Investigation of hybridized polyurethane, glass fibre reinforced cement and steel laminate in structural floor plate systems

Sandwich components have emerged as light weight, efficient, economical, recyclable and reusable building systems which provide an alternative to both stiffened steel and reinforced concrete. These components are made of composite materials in which two metal face plates or Glassfibre Reinforced Cement (GRC) layers are bonded and form a sandwich with light weight compact polyurethane (PU) elastomer core. Existing examples of product applications are light weight sandwich panels for walls and roofs, Sandwich Plate System (SPS) for stadia, arena terraces, naval construction and bridges and Domeshell structures for dome type structures. Limited research has been conducted to investigate performance characteristics and applicability of sandwich or hybrid materials as structural flooring systems. Performance characteristics of Hybrid Floor Plate Systems comprising GRC, PU and Steel have not been adequately investigated and quantified. Therefore there is very little knowledge and design guidance for their application in commercial and residential buildings. This research investigates performance characteristics steel, PU and GRC in Hybrid Floor Plate Systems (HFPS) and develops a new floor system with appropriate design guide lines

Benchmarking network propagation methods for disease gene identification

In-silico identification of potential target genes for disease is an essential aspect of drug target discovery. Recent studies suggest that successful targets can be found through by leveraging genetic, genomic and protein interaction information. Here, we systematically tested the ability of 12 varied algorithms, based on network propagation, to identify genes that have been targeted by any drug, on gene-disease data from 22 common non-cancerous diseases in OpenTargets. We considered two biological networks, six performance metrics and compared two types of input gene-disease association scores. The impact of the design factors in performance was quantified through additive explanatory models. Standard cross-validation led to over-optimistic performance estimates due to the presence of protein complexes. In order to obtain realistic estimates, we introduced two novel protein complex-aware cross-validation schemes. When seeding biological networks with known drug targets, machine learning and diffusion-based methods found around 2-4 true targets within the top 20 suggestions. Seeding the networks with genes associated to disease by genetics decreased performance below 1 true hit on average. The use of a larger network, although noisier, improved overall performance. We conclude that diffusion-based prioritisers and machine learning applied to diffusion-based features are suited for drug discovery in practice and improve over simpler neighbour-voting methods. We also demonstrate the large impact of choosing an adequate validation strategy and the definition of seed disease genesPeer ReviewedPostprint (published version

Inactivating the spindle checkpoint kinase Bub1 during embryonic development results in a global shutdown of proliferation

<p>Abstract</p> <p>Background</p> <p>Bub1 is a component of the spindle assembly checkpoint, a surveillance mechanism that maintains chromosome stability during M-phase. Bub1 is essential during the early stages of embryogenesis, with homozygous <it>BUB1</it>-null mice dying shortly after day E3.5. Bub1 is also required later during embryogenesis; inactivation of <it>BUB1 </it>on day E10.5 appears to rapidly block all further development. However, the mechanism(s) responsible for this phenotype remain unclear.</p> <p>Findings</p> <p>Here we show that inactivating <it>BUB1 </it>on day E10.5 stalls embryogenesis within 48 hours. This is accompanied by a global shutdown of proliferation, widespread apoptosis and haemorrhaging.</p> <p>Conclusion</p> <p>Our results suggest that Bub1 is required throughout the developing embryo for cellular proliferation. Therefore, Bub1 has been shown to be essential in all scenarios analyzed thus far in mice: proliferation of cultured fibroblasts, spermatogenesis, oogenesis and both early and late embryonic development. This likely reflects the fact that Bub1 has dual functions during mitosis, being required for both SAC function and chromosome alignment.</p

The evaluation of the Australian office market forecast accuracy

Purpose: Property market models have the overriding aim of predicting reasonable estimates of key dependent variables (demand, supply, rent, yield, vacancy and net absorption rate). These can be based on independent drivers of core property and economic activities. Accurate predictions can only be conducted when ample quantitative data are available with fewer uncertainties. However, a broad-fronted social, technical and ecological evolution can throw up sudden, unexpected shocks that result in the econometric outputs sceptical to unknown risk factors. Therefore, this paper aims at evaluating Australian office market forecast accuracy and to determine whether the forecasts capture extreme downside risk events. Design/methodology/approach: This study follows a quantitative research approach, using secondary data analysis to test the accuracy of economists’ forecasts. The forecast accuracy evaluation encompasses the measurement of economic and property forecasts under the following phases: (i) testing for the forecast accuracy, (ii) analysing outliers of forecast errors and (iii) testing of causal relationships. Forecast accuracy measurement incorporates scale independent metrics that include Theil’s U values (U1 and U2) and mean absolute scaled error (MASE). Inter Quartile Range (IQR) rule is used for the outlier analysis. To find the causal relationships among variables, the time series regression methodology is utilised, including multiple regression analysis and Granger causality developed under the vector auto regression (VAR). Findings: The credibility of economic and property forecasts was questionable around the period of the Global Financial Crisis (GFC); a significant man-made Black Swan event. The forecast accuracy measurement highlighted rental movement and net absorption forecast errors as the critical inaccurate predictions. These key property variables are explained by historic information and independent economic variables. However, these do not explain the changes when error time series of the variables were concerned. According to VAR estimates, all property variables have a significant causality derived from the lagged values of Australian S&P/ASX 200 (ASX) forecast errors. Therefore, lagged ASX forecast errors could be used as a warning signal to adjust property forecasts. Research Limitations: Secondary data were obtained from the premier Australian property markets: Canberra, Sydney, Brisbane, Adelaide, Melbourne and Perth. A limited 10-year timeframe (2001 – 2011) was used in the ex-post analysis for the comparison of economic and property variables. Forecasts ceased from 2011, due to the discontinuity of the Australian Financial Review (AFR) quarterly survey of economists; the main source of economic forecast data. Practical implications: The research strongly recommended naïve forecasts for the property variables, as an input determinant in each office market forecast equation. Further, lagged forecast errors in the ASX could be used as a warning signal for the successive property forecast errors. Hence, data adjustments can be made to ensure the accuracy of the Australian office market forecasts. Originality/value: The paper highlights the critical inaccuracy of the Australian office market forecasts around the GFC. In an environment of increasing incidence of unknown events, these types of risk events should not be dismissed as statistical outliers in real estate modelling. As a proactive strategy to improve office market forecasts, lagged ASX forecast errors could be used as a warning signal. This causality was mirrored in rental movements and total vacancy forecast errors. The close interdependency between rents and vacancy rates in the forecasting process and the volatility in rental cash flows reflects on direct property investment and subsequently on the ASX, is therefore justified

AFRP retrofit of reinforced concrete columns against impact loading

Structures can be exposed to impact loads as a result of an explosion, falling objects, projectiles and vehicle collisions. Within the increasing threat of these impact sources, it is very important to protect the columns that are the vital members of the structural systems to ensure structural and personal safety. This study focuses on the performance of axially loaded reinforced concrete members subjected to impact loading. A dropped-weight test set-up developed to perform impact tests on reinforced concrete members. The test set-up was used to perform low elevation impact tests on reinforced concrete (RC) columns that targets to simulate vehicular impact against ground floor columns of low-rise buildings. Since, there is limited information about the transverse impact performances of RC columns; the main objective of this research is to assess the vulnerability of RC columns under transverse impact loads and to enhance their performances by using Aramid Fiber Reinforced Polymer (AFRP) sheets. The scope is limited to 300 mm square columns with 3 m height in low to medium rise buildings which were found to be more vulnerable to lateral impacts according to previous research conducted by the authors, (Gurbuz et al. 2010, 2011). This research provides fundamental knowledge on the behavior of RC columns under low elevation impact loading and also generates new information on impact strengthening of vulnerable concrete columns by AFRP sheets

Association of rodent-borne Leptospira spp. with urban environments in Malaysian Borneo

Although leptospirosis is traditionally considered a disease of rural, agricultural and flooded environments, Leptospira spp. are found in a range of habitats and infect numerous host species, with rodents among the most significant reservoirs and vectors. To explore the local ecology of Leptospira spp. in a city experiencing rapid urbanization, we assessed Leptospira prevalence in rodents from three locations in Malaysian Borneo with differing levels of anthropogenic influence: 1) high but stable influence (urban); 2) moderate yet increasing (developing); and 3) low (rural). A total of 116 urban, 122 developing and 78 rural rodents were sampled, with the majority of individuals assigned to either the Rattus rattus lineage R3 (n = 165) or Sundamys muelleri (n = 100). Leptospira spp. DNA was detected in 31.6% of all rodents, with more urban rodents positive (44.8%), than developing (32.0%) or rural rodents (28.1%), and these differences were statistically significant. The majority of positive samples were identified by sequence comparison to belong to known human pathogens L. interrogans (n = 57) and L. borgpetersenii (n = 38). Statistical analyses revealed that both Leptospira species occurred more commonly at sites with higher anthropogenic influence, particularly those with a combination of commercial and residential activity, while L. interrogans infection was also associated with low forest cover, and L. borgpetersenii was more likely to be identified at sites without natural bodies of water. This study suggests that some features associated with urbanization may promote the circulation of Leptospira spp., resulting in a potential public health risk in cities that may be substantially underestimated

Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770