An open extensible tool environment for Event-B

Abstract. We consider modelling indispensable for the development of complex systems. Modelling must be carried out in a formal notation to reason and make meaningful conjectures about a model. But formal modelling of complex systems is a difficult task. Even when theorem provers improve further and get more powerful, modelling will remain difficult. The reason for this that modelling is an exploratory activity that requires ingenuity in order to arrive at a meaningful model. We are aware that automated theorem provers can discharge most of the onerous trivial proof obligations that appear when modelling systems. In this article we present a modelling tool that seamlessly integrates modelling and proving similar to what is offered today in modern integrated development environments for programming. The tool is extensible and configurable so that it can be adapted more easily to different application domains and development methods.

New trends in globalization of science and engineering education

Three decades ago most research and design were conducted in each country independently. But the world has become quite different since then. Global changes in technology and society changed the concept of an engineer. There is the need for engineers who can work effectively in changing global and technical environments. Less interest has been paid to the globalization of science and technology. This article reviews the stimulus, that impact the engineering profession and gives the recommendations concerning the profession of engineering, the technology and innovation

Renal clearable catalytic gold nanoclusters for in vivo disease monitoring

Ultra-small gold nanoclusters (AuNCs) have emerged as agile probes for in vivo imaging, as they exhibit exceptional tumour accumulation and efficient renal clearance properties. However, their intrinsic catalytic activity, which can enable increased detection sensitivity, has yet to be explored for in vivo sensing. By exploiting the peroxidase-mimicking activity of AuNCs and the precise nanometer size filtration of the kidney, we designed multifunctional protease nanosensors that respond to disease microenvironments to produce a direct colorimetric urinary readout of disease state in less than 1 h. We monitored the catalytic activity of AuNCs in collected urine of a mouse model of colorectal cancer where tumour-bearing mice showed a 13-fold increase in colorimetric signal compared to healthy mice. Nanosensors were eliminated completely through hepatic and renal excretion within 4 weeks after injection with no evidence of toxicity. We envision that this modular approach will enable rapid detection of a diverse range of diseases by exploiting their specific enzymatic signatures

Development of a Percutaneous Electrical Connector

Program year: 1977-1978Digitized from print original stored in HDRThe major problems with percutaneous electrical conduits are biocompatibility and infection. A system has been designed to avoid such problems and has been tested both in vitro and in vivo. The project, carried out under the Texas A&M Undergraduate Fellows Program, involved the development of an electrical connector which does not permanently pass through the skin, and yet can provide electrical continuity when and for as long as it is needed

How students process equations in solving quantitative synthesis problems? Role of mathematical complexity in students’ mathematical performance

We examine students’ mathematical performance on quantitative “synthesis problems” with varying mathematical complexity. Synthesis problems are tasks comprising multiple concepts typically taught in different chapters. Mathematical performance refers to the formulation, combination, and simplification of equations. Generally speaking, formulation and combination of equations require conceptual reasoning; simplification of equations requires manipulation of equations as computational tools. Mathematical complexity is operationally defined by the number and the type of equations to be manipulated concurrently due to the number of unknowns in each equation. We use two types of synthesis problems, namely, sequential and simultaneous tasks. Sequential synthesis tasks require a chronological application of pertinent concepts, and simultaneous synthesis tasks require a concurrent application of the pertinent concepts. A total of 179 physics major students from a second year mechanics course participated in the study. Data were collected from written tasks and individual interviews. Results show that mathematical complexity negatively influences the students’ mathematical performance on both types of synthesis problems. However, for the sequential synthesis tasks, it interferes only with the students’ simplification of equations. For the simultaneous synthesis tasks, mathematical complexity additionally impedes the students’ formulation and combination of equations. Several reasons may explain this difference, including the students’ different approaches to the two types of synthesis problems, cognitive load, and the variation of mathematical complexity within each synthesis type

Students’ conceptual performance on synthesis physics problems with varying mathematical complexity

A body of research on physics problem solving has focused on single-concept problems. In this study we use “synthesis problems” that involve multiple concepts typically taught in different chapters. We use two types of synthesis problems, sequential and simultaneous synthesis tasks. Sequential problems require a consecutive application of fundamental principles, and simultaneous problems require a concurrent application of pertinent concepts. We explore students’ conceptual performance when they solve quantitative synthesis problems with varying mathematical complexity. Conceptual performance refers to the identification, follow-up, and correct application of the pertinent concepts. Mathematical complexity is determined by the type and the number of equations to be manipulated concurrently due to the number of unknowns in each equation. Data were collected from written tasks and individual interviews administered to physics major students (N=179) enrolled in a second year mechanics course. The results indicate that mathematical complexity does not impact students’ conceptual performance on the sequential tasks. In contrast, for the simultaneous problems, mathematical complexity negatively influences the students’ conceptual performance. This difference may be explained by the students’ familiarity with and confidence in particular concepts coupled with cognitive load associated with manipulating complex quantitative equations. Another explanation pertains to the type of synthesis problems, either sequential or simultaneous task. The students split the situation presented in the sequential synthesis tasks into segments but treated the situation in the simultaneous synthesis tasks as a single event

What works with worked examples: Extending self-explanation and analogical comparison to synthesis problems

The ability to solve physics problems that require multiple concepts from across the physics curriculum—“synthesis” problems—is often a goal of physics instruction. Three experiments were designed to evaluate the effectiveness of two instructional methods employing worked examples on student performance with synthesis problems; these instructional techniques, analogical comparison and self-explanation, have previously been studied primarily in the context of single-concept problems. Across three experiments with students from introductory calculus-based physics courses, both self-explanation and certain kinds of analogical comparison of worked examples significantly improved student performance on a target synthesis problem, with distinct improvements in recognition of the relevant concepts. More specifically, analogical comparison significantly improved student performance when the comparisons were invoked between worked synthesis examples. In contrast, similar comparisons between corresponding pairs of worked single-concept examples did not significantly improve performance. On a more complicated synthesis problem, self-explanation was significantly more effective than analogical comparison, potentially due to differences in how successfully students encoded the full structure of the worked examples. Finally, we find that the two techniques can be combined for additional benefit, with the trade-off of slightly more time on task