Experimental analysis of air flow profiles in a double skin facade in a maritime climate

Glazed Double Skin Facades (DSF) offer the potential to improve the performance of all-glass building skins, common to commercial office buildings in which full facade glazing has almost become the standard. Single skin glazing results in increased heating and cooling costs over opaque walls, due to lower thermal resistance of glass, and the increased impact of solar gain through it. However, the performance benefit of DSF technology continues to be questioned and its operation poorly understood, particularly the nature of airflow through the cavity. This paper deals specifically with the experimental analysis of the air flow characteristics in an automated double skin façade. The benefit of the DSF as a thermal buffer, and to limit overheating is evaluated through analysis of an extensive set of parameters including air and surface temperatures at each level in the DSF, airflow readings in the cavity and at the inlet and outlet, solar and wind data, and analytically derived pressure differentials. The temperature and air-flow are monitored in the cavity of a DSF using wireless sensors and hot wire anemometers respectively. Automated louvre operation and building set-points are monitored via the BMS. Thermal stratification and air flow variation during changing weather conditions are shown to effect the performance of the DSF considerably and hence the energy performance of the building. The relative pressure effects due to buoyancy and wind are analysed and quantified. This research aims to developed and validate models of DSFs in the maritime climate, using multi-season data from experimental monitoring. This extensive experimental study provides data for training and validation of models.

Changes in epilithic biomasses and invertebrate community structure over a deposit metal concentration gradient in upland headwater streams

Stream bed metal deposits affect the taxon richness, density and taxonomic diversity of primary and secondary producers by a variety of direct or indirect abiotic and biotic processes but little is known about the relative importance of these processes over a deposit metal concentration gradient. Inorganic matter (IM), algal and non-photosynthetic detrital (NPD) dry biomasses were estimated for 10 monthly samples, between 2007 and 2008, from eight sites differing in deposit density. Invertebrate abundance, taxon richness and composition were also determined. Relations between these variables were investigated by canonical correspondence analysis (CCA), generalized estimating equation models and path analysis. The first CCA axis correlates with deposit density and invertebrate abundance, with lumbriculids and chironomids increasing in abundance with deposit density and all other taxa declining. Community structure changes significantly above a deposit density of approximately 8 mg cm, when algal biomass, invertebrate richness and diversity decline. Invertebrate richness and diversity were determined by direct effects of NPD biomass and indirect effects of IM. Algal biomass only had an effect on invertebrate abundance. Possible pH, oxygen, food and ecotoxicological effects of NPD biomass on the biota are discussed.

Dyslipidemia and Diabetic Macular Edema: A Systematic Review and Meta-Analysis

Topic: A systematic review and meta-analysis of dyslipidemia and diabetic macular edema (DME). 

Clinical Relevance: Diabetic macular edema causes impairment of vision in patients with diabetes, and dyslipidemia has been reported as a risk factor for its development. A systematic review with a meta-analysis was undertaken to examine the evidence of an association between dyslipidemia and DME. 

Methods: We defined eligibility criteria as randomized controlled trials (RCTs) and cohort, case-control, and cross-sectional studies reporting on the relationship between blood lipid levels and DME. We performed a literature search in MEDLINE, PubMed, and Embase from inception to September 2014. We used the NewcastleeOttawa scale to assess the quality of case-control, cross-sectional, and cohort studies, and the Cochrane risk of bias tool for RCTs. 

Results: The search strategy identified 4959 publications. After screening, we selected 21 articles for review (5 cross-sectional, 5 cohort, 7 case-control, and 4 RCTs). Meta-analysis of case-control studies revealed that mean levels of total serum cholesterol (TC), low-density lipoproteins (LDLs), and serum triglycerides (TGs) were significantly higher in patients with DME compared with those without DME (TC: 30.08; 95% confidence interval [CI], 21.14e39.02; P < 0.001; LDL: 18.62; 95% CI, 5.80e31.43; P < 0.05; TG: 24.82; 95% CI, 9.21e40.42; P < 0.05). Meta-analysis of RCTs did not show significant risk in worsening of hard exudates and severity of DME in the lipid-lowering group compared with placebo (hard exudates: relative risk, 1.00; 95% CI, 0.47e2.11; P ¼ 1.00; DME: relative risk, 1.18; 95% CI, 0.75e1.86; P ¼ 0.48). 

Conclusions: Despite evidence from the cohort studies and meta-analysis of the case-control studies suggesting a strong relationship between lipid levels and DME, this was not confirmed by the meta-analysis that included only prospective RCTs. Therefore, given the significant public health relevance of the topic, the relationship between lipid levels and DME deserves further investigation.

Raman spectra of SiC nanorods with different excitation wavelengths

With increasing excitation wavelength from 514 to 782 nm, a significant difference in the Raman spectra of SiC nanorods was observed as compared to bulk material. The intensity ratio of the LO mode to that of the IF mode increases with the excitation wavelength increasing. This has been identified as resonant Raman scattering caused by Fröhlich interaction.

LS-ADT: Lightweight and Scalable Anomaly Detection for Cloud Datacentres

Cloud data centres are implemented as large-scale clusters with demanding requirements for service performance, availability and cost of operation. As a result of scale and complexity, data centres typically exhibit large numbers of system anomalies resulting from operator error, resource over/under provisioning, hardware or software failures and security issus anomalies are inherently difficult to identify and resolve promptly via human inspection. Therefore, it is vital in a cloud system to have automatic system monitoring that detects potential anomalies and identifies their source. In this paper we present a lightweight anomaly detection tool for Cloud data centres which combines extended log analysis and rigorous correlation of system metrics, implemented by an efficient correlation algorithm which does not require training or complex infrastructure set up. The LADT algorithm is based on the premise that there is a strong correlation between node level and VM level metrics in a cloud system. This correlation will drop significantly in the event of any performance anomaly at the node-level and a continuous drop in the correlation can indicate the presence of a true anomaly in the node. The log analysis of LADT assists in determining whether the correlation drop could be caused by naturally occurring cloud management activity such as VM migration, creation, suspension, termination or resizing. In this way, any potential anomaly alerts are reasoned about to prevent false positives that could be caused by the cloud operator’s activity. We demonstrate LADT with log analysis in a Cloud environment to show how the log analysis is combined with the correlation of systems metrics to achieve accurate anomaly detection.