Building Adaptive Capacity through Learning in Project-Oriented Organisations in Infrastructure Planning

Transport infrastructure networks are currently being challenged by rapidly changing contexts, such as climate change, new IT and mobility technologies, ageing infrastructure, demographic changes and growing engagement of stakeholders. These challenges call for an adaptive management approach in infrastructure planning. Apart from making the physical infrastructure more adaptive, organisational adaptive capacity is currently being discussed in both literature and practice. The literature describes learning as one of the key elements of organisational adaptive capacity. However, it remains unclear how infrastructure network agencies learn. Most of these agencies are organised in a project-oriented way. Projects can be considered as information exchange platforms of individuals that have to align their knowledge and interpretations to collectively make sense of this information to deliver a project-result. However, projects operate relatively autonomously from their parent organisation. This article aims to enhance the understanding of how projects learn from each other and how the parent organisation learns from projects and vice versa. To this end, we have conducted an in-depth case study of a typical project-oriented organisation in infrastructure planning: Rijkswaterstaat - the executive agency of the Ministry of Infrastructure and Water Management in the Netherlands. Data was collected through documents and semi-structured interviews with members of a selection of projects of Rijkswaterstaat and other members of this organisation. We used Social Network Analysis to support the analysis of the data. Subsequently, the results were confronted with literature to understand how collective learning occurs in project-oriented organisations

Learning across teams in project-oriented organisations:The role of programme management

Purpose: Learning across teams and organisational levels enables organisations to deal with challenges that arise from changing contexts. Project-oriented organisations increasingly use programme management to cope with such challenges and improve performance. This paper aims to find out how different programme configurations affect learning across project teams and between project teams and their parent organisation in project-oriented organisations. Design/methodology/approach: A case study of a project-oriented organisation involved in five infrastructure programmes was performed. Findings: The studied programmes linked learning processes at group and organisational levels by creating relationships across project teams and their parent organisation and acting as a knowledge centre. Team learning benefits from the learning culture and stable environment that programmes create for project teams. This study indicates that a programme’s features and focus strongly determines whether a programme predominantly enhances learning across project teams or learning between project teams and their parent organisation. Originality/value: Although programme management is increasingly used by project-oriented organisations, there are few studies relating to learning in programmes. This study provides new insights into learning across teams through programmes

Best bang for your buck: GPU nodes for GROMACS biomolecular simulations

The molecular dynamics simulation package GROMACS runs efficiently on a wide variety of hardware from commodity workstations to high performance computing clusters. Hardware features are well exploited with a combination of SIMD, multi-threading, and MPI-based SPMD/MPMD parallelism, while GPUs can be used as accelerators to compute interactions offloaded from the CPU. Here we evaluate which hardware produces trajectories with GROMACS 4.6 or 5.0 in the most economical way. We have assembled and benchmarked compute nodes with various CPU/GPU combinations to identify optimal compositions in terms of raw trajectory production rate, performance-to-price ratio, energy efficiency, and several other criteria. Though hardware prices are naturally subject to trends and fluctuations, general tendencies are clearly visible. Adding any type of GPU significantly boosts a node's simulation performance. For inexpensive consumer-class GPUs this improvement equally reflects in the performance-to-price ratio. Although memory issues in consumer-class GPUs could pass unnoticed since these cards do not support ECC memory, unreliable GPUs can be sorted out with memory checking tools. Apart from the obvious determinants for cost-efficiency like hardware expenses and raw performance, the energy consumption of a node is a major cost factor. Over the typical hardware lifetime until replacement of a few years, the costs for electrical power and cooling can become larger than the costs of the hardware itself. Taking that into account, nodes with a well-balanced ratio of CPU and consumer-class GPU resources produce the maximum amount of GROMACS trajectory over their lifetime

More Bang for Your Buck: Improved use of GPU Nodes for GROMACS 2018

We identify hardware that is optimal to produce molecular dynamics trajectories on Linux compute clusters with the GROMACS 2018 simulation package. Therefore, we benchmark the GROMACS performance on a diverse set of compute nodes and relate it to the costs of the nodes, which may include their lifetime costs for energy and cooling. In agreement with our earlier investigation using GROMACS 4.6 on hardware of 2014, the performance to price ratio of consumer GPU nodes is considerably higher than that of CPU nodes. However, with GROMACS 2018, the optimal CPU to GPU processing power balance has shifted even more towards the GPU. Hence, nodes optimized for GROMACS 2018 and later versions enable a significantly higher performance to price ratio than nodes optimized for older GROMACS versions. Moreover, the shift towards GPU processing allows to cheaply upgrade old nodes with recent GPUs, yielding essentially the same performance as comparable brand-new hardware.Comment: 41 pages, 13 figures, 4 tables. This updated version includes the following improvements: - most notably, added benchmarks for two coarse grain MARTINI systems VES and BIG, resulting in a new Figure 13 - fixed typos - made text clearer in some places - added two more benchmarks for MEM and RIB systems (E3-1240v6 + RTX 2080 / 2080Ti

EICIE voorspelt 1.5% krimp

De op data van uitzendwerk en op de gereviseerde nationale rekeningen van het CBS gebaseerde EICIE voorspelt een krimp van het bbp van 1,5% in het tweede kwartaal van 2005 ten opzichte van het tweede kwartaal van 2004. Met twee achtereenvolgende kwartalen van krimp is Nederland in een recessie beland

In 2005 groeide de economie met 1.0 procent

De EICIE voor het laatste kwartaal is 1,8 procent, met een bandbreedte van 0,6. Dit betekent dat de economie in 2005 groeide met 1,0 procent

Voorspellen in ongewisse tijden

De EICIE-indicator gaf voor het tweede kwartaal van 2005 een groeicijfer voor het bbp dat behoorlijk afweek van het later verschenen officiële CBS-cijfer. De cijfers van het CBS zijn aan flinke herzieningen onderhevig. Een eenvoudige correctiefactor, zoals de auteurs gebruikten, blijkt helaas niet bruikbaar. Inmiddels is het model achter de EICIE herijkt, ook al zijn er maar bbp-gegevens voor een korte periode. Het functioneert nu weer naar behoren