Interferometric thermometry of a single sub-Doppler cooled atom

Efficient self-interference of single-photons emitted by a sideband-cooled Barium ion is demonstrated. First, the technical tools for performing efficient coupling to the quadrupolar transition of a single $^{138}$Ba$^{+}$ ion are presented. We show efficient Rabi oscillations of the internal state of the ion using a highly stabilized 1.76 $\mu m$ fiber laser resonant with the S$_{1/2}$-D$_{5/2}$ transition. We then show sideband cooling of the ion's motional modes and use it as a means to enhance the interference contrast of the ion with its mirror-image to up to 90%. Last, we measure the dependence of the self-interference contrast on the mean phonon number, thereby demonstrating the potential of the set-up for single-atom thermometry close to the motional ground state.Comment: 6 pages, 6 figure

Atom-atom entanglement by single-photon detection

A scheme for entangling distant atoms is realized, as proposed in the seminal paper by Cabrillo et al. [Phys. Rev. A 59, 1025 (1999)]. The protocol is based on quantum interference and detection of a single photon scattered from two effectively one meter distant laser-cooled and trapped atomic ions. The detection of a single photon heralds entanglement of two internal states of the trapped ions with high rate and with a fidelity limited mostly by atomic motion. Control of the entangled state phase is demonstrated by changing the path length of the single-photon interferometer

JEC Tank-to-Wheels Report v5: Heavy duty vehicles: Well-to-Wheels analysis of future automotive fuels and powertrains in the European context

In this study typical figures for fuel consumption (FC), CO2 and CO2-equivalent emissions as well as energy consumption of current and future propulsion and fuel configurations for heavy duty vehicles (HDV) have been assessed. This report covers the Tank-to-Wheels (TTW) part of a comprehensive Well-to-Wheel (WTW) analysis. The parts of the study related to Well-to-Tank (WTT) analysis and to integrated WTW view are published in separate reports. ● The following two HDV configurations have been analysed: ● Rigid truck with 18 tons gross vehicle mass rating (GVMR) designed for use in regional delivery mission (“group 4 vehicle”) ● Tractor-semitrailer combination with 40 tons GVMR designed for use in long haul mission (“group 5 vehicle”) The analysed HDV configurations are either driven with a conventional internal combustion engine (ICE) or an electrified propulsion system (xEV). ICE only configurations include the technologies: ● Direct Injection Compression Ignition (CI) ● Port Injection Positive Ignition (PI) ● LNG High Pressure Direct Injection Compression Ignition (HPDI) For CI engines the fuels Diesel B0, B7 and B100 (FAME) as well as DME, ED95, OME and Paraffinic Diesel were considered. For PI engines CNG and LNG were analysed. The electrified propulsion systems include: ● Hybrid electric vehicle (HEV) ● Battery electric vehicle (BEV) ● Catenary electric vehicle (CEV) ● Hydrogen/Fuel cell (FCEV) All considered vehicle concepts have been analysed for the model years 2016 and 2025, whereby 2016 models are representing the state of the art on the European market for the individual application purpose. Vehicle specifications for 2025 are based on a technology assessment of future improvements. For xEV concepts the it is at the moment not possible to identify typical vehicle configurations as the these systems are currently a new technology under development for HDV. As a consequence xEV vehicle specifications and related results as elaborated in the present study shall been understood as examples for these new technologies. Simulation of vehicles which are driven by an ICE only have been performed with the software Vehicle Energy Consumption Calculation tool (VECTO), the tool which is also used for the CO2 certification of HDV in the EU. Electrified propulsion systems have been simulated with the model PHEM as these propulsion concepts are not covered in the current VECTO version. Figure 1 and Figure 2 give a summary on the results on transport specific figures (i.e. per tonne-kilometre) for energy consumption and TTW CO2-equivalent emissions. The main conclusions on the comparison of different propulsion systems drawn from these results are given in chapter 7 of this report.JRC.C.2-Energy Efficiency and Renewable

Belief Propagation for Min-Cost Network Flow: Convergence and Correctness

Distributed, iterative algorithms operating with minimal data structure while performing little computation per iteration are popularly known as message passing in the recent literature. Belief propagation (BP), a prototypical message-passing algorithm, has gained a lot of attention across disciplines, including communications, statistics, signal processing, and machine learning as an attractive, scalable, general-purpose heuristic for a wide class of optimization and statistical inference problems. Despite its empirical success, the theoretical understanding of BP is far from complete. With the goal of advancing the state of art of our understanding of BP, we study the performance of BP in the context of the capacitated minimum-cost network flow problem—a cornerstone in the development of the theory of polynomial-time algorithms for optimization problems and widely used in the practice of operations research. As the main result of this paper, we prove that BP converges to the optimal solution in pseudopolynomial time, provided that the optimal solution of the underlying network flow problem instance is unique and the problem parameters are integral. We further provide a simple modification of the BP to obtain a fully polynomial-time randomized approximation scheme (FPRAS) without requiring uniqueness of the optimal solution. This is the first instance where BP is proved to have fully polynomial running time. Our results thus provide a theoretical justification for the viability of BP as an attractive method to solve an important class of optimization problems.National Science Foundation (U.S.). Career Project (CNS 0546590)Natural Sciences and Engineering Research Council of Canada (NSERC). Postdoctoral FellowshipNational Science Foundation (U.S.). EMT Project (CCF 0829893)National Science Foundation (U.S.). (CMMI-0726733

A cross-platform modular framework for building Life Cycle Assessment

In recent years the application of Life Cycle Assessment (LCA) for assessing and improving the environmental performance of buildings has increased. At the same time, the automated optimization of building designs is gaining attraction for both design and research purposes. In this regard, a number of issues persist when aiming to optimize building’s environmental impacts along the design process. Firstly, as LCA applies a life cycle perspective, many aspects have to be considered (e.g. energy demand in operation as well as consumption of resources and energy for production and end of life treatment) and a variety of specific calculations is needed (e.g. building energy performance simulation, material quantity take-off). Secondly, sophisticated software packages are available and being used for each of these calculations (e.g. software for building modelling, dynamic energy simulation, quantity surveying). Though many of these software packages are currently standalone applications that rely on human interaction, there is an increasing trend to provide an application programming interface (API) that enables customization and automation. Thirdly, the mentioned processes and calculations are influencing each other in various ways and several scenarios have to be assessed. Thus, a comprehensive and modular approach is required that promotes interconnectivity of the different software solutions and automation of the assessment. In this paper we propose a modular cross-platform framework for LCA of buildings aiming to support flexibility and scalability of building LCA. We present a conceptual framework, example data exchange requirements and highlight potential implementation strategies

Embodied GHG emissions of buildings – The hidden challenge for effective climate change mitigation

Buildings are major sources of greenhouse gas (GHG) emissions and contributors to the climate crisis. To meet climate-change mitigation needs, one must go beyond operational energy consumption and related GHG emissions of buildings and address their full life cycle. This study investigates the global trends of GHG emissions arising across the life cycle of buildings by systematically compiling and analysing more than 650 life cycle assessment (LCA) case studies. The results, presented for different energy performance classes based on a final sample of 238 cases, show a clear reduction trend in life cycle GHG emissions due to improved operational energy performance. However, the analysis reveals an increase in relative and absolute contributions of so‐called ‘embodied’ GHG emissions, i.e., emissions arising from manufacturing and processing of building materials. While the average share of embodied GHG emissions from buildings following current energy performance regulations is approximately 20–25% of life cycle GHG emissions, this figure escalates to 45–50% for highly energy-efficient buildings and surpasses 90% in extreme cases. Furthermore, this study analyses GHG emissions at time of occurrence, highlighting the ‘carbon spike’ from building production. Relating the results to existing benchmarks for buildings’ GHG emissions in the Swiss SIA energy efficiency path shows that most cases exceed the target of 11.0 kgCO$^{2}$eq/m$^{2}$a. Considering global GHG reduction targets, these results emphasize the urgent need to reduce GHG emissions of buildings by optimizing both operational and embodied impacts. The analysis further confirmed a need for improving transparency and comparability of LCA studies

On the potential of augmented reality for mathematics teaching with the application cleARmaths

Learning content in mathematics, such as vector geometry, is still predominantly taught in an abstract manner, as the visualization and interaction of three-dimensional problems are limited with classical forms of teaching such as blackboard lessons or exercise sheets. This research article proposes the use of augmented reality (AR) in mathematics education. The proposed approach aims at easing the learning process related to vector geometry currently taught in senior mathematics classes by using intuitive visualization. The article introduces the concept of AR and presents the didactic foundations and the influence on the learning process based on an extensive literature review. Although studies see great potential in the use of AR for teaching mathematics, the method has so far hardly been used in schools. This can be mainly explained by the technological entry barrier of AR and the lack of simple, robust AR applications, in particular for vector geometry. To fill this gap, the authors developed “cleARmaths”, a developed android application for augmented reality-based teaching in vector geometry that allows widespread use. As a didactical concept, some example exercises sessions with the app are proposed, demonstrating how the app could be used in a mathematics classroom. Finally, the app was evaluated in a mathematics class and the results analyzed in a detailed study. It was found by the teacher and students to be beneficial and amusing, demonstrating the potential for AR in mathematics classes