We compared music emotion ratings and their physiological correlates when the participants listened to music at home and in the laboratory. We hypothesized that music emotions are stronger in a familiar environment, that is, at home. Participants listened to their self-selected favorite and neutral music excerpts at home and in the laboratory for 10 min in each environment. They completed the questionnaires about their emotional states and gave saliva samples for the analyses of the stress hormone cortisol. We found that in the context of music listening, the participants’ emotion ratings differed between home and the laboratory. Furthermore, the cortisol levels were generally lower at home than in the laboratory and decreased after music listening at home and in the laboratory. However, the modulatory effects of music listening on cortisol levels did not differ between the home and the laboratory. Our exploratory multimethodological data offer novel insight about the psychological and physiological consequences of music listening. These data reveal the sensitivity of the current research methods to investigate human emotions in various contexts without excluding the use of laboratory environment in investigating them. Thanks to mobile technology, we have access to music listening everyday everywhere 24/7. Despite the rapid increase of the use of these online streaming services during the past decade, music recordings still have huge market; e.g., an estimation for the global trade revenue of recorded music in 2019 was US$20.2 billion [One motivation for music listening in our daily life might result from the power music has in modulating our mental state in terms of emotions and vigilance. Consequently, in music psychology, music listening has been conceptualized and empirically investigated in at least three frameworks, namely, music emotions, mood regulation, and stress reduction. These frameworks will be briefly illustrated below .First, music emotion literature is focused on investigating which emotions can be associated with a given music excerpt by its listeners. Questionnaire measures used while music listening and also retrospectively showed that music listening is linked to basic emotions , specific feelings , changes in the vigilance level (“soothing”), as well as ratings in terms of valence (“felt good”) ,5. In maSecond, in the mood regulation literature, questionnaire and interview data showed that people use music to modify their emotional state intuitively and without specific instruction, e.g., to soothe, to comfort, or to cheer up—all of this along the life span from adolescence until late adulthood ,11,12,13Third, and most importantly in the current context, in music medicine, by using questionnaires and physiological measures such as cortisol analyses, music listening has resulted in a noticeable stress reduction as indicated by psychological self-reports and concomitant biological stress measures . In thisTraditionally, listening and physiological studies have been conducted in the laboratory environment. However, one might ask whether emotional reactions of listeners are comparable in a neutral unfamiliar environment and in an environment in which listening usually takes place. Recently Azhari and others showed higher metabolic brain activity in the prefrontal cortex  if emotional infant and adult vocalizations were played in outdoor than in domestic contexts . This fiRegarding music listening, the first pioneering empirical endeavors to obtain information about listeners’ emotional responses and to record an electroencephalogram (EEG) in a concert setting were accomplished by Dolan and others . They foConsequently, from methodological perspective, it is currently challenging to get a unified perspective about the affordances of music in modulating our physiological and psychological state. While questionnaire and interview studies are conducted in laboratory settings as well as in regular listening environments, physiological studies including those with hormonal analyses have been conducted mainly in the laboratory . Thus, Here, our primary interest was to investigate whether music listening modulates music emotion ratings differentially in two different listening environments, namely, at home vs. in the laboratory. In addition to this questionnaire-based part of the study, we also compared the extent to which favorite vs. neutral music listening modifies the salivary cortisol levels of the listeners at home and in the laboratory. To this end, our specific instruction for participants was that one of the music excerpts should be their favorite music, expected to evoke more positive emotions than the other music excerpt, instructed to be neutral and not to evoke any specific emotions.Participants were adult healthy volunteers who all had some experience in music making  but were not currently actively practicing or performing music. They were recruited by using e-mail lists of local universities and other academic organizations as well as by direct contacts by the research assistants SG and HM. They were selected on the basis of their availability and interest on participation, the participants thus forming a convenience sample. In total, there were 37 participants who were 20–40 years of age .Before the commencement of the project, the study protocol was reviewed and approved by the ethics committee of the former Faculty of Behavioural Sciences, University of Helsinki (01/2011). Informed written consent was obtained from all participants and all research was performed in accordance with relevant guidelines.Before coming to the laboratory, the participants were provided with four saliva sample tubes  together with the instructions of giving the samples the night before (one sample) and in the morning (3 samples) of the laboratory visit. These samples were used for the pilot analyses of other hormonal values, not reported here. The participants also received the questionnaires for background information and the daily use of music. They were asked to bring both questionnaires and the tubes to the laboratory. Additionally, they were asked to bring two excerpts of music to the laboratory by using a CD or as wav files; one of these excerpts representing their favorite music and the other one as neutral as possible.The laboratory investigation was always conducted between noon and 4 pm. The duration of the whole experiment was 105 min on average.First the questionnaires (see below) and sound files were collected. If the files were in mp3 format, they were converted to wav by using Adobe Audition 2 . The intensity of the audio output was manually calibrated to be approximately 60 dB. Audiometry was conducted using Oscilla SM950  (15 participants) and Oscilla USB350SP (23 participants). In all these participants, their hearing was normal . For two participants the audiometry was not conducted due to unavailability of the device. However, these participants reported their hearing to be normal.Electrocardiogram  and eleAfter ECG and EDA electrodes were attached, music excerpts copied to the PC, and background questionnaires collected, the participant was seated (reclined position in an armchair) in an acoustically and electronically shielded chamber. S/he was instructed to have a rest of 10 min and to glance the magazines available. Baseline data for ECD and EDA were recorded for 5 min.After this 10-min rest, the participant gave baseline saliva samples that were kept in dry ice and was asked to fill a questionnaire about the current emotional state. Note that they spent at least 20 min at the laboratory before the first saliva collection thus minimizing the effects of physical activity on their cortisol levels. They were also instructed to not eat, drink, or smoke during one hour before entering the laboratory in order to optimize the saliva sample collection.Music listening  were given in random order. Both excerpts were played on repeat for 10 min on a headphone (Sony MDR-7506).After the listening of the first excerpt, the participant was asked to fill a questionnaire about the current emotional state. Baseline data were recorded again for 5 min. After this 20-min rest, the participant gave the saliva sample.Similarly resting data were recorded after the second excerpt . After that, they were requested to fill in the questionnaire about their emotional state and to give the last saliva sample.After the laboratory experiment was completed, instructions were given for the next identical saliva sample collection to be performed at home before and after music listening within 2–7 days. This part of the study was instructed to take place between noon and 4 pm.As in the laboratory, also at home they were requested to first give the baseline saliva sample, fill in the emotional state questionnaire, rest, and start listening to the first excerpt  for 10 min. Music listening was instructed to be uninterrupted and focused, however, everyday background sounds were not a problem. After giving the saliva sample, they were asked to wait for 20 min, fill in the questionnaire and start listening to the second excerpt for 10 min. After this, they were asked to fill in the questionnaire again and to give the last saliva sample. These data were successfully collected from 28 out of 37 participants.In the questionnaire on emotion ratings, the participants were asked to rate their current emotional state along the following attributes along a scale from 1 (least) to 5 (most): excited, peaceful, enthusiastic, happy, sad, melancholic, irritated, tired, satisfied, curious, emotionally moved, and sentimental. These attributes were carefully selected from wider sets of attributes used in other emotional state inventories. In the statistical analyses, we grouped part of them to high-arousal  and low-arousal  emotion scores as well as to positive valence  and negative valence  scores.Saliva samples were delivered to the laboratory of the Finnish Institute of Occupational Health  in a freezer bag filled with dry ice on a daily basis. Salivary cortisol was analyzed there with chemiluminescence immunoassay  based on the competition principle. An unknown amount of antigen present in the sample and fixed amount of enzyme labelled antigen compete for the binding sites of the antibodies coated onto the wells. Measuring range of the method was 0.43 to 110 nmol/L. The coefficient of variation % of intra- and inter-assay of the method was 5 and 8%, respectively.p = 0.873) or at home  = 0.042, p = 0.838). Therefore, we report the statistical analyses which have been performed for the data after pooling the cortisol data across the listening order.Normality of the cortisol sample distributions was tested with the Kolmogorov-Smirnov test. As several of the variables were not normally distributed, a logarithmic transformation was made all variables used in the analysis. After that, the distributions were found to be normal. To determine the putative effect of the order of listening to favorite vs. neutral music, we first conducted 2  × 2  ANOVA for the cortisol values. It indicated no interaction between the order and type of the music in the laboratory  = 0.026, Normality of the emotion ranking scores was tested with the Kolmogorov-Smirnov test. As several of them were not normally distributed, we used non-parametric Wilcoxon signed-rank tests separately for high- and low-arousal emotion scores as well as for positive- and negative-valence emotions scores. These analyses were separately conducted for baseline ratings (before listening) and after listening to the favorite and neutral music excerpts between the listening at home and in the laboratory.The mean values of emotion scores differed statistically significantly between different listening environments particularly at the baseline stage when low arousal and positive valence emotion scores were higher in the laboratory than at home and negative valence emotion scores were lower in the laboratory than at home . Additiop < 0.001, η2 = 0.467). In the post hoc tests, the cortisol levels after music listening were significantly lower than at the baseline , however, without a significant difference between the favorite and neutral music listening (p = 0.535).As depicted in p < 0.05, η2 = 0.156). In post-hoc tests, this difference was observed at the baseline as well as after listening to the neutral and favorite music .The music listening environment also significantly modulated cortisol levels which were lower at home than in the laboratory  = 5.190, p = 0.802, η2 = 0.008).Against our expectations, there was no interaction between listening environment and music listening in the decrease of cortisol levels caused by music listening, implying that the effect of music to reduce cortisol levels did not differ between home and the laboratory  = 0.222, Our aim was to investigate whether music listening modulates emotional state ratings and cortisol levels differentially in two different listening environments. We expected that the effects of music listening on emotional ratings and on cortisol levels would be more profound at home than in the laboratory. However, our results show that while music listening modulated the cortisol levels, there was no significant difference in this modulation as observed at home or in the laboratory. Furthermore, our results show that the baseline emotion ratings differed between home and the laboratory and that the emotion ratings after listening to the favorite music excerpts tended to differ between home and the laboratory.Thus, the preference for a given music might be considered an important factor for psychological and physiological concomitants of the emotional reactions in its listener. Supporting findings about stronger emotional reactions while listening to one’s favorite music were previously given in the musical chill paradigm by the work of Zatorre group. They showed that the occurrence of chills while listening to self-selected music was paralleled with elevated reactions of the autonomous nervous system and reward-related brain processes . Since tNotably, against our expectation, music emotions were not stronger in a familiar listening environment (home) than in a novel environment (laboratory). Although psychological and cortisol-indexed music emotions were modulated by music in both listening environments, they were not stronger at home than in the laboratory. Yet, the effect of the listening environment was seen in the general cortisol levels which were lower at home than in the laboratory. Our expectation was based on an assumption that emotionally safe and familiar environment would support the emergence of stronger emotional responses ,23,26. HIn previous studies of the field, various biomarkers have been used in the context of music listening as evidenced by a recent systematic review . It wasLimitations of the current study, specified below, are mainly caused by our intention to minimize the burden to the study participants and by the compromises by the participants themselves regarding the completion of the study. Thus, even if a study like ours takes an important step toward ecological validity of the phenomenon under interest, that is, music emotions, there are several issues to be considered as disadvantages of this novel empirical approach.First, despite the existence of valid questionnaires, we unfortunately have no information about the participants’ self-reported stress level or their personality. In this context it is noteworthy that even in the current study protocol, we were not able to obtain cortisol samples related to music listening at home from 9 out of 37 participants. Having more questionnaires might have decreased their participation even further. Second, although the participants were instructed to conduct the home listening part of the study in the afternoon, corresponding to the timing of the laboratory experiment, not all of them followed this instruction. Third, one problem in cortisol studies is the relatively long time period needed for probing optimally evoked changes in cortisol levels after music listening. In further investigations, the time between the music listening  and cortisol measurement should optimally be longer than in the current contribution in which it was 20 min. Fourth, we were not in a position for randomizing the order of the measurements at home vs. in the laboratory. To ensure that the saliva collection and music listening were properly conducted also at home environment by half of the participants before the laboratory environment, we should have had the research assistants to be present at home. However, for financial reasons this was not feasible in terms of their working hours available. Besides this resource issue there is another, even more important psychological issue, namely, the privacy of the participants. Having assistants enter the homes of the participants would have created an additional factor disturbing the listening. Admittedly, the lack of control upon the participants’ music listening, filling in the questionnaires, and saliva sample collection at homes leaves space for speculations upon the origins of the current findings which indicate that the low-arousal emotion ratings were higher in the laboratory than at home while the opposite was true for high-arousal emotion ratings . On the other hand, in the laboratory we conducted ECG and EDA recordings that might have disturbed the listening atmosphere. Furthermore, in the laboratory, research personnel was present, possibly causing some inconvenience even if the listening took place in a recording chamber without any other persons present. However, the baseline emotional ratings of the participants before any music listening in the laboratory vs. at home suggest that the laboratory environment was not experienced as particularly stressful: The high-arousal emotion ratings were lower in the laboratory than at home. The low-arousal emotion ratings were higher in the laboratory than at home. From this we might conclude that the laboratory (including the presence of personnel) was not experienced more stressful than home and could recommend that in future, home-based studies like the current one also have research personnel at home to ensure the participants’ compliance during all stages of the research.Thus, in general, as exemplified by our study limitations, conducting listening and physiological experiments in two different environments is challenging. Various issues regarding experimental design and practical procedures need to be considered. However, in our view, it is feasible and necessary to upgrade previous laboratory-based investigations by investigations in other environments, also in the fields of music psychology and neurosciences of music.In the current contribution, we show that in the context of music listening, the participants’ emotion ratings differed between home and the laboratory. Furthermore, their cortisol levels were generally lower at home than in the laboratory and decreased after music listening at home and in the laboratory. These findings promote the use of music as a tool in professional music therapy, in music medicine, and as “self-medication”. In parallel, they illuminate salivary cortisol as a sensitive index of emotional state even in non-clinical populations and listening environments.


N = 525) in southern China, with psychological capital and role conflict as the prior independent variables and the teachers’ academic entrepreneurial intent as the dependent variable. Using quantitative analysis, SPSS 22.0, and AMOS 23.0, we conducted reliability and validity tests, correlation analysis, and structural equation models on the collected data. We reached the following conclusions: (1) psychological capital has a positive effect on attitudes toward academic entrepreneurship; (2) psychological capital has a positive effect on perceived behavioral control; (3) role conflict has a negative effect on perceived behavioral control; (4) academic entrepreneurial attitudes have a positive effect on academic entrepreneurial intent; (5) perceived behavioral control has a positive effect on academic entrepreneurial intent; (6) subjective norms have a positive effect on academic entrepreneurial intent. We also provide some suggestions about academic entrepreneurship for university administrators.Because academic entrepreneurship is an innovation driving force in China’s economy, teachers are key knowledge creators in the process of entrepreneurship. Therefore, it is particularly important to give attention to the individual psychological mechanism factors at play in the process of teachers in higher education academic entrepreneurship. The purpose of this study is to identify individual psychological capital and role conflict issues among university teachers in China. To accomplish this aim, we investigated the emergence of positive academic entrepreneurial intents, continued through the process of academic entrepreneurship, and clarified the impact of psychological capital and role conflict on entrepreneurial intent. Based on the theory of planned behavior, we constructed a research model from the perspective of entrepreneurial intent prior to entrepreneurial action. We established a cohort of teachers in 17 higher education institutions ( Academic entrepreneurship is defined as an activity that transcends the traditional role of academic teaching or research and has innovative and risky elements that bring economic rewards to the individual academic or the institution . These rewards can be achieved by earning reputation, prestige, and social influence directly or indirectly . Many scMany studies have identified the connections between the specific processes of academic entrepreneurship in higher education and collaboration, including connections between individuals, organizations, and institutions. However, some scholars have argued that when investigating the entrepreneurial intent of teachers in higher education, academic entrepreneurship should be viewed from the perspectives of both economic development and individual psychology.Technology project incubation departments in eight Brazilian universities used the development of innovative and entrepreneurial universities as a theoretical basis. They found that although project incubation departments in universities preferred to work on specific academic research projects, companies created based on academic achievements were not prioritized. Meanwhile, little effort has been made to engage the academic research audience, resulting in underutilized academic research transfer channels . In the Previous studies have generally recognized that psychological capital is a core psychological element that transcends human and social capital and functions as a psychological resource for individual growth and performance enhancement , 2007. CFew researchers have investigated the work motivation and psychological capital of teachers. One study found that administrators should attend to the influence of psychological capital on teachers’ positive psychology with the aim of enhancing positive work motivation and providing high-quality education . We inveIndividuals in different social situations have different value perceptions of the roles that they play. Conflict arises when the value perceptions are inconsistent or when individuals play two or more roles . ScholarAcademic entrepreneurship is an innovative combination of risks and resources. First, it includes an element of risk because successful outcomes are not guaranteed. Second, it involves an organizational effort because it creates a new way of exploiting opportunities. Third, it must be innovative because it cannot copy what is already fully available in the market . PhilpotA study that surveyed teachers in Spanish higher education institutions found that entrepreneurial personality and talent were the two determinants of entrepreneurial intent for male and female teachers . It alsoThe theory of planned behavior suggests that the control of attitudes, subjective norms, and perceptual behaviors can accurately predict different types of behavioral intent and thus influence behavior formation . Attitudn = 496) in Germany and found that psychological factors predicted entrepreneurial intent and that economic factors indirectly influenced entrepreneurial intent through psychological factors The distribution is recorded as 5, 4, 3, 2, and 1, and the total score of each respondent’s attitude is the sum of the scores gained from the answers to each item in the questionnaire. The total value can be said to be the attitude of each respondent or the different states on each scale.The scale of psychological capital reference from N = 525, and a structural equation model was constructed. The results obtained are shown in In this study, reliability analysis, discriminant validity and convergence validity analysis, and correlation analysis were conducted on the collected valid sample data of As shown in As depicted in The method proposed by r = 0.290, P < 0.01). PBC was positively correlated with PC  and negatively correlated with RC . AEI was significantly positively correlated with SN , AEA , PBC . Therefore, the hypothesis is tentatively supported.As illustrated in According to the test method of P < 0.001). PC also had a significant positive effect on PBC , RC had a significant negative effect on PBC , and PC had a significant positive effect on AEA . AEA had a significant positive effect on AEI , PBC had a significant positive effect on AEI , and SN had a significant positive effect on AEI . Therefore, all six of the hypotheses in this study were supported.As shown in Literature on academic entrepreneurship has neglected to investigate the entrepreneurial intent of teachers in higher education from a psychological perspective. Studies that use qualitative analysis are sparse, and most were conducted on university students . Used a Based on these findings, the research model adopted in this study was constructed based on the theory of planned behavior. As shown in This study also supported the hypotheses that psychological capital on the control of attitudes and perceived behaviors have an effect on academic entrepreneurship see . We founWe also found that our hypothesis about the effect of role conflict on the control of perceptual behavior (H3) is supported. This conclusion is the same as the one obtained by With respect to spin-off business creation (a form of academic entrepreneurship), the conflict between entrepreneurship and social identity remains common . ResearcIn summary, this study showed that psychological capital is an important leading indicator that can predict academic entrepreneurial intents, and in the context of colleges and universities, managers can reduce the negative impact of role conflict caused by teachers who serve as entrepreneurs and academics through perceptual behavioral control. As The limitations of this study are twofold. First, our sample was selected from a group of university teachers living in the southern region of China, and the geographical differences may cause the findings of the study to differ. In order to address this issue, future research could take the form of a comparative study of university teachers in China and Korea. Second, designing survey questions to assess teachers’ entrepreneurial intent is a challenging task. Open-ended questions can be problematic in terms of quality data recovery and the analysis of results. Future research could use textual information mining and data analysis to obtain key terms from teacher postings and comments about academic entrepreneurship on the Internet. Third, the influencing factors of the academic entrepreneurial intents of teachers in higher education are complex and diverse. In the future, we can introduce regulatory variables or intermediary variables to further explore academic entrepreneurial intent.The original contributions presented in the study are included in the article/Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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COVID-19 pandemic is the main challenge with which the education systems in the world have ever faced. Furthermore, nursing students and nurse educators have faced the challenges in theteaching-learning process. This study aimed to explain the experiences of nursing education amid COVID-19 pandemic.  This research was conducted in 2020-2021 through qualitative content analysis approach. Participants were selected from nursing schools using purposive sampling.Data were collected using in‐depth, unstructured, and face‐to‐face interviews. The interviews were digitally recorded and transcribed verbatim. The conventional content analysismethod was used to analyze the data.  232 codes were generated which were grouped into four main categories, namely mandatory change in nursing education, change of training priorities of nurse educators,insufficient clinical competence, and opportunities during coronavirus outbreak.  COVID-19 prevalence caused many unprecedented changes in nursing education. Such changes have brought about opportunities and challenges in nursing education.  The COVID-19 pandemic has become a major crisis in global health. This pandemic deeply changed human behavior on different levels  . Depend In the education system, the nursing education had to change their routine methods and adjust their education based on health protocols  . These Nursing students and nurse educators also faced challenges in the teaching-learning process, especially students' presence in clinical wards, knowledge-based assessment,skills and attitudes, quality management in health care, and patient safety  . The chVarious studies have been conducted on the impact of pandemics on medical education  , 11 InIn Redinger’s study, adapting to a virtual classroom, embracing informal teaching, and supporting trainee mental health were the strategies that were implemented in the SARS pandemicin medical education  .Iran started to use various methods of virtual education as an alternative to in-person training as soon as possible  . To preAlthough a year has passed since the outbreak of COVID-19, and nursing education in Iran, as in other countries, has undergone changes, we have limited knowledge of the experiencesof nursing education. A qualitative study can help to shed light on the experiences of nurse educators and students in this area. Therefore, a qualitative study was conducted to explorethe experiences of nurse educators and nursing students in Iran.This qualitative study was conducted using conventional content analysis approach. The aim of this study was to explore the experiences of nursing education amid the COVID-19 Pandemic.This study was conducted in Nursing and Midwifery College affiliated to Birjand University of Medical Sciences, Birjand, Iran. Participants were 12 nurse educators and 7 nursing students in this college. Interviewees were selected purposefully from nurse educators and nursing students in the nursing faculties affiliated with Birjand University of Medical Sciences.Data were collected via in‐depth, unstructured, and face‐to‐face interviews. The time and place of interviews were selected as agreed with the participants.Interviews were recorded by digital audio recording. The interview was initiated with an open-ended primary question such as: “Could you please explain your experience of nursingeducation during the COVID-19 pandemic?” They were continued with probing questions such as “Would you please explain more?” Interviews lasted approximately 60 to 90 minutes.All interviews were audio-recorded with permission of the participants and transcribed verbatim by the researcher. Data collection and analysis were done from August 2020 to January 2021.The data collection process continued until the data were saturated and no new categories were emerged from the data. Finally, 19 nurse educators and nursing students were purposivelyselected for interview.Data were analyzed using Graneheim and Lundman's content analysis method. All interviews were listened to several times to obtain a sense of the whole, and then they were transcribed verbatim.The meaning units in quotations were extracted and labeled with a code. The various codes were compared based on differences and similarities.The codes similar in meaning were sorted into subcategories. This was done by the first author. Subsequently, subcategories were compared and combined with each other to form the main categories.To analyze the data, MAXQDA software 10 was used.Lincoln and Guba’s criteria were used to ensure the validity and reliability of the data  . To ens The data dependability was assessed, using both peer and member checking. The primary findings of the study along with the preliminary codes and categories were shared with theparticipants and their opinions were received (member check). Some parts of the data were analyzed by other colleagues who were not involved in the study (peer check).Confirmability was ensured through expert check. The codes and categories were repeatedly monitored and confirmed by supervisors (expert check). Finally, by providing a comprehensivedescription of the topics, participants, data collection and analysis procedures, and limitations of the study, we made an attempt to create transferability, so that other researchers mayclearly follow the research process taken by the researchers.The study was approved by the Ethics Committee of Birjand University of Medical Sciences, with the ethical code of IR.bums.REC.1397.386. At the beginning of the interviews,the aim of the study was explained to the participants. Participants signed written consent forms and allowed us to record their voices during the interviews.They were assured that they could leave the study at any point and that their identities would be kept confidential by researchers.Ninety nurse educators and nursing students participated in the study. Eleven participants were female and eight were male. Twelve participants were nurse educators, and the others were nursing students.The mean age of the nurse educators was 42.05±8/9 years, and that of nursing students was 22.5±3.03 years. During the process of data analysis, 232 codes were generated andcategorized into 11 subcategories and for main categories. Regarding the exploration experiences of nursing education during the COVID-19 outbreak, 4 categories and 11 subcategorieswere extracted  .Mandatory change in the nursing education was one of the experiences of the nursing students and educators. The mandatory change was experienced in the form of shifting to virtual educationand the unpredictable training conditions. Nurse educators mentioned many challenges during virtual education such as the managers’ lack of attention to the problems of virtual education,structural problems in the virtual education system, time-consuming preparation of educational content, and less teacher-student interaction.One of the educators said:“Holding classes online was very hard and time consuming. In addition, the system was problematic. The Internet was also weak and sometimes became disconnected.The content loading was very time-consuming and sometimes it took 4-5 hours. There were frequent errors in the middle of uploading.”The unpredictable training condition was another subcategory of mandatory change in the nursing education. Owing to the fact that there is no suitable prognosis for this disease,educational conditions are not predictable, and it is not possible to provide a fixed planning to continue the training process.One of the students stated:“There is no arrangement in our programs in this unstable situation. The training may be stopped for one week. Then, we again start training, but it is stopped and we haveto stay at home for two months. We are really tired of this situation.”Regarding the mandatory change in the nursing education, the authorities did not anticipate the need for more personal protective equipment. As students and educators acknowledged,there is a greater need for personal protective equipment during training in the current situation, which has created additional concerns for educators and students and passedthem round to different hospitals and the educational system to provide the protective equipment.The experience of the students is as follows:“When we were being trained, we asked for personal protective equipment. They passed us round to the hospitals and the faculty. We were stuck in this situation.”This was another experience of nurse educators and nursing students in nursing education during the COVID-19 outbreak. Changing from a teacher-centered to a student-centered education,a conservative attitude towards the disease, and lack of attention to the educational process were the experiences expressed by educators and students.They experienced changing from a teacher-centered to a student-centered context. Students were responsible for their learning and educators did not take responsibility as before.One of the educators said:“I upload the educational content for the students. Now it depends onthe student whether he/she wants to study or not.”The overemphasis on personal protection in clinical education was another priority of the educators and students in clinical settings. To protect themselves from the disease,they tried to spend the least time in the clinical setting and only did clinical procedures, and sometimes they avoided entering the patients' rooms and devoted most of theirtraining time to theoretical classes. One of the nursing students explained his/her experience as follows:“The educator used to appoint a patient for us during the training, and we had to do everything for the patient. In addition, she/he did not allow us to contact witha patient suspected of the COVID-19. Now, we are free to choose among patients and it is up to us whether to contact with COVID-19 patients or not.”The educators’ lowering of personal standards for teaching was another experience expressed by the nursing students during the training. They mentioned that their educators werenot serious in the teaching process and their goal was only to pass the time. The educator does not pay attention to the presence of the student during training and does not observeeducational regulations. One of the students explained as follows:“Educators used to explain educational materials with more details, but now they explain them briefly. They even do not ask us to present research or conference”.Most of the nurse educators and nursing students mentioned that students did not acquire the necessary clinical competence in nursing education in the current situation and consideredinsufficient clinical competence as an insufficient self-esteem in acquiring clinical competence and a weakness in providing holistic nursing care.Most of the nurse educators mentioned that the number and variety of patients admitted to the elective wards had reduced due to the COVID-19 outbreak, and they did not gainsufficient experience with different cases. In addition, the reduction of training hours is another important factor in reducing the students' clinical competencies.This change caused poor clinical competency of students.One of the nursing students explained his experience as follows:“Now, the level of learning in the training is very poor and inefficient compared with before COVID-19. We do not observe many cases. I have passed the emergency department training,but I am worried about the skills that I have not practiced even once.”Nurse educators and nursing students pointed out that nursing students were providing poor nursing care to patients. Due to the fear of COVID-19, students try to have minimal contactwith the patient and only go to the patient's room to perform nursing skills. They do not pay attention to providing nursing care for the patient psychologically.One of the nursing students explained as follows:“We are a bit afraid in the training. We used to communicate with the patient easily and do what we really can for the patient. We think our care has become superficial.I used to talk with the patient when giving him/her medications or doing a procedure, but we try not to contact with the patient in these circumstances.”Participants mentioned that regarding various problems and challenges during the disease, the current situation also created opportunities in nursing, including the ability to usevirtual education, the importance of the nurse role in the healthcare team, easier access to educational facilities and more sensitivity to the observance of health protocols.The ability of educators to use virtual education was one of the opportunities experienced by nurse educators. Nursing education was done traditionally before coronavirus andeducators rarely used virtual education because they were not familiar with e-learning methods. Therefore, with the mandatory change in the teaching process, educators becamefamiliar with these methods, and they even can present part of their educational content virtually after coronavirus pandemic. One of the educators stated:“Coronavirus made us familiar with e-learning and helped us hold at least some of the classes online in later semesters.”The importance of the role of nurses in the healthcare team was one of the opportunities created in the COVID-19 conditions. Radio, television, and other social media portrayedthe role of the nurse in the treatment of coronavirus disease, which led to a positive attitude of students and educators towards the nursing profession. One student expressed:“Corona was really good and proved something to everyone. Some people believe that nurses are just responsible for injecting ampoules and giving medications and thedoctors do the rest, but corona showed the role of nursing clearly.”Easier access to educational facilities was another opportunity experienced by nursing students. They previously faced insufficient educational facilities in the clinical settingdue to student overcrowding in various fields and even had difficulty in holding classes. In the current situation, due to the division of students and absence of many groups in theclinical settings, they have easier access to educational facilities such as classrooms and patients’ medical records. One nursing student explained as follows:"Owing to the fact that the training did not overlap with different groups, we were comfortable during the training. Medical students and other students were not present;the wards were not crowded, so we could use classes whenever we wanted. Most of the classes were empty.”Another opportunity experienced by educators and students was greater sensitivity and adherence to health protocols, such as washing hands, using masks and shields, and maintaining social distance.One nurse educator explained her experience as follows: “Students, educators and staff are more sensitive to adhere to the protocols in these situations. I urge the students to adhere to hand hygiene and wear masks and shields if necessary.Students are also very sensitive and adhere to them.”This study aimed to describe the experiences of nurse educators and nursing students in nursing education during the COVID-19 outbreak. Four main categories were extracted from thedata collected in this study, including mandatory change in the nursing education, change of the training priorities of the nurse educators, insufficient clinical competency,and opportunities in COVID-19 pandemic.The category of mandatory change in the nursing education included two subcategories of shift to virtual education and unpredictable training conditions. Participants in this studypointed to problems such as lack of infrastructure, reduced readiness of educators and students to use e-learning, time-consuming preparation of educational content,less teacher-student interaction, and managers’ lack of attention to e-learning problems. Other studies have also mentioned such challenges  - 19The unpredictable training conditions were another study finding. Iran, like other countries, had to close educational centers and stop the educational process in person at thebeginning of the COVID-19 outbreak to deal with the consequences of the disease. Therefore, in-person education gave way to online education because the continuation of in-personeducation was unpredictable  , 20. T According to the previous category, participants stated that by changing the teaching method, educational priorities also changed. In fact, change of the educational prioritiesof nurse educators was another experience of nurse educators and nursing students in the COVID-19 pandemic. In the present study, overemphasis on personal protection, educators’ loweringof personal standards for teaching, and reduced attention of students and educators to learning clinical skills were described. In the present study, like other studies,students and educators were highly stressed due to coronavirus disease  . StudieNursing educators believed that changes in nursing education led to insufficient clinical competence in students. Obviously, they believe that nursing students need to beconstantly exposed to clinical procedures and settings to gain clinical competencies. Although the effect of the prevalence of COVID-19 on students' clinical competency has notbeen objectively investigated  , we can Although the COVID-19 outbreak created various problems and challenges, it also created opportunities in nursing education, including the educators’ ability to use virtual education,the importance of the role of nurse in the healthcare team, easier access to educational facilities and greater sensitivity to adhere to health protocols.The ability of educators to use e-learning was one of the opportunities experienced by nurse educators  . SimilaThe importance of the nurses’ role in the healthcare team was another finding. For example, radio, television, and other social media portrayed the role of nurses in thetreatment of COVID-19 patients, which led to a positive attitude of students and educators towards the nursing profession. In other studies, most of the nurses reported thatpublic perceptions of their profession changed in the COVID-19 pandemic. In addition, the socially formed image made the nurses feel valued  , 29.The present study mentioned educators and nursing students’ easier access to educational facilities. One of the main challenges for nursing students in clinical education isinsufficient educational space   due to Another opportunity experienced by nurse educators and nursing students in coronavirus pandemic was greater sensitivity to adhere to health protocols. In the present study,as in other studies, many students, educators, and staff adhered to health protocols such as washing hands, wearing masks and shields, and maintaining social distance with greatersensitivity and accuracy to protect themselves and patients  , 31. TIn terms of limitation, it can be noted that due to the emergence of the corona disease, the number of published research articles was limited at the time of the study.This limitation was clearer in domestic studies. Therefore, it is suggested that more quantitative and qualitative studies should be conducted on the use of e-learning in theCorona course and the challenges ahead in nursing education.The findings of this study showed that the COVID-19 outbreak deeply affected nursing education. Educational program shifted from face-to-face to virtual education.However, this change created challenges and opportunities. Challenges and opportunities can lead the nursing education to innovation. They can also develop new approachesto implementing the content and competencies tailored to evolving social needs. Therefore, due to the change in the learning environment caused by the epidemic, the integrationof technology in education is not a choice but a need for all authorities who themselves need flexibility, creativity and innovation. What we do know is that nursing studentsneed to acquire clinical competency. Owing to the fact that acquiring clinical competency in nursing education is uncertain in the current context, an active and dynamiceducational environment should be provided with the help of appropriate and effective digital technologies to compensate for insufficient clinical competency. We would like to thank the nurse educators and nursing students of Birjand School of Nursing who helped the researcher in conducting the research.Conflict of Interest:The author(s) declared no conflict of interest with respect to the research, authorship, and/or publication of this article.


This article presents the research and results of field tests and simulations regarding an autonomous/robotic railway vehicle, designed to collect multiple information on safety and functional parameters of a surface railway and/or subway section, based on data fusion and machine learning. The maintenance of complex railways, or subway networks with long operating times is a difficult process and intensive resources consuming. The proposed solution delivers human operators in the fault management service and operations from the time-consuming task of railway inspection and measurements, by integrating several sensors and collecting most relevant information on railway, associated automation equipment and infrastructure on a single intelligent platform. The robotic cart integrates autonomy, remote sensing, artificial intelligence, and ability to detect even infrastructural anomalies. Moreover, via a future process of complex statistical filtering of data, it is foreseen that the solution might be configured to offer second-order information about infrastructure changes, such as land sliding, water flooding, or similar modifications. Results of simulations and field tests show the ability of the platform to integrate several fault management operations in a single process, useful in increasing railway capacity and resilience. The present evolution of intelligent transport systems and policies in this field points towards the reduction of environmental emissions, use of green and/or renewable energies, and increasing the efficiency of processes. The railway transport mode has been given less importance in the past years, due to the rapid growth of road transportation, its flexibility in reaching various destinations, and mobility. However, for large amounts of freight, or numerous passengers, the railway remains one of the most efficient and rapid ways of movement in land transportation. Therefore, the EU policies in the rail transport area are proposing a single European railway area . The docThe purpose of this research is to give a solution to the problem of detecting mechanical defects in the subway rail and underground train wheels.The large-scale development of urban subways raises a major problem, namely the detection of tunnel defects and running tracks, which are becoming particularly important. Due to the complexity of the tunnel environment, it is difficult for traditional tunnel fault detection algorithms to detect such faults quickly and accurately. This article presents an integrated model for the simultaneous detection of defects that can occur in the tunnel but also in the subway rail using a complex battery of LIDAR sensors, ultrasound, video cameras but also a high-performance learning algorithm PCA T2Q for the machine learning module that can detect more tunnel and tread defects quickly and accuratelyThe novelty of the work consists in developing an autonomous railway cart fitted with several kind of sensors, able to travel along the railway, detect defects by continuous measurements and integrate data via a data fusion process. The same platform can be used with few modifications for the surface railways maintenance activities. A specific method of detecting defects in the railway is proposed, based on an ultrasonic system that analyzes the internal structure of the subway line. The analysis of collected data is based on the evaluation of signal propagation and ultrasonic imaging using decompositions, based on the principal component analysis (PCA) algorithm in the classic version. Supplementary, the analysis is also based on kernel PCA (KPCA). In the first phase, defects are detected, classified, and counted by analyzing their location and geometric features. Then, depending on the maximum difference between the different types of defects and the maximum tolerance of the same type of defects, the generalized characteristics of defects are extracted. Finally, generalization features and training templates are created for the use of a machine learning architecture employing PCA. The purpose is to classify the internal defects of the subway railway. On this basic strong and generalized feature, the constraints are formulated after reducing the size, and the KPCA grouping algorithm is developed to perform data merging for defect detection. The experimental results show that the proposed method can be used to detect internal defects with an acceptable level of accuracy and detection speed.The remaining of the article is organized as following: Due to its high degree of safety, the railway is an effective mode of transportation for both passengers and freight. With the inclusion of high-speed trains with dedicated and surveilled infrastructures, land transportation became now competitive with air transportation, mostly for medium distances. In Europe, Germany began operation of high-speed concept in railway transportation with ICE trains in 1991 and in 1994 the UK was linked to the European continent via the Eurostar service, connecting Paris to London through the Channel Tunnel. The document  states tThe subway transportation is another form of railway transport, designated mostly for urban areas. It also represents the best alternative to automotive, individual transportation in large urban environments. However, there are numerous cases in which railway, or subway networks face extensive wear and require large amount of capital expenditure to maintain sufficient performance levels. Furthermore, based on these aspects, it was considered that the development of an integrated solution to make as efficient as possible the fault maintenance operations is necessary. In these conditions, the objective of the present research presented in this article was to develop an autonomous, intelligent platform for railway and/or subway stations and tunnels measurement and profiling, considering infrastructural, electrical, and mechanical components.The scientific literature is relatively rich in the field of automated measurement and non-conventional solutions for a rapid assessment of the status of different functional components of a railway, or underground network. The modern signaling systems, interlocking and concentrated control dispatching solutions now use widely standardized solutions employing fail-safe and majority (triple) redundancy principles in railway automation. These involve numerous elements with high functional responsibility, zero-tolerance to critical faults that lead to catastrophic events in railway. Therefore, the responsibility in fault maintenance systems is very high, wherefrom the intense research in this direction to achieve new solutions and procedures for fast and efficient measurements of correct state of operation. In addition, the railway mechanical infrastructure itself, represents a collection of constructions, mechanical elements, ancillary equipment always exposed to intense wear and stress, that need continuous monitoring for early detection of faulty elements. Until present, there are few developments in the field of autonomous railway vehicles designed to detect and mark defects, and the subway lines make no exception. Track alignment and other geometric measurement methods usability is analyzed in , where tA visual measurement system for track gauge evaluation was developed, experimented, and presented in , involviGNSS technologies are good for use in outdoors experiments, measurements, and recordings. However, as presented before, such technologies cannot be employed for railways in tunnels, or for subway lines, where the presence of deep concrete walls prevents the low-powered signals of GNSS to be received. Here is a novelty of our proposed solution, where the robotic cart determines its position via odometers and has the ability to integrate measured data with position. For indoor environments, different technologies are to be adopted for positioning, measurement of gauges and geolocation. One of these is, of course, laser scanning. As presented in  by Qian Things become even more complicated when specific measurements are needed to be performed to determine curvature, or elements situated in a curved section of a railway, or a tunnel railway. Arkadiusz Kampczyk  is propoThe researcher describes a method for lacing/string lining and the measurement of the perpendicularity of rail joints. He also employs an MMS device for measuring versines and differences recorded in the lengths of rails, especially in curves. A laser beam is used for this purpose with a target cross, a camera, and a surveying disk for measurement.For pro-active maintenance purposes, Pacifique Turabimana and Celestin Nkundineza propose in their article  the testRegarding the electrified line and its associated equipment for power traction, Morris J., et al.  present -Autonomy of the method: the robotic cart can travel alone, collect position-referenced information regarding tracks gauges, deviations from standards, presence of foreign objects on the rails, imaging and external gauge profiling;-Collected data can be either transmitted via Wi-Fi to dedicated access points, or locally stored for off-line analysis;-Integrates various machine learning techniques for data fusion;-Has the possibility to fine tune a wide variety of measurements parameters and speed increments;-Is designed especially for subway lines with no GPS positioning signals, but can be adapted to work in external environments, using additional GPS information for position of a location;-Reduces stress of maintenance personnel by overtaking some tasks in usual maintenance activities during subway off-service periods;-The following are comparisons with other previously published works related to the railway inspection approaches:-Rowshandel, H.  proposes-Only 3% of the maintenance operations in railway tunnels (including subway) are recently subject of robotic activities —therefor-Robotic autonomous systems have been employed mostly for cleaning purposes , rolling-Killian, K  proposes-Railway catenary and power line automatic inspection are proposed via a system with laser beams and imagistic analysis simultaneously for four wires —the solu-Railway robotic inspection in tracks maintenance operations: machine vision and classification algorithms are used to detect and/or localize cracks in the rolling surface of tracks. It is employed a laser scanner mounted on a car that uses a random Forest classification learning algorithm  and for -Ground penetrating radar is also used for some robotic inspections in tunnels .Coming back to mobile solutions for sensing remote parameters of railway equipment, it is most usual to employ specific wagons, equipped with diverse measuring instrumentation, such as in ,42,43,44Usually, these systems for automatic measurement and data collection are designed for fewer dedicated operations and/or specific activities. Our proposed solution can make use of different sensors to determine gauge parameters, detection of foreign objects on tracks, smoke and gas, and as future development, the cart will be fitted with a system to measure tracks impedance (for track circuits tuning).Another novelty that our solution brings is the integration of sensors in a complex system, and for prediction we use a machine learning algorithm based on PCA T2Q.Depending on each specific railway administration regulations, there are several types of infrastructure (railway) faults, or defects that involve significant safety concerns. The safety compliance is even more drastic when the maximum allowed speed of the train is increasing; therefore, high-speed lines have a distinct corridor, points for these lines have a longer tangent and double mechanical locking, and specific requirements are set for the catenary and vibrations. From this point of view, high-speed lines need an intensive process of maintenance, starting with the visual inspection and ending with dedicated measurements for mechanical integrity, locking of points and deformations. Even detection of foreign objects that might fall between the lines, or between the point switches arms is important. On the other side, for subway lines the same restrictions apply, with the specification that early visual detection of a foreign object on the rails might be delayed due to low intensity of light in tunnels. In addition, subway lines use in general a third rail for power supplying, and this one also needs constant monitoring for defect detection.-Rail fracturing (might be early detected employing ultrasonic and/or video solutions). In case of complete fracture, the track circuits might be also able to detect this type of defect via electric control and interdict the entrance of a train on the specific section via the covering signal;-Intensive burring of insulating joints from neighboring rail coupons might induce short-circuits between adjacent track circuits, producing delaying in trains operations;-Mechanical deformations of gauges —this can be caused by exceeding the allowed weight per axle, mud under the rails, or other types of phenomena. It can also be caused by falling of heavy objects on the respective parts, especially from freight trains. In intense and prolonged warm summers, the temperature on the rails level might exceed 50 °C, causing mechanical deformations due to dilatation. This is a very dangerous defect, that must be early detected. Therefore, in very hot summers railway administrations impose speed and weight restrictions;-Intensive erosion, rust, or wear of the rail rolling surface. This type of defect might be considered from two points of view: firstly, if the line is intensively eroded, the electrical contact between the rail and the train wheel is imperfect and might cause malfunctioning of the track circuit, which might also trigger false response on the railway signaling, with possible catastrophic effects leading to trains collisions. It is very important to use safe track circuits from this point of view (based on high voltage pulses) on such lines, or to perform regular traffic to reduce erosion by mechanical friction. On the other hand, if the rails exhibit intensively wear, the surface becomes irregular, or with undulations, causing vibrations on the rolling stock or possible loss of electrical contact at high speeds. Therefore, also these types of mechanical deformations should be detected and resolved in due time.Usual infrastructure defects that might occur in the normal railway exploitation (both for surface and underground lines) include:For determining such specific mechanical and geometrical defects, usually regular observations are performed by railway personnel, traveling by foot distances between railway stations and noting where observed such anomalies. In addition, semi-automated measurements of higher precision are performed with dedicated cars (wagons) that are periodically traveled along the rails. These methods are more difficult to perform to underground railways, however, due to lower visibility conditions and very short times of operating breaks, especially during night.From these points of view, we consider that the development of an automated platform able to displace, collect and transmit relevant information regarding the infrastructure integrity is highly necessary.The proposed solution for mobile autonomous data collection for railway applications is composed of a set of sensors mounted on a self-driving cart, enabled with self-locating techniques . The first version was initially conceived mainly for subway applications, including analysis of external gauges limits, testing of on-board/ground communications and EM beacons, obstacle and/or foreign objects detection, and early fire discovery and warning. The block diagram of the first version is depicted in Central processing unit (CPU) based on myRIO—holds all the controls and commands for the mobile platform, programmable via a notebook;Odometry module (OM)—responsible with counting pulses from the wheels, measuring traveled distance, speed and updating the information about position of the automated platform to the CPU. For computing traveled distance and speed, a NPN Hall-Effect sensor has been employed. The following formula is employed for determining velocity:n—count number of pulses transmitted from the Hall sensor, R—radius of the flange that ensures counting pulses.In the first version, the hardware equipment encompasses several functional components, such as:Obstacle detection module (ODM)—it is composed of an infrared sensor and an ultrasonic sensor combination mounted in the front of the mobile platform for rapidly detecting and/or identifying obstacles on tracks. The detection of obstacles that are present in the front of the platform is performed through an IR sensor type Sharp GP2YOA710KOF and an ultrasonic sensor XL Maxbotix EZ0. Sharp GP2YOA710KOF is an integrated distance sensor for front obstacles, that ensures IR LED detection at λ = 850 nm. Operating distance is comprised between 100 and 550 cm. Its sensitivity diagram is presented in Gauge assessment module (GAM)—composed of ultrasonic and laser sensors to check the dimensions of the external gauge of tunnels, detect and locate eventual obstacles, or protruding objects. For a much more precise gauge check, an RPLIDAR A1M8-360 laser kit with a maximum reading frequency of 10 Hz and a detection distance of approximately 6 m was also introduced. The Bucharest underground free pass gauge shape is presented in Fire detection module (FDM)—composed of dedicated IR sensor and cooperating with GAM for fire detection, heated cabling location and alarming.For the software design the LabVIEW program has been employed, whose source code was executed by the NI myRIO data acquisition module, using a combination of modular hardware and software to transform the personal computer  into a user-defined remote-control system.Below are some images with the real equipment tested in subway tunnels.In -Merging sensor measurements of different categories;-Merging measurements from different positions and angles;-Merging measurements taken at different time intervals.For most tasks assigned to a mobile system for detecting path defects, the use of a single type of sensor may not give satisfactory results. For example, in navigation, some objects in the environment can only be detected by IR sensors, lasers, and others only by ultrasonic sensors, and only few by all types of sensors. The problem is, therefore, to find a method that effectively combines information from a multitude of sensors with different categories and characteristics. The most common term in literature for this process is “sensor fusion”. However, in the context of mobile systems, the merging of data must cope with the following challenges:For the experimental tests, the robotic platform was mounted on the Bucharest subway near Straulesti station in the tunnel, with the purpose of determining by measurements the gauge to the third rail (used for power supplying the trains by a patina). The test bed setup is presented in Using the measurement and data acquisition system presented in As it can be noticed in The resulting variation of the distance measured to the third rail has been transposed in the diagram below . It can For data fusion and detection of defects  it is proposed the development of a machine learning algorithm based on principal component analysis (PCA).PCA is an algorithm for reducing the linear dimensionality and the detection of anomalies. The kernel PCA (KPCA) is the nonlinear representation of the PCA, which can be successfully applied to complex spatial structures, and the kernel function is associated with the nonlinear transformations applied on the common PCA algorithm.Application of KPCA requires high computing power and significant memory, especially if applied to large drive data sets. To apply the KPCA for large sets of training data, we propose using a reduced version of KPCA in the process of structural anomalies detection inside subway tunnels.2 model and the square predictive error Q (Q statistics) are employed. The combination of T2Q results is then used as a defect detection value (index). The T2-statistic measures the variation in each sample and indicates the distance of each sample from the center of the PCA model. The Q-statistic indicates how well each sample conforms to the PCA model by measuring the distance that a data point falls from the PCA model.The suggested methodology will be employed for reducing the dimensions of the collected data obtained with the system shown in In the first stage, the dimensions are reduced using the KPCA algorithm. To achieve this, the algorithm was implemented in LabVIEW programming language. The results obtained for reducing the database size using the KPCA algorithm are shown in 2Q model, the input matrix was divided into a training set (for developing the model), and a test set, in an odd-even manner. The input matrix was mean-centered and scaled to a unit variance. This is necessary for the PCA model development. PCA functions in LabVIEW and MATLAB were used to calculate the principal components, the eigenvalues, and the amount of variance explained by each PCA component.To construct the PCA T2Q algorithm, the training, and defect detection applications.In the second stage, the machine learning architecture was implemented based on the PCA T2Q training algorithm, and In this experimental research a machine learning algorithm is proposed with the principal component analysis (PCA), used to develop a predictive estimator based on the load state model.2 statistic, Q statistic, combined index and I2 index) of each actuation phase are used to determine the presence of an anomaly and to distinguish between them. These indices are calculated based on the analysis of the residual data matrix to represent the variability of the projected data in the residual subspace and the new space of the main components.Detection of structural damages using principal components analysis and failure indices is based on the development of an initial basic model for undamaged structure. This one is constructed by applying principal components analysis to data collected via several experiments, and after the current structure (damaged or not) is subjected to the same experiments, and the collected data are projected into the main component analysis models. Two of these projections and four damage indices , which can be either mobile or fixed. Following the measurements performed with different ultrasonic sensors on multiple types of obstacles, the dependencies between them and the different detection possibilities must be established.-Measurement errorsGross errors that result from misreading or inattention and must be eliminated;Systematic errors that occur due to some constructive characteristics of equipment, or may be caused by external factors ;Random errors that occur as a result of the diversity of processes and phenomena as well as a the interactions of the experiment with other processes and phenomena that take place simultaneously.For the measurements made with ultrasonic sensors, as for any other measurement technique, a certain amount of errors affects in the results. Performing measurement produces errors that have the same amplitudes when the process measurement is performed under identical conditions, or errors that have variable amplitudes, their variation depending on certain laws. Errors resulting from the measurement processes may be classified into:In the process of analyzing the data from the measurements performed with the ultrasonic sensors it is necessary to calculate the absolute error, the relative error and to determine the maximum admissible error. The absolute error is given by the relation:x is the absolute error and The relative error is given by the relation:d is the absolute error and Thus, for distance measurements performed with ultrasonic sensors the absolute error will take the form:The maximum permissible error will be noted as follows:-Determining the distance to obstacles of different sizesAnother problem with sensors is the detection of small obstacles. Likewise, ultrasonic sensors do not detect objects of very small dimensions—depending on the frequency of the sounding—b and if air = 345.16 m/s.The XL Maxbotix EZ0 ultrasonic sensor was used to determine the distance from obstacles of different sizes, in conditions of atmospheric pressure of 714.5 mmHg and temperature of 22.5 °C (295.65 K). In this case, the speed of sound propagation has the value cThe XL Maxbotix EZ0 sensor line provides high accuracy and high resolution ultrasonic proximity detection and ranging in air, in a package less than one cubic inch. This sensor line features 1-mm resolution, target-size compensation for improved accuracy, superior rejection of outside noise sources, internal speed-of-sound temperature compensation. This ultrasonic sensor detects objects from 1-mm to 5-m, senses range to objects from 30-cm to 5-m. Sensor operates at 42 kHz. The obstacles used to test the ultrasonic sensor were mounted on the mobile platform and a linear potentiometer guide subassembly was used for tuning the generated sounding intensity. Flat obstacles made of aluminum with a height of 150 mm and a width of 20 mm, 40 mm and 60 mm respectively were used. The distances at which the obstacle was positioned vary from 100 mm to 1000 mm, from 100 to 100 mm.It is observed that the average value of the relative error increases inversely with the width of the obstacle, which is twice as large at the obstacle with a width of 20 mm compared to that with a width of 60 mm.-Determining the distance from obstacles of different shapesThe detection of the railway equipment in subway lines may involve objects with different sizes and shapes, with different textures. As in the cases presented above, one another problem with the ultrasonic sensing occurs in the detection of obstacles having different shapes. The measured distance between the sensor and the obstacle may experience different values, depending on the shape of the obstacle.To observe the influence of the shape of the obstacle, distance measurements were performed between the ultrasonic sensor XL Maxbotix EZ0 and three obstacles with different cross sections having the dimensions shown in To simplify the expression, the obstacle with rectangular section was named Obstacle 1, one with triangular section was labeled Obstacle 2 and the cylindrical obstacle was called Obstacle 3.air = 344 m/s.Measurements with the XL Maxbotix EZ0 ultrasonic sensor were performed at atmospheric pressure of 711.4 mmHg and temperature of 20.6 °C (293.75 K). In this case, the speed of sound propagation has the value cIt can be noted that the average value of the relative error is the highest in the case of Obstacle 2. In addition, the values of the measured distance are the highest in the case of Obstacle 2, which is detected at a greater distance than the actual distance. In the case of cylindrical Obstacle 3, the measured distance values do not differ much from the values obtained for Obstacle 1.-Determining the distance to obstacles from materials with different texturesIn addition to the size and shape of the obstacles, the detection of the ultrasonic sensor can also be influenced by the texture of the material from which the obstacle is made.Stainless steel;Aluminum;Copper;Wood;Rubber;Plastic.To observe the influence of the texture of the material from which the obstacle is made, distance measurements were performed between the XL Maxbotix EZ0 ultrasonic sensor and six obstacles made of different sample materials. These were positioned in turn at the same distances between 50 mm and 250 mm from the ultrasonic sensor. The materials of which the obstacles used for the determinations are made were:air = 344.43 m/s.The field measurements with the XL Maxbotix EZ0 ultrasonic sensor were performed under atmospheric pressure of 705.6 mmHg and temperature of 21.3 °C (294.45 K). In this case, the speed of sound propagation has the value cWhen measuring the distance from obstacles made of metals, it can be observed that the values are higher than the actual distance for steel and copper  and smaller for aluminum .When measuring the distance from obstacles made of non-metals, it can be noticed that the measured values are higher than the actual distance for plastic  and smaller for wood and rubber .-Determining the distance with a sensor mounted on a rotating platform (radar)To simulate a rotary sensor, measurements were performed with the ultrasonic sensor XL Maxbotix EZ0 located on a mobile platform, which has the possibility to rotate by a maximum of 90 degrees from a fixed point. The experiment was run for 12 s for each obstacle and the sampling rate was 10 samples per second. The first set of measurements was performed without obstacles, determining the dimensions of the workspace.To simplify the expression, the obstacle with section a rectangle was called Obstacle 1, the one with section a rectangular triangle was called Obstacle 2 and the cylindrical obstacle was called Obstacle 3 see . The obsFollowing the measurements, the shape of the workspace was determined with each obstacle, as perceived by the ultrasonic sensor. By analysing the three diagrams in Based on the laboratory tests presented above, we simultaneously analyzed the two subway tracks using an area of ultrasonic sensors based on the analysis of received signals and the representation of ultrasound imaging, to determine the rail profile and the possibilty of identifying mechanical defects inside the rails. The results are shown in 2Q model, the input matrix was divided into a training set (for developing the model), and a test set in an odd-even manner. Ultrasonic imaging was employed for detecting subway rail surface quality and/or fracturing.To construct the PCA T2Q algorithm. During the development and laboratory tests we simulated the model with four ultrasonic sensors that work independently, thus using four components for the PCA T2Q algorithm. According to the tests performed to detect subway rail defects, we performed two configurations of ultrasound sensors: the four sensors worked independently and the analysis was performed on PCA 4 components or the grouping of the four sensors in groups of two sensors and PCA analysis was performed with two components. The best results were obtained by grouping the ultrasound sensors in pairs of two sensors each.In Subchapter 3.1 we described the implementation of the machine learning model with the PCA TThe algorithm proposed in this research was used to validate the analysis. The results are presented in 2Q. The prediction results were obtained by correlating the data obtained from ultrasound imaging and determining the running line profile.2Q model, the input matrix was divided into a training set (for developing the model), and a test set in an odd-even manner, for enabling the possibility to carry out ultrasonic investigations of metal parts with different geometries and sizes. As a result of the measurements obtained with this system, in repeated experiments, it can be stated that the device met the conditions for which it was designed.To construct the PCA TBased on the above considerations, during the laboratory tests, we detected a specific influence of the target edges on the accuracy of the analysis of mechanical deformations at the surface of steel, especially at sound speed measurements. Therefore, the hypothesis is that the margin effect influences the measurements of sound speed, and new experiments must be performed to find solutions that avoid or reduce the effect of edges’ sound reflections.Although it is necessary to consider the beam propagation when performing an ultrasonic inspection, it is important to note that in the remote field, the maximum sound pressure is always along the sound axis (center line) of the transducer. Therefore, the strongest reflections are likely to come from the front area of the transducer.Ultrasonic investigations allow the evaluation of the amplitude in volume, in metal components, with an acceptable accuracy. To obtain the stress values, it is necessary to consider the elastic constants of the material. Further investigations and field tests are necessary in this direction, to also determine the feasibility of using high-frequency ultrasound imaging for subway practical applications.Some manufacturing and/or operating and stressing processes  might introduce into the material of the parts residual stresses that substantially change their operating performance. These are difficult to detect with classic setup of ultrasonic sensors, a deeper investigation is necessary, probably using closer ultrasonic sensing and higher frequencies.The great advantages of ultrasonic techniques are the fast data collection, portability of instrumentation, radiation-free control, the possibility of measuring the geometric location of points or continuous time and the low costs. However, to obtain quantitative values of signal amplitude, ultrasonic techniques require the evaluation of elastic properties. Therefore, it is important to precisely know the mission of sound amplitude state analysis.Ultrasonic techniques allow the estimation of surface and volume stresses of the parts under investigation.The ultrasonic investigation method can be applied to metallic and non-metallic materials, capable of propagating ultrasonic waves with frequencies up to 20 MHz.In the analysis of internal structure stresses, decreased propagation time can be interpreted as a decrease in tension or an increase in compression stresses, the change in stress can be approximated using the relative change in time and the appropriate elastic constants.The scientific work carried out under the article allowed the analysis of a vast and complex field, the ultrasound investigation, from an applicative-technological perspective. From this perspective, the theoretical research has been further continued experimentally in the field and the results allowed the formulation of the following hypotheses:The algorithm proposed in this article responds well to the instability and to the upper or lower constraints imposed by the characteristics of the train rails during the analysis of the possible defects.The scientific approaches presented in this research allowed the analysis of the complex field of ultrasonic investigation, from an applicative—technological perspective.2Q algorithm to determine generalization characteristics. The main conclusions are as follows:(1)The proposed method avoids the instability, upper or lower constraints caused by the characteristics of the rails during the processing of anomaly detection. The experimental results showed that the proposed method can work better than similar methods presented, with an accuracy of 98.65% and an average detection time of 0.15 s. However, for better results, the speed of the automated cart should be limited and sampling intervals shortened ;(2)2Q method is used to group the generalized characteristics constrained after size reduction using KPCA. In addition, the kurtosis index, and the accuracy of the PCA T2Q algorithm are used to evaluate the results, and the detection results are obtained with an accuracy of over 95%;The PCA T(3)The experimental results show that the proposed method has a higher detection accuracy for rails defects  and present better application perspectives than the methods reported in the literature.To make reliable the detection of geometrical defects in the mechanical structure of rails, in this article we proposed a method based on the feature manipulation algorithm with KPCA, and a machine learning structure based on the PCA TFuture field experiments are still necessary for the development of the integrated solution, which will focus on integrating data from ultrasonic and acoustic sensors with video cameras to detect defects outside and inside the rails using deep learning post-processing. We also seek to investigate the possibility of using closer, higher-frequency ultrasonic sensors for analyzing the rails internal integrity.


As the pandemic evolves, information about how the virus has impacted the health of children is being steadily reported. In early April, the CDC calculated that in the US approximately 1.7%  of COVID-19 cases had occurred in children <18 years of age.1 Seventy-three percent of these cases were associated with fever, cough, and/or shortness of breath and 5.7% were hospitalized, compared to 93% and 10% respectively in adults. The median age of cases in children was 11 years (range 0−17 years) with 33% reported in children 15−17 years of age and 15% in children <1 year of age. In this dataset, 23% of children had underlying medical conditions which were present in 77% of hospitalized children and all six children admitted to the ICU. The experience in the first months of the pandemic suggests that although susceptibility to infection may be similar in children and adults, the risk for severe disease appears to be lower in children.3 Nevertheless, serious COVID-19 has been reported in children, including neonates/young infants and those with underlying medical conditions.4As of May 1, the Johns Hopkins Coronavirus Research Center is reporting that 3.127 million cases of coronavirus disease 19 (COVID-19) and 213,791 deaths related to these infections have occurred worldwide  and other research enterprises worldwide are well positioned to address these issues in collaboration with the pediatric research community. Given the urgency, it will be important to increase efficiency by establishing a clear roadmap for addressing these issues.Ensuring availability of widespread, rapid point-of-care diagnostic testing: Identification of active COVID-19 infection is important for children as well as adults. Children appear to be quite susceptible to COVID-19 infection and viral replication/shedding appears to occur in both the respiratory and gastrointestinal tracts.2 Identifying infected children is critical to implementing infection control practices in the community and in medical care settings. This is based on the experience with influenza and can be expected to be extremely important in public health strategies aimed at preventing infection in the most susceptible patients. More widespread availability of testing will also allow better understanding of the clinical course of infection in children and is needed to establish the risk of serious illness in infants and children. Restrictive criteria for testing  currently included in public health guidelines have largely been driven by limited supply of testing kits and are likely to result in substantial underestimation of the number of COVID-19 cases in children. Validation of the natural history and impact of disease in children will require broadly available point-of-care testing and possible modifications of testing methods to be acceptable for use in neonates, infants, older children, and adolescents. Pediatric health-care providers are well versed in viral diagnostics and, once given better access to testing for COVID-19, can be expected to rapidly generate the data needed to construct appropriate protocols that will test investigational candidates for prevention and/or treatment.Conducting widespread serologic testing of children as a marker for susceptibility: Antibody testing for COVID-19 is now being introduced for clinical use. It is critical to initiate widespread testing in children to assess the movement of the viral infection through children in communities and the impact of children on the spread of disease into adult populations, particularly the elderly. Although it is appropriate (until proven otherwise) to assume that the presence of anti-COVID-19 serum antibodies is evidence of prior infection, it has yet to be determined if this is associated with protection from new infections . In addition, the duration of any protection will need to be defined. Identification of protective immunity in children will be key for policymakers to decide about when and how to return children to daycare and schools and to ensure the relative safety of extended family caregivers .https://www.nih.gov/news-events/news-releases/nih-begins-study-quantify-undetected-cases-coronavirus-infection). Parallel studies in children are needed as soon as feasible and should be designed to address the potential for some immune responses to be associated with exacerbation of subsequent infections with coronaviruses. How information about seropositivity (and presumed “protection”) is utilized to make policy decisions involves serious economic, public-health, and bioethical considerations. Ensuring that scientifically valid data are collected is the responsibility of the pediatric research community and forms the basis for making these complex decisions.Antibody testing, including assessment of correlation between binding antibody titers measured by ELISA and neutralizing antibody titers, is needed for: (1) establishing research initiatives to validate “protection”; (2) establish the natural history of COVID-19 in children; and (3) optimal design of safe and effective vaccines. For all these reasons, antibody testing that is pediatric friendly can be point of care with rapid readout of results, and is available in resource poor as well as other settings is necessary. NIAID has recently begun a serological survey of 10,000 adults (>18 years of age) in the US with no history of COVID-19 symptomology to assess the frequency of unrecognized prior infection , antiviral drugs, and candidate treatments for supportive care. As these candidate therapeutics are developed and tested in adults, consideration should be given to innovative ways to ensure evaluation in children and adolescents as soon as appropriate based on risk and potential benefits. To do this, consideration of what may be needed from adult studies to allow extrapolation or simulation to be applied in children is essential. It is important for pediatricians to be at the table when adult studies are being planned to help optimize collection of standardized data elements needed in subsequent studies in children. Depending on the characteristics of a treatment candidate, investigators will need to have a good understanding of the risk of COVID-19 infection in children to ensure optimal risk/benefit deliberations. The profile of risk for children for many therapeutic agents may be very different than adults due to significant differences in pathophysiology, drug metabolism, and multiple other considerations. However, as more information becomes available, risk/benefit considerations may change substantially. The inclusion of adolescents in adult trials has been recommended in oncology and when appropriate in other drug development settings. If the pathophysiology of disease is similar in adolescents and adults, the illness in the adolescent patient being considered is severe and the risk of the new therapy is assessed to be low, adolescents should be included in adult trials. On average, the approval of innovative therapeutics for children often lags about a decade behind approval in adults. This delay is often caused by a failure to include children at very early stages and to assume that these therapies cannot be assessed in children until studies in adults are completed. While it appears so far that children represent a small proportion of the total population with serious COVID-19 disease, this should not mean that we are left without the information needed to make reasonably well-informed decisions about using new therapies in children who are critically ill. Antiviral drug development has made enormous progress over the last decades and early consideration and inclusion of children into clinical trials has resulted in a much better track record than in some other areas; a lesson clearly learned during the response to HIV infections in infants and children at the end of the last century.Evaluation of vaccines and other preventive measures for children: There is intense interest in ongoing efforts to develop vaccine candidates for the prevention of COVID-19. Pediatricians have significant expertise with the process for evaluation of the safety and efficacy of vaccines and the development of recommendations for their use. Clearly, new vaccine candidates need to be evaluated in children as soon as scientifically and medically appropriate given their likely role in COVID-19 transmission. Planning for evaluation in children will require continuous monitoring of the viability of vaccine candidates and risk/benefit assessments to determine the appropriate timing for pediatric clinical trials. In addition, for some candidate vaccines, the risk of potential exacerbation of disease  will require special attention. Development of new vaccines for respiratory viruses has historically been a lengthy process as was evident in the development of influenza vaccines in children and is evident with efforts to develop a vaccine for RSV. A roadmap for the development of a COVID-19 vaccine for children should be developed based on evolving science and experience.The main reason why children are left behind in the evaluation of new interventions is the failure to think and plan pediatric studies early in the development process. Science, commitment, funding, and risk/benefit will determine how well we as a community respond to the COVID-19 pandemic. Early consideration and planning for including children in these drug development programs is essential. In the past decade, the pediatric research infrastructures for product development in the United States, European Union, Japan and elsewhere have worked to create mechanisms for public−private global collaboration to advance product development for children. These efforts should ensure that infants, children, adolescents and pregnant women are included in the development of preventive and therapeutic approaches to stem the impact of COVID-19. This experience will not only address the specific challenges related to COVID-19, but can also define a pathway for working efficiently to address the needs of infants and children posed by pandemics we might face in the future.