With the development of the mobile phone, we can acquire high-resolution images of the skin to observe its detailed features using a mobile camera. We acquire stereo images using a mobile camera to enable a three-dimensional (3D) analysis of the skin surface. However, geometric changes in the observed skin structure caused by the lens distortion of the mobile phone result in a low accuracy of the 3D information extracted through stereo matching. Therefore, our study proposes a Distortion Correction Matrix (DCM) to correct the fine distortion of close-up mobile images, pixel by pixel. We verified the correction performance by analyzing the results of correspondence point matching in the stereo image corrected using the DCM. We also confirmed the correction results of the image taken at the five different working distances and derived a linear regression model for the relationship between the angle of the image and the distortion ratio. The proposed DCM considers the distortion degree, which appears to be different in the left and right regions of the image. Finally, we performed a fine distortion correction, which is difficult to check with the naked eye. The results of this study can enable the accurate and precise 3D analysis of the skin surface using corrected mobile images. The skin surface is composed of a complex network structure made up of wrinkles. We can analyze the skin conditions by observing the morphological  and topology changes  in the structure of the skin surface. Three-dimensional 3D) analyses of skin conditions, such as roughness and texture, are required to accurately diagnose skin aging and skin diseases ,2,3,4,5.D analyseIn addition to being cost-effective and highly usable and having excellent hardware, mobile phones are capable of obtaining high-resolution images anywhere and at any time, thus being useful in various fields of study, particularly biometrics ,10. ThroMost current mobile phones are being manufactured with wide-angle cameras that can capture wide range images in addition to high-quality and high-resolution images. The use of wide-angle cameras in mobiles has led to strong distortions in mobile images . Lens diMany studies have attempted to correct non-linear geometric image structures caused by barrel distortion in the images from wide-angle cameras. One study obtained camera parameters by the calibration of the camera to calculate the lens distortion, which was then used to correct the distortion in the image . Other sIn this study, we acquire mobile images affected by pincushion distortion of the proximity area, generate a distortion correction matrix (DCM) using the division model, and propose a correction method by pixel for the close-up mobile image. We then evaluate the performance of the proposed correction method by conducting a matching analysis of corrected mobile stereo images. The fine geometric distortions appearing on the mobile image of the skin surface are then corrected. The study’s contribution is as follows.We propose the distortion correction method for mobile images as a pre-processing step for the 3D evaluation and analysis of stereo skin surface images captured using a mobile phone.Unlike prior studies that correct the mobile image of the human face, environment, etc., we correct the distortion of skin surface images with a complex structure to demonstrate the usability of mobile cameras in the field of biometric signals, which requires precision at the µm level.Most studies deal with the correction for barrel distortion, which appears strongly over a wide area, such as in fish-eye lens images. Contrastingly, our study proposes a correcting method for fine pincushion distortion in close-up images.We employ the division model using a single parameter rather than the camera calibration approach, which is a complicated and time-consuming method, to correct the mobile image in real time. Additionally, we generate a pixel-by-pixel distortion correction matrix containing different degrees of distortion in the left and right regions of the image to increase the precision of distortion correction.We use a Galaxy A3 . Its rear camera has the specifications of 1.3 MP, aperture F1.9. It has a low-cost, high-performance camera. We zoomed in on the camera to acquire a narrow range, like the region on the back of the hand, without additional equipment.In this study, we acquired three types of mobile image with a non-convergence stereo system using the mobile phone: the vertical line pattern image in plane, the standard scalar bar image including depth, and a skin surface image.We took a zoomed-in 2.0 times image with an aspect ratio of 4:3 from a working distance of 60 mm to 80 mm between the camera and the ground. We acquired a total of 5 sets of stereo images, where the baseline—the distance between the stereo images—is 10 mm, by adjusting the working distance by 5 mm intervals. Stereo images consist of the reference image and the shift image moved by 10 mm from the baseline. For the analysis of the skin surface on the back of the hand, it is effective to obtain the center of the image, which has a small effect of the skin curvature. Thus, the baseline value of 10 mm is an adequate distance that includes a flat skin surface area.® .We adjust the geometric changes of the mobile image. Thus, for pre-processing we would not consider image information such as color other than pixel position. Therefore, our study uses mobile images converted to gray-scale using MATLABA pincushion distortion can be of two types—positive and negative . Most stIn this study, we perform distortion correction using the division model proposed by Fitzgibbon , consideWe generate the distortion correction matrix (DCM) by calculating the relationship between the ideal and distorted coordinates using the division model and distortion parameter. We then perform forward mapping from the distorted point  using the distorted and ideal points. The value of R is obtained by calculating the degree of distortion of each pixel using The traditional division model equation consists of 4th order terms, but backward mapping can ignore error terms higher than the 4th term and represent an approximate value through the 2nd order term of rd ,22,23. AFinally, the distorted point is relocated to a corrected point .We evaluated the performance of the correction method by applying DCM to each acquired mobile image. First, for the vertical line pattern image in plane, we compared the before-and-after distortion correction and performed a linear regression analysis on the distortion differences in the image according to the angle of view. Next, after correcting the standard scalar bar image including depth, we confirmed the disparity, 3D information, which varies depending on the different heights, through stereo matching.Without camera lens distortion in acquired stereo images with a vertical line pattern in the plane, the corresponding lines in the left and right images always match, regardless of changes in the stereo baseline. However, when the image is affected by distortion, the vertical lines are geometrically deformed, and a gap appears between the corresponding lines of the left and right images.In this study, we acquired a pair of stereo images which are captured from different angles of view at five different working distances. A pair of stereo images consist of the reference image and the shift image that is moved 10 mm laterally. Then, we compared the stereo images before and after the distortion correction by matching 20 corresponding lines in the shift and reference images. We matched the position of each corresponding line based on the first line at the matching line, which is the center line of the image, where the curvature of lines is the largest. If the image has no distortion, the position of the matching lines in the shift and reference images is the same—i.e., the distance = istances , the disAs we can see in From the results of To analyze the relationship between the distortion ratio and the mobile image angle, we performed a linear regression analysis using the maximum value of the distance between the corresponding lines in the stereo images acquired at each working distance. We then obtained a simple linear regression model with the equation: working distance o 98.28% . AdditioNext, we corrected the distorted stereo images for the vertical line pattern using the DCM and matched corresponding lines. In the case of images acquired using the same camera lens, we corrected the images for distortion by applying an identical DCM, since the images from different angles resulted in identical image distortion. In the graph provided in For close-up images captured using a mobile phone, a fine distortion was observed. Therefore, after distortion correction fine error values can have a significant impact on the correction rate. The maximum error of the correction was approximately 0.1 mm, which is very fine. This error has little visual impact on the image captured by the mobile phone.For the mobile stereo images of the standard scalar bar with depth , we compWe calculated the distance = The graph confirms that as a result of distortion correction, the stereo matching results—i.e., disparity—match the stereo vision principle b. FurtheAs a result, we corrected the distortion of the mobile skin image using the DCM, as verified through the previous results, and performed the interpolation of the hole region. Since it is difficult to check fine distortion correction with the naked eye, we confirmed the degree of correction using the subtraction image before and after the correction. The subtraction image is what subtracts the corrected image from the distorted image. We also corrected the skin images of different angles. A comparison shows that the interpolated image (c) is similar to the original image (a). As seen in the subtraction image (d), the distortion occurs on both sides of the image, and this corrected difference influences the accuracy of the three-dimensional analysis of the skin image. In general, people are interested in maintaining their young and healthy skin. However, it is difficult to analyze and manage their skin condition in practice. The reliability and precision of the data acquired by means of mobile cameras were low and hence were less useful for application to the field of skin analysis. With recent advances in mobile phones, we can acquire skin-related data to analyze skin conditions. However, the geometric changes in the skin’s surface structure are affected by lens distortion in the mobile images. This problem reduces the accuracy of three-dimensional information analysis, such as the depth of wrinkles and the roughness of the skin surface. However, existing distortion correction studies have dealt with barrel distortion that appears strongly across very wide areas of images containing real-world information. Hence, the previously proposed methods were not suitable for the fine distortion correction of mobile images. Additionally, existing distortion correction algorithms calculate the distortion coefficients based on the most severe distortion region under the assumption that lens distortion occurs symmetrically in the left and right regions. However, while checking the mobile images used in this study, we confirmed that the degree of distortion on the left and right sides of the image differed depending on the center of the image.Therefore, in this study we propose an adaptive distortion correction method using a division model to correct the fine distortion appearing in the pixels of the mobile image by pixels. Moreover, we generated a DCM capable of pixel-by-pixel distortion correction and performed the distortion correction of mobile images. We used a single pattern image, stereo pattern images without depth, stereo pattern images with depth information, and finally images of skin surface to verify the performance of the DCM. By comparing the results of stereo images before and after distortion correction, we obtained a corrected image that was similar to the undistorted ideal image.Additionally, we analyzed the relationship between the mobile image angle and distortion ratio by acquiring mobile images at five different working distances. It was generally observed that the wider the image angle, the larger the distortion of the outer region of the image. This same phenomenon occurred in the acquired mobile image, and we presented a simple regression model that showed the relation of each image angle and distortion ratio by performing a linear regression analysis. Next, we performed stereo matching for stereo images of the standard scalar bar containing depth information. Through the result of the corrected stereo image matching, we confirmed the feasibility of the three-dimensional skin analysis by perceiving the fine depth of the object in the mobile image. In the corrected result using the DCM, we acquired the corrected image containing the hole region in the process of relocating the pixels. The hole region degrades the image quality. Thus, we used nearest neighborhood interpolation, which can express the continuous skin surface structure well, to produce a high-quality corrected skin image. This process improves the image quickly and is easy to use for real-time analysis. Actually, the image correction performance time using the generated DCM was about two seconds and could be processed quickly.In this study, we propose a method to correct the fine distortion that appears in close-up mobile images, rarely covered in existing studies. The acquired close-up mobile image displays fine pincushion distortion, making it difficult to extract features from biometric data at the µm level. The results of our study can be used to increase the precision of the analysis of the acquired mobile biometric data and expand the utility range of mobile phones in the field of health. In future studies, a new system can be developed for the evaluation and management of skin aging or various skin diseases through the 3D analysis of the skin using corrected mobile skin images.Our study proposes a pincushion distortion correction method that adjusts geometric changes in the skin surface structure as a pre-processing step to skin analysis for extraction 3D information of skin surface, such as surface roughness and depth of wrinkles, using stereo images of the skin surface captured using a mobile phone. We tested the lens distortion correction by acquiring two types of images; one was a vertical line pattern image in-plane, and the other was a vertical line pattern image containing depth information. We analyzed each stereo image, compared the before-and-after correction distortion, and verified the performance of our proposed method. Additionally, we analyzed the relationship between the mobile image angle and the distortion ratio. By matching the stereo images, including the depth information, we confirmed that it is possible to perceive fine depth information at the µm level in the corrected mobile image. Prior studies on distortion correction dealt with significant distortions visible to the naked eye. The correction results showed considerable changes before and after correction. However, our study dealt with fine distortions which are difficult to identify with the naked eye. Additionally, prior studies on skin surface analysis performed research by selecting the region of the skin with a small distortion effect on the skin image, thus having a limitation in the observation region. To overcome this limitation, the proposed fine distortion correction method enables us to use the entire region of the mobile image and observe regions about eight times wider than the existing skin observation region . The pro


Off-design operation conditions might not only seriously affect the internal flow status of a centrifugal pump, but also result in additional energy loss and potential mechanical damage. Therefore, early-stage monitoring and predication on off-design operation conditions for centrifugal pumps have become essential. Single-value indicators have favorable factors such as a smaller amount of calculation and easier identification. As a result, industries prefer the more straightforward approach: obtaining single-value indicators directly from the signals which could be easier compared with accepted standards. The possibility of applying the single-value indicators of vibration into operation condition monitoring for a centrifugal pump is studied theoretically and experimentally, which shows that the statistical features of vibration might be suitable for hydraulic instability detection for a centrifugal pump. A centrifugal pump is supposed to keep operating under its design operation point, or best efficiency point (BEP), which is the specific combination of head, flowrate and rotational speed. Under the BEP, fluid motion of a centrifugal pump and physical contours of hydraulic passages would basically coincide in the main. Nevertheless, quite a number of centrifugal pumps usually deviate from their optimal operation points in industries . SmallerTraditional operation monitoring technologies for centrifugal pumps often require the use of many invasive sensors. Along with them are the problems of hard installation, complex cabling, high equipment costs and difficult maintenance. Given consideration of these limitations, sensors are seldom employed in reality. Therefore, a system capable of accurate detection of operation conditions and increasing the efficiency of fault diagnosis  for centPlenty of investigation and discussion have been undertaken on the development of operation monitoring and fault diagnosis methods for centrifugal pumps based on the vibration signal. Most of the proposed monitoring methods realize their function by either extracting the fault feature frequency components , or calcHowever, industries are more inclined to the simple approach, which should have the advantages of less calculation and easier identification. Single-value indicators could be directly obtained from signal, their calculation procedures are relatively simple, their change rules could reflect more clearly the operation tendency of equipment, and their values could be easily compared against accepted standards. From this point of view, single-value measurements could be more suitable for deployment in industries than formal methods.RMS) and the peak value. They are often used as standards for vibration levels definition. As these indicators could show the energy intensity characteristics, when some adverse operating conditions occur, the energy intensity fluctuation might be indicated by them. The related application could be found in cavitation detection. However, sometimes the energy fluctuation features caused by the unsteady flow regime such as recirculation, secondary flow and even cavitation onset are too weak to observe. The existing single-value indicators would be insensitive to detect these adverse flow conditions under such circumstance. In this perspective, single-value indicators still lack the ability of extracting useful information.The most widely used single-value indicators of the vibration are the root mean square  and their integer frequency.Considering that the turbulent perturbation intensity would reach the weakest level around the pump’s best performance point, the discrete frequency peaks incline to dominate the vibration spectrum, especially the low-order harmonics. If BEP during the operation of a centrifugal pump is not considered, the broadband noise results from turbulent disturbance might even exceed the tonal noise ,27,28.Dynamic characteristics of centrifugal pump could be reflected through statistical analysis. Therefore, relevant indicators could be established by means of statistical parameters.The probability density of the data samples is defined as follows:x(t) could be expressed as shown below:The expectation of a random function of time The rth-order moment about the mean RMS value happens to be the square root of the second-order moment, and the variance is the second-order central moment. The RMS reflects the intensity of the variable, and the variance gives the deviation from the mean.From Equation (3), it could be observed that the expectation of the random variable happens to be the first-order moment, the If the data is obtained in discrete form, then Equation (3) could be rewritten as:In this equation, N is the number of data samples, r represents the order of the moment.RMS and standard deviation of variables in both continuous and discrete form:The equations shown as follows demonstrate the calculation of Standard deviation:Cf) and Kurtosis (K) could be very useful. Crest factor could be expressed as shown in Equation (7), where Vp represents the peak value of signal and RMSV represents its RMS value. Crest factor reflects the sharpness of the peaks occur in the signal, which could be employed to find whether the signal contains repeated impulses.Complicated signal that might consist of a few sinusoids whose relative amplitudes and phases are unpredictable often need further processing. Under this circumstance, additional descriptions of signal like Crest factor (Kurtosis is defined in Equation (8); −3 is included in the equation for normalization so that the kurtosis of Gaussian distribution is 0, which means the natural kurtosis of a Gaussian distribution before normalization is 3. Kurtosis would excess 0 if the peaks distributed in the spectrum are sharper than the Gaussian distribution. A higher value of kurtosis often indicates larger deviation. A negative value of kurtosis usually means the distribution presents much flatter than the normal. On the contrary, a positive value of kurtosis means the distribution is sharper than the normal.Spectral entropy is described as a generalized form of information entropy by Pan et al. , which iSE is normalized to SEn according to Equation (10), where N represents the number of the data samples.In order to avoid the effects of data length, spectral entropy needs to be normalized for comparison . More coSEn has the value range from 0 to 1. The value of SEn is larger when amplitude distribution is relatively flat. This is especially the case when each frequency component of the signal has the basically same amplitude, and the value of SEn would reach the highest under that extreme condition. Conversely, SEn would become lower when a few frequency components have the relatively large amplitude, and would have its minimum value of 0 when there only exists one non-zero amplitude frequency peak in the spectrum. From the nature of SEn described above, it could be concluded that SEn could be highly suitable for analyzing the spectral structure of centrifugal pump’s vibration signal.Therefore, it could be found that the normalized spectral entropy To test theoretical predictions against experimental results, an experimental rig was built for operation simulation of a centrifugal pump. As shown in The left tank shown in the figures, which was connected to the vacuum pump, was the cavitation tank used for cavitation simulation. Meanwhile, the right tank was a stable flow tank, which was employed to stabilize the flow. The test centrifugal pump was deployed in a horizontal plane, its outlet flange was 2 m below the water surface in the cavitation tank. Two stainless steel pipes were matched and connected to the suction and discharge flanges, which have the diameters of 60 and 50 mm and used to carry the upstream and downstream of the test pump respectively. Water temperature was maintained around 1 °C for at least one hour before each experiment. The design parameters of the centrifugal pump data are introduced in H/dQ is negative, which means the pump’s head shows a downward trend as the flowrate becomes larger. Meanwhile, a positive value of dH/dQ means the centrifugal pump operates under a rather unstable condition. This unstable operation region indicates the performance of strong flow hydraulics result from the pump’s unreasonable design parameters. A centrifugal pump operating in such a region would strengthen the vibration intensity. As shown in H/dQ at the range from 3 m3/h to 20 m3/h is positive, which indicates hydraulic instability of the pump.The operation condition of centrifugal pump could be considered as relatively stable when dNPSH) is the difference between the suction head and the liquids vapor head; the net positive suction head available from the application to the suction side of pump is often named NPSHa. NPSHr, called as the Net Suction Head as required by the pump in order to prevent cavitation for safe and reliable operation of pump. As suggested in ISO 3555, the predicted cavitation characteristics between the NPSH available (NPSHa) and the NPSH required (NPSHr) for the centrifugal pump could be achieved through throttling the valve in discharge line. Meanwhile, the rotational speed of the pump should be maintained at 2900 rpm, and the valve in the suction line should be fully opened.Net positive suction head  for rotational speed measurement, LDG-SIN-CN65-Z2 electromagnetic flowmeter  which was installed in the discharge line, WIKA S-10 pressure sensors  for pump head calculation which were installed in the suction line and discharge line, respectively, and PCB MA352A60 vibration sensor  for vibration signals measurement. Their main parameters are shown in Moreover, a PXI-4472 dynamic signal acquisition card  was employed for data calculation and information aggregation. Its design parameters are introduced in Vibration of the centrifugal pump was evaluated under the following two circumstances:1. A centrifugal pump operating under different flowrates but a fixed rotational speed of 2900 rpm. The pump’s flowrate was changed by progressively adjusting the opening degree of the throttling valve in the discharge line.NPSHA but at a fixed flowrate and a fixed rotational speed. The inlet pressure was adjusted by the vacuum pump.2. A centrifugal pump operating under different In order to obtain more reliable experimental results, experiments were repeated three times, sufficient time interval existed between each test. The obtained data was processed and analyzed by Matlab.The sample time was selected at 500 rotation cycles of the test pump. Samples of the vibration signal obtained under three typical flowrates are shown in Therefore, the data sets are explored in the frequency domain. In terms of experimental analysis, accelerometers mounted on the centrifugal pump three direction as shown in RMS trend of the time domain vibration signal with the flowrate from 0 to 1.6 Qd.RMS could reveal the change law of signal energy intensity, these two indicators share a similar tendency with the pump’s flowrate as shown in the figures above. Both indicators would rapidly increase when pump’s flowrate exceeds 60 m3/h, which may also be the inception cavitation point according to Both peak and RMS was much more flatter than that of the peak, especially during the flowrate range from 10 m3/h to 18 m3/h, which is the hump area of the pump performance curve. In this area, a relatively strong flow instability phenomenon exist in the pump, peak curve has a relatively steep rise trend, yet this trend is not observed in the RMS curve. Meanwhile, the peak value would become extreme low at 53 m3/h, which indicates the highest efficiency operation point of the pump, and such tendency is also not shown in RMS curve. Furthermore, the start point of the dramatic rise of the peak occurs earlier than RMS. From these analyses, it might be concluded that the peak value is more sensitive in flow instability detection than RMS. By contrast, RMS performs better in predicting cavitation, it has strong anti-jamming capability contrast to low sensitivity.However, it was found that the change tendency of the RMS value. As the high detection sensitivity of peak and the strong anti-jamming capability of RMS, the crest factor might be highly suitable for flow instability detection. As shown in Crest factor is described as the peak factor divided by the corresponding 3/h, the other was between 55–62 m3/h. The first increase indicates the hump area of the pump performance curve; in this area, the internal flow status of the centrifugal pump is rather unstable. The second increase locates at the onset of the cavitation, which also means stronger intensity of flow instability.PDF) of the vibration signal is approximately Gaussian with the exact shape depends on its details. As the flowrate increases, the vibration would change and, obviously, the shape of the PDF curve would also change correspondingly.As can be seen from PDF curve could be evaluated by standard deviation and variances, and as a result, both mathematical parameters could be used as the single value indicators.The shape of the RMS curve, thus add nothing qualitatively new to the discussion.The change tendency of the standard deviation and the variances for the time domain vibration signal with the pump’s flowrate are shown in PDF curve for the time domain vibration signal under different flowrates, which clearly shows that there is a broad maximum value of the PDF exists at the flowrate range from 50 m3/h to 55 m3/h. When the pump’s flowrate exceeds this range, a definite decline trend occurs in the curve. Such a change law is confirmed as a universal phenomenon, and this turning point appears in the curve could detect the likely beginning of cavitation. However, the flow instability which should be reflected in the range of 10–17 m3/h is not shown, obviously.PDF is that the distribution of the vibration signal in terms of frequency would change with the pump’s operation condition. As the flowrate keeps increasing and the pump approaches cavitation, it could be seen that variations occur in the spectral structure of the vibration signal, where under the relatively low flowrates the vibration spectrum includes a few isolated low-level structural resonances. When centrifugal pump operates under the established cavitation detection point, a number of high peaks appear in the spectrum, where the PDF for the time domain vibration signal has its sharpest peak. When the centrifugal pump operates under the highest flowrate during the test, which means a severe cavitation condition, a large number of peaks appear across the spectrum, where the PDF for vibration is inclined to flatten out. Meanwhile, it should be noted that some other flow instability phenomena like backflow would cause stronger broadband noise in some areas, which would result in the change of the PDF pattern for the vibration signal. However, the degree of deformation of the PDF curve is weaker than the cavitation caused. Therefore, the PDF curve could be employed for cavitation diagnosis for centrifugal pump.The reason for such a pattern of the Previous investigations suggest that the vibration spectral of a centrifugal pump contains a broadband noise and a few discrete frequency peaks, whose intensity would be strongly affected by the flow status of the centrifugal pump. In the analysis of the frequency domain vibration signal, crest factor, kurtosis and entropy might possess the ability to evaluate the intensity change for the two components in the frequency domain.As shown in BPF and rotational frequency stand out above the spectrum, and information uncertainty is lowest. As the pump’s flowrate deviates from its design, spectral entropy would increase correspondingly due to the stronger noise. The vibration signal would increasingly resemble broadband noise and spectral entropy would finally have its highest value. The curve obtained confirms this general analysis, and further indicates that the spectral entropy of the frequency domain vibration signal could detect cavitation onset and unstable flow area of centrifugal pump.As mentioned in a previous article, the use of a single sensor is very important for the promotion of monitoring technology, Therefore, in the research and analysis of the establishment of the previous indicator, only the X-based direction was considered because the much more easy installation and the transmission of the signal.As shown in This paper explores the characteristics for the vibration signal of a centrifugal pump through statistical analysis, and some conclusions that are drawn are as follows:1. Dynamic characteristics of the centrifugal pump’s performance characteristics would change under unstable flow status, which would be reflected in the vibration signal, and as a result statistical analysis for vibration signal could be employed as an effective approach to detecting off-design operation conditions for a centrifugal pump.RMS value could indicate the unstable flow status of a centrifugal pump. The peak indicator demonstrates a higher capability in detection. Most extreme points in the crest and kurtosis curve have definite physical significance, which might be the onset and the end of the flow instability area, the onset of cavitation or the highest efficiency operation point. Meanwhile, the shape or pattern of the probability density function (PDF) for the pump’s vibration signal performs well in the detection of the early stage cavitation.2. From the statistical analysis for the time domain features of vibration signals, it is concluded that the rapid increase of the peak and 3. From the statistical analysis for the frequency domain features of the vibration signal, it is found that the kurtosis, crest factor, and entropy are very suitable for detecting the intensity change of the broadband noise and discrete frequency components in the frequency domain which are caused by the variation of the pump’s flow status.


A lack of consensus on the optimal outcome measures to assess the efficacy and effectiveness of interventions for the treatment of opioid use disorder (OUD) has hampered the pooling of research data for evidence synthesis and clinical guidelines. A core outcome set (COS) is a minimum set of outcome measures that are recommended for all studies of a particular condition. The National Drug Abuse Treatment Clinical Trials Network (CTN) Core Outcome Set for OUD (COS-OUD) is a development study to identify core constructs, meaningful outcomes, and their optimal measurement for all efficacy and effectiveness studies of OUD treatment and service delivery.Overseen by an expert workgroup, a modified, stepwise, e-Delphi methodology will be used to gain consensus among a panel of clinical practitioners and researchers involved in the treatment of OUD, who are members of the CTN. Sequential rounds of anonymous, online questionnaires will be used to identify, rate the importance of, and refine a core outcome set. A consensus threshold will be achieved if at least 70% of the panel rate the measure as critical for inclusion in the COS-OUD. Where consensus is not reached or there are suggestions for new measures, these will be brought forward to a further round of review prior to a consensus meeting. Products from this study will be communicated via peer-reviewed scientific journals and conferences.This initiative will develop a COS for OUD intervention trials, treatment studies, and service delivery and will support the pooling of research and clinical practice data and efforts to develop measurement-based care within the OUD treatment cascade.http://www.comet-initiative.org/Studies/Details/1579 Opioid use disorder (OUD) is a debilitating biobehavioral condition  with majTo address the opioid epidemic, ongoing international research efforts aim to create innovations with front-line opioid agonist, partial agonist/antagonist, and antagonist therapies for OUD. These include new developments of extended-release formulations of some medicines, evaluations of adjunctive psychosocial interventions, and applied efforts to expand patient access in priority healthcare settings .In the past four decades, evidence for the efficacy of the medication and psychosocial treatments for OUD has accumulated from randomized controlled trials and observational studies using one or more of the following direct or indirect assessments of benefit: patient-reported outcomes (PRO), clinician-reported (ClinRO), observer-reported (ObsRO), or performance outcomes (PerfO). Measures have included self-reported drug use, problem checklists, personal functioning, biomarkers, and indicators of treatment retention and completion. However, no consensus exists on the optimal ways of measuring clinical benefit and other patient outcomes. Accordingly, clinical trials use a wide array of measures and operational definitions of key outcomes, limiting the ability to make direct comparisons across studies, to aggregate data across studies, and to allow for benchmarking.Established in 1999, the National Drug Abuse Treatment Clinical Trials Network (CTN) is a network of university- and health system-based research groups and healthcare service providers that collaborate on research on OUD and other substance use disorders, and coordinates data and discovery across a multitude of trials. De-identified research data from these completed trials are distributed to the scientific community and the public at large through an electronic repository, the National Institute on Drug Abuse Data Share website , 7. One In 2000, in a more targeted effort of harmonization, the CTN recommended a common assessment battery for research . This waIn the era of measurement- and value-based care, key stakeholders have an increasing expectation that health care service providers incorporate standardized and clinically actionable measures in their EHRs to facilitate data pooling in healthcare systems. Several ongoing initiatives have this goal in mind, including the Patient-Reported Outcomes Measurement Information System (PROMIS), which includes over 300 measures organized in profile domains; the consensus measures for phenotypes and exposures initiative which includes approximately 800 measurement protocols in toolkits; and the Fast Healthcare Interoperability Resource (FHIR) standard for healthcare data exchange produced by the Health Level 7 international community. In a series of workshops and publications, the Food and Drug Administration has promoted clinical outcome assessments and called for greater use of patient-reported outcomes in research studies . NIDA anhttp://www.comet-initiative.org).In spite of these sustained and multiple ongoing efforts, implementing common measures for OUD treatment studies has been elusive. A solution to the problem of outcome measurement consensus that has gained success in many areas of medicine is to identify the absolute minimum set of measures—the so-called core outcome set (COS)—that are to be reported in all efficacy research studies on a target condition . A COS dMost COS development studies use a Delphi methodology. First developed by the RAND Corporation in the 1950s and 1960s, Delphi is a family of participatory and stepwise procedures for eliciting and refining the opinions of a panel of people who are usually experts on a particular topic. The panel consents to have their views and value judgments gathered and combined to establish a consensus on a topic of complexity where there is uncertainty, and where there are concerns that current practice or policy is an impediment to progress . Delphi Commonly, COS development studies proceed through four steps. In the first step, a concept mapping of the topic is done by a study workgroup to set the scope. A second step involves a systematic review of existing outcome measures to gather potentially relevant material. The third phase is a sequence of questionnaires  sent to the panel, beginning with an open-ended format to canvass a wide range of outcome options and progressing through a closed format with instructions to estimate the relevance and importance of potential measures. Removal and modifications of measures in successive rounds are done according to a predetermined criterion alongside opinion feedback. Further rounds of questionnaires are issued until a consensus is judged to have been reached by the achievement of the pre-determined agreement threshold. Finally, a consensus meeting is held to discuss the study products and resolve outstanding issues, prior to the communication of results. On completion of these four steps, results are publicized.The need for consensus on outcome measurement for OUD research has intensified in the context of the current opioid crisis and the large number of planned OUD-related research studies at the CTN supported by the National Institutes of Health’s Helping to End Addiction Long-term (HEAL) initiative . In MarcWhat are the core constructs or domains to be included in the COS?What are the differences in priority and perspective between different professions?What outcome items are rated most important?How should outcome items be optimally defined and captured?With only one prior OUD study registered on the COMET database, this article describes a different e-Delphi protocol for COS-OUD which has different goals than COMET protocol 1128 . The goaCOMET protocol 1128 which was funded by the Canadian Institutes of Health Research in March 2018 has produced systematic reviews , 26, buthttps://www.drugabuse.gov/about-nida/organization/cctn/clinical-trials-network-ctn].The COS-OUD will comprehensively cover adult OUD studies and include all interventions for the treatment of OUD including psychosocial and medical treatments of OUD covered by the mandate of the CTN [URL: http://www.comet-initiative.org/Studies/Details/1579]. The study will use an e-Delphi consensus method with data capture via web-based questionnaires, with final panel results reported anonymously.In the context of principles of treatment, patient involvement and low administrative burden, and informed by the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN ) and theThe size of a panel for the study will be determined following the principle of inclusivity. Membership of the panel will be open to employed professionals in all 18 CTN nodes who are either clinical addiction practitioners  or researchers and data administrators. It is anticipated that a panel size of about 40 members will be recruited. In many COS development studies, panel members are not permitted to contribute to subsequent rounds of data collection if they have not contributed to a previous round, but due to the circumstances currently taking place with the COVID-19 pandemic, we plan to relax this approach and allow for a more open process. To minimize attrition, we will clearly set out the importance of full participation and emphasize to members attrition can undermine the degree of consensus in the results. However, we will allow panel members to miss a round and will ask members who leave the study to volunteer a reason as part of efforts to assess possible validity threats to the consensus process .http://ctndisseminationlibrary.org/] which has all of the documentary data for the CTN and the parameters will be on treatment studies with OUD outcomes. In addition, members of the expert workgroup will undertake a scoping review to gather potential items for the COS-OUD that will be fed directly into the Delphi process. During this phase, panel members may also nominate specific items during the Delphi protocol.As a prelude to the Delphi process, the lead team will undertake a review of past and current CTN OUD trials to assess the current state of measures. This review will be undertaken using the CTN Dissemination Library . Should we have three rounds result in a lack of consensus or lack of movement toward it, then we would declare the process will be ended and the varied sides/camps will be reported.Panel members will see the results in the second round and with eventual consensus defined as 70% (or greater) or more of the respondents scoring an outcome from 7 to 9 and fewer than 15% scoring it 1–3. This approach has an established track record of use and is recommended by the Grading of Recommendations Assessment, Development and Evaluation (GRADE) Working Group for assessing the level of importance about research evidence [Panel members will be asked to provide recommendations regarding any additions and/or deletions to the list of proposed items, and for any other comments/suggestions. Each questionnaire should take no more than 20–40 min to complete, with the facility to complete it over several sessions and to allow participants to review their answers before the final submission of their responses. De-identified results comprising a narrative summary of findings, comments, and suggestions will be sent to each panel member after the first round. In a second round, ambiguous items or proposals collected from the first round will be compiled (with input from the workgroup as required) before being included in the new round.Due to COVID-19 challenges for all stakeholders, we plan to handle missing data with a relatively flexible approach. In the event that panel members fail to respond to a single survey, we plan to allow these members to join future rounds of the panel. Individual items that participants skip or opt to not respond to will be re-calculated by using a lower number of panel members thereby accounting for the lack of response.Prior to the final determination of the COS-OUD and communication of study results in a peer-reviewed academic journal, we plan to organize a webinar or face-to-face consensus meeting open to CTN nodes, service user representatives, and other key CTN stakeholders to discuss the results of the Delphi process. Depending on the state of travel related to COVID-19, the meeting may be in-person or virtual. Our plan is to have the workgroup leads (Karnik and Marsden) facilitate the meeting. The meeting will be open to all members of the CTN regardless of participation on the eDelphi panel.This protocol describes a study to develop a COS for OUD-related treatment efficacy and effectiveness trials and allied research. The literature on OUD includes many different outcome measures leading to a lack of clarity on which outcomes are of most importance to researchers and patient stakeholders in terms of understanding experience over the treatment cascade. This in turn has impeded evidence synthesis with missed opportunities to develop pooled research data or to compare results across studies to inform treatment policy and practice.A previous sustained effort to promote a common approach to the use of outcomes measures has favored a multi-dimensional outcome instrument approach . This stAt the point of manuscript submission, a proposal for the study had been approved, a workgroup had been formed, Institutional Review Board clearance had been received, and the study had been registered with the COMET website. The first phase of this Delphi process is now underway along with the recruitment of the panel.


Molecular overtones stretching modes that occupy the near-infrared (NIR) are weak compared to the fundamental vibrations. Here we report on the enhancement of absorption by molecular vibrations overtones via electromagnetic field enhancement of plasmonic nanoparallelepipeds comprising a square lattice. We explore numerically, using finite element method (FEM), gold metasurfaces on a transparent dielectric substrate covered by weakly absorbing analyte supporting N-H and C-H overtone absorption bands around 1.5  Near-infrared (NIR) spectroscopy is a powerful method of non-destructive material analysis based on the excitation of overtones and combination bands of molecular vibrations ,2,3,4. ALocalized surface plasmon resonance (LSPR) excited in metal nanoparticles has been known to provide substantial absorption enhancement by nearby molecules ,18,19. PTherefore, this study is devoted to planar metasurfaces consisting of nanoparallelepipeds (NPs) that can be fabricated easily and prepared specifically for overtone detection in near-infrared. The proposed structure utilizes a combination of localized plasmon resonance in isolated gold NP with Rayleigh anomalies of their periodic array. By incorporating the plasmonic metasurface into a weakly absorbing organic medium, a 22.5-fold enhancement of the first overtone of N-H stretching mode in the NIR has been demonstrated for the first time. Application of the designed gold metasurface may be beneficial in biomedicine , non-des6To obtain a realistic model we consider an infinite array of gold nanoantennas in the form of parallelepipeds arranged in a square lattice on a dielectric substrate and covered by a thin layer of analyte molecules. To be specific, as a probe molecule, we work with the well-known organic compound and derivative of Ammonia, N-Methylaniline (CNH(CH3)) ,40. ReceNH(CH3)) ,10,41. TL, while width w and height h were fixed and equal to 20 nm  were conducted in the COMSOL Multiphysics environment. The capabilities of the software allow us to calculate plasmonic properties of gold metasurface of studied geometry with high accuracy based on a straightforward solution of Maxwell’s equations under certain boundary conditions. Here, perfectly matching layers are used to absorb reflected and transmitted light from the sample when the linearly polarized wave  , α* may , Λ2 see a. Accordented in , we learx-axis) at normal incidence. L grows. At particular values of length L the transmission dip is defined by collective LSPR overlaps with and couples to the N-H and C-H overtone absorption bands in near-infrared. The transmission dip location depends linearly on the NPs length L, as can be seen in The dependence of the transmission dip location on the nanoparticle length has been investigated. We performed the simulations for the light polarized along the length of gold NPs  spectra is an important step in metasurface design for sensing purposes where the crucial feature is the detection of a small amount of analyte. In particular, the enhanced absorption of the analyte molecules in the near-field of the metasurface is still much smaller than the own absorption of the metasurface in the absence of molecules. In this regard, to reveal the contribution of the gold metasurface, the NPs absorption excluding the presence of molecules must be subtracted from the measured absorption with molecules. Furthermore, it is important to take into account that the metasurface absorption spectra shift when the permittivity of surrounding changes. Therefore, measurement of the metasurface transmission in air seems infeasible. Instead, the metasurface transmission in contact with a thin film of a transparent material should be considered. The refractive index (RI) of this material should be chosen close to the RI mean value of analyte in the actual spectral range and the film thickness should be the same as that of an analyte. Based on that, calculation of DT becomes a reliable way to reveal the presence of analyte and the spectral position of its absorption bands . Figure DT contrast’) is of paramount importance, since it is the variation magnitude that determines the metasurface sensitivity. Then, the enhancement factor (EF) can be expressed as t = 100 nm. For the sensing purposes, the difference between the DT maximum and minimum (hereinafter referred to as ’In this section, we discuss how the DT enhancement factor depends on the lattice periods. While exploring the metasurface configuration optimized for the enhancement of the particular overtone transition, the nanoantenna lengths as well as both periods of the square lattice were varied. To reach the maximum field enhancement, the nanoantenna length that maximizes the collective polarizability at the desired wavelength was found for a number of lattice periods combinations. At the final step, the enhancement factors were calculated as presented in L with the lattice period L reaches a minimum near the wavelength corresponding to the Rayleigh anomaly at about 1000 nm. Almost simultaneously, at The red curve in L was adjusted to keep the collective LSPR of the metasurface at the resonance with the overtone transition in NMA, as it is illustrated in Subsequently, we fixed In summary, we have numerically demonstrated the sensing capabilities of the rectangular lattice of gold nanoparallelepipeds on a transparent substrate while tuned on the specific transitions in the near-infrared. The shape of the metasurface unit-cells was adjusted to be readily manufacturable by electron beam lithography or focused ion beam milling. To optimize the metasurface for a particular overtone transition registration, we varied the periods of the lattice and the elements lengths simultaneously. We found that one of the optimized lattice periods is very close to that corresponding to the Rayleigh anomaly. As we are trying to anticipate the response of the actual applications, we do not place the analyte within the near-field enhancement factor maxima, but rather, as a 35 nm thick homogeneous film covering the whole metasurface. Because of that, relatively small proportion of the analyte experiences the largest field enhancement. Consequently, the structure optimized for the sensing of N-H overtone transition at 1494 nm has shown the differential transmission enhancement factor of 22.5. This rather large enhancement may be explained by the concerted action of the LSPR, the lattice resonance and the high surface density of gold nanoparticles on the substrate. Thus, we have demonstrated the way to optimize the gold metasurface for sensing the weak overtone transitions in the near-infrared. This approach may be extended for sensitive registration of other functional group overtones in the near-infrared by tailoring the lattice periods and the aspect ratios of a metasurface unit-cells.To conclude, we leverage the rapid spectral variation of the refractive index in the spectral range of the analyte anomalous dispersion. As the anomalous dispersion is associated with its overtone absorption band, by measuring the differential transmission of a metasurface tuned to the specified spectral range the tiny amount of an analyte may be detected. In practice, several arrays of nanoparallelepipeds tuned to different spectral ranges may be fabricated on a single chip . In this


Joining worldwide efforts to understand the relationship between driving emotion and behavior, the current study aimed at examining the influence of emotions on driving intention transition. In Study 1, taking a car-following scene as an example, we designed the driving experiments to obtain the driving data in drivers’ natural states, and a driving intention prediction model was constructed based on the HMM. Then, we analyzed the probability distribution and transition probability of driving intentions. In Study 2, we designed a series of emotion-induction experiments for eight typical driving emotions, and the drivers with induced emotion participated in the driving experiments similar to Study 1. Then, we obtained the driving data of the drivers in eight typical emotional states, and the driving intention prediction models adapted to the driver’s different emotional states were constructed based on the HMM severally. Finally, we analyzed the probabilistic differences of driving intention in divers’ natural states and different emotional states, and the findings showed the changing law of driving intention probability distribution and transfer probability caused by emotion evolution. The findings of this study can promote the development of driving behavior prediction technology and an active safety early warning system. Traffic safety continues to come into focus with the ever-increasing use of cars in modern society, and traffic accidents are becoming a major factor causing human injuries . AlthougIn this study, the driving intention prediction model for a driver’s natural state, and eight typical driving emotion states, based on the Hidden Markov Model (HMM), were established and trained. In the intention prediction model, we used a hidden Markov chain to describe the random process of a driver’s intentions changing with time, and took the vehicle driving data corresponding to the driver’s different intention transitions as the observed value of the hidden Markov chain. On the premise of model accuracy verification, we extracted the probability distribution of different driving intentions, the transition probability matrix between different driving intentions, and the probability distribution of driving intentions corresponding to different observation values from the HMMs. For the driver’s natural state and different emotional states, we obtained the overall differences in driving intention probability distribution and transition probability under different emotional states through the comparative analysis. For the probability distribution of driving intention corresponding to different observed values, the Bayesian formula was used to transform it into the probability distribution of driving intention under different driving environments —i.e., the probability distribution of driving intention generated by drivers under different environmental states. Finally, we compared and analyzed the probability distribution of driving intention under the same driving environment parameters but different emotional states and obtained the driving intention transition law caused by emotional evolution.ParticipantsSixty-two drivers  aged from 20 to 48  participated in this study. The participants recruited were undergraduate students, urban residents and taxi drivers in Zibo City, China. All of the participants were licensed drivers and their driving experience ranged from 1 to 16 years .Measurement of Driver’s Expected Speed and DistanceThe expected speed and expected distance are important parameters in driving behavior studies . ExpecteDriving ExperimentThe driving experiments included actual driving and virtual driving experiment. We used two experiment vehicles equipped with a multi-function speed-measuring instrument, laser range sensor, video capture system, and notebook computer for the actual driving experiments a, and usData Pre-ProcessingEach participant took part in a 10-min actual driving experiment and a 10-min virtual driving experiment. Hidden Markov Model (HMM)The HMM is a statistical analysis model that can be represented a set of hidden states (ok|qj)} ,25,26. Wov chain . An HMM Set of VariablesIn this study, we took the driving intention at  SPSS 24 . Table 3Model TrainingThe model training was to determine the value of lgorithm . Finallylgorithm . Figure We obtained the probability of intentions by model training. The probabilities of deceleration (Int1), keeping speed (Int2) and acceleration (Int3) were 0.247, 0.468, and 0.285, respectively. We used the HMM to predict the driving intention. Scilicet, the driving intention at The purposes of the experiments in this study were to collect the driving data of drivers in different emotional states. The experimental procedure related to driving was consistent with study 1. It was different in that the participants were in different emotional states.ParticipantsThe same experimental sample of 62 adult drivers volunteered to participate in this study.Driving Emotion Classification and Selection1), surprise (Em2), fear (Em3), anxiety (Em4), helplessness (Em5), contempt (Em6), relief (Em7), and pleasure (Em8).Psychologists classified emotions into different categories according to different criteria and theoretical constructions ,36. AmonExperiment MaterialsAside from the driving experiment equipment mentioned in study 1, the experiment materials also referred to emotion induction materials in this study. Emotion induction materials included pictures and movie clips derived from the network, International Affective Picture System (IAPS) and Chinese Affective Picture System (CAPS). Emotion InductionIt is difficult to induce an individual’s emotions effectively and measure the level of emotional arousal accurately . The metIn the preliminary induction of anger, surprise, fear, and anxiety, we presented the pictures with specific emotional overtones and showed the contents expressed in the pictures to the participants, respectively. In the preliminary induction of helplessness and relief, we applied the personalized recall method to awake participants’ helplessness and relief emotion. For the helplessness, we guided the participants to recall their experiences of failure to pass the examination, unable to pay huge housing or car loan, etc. In the induction of relief, we guided the participants to describe their own experiences, such as passing the test of driving skill. For contempt induction, a scene of driving competition was set on the driving simulator in the virtual driving experiment. Firstly, we constructed a number of driving routes in the simulated driving scene, and obstacles, such as the construction area, were set up in the driving routes. Secondly, we ordered five experiment organizers and one participant to drive a virtual vehicle along the routes in sequence. Thirdly, the simulator controller controlled the driving competition to let the participant always reach the end of the experiment routes with the shortest time. Fourthly, we announced the driving competition results were to the participant and praised the driving skill of the participant to let him or her produce a strong sense of superiority. In actual driving experiments, we also used the personalized recall method to preliminarily induce contempt. The participants were guided to recall their experiences of feeling very superior, such as passing the course test with a higher score than their classmates, getting more appreciation or attention than others, etc. For the pleasure emotion, we informed the participants that they would get prizes if they finished the experiment. The in-depth inductions of emotions were carried out based on the preliminary inductions at the first opportunity. The induction materials for each emotion contained two or three movie clips. We picked one of the movie clips at random to play to the participants. In order to avoid the reduction in emotional induction effect caused by the advance understanding of the movie, the movie clips applied in the actual and virtual driving experiment for the same participant were not repetitive. The driving experiment started instantly after the above two steps. The driving task attributes had an important impact on the driver’s mood ,49. For Driving ExperimentThe equipment and procedures of the driving experiments were same as in study 1. Corresponding to each emotion state, each participant participated in eight actual driving experiments and eight virtual driving experiments. Unlike study 1, an important task while driving was to measure the driver’s emotional state. As an important branch of affective computing, emotion recognition patterns based on electroencephalogram (EEG) , electroThe PAD measurement tool, which is in line with the psychological measurement theory , is the In order to be easy to use in the experiment and to facilitate the subsequent calculation, each dimension was noted from 0 to 100 based on the strength of each dimension and Each participant received eight kinds of induction. For a participant, the interval between the eight induction experiments must be long enough to ensure that his or her eight emotion experiences did not interfere with each other. We numbered the 62 participants and organized them to complete one kind of emotion induction and corresponding driving data acquisition in sequence according to the numbering order. Only after all participants completed the certain emotion induction and the corresponding driving data collection, the induction experiment of another emotion began. In the eight emotion-specific inductions, the order of 62 participants in the experiment was fixed. According to experiment records, we took the shortest time (11 days) to complete the emotion induction and corresponding driving data collection for all the participants in the relief virtual-driving experiment. This meant that the time interval between the two emotion induction experiments for the same participant was at least 11 days, and the eight emotional experiences did not interfere with each other. In addition, there were four steps involved in one emotional induction. In practice, we tried to shorten the time intervals between each step as much as possible to ensure that the emotional stimulation of each step had a positive impact on the emotional stimulation of the next step.Effect Testing of Emotion InductionIn order to ensure the data were valid, we carried out the tests on the effectiveness of emotional induction. In the driving experiments, drivers reported PAD values for their emotional state every minute. Therefore, in each emotion state, we obtained 620 emotion indicator results in the actual and virtual driving experiment, respectively. Each ser of emotion indicator data represented the driver’s emotional state within a minute. We took the Euclidean distance between the reported coordinates (reported by the participants in the driving experiments) and converted coordinates of emotion in the PAD three-dimensional space as the measurable criteria of emotion activation [The driving intention prediction model, adapting to the eight kinds of emotions, were established. For each model, the process of data processing and model training was consistent with study 1. We used 70% of the corresponding experimental data for each emotional state for model training, and applied the remaining data to the corresponding model verification.We obtained the probability distributions of the three driving intentions in different emotional states and the transition probability between the three intentions in the proposed HMMs by model training. We used the HMMs proposed in this study to predict the driving intention adapting to driver’s different emotional states. We applied the rest of the data sets, which were unused in model training, to verify the accuracy of the proposed models. We obtained the accuracy of the models in predicting the driving intention by comparing the predicted value and the actual value of the vehicle acceleration. In studies 1 and 2, we obtained and analyzed the probability distribution and transition probability of the three driving intentions in drivers’ natural states and different emotional states.t-test method [t-test results. As can be seen from In order to examine whether emotions had significant influences on the probability distribution of the three intentions, we used the t method  to compat-test method to examine the significance of emotional influence on the transition probability of the three intentions and the test result are shown in Analogously, we used the Furthermore, we applied the F-test  to verifFinally, we expected to obtain the changes in driving intention generated by the change in emotional the variable under the condition that other variables were unchanged or nearly unchanged. In the intention identification model constructed above, we got the probability distribution of driving environment parameters under different intention states . The premise of this behavior prediction is that drivers can always effectively perceive the driving environment information and make rational analysis of the traffic safety situation based on the information in the driving behavior decision. In this case, the driving behavior prediction model is likely to make wrong decisions because it ignores the driver’s current mental state. For example, when a driver is in the anger emotion, he or she is very likely to have unreasonable perceptions of the interaction between vehicles , and then adopt aggressive driving behavior. Obviously, if the behavior prediction model still follows the conventional model to predict the behavior, it will inevitably get results that are not consistent with the actual situation. In contrast, if the driver’s current emotional state of anger can be fully considered in the driving behavior prediction, and then realize that the probability of the driver taking aggressive driving behavior will be greatly increased, the accuracy of behavior prediction will be greatly improved. When it needs to be pointed out, the premise of constructing an emotional driving behavior prediction model is that the on-board intelligence system can accurately recognize the emotional state of the car driver. Finally, yet importantly, it is necessary to point out some limitations of this study. The first is the hypothesis of driving intention and emotion. In the process of constructing, the intention prediction model, based on the hidden Markov model, we assumed the transfer of driving intention as a Markov chain. In the induction and measurement of participants’ emotions, we assumed that the PAD accurately depicted the human emotions, and we deemed the eight emotions in this study to have mapping relationships with the three dimensions of PAD. In addition, as an exploratory study, the car-following driving environment with a single scene was selected, which resulted in the research results of this paper could not being applicable to a more complex driving environment.


The potential energy (EP) of AGW has enhanced from 3 to 7 March, 4–8 days before the EQ. The active region of the precursory AGW first appeared around the EQ epicenter, and then expanded omnidirectionally, but mainly toward the east, covering a wide area of 2500 km (in longitude) by 1500 km (in latitude). We also found the influence of the present AGW activity on some stratospheric parameters. The stratopause was heated and descended; the ozone concentration was also reduced and the zonal wind was reversed at the stratopause altitude before the EQ. These abnormalities of the stratospheric AGW and physical/chemical parameters are most significant on 5–6 March, which are found to be consistent in time and spatial distribution with the lower ionospheric perturbation, as detected by our VLF network observations. We have excluded the other probabilities by the processes of elimination and finally concluded that the abnormal phenomena observed in the present study are EQ precursors, although several potential sources can generate AGW activities and chemical variations in the stratosphere. The present paper shows that the abnormal stratospheric AGW activity has also been detected even before an oceanic EQ, and the AGW activity has obliquely propagated upward and further disturbed the lower ionosphere. This case study has provided further support to the AGW hypothesis of the lithosphere-atmosphere-ionosphere coupling process.The precursory atmospheric gravity wave (AGW) activity in the stratosphere has been investigated in our previous paper by studying an inland Kumamoto earthquake (EQ). We are interested in whether the same phenomenon occurs or not before another major EQ, especially an oceanic EQ. In this study, we have examined the stratospheric AGW activity before the oceanic 2011 Tohoku EQ (M It is recently agreed that precursory effects of an earthquake (EQ) could disturb the ionosphere . The precursory VLF/LF propagation anomalies usually appear a few days to about one week before an EQ ,28. We bThe papers that are mentioned above report the perturbations in the lower ionosphere, but unfortunately those observations are “indirect” (except the Doppler shift ) evidencHowever, we notice that the research in the stratosphere (middle atmosphere) is still absent in previous LAIC studies. In our preceding paper , the strw) of 9.0, and numerous studies of precursors for this EQ have been reported (see summary in )<150 nT) . There wP in Equation (1), this enhancement has remained in the temperature perturbation profile , but not dynamical processes (that include the gravity wave heating), mainly contribute the heating (energy budget) in the upper stratosphere . Once thThe negative correlation between ozone concentration and temperature, as seen in We further found that the zonal (u) component of the wind field at the stratopause had changed from westerly (eastward) to easterly (westward) on 5–6 March (the figure is not shown here), while the temperature enhancement and wave activity are both most significant. Wave breaking  might ocP (We sum up the possible mechanism of those variations here before closing the discussions of stratospheric parameters. Precursory perturbation(s) with ground and/or near-ground atmosphere triggers AGWs in the atmosphere . These AP . The wavP b, furtheP c. On theThe results in This section is intended to argue for the AGW hypothesis in the LAIC process. The most serious problem for the AGW channel is how AGWs are excited prior to the EQ, and we introduce several precursors to explain the possible solutions of exciting precursory AGW activity. Apart from medium-term (with lead time of a few months to a few years) precursors that are mainly based on crustal movements ,68,69,70We start with the results of the upper region, ionosphere. Our VLF results of the lower ionospheric perturbation presented in the previous section are strongly supported by an anomaly of ULF (f = 0.01–0.03 Hz) magnetic field depression that peaked on 6 March . This ULNext, we go to the atmospheric phenomena. A typical pre-EQ phenomenon in the atmosphere is impulsive ELF (f = 1–10 Hz) radiation, which has been really detected on 6 March on the basis of an ELF network observation in middle Japan and whose azimuthal direction was directed towards the EQ epicenter . The mecLastly, we consider the lithosphere. In , they anIn agreement with our promising AGW hypothesis of LAIC , Ref. 8 have repWe have to comment on the effect of geomagnetic activity here, because there was a moderate geomagnetic storm on 1 March with Dst = −88 nT. The period of 3–6 March is just during its recovery phase, and the geomagnetic activity was rather quiet , and/or the AGW dynamic processes as mentioned in Another important fact is about the significance of the precursory AGW. The wave activity is more significant in the Tohoku case than the Kumamoto case. Although the background values of EWe also notice that the 2011 Tohoku EQ is a reverse-fault type EQ, and this is different from the 2016 Kumamoto EQs, which are a series of strike-slip type EQs. The occurrence of precursory phenomena between the EQs with different kinds of seismic focal mechanisms  is another interesting topic in the LAIC study, since some of the specific processes in the proposed LAIC hypotheses are never tenable in at least one kind of focal mechanism . The difference in the focal mechanism between the 2011 Tohoku EQ and the 2016 Kumamoto EQs is quite essential for us. This is evidence that our theory has been applied to reverse-fault and strike-slip-fault EQs; so, the investigation of a normal-fault type EQ is necessary in the future.P, to study the stratospheric AGW activity before the oceanic 2011 Tohoku EQ.In our previous papers, we have reported the abnormal stratospheric AGW activity  and alsoP value exceeded the octuple of the reference value that was constructed by a 10-year’s mean. The present AGW activity is not correlated with any meteorological or topographical origin, and we have concluded that the AGW activity is a seismogenic effect. The active region first appeared around the Japanese Tohoku and Hokkaido area on 2 March, and then it expanded in all directions, but mainly toward the east on the following days of 3–7 March. Although there were a series of foreshocks before the main shock starting from 9 March, the abnormal AGW activity has occurred before the foreshocks. Therefore, the origin of the AGW activity is undeniably a pre-seismic, but not co-seismic, effect.Before the Tohoku EQ, the AGW activity was significantly enhanced around the stratopause from 3 to 7 March (4–8 days before the EQ). Especially on 5–6 March (5–6 days before the EQ), the precursory AGW was the most active, and the highest EIn contrast to the Kumamoto case , we haveBesides, the tempo-spatial evolution of the stratospheric AGW is found to be very consistent with the subionospheric perturbations. Both anomalies are most significant on 5–6 March, and are mainly distributed eastward of the EQ epicenter. It is highly likely that the lithosphere and lower atmosphere was really perturbed as a possible agent of AGW excitation by introducing the reported precursors in the atmosphere and lithosphere in We have evidenced the simultaneous and concentric anomalies in the stratosphere and lower ionosphere that support the AGW hypothesis of the LAIC process through the two studied cases of the 2016 Kumamoto EQs and the 2011 Tohoku EQ, even though the seismic focal mechanisms are different in these two cases. We have also coordinated some similar and dissimilar properties between these two cases in


Recent precision functional mapping of individual human brains has shown that individual brain organization is qualitatively different from group average estimates and that individuals exhibit distinct brain network topologies. How this variability affects the connectivity within individual resting-state networks remains an open question. This is particularly important since certain resting-state networks such as the default mode network (DMN) and the fronto-parietal network (FPN) play an important role in the early detection of neurophysiological diseases like Alzheimer’s, Parkinson’s, and attention deficit hyperactivity disorder. Using different types of similarity measures including conditional mutual information, we show here that the backbone of the functional connectivity and the direct connectivity within both the DMN and the FPN does not vary significantly between healthy individuals for the AAL brain atlas. Weaker connections do vary however, having a particularly pronounced effect on the cross-connections between DMN and FPN. Our findings suggest that the link topology of single resting-state networks is quite robust if a fixed brain atlas is used and the recordings are sufficiently long—even if the whole brain network topology between different individuals is variable. The fproposed . Notablyproposed . These hproposed  and theyproposed ,13,14, iproposed , while Pproposed . Typicalproposed ,18, whicproposed  but provMore recently, it has been realized that the variability in the functional networks between individual subjects in the same “group”  needs to be taken into account . In partIn this paper, we aim to address the question of how large the variability in the network links of the DMN and the FPN across different subjects is for the commonly used AAL brain atlas . Using tThe Rs-fMRI data analyzed in this paper are from the open data set published in . The setorted in  that thiwww.fil.ion.ucl.ac.uk/spm) modules for data pre-preprocessing. The preprocessing pipeline consisted of correction of head-motion by realignment of all functional images to mean functional image, coregistration of anatomical image and mean functional image, and segmentation of anatomical image in order to create subject-specific white-matter and cerebrospinal fluid (CSF) masks. Resulting images were spatially normalized to a standard stereotaxic MNI space  with a voxel size of To quantify the whole-brain pattern of functional connectivity, for each subject we computed the Pearson’s correlation matrix among the regional BOLD signal time series from 90 regions defined by the Automated Anatomical Labeling (AAL) atlas . This st regions . The Com regions . Time seDue to their general importance, in this paper, we focus predominantly on the Default Mode Network (DMN) and the Fronto-Parietal Network (FPN). In the AAL brain atlas, both of them are represented by 10 regions of interest (ROIs), see In the following, we typically consider three different cases: (i) The group case, where all recordings over all patients are concatenated; (ii) the subject case, where all recordings of a single patient are concatenated; (iii) the recording case, where individual recordings are considered .As mentioned above, to infer functional networks and direct networks we use three different similarity measures: Pearson’s correlation coefficient, partial correlation, and maximum entropy conditional mutual information (MECMI). The latter two measures both eliminate indirect effects due to other regions. In other words, they account for network effects in the sense that the functional association between two ROIs (a putative direct link) may result from other ROIs in the network with which these ROIs interact .N is the length of the time series, Pearson’s correlation coefficient (CC) between two time series, e series  for eachPartial correlations (PC) can be defined with the help of the inverse of the correlation matrix Ckl−1 ,35,(2)ρp-values for the partial correlation based on the assumption of Gaussian processes to establish significance instead, we obtain very similar results, albeit at a slightly smaller significance level.). The thresholded partial correlation matrix corresponds to the weighted direct connectivity matrix.The significance is established analogously to the case of the Pearson correlation coefficient described above . The sum over all atoms is simply One can easily visualize this approach, since the set theoretic formulation of information theory allows us to map information-theoretic quantities to the regions of an information diagram, a variation of a Venn diagram. The information diagram for three variables is shown in In general, a system of N variables results in Apart from the chosen constraints defined above, there are also general constraints on the values of the subregions that are necessary for providing a valid information diagram, i.e., such that there exists a probability distribution with corresponding information-theoretic quantities. A family of such constraints  can be inferred from the fundamental requirement that, for discrete variables,  entropies and mutual informations are necessarily non-negative: (A) e others . Not so-With the use of the information diagram formulation, the maximization of entropy of the multivariate distribution can be thus reformulated as a standard linear programming problem of finding the values of the information atoms, given linear constrains , that maximize the total entropy , the value of the maximum entropy varies by an order of This method has proven to be efficient and able to overcome fundamental issues plaguing other estimators of conditional mutual information . In partTo establish the robustness of the inferred resting-state network topology for the three considered networks , we can first look at the rank ordering: All the links between ROIs that are deemed significant see  are ordeIn While The comparison between the two conditional/partial similarity measures, partial correlations and MECMI, in Having established the existence of a well-defined backbone on the group and subject level, the question arises whether one can also detect it at the level of individual 30 min recordings. To address this question, we focus on the strongest 5 links (for better readability) for a given subject and look at their rank variation across the 10 individual recordings. This is shown in To quantify the robustness of the inferred network links more precisely, we can take advantage of precision-recall diagrams. If the ground truth is known, precision is defined as the ratio of correctly inferred links to all the inferred links, while recall is defined as the ratio of correctly inferred links to the number of links in the real network . Note tTo understand the slight drop in precision and recall for the combined case DMN+FPN in On the other hand, the precision-recall diagrams for the internetwork links shown in As discussed in Our analysis proves the existence of a robust backbone in the considered resting-state networks, which can be reliably inferred at the group level, at the subject level, and even to a meaningful degree at the level of individual 30 min recordings. In the latter case, the limited amount of data leads to a variability between recordings for the same subject that is higher than the observed variability between subjects. All these findings are largely independent of the chosen similarity measure. Since two of the applied similarity measures are partial or conditional similarity measures, this implies that the backbone consists of direct links. The maximum entropy estimate of the conditional mutual information has the advantage that no thresholding is required to establish the significance of a link, making it a particularly powerful tool.The relatively low variability in the direct network structure between subjects is not only present in DMN and FPN separately, similar to what has been observed in previous studies ,19, but Our findings shed new light on the observation that individual brain organization is qualitatively different from group average estimates and that individuals exhibit distinct brain network topologies . SpecifiGiven our findings we expect that using resting-state networks based on a fixed brain atlas as a tool for the early detection of diseases like Alzheimer’s, Parkinson’s, and ADHD should be reliable even if the brain network topology between different individuals is variable. Establishing this directly remains a challenge for the future.


Epidemiological studies have shown that environmental green space contributes to the reduction of psychosis incidence in the population. Clarifying the psychological and neuro-functional mechanisms underlying the risk-decreasing effects of green surroundings could help optimize preventive environmental interventions. This perspective article specifically aims to open a new window on the link between environmental green space and psychosis by considering its core psychopathological features. Psychotic disorders, such as schizophrenia, are essentially characterized by self-disturbances. The psychological structure of the self has been described as a multidimensional phenomenon that emerges from the reciprocal interaction with the environment through intrinsic and extrinsic self-processes. The intrinsic self refers to the experience of mental activity and environmental information as inherently related to one’s own person, which involves self-referential processing, self-reflection, memory, interoception, and emotional evaluation. The extrinsic self refers to sensorimotor interactions with the environment and the sense of agency, that is, the experience of being the source of one’s own actions and the multisensory consequences thereof. In psychosis, anomalous self-processing has been related to a functional fragmentation of intrinsic and extrinsic self-processes and related brain networks. Moreover, evidence from cognitive neuroscience suggests that green space could have beneficial effects on self-related processing. Based on the literature, it could be hypothesized that self-processing is involved in mediating the beneficial effects of green space for psychosis. Considering the multidimensionality of the self, it is proposed that urban green space design aimed at improving mental health ideally impacts the complexity of self-facets and thus restores the individual’s self. Psychotic disorders are severe mental illnesses that involve a “loss of vital contact with reality” . The mosThe high heritability of psychosis indicates the existence of genetic risk factors , althougSome of these factors may be attractive targets for global risk-reducing environmental interventions . Among tIn particular, this perspective article aims at developing a new hypothesis regarding the psychological and neuronal mechanisms that could explain the risk-decreasing effects of natural surroundings for psychosis. For the purpose of focus, an investigation of how the effects of green space on psychosis incidence can be embedded in the full complexity of genetic and environmental factors and events remains beyond the scope of the present article, although, this will be a mandatory topic for ensuing dialog. A better understanding of the underlying risk-reducing mechanisms of concrete environmental features could help in optimally exploiting these features in urban design. Given the fact that structural and direct investigations in compromising mental disorders like psychosis are still sparse, one approach to exploring these mechanisms would be to link the core psychopathological features of psychosis to green space effects.Natural environments usually refer to surroundings that include green space  but also can include blue space (water bodies), which could be developed with or without human intervention . The defThe beneficial effects of natural surroundings on daily life have been shown for physical and mental health, social well-being, academic and job performance, and happiness . AlthougTo date, the relationship between the presence of residential natural spaces and a lower risk for psychosis has been investigated by few studies. One of the questions that is derived from these studies is “What are the critical aspects of psychosis that might be influenced by green space?” From a historical, psychopathological perspective, schizophrenia and related psychotic disorders have been described as self-disorders . BuildinTo develop a more concrete understanding of the exact nature of self-disturbances in psychosis, it is useful to take a closer look at the psychological structure of the self-concept. The self and its disturbance have been increasingly portrayed from multiple perspectives in psychology and cognitive neuroscience. A major distinction is that between intrinsic and extrinsic self-processes , which cThis literature suggests that the self is composed of distinguishable but complementary processes. Such processes have been associated with largely distinct brain networks . Howevervia a disequilibrium of network states and interference between network activities due to impaired auto-excitation and collateral inhibition , a present , and a future (prospective thoughts). Green space may be an environment with auspicious qualities that can modulate mental and bodily self-experiences over time and space on various scales, transforming itself continuously as it evolves and changes over time, while at the same time remaining a familiar environment . It is tThe author confirms being the sole contributor of this work, being responsible for reviewing the literature, developing the concept, and writing the manuscript, and has approved it for publication.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.