Simulation of a data center cooling system in an emergency situation

The paper deals with keeping server rooms at reasonable air temperature in the case of an electrical power failure in a data center and with building performance simulations used to support emergency power planning. An existing data center was analyzed in detail with respect to the possibilities of emergency cooling. Based on the assumption that the thermal capacity of already chilled water can be used to prolong functionality of the cooling system when the roof chillers are out of operation, a backup power supply was designed for Computer Room Air-Conditioning and even for the cooling liquid circuit pumps (i.e. not for the roof chillers). Special models representing the data center indoor environment and cooling system, including a detailed model of the Computer Room Air Conditioning (CRAC) units, were developed in order to estimate the time period during which the internal air temperatures in the server room will not exceed the limit. The numerical model of the server room and the cooling system was built in the TRNSYS software and calibrated by measured data acquired from a real power outage situation. The results and conclusions obtained from the performed analyses and simulations helped to improve the emergency power plan of the data center. The study also forms the basis for the development of an emergency decision algorithm that will included in the novel supervisory control platform: GENi

Positional errors in species distribution modelling are not overcome by the coarser grains of analysis

The performance of species distribution models (SDMs) is known to be affected by analysis grain and positional error of species occurrences. Coarsening of the analysis grain has been suggested to compensate for positional errors. Nevertheless, this way of dealing with positional errors has never been thoroughly tested. With increasing use of fine-scale environmental data in SDMs, it is important to test this assumption. Models using fine-scale environmental data are more likely to be negatively affected by positional error as the inaccurate occurrences might easier end up in unsuitable environment. This can result in inappropriate conservation actions. Here, we examined the trade-offs between positional error and analysis grain and provide recommendations for best practice. We generated narrow niche virtual species using environmental variables derived from LiDAR point clouds at 5 x 5 m fine-scale. We simulated the positional error in the range of 5 m to 99 m and evaluated the effects of several spatial grains in the range of 5 m to 500 m. In total, we assessed 49 combinations of positional accuracy and analysis grain. We used three modelling techniques (MaxEnt, BRT and GLM) and evaluated their discrimination ability, niche overlap with virtual species and change in realized niche. We found that model performance decreased with increasing positional error in species occurrences and coarsening of the analysis grain. Most importantly, we showed that coarsening the analysis grain to compensate for positional error did not improve model performance. Our results reject coarsening of the analysis grain as a solution to address the negative effects of positional error on model performance. We recommend fitting models with the finest possible analysis grain and as close to the response grain as possible even when available species occurrences suffer from positional errors. If there are significant positional errors in species occurrences, users are unlikely to benefit from making additional efforts to obtain higher resolution environmental data unless they also minimize the positional errors of species occurrences. Our findings are also applicable to coarse analysis grain, especially for fragmented habitats, and for species with narrow niche breadth

Vegetation structure derived from airborne laser scanning to assess species distribution and habitat suitability: The way forward

Ecosystem structure, especially vertical vegetation structure, is one of the six essential biodiversity variable classes and is an important aspect of habitat heterogeneity, affecting species distributions and diversity by providing shelter, foraging, and nesting sites. Point clouds from airborne laser scanning (ALS) can be used to derive such detailed information on vegetation structure. However, public agencies usually only provide digital elevation models, which do not provide information on vertical vegetation structure. Calculating vertical structure variables from ALS point clouds requires extensive data processing and remote sensing skills that most ecologists do not have. However, such information on vegetation structure is extremely valuable for many analyses of habitat use and species distribution. We here propose 10 variables that should be easily accessible to researchers and stakeholders through national data portals. In addition, we argue for a consistent selection of variables and their systematic testing, which would allow for continuous improvement of such a list to keep it up-to-date with the latest evidence. This initiative is particularly needed not only to advance ecological and biodiversity research by providing valuable open datasets but also to guide potential users in the face of increasing availability of global vegetation structure products

Environmental movements in space-time: the Czech and Slovak republics from Stalinism to post-socialism

To demonstrate the role of space and time in social movements, the paper analyses the evolution and context of the environmental movement in the Czech and Slovak republics from 1948 to 1998. It shows that the movement's identity was formed under socialism and that political opportunity and resource availability changed markedly over time, as did its organisational and spatial structure. The movement played a significant part in the collapse of the socialist regime, but in the 1990s was marginalised in the interests of building a market economy and an independent Slovakia. Nevertheless a diverse and flexible range of groups existed by the late 1990s. The successive space-times allow analysis of the multiple and changing variables that influence the geography of social movements

Overlaps Between Autism and Language Impairment: Phenomimicry or Shared Etiology?

Traditionally, autistic spectrum disorder (ASD) and specific language impairment (SLI) are regarded as distinct conditions with separate etiologies. Yet these disorders co-occur at above chance levels, suggesting shared etiology. Simulations, however, show that additive pleiotropic genes cannot account for observed rates of language impairment in relatives, which are higher for probands with SLI than for those with ASD + language impairment. An alternative account is in terms of ‘phenomimicry’, i.e., language impairment in comorbid cases may be a consequence of ASD risk factors, and different from that seen in SLI. However, this cannot explain why molecular genetic studies have found a common risk genotype for ASD and SLI. This paper explores whether nonadditive genetic influences could account for both family and molecular findings. A modified simulation involving G × G interactions obtained levels of comorbidity and rates of impairment in relatives more consistent with observed values. The simulations further suggest that the shape of distributions of phenotypic trait scores for different genotypes may provide evidence of whether a gene is involved in epistasis

The pathophysiology of restricted repetitive behavior

Restricted, repetitive behaviors (RRBs) are heterogeneous ranging from stereotypic body movements to rituals to restricted interests. RRBs are most strongly associated with autism but occur in a number of other clinical disorders as well as in typical development. There does not seem to be a category of RRB that is unique or specific to autism and RRB does not seem to be robustly correlated with specific cognitive, sensory or motor abnormalities in autism. Despite its clinical significance, little is known about the pathophysiology of RRB. Both clinical and animal models studies link repetitive behaviors to genetic mutations and a number of specific genetic syndromes have RRBs as part of the clinical phenotype. Genetic risk factors may interact with experiential factors resulting in the extremes in repetitive behavior phenotypic expression that characterize autism. Few studies of individuals with autism have correlated MRI findings and RRBs and no attempt has been made to associate RRB and post-mortem tissue findings. Available clinical and animal models data indicate functional and structural alterations in cortical-basal ganglia circuitry in the expression of RRB, however. Our own studies point to reduced activity of the indirect basal ganglia pathway being associated with high levels of repetitive behavior in an animal model. These findings, if generalizable, suggest specific therapeutic targets. These, and perhaps other, perturbations to cortical basal ganglia circuitry are mediated by specific molecular mechanisms (e.g., altered gene expression) that result in long-term, experience-dependent neuroadaptations that initiate and maintain repetitive behavior. A great deal more research is needed to uncover such mechanisms. Work in areas such as substance abuse, OCD, Tourette syndrome, Parkinson’s disease, and dementias promise to provide findings critical for identifying neurobiological mechanisms relevant to RRB in autism. Moreover, basic research in areas such as birdsong, habit formation, and procedural learning may provide additional, much needed clues. Understanding the pathophysioloy of repetitive behavior will be critical to identifying novel therapeutic targets and strategies for individuals with autism

Simulation of a data center cooling system in an emergency situation

The paper deals with keeping server rooms at reasonable air temperature in the case of an electrical power failure in a data center and with building performance simulations used to support emergency power planning. An existing data center was analyzed in detail with respect to the possibilities of emergency cooling. Based on the assumption that the thermal capacity of already chilled water can be used to prolong functionality of the cooling system when the roof chillers are out of operation, a backup power supply was designed for Computer Room Air-Conditioning and even for the cooling liquid circuit pumps (i.e. not for the roof chillers). Special models representing the data center indoor environment and cooling system, including a detailed model of the Computer Room Air Conditioning (CRAC) units, were developed in order to estimate the time period during which the internal air temperatures in the server room will not exceed the limit. The numerical model of the server room and the cooling system was built in the TRNSYS software and calibrated by measured data acquired from a real power outage situation. The results and conclusions obtained from the performed analyses and simulations helped to improve the emergency power plan of the data center. The study also forms the basis for the development of an emergency decision algorithm that will included in the novel supervisory control platform: GENi