Effects of a single interprofessional simulation session on medical and nursing students’ attitudes toward interprofessional learning and professional identity: a questionnaire study

Background Participation in simulation-based interprofessional education (sim-IPE) may affect students’ attitudes towards interprofessional learning (through gaining experience with others) and their professional identity (by increasing the ‘fit’ of group membership). We examined this in two questionnaire studies involving students from four universities in two areas of the UK. Method Questionnaire data were collected before and after students took part in a sim-IPE session consisting of three acute scenarios. Questionnaires included the Readiness for Interprofessional Learning Scale (RIPLS) and measures of professional identity derived from the social identity theory literature. In Study 1, only identification with Professional Group (doctor or nurse) was measured, while in Study 2 identification with Student Group (medical or nursing student) and the immediate interprofessional Team worked with in the simulation were also measured. Linear mixed effects regression analysis examined the effect of the simulation session, and differences between medical and nursing students, sites and identity measures. Results A total of 194 medical and 266 nursing students completed questionnaires. A five-item subset of RIPLS (RIPLSCore) was used in analysis. In both studies RIPLSCore increased for all groups following participation in sim-IPE, although this was larger for nursing students in Study 1. Nursing students had consistently higher RIPLSCore scores than medical students at one site. Effects of the session on identity varied between sites, and dimensions of identity. Notably, while positive emotions associated with group membership (Ingroup Affect) increased for Student Group, Professional Group and Team, the sense of belonging (Ingroup Ties) and importance (Centrality) of the group increased only for Team. Nursing students had consistently higher identification scores than medical students. Conclusions Participation in a sim-IPE session can improve attitudes towards interprofessional learning. It can also enhance professional identity, particularly as related to emotional aspects of group membership, with possible benefits for wellbeing. Changes in identification with the immediate Team suggest positive psychological consequences of ad hoc Team formation in the workplace. Differences between medical and nursing students suggest their differing opportunities to work with other professions during training may change baseline attitudes and identity. However, a single sim-IPE session can still have an additive effect

Towards Full Integration Of XML And Advanced Database Concepts

Most advanced database systems courses focus on core aspects of relational design, data modeling, transaction processing, and distributed database issues. Given the ever increasing importance of web enabled databases generally but particularly the influence of XML (eXtensible Markup Language), an alternative approach would be to teach the traditional core principles while integrating an XML module into the course.  The focus of this paper is to elaborate on how such an integration would be accomplished in an advanced database course

An EfficientNet-Based Transfer Learning System for Defect Classification in Manufacturing

In semiconductor manufacturing industry, automated systems are essential for efficient and accurate identification of defects, prior to final product completion, to ensure quality and reduce waste. To achieve this, semiconductor industries are developing smart inspection systems to identify defects on the surface of wafers during manufacturing. Computer vision techniques play a crucial role in developing accurate inspection systems. However, most existing computer vision-based systems perform poorly when classifying defects, and many manufacturing companies still rely on manual inspection. To overcome this, we propose an efficient method for classifying defects in an industrial dataset using EfficientNet-B4 transfer learning along with Squeeze and Excitation block and multilayer perceptron. Furthermore, we applied data-augmentation techniques to enhance the dataset and improve the generalisation of proposed model. This proposed method is lightweight and can classify defects in real-time with an accuracy of approximately 98%

(E)-2-{Eth­yl[4-(4-nitro­phenyl­diazen­yl)phen­yl]amino}ethyl anthracene-9-carboxyl­ate

The crystal structure of the title compound, C31H26N4O4, displays a trans conformation for the nitro­phenyl­diazenyl portion of the mol­ecule. Packing diagrams indicate that weak C—H⋯O hydrogen bonds, likely associated with a strong dipole moment present in the mol­ecule, dictate the arrangement of mol­ecules in the crystal structure

(E)-4-[(4-Nitro­phen­yl)diazen­yl]phenyl anthracene-9-carboxyl­ate

In the title compound, C27H17N3O4, the azo group displays a trans conformation and the dihedral angles between the central benzene ring and the pendant anthracene and nitro­benzene rings are 82.94 (7) and 7.30 (9)°, respectively. In the crystal structure, weak C—H⋯O hydrogen bonds, likely associated with a dipole moment present on the mol­ecule, help to consolidate the packing