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Background Clinical learning environments influence the learning and performance of learners by creating learning opportunities and experiences. The maternity ward offers a major educational opportunity for midwifery students, obstetrics and gynecology residents and medical interns to acquire and improve crucial skills. Objectives This study seeks to determine the way in which the clinical learning environment of the maternity ward creates learning opportunities and enables the accumulation of experiences for the noted learners. Methods This qualitative study was conducted using inductive content analysis at the Maternity Ward of Shahid Beheshti Hospital, affiliated with University of Medical Sciences. Midwifery students, medical interns and obstetrics and gynecology residents spend a certain period of time in this ward as a mandatory part of their obstetrics and gynecology training. Data were collected through semi-structured individual interviews and observations and were then analyzed in MAXQDA concurrently with data collection. Results Three main categories emerged from the analysis of the data collected from the interviews and observations: disorganized learning opportunities, heavy emotional load and learners’ abandonment in the care-provider and learner role. Conclusion The maternity ward lacked the necessary organization to generate an environment conducive to learning and independent practice for the three groups of learners. The learners’ training and acquired skills were thus affected by the clinical learning environment.

In the path towards the intelligent industrial 4.0, the railway industry is keen to develop intelligent asset management strategies for digitalization and smart management for rail infrastructure. It aims to both reduce the cost and exposure of human-labor, associated with track maintenance risk, as well as increase the autonomy and accuracy for the railway inspection and repair job. A Robotic Inspection and Repair System (RIRS) is proposed to undertake the automated railway maintenance consisting of the autonomous off-track travel between base workshop and track, road-rail conversion, autonomous on-track inspection, and repair as well as remote communicating to railway signaling system and infrastructure system. This paper presents a localization and navigation framework for this new autonomous system; applied to the mentioned railway maintenance job. This system comprises a commercial Unmanned Ground Vehicle (UGV, named Warthog) with a robotic manipulator (UR10e), and multiple onboard sensors including Lidar, camera, RTK GNSS, IMU, wheel odometry, and multiple types of cameras. An adaptive trolley is also designed for the purpose of road-rail conversion. This research also focuses on how to increase accuracy for the support of track defect detection and localization.

Objective Food transmitted parasitic disease can be considered as one of the most common diseases in different societies and transmitted via water, soil and food. The goal of this cross-sectional descriptive-analytic research was to determine the prevalence and type of parasitic contaminated vegetables in Birjand city in 2017. Methods 196 samples were obtained from 12 kind of vegetables including leek, radish, cress, mint, basil, dill, coriander, parsley, scallion, spinach and lettuce from 20 green groceries in all parts of the city. After the washing steps, each of the samples was centrifuged, sedimented and stained. The samples were examined for the presence of metazoan and protozoan parasitic contaminations. Cysts of Giardia lamblia were partly purified via the sucrose density gradient technique. After freeze-thaw cycles, genomic DNA became extracted the usage of QIAamp Stool Mini package. The results of the study were reported as descriptive statistics (mean and percentage) and analyzed by X2 tests at a significant level of p ≤ 0.05. Results The total contamination of vegetables in the city of Birjand was reported to be 78.5%. The most elevated rate of contamination was related to Entamoeba coli parasite (46%), followed by Giardia lamblia (18.3%). The highest rate of contamination was detected in fenugreek, basil and celery vegetables, which 100% of the samples contained at least one kind of parasite. In this study, pathogens and non-pathogens were found in 52.1% and 47.9% of the samples, respectively. Conclusion Results shows that the rate of parasitic contamination in this study was relatively high, which can led to prevalence of parasitic diseases in humans.