A demand-response method to balance electric power-grids via HVAC systems using active energy-storage: simulation and on-site experiment

With the increasing popularity of renewable energy sources and the globally increasing electricity demand, the task of balancing the intermittent energy supply with varying demand becomes increasingly difficult. Instead of adjusting the supply, improving the demand response (DR) can be a more efficient way to optimize power balance. HVAC (heating, ventilation, and air-conditioning) systems, which operate on the demand side of power-grids, have a huge potential to improve the power balance. To assess their potential in a variable air volume (VAV) air-conditioning system with energy storage tank we introduce a demand response method that combines active cool-energy storage (ACES) with global temperature adjustment (GTA). To confirm the effectiveness of this combined ACES+GTA approach, we conduct measurements with the help of a full-scale VAV air-conditioning test setup. The experimental results are compared with a TRNSYS simulation. The measurements indicate that an energy-storing water-tank can effectively reduce the number of starts and stops for the heat pump. The simulation confirms that the ACES+GTA method can also effectively reduce the peak load of the power grid with little impact on the thermal comfort of the energy consumers. The cost-saving rate, compared to the conventional operating mode (no energy-storage during other periods), reaches 7.02% for an entire cooling season if the GTA method (with DR) is used

High-sensitive and rapid detection of Mycobacterium tuberculosis infection by IFN-γ release assay among HIV-infected individuals in BCG-vaccinated area

<p>Abstract</p> <p>Background</p> <p>An accurate test for <it>Mycobacterium tuberculosis </it>infection is urgently needed in immunosuppressed populations. The aim of this study was to investigate the diagnostic power of enzyme-linked immunospot (ELISPOT)-based IFN-γ release assay in detecting active and latent tuberculosis in HIV-infected population in <it>bacillus Calmette-Guerin </it>(BCG)-vaccinated area. A total of 100 HIV-infected individuals including 32 active tuberculosis patients were recruited. An ELISPOT-based IFN-γ release assay, T-SPOT.TB, was used to evaluate the <it>M. tuberculosis </it>ESAT-6 and CFP-10 specific IFN-γ response. Tuberculin skin test (TST) was performed for all recruited subjects.</p> <p>Results</p> <p>The subjects were divided into group HIV+ATB (HIV-infected individuals with active tuberculosis, n = 32), group HIV+LTB (HIV-infected individuals with positive results of T-SPOT.TB assay, n = 46) and group HIV only (HIV-infected individuals with negative results of T-SPOT.TB assay and without evidence of tuberculosis infection, n = 22). In group HIV+ATB and HIV+LTB, T-SPOT.TB positive rate in subjects with TST <5 mm were 50% (16/32) and 41.3% (19/46), respectively. Individuals in group HIV+ATB and HIV+LTB with CD4+ T cells <500/μl, T-SPOT.TB showed a higher sensitivity than TST (64.5% vs. 22.6% and 62.2% vs. 29.7%, respectively, both <it>P </it>< 0.0001). In addition, the sensitivity of T-SPOT.TB assay in group HIV+ATB increased to >85% in patients with TB treatment for less than 1 month and CD4+ T cells ≥200/μl, while for patients treated for more than 3 months and CD4+ T cells <200/μl, the sensitivity was decreased to only 33.3%. Furthermore, the results could be generated by T-SPOT.TB assay within 24 hours, which was more rapid than TST with 48–72 hours.</p> <p>Conclusion</p> <p>ELISPOT-based IFN-γ release assay is more sensitive and rapid for the diagnosis of TB infection in Chinese HIV-infected individuals with history of BCG vaccination, and could be an effective tool for guiding preventive treatment with isoniazid in latently infected people and for TB control in China.</p

Change-point multivariable quantile regression to explore effect of weather variables on building energy consumption and estimate base temperature range

Mean regression analysis may not capture associations that occur primarily in the tails of the outcome distribution. In this study, we focused on multiple weather factors to find the extent to which they impact heating-related gas consumption at higher quantiles. We used change-point multivariable quantile regression models to investigate distributional effects and heterogeneity in the gas consumption-related responses to weather factors. Subsequently, we analyzed quantile regression coefficients that corresponded to absolute differences in specific quantiles of gas consumption associated with a one-unit increase in weather factors. We found that the association of weather factors and gas consumption varied across 19 quantiles of gas consumption distribution. Heterogeneities varied between case study buildings: right tails of gas consumption for the community and educational buildings were more susceptible to weather factors than those of the healthcare building. The base temperature of the community buildings across quantiles of gas consumption indicated a flat trend, but the uncertainty ranges were relatively large compared with those for the community and educational buildings. The developed method in this study can be widely utilised to identify the most important factors and the extent to which they affect gas consumption at specific quantiles

CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction

Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a limitation of CMR is its slow imaging speed, which causes patient discomfort and introduces artifacts in the images. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have not been publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. Manual segmentations of the myocardium and chambers of all the subjects are also provided within the dataset. Scripts of state-of-the-art reconstruction algorithms were also provided as a point of reference. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community. Researchers can access the dataset at https://www.synapse.org/#!Synapse:syn51471091/wiki/.Comment: 14 pages, 8 figure

A two-step algorithm for rapid diagnosis of active pulmonary tuberculosis in entry applicants using the T-SPOT.TB and Xpert MTB/RIF assays in Shanghai, China

Emerging Microbes & Infections (2017) 6, e67; doi:10.1038/emi.2017.52; published online 26 July 201

A self-scheduling strategy of virtual power plant with electric vehicles considering margin indexes

From the perspective of a virtual power plant (VPP) with electric vehicles (EVs), a self-scheduling strategy considering the response time margin (RTM) and state of charge margin (SOCM) is proposed. Firstly, considering the response state of the state of charge (SOC) and charge-discharge state of EVs, a VPP based response capacity determination model of EVs is established. Then, RTM and SOCM indexes are introduced on the basis of the power system scheduling target and the EV users’ traveling demands. The RTM and SOCM indices are calculated and then are used to generate a priority sequence of responsive EVs for the VPP. In the process of the scheduling period and rolling iteration, the scheduling schemes of the EVs in the VPP for multiple time periods are determined. Finally, the VPP self-scheduling strategy is validated by taking an VPP containing three kinds of EV users as an example. Simulation results show that with the proposed strategy, the VPP is able to respond to the scheduling power from the power system, while ensuring the traveling demands of the EV users at the same time

Evaluation of Gamma Interferon Release Assays Using Mycobacterium tuberculosis Antigens for Diagnosis of Latent and Active Tuberculosis in Mycobacterium bovis BCG-Vaccinated Populations▿

T-cell-based gamma interferon (IFN-γ) release assays (IGRAs) using Mycobacterium tuberculosis-specific antigens have shown higher sensitivity and specificity than the routine tuberculin skin test (TST). However, the effects of Mycobacterium bovis BCG vaccination and anti-tuberculosis (TB) treatment on dynamic T-cell responses to M. tuberculosis-specific antigens in active TB cases have rarely been investigated in regions where TB is endemic. Eighty-nine patients with active pulmonary TB (ATB) and 57 healthy controls (HC) from China were recruited and tested by sputum smear and culture, TSTs, and IGRAs with M. tuberculosis-specific antigens ESAT-6 and CFP-10 (T-SPOT.TB) as well as purified protein derivative (PPD) stimulation. All 146 participants were screened by the T-SPOT.TB assay at recruitment. T-SPOT.TB-positive rates in ATB and HC groups were 87.6% (78/89) and 21.1% (12/57), respectively. Of 38 ATB patients who were both TST and T-SPOT.TB tested, the positive rates were 73.7% (28/38) and 94.7% (36/38), respectively (P = 0.0215), and those in the HC group were 62.3% (33/53) and 18.9% (10/53), respectively (P < 0.0001). The T-SPOT.TB-positive rates declined during TB treatment and were 94.4% (51/54), 86.4% (19/22), and 61.5% (8/13) for ATB patients receiving 0- to 1-month, 1- to 3-month, and 3- to 6-month anti-TB treatment, respectively. The IGRA is a most promising test for both active TB and latent TB infection (LTBI) diagnosis due to the improvement of its specificity and convenience, especially in the Mycobacterium bovis BCG-vaccinated population. Furthermore, the T-SPOT.TB assay using ESAT-6 and CFP-10 in ATB patients during anti-TB treatment could serve as a potential predictor of therapeutic efficacy