Crystal structure of dichlorido-bis(N-phenyl-2-(quinolin-8-yloxy)acetamide-κ2N,O) – acetone (2/1), C35.5H31N4O4.5Cl2Cu

Abstract C35.5H31N4O4.5Cl2Cu, monoclinic, P21/c (no. 14), a = 14.8239(3) Å, b = 16.2169(2) Å, c = 15.6515(3) Å, β = 109.889(1)°, V = 3538.16(11) Å3, Z = 4, R gt(F) = 0.0499, wR ref(F 2) = 0.1577, T = 296(2) K

Accretion disk wind during the outburst of the stellar-mass black hole MAXI J1348-630

We analyzed two observations of the low-mass black hole X-ray binary MAXI J1348-630 from Nuclear Spectroscopic Telescope Array (NuSTAR) during low hard state and hard intermediate state in the 2019 outburst. The reflection components are found in the X-ray spectra, and the spectral fittings give an inclination angle of $\sim 25^\circ-35^\circ$ for the binary system, and there is an absorption line around $\sim 7$ keV coming from highly ionized iron. The photoionization code XSTAR is used to fit the absorption line, which is attributed to outflows with a velocity of $\sim 10^{4}\rm km\ s^{-1}$ along our line of sight and the column density reaching $10^{23}\rm cm^{-2}$ in low hard and hard intermediate states. The physical mechanism launching fast disk winds from the black hole accretion system is still uncertain. These observations strongly support magnetic launching as the dominant mechanism which drives the high velocity, high ionization winds from the inner accretion disk region in hard and hard intermediate states of MAXI J1348-630.Comment: 12 pages in the authors' version, reference:Journal of High Energy Astrophysics, 37 (2023), 25-3

2-(3-Hy­droxy­benzyl­amino)­acetic acid

There are two independent 2-(3-hy­droxy­benzyl­amino)­acetic acid mol­ecules, C9H11NO3, in the asymmetric unit of the title compound. The dihedral angle between the benzene rings of the two independent mol­ecules is 58.12 (4)°. The crystal packing is stablized by inter­molecular O—H⋯O and N—H⋯O hydrogen bonds

Crystal structure of dichlorido-bis(N-benzyl-2-(quinolin-8-yloxy)acetamide-κ2N,O)copper(II) — ethyl acetate (1/1), C38H36N4O6Cl2Cu

Abstract C38H36N4O6Cl2Cu, triclinic, P1̄ (no. 2), a = 10.749(5) Å, b = 11.074(5) Å, c = 15.951(7) Å, α = 80.471(8)°, β = 80.987(8)°, γ = 77.283(8)°, V = 1812.3(14) Å3, Z = 2, R gt(F) = 0.0465, wR ref(F 2) = 0.1279, T = 296(2) K

2,2′-Dichloro-N,N′-[1,3-phenyl­enebis(methyl­ene)]diacetamide

The complete mol­ecule of the title compound, C12H14Cl2N2O2, is generated by a crystallographic twofold axis with two C atoms of the central benzene ring lying on the axis. In the crystal, N—H⋯O hydrogen bonds link the mol­ecules into chains parallel to the c axis

Crystal structure of [5,5′-((propane-1,3-diylbis(azanylylidene))bis(ethan-1-yl-2-ylidene))bis(3-(ethoxycarbonyl)-2,4-dimethylpyrrol-1-ido)-κ4N,N′,N′′,N′′′]nickel(II), C23H30N4O4Ni

Abstract C23H30N4O4Ni, triclinic, P1̄ (no. 2), a = 7.5883(9) Å, b = 12.3110(15) Å, c = 12.7718(15) Å, α = 95.621(2)°, β = 99.908(2)°, γ = 101.30(2)°, V = 1141.8(2) Å3, Z = 2, R gt(F) = 0.0433, wR ref(F 2) = 0.1239, T = 296 K

Improving Data Center Energy Efficiency Using a Cyber-physical Systems Approach: Integration of Building Information Modeling and Wireless Sensor Networks

AbstractThe increase in data center operating costs is driving innovation to improve their energy efficiency. Previous research has investigated computational and physical control intervention strategies to alleviate the competition between energy consumption and thermal performance in data center operation. This study contributes to the body of knowledge by proposing a cyber-physical systems (CPS) approach to innovatively integrate building information modeling (BIM) and wireless sensor networks (WSN). In the proposed framework, wireless sensors are deployed strategically to monitor thermal performance parameters in response to runtime server load distribution. Sensor data are collected and contextualized in reference to the building information model that captures the geometric and functional characteristics of the data center, which will be used as inputs of continuous simulations aiming to predict real-time thermal performance of server working environment. Comparing the simulation results against historical performance data via machine learning and data mining, facility managers can quickly pinpoint thermal hot zones and actuate intervention procedures to improve energy efficiency. This BIM-WSN integration also facilitates smarter power management by capping runtime power demand within peak power capacity of data centers and alerting power outage emergencies. This paper lays out the BIM-WSN integration framework, explains the working mechanism, and discusses the feasibility of implementation in future work