Structural and spectroscopic characterisation of C4 oxygenates relevant to structure/activity relationships of the hydrogenation of α,β-unsaturated carbonyls

In the present work, we have investigated the conformational isomerism and calculated the vibrational spectra of the C4 oxygenates: 3-butyne-2-one, 3-butene-2-one, 2-butanone and 2-butanol using density functional theory. The calculations are validated by comparison to structural data where available and new, experimental inelastic neutron scattering and infrared spectra of the compounds. We find that for 3-butene-2-one and 2-butanol the spectra show clear evidence for the presence of conformational isomerism and this is supported by the calculations. Complete vibrational assignments for all four molecules are provided and this provides the essential information needed to generate structure/activity relationships for the sequential catalytic hydrogenation of 3-butyne-2-one to 2-butanol

Convergence of the Many-Body Expansion of Interaction Potentials: From van der Waals to Covalent and Metallic Systems

The many-body expansion of the interaction potential between atoms and molecules is analyzed in detail for different types of interactions involving up to seven atoms. Elementary clusters of Ar, Na, Si, and, in particular, Au are studied, using first-principles wave-function- and density-functional-based methods to obtain the individual n-body contributions to the interaction energies. With increasing atom number the many-body expansion converges rapidly only for long-range weak interactions. Large oscillatory behavior is observed for other types of interactions. This is consistent with the fact that Au clusters up to a certain size prefer planar structures over the more compact three-dimensional Lennard-Jones-type structures. Several Au model potentials and semi-empirical PM6 theory are investigated for their ability to reproduce the quantum results. We further investigate small water clusters as prototypes of hydrogen-bonded systems. Here, the many-body expansion converges rapidly, reflecting the localized nature of the hydrogen bond and justifying the use of two-body potentials to describe water-water interactions. The question of whether electron correlation contributions can be successfully modeled by a many-body interaction potential is also addressed

Credence Services: Content, credibility, and usefulness of online reviews

Credence products are those whose quality is difficult or impossible for consumers to assess, even after consuming the product (Darby & Karni, 1973). For example, it is difficult to assess the technical skill and knowledge of a physician even after a visit. This research is focused on the content, structure and consumer perceptions of online reviews for credence services. We start by examining how the content and structure of real online reviews of credence services systematically differs from those of experience services (Nelson, 1970). We find that online reviews of credence services are more likely to contain unsupported claims than reviews of experience services. We experimentally examine consumer perceptions of reviews, varying both their structure and content. Consumers rationally discount the credibility of credence claims when presented with short, simple reviews but we expect more complex argument structure and inclusion of experience attributes in the review to attenuate this effect

Representing temporal dependencies in human activity recognition.

Smart Homes offer the opportunity to perform continuous, long-term behavioural and vitals monitoring of residents, which may be employed to aid diagnosis and management of chronic conditions without placing additional strain on health services. A profile of the resident’s behaviour can be produced from sensor data, and then compared over time. Activity Recognition is a primary challenge for profile generation, however many of the approaches adopted fail to take full advantage of the inherent temporal dependencies that exist in the activities taking place. Long Short Term Memory (LSTM) is a form of recurrent neural network that uses previously learned examples to inform classification decisions. In this paper we present a variety of approaches to human activity recognition using LSTMs and consider the temporal dependencies that exist in binary ambient sensor data in order to produce case-based representations. These LSTM approaches are compared to the performance of a selection of baseline classification algorithms on several real world datasets. In general, it was found that accuracy in LSTMs improved as additional temporal information was presented to the classifier

Representing temporal dependencies in smart home activity recognition for health monitoring.

Long term health conditions, such as fall risk, are traditionally diagnosed through testing performed in hospital environments. Smart Homes offer the opportunity to perform continuous, long-term behavioural and vitals monitoring of residents, which may be employed to aid diagnosis and management of chronic conditions without placing additional strain on health services. A profile of the resident’s behaviour can be produced from sensor data, and then compared overtime. Activity Recognition is a primary challenge for profile generation, however many of the approaches adopted fail to take full advantage of the inherent temporal dependencies that exist in the activities taking place. Long Short Term Memory (LSTM) is a form of recurrent neural network that uses previously learned examples to inform classification decisions. In this paper we present a variety of approaches to human activity recognition using LSTMs which consider the temporal dependencies present in the sensor data in order to produce richer representations and improved classification accuracy. The LSTM approaches are compared to the performance of a selection of base line classification algorithms on several real world datasets. In general, it was found that accuracy in LSTMs improved as additional temporal information was presented to the classifier

Tidal Evolution of the Earth–Moon System with a High Initial Obliquity

A giant impact origin for the Moon is generally accepted, but many aspects of lunar formation remain poorly understood and debated. \'Cuk et al. (2016) proposed that an impact that left the Earth-Moon system with high obliquity and angular momentum could explain the Moon's orbital inclination and isotopic similarity to Earth. In this scenario, instability during the Laplace Plane transition, when the Moon's orbit transitions from the gravitational influence of Earth's figure to that of the Sun, would both lower the system's angular momentum to its present-day value and generate the Moon's orbital inclination. Recently, Tian and Wisdom (2020) discovered new dynamical constraints on the Laplace Plane transition and concluded that the Earth-Moon system could not have evolved from an initial state with high obliquity. Here we demonstrate that the Earth-Moon system with an initially high obliquity can evolve into the present state, and we identify a spin-orbit secular resonance as a key dynamical mechanism in the later stages of the Laplace Plane transition. Some of the simulations by Tian and Wisdom (2020) did not encounter this late secular resonance, as their model suppressed obliquity tides and the resulting inclination damping. Our results demonstrate that a giant impact that left Earth with high angular momentum and high obliquity ($\theta > 61^{\circ}$) is a promising scenario for explaining many properties of the Earth-Moon system, including its angular momentum and obliquity, the geochemistry of Earth and the Moon, and the lunar inclination.Comment: Accepted for the Planetary Science Journa