Application of the WRF-Chem Model for a High Ozone Episode in SW Poland – Preliminary Results

The Weather Research and Forecasting model, integrated online with chemistry module, is a multi-scale model suitable for both research and operational forecasts of meteorology and air quality. It is used by many institutions for a variety of applications. In this study, the WRF v3.5 with chemistry (WRF-Chem) is applied to the area of Poland, for a period of 3-20 July 2006, when high concentrations of ground level ozone were observed. The meteorological and chemistry simulations were initiated with ERA-Interim reanalysis and TNO MACC II emissions database, respectively. The model physical parameterization includes RRTM shortwave radiation, Kain-Fritsch cumulus scheme, Purdue Lin microphysics and ACM2 PBL, established previously as the optimal configuration. Chemical mechanism used for the study was RADM2 with MADE/SORGAM aerosols. Simulations were performed for three one-way nested domains covering Europe (36 km x 36 km), Central Europe (12 km x 12 km) and Poland (4 km x 4 km). The results from the innermost domain were analyzed and compared to measurements of ozone concentration at three stations in different environments. The results show underestimation of observed values and daily amplitude of ozone concentrations.

Categorising Asynchronous Discussion Threads: Improving the Quality of Student Learning

My paper will focus on the generative potential of categorising asynchronous discussion threads as one strategy for improving the quality of students’ learning in a blended learning module. The approach to categorisation is based on social network analysis using intuitively simple descriptors of message posting patterns e.g. passive facilitator, dominant facilitator, unresponsive star and formulaic discussion. The intention is to produce descriptively vivid illustrative examples of the categories and to begin to suggest affordances of the different participation patterns. Looking forward to the beginning of the next module, it is anticipated that discussion during the module of approaches to participating in asynchronous discussion will contribute to effective engagement patterns and deeper learning.

Enhancing Student Learning Experiences in Sport Psychology Modules Using Practical Activities

The University of Worcester states in its most recent strategic plan (2013 – 2018) a set of enduring values that guide and direct the activities of the institution. The first listed, and perhaps the most important value is the striving to be “an outstanding university at which to be a student”. This is further supplemented by values such as “to inspire our students to reach their full potential through excellent, innovative teaching, scholarship and research” (University of Worcester 2013: p.4). One of the many ways in which the institution strives to provide this outstanding educational experience is through regular engagement, both formal and informal, with students at a number of points in each semester. Regular experiences of collating formal and informal feedback has led to the identification of a common theme amongst Higher National Diploma (HND) students in the Institute of Sport and Exercise Sciences (ISES), where they consistently request ‘more practicals’. The ISES modules however are designed to incorporate a high degree of interaction, practical activities and tasks. This is especially important for those studying at HND level as research suggests differences in learning preferences exist when compared to undergraduate students, the former preferring a more tactile style of learning (Peters et al. 2008). Using an introductory Sport Psychology HND module as an example, practical activities and tasks are fully embedded in the taught sessions to enable contextual links to be made between the learning outcomes and their subsequent use. Examples of these include: a. interviewing athletes to produce a performance profile (Butler & Hardy 1992); b. completing psychometric instruments such as the Competitive State Anxiety Inventory-2 (CSAI-2) to measure competitive anxiety in sport (Martens et al. 1990) and demonstrate data collection and construct measurement; c. performing relaxation interventions on the students to demonstrate how specific techniques (in this instance, decreasing somatic anxiety) might work in practice; d. demonstrating how observational learning facilitates skill acquisition by creating experimental conditions that the students participate in, in teaching a new skill. Nevertheless owing to the students' previously stated on-going requests for more practical activities, it became evident that assumptions about what students consider an effective means of experiential or active learning in the context of sport-related disciplines of study needed to be investigated. This is where the opportunity to undertake an action research project arose, this being a practical method commonly employed in pedagogical enquiry to aid reflection on teaching and assessment practice for the purposes of working towards continuous improvement.

Linking Research and Teaching: an Applied Soft Systems Methodology Case Study

This paper links research and teaching through an applied Soft Systems Methodology case study. The case study focuses on the redevelopment of a Research and Professional Skills module to provide support for international postgraduate students through the use of formative feedback with the aim of increasing academic research skills and confidence. The stages of the Soft Systems Methodology were used as a structure for the redevelopment of module content and assessment. It proved to be a valuable tool for identifying complex issues, a basis for discussion and debate from which an enhanced understanding was gained and a successful solution implemented together with a case study that could be utilised for teaching Soft Systems Methodology concepts. Changes to the module were very successful and resulted in significantly higher grades and a higher pass rate.

An Evaluative Case Study of a Career Coaching Approach to Improving Student Placement Success in a UK Business School

Describes the innovative approach adopted in a UK business school to improve the number of student placements. A module was designed to provide students with the skills to search, apply for and gain a year-long placement as part of a degree ‘sandwich’ course. A blend of workshops, recorded lectures, online formative feedback exercises and one-to-one career coaching created a tailored, practical approach to skill enhancement. This session provides the presentation of the adopted methodology the results of the evaluative research, a live demonstration of the coaching approach and a discussion with the audience of ideas for development of the approach.

Ensino de base de dados, através do SQL, na resolução de situações reais

Relatório da Prática de Ensino Supervisionada, Mestrado em Ensino de Informática, Universidade de Lisboa, 2015

Abstract thinking: a predictor of modelling ability?

In this paper, we describe a study of the abstract thinking skills of a group of students studying object-oriented modelling as part of a Masters course. Abstract thinking has long been considered a core skill for computer scientists. This study is part of attempts to gather evidence about the link between abstract thinking skills and success in the Computer Science discipline. The results of this study show a positive correlation between the scores of the students in the abstract thinking test with the marks achieved in the module. However, the small numbers in the study mean that wider research is needed.

Enhancing the student experience by embedding personal tutoring in the curriculum

This paper evaluates an initiative to improve the effectiveness of personal tutoring by embedding it into the curriculum. Structured group tutorials help students make the transition to learning in higher education. These tutorials are delivered within a core module and focus on enabling students to develop study skills, reflect on their learning and plan for their future. The tutor has a role in familiarising students with the practices, norms and skills required for learning at university. The system developed provides a structure and rationale for the interaction of tutors and students, with a clear place and value within the curriculum.

Curriculum and beyond: Mathematics support for first year life science students

The move into higher education is a real challenge for students from all educational backgrounds, with the adaptation to a new curriculum and style of learning and teaching posing a daunting task. A series of exercises were planned to boost the impact of the mathematics support for level four students and was focussed around a core module for all students. The intention was to develop greater confidence in tackling mathematical problems in all levels of ability and to provide more structured transition period in the first semester of level 4. Over a two-year period the teaching team for Biochemistry and Molecular Biology provided a series of structured formative tutorials and “interactive” online problems. Video solutions to all formative problems were made available, in order that students were able to engage with the problems at any time and were not disadvantaged if they could not attend. The formative problems were specifically set to dovetail into a practical report in which the mathematical skills developed were specifically assessed. Students overwhelmingly agreed that the structured formative activities had broadened their understanding of the subject and that more such activities would help. Furthermore, it is interesting to note that the package of changes undertaken resulted in a significant increase in the overall module mark over the two years of development.

Dynamic Network Mechanisms of Relational Integration

A prominent hypothesis states that specialized neural modules within the human lateral frontopolar cortices (LFPCs) support “relational integration” (RI), the solving of complex problems using inter-related rules. However, it has been proposed that LFPC activity during RI could reflect the recruitment of additional “domain-general” resources when processing more difficult problems in general as opposed to RI specifi- cally. Moreover, theoretical research with computational models has demonstrated that RI may be supported by dynamic processes that occur throughout distributed networks of brain regions as opposed to within a discrete computational module. Here, we present fMRI findings from a novel deductive reasoning paradigm that controls for general difficulty while manipulating RI demands. In accordance with the domain- general perspective, we observe an increase in frontoparietal activation during challenging problems in general as opposed to RI specifically. Nonetheless, when examining frontoparietal activity using analyses of phase synchrony and psychophysiological interactions, we observe increased network connectivity during RI alone. Moreover, dynamic causal modeling with Bayesian model selection identifies the LFPC as the effective connectivity source. Based on these results, we propose that during RI an increase in network connectivity and a decrease in network metastability allows rules that are coded throughout working memory systems to be dynamically bound. This change in connectivity state is top-down propagated via a hierarchical system of domain-general networks with the LFPC at the apex. In this manner, the functional network perspective reconciles key propositions of the globalist, modular, and computational accounts of RI within a single unified framework.

Balancing market integration in MASCEM electricity market simulator

With the restructuring of the energy sector in industrialized countries there is an increased complexity in market players’ interactions along with emerging problems and new issues to be addressed. Decision support tools that facilitate the study and understanding of these markets are extremely useful to provide players with competitive advantage. In this context arises MASCEM, a multi-agent simulator for competitive electricity markets. It is essential to reinforce MASCEM with the ability to recreate electricity markets reality in the fullest possible extent, making it able to simulate as many types of markets models and players as possible. This paper presents the development of the Balancing Market in MASCEM. A key module to the study of competitive electricity markets, as it has well defined and distinct characteristics previously implemented.

Complex market integration in MASCEM electricity market simulator

The restructuring that the energy sector has suffered in industrialized countries originated a greater complexity in market players’ interactions, and thus new problems and issues to be addressed. Decision support tools that facilitate the study and understanding of these markets become extremely useful to provide players with competitive advantage. In this context arises MASCEM, a multi-agent system for simulating competitive electricity markets. To provide MASCEM with the capacity to recreate the electricity markets reality in the fullest possible extent, it is essential to make it able to simulate as many market models and player types as possible. This paper presents the development of the Complex Market in MASCEM. This module is fundamental to study competitive electricity markets, as it exhibits different characteristics from the already implemented market types.

LMP based bid formation for virtual power players operating in smart grids

Power system organization has gone through huge changes in the recent years. Significant increase in distributed generation (DG) and operation in the scope of liberalized markets are two relevant driving forces for these changes. More recently, the smart grid (SG) concept gained increased importance, and is being seen as a paradigm able to support power system requirements for the future. This paper proposes a computational architecture to support day-ahead Virtual Power Player (VPP) bid formation in the smart grid context. This architecture includes a forecasting module, a resource optimization and Locational Marginal Price (LMP) computation module, and a bid formation module. Due to the involved problems characteristics, the implementation of this architecture requires the use of Artificial Intelligence (AI) techniques. Artificial Neural Networks (ANN) are used for resource and load forecasting and Evolutionary Particle Swarm Optimization (EPSO) is used for energy resource scheduling. The paper presents a case study that considers a 33 bus distribution network that includes 67 distributed generators, 32 loads and 9 storage units.

Multi-apprentice learning for meta-heuristics parameter tuning in a multi agent scheduling system

The scheduling problem is considered in complexity theory as a NP-hard combinatorial optimization problem. Meta-heuristics proved to be very useful in the resolution of this class of problems. However, these techniques require parameter tuning which is a very hard task to perform. A Case-based Reasoning module is proposed in order to solve the parameter tuning problem in a Multi-Agent Scheduling System. A computational study is performed in order to evaluate the proposed CBR module performance.

Case-based reasoning for self-optimizing behavior

In this paper we present a Self-Optimizing module, inspired on Autonomic Computing, acquiring a scheduling system with the ability to automatically select a Meta-heuristic to use in the optimization process, so as its parameterization. Case-based Reasoning was used so the system may be able of learning from the acquired experience, in the resolution of similar problems. From the obtained results we conclude about the benefit of its use.