19,529 research outputs found

    On site challenges for the construction of 16-storey condominium: as observed by a young civil engineering technologist

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    The difference between an engineer and an engineering technologist is that, an engineer would mainly focus and produce structural designs based on engineering calculations, while the job of an engineering technologist is to execute the design in the real working environment by adopting flexible and critical technical ideas on-site. The challenges can be divided into two categories, namely design challenges faced by an engineer and the construction challenges faced by an engineering technologist. Thus, the job scope of an engineering technologist is relatively wider when compared to that of an engineer, as the engineering technologist would be dealing with the consultant, contractors and suppliers on site, while handling the in situ construction challenges. This requires basic understanding of engineering principles and technology, critical thinking and problem-solving skills, modern tools competency in software applications, designs and construction calculations, as well as communication and leadership skills all rolled into one. I have recorded my experience as a junior civil engineering technologist engaged in the construction works of a 16-storey condominium at Langkawi, Kedah. Included in the descriptions are in situ technical problems encountered, potentially unsafe working conditions, foundations, scheduling and housekeeping on site, among others. I hope that the information shared in this entry would make a good introduction and induction for juniors entering the work site, where my personal undertakings could serve as a guide and reminder for them

    Bivalirudin versus unfractionated heparin: a meta-analysis of patients receiving percutaneous coronary intervention for acute coronary syndromes

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    OBJECTIVE: Acute coronary syndrome (ACS) encompasses ST segment elevation myocardial infarction (STEMI), with generally high thrombus burden and non-ST segment elevation ACS (NSTE-ACS), with lower thrombus burden. In the setting of percutaneous coronary intervention (PCI) for ACS, bivalirudin appears superior to unfractionated heparin (UFH), driven by reduced major bleeding. Recent trials suggest that the benefit of bivalirudin may be reduced with use of transradial access and evolution in antiplatelet therapy. Moreover, a differential role of bivalirudin in ACS cohorts is unknown. METHODS: A meta-analysis of randomised trials comparing bivalirudin and UFH in patients with ACS receiving PCI, with separate analyses in STEMI and NSTE-ACS groups. Overall estimates of treatment effect were calculated with random-effects model. RESULTS: In 5 trials of STEMI (10 358 patients), bivalirudin increased the risk of acute stent thrombosis (ST) (OR 3.62; CI 1.95 to 6.74; p<0.0001) compared with UFH. Bivalirudin reduced the risk of major bleeding only when compared with UFH plus planned glycoprotein IIb/IIIa inhibitors (GPI) (OR 0.49; CI 0.36 to 0.67; p<0.00001). In 14 NSTE-ACS trials (25 238 patients), there was no difference between bivalirudin and UFH in death, myocardial infarction or ST. However, bivalirudin reduced the risk of major bleeding compared with UFH plus planned GPI (OR 0.52; CI 0.43 to 0.62; p<0.00001), or UFH plus provisional GPI (OR 0.68; CI 0.46 to 1.01; p=0.05). The reduction in major bleeding with bivalirudin was not related to vascular access site. CONCLUSIONS: Bivalirudin increases the risk of acute ST in STEMI, but may confer an advantage over UFH in NSTE-ACS while undergoing PCI, reducing major bleeding without an increase in ST

    Nonlinear Oscillations and Bifurcations in Silicon Photonic Microresonators

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    Silicon microdisks are optical resonators that can exhibit surprising nonlinear behavior. We present a new analysis of the dynamics of these resonators, elucidating the mathematical origin of spontaneous oscillations and deriving predictions for observed phenomena such as a frequency comb spectrum with MHz-scale repetition rate. We test predictions through laboratory experiment and numerical simulation.Comment: Main text: 5 pages, 6 figures. Supplemental material: 12 pages, 8 figure

    Counting surface-kernel epimorphisms from a co-compact Fuchsian group to a cyclic group with motivations from string theory and QFT

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    Graphs embedded into surfaces have many important applications, in particular, in combinatorics, geometry, and physics. For example, ribbon graphs and their counting is of great interest in string theory and quantum field theory (QFT). Recently, Koch, Ramgoolam, and Wen [Nuclear Phys.\,B {\bf 870} (2013), 530--581] gave a refined formula for counting ribbon graphs and discussed its applications to several physics problems. An important factor in this formula is the number of surface-kernel epimorphisms from a co-compact Fuchsian group to a cyclic group. The aim of this paper is to give an explicit and practical formula for the number of such epimorphisms. As a consequence, we obtain an `equivalent' form of the famous Harvey's theorem on the cyclic groups of automorphisms of compact Riemann surfaces. Our main tool is an explicit formula for the number of solutions of restricted linear congruence recently proved by Bibak et al. using properties of Ramanujan sums and of the finite Fourier transform of arithmetic functions

    Qualitative System Identification from Imperfect Data

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    Experience in the physical sciences suggests that the only realistic means of understanding complex systems is through the use of mathematical models. Typically, this has come to mean the identification of quantitative models expressed as differential equations. Quantitative modelling works best when the structure of the model (i.e., the form of the equations) is known; and the primary concern is one of estimating the values of the parameters in the model. For complex biological systems, the model-structure is rarely known and the modeler has to deal with both model-identification and parameter-estimation. In this paper we are concerned with providing automated assistance to the first of these problems. Specifically, we examine the identification by machine of the structural relationships between experimentally observed variables. These relationship will be expressed in the form of qualitative abstractions of a quantitative model. Such qualitative models may not only provide clues to the precise quantitative model, but also assist in understanding the essence of that model. Our position in this paper is that background knowledge incorporating system modelling principles can be used to constrain effectively the set of good qualitative models. Utilising the model-identification framework provided by Inductive Logic Programming (ILP) we present empirical support for this position using a series of increasingly complex artificial datasets. The results are obtained with qualitative and quantitative data subject to varying amounts of noise and different degrees of sparsity. The results also point to the presence of a set of qualitative states, which we term kernel subsets, that may be necessary for a qualitative model-learner to learn correct models. We demonstrate scalability of the method to biological system modelling by identification of the glycolysis metabolic pathway from data
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