Navigating student transition in higher education: induction, development, becoming

Student transition into higher education (HE) has increased in importance in recent times, with the growing trend in OECD nations towards universal HE provision and the concomitant widening of participation to include previously under-represented groups. However, 'transition' as a concept has remained largely uncontested and taken for granted, particularly by practitioners but also by many researchers. Based on an analysis of recent research in the field, the chapter suggests three broad ways in which transition can be conceived and, hence, three approaches to managing and supporting student transition in HE: as (1) induction; (2) development; and (3) becoming. The third — transition as 'becoming' — offers the most theoretically sophisticated and student sympathetic account, and has the greatest potential for transforming understandings of, and practices that support, transitions in HE. It is also the least prevalent and least well-understood. Apart from being explicit about how transition is defined, this chapter argues that future research in the field needs to foreground students' lived realities and to broaden its theoretical and empirical base if students' capacities to navigate change are to be fully understood and resourced.

Functional similarities between pigeon 'milk' and mammalian milk : induction of immune gene expression and modification of the microbiota

Pigeon ‘milk’ and mammalian milk have functional similarities in terms of nutritional benefit and delivery of immunoglobulins to the young. Mammalian milk has been clearly shown to aid in the development of the immune system and microbiota of the young, but similar effects have not yet been attributed to pigeon ‘milk’. Therefore, using a chicken model, we investigated the effect of pigeon ‘milk’ on immune gene expression in the Gut Associated Lymphoid Tissue (GALT) and on the composition of the caecal microbiota. Chickens fed pigeon ‘milk’ had a faster rate of growth and a better feed conversion ratio than control chickens. There was significantly enhanced expression of immune-related gene pathways and interferon-stimulated genes in the GALT of pigeon ‘milk’-fed chickens. These pathways include the innate immune response, regulation of cytokine production and regulation of B cell activation and proliferation. The caecal microbiota of pigeon ‘milk’-fed chickens was significantly more diverse than control chickens, and appears to be affected by prebiotics in pigeon ‘milk’, as well as being directly seeded by bacteria present in pigeon ‘milk’. Our results demonstrate that pigeon ‘milk’ has further modes of action which make it functionally similar to mammalian milk. We hypothesise that pigeon ‘lactation’ and mammalian lactation evolved independently but resulted in similarly functional products.

A new framework with similarity reasoning and monotone fuzzy rule relabeling for fuzzy inference systems

A complete and monotonically-ordered fuzzy rule base is necessary to maintain the monotonicity property of a Fuzzy Inference System (FIS). In this paper, a new monotone fuzzy rule relabeling technique to relabel a non-monotone fuzzy rule base provided by domain experts is proposed. Even though the Genetic Algorithm (GA)-based monotone fuzzy rule relabeling technique has been investigated in our previous work [7], the optimality of the approach could not be guaranteed. The new fuzzy rule relabeling technique adopts a simple brute force search, and it can produce an optimal result. We also formulate a new two-stage framework that encompasses a GA-based rule selection scheme, the optimization based-Similarity Reasoning (SR) scheme, and the proposed monotone fuzzy rule relabeling technique for preserving the monotonicity property of the FIS model. Applicability of the two-stage framework to a real world problem, i.e., failure mode and effect analysis, is further demonstrated. The results clearly demonstrate the usefulness of the proposed framework.

Preparation and characterization of natural rubber/silica nanocomposites using rule of similarity in latex

Rule of similarity and latex compounding techniques were combined for the first time to prepare natural rubber/nanosilica (NR/SiO2) nanocomposite with core-shell nanosilica-poly (methyl methacrylate) (SiO 2-PMMA) particles and PMMA-modified natural rubber matrix (NR-PMMA). The microstructure of SiO2 and nanocomposites with different SiO 2 contents was characterized by fourier transform infrared spectroscopy (FTIR); the morphology of nanocomposites was investigated with scanning electron microscopy (SEM); the tensile strength was characterized by tensile testing machine and the thermal stability of composites was studied by thermal gravimetric analysis. Results showed that PMMA chains have successfully grafted onto the surface of SiO2, and the core-shell SiO 2-PMMA nanoparticles and NR-PMMA latex have been perfectly incorporated. SiO2-PMMA nanoparticles are evenly distributed over the NR matrix with an average size in the range of 60-100 nm at the low content (SiO2? 3 wt%), while aggregations are apparently observed when 5 wt% SiO2 is loaded. In addition, NR/SiO2 composities possess a considerable improvement in ageing resistance compared with the pure NR. The tensile strength of composite increases from 6.99 to 12.72 MPa, reaching the highest value at a 0.5 wt% SiO2 loading, and then the figure decreases gradually because of the aggregation of SiO2 nanoparticles. It is anticipated that the reported process is to provide a simple and economic way for preparing NR composites.

Online motor fault detection and diagnosis using a hybrid FMM-CART model

In this brief, a hybrid model combining the fuzzy min-max (FMM) neural network and the classification and regression tree (CART) for online motor detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. To evaluate the applicability of the proposed FMM-CART model, an evaluation with a benchmark data set pertaining to electrical motor bearing faults is first conducted. The results obtained are equivalent to those reported in the literature. Then, a laboratory experiment for detecting and diagnosing eccentricity faults in an induction motor is performed. In addition to producing accurate results, useful rules in the form of a decision tree are extracted to provide explanation and justification for the predictions from FMM-CART. The experimental outcome positively shows the potential of FMM-CART in undertaking online motor fault detection and diagnosis tasks.