Domiciliary Care: The Formal and Informal Labour Process

Domiciliary carers are paid care workers who travel to the homes of older people to assist with personal routines. Increasingly, over the past 20 years, the delivery of domiciliary care has been organised according to market principles and portrayed as the ideal type of formal care; offering cost savings to local authorities and independence for older people. Crucially, the work of the former ‘home help’ is transformed as domiciliary carers are now subject to the imperative of private, competitive accumulation which necessitates a constant search for increases in labour productivity. Drawing on qualitative data from domiciliary carers, managers and stakeholders, this article highlights the commodification of caring labour and reveals the constraints, contradictions and challenges of paid care work. Labour Process Theory offers a means of understanding the political economy of care work and important distinctions in terms of the formal and informal domiciliary care labour process

From Chemistry to Functionality: Trends for the Length Dependence of the Thermopower in Molecular Junctions

We present a systematic ab-initio study of the length dependence of the thermopower in molecular junctions. The systems under consideration are small saturated and conjugated molecular chains of varying length attached to gold electrodes via a number of different binding groups. Different scenarios are observed: linearly increasing and decreasing thermopower as function of the chain length as well as positive and negative values for the contact thermopower. Also deviation from the linear behaviour is found. The trends can be explained by details of the transmission, in particular the presence, position and shape of resonances from gateway states. We find that these gateway states do not only determine the contact thermopower, but can also have a large influence on the length-dependence itself. This demonstrates that simple models for electron transport do not apply in general and that chemical trends are hard to predict. Furthermore, we discuss the limits of our approach based on Density Functional Theory and compare to more sophisticated methods like self-energy corrections and the GW theory

Understanding the length dependence of molecular junction thermopower

Thermopower of molecular junctions is sensitive to details in the junction and may increase, decrease, or saturate with increasing chain length, depending on the system. Using McConnell's theory for exponentially suppressed transport together with a simple and easily interpretable tight binding model, we show how these different behaviors depend on the molecular backbone and its binding to the contacts. We distinguish between resonances from binding groups or undercoordinated electrode atoms, and those from the periodic backbone. It is demonstrated that while the former gives a length-independent contribution to the thermopower, possibly changing its sign, the latter determines its length dependence. This means that the question of which orbitals from the periodic chain that dominate the transport should not be inferred from the sign of the thermopower but from its length dependence. We find that the same molecular backbone can, in principle, show four qualitatively different thermopower trends depending on the binding group: It can be positive or negative for short chains, and it can either increase or decrease with length

Multiple Hypothesis Testing in Pattern Discovery

The problem of multiple hypothesis testing arises when there are more than one hypothesis to be tested simultaneously for statistical significance. This is a very common situation in many data mining applications. For instance, assessing simultaneously the significance of all frequent itemsets of a single dataset entails a host of hypothesis, one for each itemset. A multiple hypothesis testing method is needed to control the number of false positives (Type I error). Our contribution in this paper is to extend the multiple hypothesis framework to be used with a generic data mining algorithm. We provide a method that provably controls the family-wise error rate (FWER, the probability of at least one false positive) in the strong sense. We evaluate the performance of our solution on both real and generated data. The results show that our method controls the FWER while maintaining the power of the test.Comment: 28 page

Persistence of poor sleep predicts the severity of the clinical condition after 6months of standard treatment in patients with eating disorders

Clinical evidence suggests that eating disorder (ED) patients experience poor sleep even if they rarely complain of it. However, direct empirical evidence supporting this relationship is still sparse. In order to provide direct evidence, poor sleep, severity of the ED symptoms and depression were obtained in 562 ED patients at treatment admission (T0). For 271 patients out of them, data were also available after 6 months of standard treatment (T1). Results evidence that at T0 poor sleep predicts severity of ED symptoms through the mediation of depression. Persistence of poor sleep at T1 directly predicts the severity of the ED symptoms both directly and through the mediation of depression. These findings suggest that the treatment of ED may benefit from addressing poor sleep since its presence and persistence increase comorbidity and attrition to the standard treatment

Spectral Estimation of Conditional Random Graph Models for Large-Scale Network Data

Generative models for graphs have been typically committed to strong prior assumptions concerning the form of the modeled distributions. Moreover, the vast majority of currently available models are either only suitable for characterizing some particular network properties (such as degree distribution or clustering coefficient), or they are aimed at estimating joint probability distributions, which is often intractable in large-scale networks. In this paper, we first propose a novel network statistic, based on the Laplacian spectrum of graphs, which allows to dispense with any parametric assumption concerning the modeled network properties. Second, we use the defined statistic to develop the Fiedler random graph model, switching the focus from the estimation of joint probability distributions to a more tractable conditional estimation setting. After analyzing the dependence structure characterizing Fiedler random graphs, we evaluate them experimentally in edge prediction over several real-world networks, showing that they allow to reach a much higher prediction accuracy than various alternative statistical models.Comment: Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012

IETS and quantum interference: propensity rules in the presence of an interference feature

Destructive quantum interference in single molecule electronics is an intriguing phe- nomenon; however, distinguishing quantum interference effects from generically low transmission is not trivial. In this paper, we discuss how quantum interference ef- fects in the transmission lead to either low current or a particular line shape in current-voltage curves, depending on the position of the interference feature. Sec- ondly, we consider how inelastic electron tunneling spectroscopy can be used to probe the presence of an interference feature by identifying vibrational modes that are se- lectively suppressed when quantum interference effects dominate. That is, we expand the understanding of propensity rules in inelastic electron tunneling spectroscopy to molecules with destructive quantum interference.Comment: 19 pages, 6 figure

Single-molecule Electronics: Cooling Individual Vibrational Modes by the Tunneling Current

Electronic devices composed of single molecules constitute the ultimate limit in the continued downscaling of electronic components. A key challenge for single-molecule electronics is to control the temperature of these junctions. Controlling heating and cooling effects in individual vibrational modes, can in principle, be utilized to increase stability of single-molecule junctions under bias, to pump energy into particular vibrational modes to perform current-induced reactions or to increase the resolution in inelastic electron tunneling spectroscopy by controlling the life-times of phonons in a molecule by suppressing absorption and external dissipation processes. Under bias the current and the molecule exchange energy, which typically results in heating of the molecule. However, the opposite process is also possible, where energy is extracted from the molecule by the tunneling current. Designing a molecular 'heat sink' where a particular vibrational mode funnels heat out of the molecule and into the leads would be very desirable. It is even possible to imagine how the vibrational energy of the other vibrational modes could be funneled into the 'cooling mode', given the right molecular design. Previous efforts to understand heating and cooling mechanisms in single molecule junctions, have primarily been concerned with small models, where it is unclear which molecular systems they correspond to. In this paper, our focus is on suppressing heating and obtaining current-induced cooling in certain vibrational modes. Strategies for cooling vibrational modes in single-molecule junctions are presented, together with atomistic calculations based on those strategies. Cooling and reduced heating are observed for two different cooling schemes in calculations of atomistic single-molecule junctions.Comment: 18 pages, 6 figure