Accurate molecular imaging of small animals taking into account animal models, handling, anaesthesia, quality control and imaging system performance

Small-animal imaging has become an important technique for the development of new radiotracers, drugs and therapies. Many laboratories have now a combination of different small-animal imaging systems, which are being used by biologists, pharmacists, medical doctors and physicists. The aim of this paper is to give an overview of the important factors in the design of a small animal, nuclear medicine and imaging experiment. Different experts summarize one specific aspect important for a good design of a small-animal experiment

Expert-based development of a standard in CO2 sequestration monitoring technology

Bureau of Economic Geolog

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

End-Use Load Profiles for the U.S. Building Stock: Market Needs, Use Cases, and Data Gaps

States and utilities are developing increasingly ambitious energy goals. Part of the solution to meeting these goals is improving electric grid flexibility. This includes shifting electric demand to align with grid needs. Thus, identifying and using building energy efficiency and other distributed energy resources to produce the highest grid value requires highly resolved, accurate and accessible electricity end-use load profiles (EULPs). EULPs quantify how and when energy is used. Currently, few accurate and accessible end-use load profiles are available for utilities, public utility commissions, state energy offices and other stakeholders to use to prioritize investment and value energy efficiency, demand response, distributed generation and energy storage. High-quality EULPs are also critical for determining the time-sensitive value of efficiency and other distributed energy resources, and the widespread adoption of grid-interactive efficient buildings (GEBs).For example, EULPs can be used to accurately forecast energy savings in buildings or identify energy activities that can be shifted to different times of the day. This report serves as the first-year deliverable for a multiyear U.S. Department of Energy-funded project, End-Use Load Profiles for the U.S. Building Stock, that intends to produce a set of highly resolved EULPs of the U.S. residential and commercial building stock. The project team, made up of researchers from the National Renewable Energy Laboratory (NREL), Lawrence Berkeley National Laboratory (LBNL), and Argonne National Laboratory, ultimately will use calibrated physics-based building energy models to create these EULPs

Walking-by-Logic: Signal Temporal Logic-Guided Model Predictive Control for Bipedal Locomotion Resilient to External Perturbations

This study proposes a novel planning framework based on a model predictive control formulation that incorporates signal temporal logic (STL) specifications for task completion guarantees and robustness quantification. This marks the first-ever study to apply STL-guided trajectory optimization for bipedal locomotion push recovery, where the robot experiences unexpected disturbances. Existing recovery strategies often struggle with complex task logic reasoning and locomotion robustness evaluation, making them susceptible to failures caused by inappropriate recovery strategies or insufficient robustness. To address this issue, the STL-guided framework generates optimal and safe recovery trajectories that simultaneously satisfy the task specification and maximize the locomotion robustness. Our framework outperforms a state-of-the-art locomotion controller in a high-fidelity dynamic simulation, especially in scenarios involving crossed-leg maneuvers. Furthermore, it demonstrates versatility in tasks such as locomotion on stepping stones, where the robot must select from a set of disjointed footholds to maneuver successfully

SciTech News Volume 71, No. 2 (2017)

Columns and Reports From the Editor 3 Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division 9 Aerospace Section of the Engineering Division 12 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 14 Reviews Sci-Tech Book News Reviews 16 Advertisements IEEE