Teaching introductory undergraduate Physics using commercial video games

Commercial video games are increasingly using sophisticated physics simulations to create a more immersive experience for players. This also makes them a powerful tool for engaging students in learning physics. We provide some examples to show how commercial off-the-shelf games can be used to teach specific topics in introductory undergraduate physics. The examples are selected from a course taught predominantly through the medium of commercial video games.Comment: Accepted to Physics Education, Fig1 does not render properly in this versio

Magnetic field simulations and measurements on the mini-ICAL detector

The ICAL (Iron Calorimeter) is a 51 kTon magnetized detector proposed by the INO collaboration. It is designed to detect muons with energies in the 1-20 GeV range. A magnetic field of about 1.5 T in the ICAL detector will be generated by passing a DC current through suitable copper coils. This will enable it to distinguish between muons and anti-muons that will be generated from the interaction of atmospheric muon neutrinos and anti-neutrinos with iron. This will help in resolving the open question of mass ordering in the neutrino sector. Apart from charge identification, the magnetic field will be used to reconstruct the muon momentum (direction and magnitude). Therefore it is important to know the magnetic field in the detector as accurately as possible. We present here an (indirect) measurement of the magnetic field in the 85 ton prototype mini-ICAL detector working in Madurai, Tamil Nadu, for different coil currents. A detailed 3-D finite element simulation was done for the mini-ICAL geometry using Infolytica MagNet software and the magnetic field was computed for different coil currents. This paper presents, for the first time, a comparison of the magnetic field measured in the air gaps with the simulated magnetic field, to validate the simulation using real time data. Using the simulations the magnetic field inside the iron is estimated.Comment: 20 pages, 22 figures, latex sourc

A novel algorithm for dynamic student profile adaptation based on learning styles

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.E-learning recommendation systems are used to enhance student performance and knowledge by providing tailor- made services based on the students’ preferences and learning styles, which are typically stored in student profiles. For such systems to remain effective, the profiles need to be able to adapt and reflect the students’ changing behaviour. In this paper, we introduce new algorithms that are designed to track student learning behaviour patterns, capture their learning styles, and maintain dynamic student profiles within a recommendation system (RS). This paper also proposes a new method to extract features that characterise student behaviour to identify students’ learning styles with respect to the Felder-Silverman learning style model (FSLSM). In order to test the efficiency of the proposed algorithm, we present a series of experiments that use a dataset of real students to demonstrate how our proposed algorithm can effectively model a dynamic student profile and adapt to different student learning behaviour. The results revealed that the students could effectively increase their learning efficiency and quality for the courses when the learning styles are identified, and proper recommendations are made by using our method

A measure of individual role in collective dynamics

Identifying key players in collective dynamics remains a challenge in several research fields, from the efficient dissemination of ideas to drug target discovery in biomedical problems. The difficulty lies at several levels: how to single out the role of individual elements in such intermingled systems, or which is the best way to quantify their importance. Centrality measures describe a node's importance by its position in a network. The key issue obviated is that the contribution of a node to the collective behavior is not uniquely determined by the structure of the system but it is a result of the interplay between dynamics and network structure. We show that dynamical influence measures explicitly how strongly a node's dynamical state affects collective behavior. For critical spreading, dynamical influence targets nodes according to their spreading capabilities. For diffusive processes it quantifies how efficiently real systems may be controlled by manipulating a single node.Comment: accepted for publication in Scientific Report

Negative priming and occasion setting in an appetitive Pavlovian procedure

Rats received training in which two auditory target stimuli, X and Y, were signaled by two visual stimuli, A and B, and followed by food (i.e., A→X1, B→Y+). The test consisted of presentations of X and Y preceded either by the same signal as during training (same trials: A→X, B→Y) or by the alternative signal (different trials: A→Y, B→X). After 8 training sessions, the animals responded less on same trials than on different trials; this effect was significantly reduced after 24 training sessions. In two additional experiments, animals that had also experienced presentations of A and B alone, either before or during training, showed the opposite pattern of results, responding more on same trials than on different trials. These results are interpreted as being due to the interaction between the effects of occasion setting andnegative priming (see Wagner, 1981)

A Study of Brain Networks Associated with Swallowing Using Graph-Theoretical Approaches

Functional connectivity between brain regions during swallowing tasks is still not well understood. Understanding these complex interactions is of great interest from both a scientific and a clinical perspective. In this study, functional magnetic resonance imaging (fMRI) was utilized to study brain functional networks during voluntary saliva swallowing in twenty-two adult healthy subjects (all females, 23.1±1.52 years of age). To construct these functional connections, we computed mean partial correlation matrices over ninety brain regions for each participant. Two regions were determined to be functionally connected if their correlation was above a certain threshold. These correlation matrices were then analyzed using graph-theoretical approaches. In particular, we considered several network measures for the whole brain and for swallowing-related brain regions. The results have shown that significant pairwise functional connections were, mostly, either local and intra-hemispheric or symmetrically inter-hemispheric. Furthermore, we showed that all human brain functional network, although varying in some degree, had typical small-world properties as compared to regular networks and random networks. These properties allow information transfer within the network at a relatively high efficiency. Swallowing-related brain regions also had higher values for some of the network measures in comparison to when these measures were calculated for the whole brain. The current results warrant further investigation of graph-theoretical approaches as a potential tool for understanding the neural basis of dysphagia. © 2013 Luan et al

Subanesthetic ketamine treatment promotes abnormal interactions between neural subsystems and alters the properties of functional brain networks

Acute treatment with subanesthetic ketamine, a non-competitive N-methyl-D-aspartic acid (NMDA) receptor antagonist, is widely utilized as a translational model for schizophrenia. However, how acute NMDA receptor blockade impacts on brain functioning at a systems level, to elicit translationally relevant symptomatology and behavioral deficits, has not yet been determined. Here, for the first time, we apply established and recently validated topological measures from network science to brain imaging data gained from ketamine-treated mice to elucidate how acute NMDA receptor blockade impacts on the properties of functional brain networks. We show that the effects of acute ketamine treatment on the global properties of these networks are divergent from those widely reported in schizophrenia. Where acute NMDA receptor blockade promotes hyperconnectivity in functional brain networks, pronounced dysconnectivity is found in schizophrenia. We also show that acute ketamine treatment increases the connectivity and importance of prefrontal and thalamic brain regions in brain networks, a finding also divergent to alterations seen in schizophrenia. In addition, we characterize how ketamine impacts on bipartite functional interactions between neural subsystems. A key feature includes the enhancement of prefrontal cortex (PFC)-neuromodulatory subsystem connectivity in ketamine-treated animals, a finding consistent with the known effects of ketamine on PFC neurotransmitter levels. Overall, our data suggest that, at a systems level, acute ketamine-induced alterations in brain network connectivity do not parallel those seen in chronic schizophrenia. Hence, the mechanisms through which acute ketamine treatment induces translationally relevant symptomatology may differ from those in chronic schizophrenia. Future effort should therefore be dedicated to resolve the conflicting observations between this putative translational model and schizophrenia

Is group cognitive behaviour therapy for postnatal depression evidence-based practice? A systematic review

Background: There is evidence that psychological therapies including cognitive behaviour therapy (CBT) may be effective in reducing postnatal depression (PND) when offered to individuals. In clinical practice, this is also implemented in a group therapy format, which, although not recommended in guidelines, is seen as a cost-effective alternative. To consider the extent to which group methods can be seen as evidence-based, we systematically review and synthesise the evidence for the efficacy of group CBT compared to currently used packages of care for women with PND, and we discuss further factors which may contribute to clinician confidence in implementing an intervention. Methods: Seventeen electronic databases were searched. All full papers were read by two reviewers and a third reviewer was consulted in the event of a disagreement on inclusion. Selected studies were quality assessed, using the Cochrane Risk of Bias Tool, were data extracted by two reviewers using a standardised data extraction form and statistically synthesised where appropriate using the fixed-effect inverse-variance method. Results: Seven studies met the inclusion criteria. Meta-analyses showed group CBT to be effective in reducing depression compared to routine primary care, usual care or waiting list groups. A pooled effect size of d = 0.57 (95% CI 0.34 to 0.80, p < 0.001) was observed at 10–13 weeks post-randomisation, reducing to d = 0.28 (95% CI 0.03 to 0.53, p = 0.025) at 6 months. The non-randomised comparisons against waiting list controls at 10–13 weeks was associated with a larger effect size of d = 0.94 (95% CI 0.42 to 1.47, p < 0.001). However due to the limitations of the available data, such as ill-specified definitions of the CBT component of the group programmes, these results should be interpreted with caution. Conclusions: Although the evidence available is limited, group CBT was shown to be effective. We argue, therefore, that there is sufficient evidence to implement group CBT, conditional upon routinely collected outcomes being benchmarked against those obtained in trials of individual CBT, and with other important factors such as patient preference, clinical experience, and information from the local context taken into account when making the treatment decision

Eliciting context-mechanism-outcome configurations: Experiences from a realist evaluation investigating the impact of robotic surgery on teamwork in the operating theatre

This article recounts our experience of eliciting, cataloguing and prioritizing conjectured Context-Mechanism-Outcome configurations at the outset of a realist evaluation, to provide new insight into how Context-Mechanism-Outcome configurations can be generated and theorized. Our construction of Context-Mechanism-Outcome configurations centred on how, why and in what circumstances teamwork was impacted by robotic surgery, rather than how and why this technology improved surgical outcomes as intended. We found that, as well as offering resources, robotic surgery took away resources from the theatre team, by physically reconfiguring the operating theatre and redistributing the surgical task load, essentially changing the context in which teamwork was performed. We constructed Context-Mechanism-Outcome configurations that explain how teamwork mechanisms were both constrained by the contextual changes, and triggered in the new context through the use of informal strategies. We conclude by reflecting on our application of realist evaluation to understand the potential impacts of robotic surgery on teamwork

Extracting Message Inter-Departure Time Distributions from the Human Electroencephalogram

The complex connectivity of the cerebral cortex is a topic of much study, yet the link between structure and function is still unclear. The processing capacity and throughput of information at individual brain regions remains an open question and one that could potentially bridge these two aspects of neural organization. The rate at which information is emitted from different nodes in the network and how this output process changes under different external conditions are general questions that are not unique to neuroscience, but are of interest in multiple classes of telecommunication networks. In the present study we show how some of these questions may be addressed using tools from telecommunications research. An important system statistic for modeling and performance evaluation of distributed communication systems is the time between successive departures of units of information at each node in the network. We describe a method to extract and fully characterize the distribution of such inter-departure times from the resting-state electroencephalogram (EEG). We show that inter-departure times are well fitted by the two-parameter Gamma distribution. Moreover, they are not spatially or neurophysiologically trivial and instead are regionally specific and sensitive to the presence of sensory input. In both the eyes-closed and eyes-open conditions, inter-departure time distributions were more dispersed over posterior parietal channels, close to regions which are known to have the most dense structural connectivity. The biggest differences between the two conditions were observed at occipital sites, where inter-departure times were significantly more variable in the eyes-open condition. Together, these results suggest that message departure times are indicative of network traffic and capture a novel facet of neural activity