Fire resistance rating of light gauge steel frame walls exposed to realistic design fires : a review and development of time equivalent approach

This paper presents the details of research undertaken on the development of an energy based time equivalent approach for light gauge steel frame (LSF) walls. This research utilized an energy based time equivalent approach to obtain the fire resistance ratings (FRR) of LSF walls exposed to realistic design fires with respect to standard fire exposure [1]. It is based on the equal area concept of fire severity and relates to the amount of energy transferred to the member. The proposed method was used to predict the fire resistance of single and double plasterboard lined and externally insulated LSF walls. The predicted fire resistance ratings were compared with the results from finite element analyses and fire design rules for three different wall configurations. This paper presents the review of the available time equivalent approaches and the development of energy based time equivalent approach for the prediction of fire resistance ratings of LSF walls exposed to realistic design fires.

Shared generation of pseudo-random functions

In Crypto’95, Micali and Sidney proposed a method for shared generation of a pseudo-random function f(·) among n players in such a way that for all the inputs x, any u players can compute f(x) while t or fewer players fail to do so, where 0⩽trandom collection of functions, among the n players, each player gets a subset of S, in such a way that any u players together hold all the secret seeds in S while any t or fewer players will lack at least one element from S. The pseudo-random function is then computed as where fsi(·)'s are poly-random functions. One question raised by Micali and Sidney is how to distribute the secret seeds satisfying the above condition such that the number of seeds, d, is as small as possible. In this paper, we continue the work of Micali and Sidney. We first provide a general framework for shared generation of pseudo-random function using cumulative maps. We demonstrate that the Micali–Sidney scheme is a special case of this general construction. We then derive an upper and a lower bound for d. Finally we give a simple, yet efficient, approximation greedy algorithm for generating the secret seeds S in which d is close to the optimum by a factor of at most u ln 2.

Shared generation of pseudo-random functions with cumulative maps

In Crypto’95, Micali and Sidney proposed a method for shared generation of a pseudo-random function f(·) among n players in such a way that for all the inputs x, any u players can compute f(x) while t or fewer players fail to do so, where 0 ≤ t < u ≤ n. The idea behind the Micali-Sidney scheme is to generate and distribute secret seeds S = s1, . . . , sd of a poly-random collection of functions, among the n players, each player gets a subset of S, in such a way that any u players together hold all the secret seeds in S while any t or fewer players will lack at least one element from S. The pseudo-random function is then computed as where f s i (·)’s are poly-random functions. One question raised by Micali and Sidney is how to distribute the secret seeds satisfying the above condition such that the number of seeds, d, is as small as possible. In this paper, we continue the work of Micali and Sidney. We first provide a general framework for shared generation of pseudo-random function using cumulative maps. We demonstrate that the Micali-Sidney scheme is a special case of this general construction.We then derive an upper and a lower bound for d. Finally we give a simple, yet efficient, approximation greedy algorithm for generating the secret seeds S in which d is close to the optimum by a factor of at most u ln 2.

Burden of non-communicable diseases (NCDs) in Nepal and the availability and affordability of NCD medicines

Background: Traditionally communicable diseases were the main causes of burden in developing countries like Nepal. In recent years non-communicable diseases (NCDs), mainly cardiovascular diseases (CVDs), cancer, chronic respiratory diseases and diabetes mellitus, impose a larger disease burden compared to communicable diseases. Most elements of health and medicine policies in Nepal are still focused on communicable diseases. There is limited evidence about NCDs and NCD medicines in Nepal. Aim: To explore the gap between the burden of NCDs and the availability and affordability of NCD medicines in Nepal. Methods: Biomedical databases like Medline, Scopus, Web of Science and other online sources (including Global Burden of Diseases data) were searched for data on the burden of NCDs in term of Disability Adjusted Life Years (DALYs). The Essential Medicines List (EML) of Nepal was compared with World Health Organisation (EML) for inclusion of NCD medicines. Results: In Nepal, NCDs caused nearly 45% of the total 10.5 million DALYs in 2010. CVDs (15.2%), were the leading cause of NCDs burden followed by chronic respiratory diseases (14.7%), cancer (7.3%) and diabetes mellitus (3.2%). One hospital based national survey found that 37% of hospitalised patients had NCDs. Among them, 38% had heart disease followed by COPD (33%) , and diabetes (10%). Most (23 out of 28) non-cancer NCD medicines recommended in WHO-EML were present in Nepal's EML, theoretically indicating good availability. However, it is difficult to say whether they are accessible and affordable due to the lack of adequate data on access and pricing. Conclusion: This study gives some insight into the burden of NCDs. Although NCD medicines are available in Nepal, further research is required to determine whether they are accessible and affordable to the general population.

Modelling total duration of traffic incidents including incident detection and recovery time

Traffic incidents are key contributors to non-recurrent congestion, potentially generating significant delay. Factors that influence the duration of incidents are important to understand so that effective mitigation strategies can be implemented. To identify and quantify the effects of influential factors, a methodology for studying total incident duration based on historical data from an ‘integrated database’ is proposed. Incident duration models are developed using a selected freeway segment in the Southeast Queensland, Australia network. The models include incident detection and recovery time as components of incident duration. A hazard-based duration modelling approach is applied to model incident duration as a function of a variety of factors that influence traffic incident duration. Parametric accelerated failure time survival models are developed to capture heterogeneity as a function of explanatory variables, with both fixed and random parameters specifications. The analysis reveals that factors affecting incident duration include incident characteristics (severity, type, injury, medical requirements, etc.), infrastructure characteristics (roadway shoulder availability), time of day, and traffic characteristics. The results indicate that event type durations are uniquely different, thus requiring different responses to effectively clear them. Furthermore, the results highlight the presence of unobserved incident duration heterogeneity as captured by the random parameter models, suggesting that additional factors need to be considered in future modelling efforts.

Cost efficiency of Japanese steam power generation companies : a Bayesian comparison of random and fixed frontier models

This study analyses and compares the cost efficiency of Japanese steam power generation companies using the fixed and random Bayesian frontier models. We show that it is essential to account for heterogeneity in modelling the performance of energy companies. Results from the model estimation also indicate that restricting CO2 emissions can lead to a decrease in total cost. The study finally discusses the efficiency variations between the energy companies under analysis, and elaborates on the managerial and policy implications of the results.

A random key genetic algorithm for live migration of multiple virtual machines in data centers

Live migration of multiple Virtual Machines (VMs) has become an integral management activity in data centers for power saving, load balancing and system maintenance. While state-of-the-art live migration techniques focus on the improvement of migration performance of an independent single VM, only a little has been investigated to the case of live migration of multiple interacting VMs. Live migration is mostly influenced by the network bandwidth and arbitrarily migrating a VM which has data inter-dependencies with other VMs may increase the bandwidth consumption and adversely affect the performances of subsequent migrations. In this paper, we propose a Random Key Genetic Algorithm (RKGA) that efficiently schedules the migration of a given set of VMs accounting both inter-VM dependency and data center communication network. The experimental results show that the RKGA can schedule the migration of multiple VMs with significantly shorter total migration time and total downtime compared to a heuristic algorithm.

Greenhouse gas emissions from sub-tropical agricultural soils after addition of organic by-products

As the cost of mineral fertilisers increases globally, organic soil amendments (OAs) from agricultural sources are increasingly being used as substitutes for nitrogen. However, the impact of OAs on the production of greenhouse gases (CO2 and N2O) is not well understood. A 60-day laboratory incubation experiment was conducted to investigate the impacts of applying OAs (equivalent to 296 kg N ha−1 on average) on N2O and CO2 emissions and soil properties of clay and sandy loam soils from sugar cane production. The experiment included 6 treatments, one being an un-amended (UN) control with addition of five OAs being raw mill mud (MM), composted mill mud (CM), high N compost (HC), rice husk biochar (RB), and raw mill mud plus rice husk biochar (MB). These OAs were incubated at 60, 75 and 90% water-filled pore space (WFPS) at 25°C with urea (equivalent to 200 kg N ha−1) added to the soils thirty days after the incubation commenced. Results showed WFPS did not influence CO2 emissions over the 60 days but the magnitude of emissions as a proportion of C applied was RB < CM < MB < HC availability of C and not immobilisation of N. These findings show that the effect of OAs on soil GHG emissions can vary substantially depending on their chemical properties. OAs with a high availability of labile C and N can lead to elevated emissions of CO2 and N2O, while rice husk biochar showed potential in reducing overall soil GHG emissions.

Evolution to a chronic disease niche correlates with increased sensitivity to tryptophan availability for the obligate intracellular bacterium Chlamydia pneumoniae

The chlamydiae are obligate intracellular parasites that have evolved specific interactions with their various hosts and host cell types to ensure their successful survival and consequential pathogenesis. The species Chlamydia pneumoniae is ubiquitous, with serological studies showing that most humans are infected at some stage in their lifetime. While most human infections are asymptomatic, C. pneumoniae can cause more-severe respiratory disease and pneumonia and has been linked to chronic diseases such as asthma, atherosclerosis, and even Alzheimer's disease. The widely dispersed animal-adapted C. pneumoniae strains cause an equally wide range of diseases in their hosts. It is emerging that the ability of C. pneumoniae to survive inside its target cells, including evasion of the host's immune attack mechanisms, is linked to the acquisition of key metabolites. Tryptophan and arginine are key checkpoint compounds in this host-parasite battle. Interestingly, the animal strains of C. pneumoniae have a slightly larger genome, enabling them to cope better with metabolite restrictions. It therefore appears that as the evolutionarily more ancient animal strains have evolved to infect humans, they have selectively become more "susceptible" to the levels of key metabolites, such as tryptophan. While this might initially appear to be a weakness, it allows these human C. pneumoniae strains to exquisitely sense host immune attack and respond by rapidly reverting to a persistent phase. During persistence, they reduce their metabolic levels, halting progression of their developmental cycle, waiting until the hostile external conditions have passed before they reemerge.

A pseudo-marginal sequential Monte Carlo algorithm for random effects models in Bayesian sequential design

A computationally efficient sequential Monte Carlo algorithm is proposed for the sequential design of experiments for the collection of block data described by mixed effects models. The difficulty in applying a sequential Monte Carlo algorithm in such settings is the need to evaluate the observed data likelihood, which is typically intractable for all but linear Gaussian models. To overcome this difficulty, we propose to unbiasedly estimate the likelihood, and perform inference and make decisions based on an exact-approximate algorithm. Two estimates are proposed: using Quasi Monte Carlo methods and using the Laplace approximation with importance sampling. Both of these approaches can be computationally expensive, so we propose exploiting parallel computational architectures to ensure designs can be derived in a timely manner. We also extend our approach to allow for model uncertainty. This research is motivated by important pharmacological studies related to the treatment of critically ill patients.

Understanding public response to a congestion charge : a random-effects ordered logit approach

Public acceptance is consistently listed as having an enormous impact on the implementation and success of a congestion charge scheme. This paper investigates public acceptance of such a scheme in Australia. Surveys were conducted in Brisbane and Melbourne, the two fastest growing Australian cities. Using an ordered logit modeling approach, the survey data including stated preferences were analyzed to pinpoint the important factors influencing people’s attitudes to a congestion charge and, in turn, to their transport mode choices. To accommodate the nature of, and to account for the resulting heterogeneity of the panel data, random effects were considered in the models. As expected, this study found that the amount of the congestion charge and the financial benefits of implementing it have a significant influence on respondents’ support for the charge and on the likelihood of their taking a bus to city areas. However, respondents’ current primary transport mode for travelling to the city areas has a more pronounced impact. Meanwhile, respondents’ perceptions of the congestion charge’s role in protecting the environment by reducing vehicle emissions, and of the extent to which the charge would mean that they travelled less frequently to the city for shopping or entertainment, also have a significant impact on their level of support for its implementation. We also found and explained notable differences across two cities. Finally, findings from this study have been fully discussed in relation to the literature.