Artificial Intelligence in Education

Artificial Intelligence (AI) technologies have been researched in educational contexts for more than 30 years (Woolf 1988; Cumming and McDougall 2000; du Boulay 2016). More recently, commercial AI products have also entered the classroom. However, while many assume that Artificial Intelligence in Education (AIED) means students taught by robot teachers, the reality is more prosaic yet still has the potential to be transformative (Holmes et al. 2019). This chapter introduces AIED, an approach that has so far received little mainstream attention, both as a set of technologies and as a field of inquiry. It discusses AIED’s AI foundations, its use of models, its possible future, and the human context. It begins with some brief examples of AIED technologies

Molecular therapy for the treatment of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide. Conventional cytotoxic chemotherapy has failed to show a substantial benefit for patients with HCC. Recently, a number of new drugs targeting molecular mechanisms involved in liver cell transformation have entered into clinical trials and led to encouraging results. In this review we summarise this data and point to a number of new compounds, which are currently being tested and can potentially broaden our therapeutic arsenal even further

The effects of spatial legacies following shifting management practices and fire on boreal forest age structure

Forest age structure and its spatial arrangement are important elements of sustainable forestry because of their effects on biodiversity and timber availability. Forest management objectives that include specific forest age structure may not be easily attained due to constraints imposed by the legacies of historical management and natural disturbance. We used a spatially explicit stochastic model to explore the synergetic effects of forest management and fire on boreal forest age structure. Specifically, we examined (1) the duration of spatial legacies of different management practices in the boreal forest, (2) how multiple shifts in management practices affect legacy duration and the spatial trajectories of forest age structure, and (3) how fire influences legacy duration and pattern development in combination with harvesting. Results based on 30 replicates of 500 years for each scenario indicate that (1) spatial legacies persist over 200 years and the rate at which legacies are overcome depends on whether new management targets are in synchrony with existing spatial pattern; (2) age specific goals were met faster after multiple management shifts due to the similar spatial scale of the preceding management types; (3) because large fires can erase the spatial pattern created by smaller disturbances, scenarios with fire had shorter lags than scenarios without fire. These results suggest that forest management goals can be accelerated by applying management at a similar spatial scale as existing spatial patterns. Also, management planning should include careful consideration of historical management as well as current and likely future disturbances

Optical Imaging of Bacterial Infections

The rise in multidrug resistant (MDR) bacteria has become a global crisis. Rapid and accurate diagnosis of infection will facilitate antibiotic stewardship and preserve our ability to treat and cure patients from bacterial infection. Direct in situ imaging of bacteria offers the prospect of accurately diagnosing disease and monitoring patient outcomes and response to treatment in real-time. There have been many recent advances in the field of optical imaging of infection; namely in specific probe and fluorophore design. This combined with the advances in imaging device technology render direct optical imaging of infection a feasible approach for accurate diagnosis in the clinic. Despite this, there are currently no licensed molecular probes for clinical optical imaging of infection. Here we report some of the most promising and interesting probes and approaches under development for this purpose, which have been evaluated in in vivo models within the laboratory setting

Monitoring and data quality assessment of the ATLAS liquid argon calorimeter

The liquid argon calorimeter is a key component of the ATLAS detector installed at the CERN Large Hadron Collider. The primary purpose of this calorimeter is the measurement of electron and photon kinematic properties. It also provides a crucial input for measuring jets and missing transverse momentum. An advanced data monitoring procedure was designed to quickly identify issues that would affect detector performance and ensure that only the best quality data are used for physics analysis. This article presents the validation procedure developed during the 2011 and 2012 LHC data-taking periods, in which more than 98% of the proton-proton luminosity recorded by ATLAS at a centre-of-mass energy of 7-8 TeV had calorimeter data quality suitable for physics analysis