Development of Fuzzy Cognitive Map for Optimizing E-learning Course

Work-related stress and burnout among physicians are of increasing relevance. The aim of this study was to investigate work-related behavior and experience patterns and predictors of mental health of physicians working in medical practice in Germany. We surveyed a stratified, random sample of 900 physicians from different specialties. The questionnaire included the standardized instruments Work-related Behavior and Experience Pattern (AVEM) and the Short Form-12 Health Survey (SF-12). Only one third of physicians reported high or very high general satisfaction with their job, but 64% would choose to study medicine again. Only 18% of physicians presented a healthy behavior and experience pattern. Almost 40% presented a pattern of reduced motivation to work, 21% were at risk of overexertion, and 22% at risk for burnout. Willingness to study medicine again, fulfilled job expectations, professional years, marital status, and behavior patterns were significant predictors of mental health and accounted for 35.6% of the variance in mental health scores. Job-related perceptions also had a significant effect on burnout. The strong influence of work-related perceptions suggests a need for realistic expectation management in medical education, as well as support in stress management and coping strategies during medical training.

This paper describes the experience of teaching «Nuclear Knowledge Management» course at the National Research Nuclear University MEPhI (NRNU MEPhI). Currently, the course is implemented both in engineer and master degree programs and is attended by over 50 students. Goal, objectives and syllabus of the course are discussed in detail. A special attention is paid to practical exercises and final examination options in the case of small and large student groups. The course is supported by the Cyber Learning Platform for Nuclear Education and Training (CLP4NET), developed by the IAEA. The experience of NRNU MEPhI lecturers assisting in conducting the International School of Nuclear Knowledge Management, held annually in Trieste (Italy), is described with a special attention to the fact, that the course has passed the certification process at Academic Council of NRNU MEPhI. In 2014 and 2015 the course has been recognized as one of the best ones in NRNU MEPhI. Finally, perspectives of «Nuclear Knowledge Management» course are considered. They include increase of the course duration, introduction of the course into the learning process of other departments and institutions of the university, and transferring the course to other members of the Association «Consortium of ROSATOM supporting universities».

Prediction of humans’ behaviors during a disaster: The Behavioral Pattern during Disaster Indicator (BPDI)

Twenty-four hour activity budgets and patterns of behavior in captive ocelots ( Leopardus pardalis)

As Internet use has proliferated, Web-based learning systems have become more and more popular. Numerous researchers have spent a great deal of effort to facilitate the promotion of high quality Web-based learning environments, such as intelligent Web-based learning systems and adaptive learning. To facilitate such researches, students' behavioral patterns must be observed and experimentally analyzed. However, building a Web-based learning system and the requisite collecting of behavioral patterns usually takes a great deal of time and effort. To solve this problem, this paper proposes a learning behavioral model based on Colored Petri Nets (CPN) to model and generate students' behavioral patterns. To verify the viability of the proposed model, this paper compares actual data collected from elementary school students with the behavioral pattern generated by the proposed model. The results prove: (1) The generated behavioral pattern approaches actual student behavior; (2) The generated behavioral pattern serves as adequate test data to test whether the predicted learning content of an intelligent e-learning system is appropriate; and (3) The proposed model is capable of recommending the appropriate learning content for students utilizing e-learning systems.

Lithium chloride, cholecystokinin and meal patterns: Evidence that cholecystokinin suppresses meal size in rats without causing malaise

Metopic synostosis is a condition in which the metopic suture of the human cranium fuses prematurely and may be related to poor behavioral inhibition leading to behaviors commonly associated with Attention-Deficit Hyperactivity Disorder (ADHD). The purpose of this project was to examine the behavior patterns among children with a history of metopic synostosis. It was hypothesized that children with a history of metopic synostosis would exhibit many of the same behavioral patterns associated with ADHD. It was also hypothesized that children with a history of simple synostosis not involving the metopic suture would not evidence this type of behavioral pattern. In order to test these hypotheses, the behavior of three groups of children was compared including (1) children who had a history of metopic synostosis (M= 7.63 years, SD = 1.92 years), (2) children who had a history of simple craniosynostosis not involving the metopic suture (M= 7.54 years, SD = 1.88 years), and (3) a group of children diagnosed with ADHD (M=7.78 year, SD = 1.87 years). It was found using the Home and School versions of the Attention Deficit Disorders Evaluation Scale (ADDES) that children with a history of metopic synostosis demonstrate significantly more behavioral disturbances than children with a history of simple craniosynostosis not involving the metopic suture. Using the BASC Teacher Rating Form it was found that children with a history of metopic synostosis have a behavior pattern similar to children diagnosed with ADHD and a dissimilar behavior pattern compared to children who have a history of craniosynostosis not involving the metopic suture. Using the BASC Parent Rating Form it was found that children with a history of metopic synostosis have a behavior pattern dissimilar to children diagnosed with ADHD and a dissimilar behavior pattern compared to children who have a history of craniosynostosis not involving the metopic suture.

The possible effect of a specific overt behavior pattern upon serum cholesterol levels, blood coagulation, and incidence of arcus senilis and clinical coronary heart disease was studied in two groups of women randomly chosen for study by lay selectors after careful orientation. Women of group A were selected on the sole basis of their habitual exhibition of a behavior pattern (type A) characterized by excessive competitive "drive," ambitiousness, and immersion in "deadlines." Women of group B were selected by lay selectors on the sole basis of their exhibition of entirely opposite behavioral traits (type B). Each group of subjects included a series of nuns respectively exhibiting the type A and B behavior patterns. The women exhibiting pattern A also exhibited markedly higher serum cholesterol levels, often more rapid blood coagulation, three to five times as much arcus senilis, three to seven times more diastolic hypertension, and a four-fold higher incidence of clinical coronary disease. A history of myocardial infarction or angina pectoris only was found in group A. These striking differences were independent of differences in parental incidence of coronary disease, caloric or fat intake, obesity, physical activity, smoking, or "femininity." The strikingly higher incidence of clinical disease in pattern A women also was not found ascribable per se to differences in heredity, obesity, hypertension, caloric or fat intake, smoking, physical activity, blood coagulation, or serum cholesterol levels. The results closely resemble those previously found in men with similar behavior patterns, and strongly suggest that the type A behavior pattern is itself in some way largely responsible for the observed findings.

A Learning Management System (LMS) is nowadays a pivotal element in the education environment of a modern university. However, though it generally has a beneficial and positive impact on the education, a part of the teachers is sometimes reluctant to adopt a LMS because of the perceived usage difficulty. Therefore, it is clear that a key step in order to spread the use of a LMS is to teach to the teachers how to use it and which benefits their teaching activities can gain. In this paper, we report and analyze the experience we had at University of Perugia. An e-learning course has been released to the (approximately) 1000 teachers of the university with the aim of introducing them to the basic tools provided by the LMS. Importantly, the course has been created and delivered by means of UniStudium, i.e., the Moodle-based LMS deployed in our university. This allowed us to collect interesting quantitative and qualitative data that have been elaborated and analyzed. The analysis shows that the activities carried out reached a prominent percentage of teachers, by also providing us important suggestions and hints to guide our future activities in this direction.

Knowing the behavioral patterns of city residents is of great value in formulating and adjusting urban planning strategies, such as urban road planning, urban commercial development, and urban pedestrian flow control. Based on the high penetration rate of cell phones, it is possible to indirectly understand the behavior of city residents based on the call records of users. However, the behavioral patterns of large‐scale users over a long period of time can present characteristics such as large dispersion, difficult to discover patterns, and difficult to explain behavioral patterns. In this paper, we design and implement a human behavior pattern analysis system based on massive mobile communication data based on serial data modeling method and visual analysis technology. For the problem that it is difficult to capture the behavioral patterns of residents in cities in call records, this paper constructs base station trajectories based on users’ cell phone call records and uses users’ long‐time base station trajectories to mine users’ potential behavioral patterns. Since users with similar activity characteristics will exhibit similar base station trajectories, this paper focuses on the similarity between text sequences and base station trajectory sequences and combines the word embedding method in natural language processing to build a Cell2vec model to identify the semantics of base stations in cities. In order to obtain the group behavior patterns of users from the base station trajectories of group users, a user clustering method based on users’ regional mobile preferences is proposed, and the results are projected using the Stochastic Neighbor Embedding (t‐SNE) algorithm to expose the clustering features of large‐scale cell phone users in the low‐dimensional space. To address the problem that user behavior patterns are difficult to interpret, a visual analysis model with group as well as regional semantics is designed for the spatial and temporal characteristics of user behavior. Among them, the clustering model uses the distance between scatter points to map the similarity between users, which helps analysts to explore the behavioral characteristics of group users.

For optimal health promotion in adolescents, it is important to know their typical behavior patterns. The aims of this study were to identify and compare sex-specific health-related behavior patterns of adolescents in Germany. The KiGGS Survey and the MoMo Study assessed the health behavior of 832 boys and 811 girls by means of a questionnaire. The indices physical activity, diet quality and media use were included in cluster analyses for boys and girls separately. For each sex, 4 clusters with similar characteristics on the three included indices were identified. However, cluster size, level of the characteristics and correlates were different in boys and girls: fewer girls showed high activity levels. Overall, physical activity and media use were less pronounced in the behavior pattern of girls. There was a stronger association of behavior patterns with socio-economic status in girls. In boys, behavior patterns were significantly associated with prevalence of overweight. The similar cluster solutions for boys and girls support the assumption that there are typical health-related behavior patterns among German adolescents. This finding might facilitate the precise definition of homogeneous target groups for health promotion programs. Furthermore, it seems that sex-specific foci should be set.

Background: Changing trends in dietary habits, behavioral patterns, and prevalence of overweight/obesity among adolescents is of great concern.Methods: 6469 school children from five districts of Kerala, India were enrolled. Dietary habits and behavioral patterns were collected using a pretested questionnaire. Anthropometric measurements were recorded and compared with regional Indian Academy Pediatrics (IAP) reference charts. Data was computed and analyzed. Results: Mean age: Boys 14.51±0.93, Girls 14.42±1.04 years. Male: Female ratio 0.92:1. Practices like missing breakfast, non-compliance to weekly Iron Folic Acid supplementation and not taking exercise >2 hours/week were more in girls. Increased consumption of junk food, screen time >2 hours/week and not sharing life events with parents were more in boys. Mean anthropometric parameters were <50th percentile of reference charts. 6.1% had low BMI (boys, 4.2% vs. girls,7.6%,), 12.3% were overweight (boys, 14.9% vs. girls,10.2%) and 4.4% had obesity (boys, 4.7% vs. girls, 4.2%). Mean anthropometric parameters were higher in more urbanized districts. Daily consumption of junk food showed significant association with obesity and exercise >2 hours/week with normal BMI.Conclusions: Dietary and behavioral patterns and prevalence of overweight/obesity showed variability with respect to gender and region of residence. 12% were overweight and 4% had obesity. Daily consumption of junk food showed significant association with overweight/obesity and daily exercise >2 hours/week with normal BMI. Health education addressing behavioral change is recommended for better health.

La forma de enseñar y de aprender en la sociedad del siglo XXI ha cambiado. Actualmente, en un porcentaje alto la docencia se realiza en los Learning Management System. Estos sistemas permiten aplicar técnicas de Learning Analytics. La utilización de dichas herramientas, facilita, entre otros, conocer el patrón de aprendizaje de los estudiantes y la predicción de los alumnos en riesgo. El objetivo de este estudio fue conocer en orden jerárquico de importancia los patrones de aprendizaje más efectivos de los estudiantes en la plataforma. Se trabajó durante dos cursos académicos con una muestra de 122 estudiantes de Ciencias de la Salud. Los instrumentos utilizados fueron la plataforma Moodle v.3.1 y el análsis de los logs con técnicas de Machine Learning de regresión. Los resultados indican que el Modelo de Predicción Lineal Automático detectó en orden de importancia: las visitas medias por día, la realización por parte del estudiante de cuestionarios de autoevaluación, y la consulta al feedback del docente. El porcentaje de varianza explicada de estas variables sobre los resultados finales fue de un 50.8%. Asimismo, la efectividad del patrón conductual explicó 64.1% de la varianza de dichos resultados, hallándose tres clústeres de efectividad en el patrón conductual detectado.

PurposeThis paper aims to investigate the relationship between the students’ digital activities and their academic performance through two stages. In the first stage, students’ digital activities were studied and clustered based on the attributes of their activity log of learning management system (LMS) data set. In the second stage, the significance of the relationship between these profiles and the associated academic performance was tested statistically.Design/methodology/approachThe LMS delivers E-learning courses and keeps track of the students’ activities. Investigating these students’ digital activities became a real challenge. The diversity of students’ involvement in the learning process was proven through the LMS which characterize students’ specific profiles. The Educational Data Mining (EDM) approach was used to discover students’ learning profiles and associated academic performances, where the activity log file exemplified their activities hosted in the LMS. The sample study data is from an undergraduate e-course hosted on the platform of Blackboard LMS offered at a Saudi University during the first semester of the 2019–2020 academic year. The chosen undergraduate course had 25 sections, and the students attending came from science, technology, engineering and math background.FindingsResults show three clusters based on the digital activities of the students. The correlation test shows the statistical significance and proves the effect of the student’s profile on his academic performance. The data analysis shows that students with different profiles can still get similar academic performance using LMS.Originality/valueThis empirical study emphasizes the importance of the EDM approach using clustering techniques which can help the instructor understand how students use the provided LMS content to learn and then can deliver them the best educational experience.

This special volume contains the Proceedings of the XVII International Scientific Conference and the School of Young Scholars “Physical and Chemical Processes in Atomic Systems”. This forum was dedicated to the 150th anniversary of D.I. Mendeleev’s Periodic System of Chemical Elements. The conference was held in Moscow, Russian Federation from 28 to 30 October 2019.The organizers of the conference were• Institute of Nanotechnologies in Electronics, Spintronics, and Photonics at National Research Nuclear University “MEPhI”;• State Research Center of Russian Federation Troitsk Institute for Innovation and Fusion Research (“TRINITI”);• National Research Center “Kurchatov Institute”.This event has offered a platform in bringing together for students, postdocs, academics and industrial experts to exchange their ideas and contributing an integrative approach to research physical and chemical processes in various atomic systems. This event has been attended by more than 100 participants representing 33 organizations of Russia and foreign countries.The program of the conference consisted in oral invited lectures of the leading experts on selected topics and numerous posters for other participants.The wide range of fundamental and applied physical and chemical processes in various atomic systems has been undergone to consideration during the meeting. The main topics of the conference include the following tracks:Track 1: physics of separation processes, including separation of isotopes in rapidly rotating flows.Track 2: selective transport phenomena in membranes.Track 3: water treatment.Track 4: physics of surface and colloids.Track 5: extremal gas dynamics.Track 6: physical phenomena in porous medium.As is well known, the study of physical and chemical processes in numerous atomic systems requires using the diverse physical and chemical methods. This will explain why a fair amount of reports was focused on development of new techniques and instruments for the research of multiple phenomena in such systems.Note that some reports presented in these Proceedings have been considerably modified by their authors after discussion during presentations or in the lobby. Also, the program committee of the conference came to the opinion of possibility to include in the Proceedings a few additional posters corresponding to a scope of the meeting as a result of the carried out round table discussion.The interest shown by Russian and foreign scholars as well as students either to the forum format or the subjects of reports, gives us hope for a continuation of this meeting.Special volume is supported by MEPhI Academic Excellence Project, contracts No 02.a03.21.0005, 27.08.2013. Guest Editor Professor Vladimir D. Borman Head of the Department of Molecular Physics National Research Nuclear University MEPhI, Russia