This study investigates disciplinary variation in the relationship between syntactic complexity and rhetorical move-steps in research article (RA) introductions. Our data consisted of the introduction sections of 400 published RAs in two core social science disciplines, Anthropology and Sociology, and two core engineering disciplines, Chemical Engineering and Electrical Engineering. Each sample was manually annotated for rhetorical move-steps using an adapted version of Swales’ (2004) revised Create a Research Space model and assessed for syntactic complexity using multiple measures of global complexity, finite subordination, clausal elaboration, and phrasal complexity. Our results revealed significant disciplinary variation in terms of the syntactic complexity of sentences realizing each of six rhetorical move-steps commonly found in RA introductions. Our findings contribute to the emerging understanding of disciplinary variation in function-form mappings in RA writing and have useful implications for genre-based academic writing research and pedagogy.
Keywords: Academic writing | Disciplinary variation | Function-form mappings | Genre analysis | Linguistic features | Rhetorical goals

This quasi-experimental study examined the effects of a medication management program on nurses knowledge of medication management, three months after program completion. Fifty-seven nurses took a multiple-choice test both immediately after the program and three months later. Changes in test performance were assessed using McNemar’s test and generalized estimating equations for binary outcomes. Test results were generally consistent from immediately post-program to three months later, though four items differed significantly. From immediately post-program to three months later, fewer nurses correctly answered the items: documenting no medication administration (98.2 vs 86.6, p = 0.04); documenting opioid administration (56.1 vs 33.3, p = 0.01); and observation after opioid administration (35.1 vs 19.3, p = 0.08. Significantly more nurses correctly answered the item concerning the pharmacology of medication administered with food (64.9 vs 77.2, p = 0.09). We recom- mend both continuous medication management training and focusing on the correspondence between theory- based knowledge and clinical practice routines.
keywords: پرستار بیمارستان | برنامه آموزشی | مدیریت دارو | دانش | Hospital nurse | Education program | Medication management | Knowledge

Tissue engineering scaffolds made of biocompatible polymers are promising alternatives for nerve reparation. For this application, cell proliferation will be speeded up by electrostimulation, which required electrically-conductive materials. Here, a biomimicking scaffold with optimized conductivity was developed from electrospun polyacrylonitrile/electrically-conductive polyaniline (PAN/PANI) nanofibers doped with Ni nanoparticles. PAN/PANI/Ni was biocompatible for Schwann cells and exhibited a suitable tensile strength and wettability for cell proliferation. When compared with unmodified PAN/PANI, the electrical conductivity of PAN/PANI/Ni was 6.4 fold higher. Without electrostimulation, PAN/PANI and PAN/PANI/Ni exhibited similar Schwann cells’ proliferation rates. Upon electrostimulation at 100 mV cm
1 for one hour per day over five days, PAN/PANI/Ni accelerated Schwann cells’ proliferation 2.1 times compared to PAN/PANI. These results demonstrate the importance of expanding the electrical conductivity of the tissue engineering scaffold to ensure optimal electrostimulation of nerve cell growth. Additionally, this study describes a straightforward approach to modulate the electrical conductivity of polymeric materials via the addition of Ni nanoparticles that can be applied to different biomimicking scaffolds for nerve healing.
Keywords: Nerve tissue engineering | Electrospinning | PAN/PANI | Ni nanoparticles | Schwann cells

Aim: We compared educational outcomes associated with an active vs. passive continuing professional development activity on self-management support for respiratory educators. Background: There is a need to identify learning activities associated with the most successful continuing professional development programs for respiratory educators. Design: This was a non-randomized controlled mixed-methods study recruiting respiratory educators attending a continuing professional development activity on self-management support. Methods: In the experimental group, active learning methods (role-play simulations) were employed, whereas passive learning methods (lecture) were used in the comparison group. Educators were allocated to the comparison group (first 15 months of the study), then to the experimental group (last 17 months). Educators filled questionnaires measuring pre-/post-activity knowledge about self-management support (score 0–25) and selfreported competence (score 1–10). Scores were compared using mixed-effect models. Interviews with educators were conducted and content analysis was performed. Results: We recruited 94/94 educators (active: n = 51; passive: n = 43). Knowledge scores increased to a greater extent in the active vs. passive learning group (adjusted difference-in-difference [aDID]=2.01; 95% confidence interval [95%CI]: 0.14–3.88), although competence scores increased to a greater extent in the passive learning group (aDID=− 0.38; 95%CI: − 1.56 to − 0.04). Reflecting on their competence, educators of the active learning group identified the need to further improve their self-management support skills, whereas educators of the passive learning group did not. Conclusions: Our results show that an active learning continuing professional development activity on selfmanagement support could help educators to better apply knowledge and appears to engage them in a process of reflection on action.
keywords: Chronic obstructive pulmonary disease | Continuing education | Mixed methods | Patient education as topic | Self-management support

Biometric recognition systems have been employed in many aspects of life such as security technologies, data protection, and remote access. Physiological signals, e.g. electrocardiogram (ECG), can potentially be used in biometric recognition. From a medical standpoint, ECG leads have structural and functional dependencies. In fact, precordial ECG leads view the heart from different axial angles, whereas limb leads view it from various coronal angles. This study aimed to design a personal biometric recognition system based on ECG signals by estimating these latent medical variables. To estimate functional dependencies, within-correlation and cross- correlation in time-frequency domain between ECG leads were calculated and represented in the form of extended adjacency matrices. CNN trees were then introduced through genetic programming for the automated estimation of structural dependencies in extended adjacency matrices. CNN trees perform the deep feature learning process by using structural morphology operators. The proposed system was designed for both closed-set identification and verification. It was then tested on two datasets, i.e. PTB and CYBHi, for performance evaluation. Compared with the state-of-the-art methods, the proposed method outperformed all of them.
Keywords: Biometrics | Electrocardiogram | Functional dependencies | Structural dependencies | Genetic programming | Convolutional neural networks