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Summary: Dendrites often exhibit structural changes in response to local inputs. Although mechanisms that pattern and maintain dendritic arbors are becoming clearer, processes regulating regrowth, during context-dependent plasticity or after injury, remain poorly understood. We found that a class of Drosophila sensory neurons, through complete pruning and regeneration, can elaborate two distinct dendritic trees, innervating independent sensory fields. An expression screen identified Cysteine proteinase-1 (Cp1) as a critical regulator of this process. Unlike known ecdysone effectors, Cp1-mutant ddaC neurons pruned larval dendrites normally but failed to regrow adult dendrites. Cp1 expression was upregulated/concentrated in the nucleus during metamorphosis, controlling production of a truncated Cut homeodomain transcription factor. This truncated Cut, but not the full-length protein, allowed Cp1-mutant ddaC neurons to regenerate higher-order adult dendrites. These results identify a molecular pathway needed for dendrite regrowth after pruning, which allows the same neuron to innervate distinct sensory fields. [Copyright &y& Elsevier]

Abstract: Instrumented indentation testing (IIT), an advanced version of conventional hardness tests, is widely used to evaluate the mechanical behavior of materials. Compared to conventional mechanical tests such as tensile testing, saw-cutting, CTOD, and so on, it is a relatively simple method that measures penetration load and depth continuously. In addition, IIT needs little sample preparation and can be applied from macro- to nano-scales by controlling the load and depth range, so that it is used by many researchers on metallic materials and bio-materials at all scales. Here we introduce the fundamentals of evaluating tensile properties and residual stress using IITs as published in ISO/TR 29381. Since indentation means contact between an indenter and surface of specimen, calibrating the contact area is the key issue in estimating tensile properties. By defining a representative stress and strain, indentation load-depth data can be converted to corresponding stress and strain points from the tensile curve. In evaluating residual stress, the load difference is calculated using a stress-insensitive contact hardness model. [Copyright &y& Elsevier]

Abstract: Building automation and control network (BACnet) is a standard data communication protocol for building automation and control systems. This study investigated the relationship between network-induced delay and control performance of a BACnet-based heating, ventilating, and air-conditioning (HVAC) control system using a simulation. The model consisted of the HVAC control system model and a BACnet communication protocol model. The BACnet communication model delivered the HVAC control data using the communication services provided by BACnet. The simulation results were analysed to develop recommendations for designing and operating BACnet-based HVAC control systems. [Copyright &y& Elsevier]

Abstract: The objective of this paper is to introduce a reference model of BACnet-based fire detection and monitoring system using MS/TP and ARCNET protocols. The reference model is designed to satisfy the requirements of response time, survivability and flexibility. The reference model is operated on the basis of BACnet, a standard communication protocol for building automation systems. In this study, a simulator for the reference model of fire detection and monitoring system was developed. Using the simulator, this study examined the validity of the reference model proposed in this study. This study also evaluated the performance of the BACnet-based fire detection and monitoring system in terms of network-induced delay. Simulation results show that the reference model satisfies the requirements of fire detection and monitoring system. [Copyright &y& Elsevier]

Demand response (DR) has been acknowledged as an effective method to improve the stability, and financial efficiency of power grids. During operation of a DR program, there are usually multiple interactions among different grid entities, which complicates decision-making processes with respect to grid operations. Recently, reinforcement learning (RL) has attracted increasing attention for managing complex decision-making problems, owing to its self-learning capacity. Several theoretical RL-based approaches have been proposed for addressing various DR issues, but the practical feasibility of these theoretical approaches remains to be proven. In this paper, a conceptual architecture is firstly proposed to support DR management of a diversified facility in the context of a price-based DR environment. Secondly, exhaustive guidelines are provided to illustrate how to implement a multi-agent RL-based algorithm in the constructed DR management system. Afterwards, a laboratory-level testbed was set up to evaluate the effectiveness of the deployed DR algorithm. The experimental evaluation results show that the RL-based DR algorithm takes about 20s and 50 episodes to achieve optimal load control policy. By executing the optimal operation policy, the overall energy consumption during the highest price period (i.e., 15:00–18:00) is significantly reduced by 133.6% compared with the lowest price period (i.e., 02:00–05:00). • Explore reinforcement learning (RL-) based approaches by practical implementation. • Construct an RL-based demand response (DR) oriented experimental facility. • Provide exhaustive guidelines for practical implementation of an RL-based DR system. • The feasibility of a RL approach in practical DR implementation is fully assessed. [ABSTRACT FROM AUTHOR]