Highly Non-linear Excitonic Zeeman Spin-Splitting in Composition-Engineered Artificial Atoms

Non-linear Zeeman splitting of neutral excitons is observed in composition engineered In(x)Ga(1-x)As self-assembled quantum dots and its microscopic origin is explained. Eight-band k.p simulations, performed using realistic dot parameters extracted from cross-sectional scanning tunneling microscopy, reveal that a quadratic contribution to the Zeeman energy originates from a spin dependent mixing of heavy and light hole orbital states in the dot. The dilute In-composition (x<0.35) and large lateral size (40-50 nm) of the quantum dots investigated is shown to strongly enhance the non-linear excitonic Zeeman gap, providing a blueprint to enhance such magnetic non-linearities via growth engineering

Strong electrically tunable exciton g-factors in an individual quantum dots due to hole orbital angular momentum quenching

Strong electrically tunable exciton g-factors are observed in individual (Ga)InAs self-assembled quantum dots and the microscopic origin of the effect is explained. Realistic eight band k.p simulations quantitatively account for our observations, simultaneously reproducing the exciton transition energy, DC Stark shift, diamagnetic shift and g-factor tunability for model dots with the measured size and a comparatively low In-composition of x(In)~35% near the dot apex. We show that the observed g-factor tunability is dominated by the hole, the electron contributing only weakly. The electric field induced perturbation of the hole wavefunction is shown to impact upon the g-factor via orbital angular momentum quenching, the change of the In:Ga composition inside the envelope function playing only a minor role. Our results provide design rules for growing self-assembled quantum dots for electrical spin manipulation via electrical g-factor modulation

The Case for AI Based Web3 Reputation Systems

Initiatives such as blockchains and decentralized storage networks are pushing for a decentralized Web3 to replace the current architecture. At the core of Web3 are network resource sharing services, which allow anyone to sell spare network capacity in return for rewards. These services require a way to establish trust, as parties are potentially malicious. This can be achieved by reputation systems. In this paper we make the case for using deep reinforcement learning in Web3 reputation calculation. More specifically, we propose a model which allows for decentralized calculation of scores with high personalization for the user

Magnetism in heavy-fermion U(Pt,Pd)3 studied by mSR

We report mSR experiments carried out on a series of heavy-electron pseudobinary compounds U(Pt1-xPdx)3 (x<=0.05). For x<=0.005 the zero-field muon depolarisation is described by the Kubo-Toyabe function. However the temperature variation of the Kubo-Toyabe relaxation rate does not show any sign of the small-moment antiferromagnetic phase with TN~6 K (signalled by neutron diffraction), in contrast to previous reports. The failure to detect the small ordered moment suggests it has a fluctuating (> 10 MHz) nature, which is consistent with the interpretation of NMR data. For 0.01<=x<=0.05 the muon depolarisation in the ordered state is described by two terms of equal amplitude: an exponentially damped spontaneous oscillation and a Lorentzian Kubo-Toyabe function. These terms are associated with antiferromagnetic order with substantial moments. The Knight-shift measured in a magnetic field of 0.6 T on single-crystalline U(Pt0.95Pd0.05)3 in the paramagnetic state shows two signals for B perpendicular to c, while only one signal is observed for B||c. The observation of two signals for B perpendicular to c, while there is only one muon localisation site (0,0,0), points to the presence of two spatially distinct regions of different magnetic response.Comment: 25 pages including 12 figures (PS), J. Phys.: Condens. Matter, in prin

Facilitating pre-operative assessment guidelines representation using SNOMED CT

Objective: To investigate whether SNOMED CT covers the terms used in pre-operative assessment guidelines, and if necessary, how the measured content coverage can be improved. Pre-operative assessment guidelines were retrieved from the websites of (inter)national anesthesiarelated societies. The recommendations in the guidelines were rewritten to ‘‘IF condition THEN action” statements to facilitate data extraction. Terms were extracted from the IF–THEN statements and mapped to SNOMED CT. Content coverage was measured by using three scores: no match, partial match and complete match. Non-covered concepts were evaluated against the SNOMED CT editorial documentation. Results: From 6 guidelines, 133 terms were extracted, of which 71% (n = 94) completely matched with SNOMED CT concepts. Disregarding the vague concepts in the included guidelines SNOMED CT’s content coverage was 89%. Of the 39 non-completely covered concepts, 69% violated at least one of SNOMED CT’s editorial principles or rules. These concepts were categorized based on four categories: non-reproducibility, classification-derived phrases, numeric ranges, and procedures categorized by complexity. Conclusion: Guidelines include vague terms that cannot be well supported by terminological systems thereby hampering guideline-based decision support systems. This vagueness reduces the content coverage of SNOMED CT in representing concepts used in the pre-operative assessment guidelines. Formalization of the guidelines using SNOMED CT is feasible but to optimize this, first the vagueness of some guideline concepts should be resolved and a few currently missing but relevant concepts should be added to SNOMED CT

Excess portal venous long-chain fatty acids induce syndrome X via HPA axis and sympathetic activation

We tested the hypothesis that excessive portal venous supply of long-chain fatty acids to the liver contributes to the development of insulin resistance via activation of the hypothalamus-pituitary-adrenal axis (HPA axis) and sympathetic system. Rats received an intraportal infusion of the long-chain fatty acid oleate (150 nmol/min, 24 h), the medium-chain fatty acid caprylate, or the solvent. Corticosterone (Cort) and norepinephrine (NE) were measured as indexes for HPA axis and sympathetic activity, respectively. Insulin sensitivity was assessed by means of an intravenous glucose tolerance test (IVGTT). Oleate infusion induced increases in plasma Cort (Δ = 13.5 ± 3.6 µg/dl; P < 0.05) and NE (Δ = 235 ± 76 ng/l; P < 0.05), whereas caprylate and solvent had no effect. The area under the insulin response curve to the IVGTT was larger in the oleate-treated group than in the caprylate and solvent groups (area = 220 ± 35 vs. 112 ± 13 and 106 ± 8, respectively, P < 0.05). The area under the glucose response curves was comparable [area = 121 ± 13 (oleate) vs. 135 ± 20 (caprylate) and 96 ± 11 (solvent)]. The results are consistent with the concept that increased portal free fatty acid is involved in the induction of visceral obesity-related insulin resistance via activation of the HPA axis and sympathetic system.

Performance of prognostic models in critically ill cancer patients – a review

INTRODUCTION: Prognostic models, such as the Acute Physiology and Chronic Health Evaluation (APACHE) II or III, the Simplified Acute Physiology Score (SAPS) II, and the Mortality Probability Models (MPM) II were developed to quantify the severity of illness and the likelihood of hospital survival for a general intensive care unit (ICU) population. Little is known about the performance of these models in specific populations, such as patients with cancer. Recently, specific prognostic models have been developed to predict mortality for cancer patients who are admitted to the ICU. The present analysis reviews the performance of general prognostic models and specific models for cancer patients to predict in-hospital mortality after ICU admission. METHODS: Studies were identified by searching the Medline databases from 1994 to 2004. We included studies evaluating the performance of mortality prediction models in critically ill cancer patients. RESULTS: Ten studies were identified that evaluated prognostic models in cancer patients. Discrimination between survivors and non-survivors was fair to good, but calibration was insufficient in most studies. General prognostic models uniformly underestimate the likelihood of hospital mortality in oncological patients. Two versions of a specific oncological scoring systems (Intensive Care Mortality Model (ICMM)) were evaluated in five studies and showed better discrimination and calibration than the general prognostic models. CONCLUSION: General prognostic models generally underestimate the risk of mortality in critically ill cancer patients. Both general prognostic models and specific oncology models may reliably identify subgroups of patients with a very high risk of mortality

A compact, high resolution tracker for cosmic ray muon scattering tomography using semiconductor sensors

© 2018 IOP Publishing Ltd and Sissa Medialab. A semiconductor tracker for muon scattering tomography is presented. The tracker contains silicon strip sensors with an 80 μm pitch, precision mechanics and integrated cooling. The electronic readout of the sensors is performed by a scalable, inexpensive, flexible, FPGA-based system, which is demonstrated to achieve an event rate of 30 kHz. The tracker performance is compared with a Geant4 simulation. A scattering angle resolution compatible with 1.5 mrad at the 4 GeV average cosmic ray muon energy is demonstrated. Images of plastic, iron and lead samples are obtained using an Angle Statistics Reconstruction algorithm. The images demonstrate good contrast between low and high atomic number materials