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The last two decades have witnessed an increasing intensity of quality management in higher education institutions and quality discourses which were followed by debates about and attempts of evaluating the efficacy of quality management in the sector. Accordingly, the article presents a SWOT analysis of impact evaluation of quality management in higher education institutions. The analysis is based on a contemporary SWOT conceptualisation and on reflections of impact evaluation, ranging from theoretical models through case studies to practical experience in a European research project. The analysis reveals that certain weaknesses can be overcome (e.g. budget and process time restrictions) while others cannot (e.g. systematic limitations of methodologies). Similarly, certain threats can be tackled (e.g. proper implementation of methodologies) while others at most can only approximately be solved (e.g. attribution problem). The article concludes that a SWOT analysis may be a tool for bridging the gap between methodological challenges and the implementation of impact measurement in systematic quality management.

Tumour dendritic cells (DCs) internalise antigen and upregulate CCR7, which directs their migration to tumour-draining lymph nodes (dLN). CCR7 expression is coupled to an activation programme enriched in regulatory molecule expression, including PD-L1. However, the spatio-temporal dynamics of CCR7+ DCs in anti-tumour immune responses remain unclear. Here, we use photoconvertible mice to precisely track DC migration. We report that CCR7+ DCs are the dominant DC population that migrate to the dLN, but a subset remains tumour-resident despite CCR7 expression. These tumour-retained CCR7+ DCs are phenotypically and transcriptionally distinct from their dLN counterparts and heterogeneous. Moreover, they progressively downregulate the expression of antigen presentation and pro-inflammatory transcripts with more prolonged tumour dwell-time. Tumour-residing CCR7+ DCs co-localise with PD-1+CD8+ T cells in human and murine solid tumours, and following anti-PD-L1 treatment, upregulate stimulatory molecules including OX40L, thereby augmenting anti-tumour cytolytic activity. Altogether, these data uncover previously unappreciated heterogeneity in CCR7+ DCs that may underpin a variable capacity to support intratumoural cytotoxic T cells. Recognition of tumour antigen induces dendritic cell activation and migration to the lymph node. Here, the authors use photoconvertible mice to demonstrate that some activated dendritic cells are retained in tumours and gradually lose function, but their ability to support local anti-tumour responses can be augmented by anti-PD-L1 blockade. [ABSTRACT FROM AUTHOR]

Below-cloud scavenging (BCS) is the process of aerosol removal from the atmosphere between cloud base and the ground by precipitation (e.g., rain or snow), and affects aerosol number or mass concentrations, size distribution, and lifetime. An accurate representation of precipitation phases is important in treating BCS as the efficiency of aerosol scavenging differs significantly between liquid and solid precipitation. The impact of different representations of BCS on existing model biases was examined through implementing a new aerosol BCS scheme in the Environment and Climate Change Canada (ECCC) air quality prediction model GEM-MACH and comparing it with the existing scavenging scheme in the model. Further, the current GEM-MACH employs a single-phase precipitation for BCS: total precipitation is treated as either liquid or solid depending on a fixed environment temperature threshold. Here, we consider co-existing liquid and solid precipitation phases as they are predicted by the GEM microphysics. GEM-MACH simulations, in a local-area domain over the Athabasca oil sands areas, Canada, are compared with observed precipitation samples, with a focus on the particulate base cation NH 4+ ; acidic anions NO 3- , SO 4= , HSO 3- in precipitation; and observed ambient particulate sulfate, ammonium, and nitrate concentrations. Overall, the introduction of the multi-phase approach and the new scavenging scheme enhances GEM-MACH performance compared to previous methods. Including a multi-phase approach leads to altered SO 4= scavenging and impacts the BCS of SO 2 into the aqueous phase over the domain. Sulfate biases improved from +46% to -5% relative to Alberta Precipitation Quality Monitoring Program wet sulfate observations. At Canadian Air and Precipitation Monitoring Network stations the biases became more negative, from -10% to -30% for the tests carried out here. These improvements contrast with prior annual average biases of +200% for SO 4= , indicating enhanced model performance. Improvements in model performance (via scores for correlation coefficient, normalized mean bias, and/or fractional number of model values within a factor of 2 of observations) could also be seen between the base case and the two simulations based on multi-phase partitioning for NO 3- , NH 4+ , and SO 4=. Whether or not these improvements corresponded to increases or decreases in NO 3- and NH 4+ wet deposition varied over the simulation region. The changes were episodic in nature – the most significant changes in wet deposition were likely at specific geographic locations and represent specific cloud precipitation events. The changes in wet scavenging resulted in a higher formation rate and larger concentrations of atmospheric particle sulfate. [ABSTRACT FROM AUTHOR]

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Spatiotemporal clustering of data is an important analytical technique with many applications in air quality, including source identification, monitoring network analysis and airshed partitioning. Hierarchical agglomerative clustering is one such algorithm, where sets of input data are grouped by a chosen similarity metric, without requiring any a priori information about the final arrangement of clusters. Modern implementations of the algorithm have O(n² 10 log (n)) computational complexity and O;(n²) memory usage, where n is the number of initial clusters. This dependence can strain the resources of even very large individual computers as the number of initial clusters increases into the tens or hundreds of thousands, for example, to cluster all the points in an air-quality model’s simulation grid as part of airshed analysis (~105 to 106 time series to be clustered). Using two parallelization techniques – the Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) – we have reduced the amount of wallclock time while increasing the memory available to a new hierarchical clustering program, by dividing up the program into blocks which are run on separate CPUs but communicate with each other to produce a single result. The new algorithm opens up new directions for large data analysis which had previously not been possible. Here we present a massively parallelized version of an agglomerative hierarchical clustering algorithm which is able to cluster an entire year of hourly regional air-quality model output (538x540 domain; 290,520 hourly concentration timeseries) in 12 hours 37 minutes of wallclock time, by spreading the computation across 8000 Intel® Xeon® Platinum 8830 CPU cores with a total of 2TB of RAM. We then show how the new algorithm allows a new form of air-quality analysis to be carried out starting from air-quality model output. We present maps of the different airsheds within the model domain, identifying equally unique regions for each chemical species. These regions can be used as an aid in determining the placement of surface air-quality monitors which gives the most representative sampling given a fixed number of monitors, or the number of monitors required for a given level of similarity between airsheds. We then demonstrate the new algorithm’s application towards source apportionment of very large observation data sets, through the analysis of a year of Canada’s hourly National Air Pollution Surveillance Program data, comprising 366,427 original observation vectors, a problem size that would be impossible with other source apportionment programs such as Positive Matrix Factorization. [ABSTRACT FROM AUTHOR]

We describe a new FORTRAN 90 computer program to solve the system of equations for the NH4+-Na+-Ca2+-K+-Mg2+- SO42--NO3--Cl- system, based on the algorithms of ISORROPIA II, but containing algorithm improvements and corrections. These allow the code to deliver more accurate solution results in formal evaluations of accuracy of the roots of the systems of equations, while reducing processing time in practical applications by about 50%. The improved solution performance results from several implementation improvements relative to the original ISORROPIA algorithms. These improvements include (the use of the 'interpolate, truncate and project' (ITP) root-finding approach rather than bisection, (ii) the allowance of search interval endpoints as valid roots at the onset of a search, (iii) the use of a more accurate method to solve polynomial subsystems of equations, (iv) the elimination of negative concentrations during iterative solutions, (v) corrections for mass conservation enforcement, and (vi) several code structure improvements. The new code may be run in either a "vectorization" mode wherein a global convergence criterion is used across multiple tests within the same chemical subspace, or a "by gridpoint" mode wherein individual test cases are solved with the same convergence criteria. The latter approach was found to be more efficient on the compiler tested here, but users of the code are recommended to test both options on their own systems. We also note that implementation of inorganic chemistry within chemical transport models should take care to retain residual or "free" mass of aerosol species remaining after partitioning, to ensure mass conservation - the new code has been constructed to explicitly conserve the input mass. The new code is provided as open-source FORTRAN 90 shareware. [ABSTRACT FROM AUTHOR]