The properties of fibre reinforced cement based sandwich beams

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A Cross-Cultural Study: Middle School Students\u27 Beliefs about Matter

Turkish middle school students’ understanding of the nature of matter was examined and compared to those of US counterparts. Sixteen Turkish middle school students were interviewed using a semi-structured interview guide. The interview explored students’ understanding of the particulate nature of matter in three areas: (1) the composition of the substances; (2) the relationship between particulate structure and macroscopic properties; (3) the relationship between particulate structure and processes. The results indicated that many of the middle school students interviewed could state that matter was composed of atoms. However, the majority of them were not able to use this understanding to explain macroproperties or processes of matter. Compared to the US students, the Turkish students could use terms more appropriately in describing the microparticulate nature of matter. However, when students tried to explain the macroproperties or processes of matter, the US students offered more complex and detailed explanations

Mitigating Off-Policy Bias in Actor-Critic Methods with One-Step Q-learning: A Novel Correction Approach

Compared to on-policy counterparts, off-policy model-free deep reinforcement learning can improve data efficiency by repeatedly using the previously gathered data. However, off-policy learning becomes challenging when the discrepancy between the underlying distributions of the agent's policy and collected data increases. Although the well-studied importance sampling and off-policy policy gradient techniques were proposed to compensate for this discrepancy, they usually require a collection of long trajectories and induce additional problems such as vanishing/exploding gradients or discarding many useful experiences, which eventually increases the computational complexity. Moreover, their generalization to either continuous action domains or policies approximated by deterministic deep neural networks is strictly limited. To overcome these limitations, we introduce a novel policy similarity measure to mitigate the effects of such discrepancy in continuous control. Our method offers an adequate single-step off-policy correction that is applicable to deterministic policy networks. Theoretical and empirical studies demonstrate that it can achieve a "safe" off-policy learning and substantially improve the state-of-the-art by attaining higher returns in fewer steps than the competing methods through an effective schedule of the learning rate in Q-learning and policy optimization

Capital maintenance versus technology adoption under embodied technical progress

We study an optimal growth model with one-hoss-shay vintage capital, where labor resources can be allocated freely either to production, technology adoption or capital maintenance. Technological progress is partly embodied. Adoption labor increases the level of embodied technical progress. First, we are able to disentangle the amplification-propagation role of maintenance in business fluctuations: in the short run, the response of the model to transitory shocks on total factor productivity in the final good sector are definitely much sharper compared to the counterpart model without maintenance but with the same average depreciation rate. Moreover, the one-hoss shay technology is shown to reinforce this amplification-propagation mechanism. We also find that accelerations in embodied technical progress should be responded by a gradual adoption effort, and capital maintenance should be the preferred instrument in the short run. Copyright © 2006 The Berkeley Electronic Press. All rights reserved

Bespoke Approximation of Multiplication-Accumulation and Activation Targeting Printed Multilayer Perceptrons

Printed Electronics (PE) feature distinct and remarkable characteristics that make them a prominent technology for achieving true ubiquitous computing. This is particularly relevant in application domains that require conformal and ultra-low cost solutions, which have experienced limited penetration of computing until now. Unlike silicon-based technologies, PE offer unparalleled features such as non-recurring engineering costs, ultra-low manufacturing cost, and on-demand fabrication of conformal, flexible, non-toxic, and stretchable hardware. However, PE face certain limitations due to their large feature sizes, that impede the realization of complex circuits, such as machine learning classifiers. In this work, we address these limitations by leveraging the principles of Approximate Computing and Bespoke (fully-customized) design. We propose an automated framework for designing ultra-low power Multilayer Perceptron (MLP) classifiers which employs, for the first time, a holistic approach to approximate all functions of the MLP's neurons: multiplication, accumulation, and activation. Through comprehensive evaluation across various MLPs of varying size, our framework demonstrates the ability to enable battery-powered operation of even the most intricate MLP architecture examined, significantly surpassing the current state of the art.Comment: Accepted for publication at the 42th IEEE/ACM International Conference on Computer Aided Design (ICCAD) 2023, San Francisco, US

Two-electron singlet states in semiconductor quantum dots with Gaussian confinement: A single-parameter variational calculation

The problem of two electrons in a three-dimensional quantum dot with Gaussian confinement is investigated for the singlet pairing by a variational method with a very simple wavefunction containing only a single parameter and a Jastrow-like factor, which is shown to yield fairly good results for deep confining potentials. The calculation is also performed for a few realistic semiconductor quantum dots and the phase diagrams for the two-electron singlet states are obtained for these materials. The pair density function is calculated for several parameter values and its peak positions are obtained as a function of the confinement length and the depth of the potential to study the behaviour of the electron-pair size. The size of the bound pair of electrons is also obtained by directly calculating the average distance between the two electrons in three different ways and compared with the pair correlation results. It is furthermore shown that, other properties remaining the same, the two-electron energy and the electron-pair size depend crucially on the effective electronic mass and the dielectric constant of the material. Finally, the ways of improving the wavefunction are also indicated. © IOP Publishing Ltd

Natural Convection in an Enclosure with a Discretely Heated Sidewall: Heatlines and Flow Visualization

Natural convection inside a rectangular enclosure is investigated experimentally and numerically. One of the sidewalls is heated discretely by two flush-mounted heat sources. The other sidewall is kept at a constant temperature, while the horizontal walls are unheated. Heat dissipation rates of the heat sources are equal to each other. The aspect ratio of the enclosure (AR) is 2 and the working fluid is air (Pr=0.71). The study is focused on the validation of the two and three dimensional computations under real test conditions against experiments for various modified Rayleigh number values. Experimental study is performed for various modified Rayleigh numbers in the range of 7.7x105 and 3.1x106 while numerical part covers the values between 104 and 5x106. Temperature measurements and flow visualization studies are performed in the experimental work, and streamlines, isotherms and heatlines are presented in the numerical part of the study. From the experimental and numerical studies, it is shown that two dimensional computations reflects the general characteristics of the problem, conduction and radiation heat transfer are not negligible, surface temperatures increase with the modified Rayleigh number and heatline approach is an important tool to analyze convective heat transfer

Generalized Coordinated Multipoint Framework for 5G and Beyond

The characteristic feature of 5G is the diversity of its services for different user needs. However, the requirements for these services are competing in nature, which impresses the necessity of a coordinated and flexible network architecture. Although coordinated multipoint (CoMP) systems were primarily proposed to improve the cell edge performance in 4G, their collaborative nature can be leveraged to support the diverse requirements and enabling technologies of 5G and beyond networks. To this end, we propose generalization of CoMP to a proactive and efficient resource utilization framework capable of supporting different user requirements such as reliability, latency, throughput, and security while considering network constraints. This article elaborates on the multiple aspects, inputs, and outputs of the generalized CoMP (GCoMP) framework. Apart from user requirements, the GCoMP decision mechanism also considers the CoMP scenario and network architecture to decide upon outputs such as CoMP technique or appropriate coordinating clusters. To enable easier understanding of the concept, popular use cases, such as vehicle-to-everything (V2X) communication and eHealth, are studied. Additionally, interesting challenges and open areas in GCoMP are discussed.Comment: 11 pages, 7 figure

Deposition and Characterization of CdS, CuS and ZnS Thin Films Deposited by SILAR Method

Cadmium sulfide, copper sulfide and zinc sulfide films were grown on Si(111) substrate by successive ionic layer adsorption and reaction method at room temperature. The crystalline structure and morphology of obtained films were characterized by X-ray diffraction, scanning electronic microscope and energy dispersive X-ray analysis methods. The films were polycrystalline and showed preferred orientation. The surface morphology of these films looked relatively smooth and homogeneous in the scanning electron microscope image. The energy dispersive X-ray analysis spectra showed that the expected elements exist in the thin films