High sensitivity of 17O NMR to p-d hybridization in transition metal perovskites: first principles calculations of large anisotropic chemical shielding

A first principles embedded cluster approach is used to calculate O chemical shielding tensors, sigma, in prototypical transition metal oxide ABO_3 perovskite crystals. Our principal findings are 1) a large anisotropy of sigma between deshielded sigma_x ~ sigma_y and shielded sigma_z components (z along the Ti-O bond); 2) a nearly linear variation, across all the systems studied, of the isotropic sigma_iso and uniaxial sigma_ax components, as a function of the B-O-B bond asymmetry. We show that the anisotropy and linear variation arise from large paramagnetic contributions to sigma_x and sigma_y due to virtual transitions between O(2p) and unoccupied B(nd) states. The calculated isotropic delta_iso and uniaxial delta_ax chemical shifts are in good agreement with recent BaTiO_3 and SrTiO_3 single crystal 17O NMR measurements. In PbTiO_3 and PbZrO_3, calculated delta_iso are also in good agreement with NMR powder spectrum measurements. In PbZrO_3, delta_iso calculations of the five chemically distinct sites indicate a correction of the experimental assignments. The strong dependence of sigma on covalent O(2p)-B(nd) interactions seen in our calculations indicates that 17O NMR spectroscopy, coupled with first principles calculations, can be an especially useful tool to study the local structure in complex perovskite alloys.Comment: 12 pages, 3 figures, and 3 Table

A visual analytics tool to validate simulation models against collected data. V. 1.0.0

The validation of a simulation model is a crucial task in model development. It involves the comparison of simulation data to observation data and the identification of suitable model parameters. SLIVISU is a Visual Analytics framework that enables geoscientists to perform these tasks for observation data that is sparse and uncertain. Primarily, SLIVISU was designed to evaluate sea level indicators, which are geological or archaeological samples supporting the reconstruction of former sea level over the last ten thousands of years and are compiled in a postgreSQL database system. At the same time, the software aims at supporting the validation of numerical sea-level reconstructions against this data by means of visual analytics

A Regression-Based Differential Expression Detection Algorithm for Microarray Studies with Ultra-Low Sample Size

Global gene expression analysis using microarrays and, more recently, RNA-seq, has allowed investigators to understand biological processes at a system level. However, the identification of differentially expressed genes in experiments with small sample size, high dimensionality, and high variance remains challenging, limiting the usability of these tens of thousands of publicly available, and possibly many more unpublished, gene expression datasets. We propose a novel variable selection algorithm for ultra-low-n microarray studies using generalized linear model-based variable selection with a penalized binomial regression algorithm called penalized Euclidean distance (PED). Our method uses PED to build a classifier on the experimental data to rank genes by importance. In place of cross-validation, which is required by most similar methods but not reliable for experiments with small sample size, we use a simulation-based approach to additively build a list of differentially expressed genes from the rank-ordered list. Our simulation-based approach maintains a low false discovery rate while maximizing the number of differentially expressed genes identified, a feature critical for downstream pathway analysis. We apply our method to microarray data from an experiment perturbing the Notch signaling pathway in Xenopus laevis embryos. This dataset was chosen because it showed very little differential expression according to limma, a powerful and widely-used method for microarray analysis. Our method was able to detect a significant number of differentially expressed genes in this dataset and suggest future directions for investigation. Our method is easily adaptable for analysis of data from RNA-seq and other global expression experiments with low sample size and high dimensionality

A Social Citizen Dashboard for Participatory Urban Planning in Berlin: Prototype and Evaluation

Participatory urban planning enables citizens to make their voices heard in the urban planning process. The resulting measures are more likely to be accepted by the community. However, the parti-cipation process becomes more effortful and time-consuming. New approaches have been developed using digital technologies to facilitate citizen participation, such as topic modeling based on social media. Using Twitter data for the city of Berlin, we explore how social media and topic modeling can be used to classify and analyze citizen opinions. We develop a Social Citizen Dashboard allowing for a better understanding of changes in citizens’ priorities and incorporating constant cycles of feedback throughout planning phases. Evaluation interviews indicate the dashboard’s potential usefulness and implications as well as point to limitation in data quality and spur further research potentials

Cyclic Ferroelectric Switching and Quantized Charge Transport in CuInP2_2S6_6

The van der Waals layered ferroelectric CuInP$_2$S$_6$ has been found to exhibit a variety of intriguing properties arising from the fact that the Cu ions are unusually mobile in this system. While the polarization switching mechanism is usually understood to arise from Cu ion motion within the monolayers, a second switching path involving Cu motion across the van der Waals gaps has been suggested. In this work, we perform zero-temperature first-principles calculations on such switching paths, focusing on two types that preserve the periodicity of the primitive unit cell: ``cooperative" paths preserving the system's glide mirror symmetry, and ``sequential" paths in which the two Cu ions in the unit cell move independently of each other. We find that CuInP$_2$S$_6$ features a rich and varied energy landscape, and that sequential paths are clearly favored energetically both for cross-gap and through-layer paths. Importantly, these segments can be assembled to comprise a globally insulating cycle with the out-of-plane polarization evolving by a quantum as the Cu ions shift to neighboring layers. In this sense, we argue that CuInP$_2$S$_6$ embodies the physics of a quantized adiabatic charge pump

Materiality Thresholds: Empirical Evidence from Change in Accounting Estimate Disclosures

This paper provides empirical evidence on the materiality thresholds adopted in “change in accounting estimate” (CAE) disclosures. We also investigate the characteristics of the disclosing firms and their auditors, as well as the characteristics of the CAEs, such as the effect on income, the accounts affected, and disclosure venue. U.S. GAAP requires firms to disclose a CAE if its effect on the financial statements is deemed to be “material” (ASC 250-50-4). We analyze 4,335 CAE disclosures from 2006 to 2016 and provide the first descriptive evidence of the actual materiality thresholds used for CAE disclosures in practice. Our main finding is that quantitative materiality thresholds for CAE disclosures are significantly lower than conventional materiality thresholds, such as 5 percent of pretax income, and that firms may not only apply quantitative materiality thresholds more conservatively, but that other qualitative considerations play an important role in determining CAE materiality. Our results also show that there exists considerable variation in CAE disclosure across firm size, industry membership, auditor, financial statement account effected and the direction of the effect on income

An autonomous, multi-disciplinary sea ice - atmosphere - ocean observatory in the central Arctic

Although the polar oceans have been studied extensively during recent decades, year-round direct observations of sea ice, atmosphere and ocean are still relatively sparse. Hence, significant knowledge gaps exist in their complex interactions, and how they impact the evolution of the polar marine ecosystems. An important tool to fill these gaps has been developed and enhanced in recent years: autonomous, ice-based observation platforms. These buoys are capable of obtaining data on basin scales and year-round, including the largely undersampled winter periods. A key advantage over other observatory systems is that they send data in near-real time via satellite, contributing for example to numerical weather predictions through the Global Telecommunication Network (GTS). Here we present a concept for the implementation of a long-term strategy to monitor essential physical and biogeochemical parameters in the central Arctic Ocean year round and synchronously. We propose a combination of several new and innovative types of ice-based buoys, such as weather stations, ice mass balance buoys, ice-tethered bio-optical buoys and upper ocean profilers, with a scientific payload optimized to enable interdisciplinary research. Over the next 4 years, including the observational periods of the Year of Polar Prediction (YOPP, 2017-2019) and the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC, 2020), a network of these platforms will be (re-)deployed in the central Arctic Ocean each year, benefitting from international logistical efforts. The ultimate aim is to achieve a quasi-synoptic, basin-wide coverage of key parameters, such as air temperature, barometric pressure, wind speed and –direction, ice and snow thickness, incoming, reflected and transmitted irradiance, seawater temperature and salinity, chl-a and CDOM fluorescence, turbidity, oxygen and nitrate. Initial results from similar deployments since 2015 suggest that this approach has great potential to advance our understanding of many physical and biogeochemical processes and interactions in the polar oceans

A distributed atmosphere - sea ice - ocean observatory in the central Arctic

To understand the current evolution of the Arctic Ocean towards a less extensive, thinner and younger sea ice cover is one of the biggest challenges in climate research. Especially the lack of simultaneous in-situ observations of sea ice, ocean and atmospheric properties leads to significant knowledge gaps in their complex interactions, and how the associated processes impact the polar marine ecosystem. Here we present a concept for the implementation of a long-term strategy to monitor the most essential climate- and ecosystem parameters in the central Arctic Ocean, year round and synchronously. The basis of this strategy is the development and enhancement of a number of innovative autonomous observational platforms, such as rugged weather stations, ice mass balance buoys, ice-tethered bio-optical buoys and upper ocean profilers. The deployment of those complementing platforms in a distributed network enables the simultaneous collection of physical and biogeochemical in-situ data on basin scales and year round, including the largely undersampled winter periods. A key advantage over other observatory systems is that the data is sent via satellite in near-real time, contributing to numerical weather predictions through the Global Telecommunication Network (GTS) and to the International Arctic Buoy Programme (IABP). The first instruments were installed on ice floes in the Eurasian Basin in spring 2015 and 2016, yielding exceptional records of essential climate- and ecosystem-relevant parameters in one of the most inaccessible regions of this planet. Over the next 4 years, and including the observational periods of the Year of Polar Prediction (YOPP, 2017-2019) and the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC, 2020), the distributed observatory will be maintained by deployment of additional instruments in the central Arctic each year, benefitting from international logistical efforts

A distributed atmosphere - sea ice - ocean observatory in the central Arctic Ocean: concept and first results

To understand the current evolution of the Arctic Ocean towards a less extensive, thinner and younger sea ice cover is one of the biggest challenges in climate research. Especially the lack of simultaneous in-situ observations of sea ice, ocean and atmospheric properties leads to significant knowledge gaps in their complex interactions, and how the associated processes impact the polar marine ecosystem. Here we present a concept for the implementation of a long-term strategy to monitor the most essential climate- and ecosystem parameters in the central Arctic Ocean, year round and synchronously. The basis of this strategy is the development and enhancement of a number of innovative autonomous observational platforms, such as rugged weather stations, ice mass balance buoys, ice-tethered bio-optical buoys and upper ocean profilers. The deployment of those complementing platforms in a distributed network enables the simultaneous collection of physical and biogeochemical in-situ data on basin scales and year round, including the largely undersampled winter periods. A key advantage over other observatory systems is that the data is sent via satellite in near-real time, contributing to numerical weather predictions through the Global Telecommunication Network (GTS) and to the International Arctic Buoy Programme (IABP). The first instruments were installed on ice floes in the Eurasian Basin in spring 2015 and 2016, yielding exceptional records of essential climate- and ecosystem-relevant parameters in one of the most inaccessible regions of this planet. Over the next 4 years, and including the observational periods of the Year of Polar Prediction (YOPP, 2017-2019) and the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC, 2020), the distributed observatory will be continued and extended by deployments of additional instruments in the central Arctic each year, benefitting from international logistical efforts. The continuous data generated by this new autonomous drifting system is expected to provide new insights into the complex Arctic climate- and ecosystem on multiple scales. It is especially valuable in the context of the MOSAiC experiment, extending its coverage both in space and time

Recycling behavior of private households: an empirical investigation of individual preferences in a club good experiment

While recycling helps to limit the use of primary resources, it also requires considerable technological investments in regional circular flow systems. The effectiveness of recycling systems, however, also depends on household behavior. Therefore, current research increasingly focuses on behavioral and psychological theories of altruism, moral behavior, and social preferences. From an economic perspective, recycling systems can be understood as public goods with contributions resulting in positive externalities. In this context, the literature shows that recycling behavior highly depends on the perception of how others behave. In neutrally framed public good experiments, contributions tend to increase when alternative public goods are offered and group identity is generated. We aim to contribute to this discussion by observing household behavior concerning recycling opportunities in controlled settings. For this purpose, we study a laboratory experiment in which individuals con‑tribute to recycling systems: At first, only one public recycling system (public good) is offered. After dividing societies into two clubs, “high” and “low” according to their environmental attitudes, excludable club systems (club goods) are added as alternative recycling options for each club. The results of our pilot experiment show that adding a more exclusive recycling club option increases individual contributions to recycling compared with a pure public good framework. However, this increase in cooperation is only significant for those clubs where members with higher environmental attitudes are pooled