We present 850 $\mu$m polarization observations of the IC 348 star-forming region in the Perseus molecular cloud as part of the B-fields In STar-forming Region Observation (BISTRO) survey. We study the magnetic properties of two cores (HH 211 MMS and IC 348 MMS) and a filamentary structure of IC 348. We find that the overall field tends to be more perpendicular than parallel to the filamentary structure of the region. The polarization fraction decreases with intensity, and we estimate the trend by power-law and the mean of the Rice distribution fittings. The power indices for the cores are much smaller than 1, indicative of possible grain growth to micron size in the cores. We also measure the magnetic field strengths of the two cores and the filamentary area separately by applying the Davis-Chandrasekhar-Fermi method and its alternative version for compressed medium. The estimated mass-to-flux ratios are 0.45-2.20 and 0.63-2.76 for HH 211 MMS and IC 348 MMS, respectively, while the ratios for the filament is 0.33-1.50. This result may suggest that the transition from subcritical to supercritical conditions occurs at the core scale ($\sim$ 0.05 pc) in the region. In addition, we study the energy balance of the cores and find that the relative strength of turbulence to the magnetic field tends to be stronger for IC 348 MMS than HH 211 MMS. The result could potentially explain the different configurations inside the two cores: a single protostellar system in HH 211 MMS and multiple protostars in IC 348 MMS.
This study aims to present a smart ventilation control framework to reduce the infection risk of COVID-19 in indoor spaces of public buildings. To achieve this goal, an artificial neural network (ANN) was trained based on the results from a parametric computational fluid dynamics (CFD) simulation to predict the COVID-19 infection risk according to the zone carbon dioxide (CO2) concentration and other information (e.g., zone dimension). Four sample cases were analyzed to reveal how the CO2 concentration setpoint was varied for a given risk level under different scenarios. A framework of smart ventilation control was briefly discussed based on the ANN model. This framework could automatically adjust the system outdoor airflow rate and variable air volume (VAV) terminal box supply airflow rate to meet the needs of reducing infection risk and achieving a good energy performance.
NfsB (nitroreductase fromEscherichia coli) can catalyze nitroaromatic compounds to aromatic amines under mild conditions. Compared with the purified enzyme NfsB, we found that the crude enzyme demonstrated better thermal stability and tolerance against a wide pH range, rendering it convenient to use and cost-effective as it did not require any downstream processing. In addition, we introduced metal-organic frameworks to immobilize the crude-NfsB. The resulting composite, crude-NfsB@ZIF-90, showed excellent catalytic performance and reusability, and it also demonstrated good catalytic activity in organic solvents, rendering it more efficient for the removal of nitroaromatic contaminants in complex environments. The nitroreductase-ZIF-90 biocatalyst can be used for fluorescent labeling of carbohydrates, which is favorable for the study of the function of carbohydrates.
Abstract The vibration reduction performance of a damped two-level smart spring system is studied. The governing equations of the smart spring system are reduced to a first-order ordinary differential equation of matrix form. An active control algorithm employing the relative velocity relationships between the primary and auxiliary systems to prevent the movement of the controlled object is applied in the simulation. Different excitation types are considered to fully examine the validity of the control law. Responses of the controlled object under the control law are obtained and compared to the case of constant friction and the case of no control. The study indicates that the active control algorithm shows a good vibration reduction efficiency and a wide vibration reduction bandwidth.
The interaction of buildings and ground source heat pump systems with the surrounding ground is quite important for design and energy calculation procedures. This article describes a one-dimensional finite volume numerical model that can be used to estimate the undisturbed ground temperatures under various ground covers—short grass, tall grass, bare soil, concrete, and asphalt; and a two-harmonic analytical model, which requires minimum computational time and is intended for engineering application. The analytical model relies on five model parameters: annual average ground temperature, two temperature amplitudes, and two phase lags to estimate the ground temperatures. The parameters are estimated using the numerical model results. This article presents experimental validations of both models: Nineteen geographically and climatically diverse measurement sites, covered by short grass or tall grass, are chosen for validating the models for a 1-year period using weather data at the sites. Validation results show that both models satisfactorily predict the undisturbed ground temperatures for these sites; the mean root mean square errors of the numerical model at all sites are 1.3°C–1.6°C (2.3°F–2.9°F) at 5, 20, 50, and 100 cm depths; the mean root mean square errors of the analytical model at all sites are 1.4°C–2.4°C (2.5°F–4.3°F) at the four depths. In companion articles by Xing and Spitler (2017 Xing, L., and J.D. Spitler. 2017. Prediction of undisturbed ground temperature using analytical and numerical modeling. Part II: Methodology for developing a world-wide dataset. Science and Technology for Built Environment 23:809–825.[Taylor & Francis Online] , [Google Scholar]) and Xing et al. (2017 Xing, L., J.D. Spitler, and A. Bandyopadhyay. 2017. Prediction of undisturbed ground temperature using analytical and numerical modeling. Part III: Experimental validation of a world-wide dataset. Science and Technology for Built Environment 23:826–842.[Taylor & Francis Online] , [Google Scholar]), the authors develop automatic procedures using the two models to generate a worldwide dataset of both typical year ground temperatures and design year ground temperatures.
Novel anode materials for lithium-ion batteries were synthesized by in situ growth of spheres of graphene and carbon nanotubes (CNTs) around silicon particles. These composites possess high electrical conductivity and mechanical resiliency, which can sustain the high-pressure calendering process in industrial electrode fabrication, as well as the stress induced during charging and discharging of the electrodes. The resultant electrodes exhibit outstanding cycling durability (∼90% capacity retention at 2 A g-1 after 700 cycles or a capacity fading rate of 0.014% per cycle), calendering compatibility (sustain pressure over 100 MPa), and adequate volumetric capacity (1006 mAh cm-3), providing a novel design strategy toward better silicon anode materials.
The integration of different building technologies and network-based communication system makes grid- interactive efficient buildings vulnerable to passive threats such as equipment failure and active threats such as cyber-attacks. Modeling and simulation is an effective way to evaluate the influence of threats on the system. This paper proposes a generic and flex- ible threat injection framework for commonly-used building energy simulators such as Modelica to sup- port threat modeling and evaluation. This frame- work leverages the development of functional mock- up unit to provide a general modeling interface for threat injection and simulation. A numerical case study is conducted to demonstrate the capability of the framework for support single/multiple-order threat modeling and simulation. Besides the flexibil- ity of threat injection, the simulation also showed that cyber-attack that leads to short-term signal blocking has small influence on the system operation due to the resilience of the interactive control system for the heating, ventilation and air conditioning system, and a threat that lowers the global zone temperature set- point can lead to 31.5% more energy use and 60% more power demand.
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