Principles Of Efficient Design Research Articles

Lower carbon dioxide cements and concretes: Bringing new materials into UK industrial use

Cement and concrete are essential for global development. However, cement manufacture is responsible for around 7–8% of global greenhouse gas emissions, with significant growth anticipated. Beyond efficiencies in cement manufacturing, other strategies for using less cement in construction through applying the principles of efficient design, circular economy and reuse also have the potential to reduce emissions. A further option is to move towards cements with different compositions and inherently lower embodied carbon dioxide. This paper focuses on these materials and the challenges to their widespread adoption, drawing mainly on applied research, trials and standardisation activities conducted in the UK and the EU. In addition to modification of standards, basic technical and practical information such as strength development curves, durability, site considerations and a shared broad body of evidence are key for specifiers to consider when using any new cement. The paper describes the role of standards and specifications and the underpinning information (applied research, published case studies and experience) essential in getting any new cement adopted. It also shows how a range of evidence from research and application can feed into a simple conceptual model and evidence base.

Translating cell therapies for neurodegenerative diseases: Huntington's disease as a model disorder.

There has been substantial progress in the development of regenerative medicine strategies for CNS disorders over the last decade, with progression to early clinical studies for some conditions. However, there are multiple challenges along the translational pipeline, many of which are common across diseases and pertinent to multiple donor cell types. These include defining the point at which the preclinical data are sufficiently compelling to permit progression to the first clinical studies; scaling-up, characterization, quality control and validation of the cell product; design, validation and approval of the surgical device; and operative procedures for safe and effective delivery of cell product to the brain. Furthermore, clinical trials that incorporate principles of efficient design and disease-specific outcomes are urgently needed (particularly for those undertaken in rare diseases, where relatively small cohorts are an additional limiting factor), and all processes must be adaptable in a dynamic regulatory environment.Here we set out the challenges associated with the clinical translation of cell therapy, using Huntington’s disease as a specific example, and suggest potential strategies to address these challenges. Huntington’s disease presents a clear unmet need, but, importantly, it is an autosomal dominant condition with a readily available gene test, full genetic penetrance and a wide range of associated animal models, which together mean that it is a powerful condition in which to develop principles and test experimental therapeutics. We propose that solving these challenges in Huntington’s disease would provide a road map for many other neurological conditions. This white paper represents a consensus opinion emerging from a series of meetings of the international translational platforms Stem Cells for Huntington’s Disease and the European Huntington’s Disease Network Advanced Therapies Working Group, established to identify the challenges of cell therapy, share experience, develop guidance and highlight future directions, with the aim to expedite progress towards therapies for clinical benefit in Huntington’s disease.

Continuously Parametrized Quantum Simulation of Molecular Electron-Transfer Reactions

A comprehensive description of molecular electron transfer reactions is essential for our understanding of fundamental phenomena in bio-energetics and molecular electronics. Experimental studies of molecular systems in condensed-phase environments, however, face difficulties to independently control the parameters that govern the transfer mechanism with high precision. We show that trapped-ion experiments instead allow to reproduce and continuously connect vastly different regimes of molecular charge transfer through precise tuning of, e.g., phonon temperature, electron-phonon interactions, and electronic couplings. Such a setting allows not only to reproduce widely-used transport models, such as Marcus theory. It also provides access to transfer regimes that are unattainable for molecular experiments, while controlling and measuring the relevant observables on the level of individual quanta. Our numerical simulations predict an unconventional quantum transfer regime, featuring a transition from quantum adiabatic- to resonance-assisted transfer as a function of the donor-acceptor energy gap, that can be reached by increasing the electronic coupling at low temperatures. Trapped ion-based quantum simulations thus promise to enhance our microscopic understanding of molecular electron transfer processes, and may help to reveal efficient design principles for synthetic devices.

Analysis of Area-Efficiency vs. Unrolling for eSTREAM Hardware Portfolio Stream Ciphers

The demand for low resource devices has increased rapidly due to the advancements in Internet-of-things applications. These devices operate in environments that have limited resources. To ensure security, stream ciphers are implemented on hardware due to their speed and simplicity. Amongst different stream ciphers, the eSTREAM ciphers stand due to their frugal implementations. This work probes the effect of unrolling on the efficiency of eSTREAM ciphers, including Trivium, Grain (Grain 80 and Grain 128) and MICKEY (MICKEY 2.0 and MICKEY-128 2.0). It addresses the question of optimal unrolling for designing high-performance stream ciphers. The increase in the area consumption is also bench-marked. The analysis is conducted to identify efficient design principles for ciphers. We experimentally show that the resulting performance after unrolling may disagree with the theoretical prediction when the effects of technology library are considered. We report pre-layout synthesis results on 65 and 130 nm ASIC technology as well as synthesis results for Xilinx FPGA platform in support of our claim. Based on our findings, cipher design and implementation suggestions are proposed to aid hardware designers. Furthermore, we explore why and where area-efficiency for these ciphers saturate.

A deep learning approach for pose estimation from volumetric OCT data.

Tracking the pose of instruments is a central problem in image-guided surgery. For microscopic scenarios, optical coherence tomography (OCT) is increasingly used as an imaging modality. OCT is suitable for accurate pose estimation due to its micrometer range resolution and volumetric field of view. However, OCT image processing is challenging due to speckle noise and reflection artifacts in addition to the images' 3D nature. We address pose estimation from OCT volume data with a new deep learning-based tracking framework. For this purpose, we design a new 3D convolutional neural network (CNN) architecture to directly predict the 6D pose of a small marker geometry from OCT volumes. We use a hexapod robot to automatically acquire labeled data points which we use to train 3D CNN architectures for multi-output regression. We use this setup to provide an in-depth analysis on deep learning-based pose estimation from volumes. Specifically, we demonstrate that exploiting volume information for pose estimation yields higher accuracy than relying on 2D representations with depth information. Supporting this observation, we provide quantitative and qualitative results that 3D CNNs effectively exploit the depth structure of marker objects. Regarding the deep learning aspect, we present efficient design principles for 3D CNNs, making use of insights from the 2D deep learning community. In particular, we present Inception3D as a new architecture which performs best for our application. We show that our deep learning approach reaches errors at our ground-truth label's resolution. We achieve a mean average error of 14.89 ± 9.3µm and 0.096 ± 0.072° for position and orientation learning, respectively.

Using Mobile Eye-Trackers to Unpack the Perceptual Benefits of a Tangible User Interface for Collaborative Learning

In this study, we investigated the way users memorize, analyze, collaborate, and learn new concepts on a Tangible User Interface (TUI). Twenty-seven pairs of apprentices in logistics ( N = 54) interacted with an interactive simulation of a warehouse. Their task was to discover efficient design principles for building storehouses. In a between-subjects experimental design, half of the participants used 3D physical shelves, whereas the other half used 2D paper shelves. This manipulation allowed us to control for the “representational effect” of 3D tangibles: the first group saw the warehouse as a small-scale model with realistic shelves, whereas the second group had access to a more abstract layout with rectangular pieces of paper. Both groups interacted with the system in the same way. We found that participants in the first group (i.e., who used 3D realistic shelves) better memorized a warehouse layout, built a more efficient model, and scored higher on a learning test. Additionally, students wore eye-tracking goggles while completing those tasks; preliminary results suggest that 3D interfaces increased joint visual attention, which was found to be a significant predictor for participants’ task performance and learning gains. Implications for designing TUIs in collaborative settings are discussed.

Comparison of judgmental and optimal road pricing cordons

The performance of cordon pricing schemes is critically dependent on the location chosen for the cordon, and the toll imposed. Most cordon designs have been based on professional judgment, in the absence of guidance on the principles of efficient design. In this paper, a methodology based on Genetic Algorithms has been developed to identify the optimal location and toll level for one or more cordons, for a given objective function, with or without constraints on the design or required outcomes. To compare the performance of judgmental and optimal cordon designs, the method has been tested on a network of Edinburgh, with optimisation designed to achieve welfare maximisation with and without constraints on desired level of revenue and equity impact. The methodology is shown to generate appropriate solutions to all of these problems. The optimal single cordon achieves welfare benefits which are double those of the best judgmentally designed cordon. Constrained optima inevitably achieve lower benefits, but it is possible to design them so that this loss is minimised.