Influence of Core Density and Thickness on the Behavior of Sandwich Beams under Three-Point Bending: Analytical and Experimental

The sandwich beams with a foam core and metal face sheets fail in mechanisms such as face sheet yield, core yield, local indentation, and face sheet folding. They can be used as impact energy absorbers in the aerospace, shipbuilding, automotive, rail industries, and elevators. This paper investigated the effect of changes in foam core density and thickness of sandwich beams using analytical methods and experimental tests through the three-point bending process. A total of 21 samples with a foam core made of polyurethane foams of different densities and thicknesses were subjected to a quasi-static three-point bending load. The load-displacement diagrams were obtained at the center of the beam using the Santam testing machine. Afterwards, the impact parameters including specific energy absorption (SEA), maximum and average forces, and efficiency coefficient were examined as the objectives of the test. The analytical and experimental bending results showed that the analytical results have good agreement with the experimental results. Also, it was found that increasing foam core thickness and density can increase the energy absorption capacity. Moreover, the results of experimental tests showed that the energy absorption capacity increased by 93.12% in the samples with the same thickness when increasing the density. Likewise, examining the samples with the same density and different thicknesses revealed that the energy absorption capacity increased by 33.37%

Illusion Mechanisms with Cylindrical Metasurfaces: A General Synthesis Approach

We explore the use of cylindrical metasurfaces in providing several illusion mechanisms including scattering cancellation and creating fictitious line sources. We present the general synthesis approach that leads to such phenomena by modeling the metasurface with effective polarizability tensors and by applying boundary conditions to connect the tangential components of the desired fields to the required surface polarization current densities that generate such fields. We then use these required surface polarizations to obtain the effective polarizabilities for the synthesis of the metasurface. We demonstrate the use of this general method for the synthesis of metasurfaces that lead to scattering cancellation and illusion effects, and discuss practical scenarios by using loaded dipole antennas to realize the discretized polarization current densities. This study is the first fundamental step that may lead to interesting electromagnetic applications, like stealth technology, antenna synthesis, wireless power transfer, sensors, cylindrical absorbers, etc.Comment: 12 pages, 9 figure

Identifying Effective Factors on Technological Entrepreneurship in Iranian Nanotechnology SMEs

Technological entrepreneurship is the latest issue in the field of entrepreneurship and fostering competitive advantages in Small and Medium Enterprises (SMEs) which has been received special attention. Given the lack of coherent literature review to apply technological strategies in SMEs, and also because of the role of technology based firms which are active in industries with new technologies, such as Nano-Technology industry, while the technological entrepreneurship literature review has been investigated, this article is principally intended to identify effective factors on technological entrepreneurship in Iranian Nanotechnology SMEs. The research methodology of the current article is a mixed one; in the qualitative stage, semi-structured and open interviews and investigation of related documents have been used, and in the quantitative stage, the questionnaire has been applied to gather data. In the research’s quantitative stage, a statistical population consisted of managers, expert employees of the case study have been considered, and the simple random sampling method has been used. In addition, in this stage, the questionnaires have been used as the data collection tool and the experts in the qualitative stage measured the research’s validity, and the questionnaire’s reliability has been approved through Cronbach’s alpha of 0.81. The mean analysis has been applied in this stage for the data analysis. The findings of this paper shows that the effective factors on technological entrepreneurship in Iranian Nanotechnology SMEs are categorizes in the four issues of “Internal Processesâ€, “Individual Factorsâ€, “Institutions†and “External Networksâ€

Resistance of Cylindrical Sandwich Panels with Aluminum Foam under Blast Loading

In this research, cylindrical sandwich panels with aluminum foam core and aluminum face-sheets under explosive loading are simulated by Abaqus software. The Sandwich panel with aluminum foam core was laminated and simulated in seven cases with different densities by keeping the mass and total thickness constant. The displacement and energy results were evaluated. The results showed that the laminated foam core has a significant effect on the amount of energy absorption and displacement of the panel. The displacement of the model with the optimal laminated core was reduced by 59,8% compared to the model with an equal mass and thickness of core. It was found that reducing the density of the core along the thickness could produce higher explosive resistance. Also, the effect of parameters such as the thickness of the face-sheet and the curvature of the sandwich panel on energy absorption was investigated. According to the parametric study, it has been shown that with increasing the curvature and thickness of the face-sheet, the blast resistance of the sandwich panels increases

Towards Optimistic, Imaginative, and Harmonious Reinforcement Learning in Single-Agent and Multi-Agent Environments

Reinforcement Learning (RL) has recently gained tremendous attention from the research community. Different algorithms have been proposed to tackle a variety of singleagent and multi-agent problems. The fast pace of growth has primarily been driven by the availability of several simplistic toy simulation environments, such as Atari and DeepMind Control Suite. The capability of most of those algorithms to solve complex problems in partially-observable real-world 3D environments, such as visual navigation and autonomous driving, however, remains limited. In real-world problems, the evaluation environment is often unseen during the training which imposes further challenges. Developing robust and efficient RL algorithms for real-world problems that can generalise to unseen environments remains an open problem. One such limitation of RL algorithms is their lack of ability to remain optimistic in the face of tasks that require longer trajectories to complete. That lack of optimism in agents trained using previous RL methods often leads to a lower evaluated success rate. For instance, such an agent gives up on finding an object only after a few steps of searching for it while a longer search is likely to be successful. We hypothesise that such a lack of optimism is manifested in the agent’s underestimation of the expected future reward, i.e. the state-value function. To alleviate the issue we propose to enhance the agent’s state-value function approximator with more global information. In visual navigation, we do so by learning the spatio-temporal relationship between objects present in the environment. Another limitation of previously introduced RL algorithms is their lack of explicit modelling of the outcome of an action before committing to it, i.e. lack of imagination. Model-based RL algorithms have recently been successful in alleviating such limitations in simple toy environments. Building an accurate model of the environment dynamics in 3D visually complex scenes, however, remains infeasible. Therefore, in our second contribution, we hypothesise that a simpler dynamics model that only imagines the (sub-)goal state can achieve the best of both worlds; it avoids complicated modelling of the future per timestep while still alleviating the shortcomings resulting from the lack of imagination. Finally, in our third contribution, we take a step forward beyond single-agent problems to learn multi-agent interactions. In many real-world problems, e.g. autonomous driving, an agent needs to learn to interact with other potentially learning agents while maximising its own individual reward. Such selfish reward optimisation by every agent often leads to aggressive behaviour. We hypothesise that introducing an intrinsic reward for each agent that encourages caring for neighbours can alleviate this problem. As such, we introduce a new optimisation objective that uses information theory to promote less selfish behaviour across the population of the agents. Overall, our three contributions address three main limitations of single-agent and multiagent RL algorithms for solving real-world problems. Through empirical studies, we validate our three hypotheses and show our proposed methods outperform previous state-of-the-art.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202

Optimistic Agent: Accurate Graph-Based Value Estimation for More Successful Visual Navigation

We humans can impeccably search for a target object, given its name only, even in an unseen environment. We argue that this ability is largely due to three main reasons: the incorporation of prior knowledge (or experience), the adaptation of it to the new environment using the observed visual cues and most importantly optimistically searching without giving up early. This is currently missing in the state-of-the-art visual navigation methods based on Reinforcement Learning (RL). In this paper, we propose to use externally learned prior knowledge of the relative object locations and integrate it into our model by constructing a neural graph. In order to efficiently incorporate the graph without increasing the state-space complexity, we propose our Graph-based Value Estimation (GVE) module. GVE provides a more accurate baseline for estimating the Advantage function in actor-critic RL algorithm. This results in reduced value estimation error and, consequently, convergence to a more optimal policy. Through empirical studies, we show that our agent, dubbed as the optimistic agent, has a more realistic estimate of the state value during a navigation episode which leads to a higher success rate. Our extensive ablation studies show the efficacy of our simple method which achieves the state-of-the-art results measured by the conventional visual navigation metrics, e.g. Success Rate (SR) and Success weighted by Path Length (SPL), in AI2THOR environment.Comment: Accepted for publication at WACV 202

Recombinant expression and purification of functional vascular endothelial growth factor-121 in the baculovirus expression system

AbstractObjectiveTo express human vascular endothelial growth factor121 (VEGF121) in insect cells.MethodsA gene construct containing VEGF was cloned in the pFastBac-HTA vector, followed by transformation in DH10BAC. The recombinant bacmid was then extracted, and transfected into Sf9 insect cells. The transfected cells were harvested, and then VEGF expression was confirmed by western blotting using specific antibodies. The tube formation assay was used for functional assessment of VEGF.ResultsOur results showed that VEGF could be successfully expressed in the baculovirus system. Purified VEGF was able to stimulate in vitro tube formation of human endothelial cells.ConclusionsResults from this study demonstrated that the recombinantly-produced VEGF can be considered as a promising candidate for therapeutic purposes

Combining numerical and clinical methods to assess aortic valve hemodynamics during exercise

Computational simulations have the potential to aid understanding of cardiovascular hemodynamics under physiological conditions, including exercise. Therefore, blood hemodynamic parameters during different heart rates, rest and exercise have been investigated, using a numerical method. A model was developed for a healthy subject. Using geometrical data acquired by echo-Doppler, a two-dimensional model of the chamber of aortic sinus valsalva and aortic root was created. Systolic ventricular and aortic pressures were applied as boundary conditions computationally. These pressures were the initial physical conditions applied to the model to predict valve deformation and changes in hemodynamics. They were the clinically measured brachial pressures plus differences between brachial, central and left ventricular pressures. Echocardiographic imaging was also used to acquire different ejection times, necessary for pressure waveform equations of blood flow during exercise. A fluid-structure interaction simulation was performed, using an arbitrary Lagrangian-Eulerian mesh. During exercise, peak vorticity increased by 14.8%, peak shear rate by 15.8%, peak cell Reynolds number by 20%, peak leaflet tip velocity increased by 47% and the blood velocity increased by 3% through the leaflets, whereas full opening time decreased by 11%. Our results show that numerical methods can be combined with clinical measurements to provide good estimates of patient-specific hemodynamics at different heart rates. </jats:p