Mental health effects of social distancing in Switzerland.

This analysis examines the effect of COVID-19 on public mental health in Switzerland. Following an event-study framework, we compare helpline call volume and duration before and after the outbreak of the first and second wave. The use of administrative phone-level data allows us to i) decompose the total effects into an intensive and extensive margin and ii) calculate a measure of unmet need. For the first wave, our results show that callers with a history of helpline contacts increase calls substantially. We also identify capacity constraints leading to unmet need for psychological counseling. Finally, we find no effects in the second wave, which might be explained by a number of factors including the absence of a lockdown and less restrictive social distancing measures.

MEDEP: Maintenance Event Detection for Multivariate Time Series Based on the PELT Approach.

Predictive Maintenance (PdM) is one of the most important applications of advanced data science in Industry 4.0, aiming to facilitate manufacturing processes. To build PdM models, sufficient data, such as condition monitoring and maintenance data of the industrial application, are required. However, collecting maintenance data is complex and challenging as it requires human involvement and expertise. Due to time constraints, motivating workers to provide comprehensive labeled data is very challenging, and thus maintenance data are mostly incomplete or even completely missing. In addition to these aspects, a lot of condition monitoring data-sets exist, but only very few labeled small maintenance data-sets can be found. Hence, our proposed solution can provide additional labels and offer new research possibilities for these data-sets. To address this challenge, we introduce MEDEP, a novel maintenance event detection framework based on the Pruned Exact Linear Time (PELT) approach, promising a low false-positive (FP) rate and high accuracy results in general. MEDEP could help to automatically detect performed maintenance events from the deviations in the condition monitoring data. A heuristic method is proposed as an extension to the PELT approach consisting of the following two steps: (1) mean threshold for multivariate time series and (2) distribution threshold analysis based on the complexity-invariant metric. We validate and compare MEDEP on the Microsoft Azure Predictive Maintenance data-set and data from a real-world use case in the welding industry. The experimental outcomes of the proposed approach resulted in a superior performance with an FP rate of around 10% on average and high sensitivity and accuracy results.

Awareness and use of (emergency) sick leave: US employees' unaddressed sick leave needs in a global pandemic.

We study US sick leave use and unaddressed sick leave needs in the midst of the global severe acute respiratory syndrome coronavirus type 2 (SARS COV 2) pandemic based on a representative survey. More than half of all US employees are unaware of the new emergency sick leave options provided by the federal Families First Coronavirus Response Act (FFCRA). Awareness and take-up rates are significantly higher among Asian Americans and lower among the foreign-born. About 8 million employees used emergency sick leave in the first 6 to 8 mo. Nevertheless, the share of employees who needed but could not take paid sick leave tripled in the pandemic; unaddressed sick leave needs total 15 million employees per month and are 69% higher among women. Our findings show that access to paid sick leave significantly reduces unaddressed sick leave needs. We conclude that given the fragmented US sick leave landscape, to address the strong increase in unaddressed sick leave needs during the pandemic, federal FFCRA response was not adequate.

COVID-19 Emergency Sick Leave Has Helped Flatten The Curve In The United States.

This analysis examines whether the coronavirus disease 2019 (COVID-19) emergency sick leave provision of the bipartisan Families First Coronavirus Response Act (FFCRA) reduced the spread of the virus. Using a difference-in-differences strategy, we compared changes in newly reported COVID-19 cases in states where workers gained the right to take paid sick leave (treatment group) versus in states where workers already had access to paid sick leave (control group) before the FFCRA. We adjusted for differences in testing, day-of-the-week reporting, structural state differences, general virus dynamics, and policies such as stay-at-home orders. Compared with the control group and relative to the pre-FFCRA period, states that gained access to paid sick leave through the FFCRA saw around 400 fewer confirmed cases per state per day. This estimate translates into roughly one prevented case per day per 1,300 workers who had newly gained the option to take up to two weeks of paid sick leave.

Sickness absence, moral hazard, and the business cycle.

The procyclical nature of sickness absence has been documented by many scholars in literature. So far, explanations have been based on labor force composition and reduced moral hazard caused by fear of job loss during recessions. In this paper, we propose and test a third mechanism caused by reduced moral hazard during booms and infections. We suggest that the workload is higher during economic booms and thus employees have to go to work despite being sick. In a theoretical model focusing on infectious diseases, we show that this will provoke infections of coworkers leading to overall higher sickness absence during economic upturns. Using state-level aggregated data from 112 German public health insurance funds (out of 145 in total), we find that sickness absence due to infectious diseases shows the largest procyclical pattern, as predicted by our theoretical model.

The impact of the 1918 Spanish flu epidemic on economic performance in Sweden: an investigation into the consequences of an extraordinary mortality shock.

We study the impact of the 1918 influenza pandemic on short- and medium-term economic performance in Sweden. The pandemic was one of the severest and deadliest pandemics in human history, but it has hitherto received only scant attention in the economic literature--despite representing an unparalleled labour supply shock. In this paper, we exploit seemingly exogenous variation in incidence rates between Swedish regions to estimate the impact of the pandemic. The pandemic led to a significant increase in poorhouse rates. There is also evidence that capital returns were negatively affected by the pandemic. However, contrary to predictions, we find no discernible effect on earnings.

From highly monodisperse indium and indium tin colloidal nanocrystals to self-assembled indium tin oxide nanoelectrodes.

Indium tin oxide (ITO) nanopatterned electrodes are prepared from colloidal solutions as a material saving alternative to the industrial vapor phase deposition and top down processing. For that purpose highly monodisperse In(1-x)Sn(x) (x < 0.1) colloidal nanocrystals (NCs) are synthesized with accurate size and composition control. The outstanding monodispersity of the NCs is evidenced by their self-assembly properties into highly ordered superlattices. Deposition on structured substrates and subsequent treatment in oxygen plasma converts the NC assemblies into transparent electrode patterns with feature sizes down to the diameter of single NCs. The conductivity in these ITO electrodes competes with the best values reported for electrodes from ITO nanoparticle inks.

Infrared emitting and photoconducting colloidal silver chalcogenide nanocrystal quantum dots from a silylamide-promoted synthesis.

Here, we present a hot injection synthesis of colloidal Ag chalcogenide nanocrystals (Ag(2)Se, Ag(2)Te, and Ag(2)S) that resulted in exceptionally small nanocrystal sizes in the range between 2 and 4 nm. Ag chalcogenide nanocrystals exhibit band gap energies within the near-infrared spectral region, making these materials promising as environmentally benign alternatives to established infrared active nanocrystals containing toxic metals such as Hg, Cd, and Pb. We present Ag(2)Se nanocrystals in detail, giving size-tunable luminescence with quantum yields above 1.7%. The luminescence, with a decay time on the order of 130 ns, was shown to improve due to the growth of a monolayer thick ZnSe shell. Photoconductivity with a quantum efficiency of 27% was achieved by blending the Ag(2)Se nanocrystals with a soluble fullerene derivative. The co-injection of lithium silylamide was found to be crucial to the synthesis of Ag chalcogenide nanocrystals, which drastically increased their nucleation rate even at relatively low growth temperatures. Because the same observation was made for the nucleation of Cd chalcogenide nanocrystals, we conclude that the addition of lithium silylamide might generally promote wet-chemical synthesis of metal chalcogenide nanocrystals, including in as-yet unexplored materials.

Evaluation of ordering in single-component and binary nanocrystal superlattices by analysis of their autocorrelation functions.

Self-assembly of colloidal nanocrystals and other nanosized building blocks has led to numerous large-scale and well-ordered superstructures. To quantify the superlattice quality we present a simple and efficient method, based on analysis of the autocorrelation function to determine characteristic order parameters for short-range and long-range ordering. This provides a feedback for further improvements of deposition techniques and self-assembly processes. To show the power of this method, it is applied to various two-dimensional ordered single component and binary nanocrystal assemblies. A quantitative comparison of the normalized long-range order parameter for various colloidal or epitaxially grown superlattice structures evidences that the long-range ordering in monodisperse colloidal superlattices by far supersedes that obtained at best by epitaxially grown quantum dots. Astonishingly, for selected binary nanocrystal superlattices the long-range ordering parameter reaches almost the same values as for single component superlattices. Besides the high sensitivity of the introduced quantification method to lattice imperfections our analysis also reveals any anisotropy in the ordering of the superlattices, which again can be quantified, for example, to identify the areas of highest quality within one specific sample.

Large-area ordered superlattices from magnetic Wustite/cobalt ferrite core/shell nanocrystals by doctor blade casting.

Although a large diversity of single-component and binary superlattices from colloidal nanocrystals have been demonstrated, applications of such ordered nanocrystal assemblies are still hampered due to a lack of control over the self-assembly processes over large areas. A reel-to-reel compatible large-area coating technique for solutions is given by doctor blade casting, which is applied here to deposit colloidal nanocrystals onto various substrates. The self-assembly process is demonstrated for magnetic nanocrystals, having a high potential for applications in magnetic memory devices. Shape-controlled (spherical and cubic) and monodisperse nanocrystals with a Wustite core and a cobalt ferrite shell are used in particular. Doctor blade casting of these colloidal nanocrystals results in films exhibiting hexagonally closely packed arrangements, which are formed by a top-down growth, as is evidenced by cross sectional transmission electron microscopy. The ordering in the topmost layer extends over large areas, although some defects and irregularities are found. The degree and quality of self-assembly is quantified by analyzing plan view images of the assemblies by means of the decay of their autocorrelation function. This analysis reveals that the degree of ordering obtained by doctor blade casting outperforms those provided by alternative deposition techniques such as inkjet printing or drop casting. The results for the coherent lengths deduced from the autocorrelation analysis are shown to be consistent with those from grazing-incidence small-angle X-ray scattering, giving coherence length on the order of 1000 nm.