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We present an analysis of the adsorption geometry of THC and its analogs (cannabinol, cannabidiol and HU-210) on silver using Surface Enhanced Raman Scattering (SERS) technique along with DFT calculations. Our study shows that these cannabinoids are oriented parallel to the silver surfaces and form bonds using primarily the benzene rings and oxygen atoms. In addition, we examine the effect of four different salts: MgCl; MgSO; KNO and NaSO on SERS enhancements of these drugs. We observe that MgCl causes the greatest enhancement for most of these drugs. We attribute this enhancement to the increase of the electric field induced by Mg salts and the formation of SERS active sites. This work also provides evidence that each salt most likely affects the orientation of all four cannabinoids in a similar way on a silver surface, resulting in a production of similar SERS spectra.

Data centres are part of today's critical information and communication infrastructure, and the majority of business transactions as well as much of our digital life now depend on them. At the same time, data centres are large primary energy consumers, with energy consumed by IT and server room air conditioning equipment and also by general building facilities. In many data centres, IT equipment energy and cooling energy requirements are not always coordinated, so energy consumption is not optimised. Most data centres lack an integrated energy management system that jointly optimises and controls all its energy consuming equipments in order to reduce energy consumption and increase the usage of local renewable energy sources. In this chapter, the authors discuss the challenges of coordinated energy management in data centres and present a novel scalable, integrated energy management system architecture for data centre wide optimisation. A prototype of the system has been implemented, including joint workload and thermal management algorithms. The control algorithms are evaluated in an accurate simulation‐based model of a real data centre. Results show significant energy savings potential, in some cases up to 40%, by integrating workload and thermal management.

Data centres are part of today's critical information and communication infrastructure, and the majority of business transactions as well as much of our digital life now depend on them. At the same time, data centres are large primary energy consumers, with energy consumed by IT and server room air conditioning equipment and also by general building facilities. In many data centres, IT equipment energy and cooling energy requirements are not always coordinated, so energy consumption is not optimised. Most data centres lack an integrated energy management system that jointly optimises and controls all its energy consuming equipments in order to reduce energy consumption and increase the usage of local renewable energy sources. In this chapter, the authors discuss the challenges of coordinated energy management in data centres and present a novel scalable, integrated energy management system architecture for data centre wide optimisation. A prototype of the system has been implemented, including joint workload and thermal management algorithms. The control algorithms are evaluated in an accurate simulation‐based model of a real data centre. Results show significant energy savings potential, in some cases up to 40%, by integrating workload and thermal management.

We have used time-dependent density functional theory in conjunction with the CAM-B3LYP functional and MWB28/aug-cc-pVDZ basis set to determine non-, near-, and on-resonance Raman spectra for a complex formed by 4-mercaptopyridine (4-Mpy) binding with a Ag13 cluster via the thiolate Ag-S bond. Geometry optimizations of the Ag13-4-Mpy complex showed an on-top structure directly bound to one Ag atom with the ring of the molecule almost flat with respect to two Ag atoms of the complex. The corresponding B3LYP/MWB28/aug-cc-pVDZ geometry is also an on-top structure directly bound to one Ag atom, but the molecule is directed away from the surface. The near-resonance Raman calculations were carried out in the infinite lifetime approximation, while the on-resonant Raman excitation profiles were calculated with the complex polarization propagator (CPP) approach, introducing a half width at half-maximum spectral broadening of 0.2 eV. Calculation of the UV-vis spectra of the isolated 4-Mpy and of the Ag13-4-Mpy complex showed that binding shifts the spectra from deep in the UV to the visible region. Calculation of the near-resonance Raman spectra of the two structures of the complex at 410 (3.025 eV) and 425 nm (2.918 eV) showed a strong enhancement. A very large variation across vibrational modes by a factor of at least 103 was found for both the static chemical enhancement and charge-transfer (CT) enhancement mechanisms. This large variation in enhancement factor indicates that B-term Herzberg-Teller scattering is occurring because inactive or very low intensity modes in the static spectra of the molecule are much stronger in both the static and near-resonance spectra of the complex. From the excitation profile using the CPP method, an overall surface enhancement on the order 103 or higher was found for individual modes on excitation into a CT excited state., QC 20240110

Motivated by the increasing popularity of hosting in-memory big-data analytics in cloud, we present a profiling methodology that can understand how different memory subsystems, i.e., cache and memory bandwidth, are susceptible to the impact of interference from co-located applications. We first describe the design of the proposed tool and demonstrate a case study consisting of five Spark applications on real-life data set., QC 20161209

Motivated by the increasing popularity of hosting in-memory big-data analytics in cloud, we present a profiling methodology that can understand how different memory subsystems, i.e., cache and memory bandwidth, are susceptible to the impact of interference from co-located applications. We first describe the design of the proposed tool and demonstrate a case study consisting of five Spark applications on real-life data set., Peer Reviewed, Postprint (published version)