The ground state phase diagram of the diluted ferromagnetic Kondo-lattice model

We investigate the existence of several (anti-)ferromagnetic phases in the diluted ferromagnetic Kondo-lattice model, i.e. ferromagnetic coupling of local moment and electron spin. To do this we use a coherent potential approximation (CPA) with a dynamical alloy analogy. For the CPA we need effective potentials, which we get first from a mean-field approximation. To improve this treatment we use in the next step a more appropriate moment conserving decoupling approach and compare both methods. The different magnetic phases are modelled by defining two magnetic sublattices. As a result we present zero-temperature phase diagrams according to the important model parameters and different dilutions.Comment: accepted for publication in Journal of Physics: Condensed Matte

Economies Of Plant and Firm Size in the Unites States Pulp and Paper Industries

Statistics from the United States Bureau of the Census, census of manufacturers of 1972 for the pulp and paper industries, were analyzed with respect to labor productivity and profitability for evidence of economies of scale. In the pulp industry, profitability and productivity appeared to decrease sharply for mills with more than 500 employees. For paper and paperboard, productivity and profitability tended to level off or decline only slightly in mills with more than 500 employees. Only in the small building paper and paperboard industry did the largest mills exhibit the highest productivity. Integrated paper mills appeared more profitable than nonintegrated mills, but even the former revealed a limit to productivity gains resulting from increases in size. Employees in large mills received significantly higher wages and worked fewer overtime hours. Survivor data for pulp mills indicated a strong increase in the relative frequency of plants with 250 to 500 employees, and a large decrease in plants with 100 to 250 employees. For paper mills, a small increase in the relative number of plants with more than 250 employees was apparent. Survivor data for other industries were inconclusive. For the three largest industries, there was no evidence of economies of scale at the firm level offsetting the stagnation or decline of productivity in large plants. Size of plants appeared to explain most of the variation in productivity among firms

High speed development of new chemical synthesis and materials at molecular-level: Methods and approaches

Recent success of advanced computational chemistry, in example for the prediction of chemical reactivity and materials properties, reflects its reputation as a valuable and widely accepted means to tackle problems in academia. The development of new simulation methods and new computer architectures enables an enormous improvement of the productivity of research and development of new chemical synthesisand materials. These advances can be achieved in terms of less time, material, and staff compared to traditional lab experiments. Especially, approaches like virtual high throughput screenings (vHTS) are highly scalable and allow fast and deep insights into new promising system modifications. Consequently, the time to market and risk of new product development can be decreased significantly. These characteristicspaved the way for the successful application in industry nowadays

Constraining uncertainty in aerosol direct forcing

The uncertainty in present-day anthropogenic forcing is dominated by uncertainty in the strength of the contribution from aerosol. Much of the uncertainty in the direct aerosol forcing can be attributed to uncertainty in the anthropogenic fraction of aerosol in the present-day atmosphere, due to a lack of historical observations. Here we present a robust relationship between total present-day aerosol optical depth and the anthropogenic contribution across three multi-model ensembles and a large single-model perturbed parameter ensemble. Using observations of aerosol optical depth, we determine a reduced likely range of the anthropogenic component and hence a reduced uncertainty in the direct forcing of aerosol

Revealing exciton masses and dielectric properties of monolayer semiconductors with high magnetic fields

In semiconductor physics, many essential optoelectronic material parameters can be experimentally revealed via optical spectroscopy in sufficiently large magnetic fields. For monolayer transition-metal dichalcogenide semiconductors, this field scale is substantial --tens of teslas or more-- due to heavy carrier masses and huge exciton binding energies. Here we report absorption spectroscopy of monolayer MoS$_2$, MoSe$_2$, MoTe$_2$, and WS$_2$ in very high magnetic fields to 91~T. We follow the diamagnetic shifts and valley Zeeman splittings of not only the exciton's $1s$ ground state but also its excited $2s$, $3s$, ..., $ns$ Rydberg states. This provides a direct experimental measure of the effective (reduced) exciton masses and dielectric properties. Exciton binding energies, exciton radii, and free-particle bandgaps are also determined. The measured exciton masses are heavier than theoretically predicted, especially for Mo-based monolayers. These results provide essential and quantitative parameters for the rational design of opto-electronic van der Waals heterostructures incorporating 2D semiconductors.Comment: updated; now also including data on MoTe2. Accepted & in press, Nature Commu

A multi-model assessment of the impact of sea spray geoengineering on cloud droplet number

Artificially increasing the albedo of marine boundary layer clouds by the mechanical emission of sea spray aerosol has been proposed as a geoengineering technique to slow the warming caused by anthropogenic greenhouse gases. A previous global model study (Korhonen et al., 2010) found that only modest increases (< 20%) and sometimes even decreases in cloud drop number (CDN) concentrations would result from emission scenarios calculated using a windspeed dependent geoengineering flux parameterisation. Here we extend that work to examine the conditions under which decreases in CDN can occur, and use three independent global models to quantify maximum achievable CDN changes. We find that decreases in CDN can occur when at least three of the following conditions are met: the injected particle number is < 100 cm<sup>−3</sup>, the injected diameter is > 250–300 nm, the background aerosol loading is large (≥ 150 cm<sup>−3</sup>) and the in-cloud updraught velocity is low (< 0.2 m s<sup>−1</sup>). With lower background loadings and/or increased updraught velocity, significant increases in CDN can be achieved. None of the global models predict a decrease in CDN as a result of geoengineering, although there is considerable diversity in the calculated efficiency of geoengineering, which arises from the diversity in the simulated marine aerosol distributions. All three models show a small dependence of geoengineering efficiency on the injected particle size and the geometric standard deviation of the injected mode. However, the achievability of significant cloud drop enhancements is strongly dependent on the cloud updraught speed. With an updraught speed of 0.1 m s<sup>−1</sup> a global mean CDN of 375 cm<sup>−3</sup> (previously estimated to cancel the forcing caused by CO<sub>2</sub> doubling) is achievable in only about 50% of grid boxes which have > 50% cloud cover, irrespective of the amount of aerosol injected. But at stronger updraft speeds (0.2 m s<sup>−1</sup>), higher values of CDN are achievable due to the elevated in-cloud supersaturations. Achieving a value of 375 cm<sup>−3</sup> in regions dominated by stratocumulus clouds with relatively weak updrafts cannot be attained regardless of the number of injected particles, thereby limiting the efficacy of sea spray geoengineering

Bound-to-bound and bound-to-continuum optical transitions in combined quantum dot - superlattice systems

By combining band gap engineering with the self-organized growth of quantum dots, we present a scheme of adjusting the mid-infrared absorption properties to desired energy transitions in quantum dot based photodetectors. Embedding the self organized InAs quantum dots into an AlAs/GaAs superlattice enables us to tune the optical transition energy by changing the superlattice period as well as by changing the growth conditions of the dots. Using a one band envelope function framework we are able, in a fully three dimensional calculation, to predict the photocurrent spectra of these devices as well as their polarization properties. The calculations further predict a strong impact of the dots on the superlattices minibands. The impact of vertical dot alignment or misalignment on the absorption properties of this dot/superlattice structure is investigated. The observed photocurrent spectra of vertically coupled quantum dot stacks show very good agreement with the calculations.In these experiments, vertically coupled quantum dot stacks show the best performance in the desired photodetector application.Comment: 8 pages, 10 figures, submitted to PR

Clinical Mortality in a Large COVID-19 Cohort: Observational Study.

BACKGROUND: Northwell Health, an integrated health system in New York, has treated more than 15,000 inpatients with COVID-19 at the US epicenter of the SARS-CoV-2 pandemic. OBJECTIVE: We describe the demographic characteristics of patients who died of COVID-19, observation of frequent rapid response team/cardiac arrest (RRT/CA) calls for non-intensive care unit (ICU) patients, and factors that contributed to RRT/CA calls. METHODS: A team of registered nurses reviewed the medical records of inpatients who tested positive for SARS-CoV-2 via polymerase chain reaction before or on admission and who died between March 13 (first Northwell Health inpatient expiration) and April 30, 2020, at 15 Northwell Health hospitals. The findings for these patients were abstracted into a database and statistically analyzed. RESULTS: Of 2634 patients who died of COVID-19, 1478 (56.1%) had oxygen saturation levels ≥90% on presentation and required no respiratory support. At least one RRT/CA was called on 1112/2634 patients (42.2%) at a non-ICU level of care. Before the RRT/CA call, the most recent oxygen saturation levels for 852/1112 (76.6%) of these non-ICU patients were at least 90%. At the time the RRT/CA was called, 479/1112 patients (43.1%) had an oxygen saturation of \u3c80%. CONCLUSIONS: This study represents one of the largest reviewed cohorts of mortality that also captures data in nonstructured fields. Approximately 50% of deaths occurred at a non-ICU level of care despite admission to the appropriate care setting with normal staffing. The data imply a sudden, unexpected deterioration in respiratory status requiring RRT/CA in a large number of non-ICU patients. Patients admitted at a non-ICU level of care suffered rapid clinical deterioration, often with a sudden decrease in oxygen saturation. These patients could benefit from additional monitoring (eg, continuous central oxygenation saturation), although this approach warrants further study

Analysing Neural Network Topologies: a Game Theoretic Approach

Artificial Neural Networks have shown impressive success in very different application cases. Choosing a proper network architecture is a critical decision for a network\u2019s success, usually done in a manual manner. As a straightforward strategy, large, mostly fully connected architectures are selected, thereby relying on a good optimization strategy to find proper weights while at the same time avoiding overfitting. However, large parts of the final network are redundant. In the best case, large parts of the network become simply irrelevant for later inferencing. In the worst case, highly parameterized architectures hinder proper optimization and allow the easy creation of adverserial examples fooling the network. A first step in removing irrelevant architectural parts lies in identifying those parts, which requires measuring the contribution of individual components such as neurons. In previous work, heuristics based on using the weight distribution of a neuron as contribution measure have shown some success, but do not provide a proper theoretical understanding. Therefore, in our work we investigate game theoretic measures, namely the Shapley value (SV), in order to separate relevant from irrelevant parts of an artificial neural network. We begin by designing a coalitional game for an artificial neural network, where neurons form coalitions and the average contributions of neurons to coalitions yield to the Shapley value. In order to measure how well the Shapley value measures the contribution of individual neurons, we remove low-contributing neurons and measure its impact on the network performance. In our experiments we show that the Shapley value outperforms other heuristics for measuring the contribution of neurons