Application of large underground seasonal thermal energy storage in district heating system: A model-based energy performance assessment of a pilot system in Chifeng, China

Seasonal thermal energy storage (STES) technology is a proven solution to resolve the seasonal discrepancy between heating energy generation from renewables and building heating demands. This research focuses on the performance assessment of district heating (DH) systems powered by low-grade energy sources with large-scale, high temperature underground STES technology. A pilot DH system, located in Chifeng, China that integrates a 0.5 million m3 borehole thermal energy storage system, an on-site solar thermal plant and excess heat from a copper plant is presented. The research in this paper adopts a model-based approach using Modelica to analyze the energy performance of the STES for two district heating system configurations. Several performance indicators such as the extraction heat, the injection heat and the storage coefficient are selected to assess the STES system performance. Results show that a lower STES discharge temperature leads to a better energy performance. A sensitivity analysis of the site properties illustrates that the thermal conductivity of soil is the most influential parameter on the STES system performance. The long-term performance of the STES is also discussed and a shorter stabilization time between one and two years could be achieved by discharging the STES at a lower temperature.This research is part of the seasonal storage for solar and industrial waste heat utilization for urban district heating project funded by the Joint Scientific Thematic Research Programme (JSTP)–Smart Energy in Smart Cities. We gratefully acknowledge the financial support from the Netherlands Organisation for Scientific Research (NWO). We would also like to thank our research partners from Tsinghua University working on the project of the International S&T Cooperation Programof China (ISTCP) (project No. 2015DFG62410). Without their efforts, we would not have been able to obtain the technical data to conduct the case study

STRETCHABLE lab-on-chip device with impedance spectroscopy capability for mammalian cell studies

This paper presents the fabrication and testing of electric cellsubstrate impedance spectroscopy (ECIS) electrodes on a stretchable membrane. This is the first time when ECIS electrodes were fabricated on a stretchable substrate and ECIS measurements on mammalian cells exposed to cyclic strain of 10% were successfully demonstrated. A chemical was used to form strong chemical bond between gold electrodes of ECIS sensor and polymer membrane, which enable the electrodes keep good conductive ability during cyclic stretch. The stretchable membrane integrated with the ECIS sensor can simulate and replicate the dynamic environment of organism and enable the analysis of the cells activity involved in cells attachment and proliferation in vitro. Bovine aortic endothelial cells (BAEC) were used to evaluate the endothelial function influenced by mechanical stimuli in this research because they undergo in vivo cyclic physiologic elongation produced by the blood circulation in the arteries. Copyright © 2016 by ASME

Proprioceptive Learning with Soft Polyhedral Networks

Proprioception is the "sixth sense" that detects limb postures with motor neurons. It requires a natural integration between the musculoskeletal systems and sensory receptors, which is challenging among modern robots that aim for lightweight, adaptive, and sensitive designs at a low cost. Here, we present the Soft Polyhedral Network with an embedded vision for physical interactions, capable of adaptive kinesthesia and viscoelastic proprioception by learning kinetic features. This design enables passive adaptations to omni-directional interactions, visually captured by a miniature high-speed motion tracking system embedded inside for proprioceptive learning. The results show that the soft network can infer real-time 6D forces and torques with accuracies of 0.25/0.24/0.35 N and 0.025/0.034/0.006 Nm in dynamic interactions. We also incorporate viscoelasticity in proprioception during static adaptation by adding a creep and relaxation modifier to refine the predicted results. The proposed soft network combines simplicity in design, omni-adaptation, and proprioceptive sensing with high accuracy, making it a versatile solution for robotics at a low cost with more than 1 million use cycles for tasks such as sensitive and competitive grasping, and touch-based geometry reconstruction. This study offers new insights into vision-based proprioception for soft robots in adaptive grasping, soft manipulation, and human-robot interaction.Comment: 20 pages, 10 figures, 2 tables, submitted to the International Journal of Robotics Research for revie

A comparison of machine learning classifiers for smartphone-based gait analysis

This paper proposes a reliable monitoring scheme that can assist medical specialists in watching over the patient's condition. Although several technologies are traditionally used to acquire motion data of patients, the high costs as well as the large spaces they require make them difficult to be applied in a home context for rehabilitation. A reliable patient monitoring technique, which can automatically record and classify patient movements, is mandatory for a telemedicine protocol. In this paper, a comparison of several state-of-the-art machine learning classifiers is proposed, where stride data are collected by using a smartphone. The main goal is to identify a robust methodology able to assure a suited classification of gait movements, in order to allow the monitoring of patients in time as well as to discriminate among a pathological and physiological gait. Additionally, the advantages of smartphones of being compact, cost-effective and relatively easy to operate make these devices particularly suited for home-based rehabilitation programs. Graphical Abstract. This paper proposes a reliable monitoring scheme that can assist medical specialists in watching over the patient's condition. Although several technologies are traditionally used to acquire motion data of patients, the high costs as well as the large spaces they require make them difficult to be applied in a home context for rehabilitation. A reliable patient monitoring technique, which can automatically record and classify patient movements, is mandatory for a telemedicine protocol. In this paper, a comparison of several state-of-the-art machine learning classifiers is proposed, where stride data are collected and processed by using a smartphone(see figure). The main goal is to identify a robust methodology able to assure a suited classification of gait movements, in order to allow the monitoring of patients in time as well as to discriminate among a pathological and physiological gait. Additionally, the advantages of smartphones of being compact, cost-effective and relatively easy to operate make these devices particularly suited for home-based rehabilitation programs

Additional effects of acupuncture on early comprehensive rehabilitation in patients with mild to moderate acute ischemic stroke: a multicenter randomized controlled trial

CONSORT checklist. (PDF 46 kb