry and exit times. When multiple vehicles are running on the bridge, the time the first vehicle enters the bridge is the vehicle entry time and the time the last vehicle exits the bridge is the vehicle exit time.The angular velocity is transformed from the time domain to the frequency domain through a Fourier transform.Frequencies below 1.0 Hz are removed in order to eliminate the effect of forced displacement during vehicular passage. Although the proper high-pass filter cut-off frequency depends on the bridge length and vehicle travelling speed, since the span lengths of most girder bridges in Japan are more than 30 m and the maximum allowed speed on expressways in Japan is 100 km/h, the travelling time should be above 1.2 s, and the frequency should be below 0.9 Hz . In addiThe angular velocity in the frequency domain, which does not have the effect of forced displacement during vehicular passage, is transformed to the time domain through an inverse Fourier transform.The angular velocity under free vibration conditions obtained in Step 4, which excludes the effects of vehicular forced displacement, is numerically integrated to obtain the rotation angle, thereby producing an estimate of the initial and terminal rotation angle conditions under free vibration conditions.The total angular velocity, which includes the angular velocity under both free and forced vibration conditions, is numerically integrated to obtain the total rotation angle. The lower and upper limits of the integration are the times when the same vehicle enters and exits the bridge, respectively. The initial rotation angle is defined as the rotation angle under free vibration conditions estimated in Step 5.Because of measurement errors in the measured angular velocity, which are caused by sensor noise and the limitations of A/D conversion, the integrated rotation angle does not satisfy the terminal conditions at this point. To satisfy those conditions, the drift component is subtracted from the integrated rotation angle.The procedure used by the proposed method to determine the bridge rotation angle response from the angular velocity measured by the IMUs see  is shownIn the procedure described above, when the initial and terminal conditions of the rotation angle response in Step 3 cannot be accurately estimated, the integrated rotation angle response results include the estimated error of the initial and terminal conditions. Therefore, vehicle detection in Step 1 is of crucial importance. The measurement error, particularly the low frequency range measurement error, which includes sensor noise and quantization errors due to the A/D conversion process, corrupts the integrated results. Furthermore, since the generated angular velocity obtained over the low frequency range is small compared to the generated angular velocity over the high frequency range, low frequency range measurement data is easily affected by sensor noise. Therefore, it is important to use an IMU with low sensor noise over the low frequency range so that the angular velocity can be measured over that range with a high level of accuracy. The noise density of an IMU used in this study is 0.004 [(°/s)/√Hz]. In In order to verify the effectiveness of the method used to determine the bridge rotation angle response from the measured angular velocity, comparisons between the rotation angle response determined from the measured angular velocity and those determined by the two contact displacement gauges were conducted.Vehicle entry and exit times are detected by eight MEMS IMUs at the vertical stiffeners on both longitudinal edges of the main girders.The angle rotation response at the longitudinal center of the lower flange of G4 is determined from the measured angular velocity at the longitudinal center of that lower flange by using the detected vehicle entry and exit times and the proposed method.Accuracy verification is conducted by comparing the rotation angle response determined from the measured angular velocity and those determined by the two contact displacement gauges.The test bridge and instrument installation positions are the same as those shown in In order to apply the proposed method, precise vehicle entry and exit times must be measured. In this study, the eight above-mentioned MEMS IMUs were used for this purpose. In addition, the vehicle travel lane was estimated using each of the acceleration responses recorded at G2 to G5 by the eight MEMS IMUs. The determined rotation angle response using two contact displacement gauges, and each acceleration response measured at G2 to G5 are shown in The acceleration record in By comparing the rotation angle response determined from the measured angular velocity with that determined by the two contact displacement gauges, the accuracy of the response determined from the measured angular velocity could be verified. Then, by using an entry time (11.372 s) and an exit time (13.434 s) as the limits of the numerical integration, the rotation angle response was determined from measured angular velocity.The rotation angle response determined from the MEMS IMU and that determined by the two contact displacement gauges also placed at G4 are shown in It can be seen that the rotation angle response determined by the two contact displacement gauges contains more high-frequency components than that determined from the MEMS IMU. It is thought that this is caused by electric noise included in the displacement response measured by the contact displacement gauges.It was found that, as with the displacement response, the rotation angle response is largely classified into the response due to forced displacement, which consists of frequencies below 1.0 Hz, and the free vibration response. This result was achieved as a result of comparisons between the bridge displacement response spectrum and the bridge rotation angle response spectrum.Based on the bridge rotation angle response characteristics, which are largely classified into the rotation angle responses due to forced displacement and free vibration displacement responses, a method for determining the bridge rotation angle response from the measured angular velocity was proposed.Vehicle entry and exit times and the vehicle travelling lane could be estimated using acceleration responses measured by eight MEMS IMUs. This system worked by comparing the maximum acceleration response values measured at the longitudinal edges of each main girder.The determined rotation angle responses using IMU and the proposed method were found to be in good agreement with the reference rotation angle responses calculated by the two contact displacement gauges. This outcome was obtained by examining the results of field measurements taken from an in-service bridge using nine IMUs and two contact displacement gauges.In the present study, by conducting field measurements of an actual in-service bridge, the characteristics of the bridge rotation angle response were analyzed first. Next, based on the bridge rotation angle response characteristics, a method for determining the bridge rotation angle response from the measured angular velocity was proposed. Then, in order to detect the precise vehicle entry and exit times and assess the vehicle travel lane, a vehicle detection system using eight MEMS IMUs was constructed. Finally, by determining the bridge rotation angle response from the measured angular velocity using the proposed method, the effectiveness of the method was verified. The conclusions of the present study are as follows:In this study, by using vehicle passage time as the limits of the numerical integration, the rotation angle response was determined from the measured angular velocity. Further studies are required to verify the effectiveness of the proposed method for multiple vehicle travelling conditions. Furthermore, this study focuses solely on the bridge-axial-orthogonal direction rotation angle response. Further studies will be required to verify the effectiveness of the proposed method for the bridge-axial direction rotation angle response and the bridge rotation angle response at multiple points.


Why is the alligator heart so similar to the hearts of birds and mammals? Related research article Jensen B, Boukens BJ, Crossley DA, Conner J, Mohan RA, van Duijvenboden K, Postma AV, Gloschat CR, Elsey RM, Sedmera D, Efimov IR, Christoffels VM. 2018. Specialized impulse conduction pathway in the alligator heart. eLife7:e32120. doi: 10.7554/eLife.32120The human heart consists of four chambers – two atria and two ventricles – that expand and contract in order to drive blood containing oxygen and nutrients throughout the body. The atria, which have relatively thin walls, fill first, before squeezing the blood into the much stronger ventricles, which then contract to send blood coursing through our arteries. Most reptiles have two atria and one ventricle. The only exceptions are the 23 living species of crocodilians  who, like birds and mammals, have four-chambered hearts with two atria and two ventricles .In vertebrates, each heartbeat is initiated when a pacemaker region in one of the atria generates an electrical signal. The structure and exact location of the pacemaker region differ amongst species , but it The electrical signal from the pacemaker region spreads rapidly across the cardiac muscle cells of the atria via structures called gap junctions, and this ensures that the entire wall of each atrium contracts almost simultaneously. Neurons called Purkinje fibers are also involved in this process in birds but, in general, the mechanisms responsible for the contraction of the atria are similar in most vertebrates. However, the way in which the electrical signal travels from the atrium to the ventricle differs amongst vertebrates, and the evolution of this pathway has been the focus of considerable attention for many decades , 2013. NBack in the 17th century, William Harvey had already noticed that the atria contract before the ventricles in a number of different animals. This meant that the electrical signal generated in the pacemaker region must somehow be slowed down at the 'border' between the atria and the ventricles. In both mammals and birds, a layer of fibrous fatty tissue – which does not conduct electricity – insulates the ventricles from the atria. The only way that the electrical signal can pass from the atria to the ventricles is via a small structure called the atrioventricular node, which is positioned immediately above the septum that separates the left and right ventricles. When the electrical signal reaches this node, it activates two bundles of neurons (containing His fibers and Purkinje fibers) that swiftly relay the impulse and cause the ventricles to contract simultaneously.However, in extant reptiles, the common ancestor of both birds and mammals, there does not seem to be an insulating layer or an anatomically defined node . InsteadWhile reptiles rely on their environment to maintain their temperature , mammals produce their own heat (so they are endothermic). The high levels of metabolism needed to produce enough warmth means that the resting and maximal metabolic rates of mammals and birds are about 10 times higher than those of ectothermic animals . The carJensen et al. – who are based in Amsterdam and labs in the United States and the Czech Republic – combine electrophysiology and gene expression techniques to identify how electric impulses spread across the crocodilian heart, and to characterize the molecular phenotype of the various chambers. The experiments provided unequivocal evidence of an atrioventricular node in crocodilians. Among extant reptiles, crocodilians are the closest living sister group to birds. However, despite their four-chambered hearts and an atrioventricular node, all living crocodilians are clearly ectothermic and have low heart rates like other reptiles .With their ability to walk with their body off the ground, their peculiar respiratory muscles, their avian-like lungs and various other traits, crocodilians may have once been endothermic . AccordiThe fact that crocodilians have an atrioventricular node also sheds light on the evolution of the vertebrate heart. For example, the mere presence of a node and a division between the ventricles may be enough to prevent the electrical signal from 're-entering' the atria (which would disrupt the operation of the heart). These findings may also suggest that a nodal structure allows better fine-tuning of the heart rate by the autonomic nervous system.The next step is to characterize the electrophysiological properties of the cells in the atrioventricular node of crocodilians. Electrocardiogram recordings would also help to understand the exact timing of cardiac events, while flow and pressure measurements would capture the dynamics of the blood flow. There may still be delightful discoveries awaiting us inside the four chambers of the crocodilian heart.