Separable states and the geometric phases of an interacting two-spin system

It is known that an interacting bipartite system evolves as an entangled state in general, even if it is initially in a separable state. Due to the entanglement of the state, the geometric phase of the system is not equal to the sum of the geometric phases of its two subsystems. However, there may exist a set of states in which the nonlocal interaction does not affect the separability of the states, and the geometric phase of the bipartite system is then always equal to the sum of the geometric phases of its subsystems. In this paper, we illustrate this point by investigating a well known physical model. We give a necessary and sufficient condition in which a separable state remains separable so that the geometric phase of the system is always equal to the sum of the geometric phases of its subsystems.Comment: 13 page

Dietary patterns for adults with chronic kidney disease

This is the protocol for a review and there is no abstract. The objectives are as follows: This review will evaluate the benefits and harms of dietary patterns among adults with CKD (any stage including people with end-stage kidney disease (ESKD) treated with dialysis, transplantation or supportive care)

Dietary interventions for adults with chronic kidney disease

Background: Dietary changes are routinely recommended in people with chronic kidney disease (CKD) on the basis of randomised evidence in the general population and non-randomised studies in CKD that suggest certain healthy eating patterns may prevent cardiovascular events and lower mortality. People who have kidney disease have prioritised dietary modifications as an important treatment uncertainty. Objectives: This review evaluated the benefits and harms of dietary interventions among adults with CKD including people with end-stage kidney disease (ESKD) treated with dialysis or kidney transplantation. Search methods: We searched the Cochrane Kidney and Transplant Specialised Register (up to 31 January 2017) through contact with the Information Specialist using search terms relevant to this review. Studies contained in the Specialised Register are identified through search strategies specifically designed for CENTRAL, MEDLINE, and EMBASE; handsearching conference proceedings; and searching the International Clinical Trials Register (ICTRP) Search Portal and ClinicalTrials.gov. Selection criteria: Randomised controlled trials (RCTs) or quasi-randomised RCTs of dietary interventions versus other dietary interventions, lifestyle advice, or standard care assessing mortality, cardiovascular events, health-related quality of life, and biochemical, anthropomorphic, and nutritional outcomes among people with CKD. Data collection and analysis: Two authors independently screened studies for inclusion and extracted data. Results were summarised as risk ratios (RR) for dichotomous outcomes or mean differences (MD) or standardised MD (SMD) for continuous outcomes, with 95% confidence intervals (CI) or in descriptive format when meta-analysis was not possible. Confidence in the evidence was assessed using GRADE. Main results: We included 17 studies involving 1639 people with CKD. Three studies enrolled 341 people treated with dialysis, four studies enrolled 168 kidney transplant recipients, and 10 studies enrolled 1130 people with CKD stages 1 to 5. Eleven studies (900 people) evaluated dietary counselling with or without lifestyle advice and six evaluated dietary patterns (739 people), including one study (191 people) of a carbohydrate-restricted low-iron, polyphenol enriched diet, two studies (181 people) of increased fruit and vegetable intake, two studies (355 people) of a Mediterranean diet and one study (12 people) of a high protein/low carbohydrate diet. Risks of bias in the included studies were generally high or unclear, lowering confidence in the results. Participants were followed up for a median of 12 months (range 1 to 46.8 months). Studies were not designed to examine all-cause mortality or cardiovascular events. In very-low quality evidence, dietary interventions had uncertain effects on all-cause mortality or ESKD. In absolute terms, dietary interventions may prevent one person in every 3000 treated for one year avoiding ESKD, although the certainty in this effect was very low. Across all 17 studies, outcome data for cardiovascular events were sparse. Dietary interventions in low quality evidence were associated with a higher health-related quality of life (2 studies, 119 people: MD in SF-36 score 11.46, 95% CI 7.73 to 15.18; I = 0%). Adverse events were generally not reported. Dietary interventions lowered systolic blood pressure (3 studies, 167 people: MD -9.26 mm Hg, 95% CI -13.48 to -5.04; I = 80%) and diastolic blood pressure (2 studies, 95 people: MD -8.95, 95% CI -10.69 to -7.21; I = 0%) compared to a control diet. Dietary interventions were associated with a higher estimated glomerular filtration rate (eGFR) (5 studies, 219 people: SMD 1.08; 95% CI 0.26 to 1.97; I = 88%) and serum albumin levels (6 studies, 541 people: MD 0.16 g/dL, 95% CI 0.07 to 0.24; I = 26%). A Mediterranean diet lowered serum LDL cholesterol levels (1 study, 40 people: MD -1.00 mmol/L, 95% CI -1.56 to -0.44). Authors' conclusions: Dietary interventions have uncertain effects on mortality, cardiovascular events and ESKD among people with CKD as these outcomes were rarely measured or reported. Dietary interventions may increase health-related quality of life, eGFR, and serum albumin, and lower blood pressure and serum cholesterol levels. Based on stakeholder prioritisation of dietary research in the setting of CKD and preliminary evidence of beneficial effects on risks factors for clinical outcomes, large-scale pragmatic RCTs to test the effects of dietary interventions on patient outcomes are required

The Utilization of Heat Exchangers for Energy Conservation in Air Conditioning

This paper investigates the characteristics of heat exchanger (HPHE) as an efficient coolness recovery unit in air conditioning through experimental studies. It was conducted under a multiple-nozzle code tester based on the ASHRAE standards. The wind tunnel was subjected to airflow with considerable variation in its inlet air temperature. Among the factors being investigated are the air velocity, inlet and outlet air temperatures, overall efficiency and the number of rows in longitudinal direction. The data obtained were compared with the results predicted by previous theoretical studies. Good agreement was observed

Numerical Renormalization Group for Bosonic Systems and Application to the Subohmic Spin-Boson Model

We describe the generalization of Wilson's Numerical Renormalization Group method to quantum impurity models with a bosonic bath, providing a general non-perturbative approach to bosonic impurity models which can access exponentially small energies and temperatures. As an application, we consider the spin-boson model, describing a two-level system coupled to a bosonic bath with power-law spectral density, J(omega) ~ omega^s. We find clear evidence for a line of continuous quantum phase transitions for subohmic bath exponents 0<s<1; the line terminates in the well-known Kosterlitz-Thouless transition at s=1. Contact is made with results from perturbative renormalization group, and various other applications are outlined.Comment: 4 pages, 5 figs, (v2) final version as publishe

Spectral Graph Convolutions for Population-based Disease Prediction

Exploiting the wealth of imaging and non-imaging information for disease prediction tasks requires models capable of representing, at the same time, individual features as well as data associations between subjects from potentially large populations. Graphs provide a natural framework for such tasks, yet previous graph-based approaches focus on pairwise similarities without modelling the subjects' individual characteristics and features. On the other hand, relying solely on subject-specific imaging feature vectors fails to model the interaction and similarity between subjects, which can reduce performance. In this paper, we introduce the novel concept of Graph Convolutional Networks (GCN) for brain analysis in populations, combining imaging and non-imaging data. We represent populations as a sparse graph where its vertices are associated with image-based feature vectors and the edges encode phenotypic information. This structure was used to train a GCN model on partially labelled graphs, aiming to infer the classes of unlabelled nodes from the node features and pairwise associations between subjects. We demonstrate the potential of the method on the challenging ADNI and ABIDE databases, as a proof of concept of the benefit from integrating contextual information in classification tasks. This has a clear impact on the quality of the predictions, leading to 69.5% accuracy for ABIDE (outperforming the current state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion, significantly outperforming standard linear classifiers where only individual features are considered.Comment: International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI) 201